[ { "path": ".gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n.idea/\n\n# itlubber\n*.DS_Store\n*.virtual_documents\ntest*\n*.png\nexamples/model_report/*.png\nexamples/scorecard_samples_360.ipynb\n" }, { "path": ".readthedocs.yaml", "content": "version: 2\n\nbuild:\n os: ubuntu-22.04\n tools:\n python: \"3.11\"\n\npython:\n install:\n - requirements: docs/requirements.txt\n\nsphinx:\n configuration: docs/source/conf.py\n" }, { "path": "LICENSE", "content": "MIT License\n\nCopyright (c) 2023 itlubber\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "MANIFEST.in", "content": "include scorecardpipeline/*.ttf scorecardpipeline/*.csv scorecardpipeline/*.xlsx requirements*.txt\n" }, { "path": "README.md", "content": "# 评分卡pipeline建模包\n\n\"itlubber.png\" \n\n`scorecardpipeline`封装常用的风控策略分析和评分卡建模相关组件，API风格参考`sklearn`，支持`pipeline`式端到端评分卡建模、三方数据分析报告输出、规则集效果评估、特征有效性分析、`excel`报告输出、评分卡`PMML`文件导出、评分卡全流程超参数搜索等功能\n\n> 教程：https://scorecardpipeline.itlubber.art\n> \n> pipy包：https://pypi.org/project/scorecardpipeline\n>\n> 仓库地址：https://github.com/itlubber/scorecardpipeline\n> \n> 博文地址：https://scorecardpipeline.itlubber.art/quickstart.html\n> \n> 微信公共号推文：https://mp.weixin.qq.com/s/eCTp4h0fau77xOgf_V28wQ\n\n> PS: *新注册账号请勿 `star` 本项目*\n\n## 交流\n\n| 微信 | 微信公众号 |\n| :---: | :----: |\n| \"itlubber.png\" | \"itlubber_art.png\" |\n| itlubber | itlubber_art |\n\n\n## 引言\n\n作为一名金融搬砖工作者，评分卡建模怎么也算是基操。本文主要对笔者日常使用的评分卡建模代码进行讲解，说明如何一步步从原始数据到最终评分卡模型以及如何解读产出的模型报告文档。\n\n本文所有代码已全部提交至笔者GITHUB公开仓库，各位看官按需取用，用完记得顺带给个star以鼓励笔者继续开源相关工作。\n\n本文使用笔者对toad、scorecardpy、optbinning等库进行二次封装后的代码进行实操，文中会对仓库中的部分代码细节进行说明。本文旨在对仓库评分卡建模流程进行说明，并提供一个可以直接运行的完整示例，让更多金融从业小伙伴掌握整套评分卡模型构建方法。\n\n\n## 项目说明\n\n### 仓库地址\n\nhttps://github.com/itlubber/scorecardpipeline\n\n### 代码结构\n\n该仓库下代码主要用于提供评分卡建模相关的组件，项目结构如下：\n\n```base\n>> tree\n.\n├── LICENSE # 开源协议\n├── README.md # 相关说明文档\n├── requirements.txt # 相关依赖包\n└── setup.py # 打包文件\n├── examples # 演示样例\n│ └── scorecard_samples.ipynb\n└── scorecardpipeline # scorecardpipeline 包文件\n ├── excel_writer.py # 操作 excel 的公共方法\n ├── template.xlsx # excel 模版文件\n ├── matplot_chinese.ttf # 中文字体\n ├── processing.py # 数据处理相关代码\n ├── model.py # 模型相关代码\n └── utils.py # 公用方法\n```\n\n### 简要说明\n\n+ `processing` 中提供了数据前处理相关的方法：特征筛选方法（`FeatureSelection`、`StepwiseSelection`）、变量分箱方法（`Combiner`）、变量证据权重转换方法（`WOETransformer`），方法继承`sklearn.base`中的`BaseEstimator`和`TransformerMixin`，能够支持构建`pipeline`和超参数搜索\n+ `model`中提供了基于`sklearn.linear_model.LogisticRegression`实现的`ITLubberLogisticRegression`，同时重写了`toad.ScoreCard`，以支持模型相关内容的输出\n+ `excel_writer` 中提供了操作 `excel` 的一系列公共方法，包含设置条件格式、设置列宽、设置数字格式、插入指定样式的内容（`insert_value2sheet`）、插入图片数据（`insert_pic2sheet`）、插入`dataframe`数据内容（`insert_df2sheet`）、保存`excel`文件（`save`）等方法\n\n### `scorecardpipeline` 安装\n\n+ `pipy` 安装\n\n```bash\n>> pip install scorecardpipeline -i https://pypi.Python.org/simple/\n\nLooking in indexes: https://pypi.Python.org/simple/\nCollecting scorecardpipeline\n Downloading scorecardpipeline-0.1.5-py3-none-any.whl (36 kB)\n ......\nInstalling collected packages: sklearn-pandas, scorecardpy, sklearn2pmml, scorecardpipeline\nSuccessfully installed scorecardpipeline-0.1.5 scorecardpy-0.1.9.2 sklearn-pandas-2.2.0 sklearn2pmml-0.92.2\n```\n\n+ 源码编译\n\n```bash\npython setup.py sdist bdist_wheel\npip install dist/scorecardpipeline-0.1.11-py3-none-any.whl\n# twine upload dist/*\n```\n\n+ 特别说明\n\n`评分卡` 转 `PMML` 目前部分依赖 `java` ，如果需要解锁这部分功能，需要先安装 `jdk` 环境，推荐 `jdk 1.8 +`\n\n如果 `windows` 用户在安装过程中报错提示 `Microsoft Visual C++` 相关的异常信息，请下载安装 [`Latest Supported Visual C++ Redistributable`](https://learn.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist?view=msvc-170#latest-microsoft-visual-c-redistributable-version) 后再重新安装\n\n\n## 评分卡建模\n\n### 数据准备\n\n笔者仓库下几乎所有评分卡相关项目默认使用`scorecardpy`库中提供的`germancredit`数据集进行示例，数据集中包含类别型变量、数值型变量、样本好坏标签，共`1000`条数据，数据集中不包含缺失值，在部分示例中会随机替换数据集中的内容为缺失值，模拟实际生产中的真实数据。\n\n```python\ntarget = \"creditability\"\ndata = sc.germancredit()\ndata[target] = data[target].map({\"good\": 0, \"bad\": 1})\n```\n\n### 数据集拆分\n\n通常建模过程中会将数据集拆分成训练数据集和测试数据集集，训练数据集用于模型参数的训练，测试数据集用于评估模型好坏，且在整个建模过程中实际上只能使用一次（任何针对测试数据集调模型的，都存在模型过拟合的情况，除非你的数据不包含任何噪声数据）。而为了弥补测试数据集只能用一次用于评估模型好坏的尴尬，大多都会再拆分一个验证数据集，用于模型训练过程中判别模型是否向好的方向进行优化。\n\n在金融建模场景中，上述三个数据集通常被称为训练数据集、测试数据集（即上述三个数据集中的验证集）、跨时间验证集（即上述三个数据集中的测试集）。同时，在金融场景中，跨时间验证集的拆分方式通常使用类似时间序列数据集的拆分方法，按照时间将数据集拆分为建模集（包含训练集和测试集）、跨时间验证集（Out of Time，OOT），以保证模型在不同时间段内的泛化能力。\n\n本文为了简单起见，只拆分了训练集、测试集，OOT直接拷贝训练集来演示评分卡建模完整过程。\n\n+ 数据集拆分示例\n\n\n```python\ntrain, test = train_test_split(data, test_size=0.3, shuffle=True, stratify=data[target])\noot = data.copy()\n```\n\n### 特征粗筛（可选）\n\n通常当数据特征数量较多时，针对每个特征做分箱太耗时间的情况下，可以在分箱之前针对数据集进行一次简单的特征筛选工作。剔除数据集中空值或唯一值占比较高、变量之间相关性过高、变量`IV值`（`Information Value`）过低的特征。\n\n+ 方法简介\n\n```python\nclass FeatureSelection(TransformerMixin, BaseEstimator):\n \n def __init__(self, target=\"target\", empty=0.95, iv=0.02, corr=0.7, exclude=None, return_drop=True, identical=0.95, remove=None, engine=\"scorecardpy\", target_rm=False):\n \"\"\"\n ITLUBBER提供的特征筛选方法\n\n Args:\n target: 数据集中标签名称，默认 target\n empty: 空值率，默认 0.95, 即空值占比超过 95% 的特征会被剔除\n iv: IV值，默认 0.02，即iv值小于 0.02 时特征会被剔除\n corr: 相关性，默认 0.7，即特征之间相关性大于 0.7 时会剔除iv较小的特征\n identical: 唯一值占比，默认 0.95，即当特征的某个值占比超过 95% 时，特征会被剔除\n engine: 特征筛选使用的引擎，可选 \"toad\", \"scorecardpy\" 两种，默认 scorecardpy\n remove: 引擎使用 scorecardpy 时，可以传入需要强制删除的变量\n return_drop: 是否返回删除信息，默认 True，即默认返回删除特征信息\n target_rm: 是否剔除标签，默认 False，即不剔除\n exclude: 是否需要强制保留某些特征\n \"\"\"\n```\n\n+ 使用方法\n\n\n```python\n# 常规使用方法\nselection = FeatureSelection(target=target, engine=\"toad\", return_drop=True, corr=0.9, iv=0.01)\ntrain = selection.fit_transform(train)\n\n# pipeline 使用方法\nfeature_pipeline = Pipeline([\n (\"preprocessing_select\", FeatureSelection(target=target, engine=\"scorecardpy\")),\n ......\n ])\nfeature_pipeline.fit(train)\n```\n\n### 特征分箱\n\n在评分卡建模过程中，为了保证模型效果的稳定性，需要对变量进行一定的前处理。\n\n对于数值型变量，常规的处理是使用等频分箱、等距分箱、决策树分箱、卡方分箱、最优KS分箱等方式对数值型变量进行离散化处理，以保证特征稳定的同时，能够从业务上有一定的逻辑依据和解释性。对数值型变量进行分箱还可以减少异常值对模型的影响，降低模型复杂度。\n\n对于类别型变量，很多类别的客群属性比较相似，客户违约概率相近，将这些客群合并能够增加变量稳定性、降低模型的复杂程度和增强特征可解释性。\n\n为了对上述两类特征进行分箱，笔者将toad和optbinning两个库的分箱功能结合，合并进了processing.Combiner，能够支持变量单调分箱、“U”型分箱以及optbinning支持的所有形式的最优特征分箱方案，同时保留缺失值单独一箱，保证了评分卡模型后期上线后缺失值不知道如何填充的尴尬。\n\n+ 方法简介\n\n```python\nclass Combiner(TransformerMixin, BaseEstimator):\n \n def __init__(self, target=\"target\", method='chi', engine=\"toad\", empty_separate=False, min_samples=0.05, min_n_bins=2, max_n_bins=3, max_n_prebins=10, min_prebin_size=0.02, min_bin_size=0.05, max_bin_size=None, gamma=0.01, monotonic_trend=\"auto_asc_desc\", rules={}, n_jobs=1):\n \"\"\"\n 特征分箱封装方法\n\n Args:\n target: 数据集中标签名称，默认 target\n method: 特征分箱方法，可选 \"chi\", \"dt\", \"quantile\", \"step\", \"kmeans\", \"cart\", \"mdlp\", \"uniform\", 参考 toad.Combiner & optbinning.OptimalBinning\n engine: 分箱引擎，可选 \"optbinning\", \"toad\"\n empty_separate: 是否空值单独一箱, 默认 False，推荐设置为 True\n min_samples: 最小叶子结点样本占比，参考对应文档进行设置，默认 5%\n min_n_bins: 最小分箱数，默认 2，即最小拆分2箱\n max_n_bins: 最大分像素，默认 3，即最大拆分3箱，推荐设置 3 ～ 5，不宜过多，偶尔使用 optbinning 时不起效\n max_n_prebins: 使用 optbinning 时预分箱数量\n min_prebin_size: 使用 optbinning 时预分箱叶子结点（或者每箱）样本占比，默认 2%\n min_bin_size: 使用 optbinning 正式分箱叶子结点（或者每箱）最小样本占比，默认 5%\n max_bin_size: 使用 optbinning 正式分箱叶子结点（或者每箱）最大样本占比，默认 None\n gamma: 使用 optbinning 分箱时限制过拟合的正则化参数，值越大惩罚越多，默认 0。01\n monotonic_trend: 使用 optbinning 正式分箱时的坏率策略，默认 auto，可选 \"auto\", \"auto_heuristic\", \"auto_asc_desc\", \"ascending\", \"descending\", \"convex\", \"concave\", \"peak\", \"valley\", \"peak_heuristic\", \"valley_heuristic\"\n rules: 自定义分箱规则，toad.Combiner 能够接收的形式\n n_jobs: 使用多进程加速的worker数量，默认单进程\n \"\"\"\n```\n\n+ 使用方法\n\n\n```python\ncombiner = Combiner(min_samples=0.2, empty_separate=True, target=target)\ncombiner.fit(train)\ntrain = combiner.transform(train)\n\n# pipeline\nfeature_pipeline = Pipeline([\n......\n (\"combiner\", Combiner(target=target, min_samples=0.2)),\n ......\n ])\nfeature_pipeline.fit(train)\n\n# save all bin_plot\n_combiner = feature_pipeline.named_steps[\"combiner\"]\nfor col in woe_train.columns:\n if col != target:\n _combiner.bin_plot(train, col, labels=True, save=f\"outputs/bin_plots/train_{col}.png\")\n _combiner.bin_plot(test, col, labels=True, save=f\"outputs/bin_plots/test_{col}.png\")\n _combiner.bin_plot(oot, col, labels=True, save=f\"outputs/bin_plots/oot_{col}.png\")\n```\n\n### 特征编码\n\n特征分箱后，本质上只是将特征离散化为几箱，每一箱的值被赋予了一个标签，例如0、1、2、3、...，尽管这些标签在一定程度上也能够表征客户的风险水平，能够作为输入建立一个模型，但基于上述标签构建的模型在评估客户风险状况时比较局限，很难精准刻画客户的风险状况。\n\n对于上述离散化后的特征，常规的处理方式有ONE-HOT编码、顺序编码、TARGET编码、COUNT编码等方式。在金融建模场景，尤其是评分卡建模时，通常使用WOE编码对离散化后的特征进行编码。WOE编码将每个分箱内的坏样本比例除以好样本比例后取对数来编码，能够反映每个分箱内客户的坏客户分布与好客户分布之间的差异以及该箱内坏好比(odds)相对于总体的坏好比之间的差异性。\n\n+ 笔者基于toad.transform.WOETransformer重写了相关的方法，方法如下：\n\n\n```python\nclass WOETransformer(TransformerMixin, BaseEstimator):\n \n def __init__(self, target=\"target\", exclude=None):\n \"\"\"\n WOE转换器\n\n Args:\n target: 数据集中标签名称，默认 target\n exclude: 不需要转换 woe 的列\n \"\"\"\n```\n\n+ 使用方法\n\n```python\n# 常规使用方式\ntransformer = WOETransformer(target=target)\ntrain = transformer.fit_transform(train)\n\n# pipeline\nfeature_pipeline = Pipeline([\n ......\n (\"transformer\", WOETransformer(target=target)),\n ......\n])\nfeature_pipeline.fit(train)\n```\n\n### 特征精筛\n\n通常对特征`WOE`编码转换后，特征之间的相关性会上升、唯一值占比会增加、变量`IV`值会下降（同一特征分箱数越多`IV`值越大），为了避免入模特征`相关性过高`、`唯一值占比过高`、`IV过低`等问题的出现，需要在转换编码后进行特征精筛，筛选的限制条件相较分箱前的粗筛更严，以保证模型稳定性和泛化能力。\n\n相关方法参照特征粗筛，在此不过多赘述。\n\n\n### 逐步回归特征筛选\n\n特征在进行完前面的步骤后，在正式建模前，通常还会使用逐步回归对特征做进一步的筛选。通过假设检验的方法来得到最优的入模特征组合，能够有效的解决特征之间的多重共线性。\n\n+ 笔者基于toad.selection.stepwise重新实现了StepwiseSelection，参数与toad包中的所有参数一致，方法如下：\n\n\n```python\nclass StepwiseSelection(TransformerMixin, BaseEstimator):\n \n def __init__(self, target=\"target\", estimator=\"ols\", direction=\"both\", criterion=\"aic\", max_iter=None, return_drop=True, exclude=None, intercept=True, p_value_enter=0.2, p_remove=0.01, p_enter=0.01, target_rm=False):\n \"\"\"\n 逐步回归筛选方法\n\n Args:\n target: 数据集中标签名称，默认 target\n estimator: 预估器，默认 ols，可选 \"ols\", \"lr\", \"lasso\", \"ridge\"，通常默认即可\n direction: 逐步回归方向，默认both，可选 \"forward\", \"backward\", \"both\"，通常默认即可\n criterion: 评价指标，默认 aic，可选 \"aic\", \"bic\"，通常默认即可\n max_iter: 最大迭代次数，sklearn中使用的参数，默认为 None\n return_drop: 是否返回特征剔除信息，默认 True\n exclude: 强制保留的某些特征\n intercept: 是否包含截距，默认为 True\n p_value_enter: 特征进入的 p 值，用于前向筛选时决定特征是否进入模型\n p_remove: 特征剔除的 p 值，用于后向剔除时决定特征是否要剔除\n p_enter: 特征 p 值，用于判断双向逐步回归是否剔除或者准入特征\n target_rm: 是否剔除数据集中的标签，默认为 False，即剔除数据集中的标签\n \"\"\"\n```\n\n+ 使用方法\n\n```python\n# 常规使用方法\nstepwise = StepwiseSelection(target=target)\ntrain = stepwise.fit_transform(train)\n\n# pipeline\nfeature_pipeline = Pipeline([\n ......\n (\"stepwise\", StepwiseSelection(target=target, target_rm=False)),\n ......\n ])\nfeature_pipeline.fit(train)\n```\n\n## 逻辑回归建模\n\n逻辑回归基本上都是使用的`sklearn.linear_model`模块里的`LogisticRegression`(偏模型)或者`statsmodels.api`提供的`Logit`(偏统计)，两者各有优点。`sklearn`库提供了可以自行调整超参数的逻辑回归模型，`statsmodels`库提供了很完整的模型摘要输出，两者类似鱼与熊掌一样，不可兼得。\n\n笔者参考社区搜寻了很多评分卡建模相关的仓库，最终在`skorecard.liner_model`中`LogisticRegression`的实现上进行优化，增加了一些相关的统计信息输出，实现了类似`statsmodels`的模型输出的效果，同时保留了`sklearn`版`LogisticRegression`可以手工设置超参数或者基于超参数搜索框架搜寻最有超参数组合的特性。同时，也对`statsmodels`库中的`Logit`进行了一定的封装，以满足笔者评分卡建模的`pipeline`组件要求以及更详细的模型摘要信息输出和保存。\n\n+ 方法简介\n\n```python\nclass ITLubberLogisticRegression(LogisticRegression):\n \"\"\"\n Extended Logistic Regression.\n Extends [sklearn.linear_model.LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html).\n This class provides the following extra statistics, calculated on `.fit()` and accessible via `.summary()`:\n - `cov_matrix_`: covariance matrix for the estimated parameters.\n - `std_err_intercept_`: estimated uncertainty for the intercept\n - `std_err_coef_`: estimated uncertainty for the coefficients\n - `z_intercept_`: estimated z-statistic for the intercept\n - `z_coef_`: estimated z-statistic for the coefficients\n - `p_value_intercept_`: estimated p-value for the intercept\n - `p_value_coef_`: estimated p-value for the coefficients\n \n Example:\n ```python\n feature_pipeline = Pipeline([\n (\"preprocessing_select\", FeatureSelection(target=target, engine=\"scorecardpy\")),\n (\"combiner\", Combiner(target=target, min_samples=0.2)),\n (\"transform\", WOETransformer(target=target)),\n (\"processing_select\", FeatureSelection(target=target, engine=\"scorecardpy\")),\n (\"stepwise\", StepwiseSelection(target=target)),\n # (\"logistic\", LogisticClassifier(target=target)),\n (\"logistic\", ITLubberLogisticRegression(target=target)),\n ])\n \n feature_pipeline.fit(train)\n summary = feature_pipeline.named_steps['logistic'].summary()\n ```\n \n An example output of `.summary()`:\n \n | | Coef. | Std.Err | z | P>|z| | [ 0.025 | 0.975 ] | VIF |\n |:------------------|----------:|----------:|---------:|------------:|-----------:|----------:|--------:|\n | const | -0.844037 | 0.0965117 | -8.74544 | 2.22148e-18 | -1.0332 | -0.654874 | 1.05318 |\n | duration.in.month | 0.847445 | 0.248873 | 3.40513 | 0.000661323 | 0.359654 | 1.33524 | 1.14522 |\n \"\"\"\n\n def __init__(self, target=\"target\", penalty=\"l2\", calculate_stats=True, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver=\"lbfgs\", max_iter=100, multi_class=\"auto\", verbose=0, warm_start=False, n_jobs=None, l1_ratio=None,):\n \"\"\"\n Extends [sklearn.linear_model.LogisticRegression.fit()](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html).\n \n Args:\n target (str): your dataset's target name\n calculate_stats (bool): If true, calculate statistics like standard error during fit, accessible with .summary()\n \"\"\"\n```\n\n+ 使用方法\n\n```python\n# 常规使用方法\nlogistic = ITLubberLogisticRegression(target=target)\nlogistic.fit(woe_train)\n\nlogistic.plot_weights(save=\"outputs/logistic_train.png\")\nsummary = logistic.summary().reset_index().rename(columns={\"index\": \"Features\"})\ntrain_corr = logistic.corr(woe_train, save=\"outputs/train_corr.png\")\ntrain_report = logistic.report(woe_train)\n\n# pipeline\nfeature_pipeline = Pipeline([\n ......\n (\"logistic\", ITLubberLogisticRegression(target=target)),\n])\n\nfeature_pipeline.fit(train)\ny_pred_train = feature_pipeline.predict(train.drop(columns=target))\n```\n\n\n### 评分卡转换\n\n笔者重写了`toad.ScoreCard`以适配`pipeline`模式的端到端的评分卡建模，同时支持传入训练好的逻辑回归模型、输出评分排序性、评分分布、评分稳定性等。\n\n+ 相关代码说明\n\n```python\nclass ScoreCard(toad.ScoreCard, TransformerMixin):\n \n def __init__(self, target=\"target\", pdo=60, rate=2, base_odds=35, base_score=750, combiner={}, transer=None, pretrain_lr=None, pipeline=None, **kwargs):\n \"\"\"\n 评分卡模型转换\n\n Args:\n target: 数据集中标签名称，默认 target\n pdo: odds 每增加 rate 倍时减少 pdo 分，默认 60\n rate: 倍率\n base_odds: 基础 odds，通常根据业务经验设置的基础比率（违约概率/正常概率），估算方法：（1-样本坏客户占比）/坏客户占比，默认 35，即 35:1 => 0.972 => 坏样本率 2.8%\n base_score: 基础 odds 对应的分数，默认 750\n combiner: 分箱转换器，传入 pipeline 时可以为None\n transer: woe转换器，传入 pipeline 时可以为None\n pretrain_lr: 预训练好的逻辑回归模型，可以不传\n pipeline: 训练好的 pipeline，必须包含 Combiner 和 WOETransformer\n **kwargs: 其他相关参数，具体参考 toad.ScoreCard\n \"\"\"\n```\n\n+ 使用方法\n\n```python\n# 使用方法\nfeature_pipeline = Pipeline([\n (\"preprocessing_select\", FeatureSelection(target=target, engine=\"scorecardpy\")),\n (\"combiner\", Combiner(target=target, min_samples=0.2)),\n (\"transformer\", WOETransformer(target=target)),\n (\"processing_select\", FeatureSelection(target=target, engine=\"scorecardpy\")),\n (\"stepwise\", StepwiseSelection(target=target, target_rm=False)),\n ])\nwoe_train = feature_pipeline.fit_transform(train)\n\ncombiner = feature_pipeline.named_steps['combiner'].combiner\ntransformer = feature_pipeline.named_steps['transformer'].transformer\n\nscore_card = ScoreCard(target=target, combiner=combiner, transer=transformer, )\nscore_card.fit(woe_train)\n\ndata[\"score\"] = score_card.transform(data)\n\n# 评分效果\nclip = 50\nclip_start = max(math.ceil(train[\"score\"].min() / clip) * clip, math.ceil(train[\"score\"].quantile(0.01) / clip) * clip)\nclip_end = min(math.ceil(train[\"score\"].max() / clip) * clip, math.ceil(train[\"score\"].quantile(0.99) / clip) * clip)\nscore_clip = [i for i in range(clip_start, clip_end, clip)]\n\ntrain_score_rank = feature_bin_stats(train, \"score\", target=target, rules=score_clip, verbose=0, method=\"step\", ks=True)\nks_plot(train[\"score\"], train[target], title=\"Train Dataset\", save=\"model_report/train_ksplot.png\")\nscore_hist(train[\"score\"], train[target], save=\"model_report/train_scorehist.png\", bins=30, figsize=(13, 10))\n```\n\n\n### 超参数优化\n\n笔者根据`sklearn.pipeline`的构造规则，重写了评分卡建模相关模块，并在此基础上加入了部分笔者认为可能会有用的方法。通过对相关模块重写后，可以支持评分卡端到端`pipeline`式的超参数搜索方案。\n\n+ 使用方法\n\n```python\nfeature_pipeline = Pipeline([\n (\"preprocessing_select\", FeatureSelection(target=target, engine=\"scorecardpy\")),\n (\"combiner\", Combiner(target=target, min_samples=0.2)),\n (\"transformer\", WOETransformer(target=target)),\n (\"processing_select\", FeatureSelection(target=target, engine=\"scorecardpy\")),\n (\"stepwise\", StepwiseSelection(target=target, target_rm=False)),\n # (\"logistic\", StatsLogisticRegression(target=target)),\n (\"logistic\", ITLubberLogisticRegression(target=target)),\n])\n\n# pipeline 超参数搜索参数：{pipeline_step_name}__{param}\nparams_grid = {\n \"logistic__C\": [i / 1. for i in range(1, 10, 2)],\n \"logistic__penalty\": [\"l2\"],\n \"logistic__class_weight\": [None, \"balanced\"], # + [{1: i / 10.0, 0: 1 - i / 10.0} for i in range(1, 10)],\n \"logistic__max_iter\": [100],\n \"logistic__solver\": [\"sag\"] # [\"liblinear\", \"sag\", \"lbfgs\", \"newton-cg\"],\n \"logistic__intercept\": [True, False],\n}\n\nclf = GridSearchCV(feature_pipeline, params_grid, cv=5, scoring='roc_auc', verbose=-1, n_jobs=2, return_train_score=True)\nclf.fit(train, train[target])\n\ny_pred_train = clf.best_estimator_.predict(train)\nprint(clf.best_params_)\n```\n\n## 模型报告\n\n### 报告说明\n\n输出评分卡模型报告的意义在于能够对其他人呈现评分卡模型的好坏以及相关的模型信息。\n\n过去在评分卡建模过程中必须将各种表格和图片汇总到某个文件夹内或者几个文件中，格式每次都需要进行调整和优化，很耽误时间，如果中途有改动的话有需要重新再造一遍轮子。笔者基于日常评分卡建模工作中的需求，通过`openpyxl`实现了向`excel`文件中写入制定样式的内容（文本、图片、数据表、条件格式、设定宽度、字体对齐等），并在此基础上将评分卡建模过程中需要输出的内容以设定的格式全部保存至`excel`文件中，以减少后续建模过程中的重复性工作。\n\n+ 相关代码说明\n\n```python\nclass ExcelWriter:\n\n def __init__(self, style_excel='报告输出模版.xlsx', style_sheet_name=\"初始化\", fontsize=10, font='楷体', theme_color='2639E9'):\n \"\"\"\n excel 文件内容写入公共方法\n \n :param style_excel: 样式模版文件，默认当前路径下的 报告输出模版.xlsx ，如果项目路径调整需要进行相应的调整\n :param style_sheet_name: 模版文件内初始样式sheet名称，默认即可\n :param fontsize: 插入excel文件中内容的字体大小，默认 10\n :param font: 插入excel文件中内容的字体，默认 楷体\n :param theme_color: 主题色，默认 2639E9，注意不包含 #\n \"\"\"\n\n def add_conditional_formatting(self, worksheet, start_space, end_space):\n \"\"\"\n 设置条件格式\n\n :param worksheet: 当前选择设置条件格式的sheet\n :param start_space: 开始单元格位置\n :param end_space: 结束单元格位置\n \"\"\"\n\n @staticmethod\n def set_column_width(worksheet, column, width):\n \"\"\"\n 调整excel列宽\n\n :param worksheet: 当前选择调整列宽的sheet\n :param column: 列，可以直接输入 index 或者 字母\n :param width: 设置列的宽度\n \"\"\"\n\n @staticmethod\n def set_number_format(worksheet, space, _format):\n \"\"\"\n 设置数值显示格式\n\n :param worksheet: 当前选择调整数值显示格式的sheet\n :param space: 单元格范围\n :param _format: 显示格式，参考 openpyxl\n \"\"\"\n\n def get_sheet_by_name(self, name):\n \"\"\"\n 获取sheet名称为name的工作簿，如果不存在，则从初始模版文件中拷贝一个名称为name的sheet\n \n :param name: 需要获取的工作簿名称\n \"\"\"\n\n def insert_value2sheet(self, worksheet, insert_space, value=\"\", style=\"content\", auto_width=False):\n \"\"\"\n 向sheet中的某个单元格插入某种样式的内容\n\n :param worksheet: 需要插入内容的sheet\n :param insert_space: 内容插入的单元格位置，可以是 \"B2\" 或者 (2, 2) 任意一种形式\n :param value: 需要插入的内容\n :param style: 渲染的样式，参考 init_style 中初始设置的样式\n :param auto_width: 是否开启自动调整列宽\n \"\"\"\n\n def insert_pic2sheet(self, worksheet, fig, insert_space, figsize=(600, 250)):\n \"\"\"\n 向excel中插入图片内容\n \n :param worksheet: 需要插入内容的sheet\n :param fig: 需要插入的图片路径\n :param insert_space: 插入图片的起始单元格\n :param figsize: 图片大小设置\n \"\"\"\n\n def insert_df2sheet(self, worksheet, data, insert_space, merge_column=None, header=True, index=False, auto_width=False):\n \"\"\"\n 向excel文件中插入制定样式的dataframe数据\n\n :param worksheet: 需要插入内容的sheet\n :param data: 需要插入的dataframe\n :param insert_space: 插入内容的起始单元格位置\n :param merge_column: 需要分组显示的列，index或者列明\n :param header: 是否存储dataframe的header，暂不支持多级表头\n :param index: 是否存储dataframe的index\n :param auto_width: 是否自动调整列宽\n :return 返回插入元素最后一列之后、最后一行之后的位置\n \"\"\"\n\n def save(self, filename):\n \"\"\"\n 保存excel文件\n \n :param filename: 需要保存 excel 文件的路径\n \"\"\"\n```\n\n+ 使用方法\n\n```python\nwriter = ExcelWriter(style_excel=\"./utils/报告输出模版.xlsx\", theme_color=\"2639E9\")\n\n# 评分卡刻度\nscorecard_kedu = pd.DataFrame(\n [\n [\"base_odds\", card.base_odds, \"根据业务经验设置的基础比率（违约概率/正常概率），估算方法：（1-样本坏客户占比）/坏客户占比\"],\n [\"base_score\", card.base_score, \"基础ODDS对应的分数\"],\n [\"rate\", card.rate, \"设置分数的倍率\"],\n [\"pdo\", card.pdo, \"表示分数增长PDO时，ODDS值增长到RATE倍\"],\n [\"B\", card.offset, \"补偿值，计算方式：pdo / ln(rate)\"],\n [\"A\", card.factor, \"刻度，计算方式：base_score - B * ln(base_odds)\"],\n ],\n columns=[\"刻度项\", \"刻度值\", \"备注\"],\n)\n\nworksheet = writer.get_sheet_by_name(\"评分卡结果\")\nstart_row, start_col = 2, 2\nend_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\"评分卡刻度\", style=\"header\")\nend_row, end_col = writer.insert_df2sheet(worksheet, scorecard_kedu, (end_row + 1, start_col))\n\nend_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"训练数据集评分模型效果\", style=\"header\")\nks_row = end_row\nend_row, end_col = writer.insert_pic2sheet(worksheet, \"model_report/train_ksplot.png\", (ks_row, start_col))\nend_row, end_col = writer.insert_pic2sheet(worksheet, \"model_report/train_scorehist.png\", (ks_row, end_col))\nend_row, end_col = writer.insert_df2sheet(worksheet, train_score_rank, (end_row + 1, start_col))\n\nfor c in [\"坏样本率\", \"LIFT值\", \"分档KS值\"]:\n conditional_column = get_column_letter(start_col + train_score_rank.columns.get_loc(c))\n writer.add_conditional_formatting(worksheet, f'{conditional_column}{end_row - len(train_score_rank)}', f'{conditional_column}{end_row}')\n```\n\n### 汇总信息\n\n汇总信息包含评分卡模型相关的背景说明、取样说明、数据集划分方式以及不同时点数据分布情况。\n\n### LR拟合结果\n\n逻辑回归拟合结果主要包含了`LR`模型的拟合情况、稳定性、模型`AUC`、`KS`等指标。\n\n### 入模特征信息\n\n入模特征信息包含了入模特征的数据字典、分布情况、相关性、分箱信息以及分箱图等信息，主要为了展示评分卡入模变量相关的信息。\n\n### 评分卡结果\n\n评分卡模型结果主要包含评分卡参数信息、变量分箱及对应分数、评分`AUC`和`KS`指标、评分分布情况、评分排序性、各个评分区间相关指标信息。\n\n### 补充说明\n\n后续准备有闲暇时加上`shap`、`bad case`等相关的内容，并将支持`lightgbm`、`catboost`、`xgboost`等模型的报告输出。\n\n\n## 评分卡PMML转换\n\n### 背景说明\n\n关于评分卡上线部署方案，目前现有的大致有四种\n\n+ 提需求给公司开发部门上线部署\n+ 将评分卡模型保存为`pickle`文件提供开发部署\n+ 直接使用`python`提供`api服务`部署或者定时更新\n+ 评分卡转`PMML`文件部署\n\n前几种相对简单，提供`API服务`可能有一定技术门槛，但基本上网上有现成的方案，但评分卡转换`PMML`文件部署有一定难度，网上能够参考的实现方案比较难找，本章就笔者提供的评分卡转`PMML`相关的实现进行说明。\n\n+ 相关代码说明\n\n```python\n\nclass ScoreCard(toad.ScoreCard, TransformerMixin):\n \n ......\n\n def scorecard2pmml(self, pmml: str = 'scorecard.pmml', debug: bool = False):\n \"\"\"export a scorecard to pmml\n\n Args:\n pmml (str): io to write pmml file.\n debug (bool): If true, print information about the conversion process.\n \"\"\"\n```\n\n+ 使用说明\n\n```python\n# 保存 PMML 评分卡模型\ncard.scorecard2pmml(pmml='scorecard.pmml', debug=True)\n\n# 模型验证\nfrom pypmml import Model\n\nmodel = Model.fromFile(\"scorecard.pmml\")\ndata[\"score\"] = model.predict(data[model.inputNames]).values\n```\n\n\n## 参考资料\n\n> https://github.com/itlubber/LogisticRegressionPipeline\n>\n> https://github.com/itlubber/itlubber-excel-writer\n>\n> https://github.com/itlubber/openpyxl-excel-style-template\n>\n> https://github.com/itlubber/scorecard2pmml\n>\n> https://pypi.org/project/pdtr/\n>\n> https://github.com/amphibian-dev/toad\n>\n> https://github.com/ShichenXie/scorecardpy\n>\n> https://github.com/ing-bank/skorecard\n>\n> http://gnpalencia.org/optbinning\n>\n> https://github.com/yanghaitian1/risk_control\n" }, { "path": "docs/Makefile", "content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line, and also\n# from the environment for the first two.\nSPHINXOPTS ?=\nSPHINXBUILD ?= sphinx-build\nSOURCEDIR = source\nBUILDDIR = build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option. $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n" }, { "path": "docs/make.bat", "content": "@ECHO OFF\r\n\r\npushd %~dp0\r\n\r\nREM Command file for Sphinx documentation\r\n\r\nif \"%SPHINXBUILD%\" == \"\" (\r\n\tset SPHINXBUILD=sphinx-build\r\n)\r\nset SOURCEDIR=source\r\nset BUILDDIR=build\r\n\r\nif \"%1\" == \"\" goto help\r\n\r\n%SPHINXBUILD% >NUL 2>NUL\r\nif errorlevel 9009 (\r\n\techo.\r\n\techo.The 'sphinx-build' command was not found. Make sure you have Sphinx\r\n\techo.installed, then set the SPHINXBUILD environment variable to point\r\n\techo.to the full path of the 'sphinx-build' executable. Alternatively you\r\n\techo.may add the Sphinx directory to PATH.\r\n\techo.\r\n\techo.If you don't have Sphinx installed, grab it from\r\n\techo.http://sphinx-doc.org/\r\n\texit /b 1\r\n)\r\n\r\n%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\r\ngoto end\r\n\r\n:help\r\n%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\r\n\r\n:end\r\npopd\r\n" }, { "path": "docs/requirements.txt", "content": "sphinx>=6,<8\nmyst-parser>=2.0.0,<2.1\npygments>=2.15.1,<2.16\nsphinx-intl>=2.0,<2.2\nsphinx-design>=0.4,<0.5\nsphinx_copybutton>=0.5.0,<1.0.0\nsphinx-nefertiti>=0.1.11\nrecommonmark\nsphinx-markdown-tables\njieba3k\n" }, { "path": "docs/source/CNAME", "content": "scorecardpipeline.itlubber.art\n" }, { "path": "docs/source/conf.py", "content": "# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport sys\nimport sphinx_nefertiti\n\n\non_rtd = os.environ.get('READTHEDOCS', None) == 'True'\nif on_rtd:\n html_theme_path = [sphinx_nefertiti.get_html_theme_path()]\nelse:\n sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), \"../../\"))\n\n\n# -- Project information -----------------------------------------------------\n\nproject = 'scorecardpipeline'\ncopyright = '2023, itlubber'\nauthor = 'itlubber'\n\n# The full version, including alpha/beta/rc tags\nimport sys\nsys.path.append(\"../../scorecardpipeline\")\n\nimport scorecardpipeline\n\nrelease = scorecardpipeline.__version__\n\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n 'sphinx.ext.autodoc',\n 'sphinx.ext.viewcode',\n 'sphinx.ext.githubpages',\n 'myst_parser',\n 'sphinx_design',\n 'sphinx_copybutton',\n 'sphinx_markdown_tables',\n]\n\nmyst_enable_extensions = [\n 'amsmath',\n 'attrs_block',\n 'colon_fence',\n 'deflist',\n 'dollarmath',\n 'fieldlist',\n 'tasklist',\n]\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = 'zh_CN'\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = []\n\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\n# html_theme = \"sphinx_rtd_theme\"\n\nhtml_theme = 'sphinx_nefertiti'\npygments_style = \"sas\"\npygments_dark_style = \"dracula\"\n\nhtml_theme_options = {\n \"style\": \"indigo\",\n \"footer_links\": \",\".join([\n \"itlubber|https://itlubber.art\",\n \"github|https://github.com/itlubber/scorecardpipeline\",\n \"examples|https://github.com/itlubber/scorecardpipeline/blob/main/examples/scorecard_samples.ipynb\",\n ]),\n \"show_powered_by\": False,\n \"project_name_font\": \"Nunito\",\n \"doc_headers_font\": \"Nunito\",\n \"documentation_font\": \"Nunito\",\n \"versions\": [\n (f\"v{release}\", \"https://itlubber.github.io/scorecardpipeline-docs\"),\n ],\n \"repository_url\": \"https://github.com/itlubber/scorecardpipeline\",\n \"repository_name\": \"itlubber/scorecardpipeline\",\n \"current_version\": f\"v{release}\",\n \"show_colorset_choices\": False,\n}\n\nhtmlhelp_basename = \"scorecardpipeline\"\nhtml_last_updated_fmt = \"%Y-%m-%d %H:%M:%S\"\nhtml_permalinks = False\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\nhtml_extra_path = [\"CNAME\"]\nsuppress_warnings = []\nhtml_show_search_summary = True\n\n# 显示类的 __init__ 相关文档\nautodoc_default_options = {\n \"members\": True,\n \"member-order\": \"bysource\",\n \"special-members\": \"__init__\",\n \"undoc-members\": True,\n \"exclude-members\": \"__weakref__,set_fit_request,set_transform_request,set_decision_function_request,set_score_request,set_predict_request,Pipeline,FeatureUnion,RuleState,RuleStateError,RuleUnAppliedError,make_pipeline,make_union,RFE,RFECV,SelectKBest,SelectFromModel,GenericUnivariateSelect\",\n}\n\n# # 支持文档格式\n# source_suffix = {\n# '.rst': 'restructuredtext',\n# '.md': 'markdown',\n# }\n" }, { "path": "docs/source/index.rst", "content": ".. scorecardpipeline documentation master file, created by\n sphinx-quickstart on Fri Nov 10 13:40:57 2023.\n You can adapt this file completely to your liking, but it should at least\n contain the root `toctree` directive.\n\nscorecardpipeline\n=============================================\n\n`scorecardpipeline` 封装了 `toad`、`scorecardpy`、`optbinning` 等评分卡建模相关组件，`API` 风格与 `sklearn` 高度一致，支持 `pipeline` 式端到端评分卡建模、模型报告输出、导出 `PMML` 文件、超参数搜索等功能\n\n\n快速开始\n---------------------------------------------\n\n.. raw:: html\n :file: install.html\n\n\n.. toctree::\n :maxdepth: 2\n\n quickstart\n\n\n接口文档\n---------------------------------------------\n\n.. toctree::\n :maxdepth: 3\n\n scorecardpipeline\n\n\n快速查找\n---------------------------------------------\n\n* :ref:`genindex`\n* :ref:`modindex`\n* :ref:`search`\n" }, { "path": "docs/source/install.html", "content": "\n\n\n \n \n scorecardpipeline环境安装\n \n\n\n \n \n
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    "path": "docs/source/quickstart.md",
    "content": "## 简介\n\n`scorecardpipeline` 封装了 `toad`、`scorecardpy`、`optbinning` 等评分卡建模相关组件，`API` 风格与 `sklearn` 高度一致，支持 `pipeline` 式端到端评分卡建模、模型报告输出、导出 `PMML` 文件、超参数搜索等功能。\n\n> 在线文档: [`https://scorecardpipeline.itlubber.art/`](https://scorecardpipeline.itlubber.art//)\n> \n> `PIPY` 包: [`https://pypi.org/project/scorecardpipeline`](https://pypi.org/project/scorecardpipeline/)\n>\n> 仓库地址: [`https://github.com/itlubber/scorecardpipeline`](https://github.com/itlubber/scorecardpipeline)\n\n\n## 版本说明\n\n| **序号** | **版本号** |  **发布日期**  |                                       **版本说明**                                        |\n|:------:|:-------:|:----------:|:-------------------------------------------------------------------------------------:|\n|   01   |  0.1.0  | 2023.05.04 |                                          初始化                                          |\n|   ..   | ......  |   ......   |                                        ......                                         |\n|   26   | 0.1.26  | 2023.11.13 |         稳定版本，新增方法[`说明文档`](https://itlubber.github.io/scorecardpipeline-docs/)         |\n|   27   | 0.1.27  | 2023.12.09 |                               修复分类变量空值导致转评分卡异常报错及其他相关问题                               |\n|   28   | 0.1.28  | 2023.12.15 |                      修复sklearn版本问题、新增自动EDA方法 `auto_eda_sweetviz`                      |\n|   29   | 0.1.29  | 2023.12.29 | 新增三方数据测试报告输出方法 `auto_data_testing_report` 及示例文件 `examples/model_report/三方数据测试报告.xlsx` |\n|   30   | 0.1.30  | 2024.01.29 |                                                                                       |\n|   31   | 0.1.31  | 2024.02.28 |                新增 `rule.Rule` 规则集相关内容、修复 `processing.Combiner.load` 方法                |\n|   32   | 0.1.32  | 2024.02.29 |         合并 `pdtr` 内容，新增 `rule_extraction.DecisionTreeRuleExtractor` 决策树策略挖掘方法         |\n|   33   | 0.1.33  | 2024.03.07 |                `excel_writer.ExcelWriter` 支持多层级索引存储、多层级表头存储、多列合并单元格等功能                |\n|   34   | 0.1.34  |    预发布     |                                   修复异常、部分新功能上线 QaQ                                    |\n\n\n## 环境安装\n\n1. 安装 `python` 环境\n\n推荐 `python 3.8.13` ，参照网上教程自行安装即可，也可直接使用系统提供的 `python3` , 版本不易过高, 最低支持 `python 3.6`，最高支持 `python 3.10`\n\n2. 安装 `scorecardpipeline` 包\n\n+ 在线环境安装\n\n直接通过 `pip` 安装即可，安装后需要确认下版本是否安装正确，推荐 `-i https://test.pypi.org/simple` 指定源安装\n\n```shell\npip install scorecardpipeline\n```\n\n+ 离线环境安装\n\n离线环境安装需先找一个 `python` 版本和系统 (`windows`、`linux`、`mac`) 与生产一致的有网机器或者容器，在有网的环境中下载相关依赖项后拷贝到离线环境安装\n\n```shell\n# 在线环境依赖下载\npip download -d site-packages/ scorecardpipeline\n# 离线环境\npip install --no-index --find-links=site-packages scorecardpipeline\n```\n\n3. 安装 `jdk 1.8+` [可选]\n\n如果有将训练完成的评分卡模型直接导出 `PMML` 模型文件部署的需求，需要单独安装 `java` 环境\n\n> `mac` & `windows` 参考: [`https://developer.aliyun.com/article/1082599`](https://developer.aliyun.com/article/1082599)\n>\n> `linux` 参考: [`https://blog.csdn.net/muzi_gao/article/details/132169159`](https://blog.csdn.net/muzi_gao/article/details/132169159)\n\n\n## 使用示例\n\n本节将就 `scorecardpipeline` 的相关功能做简要介绍，并提供一个[`简单的示例`](https://github.com/itlubber/scorecardpipeline/blob/main/examples/scorecard_samples.ipynb)，旨在让读者对如何使用 `scorecardpipeline` 进行评分卡模型构建有一个简单的印象。\n\n\n### 导入依赖\n\n```python\nimport os\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom scorecardpipeline import *\n\n# 固定随机种子以保证结果可复现\ninit_setting(seed=10)\n```\n\n### 数据准备\n\n使用 `scorecardpipeline` 进行评分卡建模，需要您提前准备好一个包含目标变量的数据集，`scorecardpipeline` 提供了一个德国信贷数据集 [`germancredit`](https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data)，示例加载该数据集进行演示，读者可以直接替换为您本地的数据集，并修改目标变量名称为数据集中的目标变量列名\n\n[`germancredit`](https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data) 数据集中包含类别型变量、数值型变量、好坏标签，共1000条数据，由于数据集中不包含缺失值，为了模拟实际生产中的真实数据，笔者将在固定随机种子的情况下随机替换数据集中的部分内容为 `np.nan`\n\n\n **序号** | **类型** | **特征名**                                                  | **释义**          \n:------:|:------:|:--------------------------------------------------------:|:---------------:\n 0      | 类别型    | creditability                                            | 客户好坏标签          \n 1      | 数值型    | duration in month                                        | 月持续时间           \n 2      | 数值型    | credit amount                                            | 信用额度            \n 3      | 数值型    | installment rate in percentage of disposable income      | 分期付款率占可支配收入的百分比 \n 4      | 数值型    | present residence since                                  | 现居住地至今          \n 5      | 数值型    | age in years                                             | 年龄              \n 6      | 数值型    | number of existing credits at this bank                  | 这家银行的现有信贷数量     \n 7      | 数值型    | number of people being liable to provide maintenance for | 有责任为其提供维修服务的人数  \n 8      | 类别型    | status of existing checking account                      | 现有支票账户的状态       \n 9      | 类别型    | credit history                                           | 信用记录            \n 10     | 类别型    | purpose                                                  | 目的              \n 11     | 类别型    | savings account or bonds                                 | 储蓄账户/债券         \n 12     | 类别型    | present employment since                                 | 至今工作至今          \n 13     | 类别型    | personal status and sex                                  | 个人地位和性别         \n 14     | 类别型    | other debtors or guarantors                              | 其他债务人/担保人       \n 15     | 类别型    | property                                                 | 财产              \n 16     | 类别型    | other installment plans                                  | 其他分期付款计划        \n 17     | 类别型    | housing                                                  | 住房情况         \n 18     | 类别型    | job                                                      | 工作              \n 19     | 类别型    | telephone                                                | 电话              \n 20     | 类别型    | foreign worker                                           | 外籍工人            \n\n\n\n
\n\n```python\n# 加载数据集\ndata = germancredit()\n\n# 设置目标变量名称 & 映射目标变量值域为 {0, 1}\ntarget = \"creditability\"\ndata[target] = data[target].map({\"good\": 0, \"bad\": 1})\n\n# 随机替换 20% 的数据为 np.nan\nfor col in data.columns.drop(target):\n    for i in range(len(data)):\n        if np.random.rand() > 0.8:\n            data[col].loc[i] = np.nan\n```\n\n\n### 数据拆分\n\n通常建模过程中会将数据集拆分成训练数据集和测试数据集集，训练数据集用于模型参数的训练，测试数据集用于评估模型好坏，且在整个建模过程中实际上只能使用一次（任何针对测试数据集调模型的，都存在模型过拟合的情况，除非你的数据不包含任何噪声数据）。而为了弥补测试数据集只能用一次用于评估模型好坏的尴尬，大多都会再拆分一个验证数据集，用于模型训练过程中判别模型是否向好的方向进行优化。\n\n在金融建模场景中，上述三个数据集通常被称为训练数据集、测试数据集（即上述三个数据集中的验证集）、跨时间验证集（即上述三个数据集中的测试集）。同时，在金融场景中，跨时间验证集的拆分方式通常使用类似时间序列数据集的拆分方法，按照时间将数据集拆分为建模集（包含训练集和测试集）、跨时间验证集（`Out of Time，OOT`），以保证模型在不同时间段内的泛化能力。\n\n简单起见，示例只拆分训练集、测试集，OOT直接拷贝训练集来演示评分卡建模完整过程。\n\n```python\ntrain, test = train_test_split(data, test_size=0.3, shuffle=True, stratify=data[target])\n\noot = train.copy()\n```\n\n\n### 数据预处理\n\n在评分卡建模标准流程中，会先针对每个变量分箱后转WOE，再筛选特征训练逻辑回归模型，训练完后转评分卡，当数据集的特征个数比较多时，可以再分箱之前先进行特征初筛，以降低分箱时的计算耗时。评分卡建模完整流程可以参照如下流程:\n\n
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\n\n\n#### 特征筛选\n\n特征筛选主要从几个方面来评估特征是否有效：\n\n1. 单一值占比\n2. 缺失值占比\n3. 特征相关性\n4. 特征 `IV（information value）` 值\n5. 特征稳定性 `PSI（population stability index）`\n6. 特征重要性\n7. 方差膨胀因子 `VIF（variance inflation factor）`\n\n\n`scorecardpipeline` 提供了几种常见的特征筛选方法（`1`、`2`、`3`、`4`），可以通过实例化的方式进行配置，并在训练数据集中训练完成后应用到不同数据集中。\n\n```python\n# 初始化筛选器\nselect = FeatureSelection(target=target, engine=\"toad\", identical=0.95, empty=0.95, iv=0.02, corr=0.6)\n# 训练数据上训练\nselect.fit(train)\n# 应用到不同数据集\ntrain_select = select.transform(train)\ntest_select = select.transform(test)\n```\n\n在 `scorecardpipeline` 中，所有筛选器都包含 `dropped` 属性，用来存放特征被剔除的原因。\n\n```python\n# 输出特征筛选信息\nselect.dropped\n```\n\n|    | variable                                                 | rm_reason   |\n|:---:|:---------------------------------------------------------:|:------------:|\n|  0 | number_of_existing_credits_at_this_bank                  | iv          |\n|  1 | job                                                      | iv          |\n|  2 | number_of_people_being_liable_to_provide_maintenance_for | iv          |\n|  3 | telephone                                                | iv          |\n|  4 | duration_in_month                                        | corr        |\n\n\n#### 特征分箱\n\n\n特征分箱能够提升模型的鲁棒性，特征分箱时通常对每个箱内的样本个数或占比有要求，数据中的异常值和极端值会被分到某个箱中，在转化为 `WOE` 之后能够很大程度上避免异常值或极端值对模型训练时造成偏差。同时，特征分箱转 `WOE` 之后，所有特征的值域都被缩放到了某个固定尺度下，逻辑回归模型的系数在一定程度上能够代表特征的重要程度。\n\n+ 在 `scorecardpipeline` 中，集成了 `toad` 和 `optbinning` 两个库中提供的分箱方法，后续会陆续增加更多的分箱方法，相关内容参考: [`scorecardpipeline.Combiner`](/scorecardpipeline.html#scorecardpipeline.Combiner)\n\n\n```python\n# 初始化分箱器\ncombiner = Combiner(target=target, min_bin_size=0.2)\n# 训练\ncombiner.fit(train_select)\n# 对数据集进行分箱\ntrain_bins = combiner.transform(train_select)\ntest_bins = combiner.transform(test_select)\n```\n\n+ 为了方便调整分箱，[`scorecardpipeline.Combiner`]() 提供了输出分箱图和分箱统计信息的功能，方便快速手工调整分箱。\n\n```python\n# 查看 credit_amount 信用额度 的分箱信息，并显示分箱统计信息\ncombiner.bin_plot(train_select, \"credit_amount\", result=True, desc=\"信用额度\")\n```\n\n
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\n\n\n+ 当特征分箱不符合业务逻辑或者单调性不满足时，可以通过自定义规则的方式查看分箱效果。（此时不会对 `Combiner` 中的规则进行更新）\n\n```python\n# 通过 rule 字段传入自定义规则，实时查看分箱效果\ncombiner.bin_plot(train_select, \"credit_amount\", result=True, desc=\"信用额度\", rule=[4000.0, np.nan])\n```\n\n
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\n\n\n+ 当特征分箱调整到符合业务预期或者单调满足建模需求时，可以通过 `update` 方法更新规则 `Combiner` 中的规则。\n\n```python\n# 更新 credit_amount 的分箱规则\ncombiner.update({\"credit_amount\": [4000.0, np.nan]})\n# 打印分箱规则\ncombiner[\"credit_amount\"] # array([4000.,   nan])\n```\n\n\n#### `WOE` 转换\n\n特征分箱后，只是将特征离散化为几箱，每一箱的值被赋予了一个标签，例如0、1、2、3、...，尽管这些标签在一定程度上也能够表征客户的风险水平，能够作为训练数据训练出一个模型，但在评估客户风险状况时很难精准刻画客户的风险状况。\n\n通常使用 `WOE（Weight of Evidence）编码` 对分箱后的特征进行编码。`WOE编码` 将每个分箱内的坏样本占比除以好样本占比后取对数来编码，能够反映每个分箱内客户的坏客户分布与好客户分布之间的差异以及该箱内坏好比与总体的坏好比之间的差异性。\n\n\n特征 `WOE（Weight of Evidence）编码` 后，有如下好处：\n\n+ 能够将每个特征的值域放缩到同样的尺度下\n+ `woe` 中包含了样本好坏信息，比原始值和分箱序号更能反应客户违约概率大小\n+ `woe` 的计算公式 $woe = ln(\\frac{bad_i}{bad}/\\frac{good_i}{good} )$ 中引入了非线性，一定程度能够增强逻辑回归拟合非线性模型的能力\n\n\n```python\n# 初始化 WOE 转换器\ntransform = WOETransformer(target=target)\n# 训练\ntransform.fit(train_bins)\n# 转换分箱为WOE\ntrain_woe = transform.transform(train_bins)\ntest_woe = transform.transform(test_bins)\n```\n\n`WOE（Weight of Evidence）编码器` 提供了方法允许使用者通过如下方式查看每个分箱对应的 `woe` 值。\n\n
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\n\n在特征从原始值编码为 `woe` 的过程中，特征的值从连续值或者分类值映射到了有限的几个分箱对应的 `woe` 值，同时值域也统一到了一个基于样本好坏分布情况为基准的空间中，会造成特征之间的相关性增大、单一值占比上升、特征 `iv` 值较前筛时小幅度下降，所以推荐在 `WOE编码` 后，再进行一次特征精筛，以保证后续模型结果的稳定性和整体可解释性。\n\n\n#### 逐步回归特征筛选\n\n在正式训练 `逻辑回归（logistic regression，LR）模型` 前，通常会再使用逐步回归来剔除特征，能够一定程度上解决特征之间的多重共线性，以及在剔除尽可能多的特征的同时保证 `LR模型` 效果的有效性。\n\n```python\n# 初始化逐步回归特征筛选器\nstepwise = StepwiseSelection(target=target)\n# 训练\nstepwise.fit(train_woe)\n# 应用逐步回归特征筛选器\ntrain_woe_stepwise = stepwise.transform(train_woe)\ntest_woe_stepwise = stepwise.transform(test_woe)\n```\n\n与 `FeatureSelection` 类似，`scorecardpipeline` 中所有特征筛选器都包含了 `dropped` 属性，可以直接输出每个特征被剔除的原因。\n\n```python\n# 逐步回归特征筛选明细信息\nstepwise.dropped\n```\n\n|    | variable                    | rm_reason   |\n|:--:|:---------------------------:|:-----------:|\n|  0 | present_residence_since     | stepwise    |\n|  1 | savings_account_and_bonds   | stepwise    |\n|  2 | other_debtors_or_guarantors | stepwise    |\n|  3 | property                    | stepwise    |\n|  4 | other_installment_plans     | stepwise    |\n|  5 | foreign_worker              | stepwise    |\n\n\n### 训练 `LR` 模型\n\n对数据集预处理完成后，数据集中的特征的值域被映射到了一个与样本好坏息息相关的空间中，特征值的大小能够一定程度上反映出客户违约概率的高低，通过 `WOE编码` 方法对原始特征进行转换时引入的非线性能够在一定程度上弥补建模时使用简单模型拟合能力不足的问题，同时，数据集经过特征筛选后，冗余信息被尽可能多的剔除，多重共线性也在一定程度上得到解决，训练模型时能够在保证模型效果的前提下提升模型的鲁棒能力。\n\n在 `scorecardpipeline` 中，提供了基于 `sklearn` 重新实现的 `ITLubberLogisticRegression` 逻辑回归模型，能够满足丰富的超参数设置和丰富的统计信息输出。\n\n当然，假如您想直接使用 `sklearn` 提供的 `LogisticRegression` 去完成评分卡模型构建和结果输出，也能够支持，但转换评分卡后，有部分扩展功能会受到限制，但不会影响评分卡模型转换、评分预测以及持久化存储的相关功能。\n\n```python\n# 逻辑回归模型构建\nlogistic = ITLubberLogisticRegression(target=target)\n# 训练\nlogistic.fit(train_woe_stepwise)\n# 预测数据集样本违约概率\ny_pred_train = logistic.predict_proba(train_woe_stepwise.drop(columns=target))[:, 1]\ny_pred_test = logistic.predict_proba(test_woe_stepwise.drop(columns=target))[:, 1]\n```\n\n逻辑回归模型训练完成后，能够通过 `summary` 或 `summary2` 方法输出模型训练时的各项统计信息\n\n```python\n# 数据字典或特征描述信息\nfeature_map = {\n    \"const\": \"截距项\",\n    \"status_of_existing_checking_account\": \"现有支票账户的状态\",\n    \"credit_history\": \"信用记录\",\n    \"purpose\": \"目的\",\n    \"credit_amount\": \"信用额度\",\n    \"present_employment_since\": \"现居住地至今\",\n    \"installment_rate_in_percentage_of_disposable_income\": \"分期付款率占可支配收入的百分比\",\n    \"personal_status_and_sex\": \"个人地位和性别\",\n    \"age_in_years\": \"年龄\",\n    \"housing\": \"住房情况\",\n}\n\n# summary 仅支持输出简单的统计信息，使用 summary2 可以输出有特征描述的统计信息表\nlogistic.summary2(feature_map=feature_map)\n```\n\n| **Features**                                        | **Describe**    | **Coef.** | **Std.Err** | **z** | **P>\\|z\\|** | **[ 0.025** | **0.975 ]** | **VIF** |\n|:---------------------------------------------------:|:---------------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|\n| const                                               | 截距项             | -0.8422  |  0.0940  | -8.9567  | 0.0000   | -1.0265  | -0.6579  | 1.0464   |\n| status_of_existing_checking_account                 | 现有支票账户的状态       | 0.8083   |  0.1549  | 5.2191   | 0.0000   | 0.5048   | 1.1119   | 1.0565   |\n| credit_history                                      | 信用记录            | 0.8427   |  0.1823  | 4.6224   | 0.0000   | 0.4854   | 1.2000   | 1.0754   |\n| purpose                                             | 目的              | 1.0387   |  0.2188  | 4.7462   | 0.0000   | 0.6097   | 1.4676   | 1.0186   |\n| credit_amount                                       | 信用额度            | 1.0188   |  0.2345  | 4.3456   | 0.0000   | 0.5593   | 1.4784   | 1.0220   |\n| present_employment_since                            | 现居住地至今          | 0.7043   |  0.3693  | 1.9074   | 0.0565   | -0.0194  | 1.4281   | 1.0620   |\n| installment_rate_in_percentage_of_disposable_income | 分期付款率占可支配收入的百分比 | 1.3074   |  0.4023  | 3.2496   | 0.0012   | 0.5189   | 2.0960   | 1.0192   |\n| personal_status_and_sex                             | 个人地位和性别         | 0.8170   |  0.4786  | 1.7071   | 0.0878   | -0.1211  | 1.7551   | 1.0058   |\n| age_in_years                                        | 年龄              | 0.7926   |  0.3023  | 2.6219   | 0.0087   | 0.2001   | 1.3852   | 1.0724   |\n| housing                                             | 住房情况            | 0.7277   |  0.4115  | 1.7684   | 0.0770   | -0.0788  | 1.5342   | 1.0165   |\n\n`ITLubberLogisticRegression` 逻辑回归模型还支持直接通过画图查看模型系数稳定性\n\n```python\n# 逻辑回归系数稳定情况\nlogistic.plot_weights(figsize=(10, 6))\n```\n\n
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\n\n同时，也提供了 `report` 方法快速查看模型在某个数据集（`woe`后的数据集）上的分类效果\n\n```python\n# 查看某个数据集的分类效果\nlogistic.report(train_woe_stepwise)\n```\n\n|    | desc         | precision          | recall             |   f1-score |   support |\n|:--:|:------------:|:------------------:|:------------------:|:----------:|:---------:|\n|  0 | 好客户       | 0.7785588752196837 | 0.9040816326530612 |   0.836638 |       490 |\n|  1 | 坏客户       | 0.6412213740458015 | 0.4                |   0.492669 |       210 |\n|  2 | macro avg    | 0.7098901246327426 | 0.6520408163265305 |   0.664653 |       700 |\n|  3 | weighted avg | 0.7373576248675191 | 0.7528571428571429 |   0.733447 |       700 |\n|  4 | accuracy     |                    |                    |   0.752857 |       700 |\n\n+ 在模型分类报告中，$macro\\ avg$ 和 $weighted\\ avg$ 的计算方法如下:\n\n$macro\\ avg = \\frac{metric_1 + metric_2}{2}$\n\n$weighted\\ avg = \\frac{metric_1 \\times \\frac{support_1}{total\\ count} + metric_2 \\times \\frac{support_2}{total\\ count}}{2} = \\frac{metric_1 \\times support_1 + metric_2 \\times support_2}{2 \\times total\\ count}$\n\n\n### 评分卡转换\n\n在金融领域，如果直接使用模型预测的概率作为最终的评分，可能会对业务人员造成一定程度上的理解难度，同时，[模型预测的概率其实很难代表客户真实的违约概率](https://scikit-learn.org/stable/modules/calibration.html#calibration)，不同模型预测的概率与真实违约概率之间存在不同程度的偏差。\n\n
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\n\n不同模型预测概率与真实概率之间的偏差\n\n由于模型预测概率与真实违约概率之间存在不同程度的偏差，评分卡不直接使用违约概率，而是引入了 `odds`（客户违约概率与正常概率的比值）来刻画客户的违约情况，其中 $odds = \\frac{p}{1-p}$ ，$p$ 为模型预测的违约概率。\n\n评分卡通常基于逻辑回归模型来制作，一方面是逻辑回归模型预测的违约概率与真实概率之间的偏差相比其他模型更接近，另一方面是逻辑回归模型能够很方便的转换为评分卡。\n\n逻辑回归模型的数学公式如下:\n\n$$\np = \\frac{1}{1+e^{-\\theta^Tx}}\n$$\n\n根据逻辑回归的数学公式很容易推导出入模特征与 `odds` 之间的关系:\n\n$$\n\\begin{aligned}\n\\frac{1}{p} &= 1+e^{-\\theta^Tx} \\\\\n\\frac{1}{p}-1 = \\frac{1-p}{p} &= e^{-\\theta^Tx} \\\\\nln(\\frac{p}{1-p}) = ln(odds) &= \\theta^Tx = \\theta_0x_0 + \\theta_1x_1 + \\theta_2x_2 + ...... + \\theta_nx_n\n\\end{aligned}\n$$\n\n评分卡模型是在原有模型预测的违约概率 $p$ 的基础上计算 $odds$，再根据 $ln(odds)$ 进行平移和伸缩变换得到最终评分卡分数的模型，本质上是一种将 $[0,\\ 1]$ 概率空间映射到实数空间 $R$ 的一种方法。\n\n假设评分卡模型数学形式如下：\n\n$$\nscore = A - B \\times ln(odds) = A - B \\times ln(\\frac{p}{1-p}) = A - B \\times (\\theta_0x_0 + \\theta_1x_1 + \\theta_2x_2 + ...... + \\theta_nx_n)\n$$\n\n假设客户违约概率为 $p_0$ ，$base\\_odds=p_0/(1-p_0)$ 时，对应的评分卡分数为 $base\\_score$ ，当 $odds$ 增加 $rate$ 倍时，评分卡分数降低 $pdo$ 分，可以得到一个二元一次方程:\n\n$$\n\\left\\{\n\\begin{aligned}\nbase\\_score &= A - B \\times ln(base\\_odds) \\\\\nbase\\_score - pdo &= A - B \\times ln(rate \\times base\\_odds)\n\\end{aligned}\n\\right.\n$$\n\n根据上述二元一次方程求解可以得到:\n\n$$\n\\begin{aligned}\nB &= pdo / ln(rate) \\\\\nA &= base\\_score + \\frac{pdo}{ln(rate)} \\times ln(base\\_odds)\n\\end{aligned}\n$$\n\n> **注:** 在 `toad` 中使用的是 $woe = ln(\\frac{good_i}{good}/\\frac{bad_i}{bad})$ ，在上述推导中符号是反的，故而在 `toad` 库中的 `offset = base_score - factor * np.log(base_odds)`，形式上的差异并不会导致最终 `A` 和 `B` 的值，但在计算 $base\\_odds$ 时需要注意使用对应的方式进行计算\n\n当计算得到 $A$ 和 $B$ 后，概率转评分的模型也就确定了，下面我们正式进入评分转换方法。\n\n```python\n# 逻辑回归模型转评分卡\ncard = ScoreCard(target=target, combiner=combiner, transer=transform, pretrain_lr=logistic, base_score=50, base_odds=20, pdo=10)\n# 传入 woe 数据计算评分卡参数\ncard.fit(train_woe_stepwise)\n\n# 预测评分\ntrain[\"score\"] = card.predict(train)\ntest[\"score\"] = card.predict(test)\n```\n\n在 `scorecardpipeline` 中提供了输出评分卡刻度的方法 `scorecard_scale` :\n\n```python\n# 输出评分卡刻度信息\ncard.scorecard_scale()\n```\n\n| 序号 | 刻度项     |   刻度值 | 备注                                                                                       |\n|:--:|:----------|:--------:|:-------------------------------------------------------------------------------------------|\n|  0 | base_odds  | 20       | 根据业务经验设置的基础比率（违约概率/正常概率），估算方法：（1-样本坏客户占比）/坏客户占比 |\n|  1 | base_score | 50       | 基础ODDS对应的分数                                                                         |\n|  2 | rate       |  2       | 设置分数的倍率                                                                             |\n|  3 | pdo        | 10       | 表示分数增长PDO时，ODDS值增长到RATE倍                                                      |\n|  4 | B          | 14.427   | 补偿值，计算方式：pdo / ln(rate)                                                           |\n|  5 | A          |  6.78072 | 刻度，计算方式：base_score - B * ln(base_odds)                                             |\n\n在 `scorecardpipeline` 中也提供了输出评分卡刻度的方法 `scorecard_points` :\n\n```python\n# 输出评分卡刻度信息\ncard.scorecard_points()\n```\n\n| 序号 | 变量名称                                            | 变量分箱                                                                                                                          |   对应分数 |\n|---:|:----------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------|-----------:|\n|  0 | status_of_existing_checking_account                 | no checking account                                                                                                               |    21.3836 |\n|  1 | status_of_existing_checking_account                 | ... >= 200 DM / salary assignments for at least 1 year,nan                                                                        |     6.0824 |\n|  2 | status_of_existing_checking_account                 | 0 <= ... < 200 DM                                                                                                                 |    -0.3605 |\n|  3 | status_of_existing_checking_account                 | ... < 0 DM                                                                                                                        |    -5.0818 |\n|  4 | purpose                                             | car (used),radio/television                                                                                                       |    12.0818 |\n|  5 | purpose                                             | retraining,furniture/equipment,education,business,nan,car (new),repairs,domestic appliances,others                                |     2.3125 |\n|  6 | credit_amount                                       | [-inf ~ 1382.0)                                                                                                                   |     2.4344 |\n|  7 | credit_amount                                       | [1382.0 ~ 3804.0)                                                                                                                 |    13.6608 |\n|  8 | credit_amount                                       | [3804.0 ~ inf)                                                                                                                    |    -2.9765 |\n|  9 | savings_account_and_bonds                           | ... >= 1000 DM,500 <= ... < 1000 DM,unknown/ no savings account                                                                   |    12.0593 |\n| 10 | savings_account_and_bonds                           | nan                                                                                                                               |     5.4614 |\n| 11 | savings_account_and_bonds                           | 100 <= ... < 500 DM,... < 100 DM                                                                                                  |     2.5373 |\n| 12 | present_employment_since                            | 4 <= ... < 7 years,... >= 7 years                                                                                                 |     9.1647 |\n| 13 | present_employment_since                            | 1 <= ... < 4 years,nan,unemployed,... < 1 year                                                                                    |     3.2488 |\n| 14 | installment_rate_in_percentage_of_disposable_income | [-inf ~ 4.0)                                                                                                                      |     7.997  |\n| 15 | installment_rate_in_percentage_of_disposable_income | [4.0 ~ inf)                                                                                                                       |     0.6935 |\n| 16 | personal_status_and_sex                             | female : divorced/separated/married,nan                                                                                           |     7.4426 |\n| 17 | personal_status_and_sex                             | male : single,male : divorced/separated,male : married/widowed                                                                    |     3.122  |\n| 18 | property                                            | real estate                                                                                                                       |    10.5651 |\n| 19 | property                                            | building society savings agreement/ life insurance,nan,car or other, not in attribute Savings account/bonds,unknown / no property |     3.5272 |\n| 20 | age_in_years                                        | [-inf ~ 23.0)                                                                                                                     |     6.0181 |\n| 21 | age_in_years                                        | [23.0 ~ 35.0)                                                                                                                     |    -0.7208 |\n| 22 | age_in_years                                        | [35.0 ~ inf)                                                                                                                      |    11.0045 |\n| 23 | housing                                             | nan,own                                                                                                                           |     6.1523 |\n| 24 | housing                                             | rent,for free                                                                                                                     |     1.6267 |\n\n`scorecardpipeline` 还提供了一些方法，让您可以快速查看评分效果和分布情况:\n\n```\n# 查看 KS 和 ROC 曲线\nks_plot(train[\"score\"], train[target], figsize=(10, 5), title=\"Train Dataset\", save=\"model_report/sp_train_ksplot.png\")\n```\n\n
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\n\n```\n# 查看评分分布情况\nhist_plot(train[\"score\"], train[target], figsize=(10, 6), save=\"model_report/sp_train_histplot.png\")\n```\n\n
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\n\n通过 `scorecardpipeline` 提供的一些方法，您可以快速查看评分排序性，并得到相关的统计信息:\n\n```\n# 训练集评分排序性\nscore_clip = card.score_clip(train[\"score\"], clip=20) # 计算等频分箱的间隔\nscore_table_train = feature_bin_stats(train, \"score\", desc=\"训练集模型评分\", target=target, rules=score_clip) # 计算统计信息\nbin_plot(score_table_train, desc=\"训练集模型评分\", figsize=(10, 6), anchor=0.935, save=\"model_report/train_score_bins.png\") # 画图\n```\n\n| 指标名称   | 指标含义       | 分箱          |   样本总数 |   样本占比 |   好样本数 |   好样本占比 |   坏样本数 |   坏样本占比 |   坏样本率 |   分档WOE值 |    分档IV值 |   指标IV值 |   LIFT值 |   累积LIFT值 |   累积好样本数 |   累积坏样本数 |   分档KS值 |\n|:-----------|:---------------|:--------------|-----------:|-----------:|-----------:|-------------:|-----------:|-------------:|-----------:|------------:|------------:|-----------:|---------:|-------------:|---------------:|---------------:|-----------:|\n| score      | 训练集模型评分 | [负无穷 , 20) |          9 |  0.0128571 |          1 |   0.00204082 |          8 |   0.0380952  |  0.888889  |  -2.92677   | 0.105523    |    1.20035 |  2.96296 |      2.96296 |              1 |              8 |  0.0360544 |\n| score      | 训练集模型评分 | [20 , 40)     |        160 |  0.228571  |         63 |   0.128571   |         97 |   0.461905   |  0.60625   |  -1.27888   | 0.426292    |    1.20035 |  2.02083 |      2.07101 |             64 |            105 |  0.369388  |\n| score      | 训练集模型评分 | [40 , 60)     |        283 |  0.404286  |        197 |   0.402041   |         86 |   0.409524   |  0.303887  |  -0.0184439 | 0.000138015 |    1.20035 |  1.01296 |      1.40855 |            261 |            191 |  0.376871  |\n| score      | 训练集模型评分 | [60 , 80)     |        187 |  0.267143  |        170 |   0.346939   |         17 |   0.0809524  |  0.0909091 |   1.45527   | 0.387083    |    1.20035 |  0.30303 |      1.08503 |            431 |            208 |  0.110884  |\n| score      | 训练集模型评分 | [80 , 正无穷) |         61 |  0.0871429 |         59 |   0.120408   |          2 |   0.00952381 |  0.0327869 |   2.53699   | 0.281312    |    1.20035 |  0.10929 |      1       |            490 |            210 |  0         |\n\n
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\n\n除了在单个数据集上查看评分效果，评分卡的稳定性也是一个重要的评估指标，`scorecardpipeline` 内提供了查看两个数据集某个特征的 `PSI` 指标，同时针对评分卡模型，也提供了查看入模特征 `CSI` 的方法:\n\n```python\n# 查看某个特征的 PSI\nscore_clip = card.score_clip(train[\"score\"], clip=10)\nscore_table_train = feature_bin_stats(train, \"score\", desc=\"训练集模型评分\", target=target, rules=score_clip)\nscore_table_test = feature_bin_stats(test, \"score\", desc=\"测试集模型评分\", target=target, rules=score_clip)\ntrain_test_score_psi = psi_plot(score_table_train, score_table_test, labels=[\"训练数据集\", \"测试数据集\"], save=\"model_report/train_test_psiplot.png\", result=True)\n\n# 查看某个入模特征的 CSI\nfor col in card._feature_names:\n    feature_table_train = feature_bin_stats(train, col, target=target, desc=\"训练集分布\", combiner=combiner)\n    feature_table_test = feature_bin_stats(test, col, target=target, desc=\"测试集分布\", combiner=combiner)\n    train_test_csi_table = csi_plot(feature_table_train, feature_table_test, card[col], desc=col, result=True, plot=True, max_len=35, figsize=(10, 6), labels=[\"训练数据集\", \"测试数据集\"], save=f\"model_report/csi_{col}.png\")\n```\n\n
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\n\n\n### 模型持久化存储\n\n`scorecardpipeline` 提供了几种可选的模型持久化存储方式，可以将训练好的评分卡模型保存为 `pickle` 或 `pmml` 格式的模型文件，供后续生产部署或离线回溯使用，非常方便快捷\n\n```python\n# 将评分卡模型保存 pmml 文件\nscorecard_pipeline = card.scorecard2pmml(pmml=\"model_report/scorecard.pmml\", debug=True)\n# 将评分卡模型保存 pickle 文件\nsave_pickle(card, \"model_report/scorecard.pkl\")\n```\n\n\n### 评分卡 `pipeline` 建模\n\n在 `scorecardpipeline` 中，几乎所以的模型和数据预处理步骤都支持 `pipeline` 式构建模型，同时还可以与 `sklearn` 中其他的 `pipeline` 组件一起构建模型。\n\n```python\n# 构建 pipeline\nmodel_pipeline = Pipeline([\n    (\"preprocessing\", FeatureSelection(target=target, engine=\"scorecardpy\")),\n    (\"combiner\", Combiner(target=target, min_bin_size=0.2)),\n    (\"transform\", WOETransformer(target=target)),\n    (\"processing_select\", FeatureSelection(target=target, engine=\"toad\")),\n    (\"stepwise\", StepwiseSelection(target=target)),\n    (\"logistic\", ITLubberLogisticRegression(target=target)),\n])\n# 训练 pipeline\nmodel_pipeline.fit(train)\n# 转换评分卡\ncard = ScoreCard(target=target, pipeline=model_pipeline, base_score=50, base_odds=(1 - bad_rate) / bad_rate, pdo=10)\ncard.fit(model_pipeline[:-1].transform(train))\ncard.scorecard_points()\n```\n\n| 序号 | 变量名称                                            | 变量分箱                                                                                                                             |   对应分数 |\n|:--:|:----------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------|-----------:|\n|  0 | purpose                                             | radio/television,car (used)                                                                                                          |    12.8514 |\n|  1 | purpose                                             | business,furniture/equipment,others,education,domestic appliances,retraining,car (new),缺失值,repairs                                |     2.7818 |\n|  2 | installment_rate_in_percentage_of_disposable_income | [负无穷 , 4.0)                                                                                                                       |     7.7878 |\n|  3 | installment_rate_in_percentage_of_disposable_income | [4.0 , 正无穷)                                                                                                                       |     0.9877 |\n|  4 | installment_rate_in_percentage_of_disposable_income | 缺失值                                                                                                                               |    10.7462 |\n|  5 | savings_account_and_bonds                           | 500 <= ... < 1000 DM,unknown/ no savings account,... >= 1000 DM                                                                      |     5.9834 |\n|  6 | savings_account_and_bonds                           | ... < 100 DM,100 <= ... < 500 DM                                                                                                     |    12.1585 |\n|  7 | savings_account_and_bonds                           | 缺失值                                                                                                                               |     3.2466 |\n|  8 | present_employment_since                            | 4 <= ... < 7 years,... >= 7 years                                                                                                    |     9.4896 |\n|  9 | present_employment_since                            | 缺失值,unemployed,1 <= ... < 4 years,... < 1 year                                                                                    |     3.8957 |\n| 10 | age_in_years                                        | [负无穷 , 35.0)                                                                                                                      |     0.8917 |\n| 11 | age_in_years                                        | [35.0 , 正无穷)                                                                                                                      |    10.8457 |\n| 12 | age_in_years                                        | 缺失值                                                                                                                               |     6.7213 |\n| 13 | property                                            | real estate                                                                                                                          |    11.2541 |\n| 14 | property                                            | 缺失值,building society savings agreement/ life insurance,unknown / no property,car or other, not in attribute Savings account/bonds |     4.0428 |\n| 15 | personal_status_and_sex                             | 缺失值,female : divorced/separated/married                                                                                           |     7.8997 |\n| 16 | personal_status_and_sex                             | male : single,male : married/widowed,male : divorced/separated                                                                       |     3.7463 |\n| 17 | credit_amount                                       | [负无穷 , 2145.0)                                                                                                                    |     8.0057 |\n| 18 | credit_amount                                       | [2145.0 , 3804.0)                                                                                                                    |    14.2751 |\n| 19 | credit_amount                                       | [3804.0 , 正无穷)                                                                                                                    |    -2.6347 |\n| 20 | credit_amount                                       | 缺失值                                                                                                                               |     2.1778 |\n| 21 | status_of_existing_checking_account                 | no checking account                                                                                                                  |    22.4653 |\n| 22 | status_of_existing_checking_account                 | 缺失值,... >= 200 DM / salary assignments for at least 1 year                                                                        |     6.6694 |\n| 23 | status_of_existing_checking_account                 | 0 <= ... < 200 DM                                                                                                                    |     0.0181 |\n| 24 | status_of_existing_checking_account                 | ... < 0 DM                                                                                                                           |    -4.8558 |\n\n\n### 评分卡全流程超参数搜索\n\n```python\n# 导入超参数搜索方法\nfrom sklearn.model_selection import GridSearchCV\n\n# 构建 pipeline\nmodel_pipeline = Pipeline([\n    (\"preprocessing\", FeatureSelection(target=target, engine=\"scorecardpy\")),\n    (\"combiner\", Combiner(target=target, min_bin_size=0.2)),\n    (\"transform\", WOETransformer(target=target)),\n    (\"processing_select\", FeatureSelection(target=target, engine=\"toad\")),\n    (\"stepwise\", StepwiseSelection(target=target)),\n    (\"logistic\", ITLubberLogisticRegression(target=target)),\n])\n\n# 定义超参数搜索空间，参数命名: {pipeline名称}__{对应超参数名称}\nparams_grid = {\n    \"combiner__max_n_bins\": [3],\n    \"logistic__C\": [np.power(2, i) for i in range(5)],\n    \"logistic__penalty\": [\"l2\"],\n    \"logistic__class_weight\": [None, \"balanced\"] + [{1: i / 10.0, 0: 1 - i / 10.0} for i in range(1, 10, 2)],\n    \"logistic__max_iter\": [10, 50, 100],\n    \"logistic__solver\": [\"sag\"], # [\"liblinear\", \"sag\", \"lbfgs\", \"newton-cg\"],\n}\n\npipeline_grid_search = GridSearchCV(model_pipeline, params_grid, cv=3, scoring='roc_auc', verbose=1, n_jobs=-1, return_train_score=True)\npipeline_grid_search.fit(train, train[target])\n\nprint(pipeline_grid_search.best_params_)\n\n# 更新模型\nmodel_pipeline.set_params(**pipeline_grid_search.best_params_)\nmodel_pipeline.fit(train)\n\n# 转换评分卡\ncard = ScoreCard(target=target, pipeline=model_pipeline, base_score=50, base_odds=(1 - bad_rate) / bad_rate, pdo=10)\ncard.fit(model_pipeline[:-1].transform(train))\n```\n\n\n### 模型报告输出\n\n在 `scorecardpipeline` 中，提供了操作 `excel` 文件的写入器 `ExcelWriter`，支持将文字、表格、图像等过程内容保存至 `excel` 文件中，对相关方法抽象和封装后，能够满足日常大部分数据分析过程中结果保存的需求。\n\n`ExcelWriter`支持调整列宽、调整单元格格式、条件格式、指定位置插入数据、插入图片等功能，且提供了 `dataframe2excel` 来在日常工作中快速保存 `dataframe` 至 `excel` 文件中，并且自动设置样式。\n\n```python\n# 初始化 Excel 写入器\nwriter = ExcelWriter()\n\nstart_row, start_col = 2, 2\n\n# ////////////////////////////////////// 样本说明 ///////////////////////////////////// #\nworksheet = writer.get_sheet_by_name(\"汇总信息\")\n\n# 样本总体分布情况\nend_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\"样本总体分布情况\", style=\"header\")\nend_row, end_col = dataframe2excel(dataset_summary, writer, worksheet, percent_cols=[\"样本占比\", \"坏客户占比\"], start_row=end_row + 1)\n\n# 建模样本时间分布情况\ntemp = distribution_plot(df, date=\"date\", target=target, save=\"model_report/all_sample_time_count.png\", result=True)\nend_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"建模样本时间分布情况\", style=\"header\")\nend_row, end_col = writer.insert_pic2sheet(worksheet, \"model_report/all_sample_time_count.png\", (end_row, start_col), figsize=(720, 370))\nend_row, end_col = dataframe2excel(temp, writer, worksheet, percent_cols=[\"样本占比\", \"好样本占比\", \"坏样本占比\", \"坏样本率\"], condition_cols=[\"坏样本率\"], start_row=end_row)\n\n# ////////////////////////////////////// 模型报告 ///////////////////////////////////// #\nsummary = logistic.summary2(feature_map=feature_map)\n\n# 逻辑回归拟合情况\nworksheet = writer.get_sheet_by_name(\"逻辑回归拟合结果\")\n\nend_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\"逻辑回归拟合效果\", style=\"header\")\nend_row, end_col = dataframe2excel(summary, writer, worksheet, condition_cols=[\"Coef.\"], start_row=end_row + 1)\n\nend_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"训练数据集拟合报告\", style=\"header\")\nend_row, end_col = dataframe2excel(logistic.report(train_woe_stepwise), writer, worksheet, percent_cols=[\"precision\", \"recall\", \"f1-score\"], start_row=end_row + 1)\n\nend_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"测试数据集拟合报告\", style=\"header\")\nend_row, end_col = dataframe2excel(logistic.report(test_woe_stepwise), writer, worksheet, percent_cols=[\"precision\", \"recall\", \"f1-score\"], start_row=end_row + 1)\n\n# ////////////////////////////////////// 特征概述 ///////////////////////////////////// #\nworksheet = writer.get_sheet_by_name(\"模型变量信息\")\n\nstart_row, start_col = 2, 2\nend_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\"入模变量信息\", style=\"header\")\nend_row, end_col = writer.insert_df2sheet(worksheet, feature_describe.reset_index().rename(columns={\"index\": \"序号\"}), (end_row + 1, start_col))\n\n# 变量分布情况\nimport toad\ndata_info = toad.detect(data[card.rules.keys()]).reset_index().rename(columns={\"index\": \"变量名称\", \"type\": \"变量类型\", \"size\": \"样本个数\", \"missing\": \"缺失值\", \"unique\": \"唯一值个数\"})\nend_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"变量分布情况\", style=\"header\")\nend_row, end_col = writer.insert_df2sheet(worksheet, data_info, (end_row + 1, start_col))\n\n# 变量相关性\ndata_corr = train_woe_stepwise.corr()\nlogistic.corr(train_woe_stepwise, save=\"model_report/train_corr.png\", annot=False)\nend_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"变量相关性\", style=\"header\")\nend_row, end_col = writer.insert_pic2sheet(worksheet, \"model_report/train_corr.png\", (end_row + 1, start_col), figsize=(700, 500))\nend_row, end_col = dataframe2excel(data_corr.reset_index().rename(columns={\"index\": \"\"}), writer, worksheet, color_cols=list(data_corr.columns), start_row=end_row + 1)\n\n# 变量分箱信息\nend_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"变量分箱信息\", style=\"header\")\n\nfor col in logistic.feature_names_in_:\n    feature_table = feature_bin_stats(data, col, target=target, desc=feature_map.get(col, \"\") or \"逻辑回归入模变量\", combiner=combiner)\n    _ = bin_plot(feature_table, desc=feature_map.get(col, \"\") or \"逻辑回归入模变量\", figsize=(8, 4), save=f\"model_report/bin_plots/data_{col}.png\")\n    \n    end_row, end_col = writer.insert_pic2sheet(worksheet, f\"model_report/bin_plots/data_{col}.png\", (end_row + 1, start_col), figsize=(700, 400))\n    end_row, end_col = dataframe2excel(feature_table, writer, worksheet, percent_cols=[\"样本占比\", \"好样本占比\", \"坏样本占比\", \"坏样本率\", \"LIFT值\", \"累积LIFT值\"], condition_cols=[\"坏样本率\", \"LIFT值\"], start_row=end_row)\n\n# ////////////////////////////////////// 评分卡说明 ///////////////////////////////////// #\nworksheet = writer.get_sheet_by_name(\"评分卡结果\")\n\n# 评分卡刻度\nscorecard_kedu = card.scorecard_scale()\nscorecard_points = card.scorecard_points(feature_map=feature_map)\nscorecard_clip = card.score_clip(train[\"score\"], clip=10)\n\nstart_row, start_col = 2, 2\nend_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\"评分卡刻度\", style=\"header\")\nend_row, end_col = writer.insert_df2sheet(worksheet, scorecard_kedu, (end_row + 1, start_col))\n\n# 评分卡对应分数\nend_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"评分卡分数\", style=\"header\")\nend_row, end_col = writer.insert_df2sheet(worksheet, scorecard_points, (end_row + 1, start_col), merge_column=\"变量名称\")\n\n# 评分效果\nscore_table_train = feature_bin_stats(train, \"score\", desc=\"测试集模型评分\", target=target, rules=scorecard_clip)\nscore_table_test = feature_bin_stats(test, \"score\", desc=\"测试集模型评分\", target=target, rules=scorecard_clip)\n\nks_plot(train[\"score\"], train[target], title=\"Train \\tDataset\", save=\"model_report/train_ksplot.png\")\nks_plot(test[\"score\"], test[target], title=\"Test \\tDataset\", save=\"model_report/test_ksplot.png\")\n\nhist_plot(train[\"score\"], train[target], save=\"model_report/train_scorehist.png\", bins=30)\nhist_plot(test[\"score\"], test[target], save=\"model_report/test_scorehist.png\", bins=30)\n\nend_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"训练数据集评分模型效果\", style=\"header\")\nks_row = end_row\nend_row, end_col = writer.insert_pic2sheet(worksheet, \"model_report/train_ksplot.png\", (ks_row, start_col))\nend_row, end_col = writer.insert_pic2sheet(worksheet, \"model_report/train_scorehist.png\", (ks_row, end_col))\nend_row, end_col = dataframe2excel(score_table_train, writer, worksheet, percent_cols=[\"样本占比\", \"好样本占比\", \"坏样本占比\", \"坏样本率\", \"LIFT值\", \"累积LIFT值\", \"分档KS值\"], condition_cols=[\"坏样本率\", \"LIFT值\", \"分档KS值\"], start_row=end_row + 1)\n\nend_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"测试数据集评分模型效果\", style=\"header\")\nks_row = end_row\nend_row, end_col = writer.insert_pic2sheet(worksheet, \"model_report/test_ksplot.png\", (ks_row, start_col))\nend_row, end_col = writer.insert_pic2sheet(worksheet, \"model_report/test_scorehist.png\", (ks_row, end_col))\nend_row, end_col = dataframe2excel(score_table_test, writer, worksheet, percent_cols=[\"样本占比\", \"好样本占比\", \"坏样本占比\", \"坏样本率\", \"LIFT值\", \"累积LIFT值\", \"分档KS值\"], condition_cols=[\"坏样本率\", \"LIFT值\", \"分档KS值\"], start_row=end_row + 1)\n\n# ////////////////////////////////////// 模型稳定性 ///////////////////////////////////// #\nworksheet = writer.get_sheet_by_name(\"模型稳定性\")\nstart_row, start_col = 2, 2\n\n# 评分分布稳定性\ntrain_test_score_psi = psi_plot(score_table_train, score_table_test, labels=[\"训练数据集\", \"测试数据集\"], save=\"model_report/train_test_psiplot.png\", result=True)\n\nend_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\"模型评分稳定性指标 (Population Stability Index, PSI): 训练数据集 vs 测试数据集\", style=\"header\")\nend_row, end_col = writer.insert_pic2sheet(worksheet, \"model_report/train_test_psiplot.png\", (end_row, start_col), figsize=(800, 400))\nend_row, end_col = dataframe2excel(train_test_score_psi, writer, worksheet, percent_cols=[\"训练数据集样本占比\", \"训练数据集坏样本率\", \"测试数据集样本占比\", \"测试数据集坏样本率\"], condition_cols=[\"分档PSI值\"], start_row=end_row + 1)\n\n# 变量 PSI 表\nfor col in card._feature_names:\n    feature_table_train = feature_bin_stats(train, col, target=target, desc=feature_map.get(col, \"\") or \"逻辑回归入模变量\", combiner=combiner)\n    feature_table_test = feature_bin_stats(test, col, target=target, desc=feature_map.get(col, \"\") or \"逻辑回归入模变量\", combiner=combiner)\n    psi_table = psi_plot(feature_table_train, feature_table_test, desc=col, result=True, plot=True, max_len=35, figsize=(10, 6), labels=[\"训练数据集\", \"测试数据集\"], save=f\"model_report/psi_{col}.png\")\n    \n    end_row, end_col = writer.insert_pic2sheet(worksheet, f\"model_report/psi_{col}.png\", (end_row, start_col), figsize=(700, 400))\n    end_row, end_col = dataframe2excel(psi_table, writer, worksheet, percent_cols=[\"训练数据集样本占比\", \"训练数据集坏样本率\", \"测试数据集样本占比\", \"测试数据集坏样本率\", \"测试数据集% - 训练数据集%\"], condition_cols=[\"分档PSI值\"], start_row=end_row + 1)\n\n# 变量 CSI 表\nend_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"入模变量稳定性指标 (Characteristic Stability Index, CSI): 训练数据集 vs 测试数据集\", style=\"header\")\n\nfor col in card._feature_names:\n    feature_table_train = feature_bin_stats(train, col, target=target, desc=feature_map.get(col, \"\") or \"逻辑回归入模变量\", combiner=combiner)\n    feature_table_test = feature_bin_stats(test, col, target=target, desc=feature_map.get(col, \"\") or \"逻辑回归入模变量\", combiner=combiner)\n    train_test_csi_table = csi_plot(feature_table_train, feature_table_test, card[col], desc=col, result=True, plot=True, max_len=35, figsize=(10, 6), labels=[\"训练数据集\", \"测试数据集\"], save=f\"model_report/csi_{col}.png\")\n    \n    end_row, end_col = writer.insert_pic2sheet(worksheet, f\"model_report/csi_{col}.png\", (end_row, start_col), figsize=(700, 400))\n    end_row, end_col = dataframe2excel(train_test_csi_table, writer, worksheet, percent_cols=[\"训练数据集样本占比\", \"训练数据集坏样本率\", \"测试数据集样本占比\", \"测试数据集坏样本率\", \"测试数据集% - 训练数据集%\"], condition_cols=[\"分档CSI值\"], start_row=end_row + 1)\n\n# 保存结果文件\nwriter.save(\"model_report/评分卡模型报告.xlsx\")\n```\n\n
\n    \n
\n\n\n## 交流\n\n
\n    \"itlubber\"\n    \"itlubber_art\"\n
\n\n
\n    微信: itlubber\n    订阅号: itlubber_art\n
\n"
  },
  {
    "path": "docs/source/scorecardpipeline.rst",
    "content": "scorecardpipeline\n======================================\n\n.. automodule:: scorecardpipeline\n   :members:\n   :undoc-members:\n   :show-inheritance:\n\n\nscorecardpipeline.processing\n======================================\n\n.. automodule:: scorecardpipeline.processing\n   :members:\n   :undoc-members:\n   :show-inheritance:\n\n\nscorecardpipeline.feature_selection\n======================================\n\n.. automodule:: scorecardpipeline.feature_selection\n   :members:\n   :undoc-members:\n   :show-inheritance:\n\n\nscorecardpipeline.model\n======================================\n\n.. automodule:: scorecardpipeline.model\n   :members:\n   :undoc-members:\n   :show-inheritance:\n\n\nscorecardpipeline.auto_eda\n======================================\n\n.. automodule:: scorecardpipeline.auto_eda\n   :members:\n   :undoc-members:\n   :show-inheritance:\n\n\nscorecardpipeline.auto_report\n======================================\n\n.. automodule:: scorecardpipeline.auto_report\n   :members:\n   :undoc-members:\n   :show-inheritance:\n\n\nscorecardpipeline.utils\n======================================\n\n.. automodule:: scorecardpipeline.utils\n   :members:\n   :undoc-members:\n   :show-inheritance:\n\n\nscorecardpipeline.excel_writer\n======================================\n\n.. automodule:: scorecardpipeline.excel_writer\n   :members:\n   :undoc-members:\n   :show-inheritance:\n\n\nscorecardpipeline.rule\n======================================\n\n.. automodule:: scorecardpipeline.rule\n   :members:\n   :undoc-members:\n   :show-inheritance:\n\n\nscorecardpipeline.rule_extraction\n======================================\n\n.. automodule:: scorecardpipeline.rule_extraction\n   :members:\n   :undoc-members:\n   :show-inheritance:\n\n\nscorecardpipeline.logger\n======================================\n\n.. automodule:: scorecardpipeline.logger\n   :members:\n   :undoc-members:\n   :show-inheritance:\n"
  },
  {
    "path": "examples/auto_report.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import warnings\\n\",\n    \"\\n\",\n    \"warnings.filterwarnings(\\\"ignore\\\")\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"sys.path.append(\\\"../\\\")\\n\",\n    \"\\n\",\n    \"import os\\n\",\n    \"import re\\n\",\n    \"import six\\n\",\n    \"import random\\n\",\n    \"import joblib\\n\",\n    \"import warnings\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib import font_manager\\n\",\n    \"from matplotlib.ticker import PercentFormatter, FuncFormatter\\n\",\n    \"import seaborn as sns\\n\",\n    \"from scorecardpipeline import *\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"init_setting()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"source\": [\n    \"### 三方数据评估报告输出\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|██████████| 20/20 [00:07<00:00,  2.56it/s, feature=foreign_worker]                                          \\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"(653, 22)\"\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
\",\n      \"image/png\": 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UPR44crmrVqqtLl672qfhTgvgCtiGRAtjJmTNnNHfuXFWqVEnPPfecDAaDPvnkE+3bt09du3bVCy+8IMn8pbNu3TpVrVpVzZo105UrVzRv3jw5ODho8eLFatOmjY4dOyZvb29rl8rRo0crKipKb731lnLnzi1JqlevXqL9Ozs7a/jw4XJ92AyY/9E333yj+Ph4ffjhh3JyclLhwoVVpEiRJOW8vLwkmcfMFixYUJJUuXJlSVKZMmXUuHFjSVL58uUlSf/8848kcy+aVatWSZJatmwpScqZM6def/11GY1GTZpkvrpG0aJFk+yzWLFiSZb16dNHefLkkSQNHDhQ9evX16uvvqqSJUsqPj4+0Zm/Z0VMjLkbScIT9JbH9w/fcXVLvOzwfvMRfckX7n1FGAzSm+846sY1k1b9aFQ8eRSbWXqiOCXoA2p5fCfiEesz+PApdzfzRDxGY5x1meWxpWdKcv76K1CLf5isQV98p/LlX0m2zI6d6/VK1fopWNunh8e/Gbq4uHtxtTxOeHbZ1nJ169ZT/foN1LBhI3Xs2FFRUVHaufNeAj+jIb72ldLxjYuL08CBA1S8eAlNmOAnZ+fkrwiWURBfwDYM7QHsZMeOHTKZTNZEx/0sSYYqVarI09NTCxYskCStX79ecXFx8vT01KJFi5K87s6dO9q/f78kJeqF4u6edFbK/PnzK0eOHIqMjEyy7r+KiIiwJnQSJk+yZ8/+wNc4JThCdHFxSXQv3ev6aRmedOzYMeu6AgUKWB8XLlxYkvlSe/Hx8Y/VNdRSh4TdTC11iI6Otnk7T4sbV82JFFc3gyTz40yZzOuuXU08WVzkHSlBWBQdZb5PONFp/cYOun7VZL0sssEgZckqLoFsg6Dz5kRVlqz3lll6mpw5If1zyvz4QeszkkOHArRqzVzrc8ucJhERYdZlkf+OgSpapFSy2wi69I/mLZigr4bMVt68hSRJV66cV758hROV27N3k7p1/ipF6/+0sMy7EZbgHzgiwhzXhN8rhQsXlouLS7Llnn8+cS9Ii/z5zfPZPIufq7YivvaV0vEdN26snn++pLp27SbJfOB//fr1RHOEZCTEF7ANPVIAO7n774QUFy5ceGi5TJaj23/F/3uq/+bNm7p8+XKS8tHR0dZ5Qe5/bWqIioqy7j8+QbeElEzWJJQwWWKZH8ZkMiWZOR6JnTlpUmyMSTk97w3PyZ7DoODrJl2+aFLCE0JXLpnk7iG5/Pt2siRQblw3x/iFcgbFxcmaRJGkwsUMeu55vkIexjKEKjZW2ukv5ckva9zz/Zsf/G21dOywdOOaVDDBcX4+byn0thSwNTVrnPYqVqyhYUNmW2+NX2+lEiXK6fLls9Yy165dlKOjk2r7mOc9iYm595kYGxujGTOHq+9nftYkiiTNv+9yyTduXFZkZISKFk0+GfOsq1mzllxdXXXu3DnrsqCgIBUtWkwVKlRUVJQ5m+ri4qLatevo8uXLiokxX87r0iXzTMnNmjVLdtsXLpyXwWBQ1arJ9wTKCIivfaVkfNetWysXFxfrQb4k7du3Vzt2PN1X7nsSxBewDb+CATuxZO2PHDlinX1fMmfrT58+/cDXlS1b1powGDx4sMLDw62v++mnn+Tp6SkPD3N394sXk17+0xaWbpWWL8PH4enpae3JERRk/sKMi4t7ZMLocZQrV87ac+TSpUvW5ZYv9YoVK1rXW3qahISESEqc3PkvniQ26Ul0tLRzW7yKlTDIxUVyc5O8Cxq009+oEqUM8h3gpLIVze+zPTvMl0guW8H8lVCqjIOio00K3BUvBwfJp4GjXFwMqt/YQfUbO6jhm45q+KajoiJJZlk4OCYeRlWjjrRyi9Soifn5/BnmhEoN8/REquYj7Q2QTv4lxcdLMydJpctLufNKzi5SlVek5QuTDsPKiNp++JlCbt3QiZOHZDKZFLjfX/XqNlfOnLkVHHxFn3Sqq2nfD5Ykbdi4XNF3o7RqzVzNnD1SM2eP1NjxvZJcKvnwkV2qXKlmWjQnXciWLZs6duykgIAdioiI0O3bt3TkyGF16tRJmzdvko9PTa1evUqS1KVLV7m4uGjzZvNE3lu3blWNGjVUpkxZBQcHq2bNGvrf/2ZLkkJDQ7V8+TK1atVaxYsXT6vmpTnia18pFd/Y2FhNmjRJkZGRGj36a40e/bVGjhyhUaNGKlu2bGnYwrRFfAHbMLQHsJNatWqpYsWKOnTokHx9fVWxYkXlzp1bp06d0syZM5Pt1SFJhQoVUrNmzbRixQrt2LFDr7zyiry8vHTz5k3NnDlTknnekDlz5mjFihVq1aqVPDw8rImE+3tqWM4SJNyPZb6Sbdu2qXHjxjp//ryqV69u0+XoHBwc9Oabb2rFihVavXq1fHx8tH79euXMmVMRERGJeqZYuh7HxsZal1kSFJZ6SfeG9FjqWKBAAbVq1UoLFy7UkiVLNHr0aAUHB2vt2rVydnZW3759ra8tUqSITp8+LT8/Px05csQ6wawkXb9+3To06P59JKxXwvG9ltjs379fBw4cUGhoqIoWLZrsHDDp3b6d8TIYpBZtnSSZtGWDUSf/NqlocYNiYqTYf/8E166Y9PMSo+o2clSpsgYZjdLSeXGKvCMVKmqQR2aDylUyJNn+U55rShFlKkhvNJe8zFPwqM9gacVS6W60OT6WGF0Jkgb5Sp17S03fM1/qeMzge9vZvsncE2jwWPNVktavkX5KOrIvQyr5fEX1/cxP8+aPlwwGlSheTm0//EySeeJZNzcPububZ+zds3eTzp8/ab3az71tVEj0/K+/96t6tUap04B0qm3bdjIajeratbMMBoN69/5U9es3UEBAgNzd3eX2b9e0ggULasqUafr2Wz8tW7ZM+fPn18iRX0syJ9Zbtnxfy5b9KH//rXJ3d1f37j1Ut269h+06QyC+9pUS8T148KBCQm7ql1/WJNn+w4YrZwTEF3g0g4n+8YDdhIaGasKECdqwYYNiYmJUqVIlDRgwQOfOndPIkSN17do1eXl5qXXr1mrfvr3c3NwkmRMQEyZM0Lp16xQZGamSJUuqZ8+eevXVVyWZh9EMGzZM27dvl4eHh+rWrau4uDgtXrxY7u7u6tu3r+rXr6+vvvrKejngkiVLasSIEapQoYJu3bolX19fHTlyRN7e3urZs6def/11m9sVERGhoUOHatOmTXr55Zf11ltv6e7duxo5cqTy5MmjIUOGKCgoSN98841CQkKUKVMmvffee3r11VfVr18/hYWFydHRUS1btlTFihX13Xff6dy5c3JwcFDXrl3l6+uruLg4zZ49WytXrpSLi4uio6NVvHhx9ejRQ2XKlLHWZceOHRoyZIhu3rypV199VSNGjFDTpk31/PPPq0SJEmrWrJm+++47bdiwQSaTSXnz5tW0adO0du1aLVy4ULGxsSpUqJAGDBigevXqKSYmRp999pl27NghT09Pffzxx9bLI/9XE4bHProQHlvfIebeQ69VTeOKPKPW7zHfHz5gn2F7GV2FF80HImFhEY8oif8ia1bzREPE1z6Ir30RX/uyxDcjSqvfpJbfbM8SEilAOmAymazDef6r2NhYxcbGWiedjY+Pl8FgSLRdo9GYaLJVe4qJiZGzs7N1/9HR0XJwcFB8fLxcXFwUFxenuLi4JJPk3r17N03mfrEnEin2QSLFvkik2BeJFPviQNS+iK99EV/7IpGS+p7FRApDe4B04EmTKJJ5bo+El5RL7oo2D0ui+Pn5aePGjY/cT58+fdSgQYNHlkt4VR5JSS7D7OLikqSMlDYT6AIAAACArUikAJAk3bhxQ2fPnn1kOcvktwAAAACQEZFIASBJGjNmjMaMGZPW1QAAAACAdI3LHwMAAAAAANiIRAoAAAAAAICNSKQAAAAAAADYiEQKAAAAAACAjUikAAAAAAAA2IhECgAAAAAAgI1IpAAAAAAAANiIRAoAAAAAAICNSKQAAAAAAIBnQkhIiObMmWPXfTjZdesAAAAAAAD/QUhIiEaNGiUPDw8FBwerZ8+eKl26dJJydevW1aVLl6zPe/fubX0cEBCgRYsWydPTUw4ODho4cKBcXV2fqF4kUgAAAAAAQLrTp08fVapUSb6+vtq1a5c++ugjbdy4UVmyZElUrlKlSvr444+tzytXrixJunDhgrp166Zff/1VBQsWVL9+/TRq1CiNGDHiierF0B4AAAAAAJCuBAYGaufOnapWrZok6aWXXlJ4eLgWL16cpOyLL76oDz/80Hqz9FqZMWOGcuXKpYIFC0qSqlWrphUrViTqvfJf0CMFAAAAAADYRb169R66ftOmTcku37ZtmyTJ09NTkuTk5KTs2bNr69at6tKlS6Ky69at0zfffCMnJydVq1ZNAwcOVO7cueXv7y9vb29rOU9PT8XFxSkgIEAtWrT4z20ikQIAAAAAANKVkJAQSZKzs7N1mbOzs3V5Qg0aNNAnn3yiU6dO6fvvv9fNmze1cOFC3bp1S0WKFEn0ekm6efPmE9WNRAoAAAAAALCLB/U4eZQcOXJIkiIjI63LYmNjlTdv3iRl27dvL8k86ayDg4MmTJig8PBw5ciRI8nrpXu9XP4r5kgBAAAAAADpio+PjyTp+vXrkqS4uDiFhoaqVq1aD31diRIl5OLiIicnJ/n4+FhfL5l7uTg6Oqp69epPVDcSKQAAAAAAIF2pUqWKatasqR07dkiS9u/fLw8PD7Vo0UKdOnXSvHnzJEm///67Dhw4YH3d/v371alTJ7m5ualLly6KiIjQ8ePHJUm7d+/WW2+9pQIFCjxR3RjaAwAAAAAA0p1JkyZp6NChGjp0qK5du6bZs2fL3d1dp06dsl6JJzg4WN98843q168vb29veXt7WyeSLViwoObOnatvv/1WefLkkYODgwYPHvzE9SKRAgAAAAAA0h0PDw9NmDAhyfItW7ZYH7du3VqtW7d+4DYqVaqkGTNmpGi9GNoDAAAAAABgIxIpAAAAAAAANiKRAgAAAAAAYCMSKQAAAAAAADYikQIAAAAAAGAjEikAAAAAAAA2IpECAAAAAABgIxIpAAAAAAAANiKRAgAAAAAAYCOntK4AAAAAAACwr2Il4tO6Cs8MeqQAAAAAAADYiEQKAAAAAACAjUikAAAAAAAA2IhECgAAAAAAgI1IpAAAAAAAANjIYDKZTGldCQAAAAAAYD8rfribJvtt/kGmNNmvPdEjBQAAAAAAwEZOaV0BAHjWhYVFpHUVnklZs2aWJB0+EJnGNXk2VXjRXZL0WtU0rsgzav0e8z2fD/Zh+XwgvvZBfO2L+NqXJb7Ak6BHCgAAAAAAgI1IpAAAAAAAANiIRAoAAAAAAICNSKQAAAAAAADYiEQKAAAAAACAjUikAAAAAAAA2IhECgAAAAAAgI1IpAAAAAAAANiIRAoAAAAAAICNSKQAAAAAAADYiEQKAAAAAACAjUikAAAAAAAA2IhECgAAAAAAgI1IpAAAAAAAANiIRAoAAAAAAICNSKQAAAAAAADYiEQKAAAAAACAjUikAAAAAAAA2IhECgAAAAAAgI1IpAAAAAAAANiIRAoAAAAAAICNSKQAAAAAAADYiEQKAAAAAACAjUikAAAAAAAA2IhECgAAAAAAgI1IpAAAAAAAANiIRAoAAAAAAICNSKQAAAAAAADYiEQKAAAAAACAjUikAAAAAAAA2IhECgAAAAAAgI1IpAAAAAAAANiIRAoAAAAAAICNSKQAAAAAAADYiEQKAAAA8JS4c+eOTp8+ldbVAIAMzSmtK5CafvrpJx05ckRHjhxR6dKlNXr0aO3atUv+/v46duyYrl+/rt9//z2tqwlkeNeuXdPEiRO1Z88e3bp1S2+88YZGjhz5n7Z1+/ZtHT58WGvWrJGLi4tGjx6dwrVN30wmk+bNm6stW7ZIkt599101bfpWsmWPHDmiyZMnKj7epIIFC6pfv/7y8PCQyWRStWpVZTQak7xm7Nhxqlu3nl3b8DQI3O+vFStnS5JKlqyg1h/0kpOTc7Jljx7bp63+a3Tu3AndvRulJm+2UcMGLSRJ300fpq3+q5O85sUXa2nA55Ps14B0rnZD6YOPpM4fPLhM6bJSB1/JYJAuB0nTxktRkffWN2oivdHc/Dhgq/TjfLtWOd1Lic8GSQoPD1fdurUTlS9VqpQWLlxs1/o/DVIqxha3b9/WzJkzdOXKZfXp09fu9U/PUiq20dFRmjt3rk6fPq2jR4+qTJky+vzzfsqXL1+qtSW927Ztm+bMMX+/VahQUT179nzg91twcLBGjRqpO3ci5OjoqP79v1CRIkUkSV27dlZgYGCS17Ro0VKff97PbvUH7MVuPVJu3bqlhg0bqlmzZoqOjrbXbh7Lc889p23btunvv/+WyWSSJBUoUEAnTpzQ3r17FRsbm8Y1zNi6d++u6tWr6/jx42ldFaShW7du6Z133lGzZs00cuRIRUVFafXq1f/p/9NoNOq3337T0KFD9euvv1r/7zOSRYsWavXqVZo9+3+aPHmy/PwmaPPmTUnKBQVdlK9vD3Xs2Elz5sxVbGysvvxykCQpNDRUWbJkUeHCha23/Pnzy9PTU7Vq+aR2k9KdkycP65uJn6tHtxEaNWKBTp46ogUL/ZIte+HiaZ04cUg9uo3Q+LE/qmTJipo9Z7QCA7dKkqKiIpQ/f5FEN2dnFzWs924qtij9yJxV6v651Hug5Or64HL5vKWRk6VFs6XPOkpOzlL/4ffW16wrdeotDeol9eksNX5bat7K3rVP31Lis8EiW7ZsiT4fSpYsmVrNSNdSMsanTp3SBx+0lMFg0DffTFSBAgVTqxnpUkrFdvr06WrWrLn8/L7R999P1969e9SvX8ZOUiV05MgRDRjQT8OGDdfcufP1559H9O233yZbNi4uVr6+PVWiRAnNnDlb9es3UI8e3RQeHi5Jio6OTvQ5UbhwYTk4OKh583dSs0lAirFbIiUiIkJXr17V5cuX000ipVKlSnrllVcSLStYsKCaNGmSRjVCQqdPn9bt27d1+/bttK4K0tC0adMUHBysF198UdWrV9f06dM1a9YsOTsnf/bjYRwdHfXBBx/o7bffTvmKPgXCw8M1a9ZMVa9eQy4uLsqePYfKl6+gSZMmJUkqzZgxQ5L08stVJUm1a9fWjh3btX9/oO7evauJEyfrp59WWG9t27bTG2+8KSenDNWxMVkLl0xUvryFlD9/ERkMBr1UubY2/PGTrl67mKTszl2/q2mTdpIkg8GgGtUbSZIOHg6QJNV8tbEm+q203kYNX6CcOXKrYsXqqdegdMLJSfqoq7R0nhR66+Fl23Q03x/ca77f5S+9UlMq/6K5h0oHX+noISk8TIqNkQJ3S607SG7u9mxB+pVSnw0WHTp0TPT58OWXQ1KvMelUSsb41q1b6tWrp8qVK6++fT+XwWBI3cakMykV29DQUGXPnl158+aVJBUrVkzFixfX8ePHdfPmzdRtVDo1efJEFSxYSEWKmL/fatXy0c8//6SgoKTfb7/++qtOnTqp2rXrSJJ8fGrr2rVr+uGHJZKktm3bJfqcmDDBTy+88IKee+65VG0TkFLslkgpWLCgtm3bps2bNyt79uwpuu3Tp08rKCgoRbeZnhmNRm3fvj2tq6EDBw4oLCzMbttfvXq1tm/fniTZldLi4+O1d+9eu+4jNaWX90dKWb9+vdzc3KwH6LVr137i94Sjo2NKVO2ps337dkVFRSl//vzWZQUKFNDly5d09OhR67LY2Fht2bJZefPmtf5A9/YuIEnasGGD8uTJozJlyiTa9i+/rNHrrzdOhVakb8E3r+rEiUPyyu1tXZYnT0HFxxu1e/fGJOVbvNtFzs4u1ud37pjP1Hl5mf9GL79UN1H5gJ2/6aWX6sjBIeO9h+PipCljpZs3Hl7OyUmqXke6fuXesiv//kTwaSC9UF7Kk0+6ejnB+kuSh4f0co2Ur/fTIKU+GywyZ86cSjV/eqRkjGfPnqUbN26oe/ceGT6JIqVcbLNly6Y2bdom2nZ4eITc3NyUI0eOVGhJ+nb16lUdPnw4SZyNRqP++OOPJOUt71dL+Vy5csnV1VUbNpinTahTJ/H32y+//MLvCDzV7DrZbPbs2ZOM73xS+/bt08cff6xLly7ZVD4sLEwxMTEpWofUFBISot69e2vt2rWput/7M/EbNmxQp06d7JpIcXV1laenp922L5nn3ujatatWrFhh1/2klsd9f5hMJoWEhDyyXFr931y/fl03bjziqAk2O3nyhKTEBzmWx3///bd12fnz53X37t1HlrM4e/as7t69q+LFi9ul3k+T8+dPSpLc3e7Fzt3d/Pifs0ljd39CZPuOdXJ3z6zatZomu/2t/r/o1Rqvp1R1n0nehczDfiLv3Ft2J8J8X6KUVKyE+XFkRPLrM6KU/myYPXuWateupZo1a+jTT3sne7Y6o0mpGBuNRq1bt1aFCxfWuXPn1L59W/Xo0U2HDh1MjWakSyn5/k14ouXYsWO6cOG83nuvhRwcuB7HqVPm77fk4nf8eNLvtwf9Xc6fP6/IyMhEZY1Go37/fb3q12+Q4vUGUovd+mQbjUb9/PPP+uOPPzRjxgxdvHhRmzZt0urVqzV48GCNHz9ef//9t55//nmNGjVKzz//vCTzgdT48eN16tS92ciHDx+uIkWKaNSoUfr1118VFxenr7/+WlmzZlWZMmU0YMAASdKCBQu0ePFiZc6cWWFhYSpdurR27typVatWqUCBAjbX/fbt2/r555/1559/6syZM+rSpYu2bNmiDRs2KEeOHOratasKFCigNWvWaMeOHXJ1ddXnn3+u11+/92N306ZNmjFjhkwmk6KiolSpUiV9/vnnypo1q8LCwrR3717NmjVLHTp00LZt2/Tbb7/J1dVVXbt2VevWrSVJixcv1syZM3X16lXlypVLbdq0kSRNmjRJOXPmtLk9Fy9e1MCBA/XZZ59p8ODBun37thYvXqzChQsrODhY48aN0759+5QjRw7Fx8fLy8tL0dHRWrhwoS5duqQxY8ZYs8yfffaZMmXKJB8fH3Xo0EF3797V1KlTtWnTJmXLlk0RERFq37693nnn8cc77tmzR4MGDbJmuW/evKmdO3fq+++/1/Dhw7Vo0SL5+/srZ86c6tevnxo1avRY258+fbqmTZummJgYNW7cWKNHj7bOvVGpUiUNHjxYhQsXliSb2rV3715NnDhRvr6+6t27twoXLqwlS5bI0dFRly5d0qRJk3Tt2jUdPXpUuXPnVr9+/VSnjrm7Y2q8P/7880/5+fnp8uXLcnZ2lpeXl0JDQ1W3bl317NlTkrl3zuzZs7V8+XLlyJFDISEheuGFFxL93wQGBmrKlCkKCwuT0WhUsWLF9MUXXyhPnjyPFf+goCBNmjRJgYGBypEjh27duqWaNWuqd+/e1vfzX3/99VjbfJANGzZo5syZio6OVqZMmXT37t0U2e7TJiLC3Nsh4cG7paePZd3jlLNYv/43NWz4eP9/zypLj5KEP7odHZ0SrXuQbdt/1dFje9W/7yRlz540iXz16gXdiQxXsaKlU7DGz57MWcz3CedCjosz33tkSbA+Ppn1GbQjRUp+Njg7O6lVqw9Vvnw5HT9+XJMnT1Lnzp3044/LM3RPlZSK8YULFxQREaHKlSvLx8dHNWrUUO/evvL17amff14pLy8vu7clvbHHd1tUVJRGjhyuatWqq0uXrvap+FMmPNyccXZ0vPf9ZomfZV1CERGW8snFO0Lu7vfGUgYGBqpIkSKPdTwDpDd2Sbf+9ttvqlOnjgYPHqyoqCidP39eAwcO1JgxY3TmzBktWbJEX3zxhVq3bq0jR45o2LBh1td27dpVW7Zs0fLly7Vq1Sq98sorOnv2rLJmzaqxY8daD94GDhyohQsXWpMokydP1qhRo9SkSRP9/PPP6tq1q37//XfrBEePI3v27MqTJ49+++03nTx5Uk5OTpowYYI++ugjXb16VcOHD9fmzZs1btw4TZ06VZcuXdLgwYMV9+8vM39/f3Xr1k1lypTR8uXL9eGHH2rZsmUaMWKEJGnWrFnq3r27Dh06pEmTJsnHx0cTJ05URESERo4cqbNnz0qSWrdurXffNU8wWLNmTS1cuFALFy60+UPn+vXr6t69uxo1aqS9e/fqm2++0fnz53Xjxg0dO3ZMUVFRatOmjX7//Xf973//04oVK5QlSxZt27bNug1vb29NmTLF+vybb77RwoUL1aFDB0lS//79NXPmTI0ZM0Y//PCD3N3dNXDgQO3atcvmeJ8+fVpvvfWW2rZtq4sXzWexoqKi9M0336hv3746c+aM/Pz81KJFC40aNUpBQUEaMGDAY/9tu3TpYk1kBAYG6sUXX9TYsWOVOXNmbd++XR9//LG1F8bD2rV//369++67atOmjS5cuKDRo0fr1q1bOnTokG7duqWLFy/q3Xfflclk0vz587Vy5UplzpzZmiBKjffHqVOn1KZNG928eVMrV67U4sWL9ddffyVJVIwbN05+fn5q27atli1bpo8++ijR/83x48fVvn17ubq6asWKFfr888/122+/6dNPP32s2N+4cUMtWrTQmjVrNHbsWK1YsUK9e/fWjz/+qDZt2ig6Olp//vmnfvjhB+vff+3atVq7dq1Onz79WPtasmSJevbsKXd3d61evdq6zYzI49+jRMtnU8LHCQ9wbC1nsWHD76pXr37KV/gp5O5m7nVpNN6LneWxpWdKcv76K1CLf5isQV98p/Llkx+6tmPner1SlTg/iqUnSsLpeiyP70Q8Yn2CXiwZSUp+Nri6uqlFixYqVaq03n67mXr29NX169e1bZu/fRuRzqVUjC09gd3dzZ81Tk5Oatr0LUVFRSkgYIcdW5B+pfR3W1xcnAYOHKDixUtowgS//zQn27PIMqrA1t8GDyt//wgFfkfgWWCXREqjRo3UsGFD6/MiRYqoW7dukqScOXPKz89PFSpUsB4EHjt2zFr2zJkzCg8P19SpUxUUFKQOHTo8svv47du3NWvWLDk4OKh9+/aSzPMqPAlLhj9PnjzWng9VqlSRZO5tY0nglC9fXpJ54qvg4GBJsl5e9eDBg2rTpo0WL14sb29vay+bPn36JEoI1a9fX6+++qpKliyp+Pj4ZLvS/xe5c+fWkCFDrJcsffPNNzV+/Hi99NJLevnll7VixQr9888/8vHxUbFixSRJPj62X4Hj4MGD+u2335QpUyaNHz9eH374oW7evClvb28dOnTI5u0UL17c+v6wcHNz01dffWV9PmbMGFWvXl2NGzeWl5eXIiMjrQmFx2HJhteoUUONGjWSj4+PRo0aJcncY2Lbtm2PbFflypXVrp15ssgbN25o4sSJ6tSpk958803lypVL33zzjUJCQqzv70KFCmn58uXW/aTG+2Pq1KmKiopSy5Yt5eHhoezZs6tixYqJyoSEhGjRokVycnLSe++9J0mqXz/xl9r48eMVGxurCxcuqG3btvLz85O3t7euXr2apJvmwyxZskQ3b95U0aJF9fLLL0syvx+zZ8+u06dPa82aNSpXrpwGDx4syTwE6Y033tAbb7zxWMNHIiIiNH78eEnSJ598IkdHR7m4uKhmzZo2b+NZUqKEeUxDWFiodZnljFHCq2oULlxYLi4uyZZ7/vnEV984ceKE3NzcVKhQIbvVOz07dChAw4Z3sN6u3zBPvBERcW/YY+S/Y0iKFkl+3EjQpX80b8EEfTVktl4oXVmSdOXK+STl9uzdpFde5ofmowSdl2LuSlmy3ltm6Wly5oT0z78dXB+0PiOyx2eDRYUKFSXJ+psoo0qpGFvm6kh45bps2bJJUoadnD+l37/jxo3V88+X1IgRI+Xi4qK4uDhdvpxgUqUMYufOnercuZP1dvmyeRqF0NB732/JxdnC8ncJDU0c74IFCyZKpBiNRu3Ysd16chN4WtllaI+Dg4NeeOGFRMss3bwSdvdycTFPuJfwqj5169bV2rVrNX36dE2fPl2VK1fWkCEPn/394MGDiomJkZeXlzVDmlIT3Ca8IoWlvgkfJ1wfFxenkJAQ6wG+n5/fA2eitrzuUfF4UgmHX1SpUkXPPfecXnvtNUnmoTSSrNd3lx4vbgcOHJBk/jBduHDhE9Xz/okspcSxTS5OUVFRT7RPi+rVq8vFxUUxMTE6f/68zp83H9A8rF25c+eWZJ5Iq2jRourTp4913Y4dOxKVSSi13h+7d++W9PC/7aFDhxQbG6vcuXPL9d/rit4/R43lbzxgwIDHSrLdz5IsTTjEztHRUd7e3rp9+7YOHjyoFi1a/OftW+zfv9+a4En4JZ+wO2lGUrNmLbm6uurcuXPWZUFBQSpatJgqVKioqKgoubm5ycXFRbVr19GWLZsVExMjFxcXXbpknq2zWbNmiba5bZu/dUb+jKhixRqqWDHxDKUBu9br8uV7id1r1y7K0dFJtX3M857ExETL2TmTDAaDYmNjNGPmcPX9zE+5E0xQO3+hnwb0m2x9fuPGZUVGRqho0Qw6iYcNMrlKd6Ol2Fhpp79Uvbbk7Gx+nu/fj5rfVkunj0s3rkkFC997bT5vKfS2FLA1DSqeDqTkZ4PJZEo0AaolgVKuXPnUa1A6lFIxzp8/n3Lm9NTFixes27EkUCwHrhlNSr5/161bKxcXF3Xteu+E3r59e3Xx4kW1aNEyVduV1qpXr67q1RNfIW7jxg06f/6c9XlQUJCcnJz05pvmK55ahlAbDAY1bNhI+/fv1/nz55QrVy4FBwcrOjpab7+d+HfEoUOHVKRIUWXPzoS+eLqlu5mUxo0bp+HDh6t69epydnbW/v379dlnnz30NZYDp4SXPHucs+UpKWEd/vzzzzSpw4NkypQp0fM7//Zpjo+/N3D8ceJmed2ZM2fSLN4pwcHBQVmzmk9Vuri4PFa77o+pdC/RceHChSTrUuv9Yan3w/62lkSUMcHEAgkfJ3x9wlnwn8T9V8+x/PhOWM8ncSdBP/2Eic+MKlu2bOrYsZMCAnYoIiJCt2/f0pEjh9WpUydt3rxJPj41tXr1KklSly5d5eLios2bN0mStm7dqho1aqhMmbKJtrlr1y7VrFkrtZuSrrX98DOF3LqhEycPyWQyKXC/v+rVba6cOXMrOPiKPulUV9O+N/e22rBxuaLvRmnVmrmaOXukZs4eqbHjeyW5VPLhI7tUuVLG7EmVHAdH882iRh1p5Rapkfm3vObPMCdQavyb46vmI+0NkE7+JcXHSzMnSaXLS7nzSs4uUpVXpOULzT1ZMqKU+myIjo5Sw4b1NWXKZMXHx8toNGrJkkVq0aKlKlWqlIYtTHspFWMnJ2d98sknOnnypE6cMHeh2rhxg8qVK6fq1TPmZadSKraxsbGaNGmSIiMjNXr01xo9+muNHDlCo0aNtPb6yeh69fpU169f1+HDh2UymbRt2za9/XYz5c6dW1evXlHDhvU1bNhQSVLTpk1VosTzWr9+vSTJ33+rvLy81KxZ80Tb3L17V4btKYxni90mm/2vpkyZok8//VQtW7bUtWvX1Lx580SXOraMW0x4Vj5fvnySpFu3bunOnTvy8PDQP//8k7oV/5enp6fy58+vy5cva/LkySpXrpyee+45GY1GrVq1Sq+//vpjnR23tDelel8kZLk8WcL4Pmi4jLOzs2JjYxPFvVy5cpLMB69fffWVRowYIRcXF928eVO7du3Sm2++meJ1Tin3Jxhu3bolBwcHVatWzXo27b+2q2TJkvrzzz+tPaosvaQOHz6sChUqpMr7I3/+/Dp37txD/7Z58+aVZD6zZfm/uT/5U65cOe3bt0/z5s1T9erVValSJZlMJm3cuFHlypWz/u89SoUKFeTv75/oaltGo9FaP8uwuSeVcLhJUFDQQ+cTiouL082bNxP12rp69apy585tnTg0JCRE7u7u1h47T6O2bdvJaDSqa9fOMhgM6t37U9Wv30ABAQFyd3eXm5v5/VawYEFNmTJN337rp2XLlil//vwaOfLrRNuKjo7SlSuXVaoUvSQSKvl8RfX9zE/z5o+XDAaVKF5ObT80nwBwdHSSm5uH3N3NM57u2btJ58+ftF7t5942KiR6/tff+1W9GhP6lqkgvdFc8vr337TPYGnFUnNPlKgo800yX+54kK/UubfU9D3zpY7HDL63ne2bJDd3afBYyWSS1q+RflqU6s1JV1Lis8HV1U3vv/+B1qxZrW3b/JUnTx41bNhITZokfxWqjCalPn9btGgpo9GoL78caB1a+c03EzP0pZBTIrYHDx5USMhN/fLLmiTbT6me7U+7ChUqaPz4CfLzmyCDwaBy5cqqVy/zPHlOTk5yd3dXliyZ/33urMmTp2jUqJH65JOP5eTkpGnTvleWLFkSbfPAgQP64osvUr0tQEqzWyLFMpbTcsBquU94xjvhZESxsbFydnbWhg0bVKdOHVWsWFE5cuSQm5ubKleubC1XoEABnTt3Tr/88oty586t48ePq2nTpipUqJAuXLigX375Re+//7527dolR0dHGY3GRGfikzvzndwyS8Ig4ZjUhEkESxfBhG2w9Dbo1auX+vfvr0uXLqlx48bKkyePbt26pS5dulgPki1xSLhvy74SbrNgwYKSzEMWDhw4oNDQUBUtWjTRkI2HSdgDIuFjSWrSpImWLVumgIAABQcHy8nJSSdPmn/c37lvBr4CBQro7Nmz+vnnn9W0aVOdPXtWjRs3VtWqVbVnzx6tWrVKv/32m7Jnz647d+5o6dKlNtXv/rZL5pg4ODgkeq88Kk6P68iRI9a/4aJFi2Q0GtWmTRsVL15cxYsXf2S7LLFM7r3j6+urjh076sCBA2rYsKEqVaqk0NBQvfDCC6pQoUKqvD+aNGmiKVOmWP8fDhw4YH2d5f+hYsWKKlCggIKCgrRu3Tq999571iFBCdvSpk0bhYWF6f3335eXl5fu3Lmj119/PdE8SI/Srl07/fjjjzp16pT27dunl156SatXr9bt27dVpkwZvfXWW0nae/v27cf+IV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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n 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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\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    },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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mSb9X1zj59rTMm//OyRy6Xi3feeYc33niDS5cu8ffff/P333/f0sm9kvYuBgUFeW5Ljb2E1zvz8/Xe8xYtWtCnT5/bWlbS+3Kjk2ulpDvuuAPghv/Wjh8/Dlgn9Upyvff3VrLOly8fffv2ZcSIEcTHx/P777/z+++/s3PnTt566y0A7rrrLhYvXux57scff0z//v0ZO3Ys33zzDZkyZaJjx44cP36cYcOG4XK56Nq1K6GhoYSGhl41hpIlSxIXF0dQUBDdu3dnzpw5HDt2jDJlyniO+siZMycXL16kc+fOyQrxP/esXykuLo6YmBheeuklTp06xYgRI9iwYQMNGjSgQ4cOvPvuu4wZM4YCBQp4/n2qYIuI+BYdIi4i4gOSDn0GuHTpkufPSSdncrlcya51/W8LY1Jh3rdv301P2ORN8fHxPPLII/z000+8++67VKxYEbD2nl75ZQRc/V5kypQJSF4Sr/ztbVpLes+3b9+eostNjS8Nkk6GV6BAges+JunQ5iv3/P6X9/fy5cu0atWK1atXM3DgQMqUKQPAvHnzPI/JkyfPVcU8NjaW7777joCAACZMmEBwcLDnsPyyZct69phfS65cuZg5cyY///wzAQEBni9l5syZQ5MmTVi7di3BwcHkyJGDiRMn0qZNG4oUKYJhGFf97OGfYwoMDKRHjx7Mnz+fkiVL8sILL/DUU0+xdu1aWrduzU8//cTMmTNp0aIFoIItIuJrVLBFRHxA5cqVPXv7rjw8d9euXQBUrFiR4OBgz6HfSY+JjY3lzJkzt7UePz8/oqKiGDRokKfMR0ZGMm3atBR5LSnh8uXLDB8+nGzZstG6dWu+/vprKlWqhGEYnkNwr/deJO2F3bp1K2AVUW9eN/zRRx8FrMOcv/rqK8/e6GPHjl11hvBbkfS6jxw54jnZVlhY2L8a2z9P1pX0Xv7zbOBXSnp/t2/f7ink//YEcgCHDh1i6tSp5M2blw4dOjBr1iyKFSvmeZ3XEh0dTc+ePYmOjqZ3796evctJX2Ik7YW+mXXr1jF69GjPeQ9atWpFuXLl2LNnDzExMURGRvL222/zyy+/cPToUTJnzuz5KcW1XL58mdDQUEqVKgVY70vNmjX59ttvqVq1arLHJr13KtgiIr5FBVtExAcUL16cNm3aAPDVV18BVoFaunQpWbJk8ZzYKuk6u0uWLKFGjRrUr1+f+Pj4ZHu9k35vDfbe8KQiddddd3kua7Rw4UIqVqxIjRo1qFy5Mvnz5/c8L+lD//XOlnwrrhzTlSUi6c9X3hYXF5fstqxZs7J48WLPmctjY2O5cOEC9erV8xyie7334sknnwSss4uvXbuWUaNGeQrYjh07PGX7n5cuS5J0+5W3Xfn796Q9mFde+unK9ynpz0mvpUWLFp69qe+99x4PP/wwVatWpWXLlp7fbl85liv/nLSsK29Let0nT56kWrVqVK1a9aZn/b6e8PBwzwnwtm/fzq+//kqpUqU8/xavXHfS+9GoUSOyZMnC2bNnGTlyJOvXr+fEiROexy9ZsoSTJ08mew9ulHXOnDmZOnWq5zVcvnyZqKgoWrZsec0xx8bG0q1bN7Zt20bjxo3p2LGj576ks6k/+OCDN33tP//8M127dqV169Z07twZsI5++N///sdzzz1HZGQkxYoVY8WKFUyfPp3mzZsTHBx81REUV4qIiPCc5A6s7KdNm0ZQUBAjR470/FwA7H9H/2UbExGRtKeCLSLiI4YMGULv3r3Zvn07devWpX379lSvXp3Zs2dTsmRJwDpjduXKlcmUKRO5c+fmk08+IUeOHHTq1MmznIEDB7J8+XI+++wzli9fDljXTv78888963nuuefIkycPAQEBhIaGMnbsWGrXrk10dDRDhw71XPpr9OjRfPLJJ7f9WrZt25aspHXp0oUtW7YwdOhQzzW3Fy9ezJgxY1i9ejUTJ04ErNLYrVs3Ll++TJs2bXjjjTdo1qwZTz31FBUqVOD999/3LPNa70VoaCitW7fmtddeIzAwkN69e5M9e3Z69+7NHXfcwenTp9m/fz/bt2/3nLkcrEuX/fHHHwwfPtxz+aht27bxwQcfsHbtWt5++23PY9u0acPGjRt57rnnPLcNHjyYxYsX06lTJ88ltaZMmcLmzZsJCAhg6tSp1KtXj8yZMxMQEMB9993HtGnTKFy4MFu3bk322+yXXnqJI0eO0LNnT8+yxowZ49nb/dBDD/HUU08REhJClixZPJdV+zeyZcvG6NGjadWqFZ07d6ZFixZMnTqVoKAgjhw54rlcFcBnn33G6tWryZEjB1988QV3330306dPZ+rUqfTt25cnnniCzJkzs3DhQrJmzer5twwwdepUZs2axdy5c5k9ezZgndl+4MCB5MqVi8cee4yXXnqJFi1a0L59e5588kl69ux51XjDw8Pp1KkTa9eupUWLFgwfPpy5c+eyZ88etm3bxp49e3C5XJQvX/6Gr3v16tWMHz+eMWPGMGjQIM8XAldekzrpMmbNmjXjoYceYvDgwcTExHDp0qVrnnBw586dREVFeS6/FRkZycGDB7l8+TLPPvsslSpVYujQoXTr1o0ffvjB8xONGx1yLiIizmOYaXVmFBER8ZqEhIRkv1H959//C7fbfd0Tlt1oPH5+fslO+JU0puud0MupoqOj8ff395Sv2NhY3G430dHRZM6cGZfLxeXLlwkICEh2YrW4uDjcbney25xi7ty5DBgwgEKFCnnOup7STNNM0ax/++03evToQZYsWXj99ddp0KABAD/88AM9e/bE5XKRkJBA1apVPddIv56zZ88mO+/BpEmTGDFiBP369fN8WVW2bFnKlSvHa6+9Rvbs2Zk6darny5ctW7ZcdR3rpGUkHdoeFxdH9uzZPZcDS/pCxOVy8dtvv5EnTx727t1L27ZtGTJkSEq9TSIiksp0FnERkQzgWmfhTilXluuka0/fzPDhwz2HPwPJfjvta5JO6pUk6ezZV95+5Znfk9zoN8Qpbdu2bfTt2/eWHvvjjz+m8mgsKf1Fyv79+xk1ahQPPfRQsn+TDRs2ZNSoURw8eJDg4GB69+5902VdWa4Bz88GrjxDeOXKlWnQoIHn33Hz5s2ZPn06JUqUuKpcX+n111/n6aefJigo6Lrb4erVqz2HpRctWvSm4xUREefwzU8zIiLiSAcOHLilx12+fDmVRyJXunz58i1nA/bvqX3p979Jv63/J8MwqFu3Lj/99BP/+9//PCcYux133nknq1atomzZsp7bxo8fn+wx9913H/fcc881D10H66cD3333HdWrV79hAQeoVq0auXLlIiEhgebNm9/2eEVExHt0iLiIiIh4zJo1i4kTJ3L48GEAypQpQ/ny5Rk4cKCXR+Y9UVFRrF+/njp16qTZOhctWkSRIkW4//7702ydIiLy36lgi4iIiMe1fp8fFxeXpoe0i4iI+CoVbBEREREREZEUoMt0iYiIiIiIiKQAFWwRERERERGRFKCCLSIiIiIiIpICVLBFREREREREUoAKtoiIiIiIiEgKUMEWERERERERSQEq2CIiIiIiIiIpQAVbREREREREJAWoYIuIiIiIiIikABVsERERERERkRSggi0iIiIiIiKSAlSwRURERERERFKACraIiIiIiIhIClDBFhEREREREUkBKtgiIiIiIiIiKUAFW0RERERu2enTpzFN09vDEBFxJBVsEREfdODAAapWrcrkyZNxu9239dyOHTvSs2dPzp8/f9V9u3fv5rHHHmP58uU3XMbcuXN57LHHGDZsmD5op4Lu3bvTtm1bNmzYcNvPVb7Olh6y7d+/P23atPlXzxURSe9UsEUkQ0tI8F55SHD/+3UXL14cl8vFyJEjOXHihOf2U6dOsXz5crp06UKtWrVYs2bNVc+tW7cuP/zwA6dPnyYyMpImTZrwxRdfAPDHH39w8uRJduzYccP1P/HEE5imycyZM4mLi/vXryM1mbf5xYOT1t2hQwd+//13Jk6ceN3HxMbGcunSpatuzyj5+ipfzvbMmTNs376dcuXKsWvXLurWrUvNmjU5fPjwbS1HRCQ98/f2AEREvMnlMug95Dz7DiakyfpaNQnmmZaZWbwymga1gv/TsjJlykSVKlUoWLAgf/zxB926dcPf359GjRqRJ08eVq1aRd++ffnhhx/InTu353mhoaFkzpyZu+66C4Bq1aoxdOhQateuzfTp0/n6668pX778Ddft5+dHiRIliI2NJTAw8D+9jtRi+PkR+9WXmCdPXPN+v8qPEFCtOnE/r8H96/qUW29ofgLbd/hPyyhatCgAVapUwTRNLl++zLFjx9i/fz979uxh27ZtbNy4EcMw+Prrr7nvvvs8z80o+foqX832yJEjjB49mqioKFauXEnfvn2pUqUKxYsXZ8KECVSqVImKFSv+i3dERCR9UcEWkQxv38EEduyOT/X1vNwxM8+0zMwnEyP56ZfY/1ywIyIiePrpp/nyyy956qmnWLRoEX5+foSHh9O+fXv69u3Ls88+S1BQULLnuVwusmTJ4vl77969KVGiBDNmzKBgwYKMGDGCbt26Ub169euuOz4+HpfLRXh4OM8//zy///47uXPnZvz48dx9993/6XWlJPPkCcyjR695X8LsWXDhAgGNGhN34QIJS5ek8eiuLyAgAIACBQowZswYpkyZwj333MNdd93FnDlzePjhh+nRowfx8fH4+yefyjNSvr7IF7N97733KFWqFMOHD2fUqFHkz5+fX3/9lZ07d9KxY0emTJnC3r17qVChAoZhpMC7JCLiu1SwRUTSwMsdM/P6SyF8MjGS8VMvUfqe//af3/Pnz3P+/HnuueceXnrpJX755Rc+++wzZs2axVtvvcXYsWOpXbs2CQkJnj1Vpmly/Phxfv/9d86dO0e9evXo1q0bq1ev5vfffycsLIwnn3ySVq1aUbly5Wuud/bs2UyfPp1Dhw4RGRlJvnz5uO+++2jatCllypShRIkS/+l1pbWkUh3QqHGyv3tTREQEW7duBeCjjz7iwQcfZMuWLZw6dYpNmzYxZ84csmfPTocOyfeSZ6R8zdio23+SKwjDZW13ZkI8JMSA4YcRkOm2lmsEZrnpY67HV7Nt0KAB48ePJyIigqlTp1KtWjX27NnDqFGjmDlzJuPHj7/uekVEMhoVbBGRVPbPcv1vHT16lIkTJxIZGcm+ffvIkSMHc+fOJSYmhiZNmnDp0iXuv/9+7r33Xj755BNmzJjB5s2b8ff3Z9KkSezYsYNVq1Zx6NAh8uXLx8cff0ypUqVo2rQpf//9N0888QTR0dEsXbqUChUqXPPw0VatWlG/fn0mT57MuHHjCA8Pp2jRojRr1uy/vEVe5bSS/dNPP7Ft2zYAnnvuOVq3bg3Ajz/+yHvvvUfp0qX59NNPr3rezJkzM06+g4rf/nOengQPNLX+/NcimP4CFH8UOs+3HzOsPESdufFyhp66/XUn8tVsH3zwQR555BGCgoIYOHAg06dP5/XXX+f777+nVq1avPzyyzRp0oS33377X783IiLphU5yJiKSilKqXAMULlyYfPnykTVrVqpVq0abNm149NFHCQkJ4euvv6ZJkyZ8+eWX5M6dm927d9O+fXsmTZrE2LFjKV68OG3btuWdd97hzJkzREdHM2XKFA4cOMCsWbM8H+qzZs1KrVq1kh2G+k8xMTF8/fXX+Pv7e85G/N133/2n1+ZtCUuXEPfDQgIaNcb1eD2vjqV58+a88sorAJQoUcJzSPHBgwcBq5hd68zxytf5fDHb9evXU7VqVcLCwjBNk48++ohs2bLx66+/smLFCnbu3MnYsWOJjo7+N2+JiEi6oz3YIiKpJCXLdZKkD+fz589n7dq1rF69mgYNGvDuu+96HjNt2jTWrVvHPffcQ/78+ZM9f8iQIeTJk4eQkBCqVq3K+fPnqV27NoUKFWLVqlUEBgby008/MXPmTPLly8drr7121TIGDRpE3rx5yZ8/PwULFqRevXr069ePM2fO0KlTpxR5nd7gpD3ZSZdPmjVrFvPnz6dUqVJMmzYNgOjoaJo1a0aZMmXo1KlTspNgZZh83z5w+89xXXEugjINrWUY/9jP0G/TfxvXLfC1bMuVK8djjz3G66+/zgcffECNGjU4cOAAv/32GxMnTiRz5sxkz54df39/1qxZc8Pff4uIZAQq2CIiqSA1yvWVnnjiCRYvXszu3btp1KhRsvuSDg/952V+Ro8eTXx8PM8//zyzZs1i165dfPzxx5QoUYKcOXMC1l6w+++/37Nn7Z8nWZowYQI///wz06ZN48MPP8TtdtO8eXNWrlzJsGHD2LhxI//73/8IDQ1N8decFrxZsuPi4li1ahW7d+9m9erVhISE8MADD5ArVy4GDhxInz59GDFiBLlz52bcuHG0bduWH3/8kSlTplC5cuUMle9/+R00YP0W23X1R6D/utzr8eVs/fz86NOnD0eOHKFOnTqEhYWxe/dujhw5wuDBgzl+/DjHjx/3fHHQtWtXXnvttVR5H0VEfIEKtohICkvtcg2wYsUKNmzYwH333cfHH39M+fLlPZfnCQ8PB0h2Nt9p06ZhGAYTJkzgq6++Ij4+nn79+tGvXz8ADhw4wIIFC6hfvz533nnnNdc5ffp0Jk+ezNixY8mXLx8RERGcPXuWwYMH06hRI/744w9WrVrFL7/8wpNPPskLL7zg9SL2b3irZBuGwfz581mxYgWPPfYYY8eOZdasWUyZMoWPP/6YBx98kBEjRgBw11138emnn/L888/zyy+/sG/fPuXrYL6a7ddff81HH31E4cKFKVOmDMWLFyc0NJS8efNSoEAB3nnnHbJmzUpwcDCmaXL+/PkbHqIuIpIRqGCLSIZ3VzFXii0r6TrX0+Zc4qdfYq97tvD/ss4ZM2YwYcIExo8fT5EiRWjYsCEnT57k//7v//Dz82PZsmUAZM+e3fOctm3b4udnHQ67d+9eYmJiki0zae/T9a6LO378ePbu3cvChQtZuHAhEyZM4ODBgxQoUICAgACyZcvGxIkTmTp1KrGxsbhcLrZs2UL9+vX/9etMCUZo/ps/6BrcO/4iLls2q2Rny3Zb18n+t+v09/dn7Nix/P7775QrV47JkycTEhLCDz/8QObMmdm8eTNgXaoJ4JFHHuHDDz8kR44cVK5cOUPm6yt8Ndt27drRrl27q5b5xx9/cOjQIYoXT36yuaxZs/63N0pEJB0wzKT/MouIZEAJCSYul3eu2/pv1r1o0SL27t1L+/btyZEjBwBr1qyhbNmyxMTE8PTTT3Po0CGKFSvGkiXX3vNas2ZN8ubNy7fffuu57fDhw9StW5fffvuNbNmyJXt8TEwMZ86coWDBgp7boqOjqVixIo0aNeKDDz64rdeQVky3G8PPO+fyTI11f/LJJ4wfP54ZM2ZQrly56z4uo+Sbnvhatu+++y5bt25l9uzZ/+r5IiLpmfZgi0iG5q1y/W/X3bBhw6tuSzqpUNasWRkwYABDhgy54QfnOnXqXPVBPCQkhJw5c151O0BQUFCyD+hgnfU4Jibmqt95Oom3ynVqrbtOnTosWrSIMmXK3PRxGSHf9MTXss2SJctVJ1ATERGL9mCLiAgAX375JR06dLilx5qmydChQ3n00UepUaNGKo9MUoLyTb/SOtvvvvuOyMhInnnmmX/1fBGR9EwFW0RERERuWVxcHIDnjOUiImJTwRYRERERERFJAd77gZqIiIiIiIhIOqKCLSIiIiIiIpICVLBFREREREREUoAKtoiIiIiIiEgKUMEWERERERERSQEq2CIiIiIiIiIpQAVbREREREREJAWoYIuIiIiIiIikABVsERERERERkRSggi0iIiIiIiKSAlSwRURERERERFKACraIiIiIiIhIClDBFhEREREREUkBKtgiIiIiIiIiKUAFW0RERERERCQFqGCLiIiIiIiIpAAVbBEREREREZEUoIItIiIiIiIikgJUsEVERERERERSgAq2iIiIiIiISApQwRYRERERERFJASrYIiIiIiIiIilABVtEREREREQkBahgi4iIiIiIiKQAFWwRERERERGRFKCCLSIiIiIiIpICVLBFREREREREUoAKtoiIiIiIiEgKUMEWERERERERSQEq2CIiIiIiIiIpQAVbREREREREJAWoYIuIiIiIiIikABVsERERERERkRSggi0iIpLGYmNj2bJlC3Fxcd4eioiIiKQgwzRN09uDEBERyUiio6OpXbs2xYsX5//+7/+8PRwRERFJIdqDLSIijjV//nxq1KjBsGHDiIqK+lfLOHXqFHXq1GHQoEHExsZe8zGtW7emR48enD179obLOnjwIJUqVaJLly7s2LHjX40HIDg4mC5durBnzx5at27Nww8/zIoVK/718o4cOULlypXp2bMnhw8f/tfLeeaZZ3j55Zc5cuTINe//4IMPePLJJ9m5c+cNl2OaJvXr1+epp55i8eLFtz2OKVOmULduXSZNmsT58+eJj48HoF69ekyaNMnz99sRGRlJ2bJl+f7772/62OXLl9OwYUOmTp16zfvPnz9PpUqV+OSTT3C73Tdc1ty5c6lWrRpvvPHGTf993YolS5ZQvXp1Jk6ceEvvw48//kiNGjUYOnQoly5d4vLly4CV9UcffeT5u4iIpAwVbBERcaxmzZoRGBjIlClT2LZt23UfFxcXR1hY2DXvy5cvHw8++CBz584lMjKSyMhIatSoQZ8+fYiMjOTIkSPs3r2bsLAwzp07d8PxFCtWjFq1arFq1SqWL19+269n6tSpvPjii9SpU4d3332X2NhYgoODee655yhbtuxtLy9JkSJFqFWrFj/88ANz58697uNM0yQsLOy6xaxZs2asXLmS3bt3A/D888/Trl079u3bR1xcHL/88gsnT57k5MmTNxyPYRh06dKFLVu2MG3atFt+HdHR0URFRfHss88SHh7O7t27mThxIq1atSIsLIzQ0FBGjBjB0qVLb3mZSb7//nuCgoKoX7/+TR9bu3ZtIiMj+e677wD49ddfeeyxxxgzZgwAa9eu5fz58xw7doyYmJgbLqt58+YEBQUxe/Zsdu3adVtj7tGjB/Xr12fKlCme2+bNm8fZs2cJCwvj0qVL131uXFwcFy5coH79+gQFBbF9+3bmz59Po0aN2LVrF0WLFuWzzz7j66+/vq0xiYjIjfl7ewAiIiLXYxgGd955J6dPn6Zs2bIcPXqUqKgojh8/zuHDhzlw4AA7d+7kzz//JC4uji5dutCjR4+rllOgQAGKFi1Krly5AOjZsyf9+/enadOmfPnll7zxxhu0bt0awzBuOqYHHniAuXPnUqlSpVt+HXFxcbhcLpo2bUq2bNn4+eefiY6O5r333qNcuXJkzZqVXr16ce7cOd5++20KFSp0629SorvuuguAmjVreorfyZMnOXz4MAcPHmT37t388ccfXLx4kQoVKvDVV1/h55f8e/b8+fMD8NBDDwHQr18/2rVrxw8//EB0dDRVqlShe/fuZM6c+abjuf/++wFu633atm0bnTp14rXXXiNv3rwUL16chQsXkj9/flwuF8eOHeP++++nQYMGt7zMJN988w0tWrQgMDDwpo81DIP8+fNTunRpACpXrkz16tUZP348jRs35uuvv+brr7+mQoUKt7Ss+++/nxMnTvDwww/f1piHDBnCc889x8KFC3nuuecYOnQo586dY968eZQoUeKGzz1+/DiNGzemTZs2FCxYkKJFi7JixQpy585NQEAAx44dI3/+/LRv3/62xiQiIjemgi0iIo7mcrkoUKAA0dHRDBgwgJ07d1K2bFkKFizIjBkzeOihhzx7g5MK4rWWcWUpbNy4MTly5GDbtm1ERUXxwQcfMHv2bL799tubjid79uwAhIWF8fHHH3Pw4EFKlSrFc889R1BQ0DWfs2bNGt58803GjBlDwYIF2bdvH++88w5vvPEGRYsW5fHHHycmJoZWrVqRN2/ef/EuWa8RoFChQowZM4b58+dTsmRJ7r//fr7//ntcLhf9+vUDICAgALfbfVXB9ve3PhYkvVf33HMPU6dOZfv27cybN4/Dhw+zYMECvvnmGwoXLnzD8eTIkQOAmJgYJk+ezJ9//kn+/Plp164dBQsWvOZzKlasSNeuXTlw4AAAq1atIm/evDz//POEh4dz9OhR+vfvf0tfhFxpy5Yt7Nq1i9GjR9/yc/75b2bgwIFUqlSJyZMnEx8fz/PPP88zzzzjeU9vJHv27GTNmpU1a9awbt06Lly4QNWqVXniiSdu+rxZs2YRHh7OggULOHjwIPHx8XTu3Jl+/frx+OOPX/e5d9xxB0OGDGHRokUA/PXXX/j7+/Pcc8+RPXt2Nm/eTJ8+fQgODr61N0RERG6JCraIiDiSaZocPXqUiIgITpw4QadOnXj++edp1KgRERERrFmzhhkzZhAdHU2zZs2uWbouX77M7t272bFjBzt37qR8+fL06tWLdevWsX//fvbt20f58uUZPnz4Dfe07t+/n7///pvdu3ezbt06wNq7Gxoayt13333T38LWrl2bhQsXsnPnToYOHUpsbCxdu3bl4YcfZvTo0Zw4cYJHH32Uu++++7bLI8CZM2c4evQoAC+99BKVK1dm69atREZGcvLkSWbMmEHmzJmpXLkyRYoUuer58fHxHDp0iLVr1wJQtWpVWrZsiWEY/Pbbb+zatYtcuXLRq1cvKlWqdN097KdPn2br1q3s37+frVu3AvD555+TI0cO7rzzTnLnzn3d1xAVFcX8+fO5fPkyJ0+e5Pjx41y+fJn77ruPw4cPs2PHDu655x7y589P//79WbFiBS+88AKdO3e+6fszY8YMqlSpQtGiRW/62HPnzvHnn39y6tQppk+fznfffcfAgQOZOXMmR44cISwsjFatWtGrV68b7pH+/fff2b9/Pzt37mTNmjVERETw6quvUqRIEe655x5udI7ZhIQEnnrqKS5evEhCQoLn/StXrhxbtmxh+/btdO/endmzZ3Pfffdd8/mzZ8/mxIkTBAQEsHnzZoKCgjzv5dGjR8mRIwfly5fn3XffZcGCBTRs2JDBgwff9P0REZEbU8EWERFHWrZsGd9//z1Hjx4lb968DB482HPY8YABA1i9ejUFChTgq6++umYpPXv2LG+99Rbnz59n165d3HHHHQwcOJDKlSvTtm1bYmNjKVeuHIZh8Mcff5AnT57r/g46MjKSLVu24O/vz19//QVAr169eOmll2759QwePJgpU6bw1ltvAdbe2WeeeYbp06dTqVIlevbsSUhICLNnz/bsOR08eDA//PADY8eOve4XALt27eKzzz5jz549ALz22muex37zzTcMHz4cl8vFxIkTr1muAYYOHcqOHTsIDw8nICCAd955h+rVqxMSEgJAu3bt2L17NydOnGDTpk3XLdjx8fFs376d2NhYtm/fDkCNGjWYMGHCVXvL/ylLlixUrlyZxYsXs3btWvz8/KhUqRLNmjWjVKlSfPjhhwQFBdGqVSvq1avHkCFDqFWr1g2XCVZh/vHHH/noo49u+titW7fy6aef4na7OXr0KI8//jgvv/wypUuXplGjRmzYsIH27dvjdrtZt24dxYsXv+5REydOnOCvv/7iwoULHDt2jICAAKZOnUr58uVvOg6Xy8XAgQN56qmnePPNN2nXrp3nvgEDBhAQEMCnn356zXKd9PyqVauyZs0axo8fj2EYlC9fnhYtWlCzZk0aNGiAn58fLVu25JFHHqFPnz7UrVv3puMSEZGb00nORETEkR5//HHGjBlD6dKlPScqSypp+fLlA6yykXTI9j/lzJmTMWPG8MYbb3Dx4kWCgoI4f/48CQkJfPnll/To0QPTNPn777/Ztm0bZ86cue5YHnjgAd58803y5cvnOWv0pEmTPCfBupGEhAT69+9PvXr1KFOmDIZh8NFHH+Hn58dPP/3Ejz/+yLJly5gxYwZ33XVXspNmHTt2jIsXL95wbPfeey8fffQRzZo1A6xCm3TYb9L71LZtW8/vqq/lzTffZPLkycTFxeHv7098fDyXL1/mp59+onfv3uzdu5eoqChWrFjBiRMnrruc/Pnz06NHDxo1asSpU6cAa0/ukCFDbvo+gXVI9Ny5c3nyyScpWrQoOXPmpGfPnrz99tu8//77rFy5EoB169ZRs2ZNMmXKdNNlzps3jxw5clCzZs2bPvahhx7iiy++oE6dOoB1tveIiAiio6MZOnQoo0aNAqyTnO3fv/+GZ7ZP2iOc9JigoCC6devmOYHczZQtW5bQ0FA2btzImjVr+PbbbxkxYgQ//vgjmTJl4p577rnh83PmzMmcOXOoUqUKlStXJn/+/Lz99tv06dOHTp068fPPP5M3b142bdpEhQoVrrsdiYjI7dEebBERcbSkQrt+/XoOHz5MUFCQ57fSJ0+epGvXrjRs2JDHH3/8mieweu+993C73bjdbv73v/8RFRXFE088Qbly5di0aRMdOnSga9euxMTEcPr0afLkyXPNcZw5c4bPPvuMPHnycPr0aQYOHMiAAQOIj4+nZcuW1x1/0m+j69Spw3333UfdunV5+eWX+b//+z8KFCjgOYtzjhw5eO+999i5cycPPvggQUFBjB8/nrNnz3qK8s3eJz8/P3bv3s3q1aupVq0agwYNAqyi2K1bNypUqECzZs3ImTPnVc///PPPCQsLIyQkhKlTp/Lzzz/z7rvvkidPHs6ePUtCQgJTp04lISGBU6dOkTt3bs9ru5JpmgwbNszzPvXo0YOPPvoI0zT53//+d9092SdPnuSFF16gVatWfPfdd+TJk4cWLVrwwgsveH4rHBQURJYsWbj77rtv6URrpmkyc+ZM2rRpc82xXsu5c+c8Zws/ceIEL774IkuWLKF9+/Zs27aNTZs28fHHH1O+fHkuXrzI+fPnr1tO169fz6pVq8iTJw+hoaGUK1eODh068Pnnn1937/OVqlWrxtatW1m2bBl58uShYMGCvP/++/j7+7Nv3z5y5szpOcrgSpGRkXTu3JkHHniA3bt3c/78eXr27EnXrl15++23PfnnzJkTPz+/6x7ZICIit097sEVExLH27t3LyZMn2bhxI7NmzaJAgQIMHjzYc8bse+65h4oVK9KrVy/q169/1aW8Zs+ezd9//02NGjUoVqwY06dP5/777ycsLIyTJ08SFxfHwoULadiwIQ899BBVqlShXbt2xMXFXTWWwYMHY5omHTp0AKBu3bo0a9aMgQMHMnbs2BteD/mDDz7gf//7H4UKFeL777/n1VdfpVChQuzZs4dffvmFr776iueee45HH32Udu3a8dprrwHWychupVyfPn2aXbt24Xa76d+/Pw888AA9e/YkV65cBAYGEhQUxAsvvMBHH31ErVq1mDNnTrLn79mzh8mTJ9OoUSOCgoL44osvaNOmDcePH+fs2bNcuHCB3bt307p1a8qWLUu1atWoU6cOhw4dumosX331FRs3bqR79+4AFCxYkDfffJNvv/2WV199lYsXL17zNaxatYrRo0fTtWtX4uPjKVq0KJMnT6ZJkybUr1+fPn368McffxAVFUXVqlVv+p6AdXmto0eP0rp161t6PFhZFSpUiGLFitGgQQO+/PJLXC4Xp0+f9uy9//DDD6lRowbly5enUqVKjBw58qrlXLx4kTfeeIMKFSrw6KOPAtCnTx9y5sxJ+/btWbVq1XXH8OGHH+J2uylZsiTffPMNOXPm5PvvvycqKooGDRpQo0YNJk+eTMWKFenYsSP79u1L9vxVq1YxcOBABg0ahJ+fH6Ghofzwww/Ur1+fypUrM2zYMNavX8+pU6eoUqXKTQ/fFxGRW6c92CIi4ihut5tu3bqxfft2wsPDKViwIJ9//jkul4vu3btTrVo1OnTowLPPPotpmnTs2JG9e/cya9Yshg4dyvTp0wHrkk8ffvghI0eOZMmSJURGRvLJJ5+wdu1a7r77bkqUKEFsbCzFixf3FEuwfgucdDbtJKNHj2bFihVMmDDBczIx0zR544032LBhA6NGjeLXX3/l7bffpnjx4p7nxcbGcurUKS5cuMDRo0c9J1bbt28fu3fvJj4+nh49euDn50dwcDCBgYG43W5Wr15NWFgYefPmJSIi4role/To0SxYsIDDhw8THBzMgAEDqFWrFi+//DJut5tp06ZRt25d3G43ZcuW5d1336VPnz4MHjyYxo0bExQUxNmzZ3n11Vd58cUXKVy4MOvXr2fOnDmMGjWKQoUKUbJkSc6ePUv27Nlp3749mTJlws/PD5fLddXe/nXr1jF8+HBeeuklzwnA3G43zZs3Z8WKFSxbtoxmzZrxzjvvUKVKFc/zFixYwLJly1i0aBFhYWFEREQQFRXFnj17cLlcNG7cmLi4OJ5//nkAzyHcNzNjxgxq1qxJaGjoLT1+2rRpbNq0iS+//JKOHTty6tQpZsyYwalTpyhdujRZs2YFrLOdlylThoCAAEzTvOo36fHx8XTv3p3o6GhGjBjB0KFDMU2ToKAghg8fzlNPPUWXLl1o27YtPXv2JFu2bJ7n7t+/n8mTJ9OmTRvPlx4vv/wya9asYeTIkTz22GMcOHCAiRMn0r17d9asWcMnn3ziOUP6+vXrmT9/PgkJCZw8eZJjx47x6KOPsnnzZkzTpGbNmhQqVMjzZcetvpciInJrVLBFRMRR/Pz8aNu2Lb/99huNGjXi7bff5scff2TkyJH06tWLtm3bsmbNmmTPGTRokOckXACHDh1i7NixfPHFF5QqVYoZM2YAMGHChGTP+/HHH7nvvvto2LDhdcczdepUJk2axEcffcQDDzzAL7/8AsBbb71F9uzZ6dGjB3379mXjxo00atSIpk2b0rlzZ4oXL87OnTt55ZVXME2TMmXKULJkSSpXrkyLFi0YOnQohmEwbdo0smTJ4lmf2+0mJiaGTJky8dJLL3kKVP369a8a2xNPPMGiRYu49957+fTTT7l48SLPPvssjz/+OK+//jr+/v7ExsZ6TgLXtGlT/vzzT7788ktOnz7tOXnca6+9RoMGDZgyZQrR0dF06tSJTp06edbTpUsXYmJiaNKkyXXfp99++43u3bvToUMHOnfu7Mnoq6++YsmSJTzxxBNs3bqVY8eO0alTJ88luR555BEaNGhAWFgYmTJl4rfffiN//vx06dKFunXr0qlTJ7Jnz86zzz7LhAkTyJs37y0d0hweHs7KlSv57LPPbvpYgOXLl7NlyxZmzZpFrly5OHPmDNmyZWPBggWex/z1118sWbKEGjVqXPcM4rGxsfTt25dDhw4xZcoUDMMgPDyc48eP06NHD4oXL85LL73E2LFjmTFjBgsXLqRDhw60a9eOHDly8Oabb3p+0vDQQw8xffp0XnnlFb744gtmzZrFnXfeySuvvEJwcDDjx4+/6tJjjzzyCAcOHCAyMpKwsDDCw8P58MMPadSoEU2aNOGOO+7gjjvuYMyYMezbt++WDlUXEZFbp4ItIiKOU716dX7++WcyZcrE5s2buXTpEkuXLvXsQfz7778B+7rNgYGBTJgwwXNpqAIFCniKlWma7Nixg7vvvvuq9Zimec3fbSf5+OOP2blzJ99//z1//PEHL7/8smcPtsvlInfu3FSsWJGPP/6YxYsXYxgGISEhbNq0ieLFi/PAAw9c9WVAkqQTdF1ZrsH6giHpvgIFChASEnLN30wDFClShAULFuB2uzl79iw//PAD06ZN8xTQ33//3TPWJP369aN06dIULFgQt9vNxx9/7Ln/r7/+uu4lx270Pq1YsYJx48YxZswYsmXLRvfu3ZPt6c+dOzf58uVj0qRJTJkyhcuXL5MzZ062b9/O/fffT0hIiOeM7KVLl2b48OE89thjxMfHe64VPWrUKIKCgjhz5gytW7fmf//7HxUqVLjumGbNmkXBggU9h2ffTPXq1T17cw8cOMClS5euei+SLq11vfciKiqKXr16UaRIEb7//nsmTJjAr7/+yr59+3C5XGTNmpVs2bLRtm1bgoKC2L59O8HBwURHR7Njxw7Kly/P5s2buffeeylWrBiNGzfmtddeI0eOHDz55JOe9+ixxx6je/fu9O3bl+7du1/1+/Knn34asA79Hzp0KJUqVSI6Opphw4YBMHPmTMLCwsiSJQutW7dm0KBB2pMtIpJCVLBFRMSRkkrmww8/fNXewvXr1wMku65yrly5PJdturIA7du3j5MnT17z8kj+/v7XPEkUQEREBA0aNKBHjx4A3HHHHTRt2pQePXqwaNEiBg4cSK5cuQCoX7/+Nfcw34jL5SIgIOCGj/nf//7H//73vxs+JulLhtDQULp165bsvqT36cpDuV0ul6e0/rOY/fLLL9c8YZfL5fKcmfyfTNP0XF4saU/5lClTmDFjBkOGDKFt27Y0aNDA8/jhw4ff8PUUKlSIkJAQ8uTJQ0REBIMHD6ZQoUKcPXuW2bNns2DBAoYNG8azzz7LI488wrPPPkutWrWS/Y7Y7XYza9Ysnn322Vu+rviV/2aSjlK48tBtsN/r6/2bCQsL4+233/Yc0t+7d2/Auq549uzZefvttz2Pvd71u7t27UrTpk0B6/rpjz76KO+88w4ff/wxOXPm5Pz581y4cAGAt99+m6ioqOteLi5fvnzkzp2bzJkzkzlzZgYNGkTTpk3ZsGED3377Lbt27aJ79+5069aN+++/n3bt2tGgQYMbfpkiIiI3poItIiI+p2PHjpw7d44777zzpo8tVqwY5cqVu+ZlmkJCQrjjjjuu+bxcuXJ5CvSVkvYK/1fBwcHkyJEjRZZ1Pc2aNeO7775L9nvnG2nevDmRkZFX3R4SEnLda18bhnHNa3QnvU+3WnCPHTvG2LFj2bdvH/Xq1WP06NHs2LGDxYsX88ILL1CgQAEAnnvuOS5dusRvv/1G9uzZ2bFjBw8++CB58+b1LOunn37i9OnTtGjR4pbW/U81atSgUKFCV+39zpIlCwEBAdd9L0qUKHHVbYcPHyY8PPyWs046wR1Y793YsWOZOXMmy5cvZ8+ePVy+fJkcOXJQvHhxatWqlewa2UnOnj3LuHHj2LJlC9WrV2fRokVEREQwYcIEGjVqRJcuXQBr2xg8eDDLli0jS5Ys7NmzhwcffJBixYrd0lhFRORqhpl0vJOIiEgG8/7779O7d+/b2mM3YMAAVq5cyS+//HLLl3663nLuvPNOXnzxxX+9jLQyY8YMypYtS8mSJW/5OfPnz6dfv37Mnz+fUqVK3fTxsbGxnhOBOVV8fDxDhw7lzTffvOXnxMbG8sQTT1CsWDHGjRuXiqOzxcfHExsbe0uXMhMRkZSlgi0iInIbEhISCAsLo3Dhwv9pOatXr+buu+++7t7Q9ODw4cMULVrU28PwuosXLxIXF3fNIyJERCR9UcEWERERERERSQF+N3+IiIiIiIiIiNyMCrbDJCQkeHsIcguUk29Sbr5BOfkG5eQblJNvUE6+QTk5nxMyUsFOY5cvX2bo0KEsWrSId999l38eof/666+zePFiL40u43K73YwdO5bo6GhAOfkqt9vN8OHDWbJkCYMGDSImJibZ/cOHD2fSpEleGl3GZJom8+bNY9++fZ7blJNvUE7Oo+3Jdykn59H25Jt8ISMV7FQQERFBr169GDRoEF27duXvv//23Pf1119z+vRpGjZsyIYNG1i2bJnnvunTp3PixAnq1KnjjWFnOHPnzuXee+/l3nvvpVSpUowbN85znVfl5H2nT5+me/fujB492nNbdHQ0b731Fm+++SZdu3b1XOM3yY8//siWLVuoV68ep06dYvr06Z77fvrpJ5YvX86TTz6ZZq8hI7hWThs2bPBsWyVLlqR///7JrhusnNLW/v376dixI2XLlqVWrVpMmzYN0PbkNNfLSduTs8TExDBgwAAqVKjAo48+ytSpUwFtT05zvZy0PTnTmDFj6N+/P5A+tiVdBzsV9OrVi7Jly9K9e3fWr1/Pc889x7Jly8iaNSu///6751qY2bNnZ+PGjTz++OPs3r3bc63LgIAA776ADOS1114jW7ZsAMned+XkPQkJCcyaNYvdu3ezZMkS7r77bs997777LnFxcQwbNozDhw/TpEkTFixY4DlL8e+//46/v/WftaTcnnvuOU6dOsUbb7zBmDFjyJo1q1deV3pzo5wAOnTokOzs0Tlz5vT8WTmlnbi4OEaOHEnPnj3Jli0b7777Lm+//TYPPvggM2fO1PbkEDfKCbQ9OcmMGTNo3LgxHTp04I033mDcuHF07NhR85PDXC8n0PbkNHPmzGH06NE0b94cSB+f9VSwU9imTZv45Zdf6Nq1KwAVKlTg4sWLTJs2jS5dupApUybPYw3DIEuWLERHR9OjRw8GDhxIkSJFvDX0DKljx47XvE6ocvKe+Ph4Hn74YapWrerZgwNw9OhR5s2bx3vvvQdA0aJFyZ07N59//jnvvPMOgOcIBLBzc7vd9OnTh/bt2/PQQw+l6WtJz66XU5LWrVtfVbqTKKe0s3//fp577jkeeOABANq1a8fPP//MH3/8oe3JQa6X05kzZwgODtb25CAtW7b0fHgvWbIkDRs21PzkQNfKKYm2J+dYt26d5+eZkH4+6+kQ8RS2Zs0aAHLnzg2Av78/OXLkYPXq1QA0bNiQM2fOAHD27FkaNWrEe++9x4MPPkijRo28MuaMyjAMWrZsyQMPPECjRo2YP3++5z7l5D1BQUHXnPjWrVtHfHy8Z9sCyJMnj2fbAqhfvz7nzp0DIDw8nKZNmzJx4kT8/Px48cUXU3voGcr1ckry6quv8sADD1C3bl2mTJmS7D7llHbuvfdeypcv7/n7gQMHKFiwIPHx8dqeHOR6OVWoUAHQ9uQkWbNmxTRNJk+eTHR0NHXr1tX85EDXyimJtidn2LFjB1u3buWZZ57x3JZetiXtwU5hERERQPLDjQMCAjy3161bF8MwWLZsGYMGDWLfvn389ttvzJ071yvjzciKFClC586dCQoKYvr06fTr14/8+fNTuXJl5eRAN9u2AO677z4GDhzI8uXLeeaZZ8iaNSv/93//x7x58zAMI83HnFHlzp2bjh07kjt3bhYsWMCwYcPIkSMHLVq0AJSTtxw6dIjZs2czYcIEVq5cCWh7cqIrc8qcObO2Jwf6+uuviYmJ4dixYzRo0ICXX34Z0PbkNP/MaeLEiYSGhmp7coAjR44wb948Bg4cmOz29PJZTwU7hSX9juPSpUue2+Li4sifP7/n70knxzp69Cg9evRg0qRJ1zxMWVJX+fLlPXsMHnvsMWrWrMnKlSupXLkyoJyc5nrbVq5cuZI97pFHHgHgwoULNG/enPfee4+8efOm3UCFEiVKUKJECQBq165Ns2bNWLFihecDDCintHbgwAHGjBnDlClTyJcvH1u2bAG0PTnNP3MCbU9O1L59e8//P/LII3z55ZeAtien+WdO33zzDaNGjdL25ACffPIJq1evZt68eZ7bFi5cSFxcHOD725IOEU9hNWrUAODUqVOA9TvF8+fPU7169WSPi4+Pp3fv3nTp0oUzZ86wePFiNmzYkObjFUumTJkoVKgQWbJkSXa7cnKOKlWq4O/v79m2AM6cOXPVtpXkzTffpG7duuTIkYPFixezYsWKtBqqXMHPz4/ixYtftW0lUU6p7+DBg8yZM4dhw4Z5StuZM2e0PTnMtXL67rvvkj1G25OzhISEkCVLFh577DFtTw6WlNOVv90FbU/eNHLkSDZv3symTZvYtGkTAI0bN2b58uXpYltSwU5h5cuXp1q1aqxduxaAzZs3kyVLlmS/LwD49NNPyZkzJ+XKlWPUqFE0aNCAQYMGsXfvXm8MO8OJiIhg6tSpxMbGAnD+/HnOnz9P27Ztkz1OOTlHkSJFaNWqlWfbOnLkCOHh4df8vc3MmTM5evQo7dq1o0ePHjRo0IAvv/zS81xJPdHR0UyZMoULFy4A1pdUe/bs4YUXXrjqscop9cXExNCvXz8KFSrEvHnzmDVrFsOGDSMsLEzbk4NcL6fffvtN25ODhIeHM3HiRM/etb/++ou4uDi6dOmi7clBrpdT586dtT05XHr5rKdDxFPBp59+yuDBgxk8eDAnT55k0qRJZM+e3XP/L7/8woIFC5g3bx5ffPEFQUFBgPW7xYULF/L66697aeQZR1xcHD///DNLly6ladOmREREMGnSJM9eA1BO3rRx40a++eYbAJYsWUKBAgVo1aoVb731Fu+99x5vvvkmZ86cYezYsRQrVizZc/fs2cOoUaOYPn16sv/IhoaG8t1331G1atW0fCnp2rVyatiwIVu3bmXhwoU0b96cqKgohg8fzj333JPsucopbSxdupStW7eydevWZLd37tyZ7t27a3tyiOvl1KFDB21PDuJ2u1m5ciUrV66kcePGHDhwgJkzZ3LXXXdpfnKQ6+VUoEABbU8+ID1sSyrYqSBLlix8+OGH17zvzJkz9O/fnxEjRpAzZ85kvzFwuVxERkam1TAztNDQUCZPnnzd+5WTd1WsWJGKFSsycuTIZLf7+/szePDg6z4v6VJqffv2pVixYqxatcpzn8vl4vz586k25ozoejmNGjXqhs9TTmmnSZMmNGnS5Lr3a3tyhpvldCPKKe2EhoYyc+bMa96n+ck5bpST5idn2rVrl+fP6WFb0iHiacg0Tfr160fLli2pVKkSAGXLlvX8oD8mJoZy5cp5c4iCcvJl77//PqVLl+aJJ54A4KGHHvL8DCAmJoayZct6cXSSRDn5BuXkG5STb1BOvkE5OZ8vZKQ92Glo6dKlXLp0iVdeecVzW/369dmyZQsLFy6kVKlS1KtXz4sjFFBOvmr79u1s2LAh2aXUypYtS6tWrZg/fz6ZMmW66lwIkvaUk29QTr5BOfkG5eQblJPz+UpGhmmaprcHkVGYpklkZCRZs2b19lDkBpST77pw4QLZsmXz9jDkJpSTb1BOvkE5+Qbl5BuUk/P5QkYq2CIiIiIiIiIpQL/BFhEREREREUkBKtgiIiIiIiIiKUAFW0RERERERCQFqGCLiIiIiIiIpAAVbC+oXbs2tWvX9vYw5CaUk29QTr5BOfkG5eQblJNvUE6+QTn5Bl/KSQVbREREREREJAWoYIuIiIiIiIikABVsERERERERkRSggi0iIiIiIiKSAlSwRURERERERFKACraIiIiIiIhICjBM0zS9PYiM5t577wXA5XJ5eSRyIwkJCYBycjrl5BuUk29QTr5BOfkG5eQblJNv8HZOSevftWvXTR/rn9qDEfFV+g+tb1BOvkE5+Qbl5BuUk29QTr5BOfkGX8pJBdsLkv6B7Nixw8sjERERERERkRspXbr0LT9Wv8EWERERERERSQEq2CIiIiIiIiIpQAVbREREREREJAWoYIuIiIiIiIikABVsERERERERkRSggi0iIiIiIiKSAlSwRURERERERFKACraIiIiIiIhIClDBFhEREREREUkBKtgiIiIiIiIiKUAFW0RERERERCQFqGCLiIiISLpnut3eHoKIXEN62zb9vT2AjKz3kPPsO5hwS4/NFGzwVq8QihZyMWTERfYeuLXnpbRWTYJ5pmVmps25xOwF0V4ZQ4niLob0ycrhYwm8MzKSy9Fmmo9BediUh015WJSHTXnYlIdFedjSMo/qjwTQo3NWYr/6EvPkiZRdeGAgAS1bY+TJQ9ysbzFPhKXs8m+RX+VHCKhWnbif1+D+db1XxmDkL0BA6zaYp08TN2cWxMam/SCUh4cv5GGE5iewfYe0H1cqUsH2on0HE9ixO/6mj8uS2WDSR9kpXMBFh1fPsf3vmz8nNbzcMTPPtMzMJxMjGT/1klfGcH8pfwb1ysquvfG80PM8UZfS/sOR8rApD5vysCgPm/KwKQ+L8rCldR533uECwDx5AvPo0RRffuyoTwjs0pWAVq2JHTcW8/ChFF/HzSTMngUXLhDQqDFxFy6QsHRJmo/BPHqU2FOnCOzajYAmzYidMA5iYtJ8HMrDojy8Q4eIO1zSZHz3nf4895p3J+PXXwrx+mT8xac52LPf+x+OlIfyuJLysCgPm/KwKQ+L8rA5IY8UFxND7IRxmGFhBHbthlH0Dq8MI2HpEuJ+WEhAo8a4Hq/nlTGYhw8RO24sRoECBHbpCkFBaT8I5eGhPNKeCraDaTK2OWEyVh425WFTHhblYVMeNuVhUR42J+SRahxSIlTqEikPD+WRtlSwHUqTsc0Jk7HysCkPm/KwKA+b8rApD4vysDkhj1TnkBKhUpdIeXgoj7Sjgu1AmoxtTpiMlYdNediUh0V52JSHTXlYlIfNCXmkGYeUCJW6RMrDw5F55C+Q9mNIZSrYDqPJ2OaEyVh52JSHTXlYlIdNediUh0V52JyQR1CgkbYrVKnzcGSpUx6OySOgdZu0X38qU8F2EE3GNidMxsrDpjxsysOiPGzKw6Y8LMrD5pQ8XmyXKc3Xq1Jnc1qpUx4OyuP06bRfdypTwXYITcY2p0zGysOiPGzKw6I8bMrDpjwsysPmpDzy53Wl+boBlborOKrUKQ/H5BE3Z1barzeVqWA7gCZjm5MmY+WhPK6kPCzKw6Y8bMrDojxsTsvjs6+irBsDA9N8HCp1NqeUOuVhcUQesbFpv85UpoLtZZqMbU6bjJWH8kiiPCzKw6Y8bMrDojxsTszjyHE3AAEtW6vUqdQpjys4Io90RgXbizIFazJO4sTJWHkoD1AeSZSHTXnYlIdFedicnoeRJ49KnUqdRXl4OCKPdEQF24ve6hWiyRjnT8ZpSXlYlIdNediUh0V52JSHTXlYbpZH3KxvVepQqfNQHh6OyCOdUMH2oqKFXJqMfWAyTivKw6I8bMrDpjwsysOmPGzKw3IreZgnwrxfIlTqPBxR6pSHhyPySAdUsL1oyIiLmox9YDJOC8rDojxsysOmPCzKw6Y8bMrDcjt5OKJEqNR5KA+b8kgfVLC9aO+BBK+sV5OxRR+ObMrDpjwsysOmPGzKw6I8bL6ahyNKhEqdh/KwKQ/fp4KdwWgytujDkU152JSHRXnYlIdNeViUh83X83BEiVCp81AeNuXh21SwMxBNxhZ9OLIpD5vysCgPm/KwKQ+L8rCllzwcUSJU6jyUh015+C4V7AxCk7FFH45sysOmPCzKw6Y8bMrDojxs6S0PR5QIlToP5WFTHr5JBTsD0GRs0Ycjm/KwKQ+L8rApD5vysCgPW3rNwxElQqXOQ3nYlIfvUcFO5zQZW/ThyKY8bMrDojxsysOmPCzKw5be83BEiVCp81AeNuXhW1Sw0zFNxhZ9OLIpD5vysCgPm/KwKQ+L8rBllDwcUSJU6jyUh015+A4V7HRKk7FFH45sysOmPCzKw6Y8bMrDojxsGS0PR5QIlToP5WFTHr5BBTsd0mRs0Ycjm/KwKQ+L8rApD5vysCgPW0bNwxElQqXOQ3nYlIfzqWCnM5qMLfpwZFMeNuVhUR425WFTHhblYcvoeTiiRKjUeSgPm/JwNhXsdESTsUUfjmzKw6Y8LMrDpjxsysOiPGzKw+KIEqFS56E8bMrDuVSw0wlNxhYnTMagPJIoD5vysCkPi/KwKQ+b8rA4JQ9wSIlQqfNQHjbl4Uwq2OmAJmOLUyZj5WFRHjblYVMeFuVhUx425WFxSh5XckSJUKnzUB425eE8Ktg+TpOxxSmTsfKwKA+b8rApD4vysCkPm/KwOCWPa3FEiVCp81AeNuXhLCrYPkyTscUpk7HysCgPm/KwKQ+L8rApD5vysDgljxtxRIlQqfNQHjbl4Rwq2D5Kk7HFKZOx8rAoD5vysCkPi/KwKQ+b8rA4JY9b4YgSoVLnoTxsysMZVLB9kCZji1MmY+VhUR425WFTHhblYVMeNuVhcUoet8MRJUKlzkN52JSH96lg+xhNxhanTMbKw6I8bMrDpjwsysOmPGzKw+KUPP4NR5QIlToP5WFTHt6lgu1DNBlbnDIZKw+L8rApD5vysCgPm/KwKQ+LU/L4LxxRIlTqPJSHTXl4jwq2j9BkbHHKZKw8LMrDpjxsysOiPGzKw6Y8LE7JIyU4okSo1HkoD5vy8A4VbB+gydjilMlYeViUh0152JSHRXnYlIdNeVickkdKckSJUKnzUB425ZH2VLAdTpOxxSmTsfKwKA+b8rApD4vysCkPm/KwOCWP1OCIEqFS56E8bMojbalgO5gmY4tTJmPlYVEeNuVhUx4W5WFTHjblYXFKHqnJESVCpc5DediUR9pRwXYoTcYWp0zGysOiPGzKw6Y8LMrDpjxsysPilDzSgiNKhEqdh/KwOTKPwECvjCM1qWA7kCZji1MmY+VhUR425WFTHhblYVMeNuVhcUoeaUmlzubIUqc8HJNHQMvWXhlDalLBdhhNxhanTMbKw6I8bMrDpjwsysOmPGzKw+KUPGpXT/u9ZCp1NqeVOuXhoDzy5PHK+lOTCraDaDK2OGUyVh4W5WFTHjblYVEeNuVhUx4WJ+XRsHYmr6xbpc7mqFKnPByTR9ysb72y7tSkgu0QmowtTpqMlYfyuJLysCkPi/KwKQ+b8rA4LY9FKy57Zf2gUnclp5Q65WFxRB4nwryy3tSkgu0AmowtTpuMlYfySKI8bMrDojxsysOmPCxOzGPFmlgA/Co/4pWxqNTZHFHqlIeHE/JIb1SwvUyTscWJk7HyUB6gPK6kPCzKw6Y8bMrD4vQ8AqpVV6lTqQOUx5WckEd6ooLtRa2aBGsyxvmTcVpSHjblYVEeNuVhUx4W5WFTHrYb5RH38xqVOpU6D+Vhc0Ie6YUKthc90zKzJmMfmIzTivKwKQ+L8rApD5vysCgPm/Kw3SwP96/rvV4iVOpsTih1ysPmhDzSAxVsL5o255ImYx+YjNOC8rApD4vysCkPm/KwKA+b8rDdah5OKBEqdTblkUh5pBsq2F40e0G0V9arydimD0cW5WFTHjblYVEeNuVhUx4WX83DCSVCpc6mPBIpj3RBBTuD0WRs04cji/KwKQ+b8rAoD5vysCkPi6/n4YQSoVJnUx6JlIfPU8HOQDQZ2/ThyKI8bMrDpjwsysOmPGypmYfpjrulxykP23/NwwklQqXOpjwSKQ+fpoKdQWgytunDqkV52JSHTXlYMloe0VF/snt9aLL/Hd/VCYA7Cx/n7KGnqFC+HFWrlOPksSXJnhsfe4pjO5/lyJ/NOPJXC2Iv773ueuLjTnPq4Fsc/bst+zbdR9ieriTEnfXcHxd9iJP7+3Dkr+bs23Q/J/cPwO2OvmYeMZd2s+fX63/oNM0EIo6N5viuTuzb9CBH/mpOTNRfifeZ7F5f8KrXvHt9KBfPLLzm8o7t7MDu9aH06JwVwzDo0TkrMVE7PPefO/kVx3e/yP7ND3NoWz0unV931TLOnfyaw3824fD2+kRHbr3q/uO7X+Dcya+u+5pA28eVUmr7cEKJUKmzKY9EysNnqWBnAJqMbSoPFuVhUx425WHJqHm4AkIJCC7h+V9g5pKUKhHDn+ubcvLEWTIVWkK20Jc4vrMDUed+Aqw9rsf+foqgzKUpct93ZM3djKM72pAQf/6a6zhzZBi5C/eicKkZFLh7HBdPz+Hk/p4AuBMuE3F8LHmLvUORMvPIU3QA509O4fzx96/Kw3THcWJvN0zz+ucyiTg2miw5alHw3inc8cAyYi/v5tjOZ3AnXMYdfxaXf45kr9c/qAiugHyE5Kp/zeWVuDMT9957L/lC7/Y8xxWYD4Dzp6YTEFSEgvd8TrGHfsJ0X+b4rvbExYR5nh9z6W9O7e9LoZL/R/Z8z3J89wvJ9lifDfuc2Mt7yJan5XVfk7YPW0pvH04oESp1NuWRSHn4JBXsdE6TsU3lwaI8bMrDpjwsGTmP0DuHU7zsOs//aj4+kEfvn8vRo0eIiK7PpcsGmbM9Crg5GzYBgAvh3xBz6S9CcjUEICRXA+Jjj3E2bOJVy4+J+otMWSvh8s8BQObs1XEF5CHq7EoALp3/iWx5WuLnFwxA1tzNAcjiv/qqPM4c+5jYS7uv+1pM04074SJBWcoA4B+Yj0zZHiU+NoyYSztwu6MpVGpastebq+CrZMvbBsPwv2p5L3fMTIumD9Gl5yZy3LnW8xz/gDwAxF7eS5YcNQHwc4WQJWdd3AmRRF/c4FnGpfNrATcu/+wEBN9BfMwRYqOtvf3RUds5c2QYBe6ZiJ8ryzVfU3rePvxIoGL2jTTKu4iK2TfiR8INH59a24cTSoRKnU15JFIePkcFOx1Lz5Px7VJ5sCgPm/KwKQ9LRs/Dz5XN8+ekPDZu/BMAt5nTeox/dgCiL24G4OKZ+QAEBBUFrCJr+GXi4un5Vy0/KEsZsuZp7vm7acbjTogiILgIAFly1iM4a0XP/cGBFwEoUaJYsjwuX/wNzARcgXmv+1oMw488RQcmu80dfyFxrEUICCpIcEjZZPdfCJ9BtrytrlpWUh5b/gq+bh55ir7xj3VZe/D9g4pcMSbXlSNMvC0Ad0IUYbtfIneRAQRnue+ay0/P20fd3MtYUelxvnqwEyNL9eWrBzuxotLj1M297JqPT+3twwklQqXOpjwSKQ+fooKdTqXnyfh2qTxYlIdNediUh0V5wMn9vdizoRj7N5XAFfUqv/52jHWbcwAQF3vcepBpvS+m+zIA0Ym/afbzt8u5nysbcdF7cSdEcezvZ9i36T5iEvc2X1kyIyMWYbovkyP/i4n3GRiGVTyzZDaoXWEBAJeN5z15uBOiiDg2ltyFe101/ohjn7JnQ3EuhM+5al1xMce4fOFXsuZuhn/iYd1Xirm0G7c7mqDMpZLdfmUeK1dtZf/mh9j9a2EObKnChdPzPI+7cl3uhEgiz/5IcEh5gkPKeW7PnL0mGP7ERh8k9vLexMPM7+Tk/r4EZS5JzgLPXzUuSN/bR93cy/i0dE9CA08muz008BSflu55VclOq+3DCSVCpc6mPBIpD5+Rrgr22bNnWbRoEQ0aNGDFihXUqlWLSpUqMX/+fADWr1/P008/Tf/+/XnqqacYMGAAERERuN1uduzYwXvvvcewYcPo1asXDz30EO3atePiResb9MuXL/Puu+/y/vvv069fP7p06cK5c+e892JvID1PxrdL5cGiPGzKw6Y8LMoDXP7ZyV24BzUbL2TI4LeYP28OzZo2JChrIzD8uRg+i4T4C8THnbEeH2DtPXZ7fmttF0zDCAAgIf4CcTGHSIiLwJ2Q/DfZ8bGnOHXgLbLlbUv20PbJ7suS2eCNV44z+fP3KV22P+EXa3juCz84mNyFe2L4BV71GuJijmG6LxEfeyLZ7aaZwMl9PQjMfDf57vzwmq//4uk5ZM39RLLb/plH5uxVKXDP5xQuNR1XQC5O7OlyzROinTrwJoZfMAXumej5wgAgMFNxCtw9kZP7ehJ1dhkF753ChfBvuXzhV0Lv+via40rP24cfCQwsMRQw8TP+cZ9hvc4Bdw3zHC6e1tuHE0qESp1NeSRSHj4hXRXsY8eO8d5777F//36OHz/OlClTCA4OZvjw4Rw6dIiXXnqJfPnyMXToUMaNG8eCBQsYMGAAcXFxzJkzh6+++opNmzbRqVMnXn75ZTZu3MiCBdY36CNGjOC7774jMDAQf39/zp8/z+rVq737gq8hPU/Gt0vlwaI8bMrDpjwsysMSEFSEKtWfZv60x2jQ5DVyFnyZy5F/kpBwgUIlp+MfVJRjf7clJmorAJmyVQLAz5XVWoBpn6zLTPyzyz8rRR9Yzp3lt5EpawXP/e6EKI7tbEe2vC0JvevjZCU0S2aDoQMv06dXS+4sM5D44B6e+y6eWYh/YAGCQx645mvIV3woxcttIVehbsluP3VgAOCmcOm5uK7Y036li6fnkzV3U8/fr5VHtrytyJS1ApmzV6fQvV9h+GXh3Ikvki0n4tgoYqK2U6TMAgKCCl21nqy5G1GkzFwKlZqGYfgRfvAtCtwzwfO79Cul9+2jfPbNFAg6eVW5TuJnmBQMPkH57Ju9tn04oUSo1NmURyLl4XjpqmDfd9993HnnnQC0a9eOYsWKUaRIEc6cOcPPP/9MbGwsDz30EAC5cuXirrvuYs2aNZimyeOPPw5A9erVKVOmDA8++CAAERERACxZsoQ77riD3r1789577zFjxgyeeOKJNH+NN5LeJ+Pb4e0Pq6A8rqQ8LMrDpjxsTszDFWwV4oTYk2TJUYMiZeZQ9P4fiIuxDhXPnu9ZAIKylLYeF3/Osyx3wgUCgovj5wrBzy/YcyIwsPYmh+1+ieyhz5D3jkEYhoE7IZL42FNkyWww+l1/3hrwFAXufIdoP+uQ6fi40yTEX+D8yalcOv8TR/5qzpG/mpMQewqAI381J+rsSgzDj4CggsleV8SxTzHdMRQqNdNTrmMv70/2mOioPzFcmQnMZH1+uJU8XAE5Ccx0Nwlxpzy3XQifw6UL6yly3/cEBBW45ro875E7muO7XyJHgZe4cOobjvzZjMgI+/JnGWH7yBt4+pYe17buBa9uH04oESp1NuWRSHk4Wroq2NeS9M140v+7XK6r7nO73dd9nmlak1p8fDz79u3zHDJ+5X1OkBEm41vlxA+rykN5KA+b8rA5JY8pn2RPlkdC7EnARXDIQ57HmWYCF8K/JUvOBmTOXgXAc9Ky2Mt7AOvQb9N92VPA3e5o4uPsIhVx7FMyZ69GjisOC79wei7E/8Gkj7IzcXxv/EI6czqqtuf+s8cnEB97jMKlZ1Pkvu8pUmYeRcrM81wiq0iZeWTJWQvTND1fAABcOv8L0ZF/EHrXJ54zg8de3sv5f1xnOiriR89Z0K/MY+zk5AXwn3N+Qlw4wSEPX7Hcryl471TPWcAT4i9w5ui1D0kPPzgY/4C8iXv+TQrc8xlhu18kLuZohtk+wmPz3PxBQIPMM/lqzBavbR/gjBKhUmdTHomUh2Ol+4KdpFq1agQGBrJt2zbA+r32vn37qFmzJpkzZ77p8x977DEuXbpEnz592LRpE4sWLWLZsmuf4TKtZZTJ+FY45cOq8rAoD4vysCkPm1PyGNLzHAUL5KRp6w+JumTiTrjEuZNTyXPHWwQE2x/YIo5+jJ8rhPwlRnluy563LUGZy3Dx9FwAIiMW4wrIT/bQdgAc3vY4+zc/yOWLm4iPPcXZ4xOIjd7Hyf19OLm/Dyf2vsaZw+/zVq8iXLq4nW++XcDff6303B+25xXOnZiEyz/XTV/LqQMDOPB7WSKOjwcg/OCbYLg4daCvtbx9vTi+qyOugJzJnhd1bhUhOR9PlsdHo39m72/3cPrwMABO7u/L4e31iIs5BiR+KWD4kfeOQda6Dr2D4cpM+ME3PWM/uqM1fn5Xf764eGYhkRGLyF9iDBfPfI8rMD/+gfmty3v5fZ9hto9N5x8mLCYUt3ntY8Q9r/zgrzxzpD5D7x1A4eAjqTKWW+GEEqFSZ1MeiZSHI119sUcftnz5cvbuta4pOXnyZEqXLs2ePda36j/++CMTJkzgk08+4a233iI8PJwnn3yS1157jbNnz/Ltt98CsHTpUh5//HFmzZoFwKpVq6hfvz4DBw4kLi6ONWvWsH37dp588kleffVV77zQK+jDqs0pH1aVh0V5WJSHTXnYnJTHjl2BZM71NCcOjML/6AxcAXnJXbgnIbkaABAbfZBzJyZj4E+RMt/h58rkWYbhF0ChUjM5ub8nh/9sjGEEULj0LFyJl/MKCCpCQvwZ/FzZiDq7DHfC+av2IAM8eH8oT3f8nJjocGKir77/Vgp2QFABDL/M+AfkIzb6IDGX/iLm0l9XPc7vimW5Ey4RF3OE17tVTpaHn18wfn5Z8PO3fmOeLU9LYi7t4PC2ugRlKU1AcAnuuH8JroBcuN3RRJ1b7jnD+pWy5KiR7O9x0Yc5ub83Be7+DP/AfMTHhHnuCwrOTKWHTmaY7cONi/f39ufT0j1xm4bnxGYAJgYGsDr3G8Tu/JXH8y7nidAFNMq7mNknWjD+cGdOxYam2tiuJ2GpdRh/QKPGyf6elpJKXWDXbgR26UrshHEQE5O2g0gsdYFduhLYtRux48ZiHj6UtmNAeXgoD8cxTCcd55xBlC5t/Wbt3opr2bH7309e+rBqc9KHVeWhPJIoD5vysPlaHjGX/iYgqAh+rpAUHUNGy8M04znyZ1MyZ6/quUb3/t/Lky1vG2o+PpANSx+icdNObNjVLUNtH3VzL2NgiaEUCLriUl3ZC7Iw+E16z6oOwH0hf/JasdFUy7UOgOiEIGaEPcX4wy9xIT77La2ncd0gRv4vOzEjhmEePfqfxux6vB4BjRoT98NCr5UIo+gdBHbthhkW5p1SBxAURGCXrhgFCnit1IHy8PDRPIzChQnq0y8NRvbfJPW3HTt23PSxGeYQ8fRGH1ZtvvZhNbUoD5vysCkPi/Kw3W4eQZlLqVyngNOHPwDDRe4ifT23heSsR7bMJxk3NIjw8HBWb3osw20fy87UpfaGpbT/YwqL830CL85jVK6fPOUa4M/I+3jxz894ZuuXbDpfjmBXDG3yz8KPq8+hkxaccDisDk+2KY9EysMxVLB9kD6s2nzxw2pqUB425WFTHhblYVMetrTMI/byfi6cmkGBu8d7TrgGUK32QO4qeoqqVR8hd5E+uF2lU3Uc1+KEPNy4eLj5YzTs+TSfrniQcV9ee+/f5gsP8+wfX/LC9gm8v78f5+Lt39M3ybeATH66hFeaU6nzUB42J+ThTSrYPkYfjmz6sGpRHjblYVMeFuVhUx62tM4jMNOdFHtoLQFBhT233V/Kn2kT7mD4yAWEFFlBSN5XUn0c/+SbeRisPVuVOSdaem6pnONXRpQcwMLyzQgwYlN3sFdwQolQqbMpj0TKw+tUsH2IPhzZ9GHVojxsysOmPCzKw6Y8bN7KwxVgn1xNedhSIg9/I4HDlwuz8kxN4sxAz+1GGhxC7oQSoVJnUx6JlIdXqWD7CE3GNn1YtSgPm/KwKQ+L8rApD5vysKS3PNaerULDTQv45OBrntvKhPzFovJNaZR3UaoXbSeUCJU6m/JIpDy8RgXbB2gytunDkUV52JSHTXlYlIdNediUhyW95hFvBhCVkMXz906Fp1I880FGlurL/HItKZ2wgtS8cI4TSoRKnU15JFIeXqGC7XCajG36cGRRHjblYVMeFuVhUx425WHJSHkM2jOETw6+yoX4rNwbsodO8V1gXAOM2ENA6rz3TigRKnU25ZFIeaQ5FWwH02Rs04cji/KwKQ+b8rAoD5vysCkPS0bLIyohCxMOd6buxh/57PALxJIJjvxOwMXZBASuxvA7nSrrdUKJUKmzKY9EyiNNqWA7lCZjmz4cWZSHTXnYlIdFediUh015WDJyHufjs/Pxwdd5P3AlPPoiJi78XKcIDFqBf+AaDONsiq/TCSVCpc6mPBIpjzSjgu1Amoxt+nBkUR425WFTHhblYVMeNuVhUR6WSCMPRtP3iMvRiYT4OzFNA5crjMDgpfgH/IJhXEjR9TmhRKjU2ZRHIgfm4Vf5Ea+MITWpYDuMJmObtydjUB5XUh4W5WFTHjblYVEeNuVhc0IeRQomfuR1ZSM+rgJxMQ1IiC+KaYLL/wiugK0pvk6VukQOLHXKw0F5VKvulfWnJhVsB9FkbHPCZKw8bMrDojxsysOmPCzKw6Y8bE7Jo3P7LMluM82sxMc9QlxMPRISCpEQd98V98YAl1Nk3Sp1iZxW6pSHc/L4eY1X1p2aVLAdQpOxzSmTsfKwKA+L8rApD5vysCgPm/KwOSmPE+EJ17zfNHMQH1sV08zluc0V8BeBwT/g59qbImNQqUvkpFKnPByTh/vX9V5Zb2pSwXYATcY2J03GykN5JFEeNuVhUx4W5WFTHjan5fH519YeaSN/gZs8y8TPOI9hJGCaWVNsLCp1iRxS6pRHIofkkd6oYHuZJmOb0yZj5aE8QHlcSXnYlIdFediUh82JecTEWnkEtG5zkxJhEBf7GLExtTDdoZ5bXf67cPn/Dfz791SlLpFDSp3ySOSQPNITFWwvKlHcpck4kRMnY+WhPJSHTXnYlIdFediUh83peZinT99CiTAw3Xmv+HsMLv8/8Q/Ylnjo+B7g2oec34xKXSKHlDrlkcgheaQXKtheNKRPVk3GOH8yTivKw6Y8bMrDojxsysOmPCzKw3azPOLmzPoXJSKA+LhymO4sGEY0AYG/Exi0CD/XfsB922NUqUvkkFKnPBI5JI/0QAXbiw4fS9Bk7AOTcVpQHjblYVMeFuVhUx425WFRHrZbyiM29l+UCD/cCcWJjWlAXGw5TDMYw+8SAYG/ERD0I36uw8DtZa9Sl8ghpU55JHJIHr5OBduL3hkZqcnYFybjVKY8bMrDpjwsysOmPGzKw6I8bLeVx78uES7cCXcTG92I+LgHMc1A/PwuEhC4noCgpfj5Hed2irZKXSKHlDrlkcghefgyFWwvuhytydhnJuNUojxsysOmPCzKw6Y8bMrDojxs/yqP/1Qi/EmIL0lsdGPi4+7DNP3x8ztHQNDPBASuwPA7ectLUqlL5JBSpzwSOSQPX6WCnYFoMrbpw5FNeViUh0152JSHRXnYlIfN5/P4zyUigIT4MolFuySm6cLPdYbAoNX4+R295aWo1CVySKlTHokckocvUsHOIDQZ2/ThyKY8LMrDpjxsysOiPGzKw5Zu8kiREhFEQvyDxEY3IiG+BG53VtzuK6+3ffOMVOoSOaTUKY9EDsnD16hgZwCajG36cGRTHhblYVMeNuVhUR425WFLd3mkWInIRHzcw8TF1ANcibe5CQhagn/AeiD6hs9WqUvkkFKnPBI5JA9fooKdzmkytunDkU15WJSHTXnYlIdFediUhy3d5pGiJcLl+ZPhdwo/v0j8XCcB/5s+U6UukUNKnfJI5JA8fIUKdjqmydimD0c25WFRHjblYVMeFuVhUx62dJ9HKpQI052f2OjHiY+tgF2wTVz+O4DL13yOSl0ih5Q65ZHIIXn4AhXsdEqTsU0fjmzKw6I8bMrDpjwsysOmPGwZJo/UKNlmTtzuQp6/+/kdxT9gO4HBP+Dy/wOIueo5KnWJHFLqlEcih+ThdCrY6ZAmY5s+HNmUh0V52JSHTXlYlIdNedgyXB6pXCJMMuF258IwEvAP2JlYtP8C4pI9TqUukUNKnfJI5JA8nEwFO53RZGzThyOb8rAoD5vysCkPi/KwKQ9bhs0jFUuE6c5DXEwd4mKq4nZnxzDi8A/4M7Fo7+LKs46r1CVySKlTHokckodTqWCnI5qMbfpwZFMeFuVhUx425WFRHjblYcvweaRqiTBwuwsRF1OPuNhHcLtDMIwY/AO2Ehi8CD/XXiABUKnzcEipUx6JHJKHE6lgpxOajG36cGRTHhblYVMeNuVhUR425WFTHolSvUQYuBOKEhfTgLjYCpjuzBjGZQICNxMYtBg/1wHArVKXxCGlTnkkckgeTqOCnQ5oMrY5YTJWHjblYVMeFuVhUx425WFRHjYn5OGRJiXCD3fCncTGNCQutiymGYzhF0VA4EYCgpahkn0Fh5Q65ZHIIXk4iQq2j9NkbHPCZKw8bMrDpjwsysOmPGzKw6I8bE7I4yppViJcuBPuITa6EfFxD2CagZjuPCR9ZFepS+SQUqc8EjkkD6dQwfZhmoxtTpiMlYdNediUh0V52JSHTXlYlIfNCXlcV5qWCH8S4kslFu37PLcaxln8VvcjbvpIlTqHlDqV7EQOycMJVLB9lCZjmxMmY+VhUx425WFRHjblYVMeFuVhc0IeN5XmJSIQsMuSK+Av/FzhGL/PUKkDx5Q6lexEDsnD21SwfZAmY5sTJmPlYVMeNuVhUR425WFTHhblYXNCHrfMiyUiPrYcCfF3ER9/n6fU+T/6IK4qZdJsDFdSqbOpZCdySB7epILtYzQZ25wwGSsPm/KwKQ+L8rApD5vysCgPmxPyuG1eKxGZiY8rD2ZWwCp17ind8N8ymICih8C4mEbjsKnU2VSyEzkkD29RwfYhmoxtTpiMlYdNediUh0V52JSHTXlYlIfNCXn8a44oEW7MsGNgmviF/0pg8I/4B/wGpG2eKnU2lexEDsnDG1SwfYQmY5sTJmPlYVMeNuVhUR425WFTHhblYXNCHv+Z10uEH/FxjxIbXRd3zvsxcOPy309g8A+4ArYA0Wk2EpU6m0p2IofkkdZUsH2AJmObEyZj5WFTHjblYVEeNuVhUx4W5WFzQh4pxgElwjRzEXe8NPEP9Ifij2AYbvz9d1tF2387EJs241Cp81DJTuSQPNKSCrbDaTK2OWEyVh425WFTHhblYVMeNuVhUR42J+SR4hxSIhI2HCC+0AvQ6RvMkGIYRjz+ATsIDF6Iy38HEJfqY1Cps6lkJ3JIHmlFBdvBNBnbnDAZKw+b8rApD4vysCkPm/KwKA+bE/JINQ4pEQnLlhK/JwrjjQ0klOqG250Nw4jDP2A7gcE/4Od3JNXHoFJnU8lO5JA80oIKtkNpMrY5YTJWHjblYVMeFuVhUx425WFRHjYn5JHqHFIiEpYuIX7RD/h3GIy7+kfExVbCdIdgGDGYBKTJGFTqbCrZiRySR2pTwXYgTcY2J0zGysOmPGzKw6I8bMrDpjwsysPmhDzSjENKhKfUNW6KUbszsTENiIupgukO9TzGz7UXP9chIHXyUKmzqWQn+mce+Quk/RhSmQq2w2gytjlhMlYeNuVhUx4W5WFTHjblYVEeNifkERRopO0KHVnqGuB2FwaS3osY/AP+ICDwV/z8wlJtDI4sdY7IQyXbDAsjoHWbtF9/KlPBdhBNxjYnTMbKw6Y8bMrDojxsysOmPCzKw+aUPF5slynN1+v8UuciIb4U7oR8uN1X7kW8RErv0XZaqXNmHmnHUXmcPp32605lKtgOocnY5pTJWHlYlIdNeViUh0152JSHRXnYnJRH/ryuNF834PBS509CfGniYh/D3qsdT2DwUgICV2H4hafoGBxV6hyZR9pySh5xc2al/XpTmQq2A2gytjlpMlYeyuNKysOiPGzKw6Y8LMrD5rQ8PvsqyroxMDDNx+H8UmcfPm/4nQbi8HOFExi0Ev/ANRhGRIqNwSmlztl5pB1H5BGbNtdoT0sq2F6mydjmtMlYeSiPJMrDojxsysOmPCzKw+bEPI4cdwMQ0LK1St0NSp3pzk9sTCMS4u/ENA1crjACg5fhH7gOw7iQImNwRKnzkTzSgiPySGdUsL0oU7Am4yROnIyVh/IA5ZFEediUh015WJSHzel5GHnyqNTdrNSZmYmPq0BsTAMS4u/ANMHlOkpA0I/4B2wAI/I/j8ERpc5X8kgDjsgjHVHB9qK3eoVoMsb5k3FaUh4W5WFTHjblYVEeNuVhUx6Wm+URN+tblTpusdSZWYmPq0xcTD0SEgphGCYu/4MEBi3GP2ATcPk/jcERpc6X8khljsgjnVDB9qKihVyajH1gMk4rysOiPGzKw6Y8LMrDpjxsysNyK3mYJ8K8XyJ8rNSZZg7iY6sSG10Hd0J+DMONy38fgcE/4PLfCvz739A6otT5WB6pyRF5pAMq2F40ZMRFTcY+MBmnBeVhUR425WFTHhblYVMeNuVhuZ08HFEifLDUmWZu4mJrEBtTE3dCHgwjAZf/Qa48Sdq/oTxsKtnpgwq2F+09kOCV9WoytujDkU152JSHRXnYlIdNeViUh81X83BEifDRUme68xEXW4u4mOrEx5UDApLuwc+1D7j97UF52FSyfZ9hmmba/5cwgytdujQADdusZ9/BtC3ZrZoE80zLzEybc4nZC6LTdN1JShR3MaRPVg4fS+CdkZFcjk77f4KZgg3e6hVC0UIuhoy46LUvO5SHRXnYlIdNeViUh0152JSH5VbzqP5IAD06ZyX2qy8xT57w3G7kL0BA6zaYp09b1+P1xiWDAgMJaNkaI08e4mZ9i3kiLO3HAPhVfoSAatWJ+3kN7l/X3/bzjZjdBEQuwPTLSVyOjmDc/n485WH7r3mkhLTIwwjNT2D7Dim+3JSW1N927Nhx08eqYHvB7QQkIiIiIv+d6XZj+OngzdRi7lgCC9+Asq0x6vazb3cnYPi5vDgycTpf2DZvp7/5p/ZgRERERES8zekf4H2dUboe5j01wW0fIm7uXQPz+2HW7Qf3N1UGck3p7d9F+no1IiIiIiLiFYZ/IEZgZvuGnyfA6X0w4yUYXRtzxxJ08KykdyrYIiIiIiKS8p6eCHX7QVBWCPsLvmoH4xti7v3Z2yMTSTUq2CIiIiIikuKMoBCM2r2g3yao8SoEZILDm2FSS8zPW2Ie3uTtIYqkOBVsERERERFJNUbmnBgN3oK+G+HRF8AVCPt+hnENMac+i3n8T28PUSTFqGCLiIiIiEiqM7KGYjR9H3qvh/JPg58Ldi6FUbUwp7+EGb7X20MU+c9UsEVEREREJM0YOYtgtPoEeqyFB5tbN26bD8s/9OawRFKECraIiIiIiKQ5I+9dGG0/g9dWQekGUKeP5z7zwgnMCye9ODqRf0cFW0REREREvMYoUAaj/ZcYee+yb1z8NoyoiLl5pvcGJvIvqGCLiIiIiIhjmAlxcPYIxF2G0FLeHo7IbfH39gBERERERESSGK4AzM7fw7E/MAo/6LndXDYMgkLgkU4YAZm8OEKR61PBFhERERERRzEMAwo/5Pm7ee4orB4NCbHw8wTM2j2h/DMY/oHeG6TINegQcRERERERcbas+aH5CMhRBC6ehPn9YOSjmJtnYroTvD06EQ8VbBERERERcTTD5Y9Rvi30/gWafQBZ88HZwzCrO3xcHXPb95hut7eHKaKCLSIiIiIivsHwD8J45HnosxEaDILMOSF8D0x/AcbUxdy5HNM0vT1MycBUsEVERERExKcYgZkxarwCfX+D2r2tk58d3w5Tn4YJjTH3rfP2ECWDUsEWERERERGfZARnw6jb1yra1buBfzAc+g0+b4655ydvD08yIBVsERERERHxaUaW3BgNB0PfjVD5OesM5HdV89xvxkR6b3CSoegyXSIiIiIiki4Y2fLDE8MwE+Ix/Kx9iWZsFHz4COadVaDp+xhZcnl5lJKeaQ+2iIiIiIikK4briv2Iu1dbl/Y6ssn6rbZIKtIebBERERERSbeM+xphvroCLp/F8A8EwEyIg5UfQ+WOGFnzeXmEkp5oD7aIiIiIiKRrRqH7MUpUt2/YNANWfAjDK2L++B7mpXNeG5ukLyrYIiIiIiKSseS9GwqXhbhLsPpTGF4ec8VHOhma/Gcq2CIiIiIikqEYdz4C3X6Edl9CaCmIvgDLhlp7tH+egBkX7e0hio9SwRYRERERkQzHMAyMMg3gtVXw1ATIXRyiTsMPg2BEJcwNX1q/1Ra5DSrYIiIiIiKSYRl+fhgPtYCea6HFR5C9EFwIg3l9YOSjmFtmYboTvD1M8REq2CIiIiIikuEZrgCMis9C7/XQ5D0IyQMRh+CbbjC+Mabb7e0hig9QwRYREREREUlkBARjVHkR+v4G9d6A4Oxwdw0MP7s6mabpxRGKk6lgi/xL+hbz39H7JiIiIr7ACMyCUfM16LcJarziud3c+zN81gzz4K9eHJ04lb+3B5CRLV4ZzcSvLnll3SWKuxjSJyuHjyXwzshILken/bdwmYIN3uoVQtFCLoaMuMjeA975bUurJsE80zIz0+ZcYvaCWztjZPVHAujROSuxX32JefJEKo8wOSN/AQJat8E8fZq4ObMgNjZN1w9AYCABLVtj5MlD3KxvMU+E3dLTjND8BLbvkMqDExEREUk5RqbsyW9Y+REc/BX+mA/FKntlTOJcKtheNPGrS+zYHZ/m672/lD+DemVl1954Xuh5nqhLaV+us2Q2mPRRdgoXcNHh1XNs/zvt3weAlztm5pmWmflkYiTjp976lx133uECwDx5AvPo0dQa3jWZR48Se+oUgV27EdCkGbETxkFMTJqOASB21CcEdulKQKvWxI4bi3n4UJqPQURERCTNPTkWVn0KNXt4bjLD94I7HiO0pBcHJk6gQ8QzmPtL+fPFpznYs9/75fruO/157jXvluvXXwq57XJ9Jb/Kj6TwqG6NefgQsePGYhQoQGCXrhAUlPaDiIkhdsI4zLAwArt2wyh6R9qPQURERCSNGdkLYjwxDCNbqH3jwkHwSQ3Mb7pinjngvcGJ16lgZyAq17aUKNcAAdWq43q8XgqO7NapZIuIiIh4nxkfA/5BYJqwZTaMrII5rzfm+ePeHpp4gQp2BqFybUupcg0Q9/MaAho1VslWyRYREZEMyvAPwmj3BbyyFO6pCe542PAVjKiEufAtzMjT3h6ipCEV7AxA5dqWkuUawP3reuJ+WKiSrZItIiIiGZxR+CGMTt9A5++tk5/Fx8Daz2B4BcylQzEvn/f2ECUNqGCncyrXtpQu10kSli5RyQaVbBERERHAKF4ZOn8HnWZCoQchNso68/jwCpirPsWMjfL2ECUVqWCnYyrXttQq10lUshOpZIuIiIhgGAbGPbWsw8afnQL57oXL52DJezC8IuaOH709REklKtjplMq1LbXLdRKV7EQq2SIiIiJAYtG+rzG8vtq6vFeuOyAyHLKG3vS54ptUsNMhlWtbWpXrJCrZiVSyRURERDwMPxdG2dbQ6xd4bgZGkbKe+8yfxmBunYvpdntxhJJSVLDTGZVrW1qX6yQq2YlUskVERESSMVwBGPfW9vzdPHcUlg6FmV3g0EYvjkxSigp2OqJybfNWuU6ikp3onyU7f4G0H4OIiIiIU2XKAbV7QZmGUKyS52bz7BFMM+0/y8t/p4KdTqhc27xdrpOoZCe6omQHtG6T9usXERERcSgjKASjVg+MdlMxDAMAMyoCPqkBn7fA1F5tn6OCnQ6oXNucUq6TqGQnSirZp0+n/bpFREREfMmhjRAfC/vXwfjGmFOfwTy+3dujklukgu3jVK5tTivXSVSyE8XEEDdnVtqvV0RERMSHGKXrQ59focKz4OeCnctgVG3MaS9gntrj7eHJTahg+zCVa5tTy3USlexEsbFpv04RERERH2PkKIzR8iPouQ4eagGGAdu/h4+rYX77KmbEIW8PUa5DBdtHqVzbnF6uk6hki4iIiMjtMPLcifHUBHhtFZSuD6Ybfv8GRj6KOb8f5oWT3h6i/IMKtg9Subb5SrlOopItIiIiIrfLyF8ao/1X0O1HuLsGJMTBr1/A8AqYi4ZgxkR6e4iSSAXbx6hc23ytXCdRyRYRERGRf8MoUg7j+Vnw4jy4owLER8P2heAK9PbQJJEKtg9Rubb5arlOopItIiIiIv+WcVcV6LIQOk6Hpu9j+FsF20yIw1w/BTPW9z4fpxcq2D5C5drmhHJdpOB/33RUskVERETk3zIMA6NkHYxSj9s3bp4J3/WH8Y0wzbTvC6KC7RNUrm1OKNf3l/Knc/ssKbIslWwRERERSTGZc0LOovDwUxiGAYDpdmMmeOeze0akgu1wKtc2p5TrLz7NwYnwhBRbpkq2iIiIiKQE477G0OsXqPycfeNfP8An1TG3fYfpdntvcBmECraDqVzbnFSu9+yP5/OvL6foslWyRURERCQlGP6Bnt9kA7DucwjfC9NfhNG1Mf9eqsPHU5EKtkOpXNucVq5f6HmemNiUz0MlW0RERERSXMdpUKcvBIVA2F/w5bPWb7T3rfX2yNIlFWwHUrm2ObFcp2YeKtkiIiIikpKM4KwYdXpD301Q4xUIyASHN8HnLTAntcQ8vNnbQ0xXVLAdRuXaltHKdRKVbBERERFJaUaWXBgNBkGfjfDI8+AKgL0/w7gGmF+2wwz7y9tDTBdUsB1E5dqWUct1EpVsEREREUkNRrZQjGYfQK/18HBbMPzg7yUwqhbmjM6Y4fu8PUSfpoLtECrXtoxerpOoZIuIiIhIajFyFcVo/Sn0XAsPPAGmCX/Mg3WfeXtoPk0F2wGcUOZUrm1OyCOJSraIiIiIpCYjbwmMpydC9xVQphHU6um5z4w4hHnxpBdH53tUsL3MCWVO5drmhDz+SSVbRERERFKbUfB+jHZfYGTLb9/4/UAYXgnzj/leG5evUcH2ohLFXV4vcyrXNieW6yQq2SIiIiKSlszYKLgUAQkxUOgBbw/HZ6hge9GQPllVrlG5vlUq2SIiIiKSVozALPDyInhlGUaeOz23mwvexFwzDjPushdH51wq2F50+FiCyrXK9W1RyRYRERGRtGIYBkbB+zx/N0/thl8+h0VDYEQlzF+nYsbHem+ADqSC7UXvjIxUuVa5vm0q2SIiIiLiFbnvhBYfQ47CcOEEzO8LHz2KufkbTHeCt0fnCCrYXnQ5WuXa18u1kb9AKozs5lSyRURERCStGS5/jApPQ+/10PQDCMkLEYdh1qvwSQ3M7Qsw3W5vD9OrVLAzEJVrW0rtuQ5o3Qaj6B0pPLpbo5ItIiIiIt5g+AdhPPo89N0I9d+ETDng1G6Y9jyMfRxz1wpM0/lHhqYGFewMQuXalpKHhZunTxPYtZtKtkq2iIiISIZjBGbBeKw79NsEtXtBYBY4tg2+aAufNcXcv97bQ0xzKtgZgMq1LaV/cx03ZxZmWJhKtkq2iIiISIZlBGfDqNsP+v4G1V4G/2A4uAEmNsM88ru3h5emVLDTOZVrW6qc0Cw2ltgJ41SyUckWERERyeiMkDwYjf4HfTZApQ5QojoULuu537x0znuDSyMq2OmYyrUtVc8WHhOjkp1IJVtEREREjOwFMJqPgE7fYBgGAOalszC8AubMLpjRF708wtSjgp1OqVzb0uRSXCrZHirZIiIiIgJg+Lnsv+xcBtHn4eQu67fa6ZQKdjqkcm1L0+tcq2R7qGSLiIiIyJWMcm3g1eXQfASGn1VDzdgozB/fw4wM9/LoUo4Kdjqjcm1L03KdRCXbQyVbRERERK5kFHoAo2h5+4ZfJsPqT61Dx5e8ny5+o62CnY6oXNu8Uq6TqGR7XFWyAwO9Mg4RERERcaAi5aDwQxB7CVZ9YhXtVZ9gxkR6e2T/mgp2OqFybfNquU6iku1xZckOaNnaK2MQEREREecx7qoK3ZZAu6kQWsr6jfaS92FERcy1n2HGRXt7iLdNBTsdULm2OaJcJ1HJ9vCU7Dx5vLJ+EREREXEmwzAwyjSE11bCU+MhdzGIPA0L34IPK2Nu/BozIc7bw7xlKtg+TuXa5qhynUQl28M8fIi4Wd96Zd0iIiIi4myGnwvjoZbQcx20GAnZC8L54zC3F3xUBXPrHEy329vDvCkVbB+mcm1zZLlOopLtYZ4I88p6RURERMQ3GK4AjIrtoPev0PgdyJIHzhyEmS/DZOf/3FAF20epXNscXa6TqGSLiIiIiNwyIyAYo2pn6LsR6g2E4OxQsq7nftM0MU3nfe5XwfZBKtc2nyjXSW6xZG8/fZrI2NhUG8a/LdkXY2P58/Tp215fXELCbT9HRERERATACArBqPk69P0NKne07/hzIUx8AvPQRm8N7Zr8vT0AuT0q17aUKtcXwucQcexTij20JtntpjuO04ffJyE+gvwlPk12X8TpPf/f3p1Hx3wv/h9/TnZZBFGxxVZ7Lb2lxF61FImtodUq5bqWS+n33lJLW9TVlqLt9WvD1VK9qikqdre2UrWkKpYirTUlthCRjayTz++PSSbS0KqOzEhej3Mc8pmZz7xn3hknz3w2nnzyVTJ+icbXgI86tKe8l1eBde+6cIFOK1flXzhrDi4uLpw7cYIy4Ssxzp0FYP2ZaN4/cICQWjVp4Of3u+NOzcqiRdgy5rZvR9vKlQFLzE7avYeIS5cBmNisKd2qVy/wWPPmTWRnZ/NB1E9s+GY72fHXcTLB261b0dTfP999r6Wm8q/v93EuKYnZbdvmuy0lI4NR32wnJjmFKiV9CH2yPZ6urtbbf0lMonP4KnY925dynp6/+5pERERERG7H5FnK+m/DMCzXz77wIxz/Bqo2s9/AfkVbsB8gius8tohrc+Z1Ys+MJ/bMK2Rn538d6Td/5uKJIVy/FArkX7c5K4GvPu/Fc889x7a/j6CeXxl6r11P1m1OunA1NZUAHx9qly5l/VOuRAm616iBv2HgNnIUBFThHzu+ZeS2b/iwfXtGNm6MyWT63fFP2rWb49ev51s2bucuImOvsPOZPnzcqQP9NvyPfZcv3/bxM955h0/mzmXTvh/YvuA/BNeoQY/Va0lKz9t6fiQujqZfhGECVnYPpkYp33zreHvfD9zMyuKbviGcS0rm3f2R1tsyzWYGfr2J5+rUVlyLiIiIiM2YTCYY8Bm0/Bu0HWldblw8ghF73I4j+wOBvW7dOsaNG0fv3r1tOoDk5GQWL17MiBEjCA0Ntem6ixLFdR5bxLWRnUFczNuUqfR/OLvmv3RU+s2fSI4L56Gqb9z2sdcvziMl6YL1s9C9Rg0OXb1K2M8FP8wezi4cG/gChwfk/WlbuTIv1q1j3V18dsoNPjl6jDU9u1PPr8xdjX/buXN8/tPP+ZadvH6dj48epfvD1TGZTNQtU4bqviV5ffee264j/OQpGnl64vLNNlyDgun6/PNcT0/nVGICAFdvptJzzTqaly/Pe+3a3jb6t8ecp5S7OwDVfUuy5exZ621T9kbg7GTijcDmd/WaRERERETulqlUJUw93sbkURLI2aq9+lX4oB3G8pcwrv2S7/5Gthnj9G6MQ+GWv7Pvz2GMdx3YrVq1IjIykqioKJsOwNPTE1dXV7Zv345Zx2reluI6j612Czc5ueFfYxau7hUK3ObuWY+yVSZhMrnf9rEp8avx8vKmbM41nWv4WrbqrjhxssB9u1avhquzs/Xr62lp7Lt8mY5VAiA9nehZM5n+zjv07dOHx5o+fldjv56WxqKjUfR4uEa+5StPniLbMKhWsqR1WQ1fX3ZduMilGzcKrMff05Nvz5/n/KpwMjes52L1GtQoX576ZSyR//a+fVy6cYN/tWxxxy3qTrcsNwEuTpb/Ujb/cpb/Rv3EZ12esi4TEREREblvMm6Ajz8Y2XBgOcxpibH6VYykyxhH18PMJvBxb/hyhOXvmU0sy23srn/yLVOmDJUqVbL5AJydnalZs6ZN1nX69GmSkpJssi5HobjO4wgnNPNwu0FG6i/4+ubtKu3r7gbAwStXyDCbaRG2jPqL/8vVm6kFHr/ixEmCalTHOSc6lx89SnpGBsEtWzJgz14Cvwpn0q7dpGVZ5rn32nVU+2QRP8fHW9cxYdduprUMzBe3AIevWk5A5uuW94uBkm5uGMChK1cLjGVayxZkZmfT6svlzJs9m7UffsjO/fvx6haEOTubsJ+PU7t0KY5fT6DNshUEr1rD7osX862ja7WqRCcmYRgGJxMSCKpenUs3bjB0y1ZCOzxJFR+fP/L2ioiIiIjcE5O7N6YBi2HUJqj1BGRnQcRiS1h//lfLNbVvlXgJPh9i88i+b5uW0tPTyczMvF+rL+DixYuMHj26SAW24jqPI8S1l6eJGa9Znjc1Le+j45oTywkZGWRmZ3M+OZnYmze5mVXw+//L4yfoW7uW9evI2CsAPHwgki9nzeKDTz/l/QMHeWPPXgCiE5OIS00lIT0dgKU//Uzjh8rycKlSBdadmGG5j/Mt4W0dW87jb/VouYcI69aVlhUrMHVvBMu2biVi/jxcg4I5/UgDEjMyqF26NME1qrO9bwgAPdes42JKinUd45o25dFyD9Fp5SqalS/P6L88yl83bSGkdq0CW9hFRERERO43U8BfMA1ZDsPWWE5+Zr5Tk+b0xLrXbbq7+D2dRfzNN9/k22+/xWQyMXLkSEJCQggPD+eLL76gXLlyXL58mevXr7NkyRL8/f05cuQIYWFh1KxZk3379hEZGUmbNm14//33cXHJP4Rr167x3//+lwsXLtC6dWt69eqV7/bDhw+zcOFCypYtS1RUFC+//DJubm6MHz+emJgYJk+ejIeHB7NnzyYuLo7Zs2djNpsxm82kpqYyadIk6tSpw/Xr1/n666/Zs2cPbm5ufP/996xcuZKSJUsyZ84cnJycSExMJDExkRkzZmA2m3n77bcpVaoU0dHRBAYGMmzYsHt+43+P4jqPo8T1J+/54l/WcobsjIy8D2pmzsnNfN3c8HJ1JWrQQLKysynt4ZFvHeeSk4lJTiawfHnrsoQ0S/j6ABnzQ2k7YiR1atdmVfQvzGrbhojnniU5I5OHPEvwS2ISa06fYVlQ19uO0cfNLd948o0tZyv7rdacPs2OmPN83rULcamp9Fm3gWffepvv/f1Jf6yJZZ2ulse5ODkx8JF6bIuJYdMvZxnc4BEA3F2cmd22jXWd7+z7gYT0dN5p1er33lIRERERkfvGVKMFRqcJ8MnTv3Evw7JlOzoCHrbNz6/3tAV74sSJrF+/Hi8vL1577TUWLFjAxIkTGT16NKGhoVStWpWLObuS3rx5k88++4y1a9cSGRnJhAkT6NWrF5s3b2b37t0F1h0bG0uFChWYMmVKgbgGmDZtGiaTiUmTJjFjxgyuX79OkyZN6Nmzp/X20NBQ3NzcGDZsGIcPH+aDDz5g3rx5JCcnM2TIEFJSUli7di1Tp05l9+7d1KpVi6ysLNzd3Zk1axZr1qzBzc0NFxcXEhMT2bFjB5988gmHDh1izJgxLFiw4F7etrumuM7jCHHt4oJ1Pl5+IxsX9wDS0hKsF7ZPzDnrduOHHgIsofvruAZYc+o0PR6uke945rKeJQDIyDZbr5Pt5+1NXFoapipV8XBx4aGc+yyOiuJCSgpPha+i88pwtpw9B1jOHD5rfySNco4Jj09Ps64/KSMDE9Co7EP5xmLOzualbdvpXNVyLe6yJUowv+OTmA2D/4WFUeroUQAyy+U9zi/nNcWlpXE7uy5cZO7BQ7zS5DH6rt9An3XruXLTPt83IiIiIiKkXLm7+yXH2uwp7ymw3dzc8PT0pEePHhiGwbx58wBo1sxy/bGqOT+0A/j6+tKuXTsAunTpQq1atWjYsCEA8bccVwqwbds2+vbtS1RUFD53OHazatWqfP3117Rs2ZLQ0FAaNGhw2/udOXOG6OhoHnnkEVxdXTGZTDRp0oSrV69y8OBBXnzxRQBq1qzJiBEjiIiIoFSpUmzatImqVasyduxY3nrrLcLCwujVqxdVq1bl/PnztG7dmuHDh1tfg60prvM4QlwDtHzcLd98+Pj1wpyVxtmcM2afSUwEYPAj9QHLtaGv3yZC15+JpuevdptuVt5yzelTCZZ1kJ7O1ehoGtWpg9vIUaRXqGg9lntqi0B293uGzSFPsznkaTpVrQLArLatGde0CSG1amICTsTnXbrrTGIinatWpbKPNwA3cg7bSMrIIC4tjTRz3vdX7vHS3q6uBPx4CP9SpTidcgPnzk8BEJdqeU0Nyxa8Rve11FQGbdrMu21a8/qePUxs/jjNK1Tgb5u33tV7LCIiIiJicz7+tr3fXfhTx2CXKGHZsnYzZytVes5xnteuXfvNx+VuwcvdApirQ4cODBgwgGXLlrF9+/bbPvb111/nnXfeITAwkI0bN/Lqq6/muz13nbnP4XzLGZxzl2Xfsgutu3v+M0VnZWVx+vRpkpOT860zODiYhQsX0rt3b44ePcqwYcNIueVYVFtQXOcp1Lg2zJY/v1LCw/J94uWZnW8+ylQchXfJSnzxxRcArDtzhoZl/ehV82FuZGbyyGdLqPXpZ5y75XvoRmYmP8XH07JixXzP8WL9+lTx8WHJTz8BlpORnbx+nddr18S4dImW/11CjUWf8v2l21/L+lZ1y5RhaMMGhJ86TVZ2Nj/HxxOdmMSEZk0B+PeBg5Sbv4Bt585R2sODlhUr8J8fj5Cac0K1DdG/UNHLi761a+Hq7Mz4xo04dOgQxypVxrnzU3x18iTNyvvz1C2/QMs1fOs2ngyozMOlSnE2KZmKXl40LOvHlnPniNVWbBERERGxh+qB4FsRy/Vubsdkub16oM2e8p4C++rVq5jNZjZt2oS7uztBQUEArFixgpiYGI4cOQJAXFzcHz7R2T//+U+qV6/OhAkTiImJKXD7v//9b3r16sXcuXPp16+fNZC9vLwAiIiI4Mcff8TPz48aNWpw7Ngx6+W/Dhw4QIUKFWjSpMkdn/+JJ57g5s2bjBs3jv3797Nx40a2bNnCokWLqFOnDtOmTWP69Ok4OTnh5lbwuNZ7pbjOU1hxnZr0PZdO/p2sjItkZVzk8qkxpN84BkBa4jI8MsYCEB6+jm3/m4g5y3ICPWfX0vQdsIYtW7bwZOh8jl27xuoe3XFxcsLVyYmK3t6UK1GCEs555xeIuHSJlhUrFLhklZerK1v6PE1CWjqtvlzG8K3bWNq1C50rViRjfihV/f0pU7YspapVu6vXNLttGzpWCaDd8hX8bctWlnbtQmAFy6XIfNzc8HZ1xSPnvAdfdOtKgI8P7ZavIGjVGsJPnmJTSG/8cn5x9vfGjXi3TWv6de9OiylT8QoIILx7cIFLdv2/g4c4lZDI+0+048Itv3TyzHmec0nJiIiIiIgUNpOTM3SfnvvVr2+1/NV9uuV+NnJPJznr2bMnJUuWxNPTk9DQUBo3bkx6ejrz5s0jIiKCcePG8fLLLxMaGsqkSZPYsGEDAOHh4TRq1IiNGzcCsH79epo3b87y5csB+Oabb3jyySepXLky0dHRDBw4kFmzZjF27FjKlSvHwoUL8fLyYujQoQQEBBAbG8vkyZMB6NixI19++SXvvPMOzz//PGPHjmX+/Pm8/fbbjBkzBm9vb/z9/Xn33Xfx9vZmzZo1AJw4cYK1a9fSo0cPACZNmkRmZiY7d+7kyJEjPPvss4wePZqsrCxeeukl6tevz6VLl5gzZ47NAltxnacwt1yXKNmcEiWbU6HWvHzLvTxNhM0fTq0ao+44H6X9arJo+3bSZ83EOH/eutzN2Zm9zz1b4P4dqlShQ5Uqtx1HFR8fNoX0LnhDejrhjzfBbeFCTBUqkBH6Eca5s9abP+7UkY87dcz3EFdnZ9695aRjt/prg0f4a87JycByHexfP/7XXnq0MS892hjnzk/hGhRM5ob1mDdvst5+4MoV3vp+H1v6PI2XqyvlPT0BMAzIzpm68l6ev/kcIiIiIiL3i6lBMMYLC2Hd6/kv1eVbwRLXDYJt+3zGr/fTLmSGYeTbZdxsNuPk5IRTzpa+jIwMli9fzqpVqxg+fDidO3e+r2MoDPXrW47VrdNsF2fPmxXXORzhmOu7/WVHcCd35rzpWyCw7wt3d9xGjLxtZBemX0d2ckYGLcKW8fJjf2FoQ8u5ENKysqjz6Wd8EdSV0wkJzD98hD23/MLBVLky7uPG22X8IiIiIlJ8Gdlmy9nCk2Mtx1xXD7zrLde5/RYVFfW7972nLdi2dGvYmkymApftcnNz44UXXiAhIYF69erd9zEUphIe2nKd60GK60KXc3ZxtxEjcRs5ym6Rnbvl2jXI8lu+96dNo9FDZa1xDeDh4sLiLp15ZcdOAD7u/NtbyEVERERECoPJydlml+L6LXYP7LuxZs0a6tWrR0BAgL2HYlNvvOJN5QrOimvF9e9zwMh+LTOT1E1fF7hP+4AAIp7vV9hDExERERGxuz91FvHC0rNnTzp06GDvYdhclUqKa8X1H5AT2calS7iNHIWpSsGzeRcG8+ZNZG5Yj1ev3vgGd7fLGEREREREHNEDEdhF1dRZyYprxfUf42CR7RoUbL1OtoiIiIhIcafAtqNT0QWvvVwYFNcWNolrG16q7a4pskVEREREHJICu5hRXFvYasu1a0hfyLkWe6FSZIuIiIiIOBwFdjGiuLaw5W7hprJlcRsxUpGtyBYRERERUWAXF4prC1sfc525YjmmChUU2YpsEREREREFdnGguLa4Hyc0My5fIiP0I0U2imwREREREQV2Eae4trifZws3zp1VZOdQZIuIiIhIcabALsIU1xaFcSkuRXYeRbaIiIiIFFcK7CJKcW1RmNe5VmTnUWSLiIiISHGkwC6CFNcWhRnXuRTZeRTZIiIiIlLcKLCLGMW1hT3iOpciO48iW0RERESKEwV2EaK4trBnXOdSZOe5NbKdAlvYZQwiIiIiIoVBgV1EKK4tHCGucymy81gju01buzy/iIiIiEhhUGAXAYprC0eK61yK7DzmzZvI/G6nXZ5bRERERKQwKLAfcIprC0eM61yK7DzZEXvt8rwiIiIiIoVBgf0AU1xbOHJc51Jki4iIiIgUfQrsB5Ti2uJBiOtcimwRERERkaJNgf0AUlxbPEhxnUuRLSIiIiJSdCmwHzCKa4sHMa5zKbJFRERERIomBfYDRHFt8SDHdS5FtoiIiIhI0aPAfkAori0cJa47tHX70+tQZIuIiIiIFC0K7AeA4trCUeL674M86dahhE3WpcgWERERESk6FNgOTnFt4Uhx/X/DvNm4LdVm61Rki4iIiIgUDQpsB6a4tnC0uP5gQQrbdmbYdN2KbBERERGRB58C20Epri0cMa7v13woskVEREREHmwKbAekuLYoTnGdS5EtIiIiIvLgUmA7GMW1RXGM61yKbBERERGRB5MC24Eori2Kc1znUmSLiIiIiDx4FNgOQnFtobjOo8gWEREREXmwKLAdgCPEnOI6jyPMRy5FtoiIiIjIg0OBbWeOEHOK6zyOMB+/psgWEREREXkwKLDtqE93D7vHnOI6jyPGdS5FtoiIiIiI41Ng21H/EE/FteL6rimyRUREREQcmwLbjpauvKm4Vlz/IYpsERERERHHpcC2o6/WpdnleRXXeR6kuM6lyBYRERERcUwK7GJGcZ3HFnHtFNjCxqO6O4psERERERHHo8AuRhTXeWy15dq1TVucOz9lw5HdPUW2iIiIiIhjUWAXE4rrPLbcLTzzu524BgUrshXZIiIiIiIK7OJAcZ3H1sdcZ0fsJXPDekW2IltERERERIFd1Cmu89yvE5qZN29SZIMiW0RERESKPQV2Eaa4znO/zxauyM6hyBYRERGRYkyBXUQprvMU1qW4FNk5FNkiIiIiUkwpsIsgxXWewr7OtSI7hyJbRERERIohBXYRo7jOU9hxnUuRnUORLSIiIiLFjAK7CFFc57FXXOdSZOf4dWSXr1D4YxARERERKSQK7CJCcZ3H3nGdS5Gd45bIdu37TOE/v4iIiIhIIVFgFwGK6zyOEte5FNk5ciM7Lq7wn1tEREREpJAosB9wius8jhbXuRTZOdLTyVy5ovCfV0RERESkkCiwH2CK6zyOGte5FNk5MjIK/zlFRERERAqJi70HUJw9XM35nh9bs7ozU8f5cO6CmdmhN6ha+d7Xda9KeJh44xVvqlRyZuqsZMxmqF+78L+l+nT3oH+IJ0tX3uTbPRmFMobKFS2/mzL5l7/rx2RHHSOzZElcg4KhZEmyI/ber+H9xiDMZH61Ate+z+A25v8sW5QLMXr/yPslIiIiIvKgMRmGUfibPYu5+vXrAxAVFWXnkcifYWRnY3LSTiB/lN43EREREXmQ/JF+00+5IvdIkXhv9L6JiIiISFGln3RFREREREREbECBLSIiIiIiImIDCmwRERERERERG1Bgi4iIiIiIiNiAAltERERERETEBhTYIiIiIiIiIjagwBYRERERERGxAQW2iIiIiIiIiA0osEVERERERERsQIEtIiIiIiIiYgMu9h5AcWQ2mwGoX7++nUciIiIiIiIivyW33+6GtmCL3MJsNv+hD5AUHs2N49LcOC7NjePS3DguzY3j0tw4Ls1NHm3BtoPKlSsDsG3bNjuPRH6tQ4cOgObGEWluHJfmxnFpbhyX5sZxaW4cl+bGcWlu8mgLtoiIiIiIiIgNKLBFREREREREbECBLSIiIiIiImIDCmwRERERERERG1Bgi4iIiIiIiNiAAltERERERETEBkyGYRj2HoSIiIiIiIjIg05bsEVERERERERsQIEtIiIiIiIiYgMKbBEREREREREbUGCLiIiIiIiI2IACW0REROQ+ys7O5qOPPiItLc3eQ5EchmGwb98+wsLC7D0UESlidBbxQhYfH89bb72Fl5cXcXFxjB49mnr16tl7WMVKXFwc06ZNo1atWowePRqAtLQ03nrrLQzDID4+ngEDBtCiRQvA8oPRe++9x5UrV0hNTeWpp54iODjYni+hSDpz5gzTpk3j8OHDlC5dmiFDhtC/f3/NjQNIT09n6tSpbN26FVdXV4YNG8agQYM0Nw7kww8/5Pz588yYMUPz4iDCw8OZOHGi9WsXFxeOHTum+XEAJ0+eZPz48XTs2JFhw4bh4uKieXEAR48eJSQkpMDyAQMGMHbsWM2PHW3dupUNGzZQvnx5zpw5Q3BwMN27d9fn5k4MKVSDBg0y/v3vfxuGYRh79uwxmjdvbiQlJdl5VMVDVlaWERYWZrz55ptG7dq1jblz51pve+2114xXX33VMAzDOHv2rNGoUSPj7NmzhmEYRmhoqNG/f3/DMAwjMTHRaNKkifHDDz8U/gsowjIyMoyRI0cahw8fNqKjo40hQ4YYtWvXNo4cOaK5cQCffvqpsWvXLuOnn34ynn76aePxxx83DEOfG0fx1VdfGbVr1zbGjx9vGIbmxVGsXLnS+Oijj4wlS5YYS5YsMb788kvDMDQ/9nbixAmjSZMmxqJFi/It17zY344dO4wZM2YYH330kfVPixYtjPPnz2t+7OjEiRNGvXr1jIsXLxqGYRhnzpwx6tata8TExGhe7kC7iBei/fv3s2fPHutvdh5//HGSk5NZunSpnUdWPGRlZdGkSRP++te/5lt+/vx5Vq1aZZ2XKlWq4Ofnx8cff8yNGzdYtGgRgYGBAJQsWZJ69eoRGhpa6OMvys6cOcPgwYNp1KgR1apVY8CAAQAcPnxYc+MAQkJCaNWqFXXr1qVu3boMHz5cnxsHsXv37ny7HWteHMugQYN44YUXeOGFF3j22Wc1P3ZmGAbjx4+nbNmyvPjii9blmhfHULduXV599VVGjhzJyJEj6dChA40bN8YwDM2PHZ09exaz2UxWVhYA/v7+ODk5cfnyZc3LHSiwC9HOnTsB8PPzAyy7i5UqVYodO3bYcVTFh7u7O7Vq1SqwfPfu3WRlZVnnBaBs2bLs2LGDgwcPkpSURNmyZfPdFhERoWPpbKhOnTo0bdrU+nV0dDQVK1YkKytLc+MAfHx8MAyDhQsXkpaWRqdOnfS5cQBRUVEcOnSI/v37W5dpXhyHyWQiJCSERo0aERQUxOrVqzU/dnb48GGOHTtGSEgIYWFhTJw4kRUrVrBr1y7NiwPw9/fHZDJZv166dCnPPPOMPjd21rp1a+rWrcuYMWM4fvw4mzdv5r333uP06dOalztQYBei+Ph4AFxdXa3LXF1drcvFPn5rXu50m9lsJjExsXAHWkycPXuWr776ivnz53Pz5k1Ac+MIlixZQnp6OhcuXKBr165cvXoV0NzYS0xMDKtWrWLkyJH5luv/M8cREBDA8OHDmTlzJmXKlGH8+PGaHzv78ccfAXj44Yfp378/zz//PK+//jpxcXGA5sWR3Lhxg++//562bdvqc2NnHh4eLFiwgIYNG7Jw4ULeffddrl27pnn5DS72HkBxUrp0aQBrNABkZmZSvnx5ew1JuPO8lClT5o63OTs74+vrW7gDLQaio6P58MMPWbRoEeXKlePgwYOA5sYRDBw40Pp3ixYt+OyzzwDNjb188MEH7Nixg1WrVlmXrV+/nszMTEDz4giaNm1q3TPniSeeoH379ixatAjQ/NhL7pazEiVKANCwYUNKly7NggULAM2LI9m4cSMdOnTA2dlZP6fZWVxcHH//+98JCwvD3d2dLVu2MHr0aKZOnQpoXm5HW7ALUbt27QC4cuUKYDkmODExkbZt29pzWMVeq1atcHFxsc4LwLVr12jbti1/+ctfKFmyZIHbmjVrhoeHhz2GW2T98ssvrFy5kpkzZ1KuXDnA8l5rbhyLt7c3Xl5ePPHEE5obO5ozZw6RkZHs37+f/fv3AxAcHMzWrVs1Lw6oRIkSVKpUSZ8bO6tcuTJAgS1omhfHs3btWnr27Ano5zR727BhA66urri7uwPQqVMn6taty5YtWzQvd6DALkRNmzalTZs27Nq1C4DIyEi8vLzyHT8nhS8gIIA+ffpY5yUmJoarV68ydOhQvL29GTp0KLt378YwDFJSUoiKiiqwW6b8Oenp6YwfP55KlSqxatUqVqxYwcyZM7l06ZLmxs6uXr3KggULrL+FPnbsGJmZmYwYMUJz44D0/5ljiI+PZ/HixWRkZACWoEtMTGTcuHGaHztq3749ZcuW5YcffgDg8uXLJCYm8txzz2leHMi1a9dISEigTp06gP5fs7cqVaoQHR1NcnIygHU37379+mle7kDXwS5kN27cYMqUKXh5eREbG8uoUaNo2LChvYdVbOzbt49ly5axfv16atWqxaBBg+jTpw9ZWVm89dZbZGZmcu3aNZ5//nnatGljfdyHH37I2bNnSUtLo0OHDvTq1ct+L6IIWrduHWPHji2wfPjw4YwZM0ZzY0exsbG8/PLLgGULaXR0NP369aNWrVr63DiQOnXq0Lt3b2bMmKF5cQCxsbFMmjSJ1NRUevToQXx8PN26daNatWqaHzs7ceIEU6dOpVmzZly4cIH27dvTrVs3zYsDWb16NdHR0fzjH/+wLtP82Nfnn39OREQEDRo0ICEhgbp169KrVy/Nyx0osEVERERERERsQLuIi4iIiIiIiNiAAltERERERETEBhTYIiIiIiIiIjagwBYRERERERGxAQW2iIiIiIiIiA0osEVERERERERsQIEtIiIiIiIiYgMKbBEREREREREbUGCLiIgUY//85z8ZNGgQR48eve3tn332Gd27d2fv3r2/uZ5BgwbRq1cvli5d+ofH8PXXX9OuXTtmzJjBzZs3SU1NBaB///6899571q9FREQcnQJbRESkGAsJCWHv3r0cOHAAgNdff52QkBAiIyMB+O6777hy5QqXL1/+zfWMGjWKn376iU8++eSunzszM5OkpCS6dOmCu7s7R44cYfXq1QQFBXH8+HGqVKnCf/7zH5YsWXLvL1BERKQQudh7ACIiImI/5cuXB+DRRx8FYPTo0Tz33HMsX76cqKgo/Pz8+N///keZMmV+cz0NGzYEoHnz5nf93BcvXiQ4OJhnnnmGihUrUqVKFbZt24afnx+urq5cuHCB8uXLM3DgwHt7cSIiIoVMW7BFRESKMRcXy+/aPT09AfD392fx4sU0a9aMrVu38sMPP9C5c2cOHTr0m+vx8PDAw8MDk8nE0qVLeeWVV/jXv/7FiRMn7viYqlWrMnXqVH755RcAjh07RkpKCoMHD8bX15fIyEgGDx6Mh4eHTV6riIjI/aYt2CIiIsVQdnY2Fy5c4JtvvgGgb9++tG7dmkaNGvH1119z+vRpTCYT48aNIzAwkBo1ahRYR1JSEgcPHuT06dMcO3aMzMxMwsPD2bRpEw8//DBeXl53fH6z2cxXX33F5cuXcXV1JTIyEnd3dxo0aMC5c+c4f/48pUqVomnTpkyfPp1169bRrVs3pkyZct/eExERkT9LgS0iIlIMffzxx3z77bckJycDMG7cOLp06UKZMmUYOnQoEyZMYO3ataSkpPDdd9/dNrANw+DYsWMkJCRw/PhxzGYz9evXJyws7He3Ojs7O9O6dWt27tzJvHnzMJlMNG3alKeffpr27dvTtWtXnJycCAkJoUWLFowbN45OnTrdl/dCRETEVkyGYRj2HoSIiIgUPsMw6NOnD0ePHmXatGm0aNGC+Ph4li1bRkREBJcvX6ZmzZq0bNmSCRMmYDKZbrue2NhYOnbsSEZGBj4+Pjz22GN89NFHuLq6/ubz37x5k4EDB+Lr60t2djaVKlVi69atNG3alGbNmjFw4EBat25NQkICGzZsoGrVqvfjbRAREbEZHYMtIiJSTIWHh1svz7Vx40YmTJhAgwYNePHFFwkMDKRy5cqsW7eOCRMmEBcXR0ZGxm3XM2fOHLy9vTGZTAwZMoSTJ0/y0ksvkZaWdsfnTklJYejQoTRq1Ij09HTi4uJ49tlnCQ8PJysri9KlSwNQunRpypQpQ0BAgO3fABERERtTYIuIiBRDV69eZc6cOQQHBwMwd+5cXnrpJS5evEhCQgLx8fHExcXRv39/HnvsMVq3bk2bNm04ePBgvvVs3bqVNWvW8I9//APDMPDx8eGdd97h22+/ZfDgwcTGxt72+bdv386kSZOYPHkyTk5O+Pv7s2HDBrp06UJgYCAzZ85k7969XLlyhVatWuHkpB9ZRETE8ekYbBERkWImPT2dl19+mSeeeILevXuzfv16Nm/ezIwZM/Dz86Nu3bpcvnwZd3d3nnnmGby8vHBycsJkMuXbTfvnn3/m1VdfJSgoiODgYN544w2ys7MJDAzkxRdfZPHixfTo0YPJkyfTrVs36y7me/fuZfXq1ZjNZmJjY7lw4QItW7YkMjISwzBo3749lSpVYvTo0SQnJ9OxY0d7vVUiIiJ/iI7BFhERKWYmTZrEI488Qv/+/dm0aRNjxowhIiLCuls2wPTp09m5cyebN2++7TpOnTrF4MGDad68OVOmTOHUqVP069ePxo0bExAQQMeOHQkNDbVepqtevXqMHDmSTp06YTKZ+OKLL0hJSeHSpUtcvXqV2bNnExQURPPmzXn77bcB6NmzJ6dPn2bbtm34+/vf/zdGRETkT9IWbBERkWJm6tSpuLm5AZZrTwNkZWXlu49hGNb7/NqhQ4d48803mThxIo0aNWLcuHHExMQAlstv+fn54evry7x585g/fz7Xr1+ndOnSnDp1isaNG+Pv78/zzz8PwMmTJ5kxYwbNmzcnLS2NmTNnAvDll19y6dIlvLy86Nu3L5MnT9aWbBERcXgKbBERkWLm1nDes2cPJpMJHx+ffPdxdnbG29v7to/PzMxk+fLl1rOEz58/n507dzJ06FC6du3K3/72N+t9p0+f/ptjKVeuHH5+fnh6euLp6cnkyZPp0aMH33//PcuXL+f48eOMGTOGUaNG0bBhQwYMGEDXrl3vGP8iIiL2pMAWEREpxnr16kXp0qULXLfa29v7jpfFevzxxwssO3DgAMAdL+X1a9evXyc0NJSDBw/Stm1bNm7cSHx8PPPnzycoKIgRI0YAUK1aNaZMmcKWLVvw8vLi5MmTNG7cmGrVqv2BVykiIlI4dAy2iIiIFPDNN9/g7OxMu3bt7ur+e/fuZciQIcydO/euduXOysoiIyMDT0/PPztUERERh6HAFhEREZuIiYmhcuXKd70VW0REpKhRYIuIiIiIiIjYgJO9ByAiIiIiIiJSFCiwRURERERERGxAgS0iIiIiIiJiAwpsERERERERERtQYIuIiIiIiIjYgAJbRERERERExAYU2CIiIiIiIiI2oMAWERERERERsYH/DzIv00rSpXuDAAAAAElFTkSuQmCC\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 参考: https://scorecardpipeline.itlubber.art/scorecardpipeline.html#module-scorecardpipeline.auto_report\\n\",\n    \"# pip install scorecardpipeline -i https://pypi.org/simple\\n\",\n    \"\\n\",\n    \"from scorecardpipeline import *\\n\",\n    \"\\n\",\n    \"# 初始化设置，包含忽略warning、画图字体、[随机种子、日志输出]\\n\",\n    \"init_setting()\\n\",\n    \"\\n\",\n    \"# 加载数据集，标签转换为 0 和 1\\n\",\n    \"target = \\\"creditability\\\"\\n\",\n    \"data = germancredit()\\n\",\n    \"data[target] = data[target].map({\\\"good\\\": 0, \\\"bad\\\": 1})\\n\",\n    \"features = data.columns.drop(target).tolist()\\n\",\n    \"\\n\",\n    \"# 测试报告输出\\n\",\n    \"auto_data_testing_report(data\\n\",\n    \"                         , features=features\\n\",\n    \"                         , target=target\\n\",\n    \"                         , date=None # 传入日期列名，会按 freq 统计不同时间维度好坏样本的分布情况\\n\",\n    \"                         , freq=\\\"M\\\"\\n\",\n    \"                         , data_summary_comment=\\\"\\\"\\n\",\n    \"                         , excel_writer=\\\"model_report/三方数据测试报告.xlsx\\\"\\n\",\n    \"                         , sheet=\\\"分析报告\\\"\\n\",\n    \"                         , start_col=2\\n\",\n    \"                         , start_row=2\\n\",\n    \"                         , writer_params={}\\n\",\n    \"                         , bin_params={\\\"method\\\": \\\"dt\\\", \\\"min_bin_size\\\": 0.05, \\\"max_n_bins\\\": 10} # feature_bin_stats 函数的相关参数\\n\",\n    \"                         , pictures=['bin', 'ks', 'hist'] # 类别型变量不支持 ks 和 hist\\n\",\n    \"                         , corr=True\\n\",\n    \"                         )\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"source\": [\n    \"### 自动EDA输出报告\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Done! Use 'show' commands to display/save.   |██████████| [100%]   00:02 -> (00:00 left)                    \"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Report model_report/auto_eda.html was generated.\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# https://scorecardpipeline.itlubber.art/scorecardpipeline.html#module-scorecardpipeline.auto_eda\\n\",\n    \"from scorecardpipeline import *\\n\",\n    \"\\n\",\n    \"# 加载数据集\\n\",\n    \"data = germancredit()\\n\",\n    \"\\n\",\n    \"# 设置目标变量名称 & 映射目标变量值域为 {0, 1}\\n\",\n    \"target = \\\"creditability\\\"\\n\",\n    \"data[target] = data[target].map({\\\"good\\\": 0, \\\"bad\\\": 1})\\n\",\n    \"\\n\",\n    \"# 自动 eda 并保存文件\\n\",\n    \"auto_eda_sweetviz(data, target=target, save=\\\"model_report/auto_eda.html\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"source\": [\n    \"### 特征区分度\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"
\",\n      \"image/png\": 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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"
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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from scorecardpipeline import *\\n\",\n    \"\\n\",\n    \"# 初始化配置\\n\",\n    \"init_setting()\\n\",\n    \"\\n\",\n    \"# 加载数据集\\n\",\n    \"data = germancredit()\\n\",\n    \"\\n\",\n    \"# 设置目标变量名称 & 映射目标变量值域为 {0, 1}\\n\",\n    \"target = \\\"creditability\\\"\\n\",\n    \"data[target] = data[target].map({\\\"good\\\": 0, \\\"bad\\\": 1})\\n\",\n    \"\\n\",\n    \"use_cols = ['指标名称', '指标含义', '分箱', '样本总数', '样本占比', '好样本数', '好样本占比', '坏样本数', '坏样本占比', '坏样本率', '分档WOE值', '指标IV值', 'LIFT值', '坏账改善', '累积LIFT值', '累积坏账改善', '分档KS值']\\n\",\n    \"\\n\",\n    \"# 使用 MDLP 自动分箱查看特征区分度\\n\",\n    \"table_mdlp = feature_bin_stats(data, \\\"duration_in_month\\\", target=target, method=\\\"mdlp\\\", desc=\\\"在账月份\\\", max_n_bins=5)[use_cols]\\n\",\n    \"bin_plot(table_mdlp, desc=\\\"在账月份 MDLP \\\", save=\\\"model_report/duration_in_month_mdlp_binplot.png\\\")\\n\",\n    \"\\n\",\n    \"# 使用 step 验证特征泛化能力\\n\",\n    \"table_step = feature_bin_stats(data, \\\"duration_in_month\\\", target=target, method=\\\"step\\\", desc=\\\"在账月份\\\", max_n_bins=5)[use_cols]\\n\",\n    \"bin_plot(table_step, desc=\\\"在账月份 STEP \\\", save=\\\"model_report/duration_in_month_step_binplot.png\\\")\\n\",\n    \"\\n\",\n    \"# 初始化 Excel 写入器\\n\",\n    \"writer = ExcelWriter()\\n\",\n    \"\\n\",\n    \"end_row, end_col = dataframe2excel(table_mdlp,\\n\",\n    \"                                   writer,\\n\",\n    \"                                   sheet_name=\\\"分箱信息\\\",\\n\",\n    \"                                   percent_cols=[\\\"样本占比\\\", \\\"好样本占比\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\", \\\"坏账改善\\\", \\\"累积坏账改善\\\"],\\n\",\n    \"                                   condition_cols=[\\\"坏样本率\\\", \\\"LIFT值\\\"],\\n\",\n    \"                                   start_row=2,\\n\",\n    \"                                   merge_column=[\\\"指标名称\\\", \\\"指标含义\\\", \\\"指标IV值\\\"],\\n\",\n    \"                                   merge=True,\\n\",\n    \"                                   title=\\\"duration_in_month（在账月份） MDLP 特征区分度\\\",\\n\",\n    \"                                   figures=[f\\\"model_report/duration_in_month_mdlp_binplot.png\\\"])\\n\",\n    \"\\n\",\n    \"end_row, end_col = dataframe2excel(table_step,\\n\",\n    \"                                   writer,\\n\",\n    \"                                   sheet_name=\\\"分箱信息\\\",\\n\",\n    \"                                   percent_cols=[\\\"样本占比\\\", \\\"好样本占比\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\", \\\"坏账改善\\\", \\\"累积坏账改善\\\"],\\n\",\n    \"                                   condition_cols=[\\\"坏样本率\\\", \\\"LIFT值\\\"],\\n\",\n    \"                                   start_row=end_row + 2,\\n\",\n    \"                                   merge_column=[\\\"指标名称\\\", \\\"指标含义\\\", \\\"指标IV值\\\"],\\n\",\n    \"                                   merge=True,\\n\",\n    \"                                   title=\\\"duration_in_month（在账月份） STEP 特征区分度\\\",\\n\",\n    \"                                   figures=[f\\\"model_report/duration_in_month_step_binplot.png\\\"])\\n\",\n    \"\\n\",\n    \"# 保存到 excel 文件\\n\",\n    \"writer.save(\\\"model_report/数据有效性之特征区分度.xlsx\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"                分箱详情                      MOB1 0+                                               ...  MOB1 3+                                                                  \\n                指标名称 指标含义             分箱     样本总数   样本占比     好样本数  好样本占比    坏样本数  坏样本占比   坏样本率  ...     好样本数  好样本占比    坏样本数  坏样本占比   坏样本率  LIFT值    坏账改善 累积LIFT值 累积坏账改善  分档KS值\\n0  duration_in_month        [负无穷 , 10.5) 171.0000 0.1710 131.0000 0.1609 40.0000 0.2151 0.2339  ... 131.0000 0.1609 36.0000 0.2081 0.2156 1.2299  0.0468  1.2299 0.0468 0.0472\\n1  duration_in_month       [10.5 , 19.0) 375.0000 0.3750 303.0000 0.3722 72.0000 0.3871 0.1920  ... 303.0000 0.3722 69.0000 0.3988 0.1855 1.0582  0.0352  1.1114 0.1340 0.0738\\n2  duration_in_month        [19.0 , 正无穷) 454.0000 0.4540 380.0000 0.4668 74.0000 0.3978 0.1630  ... 380.0000 0.4668 68.0000 0.3931 0.1518 0.8660 -0.1114  1.0000 0.0000 0.0000\\n3  duration_in_month                 缺失值   0.0000 0.0000   0.0000 0.0000  0.0000 0.0000 0.0000  ...   0.0000 0.0000  0.0000 0.0000 0.0000 0.0000  0.0000  0.0000 0.0000 0.0000\\n\\n[4 rows x 27 columns]\",\n      \"text/html\": \"
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分箱详情MOB1 0+...MOB1 3+
指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率...好样本数好样本占比坏样本数坏样本占比坏样本率LIFT值坏账改善累积LIFT值累积坏账改善分档KS值
0duration_in_month[负无穷 , 10.5)171.00000.1710131.00000.160940.00000.21510.2339...131.00000.160936.00000.20810.21561.22990.04681.22990.04680.0472
1duration_in_month[10.5 , 19.0)375.00000.3750303.00000.372272.00000.38710.1920...303.00000.372269.00000.39880.18551.05820.03521.11140.13400.0738
2duration_in_month[19.0 , 正无穷)454.00000.4540380.00000.466874.00000.39780.1630...380.00000.466868.00000.39310.15180.8660-0.11141.00000.00000.0000
3duration_in_month缺失值0.00000.00000.00000.00000.00000.00000.0000...0.00000.00000.00000.00000.00000.00000.00000.00000.00000.0000
\\n
4 rows × 27 columns
\\n
\"\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 随机生成逾期天数，模拟客户逾期数据\\n\",\n    \"data[\\\"MOB1\\\"] = np.where(np.random.random(len(data)) <= 0.8, 0, np.random.randint(1, 31, size=len(data)))\\n\",\n    \"\\n\",\n    \"table = feature_bin_stats(data, \\\"duration_in_month\\\", overdue=[\\\"MOB1\\\"], dpd=[0, 3], method=\\\"mdlp\\\", max_n_bins=5, empty_separate=True, return_rules=False, del_grey=True, return_cols=['样本总数', '样本占比', '好样本数', '好样本占比', '坏样本数', '坏样本占比', '坏样本率', 'LIFT值', '累积LIFT值', '坏账改善', '累积坏账改善', '分档KS值'])\\n\",\n    \"\\n\",\n    \"percent_cols = [\\\"样本占比\\\", \\\"好样本占比\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"坏账改善\\\", \\\"累积LIFT值\\\", \\\"累积坏账改善\\\"]\\n\",\n    \"condition_cols = [\\\"坏样本率\\\", \\\"LIFT值\\\"]\\n\",\n    \"\\n\",\n    \"dataframe2excel(table\\n\",\n    \"                , \\\"model_report/数据有效性之特征区分度.xlsx\\\"\\n\",\n    \"                , percent_cols=[c for c in table.columns if (isinstance(c, tuple) and c[-1] in percent_cols) or (not isinstance(c, tuple) and c in percent_cols)]\\n\",\n    \"                , condition_cols=[c for c in table.columns if (isinstance(c, tuple) and c[-1] in condition_cols) or (not isinstance(c, tuple) and c in condition_cols)]\\n\",\n    \"                )\\n\",\n    \"\\n\",\n    \"table\"\n   ],\n   \"metadata\": {\n    \"collapsed\": false\n   }\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"outputs\": [],\n   \"source\": [],\n   \"metadata\": {\n    \"collapsed\": false\n   }\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"outputs\": [],\n   \"source\": [],\n   \"metadata\": {\n    \"collapsed\": false\n   }\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 模型增益效果评估\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"特征: duration_in_month 在账月份 KS 增益效果 0.0952%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 选择需要评估的特征集\\n\",\n    \"feature = [\\\"duration_in_month\\\"]\\n\",\n    \"\\n\",\n    \"def calculate_model_score(data, target=\\\"target\\\"):\\n\",\n    \"    # 构建 pipeline\\n\",\n    \"    model_pipeline = Pipeline([\\n\",\n    \"        (\\\"preprocessing\\\", FeatureSelection(target=target, engine=\\\"toad\\\")),\\n\",\n    \"        (\\\"combiner\\\", Combiner(target=target, min_bin_size=0.1, max_n_bins=5, method=\\\"mdlp\\\")),\\n\",\n    \"        (\\\"transform\\\", WOETransformer(target=target)),\\n\",\n    \"        (\\\"processing_select\\\", FeatureSelection(target=target, engine=\\\"toad\\\")),\\n\",\n    \"        (\\\"stepwise\\\", StepwiseSelection(target=target)),\\n\",\n    \"        (\\\"logistic\\\", ITLubberLogisticRegression(target=target)),\\n\",\n    \"    ])\\n\",\n    \"\\n\",\n    \"    # 训练 pipeline\\n\",\n    \"    model_pipeline.fit(data)\\n\",\n    \"\\n\",\n    \"    # 计算模型指标\\n\",\n    \"    return KS(model_pipeline.predict_proba(data)[:, -1], data[target])\\n\",\n    \"\\n\",\n    \"# 计算模型指标增益效果\\n\",\n    \"improvement = calculate_model_score(data, target=target) - calculate_model_score(data.drop(columns=feature), target=target)\\n\",\n    \"\\n\",\n    \"print(f\\\"特征: duration_in_month 在账月份 KS 增益效果 {improvement * 100:.4f}%\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率分档WOE值指标IV值LIFT值坏账改善累积LIFT值累积坏账改善分档KS值
0duration_in_month在账月份[负无穷 , 8.5)94.00000.094084.00000.120010.00000.03330.10641.28090.28390.3546-0.06701.00000.00000.0000
1duration_in_month在账月份[8.5 , 15.5)337.00000.3370258.00000.368679.00000.26330.23440.33620.28390.7814-0.11111.06700.64540.0867
2duration_in_month在账月份[15.5 , 34.5)399.00000.3990270.00000.3857129.00000.43000.3233-0.10870.28391.07770.05161.23610.31170.1919
3duration_in_month在账月份[34.5 , 43.5)100.00000.100058.00000.082942.00000.14000.4200-0.52450.28391.40000.04441.60780.12450.1476
4duration_in_month在账月份[43.5 , 正无穷)70.00000.070030.00000.042940.00000.13330.5714-1.13500.28391.90480.06811.90480.06810.0905
5duration_in_month在账月份缺失值0.00000.00000.00000.00000.00000.00000.00000.00000.28390.00000.00000.00000.00000.0000
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"                指标名称  指标含义             分箱     样本总数   样本占比     好样本数  好样本占比     坏样本数  坏样本占比   坏样本率  分档WOE值  指标IV值  LIFT值    坏账改善  累积LIFT值  累积坏账改善  分档KS值\\n\",\n       \"0  duration_in_month  在账月份    [负无穷 , 8.5)  94.0000 0.0940  84.0000 0.1200  10.0000 0.0333 0.1064  1.2809 0.2839 0.3546 -0.0670   1.0000  0.0000 0.0000\\n\",\n       \"1  duration_in_month  在账月份   [8.5 , 15.5) 337.0000 0.3370 258.0000 0.3686  79.0000 0.2633 0.2344  0.3362 0.2839 0.7814 -0.1111   1.0670  0.6454 0.0867\\n\",\n       \"2  duration_in_month  在账月份  [15.5 , 34.5) 399.0000 0.3990 270.0000 0.3857 129.0000 0.4300 0.3233 -0.1087 0.2839 1.0777  0.0516   1.2361  0.3117 0.1919\\n\",\n       \"3  duration_in_month  在账月份  [34.5 , 43.5) 100.0000 0.1000  58.0000 0.0829  42.0000 0.1400 0.4200 -0.5245 0.2839 1.4000  0.0444   1.6078  0.1245 0.1476\\n\",\n       \"4  duration_in_month  在账月份   [43.5 , 正无穷)  70.0000 0.0700  30.0000 0.0429  40.0000 0.1333 0.5714 -1.1350 0.2839 1.9048  0.0681   1.9048  0.0681 0.0905\\n\",\n       \"5  duration_in_month  在账月份            缺失值   0.0000 0.0000   0.0000 0.0000   0.0000 0.0000 0.0000  0.0000 0.2839 0.0000  0.0000   0.0000  0.0000 0.0000\"\n      ]\n     },\n     \"execution_count\": 72,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"table_mdlp\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 规则增益效果评估\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from scorecardpipeline import *\\n\",\n    \"\\n\",\n    \"# 初始化配置\\n\",\n    \"init_setting()\\n\",\n    \"\\n\",\n    \"# 加载数据集\\n\",\n    \"data = germancredit()\\n\",\n    \"\\n\",\n    \"# 设置目标变量名称 & 映射目标变量值域为 {0, 1}\\n\",\n    \"target = \\\"creditability\\\"\\n\",\n    \"data[target] = data[target].map({\\\"good\\\": 0, \\\"bad\\\": 1})\\n\",\n    \"\\n\",\n    \"# 随机生成逾期天数，模拟客户逾期数据\\n\",\n    \"data[\\\"MOB1\\\"] = np.where(np.random.random(len(data)) <= 0.8, 0, np.random.randint(1, 31, size=len(data)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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规则分类指标名称分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率LIFT值坏账改善准确率精确率召回率F1分数
0验证规则duration_in_month > 43.5命中700.0700300.0429400.13330.57141.90480.06810.71000.57140.13330.2162
1验证规则duration_in_month > 43.5未命中9300.93006700.95712600.86670.27960.9319-0.90480.29000.27960.86670.4228
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\"\n      ],\n      \"text/plain\": [\n       \"   规则分类                      指标名称   分箱  样本总数   样本占比  好样本数  好样本占比  坏样本数  坏样本占比   坏样本率  LIFT值    坏账改善    准确率    精确率    召回率   F1分数\\n\",\n       \"0  验证规则  duration_in_month > 43.5   命中    70 0.0700    30 0.0429    40 0.1333 0.5714 1.9048  0.0681 0.7100 0.5714 0.1333 0.2162\\n\",\n       \"1  验证规则  duration_in_month > 43.5  未命中   930 0.9300   670 0.9571   260 0.8667 0.2796 0.9319 -0.9048 0.2900 0.2796 0.8667 0.4228\"\n      ]\n     },\n     \"execution_count\": 46,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"rule = Rule(\\\"duration_in_month > 43.5\\\")\\n\",\n    \"rule.report(data[[\\\"duration_in_month\\\", target]], target=target)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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规则详情MOB1 DPD0+MOB1 DPD3+MOB1 DPD7+MOB1 DPD15+
规则分类指标名称分箱样本总数样本占比坏样本率LIFT值坏账改善坏样本率LIFT值坏账改善坏样本率LIFT值坏账改善坏样本率LIFT值坏账改善
0验证规则duration_in_month > 43.5命中700.07000.17140.8282-0.01290.17140.8791-0.00910.17141.00250.00020.08570.7585-0.0182
1验证规则duration_in_month > 43.5未命中9300.93000.20971.01290.17180.19681.00910.12090.17100.9998-0.00250.11511.01820.2415
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\"\n      ],\n      \"text/plain\": [\n       \"   规则详情                                            MOB1 DPD0+                MOB1 DPD3+                MOB1 DPD7+                MOB1 DPD15+               \\n\",\n       \"   规则分类                      指标名称   分箱 样本总数   样本占比       坏样本率  LIFT值    坏账改善       坏样本率  LIFT值    坏账改善       坏样本率  LIFT值    坏账改善        坏样本率  LIFT值    坏账改善\\n\",\n       \"0  验证规则  duration_in_month > 43.5   命中   70 0.0700     0.1714 0.8282 -0.0129     0.1714 0.8791 -0.0091     0.1714 1.0025  0.0002      0.0857 0.7585 -0.0182\\n\",\n       \"1  验证规则  duration_in_month > 43.5  未命中  930 0.9300     0.2097 1.0129  0.1718     0.1968 1.0091  0.1209     0.1710 0.9998 -0.0025      0.1151 1.0182  0.2415\"\n      ]\n     },\n     \"execution_count\": 65,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"rule = Rule(\\\"duration_in_month > 43.5\\\")\\n\",\n    \"rule.report(data[[\\\"duration_in_month\\\", \\\"MOB1\\\"]], overdue=[\\\"MOB1\\\"], dpd=[0, 3, 7, 15], filter_cols=['坏样本率', 'LIFT值', '坏账改善', '样本占比'], del_grey=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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规则分类指标名称分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率LIFT值坏账改善准确率精确率召回率F1分数
0先验规则credit_amount > 3972.25命中2500.25001450.20711050.35000.42001.40000.13330.66000.42000.35000.3818
1先验规则credit_amount > 3972.25未命中7500.75005550.79291950.65000.26000.8667-0.40000.34000.26000.65000.3714
0验证规则duration_in_month > 43.5命中110.014740.007270.03590.63642.44760.02150.74400.63640.03590.0680
1验证规则duration_in_month > 43.5未命中7390.98535510.99281880.96410.25440.9785-1.44760.25600.25440.96410.4026
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\"\n      ],\n      \"text/plain\": [\n       \"   规则分类                      指标名称   分箱  样本总数   样本占比  好样本数  好样本占比  坏样本数  坏样本占比   坏样本率  LIFT值    坏账改善    准确率    精确率    召回率   F1分数\\n\",\n       \"0  先验规则   credit_amount > 3972.25   命中   250 0.2500   145 0.2071   105 0.3500 0.4200 1.4000  0.1333 0.6600 0.4200 0.3500 0.3818\\n\",\n       \"1  先验规则   credit_amount > 3972.25  未命中   750 0.7500   555 0.7929   195 0.6500 0.2600 0.8667 -0.4000 0.3400 0.2600 0.6500 0.3714\\n\",\n       \"0  验证规则  duration_in_month > 43.5   命中    11 0.0147     4 0.0072     7 0.0359 0.6364 2.4476  0.0215 0.7440 0.6364 0.0359 0.0680\\n\",\n       \"1  验证规则  duration_in_month > 43.5  未命中   739 0.9853   551 0.9928   188 0.9641 0.2544 0.9785 -1.4476 0.2560 0.2544 0.9641 0.4026\"\n      ]\n     },\n     \"execution_count\": 47,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"prior_rules = Rule(\\\"credit_amount > 3972.25\\\")\\n\",\n    \"rule.report(data[[\\\"duration_in_month\\\", \\\"credit_amount\\\", target]], target=target, prior_rules=prior_rules)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"pd.set_option('display.max_columns', None)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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规则详情MOB1 DPD0+MOB1 DPD3+MOB1 DPD7+MOB1 DPD15+
规则分类指标名称分箱样本占比坏样本数坏样本率LIFT值坏账改善样本占比坏样本数坏样本率LIFT值坏账改善样本占比坏样本数坏样本率LIFT值坏账改善样本占比坏样本数坏样本率LIFT值坏账改善
0先验规则credit_amount > 3972.25命中0.2500480.19200.9275-0.02420.2500450.18220.9231-0.02560.2552440.17891.00830.00290.2572310.13301.06670.0231
1先验规则credit_amount > 3972.25未命中0.75001590.21201.02420.07250.75001500.20241.02560.07690.74481270.17690.9971-0.00830.7428820.12180.9769-0.0667
2验证规则duration_in_month > 43.5命中0.014720.18180.8576-0.00210.014820.18180.8982-0.00150.015320.18181.02790.00040.014910.10000.8207-0.0027
3验证规则duration_in_month > 43.5未命中0.98531570.21241.00210.14240.98521480.20271.00150.10180.98471250.17680.9996-0.02790.9851810.12221.00270.1793
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\"\n      ],\n      \"text/plain\": [\n       \"   规则详情                                MOB1 DPD0+                            MOB1 DPD3+                            MOB1 DPD7+                            MOB1 DPD15+                           \\n\",\n       \"   规则分类                      指标名称   分箱       样本占比 坏样本数   坏样本率  LIFT值    坏账改善       样本占比 坏样本数   坏样本率  LIFT值    坏账改善       样本占比 坏样本数   坏样本率  LIFT值    坏账改善        样本占比 坏样本数   坏样本率  LIFT值    坏账改善\\n\",\n       \"0  先验规则   credit_amount > 3972.25   命中     0.2500   48 0.1920 0.9275 -0.0242     0.2500   45 0.1822 0.9231 -0.0256     0.2552   44 0.1789 1.0083  0.0029      0.2572   31 0.1330 1.0667  0.0231\\n\",\n       \"1  先验规则   credit_amount > 3972.25  未命中     0.7500  159 0.2120 1.0242  0.0725     0.7500  150 0.2024 1.0256  0.0769     0.7448  127 0.1769 0.9971 -0.0083      0.7428   82 0.1218 0.9769 -0.0667\\n\",\n       \"2  验证规则  duration_in_month > 43.5   命中     0.0147    2 0.1818 0.8576 -0.0021     0.0148    2 0.1818 0.8982 -0.0015     0.0153    2 0.1818 1.0279  0.0004      0.0149    1 0.1000 0.8207 -0.0027\\n\",\n       \"3  验证规则  duration_in_month > 43.5  未命中     0.9853  157 0.2124 1.0021  0.1424     0.9852  148 0.2027 1.0015  0.1018     0.9847  125 0.1768 0.9996 -0.0279      0.9851   81 0.1222 1.0027  0.1793\"\n      ]\n     },\n     \"execution_count\": 71,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"prior_rules = Rule(\\\"credit_amount > 3972.25\\\")\\n\",\n    \"rule = Rule(\\\"duration_in_month > 43.5\\\")\\n\",\n    \"\\n\",\n    \"rule.report(data[[\\\"duration_in_month\\\", \\\"credit_amount\\\", \\\"MOB1\\\"]], overdue=[\\\"MOB1\\\"], dpd=[0, 3, 7, 15], prior_rules=prior_rules, filter_cols=['坏样本数', '坏样本率', 'LIFT值', '坏账改善', '样本占比'], del_grey=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# all_data = load_pickle(\\\"/Users/lubberit/Desktop/workspace/scorecardpipeline/examples/all_data_qhyp.pkl\\\")\\n\",\n    \"# rule = Rule(\\\"(X1 < 588.5) & (X2 < 509.5)\\\")\\n\",\n    \"# prior_rule = Rule(\\\"放款金额 < 5000\\\")\\n\",\n    \"# all_data[\\\"target\\\"] = (all_data[\\\"MOB1\\\"] > 0).astype(int)\\n\",\n    \"# table = rule.report(all_data[[\\\"X1\\\", \\\"X2\\\", \\\"放款金额\\\", \\\"MOB1\\\", \\\"target\\\"]]\\n\",\n    \"#                     , overdue=[\\\"MOB1\\\"]\\n\",\n    \"#                     , dpd=[0, 1, 2, 3, 4]\\n\",\n    \"#                     , prior_rules=prior_rule\\n\",\n    \"#                     # , filter_cols=['好样本数', '好样本占比', '坏样本数', '坏样本占比', '坏样本率', 'LIFT值', '坏账改善']\\n\",\n    \"#                     )\\n\",\n    \"# print(table)\\n\",\n    \"\\n\",\n    \"# percent_cols = [\\\"样本占比\\\", \\\"好样本占比\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"坏账改善\\\", \\\"累积LIFT值\\\", \\\"累积坏账改善\\\"]\\n\",\n    \"# condition_cols = [\\\"坏样本率\\\", \\\"LIFT值\\\"]\\n\",\n    \"# dataframe2excel(table, \\\"规则有效性验证.xlsx\\\", percent_cols=[c for c in table.columns if (isinstance(c, tuple) and c[-1] in percent_cols) or (not isinstance(c, tuple) and c in percent_cols)]\\n\",\n    \"#                 , condition_cols=[c for c in table.columns if (isinstance(c, tuple) and c[-1] in condition_cols) or (not isinstance(c, tuple) and c in condition_cols)]\\n\",\n    \"#                 )\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "examples/automl_example.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import pylab as pl\\n\",\n    \"import sys\\n\",\n    \"sys.path.append(\\\"../\\\")\\n\",\n    \"\\n\",\n    \"import os\\n\",\n    \"import datetime\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import polars as pl\\n\",\n    \"import polars.selectors as cs\\n\",\n    \"import matplotlib\\n\",\n    \"from matplotlib import font_manager\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from scorecardpipeline import *\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"logger = init_setting(logger=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    },\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
\\n\",\n       \"shape: (2, 348)
申请时间lhfv3_v1lhfv3_v3lhfv1_highirr_v6lhfv1_lowirr_v6lhfv1_tongyong_v6lhfv1_highirr_v7lhfv1_lowirr_v7lhfv1_tongyong_v7圈团2风险等级V1 疑似准入风险V1疑似被电诈V2疑似被电诈V3圈团1迭代浓度分V2圈团3浓度分V1疑似准入风险V21周内申请人逾期次数1周内申请人逾期平台数1个月内申请人逾期次数1个月内申请人逾期平台数3个月内申请人逾期次数3个月内申请人逾期平台数6个月内申请人逾期次数6个月内申请人逾期平台数12个月内申请人逾期次数12个月内申请人逾期平台数一年以前申请人逾期次数一年以前申请人逾期平台数1周内申请人逾期银行次数1周内申请人逾期银行平台数1个月内申请人逾期银行次数1个月内申请人逾期银行平台数3个月内申请人逾期银行次数3个月内申请人逾期银行平台数6个月内申请人逾期银行次数6个月内申请人逾期银行平台数12个月内申请人逾期银行次数…720天总申请夜晚平台数180天银行申请白天平台数360天银行申请白天平台数720天银行申请白天平台数180天银行申请夜晚平台数360天银行申请夜晚平台数720天银行申请夜晚平台数7天相对过去180天新增总平台数7天相对过去360天新增总平台数7天相对过去720天新增总平台数15天相对过去180天新增总平台数15天相对过去360天新增总平台数15天相对过去720天新增总平台数30天相对过去180天新增总平台数30天相对过去360天新增总平台数30天相对过去720天新增总平台数7天相对过去180天新增银行平台数7天相对过去360天新增银行平台数7天相对过去720天新增银行平台数15天相对过去180天新增银行平台数15天相对过去360天新增银行平台数15天相对过去720天新增银行平台数30天相对过去180天新增银行平台数30天相对过去360天新增银行平台数30天相对过去720天新增银行平台数最早一次查询距今的天数最近一次查询距今的天数最早一次在银行机构距今的天数最近一次在银行机构距今的天数最早一次在非银机构距今的天数最近一次在非银机构距今的天数多头申请通用分长周期多头共债子分短周期多头共债子分非银行多头共债子分银行多头共债子分target
datetime[μs]f64f64f64f64f64f64f64f64f64f64f32f32f64f64f32f32f32f32f32f32f32f32f32f64f64f64f64f32f32f32f32f32f32f32f32f32…f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64i8
2023-09-28 15:35:48734.0728.014.013.013.030.027.020.0nullnullnullNaN37.025.0nullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnull…0.01.01.01.00.00.00.0-5.0-5.0-5.0-5.0-5.0-5.0-5.0-5.0-5.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01206.01.01206.050.0905.01.056.051.066.052.054.00
2023-09-28 18:01:56713.0655.034.040.034.044.049.041.0nullnullnullNaN43.015.0nullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnull…4.00.00.01.00.00.00.0-3.0-8.0-12.0-3.0-8.0-12.0-3.0-8.0-12.00.00.0-1.00.00.0-1.00.00.0-1.01129.051.0996.0432.01129.051.041.041.044.041.046.00
\"\n      ],\n      \"text/plain\": [\n       \"shape: (2, 348)\\n\",\n       \"┌────────────┬──────────┬──────────┬────────────┬───┬────────────┬────────────┬───────────┬────────┐\\n\",\n       \"│ 申请时间   ┆ lhfv3_v1 ┆ lhfv3_v3 ┆ lhfv1_high ┆ … ┆ 短周期多头 ┆ 非银行多头 ┆ 银行多头  ┆ target │\\n\",\n       \"│ ---        ┆ ---      ┆ ---      ┆ irr_v6     ┆   ┆ 共债子分   ┆ 共债子分   ┆ 共债子分  ┆ ---    │\\n\",\n       \"│ datetime[μ ┆ f64      ┆ f64      ┆ ---        ┆   ┆ ---        ┆ ---        ┆ ---       ┆ i8     │\\n\",\n       \"│ s]         ┆          ┆          ┆ f64        ┆   ┆ f64        ┆ f64        ┆ f64       ┆        │\\n\",\n       \"╞════════════╪══════════╪══════════╪════════════╪═══╪════════════╪════════════╪═══════════╪════════╡\\n\",\n       \"│ 2023-09-28 ┆ 734.0    ┆ 728.0    ┆ 14.0       ┆ … ┆ 66.0       ┆ 52.0       ┆ 54.0      ┆ 0      │\\n\",\n       \"│ 15:35:48   ┆          ┆          ┆            ┆   ┆            ┆            ┆           ┆        │\\n\",\n       \"│ 2023-09-28 ┆ 713.0    ┆ 655.0    ┆ 34.0       ┆ … ┆ 44.0       ┆ 41.0       ┆ 46.0      ┆ 0      │\\n\",\n       \"│ 18:01:56   ┆          ┆          ┆            ┆   ┆            ┆            ┆           ┆        │\\n\",\n       \"└────────────┴──────────┴──────────┴────────────┴───┴────────────┴────────────┴───────────┴────────┘\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data = load_pickle(\\\"model_dataset.pickle\\\")\\n\",\n    \"data = data.with_columns(\\n\",\n    \"    (pl.col(\\\"FPD\\\") > 7).cast(pl.Int8).alias(\\\"target\\\"),\\n\",\n    \"    pl.col(\\\"申请时间\\\").str.to_datetime(),\\n\",\n    \").filter(\\n\",\n    \"    (pl.col(\\\"FPD\\\") > 7) | (pl.col(\\\"FPD\\\") == 0),\\n\",\n    \").select(\\n\",\n    \"    ~cs.by_name('订单编号','客户编号','授信资方','放款时间','FPD','FSPD','FSTPD','DPD'),\\n\",\n    \")\\n\",\n    \"data.head(2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    },\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train = data.filter(pl.col(\\\"申请时间\\\") > datetime.datetime(year=2023, month=9, day=1))\\n\",\n    \"train, test = train_test_split(train, test_size=0.2, stratify=train[\\\"target\\\"])\\n\",\n    \"oot = data.filter(pl.col(\\\"申请时间\\\") <= datetime.datetime(year=2023, month=9, day=1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    },\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Checking whether there is an H2O instance running at http://localhost:54321. connected.\\n\",\n      \"Warning: Your H2O cluster version is (5 months and 29 days) old.  There may be a newer version available.\\n\",\n      \"Please download and install the latest version from: https://h2o-release.s3.amazonaws.com/h2o/latest_stable.html\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\\n\",\n       \"      \\n\",\n       \"
\\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"  
H2O_cluster_uptime:21 hours 13 mins
H2O_cluster_timezone:Asia/Shanghai
H2O_data_parsing_timezone:UTC
H2O_cluster_version:3.44.0.1
H2O_cluster_version_age:5 months and 29 days
H2O_cluster_name:H2O_from_python_lubberit_d840sg
H2O_cluster_total_nodes:1
H2O_cluster_free_memory:2.739 Gb
H2O_cluster_total_cores:16
H2O_cluster_allowed_cores:16
H2O_cluster_status:locked, healthy
H2O_connection_url:http://localhost:54321
H2O_connection_proxy:{\\\"http\\\": null, \\\"https\\\": null}
H2O_internal_security:False
Python_version:3.8.8 final
\\n\",\n       \"
\\n\"\n      ],\n      \"text/plain\": [\n       \"--------------------------  -------------------------------\\n\",\n       \"H2O_cluster_uptime:         21 hours 13 mins\\n\",\n       \"H2O_cluster_timezone:       Asia/Shanghai\\n\",\n       \"H2O_data_parsing_timezone:  UTC\\n\",\n       \"H2O_cluster_version:        3.44.0.1\\n\",\n       \"H2O_cluster_version_age:    5 months and 29 days\\n\",\n       \"H2O_cluster_name:           H2O_from_python_lubberit_d840sg\\n\",\n       \"H2O_cluster_total_nodes:    1\\n\",\n       \"H2O_cluster_free_memory:    2.739 Gb\\n\",\n       \"H2O_cluster_total_cores:    16\\n\",\n       \"H2O_cluster_allowed_cores:  16\\n\",\n       \"H2O_cluster_status:         locked, healthy\\n\",\n       \"H2O_connection_url:         http://localhost:54321\\n\",\n       \"H2O_connection_proxy:       {\\\"http\\\": null, \\\"https\\\": null}\\n\",\n       \"H2O_internal_security:      False\\n\",\n       \"Python_version:             3.8.8 final\\n\",\n       \"--------------------------  -------------------------------\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import h2o\\n\",\n    \"from h2o.automl import H2OAutoML\\n\",\n    \"h2o.init()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[INFO] filtering variables ...\\n\",\n      \"Variable filtering on 95778 rows and 348 columns in 00:00:30 \\n\",\n      \"186 variables are removed\\n\",\n      \"Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\\n\",\n      \"Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\\n\",\n      \"Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"select = FeatureSelection(target=\\\"target\\\", engine=\\\"scorecardpy\\\", identical=0.95, empty=0.95, iv=0.02, corr=0.9, remove=\\\"申请时间\\\")\\n\",\n    \"select.fit(train.to_pandas())\\n\",\n    \"\\n\",\n    \"train_select = h2o.H2OFrame(select.transform(train).to_pandas())\\n\",\n    \"test_select = h2o.H2OFrame(select.transform(test).to_pandas())\\n\",\n    \"oot_select = h2o.H2OFrame(select.transform(oot).to_pandas())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y = \\\"target\\\"\\n\",\n    \"x = list(train_select.drop(y).columns)\\n\",\n    \"\\n\",\n    \"# For binary classification, response should be a factor\\n\",\n    \"train_select[y] = train_select[y].asfactor()\\n\",\n    \"test_select[y] = test_select[y].asfactor()\\n\",\n    \"oot_select[y] = oot_select[y].asfactor()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 114,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"AutoML progress: |\\n\",\n      \"22:44:26.323: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.\\n\",\n      \"\\n\",\n      \"███████████████████████████████████████████████████████████████| (done) 100%\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
Model Details\\n\",\n       \"=============\\n\",\n       \"H2OStackedEnsembleEstimator : Stacked Ensemble\\n\",\n       \"Model Key: StackedEnsemble_BestOfFamily_1_AutoML_10_20240414_224426\\n\",\n       \"
\\n\",\n       \"
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Model Summary for Stacked Ensemble: 
keyvalue
Stacking strategycross_validation
Number of base models (used / total)2/2
# GBM base models (used / total)1/1
# XGBoost base models (used / total)1/1
Metalearner algorithmGLM
Metalearner fold assignment schemeRandom
Metalearner nfolds5
Metalearner fold_columnNone
Custom metalearner hyperparametersNone
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ModelMetricsBinomialGLM: stackedensemble\\n\",\n       \"** Reported on train data. **\\n\",\n       \"\\n\",\n       \"MSE: 0.014732754309517466\\n\",\n       \"RMSE: 0.1213785578655368\\n\",\n       \"LogLoss: 0.0722913786476122\\n\",\n       \"AUC: 0.9692753119443059\\n\",\n       \"AUCPR: 0.520746595774342\\n\",\n       \"Gini: 0.9385506238886119\\n\",\n       \"Null degrees of freedom: 10024\\n\",\n       \"Residual degrees of freedom: 10022\\n\",\n       \"Null deviance: 1649.8242864782364\\n\",\n       \"Residual deviance: 1449.4421418846246\\n\",\n       \"AIC: 1455.4421418846246
\\n\",\n       \"
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Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.07301502446418714
01ErrorRate
09825.039.00.004 (39.0/9864.0)
192.069.00.5714 (92.0/161.0)
Total9917.0108.00.0131 (131.0/10025.0)
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Maximum Metrics: Maximum metrics at their respective thresholds
metricthresholdvalueidx
max f10.07301500.513011261.0
max f20.05354930.5829146108.0
max f0point50.07622200.586592257.0
max accuracy0.07622200.987132257.0
max precision0.18703121.00.0
max recall0.02540291.0299.0
max specificity0.18703121.00.0
max absolute_mcc0.07301500.517053561.0
max min_per_class_accuracy0.04188110.9130435162.0
max mean_per_class_accuracy0.04188110.9143989162.0
max tns0.18703129864.00.0
max fns0.1870312160.00.0
max fps0.01285399864.0399.0
max tps0.0254029161.0299.0
max tnr0.18703121.00.0
max fnr0.18703120.99378880.0
max fpr0.01285391.0399.0
max tpr0.02540291.0299.0
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\\n\",\n       \"
\\n\",\n       \"      \\n\",\n       \"
\\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"  
Gains/Lift Table: Avg response rate:  1.61 %, avg score:  2.98 %
groupcumulative_data_fractionlower_thresholdliftcumulative_liftresponse_ratescorecumulative_response_ratecumulative_scorecapture_ratecumulative_capture_rategaincumulative_gainkolmogorov_smirnov
10.01007480.074405940.689379540.68937950.65346530.09745630.65346530.09745630.40993790.40993793968.93794973968.93794970.4063896
20.02004990.061654211.830745326.33185010.190.06659470.42288560.08210230.11801240.52795031083.07453422533.18500660.5161904
30.03002490.055154413.698757822.13480940.220.05825980.35548170.07418120.13664600.66459631269.87577642113.48094340.6449288
40.040.05155558.094720518.63354040.130.05341350.29925190.06900220.08074530.7453416709.47204971763.35403730.7168542
50.05007480.04876826.781563216.24897920.10891090.05022830.26095620.06522500.06832300.8136646578.15632491524.89792380.7760531
60.10004990.04162201.98856949.12588320.03193610.04456670.14656030.05490620.09937890.913043598.8569445812.58832200.8262633
70.15002490.03815860.74571356.33435060.01197600.03973360.10172870.04985200.03726710.9503106-25.4286458533.43506340.8133479
80.20.03542920.24857124.81366460.00399200.03674190.07730670.04657610.01242240.9627329-75.1428819381.36645960.7751822
90.30004990.03208390.24832343.29137830.00398800.03363740.05285900.04226180.02484470.9875776-75.1676647229.13782710.6987496
100.40.02960560.06214282.48447200.00099800.03079680.03990020.03939700.00621120.9937888-93.7857205148.44720500.6034806
110.50004990.02750320.01.98737940.00.02851320.03191700.03721930.00.9937888-100.098.73793970.5017977
120.60.02565560.01.65631470.00.02659480.02660020.03544950.00.9937888-100.065.63147000.4002162
130.69995010.02381150.06214281.42867320.00099800.02474380.02294430.03392070.00621121.0-93.785720542.86732220.3049473
140.80.02171620.01.250.00.02279090.02007480.03252880.01.0-100.025.00.2032644
150.89995010.01910740.01.11117270.00.02043330.01784530.03118550.01.0-100.011.11726890.1016829
161.00.01285390.01.00.00.01709790.01605990.02977600.01.0-100.00.00.0
\\n\",\n       \"
\\n\",\n       \"
\\n\",\n       \"
ModelMetricsBinomialGLM: stackedensemble\\n\",\n       \"** Reported on validation data. **\\n\",\n       \"\\n\",\n       \"MSE: 0.015551263033921004\\n\",\n       \"RMSE: 0.12470470333520306\\n\",\n       \"LogLoss: 0.08323480175771274\\n\",\n       \"AUC: 0.615829274501485\\n\",\n       \"AUCPR: 0.030103741931861692\\n\",\n       \"Gini: 0.2316585490029699\\n\",\n       \"Null degrees of freedom: 23944\\n\",\n       \"Residual degrees of freedom: 23942\\n\",\n       \"Null deviance: 3861.537856416843\\n\",\n       \"Residual deviance: 3986.114656176863\\n\",\n       \"AIC: 3992.114656176863
\\n\",\n       \"
\\n\",\n       \"      \\n\",\n       \"
\\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"  
Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.06180198067132455
01ErrorRate
023220.0350.00.0148 (350.0/23570.0)
1347.028.00.9253 (347.0/375.0)
Total23567.0378.00.0291 (697.0/23945.0)
\\n\",\n       \"
\\n\",\n       \"
\\n\",\n       \"
\\n\",\n       \"      \\n\",\n       \"
\\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"  
Maximum Metrics: Maximum metrics at their respective thresholds
metricthresholdvalueidx
max f10.06180200.074369272.0
max f20.03252790.1024373222.0
max f0point50.06180200.074191872.0
max accuracy0.15614770.98433911.0
max precision0.15614770.51.0
max recall0.01426711.0394.0
max specificity0.17263070.99995760.0
max absolute_mcc0.06180200.059583272.0
max min_per_class_accuracy0.02927240.5806109254.0
max mean_per_class_accuracy0.03148590.5882780232.0
max tns0.172630723569.00.0
max fns0.1726307375.00.0
max fps0.013071023570.0399.0
max tps0.0142671375.0394.0
max tnr0.17263070.99995760.0
max fnr0.17263071.00.0
max fpr0.01307101.0399.0
max tpr0.01426711.0394.0
\\n\",\n       \"
\\n\",\n       \"
\\n\",\n       \"
\\n\",\n       \"      \\n\",\n       \"
\\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"  
Gains/Lift Table: Avg response rate:  1.57 %, avg score:  2.96 %
groupcumulative_data_fractionlower_thresholdliftcumulative_liftresponse_ratescorecumulative_response_ratecumulative_scorecapture_ratecumulative_capture_rategaincumulative_gainkolmogorov_smirnov
10.01002300.06627084.52294444.52294440.07083330.08015660.07083330.08015660.04533330.0453333352.2944444352.29444440.0358722
20.02000420.05849932.93885633.73255390.04602510.06230050.05845510.07124720.02933330.0746667193.8856346273.25539320.0555322
30.03002710.05388761.06422222.84187300.01666670.05594400.04450630.06613900.01066670.08533336.4222222184.18729720.0561861
40.04000840.05089331.87018132.59945720.02928870.05241940.04070980.06271630.01866670.10487.0181311159.94572030.0650098
50.05003130.04865820.53211112.18529770.00833330.04974590.03422370.06011790.00533330.1093333-46.7888889118.52977180.0602455
60.10002090.04178891.60033421.89293810.02506270.04477890.02964510.05245160.080.189333360.033416989.29380650.0907334
70.15001040.03832921.49364521.75987750.02339180.03994610.02756120.04828430.07466670.26449.364522475.98775060.1158031
80.20.03573241.22692291.62666670.01921470.03695890.02547500.04545350.06133330.325333322.692286362.66666670.1273274
90.30002090.03226491.43969941.56433560.02254700.03387840.02449890.04159460.1440.469333343.969937456.43355610.1720062
100.40.02966460.93352831.40666670.01461990.03088890.02202970.03891870.09333330.5626667-6.647173540.66666670.1652547
110.50002090.02763730.90647741.30661210.01419620.02864020.02046270.03686270.09066670.6533333-9.352261730.66120990.1557517
120.60.02574431.01354501.25777780.01587300.02669870.01969790.03516900.10133330.75466671.354497425.77777780.1571274
130.69997910.02386500.72015041.18098760.01127820.02480510.01849530.03368870.0720.8266667-27.984962418.09876100.1287032
140.80.02171520.58654421.10666670.00918580.02284810.01733140.03233340.05866670.8853333-41.345581110.66666670.0866910
150.89997910.01905350.72015041.06372840.01127820.02043210.01665890.03101130.0720.9573333-27.98496246.37283840.0582667
161.00.01284410.42657761.00.00668060.01704850.01566090.02961470.04266671.0-57.34224080.00.0
\\n\",\n       \"
\\n\",\n       \"
\\n\",\n       \"
ModelMetricsBinomialGLM: stackedensemble\\n\",\n       \"** Reported on cross-validation data. **\\n\",\n       \"\\n\",\n       \"MSE: 0.0153677690917643\\n\",\n       \"RMSE: 0.12396680641108852\\n\",\n       \"LogLoss: 0.07929611252452003\\n\",\n       \"AUC: 0.6147189694308322\\n\",\n       \"AUCPR: 0.02618197020102293\\n\",\n       \"Gini: 0.2294379388616643\\n\",\n       \"Null degrees of freedom: 95777\\n\",\n       \"Residual degrees of freedom: 95775\\n\",\n       \"Null deviance: 15447.231248486467\\n\",\n       \"Residual deviance: 15189.646130746956\\n\",\n       \"AIC: 15195.646130746956
\\n\",\n       \"
\\n\",\n       \"      \\n\",\n       \"
\\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"  
Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.02454676150565218
01ErrorRate
086328.07950.00.0843 (7950.0/94278.0)
11240.0260.00.8267 (1240.0/1500.0)
Total87568.08210.00.096 (9190.0/95778.0)
\\n\",\n       \"
\\n\",\n       \"
\\n\",\n       \"
\\n\",\n       \"      \\n\",\n       \"
\\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"  
Maximum Metrics: Maximum metrics at their respective thresholds
metricthresholdvalueidx
max f10.02454680.0535530165.0
max f20.01763420.0954986234.0
max f0point50.03929260.046998289.0
max accuracy0.19875750.98434920.0
max precision0.19875751.00.0
max recall0.00627251.0393.0
max specificity0.19875751.00.0
max absolute_mcc0.01414120.0431676285.0
max min_per_class_accuracy0.01509630.5786928269.0
max mean_per_class_accuracy0.01414120.5869169285.0
max tns0.198757594278.00.0
max fns0.19875751499.00.0
max fps0.004197894278.0399.0
max tps0.00627251500.0393.0
max tnr0.19875751.00.0
max fnr0.19875750.99933330.0
max fpr0.00419781.0399.0
max tpr0.00627251.0393.0
\\n\",\n       \"
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\\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"  
Gains/Lift Table: Avg response rate:  1.57 %, avg score:  1.57 %
groupcumulative_data_fractionlower_thresholdliftcumulative_liftresponse_ratescorecumulative_response_ratecumulative_scorecapture_ratecumulative_capture_rategaincumulative_gainkolmogorov_smirnov
10.01000230.04187663.13261383.13261380.04906050.05341180.04906050.05341180.03133330.0313333213.2613779213.26137790.0216704
20.02000460.03550281.93288942.53275160.03027140.03830740.03966600.04585960.01933330.050666793.2889353153.27515660.0311499
30.03000690.03214811.99954072.35501460.03131520.03375540.03688240.04182490.020.070666799.9540710135.50146140.0413067
40.04000920.02991062.06619212.28280900.03235910.03098940.03575160.03911600.02066670.0913333106.6192067128.28089770.0521407
50.05000100.02823691.86818812.19995410.02925810.02902410.03445400.03709930.01866670.1186.8188088119.99540610.0609536
60.10000210.02348341.59996661.89996030.02505740.02553860.02975570.03131900.080.1959.996659089.99603260.0914298
70.15000310.02101351.33330551.71107540.02088120.02215580.02679750.02826460.06666670.256666733.330549271.10753810.1083606
80.20000420.01931951.23997411.59330010.01941950.02012980.02495300.02623090.0620.318666723.997410759.33000630.1205505
90.30000630.01701781.18664191.45774730.01858430.01808940.02283010.02351710.11866670.437333318.664188845.77473380.1395120
100.40000840.01539271.16664231.38497110.01827100.01616190.02169030.02167830.11666670.55416.664230538.49710800.1564417
110.50.01407161.16009691.340.01816850.01471990.02098600.02028670.1160.6716.009689934.00000000.1727048
120.60000210.01291920.79331681.24888450.01242430.01348690.01955910.01915340.07933330.7493333-20.668323224.88845420.1517072
130.69999370.01180810.87340631.19524880.01367860.01236270.01871910.01818330.08733330.8366667-12.659371419.52487920.1388475
140.79999580.01061380.70665191.13417260.01106700.01121970.01776250.01731290.07066670.9073333-29.334808913.41725880.1090453
150.89999790.00925050.53332221.06740990.00835250.00996330.01671690.01649620.05333330.9606667-46.66778036.74098840.0616340
161.00.00376290.39332511.00.00615990.00814890.01566120.01566150.03933331.0-60.66748800.00.0
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\\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"    \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"  
Cross-Validation Metrics Summary: 
meansdcv_1_validcv_2_validcv_3_validcv_4_validcv_5_valid
accuracy0.9195740.05033040.97703210.91884710.93445360.83879540.9287420
auc0.61461450.01532530.61209110.63288220.60006140.60011080.6279272
err0.08042600.05033040.02296790.08115290.06554640.16120460.0712579
err_count1543.0967.3231436.01557.01256.03094.01372.0
f0point50.04844770.01417020.07057550.05125980.04692740.03292450.0405515
f10.05873560.00811740.05627710.07044780.06268660.04917030.0550964
f20.08734960.02463640.04679630.11259540.09438200.09706380.0859107
lift_top_group3.11346580.78839824.20335533.17228843.38312152.07536772.733196
logloss0.07930170.00240970.08188690.08186470.07845350.07721920.0770841
max_per_class_error0.84289030.08528960.95792880.81269840.85762710.72318340.8630137
------------------------
mean_per_class_error0.45557300.01800370.48271290.44082010.45539440.43789830.4610396
mse0.01536900.00060500.01596530.01607120.01511780.01480220.0148884
null_deviance3089.446392.57383158.5643214.80543048.06132999.5563026.2446
pr_auc0.02719270.00292530.02668240.03228720.02581620.02628760.0248901
precision0.04600110.02265720.08496730.04338240.04019140.02698150.0344828
r20.00310340.00105740.00296660.00479940.00265630.00193110.0031636
recall0.15710970.08528960.04207120.18730160.14237290.27681660.1369863
residual_deviance3037.874582.145383108.9193141.31053006.65192964.13572968.3555
rmse0.12395250.00243360.12635370.12677210.12295440.12166430.1220182
specificity0.93174420.05272700.99250290.93105820.94683840.84738680.9409345
\\n\",\n       \"
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[22 rows x 8 columns]
\\n\",\n       \"\\n\",\n       \"[tips]\\n\",\n       \"Use `model.explain()` to inspect the model.\\n\",\n       \"--\\n\",\n       \"Use `h2o.display.toggle_user_tips()` to switch on/off this section.
\"\n      ],\n      \"text/plain\": [\n       \"Model Details\\n\",\n       \"=============\\n\",\n       \"H2OStackedEnsembleEstimator : Stacked Ensemble\\n\",\n       \"Model Key: StackedEnsemble_BestOfFamily_1_AutoML_10_20240414_224426\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"Model Summary for Stacked Ensemble: \\n\",\n       \"key                                   value\\n\",\n       \"------------------------------------  ----------------\\n\",\n       \"Stacking strategy                     cross_validation\\n\",\n       \"Number of base models (used / total)  2/2\\n\",\n       \"# GBM base models (used / total)      1/1\\n\",\n       \"# XGBoost base models (used / total)  1/1\\n\",\n       \"Metalearner algorithm                 GLM\\n\",\n       \"Metalearner fold assignment scheme    Random\\n\",\n       \"Metalearner nfolds                    5\\n\",\n       \"Metalearner fold_column\\n\",\n       \"Custom metalearner hyperparameters    None\\n\",\n       \"\\n\",\n       \"ModelMetricsBinomialGLM: stackedensemble\\n\",\n       \"** Reported on train data. **\\n\",\n       \"\\n\",\n       \"MSE: 0.014732754309517466\\n\",\n       \"RMSE: 0.1213785578655368\\n\",\n       \"LogLoss: 0.0722913786476122\\n\",\n       \"AUC: 0.9692753119443059\\n\",\n       \"AUCPR: 0.520746595774342\\n\",\n       \"Gini: 0.9385506238886119\\n\",\n       \"Null degrees of freedom: 10024\\n\",\n       \"Residual degrees of freedom: 10022\\n\",\n       \"Null deviance: 1649.8242864782364\\n\",\n       \"Residual deviance: 1449.4421418846246\\n\",\n       \"AIC: 1455.4421418846246\\n\",\n       \"\\n\",\n       \"Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.07301502446418714\\n\",\n       \"       0     1    Error    Rate\\n\",\n       \"-----  ----  ---  -------  ---------------\\n\",\n       \"0      9825  39   0.004    (39.0/9864.0)\\n\",\n       \"1      92    69   0.5714   (92.0/161.0)\\n\",\n       \"Total  9917  108  0.0131   (131.0/10025.0)\\n\",\n       \"\\n\",\n       \"Maximum Metrics: Maximum metrics at their respective thresholds\\n\",\n       \"metric                       threshold    value     idx\\n\",\n       \"---------------------------  -----------  --------  -----\\n\",\n       \"max f1                       0.073015     0.513011  61\\n\",\n       \"max f2                       0.0535493    0.582915  108\\n\",\n       \"max f0point5                 0.076222     0.586592  57\\n\",\n       \"max accuracy                 0.076222     0.987132  57\\n\",\n       \"max precision                0.187031     1         0\\n\",\n       \"max recall                   0.0254029    1         299\\n\",\n       \"max specificity              0.187031     1         0\\n\",\n       \"max absolute_mcc             0.073015     0.517054  61\\n\",\n       \"max min_per_class_accuracy   0.0418811    0.913043  162\\n\",\n       \"max mean_per_class_accuracy  0.0418811    0.914399  162\\n\",\n       \"max tns                      0.187031     9864      0\\n\",\n       \"max fns                      0.187031     160       0\\n\",\n       \"max fps                      0.0128539    9864      399\\n\",\n       \"max tps                      0.0254029    161       299\\n\",\n       \"max tnr                      0.187031     1         0\\n\",\n       \"max fnr                      0.187031     0.993789  0\\n\",\n       \"max fpr                      0.0128539    1         399\\n\",\n       \"max tpr                      0.0254029    1         299\\n\",\n       \"\\n\",\n       \"Gains/Lift Table: Avg response rate:  1.61 %, avg score:  2.98 %\\n\",\n       \"group    cumulative_data_fraction    lower_threshold    lift       cumulative_lift    response_rate    score      cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain      cumulative_gain    kolmogorov_smirnov\\n\",\n       \"-------  --------------------------  -----------------  ---------  -----------------  ---------------  ---------  --------------------------  ------------------  --------------  -------------------------  --------  -----------------  --------------------\\n\",\n       \"1        0.0100748                   0.0744059          40.6894    40.6894            0.653465         0.0974563  0.653465                    0.0974563           0.409938        0.409938                   3968.94   3968.94            0.40639\\n\",\n       \"2        0.0200499                   0.0616542          11.8307    26.3319            0.19             0.0665947  0.422886                    0.0821023           0.118012        0.52795                    1083.07   2533.19            0.51619\\n\",\n       \"3        0.0300249                   0.0551544          13.6988    22.1348            0.22             0.0582598  0.355482                    0.0741812           0.136646        0.664596                   1269.88   2113.48            0.644929\\n\",\n       \"4        0.04                        0.0515555          8.09472    18.6335            0.13             0.0534135  0.299252                    0.0690022           0.0807453       0.745342                   709.472   1763.35            0.716854\\n\",\n       \"5        0.0500748                   0.0487682          6.78156    16.249             0.108911         0.0502283  0.260956                    0.065225            0.068323        0.813665                   578.156   1524.9             0.776053\\n\",\n       \"6        0.10005                     0.041622           1.98857    9.12588            0.0319361        0.0445667  0.14656                     0.0549062           0.0993789       0.913043                   98.8569   812.588            0.826263\\n\",\n       \"7        0.150025                    0.0381586          0.745714   6.33435            0.011976         0.0397336  0.101729                    0.049852            0.0372671       0.950311                   -25.4286  533.435            0.813348\\n\",\n       \"8        0.2                         0.0354292          0.248571   4.81366            0.00399202       0.0367419  0.0773067                   0.0465761           0.0124224       0.962733                   -75.1429  381.366            0.775182\\n\",\n       \"9        0.30005                     0.0320839          0.248323   3.29138            0.00398804       0.0336374  0.052859                    0.0422618           0.0248447       0.987578                   -75.1677  229.138            0.69875\\n\",\n       \"10       0.4                         0.0296056          0.0621428  2.48447            0.000998004      0.0307968  0.0399002                   0.039397            0.00621118      0.993789                   -93.7857  148.447            0.603481\\n\",\n       \"11       0.50005                     0.0275032          0          1.98738            0                0.0285132  0.031917                    0.0372193           0               0.993789                   -100      98.7379            0.501798\\n\",\n       \"12       0.6                         0.0256556          0          1.65631            0                0.0265948  0.0266002                   0.0354495           0               0.993789                   -100      65.6315            0.400216\\n\",\n       \"13       0.69995                     0.0238115          0.0621428  1.42867            0.000998004      0.0247438  0.0229443                   0.0339207           0.00621118      1                          -93.7857  42.8673            0.304947\\n\",\n       \"14       0.8                         0.0217162          0          1.25               0                0.0227909  0.0200748                   0.0325288           0               1                          -100      25                 0.203264\\n\",\n       \"15       0.89995                     0.0191074          0          1.11117            0                0.0204333  0.0178453                   0.0311855           0               1                          -100      11.1173            0.101683\\n\",\n       \"16       1                           0.0128539          0          1                  0                0.0170979  0.0160599                   0.029776            0               1                          -100      0                  0\\n\",\n       \"\\n\",\n       \"ModelMetricsBinomialGLM: stackedensemble\\n\",\n       \"** Reported on validation data. **\\n\",\n       \"\\n\",\n       \"MSE: 0.015551263033921004\\n\",\n       \"RMSE: 0.12470470333520306\\n\",\n       \"LogLoss: 0.08323480175771274\\n\",\n       \"AUC: 0.615829274501485\\n\",\n       \"AUCPR: 0.030103741931861692\\n\",\n       \"Gini: 0.2316585490029699\\n\",\n       \"Null degrees of freedom: 23944\\n\",\n       \"Residual degrees of freedom: 23942\\n\",\n       \"Null deviance: 3861.537856416843\\n\",\n       \"Residual deviance: 3986.114656176863\\n\",\n       \"AIC: 3992.114656176863\\n\",\n       \"\\n\",\n       \"Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.06180198067132455\\n\",\n       \"       0      1    Error    Rate\\n\",\n       \"-----  -----  ---  -------  ---------------\\n\",\n       \"0      23220  350  0.0148   (350.0/23570.0)\\n\",\n       \"1      347    28   0.9253   (347.0/375.0)\\n\",\n       \"Total  23567  378  0.0291   (697.0/23945.0)\\n\",\n       \"\\n\",\n       \"Maximum Metrics: Maximum metrics at their respective thresholds\\n\",\n       \"metric                       threshold    value      idx\\n\",\n       \"---------------------------  -----------  ---------  -----\\n\",\n       \"max f1                       0.061802     0.0743692  72\\n\",\n       \"max f2                       0.0325279    0.102437   222\\n\",\n       \"max f0point5                 0.061802     0.0741918  72\\n\",\n       \"max accuracy                 0.156148     0.984339   1\\n\",\n       \"max precision                0.156148     0.5        1\\n\",\n       \"max recall                   0.0142671    1          394\\n\",\n       \"max specificity              0.172631     0.999958   0\\n\",\n       \"max absolute_mcc             0.061802     0.0595832  72\\n\",\n       \"max min_per_class_accuracy   0.0292724    0.580611   254\\n\",\n       \"max mean_per_class_accuracy  0.0314859    0.588278   232\\n\",\n       \"max tns                      0.172631     23569      0\\n\",\n       \"max fns                      0.172631     375        0\\n\",\n       \"max fps                      0.013071     23570      399\\n\",\n       \"max tps                      0.0142671    375        394\\n\",\n       \"max tnr                      0.172631     0.999958   0\\n\",\n       \"max fnr                      0.172631     1          0\\n\",\n       \"max fpr                      0.013071     1          399\\n\",\n       \"max tpr                      0.0142671    1          394\\n\",\n       \"\\n\",\n       \"Gains/Lift Table: Avg response rate:  1.57 %, avg score:  2.96 %\\n\",\n       \"group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    score      cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain      cumulative_gain    kolmogorov_smirnov\\n\",\n       \"-------  --------------------------  -----------------  --------  -----------------  ---------------  ---------  --------------------------  ------------------  --------------  -------------------------  --------  -----------------  --------------------\\n\",\n       \"1        0.010023                    0.0662708          4.52294   4.52294            0.0708333        0.0801566  0.0708333                   0.0801566           0.0453333       0.0453333                  352.294   352.294            0.0358722\\n\",\n       \"2        0.0200042                   0.0584993          2.93886   3.73255            0.0460251        0.0623005  0.0584551                   0.0712472           0.0293333       0.0746667                  193.886   273.255            0.0555322\\n\",\n       \"3        0.0300271                   0.0538876          1.06422   2.84187            0.0166667        0.055944   0.0445063                   0.066139            0.0106667       0.0853333                  6.42222   184.187            0.0561861\\n\",\n       \"4        0.0400084                   0.0508933          1.87018   2.59946            0.0292887        0.0524194  0.0407098                   0.0627163           0.0186667       0.104                      87.0181   159.946            0.0650098\\n\",\n       \"5        0.0500313                   0.0486582          0.532111  2.1853             0.00833333       0.0497459  0.0342237                   0.0601179           0.00533333      0.109333                   -46.7889  118.53             0.0602455\\n\",\n       \"6        0.100021                    0.0417889          1.60033   1.89294            0.0250627        0.0447789  0.0296451                   0.0524516           0.08            0.189333                   60.0334   89.2938            0.0907334\\n\",\n       \"7        0.15001                     0.0383292          1.49365   1.75988            0.0233918        0.0399461  0.0275612                   0.0482843           0.0746667       0.264                      49.3645   75.9878            0.115803\\n\",\n       \"8        0.2                         0.0357324          1.22692   1.62667            0.0192147        0.0369589  0.025475                    0.0454535           0.0613333       0.325333                   22.6923   62.6667            0.127327\\n\",\n       \"9        0.300021                    0.0322649          1.4397    1.56434            0.022547         0.0338784  0.0244989                   0.0415946           0.144           0.469333                   43.9699   56.4336            0.172006\\n\",\n       \"10       0.4                         0.0296646          0.933528  1.40667            0.0146199        0.0308889  0.0220297                   0.0389187           0.0933333       0.562667                   -6.64717  40.6667            0.165255\\n\",\n       \"11       0.500021                    0.0276373          0.906477  1.30661            0.0141962        0.0286402  0.0204627                   0.0368627           0.0906667       0.653333                   -9.35226  30.6612            0.155752\\n\",\n       \"12       0.6                         0.0257443          1.01354   1.25778            0.015873         0.0266987  0.0196979                   0.035169            0.101333        0.754667                   1.3545    25.7778            0.157127\\n\",\n       \"13       0.699979                    0.023865           0.72015   1.18099            0.0112782        0.0248051  0.0184953                   0.0336887           0.072           0.826667                   -27.985   18.0988            0.128703\\n\",\n       \"14       0.8                         0.0217152          0.586544  1.10667            0.0091858        0.0228481  0.0173314                   0.0323334           0.0586667       0.885333                   -41.3456  10.6667            0.086691\\n\",\n       \"15       0.899979                    0.0190535          0.72015   1.06373            0.0112782        0.0204321  0.0166589                   0.0310113           0.072           0.957333                   -27.985   6.37284            0.0582667\\n\",\n       \"16       1                           0.0128441          0.426578  1                  0.00668058       0.0170485  0.0156609                   0.0296147           0.0426667       1                          -57.3422  0                  0\\n\",\n       \"\\n\",\n       \"ModelMetricsBinomialGLM: stackedensemble\\n\",\n       \"** Reported on cross-validation data. **\\n\",\n       \"\\n\",\n       \"MSE: 0.0153677690917643\\n\",\n       \"RMSE: 0.12396680641108852\\n\",\n       \"LogLoss: 0.07929611252452003\\n\",\n       \"AUC: 0.6147189694308322\\n\",\n       \"AUCPR: 0.02618197020102293\\n\",\n       \"Gini: 0.2294379388616643\\n\",\n       \"Null degrees of freedom: 95777\\n\",\n       \"Residual degrees of freedom: 95775\\n\",\n       \"Null deviance: 15447.231248486467\\n\",\n       \"Residual deviance: 15189.646130746956\\n\",\n       \"AIC: 15195.646130746956\\n\",\n       \"\\n\",\n       \"Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.02454676150565218\\n\",\n       \"       0      1     Error    Rate\\n\",\n       \"-----  -----  ----  -------  ----------------\\n\",\n       \"0      86328  7950  0.0843   (7950.0/94278.0)\\n\",\n       \"1      1240   260   0.8267   (1240.0/1500.0)\\n\",\n       \"Total  87568  8210  0.096    (9190.0/95778.0)\\n\",\n       \"\\n\",\n       \"Maximum Metrics: Maximum metrics at their respective thresholds\\n\",\n       \"metric                       threshold    value      idx\\n\",\n       \"---------------------------  -----------  ---------  -----\\n\",\n       \"max f1                       0.0245468    0.053553   165\\n\",\n       \"max f2                       0.0176342    0.0954986  234\\n\",\n       \"max f0point5                 0.0392926    0.0469982  89\\n\",\n       \"max accuracy                 0.198758     0.984349   0\\n\",\n       \"max precision                0.198758     1          0\\n\",\n       \"max recall                   0.00627246   1          393\\n\",\n       \"max specificity              0.198758     1          0\\n\",\n       \"max absolute_mcc             0.0141412    0.0431676  285\\n\",\n       \"max min_per_class_accuracy   0.0150963    0.578693   269\\n\",\n       \"max mean_per_class_accuracy  0.0141412    0.586917   285\\n\",\n       \"max tns                      0.198758     94278      0\\n\",\n       \"max fns                      0.198758     1499       0\\n\",\n       \"max fps                      0.00419779   94278      399\\n\",\n       \"max tps                      0.00627246   1500       393\\n\",\n       \"max tnr                      0.198758     1          0\\n\",\n       \"max fnr                      0.198758     0.999333   0\\n\",\n       \"max fpr                      0.00419779   1          399\\n\",\n       \"max tpr                      0.00627246   1          393\\n\",\n       \"\\n\",\n       \"Gains/Lift Table: Avg response rate:  1.57 %, avg score:  1.57 %\\n\",\n       \"group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    score       cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain      cumulative_gain    kolmogorov_smirnov\\n\",\n       \"-------  --------------------------  -----------------  --------  -----------------  ---------------  ----------  --------------------------  ------------------  --------------  -------------------------  --------  -----------------  --------------------\\n\",\n       \"1        0.0100023                   0.0418766          3.13261   3.13261            0.0490605        0.0534118   0.0490605                   0.0534118           0.0313333       0.0313333                  213.261   213.261            0.0216704\\n\",\n       \"2        0.0200046                   0.0355028          1.93289   2.53275            0.0302714        0.0383074   0.039666                    0.0458596           0.0193333       0.0506667                  93.2889   153.275            0.0311499\\n\",\n       \"3        0.0300069                   0.0321481          1.99954   2.35501            0.0313152        0.0337554   0.0368824                   0.0418249           0.02            0.0706667                  99.9541   135.501            0.0413067\\n\",\n       \"4        0.0400092                   0.0299106          2.06619   2.28281            0.0323591        0.0309894   0.0357516                   0.039116            0.0206667       0.0913333                  106.619   128.281            0.0521407\\n\",\n       \"5        0.050001                    0.0282369          1.86819   2.19995            0.0292581        0.0290241   0.034454                    0.0370993           0.0186667       0.11                       86.8188   119.995            0.0609536\\n\",\n       \"6        0.100002                    0.0234834          1.59997   1.89996            0.0250574        0.0255386   0.0297557                   0.031319            0.08            0.19                       59.9967   89.996             0.0914298\\n\",\n       \"7        0.150003                    0.0210135          1.33331   1.71108            0.0208812        0.0221558   0.0267975                   0.0282646           0.0666667       0.256667                   33.3305   71.1075            0.108361\\n\",\n       \"8        0.200004                    0.0193195          1.23997   1.5933             0.0194195        0.0201298   0.024953                    0.0262309           0.062           0.318667                   23.9974   59.33              0.12055\\n\",\n       \"9        0.300006                    0.0170178          1.18664   1.45775            0.0185843        0.0180894   0.0228301                   0.0235171           0.118667        0.437333                   18.6642   45.7747            0.139512\\n\",\n       \"10       0.400008                    0.0153927          1.16664   1.38497            0.018271         0.0161619   0.0216903                   0.0216783           0.116667        0.554                      16.6642   38.4971            0.156442\\n\",\n       \"11       0.5                         0.0140716          1.1601    1.34               0.0181685        0.0147199   0.020986                    0.0202867           0.116           0.67                       16.0097   34                 0.172705\\n\",\n       \"12       0.600002                    0.0129192          0.793317  1.24888            0.0124243        0.0134869   0.0195591                   0.0191534           0.0793333       0.749333                   -20.6683  24.8885            0.151707\\n\",\n       \"13       0.699994                    0.0118081          0.873406  1.19525            0.0136786        0.0123627   0.0187191                   0.0181833           0.0873333       0.836667                   -12.6594  19.5249            0.138847\\n\",\n       \"14       0.799996                    0.0106138          0.706652  1.13417            0.011067         0.0112197   0.0177625                   0.0173129           0.0706667       0.907333                   -29.3348  13.4173            0.109045\\n\",\n       \"15       0.899998                    0.0092505          0.533322  1.06741            0.00835247       0.00996325  0.0167169                   0.0164962           0.0533333       0.960667                   -46.6678  6.74099            0.061634\\n\",\n       \"16       1                           0.00376286         0.393325  1                  0.00615995       0.00814891  0.0156612                   0.0156615           0.0393333       1                          -60.6675  0                  0\\n\",\n       \"\\n\",\n       \"Cross-Validation Metrics Summary: \\n\",\n       \"                      mean          sd            cv_1_valid    cv_2_valid    cv_3_valid    cv_4_valid    cv_5_valid\\n\",\n       \"--------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------\\n\",\n       \"accuracy              0.919574      0.050330404   0.97703207    0.9188471     0.9344536     0.8387954     0.92874205\\n\",\n       \"auc                   0.61461455    0.015325267   0.6120911     0.6328822     0.60006136    0.6001108     0.62792724\\n\",\n       \"err                   0.080425955   0.050330404   0.022967918   0.08115292    0.06554639    0.1612046     0.07125792\\n\",\n       \"err_count             1543.0        967.3231      436.0         1557.0        1256.0        3094.0        1372.0\\n\",\n       \"f0point5              0.048447724   0.01417018    0.07057546    0.051259775   0.046927374   0.03292452    0.040551502\\n\",\n       \"f1                    0.058735613   0.008117401   0.056277055   0.07044776    0.06268657    0.049170252   0.055096418\\n\",\n       \"f2                    0.08734963    0.02463644    0.046796255   0.11259542    0.094382025   0.09706382    0.085910656\\n\",\n       \"lift_top_group        3.1134658     0.78839815    4.2033553     3.1722884     3.3831215     2.0753677     2.733196\\n\",\n       \"logloss               0.07930168    0.0024096703  0.081886925   0.081864655   0.078453496   0.07721919    0.077084124\\n\",\n       \"max_per_class_error   0.84289026    0.08528962    0.9579288     0.8126984     0.8576271     0.7231834     0.8630137\\n\",\n       \"---                   ---           ---           ---           ---           ---           ---           ---\\n\",\n       \"mean_per_class_error  0.45557305    0.018003713   0.48271292    0.4408201     0.45539436    0.4378983     0.4610396\\n\",\n       \"mse                   0.015368965   0.0006049513  0.015965253   0.016071161   0.01511778    0.014802203   0.014888432\\n\",\n       \"null_deviance         3089.4463     92.5738       3158.564      3214.8054     3048.0613     2999.556      3026.2446\\n\",\n       \"pr_auc                0.027192686   0.0029253333  0.026682377   0.032287188   0.025816202   0.026287604   0.024890063\\n\",\n       \"precision             0.046001054   0.022657208   0.08496732    0.043382354   0.040191386   0.02698145    0.03448276\\n\",\n       \"r2                    0.0031034066  0.001057439   0.0029666456  0.004799358   0.0026563376  0.0019311167  0.0031635754\\n\",\n       \"recall                0.15710972    0.08528962    0.042071197   0.18730159    0.14237288    0.2768166     0.1369863\\n\",\n       \"residual_deviance     3037.8745     82.14538      3108.919      3141.3105     3006.6519     2964.1357     2968.3555\\n\",\n       \"rmse                  0.12395252    0.0024335918  0.12635368    0.12677208    0.12295438    0.1216643     0.12201816\\n\",\n       \"specificity           0.93174416    0.05272704    0.9925029     0.9310582     0.9468384     0.8473868     0.9409345\\n\",\n       \"[22 rows x 8 columns]\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"[tips]\\n\",\n       \"Use `model.explain()` to inspect the model.\\n\",\n       \"--\\n\",\n       \"Use `h2o.display.toggle_user_tips()` to switch on/off this section.\"\n      ]\n     },\n     \"execution_count\": 114,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Run AutoML for 30 seconds\\n\",\n    \"aml = H2OAutoML(nfolds=5\\n\",\n    \"                , max_runtime_secs=100\\n\",\n    \"                # , balance_classes=True\\n\",\n    \"                , stopping_metric=\\\"lift_top_group\\\"\\n\",\n    \"                , stopping_rounds=5\\n\",\n    \"                # , project_name=\\\"scorecardpipeline\\\"\\n\",\n    \"                , include_algos=[\\\"XGBoost\\\", \\\"GBM\\\", \\\"DRF\\\", \\\"StackedEnsemble\\\"]\\n\",\n    \"                , sort_metric=\\\"logloss\\\"\\n\",\n    \"               )\\n\",\n    \"aml.train(x=x, y=y, training_frame=train_select, validation_frame=test_select)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 115,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"
model_id                                                  logloss     auc    aucpr  mean_per_class_error    rmse      mse
StackedEnsemble_BestOfFamily_1_AutoML_10_20240414_2244260.07929610.6147190.026182               0.4554960.1239670.0153678
GBM_2_AutoML_10_20240414_224426                         0.080128 0.6065280.0246042              0.4671630.1241280.0154077
GBM_3_AutoML_10_20240414_224426                         0.08036480.6010250.022218               0.4583460.1242730.0154437
GBM_4_AutoML_10_20240414_224426                         0.08040080.5790750.0219133              0.4738360.1241960.0154246
StackedEnsemble_BestOfFamily_2_AutoML_10_20240414_2244260.08047540.5608770.0212636              0.47342 0.12414 0.0154107
GBM_1_AutoML_10_20240414_224426                         0.08048760.6037570.0292934              0.4700590.1241480.0154126
XGBoost_1_AutoML_10_20240414_224426                     0.12758  0.6103870.0233224              0.4550010.1432650.0205249
DRF_1_AutoML_10_20240414_224426                         0.206885 0.5533590.0207998              0.4747850.1397580.0195323
XGBoost_2_AutoML_10_20240414_224426                     0.45345  0.56302 0.0191009              0.4694840.3648770.133135 
[9 rows x 7 columns]
\"\n      ],\n      \"text/plain\": [\n       \"model_id                                                    logloss       auc      aucpr    mean_per_class_error      rmse        mse\\n\",\n       \"--------------------------------------------------------  ---------  --------  ---------  ----------------------  --------  ---------\\n\",\n       \"StackedEnsemble_BestOfFamily_1_AutoML_10_20240414_224426  0.0792961  0.614719  0.026182                 0.455496  0.123967  0.0153678\\n\",\n       \"GBM_2_AutoML_10_20240414_224426                           0.080128   0.606528  0.0246042                0.467163  0.124128  0.0154077\\n\",\n       \"GBM_3_AutoML_10_20240414_224426                           0.0803648  0.601025  0.022218                 0.458346  0.124273  0.0154437\\n\",\n       \"GBM_4_AutoML_10_20240414_224426                           0.0804008  0.579075  0.0219133                0.473836  0.124196  0.0154246\\n\",\n       \"StackedEnsemble_BestOfFamily_2_AutoML_10_20240414_224426  0.0804754  0.560877  0.0212636                0.47342   0.12414   0.0154107\\n\",\n       \"GBM_1_AutoML_10_20240414_224426                           0.0804876  0.603757  0.0292934                0.470059  0.124148  0.0154126\\n\",\n       \"XGBoost_1_AutoML_10_20240414_224426                       0.12758    0.610387  0.0233224                0.455001  0.143265  0.0205249\\n\",\n       \"DRF_1_AutoML_10_20240414_224426                           0.206885   0.553359  0.0207998                0.474785  0.139758  0.0195323\\n\",\n       \"XGBoost_2_AutoML_10_20240414_224426                       0.45345    0.56302   0.0191009                0.469484  0.364877  0.133135\\n\",\n       \"[9 rows x 7 columns]\\n\"\n      ]\n     },\n     \"execution_count\": 115,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Print Leaderboard (ranked by xval metrics)\\n\",\n    \"aml.leaderboard\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 116,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.615829274501485\"\n      ]\n     },\n     \"execution_count\": 116,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# (Optional) Evaluate performance on a test set\\n\",\n    \"perf_test = aml.leader.model_performance(test_select)\\n\",\n    \"perf_test.auc()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 117,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.5807054204660588\"\n      ]\n     },\n     \"execution_count\": 117,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# (Optional) Evaluate performance on a test set\\n\",\n    \"perf_oot = aml.leader.model_performance(oot_select)\\n\",\n    \"perf_oot.auc()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 118,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"best_model = aml.get_best_model()\\n\",\n    \"# best_model.explain(oot_select);\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 119,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"stackedensemble prediction progress: |███████████████████████████████████████████| (done) 100%\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"ks_plot(best_model.predict(oot_select).as_data_frame()[\\\"p1\\\"], oot_select.as_data_frame()[y]);\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 131,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"best_model._estimator_type\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 127,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"stackedensemble prediction progress: |███████████████████████████████████████████| (done) 100%\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率分档WOE值分档IV值指标IV值LIFT值坏账改善累积LIFT值累积坏账改善累积好样本数累积坏样本数分档KS值
0score[负无穷 , 0.0184)1968.00000.08221959.00000.08319.00000.02400.00461.24210.07340.24040.2920-0.06341.00000.000023570.0000375.00000.0000
1score[0.0184 , 0.0248)6493.00000.27126426.00000.272667.00000.17870.01030.42260.03970.24040.6589-0.12691.06340.708021611.0000366.00000.0591
2score[0.0248 , 0.0266)2200.00000.09192172.00000.092228.00000.07470.01270.21040.00370.24040.8127-0.01901.23300.426415185.0000299.00000.1531
3score[0.0266 , 0.0325)6310.00000.26356213.00000.263697.00000.25870.01540.01890.00010.24040.9816-0.00661.30260.377113013.0000271.00000.1706
4score[0.0325 , 0.0579)6466.00000.27006322.00000.2682144.00000.38400.0223-0.35880.04150.24041.42200.15611.59310.24376800.0000174.00000.1755
5score[0.0579 , 正无穷)508.00000.0212478.00000.020330.00000.08000.0591-1.37240.08200.24043.77090.06013.77090.0601478.000030.00000.0597
6score缺失值0.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.0000
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"    指标名称 指标含义                 分箱      样本总数   样本占比      好样本数  好样本占比     坏样本数  坏样本占比   坏样本率  分档WOE值  分档IV值  指标IV值  LIFT值    坏账改善  累积LIFT值  累积坏账改善     累积好样本数   累积坏样本数  分档KS值\\n\",\n       \"0  score          [负无穷 , 0.0184) 1968.0000 0.0822 1959.0000 0.0831   9.0000 0.0240 0.0046  1.2421 0.0734 0.2404 0.2920 -0.0634   1.0000  0.0000 23570.0000 375.0000 0.0000\\n\",\n       \"1  score       [0.0184 , 0.0248) 6493.0000 0.2712 6426.0000 0.2726  67.0000 0.1787 0.0103  0.4226 0.0397 0.2404 0.6589 -0.1269   1.0634  0.7080 21611.0000 366.0000 0.0591\\n\",\n       \"2  score       [0.0248 , 0.0266) 2200.0000 0.0919 2172.0000 0.0922  28.0000 0.0747 0.0127  0.2104 0.0037 0.2404 0.8127 -0.0190   1.2330  0.4264 15185.0000 299.0000 0.1531\\n\",\n       \"3  score       [0.0266 , 0.0325) 6310.0000 0.2635 6213.0000 0.2636  97.0000 0.2587 0.0154  0.0189 0.0001 0.2404 0.9816 -0.0066   1.3026  0.3771 13013.0000 271.0000 0.1706\\n\",\n       \"4  score       [0.0325 , 0.0579) 6466.0000 0.2700 6322.0000 0.2682 144.0000 0.3840 0.0223 -0.3588 0.0415 0.2404 1.4220  0.1561   1.5931  0.2437  6800.0000 174.0000 0.1755\\n\",\n       \"5  score          [0.0579 , 正无穷)  508.0000 0.0212  478.0000 0.0203  30.0000 0.0800 0.0591 -1.3724 0.0820 0.2404 3.7709  0.0601   3.7709  0.0601   478.0000  30.0000 0.0597\\n\",\n       \"6  score                     缺失值    0.0000 0.0000    0.0000 0.0000   0.0000 0.0000 0.0000  0.0000 0.0000 0.0000 0.0000  0.0000   0.0000  0.0000     0.0000   0.0000 0.0000\"\n      ]\n     },\n     \"execution_count\": 127,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"feature_bin_stats(\\n\",\n    \"    pd.DataFrame({\\\"score\\\": best_model.predict(test_select).as_data_frame()[\\\"p1\\\"], \\\"target\\\": test_select.as_data_frame()[y]})\\n\",\n    \"    , \\\"score\\\"\\n\",\n    \"    , method=\\\"mdlp\\\"\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"best_model.predict(test_select).as_data_frame()[\\\"p1\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 188,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"score_transform = StandardScoreTransformer(score0=500, pdo=50, bad_rate=train[y].mean(), greater_is_better=False)\\n\",\n    \"# score_transform = BoxCoxScoreTransformer()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 189,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"stackedensemble prediction progress: |███████████████████████████████████████████| (done) 100%\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
StandardScoreTransformer(bad_rate=0.015661216563302636, greater_is_better=False,\\n\",\n       \"                         pdo=50, score0=500)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
\"\n      ],\n      \"text/plain\": [\n       \"StandardScoreTransformer(bad_rate=0.015661216563302636, greater_is_better=False,\\n\",\n       \"                         pdo=50, score0=500)\"\n      ]\n     },\n     \"execution_count\": 189,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"score_transform.fit(best_model.predict(test_select).as_data_frame()[[\\\"p0\\\"]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 190,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"stackedensemble prediction progress: |███████████████████████████████████████████| (done) 100%\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率分档WOE值分档IV值指标IV值LIFT值坏账改善累积LIFT值累积坏账改善累积好样本数累积坏样本数分档KS值
0score[负无穷 , 624.9255)508.00000.0212478.00000.020330.00000.08000.0591-1.37240.08200.24043.77090.06013.77090.0601478.000030.00000.0597
1score[624.9255 , 668.492)6466.00000.27006322.00000.2682144.00000.38400.0223-0.35880.04150.24041.42200.15611.59310.24376800.0000174.00000.1755
2score[668.492 , 683.3262)6310.00000.26356213.00000.263697.00000.25870.01540.01890.00010.24040.9816-0.00661.30260.377113013.0000271.00000.1706
3score[683.3262 , 688.4017)2200.00000.09192172.00000.092228.00000.07470.01270.21040.00370.24040.8127-0.01901.23300.426415185.0000299.00000.1531
4score[688.4017 , 710.2586)6493.00000.27126426.00000.272667.00000.17870.01030.42260.03970.24040.6589-0.12691.06340.708021611.0000366.00000.0591
5score[710.2586 , 正无穷)1968.00000.08221959.00000.08319.00000.02400.00461.24210.07340.24040.2920-0.06341.00000.000023570.0000375.00000.0000
6score缺失值0.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.0000
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\"\n      ],\n      \"text/plain\": [\n       \"    指标名称 指标含义                     分箱      样本总数   样本占比      好样本数  好样本占比     坏样本数  坏样本占比   坏样本率  分档WOE值  分档IV值  指标IV值  LIFT值    坏账改善  累积LIFT值  累积坏账改善     累积好样本数   累积坏样本数  分档KS值\\n\",\n       \"0  score            [负无穷 , 624.9255)  508.0000 0.0212  478.0000 0.0203  30.0000 0.0800 0.0591 -1.3724 0.0820 0.2404 3.7709  0.0601   3.7709  0.0601   478.0000  30.0000 0.0597\\n\",\n       \"1  score        [624.9255 , 668.492) 6466.0000 0.2700 6322.0000 0.2682 144.0000 0.3840 0.0223 -0.3588 0.0415 0.2404 1.4220  0.1561   1.5931  0.2437  6800.0000 174.0000 0.1755\\n\",\n       \"2  score        [668.492 , 683.3262) 6310.0000 0.2635 6213.0000 0.2636  97.0000 0.2587 0.0154  0.0189 0.0001 0.2404 0.9816 -0.0066   1.3026  0.3771 13013.0000 271.0000 0.1706\\n\",\n       \"3  score       [683.3262 , 688.4017) 2200.0000 0.0919 2172.0000 0.0922  28.0000 0.0747 0.0127  0.2104 0.0037 0.2404 0.8127 -0.0190   1.2330  0.4264 15185.0000 299.0000 0.1531\\n\",\n       \"4  score       [688.4017 , 710.2586) 6493.0000 0.2712 6426.0000 0.2726  67.0000 0.1787 0.0103  0.4226 0.0397 0.2404 0.6589 -0.1269   1.0634  0.7080 21611.0000 366.0000 0.0591\\n\",\n       \"5  score            [710.2586 , 正无穷) 1968.0000 0.0822 1959.0000 0.0831   9.0000 0.0240 0.0046  1.2421 0.0734 0.2404 0.2920 -0.0634   1.0000  0.0000 23570.0000 375.0000 0.0000\\n\",\n       \"6  score                         缺失值    0.0000 0.0000    0.0000 0.0000   0.0000 0.0000 0.0000  0.0000 0.0000 0.0000 0.0000  0.0000   0.0000  0.0000     0.0000   0.0000 0.0000\"\n      ]\n     },\n     \"execution_count\": 190,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"feature_bin_stats(\\n\",\n    \"    pd.DataFrame({\\\"score\\\": score_transform.transform(best_model.predict(test_select).as_data_frame()[[\\\"p0\\\"]])[\\\"p0\\\"], \\\"target\\\": test_select.as_data_frame()[y]})\\n\",\n    \"    , \\\"score\\\"\\n\",\n    \"    , method=\\\"mdlp\\\"\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 187,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"stackedensemble prediction progress: |███████████████████████████████████████████| (done) 100%\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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p1
count23945.0000
mean304.6197
std1.6628
min300.0038
25%303.4987
50%304.6367
75%305.7444
max309.0096
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\"\n      ],\n      \"text/plain\": [\n       \"              p1\\n\",\n       \"count 23945.0000\\n\",\n       \"mean    304.6197\\n\",\n       \"std       1.6628\\n\",\n       \"min     300.0038\\n\",\n       \"25%     303.4987\\n\",\n       \"50%     304.6367\\n\",\n       \"75%     305.7444\\n\",\n       \"max     309.0096\"\n      ]\n     },\n     \"execution_count\": 187,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"score_transform.transform(best_model.predict(test_select).as_data_frame()[[\\\"p1\\\"]]).describe()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 157,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from abc import abstractmethod\\n\",\n    \"import numpy as np\\n\",\n    \"from pandas import DataFrame\\n\",\n    \"from scipy import stats\\n\",\n    \"from sklearn.base import BaseEstimator, TransformerMixin\\n\",\n    \"from sklearn.preprocessing import MinMaxScaler\\n\",\n    \"from sklearn.utils import check_array\\n\",\n    \"from sklearn.utils.validation import check_is_fitted\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class BaseScoreTransformer(BaseEstimator, TransformerMixin):\\n\",\n    \"    def __init__(self, down_lmt=300, up_lmt=1000, greater_is_better=True, cutoff=None):\\n\",\n    \"        self.down_lmt = down_lmt\\n\",\n    \"        self.up_lmt = up_lmt\\n\",\n    \"        self.greater_is_better = greater_is_better\\n\",\n    \"        self.cutoff = cutoff\\n\",\n    \"\\n\",\n    \"    @abstractmethod\\n\",\n    \"    def predict(self, x):\\n\",\n    \"        pass\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class StandardScoreTransformer(BaseScoreTransformer):\\n\",\n    \"    \\\"\\\"\\\"Stretch the predicted probability to a normal distributed score.\\\"\\\"\\\"\\n\",\n    \"    def __init__(self, score0=660, pdo=75, bad_rate=0.15, down_lmt=300, up_lmt=1000, greater_is_better=True, cutoff=None):\\n\",\n    \"        super().__init__(down_lmt=down_lmt, up_lmt=up_lmt, greater_is_better=greater_is_better, cutoff=cutoff)\\n\",\n    \"        self.score0 = score0\\n\",\n    \"        self.pdo = pdo\\n\",\n    \"        self.bad_rate = bad_rate\\n\",\n    \"\\n\",\n    \"    def fit(self, X, y=None, **fit_params):\\n\",\n    \"        self._validate_data(X, reset=True, accept_sparse=False, dtype=\\\"numeric\\\", copy=False, force_all_finite=True)\\n\",\n    \"\\n\",\n    \"        score0, down_lmt, up_lmt = self.score0, self.down_lmt, self.up_lmt\\n\",\n    \"        if not down_lmt < score0 < up_lmt:\\n\",\n    \"            raise ValueError(\\\"score0 should be greater than {} and less than {}!\\\".format(down_lmt, up_lmt))\\n\",\n    \"        \\n\",\n    \"        bad_rate = self.bad_rate\\n\",\n    \"        if not 0.0 < bad_rate < 1.0:\\n\",\n    \"            raise ValueError(\\\"bad rate should be greater than e and less than 1!\\\")\\n\",\n    \"        \\n\",\n    \"        odds = bad_rate / (1. - bad_rate)\\n\",\n    \"        if self.greater_is_better:\\n\",\n    \"            B = self.pdo / np.log(2.0)\\n\",\n    \"        else:\\n\",\n    \"            B = -self.pdo / np.log(2.0)\\n\",\n    \"\\n\",\n    \"        A = score0 + B + np.log(odds)\\n\",\n    \"\\n\",\n    \"        self.A_ = A\\n\",\n    \"        self.B_ = B\\n\",\n    \"        self.odds_ = odds\\n\",\n    \"        return self\\n\",\n    \"    \\n\",\n    \"    def _transform(self, X):\\n\",\n    \"        check_is_fitted(self, [\\\"A_\\\", \\\"B_\\\"])\\n\",\n    \"        Xt = self._validate_data(X, reset=False, accept_sparse=False, dtype=\\\"numeric\\\", copy=True, force_all_finite=True)\\n\",\n    \"        # if not np.all((0 <= Xt) & (Xt <= 1)):\\n\",\n    \"        #     raise ValueError (\\\"Input should be probabilities between 0 and 1.\\\")\\n\",\n    \"        A, B = self.A_, self.B_\\n\",\n    \"        down_lmt, up_lmt = self.down_lmt, self.up_lmt\\n\",\n    \"        points =A - B * np.log(Xt / (1.0 - Xt))\\n\",\n    \"        points = np.clip(points, down_lmt, up_lmt)\\n\",\n    \"        return points\\n\",\n    \"\\n\",\n    \"    def transform(self, X):\\n\",\n    \"        data = self._transform(X)\\n\",\n    \"        if isinstance(X, DataFrame):\\n\",\n    \"            columns = X.columns\\n\",\n    \"            index = X.index\\n\",\n    \"            return DataFrame(data=data, columns=columns, index=index)\\n\",\n    \"        return data\\n\",\n    \"    \\n\",\n    \"    def predict(self, X):\\n\",\n    \"        scores = np.ravel(self._transform(X))\\n\",\n    \"        if self.cutoff is None:\\n\",\n    \"            cutoff = self._transform([[0.5]])[0][0]\\n\",\n    \"        elif not self.down_lmt < self.cutoff < self.up_lmt:\\n\",\n    \"            raise ValueError(\\\"Cutoff point should be within down_lmt and up_lmt!\\\")\\n\",\n    \"        else:\\n\",\n    \"            cutoff = self.cutoff\\n\",\n    \"        \\n\",\n    \"        if self.greater_is_better:\\n\",\n    \"            return (scores < cutoff).astype(np.int)\\n\",\n    \"        else:\\n\",\n    \"            return (scores > cutoff).astype (np.int)\\n\",\n    \"        \\n\",\n    \"    def _inverse_transform(self, X):\\n\",\n    \"        check_is_fitted(self, [\\\"A_\\\", \\\"B_\\\"])\\n\",\n    \"        Xt = check_array(X, accept_sparse=False, dtype=\\\"numeric\\\", copy=True, force_all_finite=True)\\n\",\n    \"        down_lmt, up_lmt = self.down_lmt, self.up_lmt\\n\",\n    \"        if not np.all(np.logical_and((down_lmt <= Xt), (Xt <= up_lmt))):\\n\",\n    \"            raise ValueError(\\\"Input should be points between {} and {}\\\".format(down_lmt, up_lmt))\\n\",\n    \"        A, B = self.A_, self.B_\\n\",\n    \"        probs = 1.0 - 1.0 / (np.exp((A - Xt) / B) + 1.0)\\n\",\n    \"        return probs\\n\",\n    \"    \\n\",\n    \"    def inverse_transform(self, X):\\n\",\n    \"        data = self. inverse_transform(X)\\n\",\n    \"        if isinstance(X, DataFrame):\\n\",\n    \"            columns = X.columns\\n\",\n    \"            index = X.index\\n\",\n    \"            return DataFrame(data=data, columns=columns, index=index)\\n\",\n    \"        return data\\n\",\n    \"    \\n\",\n    \"    def _more_tags(self):\\n\",\n    \"        return {\\n\",\n    \"            \\\"X_types\\\": [\\\"2darray\\\"],\\n\",\n    \"            \\\"allow_nan\\\": False,\\n\",\n    \"        }\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class NPRoundStandardScoreTransformer(StandardScoreTransformer):\\n\",\n    \"    \\\"\\\"\\\"Stretch the predict probability to a normal distributed score.\\\"\\\"\\\"\\n\",\n    \"    def __init__(self, score0=660,pdo=75, bad_rate=0.15, down_lmt=300, up_lmt=1000, round_decimals=0, greater_is_better=True, cutoff=None):\\n\",\n    \"        self.round_decimals = round_decimals\\n\",\n    \"        super(NPRoundStandardScoreTransformer, self).__init__(score0=score0, pdo=pdo, bad_rate=bad_rate, down_lmt=down_lmt, up_lmt=up_lmt,\\n\",\n    \"                                                              greater_is_better=greater_is_better, cutoff=cutoff)\\n\",\n    \"\\n\",\n    \"    def _transform(self, X):\\n\",\n    \"        points = super()._transform(X)\\n\",\n    \"        decimals = self.round_decimals\\n\",\n    \"        points = np.round(points, decimals=decimals)\\n\",\n    \"        return points\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class RoundStandardScoreTransformer(StandardScoreTransformer):\\n\",\n    \"    \\\"\\\"\\\"Stretch the predicted probability to a normal distributed score.\\\"\\\"\\\"\\n\",\n    \"    def __init__(self, score0=660, pdo=75, bad_rate=0.15, down_lmt=300, up_lmt=1000, round_decimals=0, greater_is_better=True, cutoff=None):\\n\",\n    \"        self.round_decimals = round_decimals\\n\",\n    \"        super(RoundStandardScoreTransformer, self).__init__(score0=score0, pdo=pdo, bad_rate=bad_rate, down_lmt=down_lmt, up_lmt=up_lmt,\\n\",\n    \"                                                            greater_is_better=greater_is_better, cutoff=cutoff)\\n\",\n    \"    def _transform(self, X):\\n\",\n    \"        points  = super()._transform(X)\\n\",\n    \"        decimals = self.round_decimals\\n\",\n    \"        points = np.array([[round(x[0], decimals)] for x in points])\\n\",\n    \"        return points\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class BoxCoxScoreTransformer(BaseScoreTransformer):\\n\",\n    \"    def __init__(self, down_lmt=300, up_lmt=1000, greater_is_better=True, cutoff=None):\\n\",\n    \"        super(BoxCoxScoreTransformer, self).__init__(down_lmt=down_lmt, up_lmt=up_lmt, greater_is_better=greater_is_better, cutoff=cutoff)\\n\",\n    \"    \\n\",\n    \"    def _box_cox_optimize(self, x):\\n\",\n    \"        \\\"\\\"\\\"Find and return optimal lambda parameter of the Box-Cox transform by MLE, for observed data x.\\n\",\n    \"\\n\",\n    \"        We here use scipy builtins which uses the brent optimizer.\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        # the computation of Lambda is influenced by NaNs so we need to get rid of them\\n\",\n    \"        _, lmbda = stats.boxcox(x, lmbda=None)\\n\",\n    \"        return lmbda\\n\",\n    \"\\n\",\n    \"    def fit(self, X, y=None, **fit_params):\\n\",\n    \"        X = check_array(X, accept_sparse=False, dtype=\\\"numeric\\\", copy=True, force_all_finite=True)\\n\",\n    \"        if np.min(X) <= 0 or np.max(X) >= 1:\\n\",\n    \"            raise ValueError(\\\"The Box-Cox score transformation can only be applied to strictly positive probabilities\\\")\\n\",\n    \"        if self.greater_is_better:\\n\",\n    \"            self.lambdas_ = np.array([self._box_cox_optimize(1.0 - col) for col in X.T])\\n\",\n    \"        else:\\n\",\n    \"            self.lambdas_ = np.array([self._box_cox_optimize(col) for col in X.T])\\n\",\n    \"        for i, lmbda in enumerate(self.lambdas_) :\\n\",\n    \"            X[:, i] = stats.boxcox(X[:, i], lmbda)\\n\",\n    \"        self.scaler_ = MinMaxScaler(feature_range=(self.down_lmt, self.up_lmt)).fit(X)\\n\",\n    \"        return self\\n\",\n    \"\\n\",\n    \"    def _transform(self, X):\\n\",\n    \"        check_is_fitted(self, [\\\"lambdas_\\\", \\\"scaler_\\\"])\\n\",\n    \"        X = check_array(X, accept_sparse=False, dtype=\\\"numeric\\\", copy=True, force_all_finite=True)\\n\",\n    \"        if np.min(X) < 0 or np.max(X) > 1:\\n\",\n    \"            raise ValueError(\\\"The Box-Cox score transformation can only be applied to strictly positive probabilities\\\")\\n\",\n    \"        if self.greater_is_better:\\n\",\n    \"            X = 1.0 - X\\n\",\n    \"        for i, lmbda in enumerate(self.lambdas_):\\n\",\n    \"            X[:, i]= stats.boxcox(X[:, i], lmbda)\\n\",\n    \"        return self.scaler_.transform(X)\\n\",\n    \"\\n\",\n    \"    def transform(self, X):\\n\",\n    \"        data = self._transform(X)\\n\",\n    \"        if isinstance(X, DataFrame):\\n\",\n    \"            columns = X.columns\\n\",\n    \"            index = X.index\\n\",\n    \"            return DataFrame(data=data, index=index, columns=columns)\\n\",\n    \"        return data\\n\",\n    \"    \\n\",\n    \"    def predict(self, X):\\n\",\n    \"        scores = np.ravel(self._transform(X))\\n\",\n    \"        if self.cutoff is None:\\n\",\n    \"            lmbda = self.lambdas_[0]\\n\",\n    \"            if lmbda != 0:\\n\",\n    \"                p = (0.5 ** lmbda - 1) / lmbda\\n\",\n    \"            else:\\n\",\n    \"                p = np.log(0.5)\\n\",\n    \"            scaler = self.scaler_\\n\",\n    \"            p *= scaler.scale_\\n\",\n    \"            p += scaler.min_\\n\",\n    \"            if scaler.clip:\\n\",\n    \"                if p < scaler.feature_range[0]:\\n\",\n    \"                    p = scaler.feature_range[0]\\n\",\n    \"                elif p > scaler.feature_range[1]:\\n\",\n    \"                    p = scaler.feature_range[1]\\n\",\n    \"            cutoff = p\\n\",\n    \"        elif not self.down_lmt < self.cutoff < self.up_lmt:\\n\",\n    \"            raise ValueError(\\\"Cutoff point should be within 'down_lmt' and 'up_lmt'!\\\")\\n\",\n    \"        else:\\n\",\n    \"            cutoff = self.cutoff\\n\",\n    \"        if self.greater_is_better:\\n\",\n    \"            return (scores < cutoff).astype(np.int)\\n\",\n    \"        else:\\n\",\n    \"            return (scores > cutoff).astype(np.int)\\n\",\n    \"\\n\",\n    \"    def _inverse_transform(self, X):\\n\",\n    \"        check_is_fitted(self, [\\\"lambdas_\\\", \\\"scaler_\\\"])\\n\",\n    \"        X = check_array(X, accept_sparse=False, dtype=\\\"numeric\\\", copy=True, force_all_finite=True)\\n\",\n    \"        if np.min(X) < self.down_lmt or np.max(X) > self.up_lmt:\\n\",\n    \"            raise ValueError(\\\"The Box-Cox score inverse transformation can only be applied to strictly bounded scores\\\")\\n\",\n    \"        X_inv = self.scaler_.inverse_transform(X)\\n\",\n    \"        for i, lmbda in enumerate(self.lambdas ):\\n\",\n    \"            X_inv[:, i] = self._box_cox_inverse_tranform(X_inv[:, i], lmbda)\\n\",\n    \"        return X_inv\\n\",\n    \"\\n\",\n    \"    def inverse_transform(self, X):\\n\",\n    \"        data = self._inverse_transform(X)\\n\",\n    \"        if isinstance(X, DataFrame):\\n\",\n    \"            columns = X.columns\\n\",\n    \"            index = X.index\\n\",\n    \"            return DataFrame(data=data, index=index, columns=columns)\\n\",\n    \"        return data\\n\",\n    \"\\n\",\n    \"    def _box_cox_inverse_tranform(self, x, lmbda):\\n\",\n    \"        \\\"\\\"\\\"Return inverse-transformed input x following Box-Cox inverse transform with parameter lambda\\\"\\\"\\\"\\n\",\n    \"        if lmbda == 0:\\n\",\n    \"            x_inv = np.exp(x)\\n\",\n    \"        else:\\n\",\n    \"            x_inv = (x * lmbda + 1) ** (1 / lmbda)\\n\",\n    \"\\n\",\n    \"        return x_inv\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.8\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "examples/cycle_end_vintage.sql",
    "content": "WITH loan AS (\n    # 筛选需要计算的订单数据\n    SELECT id, user_id, flow_channel, bank_channel, apply_time, loan_time, periods, amount\n    FROM database_name.loan\n    WHERE l.loan_state = 'SUCCEED'\n),\nreplan as (\n\tSELECT  l.flow_channel 流量渠道\n\t\t\t, l.bank_channel 放款资方\n\t\t\t, l.id 放款编号\n\t\t\t, l.user_id 用户编号\n\t\t\t, l.apply_time 申请时间\n\t\t\t, l.loan_time 放款时间\n\t\t\t, date_format(l.loan_time, '%Y-%m') 放款月份\n\t\t\t, l.periods 放款期数\n    \t\t, l.amount 放款金额\n    \t\t, p.period 还款期数\n    \t\t, p.plan_repay_date 应还日期\n    \t\t, date_add(p.plan_repay_date, INTERVAL 30 DAY) 观察日期\n            , date(p.act_repay_time) 实还日期\n            , p.principal_amt 应还本金\n            , p.act_principal_amt 实还本金\n\tFROM loan l INNER JOIN database_name.repay_plan p ON l.id = p.loan_id\n\tORDER BY 放款编号, 还款期数\n),\namount as (\n    SELECT date_format(loan_time, '%Y-%m') 放款月份, count(id) 放款件数, count(user_id) 放款人数, sum(amount) 放款金额\n    FROM loan\n    GROUP BY date_format(loan_time, '%Y-%m')\n)\nSELECT vintage.放款月份\n\t\t, SUM(IF(还款期数 = 1, 放款金额, 0)) 放款金额\n\t\t, SUM(IF(还款期数 = 1, case '递延逾期率' when '逾期率' then 逾期余额 / 放款金额  when '递延逾期率' then 逾期余额 / 出账金额  when '出账订单' then 出账订单  when '出账金额' then 出账金额  when '逾期余额' then 逾期余额 end, NULL)) TERM1\n\t\t, SUM(IF(还款期数 = 2, case '递延逾期率' when '逾期率' then 逾期余额 / 放款金额  when '递延逾期率' then 逾期余额 / 出账金额 when '出账订单' then 出账订单  when '出账金额' then 出账金额  when '逾期余额' then 逾期余额 end, NULL)) TERM2\n\t\t, SUM(IF(还款期数 = 3, case '递延逾期率' when '逾期率' then 逾期余额 / 放款金额  when '递延逾期率' then 逾期余额 / 出账金额  when '出账订单' then 出账订单  when '出账金额' then 出账金额  when '逾期余额' then 逾期余额 end, NULL)) TERM3\n\t\t, SUM(IF(还款期数 = 4, case '递延逾期率' when '逾期率' then 逾期余额 / 放款金额 when '递延逾期率' then 逾期余额 / 出账金额 when '出账订单' then 出账订单  when '出账金额' then 出账金额  when '逾期余额' then 逾期余额 end, NULL)) TERM4\n\t\t, SUM(IF(还款期数 = 5, case '递延逾期率' when '逾期率' then 逾期余额 / 放款金额 when '递延逾期率' then 逾期余额 / 出账金额 when '出账订单' then 出账订单  when '出账金额' then 出账金额  when '逾期余额' then 逾期余额 end, NULL)) TERM5\n\t\t, SUM(IF(还款期数 = 6, case '递延逾期率' when '逾期率' then 逾期余额 / 放款金额 when '递延逾期率' then 逾期余额 / 出账金额  when '出账订单' then 出账订单  when '出账金额' then 出账金额  when '逾期余额' then 逾期余额 end, NULL)) TERM6\n\t\t, SUM(IF(还款期数 = 7, case '递延逾期率' when '逾期率' then 逾期余额 / 放款金额 when '递延逾期率' then 逾期余额 / 出账金额 when '出账订单' then 出账订单  when '出账金额' then 出账金额  when '逾期余额' then 逾期余额 end, NULL)) TERM7\n\t\t, SUM(IF(还款期数 = 8, case '递延逾期率' when '逾期率' then 逾期余额 / 放款金额 when '递延逾期率' then 逾期余额 / 出账金额 when '出账订单' then 出账订单  when '出账金额' then 出账金额  when '逾期余额' then 逾期余额 end, NULL)) TERM8\n\t\t, SUM(IF(还款期数 = 9, case '递延逾期率' when '逾期率' then 逾期余额 / 放款金额 when '递延逾期率' then 逾期余额 / 出账金额  when '出账订单' then 出账订单  when '出账金额' then 出账金额  when '逾期余额' then 逾期余额 end, NULL)) TERM9\n\t\t, SUM(IF(还款期数 = 10, case '递延逾期率' when '逾期率' then 逾期余额 / 放款金额 when '递延逾期率' then 逾期余额 / 出账金额  when '出账订单' then 出账订单  when '出账金额' then 出账金额  when '逾期余额' then 逾期余额 end, NULL)) TERM10\n\t\t, SUM(IF(还款期数 = 11, case '递延逾期率' when '逾期率' then 逾期余额 / 放款金额 when '递延逾期率' then 逾期余额 / 出账金额 when '出账订单' then 出账订单  when '出账金额' then 出账金额  when '逾期余额' then 逾期余额 end, NULL)) TERM11\n\t\t, SUM(IF(还款期数 = 12, case '递延逾期率' when '逾期率' then 逾期余额 / 放款金额 when '递延逾期率' then 逾期余额 / 出账金额 when '出账订单' then 出账订单  when '出账金额' then 出账金额  when '逾期余额' then 逾期余额 end, NULL)) TERM12\nFROM (\n\tSELECT 放款月份, 还款期数\n\t\t\t, SUM(IF(观察点逾期 = 1, 观察点余额, 0)) 逾期余额\n\t\t\t, SUM(IF(观察日期 <= current_date(), 放款金额, 0)) 出账金额\n\t\t\t, COUNT(distinct IF(观察日期 <= current_date(), 放款编号, null)) 出账订单\n\t\t\t-- , SUM(IF(观察点逾期 = 1, 观察点余额, 0)) / SUM(放款金额) 逾期率\n\t\t\t-- , SUM(IF(观察点逾期 = 1, 观察点余额, 0)) / SUM(IF(观察日期 <= current_date(), 放款金额, 0)) 递延逾期率\n\tFROM (\n\t\tSELECT p1.放款编号, p1.放款月份, p1.放款金额, p1.还款期数, p1.观察日期\n\t\t\t\t, p1.放款金额 - sum(if(p2.实还日期 <= p1.观察日期, p2.实还本金, 0)) 观察点余额\n\t\t\t\t, if(p1.观察日期 < current_date() AND (p1.实还日期 IS NULL OR p1.实还日期 > p1.观察日期), 1, 0) 观察点逾期\n\t\tFROM replan p1\n\t\tLEFT JOIN replan p2 ON p1.放款编号 = p2.放款编号\n\t\tGROUP BY p1.放款编号, p1.放款月份, p1.放款金额, p1.还款期数, p1.观察日期, p1.实还日期\n\t\tORDER BY 放款编号, 还款期数\n\t) balance\n\tGROUP BY 放款月份, 还款期数\n) vintage\nLEFT JOIN amount a ON vintage.放款月份 = a.放款月份\nGROUP BY vintage.放款月份\n"
  },
  {
    "path": "examples/gbm_model_examples.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import warnings\\n\",\n    \"\\n\",\n    \"warnings.filterwarnings(\\\"ignore\\\")\\n\",\n    \"\\n\",\n    \"import re\\n\",\n    \"import os\\n\",\n    \"import gc\\n\",\n    \"import sys\\n\",\n    \"import time\\n\",\n    \"import json\\n\",\n    \"import joblib\\n\",\n    \"import traceback\\n\",\n    \"from tqdm import tqdm\\n\",\n    \"from urllib.parse import quote_plus\\n\",\n    \"import pymongo\\n\",\n    \"from pymongo import UpdateOne\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import polars as pl\\n\",\n    \"import polars.selectors as cs\\n\",\n    \"from loguru import logger\\n\",\n    \"from datetime import timedelta, datetime, date\\n\",\n    \"from dateutil.relativedelta import relativedelta\\n\",\n    \"\\n\",\n    \"import config\\n\",\n    \"from scorecardpipeline import *\\n\",\n    \"from dbutils.mysql_connect_pool import MysqlConnectPool\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"starrocks_uri = \\\"mysql://user:password@host:9030\\\"\\n\",\n    \"mongodb_url = \\\"mongodb://%s:%s@%s\\\" % (quote_plus(\\\"user\\\"), quote_plus(\\\"password\\\"), \\\"host:3717\\\")\\n\",\n    \"\\n\",\n    \"starrocks = MysqlConnectPool(**config.starrocks_connect_options, **config.pool_options) # starrocks 连接池\\n\",\n    \"router_data = pymongo.MongoClient(query_mongodb_url + \\\"/router_data\\\")[\\\"router_data\\\"][\\\"router_data\\\"] #三方数据结果\\n\",\n    \"\\n\",\n    \"init_setting()\\n\",\n    \"pd.set_option('display.max_columns', 50)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def deal_columns(data_name):\\n\",\n    \"    return eval(f\\\"list(set({data_name}.columns.drop('routeResponse').tolist() + list({data_name}_map.keys())))\\\")\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def flatten_dict(d, parent_key='', sep='_'):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    将嵌套的字典转换为单层的字典\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    if pd.isnull(d):\\n\",\n    \"        return {}\\n\",\n    \"    else:\\n\",\n    \"        items = []\\n\",\n    \"        for k, v in d.items():\\n\",\n    \"            new_key = f\\\"{parent_key}{sep}{k}\\\" if parent_key else k\\n\",\n    \"            if isinstance(v, dict):\\n\",\n    \"                items.extend(flatten_dict(v, new_key, sep=sep).items())\\n\",\n    \"            else:\\n\",\n    \"                items.append((new_key, v))\\n\",\n    \"        return dict(items)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def query_mongodb(query, projection=None, database=\\\"router_data\\\", collect=\\\"router_data\\\", *args):\\n\",\n    \"    current_query = pymongo.MongoClient(mongodb_url + f\\\"/{database}\\\")[database][collect]\\n\",\n    \"\\n\",\n    \"    return pd.DataFrame(current_query.find(query, projection=projection))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 获取订单数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data = starrocks.query(\\\"\\\"\\\"\\n\",\n    \"    SELECT n.id 授信编号\\n\",\n    \"        , md5(l.cust_name) 姓名\\n\",\n    \"        , md5(l.cust_mobile) 手机号\\n\",\n    \"        , md5(l.cust_cert_no) 身份证\\n\",\n    \"        , left(l.cust_cert_no, 2) 户籍省份\\n\",\n    \"        , cast(cast(substr(l.cust_cert_no, 17, 1) as int) % 2 as STRING) 性别\\n\",\n    \"        , year(u.created_time) - cast(substr(l.cust_cert_no, 7, 4) as int) 年龄\\n\",\n    \"        , l.loan_amt 放款金额\\n\",\n    \"        , l.periods 放款期数\\n\",\n    \"        , u.created_time 回溯时间\\n\",\n    \"        , l.loan_time 放款时间\\n\",\n    \"        , r.DPD, r.MOB1, r.MOB2, r.MOB3, r.CURRENT_DPD\\n\",\n    \"    FROM qy_ods.s06_zj_loan l\\n\",\n    \"    LEFT JOIN qy_ods.s01_360_360_loan n ON l.id = n.loan_no\\n\",\n    \"    INNER JOIN (\\n\",\n    \"        SELECT loan_id\\n\",\n    \"                , max(CASE WHEN repaid_date < repay_date OR repaid_date IS NOT NULL THEN 0 ELSE datediff(current_date(), repay_date) END) CURRENT_DPD\\n\",\n    \"                , max(CASE WHEN repaid_date < repay_date THEN 0 ELSE datediff(ifnull(repaid_date, current_date()), repay_date) END) DPD\\n\",\n    \"                , max(CASE WHEN repaid_date < repay_date OR period > 1 THEN 0 ELSE datediff(ifnull(repaid_date, current_date()), repay_date) END) MOB1\\n\",\n    \"                , max(CASE WHEN repaid_date < repay_date OR period > 2 THEN 0 ELSE datediff(ifnull(repaid_date, current_date()), repay_date) END) MOB2\\n\",\n    \"                , max(CASE WHEN repaid_date < repay_date OR period > 3 THEN 0 ELSE datediff(ifnull(repaid_date, current_date()), repay_date) END) MOB3\\n\",\n    \"        FROM (\\n\",\n    \"            SELECT p.*\\n\",\n    \"                    , IF(p.period >= c.period, c.act_repay_time, r.act_repay_time) repaid_date\\n\",\n    \"                    , IF(p.period = c.period, c.act_principal_amt, r.act_principal_amt) act_principal_amt\\n\",\n    \"            FROM qy_ods.s06_zj_loan_replan p\\n\",\n    \"            LEFT JOIN (\\n\",\n    \"                select loan_id, period, sum(act_principal_amt) act_principal_amt, date(max(act_repay_time)) act_repay_time, group_concat(repay_mode) repay_mode\\n\",\n    \"                from qy_ods.s06_zj_customer_loan_replan\\n\",\n    \"                where repay_purpose = 'CURRENT'\\n\",\n    \"                group by 1, 2\\n\",\n    \"            ) r on p.loan_id = r.loan_id and p.period = r.period\\n\",\n    \"            LEFT JOIN (\\n\",\n    \"                select loan_id, ifnull(period, 1) period, sum(act_principal_amt) act_principal_amt, date(max(act_repay_time)) act_repay_time, group_concat(repay_mode) repay_mode\\n\",\n    \"                from qy_ods.s06_zj_customer_loan_replan\\n\",\n    \"                where repay_purpose = 'CLEAR' AND act_principal_amt > 0\\n\",\n    \"                group by 1, 2\\n\",\n    \"            ) c ON p.loan_id = c.loan_id\\n\",\n    \"        ) t\\n\",\n    \"        WHERE repay_date < current_date()\\n\",\n    \"        GROUP BY 1\\n\",\n    \"    ) r ON l.id = r.loan_id\\n\",\n    \"    where l.loan_status = 'SUCCESS'\\n\",\n    \"\\\"\\\"\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 查询三方数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def iter_order_list(order, batch_size=50000):\\n\",\n    \"    for i in tqdm(range(0, len(order), batch_size)):\\n\",\n    \"        yield order[\\\"授信编号\\\"].tolist()[i:i+batch_size]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lasted_order = pd.DataFrame()\\n\",\n    \"for order_list in iter_order_list(data, batch_size=50000):\\n\",\n    \"    temp = starrocks.query(f\\\"\\\"\\\"\\n\",\n    \"        SELECT credit_id, create_time\\n\",\n    \"            -- , REPLACE(JSON_QUERY(parse_json(rc.credit_content), '$.zh_chnn_code'), '\\\"', '') channel_code\\n\",\n    \"            -- , JSON_QUERY(parse_json(rc.credit_content), '$.event_code') event_code\\n\",\n    \"            -- , REPLACE(JSON_QUERY(parse_json(rc.credit_content), IF(JSON_QUERY(parse_json(rc.credit_content), '$.event_code') = 50, '$.notifyContentPlain.ruleDetail.policyName', '$.rule_detail.policyName')), '\\\"', '') policyName\\n\",\n    \"            -- , REPLACE(JSON_QUERY(parse_json(rc.credit_content), IF(JSON_QUERY(parse_json(rc.credit_content), '$.event_code') = 50, '$.notifyContentPlain.ruleDetail.policyNameE', '$.rule_detail.policyNameE')), '\\\"', '') policyNameE\\n\",\n    \"            , REPLACE(JSON_QUERY(parse_json(rc.credit_content), '$.label_1'), '\\\"', '') label_1\\n\",\n    \"            , REPLACE(JSON_QUERY(parse_json(rc.credit_content), '$.label_2'), '\\\"', '') label_2\\n\",\n    \"            , REPLACE(JSON_QUERY(parse_json(rc.credit_content), '$.status_1'), '\\\"', '') status_1\\n\",\n    \"            , REPLACE(JSON_QUERY(parse_json(rc.credit_content), '$.status_2'), '\\\"', '') status_2\\n\",\n    \"            , REPLACE(JSON_QUERY(parse_json(rc.credit_content), '$.level_1'), '\\\"', '') level_1\\n\",\n    \"            , REPLACE(JSON_QUERY(parse_json(rc.credit_content), '$.level_2'), '\\\"', '') level_2\\n\",\n    \"            , REPLACE(JSON_QUERY(parse_json(rc.credit_content), '$.ovd_level_1'), '\\\"', '') ovd_level_1\\n\",\n    \"            , REPLACE(JSON_QUERY(parse_json(rc.credit_content), '$.ovd_level_2'), '\\\"', '') ovd_level_2\\n\",\n    \"            , REPLACE(JSON_QUERY(parse_json(rc.credit_content), '$.cust_type'), '\\\"', '') cust_type\\n\",\n    \"        FROM qy_ods.s06_fk_riskengine_credit rc\\n\",\n    \"        WHERE credit_id in ({str(order_list)[1:-1]})\\n\",\n    \"    \\\"\\\"\\\")\\n\",\n    \"\\n\",\n    \"    lasted_order = pd.concat([lasted_order, temp])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"temp = pd.read_excel(\\\"feature_maps/百融/百融云创零售数据产品介绍2023.08.22.xlsx\\\", sheet_name=\\\"ApplyLoanStrV2.0\\\", skiprows=14, usecols=[2, 5]).dropna().drop_duplicates(\\\"中文名称\\\")\\n\",\n    \"br_loan_intention_v2_map = dict(zip(temp[\\\"参数名\\\"], temp[\\\"中文名称\\\"].apply(lambda x: x.replace(\\\"，\\\", \\\"\\\"))))\\n\",\n    \"\\n\",\n    \"br_loan_intention_v2 = pd.DataFrame()\\n\",\n    \"for order_list in iter_order_list(data, batch_size=10000):\\n\",\n    \"    temp = starrocks.query(f\\\"\\\"\\\"\\n\",\n    \"        SELECT creditId, createTime, routeResponse\\n\",\n    \"        FROM qy_ods.s06_fk_br_loan_intention_v2\\n\",\n    \"        WHERE creditId in  ({str(data['授信编号'].tolist())[1:-1]})\\n\",\n    \"    \\\"\\\"\\\")\\n\",\n    \"    br_loan_intention_v2 = pd.concat([br_loan_intention_v2, temp])\\n\",\n    \"\\n\",\n    \"if len(br_loan_intention_v2) > 0:\\n\",\n    \"    br_loan_intention_v2 = br_loan_intention_v2.sort_values(\\\"createTime\\\").drop_duplicates(subset=[\\\"creditId\\\"], keep=\\\"last\\\").reset_index(drop=True)\\n\",\n    \"    br_loan_intention_v2 = br_loan_intention_v2.join(br_loan_intention_v2[\\\"routeResponse\\\"].apply(lambda x: json.loads(x if x and x != '\\\"\\\"' else \\\"{}\\\").get(\\\"content\\\", {})).apply(pd.Series))\\n\",\n    \"    br_loan_intention_v2 = br_loan_intention_v2.reindex(columns=deal_columns(\\\"br_loan_intention_v2\\\"))\\n\",\n    \"    br_loan_intention_v2[list(br_loan_intention_v2_map.keys())] = br_loan_intention_v2[br_loan_intention_v2_map.keys()].astype(float)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"bh_fraud_dongjian = pd.DataFrame()\\n\",\n    \"for order_list in iter_order_list(data, batch_size=10000):\\n\",\n    \"    temp = starrocks.query(f\\\"\\\"\\\"\\n\",\n    \"        SELECT creditId, createTime, routeResponse\\n\",\n    \"        FROM qy_ods.s06_fk_bh_fraud_dongjian\\n\",\n    \"        WHERE creditId in ({str(order_list)[1:-1]})\\n\",\n    \"    \\\"\\\"\\\")\\n\",\n    \"    bh_fraud_dongjian = pd.concat([bh_fraud_dongjian, temp])\\n\",\n    \"\\n\",\n    \"if len(bh_fraud_dongjian) > 0:\\n\",\n    \"    bh_fraud_dongjian = bh_fraud_dongjian.sort_values(\\\"createTime\\\").drop_duplicates(subset=[\\\"creditId\\\"], keep=\\\"last\\\").reset_index(drop=True)\\n\",\n    \"    bh_fraud_dongjian[\\\"BH_DJ_SCORE\\\"] = bh_fraud_dongjian[\\\"routeResponse\\\"].apply(lambda x: json.loads(x).get(\\\"score\\\"))\\n\",\n    \"    bh_fraud_dongjian = bh_fraud_dongjian[[\\\"creditId\\\", \\\"BH_DJ_SCORE\\\"]].join(bh_fraud_dongjian[\\\"routeResponse\\\"].apply(lambda x: json.loads(x).get(\\\"contents\\\")).apply(pd.Series))\\n\",\n    \"    bh_fraud_dongjian = bh_fraud_dongjian.replace(\\\"\\\", np.nan)\\n\",\n    \"    number_cols = bh_fraud_dongjian.columns.drop(['creditId', 'BH_G011', 'BH_G012', 'BH_G013', 'BH_G014', 'BH_G015']).tolist()\\n\",\n    \"    bh_fraud_dongjian[number_cols] = bh_fraud_dongjian[number_cols].astype(float)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 合并数据集\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"all_data = data.merge(\\n\",\n    \"    br_loan_intention_v2[[\\\"creditId\\\"] + [c for c in br_loan_intention_v2.columns if 'als' in c]].rename(columns={\\\"creditId\\\": \\\"授信编号\\\"}), on=\\\"授信编号\\\", how=\\\"inner\\\"\\n\",\n    \").merge(\\n\",\n    \"    bh_fraud_dongjian.drop(columns=[\\\"BH_G011\\\", \\\"BH_G012\\\", \\\"BH_G013\\\", \\\"BH_G014\\\", \\\"BH_G015\\\"]).rename(columns={\\\"creditId\\\": \\\"授信编号\\\"}), on=\\\"授信编号\\\", how=\\\"inner\\\"\\n\",\n    \").merge(\\n\",\n    \"    lasted_order.rename(columns={\\\"credit_id\\\": \\\"授信编号\\\"}), on=\\\"授信编号\\\", how=\\\"inner\\\"\\n\",\n    \").drop(columns=[\\\"身份证\\\", \\\"手机号\\\", \\\"姓名\\\", \\\"申请时间\\\", \\\"回溯时间\\\"])\\n\",\n    \"\\n\",\n    \"all_data.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 拆分数据集\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"mob, dpd, oot_days = 1, 15, 15\\n\",\n    \"oot_date = (date.today() - relativedelta(months=mob, days=dpd + oot_days)).strftime(\\\"%Y-%m-%d\\\")\\n\",\n    \"split_date = (date.today() - relativedelta(months=mob, days=dpd)).strftime(\\\"%Y-%m-%d\\\")\\n\",\n    \"train = all_data.query(\\\"(MOB1 <= 3 | MOB1 > 15) & 放款时间 < @oot_date\\\").assign(target=lambda x: (x[\\\"MOB1\\\"] > 15).astype(int))\\n\",\n    \"oot = all_data.query(\\\"放款时间 >= @oot_date & 放款时间 < @split_date\\\").assign(target=lambda x: (x[\\\"MOB1\\\"] > 7).astype(int))\\n\",\n    \"print(oot_date, split_date, train.shape, oot.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 特征筛选\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 当数据集比较大时，采样部分数据用于特征筛选\\n\",\n    \"feature_select_samples = pd.concat([\\n\",\n    \"    all_data.query(\\\"MOB1 > 15 & 放款时间 < @oot_date\\\").assign(target=lambda x: (x[\\\"MOB1\\\"] > 15).astype(int)),\\n\",\n    \"    all_data.query(\\\"MOB1 == 0 & 放款时间 < @oot_date\\\").assign(target=lambda x: (x[\\\"MOB1\\\"] > 15).astype(int)).sample(n=50000),\\n\",\n    \"]).sample(frac=1.0).reset_index(drop=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"selection = FeatureSelection(empty=0.95, iv=0.02, corr=0.6, identical=0.95, engine=\\\"toad\\\")\\n\",\n    \"feature_select_samples = selection.fit_transform(feature_select_samples.drop(columns=[\\\"授信编号\\\", \\\"渠道编码\\\", \\\"资产类型\\\", \\\"放款金额\\\", \\\"放款期数\\\", \\\"放款时间\\\", \\\"DPD\\\", \\\"MOB1\\\", \\\"MOB2\\\", \\\"MOB3\\\", \\\"CURRENT_DPD\\\", \\\"Extended\\\", \\\"户籍省份\\\"]))\\n\",\n    \"print(feature_select_samples.select_dtypes(object).columns, feature_select_samples.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cat_rules = {}\\n\",\n    \"for c in feature_select_samples.select_dtypes(object).columns:\\n\",\n    \"    cat_rules[c] = [[c] for c in feature_select_samples[c].unique()] + [[np.nan]]\\n\",\n    \"\\n\",\n    \"combiner = Combiner(method=\\\"mdlp\\\", max_n_bins=5, min_prebin_size=0.01, min_bin_size=0.025, gamma=0.01, target=\\\"target\\\", adj_rules=cat_rules, n_jobs=-1)\\n\",\n    \"combiner.fit(feature_select_samples)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"psi_test = all_data.query(\\\"(MOB1 <= 3 | MOB1 > 15) & 放款时间 < @oot_date & 放款时间 >= '2024-11-15'\\\")\\n\",\n    \"psi_base = all_data.query(\\\"(MOB1 <= 3 | MOB1 > 15) & 放款时间 < '2024-11-15'\\\")\\n\",\n    \"\\n\",\n    \"psi_info = {}\\n\",\n    \"for c in selection.select_columns[:-1]:\\n\",\n    \"    psi_info[c] = PSI(combiner.transform(psi_test[c]), combiner.transform(psi_base[c]))\\n\",\n    \"\\n\",\n    \"psi_info = pd.Series(psi_info)\\n\",\n    \"psi_select_columns = psi_info[psi_info < 0.01].index.tolist() + [\\\"target\\\"]\\n\",\n    \"\\n\",\n    \"feature_select_samples = feature_select_samples[psi_select_columns]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"woe = WOETransformer(target=\\\"target\\\")\\n\",\n    \"woe.fit(combiner.transform(feature_select_samples))\\n\",\n    \"\\n\",\n    \"feature_select_samples_woe = woe.transform(combiner.transform(feature_select_samples))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"selection = FeatureSelection(empty=0.95, iv=0.03, corr=0.6, identical=0.95, engine=\\\"toad\\\")\\n\",\n    \"selection.fit(feature_select_samples_woe)\\n\",\n    \"\\n\",\n    \"feature_select_samples_woe = selection.transform(feature_select_samples_woe)\\n\",\n    \"feature_select_samples_woe.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import target_permutation_importances as tpi\\n\",\n    \"from xgboost import XGBClassifier\\n\",\n    \"\\n\",\n    \"wrapped_model = tpi.TargetPermutationImportancesWrapper(\\n\",\n    \"    model_cls=XGBClassifier,\\n\",\n    \"    model_cls_params={\\n\",\n    \"        'gamma': 4.594259199851212,\\n\",\n    \"        'scale_pos_weight': 31.979731342546017,\\n\",\n    \"        'learning_rate': 0.004401689684274711,\\n\",\n    \"        'n_estimators': 144,\\n\",\n    \"        'reg_alpha': 0.8735734418747836,\\n\",\n    \"        'reg_lambda': 78.80453596258465,\\n\",\n    \"        'max_depth': 5,\\n\",\n    \"        'subsample': 0.479347123407695,\\n\",\n    \"        'colsample_bytree': 0.8195699784968611,\\n\",\n    \"        'min_child_weight': 32,\\n\",\n    \"        'objective': 'binary:logistic',\\n\",\n    \"        'eval_metric': 'logloss',\\n\",\n    \"        'verbosity': 0,\\n\",\n    \"        'importance_type': 'gain',\\n\",\n    \"        'booster': 'gbtree',\\n\",\n    \"        # \\\"device\\\": \\\"cuda\\\",\\n\",\n    \"    },\\n\",\n    \"    num_actual_runs=10,\\n\",\n    \"    num_random_runs=20,\\n\",\n    \"    shuffle_feature_order=True,\\n\",\n    \"    permutation_importance_calculator=tpi.compute_permutation_importance_by_subtraction,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"wrapped_model.fit(feature_select_samples_woe.drop(\\\"target\\\", axis=1), feature_select_samples_woe[\\\"target\\\"])\\n\",\n    \"select_columns = list(set(wrapped_model.feature_importances_df.sort_values(\\\"importance\\\", ascending=False).query(\\\"importance >= 0.002\\\")[\\\"feature\\\"].tolist())) + [\\\"target\\\"]\\n\",\n    \"wrapped_model.feature_importances_df.sort_values(\\\"importance\\\", ascending=False).query(\\\"importance >= 0.002\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 最终训练数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train_select = train[select_columns]\\n\",\n    \"oot_select = oot[select_columns]\\n\",\n    \"\\n\",\n    \"if len(train_select.select_dtypes(object).columns) > 0:\\n\",\n    \"    woe_encoder = WOETransformer(target=\\\"target\\\")\\n\",\n    \"    woe_encoder.fit(combiner.transform(train_select[train_select.select_dtypes(object).columns.tolist() + [\\\"target\\\"]]))\\n\",\n    \"\\n\",\n    \"    for c in tqdm(train_select.select_dtypes(object).columns):\\n\",\n    \"        train_select[c] = woe_encoder.transform(combiner.transform(train_select[c]))\\n\",\n    \"        oot_select[c] = woe_encoder.transform(combiner.transform(oot_select[c]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### CATBOOST 模型训练\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.metrics import *\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"\\n\",\n    \"import optuna\\n\",\n    \"import optunahub\\n\",\n    \"from catboost import CatBoostClassifier, Pool\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def objective(trial, train, test_size=0.3):\\n\",\n    \"    target = \\\"target\\\"\\n\",\n    \"    param = dict(\\n\",\n    \"        depth=trial.suggest_int(\\\"depth\\\", 2, 5)\\n\",\n    \"         , min_data_in_leaf=trial.suggest_int('min_data_in_leaf', 128, 512, 64)\\n\",\n    \"         , num_boost_round=trial.suggest_int('num_boost_round', 64, 512)\\n\",\n    \"         , early_stopping_rounds=5\\n\",\n    \"         , l2_leaf_reg=trial.suggest_float('l2_leaf_reg', 10, 64)\\n\",\n    \"         , learning_rate=trial.suggest_float('learning_rate', 1e-4, 0.01)\\n\",\n    \"         , eval_metric=f\\\"Focal:focal_alpha={trial.suggest_float('focal_alpha', 0.1, 0.5)};focal_gamma={trial.suggest_float('focal_gamma', 1.0, 10.0)}\\\"\\n\",\n    \"         # , eval_metric=\\\"Logloss\\\"\\n\",\n    \"         # , loss_function=\\\"CrossEntropy\\\"\\n\",\n    \"         , loss_function=\\\"Logloss\\\"\\n\",\n    \"         , model_size_reg=trial.suggest_float('model_size_reg', 10, 64)\\n\",\n    \"         , subsample=trial.suggest_float('subsample', 0.6, 1.0)\\n\",\n    \"         , bagging_temperature=trial.suggest_float('bagging_temperature', 0.6, 1.0)\\n\",\n    \"         , use_best_model=True\\n\",\n    \"         , rsm=trial.suggest_float('rsm', 0.6, 1.0)\\n\",\n    \"         , scale_pos_weight=trial.suggest_float('scale_pos_weight', 0.5, 32.0)\\n\",\n    \"         # , task_type=\\\"GPU\\\"\\n\",\n    \"         # , devices=\\\"0\\\"\\n\",\n    \"    )\\n\",\n    \"    \\n\",\n    \"    _train, _test = train_test_split(train, test_size=test_size, stratify=train[target])\\n\",\n    \"    clf = CatBoostClassifier(silent=True, **param)\\n\",\n    \"\\n\",\n    \"    clf.fit(\\n\",\n    \"        _train.drop(target, axis=1),\\n\",\n    \"        _train[target],\\n\",\n    \"        eval_set=(_test.drop(target, axis=1), _test[target]),\\n\",\n    \"        # cat_features=cat_features,\\n\",\n    \"    )\\n\",\n    \"    \\n\",\n    \"    _y_train_pred = clf.predict_proba(_train.drop(target, axis=1))[:, 1]\\n\",\n    \"    _y_test_pred = clf.predict_proba(_test.drop(target, axis=1))[:, 1]\\n\",\n    \"\\n\",\n    \"    # threshold = np.percentile(_y_test_pred, 0.97)\\n\",\n    \"    # return f1_score(_train[target], _y_train_pred > threshold), f1_score(_test[target], _y_test_pred > threshold)\\n\",\n    \"\\n\",\n    \"    # score = 0.3 * KS(_y_train_pred, _train[target]) + 0.7 * KS(_y_test_pred, _test[target])\\n\",\n    \"\\n\",\n    \"    return KS(_y_train_pred, _train[target]), KS(_y_test_pred, _test[target])\\n\",\n    \"\\n\",\n    \"# 启动optuna监控页面: optuna-dashboard sqlite:///catboost.db --host=0.0.0.0\\n\",\n    \"study = optuna.create_study(directions=[\\\"maximize\\\", \\\"maximize\\\"] #\\\"minimize\\\"\\n\",\n    \"    # , study_name=\\\"catboost\\\"\\n\",\n    \"    # , sampler=optuna.samplers.TPESampler()\\n\",\n    \"    , sampler=optunahub.load_module(\\\"samplers/auto_sampler\\\").AutoSampler()\\n\",\n    \"    , storage=\\\"sqlite:///catboost.db\\\"\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"study.optimize(lambda trial: objective(trial, train_select), show_progress_bar=True, n_trials=64)\\n\",\n    \"# optuna.visualization.plot_pareto_front(study, target_names=[\\\"TRAIN KS\\\", \\\"TEST KS\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"best_params = study.trials[63].params\\n\",\n    \"param = dict(\\n\",\n    \"        depth=best_params.get(\\\"depth\\\")\\n\",\n    \"         , min_data_in_leaf=best_params.get('min_data_in_leaf')\\n\",\n    \"         , num_boost_round=best_params.get('num_boost_round')\\n\",\n    \"         , early_stopping_rounds=5\\n\",\n    \"         , l2_leaf_reg=best_params.get('l2_leaf_reg')\\n\",\n    \"         , learning_rate=best_params.get('learning_rate')\\n\",\n    \"         , eval_metric=f\\\"Focal:focal_alpha={best_params.get('focal_alpha')};focal_gamma={best_params.get('focal_gamma')}\\\"\\n\",\n    \"         # , eval_metric=\\\"Logloss\\\"\\n\",\n    \"         # , loss_function=\\\"CrossEntropy\\\"\\n\",\n    \"         , loss_function=\\\"Logloss\\\"\\n\",\n    \"         , model_size_reg=best_params.get('model_size_reg')\\n\",\n    \"         , subsample=best_params.get('subsample')\\n\",\n    \"         , bagging_temperature=best_params.get('bagging_temperature')\\n\",\n    \"         , use_best_model=True\\n\",\n    \"         , rsm=best_params.get('rsm')\\n\",\n    \"         , silent=True\\n\",\n    \"         , scale_pos_weight=best_params.get('scale_pos_weight')\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"catboost_model = CatBoostClassifier(**param, random_seed=8888)\\n\",\n    \"print(param)\\n\",\n    \"\\n\",\n    \"cat_features = train_select.select_dtypes(object).columns.tolist()\\n\",\n    \"\\n\",\n    \"catboost_model.fit(\\n\",\n    \"    train_select.drop([\\\"target\\\"], axis=1),\\n\",\n    \"    train_select[\\\"target\\\"],\\n\",\n    \"    eval_set=[\\n\",\n    \"        (train_select.drop([\\\"target\\\"], axis=1), train_select[\\\"target\\\"]),\\n\",\n    \"    ],\\n\",\n    \"    # cat_features=cat_features,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"print(KS(catboost_model.predict_proba(train_select.drop([\\\"target\\\"], axis=1))[:, 1], train_select[\\\"target\\\"]), KS(catboost_model.predict_proba(oot_select.drop([\\\"target\\\"], axis=1))[:, 1], oot_select[\\\"target\\\"]))\\n\",\n    \"\\n\",\n    \"temp = oot.assign(\\n\",\n    \"    score=lambda x: catboost_model.predict_proba(\\n\",\n    \"        x[catboost_model.feature_names_].assign(\\n\",\n    \"            客群状态1=lambda x: woe_encoder.transform(combiner.transform(x.客群状态1)),\\n\",\n    \"        )\\n\",\n    \"    )[:, 1]\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"table_oot = feature_bin_stats(\\n\",\n    \"    temp\\n\",\n    \"    , \\\"score\\\", method=\\\"mdlp\\\", rules=temp[\\\"score\\\"].quantile([0.1, 0.3, 0.5, 0.7, 0.9, 0.95, 0.97, 0.98, 0.99]).unique().tolist()\\n\",\n    \"    , overdue=[f\\\"MOB{mob}\\\"], dpd=[dpd, 3, 0], max_n_bins=10, min_bin_size=0.001, return_cols=[\\\"坏样本数\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\", \\\"分档KS值\\\"]\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"ks_plot(temp[\\\"score\\\"], temp[\\\"target\\\"], figsize=(10, 5), title=\\\"OOT评分\\\");\\n\",\n    \"bin_plot(table_oot, desc=f\\\"OOT评分\\\");\\n\",\n    \"display(table_oot)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### XGBOOST 模型训练\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.metrics import *\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"\\n\",\n    \"import optuna\\n\",\n    \"import optunahub\\n\",\n    \"import xgboost as xgb\\n\",\n    \"from xgboost import XGBClassifier\\n\",\n    \"\\n\",\n    \"def objective(trial, train, test_size=0.2):\\n\",\n    \"    # 设置大范围搜索参数\\n\",\n    \"    # param = {\\n\",\n    \"    #     \\\"objective\\\": \\\"binary:logistic\\\", # for binary classification\\n\",\n    \"    #     \\\"eval_metric\\\": \\\"auc\\\", # for log loss\\n\",\n    \"    #     \\\"verbosity\\\": 0,\\n\",\n    \"    #     \\\"booster\\\": \\\"gbtree\\\",\\n\",\n    \"    #     \\\"gamma\\\": trial.suggest_float(\\\"gamma\\\", 0.0, 32.0),\\n\",\n    \"    #     \\\"scale_pos_weight\\\": trial.suggest_float(\\\"scale_pos_weight\\\", 16.0, 32.0),\\n\",\n    \"    #     \\\"learning_rate\\\": trial.suggest_float(\\\"learning_rate\\\", 0.0001, 0.01),\\n\",\n    \"    #     \\\"n_estimators\\\": trial.suggest_int(\\\"n_estimators\\\", 32, 256, 16),\\n\",\n    \"    #     \\\"reg_alpha\\\": trial.suggest_float(\\\"reg_alpha\\\", 0.0, 1.0),\\n\",\n    \"    #     \\\"reg_lambda\\\": trial.suggest_float(\\\"reg_lambda\\\", 32.0, 128.0),\\n\",\n    \"    #     \\\"max_depth\\\": trial.suggest_int(\\\"max_depth\\\", 3, 5),\\n\",\n    \"    #     \\\"subsample\\\": trial.suggest_float(\\\"subsample\\\", 0.35, 0.85),\\n\",\n    \"    #     \\\"colsample_bytree\\\": trial.suggest_float(\\\"colsample_bytree\\\", 0.4, 0.9),\\n\",\n    \"    #     \\\"min_child_weight\\\": trial.suggest_int(\\\"min_child_weight\\\", 8, 256, 4),\\n\",\n    \"    # }\\n\",\n    \"\\n\",\n    \"    # 基于最优参数缩小范围进一步搜索\\n\",\n    \"    param = {\\n\",\n    \"        \\\"objective\\\": \\\"binary:logistic\\\",\\n\",\n    \"        \\\"eval_metric\\\": \\\"auc\\\",\\n\",\n    \"        \\\"verbosity\\\": 0,\\n\",\n    \"        \\\"booster\\\": \\\"gbtree\\\",\\n\",\n    \"        \\\"gamma\\\": trial.suggest_float(\\\"gamma\\\", 4, 32.0),\\n\",\n    \"        \\\"scale_pos_weight\\\": trial.suggest_float(\\\"scale_pos_weight\\\", 22, 32.0),\\n\",\n    \"        \\\"learning_rate\\\": trial.suggest_float(\\\"learning_rate\\\", 0.004, 0.01),\\n\",\n    \"        \\\"n_estimators\\\": trial.suggest_int(\\\"n_estimators\\\", 90, 150),\\n\",\n    \"        \\\"reg_alpha\\\": trial.suggest_float(\\\"reg_alpha\\\", 0.3, 0.9),\\n\",\n    \"        \\\"reg_lambda\\\": trial.suggest_float(\\\"reg_lambda\\\", 45.0, 80.0),\\n\",\n    \"        \\\"max_depth\\\": trial.suggest_int(\\\"max_depth\\\", 4, 6),\\n\",\n    \"        \\\"subsample\\\": trial.suggest_float(\\\"subsample\\\", 0.4, 0.8),\\n\",\n    \"        \\\"colsample_bytree\\\": trial.suggest_float(\\\"colsample_bytree\\\", 0.5, 0.85),\\n\",\n    \"        \\\"min_child_weight\\\": trial.suggest_int(\\\"min_child_weight\\\", 32, 128),\\n\",\n    \"        \\\"device\\\": \\\"cuda\\\",\\n\",\n    \"    }\\n\",\n    \"    \\n\",\n    \"    X_train, X_test, y_train, y_test = train_test_split(train.drop(columns=\\\"target\\\"), train[\\\"target\\\"], test_size=test_size, stratify=train[\\\"target\\\"])\\n\",\n    \"\\n\",\n    \"    dtrain = xgb.DMatrix(X_train, label=y_train)\\n\",\n    \"    dtest = xgb.DMatrix(X_test, label=y_test)\\n\",\n    \"    \\n\",\n    \"    # pruning_callback = optuna.integration.XGBoostPruningCallback(trial, \\\"test-auc\\\")\\n\",\n    \"    # xgb_classifier = xgb.cv(param, dtrain, callbacks=[pruning_callback])\\n\",\n    \"    # xgb_classifier = xgb.train(param, dtrain, evals=[(dtrain, \\\"train\\\"), (dtest, \\\"test\\\")], callbacks=[pruning_callback])\\n\",\n    \"    xgb_classifier = xgb.train(param, dtrain, evals=[(dtrain, \\\"train\\\"), (dtest, \\\"test\\\")], early_stopping_rounds=10)\\n\",\n    \"\\n\",\n    \"    # pruning_callback = optuna.integration.XGBoostPruningCallback(trial, 'validation_0-auc')\\n\",\n    \"    # xgb_classifier = XGBClassifier(**param) #, callbacks=[pruning_callback])\\n\",\n    \"    # xgb_classifier.fit(X_train, y_train)\\n\",\n    \"\\n\",\n    \"    y_train_proba = xgb_classifier.predict(dtrain)\\n\",\n    \"    y_test_proba = xgb_classifier.predict(dtest)\\n\",\n    \"    \\n\",\n    \"    # y_train_proba = xgb_classifier.predict_proba(X_train)[:, 1]\\n\",\n    \"    # y_test_proba = xgb_classifier.predict_proba(X_test)[:, 1]\\n\",\n    \"\\n\",\n    \"    # return 0.3 * KS(y_train_proba, y_train) + 0.7 * KS(y_test_proba, y_test)\\n\",\n    \"\\n\",\n    \"    temp = pd.DataFrame({\\\"score\\\": y_test_proba, \\\"target\\\": y_test})\\n\",\n    \"    table = feature_bin_stats(temp, \\\"score\\\", rules=temp[\\\"score\\\"].quantile([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.97, 0.98, 0.99]).tolist(), target=\\\"target\\\", return_cols=[\\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\"])\\n\",\n    \"    coeffs = np.polyfit([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.97, 0.98, 0.99], table[\\\"LIFT值\\\"].values.tolist(), 2)\\n\",\n    \"\\n\",\n    \"    # trial.set_user_attr(\\\"auc-error\\\", abs(AUC(y_train_proba, y_train) - AUC(y_test_proba, y_test)))\\n\",\n    \"    # trial.set_user_attr(\\\"ks train\\\", KS(y_train_proba, y_train))\\n\",\n    \"    # trial.set_user_attr(\\\"ks test\\\", KS(y_test_proba, y_test))\\n\",\n    \"\\n\",\n    \"    return abs(coeffs[0]) / 3.0, test_size * KS(y_train_proba, y_train) + (1 - test_size) * KS(y_test_proba, y_test)\\n\",\n    \"\\n\",\n    \"    # # threshold = np.percentile(y_test_proba, 0.97)\\n\",\n    \"    # # return 0.3 * KS(y_train_proba, y_train) + 0.7 * KS(y_test_proba, y_test), 0.3 * f1_score(y_train, y_train_proba > threshold) + 0.7 * f1_score(y_test, y_test_proba > threshold)\\n\",\n    \"    # # return f1_score(y_train, y_train_proba > threshold), f1_score(y_test, y_test_proba > threshold), KS(y_test_proba, y_test)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# 启动optuna监控页面: optuna-dashboard sqlite:///xgboost.db --host=0.0.0.0\\n\",\n    \"study = optuna.create_study(\\n\",\n    \"    directions=[\\\"maximize\\\", \\\"maximize\\\"] #\\\"minimize\\\"\\n\",\n    \"    # , study_name=\\\"xgboost\\\"\\n\",\n    \"    # , sampler=optuna.samplers.TPESampler()\\n\",\n    \"    , sampler=optunahub.load_module(\\\"samplers/auto_sampler\\\").AutoSampler()\\n\",\n    \"    # , pruner=optuna.pruners.MedianPruner(n_warmup_steps=10)\\n\",\n    \"    , storage=\\\"sqlite:///xgboost.db\\\"\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# 指定某些超参数点搜索\\n\",\n    \"study.enqueue_trial(\\n\",\n    \"    {\\n\",\n    \"        'gamma': 4.594259199851212,\\n\",\n    \"        'scale_pos_weight': 31.979731342546017,\\n\",\n    \"        'learning_rate': 0.004401689684274711,\\n\",\n    \"        'n_estimators': 144,\\n\",\n    \"        'reg_alpha': 0.8735734418747836,\\n\",\n    \"        'reg_lambda': 78.80453596258465,\\n\",\n    \"        'max_depth': 5,\\n\",\n    \"        'subsample': 0.479347123407695,\\n\",\n    \"        'colsample_bytree': 0.8195699784968611,\\n\",\n    \"        'min_child_weight': 32,\\n\",\n    \"        'objective': 'binary:logistic',\\n\",\n    \"        'eval_metric': 'auc',\\n\",\n    \"        'verbosity': 0,\\n\",\n    \"        'booster': 'gbtree',\\n\",\n    \"        \\\"device\\\": \\\"cuda\\\",\\n\",\n    \"    }\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"study.enqueue_trial(\\n\",\n    \"    {\\n\",\n    \"        'objective': 'binary:logistic',\\n\",\n    \"        'eval_metric': 'auc',\\n\",\n    \"        'verbosity': 0,\\n\",\n    \"        'booster': 'gbtree',\\n\",\n    \"        'gamma': 20.618639646365324,\\n\",\n    \"        'scale_pos_weight': 28.46817520972572,\\n\",\n    \"        'learning_rate': 0.007842004642961626,\\n\",\n    \"        'n_estimators': 115,\\n\",\n    \"        'reg_alpha': 0.7437452635785256,\\n\",\n    \"        'reg_lambda': 50.96599548821085,\\n\",\n    \"        'max_depth': 6,\\n\",\n    \"        'subsample': 0.7044268776337913,\\n\",\n    \"        'colsample_bytree': 0.7708775556331752,\\n\",\n    \"        'min_child_weight': 72,\\n\",\n    \"        \\\"device\\\": \\\"cuda\\\",\\n\",\n    \"    }\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"study.optimize(lambda trial: objective(trial, train_select, test_size=0.1), n_trials=64)\\n\",\n    \"# optuna.visualization.plot_pareto_front(study, target_names=[\\\"SLOPE\\\", \\\"KS\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"trials_point = [5]\\n\",\n    \"for best_params in [study.trials[i] for i in trials_point]:\\n\",\n    \"    param = {\\n\",\n    \"        \\\"objective\\\": \\\"binary:logistic\\\",\\n\",\n    \"        \\\"eval_metric\\\": \\\"auc\\\",\\n\",\n    \"        \\\"verbosity\\\": 0,\\n\",\n    \"        \\\"booster\\\": \\\"gbtree\\\",\\n\",\n    \"        \\\"gamma\\\": best_params.params.get(\\\"gamma\\\"),\\n\",\n    \"        \\\"scale_pos_weight\\\": best_params.params.get(\\\"scale_pos_weight\\\"),\\n\",\n    \"        \\\"learning_rate\\\": best_params.params.get(\\\"learning_rate\\\"),\\n\",\n    \"        \\\"n_estimators\\\": best_params.params.get(\\\"n_estimators\\\"),\\n\",\n    \"        \\\"reg_alpha\\\": best_params.params.get(\\\"reg_alpha\\\"),\\n\",\n    \"        \\\"reg_lambda\\\": best_params.params.get(\\\"reg_lambda\\\"),\\n\",\n    \"        \\\"max_depth\\\": best_params.params.get(\\\"max_depth\\\"),\\n\",\n    \"        \\\"subsample\\\": best_params.params.get(\\\"subsample\\\"),\\n\",\n    \"        \\\"colsample_bytree\\\": best_params.params.get(\\\"colsample_bytree\\\"),\\n\",\n    \"        \\\"min_child_weight\\\": best_params.params.get(\\\"min_child_weight\\\"),\\n\",\n    \"    }\\n\",\n    \"\\n\",\n    \"    param.update({\\\"objective\\\": \\\"binary:logistic\\\", \\\"eval_metric\\\": \\\"logloss\\\", \\\"verbosity\\\": 0, \\\"booster\\\": \\\"gbtree\\\", \\\"device\\\": \\\"cuda\\\"})\\n\",\n    \"\\n\",\n    \"    print(\\\">\\\" * 10 + f\\\"\\\\tcurrent point: {best_params.number}\\\\tparams: {str(param)}\\\\t\\\" + \\\"<\\\" * 10)\\n\",\n    \"    \\n\",\n    \"    dtrain = xgb.DMatrix(train_select.drop(\\\"target\\\", axis=1), label=train_select[\\\"target\\\"])\\n\",\n    \"    dtest = xgb.DMatrix(oot_select.drop(\\\"target\\\", axis=1), label=oot_select[\\\"target\\\"])\\n\",\n    \"    \\n\",\n    \"    xgb_classifier = xgb.train(param, dtrain)\\n\",\n    \"    \\n\",\n    \"    print(KS(xgb_classifier.predict(dtrain), train_select[\\\"target\\\"]), KS(xgb_classifier.predict(dtest), oot_select[\\\"target\\\"]))\\n\",\n    \"    \\n\",\n    \"    temp = oot.assign(\\n\",\n    \"        score=lambda x: xgb_classifier.predict(\\n\",\n    \"            xgb.DMatrix(\\n\",\n    \"                x[train_select.drop(\\\"target\\\", axis=1).columns.tolist()].assign(\\n\",\n    \"                    客群状态1=lambda x: woe_encoder.transform(combiner.transform(x.客群状态1)),\\n\",\n    \"                )\\n\",\n    \"            )\\n\",\n    \"        )\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"    # 查看尾部区分度\\n\",\n    \"    display(feature_bin_stats(\\n\",\n    \"        temp\\n\",\n    \"        , \\\"score\\\", method=\\\"step\\\", rules=temp[\\\"score\\\"].quantile([0.1, 0.3, 0.5, 0.7, 0.9, 0.95, 0.97, 0.98, 0.99]).unique().tolist()\\n\",\n    \"        , overdue=[f\\\"MOB{mob}\\\"], dpd=[dpd, 3, 0], max_n_bins=10, min_bin_size=0.001, return_cols=[\\\"坏样本数\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\", \\\"分档KS值\\\"]\\n\",\n    \"    ))\\n\",\n    \"\\n\",\n    \"    # 通过自动分箱查看整体评分效果\\n\",\n    \"    display(feature_bin_stats(\\n\",\n    \"        temp\\n\",\n    \"        , \\\"score\\\", method=\\\"step\\\"\\n\",\n    \"        , overdue=[f\\\"MOB{mob}\\\"], dpd=[dpd, 3, 0], max_n_bins=10, min_bin_size=0.01, return_cols=[\\\"坏样本数\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\", \\\"分档KS值\\\"]\\n\",\n    \"    ))\\n\",\n    \"\\n\",\n    \"    ks_plot(temp[\\\"score\\\"], temp[\\\"target\\\"], figsize=(10, 5), title=f\\\"TRAIL {best_params.number} OOT评分\\\");\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 封装评分卡模型\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import toad\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from sklearn.base import BaseEstimator, TransformerMixin\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class ScorecardPredict(BaseEstimator, TransformerMixin):\\n\",\n    \"\\n\",\n    \"    def __init__(self, model, bad_rate=0.02, feature_names_in_=None):\\n\",\n    \"        from scorecardpipeline import StandardScoreTransformer\\n\",\n    \"        self.model = model\\n\",\n    \"        self.feature_names_in_ = feature_names_in_\\n\",\n    \"        self.classes_ = np.array([0])\\n\",\n    \"        self.status_1_map = {\\\"10\\\": 0.30365285, \\\"00\\\": -0.14935851, \\\"01\\\": -0.0913462}\\n\",\n    \"        self.bad_rate = bad_rate\\n\",\n    \"        self.scorecard = StandardScoreTransformer(base_score=100, pdo=200, bad_rate=self.bad_rate, greater_is_better=True, down_lmt=0, up_lmt=1000)\\n\",\n    \"\\n\",\n    \"    def processing_data(self, x):\\n\",\n    \"        import xgboost as xgb\\n\",\n    \"        return xgb.DMatrix(x.assign(客群状态1=lambda x: x[\\\"客群状态1\\\"].map(self.status_1_map))[self.feature_names_in_])\\n\",\n    \"\\n\",\n    \"    def predict_proba(self, x):\\n\",\n    \"        return self.scorecard.transform(self.model.predict(self.processing_data(x)).reshape(-1, 1))[:, 0]\\n\",\n    \"\\n\",\n    \"    def predict(self, x):\\n\",\n    \"        return self.predict_proba(x)\\n\",\n    \"\\n\",\n    \"    def transform(self, x, y=None):\\n\",\n    \"        return self.predict_proba(x)\\n\",\n    \"\\n\",\n    \"    def fit(self, x=None, y=None):\\n\",\n    \"        self.scorecard.fit(self.model.predict(self.processing_data(x)).reshape(-1, 1))\\n\",\n    \"        return self\\n\",\n    \"    \\n\",\n    \"    def __sklearn_is_fitted__(self):\\n\",\n    \"        return True\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"scorecard = ScorecardPredict(xgb_classifier, feature_names_in_=train_select.drop(\\\"target\\\", axis=1).columns.tolist(), bad_rate=train_select[\\\"target\\\"].mean())\\n\",\n    \"scorecard.fit(oot)\\n\",\n    \"\\n\",\n    \"feature_bin_stats(\\n\",\n    \"    oot.assign(\\n\",\n    \"        模型分V2=lambda x: scorecard.predict_proba(x),\\n\",\n    \"    )\\n\",\n    \"    , \\\"模型分V2\\\", method=\\\"mdlp\\\", overdue=[f\\\"MOB{mob}\\\"], dpd=[dpd, 3, 0], max_n_bins=10, min_bin_size=0.01, return_cols=[\\\"坏样本数\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\", \\\"分档KS值\\\"]\\n\",\n    \"    # , desc=f\\\"{oot_date} ~ {split_date} 样本数: {len(oot)} FPD7+: {oot['target'].sum()}\\\"\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 模型报告输出\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib agg\\n\",\n    \"# 切换为agg后在jupyter中不会显示图\\n\",\n    \"mob, dpd, oot_days = 1, 15, 15\\n\",\n    \"oot_date = (date.today() - relativedelta(months=mob, days=dpd + oot_days)).strftime(\\\"%Y-%m-%d\\\")\\n\",\n    \"split_date = (date.today() - relativedelta(months=mob, days=dpd)).strftime(\\\"%Y-%m-%d\\\")\\n\",\n    \"\\n\",\n    \"data_train = all_data.assign(\\n\",\n    \"    模型分V2=lambda x: scorecard.transform(x)\\n\",\n    \").query(\\\"放款时间 < @oot_date\\\").assign(target=lambda x: (x[f\\\"MOB{mob}\\\"] > dpd).astype(int))\\n\",\n    \"\\n\",\n    \"feature_maps = {}\\n\",\n    \"percent_cols=[\\\"样本占比\\\", \\\"好样本占比\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"坏账改善\\\", \\\"累积LIFT值\\\", \\\"累积坏账改善\\\"]\\n\",\n    \"condition_cols=[\\\"坏样本率\\\", \\\"LIFT值\\\"]\\n\",\n    \"merge_column=[\\\"指标名称\\\", \\\"指标含义\\\"]\\n\",\n    \"analyze_features = ['模型分V2']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"writer = ExcelWriter(system=\\\"windows\\\")\\n\",\n    \"\\n\",\n    \"rules = {\\\"模型分V2\\\": list(np.unique(np.quantile(scorecard.transform(oot), [0.01, 0.02, 0.03, 0.05, 0.1, 0.2, 0.4, 0.6, 0.8])))}\\n\",\n    \"auto_data_testing_report(\\n\",\n    \"    data_train\\n\",\n    \"    , features=analyze_features\\n\",\n    \"    , date=\\\"放款时间\\\"\\n\",\n    \"    , overdue=[f\\\"MOB{mob}\\\"]\\n\",\n    \"    , dpd=[dpd, 7, 3, 0]\\n\",\n    \"    , freq=\\\"W\\\"\\n\",\n    \"    , data_summary_comment=f\\\"训练样本: 放款时间 {data_train['放款时间'].min().strftime('%Y-%m-%d')} ~ {data_train['放款时间'].max().strftime('%Y-%m-%d')}，剔除 MOB1 > 3 且 <= 15 的样本，保证全部 MOB{mob} DPD{dpd}+ 有表现\\\"\\n\",\n    \"    , excel_writer=writer\\n\",\n    \"    , sheet=f\\\"TRAIN MOB{mob} DPD{dpd}+\\\"\\n\",\n    \"    , suffix=f\\\"训练样本集\\\"\\n\",\n    \"    , bin_params={\\\"method\\\": \\\"mdlp\\\", \\\"min_bin_size\\\": 0.005, \\\"rules\\\": rules, \\\"max_n_bins\\\": 10, \\\"return_cols\\\": [\\\"坏样本数\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\", \\\"坏账改善\\\", \\\"分档KS值\\\", \\\"指标IV值\\\"]}\\n\",\n    \"    , feature_map=feature_maps\\n\",\n    \"    , pictures=['bin', 'ks']\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"data_oot = all_data.assign(\\n\",\n    \"    模型分V2=lambda x: scorecard.transform(x)\\n\",\n    \").query(\\\"(放款时间 >= @oot_date) & (放款时间 < @split_date)\\\").assign(target=lambda x: (x[f\\\"MOB{mob}\\\"] > dpd).astype(int))\\n\",\n    \"\\n\",\n    \"end_row, end_col = auto_data_testing_report(\\n\",\n    \"    data_oot\\n\",\n    \"    , features=analyze_features\\n\",\n    \"    , date=\\\"放款时间\\\"\\n\",\n    \"    , overdue=[f\\\"MOB{mob}\\\"]\\n\",\n    \"    , dpd=[dpd, 7, 3, 0]\\n\",\n    \"    , freq=\\\"W\\\"\\n\",\n    \"    , data_summary_comment=f\\\"训练样本: 放款时间 {data_train['放款时间'].min().strftime('%Y-%m-%d')} ~ {data_train['放款时间'].max().strftime('%Y-%m-%d')}，不剔除灰，保证全部 MOB{mob} DPD{dpd}+ 有表现\\\"\\n\",\n    \"    , excel_writer=writer\\n\",\n    \"    , sheet=f\\\"OOT MOB{mob} DPD{dpd}+\\\"\\n\",\n    \"    , suffix=f\\\"跨时间验证集\\\"\\n\",\n    \"    , bin_params={\\\"method\\\": \\\"mdlp\\\", \\\"min_bin_size\\\": 0.005, \\\"rules\\\": rules, \\\"max_n_bins\\\": 5, \\\"return_cols\\\": [\\\"坏样本数\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\", \\\"坏账改善\\\", \\\"分档KS值\\\", \\\"指标IV值\\\"]}\\n\",\n    \"    , feature_map=feature_maps\\n\",\n    \"    , pictures=['bin', 'ks'] \\n\",\n    \")\\n\",\n    \"\\n\",\n    \"oot_table = feature_bin_stats(\\n\",\n    \"    data_oot\\n\",\n    \"    , \\\"模型分V2\\\", method=\\\"mdlp\\\", overdue=[\\\"MOB1\\\"], dpd=[7, 3, 0], max_n_bins=10, min_bin_size=0.01, return_cols=[\\\"坏样本数\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\", \\\"坏账改善\\\", \\\"分档KS值\\\", \\\"指标IV值\\\"]\\n\",\n    \"    , desc=f\\\"\\\"\\n\",\n    \")\\n\",\n    \"end_row, end_col = dataframe2excel(oot_table, writer, sheet_name=f\\\"OOT MOB{mob} DPD{dpd}+\\\", condition_color=\\\"F76E6C\\\", condition_cols=[\\\"坏样本率\\\", \\\"LIFT值\\\"], percent_cols=[\\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\"], title=\\\"MDLP自动分箱\\\", start_row=end_row + 2)\\n\",\n    \"\\n\",\n    \"# 模型上线需求\\n\",\n    \"test_units = data_oot[[\\\"授信编号\\\", \\\"模型分V2\\\"] + select_columns[:-1]].sample(n=100).reset_index(drop=True)\\n\",\n    \"feature_importances_dict = dict(zip(scorecard.model.feature_names_in_, scorecard.model.feature_importances_))\\n\",\n    \"\\n\",\n    \"features = []\\n\",\n    \"for i, f in enumerate(sorted(scorecard.feature_names_in_)):\\n\",\n    \"    features.append({\\\"数据供应商\\\": \\\"百融\\\" if f.startswith(\\\"als\\\") else \\\"其他\\\"\\n\",\n    \"                     , \\\"产品名称\\\": \\\"借贷意向验证\\\" if f.startswith(\\\"als\\\") else \\\"其他\\\"\\n\",\n    \"                     , \\\"特征名称\\\": f\\n\",\n    \"                     , \\\"特征含义\\\": feature_maps.get(f)\\n\",\n    \"                     , \\\"字段类型\\\": \\\"float\\\" if f not in data_oot[select_columns[:-1]].select_dtypes(object).columns else \\\"string\\\"\\n\",\n    \"                     , \\\"缺失值处理\\\": \\\"空字符串\\\\缺失值 转为 空值\\\"\\n\",\n    \"                     , \\\"特征贡献\\\": feature_importances_dict[f]\\n\",\n    \"                     , \\\"备注\\\": \\\"\\\"\\n\",\n    \"                    })\\n\",\n    \"\\n\",\n    \"features_info = pd.DataFrame(features).sort_values(\\\"特征贡献\\\", ascending=False).reset_index(drop=True).reset_index(names=\\\"序号\\\")\\n\",\n    \"end_row, end_col = dataframe2excel(features_info, writer, \\\"模型上线需求\\\", percent_cols=[\\\"特征贡献\\\"], title=\\\"入模变量信息\\\", condition_cols=[\\\"特征贡献\\\"], start_row=2)\\n\",\n    \"end_row, end_col = dataframe2excel(test_units, writer, \\\"模型上线需求\\\", title=\\\"测试用例\\\", start_row=end_row + 2)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"writer.save(f\\\"model_report/MOB{mob}DPD{dpd}建模报告-{date.today().strftime('%Y%m%d')}.xlsx\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"language_info\": {\n   \"name\": \"python\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "examples/loan_replan.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\n@Time    : 2024/3/22 15:25\n@Author  : itlubber\n@Site    : itlubber.art\n\"\"\"\nimport pandas as pd\n\n\nprincipal = 5000.00  # 本金\nannual_rate = 0.36  # 年化利率\nterm_rate = annual_rate / 12  # 月利率\nday_rate = annual_rate / 12 / 30  # 日利率\n\n\nrepayPattern = {\n    \"MCAT\": \"随借随还,按日计息\",\n    \"BFTO\": \"利随本清,按日计息\",\n    \"MIRD\": \"按月付息到期还本\",\n    \"EPEI\": \"等本等息,按月付息\",  # 对借款人最不利\n    \"MCEP\": \"等额本金,按月付息\",  # 借款总利息最低\n    \"MCEI\": \"等额等息,按月付息\"\n}\n\n\n# \"EPEI\": \"等本等息,按月付息\"               每期利息和本金一样,最后一期本金为上一期的剩余本金，最后一期利息为总利息-已还利息\ndef get_EPEI(num):\n    line = [[\"期数\", \"还款本息\", \"当期本金\", \"当期利息\", \"剩余本金\", \"还款方式\"]]\n    term_int = principal * term_rate  # 每期还利息（2位小数）\n    total_int = principal * term_rate * num  # 总利息（2位小数）\n    term_prin = principal / num  # 每期还本金（2位小数）\n    prin = principal  # 初始化剩余本金为借款本金\n    for i in range(1, num + 1):\n        # 如果是最后一期，还款本金为上一期的剩余本金; 利息为总利息-已还利息\n        if i == num:\n            term_prin = prin\n            term_int = total_int - (term_int * (num - 1))\n        term_amt = term_prin + term_int\n        prin = prin - term_prin  # 剩余本金=上期剩余本金-当期还本金\n        line.append([i, term_amt, term_prin, term_int, prin, 'EPEI'])\n    print('等本等息总利息为：', total_int)\n    print(pd.DataFrame(line))\n\n\n# \"MIRD\": \"按月付息到期还本\"      最后一期还本金，每期利息固定\ndef get_MIRD(num):\n    line = [[\"期数\", \"还款本息\", \"当期本金\", \"当期利息\", \"剩余本金\", \"还款方式\"]]\n    term_int = principal * term_rate\n    term_prin = 0.0\n    total_int = principal * term_rate * num  # 总利息（2位小数）\n    prin = principal\n    for i in range(1, num + 1):\n        # 如果是最后一期，还款本金为上一期的剩余本金; 利息为总利息-已还利息\n        if i == num:\n            term_prin = prin\n            term_int = total_int - (term_int * (num - 1))\n        term_amt = term_prin + term_int\n        prin = prin - term_prin  # 剩余本金=上期剩余本金-当期还本金\n        line.append([i, term_amt, term_prin, term_int, prin, 'MIRD'])\n    print('按月付息到期还本总利息为：', total_int)\n    print(pd.DataFrame(line))\n\n\n# \"等额本金,按月付息\"\ndef get_MCEP(num):\n    line = [[\"期数\", \"还款本息\", \"当期本金\", \"当期利息\", \"剩余本金\", \"还款方式\"]]\n    ## 每月本金相同，利息递减，相当于剩余本金的利息,每期利息固定：上一期本金*利率\n    term_prin = principal / num  # 每期还本金（2位小数）\n    prin = principal\n    total_int = 0.0\n    for i in range(1, num + 1):\n        term_int = prin * term_rate  # 每期利息固定：上一期本金*利率\n        # 如果是最后一期，还款本金为上一期的剩余本金;\n        if i == num:\n            term_prin = prin\n        term_amt = term_prin + term_int\n        prin = prin - term_prin  # 剩余本金=上期剩余本金-当期还本金\n        line.append([i, term_amt, term_prin, term_int, prin, 'MCEP'])\n        total_int = total_int + term_int\n    print('等额本金总利息为：', total_int)\n    print(pd.DataFrame(line))\n\n\n# \"MCEI\": \"等额本息,按月付息\"\ndef get_MCEI(num):\n    line = [[\"期数\", \"还款本息\", \"当期本金\", \"当期利息\", \"剩余本金\", \"还款方式\"]]\n    # 本金+利息保持相同，本金逐月递增，利息逐月递减，月还款数不变。\n    term_amt = (principal * term_rate * (1 + term_rate) ** num) / ((1 + term_rate) ** num - 1)  # 每期还款总额       **是幂运算\n    prin = principal\n    total_int = 0.0\n    for i in range(1, num + 1):\n        term_int = prin * term_rate  # 每期利息计算固定：上一期本金*利率\n        term_prin = term_amt - term_int\n        # 如果是最后一期，还款本金为上一期的剩余本金;\n        if i == num:\n            term_prin = prin\n        term_amt = term_prin + term_int\n        prin = prin - term_prin  # 剩余本金=上期剩余本金-当期还本金\n        line.append([i, term_amt, term_prin, term_int, prin, 'MCEI'])\n        total_int = total_int + term_int\n    print('等额本息总利息为：', total_int)\n    print(pd.DataFrame(line))\n\n\n# \"MCAT\": \"随借随还,按日计息\"\ndef get_MCAT(num, pay):\n    line = [[\"期数\", \"还款本息\", \"当期本金\", \"当期利息\", \"剩余本金\", \"还款方式\"]]\n    day_int = pay * day_rate * num\n    repay_amt = day_int + pay\n    re_prin = principal - pay\n    if pay > principal:\n        print(\"还款金额:\", pay, \"不能大于本金:\", principal)\n    else:\n        line.append([1, repay_amt, pay, day_int, re_prin, 'MCAT'])\n        print('随借随还总利息为：', day_int)\n        print(pd.DataFrame(line))\n\n\n# \"BFTO\": \"利随本清,按日计息\"\ndef get_BFTO(num):\n    line = [[\"期数\", \"还款本息\", \"当期本金\", \"当期利息\", \"剩余本金\", \"还款方式\"]]\n    day_int = principal * day_rate * num\n    repay_amt = day_int + principal\n    line.append([1, repay_amt, principal, day_int, 0.0, 'BFTO'])\n    print('利随本清总利息为：', day_int)\n    print(pd.DataFrame(line))\n\n\nif __name__ == '__main__':\n    get_EPEI(6)  # 等本等息,按月付息\n    get_MCEP(6)  # 等额本金,按月付息\n    get_MCEI(6)  # 等额本息,按月付息\n    # get_MIRD(24)  # 到期还本,按月付息\n    # get_MCAT(720, 195)  # 随借随还,按日计息\n    # get_BFTO(720)  # 利随本清,按日计息\n"
  },
  {
    "path": "examples/model_report/auto_eda.html",
    "content": "\n\n\n    \n    \n        \n            \n        \n        \n\n    \n\n\n\n\n
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坏客户
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\n            
\n             
\n                
0
\n                
DUPLICATES
\n                
0
\n            
\n            
\n                
48.6 kb
\n                
RAM
\n                
108.2 kb
\n            
\n            
\n                
\n                    21\n                
\n                
FEATURES
\n                
\n                            21\n                
\n            
\n            
\n                
18
\n                
CATEGORICAL
\n                
18
\n            
\n             
\n                
3
\n                
NUMERICAL
\n                
3
\n            
\n            
\n                
0
\n                
TEXT
\n                
0
\n            
\n            \n                \n        
\n    
\n
\n\n\n\n        
\n            \n            \n            \n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        1\n    
\n    
\n    status_of_existing_checking_account\n    
\n        
\n            
VALUES:
\n            
\n                234\n            
\n            
\n                (78%)\n            
\n                
\n                    567\n                
\n                
\n                    (81%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    66\n            
\n            
\n                    
\n                    (22%)\n            
\n                
\n                        133\n                
\n                
\n                        
\n                        (19%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                4\n            
\n            
\n                (1%)\n            
\n                
\n                    4\n                
\n                
\n                    (<1%)\n                
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        2\n    
\n    
\n    duration_in_month\n\n    
\n        
\n            
VALUES:
\n            
\n                237\n            
\n            
\n                (79%)\n            
\n                
\n                    548\n                
\n                
\n                    (78%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    63\n            
\n            
\n                    
\n                    (21%)\n            
\n                
\n                        152\n                
\n                
\n                        
\n                        (22%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                24\n            
\n            
\n                (8%)\n            
\n                
\n                    29\n                
\n                
\n                    (4%)\n                
\n        
\n            
\n            
\n            
\n                
ZEROES:
\n                
\n                        ---\n                
\n                
\n\n                
\n                 
\n                        ---\n                
\n                
\n\n                
\n            
\n    
\n\n    
\n        
\n            
MAX
\n            
72.0
\n                
60.0
\n        
\n        
\n            
95%
\n            
48.0
\n                
42.0
\n        
\n        
\n            
Q3
\n            
36.0
\n                
24.0
\n        
\n        
\n            
AVG
\n            
25.3
\n                
19.6
\n        
\n        
\n            
MEDIAN
\n            
24.0
\n                
18.0
\n        
\n        
\n            
Q1
\n            
14.0
\n                
12.0
\n        
\n        
\n            
5%
\n            
9.0
\n                
6.0
\n        
\n        
\n            
MIN
\n            
6.0
\n                
4.0
\n        
\n    
\n    
\n        
\n                
RANGE
\n                
66.0
\n                    
56.0
\n        
\n        
\n                
IQR
\n                
22.0
\n                    
12.0
\n        
\n        
\n                
STD
\n                
13.6
\n                    
11.3
\n        
\n        
\n                
VAR
\n                
185
\n                    
128
\n        
\n        
\n        
\n        
\n                
KURT.
\n                
-0.016
\n                    
1.27
\n        
\n        
\n                
SKEW
\n                
0.822
\n                    
1.16
\n        
\n        
\n                
SUM
\n                
6,001
\n                    
10,745
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        3\n    
\n    
\n    credit_history\n    
\n        
\n            
VALUES:
\n            
\n                227\n            
\n            
\n                (76%)\n            
\n                
\n                    567\n                
\n                
\n                    (81%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    73\n            
\n            
\n                    
\n                    (24%)\n            
\n                
\n                        133\n                
\n                
\n                        
\n                        (19%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                5\n            
\n            
\n                (2%)\n            
\n                
\n                    5\n                
\n                
\n                    (<1%)\n                
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        4\n    
\n    
\n    purpose\n    
\n        
\n            
VALUES:
\n            
\n                231\n            
\n            
\n                (77%)\n            
\n                
\n                    572\n                
\n                
\n                    (82%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    69\n            
\n            
\n                    
\n                    (23%)\n            
\n                
\n                        128\n                
\n                
\n                        
\n                        (18%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                10\n            
\n            
\n                (3%)\n            
\n                
\n                    10\n                
\n                
\n                    (1%)\n                
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        5\n    
\n    
\n    credit_amount\n\n    
\n        
\n            
VALUES:
\n            
\n                242\n            
\n            
\n                (81%)\n            
\n                
\n                    579\n                
\n                
\n                    (83%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    58\n            
\n            
\n                    
\n                    (19%)\n            
\n                
\n                        121\n                
\n                
\n                        
\n                        (17%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                236\n            
\n            
\n                (79%)\n            
\n                
\n                    545\n                
\n                
\n                    (78%)\n                
\n        
\n            
\n            
\n            
\n                
ZEROES:
\n                
\n                        ---\n                
\n                
\n\n                
\n                 
\n                        ---\n                
\n                
\n\n                
\n            
\n    
\n\n    
\n        
\n            
MAX
\n            
18,424
\n                
15,857
\n        
\n        
\n            
95%
\n            
12,369
\n                
7,977
\n        
\n        
\n            
Q3
\n            
5,986
\n                
3,636
\n        
\n        
\n            
AVG
\n            
4,153
\n                
3,029
\n        
\n        
\n            
MEDIAN
\n            
2,715
\n                
2,247
\n        
\n        
\n            
Q1
\n            
1,451
\n                
1,376
\n        
\n        
\n            
5%
\n            
751
\n                
706
\n        
\n        
\n            
MIN
\n            
433
\n                
250
\n        
\n    
\n    
\n        
\n                
RANGE
\n                
17,991
\n                    
15,607
\n        
\n        
\n                
IQR
\n                
4,535
\n                    
2,259
\n        
\n        
\n                
STD
\n                
3,672
\n                    
2,485
\n        
\n        
\n                
VAR
\n                
13.5M
\n                    
6.2M
\n        
\n        
\n        
\n        
\n                
KURT.
\n                
1.84
\n                    
4.64
\n        
\n        
\n                
SKEW
\n                
1.52
\n                    
1.96
\n        
\n        
\n                
SUM
\n                
1.0M
\n                    
1.8M
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        6\n    
\n    
\n    savings_account_and_bonds\n    
\n        
\n            
VALUES:
\n            
\n                241\n            
\n            
\n                (80%)\n            
\n                
\n                    553\n                
\n                
\n                    (79%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    59\n            
\n            
\n                    
\n                    (20%)\n            
\n                
\n                        147\n                
\n                
\n                        
\n                        (21%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                5\n            
\n            
\n                (2%)\n            
\n                
\n                    5\n                
\n                
\n                    (<1%)\n                
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        7\n    
\n    
\n    present_employment_since\n    
\n        
\n            
VALUES:
\n            
\n                240\n            
\n            
\n                (80%)\n            
\n                
\n                    566\n                
\n                
\n                    (81%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    60\n            
\n            
\n                    
\n                    (20%)\n            
\n                
\n                        134\n                
\n                
\n                        
\n                        (19%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                5\n            
\n            
\n                (2%)\n            
\n                
\n                    5\n                
\n                
\n                    (<1%)\n                
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        8\n    
\n    
\n    installment_rate_in_percentage_of_disposable_income\n    
\n        
\n            
VALUES:
\n            
\n                235\n            
\n            
\n                (78%)\n            
\n                
\n                    551\n                
\n                
\n                    (79%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    65\n            
\n            
\n                    
\n                    (22%)\n            
\n                
\n                        149\n                
\n                
\n                        
\n                        (21%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                4\n            
\n            
\n                (1%)\n            
\n                
\n                    4\n                
\n                
\n                    (<1%)\n                
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        9\n    
\n    
\n    personal_status_and_sex\n    
\n        
\n            
VALUES:
\n            
\n                242\n            
\n            
\n                (81%)\n            
\n                
\n                    549\n                
\n                
\n                    (78%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    58\n            
\n            
\n                    
\n                    (19%)\n            
\n                
\n                        151\n                
\n                
\n                        
\n                        (22%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                5\n            
\n            
\n                (2%)\n            
\n                
\n                    5\n                
\n                
\n                    (<1%)\n                
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        10\n    
\n    
\n    other_debtors_or_guarantors\n    
\n        
\n            
VALUES:
\n            
\n                225\n            
\n            
\n                (75%)\n            
\n                
\n                    564\n                
\n                
\n                    (81%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    75\n            
\n            
\n                    
\n                    (25%)\n            
\n                
\n                        136\n                
\n                
\n                        
\n                        (19%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                3\n            
\n            
\n                (1%)\n            
\n                
\n                    3\n                
\n                
\n                    (<1%)\n                
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        11\n    
\n    
\n    present_residence_since\n    
\n        
\n            
VALUES:
\n            
\n                235\n            
\n            
\n                (78%)\n            
\n                
\n                    558\n                
\n                
\n                    (80%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    65\n            
\n            
\n                    
\n                    (22%)\n            
\n                
\n                        142\n                
\n                
\n                        
\n                        (20%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                4\n            
\n            
\n                (1%)\n            
\n                
\n                    4\n                
\n                
\n                    (<1%)\n                
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        12\n    
\n    
\n    property\n    
\n        
\n            
VALUES:
\n            
\n                226\n            
\n            
\n                (75%)\n            
\n                
\n                    546\n                
\n                
\n                    (78%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    74\n            
\n            
\n                    
\n                    (25%)\n            
\n                
\n                        154\n                
\n                
\n                        
\n                        (22%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                4\n            
\n            
\n                (1%)\n            
\n                
\n                    4\n                
\n                
\n                    (<1%)\n                
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        13\n    
\n    
\n    age_in_years\n\n    
\n        
\n            
VALUES:
\n            
\n                242\n            
\n            
\n                (81%)\n            
\n                
\n                    566\n                
\n                
\n                    (81%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    58\n            
\n            
\n                    
\n                    (19%)\n            
\n                
\n                        134\n                
\n                
\n                        
\n                        (19%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                47\n            
\n            
\n                (16%)\n            
\n                
\n                    53\n                
\n                
\n                    (8%)\n                
\n        
\n            
\n            
\n            
\n                
ZEROES:
\n                
\n                        ---\n                
\n                
\n\n                
\n                 
\n                        ---\n                
\n                
\n\n                
\n            
\n    
\n\n    
\n        
\n            
MAX
\n            
74.0
\n                
75.0
\n        
\n        
\n            
95%
\n            
58.0
\n                
61.0
\n        
\n        
\n            
Q3
\n            
40.0
\n                
42.0
\n        
\n        
\n            
AVG
\n            
34.1
\n                
36.4
\n        
\n        
\n            
MEDIAN
\n            
31.0
\n                
34.0
\n        
\n        
\n            
Q1
\n            
25.0
\n                
27.0
\n        
\n        
\n            
5%
\n            
22.0
\n                
23.0
\n        
\n        
\n            
MIN
\n            
19.0
\n                
19.0
\n        
\n    
\n    
\n        
\n                
RANGE
\n                
55.0
\n                    
56.0
\n        
\n        
\n                
IQR
\n                
15.0
\n                    
15.0
\n        
\n        
\n                
STD
\n                
11.3
\n                    
11.4
\n        
\n        
\n                
VAR
\n                
128
\n                    
130
\n        
\n        
\n        
\n        
\n                
KURT.
\n                
0.868
\n                    
0.701
\n        
\n        
\n                
SKEW
\n                
1.18
\n                    
1.01
\n        
\n        
\n                
SUM
\n                
8,242
\n                    
20,595
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        14\n    
\n    
\n    other_installment_plans\n    
\n        
\n            
VALUES:
\n            
\n                230\n            
\n            
\n                (77%)\n            
\n                
\n                    569\n                
\n                
\n                    (81%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    70\n            
\n            
\n                    
\n                    (23%)\n            
\n                
\n                        131\n                
\n                
\n                        
\n                        (19%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                3\n            
\n            
\n                (1%)\n            
\n                
\n                    3\n                
\n                
\n                    (<1%)\n                
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        15\n    
\n    
\n    housing\n    
\n        
\n            
VALUES:
\n            
\n                234\n            
\n            
\n                (78%)\n            
\n                
\n                    566\n                
\n                
\n                    (81%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    66\n            
\n            
\n                    
\n                    (22%)\n            
\n                
\n                        134\n                
\n                
\n                        
\n                        (19%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                3\n            
\n            
\n                (1%)\n            
\n                
\n                    3\n                
\n                
\n                    (<1%)\n                
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        16\n    
\n    
\n    number_of_existing_credits_at_this_bank\n    
\n        
\n            
VALUES:
\n            
\n                231\n            
\n            
\n                (77%)\n            
\n                
\n                    569\n                
\n                
\n                    (81%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    69\n            
\n            
\n                    
\n                    (23%)\n            
\n                
\n                        131\n                
\n                
\n                        
\n                        (19%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                4\n            
\n            
\n                (1%)\n            
\n                
\n                    4\n                
\n                
\n                    (<1%)\n                
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        17\n    
\n    
\n    job\n    
\n        
\n            
VALUES:
\n            
\n                232\n            
\n            
\n                (77%)\n            
\n                
\n                    533\n                
\n                
\n                    (76%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    68\n            
\n            
\n                    
\n                    (23%)\n            
\n                
\n                        167\n                
\n                
\n                        
\n                        (24%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                4\n            
\n            
\n                (1%)\n            
\n                
\n                    4\n                
\n                
\n                    (<1%)\n                
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        18\n    
\n    
\n    number_of_people_being_liable_to_provide_maintenance_for\n    
\n        
\n            
VALUES:
\n            
\n                245\n            
\n            
\n                (82%)\n            
\n                
\n                    555\n                
\n                
\n                    (79%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    55\n            
\n            
\n                    
\n                    (18%)\n            
\n                
\n                        145\n                
\n                
\n                        
\n                        (21%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                2\n            
\n            
\n                (<1%)\n            
\n                
\n                    2\n                
\n                
\n                    (<1%)\n                
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        19\n    
\n    
\n    telephone\n    
\n        
\n            
VALUES:
\n            
\n                243\n            
\n            
\n                (81%)\n            
\n                
\n                    548\n                
\n                
\n                    (78%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    57\n            
\n            
\n                    
\n                    (19%)\n            
\n                
\n                        152\n                
\n                
\n                        
\n                        (22%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                2\n            
\n            
\n                (<1%)\n            
\n                
\n                    2\n                
\n                
\n                    (<1%)\n                
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        20\n    
\n    
\n    foreign_worker\n    
\n        
\n            
VALUES:
\n            
\n                235\n            
\n            
\n                (78%)\n            
\n                
\n                    564\n                
\n                
\n                    (81%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    65\n            
\n            
\n                    
\n                    (22%)\n            
\n                
\n                        136\n                
\n                
\n                        
\n                        (19%)\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                2\n            
\n            
\n                (<1%)\n            
\n                
\n                    2\n                
\n                
\n                    (<1%)\n                
\n        
\n    
\n    \n
\n                    \n                
\n                
\n                    
\n    
\n    
\n    
\n    \n    \n    
\n        21\n    
\n    
\n    creditability\n    
\n        
\n            
VALUES:
\n            
\n                300\n            
\n            
\n                (100%)\n            
\n                
\n                    700\n                
\n                
\n                    (100%)\n                
\n        
\n        
\n            
MISSING:
\n            
\n                    ---\n            
\n            
\n\n            
\n                
\n                        ---\n                
\n                
\n\n                
\n        
\n        
\n        
\n        
\n            
DISTINCT:
\n            
\n                1\n            
\n            
\n                (<1%)\n            
\n                
\n                    1\n                
\n                
\n                    (<1%)\n                
\n        
\n    
\n    \n
\n                    \n                
\n        
\n    
\n\n    \n        
\n                
\n
\n    \n    Associations\n    
\n        [Only including dataset \"坏客户\"]
\n         ■ Squares are categorical associations (uncertainty coefficient & correlation ratio) from 0 to 1. The uncertainty coefficient is assymmetrical,\n        (i.e. ROW LABEL values indicate how much they PROVIDE INFORMATION to each LABEL at the TOP).\n       

• Circles are the symmetrical numerical correlations (Pearson's) from -1 to 1. The trivial diagonal is intentionally left blank for clarity.\n    
\n    \n
\n
\n\n                
\n
\n    \n    Associations\n    
\n        [Only including dataset \"好客户\"]
\n         ■ Squares are categorical associations (uncertainty coefficient & correlation ratio) from 0 to 1. The uncertainty coefficient is assymmetrical,\n        (i.e. ROW LABEL values indicate how much they PROVIDE INFORMATION to each LABEL at the TOP).\n       

• Circles are the symmetrical numerical correlations (Pearson's) from -1 to 1. The trivial diagonal is intentionally left blank for clarity.\n    
\n    \n
\n
\n\n            \n            \n                
\n
\n        \n        
\n            
\n            status_of_existing_checking_account\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    66\n            
\n            
\n                    
\n                    (22%)\n            
\n                
\n                        133\n                
\n                
\n                        
\n                        (19%)\n                
\n        
\n
\n\n    \n\n    
 \n        \n        
\n            
TOP CATEGORIES
\n            
\n            
\n            \n                
\n                    \n                
\n        
\n\n        \n            
\n                \n                
... < 0 DM
\n\n                \n                
\n                    
98
\n                    
42%
\n                
\n                    
\n                        
113
\n                        
20%
\n                    
\n                \n            
\n            
\n                \n                
0 <= ... < 200 DM
\n\n                \n                
\n                    
90
\n                    
38%
\n                
\n                    
\n                        
134
\n                        
24%
\n                    
\n                \n            
\n            
\n                \n                
no checking account
\n\n                \n                
\n                    
38
\n                    
16%
\n                
\n                    
\n                        
276
\n                        
49%
\n                    
\n                \n            
\n            
\n                \n                
... >= 200 DM / salary assignments for at least 1 year
\n\n                \n                
\n                    
8
\n                    
3%
\n                
\n                    
\n                        
44
\n                        
8%
\n                    
\n                \n            
\n                
\n                
\n            
\n                \n                
ALL
\n\n                \n                
\n                    
234
\n                    
100%
\n                
\n                    
\n                        
567
\n                        
100%
\n                    
\n                \n            
\n        
\n    
\n\n    \n    
\n        \n        \n        
\n            \n            
\n                CATEGORICAL ASSOCIATIONS
\n                (UNCERTAINTY COEFFICIENT, 0 to 1)\n            
\n            
status_of_existing_checking_account
\n            PROVIDES INFORMATION ON...
\n            
\n                
\n                    
creditability
\n                    
1.00
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.07
\n                
\n                
\n                    
foreign_worker
\n                    
0.05
\n                
\n                
\n                    
purpose
\n                    
0.03
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.03
\n                
\n                
\n                    
job
\n                    
0.02
\n                
\n                
\n                    
credit_history
\n                    
0.02
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.02
\n                
\n                
\n                    
other_installment_plans
\n                    
0.02
\n                
\n                
\n                    
present_employment_since
\n                    
0.02
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.02
\n                
\n                
\n                    
number_of_people_being_liable_to_provide_maintenance_for
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n                
\n                    
property
\n                    
0.02
\n                
\n            
\n            
THESE FEATURES
GIVE INFORMATION
\n            ON status_of_existing_checking_account:
\n            
\n                
\n                    
savings_account_and_bonds
\n                    
0.06
\n                
\n                
\n                    
purpose
\n                    
0.04
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.03
\n                
\n                
\n                    
credit_history
\n                    
0.02
\n                
\n                
\n                    
present_employment_since
\n                    
0.02
\n                
\n                
\n                    
job
\n                    
0.02
\n                
\n                
\n                    
foreign_worker
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n                
\n                    
property
\n                    
0.02
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.02
\n                
\n                
\n                    
other_installment_plans
\n                    
0.02
\n                
\n                
\n                    
present_residence_since
\n                    
0.01
\n                
\n                
\n                    
housing
\n                    
0.01
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.01
\n                
\n            
\n            \n            
\n                NUMERICAL ASSOCIATIONS
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
status_of_existing_checking_account
\n                    CORRELATION RATIO WITH...
\n            
\n                
\n                    
credit_amount
\n                    
0.21
\n                
\n                
\n                    
duration_in_month
\n                    
0.07
\n                
\n                
\n                    
age_in_years
\n                    
0.06
\n                
\n        
\n    
\n
\n
\n                
\n
\n        \n        
\n            
\n            duration_in_month\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    63\n            
\n            
\n                    
\n                    (21%)\n            
\n                
\n                        152\n                
\n                
\n                        
\n                        (22%)\n                
\n        
\n
\n\n    \n    
\n            \n            \n            \n            \n    
\n\n    \n    \n\n    \n    
\n        \n            >\n        
\n            
NUMERICAL ASSOCIATIONS
\n            
\n                (PEARSON, -1 to 1)\n            
\n\n            
\n                
\n                    
credit_amount
\n                    
0.53
\n                
\n                
\n                    
age_in_years
\n                    
-0.01
\n                
\n            
\n            
CATEGORICAL ASSOCIATIONS
\n            
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
\n                
\n                    
purpose
\n                    
0.21
\n                
\n                
\n                    
present_residence_since
\n                    
0.17
\n                
\n                
\n                    
present_employment_since
\n                    
0.16
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.16
\n                
\n                
\n                    
housing
\n                    
0.14
\n                
\n                
\n                    
property
\n                    
0.14
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.13
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.12
\n                
\n                
\n                    
credit_history
\n                    
0.12
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.10
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.10
\n                
\n                
\n                    
number_of_people_being_liable_to_provide_maintenance_for
\n                    
0.10
\n                
\n                
\n                    
job
\n                    
0.09
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.07
\n                
\n        
\n    
\n    \n    
\n        \n        
\n            
MOST FREQUENT VALUES
\n            
\n                
\n                    
24.0
\n                    
\n                        
41
\n                        
17.3%
\n                    
\n                        
\n                            
99
\n                            
18.1%
\n                        
\n                
\n                
\n                    
12.0
\n                    
\n                        
41
\n                        
17.3%
\n                    
\n                        
\n                            
99
\n                            
18.1%
\n                        
\n                
\n                
\n                    
18.0
\n                    
\n                        
33
\n                        
13.9%
\n                    
\n                        
\n                            
57
\n                            
10.4%
\n                        
\n                
\n                
\n                    
36.0
\n                    
\n                        
27
\n                        
11.4%
\n                    
\n                        
\n                            
35
\n                            
6.4%
\n                        
\n                
\n                
\n                    
48.0
\n                    
\n                        
25
\n                        
10.5%
\n                    
\n                        
\n                            
17
\n                            
3.1%
\n                        
\n                
\n                
\n                    
30.0
\n                    
\n                        
12
\n                        
5.1%
\n                    
\n                        
\n                            
24
\n                            
4.4%
\n                        
\n                
\n                
\n                    
15.0
\n                    
\n                        
9
\n                        
3.8%
\n                    
\n                        
\n                            
40
\n                            
7.3%
\n                        
\n                
\n                
\n                    
9.0
\n                    
\n                        
8
\n                        
3.4%
\n                    
\n                        
\n                            
28
\n                            
5.1%
\n                        
\n                
\n                
\n                    
21.0
\n                    
\n                        
7
\n                        
3.0%
\n                    
\n                        
\n                            
15
\n                            
2.7%
\n                        
\n                
\n                
\n                    
6.0
\n                    
\n                        
7
\n                        
3.0%
\n                    
\n                        
\n                            
50
\n                            
9.1%
\n                        
\n                
\n                
\n                    
27.0
\n                    
\n                        
5
\n                        
2.1%
\n                    
\n                        
\n                            
8
\n                            
1.5%
\n                        
\n                
\n                
\n                    
60.0
\n                    
\n                        
5
\n                        
2.1%
\n                    
\n                        
\n                            
6
\n                            
1.1%
\n                        
\n                
\n                
\n                    
45.0
\n                    
\n                        
4
\n                        
1.7%
\n                    
\n                        
\n                            
1
\n                            
0.2%
\n                        
\n                
\n                
\n                    
42.0
\n                    
\n                        
2
\n                        
0.8%
\n                    
\n                        
\n                            
8
\n                            
1.5%
\n                        
\n                
\n                
\n                    
10.0
\n                    
\n                        
2
\n                        
0.8%
\n                    
\n                        
\n                            
18
\n                            
3.3%
\n                        
\n                
\n        
\n    
\n    \n    
\n        \n        
\n            
 SMALLEST VALUES
\n            
\n                
\n                    
6.0
\n                    
\n                        
7
\n                        
3.0%
\n                    
\n                        
\n                            
50
\n                            
9.1%
\n                        
\n                
\n                
\n                    
8.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
3
\n                            
0.5%
\n                        
\n                
\n                
\n                    
9.0
\n                    
\n                        
8
\n                        
3.4%
\n                    
\n                        
\n                            
28
\n                            
5.1%
\n                        
\n                
\n                
\n                    
10.0
\n                    
\n                        
2
\n                        
0.8%
\n                    
\n                        
\n                            
18
\n                            
3.3%
\n                        
\n                
\n                
\n                    
12.0
\n                    
\n                        
41
\n                        
17.3%
\n                    
\n                        
\n                            
99
\n                            
18.1%
\n                        
\n                
\n                
\n                    
14.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
3
\n                            
0.5%
\n                        
\n                
\n                
\n                    
15.0
\n                    
\n                        
9
\n                        
3.8%
\n                    
\n                        
\n                            
40
\n                            
7.3%
\n                        
\n                
\n                
\n                    
16.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
1
\n                            
0.2%
\n                        
\n                
\n                
\n                    
18.0
\n                    
\n                        
33
\n                        
13.9%
\n                    
\n                        
\n                            
57
\n                            
10.4%
\n                        
\n                
\n                
\n                    
20.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
7
\n                            
1.3%
\n                        
\n                
\n                
\n                    
21.0
\n                    
\n                        
7
\n                        
3.0%
\n                    
\n                        
\n                            
15
\n                            
2.7%
\n                        
\n                
\n                
\n                    
24.0
\n                    
\n                        
41
\n                        
17.3%
\n                    
\n                        
\n                            
99
\n                            
18.1%
\n                        
\n                
\n                
\n                    
27.0
\n                    
\n                        
5
\n                        
2.1%
\n                    
\n                        
\n                            
8
\n                            
1.5%
\n                        
\n                
\n                
\n                    
28.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
2
\n                            
0.4%
\n                        
\n                
\n                
\n                    
30.0
\n                    
\n                        
12
\n                        
5.1%
\n                    
\n                        
\n                            
24
\n                            
4.4%
\n                        
\n                
\n        
\n    
\n    \n    
\n        \n        
\n            
LARGEST VALUES
\n            
\n                
\n                    
72.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
60.0
\n                    
\n                        
5
\n                        
2.1%
\n                    
\n                        
\n                            
6
\n                            
1.1%
\n                        
\n                
\n                
\n                    
54.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
1
\n                            
0.2%
\n                        
\n                
\n                
\n                    
48.0
\n                    
\n                        
25
\n                        
10.5%
\n                    
\n                        
\n                            
17
\n                            
3.1%
\n                        
\n                
\n                
\n                    
45.0
\n                    
\n                        
4
\n                        
1.7%
\n                    
\n                        
\n                            
1
\n                            
0.2%
\n                        
\n                
\n                
\n                    
42.0
\n                    
\n                        
2
\n                        
0.8%
\n                    
\n                        
\n                            
8
\n                            
1.5%
\n                        
\n                
\n                
\n                    
39.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
3
\n                            
0.5%
\n                        
\n                
\n                
\n                    
36.0
\n                    
\n                        
27
\n                        
11.4%
\n                    
\n                        
\n                            
35
\n                            
6.4%
\n                        
\n                
\n                
\n                    
33.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
2
\n                            
0.4%
\n                        
\n                
\n                
\n                    
30.0
\n                    
\n                        
12
\n                        
5.1%
\n                    
\n                        
\n                            
24
\n                            
4.4%
\n                        
\n                
\n                
\n                    
28.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
2
\n                            
0.4%
\n                        
\n                
\n                
\n                    
27.0
\n                    
\n                        
5
\n                        
2.1%
\n                    
\n                        
\n                            
8
\n                            
1.5%
\n                        
\n                
\n                
\n                    
24.0
\n                    
\n                        
41
\n                        
17.3%
\n                    
\n                        
\n                            
99
\n                            
18.1%
\n                        
\n                
\n                
\n                    
21.0
\n                    
\n                        
7
\n                        
3.0%
\n                    
\n                        
\n                            
15
\n                            
2.7%
\n                        
\n                
\n                
\n                    
20.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
7
\n                            
1.3%
\n                        
\n                
\n        
\n    
\n\n
\n
\n                
\n
\n        \n        
\n            
\n            credit_history\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    73\n            
\n            
\n                    
\n                    (24%)\n            
\n                
\n                        133\n                
\n                
\n                        
\n                        (19%)\n                
\n        
\n
\n\n    \n\n    
 \n        \n        
\n            
TOP CATEGORIES
\n            
\n            
\n            \n                
\n                    \n                
\n        
\n\n        \n            
\n                \n                
existing credits paid back duly till now
\n\n                \n                
\n                    
126
\n                    
56%
\n                
\n                    
\n                        
293
\n                        
52%
\n                    
\n                \n            
\n            
\n                \n                
critical account/ other credits existing (not at this bank)
\n\n                \n                
\n                    
39
\n                    
17%
\n                
\n                    
\n                        
197
\n                        
35%
\n                    
\n                \n            
\n            
\n                \n                
no credits taken/ all credits paid back duly
\n\n                \n                
\n                    
21
\n                    
9%
\n                
\n                    
\n                        
12
\n                        
2%
\n                    
\n                \n            
\n            
\n                \n                
delay in paying off in the past
\n\n                \n                
\n                    
21
\n                    
9%
\n                
\n                    
\n                        
47
\n                        
8%
\n                    
\n                \n            
\n            
\n                \n                
all credits at this bank paid back duly
\n\n                \n                
\n                    
20
\n                    
9%
\n                
\n                    
\n                        
18
\n                        
3%
\n                    
\n                \n            
\n                
\n                
\n            
\n                \n                
ALL
\n\n                \n                
\n                    
227
\n                    
100%
\n                
\n                    
\n                        
567
\n                        
100%
\n                    
\n                \n            
\n        
\n    
\n\n    \n    
\n        \n        \n        
\n            \n            
\n                CATEGORICAL ASSOCIATIONS
\n                (UNCERTAINTY COEFFICIENT, 0 to 1)\n            
\n            
credit_history
\n            PROVIDES INFORMATION ON...
\n            
\n                
\n                    
creditability
\n                    
1.00
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.15
\n                
\n                
\n                    
purpose
\n                    
0.06
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.05
\n                
\n                
\n                    
foreign_worker
\n                    
0.04
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.03
\n                
\n                
\n                    
housing
\n                    
0.03
\n                
\n                
\n                    
other_installment_plans
\n                    
0.03
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.02
\n                
\n                
\n                    
number_of_people_being_liable_to_provide_maintenance_for
\n                    
0.02
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.02
\n                
\n                
\n                    
present_employment_since
\n                    
0.02
\n                
\n                
\n                    
job
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n            
\n            
THESE FEATURES
GIVE INFORMATION
\n            ON credit_history:
\n            
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.10
\n                
\n                
\n                    
purpose
\n                    
0.08
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.04
\n                
\n                
\n                    
present_employment_since
\n                    
0.03
\n                
\n                
\n                    
housing
\n                    
0.02
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.02
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.02
\n                
\n                
\n                    
present_residence_since
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n                
\n                    
other_installment_plans
\n                    
0.02
\n                
\n                
\n                    
job
\n                    
0.02
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.02
\n                
\n                
\n                    
foreign_worker
\n                    
0.01
\n                
\n                
\n                    
property
\n                    
0.01
\n                
\n            
\n            \n            
\n                NUMERICAL ASSOCIATIONS
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
credit_history
\n                    CORRELATION RATIO WITH...
\n            
\n                
\n                    
age_in_years
\n                    
0.17
\n                
\n                
\n                    
credit_amount
\n                    
0.16
\n                
\n                
\n                    
duration_in_month
\n                    
0.12
\n                
\n        
\n    
\n
\n
\n                
\n
\n        \n        
\n            
\n            purpose\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    69\n            
\n            
\n                    
\n                    (23%)\n            
\n                
\n                        128\n                
\n                
\n                        
\n                        (18%)\n                
\n        
\n
\n\n    \n\n    
 \n        \n        
\n            
TOP CATEGORIES
\n            
\n            
\n            \n                
\n                    \n                
\n        
\n\n        \n            
\n                \n                
car (new)
\n\n                \n                
\n                    
73
\n                    
32%
\n                
\n                    
\n                        
116
\n                        
20%
\n                    
\n                \n            
\n            
\n                \n                
furniture/equipment
\n\n                \n                
\n                    
41
\n                    
18%
\n                
\n                    
\n                        
100
\n                        
17%
\n                    
\n                \n            
\n            
\n                \n                
radio/television
\n\n                \n                
\n                    
39
\n                    
17%
\n                
\n                    
\n                        
181
\n                        
32%
\n                    
\n                \n            
\n            
\n                \n                
business
\n\n                \n                
\n                    
30
\n                    
13%
\n                
\n                    
\n                        
50
\n                        
9%
\n                    
\n                \n            
\n            
\n                \n                
car (used)
\n\n                \n                
\n                    
16
\n                    
7%
\n                
\n                    
\n                        
74
\n                        
13%
\n                    
\n                \n            
\n            
\n                \n                
education
\n\n                \n                
\n                    
16
\n                    
7%
\n                
\n                    
\n                        
23
\n                        
4%
\n                    
\n                \n            
\n            
\n                \n                
repairs
\n\n                \n                
\n                    
6
\n                    
3%
\n                
\n                    
\n                        
10
\n                        
2%
\n                    
\n                \n            
\n            
\n                \n                
others
\n\n                \n                
\n                    
5
\n                    
2%
\n                
\n                    
\n                        
7
\n                        
1%
\n                    
\n                \n            
\n            
\n                \n                
domestic appliances
\n\n                \n                
\n                    
4
\n                    
2%
\n                
\n                    
\n                        
5
\n                        
<1%
\n                    
\n                \n            
\n            
\n                \n                
retraining
\n\n                \n                
\n                    
1
\n                    
<1%
\n                
\n                    
\n                        
6
\n                        
1%
\n                    
\n                \n            
\n                
\n                
\n            
\n                \n                
ALL
\n\n                \n                
\n                    
231
\n                    
100%
\n                
\n                    
\n                        
572
\n                        
100%
\n                    
\n                \n            
\n        
\n    
\n\n    \n    
\n        \n        \n        
\n            \n            
\n                CATEGORICAL ASSOCIATIONS
\n                (UNCERTAINTY COEFFICIENT, 0 to 1)\n            
\n            
purpose
\n            PROVIDES INFORMATION ON...
\n            
\n                
\n                    
creditability
\n                    
1.00
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.08
\n                
\n                
\n                    
credit_history
\n                    
0.08
\n                
\n                
\n                    
housing
\n                    
0.07
\n                
\n                
\n                    
telephone
\n                    
0.07
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.07
\n                
\n                
\n                    
job
\n                    
0.06
\n                
\n                
\n                    
present_residence_since
\n                    
0.06
\n                
\n                
\n                    
present_employment_since
\n                    
0.05
\n                
\n                
\n                    
number_of_people_being_liable_to_provide_maintenance_for
\n                    
0.05
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.05
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.04
\n                
\n                
\n                    
other_installment_plans
\n                    
0.04
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.04
\n                
\n            
\n            
THESE FEATURES
GIVE INFORMATION
\n            ON purpose:
\n            
\n                
\n                    
credit_history
\n                    
0.06
\n                
\n                
\n                    
housing
\n                    
0.04
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.04
\n                
\n                
\n                    
present_residence_since
\n                    
0.04
\n                
\n                
\n                    
present_employment_since
\n                    
0.04
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.04
\n                
\n                
\n                    
job
\n                    
0.04
\n                
\n                
\n                    
telephone
\n                    
0.04
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.03
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.03
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.03
\n                
\n                
\n                    
property
\n                    
0.02
\n                
\n                
\n                    
other_installment_plans
\n                    
0.02
\n                
\n                
\n                    
number_of_people_being_liable_to_provide_maintenance_for
\n                    
0.02
\n                
\n            
\n            \n            
\n                NUMERICAL ASSOCIATIONS
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
purpose
\n                    CORRELATION RATIO WITH...
\n            
\n                
\n                    
credit_amount
\n                    
0.44
\n                
\n                
\n                    
duration_in_month
\n                    
0.21
\n                
\n                
\n                    
age_in_years
\n                    
0.20
\n                
\n        
\n    
\n
\n
\n                
\n
\n        \n        
\n            
\n            credit_amount\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    58\n            
\n            
\n                    
\n                    (19%)\n            
\n                
\n                        121\n                
\n                
\n                        
\n                        (17%)\n                
\n        
\n
\n\n    \n    
\n            \n            \n            \n            \n    
\n\n    \n    \n\n    \n    
\n        \n            >\n        
\n            
NUMERICAL ASSOCIATIONS
\n            
\n                (PEARSON, -1 to 1)\n            
\n\n            
\n                
\n                    
duration_in_month
\n                    
0.53
\n                
\n                
\n                    
age_in_years
\n                    
0.20
\n                
\n            
\n            
CATEGORICAL ASSOCIATIONS
\n            
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
\n                
\n                    
purpose
\n                    
0.44
\n                
\n                
\n                    
telephone
\n                    
0.33
\n                
\n                
\n                    
job
\n                    
0.27
\n                
\n                
\n                    
property
\n                    
0.26
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.25
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.21
\n                
\n                
\n                    
present_residence_since
\n                    
0.17
\n                
\n                
\n                    
housing
\n                    
0.17
\n                
\n                
\n                    
credit_history
\n                    
0.16
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.14
\n                
\n                
\n                    
other_installment_plans
\n                    
0.12
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.11
\n                
\n                
\n                    
present_employment_since
\n                    
0.10
\n                
\n                
\n                    
foreign_worker
\n                    
0.09
\n                
\n        
\n    
\n    \n    
\n        \n        
\n            
MOST FREQUENT VALUES
\n            
\n                
\n                    
1442.0
\n                    
\n                        
2
\n                        
0.8%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
433.0
\n                    
\n                        
2
\n                        
0.8%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
1216.0
\n                    
\n                        
2
\n                        
0.8%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
2039.0
\n                    
\n                        
2
\n                        
0.8%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
1282.0
\n                    
\n                        
2
\n                        
0.8%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
3349.0
\n                    
\n                        
2
\n                        
0.8%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
900.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
888.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
8086.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
5951.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
1922.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
4843.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
7980.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
4623.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
750.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n        
\n    
\n    \n    
\n        \n        
\n            
 SMALLEST VALUES
\n            
\n                
\n                    
433.0
\n                    
\n                        
2
\n                        
0.8%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
448.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
609.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
1
\n                            
0.2%
\n                        
\n                
\n                
\n                    
639.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
674.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
685.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
691.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
697.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
709.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
1
\n                            
0.2%
\n                        
\n                
\n                
\n                    
719.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
741.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
750.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
766.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
795.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
797.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n        
\n    
\n    \n    
\n        \n        
\n            
LARGEST VALUES
\n            
\n                
\n                    
18424.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
15945.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
15672.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
14896.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
14782.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
14555.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
14421.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
14318.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
14027.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
12976.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
12680.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
12612.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
12389.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
11998.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n                
\n                    
11938.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
0
\n                            
0.0%
\n                        
\n                
\n        
\n    
\n\n
\n
\n                
\n
\n        \n        
\n            
\n            savings_account_and_bonds\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    59\n            
\n            
\n                    
\n                    (20%)\n            
\n                
\n                        147\n                
\n                
\n                        
\n                        (21%)\n                
\n        
\n
\n\n    \n\n    
 \n        \n        
\n            
TOP CATEGORIES
\n            
\n            
\n            \n                
\n                    \n                
\n        
\n\n        \n            
\n                \n                
... < 100 DM
\n\n                \n                
\n                    
176
\n                    
73%
\n                
\n                    
\n                        
303
\n                        
55%
\n                    
\n                \n            
\n            
\n                \n                
unknown/ no savings account
\n\n                \n                
\n                    
29
\n                    
12%
\n                
\n                    
\n                        
117
\n                        
21%
\n                    
\n                \n            
\n            
\n                \n                
100 <= ... < 500 DM
\n\n                \n                
\n                    
23
\n                    
10%
\n                
\n                    
\n                        
54
\n                        
10%
\n                    
\n                \n            
\n            
\n                \n                
500 <= ... < 1000 DM
\n\n                \n                
\n                    
8
\n                    
3%
\n                
\n                    
\n                        
45
\n                        
8%
\n                    
\n                \n            
\n            
\n                \n                
... >= 1000 DM
\n\n                \n                
\n                    
5
\n                    
2%
\n                
\n                    
\n                        
34
\n                        
6%
\n                    
\n                \n            
\n                
\n                
\n            
\n                \n                
ALL
\n\n                \n                
\n                    
241
\n                    
100%
\n                
\n                    
\n                        
553
\n                        
100%
\n                    
\n                \n            
\n        
\n    
\n\n    \n    
\n        \n        \n        
\n            \n            
\n                CATEGORICAL ASSOCIATIONS
\n                (UNCERTAINTY COEFFICIENT, 0 to 1)\n            
\n            
savings_account_and_bonds
\n            PROVIDES INFORMATION ON...
\n            
\n                
\n                    
creditability
\n                    
1.00
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.06
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.05
\n                
\n                
\n                    
purpose
\n                    
0.04
\n                
\n                
\n                    
credit_history
\n                    
0.04
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.03
\n                
\n                
\n                    
foreign_worker
\n                    
0.03
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.03
\n                
\n                
\n                    
property
\n                    
0.03
\n                
\n                
\n                    
present_employment_since
\n                    
0.03
\n                
\n                
\n                    
job
\n                    
0.02
\n                
\n                
\n                    
number_of_people_being_liable_to_provide_maintenance_for
\n                    
0.02
\n                
\n                
\n                    
present_residence_since
\n                    
0.02
\n                
\n                
\n                    
other_installment_plans
\n                    
0.02
\n                
\n            
\n            
THESE FEATURES
GIVE INFORMATION
\n            ON savings_account_and_bonds:
\n            
\n                
\n                    
status_of_existing_checking_account
\n                    
0.07
\n                
\n                
\n                    
purpose
\n                    
0.07
\n                
\n                
\n                    
credit_history
\n                    
0.05
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.04
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.04
\n                
\n                
\n                    
present_employment_since
\n                    
0.04
\n                
\n                
\n                    
property
\n                    
0.03
\n                
\n                
\n                    
present_residence_since
\n                    
0.02
\n                
\n                
\n                    
job
\n                    
0.02
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n                
\n                    
foreign_worker
\n                    
0.02
\n                
\n                
\n                    
number_of_people_being_liable_to_provide_maintenance_for
\n                    
0.01
\n                
\n                
\n                    
housing
\n                    
0.01
\n                
\n            
\n            \n            
\n                NUMERICAL ASSOCIATIONS
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
savings_account_and_bonds
\n                    CORRELATION RATIO WITH...
\n            
\n                
\n                    
age_in_years
\n                    
0.12
\n                
\n                
\n                    
credit_amount
\n                    
0.11
\n                
\n                
\n                    
duration_in_month
\n                    
0.10
\n                
\n        
\n    
\n
\n
\n                
\n
\n        \n        
\n            
\n            present_employment_since\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    60\n            
\n            
\n                    
\n                    (20%)\n            
\n                
\n                        134\n                
\n                
\n                        
\n                        (19%)\n                
\n        
\n
\n\n    \n\n    
 \n        \n        
\n            
TOP CATEGORIES
\n            
\n            
\n            \n                
\n                    \n                
\n        
\n\n        \n            
\n                \n                
1 <= ... < 4 years
\n\n                \n                
\n                    
79
\n                    
33%
\n                
\n                    
\n                        
194
\n                        
34%
\n                    
\n                \n            
\n            
\n                \n                
... >= 7 years
\n\n                \n                
\n                    
60
\n                    
25%
\n                
\n                    
\n                        
160
\n                        
28%
\n                    
\n                \n            
\n            
\n                \n                
... < 1 year
\n\n                \n                
\n                    
53
\n                    
22%
\n                
\n                    
\n                        
81
\n                        
14%
\n                    
\n                \n            
\n            
\n                \n                
4 <= ... < 7 years
\n\n                \n                
\n                    
31
\n                    
13%
\n                
\n                    
\n                        
106
\n                        
19%
\n                    
\n                \n            
\n            
\n                \n                
unemployed
\n\n                \n                
\n                    
17
\n                    
7%
\n                
\n                    
\n                        
25
\n                        
4%
\n                    
\n                \n            
\n                
\n                
\n            
\n                \n                
ALL
\n\n                \n                
\n                    
240
\n                    
100%
\n                
\n                    
\n                        
566
\n                        
100%
\n                    
\n                \n            
\n        
\n    
\n\n    \n    
\n        \n        \n        
\n            \n            
\n                CATEGORICAL ASSOCIATIONS
\n                (UNCERTAINTY COEFFICIENT, 0 to 1)\n            
\n            
present_employment_since
\n            PROVIDES INFORMATION ON...
\n            
\n                
\n                    
creditability
\n                    
1.00
\n                
\n                
\n                    
job
\n                    
0.09
\n                
\n                
\n                    
present_residence_since
\n                    
0.04
\n                
\n                
\n                    
purpose
\n                    
0.04
\n                
\n                
\n                    
housing
\n                    
0.04
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.04
\n                
\n                
\n                    
property
\n                    
0.03
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.03
\n                
\n                
\n                    
foreign_worker
\n                    
0.03
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.03
\n                
\n                
\n                    
other_installment_plans
\n                    
0.03
\n                
\n                
\n                    
credit_history
\n                    
0.03
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n            
\n            
THESE FEATURES
GIVE INFORMATION
\n            ON present_employment_since:
\n            
\n                
\n                    
job
\n                    
0.07
\n                
\n                
\n                    
purpose
\n                    
0.05
\n                
\n                
\n                    
present_residence_since
\n                    
0.04
\n                
\n                
\n                    
property
\n                    
0.03
\n                
\n                
\n                    
housing
\n                    
0.03
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.03
\n                
\n                
\n                    
credit_history
\n                    
0.02
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.02
\n                
\n                
\n                    
other_installment_plans
\n                    
0.02
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.02
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.02
\n                
\n                
\n                    
foreign_worker
\n                    
0.01
\n                
\n            
\n            \n            
\n                NUMERICAL ASSOCIATIONS
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
present_employment_since
\n                    CORRELATION RATIO WITH...
\n            
\n                
\n                    
age_in_years
\n                    
0.21
\n                
\n                
\n                    
duration_in_month
\n                    
0.16
\n                
\n                
\n                    
credit_amount
\n                    
0.10
\n                
\n        
\n    
\n
\n
\n                
\n
\n        \n        
\n            
\n            installment_rate_in_percentage_of_disposable_income\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    65\n            
\n            
\n                    
\n                    (22%)\n            
\n                
\n                        149\n                
\n                
\n                        
\n                        (21%)\n                
\n        
\n
\n\n    \n\n    
 \n        \n        
\n            
TOP CATEGORIES
\n            
\n            
\n            \n                
\n                    \n                
\n        
\n\n        \n            
\n                \n                
4.0
\n\n                \n                
\n                    
123
\n                    
52%
\n                
\n                    
\n                        
249
\n                        
45%
\n                    
\n                \n            
\n            
\n                \n                
2.0
\n\n                \n                
\n                    
50
\n                    
21%
\n                
\n                    
\n                        
137
\n                        
25%
\n                    
\n                \n            
\n            
\n                \n                
3.0
\n\n                \n                
\n                    
35
\n                    
15%
\n                
\n                    
\n                        
91
\n                        
17%
\n                    
\n                \n            
\n            
\n                \n                
1.0
\n\n                \n                
\n                    
27
\n                    
11%
\n                
\n                    
\n                        
74
\n                        
13%
\n                    
\n                \n            
\n                
\n                
\n            
\n                \n                
ALL
\n\n                \n                
\n                    
235
\n                    
100%
\n                
\n                    
\n                        
551
\n                        
100%
\n                    
\n                \n            
\n        
\n    
\n\n    \n    
\n        \n        \n        
\n            \n            
\n                CATEGORICAL ASSOCIATIONS
\n                (UNCERTAINTY COEFFICIENT, 0 to 1)\n            
\n            
installment_rate_in_percentage_of_disposable_income
\n            PROVIDES INFORMATION ON...
\n            
\n                
\n                    
creditability
\n                    
1.00
\n                
\n                
\n                    
foreign_worker
\n                    
0.05
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.03
\n                
\n                
\n                    
job
\n                    
0.03
\n                
\n                
\n                    
purpose
\n                    
0.03
\n                
\n                
\n                    
property
\n                    
0.02
\n                
\n                
\n                    
telephone
\n                    
0.02
\n                
\n                
\n                    
present_residence_since
\n                    
0.02
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.02
\n                
\n                
\n                    
credit_history
\n                    
0.02
\n                
\n                
\n                    
other_installment_plans
\n                    
0.02
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.02
\n                
\n                
\n                    
present_employment_since
\n                    
0.02
\n                
\n                
\n                    
housing
\n                    
0.02
\n                
\n            
\n            
THESE FEATURES
GIVE INFORMATION
\n            ON installment_rate_in_percentage_of_disposable_income:
\n            
\n                
\n                    
purpose
\n                    
0.04
\n                
\n                
\n                    
property
\n                    
0.03
\n                
\n                
\n                    
job
\n                    
0.02
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.02
\n                
\n                
\n                    
present_residence_since
\n                    
0.02
\n                
\n                
\n                    
credit_history
\n                    
0.02
\n                
\n                
\n                    
present_employment_since
\n                    
0.02
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.02
\n                
\n                
\n                    
foreign_worker
\n                    
0.02
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.02
\n                
\n                
\n                    
telephone
\n                    
0.02
\n                
\n                
\n                    
housing
\n                    
0.01
\n                
\n                
\n                    
other_installment_plans
\n                    
0.01
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.01
\n                
\n            
\n            \n            
\n                NUMERICAL ASSOCIATIONS
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
installment_rate_in_percentage_of_disposable_income
\n                    CORRELATION RATIO WITH...
\n            
\n                
\n                    
credit_amount
\n                    
0.25
\n                
\n                
\n                    
age_in_years
\n                    
0.12
\n                
\n                
\n                    
duration_in_month
\n                    
0.10
\n                
\n        
\n    
\n
\n
\n                
\n
\n        \n        
\n            
\n            personal_status_and_sex\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    58\n            
\n            
\n                    
\n                    (19%)\n            
\n                
\n                        151\n                
\n                
\n                        
\n                        (22%)\n                
\n        
\n
\n\n    \n\n    
 \n        \n        
\n            
TOP CATEGORIES
\n            
\n            
\n            \n                
\n                    \n                
\n        
\n\n        \n            
\n                \n                
male : single
\n\n                \n                
\n                    
133
\n                    
55%
\n                
\n                    
\n                        
297
\n                        
54%
\n                    
\n                \n            
\n            
\n                \n                
female : divorced/separated/married
\n\n                \n                
\n                    
73
\n                    
30%
\n                
\n                    
\n                        
178
\n                        
32%
\n                    
\n                \n            
\n            
\n                \n                
male : married/widowed
\n\n                \n                
\n                    
25
\n                    
10%
\n                
\n                    
\n                        
46
\n                        
8%
\n                    
\n                \n            
\n            
\n                \n                
male : divorced/separated
\n\n                \n                
\n                    
11
\n                    
5%
\n                
\n                    
\n                        
28
\n                        
5%
\n                    
\n                \n            
\n            
\n                \n                
female : single
\n\n                \n                
\n                    
0
\n                    
0%
\n                
\n                    
\n                        
0
\n                        
0%
\n                    
\n                \n            
\n                
\n                
\n            
\n                \n                
ALL
\n\n                \n                
\n                    
242
\n                    
100%
\n                
\n                    
\n                        
549
\n                        
100%
\n                    
\n                \n            
\n        
\n    
\n\n    \n    
\n        \n        \n        
\n            \n            
\n                CATEGORICAL ASSOCIATIONS
\n                (UNCERTAINTY COEFFICIENT, 0 to 1)\n            
\n            
personal_status_and_sex
\n            PROVIDES INFORMATION ON...
\n            
\n                
\n                    
creditability
\n                    
1.00
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.04
\n                
\n                
\n                    
purpose
\n                    
0.03
\n                
\n                
\n                    
foreign_worker
\n                    
0.03
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.03
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.03
\n                
\n                
\n                    
other_installment_plans
\n                    
0.02
\n                
\n                
\n                    
credit_history
\n                    
0.02
\n                
\n                
\n                    
property
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n                
\n                    
job
\n                    
0.02
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.02
\n                
\n                
\n                    
present_residence_since
\n                    
0.02
\n                
\n                
\n                    
present_employment_since
\n                    
0.02
\n                
\n            
\n            
THESE FEATURES
GIVE INFORMATION
\n            ON personal_status_and_sex:
\n            
\n                
\n                    
purpose
\n                    
0.05
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.03
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.03
\n                
\n                
\n                    
credit_history
\n                    
0.02
\n                
\n                
\n                    
property
\n                    
0.02
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n                
\n                    
present_employment_since
\n                    
0.02
\n                
\n                
\n                    
present_residence_since
\n                    
0.02
\n                
\n                
\n                    
other_installment_plans
\n                    
0.02
\n                
\n                
\n                    
job
\n                    
0.02
\n                
\n                
\n                    
foreign_worker
\n                    
0.01
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.01
\n                
\n                
\n                    
housing
\n                    
0.01
\n                
\n            
\n            \n            
\n                NUMERICAL ASSOCIATIONS
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
personal_status_and_sex
\n                    CORRELATION RATIO WITH...
\n            
\n                
\n                    
duration_in_month
\n                    
0.12
\n                
\n                
\n                    
age_in_years
\n                    
0.12
\n                
\n                
\n                    
credit_amount
\n                    
0.06
\n                
\n        
\n    
\n
\n
\n                
\n
\n        \n        
\n            
\n            other_debtors_or_guarantors\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    75\n            
\n            
\n                    
\n                    (25%)\n            
\n                
\n                        136\n                
\n                
\n                        
\n                        (19%)\n                
\n        
\n
\n\n    \n\n    
 \n        \n        
\n            
TOP CATEGORIES
\n            
\n            
\n            \n                
\n                    \n                
\n        
\n\n        \n            
\n                \n                
none
\n\n                \n                
\n                    
202
\n                    
90%
\n                
\n                    
\n                        
508
\n                        
90%
\n                    
\n                \n            
\n            
\n                \n                
co-applicant
\n\n                \n                
\n                    
13
\n                    
6%
\n                
\n                    
\n                        
20
\n                        
4%
\n                    
\n                \n            
\n            
\n                \n                
guarantor
\n\n                \n                
\n                    
10
\n                    
4%
\n                
\n                    
\n                        
36
\n                        
6%
\n                    
\n                \n            
\n                
\n                
\n            
\n                \n                
ALL
\n\n                \n                
\n                    
225
\n                    
100%
\n                
\n                    
\n                        
564
\n                        
100%
\n                    
\n                \n            
\n        
\n    
\n\n    \n    
\n        \n        \n        
\n            \n            
\n                CATEGORICAL ASSOCIATIONS
\n                (UNCERTAINTY COEFFICIENT, 0 to 1)\n            
\n            
other_debtors_or_guarantors
\n            PROVIDES INFORMATION ON...
\n            
\n                
\n                    
creditability
\n                    
1.00
\n                
\n                
\n                    
job
\n                    
0.02
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.02
\n                
\n                
\n                    
housing
\n                    
0.02
\n                
\n                
\n                    
credit_history
\n                    
0.02
\n                
\n                
\n                    
present_employment_since
\n                    
0.02
\n                
\n                
\n                    
purpose
\n                    
0.02
\n                
\n                
\n                    
other_installment_plans
\n                    
0.02
\n                
\n                
\n                    
number_of_people_being_liable_to_provide_maintenance_for
\n                    
0.02
\n                
\n                
\n                    
telephone
\n                    
0.01
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.01
\n                
\n                
\n                    
foreign_worker
\n                    
0.01
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.01
\n                
\n                
\n                    
present_residence_since
\n                    
0.01
\n                
\n            
\n            
THESE FEATURES
GIVE INFORMATION
\n            ON other_debtors_or_guarantors:
\n            
\n                
\n                    
purpose
\n                    
0.04
\n                
\n                
\n                    
present_employment_since
\n                    
0.03
\n                
\n                
\n                    
job
\n                    
0.03
\n                
\n                
\n                    
credit_history
\n                    
0.03
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.03
\n                
\n                
\n                    
housing
\n                    
0.03
\n                
\n                
\n                    
other_installment_plans
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.02
\n                
\n                
\n                    
present_residence_since
\n                    
0.02
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.02
\n                
\n                
\n                    
property
\n                    
0.02
\n                
\n                
\n                    
telephone
\n                    
0.02
\n                
\n                
\n                    
number_of_people_being_liable_to_provide_maintenance_for
\n                    
0.02
\n                
\n            
\n            \n            
\n                NUMERICAL ASSOCIATIONS
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
other_debtors_or_guarantors
\n                    CORRELATION RATIO WITH...
\n            
\n                
\n                    
duration_in_month
\n                    
0.16
\n                
\n                
\n                    
credit_amount
\n                    
0.14
\n                
\n                
\n                    
age_in_years
\n                    
0.07
\n                
\n        
\n    
\n
\n
\n                
\n
\n        \n        
\n            
\n            present_residence_since\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    65\n            
\n            
\n                    
\n                    (22%)\n            
\n                
\n                        142\n                
\n                
\n                        
\n                        (20%)\n                
\n        
\n
\n\n    \n\n    
 \n        \n        
\n            
TOP CATEGORIES
\n            
\n            
\n            \n                
\n                    \n                
\n        
\n\n        \n            
\n                \n                
4.0
\n\n                \n                
\n                    
103
\n                    
44%
\n                
\n                    
\n                        
229
\n                        
41%
\n                    
\n                \n            
\n            
\n                \n                
2.0
\n\n                \n                
\n                    
69
\n                    
29%
\n                
\n                    
\n                        
172
\n                        
31%
\n                    
\n                \n            
\n            
\n                \n                
3.0
\n\n                \n                
\n                    
35
\n                    
15%
\n                
\n                    
\n                        
83
\n                        
15%
\n                    
\n                \n            
\n            
\n                \n                
1.0
\n\n                \n                
\n                    
28
\n                    
12%
\n                
\n                    
\n                        
74
\n                        
13%
\n                    
\n                \n            
\n                
\n                
\n            
\n                \n                
ALL
\n\n                \n                
\n                    
235
\n                    
100%
\n                
\n                    
\n                        
558
\n                        
100%
\n                    
\n                \n            
\n        
\n    
\n\n    \n    
\n        \n        \n        
\n            \n            
\n                CATEGORICAL ASSOCIATIONS
\n                (UNCERTAINTY COEFFICIENT, 0 to 1)\n            
\n            
present_residence_since
\n            PROVIDES INFORMATION ON...
\n            
\n                
\n                    
creditability
\n                    
1.00
\n                
\n                
\n                    
housing
\n                    
0.05
\n                
\n                
\n                    
purpose
\n                    
0.04
\n                
\n                
\n                    
present_employment_since
\n                    
0.04
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.03
\n                
\n                
\n                    
job
\n                    
0.02
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.02
\n                
\n                
\n                    
foreign_worker
\n                    
0.02
\n                
\n                
\n                    
property
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n                
\n                    
credit_history
\n                    
0.02
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.02
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.02
\n                
\n                
\n                    
other_installment_plans
\n                    
0.02
\n                
\n            
\n            
THESE FEATURES
GIVE INFORMATION
\n            ON present_residence_since:
\n            
\n                
\n                    
purpose
\n                    
0.06
\n                
\n                
\n                    
housing
\n                    
0.04
\n                
\n                
\n                    
present_employment_since
\n                    
0.04
\n                
\n                
\n                    
property
\n                    
0.02
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n                
\n                    
credit_history
\n                    
0.02
\n                
\n                
\n                    
job
\n                    
0.02
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.02
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.02
\n                
\n                
\n                    
other_installment_plans
\n                    
0.01
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.01
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.01
\n                
\n                
\n                    
foreign_worker
\n                    
0.01
\n                
\n            
\n            \n            
\n                NUMERICAL ASSOCIATIONS
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
present_residence_since
\n                    CORRELATION RATIO WITH...
\n            
\n                
\n                    
duration_in_month
\n                    
0.17
\n                
\n                
\n                    
credit_amount
\n                    
0.17
\n                
\n                
\n                    
age_in_years
\n                    
0.14
\n                
\n        
\n    
\n
\n
\n                
\n
\n        \n        
\n            
\n            property\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    74\n            
\n            
\n                    
\n                    (25%)\n            
\n                
\n                        154\n                
\n                
\n                        
\n                        (22%)\n                
\n        
\n
\n\n    \n\n    
 \n        \n        
\n            
TOP CATEGORIES
\n            
\n            
\n            \n                
\n                    \n                
\n        
\n\n        \n            
\n                \n                
car or other, not in attribute Savings account/bonds
\n\n                \n                
\n                    
75
\n                    
33%
\n                
\n                    
\n                        
179
\n                        
33%
\n                    
\n                \n            
\n            
\n                \n                
building society savings agreement/ life insurance
\n\n                \n                
\n                    
53
\n                    
23%
\n                
\n                    
\n                        
121
\n                        
22%
\n                    
\n                \n            
\n            
\n                \n                
unknown / no property
\n\n                \n                
\n                    
51
\n                    
23%
\n                
\n                    
\n                        
67
\n                        
12%
\n                    
\n                \n            
\n            
\n                \n                
real estate
\n\n                \n                
\n                    
47
\n                    
21%
\n                
\n                    
\n                        
179
\n                        
33%
\n                    
\n                \n            
\n                
\n                
\n            
\n                \n                
ALL
\n\n                \n                
\n                    
226
\n                    
100%
\n                
\n                    
\n                        
546
\n                        
100%
\n                    
\n                \n            
\n        
\n    
\n\n    \n    
\n        \n        \n        
\n            \n            
\n                CATEGORICAL ASSOCIATIONS
\n                (UNCERTAINTY COEFFICIENT, 0 to 1)\n            
\n            
property
\n            PROVIDES INFORMATION ON...
\n            
\n                
\n                    
creditability
\n                    
1.00
\n                
\n                
\n                    
housing
\n                    
0.13
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.03
\n                
\n                
\n                    
job
\n                    
0.03
\n                
\n                
\n                    
present_employment_since
\n                    
0.03
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.03
\n                
\n                
\n                    
telephone
\n                    
0.02
\n                
\n                
\n                    
purpose
\n                    
0.02
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.02
\n                
\n                
\n                    
present_residence_since
\n                    
0.02
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.02
\n                
\n                
\n                    
foreign_worker
\n                    
0.02
\n                
\n                
\n                    
other_installment_plans
\n                    
0.02
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.02
\n                
\n            
\n            
THESE FEATURES
GIVE INFORMATION
\n            ON property:
\n            
\n                
\n                    
housing
\n                    
0.10
\n                
\n                
\n                    
present_employment_since
\n                    
0.03
\n                
\n                
\n                    
purpose
\n                    
0.03
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.03
\n                
\n                
\n                    
job
\n                    
0.03
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n                
\n                    
present_residence_since
\n                    
0.02
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.02
\n                
\n                
\n                    
telephone
\n                    
0.02
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.02
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.02
\n                
\n                
\n                    
credit_history
\n                    
0.01
\n                
\n                
\n                    
other_installment_plans
\n                    
0.01
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.01
\n                
\n            
\n            \n            
\n                NUMERICAL ASSOCIATIONS
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
property
\n                    CORRELATION RATIO WITH...
\n            
\n                
\n                    
credit_amount
\n                    
0.26
\n                
\n                
\n                    
age_in_years
\n                    
0.24
\n                
\n                
\n                    
duration_in_month
\n                    
0.14
\n                
\n        
\n    
\n
\n
\n                
\n
\n        \n        
\n            
\n            age_in_years\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    58\n            
\n            
\n                    
\n                    (19%)\n            
\n                
\n                        134\n                
\n                
\n                        
\n                        (19%)\n                
\n        
\n
\n\n    \n    
\n            \n            \n            \n            \n    
\n\n    \n    \n\n    \n    
\n        \n            >\n        
\n            
NUMERICAL ASSOCIATIONS
\n            
\n                (PEARSON, -1 to 1)\n            
\n\n            
\n                
\n                    
credit_amount
\n                    
0.20
\n                
\n                
\n                    
duration_in_month
\n                    
-0.01
\n                
\n            
\n            
CATEGORICAL ASSOCIATIONS
\n            
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
\n                
\n                    
property
\n                    
0.24
\n                
\n                
\n                    
housing
\n                    
0.24
\n                
\n                
\n                    
present_employment_since
\n                    
0.21
\n                
\n                
\n                    
purpose
\n                    
0.20
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.18
\n                
\n                
\n                    
credit_history
\n                    
0.17
\n                
\n                
\n                    
job
\n                    
0.15
\n                
\n                
\n                    
present_residence_since
\n                    
0.14
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.12
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.12
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.12
\n                
\n                
\n                    
number_of_people_being_liable_to_provide_maintenance_for
\n                    
0.10
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.07
\n                
\n                
\n                    
telephone
\n                    
0.07
\n                
\n        
\n    
\n    \n    
\n        \n        
\n            
MOST FREQUENT VALUES
\n            
\n                
\n                    
25.0
\n                    
\n                        
17
\n                        
7.0%
\n                    
\n                        
\n                            
17
\n                            
3.0%
\n                        
\n                
\n                
\n                    
23.0
\n                    
\n                        
16
\n                        
6.6%
\n                    
\n                        
\n                            
20
\n                            
3.5%
\n                        
\n                
\n                
\n                    
24.0
\n                    
\n                        
16
\n                        
6.6%
\n                    
\n                        
\n                            
20
\n                            
3.5%
\n                        
\n                
\n                
\n                    
28.0
\n                    
\n                        
12
\n                        
5.0%
\n                    
\n                        
\n                            
24
\n                            
4.2%
\n                        
\n                
\n                
\n                    
29.0
\n                    
\n                        
12
\n                        
5.0%
\n                    
\n                        
\n                            
14
\n                            
2.5%
\n                        
\n                
\n                
\n                    
33.0
\n                    
\n                        
12
\n                        
5.0%
\n                    
\n                        
\n                            
17
\n                            
3.0%
\n                        
\n                
\n                
\n                    
26.0
\n                    
\n                        
11
\n                        
4.5%
\n                    
\n                        
\n                            
30
\n                            
5.3%
\n                        
\n                
\n                
\n                    
31.0
\n                    
\n                        
10
\n                        
4.1%
\n                    
\n                        
\n                            
22
\n                            
3.9%
\n                        
\n                
\n                
\n                    
27.0
\n                    
\n                        
9
\n                        
3.7%
\n                    
\n                        
\n                            
31
\n                            
5.5%
\n                        
\n                
\n                
\n                    
34.0
\n                    
\n                        
8
\n                        
3.3%
\n                    
\n                        
\n                            
19
\n                            
3.4%
\n                        
\n                
\n                
\n                    
30.0
\n                    
\n                        
8
\n                        
3.3%
\n                    
\n                        
\n                            
24
\n                            
4.2%
\n                        
\n                
\n                
\n                    
32.0
\n                    
\n                        
8
\n                        
3.3%
\n                    
\n                        
\n                            
19
\n                            
3.4%
\n                        
\n                
\n                
\n                    
22.0
\n                    
\n                        
7
\n                        
2.9%
\n                    
\n                        
\n                            
12
\n                            
2.1%
\n                        
\n                
\n                
\n                    
42.0
\n                    
\n                        
6
\n                        
2.5%
\n                    
\n                        
\n                            
14
\n                            
2.5%
\n                        
\n                
\n                
\n                    
39.0
\n                    
\n                        
6
\n                        
2.5%
\n                    
\n                        
\n                            
11
\n                            
1.9%
\n                        
\n                
\n        
\n    
\n    \n    
\n        \n        
\n            
 SMALLEST VALUES
\n            
\n                
\n                    
19.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
1
\n                            
0.2%
\n                        
\n                
\n                
\n                    
20.0
\n                    
\n                        
3
\n                        
1.2%
\n                    
\n                        
\n                            
7
\n                            
1.2%
\n                        
\n                
\n                
\n                    
21.0
\n                    
\n                        
5
\n                        
2.1%
\n                    
\n                        
\n                            
7
\n                            
1.2%
\n                        
\n                
\n                
\n                    
22.0
\n                    
\n                        
7
\n                        
2.9%
\n                    
\n                        
\n                            
12
\n                            
2.1%
\n                        
\n                
\n                
\n                    
23.0
\n                    
\n                        
16
\n                        
6.6%
\n                    
\n                        
\n                            
20
\n                            
3.5%
\n                        
\n                
\n                
\n                    
24.0
\n                    
\n                        
16
\n                        
6.6%
\n                    
\n                        
\n                            
20
\n                            
3.5%
\n                        
\n                
\n                
\n                    
25.0
\n                    
\n                        
17
\n                        
7.0%
\n                    
\n                        
\n                            
17
\n                            
3.0%
\n                        
\n                
\n                
\n                    
26.0
\n                    
\n                        
11
\n                        
4.5%
\n                    
\n                        
\n                            
30
\n                            
5.3%
\n                        
\n                
\n                
\n                    
27.0
\n                    
\n                        
9
\n                        
3.7%
\n                    
\n                        
\n                            
31
\n                            
5.5%
\n                        
\n                
\n                
\n                    
28.0
\n                    
\n                        
12
\n                        
5.0%
\n                    
\n                        
\n                            
24
\n                            
4.2%
\n                        
\n                
\n                
\n                    
29.0
\n                    
\n                        
12
\n                        
5.0%
\n                    
\n                        
\n                            
14
\n                            
2.5%
\n                        
\n                
\n                
\n                    
30.0
\n                    
\n                        
8
\n                        
3.3%
\n                    
\n                        
\n                            
24
\n                            
4.2%
\n                        
\n                
\n                
\n                    
31.0
\n                    
\n                        
10
\n                        
4.1%
\n                    
\n                        
\n                            
22
\n                            
3.9%
\n                        
\n                
\n                
\n                    
32.0
\n                    
\n                        
8
\n                        
3.3%
\n                    
\n                        
\n                            
19
\n                            
3.4%
\n                        
\n                
\n                
\n                    
33.0
\n                    
\n                        
12
\n                        
5.0%
\n                    
\n                        
\n                            
17
\n                            
3.0%
\n                        
\n                
\n        
\n    
\n    \n    
\n        \n        
\n            
LARGEST VALUES
\n            
\n                
\n                    
74.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
3
\n                            
0.5%
\n                        
\n                
\n                
\n                    
68.0
\n                    
\n                        
2
\n                        
0.8%
\n                    
\n                        
\n                            
1
\n                            
0.2%
\n                        
\n                
\n                
\n                    
66.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
2
\n                            
0.4%
\n                        
\n                
\n                
\n                    
65.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
4
\n                            
0.7%
\n                        
\n                
\n                
\n                    
63.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
7
\n                            
1.2%
\n                        
\n                
\n                
\n                    
61.0
\n                    
\n                        
2
\n                        
0.8%
\n                    
\n                        
\n                            
3
\n                            
0.5%
\n                        
\n                
\n                
\n                    
60.0
\n                    
\n                        
3
\n                        
1.2%
\n                    
\n                        
\n                            
3
\n                            
0.5%
\n                        
\n                
\n                
\n                    
59.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
1
\n                            
0.2%
\n                        
\n                
\n                
\n                    
58.0
\n                    
\n                        
2
\n                        
0.8%
\n                    
\n                        
\n                            
2
\n                            
0.4%
\n                        
\n                
\n                
\n                    
57.0
\n                    
\n                        
3
\n                        
1.2%
\n                    
\n                        
\n                            
4
\n                            
0.7%
\n                        
\n                
\n                
\n                    
55.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
5
\n                            
0.9%
\n                        
\n                
\n                
\n                    
54.0
\n                    
\n                        
2
\n                        
0.8%
\n                    
\n                        
\n                            
6
\n                            
1.1%
\n                        
\n                
\n                
\n                    
53.0
\n                    
\n                        
3
\n                        
1.2%
\n                    
\n                        
\n                            
2
\n                            
0.4%
\n                        
\n                
\n                
\n                    
52.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
7
\n                            
1.2%
\n                        
\n                
\n                
\n                    
51.0
\n                    
\n                        
1
\n                        
0.4%
\n                    
\n                        
\n                            
5
\n                            
0.9%
\n                        
\n                
\n        
\n    
\n\n
\n
\n                
\n
\n        \n        
\n            
\n            other_installment_plans\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    70\n            
\n            
\n                    
\n                    (23%)\n            
\n                
\n                        131\n                
\n                
\n                        
\n                        (19%)\n                
\n        
\n
\n\n    \n\n    
 \n        \n        
\n            
TOP CATEGORIES
\n            
\n            
\n            \n                
\n                    \n                
\n        
\n\n        \n            
\n                \n                
none
\n\n                \n                
\n                    
169
\n                    
73%
\n                
\n                    
\n                        
481
\n                        
85%
\n                    
\n                \n            
\n            
\n                \n                
bank
\n\n                \n                
\n                    
48
\n                    
21%
\n                
\n                    
\n                        
64
\n                        
11%
\n                    
\n                \n            
\n            
\n                \n                
stores
\n\n                \n                
\n                    
13
\n                    
6%
\n                
\n                    
\n                        
24
\n                        
4%
\n                    
\n                \n            
\n                
\n                
\n            
\n                \n                
ALL
\n\n                \n                
\n                    
230
\n                    
100%
\n                
\n                    
\n                        
569
\n                        
100%
\n                    
\n                \n            
\n        
\n    
\n\n    \n    
\n        \n        \n        
\n            \n            
\n                CATEGORICAL ASSOCIATIONS
\n                (UNCERTAINTY COEFFICIENT, 0 to 1)\n            
\n            
other_installment_plans
\n            PROVIDES INFORMATION ON...
\n            
\n                
\n                    
creditability
\n                    
1.00
\n                
\n                
\n                    
purpose
\n                    
0.02
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.02
\n                
\n                
\n                    
credit_history
\n                    
0.02
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.02
\n                
\n                
\n                    
number_of_people_being_liable_to_provide_maintenance_for
\n                    
0.02
\n                
\n                
\n                    
present_employment_since
\n                    
0.02
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.01
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.01
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.01
\n                
\n                
\n                    
property
\n                    
0.01
\n                
\n                
\n                    
present_residence_since
\n                    
0.01
\n                
\n                
\n                    
job
\n                    
0.01
\n                
\n            
\n            
THESE FEATURES
GIVE INFORMATION
\n            ON other_installment_plans:
\n            
\n                
\n                    
purpose
\n                    
0.04
\n                
\n                
\n                    
credit_history
\n                    
0.03
\n                
\n                
\n                    
present_employment_since
\n                    
0.03
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.02
\n                
\n                
\n                    
property
\n                    
0.02
\n                
\n                
\n                    
present_residence_since
\n                    
0.02
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.02
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.02
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.01
\n                
\n                
\n                    
number_of_people_being_liable_to_provide_maintenance_for
\n                    
0.01
\n                
\n                
\n                    
job
\n                    
0.01
\n                
\n                
\n                    
housing
\n                    
0.01
\n                
\n            
\n            \n            
\n                NUMERICAL ASSOCIATIONS
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
other_installment_plans
\n                    CORRELATION RATIO WITH...
\n            
\n                
\n                    
credit_amount
\n                    
0.12
\n                
\n                
\n                    
duration_in_month
\n                    
0.06
\n                
\n                
\n                    
age_in_years
\n                    
0.04
\n                
\n        
\n    
\n
\n
\n                
\n
\n        \n        
\n            
\n            housing\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    66\n            
\n            
\n                    
\n                    (22%)\n            
\n                
\n                        134\n                
\n                
\n                        
\n                        (19%)\n                
\n        
\n
\n\n    \n\n    
 \n        \n        
\n            
TOP CATEGORIES
\n            
\n            
\n            \n                
\n                    \n                
\n        
\n\n        \n            
\n                \n                
own
\n\n                \n                
\n                    
145
\n                    
62%
\n                
\n                    
\n                        
425
\n                        
75%
\n                    
\n                \n            
\n            
\n                \n                
rent
\n\n                \n                
\n                    
55
\n                    
24%
\n                
\n                    
\n                        
92
\n                        
16%
\n                    
\n                \n            
\n            
\n                \n                
for free
\n\n                \n                
\n                    
34
\n                    
15%
\n                
\n                    
\n                        
49
\n                        
9%
\n                    
\n                \n            
\n                
\n                
\n            
\n                \n                
ALL
\n\n                \n                
\n                    
234
\n                    
100%
\n                
\n                    
\n                        
566
\n                        
100%
\n                    
\n                \n            
\n        
\n    
\n\n    \n    
\n        \n        \n        
\n            \n            
\n                CATEGORICAL ASSOCIATIONS
\n                (UNCERTAINTY COEFFICIENT, 0 to 1)\n            
\n            
housing
\n            PROVIDES INFORMATION ON...
\n            
\n                
\n                    
creditability
\n                    
1.00
\n                
\n                
\n                    
property
\n                    
0.10
\n                
\n                
\n                    
present_residence_since
\n                    
0.04
\n                
\n                
\n                    
purpose
\n                    
0.04
\n                
\n                
\n                    
number_of_people_being_liable_to_provide_maintenance_for
\n                    
0.04
\n                
\n                
\n                    
present_employment_since
\n                    
0.03
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.03
\n                
\n                
\n                    
credit_history
\n                    
0.02
\n                
\n                
\n                    
job
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.01
\n                
\n                
\n                    
telephone
\n                    
0.01
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.01
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.01
\n                
\n                
\n                    
other_installment_plans
\n                    
0.01
\n                
\n            
\n            
THESE FEATURES
GIVE INFORMATION
\n            ON housing:
\n            
\n                
\n                    
property
\n                    
0.13
\n                
\n                
\n                    
purpose
\n                    
0.07
\n                
\n                
\n                    
present_residence_since
\n                    
0.05
\n                
\n                
\n                    
present_employment_since
\n                    
0.04
\n                
\n                
\n                    
credit_history
\n                    
0.03
\n                
\n                
\n                    
number_of_people_being_liable_to_provide_maintenance_for
\n                    
0.03
\n                
\n                
\n                    
job
\n                    
0.02
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.01
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.01
\n                
\n                
\n                    
telephone
\n                    
0.01
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.01
\n                
\n                
\n                    
other_installment_plans
\n                    
0.01
\n                
\n            
\n            \n            
\n                NUMERICAL ASSOCIATIONS
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
housing
\n                    CORRELATION RATIO WITH...
\n            
\n                
\n                    
age_in_years
\n                    
0.24
\n                
\n                
\n                    
credit_amount
\n                    
0.17
\n                
\n                
\n                    
duration_in_month
\n                    
0.14
\n                
\n        
\n    
\n
\n
\n                
\n
\n        \n        
\n            
\n            number_of_existing_credits_at_this_bank\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    69\n            
\n            
\n                    
\n                    (23%)\n            
\n                
\n                        131\n                
\n                
\n                        
\n                        (19%)\n                
\n        
\n
\n\n    \n\n    
 \n        \n        
\n            
TOP CATEGORIES
\n            
\n            
\n            \n                
\n                    \n                
\n        
\n\n        \n            
\n                \n                
1.0
\n\n                \n                
\n                    
158
\n                    
68%
\n                
\n                    
\n                        
348
\n                        
61%
\n                    
\n                \n            
\n            
\n                \n                
2.0
\n\n                \n                
\n                    
68
\n                    
29%
\n                
\n                    
\n                        
197
\n                        
35%
\n                    
\n                \n            
\n            
\n                \n                
3.0
\n\n                \n                
\n                    
3
\n                    
1%
\n                
\n                    
\n                        
20
\n                        
4%
\n                    
\n                \n            
\n            
\n                \n                
4.0
\n\n                \n                
\n                    
2
\n                    
<1%
\n                
\n                    
\n                        
4
\n                        
<1%
\n                    
\n                \n            
\n                
\n                
\n            
\n                \n                
ALL
\n\n                \n                
\n                    
231
\n                    
100%
\n                
\n                    
\n                        
569
\n                        
100%
\n                    
\n                \n            
\n        
\n    
\n\n    \n    
\n        \n        \n        
\n            \n            
\n                CATEGORICAL ASSOCIATIONS
\n                (UNCERTAINTY COEFFICIENT, 0 to 1)\n            
\n            
number_of_existing_credits_at_this_bank
\n            PROVIDES INFORMATION ON...
\n            
\n                
\n                    
creditability
\n                    
1.00
\n                
\n                
\n                    
credit_history
\n                    
0.10
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.04
\n                
\n                
\n                    
purpose
\n                    
0.04
\n                
\n                
\n                    
foreign_worker
\n                    
0.04
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n                
\n                    
telephone
\n                    
0.02
\n                
\n                
\n                    
job
\n                    
0.02
\n                
\n                
\n                    
present_residence_since
\n                    
0.02
\n                
\n                
\n                    
present_employment_since
\n                    
0.02
\n                
\n                
\n                    
number_of_people_being_liable_to_provide_maintenance_for
\n                    
0.02
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.02
\n                
\n                
\n                    
property
\n                    
0.02
\n                
\n            
\n            
THESE FEATURES
GIVE INFORMATION
\n            ON number_of_existing_credits_at_this_bank:
\n            
\n                
\n                    
credit_history
\n                    
0.15
\n                
\n                
\n                    
purpose
\n                    
0.08
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.05
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.03
\n                
\n                
\n                    
present_residence_since
\n                    
0.03
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.03
\n                
\n                
\n                    
present_employment_since
\n                    
0.03
\n                
\n                
\n                    
job
\n                    
0.03
\n                
\n                
\n                    
property
\n                    
0.02
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.02
\n                
\n                
\n                    
telephone
\n                    
0.02
\n                
\n                
\n                    
foreign_worker
\n                    
0.02
\n                
\n                
\n                    
other_installment_plans
\n                    
0.01
\n                
\n                
\n                    
number_of_people_being_liable_to_provide_maintenance_for
\n                    
0.01
\n                
\n            
\n            \n            
\n                NUMERICAL ASSOCIATIONS
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
number_of_existing_credits_at_this_bank
\n                    CORRELATION RATIO WITH...
\n            
\n                
\n                    
age_in_years
\n                    
0.18
\n                
\n                
\n                    
duration_in_month
\n                    
0.13
\n                
\n                
\n                    
credit_amount
\n                    
0.07
\n                
\n        
\n    
\n
\n
\n                
\n
\n        \n        
\n            
\n            job\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    68\n            
\n            
\n                    
\n                    (23%)\n            
\n                
\n                        167\n                
\n                
\n                        
\n                        (24%)\n                
\n        
\n
\n\n    \n\n    
 \n        \n        
\n            
TOP CATEGORIES
\n            
\n            
\n            \n                
\n                    \n                
\n        
\n\n        \n            
\n                \n                
skilled employee / official
\n\n                \n                
\n                    
148
\n                    
64%
\n                
\n                    
\n                        
333
\n                        
62%
\n                    
\n                \n            
\n            
\n                \n                
unskilled - resident
\n\n                \n                
\n                    
40
\n                    
17%
\n                
\n                    
\n                        
117
\n                        
22%
\n                    
\n                \n            
\n            
\n                \n                
management/ self-employed/ highly qualified employee/ officer
\n\n                \n                
\n                    
39
\n                    
17%
\n                
\n                    
\n                        
72
\n                        
14%
\n                    
\n                \n            
\n            
\n                \n                
unemployed/ unskilled - non-resident
\n\n                \n                
\n                    
5
\n                    
2%
\n                
\n                    
\n                        
11
\n                        
2%
\n                    
\n                \n            
\n                
\n                
\n            
\n                \n                
ALL
\n\n                \n                
\n                    
232
\n                    
100%
\n                
\n                    
\n                        
533
\n                        
100%
\n                    
\n                \n            
\n        
\n    
\n\n    \n    
\n        \n        \n        
\n            \n            
\n                CATEGORICAL ASSOCIATIONS
\n                (UNCERTAINTY COEFFICIENT, 0 to 1)\n            
\n            
job
\n            PROVIDES INFORMATION ON...
\n            
\n                
\n                    
creditability
\n                    
1.00
\n                
\n                
\n                    
present_employment_since
\n                    
0.07
\n                
\n                
\n                    
telephone
\n                    
0.07
\n                
\n                
\n                    
foreign_worker
\n                    
0.04
\n                
\n                
\n                    
purpose
\n                    
0.04
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.03
\n                
\n                
\n                    
property
\n                    
0.03
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.03
\n                
\n                
\n                    
number_of_people_being_liable_to_provide_maintenance_for
\n                    
0.03
\n                
\n                
\n                    
housing
\n                    
0.02
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.02
\n                
\n                
\n                    
present_residence_since
\n                    
0.02
\n                
\n            
\n            
THESE FEATURES
GIVE INFORMATION
\n            ON job:
\n            
\n                
\n                    
present_employment_since
\n                    
0.09
\n                
\n                
\n                    
purpose
\n                    
0.06
\n                
\n                
\n                    
telephone
\n                    
0.05
\n                
\n                
\n                    
property
\n                    
0.03
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.03
\n                
\n                
\n                    
present_residence_since
\n                    
0.02
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.02
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.02
\n                
\n                
\n                    
credit_history
\n                    
0.02
\n                
\n                
\n                    
housing
\n                    
0.02
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.02
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.02
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.02
\n                
\n                
\n                    
foreign_worker
\n                    
0.02
\n                
\n            
\n            \n            
\n                NUMERICAL ASSOCIATIONS
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
job
\n                    CORRELATION RATIO WITH...
\n            
\n                
\n                    
credit_amount
\n                    
0.27
\n                
\n                
\n                    
age_in_years
\n                    
0.15
\n                
\n                
\n                    
duration_in_month
\n                    
0.09
\n                
\n        
\n    
\n
\n
\n                
\n
\n        \n        
\n            
\n            number_of_people_being_liable_to_provide_maintenance_for\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    55\n            
\n            
\n                    
\n                    (18%)\n            
\n                
\n                        145\n                
\n                
\n                        
\n                        (21%)\n                
\n        
\n
\n\n    \n\n    
 \n        \n        
\n            
TOP CATEGORIES
\n            
\n            
\n            \n                
\n                    \n                
\n        
\n\n        \n            
\n                \n                
1.0
\n\n                \n                
\n                    
208
\n                    
85%
\n                
\n                    
\n                        
466
\n                        
84%
\n                    
\n                \n            
\n            
\n                \n                
2.0
\n\n                \n                
\n                    
37
\n                    
15%
\n                
\n                    
\n                        
89
\n                        
16%
\n                    
\n                \n            
\n                
\n                
\n            
\n                \n                
ALL
\n\n                \n                
\n                    
245
\n                    
100%
\n                
\n                    
\n                        
555
\n                        
100%
\n                    
\n                \n            
\n        
\n    
\n\n    \n    
\n        \n        \n        
\n            \n            
\n                CATEGORICAL ASSOCIATIONS
\n                (UNCERTAINTY COEFFICIENT, 0 to 1)\n            
\n            
number_of_people_being_liable_to_provide_maintenance_for
\n            PROVIDES INFORMATION ON...
\n            
\n                
\n                    
creditability
\n                    
1.00
\n                
\n                
\n                    
housing
\n                    
0.03
\n                
\n                
\n                    
purpose
\n                    
0.02
\n                
\n                
\n                    
telephone
\n                    
0.02
\n                
\n                
\n                    
job
\n                    
0.02
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.02
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.01
\n                
\n                
\n                    
other_installment_plans
\n                    
0.01
\n                
\n                
\n                    
credit_history
\n                    
0.01
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.01
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.01
\n                
\n                
\n                    
present_employment_since
\n                    
0.01
\n                
\n                
\n                    
present_residence_since
\n                    
0.01
\n                
\n                
\n                    
foreign_worker
\n                    
0.01
\n                
\n            
\n            
THESE FEATURES
GIVE INFORMATION
\n            ON number_of_people_being_liable_to_provide_maintenance_for:
\n            
\n                
\n                    
purpose
\n                    
0.05
\n                
\n                
\n                    
housing
\n                    
0.04
\n                
\n                
\n                    
job
\n                    
0.03
\n                
\n                
\n                    
credit_history
\n                    
0.02
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.02
\n                
\n                
\n                    
telephone
\n                    
0.02
\n                
\n                
\n                    
present_employment_since
\n                    
0.02
\n                
\n                
\n                    
other_installment_plans
\n                    
0.02
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.02
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.02
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.02
\n                
\n                
\n                    
present_residence_since
\n                    
0.01
\n                
\n                
\n                    
property
\n                    
0.01
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.01
\n                
\n            
\n            \n            
\n                NUMERICAL ASSOCIATIONS
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
number_of_people_being_liable_to_provide_maintenance_for
\n                    CORRELATION RATIO WITH...
\n            
\n                
\n                    
age_in_years
\n                    
0.10
\n                
\n                
\n                    
duration_in_month
\n                    
0.10
\n                
\n                
\n                    
credit_amount
\n                    
0.02
\n                
\n        
\n    
\n
\n
\n                
\n
\n        \n        
\n            
\n            telephone\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    57\n            
\n            
\n                    
\n                    (19%)\n            
\n                
\n                        152\n                
\n                
\n                        
\n                        (22%)\n                
\n        
\n
\n\n    \n\n    
 \n        \n        
\n            
TOP CATEGORIES
\n            
\n            
\n            \n                
\n                    \n                
\n        
\n\n        \n            
\n                \n                
none
\n\n                \n                
\n                    
145
\n                    
60%
\n                
\n                    
\n                        
326
\n                        
59%
\n                    
\n                \n            
\n            
\n                \n                
yes, registered under the customers name
\n\n                \n                
\n                    
98
\n                    
40%
\n                
\n                    
\n                        
222
\n                        
41%
\n                    
\n                \n            
\n                
\n                
\n            
\n                \n                
ALL
\n\n                \n                
\n                    
243
\n                    
100%
\n                
\n                    
\n                        
548
\n                        
100%
\n                    
\n                \n            
\n        
\n    
\n\n    \n    
\n        \n        \n        
\n            \n            
\n                CATEGORICAL ASSOCIATIONS
\n                (UNCERTAINTY COEFFICIENT, 0 to 1)\n            
\n            
telephone
\n            PROVIDES INFORMATION ON...
\n            
\n                
\n                    
creditability
\n                    
1.00
\n                
\n                
\n                    
job
\n                    
0.05
\n                
\n                
\n                    
purpose
\n                    
0.04
\n                
\n                
\n                    
number_of_people_being_liable_to_provide_maintenance_for
\n                    
0.02
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.02
\n                
\n                
\n                    
property
\n                    
0.02
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n                
\n                    
housing
\n                    
0.01
\n                
\n                
\n                    
foreign_worker
\n                    
0.01
\n                
\n                
\n                    
present_employment_since
\n                    
0.01
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.01
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.01
\n                
\n                
\n                    
present_residence_since
\n                    
0.01
\n                
\n            
\n            
THESE FEATURES
GIVE INFORMATION
\n            ON telephone:
\n            
\n                
\n                    
purpose
\n                    
0.07
\n                
\n                
\n                    
job
\n                    
0.07
\n                
\n                
\n                    
property
\n                    
0.02
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n                
\n                    
number_of_people_being_liable_to_provide_maintenance_for
\n                    
0.02
\n                
\n                
\n                    
present_employment_since
\n                    
0.01
\n                
\n                
\n                    
housing
\n                    
0.01
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.01
\n                
\n                
\n                    
present_residence_since
\n                    
0.01
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.01
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.01
\n                
\n                
\n                    
credit_history
\n                    
0.01
\n                
\n                
\n                    
foreign_worker
\n                    
0.01
\n                
\n            
\n            \n            
\n                NUMERICAL ASSOCIATIONS
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
telephone
\n                    CORRELATION RATIO WITH...
\n            
\n                
\n                    
credit_amount
\n                    
0.33
\n                
\n                
\n                    
age_in_years
\n                    
0.07
\n                
\n                
\n                    
duration_in_month
\n                    
0.06
\n                
\n        
\n    
\n
\n
\n                
\n
\n        \n        
\n            
\n            foreign_worker\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    65\n            
\n            
\n                    
\n                    (22%)\n            
\n                
\n                        136\n                
\n                
\n                        
\n                        (19%)\n                
\n        
\n
\n\n    \n\n    
 \n        \n        
\n            
TOP CATEGORIES
\n            
\n            
\n            \n                
\n                    \n                
\n        
\n\n        \n            
\n                \n                
yes
\n\n                \n                
\n                    
232
\n                    
99%
\n                
\n                    
\n                        
540
\n                        
96%
\n                    
\n                \n            
\n            
\n                \n                
no
\n\n                \n                
\n                    
3
\n                    
1%
\n                
\n                    
\n                        
24
\n                        
4%
\n                    
\n                \n            
\n                
\n                
\n            
\n                \n                
ALL
\n\n                \n                
\n                    
235
\n                    
100%
\n                
\n                    
\n                        
564
\n                        
100%
\n                    
\n                \n            
\n        
\n    
\n\n    \n    
\n        \n        \n        
\n            \n            
\n                CATEGORICAL ASSOCIATIONS
\n                (UNCERTAINTY COEFFICIENT, 0 to 1)\n            
\n            
foreign_worker
\n            PROVIDES INFORMATION ON...
\n            
\n                
\n                    
creditability
\n                    
1.00
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.02
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.02
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.02
\n                
\n                
\n                    
job
\n                    
0.02
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.02
\n                
\n                
\n                    
credit_history
\n                    
0.01
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.01
\n                
\n                
\n                    
purpose
\n                    
0.01
\n                
\n                
\n                    
present_employment_since
\n                    
0.01
\n                
\n                
\n                    
present_residence_since
\n                    
0.01
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.01
\n                
\n                
\n                    
property
\n                    
0.01
\n                
\n                
\n                    
telephone
\n                    
0.01
\n                
\n            
\n            
THESE FEATURES
GIVE INFORMATION
\n            ON foreign_worker:
\n            
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.05
\n                
\n                
\n                    
status_of_existing_checking_account
\n                    
0.05
\n                
\n                
\n                    
job
\n                    
0.04
\n                
\n                
\n                    
purpose
\n                    
0.04
\n                
\n                
\n                    
credit_history
\n                    
0.04
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.04
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.03
\n                
\n                
\n                    
present_employment_since
\n                    
0.03
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.03
\n                
\n                
\n                    
present_residence_since
\n                    
0.02
\n                
\n                
\n                    
property
\n                    
0.02
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.01
\n                
\n                
\n                    
telephone
\n                    
0.01
\n                
\n                
\n                    
housing
\n                    
0.01
\n                
\n            
\n            \n            
\n                NUMERICAL ASSOCIATIONS
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
foreign_worker
\n                    CORRELATION RATIO WITH...
\n            
\n                
\n                    
credit_amount
\n                    
0.09
\n                
\n                
\n                    
age_in_years
\n                    
0.04
\n                
\n                
\n                    
duration_in_month
\n                    
0.02
\n                
\n        
\n    
\n
\n
\n                
\n
\n        \n        
\n            
\n            creditability\n        
\n    
\n        
\n            
MISSING:
\n            
\n                    ---\n            
\n            
\n\n            
\n                
\n                        ---\n                
\n                
\n\n                
\n        
\n
\n\n    \n\n    
 \n        \n        
\n            
TOP CATEGORIES
\n            
\n            
\n            \n                
\n                    \n                
\n        
\n\n        \n            
\n                \n                
1
\n\n                \n                
\n                    
300
\n                    
100%
\n                
\n                    
\n                        
0
\n                        
0%
\n                    
\n                \n            
\n            
\n                \n                
0
\n\n                \n                
\n                    
0
\n                    
0%
\n                
\n                    
\n                        
700
\n                        
100%
\n                    
\n                \n            
\n                
\n                
\n            
\n                \n                
ALL
\n\n                \n                
\n                    
300
\n                    
100%
\n                
\n                    
\n                        
700
\n                        
100%
\n                    
\n                \n            
\n        
\n    
\n\n    \n    
\n        \n        \n        
\n            \n            
\n                CATEGORICAL ASSOCIATIONS
\n                (UNCERTAINTY COEFFICIENT, 0 to 1)\n            
\n            
creditability
\n            PROVIDES INFORMATION ON...
\n            
\n                
\n                    
status_of_existing_checking_account
\n                    
0.00
\n                
\n                
\n                    
credit_history
\n                    
0.00
\n                
\n                
\n                    
purpose
\n                    
0.00
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
0.00
\n                
\n                
\n                    
present_employment_since
\n                    
0.00
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
0.00
\n                
\n                
\n                    
personal_status_and_sex
\n                    
0.00
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
0.00
\n                
\n                
\n                    
present_residence_since
\n                    
0.00
\n                
\n                
\n                    
property
\n                    
0.00
\n                
\n                
\n                    
other_installment_plans
\n                    
0.00
\n                
\n                
\n                    
housing
\n                    
0.00
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
0.00
\n                
\n                
\n                    
job
\n                    
0.00
\n                
\n            
\n            
THESE FEATURES
GIVE INFORMATION
\n            ON creditability:
\n            
\n                
\n                    
status_of_existing_checking_account
\n                    
1.00
\n                
\n                
\n                    
credit_history
\n                    
1.00
\n                
\n                
\n                    
purpose
\n                    
1.00
\n                
\n                
\n                    
savings_account_and_bonds
\n                    
1.00
\n                
\n                
\n                    
present_employment_since
\n                    
1.00
\n                
\n                
\n                    
installment_rate_in_percentage_of_disposable_income
\n                    
1.00
\n                
\n                
\n                    
personal_status_and_sex
\n                    
1.00
\n                
\n                
\n                    
other_debtors_or_guarantors
\n                    
1.00
\n                
\n                
\n                    
present_residence_since
\n                    
1.00
\n                
\n                
\n                    
property
\n                    
1.00
\n                
\n                
\n                    
other_installment_plans
\n                    
1.00
\n                
\n                
\n                    
housing
\n                    
1.00
\n                
\n                
\n                    
number_of_existing_credits_at_this_bank
\n                    
1.00
\n                
\n                
\n                    
job
\n                    
1.00
\n                
\n            
\n            \n            
\n                NUMERICAL ASSOCIATIONS
\n                (CORRELATION RATIO, 0 to 1)\n            
\n            
creditability
\n                    CORRELATION RATIO WITH...
\n            
\n                
\n                    
duration_in_month
\n                    
0.00
\n                
\n                
\n                    
credit_amount
\n                    
0.00
\n                
\n                
\n                    
age_in_years
\n                    
0.00
\n                
\n        
\n    
\n
\n
\n        
\n
\n\n"
  },
  {
    "path": "examples/month_end_vintage.sql",
    "content": "WITH months_end(观察时点) AS (\n    select date(month_start)\n    FROM (\n        SELECT month_start\n        FROM (\n            SELECT CONCAT(SUBSTR(DATE_FORMAT(created_time, '%Y-%m-%d'), 1, 7), '-01') AS month_start \n            FROM qy_ods.s03_qh_loan\n            GROUP BY 1\n            ORDER BY 1 DESC\n        ) AS subquery\n        UNION ALL\n        SELECT DATE_FORMAT(DATE_ADD(LAST_DAY(NOW()), INTERVAL 1 DAY), '%Y-%m-01') AS month_start\n    ) g\n    WHERE month_start >= '2023-12-01'\n    ORDER BY month_start DESC\n)\n, loan AS (\n    SELECT l.id, l.user_id, l.flow_channel, l.bank_channel, l.apply_time, l.loan_time, l.periods, l.amount\n    FROM qy_ods.s03_qh_loan l\n    LEFT JOIN qy_ods.s03_qh_order o on o.id = l.order_id\n    LEFT JOIN qy_ods.s03_qh_user_risk_record urr on urr.id = o.risk_id\n    LEFT JOIN qy_ods.s03_qh_user_periods_rights up on urr.id = up.risk_id\n    WHERE l.loan_state = 'SUCCEED'\n)\n, cycle_replan as (\n\tSELECT  l.flow_channel 流量渠道\n\t\t\t, l.bank_channel 放款资方\n\t\t\t, l.id 放款编号\n\t\t\t, l.user_id 用户编号\n\t\t\t, l.apply_time 申请时间\n\t\t\t, l.amount 放款金额\n\t\t\t, l.loan_time 放款时间\n\t\t\t, date_format(l.loan_time, '%Y-%m') 放款月份\n\t\t\t, l.periods 放款期数\n    \t\t, p.period 还款期数\n    \t\t, p.plan_repay_date 应还日期\n    \t\t, date_add(p.plan_repay_date, INTERVAL 0 DAY) 入催观察日期\n            , date(p.act_repay_time) 实还日期\n            , p.principal_amt 应还本金\n            , p.act_principal_amt 实还本金\n\tFROM loan l \n\tINNER JOIN qy_ods.s03_qh_repay_plan p ON l.id = p.loan_id\n)\n, cycle_balance as (\n    SELECT p1.放款编号\n        , p1.还款期数\n        , p1.放款金额 - case when p1.还款期数 = 1 then if(p1.实还本金 != p1.放款金额 or p1.实还本金 is null, 0, p1.实还本金) else sum(p2.实还本金) over(PARTITION by p1.放款编号 ORDER BY p1.还款期数) end 余额\n\tFROM cycle_replan p1 LEFT JOIN cycle_replan p2 ON p1.放款编号 = p2.放款编号 AND p1.还款期数 = p2.还款期数 + 1\n)\n, replan as (\n\tSELECT  l.flow_channel 流量渠道\n\t\t\t, l.bank_channel 放款资方\n\t\t\t, l.id 放款编号\n\t\t\t, l.user_id 用户编号\n\t\t\t, l.apply_time 申请时间\n\t\t\t, l.loan_time 放款时间\n\t\t\t, date_format(l.loan_time, '%Y-%m') 放款月份\n\t\t\t, l.periods 放款期数\n    \t\t, p.period 还款期数\n    \t\t, l.amount 放款金额\n    \t\t, p.plan_repay_date 应还日期\n            , date(p.act_repay_time) 实还日期\n            , p.principal_amt 应还本金\n            , p.act_principal_amt 实还本金\n\tFROM loan l INNER JOIN qy_ods.s03_qh_repay_plan p ON l.id = p.loan_id\n)\n, amount AS (\n    SELECT date_format(loan_time, '%Y-%m') 放款月份, count(l.id) 放款件数, count(l.user_id) 放款人数, sum(l.amount) 放款金额\n    FROM loan l\n    GROUP BY 1\n)\n, balance AS (\n   select 观察时点, 放款月份, 用户编号, 放款编号 -- , 放款金额-sum(COALESCE (实还本金,0)) as 余额\n        , 放款金额 - sum(IF(实还日期 IS NOT NULL AND 实还日期 < 观察时点, 实还本金, 0)) 余额\n        , concat('MOB', TIMESTAMPDIFF(month, concat(substr(放款时间, 1, 7), '-01'), concat(substr(date_sub(观察时点, INTERVAL 1 DAY), 1, 7), '-01'))) MOB\n        , IFNULL(\n            MAX(\n                CASE\n                    WHEN 应还日期 >= IF(观察时点 > CURRENT_DATE(), CURRENT_DATE(), 观察时点) THEN 0  -- 未到还款日，当天出账不计入\n                    WHEN 应还日期 <= IF(观察时点 > CURRENT_DATE(), CURRENT_DATE(), 观察时点) AND 实还日期 <= 应还日期 THEN 0  -- 按时还款\n                    WHEN 应还日期 < IF(观察时点 > CURRENT_DATE(), CURRENT_DATE(), 观察时点) AND 实还日期 <= IF(观察时点 > CURRENT_DATE(), CURRENT_DATE(), 观察时点) THEN 0  -- 观察时点已还清, 逾期但已还的不算在内\n                    WHEN 应还日期 < IF(观察时点 > CURRENT_DATE(), CURRENT_DATE(), 观察时点) AND (实还日期 IS NULL OR 实还日期 >= IF(观察时点 > CURRENT_DATE(), CURRENT_DATE(), 观察时点)) THEN DATEDIFF(IF(观察时点 > CURRENT_DATE(), CURRENT_DATE(), 观察时点), 应还日期)  -- 观察时点时点未还款\n                    ELSE 0\n                END\n            ), 0\n        ) AS 逾期天数\n    FROM replan\n    LEFT JOIN months_end ON replan.放款时间 < months_end.观察时点\n    GROUP BY 观察时点, 放款月份, 用户编号, 放款编号, 放款金额, 放款时间\n)\nSELECT 统计口径, 放款月份, 放款金额, MOB1, MOB2, MOB3, MOB4, MOB5, MOB6, MOB7, MOB8, MOB9, MOB10, MOB11, MOB12, MOB13, `MOB1%`, `MOB2%`, `MOB3%`, `MOB4%`, `MOB5%`, `MOB6%`, `MOB7%`, `MOB8%`, `MOB9%`, `MOB10%`, `MOB11%`, `MOB12%`, (IF(坏账余额 = 0, NULL, 坏账余额) / 放款金额) as 'LOSS%'\nFROM (\n    SELECT 'CYCLE' 统计口径\n            , c.放款月份\n            , a.放款金额\n    \t\t, SUM(IF(还款期数 = 1, 净入催本金, NULL)) / SUM(IF(还款期数 = 1, 净出账本金, NULL)) MOB1\n    \t\t, SUM(IF(还款期数 = 2, 净入催本金, NULL)) / SUM(IF(还款期数 = 2, 净出账本金, NULL)) MOB2\n    \t\t, SUM(IF(还款期数 = 3, 净入催本金, NULL)) / SUM(IF(还款期数 = 3, 净出账本金, NULL)) MOB3\n    \t\t, SUM(IF(还款期数 = 4, 净入催本金, NULL)) / SUM(IF(还款期数 = 4, 净出账本金, NULL)) MOB4\n    \t\t, SUM(IF(还款期数 = 5, 净入催本金, NULL)) / SUM(IF(还款期数 = 5, 净出账本金, NULL)) MOB5\n    \t\t, SUM(IF(还款期数 = 6, 净入催本金, NULL)) / SUM(IF(还款期数 = 6, 净出账本金, NULL)) MOB6\n    \t\t, SUM(IF(还款期数 = 7, 净入催本金, NULL)) / SUM(IF(还款期数 = 7, 净出账本金, NULL)) MOB7\n    \t\t, SUM(IF(还款期数 = 8, 净入催本金, NULL)) / SUM(IF(还款期数 = 8, 净出账本金, NULL)) MOB8\n    \t\t, SUM(IF(还款期数 = 9, 净入催本金, NULL)) / SUM(IF(还款期数 = 9, 净出账本金, NULL)) MOB9\n    \t\t, SUM(IF(还款期数 = 10, 净入催本金, NULL)) / SUM(IF(还款期数 = 10, 净出账本金, NULL)) MOB10\n    \t\t, SUM(IF(还款期数 = 11, 净入催本金, NULL)) / SUM(IF(还款期数 = 11, 净出账本金, NULL)) MOB11\n    \t\t, SUM(IF(还款期数 = 12, 净入催本金, NULL)) / SUM(IF(还款期数 = 12, 净出账本金, NULL)) MOB12\n    \t\t, NULL MOB13\n    \n    \t\t, SUM(IF(还款期数 = 1, 净催回本金, NULL)) / SUM(IF(还款期数 = 1, 净入催本金, NULL)) 'MOB1%'\n    \t\t, SUM(IF(还款期数 = 2, 净催回本金, NULL)) / SUM(IF(还款期数 = 2, 净入催本金, NULL)) 'MOB2%'\n    \t\t, SUM(IF(还款期数 = 3, 净催回本金, NULL)) / SUM(IF(还款期数 = 3, 净入催本金, NULL)) 'MOB3%'\n    \t\t, SUM(IF(还款期数 = 4, 净催回本金, NULL)) / SUM(IF(还款期数 = 4, 净入催本金, NULL)) 'MOB4%'\n    \t\t, SUM(IF(还款期数 = 5, 净催回本金, NULL)) / SUM(IF(还款期数 = 5, 净入催本金, NULL)) 'MOB5%'\n    \t\t, SUM(IF(还款期数 = 6, 净催回本金, NULL)) / SUM(IF(还款期数 = 6, 净入催本金, NULL)) 'MOB6%'\n    \t\t, SUM(IF(还款期数 = 7, 净催回本金, NULL)) / SUM(IF(还款期数 = 7, 净入催本金, NULL)) 'MOB7%'\n    \t\t, SUM(IF(还款期数 = 8, 净催回本金, NULL)) / SUM(IF(还款期数 = 8, 净入催本金, NULL)) 'MOB8%'\n    \t\t, SUM(IF(还款期数 = 9, 净催回本金, NULL)) / SUM(IF(还款期数 = 9, 净入催本金, NULL)) 'MOB9%'\n    \t\t, SUM(IF(还款期数 = 10, 净催回本金, NULL)) / SUM(IF(还款期数 = 10, 净入催本金, NULL)) 'MOB10%'\n    \t\t, SUM(IF(还款期数 = 11, 净催回本金, NULL)) / SUM(IF(还款期数 = 11, 净入催本金, NULL)) 'MOB11%'\n    \t\t, SUM(IF(还款期数 = 12, 净催回本金, NULL)) / SUM(IF(还款期数 = 12, 净入催本金, NULL)) 'MOB12%'\n    \t\t\n    \t\t, (SUM(IF(还款期数 = 1, 净入催本金, 0)) - SUM(IF(还款期数 = 1, 净催回本金, 0))) + (SUM(IF(还款期数 = 2, 净入催本金, 0)) - SUM(IF(还款期数 = 2, 净催回本金, 0))) + (SUM(IF(还款期数 = 3, 净入催本金, 0)) - SUM(IF(还款期数 = 3, 净催回本金, 0))) + (SUM(IF(还款期数 = 4, 净入催本金, 0)) - SUM(IF(还款期数 = 4, 净催回本金, 0)))\n    \t\t    + (SUM(IF(还款期数 = 5, 净入催本金, 0)) - SUM(IF(还款期数 = 5, 净催回本金, 0))) + (SUM(IF(还款期数 = 6, 净入催本金, 0)) - SUM(IF(还款期数 = 6, 净催回本金, 0))) + (SUM(IF(还款期数 = 7, 净入催本金, 0)) - SUM(IF(还款期数 = 7, 净催回本金, 0)))\n    \t\t    + (SUM(IF(还款期数 = 8, 净入催本金, 0)) - SUM(IF(还款期数 = 8, 净催回本金, 0))) + (SUM(IF(还款期数 = 9, 净入催本金, 0)) - SUM(IF(还款期数 = 9, 净催回本金, 0))) + (SUM(IF(还款期数 = 10, 净入催本金, 0)) - SUM(IF(还款期数 = 10, 净催回本金, 0)))\n    \t\t    + (SUM(IF(还款期数 = 11, 净入催本金, 0)) - SUM(IF(还款期数 = 11, 净催回本金, 0))) + (SUM(IF(还款期数 = 12, 净入催本金, 0)) - SUM(IF(还款期数 = 12, 净催回本金, 0))) 坏账余额,\n    \t\tsum(if(还款期数 = 1, 净出账本金, 0)) + sum(if(还款期数 = 2, 净出账本金, 0)) + sum(if(还款期数 = 3, 净出账本金, 0)) + sum(if(还款期数 = 4, 净出账本金, 0)) + sum(if(还款期数 = 5, 净出账本金, 0))+sum(if(还款期数 = 6, 净出账本金, 0))+sum(if(还款期数 = 7, 净出账本金, 0))+sum(if(还款期数 = 8, 净出账本金, 0))\n\t\t\t+sum(if(还款期数 = 9, 净出账本金, 0))+sum(if(还款期数 = 10, 净出账本金, 0))+sum(if(还款期数 = 11, 净出账本金, 0))+sum(if(还款期数 = 12, 净出账本金, 0)) 出账金额\n    FROM (\n    \tSELECT p1.放款编号, p1.放款月份, p1.还款期数, p1.入催观察日期\n    \t       -- 出账订单，当期为首期或（上一期当前不逾期且上一期实还日期为当期应还日之前）\n    \t\t\t, if(p1.入催观察日期 < current_date() AND (p1.还款期数 = 1 OR (p2.实还日期 IS NOT NULL AND p2.实还日期 <= p1.入催观察日期) OR (p1.实还日期 IS NULL and  DATEDIFF(now(), p2.实还日期)>=0)), b.余额, 0) 净出账本金\n    \t\t\t, if(p1.入催观察日期 < current_date() AND (p1.还款期数 = 1 OR (p2.实还日期 IS NOT NULL AND p2.实还日期 <= p1.入催观察日期) OR (p1.实还日期 IS NULL and  DATEDIFF(now(), p2.实还日期)>=0)) AND (p1.实还日期 IS NULL OR DATEDIFF(p1.实还日期 , p1.入催观察日期) > {{day1}}), b.余额, 0) 净入催本金\n    \t\t\t, if(p1.入催观察日期 < current_date() AND (p1.还款期数 = 1 OR (p2.实还日期 IS NOT NULL AND p2.实还日期 <= p1.入催观察日期) OR (p1.实还日期 IS NULL and  DATEDIFF(now(), p2.实还日期)>=0)) AND DATEDIFF(p1.实还日期 , p1.入催观察日期) > {{day}}, b.余额, 0) 净催回本金\n    \tFROM cycle_replan p1 LEFT JOIN cycle_replan p2 ON p1.放款编号 = p2.放款编号 AND p1.还款期数 = p2.还款期数 + 1\n    \tLEFT JOIN cycle_balance b ON b.放款编号 = p1.放款编号 AND b.还款期数 = p1.还款期数\n    \tORDER BY 放款编号, 还款期数\n    ) c\n    LEFT JOIN amount a ON c.放款月份 = a.放款月份\n    GROUP BY 1, 2, 3\n) t\n\nUNION ALL\n\nSELECT 'MONTH' 统计口径\n    , 放款月份\n    , 放款金额\n\t, sum(CASE WHEN MOB = 'MOB1' THEN 逾期率 END) MOB1\n\t, sum(CASE WHEN MOB = 'MOB2' THEN 逾期率 END) MOB2\n\t, sum(CASE WHEN MOB = 'MOB3' THEN 逾期率 END) MOB3\n\t, sum(CASE WHEN MOB = 'MOB4' THEN 逾期率 END) MOB4\n\t, sum(CASE WHEN MOB = 'MOB5' THEN 逾期率 END) MOB5\n\t, sum(CASE WHEN MOB = 'MOB6' THEN 逾期率 END) MOB6\n\t, sum(CASE WHEN MOB = 'MOB7' THEN 逾期率 END) MOB7\n\t, sum(CASE WHEN MOB = 'MOB8' THEN 逾期率 END) MOB8\n\t, sum(CASE WHEN MOB = 'MOB9' THEN 逾期率 END) MOB9\n\t, sum(CASE WHEN MOB = 'MOB10' THEN 逾期率 END) MOB10\n\t, sum(CASE WHEN MOB = 'MOB11' THEN 逾期率 END) MOB11\n\t, sum(CASE WHEN MOB = 'MOB12' THEN 逾期率 END) MOB12\n\t, sum(CASE WHEN MOB = 'MOB13' THEN 逾期率 END) MOB13\n\n    , NULL 'MOB1%'\n\t, NULL 'MOB2%'\n\t, NULL 'MOB3%'\n\t, NULL 'MOB4%'\n\t, NULL 'MOB5%'\n\t, NULL 'MOB6%'\n\t, NULL 'MOB7%'\n\t, NULL 'MOB8%'\n\t, NULL 'MOB9%'\n\t, NULL 'MOB10%'\n\t, NULL 'MOB11%'\n\t, NULL 'MOB12%'\n\t\n\t, NULL 'LOSS%'\nFROM (\n    SELECT a.放款月份, a.放款金额, t.MOB, t.逾期余额, ifnull(t.逾期余额,0) / a.放款金额 逾期率\n    FROM (\n        SELECT 放款月份, MOB, sum(CASE WHEN 逾期天数 > 0 THEN 余额 END) 逾期余额\n        FROM balance\n        GROUP BY 放款月份, MOB\n    ) t LEFT JOIN amount a ON t.放款月份 = a.放款月份\n) v\nGROUP BY 1, 2, 3\nORDER BY 1, 2\n"
  },
  {
    "path": "examples/profitability_calculation.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\n@Time    : 2024/3/19 10:47\n@Author  : itlubber\n@Site    : itlubber.art\n\"\"\"\nimport sys\n\nsys.path.append(\"../\")\n\nimport os\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom openpyxl.utils import get_column_letter, column_index_from_string\n\nfrom scorecardpipeline import *\n\n\nlogger = init_setting(seed=8888, logger=True)\n\n\nstart_row, start_col = 2, 2\n\n\n# 基础参数配置\nirr = 0.24                          # 金融部分年化利率IRR\nloan_amount = 5000                  # 单笔放款金额\nperiods = 12                        # 放款期数\n# loan_order_rate = 1.0               # 放款比例（可忽略）\nrights_amount = 299                 # 权益金额\nrights_discount_rate = 0.9          # 权益扣款成功率\ncost_of_funds = 0.1                 # 资金成本IRR\ncost_of_rights = 50                 # 权益固定成本\ntraffic_acquisition_cost = 0.03     # 流量成本\nrisk_control_cost = 55              # 每笔放款订单数据成本\noperating_costs = 0.01              # 运营成本\nvintage = 0.05                      # VINTAGE损失\n\n\ninsert_col = get_column_letter(start_col + 2)\n\ndata1 = pd.DataFrame({\n    \"年化利率\": [irr],\n    \"资金成本\": [cost_of_funds],\n    \"放款金额\": [loan_amount],\n    \"放款期数\": [periods],\n    \"权益金额\": [rights_amount],\n    \"权益固定成本\": [cost_of_rights],\n    \"权益扣款成功率\": [rights_discount_rate],\n    \"周转率\": [f\"=24/({insert_col}{start_row+3}+1)\"],\n    \"每期应还金额\": [f\"=({insert_col}{start_row+2}*(1+{insert_col}{start_row}/{insert_col}{start_row+7}))/{insert_col}{start_row+3}\"],\n    \"综合年化利率(100%扣款)\": [f\"={insert_col}{start_row}+{insert_col}{start_row+4}/{insert_col}{start_row+2}*{insert_col}{start_row+7}\"],\n}).T.reset_index()\ndata1.index = [\"基础参数(IRR口径)\" if i == 0 else \"\" for i in range(len(data1))]\n\ninsert_row = start_row + len(data1) + 2\ndata2 = pd.DataFrame({\n    \"利息收入\": [f\"={insert_col}{start_row}/{insert_col}{start_row+7}*(1-{insert_col}{insert_row+8})\"],\n    \"权益收入\": [f\"={insert_col}{start_row+4}*{insert_col}{start_row+6}/{insert_col}{start_row+2}\"],\n    \"资金成本\": [f\"={insert_col}{start_row+1}/{insert_col}{start_row+7}\"],\n    \"流量成本\": [traffic_acquisition_cost],\n    \"数据成本\": [f\"={risk_control_cost}/{insert_col}{start_row+2}\"],\n    \"权益成本\": [f\"={insert_col}{start_row+5}/{insert_col}{start_row+2}*{insert_col}{start_row+6}\"],\n    \"运营成本\": [operating_costs],\n    \"盈亏平衡点\": [f\"={insert_col}{insert_row}+{insert_col}{insert_row+1}-{insert_col}{insert_row+2}-{insert_col}{insert_row+3}-{insert_col}{insert_row+4}-{insert_col}{insert_row+5}-{insert_col}{insert_row+6}\"],\n    \"预估资损成本\": [vintage],\n    \"利润率\": [f\"={insert_col}{insert_row+7}-{insert_col}{insert_row+8}\"],\n}).T.reset_index()\ndata2.index = [\"盈利测算(APR口径)\" if i == 0 else \"\" for i in range(len(data2))]\n\n\nwriter = ExcelWriter()\n\nworksheet = writer.get_sheet_by_name(\"盈利测算\")\n\ndataframe2excel(data1, writer, sheet_name=worksheet, header=False, index=True, start_row=start_row, start_col=start_col, merge_index=False, auto_width=False)\ndataframe2excel(data2, writer, sheet_name=worksheet, header=False, index=True, start_row=insert_row, start_col=start_col, merge_index=False, percent_cols=[0])\n\nwriter.set_number_format(worksheet, f\"{insert_col}{start_row}:{insert_col}{start_row+1}\", \"0.00%\")\nwriter.set_number_format(worksheet, f\"{insert_col}{start_row+6}\", \"0.00%\")\nwriter.set_number_format(worksheet, f\"{insert_col}{start_row+9}\", \"0.00%\")\nwriter.set_number_format(worksheet, f\"{insert_col}{start_row+7}:{insert_col}{start_row+8}\", \"0.00\")\n\nwriter.save(\"./权益类产品盈利测算表.xlsx\")\n"
  },
  {
    "path": "examples/quickstart.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import sys\\n\",\n    \"sys.path.append(\\\"../\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"import scorecardpipeline as sp\\n\",\n    \"from scorecardpipeline import *\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sp\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"logger = sp.init_setting(seed=6666, logger=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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status_of_existing_checking_accountduration_in_monthcredit_historypurposecredit_amountsavings_account_and_bondspresent_employment_sinceinstallment_rate_in_percentage_of_disposable_incomepersonal_status_and_sexother_debtors_or_guarantors...propertyage_in_yearsother_installment_planshousingnumber_of_existing_credits_at_this_bankjobnumber_of_people_being_liable_to_provide_maintenance_fortelephoneforeign_workercreditability
0... < 0 DM6.0000NaNNaN1169.0000unknown/ no savings accountNaN4.0000male : divorced/separatednone...real estate67.0000noneownNaNskilled employee / official1.0000yes, registered under the customers nameyes0
1NaN48.0000existing credits paid back duly till nowNaN5951.0000... < 100 DM1 <= ... < 4 years2.0000male : divorced/separatednone...real estateNaNnoneown1.0000skilled employee / official1.0000noneyes1
2no checking account12.0000critical account/ other credits existing (not at this bank)educationNaNNaN4 <= ... < 7 years2.0000male : divorced/separatedNaN...NaN49.0000NaNown1.0000unskilled - resident2.0000NaNyes0
3... < 0 DMNaNexisting credits paid back duly till nowfurniture/equipment7882.0000... < 100 DMNaNNaNmale : divorced/separatedguarantor...building society savings agreement/ life insurance45.0000NaNfor free1.0000skilled employee / officialNaNnoneNaN0
4... < 0 DM24.0000NaNcar (new)4870.0000NaN1 <= ... < 4 years3.0000male : divorced/separatednone...unknown / no property53.0000nonefor freeNaNNaN2.0000noneyes1
\\n\",\n       \"
5 rows × 21 columns
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"  status_of_existing_checking_account  duration_in_month                                               credit_history              purpose  credit_amount    savings_account_and_bonds present_employment_since  installment_rate_in_percentage_of_disposable_income    personal_status_and_sex other_debtors_or_guarantors  ...                                            property age_in_years  other_installment_plans   housing number_of_existing_credits_at_this_bank                          job number_of_people_being_liable_to_provide_maintenance_for                                 telephone foreign_worker creditability\\n\",\n       \"0                          ... < 0 DM             6.0000                                                          NaN                  NaN      1169.0000  unknown/ no savings account                      NaN                                               4.0000  male : divorced/separated                        none  ...                                         real estate      67.0000                     none       own                                     NaN  skilled employee / official                                                   1.0000  yes, registered under the customers name            yes             0\\n\",\n       \"1                                 NaN            48.0000                     existing credits paid back duly till now                  NaN      5951.0000                 ... < 100 DM       1 <= ... < 4 years                                               2.0000  male : divorced/separated                        none  ...                                         real estate          NaN                     none       own                                  1.0000  skilled employee / official                                                   1.0000                                      none            yes             1\\n\",\n       \"2                 no checking account            12.0000  critical account/ other credits existing (not at this bank)            education            NaN                          NaN       4 <= ... < 7 years                                               2.0000  male : divorced/separated                         NaN  ...                                                 NaN      49.0000                      NaN       own                                  1.0000         unskilled - resident                                                   2.0000                                       NaN            yes             0\\n\",\n       \"3                          ... < 0 DM                NaN                     existing credits paid back duly till now  furniture/equipment      7882.0000                 ... < 100 DM                      NaN                                                  NaN  male : divorced/separated                   guarantor  ...  building society savings agreement/ life insurance      45.0000                      NaN  for free                                  1.0000  skilled employee / official                                                      NaN                                      none            NaN             0\\n\",\n       \"4                          ... < 0 DM            24.0000                                                          NaN            car (new)      4870.0000                          NaN       1 <= ... < 4 years                                               3.0000  male : divorced/separated                        none  ...                               unknown / no property      53.0000                     none  for free                                     NaN                          NaN                                                   2.0000                                      none            yes             1\\n\",\n       \"\\n\",\n       \"[5 rows x 21 columns]\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"target = \\\"creditability\\\"\\n\",\n    \"data = germancredit()\\n\",\n    \"data[target] = data[target].map({\\\"good\\\": 0, \\\"bad\\\": 1})\\n\",\n    \"\\n\",\n    \"# 随机替换 20% 的数据为 np.nan\\n\",\n    \"for col in data.columns.drop(target):\\n\",\n    \"    for i in range(len(data)):\\n\",\n    \"        if np.random.rand() > 0.8:\\n\",\n    \"            data[col].loc[i] = np.nan\\n\",\n    \"\\n\",\n    \"data.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 2024-08-16 17:34:47,039 ][ INFO ][ 3133036894.py::3 ] 训练集数据: (700, 21), 测试集数据: (300, 21)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"train, test = train_test_split(data, test_size=0.3, shuffle=True, stratify=data[target])\\n\",\n    \"\\n\",\n    \"logger.info(f\\\"训练集数据: {train.shape}, 测试集数据: {test.shape}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"  \\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"  \\n\",\n       \"
数据集开始时间结束时间样本总数样本占比坏客户数坏客户占比备注
0建模样本2022-01-012023-01-3110001.00003000.3000
1训练集2022-01-012023-12-317000.70002100.3000
2测试集2022-01-012023-12-313000.3000900.3000
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"    数据集        开始时间        结束时间  样本总数   样本占比  坏客户数  坏客户占比 备注\\n\",\n       \"0  建模样本  2022-01-01  2023-01-31  1000 1.0000   300 0.3000   \\n\",\n       \"1   训练集  2022-01-01  2023-12-31   700 0.7000   210 0.3000   \\n\",\n       \"2   测试集  2022-01-01  2023-12-31   300 0.3000    90 0.3000   \"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 模拟实际场景中的数据， date 为数据集中的日期，为 datetime 类型，实际生产过程中可能是 申请时间｜放款时间｜入催时间｜流失时间 等\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame()\\n\",\n    \"df[\\\"date\\\"] = pd.date_range(start=\\\"2021-01-01\\\", end=\\\"2021-06-30\\\", freq=\\\"5H\\\")\\n\",\n    \"df[target] = np.random.randint(0, 2, len(df))\\n\",\n    \"\\n\",\n    \"total_count = len(data)\\n\",\n    \"dataset_summary = pd.DataFrame(\\n\",\n    \"    [\\n\",\n    \"        [\\\"建模样本\\\", \\\"2022-01-01\\\", \\\"2023-01-31\\\", len(data), len(data) / total_count, data[target].sum(), data[target].sum() / len(data), \\\"\\\"],\\n\",\n    \"        [\\\"训练集\\\", \\\"2022-01-01\\\", \\\"2023-12-31\\\", len(train), len(train) / total_count, train[target].sum(), train[target].sum() / len(train), \\\"\\\"],\\n\",\n    \"        [\\\"测试集\\\", \\\"2022-01-01\\\", \\\"2023-12-31\\\", len(test), len(test) / total_count, test[target].sum(), test[target].sum() / len(test), \\\"\\\"],\\n\",\n    \"    ],\\n\",\n    \"    columns=[\\\"数据集\\\", \\\"开始时间\\\", \\\"结束时间\\\", \\\"样本总数\\\", \\\"样本占比\\\", \\\"坏客户数\\\", \\\"坏客户占比\\\", \\\"备注\\\"],\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"dataset_summary\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"distribution_plot(df, date=\\\"date\\\", target=target)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"select = FeatureSelection(target=target, engine=\\\"toad\\\", identical=0.95, empty=0.95, iv=0.02, corr=0.6)\\n\",\n    \"select.fit(train)\\n\",\n    \"\\n\",\n    \"train_select = select.transform(train)\\n\",\n    \"test_select = select.transform(test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"combiner = Combiner(target=target, min_bin_size=0.2, empty_separate=True)\\n\",\n    \"\\n\",\n    \"combiner.fit(train_select)\\n\",\n    \"\\n\",\n    \"train_bins = combiner.transform(train_select)\\n\",\n    \"test_bins = combiner.transform(test_select)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率分档WOE值分档IV值指标IV值LIFT值坏账改善累积LIFT值累积坏账改善累积好样本数累积坏样本数分档KS值
0credit_amount信用额度[负无穷 , 4000.0)4310.61573250.66331060.50480.24590.27310.04330.11580.8198-0.28871.00000.00004902100.0000
1credit_amount信用额度[4000.0 , 正无穷)1390.1986800.1633590.28100.4245-0.54280.06390.11581.41490.10281.28870.18021651040.1585
2credit_amount信用额度缺失值1300.1857850.1735450.21430.3462-0.21130.00860.11581.15380.03511.15380.035185450.0408
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"            指标名称  指标含义              分箱  样本总数   样本占比  好样本数  好样本占比  坏样本数  坏样本占比   坏样本率  分档WOE值  分档IV值  指标IV值  LIFT值    坏账改善  累积LIFT值  累积坏账改善  累积好样本数  累积坏样本数  分档KS值\\n\",\n       \"0  credit_amount  信用额度  [负无穷 , 4000.0)   431 0.6157   325 0.6633   106 0.5048 0.2459  0.2731 0.0433 0.1158 0.8198 -0.2887   1.0000  0.0000     490     210 0.0000\\n\",\n       \"1  credit_amount  信用额度  [4000.0 , 正无穷)   139 0.1986    80 0.1633    59 0.2810 0.4245 -0.5428 0.0639 0.1158 1.4149  0.1028   1.2887  0.1802     165     104 0.1585\\n\",\n       \"2  credit_amount  信用额度             缺失值   130 0.1857    85 0.1735    45 0.2143 0.3462 -0.2113 0.0086 0.1158 1.1538  0.0351   1.1538  0.0351      85      45 0.0408\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"combiner.bin_plot(train_select, \\\"credit_amount\\\", result=True, desc=\\\"信用额度\\\", rule=[4000.0, np.nan])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"transform = WOETransformer(target=target)\\n\",\n    \"transform.fit(train_bins)\\n\",\n    \"\\n\",\n    \"train_woe = transform.transform(train_bins)\\n\",\n    \"test_woe = transform.transform(test_bins)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 初始化逐步回归特征筛选器\\n\",\n    \"stepwise = StepwiseSelection(target=target)\\n\",\n    \"# 训练\\n\",\n    \"stepwise.fit(train_woe)\\n\",\n    \"# 应用逐步回归特征筛选器\\n\",\n    \"train_woe_stepwise = stepwise.transform(train_woe)\\n\",\n    \"test_woe_stepwise = stepwise.transform(test_woe)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 逻辑回归模型构建\\n\",\n    \"logistic = ITLubberLogisticRegression(target=target)\\n\",\n    \"# 训练\\n\",\n    \"logistic.fit(train_woe_stepwise)\\n\",\n    \"# 预测数据集样本违约概率\\n\",\n    \"y_pred_train = logistic.predict_proba(train_woe_stepwise.drop(columns=target))[:, 1]\\n\",\n    \"y_pred_test = logistic.predict_proba(test_woe_stepwise.drop(columns=target))[:, 1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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FeaturesDescribeCoef.Std.ErrzP>|z|[ 0.0250.975 ]VIF
0const截距项-0.84230.0942-8.94070.0000-1.0269-0.65761.0499
1status_of_existing_checking_account现有支票账户的状态0.90180.12956.96530.00000.64801.15551.0828
2purpose目的1.00390.30823.25770.00110.39991.60791.0108
3credit_amount信用额度1.10530.25714.29880.00000.60141.60931.0346
4savings_account_and_bonds0.60720.25162.41300.01580.11401.10041.0635
5present_employment_since现居住地至今0.67390.36671.83790.0661-0.04481.39261.0612
6installment_rate_in_percentage_of_disposable_income分期付款率占可支配收入的百分比1.28560.43822.93380.00330.42672.14441.0178
7personal_status_and_sex个人地位和性别0.80990.52581.54040.1235-0.22061.84051.0106
8property0.82690.38412.15270.03130.07401.57971.0279
9age_in_years年龄0.90570.27493.29460.00100.36691.44451.0459
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"                                              Features         Describe   Coef.  Std.Err       z  P>|z|  [ 0.025  0.975 ]    VIF\\n\",\n       \"0                                                const              截距项 -0.8423   0.0942 -8.9407 0.0000  -1.0269  -0.6576 1.0499\\n\",\n       \"1                  status_of_existing_checking_account        现有支票账户的状态  0.9018   0.1295  6.9653 0.0000   0.6480   1.1555 1.0828\\n\",\n       \"2                                              purpose               目的  1.0039   0.3082  3.2577 0.0011   0.3999   1.6079 1.0108\\n\",\n       \"3                                        credit_amount             信用额度  1.1053   0.2571  4.2988 0.0000   0.6014   1.6093 1.0346\\n\",\n       \"4                            savings_account_and_bonds                   0.6072   0.2516  2.4130 0.0158   0.1140   1.1004 1.0635\\n\",\n       \"5                             present_employment_since           现居住地至今  0.6739   0.3667  1.8379 0.0661  -0.0448   1.3926 1.0612\\n\",\n       \"6  installment_rate_in_percentage_of_disposable_income  分期付款率占可支配收入的百分比  1.2856   0.4382  2.9338 0.0033   0.4267   2.1444 1.0178\\n\",\n       \"7                              personal_status_and_sex          个人地位和性别  0.8099   0.5258  1.5404 0.1235  -0.2206   1.8405 1.0106\\n\",\n       \"8                                             property                   0.8269   0.3841  2.1527 0.0313   0.0740   1.5797 1.0279\\n\",\n       \"9                                         age_in_years               年龄  0.9057   0.2749  3.2946 0.0010   0.3669   1.4445 1.0459\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 数据字典或特征描述信息\\n\",\n    \"feature_map = {\\n\",\n    \"    \\\"const\\\": \\\"截距项\\\",\\n\",\n    \"    \\\"status_of_existing_checking_account\\\": \\\"现有支票账户的状态\\\",\\n\",\n    \"    \\\"credit_history\\\": \\\"信用记录\\\",\\n\",\n    \"    \\\"purpose\\\": \\\"目的\\\",\\n\",\n    \"    \\\"credit_amount\\\": \\\"信用额度\\\",\\n\",\n    \"    \\\"present_employment_since\\\": \\\"现居住地至今\\\",\\n\",\n    \"    \\\"installment_rate_in_percentage_of_disposable_income\\\": \\\"分期付款率占可支配收入的百分比\\\",\\n\",\n    \"    \\\"personal_status_and_sex\\\": \\\"个人地位和性别\\\",\\n\",\n    \"    \\\"age_in_years\\\": \\\"年龄\\\",\\n\",\n    \"    \\\"housing\\\": \\\"住房情况\\\",\\n\",\n    \"}\\n\",\n    \"# summary 仅支持输出简单的统计信息，使用 summary2 可以输出有特征描述的统计信息表\\n\",\n    \"logistic.summary2(feature_map=feature_map)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"logistic.plot_weights(figsize=(10, 6), save=\\\"model_report/sp_lr_weight.png\\\");\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"bad_rate = train[target].mean()\\n\",\n    \"# 逻辑回归模型转评分卡\\n\",\n    \"card = ScoreCard(target=target, combiner=combiner, transer=transform, pretrain_lr=logistic, base_score=50, base_odds=(1 - bad_rate) / bad_rate, pdo=10)\\n\",\n    \"# 训练\\n\",\n    \"card.fit(train_woe_stepwise)\\n\",\n    \"\\n\",\n    \"# 预测\\n\",\n    \"train[\\\"score\\\"] = card.predict(train)\\n\",\n    \"test[\\\"score\\\"] = card.predict(test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率分档WOE值分档IV值指标IV值LIFT值坏账改善累积LIFT值累积坏账改善累积好样本数累积坏样本数分档KS值
0score训练集模型评分[负无穷 , 40)1110.1586410.0837700.33330.6306-1.38220.34510.84212.10210.20772.10210.207741700.2497
1score训练集模型评分[40 , 60)3010.43001950.39801060.50480.3522-0.23770.02540.84211.17390.13121.42390.60652361760.3565
2score训练集模型评分[60 , 80)2270.32431960.4000310.14760.13660.99680.25160.84210.4552-0.26151.07980.83614322070.1041
3score训练集模型评分[80 , 正无穷)610.0871580.118430.01430.04922.11450.22010.84210.1639-0.07981.00000.00004902100.0000
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"    指标名称     指标含义          分箱  样本总数   样本占比  好样本数  好样本占比  坏样本数  坏样本占比   坏样本率  分档WOE值  分档IV值  指标IV值  LIFT值    坏账改善  累积LIFT值  累积坏账改善  累积好样本数  累积坏样本数  分档KS值\\n\",\n       \"0  score  训练集模型评分  [负无穷 , 40)   111 0.1586    41 0.0837    70 0.3333 0.6306 -1.3822 0.3451 0.8421 2.1021  0.2077   2.1021  0.2077      41      70 0.2497\\n\",\n       \"1  score  训练集模型评分   [40 , 60)   301 0.4300   195 0.3980   106 0.5048 0.3522 -0.2377 0.0254 0.8421 1.1739  0.1312   1.4239  0.6065     236     176 0.3565\\n\",\n       \"2  score  训练集模型评分   [60 , 80)   227 0.3243   196 0.4000    31 0.1476 0.1366  0.9968 0.2516 0.8421 0.4552 -0.2615   1.0798  0.8361     432     207 0.1041\\n\",\n       \"3  score  训练集模型评分  [80 , 正无穷)    61 0.0871    58 0.1184     3 0.0143 0.0492  2.1145 0.2201 0.8421 0.1639 -0.0798   1.0000  0.0000     490     210 0.0000\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 训练集评分排序性\\n\",\n    \"score_clip = card.score_clip(train[\\\"score\\\"], clip=20)\\n\",\n    \"score_table_train = feature_bin_stats(train, \\\"score\\\", desc=\\\"训练集模型评分\\\", target=target, rules=score_clip)\\n\",\n    \"bin_plot(score_table_train, desc=\\\"训练集模型评分\\\", figsize=(10, 6), anchor=0.935, save=\\\"model_report/train_score_bins.png\\\")\\n\",\n    \"\\n\",\n    \"display(score_table_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"\\\"savings_account_and_bonds\\\" in combiner\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{0: 'unknown/ no savings account,... >= 1000 DM,500 <= ... < 1000 DM',\\n\",\n       \" 1: '... < 100 DM,100 <= ... < 500 DM',\\n\",\n       \" -1: '缺失值'}\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"feature_bins(np.array(combiner[\\\"savings_account_and_bonds\\\"]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# # 查看某个特征的 PSI\\n\",\n    \"# score_clip = card.score_clip(train[\\\"score\\\"], clip=10)\\n\",\n    \"# score_table_train = feature_bin_stats(train, \\\"score\\\", desc=\\\"训练集模型评分\\\", target=target, rules=score_clip)\\n\",\n    \"# score_table_test = feature_bin_stats(test, \\\"score\\\", desc=\\\"测试集模型评分\\\", target=target, rules=score_clip)\\n\",\n    \"# train_test_score_psi = psi_plot(score_table_train, score_table_test, labels=[\\\"训练数据集\\\", \\\"测试数据集\\\"], save=\\\"model_report/train_test_psiplot.png\\\", result=True)\\n\",\n    \"\\n\",\n    \"# # 查看某个入模特征的 CSI\\n\",\n    \"# for col in card._feature_names:\\n\",\n    \"#     feature_table_train = feature_bin_stats(train, col, target=target, desc=\\\"训练集分布\\\", combiner=combiner)\\n\",\n    \"#     feature_table_test = feature_bin_stats(test, col, target=target, desc=\\\"测试集分布\\\", combiner=combiner)\\n\",\n    \"#     train_test_csi_table = csi_plot(feature_table_train, feature_table_test, card[col], desc=col, result=True, plot=True, max_len=35, figsize=(10, 6), labels=[\\\"训练数据集\\\", \\\"测试数据集\\\"], save=f\\\"model_report/csi_{col}.png\\\")\\n\",\n    \"#     if col == \\\"savings_account_and_bonds\\\": break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# for col in card._feature_names:\\n\",\n    \"#     print(col)\\n\",\n    \"#     feature_table = feature_bin_stats(data, col, target=target, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", combiner=combiner)\\n\",\n    \"#     _ = sp.bin_plot(feature_table, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", figsize=(8, 4), anchor=0.9)\\n\",\n    \"    \\n\",\n    \"#     display(feature_table)\\n\",\n    \"#     plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'bins': array([35., nan]),\\n\",\n       \" 'woes': array([ 0.35631058, -0.40546511, -0.08982696]),\\n\",\n       \" 'weight': 0.9057217143033184,\\n\",\n       \" 'scores': array([ 0.89168832, 10.84566059,  6.7212793 ])}\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"card[\\\"age_in_years\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"save_pickle(card, \\\"model_report/scorecard.pkl\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# scorecard_pipeline = card.scorecard2pmml(pmml=\\\"model_report/scorecard.pmml\\\", debug=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pd.DataFrame({\\\"pred\\\": scorecard_pipeline.predict(train), \\\"true\\\": train[\\\"score\\\"]})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Fitting 3 folds for each of 105 candidates, totalling 315 fits\\n\"\n     ]\n    },\n    {\n     \"ename\": \"ValueError\",\n     \"evalue\": \"\\nAll the 315 fits failed.\\nIt is very likely that your model is misconfigured.\\nYou can try to debug the error by setting error_score='raise'.\\n\\nBelow are more details about the failures:\\n--------------------------------------------------------------------------------\\n315 fits failed with the following error:\\nTraceback (most recent call last):\\n  File \\\"/Users/lubberit/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\\\", line 729, in _fit_and_score\\n    estimator.fit(X_train, y_train, **fit_params)\\n  File \\\"/Users/lubberit/anaconda3/lib/python3.8/site-packages/sklearn/base.py\\\", line 1152, in wrapper\\n    return fit_method(estimator, *args, **kwargs)\\n  File \\\"/Users/lubberit/anaconda3/lib/python3.8/site-packages/sklearn/pipeline.py\\\", line 422, in fit\\n    fit_params_steps = self._check_fit_params(**fit_params)\\n  File \\\"/Users/lubberit/anaconda3/lib/python3.8/site-packages/sklearn/pipeline.py\\\", line 341, in _check_fit_params\\n    raise ValueError(\\nValueError: Pipeline.fit does not accept the error_score parameter. You can pass parameters to specific steps of your pipeline using the stepname__parameter format, e.g. `Pipeline.fit(X, y, logisticregression__sample_weight=sample_weight)`.\\n\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mValueError\\u001b[0m                                Traceback (most recent call last)\",\n      \"Cell \\u001b[0;32mIn[32], line 24\\u001b[0m\\n\\u001b[1;32m     14\\u001b[0m params_grid \\u001b[38;5;241m=\\u001b[39m {\\n\\u001b[1;32m     15\\u001b[0m     \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mcombiner__max_n_bins\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m: [\\u001b[38;5;241m3\\u001b[39m],\\n\\u001b[1;32m     16\\u001b[0m     \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mlogistic__C\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m: [np\\u001b[38;5;241m.\\u001b[39mpower(\\u001b[38;5;241m2\\u001b[39m, i) \\u001b[38;5;28;01mfor\\u001b[39;00m i \\u001b[38;5;129;01min\\u001b[39;00m \\u001b[38;5;28mrange\\u001b[39m(\\u001b[38;5;241m5\\u001b[39m)],\\n\\u001b[0;32m   (...)\\u001b[0m\\n\\u001b[1;32m     20\\u001b[0m     \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mlogistic__solver\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m: [\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124msag\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m], \\u001b[38;5;66;03m# [\\\"liblinear\\\", \\\"sag\\\", \\\"lbfgs\\\", \\\"newton-cg\\\"],\\u001b[39;00m\\n\\u001b[1;32m     21\\u001b[0m }\\n\\u001b[1;32m     23\\u001b[0m pipeline_grid_search \\u001b[38;5;241m=\\u001b[39m GridSearchCV(model_pipeline, params_grid, cv\\u001b[38;5;241m=\\u001b[39m\\u001b[38;5;241m3\\u001b[39m, scoring\\u001b[38;5;241m=\\u001b[39m\\u001b[38;5;124m'\\u001b[39m\\u001b[38;5;124mroc_auc\\u001b[39m\\u001b[38;5;124m'\\u001b[39m, verbose\\u001b[38;5;241m=\\u001b[39m\\u001b[38;5;241m1\\u001b[39m, n_jobs\\u001b[38;5;241m=\\u001b[39m\\u001b[38;5;241m-\\u001b[39m\\u001b[38;5;241m1\\u001b[39m, return_train_score\\u001b[38;5;241m=\\u001b[39m\\u001b[38;5;28;01mTrue\\u001b[39;00m)\\n\\u001b[0;32m---> 24\\u001b[0m \\u001b[43mpipeline_grid_search\\u001b[49m\\u001b[38;5;241;43m.\\u001b[39;49m\\u001b[43mfit\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[43mtrain\\u001b[49m\\u001b[43m,\\u001b[49m\\u001b[43m \\u001b[49m\\u001b[43mtrain\\u001b[49m\\u001b[43m[\\u001b[49m\\u001b[43mtarget\\u001b[49m\\u001b[43m]\\u001b[49m\\u001b[43m,\\u001b[49m\\u001b[43m \\u001b[49m\\u001b[43merror_score\\u001b[49m\\u001b[38;5;241;43m=\\u001b[39;49m\\u001b[38;5;124;43m'\\u001b[39;49m\\u001b[38;5;124;43mraise\\u001b[39;49m\\u001b[38;5;124;43m'\\u001b[39;49m\\u001b[43m)\\u001b[49m\\n\\u001b[1;32m     26\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(pipeline_grid_search\\u001b[38;5;241m.\\u001b[39mbest_params_)\\n\",\n      \"File \\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152\\u001b[0m, in \\u001b[0;36m_fit_context..decorator..wrapper\\u001b[0;34m(estimator, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m   1145\\u001b[0m     estimator\\u001b[38;5;241m.\\u001b[39m_validate_params()\\n\\u001b[1;32m   1147\\u001b[0m \\u001b[38;5;28;01mwith\\u001b[39;00m config_context(\\n\\u001b[1;32m   1148\\u001b[0m     skip_parameter_validation\\u001b[38;5;241m=\\u001b[39m(\\n\\u001b[1;32m   1149\\u001b[0m         prefer_skip_nested_validation \\u001b[38;5;129;01mor\\u001b[39;00m global_skip_validation\\n\\u001b[1;32m   1150\\u001b[0m     )\\n\\u001b[1;32m   1151\\u001b[0m ):\\n\\u001b[0;32m-> 1152\\u001b[0m     \\u001b[38;5;28;01mreturn\\u001b[39;00m \\u001b[43mfit_method\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[43mestimator\\u001b[49m\\u001b[43m,\\u001b[49m\\u001b[43m \\u001b[49m\\u001b[38;5;241;43m*\\u001b[39;49m\\u001b[43margs\\u001b[49m\\u001b[43m,\\u001b[49m\\u001b[43m \\u001b[49m\\u001b[38;5;241;43m*\\u001b[39;49m\\u001b[38;5;241;43m*\\u001b[39;49m\\u001b[43mkwargs\\u001b[49m\\u001b[43m)\\u001b[49m\\n\",\n      \"File \\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_search.py:898\\u001b[0m, in \\u001b[0;36mBaseSearchCV.fit\\u001b[0;34m(self, X, y, groups, **fit_params)\\u001b[0m\\n\\u001b[1;32m    892\\u001b[0m     results \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;28mself\\u001b[39m\\u001b[38;5;241m.\\u001b[39m_format_results(\\n\\u001b[1;32m    893\\u001b[0m         all_candidate_params, n_splits, all_out, all_more_results\\n\\u001b[1;32m    894\\u001b[0m     )\\n\\u001b[1;32m    896\\u001b[0m     \\u001b[38;5;28;01mreturn\\u001b[39;00m results\\n\\u001b[0;32m--> 898\\u001b[0m \\u001b[38;5;28;43mself\\u001b[39;49m\\u001b[38;5;241;43m.\\u001b[39;49m\\u001b[43m_run_search\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[43mevaluate_candidates\\u001b[49m\\u001b[43m)\\u001b[49m\\n\\u001b[1;32m    900\\u001b[0m \\u001b[38;5;66;03m# multimetric is determined here because in the case of a callable\\u001b[39;00m\\n\\u001b[1;32m    901\\u001b[0m \\u001b[38;5;66;03m# self.scoring the return type is only known after calling\\u001b[39;00m\\n\\u001b[1;32m    902\\u001b[0m first_test_score \\u001b[38;5;241m=\\u001b[39m all_out[\\u001b[38;5;241m0\\u001b[39m][\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mtest_scores\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m]\\n\",\n      \"File \\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_search.py:1422\\u001b[0m, in \\u001b[0;36mGridSearchCV._run_search\\u001b[0;34m(self, evaluate_candidates)\\u001b[0m\\n\\u001b[1;32m   1420\\u001b[0m \\u001b[38;5;28;01mdef\\u001b[39;00m \\u001b[38;5;21m_run_search\\u001b[39m(\\u001b[38;5;28mself\\u001b[39m, evaluate_candidates):\\n\\u001b[1;32m   1421\\u001b[0m \\u001b[38;5;250m    \\u001b[39m\\u001b[38;5;124;03m\\\"\\\"\\\"Search all candidates in param_grid\\\"\\\"\\\"\\u001b[39;00m\\n\\u001b[0;32m-> 1422\\u001b[0m     \\u001b[43mevaluate_candidates\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[43mParameterGrid\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[38;5;28;43mself\\u001b[39;49m\\u001b[38;5;241;43m.\\u001b[39;49m\\u001b[43mparam_grid\\u001b[49m\\u001b[43m)\\u001b[49m\\u001b[43m)\\u001b[49m\\n\",\n      \"File \\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_search.py:875\\u001b[0m, in \\u001b[0;36mBaseSearchCV.fit..evaluate_candidates\\u001b[0;34m(candidate_params, cv, more_results)\\u001b[0m\\n\\u001b[1;32m    868\\u001b[0m \\u001b[38;5;28;01melif\\u001b[39;00m \\u001b[38;5;28mlen\\u001b[39m(out) \\u001b[38;5;241m!=\\u001b[39m n_candidates \\u001b[38;5;241m*\\u001b[39m n_splits:\\n\\u001b[1;32m    869\\u001b[0m     \\u001b[38;5;28;01mraise\\u001b[39;00m \\u001b[38;5;167;01mValueError\\u001b[39;00m(\\n\\u001b[1;32m    870\\u001b[0m         \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mcv.split and cv.get_n_splits returned \\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m\\n\\u001b[1;32m    871\\u001b[0m         \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124minconsistent results. Expected \\u001b[39m\\u001b[38;5;132;01m{}\\u001b[39;00m\\u001b[38;5;124m \\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m\\n\\u001b[1;32m    872\\u001b[0m         \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124msplits, got \\u001b[39m\\u001b[38;5;132;01m{}\\u001b[39;00m\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;241m.\\u001b[39mformat(n_splits, \\u001b[38;5;28mlen\\u001b[39m(out) \\u001b[38;5;241m/\\u001b[39m\\u001b[38;5;241m/\\u001b[39m n_candidates)\\n\\u001b[1;32m    873\\u001b[0m     )\\n\\u001b[0;32m--> 875\\u001b[0m \\u001b[43m_warn_or_raise_about_fit_failures\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[43mout\\u001b[49m\\u001b[43m,\\u001b[49m\\u001b[43m \\u001b[49m\\u001b[38;5;28;43mself\\u001b[39;49m\\u001b[38;5;241;43m.\\u001b[39;49m\\u001b[43merror_score\\u001b[49m\\u001b[43m)\\u001b[49m\\n\\u001b[1;32m    877\\u001b[0m \\u001b[38;5;66;03m# For callable self.scoring, the return type is only know after\\u001b[39;00m\\n\\u001b[1;32m    878\\u001b[0m \\u001b[38;5;66;03m# calling. If the return type is a dictionary, the error scores\\u001b[39;00m\\n\\u001b[1;32m    879\\u001b[0m \\u001b[38;5;66;03m# can now be inserted with the correct key. The type checking\\u001b[39;00m\\n\\u001b[1;32m    880\\u001b[0m \\u001b[38;5;66;03m# of out will be done in `_insert_error_scores`.\\u001b[39;00m\\n\\u001b[1;32m    881\\u001b[0m \\u001b[38;5;28;01mif\\u001b[39;00m \\u001b[38;5;28mcallable\\u001b[39m(\\u001b[38;5;28mself\\u001b[39m\\u001b[38;5;241m.\\u001b[39mscoring):\\n\",\n      \"File \\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:414\\u001b[0m, in \\u001b[0;36m_warn_or_raise_about_fit_failures\\u001b[0;34m(results, error_score)\\u001b[0m\\n\\u001b[1;32m    407\\u001b[0m \\u001b[38;5;28;01mif\\u001b[39;00m num_failed_fits \\u001b[38;5;241m==\\u001b[39m num_fits:\\n\\u001b[1;32m    408\\u001b[0m     all_fits_failed_message \\u001b[38;5;241m=\\u001b[39m (\\n\\u001b[1;32m    409\\u001b[0m         \\u001b[38;5;124mf\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;130;01m\\\\n\\u001b[39;00m\\u001b[38;5;124mAll the \\u001b[39m\\u001b[38;5;132;01m{\\u001b[39;00mnum_fits\\u001b[38;5;132;01m}\\u001b[39;00m\\u001b[38;5;124m fits failed.\\u001b[39m\\u001b[38;5;130;01m\\\\n\\u001b[39;00m\\u001b[38;5;124m\\\"\\u001b[39m\\n\\u001b[1;32m    410\\u001b[0m         \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mIt is very likely that your model is misconfigured.\\u001b[39m\\u001b[38;5;130;01m\\\\n\\u001b[39;00m\\u001b[38;5;124m\\\"\\u001b[39m\\n\\u001b[1;32m    411\\u001b[0m         \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mYou can try to debug the error by setting error_score=\\u001b[39m\\u001b[38;5;124m'\\u001b[39m\\u001b[38;5;124mraise\\u001b[39m\\u001b[38;5;124m'\\u001b[39m\\u001b[38;5;124m.\\u001b[39m\\u001b[38;5;130;01m\\\\n\\u001b[39;00m\\u001b[38;5;130;01m\\\\n\\u001b[39;00m\\u001b[38;5;124m\\\"\\u001b[39m\\n\\u001b[1;32m    412\\u001b[0m         \\u001b[38;5;124mf\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mBelow are more details about the failures:\\u001b[39m\\u001b[38;5;130;01m\\\\n\\u001b[39;00m\\u001b[38;5;132;01m{\\u001b[39;00mfit_errors_summary\\u001b[38;5;132;01m}\\u001b[39;00m\\u001b[38;5;124m\\\"\\u001b[39m\\n\\u001b[1;32m    413\\u001b[0m     )\\n\\u001b[0;32m--> 414\\u001b[0m     \\u001b[38;5;28;01mraise\\u001b[39;00m \\u001b[38;5;167;01mValueError\\u001b[39;00m(all_fits_failed_message)\\n\\u001b[1;32m    416\\u001b[0m \\u001b[38;5;28;01melse\\u001b[39;00m:\\n\\u001b[1;32m    417\\u001b[0m     some_fits_failed_message \\u001b[38;5;241m=\\u001b[39m (\\n\\u001b[1;32m    418\\u001b[0m         \\u001b[38;5;124mf\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;130;01m\\\\n\\u001b[39;00m\\u001b[38;5;132;01m{\\u001b[39;00mnum_failed_fits\\u001b[38;5;132;01m}\\u001b[39;00m\\u001b[38;5;124m fits failed out of a total of \\u001b[39m\\u001b[38;5;132;01m{\\u001b[39;00mnum_fits\\u001b[38;5;132;01m}\\u001b[39;00m\\u001b[38;5;124m.\\u001b[39m\\u001b[38;5;130;01m\\\\n\\u001b[39;00m\\u001b[38;5;124m\\\"\\u001b[39m\\n\\u001b[1;32m    419\\u001b[0m         \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mThe score on these train-test partitions for these parameters\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m\\n\\u001b[0;32m   (...)\\u001b[0m\\n\\u001b[1;32m    423\\u001b[0m         \\u001b[38;5;124mf\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mBelow are more details about the failures:\\u001b[39m\\u001b[38;5;130;01m\\\\n\\u001b[39;00m\\u001b[38;5;132;01m{\\u001b[39;00mfit_errors_summary\\u001b[38;5;132;01m}\\u001b[39;00m\\u001b[38;5;124m\\\"\\u001b[39m\\n\\u001b[1;32m    424\\u001b[0m     )\\n\",\n      \"\\u001b[0;31mValueError\\u001b[0m: \\nAll the 315 fits failed.\\nIt is very likely that your model is misconfigured.\\nYou can try to debug the error by setting error_score='raise'.\\n\\nBelow are more details about the failures:\\n--------------------------------------------------------------------------------\\n315 fits failed with the following error:\\nTraceback (most recent call last):\\n  File \\\"/Users/lubberit/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\\\", line 729, in _fit_and_score\\n    estimator.fit(X_train, y_train, **fit_params)\\n  File \\\"/Users/lubberit/anaconda3/lib/python3.8/site-packages/sklearn/base.py\\\", line 1152, in wrapper\\n    return fit_method(estimator, *args, **kwargs)\\n  File \\\"/Users/lubberit/anaconda3/lib/python3.8/site-packages/sklearn/pipeline.py\\\", line 422, in fit\\n    fit_params_steps = self._check_fit_params(**fit_params)\\n  File \\\"/Users/lubberit/anaconda3/lib/python3.8/site-packages/sklearn/pipeline.py\\\", line 341, in _check_fit_params\\n    raise ValueError(\\nValueError: Pipeline.fit does not accept the error_score parameter. You can pass parameters to specific steps of your pipeline using the stepname__parameter format, e.g. `Pipeline.fit(X, y, logisticregression__sample_weight=sample_weight)`.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 导入超参数搜索方法\\n\",\n    \"from sklearn.model_selection import GridSearchCV\\n\",\n    \"\\n\",\n    \"# 构建 pipeline\\n\",\n    \"model_pipeline = Pipeline([\\n\",\n    \"    (\\\"preprocessing\\\", FeatureSelection(target=target, engine=\\\"toad\\\")),\\n\",\n    \"    (\\\"combiner\\\", Combiner(target=target, min_bin_size=0.2, adj_rules=None)),\\n\",\n    \"    (\\\"transform\\\", WOETransformer(target=target)),\\n\",\n    \"    (\\\"processing_select\\\", FeatureSelection(target=target, engine=\\\"toad\\\")),\\n\",\n    \"    (\\\"stepwise\\\", StepwiseSelection(target=target)),\\n\",\n    \"    (\\\"logistic\\\", ITLubberLogisticRegression(target=target)),\\n\",\n    \"])\\n\",\n    \"\\n\",\n    \"params_grid = {\\n\",\n    \"    \\\"combiner__max_n_bins\\\": [3],\\n\",\n    \"    \\\"logistic__C\\\": [np.power(2, i) for i in range(5)],\\n\",\n    \"    \\\"logistic__penalty\\\": [\\\"l2\\\"],\\n\",\n    \"    \\\"logistic__class_weight\\\": [None, \\\"balanced\\\"] + [{1: i / 10.0, 0: 1 - i / 10.0} for i in range(1, 10, 2)],\\n\",\n    \"    \\\"logistic__max_iter\\\": [10, 50, 100],\\n\",\n    \"    \\\"logistic__solver\\\": [\\\"sag\\\"], # [\\\"liblinear\\\", \\\"sag\\\", \\\"lbfgs\\\", \\\"newton-cg\\\"],\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"pipeline_grid_search = GridSearchCV(model_pipeline, params_grid, cv=3, scoring='roc_auc', verbose=1, n_jobs=-1, return_train_score=True)\\n\",\n    \"pipeline_grid_search.fit(train, train[target], error_score='raise')\\n\",\n    \"\\n\",\n    \"print(pipeline_grid_search.best_params_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[INFO] filtering variables ...\\n\",\n      \"|    | 变量名称                                            | 变量分箱                                                                                                                             |   对应分数 |\\n\",\n      \"|---:|:----------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------|-----------:|\\n\",\n      \"|  0 | property                                            | real estate                                                                                                                          |    11.2541 |\\n\",\n      \"|  1 | property                                            | unknown / no property,building society savings agreement/ life insurance,car or other, not in attribute Savings account/bonds,缺失值 |     4.0428 |\\n\",\n      \"|  2 | present_employment_since                            | ... >= 7 years,4 <= ... < 7 years                                                                                                    |     9.4896 |\\n\",\n      \"|  3 | present_employment_since                            | 1 <= ... < 4 years,... < 1 year,unemployed,缺失值                                                                                    |     3.8957 |\\n\",\n      \"|  4 | credit_amount                                       | [负无穷 , 2145.0)                                                                                                                    |     8.0057 |\\n\",\n      \"|  5 | credit_amount                                       | [2145.0 , 3804.0)                                                                                                                    |    14.2751 |\\n\",\n      \"|  6 | credit_amount                                       | [3804.0 , 正无穷)                                                                                                                    |    -2.6347 |\\n\",\n      \"|  7 | credit_amount                                       | 缺失值                                                                                                                               |     2.1778 |\\n\",\n      \"|  8 | installment_rate_in_percentage_of_disposable_income | [负无穷 , 4.0)                                                                                                                       |     7.7878 |\\n\",\n      \"|  9 | installment_rate_in_percentage_of_disposable_income | [4.0 , 正无穷)                                                                                                                       |     0.9877 |\\n\",\n      \"| 10 | installment_rate_in_percentage_of_disposable_income | 缺失值                                                                                                                               |    10.7462 |\\n\",\n      \"| 11 | age_in_years                                        | [负无穷 , 35.0)                                                                                                                      |     0.8917 |\\n\",\n      \"| 12 | age_in_years                                        | [35.0 , 正无穷)                                                                                                                      |    10.8457 |\\n\",\n      \"| 13 | age_in_years                                        | 缺失值                                                                                                                               |     6.7213 |\\n\",\n      \"| 14 | savings_account_and_bonds                           | ... >= 1000 DM,unknown/ no savings account,500 <= ... < 1000 DM                                                                      |     5.9834 |\\n\",\n      \"| 15 | savings_account_and_bonds                           | 100 <= ... < 500 DM,... < 100 DM                                                                                                     |    12.1585 |\\n\",\n      \"| 16 | savings_account_and_bonds                           | 缺失值                                                                                                                               |     3.2466 |\\n\",\n      \"| 17 | purpose                                             | radio/television,car (used)                                                                                                          |    12.8514 |\\n\",\n      \"| 18 | purpose                                             | education,retraining,others,business,furniture/equipment,缺失值,repairs,car (new),domestic appliances                                |     2.7818 |\\n\",\n      \"| 19 | personal_status_and_sex                             | 缺失值,female : divorced/separated/married                                                                                           |     7.8997 |\\n\",\n      \"| 20 | personal_status_and_sex                             | male : married/widowed,male : single,male : divorced/separated                                                                       |     3.7463 |\\n\",\n      \"| 21 | status_of_existing_checking_account                 | no checking account                                                                                                                  |    22.4653 |\\n\",\n      \"| 22 | status_of_existing_checking_account                 | ... >= 200 DM / salary assignments for at least 1 year,缺失值                                                                        |     6.6694 |\\n\",\n      \"| 23 | status_of_existing_checking_account                 | 0 <= ... < 200 DM                                                                                                                    |     0.0181 |\\n\",\n      \"| 24 | status_of_existing_checking_account                 | ... < 0 DM                                                                                                                           |    -4.8558 |\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 构建 pipeline\\n\",\n    \"model_pipeline = Pipeline([\\n\",\n    \"    (\\\"preprocessing\\\", FeatureSelection(target=target, engine=\\\"scorecardpy\\\")),\\n\",\n    \"    (\\\"combiner\\\", Combiner(target=target, min_bin_size=0.2)),\\n\",\n    \"    (\\\"transform\\\", WOETransformer(target=target)),\\n\",\n    \"    (\\\"processing_select\\\", FeatureSelection(target=target, engine=\\\"toad\\\")),\\n\",\n    \"    (\\\"stepwise\\\", StepwiseSelection(target=target)),\\n\",\n    \"    (\\\"logistic\\\", ITLubberLogisticRegression(target=target)),\\n\",\n    \"])\\n\",\n    \"# 训练 pipeline\\n\",\n    \"model_pipeline.fit(train)\\n\",\n    \"# 转换评分卡\\n\",\n    \"card = ScoreCard(target=target, pipeline=model_pipeline, base_score=50, base_odds=(1 - bad_rate) / bad_rate, pdo=10)\\n\",\n    \"card.fit(model_pipeline[:-1].transform(train))\\n\",\n    \"print(card.scorecard_points().to_markdown())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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变量名称变量分箱对应分数
0propertyreal estate11.2541
1propertyunknown / no property,building society savings agreement/ life insurance,car or other, not in attribute Savings account/bonds,缺失值4.0428
2present_employment_since... >= 7 years,4 <= ... < 7 years9.4896
3present_employment_since1 <= ... < 4 years,... < 1 year,unemployed,缺失值3.8957
4credit_amount[负无穷 , 2145.0)8.0057
5credit_amount[2145.0 , 3804.0)14.2751
6credit_amount[3804.0 , 正无穷)-2.6347
7credit_amount缺失值2.1778
8installment_rate_in_percentage_of_disposable_income[负无穷 , 4.0)7.7878
9installment_rate_in_percentage_of_disposable_income[4.0 , 正无穷)0.9877
10installment_rate_in_percentage_of_disposable_income缺失值10.7462
11age_in_years[负无穷 , 35.0)0.8917
12age_in_years[35.0 , 正无穷)10.8457
13age_in_years缺失值6.7213
14savings_account_and_bonds... >= 1000 DM,unknown/ no savings account,500 <= ... < 1000 DM5.9834
15savings_account_and_bonds100 <= ... < 500 DM,... < 100 DM12.1585
16savings_account_and_bonds缺失值3.2466
17purposeradio/television,car (used)12.8514
18purposeeducation,retraining,others,business,furniture/equipment,缺失值,repairs,car (new),domestic appliances2.7818
19personal_status_and_sex缺失值,female : divorced/separated/married7.8997
20personal_status_and_sexmale : married/widowed,male : single,male : divorced/separated3.7463
21status_of_existing_checking_accountno checking account22.4653
22status_of_existing_checking_account... >= 200 DM / salary assignments for at least 1 year,缺失值6.6694
23status_of_existing_checking_account0 <= ... < 200 DM0.0181
24status_of_existing_checking_account... < 0 DM-4.8558
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"                                                   变量名称                                                                                                                               变量分箱    对应分数\\n\",\n       \"0                                              property                                                                                                                        real estate 11.2541\\n\",\n       \"1                                              property  unknown / no property,building society savings agreement/ life insurance,car or other, not in attribute Savings account/bonds,缺失值  4.0428\\n\",\n       \"2                              present_employment_since                                                                                                  ... >= 7 years,4 <= ... < 7 years  9.4896\\n\",\n       \"3                              present_employment_since                                                                                     1 <= ... < 4 years,... < 1 year,unemployed,缺失值  3.8957\\n\",\n       \"4                                         credit_amount                                                                                                                     [负无穷 , 2145.0)  8.0057\\n\",\n       \"5                                         credit_amount                                                                                                                  [2145.0 , 3804.0) 14.2751\\n\",\n       \"6                                         credit_amount                                                                                                                     [3804.0 , 正无穷) -2.6347\\n\",\n       \"7                                         credit_amount                                                                                                                                缺失值  2.1778\\n\",\n       \"8   installment_rate_in_percentage_of_disposable_income                                                                                                                        [负无穷 , 4.0)  7.7878\\n\",\n       \"9   installment_rate_in_percentage_of_disposable_income                                                                                                                        [4.0 , 正无穷)  0.9877\\n\",\n       \"10  installment_rate_in_percentage_of_disposable_income                                                                                                                                缺失值 10.7462\\n\",\n       \"11                                         age_in_years                                                                                                                       [负无穷 , 35.0)  0.8917\\n\",\n       \"12                                         age_in_years                                                                                                                       [35.0 , 正无穷) 10.8457\\n\",\n       \"13                                         age_in_years                                                                                                                                缺失值  6.7213\\n\",\n       \"14                            savings_account_and_bonds                                                                    ... >= 1000 DM,unknown/ no savings account,500 <= ... < 1000 DM  5.9834\\n\",\n       \"15                            savings_account_and_bonds                                                                                                   100 <= ... < 500 DM,... < 100 DM 12.1585\\n\",\n       \"16                            savings_account_and_bonds                                                                                                                                缺失值  3.2466\\n\",\n       \"17                                              purpose                                                                                                        radio/television,car (used) 12.8514\\n\",\n       \"18                                              purpose                                 education,retraining,others,business,furniture/equipment,缺失值,repairs,car (new),domestic appliances  2.7818\\n\",\n       \"19                              personal_status_and_sex                                                                                            缺失值,female : divorced/separated/married  7.8997\\n\",\n       \"20                              personal_status_and_sex                                                                     male : married/widowed,male : single,male : divorced/separated  3.7463\\n\",\n       \"21                  status_of_existing_checking_account                                                                                                                no checking account 22.4653\\n\",\n       \"22                  status_of_existing_checking_account                                                                         ... >= 200 DM / salary assignments for at least 1 year,缺失值  6.6694\\n\",\n       \"23                  status_of_existing_checking_account                                                                                                                  0 <= ... < 200 DM  0.0181\\n\",\n       \"24                  status_of_existing_checking_account                                                                                                                         ... < 0 DM -4.8558\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"card.scorecard_points()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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变量名称变量分箱对应分数
0purposeradio/television,car (used)12.8514
1purposebusiness,furniture/equipment,others,education,domestic appliances,retraining,car (new),缺失值,repairs2.7818
2installment_rate_in_percentage_of_disposable_income[负无穷 , 4.0)7.7878
3installment_rate_in_percentage_of_disposable_income[4.0 , 正无穷)0.9877
4installment_rate_in_percentage_of_disposable_income缺失值10.7462
5savings_account_and_bonds500 <= ... < 1000 DM,unknown/ no savings account,... >= 1000 DM5.9834
6savings_account_and_bonds... < 100 DM,100 <= ... < 500 DM12.1585
7savings_account_and_bonds缺失值3.2466
8present_employment_since4 <= ... < 7 years,... >= 7 years9.4896
9present_employment_since缺失值,unemployed,1 <= ... < 4 years,... < 1 year3.8957
10age_in_years[负无穷 , 35.0)0.8917
11age_in_years[35.0 , 正无穷)10.8457
12age_in_years缺失值6.7213
13propertyreal estate11.2541
14property缺失值,building society savings agreement/ life insurance,unknown / no property,car or other, not in attribute Savings account/bonds4.0428
15personal_status_and_sex缺失值,female : divorced/separated/married7.8997
16personal_status_and_sexmale : single,male : married/widowed,male : divorced/separated3.7463
17credit_amount[负无穷 , 2145.0)8.0057
18credit_amount[2145.0 , 3804.0)14.2751
19credit_amount[3804.0 , 正无穷)-2.6347
20credit_amount缺失值2.1778
21status_of_existing_checking_accountno checking account22.4653
22status_of_existing_checking_account缺失值,... >= 200 DM / salary assignments for at least 1 year6.6694
23status_of_existing_checking_account0 <= ... < 200 DM0.0181
24status_of_existing_checking_account... < 0 DM-4.8558
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"                                                   变量名称                                                                                                                               变量分箱    对应分数\\n\",\n       \"0                                               purpose                                                                                                        radio/television,car (used) 12.8514\\n\",\n       \"1                                               purpose                                 business,furniture/equipment,others,education,domestic appliances,retraining,car (new),缺失值,repairs  2.7818\\n\",\n       \"2   installment_rate_in_percentage_of_disposable_income                                                                                                                        [负无穷 , 4.0)  7.7878\\n\",\n       \"3   installment_rate_in_percentage_of_disposable_income                                                                                                                        [4.0 , 正无穷)  0.9877\\n\",\n       \"4   installment_rate_in_percentage_of_disposable_income                                                                                                                                缺失值 10.7462\\n\",\n       \"5                             savings_account_and_bonds                                                                    500 <= ... < 1000 DM,unknown/ no savings account,... >= 1000 DM  5.9834\\n\",\n       \"6                             savings_account_and_bonds                                                                                                   ... < 100 DM,100 <= ... < 500 DM 12.1585\\n\",\n       \"7                             savings_account_and_bonds                                                                                                                                缺失值  3.2466\\n\",\n       \"8                              present_employment_since                                                                                                  4 <= ... < 7 years,... >= 7 years  9.4896\\n\",\n       \"9                              present_employment_since                                                                                     缺失值,unemployed,1 <= ... < 4 years,... < 1 year  3.8957\\n\",\n       \"10                                         age_in_years                                                                                                                       [负无穷 , 35.0)  0.8917\\n\",\n       \"11                                         age_in_years                                                                                                                       [35.0 , 正无穷) 10.8457\\n\",\n       \"12                                         age_in_years                                                                                                                                缺失值  6.7213\\n\",\n       \"13                                             property                                                                                                                        real estate 11.2541\\n\",\n       \"14                                             property  缺失值,building society savings agreement/ life insurance,unknown / no property,car or other, not in attribute Savings account/bonds  4.0428\\n\",\n       \"15                              personal_status_and_sex                                                                                            缺失值,female : divorced/separated/married  7.8997\\n\",\n       \"16                              personal_status_and_sex                                                                     male : single,male : married/widowed,male : divorced/separated  3.7463\\n\",\n       \"17                                        credit_amount                                                                                                                     [负无穷 , 2145.0)  8.0057\\n\",\n       \"18                                        credit_amount                                                                                                                  [2145.0 , 3804.0) 14.2751\\n\",\n       \"19                                        credit_amount                                                                                                                     [3804.0 , 正无穷) -2.6347\\n\",\n       \"20                                        credit_amount                                                                                                                                缺失值  2.1778\\n\",\n       \"21                  status_of_existing_checking_account                                                                                                                no checking account 22.4653\\n\",\n       \"22                  status_of_existing_checking_account                                                                         缺失值,... >= 200 DM / salary assignments for at least 1 year  6.6694\\n\",\n       \"23                  status_of_existing_checking_account                                                                                                                  0 <= ... < 200 DM  0.0181\\n\",\n       \"24                  status_of_existing_checking_account                                                                                                                         ... < 0 DM -4.8558\"\n      ]\n     },\n     \"execution_count\": 49,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"bad_rate = train[target].mean()\\n\",\n    \"# 逻辑回归模型转评分卡\\n\",\n    \"card = ScoreCard(target=target, pipeline=mdoel_pipeline, base_score=50, base_odds=(1 - bad_rate) / bad_rate, pdo=10)\\n\",\n    \"# 训练\\n\",\n    \"card.fit(mdoel_pipeline[:-1].transform(train))\\n\",\n    \"card.scorecard_points()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 数据字段[可选]\\n\",\n    \"feature_describe = pd.DataFrame([\\n\",\n    \"    [\\\"status_account\\\", \\\"支票账户状态\\\"], [\\\"duration\\\", \\\"借款周期\\\"], [\\\"credit_histor\\\", \\\"历史信用\\\"], [\\\"purpose\\\", \\\"借款目的\\\"], [\\\"amount\\\", \\\"信用额度\\\"], [\\\"svaing_account\\\", \\\"储蓄账户状态\\\"], [\\\"present_emp\\\", \\\"当前就业状态\\\"], [\\\"income_rate\\\", \\\"分期付款占可支配收入百分比\\\"], [\\\"personal_status\\\", \\\"性别与婚姻状态\\\"], [\\\"other_debtors\\\", \\\"他人担保信息\\\"], [\\\"residence_info\\\", \\\"现居住地\\\"], [\\\"property\\\", \\\"财产状态\\\"], [\\\"age\\\", \\\"年龄\\\"], [\\\"inst_plans\\\", \\\"其他分期情况\\\"], [\\\"housing\\\", \\\"房产状态\\\"], [\\\"num_credits\\\", \\\"信用卡数量\\\"], [\\\"job\\\", \\\"工作状态\\\"], [\\\"dependents\\\", \\\"赡养人数\\\"], [\\\"telephone\\\", \\\"电话号码注册情况\\\"], [\\\"foreign_worke\\\", \\\"是否有海外工作经历\\\"],\\n\",\n    \"], columns=[\\\"变量名称\\\", \\\"变量含义\\\"])\\n\",\n    \"feature_map = dict(zip(feature_describe[\\\"变量名称\\\"], feature_describe[\\\"变量含义\\\"]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 96,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"writer = sp.ExcelWriter()\\n\",\n    \"start_row, start_col = 2, 2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# # ////////////////////////////////////// 样本说明 ///////////////////////////////////// #\\n\",\n    \"worksheet = writer.get_sheet_by_name(\\\"汇总信息\\\")\\n\",\n    \"\\n\",\n    \"# 样本总体分布情况\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\\\"样本总体分布情况\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = sp.dataframe2excel(dataset_summary, writer, worksheet, percent_cols=[\\\"样本占比\\\", \\\"坏客户占比\\\"], start_row=end_row + 1)\\n\",\n    \"\\n\",\n    \"# 建模样本时间分布情况\\n\",\n    \"temp = sp.distribution_plot(df, date=\\\"date\\\", target=target, save=\\\"model_report/all_sample_time_count.png\\\", result=True)\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"建模样本时间分布情况\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/all_sample_time_count.png\\\", (end_row, start_col), figsize=(720, 370))\\n\",\n    \"end_row, end_col = sp.dataframe2excel(temp, writer, worksheet, percent_cols=[\\\"样本占比\\\", \\\"好样本占比\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\"], condition_cols=[\\\"坏样本率\\\"], start_row=end_row)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 98,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# ////////////////////////////////////// 模型报告 ///////////////////////////////////// #\\n\",\n    \"summary = logistic.summary2(feature_map=feature_map)\\n\",\n    \"\\n\",\n    \"# 逻辑回归拟合情况\\n\",\n    \"worksheet = writer.get_sheet_by_name(\\\"逻辑回归拟合结果\\\")\\n\",\n    \"\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\\\"逻辑回归拟合效果\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = sp.dataframe2excel(summary, writer, worksheet, condition_cols=[\\\"Coef.\\\"], start_row=end_row + 1)\\n\",\n    \"\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"训练数据集拟合报告\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = sp.dataframe2excel(logistic.report(train_woe_stepwise), writer, worksheet, percent_cols=[\\\"precision\\\", \\\"recall\\\", \\\"f1-score\\\"], start_row=end_row + 1)\\n\",\n    \"\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"测试数据集拟合报告\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = sp.dataframe2excel(logistic.report(test_woe_stepwise), writer, worksheet, percent_cols=[\\\"precision\\\", \\\"recall\\\", \\\"f1-score\\\"], start_row=end_row + 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 105,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# ////////////////////////////////////// 特征概述 ///////////////////////////////////// #\\n\",\n    \"worksheet = writer.get_sheet_by_name(\\\"模型变量信息\\\")\\n\",\n    \"\\n\",\n    \"start_row, start_col = 2, 2\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\\\"入模变量信息\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, feature_describe.reset_index().rename(columns={\\\"index\\\": \\\"序号\\\"}), (end_row + 1, start_col))\\n\",\n    \"\\n\",\n    \"# 变量分布情况\\n\",\n    \"import toad\\n\",\n    \"data_info = toad.detect(data[card.rules.keys()]).reset_index().rename(columns={\\\"index\\\": \\\"变量名称\\\", \\\"type\\\": \\\"变量类型\\\", \\\"size\\\": \\\"样本个数\\\", \\\"missing\\\": \\\"缺失值\\\", \\\"unique\\\": \\\"唯一值个数\\\"})\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"变量分布情况\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, data_info, (end_row + 1, start_col))\\n\",\n    \"\\n\",\n    \"# 变量相关性\\n\",\n    \"data_corr = train_woe_stepwise.corr()\\n\",\n    \"logistic.corr(train_woe_stepwise, save=\\\"model_report/train_corr.png\\\", annot=False)\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"变量相关性\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/train_corr.png\\\", (end_row + 1, start_col), figsize=(700, 500))\\n\",\n    \"end_row, end_col = sp.dataframe2excel(data_corr.reset_index().rename(columns={\\\"index\\\": \\\"\\\"}), writer, worksheet, color_cols=list(data_corr.columns), start_row=end_row + 1)\\n\",\n    \"\\n\",\n    \"# 变量分箱信息\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"变量分箱信息\\\", style=\\\"header\\\")\\n\",\n    \"\\n\",\n    \"for col in logistic.feature_names_in_:\\n\",\n    \"    feature_table = sp.feature_bin_stats(data, col, target=target, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", combiner=combiner)\\n\",\n    \"    _ = sp.bin_plot(feature_table, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", figsize=(8, 4), save=f\\\"model_report/bin_plots/data_{col}.png\\\")\\n\",\n    \"    \\n\",\n    \"    end_row, end_col = writer.insert_pic2sheet(worksheet, f\\\"model_report/bin_plots/data_{col}.png\\\", (end_row + 1, start_col), figsize=(700, 400))\\n\",\n    \"    end_row, end_col = sp.dataframe2excel(feature_table, writer, worksheet, percent_cols=[\\\"样本占比\\\", \\\"好样本占比\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\"], condition_cols=[\\\"坏样本率\\\", \\\"LIFT值\\\"], start_row=end_row)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 106,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# ////////////////////////////////////// 评分卡说明 ///////////////////////////////////// #\\n\",\n    \"worksheet = writer.get_sheet_by_name(\\\"评分卡结果\\\")\\n\",\n    \"\\n\",\n    \"# 评分卡刻度\\n\",\n    \"scorecard_kedu = card.scorecard_scale()\\n\",\n    \"scorecard_points = card.scorecard_points(feature_map=feature_map)\\n\",\n    \"scorecard_clip = card.score_clip(train[\\\"score\\\"], clip=100)\\n\",\n    \"\\n\",\n    \"start_row, start_col = 2, 2\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\\\"评分卡刻度\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, scorecard_kedu, (end_row + 1, start_col))\\n\",\n    \"\\n\",\n    \"# 评分卡对应分数\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"评分卡分数\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, scorecard_points, (end_row + 1, start_col), merge_column=\\\"变量名称\\\")\\n\",\n    \"\\n\",\n    \"# 评分效果\\n\",\n    \"score_table_train = sp.feature_bin_stats(train, \\\"score\\\", desc=\\\"测试集模型评分\\\", target=target, rules=scorecard_clip)\\n\",\n    \"score_table_test = sp.feature_bin_stats(test, \\\"score\\\", desc=\\\"测试集模型评分\\\", target=target, rules=scorecard_clip)\\n\",\n    \"\\n\",\n    \"sp.ks_plot(train[\\\"score\\\"], train[target], title=\\\"Train \\\\tDataset\\\", save=\\\"model_report/train_ksplot.png\\\")\\n\",\n    \"sp.ks_plot(test[\\\"score\\\"], test[target], title=\\\"Test \\\\tDataset\\\", save=\\\"model_report/test_ksplot.png\\\")\\n\",\n    \"\\n\",\n    \"sp.hist_plot(train[\\\"score\\\"], train[target], save=\\\"model_report/train_scorehist.png\\\", bins=30)\\n\",\n    \"sp.hist_plot(test[\\\"score\\\"], test[target], save=\\\"model_report/test_scorehist.png\\\", bins=30)\\n\",\n    \"\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"训练数据集评分模型效果\\\", style=\\\"header\\\")\\n\",\n    \"ks_row = end_row\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/train_ksplot.png\\\", (ks_row, start_col))\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/train_scorehist.png\\\", (ks_row, end_col))\\n\",\n    \"end_row, end_col = sp.dataframe2excel(score_table_train, writer, worksheet, percent_cols=[\\\"样本占比\\\", \\\"好样本占比\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\", \\\"分档KS值\\\"], condition_cols=[\\\"坏样本率\\\", \\\"LIFT值\\\", \\\"分档KS值\\\"], start_row=end_row + 1)\\n\",\n    \"\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"测试数据集评分模型效果\\\", style=\\\"header\\\")\\n\",\n    \"ks_row = end_row\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/test_ksplot.png\\\", (ks_row, start_col))\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/test_scorehist.png\\\", (ks_row, end_col))\\n\",\n    \"end_row, end_col = sp.dataframe2excel(score_table_test, writer, worksheet, percent_cols=[\\\"样本占比\\\", \\\"好样本占比\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\", \\\"分档KS值\\\"], condition_cols=[\\\"坏样本率\\\", \\\"LIFT值\\\", \\\"分档KS值\\\"], start_row=end_row + 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 108,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# ////////////////////////////////////// 模型稳定性 ///////////////////////////////////// #\\n\",\n    \"worksheet = writer.get_sheet_by_name(\\\"模型稳定性\\\")\\n\",\n    \"start_row, start_col = 2, 2\\n\",\n    \"\\n\",\n    \"# 评分分布稳定性\\n\",\n    \"train_test_score_psi = sp.psi_plot(score_table_train, score_table_test, labels=[\\\"训练数据集\\\", \\\"测试数据集\\\"], save=\\\"model_report/train_test_psiplot.png\\\", result=True)\\n\",\n    \"\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\\\"模型评分稳定性指标 (Population Stability Index, PSI): 训练数据集 vs 测试数据集\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/train_test_psiplot.png\\\", (end_row, start_col), figsize=(800, 400))\\n\",\n    \"end_row, end_col = sp.dataframe2excel(train_test_score_psi, writer, worksheet, percent_cols=[\\\"训练数据集样本占比\\\", \\\"训练数据集坏样本率\\\", \\\"测试数据集样本占比\\\", \\\"测试数据集坏样本率\\\"], condition_cols=[\\\"分档PSI值\\\"], start_row=end_row + 1)\\n\",\n    \"\\n\",\n    \"# 变量 PSI 表\\n\",\n    \"for col in card._feature_names:\\n\",\n    \"    feature_table_train = sp.feature_bin_stats(train, col, target=target, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", combiner=combiner)\\n\",\n    \"    feature_table_test = sp.feature_bin_stats(test, col, target=target, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", combiner=combiner)\\n\",\n    \"    psi_table = sp.psi_plot(feature_table_train, feature_table_test, desc=col, result=True, plot=True, max_len=35, figsize=(10, 6), labels=[\\\"训练数据集\\\", \\\"测试数据集\\\"], save=f\\\"model_report/psi_{col}.png\\\")\\n\",\n    \"    \\n\",\n    \"    end_row, end_col = writer.insert_pic2sheet(worksheet, f\\\"model_report/psi_{col}.png\\\", (end_row, start_col), figsize=(700, 400))\\n\",\n    \"    end_row, end_col = sp.dataframe2excel(psi_table, writer, worksheet, percent_cols=[\\\"训练数据集样本占比\\\", \\\"训练数据集坏样本率\\\", \\\"测试数据集样本占比\\\", \\\"测试数据集坏样本率\\\", \\\"测试数据集% - 训练数据集%\\\"], condition_cols=[\\\"分档PSI值\\\"], start_row=end_row + 1)\\n\",\n    \"\\n\",\n    \"# 变量 CSI 表\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"入模变量稳定性指标 (Characteristic Stability Index, CSI): 训练数据集 vs 测试数据集\\\", style=\\\"header\\\")\\n\",\n    \"\\n\",\n    \"for col in card._feature_names:\\n\",\n    \"    feature_table_train = sp.feature_bin_stats(train, col, target=target, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", combiner=combiner)\\n\",\n    \"    feature_table_test = sp.feature_bin_stats(test, col, target=target, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", combiner=combiner)\\n\",\n    \"    train_test_csi_table = sp.csi_plot(feature_table_train, feature_table_test, card[col], desc=col, result=True, plot=True, max_len=35, figsize=(10, 6), labels=[\\\"训练数据集\\\", \\\"测试数据集\\\"], save=f\\\"model_report/csi_{col}.png\\\")\\n\",\n    \"    \\n\",\n    \"    end_row, end_col = writer.insert_pic2sheet(worksheet, f\\\"model_report/csi_{col}.png\\\", (end_row, start_col), figsize=(700, 400))\\n\",\n    \"    end_row, end_col = sp.dataframe2excel(train_test_csi_table, writer, worksheet, percent_cols=[\\\"训练数据集样本占比\\\", \\\"训练数据集坏样本率\\\", \\\"测试数据集样本占比\\\", \\\"测试数据集坏样本率\\\", \\\"测试数据集% - 训练数据集%\\\"], condition_cols=[\\\"分档CSI值\\\"], start_row=end_row + 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 109,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"writer.save(\\\"model_report/评分卡模型报告.xlsx\\\")\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"scorecard\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.8\"\n  },\n  \"orig_nbformat\": 4\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "examples/rule_efficiency.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import sys\\n\",\n    \"sys.path.append(\\\"../\\\")\\n\",\n    \"\\n\",\n    \"import os\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from scorecardpipeline import *\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"logger = init_setting(seed=8888, logger=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"  \\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"  \\n\",\n       \"
status_of_existing_checking_accountduration_in_monthcredit_historypurposecredit_amountsavings_account_and_bondspresent_employment_sinceinstallment_rate_in_percentage_of_disposable_incomepersonal_status_and_sexother_debtors_or_guarantors...propertyage_in_yearsother_installment_planshousingnumber_of_existing_credits_at_this_bankjobnumber_of_people_being_liable_to_provide_maintenance_fortelephoneforeign_workercreditability
0... < 0 DM6critical account/ other credits existing (not at this bank)radio/television1169unknown/ no savings account... >= 7 years4male : divorced/separatednone...real estate67noneown2skilled employee / official1yes, registered under the customers nameyes0
\\n\",\n       \"
1 rows × 21 columns
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"  status_of_existing_checking_account  duration_in_month                                               credit_history           purpose  credit_amount    savings_account_and_bonds present_employment_since  installment_rate_in_percentage_of_disposable_income    personal_status_and_sex other_debtors_or_guarantors  ...     property age_in_years  other_installment_plans housing number_of_existing_credits_at_this_bank                          job number_of_people_being_liable_to_provide_maintenance_for                                 telephone foreign_worker creditability\\n\",\n       \"0                          ... < 0 DM                  6  critical account/ other credits existing (not at this bank)  radio/television           1169  unknown/ no savings account           ... >= 7 years                                                    4  male : divorced/separated                        none  ...  real estate           67                     none     own                                       2  skilled employee / official                                                        1  yes, registered under the customers name            yes             0\\n\",\n       \"\\n\",\n       \"[1 rows x 21 columns]\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 加载数据集，标签转换为 0 和 1\\n\",\n    \"target = \\\"creditability\\\"\\n\",\n    \"data = germancredit()\\n\",\n    \"data[target] = data[target].map({\\\"good\\\": 0, \\\"bad\\\": 1})\\n\",\n    \"\\n\",\n    \"# 目前仅支持数值型变量\\n\",\n    \"# data = data.select_dtypes(\\\"number\\\")\\n\",\n    \"data.head(1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"rule1 = Rule(\\\"duration_in_month < 4\\\")\\n\",\n    \"rule2 = Rule(\\\"credit_amount < 500\\\")\\n\",\n    \"rule3 = Rule(\\\"purpose.isin(['education', 'business'])\\\")\\n\",\n    \"\\n\",\n    \"rules = [rule1, rule2, rule3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# rule1.report(data, target=target)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# rule2.report(data, target=target)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# rule2.report(data, target=target, prior_rules=rule1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# (rule1 | rule2).report(data, target=target)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# (rule1 & rule2).report(data, target=target)\\n\",\n    \"# writer = ExcelWriter()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import itertools\\n\",\n    \"from functools import reduce\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"max_depth = 3\\n\",\n    \"\\n\",\n    \"reports = pd.DataFrame()\\n\",\n    \"\\n\",\n    \"for i in range(max_depth):\\n\",\n    \"    for rule in itertools.combinations(rules, i + 1):\\n\",\n    \"        if len(rule) > 1:\\n\",\n    \"            report = reduce(lambda r1, r2: r1 | r2, list(rule)).report(data, target=target)\\n\",\n    \"        else:\\n\",\n    \"            report = rule[0].report(data, target=target)\\n\",\n    \"        \\n\",\n    \"        reports = pd.concat([reports, report])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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\"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"  \\n\",\n       \"
规则分类指标名称分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率LIFT值坏账改善准确率精确率召回率F1分数
0验证规则duration_in_month < 4命中0.00000.00000.00000.00000.00000.00000.00000.00000.00000.70000.00000.00000.0000
1验证规则duration_in_month < 4未命中1000.00001.0000700.00001.0000300.00001.00000.30001.00000.00000.30000.30001.00000.4615
0验证规则credit_amount < 500命中18.00000.018015.00000.02143.00000.01000.16670.5556-0.00810.68800.16670.01000.0189
1验证规则credit_amount < 500未命中982.00000.9820685.00000.9786297.00000.99000.30241.00810.44440.31200.30240.99000.4633
0验证规则purpose.isin(['education', 'business'])命中147.00000.147091.00000.130056.00000.18670.38101.26980.04650.66500.38100.18670.2506
1验证规则purpose.isin(['education', 'business'])未命中853.00000.8530609.00000.8700244.00000.81330.28600.9535-0.26980.33500.28600.81330.4232
0验证规则(duration_in_month < 4) | (credit_amount < 500)命中18.00000.018015.00000.02143.00000.01000.16670.5556-0.00810.68800.16670.01000.0189
1验证规则(duration_in_month < 4) | (credit_amount < 500)未命中982.00000.9820685.00000.9786297.00000.99000.30241.00810.44440.31200.30240.99000.4633
0验证规则(duration_in_month < 4) | (purpose.isin(['education', 'business']))命中147.00000.147091.00000.130056.00000.18670.38101.26980.04650.66500.38100.18670.2506
1验证规则(duration_in_month < 4) | (purpose.isin(['education', 'business']))未命中853.00000.8530609.00000.8700244.00000.81330.28600.9535-0.26980.33500.28600.81330.4232
0验证规则(credit_amount < 500) | (purpose.isin(['education', 'business']))命中162.00000.1620105.00000.150057.00000.19000.35191.17280.03340.65200.35190.19000.2468
1验证规则(credit_amount < 500) | (purpose.isin(['education', 'business']))未命中838.00000.8380595.00000.8500243.00000.81000.29000.9666-0.17280.34800.29000.81000.4271
0验证规则((duration_in_month < 4) | (credit_amount < 500)) | (purpose.isin(['education', 'business']))命中162.00000.1620105.00000.150057.00000.19000.35191.17280.03340.65200.35190.19000.2468
1验证规则((duration_in_month < 4) | (credit_amount < 500)) | (purpose.isin(['education', 'business']))未命中838.00000.8380595.00000.8500243.00000.81000.29000.9666-0.17280.34800.29000.81000.4271
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"   规则分类                                                                                           指标名称   分箱      样本总数   样本占比     好样本数  好样本占比     坏样本数  坏样本占比   坏样本率  LIFT值    坏账改善    准确率    精确率    召回率   F1分数\\n\",\n       \"0  验证规则                                                                          duration_in_month < 4   命中    0.0000 0.0000   0.0000 0.0000   0.0000 0.0000 0.0000 0.0000  0.0000 0.7000 0.0000 0.0000 0.0000\\n\",\n       \"1  验证规则                                                                          duration_in_month < 4  未命中 1000.0000 1.0000 700.0000 1.0000 300.0000 1.0000 0.3000 1.0000  0.0000 0.3000 0.3000 1.0000 0.4615\\n\",\n       \"0  验证规则                                                                            credit_amount < 500   命中   18.0000 0.0180  15.0000 0.0214   3.0000 0.0100 0.1667 0.5556 -0.0081 0.6880 0.1667 0.0100 0.0189\\n\",\n       \"1  验证规则                                                                            credit_amount < 500  未命中  982.0000 0.9820 685.0000 0.9786 297.0000 0.9900 0.3024 1.0081  0.4444 0.3120 0.3024 0.9900 0.4633\\n\",\n       \"0  验证规则                                                        purpose.isin(['education', 'business'])   命中  147.0000 0.1470  91.0000 0.1300  56.0000 0.1867 0.3810 1.2698  0.0465 0.6650 0.3810 0.1867 0.2506\\n\",\n       \"1  验证规则                                                        purpose.isin(['education', 'business'])  未命中  853.0000 0.8530 609.0000 0.8700 244.0000 0.8133 0.2860 0.9535 -0.2698 0.3350 0.2860 0.8133 0.4232\\n\",\n       \"0  验证规则                                                (duration_in_month < 4) | (credit_amount < 500)   命中   18.0000 0.0180  15.0000 0.0214   3.0000 0.0100 0.1667 0.5556 -0.0081 0.6880 0.1667 0.0100 0.0189\\n\",\n       \"1  验证规则                                                (duration_in_month < 4) | (credit_amount < 500)  未命中  982.0000 0.9820 685.0000 0.9786 297.0000 0.9900 0.3024 1.0081  0.4444 0.3120 0.3024 0.9900 0.4633\\n\",\n       \"0  验证规则                            (duration_in_month < 4) | (purpose.isin(['education', 'business']))   命中  147.0000 0.1470  91.0000 0.1300  56.0000 0.1867 0.3810 1.2698  0.0465 0.6650 0.3810 0.1867 0.2506\\n\",\n       \"1  验证规则                            (duration_in_month < 4) | (purpose.isin(['education', 'business']))  未命中  853.0000 0.8530 609.0000 0.8700 244.0000 0.8133 0.2860 0.9535 -0.2698 0.3350 0.2860 0.8133 0.4232\\n\",\n       \"0  验证规则                              (credit_amount < 500) | (purpose.isin(['education', 'business']))   命中  162.0000 0.1620 105.0000 0.1500  57.0000 0.1900 0.3519 1.1728  0.0334 0.6520 0.3519 0.1900 0.2468\\n\",\n       \"1  验证规则                              (credit_amount < 500) | (purpose.isin(['education', 'business']))  未命中  838.0000 0.8380 595.0000 0.8500 243.0000 0.8100 0.2900 0.9666 -0.1728 0.3480 0.2900 0.8100 0.4271\\n\",\n       \"0  验证规则  ((duration_in_month < 4) | (credit_amount < 500)) | (purpose.isin(['education', 'business']))   命中  162.0000 0.1620 105.0000 0.1500  57.0000 0.1900 0.3519 1.1728  0.0334 0.6520 0.3519 0.1900 0.2468\\n\",\n       \"1  验证规则  ((duration_in_month < 4) | (credit_amount < 500)) | (purpose.isin(['education', 'business']))  未命中  838.0000 0.8380 595.0000 0.8500 243.0000 0.8100 0.2900 0.9666 -0.1728 0.3480 0.2900 0.8100 0.4271\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"reports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"score\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.11.5\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "examples/rule_extraction.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import sys\\n\",\n    \"sys.path.append(\\\"../\\\")\\n\",\n    \"\\n\",\n    \"import os\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from scorecardpipeline import *\\n\",\n    \"from scorecardpipeline.rule_extraction import DecisionTreeRuleExtractor\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"logger = init_setting(seed=8888, logger=True)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"feature_map = {}\\n\",\n    \"n_samples = 10000\\n\",\n    \"ab = np.array(list('ABCDEFG'))\\n\",\n    \"\\n\",\n    \"data = pd.DataFrame({\\n\",\n    \"    'A': np.random.randint(10, size = n_samples),\\n\",\n    \"    'B': ab[np.random.choice(7, n_samples)],\\n\",\n    \"    'C': ab[np.random.choice(2, n_samples)],\\n\",\n    \"    '时间': np.random.random(size = n_samples),\\n\",\n    \"    'target': np.random.randint(2, size = n_samples)\\n\",\n    \"})\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train, test = train_test_split(data, test_size=0.3, stratify=data[\\\"target\\\"])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pdtr = DecisionTreeRuleExtractor(target=\\\"target\\\", feature_map=feature_map, max_iter=8)\\n\",\n    \"pdtr.fit(train, lift=0., max_depth=2, max_samples=1., verbose=False, min_samples_split=8, min_samples_leaf=5)\\n\",\n    \"report = pdtr.report(valid=[test, train, data], save=\\\"model_report/决策树组合策略挖掘.xlsx\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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组合策略命中数命中率好样本数好样本占比坏样本数坏样本占比坏率样本整体坏率LIFT值
0B <= 0.495  & 时间 > 0.9432430.03471420.04011010.02920.41560.49410.8411
1B <= 0.495  & 时间 <= 0.94337580.536919330.545918250.52760.48560.49410.9828
2B > 0.495 & 时间 <= 0.87726690.381313210.373113480.38970.50510.49411.0221
3B > 0.495 & 时间 > 0.8773300.04711450.04091850.05350.56060.49411.1345
4B <= 0.495  & A <= 8.536050.515018890.533517160.49610.47600.49410.9633
5B > 0.495 & A > 4.514960.21377490.21157470.21600.49930.49411.0105
6B > 0.495 & A <= 4.515030.21477170.20257860.22720.52300.49411.0583
7B <= 0.495  & A > 8.53960.05661860.05252100.06070.53030.49411.0732
8A <= 8.5  & A <= 3.527720.396014320.404413400.38740.48340.49410.9783
9A <= 8.5  & A > 3.535260.503717750.501317510.50620.49660.49411.0050
10A > 8.5 & C <= 0.4943580.05111740.04911840.05320.51400.49411.0401
11A > 8.5 & C > 0.4943440.04911600.04521840.05320.53490.49411.0824
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"                          组合策略   命中数    命中率  好样本数  好样本占比  坏样本数  坏样本占比     坏率  样本整体坏率  LIFT值\\n\",\n       \"0     B <= 0.495  & 时间 > 0.943   243 0.0347   142 0.0401   101 0.0292 0.4156  0.4941 0.8411\\n\",\n       \"1   B <= 0.495  & 时间 <= 0.943   3758 0.5369  1933 0.5459  1825 0.5276 0.4856  0.4941 0.9828\\n\",\n       \"2     B > 0.495 & 时间 <= 0.877   2669 0.3813  1321 0.3731  1348 0.3897 0.5051  0.4941 1.0221\\n\",\n       \"3       B > 0.495 & 时间 > 0.877   330 0.0471   145 0.0409   185 0.0535 0.5606  0.4941 1.1345\\n\",\n       \"4      B <= 0.495  & A <= 8.5   3605 0.5150  1889 0.5335  1716 0.4961 0.4760  0.4941 0.9633\\n\",\n       \"5          B > 0.495 & A > 4.5  1496 0.2137   749 0.2115   747 0.2160 0.4993  0.4941 1.0105\\n\",\n       \"6        B > 0.495 & A <= 4.5   1503 0.2147   717 0.2025   786 0.2272 0.5230  0.4941 1.0583\\n\",\n       \"7        B <= 0.495  & A > 8.5   396 0.0566   186 0.0525   210 0.0607 0.5303  0.4941 1.0732\\n\",\n       \"8        A <= 8.5  & A <= 3.5   2772 0.3960  1432 0.4044  1340 0.3874 0.4834  0.4941 0.9783\\n\",\n       \"9          A <= 8.5  & A > 3.5  3526 0.5037  1775 0.5013  1751 0.5062 0.4966  0.4941 1.0050\\n\",\n       \"10       A > 8.5 & C <= 0.494    358 0.0511   174 0.0491   184 0.0532 0.5140  0.4941 1.0401\\n\",\n       \"11         A > 8.5 & C > 0.494   344 0.0491   160 0.0452   184 0.0532 0.5349  0.4941 1.0824\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"report[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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组合策略命中数命中率好样本数好样本占比坏样本数坏样本占比坏率样本整体坏率LIFT值
0B <= 0.495  & 时间 > 0.943960.0320500.0329460.03100.47920.49400.9700
1B <= 0.495  & 时间 <= 0.94316040.53478250.54357790.52560.48570.49400.9831
2B > 0.495 & 时间 <= 0.87711460.38205700.37555760.38870.50260.49401.0174
3B > 0.495 & 时间 > 0.8771540.0513730.0481810.05470.52600.49401.0647
4B <= 0.495  & A <= 8.515350.51177910.52117440.50200.48470.49400.9812
5B > 0.495 & A > 4.56170.20573060.20163110.20990.50410.49401.0203
6B > 0.495 & A <= 4.56830.22773370.22203460.23350.50660.49401.0255
7B <= 0.495  & A > 8.51650.0550840.0553810.05470.49090.49400.9937
8A <= 8.5  & A <= 3.512140.40476150.40515990.40420.49340.49400.9988
9A <= 8.5  & A > 3.514870.49577440.49017430.50130.49970.49401.0115
10A > 8.5 & C <= 0.4941470.0490810.0534660.04450.44900.49400.9089
11A > 8.5 & C > 0.4941520.0507780.0514740.04990.48680.49400.9855
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"                          组合策略   命中数    命中率 好样本数  好样本占比 坏样本数  坏样本占比     坏率 样本整体坏率  LIFT值\\n\",\n       \"0     B <= 0.495  & 时间 > 0.943    96 0.0320   50 0.0329   46 0.0310 0.4792 0.4940 0.9700\\n\",\n       \"1   B <= 0.495  & 时间 <= 0.943   1604 0.5347  825 0.5435  779 0.5256 0.4857 0.4940 0.9831\\n\",\n       \"2     B > 0.495 & 时间 <= 0.877   1146 0.3820  570 0.3755  576 0.3887 0.5026 0.4940 1.0174\\n\",\n       \"3       B > 0.495 & 时间 > 0.877   154 0.0513   73 0.0481   81 0.0547 0.5260 0.4940 1.0647\\n\",\n       \"4      B <= 0.495  & A <= 8.5   1535 0.5117  791 0.5211  744 0.5020 0.4847 0.4940 0.9812\\n\",\n       \"5          B > 0.495 & A > 4.5   617 0.2057  306 0.2016  311 0.2099 0.5041 0.4940 1.0203\\n\",\n       \"6        B > 0.495 & A <= 4.5    683 0.2277  337 0.2220  346 0.2335 0.5066 0.4940 1.0255\\n\",\n       \"7        B <= 0.495  & A > 8.5   165 0.0550   84 0.0553   81 0.0547 0.4909 0.4940 0.9937\\n\",\n       \"8        A <= 8.5  & A <= 3.5   1214 0.4047  615 0.4051  599 0.4042 0.4934 0.4940 0.9988\\n\",\n       \"9          A <= 8.5  & A > 3.5  1487 0.4957  744 0.4901  743 0.5013 0.4997 0.4940 1.0115\\n\",\n       \"10       A > 8.5 & C <= 0.494    147 0.0490   81 0.0534   66 0.0445 0.4490 0.4940 0.9089\\n\",\n       \"11         A > 8.5 & C > 0.494   152 0.0507   78 0.0514   74 0.0499 0.4868 0.4940 0.9855\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"report[1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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组合策略命中数命中率好样本数好样本占比坏样本数坏样本占比坏率样本整体坏率LIFT值
0B <= 0.495  & 时间 > 0.9432420.03461410.03981010.02920.41740.49410.8446
1B <= 0.495  & 时间 <= 0.94337590.537019340.546218250.52760.48550.49410.9825
2B > 0.495 & 时间 <= 0.87726680.381113200.372813480.38970.50520.49411.0225
3B > 0.495 & 时间 > 0.8773310.04731460.04121850.05350.55890.49411.1311
4B <= 0.495  & A <= 8.536050.515018890.533517160.49610.47600.49410.9633
5B > 0.495 & A > 4.514960.21377490.21157470.21600.49930.49411.0105
6B > 0.495 & A <= 4.515030.21477170.20257860.22720.52300.49411.0583
7B <= 0.495  & A > 8.53960.05661860.05252100.06070.53030.49411.0732
8A <= 8.5  & A <= 3.527720.396014320.404413400.38740.48340.49410.9783
9A <= 8.5  & A > 3.535260.503717750.501317510.50620.49660.49411.0050
10A > 8.5 & C <= 0.4943580.05111740.04911840.05320.51400.49411.0401
11A > 8.5 & C > 0.4943440.04911600.04521840.05320.53490.49411.0824
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"                          组合策略   命中数    命中率  好样本数  好样本占比  坏样本数  坏样本占比     坏率 样本整体坏率  LIFT值\\n\",\n       \"0     B <= 0.495  & 时间 > 0.943   242 0.0346   141 0.0398   101 0.0292 0.4174 0.4941 0.8446\\n\",\n       \"1   B <= 0.495  & 时间 <= 0.943   3759 0.5370  1934 0.5462  1825 0.5276 0.4855 0.4941 0.9825\\n\",\n       \"2     B > 0.495 & 时间 <= 0.877   2668 0.3811  1320 0.3728  1348 0.3897 0.5052 0.4941 1.0225\\n\",\n       \"3       B > 0.495 & 时间 > 0.877   331 0.0473   146 0.0412   185 0.0535 0.5589 0.4941 1.1311\\n\",\n       \"4      B <= 0.495  & A <= 8.5   3605 0.5150  1889 0.5335  1716 0.4961 0.4760 0.4941 0.9633\\n\",\n       \"5          B > 0.495 & A > 4.5  1496 0.2137   749 0.2115   747 0.2160 0.4993 0.4941 1.0105\\n\",\n       \"6        B > 0.495 & A <= 4.5   1503 0.2147   717 0.2025   786 0.2272 0.5230 0.4941 1.0583\\n\",\n       \"7        B <= 0.495  & A > 8.5   396 0.0566   186 0.0525   210 0.0607 0.5303 0.4941 1.0732\\n\",\n       \"8        A <= 8.5  & A <= 3.5   2772 0.3960  1432 0.4044  1340 0.3874 0.4834 0.4941 0.9783\\n\",\n       \"9          A <= 8.5  & A > 3.5  3526 0.5037  1775 0.5013  1751 0.5062 0.4966 0.4941 1.0050\\n\",\n       \"10       A > 8.5 & C <= 0.494    358 0.0511   174 0.0491   184 0.0532 0.5140 0.4941 1.0401\\n\",\n       \"11         A > 8.5 & C > 0.494   344 0.0491   160 0.0452   184 0.0532 0.5349 0.4941 1.0824\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"report[2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "examples/rule_test.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\n@Time    : 2024/4/18 10:15\n@Author  : itlubber\n@Site    : itlubber.art\n\"\"\"\nimport copy\nimport datetime\nimport numpy as np\nimport pandas as pd\n\nmin_num = 40\nhit_num = 30\nsub_div_bin = 0.1\nsample_type_target = {'Total': ['fpd_30_act', 'mob3_dpd_30_act']}\ntarget_ripe = {'fpd_30_act': ['agr_fpd_30'], 'mob3_dpd_30_act': ['agr_mob3_dpd_30']}\ntarget_del_col = {'fpd_30_act': ['agr_fpd_30', 'agr_mob3_dpd_30', 'mob3_dpd_30_act'], 'mob3_dpd_30_act': ['agr_mob3_dpd_30', 'agr_fpd_30', 'fpd_30_act']}\ntarget_min_rate = {'fpd_30_act': [0.015], 'mob3_dpd_30_act': [0.02]}\nsample_type_lift = {'Total': {'fpd_30_act': 1.8, 'mob3_dpd_30_act': 3}}\n\n\ndef cv(x):\n    \"\"\"计算一组数的离散系数\"\"\"\n    m = np.mean(x)\n    if m == 0:\n        return None\n    else:\n        return np.std(x) / np.mean(x)\n\n\ndef cv_type(x):\n    \"\"\"基于离散系数判断数据稳定性\"\"\"\n    if x < 0.15:\n        return '非常稳定'\n    elif x < 0.4:\n        return '相对稳定'\n    elif x < 0.75:\n        return '不稳定'\n    else:\n        return '极不稳定'\n\n\ndef get_trend(data, method=np.mean, key='', flag=['高', '低']):\n    \"\"\"判断数据的趋势\n\n    :param data: 需要判断的数据,list类型或者dataframe类型\n    :param flag: ['高','低'] 或者 ['快','慢']\n    :param method: 次数对应求和,速度对应平均\n    :param key:默认为None,如果传入数据为dataframe,则需要指定key,但是必须从小到大排好顺序\n\n    :return: 不同趋势\n    \"\"\"\n    if len(data) == 0: return '无数据'\n\n    if isinstance(data, pd.DataFrame):\n        if key != None:\n            data = list(data[data[key].notnull()][key])\n        else:\n            raise '没有指定需要判断趋势的列名'\n\n    if len(data) == 0:\n        return '无数据'\n    elif len(data) == 1:\n        return '无趋势'\n    elif len(data) > 4:\n        n1 = len(data) // 4\n        n2 = len(data) % 4\n        k1 = n1\n        k2 = 2 * n1 + (1 if n2 >= 3 else 0)\n        k3 = k2 + n1 + (1 if n2 >= 2 else 0)\n        x = [method(data[0:k1]), method(data[k1:k2]), method(data[k2:k3]), method(data[k3:])]\n    else:\n        x = data\n\n    cmax = max(x)\n    cmin = min(x)\n    cv = np.std(x) / np.mean(x) if np.mean(x) != 0 else 0\n\n    if cv < 0.15:\n        return '平稳'\n    elif len(x) == 2:\n        if cmax == x[0]:\n            return '%s > %s' % (flag[0], flag[1])\n        else:\n            return '%s > %s' % (flag[1], flag[0])\n    elif len(x) == 3:\n        if cmax == x[1]:\n            return '%s > %s > %s' % (flag[1], flag[0], flag[1])\n        elif cmin == x[1]:\n            return '%s > %s > %s' % (flag[0], flag[1], flag[0])\n        elif cmax == x[0] and cmin == x[2]:\n            return '%s > %s' % (flag[0], flag[1])\n        else:\n            return '%s > %s' % (flag[1], flag[0])\n    else:\n        if cmax == x[1] or cmax == x[2]:\n            return '%s > %s > %s' % (flag[1], flag[0], flag[1])\n        elif cmin == x[1] or cmin == x[2]:\n            return '%s > %s > %s' % (flag[0], flag[1], flag[0])\n        elif cmax == x[0] and cmin == x[3]:\n            return '%s > %s' % (flag[0], flag[1])\n        else:\n            return '%s > %s' % (flag[1], flag[0])\n\n\ndef calculate_var_lift(data, data_sub, hit_flag, target, cut_point, direction):\n    \"\"\"计算lift\n\n    data: 全量样本\n    data_sub: 满足条件的样本\n    hit_flag: 触碰标签\n    target: string 需要计算lift的列名\n    \"\"\"\n    if '>' in direction:\n        data_hit = data_sub[data_sub[hit_flag] > cut_point] if len(data_sub) > 0 else data_sub\n        bad = len(data_hit[data_hit[target] == 1]) if len(data_hit) > 0 else np.nan\n        hit_bad_rate = bad / len(data_hit) if len(data_hit) > 0 else np.nan\n    else:\n        data_hit = data_sub[data_sub[hit_flag] <= cut_point] if len(data_sub) > 0 else data_sub\n        bad = len(data_hit[data_hit[target] == 1]) if len(data_hit) > 0 else np.nan\n        hit_bad_rate = bad / len(data_hit) if len(data_hit) > 0 else np.nan\n    bad = len(data[data[target] == 1])\n    allhit_bad_rate = bad / len(data) if len(data) > 0 else np.nan\n    return hit_bad_rate / allhit_bad_rate if allhit_bad_rate > 0 else np.nan\n\n\ndef calculate_var_odds(data, data_sub, hit_flag, target, cut_point, direction):\n    \"\"\"计算odds ratio\n    \n    data: DataFrame 成熟的样本子集的补集(未触碰所有样本)\n    data_sub : 成熟的样本子集（如某个省份的数据集）\n    hit_flag: 触碰标签\n    target: string 需要计算odds ratio的列名\n    \"\"\"\n    if '>' in direction:\n        data_hit = data_sub[data_sub[hit_flag] > cut_point] if len(data_sub) > 0 else data_sub\n        bad = len(data_hit[data_hit[target] == 1]) if len(data_hit) > 0 else np.nan\n        good = len(data_hit[data_hit[target] != 1]) if len(data_hit) > 0 else np.nan\n        hit_bad_good_rate = bad / good if good > 0 else np.nan\n        # data_nohit = data[data[hit_flag] <= cut_point]\n        bad_nohit = len(data[data[target] == 1])\n        good_nohit = len(data[data[target] != 1])\n        nohit_bad_good_rate = bad_nohit / good_nohit if good_nohit > 0 else np.nan\n    else:\n        data_hit = data_sub[data_sub[hit_flag] <= cut_point] if len(data_sub) > 0 else data_sub\n        bad = len(data_hit[data_hit[target] == 1]) if len(data_hit) > 0 else np.nan\n        good = len(data_hit[data_hit[target] != 1]) if len(data_hit) > 0 else np.nan\n        hit_bad_good_rate = bad / good if good > 0 else np.nan\n        # data_nohit = data[data[hit_flag] > cut_point]\n        bad_nohit = len(data[data[target] == 1])\n        good_nohit = len(data[data[target] != 1])\n        nohit_bad_good_rate = bad_nohit / good_nohit if good_nohit > 0 else np.nan\n    return hit_bad_good_rate / nohit_bad_good_rate if nohit_bad_good_rate > 0 else np.nan\n\n\n# 1.7按月对规则进行泛化，分析规则触碰情况和风险表现情况\ndef get_month_odds(data, rule_name, rule_type, cut_point, target, rule_limit, rule_all, use_credit_flag, circle_mth, mth, direction, var):\n    \"\"\"\n    :param data: 数据框\n    :param rule_name: 规则名\n    :param rule_type: 规则类型\n    :param cut_point: 切点，cut-off\n    :param target: 目标字段\n    :param rule_limit: 泛化样本类型\n    :param rule_all: 线上已有规则触碰情况对应的字段列名\n    :param use_credit_flag: 是否用信列\n    :param circle_mth: 申请月对应的列\n    :param mth: 要泛化的具体月\n    :param direction: 变量取值方向\n    :param var: 泛化变量名\n    :return:  按月泛化分析结果\n    \"\"\"\n    data = copy.deepcopy(data)\n    data = data[data[circle_mth] == mth]\n    dic = {}\n    dic['报告日期'] = datetime.datetime.now().strftime('%Y-%m-%d')\n    if rule_limit == 'Total':\n        len_prod = len(data['product_name'].unique())\n        if len_prod >= 3:\n            dic['产品名称'] = str(data['product_name'].values[0]) + '等' + str(len_prod) + '个产品'\n        elif len_prod >= 2:\n            dic['产品名称'] = str(data['product_name'].unique()[0]) + '&' + str(data['product_name'].unique()[1])\n        else:\n            dic['产品名称'] = str(data['product_name'].unique()[0])\n    elif rule_limit == '其他':\n        data_prod = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]['product_name'].unique()\n        len_prod = len(data_prod)\n        if len_prod >= 3:\n            dic['产品名称'] = str(data_prod[0]) + '等' + str(len_prod) + '个产品'\n        elif len_prod >= 2:\n            dic['产品名称'] = str(data_prod[0]) + '&' + str(data_prod[1])\n        else:\n            dic['产品名称'] = str(data_prod[0])\n    elif rule_limit == sample_type_col[rule_limit][1][0]:\n        data_prod = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]['product_name'].unique()\n        len_prod = len(data_prod)\n        if len_prod >= 3:\n            dic['产品名称'] = str(data_prod[0]) + '等' + str(len_prod) + '个产品'\n        elif len_prod >= 2:\n            dic['产品名称'] = str(data_prod[0]) + '&' + str(data_prod[1])\n        else:\n            dic['产品名称'] = str(data_prod[0])\n    dic['样本类型'] = rule_limit\n    dic['风险类型'] = '短期风险' if target == 'fpd_30_act' else '中长期风险'\n    dic['目标字段'] = target\n    dic['规则名称'] = rule_name\n    dic['规则类型'] = rule_type\n    dic['申请月'] = mth\n    dic['申请量'] = len(data)\n    dic['通过量'] = len(data[data[rule_all] == 0])\n    dic['通过率'] = dic['通过量'] / dic['申请量'] if dic['申请量'] > 0 else None\n    dic['用信量'] = len(data[data[use_credit_flag] == 1])\n    dic['用信率'] = dic['用信量'] / dic['通过量'] if dic['通过量'] > 0 else None\n    dic['置前触碰量'] = len(data[data[rule_all] == 1])\n    ## 统计逾期成熟样本量\n    tmp15 = data[data['agr_fpd_15'] == 1]\n    tmp = data[data[target_ripe[target][0]] == 1]\n    if '>' in direction:\n        if rule_limit == 'Total':\n            dic['策略触碰量'] = len(data[data[var] > cut_point])\n            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] > cut_point)])\n            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] > cut_point)])\n            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | (data[var] > cut_point)])\n            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['整体成熟量'] = len(tmp)\n            dic['额外触碰成熟量'] = len(tmp[(tmp[rule_all] == 0) & (tmp[var] > cut_point)])  # len(tmp[tmp[var] > cut_point])\n            dic['整体成熟坏样本量'] = len(tmp[tmp[target] == 1])\n            dic['额外触碰成熟坏样本量'] = len(tmp[(tmp[rule_all] == 0) & (tmp[target] == 1) & (tmp[var] > cut_point)])\n            dic['fpd15整体成熟量'] = len(tmp15)\n            dic['fpd15额外触碰成熟量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] > cut_point)])\n            dic['fpd15整体成熟坏样本量'] = len(tmp15[tmp15['fpd_15_act'] == 1])\n            dic['fpd15额外触碰成熟坏样本量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] > cut_point) & (tmp15['fpd_15_act'] == 1)])\n            dic['整体逾期率'] = len(tmp[tmp[target] == 1]) / dic['整体成熟量'] if dic['整体成熟量'] > 0 else None\n            dic['额外触碰逾期率'] = dic['额外触碰成熟坏样本量'] / dic['额外触碰成熟量'] if dic['额外触碰成熟量'] > 0 else None\n            dic['置后触碰整体逾期率'] = (dic['整体成熟坏样本量'] - dic['额外触碰成熟坏样本量']) / (dic['整体成熟量'] - dic['额外触碰成熟量']) if (dic['整体成熟量'] - dic['额外触碰成熟量']) > 0 else None\n            dic['逾期率下降值'] = dic['整体逾期率'] - dic['置后触碰整体逾期率'] if dic['置后触碰整体逾期率'] != None else None\n            dic['逾期率下降幅度'] = (dic['整体逾期率'] - dic['置后触碰整体逾期率']) / dic['整体逾期率'] if (dic['整体逾期率'] != None and dic['置后触碰整体逾期率'] != None and\n                                                                                                            dic['整体逾期率'] != 0) else None\n            tmp_sub = tmp\n            tmp_nohit = tmp[(tmp[var] <= cut_point)]\n            tmp_sub15 = tmp15\n            tmp_nohit15 = tmp15[tmp15[var] <= cut_point]\n            dic['额外触碰Odds'] = calculate_var_odds(data=tmp_nohit, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)\n            dic['额外触碰Lift'] = calculate_var_lift(data=tmp, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)\n            dic['额外触碰fpd15_Odds'] = calculate_var_odds(data=tmp_nohit15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)\n            dic['额外触碰fpd15_Lift'] = calculate_var_lift(data=tmp15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)\n            df_ls = pd.DataFrame.from_dict(dic, orient='index').T\n        elif rule_limit == '其他':\n            dic['策略触碰量'] = len(data[(data[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))])\n            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['整体成熟量'] = len(tmp)\n            dic['额外触碰成熟量'] = len(tmp[(tmp[rule_all] == 0) & (tmp[var] > cut_point) & (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['整体成熟坏样本量'] = len(tmp[(tmp[target] == 1)])\n            dic['额外触碰成熟坏样本量'] = len(tmp[(tmp[rule_all] == 0) & (tmp[target] == 1) & (tmp[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['fpd15整体成熟量'] = len(tmp15)\n            dic['fpd15额外触碰成熟量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] > cut_point) & (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['fpd15整体成熟坏样本量'] = len(tmp15[tmp15['fpd_15_act'] == 1])\n            dic['fpd15额外触碰成熟坏样本量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] > cut_point) &\n                                                         (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])) & (tmp15['fpd_15_act'] == 1)])\n            dic['整体逾期率'] = len(tmp[tmp[target] == 1]) / dic['整体成熟量'] if dic['整体成熟量'] > 0 else None\n            dic['额外触碰逾期率'] = dic['额外触碰成熟坏样本量'] / dic['额外触碰成熟量'] if dic['额外触碰成熟量'] > 0 else None\n            dic['置后触碰整体逾期率'] = (dic['整体成熟坏样本量'] - dic['额外触碰成熟坏样本量']) / (dic['整体成熟量'] - dic['额外触碰成熟量']) if (dic['整体成熟量'] - dic['额外触碰成熟量']) > 0 else None\n            dic['逾期率下降值'] = dic['整体逾期率'] - dic['置后触碰整体逾期率'] if dic['置后触碰整体逾期率'] != None else None\n            dic['逾期率下降幅度'] = (dic['整体逾期率'] - dic['置后触碰整体逾期率']) / dic['整体逾期率'] if (dic['整体逾期率'] != None and dic['置后触碰整体逾期率'] != None and\n                                                                                                            dic['整体逾期率'] != 0) else None\n            tmp_sub = tmp[tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]\n            tmp_nohit = tmp[(((tmp[var] <= cut_point) & (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))) |\n                             (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))]\n            tmp_sub15 = tmp15[tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]\n            tmp_nohit15 = tmp15[(((tmp15[var] <= cut_point) & (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))) |\n                                 (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))]\n            dic['额外触碰Odds'] = calculate_var_odds(data=tmp_nohit, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)\n            dic['额外触碰Lift'] = calculate_var_lift(data=tmp, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)\n            dic['额外触碰fpd15_Odds'] = calculate_var_odds(data=tmp_nohit15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)\n            dic['额外触碰fpd15_Lift'] = calculate_var_lift(data=tmp15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)\n            df_ls = pd.DataFrame.from_dict(dic, orient='index').T\n        elif rule_limit == sample_type_col[rule_limit][1][0]:\n            dic['策略触碰量'] = len(data[(data[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))])\n            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['整体成熟量'] = len(tmp)\n            dic['额外触碰成熟量'] = len(tmp[(tmp[var] > cut_point) & (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['整体成熟坏样本量'] = len(tmp[(tmp[target] == 1)])\n            dic['额外触碰成熟坏样本量'] = len(tmp[(tmp[rule_all] == 0) & (tmp[target] == 1) & (tmp[var] > cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['fpd15整体成熟量'] = len(tmp15)\n            dic['fpd15额外触碰成熟量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] > cut_point) & (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['fpd15整体成熟坏样本量'] = len(tmp15[tmp15['fpd_15_act'] == 1])\n            dic['fpd15额外触碰成熟坏样本量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] > cut_point) &\n                                                         (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])) & (tmp15['fpd_15_act'] == 1)])\n            dic['整体逾期率'] = len(tmp[tmp[target] == 1]) / dic['整体成熟量'] if dic['整体成熟量'] > 0 else None\n            dic['额外触碰逾期率'] = dic['额外触碰成熟坏样本量'] / dic['额外触碰成熟量'] if dic['额外触碰成熟量'] > 0 else None\n            dic['置后触碰整体逾期率'] = (dic['整体成熟坏样本量'] - dic['额外触碰成熟坏样本量']) / (dic['整体成熟量'] - dic['额外触碰成熟量']) if (dic['整体成熟量'] - dic['额外触碰成熟量']) > 0 else None\n            dic['逾期率下降值'] = dic['整体逾期率'] - dic['置后触碰整体逾期率'] if dic['置后触碰整体逾期率'] != None else None\n            dic['逾期率下降幅度'] = (dic['整体逾期率'] - dic['置后触碰整体逾期率']) / dic['整体逾期率'] if (dic['整体逾期率'] != None and dic['置后触碰整体逾期率'] != None and\n                                                                                                            dic['整体逾期率'] != 0) else None\n            tmp_sub = tmp[tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]\n            tmp_nohit = tmp[(((tmp[var] <= cut_point) & (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))) | (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))]\n            tmp_sub15 = tmp15[tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]\n            tmp_nohit15 = tmp15[(((tmp15[var] <= cut_point) & (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))) |\n                                 (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))]\n            dic['额外触碰Odds'] = calculate_var_odds(data=tmp_nohit, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)\n            dic['额外触碰Lift'] = calculate_var_lift(data=tmp, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)\n            dic['额外触碰fpd15_Odds'] = calculate_var_odds(data=tmp_nohit15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)\n            dic['额外触碰fpd15_Lift'] = calculate_var_lift(data=tmp15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)\n            df_ls = pd.DataFrame.from_dict(dic, orient='index').T\n    else:\n        if rule_limit == 'Total':\n            dic['策略触碰量'] = len(data[data[var] <= cut_point])\n            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] <= cut_point)])\n            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] <= cut_point)])\n            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | (data[var] <= cut_point)])\n            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['整体成熟量'] = len(tmp)\n            dic['额外触碰成熟量'] = len(tmp[tmp[var] <= cut_point])\n            dic['整体成熟坏样本量'] = len(tmp[tmp[target] == 1])\n            dic['额外触碰成熟坏样本量'] = len(tmp[(tmp[rule_all] == 0) & (tmp[target] == 1) & (tmp[var] <= cut_point)])\n            dic['fpd15整体成熟量'] = len(tmp15)\n            dic['fpd15额外触碰成熟量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] <= cut_point)])\n            dic['fpd15整体成熟坏样本量'] = len(tmp15[tmp15['fpd_15_act'] == 1])\n            dic['fpd15额外触碰成熟坏样本量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] <= cut_point) & (tmp15['fpd_15_act'] == 1)])\n            dic['整体逾期率'] = len(tmp[tmp[target] == 1]) / dic['整体成熟量'] if dic['整体成熟量'] > 0 else None\n            dic['额外触碰逾期率'] = dic['额外触碰成熟坏样本量'] / dic['额外触碰成熟量'] if dic['额外触碰成熟量'] > 0 else None\n            dic['置后触碰整体逾期率'] = (dic['整体成熟坏样本量'] - dic['额外触碰成熟坏样本量']) / (dic['整体成熟量'] - dic['额外触碰成熟量']) if (dic['整体成熟量'] - dic['额外触碰成熟量']) > 0 else None\n            dic['逾期率下降值'] = dic['整体逾期率'] - dic['置后触碰整体逾期率'] if dic['置后触碰整体逾期率'] != None else None\n            dic['逾期率下降幅度'] = (dic['整体逾期率'] - dic['置后触碰整体逾期率']) / dic['整体逾期率'] if (dic['整体逾期率'] != None and dic['置后触碰整体逾期率'] != None and\n                                                                                                            dic['整体逾期率'] != 0) else None\n            tmp_sub = tmp\n            tmp_nohit = tmp[(tmp[var] > cut_point)]\n            tmp_sub15 = tmp15\n            tmp_nohit15 = tmp15[tmp15[var] > cut_point]\n            dic['额外触碰Odds'] = calculate_var_odds(data=tmp_nohit, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)\n            dic['额外触碰Lift'] = calculate_var_lift(data=tmp, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)\n            dic['额外触碰fpd15_Odds'] = calculate_var_odds(data=tmp_nohit15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)\n            dic['额外触碰fpd15_Lift'] = calculate_var_lift(data=tmp15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)\n            df_ls = pd.DataFrame.from_dict(dic, orient='index').T\n        elif rule_limit == '其他':\n            dic['策略触碰量'] = len(data[(data[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))])\n            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['整体成熟量'] = len(tmp)\n            dic['额外触碰成熟量'] = len(tmp[(tmp[var] <= cut_point) & (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['整体成熟坏样本量'] = len(tmp[(tmp[target] == 1)])\n            dic['额外触碰成熟坏样本量'] = len(tmp[(tmp[target] == 1) & (tmp[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['fpd15整体成熟量'] = len(tmp15)\n            dic['fpd15额外触碰成熟量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] <= cut_point) & (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['fpd15整体成熟坏样本量'] = len(tmp15[tmp15['fpd_15_act'] == 1])\n            dic['fpd15额外触碰成熟坏样本量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] <= cut_point) &\n                                                         (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])) & (tmp15['fpd_15_act'] == 1)])\n            dic['整体逾期率'] = len(tmp[tmp[target] == 1]) / dic['整体成熟量'] if dic['整体成熟量'] > 0 else None\n            dic['额外触碰逾期率'] = dic['额外触碰成熟坏样本量'] / dic['额外触碰成熟量'] if dic['额外触碰成熟量'] > 0 else None\n            dic['置后触碰整体逾期率'] = (dic['整体成熟坏样本量'] - dic['额外触碰成熟坏样本量']) / (dic['整体成熟量'] - dic['额外触碰成熟量']) if (dic['整体成熟量'] - dic['额外触碰成熟量']) > 0 else None\n            dic['逾期率下降值'] = dic['整体逾期率'] - dic['置后触碰整体逾期率'] if dic['置后触碰整体逾期率'] != None else None\n            dic['逾期率下降幅度'] = (dic['整体逾期率'] - dic['置后触碰整体逾期率']) / dic['整体逾期率'] if (dic['整体逾期率'] != None and dic['置后触碰整体逾期率'] != None and\n                                                                                                            dic['整体逾期率'] != 0) else None\n            tmp_sub = tmp[tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]\n            tmp_nohit = tmp[(((tmp[var] > cut_point) & (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))) |\n                             (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))]\n            tmp_sub15 = tmp15[tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]\n            tmp_nohit15 = tmp15[(((tmp15[var] > cut_point) & (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))) |\n                                 (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))]\n            dic['额外触碰Odds'] = calculate_var_odds(data=tmp_nohit, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)\n            dic['额外触碰Lift'] = calculate_var_lift(data=tmp, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)\n            dic['额外触碰fpd15_Odds'] = calculate_var_odds(data=tmp_nohit15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)\n            dic['额外触碰fpd15_Lift'] = calculate_var_lift(data=tmp15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)\n            df_ls = pd.DataFrame.from_dict(dic, orient='index').T\n        elif rule_limit == sample_type_col[rule_limit][1][0]:\n            dic['策略触碰量'] = len(data[(data[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))])\n            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['整体成熟量'] = len(tmp)\n            dic['额外触碰成熟量'] = len(tmp[(tmp[var] <= cut_point) & (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['整体成熟坏样本量'] = len(tmp[(tmp[target] == 1)])\n            dic['额外触碰成熟坏样本量'] = len(tmp[(tmp[target] == 1) & (tmp[var] <= cut_point) & (data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['fpd15整体成熟量'] = len(tmp15)\n            dic['fpd15额外触碰成熟量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] <= cut_point) & (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['fpd15整体成熟坏样本量'] = len(tmp15[tmp15['fpd_15_act'] == 1])\n            dic['fpd15额外触碰成熟坏样本量'] = len(tmp15[(tmp15[rule_all] == 0) & (tmp15[var] <= cut_point) &\n                                                         (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])) & (tmp15['fpd_15_act'] == 1)])\n            dic['整体逾期率'] = len(tmp[tmp[target] == 1]) / dic['整体成熟量'] if dic['整体成熟量'] > 0 else None\n            dic['额外触碰逾期率'] = dic['额外触碰成熟坏样本量'] / dic['额外触碰成熟量'] if dic['额外触碰成熟量'] > 0 else None\n            dic['置后触碰整体逾期率'] = (dic['整体成熟坏样本量'] - dic['额外触碰成熟坏样本量']) / (dic['整体成熟量'] - dic['额外触碰成熟量']) if (dic['整体成熟量'] - dic['额外触碰成熟量']) > 0 else None\n            dic['逾期率下降值'] = dic['整体逾期率'] - dic['置后触碰整体逾期率'] if dic['置后触碰整体逾期率'] != None else None\n            dic['逾期率下降幅度'] = (dic['整体逾期率'] - dic['置后触碰整体逾期率']) / dic['整体逾期率'] if (dic['整体逾期率'] != None and dic['置后触碰整体逾期率'] != None and\n                                                                                                            dic['整体逾期率'] != 0) else None\n            tmp_sub = tmp[tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]\n            tmp_nohit = tmp[(((tmp[var] > cut_point) & (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))) |\n                             (tmp[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))]\n            tmp_sub15 = tmp15[tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]\n            tmp_nohit15 = tmp15[(((tmp15[var] > cut_point) & (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))) |\n                                 (tmp15[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))]\n            dic['额外触碰Odds'] = calculate_var_odds(data=tmp_nohit, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)\n            dic['额外触碰Lift'] = calculate_var_lift(data=tmp, data_sub=tmp_sub, hit_flag=var, target=target, cut_point=cut_point, direction=direction)\n            dic['额外触碰fpd15_Odds'] = calculate_var_odds(data=tmp_nohit15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)\n            dic['额外触碰fpd15_Lift'] = calculate_var_lift(data=tmp15, data_sub=tmp_sub15, hit_flag=var, target='fpd_15_act', cut_point=cut_point, direction=direction)\n            df_ls = pd.DataFrame.from_dict(dic, orient='index').T\n    return df_ls\n\n\n# 1.8按周对规则进行泛化，分析规则触碰情况\ndef get_weeks_hit(data, rule_name, rule_type, cut_point, target, rule_limit, rule_all, use_credit_flag, circle_week, week, direction, var):\n    \"\"\"\n    :param data:  数据框\n    :param rule_name: 规则名\n    :param rule_type: 规则类型\n    :param cut_point: 切点，cut-off\n    :param target: 目标字段\n    :param rule_limit: 泛化样本类型\n    :param rule_all: 线上已有规则触碰情况对应的字段列名\n    :param use_credit_flag: 是否用信列\n    :param circle_week: 申请周对应的列\n    :param week: 要泛化的具体周\n    :param direction: 变量取值方向\n    :param var: 泛化变量名\n    :return:  按周泛化分析结果\n    \"\"\"\n    data = copy.deepcopy(data)\n    data = data[data[circle_week] == week]\n    dic = {}\n    dic['报告日期'] = datetime.datetime.now().strftime('%Y-%m-%d')\n    if rule_limit == 'Total':\n        len_prod = len(data['product_name'].unique())\n        if len_prod >= 3:\n            dic['产品名称'] = str(data['product_name'].values[0]) + '等' + str(len_prod) + '个产品'\n        elif len_prod >= 2:\n            dic['产品名称'] = str(data['product_name'].unique()[0]) + '&' + str(data['product_name'].unique()[1])\n        else:\n            dic['产品名称'] = str(data['product_name'].unique()[0])\n    elif rule_limit == '其他':\n        data_prod = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]['product_name'].unique()\n        len_prod = len(data_prod)\n        if len_prod >= 3:\n            dic['产品名称'] = str(data_prod[0]) + '等' + str(len_prod) + '个产品'\n        elif len_prod >= 2:\n            dic['产品名称'] = str(data_prod[0]) + '&' + str(data_prod[1])\n        else:\n            dic['产品名称'] = str(data_prod[0])\n    elif rule_limit == sample_type_col[rule_limit][1][0]:\n        data_prod = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]['product_name'].unique()\n        len_prod = len(data_prod)\n        if len_prod >= 3:\n            dic['产品名称'] = str(data_prod[0]) + '等' + str(len_prod) + '个产品'\n        elif len_prod >= 2:\n            dic['产品名称'] = str(data_prod[0]) + '&' + str(data_prod[1])\n        else:\n            dic['产品名称'] = str(data_prod[0])\n    dic['样本类型'] = rule_limit\n    dic['风险类型'] = '短期风险' if target == 'fpd_30_act' else '中长期风险'\n    dic['目标字段'] = target\n    dic['规则名称'] = rule_name\n    dic['规则类型'] = rule_type\n    dic['申请周'] = week\n    dic['申请量'] = len(data)\n    dic['通过量'] = len(data[data[rule_all] == 0])\n    dic['通过率'] = dic['通过量'] / dic['申请量'] if dic['申请量'] > 0 else None\n    dic['用信量'] = len(data[data[use_credit_flag] == 1])\n    dic['用信率'] = dic['用信量'] / dic['通过量'] if dic['通过量'] > 0 else None\n    dic['置前触碰量'] = len(data[data[rule_all] == 1])\n    if '>' in direction:\n        if rule_limit == 'Total':\n            dic['策略触碰量'] = len(data[data[var] > cut_point])\n            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] > cut_point)])\n            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] > cut_point)])\n            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | (data[var] > cut_point)])\n            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            df_ls = pd.DataFrame.from_dict(dic, orient='index').T\n        elif rule_limit == '其他':\n            dic['策略触碰量'] = len(data[(data[var] > cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] > cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] > cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] > cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))])\n            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            df_ls = pd.DataFrame.from_dict(dic, orient='index').T\n        elif rule_limit == sample_type_col[rule_limit][1][0]:\n            dic['策略触碰量'] = len(data[(data[var] > cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] > cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] > cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] > cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))])\n            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            df_ls = pd.DataFrame.from_dict(dic, orient='index').T\n    else:\n        if rule_limit == 'Total':\n            dic['策略触碰量'] = len(data[data[var] <= cut_point])\n            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] <= cut_point)])\n            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] <= cut_point)])\n            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | (data[var] <= cut_point)])\n            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            df_ls = pd.DataFrame.from_dict(dic, orient='index').T\n        elif rule_limit == '其他':\n            dic['策略触碰量'] = len(data[(data[var] <= cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] <= cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] <= cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] <= cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))])\n            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            df_ls = pd.DataFrame.from_dict(dic, orient='index').T\n        elif rule_limit == sample_type_col[rule_limit][1][0]:\n            dic['策略触碰量'] = len(data[(data[var] <= cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] <= cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] <= cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] <= cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))])\n            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            df_ls = pd.DataFrame.from_dict(dic, orient='index').T\n    return df_ls\n\n\n# 1.9按日对规则进行泛化，分析规则触碰情况\ndef get_days_hit(data, rule_name, rule_type, cut_point, target, rule_limit, rule_all, use_credit_flag, circle_day, day, direction, var):\n    \"\"\"\n    :param data:  数据框\n    :param rule_name: 规则名\n    :param rule_type: 规则类型\n    :param cut_point: 切点，cut-off\n    :param target: 目标字段\n    :param rule_limit: 泛化样本类型\n    :param rule_all: 线上已有规则触碰情况对应的字段列名\n    :param use_credit_flag: 是否用信列\n    :param circle_day: 申请日对应的列\n    :param day: 要泛化的具体日\n    :param direction: 变量取值方向\n    :param var: 泛化变量名\n    :return:  按日泛化分析结果\n    \"\"\"\n    data = copy.deepcopy(data)\n    data = data[data[circle_day] == day]\n    dic = {}\n    dic['报告日期'] = datetime.datetime.now().strftime('%Y-%m-%d')\n    if rule_limit == 'Total':\n        len_prod = len(data['product_name'].unique())\n        if len_prod >= 3:\n            dic['产品名称'] = str(data['product_name'].values[0]) + '等' + str(len_prod) + '个产品'\n        elif len_prod >= 2:\n            dic['产品名称'] = str(data['product_name'].unique()[0]) + '&' + str(data['product_name'].unique()[1])\n        else:\n            dic['产品名称'] = str(data['product_name'].unique()[0])\n    elif rule_limit == '其他':\n        data_prod = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]['product_name'].unique()\n        len_prod = len(data_prod)\n        if len_prod >= 3:\n            dic['产品名称'] = str(data_prod[0]) + '等' + str(len_prod) + '个产品'\n        elif len_prod >= 2:\n            dic['产品名称'] = str(data_prod[0]) + '&' + str(data_prod[1])\n        else:\n            dic['产品名称'] = str(data_prod[0])\n    elif rule_limit == sample_type_col[rule_limit][1][0]:\n        data_prod = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]['product_name'].unique()\n        len_prod = len(data_prod)\n        if len_prod >= 3:\n            dic['产品名称'] = str(data_prod[0]) + '等' + str(len_prod) + '个产品'\n        elif len_prod >= 2:\n            dic['产品名称'] = str(data_prod[0]) + '&' + str(data_prod[1])\n        else:\n            dic['产品名称'] = str(data_prod[0])\n    dic['样本类型'] = rule_limit\n    dic['风险类型'] = '短期风险' if target == 'fpd_30_act' else '中长期风险'\n    dic['目标字段'] = target\n    dic['规则名称'] = rule_name;\n    dic['规则类型'] = rule_type;\n    dic['申请日'] = day;\n    dic['申请量'] = len(data);\n    dic['通过量'] = len(data[data[rule_all] == 0])\n    dic['通过率'] = dic['通过量'] / dic['申请量'] if dic['申请量'] > 0 else None\n    dic['用信量'] = len(data[data[use_credit_flag] == 1])\n    dic['用信率'] = dic['用信量'] / dic['通过量'] if dic['通过量'] > 0 else None\n    dic['置前触碰量'] = len(data[data[rule_all] == 1])\n    if '>' in direction:\n        if rule_limit == 'Total':\n            dic['策略触碰量'] = len(data[data[var] > cut_point])\n            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] > cut_point)])\n            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] > cut_point)])\n            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | (data[var] > cut_point)])\n            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            df_ls = pd.DataFrame.from_dict(dic, orient='index').T\n        elif rule_limit == '其他':\n            dic['策略触碰量'] = len(data[(data[var] > cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] > cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] > cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] > cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))])\n            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            df_ls = pd.DataFrame.from_dict(dic, orient='index').T\n        elif rule_limit == sample_type_col[rule_limit][1][0]:\n            dic['策略触碰量'] = len(data[(data[var] > cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] > cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] > cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] > cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))])\n            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            df_ls = pd.DataFrame.from_dict(dic, orient='index').T\n    else:\n        if rule_limit == 'Total':\n            dic['策略触碰量'] = len(data[data[var] <= cut_point])\n            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] <= cut_point)])\n            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] <= cut_point)])\n            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | (data[var] <= cut_point)])\n            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            df_ls = pd.DataFrame.from_dict(dic, orient='index').T\n        elif rule_limit == '其他':\n            dic['策略触碰量'] = len(data[(data[var] <= cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] <= cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] <= cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1]))])\n            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] <= cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])))])\n            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            df_ls = pd.DataFrame.from_dict(dic, orient='index').T\n        elif rule_limit == sample_type_col[rule_limit][1][0]:\n            dic['策略触碰量'] = len(data[(data[var] <= cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['重复触碰量'] = len(data[(data[rule_all] == 1) & (data[var] <= cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['额外触碰量'] = len(data[(data[rule_all] == 0) & (data[var] <= cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1]))])\n            dic['置后触碰量'] = len(data[(data[rule_all] == 1) | ((data[var] <= cut_point) & (\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])))])\n            dic['置前触碰率'] = dic['置前触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['策略触碰率'] = dic['策略触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰率'] = dic['重复触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['额外触碰率'] = dic['额外触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['置后触碰率'] = dic['置后触碰量'] / dic['申请量'] if dic['申请量'] > 0 else None\n            dic['重复触碰占策略触碰比例'] = dic['重复触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            dic['额外触碰占策略触碰比例'] = dic['额外触碰量'] / dic['策略触碰量'] if dic['策略触碰量'] > 0 else None\n            df_ls = pd.DataFrame.from_dict(dic, orient='index').T\n    return df_ls\n\n\n# 1.10 按月泛化结果汇总\ndef get_mths_result(data, rule_name, rule_type, var, cut_point, target, rule_limit, rule_all, use_credit_flag, circle_mth, direction, base_lift, subset_total):\n    \"\"\"\n    :param data:  数据框\n    :param rule_name: 规则名\n    :param rule_type: 规则类型\n    :param var: 泛化变量名\n    :param cut_point: 切点，cut-off\n    :param target: 目标字段\n    :param rule_limit: 泛化样本类型\n    :param rule_all: 线上已有规则触碰情况对应的字段列名\n    :param use_credit_flag: 是否用信列\n    :param circle_mth: 申请月对应的列\n    :param direction: 变量取值方向\n    :param base_lift: 临界Lift\n    :param subset_total: 是否获取每个子集作为计算整体\n    :return:  所有泛化月泛化结果汇总\n    \"\"\"\n    if subset_total:  ##获取每个计算整体\n        if rule_limit == 'Total':\n            data = data\n        elif rule_limit == '其他':\n            data = data[\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]\n        elif rule_limit == sample_type_col[rule_limit][1][0]:\n            data = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]\n    mths = sorted(set(data[circle_mth]))\n    df_contra_monthly = get_month_odds(data=data, rule_name=rule_name, rule_type=rule_type,\n                                       cut_point=cut_point, target=target, rule_limit=rule_limit, rule_all=rule_all, use_credit_flag=use_credit_flag, direction=direction,\n                                       circle_mth=circle_mth, mth=mths[0], var=var)\n\n    for mth in mths[1:]:\n        df2_contra_monthly = get_month_odds(data=data, rule_name=rule_name, rule_type=rule_type,\n                                            cut_point=cut_point, target=target, rule_limit=rule_limit, rule_all=rule_all, use_credit_flag=use_credit_flag, direction=direction,\n                                            circle_mth=circle_mth, mth=mth, var=var)\n\n        df_contra_monthly = pd.concat([df_contra_monthly, df2_contra_monthly])\n    df_contra_monthly['额外触碰Lift大于目标值的月份占比'] = sum(df_contra_monthly['额外触碰Lift'] > base_lift) / sum(\n        df_contra_monthly['额外触碰Lift'].notnull()) if sum(df_contra_monthly['额外触碰Lift'].notnull()) > 0 else None\n    df_contra_monthly['置前触碰率离散度'] = cv_type(cv(df_contra_monthly['置前触碰率']) if df_contra_monthly['置前触碰率'].sum() > 0 else 0)\n    df_contra_monthly['策略触碰率离散度'] = cv_type(cv(df_contra_monthly['策略触碰率']) if df_contra_monthly['策略触碰率'].sum() > 0 else 0)\n    df_contra_monthly['重复触碰率离散度'] = cv_type(cv(df_contra_monthly['重复触碰率']) if df_contra_monthly['重复触碰率'].sum() > 0 else 0)\n    df_contra_monthly['额外触碰率离散度'] = cv_type(cv(df_contra_monthly['额外触碰率']) if df_contra_monthly['额外触碰率'].sum() > 0 else 0)\n    df_contra_monthly['置后触碰率离散度'] = cv_type(cv(df_contra_monthly['置后触碰率']) if df_contra_monthly['置后触碰率'].sum() > 0 else 0)\n    try:\n        df_contra_monthly['评估结论'] = '建议上线' if (df_contra_monthly['额外触碰Lift大于目标值的月份占比'].values[0] >= 0.8) \\\n                                                      & ((df_contra_monthly['额外触碰率离散度'].values[0] == '非常稳定') | (df_contra_monthly['策略触碰率离散度'].values[0] == '相对稳定')) else '不建议上线'\n    except:\n        df_contra_monthly['评估结论'] = '不建议上线'\n    return df_contra_monthly\n\n\n# 1.11 按周泛化结果汇总\ndef get_weeks_result(data, rule_name, rule_type, var, cut_point, target, rule_limit, rule_all, use_credit_flag, circle_week, direction, subset_total):\n    \"\"\"\n    :param data:  数据框\n    :param rule_name: 规则名\n    :param rule_type: 规则类型\n    :param var: 泛化变量名\n    :param cut_point: 切点，cut-off\n    :param target: 目标字段\n    :param rule_limit: 泛化样本类型\n    :param rule_all: 线上已有规则触碰情况对应的字段列名\n    :param use_credit_flag: 是否用信列\n    :param circle_week: 申请周对应的列\n    :param direction: 变量取值方向\n    :param subset_total: 是否获取每个子集作为计算整体\n    :return:  所有泛化周泛化结果汇总\n    \"\"\"\n    if subset_total:  ##获取每个计算整体\n        if rule_limit == 'Total':\n            data = data\n        elif rule_limit == '其他':\n            data = data[\n                data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]\n        elif rule_limit == sample_type_col[rule_limit][1][0]:\n            data = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]\n    td = datetime.datetime.now().strftime('%Y-%m-%d')\n    max_week = data[circle_week].max()\n    if td <= max_week:\n        weeks = sorted(set(data[circle_week]))[-11:-1]\n        data = data[data[circle_week].map(lambda x: x in weeks)]\n    else:\n        weeks = sorted(set(data[circle_week]))[-10:]\n        data = data[data[circle_week].map(lambda x: x in weeks)]\n    df_monthly = get_weeks_hit(data=data, rule_name=rule_name, rule_type=rule_type, cut_point=cut_point, target=target, rule_limit=rule_limit, rule_all=rule_all,\n                               use_credit_flag=use_credit_flag, direction=direction, circle_week=circle_week, week=weeks[0], var=var)\n\n    for week in weeks[1:]:\n        df2_monthly = get_weeks_hit(data=data, rule_name=rule_name, rule_type=rule_type, cut_point=cut_point, target=target, rule_limit=rule_limit, rule_all=rule_all,\n                                    use_credit_flag=use_credit_flag, direction=direction, circle_week=circle_week, week=week, var=var)\n\n        df_monthly = pd.concat([df_monthly, df2_monthly])\n    df_monthly['置前触碰率离散度'] = cv_type(cv(df_monthly['置前触碰率']) if df_monthly['置前触碰率'].sum() > 0 else 0)\n    df_monthly['策略触碰率离散度'] = cv_type(cv(df_monthly['策略触碰率']) if df_monthly['策略触碰率'].sum() > 0 else 0)\n    df_monthly['重复触碰率离散度'] = cv_type(cv(df_monthly['重复触碰率']) if df_monthly['重复触碰率'].sum() > 0 else 0)\n    df_monthly['额外触碰率离散度'] = cv_type(cv(df_monthly['额外触碰率']) if df_monthly['额外触碰率'].sum() > 0 else 0)\n    df_monthly['置后触碰率离散度'] = cv_type(cv(df_monthly['置后触碰率']) if df_monthly['置后触碰率'].sum() > 0 else 0)\n    return df_monthly\n\n\n# 1.12 按日泛化结果汇总\ndef get_days_result(data, rule_name, rule_type, var, cut_point, target, rule_limit, rule_all, use_credit_flag, circle_day, direction, subset_total):\n    \"\"\"\n    :param data:  数据框\n    :param rule_name: 规则名\n    :param rule_type: 规则类型\n    :param var: 泛化变量名\n    :param cut_point: 切点，cut-off\n    :param target: 目标字段\n    :param rule_limit: 泛化样本类型\n    :param rule_all: 线上已有规则触碰情况对应的字段列名\n    :param use_credit_flag: 是否用信列\n    :param circle_day: 申请日对应的列\n    :param direction: 变量取值方向\n    :param subset_total: 是否获取每个子集作为计算整体\n    :return:  所有泛化日泛化结果汇总\n    \"\"\"\n    if subset_total:  ##获取每个计算整体\n        if rule_limit == 'Total':\n            data = data\n        elif rule_limit == '其他':\n            data = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) not in sample_type_col[rule_limit][1])]\n        elif rule_limit == sample_type_col[rule_limit][1][0]:\n            data = data[data[sample_type_col[rule_limit][0]].map(lambda x: str(x) in sample_type_col[rule_limit][1])]\n    days = sorted(set(data[circle_day]))[-10:]\n    data = data[data[circle_day].map(lambda x: x in days)]\n    df_monthly = get_days_hit(data=data, rule_name=rule_name, rule_type=rule_type, cut_point=cut_point, target=target, rule_limit=rule_limit, rule_all=rule_all,\n                              use_credit_flag=use_credit_flag, direction=direction, circle_day=circle_day, day=days[0], var=var)\n\n    for day in days[1:]:\n        df2_monthly = get_days_hit(data=data, rule_name=rule_name, rule_type=rule_type, cut_point=cut_point, target=target, rule_limit=rule_limit, rule_all=rule_all,\n                                   use_credit_flag=use_credit_flag, direction=direction, circle_day=circle_day, day=day, var=var)\n\n        df_monthly = pd.concat([df_monthly, df2_monthly])\n    df_monthly['置前触碰率离散度'] = cv_type(cv(df_monthly['置前触碰率']) if df_monthly['置前触碰率'].sum() > 0 else 0)\n    df_monthly['策略触碰率离散度'] = cv_type(cv(df_monthly['策略触碰率']) if df_monthly['策略触碰率'].sum() > 0 else 0)\n    df_monthly['重复触碰率离散度'] = cv_type(cv(df_monthly['重复触碰率']) if df_monthly['重复触碰率'].sum() > 0 else 0)\n    df_monthly['额外触碰率离散度'] = cv_type(cv(df_monthly['额外触碰率']) if df_monthly['额外触碰率'].sum() > 0 else 0)\n    df_monthly['置后触碰率离散度'] = cv_type(cv(df_monthly['置后触碰率']) if df_monthly['置后触碰率'].sum() > 0 else 0)\n    return df_monthly\n\n\ndef rule_combine_results(data, rules_dict, rule_all, use_credit_flag, circle_mth, circle_week, circle_day, base_lift, subset_total):\n    \"\"\"基于数据字典泛化结果汇总并输出\n\n    :param data:  需要分析的数据\n    :param rules_dict:  阈值测算输出的规则字典\n    :param rule_all:  已有所有规则汇总后的字段名称\n    :param use_credit_flag: 是否用信列\n    :param circle_mth: 申请月对应的列\n    :param circle_week: 申请周对应的列\n    :param circle_day: 申请日对应的列\n    :param base_lift: 临界Lift\n    :param subset_total: 是否获取每个子集作为计算整体\n\n    :return:   泛化结果汇总输出\n    \"\"\"\n    for var in rules_dict.Var.unique():\n        rules_dict01 = rules_dict[rules_dict.Var == var]\n        min_index = rules_dict01.index.min()\n        starttime_init = datetime.datetime.now()\n        for row in rules_dict01.loc[min_index:min_index].iterrows():\n            print('正在泛化的规则为：' + row[1]['Rule_Name'] + '\\n' + '规则适用范围为：' + str(row[1]['Rule_Limit']) + '\\n' + '目标字段为：' + row[1]['Target'])\n            print('泛化的变量名称为:', var)\n            rule_name = row[1]['Rule_Name']\n            sample_type1 = str(row[1]['Rule_Limit'])\n            rule_type = row[1]['Rule_Type']\n            target = row[1]['Target']\n            cut_point = row[1]['Threshold']\n            var = var\n            direction = row[1]['Direction']\n            df_monthly = get_mths_result(data=data\n                                         , rule_name=rule_name\n                                         , rule_type=rule_type\n                                         , var=var\n                                         , cut_point=cut_point\n                                         , target=target\n                                         , rule_limit=sample_type1\n                                         , rule_all=rule_all\n                                         , use_credit_flag=use_credit_flag\n                                         , circle_mth=circle_mth\n                                         , direction=direction\n                                         , base_lift=base_lift\n                                         , subset_total=subset_total)\n            df_weekly = get_weeks_result(data=data\n                                         , rule_name=rule_name\n                                         , rule_type=rule_type\n                                         , var=var\n                                         , cut_point=cut_point\n                                         , target=target\n                                         , rule_limit=sample_type1\n                                         , rule_all=rule_all\n                                         , use_credit_flag=use_credit_flag\n                                         , circle_week=circle_week\n                                         , direction=direction\n                                         , subset_total=subset_total)\n            df_daily = get_days_result(data=data\n                                       , rule_name=rule_name\n                                       , rule_type=rule_type\n                                       , var=var\n                                       , cut_point=cut_point\n                                       , target=target\n                                       , rule_limit=sample_type1\n                                       , rule_all=rule_all\n                                       , use_credit_flag=use_credit_flag\n                                       , circle_day=circle_day\n                                       , direction=direction\n                                       , subset_total=subset_total)\n        if len(rules_dict01) > 1:\n            for row in rules_dict01.loc[(min_index + 1):].iterrows():\n                print('正在泛化的规则为：' + row[1]['Rule_Name'] + '\\n' + '规则适用范围为：' + str(\n                    row[1]['Rule_Limit']) + '\\n' + '目标字段为：' + row[1]['Target'])\n                print('泛化的变量名称为:', var)\n                rule_name = row[1]['Rule_Name']\n                sample_type1 = row[1]['Rule_Limit']\n                rule_type = row[1]['Rule_Type']\n                target = row[1]['Target']\n                cut_point = row[1]['Threshold']\n                var = var\n                direction = row[1]['Direction']\n                df_monthly_01 = get_mths_result(data=data, rule_name=rule_name, rule_type=rule_type, var=var, cut_point=cut_point, target=target, rule_limit=sample_type1, rule_all=rule_all,\n                                                use_credit_flag=use_credit_flag, circle_mth=circle_mth, direction=direction, base_lift=base_lift, subset_total=subset_total)\n                df_monthly = pd.concat([df_monthly, df_monthly_01])\n                df_weekly_01 = get_weeks_result(data=data, rule_name=rule_name, rule_type=rule_type, var=var, cut_point=cut_point, target=target, rule_limit=sample_type1, rule_all=rule_all,\n                                                use_credit_flag=use_credit_flag, circle_week=circle_week, direction=direction, subset_total=subset_total)\n                df_weekly = pd.concat([df_weekly, df_weekly_01])\n                df_daily_01 = get_days_result(data=data, rule_name=rule_name, rule_type=rule_type, var=var, cut_point=cut_point, target=target, rule_limit=sample_type1, rule_all=rule_all,\n                                              use_credit_flag=use_credit_flag, circle_day=circle_day, direction=direction, subset_total=subset_total)\n                df_daily = pd.concat([df_daily, df_daily_01])\n\n        df_monthly.insert(loc=0, column=\"序号\", value=df_monthly[\"规则名称\"].map({rule: index for index, rule in enumerate(df_monthly['规则名称'].unique(), start=1)}).values)\n        df_weekly.insert(loc=0, column=\"序号\", value=df_weekly[\"规则名称\"].map({rule: index for index, rule in enumerate(df_weekly['规则名称'].unique(), start=1)}).values)\n        df_daily.insert(loc=0, column=\"序号\", value=df_daily[\"规则名称\"].map({rule: index for index, rule in enumerate(df_daily['规则名称'].unique(), start=1)}).values)\n\n        # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 结果输出 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< #\n        writer = ExcelWriter()\n\n        # 相关规则说明\n        worksheet = writer.get_sheet_by_name(\"0.说明\")\n\n        end_row, _ = writer.insert_value2sheet(worksheet, \"B2\", value=\"1、规则说明\", style=\"header_middle\", end_space=\"E2\", align={\"horizontal\": \"left\"})\n\n        _, end_col = writer.insert_value2sheet(worksheet, (end_row + 1, 2), value=\"规则类型\", style=\"header_middle\")\n        end_row, _ = writer.insert_value2sheet(worksheet, (end_row + 1, end_col), value=\"规则上线标准\", style=\"header_middle\")\n\n        _, end_col = writer.insert_value2sheet(worksheet, (end_row, 2), value=\"HC\", style=\"middle\")\n        end_row, _ = writer.insert_value2sheet(worksheet, (end_row, end_col), value=\"准入类规则需结合产品和适用场景进行考虑\", style=\"middle\", align={\"horizontal\": \"left\"})\n\n        _, end_col = writer.insert_value2sheet(worksheet, (end_row, 2), value=\"FR/CR\", style=\"last_middle\")\n        # end_row, _ = writer.insert_value2sheet(worksheet, (end_row, end_col), value=f\"同时满足如下条件：\\r\\n1.近n个月额外触碰Lift>={base_lift}的月份占比>=80%\\r\\n2.按月统计的额外触碰率离散度稳定（非常稳定或相对稳定）\\r\\n注：追踪月份至少 >= 3\", style=\"last_middle\", align={\"horizontal\": \"left\"})\n        end_row, _ = writer.insert_value2sheet(worksheet, (end_row, end_col), value=f'=\"同时满足如下条件：\" & CHAR(10) & \"1.近n个月额外触碰Lift>={base_lift}的月份占比>=80%\" & CHAR(10) & \"2.按月统计的额外触碰率离散度稳定（非常稳定或相对稳定）\" & CHAR(10) & \"注：追踪月份至少 >= 3\"', style=\"last_middle\", align={\"horizontal\": \"left\", \"wrap_text\": True})\n\n        end_row, _ = writer.insert_value2sheet(worksheet, f\"B{end_row + 2}\", value=\"2、稳定性划分标准\", style=\"header_middle\", end_space=f\"E{end_row + 2}\", align={\"horizontal\": \"left\"})\n        cv_describe = pd.DataFrame({\"序号\": [1, 2, 3, 4], \"波动幅度\": [\"[0%, 15%)\", \"[15%, 40%)\", \"[40%, 75%)\", \"[75%, 正无穷)\"], \"类型\": [\"非常稳定\", \"相对稳定\", \"不稳定\", \"极不稳定\"], \"备注\": [\"波动幅度=离散度\"] * 4})\n        end_row, _ = dataframe2excel(cv_describe, writer, sheet_name=worksheet, start_row=end_row + 1, merge_column=[\"备注\"], merge=True)\n\n        end_row, _ = writer.insert_value2sheet(worksheet, f\"B{end_row + 2}\", value=\"3、统计指标含义\", style=\"header_middle\", end_space=f\"E{end_row + 2}\", align={\"horizontal\": \"left\"})\n        end_row, _ = dataframe2excel(word_desc, writer, sheet_name=worksheet, start_row=end_row + 1)\n\n        # 规则增益效果评估\n        worksheet = writer.get_sheet_by_name(\"1.规则泛化\")\n\n        # 评估规则明细\n        end_row, _ = writer.insert_value2sheet(worksheet, f\"B2\", value=\"0、规则逻辑\", style=\"header_middle\", end_space=f\"AY2\")\n        end_row, _ = dataframe2excel(rules_dict01, writer, sheet_name=worksheet, start_row=end_row + 1)\n\n        # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 每月规则泛化 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< #\n        end_row, _ = writer.insert_value2sheet(worksheet, f\"B{end_row + 2}\", value=\"1、月度规则泛化\", style=\"header_middle\", end_space=f\"AY{end_row + 2}\", align={\"horizontal\": \"left\"})\n\n        # 每月规则命中拒绝及效果评估\n        text1, text2, text3 = std_result_output(df_monthly, \"M\")\n        end_row, _ = writer.insert_value2sheet(worksheet, f\"B{end_row}\", value=text1, style=\"merge_middle\", end_space=f\"AY{end_row}\", align={\"horizontal\": \"left\"})\n        end_row, _ = writer.insert_value2sheet(worksheet, f\"B{end_row}\", value=text2, style=\"merge_middle\", end_space=f\"AY{end_row}\", align={\"horizontal\": \"left\"})\n        end_row, _ = writer.insert_value2sheet(worksheet, f\"B{end_row}\", value=text3, style=\"middle\", end_space=f\"AY{end_row}\", align={\"horizontal\": \"left\"})\n\n        end_row, _ = dataframe2excel(df_monthly, writer, sheet_name=worksheet, start_row=end_row + 1, merge_column=[\"序号\", \"报告日期\", \"产品名称\", \"样本类型\"], merge=True)\n\n        # # 规则增益效果评估\n        # end_row, _ = writer.insert_value2sheet(worksheet, f\"B{end_row + 2}\", value=\"2、规则效能\", style=\"header_middle\", end_space=f\"AY{end_row + 2}\")\n\n        # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 每周规则泛化 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< #\n        end_row, _ = writer.insert_value2sheet(worksheet, f\"B{end_row + 2}\", value=\"2、每周规则泛化\", style=\"header_middle\", end_space=f\"AY{end_row + 2}\", align={\"horizontal\": \"left\"})\n\n        text1, text2, text3 = std_result_output(df_weekly, \"W\")\n        end_row, _ = writer.insert_value2sheet(worksheet, f\"B{end_row}\", value=text1, style=\"merge_middle\", end_space=f\"AY{end_row}\", align={\"horizontal\": \"left\"})\n        end_row, _ = writer.insert_value2sheet(worksheet, f\"B{end_row}\", value=text2, style=\"merge_middle\", end_space=f\"AY{end_row}\", align={\"horizontal\": \"left\"})\n        end_row, _ = writer.insert_value2sheet(worksheet, f\"B{end_row}\", value=text3, style=\"middle\", end_space=f\"AY{end_row}\", align={\"horizontal\": \"left\"})\n\n        end_row, _ = dataframe2excel(df_weekly, writer, sheet_name=worksheet, start_row=end_row + 1, merge_column=[\"序号\", \"报告日期\", \"产品名称\", \"样本类型\"], merge=True)\n\n        # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 每日规则泛化 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< #\n        end_row, _ = writer.insert_value2sheet(worksheet, f\"B{end_row + 2}\", value=\"3、每日规则泛化\", style=\"header_middle\", end_space=f\"AY{end_row + 2}\", align={\"horizontal\": \"left\"})\n\n        text1, text2, text3 = std_result_output(df_daily, \"D\")\n        end_row, _ = writer.insert_value2sheet(worksheet, f\"B{end_row}\", value=text1, style=\"merge_middle\", end_space=f\"AY{end_row}\", align={\"horizontal\": \"left\"})\n        end_row, _ = writer.insert_value2sheet(worksheet, f\"B{end_row}\", value=text2, style=\"merge_middle\", end_space=f\"AY{end_row}\", align={\"horizontal\": \"left\"})\n        end_row, _ = writer.insert_value2sheet(worksheet, f\"B{end_row}\", value=text3, style=\"middle\", end_space=f\"AY{end_row}\", align={\"horizontal\": \"left\"})\n\n        end_row, _ = dataframe2excel(df_daily, writer, sheet_name=worksheet, start_row=end_row + 1, merge_column=[\"序号\", \"报告日期\", \"产品名称\", \"样本类型\"], merge=True)\n\n        writer.save(path_rule + var + '_single_rule_' + str(datetime.datetime.now().strftime('%Y%m%d%H%M%S')) + '.xlsx')\n\n\n# 2.9 规则合并泛化时  按月、周、日输出分析结果函数\ndef std_result_output_01(wb, sheetname, data, offset):\n    \"\"\"\n    泛化结果输出函数\n    \"\"\"\n    nrows, ncols = data.shape\n    first_title = wb.add_format(title_dic)\n    body_text_title = wb.add_format(subtitle_format)\n    body_text_center = wb.add_format(body_text_format_01)\n    body_text_left = wb.add_format(body_text_left_format_01)\n    body_text_percent = wb.add_format(body_text_per_format_01)\n    body_text_center2 = wb.add_format(body_text_format_02)\n    body_text_left2 = wb.add_format(body_text_left_format_02)\n    body_text_percent2 = wb.add_format(body_text_per_format_02)\n    ws = wb.add_worksheet(sheetname)\n    ws.hide_gridlines({'option': 1})\n    ws.autofilter(offset + 5, offset, offset + 5 + nrows, ncols)\n    ws.set_column(0, 0, 2)\n    ws.freeze_panes(7, 10)  ## 冻结单元格\n    ws.merge_range(offset, offset, offset, ncols, sheetname, first_title)\n    if '规则泛化' in sheetname:\n        data_01 = data[-3:]\n        data_02 = data[data['额外触碰成熟量'] >= 10][-3:]\n        data_03 = data[data['整体逾期率'] > 0][-3:]\n        hit_rate = sum(data_01['额外触碰量']) / sum(data_01['申请量'])\n        risk_double = (data_02['额外触碰成熟坏样本量'].sum() / data_02['额外触碰成熟量'].sum()) / (sum(data_02['整体成熟坏样本量']) / sum(data_02['整体成熟量'])) if sum(data_02['整体成熟坏样本量']) > 0 else np.nan\n        overdue_minus = sum(data_03['整体成熟坏样本量']) / sum(data_03['整体成熟量']) - sum(data_03['整体成熟坏样本量'] - data_03['额外触碰成熟坏样本量']) / sum(data_03['整体成熟量'] - data_03['额外触碰成熟量']) if sum(data_03['整体成熟量'] - data_03['额外触碰成熟量']) > 0 else np.nan\n        overdue_range = overdue_minus / (sum(data_03['整体成熟坏样本量']) / sum(data_03['整体成熟量'])) if sum(data_03['整体成熟坏样本量']) > 0 else np.nan\n        text1 = \"1.策略上线后通过率下降预估值 = 近3个月额外触碰量之和/近3个月申请量之和；策略上线后逾期率变化预估值 = （近n个有表现月逾期样本之和/近n个有表现月成熟样本之和）-（近n个有表现月置后触碰坏样本之和/近n个有表现月置后触碰成熟样本之和） ；备注：取整体逾期率大于0的近n个月计算策略上线后逾期率变化值，n默认取3，若不足3个月，有几个月计算几个月;\"\n        text2 = \"2.策略上线后额外触碰Lift预估值 = (近n个有表现月额外触碰成熟坏样本量之和/近n个有表现月额外触碰成熟量之和)/(近n个有表现月整体成熟坏样本量之和/近n个有表现月整体成熟量之和)；备注：取额外触碰成熟量大于等于10的近n个月计算策略上线后额外触碰Lift预估值，n默认取3，若不足3个月，有几个月计算几个月;\"\n        if overdue_minus > 0:\n            text3 = \"3.策略上线后通过率预计下降：{:.2f}%，逾期率下降：{:.2f}%，逾期率下降幅度：{:.2f}%，额外触碰Lift预估值为：{:.2f}。\".format(hit_rate * 100, overdue_minus * 100, overdue_range * 100, risk_double)\n        else:\n            text3 = \"3.策略上线后通过率预计下降：{:.2f}%，逾期率上升：{:.2f}%，逾期率上升幅度：{:.2f}%，额外触碰Lift预估值为：{:.2f}。\".format(hit_rate * 100, abs(overdue_minus) * 100, abs(overdue_range) * 100, risk_double)\n    if 'Weekly' in sheetname:\n        week_num = str(len(data['申请周'].unique()))\n        mean_obs = data['置前触碰率'].mean() * 100\n        cv_rate = cv(data['置前触碰率'])\n        if data['置前触碰率'].sum() == 0:\n            cv_rate = 0\n        info = cv_type(cv_rate)\n        trend = get_trend(data, key='置前触碰率')\n        text1 = \"1.近\" + week_num + \"个申请周平均置前触碰率%.2f\" % mean_obs + '%, ' + '置前触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + ';置前触碰率整体趋势：' + trend + \"；\"\n        mean_obs = data['策略触碰率'].mean() * 100\n        cv_rate = cv(data['策略触碰率'])\n        if data['策略触碰率'].sum() == 0:\n            cv_rate = 0\n        info = cv_type(cv_rate)\n        trend = get_trend(data, key='策略触碰率')\n        text2 = \"2.近\" + week_num + \"个申请周策略触碰率%.2f\" % mean_obs + '%, ' + '策略触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + '; 策略触碰率整体趋势：' + trend + \"；\"\n        mean_obs = data['额外触碰率'].mean() * 100\n        cv_rate = cv(data['额外触碰率'])\n        if data['额外触碰率'].sum() == 0:\n            cv_rate = 0\n        info = cv_type(cv_rate)\n        trend = get_trend(data, key='额外触碰率')\n        text3 = \"3.近\" + week_num + \"个申请周额外触碰率%.2f\" % mean_obs + '%, ' + '额外触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + '; 额外触碰率整体趋势：' + trend + \"；\"\n    if 'Dayly' in sheetname:\n        day_num = str(len(data['申请日'].unique()))\n        mean_obs = data['置前触碰率'].mean() * 100\n        cv_rate = cv(data['置前触碰率'])\n        if data['置前触碰率'].sum() == 0:\n            cv_rate = 0\n        info = cv_type(cv_rate)\n        trend = get_trend(data, key='置前触碰率')\n        text1 = \"1.近\" + day_num + \"个申请日平均置前触碰率%.2f\" % mean_obs + '%, ' + '置前触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + ';置前触碰率整体趋势：' + trend + \"；\"\n        mean_obs = data['策略触碰率'].mean() * 100\n        cv_rate = cv(data['策略触碰率'])\n        if data['策略触碰率'].sum() == 0:\n            cv_rate = 0\n        info = cv_type(cv_rate)\n        trend = get_trend(data, key='策略触碰率')\n        text2 = \"2.近\" + day_num + \"个申请日策略触碰率%.2f\" % mean_obs + '%, ' + '策略触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + '; 策略触碰率整体趋势：' + trend + \"；\"\n        mean_obs = data['额外触碰率'].mean() * 100\n        cv_rate = cv(data['额外触碰率'])\n        if data['额外触碰率'].sum() == 0:\n            cv_rate = 0\n        info = cv_type(cv_rate)\n        trend = get_trend(data, key='额外触碰率')\n        text3 = \"3.近\" + day_num + \"个申请日额外触碰率%.2f\" % mean_obs + '%, ' + '额外触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + '; 额外触碰率整体趋势：' + trend + \"；\"\n    ws.merge_range(offset + 1, offset, offset + 1, ncols, text1, body_text_left2)\n    ws.merge_range(offset + 2, offset, offset + 2, ncols, text2, body_text_left2)\n    ws.merge_range(offset + 3, offset, offset + 3, ncols, text3, body_text_left2)\n    ws.set_row(offset + 4, 7)\n    column = data.columns\n    need_len = get_max_len(data=data, nrows=nrows, ncols=ncols, max_len=40)\n    for i in range(len(need_len)):\n        ws.set_column(i + 1, i + 1, need_len[i])\n    data = data.replace(np.nan, '')\n    data = data.replace(np.inf, 'Inf')\n    for j in range(ncols):\n        ws.write(offset + 5, j + 1, column[j], body_text_title)\n    for i in range(nrows):\n        for j in range(ncols):\n            if '量' in column[j]:\n                ws.conditional_format(offset + 6, j + 1, offset + 6 + nrows - 1, j + 1, condition_format_green_no)\n            elif (('率' in column[j]) or ('占比' in column[j])):\n                ws.conditional_format(offset + 6, j + 1, offset + 6 + nrows - 1, j + 1, condition_format_pink_no)\n            elif 'Odds' in column[j] or 'Lift' in column[j] or '风险' in column[j]:\n                ws.conditional_format(offset + 6, j + 1, offset + 6 + nrows - 1, j + 1, condition_format_red_no)\n            elif '%' in column[j] or '比例' in column[j]:\n                ws.conditional_format(offset + 6, j + 1, offset + 6 + nrows - 1, j + 1, condition_format_blue_no)\n            value = data.iloc[i][j]\n            if int(data.iloc[i, 0] % 2) == 1:\n                if '%' in column[j] or 'rate' in column[j] or '占比' in column[j] or '率' in column[j] or '比例' in column[j]:\n                    ws.write(i + offset + 6, j + 1, value, body_text_percent)\n                else:\n                    ws.write(i + offset + 6, j + 1, value, body_text_center)\n            else:\n                if '%' in column[j] or 'rate' in column[j] or '占比' in column[j] or '率' in column[j] or '比例' in column[j]:\n                    ws.write(i + offset + 6, j + 1, value, body_text_percent2)\n                else:\n                    ws.write(i + offset + 6, j + 1, value, body_text_center2)\n    ws.set_row(offset + 5 + nrows + 1, 7)\n    ws.merge_range(offset + 5 + nrows + 2, offset, offset + 5 + nrows + 2, ncols, '2.规则效能', first_title)\n    ws.set_row(offset + 5 + nrows + 3, 7)\n    if '规则泛化' in sheetname:\n        add_combine_plot(wb=wb, ws=ws, column=column, label_x_start=offset + 5, x_start=offset + 5 + 1, x_end=offset + 5 + 1 + nrows,\n                         title='规则效能分析', x_label='申请月', y1_list=['额外触碰Lift', '额外触碰fpd15_Lift'],\n                         y2_list=['置前触碰率', '策略触碰率', '重复触碰率', '额外触碰率', '置后触碰率'],\n                         x_title='申请月', y1_title='额外触碰Lift', y2_title='规则触碰率', width=750, height=500, if_combine=True,\n                         chart1_type='column', chart2_type='line', chart_row=offset + 5 + nrows + 4, chart_col=1)\n    if 'Weekly' in sheetname:\n        add_plot(wb=wb, ws=ws, column=column, label_x_start=offset + 5, x_start=offset + 5 + 1,\n                 x_end=offset + 6 + nrows, title='规则效能分析', x_label='申请周',\n                 y1_list=['置前触碰率', '策略触碰率', '重复触碰率', '额外触碰率', '置后触碰率'],\n                 x_title='申请周', y1_title='规则触碰率', width=720, height=500, chart1_type='line', chart_row=offset + 5 + nrows + 4, chart_col=1)\n    if 'Dayly' in sheetname:\n        add_plot(wb=wb, ws=ws, column=column, label_x_start=offset + 5, x_start=offset + 5 + 1,\n                 x_end=offset + 5 + nrows, title='规则效能分析', x_label='申请日',\n                 y1_list=['置前触碰率', '策略触碰率', '重复触碰率', '额外触碰率', '置后触碰率'],\n                 x_title='申请日', y1_title='规则触碰率', width=720, height=500, chart1_type='line',\n                 chart_row=offset + 5 + nrows + 4, chart_col=1)\n\n\n# 1.14 规则合并泛化结果汇总并输出\ndef rule_combine_results_01(data, rule_all, use_credit_flag, cut_point, rule_name, rule_name_chinese,\n                            rule_type, rule_limit, circle_mth, circle_week, circle_day, target, direction, var, base_lift, subset_total):\n    \"\"\"\n    :param data:  需要分析的数据\n    :param rule_all:  已有所有规则汇总后的字段名称\n    :param use_credit_flag: 是否用信列\n    :param cut_point: 切点，cut-off\n    :param rule_name: 泛化规则英文名\n    :param rule_name_chinese: 泛化规则中文名\n    :param rule_type: 泛化规则类型\n    :param rule_limit: 泛化样本类型\n    :param circle_mth: 申请月对应的列\n    :param circle_week: 申请周对应的列\n    :param circle_day: 申请日对应的列\n    :param target: 目标字段\n    :param direction: 变量取值方向\n    :param var: 泛化变量名\n    :param base_lift: 临界Lift\n    :param subset_total: 是否获取每个子集作为计算整体\n    :return:   泛化结果汇总输出\n    \"\"\"\n    ana_date = datetime.datetime.now().strftime('%Y-%m-%d')\n    rules_dict01 = pd.DataFrame(\n        {'Ana_Date': [ana_date], 'Rule_Name': [rule_name], 'Description': [rule_name_chinese], 'Rule_Type': [rule_type],\n         'Rule_Limit': [rule_limit], 'Target': [target], 'Threshold': [cut_point], 'Direction': [direction], 'var': [var]})\n    starttime_init = datetime.datetime.now()\n    print('程序开始执行时间为: ' + str(starttime_init))\n    print('正在泛化的规则为：' + rule_name + '\\n' + '规则适用范围为：' + rule_limit + '\\n' + '目标字段为：' + target)\n    df_monthly = get_mths_result(data=data, rule_name=rule_name, rule_type=rule_type, var=var,\n                                 cut_point=cut_point, direction=direction, target=target, rule_limit=rule_limit, rule_all=rule_all, use_credit_flag=use_credit_flag,\n                                 circle_mth=circle_mth, base_lift=base_lift, subset_total=subset_total)\n    df_weekly = get_weeks_result(data=data, rule_name=rule_name, rule_type=rule_type, var=var,\n                                 cut_point=cut_point, direction=direction, target=target, rule_limit=rule_limit, rule_all=rule_all, use_credit_flag=use_credit_flag,\n                                 circle_week=circle_week, subset_total=subset_total)\n    df_daily = get_days_result(data=data, rule_name=rule_name, rule_type=rule_type, var=var,\n                               cut_point=cut_point, direction=direction, target=target, rule_limit=rule_limit, rule_all=rule_all, use_credit_flag=use_credit_flag,\n                               circle_day=circle_day, subset_total=subset_total)\n    cols = df_monthly.columns.tolist()\n    df_monthly['序号'] = 1\n    for i, j in enumerate(list(df_monthly['规则名称'].unique())):\n        df_monthly['序号'][df_monthly['规则名称'] == j] = (i + 1)\n    col1 = ['序号']\n    col1.extend(cols)\n    df_monthly = df_monthly[col1]\n\n    cols = df_weekly.columns.tolist()\n    df_weekly['序号'] = 1\n    for i, j in enumerate(list(df_weekly['规则名称'].unique())):\n        df_weekly['序号'][df_weekly['规则名称'] == j] = (i + 1)\n    col1 = ['序号']\n    col1.extend(cols)\n    df_weekly = df_weekly[col1]\n\n    cols = df_daily.columns.tolist()\n    df_daily['序号'] = 1\n    for i, j in enumerate(list(df_daily['规则名称'].unique())):\n        df_daily['序号'][df_daily['规则名称'] == j] = (i + 1)\n    col1 = ['序号']\n    col1.extend(cols)\n    df_daily = df_daily[col1]\n    endtime_init = datetime.datetime.now()\n    print('程序结束执行时间为: ' + str(endtime_init))\n    print('程序执行时间为: ' + str(endtime_init - starttime_init))\n\n    # 结果输出\n    wb = xlsxwriter.Workbook(path_rule + rule_name + '_' + str(datetime.datetime.now().strftime('%Y%m%d%H%M%S')) + '.xlsx')\n    description_output(wb=wb, sheetname='0.说明', df1=rules_dict01, df2=word_desc, offset=1, base_lift=base_lift)\n    std_result_output_01(wb=wb, sheetname='1.规则泛化', data=df_monthly, offset=1)\n    std_result_output_01(wb=wb, sheetname='2A.Weekly触碰模拟', data=df_weekly, offset=1)\n    std_result_output_01(wb=wb, sheetname='2B.Dayly触碰模拟', data=df_daily, offset=1)\n    wb.close()\n\n\n\"\"\"\n2.规则泛化分析结果输出相关函数\n\"\"\"\n\n# 2.1 excel格式设置\nbiaotou = '#44546A'\ntitle = '#44546A'\ntitle_sub1 = '#00868B'\ntitle_sub2 = '#00E5EE'\ntext = '#F2F2F2'\nxunhuan1 = '#D1D1D1'\nxunhuan2 = '#E3E3E3'\ntitle_size = 12\ntext_title_size = 9\ntext_size = 8\nsplit_color = 'white'\n\n# 条件格式: 蓝色数据条，不指定最大、最小值，绿色\ncondition_format_green_no = {'type': 'data_bar', 'bar_solid': True, 'data_bar_2010': True,\n                             'bar_color': '#65d97d'}\n# 条件格式: 黄色数据条，不指定最大、最小值，橙色\ncondition_format_red_no = {'type': 'data_bar', 'bar_solid': True, 'data_bar_2010': True,\n                           'bar_color': '#f2572d'}\n# 条件格式: 黄色数据条，不指定最大、最小值，橙色\ncondition_format_pink_no = {'type': 'data_bar', 'bar_solid': True, 'data_bar_2010': True,\n                            'bar_color': '#FF69B4'}\ncondition_format_blue_no = {'type': 'data_bar', 'bar_solid': True, 'data_bar_2010': True,\n                            'bar_color': '#1E90FF'}\ncondition_format_3_color = {'type': '3_color_scale',\n                            'max_color': '#F8696B', 'mid_color': '#FFEB84', 'min_color': '#63BE7B'}\n\n#  总标题\ntitle_dic = {'bold': True, 'font_name': 'Arial', 'font_size': title_size, 'font_color': 'white',\n             'top_color': biaotou, 'bottom_color': biaotou, 'left_color': biaotou,\n             'right_color': biaotou, 'bg_color': biaotou}\n\n# 表格正文: 边框白色，字体12，背景深灰色、居中\nsubtitle_format = {'border': True, 'font_size': text_title_size, 'font_name': 'Arial', 'font_color': 'white',\n                   'left_color': 'white', 'right_color': 'white', 'bg_color': biaotou,\n                   'align': 'center', 'valign': 'vcenter'}\n\n# 表格正文: 边框白色，字体12，背景灰色1、居中\nbody_text_format_01 = {'border': True, 'font_size': text_size, 'font_name': 'Arial',\n                       'bg_color': text, 'top_color': split_color,\n                       'bottom_color': split_color,\n                       'left_color': split_color,\n                       'right_color': split_color,\n                       'align': 'center', 'valign': 'vcenter'}\n\n# 正文左对齐\nbody_text_left_format_01 = copy.deepcopy(body_text_format_01)\nbody_text_left_format_01['align'] = 'left'\n# 正文为比率\nbody_text_per_format_01 = copy.deepcopy(body_text_format_01)\nbody_text_per_format_01['num_format'] = '0.00%'\n\n# 表格正文: 边框白色，字体12，背景灰色2、居中\nbody_text_format_02 = {'border': True, 'font_size': text_size, 'font_name': 'Arial',\n                       'bg_color': xunhuan2, 'top_color': split_color,\n                       'bottom_color': split_color,\n                       'left_color': split_color,\n                       'right_color': split_color,\n                       'align': 'center', 'valign': 'vcenter'}\n\n\n# 正文左对齐\nbody_text_left_format_02 = copy.deepcopy(body_text_format_02)\nbody_text_left_format_02['align'] = 'left'\n# 正文为比率\nbody_text_per_format_02 = copy.deepcopy(body_text_format_02)\nbody_text_per_format_02['num_format'] = '0.00%'\n\n\n# 2.2 规则稳定性说明\ntype_dic = pd.DataFrame([[1, '0%~15%', '非常稳定'], [2, '15%~40%', '相对稳定'], [3, '40%~75%', '不稳定'], [4, '>75%', '极不稳定']])\n\n\n# 2.3自动获取单元格内容的length，如果包含汉字，则一个汉字计数为2，如果是英文，则计数为1\ndef get_same_len(x):\n    \"\"\"\n    :param x: 输入内容\n    :return: 字符串长度\n    \"\"\"\n    import re\n    if type(x) != str:\n        x = str(x)\n    num = 0\n    for i in list(x):\n        if re.match(\"[\\u4e00-\\u9fa5]+\", i):\n            num = num + 2\n        else:\n            num = num + 1\n    return num\n\n\n# 2.4 自动获取每一列的最大长度\ndef get_max_len(data, nrows, ncols, max_len=60):\n    \"\"\"\n    自动计算每一列的长度\n    （1）先获得每一列的最长的一个单元格内容的长度\n    （2）如果超过最大长度限制，则只取最大程度，其余内容自动换行，否则无需限制\n    字符串length对应到excel的单元格列宽需要缩小到0.73684，具体缩放比例可以修改\n    \"\"\"\n    data = data.fillna('')\n    res = []\n    res1 = []\n    column = data.columns\n    for i in range(len(column)):\n        x = column[[i]][0]\n        res1.append(get_same_len(x))\n    res2 = []\n    for i in range(ncols):\n        tmp = []\n        for j in range(nrows):\n            tmp.append(get_same_len(data.iloc[j, i]))\n        tmp = max(tmp)\n        if tmp > max_len:\n            tmp = max_len\n        res2.append(tmp * 0.73684)\n    for i, j in zip(res1, res2):\n        res.append(max(i, j))\n    return res\n\n\n# 2.5 规则泛化说明函数\ndef description_output(wb, sheetname, df1, df2, offset, base_lift):\n    \"\"\"\n    :param wb:              excel 文件\n    :param sheetname:       sheetname\n    :param df1:            待输出数据框1\n    :param df2:            待输出数据框2\n    :param offset:          输出位置\n    :param base_lift:       最小lift\n    :return:        策略泛化结果说明\n    \"\"\"\n    nrows, ncols = df1.shape\n    first_title = wb.add_format(title_dic)\n    body_text_title = wb.add_format(subtitle_format)\n    body_text_center = wb.add_format(body_text_format_01)\n    body_text_left = wb.add_format(body_text_left_format_01)\n    body_text_percent = wb.add_format(body_text_per_format_01)\n    body_text_format_01_01 = copy.deepcopy(body_text_format_01)\n    body_text_format_01_01['text_wrap'] = 1\n    body_text_center_01 = wb.add_format(body_text_format_01_01)\n    column = df1.columns\n    ws = wb.add_worksheet(sheetname)\n    ws.hide_gridlines({'option': 1})\n    ws.set_column(0, 0, 2)\n    for i in range(len(column)):\n        x = column[[i]][0]\n        ll = get_same_len(x)\n        lll = max(8, ll)\n        ws.set_column(i + offset, i + offset, lll)\n    ws.merge_range(offset, offset, offset, ncols, '1.规则说明', first_title)\n    ws.set_row(2, 7)\n    title_01 = ['规则类型', '规则上线标准']\n    ws.write(3, 1, title_01[0], body_text_title)\n    ws.merge_range(3, 2, 3, 5, title_01[1], body_text_title)\n    rule_type01 = 'HC';\n    rule_type02 = 'FR/CR';\n    text_01 = '准入类规则需结合产品和适用场景进行考虑'\n    text_021 = \"同时满足如下条件：\"\n    text_022 = \"1.近n个月额外触碰Lift>=\" + str(base_lift) + \"的月份占比>=80%\"\n    text_023 = \"2.按月统计的额外触碰率离散度稳定（非常稳定或相对稳定）\"\n    text_024 = \"注：追踪月份至少 >= 3\"\n    ws.write(4, 1, rule_type01, body_text_center)\n    ws.merge_range(4, 2, 4, 5, text_01, body_text_left)\n    ws.merge_range(5, 1, 8, 1, rule_type02, body_text_center)\n    ws.merge_range(5, 2, 5, 5, text_021, body_text_left)\n    ws.merge_range(6, 2, 6, 5, text_022, body_text_left)\n    ws.merge_range(7, 2, 7, 5, text_023, body_text_left)\n    ws.merge_range(8, 2, 8, 5, text_024, body_text_left)\n    ws.set_row(9, 7)\n    # 增加备注\n    title = ['序号', '波动幅度', '类型', '备注']\n    for i in range(len(title) - 1):\n        ws.write(10, i + 1, title[i], body_text_title)\n    ws.merge_range(10, len(title), 10, len(title) + 1, '备注', body_text_title)\n    rshape, cshape = type_dic.shape\n    for i in range(rshape):\n        for j in range(cshape):\n            ws.write(10 + 1 + i, j + 1, type_dic.iloc[i][j], body_text_center)\n    ws.merge_range(10 + 1, cshape + 1, 10 + rshape, cshape + 2, '波动幅度=离散度', body_text_center)\n    ws.set_row(15, 7)\n    ws.merge_range(16, 1, 16, ncols, '2.规则逻辑', first_title)\n    ws.set_row(17, 7)\n    df1 = df1.replace(np.nan, '')\n    df1 = df1.replace(np.inf, 'Inf')\n    for i in range(len(column)):\n        x = column[[i]][0]\n        ll = get_same_len(x)\n        lll = max(8, ll)\n        ws.set_column(i + 1, i + 1, lll)\n    for j in range(ncols):\n        ws.write(18, j + 1, column[j], body_text_title)\n    for i in range(nrows):\n        for j in range(ncols):\n            if 'odds' in column[j] or 'Odds' in column[j]:\n                ws.conditional_format(19, j + 1, 19 + nrows - 1, j + 1, condition_format_pink_no)\n            value = df1.iloc[i][j]\n            if '%' in column[j] or 'Rate' in column[j] or '占比' in column[j] or '率' in column[j]:\n                ws.write(19 + i, j + 1, value, body_text_percent)\n            else:\n                ws.write(19 + i, j + 1, value, body_text_center)\n    ws.set_row(19 + nrows, 7)\n    ws.merge_range(19 + nrows + 1, 1, 19 + nrows + 1, ncols, '3.规则字段', first_title)\n    ws.set_row(19 + nrows + 2, 7)\n    nrows2, ncols2 = df2.shape\n    df2 = df2.replace(np.nan, '')\n    df2 = df2.replace(np.inf, 'Inf')\n    column = df2.columns\n    need_len = get_max_len(data=df2.iloc[:, 1:], nrows=nrows2, ncols=ncols2 - 1, max_len=45)\n    for i in range(len(need_len)):\n        ws.set_column(i + 2, i + 2, need_len[i])\n    for j in range(ncols2):\n        ws.write(19 + nrows + 3, j + 1, column[j], body_text_title)\n    for i in range(nrows2):\n        for j in range(ncols2):\n            value = df2.iloc[i][j]\n            ws.write(19 + nrows + 4 + i, j + 1, value, body_text_center_01)\n\n\n# 2.6 规则泛化指标说明\nword_desc = [{'评估内容': '样本类型', '内容详细含义': '测算和泛化样本细分类型', '备注': '取值可以是地域、渠道等'},\n             {'评估内容': '风险类型', '内容详细含义': '测算和泛化变量为欺诈类变量则属于短期风险；测算和泛化变量为信用变量则属于中长期风险', '备注': None},\n             {'评估内容': '目标字段', '内容详细含义': '测算和泛化的目标字段', '备注': None},\n             {'评估内容': '规则类型', '内容详细含义': 'FR/CR/HC', '备注': 'FR是欺诈类规则；CR为信用类规则；HC为强规则'},\n             {'评估内容': '申请量', '内容详细含义': '申请量', '备注': None},\n             {'评估内容': '通过量', '内容详细含义': '审批通过量', '备注': None},\n             {'评估内容': '通过率', '内容详细含义': '通过量/申请量', '备注': None},\n             {'评估内容': '用信量', '内容详细含义': '审批通过且支用量', '备注': None},\n             {'评估内容': '用信率', '内容详细含义': '用信量/通过量', '备注': None},\n             {'评估内容': '置前触碰量', '内容详细含义': '已有所有规则触碰量', '备注': None},\n             {'评估内容': '策略触碰量', '内容详细含义': '待上线规则触碰量（包含申请拒绝触碰和申请通过触碰）', '备注': None},\n             {'评估内容': '重复触碰量', '内容详细含义': '待上线规则和已有规则重复触碰量', '备注': None},\n             {'评估内容': '额外触碰量', '内容详细含义': '待上线规则在申请通过样本上触碰量', '备注': None},\n             {'评估内容': '置后触碰量', '内容详细含义': '已有所有规则和待上线规则一共触碰量', '备注': None},\n             {'评估内容': '置前触碰率', '内容详细含义': '置前触碰量/申请量', '备注': None},\n             {'评估内容': '策略触碰率', '内容详细含义': '策略触碰量/申请量', '备注': None},\n             {'评估内容': '重复触碰率', '内容详细含义': '重复触碰量/申请量', '备注': None},\n             {'评估内容': '额外触碰率', '内容详细含义': '额外触碰量/申请量', '备注': None},\n             {'评估内容': '置后触碰率', '内容详细含义': '置后触碰量/申请量', '备注': None},\n             {'评估内容': '重复触碰占策略触碰比例', '内容详细含义': '重复触碰量）/策略触碰量', '备注': None},\n             {'评估内容': '额外触碰占策略触碰比例', '内容详细含义': '额外触碰量）/策略触碰量', '备注': None},\n             {'评估内容': '整体成熟量', '内容详细含义': '观测成熟量', '备注': None},\n             {'评估内容': '额外触碰成熟量', '内容详细含义': '观测成熟且额外触碰量', '备注': None},\n             {'评估内容': '整体成熟坏样本量', '内容详细含义': '观测成熟坏样本量', '备注': None},\n             {'评估内容': '额外触碰成熟坏样本量', '内容详细含义': '观测成熟且额外触碰坏样本量', '备注': None},\n             {'评估内容': 'fpd15整体成熟量', '内容详细含义': 'fpd15观测成熟量', '备注': None},\n             {'评估内容': 'fpd15额外触碰成熟量', '内容详细含义': 'fpd15观测成熟且额外触碰量', '备注': None},\n             {'评估内容': 'fpd15整体成熟坏样本量', '内容详细含义': 'fpd15观测成熟坏样本量', '备注': None},\n             {'评估内容': 'fpd15额外触碰成熟坏样本量', '内容详细含义': 'fpd15观测成熟且额外触碰坏样本量', '备注': None},\n             {'评估内容': '整体逾期率', '内容详细含义': '观测成熟样本中逾期样本占比', '备注': None},\n             {'评估内容': '额外触碰逾期率', '内容详细含义': '观测成熟且额外触碰样本中逾期样本占比', '备注': None},\n             {'评估内容': '额外触碰后整体逾期率', '内容详细含义': '观测成熟样本中剔除额外触碰样本后逾期样本占比', '备注': None},\n             {'评估内容': '逾期率下降值', '内容详细含义': '策略上线后逾期率下降值', '备注': None},\n             {'评估内容': '逾期率下降幅度', '内容详细含义': '策略上线后逾期率下降幅度', '备注': None},\n             {'评估内容': '额外触碰Odds', '内容详细含义': '（额外触碰成熟申请单中坏好比）/（非额外触碰成熟申请单坏/好）', '备注': None},\n             {'评估内容': '额外触碰Lift', '内容详细含义': '（额外触碰成熟申请单中坏样本占比）/（全量成熟申请单坏样本占比）', '备注': None},\n             {'评估内容': '额外触碰fpd15_Odds', '内容详细含义': '（额外触碰fpd15成熟申请单中坏好比）/（非额外触碰fpd15成熟申请单中坏/好）', '备注': None},\n             {'评估内容': '额外触碰fpd15_Lift', '内容详细含义': '（额外触碰fpd15成熟申请单中坏样本占比）/（全量fpd15成熟申请单坏样本占比）', '备注': None},\n             {'评估内容': '额外触碰Lift大于目标值的月份占比', '内容详细含义': '额外触碰非空Lift大于阈值月份数/额外触碰非空Lift月份数', '备注': '阈值可根据实际情况自己设定，如取值为3'},\n             {'评估内容': '置前触碰率离散度', '内容详细含义': '置前触碰率是否稳定', '备注': '0%~15%：非常稳定\\n15%~40%：相对稳定\\n40%~75%：不稳定\\n>75%：极不稳定'},\n             {'评估内容': '策略触碰率离散度', '内容详细含义': '策略触碰率是否稳定', '备注': '0%~15%：非常稳定\\n15%~40%：相对稳定\\n40%~75%：不稳定\\n>75%：极不稳定'},\n             {'评估内容': '重复触碰率离散度', '内容详细含义': '重复触碰率是否稳定', '备注': '0%~15%：非常稳定\\n15%~40%：相对稳定\\n40%~75%：不稳定\\n>75%：极不稳定'},\n             {'评估内容': '额外触碰率离散度', '内容详细含义': '额外触碰率是否稳定', '备注': '0%~15%：非常稳定\\n15%~40%：相对稳定\\n40%~75%：不稳定\\n>75%：极不稳定'},\n             {'评估内容': '置后触碰率离散度', '内容详细含义': '置后触碰率是否稳定', '备注': '0%~15%：非常稳定\\n15%~40%：相对稳定\\n40%~75%：不稳定\\n>75%：极不稳定'},\n             {'评估内容': '评估结论', '内容详细含义': '基于策略筛选规则，评估策略是否进行上线', '备注': None}]\n\nword_desc = pd.DataFrame(word_desc)[['评估内容', '内容详细含义', '备注']]\n\n\n# 2.8 按月泛化时规则风险表现情况画图函数\ndef add_combine_plot(wb, ws, column, label_x_start=1 + 6, x_start=1 + 6 + 1, x_end=int(1 + 6 + 1 + 12 * 1 / 2), title='客户级规则效能分析', x_label='申请月',\n                     y1_list=['额外触碰Lift', '额外触碰fpd15_Lift'], y2_list=['置前触碰率', '策略触碰率', '重复触碰率', '额外触碰率', '置后触碰率'],\n                     x_title='申请月', y1_title='额外触碰Lift', y2_title='规则触碰率', width=760, height=520, if_combine=True, chart1_type='column', chart2_type='line',\n                     chart_row=1 + 6 + 12 + 4, chart_col=1):\n    \"\"\"\n    :param column: 数据框列名\n    :param label_x_start:  数据框列名开始行\n    :param x_start:   数据框取值开始行\n    :param x_end:    数据框取值结束行\n    :param title:    图表名称\n    :param x_label:  X轴数据列名\n    :param y1_list:  Y1轴数据列名\n    :param y2_list:   Y2轴数据列名\n    :param x_title:  X轴标题\n    :param y1_title:   Y1轴标题\n    :param y2_title:    Y2轴标题\n    :param width:   图表宽\n    :param height:  图表高\n    :param if_combine: 是否画组合图，默认是\n    :param chart1_type: 组合图1类型，默认是柱状图\n    :param chart2_type: 组合图2类型，默认是折线图\n    :param chart_row:  图表插入行\n    :param chart_col:  图表插入列\n    :return:\n    \"\"\"\n    x1 = list(column).index(x_label) + 1\n    y1_index_list = [list(column).index(i) + 1 for i in y1_list]\n    y2_index_list = [list(column).index(i) + 1 for i in y2_list]\n    column_chart1 = wb.add_chart({'type': chart1_type})\n    column_chart1.set_size({'width': width, 'height': height})\n    for k in y1_index_list:\n        column_chart1.add_series(\n            {'name': [ws.name, label_x_start, k],\n             'num_font': {'name': '微软雅黑', 'size': 9},\n             'categories': [ws.name, x_start, x1, x_end, x1],\n             'values': [ws.name, x_start, k, x_end, k],\n             'data_labels': {'value': False}\n             })\n    column_chart1.set_title({'name': title, 'name_font': {'name': '微软雅黑', 'size': 10, 'bold': False}})\n    column_chart1.set_x_axis({'name': x_title, 'name_font': {'name': '微软雅黑', 'size': 9, 'bold': False}})\n    column_chart1.set_y_axis({'name': y1_title, 'name_font': {'name': '微软雅黑', 'size': 9, 'bold': False}})\n    column_chart1.set_chartarea({'border': {'none': True}, 'fill': {'color': text}})\n    column_chart1.set_plotarea({'border': {'none': True}, 'fill': {'color': text}})\n    if if_combine:\n        line_chart2 = wb.add_chart({'type': chart2_type})\n        for k in y2_index_list:\n            line_chart2.add_series(\n                {'name': [ws.name, label_x_start, k],\n                 'num_font': {'name': '微软雅黑', 'size': 9},\n                 'categories': [ws.name, x_start, x1, x_end, x1],\n                 'values': [ws.name, x_start, k, x_end, k],\n                 'data_labels': {'value': False},\n                 'y2_axis': True\n                 })\n        column_chart1.combine(line_chart2)\n        line_chart2.set_y2_axis({'name': y2_title, 'name_font': {'name': '微软雅黑', 'size': 9, 'bold': False}})\n    ws.insert_chart(chart_row, chart_col, column_chart1)\n\n\ndef add_plot(wb, ws, column, label_x_start=1 + 6, x_start=1 + 6 + 1, x_end=int(1 + 6 + 1 + 12 * 1 / 2), title='客户级规则效能分析', x_label='申请周',\n             y1_list=['置前触碰率', '策略触碰率', '重复触碰率', '额外触碰率', '置后触碰率'], x_title='申请周', y1_title='规则触碰率', width=760, height=520, chart1_type='line',\n             chart_row=1 + 6 + 12 + 4, chart_col=1):\n    \"\"\"\n    插入图形函数\n    \"\"\"\n    x1 = list(column).index(x_label) + 1\n    y1_index_list = [list(column).index(i) + 1 for i in y1_list]\n    column_chart1 = wb.add_chart({'type': chart1_type})\n    column_chart1.set_size({'width': width, 'height': height})\n    for k in y1_index_list:\n        column_chart1.add_series(\n            {'name': [ws.name, label_x_start, k],\n             'num_font': {'name': '微软雅黑', 'size': 9},\n             'categories': [ws.name, x_start, x1, x_end, x1],\n             'values': [ws.name, x_start, k, x_end, k],\n             'data_labels': {'value': False}\n             })\n    column_chart1.set_title({'name': title, 'name_font': {'name': '微软雅黑', 'size': 10, 'bold': False}})\n    column_chart1.set_x_axis({'name': x_title, 'name_font': {'name': '微软雅黑', 'size': 9, 'bold': False}})\n    column_chart1.set_y_axis({'name': y1_title, 'name_font': {'name': '微软雅黑', 'size': 9, 'bold': False}})\n    column_chart1.set_chartarea({'border': {'none': True}, 'fill': {'color': text}})\n    column_chart1.set_plotarea({'border': {'none': True}, 'fill': {'color': text}})\n    ws.insert_chart(chart_row, chart_col, column_chart1)\n\n\ndef std_result_output(data, freq):\n    \"\"\"泛化结果输出函数,按月、周、日输出分析结果函数\n    \"\"\"\n    if freq == \"M\":\n        data_01 = data[-3:]\n        data_02 = data[data['额外触碰成熟量'] >= 10][-3:]\n        data_03 = data[data['整体逾期率'] > 0][-3:]\n        hit_rate = sum(data_01['额外触碰量']) / sum(data_01['申请量'])\n        risk_double = (data_02['额外触碰成熟坏样本量'].sum() / data_02['额外触碰成熟量'].sum()) / (sum(data_02['整体成熟坏样本量']) / sum(data_02['整体成熟量'])) if sum(data_02['整体成熟坏样本量']) > 0 else np.nan\n        overdue_minus = sum(data_03['整体成熟坏样本量']) / sum(data_03['整体成熟量']) - sum(data_03['整体成熟坏样本量'] - data_03['额外触碰成熟坏样本量']) / sum(data_03['整体成熟量'] - data_03['额外触碰成熟量']) if sum(data_03['整体成熟量'] - data_03['额外触碰成熟量']) > 0 else np.nan\n        overdue_range = overdue_minus / (sum(data_03['整体成熟坏样本量']) / sum(data_03['整体成熟量'])) if sum(data_03['整体成熟坏样本量']) > 0 else np.nan\n        text1 = \"1.策略上线后通过率下降预估值 = 近3个月额外触碰量之和/近3个月申请量之和；策略上线后逾期率变化预估值 = （近n个有表现月逾期样本之和/近n个有表现月成熟样本之和）-（近n个有表现月置后触碰坏样本之和/近n个有表现月置后触碰成熟样本之和） ；备注：取整体逾期率大于0的近n个月计算策略上线后逾期率变化值，n默认取3，若不足3个月，有几个月计算几个月;\"\n        text2 = \"2.策略上线后额外触碰Lift预估值 = (近n个有表现月额外触碰成熟坏样本量之和/近n个有表现月额外触碰成熟量之和)/(近n个有表现月整体成熟坏样本量之和/近n个有表现月整体成熟量之和)；备注：取额外触碰成熟量大于等于10的近n个月计算策略上线后额外触碰Lift预估值，n默认取3，若不足3个月，有几个月计算几个月;\"\n        if overdue_minus > 0:\n            text3 = \"3.策略上线后通过率预计下降：{:.2f}%，逾期率下降：{:.2f}%，逾期率下降幅度：{:.2f}%，额外触碰Lift预估值为：{:.2f}。\".format(hit_rate * 100, overdue_minus * 100, overdue_range * 100, risk_double)\n        else:\n            text3 = \"3.策略上线后通过率预计下降：{:.2f}%，逾期率上升：{:.2f}%，逾期率上升幅度：{:.2f}%，额外触碰Lift预估值为：{:.2f}。\".format(hit_rate * 100, abs(overdue_minus) * 100, abs(overdue_range) * 100, risk_double)\n    elif freq == \"W\":\n        week_num = str(len(data['申请周'].unique()))\n        mean_obs = data['置前触碰率'].mean() * 100\n        cv_rate = cv(data['置前触碰率'])\n        if data['置前触碰率'].sum() == 0:\n            cv_rate = 0\n        info = cv_type(cv_rate)\n        trend = get_trend(data, key='置前触碰率')\n        text1 = \"1.近\" + week_num + \"个申请周平均置前触碰率%.2f\" % mean_obs + '%, ' + '置前触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + ';置前触碰率整体趋势：' + trend + \"；\"\n        mean_obs = data['策略触碰率'].mean() * 100\n        cv_rate = cv(data['策略触碰率'])\n        if data['策略触碰率'].sum() == 0:\n            cv_rate = 0\n        info = cv_type(cv_rate)\n        trend = get_trend(data, key='策略触碰率')\n        text2 = \"2.近\" + week_num + \"个申请周策略触碰率%.2f\" % mean_obs + '%, ' + '策略触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + '; 策略触碰率整体趋势：' + trend + \"；\"\n        mean_obs = data['额外触碰率'].mean() * 100\n        cv_rate = cv(data['额外触碰率'])\n        if data['额外触碰率'].sum() == 0:\n            cv_rate = 0\n        info = cv_type(cv_rate)\n        trend = get_trend(data, key='额外触碰率')\n        text3 = \"3.近\" + week_num + \"个申请周额外触碰率%.2f\" % mean_obs + '%, ' + '额外触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + '; 额外触碰率整体趋势：' + trend + \"；\"\n    elif freq == \"D\":\n        day_num = str(len(data['申请日'].unique()))\n        mean_obs = data['置前触碰率'].mean() * 100\n        cv_rate = cv(data['置前触碰率'])\n        if data['置前触碰率'].sum() == 0:\n            cv_rate = 0\n        info = cv_type(cv_rate)\n        trend = get_trend(data, key='置前触碰率')\n        text1 = \"1.近\" + day_num + \"个申请日平均置前触碰率%.2f\" % mean_obs + '%, ' + '置前触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + ';置前触碰率整体趋势：' + trend + \"；\"\n        mean_obs = data['策略触碰率'].mean() * 100\n        cv_rate = cv(data['策略触碰率'])\n        if data['策略触碰率'].sum() == 0:\n            cv_rate = 0\n        info = cv_type(cv_rate)\n        trend = get_trend(data, key='策略触碰率')\n        text2 = \"2.近\" + day_num + \"个申请日策略触碰率%.2f\" % mean_obs + '%, ' + '策略触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + '; 策略触碰率整体趋势：' + trend + \"；\"\n        mean_obs = data['额外触碰率'].mean() * 100\n        cv_rate = cv(data['额外触碰率'])\n        if data['额外触碰率'].sum() == 0:\n            cv_rate = 0\n        info = cv_type(cv_rate)\n        trend = get_trend(data, key='额外触碰率')\n        text3 = \"3.近\" + day_num + \"个申请日额外触碰率%.2f\" % mean_obs + '%, ' + '额外触碰率波动幅度%.2f' % (100 * cv_rate) + '%, ' + info + '; 额外触碰率整体趋势：' + trend + \"；\"\n    else:\n        raise \"frqp must in [M\\W\\D]\"\n\n    return text1, text2, text3\n\n\nif __name__ == '__main__':\n    import os\n    import xlsxwriter\n    import sys\n\n    sys.path.append(\"../\")\n    from scorecardpipeline import *\n\n    init_setting()\n\n    # 参数值设置\n    sample_range = '202108-202110'\n    seq = 1\n    # sample_type取值只有Total，表示测算全量样本，不需要配置参数sample_type_col；若还有其他样本类型，也支持同时测算\n    sample_type = ['Total']\n\n    # 若sample_type需测算类型1和类型2的样本，类型1和类型2所在的字段为ordertype，sample_type_col参数示例如下\n    # sample_type_col = {\n    #                    '类型1': ['ordertype', ['类型1']],\n    #                    '类型2': ['ordertype', ['类型2']]\n    #                   }\n\n    sample_type_target = {'Total': ['fpd_30_act', 'mob3_dpd_30_act']}\n\n    target_ripe = {'fpd_30_act': ['agr_fpd_30'], 'mob3_dpd_30_act': ['agr_mob3_dpd_30']}\n\n    target_del_col = {'fpd_30_act': ['agr_fpd_30', 'agr_mob3_dpd_30', 'mob3_dpd_30_act'],\n                      'mob3_dpd_30_act': ['agr_mob3_dpd_30', 'agr_fpd_30', 'fpd_30_act']}\n\n    sub_div_bin = 0.1\n    target_min_rate = {'fpd_30_act': [0.015], 'mob3_dpd_30_act': [0.02]}\n    min_num = 40\n    hit_num = 30\n    sample_type_lift = {'Total': {'fpd_30_act': 1.8, 'mob3_dpd_30_act': 3}}\n\n    # 泛化结果输出路径\n    path_rule = \"rule_data/rule_result/\"\n\n    if not os.path.exists(path_rule):\n        os.makedirs(path_rule)\n\n    # 4.读入数据并进行数据预处理\n    #  读入测算环节筛选的待泛化规则\n    rules_dict = pd.read_excel(\"rule_data/规则字典.xlsx\")\n\n    # 加载数据\n    mydata = pd.read_csv(\"rule_data/rule_data.csv\", encoding=\"gbk\")\n    mydata.columns = mydata.columns.map(lambda x: x.lower())\n\n    for i in mydata.columns[mydata.dtypes != 'object']:\n        mydata[i][mydata[i].map(lambda x: x in [-999, -9999, -999999])] = np.nan\n\n    for i in mydata.columns[mydata.dtypes == 'object']:\n        mydata[i] = mydata[i].map(lambda x: str(x).strip())\n        mydata[i][mydata[i].map(lambda x: x in ['-999', '-9999', '-999999'])] = np.nan\n        try:\n            mydata[i] = mydata[i].astype('float64')\n        except:\n            pass\n\n    # 处理灰样本\n    mydata['mob3_dpd_30_act'] = mydata['mob3_dpd_30_act'].map(lambda x: 1 if x == 1 else 0)\n\n    # 5.基于加载的函数进行策略自动泛化；（需要基于阈值测算的数据字典）\n\n    \"\"\" # 泛化参数说明\n    target_ripe      获取目标字段对应的是否成熟标签，与测算环节参数一样\n    sample_type_col  若测算样本是整个样本的子集，需配置sample_type_col参数，指名样本类型从哪个字段获取，与测算环节参数一样\n    rules_dict       测算环节筛选的待泛化规则集\n    rule_all         当前已经在线上运行的所有规则对应的决策结果标签字段，1代表拒绝，0代表未拒绝\n    use_credit_flag  授信申请通过后是否用信标签字段，1代表用信，0代表未用信\n    circle_mth       申请月对应的字段，在进行策略泛化的时候的会按月进行泛化，分析策略触碰情况和风险表现情况\n    circle_week      申请周对应的字段，在进行策略泛化的时候会按申请周进行泛化，分析近10周策略触碰情况\n    circle_day       申请日对应的字段，在进行策略泛化的时候会按申请日进行泛化，分析近10日策略触碰情况\n    base_lift        策略泛化时，取额外触碰样本的Lift值与base_lift进行比较，若额外触碰Lift值大于base_lift则说明策略效果较好\n    subset_total     是否获取全量样本作为计算整体，若取值为False则分母为全样本，计算各种指标时是从全量样本维度考虑的，若取值为True则分母为样本类型对应的样本\n    \"\"\"\n\n    target_ripe = {'fpd_30_act': ['agr_fpd_30'], 'mob3_dpd_30_act': ['agr_mob3_dpd_30']}\n\n    # 在进行策略测算时，若除测算全量样本（Total）外还测算了类型1和类型2的样本，假设类型1和类型2所在的字段为ordertype，则需设置sample_type_col参数，否则不需要进行设置。\n    # sample_type_col = {\n    #                    '类型1': ['ordertype', ['类型1']],\n    #                    '类型2': ['ordertype', ['类型2']]\n    #                   }\n\n    # 对测算环节筛选的样本进行自动化泛化\n    starttime = datetime.datetime.now()\n    print('程序开始执行时间为: ' + str(starttime))\n\n    rule_combine_results(data=mydata, rules_dict=rules_dict, rule_all='apply_refuse_flag', use_credit_flag='if_loan_flag', circle_mth='apply_mth', circle_week='apply_week', circle_day='apply_day', base_lift=3, subset_total=True)\n\n    endtime = datetime.datetime.now()\n    print('程序开始执行时间为: ' + str(endtime - starttime))\n\n    # 6.基于步骤3策略泛化结果筛选泛化效果好的规则集进行合并泛化（不需要测算结果数据字典）\n\n    # 此次未筛选到足够多的泛化效果好的规则，只为跑通代码流程，代码执行结果供参考。基于泛化效果好的规则集，重构一条规则进行泛化，变量取值为1表示规则触碰，取值为0表示规则未触碰。\n\n    mydata['all_need_online_rule'] = mydata.apply(lambda x: 1 if (x['var3'] > 3 or x['var6'] > 5) else 0, axis=1)\n\n    starttime = datetime.datetime.now()\n    print('程序开始执行时间为: ' + str(starttime))\n    rule_combine_results_01(data=mydata, rule_all='apply_refuse_flag', use_credit_flag='if_loan_flag', cut_point=0, direction='>', rule_name='待上线规则合并泛化', rule_name_chinese='待上线规则合并泛化', var='all_need_online_rule', rule_type='CR', rule_limit='Total', circle_mth='apply_mth', circle_week='apply_week', circle_day='apply_day', target='mob3_dpd_30_act', base_lift=3, subset_total=True)\n    endtime = datetime.datetime.now()\n    print('程序执行时间为: ' + str(endtime - starttime))\n"
  },
  {
    "path": "examples/scorecard.pmml",
    "content": "\n\n\t
\n\t\t\n\t\t2024-08-30T06:28:54Z\n\t
\n\t\n\t\tPMMLPipeline(steps=[('preprocessing', DataFrameMapper(df_out=True, drop_cols=[],\n                features=[(['all_720_query_avg_org'],\n                           ExpressionTransformer(expr='116.31861805531601 if '\n                                                      'X[0] < 0.2568 else '\n                                                      '67.13308122593628')),\n                          (['help_loan_1080_query_times'],\n                           ExpressionTransformer(expr='118.55489075866564 if '\n                                                      'X[0] < 1 else '\n                                                      '62.89909217920857')),\n                          (['not_bank_1080_query_weekday_days'],\n                           ExpressionTransformer(expr='121...\n                                                      '108.47564228238582')),\n                          (['all_60_query_avg_org'],\n                           ExpressionTransformer(expr='88.4396327976653 if '\n                                                      'X[0] < 0.4 else '\n                                                      '81.21864819541842')),\n                          (['bank_1080_query_org_avg_times'],\n                           ExpressionTransformer(expr='68.15140311787648 if '\n                                                      'X[0] < 1.0 else '\n                                                      '101.96618280101227')),\n                          (['not_bank_180_query_avg_org'],\n                           ExpressionTransformer(expr='109.67610047709874 if '\n                                                      'X[0] < 0.381 else '\n                                                      '42.48148366452569'))])),\n       ('scorecard', LinearRegression(fit_intercept=False))])\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\n\t\n\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\n\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t0.2568\n\t\t\t\t\t\n\t\t\t\t\t116.31861805531601\n\t\t\t\t\t67.13308122593628\n\t\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t1\n\t\t\t\t\t\n\t\t\t\t\t118.55489075866564\n\t\t\t\t\t62.89909217920857\n\t\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t2\n\t\t\t\t\t\n\t\t\t\t\t121.22626170081081\n\t\t\t\t\t59.65630196796644\n\t\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t0.0\n\t\t\t\t\t\n\t\t\t\t\t98.36678169439556\n\t\t\t\t\t67.39540445245542\n\t\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t0.8333\n\t\t\t\t\t\n\t\t\t\t\t90.40493982042932\n\t\t\t\t\t74.43738758194276\n\t\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t0.0\n\t\t\t\t\t\n\t\t\t\t\t50.979707045210986\n\t\t\t\t\t108.47564228238582\n\t\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t0.4\n\t\t\t\t\t\n\t\t\t\t\t88.4396327976653\n\t\t\t\t\t81.21864819541842\n\t\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t1.0\n\t\t\t\t\t\n\t\t\t\t\t68.15140311787648\n\t\t\t\t\t101.96618280101227\n\t\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t0.381\n\t\t\t\t\t\n\t\t\t\t\t109.67610047709874\n\t\t\t\t\t42.48148366452569\n\t\t\t\t\n\t\t\t\n\t\t\n\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\n\t\n\n"
  },
  {
    "path": "examples/scorecard_samples.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# scorecardpipeline\\n\",\n    \"\\n\",\n    \"## 引言\\n\",\n    \"\\n\",\n    \"作为一名金融搬砖工作者，评分卡建模怎么也算是基操。本文主要对笔者日常使用的评分卡建模代码进行讲解，说明如何一步步从原始数据到最终评分卡模型以及如何解读产出的模型报告文档。\\n\",\n    \"\\n\",\n    \"`scorecardpipeline` 封装了 `toad`、`scorecardpy`、`optbinning` 等评分卡建模相关组件，`API` 风格与 `sklearn` 高度一致，支持 `pipeline` 式端到端评分卡建模、模型报告输出、导出 `PMML` 文件、超参数搜索等功能，各位看官按需取用，用完记得顺带给个star以鼓励笔者继续开源相关工作。\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"|  微信 |  微信公众号 |\\n\",\n    \"| :---: | :----: |\\n\",\n    \"| \\\"itlubber.png\\\" | \\\"itlubber_art.png\\\" |\\n\",\n    \"|  itlubber  | itlubber_art |\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 项目说明\\n\",\n    \"\\n\",\n    \"### 代码结构\\n\",\n    \"\\n\",\n    \"`scorecardpipeline` 仓库下代码主要用于提供评分卡建模相关的组件，项目结构如下：\\n\",\n    \"\\n\",\n    \"```base\\n\",\n    \">> tree\\n\",\n    \".\\n\",\n    \"├── LICENSE                         # 开源协议\\n\",\n    \"├── README.md                       # 相关说明文档\\n\",\n    \"├── requirements.txt                # 相关依赖包\\n\",\n    \"└── setup.py                        # 打包文件\\n\",\n    \"├── examples                        # 演示样例\\n\",\n    \"│   └── scorecard_samples.ipynb\\n\",\n    \"└── scorecardpipeline               # scorecardpipeline 包文件\\n\",\n    \"    ├── excel_writer.py             # 操作 excel 的公共方法\\n\",\n    \"    ├── template.xlsx               # excel 模版文件\\n\",\n    \"    ├── matplot_chinese.ttf         # 中文字体\\n\",\n    \"    ├── processing.py               # 数据处理相关代码\\n\",\n    \"    ├── model.py                    # 模型相关代码\\n\",\n    \"    ├── logger.py                   # 日志打印方法\\n\",\n    \"    └── utils.py                    # 公用方法\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"### 简要说明\\n\",\n    \"\\n\",\n    \"+ `processing` 中提供了数据前处理相关的方法：特征筛选方法（`FeatureSelection`、`StepwiseSelection`）、变量分箱方法（`Combiner`）、变量证据权重转换方法（`WOETransformer`），方法继承`sklearn.base`中的`BaseEstimator`和`TransformerMixin`，能够支持构建`pipeline`和超参数搜索\\n\",\n    \"+ `model`中提供了基于`sklearn.linear_model.LogisticRegression`实现的`ITLubberLogisticRegression`，同时重写了`toad.ScoreCard`，以支持模型相关内容的输出\\n\",\n    \"+ `excel_writer` 中提供了操作 `excel` 的一系列公共方法，包含设置条件格式、设置列宽、设置数字格式、插入指定样式的内容（`insert_value2sheet`）、插入图片数据（`insert_pic2sheet`）、插入`dataframe`数据内容（`insert_df2sheet`）、保存`excel`文件（`save`）等方法\\n\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### `scorecardpipeline` 安装\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# !pip install -U scorecardpipeline -i https://pypi.Python.org/simple/\\n\",\n    \"# !pip install -r /Users/lubberit/Desktop/workspace/scorecardpipeline/requirements.txt -U\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 快速开始\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"1 本地调试模式 [通过 pip 安装可直接跳过]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import sys\\n\",\n    \"sys.path.append(\\\"../\\\")\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"2 导入相关依赖文件\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"import scorecardpipeline as sp\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sp\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
ScoreCard(combiner=<toad.transform.Combiner object at 0x7fdab22e2eb0>,\\n\",\n       \"          pipeline=Pipeline(steps=[('preprocessing_select',\\n\",\n       \"                                    FeatureSelection(target='MOB6_HIST_DPD30')),\\n\",\n       \"                                   ('combiner',\\n\",\n       \"                                    Combiner(min_bin_size=0.2,\\n\",\n       \"                                             target='MOB6_HIST_DPD30')),\\n\",\n       \"                                   ('transform',\\n\",\n       \"                                    WOETransformer(exclude=[],\\n\",\n       \"                                                   target='MOB6_HIST_DPD30')),\\n\",\n       \"                                   ('processing_select',\\n\",\n       \"                                    FeatureSelection(engine='toad',\\n\",\n       \"                                                     target='MOB6_HIST_DPD30')),\\n\",\n       \"                                   ('stepwise',\\n\",\n       \"                                    StepwiseSelection(target='MOB6_HIST_DPD30'))]),\\n\",\n       \"          pretrain_lr=ITLubberLogisticRegression(target='MOB6_HIST_DPD30'),\\n\",\n       \"          target='MOB6_HIST_DPD30',\\n\",\n       \"          transer=<toad.transform.WOETransformer object at 0x7fdab2382bb0>)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
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\"\n      ],\n      \"text/plain\": [\n       \"ScoreCard(combiner=,\\n\",\n       \"          pipeline=Pipeline(steps=[('preprocessing_select',\\n\",\n       \"                                    FeatureSelection(target='MOB6_HIST_DPD30')),\\n\",\n       \"                                   ('combiner',\\n\",\n       \"                                    Combiner(min_bin_size=0.2,\\n\",\n       \"                                             target='MOB6_HIST_DPD30')),\\n\",\n       \"                                   ('transform',\\n\",\n       \"                                    WOETransformer(exclude=[],\\n\",\n       \"                                                   target='MOB6_HIST_DPD30')),\\n\",\n       \"                                   ('processing_select',\\n\",\n       \"                                    FeatureSelection(engine='toad',\\n\",\n       \"                                                     target='MOB6_HIST_DPD30')),\\n\",\n       \"                                   ('stepwise',\\n\",\n       \"                                    StepwiseSelection(target='MOB6_HIST_DPD30'))]),\\n\",\n       \"          pretrain_lr=ITLubberLogisticRegression(target='MOB6_HIST_DPD30'),\\n\",\n       \"          target='MOB6_HIST_DPD30',\\n\",\n       \"          transer=)\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"card.features_\\n\",\n    \"card #.pipeline[-1].select_columns\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"3 初始化环境\\n\",\n    \"\\n\",\n    \"+ 去除python警告信息\\n\",\n    \"+ 设置pandas显示列宽、浮点数显示精度\\n\",\n    \"+ 设置matplotlib主题、中文字体支持\\n\",\n    \"+ 固定随机种子[可选]\\n\",\n    \"+ 日志打印[可选]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"logger = sp.init_setting(seed=10, logger=True)\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"4 数据集准备\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"4.1 数据集读取\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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status_of_existing_checking_accountduration_in_monthcredit_historypurposecredit_amountsavings_account_and_bondspresent_employment_sinceinstallment_rate_in_percentage_of_disposable_incomepersonal_status_and_sexother_debtors_or_guarantors...propertyage_in_yearsother_installment_planshousingnumber_of_existing_credits_at_this_bankjobnumber_of_people_being_liable_to_provide_maintenance_fortelephoneforeign_workercreditability
0... < 0 DM6critical account/ other credits existing (not at this bank)radio/television1169unknown/ no savings account... >= 7 years4male : divorced/separatednone...real estate67noneown2skilled employee / official1yes, registered under the customers nameyes0
10 <= ... < 200 DM48existing credits paid back duly till nowradio/television5951... < 100 DM1 <= ... < 4 years2male : divorced/separatednone...real estate22noneown1skilled employee / official1noneyes1
2no checking account12critical account/ other credits existing (not at this bank)education2096... < 100 DM4 <= ... < 7 years2male : divorced/separatednone...real estate49noneown1unskilled - resident2noneyes0
3... < 0 DM42existing credits paid back duly till nowfurniture/equipment7882... < 100 DM4 <= ... < 7 years2male : divorced/separatedguarantor...building society savings agreement/ life insurance45nonefor free1skilled employee / official2noneyes0
4... < 0 DM24delay in paying off in the pastcar (new)4870... < 100 DM1 <= ... < 4 years3male : divorced/separatednone...unknown / no property53nonefor free2skilled employee / official2noneyes1
\\n\",\n       \"
5 rows × 21 columns
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\"\n      ],\n      \"text/plain\": [\n       \"  status_of_existing_checking_account  duration_in_month  \\\\\\n\",\n       \"0                          ... < 0 DM                  6   \\n\",\n       \"1                   0 <= ... < 200 DM                 48   \\n\",\n       \"2                 no checking account                 12   \\n\",\n       \"3                          ... < 0 DM                 42   \\n\",\n       \"4                          ... < 0 DM                 24   \\n\",\n       \"\\n\",\n       \"                                                credit_history  \\\\\\n\",\n       \"0  critical account/ other credits existing (not at this bank)   \\n\",\n       \"1                     existing credits paid back duly till now   \\n\",\n       \"2  critical account/ other credits existing (not at this bank)   \\n\",\n       \"3                     existing credits paid back duly till now   \\n\",\n       \"4                              delay in paying off in the past   \\n\",\n       \"\\n\",\n       \"               purpose  credit_amount    savings_account_and_bonds  \\\\\\n\",\n       \"0     radio/television           1169  unknown/ no savings account   \\n\",\n       \"1     radio/television           5951                 ... < 100 DM   \\n\",\n       \"2            education           2096                 ... < 100 DM   \\n\",\n       \"3  furniture/equipment           7882                 ... < 100 DM   \\n\",\n       \"4            car (new)           4870                 ... < 100 DM   \\n\",\n       \"\\n\",\n       \"  present_employment_since  \\\\\\n\",\n       \"0           ... >= 7 years   \\n\",\n       \"1       1 <= ... < 4 years   \\n\",\n       \"2       4 <= ... < 7 years   \\n\",\n       \"3       4 <= ... < 7 years   \\n\",\n       \"4       1 <= ... < 4 years   \\n\",\n       \"\\n\",\n       \"   installment_rate_in_percentage_of_disposable_income  \\\\\\n\",\n       \"0                                                    4   \\n\",\n       \"1                                                    2   \\n\",\n       \"2                                                    2   \\n\",\n       \"3                                                    2   \\n\",\n       \"4                                                    3   \\n\",\n       \"\\n\",\n       \"     personal_status_and_sex other_debtors_or_guarantors  ...  \\\\\\n\",\n       \"0  male : divorced/separated                        none  ...   \\n\",\n       \"1  male : divorced/separated                        none  ...   \\n\",\n       \"2  male : divorced/separated                        none  ...   \\n\",\n       \"3  male : divorced/separated                   guarantor  ...   \\n\",\n       \"4  male : divorced/separated                        none  ...   \\n\",\n       \"\\n\",\n       \"                                             property age_in_years  \\\\\\n\",\n       \"0                                         real estate           67   \\n\",\n       \"1                                         real estate           22   \\n\",\n       \"2                                         real estate           49   \\n\",\n       \"3  building society savings agreement/ life insurance           45   \\n\",\n       \"4                               unknown / no property           53   \\n\",\n       \"\\n\",\n       \"   other_installment_plans   housing number_of_existing_credits_at_this_bank  \\\\\\n\",\n       \"0                     none       own                                       2   \\n\",\n       \"1                     none       own                                       1   \\n\",\n       \"2                     none       own                                       1   \\n\",\n       \"3                     none  for free                                       1   \\n\",\n       \"4                     none  for free                                       2   \\n\",\n       \"\\n\",\n       \"                           job  \\\\\\n\",\n       \"0  skilled employee / official   \\n\",\n       \"1  skilled employee / official   \\n\",\n       \"2         unskilled - resident   \\n\",\n       \"3  skilled employee / official   \\n\",\n       \"4  skilled employee / official   \\n\",\n       \"\\n\",\n       \"  number_of_people_being_liable_to_provide_maintenance_for  \\\\\\n\",\n       \"0                                                        1   \\n\",\n       \"1                                                        1   \\n\",\n       \"2                                                        2   \\n\",\n       \"3                                                        2   \\n\",\n       \"4                                                        2   \\n\",\n       \"\\n\",\n       \"                                  telephone foreign_worker creditability  \\n\",\n       \"0  yes, registered under the customers name            yes             0  \\n\",\n       \"1                                      none            yes             1  \\n\",\n       \"2                                      none            yes             0  \\n\",\n       \"3                                      none            yes             0  \\n\",\n       \"4                                      none            yes             1  \\n\",\n       \"\\n\",\n       \"[5 rows x 21 columns]\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"target = \\\"creditability\\\"\\n\",\n    \"data = sp.germancredit()\\n\",\n    \"data[target] = data[target].map({\\\"good\\\": 0, \\\"bad\\\": 1})\\n\",\n    \"\\n\",\n    \"data.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 2023-10-13 17:48:49,261 ][ INFO ][ 3133036894.py::3 ] 训练集数据: (700, 21), 测试集数据: (300, 21)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"train, test = train_test_split(data, test_size=0.3, shuffle=True, stratify=data[target])\\n\",\n    \"\\n\",\n    \"logger.info(f\\\"训练集数据: {train.shape}, 测试集数据: {test.shape}\\\")\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"4.2 数据集概要信息\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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数据集开始时间结束时间样本总数样本占比坏客户数坏客户占比备注
0建模样本2022-01-012023-01-3110001.00003000.3000
1训练集2022-01-012023-12-317000.70002100.3000
2测试集2022-01-012023-12-313000.3000900.3000
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\"\n      ],\n      \"text/plain\": [\n       \"    数据集        开始时间        结束时间  样本总数   样本占比  坏客户数  坏客户占比 备注\\n\",\n       \"0  建模样本  2022-01-01  2023-01-31  1000 1.0000   300 0.3000   \\n\",\n       \"1   训练集  2022-01-01  2023-12-31   700 0.7000   210 0.3000   \\n\",\n       \"2   测试集  2022-01-01  2023-12-31   300 0.3000    90 0.3000   \"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 模拟实际场景中的数据， date 为数据集中的日期，为 datetime 类型，实际生产过程中可能是 申请时间｜放款时间｜入催时间｜流失时间 等\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame()\\n\",\n    \"df[\\\"date\\\"] = pd.date_range(start=\\\"2021-01-01\\\", end=\\\"2021-06-30\\\", freq=\\\"5H\\\")\\n\",\n    \"df[target] = np.random.randint(0, 2, len(df))\\n\",\n    \"\\n\",\n    \"total_count = len(data)\\n\",\n    \"dataset_summary = pd.DataFrame(\\n\",\n    \"    [\\n\",\n    \"        [\\\"建模样本\\\", \\\"2022-01-01\\\", \\\"2023-01-31\\\", len(data), len(data) / total_count, data[target].sum(), data[target].sum() / len(data), \\\"\\\"],\\n\",\n    \"        [\\\"训练集\\\", \\\"2022-01-01\\\", \\\"2023-12-31\\\", len(train), len(train) / total_count, train[target].sum(), train[target].sum() / len(train), \\\"\\\"],\\n\",\n    \"        [\\\"测试集\\\", \\\"2022-01-01\\\", \\\"2023-12-31\\\", len(test), len(test) / total_count, test[target].sum(), test[target].sum() / len(test), \\\"\\\"],\\n\",\n    \"    ],\\n\",\n    \"    columns=[\\\"数据集\\\", \\\"开始时间\\\", \\\"结束时间\\\", \\\"样本总数\\\", \\\"样本占比\\\", \\\"坏客户数\\\", \\\"坏客户占比\\\", \\\"备注\\\"],\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"dataset_summary\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mSignature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdistribution_plot\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mdata\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mdate\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'date'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtarget\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'target'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0msave\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfigsize\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m10\\u001b[0m\\u001b[0;34m,\\u001b[0m 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\\u001b[0mhatch\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m \\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m      ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/utils.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m      function\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sp.distribution_plot?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sp.distribution_plot(df, date=\\\"date\\\", target=target)\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"5 数据预处理\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"5.1 特征粗筛选[可选]\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"+ 根据特征重要性进行筛选\\n\",\n    \"\\n\",\n    \"使用 `catboost` 在数据集上训练后的 `feature_importances_` 进行排序，选出 `top_k` 个较为重要的特征，如果需要排除高 `iv` 值的变量，可以通过 `max_iv` 先筛选特征后再通过 `catboost` 进行筛选\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mInit signature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mFeatureImportanceSelector\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtop_k\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m126\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtarget\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'target'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mselector\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'catboost'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mparams\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmax_iv\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m     \\n\",\n      \"Base class for all estimators in scikit-learn.\\n\",\n      \"\\n\",\n      \"Notes\\n\",\n      \"-----\\n\",\n      \"All estimators should specify all the parameters that can be set\\n\",\n      \"at the class level in their ``__init__`` as explicit keyword\\n\",\n      \"arguments (no ``*args`` or ``**kwargs``).\\n\",\n      \"\\u001b[0;31mInit docstring:\\u001b[0m\\n\",\n      \"基于特征重要性的特征筛选方法\\n\",\n      \"\\n\",\n      \"Args:\\n\",\n      \"    target: 数据集中标签名称，默认 target\\n\",\n      \"    top_k: 依据特征重要性进行排序，筛选最重要的 top_k 个特征\\n\",\n      \"    max_iv: 是否需要删除 IV 过高的特征，建议设置为 1.0\\n\",\n      \"    selector: 特征选择器，目前只支持 catboost ，可以支持数据集中包含字符串的数据\\n\",\n      \"    params: selector 的参数，不传使用默认参数\\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m           ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/processing.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m           type\\n\",\n      \"\\u001b[0;31mSubclasses:\\u001b[0m     \"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sp.FeatureImportanceSelector?\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"+ 常规特征筛选方法\\n\",\n    \"\\n\",\n    \"通过封装 `toad` 和 `scorecardpy` 库中相应方法进行实现，补全了 `toad` 缺少 `identical` （即唯一值占比）的筛选方法\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mInit signature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mFeatureSelection\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtarget\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'target'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mempty\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.95\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0miv\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.02\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcorr\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.7\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mexclude\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mreturn_drop\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0midentical\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.95\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mremove\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mengine\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'scorecardpy'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtarget_rm\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m     \\n\",\n      \"Mixin class for all transformers in scikit-learn.\\n\",\n      \"\\n\",\n      \"If :term:`get_feature_names_out` is defined, then `BaseEstimator` will\\n\",\n      \"automatically wrap `transform` and `fit_transform` to follow the `set_output`\\n\",\n      \"API. See the :ref:`developer_api_set_output` for details.\\n\",\n      \"\\n\",\n      \":class:`base.OneToOneFeatureMixin` and\\n\",\n      \":class:`base.ClassNamePrefixFeaturesOutMixin` are helpful mixins for\\n\",\n      \"defining :term:`get_feature_names_out`.\\n\",\n      \"\\u001b[0;31mInit docstring:\\u001b[0m\\n\",\n      \"ITLUBBER提供的特征筛选方法\\n\",\n      \"\\n\",\n      \"Args:\\n\",\n      \"    target: 数据集中标签名称，默认 target\\n\",\n      \"    empty: 空值率，默认 0.95, 即空值占比超过 95% 的特征会被剔除\\n\",\n      \"    iv: IV值，默认 0.02，即iv值小于 0.02 时特征会被剔除\\n\",\n      \"    corr: 相关性，默认 0.7，即特征之间相关性大于 0.7 时会剔除iv较小的特征\\n\",\n      \"    identical: 唯一值占比，默认 0.95，即当特征的某个值占比超过 95% 时，特征会被剔除\\n\",\n      \"    engine: 特征筛选使用的引擎，可选 \\\"toad\\\", \\\"scorecardpy\\\" 两种，默认 scorecardpy\\n\",\n      \"    remove: 引擎使用 scorecardpy 时，可以传入需要强制删除的变量\\n\",\n      \"    return_drop: 是否返回删除信息，默认 True，即默认返回删除特征信息\\n\",\n      \"    target_rm: 是否剔除标签，默认 False，即不剔除\\n\",\n      \"    exclude: 是否需要强制保留某些特征\\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m           ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/processing.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m           type\\n\",\n      \"\\u001b[0;31mSubclasses:\\u001b[0m     \"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sp.FeatureSelection?\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"5.2 特征分箱\\n\",\n    \"\\n\",\n    \"集成 `toad` 和 `optbinning` 的分箱方法，支持 `chi`、`dt`、`quantile`、`step`、`kmeans`、`cart`、`mdlp`、`uniform` 等分箱方法\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mInit signature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mCombiner\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtarget\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'target'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmethod\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'chi'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mempty_separate\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmin_n_bins\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmax_n_bins\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmax_n_prebins\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m20\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmin_prebin_size\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.02\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmin_bin_size\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.05\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmax_bin_size\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mgamma\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.01\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmonotonic_trend\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'auto_asc_desc'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0madj_rules\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mn_jobs\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m     \\n\",\n      \"Mixin class for all transformers in scikit-learn.\\n\",\n      \"\\n\",\n      \"If :term:`get_feature_names_out` is defined, then `BaseEstimator` will\\n\",\n      \"automatically wrap `transform` and `fit_transform` to follow the `set_output`\\n\",\n      \"API. See the :ref:`developer_api_set_output` for details.\\n\",\n      \"\\n\",\n      \":class:`base.OneToOneFeatureMixin` and\\n\",\n      \":class:`base.ClassNamePrefixFeaturesOutMixin` are helpful mixins for\\n\",\n      \"defining :term:`get_feature_names_out`.\\n\",\n      \"\\u001b[0;31mInit docstring:\\u001b[0m\\n\",\n      \"特征分箱封装方法\\n\",\n      \"\\n\",\n      \"Args:\\n\",\n      \"    target: 数据集中标签名称，默认 target\\n\",\n      \"    method: 特征分箱方法，可选 \\\"chi\\\", \\\"dt\\\", \\\"quantile\\\", \\\"step\\\", \\\"kmeans\\\", \\\"cart\\\", \\\"mdlp\\\", \\\"uniform\\\", 参考 toad.Combiner & optbinning.OptimalBinning\\n\",\n      \"    empty_separate: 是否空值单独一箱, 默认 False，推荐设置为 True\\n\",\n      \"    min_n_bins: 最小分箱数，默认 2，即最小拆分2箱\\n\",\n      \"    max_n_bins: 最大分箱数，默认 None，即不限制拆分箱数，推荐设置 3 ～ 5，不宜过多，偶尔使用 optbinning 时不起效\\n\",\n      \"    max_n_prebins: 使用 optbinning 时预分箱数量\\n\",\n      \"    min_prebin_size: 使用 optbinning 时预分箱叶子结点（或者每箱）样本占比，默认 2%\\n\",\n      \"    min_bin_size: 使用 optbinning 正式分箱叶子结点（或者每箱）最小样本占比，默认 5%\\n\",\n      \"    max_bin_size: 使用 optbinning 正式分箱叶子结点（或者每箱）最大样本占比，默认 None\\n\",\n      \"    gamma: 使用 optbinning 分箱时限制过拟合的正则化参数，值越大惩罚越多，默认 0。01\\n\",\n      \"    monotonic_trend: 使用 optbinning 正式分箱时的坏率策略，默认 auto，可选 \\\"auto\\\", \\\"auto_heuristic\\\", \\\"auto_asc_desc\\\", \\\"ascending\\\", \\\"descending\\\", \\\"convex\\\", \\\"concave\\\", \\\"peak\\\", \\\"valley\\\", \\\"peak_heuristic\\\", \\\"valley_heuristic\\\"\\n\",\n      \"    adj_rules: 自定义分箱规则，toad.Combiner 能够接收的形式\\n\",\n      \"    n_jobs: 使用多进程加速的worker数量，默认单进程\\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m           ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/processing.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m           type\\n\",\n      \"\\u001b[0;31mSubclasses:\\u001b[0m     \"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sp.Combiner?\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"5.3 `WOE` 编码\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mInit signature:\\u001b[0m \\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mWOETransformer\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtarget\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'target'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mexclude\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m     \\n\",\n      \"Mixin class for all transformers in scikit-learn.\\n\",\n      \"\\n\",\n      \"If :term:`get_feature_names_out` is defined, then `BaseEstimator` will\\n\",\n      \"automatically wrap `transform` and `fit_transform` to follow the `set_output`\\n\",\n      \"API. See the :ref:`developer_api_set_output` for details.\\n\",\n      \"\\n\",\n      \":class:`base.OneToOneFeatureMixin` and\\n\",\n      \":class:`base.ClassNamePrefixFeaturesOutMixin` are helpful mixins for\\n\",\n      \"defining :term:`get_feature_names_out`.\\n\",\n      \"\\u001b[0;31mInit docstring:\\u001b[0m\\n\",\n      \"WOE转换器\\n\",\n      \"\\n\",\n      \"Args:\\n\",\n      \"    target: 数据集中标签名称，默认 target\\n\",\n      \"    exclude: 不需要转换 woe 的列\\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m           ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/processing.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m           type\\n\",\n      \"\\u001b[0;31mSubclasses:\\u001b[0m     \"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sp.WOETransformer?\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"5.4 特征精筛[可选]\\n\",\n    \"\\n\",\n    \"通常特征分箱转 `woe` 编码后，特征之间的相关性会增加，变量 `iv` 指标可能有一定的下降，为了避免多重共线性以及无效变量入模，会在 `woe` 编码后在筛选一次变量，筛选方法参考 `5.1` 中的 `sp.FeatureSelection` 使用方法，\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"5.5 逐步回归筛选[可选]\\n\",\n    \"\\n\",\n    \"逐步回归是以线性回归为基础的方法。其思路是将特征一个接着一个引入，并在引入一个新特征后，对已入选回归模型的旧特征逐个进行检验，将认为没有意义的特征删除，直到没有新特征引入也没有旧特征删除，从而保证回归模型中每一个特征都有意义（即统计意义上的显著）。在正式进行逻辑回归建模之前，增加一步逐步回归剔除特征，能够在一定程度上减少特征之间的多重共线性，同时也能保证逻辑回归具有更好的效果。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mInit signature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mStepwiseSelection\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtarget\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'target'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mestimator\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'ols'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mdirection\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'both'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcriterion\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'aic'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmax_iter\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mreturn_drop\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mexclude\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mintercept\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mp_value_enter\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.2\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mp_remove\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.01\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mp_enter\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.01\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtarget_rm\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m     \\n\",\n      \"Mixin class for all transformers in scikit-learn.\\n\",\n      \"\\n\",\n      \"If :term:`get_feature_names_out` is defined, then `BaseEstimator` will\\n\",\n      \"automatically wrap `transform` and `fit_transform` to follow the `set_output`\\n\",\n      \"API. See the :ref:`developer_api_set_output` for details.\\n\",\n      \"\\n\",\n      \":class:`base.OneToOneFeatureMixin` and\\n\",\n      \":class:`base.ClassNamePrefixFeaturesOutMixin` are helpful mixins for\\n\",\n      \"defining :term:`get_feature_names_out`.\\n\",\n      \"\\u001b[0;31mInit docstring:\\u001b[0m\\n\",\n      \"逐步回归筛选方法\\n\",\n      \"\\n\",\n      \"Args:\\n\",\n      \"    target: 数据集中标签名称，默认 target\\n\",\n      \"    estimator: 预估器，默认 ols，可选 \\\"ols\\\", \\\"lr\\\", \\\"lasso\\\", \\\"ridge\\\"，通常默认即可\\n\",\n      \"    direction: 逐步回归方向，默认both，可选 \\\"forward\\\", \\\"backward\\\", \\\"both\\\"，通常默认即可\\n\",\n      \"    criterion: 评价指标，默认 aic，可选 \\\"aic\\\", \\\"bic\\\", \\\"ks\\\", \\\"auc\\\"，通常默认即可\\n\",\n      \"    max_iter: 最大迭代次数，sklearn中使用的参数，默认为 None\\n\",\n      \"    return_drop: 是否返回特征剔除信息，默认 True\\n\",\n      \"    exclude: 强制保留的某些特征\\n\",\n      \"    intercept: 是否包含截距，默认为 True\\n\",\n      \"    p_value_enter: 特征进入的 p 值，用于前向筛选时决定特征是否进入模型\\n\",\n      \"    p_remove: 特征剔除的 p 值，用于后向剔除时决定特征是否要剔除\\n\",\n      \"    p_enter: 特征 p 值，用于判断双向逐步回归是否剔除或者准入特征\\n\",\n      \"    target_rm: 是否剔除数据集中的标签，默认为 False，即剔除数据集中的标签\\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m           ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/processing.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m           type\\n\",\n      \"\\u001b[0;31mSubclasses:\\u001b[0m     \"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sp.StepwiseSelection?\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"5.6 数据预处理 `pipeline`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
Pipeline(steps=[('preprocessing_select',\\n\",\n       \"                 FeatureSelection(target='creditability')),\\n\",\n       \"                ('combiner',\\n\",\n       \"                 Combiner(min_bin_size=0.2, target='creditability')),\\n\",\n       \"                ('transform',\\n\",\n       \"                 WOETransformer(exclude=[], target='creditability')),\\n\",\n       \"                ('processing_select',\\n\",\n       \"                 FeatureSelection(engine='toad', target='creditability')),\\n\",\n       \"                ('stepwise', StepwiseSelection(target='creditability'))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
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\"\n      ],\n      \"text/plain\": [\n       \"Pipeline(steps=[('preprocessing_select',\\n\",\n       \"                 FeatureSelection(target='creditability')),\\n\",\n       \"                ('combiner',\\n\",\n       \"                 Combiner(min_bin_size=0.2, target='creditability')),\\n\",\n       \"                ('transform',\\n\",\n       \"                 WOETransformer(exclude=[], target='creditability')),\\n\",\n       \"                ('processing_select',\\n\",\n       \"                 FeatureSelection(engine='toad', target='creditability')),\\n\",\n       \"                ('stepwise', StepwiseSelection(target='creditability'))])\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 构建 pipeline\\n\",\n    \"feature_pipeline = sp.Pipeline([\\n\",\n    \"    (\\\"preprocessing_select\\\", sp.FeatureSelection(target=target, engine=\\\"scorecardpy\\\")),\\n\",\n    \"    (\\\"combiner\\\", sp.Combiner(target=target, min_bin_size=0.2)),\\n\",\n    \"    (\\\"transform\\\", sp.WOETransformer(target=target)),\\n\",\n    \"    (\\\"processing_select\\\", sp.FeatureSelection(target=target, engine=\\\"toad\\\")),\\n\",\n    \"    (\\\"stepwise\\\", sp.StepwiseSelection(target=target)),\\n\",\n    \"])\\n\",\n    \"\\n\",\n    \"feature_pipeline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[INFO] filtering variables ...\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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status_of_existing_checking_accountduration_in_monthinstallment_rate_in_percentage_of_disposable_incomehousingcredit_amountpresent_employment_sincepurposesavings_account_and_bondstelephonepropertypersonal_status_and_sexcreditability
1890.3256-0.2413-0.1898-0.2064-0.3601-0.13610.14710.2877-0.19010.1784-0.22530
8850.9585-0.24130.1896-0.2064-0.05870.41570.38370.28770.11830.17840.12021
903-1.0569-0.24130.18960.4543-0.0587-0.1361-0.4490-0.8303-0.1901-0.49140.12020
604-1.0569-0.24130.1896-0.2064-0.05870.41570.14710.28770.11830.17840.12020
729-1.0569-0.2413-0.1898-0.2064-0.0587-0.13610.1471-0.8303-0.1901-0.49140.12020
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"     status_of_existing_checking_account  duration_in_month  \\\\\\n\",\n       \"189                               0.3256            -0.2413   \\n\",\n       \"885                               0.9585            -0.2413   \\n\",\n       \"903                              -1.0569            -0.2413   \\n\",\n       \"604                              -1.0569            -0.2413   \\n\",\n       \"729                              -1.0569            -0.2413   \\n\",\n       \"\\n\",\n       \"     installment_rate_in_percentage_of_disposable_income  housing  \\\\\\n\",\n       \"189                                              -0.1898  -0.2064   \\n\",\n       \"885                                               0.1896  -0.2064   \\n\",\n       \"903                                               0.1896   0.4543   \\n\",\n       \"604                                               0.1896  -0.2064   \\n\",\n       \"729                                              -0.1898  -0.2064   \\n\",\n       \"\\n\",\n       \"     credit_amount  present_employment_since  purpose  \\\\\\n\",\n       \"189        -0.3601                   -0.1361   0.1471   \\n\",\n       \"885        -0.0587                    0.4157   0.3837   \\n\",\n       \"903        -0.0587                   -0.1361  -0.4490   \\n\",\n       \"604        -0.0587                    0.4157   0.1471   \\n\",\n       \"729        -0.0587                   -0.1361   0.1471   \\n\",\n       \"\\n\",\n       \"     savings_account_and_bonds  telephone  property  personal_status_and_sex  \\\\\\n\",\n       \"189                     0.2877    -0.1901    0.1784                  -0.2253   \\n\",\n       \"885                     0.2877     0.1183    0.1784                   0.1202   \\n\",\n       \"903                    -0.8303    -0.1901   -0.4914                   0.1202   \\n\",\n       \"604                     0.2877     0.1183    0.1784                   0.1202   \\n\",\n       \"729                    -0.8303    -0.1901   -0.4914                   0.1202   \\n\",\n       \"\\n\",\n       \"     creditability  \\n\",\n       \"189              0  \\n\",\n       \"885              1  \\n\",\n       \"903              0  \\n\",\n       \"604              0  \\n\",\n       \"729              0  \"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 训练\\n\",\n    \"feature_pipeline.fit(train)\\n\",\n    \"\\n\",\n    \"# 转换\\n\",\n    \"woe_train = feature_pipeline.transform(train)\\n\",\n    \"woe_test = feature_pipeline.transform(test)\\n\",\n    \"\\n\",\n    \"woe_train.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mSignature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcorr_plot\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mdata\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfigure_size\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m16\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;36m8\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfontsize\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m16\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmask\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0msave\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mannot\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmax_len\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m35\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mlinewidths\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.1\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfmt\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'.2f'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mstep\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m11\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mlinecolor\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'white'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m \\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m      ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/utils.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m      function\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 变量相关性\\n\",\n    \"sp.corr_plot?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"_ = sp.corr_plot(woe_train, annot=False, figure_size=(9, 6), max_len=25)\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"6 逻辑回归模型训练\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mInit signature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mITLubberLogisticRegression\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtarget\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'target'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mpenalty\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'l2'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcalculate_stats\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mdual\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtol\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.0001\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mC\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m1.0\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfit_intercept\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mintercept_scaling\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mclass_weight\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mrandom_state\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0msolver\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'lbfgs'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmax_iter\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m100\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmulti_class\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'auto'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mverbose\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mwarm_start\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mn_jobs\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0ml1_ratio\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m     \\n\",\n      \"Extended Logistic Regression.\\n\",\n      \"Extends [sklearn.linear_model.LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html).\\n\",\n      \"This class provides the following extra statistics, calculated on `.fit()` and accessible via `.summary()`:\\n\",\n      \"- `cov_matrix_`: covariance matrix for the estimated parameters.\\n\",\n      \"- `std_err_intercept_`: estimated uncertainty for the intercept\\n\",\n      \"- `std_err_coef_`: estimated uncertainty for the coefficients\\n\",\n      \"- `z_intercept_`: estimated z-statistic for the intercept\\n\",\n      \"- `z_coef_`: estimated z-statistic for the coefficients\\n\",\n      \"- `p_value_intercept_`: estimated p-value for the intercept\\n\",\n      \"- `p_value_coef_`: estimated p-value for the coefficients\\n\",\n      \"\\n\",\n      \"Example:\\n\",\n      \"```python\\n\",\n      \"feature_pipeline = Pipeline([\\n\",\n      \"    (\\\"preprocessing_select\\\", FeatureSelection(target=target, engine=\\\"scorecardpy\\\")),\\n\",\n      \"    (\\\"combiner\\\", Combiner(target=target, min_samples=0.2)),\\n\",\n      \"    (\\\"transform\\\", WOETransformer(target=target)),\\n\",\n      \"    (\\\"processing_select\\\", FeatureSelection(target=target, engine=\\\"scorecardpy\\\")),\\n\",\n      \"    (\\\"stepwise\\\", StepwiseSelection(target=target)),\\n\",\n      \"    # (\\\"logistic\\\", LogisticClassifier(target=target)),\\n\",\n      \"    (\\\"logistic\\\", ITLubberLogisticRegression(target=target)),\\n\",\n      \"])\\n\",\n      \"\\n\",\n      \"feature_pipeline.fit(train)\\n\",\n      \"summary = feature_pipeline.named_steps['logistic'].summary()\\n\",\n      \"```\\n\",\n      \"\\n\",\n      \"An example output of `.summary()`:\\n\",\n      \"\\n\",\n      \"|                   |     Coef. |   Std.Err |        z |       P>|z| |    [ 0.025 |   0.975 ] |     VIF |\\n\",\n      \"|:------------------|----------:|----------:|---------:|------------:|-----------:|----------:|--------:|\\n\",\n      \"| const             | -0.844037 | 0.0965117 | -8.74544 | 2.22148e-18 | -1.0332    | -0.654874 | 1.05318 |\\n\",\n      \"| duration.in.month |  0.847445 | 0.248873  |  3.40513 | 0.000661323 |  0.359654  |  1.33524  | 1.14522 |\\n\",\n      \"\\u001b[0;31mInit docstring:\\u001b[0m\\n\",\n      \"Extends [sklearn.linear_model.LogisticRegression.fit()](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html).\\n\",\n      \"\\n\",\n      \"Args:\\n\",\n      \"    target (str): your dataset's target name\\n\",\n      \"    calculate_stats (bool): If true, calculate statistics like standard error during fit, accessible with .summary()\\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m           ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/model.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m           type\\n\",\n      \"\\u001b[0;31mSubclasses:\\u001b[0m     \"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sp.ITLubberLogisticRegression?\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"6.1 逻辑回归模型训练\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
ITLubberLogisticRegression(target='creditability')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
\"\n      ],\n      \"text/plain\": [\n       \"ITLubberLogisticRegression(target='creditability')\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 逻辑回归模型构建\\n\",\n    \"logistic = sp.ITLubberLogisticRegression(target=target)\\n\",\n    \"logistic\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 训练\\n\",\n    \"logistic.fit(woe_train)\\n\",\n    \"\\n\",\n    \"# 预测\\n\",\n    \"y_pred_train = logistic.predict_proba(woe_train.drop(columns=target))[:, 1]\\n\",\n    \"y_pred_test = logistic.predict_proba(woe_test.drop(columns=target))[:, 1]\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"6.2 模型表现\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 2023-10-13 17:48:51,466 ][ INFO ][ 842722522.py::2 ] dataset: train\\t KS: 0.49455782312925173,\\t AUC: 0.804382896015549\\n\",\n      \"[ 2023-10-13 17:48:51,471 ][ INFO ][ 842722522.py::3 ] dataset: test\\t KS: 0.41904761904761906,\\t AUC: 0.7472751322751322\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 模型 KS、AUC 指标\\n\",\n    \"logger.info(f\\\"dataset: train\\\\t KS: {sp.KS(y_pred_train, train[target])},\\\\t AUC: {sp.AUC(y_pred_train, train[target])}\\\")\\n\",\n    \"logger.info(f\\\"dataset: test\\\\t KS: {sp.KS(y_pred_test, test[target])},\\\\t AUC: {sp.AUC(y_pred_test, test[target])}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"  \\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"  \\n\",\n       \"
Coef.Std.ErrzP>|z|[ 0.0250.975 ]VIF
const-0.83760.0957-8.75520.0000-1.0251-0.65011.0496
status_of_existing_checking_account0.84080.11597.25150.00000.61351.06801.1110
duration_in_month0.69680.26022.67790.00740.18681.20681.2542
installment_rate_in_percentage_of_disposable_income1.30920.51612.53640.01120.29752.32081.0552
housing0.65220.30772.11950.03400.04911.25541.0415
credit_amount0.94420.31303.01670.00260.33071.55771.3056
present_employment_since0.63920.38951.64130.1007-0.12411.40261.0302
purpose0.84330.26873.13820.00170.31661.37001.0240
savings_account_and_bonds0.77640.20753.74200.00020.36971.18301.0772
telephone1.09980.65181.68750.0915-0.17762.37731.0511
property0.49930.33361.49690.1344-0.15451.15311.0825
personal_status_and_sex0.83250.57721.44230.1492-0.29881.96391.0159
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"                                                      Coef.  Std.Err       z  \\\\\\n\",\n       \"const                                               -0.8376   0.0957 -8.7552   \\n\",\n       \"status_of_existing_checking_account                  0.8408   0.1159  7.2515   \\n\",\n       \"duration_in_month                                    0.6968   0.2602  2.6779   \\n\",\n       \"installment_rate_in_percentage_of_disposable_income  1.3092   0.5161  2.5364   \\n\",\n       \"housing                                              0.6522   0.3077  2.1195   \\n\",\n       \"credit_amount                                        0.9442   0.3130  3.0167   \\n\",\n       \"present_employment_since                             0.6392   0.3895  1.6413   \\n\",\n       \"purpose                                              0.8433   0.2687  3.1382   \\n\",\n       \"savings_account_and_bonds                            0.7764   0.2075  3.7420   \\n\",\n       \"telephone                                            1.0998   0.6518  1.6875   \\n\",\n       \"property                                             0.4993   0.3336  1.4969   \\n\",\n       \"personal_status_and_sex                              0.8325   0.5772  1.4423   \\n\",\n       \"\\n\",\n       \"                                                     P>|z|  [ 0.025  0.975 ]  \\\\\\n\",\n       \"const                                               0.0000  -1.0251  -0.6501   \\n\",\n       \"status_of_existing_checking_account                 0.0000   0.6135   1.0680   \\n\",\n       \"duration_in_month                                   0.0074   0.1868   1.2068   \\n\",\n       \"installment_rate_in_percentage_of_disposable_income 0.0112   0.2975   2.3208   \\n\",\n       \"housing                                             0.0340   0.0491   1.2554   \\n\",\n       \"credit_amount                                       0.0026   0.3307   1.5577   \\n\",\n       \"present_employment_since                            0.1007  -0.1241   1.4026   \\n\",\n       \"purpose                                             0.0017   0.3166   1.3700   \\n\",\n       \"savings_account_and_bonds                           0.0002   0.3697   1.1830   \\n\",\n       \"telephone                                           0.0915  -0.1776   2.3773   \\n\",\n       \"property                                            0.1344  -0.1545   1.1531   \\n\",\n       \"personal_status_and_sex                             0.1492  -0.2988   1.9639   \\n\",\n       \"\\n\",\n       \"                                                       VIF  \\n\",\n       \"const                                               1.0496  \\n\",\n       \"status_of_existing_checking_account                 1.1110  \\n\",\n       \"duration_in_month                                   1.2542  \\n\",\n       \"installment_rate_in_percentage_of_disposable_income 1.0552  \\n\",\n       \"housing                                             1.0415  \\n\",\n       \"credit_amount                                       1.3056  \\n\",\n       \"present_employment_since                            1.0302  \\n\",\n       \"purpose                                             1.0240  \\n\",\n       \"savings_account_and_bonds                           1.0772  \\n\",\n       \"telephone                                           1.0511  \\n\",\n       \"property                                            1.0825  \\n\",\n       \"personal_status_and_sex                             1.0159  \"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 模型拟合情况\\n\",\n    \"logistic.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"_ = logistic.plot_weights(figsize=(10, 5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"  \\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"  \\n\",\n       \"
descprecisionrecallf1-scoresupport
0好客户0.80220.90200.8492490.0000
1坏客户0.67790.48100.5627210.0000
2macro avg0.74000.69150.7059700.0000
3weighted avg0.76490.77570.7632700.0000
4accuracy0.7757700.0000
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"           desc precision recall  f1-score  support\\n\",\n       \"0           好客户    0.8022 0.9020    0.8492 490.0000\\n\",\n       \"1           坏客户    0.6779 0.4810    0.5627 210.0000\\n\",\n       \"2     macro avg    0.7400 0.6915    0.7059 700.0000\\n\",\n       \"3  weighted avg    0.7649 0.7757    0.7632 700.0000\\n\",\n       \"4      accuracy                     0.7757 700.0000\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 模型报告\\n\",\n    \"logistic.report(woe_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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descprecisionrecallf1-scoresupport
0好客户0.78220.83810.8092210.0000
1坏客户0.54670.45560.497090.0000
2macro avg0.66440.64680.6531300.0000
3weighted avg0.71160.72330.7155300.0000
4accuracy0.7233300.0000
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"           desc precision recall  f1-score  support\\n\",\n       \"0           好客户    0.7822 0.8381    0.8092 210.0000\\n\",\n       \"1           坏客户    0.5467 0.4556    0.4970  90.0000\\n\",\n       \"2     macro avg    0.6644 0.6468    0.6531 300.0000\\n\",\n       \"3  weighted avg    0.7116 0.7233    0.7155 300.0000\\n\",\n       \"4      accuracy                     0.7233 300.0000\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 模型报告\\n\",\n    \"logistic.report(woe_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mSignature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mks_plot\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mscore\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtarget\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtitle\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m''\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfontsize\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m14\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfigsize\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m16\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;36m8\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0msave\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcolors\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m'#2639E9'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'#F76E6C'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'#FE7715'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0manchor\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.945\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m \\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m      ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/utils.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m      function\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 模型 KS、ROC 曲线\\n\",\n    \"sp.ks_plot?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"_ = sp.ks_plot(y_pred_train, train[target], figsize=(10, 5), title=\\\"Train \\\\tDataset\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"_ = sp.ks_plot(y_pred_test, test[target], figsize=(10, 5), title=\\\"Tese \\\\tDataset\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mSignature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mhist_plot\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mscore\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0my_true\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfigsize\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m15\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;36m10\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mbins\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m30\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0msave\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mlabels\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m'坏样本'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'好样本'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0manchor\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m1.1\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfontsize\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m14\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m \\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m      ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/utils.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m      function\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 概率分布图\\n\",\n    \"sp.hist_plot?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"_ = sp.hist_plot(y_pred_train, train[target], figsize=(10, 6))\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"6.3 入模特征分箱图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mSignature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfeature_bin_stats\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mdata\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfeature\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtarget\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'target'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mrules\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmethod\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'step'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mdesc\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m''\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcombiner\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mks\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmax_n_bins\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmin_bin_size\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmax_bin_size\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mempty_separate\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mreturn_cols\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mreturn_rules\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mverbose\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m \\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m      ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/processing.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m      method\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 特征分箱统计信息\\n\",\n    \"sp.feature_bin_stats?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mSignature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mbin_plot\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfeature_table\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mdesc\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m''\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfigsize\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m10\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;36m6\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcolors\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m'#2639E9'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'#F76E6C'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'#FE7715'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0msave\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0manchor\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.935\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmax_len\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m35\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mhatch\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mending\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'分箱图'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m\\n\",\n      \"简单策略挖掘：特征分箱图\\n\",\n      \"\\n\",\n      \":param feature_table: 特征分箱的统计信息表，由 feature_bin_stats 运行得到\\n\",\n      \":param desc: 特征中文含义或者其他相关信息\\n\",\n      \":param figsize: 图像尺寸大小，传入一个tuple，默认 （10， 6）\\n\",\n      \":param colors: 图片主题颜色，默认即可\\n\",\n      \":param save: 图片保存路径\\n\",\n      \":param anchor: 图例在图中的位置，通常 0.95 左右，根据图片标题与图例之间的空隙自行调整即可\\n\",\n      \":param max_len: 分箱显示的最大长度，防止分类变量分箱过多文本过长导致图像显示区域很小，默认最长 35 个字符\\n\",\n      \":param hatch: 柱状图是否显示斜杠，默认显示\\n\",\n      \"\\n\",\n      \":return Figure\\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m      ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/utils.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m      function\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 特征分箱图\\n\",\n    \"sp.bin_plot?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 数据字段[可选]\\n\",\n    \"feature_describe = pd.DataFrame([\\n\",\n    \"    [\\\"status_account\\\", \\\"支票账户状态\\\"], [\\\"duration\\\", \\\"借款周期\\\"], [\\\"credit_histor\\\", \\\"历史信用\\\"], [\\\"purpose\\\", \\\"借款目的\\\"], [\\\"amount\\\", \\\"信用额度\\\"], [\\\"svaing_account\\\", \\\"储蓄账户状态\\\"], [\\\"present_emp\\\", \\\"当前就业状态\\\"], [\\\"income_rate\\\", \\\"分期付款占可支配收入百分比\\\"], [\\\"personal_status\\\", \\\"性别与婚姻状态\\\"], [\\\"other_debtors\\\", \\\"他人担保信息\\\"], [\\\"residence_info\\\", \\\"现居住地\\\"], [\\\"property\\\", \\\"财产状态\\\"], [\\\"age\\\", \\\"年龄\\\"], [\\\"inst_plans\\\", \\\"其他分期情况\\\"], [\\\"housing\\\", \\\"房产状态\\\"], [\\\"num_credits\\\", \\\"信用卡数量\\\"], [\\\"job\\\", \\\"工作状态\\\"], [\\\"dependents\\\", \\\"赡养人数\\\"], [\\\"telephone\\\", \\\"电话号码注册情况\\\"], [\\\"foreign_worke\\\", \\\"是否有海外工作经历\\\"],\\n\",\n    \"], columns=[\\\"变量名称\\\", \\\"变量含义\\\"])\\n\",\n    \"feature_map = dict(zip(feature_describe[\\\"变量名称\\\"], feature_describe[\\\"变量含义\\\"]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"status_of_existing_checking_account\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率分档WOE值分档IV值指标IV值LIFT值累积LIFT值累积好样本数累积坏样本数分档KS值
0status_of_existing_checking_account逻辑回归入模变量... >= 200 DM / salary assignments for at least 1 year,no checking account4570.45703970.5671600.20000.13131.04230.38270.63480.43760.437639760-0.3671
1status_of_existing_checking_account逻辑回归入模变量0 <= ... < 200 DM2690.26901640.23431050.35000.3903-0.40140.04640.63481.30110.7576561165-0.2514
2status_of_existing_checking_account逻辑回归入模变量... < 0 DM2740.27401390.19861350.45000.4927-0.81810.20570.63481.64231.00007003000.0000
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\"\n      ],\n      \"text/plain\": [\n       \"                                  指标名称      指标含义  \\\\\\n\",\n       \"0  status_of_existing_checking_account  逻辑回归入模变量   \\n\",\n       \"1  status_of_existing_checking_account  逻辑回归入模变量   \\n\",\n       \"2  status_of_existing_checking_account  逻辑回归入模变量   \\n\",\n       \"\\n\",\n       \"                                                                           分箱  \\\\\\n\",\n       \"0  ... >= 200 DM / salary assignments for at least 1 year,no checking account   \\n\",\n       \"1                                                           0 <= ... < 200 DM   \\n\",\n       \"2                                                                  ... < 0 DM   \\n\",\n       \"\\n\",\n       \"   样本总数   样本占比  好样本数  好样本占比  坏样本数  坏样本占比   坏样本率  分档WOE值  分档IV值  指标IV值  LIFT值  \\\\\\n\",\n       \"0   457 0.4570   397 0.5671    60 0.2000 0.1313  1.0423 0.3827 0.6348 0.4376   \\n\",\n       \"1   269 0.2690   164 0.2343   105 0.3500 0.3903 -0.4014 0.0464 0.6348 1.3011   \\n\",\n       \"2   274 0.2740   139 0.1986   135 0.4500 0.4927 -0.8181 0.2057 0.6348 1.6423   \\n\",\n       \"\\n\",\n       \"   累积LIFT值  累积好样本数  累积坏样本数   分档KS值  \\n\",\n       \"0   0.4376     397      60 -0.3671  \\n\",\n       \"1   0.7576     561     165 -0.2514  \\n\",\n       \"2   1.0000     700     300  0.0000  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n 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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率分档WOE值分档IV值指标IV值LIFT值累积LIFT值累积好样本数累积坏样本数分档KS值
0duration_in_month逻辑回归入模变量[负无穷 , 27)7710.77105730.81861980.66000.25680.21530.03410.13370.85600.8560573198-0.1586
1duration_in_month逻辑回归入模变量[27 , 正无穷)2290.22901270.18141020.34000.4454-0.62810.09960.13371.48471.00007003000.0000
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\"\n      ],\n      \"text/plain\": [\n       \"                指标名称      指标含义          分箱  样本总数   样本占比  好样本数  好样本占比  坏样本数  \\\\\\n\",\n       \"0  duration_in_month  逻辑回归入模变量  [负无穷 , 27)   771 0.7710   573 0.8186   198   \\n\",\n       \"1  duration_in_month  逻辑回归入模变量  [27 , 正无穷)   229 0.2290   127 0.1814   102   \\n\",\n       \"\\n\",\n       \"   坏样本占比   坏样本率  分档WOE值  分档IV值  指标IV值  LIFT值  累积LIFT值  累积好样本数  累积坏样本数   分档KS值  \\n\",\n       \"0 0.6600 0.2568  0.2153 0.0341 0.1337 0.8560   0.8560     573     198 -0.1586  \\n\",\n       \"1 0.3400 0.4454 -0.6281 0.0996 0.1337 1.4847   1.0000     700     300  0.0000  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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zJN998o08c4uo17k6aEEJkBEkehBAZas6cOezcuZNnz56h1Wq5desW2bNnZ/Lkybi4uHD79m12795NwYIFOXXqFOHh4foxDf/880+CwcaXL19m1KhRaDQafvrpJyIiIvDx8WH9+vUULlxY33ieMmUKpUqV0jfwS5YsmWz5ihYtCqgzGX333XesW7eOggULsnnzZnQ6HZUqVcLR0ZGff/4ZgMDAQMAw6PhVcV1hnJyc8PX1BdSBzoqicOPGDby8vLh+/TrHjh0jNDRUP8VntWrVUlWfO3bs4OTJk7i6utKlSxcAZs2axZQpU5gxY0aK73Py5MkoiqIfZBw3jqFOnTp4eHig0+n0Xa8KFChA3rx5cXZ2pnPnzsnu9+rVq9y6dYts2bLRpk0bvLy8MDc3p2TJkri7u9OkSRPc3NwIDw/H2tqaKVOmJHkXCCB//vz6cQyHDx+mV69eODo6Uq1aNa5evcrDhw+ZNm0aPXv2ZPDgwaxZs4bTp0/rp21Nyrp165gyZQrVqlVj5syZaLVazp49i6enJ5cuXeLAgQO8ePECGxsbBg4cSOXKlTl79iwA586dY9KkSTx79oy2bdsC6t2qkJAQjh8/rk8k+/Xrh6OjIx4eHpw+fZqRI0fy/fff8/3339OhQwcuXLjA+vXrk0wedDodoN79aN++PTExMcybN4+goCCcnZ1ZtmwZ/v7+DBo0CFdXVywtLZEZ14UQGUmSByFEhurZsycVK1akePHiNG3alP79++tnlQkPD2fo0KG4urqydetWbG1t6dKlC/fv32fo0KGJZiny9PTE09MTR0dHPvroI+7cuUPHjh35/vvvAdi2bRugfiMfHBysb8jHffOflLgpV58+fYqtrS0VKlTg4sWL/PXXXxw/fpywsDC6dOlCvnz5ePnyJSEhIQC4ubklub/Q0FAAbGxsePr0KaDOthQREcGff/7JvXv3cHZ2pnnz5pQsWZL//e9/QNJ3Ml4VEBDA5MmTAfVZDVZWVnz11VesWrWKHTt2UK9ePT777LMU97Fp0yb9IO1t27bx77//0qtXLz777DM2bNhA48aNKV++PC4uLowfP56BAwemOB1siRIl6Nixo35moFy5cnHu3DnCwsI4duwYpqamLFiwgPXr1/Pjjz/SpEmTZO8CxVe7dm0qVqxIQEAArVq1wtfXl4cPH/Lzzz8zb948NBoNpqamjBgxIsnto6KiGDNmDF5eXkydOpV8+fLx6aefEhwcrF+nfv36DBw4kF27dnH27FmmTJnCnj17CAsLw8LCgnz58iUYPzFy5EguXbrEvXv39I1+gMmTJ1OzZk28vb05ffo0AQEBLF68mMjISP2sSA0bNkyynHFdkMzNzfH392f06NFcvnyZBQsWULFiRbp06cKff/7J9u3bqVmzJi9evJBuS0KIDCXJgxAiQ9nb21OnTh127dpF/fr19YlDVFQU3333HW5ubly7do2WLVsyevRoTp06RZ06dejUqVOifbVp04bnz5/Tpk0bcubMydq1a4mKiuLy5csJ1ps2bRrFihWjVq1aACnOnhOXBMQ19EuWLMmJEydYuHAhOXPmpF69elhaWgKG6TlB/Qb6VREREfoHomXLlo179+4BaoJibW3N6NGjE20zffp0zM3NKVKkSLJlBHXcwPfff09gYCBt27bVJwk2NjZ88803TJkyhdGjR+Pu7p7stK8PHz5k5syZlClThitXrvDFF19gZ2eHo6Mjbdq04c6dOyxdupTnz58zc+ZMxowZg42NDUuWLCE0NJRu3bolSugcHBz007keP36chQsX6qdpLVy4sD45e/78OXPnzqVhw4apng71008/ZerUqbi5uZErVy6uXbvG5s2bsbS0xNvbm3bt2vHxxx8nu/0PP/yQoKvYoUOHePToES1btkSr1fLRRx/x5Zdf6u80tGzZEnd392T3V6lSJf7++2/q1KlD3rx59d2XmjVrhqWlpf6u1PLlyzE1NaVOnTr6OyzJDcSPewr1f//9x8yZM/nkk0/o378/ffv2xdXVlalTpzJnzhzu37+Po6Mjpqam8uRqIUSGkuRBCJEprKysOHjwIGfPnqVy5cqcP3+eXr16Ub16da5evcqAAQOYPHky9vb2TJgwIcl9aDQaWrVqxdWrVzlz5gwRERFs3LgRX19f+vTpo+/O8dtvv+Hm5qafHjRuVqHo6GgGDhyo74IC6B8+FxAQAED37t2pUqVKkoNlPT09AbWLSVJ3M6KiogAoVKgQ2bJl49y5c0DKdxV8fHwoWLDgaxvUEyZM4MiRIzRr1ixR/XTs2JEVK1bg7+9P9+7dWblyJSVKlEiwTmRkJAMHDsTNzY0BAwbQq1cvNBoNJUuWJDg4GD8/Pz755BMuXrxIeHg406ZNw8fHJ0EXmWvXrunvlCRVN6dPn8bOzo6hQ4dSo0YNnJ2d9Xc3ypUrx//+979UJw6gDkCfNWsWa9eu1XcBi5sty8nJKcE0ua+ysLBIdMfE1taWtWvXotVqady4sb7bV1xcX/fMiPbt29O+fXuCgoJo2rSpfnmLFi0oVaoU/fr1A+Dbb7/FxcWF0NBQgoKCAPTjX14V9zdTtGhRfv75ZywtLfH19WXZsmVMmDCB58+fJ6jz9u3b6ycGEEKIjCDJgxAiU9SvXx9nZ2dWrlxJ5cqVEzxsrFSpUlSuXJldu3Yxb968JLsEDR8+XD//vUajoUKFCuh0Orp06ULVqlXx9PTk4MGDgNp48/X11XctURQFd3d3oqKiOHnyZILkIe5b3BcvXnDq1CkiIiIICgpizZo1PH36lEePHuHp6Unu3Ln1+0tuDIWDgwPTp0/HwsKCwMBArl+/jru7O3nz5kWr1fL8+XP9g+d8fHy4ffs2oaGheHp6Mm3atCS74ERHRzN27Fh27NjBd999x3fffZdoWlZLS0t69+7NxIkTCQgIoEOHDgwaNIjOnTtjZmaGVqtlyJAhODs7M2PGDE6fPq3f9uDBg0ydOpVcuXKRL18+fSO9Xr16fPHFF9jb22Nubk5kZGSK09iam5vz+PFjjh8/zpMnTyhcuDC2trbMnz8fOzs75syZk2L3saS4uLiwYMECsmfPTlRUFJ6ennTq1Al/f3+KFSv22mcovOrvv//m999/J0+ePPopcUNDQ/Hy8sLMzEw/O1ZKoqKiGDZsGDqdjsaNG7N3714mTpzInDlz9InC77//jrOzM8WKFdOPaYnfXSq+sLAwQL2r4eDgQHBwMMOHD+f27dtUrFiRQoUKJVhfq9Wi1WpRFOWNpucVQoi0kuRBCJHhIiMjuXDhAlZWVom6vcT1TT948CDz5s1Ltm/4mDFjePLkif7/jo6O1K5dGzMzMy5dusQ///xD6dKlGTduHB4eHri7u3P48GFGjBhBp06dkh30GzfD0cuXL/nhhx+wt7fHzc0NR0dH7OzsyJs3L0WLFuXQoUOcP38eQN8dKimNGzcGYOXKlWi1Wr744gtiYmJo3749169fB9RuVLly5SJnzpzUqVOH7NmzJ9ltycvLi+HDh+Pk5MT27dtT7NrUvn17/ve//+Hj40NERARTp05l3bp19O7dm/z589OuXTv91Khxd1m0Wi1t27bVJ1Pr1q3jwIEDtGnThg0bNlCzZk1q166d7DHjK1iwINOmTWPQoEGMGjWKL774gqJFi/Lw4UNmzJiR7ADzlPj5+TF//nw8PT3RaDSYmJhw6dIl8uTJk+YnLB85coRhw4Zha2vL3Llz9Xecjhw5glarpWTJksk+HyJOVFQU/fr103fv2rx5M6amplSrVo2NGzfquz+NHz+eJk2aAOpdLzAMtH9VXCziEit7e3s2btzI9OnTWbduHWvXruXHH3/Urx93ByMqKkrfnU4IId4lSR6EEBlGURR69uzJqVOn9IM8r169ysmTJ2nQoAEVK1Zk1apVFC9enF27diWYQvNV2bJl0/cxBzhx4gSgzlYzfPhwhgwZkuy2yT0dGdQpTFu2bImVlRUTJ05Mdr3y5cvTtWtXHBwcXjsoOTIykt9++408efLQvn17zMzM+P333zl+/Dj58uWjYMGCKZYJ1MHke/fuZcqUKRQrVizFdUHtpjNz5kyGDRuGj48PoDZMV6xYwVdffZVgDMmtW7cAQ5cZUL+BX7ZsGc7OzkyYMIHAwED69u3LlClTkn1AXFLc3NxYtmwZ7du35+bNm4Da5erBgwd8+eWXr33uQXwuLi5s3boVUB+Gt3nzZn755ReioqJ49OgRR48eJTQ0lHLlyqWYTGzZsoUJEyZgb2/P4sWLyZkzJ7///jutW7fW7z/u4XbJiYqKYvDgwRQqVIjZs2djb2/PggULEsQxbvrZJUuWMGnSJHQ6XaKE7VVxCamNjQ0xMTEEBATg7+9PxYoVCQ0Nxc/PjwEDBuDg4MDQoUP1A/bDw8MleRBCZAhJHoQQGUaj0TB69Gh++eUXGjduTOXKlfH09GTHjh1s2rSJlStXAnD//n08PT3Jnj07NjY2WFpaEh0dTXR0NOHh4dSsWZPPP/88wb4PHz782uPH9a9/3XMApk+fjlarTXGdihUr4ujoyKhRo5J8XkN8S5Ys4enTp6xcuVL/jbKFhQV169Z9bZnjWFtb6/vQp1b58uX5999/iYqKwsTEJNmB4nv37gUM08oqisKoUaPw9/dn2bJlWFhY8MMPP9CsWTOGDRvGsWPH+P7771PV8L98+TKTJk3i5s2buLm58fTpU0JCQliwYAFLly6lTp061KpVi8qVK1O4cOFEXW8iIyM5deoUWq2Wly9f8vz5c/z9/Tl27BgRERHUrVsXPz+/BLMdOTg4sHXr1kTPooiMjGTixIls376d5s2bM3z4cLJnz45Op2P16tVMnDhR320t7o5RchRFYebMmfq7E1qtlvPnzyeo47hB93369MHd3Z2YmBi++uor/fqv0ul0HD9+HIDPP/9cP8uTk5MTDg4O2NnZYWtri62tLZ6ennTs2FH/xOnX/b0KIcTbIsmDECJDubu7M2XKFP3v5cuXp3z58gwfPpy//vqLLVu2cOHCBX3DK77s2bPTr1+/BGMU4sQ1fHPlypXsseMaeimtE+d1dwIsLCw4fPjwa7/t3bt3Lxs2bGDhwoVUqVLltcd9F1KaWhXUb7lBfa5CZGQko0aN4vDhw8ydO5c6deoAUKBAAXr27MnixYv5888/2bt3L+3ataNHjx6JxqS8fPmSQ4cOsXnzZk6ePImZmRlfffUVw4YN4/79+/z4449cvnyZ6Oho/v33X/79918A/dS406ZN03dnMzMz4++//2bLli3kyZOHwoULkzt3bho3boyDgwNmZmaYmJgQHR1NcHAwDx8+5NmzZ9y5cydR8nDnzh2qV6/O0KFDEyQ+JiYm9O7dm+HDhwPqbEkVKlRIsc5ejfvNmzcJCQlJcMejXLlylChRgk8//VT/91mlShUOHTpE2bJlE+0zbtyCjY2NPmFKqetU27Zt0Wq12NnZpekOjhBCpIdGkafLCCGMzL1799i5cyd79+7Fx8eH4sWL06xZM9q2bZvsINsHDx7QvXt31q5dqx+U+qrLly/ToUMHTp48qe/j/i5duHCB33//nQEDBiRbJmOwa9cuVq5cyW+//caYMWPInTs33bp1I0eOHAnWi4qKYsCAARw7dozs2bNTtGhRGjZsSIcOHQD1qcjDhg3j4sWLODg4ULRoUSpXrkzLli0TdEHT6XRs2LCB//3vf/j5+VGwYEHKly9P9erVqV27dpIDsWNiYlKcYje9AgMDadKkCc2bN+f7779P0yxQoHZDqlevHnXr1mXevHnJrrdt2za2bNnCqlWrkkxQ161bx/nz55k1a9Zrj7lmzRomTZpEv379GDBgQJrKK4QQb0qSByHEByMiIoJRo0Yxe/bszC6KeA+dOnWK/PnzJ/vAwLctKCiI33//nR49erz2TpkQQrwtkjwIIYQQQgghUsUkswsghBBCCCGEyBokeRBCCCGEEEKkiiQPQgghhBBCiFSR5EEIIYQQQgiRKpI8CCGEEEIIIVJFkgchhBBCCCFEqkjyIIQQQgghhEgVSR6EEEIIIYQQqSLJgxBCCCGEECJVJHkQQgghhBBCpIokD0IIIYQQQohUkeRBCCGEEEIIkSqSPAghhBBCCCFSRZIHIYQQQgghRKpI8iCEEEIIIYRIFUkehBBCCCGEEKkiyYMQQgghhBAiVSR5EEIIIYQQQqSKJA9CCCGEEEKIVJHkQQghhBBCCJEqkjwIIYQQQgghUkWSByGEEEIIIUSqSPIghBBCCCGESBVJHoQQQgghhBCpIsmDEEIIIYQQIlUkeRBCCCGEUXv27BmKomR2MYQQSPIgRIbTajPvApiZx/4QKDrdB3lsYdwGDBhAx44dOXXqVJq37datG0OGDCEoKCjRa7dv36Zu3br8+++/Ke5j69at1K1bl2nTpr1xAjBy5Eg+//zzN9pWCPF2aRRJ5YXIcMPGB+F1T/tW9tWuuRWd2tqwbksYm3dGJLte4YKmzByf7Y2Pc/fuXTp37kz37t3p3r07Jiap/+6hW7duZM+enXHjxpEtW8Iy3L59m2+++YYxY8bQsGHDZPexdetW5s+fT5MmTfj+++/RaDRv/F7epag1q1H8nrzRtibVP8K8Vm2ijxxGd/JEqrfTuLhi0aXrGx0zzoABA3j69CmDBg2iWrVqadr2Q4pvVnTu3Dm+/PJLatasyYoVK5JcJyoqipiYGGxsbBIsX7duHRMnTmT37t24uLjQsWNH2rRpQ/fu3dm0aRNjx46lT58+DBgwINnj63Q66tWrx8uXLzl16hQWFhapLvvz58959OgRR44cYenSpbi4uBATE8Pq1avJnz9/qvcjhHh7zDK7AEJ8iLzuabl+Oybd++nTzYZObW2YuzyEJavC3kLJkleoUCFMTU2ZNWsWTZo0IXfu3AD4+/tz+fJlNm/ezO3btxk/fjy1a9dOsG2jRo2YOHEi/fr1w9TUNEED5NKlS/j5+XH9+vUUG5etWrVi3rx5bNy4kcGDB6epAZKRFL8nKL6+b7StdvMmePkS86bNiH75Eu0/f7/l0iWva9eufPnllyxfvjzZ5CG5BuaHFN+sKK6RXaNGDRRFITw8nIcPH+Lt7Y2npyeXL1/m9OnTaDQa1q5dS+nSpfXburi4YGNjQ+HChQGoVasWU6dOpUGDBqxfv561a9dSuXLlFI9vYmJCkSJFiIqKSlNcfXx8WLBgAaGhofz33398//331KhRg0KFCrF06VKqVatG1apV36BGhBDpIcmDEFlUn242DPrGLkMShzjW1tbUqFGD3Llzc+nSJfr164eZmRlNmzYlZ86cHDhwgO+//55du3aRI0cO/XaZ2QDJauISBvOmzRL8/q5l1QameD1zc3MA3NzcWLhwIStXrsTDw4PChQuzZcsWKlWqxODBg4mJicHMLGGzwNTUFFtbW/3vw4YNo0iRImzYsIHcuXMzY8YM+vXrl+gLg/hiYmIwNTXl6dOn9OjRg/Pnz5MjRw6WLFlC0aJFk9xm0qRJlChRgunTpzN//nxcXV05efIkN2/epFu3bqxcuZI7d+5QpUoVuUslRAaT5EGILCgzEgeAgIAAvvzyS1avXk2HDh3YvXs3JiYmPH36lC5duvD999/z1VdfYWlpmWC7zGqAZFWZkUBkxQZmRlKiQtO+kaklGlO1rhRtDGgjQWOCxtw6TfvVWNi+dp3kBAQEcPHiRQBmz55NuXLluHDhAv7+/pw9e5YtW7aQLVs2unZN2O1NURQePXrE+fPnefHiBZ9++in9+vXj4MGDnD9/nsePH/PFF1/Qrl07qlevnuSxN2/ezPr167l//z4hISHkypWL0qVL06JFC0qVKkWRIkWSLXeTJk1YsmQJAQEBrFq1ilq1auHp6cn8+fPZuHEjS5YsSfa4Qoh3S5IHIbKYzEocgoKCCAoKwsPDg2+++Ybjx4+zbNkyNm3axI8//siiRYto0KABWq1W/+1xZjdAsrKMTCCyagMzQ40tlPZtvvwVyrZQ/39tN6zvCYU+hm+3GdaZVhlCn6e8n6n+aT92rEOHDnH58mUAunfvTvv27QHYu3cvkyZNomTJksybNy/Rdhs3buTAgQPcv3+fXLlyMWfOHEqUKEGLFi24ceMGrVq1IiIign/++YcqVaokebeoXbt2NG7cmBUrVrB48WKePn1K/vz5admy5WvLXa5cOT766CMsLS0ZNWoU69evZ9CgQezYsYP69evTp08fmjdvzsSJE9+4boQQb0aSByGykIxOHHx9fVm+fDkhISF4eXnh6OjI1q1biYyMpHnz5oSFhVGmTBmKFSvG3Llz2bBhA+fOncPMzIxff/2V69evZ2oDJKvLqAQiqzYwxeu1bt2a2rVrs379eooUKaK/w3Tv3j1AjbcuiZm6OnbsSP369WnatCkWFhasXLmSvn37cu7cOfbv3w+Avb091atXT3DX6VWRkZGsXbsWMzMzatWqxbRp0zAzM0sxvidOnGDIkCE0a9aMAgUKMHv2bDw8PDh58iSnTp0iR44cLFq0iG3btr15xQgh3pgkD0JkEZlxxyFv3rzkypULjUZDrVq10Gg0FC5cmP3797N27VrmzJlD1apVyZEjB8eOHWP48OH06dMHrVZLoUKFKFeuXKY1QN4XGZFAZMUGZoabeDft25jG675X6jN1H5pXZikbcTZ95UqFuEkVN23axLZt2yhRogTr1q0DICIigpYtW1KqVCm+/vrrBGNZxo8fT86cObGzs6NmzZoEBQXRoEED8uTJw4EDB7CwsODQoUNs3LiRXLlyMXDgQFxdXRMce+zYsTg7O+Pq6kru3Ln59NNPGTFiBM+fP+frr79OsrwVK1akbt26DBo0iClTplCnTh3u3r3LmTNnWL58OTY2NmTLlg0zMzMOHz6cYnc4IcTbJ8mDEFlAZnVVAvjuu+8A2LZtG0ePHuXgwYM0adKEn3/+Wb/OunXrOHbsGB4eHokaD5nVAHmfZEQCkdUamBktPeMOAHXsg2niS25695uc6OhoDhw4wO3btzl48CB2dnaULVuW7NmzM2rUKIYPH86MGTPIkSMHixcvpmPHjuzdu5eVK1dSvXp1FixYQExMDD169GDTpk3cunWLOXPmUKRIEZycnAA1MSxTpow+2Xx1LMzSpUs5cuQI69atY+bMmeh0Olq3bs1///3HtGnTOH36NBMmTMDFxSXBdiYmJgwfPhwfHx8aNmzI48ePuX37Nj4+PowbN45Hjx7x6NEj/d9s3759GThw4DupRyFEYpI8CGHkMjNxiK9Vq1bs2bOH27dv07Rp0wSvxXVHCQtLWL7MbIC8b95FApGVG5giZRqNhm3btrF//37q1q3LokWL2LRpEytXrmTOnDmUK1eOGTNmAFC4cGHmzZtHjx49OH78OF5eXmg0GpYuXcqaNWuIiYlhxIgRjBgxAlCf+bJz504aN26Mu7t7ksdfv349K1asYNGiReTKlYuAgAACAwMZN24cTZs25dKlSxw4cIDjx4/zxRdf0LNnT1xcXFi7di2zZ88mb968lCpVikKFCuHi4oKzszNubm789NNP2NvbY2VlhaIoBAUFpXhXSwjx9knyIEQmKFzQNFXrxX8A3KHjUZT0ePOPbGqPmZz9+/dz6tQpSpcuzZw5c6hcubJ++s2nT58CJJgycd26dZnWAMlMGhfX16/0hnTXrxHt4KAmEA4O+gfJvekxs2oDU7yemZkZixYt4vz581SsWJEVK1ZgZ2fHrl27sLGx4dy5c4A6UxbARx99xMyZM3F0dKR69er6h0DeuXOHyMjIBPuO+8Y/uSl1lyxZwp07d/jrr7/466+/WLp0Kffu3cPNzQ1zc3McHBxYvnw5q1atIioqClNTUy5cuEDjxo3p3LkznTt3TrTPS5cucf/+fQoVSjhw3d7ePn0VJYRIM0kehMhgWq2S5ic9d2qrPgzubRzb1DTtc6Jv2LCBpUuXsmTJEvLly8dnn32Gn58fv/32GyYmJuzbtw8gwdOFO3bsmGkNkMyi6HTpftJzapnXqg21DH29FZ0OTRqe+g1Zt4EpUkej0VCpUiUAevbsmeC1I0eOAODg4KBf9tlnnyXax/Hjx3F2dk6wLO7uUfxt40RGRtKyZUv9QyR79OhBp06dqFq1KpUqVWLMmDH6dadOnZrq9/LqdMBCiMwjyYMQGexNGu+Zeezdu3fz9OlTtm/fjqOjIwALFy6kQoUKREZG8uWXX3L//n0KFixI9uzZ9duZxGvIGksD5F1La+PdGI79PjUwReo1bNiQ3bt3U6pUqdeu92oM7ezscHJySjK2lpaW+rjGuXfvHpGRkYm6rKWFra1torEyQojMIcmDECJFSTUW42Y3sbe354cffmD8+PFMmTIl2X0YSwNEpE1Wa2CK1CtdujT//PPPa9cbPXp0omXZs2enT58+qT5WsWLF6NatGx9//HGayhifu7s7uXLleuPthRBvj0aJu7cshBAZbPXq1YkePJYcRVGYOnUqH3/8MXXq1HnHJRNvg8RXvC3R0dGA4UnoQojMI8mDEEIIIYQQIlUyr4OuEEIIIYQQIkuR5EEIIYQQQgiRKpI8CCGEEEIIIVJFkgchhBBCCCFEqkjyIIQQQgghhEgVSR6EEEIIIYQQqSLJgxBCCCGEECJVJHkQQgghhBBCpIokD0IIIYQQQohUkeRBCCGEEEIIkSqSPAghhBBCCCFSRZIHIYQQQgghRKpI8iCEEEIIIYRIFUkehBBCCCGEEKkiyYMQQgghhBAiVSR5EEIIIYQQQqSKJA9CCCGEEEKIVJHkQQghhBBCCJEqkjwIIYQQQgghUkWSByGEEEIIIUSqSPIghBBCCCGESBVJHoQQQgghhBCpIsmDEEIIIYQQIlUkeRBCCCGEEEKkiiQPQgghhBBCiFSR5EEIIYQQQgiRKpI8CCHEByYqKooLFy4QHR2d2UURQgiRxWgURVEyuxBCCCEyTkREBA0aNKBQoUL89ttvmV0cIYQQWYjceRBCiGRs27aNOnXqMG3aNEJDQ99oH/7+/jRs2JCxY8cSFRWV5Drt27dn8ODBBAYGprive/fuUa1aNXr37s3169ffqDwAVlZW9O7dG09PT9q3b0+lSpXYv3//G+/Px8eH6tWrM2TIEB48ePDG++nUqRN9+vTBx8cnydenTJnCF198wc2bN1Pcj6IoNG7cmA4dOrBnz540l2PlypU0atSIX3/9laCgIGJiYgD49NNP+fXXX/W/p0VISAgVKlRgx44dr13333//5bPPPmPVqlVJvh4UFES1atWYO3cuOp0uxX1t3bqVWrVqMXr06Nf+faXG33//Te3atVm+fHmq6mHv3r3UqVOHqVOnEhYWRnh4OKDGevbs2frfhRBZhyQPQgiRjJYtW2JhYcHKlSu5fPlysutFR0fz+PHjJF/LlSsX5cqVY+vWrYSEhBASEkKdOnUYPnw4ISEh+Pj4cPv2bR4/fsyLFy9SLE/BggWpX78+Bw4c4N9//03z+1m1ahW9evWiYcOG/Pzzz0RFRWFlZUX37t2pUKFCmvcXJ1++fNSvX59du3axdevWZNdTFIXHjx8n2+hs2bIl//33H7dv3wagR48edO7cGS8vL6Kjozl+/Dh+fn74+fmlWB6NRkPv3r25cOEC69atS/X7iIiIIDQ0lK+++oqnT59y+/Ztli9fTrt27Xj8+DEuLi7MmDGDf/75J9X7jLNjxw4sLS1p3Ljxa9dt0KABISEhbN++HYCTJ09St25dFi5cCMDRo0cJCgri4cOHREZGpriv1q1bY2lpyebNm7l161aayjx48GAaN27MypUr9cv+/PNPAgMDefz4MWFhYcluGx0dzcuXL2ncuDGWlpZcuXKFbdu20bRpU27dukX+/PlZtmwZa9euTVOZhBCZzyyzCyCEEMZKo9Hg7u7Os2fPqFChAr6+voSGhvLo0SMePHjA3bt3uXnzJlevXiU6OprevXszePDgRPtxc3Mjf/78ZM+eHYAhQ4YwcuRIWrRowerVqxk9ejTt27dHo9G8tkxly5Zl69atVKtWLdXvIzo6GlNTU1q0aIGDgwNHjhwhIiKCSZMmUbFiRezt7Rk6dCgvXrxg4sSJ5MmTJ/WVFKtw4cIA1KtXT9+o9fPz48GDB9y7d4/bt29z6dIlgoODqVKlCmvWrMHEJOH3V66urgCUL18egBEjRtC5c2d27dpFREQENWrUYMCAAdjY2Ly2PGXKlAFIUz1dvnyZr7/+moEDB+Ls7EyhQoX466+/cHV1xdTUlIcPH1KmTBmaNGmS6n3G+f3332nTpg0WFhavXVej0eDq6krJkiUBqF69OrVr12bJkiU0a9aMtWvXsnbtWqpUqZKqfZUpU4YnT55QqVKlNJV5/PjxdO/enb/++ovu3bszdepUXrx4wZ9//kmRIkVS3PbRo0c0a9aMzz//nNy5c5M/f372799Pjhw5MDc35+HDh7i6utKlS5c0lUkIkfkkeRBCiBSYmpri5uZGREQEP/zwAzdv3qRChQrkzp2bDRs2UL58ef23+HGN36T2Eb/B26xZMxwdHbl8+TKhoaFMmTKFzZs388cff7y2PNmyZQPg8ePHzJkzh3v37lGiRAm6d++OpaVlktscPnyYMWPGsHDhQnLnzo2Xlxc//fQTo0ePJn/+/HzyySdERkbSrl07nJ2d36CW1PcIkCdPHhYuXMi2bdsoXrw4ZcqUYceOHZiamjJixAgAzM3N0el0iZIHMzP1khRXVx4eHqxatYorV67w559/8uDBA3bu3Mnvv/9O3rx5UyyPo6MjAJGRkaxYsYKrV6/i6upK586dyZ07d5LbVK1alb59+3L37l0ADhw4gLOzMz169ODp06f4+voycuTIVCV58V24cIFbt26xYMGCVG/z6t/MqFGjqFatGitWrCAmJoYePXrQqVMnfZ2mJFu2bNjb23P48GGOHTvGy5cvqVmzJq1atXrtdps2beLp06fs3LmTe/fuERMTw7fffsuIESP45JNPkt22QIECjB8/nt27dwNw7do1zMzM6N69O9myZePcuXMMHz4cKyur1FWIEMJoSPIghBBJUBQFX19fAgICePLkCV9//TU9evSgadOmBAQEcPjwYTZs2EBERAQtW7ZMskEZHh7O7du3uX79Ojdv3qRy5coMHTqUY8eO4e3tjZeXF5UrV2b69OkpfkPu7e3NjRs3uH37NseOHQPUb+VdXFwoWrToa/ueN2jQgL/++oubN28ydepUoqKi6Nu3L5UqVWLBggU8efKEjz/+mKJFi6a5YQzw/PlzfH19Afjmm2+oXr06Fy9eJCQkBD8/PzZs2ICNjQ3Vq1cnX758ibaPiYnh/v37HD16FICaNWvStm1bNBoNZ86c4datW2TPnp2hQ4dSrVq1ZO+MPHv2jIsXL+Lt7c3FixcB+OWXX3B0dMTd3Z0cOXIk+x5CQ0PZtm0b4eHh+Pn58ejRI8LDwyldujQPHjzg+vXreHh44OrqysiRI9m/fz89e/bk22+/fW39bNiwgRo1apA/f/7XrvvixQuuXr2Kv78/69evZ/v27YwaNYqNGzfi4+PD48ePadeuHUOHDk3xTsL58+fx9vbm5s2bHD58mICAAPr370++fPnw8PAgpblStFotHTp0IDg4GK1Wq6+/ihUrcuHCBa5cucKAAQPYvHkzpUuXTnL7zZs38+TJE8zNzTl37hyWlpb6uvT19cXR0ZHKlSvz888/s3PnTj777DPGjRv32voRQmQ+SR6EECIJ+/btY8eOHfj6+uLs7My4ceP0XWF++OEHDh48iJubG2vWrEmywR0YGMiPP/5IUFAQt27dokCBAowaNYrq1avTsWNHoqKiqFixIhqNhkuXLpEzZ85kxx2EhIRw4cIFzMzMuHbtGgBDhw7lm2++SfX7GTduHCtXruTHH38E1G/VO3XqxPr166lWrRpDhgzBzs6OzZs367/xHjduHLt27WLRokXJJje3bt1i2bJleHp6AjBw4ED9ur///jvTp0/H1NSU5cuXJ5k4AEydOpXr16/z9OlTzM3N+emnn6hduzZ2dnYAdO7cmdu3b/PkyRPOnj2bbPIQExPDlStXiIqK4sqVKwDUqVOHpUuXJrrL8SpbW1uqV6/Onj17OHr0KCYmJlSrVo2WLVtSokQJZs6ciaWlJe3atePTTz9l/Pjx1K9fP8V9gpoM7N27l9mzZ7923YsXLzJv3jx0Oh2+vr588skn9OnTh5IlS9K0aVNOnTpFly5d0Ol0HDt2jEKFCiV7t+vJkydcu3aNly9f8vDhQ8zNzVm1ahWVK1d+bTlMTU0ZNWoUHTp0YMyYMXTu3Fn/2g8//IC5uTnz5s1LMnGI275mzZocPnyYJUuWoNFoqFy5Mm3atKFevXo0adIEExMT2rZty0cffcTw4cNp1KjRa8slhDAOMmBaCCGS8Mknn7Bw4UJKliypH/Qc1wDNlSsXoDak4roRvcrJyYmFCxcyevRogoODsbS0JCgoCK1Wy+rVqxk8eDCKonDjxg0uX77M8+fPky1L2bJlGTNmDLly5dLPrvPrr7/qB9SmRKvVMnLkSD799FNKlSqFRqNh9uzZmJiYcOjQIfbu3cu+ffvYsGEDhQsXTjAA9+HDhwQHB6dYtmLFijF79mxatmwJqI31uK4ocfXUsWNH/TiGpIwZM4YVK1YQHR2NmZkZMTExhIeHc+jQIYYNG8adO3cIDQ1l//79PHnyJNn9uLq6MnjwYJo2bYq/vz+gfgM/fvz419YTqN10tm7dyhdffEH+/PlxcnJiyJAhTJw4kcmTJ/Pff/8BcOzYMerVq4e1tfVr9/nnn3/i6OhIvXr1Xrtu+fLl+d///kfDhg0BdVasgIAAIiIimDp1KvPnzwfUAdPe3t4pzgAW901+3DqWlpb069dPPxj9dSpUqICLiwunT5/m8OHD/PHHH8yYMYO9e/dibW2Nh4dHits7OTmxZcsWatSoQfXq1XF1dWXixIkMHz6cr7/+miNHjuDs7MzZs2epUqVKsp8jIYTxkTsPQgiRgrjG+okTJ3jw4AGWlpb6sQl+fn707duXzz77jE8++STJwbCTJk1Cp9Oh0+mYMGECoaGhtGrViooVK3L27Fm6du1K3759iYyM5NmzZ+TMmTPJcjx//pxly5aRM2dOnj17xqhRo/jhhx+IiYmhbdu2yZY/bixCw4YNKV26NI0aNaJPnz789ttvuLm56We7cXR0ZNKkSdy8eZNy5cphaWnJkiVLCAwM1CcBr6snExMTbt++zcGDB6lVqxZjx44F1EZwv379qFKlCi1btsTJySnR9r/88guPHz/Gzs6OVatWceTIEX7++Wdy5sxJYGAgWq2WVatWodVq8ff3J0eOHPr3Fp+iKEybNk1fT4MHD2b27NkoisKECROSvQPh5+dHz549adeuHdu3bydnzpy0adOGnj176vvmW1paYmtrS9GiRVM1aFtRFDZu3Mjnn3+eZFmT8uLFC/2sSk+ePKFXr178/fffdOnShcuXL3P27FnmzJlD5cqVCQ4OJigoKNmG94kTJzhw4AA5c+bExcWFihUr0rVrV3755Zdk7xrEV6tWLS5evMi+ffvImTMnuXPnZvLkyZiZmeHl5YWTk5P+7lB8ISEhfPvtt5QtW5bbt28TFBTEkCFD6Nu3LxMnTtTH38nJCRMTk2TvSAkhjJPceRBCiGTcuXMHPz8/Tp8+zaZNm3Bzc2PcuHH6mYU8PDyoWrUqQ4cOpXHjxommc928eTM3btygTp06FCxYkPXr11OmTBkeP36Mn58f0dHR/PXXX3z22WeUL1+eGjVq0Llz5ySf/Dxu3DgURaFr164ANGrUiJYtWzJq1CgWLVqU4nz/U6ZMYcKECeTJk4cdO3bQv39/8uTJg6enJ8ePH2fNmjV0796djz/+mM6dOzNw4EBAHdicmsTh2bNn3Lp1C51Ox8iRIylbtixDhgwhe/bsWFhYYGlpSc+ePZk9ezb169dny5YtCbb39PRkxYoVNG3aFEtLS/73v//x+eef8+jRIwIDA3n58iW3b9+mffv2VKhQgVq1atGwYUPu37+fqCxr1qzh9OnTDBgwAIDcuXMzZswY/vjjD/r3709wcHCS7+HAgQMsWLCAvn37EhMTQ/78+VmxYgXNmzencePGDB8+nEuXLhEaGkrNmjVfWyegTrHq6+tL+/btU7U+qLHKkycPBQsWpEmTJqxevRpTU1OePXumv+syc+ZM6tSpQ+XKlalWrRqzZs1KtJ/g4GBGjx5NlSpV+PjjjwEYPnw4Tk5OdOnShQMHDiRbhpkzZ6LT6ShevDi///47Tk5O7Nixg9DQUJo0aUKdOnVYsWIFVatWpVu3bnh5eSXY/sCBA4waNYqxY8diYmKCi4sLu3btonHjxlSvXp1p06Zx4sQJ/P39qVGjxmu7lAkhjIvceRBCiHh0Oh39+vXjypUrPH36lNy5c/PLL79gamrKgAEDqFWrFl27duWrr75CURS6devGnTt32LRpE1OnTmX9+vWAOu3nzJkzmTVrFn///TchISHMnTuXo0ePUrRoUYoUKUJUVBSFChXSN5pB7XsfN+tQnAULFrB//36WLl2qH5isKAqjR4/m1KlTzJ8/n5MnTzJx4kQKFSqk3y4qKgp/f39evnyJr6+vfpC2l5cXt2/fJiYmhsGDB2NiYoKVlRUWFhbodDoOHjzI48ePcXZ2JiAgINkEYsGCBezcuZMHDx5gZWXFDz/8QP369enTpw86nY5169bRqFEjdDodFSpU4Oeff2b48OGMGzeOZs2aYWlpSWBgIP3796dXr17kzZuXEydOsGXLFubPn0+ePHkoXrw4gYGBZMuWjS5dumBtbY2JiQmmpqaJ7tIcO3aM6dOn88033+gHE+t0Olq3bs3+/fvZt28fLVu25KeffqJGjRr67Xbu3Mm+ffvYvXs3jx8/JiAggNDQUDw9PTE1NaVZs2ZER0fTo0cPAH23otfZsGED9erVw8XFJVXrr1u3jrNnz7J69Wq6deuGv78/GzZswN/fn5IlS2Jvbw+os0KVKlUKc3NzFEVJNAYkJiaGAQMGEBERwYwZM5g6dSqKomBpacn06dPp0KEDvXv3pmPHjgwZMgQHBwf9tt7e3qxYsYLPP/9cn9D16dOHw4cPM2vWLOrWrcvdu3dZvnw5AwYM4PDhw8ydO1c/k9SJEyfYtm0bWq0WPz8/Hj58yMcff8y5c+dQFIV69eqRJ08efSKX2roUQhgRRQghRAKHDh1SKlWqpAwePFgJDg5WNm3apFSvXl1Zt26dotPplIMHDyoeHh7K8ePHFUVRlMjISKV9+/ZKvXr1FEVRlHv37inffPONcv36dUVRFOXbb79Vvv3220THqVChgrJ48eIUy/K///1PKVu2rLJ7924lICBAmTx5suLh4aEMGjRIGTdunLJjxw6lePHiioeHh1KiRAllxIgRire3t6IoinLp0iWlVq1aSs2aNZVvv/1WmTNnjrJ582bl+PHjSosWLZSWLVsqISEhCY6n1WqVsLAwRVEUpVevXkqxYsWUPXv2JFm2Bw8eKI0bN1ZatGih3L17V7l8+bJSq1Yt5aefflKCg4OV8PBwxcPDQ5k7d65+m0mTJikeHh6Kr6+vEhkZqfTv31/ZvXu3oiiKsmLFCqV8+fKJjvPtt98q3bp1S7GeTp8+rVSsWFGZNm2aEhwcrOzatUvx8PBQunTpogwfPlzZt2+fUqNGDcXDw0Px8PBQvvrqK338oqOjlWXLlilr1qxR+vfvr0yaNEl5/vy5UrFiRWXhwoX6dapVq6bUqFEjUZ0lxd/fXylVqpRy9OjR166rKIqyb98+ZejQocrz588VRVGU8uXLKytWrEiwztWrVxUPDw/l7Nmzye4nMjJSGThwoFKvXj3lxo0byuPHj5WOHTsqVatWVQYNGqTMmzdPmTdvnr4eKlWqpMyfP18JDAxUFEVROnbsqHh4eCh3795VBg4cqCxYsEBRFEUJCAhQli1bpkRHRyuNGjVSDh48qERHRyuzZ89WZs+enaAM69atU5YtW6aMHz9e6devnxIeHq7Ur19f+eGHH/TrtGjRQilVqpTy5MmTVNWPEMJ4yJ0HIYR4Re3atTly5AjW1tacO3eOsLAw/vnnH/03vzdu3AAMzyWwsLBg6dKl+ulB3dzcWLZsGaDeIbh+/TpFixZNdBxFUVJ8aNicOXO4efMmO3bs4NKlS/Tp00d/58HU1JQcOXJQtWpV5syZw549e9BoNNjZ2XH27FkKFSpE2bJlOXz4cJL7jhvsa2trm2C5iYmJ/jU3Nzfs7OySHKMA6pOld+7ciU6nIzAwkF27drFu3Tp9H/bz58/ryxpnxIgRlCxZkty5c6PT6ZgzZ47+9WvXriU77WxK9bR//34WL17MwoULcXBwYMCAAQnu0OTIkYNcuXLx66+/snLlSsLDw3FycuLKlSuUKVMGOzs7/cxVJUuWZPr06dStW5eYmBj9sxDmz5+PpaUlz58/p3379kyYMCHFh7Rt2rSJ3Llz67sMvU7t2rX138LfvXuXsLCwRHWhxE6vmlxdhIaGMnToUPLly8eOHTtYunQpJ0+exMvLC1NTU+zt7XFwcKBjx476pz5bWVkRERHB9evXqVy5MufOnaNYsWIULFiQZs2aMXDgQBwdHfniiy/0dVS3bl0GDBjA999/z4ABAxKN5/jyyy8BtTva1KlTqVatGhEREUybNg2AjRs38vjxY2xtbWnfvj1jx46VOxBCZCWZm7sIIUTW06VLF8XDw0Px8vJ67bqenp6Kh4eHMnjw4ESvVa5cWdm4cWOS2z1//ly5ceNGouWDBg1SPDw89N9Qv6kvv/xS6dq1a7r28ToLFy5UPDw8lA0bNqRq/erVqys1atRItLxv377KkCFDktxGp9MpJ0+eVHQ6XYLl69evVzw8PPR3NVLr8ePHytdff63Uq1dPKVeunNKjRw9l7NixSv/+/RV/f39lxYoV+m/tu3btquzbt0/RarUJ9qHVapW6desqv/76a5qOHee3335Lst5u3LiheHh46O8svcrT01Px8/NLtLxGjRrKZ599lqpjz507V79/nU6n9OzZU/Hw8FAqVqyoNGjQQKlcubL+/Xt4eCjLli1Ldl8vXrxQhg8frlSvXl2pXr260qRJE2XJkiVKt27dlLt37yp79+7V76dt27bKtm3blMjIyFSVUwiReeTOgxBCpFG3bt148eIF7u7ur123YMGCVKxYMcmpOu3s7ChQoECS22XPnp3s2bMnWh73bX56WVlZ6Z/C/K60bNmS7du3JxhfkJLWrVsTEhKSaLmdnV2yz3bQaDRJPoMirp5S+9C7hw8fsmjRIry8vPj0009ZsGAB169fZ8+ePfTs2RM3NzcAunfvTlhYGGfOnCFbtmxcv36dcuXKJXgy96FDh3j27Blt2rRJ1bFfVadOHfLkyZPoroWtrS3m5ubJ1kWRIkUSLXvw4AFPnz5NdazjBsuDWneLFi1i48aN/Pvvv3h6ehIeHo6joyOFChWifv36CZ4BEScwMJDFixdz4cIFateuze7duwkICGDp0qU0bdqU3r17A+pnY9y4cezbtw9bW1s8PT0pV64cBQsWTFVZhRCZQ6MoKTxmUgghxDszefJkhg0blmKXnFf98MMP/Pfffxw/fjzV038mtx93d3d69er1xvvIKBs2bKBChQoUL1481dts27aNESNGsG3bNkqUKPHa9aOiovSDio1VTEwMU6dOZcyYManeJioqilatWlGwYEEWL178DktnEBMTQ1RUVKqmsxVCZD2SPAghRBai1Wp5/PgxefPmTdd+Dh48SNGiRZP9Fvt98ODBA/Lnz5/Zxch0wcHBREdHJ3knSwgh0kqSByGEEEIIIUSqyJNZhBBCCCGEEKkiycMHQKvVZnYRsjypw/STOkwfqb/0kfpLP6nD9JH6Sx+pv/R7W3UoyUMWoygKf/75J15eXvplOp2O6dOn8/fffzN27FgiIyMTbDN9+nR+/fXXjC5qliJ1mH5Sh+kj9Zd6ch58N6QO00fqL32k/tImM8+DkjwYEW9vb7p160aFChWoX78+69atA+DUqVMUK1aMYsWKUbx4cUaOHImdnZ1+u71793LhwgU+/fRT/P39Wb9+vf61Q4cO8e+///LFF19k+PvJDMnV4dmzZxk4cCDTpk2jb9++rFq1KsF2UocGkZGR/PDDD1SpUoWPP/5YX1cRERH8+OOPjBkzhr59+3LixIkE20kdqpKrP39/f7777jsqVapEzZo1mTdvXoLtpP5UydWfnAdTL7k6lPNg2i1cuJCRI0cCcg58E/HrT1EUpk2bRvXq1alSpQrTp09PsK7UX9Li16GxnAcleTAS0dHRzJo1iyFDhvDnn3/i7u7OxIkTuXr1KgBdu3blxx9/1P/Ef+Lr+fPn9U+6zZYtG6dPnwbUxsro0aOZNWuW/sm477OU6rBXr1507tyZESNGMH36dGbPns2ZM2f020odGmzYsIFmzZqxdu1a3Nzc9NM7/vzzz0RFRfHzzz8zcuRIevfuzYMHD/TbSR2qkqu/adOm0blzZ7Zv306tWrVYvHgxf//9t347qT9VcvUHch5MraTqMDAwUM6DabRlyxYWLFig/13OgWnzav1t376dChUq8Ntvv1GxYkVWrFhBcHCw/nWpv8RerUMwjvOgPCTOSHh7e9O9e3fKli0LQOfOnTly5AjPnz/HysqK9u3bU7Ro0SS3tbKy0v9fo9Fga2uLTqdj+PDhdOnShfLly2fEW8h0ydXh0aNHCQsLIyYmBlAfOGVjY6P/gIHUYXxt27bVn1yKFy/OZ599hq+vL3/++SeTJk0CIH/+/OTIkYNffvmFn376CZA6jJNU/QUEBNCwYUP9w8x69uzJ1q1befbsmX47qT9VUvUXR86DqZNUHT558kTOg2lw7NgxIiIi9L/LOTBtXq0/gAYNGuj/LsuVK0fevHkTNGSl/hJKqg7BOM6DcufBSBQrVozKlSvrf7979y65c+emSpUqAPTv35+yZcvSqFEjVq5cmWDbxo0b8+LFCwCePn1KixYtWL58OSYmJlniAVBvS3J12KVLF2rWrMno0aM5e/YsR48epX///lSoUEG/rtShgb29PYqisGLFCiIiImjUqBHHjh0jJiaGHDly6NfLmTMnBw8e1P8udahKqv6yZ89OkyZN9Ot4e3tjZ2dHw4YN9cuk/lRJ1V8cOQ+mTlJ1WLx4cTkPptL169e5ePEinTp10i+Tc2DqJVV/gD5R2LFjBxcvXqR9+/YJXpf6M0iuDsE4zoPynAcjdP/+ffr168esWbMoVqwYd+7c4ezZs+TIkYOdO3fy999/M2XKFNq0aaPf5sSJE4SGhmJiYoKTkxP9+/fnzz//xNnZORPfSeZ5tQ5DQkKYPn06pqam7Nmzh6+++oq+fftiYmLIn6UODdasWUNISAiHDx/mypUr9OnThwULFrB69WqqV68OQKdOnbh48SLXrl3Tbyd1qHq1/pYvX06NGjUACAgI4Ouvv2bkyJH6uowj9adKqv5cXFzkPJgGSdVhuXLl5Dz4Gj4+PqxZs4ZRo0ah0WgoVqwYrVu3pkCBAsydO1fOga+RXP1NnToVULsu+fj4cOnSJY4cOcKkSZNo27atfvsPvf4g5To0mvagIoyKt7e3MmTIEMXPzy/J17VardKsWTOlb9++Sb4eFBSk1K9fXzl48OC7LKZRe7UOIyIilNatWyv+/v6KoijKhQsXlDJlyijz5s1LcnupQ4Pg4GCldOnSSuXKlRUPDw9l//79+tfat2+v1KxZM8ntpA5VcfXXv39/RVEU5dmzZ8qQIUMULy+vFLeT+lO9Wn9x5DyYevHrUM6DrzdkyBClYsWKSqVKlZRKlSopHh4eSqlSpRQPDw85B6ZCcvX3ySefJFgvJiZGqVOnjtK6desk9/Oh1p+ipL4OM/M8KN2WjMi9e/fYsmUL06ZNI1euXICapcdnYmJCoUKFsLW1TXIfY8aMoVGjRjg6OrJnzx7279//zsttTJKqw927d+Pv76/PusuXL0+tWrW4dOlSkvv40OswPjs7O2xtbalbty5mZmb4+/vrX3v+/Dm1a9dOcjupQ1Vc/VlZWREQEMDSpUuZOHEi7u7uQOLPdxypP1X8+otPzoOpF1eHJ06ckPNgKsyaNYtz585x9uxZzp49C0CzZs34999/5RyYCsnVX/zJIQBMTU3JkSMH1tbWSe7nQ60/SH0dZuZ5UJIHIxEZGcmIESPIkycPf/75J5s2bWLatGmcOXOGlStX8vLlSwBiYmLw9PSkZ8+eifaxceNGfH196dy5M4MHD6ZJkyasXr2ao0ePZvTbyRTJ1eHWrVsJDAzEx8dHv+7z58+pVatWon186HX49OlTli9fTlhYGADXrl0jOjqa3r17065dO309+Pj48PTp0yT7UH7IdZhc/X377bcMHz6cPHnysHv3bjZt2sTSpUvZsWNHon1I/SVdf3IeTJ3k6nDt2rVyHkyHfPnyyTkwHcLCwliyZAkBAQEA+Pn54e3tzcCBAxOtK/WXtIiICKM5D8psS0bin3/+4eLFi1y8eDHB8q5du3Lx4kX++usvWrduTWhoKNOnT8fDwyPBep6ensyfP5/169cn+ONwcXFh+/bt1KxZMyPeRqZKrg6//fZbOnbsyKRJkyhbtiyRkZF88skndOvWLcF6UofqA2b+++8//vvvP5o1a8bdu3fZuHEjhQsX5scff2TSpEmMGTOG58+fs2jRIgoWLJhg+w+9DpOrv6CgII4ePZroxN2sWbMEv0v9JV1/bm5uch5MpeTqsGjRosyYMUPOg+kg58D0OXv2LLt376Z9+/Y8ePCAX3/9lUqVKiVYR+oveTqdzmjOg5I8GInmzZvTvHnzN9o2IiKCwYMH8/3331OwYEEOHDigf83U1JSgoKC3VUyj9ro6jD/l46ukDlUuLi5s3LgxydfMzMwYN25csttKHaZcf7du3UpxW6m/lOtv/vz5KW4r9adKqQ4/++wzOQ+mUfzPrZwD0y5+/a1YsSLFdaX+kha/Do3lPCjdlt4DkydPpmTJkrRq1QpQ+7JGRUUBalee+FPxiaRJHaaf1GH6SP2lj9Rf+kkdpo/UX/pI/aVfRtWh3HnI4q5cucKpU6fYunWrflmFChVo164d27Ztw9raOsl5goWB1GH6SR2mj9Rf+kj9pZ/UYfpI/aWP1F/6ZWQdynMe3gMvX77EwcEhs4uRpUkdpp/UYfpI/aWP1F/6SR2mj9Rf+kj9pV9G1aEkD0IIIYQQQohUkTEPQgghhBBCiFSR5EEIIYQQQgiRKpI8CCGEEEIIIVJFkocsqkGDBjRo0CCzi5GlSR2mj9Rf+kkdpo/UX/pJHaaP1F/6SR2mT2bUnyQPQgghhBBCiFSR5EEIIYQQQgiRKpI8CCGEEEIIIVJFkgchhBBCCCFEqkjyIIQQQgghhEgVSR6EEEIIIYQQqaJRFEXJ7EKIxIoVKwaAqalpkq9rtdoUXxevJ3WYPlJ/6Sd1mD5Sf+kndZg+Un/pJ3WYPumtv7jtb926leptzN7oSCLTyYcs/aQO00fqL/2kDtNH6i/9pA7TR+ov/aQO0ycz6k+SByMV98dw/fr1TC6JEEIIIYR4H5UsWTLN28iYByGEEEIIIUSqSPIghBBCCCGESBVJHoQQQgghhBCpIsmDEEIIIYQQIlUkeRBCCCGEEEKkiiQPQgghhBBCiFSR5EEIIYQQHxxFp8vsIggBZL2/RXnOg5EbNj4Ir3vaRMutrTT8ONSO/HlMGT8jmDt3E6+TEdo1t6JTWxvWbQlj886ITClDkUKmjB9uz4OHWn6aFUJ4RMY/NF3iYSDxMJB4qCQeBhIPg8yMR+2PzBn8rT1Ra1aj+D1JcV2Nqxvm7T9HefaM6C2bICoqg0oZj4UF5m3bo8mZk+hNf6A8eZzxZQBMqn+Eea3aRB85jO7kiUwpw/sWD42LKxZdur7Fwr17kjwYOa97Wq7fjkmwzNZGw6+zs5HXzZSu/V9w5UZMMlu/W3262dCprQ1zl4ewZFVYppShTAkzxg6159adGHoOCSI0LOMvxBIPA4mHgcRDJfEwkHgYZHY83AuoD2JV/J6g+PqmuK7i60uUvz8Wffth3rwlUUsXQ2RkRhQzgaj5c7Ho3Rfzdu2JWrwI5cH9DC+DdvMmePkS86bNiH75Eu0/f2d4GSQemU+6LWUxcSf+ou5mdB+YuSf+Qd/YZfqF+H/zHPH0zvwLscRD4hGfxEMl8TCQeBgYQzzy5U5b80d5cJ+oxYvQuLlh0bsvWFq+o5KlIDKSqKWLUR4/xqJvPzT5C2R8GQDtP38TvesvzJs2w/STTzOlDBKPzCXJQxYiJ34DuRAbSDxUEg8DiYeBxEMl8TAoU8KMb7vYpnk7abAaSAIRy0jikdEkecgi5MRvIBdiA4mHSuJhIPEwkHioJB4GcfF48vTNxnlIg9VAEohYRhKPjCTJQxYgJ34DuRAbSDxUEg8DiYeBxEMl8TCIH49f1oa/8X6kwWogCUQsI4lHRpHkwchZW8mJP45ciA0kHiqJh4HEw0DioZJ4GLwaj8io9MVDGqwGkkDEMpJ4ZARJHozcj0Pt5MSPXIjjk3ioJB4GEg8DiYdK4mHwruIhDVYDSSBiGUk83jVJHoxc/jymcuKXC7GexEMl8TCQeBhIPFQSD4N3HQ9psBpIAhHLSOLxLknyYOTGzwiWE79ciAGJRxyJh4HEw0DioZJ4GGRUPKTBaiAJRCwjice7IsmDkcusJ39+SCf+lMiF2EDiYSDxUEk8DCQeBh9iPKTBaiAJRCwjice7IMmDSORDPPEnRS7EBhIPA4mHSuJhIPEw+JDjIQ1WA0kgYhlJPN42SR5EAh/yiT8+uRAbSDwMJB4qiYeBxMNA4iEN1vgkgYhlJPF4myR5EHpy4lfJhdhA4mEg8VBJPAwkHgYSDwNpsBpIAhHLSOLxtkjyIAA58ceRC7GBxMNA4qGSeBhIPAwkHolJg9VAEohYRhKPt0GSByEn/lhyITaQeBhIPFQSDwOJh4HEI3nSYDWQBCKWkcQjvSR5+MDJiV8lF2IDiYeBxEMl8TCQeBhIPF5PGqwGkkDEejUerm4ZX4Z0kuThAyYnfpVciA0kHgYSD5XEw0DiYSDxSD2jbLBKAmE08TBv/3nGHz+dJHn4QMmJXyUXYgOJh4HEQyXxMJB4GEg80s7YGqySQBhRPJ49y/hjp5MkDx8gOfGr5EJsIPEwkHioJB4GEg8DicebM6oGqyQQRhOP6C2bMv646STJwwdGTvwquRAbSDwMJB4qiYeBxMNA4pF+xtJglQRCZRTxiIrK+GOmkyQPHxA58avkQmwg8TCQeKgkHgYSDwOJx9tjFA1WSSD0jCIeWYwkDx8IOfGr5EJsIPEwkHioJB4GEg+D9zkeJtU/eiv7SSujaLBKAqFnFPHIQiR5+AC8zyf+tJALsYHEw0DioZJ4GEg8DN73eJjXqv1hN1glgdAzinhkEZI8vOfe9xN/asmF2EDiYSDxUEk8DCQeBh9CPKKPHJYGqyQQekYRjyxAkof32Idw4k8NuRAbSDwMJB4qiYeBxMPgQ4mH7uQJabCCJBDxGEU8jJwkD++pD+XE/zpyITaQeBhIPFQSDwOJh8GHFg9psMaSBELPKOJhxCR5eA99aCf+5MiF2EDiYSDxUEk8DCQeBh9qPKTBGksSCD2jiIeRkuThPfOhnvhfJRdiA4mHgcRDJfEwkHgYfOjxkAZrLEkg9IwiHkZIkof3yId+4o8jF2IDiYeBxEMl8TB4r+KhKBB9DRTdG22ebDyUjPv7MIZ4SIM1liQQekYRDyNjltkFEG+HXIhV0jAykHgYSDxUWSoeMZ4Q9HXCZRZ1wKodvOyfxAam4LQFTHKALgBCZ0H0OcAarL8E6/YJ1k4UjxdnIGQ8ZP8r4W61jyF0BuheAArY9ASLGobXoy9D+CpQtKAEg+UnYN0h6fekRED4WojxhpjrmFqWYMpPYynqnkONx5UbSb9n+5+T3l+ciN0QuROs2oKmVOyxdBCxAaJOxK6kAdu+YFYi4bZh/8PV6RxnD1sw4cVo1m/LEa+80fCyH1h/DRbVUy5DOhnD5yOO9p+/ATBv2izB7xkprsFq0bcfFr37ErV0MURGZmwhYhMIi959sejbj6jFi1Ae3M/YMiDxMEZy5+E9IA0jVZZqGL1jEg8DiYcqS8ZDkwNM8ht+TAupjXgTl4TLNU5gUUtNHADC14Bla7CfAhpLCFsAulD9buPHo8cgP0KfLlETByUo4fGVcHj5HehCINuvYNUegkdB1Fn1da0PvBwKVl9CtnlgPxXCVkLk3qTfT9gKsGwBDlOwdpmHifYsi+cPThiPpN5zcpQoCB6jJiT2P4FlQ8Nr4b9BxF/gMAuyLQSLmmpZ49UDUSewVLZwYP9GtCZlWb/6x1fKuxSwAPMqyZfhLTCGz8er5BvvWHIHQs8o4mEkJHnI4qRhpMqSDaN3ROJhIPFQZdl42A0Dp3WGH5seoLEAx40Jl5tXAMum6jZKuNrIt6isLreoqTbANTZA4niEPV8KVk2SbqRH7AKdP1jWBo2Juj90ELFRfT3qBBAB5qXV301zgVkhiLmReF+6l2CSDUxdsLXRsHJBeTw8inHz5nWuXPNP+T0nJ3SOenfFYS6Y5Ez4WtQBMCuqJk+g3jlQgkHnq1+lrMclcuawY/6vYZy/6gwxV0CJjUvUcYj8G+zGgsY0+TKkkzF8PpIjDdZYkkDoGUU8jMBbTx5evnxJ9erVWbhwIYqSsSeBy5cvU6VKFdq3b09UVFSGHjs+rVabIcd52w0jE7RUzXaaps67qZrtNCa8/n0Yw4k/yzaM3gGJh4HEQ5Wl46GxTbzM4mPQxOtxqwuGmOuGb8c11mCaJ/a1Z6B7AvYzQKNJOh62A8E0mcaQ9l7sPrPF/mun/htzTf3XJLv6b+Q/6r+KTj2m+ceJ92XiAFYdE8TD0/sFYA0ax5Tfc1JibkLkX2DVCkxdkjhedog+r5YHQPsETHKDaUFAjUflcpa8DNbFi4cJYArapxAyGex+UBOid8QYPh+vIw3WWJJA6BlFPDLZW08ezp07R2BgIFevXuXRo0esWrWKe/fupWkfUVFR7N+/n759+zJ06FCePn2a7Lq+vvG+RSlblhUrVmBlZcWJEyfYvHkzY8eOpX///ly6dOlN31KanT59mlWrVr3TY7zthlGjHPvYX+0T1pT7mlklvmdNua/ZX+0TGuXYl+w2xnDiz9INo7dM4mEg8VBl+XiETIfnDSGgMQRPVMcyvCrqX3UMwqvfjsfcgaC+oH0EEVso5RHD/+Y5MnLkODp+/hGhQedff/y45EAXd2cg9gsVJbavs0VdMKuojq8InQvhq8F2CFhUiy3/TAhoAtEXALC1NdPH44uvjxMV/gCs2qh3NdLyngEi9qj/mlWEl8PgRU+1+5ISW0abb4EYeNELIv6E6OPgsBA0lvp4PAupRvDLQPVug/YBmFcCzCBkojp2I/7YjrfMGD4fqSUN1liSQOgZRTwyUZqSh2LFitGqVSsWL17M/fuJB82EhoYydepUNBoNd+/epVGjRkyZMoU2bdqkmAAA+Pn5MWXKFLp160a1atUYPnw4ERERFC9eHGtr6yS3iYyMpEuXLvTu3Vv/s3r1akqWLMnNmzeJiYnBzMyMf/75h44dO7J3bzL9UFPg7e1Nt27dqFChAvXr12fdunUABAYGUqxYsUQ/vXv35qOPPiI4OJh58+al+Xip8S4Sh3klh+Bi4ZdguYuFP/NKDkkygTCGE3+Wbxi9RRIPA4mHKsvHQ2MPNl0h2yKw7gpRh9RG8quz/0TuA4sGCZcpkRB1FGy/B7MiELGeUvl/xdM7hgNH7oMSArrA15fBog5gqnbf0YXEDpoGTJxiy2gGDlPAoiFEX4fw/6ldmeLKqHuiP1b8eHT97jFeVyeBebWE3ZJS+54BtLFdo8yKg8NMdbB02HKI2Bq73APsfgbzshD2C0TuB+3NBPHYsb8k2PaDlyNA9xBsf1ATICUcbPq8vn7ekDF8PiwtNGlaXxqssSSB0DOKeGSSNM+21KBBA/r27ZtouVarZfjw4Tg4OHD06FEiIyOZOHEiL168YMKECTg7O6e4XxcXF3r27MnatWs5ceKEPulIyU8//cSjR4/YsGED+/fv59y5c4wYMYLNmzfz999/88knnxAdHU3Pnj0pX748FSpUSNN7jY6OZtasWQwZMgQHBwd+/vlnJk6cSLly5bCysqJdu3bkyZNHv/6ff/5Jz549Afj666+pV68e5cuXp06dOmk6bkreRVelUUWmAgomr5xLTTQKOkXDD4Wnsf95fXSo3+wZw4k/yzeM3iKJh4HEQ/VexMPUFUwbq/83K6b21w9fq3YZMi+nLtf6gc4PzEon3FZjCTbdAChdtgp+t8/x99697DreB63NVLB+kXiMQFLMCqtdnsJ/g+DhYNUydnns8XWBEDwO7CepXZrC10L4L2rXKdt+6gBq5QW2ds76eHQb8IxrZ0eDaWGwGwka87S95zi64Nj3qo7lwPJT9Q5I1AF1ZqnIwxBzDuwnqElP8A9oQn6kce1SzF2e1xAPq1bqD6h3SCI2qYPD45frLTKWz0evzkl/KZkSmfUnlszCpGcU8cgEb6XbUkxMDD/99BOlS5dm3bp1ODk5sW3bNm7cuEGpUqVYu3YtN2/efO1+nJ2dcXJSv9GpUqUK+/fv5+eff2bOnDlEJhGMK1eu0Lx5c+zt7VmzZg23b9/m3LlzfPvttzRu3JhffvmFH3/8keHDh9OoUSNy5kzFxSIeb29vunfvTtmyZSlYsCCdO3cG4Pnz5zg7OzNmzBj69u1L37596dChA46OjlSuXBkAW1tbmjZt+lbvPryLhlHlbOdws/RLlDjEMdEo5LZ6wrf5llPI2pvyJRSjOPFn+YbRW2IsF2KJh0riYfDW42FWRv1X99ywLOoQWNQGTdInsDIlzFg1PwfZHF0JehmuxkNjlrrEIY5FFXUmpWxLQBvbfcmqufpv+Fp1ELSJvVoGmy7qVKjRsdOjaswSJA7dB77g6tkZ6l0B+7Hq4G8lRp0ONrXvOY6JY+x/4mZpMlHvXOiC1K5LodPVOxux63b4ajyKomXKzH+TjofuhdpNyuYb9U5FUH/1uRFvkTF9Plyd32wQuHzjHUvuQOgZRTwy2FtJHszMzGjWrBlr1qyhQ4cO9OrVCx8fH1q2bIm5uTmbN29mxIgRyW6v0+l48OABe/fuZe/evWg0Glq0aMGsWbPw9PTkwYMH3L17N9F2CxcuZNiwYfTv35927doxYsQIBg0axMyZM+nVqxenTp3CwsLijd9XsWLF9MkAwN27d8mdOzdVqlQhW7ZsCbpTbd68mZYtWybYvlKlSly7du2tjLd4Vw0jZ4tnqVpvYKFF7KnSgg0uFTCbX5PARd3olWsuLXLtoLTdVWxMMqax9t42jN6AMV2IJR4Sj/jeSjxenXBD9xwwVbvpxIk6qiYPSYiLxy3PSO54PTA0pJUYwyDiNJVHq07Bal4rdtYl1Ea/8srkHCaugHptsLHWMuPHKEM8Lv6lJgw2PQ3rR59TZzZKzXtWIgyvmcU+z0HrE/uaDpSX6t0SJUyddja2bH262TByUBEATp5PosOBokDIJHWsRvRVMHUHm94QPELtwvQWGNvnY9ma0NdvkAxpsMaSBELPKOKRgd7agOkKFSrw4sULvvjiCypUqIBGo6Fdu3Z89dVXADRt2jTZbWfMmEGfPn3YvXs3V65cIW/evOTLl49ffvmF1atXM2fOHIoXL55ou3z58hESEsLkyZNp3bo1Fy9eBODMmTNUr16dRo0a8c8//7yV93f//n02b97M0qVLsbGxSfCaoihs27aN5s2bJ1hesGBBQL1Dkh7vsmH0NCp138A9jCmIYm6DRheD1cs71Hf6l975f2F68VFsrtiBfgUW69e1N33JF26/U8nh3Fst63vVMEonY7sQSzwkHnHeSjy0jyGwMYRvVn9XItRBvza9wTR37LJw0N41fDsfJ3Q+FmGd+Pn7EDy9Y+jeexUoGnVbgOAfILANRB545aCxT2ZWkpllLnyN2kXIbpRhmUUNiD6rPtAO1K5E0ZfB+gtsbTR4OP9Iv28b0KbTVq5cD4fQJWrDPmRm7M809Q6BSbbXv+foa+rg67Bf1det2qt3GiJ3q79HHQW06lgJE3swKwsRf9KrkwmDvrFj+Og9YOKceHwIQMTv6vgM2wEQdVC9M2NWWE1A9A+Ze3PG+PnwefRmT+KOIw3WWJJA6BlFPDLIW3/CtImJCbly5WLhwoVUq1aNjz76SD2QWfKHGjFiBCNGjODQoUP8/ffftG7dmvnz56fqeDExMXz++efY29vz8OFDihYtyuDBgwkODmbgwIEsXryYTz75JF3v6e7duyxcuJCVK1eSK1fiaetOnjxJ0aJFyZYtW4LldnbqtH7BwcFvfOx2za3o1NbmnTWMzgZV4nGkCy4W/phoEp/QdYqGAJ0L2cYe4+JdLWN+uIWrxht3a28K23hT2OYuhWy88Qpz129T3O4WE4r+hE94XhqdMQxS75XvV7SKKV5h7niFufMoIrd+HMXrvFcNo3QyxguxxEPiAW8xHiY51ec2hP+mNo5NnNQxDBa1DOtEX1XHAWgSXluKFG+GVcwtunTuQHBEfnTkAcfVhkHOJi7qdKhx3X5i7kLEH+p0rwAhY9WnWOvvLjyCiM2AqTqQWROvQWDVTB2cHRI75gFTsP0OW6dG/Do7G5s35ubyZVsePLIHLoESYGjsx6fJ9vr3rLFUx1LETeVq6gIO89UEJOhb9c6Dwxy10Q9g/xPFcv/C4b+/Zs82R3yfWIHDPDVRiS/mhjpI2mFR7J2N2DspGiv1X10yXapSyVg/HwXypv/ZFdLnPpaMgdAzinhkgLeWPNy/fx9FUbC1tcXLywszMzMaNmxIYGAqZrRA7bq0YMECqlatSr58+XBwcEgwGBkgJCSES5cuUaOGYfq4YsWKcejQIW7evEmrVq3o1q0bNWrU4PTp0wD06ZO+GSPu3bvHli1bmDZtmj4B2r59e4IuSjt27EjUZQnUZ14AZM+e/Y2P/y4TBwAdpky+M5J5JYegUzQJEgidokED2H4xiZv3FHoODSY0zA0v3DgWmHAKPw2Gb3GideYcfF4L/6iEidbXeVfhZP5C/3uE1pK74QXxDiuEd2xC4R3mzr3wAkQphgv0e9cwSgdjvRBnBomH6r2Mh8Zc/RbcdkDy61hUUX/iUeNRFU/vdfQcEoTOLIl42A0Dhhl+NysEdiPUn6QoEWo3I41N0q9bt1V/YsWPx22/QegcvzOsm+NI8u8HUn7PZkUg++7Eyxx/SXL1Pl/nZdA3swzxsE9iJV0oBI9XZ1Yyc4+d1ckUUNRkBAxP7X4DH8LnQxqssSSB0DOKeLxjby15OHJEPSnmzZuXNWvWULFiRezs7Dhz5gzw+gb0vHnz8Pb25o8//uDMmTNJPmBu7969hIaGJkgeQL37MHbsWIoUKUKLFi0AOH9encO7XLlyifaTWpGRkYwYMYJWrVrx559/Auog6uDgYH2yEBMTw+nTp5kwYUKi7e/cuQOoYx/e1LotYe+8YbTveSMGXp/NqCJTcbM0TNcaoHNREweLT1974lfi9YC7GFye3teWJHjdlBjWP+qAu81d3K29KWRzDyvTSErY3aKE3a0E62oVE3wj8uAd5s7apz3pP7Fe7Ik/kCs3MuYBfK+ShqrqvWyoviGJh8F7HQ8z99evEyvLxSN0ujqewkq9bqIxA/OPDA/Xw8IwViSNPqTPhzRYY0kCoWcU8XiH3lry0LRpU27evIlWq+XChQv069ePdevWUaVKFZo1a0bt2kkPaouJiWHevHns3LmT3377jSJFiuDj40NwcDCzZs3Sb3f37l3mzp1L6dKl6dq1a4J9jB8/Hi8vL9atW8e///6Li4sLu3btwsXFBVdX1xTL/eOPP7Jv3z6mTZuWaErVf/75h4sXL+rHUsT59ttv9f+/ePEi5cuXT3Jg9pkzZ/j4448pUqRIimVIyeadEa9f6S3Y97wR+5/Xp3K2czhbPMPOzYXh0xuqdxzewolfixkL7hu+gTNBS16rhxS28cY99qewzV3cbbxxMAumgLUPBax9yNe+N66xJ/6Cz7exqNosdj/9jKne3+v3ldP8Gc+icwBpm7c7td7rhlEaZLmG0Tsk8TCQeKiyXDyiz6lPqc62MuFy20EQMh6i/gO77w3dvdLgQ4yHNFhjSQKhZxTxeEfeWvKQM2dORo4cSfv27alduzbfffcd06dP56effqJu3bpER0cn2sbb25tly5ZRrFgx9u7di5WV2seydu3a9OjRg99++43ly5fr1zc3N8fe3nDvNSoqinHjxnHixAnWrl1L8eLF8ff3p1OnTiiKQq9evV5b7mbNmqEoChs2bEiUPDRv3jzRIOhXVa5cOcGMTHFCQkL477//EpTf2Okw5XRQVfXEP/Pdnvh1mPIgIj8PIvJzIKBuvFcUnC2eUdLpLmM6Pca1fDn9ib9BQW9yWT7D0sSQUFmbhHH0o7oEx9gl6P7kFebO3fBC+ITnRZuOP3NpGKmyXMPoHZJ4GEg8VFkyHuaVINtyMLFNuNzURZ2W9g19yPGQBmssSSD0jCIe70CaW1U6XdIzFNy/f59+/fpRsGBB5syZg4mJCSNHjsTKyoolS5ag1Wr55ZeEfTNz5MjBtGnTEu3L1NSU77//nsGDB3P//n1CQkKwsbGhYMGC+m/4AwICmDx5MkWLFmXs2LH6aVNr165No0aNMDMz47vvvku071dVq1YNV1dXdu3aldaqSNEvv/zCkCFD0tVtKjNk/olfQ5hZLnpPKIrTKyf+X3x6sv95fUJjDH2P81g9IkYxxd4shHIOVyjnkHBmqyidOffCC+Ad5p4gubgTVoRoJeWHIEnDSJUlG0bviMTDQOKhytLxeHXwdDpJPKTBqicJhJ5RxOMt0yhJDS5IRrFixShYsCCNGzemZcuWuLurfUHXr1/P4cOHadq0aZLf1C9fvpwrV66wYMGCt1fyt+T69eucPn2aL7/8Ml3PhIjvzJkzPHny5LV3LVJSsmRJAIpVPcr12xlz8suqJ35zTTT5rR/ou0AVtvHWj6uwMU16jvImZ3ZyN7wQANUdT5LPypezQRW5G67+TUvDSJXZF+I4Eg+VxMNA4mEg8VClJR7NGlkya0I2ImdMQ/H1fetlMf3kU8ybNiN611+Z0mAF0OQvgEXffiiPH2deg9XSEovefdG4uWVaAgHGHQ9N3rxYDk/+WWjvWlx78/r166neJk3Jg8g4GZ08ZLUTf2po0OFm+cSQUMSOqchv5UP9U//ouzNNKzaSli5/MefuAJb5fEOfbjYMbB/IlcUz+Otifv0sUI8jXRMMDH+X3sd4vClpGKkkHgYSDwOJhyqt8XjXyQMYd4M1Q0kCoZdUPLJi8vDWn/Mgsp6seOJPDQUTHkXm5lFkbo4G1kx2vWshpXAyf8GV4NL6C/GOOf/QPHgjZQob1gvTWnM3rCDe4YZpZb3DCnE/vMBru0ClxfsajzchDSOVxMNA4mEg8VAZSzxeJV1mYkkXJr0k45EFSfLwgZMTP6x52Jk1DzsnuBDv2+WKj/O3+q5QBazvY2MaTin7G5Syv5Fg+xjFFJ/wfNwJc2fg9Tn6B9+ZaaKJSWNSIfEwkIaRSuJhIPEwkHiojCUeyTHaBqskEAl+z0ivxiN65/YML0N6SfLwAZMTv0HiC3ER5t/vr3/dTBNNPitfCtncjX2ytjquwt3mLnZmoRSyuYeFSVSCJ2avLNOLAtYPGHlrEideqE9adzALwtIkiqdROXl1almJh4E0jFQSDwOJh4HEQ2Us8XgdY2ywSgJhPPEwb9s+w4+fXpI8fKDkxG+QmgtxjGLO3fBC3A0vxH/P68d7RSGXhT+FbbyxfmVwtruNNzktAngZ46Bf1iLXX4wpMoWXMfaxMz+pM0BpcxSh/w8VuH3Hmp7DQiQe0jCSeMQj8TCQeKiMJR6pZWwNVkkgjCge/V4/M6ixkeThAyQnfoP0X4g1+Ee54B/lkuiVJmf+orCNN56hhocEZjcPQKuY4GAWTHmHS5R3uGTYYDGU0pmzoUTB2DEVhfAKc+d2qAd3wt78QYOp8f7EI/3k82Eg8VBJPAwkHm/OqBqskkAYTTyiN/2BRecuGX7s9JDk4QMjJ36Dd30hDtY6cDG4fIJl8+/3Z+mDbyhofR93G28+KnCPNlV8iX50G5MAL6xMIvGw9cTD1lO/zZmgSnS+tFr/e+/8y3galZO9Tz8lVGuX7nJ+KPFIDfl8GEg8VBIPA4lH+hlLg1USCJVRxOPJ4ww/ZnpJ8vABkRO/QWZeiKMUS26HeWBZoCRNpzhy1TuGnmuCCA+LIbfVo9jxFHf1U8xefllGv62VSTgDCizERKPw3/N6hGrV5V+4/UFx25v6h+B5hxfiSaQrr46reJXEw0A+HwYSD5XEw0Di8fYYRYNVEgg9Y4hHViPJwwdCTvwGxnshNsU3Ih++Efk4RJ0kt7M0ieK3R1/iavmEwGgn/fJ62Q9SN8fhBOuGxtjgHa52fbob2wXKK8wdn4h8xCjmEo945PNhIPFQSTwM3ud4aFzd3tlzHlJiDA1WSSAMjCEeWYkkDx+A9/nEn1ZZ/UIcFJONyV4/JFr+++P23Awtpr9bkd/KB1uzMMrYX6OM/bUE60brzPCNzI95jS44uvem+8AXXL0RhY1JBGE6m3S/v7TI6vF4W+TzYSDxMJB4qN5lPMzbf06Uv/8H22CVBMLAGOKRVUjy8J5730/8afE+X4gPBNTjQEA9/e/q1LI+hmll456wbX0XW7MwCll7E2kfzlex8Shg5cPfVZviHVaIz87uIK67k7u1Fy9iHAmIzs7rukCl1fscj7SQz4eBxMNA4qF61/FQnj374BuskkAYGEM8sgJJHt5jH8KJP7U+tAuxOrWsO3fD3fn3uWG5rQ2s/imMwjbefL/IUR+PAtbqSTpCZ0n8JGF2ieEUt7vNi+hs+tmf4o+reBiRBwWTNJfvQ4tHcuTzYSDxMJB4qDIiHtFbNmHevOUH32CVBMLAGOJh7CR5eE99KCf+1JALsSouHgXdc9JloEOCeBwOrE3140dxNH8RbwsFU40WnaLB0TyIitkuUjHbxQT7jNBacje8oCGhCCvE+ZcV8Y/KlWw5JB4q+XwYSDwMJB6qDItHVJQ0WGNJAmFgDPEwZpI8vIc+qBP/a8iFWJWaeLyIceRFjGO8JRqan9uOlUm4fmrZwrHdnwrbeFPQ+h5WppGUsLtFCbtb+q1G3JzEdv+WABSzvUnzXLu4+LIc/z5vKPGIJZ8PA4mHgcRDleHxkAarniQQBsYQD2MlycN75oM88SdDLsSq9MYjQmfNzdDi3AwtnmC5KTHkiZtaNl5icTvUQ79O5Wzn6Znvf+x/Vo+izVuo8VgWTNmzX/NjEbcEs0Cpdyve7riKV70P8Xhb5POhkngYfNDxkAarniQQBsYQD2MkycN75IM+8b9CLsSqdxkPLWY8iMjPg4j8HAiom+Q6t0I9+O3hl7iWK6OPx6b1Pgz86FCidYNj7PAOK8TdcENC4R3mjk94XrRv4VT1vscjLeTzoZJ4GEg8kAZrPJJAGBhDPIyNJA/vCTnxG8iFWGUM8TgbVJkqrWvTOV48bE2tGHlrEu7WhrsV+ax9sDcLoZzDFco5XEmwjyidGffDCzD4xizuhBUBwMEsiCidBRE661SVQ+JhIJ8PlcTDQOIRjzRY9SSBMDCGeBgTSR7eA3LiN5ALscqY4xGqtWObX8sE65lroihg/YDCNt4Uih1T4W6tJhbWphEUtfXiRbSjfv1v8v3K13lXsfTBN8y/3x8AS5MIStldxzusEC9iDA/Qk3gYyOdDJfEwkHgkQRqsepJAGBhDPIyFJA9ZnJz4DeRCrMqK8YhWLLgTVkR/ZyGOBh25LR9TyOYez6Jz6Je7WvpholF4FpVTv8zD1pP15bsAEBDlhFe4Oy8sClO7RWl8jhZizHxXwsJc4A2mlk2PrBiPd0U+HwYSD5WxxCMRabDqSQJhYAzxMAaSPGRhcuI3kAux6n2Lh4IJDyPz8DAyT4Llw25O5+c7PxCjGE5h9qbBPIxwI4/VY7JbBJLd4hxwDvb9QRFgV1kI01rHDtIuhHe4Oqbiv+d1iVHM37iMKXnf4pEe8vkwkHiojCUeyZIGq54kEAbGEI/MlrFfwYm3Rk78BnIhVn1o8XgR40SI1l7/+/EXH9Pg9D4qHj3N94FbiGi5iCelBrA/sCF3Qt2J1plhYxpOKfvrtHDZxaCCC5hebCQ6xXAa7JZnNUMKzqGojWe6y/ehxSMl8vkwkHiojCUerxXbYFUeP8aibz80+QtkSjG0//xN9K6/MG/aDNNPPs2UMsQlEBo3Nyx69wVLy4wvhMTDKMidhyxITvwGciFWSTwMChdzYOysGtzwjqHnakM8zDTR5LPyTTCtrKlGiw5T/bYtXXZQwu4WF15WwDOsKAA1nY7ydd5V8R6Ep/6rdqNKempZiYeBfD4MJB4qY4lHqsk33npyB8LAGOKRWSR5yGLkxG8gF2KVxMMgpXjEKObcDVengt3/vEGS22941IHidje5EVpMv6y0/TU+djrJx04nE6wbFO0QO61sIX1S4R3mTqBJXpbPzi7xQD4f8Uk8VMYSjwa1LdK2gTRY9SSBMDCGeGQGjaIoGX/2EK9VsmRJAIpVPcr12+rJVU78BnIhVkk8DN5VPApa36Oiw3n9k7XdbbzJa/UQU40uyfV1JpboCtem4/EF+njkt3qAX1QuInVWb6VMr/M+xyMt5PNhIPEwiIsHQOSMaSi+vqnf2NISi9590bi5ZVqDFcD0k08xb9qM6F1/ZVqDVZO/ABZ9+6E8fpw5CQS8F/HQ5M2L5fAR76hkrxfX3rx+/Xqqt5E7D1mEnPgN5EKskngYvMt43AsvyL3wggmWWWgiKWhzD3drQ0JR1M6bInb3MdFFcv5qdLx4KPxRoQMOZsG0OPenfkapojae2JmF4BXmzsuYbG+tvO97PFJLPh8GEg+DuHjs3h/OZw1S95yYBOQbbz25A2FgDPHISJI8ZAFy4jeQC7FK4mGQGfGIUiy5HVqM27Hdm+LiEVZQw4ih1/H2Dteva28arP+/T0Re/f8751nH526bAXgalUM/luJumOEJ235RLiQ3riIpH2o8XiWfDwOJh0H8ePg81L5Z8gDSYI1HEggDY4hHRpHkwcgVKWTK2KH2cuJHLsRxJB4GxhkPtwSvB2sdqH7iGI5mLxJ0WwrV2vIowpXcVk9wtniOs8VzqjmeSbBtaIwN3uGGMRVngipx4WXFJMsh8VDJ58NA4mHwajyaNUrnTEHSYNWTBMLAGOKREWTMg5GK64N25uxVbt2RE79ciFUSD4P3JR62pqEUsr6rnwUq7gnb+a18MDdJuL91jzrw050xANiYhDG12Ci8wwsRVed7Bn6bTeIhnw89iYdBUvFo1siSWROypX3Mw6vegz73b4uMgTBISzxkzIN46x481MqJXy7EgMQjvvcpHqFaW66GlOZqSOkEy8000eS3epBgoPbpF1X0r7vbePOJ87+EmebA9ttJ+nhM9hhDLgt/9W5FuDvesd2gAqKzk5YuUGnxPsUjveTzofpg4iHfeOvJHQgDY4jHuyR3HoxUXCbY+quT+tmWMpK1lYYfh9qRP48p42cEc+euNsPLANCuuRWd2tqwbksYm3dGZEoZihQyZfxwex481PLTrBDCIzL+IyPxMJB4qJxM/OhbaT+Vypiw/mk3fTyWOtcnl+mjROsH67LhG1MY35jCPIwppP5fW5in2two6XheqMTDQD4fqqwQj9ofmTP4W3ui1qxG8XuS/oNZWGDetj2anDmJ3vQHypPH6d/nGzCp/hHmtWoTfeQwupMnMqUMGlc3zNt/jvLsGdFbNkFUVMYXIgvFQ+PiikWXrhlcMoM3ufMgyYORepNgCiGEcv80+N0G/9vw1BP8PeGFDyR3qjezgpZT0FTppG4f8RJe+ELOwmjMMuEJskJkEEWnQ2Py5omzEG9LZv4tSrclIYT4wGkKVIUCVRMsU6LC4JmXmkjok4o76rKYCLDJblj5zmH47WvIWwG+M9xqV67shGxukMsDjZVDRr0dId4ZSRyEschqf4uSPAghxHtOY2EDucuoP/Eo2hgIfAD2uQwLI4LB0h6cC8dbLxo2fAs6tQulYu8CzkUhV1HI5aH+61wUHFzRaN7NuAohhBDGQZIHIYT4QGlMzSCne8JllTuiVOqg3pGIEx4E7h+pdy5ePoFgP/XH+2jCHVrao8QlErmKQoV2aLLlzoB3IoQQIqNI8iCEECIBjUYD5oYHaGnsckLPLUDsmAh/T8N4Cn9PeHobnt+DyGDwOa/+ABStB7HJg3JxC1zbDaWboSnXOqPfkhBCiLdEkgchhBCpprFygPyV1J94lJhIeH7XMK7C3zNB1yfunoArOyFnYYhNHpSgR7C0haHbU67YH2cPNLbZEUIIYXwkeRBCCJFuGjNLcCmu/iSlUkfIWQQKVDYs87+tjrkIfAC39idYXbHNGZtIFDGMq8jlAQ65s9zgQiGEeJ/IVK1GSqZqFUK875TIEHh01XCn4ultdRaoFz7Jb2RhA6Mu62d8Up5cB40p5CiExswig0ouhBDvB5mqVQghRJahsbSDQtXVn3iUqFB46hU7rewdQ3Lx3BvMrRNOFbvnJ/WuResZUE190JIS8EDtJpXLA5yLoLGyz8i3JYQQ7zVJHoQQQhgVjYUt5Cmr/sSjaKMh2D/hymaWYGGrjpmI43UEtgw2bOfgZuj2FL8blF0umVpWCCHSSJIHIYQQWYLG1Bwc8yRc1nkViqIkfIK2dTZwr6HesQh5Ci8fqz93DifcoVU2w9Syecqi+bhHBrwLIYTI2iR5EEIIkaVpNBqIdwdBU7oZlG4GgBL2wjCt7NPbhtmgAh9ARBA8OKv+PLkG8ZIHZW13MDWDT35AE/ssDEVR5E6FEOKDJ8mDEEKI95bGxhEKVFF/4lGiI+CZlyGZsM1heE0bAzf3gTYKGv9o2Gj/LJTzvyfxdG0P9ThCCPEBkORBCCHEB0djbgVupdSfRBTo/D81sXDMa1jsfwsC7qs/t/5NuIVdTnD2SDy2IltuuVshhHivyFStRkqmahVCCOOihDwDv1uGKWXjukEFPUp+o7It0Xz5i7q9osCNv9XEIoe7PK9CCJHpZKpWIYQQ4h3R2OUEu5xQuEaC5UpkSMIpZf1vq+Msnt+F7AUNKwb7wZouYGIKE++BiaW6/dW/IDo89s5FEXW2KSGEMFKSPAghhBDpoLG0g7zl1Z94lJgoddxEnPAgyFMOFJ36RO44hxfBg3OG7Rzzxo6lKKofU0GuomryIoQQmUySByGEEOId0JhZQLynXmtcikH/fYlXLFgNTC3Uuxahz+CFr/pz+0CC1RSb7IaxFOXboClc812/BSGESESSByGEECITaT4br/+/EhpgGFMR1/3pqScE+kBYANw/rf7kKQOxyYPiexG2DIFC1dG0mGzYl06LxsQ0g9+NEOJ9J8mDEOmg6HQy6PEtkvoUHzqNbXawrQ4FqydYrkSFqVPLxo2tiP/6k5vw+CrYOiXc2fQqKKbmCaaUJZd650Jj5ZAB70YI8T6S5MHIDRsfhNc9bYrrWFtp+HGoHfnzmDJ+RjB37qa8/rvSrrkVndrasG5LGJt3RmRKGYoUMmX8cHsePNTy06wQwiPe3WRitT8yZ/C39kStWY3i9yTplSwsMG/bHk3OnERv+gPlyeN3Vp6UmFT/CPNatYk+chjdyROZUgaNqxvm7T9HefaM6C2bICoq4esurlh06ZopZRPC2GksbCB3GfXnVcXqQ5c1YG6lX6REBKtdn0AduH3j7wSbKPYuCaeUjZti1t5FppYVQqRIkgcj53VPy/XbMcm+bmuj4dfZ2cjrZkrX/i+4ciP5dd+lPt1s6NTWhrnLQ1iyKixTylCmhBljh9pz604MPYcEERr2bmchdi+gdgdQ/J6g+Pomu17U/LlY9O6Lebv2RC1ehPLg/jstV1K0mzfBy5eYN21G9MuXaP/5+/UbvWWKry9R/v5Y9O2HefOWRC1dDJGRGV4OId43GvtcULJxwoWWdjDq8itP1o7tAvXyiTrzU7AfeB1JuF239VC8IQCKv6d6pyN3aTRO+TLo3QghjJ0kD1lYXOJQ1N2M7gMzN3EY9I1dpicO/5vniKd3xiQOaRIZSdTSxVj07otF336Zl0DEJgzmTZsl+D0jKQ/uE7V4ERZ9+2HRu68kEEK8IxqNBhxc1Z8itRK8pkS8jJdMxHtmxfN76p2IOJe3w7/ToVJHaD9P3TYmCv6bHa8rVBE05tYZ+M6EEJlNkocsShIHA6NOHOJIAqEnCYQQmUtj5QD5K6k/8SgxkWBiblhg4wRupRN2lXrurSYP+p1pUBzzxZtaNnZchbOHOn5DCPHekeQhC5LEwSBLJA5xJIHQSzKBEEJkqgTPngA0H/eAj3skXMnUAqp2NjwQLywAAh+oP7f2J1hVsc0ZO5aiKHw6Go3NKwO6hRBZkiQPWYwkDgZZKnGIIwmE3qsJRPTO7RleBiFE2mhyukObWfrflZBnhrEU8cdXvPBVn1lx9xncOwXNfjZss2M03D8FdQeiKdNcXaaNBkVRn40hhDBqkjxkIZI4GBhD4mBp8YYzkkgCoRc/gTBv2z7Djy+ESB+NXU6wywnuHyVYrkSFwlMv9Q5FsB+aeDNB8fAiPLwMunjXsDuHYXVnlBwF4z1Zu6h+bIXG0i5D3o8Q4vUkecgiJHEwMIbEwdZGQ6/O6RgkKAmEnj6B6Pddhh9bCPFuaCxsIU9Z9edV7ReA382EYy6eeqnJxNM76s/1PQk2UbLlTjitbOwzKzT2Lu/4nQghXiXJQxYgiYOBsSQOv87OhqtzOp/cKgmEnvLgPtGb/sCic5cMP7YQImNpcrpDTveEC2v0gjLNDGMp/G8bHogX8hSCHqk/dw4btrHPBaOv6n9VLm8Hc2soWA2NdbYMejdCfHgkeTBy1laSOMQxpsShqLsZy9aEMuhb+/TtUBIIvcx6gJ4QIvNpNBrIllv9KVo3wWtK2IuEU8rGJRfZCyTcya5xaoLR5y8oUFXd9s5huHvS8EA858IytawQ6STJg5H7cagded1MJXEwssSh+8AXFMibzjsPcSSBEEKIZGlsHNVkIDYhiKPodPH+r4WC1cDvljpWIs6NfXBsWbydaVCc8sfr/lRU3w1KY+P4bt+IEO8JSR6MXP48mf/kaEkcku469taSB5AEQggh0khjYhLv/6bQcVnilQpWhchgQ3eoiCAIuK/+3NyXYFXFzllNJoo3RFPHMAZLURT1zogQApDkweiNnxEsiYMRJg7vhCQQQgjxVmnKNIe46WAVRR0/EX9K2bgpZoMeqa+FPAWnvPrtFW00TCqNkr0g9PhDP5ZCCQsES3s0ptKMEh8e+as3cnfuajPluJI4qDJ8sLokEEII8U5oNBp1kLV9LihcI8FrSmSIYSyFUz7DC8/vQlggxESBlYNh+ZYhcPMflByFEkwpq46tKKzONiXEe0qSB5GIJA6qTJvlShIIIYTIUBpLO8hXQf2JL4c7DDkKL58k7Lr0whe00bHdoW7DtV0JNlMc8yaYUhZnD3AtLk/ZFu8FSR5EApI4qDJ9elxJIIQQItNpTM1i7yp4JHyh39/w8lFs8hB/FihP9cnaL3zVn9sHDNvUHwKfjARin8x9YRO4lkDzyuxSQhg7SR6EniQOqkxPHOJIAiGEEEZJY2ICjv9v797jc64bP46/rp2PZiObzel2mBFTOcccQhHpIFIibpKIckd0ElJx3+VOP5ZS3d23RImUyiGVnEbMKdScTzOzg7GNHa7t+/vj2q7L1Ryuzezk/Xw8rgfX9/u9vt/P9+OaXe/rc6pheYTeZbfPSE+2jaU4s9/y94QDENjQdtCp3ZapZauFWlo28l+76i1wdrV1g6paF5OLe0ndlohDFB4EUHDIV2aCQz4FCBGRcsXkHQDerS1Tx16Ju69lILdfsHWTYRiw8UPISrcd5+SMEVDbbkpZy9iKBpguHYMhUoIUHkTBIU+ZCw75FCBERCoUU+2WULul/cacbLhrbN4sUHmra2emQuJhy+MP+/9zDd9ASwtFtxcw5QUVI8cMTs6aWlZuKIWHm5yCg0WZDQ75FCBERCo0k4sbdBpjfW4YBqTG29aouLQrVGq87dFtvO0k0Qvh+8kYLR7FdN8027kSD4N/LU0tK8VC76KbmIKDRZkPDvkUIEREbhomkwkqBVke9TvY7TMunstrnTgAQY1tO/JbK7C1PBgXUuDtNuDshlG1boGVtS1Ty3qVzE1JhaDwcJNScLAoN8EhnwKEiMhNz+TpB7WaWx6X6v4KtHgMXD1t21JOWJ5nX4T4Py0Pu5OZMCrXhFvq29aqqNYAQm7D5Opx429Gyh2Fh5uQgoNFuQsO+RQgRETkMkwu7hDUyH5bcFOMKUcsU8cmHLB1g8qfCerCWTh73PLY/7PthROirQvmGftWQfIxqB+B6S/nl5uPwsNNRsHBoliDg5tb8RXMUQoQIiLiIJOTEwTUsjwadrHbZ6Ql5o2puGTNirMnwS/EdtCOL+H35dBzijWcGAmH4Ke37VfXDqhjGbshFZrCw01EwcGiuFscXPv0Jeu9dyEzs3gK6CgFCBERuU4mn6rgUxXqtr3yQXXvhNxc+xW4T+2GnUvsj3NywahSJ29MRajd+AqTu88NKb+UPIWHm4SCg8WN6KpkqloVtxEjyZobqQChACEiUuGY2g6FtkPtN1a/1TLG4tJuUFnploHcCQdh3wq7ww2/YMvg7sELrFPJGplp4OatqWXLGYWHm4CCg8WNGuOQvfhLXB/uqwChACEictMwVQu1tC7kMQwDzsflBYn8blB5oSItAc6dAjcv+6Dw0cOQeBhjwMeY6kdYzpOWAJlplqllnZxL+rbEAQoPFZyCg8WNHBxtnI4jK3IObiNHKUAoQIiI3JRMJpNlxWy/YGjQ0W6fcSHFMjg7y/Y5xDAMSDgEGefA9xbbwdFfwIqp4OKRN7VsaN5MUHndoKrW0yxQpUzhoQJTcLAoiVmVjOPHFCDyKECIiMilTF6VC6yobTKZMF7ebQkQVevZdmSmgYs7mDPg9D7Lw/6FGP61LhlTEQrBTTCFhN/4GxFA4aHCUnCwKMnpWBUgbIozQGRnZ+Pq6los5RIRkbLD5OoJwU3st909EaPreMvUsdYpZQ/axlZknLNMG5t8DP780fKiBh1h6GLrOYwfplhaQJr3x+ThW5K3dFNQeKiAFBwsii04ZP4IF/8HlefbtpkPsGvL1/Trd5zTBw/yYFAgI+rXg+PHeG/oUCKjowucpr6PD0va33nFy/xwKo55hw/zdft2dtvTzWbe2PcHsRcuAvB8WCjhlStb9/94+jQ/xyew59w5XJ1MPF2/Ht0cCBAZOTn037SZlxs3omWVgAL7kzIz6bNxEwvatCHEy7PAfoB+G6OISU2127ap6114u7gUCBAvvv02IZ6ePN2gvt3xcw8eIioxCWeP3Ux/5FFq1apl3ZeVlcWAAQN45pln6NjRvhlcREQqJpOTM1T5m+XR6G7rdsMwIO1M3piKA7ZwUbu17ZiL52DdHMuT5o/Ytq9/H+L22neDCqiDyVkfhQtLNVbBKDhYFEtwyD0PF+ZB5kpwqnzJ9iTIXEl4q5eZOdWfmf0fYe6OnVRydWVAndqcS0ygtp8fTv7+YDZjpJzlzMUM+tascdnLnMvKZvaBA3x76hQBrgXnx56waze+Li78t00rNiYk8vS27XxxZxtqeHmxKTGRVLOZt5o1JTs3l1HR25m463dqeHrR6BoB4t8x+zmSnn7F25+yZx9ns7KvWkWezs7U8fayPnc1OeHtYvtvJT9A/OLqxvJTcYyoV9fu9esTEvj82HHW3tWJyPgzTJgwgYULF1r3v/POO7i5udG+ffurlkNERCo+k8kEvoGWR70r/F7INUOnMZB6BpNHJdv2mDVwcL39sc6uGFXq2k0pawkX9TC5ed+4GynnFB4qEAUHi2IJDkY2XPgQPJ+A7M32+zJ/AY9HMJmcAOhRty4f7NjJxsREBtSpTfMAf15oFIapVm3cRo7CfPIk3fv1497g6gUuk52by3sHDjC8Xl02JCQW2L85MYn1CYm8fZulL2ebqlUwG7l8cPAwr4c3YUtSMmMbWma7cHVyomtgIFuSkolKSqKRX6UrdmGKSkzi29hTV7z9pSdO8lty8jWrqW3VKpYWl6uIX/4tb+3cDYCpnn2rw+bEZLydnXEymajh68u89RtIS0vDx8eHtWvX8s0337Bs2TKcnTXjhoiIXJvJu4plCtm/ihgFddvbVtY+cxCyL8CZGMvjL4zKNaHdk5giRlie55jhYoplXYybnFNpF6C8ycnJ4dFHH6Vdu3Zs2rTpiseUNAUHi2LrqmRyBZ9x4HxLwX0eD4JzNevT1KwsAII9LV17ugQGArYxEJuOHqXx7bdTyafgAjmuTk68emtjAj0uP3PEytOnAQjxtHy772wyEezpyZr4eMy5uYwJbWB3fGp2tl1Z8sdAGHFxuI0chalWbc5nZ7P05EnuCqzG5Zy8cIHfz53j1kqVLrv/Ur4u1/7+YWbMfvpUrWIpf2goznffY93nfMmMfSYTODk54eLiQnx8PBMnTuTNN98kKCjomtcQERG5GlPDuzDdNRZT//cxjV4DUw7DxO3w90XQ63VoNRD+1ha888JBygkwcm0nSNgP0xpj/Ku13XmN2N0YyccxcnMpCiM3B+PQRoydSy1/5pb8Z8jCqhAtD+fPn2f//v2EhYXhc5kPaFdy4MABFi9eTExMDEOGDKFTp05XPD9ApUqVcHZ25uOPP2bq1Kns2bOH8+fPc+DAAX7//XciIiJ47LHH+O2334iJiWHw4MHFcHfXpuBgUWKDo03234J/d/AgLiYTj9SqWeBQ4/gxlqz5iV6jRuHWuDFZcyN5ZlMU+86d56NWLah7jfdrzHnLeAKfSz6k+7q4cDgnh6PpF6jva3t9jmGwIu40wZ4edKpmCT3PRG+3XCs1lbDxE3AbOYp3+vVjdIMGzDt0uMD1cgyDf8fsZ9Ktt/KPHTuvWRUbExOZd/gwadlm6vn68I+GobSuUsW6/8vjJ2hftSrZef+p5uzfj+vo0Za/r15FxC238MWJE1wwmzmSco62bdvi6urKuHHj6N27N3fdddc1yyAiIlJYJicnqFzD8gi1/11jpCdbwkLlS7obJ5+wfMvl/ZcxgguHQ+JhcPXEqFrPNgtU/p9V/obJxf2yZTD2fAfLX7GsgZHPLxjjvmmYmvQqrlstdoUKDw0bNqRRo0bcfffd9OzZE09PT6ZOnUpUVBSenp707duXZ599FoDbb7+dCxfsP8iGhoayfPny6y50UlISf/zxBzt37mTLli1s374ds9lM//79mTJlylVfO3/+fKKjo9m9ezdxcXGEhobSsmVLqlW7/LewAPPmzWPdunVUr27pduLq6kpAQAAZGRkkJCQQHBzMvHnz+PXXX4mKiiIyMpJt27Yxa9Ysa33cKAoOFiU5q9KltmzZwpI/Y5h0a2Ma+Bac0SHdbGbbyZNM27sHU5cuuI0YycmffiElO5vU7GuXMdVsaUlwuuQbepe87lL5+/J9dOgw8ZkZfNSyJR553XxOXrhouVb6BbLmRvJD/VBuffBBap9NhsuEh3mHDnNfSDB+bo7NbtQlMJCn69cnOSuLd/fvZ8TWaP7XpjVNK/txJC2dnSkpvBnelG9OxgJgHDpI9vffWQdRt1y9iucbNmR09A4q+/nx1uw5REZGkp6ezrhx4xwqg4iISHEyeQeAdxv7bY3vwZhyBC6ctW4zcnPB1ROcXSH7IsTtsTwu5eSMEVDbMqVs/tiK+hFwcgd8NhT4y2emc3Hw2VCMxz8uswGi0C0PXbp0YeTIkQA8//zzDBw4kIkTJzJnjuWXflhYGB06dCAiIoKwsDDr69asWcMTTzxxXYXNyspixowZbNu2DQ8PD3bu3EmDBg2IjIykSZMmVLnkG88reeSRRwgPD6dfv360atWK+fPnX/X4rVu38vHHH/Pcc8/Ru3dvxo8fz9ChQwkKCuKFF16gTp06tGvXjk6dOtGpUyfrPf/973+nc+fO3HbbbTdslhgFB4vSCg6p5w4zbtw4pnfuRLcrfNj+Of4MrasE4HYq1jqN69cLFnD2/TkUnN+ooPzBx2bDVq/mvGbUS1sjVpyK45vYU/ynVSu71ogv7mxDek4OAW5unDx7ltULFjDnk09wCg6GtWvtrrU7JYUzGRnXHMNwqT6XDAKv7e3F/es38tWJk4RV8uVff/7JG+FNC7zmr7Mw9Vu9in61amKqUYOdR4/yv//9jyVLluDmVnDwuIiISGkxuXmBm22SEJOTEzz7i2U8RPKxvLEU+y+ZDWq/Zd2KxMOWxx8rLS/s/wGsmEKB4AB520yw/BWMxj3K5CrbRe62lJycTNeuXWnd2tL3a9iwYSxdupTExEScnZ2ZPHkyAQGWj0fZ2dmsWLGCHj16XFdh3dzcePXVV63P27VrR2xsLG3btnX4g4abmxu1a9cGICIigj179rB+/XqOHz/OkCFDCA0NtTt+7969+Pr60qdPH/73v/9x9OhR1q9fz9ixY5kyZQovv/wyHTp04L333rN7nbe3Nz179mTWrFk3JDwoOFiUVnAgN4kdUZP4eN4cGq5agXHyJHEXL1LV3R1XJ9tQop/i47kvJBiwjYHwGDmKoGfHOrQORKivL3+eT+V8tq2VIdVsxsPZiTrelpkgtiUns+DYcT5r25qAvJ+DY+np1Pb2xt3ZGfe8VohlJ2M5c+ECgwYOxBQczJEky4Dof/0Zwz3Vgzials7R9HSGbtkKYJ2CdcKu3fStWYP7a4Rctax1vL3xd3MlMTOTTYlJxGdkMm7HLgASsyz3+W3sKY6kX+Cfea+5dB2IsxkZjBs3jueee453332XhIQEnn/+eW677barXldERKQ0mZxd4JZ6lkfj7tbthmFAarwtUOT/ac6076pUgGHZf2Qz1Gt3leNKR5HDQ0BAgF0YOHz4MD4+PnTt2hU3NzdrcABYvXo17du3v+5vEl955RWCgoLo168f1apVw9/fn9q1a+Pm5kZOTs41Z2Q5e/YsMTExrM37xnX27Nl89tlnBAcH4+/vz9atWwuEh379+tG9e3eWLl3Kn3/+yZIlS+jbty8rV67k119/5dtvv73idZs3b87nn3/Orl27aNas2XXd+6UUHCxKLTgApE2naaeJhIeHk7lqBQDvxhzgtSaNreEhIyeHHWdTeKuZbdVL4/gxUt+bRfbjA/F3YCG57kFBlg/caemEV65MjmFw6uJFegcH4+rkxNmsLP4ds5/I5s2tXY1yDYOZMfuZdcftZObkWFsengltwDP5A6zd3XktMJBla9bwQscOtDAXnJJ16JatbDt7lhnNwq3rPFwwm/HKa/EwDMMybV6ezJwcUrPNhFf2o2O1W+hYzTbY/JuTsUzas5feIcHWdR4ubYEwDINX3n2XDh06sHPnTkJDQxk0aBBPPfUUv/zyC15etm96REREygOTyQSVgiyP+h2s242dSx07QWr8DSrZ9SmW2ZaSk5OZM2cOc+bMITBvpplLLVq0iIcffvi6rxMUFMT//d//sWzZMsAyM0tiYiLDhg2jWbNm3HPPPVy8ePGyr12/fj1Dhw5l9uzZrFu3jltuuYVKlSoxduxYFi1axPvvv8+AAQMKvM7Lyws/Pz9atmzJrFmzSEhIIDk5GScnJx588EFuu+023nzzTTIyMgq8tk6dOgD8/vvv133v+RQcLEo2OOTmPfJkrYOc45w4vJxJkybx+oaNvLBzN2sTzlg/WANsP3uW8Mp+eP4lXD7y5Zd06NKFXQkJuI0YCe6WgVQ5GOT8pQmz3S1VibilKiviLLMubU5MwgkTT/ytDmAZo+Dq5MR7Bw7w+t59vL53H09u3UZmjqW8/aM20+2XX9mdkmJ/S5mZ5Mb8CYDL/Q9gqlX7mrWw5nQ8d675maUnTwIwKno7T2+LtraKfHb0GA19fRmcVzZH5KxeRfb33/HZmQRO5eTw0ksvsXLlSqpVq0bDhg1JSUmxhn0REZEKwbfgZ+XrOq6EXfdsS0lJSbz55pvMnDmTunXrFth/7NgxMjIyqFfP8X7UV9K8eXMA67gCs9mMh4cHXbp0YciQITRp0gRPz8uvhBsREUFERAQ5OTl06tSJ7t2789133xEbG3vN63p4ePDBBx+we/duvLy8yMrKYsyYMYSFhTFhwgQ+++wzHnzwQRo3bmz3uvyZn1L/sgJvUSk4WJRYcMjeDRnLIPeM5XnaG+DxCGT+CrmnOH7oa44fsh1+i7v9bArRyWdpW7XgfNAhnp6kZGXjsfxbTC+/wu/tO/D5rHeJz7C0QLy6ew+P16lNw0qWAdgzmoXz1r4/GBC1GWeTicgWd1Aj75v4NfHxxGdksuNsit017q0eZHctn8tNqZoXMEhOvupK1Pk8nJ3wcnbGK281zodr1mTuwUM8tGET9Xx8uNWvEh+1amHtJuWoXYu/JHLHLj5fvJiLFy+SlTf1bf7PsiM/oyIiIuXG39qAX7BlcPRlxz2YwK+65bgy6LrCQ3JyMnPnzmXq1Kl45/W//uabb7j//vutxyxfvpzevXtfXynz5A+Irly5MmAZS9GkSRMeffRRh8/x+eefc/bsWR5//HEWLVpkN6g73+rVq7n7btty6CaTiffffx+Ahx56iBYtWtC3b18AMjIy6NixY4HgALYpXi/twlVUCg4WJdri4BpueTDJfrvvqzw9+C2eG24Jh5n/moGR9238pUb/ZQ2GfLOb32H5S+p5siLn0GLkKJo3acJbV+jC5O3iwrTLDDwGWN3p6uNprNe6jNfDm/B6eBPYtAEjPLxAgPi4dUu749vfcgubunWxPr8rsNoV14r4q/trhFx2zESa2cyEnbsZW68uf4s/jaluXVxcXDAMg9y86V1vueUya22IiIiUUyYnZ4z7puXNtmTCPkDkdQe+b1qZHCwN19ltafz48YSEhPDDDz+wePFi5s6dy7fffmt3zOrVq+nevfsVzlA4+f2ePfIW1MrMzMQ979verKwsYmJiWLNmDYcOHbrs6/ft28e///1vRo4cSfXq1cnOLtjPOy0tjU8//fSyr58/fz5//PEHzz33HADx8fHExsYSHh5+2eMPHjwI2FpMikrBwaJUxzhcIv/f44efLt9FrjDyB1Gbqle368JUoi6zkFxJmbJnL7f6+dGnZg1yN0fh4uJChw4drD9b7u7uRERElFh5RERESoKpSS94/GNLC8Ol/KpDGZ6mFa6j5WH79u1s2LCBDRs22G3v1ct2s3FxcXh5eV31m8P8BdqaNm3KnDlzcPlL9wqz2czZs2e5ePEiR44cAWDlypWsXr2ac+fO8fPPP7Nx40ZOnz5tXdnZ2dmZyMhIu0XftmzZwvPPP8+IESMYPnw4YGnB+Oijj3BxccHPz4+kpCQWLlzIrl27OHPmjN3aD1FRUfzzn/9k1KhReHl5sXbtWqKjowGuOBvM1q1bufPOO6lfv/41avPKHr7PgwF9vBQcylhwePfDNE7E5nBvl8t3kyuM/ADhNnIUbg4Mor4h8gKE24iRDnVhKg5bkpLYd+48X7Rra7d90qRJjB07lhUrVvD66687NAWziIhIeWNq0gujcQ/LrEqp8ZYxDn9rU2ZbHPKZDMNw+JNgw4YNeeaZZxidt0JscUhLS2PRokUsXryYmTNncuutt9rtP3ToEAMHDiQjI4PGjRsTFhZGSEgIISEhBAQEEBAQgKenJ+7u7pjNZjIyMkhNTaV27dr4+PiQlZXFRx99xKlTpxg2bJh1EDNYBlFPmzaNo0eP2l2zfv36zJo1y/qhf/Xq1bz00ksMHz6c4cOHk56ezkMPPcTRo0epXr06P/74I66u9vP8p6Wl0aVLFz788MMizbSU3w1q3759Cg5lMDi8/+kFenVz550pflfstlRYplq1cRs5CiMurnQCBIC7O24jRmKqXr1EAkRKVhaV82ZhM9Wogfv4CTf0eiIiImJz6edNRxW65SG/H3Jx8fHxYdiwYcTFxVnXX7hUvXr1WLduHc7OznbTQjoqJyeHYcOGXXaa2IiICFatWkVcXBwJCQk4OTkRHBxsN0bhP//5DzExMSxatMgaJry9vRk/fjz/+te/eOeddwoEB7CsSv2Pf/zjuqdoXbDkgoJDGQwON8LN2AJRWQvBiYiIlCuFbnmoU6cO3bt35/7777/s7EqFlZuby8KFC2nYsCEtWrS47vOVBVu3buX06dPcd999RT5HfhJs2GoD+/aX/AdmBQebKwWH4m55yHcztkCAWh5ERERK2g1veYiJiSlciRzg5OR02fUVyrOWLVte+6AyTMHBpjQGq9+MLRAiIiJSPhTLInFScSg42JTmLFc3+yxMIiIiUjYpPIiVgoNNWZgeVwFCREREyhqFBwEUHC5VFoJDPgUIERERKUsUHkTB4RJlKTjkU4AQERGRskLh4San4GBTFoNDPgUIERERKQsUHm5iCg42ZTk45FOAEBERkdKm8HCTUnCwKQ/BIZ8ChIiIiJQmhYebkIKDTXkKDvkUIERERKS0KDzcZBQcbMpjcMinACEiIiKlQeHhJqLgYFOcwcGpTdtiKlXhKECIiIhISVN4uEkoONgUd4uDa0QHnO++pxhKVngKECIiIlKSFB5uAgoONjeiq1L2+nW49uylAKEAISIiUuEpPFRwCg42N2qMQ+7mKLK//04BQgFCRESkwlN4qMAUHGxu9ODonNWrFCBAAUJERKSCU3iooBQcbEpqViUFiDwKECIiIhWWwkMFpOBgU9LTsSpA5FGAEBERqZAUHioYBQeb0lrHQQEijwKEiIhIhaPwUIEoONiU9gJwChB5FCBEREQqFIWHCkLBwaa0g0M+BYg8ChAiIiIVhsJDBaDgYFNWgkM+BYg8ChAiIiIVgsJDOafgYFPWgkM+BYg8ChAiIiLlnsJDOabgYFNWg0M+BYg8ChAiIiLlmsJDOaXgYFPWg0M+BYg8ChAiIiLllsJDOaTgYFNegkM+BYg8ChAiIiLlksJDOaPgYFPegkM+BYg8fw0QQdVLvgwiIiJSKAoP5YiCg01ZCA41g4v+46MAkeeSAOHat1/JX19EREQKReGhnFBwsCkLwaFpIxeeGuR9XedQgMiTHyASE0v+2iIiIlIoLqVdALm6enWcqf83ZyaP9+V4bA5vR6ZTu4ZziZfD08PEq8/7UCvEmcn/SiUnBxqHlvzb5+H7PBjQx4sFSy7w66asUilD/r9HckouIZ5OmAKDinyu3H17ya5UCdeevaBSJXI3RxVjSR0tRA7ZXy3GtW8/3MY8R/aSxZCVVeLFMK//FbdHB5T4dUVERMRxJsMwSv4rbLmmxo0bA7Bv375SLolcjZGbi8lJDXjFRfUpIiJScoryeVO/pUWugz7oFi/Vp4iISNmm39QiIiIiIuIQhQcREREREXGIwoOIiIiIiDhE4UFERERERByi8CAiIiIiIg5ReBAREREREYdokbgyKicnB7DNvysiIiIiUpzyP28WhloepELLyckp0g+GFKS6LB6qx+Kjuiweqsfio7osHqrH4nMj6lItD2VUjRo1APjpp59KuSTlW5cuXQDVY3FQXRYP1WPxUV0WD9Vj8VFdFg/VY/G5EXWplgcREREREXGIwoOIiIiIiDhE4UFERERERByi8CAiIiIiIg5ReBAREREREYcoPIiIiIiIiENMhmEYpV0IEREREREp+9TyICIiIiIiDlF4EBERERERhyg8iIiIiIiIQxQeRERERETEIQoPIiIiIiLiEIUHERGRm0Rubi5z5swhIyOjtItSrhmGwW+//cbChQtLuygiJU5TtZZBycnJvPHGG3h7e5OYmMjo0aNp1KhRaRerTEtMTGTq1Kk0aNCA0aNHA5CRkcEbb7yBYRgkJyczcOBA2rZtC1h+gc6cOZMzZ85w8eJF7rnnHnr16lWat1DqDh8+zNSpU9m1axf+/v4MHTqUAQMGqB4LKTMzk8mTJ7NmzRpcXV0ZPnw4gwcPVj1eh9mzZ3Py5EmmT5+ueiyCpUuX8uKLL1qfu7i4sHfvXtVlER04cIAJEybQtWtXhg8fjouLi+qyEPbs2UOfPn0KbB84cCDjxo1TPRbSmjVr+P777wkKCuLw4cP06tWL++6778a+Jw0pcwYPHmzMmjXLMAzD2LRpk9G6dWvj/PnzpVyqsslsNhsLFy40pkyZYoSGhhrvvfeedd/LL79svPDCC4ZhGMaxY8eM8PBw49ixY4ZhGEZkZKQxYMAAwzAM49y5c0bz5s2NrVu3lvwNlBFZWVnGyJEjjV27dhlHjhwxhg4daoSGhhq///676rGQ/vOf/xgbNmww/vjjD+Ohhx4yWrZsaRiG3o9F9dVXXxmhoaHGhAkTDMNQPRbFkiVLjDlz5hjz58835s+fbyxatMgwDNVlUezfv99o3ry58cknn9htV106bu3atcb06dONOXPmWB9t27Y1Tp48qXospP379xuNGjUyTp06ZRiGYRw+fNgICwszTpw4cUPrUt2Wypht27axadMmazps2bIlqampLFiwoJRLVjaZzWaaN2/O3//+d7vtJ0+e5Ouvv7bWY61atahSpQrz5s0jPT2dTz75hDZt2gBQqVIlGjVqRGRkZImXv6w4fPgwQ4YMITw8nDp16jBw4EAAdu3apXospD59+tCuXTvCwsIICwvjqaee0vuxiDZu3GjXvUb1WHSDBw/m8ccf5/HHH+eRRx5RXRaBYRhMmDCBqlWr8sQTT1i3qy4LJywsjBdeeIGRI0cycuRIunTpQrNmzTAMQ/VYSMeOHSMnJwez2QxAYGAgTk5OnD59+obWpcJDGbNu3ToAqlSpAlialytXrszatWtLsVRll7u7Ow0aNCiwfePGjZjNZms9AlStWpW1a9eyY8cOzp8/T9WqVe32bd68+abtB9ywYUNatGhhfX7kyBGCg4Mxm82qx0Ly9fXFMAw+/vhjMjIy6Natm96PRbBv3z527tzJgAEDrNtUj0VjMpno06cP4eHh9OzZk2XLlqkui2DXrl3s3buXPn36sHDhQl588UUWL17Mhg0bVJeFEBgYiMlksj5fsGAB/fr103uyCNq3b09YWBhjxowhJiaG1atXM3PmTA4dOnRD61LhoYxJTk4GwNXV1brN1dXVul0cc7V6vNK+nJwczp07V7IFLYOOHTvGV199xdy5c7lw4QKgeiys+fPnk5mZSWxsLD169CAhIQFQPTrqxIkTfP3114wcOdJuu36ui6ZmzZo89dRTzJgxg4CAACZMmKC6LILdu3cDUK9ePQYMGMBjjz3GK6+8QmJiIqC6LIr09HS2bNlChw4d9J4sAg8PDz788EOaNm3Kxx9/zD//+U+SkpJueF26FOM9SDHw9/cHsH5oA8jOziYoKKi0ilQuXakeAwICrrjP2dkZPz+/ki1oGXPkyBFmz57NJ598QrVq1dixYwegeiysQYMGWf9s27Yt//3vfwHVo6Peffdd1q5dy9dff23d9t1335GdnQ2oHgurRYsW1pbFTp060blzZz755BNAdVkY+d/Kenp6AtC0aVP8/f358MMPAdVlUfzwww906dIFZ2dn/d4ugsTERJ5++mkWLlyIu7s7P/74I6NHj2by5MnAjatLtTyUMR07dgTgzJkzgKVP/7lz5+jQoUNpFqvcadeuHS4uLtZ6BEhKSqJDhw7cfvvtVKpUqcC+Vq1a4eHhURrFLROOHj3KkiVLmDFjBtWqVQMs9aJ6LDofHx+8vb3p1KmT6rEQ3nnnHaKjo9m2bRvbtm0DoFevXqxZs0b1eJ08PT0JCQnRe7IIatSoAVDg21nVZdF9++233H///YB+bxfF999/j6urK+7u7gB069aNsLAwfvzxxxtalwoPZUyLFi2IiIhgw4YNAERHR+Pt7W3X71eurWbNmjz88MPWejxx4gQJCQk8+eST+Pj48OSTT7Jx40YMwyAtLY19+/YV6CJxM8nMzGTChAmEhITw9ddfs3jxYmbMmEFcXJzqsRASEhL48MMPrd/o7N27l+zsbEaMGKF6LAb6uS685ORkPv30U7KysgDLB99z584xfvx41WUhde7cmapVq7J161YATp8+zblz53j00UdVl0WQlJRESkoKDRs2BPTzXRS1atXiyJEjpKamAli7HvXv3/+G1qXWeSiD0tPTee211/D29iY+Pp5Ro0bRtGnT0i5WmfXbb7/xxRdf8N1339GgQQMGDx7Mww8/jNls5o033iA7O5ukpCQee+wxIiIirK+bPXs2x44dIyMjgy5duvDAAw+U3k2UsuXLlzNu3LgC25966inGjBmjenRQfHw8zz77LGD5pvzIkSP079+fBg0a6P14HRo2bMiDDz7I9OnTVY+FFB8fz0svvcTFixfp3bs3ycnJ3HvvvdSpU0d1WQT79+9n8uTJtGrVitjYWDp37sy9996ruiyCZcuWceTIEcaOHWvdpnosvM8++4zNmzfTpEkTUlJSCAsL44EHHrihdanwICIiIiIiDlG3JRERERERcYjCg4iIiIiIOEThQUREREREHKLwICIiIiIiDlF4EBERERERhyg8iIiIiIiIQxQeRERERETEIQoPIiIiIiLiEIUHEREpNf/4xz8YPHgwe/bsuez+//73v9x3331ERUVd9TyDBw/mgQceYMGCBYUuw8qVK+nYsSPTp0/nwoULXLx4EYABAwYwc+ZM63MREVF4EBGRUtSnTx+ioqLYvn07AK+88gp9+vQhOjoagPXr13PmzBlOnz591fOMGjWKP/74g48++sjha2dnZ3P+/Hm6d++Ou7s7v//+O8uWLaNnz57ExMRQq1YtPvjgA+bPn1/0GxQRqWBcSrsAIiJy8woKCgLgtttuA2D06NE8+uijfPnll+zbt48qVaqwYsUKAgICrnqepk2bAtC6dWuHr33q1Cl69epFv379CA4OplatWvz0009UqVIFV1dXYmNjCQoKYtCgQUW7ORGRCkgtDyIiUmpcXCzfYXl5eQEQGBjIp59+SqtWrVizZg1bt27l7rvvZufOnVc9j4eHBx4eHphMJhYsWMDzzz/P66+/zv79+6/4mtq1azN58mSOHj0KwN69e0lLS2PIkCH4+fkRHR3NkCFD8PDwKJZ7FRGpCNTyICIiJS43N5fY2Fh+/vlnAPr27Uv79u0JDw9n5cqVHDp0CJPJxPjx42nTpg1169YtcI7z58+zY8cODh06xN69e8nOzmbp0qWsWrWKevXq4e3tfcXr5+Tk8NVXX3H69GlcXV2Jjo7G3d2dJk2acPz4cU6ePEnlypVp0aIF06ZNY/ny5dx777289tprN6xORETKA4UHEREpcfPmzePXX38lNTUVgPHjx9O9e3cCAgJ48sknmThxIt9++y1paWmsX7/+suHBMAz27t1LSkoKMTEx5OTk0LhxYxYuXHjN1gJnZ2fat2/PunXreP/99zGZTLRo0YKHHnqIzp0706NHD5ycnOjTpw9t27Zl/PjxdOvW7YbUhYhIeWIyDMMo7UKIiMjNxzAMHn74Yfbs2cPUqVNp27YtycnJfPHFF2zevJnTp09Tv3597rzzTiZOnIjJZLrseeLj4+natStZWVn4+vpyxx13MGfOHFxdXa96/QsXLjBo0CD8/PzIzc0lJCSENWvW0KJFC1q1asWgQYNo3749KSkpfP/999SuXftGVIOISLmiMQ8iIlIqli5dap2i9YcffmDixIk0adKEJ554gjZt2lCjRg2WL1/OxIkTSUxMJCsr67Lneeedd/Dx8cFkMjF06FAOHDjAM888Q0ZGxhWvnZaWxpNPPkl4eDiZmZkkJibyyCOPsHTpUsxmM/7+/gD4+/sTEBBAzZo1i78CRETKIYUHEREpcQkJCbzzzjv06tULgPfee49nnnmGU6dOkZKSQnJyMomJiQwYMIA77riD9u3bExERwY4dO+zOs2bNGr755hvGjh2LYRj4+vry1ltv8euvvzJkyBDi4+Mve/1ffvmFl156iUmTJuHk5ERgYCDff/893bt3p02bNsyYMYOoqCjOnDlDu3btcHLSr0sREdCYBxERKWGZmZk8++yzdOrUiQcffJDvvvuO1atXM336dKpUqUJYWBinT5/G3d2dfv364e3tjZOTEyaTya7r0J9//skLL7xAz5496dWrF6+++iq5ubm0adOGJ554gk8//ZTevXszadIk7r33Xmu3p6ioKJYtW0ZOTg7x8fHExsZy5513Eh0djWEYdO7cmZCQEEaPHk1qaipdu3YtraoSESlzNOZBRERK1EsvvcStt97KgAEDWLVqFWPGjGHz5s3WrkIA06ZNY926daxevfqy5zh48CBDhgyhdevWvPbaaxw8eJD+/fvTrFkzatasSdeuXYmMjLRO1dqoUSNGjhxJt27dMJlMfP7556SlpREXF0dCQgJvv/02PXv2pHXr1rz55psA3H///Rw6dIiffvqJwMDAG18xIiLlgFoeRESkRE2ePBk3NzfAsrYCgNlstjvGMAzrMX+1c+dOpkyZwosvvkh4eDjjx4/nxIkTgGUK1ipVquDn58f777/P3LlzOXv2LP7+/hw8eJBmzZoRGBjIY489BsCBAweYPn06rVu3JiMjgxkzZgCwaNEi4uLi8Pb2pm/fvkyaNEktECIiKDyIiEgJuzQUbNq0CZPJhK+vr90xzs7O+Pj4XPb12dnZfPnll9bZlObOncu6det48skn6dGjB8OGDbMeO23atKuWpVq1alSpUgUvLy+8vLyYNGkSvXv3ZsuWLXz55ZfExMQwZswYRo0aRdOmTRk4cCA9evS4YrAREanoFB5ERKTUPPDAA/j7+xdYl8HHx+eKU6O2bNmywLbt27cDXHE61786e/YskZGR7Nixgw4dOvDDDz+QnJzM3Llz6dmzJyNGjACgTp06vPbaa/z44494e3tz4MABmjVrRp06dQpxlyIiFYfGPIiISJnz888/4+zsTMeOHR06PioqiqFDh/Lee+851L3IbDaTlZWFl5fX9RZVROSmovAgIiIVwokTJ6hRo4bDrQ8iIlJ4Cg8iIiIiIuIQrXojIiIiIiIOUXgQERERERGHKDyIiIiIiIhDFB5ERERERMQhCg8iIiIiIuIQhQcREREREXGIwoOIiIiIiDjk/wE+LAYR2bbU3wAAAABJRU5ErkJggg==\",\n 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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率分档WOE值分档IV值指标IV值LIFT值累积LIFT值累积好样本数累积坏样本数分档KS值
0installment_rate_in_percentage_of_disposable_income逻辑回归入模变量[负无穷 , 4)5240.52403830.54711410.47000.26910.15200.01170.02390.89690.8969383141-0.0771
1installment_rate_in_percentage_of_disposable_income逻辑回归入模变量[4 , 正无穷)4760.47603170.45291590.53000.3340-0.15730.01210.02391.11341.00007003000.0000
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"                                                  指标名称      指标含义         分箱  \\\\\\n\",\n       \"0  installment_rate_in_percentage_of_disposable_income  逻辑回归入模变量  [负无穷 , 4)   \\n\",\n       \"1  installment_rate_in_percentage_of_disposable_income  逻辑回归入模变量  [4 , 正无穷)   \\n\",\n       \"\\n\",\n       \"   样本总数   样本占比  好样本数  好样本占比  坏样本数  坏样本占比   坏样本率  分档WOE值  分档IV值  指标IV值  LIFT值  \\\\\\n\",\n       \"0   524 0.5240   383 0.5471   141 0.4700 0.2691  0.1520 0.0117 0.0239 0.8969   \\n\",\n       \"1   476 0.4760   317 0.4529   159 0.5300 0.3340 -0.1573 0.0121 0.0239 1.1134   \\n\",\n       \"\\n\",\n       \"   累积LIFT值  累积好样本数  累积坏样本数   分档KS值  \\n\",\n       \"0   0.8969     383     141 -0.0771  \\n\",\n       \"1   1.0000     700     300  0.0000  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率分档WOE值分档IV值指标IV值LIFT值累积LIFT值累积好样本数累积坏样本数分档KS值
0housing房产状态own7130.71305270.75291860.62000.26090.19420.02580.08300.86960.8696527186-0.1329
1housing房产状态rent,for free2870.28701730.24711140.38000.3972-0.43020.05720.08301.32401.00007003000.0000
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"      指标名称  指标含义             分箱  样本总数   样本占比  好样本数  好样本占比  坏样本数  坏样本占比   坏样本率  \\\\\\n\",\n       \"0  housing  房产状态            own   713 0.7130   527 0.7529   186 0.6200 0.2609   \\n\",\n       \"1  housing  房产状态  rent,for free   287 0.2870   173 0.2471   114 0.3800 0.3972   \\n\",\n       \"\\n\",\n       \"   分档WOE值  分档IV值  指标IV值  LIFT值  累积LIFT值  累积好样本数  累积坏样本数   分档KS值  \\n\",\n       \"0  0.1942 0.0258 0.0830 0.8696   0.8696     527     186 -0.1329  \\n\",\n       \"1 -0.4302 0.0572 0.0830 1.3240   1.0000     700     300  0.0000  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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78zH2eOHECINV++enx448/0qhRI3788UdsbGz47bffDL/2JyUQ58+f58CBA3To0IGmTZtStWpVpkyZYpjt6WmlS5cmT548DB06lMDAQD777DPi4uLo1KkTDRo0YO/evQwdOhSdToebmxv9+vXj1KlTzJkzh7Fjxz63zDt37uT333/H09OTb775BlAHUd+4cQNLS0uqVKny3H3ExcXx+eefo9fradGiBaBOD3vv3j1DsvD777+zZs0arl27ZpgqNnnXqeSioqIANQ7Ozs7ExcUxcuRIPvzwQ2xtbVPMqKXT6dDpdDIOQgghniIJhBBCJErqD//ll19Ss2ZNAJ48eUKvXr24ffs23377LRUqVECr1XL27FlA/ZW6bdu2ae4zIiKC48ePY2trm667TKdm2LBhfPLJJxQsWJBFixbh4eFh+GXcwsKC0NBQRo4cSbFixfjiiy8AdbCxXq9n1KhRLF68GJ1Ol6JcU6dOJSAggCJFiqDX6/njjz/QaDT89NNPtG7dGm9vbywsLBgxYgQ1a9Zk8eLF5MmTJ81f+JMcPHiQzz//HAcHB2bOnImzs7NhuU6nw9vbO9VB4snFxcUxcOBArl+/zsKFC3FxcUGr1VKrVi3Wrl1rGBcxceJE/v77b6ZNm0ahQoUADN2bnhYSEgIY753h5OTE2rVr+eijj/jvv/8M3aSSJH3OpCsXQgghVJJACCEE6g3VRo4cydSpUw1jCu7du0ePHj3w8/Pjp59+wtHRkdDQUPz9/fn333+xsLCgf//+z9zvpk2biImJoV27ds9tNKfF0tKSzz//nAMHDhi6QV28eBGA4OBgunXrRuHChVm9erXhngv169encuXK6PV6pk2bRocOHdi5c6eh4T127FguXrxImTJlsLa2pmPHjkydOtVwz4jp06ezePFiNBoN9erVo3v37ri6urJ3715mzZqVZlk3bNjAwIEDcXJyYunSpeTPn5/ff/+duLg4Nm7cCGBIztISFxfHsGHD8PLyYsuWLVSqVInAwECTWaTu3bsHwIIFC6hXrx5vvfUWly5dAoyJwtNOnz4NqPfUSEhIICgoCB8fH6pVq0aHDh148OABgwcPZvz48YSGhhruf5E0A5QQQgiVjIEQQgggICCAP/74wzA70p49exg3bhy1a9dm5syZFC1alLVr19KgQQOsra1JSEigadOmeHp6prnPkJAQ5s2bh5ubG0OHDs20svr4+BAQEADA4cOHGT16dKpXN7744gv69OlDdHQ0Pj4+jBgxgg8++ICvvvrKMC7j008/BaBPnz5s27aNAQMGUK9ePdq1a0eRIkVwdHTEwsKCwMBAOnXqROfOnRk+fHiK94qNjWXSpEls3ryZtm3bMnLkSPLmzYter2f58uVMmjTJMCA5qTtSWhRF4aeffjIkXDqdjtOnT5vc8+Lhw4cA9O/fnxIlSpCQkMD//vc/w/pP0+v1HDlyBIAPPvjAMPtTnjx5cHZ2xtHREQcHBxwcHPD19aVz586GO1Ontj8hhMjNJIEQQgigXr16hn/fvn2b69evs3HjRkO3GID33nuPqVOnEhERgb29PSNGjEhzf3FxcQwaNAh7e3sWL15Mvnz5Mq2sSV1qNBoNI0aMMHTJeVqNGjU4fvw4kZGR2NjYmKzXt29fHj16RKtWrQD15nUrVqxgxIgRHDp0KNW7Q+t0Ov744w/at29PpUqVTF67fv06tWvXZsSIESYDyi0sLOjXrx8jR44E1FmUqlat+szP9/Rds69evUpERIRhmlZQ74lRtmxZmjdvbhj78Oabb7J///4UZQM1KdFoNNjb23P8+HF0Ot0zrwi9//776HQ6HB0dn3uHayGEyG0kgRBCiKcULVrU8Mt8ctbW1tSsWZOwsDDGjx+fYtBtcl9++SUVK1Zk4cKFaU4r+qIqVKhAnz59uHTpUprJQxIbG5sUDXLAcA+H5Nzd3Vm1ahVHjx7l77//5uTJk9y/fx8rKyvDTe8++OADihUrlmLb8uXLU758+VTLUL9+ffLkyUPbtm0NYzQyolChQtja2poMvK5RowabNm0yWa9Vq1ZER0fTrFmzFPvQarUMHz6c06dPp+v+GO3bt+fixYt07979uXffFkKI3EajyPQSQgiRqRRFITo6Gnt7++wuymvj+PHjFC1a1OSK0KsUFhbG77//Tu/evdO8g7cQQuRWkkAIIYQQQggh0k1mYRJCCCGEEEKkmyQQQgghhBBCiHSTBEIIIYQQQgiRbpJACCGEEEIIIdJNEgghhBBCCCFEukkCIYQQQgghhEg3SSCEEEIIIYQQ6SYJhBBCCCGEECLdJIEQQgghhBBCpJskEEIIIYQQQoh0kwRCCCGEEEIIkW6SQAghhBBCCCHSTRIIIYQQQgghRLpJAiGEEEIIIYRIN0kghBBCCCGEEOkmCYQQQgghhBAi3SSBEEIIIYQQQqSbJBBCCCGEEEKIdJMEQgghhBBCCJFukkAIIYQQQggh0k0SCCGEEEIIIUS6SQIhhBBCCCGESDdJIIQQQgghhBDpJgmEEEIIIYQQIt0kgRBCCCGEEEKkmyQQQgghhDBrjx49QlGU7C6GECKRJBBCZDGdLvtOgtn53rmBotfnyvcW5m3w4MF07tyZ48ePZ3jbHj16MHz4cMLCwlK85uPjw9tvv82///77zH1s3LiRt99+m6lTp75wEjB69Gg++OCDF9pWCJH5NIqk9EJkuc8nhnHjpi5T9tWxrS1d3rdn1YYo1v8Vk+Z6JYtr+Wmiywu/j7+/P127dqVnz5707NkTC4v0//7Qo0cP8ubNy4QJE3BxMS2Dj48Pn376KePHj6dJkyZp7mPjxo3Mnj2bli1b8sUXX6DRaF74s7xKcSuWozy4/0LbWtSug1X9BsQfPID+2NF0b6cp6I51t+4v9J5JBg8ezMOHDxk6dCi1atXK0La5Kb450alTp/j444+pV68eS5YsSXWduLg4EhISsLe3N1m+atUqJk2axLZt2yhYsCCdO3fmvffeo2fPnqxbt46vvvqK/v37M3jw4DTfX6/X88477/DkyROOHz+OtbV1usseHBzMvXv3OHjwIAsXLqRgwYIkJCSwfPlyihYtmu79CCEyl2V2F0CI3OjGTR2XfRJeej/9e9jT5X17Zi6OYMGyqEwoWdq8vLzQarVMmzaNli1b4uHhAUBQUBDnz59n/fr1+Pj4MHHiRBo0aGCybdOmTZk0aRIDBw5Eq9WaNELOnTvHgwcPuHz58jMbmB06dGDWrFmsXbuWYcOGZagRkpWUB/dRAgJeaFvd+nXw5AlWrdsQ/+QJun92ZnLp0ta9e3c+/vhjFi9enGYCkVYjMzfFNydKamjXrVsXRVGIjo7m7t27+Pn54evry/nz5zlx4gQajYaVK1dSoUIFw7YFCxbE3t6ekiVLAlC/fn2mTJlC48aNWb16NStXrqRGjRrPfH8LCwtKlSpFXFxchuJ6584d5syZQ2RkJHv27OGLL76gbt26eHl5sXDhQmrVqkXNmjVfoEaEEC9LEgghcqj+PewZ+qljliQPSezs7Khbty4eHh6cO3eOgQMHYmlpSevWrcmfPz979+7liy++YOvWreTLl8+wXXY2QnKapKTBqnUbk+evWk5tZIrns7KyAqBQoULMnTuXpUuX4u3tTcmSJdmwYQPVq1dn2LBhJCQkYGlp2izQarU4ODgYnn/++eeUKlWKNWvW4OHhwY8//sjAgQNT/GiQXEJCAlqtlocPH9K7d29Onz5Nvnz5WLBgAaVLl051m8mTJ1O2bFl++OEHZs+ejbu7O8eOHePq1av06NGDpUuXcv36dd588025WiVENpAEQogcKDuSB4CQkBA+/vhjli9fzkcffcS2bduwsLDg4cOHdOvWjS+++IL//e9/2NjYmGyXXY2QnCo7koic2MjMSkpcZMY30tqg0ap1pegSQBcLGgs0VnYZ2q/G2uG566QlJCSEs2fPAjB9+nQqV67MmTNnCAoK4uTJk2zYsAEXFxe6dzftAqcoCvfu3eP06dM8fvyY5s2bM3DgQPbt28fp06cJDAzkww8/pGPHjtSuXTvV916/fj2rV6/m1q1bREREUKBAASpUqEC7du0oX748pUqVSrPcLVu2ZMGCBYSEhLBs2TLq16+Pr68vs2fPZu3atSxYsCDN9xVCvHqSQAiRw2RX8hAWFkZYWBje3t58+umnHDlyhEWLFrFu3Tq+/PJL5s2bR+PGjdHpdIZfkbO7EZKTZWUSkVMbmVnqK6+Mb/PxL1CpnfrvS9tgdR/wegv6bjKuM7UGRAY/ez9TgjL+3on279/P+fPnAejZsyedOnUCYMeOHUyePJly5coxa9asFNutXbuWvXv3cuvWLQoUKMCMGTMoW7Ys7dq148qVK3To0IGYmBj++ecf3nzzzVSvGnXs2JEWLVqwZMkS5s+fz8OHDylatCjt27d/brkrV65MnTp1sLGxYezYsaxevZqhQ4eyZcsWGjVqRP/+/Wnbti2TJk164boRQrw4SSCEyEGyOnkICAhg8eLFREREcOPGDVxdXdm4cSOxsbG0bduWqKgoKlasSJkyZZg5cyZr1qzh1KlTWFpa8ssvv3D58uVsbYTkdFmVROTURqZ4vnfffZcGDRqwevVqSpUqZbjSdPPmTUCNtz6VGbw6d+5Mo0aNaN26NdbW1ixdupQBAwZw6tQpdu/eDYCTkxO1a9c2ufr0tNjYWFauXImlpSX169dn6tSpWFpaPjO+R48eZfjw4bRp04ZixYoxffp0vL29OXbsGMePHydfvnzMmzePTZs2vXjFCCFeiiQQQuQQ2XHloXDhwhQoUACNRkP9+vXRaDSULFmS3bt3s3LlSmbMmEHNmjXJly8fhw8fZuTIkfTv3x+dToeXlxeVK1fOtkbI6yIrkoic2MjMcpP8M76NNllXvvKt1H1onpq9bNTJlytXOiRNtrhu3To2bdpE2bJlWbVqFQAxMTG0b9+e8uXL06tXL5OxLRMnTiR//vw4OjpSr149wsLCaNy4MZ6enuzduxdra2v279/P2rVrKVCgAEOGDMHd3d3kvb/66ivc3Nxwd3fHw8OD5s2bM2rUKIKDg+nVq1eq5a1WrRpvv/02Q4cO5fvvv6dhw4b4+/vz33//sXjxYuzt7XFxccHS0pIDBw48s2ucEOLVkARCiBwgu7otAXz22WcAbNq0iUOHDrFv3z5atmzJt99+a1hn1apVHD58GG9v7xQNiOxqhLxOsiKJyGmNzKz2MuMQAHUshDblKfdl95uW+Ph49u7di4+PD/v27cPR0ZFKlSqRN29exo4dy8iRI/nxxx/Jly8f8+fPp3PnzuzYsYOlS5dSu3Zt5syZQ0JCAr1792bdunVcu3aNGTNmUKpUKfLkyQOoyWHFihUNCefTY2MWLlzIwYMHWbVqFT/99BN6vZ53332XPXv2MHXqVE6cOMHXX39NwYIFTbazsLBg5MiR3LlzhyZNmhAYGIiPjw937txhwoQJ3Lt3j3v37hn+zw4YMIAhQ4a8knoUQqROEgghzFx2Jg/JdejQge3bt+Pj40Pr1q1NXkvqmhIVZVq+7GyEvG5eRRKRkxuZ4tk0Gg2bNm1i9+7dvP3228ybN49169axdOlSZsyYQeXKlfnxxx8BKFmyJLNmzaJ3794cOXKEGzduoNFoWLhwIStWrCAhIYFRo0YxatQoQL0nzF9//UWLFi0oUaJEqu+/evVqlixZwrx58yhQoAAhISGEhoYyYcIEWrduzblz59i7dy9Hjhzhww8/pE+fPhQsWJCVK1cyffp0ChcuTPny5fHy8qJgwYK4ublRqFAhvvnmG5ycnLC1tUVRFMLCwp55dUsI8WpIAiFENihZXJuu9ZLfJG7/kTjKeb/4Vza975mW3bt3c/z4cSpUqMCMGTOoUaOGYWrOhw8fAphMp7hq1apsa4RkJ01B9+ev9IL0ly8R7+ysJhHOzoabzb3oe+bURqZ4PktLS+bNm8fp06epVq0aS5YswdHRka1bt2Jvb8+pU6cAdQYtgDp16vDTTz/h6upK7dq1DTeKvH79OrGxsSb7TvrlP63pdhcsWMD169f5+++/+fvvv1m4cCE3b96kUKFCWFlZ4ezszOLFi1m2bBlxcXFotVrOnDlDixYt6Nq1K127dk2xz3PnznHr1i28vEwHszs5Ob1cRQkhXogkEEJkMZ1OyfAdobu8r94wLjPeW6vN+Jzpa9asYeHChSxYsIAiRYrQqlUrHjx4wG+//YaFhQW7du0CMLkLcefOnbOtEZJdFL3+pe8InV5W9RtAfWPfb0WvR5OBu4NDzm1kivTRaDRUr14dgD59+pi8dvDgQQCcnZ0Ny1q1apViH0eOHMHNzc1kWdJVpOTbJomNjaV9+/aGG0327t2bLl26ULNmTapXr8748eMN606ZMiXdn+XpqYKFENlLEgghstiLNOCz8723bdvGw4cP2bx5M66urgDMnTuXqlWrEhsby8cff8ytW7coXrw4efPmNWxnkawxay6NkFctow14c3jv16mRKdKvSZMmbNu2jfLlyz93vadj6OjoSJ48eVKNrY2NjSGuSW7evElsbGyK7msZ4eDgkGLsjBAi+0gCIYR4ptQajEmznjg5OTFmzBgmTpzI999/n+Y+zKURIjImpzUyRfpVqFCBf/7557nrjRs3LsWyvHnz0r9//3S/V5kyZejRowdvvfVWhsqYXIkSJShQoMALby+EyFwaJek6sxBCZLHly5enuDlZWhRFYcqUKbz11ls0bNjwFZdMZAaJr8gs8fHxgPGO6UKI7CUJhBBCCCGEECLdsq/DrhBCCCGEECLHkQRCCCGEEEIIkW6SQAghhBBCCCHSTRIIIYQQQgghRLpJAiGEEEIIIYRIN0kghBBCCCGEEOkmCYQQQgghhBAi3SSBEEIIIYQQQqSbJBBCCCGEEEKIdJMEQgghhBBCCJFukkAIIYQQQggh0k0SCCGEEEIIIUS6SQIhhBBCCCGESDdJIIQQQgghhBDpJgmEEEIIIYQQIt0kgRBCCCGEEEKkmyQQQgghhBBCiHSTBEIIIYQQQgiRbpJACCGEEEIIIdJNEgghhBBCCCFEukkCIYQQQgghhEg3SSCEEEIIIYQQ6SYJhBBCCCGEECLdJIEQQgghhBBCpJskEEIIIYQQQoh0kwRCCCGEEEIIkW6SQAghRC4SFxfHmTNniI+Pz+6iCCGEyKE0iqIo2V0IIYQQWSMmJobGjRvj5eXFb7/9lt3FEUIIkQPJFQghhEjFpk2baNiwIVOnTiUyMvKF9hEUFESTJk346quviIuLS3WdTp06MWzYMEJDQ5+5r5s3b1KrVi369evH5cuXX6g8ALa2tvTr1w9fX186depE9erV2b179wvv786dO9SuXZvhw4dz+/btF95Ply5d6N+/P3fu3En19e+//54PP/yQq1evPnM/iqLQokULPvroI7Zv357hcixdupSmTZvyyy+/EBYWRkJCAgDNmzfnl19+MTzPiIiICKpWrcqWLVueu+6///5Lq1atWLZsWaqvh4WFUatWLWbOnIler3/mvjZu3Ej9+vUZN27cc/9/pcfOnTtp0KABixcvTlc97Nixg4YNGzJlyhSioqKIjo4G1FhPnz7d8FwIkfNIAiGEEKlo37491tbWLF26lPPnz6e5Xnx8PIGBgam+VqBAASpXrszGjRuJiIggIiKChg0bMnLkSCIiIrhz5w4+Pj4EBgby+PHjZ5anePHiNGrUiL179/Lvv/9m+PMsW7aMTz75hCZNmvDtt98SFxeHra0tPXv2pGrVqhneX5IiRYrQqFEjtm7dysaNG9NcT1EUAgMD02x4tm/fnj179uDj4wNA79696dq1Kzdu3CA+Pp4jR47w4MEDHjx48MzyaDQa+vXrx5kzZ1i1alW6P0dMTAyRkZH873//4+HDh/j4+LB48WI6duxIYGAgBQsW5Mcff+Sff/5J9z6TbNmyBRsbG1q0aPHcdRs3bkxERASbN28G4NixY7z99tvMnTsXgEOHDhEWFsbdu3eJjY195r7effddbGxsWL9+PdeuXctQmYcNG0aLFi1YunSpYdmff/5JaGgogYGBREVFpbltfHw8T548oUWLFtjY2HDhwgU2bdpE69atuXbtGkWLFmXRokWsXLkyQ2USQpgPy+wugBBCmCONRkOJEiV49OgRVatWJSAggMjISO7du8ft27fx9/fn6tWrXLx4kfj4ePr168ewYcNS7KdQoUIULVqUvHnzAjB8+HBGjx5Nu3btWL58OePGjaNTp05oNJrnlqlSpUps3LiRWrVqpftzxMfHo9VqadeuHc7Ozhw8eJCYmBgmT55MtWrVcHJyYsSIETx+/JhJkybh6emZ/kpKVLJkSQDeeecdQ8P2wYMH3L59m5s3b+Lj48O5c+cIDw/nzTffZMWKFVhYmP5+5e7uDkCVKlUAGDVqFF27dmXr1q3ExMRQt25dBg8ejL29/XPLU7FiRYAM1dP58+fp1asXQ4YMwc3NDS8vL/7++2/c3d3RarXcvXuXihUr0rJly3TvM8nvv//Oe++9h7W19XPX1Wg0uLu7U65cOQBq165NgwYNWLBgAW3atGHlypWsXLmSN998M137qlixIvfv36d69eoZKvPEiRPp2bMnf//9Nz179mTKlCk8fvyYP//8k1KlSj1z23v37tGmTRs++OADPDw8KFq0KLt37yZfvnxYWVlx9+5d3N3d6datW4bKJIQwH5JACCFEGrRaLYUKFSImJoYxY8Zw9epVqlatioeHB2vWrKFKlSqGX/OTGsCp7SN5o7dNmza4urpy/vx5IiMj+f7771m/fj1//PHHc8vj4uICQGBgIDNmzODmzZuULVuWnj17YmNjk+o2Bw4cYPz48cydOxcPDw9u3LjBN998w7hx4yhatCjNmjUjNjaWjh074ubm9gK1pH5GAE9PT+bOncumTZt44403qFixIlu2bEGr1TJq1CgArKys0Ov1KRIIS0v1dJRUV97e3ixbtowLFy7w559/cvv2bf766y9+//13Chcu/MzyuLq6AhAbG8uSJUu4ePEi7u7udO3aFQ8Pj1S3qVmzJgMGDMDf3x+AvXv34ubmRu/evXn48CEBAQGMHj06XYlecmfOnOHatWvMmTMn3ds8/X9m7Nix1KpViyVLlpCQkEDv3r3p0qWLoU6fxcXFBScnJw4cOMDhw4d58uQJ9erVo0OHDs/dbt26dTx8+JC//vqLmzdvkpCQQN++fRk1ahTNmjVLc9tixYoxceJEtm3bBsClS5ewtLSkZ8+euLi4cOrUKUaOHImtrW36KkQIYXYkgRBCiKcoikJAQAAhISHcv3+fXr160bt3b1q3bk1ISAgHDhxgzZo1xMTE0L59+1QbldHR0fj4+HD58mWuXr1KjRo1GDFiBIcPH8bPz48bN25Qo0YNfvjhh2f+Uu7n58eVK1fw8fHh8OHDgPrrfMGCBSlduvRz+6I3btyYv//+m6tXrzJlyhTi4uIYMGAA1atXZ86cOdy/f5+33nqL0qVLZ7hxDBAcHExAQAAAn376KbVr1+bs2bNERETw4MED1qxZg729PbVr16ZIkSIptk9ISODWrVscOnQIgHr16vH++++j0Wj477//uHbtGnnz5mXEiBHUqlUrzSskjx494uzZs/j5+XH27FkAfv75Z1xdXSlRogT58uVL8zNERkayadMmoqOjefDgAffu3SM6OpoKFSpw+/ZtLl++jLe3N+7u7owePZrdu3fTp08f+vbt+9z6WbNmDXXr1qVo0aLPXffx48dcvHiRoKAgVq9ezebNmxk7dixr167lzp07BAYG0rFjR0aMGPHMKwqnT5/Gz8+Pq1evcuDAAUJCQhg0aBBFihTB29ubZ82dotPp+OijjwgPD0en0xnqr1q1apw5c4YLFy4wePBg1q9fT4UKFVLdfv369dy/fx8rKytOnTqFjY2NoS4DAgJwdXWlRo0afPvtt/z111+0atWKCRMmPLd+hBDmQxIIIYR4yq5du9iyZQsBAQG4ubkxYcIEQ7eYMWPGsG/fPgoVKsSKFStSbXSHhoby5ZdfEhYWxrVr1yhWrBhjx46ldu3adO7cmbi4OKpVq4ZGo+HcuXPkz58/zXEIERERnDlzBktLSy5dugTAiBEj+PTTT9P9eSZMmMDSpUv58ssvAfXX9S5durB69Wpq1arF8OHDcXR0ZP369YZfvidMmMDWrVuZN29emgnOtWvXWLRoEb6+vgAMGTLEsO7vv//ODz/8gFarZfHixakmDwBTpkzh8uXLPHz4ECsrK7755hsaNGiAo6MjAF27dsXHx4f79+9z8uTJNBOIhIQELly4QFxcHBcuXACgYcOGLFy4MMXVjqc5ODhQu3Zttm/fzqFDh7CwsKBWrVq0b9+esmXL8tNPP2FjY0PHjh1p3rw5EydOpFGjRs/cJ6gJwY4dO5g+ffpz1z179iyzZs1Cr9cTEBBAs2bN6N+/P+XKlaN169YcP36cbt26odfrOXz4MF5eXmle9bp//z6XLl3iyZMn3L17FysrK5YtW0aNGjWeWw6tVsvYsWP56KOPGD9+PF27djW8NmbMGKysrJg1a1aqyUPS9vXq1ePAgQMsWLAAjUZDjRo1eO+993jnnXdo2bIlFhYWvP/++9SpU4eRI0fStGnT55ZLCGFeZBC1EEI8pVmzZsydO5dy5coZBkInNUILFCgAqI2ppC5FT8uTJw9z585l3LhxhIeHY2NjQ1hYGDqdjuXLlzNs2DAUReHKlSucP3+e4ODgNMtSqVIlxo8fT4ECBQyz7vzyyy+GQbbPotPpGD16NM2bN6d8+fJoNBqmT5+OhYUF+/fvZ8eOHezatYs1a9ZQsmRJk0G5d+/eJTw8/JllK1OmDNOnT6d9+/aA2mBP6paSVE+dO3c2jGtIzfjx41myZAnx8fFYWlqSkJBAdHQ0+/fv5/PPP+f69etERkaye/du7t+/n+Z+3N3dGTZsGK1btyYoKAhQf4mfOHHic+sJ1C47Gzdu5MMPP6Ro0aLkyZOH4cOHM2nSJL777jv27NkDwOHDh3nnnXews7N77j7//PNPXF1deeedd567bpUqVfj1119p0qQJoM6WFRISQkxMDFOmTGH27NmAOojaz8/vmTODJf2in7SOjY0NAwcONAxQf56qVatSsGBBTpw4wYEDB/jjjz/48ccf2bFjB3Z2dnh7ez9z+zx58rBhwwbq1q1L7dq1cXd3Z9KkSYwcOZJevXpx8OBB3NzcOHnyJG+++Waa3yMhhPmSKxBCCJGGpAb70aNHuX37NjY2NoaxCg8ePGDAgAG0atWKZs2apTpAdvLkyej1evR6PV9//TWRkZF06NCBatWqcfLkSbp3786AAQOIjY3l0aNH5M+fP9VyBAcHs2jRIvLnz8+jR48YO3YsY8aMISEhgffffz/N8ieNTWjSpAkVKlSgadOm9O/fn99++41ChQoZZsFxdXVl8uTJXL16lcqVK2NjY8OCBQsIDQ01JALPqycLCwt8fHzYt28f9evX56uvvgLUhvDAgQN58803ad++PXny5Emx/c8//0xgYCCOjo4sW7aMgwcP8u2335I/f35CQ0PR6XQsW7YMnU5HUFAQ+fLlM3y25BRFYerUqYZ6GjZsGNOnT0dRFL7++us0r0Q8ePCAPn360LFjRzZv3kz+/Pl577336NOnj6Gvvo2NDQ4ODpQuXTpdA7kVRWHt2rV88MEHqZY1NY8fPzbMtnT//n0++eQTdu7cSbdu3Th//jwnT55kxowZ1KhRg/DwcMLCwtJsfB89epS9e/eSP39+ChYsSLVq1ejevTs///xzmlcPkqtfvz5nz55l165d5M+fHw8PD7777jssLS25ceMGefLkMVwlSi4iIoK+fftSqVIlfHx8CAsLY/jw4QwYMIBJkyYZ4p8nTx4sLCzSvDIlhDBvcgVCCCFScf36dR48eMCJEydYt24dhQoVYsKECYYZh7y9valZsyYjRoygRYsWKaZ6Xb9+PVeuXKFhw4YUL16c1atXU7FiRQIDA3nw4AHx8fH8/ffftGrViipVqlC3bl26du2a6h2iJ0yYgKIodO/eHYCmTZvSvn17xo4dy7x58555P4Dvv/+er7/+Gk9PT7Zs2cKgQYPw9PTE19eXI0eOsGLFCnr27Mlbb71F165dGTJkCKAOdk5P8vDo0SOuXbuGXq9n9OjRVKpUieHDh5M3b16sra2xsbGhT58+TJ8+nUaNGrFhwwaT7X19fVmyZAmtW7fGxsaGX3/9lQ8++IB79+4RGhrKkydP8PHxoVOnTlStWpX69evTpEkTbt26laIsK1as4MSJEwwePBgADw8Pxo8fzx9//MGgQYMIDw9P9TPs3buXOXPmMGDAABISEihatChLliyhbdu2tGjRgpEjR3Lu3DkiIyOpV6/ec+sE1OlXAwIC6NSpU7rWBzVWnp6eFC9enJYtW7J8+XK0Wi2PHj0yXH356aefaNiwITVq1KBWrVpMmzYtxX7Cw8MZN24cb775Jm+99RYAI0eOJE+ePHTr1o29e/emWYaffvoJvV7PG2+8we+//06ePHnYsmULkZGRtGzZkoYNG7JkyRJq1qxJjx49uHHjhsn2e/fuZezYsXz11VdYWFhQsGBBtm7dSosWLahduzZTp07l6NGjBAUFUbdu3ed2LxNCmCe5AiGEEIn0ej0DBw7kwoULPHz4EA8PD37++We0Wi2DBw+mfv36dO/enf/9738oikKPHj24fv0669atY8qUKaxevRpQpwT96aefmDZtGjt37iQiIoKZM2dy6NAhSpcuTalSpYiLi8PLy8vQcAa1L37SbERJ5syZw+7du1m4cKFhsLKiKIwbN47jx48ze/Zsjh07xqRJk/Dy8jJsFxcXR1BQEE+ePCEgIMAwcPvGjRv4+PiQkJDAsGHDsLCwwNbWFmtra/R6Pfv27SMwMBA3NzdCQkLSTCLmzJnDX3/9xe3bt7G1tWXMmDE0atSI/v37o9frWbVqFU2bNkWv11O1alW+/fZbRo4cyYQJE2jTpg02NjaEhoYyaNAgPvnkEwoXLszRo0fZsGEDs2fPxtPTkzfeeIPQ0FBcXFzo1q0bdnZ2WFhYoNVqU1ytOXz4MD/88AOffvqpYYCxXq/n3XffZffu3ezatYv27dvzzTffULduXcN2f/31F7t27WLbtm0EBgYSEhJCZGQkvr6+aLVa2rRpQ3x8PL179wYwdDF6njVr1vDOO+9QsGDBdK2/atUqTp48yfLly+nRowdBQUGsWbOGoKAgypUrh5OTE6DOFlW+fHmsrKxQFCXFmJCEhAQGDx5MTEwMP/74I1OmTEFRFGxsbPjhhx/46KOP6NevH507d2b48OE4OzsbtvXz82PJkiV88MEHhqSuf//+HDhwgGnTpvH222/j7+/P4sWLGTx4MAcOHGDmzJmGGaaOHj3Kpk2b0Ol0PHjwgLt37/LWW29x6tQpFEXhnXfewdPT05DMpbcuhRBmSBFCCGGwf/9+pXr16sqwYcOU8PBwZd26dUrt2rWVVatWKXq9Xtm3b5/i7e2tHDlyRFEURYmNjVU6deqkvPPOO4qiKMrNmzeVTz/9VLl8+bKiKIrSt29fpW/fvinep2rVqsr8+fOfWZZff/1VqVSpkrJt2zYlJCRE+e677xRvb29l6NChyoQJE5QtW7Yob7zxhuLt7a2ULVtWGTVqlOLn56coiqKcO3dOqV+/vlKvXj2lb9++yowZM5T169crR44cUdq1a6e0b99eiYiIMHk/nU6nREVFKYqiKJ988olSpkwZZfv27amW7fbt20qLFi2Udu3aKf7+/sr58+eV+vXrK998840SHh6uREdHK97e3srMmTMN20yePFnx9vZWAgIClNjYWGXQoEHKtm3bFEVRlCVLlihVqlRJ8T59+/ZVevTo8cx6OnHihFKtWjVl6tSpSnh4uLJ161bF29tb6datmzJy5Ehl165dSt26dRVvb2/F29tb+d///meIX3x8vLJo0SJlxYoVyqBBg5TJkycrwcHBSrVq1ZS5c+ca1qlVq5ZSt27dFHWWmqCgIKV8+fLKoUOHnruuoijKrl27lBEjRijBwcGKoihKlSpVlCVLlpisc/HiRcXb21s5efJkmvuJjY1VhgwZorzzzjvKlStXlMDAQKVz585KzZo1laFDhyqzZs1SZs2aZaiH6tWrK7Nnz1ZCQ0MVRVGUzp07K97e3oq/v78yZMgQZc6cOYqiKEpISIiyaNEiJT4+XmnatKmyb98+JT4+Xpk+fboyffp0kzKsWrVKWbRokTJx4kRl4MCBSnR0tNKoUSNlzJgxhnXatWunlC9fXrl//3666kcIYX7kCoQQQiTToEEDDh48iJ2dHadOnSIqKop//vnH8AvwlStXAON9C6ytrVm4cKFh6tBChQqxaNEiQL1ScPnyZUqXLp3ifRRFeeaNxWbMmMHVq1fZsmUL586do3///oYrEFqtlnz58lGzZk1mzJjB9u3b0Wg0ODo6cvLkSby8vKhUqRIHDhxIdd9JA4AdHBxMlltYWBheK1SoEI6OjqmOWQD1DtR//fUXer2e0NBQtm7dyqpVqwx92k+fPm0oa5JRo0ZRrlw5PDw80Ov1zJgxw/D6pUuX0pyS9ln1tHv3bubPn8/cuXNxdnZm8ODBJldq8uXLR4ECBfjll19YunQp0dHR5MmThwsXLlCxYkUcHR0NM1qVK1eOH374gbfffpuEhATDvRJmz56NjY0NwcHBdOrUia+//vqZN3Jbt24dHh4ehu5Dz9OgQQPDr/H+/v5ERUWlqAslcerVtOoiMjKSESNGUKRIEbZs2cLChQs5duwYN27cQKvV4uTkhLOzM507dzbcHdrW1paYmBguX75MjRo1OHXqFGXKlKF48eK0adOGIUOG4Orqyocffmioo7fffpvBgwfzxRdfMHjw4BTjOz7++GNA7Zo2ZcoUatWqRUxMDFOnTgVg7dq1BAYG4uDgQKdOnfjqq6/kSoQQOVH25i9CCJGzdOvWTfH29lZu3Ljx3HV9fX0Vb29vZdiwYSleq1GjhrJ27dpUtwsODlauXLmSYvnQoUMVb29vwy/VL+rjjz9Wunfv/lL7eJ65c+cq3t7eypo1a9K1fu3atZW6deumWD5gwABl+PDhqW6j1+uVY8eOKXq93mT56tWrFW9vb8PVjfQKDAxUevXqpbzzzjtK5cqVld69eytfffWVMmjQICUoKEhZsmSJ4df77t27K7t27VJ0Op3JPnQ6nfL2228rv/zyS4beO8lvv/2War1duXJF8fb2Nlxhepqvr6/y4MGDFMvr1q2rtGrVKl3vPXPmTMP+9Xq90qdPH8Xb21upVq2a0rhxY6VGjRqGz+/t7a0sWrQozX09fvxYGTlypFK7dm2ldu3aSsuWLZUFCxYoPXr0UPz9/ZUdO3YY9vP+++8rmzZtUmJjY9NVTiFE9pMrEEIIkQE9evTg8ePHlChR4rnrFi9enGrVqqU6jaejoyPFihVLdbu8efOSN2/eFMuTftV/Wba2toa7Nb8q7du3Z/PmzSbjDZ7l3XffJSIiIsVyR0fHNO/9oNFoUr1HRVI9pffGeHfv3mXevHncuHGD5s2bM2fOHC5fvsz27dvp06cPhQoVAqBnz55ERUXx33//4eLiwuXLl6lcubLJHbz379/Po0ePeO+999L13k9r2LAhnp6eKa5eODg4YGVllWZdlCpVKsWy27dv8/Dhw3THOmkAPah1N2/ePNauXcu///6Lr68v0dHRuLq64uXlRaNGjUzuEZEkNDSU+fPnc+bMGRo0aMC2bdsICQlh4cKFtG7dmn79+gHqd2PChAns2rULBwcHfH19qVy5MsWLF09XWYUQ2UujKM+4JaUQQohX4rvvvuPzzz9/Zvecp40ZM4Y9e/Zw5MiRdE8NmtZ+SpQowSeffPLC+8gqa9asoWrVqrzxxhvp3mbTpk2MGjWKTZs2UbZs2eeuHxcXZxhobK4SEhKYMmUK48ePT/c2cXFxdOjQgeLFizN//vxXWDqjhIQE4uLi0jXVrRAi55IEQgghcgidTkdgYCCFCxd+qf3s27eP0qVLp/lr9uvg9u3bFC1aNLuLke3Cw8OJj49P9YqWEEK8KEkghBBCCCGEEOkmd3ARQgghhBBCpJskECJNOp0uu4uQa0hdZw2p56wh9Zw1pJ6zhtRz1pB6zhqZVc+SQOQSFy5cYNmyZSZzi+v1en744Qd27tzJV199RWxsrMk2P/zwA7/88ktWF/W1JHWdNaSes4bUc9aQes58ci7MPlLPWSOr6lkSiNeAn58fPXr0oGrVqjRq1IhVq1YZXrt//z49evRgw4YNfPjhh4abXwHs2LGDM2fO0Lx5c4KCgli9erXhtf379/Pvv//y4YcfZulnMXdp1fXJkycZMmQIU6dOZcCAASxbtsxkO6nrjImNjWXMmDG8+eabvPXWW4b6jImJ4csvv2T8+PEMGDCAo0ePmmwn9ZwxadVzUFAQn332GdWrV6devXrMmjXLZDup54xJq56T+/PPP1NMiyr1nDHPqmc5F2aetOpZzoOvzty5cxk9ejRgPudBSSByuPj4eKZNm8bw4cP5888/KVGiBJMmTeLixYs8fPiQDz/8kFKlSjFx4kTDHWaTnD592nAQdXFx4cSJE4DaeBg3bhzTpk0z3H1XPLuuP/nkE7p27cqoUaP44YcfmD59Ov/9959hW6nrjFmzZg1t2rRh5cqVFCpUyDAF5bfffktcXBzffvsto0ePpl+/fty+fduwndRzxqRVz1OnTqVr165s3ryZ+vXrM3/+fHbu3GnYTuo5Y9Kq5yQHDx7kyy+/TLGd1HPGpFXPci7MXKnVc2hoqJwHX5ENGzYwZ84cw3NzOQ/KjeRyOD8/P3r27EmlSpUA6Nq1KwcPHiQ4OJhFixYRHR3NsGHDUt3W1tbW8G+NRoODgwN6vZ6RI0fSrVs3qlSpkhUfIcdIq64PHTpEVFSU4ZK4o6Mj9vb2Jr9wSV1nzPvvv284wL3xxhu0atWKgIAA/vzzTyZPngxA0aJFyZcvHz///DPffPMNIPWcUanVc0hICE2aNDHcoK1Pnz5s3LiRR48eGbaTes6Y1Oo5ydWrVzl9+jQFChRIsZ3Uc8akVc+TJk2Sc2EmSq2e79+/L+fBV+Dw4cPExMQYnpvTeVCuQORwZcqUoUaNGobn/v7+eHh48MYbb7Br1y5atWrFnj17GD9+PEuXLiU+Pt6wbosWLXj8+DGg/kLTrl07Fi9ejIWFRY64wVRWS6uuu3XrRr169Rg3bhwnT57k0KFDDBo0iKpVqxrWlbrOGCcnJxRFYcmSJcTExNC0aVMOHz5MQkIC+fLlM6yXP39+9u3bZ3gu9ZwxqdVz3rx5admypWEdPz8/HB0dadKkiWGZ1HPGpFbPAPfu3ePPP/9k8ODBqW4n9ZwxqdVzUFCQnAszWWr1/MYbb8h5MJNdvnyZs2fP0qVLF8MyczoPSgLxGrl16xbr169n4cKFXLx4EUVRKFmyJG3btmXQoEHMmDGDuXPnGtavUKECY8eO5d9//6VLly44OTnx22+/8cMPP6DRaLLxk5i/5HVtb2/PrFmzqFu3Llu3buXzzz8nNDQUvV5vWF/qOuNWrlxJbGwsd+/epWXLljx8+BAAKysrwzpWVlaEhIQYnks9Z9zT9Xz48GHDayEhIcybN4958+ZRsGBBw3Kp54x7up63bdvGwoULGTFiRJp1JvWccU/X8/nz5+Vc+Ao8Xc9HjhyR82AmunPnDn/++ScDBgwwWZ50vjOL86AiXgt+fn7K8OHDlQcPHiiKoihbt25VvL29lT/++MOwznvvvae0a9cu1e3DwsKURo0aKfv27cuS8uZkT9d1TEyM8u677ypBQUGKoijKmTNnlIoVKyqzZs1KdXup64wJDw9XKlSooNSoUUPx9vZWdu/ebXitU6dOSr169VLdTuo5Y5LqedCgQYqiKMqjR4+U4cOHKzdu3HjmdlLPGZNUzwMHDlSqVq2qVK9eXalevbpSpkwZpWzZskr16tWV2NjYFNtJPWdMUj17e3vLufAVSn7ckPNg5hk+fLhSrVo1w/HB29tbKV++vOH/szmcB+UKxGvg5s2bbNiwgalTpxr60fr5+QHw5MkTk3WT90dMbvz48TRt2hRXV1e2b9/O7t27X22hc6jU6nrbtm0EBQXh5uYGQJUqVahfvz7nzp1LdR9S1xnj6OiIg4MDb7/9NpaWlgQFBRleCw4OpkGDBqluJ/WcMUn1bGtrS0hICAsXLmTSpEmUKFECgM2bN6e6ndRzxiTVs729PadPn+bkyZOcPHkSDw8PqlevzsmTJ7G2tk6xndRzxiTVc9LxQc6Fr0ZSPR89elTOg5lo2rRpnDp1ynB8AGjTpg3//vuv2ZwHJYHI4WJjYxk1ahSenp78+eefrFu3jqlTp3L//n3KlCljmAEhJiaGmzdv0rlz5xT7WLt2LQEBAXTt2pVhw4bRsmVLli9fzqFDh7L645i1tOp648aNhIaGcufOHcO6wcHB1K9fP8U+pK6f7+HDhyxevJioqCgALl26RHx8PP369aNjx46Gurpz5w4PHz5MtU+n1PPzpVXPffv2ZeTIkXh6erJt2zbWrVvHwoUL2bJlS4p9SD0/37PqOb2knp8vrXoePXq0nAszUVr1vHLlSjkPZoEiRYqYzXlQZmHK4f755x/Onj3L2bNnTZb37duXRYsWMXbsWKZOnUp4eDhDhgyhY8eOJuv5+voye/ZsVq9ebfKfqmDBgmzevJl69eplxcfIEZ5V1507d2by5MlUqlSJ2NhYmjVrRo8ePUzWk7pOH71ez549e9izZw9t2rTB39+ftWvXUrJkSb788ksmT57M+PHjCQ4OZt68eRQvXtxke6nn9EmrnsPCwjh06FCKk0ybNm1Mnks9p8+z/j+nh9Rz+jyrnuVcmHnSqufSpUvz448/ynkwC5jLeVASiByubdu2tG3bNs3Xf/311zRfi4mJYdiwYXzxxRcUL16cvXv3Gl7TarWEhYVlallzuufVdfKpGZ8mdZ1+BQsWZO3atam+ZmlpyYQJE9LcVuo5/Z5Vz9euXXvmtlLP6fesek5uz549KZZJPaffs+q5UKFCci7MJM+q51atWsl58BVJfkw2l/OgdGHKxb777jvKlStHhw4dALXPYlxcHKB210k+/Zp4OVLXWUPqOWtIPWcNqeesIfWcNaSes0ZW1bNcgcilLly4wPHjx9m4caNhWdWqVenYsSObNm3Czs7OZO5h8eKkrrOG1HPWkHrOGlLPWUPqOWtIPWeNrKxnjaIoSqbsSeQ4T548wdnZObuLkStIXWcNqeesIfWcNaSes4bUc9aQes4aWVXPkkAIIYQQQggh0k3GQAghhBBCCCHSTRIIIYQQQgghRLpJAiGEEEIIIYRIN0kgcpnGjRvTuHHj7C7Ga0/qOWtIPWcdqeusIfWcNaSes4bUc9bIjnqWBEIIIYQQQgiRbpJACCGEEEIIIdJNEgghhBBCCCFEukkCIYQQQgghhEg3SSCEEEIIIYQQ6SYJhBBCCCGEECLdNIqiKNldCAFlypQBQKvVvtL30el0WfI+uZ3Uc9aQes46UtdZQ+o5a0g9Zw2p56yRWfWctJ9r1649d13Ll3onkePIlzhrSD1nDannrCN1nTWknrOG1HPWkHrOGtlRz5JAmImk4F++fDmbSyKEEEIIIXKbcuXKpXtdGQMhhBBCCCGESDdJIIQQQgghhBDpJgmEEEIIIYQQIt0kgRBCCCGEEEKkmyQQQgghhBBCiHSTBEIIIYQQQgiRbpJACCGEECLXUPT67C6CEGnKKf8/5T4QZubziWHcuKl77np2thq+HOFIUU8tE38M57r/87d5FTq2taXL+/as2hDF+r9isqUMpby0TBzpxO27Or6ZFkF0TNbfXF3iYSTxMJJ4qCQeRhIPo+yIR4M6Vgzr60TciuUoD+6naxuNeyGsOn2A8ugR8RvWQVzcKy5lKqytsXq/E5r8+Ylf9wfK/cCsLwNgUbsOVvUbEH/wAPpjR7OlDK9zPDQF3bHu1j1T9vWqSQJhZm7c1HHZJ+GZ6zjYa/hluguFC2npPugxF648e/1XpX8Pe7q8b8/MxREsWBaVLWWoWNaSr0Y4ce16An2GhxEZlfUnY4mHkcTDSOKhkngYSTyMsiseJYqpN21VHtxHCQhI1zZKQABxQUFYDxiIVdv2xC2cD7Gxr7KYqYqbPRPrfgOw6tiJuPnzUG7fyvIy6NavgydPsGrdhvgnT9D9szPLyyDxMA/ShSmHSTr4ly5hSc8h2XvwH/qpY7afjH+d5YqvX/afjCUeEo/kJB4qiYeRxMPIHOKRUcrtW8TNn4emUCGs+w0AG5usL0RsLHEL56MEBmI9YCCaosWyvgyA7p+dxG/9G6vWbdA2a54tZZB4ZD9JIHIQOfgbycnYSOKhkngYSTyMJB4qicfLk0arkSQRicwkHtlBEogcQg7+RnIyNpJ4qCQeRhIPI4mHSuJhVMTj5Zo90mg1kiQikZnEI6tJApEDyMHfSE7GRhIPlcTDSOJhJPFQSTyMKpa1pG83h5fejzRajSSJSGQm8chKkkCYOTn4G8nJ2EjioZJ4GEk8jCQeKomHUVI87j/MnBmnpNFqJElEIjOJR1aRBMKMycHfSE7GRhIPlcTDSOJhJPFQSTyMksfj55XRmbZfabQaSRKRyEzikRUkgTBTcvA3kpOxkcRDJfEwkngYSTxUEg+jp+MRG5e58ZBGq5EkEYnMJB6vmiQQZkgO/kZyMjaSeKgkHkYSDyOJh0riYZRV8ZBGq5EkEYnMJB6vkiQQZsbOVg7+SeRkbCTxUEk8jCQeRhIPlcTDKKvjIY1WI0kiEplJPF4VSSDMzJcjHOXgj5yMk5N4qCQeRhIPI4mHSuJhlF3xkEarkSQRicwkHq+CJBBmpqinVg7+cjI2kHioJB5GEg8jiYdK4mGU3fGQRquRJBGJzCQemU0SCDMz8cdwOfjLyRiQeCSReBhJPIwkHiqJh5E5xAOk0ZqcJBGJzCQemUkSCDNz3T9z5qjOKDn4q+RkbCTxMJJ4qCQeRhIPI4lHStJoNZIkIpGZxCOzSAIh5OCfSE7GRhIPI4mHSuJhJPEwknikTRqtRpJEJDKTeGQGSSByOTn4q+RkbCTxMJJ4qCQeRhIPI4nH80mj1UiSiERmEo+XJQlELiYHf5WcjI0kHkYSD5XEw0jiYSTxSD9ptBpJEpHITOLxMiSByKXk4K+Sk7GRxMNI4qGSeBhJPIwkHhknjVYjSSISmUk8XpQkELmQHPxVcjI2kngYSTxUEg8jiYeRxOPFSaPVSJKIRE/Hw71Q1pfhBUkCkcvIwV8lJ2MjiYeRxEMl8TCSeBi9TvGwqF0nk0uWPmbZaJUkwmziYdXpg6x//xckCUQu8jod/F+GnIyNJB5GEg+VxMNI4mH0usXDqn4DabRKEgGYWTwePcr6935BkkDkEq/bwf9FycnYSOJhJPFQSTyMJB5Gr2M84g8ekEarJBEG5hKP+A3rsv59X5AkELnA63jwfxFyMjaSeBhJPFQSDyOJh9HrGg/9saPSaAVJIpIxi3jExWX9e74gSSBec6/rwT+j5GRsJPEwknioJB5GEg+j1z0e0mhNJEmEgVnEI4eQBOI19rof/NNLTsZGEg8jiYdK4mEk8TDKLfGQRmsiSSIMzCIeOYAkEK+p3HLwfx45GRtJPIwkHiqJh5HEwyi3xUMarYkkiTAwi3iYOUkgXkO57eCfFjkZG0k8jCQeKomHkcTDKLfGQxqtiSSJMDCLeJgxSSBeM7n14P80ORkbSTyMJB4qiYeRxMMot8dDGq2JJIkwMIt4mClJIF4juf3gn0ROxkYSDyOJh0riYZTp8dAFgD40Q5ukGQ99GOhuv1x5MiDVeChZ+3/DHL4f0mhNJEmEgVnEwwxJAvGaeC1Pxi9AGkdGEg8jiYcqx8QjwReC65s+wserr+nuQcRPEDYYQtpDxAxQYtXXYnek3C64PoS0BiXe5C2Sx6PngH+JDGidshy6BxA+AZ4Mhcd91PdVUilv/Hl4Mgxid4LGMfUPrcRA1M/wZIxa7iejsbN+kDIeugcQPgkiZ6a9L4D4ixA2AML6Q/i3puVSoiFiGjz+HzzuDlErQHnq/5sSBeFfQ9gASrtNpm9XrWk8dPfg8UcZTohelDl8P5JIozWRJBEGZhEPMyMJxGtAGkeqHNM4ygISDyOJhyrHxUOTDyyKGh9aLzVRiF4NDoPAZTbYfwqxGyFqsbqN/jFYeJpup3ECm1agsTLsOikeF6+E063XFGIeTQQlzPT9lQQIHwHawuA8E1wWQIIPRM42XS96FTz5HOy6g31vk/cxEbUEbNqB8/fq/hL+w932S9N4xJ+EsG5gWRqcJoBF3tT3pburls2up1ou4tVkIEnEZDWZcZ4FTt9B9BKIXvpUeZaBEkP/Ictwsg+iV985plceIr4Gm2ZgkSfNEGUWc/h+PE0arYkkiTAwi3iYEUkgcjhpHKlyXOPoFZJ4GEk8VDkyHo6fQ55Vxod9b4j/T23UahJP3DZN1L/xx9W/Fh6QZ63pdtoiYGO8umASj34/EK9tqSYnT9P5g+4WWFZQn2uswKoaJFwxrhP7D0QtBKevwKpK2p9F/wQsXEBbEAAH5xKULFkGf/8r/K+/X+KVh9vwZCzYtAG7zs+um6gl6l+rGupf6/oQfwTiz0DCdYjbD1YVwSIfaD3BogDErDO9ChN/kjLergz91JGImEKcPnko2f4XAxZg1+vZ5cgE5vD9SIs0WhNJEmFgFvEwE5JA5GAv2ziyQEdNlxO0dttGTZcTWKDL8D7M4eCfIxtHr4jEw0jiocqx8dA4pFxmVRcsKxqfK+HqXwt39a9NA9P1E26CogfL4kDKeCTYDAFtGo0hjStgAbG7jN1/9PfBum7ie8dA5Dw1wbCu9+zPYuEMtmpSkBSP+PhwLCzsuHo9sZtS5DxAr15VeBYlHuIOqJ9Zo1GXaT3Vv7G7QXczsfwuyT6LIyiRakKUyC2/FeW8rZi5OAKfGzrQWKovxB2H2G3gONG47BUxh+/H80ijNZEkEQZmEQ8zIAlEDvWyjaOm+Xaxu1YzVlTuxbSyX7Cici9212pG03y70r0Pczj459jG0Ssg8TCSeKhydDwifoDgJhDSQh0ToA9RG8xJjWZQrwAA2HZKfR+x28GmMaDGw7vgTBrUr0nPAXufHw+tG9h1hbhdEP45RG9QuxbZfaK+HncUlBA1oYicCY97qmXWJyY1ET9BSEv1qgCARmuIR1z0FW7fuone+j3QWKjjDOKPglVliNurjrd4MgYS/FOWS3cHiDVNsJL+nXBNveoAoA9KvpH6R4kB1Hh82KkRx07cZMGvkeo+rd4C/SO1+5PjF4arJa+KOXw/0ksarYkkiTAwi3hkM0kgcqDMSB5mlRtOQesHJssLWgcxq9zwdCUR5nDwz9GNo0wm8TCSeKhydDw0TmDfHVzmqWML4var4wySzwqUcFPtx2/XC6xrpdyHokDcbrBubIjH9RsBREWFExOdzoHB9n3A9iPAGqJmJSYNwYnvf1X9qy0KDkPB6Wt13EHE9+py/X1QIgyDkJPiUbhQHH37jQGrWmq3LFAb/ijq1RDbtur4Dv09dZxDYqPf+LkiEv+R/PRtaXzNshxYFIKEi+qYDVDHhgBY5DXEI07bhcDgUvBkkLqN3YcQ/g3YNALrp67kZDJz+H5klDRaE0kSYWAW8chGkkAk0uv1ACiKgk6nQ0m8ZJ3076TXk/5ml8zotjS21BRAwULz1Gsa9TOPKTn1md2ZzOHgn6MbR5lM4mEk8VDl+Hho3cGmBViWUccD2H0IOl9IuKS+rg+G8NFg/wnYp9HlJ+E8WLhRsUIhQzx8H02GPH+qjeTnUfTqDEzWDdWBz04/qQ39J2MSX0+80qCxTyxzUXUcRPxhUOLAaYrhvZLi4VUUWrUdRExCCXVwc9KA66f3pbEHm6agf6jOtpRc0jomx+jE+Goc1PEhztPVJCBiMsQdUweIW7jRv3cpQzwW/5YADoPBZa76N3qNWg77Ac+vm5dgDt+PFyWN1kSSRBiYRTyySa5MILZt28YHH3zAuHHjeO+99/j+++9ZtGgRZcqUoWzZspw8eZL333+fMmXK0LVrVwIDAxkwYABVq1Zl8+bNXL58mcmTJzN16lRGjBhBlSpV6Nq1K+Hh4a+03JnROKrhcopCNg9SJA9JLDQKHrb36Vd0MSXtb2ClSXvqQ2kcSWMVJB7JSTyMMjUeSeMe9MHqFKXh49WZmOwSuy7pQ0EfYbpN3H4KFWlkEo+oaC1Y5E/fe8YfhvhTYJU4iNq6Jti+B7qrancqC1d1efKrIhoXQK82xDWWYJHfJB6t2o8nPKa0OuhaY61uqwtMHG+B6SBnjXPissem5dIWBazVgdlJlMjEeiqduE5hcJoErssTuzfpqVHr3bTjEX8OYn4Hu/9B+Bh4MvqVTN9qDt8PG+s0Tn7pJI3WRJJEGJhFPLJBrksgTp48yfDhw3nzzTeZPHky3333HcuWLePhw4c0bNgQRVFQFIXZs2ej1Wpxd3fHw8ODr776iuHDh9OqVSs2bNjAihUrOHnyJL169aJ///6cOHGCv/7665WVO7NOxm7Wj9K13uDi89haoz1n6tVge402zCs3iCk1ZrKy53bunjjO0JEB0jiSxqrEIxmJh9FLx+PpexbogwEtWL4BkbPUbkVJg5kBon9/qs8/WCmHWTS7nWk8lAS1n3966O4Bpj+goHVHPW3agGX5xPUCkpX7MWjyqg8lATubYEM8OnReQ8hjK7VbVJL4UxB3BCxLoSYFyfeVOK2stmTi82j1r8ZanXVJH6he6QB1WlcAm7YpP0fsVhyd3Fk0t2/q8dCHQcQksB+kzihl10NNmiK+TU8tpZu5fD8+6Wr30vuRRmsiSSIMzCIeWSzXJRC7d+9GURSqVq0KwBtvvIGTkxP//PMP/fv3B2DZsmUULlyYwoULs3PnTgIDA9m6dSsffPABNjY2NGvWDIAGDRpQvnx5KleuDEBISMgrKXNmNo4exqXv17cbUV5EJDhgqdHhZX+Txvn30sH+F2z/HkaZf9rTOd8Sw7qulqF09VhJHdejL1W253ltGkeZwFxOxhIPlcTD6KXjoQuE0BYQvV59rsRAzJ9g30/t4x93RJ3ONeIn9RH+LcSsV2c6SuRd/B5FCzsQGVPQNB7hYyD0PYjd+9SbJnZNVZJ1C7KqqSYcMZsSX9OrYyBs2oKFgzroWFtWvXmdolcTk/jzapcqjQZt1FhiAt/llt9uug96SIDvXPXmbUnljpgKkT+o07ta5FGvbsSdUPejJEDsAbBuBJZeELsPQppDzN9qWez7qN2f4varz+MOglVtsCpn+rHiT2GnPcqq3xbxy2pSj0fE92BVXb1qoQ9Ur9BoS0L8CfVKSyYwp++Hu5s2U/YnjdZEkkQYmEU8slCuSyCSWFgYP7pGo0Gv11O1alVq167Nvn37+OWXX+jSpQsJCQnMmTMHW1tbbNL4z6BJnBVEefpXs0yQ2Y2jk2HVCYwtiF5J/TKuXtFwL8adtic3UePIMRoc283Xj38ltsV3PCzdneNPanM/tiA3okoYtinreJVxpaYyvtR3JvsaXGwO/YsupHn+nZS298VaE/vC5X5tGkeZwJxOxhIPiUdymRIPi/zqfRuif4PHvSB8HNj3ALuP1OlLlVCI3Wx8xO1EnZXIFVDj0anFVcqUrZMyHhYF1S49Sd2PEvzVhnzCZfV5xFfGWZMsvcBpqjowOqyfeqdpyzfAYYj6ukYDztPUeyyE9VYHeTsMANsOONhreKd+ERwcHFm00opLF0+pMzbFbktW9r/VqyZJ063aDwC7jupdr8M+VQc2OyaOt9DYgsZOfUBiF6XpELNBvRM1WnXa1ST6cIheS2nPnWzftpEdBwqnHo/oP9SrHg7D1PEWSTS26l/d/YxGLwVz+34sWhGZafuVRmsiSSIMzCIeWeTVTvJshho3bszSpUs5f/48jRo1wtfXlydPntCtWzcA+vXrx7Fjxzh//jyzZ89m8+bN7Ny5kzFjxmR5WV9F40iPlu+uj2ZWueHoFY1h4DRgSCq+vzEKPeqvNAVLejLip/Jc9kugz7fJD/7G7aJ09vzzsAn345JP+6fQ1XMVTpbGfsk6xYKAGE/8okrgF+WFX7RX4r9LEJaQbM7yp7xWjaOXZG4nY4mHxCNJpsVDY6UO6nUYnPI1+z6mXYCeYozHe8xckUo8HD8HPjc+t/QCx1HqIzXWNdRHWiycwNn0hxNjPL6m55BhajysgHwH094PqAmJfV/1kaIctSHvTtNlVuXBZWHq+1Ii6Na1LeOGe6Udj4RrEP0rOM9RExPDXa8VDFdkkqaEfUHm+P0oVjhzrkAk0f2jxsWqdRuT51kpqdFqPWAg1v0GELdwPsS++A92LyQxibDuNwDrAQOJmz8P5fat52+XySQeWSfXJRA1atTgp59+4tdffyUsLIxbt24xYMAAQ/elOnXqUL16dQYNGgRA9+7d8fX1xcnJCYDQ0FD++OMPAP755x+aNWvGunXrANi7dy8tWrSgdOnSL13OV9k42hXclCGXpzO21BQK2Rincr0fW5Dvb4xiV3BT4HkHf+MVjHPhlRl8ZabJe1hqElh0+xNK2PtTwt6PEvb+OFuGU8zuDsXs7vBOvv0m6wfH5cUv2otZNwdxMkw9YVtp4rGxs+Tn6a6vT+PoJZjjyVjiIfEAiUcSs4lH75LPjocSBeET1WTFspS6zLIcaPKoXah090Hr/VL3gjDXeGR2AgHSaDWQJMLALOLximmUV9HvRmRYuXJq39UyNQ/R8C3rLDkZW6Cjhssp3Kwf8TAuPyfDqhuuPGT+wV8hv1UwJe398EpMKEra++Fl54+HrfEy+f/OLTMkEJ2L/MmXpSajq/wBH+8cazgZl7K/zp2YwsTqbV+yTOkjjSOV2TSOJB6AxCM5iYdRuuIR9Yt6V2qnb0yXx5+EyPnqvx3HGpOLDDLneLRpasO0r12I/XEqSkDAc/aSMdpmzbFq3Yb4rX9nS6MVQFO0GNYDBqIEBmZfo9XGBut+A9AUKpRtSQTkzHhoChfGZmQaV0SzQFJb9PLly89dN9ddgTB3Hdva0uV9+yw5GevRciKsZorlr+bgr+FRfH4eheXn+FPvaW8RRXH7m5Sw9+NqRBlAPfj3a3UXiysx7DikNxz8HbXh/F2jA3pFk9gdygv/6BLciPIydI16nJAnE8qrksaRKkc1jl4xiYeRxEOV4+Jh150UM0wBWNUA16UvVYbcHA/55TuRXIkwMIt4vCKSQJiZrEoe0pIdB/8ovT2XI8pxOULNfJMO/g7FxzPocAcu+xq7S7nbPOBxvDOuVk8oahdAUbsA3sa0b3FIXJ7E8RVe+Ed5cSOqBP+F1SBab09GSONIleMaR6+QxMNI4qHKkfHQWKEOzMhcEg9ptBpIEmFgFvF4BSSBMDOrNkTJydjk4F/Y5PXrUaWoffQwea1C1PEVdsYxFiXs/Slse5e81qHktQ6lhstpw3aNT+zgboyaQDTP/w/eDj7sD2nA+fBKqZZDGkeq7D4ZJ5F4qCQeRhIPI4mHylziIY3WRJJEGJhFPDKZJBBmZv1fMdnyvjnr4K8hJD4fIWH5DOMlkthaRFPc7hYl7P0MYyyK2AVwL8bDsE6z/P/QusAOIhIcDQmEl50fY0r+gF+UF0WqlKFxk4osWlCIBSvtSD5gPKvkrHi8WtI4Ukk8jCQeRhIPlbnEI4k0WhNJEmFgFvHIRJJAiNfq4B+jt+Nq5BtcjXwjzXX2hTQkUufA2fDKhmVlHHxokPcQDfIegkfAYvgU+KCOszq2wtAlqgQ3okpwN8YD3Sv6+rxO8XhZ0jhSSTyMJB5GEg+VucTjadJoTSRJhIFZxCOTSAKRy+XGg/9fQW35K6itybIL4RX5N/93NHnjNjdPXUEbcgNP27u4Wj2hmstZqrmcNVk/Tm/FjagSvHd6HUri/RgL2QTyON4lw2MtksuN8UiLNI5UEg8jiYeRxENlLvFIizRaE0kSYWAW8cgEufZO1EIO/sm161yapp/3Yda9L2ixcx5N/9tB1cP/0f7UeoZd+ZE5NwewNaglVyLKEKOzwdoiHjuLGEPyADCj7OecqVeTRvn2GJZ52tyllssJ3Kwfkvzme6mReBhJ40gl8TCSeBhJPFTmEo/nkTskJ5I7VhuYRTxeklyByKXk4G+U1sk4Vm/Ltcg3uPZUdygNejxsAnG1emyy3MUyDICAaOPA72b5dzGq5E8APElwMpkZyi9a/fedmMKUfcNO4pFIGkcqiYeRxMNI4qEyl3ikl/zynUiuRBikGo8cRBKIXEgO/kYvcjJWsOBurCd3Yz1Nlrc8+Teulo8JT3A0LItXrLgZXZQitgE4W4ZTxfk8VZzPm2yXoFhh4VacJ8tKcua/ItSxK8+/UU1e/sNlQE6OR2aT74eRxEMl8TCSeLw4s220ShJh8jwrPR2P+L82Z3kZXpTcidpMJL8T9WWfV3cwlIO/UVaejK01sRSzu5043ayfYQrako7+2GqiTdY9ElqbXhd+MTyf9sbnhMbnZf7tvoTE58v0suXGeKRFvh9GEg+VxMPodYhH0p2o41auQH/yv1dUymfLiXdIfiXkjtUGhngEB2NRuPDzN3hF5E7UIlWvw8E/s2T1yThOscE3qjS+UaUNyyqWteTXKc5cvHiHBT+ew8PCjxL2fvhFlTCs46CNpHWBHQDMuvmZYfnQ4rOo43oM/8TZofwSu0XdiSlCgpL+G0Tl1nikRr4fRhIPlcTD6HWLh1WnD4gLCpJfvuVKhHnFY+Bnz1/ZTEgCkUu8bgf/l2F2J+MvHYmMegt4K8V6ekXD2GvfUMgmkHCds3F7p4tUdr5AZecLJuvH6y25E1OEG4ljLfyi1Wln/aOKE6FzMllX4mEk3w8jiYdK4mH0OsZDefRIGq2SRBiYSzzi1/2BddduWf7eL0ISiFzgdTz4v6icdjKO1tuz8cG7KZZ/c30c3g6+eNmpN8wrYe+Pl70/Dtpow125n7bibhe+uzEGABeHeFaNvEqh/GXoMcSOC1d0mfcBMyCnxeNVke+HkcTDSOKhehXxiN+wDqu27aXRKkmEgVnE435glr/ni5IE4jX3uh78X8TrdDK+GV2cm9HFn1qqUND6gUlCUdLejxJ2/hSweciD2IKAGo9lX4ZQaltnov514MKVYyTdbbt5/p0oWOAX5cWt6GLEZ6A7VEa9TvF4GfL9MJJ4GEk8VK8sHnFx0mhNJEmEkTnEI6eQBOI19lof/DMod5yMNTyIc+dBnDtHHpt2h3LUhqNBMcSjmLU/AQkleBDpQlLyADC4+FxKJl69SFC0BEQXxi/aK7EbVOJ4i2gvniS4vFRJc0c8nk++H0YSDyOJh+qVx0MarQaSRBiZQzxyAkkgXlO54uCfTnIyhgidk0k8ug8pxYUrWzC9uZ3C6bCqRCQ4UtLeD0fLSIrb36K4/S0a5dtnsr+HcfkMCcWfDzpwLrxyussi8VDJ98NI4mEk8VBlWTyk0WogSYSROcTD3MmdqF9Duerg/xxyMlalHQ9NsrU0fOk7iQ/PrqHGkWM0OLaH7ueXMMl3HL/d/ZjDobW5n9gNys06mJquJ/nIYx2etncNe6jteowNVTsxvPgMk/e31MQDEo8k8v0wkngYSTxUWR4PuUOygVncIVnikSPIFYjXTK48+KdBTsaqF4uHhqC4AgTFFeD441qm+9NG4mVnHGNxIbyC4bUyDj6Ud7rCnZgiyfak52id+uhs8+FqXYaT3xfjwYkiVHX2wi+qBGEv2R0qI3JuPDKffD9UEg+jXB0P+eXbQK5EGJlDPMyVJBCvkVx98H+KnIxVryIekToHLkZU4GJEhRSvbX/YgjsxhXmSYJxy1t3mAU6WEZAQAVdvUR2oXsa4TXBcXvyiSuAXbbyfhX+UF/diC6Fk4kXS1zUeL0K+HyqJh5HEA2m0JiNJhJE5xMMcSQLxmpCDv5GcjFXZEY+guALsCW5ksqzDRyWg8wU2/HqeCweuGGeJsvPHw/Y++axDyGcdwpuuJ02263x2JWeeVAWgguNFitjd4WJ4ee7EFM1wuXJrPFIj3w+VxMNI4pGMNFoNJIkwMod4mBtJIF4DcvA3kpOxyvzioWHB75UB08HW9hZReCVOOVvCTu0S5WXvT3G7WyZ35G5TYCs9Cq/k14DuTPUbCYCzZRj9iy423InbL9qL0Pi8Kcog8TCS74dK4mEk8UiFNFoNJIkwMod4mBNJIHI4OfgbyclYlZPiEaW351JEeS5FlDdZriUBXbLD071YD06HVeFSRFnDspL2N+hZeLnJdqHxrsaEIqoE+vylGDymCj7X7ejzeYTEQ74fEo9kJB7PII1WA0kijMwhHuZCoyhK1h8xRArlypUDoEzNQ1z2Sd8BVA7+RnIyVuWmeHjZ+fFhoXWUSBzM7Wl7L811Y/XW3Iwqhl90CcZd+4YovT2gDvDOzHEWT8tN8Xge+X4YSTxU2RGPNk1tmPa1C7E/TkUJCHj+BjY2WPcbgKZQoWxrtAJomzXHqnUb4rf+nW2NVk3RYlgPGIgSGJg9SQS89vHQFC6MzchRmba/jEpqi16+fPm568o0rjlUbj34p0ZOxqrcFg//6BJM8RvFpxcX0vjEP1Q59B8dTq1nxJUfWBc5gPhy7Yh2fYNYvTU2FnGUcfSlQd4DROntDPuYUfZzdtdsRtN8uwzL7C2iyGsVjOk9MjIut8XjWeT7YSTxUJlLPJ5LphQ1kClejcwhHtlNujDlQHLwN5KTsUriATF6O65GvoFV0Qp8/b0rF/0S6LMijOioBDxs71HCzp88VqEkv/dFSfsbeNreIzpZUtEo315+KjuKx/EuhrEVSV2i/KJKEBDjiR7tM8si8TCS74eRxENlLvFIN+k+YyDdmYzMIR7ZSRKIHEYO/kZyMlZJPIxSj4eWgJgiBCS7N0WSbud/pYSdP9civQ3L3Kwfolc0uFqFUc3lLNVczppsE6e34mZ0cZOxFtejSnI18g1A4pGcfD+MJB4qc4lHhkmj1UCSCCNziEd2kQQiB5GDv5GcjFUSD6MXiUdofF5OPTVz0693e7A68COK2d0yzAxVInGmKC+7m9hpY/B28MXbwdewjW9kSdqe2myIR7kHi1ixyIKA642BlDNDvWo5NR6ZTb4fRhKPTCCNVgNJIozMIR7ZQRKIHEIO/kZyMlZJPIwyOx6xelt8IsvgE1nGZLkGPR42geq0s4mDt0vY+eMfXcwYDy8t+t+m8YlTFH9aVjNMLdumwN+85XosWbeoEtyJLmwy21RmeB3j8SLk+2Ek8TBq3MD65XYgjVYDSSKMzCEeWU0SiBxADv5GcjJWSTyMsjIeChbcjfXkbqwnh0LrGZYnj8enQx7QOOYDStj7m9z0rq7rUd5132yyvzi9JbejixoSiqRuUf7RJYjUOWS4fLktHmmR74eRxMOofw97WjW2e/6KzyONVgNJIozMIR5ZSaZxNRNpTeMqB38jORmrJB5GOSketVyPU835DCXs/Slh54eX/U3stdFp7vd+bEG2PGjD9JvDDMvcrB/yMC4/yQeCJ5F4qOT7YSTxMEqKx7bd0bRqbJf+aVyf5TWfUjQjZIpXo5eJR06axlWuQJgxOfgbyclYJfEwymnxOP64Fscf1zI816DH3eaBevdtO3Wchfrww806GHebB9hYGE/CzpZhHKz9DuEJjtQ7to9YvS0AZRyu8n57Z7r1KifxkO+HgcTDKHk87tzVZc5VCJBfvpORKxFG5hCPrCAJhJmSg7+RnIxVEg+j1yEeChYExhYiMLYQh0LrmrzmbBmGl91NniQ4G5YVtr1LgqIlQudgSB4A5tadRpGAo+jGW9I2qgjlyyXODhVt7BIVoXN6uQ/7HK9DPDKLfD9U5hqPNk0z+d4F0mg1kCTCyBzi8apJFyYzkXTZqNUHR9FoYOJIJ27f1fHNtAiiY7I+RHa2Gr4c4UhRTy0Tfwznur8uy8sA0LGtLV3et2fVhijW/xWTLWUo5aWVeCSSeKiyKx6WxOFq8YhHeg9AjcfH4Z8Q73MEK31kmtuF6Ny4m1CCu7oSBCSU4EJcbe4klM6UMuXmeDxNvh8qc45HgzpWDOvrRNyK5SgP7mfem1lbY/V+JzT58xO/7g+U+4GZt+8MsKhdB6v6DYg/eAD9saPZUgaNeyGsOn2A8ugR8RvWQVxc1hcih8ZDU9Ad627ds6BkqctIFyZJIMxERoImhBDJKYoCTwIhyBceXocgH/XvQ194kkojqfUkNPX7qds+vA67p0HhqmjqfZrFJRci6yl6PRoLi+wuhhCpys7/nzIGQgghchGNRgMuHuqjdEOT15SYJ4lJha+aUAT5QuEqxhXuXYCzGyD0NiRLIJQlncDSFgqUBrfS4FYKCnijsXPJok8lxKshyYMwZznl/6ckEEII8RrT2DpDkWrqIzWFykPzceCY37BIiY+B6wdB0cMV0767iqObmlAkJRYF1MQCZ48cc+ITQgjxciSBEEKIXExTwFtNAJKz0ELvPxKvWFyHhz7q3yeBEPFQffgfMd3Gyh6l2zI0pd8GQHlyH6JCIF8JNFa2CCGEeH1IAiGEEMKERmsFpRqoj2SU2AhjV6jk3aIe+UF8FDi7G1c+sx62T4JKHeDjxer2eh2cWqt2h3IrjcYhbxZ+KiGEEJlFEgghhBDporFxhCJV1Ucyii4eQm5DXuOdt0mIA1tntatTkscBsMF4YzzFIX/i2IrSybpFlQLXItIdSgghzJgkEEIIIV6KRmsFbiVNlzUejtJoGOiTzf+fEAul31avXjwOgMhH6uPmMdMdWtqiuJU0Dt6u+ykae9dX/jmEEEKkjyQQQgghXgmNRgNaK+PzAt7q2AoSu0M98jPODJU03uLRDUiIgcBL6gOgfn/DPpTd0+D2SajdE03ZZuoyRVHfSwghRJaQBEIIIUSW09g4gmcl9ZGMoktQp5RNup/Fk/tobJPdSdv/iDpDVMV2xmV+R1BW9TZMNWvSLSpPETQW2iz6VEIIkTtIAiGEEMJsaLSWkL+E+ki8wmCi2VioeBG86hiXPfRVZ3y6dUJ9JGdpg5K/hDGxSBprkb8kGmv7V/thhBDiNSUJhBBCiBxDU7Q6FK1uurD6h+qyp2+Y9+iGOu7i/hX1kZydK8pX1wxdn5Rre8DKFjwrqVdHhBBCpEkSCCGEEDmaxsoOPCqqj2QUvQ5C76iJhWHqWR/1b77ipuMm/hqnJhx91humr1VunYCbJ4xXLfIUVa+QCCFELidHQiGEEK8ljYUW8hVXH280MXlNiYsy/ltR1CRBn6D+TXJlF+ybZXyutVa7QyUfa+FWGtxKylULIUSuIgmEEEKIXCf5+AeNRgPdV6RcyaMCVH7X2B0qPhoeXFUfT1FcPKFAKSjVEE3Dz15l0YUQIttJAiGEEEKkQlOpPVRqD4Ci16v3rjB0g0p2N+7IRxB2V33YuRq2V/R6+L4yuBaG7ivROOZXl0c9BhsH9f4ZQgiRA0kCIYQQQjyHxsJCvdN23qJQppHJa0pkCDxKTCZcPIwvPA6A8AcQFWqSWLBlDJzfjJLPyzjdbLJuUSbT1gohhBmSBEIIIYR4CRqHvOBQE4rVNH3BpRAM3Q9hgaaDr0Nvq+MtHibOGPUUxdk9WWKROIDbvSwaR7dX/EmEECJ9JIEQQgghXgGN1grcy6qP5Pr+BU/uJU45e930btzhQfDkvvq4ftC4TYMB0GoikNgF6vgyKFAGTfmWWfVxhBDCQBIIIYQQIgtpLCzUcRGuhcH7HZPXlOiwxITiqcSiwBvGlR5chZ3fQZ6ikCyBUHZ+B3qd8epFgdJobJ2z6mMJIXIRSSCEyKUUvV5tyAizI7HJvTR2LlC0hvpIi7UDVO0IdnlMl5/4TR3QnYziVDAxmSgFbt6Jf0uDi4fpfTCEECIDNIqiKNldCAHlypUDoNUHR7lxU5ehbe1sNXw5wpGinlom/hjOdf+MbZ9ZOra1pcv79qzaEMX6v2KypQylvLRMHOnE7bs6vpkWQXRM1v/3zgnxaFDHimF9nYhbsRzlwf0sKY/GvRBWnT5AefSI+A3rIC4uS97XhLU1Vu93QpM/P/Hr/kC5H5j1ZQAsatfBqn4D4g8eQH/sqMlrmoLuWHfrni3lEjmXotfD0aWmN8wLf5D2Btb2aiLRfAwab3VQuBIfAxoLNJbWWVRqIYQ5SWqLXr58+bnryhUIM3Pjpo7LPgnpXt/BXsMv010oXEhL90GPuXAl/dtmpv497Onyvj0zF0ewYFnU8zd4BSqWteSrEU5cu55An+FhREZlffKQU+JRopgWAOXBfZSAgCwpkxIQQFxQENYDBmLVtj1xC+dDbGyWvHdycbNnYt1vAFYdOxE3fx7K7VtZXgbd+nXw5AlWrdsQ/+QJun92ZnkZxOtFY2EBdfuYLFNinpjefTupO1SwP8RFwd1zoEl2peviX7BuMEql9mg+WmjcT8BZyFscjb1r1nwYIYTZkwQiB0tqrJYuYUnPIdnbWB36qWO2Jw+/znLF1y/7kweJR9qU27eImz8P6wEDse43IHuSiNhY4hbOx7rfAKwHDMy+JCIxabBq3cbkuRCZRWPrDEWqqY9klIQ4CLmlJhOeVYwvPPJXx1Aku6u2EhcFc5up/3bMb9oNKmmshYundLkTIpeRBCKHksaqkSQPRuYQj+eRJMJIkgiRHTSW1mrjv0Bp0xeajIRa3UFJ1u0y/IF6b4uwexDxSH34HzHdzsoexa1ksvtZlAbvd2QAtxCvMUkgciBprBpJ8mD0IvGwqF1H7U6TxSSJMHo6idBfvpTlZRACUAdVOxc0XZbPC8acRYmNSDblbLJuUcF+EB8F9y6ojyQjjkBiAqGc3wJ3TkHZ5mhKvJWVH0kI8YpIApHD5OTGamaT5MHoReNhVb8BZFMffEkijJInEfHO8qutMD8aG0coXEV9JKPoEozdoZKmnX10A/IWM650ZSecWafejTsxgVCC/WHdEJMpZ3ErBa5FpDuUEDmAJBA5SE5vrGYmSR6MXiYe8QcPZGv3GUkijJ6+EiFETqDRWoJbSfVBi9RXKt8qMXmoY1z24BrcPKY+krO0VbtDFfBWE4qkblFuJdFY2b2qjyGEyCBJIHKI16GxmlkkeTB62Xjojx0lPnE2IJAkwiySCGdn9cqQEK8JTYXWUKG16cLCVeHD+aZ34n7kBwkxEHhJfZjsRIOSpyiMOKLe4RtQHvmp3aQc8sk9LYTIYpJA5ACvS2M1M0jyYJRZ8TCHgbySRBjpjx0FSSDEa07jXFC9GV4yii4BQm8bp5tNmoI2yBdiwkCvNyQPAGwYrg7o/mghVHlP3UfIbQi6pl61yFMUjYU2Kz+WELmGJBBm7nVrrL4MSR6MMjsekkQkMpMkQojcSKO1hPwl1AfNDcsVRVHvsB3x0HSDhBjQaBLXT3TlH/hrrPpvSxuU/CWSzQ7lnfjvEmisHV79BxLiNSYJhBl7XRurL0KSB6NXFQ9JIhJJEiGEWdFoNODopj6SLx+4Q71PhTbZnbOt7cC9nDqQOyEW7l9RH09RXIsY72dRuDKaqp1e9ccQ4rUiCYSZet0bqxkhyYPRq46HJBGJJIkQIkfQWNubPn+zC7zZBUWvg9A7pmMskrpFRQbD4zvqw2cvFK8FyRII5bdeYOMEzUahcfHI6o8kRI4gCYQZyi2N1fSQ5MEoq+IhSUQiSSKEyLE0FlrIV1x9vNHE5DUlMtj0fhZ5ihpfi4+GS1tBUaDleOPyf6bApW3JukIlzRBVUp3iVohcRhIIM2Nnm7saq88iyYNRVsdDkohEkkQI8drROOQDh3xQvHZqr0LnxRDsDw75jYsDL8GDq+rjKYqLZ2J3KG9jt6gCpcGxgMwOJV5bkkCYmS9HOFK4kDZXNVZTI8mDUXbFQ5KIRJJECJFraKxsoVL7lC90mAp1ehq7QgVdh4c+EPEIwu6qD9/9ptvU+Bg6zgQSZ5i6uktNLPKXlMRC5HiSQJiZop5aug/KfY3V5CR5MMrueEgSkUiSCCFyNY2LB7h4gHcjk+VKVGjidLNPTT0bcsukaxSht2Fld7C0hUk31dmjAOXcn6CLN8wUpbF1ysJPJcSLkwTCzEz8MTzXNlZBkofkzCEeIEmEgSQRQoinaOzzQLE31UcySkKsmhgkiY0Ez0pgaYPGwsK4fO8suH/ZuJ1zIdNuUG6JD2d3uWohzIokEGbmur8uW97XHBqrkjwYmUM8kpMkIpEkEUKIdNBY2oCljfG5Z0UY9G/KFUvWB/s86tWL8CB4Eqg+rh80Xc/GESXpfhY1OqMpWe8VfwIhnk0SCGEWjVVJHozMIR6pkSQikSQRQohMomn7jeHfStRjeHTdOL4iKHEK2pCbEBsBAWfUR6n6xm38jsCfn0OpBmjaTzEuj4tKMcWtEJlJEohczhwaq5I8GJlDPJ5FkohEkkQIITKZxt4VitZQH8koCbEQfNM4xqJYTeOLD66qy/KXNK6vKPB9FRRLa9OZoZKmoHXxkO5Q4qVJApGLmUNjVZIHI3OIR3pIEpFIkgghRBbQWNpAwTLq42mV2qvJg5WtcVlkMEQ/Vv8dHgR+h0y3sbZHMdzHInGsRYHSkK8EGktrhEgPSSByKXNorEryYGQO8cgISSISSRIhhMhGGod8ULqh6TLH/CgTbyS7A3fizFAPr6v3t4iLgrvn1Udyn/wJJesCoASchcDLULQamoJvZNGnETmJJBC5kDk0ViV5MDKHeLwISSISSRIhhDAzGlsnKFJNfSSj6OJNu0MlJRZBvmpXpyQXtsD+uVCnFySOrVBiI2DbRNNuUS6eprNKiVxDEohcxhwaq5I8GJlDPF6GJBGJJIkQQuQAGq2VsctSMory1Hk4nxeUamCagAT5wvEVputZ2aO4lTSddrZAKbU7VPJuVeK1IwlELmIOjVVJHozMIR6ZQZKIRJJECCFyqKcHVWtqdoWaXU1Xss8D7ww1dosK9oP4KLh3QX2Y7MACJW9RNaH4YJ46QBxQEuJknMVrQhKIXMIcGquSPBiZQzwykyQRiSSJEEK8pjT5ikPzsYbnii4eQm4njrVI7AqVNO4i5onaVSosEJLfXXvdYJTr+6HNJDRVO6n7iY1QB367FkZjoc3aDyVemCQQuYA5NFYleTAyh3gU8cj8PquSRCSSJEIIkQtotFbgVlJ9lGthWK4oCkQEqfexiAgyTQoe+qrJgk2ypMJ3H/zWCyxtUfKXUKeadSuV7E7cJdFY2WXdBxPpIgnEa84cGquSPBiZSzz6dnN4JfuWJCKRJBFCiFxKo9GAU0H18bR+W+ChH+QrZlwWGQxaa0iIgfuX1YfpDlFciyQmFImDtwuWQVO81qv9IOKZJIF4jZlLY1WSB5U5xeP+Qx1eRV/NzBmSRCSSJEIIIUxorB3As6LpslrdUd78H4TeThxf4Zvsbty+6j0tQm+rj2u71Y3yFYeRJwz7UPbOAq0lVHkfjbN71n2gXEwSiNeUOTVWJXkwv3is/yuG78ZZvbL3el2SiHi9HquXmaJQkgghhHgujYVWnfkpnxeUbWZYriiKeoUiKbFIGmPh4mG6zsH5EBWqzhyVmEAoZ9bD5R3JukIldoeyts/yz/c6kgTiNWRujVVJHl4iHrG7IHoFuK5MfL4DIianXE/jDHk2gcYKdHcgchrEXwaLPGD/CRWrtDCJxzt1TWfB2HYvkJ/9/PizXt0Uu47X65nj40tofDzfVKyQZlE3Bdzlr7v3WFLrTSD1JGKZvz+rb93mSXwCtfPlY1LF8jhbqYnM3ahovrl0mZC4OAAGli5FwwJuqb5Xq/0HuRsdnWL58DLedPcqTkhcHEtu+OF/8hRXV/7GW++8w9gePbBftdKQREQlJDDp0mUCo2PwsLPlqwrlsdMa++oGREXR58RJVtWpRT4bmzQ/93NJEiGEEC9Eo9GAY371UaJO6ivpdVCnt5pc5C9pXO5/VL2fxVPU7lClTO9n4VYKHN1SzEYl0iYJxGtGkgdVjk8e9E8g6mc1YbBwTbb8MVh4AskGpSmhYNNKTR6UBIheA3a9wDYKIr5FGzOTX2d9lGo8wmJimXPpMlvu3SOvVcqp9a6HRzDH15d9QQ9p5+GR4vUkd6Oi+eHKVco6O5ssT55EbDt9Gttbt5lVrSq/377DnwF32XovL52LFSUqIYFeJ/4jn401a9+qzV937zH09BkW1KhO7fz5UrxfgqKnuIPxVyS9AvdjYujg6QnAPN/rDPEujbOVFceDg/l02zZiYmKYPXKk4UrEoht+xOh0LK9dk+7HTrDkhh+featzo8fr9Yw+d4HWHoVeLnlIIkmEEEK8EhqtJTT9IuULNTqriUHSjfKSBnA/vqM+fPaarm/nilLlfTTtvzcsUoJvqrNDaaW5/DSpkdeIJA+qHJ88KPEQtRjsukP8MdPXLDwgz1rTZWF9waa1+m99CNj3Awu1Ie+ctyqVyiqpxiMuLo7ZJ0/ySckSHHr4KEUxfMPD2RF4n6He3uwLephmcfWKwneXrxCp06X6elISUabTB5SsVg3dPzvpUqwomwPuUt5FLeefAXe5HxPDB0WLYKHRUCNvXvTAips3UyQQOkVhbLmyvF2ggGHZ4YeP2HT3Li7WVviEh1M1j6vhykatfPnIa23Ngf370UyfbujOdCw4GG9HdSaQwvZ2HH4UbEgg5vpeR6vR0L9USTKNJBFCCJFlNEVrQNEaJsuUyGDThCLpbtyht9WxForxPKbERsCPNUFrhfLlFTS26vlKCTinruBWEo2NY1Z9HLMjCcRrQpIHVY5PHkC9kuD4eeqv2TQwfZ5wExQ9WBZXn2uNjerSxe5QPJ8tH3X5KtV4WFtb82W9uigBAam+VWknJ0o7OXE3KmVXoeRW3LxJwwJuHHqUMglJovtnJyWbGbszbZ49m6lVKlHJ1RUAv8hIAPIkNvqdrNRD0/nHYSn2pdVoTJIHgE1379Im8QqJt5MTJR2NB/UEvZ4oXQIetnYmYyK0+w8Y1tEAlomXrg8/fMSmgLusfas2li8z/iE1kkQIIUS20TjkA4d88NQMTkp8jHpjPMtkd89+fBes7MDawZA8APDP9+CzR93OxSPZGItSiVPQlganAi/UHUrR68D/GIQ/UGex8qpttvfGyBEJxOHDh/n55585efIk1apV49dff0WrffkKjYqKYsqUKRw/fpywsDC+/vprmjdvngklzlqSPKhei+Qho2K3g03jFIu9Cp0j+OYElHBP+g1ZTry2M2i08GQkJPgQHrYAqPLSb3/tSTiB0TF0K148xWufnTrN5bAn/FKzBiUcHdH9s5OYuHgmHTnC0ZBQAqKi8HJwoLSTE/mt1e5T92NiAPUqA0BMGlc1knsSH8+Z0Md8V8k4s4c22YF7T1AQMTo9HxcrajKw+u3WbTi6YzuKonArMoqGBdx4GBPL+AsXmVChPIXsXtG845JECCGEWdFY2YJ7OdNlBcugfO0PkU/9OGbnqo7JiHgEYffUh+9+03VsnVEMSUVpKP02Gs9KzyyDcvFv+Gu8ur8kLh4obb9FU6HNS3y6V+PVzOOYifz9/enfvz/Tpk3j888/x9raOlOSB4BvvvkGrVbLb7/9RsmSJbGyenWz0rwqkjyocmXyoCgQtxusTROIN0o+pk0jf/oO+A7/O47Ehy2CmHXqi/pAUMJIiA9/6beP1emY4+vL4MRuP08LiIrmcXw84fHGWPz12wqaubkx6PPPOfY4jP4nTxOZkEAT94JYajT8fS+Q8Ph4QhMHUqdn/MG/9x/QwC1/qrMlBcfG8sOVa3Tw9KRTkcKAcXamvkOGUL5efXqdPEUlVxf+V7wYY89foLm7O40KFkixr0yVmEQogYFYDxiIpmix528jhBAiS2ksLNA4mZ4PNJ0Xohl/Gb66Bv3/hvdnQoOB8EYzdRYpjYV6J+47p+D077DjW/VmeYmUYH+UlT1Q9swwLrv4N/zW2zR5APVO3r/1Vl83M9l+BSIoKIi4uDgKFy6c6utr1qzBwsKCfPny0aNHD3r06JEp7xsaGsqWLVsYNmwYbm5urFq1KlP2m5UkeVDlyuQBIOE8WLiB1jhTkRqPUvj6FafP8DB0dl9BTBuI2wt2H4HLElCiyJP/5efJnunjS/fixXGwtORxXHyK139/qzaROh15rY2Ds98rXBju3kFbvhy3u3Xjl19+4WzoY+q65Wdu9Wos9fNn4KnTdCpSBIBqeVyfW47tgffpVcIrxfKohAQGnTpDa49CDPUubXI5Wbl9C80vPzPu669RAgOJWzifRZcu8yQhnuFveL9AbbwAuRIhhBA5lsY+DxSrqT6SURJi4ZGf6ViLItWNKwRehkvb1GSh0TC129Jf44HU2k8KoIG/xqOUa2lW3Zmy9QrE48ePGTZsGHfv3k1znStXrrySabV8fHxISEjIsVN2SfKgyrXJA0DcfrB+2/A01XhYOKhTvCpq1yA0NurUrok07oVe6K1D4uI49PARC6/foPfx/xh17jwA18LD6X38Px7FxmKj1ZokD8np/tlJocehAMRWVLse1cmfj59r1mBF7Vo8SOzK9F4aPywkCY2L43pEBG/mzWOyXKcofHHuPO8VKcywMt5oNBoiExIITnYfiKQrEZpChThfrwErb92ml5cXQ06fYfCpMybrvjJyJUIIIV4rGksbNO5l0VRsi6bxcDQfLUBTMtkU6YXKQdvJULun+tz/WMorDyYU9XX/Y89YJ+ul+wpEbGwshw4dYtasWXz44YfMnDmTKVOm0LhxY1avXs2ZM2fw8PDg0KFDfP3115QqVYozZ86wdOlSGjduzJ9//omPjw8dOnRgwoQJ3Lhxg+HDh3P16lVmzpxJnjx5GDNmDEUSf3kEWLduHefOnSM+Pp7Jkyfj4uLCZ599xqVLl5g+fTouLi4EBwfj6OjImDFjKFy4MA8ePGDz5s3cvHmTu3fvcu/ePbZu3Yp1soZMYGAg8+fPB2DXrl3cv3+ftm3bUqRIEXbs2MGRI0ewtrbm+PHjbNiwAWdnZ6ZNm4aFhQVhYWGEhYUxZcoUXF1d2blzJ9u3b6d06dLs3LmTL774gnr16mViiFKS5EGVq5MHgLhD4DwLeEY89OGgPAarxLE9SiwoUYB6BcKq0wfEBQVl+K3zWlvzVwPj//O7UdG0OnCQMk5OhvtAxOp0Ka5AJOf/30lsrayo3bcf2qNHDLM16RSFLXfv8U6BAryZL69h/aiEBOwtTQ9ZBx8+pF7+/CkGO/9yw49a+fLRsYgxAdl2L5ACtrYm95ZQbt8iaPo0Ru78hzGDBjFz4QK+r1iRU6GhjL9wkQU1qvPKPXUlIn79ulf/nkIIIbKFJp8X1P3EuCD8Qfo2TO96WSTdVyBOnjzJ+PHjuXbtGo8fP8bW1hZra2t27NjB119/jYODA7GxsVhbW/PXX38RFhbG/PnzOXDgAOfOnWPKlCnUrl2b1atXc+PGDUqWLEn37t0BGDp0KPPnzzdJHgA6depE5cqV0ev1jBs3js8++4ywsDB69+5NeHg406dP5+eff+bcuXMMGDCAuLg4li9fzrRp0zhy5IhhW8unGh2FChViwIABANStW5dx48ZRqVIltmzZwsSJEzl8+DClS5cmISEBGxsbfvzxRzZv3oy1tTWWlpaEhYWxb98+zp8/z9ChQ9FqtURFReHi4sLGjRtfNibPJMmDKvckD/rEx1N0t0FjB9pCJvH4X7cBRAb2BX3i7EXRy9WpX+3+pz4P6wOh7xL66AIAyqNHWA8YiE6rRZfq5VPQJy5P6/W0fHT0GE337uf848dcCXtCrX/+ZVOAerUxICqKrffuMaRUSZyOHsGqdRu0zdQk5+cbfjhYWvJNxfKGfa3wv0ndf/dw9FGwyXsceRSc4mZzj2JjWXnzFjcjI/nm0mW+uXSZry5cZI7vdVxTGef05fYd1HJxoXjFityLisbN2QlvJ0eOPArOmqsQYHIlwqrTB1nznkIIIbKfU8HMXS+LpPsKRN26dSlVqhQnTpygT58+DBw4EIBhw4YBMGrUKOyemrWkZs2anDhxgg4dOuDl5UXZsmXZs2cPwcHBlCz5YvOrnz59mtDQUNq2bQuoU1FWqlSJ3bt3ExAQwMf/b+/e42O68z+Ov0YSiUSkEStKXHbrXmJLkLTq8kAXSVUFbaVua5WlWK00alvU0saj1d21qMuyrJ/Vut+q1WpL6lakLhV1baqRStBU4pZkkpzfH8NEGnQwmZlk3s/Hw4M5Mznnez7fkfm+53suffuyYMECHnnkEV566SVeeuklm9c9YMAA3nzzTerWrcuwYcMYNmwYAJs3b6Z27dqMHVv00prvvPMOBQUF/PnPf6Zu3br3tD93Q+HBwi3Cg/kQZK+FguuzA5engs8z4Hn9fWY+AF4ti/VHvmdPyP0XZP7REhw8G8IDC8B0/aZr5apBQSaeXpbLnH41cwYrf84k/fplVF8/dJjn69SmQSXL/RHWp6ay9oxlajXh3Hne/vYow+o+hL8NFxyoUaECF3PNVPT0JMTXl27VH2T6sWOsOZOKj4cHb/++GS2DKltnHtJCm7F47TrK5eXxn9Yt8bnpYgl+np74enri7VH0O4/9P1/kr40bFVmWcP48l/LyWJlS/PK0D5Qv2u7/+/40P1y9Snyzpnz5f5a7fZeP6YfP/LkA/Hgt2z43krPFjZmIUX/B9CuHbomISBnx23AIqG45YfqWX9SZIOBBy+tcyD2dRO190wdq/vXLLB44cICICMttxg3DuOW5BTeWGUbRAv3y8Z3cWMfNV2K61Xq97+ND/5c/m5eXx6lTp7h06RL+/v7Wbd287zcCxO32/X4pPFi4RXgA8Aq1/GHCrZ/36X7r/igfBuUX3369ld4GwD/A8h5vHhTEIwUFvP3RR5gefLDYibzda9Sg+/W7O99JDd8KHOzyRJFlM1s0L/J4YpOHmdjkYW4l/5PNXDp7lr/8/e94b9tqDRU3RNcMIbpm8UH15vZtiy3rGRLyq+dOABzJzGLOyVMsbN0SX09PgjIyLE/85jd4RHWHHbv4jaPCww05OZhXrcB79BjHbldERJzCVM4D48kplqswYaJoiLg+nnxyikudQA12OIm6Xbt2AEyaNImEhAR27NjB4sV3GMDcxM/PD4C9e/dy7NgxUlJSbvvanOuHEjRv3pzAwEAOHbKctGk2mzl06BCNGzfmt78tfiWWX5NjwyEK7du35+rVq8TGxrJv3z42bdrEp59+at33d999l82bN7Nnzx7mzJlzV4HIFgoPFm4THmxg1/5wkRN5f/fNIby3bS1yOFNJuZKXR9zBQ4yqX4/6178UaBoQQOXy5Tkzdw4/5uTQuG5dqj0QUKLtuKXrl7AVERH3YGoSBc8vsMw03CzgQXh+Qem+D8ShQ4dITk4GYN68eWRlZQHQs2dPRowYwZUrVxgzZgwrV64kOjqao0ePkpBgudPrkiVLOHnyJNu2WW608cEHH5CVlUVERASNGzdmwYIFLFq0iODgYCIjI+natSupqamsXr2a48ePAzB+/HiOHj1KpUqVmD9/Ph4eHrz88suMHTuWRx991Dpw37RpE2A5Z2Pr1q233JeUlBTrZVvXrVvHf//7X+u/wXKFpvXr11tfP378eCIjI9m3bx+jR4/m5MmTdO7cmfDwcCZOnIiPjw/jxo1j7ty59OjRw64zEGVusHqPFB4KlUh/uEiIyP9kM+YPN5Z4iFiU/D0N/P3pU6vwvCtvDw/imzXlrS+2suRf/+KtN9+k/LDh4OhZCBERcTumJlEQlwhD1sCzcyx/xyW6ZHgAMBn2/rr8Pq1Zs4Z169bRrl07Bg0adF/rup/DiUrqUKTbadzYcgfEBq22c+S4ZXBcZgerd0nhoZC9+iOqszfT3wgg5+1pGGduOlfA25vyw4bf8nAmR/J44g94RUZh/nBjscOZ7MFcUIC5oKDYVZ1uZqpVm/LDR1jvE4GDTqg2hYTgHRvnkG2JiIjccGMseuTIkV99rcvdifrpp58mMjKSptevDX8/7icAOPv+EGVpsHo/FB4KOaQ/3GQmwqtcuTuGByh6nwjNRIiIiBRyuQDxxRdf4OXlRVhYmLOb4jRuM1j9FQoPhRzaH24SImyhECEiIlKcywWIDh060KNHD2c3w2l6PenjXoPV21B4KOSU/lCIsFKIEBERKcrlAoS7i4n2db/B6i8oPBRyan8oRFgpRIiIiBRSgHAxS1dddc/B6nUKD4VcoT8UIgopRIiIiFgoQLiYlRuynbJdVxisKjwUcoX+sFKIsFKIEBERUYAQXGOwqvBQyBX6oxiFCCuFCBERcXcKEG7OFQarCg+FXKE/bkshwkohQkRE3JkChBtzhcGqwkMhV+iPX6UQYaUQISIi7koBwk25wmBV4aGQK/SHzRQirBQiRETEHSlAuCFXGKwqPBRyhf64awoRVgoRIiLibhQg3IwrDFYVHgq5Qn/cM4UIK4UIERFxJwoQbsQVBqsKD4VcoT/um0KElUKEiIi4CwUIN+EKg1WFh0Ku0B92oxBhpRAhIiLuQAHCDbjCYFXhoZAr9Id3eZN9V6gQYaUQISIiZZ0CRBnnCoNVhYdCrtIfQ/pVsP+KFSKsFCJERKQsU4Aow1xlsKrwYOFK/VHtNx4lswGFCCuFCBERKasUIMooVxqsKjy4Xn/M/e+VktuQQoSVQoSIiJRFChBlkKsNVhUeXK8/Un4sKNkNKkRYKUSIiEhZowBRxrjiYFXhwU37QyHCSiFCRETKEgWIMsStB6u/oPBg4fT+UIiwUogQEZGyQgGijNBgtZDCg4Wr9IdCRCGFCBERKQsUIMoADVYLKTxYuEp/WClEWClEiIhIaacAUcppsFpI4cHCVfqjGIUIK4UIEREpzRQgSjENVgspPFi4Sn/clkKElUKEiIiUVgoQpZQGq4UUHizuqT/Kly/5hv2SQoSVQoSIiJRGChClUKkdrJYAhQeLe+0Pr+jezhm0KkRYFQsRzgh1IiIid0EBopQpzYNVe1N4sLif/jBVqeK8b74VIqxuDhFe0b2d0gYRERFbKUCUIqV9sGpPCg8W99sf5hXLnXv4jEKElTVEVKnilO2LiIjYSgGilCgLg1V7UXiwsEd/GGlnnX8MvkKElfHDacwrljtl2yIiIrbydHYDpKiH6ngUW1b3tx5MivXnh9R83pl9hdohxV9T0ir4mHj95YrUquHBpLcvkZ8Pjes7/u3T60kfYqJ9WbrqKtt25jqlDWWhP0KqW747MAVXw0hPw7xyBV69+1B+1F8wr1oBubkl1fTbMm9Yh1d0b8qPeBHziuUYaWcd3oaCI0mYK1XCKzIKKlWiYPcuh7cBw/FhVERE5G6YDEOfVq6gcePGABw5csTJLRF3YRQUYCqnSUhXpL4RERFHu5uxqD6hRNyUBqiuS30jIiKuTJ9SIiIiIiJiMwUIERERERGxmQKEiIiIiIjYTAFCRERERERspgAhIiIiIiI2U4AQERERERGb6UZyLiI/Px8ovAaviIiIiIij3BiL2kIzEOJW8vPz7+o/iNwb1dkxVGfHUJ0dR7V2DNXZMcpynTUD4SJCQkIA+Oyzz5zckrKtY8eOgOpc0lRnx1CdHUN1dhzV2jFUZ8coy3XWDISIiIiIiNhMAUJERERERGymACEiIiIiIjZTgBAREREREZspQIiIiIiIiM0UIERERERExGYmwzAMZzdCRERERERKB81AiIiIiIiIzRQgRERERETEZgoQIiIiIiJiMwUIERERERGxmQKEiIiIiIjYTAFCREREHK6goIBZs2aRnZ3t7KaUWYZhsGfPHpYtW+bspkgZo8u4OllGRgZTp07Fz8+PCxcuMHLkSBo1auTsZpVqFy5cYPLkydSrV4+RI0cCkJ2dzdSpUzEMg4yMDPr160dERARg+RB79913OXfuHNeuXeMPf/gDUVFRztwFl/fdd98xefJkDh48SGBgIIMHDyYmJkZ1trOcnBwmTZrEli1b8PLy4oUXXmDgwIGqcwmaOXMmZ86cIT4+XnUuAatXr+bVV1+1Pvb09CQpKUm1LgEnTpwgLi6OTp068cILL+Dp6ak629Hhw4eJjo4utrxfv36MHTu27NfZEKcaOHCg8c9//tMwDMPYuXOn0bp1ayMrK8vJrSqd8vLyjGXLlhlvvPGGUb9+fWPGjBnW5/76178ar7zyimEYhnH69GkjNDTUOH36tGEYhjF79mwjJibGMAzDyMzMNFq0aGHs3bvX8TtQSuTm5hrDhw83Dh48aCQnJxuDBw826tevb3zzzTeqs5395z//MbZv3258++23Rs+ePY2WLVsahqH3c0lZuXKlUb9+fSMuLs4wDNW5JKxatcqYNWuWsWTJEmPJkiXG+++/bxiGam1vx48fN1q0aGEsXLiwyHLV2X62bt1qxMfHG7NmzbL+iYiIMM6cOeMWddYhTE60b98+du7caU2lLVu25NKlSyxdutTJLSud8vLyaNGiBX/84x+LLD9z5gxr1qyx1rlWrVoEBQUxf/58rly5wsKFCwkPDwegUqVKNGrUiNmzZzu8/aXFd999x6BBgwgNDaVOnTr069cPgIMHD6rOdhYdHc1jjz1Gw4YNadiwIUOHDtX7uYTs2LGjyKE0qnPJGThwIM8//zzPP/88zzzzjGptZ4ZhEBcXR5UqVRgwYIB1uepsXw0bNuSVV15h+PDhDB8+nI4dO9KsWTMMw3CLOitAOFFCQgIAQUFBgGUq94EHHmDr1q1ObFXp5e3tTb169Yot37FjB3l5edY6A1SpUoWtW7eyf/9+srKyqFKlSpHndu/ereNyb6NBgwaEhYVZHycnJ1O9enXy8vJUZzvz9/fHMAwWLFhAdnY2nTt31vu5BBw5coQDBw4QExNjXaY6lwyTyUR0dDShoaFERkaydu1a1drODh48SFJSEtHR0SxbtoxXX32VFStWsH37dtXZjoKDgzGZTNbHS5cupU+fPm7zflaAcKKMjAwAvLy8rMu8vLysy8U+7lTn2z2Xn59PZmamYxtaCp0+fZqVK1cyZ84crl69CqjO9rZkyRJycnJITU2la9eunD9/HlCd7SUlJYU1a9YwfPjwIsv1e6Nk1KxZk6FDhzJt2jQqV65MXFycam1nhw4dAuChhx4iJiaGvn378tprr3HhwgVAdS4JV65c4auvvqJt27Zu8372dHYD3FlgYCCAdeAFYDabqVatmrOaVCbdrs6VK1e+7XMeHh4EBAQ4tqGlTHJyMjNnzmThwoVUrVqV/fv3A6qzvfXv39/6d0REBIsXLwZUZ3v5xz/+wdatW1mzZo112caNGzGbzYDqbG9hYWHWGcz27dvToUMHFi5cCKjW9nLjm+wKFSoA0LRpUwIDA5k3bx6gOpeETZs20bFjRzw8PNxmzKEZCCdq164dAOfOnQMsx/BnZmbStm1bZzarzHnsscfw9PS01hngp59+om3btjzyyCNUqlSp2HOtWrXCx8fHGc0tFb7//ntWrVrFtGnTqFq1KmCpm+pccipWrIifnx/t27dXne1o+vTpJCYmsm/fPvbt2wdAVFQUW7ZsUZ1LWIUKFahRo4be03YWEhICUOwbbdW55Kxfv56nnnoKcJ8xhwKEE4WFhfH444+zfft2ABITE/Hz8ytyHK7cv5o1a9KrVy9rnVNSUjh//jxDhgyhYsWKDBkyhB07dmAYBpcvX+bIkSPFDmeQQjk5OcTFxVGjRg3WrFnDihUrmDZtGmfPnlWd7ej8+fPMmzfP+k1VUlISZrOZYcOGqc4OoN8b9peRkcGiRYvIzc0FLAPczMxMYmNjVWs76tChA1WqVGHv3r0ApKWlkZmZyXPPPac6l4CffvqJixcv0qBBA8B9fnfoPhBOduXKFSZOnIifnx/p6emMGDGCpk2bOrtZpdaePXv44IMP2LhxI/Xq1WPgwIH06tWLvLw8pk6ditls5qeffqJv3748/vjj1p+bOXMmp0+fJjs7m44dO9KjRw/n7YSL27BhA2PHji22fOjQoYwaNUp1tpP09HRGjx4NWL4RT05O5tlnn6VevXp6P5egBg0a8PTTTxMfH68621l6ejrjx4/n2rVrdO/enYyMDLp160adOnVUazs7fvw4kyZNolWrVqSmptKhQwe6deumOpeAtWvXkpyczJgxY6zL3KHOChAiIiIiImIzHcIkIiIiIiI2U4AQERERERGbKUCIiIiIiIjNFCBERERERMRmChAiIiIiImIzBQgREREREbGZAoSIiIiIiNhMAUJERERERGymACEiIk7x0ksvMXDgQA4fPnzL5xcvXsyTTz7Jrl277riegQMH0qNHD5YuXXrXbfj4449p164d8fHxXL16lWvXrgEQExPDu+++a30sIiKFFCBERMQpoqOj2bVrF19//TUAr732GtHR0SQmJgLw5Zdfcu7cOdLS0u64nhEjRvDtt9/y73//2+Ztm81msrKy6NKlC97e3nzzzTesXbuWyMhIjh07Rq1atZg7dy5Lliy59x0UESmjPJ3dABERcU/VqlUD4Pe//z0AI0eO5LnnnmP58uUcOXKEoKAgPvroIypXrnzH9TRt2hSA1q1b27ztH3/8kaioKPr06UP16tWpVasWn332GUFBQXh5eZGamkq1atXo37//ve2ciEgZphkIERFxCk9Py3dYvr6+AAQHB7No0SJatWrFli1b2Lt3L0888QQHDhy443p8fHzw8fHBZDKxdOlSXn75Zf72t79x/Pjx2/5M7dq1mTRpEt9//z0ASUlJXL58mUGDBhEQEEBiYiKDBg3Cx8fHLvsqIlKWaAZCREQcqqCggNTUVD7//HMAevfuTZs2bQgNDeXjjz/m1KlTmEwmYmNjCQ8P53e/+12xdWRlZbF//35OnTpFUlISZrOZ1atXs3nzZh566CH8/Pxuu/38/HxWrlxJWloaXl5eJCYm4u3tTZMmTfjhhx84c+YMDzzwAGFhYUyZMoUNGzbQrVs3Jk6cWGI1EREpTRQgRETEoebPn8+2bdu4dOkSALGxsXTp0oXKlSszZMgQxo0bx/r167l8+TJffvnlLQOEYRgkJSVx8eJFjh07Rn5+Po0bN2bZsmW/Omvg4eFBmzZtSEhI4L333sNkMhEWFkbPnj3p0KEDXbt2pVy5ckRHRxMREUFsbCydO3cukVqIiJRGJsMwDGc3QkRE3IthGPTq1YvDhw8zefJkIiIiyMjI4IMPPmD37t2kpaVRt25dHn30UcaNG4fJZLrletLT0+nUqRO5ubn4+/vTvHlzZs2ahZeX1x23f/XqVfr3709AQAAFBQXUqFGDLVu2EBYWRqtWrejfvz9t2rTh4sWLfPjhh9SuXbskyiAiUirpHAgREXG41atXWy/fumnTJsaNG0eTJk0YMGAA4eHhhISEsGHDBsaNG8eFCxfIzc295XqmT59OxYoVMZlMDB48mBMnTvDiiy+SnZ19221fvnyZIUOGEBoaSk5ODhcuXOCZZ55h9erV5OXlERgYCEBgYCCVK1emZs2a9i+AiEgppgAhIiIOdf78eaZPn05UVBQAM2bM4MUXX+THH3/k4sWLZGRkcOHCBWJiYmjevDlt2rTh8ccfZ//+/UXWs2XLFtatW8eYMWMwDAN/f3/eeusttm3bxqBBg0hPT7/l9r/44gvGjx/PhAkTKFeuHMHBwXz44Yd06dKF8PBwpk2bxq5duzh37hyPPfYY5crpo1JE5GY6B0JERBwmJyeH0aNH0759e55++mk2btzIJ598Qnx8PEFBQTRs2JC0tDS8vb3p06cPfn5+lCtXDpPJVOQwoqNHj/LKK68QGRlJVFQUr7/+OgUFBYSHhzNgwAAWLVpE9+7dmTBhAt26dbMeArVr1y7Wrl1Lfn4+6enppKam8uijj5KYmIhhGHTo0IEaNWowcuRILl26RKdOnZxVKhERl6VzIERExGHGjx/Pww8/TExMDJs3b2bUqFHs3r3betgQwJQpU0hISOCTTz655TpOnjzJoEGDaN26NRMnTuTkyZM8++yzNGvWjJo1a9KpUydmz55tvYxro0aNGD58OJ07d8ZkMvG///2Py5cvc/bsWc6fP88777xDZGQkrVu35s033wTgqaee4tSpU3z22WcEBweXfGFEREoRzUCIiIjDTJo0ifLlywOWey8A5OXlFXmNYRjW1/zSgQMHeOONN3j11VcJDQ0lNjaWlJQUwHJ51qCgIAICAnjvvfeYM2cOP//8M4GBgZw8eZJmzZoRHBxM3759AThx4gTx8fG0bt2a7Oxspk2bBsD777/P2bNn8fPzo3fv3kyYMEEzESIiN1GAEBERh7k5GOzcuROTyYS/v3+R13h4eFCxYsVb/rzZbGb58uXWqyzNmTOHhIQEhgwZQteuXfnTn/5kfe2UKVPu2JaqVasSFBSEr68vvr6+TJgwge7du/PVV1+xfPlyjh07xqhRoxgxYgRNmzalX79+dO3a9bbhRkTEXShAiIiIU/To0YPAwMBi922oWLHibS+b2rJly2LLvv76a4DbXur1l37++Wdmz57N/v37adu2LZs2bSIjI4M5c+YQGRnJsGHDAKhTpw4TJ07k008/xc/PjxMnTtCsWTPq1KlzF3spIlL26BwIERFxKZ9//jkeHh60a9fOptfv2rWLwYMHM2PGDJsONcrLyyM3NxdfX9/7baqIiFtSgBARkVIvJSWFkJAQm2chRETk3ilAiIiIiIiIzXR3HBERERERsZkChIiIiIiI2EwBQkREREREbKYAISIiIiIiNlOAEBERERERmylAiIiIiIiIzRQgRERERETEZv8PxXipwdhPe0MAAAAASUVORK5CYII=\",\n 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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率分档WOE值分档IV值指标IV值LIFT值累积LIFT值累积好样本数累积坏样本数分档KS值
0credit_amount逻辑回归入模变量[负无穷 , 1845)3830.38302770.39571060.35330.27680.11330.00480.13150.92250.9225277106-0.0424
1credit_amount逻辑回归入模变量[1845 , 3914)3570.35702740.3914830.27670.23250.34700.03980.13150.77500.8514551189-0.1571
2credit_amount逻辑回归入模变量[3914 , 正无穷)2600.26001490.21291110.37000.4269-0.55290.08690.13151.42311.00007003000.0000
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\"\n      ],\n      \"text/plain\": [\n       \"            指标名称      指标含义             分箱  样本总数   样本占比  好样本数  好样本占比  坏样本数  \\\\\\n\",\n       \"0  credit_amount  逻辑回归入模变量   [负无穷 , 1845)   383 0.3830   277 0.3957   106   \\n\",\n       \"1  credit_amount  逻辑回归入模变量  [1845 , 3914)   357 0.3570   274 0.3914    83   \\n\",\n       \"2  credit_amount  逻辑回归入模变量   [3914 , 正无穷)   260 0.2600   149 0.2129   111   \\n\",\n       \"\\n\",\n       \"   坏样本占比   坏样本率  分档WOE值  分档IV值  指标IV值  LIFT值  累积LIFT值  累积好样本数  累积坏样本数   分档KS值  \\n\",\n       \"0 0.3533 0.2768  0.1133 0.0048 0.1315 0.9225   0.9225     277     106 -0.0424  \\n\",\n       \"1 0.2767 0.2325  0.3470 0.0398 0.1315 0.7750   0.8514     551     189 -0.1571  \\n\",\n       \"2 0.3700 0.4269 -0.5529 0.0869 0.1315 1.4231   1.0000     700     300  0.0000  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n 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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率分档WOE值分档IV值指标IV值LIFT值累积LIFT值累积好样本数累积坏样本数分档KS值
0present_employment_since逻辑回归入模变量... >= 7 years,1 <= ... < 4 years,4 <= ... < 7 years7660.76605590.79862070.69000.27020.14610.01590.06270.90080.9008559207-0.1086
1present_employment_since逻辑回归入模变量... < 1 year,unemployed2340.23401410.2014930.31000.3974-0.43110.04680.06271.32481.00007003000.0000
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"                       指标名称      指标含义  \\\\\\n\",\n       \"0  present_employment_since  逻辑回归入模变量   \\n\",\n       \"1  present_employment_since  逻辑回归入模变量   \\n\",\n       \"\\n\",\n       \"                                                     分箱  样本总数   样本占比  好样本数  \\\\\\n\",\n       \"0  ... >= 7 years,1 <= ... < 4 years,4 <= ... < 7 years   766 0.7660   559   \\n\",\n       \"1                               ... < 1 year,unemployed   234 0.2340   141   \\n\",\n       \"\\n\",\n       \"   好样本占比  坏样本数  坏样本占比   坏样本率  分档WOE值  分档IV值  指标IV值  LIFT值  累积LIFT值  累积好样本数  \\\\\\n\",\n       \"0 0.7986   207 0.6900 0.2702  0.1461 0.0159 0.0627 0.9008   0.9008     559   \\n\",\n       \"1 0.2014    93 0.3100 0.3974 -0.4311 0.0468 0.0627 1.3248   1.0000     700   \\n\",\n       \"\\n\",\n       \"   累积坏样本数   分档KS值  \\n\",\n       \"0     207 -0.1086  \\n\",\n       \"1     300  0.0000  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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devnxJr169DMlDbL3G3lETQoi0IgmEECJNzZ49mx07dvDs2TN0Oh03btzAxcWFyZMn4+bmxs2bN9m1axcFChTg1KlThIeHG8Y4/PPPP3EGIF+6dInRo0ej0Wj44YcfiIiI4OHDh6xdu5bChQsbGtBTpkyhZMmShka+p6dnouUrWrQooD7h6JtvvmHNmjUUKFCAjRs3otfrqVixIs7Ozvz4448ABAYGAsaByK+L7RaTNWtWfHx8AHXws6IoXLt2jdu3b+Pl5cWxY8cIDQ01PP6zatWqyarP7du3c/LkSdzd3enatSsAM2fOZMqUKfz0009JnufkyZNRFMUw8Dh2XEPt2rXx8PBAr9cbumHlz5+fPHny4OrqSpcuXRLd75UrV7hx4wZZsmShTZs23L59G0tLSzw9PSlUqBCNGzcmZ86chIeHY2try5QpUxK8GwSQL18+w7iGw4cP89VXX+Hs7EzVqlW5cuUKjx49Ytq0afTs2ZPBgwezatUqTp8+bXika0LWrFnDlClTqFq1KjNmzECn03HmzBm8vb25ePEiBw4c4MWLF9jZ2TFw4EAqVarEmTNnADh79iyTJk3i2bNntG3bFlDvWoWEhHD8+HFDMtmvXz+cnZ3x8PDg9OnTjBw5km+//ZZvv/2Wjh07cv78edauXZtgAqHX6wH1Lkj79u2Jjo5m7ty5BAUF4erqytKlS/H392fQoEG4u7tjbW2NPI1dCJHWJIEQQqSpnj17UqFCBYoXL07Tpk3p37+/4Wkz4eHhDB06FHd3dzZv3oy9vT1du3bl/v37DB06NN7Ti7y9vfH29sbZ2ZmPPvqIW7du0alTJ7799lsAtm7dCqjfzAcHBxsa87F3ABIS+zjWp0+fYm9vT/ny5blw4QJ//fUXx48fJywsjK5du5I3b15evnxJSEgIADlz5kxwf6GhoQDY2dnx9OlTQH0KU0REBFu2bOHevXu4urrSvHlzPD09+fXXX4GE72i8LiAggMmTJwPqXA42Njb873//Y+XKlWzfvp26devSpEmTJPexYcMGw8DtrVu38u+///LVV1/RpEkT1q1bR6NGjShXrhxubm6MHz+egQMHJvmo2BIlStCpUyfDE4Ny5MjB2bNnCQsL49ixY5ibmzN//nzWrl3Ld999R+PGjRO9G/SqWrVqUaFCBQICAmjVqhU+Pj48evSIH3/8kblz56LRaDA3N2fEiBEJbq/Vahk7diy3b99m6tSp5M2bl4YNGxIcHGxYp169egwcOJCdO3dy5swZpkyZwu7duwkLC8PKyoq8efPGGU8xcuRILl68yL179wwNf4DJkydTo0YN7ty5w+nTpwkICGDRokVERkYanpbUoEGDBMsZ2x3J0tISf39/xowZw6VLl5g/fz4VKlSga9eubNmyhW3btlGjRg1evHghXZiEEGlOEgghRJpydHSkdu3a7Ny5k3r16hmSB61WyzfffEPOnDm5evUqLVu2ZMyYMZw6dYratWvTuXPnePtq06YNz58/p02bNmTPnp3Vq1ej1Wq5dOlSnPWmTZtGsWLFqFmzJkCST9WJTQRiG/uenp6cOHGCBQsWkD17durWrYu1tTVgfHQnqN9Evy4iIsIwaVqWLFm4d+8eoCYptra2jBkzJt4206dPx9LSkiJFiiRaRlDHEXz77bcEBgbStm1bQ6JgZ2dHr169mDJlCmPGjKFQoUKJPhL20aNHzJgxg9KlS3P58mU6dOiAg4MDzs7OtGnThlu3brFkyRKeP3/OjBkzGDt2LHZ2dixevJjQ0FC6desWL6lzcnIyPOr1+PHjLFiwwPAI18KFCxsStOfPnzNnzhwaNGiQ7EelNmzYkKlTp5IzZ05y5MjB1atX2bhxI9bW1ty5c4d27drx8ccfJ7r9qFGj4nQbO3ToEI8fP6Zly5bodDo++ugjPv/8c8Mdh5YtW1KoUKFE91exYkX+/vtvateuTZ48eQxdmZo1a4a1tbXh7tSyZcswNzendu3ahjstiQ3Oj52tev/+/cyYMYNPP/2U/v3707dvX9zd3Zk6dSqzZ8/m/v37ODs7Y25uLjNcCyHSnCQQQoh0YWNjw8GDBzlz5gyVKlXi3LlzfPXVV1SrVo0rV64wYMAAJk+ejKOjIxMmTEhwHxqNhlatWnHlyhX+++8/IiIiWL9+PT4+PvTp08fQteP3338nZ86chkeHxj5tKCoqioEDBxq6owCGCeoCAgIA6N69O5UrV05wAK23tzegdjdJ6K6GVqsFoGDBgmTJkoWzZ88CSd9dePjwIQUKFHhjo3rChAkcOXKEZs2axaufTp06sXz5cvz9/enevTsrVqygRIkScdaJjIxk4MCB5MyZkwEDBvDVV1+h0Wjw9PQkODgYPz8/Pv30Uy5cuEB4eDjTpk3j4cOHcbrLXL161XDHJKG6OX36NA4ODgwdOpTq1avj6upquMtRtmxZfv3112QnD6AOSp85cyarV682dAeLfYpW1qxZ4zxC93VWVlbx7pzY29uzevVqdDodjRo1MnQBi43rm+aUaN++Pe3btycoKIimTZsalrdo0YKSJUvSr18/AL7++mvc3NwIDQ0lKCgIwDAe5nWxvzNFixblxx9/xNraGh8fH5YuXcqECRN4/vx5nDpv37694WEBQgiRViSBEEKki3r16uHq6sqKFSuoVKlSnAnJSpYsSaVKldi5cydz585NsHvQ8OHDDc/H12g0lC9fHr1eT9euXalSpQre3t4cPHgQUBtwPj4+hm4miqJQqFAhtFotJ0+ejJNAxH6b++LFC06dOkVERARBQUGsWrWKp0+f8vjxY7y9vcmVK5dhf4mNqXBycmL69OlYWVkRGBiIl5cXhQoVIk+ePOh0Op4/f26YnO7hw4fcvHmT0NBQvL29mTZtWoLdcaKiovj+++/Zvn0733zzDd988028R7ZaW1vTu3dvJk6cSEBAAB07dmTQoEF06dIFCwsLdDodQ4YMwdXVlZ9++onTp08btj148CBTp04lR44c5M2b19BQr1u3Lh06dMDR0RFLS0siIyOTfMStpaUlT5484fjx4/j6+lK4cGHs7e2ZN28eDg4OzJ49O8muZAlxc3Nj/vz5uLi4oNVq8fb2pnPnzvj7+1OsWLE3zrHwur///ps//viD3LlzGx6XGxoayu3bt7GwsDA8NSspWq2WYcOGodfradSoEXv27GHixInMnj3bkCz88ccfuLq6UqxYMcMYl1e7Tr0qLCwMUO9uODk5ERwczPDhw7l58yYVKlSgYMGCcdbX6XTodDoURXmrR/cKIcTbkARCCJHmIiMjOX/+PDY2NvG6wMT2VT948CBz585NtK/42LFj8fX1Nfzf2dmZWrVqYWFhwcWLF/nnn38oVaoU48aNw8PDg0KFCnH48GFGjBhB586dEx0IHPvko5cvXzJq1CgcHR3JmTMnzs7OODg4kCdPHooWLcqhQ4c4d+4cgKFrVEIaNWoEwIoVK9DpdHTo0IHo6Gjat2+Pl5cXoHapypEjB9mzZ6d27dq4uLgk2IXp9u3bDB8+nKxZs7Jt27Ykuzm1b9+eX3/9lYcPHxIREcHUqVNZs2YNvXv3Jl++fLRr187w2NTYuy06nY62bdsaEqo1a9Zw4MAB2rRpw7p166hRowa1atVK9JivKlCgANOmTWPQoEGMHj2aDh06ULRoUR49esRPP/2U6KDzpPj5+TFv3jy8vb3RaDSYmZlx8eJFcufOneKZmI8cOcKwYcOwt7dnzpw5hjtPR44cQafT4enpmej8EbG0Wi39+vUzdPXauHEj5ubmVK1alfXr1xu6Qo0fP57GjRsD6t0vMA6+f11sLGKTK0dHR9avX8/06dNZs2YNq1ev5rvvvjOsH3snQ6vVGrrWCSFEapMEQgiRZhRFoWfPnpw6dcow8PPKlSucPHmS+vXrU6FCBVauXEnx4sXZuXNnnMdrvi5LliyGPucAJ06cANSn2AwfPpwhQ4Ykum1isyiD+njTli1bYmNjw8SJExNdr1y5cnzxxRc4OTm9caByZGQkv//+O7lz56Z9+/ZYWFjwxx9/cPz4cfLmzUuBAgWSLBOoA8z37NnDlClTKFasWJLrgtplZ8aMGQwbNoyHDx8CauN0+fLl/O9//4szpuTGjRuAsfsMqN/EL126FFdXVyZMmEBgYCB9+/ZlypQpiU4il5CcOXOydOlS2rdvz/Xr1wG1+9WDBw/4/PPP3zgvwqvc3NzYvHkzoE6Yt3HjRn7++We0Wi2PHz/m6NGjhIaGUrZs2SQTik2bNjFhwgQcHR1ZtGgR2bNn548//qB169aG/cdOgJcYrVbL4MGDKViwILNmzcLR0ZH58+fHiWPso2kXL17MpEmT0Ov18ZK218UmpXZ2dkRHRxMQEIC/vz8VKlQgNDQUPz8/BgwYgJOTE0OHDjUM4g8PD5cEQgiRZiSBEEKkGY1Gw5gxY/j5559p1KgRlSpVwtvbm+3bt7NhwwZWrFgBwP379/H29sbFxQU7Ozusra2JiooiKiqK8PBwatSowWeffRZn34cPH37j8WP7279pnoDp06ej0+mSXKdChQo4OzszevToBOdzeNXixYt5+vQpK1asMHyzbGVlRZ06dd5Y5li2traGPvXJVa5cOf7991+0Wi1mZmaJDh7fs2cPYHzkrKIojB49Gn9/f5YuXYqVlRWjRo2iWbNmDBs2jGPHjvHtt98mq/F/6dIlJk2axPXr18mZMydPnz4lJCSE+fPns2TJEmrXrk3NmjWpVKkShQsXjtcNJzIyklOnTqHT6Xj58iXPnz/H39+fY8eOERERQZ06dfDz84vzFCQnJyc2b94cb66KyMhIJk6cyLZt22jevDnDhw/HxcUFvV7Pb7/9xsSJEw1d2GLvHCVGURRmzJhhuEuh0+k4d+5cnDqOHYjfp08fChUqRHR0NP/73/8M679Or9dz/PhxAD777DPD05+yZs2Kk5MTDg4O2NvbY29vj7e3N506dTLMTP2m31chhHifJIEQQqSpQoUKMWXKFMPP5cqVo1y5cgwfPpy//vqLTZs2cf78eUPj61UuLi7069cvzpiFWLGN3xw5ciR67NjGXlLrxHrTHQErKysOHz78xm999+zZw7p161iwYAGVK1d+43FTQ1KPXQX1225Q512IjIxk9OjRHD58mDlz5lC7dm0A8ufPT8+ePVm0aBFbtmxhz549tGvXjh49esQbo/Ly5UsOHTrExo0bOXnyJBYWFvzvf/9j2LBh3L9/n++++45Lly4RFRXFv//+y7///gtgeGzutGnTDF3bLCws+Pvvv9m0aRO5c+emcOHC5MqVi0aNGuHk5ISFhQVmZmZERUURHBzMo0ePePbsGbdu3YqXQNy6dYtq1aoxdOjQOMmPmZkZvXv3Zvjw4YD6FKXy5csnWWevx/369euEhITEufNRtmxZSpQoQcOGDQ2/n5UrV+bQoUOUKVMm3j5jxzHY2dkZkqakulG1bdsWnU6Hg4NDiu7kCCHEu9IoMgONEMLE3Lt3jx07drBnzx4ePnxI8eLFadasGW3btk104O2DBw/o3r07q1evNgxUfd2lS5fo2LEjJ0+eNPR5T03nz5/njz/+YMCAAYmWyRTs3LmTFStW8PvvvzN27Fhy5cpFt27dyJYtW5z1tFotAwYM4NixY7i4uFC0aFEaNGhAx44dAXX25GHDhnHhwgWcnJwoWrQolSpVomXLlnG6o+n1etatW8evv/6Kn58fBQoUoFy5clSrVo1atWolODg7Ojo6ycfvvqvAwEAaN25M8+bN+fbbb1P0dChQuyTVrVuXOnXqMHfu3ETX27p1K5s2bWLlypUJJqlr1qzh3LlzzJw5843HXLVqFZMmTaJfv34MGDAgReUVQoh3IQmEECLTiIiIYPTo0cyaNSu9iyI+QKdOnSJfvnyJTir4vgUFBfHHH3/Qo0ePN94xE0KI90kSCCGEEEIIIUSymaV3AYQQQgghhBAZhyQQQgghhBBCiGSTBEIIIYQQQgiRbJJACCGEEEIIIZJNEgghhBBCCCFEskkCIYQQQgghhEg2SSCEEEIIIYQQySYJhBBCCCGEECLZJIEQQgghhBBCJJskEEIIIYQQQohkkwRCCCGEEEIIkWySQAghhBBCCCGSTRIIIYQQQgghRLJJAiGEEEIIIYRINkkghBBCCCGEEMkmCYQQQgghhBAi2SSBEEIIIYQQQiSbJBBCCCGEEEKIZJMEQgghhBBCCJFskkAIIYQQQgghkk0SCCGEEEIIIUSySQIhhBBCCCGESDZJIIQQQgghhBDJJgmEEEIIIYQQItkkgRBCCCGEEEIkmyQQQgghhDBpz549Q1GU9C6GECKGJBBCpDGdLv0ugul57MxA0esz5bGFaRswYACdOnXi1KlTKd62W7duDBkyhKCgoHjv3bx5kzp16vDvv/8muY/NmzdTp04dpk2b9tZJwMiRI/nss8/ealshxPunUSSlFyLNDRsfxO17uveyr3bNbejc1o41m8LYuCMi0fUKFzBnxvgsb32cu3fv0qVLF7p370737t0xM0v+9w/dunXDxcWFcePGkSVL3DLcvHmTXr16MXbsWBo0aJDoPjZv3sy8efNo3Lgx3377LRqN5q3PJTVpV/2G4uf7VtuaVfsIy5q1iDpyGP3JE8neTuPmjlXXL97qmLEGDBjA06dPGTRoEFWrVk3RtpkpvhnR2bNn+fzzz6lRowbLly9PcB2tVkt0dDR2dnZxlq9Zs4aJEyeya9cu3Nzc6NSpE23atKF79+5s2LCB77//nj59+jBgwIBEj6/X66lbty4vX77k1KlTWFlZJbvsz58/5/Hjxxw5coQlS5bg5uZGdHQ0v/32G/ny5Uv2foQQ75dFehdAiMzo9j0dXjej33k/fbrZ0bmtHXOWhbB4Zdh7KFniChYsiLm5OTNnzqRx48bkypULAH9/fy5dusTGjRu5efMm48ePp1atWnG2/eSTT5g4cSL9+vXD3Nw8TiPk4sWL+Pn54eXllWQDs1WrVsydO5f169czePDgFDVC0pLi54vi4/NW2+o2boCXL7Fs2oyoly/R/fP3ey5d4r744gs+//xzli1blmgCkVgjMzPFNyOKbWhXr14dRVEIDw/n0aNH3LlzB29vby5dusTp06fRaDSsXr2aUqVKGbZ1c3PDzs6OwoULA1CzZk2mTp1K/fr1Wbt2LatXr6ZSpUpJHt/MzIwiRYqg1WpTFNeHDx8yf/58QkND2b9/P99++y3Vq1enYMGCLFmyhKpVq1KlSpW3qBEhxLuSBEKIDKpPNzsG9XJIk+Qhlq2tLdWrVydXrlxcvHiRfv36YWFhQdOmTcmePTsHDhzg22+/ZefOnWTLls2wXXo2QjKa2KTBsmmzOD+ntozayBRvZmlpCUDOnDlZsGABK1aswMPDg8KFC7Np0yYqVqzI4MGDiY6OxsIibrPA3Nwce3t7w8/Dhg2jSJEirFu3jly5cvHTTz/Rr1+/eF8avCo6Ohpzc3OePn1Kjx49OHfuHNmyZWPx4sUULVo0wW0mTZpEiRIlmD59OvPmzcPd3Z2TJ09y/fp1unXrxooVK7h16xaVK1eWu1VCpANJIITIgNIjeQAICAjg888/57fffqNjx47s2rULMzMznj59SteuXfn222/53//+h7W1dZzt0qsRklGlRxKRERuZaUnRhqZ8I3NrNOZqXSm6aNBFgsYMjaVtivarsbJ/4zqJCQgI4MKFCwDMmjWLsmXLcv78efz9/Tlz5gybNm0iS5YsfPFF3C5wiqLw+PFjzp07x4sXL2jYsCH9+vXj4MGDnDt3jidPntChQwfatWtHtWrVEjz2xo0bWbt2Lffv3yckJIQcOXJQqlQpWrRoQcmSJSlSpEii5W7cuDGLFy8mICCAlStXUrNmTby9vZk3bx7r169n8eLFiR5XCJH6JIEQIoNJr+QhKCiIoKAgPDw86NWrF8ePH2fp0qVs2LCB7777joULF1K/fn10Op3hW+T0boRkZGmZRGTURmaa+r5gyrf5/Bco00L9/9VdsLYnFPwYvt5qXGdaJQh9nvR+pvqn/NgxDh06xKVLlwDo3r077du3B2DPnj1MmjQJT09P5s6dG2+79evXc+DAAe7fv0+OHDmYPXs2JUqUoEWLFly7do1WrVoRERHBP//8Q+XKlRO8a9SuXTsaNWrE8uXLWbRoEU+fPiVfvny0bNnyjeUuW7YsH330EdbW1owePZq1a9cyaNAgtm/fTr169ejTpw/Nmzdn4sSJb103Qoi3JwmEEBlIWicPPj4+LFu2jJCQEG7fvo2zszObN28mMjKS5s2bExYWRunSpSlWrBhz5sxh3bp1nD17FgsLC3755Re8vLzStRGS0aVVEpFRG5nizVq3bk2tWrVYu3YtRYoUMdxpunfvHqDGW5/AE7w6depEvXr1aNq0KVZWVqxYsYK+ffty9uxZ9u3bB4CjoyPVqlWLc/fpdZGRkaxevRoLCwtq1qzJtGnTsLCwSDK+J06cYMiQITRr1oz8+fMza9YsPDw8OHnyJKdOnSJbtmwsXLiQrVu3vn3FCCHeiSQQQmQQ6XHnIU+ePOTIkQONRkPNmjXRaDQULlyYffv2sXr1ambPnk2VKlXIli0bx44dY/jw4fTp0wedTkfBggUpW7ZsujVCPhRpkURkxEZmmpt4N+XbmL/Sla9kE3UfmteeXjbizLuVKxliH7a4YcMGtm7dSokSJVizZg0AERERtGzZkpIlS/Lll1/GGdsyfvx4smfPjoODAzVq1CAoKIj69euTO3duDhw4gJWVFYcOHWL9+vXkyJGDgQMH4u7uHufY33//Pa6urri7u5MrVy4aNmzIiBEjeP78OV9++WWC5a1QoQJ16tRh0KBBTJkyhdq1a3P37l3+++8/li1bhp2dHVmyZMHCwoLDhw8n2TVOCJE6JIEQIgNIr25LAN988w0AW7du5ejRoxw8eJDGjRvz448/GtZZs2YNx44dw8PDI14DIr0aIR+StEgiMlojM629yzgEQB0LYR7/kvuu+01MVFQUBw4c4ObNmxw8eBAHBwfKlCmDi4sLo0ePZvjw4fz0009ky5aNRYsW0alTJ/bs2cOKFSuoVq0a8+fPJzo6mh49erBhwwZu3LjB7NmzKVKkCFmzZgXU5LB06dKGhPP1sTFLlizhyJEjrFmzhhkzZqDX62ndujX79+9n2rRpnD59mgkTJuDm5hZnOzMzM4YPH87Dhw9p0KABT5484ebNmzx8+JBx48bx+PFjHj9+bPid7du3LwMHDkyVehRCJEwSCCFMXHomD69q1aoVu3fv5ubNmzRt2jTOe7FdU8LC4pYvPRshH5rUSCIyciNTJE2j0bB161b27dtHnTp1WLhwIRs2bGDFihXMnj2bsmXL8tNPPwFQuHBh5s6dS48ePTh+/Di3b99Go9GwZMkSVq1aRXR0NCNGjGDEiBGAOifMjh07aNSoEYUKFUrw+GvXrmX58uUsXLiQHDlyEBAQQGBgIOPGjaNp06ZcvHiRAwcOcPz4cTp06EDPnj1xc3Nj9erVzJo1izx58lCyZEkKFiyIm5sbrq6u5MyZkx9++AFHR0dsbGxQFIWgoKAk724JIVKHJBBCpIPCBcyTtd6rk8QdOq7F0+PtP7LJPWZi9u3bx6lTpyhVqhSzZ8+mUqVKhkdzPn36FCDO4xTXrFmTbo2Q9KRxc3/zSm9J73WVKCcnNYlwcjJMNve2x8yojUzxZhYWFixcuJBz585RoUIFli9fjoODAzt37sTOzo6zZ88C6hO0AD766CNmzJiBs7Mz1apVM0wUeevWLSIjI+PsO/ab/8Qet7t48WJu3brFX3/9xV9//cWSJUu4d+8eOXPmxNLSEicnJ5YtW8bKlSvRarWYm5tz/vx5GjVqRJcuXejSpUu8fV68eJH79+9TsGDcweyOjo7vVlFCiLciCYQQaUynU1I8I3TntuqEce/j2ObmKX9m+rp161iyZAmLFy8mb968NGnSBD8/P37//XfMzMzYu3cvQJxZiDt16pRujZD0ouj17zwjdHJZ1qwFNY19vxW9Hk0KZgeHjNvIFMmj0WioWLEiAD179ozz3pEjRwBwcnIyLGvSpEm8fRw/fhxXV9c4y2LvIr26bazIyEhatmxpmGiyR48edO7cmSpVqlCxYkXGjh1rWHfq1KnJPpfXHxUshEhfkkAIkcbepgGfnsfetWsXT58+Zdu2bTg7OwOwYMECypcvT2RkJJ9//jn379+nQIECuLi4GLYze6UxayqNkNSW0ga8KRz7Q2pkiuRr0KABu3btomTJkm9c7/UYOjg4kDVr1gRja21tbYhrrHv37hEZGRmv+1pK2Nvbxxs7I4RIP5JACCGSlFCDMfapJ46OjowaNYrx48czZcqURPdhKo0QkTIZrZEpkq9UqVL8888/b1xvzJgx8Za5uLjQp0+fZB+rWLFidOvWjY8//jhFZXxVoUKFyJEjx1tvL4R4vzRK7H1mIYRIY7/99lu8yckSoygKU6dO5eOPP6Z27dqpXDLxPkh8xfsSFRUFGGdMF0KkL0kghBBCCCGEEMmWfh12hRBCCCGEEBmOJBBCCCGEEEKIZJMEQgghhBBCCJFskkAIIYQQQgghkk0SCCGEEEIIIUSySQIhhBBCCCGESDZJIIQQQgghhBDJJgmEEEIIIYQQItkkgRBCCCGEEEIkmyQQQgghhBBCiGSTBEIIIYQQQgiRbJJACCGEEEIIIZJNEgghhBBCCCFEskkCIYQQQgghhEg2SSCEEEIIIYQQySYJhBBCCCGEECLZJIEQQgghhBBCJJskEEIIIYQQQohkkwRCCCGEEEIIkWySQAghhBBCCCGSTRIIIYQQQgghRLJJAiGEEEIIIYRINkkghBBCCCGEEMkmCYQQQgghhBAi2SSBEEIIIYQQQiSbJBBCCCGEEEKIZJMEQgghRKag1Wo5f/48UVFR6V0UIYTI0DSKoijpXQghhBAitUVERFC/fn0KFizI77//nt7FEUKIDEvuQAghhIhj69at1K5dm2nTphEaGvpW+/D396dBgwZ8//33aLXaBNdp3749gwcPJjAwMMl93bt3j6pVq9K7d2+8vLzeqjwANjY29O7dG29vb9q3b0/FihXZt2/fW+/v4cOHVKtWjSFDhvDgwYO33k/nzp3p06cPDx8+TPD9KVOm0KFDB65fv57kfhRFoVGjRnTs2JHdu3enuBwrVqzgk08+4ZdffiEoKIjo6GgAGjZsyC+//GL4OSVCQkIoX74827dvf+O6//77L02aNGHlypUJvh8UFETVqlWZM2cOer0+yX1t3ryZmjVrMmbMmDf+fiXH33//Ta1atVi2bFmy6mHPnj3Url2bqVOnEhYWRnh4OKDGetasWYafhcioJIEQQggRR8uWLbGysmLFihVcunQp0fWioqJ48uRJgu/lyJGDsmXLsnnzZkJCQggJCaF27doMHz6ckJAQHj58yM2bN3ny5AkvXrxIsjwFChSgXr16HDhwgH///TfF57Ny5Uq++uorGjRowI8//ohWq8XGxobu3btTvnz5FO8vVt68ealXrx47d+5k8+bNia6nKApPnjxJtOHZsmVL9u/fz82bNwHo0aMHXbp04fbt20RFRXH8+HH8/Pzw8/NLsjwajYbevXtz/vx51qxZk+zziIiIIDQ0lP/97388ffqUmzdvsmzZMtq1a8eTJ09wc3Pjp59+4p9//kn2PmNt374da2trGjVq9MZ169evT0hICNu2bQPg5MmT1KlThwULFgBw9OhRgoKCePToEZGRkUnuq3Xr1lhbW7Nx40Zu3LiRojIPHjyYRo0asWLFCsOyLVu2EBgYyJMnTwgLC0t026ioKF6+fEmjRo2wtrbm8uXLbN26laZNm3Ljxg3y5cvH0qVLWb16dYrKJISpsUjvAgghhDAtGo2GQoUK8ezZM8qXL4+Pjw+hoaE8fvyYBw8ecPfuXa5fv86VK1eIioqid+/eDB48ON5+cubMSb58+XBxcQFgyJAhjBw5khYtWvDbb78xZswY2rdvj0ajeWOZypQpw+bNm6latWqyzyMqKgpzc3NatGiBk5MTR44cISIigkmTJlGhQgUcHR0ZOnQoL168YOLEieTOnTv5lRSjcOHCANStW9fQsPXz8+PBgwfcu3ePmzdvcvHiRYKDg6lcuTKrVq3CzCzud3fu7u4AlCtXDoARI0bQpUsXdu7cSUREBNWrV2fAgAHY2dm9sTylS5cGSFE9Xbp0iS+//JKBAwfi6upKwYIF+euvv3B3d8fc3JxHjx5RunRpGjdunOx9xvrjjz9o06YNVlZWb1xXo9Hg7u6Op6cnANWqVaNWrVosXryYZs2asXr1alavXk3lypWTta/SpUvj6+tLxYoVU1Tm8ePH0717d/766y+6d+/O1KlTefHiBVu2bKFIkSJJbvv48WOaNWvGZ599Rq5cuciXLx/79u0jW7ZsWFpa8ujRI9zd3enatWuKyiSEqZEEQgghRDzm5ubkzJmTiIgIRo0axfXr1ylfvjy5cuVi3bp1lCtXzvBtfmwDOKF9vNrobdasGc7Ozly6dInQ0FCmTJnCxo0b+fPPP99YnixZsgDw5MkTZs+ezb179yhRogTdu3fH2to6wW0OHz7M2LFjWbBgAbly5eL27dv88MMPjBkzhnz58vHpp58SGRlJu3btcHV1fYtaUs8RIHfu3CxYsICtW7dSvHhxSpcuzfbt2zE3N2fEiBEAWFpaotfr4yUQFhbqpTi2rjw8PFi5ciWXL19my5YtPHjwgB07dvDHH3+QJ0+eJMvj7OwMQGRkJMuXL+fKlSu4u7vTpUsXcuXKleA2VapUoW/fvty9exeAAwcO4OrqSo8ePXj69Ck+Pj6MHDkyWYneq86fP8+NGzeYP39+srd5/Xdm9OjRVK1aleXLlxMdHU2PHj3o3LmzoU6TkiVLFhwdHTl8+DDHjh3j5cuX1KhRg1atWr1xuw0bNvD06VN27NjBvXv3iI6O5uuvv2bEiBF8+umniW6bP39+xo8fz65duwC4evUqFhYWdO/enSxZsnD27FmGDx+OjY1N8ipECBMlCYQQQggDRVHw8fEhICAAX19fvvzyS3r06EHTpk0JCAjg8OHDrFu3joiICFq2bJlgozI8PJybN2/i5eXF9evXqVSpEkOHDuXYsWPcuXOH27dvU6lSJaZPn57kN+V37tzh2rVr3Lx5k2PHjgHqt/Nubm4ULVr0jX3R69evz19//cX169eZOnUqWq2Wvn37UrFiRebPn4+vry8ff/wxRYsWTXHjGOD58+f4+PgA0KtXL6pVq8aFCxcICQnBz8+PdevWYWdnR7Vq1cibN2+87aOjo7l//z5Hjx4FoEaNGrRt2xaNRsN///3HjRs3cHFxYejQoVStWjXROyTPnj3jwoUL3LlzhwsXLgDw888/4+zsTKFChciWLVui5xAaGsrWrVsJDw/Hz8+Px48fEx4eTqlSpXjw4AFeXl54eHjg7u7OyJEj2bdvHz179uTrr79+Y/2sW7eO6tWrky9fvjeu++LFC65cuYK/vz9r165l27ZtjB49mvXr1/Pw4UOePHlCu3btGDp0aJJ3FM6dO8edO3e4fv06hw8fJiAggP79+5M3b148PDxI6rkxOp2Ojh07EhwcjE6nM9RfhQoVOH/+PJcvX2bAgAFs3LiRUqVKJbj9xo0b8fX1xdLSkrNnz2JtbW2oSx8fH5ydnalUqRI//vgjO3bsoEmTJowbN+6N9SOEqZEEQgghhMHevXvZvn07Pj4+uLq6Mm7cOEO3mFGjRnHw4EFy5szJqlWrEmx0BwYG8t133xEUFMSNGzfInz8/o0ePplq1anTq1AmtVkuFChXQaDRcvHiR7NmzJzoOISQkhPPnz2NhYcHVq1cBGDp0KL169Ur2+YwbN44VK1bw3XffAeq36507d2bt2rVUrVqVIUOG4ODgwMaNGw3ffI8bN46dO3eycOHCRBOcGzdusHTpUry9vQEYOHCgYd0//viD6dOnY25uzrJlyxJMHgCmTp2Kl5cXT58+xdLSkh9++IFatWrh4OAAQJcuXbh58ya+vr6cOXMm0QQiOjqay5cvo9VquXz5MgC1a9dmyZIl8e52vM7e3p5q1aqxe/dujh49ipmZGVWrVqVly5aUKFGCGTNmYG1tTbt27WjYsCHjx4+nXr16Se4T1IRgz549zJo1643rXrhwgblz56LX6/Hx8eHTTz+lT58+eHp60rRpU06dOkXXrl3R6/UcO3aMggULJnrXy9fXl6tXr/Ly5UsePXqEpaUlK1eupFKlSm8sh7m5OaNHj6Zjx46MHTuWLl26GN4bNWoUlpaWzJ07N8HkIXb7GjVqcPjwYRYvXoxGo6FSpUq0adOGunXr0rhxY8zMzGjbti0fffQRw4cP55NPPnljuYQwRTKIWgghhMGnn37KggUL8PT0NAyEjm2E5siRA1AbU7Fdil6XNWtWFixYwJgxYwgODsba2pqgoCB0Oh2//fYbgwcPRlEUrl27xqVLl3j+/HmiZSlTpgxjx44lR44chqfu/PLLL4ZBtknR6XSMHDmShg0bUrJkSTQaDbNmzcLMzIxDhw6xZ88e9u7dy7p16yhcuHCcQbmPHj0iODg4ybIVK1aMWbNm0bJlS0BtsMd2S4mtp06dOhnGNSRk7NixLF++nKioKCwsLIiOjiY8PJxDhw4xbNgwbt26RWhoKPv27cPX1zfR/bi7uzN48GCaNm2Kv78/oH4TP378+DfWE6hddjZv3kyHDh3Ily8fWbNmZciQIUycOJHJkyezf/9+AI4dO0bdunWxtbV94z63bNmCs7MzdevWfeO65cqV49dff6VBgwaA+rSsgIAAIiIimDp1KvPmzQPUQdR37txJ8slgsd/ox65jbW1Nv379DAPU36R8+fK4ublx+vRpDh8+zJ9//slPP/3Enj17sLW1xcPDI8nts2bNyqZNm6hevTrVqlXD3d2diRMnMnz4cL788kuOHDmCq6srZ86coXLlyol+joQwdXIHQgghRDyxDfYTJ07w4MEDrK2tDWMV/Pz86Nu3L02aNOHTTz9NcIDspEmT0Ov16PV6JkyYQGhoKK1ataJChQqcOXOGL774gr59+xIZGcmzZ8/Inj17guV4/vw5S5cuJXv27Dx79ozRo0czatQooqOjadu2baLljx2b0KBBA0qVKsUnn3xCnz59+P3338mZM6fhKTjOzs5MmjSJ69evU7ZsWaytrVm8eDGBgYGGROBN9WRmZsbNmzc5ePAgNWvW5PvvvwfUhnC/fv2oXLkyLVu2JGvWrPG2//nnn3ny5AkODg6sXLmSI0eO8OOPP5I9e3YCAwPR6XSsXLkSnU6Hv78/2bJlM5zbqxRFYdq0aYZ6Gjx4MLNmzUJRFCZMmJDonQg/Pz969uxJu3bt2LZtG9mzZ6dNmzb07NnT0Fff2toae3t7ihYtmqyB3IqisH79ej777LMEy5qQFy9eGJ625Ovry1dffcXff/9N165duXTpEmfOnGH27NlUqlSJ4OBggoKCEm18nzhxggMHDpA9e3bc3NyoUKECX3zxBT///HOidw9eVbNmTS5cuMDevXvJnj07uXLlYvLkyVhYWHD79m2yZs1quEv0qpCQEL7++mvKlCnDzZs3CQoKYsiQIfTt25eJEyca4p81a1bMzMwSvTMlREYgdyCEEELEcevWLfz8/Dh9+jQbNmwgZ86cjBs3zvDEIQ8PD6pUqcLQoUNp1KhRvEe9bty4kWvXrlG7dm0KFCjA2rVrKV26NE+ePMHPz4+oqCj++usvmjRpQrly5ahevTpdunRJcIbocePGoSgKX3zxBQCffPIJLVu2ZPTo0SxcuDDJ+QCmTJnChAkTyJ07N9u3b6d///7kzp0bb29vjh8/zqpVq+jevTsff/wxXbp0YeDAgYA62Dk5ycOzZ8+4ceMGer2ekSNHUqZMGYYMGYKLiwtWVlZYW1vTs2dPZs2aRb169di0aVOc7b29vVm+fDlNmzbF2tqaX3/9lc8++4zHjx8TGBjIy5cvuXnzJu3bt6d8+fLUrFmTBg0acP/+/XhlWbVqFadPn2bAgAEA5MqVi7Fjx/Lnn3/Sv39/goODEzyHAwcOMH/+fPr27Ut0dDT58uVj+fLlNG/enEaNGjF8+HAuXrxIaGgoNWrUeGOdgPr4VR8fH9q3b5+s9UGNVe7cuSlQoACNGzfmt99+w9zcnGfPnhnuvsyYMYPatWtTqVIlqlatysyZM+PtJzg4mDFjxlC5cmU+/vhjAIYPH07WrFnp2rUrBw4cSLQMM2bMQK/XU7x4cf744w+yZs3K9u3bCQ0NpXHjxtSuXZvly5dTpUoVunXrxu3bt+Nsf+DAAUaPHs3333+PmZkZbm5u7Ny5k0aNGlGtWjWmTZvGiRMn8Pf3p3r16m/sXiaEKZM7EEIIIdDr9fTr14/Lly/z9OlTcuXKxc8//4y5uTkDBgygZs2afPHFF/zvf/9DURS6devGrVu32LBhA1OnTmXt2rWA+kjQGTNmMHPmTP7++29CQkKYM2cOR48epWjRohQpUgStVkvBggUNDWdQ++LHPo0o1vz589m3bx9LliwxDFZWFIUxY8Zw6tQp5s2bx8mTJ5k4cSIFCxY0bKfVavH39+fly5f4+PgYBm7fvn2bmzdvEh0dzeDBgzEzM8PGxgYrKyv0ej0HDx7kyZMnuLq6EhAQkGgSMX/+fHbs2MGDBw+wsbFh1KhR1KtXjz59+qDX61mzZg2ffPIJer2e8uXL8+OPPzJ8+HDGjRtHs2bNsLa2JjAwkP79+/PVV1+RJ08eTpw4waZNm5g3bx65c+emePHiBAYGkiVLFrp27YqtrS1mZmaYm5vHu1tz7Ngxpk+fTq9evQwDjPV6Pa1bt2bfvn3s3buXli1b8sMPP1C9enXDdjt27GDv3r3s2rWLJ0+eEBAQQGhoKN7e3pibm9OsWTOioqLo0aMHgKGL0ZusW7eOunXr4ubmlqz116xZw5kzZ/jtt9/o1q0b/v7+rFu3Dn9/fzw9PXF0dATUp0WVLFkSS0tLFEWJNyYkOjqaAQMGEBERwU8//cTUqVNRFAVra2umT59Ox44d6d27N506dWLIkCE4OTkZtr1z5w7Lly/ns88+MyR1ffr04fDhw8ycOZM6depw9+5dli1bxoABAzh8+DBz5swxPGHqxIkTbN26FZ1Oh5+fH48ePeLjjz/m7NmzKIpC3bp1yZ07tyGZS25dCmGyFCGEEEJRlEOHDikVK1ZUBg8erAQHBysbNmxQqlWrpqxZs0bR6/XKwYMHFQ8PD+X48eOKoihKZGSk0r59e6Vu3bqKoijKvXv3lF69eileXl6KoijK119/rXz99dfxjlO+fHll0aJFSZbl119/VcqUKaPs2rVLCQgIUCZPnqx4eHgogwYNUsaNG6ds375dKV68uOLh4aGUKFFCGTFihHLnzh1FURTl4sWLSs2aNZUaNWooX3/9tTJ79mxl48aNyvHjx5UWLVooLVu2VEJCQuIcT6fTKWFhYYqiKMpXX32lFCtWTNm9e3eCZXvw4IHSqFEjpUWLFsrdu3eVS5cuKTVr1lR++OEHJTg4WAkPD1c8PDyUOXPmGLaZNGmS4uHhofj4+CiRkZFK//79lV27dimKoijLly9XypUrF+84X3/9tdKtW7ck6+n06dNKhQoVlGnTpinBwcHKzp07FQ8PD6Vr167K8OHDlb179yrVq1dXPDw8FA8PD+V///ufIX5RUVHK0qVLlVWrVin9+/dXJk2apDx//lypUKGCsmDBAsM6VatWVapXrx6vzhLi7++vlCxZUjl69Ogb11UURdm7d68ydOhQ5fnz54qiKEq5cuWU5cuXx1nnypUrioeHh3LmzJlE9xMZGakMHDhQqVu3rnLt2jXlyZMnSqdOnZQqVaoogwYNUubOnavMnTvXUA8VK1ZU5s2bpwQGBiqKoiidOnVSPDw8lLt37yoDBw5U5s+fryiKogQEBChLly5VoqKilE8++UQ5ePCgEhUVpcyaNUuZNWtWnDKsWbNGWbp0qTJ+/HilX79+Snh4uFKvXj1l1KhRhnVatGihlCxZUvH19U1W/QhhquQOhBBCCABq1arFkSNHsLW15ezZs4SFhfHPP/8YvgG+du0aYJy3wMrKiiVLlhgeHZozZ06WLl0KqHcKvLy8KFq0aLzjKIqS5MRis2fP5vr162zfvp2LFy/Sp08fwx0Ic3NzsmXLRpUqVZg9eza7d+9Go9Hg4ODAmTNnKFiwIGXKlOHw4cMJ7jt2ALC9vX2c5WZmZob3cubMiYODQ4JjFkCdgXrHjh3o9XoCAwPZuXMna9asMfRpP3funKGssUaMGIGnpye5cuVCr9cze/Zsw/tXr15N9JG0SdXTvn37WLRoEQsWLMDJyYkBAwbEuVOTLVs2cuTIwS+//MKKFSsIDw8na9asXL58mdKlS+Pg4GB4opWnpyfTp0+nTp06REdHG+ZKmDdvHtbW1jx//pz27dszYcKEJCdy27BhA7ly5TJ0H3qTWrVqGb6Nv3v3LmFhYfHqQol59GpidREaGsrQoUPJmzcv27dvZ8mSJZw8eZLbt29jbm6Oo6MjTk5OdOrUyTA7tI2NDREREXh5eVGpUiXOnj1LsWLFKFCgAM2aNWPgwIE4OzvToUMHQx3VqVOHAQMG8O233zJgwIB44zs+//xzQO2aNnXqVKpWrUpERATTpk0DYP369Tx58gR7e3vat2/P999/L3ciRMaVvvmLEEKIjKJr166Kh4eHcvv27Teu6+3trXh4eCiDBw+O916lSpWU9evXJ7jd8+fPlWvXrsVbPmjQIMXDw8PwTfXb+vzzz5UvvvjinfbxJgsWLFA8PDyUdevWJWv9atWqKdWrV4+3vG/fvsqQIUMS3Eav1ysnT55U9Hp9nOVr165VPDw8DHc3kuvJkyfKl19+qdStW1cpW7as0qNHD+X7779X+vfvr/j7+yvLly83fHv/xRdfKHv37lV0Ol2cfeh0OqVOnTrKL7/8kqJjx/r9998TrLdr164pHh4ehjtMr/P29lb8/PziLa9evbrSpEmTZB17zpw5hv3r9XqlZ8+eioeHh1KhQgWlfv36SqVKlQzn7+HhoSxdujTRfb148UIZPny4Uq1aNaVatWpK48aNlcWLFyvdunVT7t69q+zZs8ewn7Zt2ypbt25VIiMjk1VOIUyF3IEQQgiRLN26dePFixcUKlTojesWKFCAChUqJPgYTwcHB/Lnz5/gdi4uLri4uMRbHvut/ruysbExzNacWlq2bMm2bdvijDdISuvWrQkJCYm33MHBIdG5HzQaTYJzVMTWU3Inxnv06BELFy7k9u3bNGzYkPnz5+Pl5cXu3bvp2bMnOXPmBKB79+6EhYXx33//kSVLFry8vChbtmycGbwPHTrEs2fPaNOmTbKO/bratWuTO3fueHcv7O3tsbS0TLQuihQpEm/ZgwcPePr0abJjHTuAHtS6W7hwIevXr+fff//F29ub8PBwnJ2dKViwIPXq1YszR0SswMBAFi1axPnz56lVqxa7du0iICCAJUuW0LRpU3r37g2on41x48axd+9e7O3t8fb2pmzZshQoUCBZZRXCFGgUJYlpGYUQQoj3bPLkyQwbNizJ7jmvGzVqFPv37+f48ePJfjRoYvspVKgQX3311VvvI62sW7eO8uXLU7x48WRvs3XrVkaMGMHWrVspUaLEG9fXarWGgcamKjo6mqlTpzJ27Nhkb6PVamnVqhUFChRg0aJFqVg6o+joaLRabbIedStERicJhBBCCJOn0+l48uQJefLkeaf9HDx4kKJFiyb6bfaH4MGDB+TLly+9i5HugoODiYqKSvCOlhDi3UgCIYQQQgghhEg2mcVECCGEEEIIkWySQAghMiydTpfeRchQpL5STuosZaS+Uk7qLGWkvlImtepLEgghhMnS6/VMnz6dv//+m++//57IyMg470+fPp1ffvklnUpneqS+Uk7qLGWkvpJ2+fJlVq5cGWcuC6mzlJH6Spn0qi9JIIQQqe7OnTt069aN8uXLU69ePdasWQPAmTNnGDhwINOmTaNv376sXLkyznZ79uzh/PnzNGzYEH9/f9auXWt479ChQ/z777906NAhLU8lTURGRjJq1CgqV67Mxx9/bKiXiIgIvvvuO8aOHUvfvn05ceJEnO0ya31B4nXm7+/PN998Q8WKFalRowZz586Ns11mrbPE6utVW7Zsife40sxaX5B0nfn6+tKtWzc2bdpEhw4dDJMtQuats8TqS/7uv9mCBQsYOXIkYLp/9yWBEEKkqqioKGbOnMmQIUPYsmULhQoVYuLEiVy5coWvvvqKLl26MGLECKZPn86sWbP477//DNueO3fOcCHOkiULp0+fBtRG4ZgxY5g5c6ZhluQPybp162jWrBmrV68mZ86chsdQ/vjjj2i1Wn788UdGjhxJ7969efDggWG7zFpfkHidTZs2jS5durBt2zZq1qzJokWL+Pvvvw3bZdY6S6y+Yh05coTvvvsu3naZtb4g8Tp7+vQpHTp0oEiRIowfP94wo3mszFpnCdVXYGCg/N1/g02bNjF//nzDz6b6d18mkhNCpKo7d+7QvXt3ypQpA0CXLl04cuQIR48eJSwszHCr38HBATs7uzjf3NnY2Bj+r9FosLe3R6/XM3z4cLp27Uq5cuXS9FzSStu2bQ1/8IsXL06TJk3w8fFhy5YtTJo0CYB8+fKRLVs2fv75Z3744Qcg89YXJFxnAQEBNGjQwDDhWs+ePdm8eTPPnj0zbJdZ6yyh+op1/fp1zp07R44cOeJtl1nrCxKvs4kTJxIeHs7gwYMT3C6z1llC9eXr6yt/95Nw7NgxIiIiDD+b8t99uQMhhEhVxYoVo1KlSoaf7969S65cuejatSs1atRgzJgxnDlzhqNHj9K/f3/Kly9vWLdRo0a8ePECUL/la9GiBcuWLcPMzCxDTAT2thwdHVEUheXLlxMREcEnn3zCsWPHiI6OJlu2bIb1smfPzsGDBw0/Z9b6goTrzMXFhcaNGxvWuXPnDg4ODjRo0MCwLLPWWUL1BfD48WO2bNnCgAEDEtwus9YXJFxn/v7+7N27lyZNmrB//37Gjh3LihUriIqKMmyXWessofoqXry4/N1PhJeXFxcuXKBz586GZab8d18SCCFEmrl//z4bN25kyZIl2NnZMXfuXKpXr87OnTsZNmwYgYGB6PV6w/qlSpVi9OjR/Pvvv3Tu3BlHR0d+//13pk+fjkajScczSX2rV68mMjKSR48e0bhxY54+fQqApaWlYR1LS0sCAgIMP2fm+oL4dXbs2DHDewEBASxcuJCFCxfi5uZmWJ6Z6+z1+tq1axdLlixh6NChiZ57Zq4viF9nly5dQlEUChcuTPPmzenfvz+zZ89mwYIFhm0yc529Xl/Hjx+Xv/sJePjwIVu2bKFv375xlsf+fTfJv/uKEEKkgTt37ihDhgxR/Pz8FEVRlIiICKV169aKv7+/oiiKcv78eaV06dLK3LlzE9w+KChIqVevnnLw4ME0K7MpCA4OVkqVKqVUqlRJ8fDwUPbt22d4r3379kqNGjUS3C6z1peiGOusf//+iqIoyrNnz5QhQ4Yot2/fTnK7zFpnsfXVr18/pXz58krFihWVihUrKsWKFVNKlCihVKxYUYmMjIy3XWatL0Ux1pmHh4fi4eGh/Pnnn4b32rRpo7Ro0SLB7TJrnb36mZS/+/ENGTJEqVChguGz5+HhoZQsWdLw+2WKf/flDoQQItXdu3ePTZs2MW3aNEO/6l27duHv74+rqysA5cqVo2bNmly8eDHBfYwdO5ZPPvkEZ2dndu/ezb59+9Ks/OnJwcEBe3t76tSpg4WFBf7+/ob3nj9/Tq1atRLcLrPWFxjrzMbGhoCAAJYsWcLEiRMpVKgQANu2bUtwu8xaZ7H1ZWdnx7lz5zhz5gxnzpwhV65cVKxYkTNnzmBlZRVvu8xaX2Css9jP38uXL+O8/2qf/ldl1jqLra8TJ07I3/0EzJw5k7Nnzxo+ewDNmjXj33//Ndm/+5JACCFSVWRkJCNGjCB37txs2bKFDRs2MG3aNDZv3kxgYCAPHz40rPv8+XNq1qwZbx/r16/Hx8eHLl26MHjwYBo3bsxvv/3G0aNH0/JU0sTTp09ZtmwZYWFhAFy9epWoqCh69+5Nu3btDOf88OFDnj59mmAf18xUX5B4nX399dcMHz6c3Llzs2vXLjZs2MCSJUvYvn17vH1kpjpLqr6SKzPVFyReZyNHjqRYsWKGpwhFRERw7949OnXqFG8fmanOEquv1atXy9/9FMibN6/J/t2XpzAJIVLVP//8w4ULF7hw4UKc5V9//TWdOnVi0qRJlClThsjISD799FO6desWZz1vb2/mzZvH2rVr4/whdHNzY9u2bdSoUSMNziLt6PV69u/fz/79+2nWrBl3795l/fr1FC5cmO+++45JkyYxduxYnj9/zsKFCylQoECc7TNbfUHidRYUFMTRo0fjXUCbNWsW5+fMVmdJ/Y4lR2arL0i6zpYuXcro0aOZNm0awcHBDBw4kHbt2sXZPrPVWWL1VbRoUX766Sf5u58Cpvp3XxIIIUSqat68Oc2bN0/0/VcfH/m6iIgIBg8ezLfffkuBAgU4cOCA4T1zc3OCgoLea1lNgZubG+vXr0/wPQsLC8aNG5fotpmxviDpOrtx40aS22bGOkuqvl61f//+eMsyY31B0nWWM2dOfv3110S3zYx1llR9NWnSRP7uv8Grf7dM9e++dGESQpisyZMn4+npSatWrQC1v6xWqwXUrlGvPvpPSH29DamzlJH6Sjmps5SR+kqZ9KovuQMhhDBJly9f5tSpU2zevNmwrHz58rRr146tW7dia2sb53nZmZ3UV8pJnaWM1FfKSZ2ljNRXyqRnfWkURVFSZc9CCPGOXr58iZOTU3oXI8OQ+ko5qbOUkfpKOamzlJH6Spn0qi9JIIQQQgghhBDJJmMghBBCCCGEEMkmCYQQQgghhBAi2SSBEEIIIYQQQiSbJBBCCJNSv3596tevn97FyDCkvlJG6ivlpM5SRuor5aTOUsYU6ksSCCGEEEIIIUSySQIhhBBCCCGESDZJIIQQQgghhBDJJgmEEEIIIYQQItkkgRBCCCGEEEIkmyQQQgghhBBCiGTTKIqipHchhBDxFStWDABzc/N0Lkna0ul0QOY777cl9ZUyUl8pJ3WWMlJfKSd1ljLvUl+x2964ceOdymDxTlsLIcR7JheQlJH6Shmpr5STOksZqa+UkzpLGVOoL0kghDBRsX8gvLy80rkkQgghhPgQeHp6vpf9yBgIIYQQQgghRLJJAiGEEEIIIYRINkkghBBCCCGEEMkmCYQQQgghhBAi2SSBEEIIIYQQQiSbJBBCCCGEEEKIZJMEQgghhBAilSl6fXoXQZi4jPQ7IvNACGHiho0P4vY9XbLWbdfchs5t7VizKYyNOyJSuWQJK1LQnPHDHXnwSMcPM0MIj0j7ye5tbTR8N9SBfLnNGf9TMLfuJq/+3jeJh0riYSTxMMpM8aj1kSWDv3ZEu+o3FD/flO/AygrLtu3RZM9O1IY/UXyfvP9CJoNZtY+wrFmLqCOH0Z88kS5l0LjnxLL9ZyjPnhG1aQNotWlfiFSIh8bNHauuX7yHwqUNSSCEMHG37+nwuhn9xvX6dLOjc1s75iwLYfHKsDQoWXylS1jw/VBHbtyKpueQIELD0r5xZG+n4ZdZWciT05wv+r/g8rU3111qkHioJB5GEg+jzBaPQvnViUEVP18UH5+32od23hysevfFsl17tIsWojy4/z6LmCy6jRvg5UssmzYj6uVLdP/8neZlUHx80Pr7Y9W3H5bNW6JdsggiI9O8HKYQj/QkXZiE+AD06WbHoF4O6X4x/nWuM9530r9xVLSQBd0Hpm/jSOIh8XiVxMNI4vGWIiPRLlmE8uQJVn37ocmXP12Kofvnb6J2/oVl02aYf9owXcqgPLiPdtFCNDlzYtW7L1hbp30hTCQe6UUSCCEyOLkYq6RxZCTxMJJ4qCQeRqYQj7dmIo1WSSJimEg80oMkEEJkYHIxVknjyEjiYSTxUEk8jEwhHu/MRBqtkkTEMJF4pDVJIITIoORirJLGkZHEw0jioZJ4GJlCPN4bE2m0ShIRw0TikZYkgRAiA5KLsUoaR0YSDyOJh0riYWQK8XjvTKTRKklEDBOJR1qRBEKIDEYuxippHBlJPIwkHiqJh5EpxMPaSpM6OzaRRqskETFMJB5pQRIIITIQuRirpHFkJPEwknioJB5GphKPr7rYpt4BTKTRKklEDBOJR2qTBEKIDEIuxippHBlJPIwkHiqJh5EpxcPd1Tx1D2QijVZJImKYSDxSkyQQQmQAcjFWSePISOJhJPFQSTyMTC0eS1eFpv4BTaTRKklEDBOJR2qRBEIIE9euuY1cjJHG0askHkYSD5XEw8gU4/HwsT5tDmwijVZJImKYSDxSgyQQQpi4zm3t5GIsjSMDiYeRxEMl8TCSeGAyjVZJImKYSDzeN0kghDBxazaFycVYGkeAxONVEg+VxMNI4vEKE2m0ShIRw0Ti8T5JAiGEidu4IyJdjisXYyNpHKkkHkYSDyOJh8pU4mFgIo1WSSJimEg83hdJIIQQ8cjF2EgaRyqJh5HEw0jioTKVeMRjIo1WSSJimEg83gdJIIQQccjF2EgaRyqJh5HEw0jioUpRPKys0q5gsUyk0SpJRAwTice7kgRCCGGQ4S7GqUgaRyqJh5HEw0jioUppPCzbts/UjVZJImKYSDzehSQQQgggY16MU4s0jlQSDyOJh5HEQ/U28dBkz57pG62SRMR4PR7uOdO+DO9AEgghRIa9GKcGaRypJB5GEg8jiYfqbeMRteFP02q0ShJhMvGwbP9Z2h//HUgCIUQml5Evxu+bNI5UEg8jiYeRxEP1LvFQfJ+YVKNVkggTSiKePUv7Y78DSSCEyMQy+sX4fZLGkUriYSTxMJJ4qN5HPEyq0SpJhMnEI2rThrQ/7juQBEKITOpDuRi/D9I4Ukk8jCQeRhIP1fuMh6k0WiWJUJlEPLTatD/mO5AEQohM6EO7GL8LaRypJB5GEg8jiYcqNeJhEo1WSSIMTCIeGYgkEEJkMh/qxfhtSONIJfEwkngYSTxUqRkPk2i0ShJhYBLxyCAkgRAiE/nQL8YpIY0jlcTDSOJhJPFQpUU8TKLRKkmEgUnEIwOQBEKITCKzXIyTQxpHKomHkcTDSOKhSst4mESjVZIIA5OIh4mTBEKITCCzXYyTIo0jlcTDSOJhJPFQpUc8TKLRKkmEgUnEw4RJAiHEBy6zXowTIo0jlcTDSOJhJPFQpWc8TKLRKkmEgUnEw0RJAiHEByyzX4xfJY0jlcTDSOJhJPFQJRgPJW1jYhKNVkkiDEwiHibIIr0LIIRIHSZ7MU4H0jhSSTyMMkQ8or0h6Mu4y6xqg+OPoD0MwWPivmfTAey/MW4bOgNQQNGB/RCwLJlgOXp21qCE/kzDJte4d+cyWJQA+0Fg7q6uoCgQvhq0R2KO0wpsmqr/j9wDIZPi71TjBFm3gsYy7nIlQt1X9B2I9jIcq3SpPEwZGUalih8lXFlZfgGLYvGXR12BsEXqeZrlBochoLFL8liG8wI1OQidB7rbmFvYM2va1Ljx0AdDUC+1zi0KJ1y2VBDbaLXq2w+r3n3RLlkEkZFpdnzAkERY9e6LVd9+aBctRHlwP23LgJpEAFg2bRbn57RkEvEwMXIHQogPUIZoHKURaayqJB5GGSoemmxgls/4Mi9ofM/M7bX38qnL9c/g5Tdg5gpZloFVdXg5AKLvxdt9n2526ENWEKprzr0XP4LTHIj6L25yErEOIndCloXg9BOEzoXIgzHHeqE23F8th8YRrJvETx4AwpaDdQtwmmI4lk3UWH6d68zFK0/RmL12vmauYF484eRB9wiCh4Jtd8iyGIiC4AlJHite0hW5A6IvY59zEdUqu7B104y48QidpiYeaZg8xDKJb77lToSBScTDhEgCIcQHJkM1jlKZNFZVEg+jDBcPh2GQdY3xZdfD+J7j1Ljv2bRQl4evAyUMrOqoP1uWB7QQsTHOrvt0s6PbZ9FcvWnH6s1O6kKLgmpjWXcT9AHqN/BhK8GyGmiswMwZLEur3/orCpjlgqzr45bDPC9YN41/LvqXYJYFzN0Mx7J1KEJE6A3+O+fLD7OCUZyWxd2X1adg0yThuglbHnN+ldR/rWpC1HGIOp/gseKcV6yoM5hZOPDLrCyUKJaPf/YeNsYjYgtE31ZjkE5MotEqSYSBScTDREgCIcQHJLmNIzN0VMlymqauu6iS5TRm6N5bGaSxapThGqupSOKhSnE8NPZJvOeQ8HLdvZj3neOuF+1lWCU2Hiv/tOCUV7u42+tDAFt1+6jjQDiY5zS+b5Yb9E/U/VnXirtt9D1Q9GBRIH65zJzAppPhx9IlLHDNFoa1tS1DJ5gToSsO5jmM6ys60O4Hq/rx96VEqd24zNxBo1GXmedW/43cF+9Y8c4rhrm5GcUKW1C0kAXb/45EpzOPOY9bELYUHMcbu0SlE5NotEoSYWAS8TABaZJAREen7gWrfPnyFCtWLM6refPmqXrM9+nBgwd06dKFzZs3v/U+UruO30Vyynb8+HEaNkyfPwbvatSoUcyfPz+9i5HsxtEn2fayr+qnrCr7JTNLfMuqsl+yr+qnfJJt7zuXQRqrRhmysZpKJB6qt4pHyHR43gACGkHwxLjfnr8cCs/rQ0AzCJ2t3nUAMHNR/9X7xawY8wWBEgGAZ545/L6sDsO/O6jGQ2Nu3Gf0NdA/BJs2oDFTG9IQN5Exi/m/7kb88kbuButXGvwhMyCgsXpXAAzHKl3CgmG9HvLg/j30Vm0IC9fE31fUf2BeSE0GXqd7CETGLVfs/6NvxDlWgueFGo+vutcn6MUTug14jr/fPbW7lxIOwePAtkfCXafSgUk0WiWJMDCJeKSzNEkgfvjhB6ZNm8a9e/fe+77Dw8OpWbMmAwcONLxKlixJz5493/ux3reIiAiWL1/OwoULOX369Fvtw9fXl3nz5jF48OD3XLp3Exoayvr16+nXrx/37yc96OratWsMGDCAqKioNCrd+zN//vx3Svzel5QkD3M9h+Bm5RdnuZuVP3M9h7xTEiGNVaMM21hNBRIP1VvFQ+MIdl+oYw9svwDtIXg5TB34a+YGdr0gywK161LEFmP/f6sG6r+RO9Rv6vUv1J/NXOjTzY5sTv4EBwezfZdv3OMp4RAyFSyrGrtKKcExb77aXIh5/oo+5LXtFdDui3vHQO8LSgjoAw2LSpewYNFUK8aOHYO5TVWiLHuQoMh/4iYjcY4Ve+wEyqW8Xq745xUbjy+7tyHaoglXTvUDjTXY9YXQWWo3LNv2CR87nZhEo1WSCAOTiEc6SpOnME2YMIH79+/z559/4uvrS+PGjalbty7m5uZv3vgNzM3NGT9+PC4u6jcuUVFR7N69m8aNG7/zvpMSEhKCg0Mit4+TKSwsjEaNGlGqVCm2bt2a7O0UReH48eP89ddf2Nvb065dOwYMGPBOZXlfvL292bBhA0FBQTRt2pT58+djZpZ4nurr68umTZsoVqwYT548SfXyvY+4xdqyZQuOjo7vZV/vIiXdlkYXmQoomL32ZZ+ZRkGvaBhVeBr7ntdDT8o+m9JYNcqwjdVUIPFQvXU8zN3BvJH6f4tiamM+fDVEXwXLssZvxy1KgM4XtHtB5wdWVcFhHERsVhMOK7WbUYWKFRnUy4GZup84cuUJmGU3HkuJVr91Ny8MDiONA6AN3/C/2s0x+rX3Yhdfihn07Gpc5jgVlBeGY5UuYcHPMx3o06cfj58VRGc3IuHB1kokRJ1OfPyBoVvRG8qVwHm9Hg//0K6Qpau6fuQeiDoHWX5N+LjpzCSeBiRPZzIwiXikkzR7jGv+/PkZPnw4Wq2WPXv28O2331KkSBHat29P9uzqH5a1a9dy6dKlRPfRuXNnSpcuHWeZlZWVIXkA+Oeff6hRowZWVlbxtl+2bBk7d+7EzMyM2bNnExUVxe+//46ZmRl9+/bl+fPnLFiwAE9PTw4fPsynn37Kl19+iVarZfbs2Vy8eBELCwsURSEwMJC//vqL6Oho/vvvPxYtWkT9+vVZtGgRgwYNomHDhkyePBlnZ2fu3r1LtWrV6NWrV5zyxJbbx8cnWXUYFBTEli1buHz5MtWqVeO7777Dzi5+38yFCxfy8OHDRPfzzTffkCdPnmQdMzmioqLYu3cvBw8eJH/+/PTo0QM3N7c3bvfy5UuWLFnCqFGjkrxjZGpxAzh8+DBarZYGDRowZcqUd6q/d5GSxlGlLGfJae2X6PtmGoVcNr58kn0vfz9rCCTQpSAB0lg1ytCN1fdM4qF6r/GwiLn+6Z/Hf8+ytJpA6J+rA4etG6gvgJDpaDRmTPuxE3OWhbBsdVTc5AHULlAWHmAX87dYiQb9UzAvEvPzS+O6+tCY8hSNuw/tIePA7VgaC9AYk4df5zozYsR3XLlVEJ3t68d6ZZxF1Cn1fBMbf2CeD7BSB0vHUhIo12vnZWerY8rICIoWyhY/HroH6hOm7AdDyBQgQn38rXnehMuQTkyi0SpJhIFJxCMdpPk8EFZWVrRo0YIWLVrw559/0qJFC/766y9cXFz4/PPP+fzzz99p/+vXr2f8+PEJvvfll1+ydu1anjx5wosXLyhXrhyurq506NABW1tbevbsSb58+QgLC8PZ2Zl///2XVq1a8euvv7J+/Xr27duHk5MT9erVw8JCrTovLy/Gjx/PvXv3KFWqFC4uLlhYWPDLL79w4cIFNm/ejL29PStWrHin8wJo1aoVHTp0YMaMGWg0iTfu+vXr987HSonRo0cTERHBlClTkv3tfmRkJDNmzGDIkCFYv+G2n6nF7dKlS1y6dIlvvvkm2clfakhp48jV6lmy9jvXcxjPtJO5GlKSq8GeXA1RX76R7ryeVEhj1eiDa6y+A4mH6p3joSjGAcIQkziYg0XxhN/T2KtPG4qzjzCsOEinrl3YvMdFjYcSHeeuAJF71Ccs2b3yRU7UWdD5gPUnEGoDulcah/pHYF4ALMvEPZb2KDjNfe346rFKl3Tn17nOrPh1C4dOQrR1AseybRt3X1Y1E6iTcNDYquW1qqkOpFa06s+6R+o61s0TPC97Ow39u17n4X0fuq/LEjceihaCx4NNR4jYps51gQaCR0GW1XHr2gSYRKNVkggDk4hHGkvzpzCFhISwZs0aBg4cSFhYGHv27IlzB+Fd3L9/n4iICAoXTvh5zRYWFnzxxRcA/Pnnn+h0OkJDQ3F1deXcuXM8ffqUpk2bMmzYMJYsWcLatWtxcXFh586dFC5c2NDIfPXb+zJlylC+fHkA2rVrx549e/jss8/Inz8/Pj4+1KhRg6+//jrenZO3sWfPHnLkyMGQIUNYtmwZz58n8C1UOpg+fTodO3Zk0qRJTJkyhVu3br1xmy1btrBjxw4aNGhApUqVOHv2LI8fP6ZSpUrxxsqYWtymTJnCypUrqVSpEi1aqI9NXLZsGd27d3/bKkyxt2kcPdVmf+M6ADrFjOxWAdR2OULf/EtZWHIgB6t+wrFqtVlWqjfVsx4DpLH6qg+isfqeSDxU7xwP3RMIbAThMY9eVSLUcQ52vdVkIbAxRB5Q39MHqo1l+xFq4/oVxXIu4KNqFbDO2scYj+BRENhG3V6JgtDF6gDskBkxr2kQOl19DKqZE9h1A+0J9c6D/gVEXQbb1ya40z1Qj/3qXYRXjtW5+Smu3Qhn8aIZREclcqxXRZ0Fq9cmlYs8CAENIeIv9We7nmr3J+0h9WftEfVxs5ae8c7LImIGVTxm8+svE1i/zTJ+PMIWxsxf0SimK1Z2tduT7r46ANsEmUQffBkTYWAS8UhDaXYH4sqVK2zatInw8HBatmxJ586d462zatUqzp8/n+g+unXrRtmyZRN9f8eOHYYGXWLat2/PwoUL2b17N1WrVqVevXoA6HRqP8qLFy/GKZuiKAQEBGBvb+xTmVjD/dVv0ps1a0aePHn4559/+Pvvv+nVqxcnTpx4p/731tbWtGnThjZt2nDlyhXmzZtHVFQUrVq1okqVKob15syZk+TA5SFDhpA37/u7JavRaKhevTrVq1fH19eXP/74g7t379KgQQM+/fTTBLuTdezYkY4dOxp+7tKlC48ePWL//v0JHsOU4rZu3TrD/318fKhfvz69evWif//+SdbT+/K2jaMzQRV5EumGm5U/Zpr42+gVDb6RbjQ9sw0P+1uUdPCipONVSjp4UcT+NtmsAqjlcpRNvm0MjaNi5qe5+f1MygXV4DJd3vepvpE0VlWSPBh9MPEwy67OpRD+O0TuArOsakPeqqY6N4NVbQidCRF/qI8ldfhOHRcRK/o2xfNuoWlDD0L0HVi66pVvQ83c1LsVZs4QfRGUAPUYr9PENOptOwN6dTI6NOps19Z1464bdQEsK8fbhUu2nGhDHAgNd+KrgYdR9G84FqhPWNI4q+ccZx2bmLsPMUmSeR5wnAVh89XxHmY5wWF8zPnHPa/oSDiwL2Y/YY7w6mVJe1hNppxXqGNIXj0eqAPB8YxfZhNgEt98y50IA5OIRxpJdgIREBDAwIED6dixI02bNmXmzJk8fvyYmTNncubMGWbMmMGPP/5IkSJF4m07duxY3N3d6d27d5J947t27UrXrl3f7kxQxz8sX748yXUcHBzo2LEjP//8M+vWrWP9+vWA+ijYLFmy8Ndff1G8eHHKly/PuXPnaNq0KZ6enpw/f55z584RHBxMaGgoUVFRBAUFkSVLlgSPs2LFCjp16kSNGjWoWbMmw4YNS7Ah/bZKlSpFqVKlCAoKYvPmzfz6668sXrwYgEGDBqV4f1qtlm+++YaPP/6Ybt26sXr1ag4dOsSiRYvw8fFh9OjRDBkyJE6ikhB3d3cGDhxoGBcxbNgw+vfvT9GiRZPc7k0yStyS+py8D+/SONJjzuRbI5nrOQS9oomTROgV9fb8lNsjCNfbczG4LBeDy0LMuHZrswg87L0p6XCVa9oKhsbR1lGH+dz+GD5OWVj9WF1Xg56FJQdwK7QwV0NKciXEk0cRuUnumIrkksaqSpIHow8qHhpLsB+gvl5n5ggOo5LcvF1zS77/9gcW/xbF0lWvxcNhGPDK4ORsR95cHtsu6isxNvG/vFPjMRnvOxPpOSSICL2SvGOZ5wXnBK7lVtXA5bVGoWVJyLIk/rqWlSDbkTfHQ+enPirX4Ts1aVNi31diXoBZtjeX+Q3q13p/1//XmUSjVZIIA5OIRxpIdhem8PBwbt68ia+v+ti3u3fv4u3tDaiNpps3bxIUFJTgtj/88APffPNNsgbWvq0nT55gZ2eHq6vrG9ft0qUL5ubmceaKcHZ2ZuHChXh4eDB37lzGjx9P6dKlcXd3Z/z48ZQoUYIePXrw33//0b17d7RaLatWreL27dtcvXoVUO+g+Pv7A1CkSBG++eYbJkyYwKZNm5g5cybm5uY0bdqUxo0b8+iR2ldz3759/PzzzwBs2LCBf/75J0XnnSVLFrp3786iRYtStN3roqOj8fb2Ngy+9vHxwdvbG61WS3BwMN7e3jx7lrw+9ACWlpY0adKEefPmJZhUvg1TiltikvqcvKv30Tja+/wTBnrNwk+bI85y30g3BnrNYu/zTxLcLlJvw+Xg0uwI6sS06UUMF+NVXg0Y5/09W/xaGtYtYHufetkO0ivfcuZ6DmFflUac/KgGy0v3ZEiB2TTM/jd5bR5guDi/BWmsqiR5MJJ4GPXpZsek7yqx+LcoiUdS8VCiIWQ8WDdTn1wFMU++KqIO6tb7gsYFLN7t7kOfbnY0qW/75hXfgUl0n5HuTAYmEY9UplEUJe0/1SagR48ezJ0797090jOWoihJDnDesmUL27Zto3bt2mnaZ/5DkZni5umpXrSKVTmKuTnv9WJsho5KWc7iavWMp9rsnAmq+MZHtya3ceRsEUhD1714OlyjpMNVPOxvYmUWf92gKCe8QkoYBmkfeF6HCP2bL7LSWFWZUmNV4iHxeFWGiUfETnWuDKcF6tOiYkXfUueNIFrtrmVZ6a3LERuPXfvCaVLflsifpqGk4sM3NPnyY9W3H8qTJ+n3zbe1NVa9+6LJmTPd7kQAmH/aEMumzYja+Ve63ImAlMVDkycP1sNHpHqZYtsWXl5eb1gzaWn+FKb0pNfruXDhAnZ2dlSoUOG9N0KBJBuhAK1btyY6OpqCBQsmuZ4wyuxxK1LQnO+HOr7Xi7Eec04HJd0d7VUpaRy9iM7KH08+M/xsqYmiqL23OqbC4SolHb0oZn+TLJYv+SjrKT7KegqAisdOGrap63IAO/NwTgdV5qnWeFdRGkcqaawaSTyMJB6qZMfDuok6R4bmtaaQRRFw/uWdy/FqPB4+0qX6XQgwke4z0p3JwCTikUoyVQLx6NEjevfuTdGiRZk/f366lOHAgQNYWlpSqdLbf6OR2WT2uI0f7siNWxngYpyIKMUSrxBPvEI82UA7QE0qitjdMgzSzmr5glCdMTH8Ms9vVHY+w8gbk9ga0z1qZOendKtynZULirJkbQ7S4SFyGatxlMqksaqSeBhluHhoNOqTl1LB6/Fo9knadWExiUarJBEGJhGPVJBpuzAJYepibzNu2naRzn1emP7F+D0aWnAWlbOcYczNH7gdVpg+3ewYWHId7BgDQEi0Pddiuj9dCfHkarAn98ILoKRiUpHhGkepSBqrKomHkcTDKKF4NPvEmpkTsqR6F6ZXSXcmo4zQnUm6MAkh3qsfZoZkuovxzLtDDP+PvRjvmWmD28uyFLe/joNFKJWdz1DZ+YxhvdBoO7xCSxgmv/MK8eRuWIE3ju1IDmkcGUljVSXxMJJ4GJlCPGKZxDffcifCwCTi8R5JAiGEiQuPkIvxnGUhLN7UEGiIOdEUtrtDSUcvSjp44engRXGHG9hbhFE5y1kqZzlr2P5EYFW6XzY+DjK/zX0eRuRJUVIhjSMjU2gcSTyMJB4qiUfiTKLRKkmEgUnE4z1J+07EQgiTZuoXYx0W3AzzYItfK368PZrPL/5OpWMnaXZmKyOuT2LVo86cDSpPmM6WW2HGWentzMLYXbkZZ6pXI4uF8ZHTzhaBmJPwOUrjyMgUGkcSDyOJh0ri8WYm8UhRecSrgUnE4z2QOxBCCIOMejHWY86tsCLcCivCNn910LUZOmzNIwzr5LF9SLjOlhCdA0HRxokEpxUfTZUs/3E9pBhXQ0riFVKCKyGe2OYrxnJpHAGm0TiSxqqRxEOVEeNhVu0jdBs3pFHJjEzim2+5E2GQYDwyGBlELYSJenUeCK+bqX9hzIgX45QyQ0d2q2f4a42TWu6p3IQCtg/iratY2BCWpQQ7rxXjQqA6ruJ2WGGiFcv3WqbEZIZ4JJc0Vo0kHqqMFo/YQdSASQ/kTRMysNrg1XhE7diG9cDBqX7M9zWIWhIIIUxUWiYQGe1i/D5p0FPA9r46T4WjF1VyeOHpcA2NNiTeupF6K66HFGPB/b4cCayZamXKzPF4nTRWjSQeqowYj9gEIurIYSxr1jKZRqskESaURDx/jlmePKl+vPeVQMgYCCEyuYx4MX6fFMy4G16Qv542ZZf1KPJP3MGFtl60vryTodems+LhF5x6UZngaAeszbSUdbqMBmOjqY7LQTaU70CvvD+/l/Jk9ni8ShqrRhIPVUaPh/7kCemDDzIm4hWGeGTPni7Hf1syBkKITCyjX4zfpziNo6HBhIbl5xr52fm0CaDeqchn85CSjle5EFzWsF1Zp0uUdrzKjVAPwzJLTRSry3bjRqiH4bGyN0OLEqVYJVkGiYeRNFaNJB6qDyUeJtsHX8ZExPk5LSkP7hO14U+sunRN82O/LUkghMikPpSL8fuQnMaRghn3I/JzPyLuN2XrH3/GjRAPfCPdDcuK2N2inNNFyjldhJzqMq3eAu9QD66GqAnF1WBPboR6GJIKiYeRNFaNJB6qDy0eptJolSRCZRLx8H2S5sd8F5JACJEJfWgX43fxro0jP607e541irPMJyI3A71mxoyruEpJBy+cLV+qc1c4GvudqklFUbwjPKnYpCJZ83Sg+8AQiYc0VgGJR6wPNR4m0WiVJMLAFOKRkUgCIUQm86FejN9GajWOgnVO/P2sIX8/i+1Tq5Db5hGlYia+ix2wndXyBSUdr1HS8RrK2W103tWUy9fUSe5a5NiOg3kohwJq8Sgy93spV1I+5HikhHw+jCQeRqkVD1NotEoSYWQK8cgoJIEQIhP50C/GKZG2jSMNjyLy8CgiD38/+zRmmUJhZ1/m97tLHv1VDvzrz7lrxhmyu+ReQ2nHqwz0cjEkEEXtvKni/B9Xgz25HlqMCL3teyld5otHwuTzYSTxMErteJhCo1WSCCNTiEdGIAmEEJlEZrkYJ4dpNI7M+HFqCXIUKk3ngbXixWPf83oERLlwJbiUYVmdbIcYWnAOANGKOXfCChkGaV8N8eRaSPEUJxUSD5V8PowkHkZpFQ9TaLRKEmFkCvEwdZJACJEJZLaLcVIySuNoyYOv4y17GJ6Hg89rUtLRC1er53jYe+Nh701rtgGgU8y4HVYIr5hB2ldCPLkeUpxwvV2C5ZB4qOTzYSTxMErreJhCo1WSCCNTiIcpkwRCiA9cZr0YJySjN472PGsUM2BbIYeVPyUdvCgVM0jb08GLHNbP8LC/hYf9LVq5bQfgWGA1elz+xbCPso4X8Q4tyhdds0s8kM/HqyQeRukVD1NotEoSYWQK8TBVkkAI8QHL7BfjV31YjSMN/lo3/APcOBBQ17A0NqnwjBmkXdLBi6vBJQ3vO1u84I/yndFjhtkXtwzxyG3jw4uorITq7N/xDJPvw4rHu5HPh0rioTKFRqskEUamEA9TJAmEEB8ouRgbZZbGkb82B/4BOTgQUMewzEITZfi/u7UvweY5cXS2Ye5vGOIxxWMslbKc5V54AcMcFVdDPPEKKUGozuG9lzOzxCM55POhknjEZQqNVkkijEwhHqZGEgghPkByMTbK7I2jaMXS8P+67Svg1OsiC5f4xYmHs+ULzDQKhezuUsjuLs1z7DS8dy8s/yuT35XkakgJQnSOb12ezB6PV8nnQyXxSJgpNFoliTAyhXiYEkkghPjAyMXYSBpHRnHisUoT570WZ7fiYvkcT4drlHS4auj+lNvmCQXs7lPA7j5Nc+w2rH8vPB9Tb3/LQcOdDgWIu8+ESDyM5POhkngkzRQarZJEGJlCPEyFJBBCfEDkYmwkjSOj5MQjICobRwNrcDSwhmFZVssASsYmFTHjKnLbPKaA7QPCdMYnOzVz3cWAAvPZ4d+M+fe/SXD/Eg8j+XyoJB7JYwqNVkkijEwhHqZAEgghPhByMTaSxpHRu8QjMMqFo4HVORpY3bDM2SKQko5eXH5lfopSjlfJZ+tDFouXhmW2ZmFsqdiO6yHFeW5bija9quJ9zZOeo8wkHvL5kHikkCk0WiWJMDKFeKQ3SSCE+ADIxdhIGkdGqRGPF9FZOfZKQgGw6MHXHAqoyXNtNsOy4g7XKWD7gAK2D4B/YC2UAbaVyh1n8rurwSV5Ee38XsqWmA85Hiklnw8jU4hHSphCo1WSCCNTiEd6kgRCiAxOLsZG0jgySst4vIzOwokXH8VZdj2kOBNe/MrINrcJu3WRkJsXyGvzkDw2j8hj84iGrnsN6z6KyBWTVJTgt0ddUzybdlIyYzwSI58PI1OIx9swhUarJBFGphCP9CIJhBAZmFyMjaRxZGQK8ShSzImhMxrjdSeantPVeDiav8TTMXZMxTVKOnhRwO4+uW0ek9vmMTVdjvLLwx6GfXTJ9Tv2FqHsedqQe+EFUlwGiYeRfD6MTCEe78IUGq2SRBiZQjzSgyQQQmRQcjE2ksaRkSnHI1jnxKkXVTn1oqphXQfz4JinP3nhYBGC7pXL0mc5N1DU/jbXQ4oZEogyjpeonvW4oRvU86jsCZZB4mEknw8jU4jH+2AKjVZJIoxMIR5pTRIIITIguRgbSePIKCPGI0TnyOmgKpwOqhLvvT+ftKe042WuvDJgu7bLYfrlX2L42S8yR5zJ766GlCTMIofEI4Z8PoxMIR7vkyk0WiWJMDKFeKQlSSCEyGDkYmwkjSOjDzEeqx//L96yqyGebPNrRkkHLwrZ3cXN2h83a3/qZTtoWCfK1g3Nf2XY/LMH2f1K4GpVkqda13cqS0p9iPF4G/L5SF2m0GiVJMLIFOKRViSBECIDkYuxkTSOjDJTPPY/r8f+5/UAsDMLo7jDdcPkd6Wd1KTCMtwPvPfymf1ePisF+5/Xoe/VBTF7UKjtcpjrIcXw07qRnAnwUiozxSMp8vkwypvLLNX2bQqNVkkijEwhHmlBEgghMgi5GBtJ48goM8cjTG/HuZcVOPeygiEeoXkimTrqFLbPL1HS0QtPBy8uvixt2MbNyo+lpfoRrZhT8dgpIvU2AHjY3+BltBO+ke68S1KRmePxKvl8GJUuYcHXXe1T9Rim0GiVJMLIFOKR2iSBEMLEFS5gjq2Nhu+GOpAvtznjfwpGpwNPj7T/+LZrbkPntnas2RTGoePadClDkYLmjB/uyINHOmYsCiV/HvM0L4PEw8j04hHOrcgK4FCBswoQDNgqeHqoSUFBi5fci/JAhwWFizgY9jHVZQIeVpcI0rlwO7okt6NKcidK/feZPifJSSokHir5fBjFxiPghZ7ctmZo3NxT7Vh6r6tEOTmpjVYnJ/QnT6TasRIvhI6ojRuwbP8ZVgMGEbVpA2i1aV6MqB3bsGzbHqt+3xC14U8U3ydpXoaUxiM1fzdSg0ZRlLT/akII8Uaenp4AeHl5pXNJhPjwKHodGjO1ca0oCixqAo8ugF4Xf2X7bJCrNOQuC7nLqP9mzYtG8/67P4kPl6LXozFLva5MIuNLi9+R99W2kDsQQgghMp3Y5AFQE4F+u1GiwuGJFzy6CI8uqS+/6xD6HLwPqq9Ydi4o7eag8WwEgKKLAjMLSSpEoiR5EG+SkX5HJIEQQgghAI2lLeSrqL5iKFER4HvtlaTiovpzWAA4vPJkp7PrYfcPKJU7o2kyzri9okhSIYT44EgCIYQQQiRCY2kDecurrxhKdKSaRLiXMK74+DKEv4BXkgUlNABmVEPJVdrY9Sl3GchWUJIKIUSGJgmEEEIIkQIaC2vIUy7uwmY/QuX/gY2jcdnjS2pScfuI+opl44SSqwzkiU0qyoJLgQzVfUEIkblJAiGEEEK8I42FlXp34VUFP4b++2K6P8V0gfL1goiXcOeo+opl7YiSu4y6j09Hqt2phBDCREkCIYQQQqQCNakorb5QZ9VWdFHqwOzY8RSPLsGTqxAZDHeOqf9vMt6wD2XvdPW9yv9D41YsfU5ECCFeIwmEEEIIkUY05pbqI2FzlYbKnYGYpML/pppMRIbEHR9x7k8IfADFP4WYBEK5dRiu7TV2gcpeOM5TpYQQIrVJAiGEEEKkI425JeQsqb5eoSgKNBiu3ql4tXvUjX1wbKnxZyu7mIHasfNUlAHXopJUCCFSjSQQQgghhAnSaDRQsYP6elXROqCLVhOLx1dAGwb3TqmvWJZ2KLlKGpOKfJXRuBZO0/ILIT5ckkAIIYQQGYjGoy541AXUGbV5eivuPBWPL6tJxf3/1BdAmVbw+bKYbfRw/k+1G5VbCXn6kxAixSSBEEIIITIojZm5OjbCrRhU+AyISSqe3VaTCZ+YpKJgVeNGz+/ChgFgYQMT7gBqAqHcPQnWDuBWTO1WJYQQiZAEQgghhPiAaMzMIYeH+irfPv4K2jD1EbMWVmjMX2kGbB0BftfAwgYlp2fcye/ciktSIYQwkARCCCGEyEQ0uUvD11vjLFP0enB0hRc+6mNjH55TX7EsrFHcX00qyqp3Kiys0rbwQgiTIAmEEEIIkclpzMyg50Y1kQi4Z5z8zueSOqN2xEvwOa++YplboXz+M5qSjQFQtKFgZqHO1C2E+KBJAiGEEEIIICaRyF5IfZVtDcQ8Tjbg3iuT38UkFhFB4JLPuPF/a2HXeJRq3dE0/9G4rU4rSYUQHxhJIIQQQgiRKI1GA9kKqq8yLYHYpOI+OOcxruh/A3RRYONkXPbSF6ZXQnErDrnKGCe/c/dEY2mTxmcihHhfJIEQQgghRIqoSUWBuAtb/QS1B8CrdxseX1aTiseX1deZNepyMwsUt2KvTH5XFnJ6orG0TbNzEEK8PY2iKEp6F0IIEZ+npycAXl5e6VwSIYR4O4qiqAOzX52n4tElCH0ef2Uzc8gRk1S0/kkGaAuRCt5X20LuQAghxGsUvT5TTq6VWc9bpB6NRgNZ86qvUs2AmKQi6BH4vJ5UPANfLwgLiJM8KNtGQWQI1OyNJmfJ9DoVIcQrJIEQwsQNGx/E7Xu6FG/XrrkNndvasWZTGBt3RKRCyd6sSEFzxg935MEjHT/MDCE8Iu1veNraaPhuqAP5cpsz/qdgbt1Nui5rfWTJ4K8d0a76DcXPN1XKZFbtIyxr1iLqyGH0J0+kyjHeROOeE8v2n6E8e0bUpg1osrpg1fWLdCmLyFw0Go06dsI5D5RqCsQmFY/VZEIbalhXURS4vA1CnkE14++ncnUXXN1l7AKVqxQaK/s0PxchMitJIIQwcbfv6fC6GZ2ibfp0s6NzWzvmLAth8cqwVCpZ0kqXsOD7oY7cuBVNzyFBhIalffJgb6fhl1lZyJPTnC/6v+DytTfXY6H85gAofr4oPj6pUi7dxg3w8iWWTZsR9fIlun/+TpXjJEXx8UHr749V335YNm9J1I5taV4GIWKpSUVu9fUqRYE2s9TEwt3TuNz7IJz7U30BaMxQXItC7tLGeSpylUJj7ZBm5yBEZiIJhBAfmD7d7BjUyyHdk4df5zrjfSf9k4eihSzoPjB5yUNaik0aLJs2i/NzWlIe3Ee7aKGaRLRNYMZiIdKZxswMPBupr1eVawP22Y1doIL91KdA+d+A8xtjNtagZC8SM0i7DBT8GE2esml/EkJ8gCSBEOIDIsmDytSTh1gmlUT0+ybNjy3E29IUqAYFqhl+Vl76qRPevTqu4uUTeOqtvi5sgoqdoP1cdX1dFBz/RX20bMGPZOyPECkkCYQQHwhJHlQZJXmIZSpJRNSGP7Hq0jXNjy3E+6BxcgOnT6D4J4ZlSrB/3MnvitQwbuB3HXaOU+esGOdt3Mbrb7C2h9xl0Lw6n4UQIg5JIIT4AEjyoMpoyUMsk0gifJ+k+TGFSE0axxxQvIH6iv8ulGwClrbq+ItYf41VJ8gDlGwF485Tkas0GjvnNCm7EKZOEgghMjhJHlQZNXmIZQpJhBCZhSZXKeiyMs4yRRcNuUqDXg8vHsLzu+rr0lbjOi754yYVucugscuatoUXwgRIAiFEBibJgyqjJw+xJIkQIv1ozC3gfysAUEKfx3R/emWeisAH6t2JgPtwebthO6XLb2hKNlb/HxYIioLG3iVdzkGItCIJhBAZlCQPqtRKHjTuOVPtMa5JkSRCiPSnsc8GHnXVVwwlNEAdqP1qYhFwH9yKGTc8vRr2/IhSrRuaVtPV7RQFwgIlqRAfFEkghMiAJHlQpeadB8v2n6H190d5cP+97TO5JIkQwvRo7F2gaB31FUMJewG2WYwrBcWMJXLOY1z2/C7MqIbinNf4SNnY7k8O2dOi6EK8d5JACJHBSPKgSu1uS8qzZ1j17Yd20UJJIoQQCXp9ULWm5RSUT0eqE+DF8o95ytOLh+rr6k7DW0qW3MaEIk/MQG3HHGlQciHejSQQQmQgkjyo0mLMQ9SmDVg2bylJhBAiRTSv3pEANJ4NUcbfgkeXjeMpHl2EZ7ch6JH68tptWF9xyqkmE51XqOMyhDBB8pspRAYhyYMqzQZMa7VolyzCqndfSSKEEO9EY+MEhaurrxhKRDA8vmycp+LRJTWpePkE/GziJA/Kuq9BGwaffIsmV+n0OAUh4pAEQogMQJIHVZo/bSkyUpIIIUSq0Ng4QqGP1VcMJTJEvVOhDTEu0+vh+r8QGQyfjjQuP/cnXNoepwuUxsk9Tc9BZF6SQAhh4to1t6FzWztJHtLrUa2SRAgh0ojG2gEKfRT/je5rwOcS5PAwLrt9FK7/o75iKI45DJPeGcZVOOWMO1meEO+BJBBCmDhJHkxgngdJIoQQ6URjZgYFqqmvV338lZooxHZ/8r8Jwf5wfa/6iuWQHSV3WTWhKFIbTUIJihApJAmEECZuzaYwSR5MYZI4SSKEECZEk7s05DaOh1C0ofDkqnqn4nHMQG3/mxDyDG7sU1/hLwx3OBRtGByYq+7Ds4maqAiRTJJACGHiNu6ISJfjSvKQAEkihBAmSmNlD/mrqK8YijYMfL3UZMLnUpw5LHhyBQ7MBsccaEo1M25zdj1Y2at3LLLmle5PIkGSQAgh4pHkIQmSRAghMgiNlR3kq6S+XmftAJU6g7V93OV7flS7QgHYZUWJHU8RM/kdLvklqRCSQAgh4pLkIRkkiRBCZHAad09oNzvOMiU6Eop/qt6x8L0GYYFw67D6imXrjJL79aSigCQVmYwkEEIIA0keUkCSCCHEB0ZjYQ1tZwExyYTvtbiT3/leU8dR3DqivmJ98TuU+FTdLugJRIVDtoKSVHzAJIEQQgCSPLwVSSKEEB8ojYU15CmnvmKoScV14+R3jy/DEy/1LkSs06tg30yo0gXazFS300VDwH01qZDB2h8ESSCEEJI8vKJ+LauUbSBJhBAik1CTipj5JWIo0Vo0Fq/83YwMAQtrcCthXOZ/A+bWBWvHmDEVZdR95C4L2QpJUpEBSQIhRCYnyYNRn252NKlvm/INJYkQQmRScZIHQNPsB5TG34Muyrgw8KGaVEQGw93j6iuWtYOaVOQqA3liZtXOXhiNmXkanYF4G5JACJGJSfJg1KebHYN6ObBrX7gkEUII8Q405pZgbmn82bMRyoQ74HdDHU8RO0/F46vqHYu7J9RXLCt7lDzloOcmuTthoiSBECKTkuTBKDZ5mLMshIePdG+XQIAkEUIIkQiNuSXkKqW++ByIGRvx9KY6R0XsuIonV0EbCmEBcZIH5ee2EB0JLaaok+iJdCUJhBCZkCQPRq8mD4tXhtHsE+t326EkEUIIkSwacwtw91RflToCsUmFN0S8NKyn6KLg/mk1gXhl3grl2C9wcbPxkbJ5yoCrh7pfkaqkhoXIZCR5MHo9eXhvJIkQQoi3oiYVJV5fCP3/Vbs+uRQwLr9/Ch6cUV+xLG1RcnrGnaciRzFJKt4zqU0hMhFJHoxSLXmIJUmEEEK8FxozM3Arpr5e9ekodf6J2C5Qjy+r3Z8enFVfsSxsUHKWVJOJEg3RFKuXtifwAZIEQohMQpIHo1RPHmJlkiQiKioKS0vLN68ohBDvkSZ7IcheCMq3B0DR6+HZbeN4ikeX1KQiMgQenlVfVnYQk0AoYS/gn8mQuxxU6iQT36WAJBBCZAKZPnmI3Avhq8B5dcLJQ/Q9COoB2fbF2SwsOpofrl4jn50tfYoWSXT3gVotU7yuczskBA3weYF8tMmTJ+bYkbxYMI/p2mj2nz6NJiKCNrlyMcCjSKIXq2NPnzHrxk18wsIo7OjAuJIlKebkaCjT7Bs3+S8gEIv/t3fv8TnX/x/HH9fOs83GMGc6mJGRcj7mS4UhfUVJOYT4OfWtCH1zKCoKISS+pJZWOSYkTUk5xZhjOROzE7PZ+XDt8/vjsmvGpik72PW83267ue1zfQ7v6+Xa9nl+3u/352My0bFSRQbem/sTXyceOsw3YRct32zaDK+8CkDbunWZW73qTeuPHTuWqlWrMnLkyBzL582bx/bt27G3t+edd96hevXq1tfS0tLo06cPI0aMoG3btnnWSESkoJns7KBCLctXw6eAa6Hi8pnsUFG7ffYGFw/CrmVQtgamxs9aFxu/LLTMtajSAHz8brpVrShAiJR4Nh0eMq9C0mJI3QR2XrmHByMDEqYAaTk23RcRwYch+wm5coWh992b9yEMg+F793E5LY21rVtyODaOQXv2YoeJ7lWrALD0j2M84u1Nr0mTeHfpUpYePkznyhWp5eFx0/7OJCSy8/JlpvrX40BsLO/+/geLTp1iZsMHAXjj0GF2XLrM+jatSDab6bbtV9IyMxmWS8BJzMigplupHMsupqbx7EsvYZ+YkKMnIvjMWdZu+ZERI0bkWP/nn38mMDCQnTt3MmfOHMaOHUtQUJD19ZkzZ+Lk5ESrVq3yrJGISFEx2dlB+fssXw/+O+eLpStCm+Hg7G5dZBgGbJmRPYnb3gmjYh3L8Kcq155TUbGO5aF6NkwBQqQEs+nwYKRD0iJw7Qfpu/BwN+U+bCn5UzCfzbHpzp07ORgVzZD77+XFPSHcyrboaI5cvcpT1ariam9PwzJe2JtMLDtz1hogHqvog6+HB/z8E11btSI8IoIqtXwhIvym/aUbmYz2s4zzreNZmgUnT1HfywuA4/HxbImMokU5b8o5W/54VXRxYfm5Pxl837043nC/9M6VK9Hex8f6/dX0dHrv2EWz+Ks4du0GWIYzXUpNZUYe73PHjh24ublhZ2dHtWrVWLhwIQkJCbi7u7N161a++eYb1q5di729HvokIncXUwVf6Dwp58KMFGja19JbceEgpMRl915ksXfE8PHLnqRdpQFUqmtToUIBQmzC+++/T2hoKC4uLixZsqTAj5eWlkZoaCgfffQRQ4cOpWnTpgV+zBvZdHgAMDmC+2gAPNxNlPawuzk8pB8GMsGuLGRGWBc3b96ch37dxm8HDv7lYU4lJAJQ5tocAAc7O0rZ23MmMZH49HQ8HB0t4QFITExkz7p1/G/KFMo0b57rnAjf63olNl4Mp0fVqvStWePasRIA8HLM7k73cHTkYkoKpxMSrcOcslwfHgA2hUfwL58KEPwD6XZ21jkRs2bMoIdfbT7ce3OIsLsulJhMJuzs7HBwcCAyMpJx48bxzjvvULFixb+sk4jI3cDk6AqdJgLXeiNizlnmUoQduPYAvIOQdMUyt+LiIdhzbcNBK+H+NpbtLp2G5FioWBeTo0u+jmtkmuHMLoiPBA8fuKdZsX4atx7vV8QMw8BsNuf5ekJCApMnT2b8+PGF2KqSp2/fvuzdu5e0tLS/XvkOOHnyJDNnzmTHjh2Fcrwb2Xx4uM7/9S9FaQ87rsZn5gwPRhIkfwGu/W97nyNC9vGvH7dyOiGBck6Wk/mIlBTr6xmGpd4p5kzrsvDkZAb+tofjcbEETp9GzLFjOA0bzpLLV2j2wxY2XszZG/H52XO8c/R3DsbGsjUqGoByTparW5E5jpV57Vh5/x7J8k1YGJ0rVQIsPQ/pG9azIu4qbdr9iwqlSuW6zSOPPEJMTAyJiYmcPn2a5s2b4+joyOjRo+nWrRv/+pfuZiIiJZPJZMLkXRNT/W6YOk3ANHAFTPgDXtsLfZbAIy9BrUfAzRsq18/e8LfPYH5H2DDRusjISMX4cy9GevJNxzEOr4fpD8PiJ+HLoZZ/pz9sWV5MKUAUkStXrrBkyRKGDx/O1atXc11nw4YNLFq0iKCgIEsKlr/N54YrsQWtbt26RTYmXOEhW9ach6vxmcQn3FCHxPlQqp+lp+I2XUhKJjY9nfj0DFqWL4eHgwNbo6KJSE4hxWwm2WzGwWTCyyl73xsuhvOSry+PVKjAyrPnGDdqFEZ4OFE1a5JsNhOVmh0KfrscQ2lHB6bWr0dUaiqv7A/lUGwc/l6eVHF1JTQ2lt+v/d64kpYOgLfzrSf5/ZmYRHx6BnU8S1uXnVy9mpD16+k+cSKm++7LdbumTZsyduxYhg4dyvnz53n33XdZsGABiYmJjB49+rZrJyJyNzOZTJjKVsfk3xVTx/9iGvg1vHEUUymv7JXsHCyhokqD7GUXD8GCzjDpXozZbTFWjMLYsQTjp9nw+QsQdzHngeLC4fOBxTZEaAhTIQsNDWX16tUYhkH37t0ZOHBgrusZhkHNmjUJCAjg448/LrT2ZY1tvhulpqaye/du2rRpU9RNKTIKD9munzB9U3hI3Qp25cChdq7b/pWvWjQj0Wym7LXeh0WNG7Hg5En+s38/g69NuK7n6ZljTsKga8ubl/Nmb0wMv0ZGEjdvLhOGjWDooEGUXbXSOpypiXdZ63YOJhPDQ/azJTISfy9PFjZ6mA9PnGDCwcP8p3YtYtPSqODsTNU8ehCyfBcezqMVs4N0emYm7//xB2/X9yd9w3rsffOuRe/evenduzcAu3fv5rPPPmPVqlU4OenOJCIiN94Fz9TxDYzH/wuZ1/UMJ1wGt3KQeAkifrd8hXx5i70agAm+fQOjbqdiN5xJAaIQJCUlsX79enbv3o2/vz+vvvoqnp6et9zGZDLxwAMP5Gv/U6ZMISQkBHd3d2bNmkV4eDiBgYFUrlyZF198kaNHjxIYGEjdunX54Ycf6NevH0888QTx8fFMmzaNCxcukJqaiqOjI46OjixdupTU1FR+/fVX5syZw9NPP83s2bOZNm0a7dtn3/7s8OHDvPnmmxw8eJDPPvuM+Ph43n//fc6ePcuWLVvw8fHh0KFDBAUFcf/99/Pbb78REhJC69at+eCDD3BwcODKlSu8++67VKpUiVOnTlGqVCmmTp3K+fPnCQ4O5qeffqJXr168//77APz3v/8lLi6ODz/8EAcHB9577z1atGjBt99+y5QpU/Dx8WHjxo0EBwdTunRpXn75Zbp27Zpr3ZYvX866deu4//77OXz4MB07dmTw4MEsXbqUDRs2YGdnxwcffEB6ejqff/45dnZ2DBs2jMuXLzNv3jzq1q3Ltm3beOyxx3jhhRfIzMzk448/ZtOmTdSoUYNjx47l8xNyZyg8ZPvL5zykrrVMso7bZ/k+M8byb9xIosIHAB1vuX9ne3ucr5s0XNezNPMefgjAOhTp39cmUOemimspDsddJS05hYxFC/EZOgxTHs+JqOJqCQZZw6Gqu5Xi/QctV7VCr8SS+RfHyrIlMoo3/bN/p+y4dJnIlFRG7z8A+w9w+dqk7DVr1nD69Gk++OCDm/YRExPD6NGj+c9//sPs2bOJjo7m1Vdf5cEHH/zL44uI2BKTyQTXPf3aVPdxjDpHLD0NWXMqTvxseTZFngzL+md2wX0tC77Rt0FDmArBoEGD+P3333n33Xfp37//X4aH2zVy5EjOnTvHvn37MAyDBg0aULZsWUaMGEFcXBxDhgwhLS2NxMREvLy8WLNmDWazmUmTJrF9+3YWL17M4sWL2b9/P+npluEQe/fu5Y033uDYsWPExsbi4uJy09XGevXq5bja36FDBxo2bGj9PikpiU8//ZR169YREhLCuHHj6N69O5s3b2b79u0AjBs3jj179mA2m3FxceH8+fPs2bMHd3d3lixZwuHDh/H29ubLL7/k6tWrTJgwAR8fHxYvXkx0dLT1dpJdunQhLi4OgLfeeos1a9YQHx/P+PHjCQsLu6lma9eu5a233uKJJ57g7bffZtSoUcyePZtPP/2UF154gbi4OI4ePUpsbCy1atWifPnyDBs2DFdXVwYNGkRMTAxJSUl4eXkRHBxMTEwMixcvZvbs2UyaNIm5c+fy0EMP3dH/51tReMiWr4fElZ4NnvPB80PLl921K/6eH1KhUvO/PEaq2UxMHvNp1oaF8UDp0nSpUjnP7c8lJfKAZ2k8nRwxUlI4/8FMjPBwnIYNx1S9xk3rArQs753rsSq5uND3nprWZcm5zIW4mJxMfEY6dUpnD19qW6E8q1q1YEnTxixp2pgXfGsB8OSTT+YaHgzDYNy4cbRp04bQ0FB8fX159dVXGTJkCElJBfgwPhGREsJkMmHyqoLpgU6YHhsHLQfnb8P4yIJt2N+gAFEIAgMDad68OePHj+eDDz7I9YT2n/Dy8qJHjx6YzWZWrlxJXFwcpUuXxsnJiW3btpGUlMSzzz7L6NGjWbp0KcuWLSMzM5PNmzfToEEDnJyc8PDwwNs7+wSlZcuW3H+/5b7ygwYN4pdffqF169a31S5PT0/rg6U6duxIrVq18Pf3ByxXMpOTk9m2bRv169dn9OjRzJgxg6CgIFq2bImPjw/u7u5UqFCBtm3bUqNGDcqXL0+ZMmXo0KED9erVAyA2NhbI7j708vLCycmJatWq8fjjj5Oens6BAwduatuPP/4IYA08jRs3BmDz5s04ODjQr18/AL7++mvMZjOJiYmUL1+effv2ER0dTUBAAKNHj2bhwoV88cUXlC1bls8++wwPDw9rcKhcOe8TyDtJ4SFb3uEh89rX7cnEUssbT8mf2bmLR3/6mYPXPn9Z1oWFcSEpmVkNH8T+2mfy87PnaP/TVo7GWeYsbI2K4mxiIuPq+AHw7u9/8NjmYBaPfpWMCxdo8/UKpp47T6ZhkGo288nps3TwqUDr8uVzHGv35cv8HBXNnIca4uZgucp1MDaWVsE/Mv/EyRzr7rh0mTY3bH+7PvnkE8LCwnj99dfZtGkTFSpUoHbt2sTGxrJ169Z/tG8REZvkkc/5mfldrxBpCFMhsLe357HHHuOxxx7j9OnTBAYGcunSJQICAmjbtm2O2yT+Xf369eOLL75g1apVeHh40L17dwDrHZ5CQ0NzPCU2Pj6e9PR0UlNTAcjIyCA2NjbHE2azODvnfV/jrBP3/E7yvn59s9lMZmYmhw4dwmw2W+8jbxhGrk/VvfF2kn+l9LWrrbdqf9Y+s/aXmWk5yezZsyfz58/nu+++o2nTptY7zWTV88CBA/Tp08e6H8MwSEpKyle77iSFh2y5hof0g5CyFjKjLN8nvA0uT4ND3k+VzhJ46DDr/jgOwNoLYaRnZvJybV8Aqri6EpuWjvu1E/dLqal89ed5Lqem8WWLZpR2zJ48/S+fCmyLjmbo3hBqebjj7eTMqpYtrHMWfJydcbW3p7y9PebFHzO0Vy+WffMNPSMiKI1Bl8qVeKpa9lOjr6ans+ZCGH9cjSeoeTMqumbfItDZzp5SDvbWdmUJiYnh8Up//1arBw8eZMGCBXzxxRckJydb72bm6uoKcMcvioiI2IR7moFnZcuEaXL7+20Cz0qW9YoZBYh8iomJ4aWXXuKZZ54hICCAmTNncvHiRWbOnMnevXuZMWMGU6dOtV61z8u9997LuHHjSE5OZv369YwcOZIpU6ZQtmzZW273V7KuuG/cuJHQ0FD69u0LWHoSHB0dWbZsGVWqVOGee+5hz549PPfcc1SqVIk9e/Zw+vRp9u3bh4uLC1evXr2tidRZJ+mHDx/Gz8+PCxcuAOR5Z6nrubu707hxY/bs2cOECRPo2bMnp0+fpmrVqn/7uQlXrlwBLCf0e/fupVq1arRo0eKm9Tp06MD333/PwYMHqV27NiEhljGIAQEB1rY988wzLF68mKCgIL780jLRqWHDhnh6erJ+/Xr8/Pxo2LAh+/btIyAggAYNGrBz507Wrl1Lly5diIqK+lvvIb8UHrLl2fPgWN/yxcQ8twWgzIqbFj3/8EM8V8Yr19Wz5jtkScjIoE+N6njlMqm4sqsrixo3yvPQA++7l4FZT7pOTaV3ZDj9li3DVKlSrnMi4tMz6FSpIv2uG7aUpXZpD35pf/NtVd9tUP+mZTd6wrcWvRb/76blCQkJvPLKK7z22mv4+vqSkZGBg4MDhmFYA3f5f9i7ISJii0x29hhdp8LnAwETOUPEtQuSXacWuwnUoCFM+ZacnMzx48eJiLA8bOrMmTOcOHECsISL48ePW8fg54erq6v1KneZMmVyXefo0aO8+eabgOXJuEuXLr3lPgcMGABAt27drMvuueceZs2ahY+PD1OmTGHGjBm0a9cOd3d33nvvPSpUqEDPnj1JSUmha9euhIWF8d1333Hw4EHOnDkDwKJFi6yBYP78+TRu3JgVKywnXI8//ji+vr58+OGHzJw5k3vvvZemTZuyadMmTp06xYYNGwBYvXo1J0+eZOPGjQCsX7+e8PBw3n//fdq0acN3333Hyy+/TGpqKk2bNmXdunVERUURFRVFUFAQwcHBREZGEh0dzebNm5k/f771/+HQoUPW93vlyhVGjx5Nv379cHV1ZdGiRTg6OjJr1iwATp06xS+//EK3bt0YP348X331FVOnTmXJkiW8/vrr1qFLAM8//zz29vY5JmF7eXkxf/58fH19mTNnDpMnT8bf35+KFSsyYcIEfH19mTBhAgMHDiQ5ORknJydWrlxp/dzcKQoP2fI15+FvcOzRE27Re3W9mm5uuYaHvyU1lbSFC/KcE1GllCsVXPL3YKI74Y033sDf359evXoB4ODgQJs2bYiMjCQsLAxnZ+fbHt4oIiIWpnpd4Lkllp6G63lWgueWWF4vhkyGHjBQovTu3Zvly5ffkWFRuTl69Cjr1q1j9+7drFmzpkCOAZYnOWdN2k5LS8MwDOzs7DCZTGRkZOBy3QlU1rq1a9emSZMmBAYG3rF2DBw4kDlz5vytW9umpaXh6Oj4t4c11a1bF4DaTX7l6PHsk3OFh2wFER66POrMzDc9MVJSMC5eJG3hArg21K9QOTvjNHRYnj0Rd5qpalWcx4zNsWznzp1MmDCBtWvX5vgZCA8P5+WXXyYxMZFBgwbxxBNPFGjbRERKusJ6EnXWucXRo0f/0X40hKkESEtL4+DBgzg5OdGpU6cCCw9g+eBVr16djIyCPWG8/o5PN979yeGG8d13+l70mZmZhIaGUqpUKR566KG//VyMgrhHvsJDtoLqeciSvuJrHJ/qidPQYUUTIq71RDgNHYZTHrd4LWjNmzdnxYoVN/0MVKpUyTqsT0RE/jmTnX2xu1XrrWgIUwlw6NAhXnzxRRYuXGgdZlBQzp49y1dffcWoUaMK9Di3a+7cuQAcOXKE6dOn/6NJnWFhYQwdOpQpU6ZYH55VHCg8ZCvo8ABgRISTtmA+pkqVcBo6LN/Dme6ovxjOVBjyGmIpIiK2S0OYpES4/s5NWR/pwr4j0p12/RAme3sUHq4p6PCQNYQp9f3pGBcuYKpeA6dhwzHCw0v0cKbchjCJiEjJcqeGMKkHQkqE68OCyWS668PD9e6/x17h4ZrC6Hm4kfHnOfVEiIiIXEcBQqSYmzzGQ+GBogkPWRQiREREsilAiBRzf4aZFR6KMDxkUYgQERGxUIAQKeamzExQeCji8JBFIUJEREQBQqTYS05ReCgO4SGLQoSIiNg6BQgRyUHh4a8pRIiIiC1TgBARK4WH/FOIEBERW6UAISKAwsPfoRAhIiK2SAFCRBQerlOt8u39WlSIEBERW6MAIWLjFB6y+ddxYEhft9veTiFCRERsiQKEiA1TeMjmX8eBT+Z4ERFt/lvbK0SIiIitUIAQsVEKD9mywsOJ0xksDkz+2/tRiBAREVugACFigxQesl0fHga9Ekdq2j977oZChIiIlHQKECI2RuEh243h4U498VshQkRESjIFCBEbovCQraDCQxaFCBERKakUIERshMJDtoIOD1kUIkREpCRSgBCxAQoP2QorPGRRiBARkZJGAUKkhFN4yFbY4SGLQoSIiJQkChAiJZjCQ7aiCg9ZFCJERKSkUIAQKaEUHrIVdXjIohAhIiIlgQKESAmk8JCtuISHLAoRIiJyt1OAEClhFB6yFbfwkKVYhoiKlQq/DSIicldSgBApQRQeshXX8JCluIUIx569Cv/4IiJyV1KAECkhFB6yFffwkKVYhYhLlwr/2CIicldSgBApARQest0t4SFLcQkR6atWFP5xRUTkrqQAIXKXU3jIdreFhyzFIkSkpRX+MUVE5K6kACFyF1N4yHa3hocsxSJEiIiI5IMChMhdSuEh290eHrIoRIiIyN3AoagbICK3dl9N+5uWubqYmPCqO9Wr2DP5/XjMZqjrW/g/zk91daFPj1IsX5XEzzvSiqQN999jz+QxHvwZZmbGgkRqVL25XrejamXLdRX7Do9hDt58J5p4ezLNpK9cgWPPXjiN+o9lbkIhDC8y+VQs8GOIiEjJYDIM4+68VCdSwtWtWxeAo0ePFnFLbI+RmYnJzvY6aG31fYuI2Io7dW6hvxQiIjew1ZNoW33fIiJye/TXQkRERERE8k0BQkRERERE8k0BQkRERERE8k0BQkRERERE8k0BQkRERERE8k0BQkRERERE8k0PkhMppsxmM5B9z2YRERGRfyLr3OKfUg+EiBRrZrP5jv3Cu9upFhaqQzbVwkJ1yKZaWKgO2QqiFuqBECmmqlatCsCWLVuKuCVFq3379oDqAKpFFtUhm2phoTpkUy0sVIdsBVEL9UCIiIiIiEi+KUCIiIiIiEi+KUCIiIiIiEi+KUCIiIiIiEi+KUCIiIiIiEi+KUCIiIiIiEi+mQzDMIq6ESIiIiIicndQD4SIiIiIiOSbAoSIiIiIiOSbAoSIiIiIiOSbAoSIiIiIiOSbAoSIiIiIiOSbAoSIiIjcNTIzM5k/fz4pKSlF3ZQiZRgGv/32G0FBQUXdFLFBuo2rSDEUExPD22+/jZubG5cuXWLkyJHUqVOnqJtVoC5dusRbb71FrVq1GDlyJAApKSm8/fbbGIZBTEwMzz//PM2bNwcsJxGzZs0iKiqK5ORkHn/8cbp06VKUb+EfO336NG+99RYHDhygTJkyDBw4kD59+thcHVJTU5k8eTLBwcE4Ojry4osv0r9/f5urw/XmzZvHhQsXmDZtmk3WYfXq1YwfP976vYODA0eOHLHJWgCcOHGCsWPH0qFDB1588UUcHBxsqhaHDx+mR48eNy1//vnnGT16tM3UIUtwcDAbNmygYsWKnD59mi5dutC1a9eC/UwYIlLs9O/f35gzZ45hGIaxY8cOo2nTpsbVq1eLuFUFIyMjwwgKCjLefPNNw9fX15g7d671tf/+97/Ga6+9ZhiGYZw7d86oX7++ce7cOcMwDGPBggVGnz59DMMwjLi4OOPhhx829uzZU/hv4A5JS0szhg0bZhw4cMA4c+aMMXDgQMPX19c4dOiQTdXBMAzjk08+MX799Vfj999/N/79738bjRs3NgzDtj4P11u5cqXh6+trjB071jAM26zDqlWrjPnz5xuBgYFGYGCg8eWXXxqGYZu1OH78uPHwww8bS5cuzbHclmqxdetWY9q0acb8+fOtX82bNzcuXLhgU3UwDMvnoU6dOsbFixcNwzCM06dPG35+fsb58+cLtBYawiRSzOzdu5cdO3ZYrxI0btyY+Ph4li9fXsQtKxgZGRk8/PDDvPDCCzmWX7hwgTVr1ljrUL16dby9vVm8eDGJiYksXbqUZs2aAVC6dGnq1KnDggULCr39d8rp06cZMGAA9evXp2bNmjz//PMAHDhwwKbqANCjRw9atmyJn58ffn5+DBkyxOY+D1m2b9+eY6iOrdYBoH///jz33HM899xzPP300zZZC8MwGDt2LOXKlaNfv37W5bZWCz8/P1577TWGDRvGsGHDaN++PQ0aNMAwDJuqA8C5c+cwm81kZGQA4OPjg52dHREREQVaCwUIkWJm27ZtAHh7ewOWrnovLy+2bt1ahK0qOM7OztSqVeum5du3bycjI8NaB4By5cqxdetW9u/fz9WrVylXrlyO13bt2nXXjouuXbs2jRo1sn5/5swZKleuTEZGhk3VAcDDwwPDMFiyZAkpKSk8+uijNvd5ADh69CihoaH06dPHuswW6wBgMpno0aMH9evXJyAggLVr19pkLQ4cOMCRI0fo0aMHQUFBjB8/nhUrVvDrr7/aVC18fHwwmUzW75cvX06vXr1s8jPRqlUr/Pz8GDVqFMeOHWPz5s3MmjWLU6dOFWgtFCBEipmYmBgAHB0drcscHR2ty23FreqQ12tms5m4uLjCbWgBOHfuHCtXrmThwoUkJSUBtleHwMBAUlNTCQsLo1OnTkRHRwO2U4fz58+zZs0ahg0blmO5rf5cVKtWjSFDhjB9+nTKli3L2LFjbbIWBw8eBOC+++6jT58+PPvss7zxxhtcunQJsK1aZElMTGT37t20adPGJj8TLi4uLFq0CH9/f5YsWcJ7773H5cuXC7wWDnfwPYjIHVCmTBkA64kjQHp6OhUrViyqJhWJvOpQtmzZPF+zt7fH09OzcBt6h505c4Z58+axdOlSKlSowP79+wHbq0Pfvn2t/zZv3pxPP/0UsJ06zJ49m61bt7JmzRrrsvXr15Oeng7YTh2yNGrUyNpD98gjj9CuXTuWLl0K2FYtsq4Ou7q6AuDv70+ZMmVYtGgRYFu1yLJx40bat2+Pvb29Tf7duHTpEv/3f/9HUFAQzs7O/PDDD4wcOZLJkycDBVcL9UCIFDNt27YFICoqCrDMEYiLi6NNmzZF2axC17JlSxwcHKx1ALh8+TJt2rShYcOGlC5d+qbXmjRpgouLS1E09444e/Ysq1atYvr06VSoUAGwvC9bq8P13N3dcXNz45FHHrGpOsycOZOQkBD27t3L3r17AejSpQvBwcE2VYfcuLq6UqVKFZv7TABUrVoV4KarxLZYiyzr1q3jiSeeAGzz78aGDRtwdHTE2dkZgEcffRQ/Pz9++OGHAq2FAoRIMdOoUSNat27Nr7/+CkBISAhubm45xkHbgmrVqvHUU09Z63D+/Hmio6MZPHgw7u7uDB48mO3bt2MYBgkJCRw9evSm4R53k9TUVMaOHUuVKlVYs2YNK1asYPr06YSHh9tUHaKjo1m0aJH1ytiRI0dIT09n6NChNlWHvNjazwVYhm0tW7aMtLQ0wHLyHBcXx5gxY2yuFu3ataNcuXLs2bMHgIiICOLi4ujdu7fN1QIsJ72xsbHUrl0bsM2fj+rVq3PmzBni4+MBrMOQnnnmmQKthZ4DIVIMJSYmMmnSJNzc3IiMjGT48OH4+/sXdbMKzG+//cZXX33F+vXrqVWrFv379+epp54iIyODt99+m/T0dC5fvsyzzz5L69atrdvNmzePc+fOkZKSQvv27enevXvRvYl/6Ntvv2X06NE3LR8yZAijRo2ymTpERkby0ksvAZYr7mfOnOGZZ56hVq1aNvV5uFHt2rV58sknmTZtms3VITIyktdff53k5GS6detGTEwMnTt3pmbNmjZXC4Djx48zefJkmjRpQlhYGO3ataNz5842WYu1a9dy5swZXn75ZesyW6zD559/zq5du6hXrx6xsbH4+fnRvXv3Aq2FAoSIiIiIiOSbhjCJiIiIiEi+KUCIiIiIiEi+KUCIiIiIiEi+KUCIiIiIiEi+KUCIiIiIiEi+KUCIiIiIiEi+KUCIiIiIiEi+KUCIiIiIiEi+KUCIiIgUsldeeYX+/ftz+PDhXF//9NNP6dq1Kzt37rzlfvr370/37t1Zvnz5bbdh06ZNtG3blmnTppGUlERycjIAffr0YdasWdbvRURupAAhIiJSyHr06MHOnTvZt28fAG+88QY9evQgJCQEgF9++YWoqCgiIiJuuZ/hw4fz+++/87///S/fx05PT+fq1at07NgRZ2dnDh06xNq1awkICODYsWNUr16djz/+mMDAwL//BkWkRHMo6gaIiIjYmooVKwLw4IMPAjBy5Eh69+7N119/zdGjR/H29ua7776jbNmyt9yPv78/AE2bNs33sS9evEiXLl3o1asXlStXpnr16mzZsgVvb28cHR0JCwujYsWK9O3b9++9OREp8dQDISIiUsgcHCzX70qVKgWAj48Py5Yto0mTJgQHB7Nnzx4ee+wxQkNDb7kfFxcXXFxcMJlMLF++nFdffZUpU6Zw/PjxPLepUaMGkydP5uzZswAcOXKEhIQEBgwYgKenJyEhIQwYMAAXF5c78l5FpORRD4SIiEghyczMJCwsjB9//BGAnj170qpVK+rXr8+mTZs4deoUJpOJMWPG0KxZM+69996b9nH16lX279/PqVOnOHLkCOnp6axevZrvv/+e++67Dzc3tzyPbzabWblyJRERETg6OhISEoKzszP16tXjzz//5MKFC3h5edGoUSOmTp3Kt99+S+fOnZk0aVKB1URE7j4KECIiIoVk8eLF/Pzzz8THxwMwZswYOnbsSNmyZRk8eDDjxo1j3bp1JCQk8Msvv+QaIAzD4MiRI8TGxnLs2DHMZjN169YlKCjoL3sN7O3tadWqFdu2beOjjz7CZDLRqFEj/v3vf9OuXTs6deqEnZ0dPXr0oHnz5owZM4ZHH320QGohIncvk2EYRlE3QkRExFYYhsFTTz3F4cOHeeutt2jevDkxMTF89dVX7Nq1i4iICO6//35atGjBuHHjMJlMue4nMjKSDh06kJaWhoeHBw899BDz58/H0dHxlsdPSkqib9++eHp6kpmZSZUqVQgODqZRo0Y0adKEvn370qpVK2JjY9mwYQM1atQoiDKIyF1McyBEREQK0erVq623b924cSPjxo2jXr169OvXj2bNmlG1alW+/fZbxo0bx6VLl0hLS8t1PzNnzsTd3R2TycTAgQM5ceIEI0aMICUlJc9jJyQkMHjwYOrXr09qaiqXLl3i6aefZvXq1WRkZFCmTBkAypQpQ9myZalWrdqdL4CI3PUUIERERApJdHQ0M2fOpEuXLgDMnTuXESNGcPHiRWJjY4mJieHSpUv06dOHhx56iFatWtG6dWv279+fYz/BwcF88803vPzyyxiGgYeHB++++y4///wzAwYMIDIyMtfj//TTT7z++utMnDgROzs7fHx82LBhAx07dqRZs2ZMnz6dnTt3EhUVRcuWLbGz02mCiNxMcyBEREQKQWpqKi+99BKPPPIITz75JOvXr2fz5s1MmzYNb29v/Pz8iIiIwNnZmV69euHm5oadnR0mkynHMKI//viD1157jYCAALp06cKECRPIzMykWbNm9OvXj2XLltGtWzcmTpxI586drUOgdu7cydq1azGbzURGRhIWFkaLFi0ICQnBMAzatWtHlSpVGDlyJPHx8XTo0KGoSiUixZzmQIiIiBSC119/nQceeIA+ffrw/fffM2rUKHbt2mUdNgQwdepUtm3bxubNm3Pdx8mTJxkwYABNmzZl0qRJnDx5kmeeeYYGDRpQrVo1OnTowIIFC6y3ca1Tpw7Dhg3j0UcfxWQy8cUXX5CQkEB4eDjR0dHMmDGDgIAAmjZtyjvvvAPAE088walTp9iyZQs+Pj4FXxgRueuoB0JERKQQTJ48GScnJ8Dy7AWAjIyMHOsYhmFd50ahoaG8+eabjB8/nvr16zNmzBjOnz8PWG7P6u3tjaenJx999BELFy7kypUrlClThpMnT9KgQQN8fHx49tlnAThx4gTTpk2jadOmpKSkMH36dAC+/PJLwsPDcXNzo2fPnkycOFE9ESJyEwUIERGRQnB9MNixYwcmkwkPD48c69jb2+Pu7p7r9unp6Xz99dfWuywtXLiQbdu2MXjwYDp16sSgQYOs606dOvWWbalQoQLe3t6UKlWKUqVKMXHiRLp168bu3bv5+uuvOXbsGKNGjWL48OH4+/vz/PPP06lTpzzDjYjYFgUIERGRQta9e3fKlClz03Mb3N3d87xtauPGjW9atm/fPoA8b/V6oytXrrBgwQL2799PmzZt2LhxIzExMSxcuJCAgACGDh0KQM2aNZk0aRI//PADbm5unDhxggYNGlCzZs3beJciUlJpDoSIiEgx8eOPP2Jvb0/btm3ztf7OnTsZOHAgc+fOzddQo4yMDNLS0ihVqtQ/baqI2DAFCBERkbvY+fPnqVq1ar57IURE/ikFCBERERERyTc9IUZERERERPJNAUJERERERPJNAUJERERERPJNAUJERERERPJNAUJERERERPJNAUJERERERPJNAUJERERERPLt/wFgGT/qEjxWTAAAAABJRU5ErkJggg==\",\n 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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率分档WOE值分档IV值指标IV值LIFT值累积LIFT值累积好样本数累积坏样本数分档KS值
0purpose借款目的domestic appliances,retraining,car (used),radio/television4040.40403200.4571840.28000.20790.49020.08680.14700.69310.693132084-0.1771
1purpose借款目的repairs,business,furniture/equipment3000.30002000.28571000.33330.3333-0.15420.00730.14701.11110.8712520184-0.1295
2purpose借款目的car (new),others,education2960.29601800.25711160.38670.3919-0.40790.05280.14701.30631.00007003000.0000
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\"\n      ],\n      \"text/plain\": [\n       \"      指标名称  指标含义                                                          分箱  \\\\\\n\",\n       \"0  purpose  借款目的  domestic appliances,retraining,car (used),radio/television   \\n\",\n       \"1  purpose  借款目的                        repairs,business,furniture/equipment   \\n\",\n       \"2  purpose  借款目的                                  car (new),others,education   \\n\",\n       \"\\n\",\n       \"   样本总数   样本占比  好样本数  好样本占比  坏样本数  坏样本占比   坏样本率  分档WOE值  分档IV值  指标IV值  LIFT值  \\\\\\n\",\n       \"0   404 0.4040   320 0.4571    84 0.2800 0.2079  0.4902 0.0868 0.1470 0.6931   \\n\",\n       \"1   300 0.3000   200 0.2857   100 0.3333 0.3333 -0.1542 0.0073 0.1470 1.1111   \\n\",\n       \"2   296 0.2960   180 0.2571   116 0.3867 0.3919 -0.4079 0.0528 0.1470 1.3063   \\n\",\n       \"\\n\",\n       \"   累积LIFT值  累积好样本数  累积坏样本数   分档KS值  \\n\",\n       \"0   0.6931     320      84 -0.1771  \\n\",\n       \"1   0.8712     520     184 -0.1295  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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率分档WOE值分档IV值指标IV值LIFT值累积LIFT值累积好样本数累积坏样本数分档KS值
0savings_account_and_bonds逻辑回归入模变量... >= 1000 DM,unknown/ no savings account,500 <= ... < 1000 DM2940.29402450.3500490.16330.16670.76210.14230.18940.55560.555624549-0.1867
1savings_account_and_bonds逻辑回归入模变量... < 100 DM,100 <= ... < 500 DM7060.70604550.65002510.83670.3555-0.25250.04710.18941.18511.00007003000.0000
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"                        指标名称      指标含义  \\\\\\n\",\n       \"0  savings_account_and_bonds  逻辑回归入模变量   \\n\",\n       \"1  savings_account_and_bonds  逻辑回归入模变量   \\n\",\n       \"\\n\",\n       \"                                                                分箱  样本总数  \\\\\\n\",\n       \"0  ... >= 1000 DM,unknown/ no savings account,500 <= ... < 1000 DM   294   \\n\",\n       \"1                                 ... < 100 DM,100 <= ... < 500 DM   706   \\n\",\n       \"\\n\",\n       \"    样本占比  好样本数  好样本占比  坏样本数  坏样本占比   坏样本率  分档WOE值  分档IV值  指标IV值  LIFT值  \\\\\\n\",\n       \"0 0.2940   245 0.3500    49 0.1633 0.1667  0.7621 0.1423 0.1894 0.5556   \\n\",\n       \"1 0.7060   455 0.6500   251 0.8367 0.3555 -0.2525 0.0471 0.1894 1.1851   \\n\",\n       \"\\n\",\n       \"   累积LIFT值  累积好样本数  累积坏样本数   分档KS值  \\n\",\n       \"0   0.5556     245      49 -0.1867  \\n\",\n       \"1   1.0000     700     300  0.0000  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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khREqQ8CCESFEzZ85kx44dPH/+HK1Wy+3bt8mQIQMTJkzA0dGRO3fusHv3bnLnzs3Zs2eJiIgwzGnYt29fvMnGV69eZdiwYahUKn7//XciIyPx8vJizZo15MuXz9B5njhxIkWLFjV08IsUKZJk+QoUKADoVzL67bffWL16Nblz52bjxo3odDrKli2Lvb09f/zxBwAvXrwA4iYdvyl2KIyDgwPe3t6AfqKzoijcvHmTe/fu4eHhwcmTJwkLCzMs8VmhQgWj3s/t27dz5swZsmbNSrt27QCYPn06EydOZOrUqW+t54QJE1AUxTDJOHYeQ7Vq1XBxcUGn0xmGXuXKlYvs2bOTOXNm2rZtm+R5r1+/zu3bt0mfPj3NmjXj3r17mJmZUaRIEfLmzUu9evVwcnIiIiKCdOnSMXHixETvAgHkzJnTMI/h2LFj/PLLL9jb21OhQgWuX7/OkydPmDx5Mp07d6Zv376sXLmSc+fOGZZtTczq1auZOHEiFSpUYNq0aWi1Ws6fP4+npydXrlzh8OHDvHz5EisrK3r37o2rqyvnz58H4MKFC4wfP57nz5/TvHlzQH+3KjQ0lFOnThmCZI8ePbC3t8fFxYVz584xZMgQBg0axKBBg2jVqhWXLl1izZo1iYYHnU4H6O9+uLu7ExMTw+zZswkKCiJz5swsXrwYPz8/+vTpQ9asWbGwsEBWXBdCpCQJD0KIFNW5c2fKlClDoUKFqF+/Pj179jSsKhMREUH//v3JmjUrmzdvxtramnbt2vHo0SP69++fYJUiT09PPD09sbe3p2LFity9e5fWrVszaNAgALZu3Qrov5EPCQkxdORjv/lPTOySq/7+/lhbW1O6dGkuX77Mzp07OXXqFOHh4bRr144cOXIQHBxMaGgoAE5OTomeLywsDAArKyv8/f0B/WpLkZGRbNmyhYcPH5I5c2YaNmxIkSJF+Pvvv4HE72S8KTAwkAkTJgD6ZzVYWlry008/sXz5crZv306NGjX4/vvv33qODRs2GCZpb926lQMHDvDLL7/w/fffs3btWurWrUupUqVwdHRkzJgx9O7d+63LwRYuXJjWrVsbVgbKkiULFy5cIDw8nJMnT2JiYsLcuXNZs2YNI0eOpF69ekneBXpd1apVKVOmDIGBgTRp0gRvb2+ePHnCH3/8wezZs1GpVJiYmDB48OBEj9doNIwYMYJ79+4xadIkcuTIQZ06dQgJCTHsU7NmTXr37s2uXbs4f/48EydOZM+ePYSHh2Nubk6OHDnizZ8YMmQIV65c4eHDh4ZOP8CECROoXLky9+/f59y5cwQGBrJgwQKioqIMqyLVrl070XLGDkEyMzPDz8+P4cOHc/XqVebOnUuZMmVo164dW7ZsYdu2bVSuXJmXL1/KsCUhRIqS8CCESFG2trZUq1aNXbt2UbNmTUNw0Gg0/Pbbbzg5OXHjxg0aN27M8OHDOXv2LNWqVaNNmzYJztWsWTMCAgJo1qwZmTJlYtWqVWg0Gq5evRpvv8mTJ1OwYEGqVKkC8NbVc2JDQGxHv0iRIpw+fZp58+aRKVMmatSogYWFBRC3PCfov4F+U2RkpOGBaOnTp+fhw4eAPqCkS5eO4cOHJzhmypQpmJmZkT9//iTLCPp5A4MGDeLFixc0b97cEBKsrKzo0qULEydOZPjw4eTNmzfJZV+fPHnCtGnTKF68ONeuXaNly5bY2Nhgb29Ps2bNuHv3LosWLSIgIIBp06YxYsQIrKysWLhwIWFhYXTo0CFBoLOzszMs53rq1CnmzZtnWKY1X758hnAWEBDArFmzqF27ttHLodapU4dJkybh5ORElixZuHHjBhs3bsTCwoL79+/TokULvvnmmySPHzp0aLyhYkePHuXp06c0btwYrVZLxYoV+fHHHw13Gho3bkzevHmTPF/ZsmX5999/qVatGtmzZzcMX2rQoAEWFhaGu1JLlizBxMSEatWqGe6wJDURP/Yp1IcOHWLatGl899139OzZk+7du5M1a1YmTZrEzJkzefToEfb29piYmMiTq4UQKUrCgxAiVVhaWnLkyBHOnz+Pq6srFy9e5JdffsHNzY3r16/Tq1cvJkyYgK2tLWPHjk30HCqViiZNmnD9+nX+++8/IiMjWbduHd7e3nTr1s0wnOOff/7BycnJsDxo7KpC0dHR9O7d2zAEBTA8fC4wMBCAjh07Uq5cuUQny3p6egL6ISaJ3c3QaDQA5MmTh/Tp03PhwgXg7XcVvLy8yJ07d7Id6rFjx3L8+HEaNGiQ4P1p3bo1S5cuxc/Pj44dO7Js2TIKFy4cb5+oqCh69+6Nk5MTvXr14pdffkGlUlGkSBFCQkLw9fXlu+++4/Lly0RERDB58mS8vLziDZG5ceOG4U5JYu/NuXPnsLGxoX///lSqVInMmTMb7m6ULFmSv//+2+jgAPoJ6NOnT2fVqlWGIWCxq2U5ODjEWyb3Tebm5gnumFhbW7Nq1Sq0Wi1169Y1DPuKbdfknhnh7u6Ou7s7QUFB1K9f37C9UaNGFC1alB49egDw66+/4ujoSFhYGEFBQQCG+S9viv0zU6BAAf744w8sLCzw9vZm8eLFjB07loCAgHjvubu7u2FhACGESAkSHoQQqaJmzZpkzpyZZcuW4erqGu9hY0WLFsXV1ZVdu3Yxe/bsRIcEDRw40LD+vUqlonTp0uh0Otq1a0f58uXx9PTkyJEjgL7z5u3tbRhaoigKefPmRaPRcObMmXjhIfZb3JcvX3L27FkiIyMJCgpi5cqV+Pv78/TpUzw9PXF2djacL6k5FHZ2dkyZMgVzc3NevHiBh4cHefPmJXv27Gi1WgICAgwPnvPy8uLOnTuEhYXh6enJ5MmTEx2CEx0dzahRo9i+fTu//fYbv/32W4JlWS0sLOjatSvjxo0jMDCQVq1a0adPH9q2bYupqSlarZZ+/fqROXNmpk6dyrlz5wzHHjlyhEmTJpElSxZy5Mhh6KTXqFGDli1bYmtri5mZGVFRUW9dxtbMzAwfHx9OnTrFs2fPyJcvH9bW1syZMwcbGxtmzpz51uFjiXF0dGTu3LlkyJABjUaDp6cnbdq0wc/Pj4IFCyb7DIU3/fvvv6xfv55s2bIZlsQNCwvj3r17mJqaGlbHehuNRsOAAQPQ6XTUrVuXvXv3Mm7cOGbOnGkICuvXrydz5swULFjQMKfl9eFSrwsPDwf0dzXs7OwICQlh4MCB3LlzhzJlypAnT554+2u1WrRaLYqivNfyvEII8a4kPAghUlxUVBSXLl3C0tIywbCX2LHpR44cYfbs2UmODR8xYgTPnj0z/L+9vT1Vq1bF1NSUK1eusG/fPooVK8bo0aNxcXEhb968HDt2jMGDB9OmTZskJ/3GrnAUHBzM0KFDsbW1xcnJCXt7e2xsbMiePTsFChTg6NGjXLx4EcAwHCoxdevWBWDZsmVotVpatmxJTEwM7u7ueHh4APphVFmyZCFTpkxUq1aNDBkyJDps6d69ewwcOBAHBwe2bdv21qFN7u7u/P3333h5eREZGcmkSZNYvXo1Xbt2JWfOnLRo0cKwNGrsXRatVkvz5s0NYWr16tUcPnyYZs2asXbtWipXrkzVqlWTvObrcufOzeTJk+nTpw/Dhg2jZcuWFChQgCdPnjB16tQkJ5i/ja+vL3PmzMHT0xOVSoVarebKlStky5btnZ+wfPz4cQYMGIC1tTWzZs0y3HE6fvw4Wq2WIkWKJPl8iFgajYYePXoYhndt3LgRExMTKlSowLp16wzDn8aMGUO9evUA/V0viJto/6bYtogNVra2tqxbt44pU6awevVqVq1axciRIw37x97B0Gg0huF0QgjxKUl4EEKkGEVR6Ny5M2fPnjVM8rx+/TpnzpyhVq1alClThuXLl1OoUCF27doVbwnNN6VPn94wxhzg9OnTgH61moEDB9KvX78kj03q6cigX8K0cePGWFpaMm7cuCT3K1WqFO3bt8fOzi7ZSclRUVH8888/ZMuWDXd3d0xNTVm/fj2nTp0iR44c5M6d+61lAv1k8r179zJx4kQKFiz41n1BP0xn2rRpDBgwAC8vL0DfMV26dCk//fRTvDkkt2/fBuKGzID+G/jFixeTOXNmxo4dy4sXL+jevTsTJ05M8gFxiXFycmLx4sW4u7tz69YtQD/k6vHjx/z444/JPvfgdY6OjmzevBnQPwxv48aN/Pnnn2g0Gp4+fcqJEycICwujZMmSbw0TmzZtYuzYsdja2rJgwQIyZcrE+vXradq0qeH8sQ+3S4pGo6Fv377kyZOHGTNmYGtry9y5c+O1Y+zyswsXLmT8+PHodLoEge1NsYHUysqKmJgYAgMD8fPzo0yZMoSFheHr60uvXr2ws7Ojf//+hgn7EREREh6EEClCwoMQIsWoVCqGDx/On3/+Sd26dXF1dcXT05Pt27ezYcMGli1bBsCjR4/w9PQkQ4YMWFlZYWFhQXR0NNHR0URERFC5cmV++OGHeOc+duxYstePHV+f3HMApkyZglarfes+ZcqUwd7enmHDhiX6vIbXLVy4EH9/f5YtW2b4Rtnc3Jzq1asnW+ZY6dKlM4yhN1apUqU4cOAAGo0GtVqd5ETxvXv3AnHLyiqKwrBhw/Dz82Px4sWYm5szdOhQGjRowIABAzh58iSDBg0yquN/9epVxo8fz61bt3BycsLf35/Q0FDmzp3LokWLqFatGlWqVMHV1ZV8+fIlGHoTFRXF2bNn0Wq1BAcHExAQgJ+fHydPniQyMpLq1avj6+sbb7UjOzs7Nm/enOBZFFFRUYwbN45t27bRsGFDBg4cSIYMGdDpdKxYsYJx48YZhq3F3jFKiqIoTJs2zXB3QqvVcvHixXjvceyk+27dupE3b15iYmL46aefDPu/SafTcerUKQB++OEHwypPDg4O2NnZYWNjg7W1NdbW1nh6etK6dWvDE6eT+/MqhBAfi4QHIUSKyps3LxMnTjT8XqpUKUqVKsXAgQPZuXMnmzZt4tKlS4aO1+syZMhAjx494s1RiBXb8c2SJUuS147t6L1tn1jJ3QkwNzfn2LFjyX7bu3fvXtauXcu8efMoV65cstf9FN62tCrov+UG/XMVoqKiGDZsGMeOHWPWrFlUq1YNgFy5ctG5c2cWLFjAli1b2Lt3Ly1atKBTp04J5qQEBwdz9OhRNm7cyJkzZzA1NeWnn35iwIABPHr0iJEjR3L16lWio6M5cOAABw4cADAsjTt58mTDcDZTU1P+/fdfNm3aRLZs2ciXLx/Ozs7UrVsXOzs7TE1NUavVREdHExISwpMnT3j+/Dl3795NEB7u3r2Lm5sb/fv3jxd81Go1Xbt2ZeDAgYB+taTSpUu/9T17s91v3bpFaGhovDseJUuWpHDhwtSpU8fw57NcuXIcPXqUEiVKJDhn7LwFKysrQ2B629Cp5s2bo9VqsbGxeac7OEII8SFUijxdRgjxmXn48CE7duxg7969eHl5UahQIRo0aEDz5s2TnGT7+PFjOnbsyKpVqwyTUt909epVWrVqxZkzZwxj3D+lS5cusX79enr16pVkmT4Hu3btYtmyZfzzzz+MGDECZ2dnOnToQMaMGePtp9Fo6NWrFydPniRDhgwUKFCA2rVr06pVK0D/VOQBAwZw+fJl7OzsKFCgAK6urjRu3DjeEDSdTsfatWv5+++/8fX1JXfu3JQqVQo3NzeqVq2a6ETsmJiYty6x+6FevHhBvXr1aNiwIYMGDXqnVaBAPwypRo0aVK9endmzZye539atW9m0aRPLly9PNKCuXr2aixcvMn369GSvuXLlSsaPH0+PHj3o1avXO5VXCCHel4QHIcRXIzIykmHDhjFjxozULopIg86ePUvOnDmTfGDgxxYUFMT69evp1KlTsnfKhBDiY5HwIIQQQgghhDCKOrULIIQQQgghhPgySHgQQgghhBBCGEXCgxBCCCGEEMIoEh6EEEIIIYQQRpHwIIQQQgghhDCKhAchhBBCCCGEUSQ8CCGEEEIIIYwi4UEIIYQQQghhFAkPQgghhBBCCKNIeBBCCCGEEEIYRcKDEEIIIYQQwigSHoQQQgghhBBGkfAghBBCCCGEMIqEByGEEEIIIYRRJDwIIYQQQgghjCLhQQghhBBCCGEUCQ9CCCGEEEIIo0h4EEIIIYQQQhhFwoMQQgghhBDCKBIehBBCCCGEEEaR8CCEEEIIIYQwioQHIYQQQgghhFEkPAghhBBCCCGMIuFBCCGEEEIIYRQJD0IIIYQQQgijSHgQQgghxGft+fPnKIqS2sUQQiDhQYgUp9Wm3j+AqXntr4Gi032V1xaft169etG6dWvOnj37zsd26NCBfv36ERQUlOC1O3fuUL16dQ4cOPDWc2zevJnq1aszefLk9w4AQ4YM4YcffnivY4UQH5dKkSgvRIobMCaIew+1H+VcLRpa0qa5Fas3hbNxR2SS++XLbcK0Menf+zoPHjygbdu2dOzYkY4dO6JWG//dQ4cOHciQIQOjR48mffr4Zbhz5w5dunRhxIgR1K5dO8lzbN68mTlz5lCvXj0GDRqESqV677p8SpqVK1B8n73XsWq3iphVqUr08WPozpw2+jiVY1bM27V/r2vG6tWrF/7+/vTp04cKFSq807FfU/t+iS5cuMCPP/5I5cqVWbp0aaL7aDQaYmJisLKyird99erVjBs3jt27d+Po6Ejr1q1p1qwZHTt2ZMOGDYwaNYpu3brRq1evJK+v0+moUaMGwcHBnD17FnNzc6PLHhAQwNOnTzl+/DiLFi3C0dGRmJgYVqxYQc6cOY0+jxDi4zFN7QII8TW691CLx52YDz5Ptw5WtGluxawloSxcHv4RSpa0PHnyYGJiwvTp06lXrx7Ozs4A+Pn5cfXqVTZu3MidO3cYM2YMVatWjXfst99+y7hx4+jRowcmJibxOiBXrlzB19cXDw+Pt3YumzRpwuzZs1m3bh19+/Z9pw5ISlJ8n6F4e7/XsdqNGyA4GLP6DYgODka779+PXLqktW/fnh9//JElS5YkGR6S6mB+Te37JYrtZFeqVAlFUYiIiODJkyfcv38fT09Prl69yrlz51CpVKxatYpixYoZjnV0dMTKyop8+fIBUKVKFSZNmkStWrVYs2YNq1atwtXV9a3XV6vV5M+fH41G807t6uXlxdy5cwkLC+PQoUMMGjSISpUqkSdPHhYtWkSFChUoX778e7wjQogPIeFBiC9Utw5W9OlikyLBIVa6dOmoVKkSzs7OXLlyhR49emBqakr9+vXJlCkThw8fZtCgQezatYuMGTMajkvNDsiXJjYwmNVvEO/3T+1L7WCK5JmZmQHg5OTEvHnzWLZsGS4uLuTLl49NmzZRtmxZ+vbtS0xMDKam8bsFJiYmWFtbG34fMGAA+fPnZ+3atTg7OzN16lR69OiR4AuD18XExGBiYoK/vz+dOnXi4sWLZMyYkYULF1KgQIFEjxk/fjyFCxdmypQpzJkzh6xZs3LmzBlu3bpFhw4dWLZsGXfv3qVcuXJyl0qIFCbhQYgvUGoEB4DAwEB+/PFHVqxYQatWrdi9ezdqtRp/f3/atWvHoEGD+Omnn7CwsIh3XGp1QL5UqREgvsQOZkpSNGHvfpCJBSoT/XulaGNAGwUqNSqzdO90XpW5dbL7JCUwMJDLly8DMGPGDEqWLMmlS5fw8/Pj/PnzbNq0ifTp09O+ffxhb4qi8PTpUy5evMjLly+pU6cOPXr04MiRI1y8eBEfHx9atmxJixYtcHNzS/TaGzduZM2aNTx69IjQ0FCyZMlCsWLFaNSoEUWLFiV//vxJlrtevXosXLiQwMBAli9fTpUqVfD09GTOnDmsW7eOhQsXJnldIcSnJeFBiC9MagWHoKAggoKCcHFxoUuXLpw6dYrFixezYcMGRo4cyfz586lVqxZardbw7XFqd0C+ZCkZIL7UDmaKGpXn3Y/58S8o0Uj//zd2w5rOkOcb+HVr3D6TXSEs4O3nmeT37td+5ejRo1y9ehWAjh074u7uDsDevXsZP348RYoUYfbs2QmOW7duHYcPH+bRo0dkyZKFmTNnUrhwYRo1asTNmzdp0qQJkZGR7Nu3j3LlyiV6t6hFixbUrVuXpUuXsmDBAvz9/cmZMyeNGzdOttwlS5akYsWKWFhYMGzYMNasWUOfPn3Yvn07NWvWpFu3bjRs2JBx48a993sjhHg/Eh6E+IKkdHDw9vZmyZIlhIaGcu/ePezt7dm8eTNRUVE0bNiQ8PBwihcvTsGCBZk1axZr167lwoULmJqa8tdff+Hh4ZGqHZAvXUoFiC+1gymS17RpU6pWrcqaNWvInz+/4Q7Tw4cPAX176xJZqat169bUrFmT+vXrY25uzrJly+jevTsXLlzg4MGDANja2uLm5hbvrtOboqKiWLVqFaamplSpUoXJkydjamr61vY9ffo0/fr1o0GDBuTKlYsZM2bg4uLCmTNnOHv2LBkzZmT+/Pls3br1/d8YIcR7k/AgxBciNe44ZM+enSxZsqBSqahSpQoqlYp8+fJx8OBBVq1axcyZMylfvjwZM2bk5MmTDBw4kG7duqHVasmTJw8lS5ZMtQ5IWpESAeJL7GCmuHEP3v0Yk9eG7xX9Xn8O1RurlA0+/2HlMkLsooobNmxg69atFC5cmNWrVwMQGRlJ48aNKVq0KD///HO8uSxjxowhU6ZM2NjYULlyZYKCgqhVqxbZsmXj8OHDmJubc/ToUdatW0eWLFno3bs3WbNmjXftUaNGkTlzZrJmzYqzszN16tRh8ODBBAQE8PPPPyda3jJlylC9enX69OnDxIkTqVatGg8ePOC///5jyZIlWFlZkT59ekxNTTl27Nhbh8MJIT4+CQ9CfAFSa6gSwG+//QbA1q1bOXHiBEeOHKFevXr88ccfhn1Wr17NyZMncXFxSdB5SK0OSFqSEgHiS+tgprQPmXcA6Oc+mCT8J/dDz5uU6OhoDh8+zJ07dzhy5Ag2NjaUKFGCDBkyMGzYMAYOHMjUqVPJmDEjCxYsoHXr1uzdu5dly5bh5ubG3LlziYmJoVOnTmzYsIHbt28zc+ZM8ufPj4ODA6APhsWLFzeEzTfnwixatIjjx4+zevVqpk2bhk6no2nTphw6dIjJkydz7tw5xo4di6OjY7zj1Go1AwcOxMvLi9q1a+Pj48OdO3fw8vJi9OjRPH36lKdPnxr+zHbv3p3evXt/kvdRCJGQhAchPnOpGRxe16RJE/bs2cOdO3eoX79+vNdih6OEh8cvX2p2QNKaTxEgvuQOpng7lUrF1q1bOXjwINWrV2f+/Pls2LCBZcuWMXPmTEqWLMnUqVMByJcvH7Nnz6ZTp06cOnWKe/fuoVKpWLRoEStXriQmJobBgwczePBgQP/Mlx07dlC3bl3y5s2b6PXXrFnD0qVLmT9/PlmyZCEwMJAXL14wevRo6tevz5UrVzh8+DCnTp2iZcuWdO7cGUdHR1atWsWMGTPInj07RYsWJU+ePDg6OpI5c2acnJz4/fffsbW1xdLSEkVRCAoKeutdLSHExyfhQYhUkC+3iVH7vf4AuKOnNBRxef+PrLHXTMrBgwc5e/YsxYoVY+bMmbi6uhqW3/T39weIt2Ti6tWrU60DkppUjlmT3+k96TxuEG1npw8QdnaGB8m97zW/1A6mSJ6pqSnz58/n4sWLlClThqVLl2JjY8OuXbuwsrLiwoULgH6lLICKFSsybdo07O3tcXNzMzwE8u7du0RFRcU7d+w3/kktqbtw4ULu3r3Lzp072blzJ4sWLeLhw4c4OTlhZmaGnZ0dS5YsYfny5Wg0GkxMTLh06RJ169albdu2tG3bNsE5r1y5wqNHj8iTJ/7EdVtb2w97o4QQ70zCgxApTKtV3vlJz22a6x8G9zGubWLy7muir127lkWLFrFw4UJy5MjB999/j6+vL//88w9qtZr9+/cDxHu6cOvWrVOtA5JaFJ3ug5/0bCyzKlWhStxYb0WnQ/UOT/2GL7eDKYyjUqkoW7YsAJ07d4732vHjxwGws7MzbPv+++8TnOPUqVNkzpw53rbYu0evHxsrKiqKxo0bGx4i2alTJ9q0aUP58uUpW7YsI0aMMOw7adIko+vy5nLAQojUI+FBiBT2Pp331Lz27t278ff3Z9u2bdjb2wMwb948SpcuTVRUFD/++COPHj0id+7cZMiQwXCc+rWO7OfSAfnU3rXz/jlcOy11MIXxateuze7duylatGiy+73ZhjY2Njg4OCTathYWFoZ2jfXw4UOioqISDFl7F9bW1gnmygghUoeEByHEWyXWWYxd3cTW1pahQ4cyZswYJk6cmOQ5PpcOiHg3X1oHUxivWLFi7Nu3L9n9hg8fnmBbhgwZ6Natm9HXKliwIB06dOCbb755pzK+Lm/evGTJkuW9jxdCfDwqJfbeshBCpLAVK1YkePBYUhRFYdKkSXzzzTdUq1btE5dMfAzSvuJjiY6OBuKehC6ESD0SHoQQQgghhBBGSb0BukIIIYQQQogvioQHIYQQQgghhFEkPAghhBBCCCGMIuFBCCGEEEIIYRQJD0IIIYQQQgijSHgQQgghhBBCGEXCgxBCCCGEEMIoEh6EEEIIIYQQRpHwIIQQQgghhDCKhAchhBBCCCGEUSQ8CCGEEEIIIYwi4UEIIYQQQghhFAkPQgghhBBCCKNIeBBCCCGEEEIYRcKDEEIIIYQQwigSHoQQQgghhBBGkfAghBBCCCGEMIqEByGEEEIIIYRRJDwIIYQQQgghjCLhQQghhBBCCGEUCQ9CCCGEEEIIo0h4EEIIIYQQQhhFwoMQQgghhBDCKBIehBBCCCGEEEaR8CCEEEIIIYQwioQHIYQQQgghhFEkPAghhBAfQKPRcOnSJaKjo1O7KEII8cmpFEVRUrsQQgghxJcqMjKSWrVqkSdPHv7555/ULo4QQnxScudBCCFEiti6dSvVqlVj8uTJhIWFvdc5/Pz8qF27NqNGjUKj0SS6j7u7O3379uXFixdvPdfDhw+pUKECXbt2xcPD473KA2BpaUnXrl3x9PTE3d2dsmXLcvDgwfc+n5eXF25ubvTr14/Hjx+/93natGlDt27d8PLySvT1iRMn0rJlS27duvXW8yiKQt26dWnVqhV79ux553IsW7aMb7/9lr/++ougoCBiYmIAqFOnDn/99Zfh93cRGhpK6dKl2b59e7L7HjhwgO+//57ly5cn+npQUBAVKlRg1qxZ6HS6t55r8+bNVKlSheHDhyf758sY//77L1WrVmXJkiVGvQ979+6lWrVqTJo0ifDwcCIiIgB9W8+YMcPwuxCfkoQHIYQQKaJx48aYm5uzbNkyrl69muR+0dHR+Pj4JPpalixZKFmyJJs3byY0NJTQ0FCqVavGwIEDCQ0NxcvLizt37uDj48PLly/fWp7cuXNTs2ZNDh8+zIEDB965PsuXL+eXX36hdu3a/PHHH2g0GiwtLenYsSOlS5d+5/PFypEjBzVr1mTXrl1s3rw5yf0URcHHxyfJTmfjxo05dOgQd+7cAaBTp060bduWe/fuER0dzalTp/D19cXX1/et5VGpVHTt2pVLly6xevVqo+sRGRlJWFgYP/30E/7+/ty5c4clS5bQokULfHx8cHR0ZOrUqezbt8/oc8bavn07FhYW1K1bN9l9a9WqRWhoKNu2bQPgzJkzVK9enXnz5gFw4sQJgoKCePLkCVFRUW89V9OmTbGwsGDjxo3cvn37ncrct29f6taty7JlywzbtmzZwosXL/Dx8SE8PDzJY6OjowkODqZu3bpYWFhw7do1tm7dSv369bl9+zY5c+Zk8eLFrFq16p3KJMT7ME3tAgghhPg6qFQq8ubNy/PnzyldujTe3t6EhYXx9OlTHj9+zIMHD7h16xbXr18nOjqarl270rdv3wTncXJyImfOnGTIkAGAfv36MWTIEBo1asSKFSsYPnw47u7uqFSqZMtUokQJNm/eTIUKFYyuR3R0NCYmJjRq1Ag7OzuOHz9OZGQk48ePp0yZMtja2tK/f39evnzJuHHjyJYtm/Fv0iv58uUDoEaNGoZOra+vL48fP+bhw4fcuXOHK1euEBISQrly5Vi5ciVqdfzvA7NmzQpAqVKlABg8eDBt27Zl165dREZGUqlSJXr16oWVlVWy5SlevDjAO71PV69e5eeff6Z3795kzpyZPHnysHPnTrJmzYqJiQlPnjyhePHi1KtXz+hzxlq/fj3NmjXD3Nw82X1VKhVZs2alSJEiALi5uVG1alUWLlxIgwYNWLVqFatWraJcuXJGnat48eI8e/aMsmXLvlOZx4wZQ8eOHdm5cycdO3Zk0qRJvHz5ki1btpA/f/63Hvv06VMaNGjADz/8gLOzMzlz5uTgwYNkzJgRMzMznjx5QtasWWnXrt07lUmI9yHhQQghRIoxMTHBycmJyMhIhg4dyq1btyhdujTOzs6sXbuWUqVKGb7Fj+38JnaO1zu8DRo0wN7enqtXrxIWFsbEiRPZuHEj//vf/5ItT/r06QHw8fFh5syZPHz4kMKFC9OxY0csLCwSPebYsWOMGDGCefPm4ezszL179/j9998ZPnw4OXPm5LvvviMqKooWLVqQOXPm93iX9HUEyJYtG/PmzWPr1q0UKlSI4sWLs337dkxMTBg8eDAAZmZm6HS6BOHB1FT/T3zse+Xi4sLy5cu5du0aW7Zs4fHjx+zYsYP169eTPXv2t5bH3t4egKioKJYuXcr169fJmjUrbdu2xdnZOdFjypcvT/fu3Xnw4AEAhw8fJnPmzHTq1Al/f3+8vb0ZMmSIUSHvdZcuXeL27dvMnTvX6GPe/DMzbNgwKlSowNKlS4mJiaFTp060adPG8J6+Tfr06bG1teXYsWOcPHmS4OBgKleuTJMmTZI9bsOGDfj7+7Njxw4ePnxITEwMv/76K4MHD+a7775L8thcuXIxZswYdu/eDcCNGzcwNTWlY8eOpE+fngsXLjBw4EAsLS2Ne0OE+AASHoQQQnxyiqLg7e1NYGAgz5494+eff6ZTp07Ur1+fwMBAjh07xtq1a4mMjKRx48aJdigjIiK4c+cOHh4e3Lp1C1dXV/r378/Jkye5f/8+9+7dw9XVlSlTprz1G/L79+9z8+ZN7ty5w8mTJwH9t/KOjo4UKFAg2bHntWrVYufOndy6dYtJkyah0Wjo3r07ZcuWZe7cuTx79oxvvvmGAgUKvHPHGCAgIABvb28AunTpgpubG5cvXyY0NBRfX1/Wrl2LlZUVbm5u5MiRI8HxMTExPHr0iBMnTgBQuXJlmjdvjkql4r///uP27dtkyJCB/v37U6FChSTvjDx//pzLly9z//59Ll++DMCff/6Jvb09efPmJWPGjEnWISwsjK1btxIREYGvry9Pnz4lIiKCYsWK8fjxYzw8PHBxcSFr1qwMGTKEgwcP0rlzZ3799ddk35+1a9dSqVIlcubMmey+L1++5Pr16/j5+bFmzRq2bdvGsGHDWLduHV5eXvj4+NCiRQv69+//1jsJFy9e5P79+9y6dYtjx44RGBhIz549yZEjBy4uLrxt7RmtVkurVq0ICQlBq9Ua3r8yZcpw6dIlrl27Rq9evdi4cSPFihVL9PiNGzfy7NkzzMzMuHDhAhYWFob30tvbG3t7e1xdXfnjjz/YsWMH33//PaNHj072/RHifUh4EEII8cnt37+f7du34+3tTebMmRk9erRhKMzQoUM5cuQITk5OrFy5MtEO94sXLxg5ciRBQUHcvn2bXLlyMWzYMNzc3GjdujUajYYyZcqgUqm4cuUKmTJlSnLeQWhoKJcuXcLU1JQbN24A0L9/f7p06WJ0fUaPHs2yZcsYOXIkoP9WvU2bNqxZs4YKFSrQr18/bGxs2Lhxo+Eb79GjR7Nr1y7mz5+fZLi5ffs2ixcvxtPTE4DevXsb9l2/fj1TpkzBxMSEJUuWJBocACZNmoSHhwf+/v6YmZnx+++/U7VqVWxsbABo27Ytd+7c4dmzZ5w/fz7J8BATE8O1a9fQaDRcu3YNgGrVqrFo0aIEdzneZG1tjZubG3v27OHEiROo1WoqVKhA48aNKVy4MNOmTcPCwoIWLVpQp04dxowZQ82aNd96TtCHgb179zJjxoxk9718+TKzZ89Gp9Ph7e3Nd999R7du3ShSpAj169fn7NmztGvXDp1Ox8mTJ8mTJ0+Sd7uePXvGjRs3CA4O5smTJ5iZmbF8+XJcXV2TLYeJiQnDhg2jVatWjBgxgrZt2xpeGzp0KGZmZsyePTvR4BB7fOXKlTl27BgLFy5EpVLh6upKs2bNqFGjBvXq1UOtVtO8eXMqVqzIwIED+fbbb5MtlxDvSyZMCyGE+OS+++475s2bR5EiRQyTnmM7oFmyZAH0HanYYURvcnBwYN68eQwfPpyQkBAsLCwICgpCq9WyYsUK+vbti6Io3Lx5k6tXrxIQEJBkWUqUKMGIESPIkiWLYXWdv/76yzCh9m20Wi1DhgyhTp06FC1aFJVKxYwZM1Cr1Rw9epS9e/eyf/9+1q5dS758+eJNwH3y5AkhISFvLVvBggWZMWMGjRs3BvSd9dihKLHvU+vWrQ3zGBIzYsQIli5dSnR0NKampsTExBAREcHRo0cZMGAAd+/eJSwsjIMHD/Ls2bMkz5M1a1b69u1L/fr18fPzA/TfwI8ZMybZ9wn0w3Q2b95My5YtyZkzJw4ODvTr149x48YxYcIEDh06BMDJkyepUaMG6dKlS/acW7Zswd7enho1aiS7b6lSpfj777+pXbs2oF8VKzAwkMjISCZNmsScOXMA/YTp+/fvv3UFsNhv8mP3sbCwoEePHobJ6MkpXbo0jo6OnDt3jmPHjvG///2PqVOnsnfvXtKlS4eLi8tbj3dwcGDTpk1UqlQJNzc3smbNyrhx4xg4cCA///wzx48fJ3PmzJw/f55y5col+TkS4mOQOw9CCCFSTGxn/fTp0zx+/BgLCwvD3ARfX1+6d+/O999/z3fffZfoZNjx48ej0+nQ6XSMHTuWsLAwmjRpQpkyZTh//jzt27ene/fuREVF8fz5czJlypRoOQICAli8eDGZMmXi+fPnDBs2jKFDhxITE0Pz5s2TLH/sXITatWtTrFgxvv32W7p168Y///yDk5OTYbUbe3t7xo8fz61btyhZsiQWFhYsXLiQFy9eGEJAcu+TWq3mzp07HDlyhCpVqjBq1ChA3wnu0aMH5cqVo3Hjxjg4OCQ4/s8//8THxwcbGxuWL1/O8ePH+eOPP8iUKRMvXrxAq9WyfPlytFotfn5+ZMyY0VC31ymKwuTJkw3vU9++fZkxYwaKojB27Ngk70D4+vrSuXNnWrRowbZt28iUKRPNmjWjc+fOhrH5FhYWWFtbU6BAAaMmbSuKwrp16/jhhx8SLWtiXr58aVhV6dmzZ/zyyy/8+++/tGvXjqtXr3L+/HlmzpyJq6srISEhBAUFJdnxPn36NIcPHyZTpkw4OjpSpkwZ2rdvz59//pnkXYPXValShcuXL7N//34yZcqEs7MzEyZMwNTUlHv37uHg4GC4O/S60NBQfv31V0qUKMGdO3cICgqiX79+dO/enXHjxhna38HBAbVaneQdKSE+FrnzIIQQIkXcvXsXX19fzp07x4YNG3BycmL06NGGlYVcXFwoX748/fv3p27dugmWc924cSM3b96kWrVq5M6dmzVr1lC8eHF8fHzw9fUlOjqanTt38v3331OqVCkqVapE27ZtE33y8+jRo1EUhfbt2wPw7bff0rhxY4YNG8b8+fPfut7/xIkTGTt2LNmyZWP79u307NmTbNmy4enpyalTp1i5ciUdO3bkm2++oW3btvTu3RvQT2w2Jjg8f/6c27dvo9PpGDJkCCVKlKBfv35kyJABc3NzLCws6Ny5MzNmzKBmzZps2rQp3vGenp4sXbqU+vXrY2Fhwd9//80PP/zA06dPefHiBcHBwdy5cwd3d3dKly5NlSpVqF27No8ePUpQlpUrV3Lu3Dl69eoFgLOzMyNGjOB///sfPXv2JCQkJNE6HD58mLlz59K9e3diYmLImTMnS5cupWHDhtStW5eBAwdy5coVwsLCqFy5crLvCeiXWPX29sbd3d2o/UHfVtmyZSN37tzUq1ePFStWYGJiwvPnzw13XaZNm0a1atVwdXWlQoUKTJ8+PcF5QkJCGD58OOXKleObb74BYODAgTg4ONCuXTsOHz6cZBmmTZuGTqejUKFCrF+/HgcHB7Zv305YWBj16tWjWrVqLF26lPLly9OhQwfu3bsX7/jDhw8zbNgwRo0ahVqtxtHRkV27dlG3bl3c3NyYPHkyp0+fxs/Pj0qVKiU7pEyIDyV3HoQQQnwyOp2OHj16cO3aNfz9/XF2dubPP//ExMSEXr16UaVKFdq3b89PP/2Eoih06NCBu3fvsmHDBiZNmsSaNWsA/bKf06ZNY/r06fz777+EhoYya9YsTpw4QYECBcifPz8ajYY8efIYOs2gH3sfu+pQrLlz53Lw4EEWLVpkmJisKArDhw/n7NmzzJkzhzNnzjBu3Djy5MljOE6j0eDn50dwcDDe3t6GSdr37t3jzp07xMTE0LdvX9RqNZaWlpibm6PT6Thy5Ag+Pj5kzpyZwMDAJAPE3Llz2bFjB48fP8bS0pKhQ4dSs2ZNunXrhk6nY/Xq1Xz77bfodDpKly7NH3/8wcCBAxk9ejQNGjTAwsKCFy9e0LNnT3755ReyZ8/O6dOn2bRpE3PmzCFbtmwUKlSIFy9ekD59etq1a0e6dOlQq9WYmJgkuEtz8uRJpkyZQpcuXQyTiXU6HU2bNuXgwYPs37+fxo0b8/vvv1OpUiXDcTt27GD//v3s3r0bHx8fAgMDCQsLw9PTExMTExo0aEB0dDSdOnUCMAwrSs7atWupUaMGjo6ORu2/evVqzp8/z4oVK+jQoQN+fn6sXbsWPz8/ihQpgq2tLaBfFapo0aKYmZmhKEqCOSAxMTH06tWLyMhIpk6dyqRJk1AUBQsLC6ZMmUKrVq3o2rUrrVu3pl+/ftjZ2RmOvX//PkuXLuWHH34wBLpu3bpx7Ngxpk+fTvXq1Xnw4AFLliyhV69eHDt2jFmzZhlWkjp9+jRbt25Fq9Xi6+vLkydP+Oabb7hw4QKKolCjRg2yZctmCHLGvpdCfBBFCCGE+ISOHj2qlC1bVunbt68SEhKibNiwQXFzc1NWr16t6HQ65ciRI4qLi4ty6tQpRVEUJSoqSnF3d1dq1KihKIqiPHz4UOnSpYvi4eGhKIqi/Prrr8qvv/6a4DqlS5dWFixY8Nay/P3330qJEiWU3bt3K4GBgcqECRMUFxcXpU+fPsro0aOV7du3K4UKFVJcXFyUwoULK4MHD1bu37+vKIqiXLlyRalSpYpSuXJl5ddff1VmzpypbNy4UTl16pTSqFEjpXHjxkpoaGi862m1WiU8PFxRFEX55ZdflIIFCyp79uxJtGyPHz9W6tatqzRq1Eh58OCBcvXqVaVKlSrK77//roSEhCgRERGKi4uLMmvWLMMx48ePV1xcXBRvb28lKipK6dmzp7J7925FURRl6dKlSqlSpRJc59dff1U6dOjw1vfp3LlzSpkyZZTJkycrISEhyq5duxQXFxelXbt2ysCBA5X9+/crlSpVUlxcXBQXFxflp59+MrRfdHS0snjxYmXlypVKz549lfHjxysBAQFKmTJllHnz5hn2qVChglKpUqUE71li/Pz8lKJFiyonTpxIdl9FUZT9+/cr/fv3VwICAhRFUZRSpUopS5cujbfP9evXFRcXF+X8+fNJnicqKkrp3bu3UqNGDeXmzZuKj4+P0rp1a6V8+fJKnz59lNmzZyuzZ882vA9ly5ZV5syZo7x48UJRFEVp3bq14uLiojx48EDp3bu3MnfuXEVRFCUwMFBZvHixEh0drXz77bfKkSNHlOjoaGXGjBnKjBkz4pVh9erVyuLFi5UxY8YoPXr0UCIiIpSaNWsqQ4cONezTqFEjpWjRosqzZ8+Men+E+BBy50EIIcQnVbVqVY4fP066dOm4cOEC4eHh7Nu3z/DN782bN4G45xKYm5uzaNEiw/KgTk5OLF68GNDfIfDw8KBAgQIJrqMoylsfGjZz5kxu3brF9u3buXLlCt26dTPceTAxMSFjxoyUL1+emTNnsmfPHlQqFTY2Npw/f548efJQokQJjh07lui5Yyf7Wltbx9uuVqsNrzk5OWFjY5PoHAXQP1l6x44d6HQ6Xrx4wa5du1i9erVhDPvFixcNZY01ePBgihQpgrOzMzqdjpkzZxpev3HjRpLLzr7tfTp48CALFixg3rx52NnZ0atXr3h3aDJmzEiWLFn466+/WLZsGRERETg4OHDt2jWKFy+OjY2NYeWqIkWKMGXKFKpXr05MTIzhWQhz5szBwsKCgIAA3N3dGTt27Fsf0rZhwwacnZ0NQ4aSU7VqVcO38A8ePCA8PDzBe6G8Wl41qfciLCyM/v37kyNHDrZv386iRYs4c+YM9+7dw8TEBFtbW+zs7GjdurXhqc+WlpZERkbi4eGBq6srFy5coGDBguTOnZsGDRrQu3dv7O3tadmypeE9ql69Or169WLQoEH06tUrwXyOH3/8EdAPR5s0aRIVKlQgMjKSyZMnA7Bu3Tp8fHywtrbG3d2dUaNGyR0I8WmlbnYRQgjxtWvXrp3i4uKi3Lt3L9l9PT09FRcXF6Vv374JXnN1dVXWrVuX6HEBAQHKzZs3E2zv06eP4uLiYviG+n39+OOPSvv27T/oHMmZN2+e4uLioqxdu9ao/d3c3JRKlSol2N69e3elX79+iR6j0+mUM2fOKDqdLt72NWvWKC4uLoa7Gsby8fFRfv75Z6VGjRpKyZIllU6dOimjRo1Sevbsqfj5+SlLly41fGvfvn17Zf/+/YpWq413Dq1Wq1SvXl3566+/3unasf75559E37ebN28qLi4uhjtLb/L09FR8fX0TbK9UqZLy/fffG3XtWbNmGc6v0+mUzp07Ky4uLkqZMmWUWrVqKa6urob6u7i4KIsXL07yXC9fvlQGDhyouLm5KW5ubkq9evWUhQsXKh06dFAePHig7N2713Ce5s2bK1u3blWioqKMKqcQ70LuPAghhEhVHTp04OXLl+TNmzfZfXPnzk2ZMmUSXarTxsaGXLlyJXpchgwZyJAhQ4Ltsd/mfyhLS0vDU5g/lcaNG7Nt27Z48wvepmnTpoSGhibYbmNjk+SzHVQqVaLPoIh9n4x96N2TJ0+YP38+9+7do06dOsydOxcPDw/27NlD586dcXJyAqBjx46Eh4fz33//kT59ejw8PChZsmS8J3MfPXqU58+f06xZM6Ou/aZq1aqRLVu2BHctrK2tMTMzS/K9yJ8/f4Jtjx8/xt/f3+i2jp0sD/r3bv78+axbt44DBw7g6elJREQE9vb25MmTh5o1a8Z7BkSsFy9esGDBAi5dukTVqlXZvXs3gYGBLFq0iPr169O1a1dA/9kYPXo0+/fvx9raGk9PT0qWLEnu3LmNKqsQxlIpylseiyiEEEJ8ISZMmMCAAQPeOiTnTUOHDuXQoUOcOnXK6OU/kzpP3rx5+eWXX977HCll7dq1lC5dmkKFChl9zNatWxk8eDBbt26lcOHCye6v0WgMk4o/VzExMUyaNIkRI0YYfYxGo6FJkybkzp2bBQsWfMLSxYmJiUGj0Ri1nK0QKUHCgxBCiK+WVqvFx8eH7Nmzf9B5jhw5QoECBZL8FjstePz4MTlz5kztYqS6kJAQoqOjE72TJcTXQMKDEEIIIYQQwijyJBEhhBBCCCGEUSQ8CCG+alqtNrWLkOKkzl8HqfPXQer8dfic6izhQQiRpul0OqZMmcK///7LqFGjiIqKivf6lClT+Ouvv1KpdJ+G1Dnt1VlRFLZs2cK9e/cM29J6nRMjdZY6w5df5y/98yzhQQjxxXj+/Dm9evVi7ty5hm3nz5+nd+/eTJ48me7du7N8+fJ4x+zdu5dLly5Rp04d/Pz8WLNmjeG1o0ePcuDAAVq2bJlSVXgn9+/fp0OHDpQuXZqaNWuyevVqACIjIxk5ciQjRoyge/funD59Ot5xabHOiqIwefJk3NzcKFeuHFOmTIl3XFqs89mzZylYsCAFCxakUKFCDBkyBBsbG8NxabHOafnzHBUVxdChQylXrhzffPONoW5p+fOcVJ3T8uc5qTqnpc+zhAchxGdPq9Wybt06FixYwL///mvY/uLFC3755Rfatm3L4MGDmTJlCjNmzOC///4z7HPx4kXDk4vTp0/PuXPnAPDz82P48OFMnz7d8KTjz0l0dDTTp0+nX79+bNmyhbx58zJu3DiuX7/OH3/8gUaj4Y8//mDIkCF07dqVx48fG45Ni3Xetm0bpUuX5p9//qFMmTIsXbqUkJAQw7Fpsc4A7du3Z+TIkYaf159OnRbrnFY/z6BfIrdBgwasWrUKJycnw1KvafXzDEnXOa1+niHpOkPa+TzLQ+KEEJ+9mJgYypYtS+XKlQ3fUAI8e/aM8PBwYmJiAP3Dr6ysrAx/AYP+4V2xVCoV1tbW6HQ6Bg4cSLt27ShVqlSK1eNd3L9/n44dO1KiRAkA2rZty/Hjx7ly5Qpbtmxh/PjxAOTMmZOMGTPy559/8vvvvwNpr84BAQHUqlXL8A9nyZIlyZ49e7x/SNNinS0tLXF3d6dAgQKJHpvW6nzixIk0+3kGaN68ueHPbKFChfj+++/x9vZOs59nSLzOQJr9PEPSdQbSzOdZ7jwIIT57FhYWif6FW6hQISpXrszw4cM5f/48J06coGfPnpQuXdqwT926dXn58iUA/v7+NGrUiCVLlqBWqz/rB3oVLFgQV1dXw+8PHjzA2dmZmJgYYmJiyJgxo+G1TJkyceTIEcPvaa3O5cqVM/xjvH37di5fvoy7u3u8Y9NinQF69uxJiRIl+Pbbb1m2bFm8Y9Nandu1a5dmP88Atra2KIrC0qVLiYyM5Ntvv+XkyZNp9vMMidc5djukvc8zJF1nSDufZ3nOgxDii+Ht7U2tWrX47bff6NmzJwChoaFMmTIFExMT9uzZw08//UT37t1Rq+O+Gzl9+jRhYWGo1WocHBzo2bMnW7ZsIXPmzKlVlXfy6NEjevTowfTp0zl06BCzZs1ixYoVuLm5AdCmTRsuX77MjRs3DMekpToXLFgQ0A918PLy4sqVKxw/fpzx48fTvHlzwzFprc53797l/PnzZMyYkR07dvDvv/8yceJEmjVrZjgmrdU5rX+eV65cSWhoKMeOHePatWt069aNuXPnpunP85t1XrJkCZUqVUrTn+fE6uzo6Jh2Ps+KEEJ8Iby8vBQXFxdlzpw5iqIoSmRkpNK0aVPFz89PURRFuXTpklK8eHFl9uzZiR4fFBSk1KxZUzly5EiKlflD3b9/X+nXr5/i6+urKIqirF27VnFxcVEOHjxo2Mfd3V2pXLlyosenhTq/KSYmRqlWrZrStGnTRF9Pi3XWarVKgwYNlO7duyf6elqo89fweY4VEhKiFCtWTHF1dU3zn+dYsXXu2bNnvO1p8fMcK6k6f+mfZxm2JIT4Yh0/fhw/Pz/DtzKlSpWiSpUqXLlyJdH9R4wYwbfffou9vT179uzh4MGDKVncd/bw4UM2bdrE5MmTyZIlCwABAQGYmpri5+dn2C8gIICqVasmeo60UOdt27bF28fExISMGTOSLl26RM+RFuusVqvJkycP1tbWiZ4jLdR59+7dafrz/DobGxusra2pXr16mv48vy62zq+P7Ye093l+XVJ1/tI/zxIehBBfrOzZs/PixQu8vLwM2wICAqhSpUqCfdetW4e3tzdt27alb9++1KtXjxUrVnDixImULLLRoqKiGDx4MNmyZWPLli1s2LCByZMn4+PjQ4sWLQzl9vLywt/fP9ExsWmlzv/99x8LFy4kMDAQAF9fX+7fv0/v3r0TnCMt1XnZsmUEBwcD+kUDPD096dy5c4JzpJU6b968Oc1+nv39/VmyZAnh4eEA3Lhxg+joaLp27ZpmP89J1fmXX35Js5/npOr866+/pqnPs6y2JIT4Ipw7d47169cD8O+//+Lk5ESLFi2YOnUq48ePp0SJEkRFRfHdd9/RoUOHeMd6enoyZ84c1qxZE+8vYEdHR7Zt20blypVTsipG2bdvH5cvX+by5cvxtv/666/06tWL8ePHM2LECAICApg/fz65c+eOt19aqnP79u05f/48u3fvxt3dncePH/PXX39RtmzZePultTpfvnyZnTt30rRpU8LCwpgyZQouLi7x9ktLdf71119p3bp1mvw863Q6Dh06xKFDh2jQoAEPHjxg3bp15MuXj5EjR6bJz3NSdc6WLVua/TwnVWcnJ6c09XmW8CCE+CKUL1+e8uXLM3369Hjbv//++3hL4b0pMjKSvn37MmjQIHLnzs3hw4cNr5mYmBAUFPTJyvwhGjZsSMOGDZN8ffTo0Um+llbr/DZS57RT57T4eXZ0dGTdunWJvmZqapomP89vq/PSpUvfemxarPOcOXPeeuyXVGcZtiSESNMmTJhAkSJFaNKkCaAfR63RaAD98InXl4FMK6TOUmepc9ohdZY6f251ljsPQog069q1a5w9e5bNmzcbtpUuXZoWLVqwdetW0qVLR5s2bVKxhB+f1FlP6ix1TgukznpS58+rzvKcByFEmhYcHIydnV1qFyNFSZ2/DlLnr4PU+evwJdVZwoMQQgghhBDCKDLnQQghhBBCCGEUCQ9CCCGEEEIIo0h4EEIIIYQQQhhFwoMQIs2pVasWtWrVSu1ipCip89dB6vx1kDqnfV9yfSU8CCGEEEIIIYwi4UEIIYQQQghhFAkPQgghhBBCCKNIeBBCCCGEEEIYRcKDEEIIIYQQwigSHoQQQgghhBBGUSmKoqR2IYQQn7eCBQsCYGJiksolMY5WqwW+nPJ+DFLnr4PU+esgdU77Psf6xpbp9u3bb93PNCUKI4QQKelz+ss4pUidvw5S56+D1Dnt+5LrK+FBCJGs2L/kPDw8UrkkQgghhPgUihQpYtR+MudBCCGEEEIIYRQJD0IIIYQQQgijSHgQQgghhBBCGEXCgxBCCCGEEMIoEh6EEEIIIYQQRpHwIIQQQgghhDCKhAchhBBfPEWnS+0ifBZlEEKIT02e8yCEMNqAMUHce6h96z7585gwZqAtj59o+X16KBGRKf8Q+3SWKkb2tyFnNhPGTA3h7oO3l/lTadHQkjbNrVi9KZyNOyJTpQxfQ3tUrWhG319t0axcgeL77IPOpXariFmVqkQfP4buzGmjj1M5ZsW8XfsPurYQQnwJJDwIIYx276EWjzsxSb5evLApo/rbcvtuDJ37BREWnvIdVWsrFX/NSE92JxPa93zJtZtJl/dT6tbBijbNrZi1JJSFy8NTpQxfS3vkzaV/iKHi+wzF2/uDzqXduAGCgzGr34Do4GC0+/79GEUUQog0Q8KDEOKjKF7YlL9n2+N5P/U7qgXymtKxd+oGhz5dbFI9OEh7vJ/YwGBWv0G834UQQkh4EEJ8BNJRjSPBQe9zaY/3JQFCCCESJ+FBCPFBpKMaR4KD3ufSHh9KAoQQQiQk4UEI8d6koxpHgoPe59IeH4sECCGEiE/CgxDivUhHNY4EB73PpT0+NgkQQggRR8KDEOKdSUc1jgQHvc+lPT4VCRBCCKEn4UEI8U6koxpHgoPe59Ien5oECCGEkPAghHgH+fOYMKq/rXRUkeAQ63Npj5QiAUII8bWT8CCEMNqYgZ/HA8dSu6MqwUHvc2mPWlXNU/R6EiCEEF8zCQ9CCKM9fqL96juqEhz0Pqf2+L5WuhS/7psBQudxI8XLIIQQqUHCgxDCaL9PD/3qO6oSHD6/9th9MCLVA0S0nV2KX18IIVKDhAchhNEiIqWjKsHh82sPryfaVAkPkPAOhBBCpHXq1C6AEEIk5XPsqEpw+HzbQ5XVKVXKo933L9HHj6XKtYUQIqVJeBBCfJY+945qSpLgEOdt7WHm/gOqnLlSpVy6M6dT5bpCCJHSJDwIIT47X0JHNaVIcIiTXHsoz59j3r1HqgUIIYT4Gkh4EEJ8Vr6UjmpKkOAQx5j2iN60AcXHRwKEEEJ8QhIehBCfjS+po/qpSXCIY3R7aDRoFi2QACGEEJ+QhAchxGfhi+uofkISHOK8c3tERUmAEEKIT0jCgxAi1X2xHdVPQIJDnPduDwkQQgjxyUh4EEKkqi++o/oRSXCI88HtIQFCCCE+CQkPQohUk2Y6qh+BBIc4H609JEAIIcRHJ+FBCJEq0lxH9QNIcIjz0dtDAoQQQnxUEh6EECkuzXZU34MEhzifrD0kQAghxEcj4UEIkaLSfEf1HUhwiPPJ20MChBBCfBQSHoQQKear6agaQYJDnBRrDwkQQgjxwSQ8CCFSxFfXUX0LCQ5xUrw9JEAIIcQHkfAghPjkvtqOaiIkOMRJtfaQACGEEO9NwoMQ4pP66juqr5HgEOed2kOJgpjbRp/7TkgI4THJ1OsdA0S0Tmf09YUQIi0zTe0CCCHSri+yo/qJfLLgoHsBEatA+xhiPMGsLFj3AbVdwv2C2jFx3DoK5M2ob4+LayB8bvz9rHpDuhbJXzd0PKidwOpn/e9KBIQtgJhLgAmY14J0bUGlSnBobHtMnOXF8r+GgnltsPw+4TWUSH3doq+BdY9ki3T40WOWnz/Pd1mzUiCnDQCdz/3Hf4EvEuzbKmcOhhYpjGbRAsy7dmeJ2oSTl66g1mgYW7woOaysDPtG63R0OHuObvnzUTlz5uTfGyGESMMkPAghPgkJDnE+6R2H8KVg9SuobSH6PAT3BaLAdny83UwiJqPVvSR3zjfaQ50NMHltR+fkrxl1FKL2QrqOcdtCx4PmHDis1Xf6X/4IRINVp3iHxrZH74E72LtrCWjv6MPDm7S+ENIf1DnBbgaokv7nSlEUfv/9d3YdP8FS1zLks7ExvBap1ZHb2ire/o/DwnHPkeNVXaI4OHY0q27c5PTRo8zs9DMjrl5nhVt5w/6zbntirlZTMVOm5N8bIYRI4yQ8CCE+OgkOcT5pcIi5C2bF9cEBwMwVVA6gORNvN3PdTlTKRbTAwLHB3H5gEfdi+qWgtjb+mroACJ+XsByao2BWHtQZ9dvUWSByA6RrByozIK49evTdyIGjoWDZHMImJryGEgUhg0BlA7Zj3xocAO56/M29m+v4p8H35IuMjPdax7y5qeXoaPj9QWgYI69dJ79tXMA44+OLdUwMKl9fcjduwp9jxhAWE4O1qSnH/PzZ9fQp6ytVxCSRuyhCCPG1kTkP4osTGBjIvHnziHyjkyA+D+8SHNRoKZ/+HPUz76Z8+nOo0X60cqT54ABgmj/+t/ZKjH74kImTYVM686dUKnOXosWKAXD77uvtoQZV/G/lkxW2ACwaxt+mfaj/ryp93DaVDShhoH0ExG+PA2erJj5MKVbkFtDeh3Q/G4JHkrT+eN5YRt26dSmSyJ2B14MDwNYnT6jv7BRvm4kKUBQ0ixZAUBBqtRrTnLnwjYxk1LXrjCleFEdLy7eXQwghvhISHlJBzGsT+RQl8c6EoijoXpugp9V+vE4VQEhICMuXL6dr164sWLDgo577U1u/fj1nz57FUv4x/+y8S3D4NuN+Dlb4jpUlf2Z64UGsLPkzByt8x7cZ939wOb6K4BBL9dqQI81xIBIs9XMWrNLpKJ7zL0YMH8BtzyTa4uVPEFATAptA+DJQovXbI1ZBwHcQ9Vp7RG4BczdQv9FJj73boPN7beOrv7OUSBrWvM4/S6rT7IcRxrVH1G4gHajSQVA3CPoNNMcS31dzAJ1OQ82aNRlw8BA/nDzNzNt3iErk70ytorDX5xnfZY0fKKpkzkxgtIbwsDA8d2zHrWRJrHv8xrDbntR3dqJ6lizJl1kIIb4SKRIeYpJb9eIj0Gg0zJkzh44dOyZ4bfny5fTp04e+ffuyfPnyeK/t2LGD3377jcGDBzNjxox4HfaTJ0/SrVs3RowYwahRo5L8pnvmzJl88803FCxYkEaNGjFp0iQGDx5M27Zt6dSpE1u2bIl33g0bNtC6dWsKFizIxImJ3LIH1q1bR+HChSlatChDhw7lxo0b7/GuJM3KygozMzMOHz780YPJp6TValm/fj1t2rRJ7aK8lytXruDq6sqhQ4dSuygf3bsGh9lF+uFo7htvu6O5H7OL9PugAPFVBYfX6QIhbA5Y1AeLxlhbqfj+m/W0atWUPqNIvAymecC6F9jNBfNvIOLvuCFJWl8gAnT+r35/BNHXweLbRM5TRD95OuY6xNx5VZ6XALRxd6bmN5GEhIRww8Mr+XooUaB9ACbZ9EOy0i/U/3/IyLhzvy7mFgB58+ZlWq2aDCpckOUPHjLnjmeCXf8LCCS3tTUZLSzibS+XMQP9Cxak54VLeIeEMCZzRhbMmEGEgwN9aycyH0MIIb5iKTLn4ffff8fKyoqWLVuSO3fuj37+EydOcOLECbZu3UqBAgXivbZ9+3ZWr17Nnj17AKhbty4ZM2akYcOGnD9/nnHjxnHo0CFsbW1p06YNS5YsoWvXrjx+/Jju3buzc+dOcuTIwaBBgxg/fjy///57guv37dsXlUrFwoULadOmDS1btgT0dw+2bt3KyJEj2bp1K4sXL8bS0pLWrVvz/Plzrl69ypo1a2jTpg25csUtE6jRaPj7778BKFmyZJIB40OYmJiQP3/+j37eT+3gwYPodDpqf6H/oFtYWJAzZ07s7OyS3/kL8q5DlYblnwQoqN8YQq5WKegUFUPzTeZgQE10r0/kNcJXGxyUcAgerO/YW3XD2lrNkG6PuPifP39vKp10e5iVfe3/i+o77ZE7waonWPfTz1cwyaIfDhU2F2xGJH4elYV+UnP4Ev3EaatuoARhbZOFUYMKMWtJdnDYAip7I+oS8uqcrw2nsmikvxuhOQqmLonub22tn7fhmiEDua2t2PfMl4GFC8XbdU8idx1i/ZAzBz/k1E+i/i8gkJWHj7Jx0SKsy5ZFs2A+yuNHyZddCCG+Aily52Hs2LG0atWKDRs20L9/fw4cOPBRv+22t7dnyJAhWFnFH7urKArz5s3D1dUVU1NTTE1NKVeuHPPm6b9ZmzdvHoULF8bWVj/ZsGLFiixbtozw8HAWL15MpkyZyPFqRY6KFSuyefNmnjx5kmgZTEz0nRy1Ou4tValUNG3alJ9//pkzZ84wf/78eMc0b96c6Ohopk2bFm/7pk2bqFixYrzzfkkURTEMx9JqtYa7LrFDsV7//V2tXr2ali1bYmr6ec31Dw0NBYh3h+n132Pfk0KFCrF582ZcXV0/+Fqfi3edHO2a/gJOFr4JgkMstUrB2fIZ1TMceadyfL3BQQshY8CyIVh3x9pazdzxpuzZuYi9B25y7Uw3COoJ2rv6/UPHQOTuxM9lWhzQ6DvkKrU+OABEn9XfgQgZqT9XxGr99qg9EDJa//8m2cF2HNivAJU1oKNjhx/i2kOdKdmJz8CreRMqIDpuW+yys7qgRPa31xcxOm5/ezNzXr72O+iHLB3z96em49uHIL3QaBh69Ro9C+Rj5qiRtG3bFo/KVeRBckII8UqK9cBy5crFwIED0Wg07N27l0GDBpE/f37c3d3J9GqS25o1a7h69WqS52jTpg3FixdPsL3Yq4mAb3r48CGPHj2iTp06hm0ZM2bk4cOH3L59m3PnzlG3bt14rwUFBXHp0iWOHj1KtmzZ4r0WExPDyZMn+eGHH96p7rVr12bx4sXs2LGD/v37G7a3a9eOQ4cOsW/fPs6fP4+rqyvR0dEcOnSIn3/+mXXr1iV5zuvXrzN27FiuXr3KypUrCQkJYerUqTx8+JCDBw/i6OjItWvXWLt2Lfnz5+fcuXNcuHCBKlWqMHPmzASd74CAAFauXMmTJ0+oXLky9evXT/b458+fM336dPz9/bGxscHb25vBgweTM2dOOnTowP3792nQoAFlypRhwoQJqFQqpk+fTkREBFOmTCFfvnzMmTOH06dPM3fuXAYMGMD48eMJCwtj6NChNGnSJF4Z7927x4ULFxKErdfdvXuXvXv38uTJE6ytrdm0aRO5c+dm3rx5ZMuWjejoaJYsWcLJkyfJlSsX169fp02bNrRq1SrBuQICApgwYQL29vY8ePAANzc3unTpQkhICJMmTcLb25uoqCjMzMwwMzNj0aJF/PXXX8yfP5+YmBhmzpxJunTpmDRpEnXr1qV169b4+PgwY8YMJk6caFQbvXz5kokTJ+Lv709YWBjm5uaYmZkxderURMuW0t5nVaXM5s+NOveCYr3xisjO1ZBiXAspztWQ4twMLUSELuEE3682OIB+boJZWbBsZGiPq5c28eRlIyLM3cD81X5BPSHmMtiMiZtQrSjxn8OgC9Av3aqy17+m89cHCPNK+p9Ykbv1qyRZ1It7zsNrCuX8l+BAZyJUP7A0tj10z/XnTS5AqMzApABoX/uiJjY0mOZ7Ve4owFxfdtMioNnPw4cPyf5q9xcaDQVeW00J4NKLF+S1tsbB3JykKIrCyGvXqZw5E1deBlHA2po29unp+ttvHPr3X0z/XiZ3IIQQX70UnzBtbm5Oo0aNmD59OhkzZqRRo0YEBgYC8OOPPzJp0qQkfxILDm8Te14zs7jVOmL/PzQ0FK1Wm+hrAQEBvHjxIsnX3lXs7fQ3j7W0tOS3334DYNKkSSiKwqZNm2jatGm8OxiJKVasGFWrVjX8Xrt2bUqXLm34PTw8nBUrVrB9+3YuXLjAkCFDaNKkCfv27ePkyZMJzufr64uTkxOjR4+mSZMmRh3fv39/du3axfTp05k1axZZs2bll19+QavVMnnyZMN527RpQ+PGjYmJiSF//vw0adKEli1b8ueff/LkyRPGjx/P/fv3efr0KcuWLcPS0pIpU6YkKOOaNWuoU6cOmd/ykKb9+/czd+5cLly4QJ06dRgzZgweHh6sWbMGgCVLljBnzhx+/fVXJk6ciLu7O6NHj2bv3r0JzvXXX39x+fJlevXqxZIlSwzbR48ezcmTJ/nzzz/5888/uXTpEtHR0Zibm9O9e3caNGgAgK2tLTVq1MDNzY2+ffsSGBjIwIEDOXfunNFtNGrUKM6fP8+SJUtYtGgRFy9eJDo6OsmypaT3XY7VX2P8Wvk50nlTP8tehuSbyppS7ThfyY2tZZoxrsBoKqTXv49fdXDQBUDketB6YRo5DbdCs1mzajRTpszgkbfN24+NOgAvGkP0Jf3vWi+IPgM2w/Wd8rCZ8LI5RCT9JUZiGta8jr/PCSrVms7SNa+CSdQheNEUQoa/WYE3/vuKVUf93Q/Ncf3vmoP6ORUW3+vnYgQ21A+PArCsTzorJ7Zs2QLAzeBgHoWH0+ONYZmnngdQNcvbH/C28uEjnkREMKhQQfY/8yWzhQUFLC14GRzMke3bjXoStRBCpHUpHh5CQ0NZvXo1vXv3Jjw8nL1795IhQ4ZPci0HBwdA30mLFXtr29raGhMTk0Rfy5gxIw4ODkm+9q58ffWTQhOb79GiRQty587NtWvX2LJlC4cOHYp3N+R9pU+fnmrVqgH6eR4FChQwhK/YUBXr4MGDuLu74+HhYRjCldzxYWFhnDlzhjx58pA+vX55RldXVyIiIjh27BglSpSgcuXK/Pfff9y4cYPy5cujKAorVqwgICCALFmyYGlpSbFixcibNy8Abdu2JXfu3OTIkSNB0AoLC2Pr1q38+OOPb613w4b6JSTLlClDuXLlKFOmTLw6Hzx4EMAQtMqVKwfAvn37EpwrV65ceHt7U7lyZX799VeKFy9OdHQ0+/bto2TJkpibm2Nra5vgz0Tnzp1RqVSsWLGCq1evGq5RpEgRQ/mMeY+jo6M5ePAgpUqVwtTUFAcHB8NdusTKlpI+5DkO54PK4hPliE5JfNySTlHxNDIrFU4ep+PVP5nxoDf7n9fENyoLJiodhWzu8IPTJlys7xiCw9/zb5D+2BhqZzzwkWpovFQLDgCa06CEQtQ2YsK2cXD/BnZs34w2JhjU6d9+rGkJMCuh79AH/aafr2A3Rz9JGcAkM5AubiWl5OhCqFhsE0Tu5vtmK9mwO3vca+r0+qVb1a/NNwj/EyL+0f9/xNq4oVAA5pXBZpS+TEFd9MHGbo5+boXKRL8Kk/pVOFKl45taiwkKCqLV1m2MvnaDaaVKUClz/JB6IfAF32RKui7Xg4L48959ppQsQYRWi+bVkMN0r4aOPt62FcXHRwKEEOKrl2LDlq5fv86mTZuIiIigcePGia6Ws3LlSi5dupTkOTp06EDJkiWNvmaePHnIlSsXfn5xywcGBASQK1cuChYsSPny5RO8ZmtrS6lSpahWrRpHjx41vBYYGIiJiQnffPON0dePdfjwYUA/TOlNpqam9OnThz59+jBq1CimTJmS7F2HWKpXww2MnTuQ1P61atXCzc2Nv//+mxo1alCjRg2jjlepVPHmZMS+HjvOv1u3bpw4cYK///6bXLly4ebmxrZt27Czs6N79+7JlvN1W7duJUeOHJQtWzaRI5KWWJkhbm7Km2V+XYMGDciePTv79u3j33//pUuXLuzbt4/o6GiioqIA/UpiL1++JGfOnIbjChQoQI0aNTh8+DDp06dn0qRJhteSa9vXyxsSEkJMTAwRERGG18PCwpIs2+nTp7GxSebb5o/gQx8Ap8OECXeHMLtIP3SKCrUq7s9jbKCYeG8wQVoHTr+syOmXFQ2vZzH3pbjtdUrYXiNn9eq0e3XH4dmBk0wsuJKiNh4cCIibTN8u2yoeReTkWkgxAqPfPfgnJ1WDA4BlA6wzNDSuPdLPjf+7SRaw/SPpc6drq/9J9LrfJ3hOQ5vmWn5t14y126wT3gEyKwsZ9sTfZvWL/icpFrX1P29SZ4IM2+IX1TorC1atImrqZBRv70RP9/oTo98UGhPD4MtX6VvQhQK2tsTodJiqVCiA7tXfHZlMTNAsWoB51+6Yd+8hk6iFEF8to+88BAYG0rZtW3bt2gXA9OnTDeP3z58/T6tWrbh7926ix44YMYIjR47QtWtXJk2aZJgM/KZ27doxc+bMJH/eJTiAviPWu3dvzp07R1RUFDExMVy4cIHu3bujUqno2bMnt2/fNnwrffbsWTp06IC1tTVdu3YlNDSUW7f0ywCeOXOGxo0bkz179kSvFdv5fPr0abztly9fZsOGDbRp04YWLVrEey22Q1u3bl2KFStG7ty5qVevXrzX3hYMYlfsuX79Oi9fvsT71T+awcHBxr9Jr/Tr1488efIwZMgQvLySX07R2tqaihUrcv/+fcME3gsXLmBjY0P16tUB/Z0IV1dXdu3axbfffkv79u2JjIzEwsIiweT25Kxdu/ajLM8au0rTtWvXDGUGDEONXrds2TIKFizIuHHj+OOPP1Cr1WTMmBEnJyf+++8/7t+/z9atW7G0tCQ4ODjeROYuXbqgKAp58+aNN/ztXWTIkAEnJyfOnz+Pn59fvOVdEyubeSJjud/lc2qMdJYf58nR+wO+pbfHDHw18SevPotypLfHDPYHJLIcKOCnceRgQC0iqw+j3W9lDEOV7oXn5Z8nP7L3+XeGfW1MQhiWbzKLi/XgVMVqHCz/HTML9+fn7H9TLv1/WKk/bIhTqgcHPq8neY8a6JJ4cPgCjL1+g6Lp09M8h/7vd1O1msqZM+EXFcXTiAgs1Gr9XYuoKDSLFsgdCCHEV83oOw8RERHcuXOHZ8+eAfDgwQMeP34M6IPFnTt3CApKZCUM9Eu1JvZt8sfy+PFjNm7cyLNnzwgODmbRokW0bdsWa2tr6tevT2hoKAMHDsTU1JQffvjBMBG3bNmyTJ8+neHDh2Nvb0+xYsXo1q0bADly5ODvv/9m5syZODo6olarGTlyZKLXHzVqlGHYy+LFi7lx4wbZsmUjODiY8PBwZs+ebRieEhISwp9//snu3bu5ffs2rq6udOjQgQEDBvDy5UtUKhX79u3jn3/0t/OvXbvG6NGjadasGSdOnGD58uUMGjQId3d36tSpw4YNG5g7dy6PHj0ib968qNVq9u7di4WFhSHobd68mRIlSrB7t36FlZ07d1KhQgX+97//AXDo0CFq1qxJ9uzZefDgAe3atWPq1KlvPb5ixYpMnTqViRMn0qdPH5ydnQkJCeHvv//G2dnZ8N5069aNjRs3UqhQIQoWLEjhwoX56aefDK8fOHDA0JldunQpRYoUwdNTvz77kiVL6NKlC2fPnsXPzy/ekJ/EaDQaw3M8zp07x7Vr19ixYweg7zhfuHCBzp07ExkZyezZszl48CA3b95k6tSpiS79mj9/fn777TeKFCmCj48P06dPx9zcnClTpjB69Gjc3d3p27cvDRs2ZOvWrezZswd3d3dAPyyqcuXK8SZiX7p0yfDnZNmyZbRp0ybZ93jixImMGTOGhg0b0r17d0NgTKpsb3qXz6kxRva3IbuTyUfpqO4P+JaDATVxTX+BzObP8ddk4nxQ2WSXZ01sjsPVkBJcDSkRbz8rkwi2PGtMcdtr5LV6QDbLp2SzfEq9zP8CoFXU3AvPx7WQolwNKc61kOLcCStAjJJ82JPgEOdzmHOSw/n9R+CeDQjAIyiY9ZXif6k1tHBhBl+5yj6fZ4wqViTu2RCvAoTcgRBCfK1UyvuslylShYeHB9u3b+fs2bOGyYHvS1GUeENktFotarXaMKxGq9V+NsvE9urVC2dnZ4YMGZLaRfkgOp0u3vurVquTDdX37t0jY8aM2NvbA1CpUiXKlSvHrFmzPnFp4ytSpAgA/52/TvueX15H1doklGI2HhS3vUYJ22sUs72Os+WzBPtF6cz5/r8dPInKZjguXGuF8tpNWgkOcT6H4FC8sCmr5tmTLp36rcOW3ualRoP9W1ZhSpSFBeZdu6NyckKzYD7otFgMHPzO1xZCiM9F7L/1Hh4eb93v81osX7xVkSJFyJkz50d5YvfrnVaVSpVg6dbPJTj4+vpy6NAhwzfyX7LX5zsY+/726NGD4sWLM3XqVHx9fQkMDEx0iFVKGTM15IvsqIZpbTgbVJ6zQXHj3jOb+1PM5jol7K5R3OY6xW2vo1bpeBrlZNhnXIGxVHY4ye/3hrHTr4E+OMxKL8GBzyc4/D3bnmf+WvLkfP+7D+8cHCDBHYjojRve+/pCCPElkTsPX5DYZzi4u7unuScUi8Rt2LCBhQsXUrlyZQIDA6lZsybNmjVL8XLEfhtRsPwJPO6kfGc1ZTqqClnM/fDTxK0ItKNsYwpY3+Pnq0sIcarK37Pt8TuyDatDo7kaXNTw/IkbIUUI1dp+onLFkeAQ5/U7QBt3RDJhuN1733n4ILF3IJydUVlapuy1hRDiIzL2zoOEByFEslIzPKRmR9VUFU0Bq7tY58zHotnOeN6P4cr0UXRwWhZvP52i4kFEbn2YCC7OtdBi3AotSLTyHt9oJ0GCQ5w3h47VqGTO9LHpiZo9E+X+/ZQvkIUF5r36oE5iQQ0hhPgSyLAlIcQXL7U7qjGKGaY5i7PotY6qEtmVA35V9XMnbK5Twu462S2fkM/qAfmsHtDEcTsAGp0pt0ILcS2kOKdeunEwoNZ7l0OCQ5y3zTkxa+6OZs4seLWUcoqJiiJ60wYsevdN2esKIUQqkPAghPgsfb4dVSvOB7lyPsjVsF8GswCK2143/JSwvYaD2UtK2OnDRUbzgNfCg0LPXPO5FVqQI4HVkr07IcEhTnKT1VWZMmHetTuaRQtSPkBoNCl7PSGESCUSHoQQn50voaP6usDojBwNrMbRwGqvtihks3xCCdtrlLC9zrWQooZ9s1t60yPXIjQ6M8qePGvYXtXhGDGKKddDixIco386tASHOMa0R/SG/2HWwj31AoQQQnwFJDwIIT4rX0pH9e1UPInMzpPI7Ozxr5fg1f/5tMBcHRXvrkOfPHMoYqN/KOWD8Nx4RBSn5HeuZDB3pXPv7Fy7mToroH1J7aE880GzYD7m3XtIgBBCiE9EwoMQ4rPxJXVU35d3ZA5GeY55Y6uCZ1h+bExCyZnOmzxWD8lj9RAu7IALsCKjKbdLu3AtpJhhhaf74XmSfaDeh/oS20N5/EgChBBCfEISHoQQn4UvsaP68agYfHsSANnsXrK4zwNy6K5yff85cquukdE8kGK2HhSz9aA1+ifDh8VYMeDWZA4H1gDAhBi0mABvf/Cgsb7k9pAAIYQQn46EByFEqvuSO6ofk7WVimlTcpM1b35+6l2OazfbAwrOFj6Gp2MXt71OUdsbWJuG4/PaA+3cnTbyW64FrPNpybxHPT6oHGmhPSRACCHEpyHhQQiRqtJCR/VjSHpytIqnUc48jXLm3+d1AFCjJZ/Vfe6H54mrg+11MpkHokZn2JbR7DlrSrXlekgxroYU52pIMW6GFiZSly7JcqSl9pAAIYQQH5+EByFEqklLHdUP8a6rKukwwTO8QLxtYz1H8j8fdwKiMxi2Fbe9Tq50XuRK50X9LHsAiFFM8AzL/2ruRDGuhRTjblh+tJimyfaQACGEEB+XhAchRKpIix3V9/GxlmPVKBZcCSkZb9t/QeX4+eoSitteo7jtDUrYXiWLxXMK29ymsM1tfnDaCECE1pJA62Jkcy7L7hlFWLyxOnziydiJ+VTtIQFCCCE+HgkPQogUJ8FB71M/xyFMa82pl99w6uU3r7YoOJr7Gh5kF/tQOxvTMLJFnocT5ymnyYCOmoZz1M+8m1CtNeeDyhKmtfmo5Xvdp24PCRBCCPFxSHgQQqQoCQ56qfMAOBW+mqz4BmTlQEBtALq1t6R3U1/+XXWa55cvEKUz5/UVmwblnYajhR8/Xl7JxeAyAOS3uou92Us8QooQrrP64FKlVHtIgBBCiA8n4UEIkWIkOOh9fk+OVrNwU12gbrzXzVQaTr90o4iNBzdDCxm2t3T6H22zrUGrqLkXnu/V3IniXAspxp2wAsQoZkaXIaXbQwKEEEJ8GAkPQogUIcFB7/MLDkm3R7RizpDbExJsD9da4RPliJOFLy7WnrhYe9Ii6xYAIrUW3AwtxNWQ4oZA8SgyJ4k9fyK12kMChBBCvD8JD0KIT06Cg96XFBzeZubDPsx82IfM5v4Us7lOCTv9MyiK2dwgvVkwpdNfoXT6K4b9X0bb8ZdXJ/7y7mTYltrtIQFCCCHej4QHIcQnJcFBL60Eh9f5azJzOLCG4SnXKnTktPSi+KswUcL2OoVtbmJvFkykztJwXL1iD5ieuysvVn9D5w1jUqU9QAKEEEK8DwkPQohPRoKDXloMDolRUPMoMhePInOx068BAGaqaApYe+KvyQzo22Niu7uodz7hwZOH8dpjYdEevIy2fzXkqRi3wwoS/Q7zJ96rzBIghBDinUh4EEJ8EhIc9L6W4JCUaMUMj9AiQFx73L79PfNuORAVFfc0bFuTYGpkPApA06zbANDozLgZWijeA+0eRuRGQf1RyygBQgghjCfhQQjx0Ulw0Pvag8Pr4rXH4BjCwl3jvR6ls6Dr9XmGZ0+UsL2GvVkQJe2uUdLummG/kBgbroUU43pIUa6GFOf0y4qAxQeXTwKEEEIYR8KDEOKj+uw6qhIcvoj20CgWHAmszpHA6q+2KOSw9HoVJK5T3PYaRWxuYmsayjcOZ/jG4QwAdf/bCWQAQBXtA+pnKLoMgPk7l1MChBBCJE/CgxDio/lSOqqfmgSHOO/fHiq8InPiFZmT3f7fA2BCDPmt7xrCRD6r+zyKyEmxV0eoIy9gYnGbmOjiaGOKvNqqQaUKRlEcAJNkryoBQggh3k7CgxDio/iyO6ofjwSHOB+7PbSYcjusELfDCrHhWYuEO6htUHTW6HQZ4japfTGzOIWiqFAUexRdBnS6jCi6DCiKLSQyf0IChBBCJO3jzjoTQnyV0mJH9X1IcIiTGu2hta6OJqoBis4xbqMqGkWxQKVSUKtfYGJ6DzPzc5hb7sXccgtm5ocxMb2CWu0FqjBAX87YAKFycsK8a3ew+PB5FUIIkRbInQchxAf5Wjuqb5LgECf12yPuadY6bV402jygCketCkSlDkCtDkSlfoFKFYPKxA+1iZ9hf0WxRKd1Jia6nNyBEEKIREh4EEK8N+mo6klwiPM5tEdCKlCs0SnWoMuBFgAdKlUwKnXgqzARgEoVhEoVCUQbjlQeP0Q3vgLqQuUx79AdzfI1EiCEEF81CQ9CiPciHVU9CQ5xPof2MJ5aPwdCa49Om/fVthhU6pegvDaiVxWKOuYZyvVdqJpMN9yBMNFeQqUKM8yfQHFOjUoIIUSKk/AghHhn0lHVk+AQ53Nojw9niqLLFH+Tkg5NVDVUqnCUxX8ahjApi+uhVr3AhPv63QIPoiw6DTlKQ44ykL0UOOREpVIlvIwQQnzBJDwIId6JdFT1JDjE+Rza49MxRdFl1U+jfm0OBHWGEPPvStQ6f1TqQFSqGHh4Rv8TyzojSvbS+iCRowzkdEVlZZ861RBCiI9EwoMQwmhd2llRr6YlqzeFc/SUhiIuKf9XSP48JowZaMvjJ1qmLQgjV/bk1+7/2NJZqhjZ34ac2UwYMzUErZZUeS9aNLSkTXOrr7498ubUX1PlmPXTX0ynJXrjBszcW2Pi8i3RmzZAVCQqBxPMyuQEr0vgfQl8bkBYANw+oP8BaDYDyv8EgBL8DAIeQrbiqMytP325hRDiI1EpipKWviISQnwCRYroH7jl4eGRyiURInGKTodKnbqrj79eBiUmSh8gvC6B10Xwvgw/LkHlVFT/+ullsG0IuNRE9fO6uHP43IAsLqhMzFKjCkKIr5ix/9bLnQchhBBfvNQODm+WQWVqoR+qlKMM0Cnhzjod2Dnp50i8ooT4wuwaYGqJkq04ZC+tfz17aciYR+ZPCCE+CxIehBBCiBSmqtQZKnVG0b42VybwMVjaQWQwPPpP/xMrnT1K7NyJ7KUhRylUto4JziuEEJ+ahAchhBAilahM4v4ZVuUqhzLqDgQ8AO+LcfMnnl6HiJfgeUT/84qSPpv+zsQP81CZW6V42YUQXycJD0IIIcRnQqVWQ+Z8+p/S7gAoMRp4dvNVoLisDxR+tyHoCeiiwSyd4Xhlxwj9nYtKXVA5F0ulWggh0jIJD0IIIcRnTGVqDtlL6n/c9NuUqFB4chXCAgxzIRRFgatbIcQPyrUxHK/cPqRf8SlHacheRj9/4jOYIyKE+DJJeBBCCCG+MCoLG8j7TfyNigJNp+vvTDgXj9t+az+cXhr3u6Xda/Mn9P9V2aXAMrdCiDRBwoMQQgiRBqjUaihSR//zusLfgdpUP+zpyTX9sKa7x/Q/ryixKz8ZVngqhcrSLoVrIIT4Ekh4EEIIIdIwlUsNcKkBgKKNhme39HcnvC7pA4XvbQj2gRs+cGO3/qAyP8AP8/TH6LTw5Ao4FdUvQSuE+KpJeBBCCCG+EioTM8hWXP9ToR0AiiZMf0fC66I+VHhf0t+BiOV3G+bXBSsHlJG34uZYBPuCTWaZPyHEV0bCgxBCCPEVU5lbQx43/c8riqLE7RD8DKwzgmOh+A+qW9IEQnxRspWCHKVeDXkqA+md5YF2QqRhEh6EEEIIEc/rnX+VS02UER4QFWLYpmjC9KFCEwb3T+h/YtlmQcn+xvwJK4eULL4Q4hOS8CCEEEKIt1KpVPqnX8f+bm6NMtoT/O/o5054XQTvy/DMQ79U7M1/9T+vKBnz6INErQGoMudPhRoIIT4WCQ9CCCGEeGcqE1PIWkT/8+q5EoomHHyuv3qY3aunZAc8iPupPchwvHJpIzw8A8UbocpfNZVqIYR4VxIehBBCCPFRqMytIFd5/c8rSvgL/V2JJ1chY564nW/+C1e3gUNOeBUelKCncHKJ/mF2OUqDfXaZPyHEZ0bCgxBCCCE+GZWVg36p2FfLxRq4tgaHHJC/Wty2R//BsQVxv1tnQnnz+RPWGVOm4EKIREl4EEIIIUSKU7nUBJea8Tc65AS3jvrlYn1uQNhz/ROyb+037KJkyPXa07FLg3MJ/R0PIUSKkPAghBBCiM+CKserOwyAEh2pDxDel+OeQeF/FwIf6X+ubNEflDE3DDxnOIcS8FA/3MlEujhCfAryyRJCCCHEZ0dlZgk5y+p/6ASAEhGkDxOxT8j2ugjZShqOUXQ6mPstaDUovQ4aVnZSNGFgZiXzJ4T4CCQ8CCGEEOKLoEqXHgpU0/+8okRHxO0Q4guKTv+TIVfc9u0jwGP3q+dPlDIMe1LZZkHR6eQp2eKL8Tn8eZXwIIQw2oAxQdx7qDV6//x5TBgz0JbHT7T8Pj2UiEgl+YM+snSWKkb2tyFnNhPGTA3h7gPjy/8xtWhoSZvmVqzeFM7GHZGpUgZpjzjSHnpppz1iA4QFKs6SSe2Df6e4h9pNzXiJfGYv4M4h/c8rQSbO2BUpS4yvDl2kNYqpI6jMUWV1wsz9B5Tnz4netAE0mg+r4PswN8esuTuqTJmI3vA/lGc+KV8GQO1WEbMqVYk+fgzdmdOpUgZpDz2VY1bM27VP8esmKIcS7xn0QgiRUJEiRQAoWP4EHndijDqmeGFT/p5tj+f9GDr3CyIsPOX/qrG2UvHXjPQUyGtKx94vuXbTuLJ/bN06WNGniw2zloSycHl4qpRB2iOOtIfe19QeZqpoClrfprjtdUrYXqOY7XXyWd1HrYr/visKKIodii4juowVMOszGcXHB82iBRAV9UnK9lYWFph37Y7KyQnNgvkojx+lfBkAk+/qYFa/AdG7dqLd92/yB3wCqpy5MO/e46tuD1X27FgMHPzJzh/7b72Hh8db95P7dEKIj046RnGko6on7RFH2iNOSrVHtGLG9dBirPVpxdA742l4YRvlTp1modkqqDcSnXkBFJ0VKhWo1cGYmD4A/3toFsxH5eSE+Q91MbG8glrt/cnKmKioKDSLFqD4+GDevQeqnLmSP+YT0O77l+hdOzGr3wCT7+qkShmUx4/i2qNrd7CwSPlCfCbtkdokPAghPirpGMWRjqqetEccaY84qd0eYVobNNm/QVWtJzG2jdBENSQqohHRUZWJiS6CTpclrsMa5omp6hZq84fxzmFi6oFa/YS4YVOfwGfSYZUA8cpn0h6pScKDEOKjkY5RnNTuGIG0x+ukPfSkPeIUL2zKr+2s39iaDp0uG9qY4qDYAPoOa/TpO1DhZ9TVO77WYY3A1OwaZhYnsEi3HXPL7Zian8DE9CYqtS/wEcflfyYdVgkQr3wm7ZFaJDwIIT4K6RjF+Vw6RtIeetIeetIecWLb45m/cRPEdT4xaO5nQlX9l7gOq0qHNiYPOl16FEWFShWBickTTM2uYm5xBHPLLZhZ7MbU7CxqE09UqgDgAyakfyYdVgkQr3wm7ZEaJDwIIT6YdIzifE4dI2kPaY9Y0h5xXm+PP1cZP9woQYfVPAMx0eWJjqqLJrIpmqgaxESXRBuTA0Vn/Wr+RAgmpg8xM7+IueUBzC03A6+vJqUB3uHPw2fSYZUA8cpn0h4pTcKDEOKDSMcozufWMZL2kPYAaY/XvdkeUZp3a4+kO6xmKLosaGMKERP9DZqoBkRFNCY6qgox0UXRap1QFHPAHIjr5Jqan8XccgtqtdfrV+GtgeIz6bBKgHjlM2mPlCThQQjx3qRjFOdz7BhJe0h7SHvE+VjtYXyH1RKdzhltTDFiNFXRRDZBE1kHiHvKtVoVhEoVjUK6uG0mD1+bP+GBSv2MBPMnPpMOqwSIVz6T9kgpEh6EEO9FOkZx0lLH6ENIe8SR9oiTFtvj/TqsKsAy3hZN1PdoIuug6BwM29TqQFSqyFfzJ65hbnEUi3RbMLPYhanZGUxM7qBSPYeo8M+iwyoB4pWvKEBIeBBCvDPpGMVJix2j9yHtEUfaI05abo+P02FVoyj2gIlhS0x0STRRNYnRlEIbkxNFp1/1Sa0OxcT0EabmlzC3PIi55SbM2IFucTuU0+slQEiASDESHoQQ70Q6RnHScsfoXUh7xJH2iPM1tMen6bCaougyo9UWJCa6Ipqo+v9v777DoyjXPo5/Z0t6CCFIEQigNOkoVbo06dJRQEGKHpCiiIAFOBx4BRUEBUQ5cvAELHRpgoIH6aIRUAGB0KRDEgikbZ33j0l2ElJYIGXD3p/rypVkZ3d25vkt5L535pnFkvQMVksz7LbqOBwPo6q+KIqKwXAdo3Ic55ZP9YK1TBmMpkMp8yicObA97pEGIoUXNBDSPAgh3FahvFEKoxTeUBi5Q/LQSR46b8ojbwpWX1RnSRz2atitTbEmd8WS3Amb5Unstio4rSX1grVna0zmvzD57Cfd/ArDeRTDJcCSC9unkQYixQPeQEjzIIRw25RxwVIY4V2FUXYkD53kofPGPPK+YFVADcTpLIPDXguns5ResMbehLr9cfpXJ23zYDQfwsd3B77+a/Hx3YjJvBej8RiK4RqQc68VaSBSPMANhDQPQgi3/X3BIYWRFxZGmZE8dJKHzpvz8JiC9esNqI+/gnH8t2kKVieqMwynMxgAxRCP0fQ3Jp+D+Pj+iI/fasy+WzCZf8FgPImi3OB+TnmSBiLFA9pASPMghHDbv2bFS2HkpYVRWpKHTvLQSR6eXLAasNsaYrN0wJLUDaulOXZbDRyOUqiqX8r8iRsYTacw+/yKj98WfPxWp1wmNpWDu/lAO2kgUjyADYQ0D0IItyUlS2HkzYURSB5pSR46yUPn+QWrD6qzBA57VezWJtr8iaTO2CyNsdsew+kojqqaURQHqhrkepTRdAIfv7UYTUfc3gxpIFI8YA2ENA9CCI8lhZHOEwojyUMneegkj4wKXsEagNNZGoe9JjZrC6zJ3bAmtwc10HUPRYlFUayknUeBEo+P7wZM5j0YTX+hGK4CtnRrlgYixQPUQEjzIITwSFIY6TyhMJI8dJKHTvLIWsEuWBVUtRBpGwW7rQHW5NY4HPo6DIZYFEMCRtM5TOZD+Pj+Dx+/NZh9N2My78dgjEJRYnF8/500EPDANBDSPAghPI4URjpPKIwkD53koZM87uzBKliNqGoYqAGuW5yOklgtLbDbauJwlEZ1+qfMn4jDaDqN2ScSH78f8PFbjWH7OBwfPYeplAVjm7Y5s2936cHKI/9I8yCE8ChSGOk8oTCSPHSSh07ycN+DXbCaUZ3Fcdgfw25tjNXSBUtSF2yWJthtVdPNnzAYYzBc3Iq6dhLmTp1dRyAMxlMYDBe4/XSn3PJg55E3pHkQQngMKYx0nlAYSR46yUMnedw97ypY/XE6S+Gw10gzf6IDNmsD7PaKOG4V009hatMGk/kgZt9dKEq8aw2Kch3FcIXcaii8K4+cJ82DEMIjSGGk84TCSPLQSR46yePeeW/BqqCqwTgd5XDYHsdhr+6aRG1q8xRqiQY4naGoaojrEUbTCXx8t6d8/sR3mMw/YzCeQFFi0C4Ze/+8N4/7J82DECLfSWGk84TCSPLQSR46yeP+ScGqc3y/BfvW7RhfXY2z+SzSlqSq6ovqDEBRwGC4idF0BrPPb/j4bU1pKH7AZI7EYDyNotzkbj5/Ii3J495I8yCEyFdSGOk8oTCSPHSSh07yyDlSsOqyuoyrw14Lq6UzlqSu2CxNsduq4XSUQFV9UBQnBkMsRlMUZp/9+Ph9h4/fGozGY2nW7P5rQ/K4e9I8CCHyjRRGOk8ojCQPneShkzxynhSsuuw/B8IPp/NhHPbq2KzNsSY/gyW5IzZrI+z2SjgdRVFVI4piQ8XsepSixOLjtw6Teb9b2yB53B1pHoQQ+UIKI50nFEaSh07y0EkeuUcKVp37HySngBqE0xGOw1YHm7UV1uTuWJPb4nSU0u9liEVRkkBJTvdos8//MJn3YTAezzB/QvJwnzQPQog8J4WRzhMKI8lDJ3novCkPQ8NGubLeO5GCVXfvn0RtQFVDAX3snI7yWC1P4bBVTXO/JAzGqxhNZzH7HEgzf+J7TOZfMRhPw7nfsc7/WPK4A2kehBB5SgojnRSqOslDI3no8jIPc9Nm8snHHlCw3nsDcTsTqvMhVLVomtvM2CzNsNuq4XCURFV9U+ZPXMdoOpkyf2Iz5mtzcP5fE5Q/l+DTtgr45kOp7CF5ZEWaByFEnpHCSCeFqk7y0EgeurzOw7ZzRw4UrHfP7nQC0kCklXMNxO1MOJ0lcdirY7c2w5rcFUtyJ2yWJ7HbKuN0PISqmlAUOwbbOdizEGXrm/gMeNaVh2K4hmK4BFhzcLuy4CF5ZMaU3xsghPAOUhjppFDVSR6afMtDtUDcYAgcC+Y6PN34Zz6Z9TqfzEpzH78+EPiK9nPSckj8OP06AkaDf89M1u2A5K/BfhRsh8FYBgJHgamCttx5C653SP8YYyVqNPoifR7Xv4ek/0LhiOz3xXYEEueC/SQYH4bA0WB+Ql8e2x5U/YPIUAoBW7VN2bcX282bmDt2AuCLzz7j/b/SXr0Hxj9WhefKhgPw3tG/WHb273TL5z5emxbFirl+//3GDeYcO0Giw86z4eF0LV0q3f0jzpzljxtxvFe7pjZcKQ2Ez/AR+Lw8HOvCBWCxZL/POS2lYPV5eTg+w0dgXTAf9e+zebsNaA0E4Moj9fecpYAaiFMNBGeZlNkPThTlJoohFoMhFsXfgaFCLXxeLoZ14QKM6l8YjRexW+vgcFRKWU8yiiEe1VmYHC+rb8vDtnJFzq7/HsmRByFErpNCVefVheptJA9NvuaRuAAcWnH4j4EBdG7nT1BwCTCE61/G8PSPMZS6bfnDma87aRmYG0DwNCj8b3CcgVtvaA1LKiUk3bpCi1Z25TF49DkSrn4A8e+Bmpz5c6RyxoFlk9bIBE0Ex3lI+CT9fZSg9Ntteizd4rTveBsqV6FMgD/lAgNcX6X9/fVVoVA2ICDd8hJ+fq7lNqeT1w8eon+5srxXqxZTDx/hdHyCa/mRuJssjDrJkEfLp9sGOQKhy70jENkxoKqFcToewW6ri+1mg3R5qIZCOJ1BOJ1F9EcYL+Hjuy1l/sQWTOZfMBhPoSg3AOf9b1KaPMy9et//+nKAHHkQQuQqKVR1Xl+opiF5aPI1D+svkPwdAF2e9mXMsCBGj0si3vAuhFbI+nEhn4MhMPt1q05QE/WjDIYwMNcB649gjwJzNe12/4GuoxZp8xj8ajSJMZ+C/wtg23fnfVFvaEcaFDNQFZK+BFO19Pfx7QABg7JdTeo73IaqVVnRoD7+O37K9H7BZhPrmjXJcj2nExK4kmwh2GSidIA/dlVlX0wM5YMCSbDbeePQIUZXqkil4OCMuyJHIFzy5ghE9tLmYXxpiZZH2gYYB6rqh6Ikaw2D4QZGTmmPVY2oziIpn6AdpjUdaiCg3N1GpOYxagxK6dI5tWv3TI48CCFyjRSqOq8vVNOQPDT5mofzFljWg08zAHp28mfOZ/Fs/p9Fe4c+SwZQAu68fsUAAUPT36be0r4bS6a5n9aE3J5HYpIJgl4H40Pu7Y+xbErjANgOgKmifqqV67my2y+d4/st8NdRCnfvkeU73sGm7N97NaQpDlN/MinaT1P/PELl4GB6h5fJ8vFyBEKXP0cg0ssuD6ejAtbkLliSO2OzNMZuq4LTUSxl/oQDg/EaJvNxzD578fXbiI/ft5jMkXe/ERYLtpXfoJ7cjXpwtfbd6bjz43KBNA8FiKqqOFMnV6lZ/6FLvU/qz2l/FzknPj6eBQsW5Os2OBxprlGdzWsi7f3s9rwpUKRQ1Xl9oZqG5KHJ9zwS50HAMCpX1ArulRuS9DxujoWYVhDbCRI+1I4gpHWjP8Q8BbHPQOJiUG3a7UkRENMWLD9ovytG/TGOK2A7BD5PgUE/5YOkJRhutOPEr08xbNjLvDjqiHt5WLZoz5W07Lbbt8Gtt7RTsSybb1u2AWI7aPsW9wrY089pSMv511+0b9aUOuMn0HrPPj45EYUtzd/SWKuV9j/t4IktP9B++w6+PPt3uv+DywUGUCbAn/NJSZxOSMCkKDxZtCirz53n97gbTK5eLbOnTUcaCJ2nNxDa/IkAnM7SOOy1sFlbpnz+RHts1vo47BVwOougqgYUxULaz5cAJ2bf7zCZ95DdRGyD4Tzmq/NhUTf4+mXt+8wnUP/ckEt7nDWvP20ptSA3Go2ZLo+Pj+eDDz7AYrHw7rvvum53Op3Mnj2bq1evkpSURLt27ejUqZNr+ZIlSzh48CCKolCrVi0GDhyYYd0JCQlMmjSJLVu2YLPZaNasGeXLl+fatWtcvHiRUqVK0adPHxo0aOC6f0REBF9//TWXL19m/vz5tG7dOsN6x44dy6ZNmyhSpAhdunRhyJAhPPRQxndvoqOjmTp1KhUrVmTkyJGu25OTk5k+fTqqqhIbG8uAAQNo1KiRW/t9N+x2O6Y7vHuTH7LbrgEDBrB/v/6Jlb169XL9fOTIET7++GMeeughEhMTeeuttwgNDQXg3LlzvPfee4SGhnLjxg3eeOMNSmdy6HHDhg0sXLiQEydOULhwYbp27UpSUhJXr17lxo0btGjRggEDBhAUpL2Dtnv3btatW8f69eupVq0aq1atQlHSHw6Nioqic+fOOJ1OmjRpQvfu3enYseN9j1N28r0wSiGFqkby0EkeaEW1sSL/GFyJy6fMHDsM6zZbwAwYikPAMDAWA+tOSFoKjstQaKb2WFN5bdKzEgSWjZD0H1DjIPBVrUEgCZzX0j+f6oCEmdrRgcBx+u2KmYfLPct7Uxvx064/WfTpB6C8AiERdz4tyhmtPZfjin6b/aS2LUGTIWkxJLwHSjD4ttCW+3YGcw1tPkTifIgbSWLC10BIhtU/GhTIuIAAQvo+y7eXL7Nw+XJu2GxMrKrNk6gWEkLDsDCMBoX/nj7LzKN/YXM6eaF8OW2YDAbm1KnDnOPH2XjxIu/XrkWy08HsY8dZUPdxCpnNGZ4zM3IKk87TTmG6cx4KqloI1VEIpyN1bosjZS6EXnMqShwGw01UJQls+uvCaPodRUlOmV9hx2Q+lHEKRdwlWDoYtf/nKNXvrRa7F55XueWR69evs3r1aiIjI5k+fbqryEtr48aNHDt2jK+++opu3bqlW/bpp59y8OBBli5dys2bN3nqqacoUaIEdevWZd26dSxbtozvvtPOJX366acJCwujc+fO6dYRGBjIrFmzuHTpEpGRkbz55puUL6+9wJKTk5k1axbPP/88Q4cO5fXXXycoKIh//OMf7N+/n8uXL/P+++/TvHlzzGn+E4qKimLbtm0A9OnThzFjxmTYL4fDwYoVKzh+/DhbtmyhYsWK6ZZPmzYNm83GzJkz+fvvv+ncuTPr168nPDw82/12h9VqZcuWLfzwww8MGDCAevXqufW4vLB//37WrFlD9erV6devX6b3KV++PO3a6e96VKlSBYCbN2/y4osvMmfOHBo2bMjcuXMZO3Ysixcvxm63M2zYMIYMGUKPHj1YuXIlw4YNY/369Rma1k6dOhEWFsbAgQNp0qQJb775pmvZwYMHGTNmDN988w1LliyhXLlyNGvWDF9fX7777jsOHz7Mt99+yzPPPJNunYsWLXIdffrss8+ybJRzSr4XRimkUNVIHjrJA3BcBMsOXh75IWOGBdGluy39clNl7Qu0CcWOy2D9QSvSjcXTX73IXA0cpyF5AwSMhMDXwP95rfFIK+FDwAmFPgKDfupQjapB/GfuEE6csvPlxhIQkAAJ74NtJ/g+nf1++PcDn9ZgSPNcpke1L9CaoLgBYP2f3jykXhHKVFm70tKt17h8bhNQJcPqG4SFaT8c/4tabdtx4sQJVv/xB+OqVMZkMPBUcf15a4SE0GXnLpb/fc7VPABUCA5i3hOPA5DscNBv788MebQ8NQsXzn7fbiMNhK7gNRC3M6KqYenXpwZjtbRAUZJJOxfCaDyHYojHyOmU+2U2U0LVHrP+bdSq7VEMufv3PZXXnbZ08OBBJk2axOzZs6lduzYLFizItHFQVZVy5crx2muvZViWkJDA4sWLadiwIQCFChXiscceY8GCBaiqyrx586hbty4mkwmTyUS9evWYN29eltuUWswZDHocfn5+vPXWW9SpU4dFixaxc+dO17LixYvTpEkTzpw5w9dff51uXYsWLXIdCciqSLTb7TzxxBO8+OKLGZadP3+eNWvWuI40hIeHExYWxqJFi7Ld7zs5f/48H374IRMnTsRkMjFr1iyPaBzi4+NZunQpo0aN4ujRo0yYMCHLxgGgcuXK9O/f3/WV2jQtXbqU+Ph41++NGjVi9+7d/Pbbb2zcuJFTp065xrRRo0acPHmSTZs2Zfocqa+D248g1K5dm2nTpnHp0iXGjx+fbln37t0B+PDDD0lO1q9KEhUVRWCg/g6eNA55RwpVneSh8Yg8LBt5qEgMv+0aTstWz3HsyB7t9oSPtKMMtzPX0L47YzJfn6kGYNXmMyiGjI1DUoS2PPgDvXFwnMs8D9Mdnut2xuKgZDHxNHVehZpFUZeyX4+UuXHHp3F8v4XahUOw2u3EN844SdpsMFA9JITobArI947+RXE/XxQUBv28n0+jTt7xedOSU5h0nn8K090yoTqL43SkHU8Vu602dttjOJ1ajZrVSx1UiLsIp924sEAO8YojD4mJiWzYsIGff/6ZGjVqMHbsWEJCMh6mTEtRFKpVy/ycxAMHDnDz5k2KFtU/ubBo0aJs2bKFY8eOcfbs2XTvToeFhXHmzBnOnDlDuXLl7mrbW7VqxYEDB1i/fj1NmzZ13f7aa6+xe/du5s2bR9euXSlUqBAnT54kMDAw02YoLV9fXypWrMj58+czLNu9ezd2u52wML0zLlq0KNu3b6ddu3ZZ7ndycjJ+aS5TB9opTjt37mTz5s2EhYXRq1cvypbN+J/Nvn37WLt2bZbb26hRI7p27ZrtPt2to0ePsmrVKqxWK126dKF///5uPS4yMpKPP/4Yu91O7dq1eeuttyhfvjw7duygcOHCrtOdUsfvp59+co1z6rilft++fXuGo1F38uSTTxIQEMDBgwc5d+4cZcpoE+7q1KlDTEwM27ZtY/HixQwfPhyAf//734wePZply5Zlt9oc4RGFEVKoppI8dJKH7h/DR7vy2L8kEXyma6cxBY7SroakqumrFGeMNqnZlHLaRWbLDaVAKawtc17TGwjbAW1eQdC/9Mc4/qZo4Ab+M3cSx0/aGDr2pp6HmtI0mKq7tzOOq2B4KPOqynFO++7TINPt9veNJQlo2KAOAFaHA4OqYky5j6qq6d7AuXrsGGXCwijepy/24GDsWzanW37NYsnyiMKWS5fZcS2asZUr8UnUSdY2bUyPXXsIDwygfcmSmT4mM3IEQlfwj0DciYLTWQqcpVDVEAw+bjQGt67c+T45xCuahyFDhlC5cmXeffddfHx87nt9sbGxAOlOFzKbzTgcDhISEjJdBhATE3PXzUPque0xMenfialWrRodOnRg48aNfPLJJ4wfP55FixYxevRoVq5cedf7lCqrfYuNjc12v+Pi4jI0Dx999BH79+9n7ty5mc65SNWwYUPX0Yy8sGvXLt555x3mzp1LzZo17+qxDRo0oGPHjly8eJF58+bx0ksvsXHjRmJjY9ONS+rrLCYmJsO4pX5Pvf1uGAwGAgICSExMJCYmxtU8gNZQbt++nUWLFtG7d2/i4uIICAig5F38cbpXHlMYSaEKSB5pSR66O+Zh+wNujYPA8eDbEpzXUxqL8aD4g2WrdoQi+J9ao+E4p11GNXiGVpjHzwbLGggYAf59tfsawyEh9RPnnPgafqdPv+78efQWA59vCz4dtTkWqJD0Dfh1B3Ot2zYsk4t+JH0JiZ9o9w98FW4MAkMJCJ4CmLUPlTNVB9+U88DjBminYQW+TmCgHw2rrMRasSXnY1qRlJTE018vJ9zPl/80qM93ly7x3tFjvF+7JnWLFOFsQgK7rkXz0eN1sG/ayH8uXOSbX39jTpVKVAwO5uD1G5yKT2BpwwYZNvN8YiLTjhzhg9q12HzpMg/5+mJQFCoEB7HhwqW7ah5AGoi0HvwGIuU5VP873wkguHiOPm92vKJ5iIiIYNu2bUycOJHSpUvTu3dvSpUqdecHZiH1nf3ERP0/X5vNhtFodJ0icvsyIN27+e66fPkygGsuRFpjxozh+++/Z+nSpTRs2DBHCsWs9q1IkSLZ7ndmR3LGjBnDwYMH+fjjjzEYDHTr1o1atW7/o5D3mjRpQkREBMuXLyciIoK2bdvSsmVLtyZv9+nTx/VzWFgYr776KidPniQ0NJQzZ864llmtVtd9UscrISGBoKAg1+uhSJE0Vxxxk9VqJSYmBqPRSHh4+g9uqlChAl27dmX16tXMnTsXm83G6NGj7/o57laBKYzygBSqOslDU6DyMJYDn+ZasZ/8jXY0IegdvZg31QRzTe1qRsZHwBCqzWNInWdgfAjw1z7TwXEBHFHaVxoWwGIvxIiJVvDtqU26tu3S5i74tNI+iyGV7XdIXgvOq9rv8dO1T7s2VdCeA399zoN/P0j8HG4MTPlMifratqdevtWvNyR9gxI3gEdKl6Na1cb8+Msozl9SMRgMBPqYXZOYHw8N5fHQwrx64CAVgoII8/Hl8/p1qRgcjOP7LTStWYt9Vaow+MABKvn5UsLPj68aNaRUQPoiz+Z08sah3+kbHk6DsDC+OH3GtczPYCQqKZ57IQ2EzhsaCNVZFNXpD0pSFqcuKRBSEsrn3ZuwXtE8GI1G2rZtS9u2bTl16hQRERFER0fTsWNHmjdvnm6ugTvq1KlDoUKFuHr1quu2mJgY6tevT+XKlSlbtmyGZWXLls30lJ07+emnnzCbzfTt2zfDsvDwcHr16sWXX37JmDFjXBO070fjxo0xmUwZtr9Zs2bZ7vftRx1S1a5dm9q1a3P9+nVWrlzJ559/TuPGjenUqZOr0dqzZw8rVmT9ketNmjShR48e971vaZUuXZrXXnsNq9XK5s2bGTt2LBUqVKBXr16UKFHCrXWkTjQPDAykefPm/PHHHyQlJeHv7+86qtC0aVMuXLjAxo0buXr1KkFBQa6jSM2aNbvr7d6xYweqqtKhQ4dMm49Ro0axYcMGVq1axbPPPpvrRx0KVGGUy6RQ1UkemgKRR9Bb2pfr94lZr8hYTPu06Kz4D9C+UoXpc/UyzSPgBe0rK+aUZoVJGZf5ttO+XL+31r6y4teFwCJd0+Xx5zE75cK1U3nX9+qJmnKKaXE/P2bVqZ3lqsr/fohPe3THvHgxto0bsixYPzp+Al+DgZcraM1VmK8vFxKTAHCiUvQ+zpOXBkL34DcQBuy2xzH57E6dHp1Gym+dp+XZZGltiwqI1EuGbty4EYBZs2YxduxYAH799Vf69u1LVFRUdqsA4JFHHmHChAn861//Ijo6mpEjR9716SNBQUEMHTqU3bt3o6oq8fHxHDlyhOHDh6MoCqNHj2b//v1YLBbsdjuRkZGuZZlJvRLOxYsX090eERFBVFQU06dPp0KFChnuDzB8+HACAgLo2rWrq+hNvdZ0dtf9z0qZMmXo2bMnu3btArRLjF67do2hQ4dmu993EhoaytChQ5kzZw4lSpRg0qRJ7N27F9DO4//www+z/MqqcYiIiGDIkCFYrVZOnTpF3759XZdRHT9+PDNnzrzjdvn4+NClSxfmzp1LmzZt+PTTT/nvf/+b6X337t3L9u3bXb9HRkbSrVs3ypQpw4ABAwgNDWXPHm3y4b59+2jQoAF169alU6dOVKxY0TWm+/bto1y5clleLjU130uXLqW7PTo6mtmzZ1OnTh0mT56cbllq1iVLlqR///4YDAaGDh2abtntP9+vAlEY5REpVHWSh0by0D2Iedxp0u7Oa9dYf+EiM2rVdM2jaFHsIa5atAtaXEpKpkUxNz8ALwsyiVr34E2iTs/pLI3d2jjd1coA7YhDHl+mFQrQkYekpCSOHz/uOo3n9OnT/P3334DWWBw/fpy4uDi31+fv70+vXr3o1atXlgXVkSNHXO+I7927l8WLF7uuUDRs2DCsVitvvPEGycnJvP3229SvXx+Ajh07Eh8fz7hx4zCZTPTu3TvD5TNBuwLR7NmzOXToEAAjRoygZcuWhISEcPnyZUJCQli7dq2rcTh+/DjLly9n27ZtTJ8+nfr169OmTRuGDh3qutrOokWLXA3WmjVrsFgsvPDCC0ycOJGoqCjmz59P9erV2b9/P9988w0AW7ZsoWTJkvTsqV3G7p133mH69Om8/fbbxMTEMH/+fNdcjez22x0Gg4HmzZvTvHnz+y5kz58/z4kTJ7Bardy6dYsTJ04QHR0NwIkTJyhWrNgd1pBelSpVmDx5cpbbdfPmTebOncuPP/5IjRo1SEpKYurUqQAEBwezdOlSZs6cyc6dO4mLi2POnDmAduRryZIlTJkyhVOnTnHt2jUWLVqU6WlSS5cu5csvvwS0prhnz55Uq1YNm83GlStX6NevH3369HE99osvvnBNWL9y5Qq9e/fmpZdewmw2U6JECQ4fPszSpfoVVEaPHk27du3u+3MepDDSPYiF0b2SPDSSh+5BziOrd7ztTiczjvzFlBrVKJ7mqPxTxYoRGXudPnv2Eh4QkO0nTLtLjkDoHvQjEE5naWyF6+PTo5k2OTq4OJRvmKdHHFIpak6+FSk81tatW1m3bh3FihXj7bffzu/NEQVM1apVAahcfxdnzzukMErxIBdGd0vy0EgeuoKQR6c2vsz6ZwiW92e6Tlu6W8a27TB37JTuFKYbViuFc+ACLe5SwsviM3wE6qVL+dNAAPj64vPycJSSJfOtgYDM88hruZWHUro0vuPG3/mO9yj1b/2RI0eyvV+BOW1J3J/WrVvTt29fatSokd+bIgowfz8pjFIVhMIor0geGslD5015ZHbKTF42DiCnMKX1oJ/C5AmkefASv/zyC6dPn6ZLly75vSmiAHtnbJAURnhXYXQnkodG8tB5Yx5SsKaQBsLFI/LIJdI8eIl69erRr1+/LCdtC+GO8FJGKYy8sDDKiuShkTx03pyHFKwppIFw8Yg8coE0D0IIt015/5YURl5aGN1O8tBIHjrJQwpWF2kgXDwijxwmzYMQwm1Rpx358rxSGGnyuzBKJXloJA+d5KGTgjWFNBAuHpFHDpLmQQjh0aQw0nhKYSR5aCQPneSRkRSsKaSBcPGIPHKINA9CCI8lhZHGUwojyUMjeegkj6xJwZpCGggXj8gjB0jzIITwSFIYaTylMJI8NJKHTvK4MylYU0gD4eIRedwnaR6EEB5HCiONpxRGkodG8tBJHu6TgjWFNBAuHpHHfZDmQQjhUaQw0nhKYSR5aCQPneRx96RgTSENhItH5HGPpHkQQngMKYw0nlIYSR4ayUMnedw7KVhTSAPh4hF53ANpHoQQHkEKI42nFEaSh0by0Eke908K1hTSQLh4RB53SZoHIUS+k8JI4ymFkeShkTx0kkfOkYI1hTQQLh6Rx12Q5kEIka+kMNJ4SmEkeWgkD53kkfOkYE0hDYSLR+ThJmkehBD5RgojjacURpKHRvLQSR65RwrWFNJAuHhEHm6Q5kEIkS+kMNJ4SmEkeWgkD5035aGUKJkr670TKVhTSAPh4hF53IE0D0KIPCeFkUYKVZ3koZM8NHmZh7lXbylY87tglQbCxSPyyIY0D0KIPCWFkUYKVZ3koZM8NHmdhxodLQWrJxSs0kC4eEQeWZDmQQiRZ6Qw0kihqpM8dJKHJj/ysK1aIQUrHlKwSgPhkiEPH5982Y7bSfMghMgTUhhppFDVSR46yUOTb3lYrVKwppAGQudpeZh79MqXbbidNA9CiFwnhZFGClWd5KGTPDT5nocUrC7SQOg8Ko+iRfPl+W8nzYMQIldJYaTJ98IoheShkTx0kkcaUrC6SAOh85Q8bCuW58tz306aByFErpHCSOMphZHkoZE8dJJHJqRgdZEGQucReVy+lC/PeztpHoQQuUIKI42nFEaSh0by0Eke2ZCC1UUaCJ0n5OEJpHkQQuQ4KYw0nlIYSR4ayUMnebhBClYXaSB0npBHfpPmQQiRo6Qw0nhKYSR5aCQPneSha9XsDpe+lILVRRoInSfkkZ+keRBC5BgpjDSeUhhJHhrJQyd56P4xMIAOrfzvfEcpWF2kgdB5Qh75RZoHIUSOkMJI40mFkeQheaQleehS89i0Lcm9B0jB6iINhM4T8sgP0jwIIe6bFEYaTyuMJA/JI5XkoUubx7YdVvcfKAWrizQQOk/II69J8yCEuC9SGGk8sTCSPCQPkDzSuu88pGB1kQZC5wl55CVTfm+AEKLgeLScMd3vPTv70a9HAMtWJfLTHitVK+X9fykVyhuZMi6Yvy84+GBBAmVLG+/8oBzm76fwztggwksZmfL+LRwO8mUsJA+N5KGTPHSZ5VH6Ye09VKV4ibtal239t5h79MJnxCvYVizPl+vvO48cxlaoEOaOnaBQIZz79ub5NuB0YFu5AnOv3viMGoNt1Qqw3sXRnBziLXnc7es0tyiqqub9WxBCiAKlatWqABw5ciSft0QIIXKW6nSiGOREDFEw5Obr1d2/9fKvRQghhBBeSxoHUZB4wus1/7dACCGEEEIIUSBI8yCEEEIIIYRwizQPQgghhBBCCLdI8yCEEEIIIYRwizQPQgghhBBCCLdI8yCEEEIIIYRwi3xInBDijhwOB6BfA1oIIYQQD5bUv/V3IkcehBDiHjgcDrf/o/UGMh7pyXhkJGOSnoxHejIeGXnqmMiRByHEHZUuXRqAbdu25fOWeI5WrVoBMiapZDzSk/HISMYkPRmP9GQ8MvLUMZEjD0IIIYQQQgi3SPMghBBCCCGEcIs0D0IIIYQQQgi3SPMghBBCCCGEcIs0D0IIIYQQQgi3SPMghBBCCCGEcIuiqqqa3xshhBBCCCGE8Hxy5EEIIYQQQgjhFmkehBBCCCGEEG6R5kEIIYQQQgjhFmkehBBCCCGEEG6R5kEIIYQQQgjhFmkehBBCCCFykdPpZP78+SQnJ+f3puQ7VVXZv38/X331VX5virhHcqlWIUS2YmNjmT59OoGBgURHRzNy5Egee+yx/N6sPBEdHc3UqVOpWLEiI0eOBCA5OZnp06ejqiqxsbEMGDCARo0aAVqBMHv2bK5evUpSUhLt2rWjU6dO+bkLOebUqVNMnTqVQ4cOERoayuDBg+nXr5/XjofFYmHKlCls3boVs9nMsGHDGDhwoNeOR1rz5s3j/PnzzJgxw6vHY/Xq1UycONH1u8lk4vDhw149JidOnGD8+PG0bt2aYcOGYTKZvHY8/vzzT3r06JHh9gEDBvD666979pioQgiRjYEDB6pz585VVVVV9+zZozZo0EC9efNmPm9V7rLb7epXX32l/vOf/1QrVaqkfvTRR65lb731lvrGG2+oqqqqZ8+eVWvWrKmePXtWVVVVXbBggdqvXz9VVVU1Li5OfeKJJ9Rffvkl73cgh1mtVnX48OHqoUOH1NOnT6uDBw9WK1WqpP7xxx9eOR6qqqr/+c9/1F27dqlHjx5Vu3fvrtarV09VVe98faS1cuVKtVKlSur48eNVVfXu8Vi1apU6f/58NSIiQo2IiFC//vprVVW9d0yOHz+uPvHEE+rixYvT3e6t47F9+3Z1xowZ6vz5811fjRo1Us+fP+/xYyKnLQkhsvTrr7+yZ88e1zse9erV49atWyxbtiyftyx32e12nnjiCV588cV0t58/f541a9a4xiM8PJywsDAWLVpEQkICixcvpmHDhgAUKlSIxx57jAULFuT59ue0U6dOMWjQIGrWrEm5cuUYMGAAAIcOHfLK8QDo0aMHjRs3pkqVKlSpUoWXXnrJa18fqXbv3p3utBxvHw+AgQMH0r9/f/r370+fPn28dkxUVWX8+PEULVqUF154wXW7t44HQJUqVXjjjTcYPnw4w4cPp1WrVtSqVQtVVT1+TKR5EEJkaceOHQCEhYUB2mH3woULs3379nzcqtzn6+tLxYoVM9y+e/du7Ha7azwAihYtyvbt2zlw4AA3b96kaNGi6Zbt27evwJ/nXLlyZerWrev6/fTp0zz88MPY7XavHA+A4OBgVFXl888/Jzk5mTZt2njt6wPgyJEjHDx4kH79+rlu8+bxAFAUhR49elCzZk06duzI2rVrvXZMDh06xOHDh+nRowdfffUVEydOZMWKFezatcsrxwOgePHiKIri+n3ZsmX07t27QLxGpHkQQmQpNjYWALPZ7LrNbDa7bvc22Y1HVsscDgdxcXF5u6G56OzZs6xcuZKFCxeSmJgIeO94REREYLFYuHDhAu3bt+fatWuA943HuXPnWLNmDcOHD093u7f/eylTpgwvvfQSM2fOpEiRIowfP95rx+T3338H4NFHH6Vfv34899xzvP3220RHRwPeNx63S0hI4Oeff6ZZs2YF4jViyrNnEkIUOKGhoQCuIhHAZrNRokSJ/NqkfJXVeBQpUiTLZUajkZCQkLzd0Fxy+vRp5s2bx+LFiylWrBgHDhwAvHc8nn/+edf3Ro0a8cUXXwDeNx5z5sxh+/btrFmzxnXbhg0bsNlsgPeNR6q6deu6jti1aNGCli1bsnjxYsD7xiT1XXF/f38AatSoQWhoKJ999hngfeNxu02bNtGqVSuMRmOB+DsjRx6EEFlq3rw5AFevXgW0uQBxcXE0a9YsPzcr3zRu3BiTyeQaD4CYmBiaNWtGnTp1KFSoUIZl9evXx8/PLz82N0edOXOGVatWMXPmTIoVKwZo++et45FWUFAQgYGBtGjRwivHY9asWURGRvLrr7/y66+/AtCpUye2bt3qleORGX9/f0qVKuW1r5HSpUsDZHh33FvH43br1q2ja9euQMH4OyPNgxAiS3Xr1qVp06bs2rULgMjISAIDA9Od1+xNypQpQ8+ePV3jce7cOa5du8bQoUMJCgpi6NCh7N69G1VViY+P58iRIxlO5SiILBYL48ePp1SpUqxZs4YVK1Ywc+ZMLl265JXjce3aNT777DPXu3+HDx/GZrPx8ssve+V4ZMVb/72AdsrWkiVLsFqtgFY0x8XFMW7cOK8ck5YtW1K0aFF++eUXAC5fvkxcXBzPPvusV45HWjExMdy4cYPKlSsDBePfjXzOgxAiWwkJCUyePJnAwECuXLnCiBEjqFGjRn5vVq7bv3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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率分档WOE值分档IV值指标IV值LIFT值累积LIFT值累积好样本数累积坏样本数分档KS值
0telephone电话号码注册情况yes, registered under the customers name4040.40402910.41571130.37670.27970.09860.00390.00640.93230.9323291113-0.0390
1telephone电话号码注册情况none5960.59604090.58431870.62330.3138-0.06470.00250.00641.04591.00007003000.0000
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"        指标名称      指标含义                                        分箱  样本总数   样本占比  \\\\\\n\",\n       \"0  telephone  电话号码注册情况  yes, registered under the customers name   404 0.4040   \\n\",\n       \"1  telephone  电话号码注册情况                                      none   596 0.5960   \\n\",\n       \"\\n\",\n       \"   好样本数  好样本占比  坏样本数  坏样本占比   坏样本率  分档WOE值  分档IV值  指标IV值  LIFT值  累积LIFT值  \\\\\\n\",\n       \"0   291 0.4157   113 0.3767 0.2797  0.0986 0.0039 0.0064 0.9323   0.9323   \\n\",\n       \"1   409 0.5843   187 0.6233 0.3138 -0.0647 0.0025 0.0064 1.0459   1.0000   \\n\",\n       \"\\n\",\n       \"   累积好样本数  累积坏样本数   分档KS值  \\n\",\n       \"0     291     113 -0.0390  \\n\",\n       \"1     700     300  0.0000  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率分档WOE值分档IV值指标IV值LIFT值累积LIFT值累积好样本数累积坏样本数分档KS值
0property财产状态real estate2820.28202220.3171600.20000.21280.46100.05400.07260.70920.709222260-0.1171
1property财产状态building society savings agreement/ life insurance,car or other, not in attribute Savings account/bonds,unknown / no property7180.71804780.68292400.80000.3343-0.15830.01850.07261.11421.00007003000.0000
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"       指标名称  指标含义  \\\\\\n\",\n       \"0  property  财产状态   \\n\",\n       \"1  property  财产状态   \\n\",\n       \"\\n\",\n       \"                                                                                                                              分箱  \\\\\\n\",\n       \"0                                                                                                                    real estate   \\n\",\n       \"1  building society savings agreement/ life insurance,car or other, not in attribute Savings account/bonds,unknown / no property   \\n\",\n       \"\\n\",\n       \"   样本总数   样本占比  好样本数  好样本占比  坏样本数  坏样本占比   坏样本率  分档WOE值  分档IV值  指标IV值  LIFT值  \\\\\\n\",\n       \"0   282 0.2820   222 0.3171    60 0.2000 0.2128  0.4610 0.0540 0.0726 0.7092   \\n\",\n       \"1   718 0.7180   478 0.6829   240 0.8000 0.3343 -0.1583 0.0185 0.0726 1.1142   \\n\",\n       \"\\n\",\n       \"   累积LIFT值  累积好样本数  累积坏样本数   分档KS值  \\n\",\n       \"0   0.7092     222      60 -0.1171  \\n\",\n       \"1   1.0000     700     300  0.0000  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率分档WOE值分档IV值指标IV值LIFT值累积LIFT值累积好样本数累积坏样本数分档KS值
0personal_status_and_sex逻辑回归入模变量male : divorced/separated,female : divorced/separated/married3600.36002590.37001010.33670.28060.09440.00310.00490.93520.9352259101-0.0333
1personal_status_and_sex逻辑回归入模变量male : single,male : married/widowed6400.64004410.63001990.66330.3109-0.05160.00170.00491.03651.00007003000.0000
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\"\n      ],\n      \"text/plain\": [\n       \"                      指标名称      指标含义  \\\\\\n\",\n       \"0  personal_status_and_sex  逻辑回归入模变量   \\n\",\n       \"1  personal_status_and_sex  逻辑回归入模变量   \\n\",\n       \"\\n\",\n       \"                                                              分箱  样本总数   样本占比  \\\\\\n\",\n       \"0  male : divorced/separated,female : divorced/separated/married   360 0.3600   \\n\",\n       \"1                           male : single,male : married/widowed   640 0.6400   \\n\",\n       \"\\n\",\n       \"   好样本数  好样本占比  坏样本数  坏样本占比   坏样本率  分档WOE值  分档IV值  指标IV值  LIFT值  累积LIFT值  \\\\\\n\",\n       \"0   259 0.3700   101 0.3367 0.2806  0.0944 0.0031 0.0049 0.9352   0.9352   \\n\",\n       \"1   441 0.6300   199 0.6633 0.3109 -0.0516 0.0017 0.0049 1.0365   1.0000   \\n\",\n       \"\\n\",\n       \"   累积好样本数  累积坏样本数   分档KS值  \\n\",\n       \"0     259     101 -0.0333  \\n\",\n       \"1     700     300  0.0000  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for col in logistic.feature_names_in_:\\n\",\n    \"    print(col)\\n\",\n    \"    feature_table = sp.feature_bin_stats(data, col, target=target, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", combiner=feature_pipeline.named_steps[\\\"combiner\\\"])\\n\",\n    \"    _ = sp.bin_plot(feature_table, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", figsize=(8, 4), anchor=0.9)\\n\",\n    \"    \\n\",\n    \"    display(feature_table)\\n\",\n    \"    plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"7 评分卡模型转换\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mInit signature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mScoreCard\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtarget\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'target'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mpdo\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m60\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mrate\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mbase_odds\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m35\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mbase_score\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m750\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcombiner\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtranser\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mpretrain_lr\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mpipeline\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m     \\n\",\n      \"Base class for all estimators in scikit-learn.\\n\",\n      \"\\n\",\n      \"Notes\\n\",\n      \"-----\\n\",\n      \"All estimators should specify all the parameters that can be set\\n\",\n      \"at the class level in their ``__init__`` as explicit keyword\\n\",\n      \"arguments (no ``*args`` or ``**kwargs``).\\n\",\n      \"\\u001b[0;31mInit docstring:\\u001b[0m\\n\",\n      \"评分卡模型转换\\n\",\n      \"\\n\",\n      \"Args:\\n\",\n      \"    target: 数据集中标签名称，默认 target\\n\",\n      \"    pdo: odds 每增加 rate 倍时减少 pdo 分，默认 60\\n\",\n      \"    rate: 倍率\\n\",\n      \"    base_odds: 基础 odds，通常根据业务经验设置的基础比率（违约概率/正常概率），估算方法：（1-样本坏客户占比）/坏客户占比，默认 35，即 35:1 => 0.972 => 坏样本率 2.8%\\n\",\n      \"    base_score: 基础 odds 对应的分数，默认 750\\n\",\n      \"    combiner: 分箱转换器，传入 pipeline 时可以为None\\n\",\n      \"    transer: woe转换器，传入 pipeline 时可以为None\\n\",\n      \"    pretrain_lr: 预训练好的逻辑回归模型，可以不传\\n\",\n      \"    pipeline: 训练好的 pipeline，必须包含 Combiner 和 WOETransformer\\n\",\n      \"    **kwargs: 其他相关参数，具体参考 toad.ScoreCard\\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m           ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/model.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m           type\\n\",\n      \"\\u001b[0;31mSubclasses:\\u001b[0m     \"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sp.ScoreCard?\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"7.1 评分卡转换\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
ScoreCard(combiner=<toad.transform.Combiner object at 0x7fd2c5a62380>,\\n\",\n       \"          pipeline=Pipeline(steps=[('preprocessing_select',\\n\",\n       \"                                    FeatureSelection(target='creditability')),\\n\",\n       \"                                   ('combiner',\\n\",\n       \"                                    Combiner(min_bin_size=0.2,\\n\",\n       \"                                             target='creditability')),\\n\",\n       \"                                   ('transform',\\n\",\n       \"                                    WOETransformer(exclude=[],\\n\",\n       \"                                                   target='creditability')),\\n\",\n       \"                                   ('processing_select',\\n\",\n       \"                                    FeatureSelection(engine='toad',\\n\",\n       \"                                                     target='creditability')),\\n\",\n       \"                                   ('stepwise',\\n\",\n       \"                                    StepwiseSelection(target='creditability'))]),\\n\",\n       \"          pretrain_lr=ITLubberLogisticRegression(target='creditability'),\\n\",\n       \"          target='creditability',\\n\",\n       \"          transer=<toad.transform.WOETransformer object at 0x7fd2c5a624d0>)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
\"\n      ],\n      \"text/plain\": [\n       \"ScoreCard(combiner=,\\n\",\n       \"          pipeline=Pipeline(steps=[('preprocessing_select',\\n\",\n       \"                                    FeatureSelection(target='creditability')),\\n\",\n       \"                                   ('combiner',\\n\",\n       \"                                    Combiner(min_bin_size=0.2,\\n\",\n       \"                                             target='creditability')),\\n\",\n       \"                                   ('transform',\\n\",\n       \"                                    WOETransformer(exclude=[],\\n\",\n       \"                                                   target='creditability')),\\n\",\n       \"                                   ('processing_select',\\n\",\n       \"                                    FeatureSelection(engine='toad',\\n\",\n       \"                                                     target='creditability')),\\n\",\n       \"                                   ('stepwise',\\n\",\n       \"                                    StepwiseSelection(target='creditability'))]),\\n\",\n       \"          pretrain_lr=ITLubberLogisticRegression(target='creditability'),\\n\",\n       \"          target='creditability',\\n\",\n       \"          transer=)\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 逻辑回归模型转评分卡\\n\",\n    \"card = sp.ScoreCard(target=target, pipeline=feature_pipeline, pretrain_lr=logistic)\\n\",\n    \"card\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 训练\\n\",\n    \"card.fit(woe_train)\\n\",\n    \"\\n\",\n    \"# 预测\\n\",\n    \"train[\\\"score\\\"] = card.predict(train)\\n\",\n    \"test[\\\"score\\\"] = card.predict(test)\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"7.2 评分卡信息\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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刻度项刻度值备注
0base_odds35.0000根据业务经验设置的基础比率（违约概率/正常概率），估算方法：（1-样本坏客户占比）/坏客户占比
1base_score750.0000基础ODDS对应的分数
2rate2.0000设置分数的倍率
3pdo60.0000表示分数增长PDO时，ODDS值增长到RATE倍
4B442.2430补偿值，计算方式：pdo / ln(rate)
5A86.5617刻度，计算方式：base_score - B * ln(base_odds)
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"          刻度项      刻度值                                               备注\\n\",\n       \"0   base_odds  35.0000  根据业务经验设置的基础比率（违约概率/正常概率），估算方法：（1-样本坏客户占比）/坏客户占比\\n\",\n       \"1  base_score 750.0000                                      基础ODDS对应的分数\\n\",\n       \"2        rate   2.0000                                          设置分数的倍率\\n\",\n       \"3         pdo  60.0000                         表示分数增长PDO时，ODDS值增长到RATE倍\\n\",\n       \"4           B 442.2430                          补偿值，计算方式：pdo / ln(rate)\\n\",\n       \"5           A  86.5617           刻度，计算方式：base_score - B * ln(base_odds)\"\n      ]\n     },\n     \"execution_count\": 40,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 获取评分卡刻度信息\\n\",\n    \"card.scorecard_scale()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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变量名称变量含义变量分箱对应分数
0status_of_existing_checking_accountno checking account,... >= 200 DM / salary assignments for at least 1 year123.7185
1status_of_existing_checking_account0 <= ... < 200 DM23.0998
2status_of_existing_checking_account... < 0 DM-22.9661
3duration_in_month[-inf ~ 27)61.3498
4duration_in_month[27 ~ inf)7.2134
5installment_rate_in_percentage_of_disposable_income[-inf ~ 4)68.3050
6installment_rate_in_percentage_of_disposable_income[4 ~ inf)25.3142
7housing房产状态own58.4474
8housing房产状态rent,for free21.1485
9credit_amount[-inf ~ 1845)51.5913
10credit_amount[1845 ~ 3914)76.2287
11credit_amount[3914 ~ inf)4.5683
12present_employment_since4 <= ... < 7 years,... >= 7 years,1 <= ... < 4 years54.3246
13present_employment_sinceunemployed,... < 1 year23.7916
14purpose借款目的domestic appliances,retraining,car (used),radio/television79.5717
15purpose借款目的furniture/equipment,repairs,business36.0542
16purpose借款目的education,car (new),others18.7831
17savings_account_and_bonds... >= 1000 DM,500 <= ... < 1000 DM,unknown/ no savings account102.5976
18savings_account_and_bonds100 <= ... < 500 DM,... < 100 DM27.4615
19telephone电话号码注册情况yes, registered under the customers name64.8950
20telephone电话号码注册情况none35.5318
21property财产状态real estate68.0330
22property财产状态building society savings agreement/ life insurance,car or other, not in attribute Savings account/bonds,unknown / no property39.0842
23personal_status_and_sexmale : divorced/separated,female : divorced/separated/married63.0338
24personal_status_and_sexmale : single,male : married/widowed38.1292
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"                                                   变量名称      变量含义  \\\\\\n\",\n       \"0                   status_of_existing_checking_account             \\n\",\n       \"1                   status_of_existing_checking_account             \\n\",\n       \"2                   status_of_existing_checking_account             \\n\",\n       \"3                                     duration_in_month             \\n\",\n       \"4                                     duration_in_month             \\n\",\n       \"5   installment_rate_in_percentage_of_disposable_income             \\n\",\n       \"6   installment_rate_in_percentage_of_disposable_income             \\n\",\n       \"7                                               housing      房产状态   \\n\",\n       \"8                                               housing      房产状态   \\n\",\n       \"9                                         credit_amount             \\n\",\n       \"10                                        credit_amount             \\n\",\n       \"11                                        credit_amount             \\n\",\n       \"12                             present_employment_since             \\n\",\n       \"13                             present_employment_since             \\n\",\n       \"14                                              purpose      借款目的   \\n\",\n       \"15                                              purpose      借款目的   \\n\",\n       \"16                                              purpose      借款目的   \\n\",\n       \"17                            savings_account_and_bonds             \\n\",\n       \"18                            savings_account_and_bonds             \\n\",\n       \"19                                            telephone  电话号码注册情况   \\n\",\n       \"20                                            telephone  电话号码注册情况   \\n\",\n       \"21                                             property      财产状态   \\n\",\n       \"22                                             property      财产状态   \\n\",\n       \"23                              personal_status_and_sex             \\n\",\n       \"24                              personal_status_and_sex             \\n\",\n       \"\\n\",\n       \"                                                                                                                             变量分箱  \\\\\\n\",\n       \"0                                                      no checking account,... >= 200 DM / salary assignments for at least 1 year   \\n\",\n       \"1                                                                                                               0 <= ... < 200 DM   \\n\",\n       \"2                                                                                                                      ... < 0 DM   \\n\",\n       \"3                                                                                                                     [-inf ~ 27)   \\n\",\n       \"4                                                                                                                      [27 ~ inf)   \\n\",\n       \"5                                                                                                                      [-inf ~ 4)   \\n\",\n       \"6                                                                                                                       [4 ~ inf)   \\n\",\n       \"7                                                                                                                             own   \\n\",\n       \"8                                                                                                                   rent,for free   \\n\",\n       \"9                                                                                                                   [-inf ~ 1845)   \\n\",\n       \"10                                                                                                                  [1845 ~ 3914)   \\n\",\n       \"11                                                                                                                   [3914 ~ inf)   \\n\",\n       \"12                                                                           4 <= ... < 7 years,... >= 7 years,1 <= ... < 4 years   \\n\",\n       \"13                                                                                                        unemployed,... < 1 year   \\n\",\n       \"14                                                                     domestic appliances,retraining,car (used),radio/television   \\n\",\n       \"15                                                                                           furniture/equipment,repairs,business   \\n\",\n       \"16                                                                                                     education,car (new),others   \\n\",\n       \"17                                                                ... >= 1000 DM,500 <= ... < 1000 DM,unknown/ no savings account   \\n\",\n       \"18                                                                                               100 <= ... < 500 DM,... < 100 DM   \\n\",\n       \"19                                                                                       yes, registered under the customers name   \\n\",\n       \"20                                                                                                                           none   \\n\",\n       \"21                                                                                                                    real estate   \\n\",\n       \"22  building society savings agreement/ life insurance,car or other, not in attribute Savings account/bonds,unknown / no property   \\n\",\n       \"23                                                                  male : divorced/separated,female : divorced/separated/married   \\n\",\n       \"24                                                                                           male : single,male : married/widowed   \\n\",\n       \"\\n\",\n       \"       对应分数  \\n\",\n       \"0  123.7185  \\n\",\n       \"1   23.0998  \\n\",\n       \"2  -22.9661  \\n\",\n       \"3   61.3498  \\n\",\n       \"4    7.2134  \\n\",\n       \"5   68.3050  \\n\",\n       \"6   25.3142  \\n\",\n       \"7   58.4474  \\n\",\n       \"8   21.1485  \\n\",\n       \"9   51.5913  \\n\",\n       \"10  76.2287  \\n\",\n       \"11   4.5683  \\n\",\n       \"12  54.3246  \\n\",\n       \"13  23.7916  \\n\",\n       \"14  79.5717  \\n\",\n       \"15  36.0542  \\n\",\n       \"16  18.7831  \\n\",\n       \"17 102.5976  \\n\",\n       \"18  27.4615  \\n\",\n       \"19  64.8950  \\n\",\n       \"20  35.5318  \\n\",\n       \"21  68.0330  \\n\",\n       \"22  39.0842  \\n\",\n       \"23  63.0338  \\n\",\n       \"24  38.1292  \"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 获取评分卡明细\\n\",\n    \"card.scorecard_points(feature_map=feature_map)\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"7.3 评分效果\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 评分卡 KS、ROC 曲线\\n\",\n    \"_ = sp.ks_plot(train[\\\"score\\\"], train[target], figsize=(10, 5), title=\\\"Train Dataset Scorecard\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 评分分布情况\\n\",\n    \"_ = sp.hist_plot(train[\\\"score\\\"], train[target], figsize=(10, 6))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率分档WOE值分档IV值指标IV值LIFT值累积LIFT值累积好样本数累积坏样本数分档KS值
0score训练集模型评分[负无穷 , 400)720.1029160.0327560.26670.7778-2.10010.49141.30722.59262.592616560.2340
1score训练集模型评分[400 , 500)1870.2671940.1918930.44290.4973-0.83660.21001.30721.65781.91761101490.4850
2score训练集模型评分[500 , 600)2240.32001810.3694430.20480.19200.59000.09711.30720.63991.32512911920.3204
3score训练集模型评分[600 , 700)1790.25571630.3327160.07620.08941.47390.37801.30720.29801.04734542080.0639
4score训练集模型评分[700 , 正无穷)380.0543360.073520.00950.05262.04300.13061.30720.17541.00004902100.0000
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"    指标名称     指标含义           分箱  样本总数   样本占比  好样本数  好样本占比  坏样本数  坏样本占比   坏样本率  \\\\\\n\",\n       \"0  score  训练集模型评分  [负无穷 , 400)    72 0.1029    16 0.0327    56 0.2667 0.7778   \\n\",\n       \"1  score  训练集模型评分  [400 , 500)   187 0.2671    94 0.1918    93 0.4429 0.4973   \\n\",\n       \"2  score  训练集模型评分  [500 , 600)   224 0.3200   181 0.3694    43 0.2048 0.1920   \\n\",\n       \"3  score  训练集模型评分  [600 , 700)   179 0.2557   163 0.3327    16 0.0762 0.0894   \\n\",\n       \"4  score  训练集模型评分  [700 , 正无穷)    38 0.0543    36 0.0735     2 0.0095 0.0526   \\n\",\n       \"\\n\",\n       \"   分档WOE值  分档IV值  指标IV值  LIFT值  累积LIFT值  累积好样本数  累积坏样本数  分档KS值  \\n\",\n       \"0 -2.1001 0.4914 1.3072 2.5926   2.5926      16      56 0.2340  \\n\",\n       \"1 -0.8366 0.2100 1.3072 1.6578   1.9176     110     149 0.4850  \\n\",\n       \"2  0.5900 0.0971 1.3072 0.6399   1.3251     291     192 0.3204  \\n\",\n       \"3  1.4739 0.3780 1.3072 0.2980   1.0473     454     208 0.0639  \\n\",\n       \"4  2.0430 0.1306 1.3072 0.1754   1.0000     490     210 0.0000  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 训练集评分排序性\\n\",\n    \"score_clip = card.score_clip(train[\\\"score\\\"], clip=100)\\n\",\n    \"score_table_train = sp.feature_bin_stats(train, \\\"score\\\", desc=\\\"训练集模型评分\\\", target=target, rules=score_clip)\\n\",\n    \"sp.bin_plot(score_table_train, desc=\\\"训练集模型评分\\\", figsize=(10, 6), anchor=0.935)\\n\",\n    \"\\n\",\n    \"display(score_table_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率分档WOE值分档IV值指标IV值LIFT值累积LIFT值累积好样本数累积坏样本数分档KS值
0score测试集模型评分[负无穷 , 400)39.00000.130017.00000.081022.00000.24440.5641-1.10510.18071.30641.88031.880317.000022.00000.1635
1score测试集模型评分[400 , 500)86.00000.286747.00000.223839.00000.43330.4535-0.66070.13841.30641.51161.626764.000061.00000.3730
2score测试集模型评分[500 , 600)83.00000.276763.00000.300020.00000.22220.24100.30010.02331.30640.80321.2981127.000081.00000.2952
3score测试集模型评分[600 , 700)79.00000.263370.00000.33339.00000.10000.11391.20400.28091.30640.37971.0453197.000090.00000.0619
4score测试集模型评分[700 , 正无穷)13.00000.043313.00000.06190.00000.00000.000011.03340.68301.30640.00001.0000210.000090.00000.0000
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"    指标名称     指标含义           分箱    样本总数   样本占比    好样本数  好样本占比    坏样本数  坏样本占比  \\\\\\n\",\n       \"0  score  测试集模型评分  [负无穷 , 400) 39.0000 0.1300 17.0000 0.0810 22.0000 0.2444   \\n\",\n       \"1  score  测试集模型评分  [400 , 500) 86.0000 0.2867 47.0000 0.2238 39.0000 0.4333   \\n\",\n       \"2  score  测试集模型评分  [500 , 600) 83.0000 0.2767 63.0000 0.3000 20.0000 0.2222   \\n\",\n       \"3  score  测试集模型评分  [600 , 700) 79.0000 0.2633 70.0000 0.3333  9.0000 0.1000   \\n\",\n       \"4  score  测试集模型评分  [700 , 正无穷) 13.0000 0.0433 13.0000 0.0619  0.0000 0.0000   \\n\",\n       \"\\n\",\n       \"    坏样本率  分档WOE值  分档IV值  指标IV值  LIFT值  累积LIFT值   累积好样本数  累积坏样本数  分档KS值  \\n\",\n       \"0 0.5641 -1.1051 0.1807 1.3064 1.8803   1.8803  17.0000 22.0000 0.1635  \\n\",\n       \"1 0.4535 -0.6607 0.1384 1.3064 1.5116   1.6267  64.0000 61.0000 0.3730  \\n\",\n       \"2 0.2410  0.3001 0.0233 1.3064 0.8032   1.2981 127.0000 81.0000 0.2952  \\n\",\n       \"3 0.1139  1.2040 0.2809 1.3064 0.3797   1.0453 197.0000 90.0000 0.0619  \\n\",\n       \"4 0.0000 11.0334 0.6830 1.3064 0.0000   1.0000 210.0000 90.0000 0.0000  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 测试集评分排序性\\n\",\n    \"score_table_test = sp.feature_bin_stats(test, \\\"score\\\", desc=\\\"测试集模型评分\\\", target=target, rules=score_clip)\\n\",\n    \"sp.bin_plot(score_table_test, desc=\\\"测试集模型评分\\\", figsize=(10, 6))\\n\",\n    \"\\n\",\n    \"display(score_table_test)\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"7.4 评分卡稳定性\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mSignature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mpsi_plot\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mexpected\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mactual\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mlabels\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m'预期'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'实际'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mdesc\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m''\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0msave\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcolors\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m'#2639E9'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'#F76E6C'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'#FE7715'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfigsize\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m15\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;36m8\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0manchor\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.94\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mwidth\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.35\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mresult\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mplot\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmax_len\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mhatch\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m \\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m      ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/utils.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m      function\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sp.psi_plot?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mSignature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcsi_plot\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mexpected\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mactual\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mscore_bins\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mlabels\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m'预期'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'实际'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mdesc\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m''\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0msave\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcolors\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m'#2639E9'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'#F76E6C'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'#FE7715'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfigsize\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m15\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;36m8\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0manchor\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.94\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mwidth\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.35\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mresult\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mplot\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmax_len\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mhatch\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m \\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m      ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/utils.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m      function\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sp.csi_plot?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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49wtfXl2zZslG7dm2KFStG6dKlY03bGOz26dOHjBkz8vz5czw8PLh16xYGg4GzZ89GCWg8PT2pVasWOXLkiJKOubk5v/32GwDt2rXj9evX6kOE27dvc/DgQZYvX46DgwNt27alRo0aZM2alUOHDlGvXr0Y74HQ0FAmTZqEXq/n1KlTZM2alTJlytChQwccHR3VYDk0NJSaNWtiYWFBvnz54ryfICKQPXfuHNmyZSNPnjycOXOGZcuWMWbMGPX7NaYdXxqNBq1Wy+rVq6lcuTIWFhYEBASQJUsWdu3aFSWgb9KkCdu2bYsxn7a2tvTq1Yvz588DEUGaMVCbNWuW2oOiV69ehIWFsWPHDjUIN96vK1euBCIeEsXXh2W2lZUVoaGhdOjQgYEDB9K4cWMsLCxwdHSMd5oAlSpV4siRI6xfv55+/frF6xjjXBIf9j4x/n348GHKlCmToONKly6t9lR5fz9jUHzx4kX8/PywsbGhUaNGUdJJlSoVANbW1qRNm5br16+rr73fi8x4Dd27dy8+b1cIYWISYAuRTJw8eZKVK1fy9OlTXrx4QalSpZg0aZIaaF29epVhw4Yxb948unfvjlarZfPmzaRKlYqXL18yc+ZMLl68iKIo5M6dm8mTJ0eplF+5coXZs2fj6emJra1trF2Dg4ODmTJlCkWKFKFp06YAnD9/noEDB360Ze9j9u3bx7x581AUhcDAQAoVKsSlS5eYMWMGpUuXJjAwkKtXr7Jnzx4gouvklClT8PLyIl++fGpr2dixY7l16xbZs2dn/PjxFCtWTD3H1atXmT9/Pnfu3MHOzg4zMzNatmxJu3bt0Gg07NmzRw027Ozs6NSpEzt37lSDRYC+ffuq/9fpdEyePJnChQuTIUMGJk6ciK+vL82aNePnn39W97t79y4DBgyId6uLt7c3586d4+jRo+zatYuwsDDOnz9P7ty5qVChAhYWFpiZmWFlZUW6dOnw9fVVgw9joP2hly9fMnv2bLZv307Xrl1Zs2YNc+bMoWjRoixevBgLCwt18p4dO3Zga2uLg4NDrHk0GAycOnWKP//8kwIFCqhBeooUKQgNDeXp06cA3Lx5k8GDB5M7d25Gjx6ttgTHJjAwUM2PUYoUKfjhhx84deoUGo0GRVEoVKgQP/74I7///jvHjx/HYDAQFhaGq6srTZo0ifMcNWvWxNHRkdGjR5M1a1YURcHMzIysWbOyfv16NYDMnz8/EFFxNubnwwmJPnT06FEyZMgQpTXLYDBw+fJlzM3N8fT0ZOjQoVhYWDBt2jTq1q0bLY0XL14QFBTEvn37yJQpE5cvXyZFihSYm5vz7t07dSZzd3d3zpw5Q+nSpT/acvu+cuXKsWzZMsqUKUPFihVp3bo1lSpVYvHixfz444/Uq1dP7dJ+6NAhevXqRY4cOWjTpg2pU6cmZcqUMab74MEDdu3aRapUqWjbti0zZsxg2bJlpEmTRu0BYWFhwZw5c/jhhx9InTp1rNerkY+PD+Hh4YSHh9OnTx+KFCmCmZkZvXr14tixY5w5c4bLly8DsH//fgA2bNjAggUL1ADGaO3atWq38qlTp5I2bVr1tbCwMM6ePcuLFy9InTo17du3p2nTplH2eZ+fn5+avkajoXfv3uh0OlavXs2rV69ImTIlRYoUYdeuXWi1WrZt28bp06fJkiULQ4YMiTEYDgoK4saNGxw+fBgvLy+CgoKYMWMGISEhWFtb891339G7d+8ox5w4cYJbt25x4cIFSpYsqW5XFIUhQ4Zw6dIlnj9/rvZMKF26NHXr1qVo0aLkzJmTcePGUbBgQS5evMjAgQN59uwZWq2WbNmy0ahRI169esWFCxewt7dXu/vHh/EhkDFwrVSpEn/99Rdv3rzBzc2NefPm4erqSvPmzeM9ZABQe+f8/fff9OvXjz179jBq1CiaN2/O8OHDYzzGOBngh2WP8e+bN28m+DgvLy914rn3HyoY9zEYDNy6dSvGAP7OnTtARE8SCwuLKL+hfn5+6v8NBgMQUTYKIZI+CbCFSAa2bduGm5sba9asoXjx4ixYsIC5c+dy6tQpMmbMyLRp09Qn35MnT+bZs2coisLDhw/JlSsXbdq04fXr1+zevRuNRkO1atXo2rUrf/31FxDRbblbt25kzJiRv/76CysrK1q1ahUtH/PmzWPDhg28fv2aIkWKALB582ZmzpyJt7e3Ou4MIrr9Gcesxcfu3bsZPHgwnTp14pdfflHHwL7v1q1bDB8+nBcvXpAxY0bs7OyYMGECv/zyCzdv3mTIkCFkzZqVoUOHMn/+fE6cOMHEiRPVMZRnz56lS5cu2NjY4O7uTqZMmRg8eDATJ07E09MTNzc3KlasqI7lzJo1K506deLHH3/k8ePHzJ07F4gIsBVFYcaMGezevZtnz57RuHFj9u3bR8qUKfH19WXZsmX88MMPVK9enaNHjzJq1ChevnwJoH5GlSpVivYejdzd3Zk6dSolSpSgefPm/Pnnn1SuXDnKOE9jZczCwkIN/uJqaXvw4IE6S/K+fft4+vQpuXPnZunSpWrwdvHiRfLkycMvv/zCvXv36NatW6xjbs3MzNQujhARCEFEMFypUiVy5szJ8uXL1eWtbty4wb59+6hZs2acQbaNjQ0tWrRgyJAhauB05swZ7O3tsba2pm3btqxatYqCBQsyePBgjh8/zvHjx7G2tmb16tVcuHDhowF2o0aNWLBgAQ4ODrRu3RofHx8sLCywtrYmR44cardeY6XX0tJS/Yxjmhk4JCSEW7dusXbtWnbv3o2ZmRkjRowgT548LFq0iFu3bqHVatWHWq6urnTq1CnWFv3MmTPj6uoa5bNNmTIlFhYWNGnSBEtLS/bu3UuvXr2oVq0aV65cYc+ePdSuXTtewUqpUqXU7utXrlzh3bt3+Pn54eLiwtu3b9myZQtdu3YlLCxMneHZuFSYvb09J06cQKvVRkt32rRphIeH4+fnxz///MOQIUPo168fFy5cUMf36vV65syZw/r162MMro33oo+PD2/evCEgIACdTkeRIkUoX74833//PXq9nqVLl5I5c2YaNmzIgAEDyJo1K1OnTmXDhg34+/vHeC8YH0gOHDiQVKlScfnyZa5fv86FCxc4ceIEgYGBpE2blm7duqkPGGLTrVs3li1bhqIopE2blvnz5zN9+nRu377N69evcXZ2ZsOGDdy9exedTsfIkSPJnTs3xYsXjzFvd+/eZcKECbx+/ZqcOXOqrbtbtmwhRYoUvHr1irVr19KvXz8yZMjAu3fv8PLyUsv+P//8M0qArdFomDZtGm/fvqVfv35cvnwZRVEwGAy8efOGLFmysGHDBgIDA/Hw8GDq1KkULFiQPn36UKVKFfXBwtWrV1EUJV4tzeHh4Zibm7N27Vru3LlDrly51OEMgwYN4vz58zx79gyA58+fM2rUKLZv387ChQvj3bvF+NDrwYMHvHv3jh07dhAQEMCmTZtiDbDff7jzPuPfsQ1/ic9x7x/7/j3x/jExzTBvMBhYvnw5OXLkUO/1PHnyYGVlRXBwMJcvX1a72fv6+gJEe2AkhEiaJMAWIol79+4dEydOJG/evOrYrd69e9OkSRMyZcqEubk5P/30k1rJatOmDRUrVuTo0aPkz5+fJUuW4OXlRY0aNXBwcCA8PBwLCwvu3LnDxYsXKVasGKNHj0an09GhQwc18Kldu3aU7moQMb4sKCgoyrYWLVqQPXt2OnToQMaMGdm0adMnv0edTsevv/6KoihqJbxy5cpYWlpGWX6oZMmSlClThh07dpA1a1aGDRsGQPPmzZk1axZPnjxh0aJFZM2alZYtW3LixAn+/fdf9fiZM2ei0+moWbOm2pWvVatW7Nq1izVr1tCxY0eyZcsWpeURIirl71dcIaLyWrFiRbXr5OPHj9m3bx/p0qWjadOm3L59W53IqlKlSvz5559Uq1YNIF6fUadOnWjTpg2WlpYcO3aMP//8k0qVKkX5zIytJYqiqBW7uCbrKVu2LIMGDaJixYocP36c1atXs3jxYuzs7Dhz5gy9evUiNDSUQoUKERoaqlbE48vY9f369eucPn1a/ewPHDjAnj171JbfQ4cOMX/+/DjTypYtGwMHDlTHBt+/f5+SJUtiZWVFnTp11AmjDh06hE6n4/z581SqVIl27drFawm5bt26cefOHUqVKsWIESMoUKAAKVOmxM/Pj7Rp06LRaNQu8VqtlrCwMDUo+jDAHjVqFFu3bkWv16uBucFgwNramh9//JGCBQsSHh5OunTpeP78OZUrV0an08U7oPD39+fKlStkzJiRQ4cO4eXlpY7lnzJlCiNHjlT3ff36NR07doxXuocPH8bd3V3tOmxmZkaqVKkoXrw4NWvW5Nq1axw+fFhtxStQoADNmzenevXqMQbXe/bs4dChQ1SpUoXDhw+rrXqWlpYsWrQICwsLNBoNZcuW5fbt2/Tp04eFCxdGaw3Pnj07PXv25MiRI/z4448oikKvXr3IkycPtWrVIk2aNLx8+ZKaNWvy6NEj1q1bx5UrVyhSpAjm5uZYWVmxaNGiGB/iNGjQgIIFC5IvXz4CAgI4fvw4jx8/5uzZswQGBjJ9+nQaNGjw0c/O29ubK1eucOjQIQwGAxYWFhQuXJgFCxbQpEkTSpUqRf369QkKCsLOzo7Q0FCOHTsWa2s4RIy5Xbt2rfp3rVq1sLKyYvv27eTMmZP+/fsTHh7OqVOnOHr0KDqdDjs7O3UZv3bt2kVL0zh2/9KlS4wZM4bx48eTKVMmFi1axNOnTzl79ixr166laNGiFC9enJs3b+Lj4xMln8aeKPb29h/9XNatW8fu3bsJCQmhV69edO3aVS2nMmfOzNatW5k1axbbtm1T7y9jL6Xx48d/NH2IuJ5sbGwIDAzk+fPnNGnShOvXr6s9quLy4YMN499xlZsfOy62Y98/JqZ9/vjjD16/fs3atWvVwDllypR07dqVuXPnsnLlSho1akTq1KnVAD1nzpxx5lMIkTRIgC1EEnf06FECAwPJnTt3lO3fffed+v/s2bOr/y9ZsiTVq1enU6dOQNSgp2XLlgQHB6utOC9fvuTGjRt4enoCUSfhiWnSmapVq1KgQIEEreMbl+vXr/P69Wvs7e3VwNfCwgJ7e3u1K6yRsdLyfkXD2CqYLl06smbNCqAGL8Z1XYODg9XJdt4fu2icSEZRFE6fPv1Jre6lSpUiQ4YMPHv2jJYtW6r5yJcvH7dv31ZbHRLK2EJtfMjw/my9QUFB6nfk5+f30cqcUYcOHVi2bBkLFy5kyJAh7Nu3D29vb9q1a4ejoyOFCxdmxIgRbN26lZEjR2IwGBg9ejRPnz6lWrVqtGnTJsZ09Xo9169fx8zMDE9PT9avX4+9vT0zZsygYMGCZMiQAWtra8LCwjA3N0ev13+0a3Dp0qVxcnLi7t27bNiwgZIlS+Lu7h5lLLSDgwP379+ne/fu5MyZkwYNGsQrwLS2tuaPP/5g3rx5ODg4cOHCBdKkSYOvry+2trZ4e3sTFhaGt7c3QUFBmJmZqZ/xh2sFDxs2jMyZM1O2bFkyZMhA9erVcXZ2plmzZkDEtfju3TtWr17Ns2fPyJo1K0ePHo02Rjs2t27dQqfTYWlpyZUrV1i2bBnly5dnxYoV5MyZk7Rp06LVatHpdJ/U1bZKlSqUK1eOihUrUrJkSUaNGqUOJdFqtTRo0ID+/ftz/fp1Dh8+TIUKFWjYsCFnzpzh+vXr1KlTR21NvH//PuPGjWPEiBE4OTlx+PBhNQg/ePAgZ86coVWrVuzduxdra2t+/vlnhg4dSq9evZg3b160VQvKlStHuXLl0Ov16j158OBB/vrrL6ZNm0bZsmV5+fIler2e1q1bM27cOOzt7alSpQo1a9ZUx79+yNzcnHz58nH+/Hk2b95Mv379yJYtG0OGDGHnzp3xbiE8cOAAEBFoGwwGzM3NURSF4cOH8/333zNx4kQ1sNyxYwdv377F1taWoKAgrK2t43UORVG4evUq+fPn56efflKHvxg9f/6czZs3kz17dho1ahRjq/iSJUtYvHgxzZs3Z8uWLRQvXpyff/6Zn3/+mSVLltC2bVv1O0ybNi0BAQHky5cvShrGiQmtrKw+mueOHTvGef+lTZuWCRMm4OrqyrJly9iwYQM6nY7t27czcuTIeA8zsrOzIzAwkMDAQOrUqUOdOnXi3N9YVn64XrzxYZnxNzEhx304zODDfYBoD1b++usv9uzZw4YNG6L8lkPE3AVp0qRh27ZtuLi4MGLECPW3JKZu5kKIpEdmERciiTN2p/uwUh+bD1vFjE++e/TowaZNm9i5cyfHjh3j2LFj1K1bFy8vL3Xf94Pq2Loaf0oFPr6M7/HDcd8fa1UwiimvH2579+6dOo7t/Zat98fMxdZN8FPPbQwc45v/jzGOu7OyssLDw4M9e/aoXTzDwsK4efOm+t6AKP//UPfu3Zk7dy46nQ53d3csLCyoW7cuTZo04cqVK+zcuZNy5crxxx9/APDq1St0Oh1p06blxo0b0XowvP+eu3btSt26denZsyeNGzfmzp07bNq0CQ8PD9atW8fQoUNp0qQJzs7OlC1bNl6TvRmXINq7dy9v3rzh9u3bFC1aVH199OjR6nX76NEj5s6dS7t27T46ThoiWuaWLFnCjz/+yL1798iePTt37twhbdq0PHz4EEtLS3Xsavbs2dU0P7wXbW1t6d27Nz/88INawTZWhPfu3Uu3bt2oUKECs2fPJjQ0lBo1anDv3r1YZ37+ULFixfjxxx9p164dgwYNwtnZmSNHjnD06FEuXrzIH3/8Qe/evalVqxaFCxembt26aqvjx+zYsQMfHx8uXLhAhw4dCAoKwsLCgjNnzrB582ZOnDjBP//8A0T0vChTpgy9e/dm4cKFtG3bFm9vbwICApg2bRrz58+nY8eO6meg1Wrx9vZm7NixFC1alF9++QWNRoNer6dhw4aULl2aU6dO0bx58yg9Td63ePFidXxss2bNqFOnDrdu3eLSpUvqZH6HDx/m559/pkqVKoSEhHDjxo2Pvu+goCD++usvmjVrhpeXl/owYO/evdStW5dSpUqxY8eOWI83Dq8pUqSI2i06MDCQrl27Mnv2bPUhzalTp/D19SUsLIwiRYpQpkyZjy61pNfrWbduHS9evKBo0aKMHz+eCxcu0KZNG/r370+XLl1o3bo1PXv2ZPXq1QwbNgwXF5doPStmzZqFh4cHu3btonPnzty8eZPMmTNz4sQJOnXqRIYMGVi/fj0DBgxAURT8/PywsrKibNmyUdIxPhCI729QbO7fv6/+P0uWLIwcOZLVq1erPUSMQ0ziw5iX2JaT/JDxIcKHDz2NvSyMM34n5DgHBwc1H+/vZ9xHo9Go6UDEUIu1a9eydu3aaMG1Udu2bdm6dSvbtm3D3NwcnU5HtmzZ4pxcUAiRdEgLthBJnLFyc+PGDcLCwj55IjFji8yH3b0/TB/4z0soxRXYxcXYRdTPzw9FUdBoNOh0uv/cAvy+1KlTY2ZmhsFgiBIkBgQEqP//sNU+sQLkDxkMhk96UGGcGG3atGk8f/6cP/74Aw8PD9q2bcuTJ08ICQlRW5f0en2MY4SN+vfvz8SJExk8eLA6ntvf358GDRqg1Wpp0aIFDg4O7Nixg2HDhtG1a9dYl+4xGAw8efKEt2/f8uzZM6ysrDA3N6dZs2bqpHBnz57Fw8MDOzs7NYj57rvvCAwMZP369eo6sLFp2LAhM2bMwMfHh+HDh0db3zt37twcPnxY7YZ++PBh7ty5w507dyhYsGCs6YaFhTFixAgA6tSpw65duyhRogQHDhwgb968WFpaEhoaSsGCBenbty+NGjVSe0AYxyTHxNil3rge+Q8//ED69Ok5e/YsBoMBNzc3vLy8WLVqFUuXLuXXX3+NMZ2QkBC8vLx48+YNXl5eFC5cmHPnzrF+/Xq1x8nq1avJmDEjNjY2mJubkzJlSr777jvevn3L33///dExxIGBgSxYsEDtbrtp0yYcHBwoWrQo+fLlY9OmTZw8eVLtGlyyZElatGiBtbU1wcHBaDQa7O3tCQ4OZv78+eqY0/fvqcGDB+Ps7MyUKVOwsrJCURT+/fdfBg8eTJEiRbhy5QoPHjxQ13Xv1auXWmadOnWKuXPn0r59e/bu3avOewARD36MDweLFSuGi4sLadKkITAwkB07drB9+3YqVqwYa8ukMdjLmDEjadKkUR+ehISEULZsWfLlyxfnWvJ2dnZ8//33FC1aVO05YGtrqw5d2Lx5M3PmzCEwMFDtteHq6kqpUqWiLL/0Pj8/P44ePcqSJUvUB6PGB4CFChXi5s2b2NnZRekd8PvvvzN//nz8/PyilCn37t2jYsWKaLVa/v33Xw4ePAhEjFu+ePEiz58/58KFC6xYsYJmzZoxZswYfH191TW332ec3T0hDyDfN27cOHUiRKMffviBkiVLcubMmY9OfmgUFhaGr68vZmZm8Z50rUqVKkybNk19mGtknBejatWqCT7OzMyMypUrq/NxFC9ePMo+xYoVU39bPD09mT59OkuXLo3ye3Pz5k0OHz4cZU4Lo927dwMRQ8M+xwNuIUTikztViCTOOBHR69evGT16tFp5ffToEVu2bPno8cbgyN3dXW2JgogK2NGjR9VAwLgtIYyVvTdv3qhB8YcVkrgYu3sHBwerrTsXLlxIcMAeEysrK3VitvdbDo2zuJqbm1OuXDl1X4ho/Te2lLzf9S8hgff7Y1aNlfv4fEaBgYFqa9mLFy9Ys2YN4eHhBAUFUb16dXLnzk2pUqXUCYh0Oh06nS7Wz65EiRLs2LGDvHnzsnr1atq0acPPP/9Mz549efbsmRogGoP058+f06VLFzZs2BDtAUxQUBDjx4+nS5curF27ljdv3lCoUCGCgoLU6zRlypQ4Ojqybt06du7cyd9//612Gf7YOGyI6NXw008/ARHBesOGDaO8Pm7cOKysrGjQoAF//PGHOrlZXGtCHz16lEqVKqmzb6dMmZJly5aRMmVKzM3NOX/+POXKlWPy5MlARJdNBwcH9RqIrRUfIr4jiBhvun//ftatW0fx4sXJnj07uXPnxsLCQu1OvnPnzigtze+3mHp6etKnTx8GDBjA33//TXh4OEWLFuXZs2dqPlKkSEHt2rX566+/2LlzJ3v37uXw4cOcPXv2o8E1RCzXFh4erk4MdeHCBZYsWUJoaCh16tTh0KFDrFmzRk0rT548VKhQgeLFi1O+fHnKlSuHmZkZNjY2UT7vd+/eAbBz504aNWpE7ty56d27N2XLlsXHxwcvLy88PT1xcHBg1KhRmJmZodPpWL58OS1atODZs2fcuXOHYcOGMXLkSEaOHImfnx9hYWEEBQUxbdo07t69S926ddVl1FauXEmWLFnImzcvfn5+/PLLL9StW1fNy/sMBgMbN24EIu7rc+fOqQH2wIEDGTVqFK1atYpzvPTs2bNZs2YNGo2GoKCgaD1ZWrRowYkTJ7h8+TJZs2ZVJwR8f130923YsIFSpUoxZMgQQkJC2LhxozohGUSUSdmyZSMsLExdB33FihVqj6VKlSpFGXLx8OFDOnXqRIsWLRg/fjzbtm2jQ4cOzJs3jwIFCmBubs6IESNwcHBg5MiR6ucR01J0xrHtd+/e/U8PHS9fvhxrr40SJUrEu+u8MR+Ojo7Y2tqyb98+ypcvz8yZM2M9xnjtPn78OMqEY3fu3CFfvnxqq/2JEydo3rw5p06d+qTjjN3ir1y5ou5jXP7MOI+Eoii4ubkxcuTIKMG1Xq9nxYoVMb7/ly9fsnXrVqpWrRqvMeZCiKRBAmwhkrj8+fNTv359ALZv307p0qWpWLEizZs3VwPG97vDfjhWrHPnztjb2xMeHk7//v0pXbo0pUuXpnfv3hQrVgwHBwe1JXPNmjXq8cbWoQ8rVMbg4v01kbNmzaq2OlerVk2dZC2+8ubNq47/njJlCteuXWP79u3kypULiGjJMlbMjAHv+62Ixq54sY1/M/5/8ODBaLVa/vnnH169eoWiKKxYsQKI6EJvbA0xtjCFhITQs2dPJk6cyOrVq9X0RowYoX4+xs/h/e/AeL73P6P06dOrFaiWLVtStWpV/vzzz49+Nps3b+bevXtotVoWLlyIo6MjGzdupFChQhQtWhSNRsPSpUvVIMkYUHwYDHt7e7No0SLGjx9Po0aNqFy5Mhs2bKBnz5788ccfnD9/nl27dqmVQuNnmSpVKgoVKsSYMWOoV69elGXGbG1tWbZsGZcvX2bDhg2MHj0aHx8fHjx4oD6saNu2LW/evKFRo0bqEkqfqk2bNlhYWFCrVq1oQyD+/fdfNTBVFAUfHx+cnZ3jnPE4Y8aM6udkrCCHhISwZMkSmjVrRrFixejTpw+jR4+Ocpzx2o8rwDZWsI1L5hkr18HBwfj7+6sPNNq1a4dOp2PSpElAxHW9fv16NR1HR0f++ecfdT3l/v37c+LECYKDg9WHZkOHDmXz5s20bt36o92OP/TXX39x9OhRVq5cqd5nixcv5sSJE9SpUydKK6YxyPvYWtBGxpa7tm3b0rhxY/r27cuIESNYsWIF5ubmFCtWjM2bN9OyZUtatGjBoUOH1NbyvXv38t1333Hp0iW2bdtGmzZt8PPzIyQkhLCwMPbt28fKlSvx9PRk1qxZVKtWjZ9++on9+/cTEhJCQEAA48aNo1+/foSHh8e4LN7WrVt5/PgxNWrUoEmTJri6uqrrQse3h1DKlCnV1uXXr1/H+dmEh4ejKApPnjzhwIEDLFy4kBkzZkR5uNKyZUv69u1LxowZWbRoEenSpSMsLCxKmWZlZUVQUBAbNmwgNDSU1KlTqw82Ppx0rlatWuzatYudO3eyYsUKDAYD9vb26hJgderUUb/3IkWKqMc/efIkWv7TpElD2bJl8fPz4+HDh9FeN5Y1xnI4Njqdjn79+kUJsnft2sXNmzdxc3NTt73/mxNTQG/sjWVc3m7Lli28fv06Wuv4h8aMGUPq1KnV/Q4dOsSbN2+YMGGC+v2tXr2a69evR0krPscVKVIEFxcXdu3axdu3b9HpdGzevJmqVauq48OPHz/O+fPn6dChA4ULF47yb+fOndEmkQsJCWHgwIEULVo0zocHQoikRwJsIZKBSZMmqWPmLC0tyZEjB6tWrSJfvnzcvHmTefPmqft27NiR48ePq3/b29uzZs0aypUrpy7zU6FChSgzl/72229UrlyZBw8e0LBhQyZOnKhW/nx9fVm1ahW+vr506dJFHd84Y8YMdTbsTJky0a9fP+zt7bGysqJdu3a0bt36k97jvHnzKF++PJcuXWLkyJF07tyZdu3aYWNjw4kTJzA3N2fq1Knq5EJ79+6lf//+nDx5Un3/r1+/pkuXLuzatStKcDRw4EDCwsIoWbIkK1euJF++fDRp0oTatWvz9u1bpk+fri4lA6gtbvb29ty6dYtUqVLRo0cPnJ2d6dq1K7Vr10ar1dKnTx81mJg2bRpHjhxhyZIlHDp0CIhYk3f27NlARAu5m5sbGTJkIEWKFNSoUSNe6xbXr18fe3t7unXrRtGiRbl06RJnzpxhzpw56j4pUqQgVapUeHt78/z5cyB6C66trS2HDh1i3bp1eHh4MHjwYNzd3alYsSL+/v70798fRVHU3gTG1hpfX1/69u1LsWLFePr0KUeOHIkxn3q9nl9//ZUFCxZQt25dtaujoigsWrQIiGgJ7ty5M6dPn/6klrBMmTJRq1atGFvXmjVrxvz586lfvz4tWrQgTZo0LFiwIM6Ap0CBAgwdOhSImBnfYDAwfPhwrK2tGTZsGJ07d6ZYsWJq0LVlyxb69u2r9gB5f33a9x08eJDNmzcDEQ8mWrRowezZs2natClPnz4lW7Zs/Pzzz/z+++9Uq1aNUqVKcfDgQVatWsWaNWti7drv6+tLt27dOHXqFCNGjFAD7KxZszJr1ixu3bpF06ZNGTlyZLzGdQcHB3Pr1i22bdtG3rx5yZs3L7ly5WL79u2sWbOG2bNnq7P5Q2SAHd/uqcZZx41Bm6WlJfnz5yd9+vTo9fpoDyiyZMlCkSJFSJ8+vbqtTZs26qSBxjHY4eHh/P333wDqagKnT59m7NixrFixAjMzM1xdXfHy8qJ3795cvHgxyoSGEDHb/5QpU8iSJQtjx46lZ8+eLFmyRA2W+/Tpw/bt26N0c4+Lh4cH3t7eaDQaDh06xOLFi1m4cCEzZ87Ezc2NLl268OrVK8LDw6levTq9e/dm1qxZLF68mJ9++kkdd2xmZkbv3r3566+/0Gq1ak+S9x+UGQwG0qZNy86dO1m4cOFHWzRz5MiBo6MjuXLlYvHixRw+fJh+/fqhKIrajfnp06f06NGD/PnzkyFDBrVHirFnj1Hnzp0Botz/+/fvp0GDBmpZMXPmTLp27RrrNTh27FjSp09Ply5daNq0KS1atOD48eNs2bJFHaM8ZMgQunTpoh4zffp0unTpEuVByalTp7CxsVF7tjRo0AAbGxuaN28e5+fh4ODAn3/+yeXLl2nWrBmLFi1ixYoVFCtWTN2nRo0a2NjYUKNGjU86DiIeqnXp0oVOnTrRtGlTnJ2do5TTx44dQ1EU9cGJ8Z/x4awxwDYYDBw9ehQXFxcqVqzI0qVL4zXBnBAiCVGEEF8dg8Hwn9MIDg5WAgICoqQZHh4eZR+dTvefz/Mp/P39o7w3f39/JTg4WPH391fCw8OVkJAQxdvb+6PHJTdPnjxRP+uZM2cqnp6eMe63f/9+xdHRUSlatGiMr797904ZNGiQcurUqSjb9Xq9Uq5cOWXChAnKwYMHlVKlSinOzs6Ko6OjsnHjRjUP9evXVy5duhQt3adPnyodOnRQihYtqixZskTR6/XK/PnzFUdHR2XkyJGKoijKjRs3lGLFiimOjo6Ko6OjUqVKFWXatGnK/fv3E/y5/FdHjhxRvL29lR49eihdunSJcu28evVKWb16taIoEZ9Ply5d1LwPGDAgxvQWL16sODo6Kj/88IPy+PFjZf/+/Yqzs7NSrVo15ejRo9H2v3//vlKkSBE13ebNm0fb5/z580rVqlWVKlWqKEeOHFEURVE6duyoODo6Ku7u7oqiKMrGjRvVNBwdHZVmzZopy5cvV96+ffufPyNFUZSLFy8qjo6Oyrp16+K1f9GiRRVHR0flypUrUbYfO3ZMcXR0VOrVq/dJ5587d67i6OioTJgwQZkxY4by66+/KoqiKL1791aKFy+uHDp0SN33t99+U8qUKaMcPHgwWjpv375VatWqpVSvXl158OBBlNdevHihfq6Ojo5KoUKFlM6dOytz5sxR/vnnH+X27dtRykOjFStWKI6OjkrTpk2VW7duKTVr1lTKlCmjdOzYUZkyZYqydu1aZf/+/crFixeVW7duKbdv31YuXLig7Nq1S9m8ebMSFBQUJT1fX1+lVq1aSqFChRRHR0elVatW6mv169dXKlasqPzzzz/KpEmTlDZt2ihNmzZVHB0dlYULF8b5GXp4eCjVqlVTKlSooJQqVUpxcnJSdu3apdSoUUOZM2eOEhISopw+fVrJnz+/4ujoqDg5OSmdO3dW9u3bp5b7/fv3V2rVqvWRb+u/CQ0NjbZNp9Mper1eURRFefPmjVKoUCFl/fr1nzUfpvTu3Tvl4MGDir+/v6mzIoRIIJnkTIivUHy7csblw7VpNRpNtGWV4hrn+vLly3ivx7tq1Sp1ea64vD/jd0x/m5ubR5uJPKb9kpv3J/KJq9XbuDTR+zPWvi9VqlTqJFHvMzMz4+jRo+r326VLF2bMmIFGo6FQoUJqHnbu3Bnt2A0bNnDgwAGqVq3K77//rnbhNo5FNvaSKFiwIBs3bmTnzp3odDrSpUtHlixZPrpU1+dUqVIltm3bRpcuXaKtc54hQwZ1BnMzMzOmT59O9erV8ff3V9ej/5Cx90ibNm3Inj072bNnZ//+/djb28fY9ThPnjxMmzaNgQMHotPpoozxDA4OZu7cuXh6ejJ48GBq1Kihtgh/+Nm2bNmSTJkycfr0aSwtLaMsV5cYjEMb4juhVNq0aQkJCVG7IBsZW/4/9X40ljNOTk5RltEbPnw4nTp1omfPnqRJk4ZUqVLx4sULQkND6dWrF4sXL1bXjg8MDKR///5UqVIFV1fXaBNqZcqUiRUrVrBmzRpmzJhBSEgIJ06ciDLTffr06XFzc1O7JkNkq37mzJkpUKCA2ushoVKnTs38+fPVuQZiGrN97tw50qVLR8OGDdUhLrFN5hYWFsaqVatYuHAhuXLlYt68eQQEBNCzZ0++++47duzYoX6/ZcqUYdKkSSxatIjw8HCCg4M5duwYDg4O5M+fnwkTJtCoUSN1DoPPIab75P3fmaVLl1K9enVatWr1Wc6fFKRKlSrWSdeEEMmDRlE+0zS5QohvmpeXF9WqVYvXvgcPHox35V3ErXfv3pQuXVod+5sQISEhNGjQgBYtWtC9e/eP7vvhwxiIeMAyfPhwfv7551gD/uRm6tSp3LhxgyVLlsT4ICeh7ty5w6+//oqPjw/u7u5AxHhVjUYT40OsM2fOsGTJEubPnx/jZ5/YAgICqFixIkeOHInXOtHu7u4cPHgwSvdYiAj2OnfuTMWKFT96Xb3Py8uLTp06sXHjxmiBpJ+fHytXrmTv3r08efIEGxsbihQpQvPmzalevXqUc0P8xlh7enoyefJkTp06RZYsWShevDg1atSgfPny0cY6BwcH065dOzp27BhtAr7/YtCgQQQFBTFz5kw1AJ4zZw6KojBgwAB1vzVr1rBs2TL27NkTbZIs40R53333HU2aNKFmzZr/+eHrvXv3GDduHH/88Ue81wxPLNeuXWPOnDn8/vvv8V6eSwghTEECbCGEEELEycPDI8qKAyJpMBgMBAYGxnuJq8RgnIuhbdu2X+ycEPGAoVevXp+8VKUQQnxpEmALIYQQQgghhBCJQGYRF0IIIYQQQgghEoEE2EIIIYQQQgghRCKQAFsIIYQQQgghhEgEEmALIYQQQgghhBCJQAJsIYQQQgghhBAiEUiALYQQQgghhBBCJAIJsIUQQgghhBBCiEQgAbYQQgghhBBCCJEIJMAWQgghhBBCCCESgQTYQgghhBBCCCFEIpAAWwghhBBCCCGESAQSYAshhBBCCCGEEIlAAmwhhBBCCCGEECIRSIAthBBCCCGEEEIkAgmwhRBCCCGEEEKIRCABthBCCCGEEEIIkQgkwBZCCCGEEFHo9Xq8vb1NnQ0h/jO5lsWXJgG2EEIIIb4Zd+/epXr16uzateuj+968eZMaNWpw9OhR/vnnH0qWLMnZs2fjPEan01G1alX69++Pl5fXJ+fP29ubWrVqMXPmzBhfX7duHc2bN+f+/ftxpjNixAgaNGjA9u3bPzkPAMHBwdSvX59p06Yl6Hjx6aZOnUqLFi04c+ZMoqct17Jcy+LLkQBbJBq9Xvkmzx2Tz12BAzh69CgNGjRgz549Mb4+cOBABgwYQEBAQKxpGAwG6tSpQ/v27bl69epHz/mhefPmUbVqVX777TdCQkI++fikTDEYvspzJ7UKHMi1LGL39OlTSpcuTfny5dm9e3eM+9SqVYvu3bvHGAD8+eeftG/fPkoFPkeOHDx58oS1a9d+9PxbtmzB09OTu3fvUrt2bWbOnEmGDBm4cuUKHh4eMR6j1Wpp0aIF//zzT6zXNEBgYCDh4eHRtqdNm5b8+fOzdetWFEXh9OnT/PTTTxw/fhyAy5cv8+jRIx4/fhxn3rt37869e/fi9Tv0vvDwcJ49e8bt27cpXbo0O3bsoGzZsrRt2xa9Xv9JaYm4GQyGKOVa69atuXbtWoyBZL9+/ahRo4b62vz586lZsyZz5szh7du3PHr0CEMcvx1yLcu1LL4cC1NnQHw9zM01/H0ohDpVU7JuaxBbdn6ZSmqenOZMH5s6wcc/ffqUpk2botVqGT58OPXq1Yu2T61atciRIwejR48mW7ZsUV77888/+fvvvxkzZgx58+YFolbg6tevH+f53//R6969OzY2NuqPnp2dHXny5InxuIoVKzJ8+HD27dtH3bp1+eOPP7hy5Qpubm5kzpyZ69evY2FhwevXr7G1tY0xDTMzM1xcXBg1ahRnzpyhSJEi8fnIVJ06dWLhwoUsX76c5s2bx5rX2AQEBFC3bl3y58/PoEGDyJ8//ycd/zlpzMwIW70K5eWLRE3XrExZtBUqojt+DMOZ09HPmykzlh06Jtr5DAYDQUFB6jXQunVrli1bxvbt2ylTpkyUffv168ft27dxdXWlSZMmzJ8/n7/++ot69erRrl07/P39yZ49O2ZmMT+blWs5aV7LyVnWrFlZtWoVAwcOZOfOnWTNmpVDhw7h4uJCmjRpAGjZsiVTp07l2bNn0cpnHx8fXrx4QVhYmLrt0aNHQESrWFyuXbvGpk2b6NSpE2nSpGHq1KncvXsXRVHIlCkTZcqUifU6yZ49OwA//vgjBoMBPz8/PD09efDgAXfv3uXy5ctcvXqVHDlysGXLlmjXdcaMGSlcuDAajYZSpUqh0WiYO3cuiqLw8uVLdu7cSZYsWeLMf86cObG0tKRYsWJx7vehc+fOsXHjRl6+fImXlxcjRozA2dmZTJkyMW7cOLp164aDg8MnpSng4MGDLF++nD59+nDmzBkCAwM5duwYL168YPLkydSvX59s2bKRIkUKcubMGe34kSNH0q5dO/7880+aNGlCz549+eeff9i7dy8//vgjLi4utGzZktGjR0c7Vq5luZbFlyUBtkhUdaqmZPbiAP5YGWTqrMRbcq3AaTQaMmTIgLOzMwCNGzdm5cqVbNq0CTMzMypUqMCwYcNImTJlnHlwdHQE+OQfLgBbW1syZ85MeHj4RwOSly9fsmvXLkqWLKnm2dbWlmbNmrFgwQIaNmyY5IIS5eULlAR0i4uLfstm8PNDW68+Oj8/9Pv2JlraybUCJ9eyiEv+/PnZvXs3QUFBaLVaOnToQEhICB4eHjRs2JCMGTNiYWFBiRIlohwXFBTEoUOHWLlyJa6urnTp0oWGDRty+vRpMmTIwPnz51m4cCHW1taMHj2aVKlSqce+ffuWwYMHYzAY8Pb2pnr16pw/f55ly5ZRpUoVsmbNSt26dWPNs1arBSBDhgx0796dq1evUrhwYSwtLTl8+DBNmzaldu3aKIoS5bfDyMLCAmtrawDMzc1ZtGgRBw8eZNu2bVhbW/Pzzz8zderUOAOToKAgrKysOHv2LIcPH+b+/fvkz5+fNWvWYGlpGW3/kJAQhgwZQpcuXZg9ezadO3dm0KBBTJ48mf79+6PX69m5cycODg5069Yt7i9NRFOpUiWWL1/O0aNH2bdvH8OHD2fAgAEUL16cmzdv8vjxY9q3b0+qVKli/H4yZszItm3bePnyJRBxXdSvX58///yTkiVLMm/ePC5evBjtOLmW5VoWX54E2CJRrdsalKyCa6PkWIGDiB8r4w9XlixZWLt2LTdv3sTd3R2dTscvv/zC7Nmz40zD2KVs7dq1jBw5ktevX9OkSZMYg6iYpEiRAhsbGw4dOsSJEyc4f/48xYoVY/z48VH2S5cuHbt27WLq1Kns379ffSpu/FEtU6YMFy5c4OTJk/Tt2zfWVtKvgTGo1tarH+Xv/yq5VuCM55JrWXxIURRWrVrF4cOH6d+/Pz/88ANp0qTB0dERLy8vzp49y48//ohWq43yOYeGhjJ27FhKlizJ2rVruXv3Lq9fv+bAgQPMnj2b0qVL8+rVK2rWrEmePHmilM0BAQH8/PPPNGrUiMDAQG7cuIGNjQ2ZMmUCoGbNmtjZ2cX6vT59+pQ7d+4A0LVrV1q0aMHSpUvx9PTkzz//BCIeBnXq1CnaseHh4Tx48AAPDw+uXLlCgwYNGDhwIGvWrOHWrVsEBwfj6upK6dKlYwxIDAYDv/32G8eOHePp06eEhoYSHBxMjRo16NmzJ87OzjHe+wApU6akVKlSHDt2jMWLF/P48WN27NhB7ty5KVasGIsXL8bd3V1a/BLA19eXBw8esGbNGnQ6HRcuXGDRokXs3LmTlClTUqxYMYYNG4atrS0pUqSItau3ra1tlBbi0qVLM2vWLJo3b06uXLmYOnVqlP3lWpZrWZiGBNgiUX2pbuGJKTlW4IKDg7l58ya+vr7Mnz+fgwcP0rBhQzUoyZw5My4uLpQrVy7G41+/fs3EiRO5du0ar169Ure3adOGvHnzflILoKIo+Pn58ddff5E6dWpatmwZ43ktLCyoXr06//77L8+fP2fr1q04ODhgMBjQarWEh4czbtw47t27h5WVFd27d493HpKjxA6yk2sFTq7lLyMoOH7zVGi1oLXQRNseHq4QpkvYua2toqf3MXq9ntGjR3Pq1ClCQ0N5+/YtP/30ExAxHMDGxobUqVNjZ2eHRqNBo4l6jgEDBnDo0CGyZcuGl5cXY8eOpVmzZly9ehWtVsvr16+5fPkymzZtIleuXFGOtbKyYsmSJZiZmTFnzhxSpkyJj48P165dA8De3p4iRYqg1+sxNzePcmxwcDC7du3i9u3bAEyfPh0nJycA9YFMmzZtcHFxifF9T548mWfPnnHp0iVKlCjByJEjyZYtG1WrVmXlypX89ttvvH37lhMnTsR4bZuZmTF8+HD69u1Lz549OX/+PI8fP6ZSpUoUKFDgo597o0aNWLRoEdWrV6dkyZKcOnWKChUqcPLkSQoWLEjt2rWZNGkSjRs3/mhan5MSGhq/HS0s0HzwHQEoej3EMGY4PjQpUnzyMXq9nk6dOtGhQwf69etHaGgoBQsW5OnTp6RMmRKtVkuaNGlImzYtFhYW6HRRbzYPDw9+/vlntFotkyZN4vr161y5coX9+/ejKAqFChWiW7du0e4DuZaT/rUsvk4SYItkL2+u6D+e8ZFcK3CvXr1i0qRJhIaG8vTpU/r06UOHDh1InTo1DRo0wNXVlX///ZebN2+SNm3aGLsCZ8iQgRkzZvD27Vvq1auHv78/np6eTJw4McqDgPh+jsWLF2f69Okf3TdNmjSYmZmxdu1adfIT49IZXbp0oVKlSowcOTJBXXyTo8QMspNjBU6u5S+nWLXX8dpv9GBb2jazjrZ9/7FQBoz0S9C5757K+MnHmJub4+LiQvfu3Tlx4gR//PGH2gsiPDxc7e1gYWGBokR/eNCvXz969uzJH3/8QdOmTWndujWnT5/G1dWVUqVKERQURFhYGBkyZEBRlCjX9fvX6YULF7CwsMDDwwN/f38AFi9ezMOHDwkLC2PLli1RhhRYWVnRo0cP9uzZw6FDh9SAJDQ0lOfPnwPg4uKCwWCI8aHT6NGjOXLkCIcPH+bp06csXryYQYMGsXLlSk6cOEGKFCnIkCEDpUqVivPzu3z5MhcuXMDMzIyKFSvSpUsXli9fHufQhcWLF7NgwQImTJjAlStX2Lp1KxUrVuTevXvs2bOH5cuXM3DgQHx9feM895cQ+vOQeO1n0bwFFhUqRttuuHYN3crlCTp3yjlzP/mYdOnS4ebmRlhYGDNnzsTb25vw8HC+++47Hj58qD6c0+v1aDQaQv//AOHAgQPMnTsXvV7Po0ePyJs3L5kyZcLb2xs7Ozt69erF4MGDCQsLI2vWrNHOK9dy0r+WxddJAmyRrBUuYMHYoXYJOja5VuAyZszInDlz+O233zh8+DAXLlwgRYoU1KlTh+XLl3Pnzh1SpEhBgQIFKFSoUKzv38LCgrVr1xIcHAxAnjx56Nq1K0uWLCF16vhPGqfT6eK9v1arJX369MydG1lBWbNmDenTp2fHjh3qWK9vyYdBtuHWzQSlkxwrcHIti7gYJ408dOiQ2iMCImYsTp8+PT4+PqRLlw5FUaL1yNBoNIwePRovLy+sra2ZN28eOp0OCwsLSpcuzdmzZ1EUhZ9++onHjx/TsWNHhg4dGi0Pvr6+ODo6smvXLk6fPs3s2bMpW7YsAQEBPHnyhPv375MpU6YYJ9/TaDRMnDiRdOnS8fDhQ27cuAHAvXv3aN++PbVr16ZLly5kzBj5AMLf359x48aRM2dOypQpQ9q0aQkPD6d169akSJGCBw8eEB4ezrp16/j999/5/vvv6devX5SusgEBAYwePZqmTZuyZ88eatSogaWlJe3atWPevHnRJjc0qlWrlvqgatmyZfTo0YPFixdTuXJl1q1bR548eciePTs3btzgxo0bcd6TIrqffvqJ33//nfDwcN68eUNYWBgZM2bE19eXgIAAXrx4gVarRVEUdSWD6tWr4+zsTMaMGalcuTItWrRQy+oLFy6wbNkyHBwcOHjwICEhIXHOUyHXslzL4suRAFskW4ULWLBijj2eT/UUyJewMY7JtQJ37do1duzYQdq0aalfvz5v374lQ4YMdOnShRcvXvDy5UtevXrFtGnTCAwMpHbt2rRo0SLKeW/cuMHy5ctp3bo1a9asUScEadeuHYsWLeK7776L8TPT6/WYmZmh0WgIDg4mNDSUFy9eMH/+fE6ePKm+VxcXl2hBhsFgwMIisth58uQJBoMBKyuraPv+/fff1KlTJ66v76vxfpCt+8RW1/clxwqcXMtfxuWDGeK1X2zPBWpUTBHvNBKbr68v4eHhzJkzB3NzcwIDA8mePTuXL18mX7586PX6aEvuZMiQAXNzcwYOHEjTpk3RaDSMHDmS1q1bU7BgQUJDQ3n06FGsS3+FhYXx/PlzunTpwuPHj7l9+zbPnj1j5MiRBAcHR/lNKF++PMuWLcPb25vjx49z+/Zt9u3bR44cOahQoQJnzpzhyJEj9OzZk4ULF1KmTBmGDh3KkCFDcHd3Z8+ePaRJk4awsDAGDBig/mYYDAbOnz/Ptm3bcHJyIiAgAEVRyJIlC3ny5MHMzCzKNQgR1/TAgQOxtLRk2LBh7NixA4PBgJubG2fPnsXFxYWuXbvSu3fvaPdy5syZ6dy5Mzdu3MDV1ZXnz59jaWnJqVOnePr0KV5eXrx9+xaI6OW1dOnSWIdufG4ppn68lwkAFjFXc82cneOfRiK5desWy5YtY8mSJfz999+8evUKZ2dntFotTk5OFC5cmMKFC6PX69WHhYAatL579w4nJyfevHlD6dKl+eOPP6hXrx5dunRh3759rFu3ji5dugAREzEa6zRyLSfta1l8nSTAFsmSMbj+90E40xcE8ufCNP8pveRSgQN48eIFQ4cOZcaMGYwcORI/Pz927tzJnj17cHR05NGjR1hZWZE3b14KFy4MRHTTfd+rV6/o27cv1atXp1atWupMnFOnTqVVq1Y0atSIkSNH0rBhw2hdgtevX8+UKVOwt7cnY8aM/PDDD2TJkgUrKysaNGjA2LFjmTFjBs+fP2fMmDHR3rO5uTkPHz7kt99+o3r16tG6DUNED4I///zT5EHJl/RhS3ZCJKcKHMi1/CUlZBz0+ywsNLHFKp+VTqfj2LFj3LlzhzJlylC4cGGKFi2KnZ0dTk5OZMuWjaCgIHQ6HTqdTn2ApNVqGT16NOfOnaNPnz5kz56dn3/+mVGjRjFo0CDu3r1LeHg4CxYs4O3bt3Ts2FGdrO7Jkye4urqSIkUKChUqRGhoKIcPH/7/52DBtGnTqFevHgaDgZCQEDUw0Ol0LFiwAE9PT7p06ULnzp0ZMWIEvr6+rF+/nrt376rvq379+jx69Ii5c+dy8+ZNypYty6+//kqLFi2oXbs2PXv2xN7enuXLI7sxb9y4kfv379OkSZMYP6vw8HCGDx/Os2fPWLRoEa9fv0av17Nt2zauXLlCv379GDFiBIsXL2b79u107dqVVq1akTJlSgYOHMjRo0dxdHTk+++/J1u2bGTOnBkbGxsKFSrE0KFDsbGxwdLSkvDwcN69exflofSXlpBx0FGONzeHGO7Xz2XVqlXMmDGDQoUKUapUKZYtW0bz5s1Jnz49EyZMIH/+/Oq67GFhYVHKZ4A3b94QFBTEjBkzyJ8/P6NHj8ba2ponT54wY8YMateuzeLFi2nYsCE2Njbqg0a5lpP+tSy+ThJgi2Tn/eC666B35Mj2334kk1MF7uXLl8ycOZM5c+aQP39+3r17R+7cuaME8YMHD0ZRFBo1ahTj+33z5g3dunWjcOHCjB07lv379wMRE5hkzZqVrl278scff/Dzzz+zYsUK+vTpQ7Vq1dTgpGXLlixevJgcOXKwZs2aKGl7enoCEbOK9u3bN9q5Hz9+zLNnz2jYsCHDhw+nefPmbNmyhWfPnuHj46Mui7Znzx6ePn2a4O80udLv2wupUqGNYczgxyS3CpxcyyI+zp8/z61bt6hfvz7Dhw+nQ4cO9O7dG4gYfmBmZsakSZPUnggQMWazbdu2GAwGSpUqhZubG/nz58fFxQUPDw98fHzw9vYmICCAdu3a0bhxY3bt2sXJkyexsLDAwcGBnTt3AvDw4UPatm1Lw4YNcXd3p0+fPowbN46TJ08yZMgQ0qZNq+Y1U6ZMbN26lSdPnuDg4MD8+fNp3LgxtWrVQqPRsG/fPgB1vKrxfeTLlw9zc3NGjx6tvgcPD49oMysrihLrrMnBwcG4ubmRI0cORo0axW+//abO/hwcHIxWq6VIkSLMnj2bf/75BwsLC3x8fLh37x7Ozs7MmjUrxnTnz59PmjRpos22/OGDLhG3YsWKoSgKZcqUISwsjEmTJtG5c2ecnJwYPHgwOXPmVHu/+Pv7ExAQEOX4KVOmAKjzubRt2xZfX19++OEHWrVqRUBAAIcOHWLAgAEULlxYLWflWo4k17L4kiTAFokqT87P+0Q4by5zxg61w/OpnukLAsmRzfw/nzM5VeAyZMigzuL84sULAgIC1K6+RnH9cD158oRRo0bRuXNnihYtyvDhw3n48CEQMXGJubk5HTt2xNramps3b2Jra8u9e/coXLiw+oTX0tKSypUrc+LEiShpGwwGzp49C8Cvv/4aJd8AFy9eZMOGDeh0OgYMGECbNm0AyJkzJyEhIVStWpUMGTLg7++vThZ1/vx5SpYs+SlfZ6LSZMr8xc+pPHwICQiwk1sFTq5lER9ly5alW7du9OjRg4MHD9KsWTMqVoy4P4zdQm/fvk369OnVa/KHH37g119/5fnz5/Ts2VMttx8+fMjAgQOZOHEiu3btIn369KRKlYrZs2fj5uYWrYfD/v37GT16NPXr16dJkya4u7uTPn165syZQ7du3dizZw8NGzakYcOGFC9eHI1Gg62trTrD8bBhw6Kkd/z4cSwtLdV8azQa+vTpo75uPP+TJ0/w9PTExsYmyvHm5ubY2cU850hgYCDjx49Xh19MmjSJs2fP0qFDB1q0aKHOlJwrVy5q1qwZ78/fOOGn+G+cnZ1ZtmwZOp2OgQMHUq1aNXVpq1evXqmTqnbs2JGgoCDSp08f5XhfX1/1QeLBgwdJmTIlixYtUq+HtGnTMnXqVFxdXblw4QIdO3aMcrxcy3Itiy9LAmyRaPR6helj4z+h0H9RIJ9ZlG7her2CuXnCukAmpwrc+zN1njx5EuCTfrgAli5dqr6PhQsXMnfuXObNm8eQIUPUsaofW1aoSJEibN26lUGDBvH27VtevnyJl5cX4eHhpEiRgtu3b0ebNKRAgQLkypWLjBkz0qtXL3V7iRIlGDx4MP/88w/Pnj0jICCAbNmyUbZs2Rhnjf5SFIMByw4dP77jZzq35hPXTk5uFTi5lkV8aDQahgyJmDG6WrVqMe7TtGlTddIlo5iW3jl8+DAajYZ69epx48YNunbtCkTcO8aHRBDRSrZ8+XJ8fHxYuXIlTk5OHDt2DIh4MFSiRAkWLFiAu7s7BoOBM2fOYG9vr87pEZtGjRphb2//0XXRs2TJgrOzc7Qxoba2tuTIkSPGYz68nwG11S+moQvxZWNjQ+bMX/5B49eoVKlSeHl5MWvWrCgPDt9f5mrGjBl07dqVDh06RDl29uzZhISEYGlpSZ06dWIcclKpUiVWrlzJlClTCP//EmRyLUeSa1l8UYoQ4qPWr1+vuLm5fXQ/g8GgKIqihIaGKjVr1lQ2bdoU435BQUHKvHnzlAkTJih37txRFEVRjh49qjg6Oirnz59X/x48eLDi5uamzJ07V/n333+jpOHp6alUr15d8fX1jbJ97Nixytq1a+P93lxdXRVHR0flxYsX8T7G29tbqVevnnLz5k3lyZMnio+PjxIaGvrR4/z8/BQfH594n0d8uidPnsT5Xdy9e1epUKGCcu/evSjb/f39ldevX380/XPnzilNmzZVxo0bpyiKXMvi23Hq1ClFr9eb7PyPHz9WDh06FO/9fXx8lAEDBihPnz5N8Dnd3Nyi3a8i+ZNrWYjPS6MoMaw/JIQwidOnT1O6dOmPPhWOy969eylSpEi8n9TeuHGDFStWMH369Ggt7EIklFzLQiR/QUFB6pKVQiRnci2LL0kCbCGEEEIIIYQQIhEkvGlBCCGEEEIIIYQQKgmwhRBCCCGEEEKIRCABthBCCCGEEEIIkQgkwBZCCCGEEEIIIRKBBNhCCCGEEEIIIUQikABbCCGEEEIIIYRIBBJgCyGEEEIIIYQQiUACbCGEEEIIIYQQIhFIgC2EEEIIIYQQQiQCCbCFEEIIIYQQQohEIAG2EEIIIYQQQgiRCCTAFkIIIYQQQgghEoEE2EIIIYQQQgghRCKQAFsIIYQQQgghhEgEEmALIYQQQgghhBCJQAJsIYQQQgghhBAiEUiALYQQQgghhBBCJAIJsIUQQgghhBBCiEQgAbYQQgghhBBCCJEIJMAWQgghhBBCCCESgQTYQgghhBBCCCFEIpAAWwghhBBCCCGESAQSYAshhBBCCCGEEIlAAmwhhBBCCCGEECIRSIAthBBCCCGEEEIkAgmwhRBCCCGEEEKIRCABthBCCCGEEEIIkQgkwBZCCCGEEEIIIRKBBNhCCCGEEEIIIUQikABbCCGEEEIIIYRIBBamzkBS4+TkBIC5ubmJcyKEEEIIIYQQSYterwfg7t27Js5J0iQt2EIIIYQQQgghRCKQFuwPGFuub926ZeKcCCGEEEIIIUTS8v3335s6C0mayQNsb29vJk2ahI2NDW/evKFv374UKFAg1v1fvHhBq1atWLt2LdmyZQPg5MmTrF27lnTp0mFmZsaIESNImTLll3oLQgghhBBCCCGE6buIDx48mBw5cjB+/Hjat2+Pi4sL/v7+Me7r7+9P9+7def78ubrN09MTV1dXRowYwcSJEwkJCWHSpElfKvtCCCGEEEIIIQRg4gD7woULnDp1irJlywJQsmRJ/P39WbduXbR9dTod06dPp0SJElG2L1q0iPTp0+Pg4ABA2bJl2bZtG0+fPv38b0AIIYQQQgghhPg/k3YRP3bsGADp0qWLyIyFBfb29hw5coSePXuq+ymKwsyZM+natSs7duyIksbRo0fJmjWr+ne6dOkIDw/n5MmTtGzZMsbzVqtWLdY86fV6mUFcCCGEEEIIIZKBgIAAVq9ejaurq6mzApi4Bdvb2xsArVarbtNqtep2o/nz51OnTh21lfp9Pj4+0Y4HePv27efIshBCCCGEEEKIBPL29mbw4MGMHj0aV1dXbt++Hef+L168oHLlynh5eanb2rdvj5OTE05OThQvXpxnz5597mzHm0lbsNOkSQNAUFCQuk2n05E5c2b1bw8PD5YuXcrKlSsBCA0NBaBhw4a4ubmRJk2aaMdDZKt4TA4ePBjrazIrnhBCCCGEEEJ8HoMHD6ZYsWL069eP06dP4+Liwv79+7Gzs4u2b0xzcAHkypWLWrVqqX/nz5//s+c7vkzagl2pUiUAXr16BUB4eDjv3r2jYsWK6j558uThypUrXLhwgQsXLtC9e3cA3N3dadasGZUqVVKPh4gnIubm5vz4449f8J0IIYQQQgghhIhLYszBBeDk5ES7du3UfzHtYyomDbBLlChBhQoVOHHiBAAXL17ExsaGli1b0r17d7XVOi49e/YkICCAO3fuAHDmzBkaNWqkLuElhBBCCCGEECLx6PV6qlWrFuu/2MQ1B9f73p+Dy9jr+X0XL16kTJkylChRgq5du/Lw4cPEe3P/kcnXwZ4zZw5jxoxhzJgxvHz5kqVLl2Jtbc2///4b45jrDzk4OLBixQpmzZpFpkyZMDMzY9SoUV8g50IIIYQQQggh4isx5uACKF26NPXq1ePZs2fMmzePHj16sHv37ijpmopGURTF1JlISoxjsG/dumXinAghhBBCCCFE0vJf4qUZM2awePFi3N3dcXJyAqBcuXI4ODiwYcMGIGIOrmbNmmFhEdEWHBoaSlhYGDY2Nri5udGsWbMoae7Zs4eBAwfy119/JYmx2CbtIi6EEEIIIYQQ4tuQGHNwfShfvnwA2NjYfO7sx4sE2EIIIYQQQgghPrvEmIPr9OnTUcZsX7x4kSZNmsRrePGXYPIx2EIIIYQQQgjTC/d7i877Gdq032GRKvYlb4X4L/7rHFx+fn7MmTOHQ4cOUbhwYYKDgxk/fvwXyHn8yBjsD8gYbCGEEEII8a3xu3KAN3sWgqKARkP6uj1JVbS6qbMlkiCJl+ImXcSFEEIIIYT4hoX7veXNnoVYhJmjUTSgKLzZs4hwv7emzpoQyY4E2EIIIYQQQnzDdN7PQFHQW+ixCbJBY9CAYkDn89zUWRMi2ZEAOxnR66U3f1Ii34cQQgghvgbatN+BRoOiUQiwDkDRKKAxQ5smi6mzJkSyI5OcJSPm5hqGjH2HxyN9nPvlzWXO2KF2eD7VM2FGAMEhXz4QtEqpYdRgW7JnNWfsNH/uP4w7z59L8wYpadvMmnVbg9iyMyTR0s2T05zpY1MnWnpCCCGEEKZikSod6ev25M2eRYABNGakr9tDJjoTIgEkwE5mPB7puXUvPNbXCxewYPRgO+7eD6froHcEBn354NrGWsPSmanJlsWcjn19uX479vx+Tr06WdO2mTWzFwfwx8ogk+RBCCGEECI5SFW0Ota5i6HzeY42TRYJroVIIAmwvyKFC1iwYo49/z4wfXCdL7cFLv1NG1wP6G4rwbUQQgghRAwURUF58ACzPHnUbRap0klgLcR/JGOwvxISXEeS4FoIIYQQIm76/fsI+3024f/8beqsCPFVkQD7KyDBdSQJroUQQggh4qa/dJHw3bsi/rC1NW1mhPjKSICdzElwHUmCayGEEEKIuBkePEC3bi0A5pWrYFG+golzJMTXRQLsZEyC60gSXAshhBBCxM3w+jVhSxdDeDhmhZ2xaNTY1FkS4qsjAXYyJcF1JAmuhRBCCCHipgQGolv0BwQGonHIjrZ9BzRmEgoIkdjkrkqGJLiOJMG1EEIIIUTclHAdYcuWorx+DWnSYNm9B5oUKUydLSG+ShJgJzN5c5lLcP1/ElwLIYQQQsRNURR069ejeNyHlCmx7N4TTapUps6WEF8tCbCTmbFD7SS4RoJrIYQQQoj40P/zN4YL58HMDK1LF8y++87UWRLiqyYBdjLj+VQvwXUSCK7z5jI3yXmFEEIIIeJLf/68us61RYuWmOfPb+IcCfH1kwA7mZkwI0CCaxMH14ULWDDuZzsUg8Ek5xfRyXchhBBCRGW4fx/d+nUAmFerjsWP5UycIyG+DRamzoD4NMEhElybOrheMceel6/15MquJWz1KpSXL/5zumZlyqKtUBHd8WMYzpxOhJx+Ok3mLGhbtER58wbd1s0QFvblM2FpibZZCzTp06PbvAnlxfOPHqLJlBnLDh2/QOaEEEKI5MHw8iVhy5aAXo9Z0aJY1G9g6iwJ8c2QAFvESYLrSO/P3r5lZwiT3bQoL1+geHn957T1WzaDnx/aevXR+fmh37c3EXL8aRQvL8JevcLStTfaBo0IW7gAQkO/eD7Cfp+NZU9XtM1bELZgPorn4y+eByGEECK5UgL80S1eCEFBaHLkRNu2vSzHJcQXJHebiJUE15E+XBotNCzxexLo9+1Ft3sX2nr1Ma9ZK9HTjw/F8zFhC+ajyZIFy56uYIolPEJDCVu4AOX5cyxde6PJnuPL50EIIYRIhhSdjrClS1DevEGTNi2W3bqjsbQ0dbaE+KZIgC1iJMF1pC+57rgE2f8nQbYQQgjxSRSDAd26tSgPH4KVFdoevdDY2Zk6W0J8cyTAFtFIcB3pSwbXRhJk/58E2UIIIUS8hf+9B8PlSxHLcXXpilnmzKbOkhDfJAmwRRQSXEcyRXBtJEH2/0mQLYQQQnyUEhKC4dIlACxatcY8n6OJcyTEt0sCbKGS4DqSKYNrIwmy/0+CbCGEECJOmpQpsRw4CItWbbAoXcbU2RHimyYBtgAkuH5fUgiujSTI/j8JsoUQQog4aWxtsShb1tTZEOKbJwG2kOD6PUkpuDaSIPv/JMgWQgghhBBJnATY3zgJriMlxeDaSILs/5MgWwghhBBCJGESYH/DJLiOlJSDayMJsv/vwyA7c5YvnwchhBDCRIzLcelv3zJ1VoQQMZAA+xslwXWk5BBcG0mQ/X/vBdnaFi2//Pm/EXp90r0XvjXyXQghjMJ3uqM/dxbd8mUo/v6mzo4Q4gMWps6A+PIkuI6UnIJrI/2+vQBo69WP8veXZAyyLV17Y9nTlbCFCyA09Mtm4v9BtmW/AWiyZfuy5/5GmJtrCA4x8OiJngkzAggO+fL3h1VKDaMG25I9qzljp/lz/6H+i+cBoHmDlLRtZs26rUFs2RnyRc+dJ6c508em/qLnFEIkTeEnT6A/dBAA7U+t0NjZmThHQogPSYD9jZHgOlJiBNeazFlQvLw+Q+7iJkH2/4WGotu6mRT9B37Z835DHj3R07aXr0kePhnLq2xZzOnY17TlVdtm1iYtr4QQQn/7NuFbNgNgUacu5iVKmjhHQoiYSBfxb4gE15ESq+Va26KlySbaku7i/xcW9uXP+Q2ZMCPApMG1lFeQN5e5Sc4rhEg6DM+eoVuxHAwGzEqWxLxWbVNnSQgRi68uwA4ICGDBggWmzkaSI5XVSInZLVx588aks1lLkC0+N1N0C5fyKlLhAhaMHSpdQIX4linv3hG2aCGEhqDJmxdtq9ZoNBpTZ0sIEQuTB9je3t4MHjyY0aNH4+rqyu3bt2Pc78SJE9SrVw9nZ2fatGnDixcv1Nfat2+Pk5MTTk5OFC9enGfPnn2p7CcLUlmNlNhjrnVbN5t8ySgJssXXRMqrSMbyyvOpacadCyFMTwkNJWzJIvD1QZMxI5adu6Kx0Jo6W0KIOJg8wB48eDA5cuRg/PjxtG/fHhcXF/w/mBHx1atXnD9/nt9//53hw4dz8eJF9u3bp76eK1cuRo0apf5r3LjxF34XSZdUViN9lgnNwsKSxLrMEmSLr4GUV5HeL68mzAgwSR6EEKalGAzo1qxCefIEbGzR9uiJxsbG1NkSQnyESQPsCxcucOrUKcqWLQtAyZIl8ff3Z926dVH2s7a2ZsCAAeTJk4cqVaqQPXt26tatq77u5OREu3bt1H8lSpT4ou8jqZLKaqTPOlv4h+syS5AtQbb4ZFJeRfqwvDJFN30hhOmF/7UDw/XrYGGBZbdumKXPYOosCSHiwaSziB87dgyAdOnSRWTGwgJ7e3uOHDlCz5491f1sbW0BePLkCaNGjaJPnz6kSZNGff3ixYvMnTuX8PBwihYtipubG7ly5Yr1vNWqVYv1Nb1ej7l58p9QRiqrkb7IUlzGJaN6umLp2puwBfNRPB8n/nk+QmYXF8mRlFeRkuPSgUKIxBd+/Bj6I4cB0LZth1mu3CbOkRAivkzagu3t7Q2AVhs5lkSr1arb3/fgwQM2b95MgQIFGDVqFC4uLhgMBgBKly7NpEmT6N+/P9evX6dHjx7odLov8yaSIKmsRvqilVVpyVZJS7aILymvIsVWXqVPa4by/987kTTI9yE+J/3NG4Rv3QKARf0GmP9Q3MQ5EkJ8CpO2YBtboYOCIiszOp2OzJkzR9s3d+7cDBo0CIgIwhctWsT9+/dxdHTkp59+UvdLly4dAwcOxMPDg/z588d43oMHD8aap++//z5B7yWpkMpqJJO0BElLtkpassXHSHkVKa7yKpWdBo2ZGWGrV6G8fBFHKglgaYm2WQs06dOj27wJ5cXzxE0/nszKlEVboSK648cwnDltkjxoMmdB26Ilyps36LZujnUJQE2mzFh26PiFcye+FQYvL3QrV4CiYF6mLObVa5g6S0KIT2TSALtSpUosXryYV69e4eTkRHh4OO/evaNt27ZxHpcpUyYArKysor2WL18+AGy+wUkgpLIayaTdLCXIVkmQLWIj5VWk+JZXyssXKF5eiX7+sN9nY9nTFW3zFqYrr7ZsBj8/tPXqo/PzM0155eVF2KtXWLr2RtugkZRX4otTfH0IW7wQwsIwc3TEouVPshyXEMmQSbuIlyhRggoVKnDixAkgYiy1jY0NLVu2pHv37qxcuRIAd3d3zp49qx53/PhxWrZsiYODA6dPn+bIkSPqaxcvXqRJkyY4ODh8ybdiclJZjZQkxjBKd3GVdBcXH5LyKpKUV5GkvBLfMiUkhLDFi+HdOzSZM6N16YLmK5gTSIhvkcmX6ZozZw5v375lzJgxrFixgqVLl2Jtbc2///7LkydPAHj79i1Dhw5l5syZ/P7775QpU4bRo0cD4Ofnx9SpUxk9ejSbN28mODiY8ePHm/ItfXFSWY2UJCqrRlJpVUmlVRhJeRVJyqvopLwS3yJFr0e3agXKUy+wtUPbvScaa2tTZ0sIkUAm7SIOEV25p0+fHm374cOH1f+7uLjg4uIS4/G1atWiVi3T/AgnBVJZjZSkKqtG0l1cJd3FhZRXkaS8ip2UV+JbE+7+F4Zbt0CrxbJ7d8z+v7qOECJ5MnkLtkg4qaxGSpKVVSNpGVJJy9C3S8qrSFJefZyUV+JbYl7sB0iVCm37jpjlyGnSvLzxDeba/de88Q02aT6ESM4kwE6mpLIaKUlXVo2k0qqSSuu3R8qrSFJexZ+UV+JbYZYzJylGjsa8SBGT5mPf2cd0nrgPtz9O0XniPvad/fI9WIT4GnyWANvPzy/KpGQicUllNVKyqKwaSaVVJZXWb4eUV5GkvPp0SbK8srQ0ST7E101j4t/BN77BzNt8hZAAaxSDBkWB+ZuvSku2EAmQoABbp9PF+frGjRvZv39/gjIk4maVUiqrRsmqsmoklVaVBNlfPwmuI0l5lXBJrbzSNmthkjwI8Tk9exNAkJ8tnjcK8eyeIwaDBoOi8PxNoKmzJkSyk6AAu127dnjFshanTqfjzz//jPV18d+MGmwrlVWSaWXVSCqtKgmyv14SXEdKtPLKFC2nUl6p1PIqfXqTnF98HZSQYBRvb1NnI5pAPyue3i6AYjBHMUSEB2YaDVnS25g4Z0IkPwkKsF+/fs2BAwdwd3fn8eOo4zNWrVrF8+fPEyVzIrrsWc2lspoEgusUlpr/loBUWlUSZH99JLiOlJjllbZZC9PcH1JeqRTPx+g2bzLJuUXyp+j16FYsJ3TmdAyPk874Zo9H4QwaGYxBb4GVnT/fOd3Fwhx6tyhCensrU2dPiGQnXgH28+fP6d69O//++y8Q0YK9ceNGhg0bRu3atalRowa//fYb+/btY+7cudjY2NClS5fPmvFv1dhp/lJZNXFwbWOtoVv7RPjBkUqrSoLsr4cE15ESu7zSpE9vuvtDyiuV8kIaEcSnUxSF8C2bMdy5E7Hkm1nSmGf4yTM9Lv198fZV+N7Rgu3LsjClT1mWjaxBzdKmuc+FSO7idXdPmzaNY8eO0ahRI/r06UPevHl5+vQpdevWpW3btuTPn59NmzbRv39/MmbMiLu7OyVLlvzcef8m3X+oN8l5v8bKakIYg4fMGcwTJ0GptKokyE7+JLiO9DnKK93mTaa9P6S8EiLB9IcOoj91EjQatB07YebgYOos8fK1Hpd+Prx8bSBvLnOWzbYnVzZrCudNLy3XQvwH8QqwM2XKxJQpU+jduzdPnjyhR48e2NjYMG3aNEaMGEH+/PkxNzenffv2+Pj4ROs2LpK3r7Wy+qneDx4WrU7EST+k0qqSIDv5kuA60ucqr5QXz01/f0h5JcQn01+5TLj7XwBYNGmKeaHCJs4RePsY6NTPlyfPDGTPas7y2faktU8arepCJHfxupOGDRuGpaUlhQoVYvPmzSxYsICSJUsydOhQFixYwKlTp3B3d2fEiBGsXLmS6dOnf+58iy/ka66sfooPg4cnzwyJewKptKokyE5+JLiO9LnLqyRxf0h5JUS8GR49Qrd2DQDmFSthUamyaTME+Pkb6DzAlweP9WTOaMaK3+3JlFg984QQ8Quwvb29cXNzY/To0axZs4azZ8/y22+/ERYWRlBQEM7Ozly4cAGAQoUK0aFDB+7cufNZMy4+v2+hshofXyx4kEqrKkkEESJeJLiO9KXKqyRxf0h5JcRHGd6+IWzJYtDpMCtYCIsmTU2dJQKDDHQf7Mvtf8NJl0bDijn2ZMsiwbUQiSleAbZWq2Xt2rW0bduWXbt2cf36df766y+8vLyoV68eK1asYOjQodSqVYuFCxcycuRIFixY8LnzLj6jb6myGpcvHjxIpVWVJIIIEScJriN96fIqSdwfUl4JESslKAjdokUQ4I8mWza0HTuhMfHEZqGhCq7D3nH5Rjip7TQsn5OG3DksTJonIb5G8brTf/nlF9q0acOOHTsICgri5s2brFq1igwZMvDq1SumTJlC1apVKVy4MHPmzCFbtmxMmTLlc+ddfCbfYmU1JiYLHqTSqkoSQYSIkQTXkUxVXiWJ+0PKKyGiUcLD0S1fhvLyBdjbY9m9JxoT/37pwhX6j3zHmYs6rK01LJ1lT/68ElwL8TnEK8C+evUqDRo0oHbt2lSrVg1bW1s8PT3JmzcvEyZMYNasWfj6+jJp0iSsra159eoVL168+Nx5F5/Bt1xZfZ/JgweptKqSRBAhojD5/fF/Ul4lkftDyishVIqiEL5pI4Z/70GKFFh274EmdWqT5kmvV/h5nB+HT4aRwhIWTUuN8/dak+ZJiK9ZvALsrVu3MnHiRI4dO8bhw4cpW7YsdnZ27Ny5kypVqtCyZUtu3rxJjRo1aNCgAb1792bVqlWfO+8ikUllNUJSCR6k0hopSQQRAkg694eUV5GSxP0h5ZUQAOj370N/9kzEclydXDDLms2k+TEYFEZO8WfPwVC0FjBvSmpKFbM0aZ6E+NrFe5kub29v/Pz8mDlzJnnz5mXq1KlUr16df//9l969e5MrVy569uzJ4MGD6dixI7dv3/7ceReJSCqrEZJK8KCSSqsqSQQR37ikcn9IeRVdkrg/pLwS3zj9pYuE794FgEXzFph/X9DEOYLte0LYtjsEMzOYMS4VFcvIb6cQn1u8Z1tImzYte/fupUCBAjg6OlKsWDHGjBmDk5MTBw4cIF26dLx8+RI7OzssLCyoXbs2AQEBnzPvIpFIZTVCUgkeopFKqypJBBHfqKRyf0h5FbskcX9IeSW+UYYHD9CtWwuAeeUqWJSvYOIcRWhUOyVN6qbkVzc7alVJaersCPFN+KTpDDUaDQBVqlTBzs4OADc3N9KmTYuZmRllypRR93VxccHW1jYRsyo+B6msRkgqwUOspNKqihZEWEpXt88tqdwfUl59nATZkZJCeSW+DYbXrwlbuhjCwzFzdsaiUWNTZ0llYaFhyshUNK5jZeqsCPHNSJT1An744QcmT55M2bJl1W3GYFwkXVJZjZBUgoePkkqr6v0gQtushUny8K2wSpk07g8pr+JPguxISaG8El83JTAQ3aI/IDAQTfbsaNt3NPlyXEII00q0EiB9+vSJlZT4AqSyGiHZBNdGUmlVqUGElD2f1ajBtia/P6S8+nQSZEdKCuWV+Dop4TrCli1Fef0a0qTBslsPNNKrSoivQnh4OJ6engk6Vh6xfYOkshohUYJrU/yQSqVVpXg+Rrd5k0nO/a3IntVcguskUF4lhATZkZJCeSW+PgYPD5QHHpAyZcRa16lSmTQ/fx8KYf6KQBQleZRRQiRlf//9N1u2bEnQsYkSYB88eJAyZcrQq1evxEhOfEZSWY2QWC3X2mYtpNJq6iD7xXOTnPdbMXaavwTXJi6vUlgmfMiVBNmRkkJ5Jb4u5k750XbrgdalC2bffWfSvBw5GcqQMX78viSQ/UdDTZoXIZKDCRMm4O/vH+vrq1ev5tGjRwlKO1EC7NmzZ+Pr60uDBg3w9/ene/fuvHz5MjGSFolIKqsRErNbuCZ9eqm0SqX1q3b/od4k55XyKoKNtYZu7f/b5EQSZEeS8kokNvOCBTHPn9+keTh9IYy+bu8I10P9mimoVkFW2BDiY3bt2sX+/ft58uRJtNcOHDjA9evXCQwMTFDa8QqwfXx88PDwAMBgMLBy5Ur1tZcvX/Lvv//Sp08f6taty+zZszl+/HiUfYTpSWU1QmKPudZt3iSVVqTSKhKXlFcRjOVV5gzm/zktCbIjSXklviaXr+twHfaOsDCoVsGSKSNTYW4uEw0L8aGQkBCWLl1KWFgYAA0bNmTXrl3Uq1ePypUr4+bmxpEjR3j9+jUTJkwAoEaNGgk6V7wC7NmzZzN9+nQAFi9ezG+//cbs2bM5cOAAp0+fpnv37vTp04dZs2axbt06AE6cOJGgDInEJ5XVCJ9jQjPlxXOptP6fVFpFYpDyKsL75dWi1Ql7gv4hCbIjSXklvga37+noNtiXoGCFciW1zJ6QGq2FBNdCxOSPP/5g+vTp1KpVi9WrV9O4cWNOnTpFqlSpSJUqFQcPHqRXr15Uq1aN0NBQfv/9d1q1apWgc8UrwN69ezdHjhzh0KFDrF69GkVRWLhwIX379iVTpkwMGjSIdevW8eLFC+bPnw/A8+cyLjIpkMpqhM85W7hUWiNJpVX8F1JeRfiwvHryzJBoaUt5FUnKK/EplAB/wtauRklgl9HE5vEonM4DfPEPUCheRMu8KfZY/of5GoT42l28eJGmTZuSPXt2pk6dSo8ePciUKRMnTpzA3d2d2bNnY2trS+7cuVEUhfz/YehHvALsffv2MWTIEKZMmUKBAgVwcXFhypQp5MiRg7dv3xISEkLGjBkZOnQo1apVI23atGrzuzAdqaxG+BJLcUmlNZJUWkVCSHkVQcqrL0vKKxEfik5H2NIlGM6fR7d6lamzw5Onejr188XbV6FgfgsWTUuNtZUE10LEZe3atbi4uDBu3DiOHTtGhw4dMDMzY926dezevZuhQ4fy66+/smPHDgYOHMi4ceMSfK54Bdhp06alS5cuDBo0CHNzc6ysrGjcuDENGjTg7du3TJo0iX79+rFnzx4ArK2tSZs2bYIzJf47qaxG+JLrXEulNZJUWsWnkPIqgpRXUl6JpEcxGNCtW4vy8CFYWWHRpKlJ8/PilZ5O/X149cZAvlzmLJtlj52trLorxMeEhITQs2dPhg0bxrlz54CIpbjOnDnDzp076datG05OTgC0atWKUqVK8fTp0wSdK953pF6vp1y5cvTr148HDx4AkC1bNt68ecO+ffto0aIF7du3x8/PjxcvXuDs7JygDIn/TiqrEb5kZdVIKq2RpNIq4kPKqwhSXkl5JZKm8D27MVy+BGZmaLt0xSxzZpPl5a23gU79fPF6ZiBHNnOWz7EnTWoJrkXy4+3tzeDBgxk9ejSurq7cvn07zv1fvHhB5cqV8fLyUredPHmSXr16MXLkSEaPHk1ISMhHz9u1a1fs7OxYuXIl58+f58iRI1y8eJExY8YwY8YM6tevz6hRo7h06RLu7u78/vvvCXp/8b4rR44cSdmyZcmYMSOPHz8GIEuWLLx9+5aZM2cyfvx4NBoNU6dORafT0ahRowRlSPw3UlmNYIrKqpFUWiNJpVXERcqrCFJeSXklkqbwM6fR798HgEWr1pjnczRZXt75Geg80JeHnnqyZDJjxRx7Mqb/7ysMCGEKgwcPJkeOHIwfP5727dvj4uIS65rUxiWg35/fy9PTE1dXV0aMGMHEiRMJCQlh0qRJcZ5zzpw57Nmzh2zZshEeHs7169fZtm0b1atXx9vbmzZt2uDk5MTp06dp27Ytvr6+9OjRI0HvL94B9qlTp2jSpAkZM2YkKCiIfv36MWLECI4cOULevHk5ePAgHTt2xN3dnSFDhlCtWrUEZUgknFRWI5iysmokldZIUmkVMZHyKoKUV/8n5ZVIYvR37xK+cQMA5jVrYVG6jMnyEhBooPtgX+78G076tGas/N2erFkkuBbJ04ULFzh16hRly5YFoGTJkvj7+6srUb1Pp9Mxffp0SpQoEWX7okWLSJ8+PQ4ODgCULVuWbdu2xdml+++//+bVq1d4enpiZ2dHaGgoJ0+e5OnTp4wYMQIPDw/Sp0/P7t27SfH/38F06dIl6D3GO8Du3r27uiaYXq/n7NmzFCtWjF9++YXTp0+ze/duateuzeHDh+natWuCMiMSTiqrEZJCZdVIKq2RpNIq3iflVQQprz4g5ZVIIgwvnqNbsQwMBsx+KI5F3Xomy0tIqEKvYe+4cjMc+1Qals+xJ6eDhcnyI4SRXq+nWrVqsf6LzbFjx4DI4NXCwgJ7e3uOHDkSZT9FUZg5cyZdu3YlTZo0UV47evQo6dOnV/9Oly4d4eHhnDx5MtbzDh8+nL1795I7d27Mzc0pU6YMBoMBMzMzUqVKRWhoKKdOnWLo0KGUKFGC8uXLs2bNmk/9WIBPCLDbtm2r/r9KlSocPXqUadOm0bBhQ4oUKcKPP/6IhYUFBkPiLSci4kcqqxGSUmXVSCqtkaTSKkDKKyMpr2Ih5ZUwMcXPD92ihRAcjCZXbrRt2qLRmGaG7jCdQr8R7zh3SYeNtYYlM+1xyiPBtUjevL29AdBqteo2rVarbjeaP38+derUUVup3+fj4xPteIC3b9/Get5atWrh7+/Ppk2bqFu3LgUKFGDMmDH4+vri5OTEqlWr+O6778iQIQPDhw/Hzc1NfRjwqRJ0l7q5uUX5+9KlSyxatAgvLy9SpEjB3LlzqVChQoIyJD6NVFYjJMXKqpGx0mrp2hvLnq6ELVwAoaFfNhP/r7Ra9nTF0rU3YQvmo3g+/rJ5IKLSCqCtVz/K3+LbIOVVBCmvPkLKK2EiSlgYYUsXo3h7o0mfAcuu3dC8V4n/ksLDFYaO8+Po6TBSpoDF01Pj/L1p8iJETMzNzTl48OAnH2dsjQ4KiqwH6HQ6Mr83gaCHhwdLly5l5cqVAIT+/3eoYcOGuLm5kSZNmmjHw8e7dFtZWbFr1y6yZ8/Opk2bqFChAg0bNqR79+7cvn2bTJkykSVLFvLkyQNA0aJFCQkJIWXKlJ/0HhNl6sFmzZqxZ88eevToQYECBaI9gRCfh1RWIyTlyqqRtAxFkpahb5OUVxGkvIonKa/EF6YYDOjWrkF5/BisrdH26InG1tYkeTEYFEZO8eefQ6FotTDv19SUKGppkrwIkdgqVaoEwKtXrwAIDw/n3bt3VKxYUd0nT548XLlyhQsXLnDhwgW6d+8OgLu7O82aNaNSpUrq8RDRKm5ubs6PP/4Y57ktLCzInj07AM2bNydLlixYW1uzaNEiPDw8MDMzw/a9+97V1RVLy0+/9xJtbn+tVsuAAQNYv369zCD+BUhlNUJyqKwaSaU1klRavy1SXkWQ8uoTSXklvqDwne4Yrl4Bcwssu3bDLGNGk+XF953CpWs6zM1h1vjUVChjgvtPiM+kRIkSVKhQgRMnTgBw8eJFbGxsaNmyJd27d1dbrePSs2dPAgICuHPnDgBnzpyhUaNGZMuWLd75MDOLDINtbGyoX78+3bp1o1WrVup2e3v7KPvFO+1PPiIOjx8/ZsuWLZ90THzXQTtx4gT16tXD2dmZNm3a8OLFC/W1hKyDlpxJZTVCcqqsGkmlNZJUWr8NUl5FkPIqgZJgeWVWpqxJ8iA+n/CTJ9Afiujqqm3TBrM8eU2an7RpzFi7wJ7fJ6WmRiUJrsXXZ86cObx9+5YxY8awYsUKli5dirW1Nf/++y9Pnjz56PEODg6sWLGCWbNmMXr0aMzMzBg9evR/zleZMomzWoBGUZREq2kMHz4cOzs7RowYEe9jXFxcKFasGP369eP06dMMHDiQ/fv3Y2dnp+7z6tUr1q1bR8OGDTl37hxjx47Fzc2NDh064OnpSYMGDdi1axcODg78/PPPpEiRQp3x/FN9//33ANy6dStBx39u67YG0baZtVRWTVxZrV8jBTPGpSZ02m8o7y16H1+a7DmwdO2N8vy5acY4AqRIgWVPVzRZsphsjCNELH+irVcf3e5dCRrjqMmWjRRDh32GnAmAJp28uXUvYfeXBNcR/mt59V/Lm/9KyqtIxvJKfD30t2+jW7wQDAYs6tTFonYdU2dJiCQvqcdLppZoLdiPHj3C3d2dN2/exPuY+K6DZm1tzYABA8iTJw9VqlQhe/bs1K1bF0jYOmjJmQTXpg+uE4O0DEWSluyvkwTXERKzvDJVy6mUV5H0+/aiO56wWWVF0mN49gzdiuURy3GVLIl5rdqmzpIQwkQ8PDzo168fs2fP/s9pfXKAHRYWRlhYWJRtiqIwatQoDAYDn9IgHt910GxtbdFoNDx58oRffvmFPn36qDPQJWQdtORs3dYgqawmgeC6WsX/PtmIVFojSZD9dZHgOkJil1faChVNdn9IeRXJcOa0Sc4rEpfy7h1hixZCaAiavHnRtmptsuW4vH1liVshTG3ixIns378fjUaDoigsXbpUnb38U31ygL1y5UqWLl0aZdvChQs5f/48ZmZm1KkT/6418V0HDeDBgwds3ryZAgUKMGrUKFxcXDAYDAlaBy2uRdH1en28828KW3aaZnz511hZTahenaypW80qUdKSSmskCbK/DhJcR/gc5ZXu+DGT3h9SXomvhRIWRtiSReDrgyZjRiw7d0VjYZolsHbvD6F687ecOh/28Z2FEAlmMBgICAhQ/36/e3tAQADnzp2jYsWK9O3bl/Xr1zNjxgzWrl2boHN9coB969YtLl26pP69fft25syZw3fffcfUqVMpVqxYvNOKbR20tGnTRts3d+7cDBo0iGHDhtGpUyfOnj3L/fv3E7wOmoi/r7WymhDG4GHPweBES1MqrZEkyE7eJLiO8LnKK8OZ0ya/P6S8El8FCwvMCnwPNrYRy3HZ2JgkGwePh/LzeD8CgxQOHjfB3AZCfEOWLVvGrFmzANi/fz/NmzfnyJEjeHl5cfHiRX744Qd+//13Tp48ydSpUwEStM43JCDAzpEjB/fv3wci1iIbOXIkTZo0wd3dnQsXLrBx48Z4pxWfddBikilTJiBisfCErIN28ODBWP+Zm5vHO//fgq+5svqp3g8eDh5L3CfNUmmNJEF28iTBdYTPXV4lhftDyiuR3GnMzNDWq0+KEW6Ypc9gkjycOh9G/5HvCNdDg5opcBtgmjW3hfhWrF27li1btuDh4cFvv/2GwWCgb9++NGvWDJ1Ox/Llyzl69CgTJ07kp59+QlEUHjx4kKBzxSvAvnPnDvPmzeP8+fPkypWLly9fsmTJEiZNmsSvv/7K6NGj0Wg0vHv3Dk9PT3x8fPD19f3oeOz4roPm7u7O2bNn1eOOHz9Oy5YtcXBwSJR10ETMvoXKanx9ieBBKq2RkkIQIeJPgusIX6q8Sgr3h5RX4mugsTVNUHvxWhiuw3zR6aBGpRRMGZkKMzPTjP8W4lsxYsQIypYty6hRo0iTJg1VqlShQYMG6HQ69Ho9Wq2W3Llzs2fPHoYPH06qVKkIDAxM0Lks4rPTnDlzOHz4MPPnzwciJjWbOXMmAMOGDWPYsKhL5OzcuROIWJx769atfPfdd3GmPWbMGMaMGcPLly+jrINmnBn87du3TJ8+ncaNG2NhYUGZMmVo27YtEHUdtEyZMmFmZsaoUaM+8WMQH/qWKqsf8yWDB2Ol1dK1N5Y9XU2zJM7/K62WPV2xdO1tsiVxjEt2GZfEScgSXuLzk+A6wpcur5LC/SHllRCf7uZdHd0HvyM4BMqXtmTmuFRYWEhwLcTnVqtWLWrVqsWvv/7K48ePKVasGN27dydt2rS8ffuWpUuXMmPGDIYMGUKXLl1InTp1lHm+PkW8AuxLly7RtGlTnJ2dSZ06NcuXL+fmzZsYDAbSp09Pp06dsLa25vHjx+zevZtSpUpx6dIlfvzxR7JkyRJn2jY2NkyfPj3a9sOHD6v/d3FxwcXFJdY0ihUrxqJFi+LzVkQ8fIuV1diYIniQSmukpBBEiNhJcB3BVOVVUrg/pLwSyYHi4wOpU6MxS7TVaRPk3wfhdB7gS0CgQokiWub9mhpLSwmuhfiShg8fzs6dOzl16hQQMfz5+fPnuLu7kz9/fpo3b054eDg+Pj4UKlQoQeeIV0mzadMmJk+eTKtWrahTpw4//vgjHTt2ZNiwYYSHh7NkyRIcHBzo1asXDg4OzJw5kyNHjjB58mSTLXkgEuZbrqx+yJTBg3S/jJQUusOK6CS4jmDq8iop3B9SXomkTPH1IXTWDHQrlqOEmW6mbk+vcFwG+OL7TqFQfgsWTU+NVUqpIwvxJa1YsYLmzZtTqlQpdXx15syZefPmDfXq1WP9+vWkTp2ajRs3EhAQQPXq1RN0nngF2DlyRP2hcnJyQq/X4+Liwu7duylZsiQ9e/bk3LlztGnTJkEZEaYnldVISSF4kEprpKQQRIhISeH+kPIqUlK4P6S8EkmREhJC2OJF8O4dyquXEG6ae/T5Sz2d+vny+o0Bx9zmLJ1lj62NaVvThfgWbdq0CYAMGTLg4+PDunXr2Lx5M5cvX6Z79+4EBwczb948fv31VypVqkSrVq0SdJ4E3d0FChSgfv2ILmnp0qVj3rx59OzZk2HDhlG0aNEEZUSYllRWIyWF4MFIKq2RkkIQIZLG/SHlVXRJ4f6Q8kokJYpej27VCpSnT8HWDm33nmisrb94Pt54G+jU35enLwzkdDBn+Rx70qSW4FoIUyhQoAArVqzAzMwMc3NzJkyYwNWrVylSpAjbt2+nadOmeHh4MGfOHBYtWoSFRbxGU0eToKNy5coVbVvfvn2xs7Nj0qRJLFy4MEGZEaYhldVISSF4+JCMcYyUFMacfsuSwv0h5VXsksL9IeWVSAoURSF821YMt26BVotl9+6YpUv3xfPh62fApb8Pjzz1fJfJjBVz7MmQTpaDFcJUjJN0A9jZ2bFhwwa1cVhRFCpVqoSiKGTPnv0/nSdRH6F16tQJJycn7t27l5jJis9IKquRkkLwEBtpGYqUFFrqvkVJ4f6Q8urjksL9IeWVMDX90SPoTxwHjQZtuw6Y5cj5xfMQEGig2yBf7nnoyZDOjJVz7f/H3n2HN1W2cRz/pmnT0hYotGzK3qgIggIyRUWWiizZLSKjgIiAgLJkiYBMZQuILKmMFwEFBNl7ORiy925pKdA28/2jbUKBQpp52tyf6+K6SJqc56ny/O7nzjiH/HmluRZCKR5triGp+U4503jTpk25eNH2F2Yd/hmVPn36EBIS4ujDCieQzaqFEpqH55FNq8WjTYRX1WpumYMnUcL6kLyynjTZyRSSV8K1DP/8jX71KgC8330PtRu+uhifYKL757H8fVxPUDYV8yYHUbigbR81FUI4h9djVxXo27cva9eupWLFipw4cYI1a9bYfmx7JwdgMBhS3c6ZM6cjDiucSDarFkpoHqwlm1YLcxNRs5ZbxvcUzZv4uX19SF6lnzTZyRSSV8I1jJcuoVv4I5hMqF+vgbruGy6fg1ZrotcXsew/oiMwQMUPk4MoVVyaayEygmLFirFkyRL++ecfevXqZfNxHNJgh4eHs3//fkccSriAbFYtMlJznUI2rRaGjRvQ7djulrE9Rdtm/tJcKySv0kua7GQKySvhXKboaLRzZoFWi1fZsng3a+7yS8Xq9Sb6Dr/Hjr1a/Hxh1oTsvFDGx6VzEELY5969exw8eNCuY1jVYMfGxqb5OfSoqCj279/PgwcP7JqIcA3ZrFo4orl218eTZdNqYdy7xy3jeorFKx5Kc62AvLKVNNnJFJJXwjlM8fFoZ8+Ee/dQ5cuPT1g4KrXrv+88fHwcG7cm4uMD078JonIFjcvnIISwz8KFC9m9e7ddx7Cqwf7qq68YNGgQAB9++CH379/n6NGjbNmyhaxZsz7xGXahTLJZtXDUO9c+NWvJplU2rZnaL78muGVcySuLerXs26RLk51M8ipTMhkM6ObPw3T9OmTLhqZrN1R+Wdwyl7fr+hLgr2LyyOy8/qo010JkNHFxcfz444/cvHnTruNY1RlfvnyZ48ePExkZyalTp9i7dy+bN29m4cKFaDQaihYtSqKrL8Mh0kU2qxaO/Fi4bsd22bTKplU4mOSVRfcwfxrWs79ZkCY7meRVpmIymdBHLsf430nQaNB06YoqRw63zadWVV82rwjmzVpu+LcthLDbxIkTiYuLIyHBvjcXrGqwIyMj+fXXX9m4cSNlypThxx9/5OjRoxw7dsx8zet169Yxa9Ys5s+fT2RkJPv370evz1gfpcusZLNq4ejvXBv37pFNK8imVTiM5JVFSl6t3xzvkONJk51M8irTMGzZjGHP7qTLcXUIwyvUvmvXOkKO7PKpTiGUbs2aNaxevTrVfVu3bmXp0qWoVCpeeOEFu45vdQoEBwcTFhZGjRo1OHDgAEePHiU+Pp4FCxZw48YNtm7dyuzZs5k2bRrjx49n4MCBTJgwwa7JCfvJZtXCWSc0k01rMtm0CjtJXlk8mlebt2sddlzJq2SSVxme4egR9Gv+B4D3+x+gfvFFN89ICJFR7Nixgy1btphvHz9+nL59++Lt7U2PHj0ICwuz6/hWN9jDhw+nS5cuvPDCC5QoUYJDhw5Rp04d5s6dy7hx43jvvfc4dOgQhw8fZv/+/WzZsoWBAwfaNTlhH9msWjj7bOGyaU0mm1ZhI8krC8krF5G8yrCMF86jW/QTAOqatVDXru3yOfy+JYGoaKPLxxVC2C9btmycPn0agNOnT9O5c2fy5s3Lzz//jMlkYtmyZXYd3+oGe//+/YwdO5YaNWpQtGhRNBoNr776KoULF6ZgwYKcOXPGrokIx5LNqoWrLsUlm9ZksmkV6SR5ZSF55WKSVxmOMeoO2jmzQafDq1x5vJt+4PLLca3dmMCnQ+7RNuIusfekyRYiI4iNjWX//v0YDAZef/11Ll++zKFDh+jQoQONGzdm1apVlC9fnvPnz3P27Fm7xrL6yvfr1q0jICAAgBo1amAymWjdujVr1qzhhRde4NSpU3ZNRDiObFYtXH2da8PGDQD4NGqc6rYrpWxaNRE90HSLQDtzOrj6JITJm1ZNtwg0ET3QTv8e06WnX+pPeDbJKwvJK8kr8XyGP/6A+/dRFSzolstx/bE9kc9H3sNkgmqVNWTL6trmXghhm/Hjx7NixQq8vb3Jli0ber2esLAw/Pz8OHz4MG3atMFoNHL9+nUMBgOHDx8GoEiRIowcOZKgoCCrx7LqHWydTodOpzPf/vbbb9m7dy+bNm1i/PjxaDQaatSokb7fUjiFbFYtXL1ZTSHvDCWTd4bEc0heWUheSV4J63g3a476jXpoPu6KysX/Vnbt1/LpkFgMBni/gR9DPgt0+bvnQgjbbN++nYIFC/LWW2/x9ttvkz9/fnQ6HXFxcdy4cYNixYpRqFAhihUrhk6n4+7du5w+fZrz58/j7W31e9KAle9gjxo1ilWrVlGoUCH8/f2Jj49nzJgx+Pr6kj9/fqZNm4aXlxfdu3dHr9eTLVs23njjDRo1amTTfwBhG9msWrhrs5pC3hlKJu8MiTRIXllIXkleCeupvL3xee99l4978C8tPQbGoNNB/Tq+jB6UFS+vzNlcm4xGVF5yNnSlkP8fjtGnTx+aNm1qvj169Giio6N58OABW7du5fr160yYMAGTyUSvXr2IjIzk8uXL5MmTB40mfde1t6rB3r17N2+//Tb58uVDrVYTFBTEX3/9RVxcHCaTidy5cxMcHExCQgKnTp3i5s2b3LhxQxpsF5LNqoW7N6spZNOaTDat4jGSVxaSVxaSV0Kp/j2po2u/WOIToFY1DRO+yoa3d+ZsrgFUXl5oF/6I6eaNJ3+o0eDTrAWqkBB0kcsx3bju+gkCXlWr4VOzFrod2zHu3eOWOajy5sOnRUtMd+6gWxEJWsdd8cE8Rp68aDp0dPhxPdGjzTVAmTJlOH78ON9++y179uzhyy+/pFmzZsybN4+qVasCEBoaatNYVjXYS5YsIVeuXObbf//9NxMmTOCLL75g9OjRHD58mG+//ZaaNWsCcPbsWYoUKWLThET6yWbVQimb1RSyaU0mm1aRTPLKQvLqSZJXQmlOndXz0acx3H9g4tWKPkwbkx2NT+ZtrlOYbt7AdOXKU3+mnToZTbcIfJq3cNv6MPwSCffu4dOoMbp799yTV1euoL11C01ED3yavOeevBI2K1asGFmyZAGgWrVqrFmzhl69etG9e3fWrFlj17Gt+rzBo801QKlSpciePTtlypThp59+4vPPP2fAgAH8999/ABQvXhy1i0864alks2qhtM1qCvmOYzL5jqPHk7yykLxKm+SVSGE8dw791j8xmVyfFQAXLuvp9GkMMfdMVCjvzYxx2fHzzfzN9XMpZH1IXgl7VKxYkYYNG5pvBwYGMnfuXF588UWmTZtm17Ft+kC/n58fI0eONN9u3rw5M2fOZNKkSXZNRqSPbFYtlLpZTSFFIJlCirJwPckrC8mr55O8Esbbt9HOnY1+1UoMbvgI8LUbBsI/ieF2lJHSJbyZ820QgQHyPVgzhawPySvhSGq1mvHjx3PhwgXu3Llj83FsTorHT1X+0ksv8cUXX6Q627hwHtmsWih9s5pCikAyhRRl4TqSVxaSV9aTvPJcpgcP0M2aAQ8eoAothLrSKy4d/3aUgfDeMVy7aaRIITXzJgeRPZs0109QyPqQvBKO5OPjw8SJE4mNjbX5GA5Ni0KFCuHj4+PIQ4qnkM2qRUbZrKaQIpBMIUVZOJ/klYXkVfopMq/y5nP9HDyISa9D+8NcTLdvQ44caLq49nJcd2ONdOodw4XLBgrk9WLBlCBCckpznSaF1HPJK+FI/v7+FC9e3ObnS2JkMCWKqmWzmiyjbVZTSBFIppCiLJxHmmsLySvbKS2vfFq0dP34HsJkMqFbuhTT2TPg54emSzdU2bK5bPz7D4x07hPDqXMGcoV4sWBqEPnyyDmFnksh9VzySthj0qRJ5vOJ2Usa7AxmeP+sslkl425WU0gRSKaQoiwcT5prC0fllbveOZW8SpaSV3Z8L088m+H33zAePABeXviEd8Irf36XjR2fYKJrv1j+PaknR5CKBVOCKFTQqovtCFBMPZe8Es+SmMZZ3u/du8ecOXO4du2aQ8aRBjuDuXTVIJtVBTTXofntXzpSBJIppCgLx5Hm2sKReeXToqVsWhWQV7oVka4f1wMYDhxA//tvAHi3aIm6TFmXja3Vmug5MJaDf+kIDFDxw6QgShSV5jrdFFLPJa/E00ydOpVhw4YBMG7cOPR6PTdv3uTcuXN4eXlhNBpRqRxzlQBpsDOYkd/el82qm5vrF8t607VDgEOOJUUgmUKKsrCfNNcWjs4r0507smlVQl5pta4fM5MznjmDbuliANT13sS7+usuG1uvN/HZsHvs3K8lix/MmRhE+dJyPiGbKaSeS16Jxy1evJg1a9Zw8OBBlixZwqFDh1i0aBETJ04kMDCQvHnzOuxk3VY12EOHDuXkyZMOGVDYJz5BNqvubq7nTwnixm2Dw44pRSCZQoqysJ001xbOyCvdiki3rw/JK+Foxps30f4wBwwGvF5+Ge/GTVw6/vrNiWzalohGAzPGBVHpRWmu7aaQei55JR61adMmxo8fz7fffkvJkiVZu3Ytd+7c4Z9//uHw4cPkzZuXf//9lyNHjnDs2DEuXryIXm/bHsKqBvv8+fM0bdqUli1bsnr1arTy6q3HyMyb1fR6tHmY81O8Q48tRSCZQoqySD9pri2clldarSLWh+SVcBTT/fvoZs+Ehw9RFS6CT9v2qLxc++HKJm/70qOTP1NGZadaZY1Lx87UFFLPJa9EimzZstGoUSPGjh1L2bJliYyMZNWqVdy8eZO2bdty9OhRZs+eTevWrWnevDnvvPMOFStWZMyYMekey6oU++mnn1i7di0vvfQSY8aMoWbNmowdO5YLFy6ke0CRcWT6zWo6PN48JGod3zxIEUimkKIsrCfNtYXT80oh60PyStjLpNOhnTsb0507qHLmRPNxF1Qa1ze4KpWKTzoH8kYN+ffjcJJXZpJXyvD999/TqFEjqlatSr58+Vi4cCGVK1dm8uTJDBw4kLfeeouVK1fyyy+/sGrVKn777TcGDBiQ7nGsfpmwePHiDB48mO3bt9O/f38OHTpEgwYN6NixIxs2bMBgcNxHZoX7ecxm1QqubB6kCCRTSFEWzyfNtYXL8koh60PyStjKZDSiW7wI0/nzkCULPl27o8qa1d3TEs4geWUmeeV+v/zyC82aNaNevXqEhoby6quv8uKLL1KhQgVeeeUVHjx4QLly5ShfvjxlypShYMGCqNXpv1Rfuj+H4+fnR/PmzYmMjOSXX36hUKFCDBw4kNq1azNlyhSuX7+e7kkIZfG4zeozuKN5kCKQTCFFWaRNmmsLl+eVQtaH5JWwhf639RiPHE66HNdHnfHKm9dlY5tMrs8pjyd5ZSZ55V5jx47lq6++wtfXlw8//BCATz/9FJ1OR3BwMMeOHXPIOHZ90aV8+fKMHDmSHTt20L17d/744w/efPNNunXrxrZt2xwyQeFaHrtZfQp3Ng9SBJIppCiLJ0lzbeG2vFLI+pC8Eumh37sXw8YNAHh/2Bp1yVIuG3vpqni+GBOHwSBNtstJXplJXrnPa6+9Zv77mDFjOHnyJAcPHuSdd97h2rVr5MqVyyHjOORMEoGBgbRt25Zff/2VH3/8kcDAQD755BNHHFq4kMdvVh+hhOZBikAyhRRlYaGE9SF5lUwh60PySljDcOo/9D8vBUD9dn28X6vqsrH/93sCX02IY+W6BNZvTnTZuOIRkldmkleut3DhQj744ANGjBjBhAkTePDgAdOnT2fNmjVUrlyZM2fO8NZbb7FgwQLmzJnD8uXLuXz5sk1jeTt47lSuXJnKlSsTExPj6EMLJ5LNqoUSmocUKa/y+zRqnOq2K6UUAU1EDzTdItDOnA6JLt6cJBdlTbcINBE90E7/HtOli66dgwCUsT4krx6jkPUheSWexXjjBrp5P4DRiFelV/Bu2MhlY2/alsig0fcwmaBtsyw0fkuaGbeRvDKTvHKtn3/+mfv373Ps2DHUajXBwcFs3LgRSDrZ4a1bt9Dr9URFRfHwYVJNL1iwIH/88Ue6x3LatRCCgoKcdehnun//PtOnT3fL2BmVbFYtlNA8PE5eaU2mkFe+PZkS1ofkVRoUsj4kr0RaVH6+qHLmRFWsGD5t2qJSqVwy7o69ifQZEovBAB809GNwn0CXjS3SIHllJnnlOp999hnbtm3j559/ZsmSJQwfPpyqVasyePBgsmXLhr+/Pz/++COHDx/mt99+Y+jQofz44482jeXaiw0+RXR0NH379mXo0KFERERw4sSJpz5u9uzZ1KlTh4oVK9K5c2euXbtm/ln79u0pXbo0pUuX5pVXXkn1M/Fsslm1UELzkBYpAskUUpQ9kRLWh+TVcyhkfUheiadRBeVA88mnaDp3QeXj45IxDxzR0nNQLDo91K/ry8iBWfHykuZaESSvzCSvXKNevXqpbr/44ot4eXnRrl07Nm7cSIkSJejYsSOxsbEULVqUNm3aUKBAAZvGcnuD3bdvXwoXLsyIESNo37494eHhxMXFpXpMZGQkXl5eLFmyhDFjxrB3715GjBhh/nnRokUZMmSI+c/777/v4t8iY5LNqoUSmofnkSKQ7PGinDef6+fgYZSwPiSvrCSbVjNF5JVIReXnhyogwCVj/X1cR9f+sSQkQu1qGiYMz4a3tzTXiiJ5ZSZ55XrZs2end+/e5r9/8803hIWFMXr0aLuP7ZQGOyEhgePHjz/3cQcPHmT37t1Uq1YNgCpVqhAXF8fixYtTPU6r1dK5c2fy589PgwYNKFu2LHfu3DH/vHTp0rRr1878p3Llyo79hTIh2axaKKF5sJYUgWSPFGWfFi1dP74HKVFU7fb1IXmVTrJpNVNEXgmrOPLM3v+d1dO5TwwPHpp4rZIPU8dkR+MjzbUiSV6ZSV65XoUKFVLdbtu2LW+//TZ6vX37DIef5Axg1apVXLp0iXLlyj3zcdu3bwcgODg4aTLe3gQFBbF161a6detmflzbtm3Nf9dqtVy+fJmePXua7zt06BDTpk1Dr9fz8ssv8+WXX1K0aNE0x338IwKPMhgMNl1QPCORzapFRmquU8iJOZKlnCjlk09RFSzo2rE9yPD+WfnvjDTXSsirdJETCZkpIq88kOn+fVSBgVY/Xq1W0W94LGcvGGwar0RRNcP7Z2XPIS29v7yHXg9Z/CDmnpHWXe/adMz0yuKnYkjfQAoVUDN8fBxnztv2u9ireRM/2jbzZ/GKh/zya0K6nlurmg99umZ10szSIHll9nhe6X79n8vn4OnefPNNu49hU4MdERHBiBEjCAkJeeJnJpOJn376iSJFijz3ONHR0QD4PPJdHB8fH/P9TzNu3DgaN26cqul+7bXXaNSoEdeuXeO7776ja9eurFu3LtVxRRLZrFo4orlW5c2H6coVJ8zu2ZRYBNzVZOtWROLbu49rx/Ugl64apLlWQF7ZRDatZorIKw9iOHEC3by5+HzYGvUr1n+q8OwFA8dPpX+dv1jWm6F9s7Jzn5beg5POFg4QnwD/nXFNk5uSVwXzqenYy7151baZv815Vaywm95kkrwyezSvfJq1cPn4wn42Ndh//fUXmzZtomTJkpQrVw5/f3/zz1asWMG5c+fInz//c4+TI0cOAPOp0AF0Oh158+Z96uOnTZtG6dKladEi9T+2Vq1amf8eHBxMnz59OHv2LGXKlHnqcTZv3pzmnJ73rntGJptVC0e9c+3ToiXaW7ekCLhz06rVunY8DzPy2/vSXLs5r0Lz2/FtLtm0mikirzyA8do1dPPngVaL4eSJdDXYtkip5/uPpG6uXSlT5pVG45hJpYfklZk5r3r0fP6DheJYVbVjYmL46quvzO8st2jRgpUrV9KxY0eqVatGWFgYixcv5tixY4wbNw5vb29atnz+9yJr164NwK1btwDQ6/XExsZSq1atJx47Y8YMatWqZW6uDxw4wPXr1594XMmSJQEIcNFJNDKKTBn+NnLkx8JNd+7Id4bkO0OZWnyCNNfuzquuHeysZ/IdRzPJK+cyxcainTUTEhPwKlESn1atnTpeSj0/9LeWHgOluXZkXvk0a+H2c6xIXl1EF7ncLWML+1jVYE+aNImlS5dSr149vv76a+rUqcPx48d58cUXqV69Ovfu3WPMmDE0b94cf39/Fi9ezNtvv/3c41auXJmaNWuyc+dOIOm71AEBAbRs2ZIuXbqwYMECAFauXMmZM2c4deoUkZGRLF26lOHDh+Pl5cWePXvYunWr+ZiHDh2iadOmhIaGpv+/RiaVWcPfFo7+zrVuRaQUAWTTKhxH8soiJa9u3HbAR1xl02omeeUcpsREtHNmQcxdVLlz49PpI1TeTjnVD2BZH38d09H9c2muHZ1XqpAQRZzI1OPz6saTbyYK5bOqwY6Li6NXr140aNCA//3vf4SFhZEjRw6WLVvGjBkz6NChAwEBAbzxxhvExsbinY5AnTJlClFRUQwbNoz58+czd+5c/P39OX36NJcvXwZg1qxZrF27lsGDBzN48GCGDx/OmTNnyJEjB/fu3WPcuHEMHTqUyMhI4uPjU13Cy9Nl5vBPL6ec0EyrlSKQTDatwl6SVxaP5tWcn+Idc1DZtJpJXjmWyWhEt/BHTJcvQ0AgPl27OfVyXCnr458TOj7uG4vR6LSh0pTZ80oXuVwxVwvx9LwSjrF27Vp0Op1LxlKZTNa95rd//378/PwoU6YMa9euZcmSJVSpUoUCBQqwZMkSxo8fT/ny5dm2bRszZsxg2bJlzp67U6R8B9uay4y5Q9OwaKtPAJLZwz89HN1cN37Ll2+/yk7i+G+STnLm64umWwSqfPnc9p0hAPXb9fFp1BjdurVu+c4QgKpQYTQRPTBdv+6S7ziqChbEt/8Ap47hydKTOfaQvLJ4PK/qvq5JnTf2krwysyWvJHOepFu5AsO2reDtjaZnL7yKFrPpONbkTcr6OPafjvDe0lw7Oq8e3d/gpXZpPX8qD88rpeaN0vulp6lUqRJ+fn40a9aMli1bOvXTzla/g927d28GDBjA6tWruXjxIkuXLuXKlSscO3aMd999l4SEpMsA1K5dm2bNmnHFDWdWFhaZOfzTyyWX4pJXWs3knSGRXpJXFpJXriV5ZT/99m1JzTXg07adzc21NVLWx4nTOjp9Ks21s/NKEetD8ko4yI4dO+jZsyc7duygfv36dO7cmS1btmDle83pYlWD7evry9ixY6lSpQrLli3jr7/+Yt26dRw/fpzOnTszdepU2rVrR6dOnfj1118ZP348Y8aMcfhkhXU8Kfyfx6XXuZYiYKaIoiwyBMkrC8kryauMxnDsX/QrVwDg3bgJ6kqvOG2sR9dH136xGNxwiWlPzCtFrA/JK+EAAQEBtGnThtWrV7N48WJy5sxJnz59qFu3LtOnT+f27dsOG8uqBnvIkCF89dVXXL9+HZPJxL///suyZct4+eWXiYmJoV+/flStWpX4+Hg+//xzAgMD5XvQbuKJ4Z8Wl25WU0gRMFNEURaKJnllIXkleZXRGK9cQbdgPphMqKtWQ/3mW04b6/H18dBBpyVID0/OK0WsD8kr4UAVK1Zk3LhxbN++nY4dO7JmzRrq1q3LJ598wp49e+w+vlUN9s6dOylcuDB58+alfPnyqNVq/vnnHzQaDYMGDeLXX39FrVbz008/4efnx71799Dr3RM8nsyTw/9xbtmsppAiYKaIoiwUSfLKQvJK8iqjMcXcRTt7Jmi1eJUqhXfLVqhUKqeM5db1kUzySiHrQ/JKOFj27NkJDw/n999/Z86cOahUKj7++GPq16/PggULiI2Ntem4VjXYM2fOZP78+URFRfHvv/9StWpV1Go1x44do1y5cpQtW5Z9+/bRunVr3njjDVq0aMGPP/5o04SEbST8LZRQjKUIWCiiKAtFkbyykLyykLzKGEwJCWhnz4bYWFR58+IT/hEqtdopYylhfUheWShifUheCSepVq0aU6ZMYevWrbz33nssXLiQWrVq2XQsqxrsF198kZiYGA4dOkS/fv0oVaoU33zzDQUKFABgzJgxFClShDp16vD555/Tu3dvDh48aNOERPpJ+FsooRibSREwU0RRFoogeWUhefUkyStlMxkM6H6cj+nqFQjMik+Xbqj8/Z0y1qPro9OnMdJcuzmvUihifUheCScKCQkhIiKCzZs3M2XKFJuOYVWDDUlvof/xxx/UqFGDQoUK8eqrr/Ldd99hNBrZv38/uXPnBiBPnjz4+flRo0YNHjx4YNOkhPUk/C0UtVlNIUXATBFFWbiV5JWF5FXaJK+UyWQyoV+5AuPx4+Djg6ZLF7yCg50y1qPro2OvGOITnDLMM0lepU0R60PySjiZSqWiTp06Nj3X6gZbpVKRNWtWAJo0aUJwcDBqtZqJEyfy8GHSgi9evLj58d26dcPfSa9qiiQS/haK3KymkCJgpoiiLNxC8spC8ur5JK+Ux7BtK4adO0Clwqd9B7wKF3HKOKma609i0OqcMswzSV49nyLWh+SVUCirG+y0eHt7U6dOHQYNGsQ777xjvt/X19dpJ7wQEv6PUvRmNYUUATNFFGXhUpJXFpJX1pO8Ug7DP3+jX70KAO9330Nd4WWnjFOiqNq8PsI+iUGrdcowzyR5ZT1FrA/JK6FAdjfYKUqUKIHBYOD8+fOOOqRIg4S/RYbYrKaQImCmiKIsXELyykLyKv0UmVcajVvm4S4mnQ5d5PKky3G9XgN13TecNtbw/lmTmuveMSRKc63o5jqFIuq55JVwkqioKJue57AGG2DNmjWsW7fOkYcUj8niJ+GfIkNtVlNIETBTRFEWTiWbVQvJK9spLa98mrVwyxzcReXjgyaiB+pq1fFu1typn068dNVAeO8YEhOdNkSaJK9sp4h6LnklHGzLli0sXbrUpuc6rME2GAzMmDGD69evO+qQ4imG9A2U8CeDblZTSBEwU0RRFk4hm1ULh+WVO945lbwyM+dVSIhbxncnr7z58PmwtdMux5Vi4Mh7JEhz7da8Cs1vW2ugiHoueSXSYfbs2SQ+49W8n376ibNnz9p0bIc12MuWLePSpUvcu3fPUYcUT1GogNrjw18JzbWvxs5X8KUImCmiKAuHks2qhSPzyqdZC9m0KiCvdJHL3TJ2ZnfyjJ5T5wwuH1fyyuLFst507RBg8/MVUc8lr4SV5s6dy5YtW0hIePIyBQcPHmTPnj3ExMTYdOx0N9iHDx/m8OHDqe47f/48EyZMQKVSkTdvXpsmIqwzfHycx4e/u5vrAH8VH7fPYv+BpAiYKaIoC4eQzaqFo/NKFRIim1Yl5NUN+aSeo527qKdT77sYja4dV/LKIiWvbty270UORdRzySuPc//+faZPn/7MxxgMBn777Tfz7Xr16vH7779Ts2ZNWrduzYwZMzh16hTx8fEMGzYMgCpVqtg0n3Q32KtXryYyMtJ8OyYmhp49exIfH0/16tXp2bOnTRMR1jlz3vWv7oKywt/dzfXcidnJm8tBH5OTImCmiKIs7CKbVQtn5JUucrlsWlFGXmVm+t27MLrwRYQr15O+dx1114SfC/9ZS15ZPJpXc36Kt/t4iqjnkleKFh0dTd++fRk6dCgRERGcOHHiqY/buXMnjRo14qWXXqJNmzbcuHHD/LP27dtTunRpSpcuzSuvvMK1a9eeOeaCBQv47LPPaNWqFZs3b6Zt27Zs2LCBuLg4Tp06xZQpU3jvvfd4++23uXz5MgMGDCAiIsKm3y/dDbZOp+O///4Dkl4t6NKlC1evXuXLL7+kcePG7Nu3z6aJCOVSWvi7u7kuWcybWQsfOO7AUgTMFFGUhU1ks2rhrLwy3bju/vUheZWpGY4eRf/zMrSTJ2G6e9fp4928bSCs111u3DJSvIiaQgWc+x3vFJJXFo/nVaLWQXmlhHoueaVYffv2pXDhwowYMYL27dsTHh5OXFxcqsfcunWLAwcOMHXqVAYNGsShQ4fYuHGj+edFixZlyJAh5j/vv//+M8dcvnw5r776KlFRUfTo0YMvv/ySkJAQ/v77bw4dOsTy5csJDAxEr9cTHBzMu+++a/PvZ3WDrU2+GGGVKlU4f/480dHRdOzYEaPRyKpVq2jfvj07duxg//79Nk9GKI8Sw9/dzXV47xguX3Pw59ikCJgpoiiLdJHNqoWz80oR60PyKlMyXriAbtFCANRVXkWVI4dTx4u+a6TTp0n1NDS/F/OnBOHt7bwzlKeQvLKQvHIdySuLgwcPsnv3bqpVqwYk9ZZxcXEsXrw41eP8/f359NNPKV68OHXr1qVQoUI0bNjQ/PPSpUvTrl0785/KlSs/c9wNGzYwbtw4li5dysqVK3n55ZdJTExk+/bt7Ny5ky5dutCzZ0927txJkyZNGDlypM2/o1UN9vfff0+VKlXo0KEDGzZsID4+nubNm3P58mUqVapEZGQk33zzDWfOnGHPnj2MHDmSUaNG8dNPP6HXuye4hP08Ifyt4bJiLEXATBFFWVhFNqsWrsorRawPyatMxRh1B+2c2aDT4VX+BbybfuDU8e7FGfmoTwxnzhvIk8uLBdNykMdRX716BskrC8kr15O8SrJ9+3YAgoODAfD29iYoKIitW7emelxgYCAqlYrLly8zcOBAevbsSY5HXvg7dOgQVatWpXLlynTu3Jnz588/c1yDwUC3bt348ssviY2NpXz58vz222+sWbOGOXPm8Mknn/DBBx+gVqv57LPPCAoK4ubNmzb9jlY12OvXrycxMZFz585x5coVsmTJwrVr14iLi2PVqlWsX7+e//3vf1y6dImzZ8+yePFiFi1axKRJk2y+QLdwL08K/2dxeTGWImCmiKIsnkk2qxauzitFrA/Jq0zB9PAhulmz4H4cqoIF8ekYhsrLYReZecLDeBNd+sVy/JSe4BwqFkwNomA+aa5dSfJK8soRDAYD9erVS/NPWqKjowHw8fEx3+fj42O+/1Hnzp0jMjKSsmXLMmTIEMLDwzEmnw3xtddeY/To0fTu3Zt//vmHrl27otPp0hz3wYMHvP7661y7do05c+awdetWTp48yd69exk3bhyTJ0+mfv36zJw5k+vXr3P16lXmzJlj038bqxK0QoUKbNmyhZ07d7Ju3TreeecdKlWqRHBwMFqtlmbNmrFjxw5Wr15NuXLlWL9+PaNGjSIyMpI8efLYNDHhPp4Y/k/jtmIsRcBMEUVZPJVsVi3clVeKWB+SVxmaSa9HN+8HTDdvQPYgNB93ReXEf0eJiSYiBsRw5B8d2bKqmDc5iGKFvZ02XgrJKwvJK8krd0t5F/rhQ8sa0Ol05MyZ84nHFitWjM8++4wBAwYQFhbGvn37OHPmDACtWrWiXr16tG/fnmHDhnHx4sVnXrf6l19+4c6dOzRp0oQHDx7w77//snr1at577z3u3r1LrVq1CAoKYu7cubz11lscPHiQBg0a2PQ7WtVgjxkzhvz585tvly9fnvLly7N582Y6derErFmzCAsLI3v27BQpUoRixYrRvHlzihcvbtOkhPt4cvg/yu3FWIqAmSKKskjF7esjmeSVQtaH5FWGZDKZ0C//GePpU+Dri6ZrV1RBQU4dc/DYe+w5qMPfX8XciUGUKenz/CfZSfLKQvIKySsHUqvVbN68Oc0/aalduzaQdBIzAL1eT2xsLLVq1XrmeClv2mbJ8uSlckuWLAlAQEDa13FftmwZGzZsYOXKldy9e5fo6Gh+++03duzYwejRo9FoNJQqVYqNGzeiVqsJDg6mfPnyz/6PkAabPgNUrFgxChQogK+vL71792bp0qVcunSJfv36ma8bJjIeCf8kSinGUgQsFFGUBaCc9SF5ZaGI9SF5leEYNm3EsG8vqFT4hIXjVaCg08cM/9Cf/Hm8mDkuOxXKS3PtSpJXj5C8cqvKlStTs2ZNdu7cCSR9lzogIICWLVvSpUsXFixYAMCaNWtSXZ1qx44dtGzZktDQUPbs2ZPqO9uHDh2iadOmhIaGpjlu69at2bdvHy1atKBKlSq89tprGAwGXnvtNW7fvs3+/fvZsWMHs2fPpmzZshQpUoRly5bZ9Dva1GBXqlSJ1q1bm2+/+OKLLF++nJs3b6a6gLfIOCT8kyilGJtJETBTRFH2cEpZH5JXT1LE+pC8yjAMhw+hX7cWAO/mLVCXs+1dmvQqV9qHDT8H81oljdPHkryykLx6Cskrt5oyZQpRUVEMGzaM+fPnM3fuXPz9/Tl9+jSXL18GICoqiv79+zNx4kSmTp1K1apVGTp0KAD37t1j3LhxDB06lMjISOLj4xkxYsQzxwwPD0ev1zN58mRCQ0MpX748vXr1YufOnbz//vv88ccfBAcHc+PGDQYMGMDo0aP5/fffbfr9bPrii5+f3xP35cmThx9++IEePXrQtGlTfGXzm2FI+CdRSjF+QnIR0HSLQBPRA+307zFduujyaRg2bgDAp1HjVLddKaUoayJ6oOkWgXbmdJfPwVMpZX1IXqXtqesjMdG1k5C8UjzjuXPoFi8CQF2nLt41arp0fI1GLsXlSpJXzyB55TYBAQFMmDDhifv//PNP89/Dw8MJDw9/6vPr169P/frpf0FCrVYzf/58qlSpwuLFi6levTqtWrWiU6dONGzYkHz58lGlShUqVqwIQPHixdFqtWg06XtR0KGnicyTJw8TJkzg5MmTjjyscCIJ/yRKKcZpkldazZ545TudoSfSTynrQ/Lq+eSdIQsl5JXSGG/fRjt3Nuj1eL34Et7vve/uKTmc5JWF5JUVJK88iq+vL1WqVAGgadOmFC1alJCQEObOncuePXvw8fFJdfK1Hj16oFKl/0VBh1+HoUiRIlSoUMHRhxVOIOGfRCnF+LmkCJg9WpR9mrVwyxw8RRY/ZawPySvryabVQgl5pRSmBw/QzZ4JDx6gCi2ET/sOTr0c1+IVDzl30bV5oZR6LnllPckrC8kr1/L39zf/PXfu3LRq1Yr333+fTp06me/Pnz9/qsuJWcshyfrHH3/YfCFu4R4S/kmUUoytJkXAzFyUQ0LcMr6nGNI30O3rQ/Iq/WTTaqGEvHI3k16H9oe5mG7dghw50HRx7uW4Fv3ykBHf3qddxF2i7xqdNs6jlFLPJa/ST/LKQvLKvRo3boxarebBgwd2HcfuBjsxMZHPP/+c48eP23so4SIS/kkcUozd8fFkKQJmpksX0UUud8vYnqJQAbVsVhWQV7aQTauFEvLKXUwmE7qlSzGdPQN+fmi6dEOVLZvTxlu5Lp6RE+8D0PK9LOTM4bx3yVNIc20heWUHBeaVV9VqbpmDJ9u3bx9Lly616xhWpd7vv//O3LlzAdi0aRMABoOBhIQEHjx4wMOHD236fLpwPQn/JI4qxj7NWkgRcHeTfeO6W8b1FMPHx8lm1c155WvHSaFk02qhhLxyB8Pvv2E8eAC8vPAJ/wiv/PmdNtZvWxL48us4ADq0zELvj9O+Jq2jSHNtoYS8sofklYU5r2o++9rQwvG+++47rly5YtcxrGqwJ0yYwPfff8/169cZMGAAJ0+eZPbs2YwZM4acOXOSLVs29HqFf7RWSPgnc2QxVoWESBHw0E2rpzhz3uCWcSWvkgT4q/i4fRa7jiGbVgtPyyvDgQPof0+6fKp3i5aoy5Rx2lhbdyXSb9g9jEZo0cSPL3oHOv3NF2muLRydV+5651TyysKwcQO6HdvdMran2r17NwcOHCA6Otqu41jVYA8aNIh3332Xr776irx587Jz5050Oh0HDx4E4IUXXkh1xjWhPJkx/G3h6GKsi1wuRQDP27QK55K8SpKSV3lzqe0+lmxaLTwqr9ReoPZGXe9NvKu/7rRh9h7S0uvLWPQGaPyWL199nlWaaxdyRl751Kzl/nOsSF5h3LvHLeNmdjdu3ODGjRup7rt//z6DBw9GpVKlOgGaLaxqsOvVq8dXX31Fy5YtKVSoEJMnT2bGjBmcP3+eF154gT179jBgwABeeOEFKlWqRK1atWjfvj2RkZF2TU44RmYN//RyRjE23bguRSCZR21ahdNIXiV5NK9mLbTvZCspZNNq4Sl5pa70Cpr+n+PduInTxjjyj47un8ei1cIbNTSMHZINtVqaa1dxVl7pdmx3+zlWJK+Es/z444/MmzfPfNtkMjFo0CCuXbtGSEhImtfftpbVZ57YvHkz8+fPp379+vj7+9OjRw+KFi3KRx99RNOmTalYsSL9+vWjV69edO7cmaZNm/LCCy/YNTlhv8wc/unhzGIsRcDCUzatwjkkr5I8nleXrznuLMySVxaeklde+fI57XJcJ07p+LhvDA/jTVSv4sPkkdnx8Zbm2lWcmVfGvXvcvj4kr4Sz3Lx5k2PHjplvDxs2jE2bNvHWW28xfPhwdDqdXce3OnEnT55MfHw8NWvWpGjRovTs2ZPKlSvz7rvv8sEHH5AzZ07CwsIIDw+nQ4cOfPDBB5QtW9auyQn7ZPbwt5YrirEUAQtP2bQKx5K8SiJ55VqSV7Y7e0FPp09jiLtvotJLPnw/NghfX2muXcUVeaWE9SF5JZzhhRde4OzZswAMHTqU9evXM3bsWKZNm8bGjRv57bff7Dq+1Q12gwYNWLhwISEhIbRu3RqA8PBwihcvTv78+eUyXQrjKeH/PK4sxlIELJRQlEXGIXmVRPJK8soeJqMRU2KiS8a6fNVA2CcxRMeYKF/am9kTsuOfRZprV3FlXilhfUheCUf4/fffad++Pd9//z0xMTHExsbSrVs3fv/9dz777DNy5MjBn3/+aX53e8OGDWzcuJGTJ0+meyyrG+yIiAjzF74XL17MzZs30Wq11K1bl3PnzpGQkJDuwYVzeFr4p8UdxViKgIUSirJQPsmrJJJXklf20v+2Hu2UyZhiYpw6zs3bBsJ63+XWHSMliqqZOymIrIHOvda1NNcW7sgrJawPySthrwULFnDgwAGmTZvG7NmzMZlMbNu2jbi4OEaOHEm3bt3o3r07e/fuZd++fXz66af07t2bpk2bcvny5XSNZVUi7tmzh2+++YYNGzawfft2zp49S2RkJNu2baNIkSLky5ePnj17cvz4cf7++28uXLhg9QSio6Pp27cvQ4cOJSIighMnTjz1cbNnz6ZOnTpUrFiRzp07c+3aNfPPdu3aRffu3Rk8eDBDhw716GbfU8P/ce4sxlIELJRQlIVySV4lkbySvLKXfu9eDBs3YLp6BeOZM04bJyraSNgnMVy5ZqRQATXzpwSRM0iaa1dxZ14pYX1IXgl7xMbGMnbsWNavX8+uXbuoV68euXLlwmQyERwczIgRI1i3bh0TJ06kUKFCDBs2jCZNmvD1118TGhqarrG8rXnQlClTOHr0aKr7vv/+e/PfGzdujMlkSnVJhtKlS7N69ernHrtv375UrFiRTz75hD179hAeHs6mTZvImjWr+TGRkZF4eXmxZMkS/vrrL/r378+IESOYOXMmly5dIiIigrVr1xIaGsrnn3/O6NGjGTlypDW/Wqbi6eGfQgnFOKUIaCJ6oOkWgXbmdHDRR/fMkouAplsEmogeaKd/j+nSRdfOgaSiDODTqHGq28KzSV4lkbxKJnllM8N//6H/eSkA6rfro65c2Snj3Isz8lGfGM5dNJA3txcLpgaRO8T+S8g9ixLWB0hepVDC+pC8ErZ6/HvVFSpUoHDhwjRq1IixY8cydOhQunTpQpcuXVixYgUffvghH374oU1jWfWy4yuvvMKGDRv4+++/OXbsGBMmTKB8+fK89dZbmEwmGjVqxMaNG/nll18YPnw49evXp1evXs897sGDB9m9ezfVqiVdzL5KlSrExcWxePHiVI/TarV07tyZ/Pnz06BBA8qWLcudO3cAmDVrFiEhIeZXFqpVq8bKlSu5evVquv5DZHQS/kmUUoxBXml9lBJe+RbKIXmVRPLqMZJX6Wa8cR3d/B/AaMSr0it4N2zklHEePDTy8WcxnDitJyRnUnNdIJ80166ihLxKoYT1IXklHKF48eJoNBrKly/PTz/9xMCBA/nhhx+YOXMmffv2tevYVjXY/fv3p3Dhwmg0GtRqNZUrVyZLlixMnTqVBQsWcOLECUaNGkW5cuVo1aoVkydPpl69es897vbt2wEIDg4GwNvbm6CgILZu3ZrqcW3btjX/XavVcvnyZd5//30Atm3bRkhIiPnnwcHB6PV6du3aZc2vlilI+CdRSjF+lBQBCyUUZeF+kldJJK/SIHllNdO9e+hmzYT4eFRFi+HTpm2qTxI6SkKiie6fx3L0mJ7sWVXMmxJE0UJWfQDSZkpZH5JXT6eE9SF5Jez18ssvm0/cDdCxY0dmzZrF0qVLiYuLs+vYNn1xJm/evLz33nsAVK1alcjISAICAlJ9bNwa0dHRAPj4+Jjv8/HxMd//NOPGjaNx48bmpvvu3btPPB8gKioqzWPUq1cvzT8GgyFdv4O7SfgnUUoxfhopAhZKKMrCfSSvkkhePYfk1XOZtFq0c2djio5GFZILTeePUT2yF3KkXfu17Dusw99fxdxJQZQuLs21qyghr9KihPUheSXsERwcTJ48eVLd9/rrrzNjxgwmTJhg17FtPjNF8+bNzX/39/dn0qRJ6HQ69HrrgzBHjhwAPHxoCS6dTkfOnDmf+vhp06ZRunRpBg8ebH6VNkeOHE88HyzvimdmEv5JlFKMn0WKgIUSirJwPcmrJJJXVpK8SpPJaES36CdMFy+Cvz8+XbuhCgx02nj1avoy+ouszBqfnZfKOaeJT5HFTxnrQ/LKOkpYH5JXwtGqVKlC9+7dOXDggM3HcOipH/v06YO3t/WvbNauXRuAW7duAaDX64mNjaVWrVpPPHbGjBnUqlWLFi1aAHDgwAGuX79O7dq1zc+HpHfF1Wo11atXT3PczZs3p/lHrXbud4ocRcI/SUbYrKaQImChhKIsXEfyKonkVTpJXj2Vfu2vGP86CmpvNJ0/xit3bqeP2bxxFl6tqHH6OEP6Brp9fUhepY8S1ofklXC0N954gypVqtj8fOdeW+E5KleuTM2aNdm5cycAhw4dIiAggJYtW9KlSxcWLFgAwMqVKzlz5gynTp0iMjKSpUuXMnz4cLy8vOjWrRv37983XwR87969vPfeexQsWNBdv5bTSfgnyUib1RRSBCyUUJSF80leJZG8spEC88qrajW3zAFAv3sXhs1/AODTpg1exUu4bS7OUKiAWpprBeRVeimhnkteCXtdvnyZ+Ph4hxzLrQ02JF0CLCoqimHDhjF//nzmzp2Lv78/p0+fNl/Ue9asWaxdu5bBgwczePBghg8fzpkzZ8iRIwehoaHMnz+fSZMmMXToULy8vBg6dKibfyvnad7ET8KfjLlZTSFFwEIJRVk4j2xWk0he2UlpeVXzyU/ZuWT8EyfQRy4HwLtBQ9SVbX935Vn0evc1dMPHx0lz7ea88tXYdqI8JdRzySthK6PRSMeOHe36WPij0nWmCoPBwM6dO3n99ddTfRTcaDTy8OFDHjx4gI+PT5rfoX6agICAp36R/M8//zT/fcOGZ19nr2LFisyaNcvqMTOyts38PT78M/JmNYVcx9FCCdfVFI4nm9Ukjswrr6rVMPwS6cDZWUfyysKwcQNky+byJtt47Rq6+fOSLsdVpQrq+u84ZZyZPz5g/2Et340Nwj+L489I/jxnzrvnRLOSV0kC/FV83D6Lzc9XQj2XvBJp+e+//7h58ya1atXi2rVr5M+f3/yzqKgorl27htFodMhY6XoHe/bs2XTr1o2KFStStWpVXn31VSpUqED58uWpUqUKderU4fXXX6d+/fps27bNIRMUqS1e8dDjw18JzXW9WvZ/F01eabVQwivfwnFks5rE0XnlU7OWvDOkgLwy7t3j0vFMsbFoZ82ExARUJUrg82Frp1yOa+Hyh0ya9YBdB3Rs3uHihsSNJK+SpORV3lz2nYtICfVc8ko8zbBhwxg+fDgJCQm0bNmS69evs2bNGmbPnk1wcDC+vr4Ou5pUuhrsZcuW4eXlRcmSJalQoQI1a9akcePGtGrVijZt2vDhhx+SL18+Ll68yIgRIxwyQZHaL78muGVcJYW/u5vr7mH+NKxn+yu8j5IiYKGEoizsJ5vVJM7IK92O7bJpVUheuYopMRHtnFkQcxdV7txoOnVG5e34M3n/sjae0ZPvA9Czkz9N3vZz+BhKJHmV5NG8mrXwgd3HU0I9l7wSj6tQoQKBgYFMmTIFgH379nH16lU2bdqEl5cXpUuXJiHBMX1Wuhrs0qVL8+6777Jy5UpmzZrFqFGjMBgM/PPPP4SFhTFs2DDq1q1LlSpVWLZsmUMmKNxPaeHv7ub60y6BrN/smJMggBSBRymhKAvbyWY1ibPyyrh3j9vXh+SV65iMRnQ//Yjp8mUICEy6HFdAgMPHWf9HAoO/jgMg/MMs9PzI8WMokeRVksfz6vI1x3xEVgn1XPJKPGrQoEGsXLkSLy8vChUqxKRJk1i4cCEnT56kU6dOXL16lenTp/PRRx/RrVs3+vXrx3fffcepU6fSPVa6GuyXX3451TWnZ8+ezerVqylRogQFChQAIDQ0lNmzZ5MrV650T0YojxLD393N9eTZ99m8XevQY0sRsFBCURbpJ5vVJM7OKyWsD8kr19CvXoXxn3/AO/lyXCGO31dt2ZlI/6/uYTJBq/f9GNAr0CkfP1cayaskklcu4gF5lVFcunSJLFmy0LRpU6KioihRogRZs2bFy8uLAgUKoNVq0WiSvgaakJDAlStXOHLkSLrHsarB/vrrr/n000+5evUqV69eRafTodVqWbp0Kf369eObb77ByyvpUGFhYWTJ4piPzwr38oTwt5YrirEUAQslFGVhPdmsJnFVXilhfUheOZfh9GkM27YC4NO2HV7Fijl8jD0HtfQeHIveAE3e9mV4v6zSXLuI5JVrSV6JFMOGDWPRokW8+OKLlCpVip9++onXX3+dzz77jN69e1OhQgVmzJjBzJkz+e677xg7diytWrVK9zhWNdhbtmzh999/Z8WKFRw7dowqVarQokUL6tevT+fOndM9qFA+Twr/53FlMZYiYKGEoiyeTzarSVydV0pYH5JXzuNVogTe7zfFu3ET1JVecfjxD/+jo/vnMWi18GYtDWMHZ8PLS5prV5C8krzKbHmVkfj6+vLTTz9RpkwZXn31VQCaNGli/jT2f//955BxrGqwW7duzZ49e9i6dSv58uWjV69e5MiRg8jISPr27YtOp3PIZIQyeGL4p8UdxViKgIUSirJIm2xWk7grr5SwPiSvnEOlUuFd9w2833rb4cc+9p+OLn1jiE+AGq9qmDQiO97e0ly7guSV5FVmzKuMZO7cuZQsWdJ8++HDh5QrV44hQ4Zw584drly54pBxrGqwO3XqRI4cOcibNy85c+akXbt2LFiwwHx96i+//NIhkxHu58nh/zh3FmMpAhZKKMriSbJZTeLuvFLC+pC8yjjOnNfzUZ8Y4u6bqFzBh+/GZkejkebaFSSvJK/MJK/c4sqVK/z555/cvXuXxMREVqxYwY4dO9i7dy9nz56ldOnSfPLJJw4ZK10nOQPIli0b06ZNY86cOWzZsoUqVapw7949Fi5cyMGDBx3W+QvXk/C3UEIxliJgoYSiLCyUsD4kryyUsD4kr5Tv0hU9Yb1juBtj4oUy3syakJ0sftJcu4LklYXkVTLJK5cbM2YM3bt3p3r16rz88svExcXRr18/hgwZwsWLF2nQoAFz587llVdeMV+Oevjw4TaN5Z3eJ5w7d47du3c/cf/WrVvNJ8coVqwYa9eu9YiTZWQWEv4WSijGKVKKgCaiB5puEWhnTofERNdOIrkIaLpFoInogXb695guXXTtHEgqygA+jRqnui1cSwnrQ/LqSUpYH5JXtjEZDBh27UT9eg1UarVTxrhxy0DYJzHcvmOkVDE1cycFERiQ7vdYMhzJqySSV0+SvPI89+7dY+TIkeTNmxdvb2927tzJpk2b0Ol0XL9+nVq1alGrVi2io6P5999/2bNnD4GBgTaNle4G+6OPPuKtt94iKCgIvV7Pw4cPefjwIdevX+fQoUMcP36cd999V5rrDETC30IJxfhxUgQslFCUPZkS1ofkVdqUsD4kr9LHZDKhX7UCw44dGP87iU/nLg7fP0VFGwn7JIarN4wULqhm3pQgcmSX5toVJK/SJnmVLAPlVUa3aNGiVLdz587NX3/9xdy5c5k9ezbz5s2jePHiRERE2D1WuhO2ffv25M2bFz8/PwIDA8mdOzdFihShWrVq9OzZk+nTp/POO+/YPTHhGhL+FkooxmmRjzNZKOHjZZ5ICetD8ur5lLA+JK+sZ9i2FcOOHaBSoa7ymsOb65h7RsI/jeH8JQP583ixYGoQuYKd8y65kkheJZG8ej7JK89VpEgRypYti5+fH5988gmRkZH8+uuvbNy40e5jZ/6XMEWaJPwtlFCMn0eKgMWjRdmrajW3zMGTKGF9SF5ZTzatyRSSV2kx/PM3+tWrAPB+9z3UL7/s0OPff2Dk489i+O+MnpCcXsyfGkT+vNJcu4LklfUkr5IpPK8yI7VanepE3SVLlmTJkiWsXbsWk8m+NSsNtoeS8LdQQjG2lhQBC3NRrlnLLeN7iuZN/Ny+PiSv0k82rckUklePM166hG7hj2AyoX69Buq6bzj0+AmJJroPiOXv43qCsqmYPyWIIqHp/lZghqOEei55lX6SV8kUmleeJEeOHEyYMEEabJF+Ev4WSijG6SVFwMKwcQO6HdvdMranaNvMXzarCsmr9JJNazKF5FUKU3Q02jmzQKvFq2xZvJs1d/hHw71UkCObV9K/3UlBlCouzbUrSF7ZTvIq2eN5lTef6+fgAYxG41Pv12q1REVF4eVlX4ts1bMfPHjAjBkzUt2Xch3sf//9l4MHD7J161ZWrlzJggUL2LhxI3p9xljQnkbC38IRxdhdH09WZBFw06bVuHePW8b1FItXPJTNqgLyylayaU2mkLwyJcSjnT0T7t1DlS8/PmHhTjlzuEajYuKIbCyfk4MXy/o4/PhKI811Eskr+yktr3xatHT9+JmYwWCgffv2rF279omfrVy5kldeeYU33niDNWvW2DWOVQ32zp07mTp1KpcvXwbg77//ZuzYsXz66ae0aNGC9u3b061bN7744gu++eYbevfuTcuW8g9CaST8LRxVjH1q1pIioIBNq3CeX35NcMu4klcW9Wpp7Hq+bFqTuTmvTAYDuvnzMF2/DtmyoenaDZVfFqeN5+2tokRReefaFTJjXrnrnVPJq2QpeXXnjuvHzsRu377NgQMH0Ol0qe6PiYlh1KhR6HQ6OnToQOPGje0ax6oGu27dupQqVYozZ84wcuRI/vzzTwBatGjB1KlTGTVqFLNmzWLFihX07t0bk8lEWFiYXRMTjpUZw99WjizGuh3bpQhIky0cTPLKonuYPw3r2d+EyaY1mZvyymQyof8lEuPJk6DRoOnSFVWOHC4ZOzOT5jqJM/LKp0VL959jRfIK3YpI14+bieXNm5esWbMCcOXKFfP9q1evxmAwMH78eAYNGuSaj4jPnDmTW7duodfrWbduHe3atSM0NJRChQrx1ltvcfnyZQIDAwkMDKRNmzb4+vry7rvv2jUx4TiZNfxt4ehibNy7R4oASJMtHEbyyiIlr9ZvjnfI8WTTmswNeWX4cwuG3btApcKnQxheoYUcdmyTycSBI1qHHS+jkOY6ibPyynTnjvvPsSJ5BVrPW9vO9tJLLzFp0iSaN2/O33//DcDBgwdZuHAhTZo0ccgYVjXYy5cv5+7du8ycOZPJkycTHBxMrVq1WL9+PX///Tc3b94kJiaGCxcukJCQQGBgIFr5B6EImTn808tZxViKQDJpsoWdJK8sHs2rzdsdV08lr5K5MK8MR4+i/99qALybfoD6xRcdevwZCx7SrkcM3/3wwKHHVTJprpM4M690KyLdXs8lr4QzlCpVCp1Ox/3792nTpg2jRo2iaNGi7Nixg6lTpzJt2jSmT5/O8uXLuXr1qk1jWP0d7A0bNpA7d25+/PFHAAoVKsTx48dp1aoVq1atokePHnTt2pXatWsTFRXF7NmzbZqQcJzMHv7p4exiLEUgmTTZwkaSVxaSVy7igrwyXriAbtFCANS1auNdu45Dj7/g54dMmZPUWAf4O/ZM5EolzXUSp+eVVquIei55JRytaNGivP/++6xZswa9Xs+iRYuYO3cuc+bMYe7cuXz//fdMnTqVYcOG0alTJ5vGsPoD5v/99x8VK1akRo0a7Nq1i0KFClGnTh1mzpzJli1bmDRpEqtXr2b69OlMmDCBNm3a2DQh4RgeEf5WclUxliKQTJpskU6SVxaSVy7mxLwyRt1BO2c26HR4lX8B76YfOOzYAJFr4vl6yn0AenUOILy1v0OPr0TSXCdxWV4ppJ5LXglHCg0N5Z9//iFPnjx8/vnnTJgwAT8/P3Lnzs2mTZv466+/WLJkCb169WLevHk2jWF1g12oUCHOnTtHxYoVKVSoEHXr1uXIkSOsW7eOBw8e8Nlnn1GwYEHq1q1L48aNyZkzp00TEvbzqPB/DlcXYykCyRRSlIXySV5ZSF5lnrwyPXyIbtYsuB+HqmBBfDqGobLzpDmPWrsxgSHfxAHQqY0/PcKluXYFj8wrhdRzySvhKHnz5uXEiRMEBATQqVMnGjduzNKlS/Hx8SE8PByDwUClSpWIiIigQIECNo1hVdrPmzePr7/+Gp1Ox/Dhw1myZAnNmzdHo9GwceNGZs6cSZYsWViwYAEzZsxgzpw5rFixgtjYWJsmJWznkeGfBncVYykCyRRSlIVySV5ZSF5lnrwy6fXo5v2A6eYNyB6E5uOuqBz4O/2xPZHPR97DZIIPm2bh8x4BqFSZ++Ph0lwncVteKaSeS14JR/D19eW9995jwoQJ5nOGbd++nWHDhuHl5cWUKVPsHsOqBnvRokXs27ePffv2UatWLU6fPs2pU6coUKAAKpWKdevWkZCQwJ9//smqVauYOHEigwcPZsCAAXZPUFjPo8P/Me4uxlIEkimkKAvlkbyykLzKPHllMpnQL/8Z4+lT4OuLpmtXVEFBDpvirv1aPh0Si8EA773jx7C+gdJcu4DkFYqp55JXwlaXL19m6tSpREVF0atXL/bu3cv+/fuBpEtSjx8/nhkzZvD777/bfHKzFFY12P3792fevHm88cYb/PTTTzRt2pTdu3ezbNkyXn75ZT788ENy5syJWq3mxx9/ZMeOHebvYgvXkPC3UEIxBikCZgopykI5JK8sJK8sMkVeGQyY4uOTLscVFo5XgYIOm9rBv7T0GBiDTgdv1/FlzBdZ8fKS5trZJK8eoZB6LnklbDF58mSmT59Oy5YtqVGjBv/++y8ff/wxZcuW5f333+fYsWNs3bqV77//njVr1tg1llUNdoMGDahevTojRoxg5MiR7Nu3j8DAQCDpVOclS5bk999/J1euXHTp0oUsWbJQt25d82OEc0n4WyihGD9KikAyhRRl4X6SVxaSV0/K6Hml8vbGJ7wTmt6foi5X3mFT+vekjq79YolPgJpVNXw7PBve3tJcO5vk1VMopJ5LXon02r17N82aNaN///4MHDiQdu3aUaJECfr168enn35Kw4YNmThxItmzZ6dZs2Z2jZXuM268/fbbtG7d2nw7f/78AAQGBjJt2jReeOEFvvvuO7smJawn4W+hhGL8NFIEkimkKAv3kbyykLxKW0bPK5WXF15FizlsKqfP6fmoTwz3H5io8rIP08ZkR6OR5trZJK+eQSH1XPJKpMfy5csZPXo0nTp1omPHjkRERFCqVCk++ugjPv74Y7744gsSEhLo3bu33SfrtumUlmXLljX/vXbt2uYu38vLi6+//trcdAvnkvC3UEIxfhYpAskUUpSF60leWUhePZ/kVZKLV/SE944hJtbEi2W9mTk+O1n8pLl2NskrKyhgfYDklbBeaGhoqts5c+aka9eu5tvBwcEUKlSIxMREvOy86oNdzz516hRFihTBz88v1f3t27e3a1Li+ST8LZRQjK0hRSCZQoqycB3JKwvJK+tlhLwyGQxOG/r6TQNhvWK4HWWkVHE1cycFERjguEt9KVHzJn5uXx+SV+mgkHoueSVsVapUqVS3mzRpwqxZs9zXYCcmJvLZZ5/ZNbiwjYS/RUbZrKaQIpBMIUVZOJ/klYXkVfopMq/y5kuam16H9vvv0K1bi8nk2H/Xt6MMhH0Sw7WbRooUUjN/Sg6CsmXu5hqgbTN/aa4VkldWU0g9l7wSjtCzZ08KFrT/5JQ2p/WaNWs4e/Ysd+/etXsSwnoliqol/JNltM1qCikCyRRSlIXzyGbVQvLKdkrLK58WLTGZTOiWLsV09gyGbdswRUc7bKiYe0Y6fRrDhcsGCuT1YsGUIEJyZv7mGmDxiofSXCsgr9JNIfVc8koohU2JnZCQwIwZMwC4efOmQycknm14/6wS/mTczWoKKQLJFFKUhePJZtXCUXmV8s6pq0leJUvJqzt3MPz+G8aDB8DLC59OnfAKDnbIEPcfGOncJ4ZTZw3kCvZi/tQg8uVRO+TYGcEvvya4ZVzJK4t6tTS2PVEh9VzySiiBTQ32hAkTuHbtGn5+fuTNm9fRcxLPcOmqwePDXwnNdWh++99NkCKQTCFFWTiObFYtHJlXPi1ayqZVCXm1YB76338DwLtFS9Rlyj7nSdYbPj6Of07oCcquYv6UIAoX9HbYscXTSV5ZdA/zp2G9LLYfQCH1XPJKuFu6u4T58+ezaNEiQkJC6NixI927d2fPnj0YnHiiD2Ex8tv7Hh/+7m6uXyzrTdcOAQ45lhSBZAopysJ+slm1cHReme7ckU2rEvLq9m0A1PXexLv66w499GfdAnmhjDfzJgdRspg0184meWWRklfrN8fbdyCF1HPJK+FO6WqwFyxYwDfffMMrr7zCsmXLWLNmDUeOHKFTp05UrlyZjh07MmfOHKId+F0kkVp8goS/u5vr+VOCuHHbcS8oSRFIppCiLGwnm1ULZ+SVbkWk29eH5FUSr5dfxrtxE4cfN39eNb/8kIPypX0cfmyRmuSVxaN5tXm71v4DKqSeS16J5zlw4AC///57qvs2btzIgQMH7Dqu1Q32d999xzfffMPHH3/MwoUL8fHx4caNGzRv3pyPP/6YF198kUOHDjFx4kQaNmzI8ePH7ZqYre7fv8/06dPdMnZmpMTwd3dzffqcnjk/2fkK72OkCCRTSFEW6SebVQun5ZVWq4j14fF5pdHg07Y9Kjsv45IWlSpzX+daCSSvLJyWVwqp5x6fV+KZIiIi6NOnD3fu3AHg9u3b9O7dm549e9p1XKuqw/fff8/8+fOZPXs2ffv2Ra1WkydPHurVq0epUqXo3bs3Cxcu5M8//6R69erExMQwcOBAqyYQHR1N3759GTp0KBEREZw4cSLNx65fv56qVas+cX/79u0pXbo0pUuX5pVXXuHatWtWjS2eLdOHfzo8XowTtY4vxlIEkimkKAvryWbVwul5pZD14dF5FRyMSmPjiaCE20leWUheuY4i9lfiCQ0bNqRGjRrkyJEDgJw5c1KzZk0aNGhg13GtarAXLlzI5MmTuXfvHocPHzbfX61aNSZOnEiXLl24f/8+uXLlQqPRULZsWZo2bWrVBPr27UvhwoUZMWIE7du3Jzw8nLi4uFSPOX/+PF9//TXz5s176mXBihYtypAhQ8x/3n//favGFmnzmPC3giuLsRSBZAopyuL5ZLNq4bK8Usj68NS8UqntP6u3yWRiypz7HPzLAR/HFVaTvLKQvHI9ReyvRCpfffUVc+bMQZ2c63fu3GH27NkMHz7cruNa1WA3adKEXLlyMWDAALp06cKuXbsAKFOmDAkJCezatYuWLVty+fJl2rVrx6pVqwgPD3/ucQ8ePMju3bupVq0aAFWqVCEuLo7FixenelxiYiL9+vWjRIkSTz1O6dKladeunflP5cqVrfm1RBo8LvyfwR3FWIpAMoUUZZE22axauDyvFLI+JK9s8/28h0yf/5CPP4vl1h05SawrSF5ZSF5JXonUtFot3bp144033iAxMdHu41nVYA8ePJj//vuP6tWro9Pp6Nq1K6tXr6ZIkSL4+PgwZ84cEhISCAsLo0yZMlYPvn37dgCCk68f6e3tTVBQEFu3bk31uDJlyuDjk/YJPw4dOkTVqlWpXLkynTt35vz5888ct169emn+8fSzoXts+D+FO4uxFIFkCinK4kmyWbVwW14pZH1IXqXP/KUPmfbDAwA+7RJA7hDPuc61u0heWUheSV55uuvXr6PT6QBISEggNjaWv/76i61bt6JSqfB1wP8Pq8/Q8d577zF79mzWrl1LhQoV+OKLL9i/fz9lypShZs2a/PLLL2TJkoVhw4ZZPXjK2cYfbZ59fHzSfRby1157jdGjR9O7d2/++ecfunbtav4PJ6zn8eH/CCUUYykCyRRSlIWFEtaH5FUyhawPySvr/Lw6nrHT7gNJzXXHVv5unlHmJ3llIXmVRPLKsw0ZMoQ///wTgObNmzNkyBDKlCmDt7c3fn5+Dhkj3afADA0NZdGiRbRp04YvvviC8ePHA0lfCv/hhx84cuQIJ0+etOpYKV8of/jQssh1Oh05c+ZM15xatWpFvXr1aN++PcOGDePixYucPXs2zcdv3rw5zT9qB3y3KiOS8LdQQjFOIUUgmUKKslDG+pC8eoxC1ofk1bOt2ZDAsPFJ55j5uJ0/3TpKc+1sklcWklepSV55nj///JO7d++SPXt21q5dy+nTp4mLi2PQoEFkzZqVKlWqYDI5JiNsusaESqVi8ODBtGrVilmzZpnvz5MnD1999RW//fabVcepXbs2ALdu3QJAr9cTGxtLrVq1bJkWACVLlgQgICDA5mN4Ggl/CyUU48dJEUimkKLsyZSwPiSv0qCQ9SF59XSbtiUycNQ9TCZo2ywLfbsHyOW4nEzyykLy6ukkrzxL//79WbZsGd9++y2vvPIKBoOBiRMnsnDhQu7du0fFihVJSEhwyFh2XcTx888/JyEhIVW3/+abbz71UlpPU7lyZWrWrMnOnTuBpO9SBwQE0LJlS7p06cKCBQuee4w9e/ak+s72oUOHaNq0KaGhoen6XTyVhL+FEopxWqQIJFNIUfZESlgfklfPoZD1IXmV2s59ifQZGovBAE0b+jG4T6A0104meWUhefVskleeo1ixYuzZs4f79++zadMm8uXLx759+8wn7y5ZsiRGo9EhTbZdDbZKpWLMmDFPFIqUs4JbY8qUKURFRTFs2DDmz5/P3Llz8ff35/Tp01y+fBlI+gL67Nmzzf8BRo8ebT6R2b179xg3bhxDhw4lMjKS+Ph4RowYYc+v5TEk/C2UUIyfR4pAsseLct58rp+Dh1HC+pC8spJsWs2UkFcHj2rpMTAWnQ7q1/Vl1MCseHlJc+1MklcWklfWkbzK3PR6Pe3bt+fixYv8+++/HD58mIsXL9KrVy9efvllqlatSlhYGP379wegTZs2tG7dmk6dOtGvXz9++OEHrl+/nq4xve2ddJYsWex6fkBAABMmTHji/pQvnwP4+fnRpUsXunTp8sTj6tevT/367lkMGZmEv4USirG1DBs3AODTqHGq266UUgQ0ET3QdItAO3M6OOCSBumSXJQ13SLwadHStWN7mBJF1Qztm1U2qygjr6zyyPrQRPRAO/17TJcuunwanp5Xfx/X0aVfLAmJULuahgnDs+HtLc21MymhnktepZPkldlT80rY7d9//+X8+fP06tWLUaNGMXjwYEaOHElERASLFy/Gy8uLq1evUqpUKU6dOoXRaASSLt0VExPD2bNnOXXqFPnyWf+Gjl3vYIuMScLfQgnFOL3kldZkKa9837nj+rE9yPD+0lyDMvIqXeSdITN35NWps3o+/iyGBw9NvFrJh6ljsqPxkebamZRQzyWvbCR5ZfZEXmk0bplHZlK+fHk2b95Mu3btCA0NpXLlykyaNAmTyUSdOnU4ePAg7du3Z+XKlZQqVYqBAweydOlSFi1axNy5cxkzZoz5vGHWkgbbw0j4WziiGLvr48mKLAJuarJ1KyJdP64HuXTVIJtVBeSVTWTTaubKvLpwWU947xhi7pmoUN6bGd9kx89XmmtnkubaQvLKPkrLK59mLdwyB2eLjo6mb9++DB06lIiICE6cOPHUx+3cuZNGjRrx0ksv0aZNG27cuGH+2a5du+jevTuDBw9m6NChaX532sfHx3xt66JFi1KrVi0GDRpkvkz0Z599xsyZMzl79iwlSpRI98fBn0YabA8i4W/hqGLs06KlFAF3N9larevH9CAjv70vm1U351VofjtKtWxazVyRV1evGwj7JIY70UbKlPRmzrdBBAbIVsuZpLm2cGheueOdU8krM3NehYS4ZXxn69u3L4ULF2bEiBG0b9+e8PBw4uLiUj3m1q1bHDhwgKlTpzJo0CAOHTrExo0bAbh06RIRERF88cUXjBo1ioSEBEaPHv3ccV9//XVq166Nr68vAwYM4MqVKwQFBbFx40aKFy9O8eLFzecAs4ekvofIlOFvI0cWY9OdO1IElNBkC6eJT5DNqrvzqmsHOy87KZtWs5S8IiAA7waNHJpXt+4YCO8dw/WbRooWUjNvUhDZs8k2y5mkubZwdF75NGuhjBOZenhe6SKXu2VsZzp48CC7d+82nxS7SpUqxMXFsXjx4lSP8/f359NPP6V48eLUrVuXQoUK0bBhQwBmzZpFSEiI+apR1apVY+XKlVy9evWZY3fs2JGgoCB69OjB4cOH6dSpE4MHD2bmzJkYjUaKFSvGyZMn7f4dbU7+AwcO8Pvvv6e6b+PGjRw4cMDuSQnHyqzhbwtHF2PdikgpAkiTLRxH8soiJa9u3DbYfzDZtJqZLl1EO2Ec+v+tQl2nrkPy6m6skU69Y7h4xUCBfF4smBpEcE5prp1JmmsLZ+SVKiTE/edYkbzCdMP+jys7i8FgoF69emn+Scv27dsBCA4OBsDb25ugoKBUl10GCAxMuqTh5cuXGThwID179iRHjhwAbNu2jZBH3t0PDg5Gr9ebrzj1vHmPGjWKb7/9luzZs1O3bl10Oh1eXl4ULFiQY8eOpfc/xRNsTv+IiAj69OnDneQTDN2+fZvevXvTo0cPuyclHCczh396OaUYa7VSBJJJky3sJXll8Whezfkp3jEHlU2rhVYLKhXqlyrYnVdx94181CeG0+cN5A7x4sepOcibW+3AyYrHSXNt4ay80kUud/s5ViSvMqfo6GgA83egU/6ecv+jzp07R2RkJGXLlmXIkCGEh4djNBq5e/fuE88HiIqKeu743t7evPHGG+bbY8aM4euvvwYgW7Zstv1Sj7G5wW7YsCE1atQwv5KQM2dOatasaX7rXrhfZg//9HBqMZYiYCZNtrCV5JXF43mVqJW8cgqjEd3Py+zOq39P6jl1Rk+OIBULpgYRWkCaa2eS5trCmXllunHd/fVc8krR1Go1mzdvTvNPWlJ6x4cPLf9mdTodOXPmfOKxxYoV47PPPmPAgAGEhYWxb98+zpw5Q44cOZ54PljeFU8PLy9LO5wrVy6mTZuW7mM8cUxbn/jVV18xZ84c1OqkQqJWq5k9ezYDBgywe1LCfp4Q/tZySTGWImAmTbZIL8krC8kr13JEXlWrrGHm+OzMmxRE8SLeTpilSCHNtYUr8koR9VzyKtNJueTVrVu3ANDr9cTGxlKrVq1nPi9PnjwAZMmShdq1a5ufD0nviqvVaqpXr27X3DQaDS+99JJdxwAHnOQsOjqaH374gVmzZjFs2DBef/11IiPlsjnu5Enh/zwuLcZSBMwUUZRFhiB5ZSF5lXHzqsZrvpQr7fP8BwqbSXNt4cq8UkQ9l7zKVCpXrkzNmjXZuXMnAIcOHSIgIICWLVvSpUsXFixYAMCaNWvYt2+f+Xk7duygZcuWhIaG0q1bN+7fv28+IdnevXt57733KFiwoN3zu3jxIr/88otdx7CpwY6Li8NoNAJJHw1/88038fb2Zu/evTx8+JA1a9bYNSlhO08M/7S4pRhLETBTRFEWiiZ5ZSF5JXkl0ibNtYU78koR60PyKlOZMmUKUVFRDBs2jPnz5zN37lz8/f05ffq0+TJZUVFR9O/fn4kTJzJ16lSqVq3K0KFDAQgNDWX+/PlMmjSJoUOH4uXlZf6ZvWbOnMmpU6fsOka6P8t07tw5wsLCGDx4MG+//TYAhQsX5qOPPuKtt97i7bfftntSwjaeHP6Pc2sxTi4Cmm4RaCJ6oJ3+PaZLF103fjLDxg0A+DRqnOq2K6UUZU1EDzTdItDOnA6JiS6fh1AeySsLyauMk1cmEzzUeSPtt+tIc23hzrxSRD2XvMo0AgICmDBhwhP3//nnn+a/h4eHEx4enuYxKlasyKxZsxw6rwsXLrBmzRrq17fvxZN0v4M9ffp0bt26xZ49e574WaFChVCr1Tx48MCuSYn0k/C3UEIxlldaLRTxyrdQFMkrC8krC6XnlckEEy6/RcftjbgTbXTL/DyNEtaH5JWFIuq55JVwAK1Wi1arTXWfyWRiyJAhGI1GTCb7sibdDfaECRMYMGAAGzZsQK9/MmRUKhVBQUF2TUqkj4S/hRKKsZkUATNFFGWhCJJXFpJXT1JyXs24VptFN6ty6l5O9h/RPucowl5KWB+SV09SRD2XvBJ2WrBgAXPnzk1138yZMzlw4ABeXl40aNDAruNb1WAfOXKETp06MX/+fLZt28Yrr7xC2bJlWb16Nffu3ePhw4fExsYSGRmJXq+nbNmydk1KWE/C30IJxfgJUgTMFFGUhVtJXllIXqVNiXn14+0azLqWdIbbgS/tpWE9P7fMy1MoYX1IXqVNEfVc8krY4fjx4xw+fNh8e9WqVUyZMoX8+fMzbtw4KlasaNfxrWqwZ8yYwe7du/nmm2/o2rUrrVq1YteuXQwePJjXXnuNV155hapVqzJkyBA0Gs0zPy8vHEfC30IJxThNUgTMFFGUhVtIXllIXj2fkvJq2YFgJl6oC8AnBbbQpvgJt8zHUyhhfUhePZ8i6rnklbBR4cKFOXPmDJB0tvLBgwfTtGlT1qxZw8GDB/n555/tOr5VJzk7fvw4PXr0oHz58nh7e2M0GjEYDJhMJoxGIyqVCm9vb7JmzUrp0qXJmjWrXZMSzyfhb6GEYvxccmIOM0WcKEW4lOSVheSV9ZSQV2sOZ+OrFUn51KWJlo/uHgRyuXwenqJEUTVD+2aV5hpl5NXzKKKeS14JK508eZI//viD1157jaJFi3Lz5k3mzJnD3Llz+frrr3nrrbcwGo3Exsby4MED7t69i0qlInv27KhUqnSNZdU72Js3b6ZXr1688cYb1KpViyxZsrB9+3befPNN3n77ba5evUrx4sWpXLmyNNcuIOFvkSE2qynklVYzRbzyLVxC8spC8ir93JlXf0SXYej5dwFoU/wEfXrnTMorjcal8/Akw/tLcw3KyCtrKaKeS14JK0yZMoXvvvuODh06MGjQIEwmExMnTuTevXsMGDCASpUqUblyZX777Td+/fVXqlevTrVq1ahevTrXrl1L11hWNdi+jy2WixcvsmPHDvPtunXrEh4eztmzZ9M1uEi/LH4S/iky1GY1hRQBM0UUZeFUslm1kLyynTvyaldscQac+wAjXrwXcpT+OX5BN2M6qnz58GnWwiVz8ESXrhqkuVZAXqWXIuq5B+eVsM7hw4f54IMPGDZsGBMnTuTFF19EpVJhMpkIDg6mb9++DBkyhA4dOhAcHEyDBg3IkycPdevWJV++fOkaK91nEQd49dVXuXnzJkZj0mUqChcuTOPGjWnXrh1///23LYcUVhrSN1DCnwy6WU0hRcBMEUVZOIVsVi0cllfueOfUA/Pq4L1C9DndAr1Jzds5jjGsyFq8VI/kVUiIU8f3ZCO/vS/NtZvzKjS/Ta2BMuq5B+aVsN7y5csZM2YMH374IQ0aNKB69ep07NiRAQMGoNfrmTNnDqGhoXTv3p3Q0FAmTpzI1q1bGTNmjHM+Ip5i9erVVKtWjS+//BJvb2/69+/PwIED+fLLL7lx4wZ3796lQ4cOLFy40O7rh4mnK1RA7fHhr4Tm2leTvoX2BCkCZoooysKhZLNq4ci88mnWQjatTs6rf+7np9fpD0k0+VAz+ynGFFuNWmX5f2a6dBFd5HKnjC0gPkGaa3fnVdcOATY/XxH13IPySqRP4cKp/y2ULl0ag8FAeHg469ato0qVKnTr1o39+/fTpk0bu8ZKV4P966+/EhcXR1xcHC+++CJRUVHcvHmTS5cucerUKTQaDQkJCXz77bdcvOj6Ewx4guHj4zw+/N3dXAf4q/i4fRb7DyRFwEwRRVk4hGxWLRydV6qQENm0OjGvTj3MTcSpNjw0+vJq1vNMKLECHy/jE48z3bju0HGF+0heWaTk1Y3bBruOo4h67gF5JexXtmxZGjdOOildcHAw3333Hd26dWPAgAG8/PLLdh07XQ32yy+/zJYtW1izZg2LFi1iwYIFzJ8/n59++olVq1axadMmXn/9debNm0eRIkXsmph4ujPn7Qs+Wykp/N3dXM+dmJ28udSOOaAUATNFFGVhF9msWjgjr3SRy2XTinPy6kJCTrr+15Z7hixUCLjMlJI/4+flnn+/wjUkrywezas5P8XbfTxF1PNMnFfCMYoWLcpLL72U6r5evXrRu3dvRo8ebdex09Vg9+rVi9y5c6f58zx58vDDDz/wyiuv2DUpoSxKC393N9cli3kz5Yco/j5zmyiDVVe6ezYpAmaKKMrCJrJZtXBWXpluXHf/+siEeXU1MTtdTrYjWh9Iaf8bfFdqKf5qnYNmKpRI8sri8bxK1Door5RQzzNhXgnnCwsLo3Tp0pw6dcrmY9h2JgPhMZQY/u5urjv0P8nOs3voP+kQ3W4XZ7Mqv/0DSBEwU0RRFukim1ULZ+eVItZHJsqr29pAuv7Xjpu67BTzu83MUovJ5u3i6/gKl5K8spC8ch0l7K+E9fr06UOIHSe0lAZbpMkTwt8ajxbjsE9vostymof3snHhSCUu/FWBMdfe4mB8fuw+r58UATNFFGVhFdmsWrgqrxSxPjJBXt3VZaHLf+24nJiTAr53mVl6MTl9MsZlkYRtJK8sJK9cTwn7K5E2gyH113Bz5sxp87GkwRZP5Unh/yyPF+OzV+6jUoE2PgtgIvFhAHeuFOKjfz/i/X+7M/VKXY4/yGt7sy1FwEwRRVk8k2xWLVydV4pYHxk4rx4afOh+qi3nEnKR2+ces0svIo8mzskzFe4keWUheZWx8kq4Rnh4OPv373fIsRzSYK9bt44RI0Y44lBCATwx/J/macVYl+CHyQQ58t2geOWD5Cl+hoCgaHxUei4khPDD9Rq0Pv4xDf/uxfhLb/HX/QLpH1iKgJkiirJ4KtmsWrgrrxSxPjJoXmXx0vFK1ovk8H7A7NKLKOgb4/xJCreRvLKQvMp4eSUcIzY2Ns2rXEVFRbF//34ePHjgkLHS3WB36NCBq1evmm/fvn2bIUOGsHTpUj799FMuXbrEsmXLHDI54XqeHP6PSqsY67W+3DxXDAC1j54cuW8xstQqtlb8lrHFVvJWjuP4eWm5pg1i0c2qzL5W07YJSBEwU0RRFqnIZtXC3XmliPWRAfNKpYJ+oZuILD+bolmiXDRD4Q6SVxaSV2TIvBKO8dVXXzFo0CAAPvzwQ+7fv8/Ro0fZsmULWbNmxcvLcR/sTteRTCYThw4dYufOneb7Fi5cyMOHDzGZTPz+++/Ur1+fMWPGOGyCwnUk/JM8rxjfu5WHov5VGdP9dWbkOks90zUC1VoaBB9jQokVbH35WyaVWE6j4L9pFPzvU8dINKrRGp9zqS8pAmaKKMoCkM3qo5SQV6CQ9ZEB80qlglya+y6amXAHySsLyatHZMC8Eva7fPkyx48fJzIyklOnTrF37142b97MwoUL0Wg0FC1alMREx5zkMl0Ntkql4qWXXuLEiRPm+0JCQsiRIwcrV65kypQpvP/+++h0cnmLjEbCP4m1xdjHy48XS4QQrH7y51nUet7I8R9jiv2Phmk02Buiy1PnSF8Gnm3KpuiyPDT4PH1CUgTMFFGUPZxsVi2UkFePUsT6kLwSCiJ5ZSF59RSSVx4nMjKSX3/9lY0bN1KmTBl+/PFHjh49yrFjx5g5cyaQ9LXnWbNmMX/+fCIjI9m/fz96ffqzI93vhTdq1Ihbt26Zb7/xxhskJiZSrlw56tevT7du3QDQarXpnoxwDwn/JK4sxvvvFeGB0Zffol+g39nm1DnSl09Pt2DtnRe5p3+s0EgRMFNEUfZQslm1UEJePY0i1ocC80r1Vn1+iyqPURn/m4QLSF5ZSF49gwLzSpps5woODiYsLIwaNWpw4MABjh49Snx8PAsWLODGjRts3bqV2bNnM23aNMaPH8/AgQOZMGFCusexqsGOjIykfv36jB07ltOnTxMdHc3WrVs5ePAgDx48wMvLi/v3kz5mVbBgQdRqNQkJCemejHA9Cf8kri7GI4quYWHZeXTMu5sCvndJNPnwZ0wZvjz/PnWP9iXiVGtW3n6ZaJ1/0hOkCJg9UZQ1GrfMw5PIZtVCCXn1LLJptTBs3IB27VomnKnNwHMfMOJCY7fMQ7iW5JWF5JUVFJRX7t5feYLhw4fTpUsXXnjhBUqUKMGhQ4eoU6cOc+fOZdy4cbz33nscOnSIw4cPs3//frZs2cLAgQPTPY5VDfaKFSu4ePEiCxYs4Oeff+bo0aN0796d9u3b07RpUx48eMD169cBUKvV5M2bl4cP5VqSSifhn8QdxdhLBRUCr/JZ6GbWvfgdy8rN4eN8Oyjmdxu9Sc2u2BJ8daEJ9Y72ofPJdiy9WZmbcRopAskeLco+zVq4ZQ6eIoufbFZTKCGvrCGbVoup8xNZuDwegMrVsrtlDsJ1pLm2kLxKB4XklRL2V5nd/v37GTt2LDVq1KBo0aJoNBpeffVVChcuTMGCBTlz5oxDxvG25kHnzp2jc+fOlCtXDj8/PwCMRiM6nQ6dTsfKlSu5evUqJUuWBJLefo+LiyNv3rwOmaRwPAn/JEooxioVlA24QdmAG/QsuJVz8SFsvluGP+6W4eTDfByIK8qBuKKMvdSACgGXqTf4AA0+eZXQiB5op3+P6dLTLzngTIaNGwDwadQ41W1XSinKmh49XT62JxnSN5CC+dSyWVVAXqWHeX1E9EDTLQLtzOngoJO3WC1506rpFoHGDXk173p15lxPupLD4PoXaD6oKrp1d9ySV8L5lFDPQfLKFpJXFo/vr4zHj7l8DpnZunXrCAgIAKBGjRqYTCZat27NmjVreOGFFzh16pRDxrGqwf7999/JmTNnmj+/fPkyJ0+eJH/+/Fy9epWEhAR27dplbriFskj4J3FIMXbCx5OLZblDsSw7+Tj/Tq4kBrH5bhk2R5fhrweh5j8TP4OyhWN4+43O1A2KpGjM3w6fx/MopcnWRS5H076Dy8f2FIUKqOnYSzar7s4rW3jypnXZzcpMuVIPgN4FN9Mieje6dbfcmlfCeaS5tpC8soMCm2xdtmwuHz+zSnljOMW3335LoUKFuHv3LuPHj2fp0qXUqFHDIWNZ1WA/q7kGCAoKYtSoUUyZMgWTyYRarebkyZMOmaBwLAn/JI4qxj7NWqCdOtlpRaCgbwwd8+6lY9693NIGsiX5ne1DcYU5cVHNiflapvAeXcsUISLrGqfM4VkU0WTfuO7yMT3J8PFxsll1c175alQ2P9cTN61r7rzE15caAPBxvh10yrcbUEZeCceT5tpCCXllD0/Mq7Q8nlfCfqNGjWLVqlUUKlQIf39/4uPjGTNmDL6+vuTPn59p06bh5eVF9+7d0ev1ZMuWjTfeeINGjRqleyyHXFH79u3bZMuWjS5durBgwQL27t3L2LFjrXpudHQ0ffv2ZejQoURERKS6BNjj1q9fT9WqVZ+4f9euXXTv3p3BgwczdOhQOcFaGiT8kziyGKtCQlz2naHcmvt8mOcgc8ssYsvLExlW5Fdq5DiLj9rEa52qyneGhFOcOW9wy7iSV0kC/FV83D6LXcfwpO84boouy7DzTQBom2cfPQpsTfVzyavMRZprC0fnlVfVag6aWfp4Ul49j2HjBnQ7trtl7Mxo9+7dvP3229StW5fq1atTrVo1bt26xfHjxzlx4gQPHz4kICAAk8nEqVOnWLduHUuWLLFpLIc02A0bNmTz5s306dOHqlWrkjVrVquf27dvXwoXLsyIESNo37494eHhxMXFpXrM+fPn+frrr5k3bx53795N9bNLly4RERHBF198wahRo0hISGD06NGO+LUylcwY/rZwdDHWRS53SxHI4RPPB7mO8n2JJfxZZQpV8tx+ahFYdrMyX1+sz/57hdGbbH8X7Hlk0yocSfIqSUpe5c2ltvtYnrBp3RFTgoHnmmLEi6YhR+gfuhHVU2JP8ipzkObawhl55VOzliJOZJpZ88paxr173DJuZrRkyRImTJhA3759+fTTT+nZsyelS5dm5cqVVK5cmcOHD/POO+8wc+ZMtm3bxrp161i4cKFNYzmkwS5VqhSBgYHpft7BgwfZvXs31aolvUpWpUoV4uLiWLx4carHJSYm0q9fP0qUKPHEMWbNmkVISAihoaEAVKtWzXzSNZEks4Z/ejmjGJtuXHd7EchqiMM45+lFYMXtSiy79Sof/9eBN4/24avzjdgZUxyd0SFLPxXZtApHkLxK8mhezVr4wCHHVOKmlXz5HXLYA/cK0/dMc/QmNe/k/JchRdY9tblOIXmVsUlzbeGsvNLt2K6Yq4UoJa/c1WQLx8iVK1eq26VKlSJ79uyUKVOGn376ic8//5wBAwbw33//AVC8eHHUatte4Hb8Ljsdtm9P+thDcHAwAN7e3gQFBbF169ZUjytTpgw+Pj5PPca2bdsICQkx3w4ODkav17Nr1y7nTDqDyczhnx7OLMZKLQImE/QquIX3Q44Q5P2Qu/oAVt6pRI/Tbah7tC+Dzr7P5ruliTdYdSoGq8imVdhD8irJ43l1+ZrRYcdWUl4Zr1xBpdc9//HP8ff9AvQ6/SGJJh9qB51iVNH/oVY9//+b5FXGJM21hTPzyrh3j9vXh5LySprszMfPz4+RI0eabzdv3pyZM2cyadIku4/tuJ11stu3bz/xCkFaoqOjAVI1zz4+Pub7rXH37l2KFCmS6vkAUVFRaT6nXr16af7MYDDY/GqF0mT28LeWK4qxUk/MUevSGWoFnUFvWsehuMJsvluGLXfLcFuXlfXRL7I++kX8vLTUyH6WN3OcoGbQaQLVWrumIScSEraQvErytLwqXNCxNUkpeaWbMwvvdxpg2LcPk1oNV6+k+zAnH+Yh4lRr4o0aXst2jvHFf8HHy/oXJCSvMhZpri1ckVdKWB9KySslnPhMOF5QUFCq2y+99BJffPEFOp0uzTd3reHQd7D/+OMPVqxYYfXjc+TIAcDDh5Zw0ul0zz1r+ePHePz5YHlX3FN5Svg/jyuLsZJfafVWmXgt2wW+KPw7GytMZkGZ+bTPs4f8mhgSjBr+uFuWgec+oM6RvvQ81YrVtysQo7f9xEryzpBID8mrJJ6YV/rff8O7ZSt8e32S7neGzscH0+2/tsQZsvBy4GUml1iOr1f6T8oneZUxSHNt4cq8UsL6UEpeyTvZnqFQoUJ2Ndfg4Ab7+++/59q1a1Y/vnbt2gDcunULAL1eT2xsLLVq1UrXMVKeD0nviqvVaqpXr57mczZv3pzmn8zw7rWnhX9a3FGMM0IR8FJBxaxX6FfoD9a/NI2l5ebQOd9OivjdQWfyZkdsKYZdeJc3jnxGl//a8vOtV7itTf85FpRQlIXySV4l8eS80s2ake5N65XEILr81467+gDK+l9nWsml+Ktt/7i55JWySXNt4Y68UsL6UEpeSZOd8U2ZMoWdO3c+cf+///5Lu3btePPNN5/4unJ6OazBXr9+PSdOnHjiLN/PUrlyZWrWrGn+JQ8dOkRAQAAtW7Y0X/Lrebp168b9+/fN193eu3cv7733HgULFrTp98joPDX8H+fOYpyRioBKBeUCbtCr4J+sfmEGK1+YQUSBrZTOcgMDXuy7V4wxFxvy1l+f0vFER/69ny9d01BCURbKJXmVRPIqfZvWm9qsdP2vLbd02Sjmd5sZpRaTzdv+j4xKXimTNNcW7swrJayPjJhXQllu3LjBzJkzuX79eqr7dTodvXv35uDBgwQHB1OhQgW7xkl3g33mzBnOnDmT6r5bt24xYsQIVCqV+WPf1poyZQpRUVEMGzaM+fPnM3fuXPz9/Tl9+jSXL18GICEhgdmzZ5tPXDZ69GjOnz8PQGhoKPPnz2fSpEkMHToULy8vhg4dmt5fK1Pw9PBPoYRinBGLgEoFxbPcoWv+HSx/YQ5rX/yOPgX/4MWAK5hQcfR+IbLasIlVQlEWyiN5lUTyKpmVeRWt86frf+24kpiTUN9oZpVeRA6feIdNQ/JKWZSwPkDyKoUS1kdGyiuhPCEhIXh7e6PRaFLdv3HjRq5evUrLli1ZtGhRuvvZx6X7JGfLli1Dp9Px1VdfAUmX0OrTpw8xMTGUKlWKbt26pet4AQEBTJgw4Yn7//zzT/Pf/fz86NKlC126dHnqMSpWrMisWbPSNW5mI+GfRCnFGDL+iTlC/e4Slm8PYfn2cFOblf33ilLY7+knINxytzQFfGMoleVmmtedBTmRkEgieZVE8uoxVuTVpMtvcj4hhDw+scwqvYjcmvsOn4bklTIoZX1IXqWmhPWRUfJKKI+3tzfly5dn6dKl/PjjjyxcuJDAwEDWr1/PwIEDCQsLc8g46X4HOyYmxvxxbL1eT+/evTl8+DBhYWFERERw8aL843I1Cf8kSinGj8osr7Tm0cTRJOTvp/5MZ/Ri+PnGtDzWhSb/9GDS5Xr8fT8/psf+CSjhlW/hfpJXSSSv0vCcvOpfaANvBJ1kdunFFPCNddo0JK/cSynrQ/Lq6ZSwPjJCXgllKlu2LEePHuX48eO8++67bNq0iWrVqlGuXDn27t3Lvn37OHjwIBcuXLB5jHQ32BUrVuTs2bNotVp69OjBqVOn+PHHHxk4cCCbNm1i27ZtNk9GpJ+EfxKlFOOnyexF4J4hC5WyXsJXpeNyYk4W3KhO+xMfUf+vTxh7sT4H7xXCYEp6W1sJRVm4j+RVEsmr53hGXmXzTmRSyUiKZEn7UpyOInnlHkpZH5JXz6aE9aH0vBLKVLRoUVq3bs0333zDtWvX+OSTTxg9ejQdO3YkLCyMsLAw2rdvT4MGDWjatKlNY1jVYC9ZsoQ33niDL774guPHj3P//n0+/PBDjhw5QqtWrTh9+jSLFi3iypUrHDp0iHnz5jF//nw2b95s06SEdST83vePiQAATZFJREFUkyilGD9LZi4CwT4PmFwykq0Vv2V88V94J+e/+HslclOXnaW3XuWj/zpS72gfRlxoxK7YYiT8vsntRVm4nuRVEskrKylk06qEJsKTZPFTxvqQvLKOEtaH5JVIr0KFCnH9+nVq1apFnTp1aNGiBSaTieLFi7No0SJmzZpF7969ee211+jdu7dNY1j1HezIyEiuXbvGypUr8fPzQ61Wc/z4cVQqFVOmTMFoNJofq1Kp+Oeff5IO7u3Nhg0bKFCggE2TE2mT8E+SETarKTL7d4b81TreznmCt3OeINGoZs+9Ymy+W4atd0tzVx/AituVWHG7ElnV8dS+cJq3/9tJ7R6NUCPfcczsJK+SSF6lk0K+4/j4d06Nx4+5fA6eYkjfQArmU0tzrYC8spZ8JzuZQvJKPF/+/Pk5cuQIOXLkYObMmQBUqlSJ4cOHs3DhQqZOnUqtWrXSfV6xR1n1DnZQUBCLFi3ir7/+4ujRozRo0ICSJUvi4+NDjhw5GDlyJEeOHGHp0qUUL16cOXPm0L17d+bOnSvNtRNI+CfJSJvVFJ7ySquvl4E6QacZWfRXtrw8kZmlFtEi10GCve8TZ8jC2qiX+OSXUlR9+wb99tRkY8HWPDBonn9gkeFIXiWRvEofowlGXmjIxuvFFPHO0KPv1HlVreaWOXiCQgWkuVZCXqWXvJOdTN7JzhACAgIoVaoUv/32m/k+rVbLtGnT2L17Nz///LPdY1jVYM+fP5/KlSvjm/yPtXz58rz++uv88ccfvP766wwdOpQhQ4ZQsmRJQkNDqVmzJr1796Zq1ap2T1Ck1ryJn4Q/GXOzmsLTioCPl5Fq2c8zuMhvbHp5MvPLLKBdnr3k08QQr1Pz+5ZE+q8qQZ2/+vPJ6Vb8785LxBvSfYEDoUCyWU0ieZXOMU0w/tLb/HL7Fb449z437vkqYtNqbiJq1nLL+J5g+Pg4aa7dnFe+mqdcCsQK0mQnkyZbseLj49m7dy+5cuXiu+++44cffjB/6rpQoUKsXr2aqVOnMnXqVO7ft+8KFek+yRlAsWLFyJ49O7lz52bcuHFMmzaNP//8k5EjRzJkyBC7JiSerW0zf48P/4y8WU3hqUVArTJRKetl+hfaxG8vTWNx2bl0yruLwjkT0Bq82BZTipEXGmG0LZqEgshmNYkj88pd75y6Oq++u1qXJbdeA2BYkbXk9b2nmE2rYeMGdDu2u2VsT3DmvMEt40peJQnwV/Fx+yw2P1+a7GQKySuR2tSpUwkPD6dq1aq0bduW8+fP079/fzp06MCMGTNYv349RqORzz77jNWrV9s1lk272EqVKtG2bVvz7TfffJPFixezZ88e/v776ZfyEY6xeMVDjw9/JTTX9WrZ/3FmTy8CKhW8EHid3qFb+F+x8az8+B96fRTAh1XuEKDWPvU50Tp/l81P2E42q0kcnVc+NWtl+k3rD9erM/d6DQC+LLyeJiH/WH6okE2rce8et4wrnEPyKklKXuXNpbbrONJkJ1NIXgmL3377jdDQUF599VUKFy5MyZIlefDgAVmyZEGj0VCiRAmGDh1KgwYNKFeunF1j2dRgZ82alWzZsqW6r0yZMsybN4/Zs2djMLjnFUhP8MuvCW4ZV0nh7+7munuYPw3r2f4K76OkCCRRqaD40dV0zfsngye/9NSifFeXhbf++pQ2xzsx73p1LibkdPk8xfPJZjWJM/JKt2N7pt60Lr1ZmalX6gHwWcFNtMx96MkHKSCvROYheZXk0byatfCB3ceTJjuZ5JWi9OnTh40bNzJz5ky+//57xo8fz8svv8ysWbOYM2cOkyZN4tq1awwdOpRKlSrZNZZDP4dZokQJhg0bxpEjRxx5WOFmSgt/dzfXn3YJZP3meIcdU4qAxbOK8l8PCmIweXHsQQGmXKnHu//0oPm/XZhx4mX+O6vHZFL+iWAyO9msJnFWXhn37sm0m9bVtysw9lIDALrm307HfHvTfrBC8kpkbJJXSR7Pq8vXjM9/khWkyU4meaUY7733XqrboaGhvPvuu+bbJUqUICQkhO3bt5No51noHf5FxwoVKlC5cmVHH1a4iRLD393N9eTZ99m8/ekfYbaVFAGLtIpynaDTbH55EoMLr6NatrN4qwycjs/DjJMVebd9NO98GM2E6ff5+7hOmm03kM1qEmfnlSI3rWr7PlK6MbosX11IusRPuzx76Z5/2/OfpJC8EhmT5FUSj8wrD95fiSe99dZbqW6/9NJLfPvtt+YTe9vKIQ32X3/9RUxMjCMOJRTEE8LfWq4oxlIELNIqysE+D2iR+zAzSy9hy8sTGVn0f9TJewmNBi5cNjBn0UNadL5L3Q+iGD05joNHtRgM0mw7m2xWk7gqr5S0aTVeOA92fC1se0wJBp1rihEvmuU6RL/QTaisPYmxQvJKZCySV0k8Ma9kfyWe5/vvv6dmzZp2H8fuBlun09GtWzf++usvuycjlMOTwv95XFmMpQhYPK8oZ/dO4N2Qv5labTN71oUwaUQ2GtTzxT+Lius3jSxcHk/biBhqvRfF0HH32LVfi04vzbajyWY1iavzSimbVv3/VgOgrvZ6uvNq370i9D3TAr1JTcOc//Bl4d+sb65TKCSvRMYgeZXEU/NK9lfieVTpLkJPZ1WDvW/fPvPpyh8/S3hsbCx3796Vj2RmIp4Y/mlxRzGWImBhbVEODPCi4Zt+TB6ZnT3rQ/h+bHbeb+BHtqwq7kQb+Xl1Ap0+jeH1RncYOOoem3ckkpgomWUv2awmcVdeKWHTCkBAAOp3301XXv11vwC9T7dCa/KmbtB/jCi6BrXKxv93CskroWySV0k8Oa9kfyVcxaoGe9SoUYwbN46YmBg6d+7MhQsXWLhwId988w0hISH4+/vLmcMzCU8O/8e5sxhLEbBIb1H281XxZi1fvhmSjV1rQ5g7MTut3vMjZ5CK2DgTq9YnEDEglvfDol0w+8xLNqtJ3J1XSti08uABuhnTrc6rEw/y0uNUG+KNGqpmO8u44ivw8bLzxEoKySuhTJJXSSSvZH8lXMOqBrt58+aUL1+eb775hqxZs7J3714SEhLYvXs3AOXKlePhQ/cElnAcCX8LJRRjKQIWthZljY+KmlV9GTEgGzt/DeGn74No3yILeXN7Ub2K/dcy91RKWB+SVxYZadN6Lj6E7qfaEGfwo2LgJSaViETj5aAX6BWSV0JZJK+SSF5ZyP5KOJtVDXbHjh2ZM2cO5cuXJzQ0lPHjxzN16lTOnDlD/fr1OX78OF9//TX169enSZMmtG7dmkGDBrFjxw5nz184iIS/hRKKcQopAhb2FmW1WsWrFTUM7pOVP1cG81m3gKc+7uQZPR/1iWH5/+KJinbM5UoyEyWsD8mrJ2WETeuVhCC6/NeWu/oAyvlfY1rJZfirdY6dhELySiiD5FUSyasnyf5KOJPVJzk7cuQIx44d491338VoNNKkSRPy5MlDpUqVePXVV8mdOzc1a9bktdde46WXXiJ//vzysfEMQsLfQgnF+HFSBCwcVZS9vFQE+D89/jb8mcDOfVqGfBNHjXfv0L7HXX6KfMiNW5JnSlgfkldpU/Km9aY2Kx//147bumwUz3KLGaWXkNXbvuuMpkkheSXcS/IqieRV2mR/JZzF6gZ79OjRHDhwgCpVqlC8eHG+/vprXnvtNTp27Eh4eDhFihRh8ODBDB48mEGDBtGrVy/q1KnjxKkLR5Dwt1BCMU6LFAELZxfl9xv40adrAOXLeGM0wv4jOkZNuk/t96No+XE0cxc/4PJVz2u2lbA+JK+eT4mb1ihVDrr8145r2hwU8o1iVqnFBHnHO3cSCskr4R6SV0kkr55P9lfCGaxusMuWLcuSJUsIDQ3lnXfeAeDDDz+kVKlS5M+fnxMnTjhtksI5JPwtlFCMn0eKgMWjRdmrajWHHrtwQW+6dQxg5bycbF4RzKBPAqn0kg8qFfx1TM/47x/wZoso3usYzffzH3DmvD7TX0VBCetD8sp6Stu0RjfpRJQukLyaWGaVXkwuzX3XTEIheSVcS/IqieSV9WR/JRzN6gZ75MiR5M6dG0i6VNfdu3fJmTMnHTp04MqVK0RFRTltksLxJPwtlFCMrSVFwMJclGvWctoYBfOpCfvQn6Uzc7D9f8EM6xdItco+qNVw8rSeqXMe0KhtNA1aRzNp5n3+PanLdM128yZ+bl8fklfpp6RN6wvV8/DjCCOzX1xOft9Y105CIXklXEMJ9VzyKv2UlFeyvxKOYFWDffz4cRYtWsS///7L2bNn2bVrFxs3bmTv3r34+PjwwgsvMHDgQGJjY4mKikKr1Tp73sIOEv4WSijG6SVFwMKwcQO6HdtdMlbuEDVtPvBnwdQc7Pw1hDFfZKVOdQ0+PnD+koGZCx/SrNNd6jWPYuzUOA79rcVozPjNdttm/rJZVUhepZeSNq3lquahZJ/WHp1XwrmUUM8lr2ynpLxS1P4qbz7Xz0HYTWWy4u2W8PBw9uzZg0qlAsBkMpn/nuLR+1QqFTVr1mTWrFlOmLJzlStXDkh6UUGJmoZFc/yU7WEp4W9hTzFu/JYv336VHd2O7Rh+iXTSDJ9NVagwmogemK5fRztzOiQ66YRBz+Lri6ZbBKp8+dBO/x7TpYsun4KqYEF8+w9w+bgp7j8wsnWXlo1bE9m+N5H4BMvPcoV40bmNP2Ef+rttfvZavOIhI7510Ud6HyN5lSQlbxLHf4PpypV0P1/9dn18GjVGt24tho0bnDDD58tMeeXuzMnMbN3jSHOdxBF5ZW/e2EvyKllKXuXPj8rPz/XjP4fS+yV3s+od7Jw5czJv3jzWrVvHhg0bGDhwICVKlKB8+fKYTCYaNWrEokWLmDx5Mh9//DHly5enVi3nfWxT2CazhL8jOKoY+9SsJa+0evg7Q4EBXjR+24+pY7KzZ30uvvs6O+/W9yVroIrbd4wYMviVvn75NeH5D3ICySuLerXsu2a7K98Z0ptU3NIGPnG/5JVwFmmukzg6r9z1zqm8k50sJa/u3HH92MJuVjXY3377LdWrV6dYsWIUKlSIevXqkT17dn755RdGjx7Nnj17+N///kf9+vX57LPPiIyMpG3bts6eu0iHzBj+tnJkMdbt2C5FQDatZln8VLxV25fxw7Kze10Is7/NTuO3nv7/5J8TOjb8mcDD+Iz/MXJHk7yy6B7mT8N6Wew+jis2rUYTfHW+CW2Pf8T5+OAnfi55JRxNmuskzsgrnxYt3X+OFdlfoVvhnk9JCvtYfZKzRxUsWND8DnWzZs1YvXo1586dY+nSpQ6dnHCMzBr+tnB0MTbu3SNFAGTT+hQaHxW1q/mSJ5f6qT//KTKeT768R7WGt+k5KJY1GxKIu5/B3+52AMkri5S8Wr/ZMZe0cuam1WSCsZfeYU1UBaJ0gVxMeLLBBskr4TjSXCdxVl6Z7txx/zlWZH8Fcl6rDMmmBlulUtGlSxfz7Vy5cjF//nz+/fdf9PqMcTIFT5GZwz+9nFWMpQgkk01ruhQqqKZAPi8SEmHTtkT6f3WPag3v8HHfGCJ/jSc6xvOabckri0fzavN2x22wnJFXJhNMufIGP9+qggoTI4v9jzo5TqX9eMkrYSdprpM4M690KyLdvj5kfyUyKpsa7KfRaDSMHj0ab29vRx1S2Cmzh396OLsYSxFIJptWq/XsFMDmX4JZNT8H3Tr6U7yIGp0etu/RMvjrOF5vfIcOPe+yeMVDbt42uHu6Tid5ZZHR8mru9RrMv/E6AIMLr6NR8L/PfY7klbCVNNdJnJ5XWq0i1ofsr0RG5LAGWyiLR4S/lVxVjKUIJJNNq9VUKhXlSvvQp2sg65cEs35JTj7tEkC5Ut4YjbDvsI4R396n1ntRfNglmnlLHnL5WuZrtiWvLNyRV141atp8nMU3XuW7q3UB6Bu6kea5j1j9XMkrkV7SXCdxWV4pZH3I/kpkNNJgZ0IeFf7P4epiLEUgmUKKckZTvIg33cMCWLUgJ39EBjOgZyAVX0j6VNCRf/V889193mweRdOwaGYseMDZCxn/KzmSVxbuyCvt6lWYTp+26fkrb7/MuMtJOdc9/zY65N2X7mNIXglrSXOdxOV5pZD1IfsrkZFIg53JeGT4p8FdxViKQDKFFOWMKrSAmk5t/Fk2Oyfb/xfM0L6BvFbJBy8vOH5Kz+TZD2jYJpqGbaKYPPs+F69kvGZb8srCXXll/HMLpps3IHsQXrVqW/2836LKM+JCYwA65t1N1/zbbZ6D5JV4Hmmuk7gtrxSyPmR/JTIKabAzEY8O/8e4uxhLEUimkKKc0eXJpaZtM38WfpeDXWtDGDUoK7WqafDxhrMXDMxY8JATpzJWgy15ZeHuvALwKlcWTbPmVuXV1rulGHz+PUyoaJHrIH0Kbkalsm98ySuRFiWsD8krFLM+ZH8lMgJpsDMJCX8LJRRjkCJgppCinFnkDPKiRZMszPk2iD3rQxg/LBvvvOFLrWpP/39787YBvV5Z19qWvLJQSl4Z91h3ycG9sUXpd7YZepOaRsF/80Xh3+xurlNIXonHKWF9SF49QiHrQ/ZXQumkwc4EJPwtlFCMHyVFIJlCinJmkzXQi3fr+zFlVHb8szy9y+k77B413r3Dl1/fY9ueRLQ69zbbklcWGS2vjsQVpPeZluhM3rwRdJIRRdfg5aDmOoXklUihhPUhefUUClkfsr8Sj7p//z7Tp0939zTMpMHO4CT8LZRQjJ9GikAyhRRlTxKfYOLsBT13Y0z88msCXfrGUr3RHfoNj2XTtkTiE1ybF5JXFhktr048yEvP061JMGqonu0M3xRfibfKOf//JK+EEtaH5NUzKGR9yP4q44qOjqZv374MHTqUiIgITpw48dTHzZ49mzp16lCxYkU6d+7MtWvXzD9r3749pUuXpnTp0rzyyiupfuZu0mBnYBL+Fkooxs8iRSCZQoqyp8jip2L7mhAWTA2iddMs5Ar2Iu6+iV83JtJzUCzVGt7mky9iWbsxgfsPjE6di+SVRUbLqzPxueh2qi33DX68kvUiE0tEovFy7uXiJK88lxLWh+SVFRSyPmR/lTH17duXwoULM2LECNq3b094eDhxcXGpHhMZGYmXlxdLlixhzJgx7N27lxEjRph/XrRoUYYMGWL+8/7777v4t0hbpmuwlfYRAWeR8LdwRzEOIJr4C/9gMGmtfo4UgWQKKcqewsdbRbXKGob3z8r2/wWzZGYQYa2yUCCvF/EJsGFrIn2H36Nqwzt07RfDirXx3I11bLMteWWhhObBGil5db3CO3S70IkYvT/lA64yteQysqhd899O8srzNG/i5/b1IXmVDgpZH7K/ylgOHjzI7t27qVatGgBVqlQhLi6OxYsXp3qcVqulc+fO5M+fnwYNGlC2bFnu3Llj/nnp0qVp166d+U/lypVd+ns8i9sbbGs/IvDrr7/Ss2dPBgwYwMSJEzEaLRtAJX9EwBkk/C3csVl9O3QXHbz7c+Onr7ihPcoDvwdWP1eKQDKFFGVP4+Wl4pWXNAzqnZXNK4JZMS8HXTv4U6SQGp0Otu7W8sWYOF5vfIfw3ndZsvKh3SdIk7yyyCjNdYqrv+6h48fXuX1fQ8ncD5lRagmBautfVHQEReZV3nyun4OHaNvMX5prheSV1RRSz2V/lXFs3550Wcfg4GAAvL29CQoKYuvWrake17ZtW/PftVotly9fTvUu9aFDh6hatSqVK1emc+fOnD9/3ulzt5a3uyfQt29fKlasyCeffMKePXsIDw9n06ZNZM2a1fyYgwcPMmLECLZs2ULWrFlp27Yts2fPplu3bkDSRwTq17cspjJlyrj893CVEkXVDO2bVcIf92xWQ/zu0vvFxWTRagiOTQoGo8qICRMqrDvbj2HjBgC8GzZKdduVUoqAJqIHmm4RaGdOh8RE104iuShrukWgieiBdvr3mC5ddO0cPJhKpeKFMj68UMaHPl0DOHPewIatiWzalsjJ03p2H9Cx+4DOrjFks2qR0ZrrBKM3XU+15VqCL4VzJrBgXiGy76ktedUtAp8WLV07tgdZvOKhNNcKyKt0U0g9T8knn0aNU912JUXklYsYDAbq1auX5s83b9781Pujo6MB8PHxMd/n4+Njvv9pxo0bR+PGjVM13a+99hqNGjXi2rVrfPfdd3Tt2pV169alOq67uPUdbGs/IvDdd99RtmxZc9NdrVo15s2bx8OHSSGs5I8IONrw/tJcg/s2q/kDbuGlMuFlsiwdL5OX1c11CsPGDWi/HoNh+zbw83P0NK2iiFdaFfLKt6dTqVSULOZNz04B/O/HnGxcnpN+EQG8VM7212Bls2rhqLxy5Tunfl56WuU+SH7NXWYVnEX2PevlnaGUvHrkI4rCsX75NcEt40peWdSrpbHtiQqp5/JOtvLlyJEDwNzHAeh0OnLmzPnUx0+bNo3SpUszePBgVI9cF7JVq1bUq1eP9u3bM2zYMC5evMjZs2edO3krubXBtuYjAgkJCezfv5+QkBDzfcHBwcTGxnLkyBEg/R8RqFevXpp/DAbnnrjFXpeuGjw+/N35TtC1B7kxmlTE+8VzLdc1boTc4GaOmxhU6f93Y7p5AxISkv64iSKKgEKKsrAoXNCbj9sFEDk3JyWLqtP9fNmsWjgyr3xatHTp+miT5wArXphFPt97smlNkZiIbkWk68cVTiN5ZdE9zJ+G9bLYfgCF1HPJK9dQq9Vs3rw5zT9pqV27NgC3bt0CQK/XExsbS61atZ547IwZM6hVqxYtWrQA4MCBA1z/f3t3HhdVvf4B/DMz7JssKi6A4IqWmguk3lzKyiW3NLUiTa9LipaaW/40NdPyauq9lppmuac3xcwFlzRvivueFlBuKISIoLIOzHJ+f6AzItsAw3zPDJ/369UrmHNgHtTzec7D2RITC6zXoEEDAICrq2u5fy5zEDpgm3KKwMOHD6HT6QqsAwApKSkA8k4RmDdvHsaNG4fLly/jvffeg0ZTvlMb5erTRRmVPvxFnmZ5T+2FiHuDAIUSUAB6pR5u2W5QSaUfQh5Thj4P+/dGQdG0qRkrNZ10Kw45n8yG9vgxKJ99FlCV/WcpM5k0ZSrI3r50Z2fUC1Thu39X4c4qzJ9X0r17Ft8+XFTGXsqd1kdyLXsdOlUcDtdGj/Mq8lB2+b6RTPo580q+Wrdujfbt2yMqKgpA3oFSV1dXDBgwACNHjsTatWsBANu3b8fVq1fx559/YuvWrdi8eTNmz54NpVKJEydO5Dsge+7cObz++uvw9/cX8BMVJPQa7KJOEahRo4bhcw8PD6hUqgLrAMYj3wMHDjQs8/HxwYQJE3Dt2rUir8Uu7rcqTZo0KcNPYjmWfm4tIL/wF30DlPC5PZF8+3nUdLkH6b8/Qqm+W67vqT99CgpPTzgOHwnNnt1CrhlCdhb0Z05b/n2fJJNruKh8rt3UocubqUhLl6AVEBW2nFeaiK2w79mb1zhWomscqeJwuDZ6Mq9uJ+jKdxQbkE0/Z17J13/+8x/MmjULs2bNQlJSElavXg0XFxf89ddfhiF55cqVuHnzJnbv3p3va728vJCWlob//Oc/+OWXX9C0aVNkZ2fne4SXaEIH7I4dO2LVqlW4e/cuGjVqZDhF4MkL2J2dnREaGmo4jQDIO3Lt7u6O5557rsD3lNspAtZOjuEvhxugbNvlgs+mP4scxR6YoyXLoQkAANzdoWzYCNKD+5CuXwckC+9w5OQgd9mXsOvRC3Y9e0GzPQJItO2nAtiKgNpKNKpvh6Mnc5F63/I7qkAlyKvc3ArZadVKCmy68zze9D0DRxOeby2HvOJOK5UHh2ujp/OqxytmOsrKIduAeVWQq6srvvjiiwKvHz582PDx/v1F/1116dIl3w2u5UboKeKmniLw/vvvIzY21nDq+KlTpzBkyBC4urrK/hQBaybX8Bfh6Wack2v+ZiyH05mQng4pORkOI96DwwfjxZzOpNVCu2M7NMu+NGm4lh48gPbAfmijjkJ3/hx00dHQx8VBn5wMKTMTkt68z3Smgpo2tsP2Nd7451suUArqKpUmr8x8+qVeAmbd6IXF8a9g0tU3TP6dmhzyiqdfUllwuDaytrwqK+YVWZrwx3SZcopAq1atsGjRIkyfPh2enp549tlnMXr0aACQ/SkC1qrShL8JLNmM+ZvWp9jbAxoNUKs28HdC4etkZEC7Z3fhyx5zcgJcXKBwdoHCxRlwdoHCxeXRa855/3/0uTIwCApBd3a3Rk9vH1nlvHyvLCpdXpnpyJAkAZ/HdcPulGZQQY++1S5AUYpL7plXZG04XBtZW16VF/OKLEn4gG3KKQKA8c7fT5P7KQLWqNKFfzFENGM2gScolbAf+wEUtWtDs2J54U3ZzQ2qZ56FlJUFKTsLyMqClJUNZGcZa350t3YJqSWe0u8w5SMoatcu8Lr2l0PQ//F70cP5k8O7qyvg7AyFqMO5FsKdVSOL59VTO605n80F0tJM/nJJAv4d3xk/JLeGAhLm1d2BF73+LHUZzCuyFswrI9F5xSGbeWXrhA/YJC+VNvwLIbIZswk8kpMDzTcri23KCk9P2L8dVuiXSzodkP1o4M43gD/x/+zsfMO5ws2t0O+lT/wb+r/+Kl39JRw5Vz3fBooqVQrWLUn5nvUoR9xZNRKWV492WlXtO5ZquAaAVYntsfZOOwDAzMDd6Obze5nLYF6R3DGvjETnFYds5lVlwAGbDCp9+D9BDs2YTeCRcjRlhUoFuLlD4eZe7jLsOnSE1Khx/iH9qeG8tEfOlU2eKXTA1u78Cbrjx4oezp88Yv5oHePrFX/kXA7bB/PqkZwc6A4eAACoXuoM/dWrJW4fG++EYnlCJwDAZP/96FvtYrnLYF6RXDGvjOSQVxyy8zCvbBsHbALA8H+SHJrxY2wCj8igKSv9AwD/AJPWLfbIeXY2pMxMIDsbCg+Pwr9BZmapTmsvoJAj53av9YDyiUcgGmpNS4OUk2PycC6H7cPZCcyrpymVUD7bFHavdil2+4hIboGFt/MuqxpT+zDeqWG+x/Mxr0hu5JBX3L96igz6OcC8oorFAZsY/k+QQzN+GpvAIzJpyqYo75Fzuz6vQ9X55UdDehaQlV3+I+evvlroe2kP/wLdL4eMLzw1nI93sUNCoCPStM5w8XZBv/7eSPzZEd+s1SNI4YQ0Ryek6ZyRrnWCzkIPpshWAzMXpONhmoS798TcJV4OeZWPXg/NyhXFbh+RKc/i05uvAQCG1DiOETWjzF4G84rkQg79nPtXRZBJP2deUUXhgF3JMfyN5NCMi8Im8EghTRn6kp/ba20Uj0/3LqXijpwrvH2K/kJHxyKH8+ftAVR7Yt2dgB+AJYEFv02GzgFpWmd8dP11XMoo+KjERs53EOR8D2laZzzUOSNN64Q0rRPSdU7QmzCcOzkCDera4Y9YLf66Lu7vXQ55VahidloP32+IGdd7Q4ICA6ufwXi/Q6W6Y3hpMK9INDn0c+5flYBDtgHzyvZwwK7EGP5GcmjGJWETeOSppqzZttWy7y9jZTlybt+7D+x79ylyOF+58h7clNno11lCWlIGfjuXBheo4WGXDQ+VGh52ariqcgEAbqpcuKlyoZMKH5a7+PyOYTWPF7osXeuINJ0T0rV5R8TTtE5I0zkhTeuMNJ0TcpTOeOttb9QIdMOsr7T47bqDYT1ThnNzkUNeFauQndbjl5WYfK0fdFCip88lfBSwr8KG68eYVySKHPo5969MxCHbgHllWzhgV1IMfyM5NGNTsQk88kRTtu8/wLLvbaOKGs5v1H6ImRPdEXtdi+ErC98+7BQ6uKvUhoH7Wna1AusAQGJOFZxJq1PocO5ulwN3uxzA8WHRRf4v73/z3AA0y/s4/M+3cOxh/QKrtvW4hjYeNx4N6XlHyct65PwxOeSVSZ7YPi63fw/jd6RDI9nhZa9ozA7aBaWFblDPvCJLk0M/5/5VKXHINig0r8gqccCuhBj+RnJoxqUl2yYgasj+YDwUfn6Wfe9KZPZkd8ReLX770Eoq3Ne64r7WFSjmn8HW5NbYmtw632tPD+fuqmxUscuGh13eaz5OanRtq0MVOzVir6RDmZOVbzhP0zoV+l6t3OMwpOaJEn++ko6cpz16vcNLVdDv7YZC80rKyDB95ZwcXPwiAu/FDoZaZ4cXfOMx32877BSWzTjmFVmKHPo596/KiEO2wdN5pdn1k8VroPLjgF3JMPyNzNGMFTVqQoqPr4DqiifHJiDsOdkRW+E4boJl37cSuZWgq9Cd1eKGc1cXBVbPqgK3unYYXEhe2Sl00EuFH449nx6AdXd0qKLKfjS4F35au0lHzgEgDvhx5WCsiKhTYFE378voW+1iocN5+lOfP9Q6l/rIuStSkX39JpCZAVMPPv+VVQ2jYvojQ6dASLAOXy0OhvJbf+60csi2SRyujeSwf1UmHLINnswr+379Lf7+VH4csCsRhr+RuZqxff8ByL17l01A5E5rbq5l36+S+XRRhmx3VrWSqsivP55WH8fTCp46/lhhR84fHzU3HkHPRvO6GtT1zUXy7Qxs2Gtf6PcKckpBqMfNUv18JR05P3g/GHHqqnjV/xjeddiExM0SFFUU8H7gDUetY7HfO07tjVF/huGhzgVNXePxH89tcEwdDgV3WsXnFZkdh2sjs+5fOTiYp6jS4JBtYMirMWMt/t5UfhywKwmbDP8yMmczlu7dYxPgTqtNy1bb5s6qKae1jx7igjcf51VU0Xm1L/UZXFdXfTSsGwd14xHz0h85v5ZdDZlQYVzTTVA8etiapJSQ4pUC3xRfqPSF/3IhMccD78WG4Z7GHQ2dk7C84Wa46tTcaX2EeWVbOFwbmXv/yr5ff+Qu/bfwG5lW9rzSbP0BDoMGW/y9qXw4YFcCthr+ZWHuZqyJ2Ar7nr3ZBLjTSmYil7xSKIBzv2kwbMJ9RJ3SFLvudXU1XFcXfmO3pxV35PzJ4fy22gsvNE6B8ulrphWAVqUtcsBec6cdEnM9UccpBV832gQPO3XeAu60GjCvbAOHa6OK2L9SVK0q/h4rzCtIdxIt/p5UfpZ7tgkJYcvhX1oV0oxzc5H79XJIiYlwCB8DRUDB6zMtQXdgPzR7dsP+tR5QvdpFSA2Pd1oVNWvCYVR43rOViUpBLnkV3EAFSQJOn9eUOFyX1uMj53E5PricWRvH0+pjX+qz+CG5NVYntsfi269g9s2ecAmqhWkzGkB6+qprCbDTFf278cn+B/B29VNY1XAjfOwz8y98tNPKvGJeWTsO10YVtX+l2fqD2O2DeUVWjAO2DbP18C+NCm3GbAIG3GmlspJTXv20zgdh/Qq/O7klPM6r68lVsCLmHeifeK54lfQqRR69BgB7pR5T6xxADce0wldgXhkwr6wTh2ujity/ku4kit8+mFdkpThg26jKEP6mskgzZhMw4E4rlZYc82pThFpIDU/n1a5r7bBeswA13/kENeyfg6vatfxvwrwyYF5ZFw7XRpbYv5LF9sG8IivEAdsGVabwL4lFmzGbgIEsmjJZBeaVUVF5lQlvONd5FipF/rv66ssTZ8wrA+aVdeBwbWTJvJLF9sG8IivDAdvGVMbwL4qQZswmYCCLpkyyxrwyKm1eafRKTLzaH+sS25T9TZlXBswreeNwbSQir2SxfTCvyIpwwLYhlTn8nya0GbMJGMiiKZMsMa+MSptXegmYeaMXfnkQjK8SXkS82rPsb868MmBeyROHayOReSWL7YN5RVaCA7aNYPgbyaEZswkYyaIpk6wwr4xKm1eSBMyN647I1KawU+iwsP42+Dk9KF8RzCsD5pW8yKGfM6+MZLF9MK/ICnDAtgEMfyM5NGMDNgEDWTRlkgXmlVE1HwWq+iixZnMWhk14YNJwvej2y4hIbgUFJMwL2oFOnn+ZpxjmlQHzSh7k0M+ZVwXJYvtgXpHMccC2cgx/Izk04wLYBAxk0ZRJKOaV0bPBKigUChyOysWyNVnIyi75a1bEPIcNSW0BALMCd6Grzx/mLYp5ZcC8EksO/Zx5VTRZbB/MK5IxDthWjOFvJIdmXCQ2AQNZNGUSgnll9Divxo90gb1dyetr9GrMXXoXX8e0AABMCdiH16tdqpjimFcGzCsx5NDPmVclk8X2wbwimeKAbaUY/kZyaMYlYhMwkEVTJotiXhk9zqurN3SY9+9MaEz4o/jfyUxs2JL3cXe/swjzPVOxRTKvDJhXllU/SCW8nzOvTCeL7YN5RTLEAdsKMfyNrGK4foxNwEAWTZksgnllVOq8UugBADmZbgAAr1oJuO6XixRYYHthXhkUyCsHh5K/iMpk9mR3DteQR16ZShb9nHlFMsMB28o4OzH8H7Oq4foxNgEDWTRlqlDcWTUqS145u6cjqOU5VA+6Dt+611A14Bb0CgXuKFwsUDGYV094Mq/s+/UXUkNlcCtBx+FaBnlVWrLo58wrkhEO2Fbm44luDH9Y6XD9GJuAgSyaMlUI7qwalTWvNGon2DnkwrNGEqr43oVCASglCTUkC/4czCsDQ15VrSrk/SuDTxdlcLgWnFf+tco2GsiinzOvSCY4YFuZgNqqSh/+chiuHR0U5fsGbAIGsmjKZFbcWTUqT15pcx2RdL2u4XOlAhip/As+yKmIUovGvDKQbsVBs/UHIe9dGWSrOVyLzqv3BruW+etl0c+ZVyQDHLCtzOyF6ZU+/EUP164uCowY5Fz+b8QmYCCLpkxmwZ1VI3PkVdpdXwS5tMFno/+B1ZM7olt4P+60is6rO4lC3pfMj3ll9Div7iTryvV9ZNHPmVckGAdsK3P1RvmCr6zkFP6ih+vVi6ugRjWVeb4hm4CBLJoylQt3Vo3MmVf2Sic0rV8VVQ7s5E4r5JFXZP2YV0ZP5tU3G7LL/f1k0c+ZVyQQB2wqkdzCX/Rw3aCuHVauzzTfN2YTMJBFU6Yy4c6qUUXllXQnUfz2wbwiG8C8Mno6r3JyzZRXcujnzCsShAM2FUuO4S96uB467gFu/6037xuwCRjIoilTqXBn1aii80oW2wfziqwY88qIeWU5zKvKhQM2FakyhL8pLNaM2QQMZNGUySTcWTWyVF7JYvtgXpEVYl4ZMa8sj3lVeXDApkJVpvAvjsWbMZuAgSyaMhWLO6tGls4rWWwfzCuyIswrI+YV84oqFgdsKqAyhn9hhDVjNgEDWTRlKhR3Vo1E5ZUstg/mFVkB5pUR84p5RRWPAzblU5nD/0nCmzGbgIEsmjLlI3z7eIR5JZPtg3lFMsa8MmJegXlFFsEBmwwY/nnk0ozZBIxk0ZQJgHy2D+aVkSy2D+YVyRDzyoh59QTmFVUw4QN2amoqJk6ciJkzZyI8PBzR0dGFrrdr1y6MHTsWU6dOxeLFi6HXG+/ifOzYMYwePRozZszAzJkzoVarLVW+zWD455FLMzZgEzCQRVOu5OSyfTCvCpLF9sG8IhlhXhkxrwrBvKIKJHzAnjhxIurUqYM5c+Zg0KBBGDp0KNLT0/Otc/bsWcyZMweff/45/vWvf+HcuXNYtWoVAODWrVsIDw/H//3f/2Hu3LlQq9WYN2+eiB/FajH888ilGRfAJmAgi6ZcScll+2BeFU0W2wfzimSAeWXEvCoG84oqiNAB++zZszh+/Djatm0LAAgJCUF6ejo2bdqUb72vvvoKjRs3hru7OwCgbdu2+O6775CVlYWVK1eiatWq8Pf3Nyzbvn07EhISLPvDWCmGfx65NOMisQkYFGjKDg5C6qhM5LJ9MK9Kxp1WIznkFVke88qIeWUC5hVVAKED9pEjRwAAPj4+AAA7Ozt4enrif//7n2EdtVqN06dPo2rVqobXfHx88PDhQ1y4cAG//vprgWVarRbHjh2zzA9hxRj+eeTSjEvEJmDwZFO279dfSA2VhbOTPLYP5pXpuNNqJIe8IsuRSz9nXpmOeWXEvLIdCkmShG1xM2bMwNatW3Hw4EHDEehOnTrBwcEBBw4cAAAkJSWhQ4cO6NOnD/71r38BALZt24bp06dj4cKFmDZtGlq0aIGNGzcCAE6cOIEhQ4Zg/PjxGD16dKHv27lz5yJrio+PBwCoVCqz/ZzmpNcD5vgbUyoBhSLvez1xObtFKRR5dQCATg9AxL9EBaB6VENp/mwNtUuSef5CSuvxH5yo9wfy/hAe/yMSXQNVOHNlT1lU9rwqc948uX2I+oMDbC+vmDsVplw5U8Z+bm7WnlfC9m+YV0ZP5tXjz2VGp9MBAGJjYwVXIk92It/cy8sLAJCVZfztnkajQY0aNQyfe3h4QKVSFVgHyDta7eXlVeQyW6Q08zkHCgUgh98lqITfDaCMf7aid7REv79caqAKZ+7sKYtKn1fl2dbk8hcoOivkUAMVyVz/TOXyz92q80rktiKXv0DRWSH6/anMhA7YHTt2xKpVq3D37l00atQIWq0WDx8+RFhYmGEdZ2dnhIaG4u7du4bXUlJS4O7ujueeew4dO3bEr7/+aliWmpoKlUqFdu3aFfm+hw4dqpgfiIiIiIiIiCotob8iat26Ndq3b4+oqCgAwLlz5+Dq6ooBAwZg5MiRWLt2LQDg/fffR2xsLFJTUwEAp06dwpAhQ+Dq6opRo0YhIyMDMTExAICTJ0+id+/e8PPzE/IzERERERERUeUk9BpsAMjMzMSsWbPg6uqKpKQkjBkzBvXq1cNrr72Gl156CR9//DGAvKPO27Ztg6enJzw9PTFp0iTDddIXLlzA119/DV9fX2i1Wnz88cdwdnYW+WMRERERERFRJSN8wCYiIiIiIiKyBTK4iwARERERERGR9eOATURERERERGQGHLCJiIiIiIiIzIADNhEREREREZEZcMAmIiIiIiIiMgMO2ERERERERERmwAGbiIiIiIiIyAw4YBMRERERERGZAQdsIiIiIiIiIjOwE10AkbVITU3F9u3bcfnyZWRkZMDLywshISHo1asXnJ2dRZdHRDZEo9Hg4MGDBfLmhRdeEF0aEdmgP//8E5s3b8bly5eRnp4Ob29vhISE4K233kLNmjVFl0dkVRSSJEmiiyCSM71ej8WLF2PDhg3IycmBi4sL3NzckJaWBrVajSpVqmDs2LEYNGiQ6FKJyAZs2bIFy5cvx927dwssCwoKwqRJk9C5c2cBlRGRrcnKysL06dOxd+9eODs7w8/Pz7CPc/v2bej1erzzzjuYPHkyVCqV6HKJrAIHbKJiqNVqjBo1CgqFAgMHDkSrVq1QrVo1w/KEhAScOXMGERERqFGjBhYsWACFQiGwYiKyVjqdDtOnT8eFCxcwYMAAtGrVCoGBgYad3atXr+L06dP46aef0L17d0yYMEF0yURkxVJTUzF8+HA0adIEAwcOxDPPPAOl0nj1aG5uLi5duoSIiAjcu3cPy5cvh4ODg8CKiawDB2yiYnz++efo3LkzQkNDS1z32LFjuHDhAsaOHWuByojI1ixduhT+/v7o06dPsb+okyQJW7duRU5ODs+cIaIy0ev1mDt3LgYNGoSgoKAS14+NjcXu3bsxceJEC1RHZN04YBMVQa/X4969e6hevbrJX5Oamgpvb+8KrIqIbJFer8dff/2FRo0aFVh269YtBAQEFHj92rVrqFevniXKIyIbpFarkZycDF9fX5OOTGs0Gtjb21ugMiLrxruIExVBqVTi119/xe7duxEfHw8AyMjIwLRp0/D888/jpZdewo4dO/J9DYdrIioLpVJZ6HANAG+88Uahr3O4JqLyePjwIQYMGIAzZ86YtD6HayLT8C7iRMUIDQ1F165dMWPGDISFhWHBggXYuXMnBgwYgPr16+Po0aNwdHREt27dRJdKRDagS5cu0Gq1+V5LT0833NRMoVDg4MGDIkojIhuzbNky3L9/HzExMfjHP/4huhwim8Ej2ETFqFOnDp68iuLw4cMYPnw4Zs2ahbCwMCxatAgpKSkCKyQiWzJr1iykpaWhZs2aqFWrFmrVqgWlUmn4mI/LISJzyczMRNu2bXHs2DHDa1evXsX8+fPx8OFDgZURWTcewSYyweMbDmVkZKBFixb5lqWnp4soiYhsULt27bB+/XpERERgypQpcHBwQEhICDZs2CC6NCKyMS1btoSTkxOcnJzw7rvvYt26dZgxYwYuXryI9evXw9/fH82bN0evXr3wwgsviC6XyGrwCDaRCY4cOYJNmzbBx8cHMTExhtevXbuGXbt2CayMiGxN48aNMWLECMyfPx9paWl89B8RVYiAgAA8ePAAW7ZsQc2aNaHX6/HHH39gxIgRCA8Ph5+fH/bt24cRI0Zg9erVosslshq8izhROURERCA+Ph7jxo0TXQoR2Zj09HQsXLgQe/bswblz50SXQ0Q2ZO7cuUhOToa/vz+uX7+OBQsW4OzZs1izZg0++OADtGrVCgBw4sQJDB06FAEBAThw4IDgqomsAwdsojIo6rE5RETmlJubix9//BEDBw4UXQoR2ZAWLVpg1qxZOHXqFObNm4erV6+id+/eAIDu3btj/Pjx8Pf3x9atW3H79m2MGTMGjo6Ogqsmsg4csInKIDQ0FKdPnxZdBhEREVGpff/993j77bcxZswY1KtXD7///juaNWuGmJgYHD58GEqlEm+88QamTJkCNzc30eUSWRXe5IyoBHxsDhHJxdq1azFkyBDRZRCRlXv77bcBADqdDg8ePMCZM2cwdOhQ9O/fH0lJSejbty+WLl2KS5cuYePGjXB3dxdcMZH14BFsohIcP34c48aNQ6NGjQw3Gzp//jxatmxpWId3+CUic5g+fTr0en2Ry0+ePInDhw9bsCIismVjxozBsmXLcPXqVUyfPh3fffcdXnrpJZw6dQqJiYkYM2YM3NzcsH79etGlElkNDthEJoiOji7w2JwzZ86ILouIbMzs2bOxZcuWIpcrFApER0dbsCIislUJCQno168ffHx8MGzYMDRp0gQ5OTm4desWevbsCSDv8aRDhw7F0KFD0b17d8EVE1kHDthEJkpKSsLKlSsxfvx4vPzyy7wGm4jMLiMjA9988w0mTJhQ6PI5c+Zg5syZFq6KiGzV1atXMW7cOFy/fh2RkZEICgoqsM6dO3cwb948fPnllwIqJLI+HLCJSoGPzSGiivZ4J7dx48YFlkVHRxf6OhFRWWk0GmRnZ8PDw6PIdX7//Xc888wzFqyKyHpxwCYqJT42h4iIiIiICsO7iBOZ4OzZs7C3t0fz5s3h4OCA5ORkDB8+HB07dsSgQYNEl0dENiQ3NxdHjhzBrVu34OLigtatW6N+/fqiyyKiSiQtLQ2JiYk4duwY7O3tua9DVAo8gk1UgnXr1mHx4sVwdXXF+++/j9TUVKxatQrt27dHSkoKunTpwsfmEJFZ3LhxA8OGDUNiYiIkSTI8uaBdu3b49NNPUatWLcEVElFlcObMGWzbtg27d++Gm5sbTp06JbokIqvBI9hEJTh9+jSOHTsGNzc3HDt2DF9++SXWr1+P5s2bAwCWLFkiuEIishWLFi1Cz5494efnB0mSkJKSgjt37uDkyZN444038P333yMwMFB0mURkg7KysuDi4gIACAkJQUhICPz9/bFs2TLcunULAQEBgisksg4csIlKEBgYCDc3NwCAo6MjunbtahiuAcDZ2VlUaURkY15++WX06dMn32sRERH45JNPsGvXLnz88cfYsGGDmOKIyKY8eZbM1q1bcfHiRcybNy/fOq1atQIAPHjwgAM2kYk4YBOVICEhAefPn4dWq8X06dPRrl07pKSkwMfHBwkJCbh06ZLoEonIRhw9ehRubm6oX78+1Go19u7di8TERPTr1w89e/aEu7s7jhw5gg4dOogulYis3KJFi1C/fn306dMHly5dwt27dwus4+TkBABwcHCwdHlEVosDNlEJ3nnnHQwfPhzZ2dl44YUXMHnyZLz77ru4e/cu7t+/j4ULF4oukYhsRJ8+fTB69GjodDoAgEKhwOrVqw3LO3XqhIMHD4oqj4hsyJUrV5CQkIA+ffqgRYsW2LRpU4F1rl69CpVKxaPXRKXAm5wRmeDBgwe4c+cOgoODAeTd5ffo0aPw8/NDo0aNBFdHRLYkNjYWkZGRyM3NRefOndG6dWvRJRGRDdq8eTNWrFiB77//Hk5OTujWrRvatGmD6OhoNG7cGJIk4dixY/jHP/6Br776SnS5RFaDAzYREZFM3bt3D/v374eLiwu6d+8OR0dH0SURkY1IT09H27ZtIUkSgoKCEBcXh1q1aqFjx47Izs5GZmYmfH19MXLkSHh5eYkul8hqcMAmKqft27ejb9++ossgIhtw6tQpbNq0CYMHD0ZwcDC6du2Ke/fuAQAaN26MNWvWwNPTU2yRRGQzBgwYAA8PD9jb2+PSpUtITU2Ft7c3wsLCMHz4cP5Sj6gMeA02UQlKOi1q//79HLCJyCxmzZqFlJQUxMfH46WXXoKdnR2+/fZb+Pr6IioqCt988w0mT54sukwishHBwcEYNmwY6tSpg4iICOzduxfBwcFYs2YNfv75Z2zYsAHu7u6iyySyKjyCTVSCcePGYf/+/UUuVygUiI6OtmBFRGSrWrRoga+//hqhoaH46KOPEBgYiNGjRxuWr1u3Du+++67AConIlly6dMnw6NGYmBhMnToVP/30E5KSkjBlyhRIkoT169cLrpLIuvAINlEJZs6ciapVq+Kf//xngWWSJGHJkiUCqiIiW9SsWTO4u7tDoVBg9uzZWLFiRb7lly9fFlQZEdmix8M1ANStWxd16tQBAPj6+uKbb75BeHg4fv75Z7zyyiuiSiSyOkrRBRDJnY+PDxo2bAi1Wo3atWvn+8/Pzw9vvvmm6BKJyEbMnTsXkZGRyMrKgrOzM8aOHWtYFhERwQGbiCqMg4MD5syZk+/zL774Avv27RNYFZH14SniRCbQ6/XQarVwcHAQXQoRVQKSJEGhUOR77eLFi1Cr1WjTpo2gqoioMjp69Ciee+45XotNZCIO2ERERERERERmwFPEiYiIiIiIiMyAAzYREREREeUjSRImTpyIhIQE0aUQWRUO2ERERERElE9UVBQiIyORmZkpuhQiq8IBm6icjhw5gvDwcHz99dfQaDSiyyEiG5aYmIi1a9fi3LlzokshIhvw8OHDfJ//73//M3xsZ5f3NF8PDw9LlkRk9ThgE5XT1KlTUbVqVXTv3h2bN28WXQ4R2bDhw4fjyJEjuH79Onbu3Cm6HCKyYvHx8ejTp4/hc0mSMGHCBMTFxQEAAgMDAYBPUCEqJQ7YROX0yiuvoGfPnggICEBISIjocojIhikUCvTq1Qv9+/fnUSUiKpcHDx4gMTER165dA5CXL66uroiNjQUA1KxZE05OTiJLJLJKdqILILJ2c+bMMXzcuHFjgZUQka3buXMnlMq834136tRJbDFEZNWaNGkCT09PHDt2DDExMWjWrBnatWuH+Ph4wzr+/v5Qq9UCqySyPnwONhERERFRJTRx4kQcOnQIOTk5APJOE1coFADyrsHW6/X48ccf0bBhQ5FlElkVHsEmIiIiIqqEQkJCcPLkSQwaNAheXl5QKpXQarXQaDRQq9U4ceIEUlJSRJdJZFU4YBMRERERVUJNmzZFjRo1MHHixEKXu7u7Izk52cJVEVk3DthEJThw4ABat24Nb29v0aUQUSWUkZEBe3t7ODo6ii6FiGxM/fr1Ddly+vRpfPLJJwgODoanpyccHR1x9epVPPfcc2KLJLIyvIs4UTF+++03fPjhhzh//rzoUojIxq1ZswYrV65ETEwMACA5ORmDBw9GSEgIQkND8dVXXwmukIhsjaOjIwYPHgwA2Lt3L65du4bz58/j4sWLuHz5Mu7fv49bt24JrpLIuvAINlExVq9eDa1Wi0uXLuHll18WXQ4R2bDevXujXbt2cHNzQ3BwMD777DNcvnwZEydORIMGDXDu3Dn88MMPGDBggOhSiciGdO3aFQDQokULjBgxArVq1TIsi4mJweTJk0WVRmSVeASbqBheXl4ICwvDw4cPodPpAAA///wz+vbti4MHDyIpKUlwhURkK56+DOXEiRMYPXo0hg8fjo4dO+LDDz805BARkbn16tUr33ANAJ6enrh+/Tq0Wq2gqoisD49gExWjWbNm0Ol0uH//Ptq0aYNTp07h888/x99//42xY8dCoVDA19cXvXv3xpgxY+Dg4CC6ZCKyco8fkaPVags8Gufvv/8WURIRVVJOTk7o2LEj7Ow4MhCZilsLURG0Wi2qV6+O6Oho/PzzzxgxYgSUSiWysrKwdOlSeHl54dy5c4iMjMSqVasQHx+PRYsWiS6biKxYrVq1cP78eUiShODgYMTFxRmWXbhwAQcPHizybr9ERObm6emJ5cuXiy6DyKpwwCYqQlhYGBwdHfHMM8+gc+fOGD58OBYsWICAgAC4u7sjJCTEcPOhsLAwaDQa0SUTkZX75ZdfDB+HhYXlW5acnIxBgwZZuiQiIiIqBYUkSZLoIojkKDQ0FN9++y3WrVuHjz76CGfPnsX48eOhUChQv359/POf/0SPHj1w8OBBuLm5oX379qJLJiIbdPLkSbRp00Z0GURERGQCDthERbh8+TKaNm2K0aNHw83NDX/++SemTJmCCxcu4Ntvv4VarUaDBg3wxRdfFLhOkojIXEJDQ3H69GnRZRAREZEJeBdxoiI0bdoUACBJElq2bInc3Fz8+eefePvtt+Hn54cdO3bAyckJYWFhhufWEhGVR0hICBo3bpzvv7S0NMPHTZo0EV0iERERFYMDNlEJFAoFXnnlFWzfvh1RUVHQarVIT09Ho0aNsHnzZnTp0gXh4eHIyckRXSoRWbkNGzbA19cXXbt2RZ8+fdC7d2/Y2dmhT58+hs+JiCzljz/+wLx587Br1y7RpRBZDZ4iTlSMhIQEDB48GImJiejVqxc+/PBDpKenQ6fT5TstfNq0aQgKCsLIkSMFVktEtuDOnTtYvnw5Jk6ciCpVqiAkJARnzpwRXRYRVUKdOnVC8+bN8eabb+LmzZt46623RJdEJHs8gk1UjNq1a2Pv3r3o27cvYmJioNFoUK9evQLXXH/yySf466+/BFVJRLakRo0amDJlCr788kskJCQYnotNRGRp9evXR+/evdG2bVs0aNBAdDlEVoFHsInMJDU1Fd7e3qLLICIbodFosHDhQmzduhUXLlwQXQ4RERGZgAM2ERGRjB09epSPASQiIrISPEWcqIyOHz+ODRs2YNSoUZgyZYrocojIRvz000+IjIwEkHcUOzIyEt26dcP8+fOh0+kEV0dERETF4RFsojLKzMzEvn37sHjxYqSnp+PcuXOwt7cXXRYRWbElS5Zg586dqFq1Kl588UWkpqYiMjISPXv2REpKCgICAvDBBx+ILpOIiIiKwCPYRCbKzc1FbGys4XNXV1f069cP48aNg0ajQVxcnMDqiMgWxMXF4dChQ9i6dStefPFF7NmzB5s3b8a0adPwxRdfQKVSiS6RiGzcsmXLRJdAZNXsRBdAJGcpKSlwcnKCq6srli1bhvj4eCxatCjfOnXr1gUAZGVliSiRiGxI7dq1oVTm/e47MTERffv2RZ06dQzLedIZEZlTYY8A/Pbbb/H8888b8iYkJMTSZRFZNQ7YRMX4/PPP0bhxYwwbNgxZWVlITU0tcl1XV1cLVkZEtujvv//Gzp07odPpsGDBAvTo0QO///47GjZsiEuXLuHKlSuiSyQiGzJv3rx8Z+cBeb/IGzRoECRJgkKhQHR0tKDqiKwTB2yiYmRnZ+P48eMYNmwY2rRpgxMnTiAxMREAULNmTQDAkSNH4OzsjMDAQIGVEpEtCA8Px/Dhw5GUlISBAwdi6tSpGDVqFKKiouDk5ISvv/5adIlEZEM2btyIDz74AM2aNUOdOnUgSRLmzJmDWbNmiS6NyGrxJmdExTh06BCmTp2KGTNmoHXr1ujVqxcCAgIQGxsLDw8PSJKE9PR0DBs2DJMmTRJdLhHZgMe54uHhYXgtJiYG1atXh7e3t8DKiMgWabVaLF68GN26dUPTpk0REhJS6KnjRGQaDthExdBoNHj++eeRnZ1teM3f3x+vv/46/Pz8kJmZiRo1aqBDhw6G6yaJiIiIrM2qVavQoEEDTJ06FadPnxZdDpHV4kRAVAx7e3s0b94cI0eOxNSpU9G8eXMkJCRg6dKliIiIQEhICDp16sThmogsYu3ataJLICIbNXLkSCQlJUGj0Yguhciq8Qg2UQnmz5+PESNGwMfHB/v370dkZCT69euHpUuX4ubNm9i4cSOCg4NFl0lENmD69OnQ6/VFLj958iQOHz5swYqIqLLJysqCi4uL6DKIrBYHbKIS3L17F9WrVwcA3L59G+Hh4di1axckScK///1v7Nq1C/v27YODg4PgSonI2s2ePRtbtmwpcjnv6EtElhQXF5fvUYFEVDLeRZyoBI+HawDw8/ND+/btAeTt6E6YMAGSJOH777/HkCFDBFVIRLZi0qRJqFKlCiZMmFDo8jlz5li4IiKqzL7//ntMmzZNdBlEVoVHsInKSafTYfr06Zg/f77oUojIBkRGRiIoKAiNGzcusCw6OrrQ14mIyqJt27Z48OBBsevwrBmi0uGATWQGN27cQNWqVeHu7i66FCKyYrdv34a/v79J6968eROBgYEVWxAR2bTIyEisWbMG9evXL7BMkiScOHECv/76q4DKiKwXTxEnMoOgoCDRJRCRDdizZw9GjRpl0ro7duzA+PHjK7YgIrJp3bt3x2+//YbJkydDpVIVWP7NN98IqIrIuvEINhERkUz07dsXnTt3RkmtWZIk7NmzB/v27bNQZURkq27evInc3Fw0bNiwwLKcnBw4OjoKqIrIenHAJiIikonSPPKPdxQnIiKSHw7YRERERESV1JEjR/Df//4Xt27dgouLC1q3bo233noLfn5+oksjskocsIlKkJKSAh8fH8TExGDatGl47bXXYGeXd/sCb29vdOjQATk5OZg5cyYcHR2xdOlSwRUTERERlWzbtm2YMWNGgdcdHBwwdepUhIWFCaiKyLrxJmdExYiLi0O/fv2wadMmbNy4EdHR0flOyVQoFOjRowcyMjLw66+/YuLEiQKrJSIiIjLdlStXsH//ftSuXRuSJCE1NRVXrlxBbGwsVqxYAWdnZ/Tt21d0mURWhQM2UTEOHz6MjIwMzJo1C88++yyGDBmCd955Bw4ODsjJycG1a9dQu3Zt9OrVC+Hh4RgxYoTokomIiIhMEhwcjDp16hg+9/LywtmzZw1HrydNmoRXXnmFjyElKgWl6AKI5OzVV1+FSqWCp6cnDh48CEdHR8NzIatWrYpOnTrh9OnTGDJkCD744APR5RIRERGZ7OzZswgPD8eSJUvw2Wef4bXXXjM8xaBKlSqYO3cuDhw4ILhKIuvCa7CJirB8+XJcuXIF169fx3fffYcff/wR8fHxUCgU2L59O+zs7NCyZUuEhoZi7NixosslIiIiKpXk5GS8//77uHjxIgCgXr16WLt2LapVq2ZY59q1a6hXr56gComsDwdsoiK0bdsW9+/fh1KpRFRUFDw9PTFixAi0aNEC1atXx927dxEVFYWLFy+iU6dOWLJkCZydnUWXTURERFQqt27dglqtRr169aBSqUSXQ2TVeIo4UREOHDiAFStWwN/fH+Hh4dDpdLh//z58fX2hUqlQr149zJgxA1u2bMGNGzcwYcIE0SUTERERlUpGRgYCAgLQsGFD7Ny5E2+88QYGDRqEs2fPii6NyCrxCDZRCTIzMzFlyhQ0bdoU69atw4MHDwAAkiRBoVCgb9++hpuBjB49Gt27dxdbMBEREZEJFi5ciO+++w7vvPMO+vfvj969e6NGjRrw8fHBjRs3sHr1arRo0UJ0mURWhQM2kQn0ej0+/fRT9OjRw9Bobt68iaioKLz22mvw8fFBXFwc5s+fjxUrVgiuloiIiKhkoaGhaNmyJZo3b264DnvFihVQKpVISkrC6tWrMX36dLFFElkZPqaLyARKpRIzZszId11S3bp1UbduXcPnderUQYcOHUSUR0RERFRqKpUKkydPRr169bBr1y7cvn0bSmXeFaS+vr75HuFFRKbhNdhEJjLlph9vvfWWBSohIiIiKr/XX38dN27cAAD07NkTfn5+hmWpqan44YcfRJVGZLU4YBMRERERVUJTpkxBRkYGMjIyAAC9evUyLPvhhx9QvXp1UaURWS1eg01Ugtu3b8Pf39+kdW/evInAwMCKLYiIiIjIDIrbx8nOzgYAwyNIuY9DZBoewSYqwZ49e0xed8eOHRVXCBEREZEZFbeP4+zsbBiuAe7jEJmKNzkjKsGBAweg0+lQ0skekiRh3759GD9+vGUKIyIiIioH7uMQmR9PEScqQXBwsMnrKhQKREdHV2A1RERERObBfRwi8+OATURERERERGQGvAabiIiIiIiIyAw4YBMRERERERGZAQdsIiIiIiIiIjPggE1ERERERERkBhywiYiIiIiIiMyAAzYRERERERGRGXDAJiIiIiIiIjIDDthEREREREREZsABm4iIiIiIiMgM/h9VHExWmpxxoQAAAABJRU5ErkJggg==\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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7dGicnJ7X1UFEUWrVqhUajIX/+/KRNm5aYmJi3vsc3b94kJiaGggULcuPGDWbOnMnNmzfVFlV7e/sUfDLxjH/Pbm5uVKpUCVNTUwwGA1qtlg0bNiRo0e7UqROLFy/GxsYmUTlmZmZ069aN69evExwczJkzZzhz5gwAQ4YMwcbGhpiYGFxcXFAUhT/++IM7d+7Qvn17tdeFq6sriqLwv//9L8X1f7MuxuA9cOBA+vXrR/v27bG0tEzUA+Zdqlevzvr163F1dU1xS/SZM2eIiopK9F1vPPbRo0fV1uD32c84r8HrvVMyZsyIhYUFjx8/5vbt2xQsWDBRL4QjR44wcuRISpUqpT5ma2ub4Hvy8uXLFChQIEU9K4QQ/z0SoIX4D3J3d2f37t38+eefaDQaTE1NmTVrFg8ePCB79uwMGjSIOnXqcP/+ffbs2aPu17x5cyIjI/njjz/Ux2rXrq3e9VcUhd27d7Nv3z4aNWrEzz//TMWKFZk8eTJbt27lyJEjXLhwgRkzZjBt2jROnjyJubk5jRo1Yvjw4eqFcnBwMAsWLODQoUPY2NgQHBxMlSpVGDBggHqxFBAQwJw5c7h69SrR0dGYmpoyfPhwtRv106dPmTVrFpcvX0ZRFPLmzcvkyZPfekEdEhLCpUuXOHXqFLt27SI0NJTz589jb29Pu3bt1HBgZWWlhorXL96SCmXBwcEsXLiQdevW0bJlS3bs2MGSJUvInTs3q1atws7ODg8PDwIDA1mzZg12dnbv7G595MgR5s2bR9myZdWLSAsLC6Kjo/Hz80Ov1+Pj48PQoUPJnj07w4cPJ1u2bG8tE0g0y69Go+F///sfR44cUQNOzpw5adKkCcuXL1fPjZCQENq3b0/Pnj3fWn7Xrl3RarWMGjWKggULqhP05MmTh7Vr11K9enUgrnXRzs4OR0dHNQi9awZj402JNwOhu7s7UVFRREZG0r9/f7RaLUOGDMHZ2TlRGaGhody5c4c9e/aQK1cupkyZAsSFkNDQUPz8/AA4deoU9+7do1ChQowbNy7ZMP6mSpUqce7cOYoXL06fPn0YNmwYdnZ27N27l1atWlGsWDGGDBkCwJ07d2jSpAlp06alR48emJmZkS5duiTLDQoKYs2aNWi1Wnr27Mm6deuYMmUKlpaWREREqDdhxo8fT506dUifPn2CLrtJCQkJITIyEgsLC4YOHYqjoyP58+enR48euLu7s2nTJv78808ALl68CMS13C9ZsoSsWbMmKOvAgQPquTJu3DicnJzU52JjY9WJ8LJmzUr16tVZsGABWbJkSbJewcHBCd6H8ePHEx4ezrRp03jy5AkajYayZcuyd+9eAI4dO8bff/+NjY0NQ4YMUXuLvC46Ohp3d3dOnDihttzOnDmTx48fY21tjaWlJYMGDUqwj6enJ1euXOHQoUM0a9YswXNTpkzh4MGD+Pr6qq38hQsXpnnz5pQoUYKiRYvStGlTsmTJQmBgII0bN+bevXtotVqsrKzo1asXer1evUFXpEiRt35Wr7t9+zaA2sW9evXq3Lx5k7CwMKZOncrSpUtxdnama9euqbr5YxwScPDgQSZNmsSVK1fo378/5cuXZ968eUnuY7zhZOxFYmT8t3HYyvvsFx0djaenZ7LbRUVFcfPmTQoWLJjguQULFuDr60uDBg2Sfa03btxg3LhxdO3aNdUrIggh/hukC7cQ/0EFCxbE2tqaJ0+e4O/vz759+/juu+/IlSsXXl5eDBgwAA8PD3LmzEnFihVZsGABCxYs4PHjx+TMmZPKlSuzatUqFixYoF7srF+/nlq1ajF06FAePXrE9OnTCQ4O5tixY9jY2FC7dm0uXLgAwLJly6hcuTKNGjUiKCiIdevWqWP2oqKi6NSpE6tXr6ZXr164ubkxceJEtm7dSrt27dQZa11cXDh//jw7d+5k//79VKlShRs3bgBxF9kdOnTgjz/+YNWqVaxevZoTJ07Qo0ePt74vJ0+eZMCAAVy9elUde6fT6WjTpk2C7SwsLDA1NVUD9dvGpgYGBrJ69Wr0ej0nTpxg1qxZaDQaVq1ahb29PRqNhuvXr5MjRw4WLFhAgwYNGDp06Fvr6ezsrAZF40y6FhYWlCtXjg4dOmBqaoqTkxObNm3C2dmZs2fP4u/v/9YyAdq3b8+dO3fUz7tGjRoULVqU5s2bs27dOkqUKEHDhg2ZOXMmM2fOZMmSJbi5uTF69GjOnj37zvKbN2/OhQsXmDdvHk+fPuXRo0dA3PhkS0tL9SId4m5SmJmZqeE0qZb46Ohobt68yS+//EKnTp3o1KkTU6dOxd3dnZ49e1KpUiW6du1KSEgIAK1bt+bkyZNJhmeIa70bMGAAuXLlSvTeNmjQQL3obtGiBStXrqRu3brs3r07xbOc63Q6Fi9eTJ8+fciYMSN3795Fq9Xi4uLCrVu32LdvH6Ghoej1enWcp7e3N9WqVaNatWrJfoYLFiwgJCQEg8HA9u3b6dSpE1evXmXz5s3qUmYajYbffvuN2NjYJMPzy5cvcXZ2pkmTJlSpUoUKFSpw8OBBMmbMSJkyZfjf//5HqVKluHTpErdu3aJy5crs2rWLixcvqjOkh4SEJPm3YLzZ1KlTJ3Q6HTdv3mTTpk0MGzaMSpUq0bZtWyZOnMiRI0cYMGBAsuEZUL9fjF27t23bRvXq1QkPD+fvv/8mV65cnDx5Uj0fJ06cyKVLl7C1tSVNmjSJyvPz81NvZvz111+Ym5uTLl06Lly4wJgxY3ByciIoKAhnZ2cmTJjA8OHD6dy5M82bNyc2NpYNGzYkKnP48OFs2bKF+vXrq++9oii8ePECBwcHjhw5gqenJ0FBQUycOJGYmBjGjh3L4cOH6dWrFwAeHh6Eh4eTNWvWJFu4X2f8jA8dOsSxY8ewtbVl7NixQNx3hfEGJ8Sd0zNmzKBZs2Y8fvz4reW+ztbWFnt7e8LCwrh79y5//PGHOmwgICAgyX1evHgBkKhXgvHfxuffZ7+XL1+q4TYl5UdHRzN06FCWLFnCyZMn6datG3379k10Y+7PP/+ka9eueHp6Mnr0aBo0aICHh0cy74oQ4r9KWqCF+A/SarVqK5GdnR3Tp08H4lrIateuTXR0NK6urkyePJkyZcok2Nfc3JzSpUuTIUOGBOvKduzYkatXr/L48WPu37/PyZMnmTJlinrx/PpYthEjRlCxYkXatm2Lv78/x44dY8OGDfzwww/s3LmTO3fuYGVlRfPmzQGoUqUK2bNnx8fHhzVr1tCuXTuuXbtG2rRpcXd3V1v0jGPj1qxZg4+PD3Xq1CFHjhzo9XpMTU25ffs2ly9fVscJvqlBgwbUrl0bc3NzPD09Wbp0KdWqVVPHGxpDnI2NDYqiqEEkuXGaEDeh0ejRoylYsCBPnz5lzJgxLF26lKxZs3L37l26du3KixcvKFKkiPp+piTsGhnH8t69e5fHjx/j7e1NVFQUN27coHTp0mqdCxUqlGByrqTY2dkxfPhwDh8+jMFg4O7duxQpUoSMGTNSvHhxtYXY2Op98uRJWrVqRa1atbh79+4769qiRQsuXbrEN998w/Dhw9WulTExMWTMmFENX8aQFxMToz72ZoD+7bff+O2334iJicHBwUH9DCwtLSlcuLC6fq2joyPR0dGUK1eOsLAw7OzsUvK2YjAYOH36NObm5pw8eZLHjx+rrZNr165lyZIl6jHd3d0ZOXJkisq9dOkSW7du5dKlSxgMBszMzDA3N6dEiRKUL1+eJ0+ecPDgQby8vIC4v5sOHTpQs2bNJCdSu3LlChs2bKBGjRocPXpUvVlgZmbG8uXLURQFKysr8ubNS0xMDN9//z2rV68mY8aMCcpJnz49AwcO5Pfff6d06dJkz56dtm3bki1bNurWrYu9vT0vX76kdu3aeHt78/vvv3PlyhUcHBwoWrQoGo0m2ZbjsmXL8vvvv+Pk5ERMTAxnz57l1q1bXLlyhaCgIAYPHkyPHj2SHdf7+mdy+fJl9uzZowaffPnysWjRIjp16kT+/Pnp1q0bISEhZMqUiWfPnvH7778naoV8XebMmRPMReDs7IyHhwfHjx9Hq9XSoUMHXrx4weHDh7ly5QqRkZFYW1uTL18+SpUqRfv27ROVqdFoOHnyJHv37qV///5s2rSJHDlysGHDBu7du6cuK1arVi2aNGnCnTt3uHfvXoIu9k+ePAEgQ4YMb31PAPbt28fWrVsJCAigY8eO9O7dW/3etbGxYePGjSxYsID169erN2Y8PT0ZO3YsK1aseGf5RhkzZuTZs2c8fvyYhg0bcujQIcqVK/fOISJv3lQx/vtt35vv2u/1fZPb7nVeXl7qZHyXLl2iZ8+eHDp0iG3btqk3SBVFITQ0lLNnzxIeHs4PP/zAuXPnGDt2LJs2bXprXYUQ/y0SoIX4j3v9otXR0ZGqVaty+PBhtXtcahhb7vLnz0+mTJnUYP62Y7Zu3Zpjx44RERHB48ePOX/+vFrW610MnZyc8PHx4fTp0/Tu3RsbGxtCQkJo3bo1hQoVolmzZmqrsTFU/v3337Rp04aIiIgE4zjfxnhMY6vi612+IyIisLKywsLCguDg4EQXasldELZt25atW7fy66+/0qNHD65evcq+ffto27YtZcqUISYmht9++42zZ8/SvXt3rKysmDBhAg8ePKB06dL06dMn2VZuY6h79uwZhw8fJjo6mjFjxvDNN9/g4OCAtbU1er0erVZLTEzMO7vu5siRQ+22vWnTJpo1a8aff/6ZoJtqjhw58PLyYsyYMSxcuJAGDRq8dSZhI61Wy5QpU9i1axfR0dF4eHhgbW1NWFgYNjY2aktWaGgo/v7+5MuXT33dUVFRCcr6/vvvMTc3p0CBAlSpUoXChQtjb29Pv379gLjZ0bVaLdu2bePWrVvkzZuX06dPExsbm6Iu10+ePOHZs2dkzJiR69evs3r1anQ6Hb/99hs6nY5MmTKRJk0aoqOj0Wq1KIryzlmyAcqUKUOZMmVo3LgxOXLkYMaMGSxbtoz9+/cTHh5Os2bN6Nu3LwEBAezZs4dixYrRvn17Lly4wM6dO6lUqZLa++DZs2cMGjSIbt260b59e44ePap+vu7u7uzcuZOqVasSGBiIRqNh7NixdOrUiW7durFixYpENxOKFSum9gLo3bs3BoOBv//+mypVqjB06FA6dOjA8+fPCQ0NpXHjxgwbNoxs2bLRuHFjSpcurc58nZSCBQty//59Zs+eTZ8+fejRowezZ89m8eLF6mf1LmfPniUkJISAgAAMBoP6Oc6YMQNzc3NWrVql3mS4dOkSt2/fJn369ISGhr6zFdfIYDDg7++Pm5sb8+bNw9zcnMGDB6ut7AEBAWzbtg1LS0vatm2bZDfo3bt3M2bMGNq3b8/u3bvJkycPffv2Zfz48WzcuJF8+fKpn6ExfL45q7Zxhu83Jw9LSpMmTWjSpEmyz1taWjJ06FCcnZ1Zt24dq1atIjw8nFOnTvHkyZNEXe6TY+wqHR4eTu3atTl58uRbtzfepHmzh4bxZpjxO/l99suQIQNarTbJde6N270e7F+/iVKmTBlat26Nq6srx44dUwO0RqNRJ3QzNzdnxIgRNG3alKtXryYaOiCE+G+TAC2ESMAYgoOCgt67jNQsl2M8HsR1IzV2m01u/FtAQAAmJiZMmDCBUaNGER4ezq1bt9TZsRcvXqwGsV69eqmhOrWMrcFWVlY8efKEM2fO8OzZM7WF7cqVK4m6/yU3Xm7s2LHqDMr79u2jVatWtGnTBmdnZx48eED69OmpUKGCeoEWEBBAhgwZyJQpE97e3vj5+SXbrdXZ2Zk//viDH3/8kRw5cjB69GhWr16Ng4MDx48f5+7du7i7u+Pl5YWJiQnjx49XW/aT06VLF44cOaJOSvbmZF5Dhgzh3r17PH78GD8/P1auXMnevXvZt2/fO4NKaGgoM2bMoEePHvz222/kzJmT27dvY2trq06qFRMTw8uXL8mZM6f6nr4ZoM3MzNQu+ZGRkQDqTNFnz55l48aNnDp1ipiYGOrWrUv9+vWZOXMmp0+ffudyYxC3VFCTJk1wcHCgf//+PHz4kN27d5MlSxZ1/KWHhwfu7u74+Pjg6OjIokWLEswInJzTp0/j4eGBRqOhdevW6HQ6rK2tuXXrFqtXr6ZOnTrq7MknT56kbNmy6utftmwZu3btIk+ePEyYMIHRo0dTu3Zt9YaXmZkZUVFR/PTTT+TIkYOpU6fSq1cvYmNjKVOmDM2bN2fHjh00b96c2bNnJ+phAnETaBl7GVStWhV7e3tu3brFtWvXUBQFCwsLzp8/z+bNm7lz5w5hYWFERka+M6gaDAb279/PsWPHWLdunRr2L126xPbt27l//z69e/dOdiz977//DkCJEiU4ffo0JiYmGAwGqlevrk58FhwczJ07d9QW3Fq1agGwePHid37u+/btw93dncyZMzN//nx8fHwYMWIEDg4OREREEB4eTkhICM+ePSMgIIDt27fj6uqaoJV4/fr1nDt3jq1bt5I7d25KlChBxowZCQoKolevXrRp04ZTp05x6tQpNm3apA5JeX3meEAdq/3meZ9a9+7dU2cqt7W1ZcCAATRp0oQ2bdrw8uVLfH19UxygjXWxtrZO0fbG0Prmb4mxl0RyPQNSsp+5uTl58+bl3r17yW73etf1N+XOnRtI/jsb4uZmMDJ2kxdCCJAALYR4g/GCIqnA9q4ud//keMZjGlsfXu8eDvEXRRkzZkSv11OxYkWOHz/O/v37cXNz48KFCxw9epTAwEA1iP7999/vXS/j0k9r167l119/ZebMmXh4eFCwYEEiIyPx8fFR62p8DcldZPXs2ZObN2/y/fff06hRIzQaDeHh4bRs2ZKnT5/Srl078ufPz+XLl+nYsSOtWrWidevWydbt0aNHBAQE8OTJE8LCwsiZMyft27dXA6inpyc//fQTGTJkwNzcnNjYWDJnzkx4eDhr1659Z4CuWLEiOp0ODw8PZsyYQYECBRK0tGXMmJH9+/dz/Phx/vjjD/7880+ePn3KmTNnqFu3brLlxsbGqstq1a5dm3v37pE1a1aWLVtGixYtcHR0xGAwoNPpGD16NMWLF1ffU2NITsrz588ByJs3LxB34dy9e3du3LiBr68vQ4cOxdramkWLFrFs2bJkg1R0dDQ+Pj68ePGCx48fkyNHDh48eEDTpk3VGcP37NnDxYsXsba2VsfBZ82albCwMHbs2PHOAB0bG8vMmTPVlvf58+dTqVIl6tWrh4ODAydPnuTmzZvqBX6BAgXo2bMnNjY2REZGEhsbS44cOYiNjWXatGnquF7j34uZmRnjxo3DxsaGZcuWkSlTJrVVdfjw4WTKlAlbW1v8/f3p3LkzzZo148cff1Rbbj08PBg3bhx169bF19eX6OhofvrpJyAunHp5eaHRaChQoAAdOnTA1tYWU1NTVq9ezd69e6lSpUqyE9YZu/mnT5+eLFmyqH83ERERFC9enKZNm771/DGup1yrVi2OHj2qLktUoUIFAI4fP87PP//M8+fP1e+qzp07U6lSJapUqZJkmeHh4Zw9e5YVK1Zw+/ZtzMzMSJMmDWZmZuTOnRt/f3+uXr3KlStX1OC4fft2Ro4cSVBQUILxtw8fPsTJyYlixYrx9OlTdSKzkJAQxowZg5+fH3v37mXjxo20aNGCcePG4eHhQeHChdWZt42MNxaTGyecUj///HOCJZ0gLjzWrVuXrVu3pqiLuJFxaEnOnDlTtH2VKlUwNzfH19dXncn89XKMNzfed7+aNWty79499WYJxIXuqKgoMmfO/Na/RWPoflvINm6TJUuWL2YlAyHEl0EmERPiP+7N7m/GlqzXL26M3QiN654aDAa1m9z7hOrXj2k8XpEiRcicObM6e6y3t3eCQHrnzh0AqlWrRkREBNOmTSNdunS0bt2atWvXUr58eXVG8UqVKgFxLWnGmYIBdWzju+j1erZs2aLWY9GiReh0Ory8vKhduzZp0qShefPm6HQ6IL7LYHKtRXny5MHNzY3KlSuzbds2nJ2d6dWrFw0bNsTGxoY7d+6g0WjUcsLDw3F2dmblypW8fPkyUd3mzZtHly5dWLZsGY8ePaJAgQJoNBr1gi9NmjRqMDXOin7o0CHOnDnz1rWEX9e5c2cgfpzz6yZMmICpqSm1a9dm5syZ9O7dG0g8mc/r3N3dqVWrFr///jv58+cne/bsTJw4kaJFixIeHo6npycODg7qmMxOnTpRvHhx9VwxdmlNinF27GzZsnH27FkWLlxI8eLFKVCgAOnSpSNz5sxYWFjw7bffcuHChQRr4u7evVsNcgEBAQwbNgwXFxe2b99OaGgoJUuW5Pnz5+rY0TRp0lC2bFm2bdumzmR/5MgRzp8/z+jRo9/5vq5YsYL79+8zadIkIG7G3y1btuDt7U25cuU4ePAgmzdvVieSy5YtG9WrV+ebb76hcuXKVKtWDQsLC8zNzRNMimX87I8ePUqxYsWoXr06I0eOpFq1aty8eZMXL15w69Yt0qVLx6+//oqFhQUGg4EdO3bQokUL3N3d8fX1pXfv3nz33XfMmTOH8PBw9f3/5Zdf8Pb2pkyZMtSqVYsZM2YwZcoUsmXLhqOjI+nTp2fs2LE0aNBAvZHzJuOEW4qicPr0afXvu1u3bowfP56uXbu+dQKxn376CTc3NywtLQkPD0/UZb569eocOXKE69evqy3rzs7OVK9ePcku4sePH6dMmTL06dOH+/fvs379ejJkyKB+pxmX04K41u8hQ4Ywffp0tYdNuXLlErS4P3/+HBcXF1q3bs2IESNYu3Yt3377LQsWLFDnDxgzZgzp06dn6tSp7Nq1i5iYmESTFELc2O4MGTLw5MmTRN8BqfH48WN1+aw3Zc+eXb3p9C4BAQH4+/uTIUMG8uXLx5UrV6hRo4ba8p+UtGnT0qJFC3WWcyN3d3dsbW1p2LCh+u+2bduqPQxSul/79u0xNzfnr7/+UrcxTmrZuXNndfK2I0eOJJro7PTp06RJk0Z972/fvp3ohqtxKJDxu1AIIYwkQAvxH/fs2TN1HO3ff//NuXPnKFSoUIKLOmOXulmzZjF16lSGDh2qXlivX7+eAwcOAPEtsO+alfjw4cPqdmvWrMHMzEydhKlZs2Y4OTkREhLCzp07gbi1eX18fChQoABdunQhbdq0/PHHH+pMu9HR0QQHB1OvXj3Spk3Ld999R4YMGdDr9fzwww+UL1+e8uXL07dv3wRrfybn6NGj6kXntGnTqFChAlu2bCFbtmzUqFEDgEmTJqkzhxsvcI0hyygqKoply5YxefJk2rZtS+XKlVmwYAHNmjVjzZo1PHv2jNWrV6vLuRgDuKIo1K9fn6lTp1KvXr0Es8Campoyffp0rl27xo4dO5gwYQI2NjZcuXKFypUrA1C/fn0yZMhA8+bN2bJly3vd5GjSpAkZMmTgm2++SbSsVkhICL/99pv67xcvXpA9e3bKly+fbHl2dnZqd1XjTRJFUVi4cCGVK1emdevWDB8+PFH3XeN7+rYAbbyAnjNnDiNHjqRz586YmJgQERGBVqvF2dmZ77//nhYtWqDVapk4caJ6rq5cuVIdS5s5c2a2bdvG5cuXWbt2LT/99BPu7u48f/5cfW/79u3LxYsXadGihbp8U0qdP3+elStXsmzZMvU83LRpE7///jt16tQhY8aM6mdlHB6QknHVED+2/9tvv6Vjx444OzszYsQIlixZQpYsWciSJQtubm64uLjwv//9j6NHj7Jp0yaOHz/OyZMnKVy4sFq//v37Y2Jigr+/P9HR0Vy6dIn169dz8+ZNJk6cSOHChRk4cCCXLl0iMDCQFy9e0KtXLyZMmKCuwfum06dPc/78eYoVK8YPP/zA2LFjWbVqFcA7x+UbmZqaqr0+nj179tb3xvj5+vr6cvz4cZYvX8706dPVG3EQF7jHjRuHvb098+bNo1ChQoSEhCT4/jLePNy4cSMBAQFkypRJre+b9f7mm2/Yu3cv27Zt49ChQ0RHR5MpUyaePHnC1q1bKVWqlHru58uXT30tPj4+iYaDaLVa6tWrB8D169cTvT7j34OxZ87b/PTTT1y9elX999mzZ9m/fz/jx49X38PXj5/U94UxXNarVw+tVsvu3bt58uQJu3btSnYWboBBgwaRM2dO1qxZA6AOBRg3bpzaTX3r1q389ddfCSY0S8l+WbNmZdiwYZw6dQpPT08URWHjxo0ULVqULl26AHFd2Hv37k3r1q3Vlupdu3Zx/fp1pk2bpnZfHzVqFG3atFF/c/z8/Fi4cCHffvttiuZ3EEL8t0gXbiH+49KlS8f8+fMJCgriyZMntGjRgsGDB2NhYaFuM2LECIYPH86TJ0+4c+cOkydPxt/fn6xZs1KuXDmKFy/On3/+iZubGxB3wTdo0CD69euXZAuHt7c3bdu25cmTJ2TPnh1XV1e1xcjc3Jw1a9Ywe/Zs5s2bx6pVq4iKiqJ79+706dNHbfFp06YNo0aNIm3atJiYmFC2bFkGDhwIxM1cu3btWqZMmcLly5cxNTWlYsWKDB06NEUTwVSrVk0dv1ivXj0ePXrEli1bWLp0qXrxZmJigq2tLXq9Xr0of3NyKgsLC65fv66u59q9e3cGDBigXpS7uLgQERFB3rx5URRFvRANDAykW7dunDp1ij///JO9e/eqrd1vWrVqFVOnTqVcuXLqOr8RERHMnTuXdu3aMWbMGLZs2UKvXr2oWbNmitcsNrbOJLUmdePGjVm+fDlubm5kyJABR0dHVqxY8daxkQ4ODkyaNIlBgwapXSunT5/O48eP2bZtG5kzZ+bAgQP8+eefhIaG8tdff7Fq1Sr1PDSG7zddv36dRYsWAXEX/t9//z2urq7qBXehQoXo0qWL2gLYtGlTdu7cyfTp0ylRokSC7p+vi4yMZMSIEezbt4/u3btTvHhxTp8+jbW1NYsXL6Zjx4507tyZevXq4ezsrM4o/jZHjx5l+/btZMuWTe2qvnjx4iS7PBtDTEom14L4ljdjV3tTU1Py58+PwWAgKCgowd8zxE3E9OYkTq9PFPfo0SNCQkLQ6/Xq7PbFixdn6tSpbNu2jZ49e1KmTBkyZsyIi4sL/fv3p02bNkm2pgYGBjJ69GjSpUvHlClTyJ8/P4UKFeKHH37Ax8eHMWPG0KFDBxo2bJhodvCkhIaGcuvWLczNzbl48SJXr15FURTCw8MJDAzEz89PvSn1Zn02bNjAjh071PGtbdu2pVGjRvj5+alj719vVTYGy507d6o9LN7Wi8XR0RFHR0cg7ubi/Pnz+e677wDU2f+DgoLU77KMGTOyfPlyzp8/z08//ZRgTHq3bt3YunUrx44do2rVqgBcvnyZqVOnqkv2rV+/ntu3bzNgwIAkx7MPGTKE33//nUGDBpEuXTosLCxwdHRk9erV6t/hlClTOH36tLrP6tWruXjxIj/99JN68/TMmTNotVq6desGxAXpffv2UbZs2bd2b06fPj3r16/nl19+oXnz5mg0GmbPnp2gq36NGjXYvXu3esMgpftBXOuwlZUVgwYNUocWrFq1Sv07KFCgAEOHDmXz5s20aNECnU6Hvb0927ZtS/CdOnbsWKZNm8aECRPYuHGjuqRd06ZNU3wTSwjxH6IIIf6Ttm/fruh0OqVGjRof7RgGgyHBv3U6naLT6ZRz5859tGN+KE+fPlXCwsIURVGUZcuWKe7u7klud+vWLfV1BQQEJHo+KipKGTNmjLJ79+5Ez7Vq1Urp16+f8tdffykVK1ZUSpUqpeh0OmXWrFmKoijKy5cvlbZt2yr79u1LtG9gYKAycOBApUiRIsrUqVOVqKgoZdeuXYpOp1O6d++uKIqi+Pj4KFWqVFHrV6lSJeWXX35Rrl279t7vyz914cIF5dmzZ8ro0aOVli1bKo8ePVKfCw0NVZYsWaL+e8yYMWrdW7dunWR5e/fuVXQ6nVK4cGHlypUrytWrV5Vy5coplSpVUn7//fdE5+CLFy+USpUqqeVWrFgxUZkeHh5K06ZNlfLlyys7duxQFEVRRo4cqeh0OmXRokWKoijKsWPHlMKFC6vlfPvtt8qCBQuUx48f/+P3SFEUxc/PT9HpdMq0adNStH2jRo0UnU6n7N27N8Hjnp6eik6nU0qWLJmq4+/YsUPR6XSKi4uLsnHjRqVfv36KoijK5MmTlSJFiijbtm1T39t169YpJUuWVLZt25aonPDwcKV9+/ZKxYoVlStXriR4Ljg4WPnxxx/V97BQoUJKhw4dlOnTpyt79uxRbty4oQQHBycq8+DBg4pOp1PKly+v+Pj4KC1btlTKli2rtG/fXpkwYYLi6uqq7N+/X7l48aJy48YN5datW8qVK1eUP/74Q9myZYvy4sWLBOVFRUUpHTp0UIoWLarodDqlatWq6nO9evVSChcurBw6dEiZNWuW0qlTJ6V9+/aKTqdTxowZ89b30N/fX2nevLlSpkwZ5X//+5+i0+mUzZs3Kw0bNlR++eUX5eXLl8q9e/eUkiVLqu9B27ZtlZ07dyqRkZGKoijKtGnTlDJlyigREREp+NTeT3R0dKLHYmNjFb1eryhK3PtTuXJlZcaMGR+tDkII8TWRFmghxEfzNd+5f33NXeNsz0mxs7PDxMQEOzu7JFvPzM3N+eWXX5Lcd9OmTWqL8KhRoxg0aBCAupRQunTpklx/dP/+/WzdupUyZcrw008/qS1exm68xlb2bNmysXnzZrZt20Z4eDi2trY4ODgkufTOp1K2bFkOHjxIzZo1+eWXXxKcI9bW1gm6cI8ePZqzZ8/i7e391rW7Dx8+TLFixdRu0X/++SdmZmZJzghta2vLggUL+P777wkJCeHly5fq0laxsbEsW7aMq1ev0rFjRxo1aqT2FjCOsza+t9WrV2ft2rUcOnQIjUaDnZ0dWbJkSXGL8bsYW/OzZ8+eou2N596bPT6MLfcpXcbJyNja6uTkRLt27WjXrh0A/fr149KlS4wcOZJff/2VTJky4e/vT3h4OCNHjiQyMlJdzzg2NpYhQ4aQJ08e5syZk2gd67Rp0zJr1ixq1qzJhAkTCAoK4tKlS1y6dCnBNj/88EOCcajGcyZz5sxky5aNbdu2peq1vcnc3Jz58+dTv359oqOj1SWmjKysrDh16hSZMmWiQYMG6ljd5JZhio2NZfv27cybNw9ra2t1bHX37t2xt7dn3bp16uRd6dKlY86cOUyfPp3w8HAUReHcuXNky5ZN7VVz/vx5duzY8d4rCrxLUl3oXz+Pt2zZQo4cOfjhhx8+yvGFEOKr87kTvBDi89i2bZui0+mUypUrf5LjGQwGtZXlxIkTn+SYn8qkSZOUyZMn/+NyWrVqpfzyyy/v3C48PDzJx0NCQpTevXsrp06d+sd1+VKsW7dOady4caJWw3/K29tb6du3r1K5cmW1tU+v1ytRUVFJbn/nzh2lc+fOir+//wetx9tUrlxZuXfvXoq2PX/+vNK2bdsknxs4cKAyduzYVB07ODhYqV+/vnL79u1Ez0VFRSnLly9XmjdvrpQsWVIpW7as0rVrV2XHjh1KbGysup1er1d7cbzLs2fPlCFDhiglSpRQatSooQwaNEjZs2dPki2vBoNB+f7779XeAB/K9OnTlbZt2yrPnj1TH9uyZYsydOjQBNsdPnxYKVWqlOLr65uojKCgIKVNmzZKly5dlG3btikxMTH/uF5+fn5K+/btFR8fn39cVmp5e3srHTt2/KTnvRBCfOk0ivIR1qURQnzRtm7dytKlS/H29gbiZsAuU6aMOpHXh3bv3j2mTp3KiRMnALC3t+ebb76hf//+6iy3QoiEPD09kxyDLj6/oKCgVC0B9SGOt2XLlmTXyP5YlixZQocOHUibNu0nPa4QQnzJJEAL8R9k7LL6upiYmBTPhptar6/l+fpjiqKkeFIrIYQQQgghPjcJ0EIIIYQQQgghRArIOtBCCCGEEEIIIUQKSIAWQgghhBBCCCFSQAK0EEIIIYQQQgiRAhKghRBCCCGEEEKIFJAALYQQQgghhBBCpIAEaCGEEEIIIYQQIgUkQAshhBBCCCGEECkgAVoIIYQQQgghhEgBCdBCCCGEEEIIIUQKSIAWQgghhBBCCCFSQAK0EEIIIYQQQgiRAhKghRBCCCGEEEKIFJAALYQQQgghhBBCpIAEaCGEEEIIIYQQIgUkQAshhBBCCCGEECkgAVoIIYQQQgghhEgBCdBCCCGEEP9BsbGxBAQEfO5qCPGPybksPiUJ0CJVYmOV/+Sxxb+TYjD8J48txL/dnTt3qF27Nnv27Hnntjdv3qROnTocP36cP//8k7Jly3L+/Pm37hMTE0PNmjX54Ycf8PHxSXX9AgICqFevHrNmzUry+fXr19OqVSvu3bv31nJGjhxJ48aN2blzZ6rrABAREUGjRo2YPn36e+0vUm/atGm0bt2ac+fOffCy5VyWc1l8GqafuwLi62JiomHI+Jd4PohNdptWjdPQsaUV67eHs2135Ac5br7cJswYn/6DlPWh3Llzh759+zJw4EAaNWr01m1v3rzJwIEDGT16NBEREYwZM4YFCxZQvnz5t+53/PhxZsyYQe/evWnQoEGi53/88UcURWHixInY2NgkWYbBYKBhw4bY2dkxZMgQSpQokfIXCSxYsIAdO3ZQr149fvjhB9KkSZOq/b9kGq2W6DWrUZ76fZLjaStUxKxqNfRXr2JaqtRHO860adO4ePEigwcPpkKFCh+0bDmXxYf2+PFjWrRogZmZGSNGjKBhw4aJtqlXrx65cuVi7NixZM+ePcFzGzZs4I8//mDcuHHkz58fgFy5cvHo0SPWrVv3zu/nbdu24e3tzZ07d+jZsyfW1tbY29vz119/kTZtWvLly5doHzMzM1q3bs2cOXMoUqQIPXv2TLLssLAwLCwsMDVNeLlla2tLwYIF2b59Oz/++CPnzp1jzpw59OvXj6pVq3L16lUePHjAw4cP1deUlJ49e1KvXj327NlD8+bN3/o6X6fX6/H39+fx48eUL1+eXbt2sWPHDvLmzcuaNWswMTFJcVni7QwGA+Hh4er3Wvv27VmxYgU7d+5M9P08YMAAbt26RZ8+fWjevDkLFy7k999/p2HDhnTq1ImQkBBy5syJVpt0+5ecy3Iui09DArRINc8Hsbh76JN8rnc3Kzq2tGLO0lAWuYZ/4pol72u8QAOoVq0aI0aM4MCBAzRo0IBFixbx119/MWrUKDJnzszff/+Nqakpz549SzZ0aLVaunfvzpgxYzh37lyqQ0e3bt1YvHgxK1eupFWrVsnWNTmhoaE0aNCAggULMmjQIAoWLJiq/T825akfynvcdX8fsdu2QnAwZg3ffr6k1pd+gQZyLovkZcuWjdWrV/Pjjz+ye/dusmXLxpEjR+jevTsZM2YEoE2bNkybNo0nT54k+n4ODAzEz8+P6Oho9bEHDx4Aca1ab3P9+nW2bNlCt27dyJgxI9OmTePOnTsoioKjoyMVKlRI9jzJmTMnAJUqVcJgMBAcHIy3tzdeXl7cuXOHq1evcu3aNXLlysW2bdsSndcODg4UK1YMjUZDuXLl0Gg0zJ8/H0VRePr0Kbt37yZLlixvrX/u3LkxNzenVCpvyF24cIHNmzfz9OlTfHx8GDlyJMWLF8fR0ZGff/6Z77//nhw5cqSqTAGHDx9m5cqV9OvXj3PnzhEWFsaJEyfw8/Nj8uTJNGrUiOzZs2NhYUHu3LkT7T969Gg6derEhg0baN68OS4uLvz555/s37+fSpUq0b17d9q0acPYsWMT7SvnspzL4tORLtzig+ndzYqBPW2+uPAM8RdoadOmZffu3fz111/MmjWLwMBAdZs2bdpw/Phxnjx5kmj/D32B5urqysSJE9m8eTM3btxIdl+NRoO9vT3FixcHoFmzZvz1119s2bKFBQsWULVqVXbt2kWePHneWgedTgeQ6h8mABsbGzJnzkyWLFneGTiePn3KihUruH79eoL9W7ZsyfHjx9/Zheu/IPbAfmJOnnjv/Q8fPkzHjh05e/Yss2fPZuLEidSvX59KlSqp3VXfdYGm0WjYsGEDAC4uLlhYWLB//368vLxo1KgREydOTPLYci7LufyxFCxYkL179zJjxgwKFSqEq6srixYtwtnZmd9//x0HBwdMTU0pU6ZMgv3Cw8M5cuQIrq6ujBgxAjc3NwDOnj2Lvb09Fy9epE+fPgwZMoTg4OAE+7548YLBgwdjMBgICAigdu3alCxZklOnTmFubk62bNmS7C1hZGZmBoC9vT09e/akTp06zJkzhz///JOVK1eSJ08ehg8fTtu2bRP8dhiZmppiZWUFgImJCUuWLKFdu3bs2LEDKysrhg0bhq+v71vft/DwcCwtLTl//jwtW7akRIkSyR4PIDIykn79+mFpacmcOXOwtLRk0KBBTJ48mbNnz7J9+3Z2797Nn3/++dbjiqRVr14diOtxs3v3bsqXL8+OHTuIiori5s2bLFy4kJCQENKlS4e5uXmi/R0cHNixYwdTpkwB4s6LRo0aERYWRtmyZVmwYAHW1taJ9pNzWc5l8WlJC7T4IL7k8GxkvEALDw/HzMyMLl26EBkZiaenJ02aNEnRBVqfPn1wdnamSZMmCS7QFi9ejJWVFWPHjiVdunTqvkn9qF28eJEVK1ZQo0aNd/6oQdyPkfGHKUuWLKxbt46bN2/i5uZGTEwMP/30E3PmzHlrGYZX423XrVvH6NGjefbsGc2bN0/yLnZSLCwssLa25siRI5w6dYqLFy9SqlQpfvnllwTbZcqUiT179jBt2jQOHjyo3tU23nmuUKECly5d4vTp0/Tv3z/ZVs5/O8O5s1C12nvtW716dVauXMnx48c5cOAAI0aMYODAgXzzzTfcvHmThw8f0rlz53deoD19+hSIv0DbsGGDeoF2+fLlRPvJufzln8vhESmbJ8LMDMxMNYke1+sVomPe79hWlonLSylFUVi9ejVHjx7lhx9+oHTp0mTMmBGdToePjw/nz5+nUqVKmJmZJXifo6KiGD9+PGXLlmXdunXcuXOHZ8+ecejQIebMmUP58uXx9/enbt265MuXL8F3c2hoKMOGDaNp06aEhYVx48YNrK2tcXR0BKBu3bqkTZs22c/18ePH3L59G4AePXrQunVrli9fjre3t3pzSqfT0a1bt0T76vV6vLy88PT05K+//qJx48b8+OOPrF27Fnd3dyIiIujTpw/ly5dPstXOYDAwdepUTpw4wePHj4mKiiIiIoI6derg4uJC8eLFk/zbB0iTJg3lypXjxIkTLF26lIcPH7Jr1y7y5s1LqVKlWLp0KW5ubl9Ei50SFZWyDU1N0STRTVeJjQV90r3l3kVjYZHqfYKCgvDy8mLt2rXExMRw6dIllixZwu7du0mTJg2lSpVi+PDh2NjYYGFhoX6XvcnGxiZBC2/58uWZPXs2rVq1Ik+ePEybNi3B9nIuf/nnsvj3kQAt/rGvITx/jRdoERER3Lx5k6CgIBYuXMjhw4dp0qSJGjoyZ85M9+7dqVy5cpL7P3v2jIkTJ3L9+nX8/f3Vxzt06ED+/PlT1YKnKArBwcH8/vvvpE+fnjZt2iR5XFNTU2rXrs3du3fx9fVl+/bt5MiRA4PBgJmZGXq9np9//hkPDw8sLS2THWv1KWkyZ/lkXbj/qa/1Ak3O5U+jVK1nKdpu7GAbOra0SvT4wRNRDBwdnMQe73bnjEOq94mNjWXs2LGcOXOGqKgoXrx4Qdu2bYG47vrW1takT5+etGnTotFo0GgShvSBAwdy5MgRsmfPjo+PD+PHj6dly5Zcu3YNMzMznj17xtWrV9myZUuing2WlpYsW7YMrVbL3LlzSZMmDYGBgWqPgwwZMlCiRAliY2MTjaGMiIhgz5493Lp1C4AZM2bg5OQEoN5w6dChA927d0/ydU+ePJknT55w5coVypQpw+jRo8mePTs1a9bE1dWVqVOn8uLFC06dOpXkua3VahkxYgT9+/fHxcWFixcv8vDhQ6pXr06hQoXe+b43bdqUJUuWULt2bcqWLcuZM2eoWrUqp0+fpkiRItSvX59JkybRrFmzd5b1MUUNG5Ki7UxbtcY0iRuShuvXiXFd+V7HTjN3fqr3iY2NpVu3bnTp0oUBAwYQFRVFkSJFePz4MWnSpMHMzIyMGTNia2uLqakpMTEJ71Z5enoybNgwzMzMmDRpEn///Td//fUXBw8eRFEUihYtyvfff5/o70DO5S//XBb/Pp//lrn4qn3p4Tk2NpZRo0ZRs2ZNli5dyrlz5/Dzi5swKqUXaL///jsHDx5k5cqVjBs3js6dO5M+fXr1Am3Hjh2UKFGCYsWKJdjX+KPWr18/0qRJk+SPWunSpYmNTTwhm7+/Pz/99BPLly/n8ePHtGnThhkzZtC4cWM2b95MtWrV0Gg03Lx5U/3he5O9vT0zZ85k06ZNWFpaAuDt7U2LFi2oUqVKkt3A3vY+fvPNN8ydO5dffvmFzp07kzdv3iS3zZgxI1qtlnXr1hEUFIS1tbX6Gp2dnalatSpr1qxJ8m7252DWug2anLk+dzVSxHiBNmvWLDQaDVFRUeTPn5/AwMAUX6C1bNmSdu3a4enpya5duxg/fjx9+/ZVL9AGDhyY7AWanMtf9rn8tTExMaF79+64urrSt29f7O3t1V4Mer1e7a1gamqKoiRuXR8wYABbtmyhQIECDBgwgPbt23P58mV69uzJN998g42NDdHR0djb2yfa38TERL3hc+nSJfR6PZ6enoSEhACwdOlSGjRoQPny5fH09Eywr6WlJb169aJx48YAauCIiopSu6l279492RtYY8eOpV27doSEhPD48WOWLl1KUFAQc+bMYc+ePVhYWGBvb0/VqlXf+v5dvXqVS5cuodVqqVatGs7OzmpLYnKWLl2qhhN3d3fmzZuHlZUVHh4ezJ07lxIlSvDjjz8SFBT01nJEYpkyZWLUqFE4Ojoya9YsAgIC0Ov1ZM2aFTMzM/XmW2xsrPr9DXDo0CGaNm3KDz/8wJ07d4iMjMTR0ZFs2bJRtWpVduzYQdmyZYmOjiZbtmyJjivnspzL4tOTFmjx3r708AzxF2g9e/bk1KlTLFq0KNUXaC4uLixatIgWLVrQvn17zp49S58+fShXrhzh4eEJLtBeDx6v3+m9dOkSpqamiX7U7t+/T3R0NNu2bUswJtPBwYG5c+cydepUjh49yqVLl7CwsODbb79l5cqV3L59GwsLCwoVKkTRokWTff2mpqasW7eOiIgIAPLly0ePHj1YtmwZ6dOnfFbzmJiYFG9vZmaGnZ0d8+fH38Ffu3YtdnZ27Nq1Sx1r9aVQnj/HvE9fon9biOL98HNX562MF2jR0dGJLtDu37//1gu0+fPnExsby4MHD8ifPz+Ojo4EBASQNm1aevfuzeDBg996gWYk5/KXey5fPWyfou2Sq3adahYpLuNDMU7KeOTIEbVHA8TN+GtnZ0dgYCCZMmVCUZREF/EajYaxY8fi4+ODlZUVCxYsICYmBlNTU8qXL8/58+dRFIW2bdvy8OFDunbtytChQxPVISgoCJ1Ox549ezh79ixz5syhYsWKhIaG8ujRI+7du4ejo2OSk9tpNBomTpxIpkyZuH//vjoPgIeHB507d6Z+/fo4Ozvj4BDfQh8SEsLPP/9M7ty5qVChAra2tuj1etq3b4+FhQVeXl7o9XrWr1/PvHnzKFy4MAMGDEjQlTU0NJSxY8fSokUL9u3bR506dTA3N6dTp04sWLAg2dn369Wrx4sXL2jYsCErVqygV69eLF26lP/973+sX7+efPnykTNnTm7cuMGNGzfe+jf5sVlMm5GyDU2TvpTVFi+e8jI+kLZt2zJv3jz0ej3Pnz8nOjoaBwcHgoKCCA0Nxc/PDzMzMxRFITIybpWS2rVrU7x4cRwcHPjf//5H69at1e/qS5cusWLFCnLkyMHhw4eJjIx86+oBci5/meey+PeRAC3ey9cQno2+1gu069evs2vXLmxtbWnUqBEvXrzA3t4eZ2dn/Pz8ePr0Kf7+/kyfPp2wsDDq169P69atExz3xo0brFy5kvbt27N27VqGDBmCs7MznTp1YsmSJWTNmjXJ9yw2NhatVotGoyEiIoKoqCj8/PxYuHAhp0+fVl9r9+7dE4UIg8GQYJmLR48eYTAYsLS0TLTtH3/8wbfffvu2j++ji9m+FbPGTb+aEP01XqDJufxp/JNxyACmpprksshHFxQUhF6vZ+7cuZiYmBAWFkbOnDm5evUqBQoUIDY2NlEPB3t7e0xMTPjxxx9p0aIFGo2G0aNH0759e4oUKUJUVBQPHjxg7969SR4zOjoaX19fnJ2defjwIbdu3eLJkyfqEm2v/yZUqVKFFStWEBAQwMmTJ7l16xYHDhwgV65cVK1alXPnznHs2DFcXFxYvHgxFSpUYOjQoQwZMgQ3Nzf27dtHxowZiY6OZuDAgepvhsFg4OLFi+zYsQMnJydCQ0NRFEWd6E6r1SZaNig2NpYff/wRc3Nzhg8fzq5duzAYDIwaNYrz58/TvXt3evToQd++fRP9LWfOnJnvvvuOGzdu0KdPH3x9fTE3N+fMmTM8fvwYHx8fXrx4AcT10lq+fHmyQys+tvcZh5xgfxMT+MRLGLm7u7NixQqWLVvGH3/8gb+/P8WLF8fMzAwnJyeKFStGsWLFiI2NVW8GAmooffnyJU5OTjx//pzy5cuzaNEiGjZsiLOzMwcOHGD9+vU4OzsDcRMdGq9p5Fz+ss9l8e8jAVqkmnGd568hPL/ua7lAA/Dz82Po0KHMnDmT0aNHExwczO7du9m3bx86nY4HDx5gaWlJ/vz51a7jGTJkSHBsf39/+vfvT+3atalXrx5r167F3NycadOm0a5dO5o2bcro0aNp0qRJoi67GzduZMqUKWTIkAEHBwdKly5NlixZsLS0pHHjxowfP56ZM2fi6+vLuHHjEr1mExMT7t+/z9SpU6ldu3aS6zDq9Xo2bNjw2UMH0dFEL/4Nc5c+X0WI/pou0EDOZfFuMTExnDhxgtu3b1OhQgWKFStGyZIlSZs2LU5OTmTPnp3w8HBiYmKIiYlRbxCZmZkxduxYLly4QL9+/ciZMyfDhg1jzJgxDBo0iDt37qDX6/ntt9948eIFXbt2VSeDe/ToEX369MHCwoKiRYsSFRXF0aNHgbjeDtOnT6dhw4YYDAYiIyPVC/+YmBh+++03vL29cXZ25rvvvmPkyJEEBQWxceNG7ty5o76uRo0a8eDBA+bPn8/NmzepWLEiv/76K61bt6Z+/fq4uLiQIUMGVq6MH6e7efNm7t27l+w6uHq9nhEjRvDkyROWLFnCs2fPiI2NZceOHfz1118MGDCAkSNHsnTpUnbu3EmPHj1o164dadKk4ccff+T48ePodDoKFy5M9uzZyZw5M9bW1hQtWpShQ4dibW2Nubk5er2ely9fJrjpLN5u9erVzJw5k6JFi1KuXDlWrFhBq1atsLOzY8KECRQsWJB169YBcd8tr38/Azx//pzw8HBmzpxJwYIFGTt2LFZWVjx69IiZM2dSv359li5dSpMmTbC2tlZvJMq5LOey+PQkQItU69jSivXbwzl+JprCuk9zCuXL/c/uIn9NF2hPnz5l1qxZzJ07l4IFC/Ly5Uvy5s2bIKQPHjwYRVFo2rRpkq/3+fPnfP/99xQrVozx48dz8OBBIG6CkGzZstGjRw8WLVrEsGHDWLVqFf369aNWrVpq+GjTpg1Lly4lV65crF27NkHZ3t7eQNysnP3790907IcPH/LkyROaNGnCiBEjaNWqFdu2bePJkycEBgaq67ru27ePx48fv/dn+qFoHDMDELP7d8xatsa8bz9itm5B8Xv7khsf4pip9bVdoMm5LFLi4sWLuLu706hRI0aMGEGXLl3o27cvEDc8QKvVMmnSJLUnAcSNmezYsSMGg4Fy5coxatQoChYsSPfu3fH09CQwMJCAgABCQ0Pp1KkTzZo1Y8+ePZw+fRpTU1Ny5MjB7t27Abh//z4dO3akSZMmuLm50a9fP37++WdOnz7NkCFDsLW1Vevq6OjI9u3befToETly5GDhwoU0a9aMevXqodFoOHDgAIA6JtX4OgoUKICJiQljx45VX4Onp2eimYkVRUl21uGIiAhGjRpFrly5GDNmDFOnTlXHiUZERGBmZkaJEiXU5YdMTU0JDAzEw8OD4sWLM3v27CTLXbhwIRkzZkw0W/GbN7LE25UqVQpFUahQoQLR0dFMmjSJ7777DicnJwYPHkzu3LnV3ishISGEhoYm2N+4dJVxwruOHTsSFBRE6dKladeuHaGhoRw5coSBAwdSrFgx9XtWzuV4ci6LT0UCtEgVg0FBq9XQsaVVkrO4fkyxsQomJu/XRfFrukCzt7dXZ0H28/MjNDRU7Ypr9LYfpkePHjFmzBi+++47SpYsyYgRI7h//z4QNzGIiYkJXbt2xcrKips3b2JjY4OHhwfFihVT79Cam5vzv//9j1OnTiUo22AwcP78eQB+/fXXBPUGuHz5Mps2bSImJoaBAwfSoUMHAHLnzk1kZCQ1a9bE3t6ekJAQAgIC1M+mbNmyqfk4PxjFYMC8S9dEj5t37vJJjq1J5dJHX9sFmpzLIiUqVqzI999/T69evTh8+DAtW7akWrW4WZWN3TZv3bqFnZ2dek6WLl2aX3/9FV9fX1xcXNTv7fv37/Pjjz8yceJE9uzZg52dHenSpWPOnDmMGjUqUQ+FgwcPMnbsWBo1akTz5s1xc3PDzs6OuXPn8v3337Nv3z6aNGlCkyZN+Oabb9BoNNjY2KgzBA8fPjxBeSdPnsTc3Fytt0ajoV+/furzxuM/evQIb2/vRBPgmZiYkDZt2iTfp7CwMH755Rd1eMSkSZM4f/48Xbp0oXXr1upMw3ny5KFu3bopfv+NE2qKf6Z48eKsWLGCmJgYfvzxR2rVqqVOMOjv769OWtq1a1fCw8Oxs7NLsH9QUJB6o/Dw4cOkSZOGJUuWqOeDra0t06ZNo0+fPly6dImuXRP+dsm5LOey+HQkQItU0Wr/2Ri7f+J9wzN8XRdory8FdPr0aYBU/TABLF++XH0dixcvZv78+SxYsIAhQ4aoY0XftexOiRIl2L59O4MGDeLFixc8ffoUHx8f9Ho9FhYW3Lp1K9GkHIUKFSJPnjw4ODjQu3dv9fEyZcowePBg/vzzT548eUJoaCjZs2enYsWK5M6d+631+JhSG2A/97G/tgs0OZdFSmg0GoYMiVuyqFatWklu06JFC3VSI6OklqY5evQoGo2Ghg0bcuPGDXr06AHE/e0YbwJBXCvXypUrCQwMxNXVFScnJ06cOAHE3fgpU6YMv/32G25ubhgMBs6dO0eGDBnUOTWS07RpUzJkyPDOdcGzZMlC8eLFE43JtLGxIVeupFcFePPvGVBb7ZIaWpBS1tbWZM78fr1iRELlypXDx8eH2bNnJ7gx+PoyUDNnzqRHjx506ZLwRu2cOXOIjIzE3Nycb7/9NskhIdWrV8fV1ZUpU6agf7XGtZzL8eRcFp+MIoRQFEVRNm7cqIwaNeqd2xkMBkVRFCUqKkqpW7eusmXLliS3Cw8PVxYsWKBMmDBBuX37tqIoinL8+HFFp9MpFy9eVP89ePBgZdSoUcr8+fOVu3fvJijD29tbqV27thIUFJTg8fHjxyvr1q1L8Wvr06ePotPpFD8/vxTvExAQoDRs2FC5efOm8ujRIyUwMFCJiop6537BwcFKYGBgio8jUu/Ro0dv/Szu3LmjVK1aVfHw8EjweEhIiPLs2bN3ln/hwgWlRYsWys8//6woipzL4r/jzJkzSmxs7Gc7/sOHD5UjR46kePvAwEBl4MCByuPHj9/7mKNGjUr09yq+fnIuC/HxaBQlibV7hBAfzdmzZylfvvw77+q+zf79+ylRokSK77TeuHGDVatWMWPGjEQt5EK8LzmXhfj6hYeHq0s6CvE1k3NZfCoSoIUQQgghhBBCiBT4fAMAhRBCCCGEEEKIr4gEaCGEEEIIIYQQIgUkQAshhBBCCCGEECkgAVoIIYQQQgghhEgBCdBCCCGEEEIIIUQKSIAWQgghhBBCCCFSQAK0EEIIIYQQQgiRAhKghRBCCCGEEEKIFJAALYQQQgghhBBCpIAEaCGEEEIIIYQQIgUkQAshhBBCCCGEECkgAVoIIYQQQgghhEgBCdBCCCGEEEIIIUQKSIAWQgghhBBCCCFSQAK0EEIIIYQQQgiRAhKghRBCCCGEEEKIFJAALYQQQgghhBBCpIAEaCGEEEIIIYQQIgUkQAshhBBCCCGEECkgAVoIIYQQQgghhEiBjxKgg4ODOX/+/McoWgghhBBCCCGE+CzeK0DHxMS89fnNmzdz8ODB96qQEEIIIYQQQgjxJXqvAN2pUyd8fHySfC4mJoYNGzYk+7wQQgghhBBCCPE1eq8A/ezZMw4dOoSbmxsPHz5M8Nzq1avx9fX9IJUTQgghhBBCCCG+FKYp2cjX15dx48YxdOhQChQoQKdOndi8eTMPHjwAIHv27NSuXZtSpUoxf/58rK2tcXZ2/pj1FkIIIYQQQgghPimNoijKuzYaNGgQ+/btQ6vVUrNmTdq0aUO/fv2oU6cOGTNm5OnTp5w5c4bw8HCyZ8+Oq6sr2bJl+xT1F0IIIYQQQgghPokUdeF2dHRkypQp9O3bl0ePHtGrVy+sra2ZPn06I0eOpGDBgpiYmNC5c2cCAwMTdesWQgghhBBCCCG+dilqgQbYt28f1tbWVKxYkdOnT7Nz507MzMzIkycPZ8+eZebMmWTOnJkbN24wduxYduzY8bHrLoQQQgghhBBCfDIpCtABAQHUqlWLdOnS0aVLF549e8YPP/zAsGHDyJEjB4qiUKRIERo1agTArl27KFiwIAULFvzoL0AIIYQQQgghhPgUUhSgQ0JC8Pb25vTp0/zxxx9YWVnRuHFjNm/ezMSJE2nZsiUajYacOXPSvHlzFixYQM2aNZk3b96neA2p5uTkBICJiclnrokQQgghhBBCfFliY2MBuHPnzmeuyZcnRQG6b9++nDp1imzZshEbG8vTp0/JkiULOXLkoH379rx8+ZKDBw9iaWnJ3r17yZUrFzt27MDKyupTvIZUkwAthBBCCCGEEEmTAJ28FC1jde3aNRo3boyDgwORkZG4ubnh7e1NjRo1mDBhArGxsWTPnp1Zs2Zx9OhR/P398fPzI2/evB+7/u/FGJzd3d0/c02EEEIIIYQQ4stSuHDhz12FL1aKWqCfPn2Ko6MjrVq1IiwsjKJFi3Ly5EnMzMyoW7cutra2LFu2jHTp0lGzZk1y5MiBt7c3P//886d4DalmPCEkQAshhBBCCCFEQpKXkpeiFmhHR0cCAgIIDg5m7ty5nDhxgsaNG3P06FHu3r3LmjVrOHToEK1bt6Zx48ZYWlrSoUOHj113IYQQQgghhBDik0lRgAawtbVl//79aDQa/Pz8KFWqFNWqVWPSpEkcOnSITJky8fTpU9KmTQtA/fr1CQ0NxcbG5qNVXgghhBBCCCGE+FS0qdlYo9EAUKNGDTUojxo1CltbW7RaLRUqVFC37d69u4RnIYQQQgghhBD/GilugX6b0qVLM3nyZOzs7NTHjGFbCCGEEEIIIYT4kuj1ep48eULOnDlTtV+qWqDf5vXwLIQQQgghhBBCfKn++OMPtm3blur9PliAFkIIIYQQQnzZ9MEviHjwN/rgF5+7KkJ8VBMmTCAkJCTZ59esWcODBw9SXe4HCdCHDx+mQoUK9O7d+0MUJ4QQQgghhPjAgv86hPeCXviuH4/3gl4E/3Xoc1dJiI9mz549HDx4kEePHiV67tChQ/z999+EhYWlutwPEqDnzJlDUFAQjRs3JiQkhJ49e/L06dMPUbQQQgghhBDiH9IHv+D5vsWgKHEPKArP9y2RlmjxrxEZGcny5cuJjo4GoEmTJuzZs4eGDRvyv//9j1GjRnHs2DGePXvGhAkTAKhTp06qj5OiAB0YGIinpycABoMBV1dX9bmnT59y9+5d+vXrR4MGDZgzZw4nT55MsM3bBAQEMHjwYMaOHUufPn24devWW7f38/Pjf//7Hz4+Pikq/2sTG6t87iqI18jnIYQQQoivhaIoGPx80Z85TfTaNcTs26s+FxPwJD48qzsYiAn0/cS1FOLjWLRoETNmzKBevXqsWbOGZs2acebMGdKlS0e6dOk4fPgwvXv3platWkRFRTFv3jzatWuX6uOkaBbuOXPm4O/vz6JFi1i6dClz584lKCiIokWLEhoaSs+ePenXrx+zZ89m/fr1aDQaTp06xfDhw99Z9uDBgylVqhQDBgzg7NmzdO/enYMHD6rLZL3O2Lrt6/vv/UM3MdEwZPxLPB/EfrRj5M9jwvihafF+HMuEmaFERH76kGiZRsOYwTbkzGbC+Okh3Lv/8V7v27RqnIaOLa1Yvz2cbbsjEzyXL7cJM8an/yz1EkIIIYR4F0WvR/HxweDlicHTE8N9L3itS6omc2Zo0BAAM9usoNEkDNEaLWYZs3zqaov/gICAACZNmoS1tTXPnz+nf//+FCpUKNnt/fz8aNeuHevWrSN79uwAnD59mnXr1pEpUya0Wi0jR44kTZo0yZZx+fJlWrRowePHj5k2bRoZMmTA0dGR48ePA3Du3Dn69+9PtmzZ8PX1pWDBgu/12lIUoPfu3UtYWBhHjhxhzZo1KIrC4sWL0Wg0rFy5kmbNmrF+/Xr8/PxYuHAh/fr1S1HIvXTpEmfOnKFPnz4AlC1blpCQENavX4+Li0uCbWNiYpgxYwZlypThzp077yy7Vq1ayT4XGxuLiYnJO8v4XDwfxOLuof8oZRcrZMrYwWm5c09Pj0EvCQv/9OHZ2krD8lnpyZ7FhK79g/j71sd5re/Su5sVHVtaMWdpKItcwz9LHYQQQgghUkqJjMTw4AEGL08UL08MDx5ATEzCjczM0ObOjSZvPrT58qkPm6bLhF0DF57vWwKKATRa7Br0wjRdpk/7IsR/wj9tJPX29qZPnz7s2bOHHDlyMGzYMCZNmqR2vU7KunXruHv3LmZmZqRLl45t27axceNG1q9fT4YMGZgyZQq//vortWvXZtOmTfz888+sWLEi1a8tRQH6wIED7Ny5kylTplCoUCF0Oh1OTk4sXryYFy9eEBkZiYODA0OHDsXOzg5bW9u3znhmdOLECQAyZYr7wzU1NSVDhgwcO3YsQYBWFIVZs2bRo0cPdu3aleoXKeIUK2TKqrkZuOv1+cNzgbymdP/h84bngT1t3hqe7Wy1KAYDGq1MVv8lkM9CCCHEf40SHIzByyuuhdnLE+XxYzAYEm5kZYX2VVjW5s2LJnsONKZJX+KnK1kbq7yliAn0xSxjFgnP4q1iY2Pf2ih5+PDhJB//EI2kS5Yswc7Ojhw5cgBQsWJFRo8ejYuLC9myZUvyuJGRkbi4uGBnZ0f37t2BuKWqhg4dSkxMDN9//z1OTk4AtGvXjpcvX/L48eNky0tOigK0ra0tzs7OZMuWjR07dmBpaUmzZs14/PgxL168YNKkSWzbto0RI0bQpUsXrKysMDMze2e5AQEBAAm2NTMzUx83WrhwId9++636BqZEch8oQOHChVNczr+FhOd4KQnPAOnSatBotUSvWY3y1C/JbbQVKmJWtRoxJ09gOHf2Y1X5rTSZs2DWug3K8+fEbN8KryZO+KTMzTFr2RqNnR0xW7eg+H3YYRYax8yYd+n6QcsUQgghvjSGF88x3L0X17rs5Yny7FnijWxtEwZmB8dU3WA2TZdJgrP4qD5EI+nx48cTBNtMmTKh1+s5ffo0bdq0SfbYPXr04PDhw7i6upI2bVpy5szJ5cuX2b59O/Xr12fmzJk0adKE5s2b4+bmhpeXF1OnTk3V60tRgIa4OxCVK1cme/bsLF++HIDs2bNz7949Dhw4QOvWrencuTPBwcH4+flRo0aNd5aZMWNGAMLD40NMTEwMmTNnVv/t6enJ8uXL1UnJoqKigLhZ1UaNGkXLli1T+hL+syQ8x0tpeH6d8tQPJZlJ62K3bYXgYMwaNiImOJjYA/s/ZHVTRPHxIdrfH/M+fTFr3JToxb/Bq7+TTyl63hzMXfpg1qo10b8tRPF++MnrIIQQQnzN9H/sw3DxYvwDGg2azFnUsKzNmw/Nq+tnIT42ExOTtzZKJudDNJIGBgaSO3fuBPsDvHiR/Kzxc+fO5caNG+TLl48bN27w999/o9FoqF27NgEBAXTo0IHLly9z9uxZtm3bhq2tLb169Ur160txgB49ejS7d+/myJEjPHwYd2GcJUsWzp8/z6xZs6hcuTIA06ZNIyYmhqZNm76zzOrVq7N06VL8/f1xcnJCr9fz8uVLOnbsqG6TL18+/vrrL/Xf8+fPZ8GCBbi5uakDzEXyJDzHe5/wnBLG0GzWsFGCf39KivdDon9biHmfvpi79Pk8IToqiujFv2Hu0gfzPn0lRAshhBCvUaKjUbwfYvDyQluyFFoHh0TbmOicUJ6/iAvL+fKhzZMXjZXVZ6itEO/vQzSSZsyYMdH+EN+qnZQ//vgDCwsLzMzMSJs2LVFRUZw+fZoKFSowcuRIHB0dsbOzY+3atZQvX/6d5SUnxf09zpw5Q/PmzXFwcCA8PJwBAwYwcuRIjh07Rv78+Tl8+DBdu3bFzc2NIUOGvLW/vFGZMmWoWrUqp06dAuJmTrO2tqZNmzb07NkzxUthiaRJeI73scKzUeyB/cTs3YNZw0aY1K33wctPCWOI1mTJgrlLH7Cw+PSVeBWiFV9fzPv0RZMz16evgxBCCPEFUMLCiL1xgxi334maPYuon4YRPX8e+r17MNxOetlWk3LlsRj4I2ZNmmJSpKiEZ/FVql69OgD+/v4AaiNptWrV1G2MjaSXLl3i0qVL9OzZEwA3NzdatmxJ9erV1f0hrlXbxMSESpUqJXvcESNGsH//fvLmzYuJiQkVKlTAYDCg1WpJly4dUVFRnDlzhqFDh1KmTBmqVKnC2rVrU/36Uhyge/bsqc56Fhsby/nz5ylVqhQ//fQTZ8+eZe/evdSvX5+jR4/So0ePFFdg7ty5vHjxgnHjxrFq1SqWL1+OlZUVd+/e5dGjR6l+QSKOhOd4Hzs8G0mIfkVCtBBCiP8gJSCA2EuXiNmymagpk4ka+RMxy5YQe/gQyoP7EBsL6dOjLVUajX3i1mch/i0+RCOpi4sLoaGh3L59G4hbgqpp06Zv7YFcr149QkJC2LJlCw0aNKBQoUKMGzeOoKAgnJycWL16NVmzZsXe3p4RI0YwatQodbx2aqS4C/fr3apr1KjB4MGD1XW47t+/j16vj1u8/c2ZAd/B2tqaGTNmJHr86NGjSW7fv39/+vfvn6pj/NdIeI73qcKzkXTnfkW6cwshhPgXUwwGlKdP49ZefjXhF4GBibbTODi8Gr+cD03efGgyZUKj0XyGGgvxac2dO5dx48Yxbtw4nj59mqCRNCUTQ+fIkYNVq1Yxe/ZsHB0d0Wq1jBkz5p37WVpasmfPHnLmzMmWLVuoWrUqTZo0oWfPnty6dQtHR0eyZMlCvldLvJUsWZLIyMi3ri/9Jo2iKP84XW3fvp3Fixfj4+ODhYUF8+fPp2rVqv+02I/GOAu3u7v7Z65J0pp3C3jvdaAlPMf7p+G5UR0LZv6cnqjpU5OdRCw5JnXrxU0stnfPZwnRAJqcuTDv0xfF1/ezTSyGhQXmLn3QZMnyj0K0Jnt2LIYO/8CVE0IIIVJG0etRHj1Sw7LBywvC37i20GrRZM8eN0N23nxo8+VFY5N4zVshvgZfel5KCWP3bYCwsDCOHj3Kzp07qVOnDu3atQMgKCiIdOnSqdulRIpboN+mZcuWNGnShIULF3L+/PlEM6yJT0PCc7xP3fL8JmmJfkVaooUQQnzllOhookaNSLxMpLk52ty50RgDc+7caD7H0CkhRJJeD8XW1tY0atQIOzs7KlSooD6eIUOGVJf7QQI0xE0tPnDgwA9VnEglCc/xPnd4NpIQ/YqEaCGEEF84JTgYg68vJk5OiZ7TmJujsXdACQqKnx07b1402XOgMTH5DLUVQryv18Pz+/pgARrg4cOHXLx4kVatWn3IYsU7SHiO96WEZyMJ0a9IiBZCCPGFUBQF5dkzDF6eKK/GMCvPn4OJCdop09CYmyfax7xPX7C2lvHLQogPG6AXL15M2rQy1uNTkvAc70sLz0YSol+REC2EEOIzUGJjUZ48jpvwy/PV+OXQkIQbaTRoMmdGCX6Jxs4+URkaG5tPVFshxMfi6enJ3LlzyZs37z/qOf3BAvSDBw9wc3OjXr3Ps3zPf5GE53hfang2khD9ioRoIYQQH5kSHY3h4QMUL6+4wPzgfuLfO1NTNLlyoc3zqkt2nrxoLC0/T4U/sedBETx5HkpWOxvsMvw3XrMQABMnTuTcuXO4uLigKAorVqygc+fOWKRy7oJUB+joVxMomL/WvUVRFMaMGYPBYOADTOotUkDCc7wvPTwbSYh+RUK0EEKID0gJC8Pg5RU3O7anJ8ojb3hzWVVLS7R58ryaHTs/mhw50JiZfZ4Kf0YHzj9kwda/MBhAq4V+rUtSt3yuz10tIT4Ig8FAeHg4Nq96jLi7u6uziYeGhnLhwgWqVatG//792bhxIzNnzkSj0eDs7Jyq46Q6QLu6uqLX6+nTp4/62OLFi7l48SImJiZ8++23qS1SpJKE53hfS3g2khD9ioRoIYQQ/1Ds7dvod25H8fNL/GT6DGjz5VWXlNJkyYImFcvU/FsoioKPr4FbHjFcuh7Jtv2RRIZ+Q5YCd7FKH8zCrdco7eQgLdHiX2HFihX4+fkxZswYDh48yA8//MBvv/1G/vz58fT0pHTp0sybN4/Tp08zbdo0AA4fPvzxA7S7uzuhoaHqv3fu3MncuXPJmjUrgwcPplSpUqktUqSChOd4X1t4NpIQ/YqEaCGEEO+gGAwQHYUmTeKAp0ljoYZnjWNmdYZsTd58aGxt/3MTfun1Cl4PY3H3iOGWhx73u3pu39UTHPL6tWJGACLDrLFKH4xBUfB9HiYBWvwrrFu3jqCgIDp06MDUqVMxGAz0798fKysrJk2axMqVKzl69CgzZ86kbdu2rF69Gi8vr1QfJ9UBOleuXPz+++8AuLm5MXr0aJo3b86oUaOYOXMm9+/fp1+/fqmuiHg3Cc/xvtbwbCQh+hUJ0UIIIV6j6GNQvB+p3bEN970wKVUas7btEm2ryZ4DM+cecUtK2fy3J7HduS+CcdNCiIpO/JyZKRTIa0re3Bou3PXAwioMC+swALQaDVnsrD9xbYX4OEaOHMnOnTsZM2YMGTNmpECBAmTMmJE///yT2NhYzMzMyJs3L/v27cPExISdO3cSFhaW6uOkKEDfvn2bQ4cOUb58efLkycPTp09ZtmwZy5cv59dff6VOnToYDAZevnxJWFgYgYGBaDQa0qdP/5+7+/exSHiO97WHZyMJ0a9IiBZCiP8sJSICw4P7r2bH9kR5+BD0Ca8vDI+8k9xXY2qKSfESn6Kan1VwiIFbd/Xc8tBTtJApZUokXmbLwc6EqGiwstJQqIAphQuYUkhnSmGdKfnymGJuFnc9fuB8ZhZuvYZBUdBqNPRtXUJan8W/Rr169ahXrx6//vorDx8+pFSpUvTs2RNbW1tevHjB8uXLmTlzJkOGDMHZ2Zn06dNj9h5zIWiUFMz61bt3b44ePaqGYUVRUhSMM2TIwPbt28maNWuqK/YxGQeTu7u7f+aaJK15twDcPeJ/PCQ8x/uU4blRHQtm/pyeqOlTUXx8PtpxTOrWw6xhI2L27vksIRpAkzMX5n36ovj6fp4QDWBhgblLHzRZsiQK0Zrs2bEYOvzT10kIIcQHpbx8Gde6/GqGbOXJY3jzUtTG5tXY5bxxE35ly4bGxOTzVPgT838eyy0PPTfv6Ll1V4+7Rww+T+InROvSxpJRAxO3tkdEKvj5x5Iruwla7duv0Z8HReD7PIwsdtYSnkWyvvS89C67d+/mzJkz/Prrr2zduhVfX1/c3NxImzYtrq6uWFtbU6FCBYoWLYqrq2uqyk5RC/SVK1do0aIFxYsXJ3369KxcuZKbN29iMBiws7OjW7duWFlZ8fDhQ/bu3Uu5cuW4cuUKlSpVIkuWLO/zmsUrEp7j/Vtant8kLdGvSEu0EEL8qyiKguLv/2qG7Hsonl4oL54n2k5jZ4cmb160efPHjWG2t//X92A0GBQePYnF3SOuZdn43+cBhiS3z5ZZSyGdKUWckr50t0yjIU/OlI3MtMtgKcFZ/GutWrWKvXv3snDhQtatWwdA5syZ+fvvv2nYsCG9e/cmTZo0rF+/ntDQUGrXrp3qY6ToL23Lli3kyhU/xf3t27cpU6YMDg4OLFmyhGXLljFjxgwaNGjA9evXmTVrVqorIhKT8Bzv3xqejSREvyIhWgghvnqxF84T+/ffGLy8IDQk4ZMaDZqs2eLWXs4bN0u2Jn36z1PRTyRGr+D5IJZbHjG4e+hxf9W6nNR1nVYLeXKaxIVlnRmFdHFdsTOk++/NIC7E+9iyZQvW1tbY29sTGBjI+vXrOX/+PPfv32fTpk1ERESwfPlyFi9eTPXq1WnXLvH8Cu+SogD9engGcHJy4q+//qJ79+40adKEcePG4eLiwuzZs+nQoUOqKyESk/Ac798eno0kRL+SRIjGEPtp6yCEEOK9xXp4YLh+Le4fpqZocuVWZ8jW5s6DxvK/0foZGaXQoXcgd730RCcxuZe5Oejyxo1TjhuvbIZTflMs0/y7W9+F+JgKFSrEzz//jFarxcTEhAkTJuDo6EjVqlXZuXMnK1asoGTJksydO5datWq91zFSPQu3sWLZs2cHIFOmTCxYsID58+czfPhw3Nzc3qsiIl7+PCaMHZxWwjP/nfBsJCH6lTdCdMy2rZ/2+EIIIRJRQkPjxy97eWLWth3abNkTbWdSpgzazJnjWpdz5kBjmvpJer4GQcEGbnnoCQg00LBOmkTPp7HQ8CLAQHQ02FjHTe5lnNirkM6MfLlNMDOVsCzEh/R6T+i0adOyadMmSpYsCcQNK6levTqKopAzZ873PkaKJhFLKVdXV86dO8fixYs/VJEfxZc+KD4s3MCdexKeP3d4/lSTiCVFJhZ7xTixWNasaNIkvjgRQgjxcSiKghLwAuXVZF8GL0+Up08TbGPashWm1ap/php+Ooqi8PSZIa77tYeeW6/WWX7sFzde2cZaw8X9dklO3nX5ejT2tlqyZ3335F5CfEm+9LyUEgaDAa02fvjDzJkzWbFiBYqiULBgQebMmZOop3VKvFcLdHK6detGYGAgHh4e6HS6D1n0f4r341gJz19Ay3OOrJ9vvJG0RL9ibIkeMBBN9sStHEIIIT4MxWBA8fWNX3/ZywteBiXaTvOqZVmbLx/aAv++az2DQeGhT6w6sZf7nRjc7+oJDEr6mixHVi2FdWaERyjYWCcOyN8UT7zklBDi03g9PAMMHjyY5s2bM3r0aK5cuYKbmxv9+/dPdbkftAXaKCAgAFtb2w9d7Afzpd9R6eASyOXrMZ/8uBKe4xUrZMraBRmwtNQSvXYNhksXP0s9pCX6VR3y5sXihx8/+XGFEOLfStHHoDz0ftUl2xPD/fsQEZFwI60WTc6cr5aUipv0S2Nt/Xkq/BFExyjc89Ljfjd+Juzb9/SEJ9GAYWIC+XKZUNjJLL4bdgFT0trI5F7i3+lLz0v/VExMzHutAQ0fqAU6NjYWk9fW5/uSw/PXICJSWp4/d3heNTcDfs9iyZNTi1nrNkT7+3+W2aClJfqVpGZfEUIIkWKKPgaDh0d8d2xvb9C/8VtvYYE2d564paTy5kObKxca839fC+q6beFs3xPJXS89MUlc7liYQ8ECceOUC78at6zLZ0oaC+mCLcS/QXBwMDdv3qRixYrvtf8HCdDdu3enX79+lCtX7kMUJz4xCc/xXp/9fNvuSCaPMkN5/vyzLqkkIVoIIcQ/ptcTs3QJvN7x0CZt/HJS+fKhyZoNzWsNIl+rgEAD7h4xFCloRsb0iVuIA4LixjMDpEsbN7mXcWKvwjpT8uQ0wVQm9xLiX2vNmjVERUV93AD98uVLgoKCkhxk/eLFCy5cuEBYWNh7VUB8XhKe4725dFiNynF33WO2b8WscVMJ0RKihRDii6QoCor/UwyenvDyJabfNki0jSaNJdpixdGkSYMmX1yXbI29PRrN1xsUFUXhiV/cTNjud2NeTfClx88/bnKvuRPTUb9m4gkoG9RKQ8H8cYE5exbtV/0eCCFSJyQkhNWrV1OjRo33LiNFAfrnn3/Gz8+PDRs20K5dO5YvX869e/cICAigSpUqiQZoi6+DhOd4b113Ozo60brEEqIlRAshxOeixMai+DxSJ/syeHlBWGjck1otJrVqJ9n12ty5xyeu6YcTG6vw4FFsopmwg4KTHvaWO4cJsbFJl5U/jyn583zQeXSFEF+JWbNmERISQmRk5HuXkaJvj0ePHnH37l22bt2Kh4cH586d49q1a/z999/UrFmTPHnyECUX0V8VCc/x3hqejd5Yl1hCtIRoIYT4VJSoKAwP7mPw8kLx9MTw8EHiuSHMzNDkyoU2b764sc1f8djl6GgFDy99gpmw73jqiUjietfUBPLnNVXHKhfWmVKwgCk21tK4I8R/mZubGwaDgWbNmqmPHTt2jI0bN6LRaChatOh7l52iAL1161YePXrEL7/8QsGCBVm9ejUAt2/fVtd83rt3Lw8fPsTc3BwbGxty5cpF6dKlMTWVO3xfGgnP8VIUno0kRKskRAshxMejhIZg8PRSZ8hWfHzAYEi4kZVV3NjlVzNka3LkQPMVX3M9ehzLb6vCuOmhx/O+Hn0SrceWaaBgftMEM2EXyGOKubl0wRZCJHTy5EmioqLUAO3u7s7gwYMxNTXFxcWFbt26vXfZKf6mzZQpE926dePatWvMmzcPMzMzFEXB1dWVmJgYvL29OXPmDIqiYGpqio2NDXXr1uWnn35678qJD0/Cc7xUhWcjCdEqCdFCCPHPKYqC8uIFinE5KU9PFH//xBtmzBi/nFS+fGgcHdF8ZUPongcYCAs3kCt74stPrRZ27ItvYs6QXhM/sder1uXcOUwwMZGwLIR4t3Tp0nHmzBkA7t69S48ePcicOTPTpk3jyJEjbNq0iS5durxX2SkO0OPHj2fv3r0sWrSI/Pnzs2PHDgYNGoSLiwtPnz7l6NGjTJw48b0qIT4NCc/x3is8G0mIVkmIFkKIfyb25An027clelyTOUv8DNl586H5ipYIVRQFH18DtzxiuHnnVVfsu3qePTdQvaI5S2dmSLRP1sxaBva0pkBeU4o4mZLZQSb3EkKkzsuXL7lz5w7ffPMNlStXZvPmzVy+fJl+/frRuHFjhgwZgrm5OStWrCBt2rTvfZwUB+gLFy4wZcoUqlSpwtatWzE3N6dcuXLkypULMzMz7t27996VEB+fhOd4/yg8G0mIVkmIFkKI5CkxMSgPH4KlJdps2RI9r82ZC0xM0OTIqS4npc2TF4219Weoberp9QpeD2NxfzWpl/tdPbfv6gkOSfzbqtFARGTSv7kajYbe3b6O1yyE+DJNnz6d7du3Y2pqSrp06dDr9XTr1o00adJw5coVOnTogMFgwNfXl9jYWK5cuQJA7ty5mTBhAhkyZEjRcVIcoPfu3Yv1qy/zKlWqoCgK7du3x83NjaJFi+Lh4ZH6Vyk+CQnP8T5IeDaSEK2SEC2EEHGU8HAM973UGbIVb2+I1WNSsRLadu0Tba/JmROLKdOSnDX7SxMZpeDhGT+xl7uHHg9PPVHRibc1MwVdvviJvQrpzHDKZ4K11dfV7VwI8fU4ceIE2bNnp1ixYqRPn57jx4/z5MkTYmJi8PPzo1KlSkRHR2NpaYm7uzuBgYE8f/5cHYKcUinaMiYmhpiYGPXfM2fOJGfOnAQGBjJ9+nQ2btxIlSpVUv8qxUcn4TneBw3PRhKiVRKihRD/RUpQYPxyUp6eKH6+oLzx+5I2LaRJvB4xEDeO+QsOz5t3RXDpWjS3PPR4eccmuTSUlZWGQgUSzoSdL48p5mbSBVsI8en8+OOPNG/eXP33pEmTCAgIICwsjGPHjuHr68uMGTNQFIX+/furE2U7Ojpinorv4RQF6IkTJ7Jz505y5syJlZUVERERTJ48GQsLC7Jmzcr8+fPRarX07t0bvV5PunTpqFmzJg0bNkz9KxcfjITneB8lPBtJiFZJiBZC/JspioLy1C8+LHt5ogQEJNpOY28fN24536sZsu3svujxvM9exGKfySTJ5/YeiuT8lfhGFNsMGgrrXs2C7RQXlnNmM0Gr/XJfnxDiv+H18AxQsGBB3N3dmTlzJmfPnmXUqFG0bNmSlStXUqFCBQBy5MiR6uOkKECfOXOGunXrkiVLFkxMTMiQIQPXrl0jJCQERVFwcHAgU6ZMREZG4uHhwdOnT/Hz85MA/RlJeI73UcOzkYRolYRoIcS/hRIbi/LokbqclMHLC8LCEm6k0aDJnj1+huy8edGkS/d5KvwOBoPCoyexuHvEr7F8y0PP8wADp/fYYWebuHt18wZpKP+NOYVftSw72MnkXkKIr0PevHmxtLQEoGLFiri5udG/f3969+6Nm5vbe5ebogC9YcMG7O3t1X9fv36dGTNmMHLkSCZNmsSVK1eYOXMmVatWBcDT05PcuXO/d6XEPyPhOd4nCc9GEqJVEqKFEF+76PVrMVy9Cq8NYQPAzAxt7txojIE5d240yXTP/pxi9AqeD2K55RHzasyynlt39Un+Dmq14PVQj51t4i6MzRtYforqCiHEB1eqVClKlSql/tvGxobly5czePBg5s+f/97LLacoQL8engF0Oh3p06enYMGCrF27lm3btjF8+HBWrVqFk5MT+fLle6/KiH9OwnO8TxqejSREqyRECyG+dEpwMNjYJL+eckwMWFmpay9r8+ZFkz0HmlRMNvMphEco3LmnTzAT9l0vPdFJTO5lbg66vMaJvUwprDPDKb8plmmkVVkI8e9nYmLC9OnT6d+/P8+fP8fOzi7VZbzXL0CaNGmYMGGC+u9WrVqh0+mYPXs2ixcvfp8ixQcg4TneZwnPRhKiVRKihRBfCkVRUJ4/x+DlieIZ1yVbefYM86HD0GRPPAbOtHYdqFUbjYNj8gH7M7nvrefIqWg1MN/3jsVgSLydjXXc5F7Gib0KO5mRN5cJZqYSloUQ/11mZmbMmjULX1/fTxeggUTrZBUvXpyRI0cSExODmZnZ+xYr3pOE53ifNTwbSYhWSYgWQnwOisGA8vhxwvHLwcEJN9JoUHx9IYkArXXM/IlqmjRFUdDHkmTYve6uZ9qC0ASP2WfSxgXl1wJz9qwyuZcQQiTFysrqvXtNf9A+SDlz5vyQxYkUkvAc74sIz0YSolUSooUQH5sSHY3i/TB+San79yEqMuFGJqZocuZEmzdvXJfsPHnRWFl9ngq/xmBQeOgTq07s5X4nBve7erq3s6JXF+tE2xcvbEq9/1m8tsayKQ52Sc+iLYQQIs7s2bNp0KABTk5O/6icL2sQj0g1Cc/xvqjwbCQhWiUhWgjxISlhYRjue6mBWXnkTaJFitOkQZsnrxqYNTlzofnMveSiYxTuecWNUzYG5tv39IQn8Zvl7pH0b3qenKbMm5z+Y1dVCCG+OlFRUVhYWCR6PDg4mGXLllGyZEkJ0P9lEp7jfZHh2UhCtEpCtBDin1CCg9H/+UfcGsx+vok3SJfu1WRfr9Zfzpr1s45fDgs3cPtewiWj7nrpiUni5zqNBTjlN6WQzkzthu2UTy7ThBAipebNm8eTJ0+YMmUK06ZNY9CgQbx48YKwsDAcHBwwGAwfZBk++Wb+Skl4jvdFh2cjCdEqCdFCiPdmbkbsmdOgxH3Paxwc1MCsyZsPTaZMn22NYr1e4fyVGNw9YnC/E9fC/PBRrLGqCaRLGze5V1z3azOKOJmSO4cJpjK5lxBCvLf169cTEhJCq1at2LBhA9WrV+fUqVPcv3+fBQsWkDlzZmLeXJrwPUiA/gpJeI73VYRnIwnRKgnRQojXKXo9yqNH6oRfGAyY9+qdaDtNGktMmzRFk8kObb68aGzSfobaJq/3sCCi3lg6ysFOq45TLuJkRiGdKdkyaz9b0BdCiK9RaGgoa9asoU+fPsluc/DgQU6ePMnMmTMpUKAAe/bsQa/X8/fff3PlyhUyZ87MjRs3sLOzw9zcHBsbG7Jly4ZpKpcmTNHWY8eOpUOHDhQsWDBVhYsPT8JzvK8qPBtJiFZJiBbiv0uJjMBw/4EamJWHD+PWXDbSalGio9GYmyfa17RmrU9Wz9hYhQePYuMn9vLQEx6hsHW5beJ6mWqoUSVu3F2R19ZYzmT7ZS2BJQTEzVL/pS3P9l/2b/w8AgICmDRpEtbW1jx//pz+/ftTqFChRNudOnWKX3/9lUePHlG0aFFmzZpF5sxxqyB07tyZCxcuqNu2bt36rcdMly4dDRs2pGjRoqxYsYItW7aoz3Xs2BFFUbh27RpLlixRb2KamprSvn17Ro4cmeLXlqIAff/+fZo3b06xYsXo0KEDDRo0wDyJHzXxcUl4jvdVhmcjCdEqCdFC/DcowcGvJvt6FZgfPyZR32Zrm7jJvl5N+EUqWwT+qehoBQ8vfVz3aw89tzxiuOOpJyIy8bahYQZsrBNf7M6dKBN7ia+DRqsles1qlKd+H67MzFkwa90G5flzYrZvhejod+/0oZmbY9ayNRo7O2K2bkl6roRPQFuhImZVqxFz8gSGc2ffuq3GMTPmXbp+opp9OoMHD6ZUqVIMGDCAs2fP0r17dw4ePEjatPE9h/z9/bl48SLz5s3jwoULjB8/ngMHDtClSxcA8uTJQ7169dTtU9KYu3DhQhYtWsS0adM4efIkU6dOZd68eXTu3BlfX1+uXLmCi4sLiqJgYmKCjY0NWbJkSdVrS9Gv09q1a/H09GTjxo1MnjyZX3/9lebNm9OuXTty586dqgOK9yPhOd5XHZ6NJESrJEQL8e+iKArKs2dxQdk4Q/bzZ4m202TKhCZvvvgZsh0cP1m35tAwQ/zEXnfj/ut5X48+NvG2lmmgYAEztRt2YZ0paSyk+7X4+ilP/VB8fD5ceT4+RPv7Y96nL2aNm3623/PoeXMwd+mDWavWn+/6attWCA7GrGEjYoKDP8v11YcQGxtLrVrJ9/o5fPhwko9funSJM2fOqN2ty5YtS0hICOvXr8fFxUXdzsrKioEDB6LRaLC2tmblypU0aNBAfd7JyYmOHTumqs7btm2jZcuW1KpVi02bNlGuXDmKFStGiRIlyJo1KydPnqRw4cKpKvNNKb69my9fPkaPHs2QIUPYs2cPmzdvZvXq1ZQrV44OHTpQu3ZtTExkDcKPQcJzvH9FeDaSEK2SEC3E183g74/B/Wbc+stenhASknADjQZN1qyvWphfTfqVIcMnqVtAoIEbd2ISzIT90CeJpAxkSK9RJ/YyzoSdO4cJJiYSmIVIiS/i91yurz67EydOAJApUyYgrpt0hgwZOHbsWIIAbWNjA8CjR48YM2YM/fr1I2PGjOrzly9fZv78+ej1ekqWLMmoUaPIkyfPW489ZcoUypcvD0C7du0AGDhwIP7+/mTKlImbN2/+49eX6v5RadKkoVWrVrRq1YqbN2+yadMmfvrpJ6ytrWndujVt2rRJVTP4h+gf/28m4Tnevyo8G8mXvOqL+NH9j4mNVSQYfEG+5s/DcONv9L/vin/AxBRNrpxxYTlfPrS586CxsvosdZuzLJTNuxL3w87iqFVnwi7sFNfCnNlBJvcS4p/6In7P5frqgzAxMUm2lfltAgICADAzM1MfMzMzUx9/nZeXF7t27aJQoUKMGTOG7du34+rqilarpXz58jRs2JAnT56wYMECevXqxd69exOU+yZjeAaYPHkyefPm5cWLF/Ts2ZPVq1djb2+f6tfzpn80wKhIkSJMmDCB4cOH8/vvv7Np0yaWLl1K1apVad++PdWrV39nGR+if/y/lYTneP/K8GwkX/KqL+JH9z/ExETDkPEv8XyQdGtcalim0TBmsA05s5kwfnoI9+7/8zLfR6vGaejY0or128PZtjuJwaufQP48Jowfmhbvx7FMmBlKROS7v6/y5TZhxvgvc/ysEhamtixrdTpMCiXu+qYtoENbuHB863LOnGjecoHzT+n1Cl4PY3H3eNWyfFfPkN42lCiS+JjFCppxMVfMa12wzShYwBTbDP+uCXuE+JJ8Eb/ncn312RhbkcPD43NDTExMko2fefPmZdCgQUBcyF6yZAn37t1Dp9PRtm1bdbtMmTLx448/4unpmexY6DVr1rBr1y5KliyJlZUVYWFh/Pbbb1haWlKmTBnu3btHnTp1cHV1JSYmhvTp01OxYkVy5MiRqtf3QWbosLGxoWPHjnTs2JFLly6xadMmBgwYwLVr196634fqH/9vJOE53r86PBvJl7wqyR9d8dF4PoibYfifMH5fZc9iQtf+n/f7qmNLq8/+fTV2cFru3Ps6v68URYHAwIQTfvm9NslQRETSATpHjiSXnfoQIqMUPDz13LwTN7GXu4ceD099ouWirrvHJBmgWzexpHUTy49SNyFE8iREx/sSrq8+perVq7N06VL8/f1xcnJCr9fz8uXLd45ndnR0BMDSMvF3doECBQCwtrZOdv/NmzcTGhrKzZs3MTExIVOmTBw4cAAAjUaDv78//2fvvsObKts4jn+TdA9Wy6Zl7z1lyAYRZMlSERAcyEZBRGQKgoKIArIREGUIgrws2XtvEMoGoWxoS+keSd4/0iYtM0nT5DS9P9fldZnT9py7wPk9z51z8pzExERCQkKMzX2BAgXYtm2bRb+fzZe4rFatGtWqVePx48ev/F5b3R//PC/7wLtWq1X057U9PaR5TpYpmudkEvJGTw+6Cev+Z/cahHnkzT6TjJhXep0O/b176K5eMX1++Tnjtyp3bsPV5XLl0rWe8Cc6zl9OTPq8suHq8rWbWrTPuaHBy0tluAU76bPKr1WVp4MIoTTSRJsoYX5lL9WqVaNu3brs27ePunXrcvz4cby9venUqRM9e/akdu3adO/enbVr15I7d27jbdd79+6lU6dOBAQEcPDgQeLi4mjQoAFg+Dz022+//dKrxYMGDUrVA+7bt4/58+fTpEkTpk+fjpeXF9OnTydfvnxcv36dgwcPmnXH9NPS7RkR2cxYHMRW98c7m5GDfSiQVyOT0Qw4GU0zCXmjlIOua/uXP/dPOIY0zyYZJa/0iQnobwYbri5fvYru+jWIiUn9TWo1qoCApNuxDY+VUvn4Pn+H1tah1/PgUeqVsM9dTOD2Xd1zvz9HNhVlSrgabsEuafjccmB+DWq1fF5ZCKWTJtpECfMre5k6dSqjR49m9OjR3L9/n/nz5+Pl5cXly5eNTXBISAiTJ0+mbdu2uLi4ULNmTeNV6idPnjB16lR27NhB+fLliYmJYezYsS895tMXUMuXL49araZLly60atWKCRMm8MEHH/DXX39RuHDhVy5I9iIqvf7pBzHaz48//sjcuXNZu3YtJUuWBKBOnToEBASwfPnyF/7clClTmDNnDuvWraNEiRIWHzd56fKgoCDrCk9nUdE6h98GKZNRaP+WBxOGZyHuh4k2fcyDWdzdcevVB1XevA4LeQDNG80Mj2DYsN5hIa8KLIhb336oPDwccnxn93b3UKtu4Zbm2cQWeVWmhAt/L8ph89r0MTHo/rtuvCVbf+MGJD71d+XmhrpQYUOjXLQo6oKFULm727yWlP49n0CHj8Ke+7X8edWmZjnpv1z+sriXELZm7/mNKrAgbn36or9713FrnChwfqULOof7kKEOqeNllN4vmev06dNUrFjR+HrJkiWcPn2aSZMmWb3PdLsCbY70uD/eGYz5IUImow5unr29VHzSNenfl5sDbguUd0qN9DdvkLByBW5dnXvRwIxEmmcTJeTVy8T/+AP6h089g9nHx3R1uWgxVPnzo7LRx5oSEvVc/U/L+UsJnLuYSO6caj7p8uzn1YoXccHVFQoW0CSthG1omEuXcCFbFue7s0wIIVeiU0o5v0rIksXux89MUjbPAO+//z65c+cmMTERFxfrWuF0aaBjY2O5du3aKx9SbYv7452Ro1avlcmoQXJzkCenYULp2r4j8dN+lpB3ZBN9767djymeT5pnE0fnlV6vR//gAbrr19DUeA3Vcz7SpCpcGPR6VEWKoC5SzHCVOVcum1zNjY7Rc/FKYqqVsC9fSyQ+xeJepYu7PLeB9nBXcXxLTtzd5aqyEJmJNNEmT8+vhP00adIkTT+fLg3033//zc2bN1/ZQEPa748XtiGTUYOUzcGcxVF89qkvKn9/CXkFNNHC8aR5NnFEXum1WvS3b6dY8OsaREYAoC5QAFWBZ99Udn3nPVRWvsOe0uMnps8rB11KIOhiIv8Fa9E95yPLPt6Gxb1Kl3ChQpkXP8pKmmchHEtdsxbav1ba/bjSRJtot2yGLFlwrVvP7scW1rNqVO3Tpw9jx47F39//ma/p9Xp+//13ChUqZNa+vL29mTx58jPbd+7cafz/Hj160KNHD2tKFWbIrJPRpz3dHBQsYLgCnbByBa4dOkrISxOdqUnzbJIeeZXVJZSY/27jmiMfLlkMT6bQx8ej++86+mvXDJ9h/u86qS7vAri4oCpYEBKe//dhafOs1+u5/1CX9Mgo09XlO/efv7hXTj+14bPKxV2Mn1kukE8W9xIiI3CtWw+ePHH4QqGZfX6lO3QQpIHOUKxqoE+fPs3WrVspXrw4ZcqUwcvLy/i1VatWce3aNfLly2ezIkX6cdbJqKWe1xwkN9D6e3cl5JNIE505SfNskh559UbAfgYWWsL931W4JbiRtUB1XCK16INv8swlXk9P1IWLoC5aFHWRoqgCA1C5vPgqr6W69HnMsdMJz/1aQL7Ui3uVLuFCLn/lPhJSKI9Wq0ejkTdXlCJh7x6Hr3Ei8yuREZnVQD9+/JipU6fSv39/cuTIQceOHVm9ejVBQUG4uLhQuXJlmjZtSqVKlZg0aRIuLi506tQpvWsXaeSsk1FLmdMcSMibSBOduUjzbJIeeeXvEcrQEn+RJcwPt8SkBQvDr2Lcc7ZsqRf8ypPnuZ91fpX4BD1Xrhk+p3zhUiJf9vfBzfXZRiZfHjWas1C0oIbSJVyNj4wqXdwFX5/Ms7iXXqez6s9ZvJxGo+KLMeFc/S991nrx9FAxcrAPgfk1jPkhwmFrynRo5cH77b1Ysiqav9bFOqSGYoU1jBniy83bWsb9GElMrCmv6tVy5fNPfdEdOkjCkyfSRCtkfiUyDrMa6J9++ok///yTNWvW0KlTJ5o3b868efMoX7482bNn5/79+0yYMAGdTkfu3LmZN2/eMyueCWVx1smopSxpDiTkTaSJzhykeTaxfV7pqZXlGkMKbsL/ienxVQmaBOJd43H38MPFIxtoNOgf3Ef74D7aQwfN2nN0ogsXw3NwQVeYS74VCboQx+UbehJ1pqvFLUOWUzpb6DM/+7nGgxFvJeChSWo8bib9tw2sSjs3N1zbd0Tl70/CyhUOWxBQXbMWrnXrkbB3j+F2yZdQ5c6DW7cP7FRZ5nP1P61Vj857leS8KpBX4/BHgb7f3svheTVqsC8Xrzw/r4oUNGWBEsZzmV8JW1m/fj3NmjXD1dV2d2Y9j1kNdEREBP379+f27dv873//488//yR79uzGZzWvWbOGCRMmUL16dQ4cOGD1kuDCPpxzMmo5a5oDCXkTJQy6Iv1I82xi67xyUWmZXWIJ1bMYzlsdOqK8oojyikKn1oEecod4wGPDlauXHS0swZML0Xk4H52XC9F5uBCdh5uxOdCTfHU5ATBcSfXVxFLK6y6lvO7h9SgYfeSzz2FObuVtmcjx037GrVcfXDt0dFxe/bUSkq60JTjoM58i/UhemViTV0oYz2V+JWxh1KhRTJgwgfbt26frE5vM6nSnTJnCkSNHqFu3LmPGjGH9+vUsXbqUiRMnkj9/fpYuXcrChQspW7Ysu3fvZty4ccbmWihLRg13W0vLYCshb6KEQVfYnkxGTdIjrxL1Gh4k+BKn07DiQTWuqV35qOxfqFWGzztnjciKRpf6s8V6PdyNz2pski9E5+FCVG7uJ2R97jFyuj6hlNc9Snndp3RBLRX6vUU+fSQJc1ZKXkleORXJK5O05JUSzg+ZX4m02rt3L//73/9YsWIFv/76K7Vr16Zz5840bNjQJo9vTGb2FeiBAweSLVs2evTowe3bt1m2bBmDBg3i8ePHtG7dmthYwzvl9evX58GDB9y6dYsCBQrYrFCRdhk93G3FFoOthLyJEgZdYTsyGTVJz7z6ObgxPwc35kFCFgBUxSryfvNHZNu9B+/Y1Lc5bw4tw/j/mhOu9Xrergh0D6GU131Ked9Naprv4eea4s9MC6q/b6KWvJK8cjKSVya2yCslnB8yvxJp4e3tTefOnencuTMnT55k2bJlfP7552TPnp1OnTrRsWNHcubMmebjmLVChru7O99//z3Vq1dn+fLlnD59mg0bNhAUFMTHH3/MtGnT6NKlCx9++CHr1q3jhx9+YMKECWkuTtiOs4R7WtlysE0OeVXevLj16gPu7jas1ExJIa+/exe3Pn1RBRa0fw0YBtmEDetxfaslmjeaOaQGkXYyGTWxRV4V83xAVd/nT7oeJGQxNs++Oe+z/+YjBi6Ko/ejMmxXpX6KRTaXaMK1XriotJT0vEdr/1N8GbiJhaUWsb/KRNZVmMkPxVbxUd4D1Ml6LXXznETyykTyyjlIXpnYcn6lhPND8krYQuXKlZk0aRJ79uzhgw8+YO3atTRs2JABAwZw8KB5a4q8iFkN9MiRI/nmm2+4e/cuer2es2fPsnz5cipVqsTjx4/54osvqFmzJjExMXz55Zf4+PgwduzYNBUmbMfZwt1a6THYSsibKGHQFdaTyahJWvOqoHsI3xVZzcqycxhTaB0uqhevBKxSJxIdloN7l0sQ8cgfPSrmqksTgilLynvfZnmZeRysMpEV5eYxrvA63s99lCq+wfho4l+476dJXplIXmVsklcm6TG/UsL5IXklbCVr1qz06NGDTZs2MW/ePFQqFZ988gnNmjVj0aJFhIeHW7xPsxroffv2UbBgQfLkyUPZsmXRaDT8+++/uLm5MWzYMNatW4dGo+H333/Hw8ODJ0+ekJjomDATqTlruFsqPQdbCXkTJQy6wnIyGTVJS17ldwtjbKG1/F1+Fi38zqFWwYXoPHirX3z7oV7ngjbRFRe3ONw8YwDQqVTcU5lu1/bSJFDa+x5u6rQ/kkfyykTyKmOSvDJJz/mVEs4PyStha7Vq1WLq1Kns2rWLNm3asHjxYurVq2fxfsxqoGfPns3ChQsJCQnh7Nmz1KxZE41Gw7lz5yhTpgylS5fm8OHDvPfeezRq1IiOHTvy22+/WVyMsC1nD3dz2WOwlZA3UcKgK8zn6SGT0WTW5lUu1yeMKLiB/5WfSZucp9Go9OwKK06ns58w5GqHF352GUCl0pG3xAUKVzlBtjz3AVDr9eTRp9+fgeSVieRVxiLNs4k95ldKOD8kr0R68Pf3p0+fPmzfvp2pU6da/PNmNdDly5fn8ePHHD9+nC+++IISJUoYV+AGmDBhAoUKFaJBgwZ8+eWXDBw4kGPHjllcjLCdzBLur2LPwVZC3kQJg64wz8jBPjIZxbq88nOJ5MvAzayv8Asdc53AVa3jQHgRugR9yMAr73IxJs8r96HXq4l6nJ3kxUHV6OmpO4+fdU9dNpvklYnkVcYgzbOJPedXSjg/JK9EelGpVDRo0MDinzOrgQbD/ePbtm3j9ddfJzAwkBo1avDLL7+g0+k4cuQIuXLlAiB37tx4eHjw+uuvExUVZXFBIu0yW7i/iCMGWwl5EyUMuuLVAvNrZDJqYV5lc4nmswLb2FBhOu/nPoK7WsuxJ4F0P/8BvS+9z79R+S06/pMHuSmVrRYTetdhVs6rNNbfScuvYzbJK5OUeaWuWcshNYgXk+bZxBHzKyWM55JXQknMbqBVKhW+vr4AtGrVCj8/PzQaDVOmTCE62hAiRYsWNX5/r1698PJ68W1rIn1k1nB/miMHWwl5EyUMuuLlxvwQIZNRM/PKVxNL3/w72VhhOj3yHsRTk8jpyPz0vPg+H13sxsnIQKtq8PZS0eeD7JQv5o+fxr5/F5JXJsa8qmv55+FE+pHm2cSR8ysljOeSV0IpzG6gX8TFxYUGDRowbNgw3nzzTeN2d3d3mz6wWrxaZg/3ZEoYbCXkTZQw6IoXu3I97QtTWSMj5ZWXOo5P8u5lY4Xp9My3D29NPOej8tDv0rt0O9+Dw0+KANaNd8l5lSenJg2/SdpIXplot2wmYe8ehxxbPEsJ4zlkrLxKT0oYzyWvhK2FhIRY/DNpbqCTFStWDK1Wy/Xr1221S2EBCXcDpQy2ICGfkhIGXaEcGSWvPNQJdMtzkI0VptOvwC6yuMRyJTong6504N2gj9kbXhxrG2dInVdzFhs+8uSo24clr0x0h9L2fFBhG0oZzzNKXtmLEsZzySthKzt27GDZsmUW/5zNGmiAtWvXsmHDBlvuUphBwt1AKYNtShLyJkoYdIXjZYS8clFpeS/XEdaX/4XBAdvI7hrDfzE5GHr1bTqe68n2sNKkpXGGZ/Mq+I4OANe69WRSqoC8Eo6llPE8I+SVIyhhPJe8EuaYO3cucXEvXpTz999/5+rVqxbv12YNtFarZdasWdy9e9dWuxRmkHA3UMpg+zwS8iZKGHSF42SUvNKg48O8B8jpFsntuGyMvNaadmd7sym0HDobDJsvy6uEvXtkUqqQvBKOoZTxPKPklaMoYTyXvBKvMn/+fHbs2EFsbOwzXzt27BgHDx7k8ePHFu/XZg308uXLuXnzJk+ePLHVLsUrSLgbKGWwfRkJeRMlDLrC/jJSXsXpXZkS3Jhx/7Wg9b99WBtSEa2NhstX5ZXu0EGHnx+SV8JRlDKeZ6S8ciQljOeSVyIlrVbLP//8Y3zduHFjNm3aRN26dXnvvfeYNWsWly5dIiYmhtGjRwNQvXp1i49j8YzgxIkTnDhxItW269evM3nyZFQqFXnyvPq5lyLtJNwNlDLYmkNC3kQJg66wH6XmlQo9nur4537/P6Hl+ethVRL1tlvcy9y8UsL5IXkl7E0p47lS80qpJK+SSF4pwqJFixg0aBDvvPMO27dv5/3332fz5s1ERERw6dIlpk6dSps2bXjjjTcIDg5m6NCh9OnTx+LjWNxAr1mzhpUrVxpfP378mH79+hETE0Pt2rXp16+fxUUIy0i4GyhlsLWEhLyJEgZdkf6UmVc6GmW7wMqycxgUsM0uNViaV0o4PySvhL0oZTxXZl4pt3lOJnmVRPLK4VasWEGNGjUICQmhb9++DB8+HH9/f86cOcPx48dZsWIFPj4+JCYm4ufnR+vWra06jsUNdEJCAhcvXgQgMjKSnj17cvv2bYYPH07Lli05fPiwVYUI80i4GyhlsLWGhLyJEgZdkX6Ul1ePqex6iWVl5vNT8ZUU93pIk+znX3gV2laszSslnB+SVyK9KWU8V15eZYzmOZnkVRLJK4favHkzkyZNYtmyZaxevZpKlSoRFxfHnj172LdvHz179qRfv37s27ePVq1aMW7cOKuOY3YDHR9vmGBUr16d69evExoaygcffIBOp+Pvv/+ma9eu7N27lyNHjlhViHg1CXcDpQy2aSEhb6KEQVfYnqLy6moCs0aeZFbBBcwosZwy3veI0rox987rtP63DzE6t3SrIa15pYTzQ/JKpBeljOeKyqsM2Dwnk7xKInnlMFqtll69ejF8+HDCw8MpW7Ys//zzD2vXrmXevHkMGDCAdu3aodFoGDRoENmyZeP+/fsWH8esBnrGjBlUr16dbt26sXnzZmJiYujQoQPBwcFUqVKFlStXMnHiRK5cucLBgwcZN24c3377Lb///juJiRmvuVEiCXcDpQy2tiAhb6KEQVfYjpLy6taBS8RMn870Qn9Q0ec2sToXFt2txVtn+jHjdkMitJ7pVoOt8koJ54fklbA1pYznSsqrjNw8J5O8SiJ55RBRUVHUqVOHO3fuMG/ePHbt2sWFCxc4dOgQkyZN4ueff6ZZs2bMnj2bu3fvcvv2bebNm2fxccxqoDdu3EhcXBzXrl3j1q1beHp6cufOHSIiIvj777/ZuHEj//vf/7h58yZXr15lyZIl/PHHH/z000+EhIRYXJRITcLdQCmDrS1JyJsoYdAVaaeUvFo0JJy4mTMpvGEGVb1vEK/TsPR+dd4604+fbjUhLNE7XWuwdV4p4fyQvBK2opTxXCl55ej5la1JXiWRvLK7v/76i0ePHtGqVSuioqI4e/Ysa9asoU2bNoSFhVGvXj2yZcvG/Pnzadq0KceOHaN58+YWH8esBrpixYrs2LGDffv2sWHDBt58802qVKmCn58f8fHxtG/fnr1797JmzRrKlCnDxo0b+fbbb1m5ciW5c+e2uChhIuFuoJTBNj1IyJsoYdAV1lNCXr1V9hGLq63EZdYUfG5fJEGn5q8HVWj5b18m3nyTRwm+6V5DeuWVEs4PySuRVp4eyhjPlZBXSphfpRfJqySSV3a1fPlyNm/ezOrVqwkLCyM0NJR//vmHvXv3Mn78eNzc3ChRogRbtmxBo9Hg5+dH2bJlLT6OWQ30hAkTyJcvn/F12bJlKVu2LNu3b+fDDz9kzpw5dO/enaxZs1KoUCGKFClChw4dKFq0qMUFCRMJdwNnbp6TScibKGHQFZZzdF4V9njI3EqrmeA1C/WFs2j1Kv73qAJtzvZh3I23uB+f1S51pHdeKeH8kLwSaTFysI/Dx3NH5xUoY36V3iSvkkhe2c17773H4cOH6dixI9WrV+e1115Dq9Xy2muv8fDhQ44cOcLevXuZO3cupUuXplChQixfvtzi41i8CjdAkSJFyJ8/P+7u7gwcOJBly5Zx8+ZNvvjiC+NDqUXaSLgbZIbmOZmEvIkSBl1hPkfmVYB7KOMLr2F1uTm85noOPSq2hpel3dlejLrehttx2e1Wi73ySgnnh+SVsFZgfo00zwqYX9mL5FUSySu76NGjB4mJifz8888EBARQtmxZ+vfvz759+2jbti3btm3Dz8+Pe/fuMXToUMaPH8+mTZssPo5VDXSVKlV47733jK/Lly/PihUruH//Pv/88481uxQpdGjlIeFO5mqek0nIm6QcdNU1azmkBvFqjpqM5nV7zKhC61lTfiYt/f9FrdLzpGB5ul7pyReX2vFfrL/dagH755VMSpMoJK+E+cb8ECHNs4PnV+5uKrseT/IqieSVXWg0GhYuXEivXr3InTs3LVq04M8//2Tjxo3cvHmTvHnzUr16dSpXrkzu3LkpWrSo8WlT5rKqgfbw8MD9qX94uXPn5tdff+Wvv/4iLi7Omt2KJO+398r04Z4Zm+dkEvImxkG3bj2HHF+8nKMmo7lcn7C2/Eza5zyJi0pPkLoE19p8xpsb3+bfsFx2qyOZo/JKJqVJFJJXwjxXrmsdclxpng28vVR80jX9nj7wIpJXSSSv0p27uzvVq1cH4O2336Zw4cL4+/szf/58Dh48iKurK9HRpgzo27cvKpVlbypZ1UC/SO7cuZk8eTIXLlyw5W4znSWrojN9uCuheW5cL/2eDfsqEvIm2i2bSdi7xyHHFi/myMnog4QsHAgvwuEnhVmSpxf33vqYLhOyZMq8kklpEoXklVAmaZ4NkvMqT06N3Y8NkldGT+dVnrz2ryGT8PLyMv5/rly5eOedd2jbti0ffvihcXu+fPlwdXW1aL82baABChUqRMWKFW2920zlr3WxDjmuksLd0c1z7+5etGhseIfWUbcPKzLkHTQp1R066JDjiudTwmR0WnRHykweSOW3ijPi+8hMnVcyKU2ikLwSyqKEvFLa/GrO4ii7Hz+Z5FWSFHnl2rGT/Y+fibVs2RKNRkNUlPXngU0a6G3btnH//n1b7Eo4iNLC3dHN82c9fdi4PQYA17r1JORlUiqS2Gsy6qOJpVe+3dTJeuWZr5Uv7cKfi3ITfFub6fMqmUxKk0heiRSkeTZ4Oq+C7+gAHHblU/IqSXJePXpk/2NncocPH2bZsmVW/3yaG+i4uDi+/PJLgoKC0ror4SBKDHdHN88/z41k+x7DggIJe/dIyMukVGCfyainOp4P8+xnY4Xp9M6/h4EFtqPClEmSVy8mk9IkklcCaZ6TvSyvXDt2cvwaJ5JXJKxaaf/jZnK//PILt27dsvrnzWqgN23axPz58wHYunUrAFqtltjYWKKiooiOjrb4w9dCGZQe7vb0osFWd+ighDzIpDSTS+/JqLsqga65D7GxwnQGBuwgq0ssV2P8mXenrvF7JK9eTSalSSSvMjVpng1elVf6R48cv8aJ5BVYuAK0SJsDBw5w9OhRQkNDrd6HWQ305MmTmTFjBnfv3mXo0KFcuHCBuXPnMmHCBHLkyEGWLFlITFTWJEK8WkYId3t51WArIZ9EJqWZUnpORl1VibyT6yjrK/zCF4FbyeEazc3Y7Hx9rQ0dzn7K1rAy6FFJXllA8iqJ5FWmJM2zgTl5lbBqpcPPD8krkV7u3bvHvXv3Um2LjIxkxIgRqFSqVAuMWcqsBnrYsGG0bt2ab775hjx58rBv3z4SEhI4duwYAOXKlUu1HLhQvowS7vZg7mArIZ9EJqWZSnpNRl1UWt72P8na8jP5uuAmcrlFcicuK2Out+Tts73ZEFIBXdIQJXllOcmrJJJXmYo0zwZm51V8vCLOD8krkR5+++03FixYYHyt1+sZNmwYd+7cwd/fnx49eli9bxdzvqlx48Y0btyYHTt2sGLFCn7++We0WsNz/MqVK4dWq+XgwYN8/fXXuLm54ePjQ8GCBWndujUdO3a0ujiRPjJUuKczSwdb7ZbNALi+1TLVa3tKDnm3Pn1x69WH+Nkzwd7PXk+alLr16oNbn77Ez5yB/uYN+9Yg0l16TEbV6Gjud5Ze+fYQ6BEGwIN4H+bdqcvfjyqRoE89LEleWU/yKonkVaYgzbOBxXmlkPND8sr5hIaGMn78eLy9vXn06BH9+/endOnSz3zfvn37+O677wgODqZcuXJMmTKFPHnyALB//37++OMP/Pz8UKvVfP3113h4eJh1/Pv376da5Hr06NFs3bqVpk2b0rZtWxISEqz+3cxeRGz79u0sXLiQZs2a4eXlRd++fSlcuDAfffQRb7/9NpUrV+aLL76gf//+fPzxx7z99tuUK1fO6sJE+siQ4Z5OrB1s5Z3SJHJlx6nZejKqQk/T7EH8VW4OE4r8j0CPMEITvPjhZlNanunHiofVpHlOB5JXSSSvnJo0zwZW55VCzg/JK+cyePBgChYsyNixY+natSs9evQgIiIi1fc8ePCAo0ePMm3aNIYNG8bx48fZsmULADdv3qRPnz58/fXXfPvtt8TGxjJ+/Hizj1+uXDmuXr0KwKhRo9i4cSPff/8906dPZ8uWLfzzzz9W/25mXYEG+Pnnn3F3d6du3bosX76cfv368eDBA1q3bk1YWBiLFi2ie/fuVhci0l+GDncbS+tgK++UJlHIO9fCtmw7GdVTP9sl+ubfRUmvBwCEJ3qw6G5tlj2oTozO7bk/JXllO5JXSSSvnJI0zwZpziuFnB+SV8qi1Wpp3LjxC7++ffv2524/duwYBw4coE+fPgBUr16diIgIlixZQq9evYzf5+XlxWeffYZKpcLb25sFCxbQokULAObMmYO/vz8BAQEA1KpVixEjRtCrVy/y58//3ONu2rSJJUuWULNmTeLi4ggPD6dXr16cOHGCQYMGkT17dnbu3Gm8Or1582ZUKhWBgYGUKlXK7D8Xsxvo5s2b0717d7y8vHjvvfcA6NGjB4ULF+bOnTvyGCuFc4pwtxFbDbYS8kkUMugK27DdZFRP7SzX6Jt/F+V87gAQkejO7/dfY8n914jUvvgWLMkr25O8SiJ55VSkeTawWV4p5PyQvMr49uzZA4Cfnx8ALi4uZMuWjV27dqVqoH18fAAIDg5m5MiR9OvXj+zZswOwe/fuVI2yn58fiYmJ7N+/n06dOj33uIsWLeLUqVMcPXrUuG337t0AjBs3DjB8FhpApVJx+PBh4/dt2bLF2Ky/itm3cPfp08e4WtmSJUu4f/8+8fHxNGzYkGvXrhEbG2vuroSdOVW4p5GtB1u53SiJQm7/Emljq/OjgHsoC0v9xqySSynnc4cYrSvz79ShxZn+zLlTX5pnB5G8SiJ55RSkeTaweV4p5PyQvFIGjUbD9u3bX/jfiyQ/IsrV1dW4zdXV9bmPjrp27RorV66kdOnSjBw5kh49eqDT6QgLC3vm5wFCQkJeeNzw8HC+//57Nm7cyP79+2ncuDE5c+ZEr9fj5+fH2LFj2bBhA1OmTCEwMJDRo0fTqlUrvvvuO7ObZzCzgT548CATJ05k8+bN7Nmzh6tXr7Jy5Up2795NoUKFyJs3L/369SMoKIgzZ87w33//mV2ASF9OGe5WSq/BVkI+iUIGXWEdW54fYQneFPF8RJxOw+/3XqPFmf5Mv92IJ1rPl/6c5FX6k7xKInmVoUnzbJBueaWQ80PyKuNKvoqc8ilNCQkJ5MiR45nvLVKkCIMGDWLo0KF0796dw4cPc+XKFbJnz/7Mz4Ppqvbz/PPPP7Rt25YiRYrg5+dHxYoVadmyJatWraJw4cKMGjWKtWvXUr9+fQoUKMC7777LpEmTaNu2rUW/n1m3cE+dOpVTp06l2jZjxgzj/7ds2RK9Xo9KpTJuK1myJGvWrLGoGGFbTh3uFkrvwVZuN0qikNu/hGVsfX5E6dz56urbXI3JyYOELGb9jOSV/UheJZG8ypCkeTZI97xSyPkheZUx1a9fn7lz5/LgwQNKlixJYmIi4eHhvP/++y/9udy5cwPg6elJ/fr1jbdfg+GqtkajoXbt2mbXUbRoUf7991/Kli3L77//zm+//cYPP/yATqdj8ODB1v1ymHkFumrVqmzevJkzZ85w7tw5Jk+eTNmyZWnatCl6vZ633nqLLVu28NdffzFmzBiaNWtG//79rS5KpF2mCHcz2WuwlXdKkyjknWthng6tPKw+P4p53ier5vk/c/BJUWmeFUzyKonkVYYizbOB3fJKIeeH5FXGU61aNerWrcu+ffsAOH78ON7e3nTq1ImePXuyaNEiANauXZvqc8h79+6lU6dOBAQE0KtXLyIjI7lw4QIAhw4dok2bNhQoUMDsOipVqmRcuwvggw8+YM6cOSxbtuyZFcEtYVYDPWTIEAoWLIibmxsajYZq1arh6enJtGnTWLRoEefPn+fbb7+lTJkyvPPOO/z8888vXbFNpK9MFe6vYO/BVkI+iUIGXfFq77f3svj8KOTxiIlFVrGy7Fx65D2QpuNLXjmO5FWSp/MqT1771yBeSZpnA7vnlULGc8mrjGfq1KmEhIQwevRoFi5cyPz58/Hy8uLy5csEBwcDhs8zDxkyhClTpjBt2jRq1qzJqFGjAAgICGDhwoX89NNPjBo1CrVabfyaufz8/IxXtZPVqVOHWbNmMXnyZKt/N5U+eSkyC/3111906NABMNzfPnz4cIoWLUq/fv0s2o+5D9meO3cuS5cuJTw8nKpVqzJ27Fjy5ctnTemUKVMGQLErh7/dPZSgS9YFYqYM9xdI62Dbsqk7P36TlbgfJqK/dcuin9W80QzXt1qSsGG9Q243AlAFFsStT1/0d+867nYjd3fcevVBlTdvmm7/UhUogPuQoTYuTgAsWRXN2B8jzfre/O5h9Mq3h7f8/kWjMmTL/x5WZNR/rQDVy3/4OSSvTH4a50uLxp5W5U1aSV4lSc6rfPlQebx4oTuRNtbMcaR5NrBVXlk1v7HReJ5WzphXSp3jKL1fSqsdO3bg6+tL9erVLf5Zs1fhflpy8wyGZ3j99NNPJCQkkJho2clszkO2V65ciVqtZunSpUyYMIFDhw4xduxYa0t3Ws4U7mnl6MFW3ilNopB3rsWL/bXu1U9QyO0WzsiCG/hfuZm09j+DRqVnZ1gJOp39hFH/tUaa57Tp3d2LFo1fvsBaepK8SpKcV48e2f/Y4oUcPZ6D5BWgmPFc8krYSqNGjaxqniENDfTzfP7557i4mP1oaeNDtmvVqgWkfsh2SvHx8Xz88cfky5eP5s2bU7p0aR7JAJeKhLuJEgZbkJA3UsigK1ILCdWRmPjynPB3jWBo4CbWl59Bh1wncFXr2B9elPeDPuSzK+9wMSaPVceWvDJJzquN22MAHHb7sORVkrg4ElattP9xxXMpYTyXvEpBIeO55JWwVnBwMDExMWnej00baEu97CHbKaVcsS0+Pp7g4OBXLjfeuHHjF/6n1Wpt+ns4moS7iRIG25Qk5JMoZNDN7PR6PcdOxzN4dDj12z5ix77n33aW3SWKzwtsY335X+ic+yhuai1HnxSk+/kP6HOpM2ej8ltdg+SVScq82r4nHgDXjp1kUurovIqPt/8xxTOUMJ5LXj2HQsZzySthKZ1OxwcffMDRo0fTvC+HNtCWPGQ72aRJk2jZsuUrl0HPLCTcTZQw2D6PhHwShQy6mVFklI6lq6Np3S2U93s/Zv3WOBIS4ciJhFTf56uJoW/+nWyo8Avd8x7EU5PIqcgCfHKhCx9f7MrJyMA01SF5ZfKivNI/eiSTUiXklXAoJYznklcvoZDxXPJKPO3ixYvGC7R37txJ9bWQkBDu3LmDTqdL83HMv98a0Gq17Nu3jzp16qS6VVun0xEdHU1UVBSurq7PfUj287zoIdt58jz/tsDp06dTsmRJOnbs+Mp9b9++/YVfS/5QfEYn4W6ihMH2ZeQ5hkkU8lzJzOLS1USW/R3Dmk2xRCflg4c7tHzDg/fe9qRcKVfe7h6KtzqO93MfpmueQ2RxMfybCIrKwy+3G7I/vCjWfMb5aZJXJi/Lq4RVK3Ft1Uaeu6qEvBIOoYTxXPLKDAoZzyWvREqjR4/mwYMHbNy4kU6dOrFy5UqOHj3KvXv3+Pjjj3F3d7fJncgWXYGeO3cuvXr1onLlytSsWZMaNWpQsWJFypYtS/Xq1WnQoAF16tShWbNmqR58/SL169cH4MGDBwDGh2zXq1fvme+dNWsW9erVMzbPR48e5e7du5aU71Qk3E2UMNiaQ94pTaKQd66dVXyCng1bY+nSJ4xWXUNZujqG6Gg9hQI1fD3Qhz3/82f8sCyUK+WKPi6OVm772FBhOn0L7CaLSxyXo3Py2eWOvBf0MfvDiyHNs229Mq/i4xVxfkheCUdQwngueWUBhYznklciWcWKFfHx8WHq1KkAHD58mNu3b7N161bUajUlS5YkNvbVi6e+ikVXoJcvX45araZ48eLkzJkTHx8fPDw8cHV1RaPRoNPp2L17Nzdu3GDs2LEvvQoMqR+yXbdu3Wcesl27dm26d+/O6tWruXLlCv7+/ly6dInExET++OMPFixYkKZfPqOScDdRwmBrCXmnNIlC3rl2JnfuafnzfzGsXBtDSJghEzQaaFLXnffaeVKzqisqlaEZ1ickoN2/n8RtW+jsaXjqwfUYP2bfqcfm0LLobdA0J5O8MjE7rxRyfkheCXtSwngueWUFySsjySvHGzZsGImJifz0008EBgby008/ER8fT2RkJB9++CG3b99m5syZrF69GldXV3x8fChUqBBvvPEGJUqUMPs4Fl2BLlmyJK1bt2b16tXMmTOHb7/9Fq1Wy7///kv37t0ZPXo0DRs2pHr16ixfvtysfZrzkO05c+awfv16RowYwYgRIxgzZgxXrlwx3gKemUi4myhhsLWGvFOaRCHvXGdkOp2evYfi6P3lYxp3CGH2b9GEhOnJ5a+m34de7Fztx7QJWalVzc3YPAPo/rtO4t+rICKC+7rsDL/WmvZne7EptJw0z+nE4rxSyPkheSXsQQnjueRVGkheGUleOd7Nmzfx9PTk7bffJiQkhGLFiuHr64tarSZ//vzEx8fj5uYGQGxsLLdu3eLkyZMWHcOiK9CVKlXi4sWLxtdz585lzZo1tGnThvz5DauyBgQE8MUXX+Dpad4zLb29vZk8efIz23fu3Gn8/82bHfOgdKWRcDdRwmCbFvJOaRKFvHOd0YSF61i9IZblf8dw87bpszw1q7rSuZ0njeq64+ry4kZYXaw46mrVURctyuC5Jfg3xPZZInllYnVeKeT8kLwS6UkJ47nklQ1IXhlJXjnW6NGjuXz5MosWLaJEiRL8/vvvDBkyhB49ehAWFsbff//93N7TEmZdgf7uu+/47LPPuH37Nrdv3yYhIYH4+HiWLVvGF198wcSJE1GrDbvq3r272c2zMJ+Eu4kSBltbkHdKkyjkneuMICxcx1ffPqFem0dM+iWSm7e1+Pqo6NbJk41Lc/Db9Ow0a+hhbJ71L1hpUqVS4da1Gy6166BFY/M6Ja9M0pxXCjk/JK9EelDCeC55ZUOSV0aSV47j7u7O77//TqlSpahRowYArVq1olixYuTPnz/VxWBrmdVA79ixg02bNrFq1SrOnTtH9erV6dixI82aNePjjz9OcxHi5STcTZQw2NqShHwShQy6SufjrWLf4Xji46FMCRe+/cqXPf/zZ/hnvhQtZLqhSK/Xoz19iviJ36O9lPaBwhKSVyY2yyuFnB+SV8KWlDCeS16lA8krI8krx5g/fz7Fixc3vo6OjqZMmTKMHDmSR48ecevWrTQfw6wG+r333uPgwYPs2rWLvHnz0r9/f7Jnz87KlSsZPHgwCQkJr96JsIqEu4kSBtv0ICGfRCGDrpK5uqgY/YUvK+ZlZ/XC7HRs7YmXp+lWbb1ej/bcWeInTyJhwa/o791Fu2OH3eqTvDKxeV4p5PyQvBK2oITxXPIqHUleGUle2detW7fYuXMnYWFhxMXFsWrVKvbu3cuhQ4e4evUqJUuWZMCAAWk+jlkN9Icffkj27NnJkycPOXLkoEuXLixatMj42eThw4enuRDxLAl3EyUMtulJQj6JQgZdR0lM1LNlVyzdB4Rx7cbzz7Wm9d2pWNY11aJger0e7YULxP80hYS5c9DfugXuHmiavYlrtw/sUrvklUm65ZVCzg/JK5EWHVp5OHw8l7yyA8krI8kr+5kwYQK9e/emdu3aVKpUiYiICL744gtGjhzJjRs3aN68OfPnz6dq1apUrFiRunXrMmbMGIuPY9Eq3ABZsmRh+vTpzJs3jx07dlC9enWePHnC4sWLOXbsmE0uiwsJ95ScvXlOJiGfRCGDrj3df6jll1+jaNQ+hP5fP+HgsQSWr4kx62d1V64QP30aCbNmoL/xH7i6omncBPfRY3Bt8RYqL6/0LR7Jq5TSPa8Ucn5IXglrvd/eS5pnheRVupO8MpK8so8nT54wbtw45s6dy4IFC/joo4/Imzcv2bJlIyIigtdee40vv/ySfv360aRJE7RaLT4+PhYfx6JVuAGuXbvGgQMHntm+a9cu4xWRIkWKsH79+lRXSIT5ihXWMGqwr4Q7mad5TiarRyZ5ajXPhL9W2vf4dqDX6zl8IoGlq2PYticObdJi2jmyqejU2pNObV6+GKPuv/9I3LgB3cULhg0uLmjqvI5Lk6aosmRJ5+pNZDJqYre8ktVujRSRV8IiS1ZFS/OsgLyyG8krI8mr9PfHH3+kep0rVy5Onz7N/PnzjU110aJF6dOnT5qOY3ED/dFHH9G0aVOyZctGYmIi0dHRREdHc/fuXY4fP05QUBCtW7eW5jkNxgzx5eIVCffM1jwnk5BPkmLQde3Yyb7HTkdPInSs+SeWZX/HcO2G6RFUVSu60vltT95o4I6b24vzU3crmMSNG9GdO2vYoFajqVUblzfeQJUte3qXn4pMRk3snlcyKTVSRF4Js/21LtYhx5W8Mmlcz82+B5S8MnpuXol0U6hQIUqXLo2HhwcDBgygefPmDBgwgGLFivHGG29YvV+LG+iuXbumep182btQoULUqlXL6kKEyc3b2kwf7kpongPyWfwJB5tRbMg7qoke8BmqAgXse2wbC7pouNq8fmssMUnzRy8vFW2aefBeO09KFn15HOvu3iXxn43oTp8ybFCp0NR4DU2zZqj9/NO3+OeQyaiJw/JKJqVGisgroViSVya9u3vRorEDHjcreWX0dF4lrPuf3WvILDQaTaq1uooXL87SpUsZPXo0TZs2tfqCr+M6BPFC436MzPTh7ujmuXxpFz7t5g2AKk9eh9Qgn9lJEhdHwqqMeQt3XJyeNf/E8M4nobzdI4yV6wzNc4kiGkZ/4cPe//kxZojvS5tn3YMHxC/+jfiJ3xmaZ5UKddVquH09AtfO70vznNnzSj5jaKSIvBKKI3llkpxXG7ebt8aGzUleGaXMK9f2HR1SQ2aVPXt2Jk+ejF5vfRZIA61AMbES7o5unhdOzca9h4bba107dpKQd/SkND7e/sdMg5u3Epk0I5J6bR8xdFwEp84l4uoCbzVxZ8nMbKz9PQed23nh4/3iCNaFhJCwdAnx341Hd/wY6PWoK1bC7cuvcOv2Aepcuez4G5nIZNRECXkFyKQ0BUXklVAMySuTlHm1fU/SmOpm51u5QfIqBWNe+dv/jfDMQKfTPXd7fHw8ISEhqNXWt8HSQAtFhrujm+fL1xKZ97vhHVr9o0cS8jIpfSWtVs+OfXF8POgxb7wTyq9LonkcridvbjWff+rNrjX+TBmblWqV3F55y5D29Cnix49De/gQ6HSoy5bDbciXuH34Eep8+ez0Gz1LJqMmSsirVGRSaiR5JUDyKqUX5ZVr+46Z+mkbSsmrhJUrHHJsZ6XVaunatSvr169/5murV6+matWqNGrUiLVr11p9DLMa6KioKGbNmpVqW/JzoM+ePcuxY8fYtWsXq1evZtGiRWzZsoXERCdfVdBJKD3c7enpwTYu3jDYJqxaKSGPTEpf5VGojn7Dwtl7KB69HurWdGPmxKxs/8uPXh9445/D/Pcr1UWLgkaDumQp3D4fhFvPT1EXCEjH6l9NJqMmSsir55JJqZHkVeYmeWXysrxS+ftn+kdWKiKv7t11yHGd1cOHDzl69CgJCQmptj9+/Jhvv/2WhIQEunXrRsuWLa0+hlmLiO3bt49p06bRsmVLAgICOHPmDN9//32qqyjJ95EnbytdujSrV6+2ujCR/jJCuNvLSwfb+HhZ+CKJLNTzYrlzamjXwgNfXzXvtvWgYAGL12g0Uvn44j5sOKocOWxYofVkMmqihLx6KVmox0jyKnOSvDJ5VV4lrFyBa4eOjl8oVPJK2FCePHnw9fUF4NatWxRIWoR2zZo1aLVafvjhB1q1apWmY5h1SaRhw4aUKFGCK1euMG7cOHbu3AlAx44dmTZtGt9++y1z5sxh1apVDBw4EL1eT/fu3dNUmEhfGSXc7cGswVbeKTXKzFd2IqN0LF8TQ3TM8ydk3w7LwtB+PmY1z/qYGBI3b0If+/zFXKR5NpG8spDklVFmzqvMSPLKxJy80t+76/jzQ/JKpIMKFSrw008/0aFDB86cOQPAsWPHWLx4cZqbZzCzgZ49ezYPHjwgMTGRDRs20KVLFwICAggMDKRp06YEBwfj4+ODj48PnTt3xt3dndatW6e5OJE+MlK4pzeLBlsJeaPMNim9dDWRbyZHULdNCKMnRbBhm/XPMdXHxZG4ZTNxY8eQuHED2t27bVeojclk1EQJeWURySujzJZXmZXklYkleaWI80PySthYiRIlSEhIIDIyks6dO/Ptt99SuHBh9u7dy7Rp05g+fTozZ85kxYoV3L592+L9m3WP4YoVKwgLC2P27Nn8/PPP+Pn5Ua9ePTZu3EiNGjW4f/8+jx8/JjIyEk9PT3x8fIiPj8fNEav7iZfKiOGeXqwabOV2IyNnvz0yPkHP1l1xLF0dw7HTps/RFArU4O1p+XMD9fHxaPftJXH7NoiMBECVJw+q/PltVrMtyWTURAl5ZRXJKyNnz6vMTvLKxJq8UsT5IXklbKhw4cK0bduWd955hxYtWvDHH3+gUqlwc3NDr9cTn/R0F5VKRWBgIJs3W/b3bNYV6H379rF582Zy5crFb7/9BkBgYCBBQUG88847/P333/Tt25dPP/2U+vXrExISwty5cy38VUV6y8jhbmtpGmzlnVIjRbxzbWN37mn5aXYkDdo+YtDoJxw7nYBGA80auLNoWjY2LctBiyYeZu9Pn5hA4p7dxI37hsT/rYHISFT+OXHt+gFuQ4ehKVc+/X4ZK8lk1EQJeZUmkldGzphXQvIqpbTklSLOD8krYSMBAQH8+++/5M6dmy+//JLJkyfj4eFBrly52Lp1K6dPn2bp0qX079+fBQsWWLx/s1e5uXjxIpUrV8bb25v9+/cTGBhIgwYNeO+99yhevDinT5+mcOHC3L17l6ioKGrXrm1xMSL9OEO424pNBlt5p9RIEe9cp5FOp2ffkXiWrY5h14F4kh8dmMtfTafWHnRq40nunBqL9qnXatEeOUzipk3wOMywMUcOXJo1R1O9OiqNZfuzF5mMmighr2xC8srIGfJKmEhemdgirxRxfkheCRvIkycP58+fx9vbmw8//BCAYsWKMWjQIHr06MFff/1FlSpVqFKlilX7N/u5KoGBgVy7do3KlSsTGBhIw4YNOXnyJBs2bCAqKopBgwZRoEABGjZsSMuWLcmhkAVwhHOFe1rZdLCVd0qNFPHOtRXCwnX8ujSaZu+E8smgcHbsMzTPNau6Mm18Fnas9qP/xz4WNc96nQ7tkcPEj/+WxOXLDM1z1my4dHwH9+EjcalZU5rnl5C8SieSV0YZNa9EapJXJrbMK0WcH5JXIo3c3d1p06YNkydPNt6uvWfPHkaPHo1arWbq1Klp2r9ZDfSCBQv47rvvSEhIYMyYMSxdupQOHTrg5ubGli1bmD17Np6enixatIhZs2Yxb948Vq1aRXh4eJqKE2nnjOFurXQZbCXkjRQx6JpBr9dz+lwCQ8c9oV6bR0z6JZKbt7X4+qjo1smTjUtz8Nv07DRr6IGri/mfddbrdGhPHCf++wkkLPkDfcgj8PHF5e32uI8chcvrr6Nysf7RVulNJqMmSsirdCF5ZZRR8ko8n+SVSXrklSLOD8krYYXg4GCmTZtGSEgI/fv359ChQxw5cgQwPFXqhx9+YNasWWzatMmqxcOSmTWb++OPP7hz5w7+/v68++67nDp1ikuXLlGmTBkuXrzIhg0bUKvV7Ny5k4iICIKDgwHYunUrs2fPtro4kTbOHO6WStfBVm43MlLE7V8vEB2jZ8PWWJb9HcO5i6ZzoUwJFzq38+Stph54WbM4mF6P7uy/JG7cgP7OHcNGLy9cmjRF83pdVBlgYi6TURMl5FW6krwyUnJeiReTvDJJz7xSxPkheSUs9PPPP7NhwwZmzZoFGOZon3zyCQBqtRqdTseuXbuYMWMGa9eupXfv3lYdx6wGesiQIWTNmpVNmzbx+++/M2rUKH7++Wd8fHzo0aMHBQsWZNu2bWg0Gn777TdcXV35999/qV69ulVFibTLDOFuLrsMthLyRooYdJ9y41Yi7T8MIyLS8Hfv5gYtGnvQuZ0nFcq4oFJZ2TifDyJx40b0wTcNGz08cGnYCE2DBqg8PG35K6SbYoU1jBrsK5NRlJFXdiF5ZfTcvBKKJc2ziT3yShHjueSVsMCBAwdo3749RYsWRaPREBwczOHDh2nbti06nY4LFy4wZcoU1q5dS/v27a0+jlkNdPPmzQGoXbs2r7/+Ovv27eOtt94CDM/ZCgwM5IsvvmDo0KH07NmT5cuX07BhQ6uLEmmTmcL9Vew62ErIGyli0E0hML8G/xxqsmXR8147L9q95UH2rGYvAfEM3X/XSVjzN/rr1w0b3NzQ1G+AS8NGqLy9bVS1fYwZ4svFKzIZVUJe2ZXkldHTeZWw7n92r0G8mjTPJvbMK0WM55JXwkwrVqwgICDA+Do0NJSwsDA++ugjAEJCQti4cSMDBw5kxYoVVh/H4hnkG2+8wXvvvWd8nS9fPgB8fHyYPn065cqV45dffrG6IJE2mTHcX8Qhg618ZsfIEZ+hehSqQ69/9u9ZpVKxcGo2tqzw46POXmlqngH0T54YmmdXVzQNG+E+egyuLVtluOYZ4OZtrUxGFZBXAfnS9m/SKpJXRinzyrV9R4fUIF5MmmcTR+SVfCbaRAl5JV4sZfMMkCNHDj799FPjaz8/PwIDA4mLi0Ottn7cteonS5cubfz/+vXrGy+Bq9VqvvvuO2NTLewrM4f70xw62ErIG9lj0NXr9Rw8Fs+Ar8Op1+YRZ4Ke/+8+b24NarXlt2o/j7p8BVxatsJ95Ghc276NysfXJvt1hHE/RspkVAF59Wk3B735InllZMwrf3+HHF88nzTPJo7MK2miTZSQV8J8JUqUSPW6VatWzJkzx/4NdLJLly5RqFAhPDw8Um3v2rVrWnYrrCDhbqKEwVZC3iS9Bt0nETp++zOa5u+F0n3AYzbvikOrhf1H4m2yfwBdSMhzt6tUKlyavoEqa1abHctRYmJlMqqEvLr3UGvY4OZm/yIkr4z0N2+QsNL62/qEbSlhPJe8MpEm2kQJeSWs069fPwoUKJCmfVjdQMfFxTFo0KA0HVzYhoS7iRIGWyMJeSNbDrrnLiYw4rsn1G39iAlTI7l+U4uXl4r33vZk7e856NMj7VfydPfuEb9oAfHjvkF3w/6fs3JmklcmKfNq3u8xAIbbh2VS6ti8unfXIccVqSlhPJe8epY00SZKyCvhGFY30GvXruXq1auEhYXZsh5hIQl3EyUMts+QkDdKy6Abp9Ww5p8YOn0SSrseYaxcF0tsHJQoomH0Fz7s/Z8fY4b4UrJo2p6zrHv0kPg/FhP//QR0J0+CXo/uyuU07VOYSF6ZPJ1XcfGGvFL5+8ukVAF5JRxLCeO55NWLSRNtInmVOVnVQMfGxhqfr3X//n2bFiTMJ+FuooTB9oUk5I0sHXSDY7MzJbgxTTd1Yui4CE6fS8TVBVo2dWfJrGys/T0Hndt54eOdxkXBQkNJWL6U+PHfojt6FPR61OXL4/blV7g0bpKmfQsDySuTl+VVwsoVMilFGXklHEMJ47nk1atJE20ieZX5WDXrnDx5Mnfu3MHDw4M8efLYuiZhBgl3EyUMtq8kIW/0qkFXq1ex63Fx+lx6j5b/9uO3e7V5HO9BvtxqBvXyZtcaf378JivVKrpZ9fzmVLWEh5Pw1wrivh2H9uBB0OlQly6D2+AhuH3cE3X+/GnavzCQvDJ5VV7p792VSWkSJeSVsC8ljOeSV+aTJtpE8ipzsbiBXrhwIX/88Qf+/v588MEH9O7dm4MHD6LVatOjPvEcEu4mShhszSYhb/S8QTckwZv5d+rw1pl+DLz8LvvDi6FCT52sV5hWcxvb/vLj027e+OdI++N+9BERJPy9mrhx36Dduxe0iaiLl8Dts89x69UbdWCgDX5LAZJXKZmbVzIpNVFCXgn7UMJ4LnllOckrE8mr9BUZGcnMmTMt/rmjR4+yadOmVNu2bNnC0aNHra7Fog8MLlq0iIkTJ1K1alUmTpxIt27duHPnDh9++CEeHh5UqFCB119/nfbt25MjRw6rixIvJuFuooTB1mJJIe/Wqw9uffoSP3MG+pv2X6RKu2UzAK5vtUz12p6SB92QDr2ZrP2czafVJOo1AGTVRPN2zlN0yHmCAI8wVHkLoNGk/RFU+qgoEnfuQLt7F8QbVutWFS6MS4u30JQomeb9i9Qkr0wszavk88OtT1/cevUhfvZMiIuzU7VJJK+EnShhPJe8sp7klYnklUloaCjjx4/H29ubR48e0b9//1SPQk42d+5cli5dSnh4OFWrVmXs2LHGRyJ37dqVI0eOGL+3Y8eOFtfRp08fIiMjqVatGv7+/jx8+JCBAweSJUsWDh8+bNXvZnYD/csvvzBjxgw++eQTPvvsMx49esS9e/fo0KEDOXLk4NSpUxw/fpwjR47w66+/smDBAsqUKWNVUeL5JNxNlDDYWk1C3kh/8wauS+ez/cR7JOqhgu9tOvkf5Y0cQbirbXdXiz42Bu2uXSTu3AGxsQCoAgJweasl6lKl03wruHiW5JWJtXklk1ITJeSVSB9KGM8lr9JO8spE8spg8ODBVK5cmQEDBnDw4EF69OjB1q1b8fX1NX7PypUrUavVLF26lNOnTzNkyBDGjh3L7NmzAShcuDDNmpmu5pcqVcriOlq0aMGdO3fInj07ADly5KBu3brGJt0aZjXQM2bMYOHChcydO5e6desCkDt3bho3bkyJEiV4//330Wg0PHz4kK+++or9+/fz1VdfsXbtWqsLE6lJuJsoYbBNMwl5I997Vxhefh9lPnqDMr5ZiZ99CeJs0zzr4+LQ7t1D4vZtEG3496rKmw+Xt95CXa68NM7pRPLKJK15JZNSEyXklbAtJYznkle2I3llktnz6tixYxw4cIA+ffoAUL16dSIiIliyZAm9evUyfl98fDwff/wxAPny5WPBggU8evTI+PWSJUvy/vvvp6mWb775JtXrR48eMXfu3DTt06wPEy5evJiff/6ZJ0+ecOLECeP2WrVqMWXKFHr27ElkZCQ5c+bEzc2N0qVL8/bbb6epMGEi4W6ihMHWZjLJZ3YSdGr+CSlLz4vvE5bg+dzvaa3aSfFts232GSp9QgKJu3YSN/YbEtethehoVLlz49q9B25fDkVTvoI0z+lE8srEVnklnzE0kc8YOg8ljOeSV7YneWXiDHml1Wpp3LjxC/97kT179gDg5+cHgIuLC9myZWPXrl2pvi9lcxwfH09wcDBt27Y1bjt+/Dg1a9akWrVqfPzxx1y/ft3q3yU+Pp5evXrRqFEj4tL4xo5ZDXSrVq3ImTMnQ4cOpWfPnuzfvx8wXEaPjY1l//79dOrUieDgYLp06cLff/9Njx490lSYMJBwN1HCYGtzThzyd+KyMv1WQ944PZCvrrXj8JMirH1U8YXfb8tBN3HDehL/Xg2REaj8/HHt0hW3r75GU7kKKnXaFyETzyd5ZWLrvJJJqYkzTEozOyWM55JX6UfyyiSz5lVoaCgArq6uxm2urq7G7c8zadIkWrZsmaqpfu211xg/fjwDBw7k33//5dNPPyUhIeGVx797967x+2JjYwkPD+f06dPs2rULlUqFexr/TZo1kxwxYgQXL16kdu3aJCQk8Omnn7JmzRoKFSqEq6sr8+bNIzY2lu7du1t1b7p4Pgl3EyUMtunGiUJep4f94UUZcPkd3jrTj/l3Xyc00Yecrk/onW83b/qde+nP22rQdanfAFXOnLi8+x5uw0egqV5DGud0Jnllkl55JZNSk8w6KXUGxQprHD6eS16lP8krk4ycVxqNhu3bt7/wvxdJ/rxxdLTp33VCQsILF5mePn06JUuWZMSIEanuEHznnXdo3LgxXbt2ZfTo0dy4cYOrV6++su6RI0eyc+dOADp06MDIkSMpVaoULi4ueHh4mPW7v4zZM8o2bdowd+5c1q9fT8WKFfn66685cuQIpUqVom7duvz11194enoyevToNBclJNxTcurmOVkGD/nHiZ78drcmrf/tS59Lndn9uAQ61LyW5Ro/Fl3JPxWm0yv/HnK7RbxyX7YYdFXZs+P29QhcatVGpdFY/PPCMpJXJumdVzIpNcnIk9LMbMwQX2meUUZepTfJK5PMllf169cH4MGDBwAkJiYSHh5OvXr1nvneWbNmUa9ePeMK20ePHuXu3bvPfF/x4sUB8Pb2fuFxd+7cSVhYGFmzZmX9+vVcvnyZiIgIhg0bhq+vL9WrV0evT3vuWHxJJiAggD/++IPOnTvz9ddf88MPPwCGFc1+/fVXTp48yYULF9JcWGbm6SHhnixTNM/JMljI6/VwJjIfI661pumpz5hyqynBcTnw1cTyfu7DrCk3k7kll9AkxwVc1TqLanhm0HVzS/11nQ7tqZOGxUFecCuPXHG2D5mMmtgrr2RSapLZJqXO4OZtrTTPCsgre5G8MslMeVWtWjXq1q3Lvn37AMNnmb29venUqRM9e/Zk0aJFAKxevZorV65w6dIlVq5cybJlyxgzZgxqtZqDBw+m+sz08ePHefvttwkICHjhcYcMGcLy5cv58ccfqVq1KlqtlilTprB48WKePHlC5cqViU16GktaWDXDVKlUjBgxgnfeeYc5c+YYt+fOnZtvvvmGf/75J82FZWYjB/tIuJPJmudkGSDko7WurH5YifeCPqbr+Y9YF1KReL0LpbzuMrrQOrZU/JkvA7dQ2DMkTTWkHHRd2xveldTr9WjP/kv85B9IWLgA3cULaA8eSNNxhPVkMmpi77ySSalJZpqUOoNxP0ZK8+zgvArIZ983mCWvTDJTXk2dOpWQkBBGjx7NwoULmT9/Pl5eXly+fJng4GAA5syZw/r16xkxYgQjRoxgzJgxXLlyhezZs/PkyRMmTZrEqFGjWLlyJTExMYwdO/alxyxSpAgHDx4kMjKSrVu3kjdvXg4fPmxcv6t48eLodLo0N9FmPwf6eb788ksGDRqEXq833q/epEmTl15aF68WmF/DB/0zd7groXl2d3PQKs0KfQTDlf8dY+XDqqx9VJEIreHzI26qRN7McY6OuY5R3vsOtl7YOnnQde3TF+2F8yRu3ID+RtKfhbsHmoYN0VSvYduDCrPIZNTEUXklj4wxyeyPjMlIYmKleXZ0Xn3azf7zdMkrk6fzShf08vVhMipvb28mT578zPbkzyYDbN784qxu1qxZqmdAv0xiYiI9evTgxo0bJCQkcOLECW7cuEH//v3p1asXjx8/pnv37ly6dAmAzp074+7ujqenJzly5KB06dK0aNGCvHnzmnW8NL0FpVKpmDBhwjOPg6lVq1ZadpvpjfkhItOHu6ObZ28vFZ90TXrk0lO3D9uFQt4pjd20lY0/7ufjTXVoe7YPS+6/RoTWgwLuoQwqsJWtlX5mXJG1VPCxffOcTH/zBvHfjiVh1kxD8+zmhqZJU9xHj8G1eQtUns9/NJZIPzIZNXF0XsmVHZPMdGVHmE/yyiQ5r+491Drk+JJXJinzSl1T+qa0Onv2LNevX6d///5ER0czYsQIxo0bx7Fjx1iyZAl3797l9u3blChRAo1Gg05n+GhhfHw8jx8/5urVq8bm2hxpvofDUyavNnflumOCTUnh7ujmef6UrOTJaVh8yrV9x0wb8ldicvH5quIcOp6AWg0Ni4cxs8QS1pWfwQd5D5HNJcY+hURGgosLmvoNcB85GtdWrVHJnS4OIZNREyXkFcikNCVpokVKklcmKfNq3u92GrufQ/LKxJhXdZ9dWEtYpmzZsmzfvp0uXboQEBBAtWrV+Omnn9Dr9TRo0IBjx47RtWtXVq9eTYkSJfjqq69YtmwZf/zxB/Pnz2fChAnGhc/MIavsCEB54e7o5rl4ERfmLI4CQOXvn2lDvrT3Pepnu8THefexqc8pZv9Wknodi6O2993t3t6Gxrlde1RZstj54CKZTEZNlJBXKcmk1ESaaAGSVyk9nVdx8Ya8ctSVT8krE+2WzSTs3eOQYzsTV1dX47OdCxcuTL169Rg2bJjxOdSDBg1i9uzZXL16lWLFij13lW9LSAMtFBnujm6eewx8TPAdw+0dCStXOHXIP0l0568HldG94I98WvE/6V9gJ7kPr3PYpFSVPTuqbNnsekyRmkxGTZSQV88jk1ITaaIzN8krk5fllWvdeg47PySvTHSHDjrkuM6qTp061K9fH3d3d4YOHcqtW7fIli0bW7ZsoWjRohQtWtS4iJm1HN5Ah4aGMnjwYEaNGkWfPn04f/78C79348aN1KxZ047VOT+lh7u9vGyw1d+765Qhfz4qD2Out6Tp6c8Zd6MlB8KLvvJnZFKaOclk1EQJefUyMik1kbzKnCSvTF6VVwl79zj0/JC8Eunhgw8+IFu2bPTt25cTJ07w4YcfMmLECGbPno1Op6NIkSJpfuSywxvowYMHU7BgQcaOHUvXrl3p0aMHERERqb7n+vXrfPfddyxYsICwsDAHVep8MkK424M5g62zhHyszoW1jyrQJagH7wZ9wt+PKhOrc6W4532zFwGTSWnmIpNREyXklTmcJa9sQfIqc5G8MjEnr3SHDjr8/JC8EulBq9Xy7bff8uOPP5I1a1YaNmxIQkICarWaAgUKcO5c2lY+t7qBPnr0KJs2bUq1bcuWLRw9etTsfRw7dowDBw4YV+2uXr06ERERLFmyJNX3xcXF8cUXX1CsWDFryxVPySjhnt4sGWwzcsgHx2ZnSnBj3jg1kJHX2/BvVAFcVFpa5PiXhaUWsbLsXOpkvWp2GTIpzRxkMmqihLyyREbOK1uTvMocJK9MLMkrJZwfklfC1lxcXGjUqJHx9YQJE/juu+8AyGKDtXSsfg50nz59iIyMpFq1avj7+/Pw4UMGDhyIr68vR44cMWsfe/YYPjTv5+dnKMbFhWzZsrFr1y569epl/L5SpUpZXF/jxo1f+DWtVotGo7F4n84io4V7erFmsM1IzzHU6lXsfVycPx9U5cAT05tP+dwe0yHnCdrmPImfq/V///LcVecmk1ETJeSVNTJSXqU3ySvnJnllYk1eKeH8kLwS6UmtNl0zzpkzJ9OnT0/b/qz9wRYtWvD666+TPXt2AHLkyEHdunVp0aKF2fsIDQ0FMK6Qlvz/yduF7WXUcLe1tAy2Sn+nNCTBm3l3XqfFmf4MvPIOB54UQ4We17NeZlrx5ayv8Asf5dufpuY5mRLeuRa2J5NREyXkVVooPa/sSfLKOUlemaQlr5RwfkheCXtwc3OjQoUKadqH1Vegv/nmm1SvNRoNc+fOJSbG/GfLJTff0dGmoElISCBPnjzWlmW0ffv2F36tTJkyad5/RpTRw91WbDHYKu2dUtfefTg49i9WnC3AtrDSJOoNd1hkc4mmrf8pOuY8TgGPx+lShhLeuRa2I5NREyXklS0oLa/kSrSwFckrE1vklRLOD8krYQ83btzg6NGjdOjQwaqfT/MiYqGhofz666/MmTOH0aNHU6dOHVauXGnWzyY/sPrBgwcAJCYmEh4eTr168kBxW3OWcE8rWw62SninNDJaz++jD9Cqezgf7nmTTaHlSNRrqOB9i28Lr2FLxZ/5PGB7ujXPyVK+c61u9OKPTwhlk8moiRLyypaUkFdKubKjhCttIu0kr0xsmVdKOD8kr0R6mz17NpcuXbL6561qoCMiItDpDM/IzZEjB02aNMHFxYVDhw4RHR3N2rVrzdpPtWrVqFu3Lvv27QPg+PHjeHt706lTJ3r27MmiRYusKU88xdnC3VrpMdg6MuTPROaj6anPmHD1Da7c0uDprqfjW6782Xg9v5dZSCv/f3FXa+1Wj3bLZuJ+mY7u8CG7HVPYjkxGTZSQV+lBJqUmSmgShPUkr0zSI6+UcH5IXon08t9//7F27VoePXpk9T4sbqCvXbvGW2+9xbZt24zbChYsyEcffcS8efMALOrop06dSkhICKNHj2bhwoXMnz8fLy8vLl++bHzIdWxsLHPnzmX//v0AjB8/nuvXr1taeqbkrOFuqfQcbB0V8iW97uOq0lLY4xFDAzexpfI0xr4XRsWv33NYyOsvX4KoKFS5cqEqn7bPlwj7kcmoiRLyKj3JpNRECU2CsJzklUl65pUSzg/JK5EW8fHxxMfHp9qm1+sZOXIkOp0Ovd7688XiBnrmzJk8ePCAgwcPPvO1wMBANBoNUVFRZu/P29ubyZMn88033zB79mzKly+Pl5cXO3fuZOTIkQB4eHjQs2dP9u7dy8WLFxk+fDiFCxe2tPRMx9nD3Vz2GGzTM+TDEjyfu91drWVp2V/5u9wsOuc+ShbtE8WEvKpCRdw//kQmpRmAp4dMRpMpIa/sQSalJkpoEoT5pHk2sUdeKeH8kLwS1lq0aBHz589PtW327NkcPXoUtVpN8+bNrd63xQ305MmTGTp0KJs3byYx8dngUqlUZMuWzeqChG1klnB/FXsOtrYMeZ0e9j0uSv9L79Dk9Ofcjsv63O8r4P4YlSrFBoWEvG7bVocPusI8Iwf7yGQUZeSVPcmk1EQJTYJ4NWmeTeyZV0o4PySvhDWCgoI4ceKE8fXff//N1KlTyZcvH5MmTaJy5cpW79usBvrkyZN8+OGHLFy4kN27d1O1alVKly7NmjVrePLkCdHR0YSHh7Ny5UoSExMpXbq01QWJtMts4f4ijhhs0xryYQmeLLpbi1b/9qXv5c7sCS9Bol7DoSdFzN+JQkJeCYOueLXA/BqZjCogr9zdVK/+JhuTSalJqoUQa9ZySA3ixaR5NnFEXilhPJe8EpYqWLAgV65cAWDt2rWMGDGCt99+m7Vr13Ls2DH+/PNPq/dt1mOsZs2axYEDBzhw4ABguMqs1+vZv3+/8TZrMNxX7ubmRo8ePawuSKRNZg33pzlysLX0EQx6PfwblZ8/H1RjS2gZ4vWG09JXE0sb/1N0zHmCQp4hlhWhkEcwKOGRGOLlxvwQIZNRBeTVJ12f/1GN9CaPjDF5Oq+EMkjzbOLIvFLCeC55JV7lwoULbNu2jddee43ChQtz//595s2bx/z58/nuu+9o2rQpOp2O8PBwoqKiCAsLQ6VSkTVrVlQq89/INquBDgoKom/fvpQtWxYXFxd0Oh1arRa9Xo9Op0OlUuHi4oKvry8lS5bE19fX6l9cWC+zh3syJQy25oR8tNaVf0LLseJBVS5E5zVuL+11l3dyHeXNHOfw1KShdoWEvBIGXfFiV67bb6X2lCSvDJLzKk9Ojd2PnUwmpSbaLZshSxZc68rjNJVACeM5SF4lU8J4LnklXmbq1Kns3LmTGTNmAIaLu1OmTAFg6NChDB06NNX3r1u3DoBs2bKxatUq8uXLZ9ZxzLqFe/v27fTv359GjRpRr149PD092bNnD02aNOGNN97g9u3bFC1alGrVqknz7CAS7gZKGWzhxbcbXY/xY+KNN3jj9GeM/a8lF6Lz4qZKpLXfaf4o/SvLyszn7Zyn09Y8J1PI7UZKuP1LKIfklUHKvJqz2LD4pqNuH5bbI010h55dJFXYn1LGc8mr1JQwnkteiRc5ceIE7dq1Y/To0UyZMoXy5csb75z28/Nj8ODBjBw5km7duuHn50fz5s3JnTs3DRs2JG/evK8+QBKzGmj3p/5h3rhxg7179xpfN2zYkB49enD16lWzDyxsR8LdQCmDbUrJIZ+YMw87q/Tn40vdaHu2D0sfvEaE1oMA91AGBWxla6WfGVdkLeV97mDBHSTmUUjIK2HQFY4neWXwdF4F39EB4Fq3nkxKFZBXwrGUMp5LXj2fEsZzySvxPCtWrGDChAm8++67NG/enNq1a/PBBx8wdOhQEhMTmTdvHgEBAfTu3ZuAgACmTJnCrl27mDBhgkW3cFu8CjdAjRo1uH//PjqdYcAvWLAgLVu2pEuXLpw5c8aaXQorSbgbKGWwfdr9eF9mHixIozYP+GyaJ0fDC6JGR8NsF5lZYglry8/ggzyHyOYSk76FKCTklTDoCseRvDJ4WV4l7N0jk1KF5JVwDKWM55JXL6eE8VzySjytYMHUf/4lS5ZEq9XSo0cPNmzYQPXq1enVqxdHjhyhc+fOVh/HogZ6zZo11KpVi+HDh+Pi4sKQIUP46quvGD58OPfu3SMsLIxu3bqxePHiND2cWphHwt1AKYNtSv/F+DHoSgeanx7A7Dv1eRjrhZ97DL3ed2PrzzH8XO5/1Ml6DbU9F95VSMgrYdAV9id5ZfCqvNIdOujw80MmpcJRlDKeS16ZRwnjueSVeJnSpUvTsqXhM/t+fn788ssv9OrVi6FDh1KpUiWr92tRA71u3ToiIiKIiIigfPnyhISEcP/+fW7evMmlS5dwc3MjNjaWH3/8kRs35MP06UnC3UApg+3TNCodO8JKoUVNVd8bTCyyis3lptAndj75y+dxeMjrzp5FlTUruJi1jqDNKWHQFfYjeWVgbl4p4fyQSamwN6WM55JXlpG8SiJ5pUiFCxemQoUKqbb179+fgQMHMn78eKv3a9HsuVKlSnz33XfkypXruV+/f/8+X3/9NX369KFQoUJWFyVeTsLdQCmD7fMEeITxVeAmqvreoLjXQ+N2pawemfDHYsP/q1SQKzc8uG/fGlDGap4i/UleGViaV0o4P5SSV7LarfNTyngueWUdyaskklcZRvfu3QkLC+PSpUuUKFHC4p+36Ap0//79X9g8A+TOnZtff/2VqlWrWlyIMI+Eu4GjB9tYnQtrH1Wg76V3idc9//Ez7+Y+lqp5TqaId0oB1GpcOnfBfdBguZ1bpAvJKwNr80oJ54ci8kqu7Dg1R4/nySSv0kbyKonkVYbx+eef4+/vb9XPWrWImHAMCXcDRw62wbHZmRLcmDdODWTk9TbsCy/OtrDSFu9HESGv05H41wqHh7wSBl1he5JXBmnNKyWcH4rIK5mUOiVpnk2UkFdpJXmVRPJKsbRabarXOXLksGo/0kBnEBLuBvYcbBN0sZy58pAHia7sCitB74vv0fLffvx2rzbhWi/yuT1mQP4dvJblmlX7l5A3UcKgK2xH8srAVnmlhPND8krYmjTPJkrIK1uRvEoieaVIPXr04MiRI2nejzTQGYCEu4E9B9ssue5z+fFxeg2/wZubOzLwyjsceFIMFXpez3qZacWXs77CL3yUbz9+rtb/nUjImyhh0BVpJ3llYOu8UsL5IXklbEWaZxMl5JWtSV4lkbyyu/Dw8BcuZB0SEsKRI0eIiopK83Fs0kBv2LCBsWPH2mJX4ikS7gb2HGxd3OLIXeQaD64XJiQ4kMR4dzQuCbyb5zDry//CjBLLqZ/tMhqVbf4cJORNlDDoCutJXhmkV14p4fyQvBJp5ekhzXMyJeRVepG8SiJ5ZVfffPMNw4YNA+Ddd98lMjKSU6dOsWPHDnx9fVGrbXPt2OK9dOvWjdu3bxtfP3z4kJEjR7Js2TI+++wzbt68yfLly21SXGYn4W5g73eqXT1iUakga+77ePhEkKfYZQpXPU7rwKMU8HicLseUkDdRwqArLCd5ZZDeeaWE80PySqTFyME+0jyjjLxKb5JXSSSv7CY4OJigoCBWrlzJpUuXOHToENu3b2fx4sW4ublRuHBh4mywOrtFDbRer+f48ePs27fPuG3x4sVER0ej1+vZtGkTzZo1Y8KECWkuLLOTcDdwxG1eCbEe6PXglTWcwPJnyZLzES4qHXn06fv3ICFvooRBV5hP8srAXnmlhPND8kpYKzC/RppnBeSVvUheJZG8souVK1eybt06tmzZQqlSpfjtt984deoU586dY/bs2YDhzuk5c+awcOFCVq5cyZEjR0hMtCyPLGqgVSoVFSpU4Pz588Zt/v7+ZM+endWrVzN16lTatm1LQkKCRUWI1Dq08pBwx3GfkUqMd+f+tSKoVIbXavT01J3Hj/R/nqCEvEnKQVdds5ZDahCvJpNRA3vnlUxKkygkr4T5xvwQIc2zg/PK3U1l1+NJXiWRvLILPz8/unfvzuuvv87Ro0c5deoUMTExLFq0iHv37rFr1y7mzp3L9OnT+eGHH/jqq6+YPHmyRcew+Bbut956iwcPHhhfN2rUiLi4OMqUKUOzZs3o1asXAPHx8ZbuWiR5v71Xpg93Ry8w8uRBbgp71WRC7zrMynmVxvo7dju2hLyJcdCtW88hxxcvJ5NRA0fllUxKkygkr4R5rlzXvvqb0oHklYG3l4pPunra/biSV0kkr9LdmDFj6NmzJ+XKlaNYsWIcP36cBg0aMH/+fCZNmkSbNm04fvw4J06c4MiRI+zYsYOvvvrKomOY1UCvXLmSZs2a8f3333P58mVCQ0PZtWsXx44dIyoqCrVaTWRkJAAFChRAo9EQGxtr+W8sAFiyKjrTh7sSFhh5s0EWyhfzx09j/+NLyJtot2wmYe8ehxxbvJhMRg0cnVcyKU2ikLwSyiR5ZZCcV3lyaux+bJC8Mno6r/LktX8NTuzIkSN8//33vP766xQuXBg3Nzdq1KhBwYIFKVCgAFeuXEnzMcxqoFetWsWNGzdYtGgRf/75J6dOnaJ379507dqVt99+m6ioKO7evQuARqMhT548REc7JqCcwV/rHPPmg5LC3dHNc+/uXrRobHiH1lG3D+tv3iDu+wnKCnkHTUp1hw465Lji+WQyaqCUvJJJaRKF5JVQFskrg5R5NWdx2h/jYy3JqyQp8sq1Yyf7H9+JbdiwgVatWqFWq3n99dfR6/W89957bNmyBYBLly6l+RhmNdDXrl3j448/ZsqUKcycOZOZM2cyffp0pkyZwsSJE3nttddSrczt5+dHREREmosT9qO0cHd08/xZTx82bo8BwLVuPcctZPX4MXHTpiom5GVSKmQyaqCUvEomk9IkklciBckrg6fzKviODsBhVz4lr5Ik59WjR/Y/tpNKSEhItRbXjz/+yKFDh9i6dSs//PADbm5uvP7662k+jos537Rp0yZy5Mjxwq8HBwdz4cIF8uXLx+3bt4mNjWX//v0UL148zQWK9KfEcHd08/zz3EiCb2tp0diThL17cH2rJWAIfbu7c5v4mTNw69MXt159iJ89E2ywBL9FkkLerVcf3Pr0JX7mDPQ3n/+geuG8ZDJqoJS8elpyPjkyr5InpZJXwtEkrwyel1cFCxhu4Xbt2In4Bw8ccn5IXiWJiyNh1UrcB35u3+M6qW+//Za///6bwMBAvLy8iImJYcKECbi7u5MvXz6mT5+OWq2md+/eJCYmkiVLFho1asRbb71l0XHMugL9suYZIFu2bEydOpU2bdrQu3dvrly5woULFywqRDiGUsPdEV402OoOHZR3SkGu7GRyMhk1UEpevYhc2UkieZWpSV4ZvCqv9I8eOX6NE8krkIWXbebAgQO88cYbNGzYkNq1a1OrVi0ePHhAUFAQ58+fJzo6Gm9vb/R6PZcuXWLDhg0sXbrU4uNYvAr38zx8+JAsWbLQs2dPFi1axKFDh/j+++9tsWuRjjJCuNvLqwZbCfkkMinNlGQyaqCUvHoVyaskkleZkuSVgTl5lbBqpcPPD8krYUtLly5l8uTJDB48mM8++4x+/fpRsmRJVq9eTbVq1Thx4gRvvvkms2fPZvfu3WzYsIHFixdbfBybNNAtWrRg+/btfP7559SsWRNfX19b7Fako4wS7vZg7mArIZ9EJqWZikxGDZSSV+aSvEoieZWpSF4ZmJ1X8fGKOD8kr4St5MyZM9XrEiVKkDVrVkqVKsXvv//Ol19+ydChQ7l48SIARYsWRaOxfFV6mzTQJUqUwMfHxxa7EnaQocI9nVk62ErIJ5FJaaYgk1EDpeSVpSSvkkheZQqSVwYW55VCzg/JK+cTGhrK4MGDGTVqFH369OH8+fPP/b65c+fSoEEDKleuzMcff8ydO3eMX9u/fz+9e/dmxIgRjBo1yuLHJHt4eDBu3Djj6w4dOjB79mx++ukn636pJDZpoEXGkSHDPZ1YO9hKyCdRyKAr0odMRg2UklfWkrxKInnl1CSvDKzOK4WcH5JXzmXw4MEULFiQsWPH0rVrV3r06PHMU5pWrlyJWq1m6dKlTJgwgUOHDjF27FgAbt68SZ8+ffj666/59ttviY2NZfz48RbXkS1btlSvK1SowNdff51qtW5L2byBfvjwoa13KWwkQ4e7jaV1sJWQT6KQQVfYlkxGDZSSV2kleZVE8sopSV4ZpDmvFHJ+SF45h2PHjnHgwAFq1aoFQPXq1YmIiGDJkiWpvi8+Pp6PP/6YfPny0bx5c0qXLs2jpMd6zZkzB39/fwICAgCoVasWq1evTvXoZGsFBgbi6upq9c/btIHetm0bq1atsuUuhY04RbjbiK0GWwn5JAoZdIVtyGTUQCl5ZSuSV0kkr5yK5JWBzfJKIeeH5JVyaLVaGjdu/ML/XmTPnj0A+Pn5AeDi4kK2bNnYtWtXqu97//33jf8fHx9PcHAwbdu2BWD37t34+/sbv+7n50diYiL79+9/Zd1Tp05l3759z2w/e/YsXbp0oUmTJs/UYgmbNtAzZsxIdd+6UAanCvc0svVgKyGfRCGDrkgbmYwaKCWvbE3yKonklVOQvDKweV4p5PyQvMrYQkNDAVJd5XV1dTVuf55JkybRsmVLY1MdFhb2zM8DhISEvPTY9+7dY/bs2dy9ezfV9oSEBAYOHMixY8fw8/OjYsWKlv1SKdisgd64cSPnz58nLCzMVrsUNuCU4W6l9BpsJeSTKGTQFdaRyaiBUvIqvUheJZG8ytAkrwzSLa8Ucn5IXjmeRqNh+/btL/zvRbJnzw5AdLTp/ExISCBHjhzP/f7p06dTsmRJRowYgUqlMu7j6Z8H01XtF/H398fFxQU3N7dU27ds2cLt27fp1KkTf/zxh7FGa1jcQF+5coUrV66k2vbgwQPGjh2LSqVKUzHCtpw63C2U3oOthHwShQy6wjIyGTVQSl6lN8mrJJJXGZLklUG655VCzg/Jq4ypfv36gKFHBEhMTCQ8PJx69eo9872zZs2iXr16dOzYEYCjR49y9+5d6tevb/x5MFzV1mg01K5d+6XHdnFxoWzZsixbtox27doRGRkJGC72fvXVV4wdOzZNn38GKxro5cuX8/vvvxtfx8XF8fnnn/P48WOKFy9Or1690lSQsI1MEe5mstdgKyGfRCGDrjBPh1YeMhlFOXllL5JXSSSvMhRpng3sllcKOT8krzKeatWqUbduXePnkI8fP463tzedOnWiZ8+eLFq0CIDVq1dz5coVLl26xMqVK1m2bBljxoxBrVbTq1cvIiMjuXDhAgCHDh2iTZs2FChQ4JXHL126NKdOnSIoKIjWrVuzdetWatWqRZkyZTh06BCHDx/m2LFj/Pfff1b9fi6W/sDjx48JDg4GDO8mDBw4kBMnTtC9e3cqVarEjRs3yJcvn1XFCNvIVOH+CvYebLVbNgPg+lbLVK/tKTnk3fr0xa1XH+Jnz4S4OPsWkTTouvXqg1ufvsTPnIH+5g371iDM8n57L5mMKiSv7E3yKslTeZXw10r7Hl+YRZpnA7vnlULGc8mrjGfq1KmMHj2a0aNHc//+febPn4+XlxeXL182rqw9Z84c/vvvP9avX5/qZ7Nnz46bmxsLFy7kp59+Infu3KjVakaOHGnWsQsXLsx7771HpUqVGDp0KAMGDDB+Ta/XG28TByhVqhR///23Rb+bxQ105cqV2bVrF/Hx8fTv35/Lly/z22+/UaNGDQYPHkzOnDmNS5YL+8uU4f4CaR1svQkl5r+b6HVxFt2qISGfRCGDrni5JauiZTKqgLxqXM/t1d+UDiSvkqTIK9eOnex7bPFK0jwbOCyvFDKeS15lLN7e3kyePPmZ7Tt37jT+/+bNL/87rFy5MnPmzLH42IGBgRw4cIABAwbQoEEDcuXKxYoVKyhWrBhjxowhOjqaoKAgDh06RPfu3S3ev1l9wdKlS2nUqBFff/01QUFBREZG8u6773Ly5EneeecdLl++zB9//MGtW7c4fvw4CxYsYOHChS/9cLmwvUwd7k9J62D7RsB+urkMIXz+JPT37ln883K7URKF3P4lXuyvdbEOOa7klUnv7l60aOzpkGOD5JVRcl4lPYNUKIM0zwYOzyuFjOeSV8Ic+fLl4+TJk2TPnp3Zs2czduxYvv/+e27dusXixYupV68evXr1YtGiRTRo0MDi/ZvVQK9cuZI7d+6wevVqNmzYgEajISgoiIiICKZOncq4ceP49ttvOX36NGfPnmXSpElMnDiRgQMH2uRh1+LVJNxN0jrY+nuEMbD8EvyfZCdbRDY0Og1atRadSmfRfiTkkyhk0BXKIXllkpxXG7fHAKDKk9chdUheJYmLI2GV3MKtFNI8Gyglr5QynkteiVfx9vamRIkS/PPPP8Zt8fHxTJ8+nQMHDvDnn3+maf9mNdDZsmXjjz/+4PTp05w6dYrmzZtTvHhxXF1dyZ49O+PGjePkyZMsW7aMokWLMm/ePHr37s38+fPJnz9/mgoUrybhbmKLwTaf9wPUKj3RHtFo1VrCfcK573efBJcEi/clIZ9EIYOucDzJK5OUebV9TzwArh07yaTU0XkVH2//Y4pnSPNsoJS8MlLIeC55JZ4nJiaGQ4cOkTNnTn755Rd+/fVX/v33X8BwW/eaNWuYNm0a06ZNM67ObQ2zGuiFCxdSrVo13JP+YZQtW5Y6deqwbds26tSpw6hRoxg5ciTFixcnICCAunXrMnDgQGrWrGl1YcI8Eu4mthps70TlQqdXEeseywO/B0R5RQHgorV4yQBAQt5IIYOucBzJK5MX5ZX+0SOZlCohr4RDSfNsoJS8eoZCxnPJK/G0adOm0aNHD2rWrMn777/P9evXGTJkCN26dWPWrFls3LgRnU7HoEGDWLNmjdXHsfgxVgBFihQha9as5MqVi0mTJjF9+nR27tzJuHHjzF4dTaSdhLuJLQfbR7HZmfrv++hQo1cZ/kyzRmRFo9NYvU8J+SQKGXSF/UlembwsrxJWrXT4+SF5JRxJmmcDpeTVCylkPJe8Ein9888/BAQEUKNGDQoWLEjx4sWJiorC09MTNzc3ihUrxqhRo2jevDllypSx+jhWNdBVqlTh/fffN75u0qQJS5Ys4eDBg5w5c8bqYoT5JNxN0mOw3RJch8UJk8jb5RvyuFbCO9Y7zfuUkE+ikEFX2I/klckr8yo+XhHnh+SVcARpng2UklevpJDxXPJKJPv888/ZsmULs2fPZsaMGfzwww9UqlSJOXPmMG/ePH766Sfu3LnDqFGjqFKlitXHsaqB9vX1JUuWLKm2lSpVigULFjB37ly0Wq3VBYlXk3A3Sc/BNooceBYsh0Zlu8fLSMgnUcigK9Kf5JWJ2XmlkPND8krYkzTPBkrJK7NJXhlJXjlemzZtUr0OCAigdevWxtfFihXD39+fPXv2EJeGx49Z1UC/SLFixRg9ejQnT5605W5FChLuJkoYbK0hIZ9EIYOuSD+SVyYW55VCzg/JK2EPShjPJa/SQPLKSPJKeZo2bZrqdYUKFfjxxx+Na3tZw6YNNEDFihWpVq2arXcrkHBPSQmDbVpIyCdRyKArbE/yysTqvFLI+SF5JdKTEsZzySsbkLwykrxSthkzZlC3bt007cMmDfTp06d5/PixVT8bGhrK4MGDGTVqFH369OH8+fPP/b5169bRr18/hg4dypQpU9DpLHsmb0Yn4W6ihMHWFiTkkyhk0BW2I3llkua8Usj5IXkl0oMSxnPJKxuSvDKSvFIulUqV5n2kuYFOSEigV69enD592qqfHzx4MAULFmTs2LF07dqVHj16EBERkep7jh07xtixY/nuu++YOHEix48fZ+7cuWktPcOQcDdRwmBrSxLySRQy6Iq0k7wysVleKeT8kLwStqSE8VzyKh1IXhlJXjkvsxrow4cPG5+V9fQq2+Hh4YSFhaHXWx46x44d48CBA9SqVQuA6tWrExERwZIlS1J93y+//ELp0qXx9fUFoFatWixYsIDo6IzfQL2KhLuJEgbb9CAhn0Qhg66wnuSVic3zSiHnh+SVsAUljOeSV+lI8spI8so5qfRmdL6tWrUiJCSEjRs38sYbb7BixQr27NnD3bt3GTp0KFWqVOGHH36gcePGFh18ypQpzJkzh3/++YciRYoAUKdOHQICAli+fDkAsbGxVKlShTfffJMpU6YAsGzZMsaMGcOCBQuoU6fOc/f9slpu3boFgEZj/XN905NOB3o9qFSgTnqLQ6sD7J/toAKNOnVdjqBWG/489HpDHfZg/PPX6+3zi6tUpl/SUX/QyTWA/f6gnyf5H37yn0XKuoRN2eq8lrwysSavLMqbp88PR3DmvJK8SVfJkW7P8fxpmT2v7Dq/kbxKXQM8+w9foZmT/FSlixcvOrgS5XEx55s6dOjAvn37mDhxIr6+vhw6dIjY2FgOHDgAQJkyZay6GhwaGgqAq6urcZurq6txOxiucGu12me+ByAkJMTiY2YE6ufcF6Cx+XJvlnteXfamUoHd3/ewd7ApJUiV8heuhD8LJ5Yef82SVwZW5ZUl/+aVcH4ooQZQxl+4MEvyPxeHjOfPkanzyp7nrxKyQgk1gOSVEzCrgf7ggw/44IMP+OOPP7h79y4//PADcXFx6PV6mjVrxsOHD/nuu+/45ZdfcHNzw8fHh0KFCtGiRYuXrnKWPXt2gFTNd0JCAnny5DG+zpIlCxqN5pnvAfDz83vhvrdv327OryaEEEIIIYQQQpjF7LdATp48yblz52jdujU6nY5WrVqRO3duqlSpQo0aNciVKxd169bltddeo0KFCuTLl8946f9F6tevD8CDBw8ASExMJDw8nHr16hm/x9PTkxo1ahi/BwxXnn19falUqZIlv6sQQgghhBBCCGE1s65AA4wfP57Hjx/Tp08fihYtynfffcewYcP44IMPCA8PZ9myZYwYMcKig1erVo26deuyb98+6taty/Hjx/H29qZTp0707NmT2rVr0717d/r3789HH31EaGgoOXLk4PDhw3Tv3h1vb2+Lf2EhhBBCCCGEEMIaZjfQpUuXpn///uTKlYs333wTgHfffZcSJUpw+/btFz6/+VWmTp3K6NGjGT16NPfv32f+/Pl4eXlx+fJlAgICAKhatSo//vgjw4cPJ1u2bJQrV47evXtbdTwhhBBCCCGEEMIaZq3C/bQBAwbwzTffEBkZybBhw+jbty/9+/fn2LFj6VGjEEIIIYQQQgjhcGZdgQ4KCuLEiRNUqlQJT09P9u/fz5YtW1Cr1bi6ulKuXDm++uorwsPDSUxMxNfXFzc3t/SuXQghhBBCCCGEsBuzrkD36NGDgwcPokpa+l2v1xv/P1nKbSqVirp16zJnzpx0KFkIIYQQQgghhLA/s65A58iRgwULFpAnTx5cXFzYsWMHq1atwt3dnbNnz9KyZUveffddQkJCOHfuHAcPHky1krYQQgghhBBCCJHRWfUZ6ODgYIYNG8Yff/zBqlWrmDJlCo0aNWLcuHHpUaMQQgghhBBCCOFwZj8HOqUCBQoYrzC3b9+eNWvWcO3aNZYtW2bT4oQQQgghhBBCCKWw6gr088THx/PNN9/wzTff4OJi9tOxhBBCCCGEEEKIDMFmDbQQQgghhBBCCOHMrLqFWwghhBBCCCGEyGykgRZCCCGEEEIIIcwgDbQQQgghhBCZ0O7dux1dghAZjqz2JcRLTJo0iRs3bjBjxgxHlyKEyAQSEhJISEjAy8vL0aUIIZzMhQsXmDt3LiEhISQvgXT58mUOHjzo4MqEyFikgRbiJUJCQrh7966jyxBCZBJ9+vThypUr7Ny509GlCCGczMCBA7lx40aqbSqVykHVCJFxSQMtxEtMnDjR0SUIITKRevXqUbRoUUeXIYRwQmFhYfzxxx9Uq1bNuG3x4sUOrEiIjEkaaCFe4tixY4SEhNCsWTNHlyKEyAS6du3q6BKEEE6qR48eREZGptomb9gJYTl5DrQQL/Hee+9x5coVjh496uhShBCZwG+//catW7cYPny4o0sRQjiZPn36cObMGYoUKWLcJp+BFsJycgVaiJeYMGECERERji5DCJFJ7N+/nytXrkgDLYSwuRo1arBjxw4ePXpk3CafgRbCcnIFWoiXkBVxhRD2lJCQQGJiIp6eno4uRQjhZG7dusXJkydp1aqVcdvcuXPp2bOnA6sSIuORBlqIl/jkk09kRVwhhN1cvXqVJ0+eULlyZUeXIoRwQtHR0Zw8eRK9Xk/lypXx9vZ2dElCZDhyC7cQLyEr4goh7Gn48OFcuXKFY8eOOboUIYSTuXTpEh9//DEPHz4EIG/evCxcuJCCBQs6uDIhMha5Ai2EEEIoxLFjxwgLC6Np06aOLkUI4WS6d+/OkydPqFatGiqVihMnTpAnTx6mT5/u6NKEyFDkCrQQQgihENWqVePkyZOOLkMI4YQ8PDxYtGhRqm1fffWVY4oRIgOTBloIIYRwkLt377J06VJCQkJIviHs+PHjbNmyxcGVCSGcjaurKzqdDrVaDYBerycqKsrBVQmR8UgDLYQQQjhI3759CQoKSrVNHisjhEgPKpWKxo0bU7ZsWdRqNefOnaN48eKOLkuIDEcaaCFSePDgAR4eHmTJksXRpQghMoH//vuPH374gcqVK6NSqdDr9fz++++OLksI4YSGDBnCJ598wrZt2wAoUqQIX3/9tYOrEiLjkQZaiBTatWuHTqfjwIEDji5FCJEJtGvXjrx581KgQAHjttatWzuwIiGEswoICGDDhg1cu3YNlUpF4cKF0Wq1ji5LiAxHGmghUqhZsyYlSpRItW39+vW0bNnSQRUJIZxZdHQ0w4cPp0qVKsZtR48eNV4hEkKItNi1axe+vr5UrVqVO3fuABif/Xz//n0WLFjA8OHD5aMjQlhAHmMlRAoDBgzgwIEDFCtWDDc3N8Dw3MRDhw45uDIhhDP65Zdf+OWXX1JtU6lUnD9/3kEVCSGcSfXq1QkMDGTVqlV069aNo0ePPvM9kjdCWEauQAuRQq1atdiyZQunTp0ybpN3ZYUQ6aV58+a4uLjQqlUrwLAq7tOPmRFCCGv9+uuvuLu7A/D555/z5Zdf4GcTagAAOShJREFU0qpVK9RqNXq9nq1btzq4QiEyHrkCLUQKjx8/Zu3atXTr1s24be3atfKZRCFEunn8+DHZsmUDDLdUZs+e3XgHjBBC2NL58+cpXbo0APHx8QQFBVGpUiXHFiVEBqN2dAFCKEm2bNlSNc9xcXEUK1bMgRUJIZzZwoUL6devn/H1hg0bOHPmjAMrEkI4qyFDhhibZ4DTp0/z77//OrAiITImuYVbiCRRUVEsWrSIlDdlPH78mO3bt7Nz504HViaEcFZbtmwxXn0GqFKlCt988w1///2344oSQjilmJiYVK9jY2OZMWMGXbt2dVBFQmRM0kALkcTb25v9+/dz4sSJVNvLly/voIqEEM6uXr16qSa1J0+e5L///nNcQUIIp3Pq1Cn69+/Po0ePUl2BBsiVK5eDqhIi45IGWogUoqOjmTdvHoUKFeLhw4fs2LGDBg0aOLosIYSTCgoKIiwsjGXLlnH69GnWr1//zKP0hBAiLSpVqsT8+fP58MMPqVevnnG7t7e3cQFDIYT5ZBExIVLo27cvM2bMML4OCwujXbt2cgu3ECJdXLhwgU8++YSHDx8CkDVrVmbMmEG1atUcXJkQwtls27aNJk2aOLoMITI8aaCFSOGjjz4iS5Ys1KhRg7i4ODZu3Mi9e/fYs2ePo0sTQjipqKgoTpw4gVarpXLlymTJkkUenyeESHcRERH89ttvqRYyFEK8mjTQQqRw9epVPv30U27dugUYngE9fPhwunTp4uDKhBDO6vLly4SHhxsXMPznn3/4+uuvcXGRT1kJIWwjKiqKd999l+joaOO26Oho4uPjOX78uAMrEyLjkQZaiKfExcVx+vRpQkNDKV26NAULFnR0SUIIJzVgwAC2bt36zPbz5887oBohhDPr2rUrN2/epECBAjx69Ijo6Gg+/PBDevTo4ejShMhQ5O1tIVJYvHgxefPmpWnTpgAsWrSIxo0bExAQ4ODKhBDOaM+ePTRq1IhSpUqhUqnQ6/Wy5oIQIl1oNBp27NiBRqMBYPfu3XL1WQgrSAMtRAqzZs1i5syZxteVK1dm5MiRLFq0yHFFCSGcVsWKFZk4cSI+Pj7GbTVq1ECv18vnoIUQNpWQkEBERITx2fNZsmRhyZIlDBo0yLGFCZHBSAMtRAr16tWjcuXKxtcPHjzg9OnTDqxICOHMxo4dy+DBg2nevLlx24YNG6hUqRLu7u4OrEwI4WwqVKhAo0aNKFasGHFxcVy5cuWZ50ILIV5NGmghUtBqtaxdu5YKFSoQHBzMpEmTyJcvn6PLEkI4qW+++YYDBw4YV/pPvvIszbMQwtY+//xzXFxc2LBhA2FhYVSqVIkxY8Y4uiwhMhxZREyIFK5cucIHH3xAaGgoAGq1mh9//JE333zTwZUJIZxRhQoVaNWqFVWqVEGtVqPX61m/fj2zZs2SJloIYVMJCQloNBrUajUAwcHBssaLEFaQK9BCpFCsWDHWrVvHP//8g06no1atWhQrVszRZQkhnNRrr73GsGHDUn0GulixYsYJrhBC2ErPnj2pXbs2n3zyCQB79+7Fx8eH1q1bO7gyITIWuQItxCvs2bOH5cuXU6FCBT766CNcXV0dXZIQwkn8+uuvHDlyJNVnoNeuXcuCBQscWJUQwhk1adKE1atXkyVLFgD+/fdfBg0a9NxH6QkhXkyuQAvxCkOHDqVp06a0aNGCZcuW0a1bN0eXJIRwEqdPn2b37t3PfAZaCCFsrU6dOsbmGWDfvn2EhYU5sCIhMiZpoIV4haZNm9KqVSsCAwOpXr26o8sRQjiRli1bUrhwYQoVKgQYGugNGzZIIy2EsLksWbLQvXt3KlasSHBwMJs3b+b11193dFlCZDhyC7cQSaKioli0aBHPOyX8/f1p0KABefLkcUBlQghnFR8fj6ura6pm+f79++TMmVM+By2EsKnY2FjGjBnD2rVr0el0VKpUiSlTpsjTRoSwkDTQQqTQuXNnTpw48dyv+fr68uuvv1KhQgU7VyWEcFa3b99m9OjRHD9+HDc3N95991369++Pi4vcICaESB+RkZEA+Pj4EBoaSo4cORxckRAZi4zQQqQQFRXFzJkzKV68OJGRkezatYt8+fJRvnx5Lly4wKxZs5g1a5ajyxRCOIlhw4Zx5MgR3N3d0Wg0zJkzh8TERIYMGeLo0oQQTubo0aOpXoeHh3P8+HGGDh3qoIqEyJikgRYihcDAQBo1amR8XaxYMZo3b862bdsoUqQIp0+fdmB1Qghnc+bMGb766iu6du2KRqPh8uXLfPnll44uSwjhhLp27frM2gqVK1d2UDVCZFzSQAuRwpMnTxg2bBhVqlQhJiaGrVu3Ehsbi06nY/Xq1ezdu5dhw4Y5ukwhhJMoW7Ys3bt3N74uXrx4qmfPL168WFb+F0LYRPny5alfv77x9bVr1wgICHBgRUJkTNJAC5HC119/Te/evfn7778BUKlUDB06lLNnzzJixAjef/99B1cohHAWFy9eJH/+/MydO5dcuXIBEBoaSnBwMGvWrAFgzZo10kALIWxiyJAh1KhRw/g6KipKLgoIYQVZREyIp8TGxnL69GnCwsIoXbo0AQEBqNVqHj16hL+/v6PLE0I4ieDgYJo1a5Zq5f/k/1epVMZHWZ0/f95RJQohnEjyG3NgyJpz586xcuVK+XiaEBaSK9BCPEdgYKDxtqYJEyYwYsQIaZ6FEDYVEBBAhw4dqFSp0nOf+Zz8TGghhLCFr776yvjmXLKqVas6sCIhMia5Ai1ECnPnzmXq1KnodLpU2+UKkBAiPTx58oQsWbKk2hYfH4+bmxsA169fp3Dhwo4oTQjhZD7++GPeeustwHCXS5YsWahduzYeHh4OrkyIjEUaaCFSqFatGgULFqR48eLGd2lPnjzJ5s2bHV2aEMJJ7Nq1C19fX6pWrcqdO3ee+fqCBQsYPnz4c69KCyGEtW7evElgYGCqbZGRkfj4+DioIiEyJmmghUihVatWzJw5M9WqlOfOnaNs2bIOrEoI4UyqV69OYGAgq1atolu3bs88mxXkrhchRNo97w26py1YsIARI0bYoRohnIc00EKksGHDBi5dusS7775r/IyQDC5CCFs6c+YM7u7ulCxZkpMnT/Lll1/SqlUr1Go1er2erVu3snbtWkeXKYTI4Nq3b09QUNBLv8fDw4OTJ0/aqSIhnIM00EKk8M4773DmzJlntsvVICFEejl//jylS5c2vj516hSVKlVyXEFCCKewe/duJkyYQJUqVQA4dOgQgYGB5MuXD4BHjx6hVquZPn26cd0FIcSrySrcQqTw6aefMmXKFMqXLw9g/Ay0EEKkl5TNc0REBEeOHJEGWgiRZjVr1mT8+PFUq1YNgIEDBzJ16lTj1xMSEvjiiy+keRbCQtJAC5FCjRo1mDFjBgULFjRue97nE4UQwhZSNs/J6tatS8+ePR1QjRDCmbi7uxubZzA8e/7evXvkyZMHMNztsmvXLgdVJ0TGJQ20yPTu37+Pu7s72bJlw8fH55nVKG/cuEHVqlVRq9UOqlAI4axy5MhBkSJFjK8fPXqEu7u7AysSQjir6tWr06RJE4oXL45Wq+XKlSupFk0VQphHGmiR6bVr146CBQuydOlSunXrxu3bt1N9/dGjR3To0MFB1QkhnNngwYNp166d8fWDBw9YtmyZAysSQjirQYMGERMTw6pVq9BqtRQsWJDx48c7uiwhMhxZRExkeosWLSJbtmy0bduWzZs3M3ToUMqVK2d8BuulS5c4fPiwg6sUQjijlI+Z0ev1/PvvvwwfPpzjx487sCohhLPS6/XExsYSExODu7s73t7eji5JiAxHGmghUkhISGDr1q20aNHCuG3t2rW0bt3agVUJIZxVqVKljG/WJcuXLx/bt293UEVCCGe1du1a1qxZw4IFCwCYO3cuDRs2pHjx4g6uTIiMRW7hFiIFV1fXVM0zgE6nc1A1QghnV7BgQeMjZgCyZs2a6pZuIYSwlUWLFpE9e3bj62rVqjFmzBiWLFniwKqEyHikgRYihf379zNt2jRCQkJIvjnj0aNHtG3b1rGFCSGc0jfffEPNmjUdXYYQIhN44403iI6ONr6+fv06QUFBDqxIiIxJGmghUvj66695/Pgxfn5+xm3yKQchRHp5VfO8ZMkSOnfu/Mxt3kIIYamgoCDi4+PZs2cPp0+fZuHCheTNm9fRZQmR4UgDLUQKWq2WVatWUaxYMeO2f/75x4EVCSGcWe/evbl06dJzv6bX63n48CHvv/++nasSQjijLl268Omnn7J79270ej0uLi4MGjTI0WUJkeFIAy1ECgMHDuTcuXOpGujExEQHViSEcGb58+fn2LFjFC1aFIDLly/j5+dH7ty50Wq1hIaGOrhCIYSzqFGjBhs2bGDPnj1otVrq1KlDoUKFHF2WEBmONNBCpLB69WouXrzItGnTjNtCQkJo1aqVA6sSQjirBw8esHfvXjw8PAB4/Pgxw4YNY9asWQBMmjTJkeUJIZxMvnz5ePfdd42v9+zZw/Lly6lQoQIfffQRrq6uDqxOiIxB7egChFCSdu3aodVq0ev1xv+EECK9qNVqY/MM4O3tzYULF4yv+/Xr54iyhBCZxNChQ/H396dFixYsW7bM0eUIkSHIFWghUmjQoAEFChSgVq1axm2rVq1yYEVCCGcWGxvLkCFDqF69OlFRUWzYsAEXF9PQ7OXl5cDqhBDOrmnTprRq1YrAwECqV6/u6HKEyBBUernEJsRLHT16lKpVq6JWyw0bQgjbun37Np988gnXrl0DDA3zL7/8Qu3atR1cmRDCmR08eBA3NzeqVq3q6FKEyHCkgRaZ3rvvvktgYCCTJk1i7NixxMTEpPr6iRMn2Lx5s4OqE0I4O51Ox5EjR3jy5AnVqlUjR44cji5JCOGE+vfvT9myZcmePTujR49GpVIxcOBAevXq5ejShMhQ5BZukenlzJmTLFmyABAYGMj333+f6uvy/FUhRHoJCgpi3bp1DB06FIDffvuN9u3b4+Pj4+DKhBDOxt3dnV69etG+fXsKFSrEihUrGDVqlKPLEiLDkQZaZHrTp083/n+7du24d+8eQ4cONTbOU6ZMcVRpQggnN2rUKNzd3Y2vixcvzujRo/nxxx8dWJUQwhl5enpy8eJFgoKC6NmzJ1myZCFr1qyOLkuIDEcaaCFSyJIlC1999ZXxdVxcHE2aNHFgRUIIZ1anTh3i4+ONr7VaLXv27HFgRUIIZ+Xr60ubNm1wcXGhZcuW7Nq1i7Nnzzq6LCEyHGmghUgSFRXFokWLUj266vHjx2zfvp2dO3c6sDIhhLO6e/cuPj4+xMXFcerUKcaPH4+np6ejyxJCOKHBgwfz2muvkS9fPvLnz09wcDCTJ092dFlCZDjSQAuRxNvbm/3793PixIlU28uXL++gioQQzq5SpUqMHTvW+PxVvV4vz34WQqSLr776CpVKxaRJkwBo1KiRgysSImOSBlqIFKKjo5k3bx6FChXi4cOH7NixgwYNGji6LCGEk+rcuTM5cuRgy5YtJCYm0qBBA9q1a+fosoQQTujEiROULVs21baLFy9SsmRJB1UkRMYkj7ESIoW+ffsyY8YM4+uwsDDatWsnt3ALIdIsJCQEPz8/R5chhMikfv75Z4KCgmjcuLFx8cINGzYwb948B1cmRMYiV6CFSCE2NpbPP/+cGjVqEBcXx8aNG9FqtY4uSwjhBJYvX87//vc/OnfuTLt27YyPzxNCCHsIDQ1lz5497N27FzB8ZEQe1SmE5eQKtBApXL16lU8//ZRbt24BhmdADx8+nC5duji4MiFERpeQkMA///zD0qVLuXjxIm+99RZdunShVKlSji5NCJEJXLp0iSlTptCsWTPA0EDv27dPHtcphIWkgRbiKXFxcZw+fZrQ0FBKly5NwYIFHV2SEMLJBAUFsWTJEjZs2EDp0qXp0qULzZo1w8VFbgwTQqSf+/fvkzt3buPrx48fky1bNscVJEQGJA20ECkMGTIEtVrNxIkTHV2KECITePLkCX/99RfLly8nOjqaTp068c4776Sa4AohhC0cOHCAVatW8eOPPwIwZ84c2rZtK3kjhIXUji5ACCU5ceIEMTExqbZdvHjRQdUIIZxdlixZ+PDDD9myZQvjx4/n3LlzNGnShAEDBnD06FFHlyeEcCITJ07k4cOHxtdVqlRhxIgRDqxIiIxJ7hUTIoVWrVoRFBTEn3/+KStUCiHsqn79+tSvX5/g4GCWLl1K//79OXTokKPLEkI4iWbNmqW6SBAWFsbx48cdWJEQGZM00EKkICtUCiEcLSAggKFDh/LZZ585uhQhhBO5fPkyLi4uXL16ldOnTzNlyhSyZ8/u6LKEyHCkgRYihS5duvDgwQPjCpWAsZkWQghbCwoKYt26dQwdOhSA3377jfbt2+Pj42O8C0YIIWzhzTff5PPPP2f9+vWA4SLBmDFjHFuUEBmQLCImRAqxsbGEh4cbF9QICwtDpVLJCpVCiHTRoUMH3N3dWbJkCfDsIj9CCGFLZ86cYcuWLWi1Who0aMBrr73m6JKEyHDkCrQQKXzxxRf88ssvxte3bt1i69atDBo0yIFVCSGcVZ06dYiPjze+1mq17Nmzx4EVCSGcWYUKFahQoYLx9eLFi+nWrZsDKxIi45EGWgggMjKSJ0+eEBMTw927d9Hr9ej1ei5cuMDSpUulgRZCpIu7d+/i4+NDXFwcp06dYvz48Xh6ejq6LCGEE1q6dCnTp0/n8ePHqbZLAy2EZaSBFgK4f/8+AwYM4Nq1azRq1CjV1wIDAx1UlRDC2VWqVImxY8eybNkywPCZxH79+jm4KiGEM/rxxx9xcXGhWrVqxm3yqE4hLCefgRYiyZ07d/jggw9o06aNcZu3tzdNmjQhICDAgZUJIZzZpk2b2LJlC4mJiTRo0IB27do5uiQhhBNq1KgRc+bMoXjx4sZtBw8epFatWg6sSoiMRxpoIVI4ceIEVapUcXQZQohMYufOnbi5uVGnTh1HlyKEcHLr1q0jJCSE7t27G7ctXbqUzp07O64oITIgaaCFSOFlj5QRQghbq1GjBp6enuzevdu4LTIyUjJHCJFm77zzDo8ePUq17eHDh/j7+6NSqQDDR9jOnj3riPKEyLDUji5ACCUZNWoUZ86cMb4uXrw4o0ePdmBFQghnNnDgQN59991UK3Enfx5aCCHS4u233+b+/fvGhVH1ej3+/v4AxtfJjbQQwnyyiJgQKcgjZYQQ9rRixQouXbrEtGnTUm3/5JNPHFSREMJZtGjRAnd3d95+++0Xfs+WLVukkRbCQnIFWogU7t69S1xcHHFxcRw+fFgeKSOESFcDBgwge/bsVKtWzfhf8hUiIYRIiyxZsqRqnletWpXq66dPn6Zw4cLSPAthIbkCLUQK8kgZIYQ91a1bl8mTJ1O7dm3jtpQfIxFCCFvZuXMn7du3N77Onj07AwYMYM2aNf9v786jsqzz/4+/7lsFEcNRUzQVMRdEGrBSzFxwHSsXyKWs1JrB1Mo0JXNfv3omnbCZtKZsvpNLjbliZfZNcwlzBxVUNsclZdJGRDQUQbmv3x/W/btJsU39yMXzcc6c4/XhnnOe/wS8ua7r8zEXBZRAbCIG/AhHygAwxeVyad26derSpYvpFAA2sXfvXi1ZskQ7duxQixYt3OtpaWk6evSo9uzZY7AOKHm4Aw142Lhxo+644w7Nnj3bdAqAUmDAgAFFri9cuKCAgAAGaAA3TNOmTZWYmKi4uDjFxcW51ytUqHDV9yAAP4070IAHjpQBcCs1btz4qrVatWpp/fr1BmoA2NnkyZM1depU0xlAiccdaMDD8OHDde7cORUUFMjLy0vSlSNl2BEXwM3w/PPPa9iwYe7rlJQUZWZmGiwCYFdTp07VhQsXtGfPHlmWpXvvvVe+vr6ms4AShzvQgIfIyEhlZGRctZ6ammqgBoDdXbp0SeXKlXNfnzhxQn379i3yFAwA3AgZGRkaOHCgTp06JUmqWbOm/vnPfyowMNBsGFDCMEADHtavX6+JEyeqfv367rWjR49q8+bNBqsA2FXHjh3d/7YsS6dPn5a3t7d27txpsAqAHT3zzDM6d+6cmjVrJofDod27d6tGjRqaM2eO6TSgROERbsADR8oAuJWysrJUtWpVSZLD4VD9+vX1zDPPmI0CYEvly5fX/Pnzi6yNGTPGTAxQgjFAAx68vLyKDM+SVLZsWc2YMUOhoaHq3r27oTIAdjRhwgT16dPHdAaAUqBcuXJyuVxyOp2Srjz1cv78ecNVQMnDAA38hOeff15hYWG68847tXjxYj3xxBOmkwDYxA+/yP4gKSlJFSpUUMOGDQ0VAbArh8Ohjh07KiQkRE6nUwcOHOB7DfArOH/6I0Dp1qBBA0VGRqply5b8oAFwQ23cuLHIdeXKlTVq1ChDNQDsbNSoUfL29tYXX3yhtWvXytvbW+PGjTOdBZQ4bCIGFOPrr7+Wt7e3atSoYToFgM3s3btXS5Ys0Y4dO9SiRQv3elpamo4ePao9e/YYrANgV4WFhTp8+LAcDofq1aunMmXKmE4CShwe4QY8TJ8+XXfeeafq1aun4cOHy+FwaNq0abyjCOCGatq0qRITExUXF6e4uDj3eoUKFTRgwACDZQDsrEyZMkWepnvzzTe1dOlStWnTRi+++KL8/f0N1gElA49wAx5OnDihIUOGaNGiRapRo4ZWrlyp+Ph401kAbCg6OlqPP/640tLS3P/bvXu3RowYYToNQCmxYMEC9ezZU1OnTtWaNWtM5wAlAgM04KFGjRr673//qz179qhLly4KDg5WtWrVTGcBsKmpU6detZaWlibergJwK4wZM0Y9e/ZUmTJl1K1bN9M5QInAI9yAh4KCAj300ENyuVzq2rWrMjIyOAcawA0VExOju+66SzExMXr77bd1+fLlIl/ftGmTli9fbqgOgB1lZ2dr5cqV2rdvn3Jzc1W5cmU1b95cPXr0kI+PjyRxwwD4mRigAQ/jxo1T48aNFRAQoPr162vVqlUaM2aM6SwANnLkyBEVFBRIki5evKi33367yNcdDoeJLAA25HK5NHv2bC1atEj5+fmqUKGCKlasqHPnzmn16tWaPXu2hg4dqv79+5tOBUoMBmjAQ3Z2ti5evKg2bdpIkry9vdW0aVOzUQBsZeXKle5/P/XUUzp69KhiYmLkdDplWdZVAzUA/BoXL17UkCFD5HA4NHPmTN1///1F7jL/5z//0a5du7RixQolJydr1qxZ/AEP+Bk4xgrw0L9/f7lcLn3wwQeSrpzRunnzZk2aNMlwGQC7KiwsLHKUTHp6uoKCggwWAbCDP//5z+rYsaPCw8N/8rNbtmzRnj17NHTo0FtQBpRs3IEGPAQFBRXZvMfX11dr1qxhgAZwU+Tm5mr16tU6ffq0+3sP70AD+K1cLpeio6NVvXp1SZJlWfrkk0/Uo0ePa36+VatWCg4OvpWJQInFAA14KCgokK+vryTp+PHjio2NldPJZvUAbo7nnntOu3btKrLGI5QAfiun0+kenqUr31emTJmi7777Tn369JGXl9dV/58qVarcykSgxGKABjxUr15d77zzjj7++GNlZ2fLsiw99dRTprMA2NT+/fs1cuRIhYWFyeFwyLIs7j4DuOEsy1KLFi3UoUMHLVy4UD4+PoqMjFTFihVNpwElDu9AAx5cLpfee+89rV27VpcvX1a7du00ePDga/6lFgB+q5iYGA0cOLDIo5MHDx5Uw4YNDVYBsKOTJ0+qRo0aOnnypObMmaO1a9fqiSee0MiRI02nASUKd6ABDzk5OXrssccUHR0tSdq3bx/DM4Cbpnr16poyZYp7539JWrdunT766CODVQDsxuVy6eWXX5a/v78+//xzXb58WU2aNOGPdcCvwAANeBg0aJC6du2qP/7xj5KunNeakJDgvgaAG6mwsFBJSUlKSkpyr/EONIAbzel0KiEhQWXKlFHt2rXVr18/zn4GfiV2RwI85OfnF3nnOTAwUO+9957BIgB29uijj6pfv35auHChFi5cqAULFqhr166mswDYUEhIiNatW6fPP/9cBQUFeumll3To0CHTWUCJwx1owENoaGiRR7ZXr16t/Px8g0UA7KxBgwYaNWqUvL293WucAQ3gRnO5XBozZowyMzM1ffp0bdq0SS6XS1lZWXr//fdN5wElCgM04KFevXp66KGHFBISoszMTCUnJ+uRRx4xnQXApn6427x27Vr32u9+9ztDNQDsyul06umnn5ZlWbIsSw888ICGDBmiBx54wHQaUOIwQAMe/vSnPykvL0//+te/5HK59Mgjj2jixImmswDY1F133aWwsLAia19++aUiIiIMFQGwK5fLpYiICD333HNq2rSp6RygxOIYK+AnHDp0SPXr1zedAcCGxo8fr23btunee+91vz6SmJhY5I40APxWlmVp/vz5bIoK3ADcgQY85ObmavXq1Tp9+rR++NvSpk2btHz5csNlAOwoMDBQK1as0DfffONeYxduADeaw+FQo0aNFBMTo9jYWEnSO++8o6ioKPn7+xuuA0oWBmjAw3PPPaddu3YVWeOXWQA3S48ePZSTk6Mnn3xS0pW7RGvWrDFcBcCOZs2apUqVKrmv77vvPk2YMEHvvvuuwSqg5GGABjzs379fI0eOVFhYmBwOhyzL4u4zgJvG399fI0aMUNmy///H8R/+8AeDRQDsqkuXLsrLy3NfnzlzRomJiQaLgJKJARrw0KFDB7Vp00bBwcHutSpVqhgsAmBnq1atKnJ97tw5HTt2TBMmTDATBMC2Dh48qLJly+rQoUNKSkrS7NmzVblyZdNZQInDAA14qF69uqZMmaI2bdq419atW6ePPvrIYBUAuxozZoz7NRHLsuRwOBQSEmK4CoAdPfTQQxoxYoRWr14t6cr3nClTppiNAkogBmjAQ2FhoZKSkpSUlORe4x1oADdLq1at1K1bN/d1RkaGqlatarAIgF116dJFH374odauXavCwkK1a9dOLVq0MJ0FlDgcYwV4SE1N1YoVK9zvIFqWpWXLlum1114zXAbAjlJTU4u8MpKXl6fBgwdr4cKFBqsAlBarVq1SVFSU6QygRGGABjxYlqVLly65z2MtKCjQ6dOnVbNmTcNlAOxo7ty5Ra5TUlK0detW7d2710wQANvasmWL3njjjSJHdZ46dUrJycmGy4CShUe4ge9dvHhR+/btK7KWnZ2tf/7zn1qyZImhKgB29uMBWpI6depkoASA3Y0bN045OTm8JgL8RgzQwPe8vLw0ZcoUHT58uMg6P2gA3CjHjh2Tt7e3/P39JUndunXTY489JunKfgt+fn4KCgoymQjApgoLC7VixQo1aNDAvfbZZ58ZLAJKJgZo4HtOp1PlypXTwIEDVbduXWVlZWnHjh0aNmyY6TQANvHss8+qUqVKWrp0qSRp7Nix/JEOwC0xfPhwHThwoMgAffnyZYNFQMnEAA14CAgIUExMjPu6d+/eev75592/7ALAb5Gbm6vp06e7r+fNm6exY8cW+cyXX36piIiIW50GwOZWrlyp9PR0vfHGG+6106dPq3v37gargJKHARrwcPLkSX344Ydq3ry5CgoKtGLFCh09etR0FgCbaNq0qbKzs7V//345HA6dOnVKu3btcn89Pz9ff//73xmgAdxwPXv21PTp08X+wcBvwwANeHj++ec1dOhQFRYWSrqyK3fv3r0NVwGwizFjxmjQoEFF/jDn+Q6iZVmcPQ/gpmjXrp1q166tli1butdWrFhhsAgomTjGCviRjIwMff755zpz5oyaNGmiyMhIlStXznQWAJu4dOmS4uPjlZKSolWrVunRRx91f82yLG3YsEFxcXEGCwGUdC6XS7NmzdILL7ygO+644yc/f/r0aS1atEgvvfTSzY8DSjgGaJR6+fn5KlOmjMqWvfYDGV988YU6dOggp9N5i8sA2F1iYqLuv//+ImsJCQlq1qyZoSIAdvH111/rpZdeUvfu3dW7d2/5+fld9ZmsrCwtX77cfUZ05cqVDZQCJQsDNEq9zp07q27duvrHP/6h4cOH68yZM0W+/u9//1tbt241VAegtImPj9eHH36o0NBQRUdH8wQMgF/txIkTGjp0qNLS0nT33XcrMDBQPj4+ys3N1aFDh3Ts2DFFREToL3/5y8+6Uw1A4pYaSr0OHTooPDxcktS2bVvt3LlTx48fV2ZmpjIzM/Xdd98ZLgRQmowePVp33nmnHnnkES1evNh0DoASrGbNmlq+fLn++te/yt/fXzt37tSaNWuUnJysoKAgLViwQG+//TbDM/ALcAca8JCXl6dly5ZpwIAB7rUFCxbo6aefNlgFoDSZNGmSunfvrubNmys1NVXBwcGmkwAAwPcYoIGfsGXLFrVq1cp0BgCb27Ztm7y8vK56JxoAANw+OMYKpdrAgQNVUFBw3c8cPnxYX3311S0qAlCavPjiiwoJCVHlypU1efJkORwODR8+XEOGDDGdBgAAroEBGqXaxYsXlZCQcN3PcCYrgJvF29tbQ4YMUa9evRQYGKilS5dq0qRJprMAAEAxGKBRqjVt2lSzZ89W9erVi/3M66+/fguLAJQmPj4+Sk9PV0pKigYNGiQ/Pz9VqlTJdBYAG7p06RK7+gM3AAM0SrWXX375qrUTJ04oKSlJ5cuXV8uWLTVs2DADZQBKAz8/P0VGRqps2bLq1q2bNm3apP3795vOAmBDXbt2lWVZWrdunekUoERjgAY8rF69WuPGjdOlS5ckSQEBAZo3b57q1q1ruAyAHY0cOVLh4eG66667VLt2bWVmZuq1114znQXAhu666y6FhYUVWfvyyy8VERFhqAgomRigAQ+vv/66ypcvr8jISPn5+SkhIUFTpkzRe++9ZzoNgA3t2rVLeXl5Kl++vKKjo+Xt7a2JEyeazgJgQ7Vq1dInn3yizMxMeXl5SZISExMZoIFfiAEa8HDu3DnFxcWpdu3akiTLsvTMM8+4v37ixAnVrFnTUB0Au/nb3/6mV199VXPnztW+ffvUt29fxcbG6s033zSdBsBmAgMDtWLFCn3zzTfuNTZKBX45BmjAQ6dOneTv7+++djgcRa4XLVqkV155xUQaABsKCQlRnTp1tGnTJnXo0EHjx4/X1KlTTWcBsKEePXooJydHTz75pKQrNwnWrFljuAooeRigge9t3rxZKSkp6tWrl373u99Jks6cOaMzZ85owIABkqSUlBQGaAA3zKlTpxQTE6OzZ8+qffv2kqTjx48brgJgR/7+/hoxYoTKlr3y639+fr4efvhhw1VAycMADXyvRYsWOnnypM6ePXvV17KysiTxqBOAG+tPf/qT/ud//kddu3ZVRESEpkyZoqCgINNZAGzm/PnzV+2+nZOTo6VLl3IXGviFHJZlWaYjgNvF6tWr1a1bt2K/PmfOHL344ou3sAiA3R08eFANGzaUJGVkZKhRo0aGiwDYUa9evXTgwAH3tcPhUO3atTnWCviFGKCBH/nxOdDe3t6mkwDY1MyZM5WcnKwPPvhAkvTBBx+oevXq6ty5s+EyAHYTGRmpJ554QnXr1lVWVpbi4+PVv39/hYaGmk4DShQe4QY8XOsc6HfeeUeBgYFmwwDYUkZGhqpVq+a+vueeezRmzBgGaAA3XN26ddW3b1/3devWrfXUU0/xCDfwCzFAAx6udQ701KlTOQcawE1x//336+LFi+7r9evX6+TJkwaLANhVVlaWYmNj1bx5cxUUFOijjz5STk6O6SygxGGABjxwDjSAWyk9PV1Hjx6Vy+VSUlKSEhISdN9995nOAmBDr7zyigYNGqR//OMfkq78jjNo0CDDVUDJwwANeOAcaAC30ujRozVkyBD3L7QBAQGcAw3gpmjatKnWrFmjzZs3Kzs7W02aNFHLli1NZwElDpuIAd/bvHmzXnvtNVmWddU50HfffbekK+dAJyQkGKwEYDeWZenIkSMqLCzU3Xffrby8PFWsWNF0FgCb+eyzz1SlShW1aNFCkrR06VJ17txZlStXNlwGlCwM0MD3CgoK1KZNm2ueA/0Dh8Oh1NTUW1gFwO4OHz6s7Oxs/fDj+MMPP1RsbKzhKgB207p1a82YMUMRERGSpISEBM2fP19z5841XAaULDzCDXzPy8tLEydO/MlzoAHgRpk2bZoWL1581ToDNIAb7b777nMPz5KUl5enrVu3GiwCSiYGaMDDj4fn/Px8HTp0SE2aNJEkvfjiiyayANhUXFyc2rdvr+DgYDkcDlmWpfj4eNNZAGyoXLlySkxMVGhoqI4fP67XX39dVatWNZ0FlDgM0MD3zp8/r/nz58vzrYacnBytX79eGzduNFgGwK6CgoI0evRo1a1b1732yCOPGCwCYFdPPvmkoqOjlZ+fL+nK/gsTJ040XAWUPLwDDXh48skntXv37iJrv//977Vs2TJDRQDsbMmSJUpOTlZUVJR7bdGiRXrjjTfMRQGwrUOHDmn58uWyLEutW7dW69atTScBJQ4DNOAhKipKMTExCgwM1KlTp7Rhwwa1a9dOzZo1M50GwIYGDRqk+Ph4ORyOIutsVggAwO2JR7gBD7Vq1VKbNm0kSXXq1FG9evXUs2dPHuEGcFP88Y9/VF5envtYGcuytHnzZsNVAACgONyBBjxER0fLz89P4eHhys/P15o1a3Ty5Ek29QFwU+Tn5ysnJ0f+/v7utbS0NDVu3NhgFQAAKA53oAEP48eP1+DBg/XZZ59JunLu8/jx4w1XAbCTlJQUVahQQYGBgfL29i4yPEvSxo0b1ahRIzmdTkOFAACgONyBBn4kPz9fSUlJys7OVnBwcJHdcQHgtwoPD1dAQICWL1+uqKgopaenX/UZ3oEGAOD2xAANAMAttG7dOvn6+urBBx/UV199pVGjRqlt27ZyOp2yLEs7d+7Uhg0bTGcCAIBrYIAGAMAQy7K0fft2tWzZ0r0WHx+vtm3bGqwCAADF4R1oAAAMOXz4sHx8fJSdna0///nPKl++vIYPH246CwAAFIMdSgAAMGTs2LHKz8/XnDlztHr1ap0+fVrTpk0znQUAAIrBAA14+Mtf/qKVK1e6r+fNm6cDBw4YLAJgZ/fcc49atGihdevWqU2bNnrrrbdUuXJl01kAAKAYDNCAh7i4OIWGhrqvW7ZsqalTpxosAmBn2dnZmjlzprKystS+fXv3GgAAuD0xQAMeOnXqpAYNGrivDx06pIMHDxosAmBnjz76qFatWqWmTZuqQ4cOmjRpkry8vExnAQCAYrALN+Bh7NixqlOnjsLCwnT8+HG98cYbqlGjRpHHugHgZjp27JgCAgJMZwAAgGtggAY8nDhxQtHR0Tpy5Igsy1LFihX11ltvKTw83HQaABtatWpVketz587p2LFjmjBhgpkgAABwXQzQwI8UFBRo69atcrlcatq0qapUqWI6CYBNNW7cWA6HQz/8KHY4HAoJCdHy5csNlwEAgGvhHGiUeikpKapQoYICAwMlSV5eXmrXrp3763//+981ePBgOZ1sGQDgxmrVqpW6devmvs7IyFDVqlUNFgEAgOvhDjRKvfDwcAUEBGj58uWKiopSenr6VZ9JTU01UAbA7lJTUxUcHOy+zsvL0+DBg7Vw4UKDVQAAoDjcgUapN2PGDPn6+kqSXn75ZY0aNUpt27aV0+mUZVnauXOn4UIAdrV+/XqtX7/efZ2SkqLk5GSDRQAA4Hq4Aw38yLZt29SyZUv3dXx8vNq2bWuwCIBdNW7c+Kq1Tp06ae7cuQZqAADAT+EONODh1Vdf1ZgxY9zX27dv1/nz5w0WAbCzbt266bHHHpN0ZQMxPz8/BQUFGa4CAADFYYAGPGRmZha59vPz0+jRo/Xwww8bKgJgZ2PHjlXVqlV19uxZeXl5ycfHx3QSAAC4DgZoQNLevXs1ceJEZWZmqmPHjpIky7KUlZWlChUqGK4DYFffffednn32WaWmpsrhcKhly5aaNWsWO3EDAHCb4h1o4Hvbt2/XCy+8oCZNmrjXfH191bt3b3Xq1MlgGQC76t+/v3bt2qUGDRqofPnySktL00MPPaTXXnvNdBoAALgG7kAD33vggQc0evRo9/uIAHCzpaen6/3331ezZs0kSUeOHNHAgQMNVwEAgOIwQAMeOnfurG+//VZ+fn565513VL58eT399NO8lwjgpnjwwQfdw7Mk1atXj+83AADcxniEG/DQp08fPfXUUzp06JDeffddBQUFqXHjxpo5c6bpNAA28Prrr+u///2v+/rAgQNq2LChvLy8JElZWVn66quvlJqaaioRAABcB3egAQ9NmjRRVFSUOnbsqGbNmun999/XhAkTTGcBsIn09HRt2rSpyFpGRkaRa4fDcQuLAADAL8EADXjIzc3VokWL9J///Ef9+vWTJF28eNFwFQC7aNiwoSIjIxUaGmo6BQAA/AoM0ICH1q1ba/z48apZs6a6dOmimTNn6uTJk6azANjE0KFD5e3tfd3PuFwupaamqm7duhyjBwDAbYZ3oIEfyc3NlY+PjxwOhy5cuKCKFSuaTgJQigwZMkRff/21WrVqpYcfflj333+/6SQAAPA9p+kA4HbyySefaNiwYSpTpoycTqf+9a9/6eDBg6azAJQi+/btU58+fTRhwgSlp6ebzgEAAB64Aw146NmzpypXrqz//d//lSTt3r1bsbGx+uCDDwyXASgtMjIyVKtWLfn6+sqyLDYVAwDgNsI70ICHP/zhD7pw4YL7+siRI0pJSTFYBMDOtm/frpycHIWEhGj06NHy9vbWxIkT5evrK4kduQEAuN3wCDfgISUlRRkZGYqPj9ecOXM0Y8YM1axZ03QWAJv629/+puDgYM2dO1f79u1TgwYNFBsbazoLAAAUgwEa8NCvXz/t2LFDgwcP1ptvvqmCggKNHDnSdBYAmwoJCVGdOnW0adMmdejQQePHj1f16tVNZwEAgGLwCDfgITw8XJ9++qni4+NVWFioVq1aKTAw0HQWAJs6deqUYmJidPbsWbVv316SdPz4ccNVAACgONyBBn6kUqVKioiIUPv27eXl5aV58+bp8uXLprMA2FB0dLSOHz+url27KiIiQlOmTFFQUJDpLAAAUAx24QY8TJ8+/Zo7bqemphqoAWB3KSkp8vb2Vv369U2nAACAn4EBGvBw7733ql69emrUqJEcDocsy1JCQoK++OIL02kAbKhFixZyOp3atm2be+3SpUsqV66cwSoAAFAc3oEGPNSrV0/vv/++KlSo4F7bunWrwSIAdvbYY4/pjjvuKLK2atUq9enTx1ARAAC4Hu5AAx4OHjyoBQsWKCoqSj/8p7Fs2TJNmzZN5cuXN1wHwG4GDBigpKQk3XHHHfL29pYkffvtt9q/f7/hMgAAcC0M0ICHmJgYffrpp3I4HEXWeQcawM3w8ccfa/z48apWrZp77cyZM9qzZ4/BKgAAUBwe4QY8fPHFF2rVqpXCwsLkdDplWZY2btyogoICeXl5mc4DYDMdO3ZUuXLl9PDDD7vXtmzZYrAIAABcDwM04CE4OFhz586Vj4+Pey0iIsJgEQA78/X1LTI8S5KXl5dcLpecTk6aBADgdsMj3ICHZcuWKTk5WT169HCvLV68WLGxsVc91g0Av0bfvn0VEBCgWbNmadq0acrLyyvy9d27d+vzzz83VAcAAK6HO9CAh08//VTbt2/X8uXLi6zPnj3bUBEAu6lWrZr8/PwkSQEBAXr11VeLfJ0/1gEAcPtigAY89OrVS/7+/qpTp44kybIsbdq0yWwUAFuZM2eO+989e/bUyZMnNXr0aPfgzB/sAAC4ffEIN+AhNzdXXl5eRTYMO3TokAIDA1WmTBmDZQBKg/z8fKWnpys0NNR0CgAAuAYGaMCDy+XSggUL3O8fdu3aVf379zdcBcCOzp8/r/nz58vzx3BOTo7Wr1+vjRs3GiwDAADF4RFuwMO8efP017/+1X2dlJQkh8Ohfv36mYsCYEu+vr7asmWLdu/eXWT997//vaEiAADwUxigAQ//93//p7feekvNmzeXw+FQQkKC3nvvPQZoADfFhQsX9O677yowMFCnTp3Shg0b1K5dO9NZAACgGAzQgId77rlHHTp0cF+3a9dOW7ZsMVgEwM5q1aqlNm3aSJLq1KmjevXqqWfPnjzCDQDAbYoBGvDw73//W0uWLFFYWJjKlCmj5ORkJSUlmc4CYFMXL17UiBEjFB4ervz8fK1Zs0aFhYWmswAAQDEYoAEP3bt31+TJk4ucwzp27FiDRQDsbNy4cRo8eLA+++wzSVfOgB4/frzhKgAAUBx24QZ+ZNWqVdqwYYMcDoc6deqk7t27m04CYGP5+flKSkpSdna2goODVbduXdNJAACgGAzQwE/YvHmz+x1FALiRzp49q9mzZysxMVFeXl564okn1KdPH9NZAACgGAzQKNUmTZqkS5cuXfczaWlpiouLu0VFAEqTF154QevXr3dfOxwOvfzyy4qOjjZYBQAAisM70CjVqlSporfffvu6n3E6nbp8+bLKluU/FwA31rZt29S/f38NHDhQlSpV0o4dOxQbG8sADQDAbYqJAKVa79695XK59Pjjj8uyLE2ePFnPPfecatasKUk6ceKEEhMTGZ4B3BSBgYFFNg2LiIjQxx9/7L7+6KOPFBkZaSINAABcA1MBSrXatWtr4MCB8vPzkyR5eXmpWbNm7q/feeedeuWVVzR48GBTiQBs6ptvvlFoaKjWrFmjatWqSZKys7N15swZJSQkyLIsrVixggEaAIDbCO9AAx4ef/xxNW7cWG3btpXL5dKKFSu0a9cuJSYmmk4DYDNpaWmKiooqcmyeJFmWVWQtNTX1VqcBAIBicAca8DBq1Cg9++yzWrp0qaQrv8g++uijhqsA2FHjxo3VuXNnNWrU6KohWrry/efLL780UAYAAIrDHWjgR77++mstW7ZMeXl5CgsLU/fu3a/5yy0A/Fbffvut/P39i/16cnKyQkNDb2ERAAC4HgZoAAAMSUlJ0SeffKLRo0dLkhYsWKBevXqpYsWKhssAAMC1OE0HAABQWk2aNEnJycnu64YNG2ry5MkGiwAAwPXwDjQAAIa0atVKBQUF7uvCwkLFx8cbLAIAANfDAA0AgCEnTpxQxYoVlZ+fr71792rGjBny8fExnQUAAIrBAA0AgCH33nuvpk6dqsWLF0u6svP20KFDDVcBAIDisIkYAACGbNy4UXl5eVq3bp0KCwvVrl079ezZ03QWAAAoBgM0AACGhIeHy8fHp8h5z7m5uezCDQDAbYpduAEAMGT48OHq27evLl265F774XFuAABw++EONAAAhkRGRiojI+Oq9dTUVAM1AADgp7CJGAAAhgwbNkwTJ05U/fr13WtHjx41FwQAAK6LO9AAABhSUFCghIQEPfjgg+615ORkhYaGGqwCAADFYYAGAAAAAOBnYBMxAAAAAAB+BgZoAAAAAAB+BgZoAAAAAAB+BgZoAAAAAAB+BgZoAAAAAAB+BgZoAAAAAAB+BgZoAAAAAAB+hv8HJpH852M9AwwAAAAASUVORK5CYII=\",\n 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\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n 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\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for col in card._feature_names:\\n\",\n    \"    feature_table_train = sp.feature_bin_stats(train, col, target=target, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", combiner=feature_pipeline.named_steps[\\\"combiner\\\"])\\n\",\n    \"    feature_table_test = sp.feature_bin_stats(test, col, target=target, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", combiner=feature_pipeline.named_steps[\\\"combiner\\\"])\\n\",\n    \"    \\n\",\n    \"    psi_table = sp.psi_plot(feature_table_train, feature_table_test, desc=col, result=True, plot=True, max_len=35, figsize=(10, 6), labels=[\\\"训练集\\\", \\\"测试集\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n 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\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n 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\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for col in card._feature_names:\\n\",\n    \"    feature_table_train = sp.feature_bin_stats(train, col, target=target, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", combiner=feature_pipeline.named_steps[\\\"combiner\\\"])\\n\",\n    \"    feature_table_test = sp.feature_bin_stats(test, col, target=target, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", combiner=feature_pipeline.named_steps[\\\"combiner\\\"])\\n\",\n    \"    \\n\",\n    \"    csi_table = sp.csi_plot(feature_table_train, feature_table_test, card[col], desc=col, result=True, plot=True, max_len=35, figsize=(10, 6), labels=[\\\"训练集\\\", \\\"测试集\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mSignature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcsi_plot\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mexpected\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mactual\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mscore_bins\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mlabels\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m'预期'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'实际'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mdesc\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m''\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0msave\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcolors\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m'#2639E9'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'#F76E6C'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'#FE7715'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfigsize\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m15\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;36m8\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0manchor\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.94\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mwidth\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.35\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mresult\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mplot\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmax_len\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mhatch\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m \\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m      ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/utils.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m      function\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sp.csi_plot?\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"7.5 评分卡模型导出 pmml 文件\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mSignature:\\u001b[0m \\u001b[0mcard\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mscorecard2pmml\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mpmml\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mstr\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m'scorecard.pmml'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mdebug\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mbool\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m\\n\",\n      \"export a scorecard to pmml\\n\",\n      \"\\n\",\n      \"Args:\\n\",\n      \"    pmml (str): io to write pmml file.\\n\",\n      \"    debug (bool): If true, print information about the conversion process.\\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m      ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/model.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m      method\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"card.scorecard2pmml?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"python: 3.10.8\\n\",\n      \"sklearn2pmml: 0.99.0\\n\",\n      \"sklearn: 1.2.2\\n\",\n      \"sklearn_pandas: 2.2.0\\n\",\n      \"pandas: 2.1.1\\n\",\n      \"numpy: 1.23.5\\n\",\n      \"dill: 0.3.7\\n\",\n      \"joblib: 1.3.2\\n\",\n      \"java: 1.8.0_221\\n\",\n      \"Executing command:\\n\",\n      \"java -cp /Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/sklearn2pmml-1.0-SNAPSHOT.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/gson-2.10.1.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/guava-32.1.1-jre.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/h2o-genmodel-3.42.0.3.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/h2o-logger-3.42.0.3.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/h2o-tree-api-0.3.17.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/istack-commons-runtime-4.0.1.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/jackson-annotations-2.13.3.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/jakarta.activation-2.0.1.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/jakarta.xml.bind-api-3.0.1.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/jaxb-core-3.0.2.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/jaxb-runtime-3.0.2.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/jcommander-1.72.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/pickle-1.4.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/pmml-converter-1.5.5.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/pmml-h2o-1.2.9.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/pmml-lightgbm-1.4.6.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/pmml-model-1.6.4.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/pmml-model-metro-1.6.4.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/pmml-python-1.1.17.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/pmml-sklearn-1.7.40.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/pmml-sklearn-extension-1.7.40.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/pmml-sklearn-h2o-1.7.40.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/pmml-sklearn-lightgbm-1.7.40.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/pmml-sklearn-statsmodels-1.7.40.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/pmml-sklearn-xgboost-1.7.40.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/pmml-statsmodels-1.0.5.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/pmml-xgboost-1.7.4.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/serpent-1.40.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/slf4j-api-1.7.36.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/slf4j-jdk14-1.7.36.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/ubjson-0.1.8.jar:/Users/lubberit/anaconda3/envs/py10/lib/python3.10/site-packages/sklearn2pmml/resources/ubjson-gson-0.1.8.jar com.sklearn2pmml.Main --pkl-input /var/folders/g0/cmfm36_10sgb974xjrwhp8n40000gn/T/estimator-eff9ml71.pkl.z --pmml-output model_report/scorecard.pmml\\n\",\n      \"Standard output is empty\\n\",\n      \"Standard error:\\n\",\n      \"十月 13, 2023 5:49:11 下午 sklearn2pmml.pipeline.PMMLPipeline encodePMML\\n\",\n      \"警告: Model verification data is not set. Use method 'sklearn2pmml.pipeline.PMMLPipeline.verify(X)' to correct this deficiency\\n\",\n      \"\\n\",\n      \"Preserved dump file(s): /var/folders/g0/cmfm36_10sgb974xjrwhp8n40000gn/T/estimator-eff9ml71.pkl.z\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"scorecard_pipeline = card.scorecard2pmml(pmml=\\\"model_report/scorecard.pmml\\\", debug=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from pypmml import Model\\n\",\n    \"\\n\",\n    \"pmml_card = Model.fromFile(\\\"scorecard.pmml\\\")\\n\",\n    \"# pd.DataFrame({\\\"PMML\\\": pmml_card.predict(test)[\\\"predicted_score\\\"].tolist(), \\\"SCORECARDPIPELINE\\\": test[\\\"score\\\"].tolist()})\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"8 `pipeline` 超参数搜索\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 训练集 & 测试集数据，为了不影响 第9节 输出评分卡模型报告，这一节重新加载下数据并且变量命名全部以 _ 开始\\n\",\n    \"_data = sp.germancredit()\\n\",\n    \"_data[target] = _data[target].map({\\\"good\\\": 0, \\\"bad\\\": 1})\\n\",\n    \"_train, _test = train_test_split(_data, test_size=0.3, shuffle=True, stratify=_data[target])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"NameError\",\n     \"evalue\": \"name 'target' is not defined\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mNameError\\u001b[0m                                 Traceback (most recent call last)\",\n      \"Cell \\u001b[0;32mIn[10], line 3\\u001b[0m\\n\\u001b[1;32m      1\\u001b[0m \\u001b[38;5;66;03m# 构建 pipeline\\u001b[39;00m\\n\\u001b[1;32m      2\\u001b[0m _model_pipeline \\u001b[38;5;241m=\\u001b[39m sp\\u001b[38;5;241m.\\u001b[39mPipeline([\\n\\u001b[0;32m----> 3\\u001b[0m     (\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mpreprocessing_select\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m, sp\\u001b[38;5;241m.\\u001b[39mFeatureSelection(target\\u001b[38;5;241m=\\u001b[39m\\u001b[43mtarget\\u001b[49m, engine\\u001b[38;5;241m=\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mtoad\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m)),\\n\\u001b[1;32m      4\\u001b[0m     (\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mcombiner\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m, sp\\u001b[38;5;241m.\\u001b[39mCombiner(target\\u001b[38;5;241m=\\u001b[39mtarget, min_bin_size\\u001b[38;5;241m=\\u001b[39m\\u001b[38;5;241m0.2\\u001b[39m)),\\n\\u001b[1;32m      5\\u001b[0m     (\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mtransform\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m, sp\\u001b[38;5;241m.\\u001b[39mWOETransformer(target\\u001b[38;5;241m=\\u001b[39mtarget)),\\n\\u001b[1;32m      6\\u001b[0m     (\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mprocessing_select\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m, sp\\u001b[38;5;241m.\\u001b[39mFeatureSelection(target\\u001b[38;5;241m=\\u001b[39mtarget, engine\\u001b[38;5;241m=\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mtoad\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m)),\\n\\u001b[1;32m      7\\u001b[0m     (\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mstepwise\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m, sp\\u001b[38;5;241m.\\u001b[39mStepwiseSelection(target\\u001b[38;5;241m=\\u001b[39mtarget)),\\n\\u001b[1;32m      8\\u001b[0m     (\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mlogistic\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m, sp\\u001b[38;5;241m.\\u001b[39mITLubberLogisticRegression(target\\u001b[38;5;241m=\\u001b[39mtarget)),\\n\\u001b[1;32m      9\\u001b[0m ])\\n\\u001b[1;32m     11\\u001b[0m _model_pipeline\\n\",\n      \"\\u001b[0;31mNameError\\u001b[0m: name 'target' is not defined\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 构建 pipeline\\n\",\n    \"_model_pipeline = sp.Pipeline([\\n\",\n    \"    (\\\"preprocessing_select\\\", sp.FeatureSelection(target=target, engine=\\\"toad\\\")),\\n\",\n    \"    (\\\"combiner\\\", sp.Combiner(target=target, min_bin_size=0.2)),\\n\",\n    \"    (\\\"transform\\\", sp.WOETransformer(target=target)),\\n\",\n    \"    (\\\"processing_select\\\", sp.FeatureSelection(target=target, engine=\\\"toad\\\")),\\n\",\n    \"    (\\\"stepwise\\\", sp.StepwiseSelection(target=target)),\\n\",\n    \"    (\\\"logistic\\\", sp.ITLubberLogisticRegression(target=target)),\\n\",\n    \"])\\n\",\n    \"\\n\",\n    \"_model_pipeline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.model_selection import GridSearchCV\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Fitting 3 folds for each of 105 candidates, totalling 315 fits\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 2023-10-13 17:49:51,056 ][ INFO ][ 3098908083.py::13 ] {'combiner__max_n_bins': 3, 'logistic__C': 1, 'logistic__class_weight': None, 'logistic__max_iter': 10, 'logistic__penalty': 'l2', 'logistic__solver': 'sag'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"_params_grid = {\\n\",\n    \"    \\\"combiner__max_n_bins\\\": [3],\\n\",\n    \"    \\\"logistic__C\\\": [np.power(2, i) for i in range(5)],\\n\",\n    \"    \\\"logistic__penalty\\\": [\\\"l2\\\"],\\n\",\n    \"    \\\"logistic__class_weight\\\": [None, \\\"balanced\\\"] + [{1: i / 10.0, 0: 1 - i / 10.0} for i in range(1, 10, 2)],\\n\",\n    \"    \\\"logistic__max_iter\\\": [10, 50, 100],\\n\",\n    \"    \\\"logistic__solver\\\": [\\\"sag\\\"], # [\\\"liblinear\\\", \\\"sag\\\", \\\"lbfgs\\\", \\\"newton-cg\\\"],\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"_pipeline_grid_search = GridSearchCV(_model_pipeline, _params_grid, cv=3, scoring='roc_auc', verbose=1, n_jobs=-1, return_train_score=True)\\n\",\n    \"_pipeline_grid_search.fit(_train, _train[target])\\n\",\n    \"\\n\",\n    \"logger.info(_pipeline_grid_search.best_params_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"NameError\",\n     \"evalue\": \"name '_model_pipeline' is not defined\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mNameError\\u001b[0m                                 Traceback (most recent call last)\",\n      \"Cell \\u001b[0;32mIn[11], line 1\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0m \\u001b[43m_model_pipeline\\u001b[49m\\u001b[38;5;241m.\\u001b[39mset_params(\\u001b[38;5;241m*\\u001b[39m\\u001b[38;5;241m*\\u001b[39m_pipeline_grid_search\\u001b[38;5;241m.\\u001b[39mbest_params_)\\n\\u001b[1;32m      2\\u001b[0m _model_pipeline\\u001b[38;5;241m.\\u001b[39mfit(_train)\\n\",\n      \"\\u001b[0;31mNameError\\u001b[0m: name '_model_pipeline' is not defined\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"_model_pipeline.set_params(**_pipeline_grid_search.best_params_)\\n\",\n    \"_model_pipeline.fit(_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"_y_pred_train = _model_pipeline.predict(_train.drop(columns=[target]))\\n\",\n    \"_y_pred_test = _model_pipeline.predict(_test.drop(columns=[target]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"_ = sp.ks_plot(_y_pred_train, _train[target], figsize=(10, 5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"_ = sp.ks_plot(_y_pred_test, _test[target], figsize=(10, 5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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变量名称变量分箱对应分数
0status_of_existing_checking_accountno checking account125.0441
1status_of_existing_checking_account... >= 200 DM / salary assignments for at least 1 year,0 <= ... < 200 DM30.6659
2status_of_existing_checking_account... < 0 DM-11.9486
3duration_in_month[-inf ~ 16)81.0247
4duration_in_month[16 ~ 45)32.3654
5duration_in_month[45 ~ inf)-17.5272
6credit_historycritical account/ other credits existing (not at this bank)77.8221
7credit_historyexisting credits paid back duly till now,delay in paying off in the past46.6001
8credit_historyall credits at this bank paid back duly,no credits taken/ all credits paid back duly-24.1878
9purposeretraining,car (used),radio/television97.2919
10purposerepairs,furniture/equipment,domestic appliances,business,education32.6119
11purposecar (new),others2.8807
12credit_amount[-inf ~ 1374)39.2184
13credit_amount[1374 ~ 3914)73.3016
14credit_amount[3914 ~ inf)11.8186
15savings_account_and_bonds... >= 1000 DM,500 <= ... < 1000 DM98.2947
16savings_account_and_bondsunknown/ no savings account79.4525
17savings_account_and_bonds100 <= ... < 500 DM,... < 100 DM33.1492
18installment_rate_in_percentage_of_disposable_income[-inf ~ 2)75.7922
19installment_rate_in_percentage_of_disposable_income[2 ~ 4)63.8251
20installment_rate_in_percentage_of_disposable_income[4 ~ inf)24.9286
21other_debtors_or_guarantorsguarantor75.2848
22other_debtors_or_guarantorsnone46.6128
23other_debtors_or_guarantorsco-applicant14.7865
24age_in_years[-inf ~ 34)28.4249
25age_in_years[34 ~ 37)114.1148
26age_in_years[37 ~ inf)54.5930
27other_installment_plansnone53.8884
28other_installment_plansbank19.8446
29other_installment_plansstores15.6862
30housingown54.0833
31housingrent30.7253
32housingfor free26.5866
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"                                                   变量名称  \\\\\\n\",\n       \"0                   status_of_existing_checking_account   \\n\",\n       \"1                   status_of_existing_checking_account   \\n\",\n       \"2                   status_of_existing_checking_account   \\n\",\n       \"3                                     duration_in_month   \\n\",\n       \"4                                     duration_in_month   \\n\",\n       \"5                                     duration_in_month   \\n\",\n       \"6                                        credit_history   \\n\",\n       \"7                                        credit_history   \\n\",\n       \"8                                        credit_history   \\n\",\n       \"9                                               purpose   \\n\",\n       \"10                                              purpose   \\n\",\n       \"11                                              purpose   \\n\",\n       \"12                                        credit_amount   \\n\",\n       \"13                                        credit_amount   \\n\",\n       \"14                                        credit_amount   \\n\",\n       \"15                            savings_account_and_bonds   \\n\",\n       \"16                            savings_account_and_bonds   \\n\",\n       \"17                            savings_account_and_bonds   \\n\",\n       \"18  installment_rate_in_percentage_of_disposable_income   \\n\",\n       \"19  installment_rate_in_percentage_of_disposable_income   \\n\",\n       \"20  installment_rate_in_percentage_of_disposable_income   \\n\",\n       \"21                          other_debtors_or_guarantors   \\n\",\n       \"22                          other_debtors_or_guarantors   \\n\",\n       \"23                          other_debtors_or_guarantors   \\n\",\n       \"24                                         age_in_years   \\n\",\n       \"25                                         age_in_years   \\n\",\n       \"26                                         age_in_years   \\n\",\n       \"27                              other_installment_plans   \\n\",\n       \"28                              other_installment_plans   \\n\",\n       \"29                              other_installment_plans   \\n\",\n       \"30                                              housing   \\n\",\n       \"31                                              housing   \\n\",\n       \"32                                              housing   \\n\",\n       \"\\n\",\n       \"                                                                                    变量分箱  \\\\\\n\",\n       \"0                                                                    no checking account   \\n\",\n       \"1               ... >= 200 DM / salary assignments for at least 1 year,0 <= ... < 200 DM   \\n\",\n       \"2                                                                             ... < 0 DM   \\n\",\n       \"3                                                                            [-inf ~ 16)   \\n\",\n       \"4                                                                              [16 ~ 45)   \\n\",\n       \"5                                                                             [45 ~ inf)   \\n\",\n       \"6                            critical account/ other credits existing (not at this bank)   \\n\",\n       \"7               existing credits paid back duly till now,delay in paying off in the past   \\n\",\n       \"8   all credits at this bank paid back duly,no credits taken/ all credits paid back duly   \\n\",\n       \"9                                                 retraining,car (used),radio/television   \\n\",\n       \"10                    repairs,furniture/equipment,domestic appliances,business,education   \\n\",\n       \"11                                                                      car (new),others   \\n\",\n       \"12                                                                         [-inf ~ 1374)   \\n\",\n       \"13                                                                         [1374 ~ 3914)   \\n\",\n       \"14                                                                          [3914 ~ inf)   \\n\",\n       \"15                                                   ... >= 1000 DM,500 <= ... < 1000 DM   \\n\",\n       \"16                                                           unknown/ no savings account   \\n\",\n       \"17                                                      100 <= ... < 500 DM,... < 100 DM   \\n\",\n       \"18                                                                            [-inf ~ 2)   \\n\",\n       \"19                                                                               [2 ~ 4)   \\n\",\n       \"20                                                                             [4 ~ inf)   \\n\",\n       \"21                                                                             guarantor   \\n\",\n       \"22                                                                                  none   \\n\",\n       \"23                                                                          co-applicant   \\n\",\n       \"24                                                                           [-inf ~ 34)   \\n\",\n       \"25                                                                             [34 ~ 37)   \\n\",\n       \"26                                                                            [37 ~ inf)   \\n\",\n       \"27                                                                                  none   \\n\",\n       \"28                                                                                  bank   \\n\",\n       \"29                                                                                stores   \\n\",\n       \"30                                                                                   own   \\n\",\n       \"31                                                                                  rent   \\n\",\n       \"32                                                                              for free   \\n\",\n       \"\\n\",\n       \"       对应分数  \\n\",\n       \"0  125.0441  \\n\",\n       \"1   30.6659  \\n\",\n       \"2  -11.9486  \\n\",\n       \"3   81.0247  \\n\",\n       \"4   32.3654  \\n\",\n       \"5  -17.5272  \\n\",\n       \"6   77.8221  \\n\",\n       \"7   46.6001  \\n\",\n       \"8  -24.1878  \\n\",\n       \"9   97.2919  \\n\",\n       \"10  32.6119  \\n\",\n       \"11   2.8807  \\n\",\n       \"12  39.2184  \\n\",\n       \"13  73.3016  \\n\",\n       \"14  11.8186  \\n\",\n       \"15  98.2947  \\n\",\n       \"16  79.4525  \\n\",\n       \"17  33.1492  \\n\",\n       \"18  75.7922  \\n\",\n       \"19  63.8251  \\n\",\n       \"20  24.9286  \\n\",\n       \"21  75.2848  \\n\",\n       \"22  46.6128  \\n\",\n       \"23  14.7865  \\n\",\n       \"24  28.4249  \\n\",\n       \"25 114.1148  \\n\",\n       \"26  54.5930  \\n\",\n       \"27  53.8884  \\n\",\n       \"28  19.8446  \\n\",\n       \"29  15.6862  \\n\",\n       \"30  54.0833  \\n\",\n       \"31  30.7253  \\n\",\n       \"32  26.5866  \"\n      ]\n     },\n     \"execution_count\": 62,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"_woe_train = _model_pipeline[:-1].transform(train)\\n\",\n    \"\\n\",\n    \"_card = sp.ScoreCard(target=target, pipeline=_model_pipeline)\\n\",\n    \"_card.fit(_woe_train)\\n\",\n    \"\\n\",\n    \"_card.scorecard_points()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"9 模型报告输出\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mInit signature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mExcelWriter\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mstyle_excel\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'/Users/lubberit/Desktop/workspace/scorecardpipeline/scorecardpipeline/template.xlsx'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mstyle_sheet_name\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'初始化'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmode\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'replace'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfontsize\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m10\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfont\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'楷体'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtheme_color\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'2639E9'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mopacity\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.85\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m      \\n\",\n      \"\\u001b[0;31mInit docstring:\\u001b[0m\\n\",\n      \"excel 文件内容写入公共方法\\n\",\n      \"\\n\",\n      \":param style_excel: 样式模版文件，默认当前路径下的 template.xlsx ，如果项目路径调整需要进行相应的调整\\n\",\n      \":param style_sheet_name: 模版文件内初始样式sheet名称，默认即可\\n\",\n      \":param fontsize: 插入excel文件中内容的字体大小，默认 10\\n\",\n      \":param font: 插入excel文件中内容的字体，默认 楷体\\n\",\n      \":param theme_color: 主题色，默认 2639E9，注意不包含 #\\n\",\n      \":param opacity: 写入dataframe时使用颜色填充主题色的透明度设置，默认 0.85\\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m           ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/excel_writer.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m           type\\n\",\n      \"\\u001b[0;31mSubclasses:\\u001b[0m     \"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sp.ExcelWriter?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mSignature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mExcelWriter\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0minsert_value2sheet\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mworksheet\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0minsert_space\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mvalue\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m''\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mstyle\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'content'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mauto_width\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m\\n\",\n      \"向sheet中的某个单元格插入某种样式的内容\\n\",\n      \"\\n\",\n      \":param worksheet: 需要插入内容的sheet\\n\",\n      \":param insert_space: 内容插入的单元格位置，可以是 \\\"B2\\\" 或者 (2, 2) 任意一种形式\\n\",\n      \":param value: 需要插入的内容\\n\",\n      \":param style: 渲染的样式，参考 init_style 中初始设置的样式\\n\",\n      \":param auto_width: 是否开启自动调整列宽\\n\",\n      \":return 返回插入元素最后一列之后、最后一行之后的位置\\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m      ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/excel_writer.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m      function\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sp.ExcelWriter.insert_value2sheet?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mSignature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mExcelWriter\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0minsert_pic2sheet\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mworksheet\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfig\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0minsert_space\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfigsize\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m600\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;36m250\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m\\n\",\n      \"向excel中插入图片内容\\n\",\n      \"\\n\",\n      \":param worksheet: 需要插入内容的sheet\\n\",\n      \":param fig: 需要插入的图片路径\\n\",\n      \":param insert_space: 插入图片的起始单元格\\n\",\n      \":param figsize: 图片大小设置\\n\",\n      \":return 返回插入元素最后一列之后、最后一行之后的位置\\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m      ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/excel_writer.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m      function\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sp.ExcelWriter.insert_pic2sheet?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mSignature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mExcelWriter\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0minsert_df2sheet\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mworksheet\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mdata\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0minsert_space\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmerge_column\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mheader\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mindex\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mauto_width\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfill\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmerge\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m\\n\",\n      \"向excel文件中插入指定样式的dataframe数据\\n\",\n      \"\\n\",\n      \":param worksheet: 需要插入内容的sheet\\n\",\n      \":param data: 需要插入的dataframe\\n\",\n      \":param insert_space: 插入内容的起始单元格位置\\n\",\n      \":param merge_column: 需要分组显示的列，index或者列名\\n\",\n      \":param header: 是否存储dataframe的header，暂不支持多级表头\\n\",\n      \":param index: 是否存储dataframe的index\\n\",\n      \":param auto_width: 是否自动调整列宽\\n\",\n      \":param fill: 是否使用颜色填充而非边框\\n\",\n      \":param merge: 是否合并单元格，配合 merge_column 一起使用，当前版本仅在 merge_column 只有一列时有效\\n\",\n      \"\\n\",\n      \"返回插入元素最后一列之后、最后一行之后的位置\\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m      ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/excel_writer.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m      function\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sp.ExcelWriter.insert_df2sheet?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mSignature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mExcelWriter\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0madd_conditional_formatting\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mworksheet\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mstart_space\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mend_space\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m\\n\",\n      \"设置条件格式\\n\",\n      \"\\n\",\n      \":param worksheet: 当前选择设置条件格式的sheet\\n\",\n      \":param start_space: 开始单元格位置\\n\",\n      \":param end_space: 结束单元格位置\\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m      ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/excel_writer.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m      function\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sp.ExcelWriter.add_conditional_formatting?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mSignature:\\u001b[0m \\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mExcelWriter\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mset_number_format\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mworksheet\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mspace\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0m_format\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m\\n\",\n      \"设置数值显示格式\\n\",\n      \"\\n\",\n      \":param worksheet: 当前选择调整数值显示格式的sheet\\n\",\n      \":param space: 单元格范围\\n\",\n      \":param _format: 显示格式，参考 openpyxl\\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m      ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/excel_writer.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m      function\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sp.ExcelWriter.set_number_format?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mSignature:\\u001b[0m\\n\",\n      \"\\u001b[0msp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdataframe2excel\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mdata\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mexcel_writer\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0msheet_name\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtitle\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mheader\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtheme_color\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'2639E9'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfill\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mpercent_cols\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcondition_cols\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcustom_cols\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcustom_format\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'#,##0'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcolor_cols\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mstart_col\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mstart_row\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmode\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'replace'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mwriter_params\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m\\n\",\n      \"向excel文件中插入指定样式的dataframe数据\\n\",\n      \"\\n\",\n      \":param data: 需要保存的dataframe数据，index默认不保存，如果需要保存先 .reset_index().rename(columns={\\\"index\\\": \\\"索引名称\\\"}) 再保存，有部分索引 reset_index 之后是 0 而非 index，根据实际情况进行修改\\n\",\n      \":param excel_writer: 需要保存到的 excel 文件路径或者 ExcelWriter\\n\",\n      \":param sheet_name: 需要插入内容的sheet，如果是 Worksheet，则直接向 Worksheet 插入数据\\n\",\n      \":param title: 是否在dataframe之前的位置插入一个标题\\n\",\n      \":param header: 是否存储dataframe的header，暂不支持多级表头\\n\",\n      \":param theme_color: 主题色\\n\",\n      \":param fill: 是否使用单元个颜色填充样式还是使用边框样式\\n\",\n      \":param percent_cols: 需要显示为百分数的列，仅修改显示格式，不更改数值\\n\",\n      \":param condition_cols: 需要显示条件格式的列（无边框渐变数据条）\\n\",\n      \":param color_cols: 需要显示为条件格式颜色填充的列（单元格填充渐变色）\\n\",\n      \":param custom_cols: 需要显示自定义格式的列，与 custom_format 参数搭配使用\\n\",\n      \":param custom_format: 显示的自定义格式，与 custom_cols 参数搭配使用，默认 #,##0 ，即显示为有分隔符的整数\\n\",\n      \":param start_col: 在excel中的开始列数，默认 2，即第二列开始\\n\",\n      \":param start_row: 在excel中的开始行数，默认 2，即第二行开始，如果 title 有值的话，会从 start_row + 2 行开始插入dataframe数据\\n\",\n      \":param mode: excel写入的模式，可选 append 和 replace ，默认 replace ，选择 append 时会在已有的excel文件中增加内容，不覆盖原有内容\\n\",\n      \":param writer_params: 透传至 ExcelWriter 内的参数\\n\",\n      \":param **kwargs: 其他参数，透传至 insert_df2sheet 方法，例如 传入 auto_width=True 会根据内容自动调整列宽\\n\",\n      \":return 返回插入元素最后一列之后、最后一行之后的位置\\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m      ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/excel_writer.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m      function\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sp.dataframe2excel?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from scorecardpipeline import get_column_letter, ColorScaleRule\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"writer = sp.ExcelWriter()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"\"\n      ]\n     },\n     \"execution_count\": 72,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"plt.ioff()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# # ////////////////////////////////////// 样本说明 ///////////////////////////////////// #\\n\",\n    \"worksheet = writer.get_sheet_by_name(\\\"汇总信息\\\")\\n\",\n    \"\\n\",\n    \"# 样本总体分布情况\\n\",\n    \"start_row, start_col = 2, 2\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\\\"样本总体分布情况\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = sp.dataframe2excel(dataset_summary, writer, worksheet, percent_cols=[\\\"样本占比\\\", \\\"坏客户占比\\\"], start_row=end_row + 1)\\n\",\n    \"\\n\",\n    \"# end_row, end_col = writer.insert_df2sheet(worksheet, dataset_summary, (end_row + 1, start_col), header=True)\\n\",\n    \"# writer.set_number_format(worksheet, f\\\"{get_column_letter(end_col - 2)}{end_row - len(dataset_summary)}:{get_column_letter(end_col - 2)}{end_row}\\\", \\\"0.00%\\\")\\n\",\n    \"# writer.set_number_format(worksheet, f\\\"{get_column_letter(end_col - 4)}{end_row - len(dataset_summary)}:{get_column_letter(end_col - 4)}{end_row}\\\", \\\"0.00%\\\")\\n\",\n    \"\\n\",\n    \"# 建模样本时间分布情况\\n\",\n    \"temp = sp.distribution_plot(df, date=\\\"date\\\", target=target, save=\\\"model_report/all_sample_time_count.png\\\", result=True)\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"建模样本时间分布情况\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/all_sample_time_count.png\\\", (end_row, start_col), figsize=(720, 370))\\n\",\n    \"end_row, end_col = sp.dataframe2excel(temp, writer, worksheet, percent_cols=[\\\"样本占比\\\", \\\"好样本占比\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\"], condition_cols=[\\\"坏样本率\\\"], start_row=end_row)\\n\",\n    \"\\n\",\n    \"# end_row, end_col = writer.insert_df2sheet(worksheet, temp.T.reset_index(), (end_row, start_col), header=False)\\n\",\n    \"# writer.set_number_format(worksheet, f\\\"{get_column_letter(start_col)}{end_row - 1}:{get_column_letter(end_col)}{end_row - 1}\\\", \\\"0.00%\\\")\\n\",\n    \"# writer.set_number_format(worksheet, f\\\"{get_column_letter(start_col)}{end_row - 2}:{get_column_letter(end_col)}{end_row - 2}\\\", \\\"0.00%\\\")\\n\",\n    \"# writer.set_number_format(worksheet, f\\\"{get_column_letter(start_col)}{end_row - 4}:{get_column_letter(end_col)}{end_row - 4}\\\", \\\"0.00%\\\")\\n\",\n    \"# writer.set_number_format(worksheet, f\\\"{get_column_letter(start_col)}{end_row - 6}:{get_column_letter(end_col)}{end_row - 6}\\\", \\\"0.00%\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# ////////////////////////////////////// 模型报告 ///////////////////////////////////// #\\n\",\n    \"summary = logistic.summary2(feature_map=feature_map)\\n\",\n    \"\\n\",\n    \"# 逻辑回归拟合情况\\n\",\n    \"worksheet = writer.get_sheet_by_name(\\\"逻辑回归拟合结果\\\")\\n\",\n    \"\\n\",\n    \"start_row, start_col = 2, 2\\n\",\n    \"\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\\\"逻辑回归拟合效果\\\", style=\\\"header\\\")\\n\",\n    \"# worksheet.merge_cells(f\\\"{get_column_letter(start_col)}{start_row}:{get_column_letter(start_col + len(summary.columns) - 1)}{start_row}\\\")\\n\",\n    \"# worksheet[f\\\"{get_column_letter(start_col)}{start_row}:{get_column_letter(start_col + len(summary.columns) - 1)}{start_row}\\\"].style = \\\"header\\\"\\n\",\n    \"# end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/logistic_train.png\\\", (end_row + 2, start_col))\\n\",\n    \"\\n\",\n    \"end_row, end_col = sp.dataframe2excel(summary, writer, worksheet, condition_cols=[\\\"Coef.\\\"], start_row=end_row + 1)\\n\",\n    \"# end_row, end_col = writer.insert_df2sheet(worksheet, summary, (end_row + 1, start_col))\\n\",\n    \"# conditional_column = get_column_letter(start_col + summary.columns.get_loc(\\\"Coef.\\\"))\\n\",\n    \"# writer.add_conditional_formatting(worksheet, f'{conditional_column}{end_row-len(summary)}', f'{conditional_column}{end_row}')\\n\",\n    \"\\n\",\n    \"# worksheet.merge_cells(f\\\"{get_column_letter(start_col)}{end_row + 2}:{get_column_letter(start_col + len(train_report.columns) - 1)}{end_row + 2}\\\")\\n\",\n    \"# worksheet[f\\\"{get_column_letter(start_col)}{end_row + 2}\\\"].style = \\\"header\\\"\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"训练数据集拟合报告\\\", style=\\\"header\\\")\\n\",\n    \"# end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/lr_ksplot_train.png\\\", (end_row, start_col), figsize=(480, 270))\\n\",\n    \"end_row, end_col = sp.dataframe2excel(logistic.report(woe_train), writer, worksheet, percent_cols=[\\\"precision\\\", \\\"recall\\\", \\\"f1-score\\\"], start_row=end_row + 1)\\n\",\n    \"# end_row, end_col = writer.insert_df2sheet(worksheet, logistic.report(woe_train), (end_row + 1, start_col))\\n\",\n    \"\\n\",\n    \"# worksheet.merge_cells(f\\\"{get_column_letter(start_col)}{end_row + 2}:{get_column_letter(start_col + len(test_report.columns) - 1)}{end_row + 2}\\\")\\n\",\n    \"# worksheet[f\\\"{get_column_letter(start_col)}{end_row + 2}\\\"].style = \\\"header\\\"\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"测试数据集拟合报告\\\", style=\\\"header\\\")\\n\",\n    \"# end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/lr_ksplot_test.png\\\", (end_row, start_col), figsize=(480, 270))\\n\",\n    \"end_row, end_col = sp.dataframe2excel(logistic.report(woe_test), writer, worksheet, percent_cols=[\\\"precision\\\", \\\"recall\\\", \\\"f1-score\\\"], start_row=end_row + 1)\\n\",\n    \"# end_row, end_col = writer.insert_df2sheet(worksheet, logistic.report(woe_test), (end_row + 1, start_col))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# ////////////////////////////////////// 特征概述 ///////////////////////////////////// #\\n\",\n    \"worksheet = writer.get_sheet_by_name(\\\"模型变量信息\\\")\\n\",\n    \"\\n\",\n    \"start_row, start_col = 2, 2\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\\\"入模变量信息\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, feature_describe.reset_index().rename(columns={\\\"index\\\": \\\"序号\\\"}), (end_row + 1, start_col))\\n\",\n    \"\\n\",\n    \"# 变量分布情况\\n\",\n    \"import toad\\n\",\n    \"data_info = toad.detect(data[card.rules.keys()]).reset_index().rename(columns={\\\"index\\\": \\\"变量名称\\\", \\\"type\\\": \\\"变量类型\\\", \\\"size\\\": \\\"样本个数\\\", \\\"missing\\\": \\\"缺失值\\\", \\\"unique\\\": \\\"唯一值个数\\\"})\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"变量分布情况\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, data_info, (end_row + 1, start_col))\\n\",\n    \"\\n\",\n    \"# 变量相关性\\n\",\n    \"data_corr = woe_train.corr()\\n\",\n    \"logistic.corr(woe_train, save=\\\"model_report/train_corr.png\\\", annot=False)\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"变量相关性\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/train_corr.png\\\", (end_row + 1, start_col), figsize=(700, 500))\\n\",\n    \"\\n\",\n    \"end_row, end_col = sp.dataframe2excel(data_corr.reset_index().rename(columns={\\\"index\\\": \\\"\\\"}), writer, worksheet, color_cols=list(data_corr.columns), start_row=end_row + 1)\\n\",\n    \"# end_row, end_col = writer.insert_df2sheet(worksheet, data_corr.reset_index().rename(columns={\\\"index\\\": \\\"\\\"}), (end_row + 1, start_col))\\n\",\n    \"# conditional_column = f\\\"{get_column_letter(start_col + 1)}{end_row - len(data_corr)}:{get_column_letter(end_col - 1)}{end_row - 1}\\\"\\n\",\n    \"# worksheet.conditional_formatting.add(conditional_column, ColorScaleRule(start_type='num', start_value=-1.0, start_color='2639E9', mid_type='num', mid_value=0., mid_color='FFFFFF', end_type='num', end_value=1.0, end_color='2639E9'))\\n\",\n    \"\\n\",\n    \"# 变量分箱信息\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"变量分箱信息\\\", style=\\\"header\\\")\\n\",\n    \"\\n\",\n    \"for col in logistic.feature_names_in_:\\n\",\n    \"    feature_table = sp.feature_bin_stats(data, col, target=target, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", combiner=feature_pipeline.named_steps[\\\"combiner\\\"])\\n\",\n    \"    _ = sp.bin_plot(feature_table, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", figsize=(8, 4), save=f\\\"model_report/bin_plots/data_{col}.png\\\")\\n\",\n    \"    \\n\",\n    \"    end_row, end_col = writer.insert_pic2sheet(worksheet, f\\\"model_report/bin_plots/data_{col}.png\\\", (end_row + 1, start_col), figsize=(700, 400))\\n\",\n    \"    end_row, end_col = sp.dataframe2excel(feature_table, writer, worksheet, percent_cols=[\\\"样本占比\\\", \\\"好样本占比\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\"], condition_cols=[\\\"坏样本率\\\", \\\"LIFT值\\\"], start_row=end_row)\\n\",\n    \"    \\n\",\n    \"    # end_row, end_col = writer.insert_df2sheet(worksheet, feature_table, (end_row, start_col))\\n\",\n    \"\\n\",\n    \"    # for c in [\\\"坏样本率\\\", \\\"LIFT值\\\"]:\\n\",\n    \"    #     conditional_column = get_column_letter(start_col + feature_table.columns.get_loc(c))\\n\",\n    \"    #     writer.add_conditional_formatting(worksheet, f'{conditional_column}{end_row - len(feature_table)}', f'{conditional_column}{end_row}')\\n\",\n    \"\\n\",\n    \"    # for c in [\\\"样本占比\\\", \\\"好样本占比\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\"]:\\n\",\n    \"    #     conditional_column = get_column_letter(start_col + feature_table.columns.get_loc(c))\\n\",\n    \"    #     writer.set_number_format(worksheet, f\\\"{conditional_column}{end_row - len(feature_table)}:{conditional_column}{end_row}\\\", \\\"0.00%\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# ////////////////////////////////////// 评分卡说明 ///////////////////////////////////// #\\n\",\n    \"worksheet = writer.get_sheet_by_name(\\\"评分卡结果\\\")\\n\",\n    \"\\n\",\n    \"# 评分卡刻度\\n\",\n    \"scorecard_kedu = card.scorecard_scale()\\n\",\n    \"scorecard_points = card.scorecard_points(feature_map=feature_map)\\n\",\n    \"scorecard_clip = card.score_clip(train[\\\"score\\\"], clip=100)\\n\",\n    \"\\n\",\n    \"start_row, start_col = 2, 2\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\\\"评分卡刻度\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, scorecard_kedu, (end_row + 1, start_col))\\n\",\n    \"\\n\",\n    \"# 评分卡对应分数\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"评分卡分数\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, scorecard_points, (end_row + 1, start_col), merge_column=\\\"变量名称\\\")\\n\",\n    \"\\n\",\n    \"# 评分效果\\n\",\n    \"score_table_train = sp.feature_bin_stats(train, \\\"score\\\", desc=\\\"测试集模型评分\\\", target=target, rules=scorecard_clip)\\n\",\n    \"score_table_test = sp.feature_bin_stats(test, \\\"score\\\", desc=\\\"测试集模型评分\\\", target=target, rules=scorecard_clip)\\n\",\n    \"\\n\",\n    \"# sp.bin_plot(score_table_train, desc=\\\"测试集模型评分\\\", figsize=(10, 6))\\n\",\n    \"# sp.bin_plot(score_table_test, desc=\\\"测试集模型评分\\\", figsize=(10, 6))\\n\",\n    \"\\n\",\n    \"sp.ks_plot(train[\\\"score\\\"], train[target], title=\\\"Train \\\\tDataset\\\", save=\\\"model_report/train_ksplot.png\\\")\\n\",\n    \"sp.ks_plot(test[\\\"score\\\"], test[target], title=\\\"Test \\\\tDataset\\\", save=\\\"model_report/test_ksplot.png\\\")\\n\",\n    \"\\n\",\n    \"sp.hist_plot(train[\\\"score\\\"], train[target], save=\\\"model_report/train_scorehist.png\\\", bins=30)\\n\",\n    \"sp.hist_plot(test[\\\"score\\\"], test[target], save=\\\"model_report/test_scorehist.png\\\", bins=30)\\n\",\n    \"\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"训练数据集评分模型效果\\\", style=\\\"header\\\")\\n\",\n    \"ks_row = end_row\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/train_ksplot.png\\\", (ks_row, start_col))\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/train_scorehist.png\\\", (ks_row, end_col))\\n\",\n    \"end_row, end_col = sp.dataframe2excel(score_table_train, writer, worksheet, percent_cols=[\\\"样本占比\\\", \\\"好样本占比\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\", \\\"分档KS值\\\"], condition_cols=[\\\"坏样本率\\\", \\\"LIFT值\\\", \\\"分档KS值\\\"], start_row=end_row + 1)\\n\",\n    \"\\n\",\n    \"# end_row, end_col = writer.insert_df2sheet(worksheet, score_table_train, (end_row + 1, start_col))\\n\",\n    \"# for c in [\\\"坏样本率\\\", \\\"LIFT值\\\", \\\"分档KS值\\\"]:\\n\",\n    \"#     conditional_column = get_column_letter(start_col + score_table_train.columns.get_loc(c))\\n\",\n    \"#     writer.add_conditional_formatting(worksheet, f'{conditional_column}{end_row - len(score_table_train)}', f'{conditional_column}{end_row}')\\n\",\n    \"# for c in [\\\"样本占比\\\", \\\"好样本占比\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\", \\\"分档KS值\\\"]:\\n\",\n    \"#     conditional_column = get_column_letter(start_col + score_table_train.columns.get_loc(c))\\n\",\n    \"#     writer.set_number_format(worksheet, f\\\"{conditional_column}{end_row - len(score_table_train)}:{conditional_column}{end_row}\\\", \\\"0.00%\\\")\\n\",\n    \"\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"测试数据集评分模型效果\\\", style=\\\"header\\\")\\n\",\n    \"ks_row = end_row\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/test_ksplot.png\\\", (ks_row, start_col))\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/test_scorehist.png\\\", (ks_row, end_col))\\n\",\n    \"end_row, end_col = sp.dataframe2excel(score_table_test, writer, worksheet, percent_cols=[\\\"样本占比\\\", \\\"好样本占比\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\", \\\"分档KS值\\\"], condition_cols=[\\\"坏样本率\\\", \\\"LIFT值\\\", \\\"分档KS值\\\"], start_row=end_row + 1)\\n\",\n    \"\\n\",\n    \"# end_row, end_col = writer.insert_df2sheet(worksheet, score_table_test, (end_row + 1, start_col))\\n\",\n    \"# for c in [\\\"坏样本率\\\", \\\"LIFT值\\\", \\\"分档KS值\\\"]:\\n\",\n    \"#     conditional_column = get_column_letter(start_col + score_table_test.columns.get_loc(c))\\n\",\n    \"#     writer.add_conditional_formatting(worksheet, f'{conditional_column}{end_row - len(score_table_test)}', f'{conditional_column}{end_row}')\\n\",\n    \"# for c in [\\\"样本占比\\\", \\\"好样本占比\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\", \\\"分档KS值\\\"]:\\n\",\n    \"#     conditional_column = get_column_letter(start_col + score_table_test.columns.get_loc(c))\\n\",\n    \"#     writer.set_number_format(worksheet, f\\\"{conditional_column}{end_row - len(score_table_test)}:{conditional_column}{end_row}\\\", \\\"0.00%\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# ////////////////////////////////////// 模型稳定性 ///////////////////////////////////// #\\n\",\n    \"worksheet = writer.get_sheet_by_name(\\\"模型稳定性\\\")\\n\",\n    \"start_row, start_col = 2, 2\\n\",\n    \"\\n\",\n    \"# 评分分布稳定性\\n\",\n    \"train_test_score_psi = sp.psi_plot(score_table_train, score_table_test, labels=[\\\"训练数据集\\\", \\\"测试数据集\\\"], save=\\\"model_report/train_test_psiplot.png\\\", result=True)\\n\",\n    \"\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\\\"模型评分稳定性指标 (Population Stability Index, PSI): 训练数据集 vs 测试数据集\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/train_test_psiplot.png\\\", (end_row, start_col), figsize=(800, 400))\\n\",\n    \"end_row, end_col = sp.dataframe2excel(train_test_score_psi, writer, worksheet, percent_cols=[\\\"训练数据集样本占比\\\", \\\"训练数据集坏样本率\\\", \\\"测试数据集样本占比\\\", \\\"测试数据集坏样本率\\\"], condition_cols=[\\\"分档PSI值\\\"], start_row=end_row + 1)\\n\",\n    \"\\n\",\n    \"# end_row, end_col = writer.insert_df2sheet(worksheet, train_test_score_psi, (end_row + 1, start_col))\\n\",\n    \"# conditional_column = get_column_letter(start_col + train_test_score_psi.columns.get_loc(\\\"分档PSI值\\\"))\\n\",\n    \"# writer.add_conditional_formatting(worksheet, f'{conditional_column}{end_row-len(train_test_score_psi)}', f'{conditional_column}{end_row}')\\n\",\n    \"# for c in [\\\"训练数据集样本占比\\\", \\\"训练数据集坏样本率\\\", \\\"测试数据集样本占比\\\", \\\"测试数据集坏样本率\\\"]:\\n\",\n    \"#     conditional_column = get_column_letter(start_col + train_test_score_psi.columns.get_loc(c))\\n\",\n    \"#     writer.set_number_format(worksheet, f\\\"{conditional_column}{end_row - len(train_test_score_psi)}:{conditional_column}{end_row}\\\", \\\"0.00%\\\")\\n\",\n    \"\\n\",\n    \"# 变量 PSI 表\\n\",\n    \"for col in card._feature_names:\\n\",\n    \"    feature_table_train = sp.feature_bin_stats(train, col, target=target, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", combiner=feature_pipeline.named_steps[\\\"combiner\\\"])\\n\",\n    \"    feature_table_test = sp.feature_bin_stats(test, col, target=target, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", combiner=feature_pipeline.named_steps[\\\"combiner\\\"])\\n\",\n    \"    psi_table = sp.psi_plot(feature_table_train, feature_table_test, desc=col, result=True, plot=True, max_len=35, figsize=(10, 6), labels=[\\\"训练数据集\\\", \\\"测试数据集\\\"], save=f\\\"model_report/psi_{col}.png\\\")\\n\",\n    \"    \\n\",\n    \"    end_row, end_col = writer.insert_pic2sheet(worksheet, f\\\"model_report/psi_{col}.png\\\", (end_row, start_col), figsize=(700, 400))\\n\",\n    \"    end_row, end_col = sp.dataframe2excel(psi_table, writer, worksheet, percent_cols=[\\\"训练数据集样本占比\\\", \\\"训练数据集坏样本率\\\", \\\"测试数据集样本占比\\\", \\\"测试数据集坏样本率\\\", \\\"测试数据集% - 训练数据集%\\\"], condition_cols=[\\\"分档PSI值\\\"], start_row=end_row + 1)\\n\",\n    \"\\n\",\n    \"# 变量 CSI 表\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"入模变量稳定性指标 (Characteristic Stability Index, CSI): 训练数据集 vs 测试数据集\\\", style=\\\"header\\\")\\n\",\n    \"\\n\",\n    \"for col in card._feature_names:\\n\",\n    \"    feature_table_train = sp.feature_bin_stats(train, col, target=target, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", combiner=feature_pipeline.named_steps[\\\"combiner\\\"])\\n\",\n    \"    feature_table_test = sp.feature_bin_stats(test, col, target=target, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", combiner=feature_pipeline.named_steps[\\\"combiner\\\"])\\n\",\n    \"    train_test_csi_table = sp.csi_plot(feature_table_train, feature_table_test, card[col], desc=col, result=True, plot=True, max_len=35, figsize=(10, 6), labels=[\\\"训练数据集\\\", \\\"测试数据集\\\"], save=f\\\"model_report/csi_{col}.png\\\")\\n\",\n    \"    \\n\",\n    \"    end_row, end_col = writer.insert_pic2sheet(worksheet, f\\\"model_report/csi_{col}.png\\\", (end_row, start_col), figsize=(700, 400))\\n\",\n    \"    end_row, end_col = sp.dataframe2excel(train_test_csi_table, writer, worksheet, percent_cols=[\\\"训练数据集样本占比\\\", \\\"训练数据集坏样本率\\\", \\\"测试数据集样本占比\\\", \\\"测试数据集坏样本率\\\", \\\"测试数据集% - 训练数据集%\\\"], condition_cols=[\\\"分档CSI值\\\"], start_row=end_row + 1)\\n\",\n    \"\\n\",\n    \"    # end_row, end_col = writer.insert_df2sheet(worksheet, train_test_csi_table, (end_row + 1, start_col))\\n\",\n    \"    # conditional_column = get_column_letter(start_col + train_test_csi_table.columns.get_loc(\\\"分档CSI值\\\"))\\n\",\n    \"    # writer.add_conditional_formatting(worksheet, f'{conditional_column}{end_row-len(train_test_csi_table)}', f'{conditional_column}{end_row}')\\n\",\n    \"    # for c in [\\\"训练数据集样本占比\\\", \\\"训练数据集坏样本率\\\", \\\"测试数据集样本占比\\\", \\\"测试数据集坏样本率\\\"]:\\n\",\n    \"    #     conditional_column = get_column_letter(start_col + train_test_csi_table.columns.get_loc(c))\\n\",\n    \"    #     writer.set_number_format(worksheet, f\\\"{conditional_column}{end_row - len(train_test_csi_table)}:{conditional_column}{end_row}\\\", \\\"0.00%\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"writer.save(\\\"model_report/评分卡模型报告.xlsx\\\")\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"+ 报告样例\\n\",\n    \"\\n\",\n    \"\\\"itlubber.png\\\" \"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.8\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "examples/third_party_data_describe.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import warnings\\n\",\n    \"\\n\",\n    \"warnings.filterwarnings(\\\"ignore\\\")\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"sys.path.append(\\\"../\\\")\\n\",\n    \"\\n\",\n    \"import os\\n\",\n    \"import re\\n\",\n    \"import six\\n\",\n    \"import random\\n\",\n    \"import joblib\\n\",\n    \"import warnings\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from datetime import datetime, timedelta\\n\",\n    \"from matplotlib import font_manager\\n\",\n    \"from matplotlib.ticker import PercentFormatter, FuncFormatter\\n\",\n    \"import seaborn as sns\\n\",\n    \"from scorecardpipeline import *\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"init_setting()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data = load_pickle(\\\"人行征信特征筛选.pkl\\\", engine=\\\"pickle\\\")\\n\",\n    \"data[\\\"申请日期\\\"] = [datetime.now() - timedelta(days=np.random.randint(0, 7)) for i in range(len(data))]\\n\",\n    \"data[\\\"申请日期\\\"] = data[\\\"申请日期\\\"].dt.strftime(\\\"%Y-%m-%d\\\")\\n\",\n    \"data[\\\"流量渠道\\\"] = [[\\\"RONG360\\\", \\\"LEXINGJIE\\\"][np.random.randint(0, 2)] for i in range(len(data))]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pd.DataFrame(temp, columns=pd.MultiIndex.from_product([p]) if isinstance(p, tuple) else [p])\\n\",\n    \"# describe.columns = pd.MultiIndex.from_tuples(describe.columns)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# describe = pd.DataFrame(index=pd.MultiIndex)\\n\",\n    \"# by = [\\\"流量渠道\\\", \\\"申请日期\\\"]\\n\",\n    \"# test_data = data[[\\\"流量渠道\\\", \\\"申请日期\\\", \\\"QYQUERY_180_NUMS_DK_XYK\\\", \\\"QYDK_X111\\\", \\\"QYDK_X1975\\\", \\\"QY_NEWVAR_507\\\", \\\"QYDKD1_X870\\\", \\\"QYDK_X2254\\\"]]\\n\",\n    \"\\n\",\n    \"# for p, group in test_data.groupby(by=by):\\n\",\n    \"#     _describe = pd.DataFrame()\\n\",\n    \"    \\n\",\n    \"#     for f in group.columns:\\n\",\n    \"#         if f not in by:\\n\",\n    \"#             temp = feature_describe(group[f])\\n\",\n    \"#             temp.index = pd.MultiIndex.from_product([[f], temp.index])\\n\",\n    \"#             temp = pd.DataFrame(temp, columns=pd.MultiIndex.from_tuples([p]) if isinstance(p, tuple) else [p])\\n\",\n    \"#             _describe = pd.concat([_describe, temp])\\n\",\n    \"    \\n\",\n    \"#     describe[pd.MultiIndex.from_tuples([p])] = _describe[p]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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LEXINGJIERONG360
2024-12-032024-12-042024-12-052024-12-062024-12-072024-12-082024-12-092024-12-032024-12-042024-12-052024-12-062024-12-072024-12-082024-12-09
特征名称统计指标
QYQUERY_180_NUMS_DK_XYK样本数7986.00008018.00008099.00007975.00007983.00007992.00008082.00007818.00007875.00007972.00007960.00007913.00008003.00007907.0000
非空数7986.00008018.00008099.00007975.00007983.00007992.00008082.00007818.00007875.00007972.00007960.00007913.00008003.00007907.0000
查得率1.00001.00001.00001.00001.00001.00001.00001.00001.00001.00001.00001.00001.00001.0000
最小值0.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.0000
平均值27.959127.980727.958927.750028.060628.069828.272028.063727.973528.136727.816527.980727.839828.0990
................................................
QYDK_X225495%29947.800029382.300029032.700029054.000029329.800029233.100029052.000029916.300028783.900030417.250029231.100029303.600029818.800029435.4000
97%34897.760034083.210033821.200033315.260033334.280034513.580033467.600034588.320032846.300034936.400033686.140033690.040034382.040033659.2200
98%39024.120037791.280037171.280037228.600036786.080038009.220037530.000038717.120036996.540038295.740037988.520037900.440038091.040037540.1600
99%48173.880044534.510043482.460045641.900043996.020043839.390043182.600046566.880044886.960044902.800044844.240044813.520045889.840044828.2200
最大值3223069.00008054181.00001497655.0000862547.0000529136.0000623917.0000632285.00001489341.00001920824.00001502390.0000999885.00002668455.0000808231.00001017479.0000
\\n\",\n       \"
138 rows × 14 columns
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"                                LEXINGJIE                                                                                RONG360                                                                            \\n\",\n       \"                               2024-12-03   2024-12-04   2024-12-05  2024-12-06  2024-12-07  2024-12-08  2024-12-09   2024-12-03   2024-12-04   2024-12-05  2024-12-06   2024-12-07  2024-12-08   2024-12-09\\n\",\n       \"特征名称                    统计指标                                                                                                                                                                                \\n\",\n       \"QYQUERY_180_NUMS_DK_XYK 样本数     7986.0000    8018.0000    8099.0000   7975.0000   7983.0000   7992.0000   8082.0000    7818.0000    7875.0000    7972.0000   7960.0000    7913.0000   8003.0000    7907.0000\\n\",\n       \"                        非空数     7986.0000    8018.0000    8099.0000   7975.0000   7983.0000   7992.0000   8082.0000    7818.0000    7875.0000    7972.0000   7960.0000    7913.0000   8003.0000    7907.0000\\n\",\n       \"                        查得率        1.0000       1.0000       1.0000      1.0000      1.0000      1.0000      1.0000       1.0000       1.0000       1.0000      1.0000       1.0000      1.0000       1.0000\\n\",\n       \"                        最小值        0.0000       0.0000       0.0000      0.0000      0.0000      0.0000      0.0000       0.0000       0.0000       0.0000      0.0000       0.0000      0.0000       0.0000\\n\",\n       \"                        平均值       27.9591      27.9807      27.9589     27.7500     28.0606     28.0698     28.2720      28.0637      27.9735      28.1367     27.8165      27.9807     27.8398      28.0990\\n\",\n       \"...                                   ...          ...          ...         ...         ...         ...         ...          ...          ...          ...         ...          ...         ...          ...\\n\",\n       \"QYDK_X2254              95%    29947.8000   29382.3000   29032.7000  29054.0000  29329.8000  29233.1000  29052.0000   29916.3000   28783.9000   30417.2500  29231.1000   29303.6000  29818.8000   29435.4000\\n\",\n       \"                        97%    34897.7600   34083.2100   33821.2000  33315.2600  33334.2800  34513.5800  33467.6000   34588.3200   32846.3000   34936.4000  33686.1400   33690.0400  34382.0400   33659.2200\\n\",\n       \"                        98%    39024.1200   37791.2800   37171.2800  37228.6000  36786.0800  38009.2200  37530.0000   38717.1200   36996.5400   38295.7400  37988.5200   37900.4400  38091.0400   37540.1600\\n\",\n       \"                        99%    48173.8800   44534.5100   43482.4600  45641.9000  43996.0200  43839.3900  43182.6000   46566.8800   44886.9600   44902.8000  44844.2400   44813.5200  45889.8400   44828.2200\\n\",\n       \"                        最大值  3223069.0000 8054181.0000 1497655.0000 862547.0000 529136.0000 623917.0000 632285.0000 1489341.0000 1920824.0000 1502390.0000 999885.0000 2668455.0000 808231.0000 1017479.0000\\n\",\n       \"\\n\",\n       \"[138 rows x 14 columns]\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_data = groupby_feature_describe(data[[\\\"流量渠道\\\", \\\"申请日期\\\", \\\"QYQUERY_180_NUMS_DK_XYK\\\", \\\"QYDK_X111\\\", \\\"QYDK_X1975\\\", \\\"QY_NEWVAR_507\\\", \\\"QYDKD1_X870\\\", \\\"QYDK_X2254\\\"]], by=[\\\"流量渠道\\\", \\\"申请日期\\\"])\\n\",\n    \"# test_data.index.names = [\\\"特征名称\\\", \\\"统计指标\\\"]\\n\",\n    \"test_data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(142, 16)\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dataframe2excel(\\n\",\n    \"    groupby_feature_describe(data[[\\\"流量渠道\\\", \\\"申请日期\\\", \\\"QYQUERY_180_NUMS_DK_XYK\\\", \\\"QYDK_X111\\\", \\\"QYDK_X1975\\\", \\\"QY_NEWVAR_507\\\", \\\"QYDKD1_X870\\\", \\\"QYDK_X2254\\\"]], by=[\\\"流量渠道\\\", \\\"申请日期\\\"]),\\n\",\n    \"    \\\"数据分布情况.xlsx\\\",\\n\",\n    \"    sheet_name=\\\"TEST\\\",\\n\",\n    \"    index=True,\\n\",\n    \"    fill=False,\\n\",\n    \"    percent_rows=\\\"查得率\\\",\\n\",\n    \"    condition_rows=\\\"平均值\\\",\\n\",\n    \"    )\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[0;31mSignature:\\u001b[0m\\n\",\n      \"\\u001b[0mdataframe2excel\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mdata\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mexcel_writer\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0msheet_name\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtitle\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mheader\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mtheme_color\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'2639E9'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcondition_color\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfill\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mpercent_cols\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcondition_cols\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcustom_cols\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcustom_format\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'#,##0'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcolor_cols\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mpercent_rows\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcondition_rows\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcustom_rows\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mcolor_rows\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mstart_col\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mstart_row\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mmode\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'replace'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfigures\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mfigsize\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m600\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;36m350\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0mwriter_params\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m    \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n      \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mDocstring:\\u001b[0m\\n\",\n      \"向excel文件中插入指定样式的dataframe数据\\n\",\n      \"\\n\",\n      \":param data: 需要保存的dataframe数据，index默认不保存，如果需要保存先 .reset_index().rename(columns={\\\"index\\\": \\\"索引名称\\\"}) 再保存，有部分索引 reset_index 之后是 0 而非 index，根据实际情况进行修改\\n\",\n      \":param excel_writer: 需要保存到的 excel 文件路径或者 ExcelWriter\\n\",\n      \":param sheet_name: 需要插入内容的sheet，如果是 Worksheet，则直接向 Worksheet 插入数据\\n\",\n      \":param title: 是否在dataframe之前的位置插入一个标题\\n\",\n      \":param figures: 需要数据表与标题之间插入的图片，支持一次性传入多张图片的路径，会根据传入顺序依次插入\\n\",\n      \":param figsize: 插入图像的大小，为了统一排版，目前仅支持设置一个图片大小，默认: (600, 350) (长度, 高度)\\n\",\n      \":param header: 是否存储dataframe的header，暂不支持多级表头\\n\",\n      \":param theme_color: 主题色\\n\",\n      \":param condition_color: 条件格式主题颜色，不传默认为 theme_color\\n\",\n      \":param fill: 是否使用单元个颜色填充样式还是使用边框样式\\n\",\n      \":param percent_cols: 需要显示为百分数的列，仅修改显示格式，不更改数值\\n\",\n      \":param condition_cols: 需要显示条件格式的列（无边框渐变数据条）\\n\",\n      \":param color_cols: 需要显示为条件格式颜色填充的列（单元格填充渐变色）\\n\",\n      \":param custom_cols: 需要显示自定义格式的列，与 custom_format 参数搭配使用\\n\",\n      \":param custom_format: 显示的自定义格式，与 custom_cols 参数搭配使用，默认 #,##0 ，即显示为有分隔符的整数\\n\",\n      \":param start_col: 在excel中的开始列数，默认 2，即第二列开始\\n\",\n      \":param start_row: 在excel中的开始行数，默认 2，即第二行开始，如果 title 有值的话，会从 start_row + 2 行开始插入dataframe数据\\n\",\n      \":param mode: excel写入的模式，可选 append 和 replace ，默认 replace ，选择 append 时会在已有的excel文件中增加内容，不覆盖原有内容\\n\",\n      \":param writer_params: 透传至 ExcelWriter 内的参数\\n\",\n      \":param **kwargs: 其他参数，透传至 insert_df2sheet 方法，例如 传入 auto_width=True 会根据内容自动调整列宽\\n\",\n      \"\\n\",\n      \":return: 返回插入元素最后一列之后、最后一行之后的位置\\n\",\n      \"\\n\",\n      \"**参考样例**\\n\",\n      \"\\n\",\n      \">>> writer = ExcelWriter(theme_color='3f1dba')\\n\",\n      \">>> worksheet = writer.get_sheet_by_name(\\\"模型报告\\\")\\n\",\n      \">>> end_row, end_col = writer.insert_value2sheet(worksheet, \\\"B2\\\", value=\\\"模型报告\\\", style=\\\"header\\\")\\n\",\n      \">>> end_row, end_col = writer.insert_value2sheet(worksheet, \\\"B4\\\", value=\\\"模型报告\\\", style=\\\"header\\\", end_space=\\\"D4\\\")\\n\",\n      \">>> end_row, end_col = writer.insert_value2sheet(worksheet, \\\"B6\\\", value=\\\"当前模型主要为评分卡模型\\\", style=\\\"header_middle\\\", auto_width=True)\\n\",\n      \">>> # 单层索引保存样例\\n\",\n      \">>> sample = pd.DataFrame(np.concatenate([np.random.random_sample((10, 10)) * 40, np.random.randint(0, 3, (10, 2))], axis=1), columns=[f\\\"B{i}\\\" for i in range(10)] + [\\\"target\\\", \\\"type\\\"])\\n\",\n      \">>> end_row, end_col = writer.insert_df2sheet(worksheet, sample, (end_row + 2, column_index_from_string(\\\"B\\\")))\\n\",\n      \">>> end_row, end_col = writer.insert_df2sheet(worksheet, sample, (end_row + 2, column_index_from_string(\\\"B\\\")), fill=True)\\n\",\n      \">>> end_row, end_col = writer.insert_df2sheet(worksheet, sample, (end_row + 2, column_index_from_string(\\\"B\\\")), fill=True, header=False, index=True)\\n\",\n      \">>> end_row, end_col = writer.insert_df2sheet(worksheet, sample, (end_row + 2, column_index_from_string(\\\"B\\\")), merge_column=\\\"target\\\")\\n\",\n      \">>> end_row, end_col = writer.insert_df2sheet(worksheet, sample.set_index(\\\"type\\\"), (end_row + 2, column_index_from_string(\\\"B\\\")), merge_column=\\\"target\\\", index=True, fill=True)\\n\",\n      \">>> end_row, end_col = writer.insert_df2sheet(worksheet, sample, (end_row + 2, column_index_from_string(\\\"B\\\")), merge_column=[\\\"target\\\", \\\"type\\\"])\\n\",\n      \">>> end_row, end_col = writer.insert_df2sheet(worksheet, sample, (end_row + 2, column_index_from_string(\\\"B\\\")), merge_column=[10, 11])\\n\",\n      \">>> end_row, end_col = dataframe2excel(sample, writer, theme_color='3f1dba', sheet_name=\\\"模型报告\\\", start_row=end_row + 2, percent_cols=[\\\"B2\\\", \\\"B6\\\"], condition_cols=[\\\"B3\\\", \\\"B9\\\"], color_cols=[\\\"B4\\\"])\\n\",\n      \">>> end_row, end_col = dataframe2excel(sample, writer, theme_color='3f1dba', sheet_name=\\\"模型报告\\\", start_row=end_row + 2, percent_cols=[\\\"B2\\\", \\\"B6\\\"], condition_cols=[\\\"B3\\\", \\\"B9\\\"], color_cols=[\\\"B4\\\"], title=\\\"测试样例\\\")\\n\",\n      \">>> end_row, end_col = dataframe2excel(sample, writer, theme_color='3f1dba', sheet_name=\\\"模型报告\\\", start_row=end_row + 2, percent_cols=[\\\"B2\\\", \\\"B6\\\"], condition_cols=[\\\"B3\\\", \\\"B9\\\"], color_cols=[\\\"B4\\\"], title=\\\"测试样例\\\", figures=[\\\"../examples/model_report/auto_report_corr_plot.png\\\"])\\n\",\n      \">>> # 多层索引保存样例\\n\",\n      \">>> multi_sample = pd.DataFrame(np.random.randint(0, 150, size=(8, 12)), columns=pd.MultiIndex.from_product([['模拟考', '正式考'], ['数学', '语文', '英语', '物理', '化学', '生物']]), index=pd.MultiIndex.from_product([['期中', '期末'], ['雷军', '李斌'], ['测试一', '测试二']]))\\n\",\n      \">>> multi_sample.index.names = [\\\"考试类型\\\", \\\"姓名\\\", \\\"测试\\\"]\\n\",\n      \">>> end_row, end_col = dataframe2excel(multi_sample, writer, theme_color='3f1dba', sheet_name=\\\"模型报告\\\", start_row=end_row + 2, title=\\\"测试样例\\\", index=True, header=False)\\n\",\n      \">>> end_row, end_col = dataframe2excel(multi_sample, writer, theme_color='3f1dba', sheet_name=\\\"模型报告\\\", start_row=end_row + 2, title=\\\"测试样例\\\", index=True)\\n\",\n      \">>> end_row, end_col = dataframe2excel(multi_sample, writer, theme_color='3f1dba', sheet_name=\\\"模型报告\\\", start_row=end_row + 2, title=\\\"测试样例\\\", index=True, fill=False)\\n\",\n      \">>> end_row, end_col = dataframe2excel(multi_sample.reset_index(names=multi_sample.index.names, col_level=-1), writer, theme_color='3f1dba', sheet_name=\\\"模型报告\\\", start_row=end_row + 2, title=\\\"测试样例\\\", index=False, fill=False, merge_column=[('', '考试类型'), ('', '姓名')])\\n\",\n      \">>> end_row, end_col = dataframe2excel(multi_sample.reset_index(names=multi_sample.index.names, col_level=-1), writer, theme_color='3f1dba', sheet_name=\\\"模型报告\\\", start_row=end_row + 2, title=\\\"测试样例\\\", index=False, fill=False, merge_column=[('', '考试类型')], merge=True)\\n\",\n      \">>> end_row, end_col = dataframe2excel(multi_sample.reset_index(names=multi_sample.index.names, col_level=-1), writer, theme_color='3f1dba', sheet_name=\\\"模型报告\\\", start_row=end_row + 2, title=\\\"测试样例\\\", index=True, fill=True, merge_column=[('', '考试类型')], merge=True)\\n\",\n      \">>> writer.save(\\\"./测试样例.xlsx\\\")\\n\",\n      \"\\u001b[0;31mFile:\\u001b[0m      ~/Desktop/workspace/scorecardpipeline/scorecardpipeline/excel_writer.py\\n\",\n      \"\\u001b[0;31mType:\\u001b[0m      function\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dataframe2excel?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "examples/同盾百融三方数据评分卡建模样例.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"id\": \"06bac5a1-08b0-41c4-87bb-400afc0dec87\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import re\\n\",\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import time\\n\",\n    \"import json\\n\",\n    \"import joblib\\n\",\n    \"import traceback\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from tqdm import tqdm\\n\",\n    \"from datetime import timedelta, datetime\\n\",\n    \"from dateutil.relativedelta import relativedelta\\n\",\n    \"\\n\",\n    \"from tools import *\\n\",\n    \"from scorecardpipeline import *\\n\",\n    \"\\n\",\n    \"logger = init_setting(seed=999, logger=True)\\n\",\n    \"use_cols = ['指标名称', '指标含义', '分箱', '样本总数', '样本占比', '好样本数', '好样本占比', '坏样本数', '坏样本占比', '坏样本率', 'LIFT值', '累积LIFT值']\\n\",\n    \"json_cols = [\\\"app_id\\\", \\\"supplier_code\\\", \\\"data_source_code\\\", \\\"result_value\\\", \\\"apply_result\\\", \\\"reason_code\\\", \\\"expire_date\\\"]\\n\",\n    \"feature_describe = pd.read_excel(\\\"common/百融征信打包字段.xlsx\\\").rename(columns={\\\"var_name_en\\\": \\\"变量名\\\", \\\"note\\\": \\\"字段\\\"})[[\\\"变量名\\\", \\\"字段\\\"]].drop_duplicates(\\\"变量名\\\")\\n\",\n    \"\\n\",\n    \"feature_map = dict(zip(feature_describe[\\\"变量名\\\"], feature_describe[\\\"字段\\\"]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"id\": \"b7126ff2-4d56-4d9f-9d92-adf0545c0353\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def timestamp2datetime(stamp,format='%Y-%m-%d %H:%M:%S'):\\n\",\n    \"    if not isinstance(stamp, int):\\n\",\n    \"        try:\\n\",\n    \"            stamp = int(stamp)\\n\",\n    \"        except ValueError as error:\\n\",\n    \"            raise Exception(f\\\"timestamp is not digital. error info {error}\\\")\\n\",\n    \"\\n\",\n    \"    if len(str(stamp)) == 10:\\n\",\n    \"        _ = time.localtime(stamp)\\n\",\n    \"        _ = time.strftime(format, _)\\n\",\n    \"        return datetime.strptime(_, format)\\n\",\n    \"    elif 10 < len(str(stamp)) < 15:\\n\",\n    \"        _ = len(str(stamp)) - 10\\n\",\n    \"        _ = datetime.fromtimestamp(stamp/(1 * 10 ** _))\\n\",\n    \"        return _.strftime(format)\\n\",\n    \"    else:\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def asnumeric(data):\\n\",\n    \"    numeric_columns = []\\n\",\n    \"    for col in data.columns:\\n\",\n    \"        try:\\n\",\n    \"            data[col] = pd.to_numeric(data[col])\\n\",\n    \"        except:\\n\",\n    \"            pass\\n\",\n    \"    \\n\",\n    \"    return data\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def deal_td_risk_items(items):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    同盾数据解析方法\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    results = {}\\n\",\n    \"    \\n\",\n    \"    for item in items:\\n\",\n    \"        group_name = item.get(\\\"group\\\") or \\\"\\\"\\n\",\n    \"        item_name = item.get(\\\"item_name\\\") or \\\"\\\"\\n\",\n    \"        \\n\",\n    \"        results.update({f\\\"{group_name}_{item_name}_score\\\": item.get(\\\"score\\\")})\\n\",\n    \"        # results.update({f\\\"{group_name}_{item_name}_level\\\": item.get(\\\"risk_level\\\")})\\n\",\n    \"        \\n\",\n    \"        item_detail = item.get('item_detail', {})\\n\",\n    \"        \\n\",\n    \"        if item_detail:\\n\",\n    \"            try:\\n\",\n    \"                results.update({f\\\"{group_name}_{item_name}_count\\\": item_detail.get(\\\"platform_count\\\")})\\n\",\n    \"                \\n\",\n    \"                high_risk_areas = item_detail.get('high_risk_areas', {})\\n\",\n    \"                frequency_detail_list = item_detail.get('frequency_detail_list', {})\\n\",\n    \"                platform_detail = item_detail.get('platform_detail', {})\\n\",\n    \"                platform_detail_dimension = item_detail.get('platform_detail_dimension', {})\\n\",\n    \"                \\n\",\n    \"                if high_risk_areas:\\n\",\n    \"                    results.update({f\\\"{group_name}_{item_name}_high_risk_areas\\\": high_risk_areas, f\\\"{group_name}_{item_name}_high_risk_areas_count\\\": len(high_risk_areas)})\\n\",\n    \"                \\n\",\n    \"                if frequency_detail_list:\\n\",\n    \"                    for detail in frequency_detail_list:\\n\",\n    \"                        detail_info = detail.get(\\\"detail\\\", \\\"：\\\").split(\\\"：\\\")\\n\",\n    \"                        results.update({f\\\"{group_name}_{item_name}_{detail_info[0]}_count\\\": int(detail_info[1])})\\n\",\n    \"                \\n\",\n    \"                if platform_detail:\\n\",\n    \"                    for detail in platform_detail:\\n\",\n    \"                        detail_info = detail.split(\\\":\\\")\\n\",\n    \"                        results.update({f\\\"{group_name}_{item_name}_{detail_info[0]}_count\\\": int(detail_info[1])})\\n\",\n    \"                \\n\",\n    \"                if platform_detail_dimension:\\n\",\n    \"                    for dimension in platform_detail_dimension:\\n\",\n    \"                        dimension_name = dimension.get(\\\"dimension\\\") or \\\"\\\"\\n\",\n    \"                        results.update({f\\\"{group_name}_{item_name}_{dimension_name}_count\\\": dimension.get(\\\"count\\\")})\\n\",\n    \"                        \\n\",\n    \"                        for detail in dimension.get(\\\"detail\\\", []):\\n\",\n    \"                            detail_info = detail.split(\\\":\\\")\\n\",\n    \"                            results.update({f\\\"{group_name}_{item_name}_{dimension_name}_{detail_info[0]}_count\\\": int(detail_info[1])})\\n\",\n    \"            except:\\n\",\n    \"                traceback.print_exc()\\n\",\n    \"                print(\\\"item_detail\\\", item_detail, \\\"\\\\n\\\")\\n\",\n    \"    \\n\",\n    \"    return results\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def td_parase_data(path):\\n\",\n    \"    if \\\"xlsx\\\" in path:\\n\",\n    \"        td_data = pd.read_excel(path)[json_cols]\\n\",\n    \"    else:\\n\",\n    \"        with open(path, \\\"r\\\", encoding=\\\"utf8\\\") as f:\\n\",\n    \"            td_data = pd.DataFrame(json.load(f)[\\\"data\\\"])[json_cols]\\n\",\n    \"    \\n\",\n    \"    td_data[\\\"result_value\\\"] = td_data[\\\"result_value\\\"].apply(json.loads)\\n\",\n    \"\\n\",\n    \"    cols = [\\\"final_score\\\", \\\"final_decision\\\", \\\"report_time\\\", \\\"report_id\\\", \\\"apply_time\\\", \\\"application_id\\\"]\\n\",\n    \"    td_data[cols] = td_data[\\\"result_value\\\"].apply(lambda x: [x.get(c) for c in cols]).apply(pd.Series)\\n\",\n    \"\\n\",\n    \"    cols = [\\\"mobile_address\\\", \\\"id_card_address\\\"]\\n\",\n    \"    td_data[cols] = td_data[\\\"result_value\\\"].apply(lambda x: x.get(\\\"address_detect\\\", {})).apply(lambda x: [x.get(c) for c in cols]).apply(pd.Series)\\n\",\n    \"    td_data[\\\"risk_items\\\"] = td_data[\\\"result_value\\\"].apply(lambda x: x.get(\\\"risk_items\\\", {}))\\n\",\n    \"    td_data[\\\"apply_time\\\"] = pd.to_datetime(td_data[\\\"apply_time\\\"].apply(timestamp2datetime))\\n\",\n    \"    td_data[\\\"report_time\\\"] = pd.to_datetime(td_data[\\\"report_time\\\"].apply(timestamp2datetime))\\n\",\n    \"\\n\",\n    \"    td_data = td_data.join(td_data[\\\"risk_items\\\"].apply(deal_td_risk_items).apply(pd.Series))\\n\",\n    \"    td_data = td_data.sort_values(\\\"report_time\\\").drop_duplicates(\\\"app_id\\\", keep=\\\"last\\\")\\n\",\n    \"    td_data = td_data.drop(columns=[\\\"result_value\\\", \\\"risk_items\\\", \\\"supplier_code\\\", \\\"data_source_code\\\", \\\"apply_result\\\", \\\"reason_code\\\", \\\"expire_date\\\", \\\"report_id\\\", \\\"apply_time\\\", \\\"application_id\\\"])\\n\",\n    \"    \\n\",\n    \"    td_data = asnumeric(td_data.rename(columns={\\\"app_id\\\": \\\"订单编号\\\"}).replace(\\\"\\\", np.nan))\\n\",\n    \"    \\n\",\n    \"    return td_data\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def br_parase_data(path):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    百融征信数据解析\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    with open(path, \\\"r\\\", encoding=\\\"utf8\\\") as f:\\n\",\n    \"        br_data = pd.DataFrame(json.load(f)[\\\"data\\\"])[json_cols]\\n\",\n    \"        br_data[\\\"result_value\\\"] = br_data[\\\"result_value\\\"].replace(\\\"\\t(N/A)\\\", \\\"{}\\\").replace(\\\"(N/A)\\\", \\\"{}\\\").apply(json.loads)\\n\",\n    \"        br_data = br_data.join(br_data[\\\"result_value\\\"].apply(pd.Series))\\n\",\n    \"        # br_data[\\\"expire_date\\\"] = pd.to_datetime(br_data[\\\"expire_date\\\"])\\n\",\n    \"        \\n\",\n    \"        br_data = br_data.sort_values(\\\"expire_date\\\").drop_duplicates(\\\"app_id\\\", keep=\\\"last\\\")\\n\",\n    \"        br_data = br_data.drop(columns=[\\\"result_value\\\", \\\"expire_date\\\", \\\"supplier_code\\\", \\\"data_source_code\\\", \\\"apply_result\\\", \\\"reason_code\\\", \\\"code\\\", \\\"swift_number\\\"])\\n\",\n    \"        \\n\",\n    \"        br_data = asnumeric(br_data.rename(columns={\\\"app_id\\\": \\\"订单编号\\\"}).replace(\\\"\\\", np.nan))\\n\",\n    \"    \\n\",\n    \"    return br_data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"id\": \"fed27290-cb40-4e1c-8d32-8de46c2ce33b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"((2747, 10), 487)\"\n      ]\n     },\n     \"execution_count\": 67,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data = pd.read_sql_query(\\\"\\\"\\\"\\n\",\n    \"                        SELECT C_EXT_ID\\n\",\n    \"                                , D_APPLICATION\\n\",\n    \"                                , N_AGE\\n\",\n    \"                                , N_MAX_OVERDUE_DAYS\\n\",\n    \"                                , N_OVERDUE_DAYS\\n\",\n    \"                                , CASE WHEN N_MAX_OVERDUE_DAYS > 30 THEN 1 ELSE 0 END TARGET\\n\",\n    \"                                , N_TENOR\\n\",\n    \"                                , C_INDUSTRY_TWO\\n\",\n    \"                                , C_EDUCATION\\n\",\n    \"                                , XGB_SCORE\\n\",\n    \"                        FROM DATAMART.DM_MID_LOAN_BASE l\\n\",\n    \"                        left join (\\n\",\n    \"                            SELECT C_APP_ID, MIN(X492) xgb_score\\n\",\n    \"                            FROM DATAMART.DM_MID_URULE_PARAM\\n\",\n    \"                            WHERE C_TARGET_NAME like 'education%' AND X492 IS NOT NULL\\n\",\n    \"                            GROUP BY C_APP_ID\\n\",\n    \"                        ) s ON s.C_APP_ID = l.C_EXT_ID\\n\",\n    \"                        WHERE C_EXT_ID IN (\\n\",\n    \"                                                SELECT C_EXT_ID FROM (\\n\",\n    \"                                                        SELECT C_CUST_IDNO, C_EXT_ID, ROW_NUMBER() OVER(PARTITION BY C_CUST_IDNO ORDER BY D_APPLICATION) rk FROM DATAMART.DM_MID_LOAN_BASE\\n\",\n    \"                                                        WHERE D_APPLICATION >= DATE '2022-01-01' (N_MAX_OVERDUE_DAYS > 30 OR N_MAX_OVERDUE_DAYS <= 0)\\n\",\n    \"                                                ) t WHERE rk = 1\\n\",\n    \"                                        )\\n\",\n    \"                                        AND D_APPLICATION >= DATE '2022-01-01' AND D_LOAN_DATE <= DATE '2022-12-31' AND C_FINANCE_PRODUCT_CODE NOT LIKE '%DDG%' AND (N_MAX_OVERDUE_DAYS > 30 OR N_MAX_OVERDUE_DAYS <= 0)\\n\",\n    \"                        \\\"\\\"\\\", cx.connect(\\\"zhangluping/zhangluping#0210@172.16.104.109:1521/geexdb\\\")\\n\",\n    \"                        )\\n\",\n    \"\\n\",\n    \"data.shape, data[\\\"TARGET\\\"].sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"id\": \"b6bf9a33-024c-48fe-91ed-648393bc364a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"((920, 10), 54)\"\n      ]\n     },\n     \"execution_count\": 68,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 验证数据集\\n\",\n    \"oot_data = pd.read_sql_query(\\\"\\\"\\\"\\n\",\n    \"                        SELECT C_EXT_ID\\n\",\n    \"                                , D_APPLICATION\\n\",\n    \"                                , N_AGE\\n\",\n    \"                                , N_MAX_OVERDUE_DAYS\\n\",\n    \"                                , N_OVERDUE_DAYS\\n\",\n    \"                                , CASE WHEN N_MAX_OVERDUE_DAYS > 30 THEN 1 ELSE 0 END TARGET\\n\",\n    \"                                , N_TENOR\\n\",\n    \"                                , C_INDUSTRY_TWO\\n\",\n    \"                                , C_EDUCATION\\n\",\n    \"                                , XGB_SCORE\\n\",\n    \"                        FROM DATAMART.DM_MID_LOAN_BASE l\\n\",\n    \"                        left join (\\n\",\n    \"                            SELECT C_APP_ID, MIN(X492) xgb_score\\n\",\n    \"                            FROM DATAMART.DM_MID_URULE_PARAM\\n\",\n    \"                            WHERE C_TARGET_NAME like 'education%' AND X492 IS NOT NULL\\n\",\n    \"                            GROUP BY C_APP_ID\\n\",\n    \"                        ) s ON s.C_APP_ID = l.C_EXT_ID\\n\",\n    \"                        WHERE C_EXT_ID IN (\\n\",\n    \"                                                SELECT C_EXT_ID FROM (\\n\",\n    \"                                                        SELECT C_CUST_IDNO, C_EXT_ID, ROW_NUMBER() OVER(PARTITION BY C_CUST_IDNO ORDER BY D_APPLICATION) rk FROM DATAMART.DM_MID_LOAN_BASE\\n\",\n    \"                                                        WHERE D_APPLICATION >= DATE '2022-01-01' AND (N_MAX_OVERDUE_DAYS > 30 OR N_MAX_OVERDUE_DAYS <= 0)\\n\",\n    \"                                                ) t WHERE rk = 1\\n\",\n    \"                                        )\\n\",\n    \"                                        AND D_LOAN_DATE >= DATE '2023-01-01' AND D_LOAN_DATE <= DATE '2023-04-30' AND C_FINANCE_PRODUCT_CODE NOT LIKE '%DDG%' AND (N_MAX_OVERDUE_DAYS > 30 OR N_MAX_OVERDUE_DAYS <= 0)\\n\",\n    \"                        \\\"\\\"\\\", cx.connect(\\\"zhangluping/zhangluping#0210@172.16.104.109:1521/geexdb\\\")\\n\",\n    \"                        )\\n\",\n    \"\\n\",\n    \"oot_data.shape, oot_data[\\\"TARGET\\\"].sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"b0c5dde5-ad70-493f-b9b2-21b39d9844d6\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"id\": \"09d51357-469c-4e61-9ffd-ea672cab9152\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 职培同盾百融数据\\n\",\n    \"br_data1 = br_parase_data(\\\"export_result (69).json\\\")\\n\",\n    \"td_data1 = td_parase_data(\\\"export_result (71).json\\\")\\n\",\n    \"\\n\",\n    \"train = data.rename(columns={\\\"C_EXT_ID\\\": \\\"订单编号\\\"}).merge(br_data1, on=\\\"订单编号\\\", how=\\\"inner\\\").merge(td_data1, on=\\\"订单编号\\\", how=\\\"inner\\\")\\n\",\n    \"\\n\",\n    \"del_cols = [\\n\",\n    \"#             \\\"多平台借贷申请检测_12个月内申请人在多个平台申请借款_借款人身份证详情_银行消费金融公司_count\\\", \\\"多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_借款人手机详情_大型消费金融公司_count\\\",\\n\",\n    \"#             \\\"ir_id_x_name_cnt\\\", \\\"多平台借贷申请检测_12个月内申请人在多个平台申请借款_银行消费金融公司_count\\\", \\\"多平台借贷申请检测_6个月内申请人在多个平台申请借款_银行消费金融公司_count\\\", \\n\",\n    \"#             \\\"als_lst_id_bank_inteday\\\", \\\"多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_大型消费金融公司_count\\\", \\\"多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_借款人身份证详情_大型消费金融公司_count\\\",\\n\",\n    \"#             \\\"多平台借贷申请检测_18个月内申请人在多个平台申请借款_借款人手机详情_一般消费分期平台_count\\\", \\\"多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_银行消费金融公司_count\\\",\\n\",\n    \"#             \\\"多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_借款人身份证详情_银行消费金融公司_count\\\", \\n\",\n    \"            \\\"C_EDUCATION\\\", \\\"C_INDUSTRY_TWO\\\"\\n\",\n    \"           ]\\n\",\n    \"for col in train.columns:\\n\",\n    \"    if len(train[col].value_counts()) < 2:\\n\",\n    \"        del_cols.append(col)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train = train.drop(columns=[\\\"个人基本信息核查_身份证归属地位于高风险较为集中地区_high_risk_areas\\\", \\\"id_card_address\\\", \\\"mobile_address\\\", \\\"final_decision\\\", \\n\",\n    \"                            \\\"D_APPLICATION\\\", \\\"N_MAX_OVERDUE_DAYS\\\", \\\"N_OVERDUE_DAYS\\\", \\\"订单编号\\\", \\\"N_TENOR\\\", \\\"report_time\\\"] + del_cols)\\n\",\n    \"\\n\",\n    \"train = train.reset_index(drop=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"id\": \"c7809843-31f4-429b-a54b-6bc9e5073658\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 职培同盾百融数据\\n\",\n    \"br_data1 = br_parase_data(r\\\"export_result (76).json\\\")\\n\",\n    \"td_data1 = td_parase_data(r\\\"export_result (75).json\\\")\\n\",\n    \"\\n\",\n    \"oot = oot_data.rename(columns={\\\"C_EXT_ID\\\": \\\"订单编号\\\"}).merge(br_data1, on=\\\"订单编号\\\", how=\\\"inner\\\").merge(td_data1, on=\\\"订单编号\\\", how=\\\"inner\\\")\\n\",\n    \"oot = oot.drop(columns=[c for c in oot.columns if c in [\\\"个人基本信息核查_身份证归属地位于高风险较为集中地区_high_risk_areas\\\", \\\"id_card_address\\\", \\\"mobile_address\\\", \\\"final_decision\\\", \\n\",\n    \"                            \\\"D_APPLICATION\\\", \\\"N_MAX_OVERDUE_DAYS\\\", \\\"N_OVERDUE_DAYS\\\", \\\"订单编号\\\", \\\"N_TENOR\\\", \\\"report_time\\\"] + del_cols])\\n\",\n    \"\\n\",\n    \"# oot = oot.drop(columns=[\\\"C_INDUSTRY_TWO\\\"]).reset_index(drop=True)\\n\",\n    \"oot = oot.reset_index(drop=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"d7c0d3a7-c2b6-4be5-b13d-d240c625d965\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"id\": \"7ab1ed2e-2cf9-4817-bd55-c68e9e456fd0\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"target = \\\"TARGET\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 209,\n   \"id\": \"8891e5fa-6c3b-4319-97b9-ecc81ec22886\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 构建 pipeline\\n\",\n    \"pre_select = FeatureSelection(target=target, engine=\\\"toad\\\", identical=0.95, empty=0.95, corr=0.9, iv=0.05)\\n\",\n    \"temp_train = pre_select.fit_transform(train.drop(columns=[\\\"XGB_SCORE\\\"]))\\n\",\n    \"temp_oot = pre_select.transform(oot)\\n\",\n    \"\\n\",\n    \"combiner = Combiner(target=target, empty_separate=True, method=\\\"cart\\\", min_bin_size=0.12, max_n_prebins=5, min_prebin_size=0.05, max_n_bins=2)\\n\",\n    \"combiner.fit(temp_train)\\n\",\n    \"\\n\",\n    \"combiner.combiner.update({\\n\",\n    \"    \\\"C_EDUCATION\\\": [[\\\"本科\\\", \\\"研究生\\\"], [\\\"大专\\\"], [\\\"中专、职高、技校\\\", \\\"高中\\\", \\\"技术学校\\\"], [\\\"初中及以下\\\", \\\"初中\\\", \\\"小学\\\", \\\"文盲或半文盲\\\"], [\\\"其他\\\", np.nan, None, \\\"其它\\\"]],\\n\",\n    \"    \\\"als_m3_id_bank_min_inteday\\\": [8, np.nan],\\n\",\n    \"    \\\"als_m3_cell_pdl_allnum\\\": [2, np.nan],\\n\",\n    \"    \\\"als_m1_id_caon_allnum\\\": [np.nan],\\n\",\n    \"    \\\"als_m3_cell_nbank_else_allnum\\\": [2, np.nan],\\n\",\n    \"    \\\"als_m3_id_nbank_nsloan_orgnum\\\": [2, np.nan],\\n\",\n    \"    \\\"ir_id_x_cell_lastchg_days\\\": [106.0, np.nan],\\n\",\n    \"    \\\"多平台借贷申请检测_24个月内申请人在多个平台申请借款_银行消费金融公司_count\\\": [2, np.nan],\\n\",\n    \"    \\\"als_m1_id_nbank_nsloan_orgnum\\\": [1, np.nan],\\n\",\n    \"    \\\"als_m6_id_cooff_orgnum\\\": [1, np.nan],\\n\",\n    \"    \\\"多平台借贷申请检测_18个月内申请人在多个平台申请借款_大型消费金融公司_count\\\": [1, np.nan],\\n\",\n    \"    \\\"多平台借贷负债检测_近60个月以上申请人在多个平台被放款_借款人身份证详情_一般消费分期平台_count\\\": [2, np.nan],\\n\",\n    \"    \\\"多平台借贷申请检测_6个月内申请人在多个平台申请借款_借款人身份证详情_O2O_count\\\": [1, np.nan],\\n\",\n    \"})\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for k, v in combiner.rules.items():\\n\",\n    \"    if len(v) <= 1 and pd.isnull(v[0]):\\n\",\n    \"        combiner.update({k: [1.]})\\n\",\n    \"\\n\",\n    \"temp_train = combiner.transform(temp_train)\\n\",\n    \"temp_oot = combiner.transform(temp_oot)\\n\",\n    \"\\n\",\n    \"transform = WOETransformer(target=target)\\n\",\n    \"woe_train = transform.fit_transform(temp_train)\\n\",\n    \"woe_oot = transform.fit_transform(temp_oot)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"98e4014a-66b6-4ce7-b304-d4e8737ce0dd\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 210,\n   \"id\": \"3e54ccb1-aaeb-4609-be95-44eaf7ebfc77\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import joblib\\n\",\n    \"from automl.aml_main import auto_lightgbm\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"\\n\",\n    \"woe_train['订单编号'] = [i for i in range(len(woe_train))]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 211,\n   \"id\": \"90d9c38a-69c8-42c0-93d3-c8b20facf835\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"uid, dep = \\\"订单编号\\\", \\\"TARGET\\\"\\n\",\n    \"var_names = list(woe_train.columns)\\n\",\n    \"var_names.remove(dep)\\n\",\n    \"# woe_train, woe_oot = train_test_split(woe_train, test_size=0.25, random_state=328)\\n\",\n    \"datasets = {\\\"dev\\\": woe_train, \\\"off\\\": woe_train}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 212,\n   \"id\": \"792ae137-a09e-4363-87f1-9556ba317a8a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"开始自动建模...\\n\",\n      \"--------------------------------------------------\\n\",\n      \"Shap阈值 0.001\\n\",\n      \"shap删除特征个数： 172 shap保留特征个数： 142\\n\",\n      \"--------------------------------------------------\\n\",\n      \"相关性阈值: 0.35 相关性删除特征个数： 124 相关性保留特征个数： 18\\n\",\n      \"--------------------------------------------------\\n\",\n      \"PSI阈值 0.1\\n\",\n      \"PSI删除特征个数： 0 PSI保留特征个数： 18\\n\",\n      \"--------------------------------------------------\\n\",\n      \"['ir_id_x_cell_lastchg_days', 'als_m6_id_bank_night_orgnum', 'als_fst_id_bank_inteday', 'als_m6_id_nbank_else_orgnum', 'als_m3_id_bank_min_inteday', 'als_m3_cell_pdl_allnum', 'als_m1_id_caon_allnum', 'als_m6_cell_cooff_allnum', 'als_d15_id_rel_allnum', 'als_m12_cell_coon_allnum', '多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_借款人身份证详情_P2P网贷_count', '多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_信用卡中心_count', '多平台借贷申请检测_6个月内申请人在多个平台申请借款_借款人身份证详情_O2O_count', '多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_借款人身份证详情_银行个人业务_count', '多平台借贷申请检测_3个月内申请人在多个平台申请借款_借款人身份证详情_一般消费分期平台_count', '多平台借贷申请检测_24个月内申请人在多个平台申请借款_小额贷款公司_count', '多平台借贷申请检测_24个月内申请人在多个平台申请借款_大型消费金融公司_count', '多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_借款人身份证详情_财产保险_count']\\n\",\n      \"开始参数搜索,目标函数 offks\\n\",\n      \"train_number: 0, devks: 0.44778519990720345, offks: 0.44778519990720345, params: {'boosting_type': 'gbdt', 'objective': 'binary', 'metric': 'cross_entropy', 'reg_lambda': 3, 'reg_alpha': 0.85, 'num_leaves': 32, 'learning_rate': 0.02, 'min_data': 50, 'min_hessian': 0.05, 'num_threads': 4, 'feature_fraction': 0.9, 'bagging_fraction': 0.8, 'bagging_freq': 2, 'verbose': -1, 'num_boost_round': 100}\\n\",\n      \"(Good) train_number: 1, devks: 0.4495131768706994, offks: 0.4495131768706994, params: {'boosting_type': 'gbdt', 'objective': 'binary', 'metric': 'cross_entropy', 'reg_lambda': 13, 'reg_alpha': 0.85, 'num_leaves': 32, 'learning_rate': 0.02, 'min_data': 50, 'min_hessian': 0.05, 'num_threads': 4, 'feature_fraction': 0.9, 'bagging_fraction': 0.8, 'bagging_freq': 2, 'verbose': -1, 'num_boost_round': 100}\\n\",\n      \"(Good) train_number: 9, devks: 0.45026296341472166, offks: 0.45026296341472166, params: {'boosting_type': 'gbdt', 'objective': 'binary', 'metric': 'cross_entropy', 'reg_lambda': 3, 'reg_alpha': 1.4000000000000001, 'num_leaves': 32, 'learning_rate': 0.02, 'min_data': 50, 'min_hessian': 0.05, 'num_threads': 4, 'feature_fraction': 0.9, 'bagging_fraction': 0.8, 'bagging_freq': 2, 'verbose': -1, 'num_boost_round': 100}\\n\",\n      \"(Good) train_number: 21, devks: 0.48904857229640364, offks: 0.48904857229640364, params: {'boosting_type': 'gbdt', 'objective': 'binary', 'metric': 'cross_entropy', 'reg_lambda': 3, 'reg_alpha': 1.85, 'num_leaves': 32, 'learning_rate': 0.22, 'min_data': 50, 'min_hessian': 0.05, 'num_threads': 4, 'feature_fraction': 0.9, 'bagging_fraction': 0.8, 'bagging_freq': 2, 'verbose': -1, 'num_boost_round': 100}\\n\",\n      \"(Good) train_number: 23, devks: 0.5042416154252737, offks: 0.5042416154252737, params: {'boosting_type': 'gbdt', 'objective': 'binary', 'metric': 'cross_entropy', 'reg_lambda': 3, 'reg_alpha': 1.85, 'num_leaves': 32, 'learning_rate': 0.32, 'min_data': 50, 'min_hessian': 0.05, 'num_threads': 4, 'feature_fraction': 0.9, 'bagging_fraction': 0.8, 'bagging_freq': 2, 'verbose': -1, 'num_boost_round': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"  0%|                                                                                          | 0/18 [00:00\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"      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  \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      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Coef.Std.ErrzP>|z|[ 0.0250.975 ]VIF
als_m6_id_nbank_else_orgnum0.09090.15670.58020.5618-0.21620.39791.4404
als_d15_id_rel_allnum0.11330.21290.53210.5946-0.30390.53051.1416
多平台借贷申请检测_24个月内申请人在多个平台申请借款_大型消费金融公司_count0.14110.14230.99160.3214-0.13780.41991.2459
多平台借贷申请检测_3个月内申请人在多个平台申请借款_借款人身份证详情_一般消费分期平台_count0.15020.15880.94590.3442-0.16110.46161.2451
als_m12_cell_coon_allnum0.15320.21340.71790.4728-0.26500.57131.1848
多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_借款人身份证详情_财产保险_count0.19090.21360.89370.3715-0.22780.60961.1524
als_m1_id_caon_allnum0.33840.16012.11290.03460.02450.65221.2692
多平台借贷申请检测_24个月内申请人在多个平台申请借款_小额贷款公司_count0.35340.13842.55450.01060.08230.62461.2543
ir_id_x_cell_lastchg_days0.35920.11473.13290.00170.13450.58391.1821
als_m3_cell_pdl_allnum0.38980.14182.74860.00600.11180.66781.2505
多平台借贷申请检测_6个月内申请人在多个平台申请借款_借款人身份证详情_O2O_count0.40420.12253.30070.00100.16420.64421.2678
多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_信用卡中心_count0.43430.12853.37940.00070.18240.68611.1229
多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_借款人身份证详情_P2P网贷_count0.54920.08186.71460.00000.38890.70951.3770
als_m6_cell_cooff_allnum0.57720.24292.37610.01750.10111.05331.1363
const0.67210.053212.62530.00000.56780.77651.0703
\\n\",\n       \"\"\n      ],\n      \"text/plain\": [\n       \"                                                    Coef.  Std.Err       z  \\\\\\n\",\n       \"als_m6_id_nbank_else_orgnum                        0.0909   0.1567  0.5802   \\n\",\n       \"als_d15_id_rel_allnum                              0.1133   0.2129  0.5321   \\n\",\n       \"多平台借贷申请检测_24个月内申请人在多个平台申请借款_大型消费金融公司_count         0.1411   0.1423  0.9916   \\n\",\n       \"多平台借贷申请检测_3个月内申请人在多个平台申请借款_借款人身份证详情_一般消费分期平台_count 0.1502   0.1588  0.9459   \\n\",\n       \"als_m12_cell_coon_allnum                           0.1532   0.2134  0.7179   \\n\",\n       \"多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_借款人身份证详情_财产保险_count  0.1909   0.2136  0.8937   \\n\",\n       \"als_m1_id_caon_allnum                              0.3384   0.1601  2.1129   \\n\",\n       \"多平台借贷申请检测_24个月内申请人在多个平台申请借款_小额贷款公司_count           0.3534   0.1384  2.5545   \\n\",\n       \"ir_id_x_cell_lastchg_days                          0.3592   0.1147  3.1329   \\n\",\n       \"als_m3_cell_pdl_allnum                             0.3898   0.1418  2.7486   \\n\",\n       \"多平台借贷申请检测_6个月内申请人在多个平台申请借款_借款人身份证详情_O2O_count      0.4042   0.1225  3.3007   \\n\",\n       \"多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_信用卡中心_count          0.4343   0.1285  3.3794   \\n\",\n       \"多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_借款人身份证详情_P2P网贷_count 0.5492   0.0818  6.7146   \\n\",\n       \"als_m6_cell_cooff_allnum                           0.5772   0.2429  2.3761   \\n\",\n       \"const                                              0.6721   0.0532 12.6253   \\n\",\n       \"\\n\",\n       \"                                                    P>|z|  [ 0.025  0.975 ]  \\\\\\n\",\n       \"als_m6_id_nbank_else_orgnum                        0.5618  -0.2162   0.3979   \\n\",\n       \"als_d15_id_rel_allnum                              0.5946  -0.3039   0.5305   \\n\",\n       \"多平台借贷申请检测_24个月内申请人在多个平台申请借款_大型消费金融公司_count         0.3214  -0.1378   0.4199   \\n\",\n       \"多平台借贷申请检测_3个月内申请人在多个平台申请借款_借款人身份证详情_一般消费分期平台_count 0.3442  -0.1611   0.4616   \\n\",\n       \"als_m12_cell_coon_allnum                           0.4728  -0.2650   0.5713   \\n\",\n       \"多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_借款人身份证详情_财产保险_count  0.3715  -0.2278   0.6096   \\n\",\n       \"als_m1_id_caon_allnum                              0.0346   0.0245   0.6522   \\n\",\n       \"多平台借贷申请检测_24个月内申请人在多个平台申请借款_小额贷款公司_count           0.0106   0.0823   0.6246   \\n\",\n       \"ir_id_x_cell_lastchg_days                          0.0017   0.1345   0.5839   \\n\",\n       \"als_m3_cell_pdl_allnum                             0.0060   0.1118   0.6678   \\n\",\n       \"多平台借贷申请检测_6个月内申请人在多个平台申请借款_借款人身份证详情_O2O_count      0.0010   0.1642   0.6442   \\n\",\n       \"多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_信用卡中心_count          0.0007   0.1824   0.6861   \\n\",\n       \"多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_借款人身份证详情_P2P网贷_count 0.0000   0.3889   0.7095   \\n\",\n       \"als_m6_cell_cooff_allnum                           0.0175   0.1011   1.0533   \\n\",\n       \"const                                              0.0000   0.5678   0.7765   \\n\",\n       \"\\n\",\n       \"                                                      VIF  \\n\",\n       \"als_m6_id_nbank_else_orgnum                        1.4404  \\n\",\n       \"als_d15_id_rel_allnum                              1.1416  \\n\",\n       \"多平台借贷申请检测_24个月内申请人在多个平台申请借款_大型消费金融公司_count         1.2459  \\n\",\n       \"多平台借贷申请检测_3个月内申请人在多个平台申请借款_借款人身份证详情_一般消费分期平台_count 1.2451  \\n\",\n       \"als_m12_cell_coon_allnum                           1.1848  \\n\",\n       \"多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_借款人身份证详情_财产保险_count  1.1524  \\n\",\n       \"als_m1_id_caon_allnum                              1.2692  \\n\",\n       \"多平台借贷申请检测_24个月内申请人在多个平台申请借款_小额贷款公司_count           1.2543  \\n\",\n       \"ir_id_x_cell_lastchg_days                          1.1821  \\n\",\n       \"als_m3_cell_pdl_allnum                             1.2505  \\n\",\n       \"多平台借贷申请检测_6个月内申请人在多个平台申请借款_借款人身份证详情_O2O_count      1.2678  \\n\",\n       \"多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_信用卡中心_count          1.1229  \\n\",\n       \"多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_借款人身份证详情_P2P网贷_count 1.3770  \\n\",\n       \"als_m6_cell_cooff_allnum                           1.1363  \\n\",\n       \"const                                              1.0703  \"\n      ]\n     },\n     \"execution_count\": 224,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"logistic = ITLubberLogisticRegression(target=target, class_weight={1: 0.9, 0: 0.1}, C=10, max_iter=50)\\n\",\n    \"logistic.fit(woe_train[new_var_names + [target]])\\n\",\n    \"logistic.summary().sort_values([\\\"Coef.\\\"], ascending=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 216,\n   \"id\": \"c7aaca64-af3d-4df3-bcc7-0a70665a5830\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# col = \\\"多平台借贷申请检测_近60个月以上申请人在多个平台申请借款_借款人身份证详情_财产保险_count\\\"\\n\",\n    \"# feature_table = feature_bin_stats(train, col, target=target, desc=feature_map.get(col, col), combiner=combiner)\\n\",\n    \"# _ = bin_plot(feature_table, desc=feature_map.get(col, col), figsize=(8, 4), save=f\\\"model_report/bin_plots/data_{col}.png\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"0616db85-0565-4ecf-8177-b5c275d24fad\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 225,\n   \"id\": \"2767aa81-28ab-41b5-b89b-44130e6ac6f3\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n 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\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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a0a9cONzc3mjRpkuFr8CTPsz5Zeb8lMxgM2NjY0LdvX7Zv386CBQtMv77B+KVeqlQpihYtyq5du2jcuDE//PADly9fplixYoSGhvL222/Tq1cvxo8fT4sWLdi5c6dZMjR37lw++eQTIiIiWLNmDY0aNTIbDevRowdDhgwxq5OHhwedO3emX79+gPHLOPUPRIPBwObNm00Jy9WrV7GwsKBbt264u7tz4cKFTM2hS8+SJUtYtGgRLi4ufPLJJ6ZRtuREcenSpabPwjt37lCzZk0URaFt27b07t0bMH7G29nZUaNGDU6dOkViYiJhYWEEBwcTGxuLnZ2d6flsbGwoUaKEaURlxowZWFhYEBgYyOXLl7l9+zaRkZEsXboUW1vbLLdn3759zJ07F0dHR1asWMHmzZtZtWoVJUuWZOnSpezfvx8/Pz/Gjx/PqFGjGD58OHv37uX8+fOcOnWKdevWsXTpUv766y92797N77//TtmyZfnuu+/47rvv2LdvH/v37+fMmTMkJSUxbtw4Vq5cybp16xg1apTpdfj11185cuQIx48fp2rVqpQvX55//vmHzZs3c/PmTX777Tfq1atnqndyPzVs2JA1a9bw4YcfMmbMGEqXLs2NGzcydQJFppOhhIQEmjdvztmzZ9m7dy+bN2/miy++YNSoUWZv1ps3b7Jp0ybc3d1p27Yte/bsoW3btrz22mtYWlpibW2dZsa7u7s7TZs2pVGjRjRv3hwvLy/CwsIYM2YMtWvXNn3hBwcHM2XKFNatW4dGo6Fly5ZUrlyZZcuWmZ7777//NsukwfgGWrBgAaNHj2bEiBHUqFGDI0eOsGzZMtatW8fNmzfNyteoUQODwcCFCxe4d+8eU6dOpWLFinz00Udp+iX5Q6RcuXKUK1eO119/nZYtW+Ll5cXJkyfTfPl98803dOnShaioKOzt7UlISGDPnj14enry+eefs2fPHj788EMmTJiAl5cXzZo1o1q1ahw9ehRVVbl48SJt2rTBwcGBkiVLYjAYnunMDltbW4YOHcqxY8dwc3OjatWqgDFzf3TS2d27d+nduzdTp07FYDAwaNAgLl26hKqqxMXFmcrFxMSYPgDc3Nx46623TI/p9fpMTcgNCAjg5s2bdOnSBU9PT95+++1039AGgwELCwvTBG6At99+m++//z5T7be2tn7i3IGcfD8kmzdvHn379qVs2bKMHTuWOXPmpHkfJ9NoNIwfP57x48dz4MAB/vrrL06ePMm2bdv46aef0hxTz+y+C1l/vXJyf0+WU/3/NJ8nGo0GKysrEhIS+PPPP6lcuTKTJk1i+PDh9OjRgwYNGmTYV0/yPOuTlffb/fv3+e233wgJCWHRokXcunXLlLilTraKFStGy5YtmT59Oi1atMDBwQErKyucnJz4/PPP8fb2plevXnTs2NGUrGg0GooUKcLFixepWbMmV69eBaBIkSLpjhi4ublhb2/PkiVLaN68ORcvXjQlOIcPH0an09GoUSO+++47AKZNm0bZsmXp2bMnXbp0Yc6cObRv3x4HBwdOnDjB119//RSvlHk/tm7dGkVRTJ95qqqaDukkf54CtGnThkWLFqHX69P0dWRkJIcPH8bZ2ZnJkyczduxY3Nzc2Lp1q2kCdmre3t4cOXKE06dPU716derVq8fVq1fp1KkTa9aswd/f3zT1IbMMBgM//fQTv//+O3q9Hr1ez8aNG9m+fTvbtm1j9+7dlC5d2nQoLiIiAjs7O+bOncuoUaMoX748Op2OmTNnEhISYjo54s6dO8yYMQMHBwd+++037t+/z8aNG5k/fz5gHClOTEw0S2A7dOhAv379GDp0KN27d6devXrcvHnTlPA+ejgwKSmJ6OhovvrqK+rWrcuZM2f44YcfCAsLY+bMmeme0POoTCdD9vb2eHh44OrqyuDBg+ncuTM6nc40NJfM09OTMWPGmG4HBQVRsWJFs+PSFStWTBO/SJEifPLJJ6Zh7z/++IMWLVqYfTBWqVIFML5oSUlJ1KtXj8OHDzN8+HBKly7NnDlzCAoKMg0/pubh4YFOp2PXrl0YDAZKlCiBvb292a+MZIqisGHDBsA4oW/06NFpRj5S0+l0/Pfff7Ro0YIiRYpQvHhx7Ozs0p1g+MMPP6DT6bh+/To9e/YkPj4eX19fKleuzC+//MKgQYPMRqnA+Gtp7NixeHt7s3btWt5//32cnZ1p06YNCxYseKZkKCwsjMqVKzNr1iy6detmGlpUVTXNKImjo6Nph7ewsMDd3Z2YmBiqVq3K0aNHTXOwDh48SJ06ddJ9Pq1Wm6ks3cfHhy+//NL05ZWYmJjudklJSYSGhjJjxgxmzZpFQEAALi4unDlzhoCAAAYPHvzYX72Z+UWck+8HMB7aOXXqFP/73/+wtrbGz8+PMWPGMHv2bLNfhWB870+bNo3g4GAqVKjAnTt3qFSpEhUrVuSzzz5LN35m9930POn1ysn9PVlO9X9WP0/u37/P8OHDTb/EZ82aRZ06dbh27Rp3797N8D2fWc+rPll5v4Fx5K9WrVrs37+fU6dOsWHDBqpWrZrh4XmDwYCDgwMajYY///yTBQsWEB0dbZq34eDgYCqr0+k4fvw4lSpVombNmsTExKDVah87EfjWrVvodDqCg4MpVaoUrVu3pk6dOunOJalYsaLZiHJ4eDj79u3DyckpU/NIMiN53lCy1atXY29vz6BBg9KUVRQl3UQoLi6OFStWpPkh8+GHH/LLL7+kiWNvb2/qxwULFvDBBx/g6emJl5dXmkOXmRUeHk65cuVM7wFfX18qVqyIjY0NXl5eHD9+nKSkJMqXL09wcDBg/FyuU6cOH374IV9++aXpcG3qz4zSpUub/g4KCuLrr7/G0dGRa9eumZKbR9937u7urFu3jmPHjlG7dm02bNiAoiiEh4dTvXr1NMlN8hy1efPmER4ejr+/P1qtlqpVq1KpUqVMfedkac6Qk5MT7dq1w8rKiqJFi9K7d+80b4T0ZOYLp0yZMqZjjLGxsZw4cYJ33nkn3bKpO8LS0pKxY8cyffp0bt269djJxHXq1OHQoUO8+OKLDB8+/Il1AuPIwZPaqNFoTL+w4+LiOHXqFJcuXUqTpAQEBNCyZUvKli3LV199RbFixVi7di27du1i7dq1tGnTJk3sEydOoNPpcHFxwdfXFzc3N8aMGWM2QTirpxonS0pK4ptvvuGjjz7C39+fTZs2cfnyZTQaDXFxcaYP52R169bl33//JSAgAD8/P44dO0bNmjVp3bo1//77L+fPnyckJIQlS5bQvXv3dJ9Tq9WmOxE6NVVVOX78uNkhmeS5H48KCQnh2LFjvPbaawDMnj2boUOH0rZtWxITE+nZs6fp12Z6bGxsiI2NJSkpiaSkpCeOWmXn+wGMSd+WLVtYuXKl6Qtg/PjxeHh48Prrr7Nnzx6z7ZYvX07//v0pV64c8fHxvPDCC9SqVYuYmBiCgoK4du1aum142n03M6/Xo7J7f08tu/sfMv95UqJECQYPHkyDBg2oUKECPj4+TJkyhRkzZtCrVy+mTZv2xPpnRk7WJ6vvNwAvLy/q1q3L4sWLCQ8PZ9myZbi6uqb7PouKijIlGXPnzuW9996jaNGinDx5koYNG6Ypb2Fhwf379+natStg3PcjIiJISEggPDw83eewsbGhT58+dO7cmVatWlGyZMk0I5zJBgwYgFarNc2jSUxMpEyZMlhbW2fL2l+P1i8gIIBVq1Yxfvx4oqOjiY6ONj13Rp8tRYoUoX79+maJUHBwMKqqoqpqugkqGA+XBwQEUK9ePRo1avTEkyKexNnZGX9/f+Li4oiMjMTV1ZVr166RmJjIzp07qVChAg8ePCAqKorFixdjZWXFvXv3TEm3tbW12VzRq1evmk2y//XXX6lXrx7Ozs5oNBpu3LiRZs5fssDAQGJiYujbty9dunShe/fuVKtWjdmzZzNu3Lg0ybKNjQ1t2rTBycmJ27dvM2/ePNPrmzxN5kmyNGcI4MqVK8yaNYvly5fj7u6e1c0z9PLLLzNy5Eji4+NNZ6xUqlQp3RfYYDCYjodaWloyZcoUunfvnuFwmMFg4NKlS5w7d44PP/yQqKgo/vvvP5ydndMdRUrNycnpiR/uXl5eNGjQAI1Gg6WlJbNnzyYkJCRNhu7l5UXZsmVZvnw5YBzKT0xMxNnZGUVR0v1VvHv3bj744AP+/fdfvLy8mDZtGv/99x96vZ7bt29z4sQJOnbsmGY7Hx8fhg0bZkqaFEXBx8fHNLIDxkNkc+bMAYy/Tnbs2MG8efPw8/OjatWqXL9+ncuXL1O6dGlefvllihcvzogRIxgwYAD29vZ8+eWXpjkbn3zyCQMHDkRRFCZNmpThXCaNRvPEs6oOHDhAly5dTB9wQUFB7Nu3L90vZb1eT69evWjSpAnLli1j0KBBpuHhkSNHmubXJI8snDt3ju3btxMUFMT9+/eJiIhIcyjh66+/pm/fvunWLTvfD2D81T1//nyzX9mKovDFF1+wf/9+1qxZw969e03LMQwbNgwwvl4//PADN2/eJCgoCI1Gw6FDh7C2tuall15iypQpaZ7rafbdzLxeTyMr+3tq2d3/kPnPE71ez6VLlxg5ciS9e/fmvffeY9CgQQwePBhvb2/T4YLJkyfj5uZm9hyZ2R+fR32y+n4D42GMyMhIRo0aZfrCTkpKSvckBb1eT/PmzU3LVpQrV44pU6YQFBRkmvScmr29PS+++CIRERFs3ryZa9eu0a1bNx48eMBLL72EpaUl3bp1Y+bMmaZtPv/8c7P3r42NTYbJEJiPKmu1WgYNGkSTJk3MDgc/KjOvV/ny5dMcwjp69Ci3b982+2Hbvn17FixYkOFJHcn7dGqRkZGMGzeOli1bUrx48TSPx8fHc/nyZa5evcr69etNozYdO3bMcBL5V199xZYtW0y3vb29zQ4T2tra0rNnT9q0aWOaI9S4cWNeffVVWrRoQdu2bfHy8mL06NHUrVsXW1tbAgMDTUcQ2rZty6hRo9i0aZNp5HfTpk2AcXkDS0tL+vXrx8iRI/Hw8KB+/frpvv/B+FkwbNgwJk2aBBg/hy5cuMD48eNN8wBTq1ixIsePH6dChQokJibSrl07/vjjDzp27Eh8fHzmJpWrWXDgwAH1iy++UGNjYzO9zapVq9Tz588/sZxer1fv37+fqZiTJk1Sw8LCVFVV1a1bt6o9e/ZUP/30U/Xll19Wg4KC0pTftGmT+sorr6irVq0y3Xfx4kV15MiR6vTp0x/7XDNnzjQ9V2b4+/urqqqqu3btUqdOnZrm8Tt37qiTJ09Wr127po4YMUJt2LChWrVqVfWFF15Qmzdvrk6aNMmsvF6vz/C5EhMT1REjRqh//fVXpuuXGdHR0er+/fvVxYsXq19++aX6zTffqLdu3XridjExMWp0dPRjy3z33XdPjHP58mXVYDCY3bd69Wq1d+/ej90uMjIy3ftT1+nq1avq8ePH1StXrqj37t1TExISnlif1LL7/fC0evbsqd65c0cNDQ1VNRrNE8s/zb6rqpl7vVLLif09tZzo/6x+nhw8eFDt0KGDevjwYfXUqVNmj/30009qixYtTM/7NPJafRITE01/3759Wx05cqTaqlUr9fr165na/sGDB+rSpUszfDwgIEC9efOm+uuvv6rHjh1Tg4KC1AcPHqhJSUlqYmLiE9/fu3fvVtesWZPh40uXLlX37dunqqqqTp8+Xb18+bL6008/qa+//nqm6p9VT/oMzKxLly6pb7/9tjp79uw0jw0ePFh97bXX1KtXr6qqqqo6nU79448/1D59+qgnTpzIlufPTqnfQ/fv31f79u2rnj179onbzZkzRw0PD1cNBoOq1WpVVVXVw4cPq8uXLzcrl5SUpM6YMUOdOXOmunTpUtO/AwcOqIMHD1aPHTv2xOdSVDVzS/UGBwdz4cKFdEchnje9Xp+pIf7MSExMzHAYEmDz5s307NnzmU5fz8j9+/dNx34zypBF3pKT74eckpf23WeVE/2f1c+TpKQkfH19M3XGYkGoz6PCw8PZvXs3b7zxxnN5vic5c+YMlpaWWZqzde/ePQ4ePMjrr7+egzXLHUlJSU91JllBdeTIEerUqZPhiFmyTCdDQgghhBAFkQxHCCGEEKJQk2RICCGEEIWaJENCCCGEKNQkGRJCCCFEoSbJkBBCCCEKNUmGhBBCCFGoSTIkRA550mUsLl++zLZt255TbQoO6deckZ39evHiRX7//fc016kSIq+SZEjkWUlJObMElkab+bghISFcvHgRgJiYGIYPH0779u3ZtWuXWbkdO3Zw9uxZwHgx3oiIiHSX2U9NVVVu3LiRpbpnB/UZLuybXXFXr14NGBe/mzt3LlOmTKF3796m5fuT5ad+zQtyu19DQkJ4//33mTlzJvPmzWPQoEF89NFHmbpquBC5KcvXJhPiebG1Vaj2Umi2xWtcz5p501wo5pr53wBBQUH4+/tTs2ZNnJycmDZtGh988EGai+omJCSYfllXr16dd955h06dOj029p07d7h27RobNmzglVdeoUSJEllv1FNQrK1JHDUiS9tYVK6M9dtD0P68EoO/f7pl7OYtyHS8ffv2MXDgQJycnHB1dWXjxo38/PPPaa4VmJ/6NS/I7X51dHRkyZIlLF++nHHjxlGjRg327dsnK+yLPE+SIVEoJCdCoyZGsXrRky88qtFo+Oeff9i0aROBgYH4+PgwZ84cFi1axIABA8yumuzn58fff/9NWFgYo0ePZtGiRVy/fj3DywOEh4ezaNEiDh8+THR0NC+99FKeXz7f4O+P9ueVT0yIMiMwMJCQkBCGDx/OwoULcXZ2pnv37lStWtWsXGHo1+yUF/p1w4YNxMXF8csvv7Bo0SJee+01mjVrhlarTXOlcSHyEkmGRIGXOhE6cSZzh3Lu3buHTqcjMTGR0aNHc/bsWRYsWMC1a9fYsmULq1evRlEUpk6dStGiRUlISGDYsGG88sorODo68umnn1K0aNF0Y7u5uTF8+HD27t1LkSJFiIyMNF0NPC/LroToxIkTREVFMWjQIADWrFnDq6++yvXr16lYsaKpXH7qV3VpjycXqvEqSsuPUso36IfSsB9qXDiseSfdTZT3/sx0HfJCvzZs2JB//vmHwYMHU61aNTw8PNi8eTPLly9n+vTpVKpUKdPtEeJ5krFLUaA9TSIE4OnpSfv27bGzs+PIkSP069ePNm3aMGfOHCpUqMDKlStZv349VapUwdHRkYsXL/Lrr78SFhYGgL29fYZzMAwGA59++int27fH29ub4OBg1q9fny3tzWmpEyKLypWfKka/fv2oXLky169fZ+PGjbRo0YIjR45w8eJF+vXrh4+PD0Ch6tfskNv9evPmTbZv3461tTW7d+/mxIkTLF26lFGjRrFu3TpJhESeJiNDosB62kQomaIo3L17F3t7e6pXr266v3z58oSGhpp+ba9atYpq1aoxbdo0AgIC2L9/PyEhIXzwwQdoNBqqVq3K119/bdp+7ty5NGnSBE9PT1auXMmQIUPYsWMHlpaW9OnT59kbnsOedYTI19eXu3fvoigKR44cYcaMGfTo0YMuXbpQq1YtVq9eTcOGDfNVv2ZlBOfR8koRN8ji9unJ7X719PTkjTfewMfHhxYtWrB//35q1qzJ1KlTAWjfvj2vvvrqM7dTiJwgyZAokJ41EdJqtSxcuJAePXoQEhLCwoULeeutt4iPj8dgMKBNdebUO++8Q3h4OImJiYSHh9O5c2cuXrzIL7/8kibuP//8Q4UKFYiPj2fWrFm4uLhQsmRJ5s6dy9y5cxk7diwffvhhnv8V/bQJkU6nY+3atSxYsIDixYvj7e2NoiioqvEMvwoVKjBp0iSgcPbr08oL/XrlyhXOnj2Ll5cXderU4eTJk0yZMgU7Ozvi4+PR6/XPtU+EyApJhkSB86yJEIBer6d///6ULl0agH///ReAoUOHEh0dzZQpU0xlnZyciIuLw8XFhc6dOwPg4uKSbtzUv4zd3Nw4fPgw9evXB2Ds2LGEhobmmy+NRxOizLCysuLbb781uy8uLi7dsoW1X59GXujXatWqUa1aNVP5kiVLYmdnB4CDg8NTtkyI50OSIZFnJSWpXDlS8qm3z+issaQkFVtb5bHb2tnZmRIhwHQq/bRp0/jxxx9xdHQ0K9+5c2eKFStmul2mTJkn1q9EiRJm24DxCySnqVptlk6DzwybEaNQtVqUpzhjyMbGJs0ZT8nyU7/mNbndr05OTpmsqRC5T1GTx1GFENkmIiIizReHeHbSrzkjJ/rVx8eHhg0bZmtMIXKKJENCCCGEKNTk1HohhBBCFGqSDAkhhBCiUJNkSAghhBCFmiRDQgghhCjUJBkSQgghRKEmyZAQQgghCjVJhoQQQghRqEkyJIQQQohCTZIhIYQQQhRqkgwJIYQQolCTZEgIIYQQhZokQ0IIIYQo1CQZEkIIIUShJsmQEEIIIQo1SYaEEEIIUahJMiSEEEKIQk2SISGEEEIUapIMCSGEEKJQk2RICCGEEIWaJENCCCGEKNQkGRJCCCFEoSbJkBBCCCEKNUmGhBBCCFGoSTIkhBBCiEJNkiEhRIa0Wu0TyyxcuDBLMQMCAtDr9cTHx2MwGJ62as8sM21bunRppso9i6SkJKKiojJdPjP1OXXqFEePHn2WaglRqEgyJIQgJCSEn376CUj5Ij179izff//9E7c9duxYpp8nIiKC5cuX0717d4YMGcLYsWOfus6ZZTAYmD59OgDBwcFs2bKFO3fu8Nlnnz1x20uXLj0xUdHpdJw8eZKNGzdy9+7dLNdv3bp1GfbhtGnTTH8nJ50jRozgwYMHj40ZGxvL+fPnM/X8169fByAqKooBAwY8tqxOp+Orr74iMDAwU7GFyC+scrsCQojst2bNGpo1a4aXlxc//fQTgYGBpn8LFy6kadOmZuW1Wi1xcXEAVKxYkXnz5nHt2jWGDRv2xOdKHqmIjo7G2dk5w3Jjx46lePHi+Pv7s3nzZmxsbEhMTMxy23bv3o2TkxPNmjVjw4YNnDp1itu3b3Px4kUmT55M9+7dzcorimJKHkqVKsXBgwc5ePAgDRo0yFTbVFUlKioKFxeXdMv8+eef+Pn5sXnzZt59910+/PBDvv32W7744otMtefvv/82JaKPio2NRafTYWVlRXR0NJs2beL+/fsULVr0ifUG46iTqqrY2dllWHbLli00a9aM06dPU79+/QzLJSUlMXHiRI4dO4afnx+LFi1i9+7dtGrVCk9Pzyc3VIg8TEaGhCiAIiMjCQ4OBqBt27bcv38fT09PDh48mCYRAkxJiaqq2Nvbo9Fo8PPzIz4+PsPn2L17N9OnT8fX15eXX36ZSZMmZVj25s2bzJo1i5YtW9KjRw8iIyPx9fXlv//+y3LbYmNjTSMTzZo1w2AwoCgK+/btS5MIJbdNVVVUVcVgMFCuXDm2bdv22MNN//33H/PmzePgwYN07dqV9957L91yoaGhHD16lPXr11O3bl1KlSrF5cuXOXPmDEuWLOGXX355bFtCQ0OxtrbOMNFKSEhAVVUA2rdvz6xZs3B1dSUsLCzd8kFBQfz222/88MMPLFy4kB49enD69OkMn1+r1dKmTRuuX7+OTqejRYsWGb7mv//+O//++y9arZZXX32VxMRE/vjjD/7991+++uorUzItRH4kI0NCFDDXr1/H19eXbdu2YW9vT+PGjbl9+zZffPEFDg4OacqfPn2a+fPnc+nSJWJiYmjatCm+vr7UrFmTkiVLZvg8Tk5OeHh4YDAY8Pb2fuxhp5CQELZv385vv/1GdHQ0+/fvp3z58vTp0weNRoONjU2m2hYSEsKxY8c4efIkLi4udO3albCwMIYNG5buqFRAQABz587l8OHDjBo1ioEDB3Lw4EEqVqxIxYoVM3weBwcHPD090ev1NG3alB9++CHDsp06dWLbtm0UKVKEKlWqsGjRIhwcHPj1119ZtWrVY9tz4MABXnnllXQfmzFjBj4+PvTt25cvv/ySZcuWodVqady4cbqvI4CVlRUVK1ZEVVWKFCnCsmXLKFu2bIbPf+7cOZYvX87EiRNxd3enR48ezJw5k9q1a6cpW7t2bcqUKUN4eDienp6cOXOG+/fvs3XrVtq3b4+1tfVj2ypEXiYjQ0IUMHfu3CEhIYGBAwfSpEkTtm/fzs2bN9FoNJw7dy5N+bp161KtWjU6duzI5MmTqVOnDhUqVCA4OJibN29m+DzVq1dn7dq1lChRgpiYmMd+8VeqVInQ0FCmT59O9+7d+fzzz2nTpg3z58/nrbfeIjo6OlNtu3fvHnFxcXTs2JGuXbty5swZjh07hqWlJcePH08zIbtSpUo0atSIhg0bMm/ePGrWrEm1atUIDw/nypUrGT5P7dq1+fXXX3FycqJ48eKmOUePcnJyYseOHfTq1QutVsvbb79NmTJlWLJkCaVLl6Zq1aqPbc/u3btp165duo8NHTqU+Ph4Fi1aRN26denQoQO2trYcOnQIRVHS3aZMmTL4+fkRFhZGy5YtGTlyJOHh4Rk+f506ddBoNJQtWxatVktMTAw1a9ZMt6yPjw9dunTB1taWn376icWLF7N582YqVKhA69atM53QCpEXSTIkRAHTvHlzmjRpgq+vL/v37+eXX35h6NChnDlzBh8fH3r37m12plFiYiLbt2/n+PHjbNmyhWLFilGrVi369etHr169UFUVvV5v9hyqqjJu3DgGDhyIu7s7kydP5uDBg2zYsCFNfaKjo5kzZw4vv/wyEyZMwGAwsGLFCv777z8WLVrE+vXrzUZ1ZsyYwdatW9NtW926dWnbti03btxgz549zJo1izFjxnD48GHu3bvHa6+9xo4dO8zquXnzZgICAlixYgV2dnY0atSItm3bmuZDPdo2ME5cbtCgAY0aNeKdd94hNjaWefPmpSm3ZcsWhg8fzvHjx/H29mb06NH06tWLc+fO0bhx48e+TtHR0YSHh1OhQoV0H9+6dStarZbp06cTFxdHkyZNsLGx4ddff8Xe3h6dTpdmGx8fHzZu3MjYsWPx9PRkzJgxDBs2LMMJ1zY2NlhZWfHGG2/QpUsXevTogaWlZZrDcAEBAZQuXZr79+/TtGlTBg0axPvvv0+JEiUICgp6YtInRF6nqMkHpIUQBcLRo0f56quvaN68OdHR0bz77rscO3aM2NhYhg8fzu3bt9mxYwdDhw4FYPHixVy6dImmTZtSp04d9uzZw6FDhwgNDaV06dLExcXRokULxo0bZ3qO7777DkVRaNWqFaNGjcLLy4sPPviApUuX0qlTJ/r27WtWp/j4eM6dO8eVK1f4559/KFWqFFZWViQlJeHi4kLPnj2pW7cuAO+99x5NmjRhyJAhadrm5+fHlClTKFWqFG5ubrRv3564uDj27t3LjBkziIiIYM2aNYwcORKADRs2cOLECUqWLMlrr73Grl27OHr0KFeuXKFSpUrExcVRpUoVs7Pm1q1bx9mzZ+nfvz8TJ07Ezs6O9957j7///pty5coxevRowHhm3Jw5c/j3339JSEhAURTmzp3L2rVr0ev1jBs3jmrVqmX4Om3ZsoXg4GCGDx+e5rH79+8ze/ZsLly4wIYNG9ixYwenTp3i77//pnr16mg0GjQaDYsXL6ZcuXIA3L59m1GjRjF+/Hh2797N33//TceOHWncuDErVqxg0aJFuLm5mZ5j+/btdOrUiaioKK5evUpgYCBvvPEGAO+++y49e/akS5cuAPz6669s3boVPz8/ihcvTrNmzahTpw5nz56lRIkSjB8/PsN2CpEfyJwhIQqYH374AW9vbz766CO0Wi02NjYcPnzYNBG3TJkypkQIjF9869evx9LSEmtra7y9valUqRIhISFm5ZIdPXqU8uXLU61aNRYsWEB0dDT9+vWjUaNGNGjQgLlz55pOm69ZsyZBQUFs3LgRT09PqlevzrFjx2jatCkdOnQAjJOIU48MLV26NMO2rVixgurVq/PVV1+h1+uxsbFh586dprYVK1bMlAgB9OjRAzc3N86ePQtAu3btaNiwIdu3b+err75KE//KlSvExMQwfPhwpk2bxr179/jwww9p0aIFbdu2Zfny5QwYMICPPvqIWrVq0aVLFw4dOsSYMWO4cOECrVq14uLFi6xcuZLg4ODHJkN79uxh1KhR6T7m6urKt99+S7du3dBoNNSoUYOWLVvi7+/P+vXr05TX6XSsX7+eH3/8keXLl3PkyBEqVKjAu+++i7u7O+7u7owaNYoGDRowZswYwJgoOjo6UqpUKZo0acK1a9cIDw8nMDCQuXPnsmnTJlP82rVrc+rUKZo0acLNmzcZN24c8fHx/Pjjj6ZJ1xnNYxIiP5BkSIgC5n//+5/p7+R5HOkdCkpmbW1NbGwsJUqUMH15X7lyJcPTt5s1a2b6e9myZdSvX58mTZpgZWX8OPnss8+IjY01lSlfvjyffPKJ6fbRo0epWrUqTk5OAKb/k40cOZLmzZunGV0CmDt3rulvS0vLTLfN0tKSSpUqAcZJwxmdvVWtWjVTHyxduhRvb28aNGiAra0tYJzHM3DgQBITE3FxcaFOnTq4u7uzc+dOgoODGThwIOXLl2fXrl0MGzaMlStX8vbbb9O6dWtT/wBoNBpCQ0OpXLlyuvVILpuQkEDRokVNr4Wjo2OG5ZP7+PPPP+fnn38mKCgId3d3wDg3aM2aNYSEhJi2iYuLo1q1aowaNYrRo0fz+uuvY2Njw8qVK9FqtQwePNhUtl69epw+fZq//vqL+Ph4Jk2axJ07d/j9999NE6gHDhxInz59KFasWIavhxB5lSRDQhQCFStW5MaNGxk+3qhRI+zt7U23HRwczL68H6d8+fJpEqeMvrTB+MX9uMm2Go0mS6dplytX7rHPV6tWLbPDQw4ODk9cpyd1bFdXV7P77OzsTOv2TJ48mffff5/WrVvz119/ERgYaBrt+e2331i0aBHTp0/HxsaGVq1amWKEhYWlm+w9atCgQWa3S5Uqlal6ly1blpiYmDT3JydHYExaPTw8+OGHH/jxxx/54osviI2NJTY2Fq1Wy0svvWQq++effxIWFsbvv/9OSEgICxYsYOrUqZQoUYKPPvqIF154gfnz5xMQEMCsWbMyVUch8hKZMySESCMmJgaNRmOWRGSX1atX07Vr1zRJxvOi1WoJCQl57CnnedWNGzcynHAthHh6kgwJIYQQolCTU+uFyKNkRd+no9FocrsKBcajfSnvSZGXZOf7UZIh8UQ+Pj706NGDfv36pXsNpYULF3L48OFMly+sstqPAwcOxNvbm44dO8o8jFQy6scDBw7Qu3dvPvjgA7y9vU1r5QQEBNC7d2/69+9vduFTkfW+/OSTT/D29qZDhw5mk+ILu4z6MTAwkHfeeYfevXubLUoqn5Hpy2o/ZutnpCrEY8TGxqrt27dXb926pRoMBnXcuHHqpUuXzMqMGDFCDQoKynT5wiir/ZiQkKC++eabuVHVPO1x/Th//nw1KSlJVVVVnT59urpv3z5Vr9er3bt3V319fVVVVdU5c+aoe/bsybX65yVZ7UtVVdU+ffrkVnXzrMf1o7+/v5qUlKSeO3fO1HfyGZm+rPZjdn9GysiQeKw9e/bQqVMnypQpg6Io9OjRgyNHjpiVCQ4ONi38lpnyhVFW+/Hq1auPvXZWYfW4fhwxYgQ2NjZcu3aNS5cu0bx5c06fPk2VKlVM19rq3bu3vB8fympfhoeHp1kGQTy+H728vLhz5w5Tp07lww8/fGL5wiyr/Zjdn5GSDInH8vf3p0GDBqbbGo3G7BTsxMREbG1tTddKelL5wiqr/Vi1alVef/119Ho9CQkJ/PHHH8+9znnRk/oxLCyMzz77jOnTp2NtbS3vx8fIal+6uroyYsQINBoNqqqybt263Kh2nvO4fvz999/p2bMnly9fZuvWreh0OnlPZiCr/Zjdn5GSDInHcnFx4e7du6bbp06dMlso7tq1a2a3n1S+sMpqP9rZ2fHCCy9gaWmJvb09K1eufK71zase149JSUmMGTOGzz//HE9Pz3TL+/j4yPvxoaz2pYWFBS+++CI2NjYoisIff/xBfHx8rtQ9L3lcP5YrV46///6bnTt3cuPGDY4fPy6fkRnIaj9m92ekJEPisdq1a8evv/5KZGQkAQEBnDhxwix7v3LlitlFGp9UvrDKaj+uWrWKf//9F4Bjx47JWTwPPa4fJ02axIABA2jYsKGpfPPmzdmxYwfBwcGEhISwadMm2rZtm1vVz1Oy2pfbtm1j06ZNGAwGrl27xq1bt3Kr6nnK4/qxWbNmeHh4UKZMGV566SWioqLkMzIDWe3HbP+MzLbZR6LA2rRpk9qtWzf17bffVq9fv2722PTp09Vjx45lunxhlpV+jIqKUkeMGKF26NBB7d+/v3r8+PHnXNu8K71+vHjxolq3bl21Q4cOaocOHdSOHTuqGzZsUFVVVffv36/26tVL7devn3ru3LlcrHnek5W+TEpKUidNmqS2b99e7d27t7pz587crXwekl4/BgYGqnfv3lVVVVVDQkLU1q1bq/7+/hmWF1nrx+z+jJRFF0WW9erVi3HjxtG0aVOOHDlC7dq1ZWLlU5B+zB7J/fjiiy+aLpMhno70Zfbo1asXY8aMYevWrVy7do2kpCQGDhxI//79c7tq+crz7EdJhkSWRUVF4ezsbJrsK56O9GP2kH7MPtKX2UP6MXs8z36UZEgIIYQQhZpMoBZCCCFEoSbJkBBCCCEKNUmGhBBCCFGoSTIknkl0dDQLFiwgOjo6t6uSr0k/Zg/px+wh/Zh9pC+zR073oyRD4plER0ezcOFC2dGfkfRj9pB+zB7Sj9lH+jJ75HQ/SjIkhBBCiEJNkiEhhBBCFGqSDAkhhBCiULPK7QoUFA8ePGDbtm14eXlhbW2d29V5bu7fvw/A+fPnuXfvXi7XJv+Sfswe0o/ZQ/ox+0hfZo+s9KNWqyUgIICuXbtStGjRTMWXFaizyZo1a5g6dWpuV0MIIYQQwKRJk3jzzTczVVZGhrJJpUqVAGPnV69ePZdrI4QQQhROly9fZurUqabv5cyQZCib2NjYAFC9enUaNmyYy7URQgghCrfk7+XMkAnUQgghhCjUJBkSQgghRKEmyZAQQgghCjVJhoQQQog8TNVoc7sKOULV5p12yQTqbDbg/UiwCTW7r3E9a+ZNc2HUxChOnMn+F1/iS3yJL/ElfsGNf+VISRJHjXjmulhUroz120PQ/rwSg7//M8d71vh28xZkex2elowM5bD8sKNJfIkv8SW+xM9/8bPK4O+P9ueVWL89BIvKlfNd/JwkyVAOyu87msSX+BJf4kv8vBn/aUlClD5JhnJIft/RJL7El/gSX+LnzfjPShKitCQZygH5fUeT+BJf4kt8iZ8342cXSYjMSTKUzWpUtcrXO5rEl/gSX+JL/LwXPz8mLPkpIZJkKJuNGuqYL3c0iS/xJb7El/h5N35+TVjyS0IkyVA2m7csNl/uaBJf4kt8iS/x82Z8IF8nLPkhIZJkKJtduqrL9pj5fUeW+BJf4kt8if/08SH/Jyx5PSGSZCiPKwg7ssSX+BJf4kv8p4+fLL8nLHk5IZJkKA8rKDuyxJf4El/iS/ynj59afk9YUsfPSyQZyqMK0o4s8SW+xJf4Ej/74heUhCgvkWQoD8rtHU3iS3yJL/Elft6OXxASorxEkqE8Jq/saBJf4kt8iS/x83b8/J4Q5SX5Khlau3YtcXFxAGi1WvR6fS7XKHvltR1N4kv8Qhlf1YFqyP74akKWY2YpfjJDbM7Gf0oSP2fiS0KUPayyO+DOnTuxsLCgffv2JCYmYmdn98RtIiMjcXFxwcIi/dxs8eLFxMbGcvPmTY4fP06RIkXYv38/HTp04KuvvsqWeickJGBra5thHXJaXt3RJL7EL0jxB3+wmytXAsG+N8TNB10AWFYAxzGgxkPMF4AVGO6B0wywLAvxK0F3GdRYsH8TbF5KN751wnhGTZxprH/sTNCeAiyN27luhbh5oLsIGMCuN9j1SL+y0Z+B87dmdxVVxjNv2nLz/lENkLASNEeg6MP5F6oKcXPBYTCocWBRBhI3gn0/0N8GfRDYts2wf/L761tY46dOWLQ/r8z2Q1A5HT8veKpv/lu3blGtWjXef//9NI8dP36cFStW4O3tTePGjbl06VKGcVasWEG7du0YNGgQfn5+GZY7e/Ysn376KQsWLGD+/Pm8++671KxZkxEjRmS57lqtloEDB6a5f/369XTo0IEOHTrQp08foqKi0Gq1fPPNN1l+jqzK6ztaXoj/4xt70M+ozyrH6uxt/CpdSmzL1vj5vX8kfubiX7kSCJbuoCYZExLn2aA9bCyoPQ0OI4332b0GOj/jaI7+DjjPAqfZkPhXuvG/mWDJxatqSv0dx4PrBuNz2LwMigUYQqHor1B0TcaJEICamCa+VwV92v7R7AXtWWN9k2mPgVUl0PqCIRySdgAWoD0DSdszTOQKyutbmOPLCNGzeephkJ49e7JkyRKz+wICAoiMjOT777/H0dGRmTNnUqNGjQxjDB48GCcnJ/7880/q1KmDTpf+goX16tVj06ZN+Pn5cfr0afbv38+UKVNwc3PLcr2vX7+Oh4dHmvv9/PxYsWIFu3btYsOGDbi4uGBtbY2joyOnTp3K8vNkVn7Z0XIz/o9v7MH6r08obnEHC0WljN1dplX9KlsSooLQPxI/C/H1N8CyPCi2YOEGsdPBvr+xsM3LYFURDHGQtA9sWgIWoEYaR40MIaBYpxt/5PhzRMaWS1uBpN3g8KFxFEfNxIKshnCwKGYWf/IYDdeDndP2T+IGcBxnbIfp+baDzaugDwTLcmD9IiTtAotSoBQBxf7x/ZPNJP7zjS8J0dPL1mNCyYexpk6dSq1atVi8eHG65UJDQzl79izbt2/n2rVrjBw5ko4dO9KxY0euX7+epnzHjh05d+4cFhYW2NvbU716dXbs2PFUdbxy5QpVq1ZNc39AQADlyqX9MKtbt26OJUP5bUfLrfiJW6djq5j/Wra3TOTrKlMYVm4ZnUvsoI6TL8WswwE1y/Hze/9I/CzEN9wxHjrSh0DUMNDsh6QDxgQoWdxM42EmxcGYNNm0gqiRED0GrOulG//iRX9jkpWa9jxYVQcLZ+PIUJGPQNWCPhQ0/6VfaX2QKU5y/LGTzhMR/chnkz7EOGKV+CdEjwftBeNcIUM0WJYEfTBYlADLquD6mzEhsuv55P7JRhI/d+JLQvR0snXOUJMmTejUqRM9e/ZEr9dz+fLldMutXbuWW7ducf36dWrWrInBYGDHjh0Zzte5dOkSI0eO5Msvv6RkyZKUL1+epk2bPlUdr169SqNGjdLc/8033xAVFYWDgwNbtmyha9eu2Nvb8+DBA4oUKfJUz/U4+XVHy434qxzvplvGwTKBjyvOM7svTm/Pl1e/Ytv9LpS0CaVjiV3suv8qIRoPjImS8tzrL/HzUHxVD4qlMdFx+ACsakHcLND+B7YdIWGdMYFJlfRg182YEEWPBdsOADirY3ir53BGTayeMuJk2868Epq9pvIAWFUz/m9RFGLXgk0LiJ0BVi+CXSfjY/obYFnBrP6Xr/gb5zWlpkaDbSdjgqUPgYTlxtu6yxDZBwwRENkbXJaC4gTojKNdmr3gsrjgvr4SH5A5RE8jW0eG7O3tiYyMxM/Pj3v37hEVFZVuuTFjxvDtt9+i0+l46623aNCggVkiFBYWZla+Vq1aLF++nOvXr9OqVSvKli3LggULnqqOV69epVq1amnur1mzJkWLFsXGxoagoCCOHz8OwOHDh2nYsOFTPVdG8vuO9rzj301Ke1gT4E5SKeofOoG3zx984LeQ6f6fsfHua1xPqABADcdLfO71LaVs7wHQreRfHG32Mtubv8HyRp9y+OtJVLy3luauh/G0u4m1kj1tKWj9X2DiGx6AxcNLG1g4gU1T4//WL4EhxjiSo7sG9gPMA6pJEDsFinwIigON61nz+7r5/LK5Skp8/U2w9DTfTnsBrB5OE9CegsQ/jIfLNIcBW+P9DsPNkyhdELVfqGxef11Q2lEnC3fQXTXG010Bi+JgXR+K7YKiv4FNMyi23TgBPHGTcVRIfxUsK9OwrlIwX1+Jb0ZGiLImW5OhGzdu4OrqyrZt26hbty5ffvllhmV//vlnevbsiY2NDZaWlqb79Xo9K1asMCvr7u5OWFgYK1as4JVXXiEwMJBWrVplGPvy5ctcvHgx3cdCQkJwd3c3uy8wMJBvv/2WuLg4Hjx4wJEjR7CzsyMwMJDIyMh0k6enld93tNyIP+f6KBL05mclJujtmHN9FPEGB67FV2FfxCusvjOQGYHjuRBbC4ADES1peuQ/0+3gxLKcUTvg6VWMiIvnaG/9M1OqfM2K2u+xu3Fnzr7cgH8bt6O8XRAANR0v0r3kliwlSQWx/wtM/OT5QmBMfMB42Cppp3GuUMJvoPOFyAHGfwkbjaMuMV8YJ0Jb1zPFf2PgRE6ePJsSW00wn4+jv2lMRJSHH7FW9Y2Hrh68aUyKigw33p/wM2gOmjZzLnKbH7+r9cihvWDj/J/ULJzBpjFEDTGeLWbXx3i/ooAhDJSiD7eNMo4SWZYB+7d44cXXWPCNW8F8fSV+GpIQZV62Hiazt7fnzp07tGjRAo1Gw+HDh3Fzc6NixYpm5fz9/fH39+fbb7/F19eXP//8k7Zt26IoCqdOnSI0NNRUNj4+nh9//JFPPvkEjUbDunXr2LRpE3/88UeG9dizZw/x8fHUrFnT7P7o6GicnJzSlK9UqRJlypShd+/e2NjY0K5dO5o0acLYsWOZPHnyM/ZKivy+o+VW/O33uwLwccV5lLK9x90kD+ZcH2W6P2MKD3SuplvWXk2oN6k97zyMb4GekrahlLO7RVm725S1u0UZu9uEa40TWDsU382QsqvYFtoFgPGVvqVVsf+4lViGW4llCE4sy+3Esg9vl6VabbcC2f8FJr5FKePhLjAmCFHvGefY2DQH64ZgVRdQQEn1sajqwOkbUGzM4kfGVgOblInO2Pd95LlKpkzMBmOSUmSk8V9qll5g4WGqf5+Og/jkq3jz+tu9llInQyxEDTWeSm8/IO0oFhhHv2zbPywfaapb44YVC/brW8jjZ0QOmWWOoqpq5mecPnT37l0GDBhA0aJF2bhxIxYWFkRFRTFhwgQ6dOhA9+7dAeNk5WXLllG1alWGDRtm2n7Pnj00b94ce3vjL6mvv/6aP/74g/j4eBwcHHjzzTf55JNPAONIUeqRo88++4wXXngh3dPjk8vPmDGDMWPGpJnrExcXx+nTp2nRogUAiYmJdOnSheXLl6dJ2LLKx8eHAQMGgNNCsKmb5vH8vqMVxvg2ShIlbUO5lWj8Vd7LfTMtix2i7MPkqai1+WFg1caJawmV6XZ4NQDNih7FgAXHHzTJlfpL/AIa3xBjPLyXU/GfgsTP2fhXjpQkcVTWl5JJzaJy5RxNWJ4mvt28p5vu8iTJ38dr167N9DSXp0qGCpLIyEhcXV2fXPAJHpcM5fUdTeI/HUfLGMra3aZ1zXsM7RzG4V3+hIVpmOJvHE38tc5gLBU9A84Zk6MVtd/FwSKBW0llTaNLtx6OLN1L9ECfwUBtfu0fiS/xJX72xM+OZAjyXkKUl5KhbF+BOr/JjkTocfLDjibxn06s3gnnqrV566uXeT+d+KMvfU8Ry5RTtv3jKlO1yDVedDpHpxI7sVJSLiejUy25l+TB7vvtmHX9UwBaF9uPUwUvJkytly/7R+JLfImfO4fGMiKHzDJW6JOhnJTfdzSJ/2zxI7RuRGhTFsSbETje9LclOjzs7pnmKhn/v03kwzlOCgbm1foYQ5N3GDaxEufPRbGtQb+HI0lpR5Zi9Vk7bJKZ+j8riS/xJX7eSYSSSUKUPkmGckh+39Ekfs7G12PF7YcTsI+Tdk5R43rWJLz7D1/N03PijBY363gCEypSzu4WDVxO42RlfjHOB1oXbiWWYeWtwey43xk7iwQaupzCL+YFHuiKZnv9n0TiS3yJn/cSoWSSEKUlyVAOyO87msTP/fg/THNhxEQLU/xwbXFGXkxeYFLFxSraNKJUJtXIksZgA4CXQyDLa7/P8As/sCe8HfWdT/NxxR+4lVgGtagn7XtW4Ycv3Qi+6IEFJTFgmUFtnq7++b3/Jb7Ez6vxs4skROYkGcpmNapa5esdTeLnh/gKUToXomJdTGsoPSowviL9z/5KYHwlACwVPQbVgpdLnMLNYhvKDpUvHOCLJqAxWHE3qRS3EssyI+Az/OMrU9ImFHfbEC7HVkerWqf7HE9f/6cn8SV+YY1vUblyvktY8lNCJMlQNhs11DFf7mgSv2DFTzA4cDq6vun2yahGLLRcy7wJLgyZeJ/bl4JTzVW6ZfpbazAmPu2L/8PEyjNofnQ/4drivO6xgVbFDprNVbr98P94g0O21z89El/iF+b4+TVhyS8JkSRD2Wzeslgu3ch/O5rEL0zxFcCTm4meGZbfHdaO24mlCX84AdzWIpFy9rdo6nqMIpYJZmXDNcV4YFmWcrUq8t7E6Zw4A+XsbqKimNZoyt765/f+l/gSP+vyc8KSHxIiSYay2aWrOrDJ3pj5fUeW+PkvfqjGndCIlMvWrL4zkNV3BgIqrtaRZmfB1StzlxZe97h1wZ+jZ4zlR1dYQG2n87Q/uROAsRW/p5h15CNnwZXlvqY46hOuCpQX+0fiS/znGR/yf8KS1xMiSYbyuIKwI0v8ghRfIVJbjEhtMc7H1KZxPWuGTHIxXeIk2fLgIbjZhJtuu9uG0tjlJO62oWbRkgw2Dw+3leFUVD2WBr8HQAX7G4RrilGjjlziROJL/GT5PWF5NH5eIslQHlZQdmSJX/jiX4qrASnrTfLp5W8B4+G20rZ3TGe/pV5jqXKRQFP5FbWHcsOyETUn/sSoiQ/omzCG1pVKplwXLqEst5PKkGQwv4BvdtU/O0h8iZ9d8VcvSlkcuCAlRHmJJEN5VEHakSW+xE+WZLDjekIlridUemy5DepEhgwux/CJUfidi2ZyvSu0dfsXO8sks3KhScWNI0tJZdl5vz17w9uiYKC07V3uJbnLJU4kfoGMX1ASIpsRo7I17rOQZCgPyu0dTeJL/NyOP+ir1xhuiu9A11NbAZXi1uGUsw+mzCMjS/Wdz3AltioAJWzus7dJB6Zcm8i6u/0oaxfMe+WWPRxVKoezZ3lGfVaTkRMfcOKMLkfqn9/7X+Ln/fgFISHKSyQZymPyyo4m8SV+3ouvEKYtTpi2OGei62UYI17vwBdXp3AqqgEA7jahtHbbT3GbiJRCC2GJgz23G5Q2LRGw/u7r+MdXxtYiEStFR5zeMZvr/+wkvsRPLb8nRHlJob9qfXZ5mqvkCiGeH1UTB5HBEHHT+C8y6OH/D2+/9SuKV3NU363w27swej+KR03Uawfg2n4o5gnFyoNreXAti2Jlm9tNEoWEqtGi2GR+8dP8QtVqUayzv11y1XohhMiAYlME3Ksb/z3C7DehRw3oNAlcH67DdNcPDi8DvSZVMAXVySNVguQJLT9EsXVE1cSDlS2KRfZd4kQUbgUxEQJyJBF6WpIMCSEKPUVRUv4uWQVKVkm53fIj1Jc/gJh7D0eVglJGkyKCIOAQRN+DV0YaN9g1A07/D3XyFRRFQT21HmJCU5KmYp7gUMzsOYUQuUuSISGEeALFwgJcShv/VWya5nFVp0GxerjaarU24FIqJdk5vw0u7zbfwKYIajFP4yG3Yp7gXh2l8ZvGWHotimXe+cUsRGEgyZAQQjwjUyIEKFVbQ9XWKbcHr0FNijWOIqWeoxQRBBE3wP+gcSTqYTLEkm6ozh4oA38GQD24COycU0aVipaVZEmIbCbJkBBC5DDF1hFK1TL+e4SqqqBJtUJlvd5g55Ty2J7ZoIlPFcwC1aW0+WG3Ck1RvJqbtpFDcEJkjSRDQgiRixRFAduU0/iVl94xe0z90h+i76YaVQpKmbd09V+ICYFm74BXc1SdBr6uitr+c5SXh6EmRsOJ1Q+TJmPypDgUzYVWCpG3STIkhBB5mGJpBa7ljP9onuZxVZsAuodnuuk10GQQlKppvB0WCDummJdPfcit2MM5S1VaoxR//KrgQhRkkgwJIUQ+pljbg7W98W9bR+iSkvwoZV9E/fLawzlKN1LmK0XehNBrcOVf0CVCvyVQvBLqjWOwdigM/BnFsyFq6FUI8jEmTK6e4FLamJwJUcDIu1oIIQowxd4FytQ2/nuEajBA7H2wLWK8w84ZqrUFZw/j7av7YdvElA0srFCLlk5ZfDI5Sar+KsrDeU5C5EeSDAnxnKlaHYq17HqPk1Mr0wpzioUFOLun3PaoCb1/SCnQ7G2o0d78DLjkvy/thNgwY7nPToGdE+rh5XDiVxi+G8XazjjSFBdhmrMkCZPIq+QTOZsNeD8SbEJNt/PatWwkfu7Hv3KkJGpsTI5d68eicuUcvZbQ84ifl65mXZgpltbgVsH4Lx1qUqzxEicupY13OLtDqZoo1nbG20dWgO+WlPIOxVJGk5L/d6uAUuWVHG2HEE8iyVAOyotfxBI/b8TPzxdXLGxXsxYZU2wdjZcvSb5d2xtqe6cU6DELWnyYchZc8qjSXT+4uNM44dvZAz73BUDd/AnotSh95htvX/kXbByMiZOTh3EkS4gcIMlQDsnLX8QSP/fjF4SEpbBczVo8PcXBFRxcoVy9NI+pBoPxEidxESl3OpYAgy7l9uaPIeqO8W9LG1TXsuajSsUqgHs1FPdqOdsQUeBJMpQD8voXscTPG/Hze8IiCZF4FmaXOEm+r/1480JDN6caUUq1gvd5X4h/mETV9oYBKwBQf+oJtb1Rmg0xJltX9jxMnMoZL9QrRAYkGcpmNapa5YsvYomfN+Ln94RFEiKRk5TilSCD9Y/UxBhjgvTwVH9VrzMuXmllaywQfRd+eTOlvGPxh2fAlTeft+RRA8WxRI63ReRtcgA2m40a6phvvoglft6InzqhsKhcOdviFpT4QqRHsXNCKf0Cint1421LK5RBq1EaDTAWcCwOH+4wrqHU4XOo0RFsHSD4FBxYaDwEt7w3nNkEgBpxE/Wnnqg3jhtvx4WjBp1AjQ4xXhZFFGgyMpTN5i2L5dKN/PNFLPHzRvz8PoIjI0Qir1GsbMGzofHfI1S97uElToKMI0VgvD6cXgMWlsbb/v/BumHGv63sUB89C66Yp3GkqWSVlLPnRL4lyVA2u3RVBzZPLpcV+f2LXuJnTn5PWCQhEvmF+SVOHt7nUQM+2J5SyOtlGPwbRD6cq5Q8XynoBCRGp5T78G/wbIB6eQ+cXAu9vkcpUgw1Mhi0icb5SpIs5XmSDOVx+f2LXuJnTX5PWCQhEgWF4lgcqrdL9zE1/kHKpO7kM9kSHkDo1ZTVvP9bAkeWGcs7uT8yV6n8wzWWyoNLGePFekWukmQoD8vvX/QS/+nk94RFEiJR0CkORcGhKJSpk3Jfvd5Qr3dKoSZvQbkXzUeVrh+Ds5tBNRjLWFrD1JugWKIeXgZRt1E6fwWAGn7dOCG8SHFJlp4DSYbyqPz+RS/xn01+T1gkIRKFneJeLWXUKBVVr4UHt4wJUlw4SvIcpfAbEJZqP1n3Ptw6AzYOqK7l067cXezhJU5sHZ9Pgwo4SYbyoPz+RS/xs0d+T1gkIRIiLeMlTioa/6W+v9t084IdJkDotYejSg/nLQUcMk70TlahCbz/FwDqjq/AvQZKg77G2xFB4FwKxSqbJ7EWUJIM5TH5/Yte4j85flbk94RFEiIhno5S5RV45JptqqoaF5tMPuxmbZ/yoP9/KeV0STC7sfFv59LpXA+uvPFMOCd3ucTJQ5IM5SEF4Ys+X8U3xILFwyFmNYHG9Z2zHl81AAZQnrwrJdc/s2K0WpysrTOVUMTrdDhYPd3uLAmREPmDoihQxM3475FLnCgj96bcUFXoPc981W7/g8bLn6ReM6nVcOg02XjB3R1ToEE/FM8GxmRKEw/2RQvNfKVCnwxt3LiRNm3aUKxYMXQ64zVxrJ7yS+VZ5LtEIr/H14dA4h9gXQes6mKv/Yj7AdCmLSRYjgPrmg/LBUPin1BkRNoYsd+D4TYYIsD+LbBtA5ojkLgB1CRw+hosigNQvvif+J/+hVETt7F6katZGL+oKBZe9SdOp2PSCzWp6uQEwMJr/nxQ2Yuj4eF0IuXirnu/nMzqo8dI1BuY/WIdStrZMevyFXwfPMCgQv/ynnQs5cFvQTd5q0J5biUkEBgbR8dSHhl2hyREQhQcirUdNOiX5n5Vm5gyXykyCEq9YHwgJhTOb4VKzcGzAdw8DT91B1unR9ZXejh36eF9io3Dc25Zznku3/rx8fE4ODx7pwUHB3PhwgWqVKmCl5fXY8vGxMRga2uLjU36x0vXrVvHzZs3CQ0NZf/+/Tg5OXHixAkqVarEsmXLnrmuWZHvEomCED9xE9i8AombadCgIc6KF3GWX3M9+pH4+iCwSCeJ0IcYEynbT4xlEtYZkyGLUuA8F2JngO4a2BSnRsVLJIb/Sdnqi9OtfxFLK35sWJ+fAgI5ExlJVScnHmg0hCYlEavTcSEqmk6lSpkSivKDBrOsQgUm/vEnV2JiKGlnx72ERDa/3NwUc/WNICwUBZ+ISI6GhzOiypNXhpaESIiCTbG2gxKVjf9S31+8Eky+krLSdrFy0GVKyqhSWCBc2w/aBLPt1MFrUaq/inr3AvhugZfeQXFyR9XEg6W1cX5UBtQzm2DXdHhwG4qWgQ5foNR7LbubnGlPlQzdunWLtm3b0rp1a5YsWWK6//bt28yZM4fvv//erPycOXM4fPgwBoOBkiVLsnr16iw938cff0xAQADR0dG89tprVKlSJcOyW7ZsYenSpej1eiZMmMArr7ySbrkDBw6Y1T0mJoZhw4YxceJE031arZbZs2fz+eefZ6m+WZEvE4n8Hl/Vge4CFPkQF5tfef/NUCZPdedubDrx9TfAsmra+y3dwfJV49/xv4C9cdIiVhUh6R/Q3wPrRjSuZ4299ndCDB/je9kd0hlxruhYhOPh4Ry6H8bCBsah7+137vKquzv+sbFUKJJygUmDvz/lgZ0epbmLQlM3Nwyqijb5VN2HGrq68tk5X9rWfxHHKKtMH0KThEiIwiv5kJhStCy0+MDsMVVVIfZ+qgvn3gSPGsYH712CAwugySDj7SPLYfcMVJcy6cxV8oTb52HHV6BLNJZ/cAs2f4wKuZYQPfXIUM+ePZk5c6bZfVevXqVcuXJpyl65coUtW7ZkOErzJJ9//jnFixenX79+DB8+/LFlu3Xrxq5du5gwYQLlypVDp9Ole9jrxRdf5OjRo8TGxlKpUiV27drF119/Tfny5U1lrK2tcXR05NSpUzRo0OCp6v44+TKRKAjxtafAug6NXrTEKt6CpWtLcfdBG+PeoDkBlh5g6Wksqw8Cm1czjpWwwVjW6uEptHHzIHEjKMWpVsGP2ZNb0rbtSXQWzqD+D2w7Az3NQvxy/Qbzrl7DycqKQ2FhdC1dmj0hISxqUJ91QTepU7QoGoMBm4cTHWf8tY3fgm5SskQJztg70DgpkU+qVUNrMBCh0XAxKprW7iXZ2vJlfrhylTcrlCcrJCESQjxKURRwKmn8V76R+WP1eqPW6ZFyKZMKTaDVyJSz4C7sMC5K+TjaBONIUS4lQ9k6jfzKlStUrWr+K1pVVWJjY586EQIoXtw478JgMBAeHk58fHyaMhEREfj6+rJr1y7Onj3L119/TdeuXXn11Vc5ceJEmvJ9+vThzz//pGrVqoSGhtK8eXN27tyJVmv+pVu3bl1OnTr11HXPSL5NJApCfJ0fVrq/uOn3GsePn8DnwECwSh42ViFxR0pZfahxFChZ5ADjPCIArR9oTxvnCyWzbgCuO/B6YTSOyhrGTApFp9QDxzHg+JXx8Bww+bwfW2/fBqCKkyP/vNKSufVeZKl/IEl6PRejo3nt0BFWBF5nzJmz/H3nrukpmrgV47+2rfm0fj2W376DReXK1HRxxtrCAlcbG1YGXgfggUaDTlXZfS+Et44dz1IXycVdhRCZpUbfgxvH4exG1P3z4dwfxtGi+/4QcSMlERp9AAavzTjQg9vPo7rpytY5Q1evXuXVV81/RSuKwpQpU4iLi8PGxoYNGzbQr18/LLJwOt/Bgwdp2bIl4eHhDB48mM8++4yXX37ZrMy2bdvw8fEhMjKSsmXLEh4ezubNmzNMws6cOcOnn37KokWLiIqKom7dujRo0ABra/NjnA8ePKBIqsMU2SFfJxIFIX7z9/lh6jjeGX6QkJizgA1oz4BVXdAeA+UxZ3y5LAQLVzBEQ/wycJoOSqr3ss3LNK5nzQ9Te9Gu/WrirW3AcN84oVp/A5SiAIytXg17S+OvqJceJvtutrbYW1pia2nJsXZtUYGPz5xjbr26Zmd0vFKyJACvqiorg4I49c47XDvvR287Ww6EhmL3MO66oJv09SzHTwGBVHNyQmswYJ2F/U5GiIQo3FRNHETdhag7xn81O6HYu6Ce+xP2z4MPd6BY28OBRXB4acqG9kXBpRS4lIYydcH54d/FvVA8ahgPn0Wlk/gULfO8mpZGtiZDN27coEKFCmnur1MnZcnykydP0qhRo8fO+3nUjh07aNmyJaVKlWLNmjXplnnrrbd46623GDhwIMOGDePIkSNmiVBYWJhphAmgfv36/Pbbb+zZs4cvvviC0qVLM378eHbs2GEW9/Dhw7zzzjuZruuT5PtEogDFv3T54eRo64YQOw0M34NlZSgy2FhYHwKWJc0DxHwOjpNA8x/oAyDqPeP9VtXAfgAN6nkyb1px+r+9mvik8mBjAXY9jeUUW3A0zj9b4h9AnaJFqensTBErS9xsbTkWFo6DlTGRURQFBdAY9GaJ0LWYGMrY2+NgZcUft29TydqK+n6+/Fu6DD3//pviFgqfVq/GA42GcI2Gcg4ODK1UiQS9PkuJUDJJiIQomNTEGLCyRbGyQb13Cfy2Q3SqxOfBHUiMMt/o4UVpsXEAlzLG0++t7aHxAKjxqvE+Fw8UmycMIHScCJs/Np+QbW0PHb7I/oZmUrYlQxqNBktLSywf/ipNduzYMc6fP8+gQYMIDw/n3Llz2NmZX8H38uXLGAwGatasmW7skJAQVFVFq9USFhaGhYUFNjY2ODqaL0O+Y8cOqlWrRqlSpdLU46effjKbCF20aFFCQ0NZtmwZ1atXZ8eOHbRq1cpsm8DAQCIjI6lWLe2S6k+jICUSBSK+VQ1QHMDCBZxnp91AcQBbb/P7rF8EpYhxwrRdd1BS3sv1asVilTiZNm3vkpDkCI4PJ+PbdjD+S6WKkxOl7e1wt7PlC18/guLjsFIs+PIF832gf3nj3CVVVel9+ChTatfik7PnCE1Mwtnaiul1aqMGBPBp5cp8vnePKaEIjI1l4MO5Qp5Fnu1MTkmIhMg/VFWFxGjjyEvqUZ2ou8Z1hCo2RQ04DMt6wtA/wKu58QKze2aBYwnjCE6x8lCxmfFvl1IPk5xSULQsAEqN9lCjvek5Fffq4F4903VU6r2GCvn/bLL0BAYGpnu6e5MmTTh69Cje3t4UKVKEd999N80k6z179hAfH58mGVq5ciVnzpzh/PnztG3blrt37/Lqq6+aEqNWrVrx448/AsaRn+3btzN37lyio6Px9fXl+vXrWFtbExwczPnz501x9Xo9P/74I/3796dUqVL89ttvrFq1Ks1ZbosWLWLy5MnZ0j8FLpEoCPGtKj5+IwsnsKhjfp/Deyl/p0qEjPErMGridyTYacE830+jZ9mU4eDv6tXNsNzLJUoYn0pR+LlJI5ytrVncMO1k/kcTikr5LGF5NL4QImOqTmM81b24F0oJL+NFXf8Ym5L8aB6ZV6so4OQOlVsYb5esAp0mg+vD7+KanWDared66Q6l3mu5Nlk6PU+VDFlaWnLixAl69erFxo0bsbCwoHjx4vTunXLF3rt379K/f3+2bt3KmDFjGDNmTLqx9Ho9Dx48SPfxF198kQYNGvDZZ5/h6uqKg4OD6ZCBqqokJKQMsV29epWvv/4aGxsbihcvTps2bejTp49pvaH27VOyWAsLCz766CPTvKWwsDDatGmDu7u72fM/ukTA0yqQiYTEf6b4FpUrZzmhcLZ+/KU88vsITur4QhQ2qsEAcWEpCU30nVQjOw//r90NpeMXoOrhlzehw+fQerTxEJMmHjxqQrW2D0d0Uo3qOJU0W/NHcSppXH06+bZcvwxFVVOvzZ29IiMjcXV1fXLBAsDHx4cBAwaA00KwSfmlnxe/iCV+7sa/cqQkamxMjh0SsqhcOUcPOT2P+DYjRmV7XCFyi2rQG9fo0cQbFzgE1L1zwNkDpVF/YyL0ZcU0ixpiYWWcfFz0YXJTrS1K/deN2wefMV61vkix592cPC/5+3jt2rU0bNgwU9vk6ArUhSURykhe/CKW+HkjfkEZwcmp+ELkN+rt88bTyB/cfjiyk2pUJ/oeGHTGyccf/m3c4Mo/ULI6NOqPYmGB2no02DmnnIXlUgqKlMjwQqrKI9cmE8+m0F+bLKfk5S9iiZ/78QtCwiKTnkVBp+o0EBOSktQkRKI0fdv42PYv4d4llHd+NxbeOh6CThr/trZPOVRV6aWU5Matkim2kpwUJd9uk/5UEvF8SDKUA/L6F7HEzxvx83vCIgmRyM9UXRJE30MpZjzrUr24E64dMB/Vib1vfpV3xQK10UAUSysoWg4M+pTHus0ES0twLg32LoXmau8FhSRD2axGVat88UUs8fNG/PyesEhCJPIiVRNvflp56kNWPWejOHvAf0tg13TUr68b18W5fhTObEgZ0Sn1gvkhK5cy4FLamAgBSvN3zZ5TKVM7N5oqsokkQ9ls1FDHfPNFLPHzRvz8nrBIQiSeJ1VVURQFNToErv4L1V9FcSyOeuFv+OdbY+ITH5l2QwdX42TkxGhw9oDq7YynmyevIN9pMkqXKc+3MSLPkGQom81bFsulG/nni1ji5434+T1hkYRIPCtVVSEpxnwFZNME5If/P7gNvb6Huj0gLAA2joJ3N0LllmDraLwieoUmqUZzHo7yOHug2JgvPqqUqgWlaqXctrBEFF6SDGWzS1d1kM1LNuT3L3qJnzn5PWGRhEhkRFVV48U6o+6ArSNKsfKoCVGwbTLU7Y5StQ3cOguLzFdqR1HAseTD61pVAq+XoZhxVXbKvgifnjBdz0qp3CJlUUEhskiSoTwuv3/RS/ysye8JiyREhZeq18GVvcZRnPRGdZLX0Gk+DLyngbUd+B+ACo2N97tVhM5fmY/qOLlnuCCgYuMAbhWeS9tEwSfJUB6W37/oJf7Tye8JiyREBYdxVeT7KYeoou6CvXPKwn+LOkHZF1G6zzDOvVnztnE9HQsr47wcl1JQug7U6JByyMqjBgCKlS1MOGt6LsWhKLT8MBdaKYQkQ3lWfv+il/jPJr8nLJIQ5Q9qTCgkRKGUrGK8vX8+3PFLNapz15jcpObZEB4mQ3i9bBzRAePCgcN3GQ9rORaXOTgiX5FkKA/K71/0Ej975PeERRKi3KPqtSmLBT56yEqbiDJ4rbHgH59C+A0Yc8B4O+A/iLhpHMGp2DTtRGSXUuDgZnoepeMXZs+rlJbTy0X+JMlQHpPfv+gl/pPjZ0V+T1gkIcp+qi4JLKyNIzG3zsH1oygt3jc+tmsG+PwGsaHmiwUC2DgYE5qiZVENBuNlHlp+ANpEUxHlnQ3PsylC5Bk5eqHWwuRpLgwnCidVo0WxyVpSVNioWi2KdeHrI1WbmHbicfLoTvTDEZ7YMBjng1LME/XAAvh7KkwJRLF1RD25FoJ80o7muJQGO2dZFVkUCnnuQq1CiLQkEXqygpgIqZq4lOTGvRqKkzvqrbOwZzZ4T0Nxqwgn1sBfn5tvaF80ZQXkMnWNiY21nfGxpm9D07dRbB0BUBoNgEYDnmu7hCgIJBkSQohnlHzYSY29D5d2p4zupP4/MSplg35L4MVeoBqMjyfGGO+v+gr0mW+69AMuHsZLRWQgOQkSQjwbSYaEECIDxsUCo8xXQI66A54NUaq3Q42+B9+/BF2+hsZvGg9nbXp49XHHEsaExq08VGqW6rBVqtPLy9WHUf+ank8pURlKVM6NpgpRqEkyJIQo1NQ7fmBtj1LCC1WbAH+MS7Vw4F3QxptvoCjQaqTx2lZF3KDBG/Dw1HQ8qsM4H+PlHzJYLFAIkfdIMiSEKHCMiwWGpRyiin7ksFXJKig9ZhkL//wGVGsLvX8AS1sIOm4c1Sn9AlR/Ne1EZKeSKJbGOU2KpTV0m256XsXKNuVyEUKIfEOSISFEvqIa9BAfieJY3HjbdyskRqE0Hmi8/VNPCDoB+keWRrC0Nl613KUU2Lum3N/3R+NqyRgXDuTTE8+lHUKIvEOSISFEnqLGhEJEUKqRnNvmozox98DBFSZeNG7g+yeEX4eHyRBeLaBcffM5Oi6loEhxY7LzCEUu7ilEoSfJkBDiuVB1Goi+l3LIqlZnFCtbVJ91cOp/MOwP4zo4O76CMxtTNrS2T0lqvJqb/lZV1Vj+9YXGMg8pbT9+/o0TQuRrkgwJIZ6Zqk0ES2sUC0vUO+fh6r5UoznJiwXeN9/o0+PG61opClhZgy7JuH7OS+/Ci68ZR3OcS4O9y2MXC1RsHHK4dUKIgk6SIZFp6a2cXFhXCi5MVE28eWIT/fDvJoNQStVCvbADVg+G0QeMp4wHnYSd08DOJeUQVenaKQsHmlZHLgOA0qAfNOhnej6lXP1caqkQorCSZCibDXg/EmxCTbcLwrW0kuOvXuRK4qgRZo/bzVuQ7c8pni9VE2+ccFyiCkrRMqj3LsLf01ISn/jItBs5FIOqbaFULWMC1H6CcaVkgAZ9of7rsiCgECLfkGQoBxWkRCg3r94usk5VVUiMfmQl5EeuedVoAMrL7xmTnRWvQ8/voMlboFgYL/Tp6gkVmqQ6rTx5VKcUSuo5Om4Voc2YlNuPWTFZCCHyIkmGckh+T1QkEcq7VFU1JjC6JBSXUsb7/pkFpWqivNAVNSEKZtQFTTqLBTqWNCY3xSuZTifHyR2GbQH3qsZi7tVhxJ7n2SQhhMhVkgzlgPyeqEgilPvUuxcgMjida1w9PM1clwjV28PgNcYNTv0PanWCF7qCnTM0fsuY7JgtFuie7qrIiqWV8XIRQghRSEkylM1qVLXK14mKJEI5x7gq8v2U5EabgPLia8bH/vwMkmJR+i4yFl77LoQFGP+2sEpZLLBMXajZ0ZjcuFdPCT7upGkNHUVRoOvXz7NpQgiRr0kylM1GDXXMt4mKJEJPTzXoIS4MxcndeNtvm/GsqtSjOtF3waBL2cjB1XgKORivcWWTMg+H3nPByu7hYoEl0l0sMLUnPS6EECJjkgxls3nLYrl0I/8lKpIIZUzVa42LBT56yCr6DvRZYFznZscUOP4L6tc3jCMzF3eB7xYoWtq4Vk7FpuaHrFzKgIuH6TmUV8eZPadSoenzbqYQQhRakgxls0tXdZDNF6uWRCjnqVF34PpRqNEBxdYR9exmOLTUOEcn9j6oqvkGNg7GpCYhyvh37a5QwgsMerC0gp6zoc/8xy4WKIQQIm+QZCiPk0To6anahIcjOakmHqce1XlwB95YglK5Jdw8Bes/gJH/Gq9WbmkNDkXBo2aq0ZxUIzt2zmaJjlK+MZRvnHLb2i4XWiyEEOJpSDKUh0kilDFVE2dMamydUJzdUaNDYO9saPAGimcD1Kv7YGXftBs6uD6cjFzaOBnZoZjx/sot4eNDUKwCAEptb6jt/fwaJIQQItdIMpRHFfZESNUlQeAR88UCU/+fGGUs+Op4aPsxoILfDuMVyz0bgHs16PB5SuJTtAw4e2R4HSvF3gXsXZ5fA4UQQuQZkgzlQQU1EVJVFRIemCc1TiVRanY0PjavNdTuZrzquDbRfGTHsYQxoXGrAJVeSjlcVbYeAIqzB0y6aCquuJSG1qOfW9uEEELkX5IM5TH5ORFS4yJAE4fiWs54e+8cCA80H9XRPrIqcrV2ULMjiqKgejaAYsZtsXOG97caz8Ry9kh3sUAhhBAiO0gy9IitW7fSuXNnrKyef9fk1URIwUAx6wjUW3exLKNHvReIosSjKAmoG0ai9JlvLLh6kHGBwGF/GG/7boGkmIdXLX8Bqr+adiKyU8mU5+n1fcrfigJyerkQQojnIF8lQyEhIbi7u5tujxw5kvnz56dbVqPRkJCQwMyZM5kxY0amn2PHjh3UrVuXcuXKYfEcF7LLrUTIAj0GLAGo7XSeKg7+bA7pCcD4SrNoV3wvJW1CsLHQwcKHbxgbUFULUO0hLjzlSVqPNl7kM9no/SQkJODgkP48HSGEECIvyDfJkKqqDBgwgP79+zNkyBAAkpKSUFUVVVXTJC4+Pj74+flx/vx5Dh8+TPPmzdONe/ToUc6fP4+Hhwdbt27l7NmzzJgxg44dO9KjR4+cbhaQcaLSpcQ2Pq44j1K297ib5MGc66PYfr/r44NpToI+COx7Yxk3kxLKP1QpXZ3v3ujLtinBtAnbxhuVQ/CwB3d7C0ra3qfJkSPE6YvQofhu3iqzhs13GkDiFu5rinMqqh4h0T7cS7DCsqg9vSrWosaICeh+34IhIAC7wQt45513uH79Onq9nuLFi7NpU2sA9Ho97du3x8nJCTs7OxYsWEDZsmWZNGkS27Zto0WLFnz33XecO3eO27dv06NHDxYsWMD777+PtbV1Tna5EEIIYZJvkqEDBw4QERGBp6cnAIsWLeLMmTO0a9eOCRMm0K5dO7PyO3fu5Pz580RFReHv759uMhQaGsry5cuZP38+qqqyfft2Pv74Y4KCgvJEIjSt6lfYWyYCUMbuLtOqfgXA9vtdsVa0lLQJxd02hGtxlYnRO9PA+RSDSs1i8uUhPDA84MNK//JRtRAgBLYdYIAdxHvaEpLkwd24+xx/8Cr3NO4oGABYHjyE5cFDQO8HFh6suNUH1HiI/QacpnFlR0kSR41Au2Er1kPeQfvzSgBWrFgBwMyZM3FycjK14fr163Tp0oUJEyaY7ouIiMDPz4+9e/fy3XffcfjwYfbt24dGo6FNmzbEx8dLIiSEEOK5yjfJkFarpV+/fvj4+GBhYcGwYcO4cuUKbm5uNG1qPrfk2LFjNGjQgG7durF9+3YGDBhAZGSkaeQCICEhgTlz5uDu7k6RIkW4dOkSMTExVKhQgT/++IPw8HDc3NxytE2POzT2ccV5pkQomb1lIjOqTWK812xK2KQcnhri+xNHHrxEEcs4KjncwdnWlQeJBo7HejK4w8dMW3yUCyHVuKd2IVprC7EzwLou2PUyi/9A52r8Qx8EllWNf+uug0VZs3IGf3+0P6/E+u0hpvvi4+M5ceIE69evN9139epVKlSoYL6twUDRokUpVqwYpUuXRqvV4uTkhFarZf369fTu3TtLfSiEEEI8q3yRDPn4+LBhwwaCgoJ44YUXsLCwYP369fj5+REREcGJEyfo06cPgwcPJjAwEF9fX65du8bhw4eJiYnhxo0bqKqKpaUl3333Ha6urgQEBDB58mQ+//xzABwcHLh16xaHDh1CURTi4uJyNBl60hyhUrb30t3OWtGyL/wVQpLcuadx516SBxdiawFwMLIlB4OagFMjGjewo4ihAq+M30FMrAacPwfDA4gZCfobxr9tOoBFkbRPog8Cm1eNf1tVBsUWgEOHDlE8No4KjkVMCZHNiFEA7N27l86dO2Njk3LW10svvWRKQnfu3EnTpk0pXrw4Tk5OvPnmm2i1Wt59913at2+PRqNh2rRpeHl5PXWfCiGEEE8jX1zqumHDhrz00ks0adIET09PYmNjOXjwIHZ2dkydOpXt27fTv39/wJjUNGvWjE8++QQ3Nzf69etHq1atWLVqFStWrMDV1Tj68cILL6CqKgaDwfQ8lpaW+Pr6Urx4ceLi4nKsPZmZLH03ySPd++8klWLytSksuvkhm+69xuHI5kTpUi0WqOppXN+OiaOSOH7yOjG6boDBmAApDuDwAbhuAwtn0P5nPAwW0R3UVKNQ+lCwfDhRXbE1JkQY521tuX0HgO7/HeL6uXOmTf7++2+6d+9uVldXV1cqVaqEpaUlMTEx/PPPP4SEhBAcHMwbb7yBwWAgICAAgM2bN9OjRw/69u3L2rVrs9CbQgghxLPJF8kQGM/y8vHx4ccffyQ2NhZ7e3t+/fVX/vnnHyIiIkwjEh4eHtSuXZt169YxbNgwJkyYwMmTJ9mxY0eamLGxsWa3nZycGDhwIKVKlSIpKSlH2pHZs8bmXB9Fgt78+lYJejvmXB+VcXDDA4oVc2XeNBdGjP2LeLUv2LYD+0GQuAMsnMCmqfF/65fAEGNMkIr+DEoG19JK2ADaM6AaOHDgAHaWxrfMysaNKF/EOKqUmJhIZGQkJUqUMNt0/vz5XLlyBY1Gw6FDh7Czs2PPnj28/fbbdOnShQ8++IBNmzah0+k4d+4cXl5elC5dmoiIiEz0pBBCCJE98k0yNHLkSH788Ud27txJsWLFcHBwoHjx4nz22Wd88cUX3Lx501R29erVFCtWDG9vbywsLJg9ezZr165l4cKF6HQ6U7mEhASzv0NCQjhw4AA3b94kMdF8vk52yMrp89vvd2Xi1a+4nVgKg6pwO7EUE69+9dizyapXukUP72qMmhhF0C0FDPdBfTgqZOEA2vPGgqoWknaCVUXjiFD0GFAf9oU+BCxT1v7Btj0k/AZRbxEREUFfT+OiiB+fOceteOMCij4+PjRq1ChNfXr27MmUKVPo1asXxYoVo2PHjlhZWRESEoLBYMDf3x9HR0f++OMPunTpgouLCz179uSdd97JfKcKIYQQz0hRVVXN7UpkRVRUFG+99RbLli2jZEnjl3ZwcDCTJ0+madOmdOjQgdjYWF544QWz7RISEpgxYwZarda07tCVK1dYtGgRrVq1YteuXZw7dw5PT08uXryIoii88MILDBkyhPbt2z+xXj4+PgwYMACcFoJN3TSPP491hD4fEc/Eb27j51/BePgr+mPQXQbLiuA0A3RXIPE3MESDTXMoMtyYGMUvBocPQbEyjhbpr4N1nTTxVy9yJXHUCADmX73GWxXK47F4KREREcTExFC+fHnAuFzBqlWrWLp0aZp6xsbG8s4773D+/HmqVKnCjz/+yOXLl2nbtm2294kQQojCJ/n7eO3atTRs2DBT2+S7ZCi7xcfHExMTg5WVFVZWVlhbW5v+VxQl03Eelwzl1ZWlsxq/mKuFKRlKZjdvQbrbREdH4+zsnO11EUIIIR7naZKhfHOYLKc4ODjg7u6Om5sbLi4uODg4YGNjk6VE6HEKSiI0amJUlraTREgIIUR+UeiToZxUkBKh53l1eyGEEOJ5kmQoh+T3REUSISGEEIWFJEM5IL8nKpIICSGEKEwkGcpmNapa5etERRIhIYQQhY0kQ9ls1FDHfJuoSCIkhBCiMMoX1ybLT+Yti+XSjfyXqEgiJIQQorCSkaFsdumq7smFskgSISGEECLnSDKUx0kiJIQQQuQsSYbyMEmEhBBCiJwnyVAeJYmQEEII8XxIMpQHSSIkhBBCPD+SDOUxkggJIYQQz5ckQ3lIfkiELCpXzuZaCSGEELlL1hnKZmuXuNKwYclnirF6kWs21SZ746taLTYjRqW5T7G2zo5qCSGEELlCRoZEpqWX9EgiJIQQIr+TZEgIIYQQhZokQ0IIIYQo1CQZEkIIIUShJhOos4lGowHg8uXLuVwTIYQQovBK/h5O/l7ODEmGssnFixcBmDp1ai7XRAghhBCBgYG89NJLmSqrqKqq5nB9CoXLly/TvXt3Zs+eTenSpXO7Os/F/fv3GT16ND/88AMlSpTI7eo8N4Wx3dLmwtFmKJztljYXrDZrtVoCAgLo2rUrRYsWzdQ2MjKUTRwdHQGoX78+ZcuWzeXaPB+3bt0CoHbt2oWmzVA42y1tLhxthsLZbmlzwWtzs2bNslReJlALIYQQolCTZEgIIYQQhZokQ0IIIYQo1CQZyibOzs4MHz4cZ2fn3K7Kc1MY2wyFs93S5sKjMLZb2izkbDIhhBBCFGoyMiSEEEKIQk2SISGEEEIUapIMiUxRVRWtVpvb1XjuYmJicrsKQogckpXLNRQEqqoSGxub29XIkyQZEo+l0Wj46KOPGDp0KK+99ho//fQTAHPnzqVXr1688cYbpkuRhIaG8tZbb/HGG2/w8ccf5/sPGo1Gw8SJE4mJiWHPnj1AwW+3Tqdjzpw5vPzyy4BxJdeJEyfSp08f3nrrLdNCbQEBAfTu3Zv+/fszbdq03KzyM/v222+pX78+3bt35/LlywW+zVqtloEDB5r+zkpb165dS/fu3enbty+HDh3Klfo/rdTtPnDgAL179+aDDz7A29ubsLAwoOC1O3WbATZu3MjJkyfZvn276XOqoLX5qakiW5w8eVLt3r272rdvX3Xp0qW5XZ1sc/DgQfXYsWOqqqpqZGSk2qdPH3XHjh3q8OHDVYPBoIaEhKhDhgxRVVVVhw4dqv7zzz+qqqrq77//rv7yyy+5Vu/s8L///U9ds2aNeujQIfXHH38sFO1etmyZOnDgQPXu3bum29OnT1dVVVWvXr2qfvzxx6per1e7d++u+vr6qqqqqnPmzFH37NmTa3V+FpGRkWrLli3V4OBgdfv27eqIESMKfJuvXLmijh07VlXVrL2+Z8+eVfv27asmJSWpMTEx6ptvvqlqtdpca0dWpW73/Pnz1aSkJFVVVXX69Onqvn37CmS7U7dZq9Wqffr0UTUajfrWW2+pOp2uQLb5acnIUDaIi4vjiy++YNGiRaxbt46AgIACc/X6Fi1a0KRJEwBmz57NkCFD2LBhA2PHjkVRFEqWLImbmxu3b98mLi6Odu3aAfDaa69x5MiR3Kz6M9uwYQPdunXj4sWLVKlSpcC3W6PRsHbtWmbMmIG7uzsAf/31FyNHjgSgSpUqREdHc/r0aapUqULt2rUB6N27d75ts16vx8nJiTJlyuDp6YlOpyvwbb5y5QpVq1YFsvb6/v7774waNQobGxscHR1p2LBhvvqcS93uESNGYGNjw7Vr17h06RLNmzcvkO1O3ea9e/fSrFkzLC0t0Wg0WFpaFsg2Py1JhrLBnj176NSpE2XKlEFRFHr06JFvPygz8tNPP1GkSBE6duxIREQE5cuXNz2m0Wi4dOkS9evXN7vP3t4+N6qaLc6ePUvp0qVxcnLi8uXLeHl5Ffh2HzlyBFVVWb16Ne+99x47duzAzs7OdN09ACsrK/z9/WnQoIHpvvzcZjc3N+rXr88bb7zBiBEjeO+99wp8m69evUq1atXQaDRZamt+74PkdicLCwvjs88+Y/r06VhbWxfIdqdu8++//463tzdBQUGULl0ajUZTINv8tCQZygYF/Y3zzz//cPbsWT777DMAFEUhPj4eAIPBwP379ylZsiR37twxbePj44OXl1eu1Dc7nDhxgsOHD9OmTRt2797N66+/zp07dwp0uyMiInj33XcZP348CxcuZOXKlURHR5sej42NRavV4uLiwt27d033+/j4ULly5dyo8jOLiIjg1KlT9O7dm8aNG7N3794C3+bkL0grK6sstdXFxcXsvX7x4kXKlCnzXOv+LFInBklJSYwZM4bPP/8cT09PgALZ7uQ263Q6Tp8+zbBhw3jzzTf5999/6dChQ4Fs89OSZCgbPPqGOnXqVL79oHzU5cuXWb16NXPmzMHS0hKA1q1bs3DhQlRVZeXKlbRo0YJatWpx+fJlzp8/T0xMDD/99BPdunXL5do/vWHDhnHy5El27dpFnTp1OHnyJG+++WaBbneFChU4ceIEAH5+fpQoUYI6deqwdu1adDodc+fOpXv37jRv3pwdO3YQHBxMSEgImzZtom3btrlc+6ezc+dOunTpQu/evZk6dSqbNm0q8G0OCQnB3d0dCwuLLLW1Q4cOzJ8/H71ez44dOyhbtix2dna53ZxMS243wKRJkxgwYAANGzY0PV4Q253cZisrK06fPs3evXt59913mTFjBvv27SuQbX5quTxnqUC4fv262qVLFzUiIkL19/dX+/btq+r1+tyuVrYYMWKE2rx5c7VDhw5qhw4d1N69e6vR0dHquHHj1I4dO6rffvutqa2+vr5q37591Z49e6r79+/P5Zpnj3v37qnvv/++qqqqGh8fX+DbPXXqVNXb21vt27ev6u/vr4aHh6vvv/++2rlzZ3X58uWmcvv371d79eql9uvXTz137lwu1vjZ/P777+qkSZNUnU6nXrhwQW3Tpk2BbnNUVJT65ptvmm5npa06nU795ptv1E6dOqkTJkxQExISnnv9n1bqdl+8eFGtW7eu6TOtY8eO6oYNG1RVLVjtfvS1TvbFF1+oFy5cMN0uSG1+FnI5jmyyefNmfvnlF9zc3Jg8eTIVKlTI7SplC51Oh8FgwMbGJrerkivi4+M5ffq06VRzUbAkJCTw4YcfcuzYMdzc3JgwYQJdunTJ7WrlmLi4OE6fPk2LFi1yuyrP1aPtTkxMLPAjHRm91ocOHaJ+/fo4ODjkUs3yJkmGhBBCCFGoyZwhIYQQQhRqkgwJIYQQolCTZEgIIYQQhZokQ0KIQuFJFxpeuHDhc6qJECKvkWRICFGghISEmC4ofOrUKY4ePcrZs2f5/vvvH7vdsWPHnhhbp9OxZcsW9u3bR0RERLbUVwiR+yQZEkIUKFqtlri4OAAqVqzIzp07mTVrFk2bNn3idoDZqsyPWrJkCadOnWLUqFH8888/aDQavvvuu+yrvBAiV1jldgWEECI7JSYmAqCqKvb29mg0Gvz8/EyXUnnU7t27OXnyJL6+vrz88ss0aNCAefPmpSkXGBjImTNnTJdpKVKkCP/99x9Xrlxh/vz5VKhQId+uPi5EYSfJkBCiwDh9+jTz58/n0qVLxMTE0LRpU3x9falZsyYlS5ZMdxsnJyc8PDwwGAx4e3ubrsH3KGtra1599VUOHTqEi4sLFStWZNKkSZQsWZItW7awatWqHGyZECInyWEyIUSBUbduXapVq0bHjh2ZPHkyderUoUKFCgQHB3Pz5s10t6levTpr166lRIkSxMTEZJjUODg4cOLECdq0aUNkZCSvv/46Xbt2Zdy4cdSsWZNy5crlYMuEEDlJkiEhRIGRmJjI9u3bOX78OFu2bKFYsWLUqlWLfv360atXL1RVRa/Xm8qrqsq4ceMYOHAg7u7uTJ48mYMHD7Jhw4Y0sf/8808++ugjzp07R8+ePRk7diyvvvoqp0+ffuJ8JCFE3iaX4xBCFBiLFy/m0qVLNG3alDp16rBnzx4OHTpEaGgopUuXJi4ujhYtWjBu3DgAvvvuOxRFoVWrVowaNQovLy8++OADli5dSqdOnejbty8AQUFBLFy4kH379mFpaYlGo2HVqlX88MMPaLVaFixYgKura242XQjxDGTOkBCiwHj33XdZv349lpaWWFtb4+3tTaVKlQgJCWHo0KFmZY8ePUr58uWpVq0aCxYsIDo6mn79+tGoUSMaNGjA3LlzGTJkCGPHjsXNzY0OHTpw5swZBg8eTFRUFHXq1KFcuXIcOHCAgIAAGjZsmEutFkI8KxkZEkIUKIsXL6ZEiRL07t0bgB07dhAXF0efPn0eu139+vX5559/cHNzM90XGxsLgKOjI+Hh4fTv35/SpUsTHh6Ora0trVq1okePHgwZMoSKFSsyePBgmjZtiqIoOddAIUS2k5EhIUSB0qhRI+zt7U23HRwcsLJ68kdd+fLlKVq0qNl9jo6Opr/HjRvHzJkzqVevHqtWrcLa2poBAwYAsGnTJr7//nsmTJjAkiVLqF69evY0RgjxXMjIkBCiQIuJiUGj0ZiN+AghRGqSDAkhhBCiUJNT64UQQghRqEkyJIQQQohCTZIhIYQQQhRqkgwJIYQQolCTZEgIIYQQhZokQ0IIIYQo1CQZEkIIIUSh9n/kz3v4MhgdDAAAAABJRU5ErkJggg==\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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duXXQ6HS+88AJvvvkmZcqUwc7OjqtXr/Lll1+yZMkSqwOVo6Mjc+fOZePGjTRr1oxBgwZRvnx5Fi9eTOXKlbl69apVfapVq4bJZOLMmTPcunWLyZMnExUVZdkxUko6EJUqVYpXXnmF7du3U7BgQSpUqJDmAL/PP/8cNzc3IiIicHFxIS4ujm3btnH79m0+/vhj/ve//zFkyBCCg4MBeO655+jevTv79+9HKcXZs2cpXbo0rq6uFC1aFJPJlGaXdUY5OTkxcOBAzpw5g7+/P5UrVwbAxcUl1UDhmzdv0q1bN77//ntWrlzJqVOnCAgIQClFTEyMpVxUVJQlwfH29ub111+3PH7Ut8okwcHBXL16lfbt2/P1118THByc5pgLk8mEVqu1DHJu2bIl+/bto1WrVhlqv4ODA8ePH+f3339PdzxLdv4/JJk9ezY9evSwfJA+7KofnU7Hhx9+yNixY9m1axd//vkns2bNomfPnkRGRqYqn9F9FzL/fmXn/p4ku7b/43ye6HQ67O3tiYuL448//mDDhg2MHz+eq1ev0rRp00ee+nuU0qVLYzAYLKdjdTodnp6evPrqqwwYMIBTp06lud6FCxd49913+eyzz7h69Spbt24lPDw81WndwoUL07NnT1asWMHBgwcpXbo0ixYt4s0332TXrl0UKlQoVdJYvnx5AgMDGTJkCCdOnECv19O4ceM0ewwDAgLw8PBINQYvMDCQiIgIhg0bhr39433nX7RoEYMHD2bNmjXUr1+f7777jnHjxvHbb7/x22+/4e3tbenRCwgIwMfHh3PnzvH3338zY8YMIiIiaNiwIUOGDGHlypWMGjUKgBYtWtC1a1cMBgMrV67k448/ZsmSJZw6dYpPP/2UHTt2MGTIEMs+UaVKFcaOHcuQIUOoWbMmAwcOJCwsjOnTp9OzZ0/OnDlj9WUsISEBR0dHxo8fT6lSpTh8+DAdO3ZkxowZlCtXLsvHb+W0fNGD4+Ligq+vL4UKFaJ///60a9cOg8GQasR56dKlLf84AFeuXKFcuXJ4eXlZlpUrVy5V/AIFCvD+++9b/kF///13mjZtatVDUalSJcB8UEtISKB27drs3buXYcOGUbx4cWbOnMmVK1eoUKFCqvi+vr4YDAa2bt2KyWSiSJEiuLi4pPkhqdFoWL16NQBvv/027777bqoeipQMBgP//fcfTZs2pUCBAhQuXBhnZ2eOHj2aquw333yDwWDg0qVLdOnShdjYWE6ePEnFihX54Ycf6NevX6rzuLNmzWL06NF07NiRFStWMHjwYDw8PGjevDlz5859ogTnzp07VKxYkS+//JJOnTpZxn8opVId8N3c3Cw7uVarxcfHh6ioKCpXrsz+/fvp3r07ALt376ZmzZppvp5er8/QJbv+/v58+umnlm/c8fHxaa6XkJBAWFgY06ZN48svvyQ4OBhPT0+OHTtGcHAw/fv3f2hvSEa6ibPz/wHMp1WOHDnCb7/9hoODA6dPn2bUqFF89dVXqU5HmEwmpkyZQkhICGXLluXGjRuUL1+ecuXKMXbs2DTjZ3TfTcuj3q/s3N+TZNf2z+znye3btxk2bBharZaLFy/y5ZdfUrNmTS5cuMDNmzfT/Z/PLIPBYPlysXTpUs6dO4eXlxc+Pj5MnjyZmTNn0qBBA0v5CxcuULRoUVauXMnx48dZtmwZn3zyCcePH7c6FQNgZ2dnldQ6ODjw559/cu/evYdeWr5+/Xq0Wi3ffvst9vb29O3bN81yhQoVIiEhgRo1ajB//nyGDh0KmD8vXn31VX777beHvncPc/78eT744APAfBr85MmTTJw4EUdHR5RSxMfHW/bngwcP0rVrV+Li4vjyyy+Jjo6mYMGCgPmLWuHChS1xK1asCJi/VL311lvUrFmTDRs2UKVKFcs2S5n0ubi4cPfuXWbMmEHFihX5/vvvuX37NvHx8URHR1OpUiWrpMVoNHLlyhVGjRpF1apV2bdvH66urhQqVIiGDRs+1tQHeUm+6MEBcHd3p2XLltjb21OwYEG6deuW5gCsB2XkIFKiRAnLDhYdHc2hQ4d488030yybsmfBzs6O0aNHM3XqVK5du/bQAbc1a9Zkz549/O9//2PYsGGPrBOYPwAe1UadTmf50IiJieHIkSMEBASkSjyCg4N54YUXKFmyJJ999hleXl6sWLGCrVu3smLFCpo3b54q9qFDhzAYDHh6enLy5Em8vb0ZNWqUVbdmZi+bTZKQkMDnn3/O0KFDCQoKYu3atQQGBqLT6YiJibEcAJLUqlWL7du3ExwczOnTpzlw4ADVq1fnpZdeYvv27Zw6dYrQ0FC+++47OnfunOZr6vX6R87RoZTi4MGDVqdDksZSPCg0NJQDBw7wyiuvAPDVV18xcOBAWrRoQXx8PF26dOH8+fPpvpajoyPR0dEkJCSQkJDwyN6lrPx/AHMit379epYtW2bpzv/www/x9fXl1VdfZdu2bVbrLVmyhF69elGqVCliY2OpUaMGzzzzDFFRUVy5coULFy6k2YbH3Xcz8n49KKv395SyevtDxj9PihQpQv/+/albty5ly5a1DHKeNm0aXbt2ZcqUKY+sf0YYjUZLL8e9e/cYPnw4X331FQMGDKB+/fpWyQ3AsmXLLGN/krZ9VFQU8fHxj+wd0Gg0aLVafH19KVOmTLrlkr7YODg4pDrtlNKMGTPw9PTk9ddfJywsjNdee43333+fCRMmcOXKlSe64rNIkSIcOnQIk8nE6dOn8fLy4uTJk5w9exatVktERARxcXHs2bOHI0eO4ODgQHR0NBUqVMDBwcEyLlCj0aDT6ayu8Lp27RobN260XDxx+PBhatWqlW5d1q1bxyeffEKlSpUsXzjHjh3LunXrsLe3t9pGjo6OlCtXzjJAfMGCBZbT/wkJCTY/Z1q+6MFJcu7cOUu3rY+PT5bFbdKkCSNGjCA2NpawsDDA3DWaVpevyWTCyckJMH8gTZw4kc6dOzN9+vQ0518xmUwEBARw4sQJhgwZQkREBP/99x8eHh5p9vak5O7u/sgP7AoVKli6ue3s7Pjqq68IDQ3l008/TVWuZMmSLFmyBDB3o8fHx+Ph4YFGo0nz2+vff//NO++8w/bt26lQoQJTpkzhv//+w2g0cv36dQ4dOkSbNm1Srefv78+gQYMsiZBGo8Hf39/yQQXm01NJA+LCw8PZvHkzs2fP5vTp01SuXJlLly4RGBhI8eLFadKkCYULF2b48OH07t0bFxcXPv30U8s3m/fff5++ffui0WgsXbFp0el0j7yaaNeuXbRv395ykLpy5Qo7duxI80BrNBrp2rUrDRs2ZPHixfTr149nnnkGgBEjRljGqyT1AJw4cYJNmzZx5coVbt++zb1796hbt65VzEmTJtGjR48065aV/w9gHr8yZ84cq3EVGo2GTz75hJ07d/Lzzz/z77//WqYWGDRoEGB+v7755huuXr3KlStX0Ol07NmzBwcHB55//nkmTpyY6rUeZ9/NyPv1ODKzv6eU1dsfMv55YjQaCQgIYMSIEXTr1o23336bfv360b9/fzp27MisWbMYOXIkEyZMSHUlWEb2xyRKKcuVanXq1GHy5MmW8XdJ739KZcqUsYy/Stof//rrL2bMmGHpVU1PylPLGeHo6PjQU0xJ741Wq2XixImWKywdHByoUaNGuv93Gdk+AwcOZMiQIcTExDBy5EiGDBnCBx98gJeXF19++SVFixbF0dGRmTNnMmzYMLZu3WpZt1KlSuzZs4e6devStGlTevTowdy5cwHz+KUffviBr776isWLFxMVFcXOnTvT7REF82dLyh7BhIQEpk6dilarJTIy0up/1MPDg6ioKC5evMitW7fo1q0bZ86cwcPDA3d39zSPWTZF5RO7du1Sn3zyiYqOjs7wOsuXL1enTp16ZDmj0ahu376doZjjx49Xd+7cUUoptWHDBtWlSxf1wQcfqCZNmqgrV66kKr927Vr14osvquXLl1uWnT17Vo0YMUJNnTr1oa81ffp0y2tlRFBQkFJKqa1bt6rJkyenev7GjRtqwoQJ6sKFC2r48OGqXr16qnLlyqpGjRqqcePGavz48VbljUZjuq8VHx+vhg8frv78888M1y8jIiMj1c6dO9WCBQvUp59+qj7//HN17dq1R64XFRWlIiMjH1pmxowZj4wTGBioTCaT1bKffvpJdevW7aHrhYeHp7k8ZZ3Onz+vDh48qM6dO6du3bql4uLiHlmflLL6/+FxdenSRd24cUOFhYUpnU73yPKPs+8qlbH3K6Xs2N9Tyo7tn9nPk927dys/Pz+1d+9edeTIEavnFi1apJo2bWp53ccRHR2t4uPjlVJKJSQkqFOnTqmEhIQMr/vFF18opZTS6/WPfL8bNGigDAZDhuu2cuVKtWrVqgyXtwUPbttvvvkmQ/vq4cOH1e+//66UUlb74IABA1KV3bRpkxo3bpz67rvv1MKFC9XChQvV0qVL1e+//67Gjh37ZA3IZRqlbHyYNOYBeWfOnEmztyCnGY3GDHWvZ0R8fHyal10mWbduHV26dHmiS7HTc/v2bVxcXHB1dU3zm5zIe7Lz/yG75KV990llx/bP7OdJQkICJ0+ezNCVenndqVOn0p0PLC1btmzB2dk5QzNhi0cLDw8nODjYah4vW5MvEhwhhBBCiJTkq7kQQggh8h1JcIQQQgiR70iCI4QQQoh8RxIcIYQQQuQ7kuAIIYQQIt+RBEcIIYQQ+Y4kOEJkgUfdMiAwMJCNGzfmUG3yD9muWS8rt+nZs2dZtWoVJpPJ5qf1F/mPJDgi1yUkZM9UTJmNGxoaytmzZwHz/XKGDRtG69atraZVB9i8eTPHjx8HzDcovXfvXprT1KeklLLc4yWnqCe40WlWxv3pp58AuHXrFrNmzWLixIl069aNtWvXWpWzle2aF+T2Ng0NDWXw4MFMnz6d2bNn069fP4YOHWr7U/uLfCVf3YtK2CYnJw1Vng+jQW0HZk/xZOS4CA4de/KD87l9RTNV/sqVKwQFBVG9enXc3d2ZMmUK77zzTqobjcbFxVm+BVetWpU333yTtm3bPjT2jRs3uHDhAqtXr+bFF1+kSJEimWvMY9A4OBA/cniWxNJWrIjDGwPQf78Mx+EjM7Xujh076Nu3L+7u7hQqVIg1a9bw/fffp7rXmq1s17wgt7epm5sb3333HUuWLGHMmDFUq1aNHTt2yKznIk+RBEfkCVmd3GSGTqfjn3/+Ye3atVy8eBF/f39mzpzJ/Pnz6d27t9Xdd0+fPs1ff/3FnTt3ePfdd5k/fz6XLl2iZs2aaca+e/cu8+fPZ+/evURGRvL8889bbp5oS0xBQei/X4bDGwMytd7FixcJDQ1l2LBhzJs3Dw8PDzp37kzlypWtyj2t2/Vx5IVtunr1amJiYvjhhx+YP38+r7zyCs899xx6vf6hd/QWIidJgiPyhNxKbsDczW8wGIiPj+fdd9/l+PHjzJ07lwsXLrB+/Xp++uknNBoNkydPpmDBgsTFxTFo0CBefPFF3Nzc+OCDDyhYsGCasb29vRk2bBj//vsvBQoUIDw8HA8Pj5xtYBZJSnIy04Nz6NAhIiIi6NevHwA///wzrVq14tKlS5QrV85Szpa2q1r48qMLVWuF5oWhyeXr9kRTrycq5i78/Gaaq2je/iNDr58Xtmm9evX4559/6N+/P1WqVMHX15d169axZMkSpk6dSvny5TPUFiGyk/Qnijwht5IbgNKlS9O6dWucnZ3Zt28fPXv2pHnz5sycOZOyZcuybNkyVq5cSaVKlXBzc+Ps2bP8+OOP3LlzBwAXF5d0xzWYTCY++OADWrduTceOHQkJCWHlypU52bwsZQoKylT5nj17UrFiRS5dusSaNWto2rQp+/bt4+zZs/Ts2RN/f3+Ap367ZkZub9OrV6+yadMmHBwc+Pvvvzl06BALFy5k5MiR/Prrr5LciDxDenBEnpBbyU0SjUbDzZs3cXFxoWrVqpblZcqUISwszPLNePny5VSpUoUpU6YQHBzMzp07CQ0N5Z133kGn01G5cmUmTZpkWX/WrFk0bNiQ0qVLs2zZMgYMGMDmzZuxs7Oje/fu2d4ubcWKmU5KstLJkye5efMmGo2Gffv2MW3aNF5++WXat2/PM888w08//US9evVsartmtKclrfKaAt6QyfUflNvbtHTp0rz22mv4+/vTtGlTdu7cSfXq1Zk8eTIArVu3plWrVk/URiGygiQ4Il9qUDvj4wD0ej3z5s3j5ZdfJjQ0lHnz5vH6668TGxuLyWRCn+KqoTfffJO7d+8SHx/P3bt3adeuHWfPnuWHH35IFfeff/6hbNmyxMbG8uWXX+Lp6UnRokWZNWsWs2bNYvTo0QwZMiRbv/EmDQzOjSTHYDCwYsUK5s6dS+HChenYsSMajQalzFe3lS1blvHjxwO2t11zS17YpufOneP48eNUqFCBmjVrcvjwYSZOnIizszOxsbEYjcYc3SZCpEcSHJHvJA1Yziij0UivXr0oXrw4ANu3bwdg4MCBREZGMnHiREtZd3d3YmJi8PT0pF27dgB4eqb9Wim/xXp7e7N3717q1KkDwOjRowkLC8v2g0HSwODcSHLs7e354osvrJbFxMSkWdbWtmtuyQvbtEqVKlSpUsVSvmjRojg7OwPg6ur6mC0TIutJgiNyXUKCyvQl3RmN6+SkeWQ5Z2dnS3IDWC4LnzJlCt9++y1ubm5W5du1a4eXl5flcYkSJR75GkWKFLFaB8wHhuyk9HrLgODMXtr9qLiax7xSxtHRMdXVPklsZbvmNbm9Td3d3TNYUyFylkYl9W0KIR7LvXv3Uh0QxJOT7Zr1smOb+vv7U69evSyNKURWkARHCCGEEPmOXCYuhBBCiHxHEhwhhBBC5DuS4AghhBAi35EERwghhBD5jiQ4QgghhMh3JMERQgghRL4jCY4QQggh8h1JcIQQQgiR70iCI4QQQoh8RxIcIYQQQuQ7kuAIIYQQIt+RBEcIIYQQ+Y4kOEIIIYTIdyTBEUIIIUS+IwmOEEIIIfIdSXCEEEIIke9IgiOEEEKIfEcSHCGEEELkO5LgCCGEECLfkQRHCCGEEPmOJDhCCCGEyHckwRFCCCFEviMJjhBCCCHyHUlwhBBCCJHvSIIjhABAr9c/ssy8efMyFTM4OBij0UhsbCwmk+lxq/bEMtK2hQsXZqjck0hISCAiIiLD5TNSnyNHjrB///4nqZYQ+ZIkOEI8ZUJDQ1m0aBGQfHA8fvw4X3/99SPXPXDgQIZf5969eyxZsoTOnTszYMAARo8e/dh1ziiTycTUqVMBCAkJYf369dy4cYOxY8c+ct2AgIBHJh8Gg4HDhw+zZs0abt68men6/frrr+luwylTplj+Tkokhw8fzv379x8aMzo6mlOnTmXo9S9dugRAREQEvXv3fmhZg8HAZ599xsWLFzMUW4i8xj63KyCEeDI///wzzz33HBUqVGDRokVcvHjR8jNv3jwaNWpkVV6v1xMTEwNAuXLlmD17NhcuXGDQoEGPfK2kHoXIyEg8PDzSLTd69GgKFy5MUFAQ69atw9HRkfj4+Ey37e+//8bd3Z3nnnuO1atXc+TIEa5fv87Zs2eZMGECnTt3tiqv0WgsCUGxYsXYvXs3u3fvpm7duhlqm1KKiIgIPD090yzzxx9/cPr0adatW8dbb73FkCFD+OKLL/jkk08y1J6//vrLklw+KDo6GoPBgL29PZGRkaxdu5bbt29TsGDBR9YbzL1DSimcnZ3TLbt+/Xqee+45jh49Sp06ddItl5CQwLhx4zhw4ACnT59m/vz5/P333zRr1ozSpUs/uqFC5AHSgyOEjQsPDyckJASAFi1acPv2bUqXLs3u3btTJTeAJdFQSuHi4oJOp+P06dPExsam+xp///03U6dO5eTJkzRp0oTx48enW/bq1at8+eWXvPDCC7z88suEh4dz8uRJ/vvvv0y3LTo62tKD8Nxzz2EymdBoNOzYsSNVcpPUNqUUSilMJhOlSpVi48aNDz3V899//zF79mx2795Nhw4dePvtt9MsFxYWxv79+1m5ciW1atWiWLFiBAYGcuzYMb777jt++OGHh7YlLCwMBweHdJOnuLg4lFIAtG7dmi+//JJChQpx586dNMtfuXKFX375hW+++YZ58+bx8ssvc/To0XRfX6/X07x5cy5duoTBYKBp06bpvuerVq1i+/bt6PV6WrVqRXx8PL///jvbt2/ns88+syTIQuRl0oMjhA27dOkSJ0+eZOPGjbi4uNCgQQOuX7/OJ598gqura6ryR48eZc6cOQQEBBAVFUWjRo04efIk1atXp2jRoum+jru7O76+vphMJjp27PjQUz6hoaFs2rSJX375hcjISHbu3EmZMmXo3r07Op0OR0fHDLUtNDSUAwcOcPjwYTw9PenQoQN37txh0KBBafYeBQcHM2vWLPbu3cvIkSPp27cvu3fvply5cpQrVy7d13F1daV06dIYjUYaNWrEN998k27Ztm3bsnHjRgoUKEClSpWYP38+rq6u/Pjjjyxfvvyh7dm1axcvvvhims9NmzYNf39/evTowaeffsrixYvR6/U0aNAgzfcRwN7ennLlyqGUokCBAixevJiSJUum+/onTpxgyZIljBs3Dh8fH15++WWmT5/Os88+m6rss88+S4kSJbh79y6lS5fm2LFj3L59mw0bNtC6dWscHBwe2lYh8gLpwRHCht24cYO4uDj69u1Lw4YN2bRpE1evXkWn03HixIlU5WvVqkWVKlVo06YNEyZMoGbNmpQtW5aQkBCuXr2a7utUrVqVFStWUKRIEaKioh56MC9fvjxhYWFMnTqVzp078/HHH9O8eXPmzJnD66+/TmRkZIbaduvWLWJiYmjTpg0dOnTg2LFjHDhwADs7Ow4ePJhq0HL58uWpX78+9erVY/bs2VSvXp0qVapw9+5dzp07l+7rPPvss/z444+4u7tTuHBhyxieB7m7u7N582a6du2KXq/njTfeoESJEnz33XcUL16cypUrP7Q9f//9Ny1btkzzuYEDBxIbG8v8+fOpVasWfn5+ODk5sWfPHjQaTZrrlChRgtOnT3Pnzh1eeOEFRowYwd27d9N9/Zo1a6LT6ShZsiR6vZ6oqCiqV6+eZll/f3/at2+Pk5MTixYtYsGCBaxbt46yZcvy0ksvZThJFSI3SYIjhA1r3LgxDRs25OTJk+zcuZMffviBgQMHcuzYMfz9/enWrZvVFTbx8fFs2rSJgwcPsn79ery8vHjmmWfo2bMnXbt2RSmF0Wi0eg2lFGPGjKFv3774+PgwYcIEdu/ezerVq1PVJzIykpkzZ9KkSRM++ugjTCYTS5cu5b///mP+/PmsXLnSqvdl2rRpbNiwIc221apVixYtWnD58mW2bdvGl19+yahRo9i7dy+3bt3ilVdeYfPmzVb1XLduHcHBwSxduhRnZ2fq169PixYtLOOLHmwbmAf31q1bl/r16/Pmm28SHR3N7NmzU5Vbv349w4YN4+DBg3Ts2JF3332Xrl27cuLECRo0aPDQ9ykyMpK7d+9StmzZNJ/fsGEDer2eqVOnEhMTQ8OGDXF0dOTHH3/ExcUFg8GQah1/f3/WrFnD6NGjKV26NKNGjWLQoEHpDkp2dHTE3t6e1157jfbt2/Pyyy9jZ2eX6hRYcHAwxYsX5/bt2zRq1Ih+/foxePBgihQpwpUrVx6ZyAmRV2hU0klfIYTN2b9/P5999hmNGzcmMjKSt956iwMHDhAdHc2wYcO4fv06mzdvZuDAgQAsWLCAgIAAGjVqRM2aNdm2bRt79uwhLCyM4sWLExMTQ9OmTRkzZozlNWbMmIFGo6FZs2aMHDmSChUq8M4777Bw4ULatm1Ljx49rOoUGxvLiRMnOHfuHP/88w/FihXD3t6ehIQEPD096dKlC7Vq1QLg7bffpmHDhgwYMCBV206fPs3EiRMpVqwY3t7etG7dmpiYGP7991+mTZvGvXv3+PnnnxkxYgQAq1ev5tChQxQtWpRXXnmFrVu3sn//fs6dO0f58uWJiYmhUqVKVleL/frrrxw/fpxevXoxbtw4nJ2defvtt/nrr78oVaoU7777LmC+ImzmzJls376duLg4NBoNs2bNYsWKFRiNRsaMGUOVKlXSfZ/Wr19PSEgIw4YNS/Xc7du3+eqrrzhz5gyrV69m8+bNHDlyhL/++ouqVaui0+nQ6XQsWLCAUqVKAXD9+nVGjhzJhx9+yN9//81ff/1FmzZtaNCgAUuXLmX+/Pl4e3tbXmPTpk20bduWiIgIzp8/z8WLF3nttdcAeOutt+jSpQvt27cH4Mcff2TDhg2cPn2awoUL89xzz1GzZk2OHz9OkSJF+PDDD9NtpxB5iYzBEcKGffPNN3Ts2JGhQ4ei1+txdHRk7969lsGqJUqUsCQ3YD6YrVy5Ejs7OxwcHOjYsSPly5cnNDTUqlyS/fv3U6ZMGapUqcLcuXOJjIykZ8+e1K9fn7p16zJr1izLJeDVq1fnypUrrFmzhtKlS1O1alUOHDhAo0aN8PPzA8wDbVP24CxcuDDdti1dupSqVavy2WefYTQacXR0ZMuWLZa2eXl5WZIbgJdffhlvb2+OHz8OQMuWLalXrx6bNm3is88+SxX/3LlzREVFMWzYMKZMmcKtW7cYMmQITZs2pUWLFixZsoTevXszdOhQnnnmGdq3b8+ePXsYNWoUZ86coVmzZpw9e5Zly5YREhLy0ARn27ZtjBw5Ms3nChUqxBdffEGnTp3Q6XRUq1aNF154gaCgIFauXJmqvMFgYOXKlXz77bcsWbKEffv2UbZsWd566y18fHzw8fFh5MiR1K1bl1GjRgHm5M/NzY1ixYrRsGFDLly4wN27d7l48SKzZs1i7dq1lvjPPvssR44coWHDhly9epUxY8YQGxvLt99+axmYnN64ICHyEklwhLBhv/32m+XvpHERaZ2GSeLg4EB0dDRFihSxHJDPnTuX7qXIzz33nOXvxYsXU6dOHRo2bIi9vfmjY+zYsURHR1vKlClThvfff9/yeP/+/VSuXBl3d3cAy+8kI0aMoHHjxql6gQBmzZpl+dvOzi7DbbOzs6N8+fKAeWBtelctValSxbINFi5cSMeOHalbty5OTk6AeVxM3759iY+Px9PTk5o1a+Lj48OWLVsICQmhb9++lClThq1btzJo0CCWLVvGG2+8wUsvvWTZPgA6nY6wsDAqVqyYZj2SysbFxVGwYEHLe+Hm5pZu+aRt/PHHH/P9999z5coVfHx8APNYm59//pnQ0FDLOjExMVSpUoWRI0fy7rvv8uqrr+Lo6MiyZcvQ6/X079/fUrZ27docPXqUP//8k9jYWMaPH8+NGzdYtWqVZZBx37596d69O15eXum+H0LkNklwhMhnypUrx+XLl9N9vn79+ri4uFgeu7q6Wh2QH6ZMmTKpkqH0DsRgPhg/bECqTqfL1CXHpUqVeujrPfPMM1anZlxdXR85j0zK2IUKFbJa5uzsbJlXZsKECQwePJiXXnqJP//8k4sXL1p6ZX755Rfmz5/P1KlTcXR0pFmzZpYYd+7cSTOBe1C/fv2sHhcrVixD9S5ZsiRRUVGpliclPGBORH19ffnmm2/49ttv+eSTT4iOjiY6Ohq9Xs/zzz9vKfvHH39w584dVq1aRWhoKHPnzmXy5MkUKVKEoUOHUqNGDebMmUNwcDBffvllhuooRG6QMThCPOWioqLQ6XRWiUFW+emnn+jQoUOqxCGn6PV6QkNDH3r5dF51+fLldAclCyEeTRIcIYQQQuQ7cpm4eOpnJdXpdNl+k8WcoNPpcrsKOSqt9iqlUr2X+eH/+8G25oc2PUxG/peNRuNj3f5D5C0P7rNZ+b5KgpMP+fv78/LLL9OzZ88073szb9489u7da3nct29fOnbsSJs2bazOqc+aNYuuXbvy2muvcfbs2Rype2Zlpq0JCQn4+fnRtGlTmjdvTt26dUlISGDnzp20atWKNm3a0KVLlzx7c8H02rpr1y66devGO++8Q8eOHS3zmgQHB9OtWzd69epldSPHFStW0LlzZ3r06MGePXtyvB0Zldn2jh8/nv79+/Paa68xceJEAO7fv0/z5s1p164d7du3t5o3Jy/JbFvff/99OnbsiJ+fn9Wg7vz63vbs2ZMmTZrg5+dHtWrVuHnzJoGBgbRs2ZK2bdvSsWNHDh8+nFvNeaj02nrx4kXefPNNunXrZjVxZnrlbfnzOK226nQ6hg4dysCBA3nllVcs5bP0fVUiX4mOjlatW7dW165dUyaTSY0ZM0YFBARYlRk+fLi6cuWKUkqpuLg41adPn1RxNm/erIYNG6ZMJpMKDQ1VAwYMyJH6Z0Zm25rS7Nmz1ZgxY5RSSs2cOVPt27cvR+r8uB7W1jlz5qiEhASllFJTp05VO3bsUEajUXXu3FmdPHlSKWVu47Zt29Tx48dVjx49VEJCgoqKilJ9+vRRer0+19qVnsy2NyAgQP35559KKaWMRqPy8/NTSim1Z88eNWvWrFxpQ0Zltq1KKdW9e/dUcfLre5vSmjVrVJ8+fZTJZFK//fabWrVqVU5XP1Me1tagoCCVkJCgTpw4YXk/0ytv65/HabV19+7d6sCBA0oppcLDwy3Ls/J9lR6cfGbbtm20bduWEiVKoNFoePnll9m3b59VmZCQEMuEYefPn0/zPj2rV69m9OjRaDQaihYtire3N+Hh4TnShozKbFuTxMbG8tdffzFu3DgAzp49S4UKFXKs3o/jYW0dPnw4jo6OXLhwgYCAABo3bszRo0epVKmS5T5D3bp1Y9++faxatYqRI0fi6OiIm5sb9erVIzAwMDeblqbMtrdq1ap06NABME9m2L17d8D83qZ3eXZekdm23r17N9Xl9kC+fW+TKKX44YcfmD59OhqNxubf2woVKnDjxg0mT57MkCFDHlre1j+P02pr06ZNadiwIQBfffWVZbLPrHxfJcHJZ4KCgqhbt67lsU6ns7okOD4+HicnJ8v9bSpXrsyrr76K0WgkLi6O33//HTDP3FqmTBmrOHltcq/MtjXJX3/9Rbt27SwHibFjx6LRaNDpdGzevPmh9/PJLY9q6507dxg7dixTp07FwcEh3fKPipNXZLa9STZt2kRgYKDlw7JTp07UqFEDo9HImTNnOHbsWM41IoMy29ZChQoxfPhwdDodSil+/fXXDMXJKx73vT148CDVq1enRIkSAAwYMAAfHx90Oh179uzh0qVLOdeIDHpYW1etWkWXLl0IDAxkw4YNGAyGdMvb+udxWm1NsmjRIgoUKECbNm2ArH1fJcHJZzw9Pbl586bl8ZEjR6yy4QsXLlg9dnZ2pkaNGtjZ2eHi4sKyZcsA0Gg0xMbGAmAymbh9+7ZlArS8IrNtTfLHH3/w6quvWh5XrFiRIkWK4OjoSEREBFu2bMneij+Gh7U1ISGBUaNG8fHHH1O6dOk0y/v7+1OxYkU8PT25ceOGZfnZs2ctB4y8JLPtBTh+/Di//PILX3zxhSWp9fHxoWzZstjZ2eHs7Mz333+fsw3JgMy2VavV8r///Q9HR0c0Gg2///47sbGx+fq9Bfj999+t9tvSpUtTvHhxHB0dMZlMrFq1KmcakAkPa2upUqX466+/2LJlC5cvX+bgwYPplrf1z+O02grwzz//cPz4ccaOHWtZLyvfV0lw8pmWLVvy448/Eh4eTnBwMIcOHbLKqs+dO2d1s7zly5ezfft2AA4cOGC5OuOll15i3rx5KKVYtmwZTZs2zdmGZEBm2wrm+/7o9XrLJGiRkZFMmDCBe/fuERcXx86dOy0Tu+UlD2vr+PHj6d27N/Xq1bOUb9y4MZs3byYkJITQ0FDWrl1LixYt8PPzY86cORiNRjZv3kzJkiXzRXtv3brF9OnTmTt3rtU320mTJhEUFITJZGLr1q15skcjs23duHEja9euxWQyceHCBa5duwaQb99bMM9ndPLkSWrXrm1Z9uGHH3Ljxg30ej3btm2zuff2ueeew9fXlxIlSvD8888TERGRbnlb/zxOq62BgYH89NNPzJw50zJTOWTx+5olI3lEnrJ27VrVqVMn9cYbb6hLly5ZPTd16lTLwC6llIqIiFDDhw9Xfn5+qlevXurgwYNKKaViY2PVmDFjVJs2bdQXX3yhjEZjTjYhwzLTVqWUWrVqlfruu++slm3evFl16NBBdejQQU2ePFnFx8dnd7UfS1ptPXv2rKpVq5by8/NTfn5+qk2bNmr16tVKKaV27typunbtqnr27KlOnDihlFLKYDCozz//XLVt21Z99NFHKi4uLrea80iZae/06dNVw4YNLcs7deqkQkNDVWBgoOrRo4dq166dGjp0qLp27VruNiodmWlrQkKCGj9+vGrdurXq1q2b2rJli1Iq/763Sim1d+9eNX78eKsYBw4cUF26dFHt27dXY8eOVeHh4TncioxJq60XL15UN2/eVEopFRoaql566SUVFBSUbnlb/jxOr63Dhw9XjRs3trzf3bp1UzqdLkvfV0lwngJdunRR+/fvV0qZPygiIyNzuUbZ51FtvXr1qmVns3VJbc3LB7Ks9LD2mkymfLUd5L21dvfuXXXhwoUcrlX26NKli9q9e7caPXq06ty5s2rTpo1asWJFblcrWzysrXq93nLFXHaRmYyfAhEREXh4eKQabJsfSVvzr6epvU9TW+Hpaq+0NedIgiOEEEKIfEcGGQshhBAi35EERwghhBD5jiQ4QgghhMh3JMF5SkVGRjJ37lwiIyNzuyo54mlqr7Q1/3qa2vs0tRWervbmVFslwXlKRUZGMm/evKdiZ4Knq73S1vzraWrv09RWeLram1NtlQRHCCGEEPmOJDhCCCGEyHckwRFCCCFEvmOf2xWwRffv32fjxo1UqFABBweH3K7OY7l9+zYAp06d4tatW7lcm+z3NLVX2pp/PU3tfZraCk9Xe7OyrXq9nuDgYDp06EDBggWtnpOZjB/Dzz//zOTJk3O7GkIIIYTAfCf6Pn36WC2THpzHUL58ecC8QatWrZrLtRFCCCGeToGBgUyePNlyXE5JEpzH4OjoCEDVqlWpV69eLtdGCCGEeLolHZdTkkHGQgghhMh3JMERQgghRL4jCY4QQggh8h1JcITIRkqnz+0qAKD0eaMeQgiRU2SQ8RPoPTgcHMMeWa5BbQdmT/Fk5LgIDh3L+gONxM+78c/tK0r8yOGWx9qKFXF4YwD675dhCgrK8rqkF9959twsfy0hhMjLpAcnm+Xng7fEz3x8U1AQ+u+X4fDGALQVK2Z5fbI7vhBC2ApJcLJRXju4Svy8EV+SHCGEyH6S4GSTvHpwlfh5I74kOUIIkb0kwckGef3gKvHzRvycTHKEEOJpIwlOFrOVg6vEzxvxcyrJEUKIp40kOFnI1g6uEj9vxM+JJEcIIZ42kuBkEVs9uEr8vBFfxswIIUTWkgQnC9j6wVXi5278JJLkCCFE1pEE5wnZ+sFV4udu/AdJkiOEEFlDEpwnUK2yvU0fXCV+7sZPjyQ5Qgjx5CTBeQIjB7rZ7MFV4udu/EeRJEcIIZ6MJDhPYPbiaJs8uEr83I2fUZLkCCHE45ME5wkEnDdkeUxbP3hL/KwlSY4QQjyePJngrFixgpiYGAD0ej1GozGXa5QzbP3gnaXxTdGpHttU/bOQJDlCCJF59o+74pYtW9BqtbRu3Zr4+HicnZ0fuU54eDienp5otWnnVQsWLCA6OpqrV69y8OBBChQowM6dO/Hz8+Ozzz573KpaiYuLw8nJKd065BZbP3hnaXxjKMT/Dg41wb4WaLQUcf2Ot3r0YPD7zpwILAKR74MxBDCCxgsKLk47VuRY8PjC/LfpPkR9CioWnFqAS08wBEDMt3gWLICvazFGjhuddv1jF4Fjc7CvaE6+oieAxhVMkeA+DbQFksvqj0H0dHCbABTFpBRTzwYQEBFJKVdXptV6lj+uX6eimxs1CxZk7vkLDK9c6aGbJGWSo/9+mUzeJ4QQj/DQo/y1a9eoUqUKgwcPTvXcwYMHWbp0KR07dqRBgwYEBASkG2fp0qW0bNmSfv36cfr06XTLHT9+nA8++IC5c+cyZ84c3nrrLapXr87w4cMz0SQzvV5P3759Uy1fuXIlfn5++Pn50b17dyIiItDr9Xz++eeZfo2sYlPJR07Ej18Lji9AwnbQaChT5G9GvNOMzz7fxonTkeYyHl9DoVXg+BI4Nk47jikGsEt+HPstuHQHz0VgDAbjbYhfR90mX7Nl4xL+23+bQ0fupR3LeAW0vua/ddvA+TVwn2Kup/5IcjkVC9FTwe0DsK8OwMYbN3DW2vHL842o51WIf26FciEqmo03bnLw7l0quLllbLtc+hc1rTYO1z/H0e0vtHZXMraeEEI8hR7ZjdGlSxe+++47q2XBwcGEh4fz9ddf4+bmxvTp06lWrVq6Mfr374+7uzt//PEHNWvWxGBIe+xK7dq1Wbt2LadPn+bo0aPs3LmTiRMn4u3tnclmwaVLl/D19U21/PTp0yxdupStW7eyevVqPD09cXBwwM3NjSNHjqQRKXvZXPKR3fGVAQxnwKE6qEga1PHEy3UX6/+ty43rF8GuZIqycebeEpfeaccyXgb7MolldWC8CY5NQKMBJz8wnKawlz3vvhXN8I9CCQ+/DxqHtGOZokGblIjYg+m6ua6mO9brxG8Gp06JPU8aAP6+FcqgCuUB6FqqJEfCwzkbEUkDLy+23LxFa1+fR24Wrd0V7B0OozFGAgqNMRJ7J39JcoQQIh2PdZ4m6RTS5MmTeeaZZ1iwYEGa5cLCwjh+/DibNm3iwoULjBgxgjZt2tCmTRsuXbqUqnybNm04ceIEWq0WFxcXqlatyubNmx+nipw7d47KlSunWh4cHEypUqVSLa9Vq1aOJzg2l3zkRHz9EfOpKWXC0wNGvXWXKzcK4X9SCyrGuqxuj/lUU3pJifEy2CUmOKbbYFc2+TllpHLFAsz7ZghDR0zGf3c/QAeaNE61KgNoUpzNdWoO+tMQ8TbErwP7Z1LU6T9z71DM1xT3WG5ukknh6Wiuo8Fkwlmr5YdGDSjp6kIFNzfsM3C61N7+JBqN9Vg0jTJg73QMrfYaGk0kYHpkHCGEeFo81hichg0b0rZtW7p06YLRaCQwMDDNcitWrODatWtcunSJ6tWrYzKZ2Lx5c7rjXwICAhgxYgSffvopRYsWpUyZMjRq1Ohxqsj58+epX79+quWff/45ERERuLq6sn79ejp06ICLiwv379+nQIECaUTKHjaZfOREfMNpiP8TR9M/xBNBr9deRuEEmu5gugfhr0ChNeZEJGE7uI1OXjfyXXB5HRzqmB8bL4NTS/PfGjdzb0uiEkUCmfxxV75e5EWkZhbYTzf37gDEbwLDKXD7MDFOiHXPkcYV3MdBwhYwXACtR4rntOA+kQa1HTDdfxej0YjOyRGjUthpNJyKiKB04v/ZumvXebVUKVrv3MXsOrWp5pEizoM0sWkvVgk4OO0FQCkNSrmhlAdGfQ2UKggYAPXQTS6EEPnRY/XguLi4EB4ezunTp7l16xYRERFplhs1ahRffPEFBoOB119/nbp161olN3fu3LEq/8wzz7BkyRIuXbpEs2bNKFmyJHPnzn2cKnL+/HmqVKmSann16tUpWLAgjo6OXLlyhYMHDwKwd+9e6tWr91ivlVk2m3zkRHzXN6nffAuzZ8+gaKk3UV7bodBm8JgHTu3Aa6M5uVEJoCJAm+L0pdtE86mhJMarYFfa/LfWE1QkGM5Rq9o9yhS7wNeLvMz1j18H2CcnOE6twXVoijiXrXt/APRnIH4juA6yXq5x49nKN5j2sT0B5yOws7OjTqtWrImLJ95o5OfLV3mxaBEux8Tg7ejI1dhYmhQuwo24uIdvF+Wa9mKtO7z5B4ZCnTAaqqKUR2Jvjjmp0dpdxcllHeqe+VSWurgPdWC5+XdUGEpJ8iOEyJ8eqwfn8uXLFCpUiI0bNzJjxgw+/fTTdMt+//33dOnSBUdHR+zskgd8Go1Gli5dytixYy3LfHx8uHPnDkuXLqVYsWIsWrSIZs2apRs7MDAQk8lE9erVUz0XGhqKj4/12IaLFy+yevVqhg0bhl6vZ9++fTRp0oSLFy8SHh6eZkKU1Ww6+cjB+L0GnCPkVhFwwjyWxXQD7FKMqdKfAIda1ivHzADn7uZTXGAeo6NxSX6+wHu48TWaaEeiXMZw6pgeYhaAiocC7yWX0/1n7sEpMNL82HjZ+rUS/jX33rhPAY2TVRVq1H4Tw/3JdOgUTxz9ARjg6MAErR1rjx1nSOlSFHZyYkFQMCMqVcLZTou9RsMLRYs8dLsYDDXNY3BSnKZSyg5D/DOw5TAOQ2eh/34ZxgeurlImLwz6Z7EvmNgDdWoD7F+WXMClIKpoJShSGYpWMv8UqQReZdAkjiESQghb9FgJjouLCzdu3KBp06bodDr27t2Lt7c35cqVsyoXFBREUFAQX3zxBSdPnuSPP/6gRYsWaDQajhw5QlhYmKVsbGws3377Le+//z46nY5ff/2VtWvX8vvvv6dbj23bthEbG5sqwYmMjMTd3T1V+fLly1OiRAm6deuGo6MjLVu2pGHDhowePZoJEyY8zqbIlPySfORE/EvXK4N9il4LbUlwSPGe2lcCu+LWAeyrgdYr+bFLD+v49asye8oq6/q79jOfckpJWxTsUly27VAX7FL8bzs+Zx77k1L0dCpUasji2d0ZOW4JcWHJ26dASAizalTH4esZlku8mxYpbBmX86jkBsBkLIMB81gcNLGgXDEYamIyloGHXEKuVEGMhoI4aBO/XHT8HJoNg7AL5p/b582/A7aC/4rE7egMky6Bxg516GfQxaBp8rY5nsmEJo9NsSCEEGl5aIJjZ2fHoUOH6Nq1K2vWrEGr1RIREcGkSZNo27YtnTt3BsyDg+fPn0/lypUZNCi5y/7y5cuW+Wtq1qyJr68vnTp1IjY2FldXV/r06WMp6+TkxOjRyeMpvv32W/r06YOra9pd80ajkfv37zNq1Kg0652yHvHx8bRv354lS5bQp08fq9cF+Prrrx+2GbJEfko+ciS+vXWyjF0RIEUioC0EFLIu49LL+nGKy8fTrf+DyQ2AQw3zj+VxTevn01in7nPv8c2UoulunwfnsXkx9as+kslYBp2xTNrPZXCeHI1WCwVLmn8qv2T1nIq5Z054om6jSUqILuyE6NuQmOAw3w+VEJW6x6doZTTOqb9UCCFEbtGop+QkfHh4OIUKFXp0wQzw9/end+/e4D4PHGs9sny+Sz4kfobjn9tXlPiRyfM4aStWzNbJ+tKL7zz78cayASiT0ZLwqO2z4OZpc6/PnWAwpmivh6854anWFk3jt8zlY+6Cq5ec7hJCZIuk4/GKFStSjaN97JmMbU1WJTeZlZ8P3hI/8/Gze0bi7Ihv6c0BNM2Te0yV0QD3rkDYebiddMrrAtwPMT9vMsK0/0GTwdDmE5Q+DvYtSe798SpjFVsIIbLSU5Pg5Ia8dnCV+Hkjvi0mOWnR2NlDkQrmH9qmUREDtPsMSjxrfnz3Evw1Ofl5eydU4fKJp7jMp7koUgmKVETj8OhbvwghxMNIgpNN8urBVeLnjfg5neTkBo29Ezz/ZvJj3+qoTy8k9vgEmX+HXYAbJ+H0RlCJExV2mwP1eqLuXISDP8Bzb6LxKi0DnIUQmSIJTjbI6wdXiZ834udkkpNXaFw8oUx9808KSh9vHtNzOwhKJ55Hvx1kvqS9XuLg8UM/orZ9Zd3jk/Tbo5iM8xFCWJEEJ4vZysFV4ueN+DmV5DgOH5mlcbOaxsEZij1j/klaVq01atJlIDFxKVwBqrYy9/yc+APiU0ww6uSGSkp8OkxB41oQFR8FDi7mU2lCiKeO7PlZyNYOrhI/b8TPiSTHVlkNcK7YFCo2BTDPwBwdljifz/nkOX0u7genxFuubP0cTvyOGh+ARqNBnd4IhgTzIOciFdA4pj0FhRAif5AEJ4vY6sFV4ueN+Dk1MDi/0Gg04O5j/qnQJO1C1f2gcIXkU1e758PV5BvqqoKlkufySRrgXLQymgJeaccTQtgUSXCygK0fXCV+7sZPIklO1tJUehEqvZi8YNAf5iu5rHp9LsCl/aBPvBdYqbow9C8A1I5voEglNDXamx8rJeN8hLAhkuA8IVs/uEr83I3/IElyso/G3gl8qpp/UlAmE9y/Zk52EhMYpRQc+hmqtYYa7VEGHUx5BuVdxtLTQ5GK5t/e5dDYO+ZGk4QQDyEJzhOoVtnepg+uEj9346dHkpycpdFqwau0+SdpmUYDY/1RSTM1G+Khbg9zEnT5IBxfmxxAa4fyLmdOfOr3Ng+ONhlBH4fGyS2HWyOESCIJzhMYOdDNZg+uEj934z+KJDl5g8bOfENUjbMHdJxiWa4SouF2cPLNSpNOd0XfMRe4fQFmvYDqswxNjQ6ou5fhwo7kWZzdisjpLiGymSQ4T2D24mgCLtvewVXi5278jJIkJ+/SOLlByVrmn7Q4e4Lfx1A8cRbnSwfgj7HJz7sURBWtlOKmpYm/C5aSyQyFyCKS4DyBgPMGyOJT77Z+8Jb4WUuSHNuk8SwGL72bvKDOq1CxSeLg5qDknp+AreC/IrnciO1QvAYqeI85KWo2FI2DiwxwFuIxPDV3E89KD7t7qRApKZ0ejaNDblcDpdejccj9eojUVMy95JuV1n7FnND8OxN2zoaJF9Fo7VB/jIWgXcmzOFt6fiqZT58J8ZSSu4kLkUvyQnIDSHKTh2kKeEGBhlC2YfKyFu+hmg1Nnuiw5P8g5o45CTq/HYzJvYvK3cd8iqvEs2jafWZelhANjgWk10c81STBEUKIPEhj75T8d73XoN5rACijAe5dSez1STGfT8jx5JWX9TTP6DzgN/M6h3+BAt7mXh+vMlYzRAuRX0mCI4QQNkRjZw9FKph/qrdJu1D93pA4N48ymWD9h+ZL3QHsHFGFK6SexblIBTQOLjnUCiGynyQ4QgiRz2gSe3vAPM+P+uRU8jifpF6fGyfh9EZQJnPBhv2gy1fmHqKtU+DZTmhK1cmlFgjx5CTBEUKIfE7j4gml65l/UlD6eLgTbE54CpUyL4y6BfuWQtEqUKoO6vop+L5n8qXsRVL0/HgUk3E+Is+SBEcIIZ5SGgdnKPaM+SdpWcGSqEmXwWQ0L3BwgiotzT1Ax3+H+IjkAE5uqKSE54UhaHyrowwJoLEzn0oTIhfJf6AQQggrGq0dJA5E1hStDN1nA4n36IoOSz7VdTvI/DvoP3hugHnlUxtgzbuo9/ag8S6HunrEfJPTopXN43wcC+RWs8RTRhIcIYQQGaLRaMDdx/xToUnahYpWgSaDwbOE+fHxtbBvieVpVbBU6lNdRSuhKeCdAy0QTxNJcIQQQmQZTYmaUKJm8oJ2n0LD11PcsytxkPOl/aCPM5fR2qMmX0Fj54A68TvER6Fp+HruNEDkG5LgiCyn9AY0DnnjX0tm8BUid2nsncCnqvknBWUywf1r5tNcUaGWG5ty4neICjMnRYBa1AXiIx+YwbkyeJdDY5/F98oR+UreOArZqN6Dw8Ex7KFlbP3eSY8T/9y+oqjoqGy7d5K2YsUM35vJefbcLH99IcST02i14FXa/JNy+es/onQxyQvK1Idrx+HSQTi+Lnm51h7lXdZ8qqtKC0uPj9LHyXw+ApAEJ1vlxeQjp+Jn5w0i5QaUQuRvKQcia/w+tvytEqLhdnDyaa6kOX3cCpufN5lgcnVUk7fRtP4QZdCB/y+JA5wrglsRuaz9KSIJTjbJy8lHTsTP7iREkhwhnj4aJzcoWcv8k4LlntEmPbw0EkrVNT++ewn+GJNc0KUg6sFTXUUqQqHS5h4lka9IgpMN8nrykVPxJckRQuSEpF4Zjb0TvPRu8hNFK8OHx1Lcsyvxd+BW8F+RXK7r19CgLyo8BI78BvV6oilYMmcbIbKcJDhZzFaSj5yKL0mOECK3aDQaKFjC/FP5JavnVMy95NtXVGhsXnjzLPz7FVT3g4IlUUdWwvZZac7irHH2yIUWicyQBCcL2VrykVPxJckRQuQ1mgJeUKAhlG2YvKy6H2riJUi6k7u7DxSvYU6Czu8Ao85SVrn7JCc+rcaicS1knsXZzlHG+eQRkuBkEVtNPnIqviQ5QghboHF0Tf678kuWnh9lNED41cTTXUmnvC7AsbXQ7jPzClumwonfUR+fRKPRoM5vB4POnAgVKi23r8hhsrWzgK0nH9kdP4kkOUIIW6Wxs4fC5c0/1dtYliulkntsKjSBAl7Jj7fPgssHzX/bOaIKV0g8xZU0wLmS+fYVcll7tpAE5wnZevKRU8lNkpxOcoQQIjulPB2lqdYaqrVOfrLfz9b37Aq7ADdOwumNoEzmMiVqwvBtAKi9S6BwOTRVWuRkE/ItSXCeQLXK9jadfOR0cpMkJ5McIYTILRoXT/NEhWXqWy1X+ni4E2xOfDQpLk/fNQeq+UGVFiiTEb6sD4VKW92zi6KVwaOYjPPJAElwnsDIgW42m3zkVnKTJKeSHMfhI7M0rhBCPCmNgzMUe8b8k9KHx5Lvz6WLhYovmHt+TvwB8RHJ5RwLJM7nUwlqv4KmcnPzJIfKJON8UpAt8QRmL44m4LLtJR+5ndwkyYkkRwghbIVGawdObua/nd2h2zdA4kSG0bet5/IJOw/Be6BUHaiMeVLDb15AvbYQTY0OqIgbcHFf4qXtFa1mh35aSILzBALOGyCL7/Vm68lNg9qZu7GlDAwWQoiH02g04F7U/JM0Z08iyyzODs7Q9J3km5pe3Au/DU0uV7Ck9Q1LE+f00STe5iI/kgQnD8kzyY0yAFrrc8OpyhgBBZrkf6Gk+Jn1OEmO3mTCQaZWF0I85SyzOBcsAW3GJT/xbGcoXjP1LM6XDiSfBgPUsL/RlPwf6sphuHoEGr5udam8LXtqEpw1a9bQvHlzvLy8MBgMANjb553m54nkRsVC1CeAPZhugfs0SNgOhmOgKQBukwETRH8KKsFcpsAH4FDTKv5P8wsBMPzIUebWrWP1EukuI/UNOk9HRDDvfBAxBgPja1Snsrs7f16/wZqQawDU8PTgg2pVLXF2h91mxZUrxBtNfPW/mhR1dgbg559/ZsGCBezdu5eIiAh++uknBgwYwNWrV7l48SLt2rXLkm0shBB5hcbeEXyqmH9SUCYT3L+WOIvzefO9uAAu7DRf1v78m+Zyf02CoN2JPT2J9+wqWhm8y5lj24AsPcLHxsbi6vrkmV9ISAhnzpyhUqVKVKhQ4aFlo6KicHJywtEx7Q3+66+/cvXqVcLCwti5cyfu7u4cOnSI8uXLs3jx4ieua1bIE8kNgP4ouI4A+3IQ/wcYToNjU3DpC/f7mBMg42VwfgUcaoPuIOiP0qBB3VTxow0G7B7oAXrUsgd7cgocP8G39eqwKPgix8LDqezuzu2EBL5vWB+tRkO/A4esYpVwdWFh/XpMOHWac1FRFHV25ujRo/z666/88ssvAPz+++9otVoOHTrEnj17eO+9955s4wohhA3RaLXgVdr8k+JydE3LD1DPD0RjlzjMoFBpKOBtnsfn+NrkAFp7lHdZc+JT7Bk0rcw3M1VGQ7oDnNWxtbB1Kty/br5tht8naGq/kl1NtHhognPt2jVatGjBSy+9xHfffWdZfv36dWbOnMnXX39tVX7mzJns3bsXk8lE0aJF+emnnzJVmffee4/g4GAiIyN55ZVXqFSpUrpl169fz8KFCzEajXz00Ue8+OKLaZbbtWuXVd2joqIYNGgQ48Yld+Xp9Xq++uorPv7440zVNyvkmeQGwLGJ+bcpBhJ2gMc0wAliZ5sTHW0B0CaO+lcGiF9NrYYfpxn/YnQ05d2sB7VlZFnKJKfC98vYf/Age27fYV7d2gAMKF8OgFVXQ2jl62MVq4KbG5tv3ORGXByNvL0BWLZsGRMmTKB06dIA1K9fn/fee4/WrVvj7u6eJQm5EELkBxrXgsl/N+oPjfoDoBKi4XZw4mmuxDl9bl+A8BBITHBY1gPl6Iqmn/m4r079Ca5e5rKbPgVDvLnc/Wuw7j0UZHuS88genC5dujB9+nSrZefPn6dUqVKpyp47d47169en25vyKB9//DGFCxemZ8+eDBs27KFlO3XqxNatW/noo48oVaoUBoMhzVNO//vf/9i/fz/R0dGUL1+erVu3MmnSJMqUKWMp4+DggJubG0eOHKFu3bqPVffHkaeSm5RipoNrf1AKokaC4QTYVQFjGNgVNZeJ/Zbyldvw3YzKaca/GB1DuQIPJjMZW5aU5Pxo58BM/6O429uz584dOhQvDsCp+xHsvn2bOXVqW633RUAgv1y5ShEnJ46Gh1O7UCEOH/a3JNtdu3alefPmbN26lRkzZtCvX7+MbxMhhHhKaZzcoGQt808KymRKflCjPST2/qiIm7DizfQD6uPMPTrZnOA81ijNc+fOUblyZatlSimio6MfO7kBKFzYPJrbZDJx9+5dYmNjU5W5d+8eJ0+eZOvWrRw/fpxJkybRoUMHWrVqxaFDh1KV7969O3/88QeVK1cmLCyMxo0bs2XLFvR66wNyrVq1OHLkyGPXPbPybHIT9yvYVzWfgtI4gXN3KLQFHGpCwiZzmYQdFC1sZMXSnsnxI981n+JKdDE6mgpublahM7JswqnTbLh+HVNQEBUvBbP7761807E9C4MuAhCh0zPz3DkmPVsD7QMTXTX09uK/Fi/xQdUqLA6+SLzRSMOGDZkwYQIzZ87k559/BiA8PByDwcCWLVvo2bNnxreNEEI8pZTJhIoKQ107gTrzF2r/Urh32fxc8B7Y8x2UrmcuHLDl0QHvX8++yiZ6rDE458+fp1WrVlbLNBoNEydOJCYmBkdHR1avXk3Pnj3RZuJKl927d/PCCy9w9+5d+vfvz9ixY2nSpIlVmY0bN+Lv7094eDglS5bk7t27rFu3Lt3E6tixY3zwwQfMnz+fiIgIatWqRd26dXFwsL6c+f79+xR4oCchu+TZ5EZ/CgwXwH2C+bHGHpyamf92bAq6/8B4HU+nv1i7cqF1fLeJoElOVC7FxFC2gPXpn4wsG121Ci52dgA00unQrv+DRp+Mw3X3HpRSTDpzhjHVquKVxvv9YlFz71JrXx++v3QJN3t7QkNDSUhIICgoiEKFzIOff/75Z3r16sWCBQuoWrUqer0+1f+DEEI8LZTJBNFh4OCCxsUTde8qHFgGETch4ob5d+RNMD5wPOk+13xvrgLeULIOaBNTiurtoEQt8CwO89tCRBrJTMES2d6ux0pwLl++TNmyZVMtr1mzpuXvw4cPU79+/YeOo3nQ5s2beeGFFyhWrJjl2/aDXn/9dV5//XX69u3LoEGD2Ldvn1Vyc+fOHUtPEECdOnX45Zdf2LZtG5988gnFixfnww8/ZPPmzVZx9+7dy5tvPqRLLYvk2eQGIO4XMF6A8N7mx04twaW/+e/4TeDwP3zc12GMC6JV647ExyvzuJ0C70DMDHNvj4P5fyDWaMT1gVOGGVn2XVAwNQsWpLqHBwXs7fAOCmL3pIkUKFmSQA9P9t25S1B0NADOdnZ836A+rvb2XIiKooSLC6729vx+/Trl3dzQajT06tWL7t274+zszLRp0wgPD+fOnTuULl2awYMHExcXJ8mNECLfUkYDRIUmJiopf25C1ZZo6vaEqFsw7X/Q5Sto2A/iI2HfUvAsBh7FoWwDc7KS9NizOBQsDq7msY4a32rwWvJYV42HD3iYx0iqNuNg3XtWl6bj4AJ+n2R72zOd4Oh0Ouzs7LBL/Jad5MCBA5w6dYp+/fpx9+5dTpw4gXPiZbpJAgMDMZlMVK9ePc3YoaGhKKXQ6/XcuXMHrVaLo6Mjbg+c1ti8eTNVqlShWLFiqeqxaNEiq8HCBQsWJCwsjMWLF1O1alU2b95Ms2bNrNa5ePEi4eHhVKlifTldVsvTyQ2A+yRAkzy3jf4kRA4BUyTYV6Neww58M7kQ744biv9JLaS8Aa59NdB6WR6+nkYCnJFlldzdKe7ijI+zE5+cPM2V2BjsNVomtm3Ds2PGsNOrEE5XrljKK6V4Zc8+Jj77DO8fP0FYfAIeDvZMrfksAJ07d6Zz586W8sHBwbzxxhsAVuOwhBDC1iiTyXxVFKBOrgd3HzTlGqF0sbC4qzmRiQpLvrFnEgcXc5KSdErJrSi8/AWUe9782Lc6TL6aJfe70tR+BQV57yqqtFy8eDHNS7cbNmzI/v376dixIwUKFOCtt95KNRB527ZtxMbGpkpwli1bxrFjxzh16hQtWrTg5s2btGrVypLsNGvWjG+//RYw99Bs2rSJWbNmERkZycmTJ7l06RIODg6EhIRw6tQpS1yj0ci3335Lr169KFasGL/88gvLly9PdXXX/PnzmTBhQmY3Rabk+eQGQPNAT4ZDTfBckCq+/8k0Tju69LJ62KxokVRFMrKsS8nkbssZtVMMaIu4j/77ZXgMettqMkCNRsP3Devj4eDAgnqPHiD+qGkHhBAiL1D6ePNpIctpogd6XyJumG/TkHjVEps+M89yXK6ROYEp4G2e1dizWGLvS4oeGBdP67ug29lDozeSH2fxJKqa2q9k+4DitDw0wbGzs+PQoUN07dqVNWvWoNVqKVy4MN26dbOUuXnzJr169WLDhg2MGjWKUaNGpRnLaDRy//79NJ//3//+R926dRk7diyFChXC1dXVsvGVUsTFJXdtnT9/nkmTJuHo6EjhwoVp3rw53bt3t8yH07p18q3qtVotQ4cOtYwDunPnDs2bN8fHx/ry4gcvd89qNpHcZHF8bcWK2XqX8JRJjoecYhJC2BClizUnKXH30ZQ2fzFTu781P/ZLPAMxp7n5buMpOXuaTw15FocSNc3jXJK8/Yc5qSFxduP+K3KgJXmbRlluZPH4wsPDLQM4nwb+/v707t0b3OeBY62Hls2LyUd2xz+3rygqOirb7i2lrVgxw7d1cJ49N8tfXwghHkVdOwG3zqbd+xJ331zItRCaCefM5de9D1G30fT70fz42GowGZN7Xzx8zZdrCytJx+MVK1ZQr149q+eyZCbjpym5yYy8mHzkVPzsvku43KBTCJGTlFLmwbcpTxFFXIdmw9E4uqJ2zYP/FsAnp809KPuXwpGV5pXdCpuTlEJloNxzVqeLlFJoNBo0Xa3PJGhqd8+FVuYveedmTPlMXk4+ciJ+dichkuQIIbKS0sXAnUtQpCIaB2dU0G44uhoiUyQ0ugfmZtNooHZ386XSRStDzc5g1IG9E7T8AFq8b+55sXfKnUY95STByQZ5PfnIqfiS5AghcpsymSD2bvqDdf0+QlOmAZzfAT8PgOH/QolnzbchCP7P3NPiWw0qtzD/XbBE8sBddx/LvZs01VpDteQxoJpCqWf7FzlLEpwsZivJR07FlyRHCJFdlMkERp25xyUuwtzjUrEJGp+qqKv+sHIwRNwy96qkpLUHj2LmRMWQ+DlXuj70XgqFSgKgqd8b6vfO4RaJrCQJThayteQjp+JLkiOEyCxlMpknoLMa8/LA35G3zKeBWrwP+nj482PoPN18eXQBb3PSkvIS6aTfBYqkuhRa4+EDz3bMpdaK7CAJThax1eQjp+JLkiOESKKMenNyEnED7BzRlDLfOFetHAxlG6Jp9Aboos2z66bk4JI8l0v5580JS9lG5ufcisC4s8mXSnuXg54LcrBVIq+RBCcL2Hrykd3xk0iSI0T+p0xGNFrzDPPq1J/mGzLeT+pxSex9ib4NSTOUVG4OAxKvNoq+DfFR5r+d3KHr1+Dhm9wL41Iw3dl1NVqt+WolIRJJgvOEbD35yKnkJklOJzlCiKyjdLHJPS9Jp4tMBjQt3jc//0NfSIiGQb+bV/jnCwg7D84eyaeHfJ+xnl3Xu6wlvuattcl/azTQoG9ONk/kM5LgPIFqle1tOvnI6eQmSU4mOUKIjFMRNyH8CprE0z5q7xK4sCM5oYkNT72SV2nzGBiAan7WA3oH/Ga+LYBMUCdygSQ4T2DkQDebTT5yK7lJklNJjuPwkVkaVwhbo5SChKg0Bumm+P3ORjTO7rBvMexZhJoSYu5BCb9q7rEpWBLK1AfPEta9Lx6+aBxdLa+ladDH6rU1BUs8WB0hcowkOE9g9uJoAi7bXvKR28lNkpxIcoTIz5RS5mn/HVzMl0qHBsKJP6DJYDSuBVH7lsKWKaCLsV5RozEPyk06RaSPA2d3qNcbqrQyj4/RaNB0mJQLrRIia0iC8wQCzhvAMWtj2npy06B25m58KQODhUif0sXC7eDEAboP9r5cN//Wx5kH6VZubp6Jd8c3UKMDuBaEopXMc7mkvETas4R5gjr71B9emiIVoEiFHG+nENlBEpwnsOK7QtSrVzRbYv80P3vv75Wd8ZVO/1g3ucyO00lKr0cjdxsXeYwymSDmdnLCUri8eXK68BBYNQyav4emUjO4fBCW9UheUWuXPEFd8WehauvEXpjy5uertoQpIcmz61Z8ASq+kAstFCL3SYIjspzGMe8kFJLciJymTMbE2XVdUAnRcPiX1L0vkTfBmKIHtcVoaFXVPM+LUqCM5uUlakKfZYm9LyXArbDlEuy0JCU2QghJcIQQIsOU0QBRoanvaVS4PJpGb5jHxHxWAZ4bAG0nmFfaOA7snZNPE5VtYHU3aQqWgEKlAdC4FYbBGyyvpyngbT7dJITINElwhBAiBXXtuLkHpkwD8+PVI8xzuUTcgKgwUCbrFRxczXeRbmSeu0W1/MB8+gjQOLmhxgeCa6F0J6gTQmQPSXCEEPme0sclniK6+cDposS/HVzQJPWcbPrMfIpo8J/mx3H3zRPV+VS1vkQ6acCus4dV8qJ5YajVa2sKeOVMI4UQViTBEULYNKWLMScphSuYe1DOboGrR9C0+cT8/C8D4eT61Cs6e0LBxGSlcIorhzp/bj6llEjz+o/Z3QQhRDaQBEcIkaepyFsQGpj6fkYRN8zL4iPMBT+9AC6ecPUIHFmJ8vvY3LNSzQ98q6eeoC6d2XU1vtVzsHVCiOwiCY4QIscppSA+0npG3aqt0Hj4oM5uNU9O99YaNB4+5quQ/pmevHLSBHVeZaDcc8mni+wSP878Prb03gBoanfL4dYJIfICSXCEEFlKKWW+Z5GDMxpHV9TtIDi6OvVkdbpY6xXf+BU8fMDVE4pUBFPiZdT/6wrln0/sefFBY+/00NeXwbxCCJAERwiRCcpkgti7KU4XpUhYar6MplpruHka5rSA3kvh2Y7m53bOBg/fxLtJV4cqLVIP1nX3ATDf6DHxZo8AGu+yVnecFkKIjJAERwhhRZlMcHYzFCqDpsSzqKgwWPFWYjJzy/pu0QBae/PsuuWeMz/2KgsdpkCxZ8yPyz0PU66hsZOPG/H0Ujp9npoENaflxqzy8onzBHoPDgfHsIeWyQ/3lpL4th/fDgOFHe/g6xSa+HMLX6dQqvuEUbfsHe6HXGd76HNMuDARUBxrPISVN3vwxcUPcNHGsrCGgVsJz3IroRWhOh9uJfgQmuDDrQRf7uq9UGhhA0DS/vByYv3DbWL7SHyJn93xf5pfiPiRwx+5jrZixWy9N19uxX+c2/c8KUlwslF+2jklft6Nb6/RU9TxNgXsYrgQWwmAgaWWoDc5sPx6PwB2N2qOt+M9q/USlDP23iUIvFKEC3frcTyyVuIzGrofW0mYrggAcSZXXj+5PNvq/7gkvsTPj/Gz+wbEth4/MyTBySZ5deeR+LYZv7rbWcq7XLL0vPgk/XYMpbDjXbQaxaXYsrT13wjA/9xPEG9Knsvl26uD0ZscuJXgwy2dDyUql+TzyaUZOT4yzfoHxVbM0vpnNYkv8fNzfFtPQvJKkiMJTjbI6zuPxM8b8Z21cfg4hiWeMrpJUafbLA55kwa1HVnot5Cwz//i0LHVAIwoM48XvXcDEGlwNycqCb4ERFc1nyrS+XA9voQl9tCz1t3BK270sqr/5zawfSS+xH+a49t6EvJg/NwgCU4Ws5WdR+Jnb3wnbTwlnK5zOa4sJuxoWug/Whbejo/jLcs4mIIOEanWCyr8Gp9PKcIPn/ngEV4TUICGLy6O5ouLownT+RBjLJDt9Zf4El/i5378/JTk5AZJcLKQre08Ev8x4k/24MNx17h37hpNCpl7XpJOFfk6hfJ58FguxZWnc9E/mVR5Is0ObCNU50s1t0Baev/LrQQfbiQU50hkncRBuj6EJvhyK8GHUtVK8NUU38T6twfaW177Ulz5rKm/rW9/iS/xn7L4+SXJcRw+MkvjZoQkOFnEVnceiZ+Swg4jRuwp5HCPVt7/sie8MTcSivNGvcOMLvk5ui+us9A9Fuolr2VSGu7ovAnV+VDAzjx53f77DRkd8AXRRvPtABaFvMWikIEPrf9XeX77SHyJL/FzI35+SHJygyQ4WcDWd56nI76ikEM4vok9LUmDdC2XTDuG4uMUyhcXR7PyZk+KONxhUuWJvHt2BiWrl+Gdd4pzZEl5zoY2sox5Sep9ua0rgl5Zz+8QEl+akPjSKZakP7tu3tg+El/iS/y8GD+JrSc5uUESnCdk6ztPvog/2Z0J44OIvXCNCq4uBMdWwEGj5/Mq4/j3zktsudOGUs4h/NOgndW6epM9YboihCb4cCa6Gtvvvsi5mMoAXIwrR7MD2yj3TDFmT/Fk2LiyHDr2dfbU39a3v8SX+BI/W5ObJJLkZI4kOE+gWmV7m955bCO+eZAtQCvvfyjmfAtfx1v4OIVSySuMCl5hqC9uMdfNAHVgfWgHxp6bjl7ZU6XAOU5E1gTgVoIvU4M+5FaCDzcTfAnV+XJX54UJuzRf1aAcKPNMKWbl+e0j8SW+xM+P8dMjSU7GSYLzBEYOdLPZnScvxLfX6CnieAdfp1v4OIZSzOkWOuVouaR5ec0B3NV58X7gDAA+qzQZb8d7xBuduK/xxbtsCQ4E1+HMzSLcSvDlls6HS7HlEqNr6HTkD8tr6ZUjP93ok6X1fxISX+JLfIn/uCTJyRhJcJ7A7MXRBFy2vZ0np+JPGH+BqPO3gGoAvFFiObU9jlsmqyvseAetRlmtez6mkiXB2X2vCTGJg3QB+pz4gXB9ISo/W5jZUwrypo1vH4kv8SW+xH9ckuQ8miQ4TyDgvAEcszamLeycTtr4xAnqzKeKfJ1CLYN0yxa6i/dHWxg5LoJOMTNpWmMvzQ5uB6CaWwDlXC8RmuDDuZjKlnsa3UrwtVwyHWV0t7zOsmvWcydciitnE9tH4kt8iS/xc4IkOQ/3VCc4GzZsoF27dtjb543NkBd2ThdtLDqTI0bsqVogkGZeu1h2bQB65cCgUot5o+QPFHK4n2q9+3oPIrW+FKtUknfG3+LQMQeiCvRiU1jywN4x577I9vpLfIkv8SV+Xo2vrVjR5pIQW05y8saRPQ2hoaH4+PhYHo8YMYI5c+akWVan0xEXF8f06dOZNm1ahl9j8+bN1KpVi1KlSqHVap+4zk8iR3bOTx35YsIRHC5fo6tPaKo7S/s4huLpEMkrR3/jTPQz1HA/zahyc9kQ1ombCcW4HFeWLbdbmy+P1vmaB+sm+BCaUJRna3kye4qn1WmjgJhqWVv/PP7hJfHzdvyvPyvAiE/COXzc+PDCpmjQuqX/vFKAETTJH5/5YftI/OyPb6tJiK0mOXkywVFK0bt3b3r16sWAAebTFAkJCSilUEqlSkb8/f05ffo0p06dYu/evTRu3DjNuPv37+fUqVP4+vqyYcMGjh8/zrRp02jTpg0vv/xydjcrXQ1qO/Dta9uInzaV5W43udnAl5mXRrLpdocMRlB42EdaThNdjC3P9YQSVHK9wIcVvmSL4we8N+l5Vn72M9MLjIZnk9e8rfMmNMGHq3GlOHy/Hrd0PtzVewHwZ1gH/gzrQELiTRv/vtOKv++0SrP+tvDhIvGfsvi6w2C8Qt0GnVARY+jQWUPE/RvgPg3sSkLs9xC/Euyqgvtk0HqAioPYb8GpHWi9wa6Ydcy4n0B3CFQ8OLUAl57Uq2nE1TCJlq2vEaN7FgoMN5fVH4Po6eA2ARyeSbuOsYvAsTnYVwRTFMRMB9NdcH4ZnNpYts87fa7R7MWX0emMUGAMODYCQwDELgYVCwVGg11piPsFnDsBGkjYBi7dH7mZbPb9tcH4tpyE2GKSkycTnF27dnHv3j1KlzZPlDZ//nyOHTtGy5Yt+eijj2jZsqVV+S1btnDq1CkiIiIICgpKM8EJCwtjyZIlzJkzB6UUmzZt4r333uPKlSt5Irlx+PN93LTxAJRwvsmUyp8BsOl2e+w1BgzKAU/7CNoU2WI15qVY4jgYV7s4S8zJQR+z4kYv9MoBX48YPnjdjmHjIgg5W4sgjy8tPS9huiLoVfqDiBJS3I36YfW3lQ8Xif+UxTdeoVLFEvRoF8hP60YQQUlw/gMMp80Jju5f8PwREjZDwj/g8gokbAGHeqA/AI4vWcdTCYATeM41JxVR42nwfF9qlv2Nv3fXIMZhAui+A/1ZsC8L0VPB7UOwr/7QOqL1TYwfBwU+BuMFiF8HTm0s26dztxno7AaAR3Vz0uTYCDSu4D4D4n4Awykw3gA0oNsJpghzkvYINv3+2mB8W09CbC3JyZMJjl6vp2fPnvj7+6PVahk0aBDnzp3D29ubRo0aWZU9cOAAdevWpVOnTmzatInevXsTHh6O0WikcOHCAMTFxTFz5kx8fHwoUKAAAQEBREVFUbZsWX7//Xfu3r2Lt7d3jrczaeeJnzbVktwkcbGL5/1ys5ha+VPmXBnKsmsDcLOLZmKlyRiUHWEJ5gnqAmOqsOveC8nzuyT4cCnOfKl00aqV8Pr4b4ZZds5ibLpdLI2aPFn9beXDReI/XfGLFLrKlHF+fL3Ii1MX9GCKgYQd4JF0GtsO7HzMCYZKvPFpwnbwmAHRO8xJUEoaJ3B51fx37A9UqvYKs6d40qrNf0RrvzFP1+TcEXS7wXAWnDqBfS3QpD+LtdXpMLuiYAyF2IXgOsRq+4TdNoBTCdD6YJ4bCrArA/oj5mTM/Utzr1Lst+Yeqvg/zG17CFt/f201vq0nIbaU5OS5BMff35/Vq1dz5coVatSogVarZeXKlZw+fZp79+5x6NAhunfvTv/+/bl48SInT57kwoUL7N27l6ioKC5fvoxSCjs7O2bMmEGhQoUIDg5mwoQJfPzxxwC4urpy7do19uzZg0ajISYmJscTnJQ7z3K3m2mW8XUKY3HIm5yOqgHAzQRfXjiwnTs673QnqEsrvi3t/BJf4mdVfCJDmbHQk8PHE+PHTAfX/uaeDwCHBhDxtrnnxGNOYm9KYXMio2LSD67bTRGvKH5c1IkRH98lOsYFPJI+Sg3m9RN2gbYgxHwN2qLgmsbdlJXBahwPen+I+hRUAj6eO5g95aXk7ePcGSJHg9YLXMzTKBD3qzkZ0riZkxwnPyi0EuJWg3P7R24fW39/bTm+rSchtpLk5LkEp169epw+fRpfX1+8vb2Jjo5m9+7dODs7M3nyZDp27IhOpwPMicpzzz1Hp06dCAwMpH379pQoUYL+/ftbxaxRowYxMTGYTCbLMjs7O06ePEnhwoWJiXnIh1k2eHDnudnAlxLOqZOcmwm+zLr8ruWxCTvCdEUzHT+rSXyJbwvxW7dNIEqbuM/H/Qr2VcGhtvmxigf98cTTUlvBcAZM982JQnh3MN2D8Feg0BqI+hBcXgeHOmC8jrv9Olb9soSR4yI4fNxknQzpz4K2JGi04D7RvCzyXVBG8+kvwynzaSsAY4h1L5GmIHjMo1YNN8JD3mPw6HOcCEj84pWwDZxeMidgul3g1BLsKkCh38F41XzaysnPnDQZL5p7b8I/hYLLzQlXGtvH1t9fW42fxNaTEFtIcnL30qF0bN68GX9/f7799luio6NxcXHhxx9/5J9//uHevXs4OprHjfj6+vLss8/y66+/MmjQID766CMOHz7M5s2bU8WMjo62euzu7k7fvn0pVqwYCQkJOdIuSHvnmXlpJHFG6/EucUZnZl7K/O3lbX3nl/gSPyviDxlzhagYD/NC/SkwXACX3skF9UfBsal5IK/rMIhfb+71KLQZPOaZx694bQSNM7hNNJ9qUjo8tDP5aflXjJ2SYK6/RgPaIqDbax4krPvHnERp3MyJh4o1nxrT2IFTa3AdmlwH42WwK5v82L4iDepX5ruvqxGrf4YTp8KTnzOcgAIjzW0w3gBTODg2AG0hcKgFGhdzuYQt5tfRHwfHhqCsP/fyy/trq/EflDJJ0FasKPGzWJ5McEaMGMG3337Lli1b8PLywtXVlcKFCzN27Fg++eQTrl69ain7008/4eXlRceOHdFqtXz11VesWLGCefPmYTAYLOXi4uKs/g4NDWXXrl1cvXqV+Hjr8S/ZJb2dZ9PtDow7/xnX44thUhquxxdj3PnPMnEV1cPjZxWJL/FtJf6x4xfMY1TAfGWR4SSE9zb/xK0B7MF0B5QJjJdAU8BcVqMB0w2w800OHDMDDGeoWPIQJl0APV97g0PbX4WIEebnXYdC/GqIfB9cBoDGAVz6QdRkiBgCLom3CNH9B3HLkuMaLyfXUcVTo+JlZk/x5J0PgrgTegTsiieXVQYwRZp7llSU+bcpMQHS+ZsTHKU3J24Otc2nsRxbmK8ES2P72Pr7a4vx02PrSUheTnLy3CkqgCZNmgAQERHB/PnzWbx4MQAlSpTg448/ZsKECTRq1Ag/Pz9q165NjRo1LOu6urqyZMkSpk2bxvjx4y3z4iT10qxdu5atW7cCcOHCBc6ePcv+/fupUaMGAwYMoHXr1tnSpkftPJtud8h0QpOZ+E9K4kt8m4qvLQaOzcxPuk8CNNbjXZQB4n+Hey+ZyyadTgLzKSaH5Bm1sa9GzRpFWTizOcM+asSRk/bgkuLF7YqCxzfWFbKvCAUXWy/TFgW7SsmPHeqCnfmCgAZ13KlR5ndatJpCbEwcuA40jxWKfA9c+oNzFwjvZk7AnHuZT21FTzGf5sIe3MaaB0onXRauLWz+SW/7ZDGJ/+j4D2Prp5Py6ukqjVJKPbpY/hAbG0tUVBT29vbY29vj4OBg+a152JUOD/D396d3797gPg8caz2yfH7YOSW+xJf4uRDfFGU+3ZWJz6dMxc8CEj9j8b0KaYkfOfyhZbUVK2ZrkpCb8Z1nz83y14Pk4/GKFSuoV6+edX2y5RXzKFdXV3x8fPD29sbT0xNXV1ccHR0zldxkVn7ZOSW+xJf4uRBf6y7JTT6JnxG2fjopr52ueqoSnJyWn3ZOiS/xJb7El/jZH9/Wk5C8lORIgpNN8urOI/ElvsSX+BI/b8e39SQkryQ5kuBkg7y+80h8iS/xJb7Ez9vxbT0JyQtJjiQ4WcxWdh6JL/ElvsSX+Hk7vq0nISnj5wZJcLKQre08El/iS3yJL/Hzdvz8kuTkBklwsoit7jwSX+JLfIkv8fN2/PyQ5OQGSXCygK3vPBJf4kt8iS/x82b8JLae5OQGSXCekK3vPBJf4kt8iS/x82b8B0mSkzmS4DyBapXtbXrnkfgSX+JLfImfN+OnR5KcjJME5wmMHOhmszuPxJf4El/iS/y8Gf9RJMnJGElwnsDsxdE2ufNIfIkv8SW+xM+b8TNKkpxHkwTnCQScN2R5TFvfOSW+xJf4El/i5wxJch5OEpw8xNZ3Tokv8SW+xJf46bPFJMSWkxyNUkrldiVszcNuzy6EEEI8SOn1aBwccrsauSa72v+w47H04AghhBDZ7GlObiB32i8JjhBCCCHyHUlwhBBCCJHvSIIjhBBCiHzHPrcrYIt0Oh0AgYGBuVwTIYQQ4umVdBxOOi6nJAnOYzh79iwAkydPzuWaCCGEEOLixYs8//zzVsvkMvHHEBgYSOfOnfnqq68oXrx4blcnR9y+fZt3332Xb775hiJFiuR2dXKMtFva/TSQdku7bZVeryc4OJgOHTpQsGBBq+ekB+cxuLm5AVCnTh1KliyZy7XJGdeuXQPg2WeffWraDNJuaffTQdot7bZlzz33XJrLZZCxEEIIIfIdSXCEEEIIke9IgiOEEEKIfEcSnMfg4eHBsGHD8PDwyO2q5Jinsc0g7ZZ2Px2k3dLu/EiuohJCCCFEviM9OEIIIYTIdyTBEUIIIUS+IwmOSEUphV6vz+1q5LioqKjcroIQIpulNaV/fqeUIjo6OrerkeMkwREWOp2OoUOHMnDgQF555RUWLVoEwKxZs+jatSuvvfaa5TYVYWFhvP7667z22mu89957Nv+hodPpGDduHFFRUWzbtg14OtptMBiYOXMmTZo0Acyzgo4bN47u3bvz+uuvWyYECw4Oplu3bvTq1YspU6bkZpWzxBdffEGdOnXo3LkzgYGBT0W79Xo9ffv2tfydmfauWLGCzp0706NHD/bs2ZMr9X8cKdu8a9cuunXrxjvvvEPHjh25c+cOkP/aDNbtBlizZg2HDx9m06ZNls+s/NjuVJTIlMOHD6vOnTurHj16qIULF+Z2dbLU7t271YEDB5RSSoWHh6vu3burzZs3q2HDhimTyaRCQ0PVgAEDlFJKDRw4UP3zzz9KKaVWrVqlfvjhh1yrd1b47bff1M8//6z27Nmjvv3226em3YsXL1Z9+/ZVN2/etDyeOnWqUkqp8+fPq/fee08ZjUbVuXNndfLkSaWUUjNnzlTbtm3LtTo/qfDwcPXCCy+okJAQtWnTJjV8+PCnot3nzp1To0ePVkpl7n0+fvy46tGjh0pISFBRUVGqT58+Sq/X51o7MiNlm+fMmaMSEhKUUkpNnTpV7dixI1+2WSnrduv1etW9e3el0+nU66+/rgwGQ75t94OkBycTYmJi+OSTT5g/fz6//vorwcHB+eqO4k2bNqVhw4YAfPXVVwwYMIDVq1czevRoNBoNRYsWxdvbm+vXrxMTE0PLli0BeOWVV9i3b19uVv2JrV69mk6dOnH27FkqVar0VLRbp9OxYsUKpk2bho+PDwB//vknI0aMAKBSpUpERkZy9OhRKlWqxLPPPgtAt27dbLrdRqMRd3d3SpQoQenSpTEYDE9Fu8+dO0flypWBzL3Pq1atYuTIkTg6OuLm5ka9evVs5nMvZZuHDx+Oo6MjFy5cICAggMaNG+fLNoN1u//991+ee+457Ozs0Ol02NnZ5dt2P0gSnEzYtm0bbdu2pUSJEmg0Gl5++WWb/sBLz6JFiyhQoABt2rTh3r17lClTxvKcTqcjICCAOnXqWC1zcXHJjapmiePHj1O8eHHc3d0JDAykQoUKT0W79+3bh1KKn376ibfffpvNmzfj7OxsudcagL29PUFBQdStW9eyzNbb7e3tTZ06dXjttdcYPnw4b7/99lPR7vPnz1OlShV0Ol2m2mvL2yGpzUnu3LnD2LFjmTp1Kg4ODvmyzWDd7lWrVtGxY0euXLlC8eLF0el0+bbdD5IEJxPy25ufln/++Yfjx48zduxYADQaDbGxsQCYTCZu375N0aJFuXHjhmUdf39/KlSokCv1zQqHDh1i7969NG/enL///ptXX32VGzdu5Pt237t3j7feeosPP/yQefPmsWzZMiIjIy3PR0dHo9fr8fT05ObNm5bl/v7+VKxYMTeqnCXu3bvHkSNH6NatGw0aNODff/99KtqddNCzt7fPVHs9PT2t/u/Pnj1LiRIlcrTujyvlgT4hIYFRo0bx8ccfU7p0aYB82WZIbrfBYODo0aMMGjSIPn36sH37dvz8/PJtux8kCU4mPPhPceTIEZv+wHtQYGAgP/30EzNnzsTOzg6Al156iXnz5qGUYtmyZTRt2pRnnnmGwMBATp06RVRUFIsWLaJTp065XPvHN2jQIA4fPszWrVupWbMmhw8fpk+fPvm+3WXLluXQoUMAnD59miJFilCzZk1WrFiBwWBg1qxZdO7cmcaNG7N582ZCQkIIDQ1l7dq1tGjRIpdr//i2bNlC+/bt6datG5MnT2bt2rVPRbtDQ0Px8fFBq9Vmqr1+fn7MmTMHo9HI5s2bKVmyJM7OzrndnAxJajPA+PHj6d27N/Xq1bM8nx/bDMnttre35+jRo/z777+89dZbTJs2jR07duTbdqeSy2OAbMqlS5dU+/bt1b1791RQUJDq0aOHMhqNuV2tLDN8+HDVuHFj5efnp/z8/FS3bt1UZGSkGjNmjGrTpo364osvLO09efKk6tGjh+rSpYvauXNnLtc8a9y6dUsNHjxYKaVUbGzsU9HuyZMnq44dO6oePXqooKAgdffuXTV48GDVrl07tWTJEku5nTt3qq5du6qePXuqEydO5GKNn9yqVavU+PHjlcFgUGfOnFHNmzfP9+2OiIhQffr0sTzOTHsNBoP6/PPPVdu2bdVHH32k4uLicrz+jyNlm8+ePatq1apl+Wxr06aNWr16tVIqf7VZqdTvdZJPPvlEnTlzxvI4v7U7LXKrhkxat24dP/zwA97e3kyYMIGyZcvmdpWyjMFgwGQy4ejomNtVyRWxsbEcPXrUcsm0yJ/i4uIYMmQIBw4cwNvbm48++oj27dvndrWyVUxMDEePHqVp06a5XZUc82Cb4+Pjbbs3IoPSe6/37NlDnTp1cHV1zaWa5TxJcIQQQgiR78gYHCGEEELkO5LgCCGEECLfkQRHCCGEEPmOJDhCiHznUTeLnTdvXg7VRAiRWyTBEULYrNDQUMtNYY8cOcL+/fs5fvw4X3/99UPXO3DgwCNjGwwG1q9fz44dO7h3716W1FcIkXMkwRFC2Cy9Xk9MTAwA5cqVY8uWLXz55Zc0atTokesBVjP6Pui7777jyJEjjBw5kn/++QedTseMGTOyrvJCiGxln9sVEEKIxxUfHw+AUgoXFxd0Oh2nT5+23GbjQX///TeHDx/m5MmTNGnShLp16zJ79uxU5S5evMixY8cst/AoUKAA//33H+fOnWPOnDmULVvWpmexFuJpIAmOEMImHT16lDlz5hAQEEBUVBSNGjXi5MmTVK9enaJFi6a5jru7O76+vphMJjp27Gi559qDHBwcaNWqFXv27MHT05Ny5coxfvx4ihYtyvr161m+fHk2tkwIkRXkFJUQwibVqlWLKlWq0KZNGyZMmEDNmjUpW7YsISEhXL16Nc11qlatyooVKyhSpAhRUVHpJiqurq4cOnSI5s2bEx4ezquvvkqHDh0YM2YM1atXp1SpUtnYMiFEVpAERwhhk+Lj49m0aRMHDx5k/fr1eHl58cwzz9CzZ0+6du2KUgqj0Wgpr5RizJgx9O3bFx8fHyZMmMDu3btZvXp1qth//PEHQ4cO5cSJE3Tp0oXRo0fTqlUrjh49+sjxPUKIvEFu1SCEsEkLFiwgICCARo0aUbNmTbZt28aePXsICwujePHixMTE0LRpU8aMGQPAjBkz0Gg0NGvWjJEjR1KhQgXeeecdFi5cSNu2benRowcAV65cYd68eezYsQM7Ozt0Oh3Lly/nm2++Qa/XM3fuXAoVKpSbTRdCZICMwRFC2KS33nqLlStXYmdnh4ODAx07dqR8+fKEhoYycOBAq7L79++nTJkyVKlShblz5xIZGUnPnj2pX78+devWZdasWQwYMIDRo0fj7e2Nn58fx44do3///kRERFCzZk1KlSrFrl27CA4Opl69ernUaiFERkkPjhDCZi1YsIAiRYrQrVs3ADZv3kxMTAzdu3d/6Hp16tThn3/+wdvb27IsOjoaADc3N+7evUuvXr0oXrw4d+/excnJiWbNmvHyyy8zYMAAypUrR//+/WnUqBEajSb7GiiEeGzSgyOEsFn169fHxcXF8tjV1RV7+0d/rJUpU4aCBQtaLXNzc7P8PWbMGKZPn07t2rVZvnw5Dg4O9O7dG4C1a9fy9ddf89FHH/Hdd99RtWrVrGmMECJLSQ+OECLfiIqKQqfTWfXMCCGeTpLgCCGEECLfkcvEhRBCCJHvSIIjhBBCiHxHEhwhhBBC5DuS4AghhBAi35EERwghhBD5jiQ4QgghhMh3JMERQgghRL7zfysZODKIxs0jAAAAAElFTkSuQmCC\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n 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\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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wICAggKCgIrVZLzZo1uX37Nq6urkyZMiXV+q9cuZIHDx688pAiwMSJE7l48SKzZ8/GxMSEqVOnpnoYA3RzHDw9PZk4cSJffvklDRo0YPDgwWzatImiRYvqy71O/6vVaq5fv06VKlX4/fffOXDgAFWqVKFPnz7ExcVRqlQpfv75ZywtLQHdCMvu3buJiYkhMDCQ27dv8+TJEywsLNi0aVO29v/06dMpWLAgarWaP//8k6NHj/LgwQP27dtH4cKFuXbtGjExMXz22WfUrl0b0E3Urlq1qn6U8OzZs/Tp04dChQoRHh7+yrkYr+Lj48Pu3bv58ssvMTU11ScQZ8+eZfv27UyZMkV/WDJ//vyMHz+eRYsWERoaio2NDZs2baJ58+a0atWKzZs3G3x5Jv6YCAoKwsfHhzVr1mBlZcXly5f55ptvaNSoEfHx8UycOFG/Tkbbe/v2bXbv3s2yZcuIjo7GwcGBqVOnMnToUD7//HPat29vMKp0//59Nm/ejJeXF7GxsQQFBTFs2DBMTEwyNDfxZVqtlilTpnD58mXef/99hgwZwuDBg7l79y6dOnVi6NChDBo0iFGjRlGkSBFmz55N4cKFKVq0KEuWLKFYsWIsWbKE2rVro9FoePDgARs2bODEiRPcv3+fIUOGsHr1ak6cOMGNGzdwd3fnk08+oX///tSoUYPx48fj7OzM48ePWbJkCZcvX8bb25vChQsTFhZGjRo1GDt2LD///DPXr183GPGPiIjAzs6OIkWKUKlSJZydnenduzc1a9Y06pME0pKnR3BiYmJo0KABTk5OHDhwQD/PZOTIkQaHhe7cucOqVau4desWzZo1o3jx4owYMYIFCxawatUq1q1bl2zYz8XFhY4dO7J48WLCwsIoW7YsJUuWZNSoUQQFBenLBQUF8f333+s/RO+99x6fffYZJUuWxNTUlDt37vDzzz+zcuVKg2OtFhYWLFy4kO3bt9O4cWMGDRpEmTJlWLFiBe7u7ty5c8egPhUqVECr1XL16lUePnzI1KlTiYiI4NNPP03WL4mJSPHixfnwww85ePAgjo6OlC1blrNnzyYr/+OPP2JnZ0dYWBjW1tbExMSwf/9+Hj16xIQJE6hWrRpDhw4lICAAgPr169OtWzdOnjyJoihcu3aNEiVKYGNjg7OzM1qtFo1G8xrvqM6zZ8+YOHEiP/30k/59vHTpEo8ePaJp06aMGzeObdu20aRJEzQaDSqVikGDBun7N72vHxkZyaNHj+jSpQsTJ07kv//+w8zMLMV14+LisLGxYcWKFXzzzTfs2rWLAQMGcOjQoRQnxZqYmFCtWjWuXbuGr68va9euZdeuXXz//ffJypqZmWFqaoqFhQW9evXi3r17hIeHU7VqVU6ePJlq/ffv36+fyzVixAgeP36cZnt/+eUX2rRpw5MnT1i5ciWHDh2ibdu2XLhwIcXytra2fPzxx8ydO5fFixfj7u5Ohw4dkv0qzGj/x8bGsnz5ci5cuEDVqlWpVq0azZs359dff2X58uX89ddfjB492mAUMHH+3OjRo7l9+zZFihTh3XffZe7cuezcuTPZa2RV/6vVat5//3369OlDWFgYVapU4fPPP6dKlSrY2NgQEBDA559/ztKlS/XJjVarxdzcnIiICK5evcpPP/3EqFGjePjwIc7OzgwbNizVvkqvmTNn0qpVK0A3epWYoDRt2pRmzZrx4YcfGpw9V7lyZQoVKsS8efOIjo7m/PnzxMbGMmnSJGxsbFIcYZk3bx6HDx+mfv36tG3bFg8PD5YuXUrlypUNfhRktL2hoaHMnj2b2bNnExUVpR8JL126NL///junTp2iTZs2+tEigIsXL3Lr1i1Wr17NunXrcHV1pVatWoSGhiabEpARR48eJX/+/Pzzzz/079+fnTt3Uq1aNXbv3s358+dRq9U4OTlRokQJrl+/jrOzM2q1ml9++YWpU6diZWWFhYUFK1asYPTo0WzYsAEAZ2dnJk+ejImJCZ6enrRq1YrDhw/j5+dHz549OXLkCFOnTtUnk05OTgwcOJAFCxbg4ODA6NGjSUhIYNu2bXTv3p2dO3cmOzMw8YfGzZs3CQkJoWDBgqxevZo6deq89oh6bpenR3Csra1xdXUlf/789O/fn7Zt2xIfH8/cuXMNypUoUYJRo0bpH9++fZvSpUsbHPdMafKkra0tY8aM0R8C+ueff2jUqJHBCEW5cuUA3UYdFxdH9erVOX78OMOGDaNIkSLMmTOH27dvp3hGgqurK/Hx8ezZswetVkuhQoWwtrZOMWlRqVT6Ic/PP/+cL7/8Mtm8g6Ti4+P577//aNSoEba2thQsWBArKyvOnz+frOy8efOIj48nMDCQzp07Ex0djbe3N25ubqxdu5Z+/foZjCaB7kynsWPH0qFDB9atW8fgwYPJly8fTZs2ZeHChW+U4EyePJn+/ftTpUoV/bKWLVty4cIFTExM+Omnn3j33XdTPbyo0WjSdbro+fPn6dChAzNmzNAv27RpU4qn02s0Gnx9falXrx4NGzakYcOGtGjRgvHjxyc7XJYocUe9atUqTE1N0xwt0Wg0HDt2TD8x29XVlfLlyxMaGppi+bt37zJjxgzWrFlD8eLF+fzzz+nfvz/Lli0zGF1JtH79eg4fPsyYMWNwcnLST6D19PRMdedXqVIltm3bxsyZMxk8eDA+Pj4pjpak1Ja0+t/KysrgS+7ixYs8fvzYoB9LliyZbD1bW1t69uxJ9+7dAd1Zi1ZWVqlOzM+K/rewsKBly5bs2LFDPyn57NmzVK1alZ49e+Lp6cmFCxcM5uGMGjWKwMBAEhISaNeuHR07diR//vwcOXKEESNGpFqn9FKr1fTu3Vu/PzA1NTUY7WjXrh3u7u76/Z1arWbEiBHcvHkTe3t7Dh8+zEcffcTDhw8pWLAgtWrVSvEXv6urKxqNhuvXr3P58mU0Gg1FihShWLFitG3b9rXbGxERwQ8//ICjoyMzZsygZcuWgO6wV6FChZg5cyYajcYgiapWrZo+4b1+/ToNGzakXLlyeHp6pvvswJTcuHFDf9aXg4MDly5domnTppiYmFC0aFHCwsKIjIzE1taW06dPU7x4cfz8/BgyZAi1a9fmt99+A3SHi5J+nhMPi0ZHR1O2bFm6detGcHCw/jA/YDASr1KpcHJy4rPPPsPGxgZPT0/8/f1JSEggKChI/4M3KY1Gg5WVFd999x1nzpwhLCwMS0tLGjZsmGcTnDw9ggNgb29P8+bNMTMzw9HRka5du6br9ORXnUIJumOgiRtVZGQkZ86cSXVSXdIPm6mpKWPHjmX69OncvXs3zQm3VapU4dixY1SrVi3dv+TMzc1f2Ua1Wq3/JRMVFcW5c+e4fv16ssQjICCA9957j2LFijFlyhQKFCjAunXr2LNnD+vWrdNP6k3qzJkzxMfH4+DggLe3N05OTowaNcrgSy3xlOuM2rx5M5GRkXTq1Inw8HCDvjMzM8PExITg4OA0D8ckzpN5ld27dyebbGpmZpbiISqtVkvp0qUNJl0XL16c77777pU7D3Nz8xTPekgq6fsVGxuLn58fe/fu1R+iSerRo0f88MMPLF68WJ9st2jRgqFDh/LRRx+xbt06g/ku+/fvJ1++fLRq1Yro6GhcXFx4//33iYuL486dOwQEBOjnbLysY8eO+vklnTp1wtnZOc12QPr7P6mMbo+xsbGcOHGCnj17pnhWVFKZ3f9gOOfExMSENWvWMGrUKLy8vJJt74mjIk2bNuXZs2eMGzeOb775ho8++ogpU6YQGRn5yranxcLCghYtWgC6/VRCQkKyUYxy5cphZWVFbGwsa9asoUePHlSsWJGhQ4dSqlQpGjduTFhYGK1bt8bU1DTViboeHh4cPHiQ+vXr6w8JvSyj7S1evDiOjo5cvXqVa9euUa9ePe7du0f//v31c3DMzc0NEoAiRYrw448/8sknn7B+/XomT54M6BL/9MxHS03BggX1o9yJ+zZvb2/CwsK4du0aBQoUIDQ0lICAADw9PTEzMyM4OFj/YyyxbSqVCq1Wi5eXlz62Wq1m2rRpDBgwANDtR6tWrZpqXbZu3crgwYMpXrw4AwcOxN3dna5du/Lnn39SrVo1/aUzEtnY2NCxY0dAd1JK0sulpDSvMC/I8wkOgK+vr/4w0JgxYzItbsOGDRkxYgSNGjWiZs2abN682WCYNCmtVqvfGZqamvL999+zbt063n///WSZdmL5q1evcunSJYYOHUpYWBj//fef/jBQWuzt7V/5hVC2bFn9YTNTU1N++eUXxo0blywxKlu2LI0aNdLXvXjx4sTGxpIvXz5UKlWqp4sOGTKEgwcPUrZsWVavXs2gQYNISEjg3r17nDlzJsVfgF5eXtSoUYPq1atTvXp1atSokaxvDh8+zMmTJ6lduza1a9emVq1a/PHHHwZl7t69m+apxHFxca88tfrp06fExsbqd0xxcXGsWbOGx48fp/rlnLTPIyIi2LJlC/fv30+2o3lZet4vKysr/ZlYt2/fZsaMGcyYMSPFdvj7+zN37txkZ+u0bduW3377jfPnzzNo0CD9Tq158+a0a9eOZ8+esXfvXv0oyO3bt+nduzeDBw9m0KBBKY66RUREMHr0aMaNG8f06dPTdU2e9PT/62jQoAHLli2jVatWVK9enUmTJnH16tVXrpfZ/Q+G27uZmRldunRh8+bNDBo0KNln+t9//2XOnDn6Q+oVK1akYMGCjBo1ir59+9KvXz+uX7+e7DXUajX169fXby/Vq1dP8RBzUsuWLWPw4MEpnhWX2M5BgwZRt25dAgIC9IdWAgICiI6OpmPHjlSqVImvvvqK//3vfwbr3r9/Hx8fH8qVK0etWrU4fPgwp0+fTjZa9zrtDQkJYciQIXh7e9OhQwcGDx5MSEgIa9euTbWtjRs3pmrVqtjY2PD06VMUReHq1au4u7unWD49/dmmTRsuXbpEnTp18PT0pFu3buzdu5fu3bszYMAATE1Nad68OUOGDKFfv36cO3eOhw8f6uc3FStWjFmzZlGrVi1mz57N6dOn9bEXLVrEmDFjePr0KUOHDmXt2rW89957qbavW7du1K9fX78vjYuLY8OGDQwcOJDly5cnOzOqcOHCnD9/Hi8vL+rXr4+JiQl//fUXkZGRr/2DM9dT8rgjR44oEydOVCIjI9O9zpo1a5TLly+/slxCQoLy6NGjdMWcPHmy8vjxY0VRFGXbtm1K586dla+++kpp2LChcvv27WTlN23apLz//vvKmjVr9MuuXbumjBgxQpk+fXqarzVz5kz9a6WHv7+/oiiKsmfPHmXq1KnJnr9//77y7bffKn5+fsrw4cOVWrVqKe7u7so777yjNGjQQJk8ebJB+YSEhFRfKzY2Vhk+fLjy77//prt+ScXHxysRERFpljl06JASHx+f6vP//POPvs2puXfvnhIaGmqw7MSJE0q7du2Ue/fuJSsfFxeneHl5GSy7fv260qZNG+Xnn39O87UOHjyo7NixI80ySSXW/c6dO0rv3r3Tvd6rzJw5U9m1a5fy8OFDJTo6+pXlb926pYwePVoJDAzM0Oukp/+Tunr1qvLbb7+lq2xISIii1WozVJ+s6P/t27frY967d09p1qyZMnr0aKV9+/bKP//8k6x8UFCQ0rJlS+W///5L9vzhw4eVli1bKp6enumuY1rWrVun7Nu3L93l16xZo6xevdpgu46OjlbWrl2rfxwUFKTUrl1bmTBhgqJWqxVF0b0XM2fOVHr27JksZkbbGxcXl2xfGxERYbB/TE1sbKwyadIkpX79+kq3bt3S1eacEBsbq//7wIEDSo8ePdLclyqKrg9mzpypKIqiaDQa/Wd/zJgxypMnTwzKXr16VRk/fryyaNEiZdmyZfp/Fy9eVJo0aZLJrckdVIqSd69dHRQUxNWrV9N1cbyslpCQ8NpX7n1ZWhcfBN1hnM6dO2fJcdVHjx5hbW2NjY1NmleUFToxMTGo1eo0rzNx9epVTE1NX3nhstxEURTWrVtH9+7d03X4KDfLiv5XFAVFUTK0jRw7dizdF2M0Jqntr7K7vRcuXMDFxeWVZxO+bRRF4cCBA8nmUeYFeTrBEUIIIcTbSX6CCyGEECLPkQRHCCGEEHmOJDhCCCGEyHMkwRFCCCFEniMJjhBCCCHyHElwhBBCCJHnSIIjRCZI6fYNSfn4+LB9+/Zsqk3eIf2a+TKzT69du8bff/+NVqtN8R5tQuQkSXBEjouLy5pLMWU0bnBwMNeuXQN0tyAYNmwYLVu2ZM+ePQbldu7cycWLFwHdjUifPn3KoEGD0oytKAq3bt3KUH3elPIGNzTNzLiJt9J4+PAhc+fO5fvvv6dr167JbsxpLP2aG+R0nwYHBzN48GBmzpzJ/Pnz6devH1988UWKt50RIqfk6buJC+NgaanC490Q6lQ3Z/40B0ZOCuPMhTf/cvY98eobPyZ1+/Zt/P39qVixIvb29kybNo0hQ4Yku6FoTEyM/ldw+fLl+eyzz2jTpk2ase/fv4+fnx+enp68//77FCpUKGONeQ0qc3NiRw7PlFgmbm6Yf/Ipmt9WYzF8ZIbWPXToEH369MHe3p78+fOzceNGfvvtN8qWLWtQzlj6NTfI6T61s7Nj6dKlrFy5knHjxlGhQgUOHTokVzcXuYokOCJXyOzkJiPUajX79u1j06ZN3Lx5Ey8vL+bMmcPixYvp3bu3wc0yr1y5wq5du3j8+DFffvklixcvJjAwUH9Tzpc9efKExYsXc/z4ccLDw3n33XdTvQN1bqb190fz22rMP/k0Q+vdvHmT4OBghg0bxqJFi8iXLx8ffPBBshsevq39+jpyQ596enoSFRXF2rVrWbx4MR9++CH169dHo9G88uayQmQXSXBErpBTyQ3ohvnj4+OJjY3lyy+/5OLFiyxcuBA/Pz+2bt3KH3/8gUqlYurUqTg6OhITE8OgQYN4//33sbOz46uvvkr17sxOTk4MGzaMAwcOYGtry7Nnz/R3FjY2iUlORkZwzpw5Q1hYGP369QPgzz//pEWLFgQGBlK6dGl9OWPqV2VZp1cXqtAC1XtfvChfsyeqWj1Rop7An5+luIrq8y3pev3c0Ke1atVi37599O/fHw8PD1xdXdm8eTMrV65k+vTplClTJl1tESIryXiiyBVyKrkBKFGiBC1btsTKyooTJ07Qs2dPmjZtypw5cyhVqhSrV69mw4YNlCtXDjs7O65du8bvv//O48ePAbC2tk51XoNWq+Wrr76iZcuWdOjQgaCgIDZs2JCdzctUWn//DJXv2bMnbm5uBAYGsnHjRho1asSJEye4du0aPXv2xMvLC+Ct79eMyOk+vXPnDjt27MDc3Jy9e/dy5swZli1bxsiRI1m/fr0kNyLXkBEckSvkVHKTSKVS8eDBA6ytrQ3uKl2yZElCQkL0v4zXrFmDh4cH06ZNIyAggMOHDxMcHMyQIUNQq9W4u7vzww8/6NefO3cudevWpUSJEqxevZpPP/2UnTt3YmpqSrdu3bK8XSZubhlOSjKTt7c3Dx48QKVSceLECWbMmEGnTp1o164dlSpV4o8//qBWrVpG1a/pHWlJqbzK1gkyuP7LcrpPS5QowUcffYSXlxeNGjXi8OHDVKxYkalTpwLQsmVLWrRo8UZtFCIzSIIj8qQ61dM/D0Cj0bBo0SI6depEcHAwixYtom/fvkRHR6PVatEkOWvos88+48mTJ8TGxvLkyRPatm3LtWvXWLt2bbK4+/bto1SpUkRHR/Pzzz/j4OCAs7Mzc+fOZe7cuYwdO5ahQ4dm6S/exInBOZHkxMfHs27dOhYuXEjBggXp0KEDKpUKRdGd3VaqVCkmT54MGF+/5pTc0Ke+vr5cvHiRsmXLUqVKFc6ePcv333+PlZUV0dHRJCQkZGufCJEaSXBEnpM4YTm9EhIS6NWrF0WKFAHg4MGDAAwcOJDw8HC+//57fVl7e3uioqJwcHCgbdu2ADg4pPxaSX/FOjk5cfz4cWrUqAHA2LFjCQkJyfIvg8SJwTmR5JiZmfHTTz8ZLIuKikqxrLH1a07JDX3q4eGBh4eHvryzszNWVlYA2NjYvGbLhMh8kuCIHBcXp2T4lO70xrW0VL2ynJWVlT65AfSnhU+bNo0lS5ZgZ2dnUL5t27YUKFBA/7ho0aKvfI1ChQoZrAO6L4aspGg0+gnBGT21+1VxVa95poyFhUWys30SGUu/5jY53af29vbprKkQ2UulJI5tCiFey9OnT5N9IYg3J/2a+bKiT728vKhVq1amxhQiM0iCI4QQQog8R04TF0IIIUSeIwmOEEIIIfIcSXCEEEIIkedIgiOEEEKIPEcSHCGEEELkOZLgCCGEECLPkQRHCCGEEHmOJDhCCCGEyHMkwRFCCCFEniMJjhBCCCHyHElwhBBCCJHnSIIjhBBCiDxHEhwhhBBC5DmS4AghhBAiz5EERwghhBB5jiQ4QgghhMhzJMERQgghRJ4jCY4QQggh8hxJcIQQQgiR50iCI4QQQog8RxIcIYQQQuQ5kuAIIYQQIs+RBEcIIYQQeY4kOEIIIYTIcyTBEUIAoNFoXllm0aJFGYoZEBBAQkIC0dHRaLXa163aG0tP25YtW5aucm8iLi6OsLCwdJdPT33OnTvHyZMn36RaQuRJkuAI8ZYJDg5m+fLlwIsvx4sXLzJ79uxXrnvq1Kl0v87Tp09ZuXIlH3zwAZ9++iljx4597Tqnl1arZfr06QAEBQWxdetW7t+/z9dff/3Kda9fv/7K5CM+Pp6zZ8+yceNGHjx4kOH6rV+/PtU+nDZtmv7vxERy+PDhhIaGphkzMjKSy5cvp+v1AwMDAQgLC6N3795plo2Pj2fKlCncvHkzXbGFyG3McroCQog38+eff1K/fn3Kli3L8uXLuXnzpv7fokWLqFevnkF5jUZDVFQUAKVLl2b+/Pn4+fkxaNCgV75W4ohCeHg4+fLlS7Xc2LFjKViwIP7+/mzevBkLCwtiY2Mz3La9e/dib29P/fr18fT05Ny5c9y7d49r167x7bff8sEHHxiUV6lU+oSgcOHCHD16lKNHj1KzZs10tU1RFMLCwnBwcEixzJYtW7hy5QqbN29mwIABDB06lJ9++omJEyemqz27du3SJ5cvi4yMJD4+HjMzM8LDw9m0aROPHj3C0dHxlfUG3eiQoihYWVmlWnbr1q3Ur1+f8+fPU6NGjVTLxcXFMWnSJE6dOsWVK1dYvHgxe/fupXHjxpQoUeLVDRUiF5ARHCGM3LNnzwgKCgKgWbNmPHr0iBIlSnD06NFkyQ2gTzQURcHa2hq1Ws2VK1eIjo5O9TX27t3L9OnT8fb2pmHDhkyePDnVsnfu3OHnn3/mvffeo1OnTjx79gxvb2/++++/DLctMjJSP4JQv359tFotKpWKQ4cOJUtuEtumKAqKoqDVailevDjbt29P81DPf//9x/z58zl69Cjt27fn888/T7FcSEgIJ0+eZMOGDVStWpXChQvj4+PDhQsXWLp0KWvXrk2zLSEhIZibm6eaPMXExKAoCgAtW7bk559/Jn/+/Dx+/DjF8rdv3+Z///sf8+bNY9GiRXTq1Inz58+n+voajYamTZsSGBhIfHw8jRo1SvU9//vvvzl48CAajYYWLVoQGxvLP//8w8GDB5kyZYo+QRYiN5MRHCGMWGBgIN7e3mzfvh1ra2vq1KnDvXv3mDhxIjY2NsnKnz9/ngULFnD9+nUiIiKoV68e3t7eVKxYEWdn51Rfx97eHldXV7RaLR06dEjzkE9wcDA7duzgf//7H+Hh4Rw+fJiSJUvSrVs31Go1FhYW6WpbcHAwp06d4uzZszg4ONC+fXseP37MoEGDUhw9CggIYO7cuRw/fpyRI0fSp08fjh49SunSpSldunSqr2NjY0OJEiVISEigXr16zJs3L9Wybdq0Yfv27dja2lKuXDkWL16MjY0Nv//+O2vWrEmzPUeOHOH9999P8bkZM2bg5eVFjx49+O6771ixYgUajYY6deqk+D4CmJmZUbp0aRRFwdbWlhUrVlCsWLFUX//SpUusXLmSSZMm4eLiQqdOnZg5cyaVK1dOVrZy5coULVqUJ0+eUKJECS5cuMCjR4/Ytm0bLVu2xNzcPM22CpEbyAiOEEbs/v37xMTE0KdPH+rWrcuOHTu4c+cOarWaS5cuJStftWpVPDw8aN26Nd9++y1VqlShVKlSBAUFcefOnVRfp3z58qxbt45ChQoRERGR5pd5mTJlCAkJYfr06XzwwQdMmDCBpk2bsmDBAvr27Ut4eHi62vbw4UOioqJo3bo17du358KFC5w6dQpTU1NOnz6dbNJymTJlqF27NrVq1WL+/PlUrFgRDw8Pnjx5gq+vb6qvU7lyZX7//Xfs7e0pWLCgfg7Py+zt7dm5cyddunRBo9HwySefULRoUZYuXUqRIkVwd3dPsz179+6lefPmKT43cOBAoqOjWbx4MVWrVqVVq1ZYWlpy7NgxVCpViusULVqUK1eu8PjxY9577z1GjBjBkydPUn39KlWqoFarKVasGBqNhoiICCpWrJhiWS8vL9q1a4elpSXLly/n119/ZfPmzZQqVYomTZqkO0kVIidJgiOEEWvQoAF169bF29ubw4cPs3btWgYOHMiFCxfw8vKia9euBmfYxMbGsmPHDk6fPs3WrVspUKAAlSpVomfPnnTp0gVFUUhISDB4DUVRGDduHH369MHFxYVvv/2Wo0eP4unpmaw+4eHhzJkzh4YNGzJ+/Hi0Wi2rVq3iv//+Y/HixWzYsMFg9GXGjBls27YtxbZVrVqVZs2acevWLfbv38/PP//MqFGjOH78OA8fPuTDDz9k586dBvXcvHkzAQEBrFq1CisrK2rXrk2zZs3084tebhvoJvfWrFmT2rVr89lnnxEZGcn8+fOTldu6dSvDhg3j9OnTdOjQgS+//JIuXbpw6dIl6tSpk+b7FB4ezpMnTyhVqlSKz2/btg2NRsP06dOJioqibt26WFhY8Pvvv2NtbU18fHyydby8vNi4cSNjx46lRIkSjBo1ikGDBqU6KdnCwgIzMzM++ugj2rVrR6dOnTA1NU12CCwgIIAiRYrw6NEj6tWrR79+/Rg8eDCFChXi9u3br0zkhMgtVEriQV8hhNE5efIkU6ZMoUGDBoSHhzNgwABOnTpFZGQkw4YN4969e+zcuZOBAwcC8Ouvv3L9+nXq1atHlSpV2L9/P8eOHSMkJIQiRYoQFRVFo0aNGDdunP41Zs2ahUqlonHjxowcOZKyZcsyZMgQli1bRps2bejRo4dBnaKjo7l06RK+vr7s27ePwoULY2ZmRlxcHA4ODnTu3JmqVasC8Pnnn1O3bl0+/fTTZG27cuUK33//PYULF8bJyYmWLVsSFRXFgQMHmDFjBk+fPuXPP/9kxIgRAHh6enLmzBmcnZ358MMP2bNnDydPnsTX15cyZcoQFRVFuXLlDM4WW79+PRcvXqRXr15MmjQJKysrPv/8c3bt2kXx4sX58ssvAd0ZYXPmzOHgwYPExMSgUqmYO3cu69atIyEhgXHjxuHh4ZHq+7R161aCgoIYNmxYsucePXrEL7/8wtWrV/H09GTnzp2cO3eOXbt2Ub58edRqNWq1ml9//ZXixYsDcO/ePUaOHMk333zD3r172bVrF61bt6ZOnTqsWrWKxYsX4+TkpH+NHTt20KZNG8LCwrhx4wY3b97ko48+AmDAgAF07tyZdu3aAfD777+zbds2rly5QsGCBalfvz5VqlTh4sWLFCpUiG+++SbVdgqRm8gcHCGM2Lx58+jQoQNffPEFGo0GCwsLjh8/rp+sWrRoUX1yA7ovsw0bNmBqaoq5uTkdOnSgTJkyBAcHG5RLdPLkSUqWLImHhwcLFy4kPDycnj17Urt2bWrWrMncuXP1p4BXrFiR27dvs3HjRkqUKEH58uU5deoU9erVo1WrVoBuom3SEZxly5al2rZVq1ZRvnx5pkyZQkJCAhYWFuzevVvftgIFCuiTG4BOnTrh5OTExYsXAWjevDm1atVix44dTJkyJVl8X19fIiIiGDZsGNOmTePhw4cMHTqURo0a0axZM1auXEnv3r354osvqFSpEu3atePYsWOMGjWKq1ev0rhxY65du8bq1asJCgpKM8HZv38/I0eOTPG5/Pnz89NPP9GxY0fUajUVKlTgvffew9/fnw0bNiQrHx8fz4YNG1iyZAkrV67kxIkTlCpVigEDBuDi4oKLiwsjR46kZs2ajBo1CtAlf3Z2dhQuXJi6devi5+fHkydPuHnzJnPnzmXTpk36+JUrV+bcuXPUrVuXO3fuMG7cOKKjo1myZIl+YnJq84KEyE0kwRHCiP3111/6vxPnRaR0GCaRubk5kZGRFCpUSP+F7Ovrm+qpyPXr19f/vWLFCmrUqEHdunUxM9PtOr7++msiIyP1ZUqWLMmYMWP0j0+ePIm7uzv29vYA+v8TjRgxggYNGiQbBQKYO3eu/m9TU9N0t83U1JQyZcoAuom1qZ215OHhoe+DZcuW0aFDB2rWrImlpSWgmxfTp08fYmNjcXBwoEqVKri4uLB7926CgoLo06cPJUuWZM+ePQwaNIjVq1fzySef0KRJE33/AKjVakJCQnBzc0uxHollY2JicHR01L8XdnZ2qZZP7OMJEybw22+/cfv2bVxcXADdXJs///yT4OBg/TpRUVF4eHgwcuRIvvzyS7p3746FhQWrV69Go9HQv39/fdnq1atz/vx5/v33X6Kjo5k8eTL379/n77//1k8y7tOnD926daNAgQKpvh9C5DRJcITIY0qXLs2tW7dSfb527dpYW1vrH9vY2Bh8IaelZMmSyZKh1L6IQfdlnNaEVLVanaFTjosXL57m61WqVMng0IyNjc0rryOTNHb+/PkNlllZWemvK/Ptt98yePBgmjRpwr///svNmzf1ozL/+9//WLx4MdOnT8fCwoLGjRvrYzx+/DjFBO5l/fr1M3hcuHDhdNW7WLFiREREJFuemPCALhF1dXVl3rx5LFmyhIkTJxIZGUlkZCQajYZ3331XX3bLli08fvyYv//+m+DgYBYuXMjUqVMpVKgQX3zxBe+88w4LFiwgICCAn3/+OV11FCInyBwcId5yERERqNVqg8Qgs/zxxx+0b98+WeKQXTQaDcHBwWmePp1b3bp1K9VJyUKIV5MERwghhBB5jpwmbgTkqqGZR61W53QV8qyX+1Y+t8JYyWc3cyiKYjBHL7vJCE4O8PLyYtq0aVhZWdG0adNk9wBatGgR1atXp0GDBgD6C4slXmo96Sm8wlBqfXvkyBEWLlyIg4MDISEh/PbbbxQsWJCAgAC+/vprLCwsqFixIpMmTcrhFuReGe3bwYMHc+/ePdRqNe+88066bub5Nkutf2/evMn06dMJCwujffv2+gnBr9qPiBcy2reyz02/tD6Hnp6eFCxYkOjoaFq0aIGFhUX27nMVka0iIyOVli1bKnfv3lW0Wq0ybtw45fr16wZlhg8frty+fVtRFEWJiYlRPv7445yoqtFJq28XLFigxMXFKYqiKNOnT1cOHTqkJCQkKB988IHi7e2tKIqizJkzR9m/f3+O1T83y2jfKoqidOvWLaeqa3TS6l9/f38lLi5OuXTpkr5P07MfEToZ7VvZ56ZfWn2r0WiUbt26KWq1Wunbt68SHx+f7ftcOUSVzfbv30+bNm0oWrQoKpWKTp06ceLECYMyQUFB+gt63bhxI8376IgX0urb4cOHY2FhgZ+fH9evX6dBgwacP3+ecuXK6e/F07Vr12TvhdDJaN8+efIk2SnhInVp9W/ZsmW5f/8+U6dOZejQoa8sLwxltG9ln5t+afXtgQMHqF+/PqampqjVakxNTbN9nysJTjbz9/enZs2a+sdqtdrglN3Y2FgsLS31959xd3ene/fuJCQkEBMTwz///JPtdTYWr+rbx48f8/XXXzN9+nTMzc1fWV68kNG+zZ8/P8OHD0etVqMoCuvXr8+JahuNtPr377//pnPnzvj4+LBt2zbi4+Pls5sBGe1b2eem36v6tkOHDty+fZsiRYqgVquz/XMrCU42c3Bw4MGDB/rH586dM7gAmJ+fn8FjKysr3nnnHUxNTbG2tmb16tXZWl9jklbfxsXFMWrUKCZMmECJEiVSLO/l5ZXqxdjedhntWxMTE6pVq4aFhQUqlYp//vmH6OjoHKm7MUirf4sXL86uXbvYvXs3t27d4vTp06/cj4gXMtq3ss9Nv9T6Nj4+nvPnzzNo0CA+/vhjDh48SKtWrbJ/n5tlB79EigIDA5V27dopT58+Vfz9/ZUePXooCQkJ+uc9PT2VtWvX6h//9ttvyoEDBxRFUZSTJ08qTZo0yfY6G4u0+varr75Sdu3aZVA+LCxMad68uXLnzh3l4cOHSrdu3ZTw8PCcqHqul9G+/ffff5WNGzcqCQkJyo0bN5T69esrUVFROVF1o/Cq/UKiX375RdmxY0e6y4uM963sc9Mvrb7VarWKVqtVVq9erd8/ZPc+VxKcHLBp0yalY8eOyieffKIEBgYaPDd9+nTl1KlT+sdhYWHK8OHDlVatWim9evVSTp8+nc21NS4p9e21a9eUqlWrKq1atVJatWqltG7dWvH09FQURVEOHz6sdOnSRenZs6dy6dKlHKx57peRvo2Li1MmT56stGzZUunatauye/funK28EUipf2/evKk8ePBAURRFCQ4OVpo0aaL4+/unWl6kLCN9K/vcjHnV53DixInK1atX9Y+zc58rp4nnAl26dGHcuHHUq1ePEydOULlyZZmgmUkS+7ZatWr6S+6LzCF9m7W6dOnCqFGj2LZtG35+fsTFxdGnTx969eqV01UzetK3WSfp9xnAsWPHqFGjRo7coFUSnFwgLCyMfPny6ScWi8wjfZt1pG+zlvRv1pG+zTq5qW8lwRFCCCFEniNnUQkhhBAiz5EERwghhBB5jiQ4QgghhMhzJMHJZcLDw1m4cCHh4eE5XZU8R/o260jfZh3p26wl/Zt1crpvJcHJZcLDw1m0aJFsbFlA+jbrSN9mHenbrCX9m3Vyum8lwRFCCCFEniMJjhBCCCHyHElwhBBCCJHnmOV0BYxRaGgo27dvp2zZspibm2dq7EePHgFw+fJlHj58mKmx33bSt1lH+jbrSN9mLenfrJMdfavRaAgICKB9+/Y4OjoaPCdXMn4Nf/75J1OnTs3pagghhBACmDx5Mh9//LHBMhnBeQ1lypQBdB1avnz5HK6NEEII8Xby8fFh6tSp+u/lpCTBeQ0WFhYAlC9fnlq1auVwbYQQQoi3W+L3clIyyVgIIYQQeY4kOEIIIYTIcyTBEUIIIUSeIwmOEEIIkYkUtSanq5DrKJrs7xOZZPwGeg9+BhYhryxXp7o586c5MHJSGGcuZP6bLPElvsSX+BI/98T3PeFM7MjhGV7PxM0N808+RfPbarT+/plSl9wS32r+wkx/vVfWJ9tf8S1jjBunxJf4El/iS/zsp/X3R/Pbasw/+RQTNzeJ/4YkwclCxr5xSnyJL/ElvsTPXsaehOSmJEcSnCxi7BunxJf4El/iS/ycYexJSG5JciTByQLGvnFKfIkv8SW+xH/9+JnB2JOQ3JDkSIKTyfLCxinxJb7El/gS//XjZxZjT0KSxs8JkuBkoryycUp8iS/xJb7Ef/34mSmvJDk5QRKcTJKXNk6JL/ElvsSX+Lknfl5IcnKCJDiZwNg3Hokv8SW+xJf4uTN+ImNPcnKCJDhvyNg3Hokv8SW+xJf4mR/fGJOQvJbkSILzBiq4m+XZjVPiS3yJL/El/uvHN9YkJC8lOZLgvIGRA+3y7MYp8SW+xJf4Ev/14xtzEpJXkhxJcN7A/BWReXbjlPgSX+JLfIn/+vGNPQnJC0mOJDhv4PqN+EyPmVs2Tokv8SW+xJf4bxbf2JMQY09yJMHJRXLbxinxJb7El/gS/83iG3sSYsxJjiQ4uURu3TglvsSX+BJf4r9ZfGNPQow1yZEEJxfI7RunxJf4El/iS/w3i2/sSYgxJjmS4OQwY9k4Jb7El/gSX+K/GWNPQowtyZEEJwcZ28Yp8SW+xJf4Ev/NGHsSYkxJjiQ4OcRYN06JL/ElvsSX+G/G2JMQY0lyJMHJAca+cUp8iS/xJb7EfzPGnoQYQ5IjCU42M/aNU+JLfIkv8SV+5jD2JCS3JzmS4GQjY984Jb7El/gSX+JnLmNPQnJzkiMJTjYx9o1T4kt8iS/xJX7WMPYkJLcmOZLgZANj3zglvsSX+BJf4mctY09CcmOSkysTnHXr1hEVFQWARqMhISEhh2v0+ox945T4El/iZ2N8bWTGAryifJ7rHyOLn1HGnoTktiTntROc3bt3s3fvXgBiY2PTtc6zZ8/QarWpPv/rr7/yyy+/cOrUKcaPH8/48eN57733mDp16utWM0dlZONpV2g7B+q04Fqjyhyo04J2hbZnavzXIfElvlHHV5+FmI2gDYNnPXX/nrSE6GW65yN/gvAJEDYE4n10y6JXQ/g4CBuKe/EzyeOrT0D4KAgbCtrHhq8X/rXh45hN8PSDNOtvHvONYfyYFbr6xu1/qS0nIGwEhI/W1VH/GknKKwrErIeEO6CoKerwe95+f3N5/Ndl7ElIbkpy0kxw7t69i4eHB4MHD0723OnTp1m1ahUdOnSgTp06XL9+PdU4q1atonnz5vTr148rV66kWu7ixYt89dVXLFy4kAULFjBgwAAqVqzI8OHDM9AkHY1GQ58+fZItj4yM5IsvvqBnz558/vnnhIWFodFo+PHHHzP8GmnJaHIzzX0KRa0eYKJSKGr1gGnuU9JMcox945f4Ej/L4yfcBlMXMHGA/Bt0/0xLgnld3XMm+SHfj2A7BuL2gBIDCfch38/UbDgPF/sdyeObFIZ8c8G0OMT7vViujQJMXzzWXIa4LeCwOM36X74W+SK+NkyXNClRLxKuRHG7da+bbw5oLqZcPsFP14bYnZR2OcC33zTJ2+9vLo7/pow9CcktSc4rR3A6d+7M0qVLDZYFBATw7NkzZs+ejZ2dHTNnzqRChQqpxujfvz/29vZs2bKFKlWqEB8fn2K56tWrs2nTJq5cucL58+c5fPgw33//PU5OThlsFgQGBuLq6pps+ezZs6lTpw4bNmygX79+/Prrr5ibm2NnZ8e5c+cy/DopyejGM7r0fKxNDUfBrE1jGV16fqbEzyiJL/HzRPyEW7qEJpHG+3mCUw0wg4QQUNSgDQHMARNQnlGzsoZRAyO56qtKHt+sNMTtg4SHYF7b8LXMkrxW7AawHQ0mRVOt/5CvbhIe5fjiibh9YPE+JNwE0xKGKynxoH3y/JCUOuXypsVAfZCKlZtQsawPq/4uk7ff31wcPzMYexKSG5Kc1zpEdefOHU6fPs3UqVOpVKkSv/76a4rlQkJCuHjxIjt27MDPz48RI0bQunVrWrduTWBgYLLyrVu35tKlS5iYmGBtbU358uXZuXPn61QRX19f3N3dDZbFxcVx9uxZ/cjOu+++S0BAAABVq1bNlATndTaewpYPU1xexPIBw0ouplXBvZS2vokp8Xli45f4Ej9b4mvvGyYYcbvB+kPd36ZFdaMwYYMhchqY1wCVJaXcmhL1cDiffPIpT6OqJo8ZNR8if9AdBoq/+GJ50mRKUYPmEsQdhshJoD6eYv0vXvI3TMDUh8GiASQE6uqmqF88Z/0RRH4PYQPBxCXl8phRp8lf9O0ag++dOjnf/29x/Mxi7ElI0vg5wex1Vqpbty5t2rShc+fOJCQk4OPjk2K5devWcffuXQIDA6lYsSJarZadO3diYpJyXnX9+nVGjBjBd999h7OzMyVLlqRevXqvU0Vu3LhB7dq1DZbdu3cPd3d3g9c3M9N1QWhoKLa2tq/1Woled+N5EOdKUasHyZYnKKYMLbEUE5UCgEYxR+VcjhGTV3Hmgj3FrIIAuBtbDFC9Ud3fpP4SX+LnyvhKAqieHzZS4nWJgFmSkWabfmDZFCJngnmd5/H7MXRcEzR+I8Gula5c+Jdg3VeXBJnXBOtPQXMWov8Ah1q6Mgm3wLL589eKA/PqYDcKFA1EfA0WDSho8RMt6zdg5KQmuvon3ALTUi/WifeF0H6ghAMmYDMcrNrqnjevrDvcFT4GrHulWL50hS+ZP603HTrv4XFUe4j8EPItBdNCb9Lternu/c3F8f9YnD/T4iZNEjS/rUbr759psbMzvsXwkZkaNz1eawTH2tqaZ8+eceXKFR4+fEhYWFiK5UaNGsVPP/1EfHw8ffv2pWbNmgbJxePHhpP0KlWqxMqVKwkMDKRx48YUK1aMhQsXvk4VuXHjBh4eHgbLHB0duX//vv5xYGAgjo6OABw/fpxatWq91mvBm208cwJHEpNgZbAsJsGKr32nU+P4Gbqc/5ulMT+RUG8Ql+4X5tB5XSL2efEV/FWtN4nJTY/Cf/NJ0TU0zH8cZ4tgQMmW+kt8iZ/r4mtDdXNvEsVfBrOKyctE/gi2X1GnhiXzpzkwfHwwF05OAtuhoLLRlbP7Hsyej+ZYNAQTe7BoAkqSM5gS7rw4rKSyBe0jXRKScBNUjtSpbs7/fp/M7uMNX9Q//vaLERyVJRTYC45/gVlNyL/zRXKTKGqxLsky80hWvnbTffxvdW/6D93P4/DKEH8VzOuD8jQ93ftKue79fcvi54WRnJzwWiM4t27dIn/+/Gzfvp1Zs2bx3XffpVr2t99+o3PnzlhYWGBq+mISXkJCAqtWreLrr1+ceeDi4sLjx49ZtWoVhQsXZvny5TRu3DjV2D4+Pmi1WipWrJjsueDgYFxcXAyWFShQAHNzcw4dOsS7777L3Llz6du3Lzdv3uTZs2fJEqL0etMP945H7QHdXJzClg95EOfKnMCR+uV25arSY8p7DHop/h/3PubAk6b6x20L7aKu41n94/B4e/yi3PCLcsM/2g2/qHLciHbjmaZAptb/VSS+xM/2+Mnm35zRTS5OFB8A0QvBZjh1apdj/jQHBo+9waXTP4JVV90ITKKoWWDVTZe4mBbWJT5xO16MvoBucq/KWve3ygSsOkPY56CypHLtb5k/zYFO3aYQ/KwCWDbTldMGPT+09JxKhe7Hivr534mxNbo6mBQB697Jyjvk07BguiMjJ4Xhe2Ub2E3Q1SfeV5cMvaFc+f6+RfETGftITk54rQTH2tqa+/fv06hRI9RqNcePH8fJyYnSpUsblPP398ff35+ffvoJb29vtmzZQrNmzVCpVJw7d46QkBB92ejoaJYsWcKYMWNQq9WsX7+eTZs28c8//6Raj/379xMdHZ0swQkPD8fe3j7FdX788UcmT57MnDlz+Oyzz6hVqxZjxozh22+/fZ2uyLQP945H7fUJTXrj34h250b0i3lG/bx/w9HsGeVs/Sln44+brT/lbP1pXWgPjuaeAJwJrUVf7zUADCq+ggSXdxjwfQej3fglvsRPkUlhsEjy48iiMZiWffHYtBjYz6JODWt9/EvX8oH9j6CyMIxlVgFMCugSnIjJujOXVPZgN+lFGesehutYtgLLVgb1D35aGkyTnPhg9SGoUtgFW3XV/a8oENYf7GeA7cgXI0pJ1KluTs+2n+j653wkWLbVxVTZg8Xrj0gnjZ8r318jiG/i5mZ0SUheS3LSTHBMTU05c+YMXbp0YePGjZiYmBAWFsYPP/xAmzZt+OAD3TUeWrduzeLFi3F3d2fQoEH69W/dusWUKVMAqFKlCq6urnTs2JHo6GhsbGz4+OOP9WUtLS0ZO3as/vGSJUv4+OOPsbFJvlGDbgQoNDSUUaNGpVjvpPWIjY2lXbt2rFy5ktKlS7NmzRqD8rNnz06rG1JVwd0s122cofH5ORtWm7NhSecfKRSyeEw5Gz8Snp/KqkLLwJJrMa//MYMmvcflS2EcqPMBAdFldaM+0eXwjypLQHQZYrQpvwdZUX+JL/EzJb6pC5BkBNesvOHzKsvk8VNKNkA35yVRvlT2FRYNXl1/q3avXEe3/Pm8Q5UK8i3SHRJLwYv4VZ/X3zJTkppU65/J8np8Y01C8lKSo1IUJf0TNYzYs2fPyJ8/cyZ+eXl50bt3b5Yu/ZPVG92Md+Ocas/YyY84fsGUAuZPGFdm1vORnwAsTXRncGgVFUGxxfCPcsMv2o09j1pyPSr1SwJka/0lvsSX+BI/F8b3PeGMeuH8LE0STNzcjCq+1fzXm0/7Konfx+vWrUs2jzZX3qohK2RWcpPU/BWRxr1xTo7g+AXdiM5TjRPf+M7gwwueVD92ltZntzPs6jwW3v6CqxEVKWF9h8+K/UY5Wz8AKtheZ3vNjlSx9wYgv/lTOZVd4kt8iS/xn8sLE4Nz+jo2b+q15uAInes34sHi1eUyIjdsnFpMuRVTilsxpdj/pLl+ublKg4oXt9q4FVOKpxpd4ti64F6+KzdNdyq7izunp5Wh6uOy2BZw40a0G/dji6BkQj6dG/pH4kt8iS/x0xPf2A8nGfvhqrdmBMcY5LaN82UaxRy1YgnA9agKDLu2gLuxurNAjj5txPKYmSTUHcDV+06UTjjLmNLz+PWdYRyo0xqvBnX5u1pPnMx1lwZwsXgop7JLfIkv8fN8fGMfaTHmkRwZwcklcuvGmV5FK5ai25SqBqey25lGUM7WHzebAMrZ+lHW5iahGkcABpVYSUfn7dQ+cRKATi5bsTGJxi9ad1p7aLzhIUVj7x+JL/El/tsb39hHWox1JEcSnFwgt2+crxs/MsGeC+HVuRBePdk6Gx98yJnQ2iRepLCLyz/UcfTSP/9I7YRfVDn8otxIKOhB1wHVGTupMGcuWGZb/SW+xJf4Ej+z4ht7EmKMSY4kODnMWDbOzI5/PaqCwdlYfb1/w9niEeVs/V5cw8fGnx5FN2OlioY/YYi2KkdZpytf9A/8o8pyIvTdHKm/xJf4El/iZ5SxJyHGluRIgpODjG3jzNr4KkLUzoSonTn+rMGL+F/bM+Hba0Tf8iFBSbwStsLQEr+yJfgDToS+i7lKzbaaXQiILqM/xOUf5UZgTGk0Sup39jWu/pH4El/i54X4xp6EGFOSIwlODjHWjTNn4rsCSa4Ai4oGJ49iZRoHgJ1ZJL5R7rjZ+PO+0xHMVAkAaLRm3Iopqb9VxYEnTfCNKp8D9Zf4El/iS/wXjD0JMZYkRxKcHGDsG2duiJ+AGVEJuo/vM00Bvrw+BwBzlZrSNoG4JznM9Y79Vdo67yFEXQjfqPJ0rnmLqaW/4cdvJ3HmwjvkMwvDzjSK+3GFkbuyS3yJL/GzMn4iY09CjCHJkQQnmxn7xpnb42sUC25EeXAjygMevVhubRKtjz/+CyuurszPqWu6myO2LLiPae5TiIq3eXGIK8n/IepCpDfxye39I/ElvsTPufgvM/YkJLcnOZLgZCNj3ziNOX6M1kYff9gkOHPhV/1zp0Pr8O2N7yhn64ebTQBNnA7TrfBm/fOhmnz4RZdjzPVfCFE742T+mATFjNB4x2yrv8SX+BLfuOOnxtiTkNyc5EiCk02MfePMy/GDYksQ9LCEwbIC5k8oZxPw/DCXH262ATx7fg2fgcVX0b3wRmoeP42CCW0K7eKd0nF0G1CdsZNc5VR2iS/xJX6GGHsSkluTHElwsoGxb5xvY/ynGidOhzlxOqxOsue2h7TlemR5/a0nBpbzpKLZGfgTltvDvTqF9XdkTzzMFRBdhjitVbbVX+JLfImfO+Knl7EnIbkxyZEEJ4sZ+8Yp8ZO7ElmZK5GV9fELT9jCmG+vEX3rOu62utGecjZ+vJv/FBYmute8HFGJbhf+AqBH4b+5FV0qxeQpO+ov8SW+xM+e+Bll7ElIbktyJMHJQsa+cUr8jMTXncp+6GkT/fOmxFPC+g7utv4kKIm3fVMYVWo+Ox614XRYHUyJx7NGT25FlzQY8bkTU5xa1a3yUP9IfIn/dsV/XcaehOSmJEcSnCxi7BunxH/z+AmYERhThsCYMkmWqnjv1EGsTWMBsDeL5GGcK5Wfn8qeSK1YYuLqzqlpZajyqAyhNo24Ee2erfWX+BJf4ucMY09CckuSI3cTzwLGvnFK/KyNr1YsCYt3ACA03pGhVxfR4uxuahw7Q9fz61kR8yPaOv25ds8BN+0pxpaZS02H8wCUsLrDhmq9qJ7vAgC2ppEUNH+M3JVd4kv83BM/Mxj7XcJzw13IZQQnk+WFjVPi50z8aK0NNuVq0HVKE4O7suczC0P7/BCXtWk0sVorohN01/Bp7nSAn8pP5JnGEf8oN248v36PX1Q5/KPd9IlUdtRf4kt8ia+Ln1mMfaQlafycIAlOJsorG6fEz13xw5MkKb5R5envvVr/+GJ4Nab5j6fc89PZOzjvIJ9ZhP75kLhC+EW7McF3KiUrFWfBxATGTbonp7JLfImfhfH/WJw/0+LmlSTHYvjITI2bHpLgZJK8tHFKfOOJfzu2JLfvl0yyRMHFIphytv66M7psAnR3aK/kxC/THDj2wyQW2v5ODc6QgBlNnQ5iZxqJX1Q5bsaUllPZJb7Ez4Xx80KSkxMkwckExr7xSPy8FF9FsNqVYLUrx541TBY/zr8JZ2yLkvB80/+4yP94N/8pABIUE4Jiij8/zPXijK5bMSWJl7uyS3yJnyPxExl7kpMTJMF5Q8a+8Uj8ty1+VS5FVNU/P+jKr5SwuqMf8Sln44+bbQDNnA5hqtICcDWiAh9e8ASgo/M27scVwSusVg7VX+JLfOOIb+LmZnRJSF5LciTBeQMV3M3y7MYp8d+O+PGKOTdjynIzpix7HrfSL7dQxVHGJpBytn5oFVP98nFlZnHoyft4hdWibnVTVpXvw6lpRXjnURks85fDL9qNh3GuyF3ZJf7bHt9Yk5C8lOSoFEVJ//mlAgAvLy969+7NunXrqFWrVk5XR4hso8RFgiYGlV0hlJgw+N9ACPaB8IcvClnag4sHuFYAl/Lg9h4qF4+cq7QQ2UxRa1BZZM7p4nmFotGgMs/8Pknr+1hGcIQQ6aaytANLO93f1g7w2d8AKNGhEOIDD310Cc9DH7iyA878Ae2ngosHyrMg+Hs4tBqPqlRdFHUUxGtQ2TjmXIOEyAKS3CSXFcnNq0iCI4R4YyobRyhVT/fvOUVRIPIRmD7fscWGQ4L6xePre2H95yj5XHUjPS4e4FIBXMuDs7sumRJCiNckCY4QIkuoVCqwd37xuHAlGLrzRYEilaHNt7oRn2AfOP07aGL0Tyv5S+gSn04zUTkWQ4kNB1MLVOavdyq7EOLtIgmOECJHqAq5QeNh+seKNgGe3n6e8Pi+ONRlaa8rcHQJHFmE8v1NVGYWKDcOQmykbsTHqTQqUzksIIR4QRIcIUSuoDIxhYJldP8qtU1ewL0JWDuiMrPQPf5vKfgd1v1taoFSqKzuEJeLh27kx7U85C+JykRuuSfE20gSHCGEUVCVqgul6r5Y0HcthPgbTm6+fRYubX5RpnAlGHkIAMV7Gzi4oipZJ5trLoTICZLgCCGMksrcGopW1v1LQomLfHGIS5Vk9Gb7ZHBrBCXr6CZAr+4B+Ys/n+CsG/FR2RXK5lYIIbKKJDhCiDxFZWkHJWrq/iX15WHQxOr+VkdBvBqubNedyv6cYlvQ8BBXmQa6uUJCCKMjCY4Q4q2gsnlxh2eVpR18vuX5qewhuhGfxMNcwdfh/F8QFwmtJ8H7I1DCg2HjCGg6WncNH00saOPlVHYhcjFJcIQQby3dqewuun9u7+mXK4oCYffAzFK3IPopRD4Gnl/43fcA/PkJSoEShhObXcqDczlUiesJIXKMJDhCCPESlUoFjsVePHatACMOvCjgXA5afP1iro/vAdDG654zMUVxKqNLetp9jyp/cd1Vm00tUZnKLleI7CJbmxBCZJDK2R2ajdE/VuLV8DggyfV7rsPDa2BurSvw3zI4NA/lO19U5tYoAcchLkI34pO/hJzKLkQWkARHCCHekMrMQndzUdcKKRcoXR9MTHRnfgH89yv47NX9bW6D4uJhOLnZpQLkc9WNJAkhXoskOEIIkcVUZepDmfovFvT8FUJ8k0xuvg43DsK5DS/KJL2Gz7U9YF8IVfEa2VxzIYyXJDhCCJHNVFb2UKKW7l8SStTT54e5rhuusH0iFKsBvZbryv3RHxyKGExuVlk7ZFPthTAOkuDkAYpag8oi6+7Do2g0OXKreyHeNirbArqRnqSjPQCDt0N8HACKOhrCg3W3qVBH64soDkUM78peqg6qgmWysfZC5C6S4LyB3oOfgUVIsuV1qpszf5oDIyeFceaCJtNf9+X4vieciR05/LVimbi5Yf7Jp2h+W43W3z/FMlbzF75JdYUQb0iVz/XF3xY28MUuFK0WQu++uBt74s1Jbx7XJUMtvoZmY1CinsDGUdB4KKpS9XQTolHkVHaR50mCk8myO7l5U1p/fzS/rX5lkiOEyF1UJiZQoITuX4WW+uVKQrzuruyWtroFkY/hSSBodCNA+B+B3/uiFCybZMTn+TV8nErLqewiz5BPciYytuQmkSQ5QuQdKlMzKFT2xWMXDxj934sC+UtA4xG6EZ/7l+HKv6A8v4ChqQWKczld0tNyPKoCJVHi48DEXE5lF0ZHEpxMYqzJTSJJcoR4O6hcPKDVeP1jRR0NIX6Gh7oCT4Pp83l3x5fDgTkoEy+jsrRDuX0G4qJ0Iz5yKrvIxSTByQTGntwkkiRHiLePysIGilXV/UtJsRrQYNCL+24d/RWu7tD9beWA4lr+xSGuxLuy2zplT+WFSIMkOG8oryQ3iSTJEUIkpSrbAMo2eLGgyyx4d4DhiM+lLRAbpi+iFH4H1ciDur/9DoONE6qilbO34uKtJwnOG6jgbpankptELyc5QgiRSGXrpEt4kiQ9iqJARLDuFhXBvqBoX6ywbQI4u0OfNbqyG4ZAPpfnIz4VdDcntbDJ5laIt4EkOG9g5EC7PJfcJEqa5AghRFpUKhXkc9X9c29i+OQnGyBBtw9T4uN0CdCVF9f1QaVCKVDS8DBXiZqoCpTM5laIvEYSnDcwf0Uk12/ljuTGxM0t0w8nJSY5FsNHZmpcIcTbQ1WgxIu/zSxh5MHnp7Lfej7i4/PiJqU++0CbAE1HQ8tvUGLDYdNoaDAQVam6uvVUKlQmpjnXIGE0JMF5A9dvxINF5sZ83ZGbrJozI3NwhBCZTXcqu5vuX+UO+uVKfBw88gerfLoF4cFw3xtiw3WPbx6HtR+jFCr3/Kak5XU3OHX2AMdiciq7MCAJTi7yJoelZGKwEMLYqcwsdTcZTXzsXA6+OvOiQD5XePez51dsPgkXNr54zsIWJfEQV9MvddfwSYgHE1M5lf0tJQlOLvGmc27k7CchRF6ncvGAtlP0j5XY8Be3qNAf5toDTb/UFTi5Cg7MRhl3FpW1A8rdS6COAhcPOZX9LSAJTi6QWROKJckRQrxNVFb5oGQd3b8klMQrM7tWhBo9Xtxp/b8lcOkfXRm7Qs8Pc1UwuGWFyso+O5sgspAkODlM7i0lhBCZK/GQlMqtEbg1evFEux+gZo/nIz7PR33O/AmaJHdlL1oF1fD9ur8DT4G1AyrXCtlaf5E5JMHJQXJvKSGEyD6qfC66a/C4N9UvU7RaeHbnRcKjjX+xwtavwbEY9F+nK/vPWLAtqL9iM05lUJll8pkmItNIgpND5N5SQgiR81QmJuBUSvevYmvDJ3uvggQ18Pwu7YGn4bG/7lR2ABMzlEJuSa7h4wHFqqFyLJqtbRApkwQnB8i9pYQQIvdTFXJ78bepGYz+78Wp7EknN9+9AN5bdAUbD4M23+puYvrPV1C3r+4aPlqt7ho+ckZXtpEEJ5vJvaWEEMJ46U9lT3I6O4ASF6m7K3vihObwh7rr9lRqo3t865TuGj4uHuBc/sV1fFzKg72zJD5ZQBKcbJRb7i0lSY4QQmQulaUdFK/+4nHBMjD+4oszumwLQI0eupGf67vBa92LlW3yv7iGT6MhqJxKoWi1cuHCNyQJTjbJTfeWkiRHCCGyh/6MLpfy8MEM/XIl8lGS6/c8v2XFxU3QYKCuwJm1KAdmw5dHUNk6oTy8BuoY3TV8LO1yoilGRxKcbJDTyU0iSXKEECJ3UNkVArdCBqex60d7AJzKQIVWYFNA9/jor3D+L105x+LPD3F5JLkruxsqc+vsbEKuJwlOFsvq5KZOdfMMlZckRwghcqek83BU5RpDucYvnmz5NbzTLsnk5uvgd1h/p3ZUJrpr+AzbC4ASdB6s8hlMlH7bSIKThbIjuZk/zSHD60mSI4QQxkXlWEx3TZ4kp7IrCRp4Evgi4UlIeg2fb3Q3LR2gu1+Xsn0yWOd/Mbm5QMk8f1d2SXCySHYlNyMnhfHH4vwZXl+SHCGEMG4qU3Nwdtf9o6Phk13nQbzuu0fRJoDPPnh888Xz5tYozuWSXMOnPBSprLsYYh7x1kzR3rhxI0+fPgUgPj6e+Pj4V6zx+rIzuUkrfoQm7ddOmuSYuL3+MGZERMRrrwsQExPzRusLIYQwpHKtiKpYVd3fJqaoxp6C72/CF3ug63yo2083v8f/P9j1A6zpBUcWAqDEx6FsGo1yW3cnd0VRDOcHGYlcOYITFBTE1atXKVeuHGXLlk2zbEREBJaWllhYpHy57PXr13Pnzh1CQkI4fPgw9vb2nDlzhjJlyrBixYpMr3t2JTf9h+zF1/cmWHclLCyM8Rcu4mRhwcRKFQF4GBPLX3eCqJ7fkRoF8mNnlvytjktIYKLnJuaAfiQnxteXid5XmFVdt2EERUczsXt34uPj+eyzz2jXrl2yOPPmzWPYsGGcOHGCdu3aodVqmTJlClevXqVkyZLMmjULk5dOdxw8eDBLly4F4LfffmPr1q2oVCpat27N0KFDWbFiBT169EClUrF9+3b69euXyT0phBBvF/2p7ElOZwdQokMhxOfFhOawB3B1J5RtqLuR6R0vWNvn+ansHkluUuqByibjRxCyS5oJzt27d2nWrBlNmjTRfxkB3Lt3jzlz5jB79myD8iEhIYwdOxaNRkPhwoWZOXNmqolHSkaPHk1AQADh4eF8+OGHlCtXLtWyW7duZdmyZSQkJDB+/Hjef//9FMsdOXLEoO4REREMGjSISZMm6ZdpNBp++eUXJkyYkO66piQ7R258fW+CqW4ocdasWRS2smK4+4v+Wn/nDs1dXNhw5w41C6T8AbwdHY2LlaXBSI7vLz/jYmWpLzPt6jW+W7ESd3d3RowYQYsWLQze02fPnhEcHExkZCSXL1+mXbt2bN26FWtrazZt2sSGDRvYvXs3bdu2NXjtpKM2vr6+bNy4EXt73V189+3bh0qlYs+ePTx79owuXbq8eecJIYRIkcrGEUrVe/HYqRTK5OuQOGpjZa+b4PzwOlzwhLhIfVnF3uXFvJ76n+nWVRSDCdPKhU2wZzqE3gPHotBqIqrqH2Z5u155iKpz584GCQLAjRs3KF68eLKykyZNom/fvqxfv5769euzYcOGDFVmwoQJbN26FRcXF4YNG5bm6E3Hjh0pVaoUK1eu5P3330/1kFO1atU4efIk+/btIyAggD/++IMffviBkiVL6suYm5tjZ2fHuXPnMlTfpLL9sFTCLTAtCdowLl26xBfl3LAy1U0Yi9dquRQaSmVHB8I0GmxTGL0BCIiMpLSd7noKiUnO7SpVKVO6NADBsbHYm5lTqVIlzM3Nadq0Kb6+vgYx/v33X1q1asWNGzco/Xy93bt3M2TIEAC6deuGl5eXwTqPHj2iYMGC+sdPnjzRJzcA77zzDv/88w9169bl6dOnFClS5A16TgghREapVCr9hQZVLuVRdZmNauhOmBIA31yAT9ZDm291Z3pFh8Lp30EdpVvZaz3KT7VQIkJ0yc2mLyH0LqDo/t88Wrc8i73WHBxfX1/c3d0Nlj148ICoqCiaN28OwIcffsiJEycyFDfxS0+r1fLkyROio6OTlXn69Cne3t7s2bOHixcv8sMPP9C+fXtatGjBmTNnkpXv1q0bW7Zswd3dnZCQEBo0aMDu3bvRvDQ/pWrVqq+d4OTInBvtfTApCpqTaLVaFvv58+X5C4SpNZx+8pQa+fOjVRQURXcoKiWBkVGUsbXVP9b6++P3zz+4f/wxJm5u3IqKopJDPv3z8fHxWFlZGcTYs2cPTZs2xd/fn1KlSqFWq9FoNDg6OurXsbS0NFgnICCAMmXK6B9PmjSJ6OhoYmNj2bRpE4ULF2bfvn0cP36crl27vknXCSGEyEQqlQqVY1FUHs1QNR6GqvsiVMP36eb3uFTQFXIsAqXq6O68vmc6xMcZBtHE6JZnsddKcG7cuIGHh4fBMn9/f2rUqKF/rFarsbbO2EWHjh49Cuh+0ffv35/z588nK7N9+3ZWrlzJunXrKFasGE+ePGHz5s0cOnSIOnXqJCt/4cIFvvrqK9asWYOnpycXL16kZs2amJsbXj8mNDQU2yRf9umVYxOKlQRQmYI2nLFjx/JVhfI0c3HhUEgIl0JD2RR0lzZH/sPr6VO6HT8JwAf/HeN2VJQ+RGBUFGXsDNt8MyiIkidPYv7Jp+QrXZrg2BcfzOvXr1OkSBHGjx/PP//8Q1xcHFevXqVDhw4sW7aMYcOGsX37duLi4kh4nlRdunSJUqVKGbxGQEAAbkkmNZcvXx4bGxusrKz4888/Ad1hQz8/P+7fv0+rVq2Ii3tpAxFCCJFrqExMX4z4lHsfVY8luseh91JeIbXlmei1Epxbt24l+9JycHDg/v37+sdeXl6vnCD8sp07dwJQuHBh/v33Xxo2bJisTN++fVmwYAEAgwYNolq1agZzQh4/fmxQvkaNGmzYsIH9+/fTsmVLqlevzrRp05LFPX78OLVq1cpQfSu4m+VMcqMNBZPn178xLcqFCxcAuBYejrOVJUPLuXG46fvMqFKZwW5l2faerh9X16lNySRJ3FO1GseX5kg9VavJd+8umt9W886YsXhFRXL//n18fHyIjY3F1taW8ePH065dOywtLblw4QL79++nfv36nDlzhi5dulCtWjU2bNhAbGwsa9eupWnTpgavERAQoP9sBAQEsHDhQtRqNd7e3kQ9T8C2bNlChw4dOHPmDI0aNSI8PDwzu1YIIUR2cCyaseWZKMMJjlqtxtTUFFNTwwsEVapUCR8fHy5fvkxERATLly+nY0fD8/J9fHy4du1aqrGDg4NRFAWNRsPjx495+vQpkZGRycrt3LkTDw8PChcunKwey5cvN3js6OhISEgIK1asoHXr1ty9e5fGjRsblLl58ybPnj1LNir1KiMH2uXMqeCJ828AzOsSFBRE9+MnidTEU9/JCdANIwZFR1M4ySja6AuXuPv8sF+8VouZyvDtT7pM6++P8sfvTJw2nWHDhjF79mzGjRsHwMKFC9m/f7/+dUxMTIiLi9NPKhs8eDAnTpygR48efPjhhxQqVMjgdW7fvq2fA1W2bFksLS3p0KED06dP5/vvv0etVnP69Gnq1q3LgAEDaNu2bbIYQgghjECrifDyLSTMrXXLs1iGTxO/efNmiiMzpqamzJw5k+nTp6NWqxk5ciQlSpQwKLN//36io6OpWLGiwfLVq1dz4cIFLl++TLNmzXjw4AEtWrTQJzuNGzdmyZIlgG6EZseOHcydO5fw8HC8vb0JDAzE3NycoKAgLl++rI+bkJDAkiVL6NWrF4ULF+Z///sfa9as4Y8//jB4/cWLF/Ptt99mtCuYvyKS67dy4Do3JoXB4nmSpjJjzpw5qBfOT3axvkoODtiavUgAayY5XVwB+pUuaVD+5WVaf39qAps3bzYo5+HhQdGihtl3nz599H/b29uzePFig+c/++wzhgwZQq1atfj4448NDhEOGjSIQYMG6R+HhITQt29fAJydnXF2dk7eB0IIIXI9VfUPUSBHzqJSKWlcvefBgwf07t0bR0dHNm7ciImJCY8fP+bWrVv6wzkPHjygV69ebNu2zeBMmJclJCQwY8YMRo0alWyuy/nz5zE1NcXJyYn8+fNjY2OjHw1QFIWYmBhsbGwAOHHiBB4eHjg9H6lYtmwZK1as0F8Pp2XLlsyaNUu/rqIo+muwLFiwgOjoaL755pvX7S9Ad/itd+/eYL8ILKq+UayXvc6cHt8TziiREVl2RWKr+QvfOEZ4eDj29vYGpw4KIYQQbyLx+3jdunXJppmkmeCk17Nnz8ifP/de7CezZVWC87oTln1POKNeOD/LbruQGQmOEEIIkdnSSnAy5VYNb1Nyk1Xe9GyszLrtghBCCJEXvDX3osrNMutUc0lyhBBCCB1JcHJYZl9HR5IcIYQQQhKcHJVVFwmUJEcIIcTbThKcHJLVV0CWJEcIIcTbTBKcHJDVyU0iSXKEEEK8rSTByWbZldwkkiRHCCHE20gSnGyU3clNIklyhBBCvG0kwckmOZXcJJIkRwghxNtEEpxskNPJTSJJcoQQQrwtJMHJYlmd3NSpbv7qQklIkiOEEOJtIAlOFsqO5Gb+NIcMrydJjhBCiLxOEpwskl3JzchJYa+1viQ5Qggh8jJJcLJAdiY3cu8qIYQQIjlJcDKZsSQ3iSTJEUIIkRdJgpOJjC25SSRJjhBCiLxGEpxMYqzJTSJJcoQQQuQlkuBkAmNPbhJJkiOEECKvkATnDeWV5CaRJDlCCCHyAklw3kAFd7M8ldwkkiRHCCGEsZME5w2MHGiX55KbREmTHCGEEMLYSILzBuaviMw1yU1WjLQkJjlCCCGEsZEE5w1cvxGf6TFfd+Qmqw4naf39Mz2mEEIIkdUkwclF3uSwlMyZEUIIIV4wy+kKGLN1S/NTq5Zzpsf9Y3H+DJVX1Bosho8E0P+fmRSNBpV5xu5aLoQQQuQkGcHJA1QWWZt8SHIjhBDC2EiCI4QQQog8RxIcIYQQQuQ5kuAIIYQQIs+RScavQa1WA+Dj45PDNRFCCCHeXonfw4nfy0lJgvMarl27BsDUqVNzuCZCCCGEuHnzJu+++67BMpWiKEoO1cdo+fj48MEHH/DLL79QpEiRnK5Opnn06BFffvkl8+bNo1ChQjldnUyVV9uWV9sFebdtebVdkHfbJu3KvTQaDQEBAbRv3x5HR0eD52QE5zXY2dkBUKNGDYoVK5bDtck8d+/eBaBy5cp5ql2Qd9uWV9sFebdtebVdkHfbJu3K3erXr5/icplkLIQQQog8RxIcIYQQQuQ5kuAIIYQQIs+RBOc15MuXj2HDhpEvX76crkqmyqvtgrzbtrzaLsi7bcur7YK82zZpl3GSs6iEEEIIkefICI4QQggh8hxJcIQQQgiR50iC85ZSFAWNRgNAQkICsbGxOVyjNxcVFZXTVchWarVa/x6K3O/lS8kb++f1bfz8Gdt7lnQ//zaSBOcto1ar+eKLLxg4cCAffvghy5cvx8fHh+bNm9OmTRs6dOjA2bNnAQgJCaFv37589NFHjB49OsV7feQmffr0oUOHDrRu3Zqff/4ZgLlz59KlSxc++ugj/S02jK1dAHFxcbRq1YpGjRrRtGlTatasyZ49e2jRogWtW7emc+fO3Lx5E4CAgAC6du1Kr169mDZtWg7XPHUajYY+ffro/540aRLdunWjb9+++guQpdaWdevW8cEHH9CjRw+OHTuWI/VPS9K2HTlyhK5duzJkyBA6dOjA48ePARgzZgwdOnSgVatWjBkzRr9ubm5b0nYdPnw4Q5+/3NwuMGzbmjVraNiwIc2bN6dixYqsWbMGSHkfAynvZ3JSSvt5yNj+cM+ePXTs2JEePXqwZcuWnGrKm1FEhpw9e1b54IMPlB49eijLli3L6epk2NGjR5VTp04piqIoz549U7p166b89ddfyt9//52s7MCBA5V9+/YpiqIof//9t7J27dpsrWtGxMTEKB9//LHBsp07dyrDhg1TtFqtEhwcrHz66aeKohhXu1Iyf/58Zdy4ccqcOXOUEydOGDyXkJCgfPDBB4q3t7eiKIoyZ84cZf/+/TlRzVfy9fVVxo4dqyiKoqxYsUKZPn26oiiKcuPGDWX06NGptuXixYtKjx49lLi4OCUiIkL5+OOPFY1Gk2PtSEnSti1YsECJi4tTFEVRpk+frhw6dEhRFEXp1q1bsvVye9uStisjn7/c3i5FMWxborCwMOXdd99V/P39U9zHKErq+5mclNJ+PiP7w3v37ilt27ZVQkNDFbVarQwYMEB5/PhxjrXndckITgZERUUxceJEFi9ezPr16wkICDC6O4o3atSIunXrAvDLL7/w6aefcu3aNdzc3AzKPXjwgKioKJo3bw7Ahx9+yIkTJ7K9vul148YNSpcubbDM09OTsWPHolKpcHZ2xsnJiXv37hlVu14WHR3Nrl27mDRpEteuXaNs2bIGz58/f55y5cpRuXJlALp27Zpr2+fr64u7uzsA//77LyNGjACgXLlyhIeHp9qWv//+m5EjR2JhYYGdnR21atXKddth0rYNHz4cCwsL/Pz8uH79Og0aNODJkyfY29snWy+3ty1puzLy+cvt7QLDtiX63//+R79+/ShbtmyK+xhIeT/z7Nmz7Kp2ilLaz2dkf7hlyxb69++Pg4MD5ubmtGrVCi8vr5xs0muRBCcD9u/fT5s2bShatCgqlYpOnTrl2i+PV1m+fDm2tra0bt2aTz/9FBcXF9RqNceOHSMwMBB/f39q1KihL69Wq7G2ts7BGqfN3d2d7t27k5CQQExMDP/88w9Pnz6lZMmS+jJqtZrr168bVbtetmvXLtq2bYu9vT1ff/01KpUKtVrNzp07efLkCf7+/tSsWVNfPje378aNG3h4eKBWq7GystLf4w3AzMws1bYYQxsT25bo8ePHfP3110yfPh1zc3Py58/P8OHDUavVKIrC+vXrAXJ925K2KyOfv9zeLkj+nimKwo4dO+jVqxeQ8j4GSHE/Y2Njk72VT0XS/XxG9ofG8H6lhyQ4GZBX3vR9+/Zx8eJFvv76awBKlChBkSJFsLCwQKvV8vfff+Pg4MD9+/f163h5eSX7tZabWFlZ8c4772Bqaoq1tTWrV69GpVIRHR0NgFar5dGjRzg7OxtVu162ZcsWunfvDoCbmxuFChXCwsKCsLAwdu/ejYODAw8ePNCX9/LySjY6l1skfqGYmZkRHh6uXx4ZGYlGo0m1LS9/Nq9du0bRokWzte6vkvTLMi4ujlGjRjFhwgRKlCgBgImJCdWqVcPCwgKVSsU///xDdHR0rm9b0nZl5POX29sFyROc06dPU61aNX3indI+BkhxP2NpaZn9DXjJy/v5jOwPX34fz507Z1T7yUSS4GRASm96bv3ySI2Pjw9//PEHc+bMwdTUFIBvvvmG+/fvo9Fo2L9/P9bW1lSqVAkfHx8uX75MREQEy5cvp2PHjjlc+9StWbOGgwcPAnDq1CmioqJo0qQJixYtQlEUVq9eTaNGjYyuXUk9evQIjUaDi4sL4eHhfPvttzx9+pSYmBgOHz6MlZUVDRo0YOfOnQQFBREcHMymTZto1qxZTlc9RcHBwbi4uGBiYkKVKlVYt24d8fHxzJ07lw8++CDVtrRq1YoFCxaQkJDAzp07KVasGFZWVjndHAOJbQOYPHkyvXv3platWvrnt2/fzqZNm9Bqtfj5+eknVef2tiW2K6Ofv9zeLjB8zwB2796tP3QDKe9jgBT3Mzktpf18RvaHrVq14tdffyUmJgYvLy8iIyNzXUKaLjk6A8jIBAYGKu3atVOePn2q+Pv7Kz169FASEhJyuloZMnz4cKVBgwZKq1atlFatWildu3ZVjh07pnTu3Flp166d8vXXXyvPnj1TFEVRvL29lR49eiidO3dWDh8+nLMVf4WwsDBl+PDhSqtWrZRevXopp0+fVqKjo5Vx48YprVu3Vn766Sf9e2VM7Urq77//VpYuXap/vHPnTqV9+/ZK+/btlalTpyqxsbGKoijK4cOHlS5duig9e/ZULl26lFPVTVNYWJjBhM0nT54ogwcPVtq2bausXLlSvzyltsTHxys//vij0qZNG2X8+PFKTExMttc/LUnbdu3aNaVq1ar67a1169aKp6enEhcXp0yePFlp2bKl0rVrV2X37t2KouTutr38nmXk85eb26UoydumKIrSpk0b/eTwxDIv72MURUl1P5OTUtrPh4eHZ2h/uGzZMqVdu3bKsGHDlCdPnuRUU96I3KohgzZv3szatWtxcnLi22+/pVSpUjldpQyJj49Hq9ViYWGR01URGRQUFIS5uTmurq45XZU3FhUVxfnz53PFr93M9nLbYmNjc91oxet4m94zgLNnz1K7du0crNXrk/28jiQ4QgghhMhzZA6OEEIIIfIcSXCEEEIIkedIgiOEEEKIPEcSHCFEnvOqGwwuWrQom2oihMgpkuAIIYxWcHCw/kaC586d4+TJk1y8eJHZs2enud6pU6deGTs+Pp6tW7dy6NAhnj59min1FUJkH0lwhBBGS6PR6C+4Vrp0aXbv3s3PP/9MvXr1XrkeYHAF5ZctXbqUc+fOMXLkSPbt24darWbWrFmZV3khRJYyy+kKCCHE64qNjQV09w2ytrZGrVZz5coV/SXpX7Z3717Onj2Lt7c3DRs2pGbNmsyfPz9ZuZs3b3LhwgWOHz9O06ZNsbW15b///sPX15cFCxZQqlQpo7kCthBvK0lwhBBG6fz58yxYsIDr168TERFBvXr18Pb2pmLFijg7O6e4jr29Pa6urmi1Wjp06KC/T8/LzM3NadGiBceOHcPBwYHSpUszefJknJ2d2bp1K2vWrMnClgkhMoMcohJCGKWqVavi4eFB69at+fbbb6lSpQqlSpUiKCiIO3fupLhO+fLlWbduHYUKFSIiIiLVRMXGxoYzZ87QtGlTnj17Rvfu3Wnfvj3jxo2jYsWKFC9ePAtbJoTIDJLgCCGMUmxsLDt27OD06dNs3bqVAgUKUKlSJXr27EmXLl1QFIWEhAR9eUVRGDduHH369MHFxYVvv/2Wo0eP4unpmSz2li1b+OKLL7h06RKdO3dm7NixtGjRgvPnz79yfo8QIneQWzUIIYzSr7/+yvXr16lXrx5VqlRh//79HDt2jJCQEIoUKUJUVBSNGjVi3LhxAMyaNQuVSkXjxo0ZOXIkZcuWZciQISxbtow2bdrQo0cPAG7fvs2iRYs4dOgQpqamqNVq1qxZw7x589BoNCxcuJD8+fPnZNOFEOkgc3CEEEZpwIABbNiwAVNTU8zNzenQoQNlypQhODiYgQMHGpQ9efIkJUuWxMPDg4ULFxIeHk7Pnj2pXbs2NWvWZO7cuXz66aeMHTsWJycnWrVqxYULF+jfvz9hYWFUqVKF4sWLc+TIEQICAqhVq1YOtVoIkV4ygiOEMFq//vorhQoVomvXrgDs3LmTqKgounXrluZ6NWrUYN++fTg5OemXRUZGAmBnZ8eTJ0/o1asXRYoU4cmTJ1haWtK4cWM6derEp59+SunSpenfvz/16tVDpVJlXQOFEK9NRnCEEEardu3aWFtb6x/b2NhgZvbq3VrJkiVxdHQ0WGZnZ6f/e9y4ccycOZPq1auzZs0azM3N6d27NwCbNm1i9uzZjB8/nqVLl1K+fPnMaYwQIlPJCI4QIs+IiIhArVYbjMwIId5OkuAIIYQQIs+R08SFEEIIkedIgiOEEEKIPEcSHCGEEELkOZLgCCGEECLPkQRHCCGEEHmOJDhCCCGEyHMkwRFCCCFEnvN/QQdK9h2O25IAAAAASUVORK5CYII=\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for col in logistic.feature_names_in_:\\n\",\n    \"    feature_table = feature_bin_stats(train, col, target=target, desc=feature_map.get(col, col), combiner=combiner)\\n\",\n    \"    _ = bin_plot(feature_table, desc=feature_map.get(col, col), figsize=(8, 4), save=f\\\"model_report/bin_plots/data_{col}.png\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"55dd92b8-1536-44ee-9756-28d4e3565d7f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 226,\n   \"id\": \"a80cd2b7-bdd9-43f5-a896-2e137c2a2990\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
ScoreCard(base_odds=0.2, base_score=600,\\n\",\n       \"          combiner=<toad.transform.Combiner object at 0x000002615303D400>,\\n\",\n       \"          pdo=50,\\n\",\n       \"          pretrain_lr=ITLubberLogisticRegression(C=10,\\n\",\n       \"                                                 class_weight={0: 0.1, 1: 0.9},\\n\",\n       \"                                                 max_iter=50, target='TARGET'),\\n\",\n       \"          target='TARGET',\\n\",\n       \"          transer=<toad.transform.WOETransformer object at 0x000002614E3CBD00>)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
\"\n      ],\n      \"text/plain\": [\n       \"ScoreCard(base_odds=0.2, base_score=600,\\n\",\n       \"          combiner=,\\n\",\n       \"          pdo=50,\\n\",\n       \"          pretrain_lr=ITLubberLogisticRegression(C=10,\\n\",\n       \"                                                 class_weight={0: 0.1, 1: 0.9},\\n\",\n       \"                                                 max_iter=50, target='TARGET'),\\n\",\n       \"          target='TARGET',\\n\",\n       \"          transer=)\"\n      ]\n     },\n     \"execution_count\": 226,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"card = ScoreCard(target=target, combiner=combiner, transer=transform, pretrain_lr=logistic, base_score=600, base_odds=0.2, pdo=50)\\n\",\n    \"card.fit(woe_train[new_var_names + [target]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 227,\n   \"id\": \"202b9d76-c099-42e1-96f1-de18a0953726\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率LIFT值累积LIFT值
0score[负无穷 , 575.7535)260.00000.1088135.00000.0688125.00000.29340.48082.69612.6961
1score[575.7535 , 606.071)188.00000.0787123.00000.062765.00000.15260.34571.93892.3784
2score[606.071 , 624.5664)142.00000.0594100.00000.050942.00000.09860.29581.65872.2052
3score[624.5664 , 648.416)211.00000.0883167.00000.085144.00000.10330.20851.16941.9323
4score[648.416 , 680.9749)394.00000.1649336.00000.171258.00000.13620.14720.82551.5674
5score[680.9749 , 692.0589)225.00000.0942203.00000.103422.00000.05160.09780.54831.4059
6score[692.0589 , 699.3025)237.00000.0992219.00000.111618.00000.04230.07590.42591.2658
7score[699.3025 , 正无穷)732.00000.3064680.00000.346452.00000.12210.07100.39841.0000
8score缺失值0.00000.00000.00000.00000.00000.00000.00000.00000.0000
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"    指标名称 指标含义                     分箱     样本总数   样本占比     好样本数  好样本占比     坏样本数  \\\\\\n\",\n       \"0  score            [负无穷 , 575.7535) 260.0000 0.1088 135.0000 0.0688 125.0000   \\n\",\n       \"1  score        [575.7535 , 606.071) 188.0000 0.0787 123.0000 0.0627  65.0000   \\n\",\n       \"2  score        [606.071 , 624.5664) 142.0000 0.0594 100.0000 0.0509  42.0000   \\n\",\n       \"3  score        [624.5664 , 648.416) 211.0000 0.0883 167.0000 0.0851  44.0000   \\n\",\n       \"4  score        [648.416 , 680.9749) 394.0000 0.1649 336.0000 0.1712  58.0000   \\n\",\n       \"5  score       [680.9749 , 692.0589) 225.0000 0.0942 203.0000 0.1034  22.0000   \\n\",\n       \"6  score       [692.0589 , 699.3025) 237.0000 0.0992 219.0000 0.1116  18.0000   \\n\",\n       \"7  score            [699.3025 , 正无穷) 732.0000 0.3064 680.0000 0.3464  52.0000   \\n\",\n       \"8  score                         缺失值   0.0000 0.0000   0.0000 0.0000   0.0000   \\n\",\n       \"\\n\",\n       \"   坏样本占比   坏样本率  LIFT值  累积LIFT值  \\n\",\n       \"0 0.2934 0.4808 2.6961   2.6961  \\n\",\n       \"1 0.1526 0.3457 1.9389   2.3784  \\n\",\n       \"2 0.0986 0.2958 1.6587   2.2052  \\n\",\n       \"3 0.1033 0.2085 1.1694   1.9323  \\n\",\n       \"4 0.1362 0.1472 0.8255   1.5674  \\n\",\n       \"5 0.0516 0.0978 0.5483   1.4059  \\n\",\n       \"6 0.0423 0.0759 0.4259   1.2658  \\n\",\n       \"7 0.1221 0.0710 0.3984   1.0000  \\n\",\n       \"8 0.0000 0.0000 0.0000   0.0000  \"\n      ]\n     },\n     \"execution_count\": 227,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"_ = ks_plot(card.predict(train), train[target], figsize=(10, 5))\\n\",\n    \"feature_bin_stats(pd.DataFrame({\\\"score\\\": card.predict(train), \\\"target\\\": train[target]}), \\\"score\\\", max_n_bins=10, method=\\\"cart\\\")[use_cols]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 248,\n   \"id\": \"027bd880-ae3c-412f-a8fb-c6a329fc767a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
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指标名称指标含义分箱样本总数样本占比好样本数好样本占比坏样本数坏样本占比坏样本率LIFT值累积LIFT值
0score[负无穷 , 570.7376)62.00000.067548.00000.055414.00000.26420.22583.91543.9154
1score[570.7376 , 603.9725)61.00000.066453.00000.06128.00000.15090.13112.27403.1014
2score[603.9725 , 629.6114)61.00000.066452.00000.06009.00000.16980.14752.55832.9213
3score[629.6114 , 647.55)61.00000.066458.00000.06703.00000.05660.04920.85282.4063
4score[647.55 , 661.5176)61.00000.066460.00000.06931.00000.01890.01640.28431.9833
5score[661.5176 , 674.2249)62.00000.067560.00000.06932.00000.03770.03230.55931.7434
6score[674.2249 , 683.715)51.00000.055547.00000.05434.00000.07550.07841.36001.6967
7score[683.715 , 688.085)46.00000.050145.00000.05201.00000.01890.02170.37691.5662
8score[688.085 , 693.7907)57.00000.062055.00000.06352.00000.03770.03510.60841.4616
9score[693.7907 , 698.4955)87.00000.094787.00000.10050.00000.00000.00000.00001.2528
10score[698.4955 , 701.0486)9.00000.00989.00000.01040.00000.00000.00000.00001.2345
11score[701.0486 , 750.8179)219.00000.2383211.00000.24368.00000.15090.03650.63341.0773
12score[750.8179 , 正无穷)82.00000.089281.00000.09351.00000.01890.01220.21151.0000
\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"     指标名称 指标含义                     分箱     样本总数   样本占比     好样本数  好样本占比    坏样本数  \\\\\\n\",\n       \"0   score            [负无穷 , 570.7376)  62.0000 0.0675  48.0000 0.0554 14.0000   \\n\",\n       \"1   score       [570.7376 , 603.9725)  61.0000 0.0664  53.0000 0.0612  8.0000   \\n\",\n       \"2   score       [603.9725 , 629.6114)  61.0000 0.0664  52.0000 0.0600  9.0000   \\n\",\n       \"3   score         [629.6114 , 647.55)  61.0000 0.0664  58.0000 0.0670  3.0000   \\n\",\n       \"4   score         [647.55 , 661.5176)  61.0000 0.0664  60.0000 0.0693  1.0000   \\n\",\n       \"5   score       [661.5176 , 674.2249)  62.0000 0.0675  60.0000 0.0693  2.0000   \\n\",\n       \"6   score        [674.2249 , 683.715)  51.0000 0.0555  47.0000 0.0543  4.0000   \\n\",\n       \"7   score         [683.715 , 688.085)  46.0000 0.0501  45.0000 0.0520  1.0000   \\n\",\n       \"8   score        [688.085 , 693.7907)  57.0000 0.0620  55.0000 0.0635  2.0000   \\n\",\n       \"9   score       [693.7907 , 698.4955)  87.0000 0.0947  87.0000 0.1005  0.0000   \\n\",\n       \"10  score       [698.4955 , 701.0486)   9.0000 0.0098   9.0000 0.0104  0.0000   \\n\",\n       \"11  score       [701.0486 , 750.8179) 219.0000 0.2383 211.0000 0.2436  8.0000   \\n\",\n       \"12  score            [750.8179 , 正无穷)  82.0000 0.0892  81.0000 0.0935  1.0000   \\n\",\n       \"\\n\",\n       \"    坏样本占比   坏样本率  LIFT值  累积LIFT值  \\n\",\n       \"0  0.2642 0.2258 3.9154   3.9154  \\n\",\n       \"1  0.1509 0.1311 2.2740   3.1014  \\n\",\n       \"2  0.1698 0.1475 2.5583   2.9213  \\n\",\n       \"3  0.0566 0.0492 0.8528   2.4063  \\n\",\n       \"4  0.0189 0.0164 0.2843   1.9833  \\n\",\n       \"5  0.0377 0.0323 0.5593   1.7434  \\n\",\n       \"6  0.0755 0.0784 1.3600   1.6967  \\n\",\n       \"7  0.0189 0.0217 0.3769   1.5662  \\n\",\n       \"8  0.0377 0.0351 0.6084   1.4616  \\n\",\n       \"9  0.0000 0.0000 0.0000   1.2528  \\n\",\n       \"10 0.0000 0.0000 0.0000   1.2345  \\n\",\n       \"11 0.1509 0.0365 0.6334   1.0773  \\n\",\n       \"12 0.0189 0.0122 0.2115   1.0000  \"\n      ]\n     },\n     \"execution_count\": 248,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"_ = ks_plot(card.predict(oot), oot[target], figsize=(10, 5))\\n\",\n    \"feature_bin_stats(pd.DataFrame({\\\"score\\\": card.predict(oot), \\\"target\\\": oot[target]}), \\\"score\\\", max_n_bins=15, method=\\\"quantile\\\")[use_cols]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"11437567-88db-4c1e-af01-204ef6ecc765\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 249,\n   \"id\": \"dff15bc9-5686-490f-8ac5-359cd3b7ebd2\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"\"\n      ]\n     },\n     \"execution_count\": 249,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"init_setting()\\n\",\n    \"plt.ioff()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 250,\n   \"id\": \"9cb1166f-90e4-4cae-81f7-588992676977\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"writer = ExcelWriter(theme_color=\\\"0000ff\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 251,\n   \"id\": \"234f2d4b-dc67-4385-86a1-01cbf7e2bff7\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"total_count = len(train) + len(oot)\\n\",\n    \"dataset_summary = pd.DataFrame(\\n\",\n    \"    [\\n\",\n    \"        [\\\"全量样本集\\\", \\\"2022-01-01\\\", \\\"2023-04-30\\\", total_count, total_count / total_count, train[target].sum() + oot[target].sum(), (train[target].sum() + oot[target].sum()) / total_count, \\\"除舞蹈培训、财会以外的职培订单数据，剔除历史逾期(0,30]的的样本\\\"],\\n\",\n    \"        [\\\"跨时间验证集\\\", \\\"2023-01-01\\\", \\\"2023-04-30\\\", len(oot), len(oot) / total_count, oot[target].sum(), oot[target].sum() / len(oot), \\\"\\\"],\\n\",\n    \"        [\\\"建模样本集\\\", \\\"2022-01-01\\\", \\\"2022-12-31\\\", len(train), len(train) / total_count, train[target].sum(), train[target].sum() / len(train), \\\"\\\"],\\n\",\n    \"#         [\\\"训练集\\\", \\\"2022-01-01\\\", \\\"2022-12-31\\\", len(dev), len(dev) / total_count, dev[target].sum(), dev[target].sum() / len(dev), \\\"\\\"],\\n\",\n    \"#         [\\\"测试集\\\", \\\"2022-01-01\\\", \\\"2022-12-31\\\", len(off), len(off) / total_count, off[target].sum(), off[target].sum() / len(off), \\\"\\\"],\\n\",\n    \"    ],\\n\",\n    \"    columns=[\\\"数据集\\\", \\\"开始时间\\\", \\\"结束时间\\\", \\\"样本总数\\\", \\\"样本占比\\\", \\\"坏客户数\\\", \\\"坏客户占比\\\", \\\"备注\\\"],\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# ////////////////////////////////////// 样本说明 ///////////////////////////////////// #\\n\",\n    \"worksheet = writer.get_sheet_by_name(\\\"汇总信息\\\")\\n\",\n    \"\\n\",\n    \"# 样本总体分布情况\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (2, 2), value=\\\"样本总体分布情况\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, dataset_summary, (end_row + 1, 2), header=True)\\n\",\n    \"\\n\",\n    \"writer.set_number_format(worksheet, f\\\"{get_column_letter(end_col - 2)}{end_row - len(dataset_summary)}:{get_column_letter(end_col - 2)}{end_row}\\\", \\\"0.00%\\\")\\n\",\n    \"writer.set_number_format(worksheet, f\\\"{get_column_letter(end_col - 4)}{end_row - len(dataset_summary)}:{get_column_letter(end_col - 4)}{end_row}\\\", \\\"0.00%\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 252,\n   \"id\": \"80e3b390-d00d-45d2-9059-ac77d309d9da\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 样本时间分布情况\\n\",\n    \"temp = distribution_plot(data, date=\\\"D_APPLICATION\\\", target=\\\"TARGET\\\", save=\\\"model_report/建模集样本分布情况.png\\\", result=True)\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, 2), value=\\\"建模样本时间分布情况\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/建模集样本分布情况.png\\\", (end_row, 2), figsize=(720, 370))\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, temp.T.reset_index(), (end_row, 2), header=False)\\n\",\n    \"\\n\",\n    \"writer.set_number_format(worksheet, f\\\"{get_column_letter(2)}{end_row - 1}:{get_column_letter(end_col)}{end_row - 1}\\\", \\\"0.00%\\\")\\n\",\n    \"writer.set_number_format(worksheet, f\\\"{get_column_letter(2)}{end_row - 2}:{get_column_letter(end_col)}{end_row - 2}\\\", \\\"0.00%\\\")\\n\",\n    \"writer.set_number_format(worksheet, f\\\"{get_column_letter(2)}{end_row - 4}:{get_column_letter(end_col)}{end_row - 4}\\\", \\\"0.00%\\\")\\n\",\n    \"writer.set_number_format(worksheet, f\\\"{get_column_letter(2)}{end_row - 6}:{get_column_letter(end_col)}{end_row - 6}\\\", \\\"0.00%\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 253,\n   \"id\": \"c41d1079-8868-469f-bbb1-8613cccc0629\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 样本时间分布情况\\n\",\n    \"temp = distribution_plot(oot_data, date=\\\"D_APPLICATION\\\", target=\\\"TARGET\\\", save=\\\"model_report/跨时间验证集分布情况.png\\\", result=True)\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, 2), value=\\\"跨时间验证样本分布情况\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/跨时间验证集分布情况.png\\\", (end_row, 2), figsize=(720, 370))\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, temp.T.reset_index(), (end_row, 2), header=False)\\n\",\n    \"\\n\",\n    \"writer.set_number_format(worksheet, f\\\"{get_column_letter(2)}{end_row - 1}:{get_column_letter(end_col)}{end_row - 1}\\\", \\\"0.00%\\\")\\n\",\n    \"writer.set_number_format(worksheet, f\\\"{get_column_letter(2)}{end_row - 2}:{get_column_letter(end_col)}{end_row - 2}\\\", \\\"0.00%\\\")\\n\",\n    \"writer.set_number_format(worksheet, f\\\"{get_column_letter(2)}{end_row - 4}:{get_column_letter(end_col)}{end_row - 4}\\\", \\\"0.00%\\\")\\n\",\n    \"writer.set_number_format(worksheet, f\\\"{get_column_letter(2)}{end_row - 6}:{get_column_letter(end_col)}{end_row - 6}\\\", \\\"0.00%\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 254,\n   \"id\": \"d9829d4c-90f1-44ba-be65-5e14c23fe3a5\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"_ = logistic.plot_weights(save=\\\"model_report/logistic_train.png\\\")\\n\",\n    \"\\n\",\n    \"# 预测\\n\",\n    \"y_pred_train = logistic.predict_proba(woe_train[card.features_])[:, 1]\\n\",\n    \"y_pred_oot = logistic.predict_proba(woe_oot[card.features_])[:, 1]\\n\",\n    \"\\n\",\n    \"_ = ks_plot(y_pred_train, woe_train[target], figsize=(10, 5), save=\\\"model_report/lr_ksplot_train.png\\\", title=\\\"TRAIN \\\\t\\\")\\n\",\n    \"_ = ks_plot(y_pred_oot, woe_oot[target], figsize=(10, 5), save=\\\"model_report/lr_ksplot_oot.png\\\", title=\\\"OOT \\\\t\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"8ffcc0b6-6e4c-4ad0-835f-24a18c75b5b7\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 255,\n   \"id\": \"835df131-53c4-4e81-ad6b-694b54095c17\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# ////////////////////////////////////// 模型报告 ///////////////////////////////////// #\\n\",\n    \"summary = logistic.summary2(feature_map=feature_map)\\n\",\n    \"\\n\",\n    \"# 逻辑回归拟合情况\\n\",\n    \"worksheet = writer.get_sheet_by_name(\\\"逻辑回归拟合结果\\\")\\n\",\n    \"start_row, start_col = 2, 2\\n\",\n    \"\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\\\"逻辑回归拟合效果\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/logistic_train.png\\\", (end_row + 2, start_col))\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, summary, (end_row + 1, start_col))\\n\",\n    \"\\n\",\n    \"conditional_column = get_column_letter(start_col + summary.columns.get_loc(\\\"Coef.\\\"))\\n\",\n    \"writer.add_conditional_formatting(worksheet, f'{conditional_column}{end_row-len(summary)}', f'{conditional_column}{end_row}')\\n\",\n    \"\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"建模集拟合报告\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/lr_ksplot_train.png\\\", (end_row, start_col), figsize=(480, 270))\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, logistic.report(woe_train[card.features_ + [target]]), (end_row + 1, start_col))\\n\",\n    \"\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"跨时间验证集拟合报告\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/lr_ksplot_oot.png\\\", (end_row, start_col), figsize=(480, 270))\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, logistic.report(woe_oot[card.features_ + [target]]), (end_row + 1, start_col))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"990eb408-733f-47c2-9f95-a5152dc76546\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 256,\n   \"id\": \"7f720b1d-180f-4f41-b9b6-f18bd8eb16b3\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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3atXzzzTfUqFGDnj17smvXLoYMGUJ8fHymUyuxsbGo1WoiIyOxsbEhNjaWsmXLcuzYsQzBvHjx4hw/fpw333yTkiVLUrNmTR48eMCsWbPo27cv0dHRGf4/1Gq1+Pr68uOPP+Lo6MjYsWPx9vbOdv1t27aNIUOG0KdPHyZOnEjdunVp0aIFp0+fzrA9nnd5S5UqxdChQwkNDWXy5MkMHToUX19fNm7cyPbt2wkNDSUqKipDwFGpVMydOxeVSsW1a9fo2rUro0eP5siRI/qxVELkhAQckSN37tzhypUr9OnTBwsLCyZNmsTo0aMZPXo0Fy9eZMCAARnGQfTp0yfD/KmpqYSHh+tfPz4a8JhGo2Hp0qVER0dTo0YNrl+/TqlSpbL90i1ZsiSgO61VtWpVqlatysCBA/n111+5fPlypoDj5OTE4sWL+fXXXylRogQeHh5cunQJgIYNG2Jvb0+XLl0yzJOSkpLhku2QkBBOnjxJly5dCAkJyfILIKciIiLw9PRk5syZdOjQgU8//RTQXUXj6OiYoa2Dg4N+3IKZmRlubm4ZwuGTNBpNjk6d+fn5MXHiRH3gSE5OznY+lUrFxo0bAfjkk08YMWIEFStWzLZ2amoqf/31F40aNcLe3p7ChQtjY2PD+fPnM7X19PRk+/bt7Nu3jxIlSuDp6cn9+/eJjo7Wb+On0Wg0TJ8+HU9PT06ePJlluEtMTGTFihXMmjWL5ORkWrRooQ+r5ubmWfapRYsWeHl5ERISQoECBRg2bBi1a9fOsi3oBv62bNmSJk2acO7cOX2oUKlUGbZnamoqV69e5dixY7z33nv8+uuvjBgxgtDQUN58800aN27MmTNn9O2tra3x8PBg5cqVJCQksHr1arp27cr27du5ceMGUVFRGdbT2rVrqVu3Lo6OjoSEhLBnzx5iYmJwd3enWbNmGfp+69Yt/P39WbRoEebm5nz11VesWrWKCRMmoNFomD9//gsvr6WlJdbW1kyYMAErKyvWrl1LSEgIKSkp3Lp1iwoVKmQ6KhMeHk6NGjVo1KgRR48eBXT7wFtvvfXSrqwTpknG4IgcKVGiBEOHDkWtVlO2bFnMzc0JCgri0KFD/Pzzz/ojM9lJS0t76r/G582bR4kSJZg0aRK2trYUK1YMlUqV4TLeY8eOZZqvQoUKzJw5k+bNm+Pg4ICfnx+NGzfO1K5JkyaYm5vj5OTE2rVruXr1Kk5OTpQqVSrbgYsJCQn6gZBDhw6lSpUq9OzZk4oVK3Lp0iX9+Jkn/fPPP/qB0tlJSUnhm2++YciQIQQGBrJ582b8/f1Rq9UkJCRkGsRarVo1Dh48SFBQEFeuXOHUqVNUqlQpy9oajSbbwcKPKYrC6dOnM5y+ejxW5lksLS2zDAVPUqvV+vCakJDAuXPnuHbtWpbjh06cOMHkyZNp0qQJnp6eXLhwgdmzZ7N06VJ+++03EhISnnplXVpaGiqVSj++5r9X6anVaoYNG6YPcmlpady/f59y5crh5uaW7SkPd3d3/v33XwoUKIC5uTl2dnbZ/v8bExODr6+vPtSfPXuWatWqZdm2V69ejBkzhujoaOzs7DA3N6dt27Z06dKFQYMGUa9evQzbwcrKChcXF2xsbIiMjMTDwwMzMzM6d+6sDzLW1taALsht3LhRf2Rn2LBhfPPNN/zwww/MmTMn0yXWNjY2tG7dGmdnZ4YNG8bHH39M1apV9WPrgoKCXnh5QXfl2vjx4ylevDiffPKJ/r+bNm3Czc0NS0vLDO2LFy9Oo0aNUBSFNWvWcPnyZUC337zIuDfx+pEjOCLHZs6ciUajoUyZMkRFRdGhQwdWrFiBjY0NEyZM4LPPPsvyiqTFixfrg1F2PvvsM3755RdAd6+Vx388raysWLNmDbVq1cLX15e33nor07zBwcEoisKDBw+4efNmlsHjsdKlSxMfH8+pU6ewsLBg3Lhx2bYdOnQob7zxBoD+1ERYWBgffvgh1apV03+xPGnSpEk0a9ZMf0QmK9bW1syePRuAqKgodu/ezdy5c7ly5Qrly5cnJCQEf39/ihcvzltvvUXhwoUZNmwYvXv3xtbWlokTJ2Ya+/KYWq3O9mqlx44cOULbtm31IeTWrVscOnTomcEIdKd8nnXaqFy5ctSsWRO1Wo25uTmzZs0iLCyMiRMnZmr71ltv6a8MS0hI4MGDB8yYMQOVSsWIESP4+OOPKVKkSLb3c0lNTX1q4IqKiqJBgwY0bNgQ0AW5xwNb79y5k23AiY2N5ciRI8yePZuCBQvi7u6eZTu1Ws2CBQuYPn06Bw8eZMmSJVy4cIElS5Zk2X7Dhg1cvHiRNWvWMGDAAOrUqcPw4cPZvHkzly5dIjo6OsNRKDMzM8LDw/nggw9ISEhg+vTpAEyZMgV3d/cMRxHt7Oz0g6ZnzJjB2LFj9cu9cOFC+vXrx/Hjx/nkk0/w9PTEzc0NCwsLzM3N+fPPP4mMjOTw4cM0adKEtWvX0r9/f8qUKUO/fv2oV68eKpUq18sLMGDAgAxHQlNSUli4cCErVqzg/v37mQYaW1paEhwczIMHD2jXrh3nzp1j69at+ivHhMgxRYgcSk5OVvr166ccOnRI2b9/v3LmzBlFo9EoWq1W2b17t9KiRQslIiIi03wnTpxQatasqVy7du2p9RcuXKj89ddfyo4dO/TTLl26pPTp00dp2LCh0q1btyznCw4OVvr27auUL19e2bBhQ7b179+/r+zYsUOZPXu2MnLkSKVXr15Ku3btlF69eilXr17N4VpQlMTERCU6OjrT9NTUVGX06NFZroOciI2NVQ4fPqwsWrRImThxovLNN98o//77b65qfP/9989s4+/vr2i12gzT1qxZk+36fdJ3332Xq+ULDAxUFEVR9u7dq0ydOjXH8z22a9cu5fPPP8/2fR8fn1z1Z9y4cfrfp0yZonTv3j3LdvPnz1d8fX0VRVGUCxcuKP3791eWLFmSabtrNBolNTVV/3r9+vXKkCFDntqHGzduKPHx8YqiKIpWq1W2bdumKIqi3L17V+ncubMyYMCAZy7HhQsXlKZNmyq3bt3K9F5aWlqW80RGRioTJ05U3n77beXChQuKoihK//79lfPnzyuKoig//fST8ssvv+jbx8bGKhMnTlSaNGmi33efZ3kVRVFu3rypLF26VFEURVGr1frp/fv3z9T2xIkTytixY5VFixYpS5YsUZYsWaIsXbpUOXz4sPLBBx8887OEeEylKNncOlOIXIqOjsbJySnLm3c9fPiQggULZvleXvn3338pUaJEtp8RHh5OSEgIJUuWxMnJCRsbmxzdFDCnHj58iJWVVbZHV0zBli1b6Ny580vdjoa2b98+ChUqRK1atfTTkpOTWbJkCceOHdOPR3oZ0tLSuHnzJuXKlXtpn2GsHt809Gn3PBLiSRJwhBAihxRFMelwJ4QpkUHGQgiRQxJuhDAeEnCEEEIIYXIk4AghhBDC5EjAEUIIIYTJkYAjhBBCCJMjAUcIIYQQJkcCjhB54Fl3Afb392fnzp2vqDemQ9Zr3svLdXr16lU2bNiAVquVxyiIfEcCjjC4lJSXcyum3NYNCwvj6tWrAMTFxTF06FBatWrF3r17M7TbvXs3Fy9eBODHH3/k4cOHDBo06Km1FUXh5s2buerPi1KyePaTIequWbMGgPv37zNnzhwmT55Mt27d2Lx5c4Z2xrJe8wNDr9OwsDAGDx7Md999x9y5c/nwww8ZMmSIPEpB5CvyLCphcNbWKrwbhFOnuiVzpznhMz6GMxde/Mv5+gnXXLW/desWgYGBVKpUCUdHR6ZNm8ann36a6SnMSUlJ+n8FV6hQgQEDBvDOO+88tfbdu3cJCAhg48aNvP322xQpUiR3C/McVJaWJPsMy5NaZp6eWH7UH81PK7Ea5pOreQ8dOkSfPn1wdHSkYMGCbNq0iZ9++inT3XqNZb3mB4Zepw4ODixevJjly5czevRoKlasyKFDh/RPvRciP5CAI/KFvA43uaFWq9m/fz+bN28mODgYPz8/Zs+eja+vL717987wtOMrV67wxx9/EBERwYgRI/D19SUkJISqVatmWTsyMhJfX1+OHz9ObGwsDRo0yPIhnfmdNjAQzU8rsfyof67mCw4OJiwsjKFDh7JgwQIKFChAx44dKV++fIZ2r+t6fR75YZ1u3LiRhIQEVq9eja+vL127dqV+/fpoNJpMTwcXwlAk4Ih8wVDhBnSH+VNTU0lOTmbEiBFcvHiR+fPnExAQwPbt21mzZg0qlYqpU6fi7OxMUlISgwYN4u2338bBwYEvv/wSZ2fnLGu7uLgwdOhQ/vzzT+zt7YmKiqJAgQKvdgHzyOOQk5sjOGfOnCEmJoYPP/wQgF9++YWWLVsSEhJCmTJl9O2Mab0qSzo9u1HFlqgaD0lvX7Mnqlo9URIi4ZcBWc6i+mRbjj4/P6zTWrVqsX//fvr164e3tzdFixZly5YtLF++nOnTp1O2bNkcLYsQL5McTxT5gqHCDYC7uzutWrXCxsaGEydO0LNnT5o1a8bs2bPx8PBg5cqVrF+/Hi8vLxwcHLh69So///wzERERANja2mY7rkGr1fLll1/SqlUr2rdvT2hoKOvXr3+Vi5entIGBuWrfs2dPPD09CQkJYdOmTTRq1IgTJ05w9epVevbsiZ+fH8Brv15zw9Dr9Pbt2+zatQtLS0v27dvHmTNnWLJkCT4+Pqxbt07Cjcg35AiOyBcMFW4eU6lU3Lt3D1tbWypUqKCfXrp0acLDw/X/Ml61ahXe3t5MmzaNoKAgDh8+TFhYGJ9++ilqtZry5cszZcoU/fxz5syhbt26uLu7s3LlSvr378/u3bsxNzene/fuL325zDw9cx1K8tLly5e5d+8eKpWKEydO8O2339KpUyfatm1L5cqVWbNmDbVq1TKq9ZrTIy1ZtVfZu0Au5/8vQ69Td3d33n//ffz8/GjUqBGHDx+mUqVKTJ06FYBWrVrRsmXLF1pGIfKCBBxhkupUz/k4AI1Gw4IFC+jUqRNhYWEsWLCAvn37kpiYiFarRfPEVUMDBgwgMjKS5ORkIiMjeffdd7l69SqrV6/OVHf//v14eHiQmJjIzJkzcXJywtXVlTlz5jBnzhxGjRrFZ5999lL/xft4YLAhQk5qaipr165l/vz5FC5cmPbt26NSqVAU3dVtHh4efP3114DxrVdDyQ/r9Pr161y8eJFy5cpRtWpVzp49y+TJk7GxsSExMZG0tLRXuk6EyI4EHGFyHg9Yzqm0tDR69epF8eLFATh48CAAAwcOJDY2lsmTJ+vbOjo6kpCQgJOTE++++y4ATk5Zf9aT/4p1cXHh+PHj1KhRA4BRo0YRHh7+0r8MHg8MNkTIsbCwYMaMGRmmJSQkZNnW2NaroeSHdert7Y23t7e+vaurKzY2NgDY2dk955IJkfck4AiDS0lRcn1Jd07rWlurntnOxsZGH24A/WXh06ZNY+HChTg4OGRo/+6771KoUCH96xIlSjzzM4oUKZJhHtB9MbxMikajHxCc20u7n1VX9ZxXylhZWWW62ucxY1mv+Y2h16mjo2MOeyrEq6VSHh/bFEI8l4cPH2b6QhAvTtZr3nsZ69TPz49atWrlaU0h8oIEHCGEEEKYHLlMXAghhBAmRwKOEEIIIUyOBBwhhBBCmBwJOEIIIYQwORJwhBBCCGFyJOAIIYQQwuRIwBFCCCGEyZGAI4QQQgiTIwFHCCGEECZHAo4QQgghTI4EHCGEEEKYHAk4QgghhDA5EnCEEEIIYXIk4AghhBDC5EjAEUIIIYTJkYAjhBBCCJMjAUcIIYQQJkcCjhBCCCFMjgQcIYQQQpgcCThCCCGEMDkScIQQQghhciTgCCGEEMLkSMARQgghhMmRgCOEEEIIkyMBRwgBgEajeWabBQsW5KpmUFAQaWlpJCYmotVqn7drLywny7ZkyZIctXsRKSkpxMTE5Lh9Tvpz7tw5Tp48+SLdEsIkScAR4jUTFhbG0qVLgfQvx4sXL/LDDz88c95Tp07l+HMePnzI8uXL6dixI/3792fUqFHP3eec0mq1TJ8+HYDQ0FC2b9/O3bt3GTNmzDPnvXbt2jPDR2pqKmfPnmXTpk3cu3cv1/1bt25dtutw2rRp+t8fB8lhw4YRHR391Jrx8fH8/fffOfr8kJAQAGJiYujdu/dT26ampjJp0iSCg4NzVFuI/MbC0B0QQryYX375hfr161OuXDmWLl1KcHCw/mfBggXUq1cvQ3uNRkNCQgIAZcqUYe7cuQQEBDBo0KBnftbjIwqxsbEUKFAg23ajRo2icOHCBAYGsmXLFqysrEhOTs71su3btw9HR0fq16/Pxo0bOXfuHHfu3OHq1atMmDCBjh07ZmivUqn0gaBYsWIcPXqUo0ePUrNmzRwtm6IoxMTE4OTklGWbbdu2ceXKFbZs2cLHH3/MZ599xowZMxg3blyOluePP/7Qh8v/io+PJzU1FQsLC2JjY9m8eTMPHjzA2dn5mf0G3dEhRVGwsbHJtu327dupX78+58+fp0aNGtm2S0lJYfz48Zw6dYorV67g6+vLvn37aNKkCe7u7s9eUCHyATmCI4SRi4qKIjQ0FIDmzZvz4MED3N3dOXr0aKZwA+iDhqIo2NraolaruXLlComJidl+xr59+5g+fTqXL1/mrbfe4uuvv8627e3bt5k5cyaNGzemU6dOREVFcfnyZf76669cL1t8fLz+CEL9+vXRarWoVCoOHTqUKdw8XjZFUVAUBa1WS6lSpdi5c+dTT/X89ddfzJ07l6NHj9KuXTs++eSTLNuFh4dz8uRJ1q9fT7Vq1ShWrBj+/v5cuHCBxYsXs3r16qcuS3h4OJaWltmGp6SkJBRFAaBVq1bMnDmTggULEhERkWX7W7du8euvv/Ljjz+yYMECOnXqxPnz57P9fI1GQ7NmzQgJCSE1NZVGjRplu803bNjAwYMH0Wg0tGzZkuTkZLZu3crBgweZNGmSPiALkZ/JERwhjFhISAiXL19m586d2NraUqdOHe7cucO4ceOws7PL1P78+fPMmzePa9euERcXR7169bh8+TKVKlXC1dU1289xdHSkaNGiaLVa2rdv/9RTPmFhYezatYtff/2V2NhYDh8+TOnSpenevTtqtRorK6scLVtYWBinTp3i7NmzODk50a5dOyIiIhg0aFCWR4+CgoKYM2cOx48fx8fHhz59+nD06FHKlClDmTJlsv0cOzs73N3dSUtLo169evz444/Ztn3nnXfYuXMn9vb2eHl54evri52dHT///DOrVq166vIcOXKEt99+O8v3vv32W/z8/OjRowcTJ05k2bJlaDQa6tSpk+V2BLCwsKBMmTIoioK9vT3Lli2jZMmS2X7+pUuXWL58OePHj8fNzY1OnTrx3Xff8cYbb2Rq+8Ybb1CiRAkiIyNxd3fnwoULPHjwgB07dtCqVSssLS2fuqxC5AdyBEcII3b37l2SkpLo06cPdevWZdeuXdy+fRu1Ws2lS5cyta9WrRre3t60adOGCRMmULVqVTw8PAgNDeX27dvZfk6FChVYu3YtRYoUIS4u7qlf5mXLliU8PJzp06fTsWNH/ve//9GsWTPmzZtH3759iY2NzdGy3b9/n4SEBNq0aUO7du24cOECp06dwtzcnNOnT2catFy2bFlq165NrVq1mDt3LpUqVcLb25vIyEiuX7+e7ee88cYb/Pzzzzg6OlK4cGH9GJ7/cnR0ZPfu3XTp0gWNRsNHH31EiRIlWLx4McWLF6d8+fJPXZ59+/bRokWLLN8bOHAgiYmJ+Pr6Uq1aNVq3bo21tTXHjh1DpVJlOU+JEiW4cuUKERERNG7cmOHDhxMZGZnt51etWhW1Wk3JkiXRaDTExcVRqVKlLNv6+fnRtm1brK2tWbp0KYsWLWLLli14eHjQtGnTHIdUIQxJAo4QRqxhw4bUrVuXy5cvc/jwYVavXs3AgQO5cOECfn5+dOvWLcMVNsnJyezatYvTp0+zfft2ChUqROXKlenZsyddunRBURTS0tIyfIaiKIwePZo+ffrg5ubGhAkTOHr0KBs3bszUn9jYWGbPns1bb73F2LFj0Wq1rFixgr/++gtfX1/Wr1+f4ejLt99+y44dO7JctmrVqtG8eXNu3rzJgQMHmDlzJiNHjuT48ePcv3+frl27snv37gz93LJlC0FBQaxYsQIbGxtq165N8+bN9eOL/rtsoBvcW7NmTWrXrs2AAQOIj49n7ty5mdpt376doUOHcvr0adq3b8+IESPo0qULly5dok6dOk/dTrGxsURGRuLh4ZHl+zt27ECj0TB9+nQSEhKoW7cuVlZW/Pzzz9ja2pKampppHj8/PzZt2sSoUaNwd3dn5MiRDBo0KNtByVZWVlhYWPD+++/Ttm1bOnXqhLm5eaZTYEFBQRQvXpwHDx5Qr149PvzwQwYPHkyRIkW4devWM4OcEPmFSnl80lcIYXROnjzJpEmTaNiwIbGxsXz88cecOnWK+Ph4hg4dyp07d9i9ezcDBw4EYNGiRVy7do169epRtWpVDhw4wLFjxwgPD6d48eIkJCTQqFEjRo8erf+M77//HpVKRZMmTfDx8aFcuXJ8+umnLFmyhHfeeYcePXpk6FNiYiKXLl3i+vXr7N+/n2LFimFhYUFKSgpOTk507tyZatWqAfDJJ59Qt25d+vfvn2nZrly5wuTJkylWrBguLi60atWKhIQE/vzzT7799lsePnzIL7/8wvDhwwHYuHEjZ86cwdXVla5du7J3715OnjzJ9evXKVu2LAkJCXh5eWW4WmzdunVcvHiRXr16MX78eGxsbPjkk0/4448/KFWqFCNGjAB0V4TNnj2bgwcPkpSUhEqlYs6cOaxdu5a0tDRGjx6Nt7d3tttp+/bthIaGMnTo0EzvPXjwgFmzZvHPP/+wceNGdu/ezblz5/jjjz+oUKECarUatVrNokWLKFWqFAB37tzBx8eHr776in379vHHH3/Qpk0b6tSpw4oVK/D19cXFxUX/Gbt27eKdd94hJiaGGzduEBwczPvvvw/Axx9/TOfOnWnbti0AP//8Mzt27ODKlSsULlyY+vXrU7VqVS5evEiRIkX46quvsl1OIfITGYMjhBH78ccfad++PUOGDEGj0WBlZcXx48f1g1VLlCihDzeg+zJbv3495ubmWFpa0r59e8qWLUtYWFiGdo+dPHmS0qVL4+3tzfz584mNjaVnz57Url2bmjVrMmfOHP0l4JUqVeLWrVts2rQJd3d3KlSowKlTp6hXrx6tW7cGdANtnzyCs2TJkmyXbcWKFVSoUIFJkyaRlpaGlZUVe/bs0S9boUKF9OEGoFOnTri4uHDx4kUAWrRoQa1atdi1axeTJk3KVP/69evExcUxdOhQpk2bxv379/nss89o1KgRzZs3Z/ny5fTu3ZshQ4ZQuXJl2rZty7Fjxxg5ciT//PMPTZo04erVq6xcuZLQ0NCnBpwDBw7g4+OT5XsFCxZkxowZdOjQAbVaTcWKFWncuDGBgYGsX78+U/vU1FTWr1/PwoULWb58OSdOnMDDw4OPP/4YNzc33Nzc8PHxoWbNmowcORLQhT8HBweKFStG3bp1CQgIIDIykuDgYObMmcPmzZv19d944w3OnTtH3bp1uX37NqNHjyYxMZGFCxfqByZnNy5IiPxEAo4QRuy3337T//54XERWp2Ees7S0JD4+niJFiui/kK9fv57tpcj169fX/75s2TJq1KhB3bp1sbDQ/ekYM2YM8fHx+jalS5fmiy++0L8+efIk5cuXx9HREUD/38eGDx9Ow4YNMx0FApgzZ47+d3Nz8xwvm7m5OWXLlgV0A2uzu2rJ29tbvw6WLFlC+/btqVmzJtbW1oBuXEyfPn1ITk7GycmJqlWr4ubmxp49ewgNDaVPnz6ULl2avXv3MmjQIFauXMlHH31E06ZN9esHQK1WEx4ejqenZ5b9eNw2KSkJZ2dn/bZwcHDItv3jdfy///2Pn376iVu3buHm5gboxtr88ssvhIWF6edJSEjA29sbHx8fRowYwXvvvYeVlRUrV65Eo9HQr18/fdvq1atz/vx5fv/9dxITE/n666+5e/cuGzZs0A8y7tOnD927d6dQoULZbg8hDE0CjhAmpkyZMty8eTPb92vXro2tra3+tZ2dXYYv5KcpXbp0pjCU3Rcx6L6MnzYgVa1W5+qS41KlSj318ypXrpzh1Iydnd0z7yPzZO2CBQtmmGZjY6O/r8yECRMYPHgwTZs25ffffyc4OFh/VObXX3/F19eX6dOnY2VlRZMmTfQ1IiIisgxw//Xhhx9meF2sWLEc9btkyZLExcVlmv448IAuiBYtWpQff/yRhQsXMm7cOOLj44mPj0ej0dCgQQN9223bthEREcGGDRsICwtj/vz5TJ06lSJFijBkyBCqVKnCvHnzCAoKYubMmTnqoxCGIGNwhHjNxcXFoVarMwSDvLJmzRratWuXKTi8KhqNhrCwsKdePp1f3bx5M9tByUKIZ5OAI4QQQgiTI5eJi3xLrVYbugsiG//dNnJnWyGej+w7L48cwREG4+fnx7Rp07CxsaFZs2b6e5UcOXKE+fPn4+TkRHh4OD/99BOFCxcmKCiIMWPGYGVlRaVKlRg/fryBl8B05XbbDB48mDt37qBWq6lSpUqOHtwpnl922yc4OJjp06cTExNDu3bt9IOHs2sv8l5ut83jG0c+fpTGk7doEC9IEcIA4uPjlVatWin//vuvotVqldGjRyvXrl1TFEVR5s2bp6SkpCiKoijTp09XDh06pKSlpSkdO3ZULl++rCiKosyePVs5cOCAwfpvynK7bRRFUbp3726o7r52nrZ9AgMDlZSUFOXSpUv6bfK09iJv5XbbJCUlKR988IEhu2zS5BSVMIgDBw7wzjvvUKJECVQqFZ06deLEiRMADBs2DCsrKwICArh27RoNGzbk/PnzeHl56Z+b061bN317kbdyu20iIyMzXf4tXp6nbZ9y5cpx9+5dpk6dymefffbM9iJv5Xbb3Lhx46nPSRMvRgKOMIjAwEBq1qypf61WqzNcuhwREcGYMWOYPn06lpaWz2wv8k5ut03BggUZNmwYarUaRVFYt26dIbr92nja9tmwYQOdO3fG39+fHTt2kJqaKvvOK5TbbVO+fHnee+890tLSSEpKYuvWrYbqukmSgCMMwsnJiXv37ulfnzt3Tn8jtJSUFEaOHMn//vc/3N3ds2zv5+eX7Y3TxIvJ7bYxMzPjzTffxMrKCpVKxdatW0lMTDRI318HT9s+pUqV4o8//mDPnj3cvHmT06dPP7W9yFu53TY2NjZUqVIFc3NzbG1tWblypaG6bpIk4AiDaNGiBT///DNRUVEEBQVx5swZ/b98vv76a3r37k2tWrX07Rs2bMju3bsJDQ0lLCyMzZs307x5c0N136Tldtvs3LmTzZs3o9VqCQgI4N9//zVU118LT9s+9evXp2jRopQoUYIGDRoQExPz1PYib+V226xatYqDBw8CcOrUKbmiKo/JVVTCYLZs2cLq1atxcXFhwoQJeHh4cO3aNd5//32KFi0KgEqlYsCAAXTr1o0jR44wb948rKysGDt2LFWrVjXwEpiu3GybDh06MG3aNE6fPk2BAgX4+OOP9c+eEi9HVtsnJCQEW1tbihYtSnh4OD179mTZsmWUK1cuy/bi5cjNtilSpAjjx4/nxo0buLi44OPj88wn04uck4Aj8o0uXbowevRo3nzzTf3t8UX+INsmf+vSpQsjR45kx44dBAQEkJKSQp8+fejVq5ehu/bak21jOBJwRL4RExNDgQIFUKlUhu6K+A/ZNvmbbJ/8S7aN4UjAEUIIIYTJkUHGQgghhDA5EnCEEEIIYXIk4AghhBDC5EjAEflSbGws8+fPJzY21tBdEf8h2yZ/k+2Tf8m2ebUk4Ih8KTY2lgULFsgfgnxItk3+Jtsn/5Jt82pJwBFCCCGEyZGAI4QQQgiTIwFHCCGEECbHwtAdMEbR0dHs3LmTcuXKYWlpaejumKQHDx4A8Pfff3P//n0D90Y8SbZN/ibbJ/+SbZP3NBoNQUFBtGvXDmdn5wzvyZ2Mn8Mvv/zC1KlTDd0NIYQQQgBff/01H3zwQYZpcgTnOZQtWxbQrdAKFSoYuDdCCCHE68nf35+pU6fqv5efJAHnOVhZWQFQoUIFatWqZeDeCCGEEK+3x9/LT5JBxkIIIYQwORJwhBBCCGFyJOAIIYQQwuRIwBFCCCFySNFoDN0Fo2SI9SaDjF9A78FRYBX+zHZ1qlsyd5oTPuNjOHMh7zey1Jf6Ul/qS/1XU//6CVfU8+eiDQzM876YeXpi+VF/ND+tNLn6NnPn5/nnPbM/r/wTXzP5beeU+lJf6kt9qf9i9S0/6o+Zp2ee90cbGIjmp5VSP49IwHmJ8uvOKfWlvtSX+lL/+esbc0gw9vq5IQHnJcnPO6fUl/pSX+pL/eevb+whwdjr55QEnJcgv++cUl/qS32pL/VfrL6xhwRjr58TEnDymLHsnFJf6kt9qS/1X4yxh4RXWd8QJODkIWPbOaW+1Jf6Ul/qvxhTCiEvs74hSMDJI8a6c0p9qS/1pb7UfzGmEkJeZn1DkICTB4x955T6Ul/qS32p/2JMIYQYesxMXpOA84KMfeeU+lJf6kt9qZ83jD2EmFrIkYDzAiqWtzDqnVPqS32pL/Wlft4y9hBiSiFHAs4L8BnoYLQ7p9SX+lJf6kv9l8PYQ4iphBwJOC9g7rJ4o9w5pb7Ul/pSX+q/XMYeQkwh5EjAeQHXbqTmeU1j3/mlvtSX+lLflOvnhrGHEGMPORJw8hFT2PmlvtSX+lLflOvnlrGHEGMOORJw8glT2fmlvtSX+lLflOs/D2MPIcYaciTg5AOmtPNLfakv9aW+1M/M2EOIMYYcCTgGZiw7p9SX+lJf6kv9F2PsIcTYQo4EHAMytp1T6kt9qS/1pf6LMfYQYkwhRwKOgRjrzin1pb7Ul/pS/8UYewgxlpAjAccAjH3nlPpSX+pLfan/Yow9hBhDyJGA84oZ+84p9aW+1Jf6Uj9vGHsIye8hRwLOK2TsO6fUl/pSX+pL/bxl7CEkP4ccCTiviLHvnFJf6kt9qS/1Xw5jDyH5NeRIwHkFjH3nlPpSX+pLfamfzhhDgrHXfx75MuCsXbuWhIQEADQaDWlpaQbu0fPLjzun1Jf6Ut8E6itJL7d+Dryu9Y01JBh7/dx67oCzZ88e9u3bB0BycnKO5omKikKr1Wb7/qJFi5g1axanTp1i7NixjB07lsaNGzN16tTn7aZB5WbnaVtkJ3/WacnVRm/wZ52WtC2yM0/rPw+pL/WNur76LCRt0v0ePxMiW0HceFA0OZpWqxqZ6yf+DLEjH82jPJr2EzxsDTE+oI3VTdNchegBED0QNBey7b9l0lcZ6ydthocdMzeO7gtR78HDdhA3UTctZjBE94HoD3WflxqUvrzJW3izUrRpb18D1jfmkGDs9XPjqQHn33//xdvbm8GDB2d67/Tp06xYsYL27dtTp04drl27lm2dFStW0KJFCz788EOuXLmSbbuLFy/y5ZdfMn/+fObNm8fHH39MpUqVGDZsWC4WSUej0dCnT59M0+Pj4xkyZAg9e/bkk08+ISYmBo1GwzfffJPrz3ia3IabaeUnUcLmHmYqhRI295hWftJTQ05+3vmlvtTPF/XTboG5G2ijIdUfCv4GKgfQnHnmtMKFC9Dj3b8z17dqBI6zITUElETdNPWf4PQzWL4JKftBSYOE2eD4DTjNg6Tfsuz/N2PNuXpDSa+v+RtStoGTb+Zlcf4ZCm7QfYZVfd1nm5cC5zXgvBosK0HKDkgLAW0cRZxDWDTL07S3rwHrG3tIMPb6OfXMIzidO3dm8eLFGaYFBQURFRXFDz/8gIODA9999x0VK1bMtka/fv1wdHRk27ZtVK1aldTU1CzbVa9enc2bN3PlyhXOnz/P4cOHmTx5Mi4uLrlcLAgJCaFo0aKZpv/www/UqVOH9evX8+GHH7Jo0SIsLS1xcHDg3Llzuf6crOR25/m8zFxszTMeBbM1T+bzMnPzpH5uSX2pbxL1026CeWlAC2ZOYFYQzNxASX3qtDo1Xen0bmmW/JxFfXN3SJyrCzpm9o8n6oKUWVFAA6n/gGV13TSVLVh46sLTf/o//KtLRMWXSq+dvB7sPwezEtksz33QxoF1G13AMiv5n755gDaKciXO8b8v2xp+/Zt4fWMPCcZePyee6xTV7du3OX36NFOnTqVy5cosWrQoy3bh4eFcvHiRXbt2ERAQwPDhw2nTpg1t2rQhJCQkU/s2bdpw6dIlzMzMsLW1pUKFCuzevft5usj169cpX758hmkpKSmcPXtWf2SnQYMGBAUFAVCtWrU8CTjPs/MUs76f5fTi1vf41H0xLVwOUNrmFmakGc3OL/WlvsHra+/qwoJZId1RmpihoDkNVvWynVawYAHUkUNZt/Ewgf/W/k+9BIj1geTNoPGDtHDddMs6EPMJJK/ThY+022DxxD/4lFRQWWfq/9WrgY8CGKCoQXMJUg5D/HhQH8+8PCn7wKaT7ncLT7BuoftdfUb3mTadqdNkFhVKn2fdzjcMv/5fg/rGHhJeZX1DeK6AU7duXd555x2GDh1Ku3btcHJyyrLd2rVrWbNmDatWraJSpUpotVp2797NgQMHKFOmTKb2165dY/jw4fj6+rJhwwYCAgKoV6/e83SRGzdu4O3tnWHanTt3KF++PGZm6YttYWEBQHR0NPb29ryI59157qVkPtIEkKaY4+OxgAWVR7C3TlsuNqrDSs9ujPs6hDMXNJSwvkNx67uA8kL9ftH+S32pny/rK2mgMgdtBKTdeRQOtLojO1lMe7NSNEWc7xGn6UhCQpqunZKoGxOjJOtCik13KLgHLKtCyi7ddM1FsOkMZi66ozcqB9CGp/dDGw4qWwooI+nb2T+9/2k3wfzR30ElRXfUx2EkOEzShaj/0pwBq7d0v6usdSFHNzMk76ZOdUveb/c3/reqc+boWEjI+h+ezyvfbd98Ut+UQsjLrG8IzxVwbG1tiYqK4sqVK9y/f5+YmJgs240cOZIZM2aQmppK3759qVmzZoZwERERkaF95cqVWb58OSEhITRp0oSSJUsyf/785+lilgHH2dmZu3fv6l+HhITg7OwMwPHjx6lVq9ZzfRa82M4zO8SHpDSbDNOS0mwYc306NY6dodv5dSxN+hZtnY/4+05hDp3XBbFP3JeyqXoPQAVA96Kb6FtiDQ2cT1DE6gG5CT7G+sdF6kv9LGmjdaegANRHwban7oiH7YeQvDvTNFfHPTSve5pUyx4E3Xs7vZ3KDpx/ApUNqCzAuonu1JRVI1DiQHNe97t1G7AbCsnbdUElZY/udJL6LJi5UKe6JRvWzWP1Fq/0/qfd1p3yAlDZg/aBLuikBYPKOePypN0HM2dQWepeJ23UDV5WtKA5RYni9syd5sT0GVsJutv40Sm3vPmHD+TD7ZvP6ptKCHmZ9Q3B4nlmunnzJgULFmTnzp18//33TJw4Mdu2P/30E507d8bKygpzc3P99LS0NFasWMGYMWP009zc3IiIiGDFihUUK1aMpUuX0qRJk2xr+/v7o9VqqVSpUqb3wsLCcHNzyzCtUKFCWFpacujQIRo0aMCcOXPo27cvwcHBREVFZQpEOfWiO8+uB+0A3VicYtb3uZdSlNkhPvrpdl416D6pKYP+U3/t3fc5/DB9/XRw3UltZz/962iNEwEJXgQklnv0X08CEzyJTnXO0/4/i9SX+q+8vn78DYDFo/Dw6OiNmV2GaSXdbtOsYSE270zl5u0IsHminZKsu2rKaTGkBoPFo781ybt0A36x0B0NUrS6Ab4qe12wsv0AYoeBuQc1G37N3GlOvNthCFHJXXVHf0B3mbfKVve7ykx3FCjmE93RGYf/ZVwezRndqbDHrFtB/DTQ/kChwuVZsehz+n26n4jYymBjDrbvPbH8LyZfbt98VP+xJ0OC5qeVef6lbuz1DeG5Ao6trS13796lUaNGqNVqjh8/jouLS6bTToGBgQQGBjJjxgwuX77Mtm3baN68OSqVinPnzhEenn4YNzExkYULF/LFF1+gVqtZt24dmzdvZuvWrdn248CBAyQmJmYKOLGxsTg6OmY5zzfffMPXX3/N7NmzGTBgALVq1eKLL75gwoQJz7Mq8mzn2fWgnT7Q5LT+9YQKXE+ooH/d5/IqCllG4mUfSHm7ADztg/CyC6C96y4cLeIBOBNdi76XVwHwcckVaItWYeBkGZAo9U2svlkxsHoU/q1bQOznkLhMd0rI8VswK6CblrQMWxcvLgV+y81wO1D/px3mYFkTsATSIPYz3aXgFhXBujWgguSt8LCp7jMdJ6d/pnWLDP2PivcGq0LpfbTtkbHP1q0f1Xwk7bbuknDnn8Cyhi486ZfPCQrMylD/eqAarN/RvW/5Zs7X1VPk2+2bT+r/l7GHEFMLOSpFUbI9jnnv3j169+6Ns7MzmzZtwszMjJiYGMaOHUvr1q3p2FF3v4br16+zbNkyypcvz6BBg/TzHzhwgIYNG2Jrq/tXypQpU9i6dSuJiYnY2dnxwQcf8MUXXwC6IzpPHuEZM2YMVapUyfJS78ftv/32W0aOHJlp7ExCQgLnz5+nUaNGgO4+PW3btmX58uVZjv3JLT8/P3r37k3FmotZ6dvECHZOBTerMLzsA0lTzDkZXR8VWs42aoRFvQ8YdGA4f1+KYUfNLgQlluVGgheBj476BCeVIUVr8+yPeKn9l/pS/zWtr40Ds6z/sWYU/TfB+tdPuJLs8/Rbl5h5er7UkGCM9W3mPt9wk2d5/H28du3aTMNMnhpwTElUVBQFCxbMk1qPV+jixb+wcpOnUe2cGepPdeTLr8M5dsECF8sIvio3Cy+7AMraBWNlpruUP00x43aSu/40176IFhmOGhm0/1Jf6kt9qf+K6+ck4IBxhpCXWd8QASdfPqrhZcircPOkucvijW7nzFD/6ziOXdCdpYzUFOZL/xl0Or+FGsfP8u7Z7fhc/YFFtz/hRoIXnnbBDHZfSgX76wBUsPdnR83OVHW8DICzRZRcyi71pb7UN/n6OWUKA4MNfR+bF/VcY3CEzrUbqWCVtzXzw86fqlgSnFSO4KRy7I1IHxNgpUpBpdId8DNTabmTXJwojTMA7xTZy0SvaagVa8yKenNqWlneeFAWm4KeBCR6PboUXvVK+i/1pb7Ul/ovq35uGPuYGWMfk/PaHMExBvl951cr1vrxOFfjK/HpP76EJusuc/0rqiHLk6ajrdOPa3cK4KU9wZdlZ7P0jc84VLclfg3qsf7N3rhY6m4N4GoVRmHLCORSdqkv9aW+MdXPLWM/0mLMR3LkCE4+Yew7f/GKZek6qXqGS9kLWMTgaReEl30AXnZBlLULJvrREZ9P3JfpLms/cRKADq47sDVPIjDBk8BET2JSM/4hMfb1I/WlvtQ3jfprfHM/3MHYj7QY65EcCTj5gCnt/E/Wj0114nxsDc7H1sg0z5b7nfGLqUn6TQq3ZLiHT1iKq/5KrlQXb94bVJ1R4904c8H6lfVf6kt9qS/186q+sYcQYww5EnAMzFh2zryu/098Zf6Jr6x/3efyTxS1vo+XXaD+iI+XfQDvl9iAtSoZ1sCn2mocZS0AHxRfS2BiOU5FP9+jPF60/1Jf6kt9qZ9bxh5CjC3kSMAxIGPbOV9ufRX3U4pxP6UYf0U1Sq8/2oFxE/8h8ZY/acrj+yQpDCvty9awjpyKroelSs3WGt0ITixLQKLno/v4eHIryZ1UJftBgca1fqS+1Jf6plDf2EOIMYUcCTgGYqw7p2HqFweKP/GuioanjmBtlgKAg0U8wYll8bQPonnhg5irtACotRaEJJYhINGLgIRyHHr4NjcSvA3Qf6kv9aW+1E9n7CHEWEKOBBwDMPadMz/UT1UsSU3THZ2J0hRi+LUfAd2l7GXtQh6d5grEyz6QNwtcop3rbiI1LtxI8KZTzVtMKzeW6RPGceZCZRzNY7G3SOC+XMou9aW+1H9F99Ex9hBiDCFHAs4rZuw7Z36vr1as8U+ogP9/7rZsb56AoqioU92S/w2x4tpyR05f1V3y3qrIfqaXn0hcqgOBCeUeHfHxJCDRk4AETyI1LuQ0+OT39SP1pb7UN1z9/zL2EJLfQ44EnFfI2HdOY66fkGavrz90vDtnLizRv3c2ujaTAsbjZR+Il10grQrv571im/TvP1QXJCDRk1H+M3mgLkIhy0hSFQti5VJ2qS/1pf4LMvYQkp9DjgScV8TYd05Trn872Z3b99yfmKJQ2DJSd5rLPhBPO13widboAs2gUivoUWwDNY6fQcGM1oX3UqVMCj0GvckX44ty5kIe3976Gf2X+lJf6ufv+s9i7CEkv4YcCTivgLHvnK9ffRURmsJERBfmZHT9TO/uevAONxK8UB7dCHxw+d+oaHEG1sAyRwitXfLRPXw8uZHoRWCCJ8GJZVArz3cPn/y3fqS+1H+965t5ehpdSDD2+s9DHtXwkuXHnVPqv5i/495gS1hnff2iY7cyKn4fQ/6Zy483h3E5rgolbO7yUcnVfF/hK7bV7MbaN/vq5+9WdBN1nM4YrP9SX+pL/Rerb6yPRTD2+rklR3Beovy6c0r9l1Ffdyn7n5HN9e9bqjSUtr2Fp10gWtLv4TOqzBx2P2jDmZg6mJPKb9V7cTPJQ3fEJ8GLgERP7iSXoHZ1axNaP1Jf6ptOfWM+EmLs9XNDAs5Lkp93Tqn/auprFEsCE3XP1kqnosnpP7E1SwbA0SKeCHVhqhe4SDvX3fpWKYot5sXKc2JaOao8KEOU3VsEJHq90v5Lfakv9bNm7CHB2OvnlJyiegny+84p9Q1bP0VrQ3SqMwDRqc4M/mchzc/so+bx07x34VeWJ09Dqd2Ha/86UEH7F6PL/kBtJ91zutxtbvNrtQ+oXuACoLv8vZBl5Cvtv9SX+lLf+E/3GHv9nJAjOHnMWHZOqZ//6iek2WPjWZOuE5tleCq7s0U0qY8eU2FnnkiaYkFimi0ALVwOMKPCOCLVhfQPJ9U9rsKTwARP4tIKvLL+S32p/7rVN/YjIa+yviFIwMlDxrZzSn3jqP/4aA+Af0IF+lxepX99Ka4a3wSNwctOd0l7Z7dt2Fsk6t+/n+JGQIIn425MwaNySeaN0zJq/D3OXMj+GV153X+pL/VNub4phZCXWd9qmE+e1s0JCTh5xFh3Tqlv3PVvJnlw846H/rUKLcWs7+sfVeH56OaF5SsXYtY0J45N+Rpf+9XU4AxpWNC00GHszRO4kehJSGIZNMrz3cMnv64fqS/1X0V9UwkhL7O+IUjAyQPGvnNKfdOpr2DG3ZTi3E0pzpGHTTLV1wQ1xs/ejbRHu37fEmuoX/A0AKmKObeS3AlI8NKf7rqR4MntJHd9+5fdf6kv9Y2t/mOmEELyw8DgvCQB5wUZ+84p9V+3+tW5EFtd//6gK4vxsL2pP9LjZR9ABfvrtCq8HzOVAsA/cRXpemEjAO1cd3I3uTjnY2sYqP9SX+rnn/r/ZewhxNRCjgScF1CxvIVR75xSX+prFEvdw0UTvfjjiek2Zkm6p7LbBZCmv4cPfFV2Joci3+Z8bA3qVjdnecUPOTmtOJXDy2JVUHfEJ1ztijyVXeqbev3sGHsIMaWQo1IURTF0J4yNn58fvXv3Zu3atdSqVcvQ3RHilVGS40CThMrRFSUpBtYOgDB/iAtPb2RTANwqQNEK4FYRPBuhci1vuE4LkYcUtQaVVd4P0jd1ikaDyjLv19vTvo/lCI4QIsdUNo5g46j73dYJPtY9dV1JeAhh1yHsmi7w3PeHyzsg6WdoOwVcy6NE/wsbhkOrMag86qKoEyFNo6sjhJGQcPN8Xka4eRYJOEKIF6ayLwRl6+t+HlEURXdkx/zRH7akGNAkpb++tg/WDUJxKq474uPmrTviU7QCuHqhsrI3wJIIIUyFBBwhxEuhUqmggFv662KVYcgTI32KVYY249OP+AQfh9SUxzOjFHTXBZ6O36ByLomSEg/mlqgsnu+p7EKI14sEHCGEQahcvcA1/flaSloqPLylO811318XfML8wVp3SowjC+CoL8qkIFQWVig3DkFKPBStCIU8UJnLnzMhRDr5iyCEyBdU5hZQpJzup0q7zA3Kvw02BVBZPLoZ4V+LIOCw7ncLa5Qino9OdVVIH+TsXAqVmTxyT4jXkQQcIYRRUHnUA4966RP6rILwgPQjPWH+EHIKLm5Ob1OsMvgcAkD5+3dwKobKXa58FOJ1IAFHCGGUVFZ2ULKa7ucJSnLsoyu6/EH1xNGb38dDubfAvZZuAPRP70PBUhmO+KjsXV7xUgghXhYJOEIIk6KyKQCla+t+nuRzMH0QszoB1IlweTskrdY3URyKZDzFVaYBqiLlXmHvhRB5RQKOEOK18OTRGZW1Awze8ehS9rD0K7ken+ryW6sLQG3Gw9vDUWLDYJMPNBupu4ePJhkUre4okhAiX5KAI4R4bekuZS+q+/F6Wz9d0Woh+l+wtNFNSIzUBaHHN36//ies7Y9SqHTmgc2FPdMHQgshDEYCjhBC/IfKzAwKuae/LlpJP1gZgCKe0HxU+lgf//2gTdO9Z2aBUrisLvC8OxFVwVK6uzZbWKMyM0cI8WpIwBFCiFxSuXmD25f610pqCjwITA889/3h7uX0I0B/LYbDc1Em+KOytEUJPqG7h49bBXAuKZeyC/ESSMARQogXpLKw1l2SXqxy1g3K6C5vV1na6l4fXQj++3S/W9mjuHk/carLW3fzQkc33Sk0IcRzkYAjhBAvmapsAyjbIH1Cz4WPBjRfTx/c7L8P/H5Nb1Osiu7KL0Dx3w8ORVCVfPPVdlwIIyYBRwghXjHdpex1dD9PUOIjnriHzxNv7PgflKwOvZbq2v3SHzI8pLSC7knvQgg9CTgGoKg1qKye/uh4RaMxyOPlhRCGo3IoDA6FoVzDjG98skN/Dx9FnQjRd+D6QdAk6psoziUzBB486qByKfMquy9EviIB5wX0HhwFVuGZptepbsncaU74jI/hzAVNpvevn3Al2WfYU2vbzJ2fZ/0UQhg3lVOx9N+t7GDoXt2l7FG304/4PP4JPAppamgxGlqMQkl4CJtHQuPPdPfwSVXr6sil7MLEScDJY88KN0IIkRdUZmbg4qH7qdRaP133VPabYGWvmxD/QHeFlyZZ9zrwCPz8IUrhcrr79uiP+lQEFw+5lF2YDAk4eUjCjRDC0HRPZfdMf+3mDV8cT29QsBQ0HqI78vPvRd3jKh6zsEFx9dIFnpZjUBUqrbsE3txKrugSRkcCTh6RcCOEMAYqtwrQZpz+tZIS/+ip7Nch7JruNFfQcWjzaAzg8aVwcA7K/y6jsnZAue336B4+FcHRVYKPyLck4OQBCTdCCGOlsnaAUtV1P1kpWR3qD9C1A909fK7s1P1uVxDF1fuJU126H5V9oVfTeSGeQgLOC5JwI4QwZapyb0G5t9IndJoJ9fvr7t8T/uiuzRe3QHKsvolS/A1Uw//U/R54FOwKoSpe5VV3XbzmJOC8gIrlLSTcCCFeK7pL2TOGHkVRIPZe+mMqUNJn2P4VuJaHPqt0bTcMBUfX9CM+rl7pd3gWIg9JwHkBPgMdJNwIIV57KpVKd+NBp+JQvlnGN/v9CtpU4NEzu+5egQcBkPbo76bKDMXFI+NT2UvVQPXEw06FeB4ScF7A3GXxXLsp4UYIIbKjcvFI/93CGkYcRknTQGTIo0dVXEt/bMW1vbqnsjf7HFp9hZIcB1u+gAYDdPfwSUsFlUouZRc5IgHnBVy7kQpyrywhhMgVlbml7rSVa3l4o71+uqJJ1t2zx9ZJNyH2Pvx7HpK66V4HH4fVfR5dyl7h0eDmirrL2p1LyhVdIgMJOEIIIfIFlaUNPDEYWeXqBaP90hsUcIN6/R5dyn4MLmxMf8/a4dFT2StCUx/dPXzSUsHMXILPa0oCjhBCCKOgcqsA7aboXyuJ0elXcj1+ZMU/u+Ht4boGJ1fCwR9QvjyDytYJ5c5lUCeCmzcqu4KGWQjxykjAMRAzT0+0gYGG7oYQQhgtlZ0zeNTT/TyiKE9cwVW0ArzZDWwK6F4fXQiXtujaObo9cf+eR6e53LzT7/cjjJ4EHAOx/Kg/mp9WSsgRQog89OTpKJVnY/BsnP5m20lQ4z3dHZvv+0P4dTj9M2iS9E2UEtVQDduv+/3mKbB11h05EkZHAo6BaH5aKSFHCCFeIVWBolCgKHinX8queyr7rfTAk5aaPsO2r3SXvn/0q67t1i/B3gWKVtQd+SlcVjdgWuRLEnAMRBsYKCFHCCEMTPdU9jK6n8rvZHyz1zJIUwOPntIefAIigkDR6t43t0Qp7Jnxqewl30TlVPwVL4XIigQcA5KQI4QQ+ZfK1Sv9d3ML+OL4o0vZA9Lv3XPfH26fg0tbdQ0bD4F3J6KoE2HbaKjbF1XpOrojRSqVXNH1CknAecXqVM94OFNCjhBCGA/dpexv6H6eoHsq+w2wddZNiL0PgUehUhvd65unYXXv9EvZ9ffx8QYHeSr7yyAB5xV6/GDO/5KQI4QQxk33VPYa6a8Ll4X/XU6/qsu+oG6A8/1Hl7Kf/SV9ZrtCKI8Dz1uDUbl4oCiKhJ4XJAHnFXnyqeNrfDPff+G/IUcIIYTxexxSVG4VoON3wKNL2eMfpD+cNPzRIyvOb4D6A3Qznl6FcnAO+BxCZe+Ccv+a7mov1/JyKXsOScB5BZ4MN097MOeTIUcIIYRpUqlUuieqO7pmuIw9wz18XMqCdwuwK6R7fXQhnP9N166QO7hWyDi4uYiX7vSZ0JOA85LlNNw89jjkWA3zeQW9E0IIkV9kuIePVxPwapL+ZqsxUPndR4ObHx35CTiU8ansJaqhGroXACX0Atg66U6VvaYk4LxEuQ03j8kYHCGEEE9SOZcE55IZLmVX0jQQEZweetKe+J7ZPkZ3B+ePN+na7pwAdgXTBzcXLK27RN6EScB5SZ433AghhBA5oTK31D9iAjpmfLPrHH3gUbRpcG0vRIakv29p95+nsleA4lVQObq9ugV4yV6bgLNp0yaaNWtGoUKFSE3V3anSwuLlLH5uwk1iaip2z+hHXFwcjo6OednFHNFqtaSlpWFpKXfqFEIIY6IqVjn9dzNz+PK07lL2xw8lffwTcFg/toeGg6D9NJTUFNjxP6jZQ3cPn0djg4ztqq58GXBCQ0P5559/8PLyoly5ck9tGxcXh7W1NVZWVlm+v27dOm7fvk14eDiHDx/G0dGRM2fOULZsWZYtW5bnfX9quNHGQMwntGxpTsSdO7xf2p3td+7gaGGJtbkZs998kxJ2thlmSUxMZObMmXTp0oUiRYpQsmRJtFot8+bN48iRI2zdujVTHw4fPszq1atJTk7mxx9/xM3NjSNHjjB27FgcHR354YcfqFKlir795cuXmTRpEiqVitGjR1O3bl0mTZrE7du3iYiIYPDgwTRs2JA1a9bQv39/bt++TXBwMO+++26erz8hhBAvh8raAdxr6n6eoCRG6YLP4yesx9yDKzuhbEMoXQdCz8HqPrpL2d2804/6uFbQPfA0n3pqwPn3339p3rw5TZs2ZfHixfrpd+7cYfbs2fzwww8Z2oeHhzNq1Cg0Gg3FihXju+++yzZ4ZOXzzz8nKCiI2NhYunbtipeXV7Ztt2/fzpIlS0hLS2Ps2LG8/fbbWbY7cuRIhr7HxcUxaNAgxo8fr5+m0WiYNWsW//vf/3Lc16w888iNmRMUXM/+/a50qV2bBoVdSEnT8mVF72xrbt26lfr163P06FHatNHdMGrXrl34+fmxdOnSLOcpWbIkP/30E2PHjsXf3x83Nzd+/fVXfvzxR9LS0li6dCnz5s0DIC0tjUmTJuHr64uTkxOff/45pUqVombNmkyaNImgoCBWrFhBeHg4ZmZmnDlzhmPHjvH555+/0LoSQgiRP6jsCkKZ9Ceyq1w8UL72T38khbUDVHpH95DSc7+BOkHfVilQ9NGVXBWgfn9ULh6Z6isXNsPe6RB9B5xLQOtxqKp3fdmL9ewjOJ07d+a7777LMO3GjRuUKlUqU9vx48fTt29fWrRowcaNG1m/fj19+/bNcWf+97//UbhwYXr27MnQoUOf2rZDhw7s3buXsWPHUqpUKVJTU7M85fTmm29y8uRJ4uPjKVu2LHv37mXKlCmULl1a38bS0hIHBwfOnTtHzZo1M9XIidyclvLz86OMgz0PUlIobW/31LZ79uxh+fLl7NmzBw8PDwB+/vlnZs6cSZEiRbKcx9PTk99//507d+7QoEEDQHe6yd3dndTUVDSa9P5dvHiRunXrUqxYMQAqVKiAjY0N7du3B2DhwoV89NFHqFQqPv/8c1q1aoWjoyN2dk/vtxBCCOOlUqlAZa773a0CdJ0NPLqUPeaO7iqux09lD/OHU6ugZg9dG791cPAH+HQ3BP4Fm0dAaoqucPS/sOVzFHjpIee5hlBfv36d8uXLZ5h27949EhISaNGiBQBdu3blxIkTuapbuHBhQPdlHBkZSWJiYqY2Dx8+5PLly+zdu5eLFy8yZcoU2rVrR8uWLTlz5kym9t27d2fbtm2UL1+e8PBwGjZsyJ49ezJ8yQNUq1aNc+fO5aq/j+V2QPH27dt5v7Q79V1cqFOoEGmKwh/37hGZkpKhXVBQEK6urlhbW5OQoEvM9+7d4/bt2/z6668MHjyYS5cuZao/bdo0Ro0aRUhICH5+fgD07t2b3r17M2DAAAYOHKhvGxwczBtvpN9yPDU1FRsb3b0UVq9eTdmyZalSpQqVK1dm7969bNu2jV69euV+JQkhhDB6KpUKlXNJVBVaoGoyDFUPX1TD/4QpIbpHUIDuie3utcC+sO7ITWrG7zY0SbrpL9lzBZwbN27g7Z3xtEpgYCA1aqTfplqtVmNra/vfWZ/q6NGjAERGRtKvXz/Onz+fqc3OnTtZvnw5a9eupWTJkkRGRrJlyxYOHTpEnTp1MrW/cOECX375JatWrWLjxo1cvHiRmjVrZho4Gx0djb29fa76C89xtZSSSkBAAFWcnHC2ssLDwR5zlYp4TSoHw8JJTE2l+aHDJCUlcfHiRY4cOUKzZs04e/YsjRs35t69e3Tp0oVx48YxYcIEfv31VwD69evHqVOnAKhfvz5nzpxh7NixLFq0CIDff/+dbt260aRJE/bv36/vToECBQgLC9O/vnfvHnZ2dly4cIHTp0/z6aef6t+LiooiNTWVPXv20LNnz1yvKyGEEKZJZWauv+xcVb4pqp6Lda+j72Q9Q3bT89BzBZybN2/qT5c85uTkxN27d/Wv/fz8njlA+L92794NQLFixfj999956623MrXp27evfvzIoEGDePPNNzOM84mIiMjQvkaNGqxfv54DBw7QqlUrqlevzrRp0zLVPX78OLVq1cpVfyuWt8j9peCpf1OtWjUAFgYEEhAXh0ar5URkJDbm5thZWLChQX1sbW3p2rUrZ8+eZe3atXTp0oXTp09TtmxZrl69ilar5Z9//sHNTXdJ35w5c6hduzYAzZs3x8nJiXfeeYf4+Hji4+OJjo7m008/5auvvmLv3r367tSpU4etW7cSExPD8ePHcXV1JTo6mh9//JEZM2Zg9sR9En755Rd69erF1atXqVChQqajYEIIIUQGziVyNz0P5TrgqNVqzM3NMTc3zzC9cuXK+Pv78/fffxMXF8fSpUvp0KFDhjb+/v5cvXo129phYWEoioJGoyEiIoKHDx8SHx+fqd3u3bvx9vamWLFimfrx34G3zs7OhIeHs2zZMtq0acO///5LkyZNMrQJDg4mKioq01GpZ/EZ6JD7+9xoztCoUSMAOpQozvSr1+hx4iSFrKxoWdSNpLQ0Pjl7Tn96TqVScfv2bYoXL65fnrfeeotOnTrx888/68c4TZw4kYsXL3L9+nX96axNmzZRrlw5VCoV8fHxJCcnc/v27QxjlQoWLMigQYPo06cPmzdvZvjw4Wzbto3r16/TvXt32rRpw6hRo4iKiiIiIgJ3d3cGDx5Mz5495fJxIYQQT9d6HFj+52yOpa1u+kuW68vEg4ODszwyY25uznfffcf06dNRq9X4+Pjg7u6eoc2BAwdITEykUqVKGaavXLmSCxcu8Pfff9O8eXPu3btHy5Yt9WGnSZMmLFy4ENAdodm1axdz5swhNjaWy5cvExISgqWlJaGhofz999/6umlpaSxcuJBevXpRrFgxfv31V1atWsWaNWsyfL6vry8TJkzI7apg7rJ4rt3M5VEMqybUqVMH7cbfKGlnx6q6GU+rabRa6roUynBUysPDAyen9KeQDxw4MMM4GoCqVatSuHBhHB0dGT58OOHh4RQoUICZM2dib2/P22+/Te3atbGzs2P06NGEhIQwYsQItm/fTrt27WjXrp2+Vr9+/ejRo0eGU4xBQUF89NFHABkGaAshhBDZUVXvigIGuYpKpWR4uldG9+7do3fv3jg7O7Np0ybMzMyIiIjg5s2b+tM59+7do1evXuzYseOpN6NLS0vj22+/ZeTIkZnGupw/fx5zc3NcXFwoWLAgdnZ2+hsKKYpCUlKS/qqdEydO4O3tjYuLCwBLlixh2bJl+vvhtGrViu+//14/r6Io+tMs8+bNIzExka+++up51xegO/3Wu3dvcFwAVtVyPf/1E64k+wx7ahubufOft3s5FhMTkyE4CSGEEMbk8ffx2rVrMw0zeWrAyamoqCgKFiz4omWMhqkEHCGEEMKYPS3g5MmTtl6ncCOEEEKI/M+0HyWaj5l5ehq6C0IIIYTJkoBjIJYf9ZeQI4QQQrwkEnAMRPPTSgk5QgghxEsiAcdAtIGBEnKEEEKIl0QCjgFJyBFCCCFeDgk4r1id6hnv/ishRwghhMh7EnBeoccP5vwvCTlCCCFE3pKA84o8+dTxrEjIEUIIIfKOBJxX4Mlw87QHcz4ZcoQQQgjx/CTgvGQ5DTePPQ45QgghhHh+EnBeotyGm8e0gYEvsVdCCCGE6ZOA85I8b7gRQgghxIuTgPMSSLgRQgghDEsCTh6TcCOEEEIYngScPCThRgghhMgfJODkEQk3QgghRP4hAScPSLgRQggh8hcJOC9Iwo0QQgiR/0jAeQEVy1tIuBFCCCHyIQk4L8BnoIOEGyGEECIfsjB0B4zZ3GXxXLsp4UYIIYTIb+QIzgu4diPV0F0QQgghRBYk4AghhBDC5MgpqhewdnFBatVyzfV8ikaDzdz5z2yjsrR83q4JIYQQrzU5gmMAOQkuEm6EEEKI5ycBRwghhBAmRwKOEEIIIUyOBBwhhBBCmBwZZPwc1Go1AP7+/gbuiRBCCPH6evw9/Ph7+UkScJ7D1atXAZg6daqBeyKEEEKI4OBgGjRokGGaSlEUxUD9MVr+/v507NiRWbNmUbx4cUN3RwAPHjxgxIgR/PjjjxQpUsTQ3RGPyHbJf2Sb5E+yXZ6PRqMhKCiIdu3a4ezsnOE9OYLzHBwcHACoUaMGJUuWNHBvBMC///4LwBtvvCHbJB+R7ZL/yDbJn2S7PL/69etnOV0GGQshhBDC5EjAEUIIIYTJkYAjhBBCCJMjAec5FChQgKFDh1KgQAFDd0U8Itskf5Ltkv/INsmfZLvkPbmKSgghhBAmR47gCCGEEMLkSMARQgghhMmRgCOMSkJCgqG7IIRRUavVaDQaQ3fjtacoimyHV0wCjjAqffr0oX379rRp04aZM2cCMGfOHLp06cL777+vf4xGeHg4ffv25f333+fzzz/P8jkl4sVpNBr69Omj/338+PF0796dvn376m9cFhQURLdu3ejVqxfTpk3Tz7t27Vo6duxIjx49OHbsmEH6b6qe3C6HDx+mZcuWtGnThs6dOxMcHAzIdnlV1Go1Q4YMYeDAgXTt2pWlS5cCufu7tXfvXjp06ECPHj3Ytm2boRbF+CgiV86ePat07NhR6dGjh7JkyRJDd+e1kpSUpHzwwQcZpu3evVsZOnSootVqlbCwMKV///6KoijKwIEDlf379yuKoigbNmxQVq9e/cr7+zq4fv26MmrUKEVRFGXZsmXK9OnTFUVRlBs3biiff/65kpaWpnTs2FG5fPmyoiiKMnv2bOXAgQPKxYsXlR49eigpKSlKXFyc8sEHHygajcZgy2Fqntwus2fPVk6cOJHhfdkur87Ro0eVU6dOKYqiKFFRUUr37t1z9Xfrzp07yrvvvqtER0crarVa+fjjj5WIiAiDLY8xkSM4uZCQkMC4cePw9fVl3bp1BAUFyRPFX6EbN25QpkyZDNM2btzIqFGjUKlUuLq64uLiwp07d0hISKBFixYAdO3alRMnThiiyybv+vXrlC9fHoDff/+d4cOHA+Dl5UVsbCznz5/Hy8uLN954A4Bu3bpx4sQJNmzYgI+PD1ZWVjg4OFCrVi3Zl/LQk9vl6tWrlCtXLsP7sl1enUaNGlG3bl0AZs2aRf/+/XP1d2vbtm3069cPJycnLC0tad26NX5+foZcJKMhAScXDhw4wDvvvEOJEiVQqVR06tRJvjhfofLly/Pee++RlpZGUlISW7du5eHDh5QuXVrfRq1Wc+3aNWrUqJFhmq2trSG6bPJu3LiBt7c3arUaGxsb/XPaACwsLAgMDKRmzZr6aY+3RXbTRd54vF0AxowZg0qlQq1Ws3v3biIjI2W7GMDSpUuxt7enTZs2ufq7Jdvk+UnAyQX5H82wbGxsqFKlCubm5tja2rJy5UpUKhWJiYkAaLVaHjx4gKurK3fv3tXP5+fnl+lfsCJvPP4itbCwIDY2Vj89Pj4ejUaDk5MT9+7d00/38/PD09MTJyenDNvo6tWrlChR4pX23ZQ9GXA8PT0pUqQIVlZWxMTEsGfPHtkur9j+/fu5ePEiY8aMAcjV363/bqtz587J37MckoCTC1n9j+bp6WnAHr1eVq1axcGDBwE4deoUCQkJNG3alAULFqAoCitXrqRRo0ZUrlwZf39//v77b+Li4li6dCkdOnQwcO9NU1hYGG5ubpiZmVG1alXWrl1Lamoqc+bMoWPHjjRs2JDdu3cTGhpKWFgYmzdvpnnz5rRu3Zp58+aRlpbG7t27KVmyJDY2NoZeHJPxeLvExsYyYcIEHj58SFJSEocPH8bGxka2yyvk7+/PmjVrmD17Nubm5gC5+rvVunVrFi1aRFJSEn5+fsTHx0vozClDDwIyJiEhIUrbtm2Vhw8fKoGBgUqPHj2UtLQ0Q3frtRETE6MMGzZMad26tdKrVy/l9OnTSmJiojJ69GilTZs2yowZM/Tb4/Lly0qPHj2Uzp07K4cPHzZwz01TTExMhkHfkZGRyuDBg5V3331XWb58uX764cOHlS5duig9e/ZULl26pCiKoqSmpirffPON8s477yhjx45VkpKSXnn/TdV/t8vu3buVdu3aKe3atVOmTp2qJCcnK4oi2+VVGTZsmNKwYUOldevWSuvWrZVu3bopsbGxufq7tWTJEqVt27bK0KFDlcjISEMtitGRRzXk0pYtW1i9ejUuLi5MmDABDw8PQ3dJCINISEjg/PnzNGrUyNBdEU+Q7ZK/pKamotVqsbKyMnRXXjsScIQQQghhcmQMjhBCCCFMjgQcIYQQQpgcCThCCCGEMDkScIQQJudZDzVcsGDBK+qJEMJQJOAIIYxWWFiY/uGF586d4+TJk1y8eJEffvjhqfOdOnXqmbVTU1PZvn07hw4d4uHDh3nSXyHEqyMBRwhhtDQaDQkJCQCUKVOGPXv2MHPmTOrVq/fM+YAMd1/+r8WLF3Pu3Dl8fHzYv38/arWa77//Pu86L4R4qSwM3QEhhHheycnJACiKgq2tLWq1mitXruhvg/9f+/bt4+zZs1y+fJm33nqLmjVrMnfu3EztgoODuXDhAsePH6dZs2bY29vz119/cf36debNm4eHh4fcHVuIfE4CjhDCKJ0/f5558+Zx7do14uLiqFevHpcvX6ZSpUq4urpmOY+joyNFixZFq9XSvn17/bOB/svS0pKWLVty7NgxnJycKFOmDF9//TWurq5s376dVatWvcQlE0LkBTlFJYQwStWqVcPb25s2bdowYcIEqlatioeHB6Ghody+fTvLeSpUqMDatWspUqQIcXFx2QYVOzs7zpw5Q7NmzYiKiuK9996jXbt2jB49mkqVKlGqVKmXuGRCiLwgAUcIYZSSk5PZtWsXp0+fZvv27RQqVIjKlSvTs2dPunTpgqIopKWl6dsrisLo0aPp06cPbm5uTJgwgaNHj7Jx48ZMtbdt28aQIUO4dOkSnTt3ZtSoUbRs2ZLz588/c3yPECJ/kEc1CCGM0qJFi7h27Rr16tWjatWqHDhwgGPHjhEeHk7x4sVJSEigUaNGjB49GoDvv/8elUpFkyZN8PHxoVy5cnz66acsWbKEd955hx49egBw69YtFixYwKFDhzA3N0etVrNq1Sp+/PFHNBoN8+fPp2DBgoZcdCFEDsgYHCGEUfr4449Zv3495ubmWFpa0r59e8qWLUtYWBgDBw7M0PbkyZOULl0ab29v5s+fT2xsLD179qR27drUrFmTOXPm0L9/f0aNGoWLiwutW7fmwoUL9OvXj5iYGKpWrUqpUqU4cuQIQUFB1KpVy0BLLYTIKTmCI4QwWosWLaJIkSJ069YNgN27d5OQkED37t2fOl+NGjXYv38/Li4u+mnx8fEAODg4EBkZSa9evShevDiRkZFYW1vTpEkTOnXqRP/+/SlTpgz9+vWjXr16qFSql7eAQojnJkdwhBBGq3bt2tja2upf29nZYWHx7D9rpUuXxtnZOcM0BwcH/e+jR4/mu+++o3r16qxatQpLS0t69+4NwObNm/nhhx8YO3YsixcvpkKFCnmzMEKIPCVHcIQQJiMuLg61Wp3hyIwQ4vUkAUcIIYQQJkcuExdCCCGEyZGAI4QQQgiTIwFHCCGEECZHAo4QQgghTI4EHCGEEEKYHAk4QgghhDA5EnCEEEIIYXL+DyvdffCLg1TLAAAAAElFTkSuQmCC\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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W8efMYOnQo7777rlxvS5QocrFNUeysrVX4tginSX1L5s1wwn9yDMdO5//L+fIht1y1v3HjBkFBQdSqVQtHR0dmzJjB6NGjadOmTaZ2SUlJhr+Ca9SowZtvvknnzp0fW/vOnTtcvXqVDRs28NJLL+Hq6pq7weSBytKSZP8xBVLLzNsbyzeGo/lhFVZj/HP13L179zJ48GAcHR0pXbo0Gzdu5IcffqBatWqZ2hnL+1oSFPd76uDgwPfff8+KFSsYN24cNWvWZO/evYbrbwlREkjAESVCQYeb3FCr1ezevZtNmzZx7do1Tpw4wezZs1m0aBGDBg0yXFwR4MKFC2zfvp2IiAjef/99Fi1aREhICHXr1s22dmRkJIsWLeLgwYPExsbSokULrK2ti2poBUYXFITmh1VYvjE8V8+7du0aYWFh+Pn5sXDhQkqVKkXPnj3x8fHJ1O5ZfV/zoiS8pxs2bCAhIYEff/yRRYsW8corr9C8efNMFyMVorhJwBElQnGFG9Bv5k9NTSU5OZn333+fM2fOsGDBAq5evcqWLVtYs2YNKpWK6dOn4+zsTFJSEqNGjeKll17CwcGBjz/+GGdn52xru7i44Ofnx19//YW9vT3R0dGUKlWqaAdYQNJCTm624Bw7doyYmBiGDh0KwM8//0z79u0JCQnBy8vL0M6Y3ldlaa8nN6rZHtWL76a3bzgAVaMBKAmR8POb2T5F9dbmp3r9kvCeNmrUiN27dzNs2DB8fX0pV64cv/32GytWrGDmzJlUrVr1qcYiRGGS7YmiRCiucAPg4eFBhw4dsLGx4dChQwwYMIA2bdowe/ZsPD09WbVqFevWraN69eo4ODhw8eJFfvrpJyIiIgCwtbXNcV6DTqfj448/pkOHDnTv3p3Q0FDWrVtXlMMrULqgoFy1HzBgAN7e3oSEhLBx40ZatWrFoUOHuHjxIgMGDODEiRMAz/z7mhvF/Z7evHmTbdu2YWlpya5duzh27BhLly7F39+fX3/9VcKNKDFkC44oEYor3KRRqVTcvXsXW1tbatSoYVhepUoVwsPDDX8Zr169Gl9fX2bMmEFwcDD79u0jLCyM0aNHo1ar8fHx4fPPPzc8f86cOTRt2hQPDw9WrVrF8OHDCQgIwNzcnH79+hX6uMy8vXMdSgrSuXPnuHv3LiqVikOHDvHll1/Sq1cvunbtSu3atVmzZg2NGjUyqvf1abe0ZNdeZe8CuXz+o4r7PfXw8OC1117jxIkTtGrVin379lGrVi2mT58OQIcOHWjfvn2+xihEQZCAI0xSk/pPPw9Ao9GwcOFCevXqRVhYGAsXLmTIkCEkJiai0+nQZDhq6M033yQyMpLk5GQiIyPp0qULFy9e5Mcff8xSd/fu3Xh6epKYmMjXX3+Nk5MTbm5uzJkzhzlz5jB27FjeeeedQv2LN21icHGEnNTUVNauXcuCBQsoW7Ys3bt3R6VSoSj6o9s8PT2ZMmUKYHzva3EpCe/p5cuXOXPmDNWqVaNu3bocP36czz77DBsbGxITE9FqtUX6ngiREwk4wuSkTVh+WlqtloEDB1KhQgUA/v77bwBGjhxJbGwsn332maGto6MjCQkJODk50aVLFwCcnLJ/rYx/xbq4uHDw4EEaNGgAwNixYwkPDy/0L4O0icHFEXIsLCyYNWtWpmUJCQnZtjW297W4lIT31NfXF19fX0N7Nzc3bGxsALCzs8vjyIQoeBJwRLFLSVFyfUj309a1tlY9sZ2NjY0h3ACGw8JnzJjB4sWLcXBwyNS+S5culClTxnC/YsWKT3wNV1fXTM8B/RdDYVI0GsOE4Nwe2v2kuqo8HiljZWWV5WifNMbyvpY0xf2eOjo6PmVPhShaKiVt26YQIk+ioqKyfCGI/JP3teAVxnt64sQJGjVqVKA1hSgIEnCEEEIIYXLkMHEhhBBCmBwJOEIIIYQwORJwhBBCCGFyJOAIIYQQwuRIwBFCCCGEyZGAI4QQQgiTIwFHCCGEECZHAo4QQgghTI4EHCGEEEKYHAk4QgghhDA5EnCEEEIIYXIk4AghhBDC5EjAEUIIIYTJkYAjhBBCCJMjAUcIIYQQJkcCjhBCCCFMjgQcIYQQQpgcCThCCCGEMDkScIQQQghhciTgCCGEEMLkSMARQgghhMmRgCOEEEIIkyMBRwghhBAmRwKOEEIIIUyOBBwhBAAajeaJbRYuXJirmsHBwWi1WhITE9HpdHntWr49zdiWLl36VO3yIyUlhZiYmKdu/zT9OXnyJIcPH85Pt4QwSRJwhHjGhIWFsWzZMiD9y/HMmTN89913T3zukSNHnvp1oqKiWLFiBT179mT48OGMHTs2z31+WjqdjpkzZwIQGhrKli1buHPnDuPHj3/icwMDA58YPlJTUzl+/DgbN27k7t27ue7fr7/+muN7OGPGDMPttCA5ZswYHjx48Nia8fHxnD9//qlePyQkBICYmBgGDRr02LapqalMmzaNa9euPVVtIUoai+LugBAif37++WeaN29OtWrVWLZsGdeuXTP8W7hwIc2aNcvUXqPRkJCQAICXlxfz5s3j6tWrjBo16omvlbZFITY2llKlSuXYbuzYsZQtW5agoCB+++03rKysSE5OzvXYdu3ahaOjI82bN2fDhg2cPHmS27dvc/HiRaZOnUrPnj0ztVepVIZAUL58efbv38/+/ftp2LDhU41NURRiYmJwcnLKts3mzZu5cOECv/32GyNGjOCdd95h1qxZTJo06anGs337dkO4fFR8fDypqalYWFgQGxvLpk2buH//Ps7Ozk/sN+i3DimKgo2NTY5tt2zZQvPmzTl16hQNGjTIsV1KSgqTJ0/myJEjXLhwgUWLFrFr1y5at26Nh4fHkwcqRAkgW3CEMHLR0dGEhoYC0LZtW+7fv4+Hhwf79+/PEm4AQ9BQFAVbW1vUajUXLlwgMTExx9fYtWsXM2fO5Ny5c7zwwgtMmTIlx7Y3b97k66+/5sUXX6RXr15ER0dz7tw5/v3331yPLT4+3rAFoXnz5uh0OlQqFXv37s0SbtLGpigKiqKg0+moXLkyW7dufeyunn///Zd58+axf/9+unXrxltvvZVtu/DwcA4fPsy6deuoV68e5cuX59KlS5w+fZrvv/+eH3/88bFjCQ8Px9LSMsfwlJSUhKIoAHTo0IGvv/6a0qVLExERkW37Gzdu8MsvvzB37lwWLlxIr169OHXqVI6vr9FoaNOmDSEhIaSmptKqVascf+br16/n77//RqPR0L59e5KTk/n999/5+++/mTZtmiEgC1GSyRYcIYxYSEgI586dY+vWrdja2tKkSRNu377NpEmTsLOzy9L+1KlTzJ8/n8DAQOLi4mjWrBnnzp2jVq1auLm55fg6jo6OlCtXDp1OR/fu3R+7yycsLIxt27bxyy+/EBsby759+6hSpQr9+vVDrVZjZWX1VGMLCwvjyJEjHD9+HCcnJ7p160ZERASjRo3KdutRcHAwc+bM4eDBg/j7+zN48GD279+Pl5cXXl5eOb6OnZ0dHh4eaLVamjVrxty5c3Ns27lzZ7Zu3Yq9vT3Vq1dn0aJF2NnZ8dNPP7F69erHjueff/7hpZdeyvaxL7/8khMnTtC/f38+/fRTli9fjkajoUmTJtn+HAEsLCzw8vJCURTs7e1Zvnw5lSpVyvH1z549y4oVK5g8eTLu7u706tWLr776ijp16mRpW6dOHSpWrEhkZCQeHh6cPn2a+/fv88cff9ChQwcsLS0fO1YhSgLZgiOEEbtz5w5JSUkMHjyYpk2bsm3bNm7evIlarebs2bNZ2terVw9fX186derE1KlTqVu3Lp6enoSGhnLz5s0cX6dGjRqsXbsWV1dX4uLiHvtlXrVqVcLDw5k5cyY9e/Zk4sSJtGnThvnz5zNkyBBiY2Ofamz37t0jISGBTp060a1bN06fPs2RI0cwNzfn6NGjWSYtV61alcaNG9OoUSPmzZtHrVq18PX1JTIyksuXL+f4OnXq1OGnn37C0dGRsmXLGubwPMrR0ZGAgAD69OmDRqPhjTfeoGLFinz//fdUqFABHx+fx45n165dtGvXLtvHRo4cSWJiIosWLaJevXp07NgRa2trDhw4gEqlyvY5FStW5MKFC0RERPDiiy/y3nvvERkZmePr161bF7VaTaVKldBoNMTFxVGrVq1s2544cYKuXbtibW3NsmXLWLJkCb/99huenp68/PLLTx1ShShOEnCEMGItW7akadOmnDt3jn379vHjjz8ycuRITp8+zYkTJ+jbt2+mI2ySk5PZtm0bR48eZcuWLZQpU4batWszYMAA+vTpg6IoaLXaTK+hKArjxo1j8ODBuLu7M3XqVPbv38+GDRuy9Cc2NpbZs2fzwgsvMGHCBHQ6HStXruTff/9l0aJFrFu3LtPWly+//JI//vgj27HVq1ePtm3bcv36dfbs2cPXX3/NBx98wMGDB7l37x6vvPIKAQEBmfr522+/ERwczMqVK7GxsaFx48a0bdvWML/o0bGBfnJvw4YNady4MW+++Sbx8fHMmzcvS7stW7bg5+fH0aNH6d69O++//z59+vTh7NmzNGnS5LE/p9jYWCIjI/H09Mz28T/++AONRsPMmTNJSEigadOmWFlZ8dNPP2Fra0tqamqW55w4cYKNGzcyduxYPDw8+OCDDxg1alSOk5KtrKywsLDgtddeo2vXrvTq1Qtzc/Msu8CCg4OpUKEC9+/fp1mzZgwdOpS3334bV1dXbty48cQgJ0RJoVLSdvoKIYzO4cOHmTZtGi1btiQ2NpYRI0Zw5MgR4uPj8fPz4/bt2wQEBDBy5EgAlixZQmBgIM2aNaNu3brs2bOHAwcOEB4eToUKFUhISKBVq1aMGzfO8BrffvstKpWK1q1b4+/vT7Vq1Rg9ejRLly6lc+fO9O/fP1OfEhMTOXv2LJcvX2b37t2UL18eCwsLUlJScHJyonfv3tSrVw+At956i6ZNmzJ8+PAsY7tw4QKfffYZ5cuXx8XFhQ4dOpCQkMBff/3Fl19+SVRUFD///DPvvfceABs2bODYsWO4ubnxyiuvsHPnTg4fPszly5epWrUqCQkJVK9ePdPRYr/++itnzpxh4MCBTJ48GRsbG9566y22b99O5cqVef/99wH9EWGzZ8/m77//JikpCZVKxZw5c1i7di1arZZx48bh6+ub489py5YthIaG4ufnl+Wx+/fv88033/Dff/+xYcMGAgICOHnyJNu3b6dGjRqo1WrUajVLliyhcuXKANy+fRt/f38++eQTdu3axfbt2+nUqRNNmjRh5cqVLFq0CBcXF8NrbNu2jc6dOxMTE8OVK1e4du0ar732GgAjRoygd+/edO3aFYCffvqJP/74gwsXLlC2bFmaN29O3bp1OXPmDK6urnzyySc5jlOIkkTm4AhhxObOnUv37t1599130Wg0WFlZcfDgQcNk1YoVKxrCDei/zNatW4e5uTmWlpZ0796dqlWrEhYWlqldmsOHD1OlShV8fX1ZsGABsbGxDBgwgMaNG9OwYUPmzJljOAS8Vq1a3Lhxg40bN+Lh4UGNGjU4cuQIzZo1o2PHjoB+om3GLThLly7NcWwrV66kRo0aTJs2Da1Wi5WVFTt27DCMrUyZMoZwA9CrVy9cXFw4c+YMAO3ataNRo0Zs27aNadOmZal/+fJl4uLi8PPzY8aMGdy7d4933nmHVq1a0bZtW1asWMGgQYN49913qV27Nl27duXAgQN88MEH/Pfff7Ru3ZqLFy+yatUqQkNDHxtw9uzZg7+/f7aPlS5dmlmzZtGjRw/UajU1a9bkxRdfJCgoiHXr1mVpn5qayrp161i8eDErVqzg0KFDeHp6MmLECNzd3XF3d8ff35+GDRvywQcfAPrw5+DgQPny5WnatClXr14lMjKSa9euMWfOHDZt2mSoX6dOHU6ePEnTpk25efMm48aNIzExkcWLFxsmJuc0L0iIkkQCjhBG7H//+5/hdtq8iOx2w6SxtLQkPj4eV1dXwxfy5cuXczwUuXnz5obby5cvp0GDBjRt2hQLC/1Hx/jx44mPjze0qVKlCh999JHh/uHDh/Hx8cHR0RHA8H+a9957j5YtW2bZCgQwZ84cw21zc/OnHpu5uTlVq1YF9BNrczpqydfX1/AeLF26lO7du9OwYUOsra0B/byYwYMHk5ycjJOTE3Xr1sXd3Z0dO3YQGhrK4MGDqVKlCjt37mTUqFGsWrWKN954g5dfftnw/gCo1WrCw8Px9vbOth9pbZOSknB2djb8LBwcHHJsn/YeT5w4kR9++IEbN27g7u4O6Ofa/Pzzz4SFhRmek5CQgK+vL/7+/rz//vu8+uqrWFlZsWrVKjQaDcOGDTO0rV+/PqdOneLPP/8kMTGRKVOmcOfOHdavX2+YZDx48GD69etHmTJlcvx5CFHcJOAIYWK8vLy4fv16jo83btwYW1tbw307O7tMX8iPU6VKlSxhKKcvYtB/GT9uQqparc7VIceVK1d+7OvVrl07064ZOzu7J55HJmPt0qVLZ1pmY2NjOK/M1KlTefvtt3n55Zf5888/uXbtmmGrzC+//MKiRYuYOXMmVlZWtG7d2lAjIiIi2wD3qKFDh2a6X758+afqd6VKlYiLi8uyPC3wgD6IlitXjrlz57J48WImTZpEfHw88fHxaDQaWrRoYWi7efNmIiIiWL9+PWFhYSxYsIDp06fj6urKu+++y3PPPcf8+fMJDg7m66+/fqo+ClEcZA6OEM+4uLg41Gp1pmBQUNasWUO3bt2yBIeiotFoCAsLe+zh0yXV9evXc5yULIR4Mgk4QgghhDA5cpi4ECZErVYXdxeKVHbjVRSl0C+aWVyyG292u6dMhfw+i/yQgGNiTpw4Qa9evRgwYEC217xZuHAhBw8eNNxPTU01nLfEGOV2vLNmzaJBgwb07NmTS5cuFWVXC0RO4/3nn3/o27cvo0ePpnv37oZzmwQHB9O3b18GDhyY6WKOxiK3450yZQrDhg3jtdde47PPPiuubudZbscL8Mknn6DT6di8eXMx9Dh/8jLe1atX07RpU6Kiooqjy/mS2/GuXr2axo0b06FDB6O9YnxOY7527Rpvvvkmffv2zXTi0Cd9pueKIkxGfHy80qFDB+XWrVuKTqdTxo0bpwQGBmZqM2bMGOXGjRuG+8uXL1cGDx6s3L17t6i7m2+5HW90dLTy4osvKqGhocq2bduUMWPGFEe38+xx450/f76SkpKiKIqizJw5U9m7d6+i1WqVnj17KufOnVMURVFmz56t7Nmzp9j6n1u5HW9gYKDy559/KoqiKFqtVunYsWOx9T0vcjteRVGUAwcOKF999ZUSEhKiTJo0qbi6nid5GW9AQIDSrVs3JSgoqLi6nWd5GW/Lli2VwMBA5fTp00rfvn2Lq+t59rgxBwUFKSkpKcrZs2eVfv36PbF9XsgWHBOyZ88eOnfuTMWKFVGpVPTq1YtDhw5lahMaGmo4WZharWbt2rV8+eWXmY64MBa5Ha9Wq8XR0ZGKFSvi4eGR7dlhS7LHjXfMmDFYWVlx9epVAgMDadmyJadOnaJ69eqGaw317ds3y/tTkuV2vDVq1KBbt26A/oSG/fr1K87u51puxwv6SdyvvvoqFy9ezPEw9JIqL+NdvHgxX375pVFOvs7LeM3MzKhatSqVKlV67CkSSqrHjblatWrcuXOH6dOn88477zyxfV5IwDEhQUFBNGzY0HBfrVZnOhw4OTkZa2trw7VtDh06hKIorFmzxnD2VmOS2/G6uLjQoEEDXnvtNcaMGWNYqYzFk8YbERHB+PHjmTlzJpaWlk9sX9Lldrxptm3bxqVLl7I9O3JJltvxhoWF8eDBA7y8vLh06RLe3t5GNYcjt+O9evUq4eHhbNmyBX9/f3766afi6Hae5eX3+fXXX6dfv34MHDgw27Ngl3SPG/P69evp3bs3ly5d4o8//iA1NbXAP7Mk4JgQJycn7t69a7h/8uTJTH/VXb16NdP9qKgoRowYwSeffMLChQtZuXJlkfY3v/Iy3pMnT9K3b1+aNGnC7t27i7S/+fW48aakpPDBBx8wceJEPDw8sm1/4sQJo/orP7fjBThz5gy//PILs2bNyvEilSVVbsd74sQJLl++TJs2bVizZg3vv/8+ixYtKpa+50VuxxsVFUWfPn2YNGkSCxcuZOPGjcXS77zK7XjVajU7d+6kd+/etG/fPtN114zF48ZcuXJltm/fzo4dO7h+/TpHjx594md6rhXEfjZRMoSEhChdu3ZVoqKilKCgIKV///6KVqs1PL5hwwblxx9/NNw/efKkYR7KyZMnlbfffrvI+5wfuR3v2rVrlUWLFimKoigpKSlKixYtirzP+fG48X788cfK9u3bM7WPiYlR2rVrp9y8eVO5d++e0q9fPyU2NrY4up4nuR3v3bt3lf79+yuRkZHF0d18y+14FUVRdDqdotFolIEDByo6na6ou5wvef35ajQa5ebNm0Y3JyW34923b58yceJEw/0OHToY3e/2kz6j03zzzTfKtm3bnrr905IzGZsQT09Phg8fzrBhw3BxceGrr77CzCx9I92VK1do27at4X6DBg0ICAigR48e2NnZMXPmzOLodp7ldryWlpbcu3cPrVZLUFCQ4Qy1xiKn8QYGBrJr1y7OnTvH3LlzUalUhqMTJk+ezPvvv4+VlRWTJ0/OcqmEkiy34w0ODub69esMHDgQAGtra5YvX46bm1sxj+Tp5OXnq1KpUKlUaDQao9tilZfxtmnThj59+mBhYcHEiROLewi5ktvxVqxYkYiICMOlPhISErC3ty/uYeRKTmMOCQnB1taWcuXKER4eTkBAAL17937iZ3qu5SediZKvd+/eyuHDhxVFUZSDBw8a1V/wefG48SYmJirDhg1TatSoobRs2VLZunVrcXWzwKSNNykpqbi7UiQeN16dTmdy78PT/Hy1Wq2ye/fuIuxV4ZHf53RarVb58MMPlZo1ayqNGzdWfvjhh6LvYCHo3bu3sn//fmXs2LFKz549lU6dOilr164tlNeSMxmbuJiYGEqVKmV0f93llYzXtMl4TZuM1/QV5Zgl4AghhBDC5MhRVEIIIYQwORJwhBBCCGFyJOAIIYQQwuRIwHmGxMbGsmDBAmJjY4u7K0VCxmvaZLymTcZr+gp7zBJwniGxsbEsXLjwmVmBZLymTcZr2mS8pq+wxywBRwghhBAmRwKOEEIIIUyOBBwhhBBCmBy5FlUePHjwgK1bt1KtWjXDZe2Nwf379wE4f/489+7dK+beFD4Zr2mT8Zo2Ga/pK4gxazQagoOD6datG87OzpkekzMZ58HPP//M9OnTi7sbQgghhACmTJnC66+/nmmZbMHJg6pVqwL6N7RGjRrF3BshhBDi2XTp0iWmT59u+F7OSAJOHlhZWQFQo0YNGjVqVMy9EUIIIZ5tad/LGckkYyGEEEKYHAk4QgghhDA5EnCEEEIIYXIk4AghhBAFQFFrirsLJZaiKfr3RiYZ58Ogt6PBKvyJ7ZrUt2TeDCf8J8dw7HTB/5ClvtSX+lJf6hd//cuH3Ej2H5Pn55t5e2P5xnA0P6xCFxSUr76UtPo28xYU+Os9sT9F/orPGGNaOaW+1Jf6Ul/qFx9dUBCaH1Zh+cZwzLy9pX4+ScApRMa+ckp9qS/1pb7UL1rGHkJKUsiRgFNIjH3llPpSX+pLfalfPIw9hJSUkCMBpxAY+8op9aW+1Jf6Ur94GXsIKQkhRwJOATP2lVPqS32pL/Wlfslg7CEkY/3iIAGnABn7yin1pb7Ul/pSP+/1C4OphJziIAGngJjCyin1pb7Ul/pSP+/1AaMOIYVZvzhIwCkAprJySn2pL/WlvtTPe33AqENIcc+ZKWgScPLJlFZOqS/1pb7Ul/r5q2/MIcTUQo4EnHyo6WNhciun1Jf6Ul/qS/281zf2EGJKIUcCTj74j3QwuZVT6kt9qS/1pX7+6ht7CDGVkCMBJx/mLY83yZVT6kt9qS/1pX7+6ht7CDGFkCMBJx8Cr6QWeM2SsnJKfakv9aW+1M9ffWMPIcYeciTglCAlbeWU+lJf6kt9qZ+/+sYeQow55EjAKSFK6sop9aW+1Jf6Uj9/9Y09hBhryJGAUwKU9JVT6kt9qS/1pX7+6ht7CDHGkCMBp5gZy8op9aW+1Jf6Uj9/jD2EGFvIkYBTjIxt5ZT6Ul/qS32pnz/GHkKMKeRIwCkmxrpySn2pL/WlvtTPH2MPIcYSciTgFANjXzmlvtSX+lJf6uePsYcQYwg5EnCKmLGvnFJf6kt9qS/1C4axh5CSHnIk4BQhY185pb7Ul/pSX+oXLGMPISU55EjAKSLGvnJKfakv9aW+1C8cxh5CSmrIkYBTBIx95ZT6Ul/qS32pX7iMPYSUxJBTIgPO2rVrSUhIAECj0aDVaou5R3ln7Cun1Jf6z1x9JRUUXc71FZ2+TQHIVP9UvOF1s++XkuvXNcr334Tq55axh5CSFnIs8vrEHTt2YGZmRocOHUhOTsbGxuaJz4mOjsbJyQkzs+xz1ZIlS4iPj+fmzZscPXoUe3t79u3bR8eOHZk2bVpeu1pscrPydHXdyode8yhvfY+7KeWYHeLPtvvdCqx+Xkh9qW/U9dXHQXsDbPuCLg4SZoGqNDh8pH/80WVKIsRNAixAd4+6Tb9h3oznMteP/w50t0EXBbZDwLoNqA9B0jpQWYDFc2A3HFIvQ9xkQAv248CqWbb9L20+E79P3ufkeStQ74PElYAlWNYBh0/SGyetAfUxUJLBui3YDgAlBeKngzYMLOuCnR8krwOrlmBWjorO65g340PT/fmW8Pp5lTEkaH5YhS4oSOrn0WO34Ny6dQtfX1/efvvtLI8dPXqUlStX0r17d5o0aUJgYGCOdVauXEm7du0YOnQoFy5cyLHdmTNn+Pjjj1mwYAHz589nxIgR1KpVizFjxuRiSHoajYbBgwdnWR4fH8+7777LgAEDeOutt4iJiUGj0fDFF1/k+jUeJ7fhZobPNCra3MVMpVDR5i4zfKbR1XVrgdTPC6kv9Y2+vvYGmLvrbycuATM3sPdLf/zRZZpTYPcelPoGz+qv0qt9cOb6aUGi1Gxw/Aw0x/TLU3ZAqTn65Zoz+mXJAWA7XL886ccc+3/42F19uAFIDQLHL6D02szhRkkBrMFpATjNA81x/fKkX8DieXBeDlhC6kVQkiA5AC/3v5j6ycum/fMtwfXzy9i3tJSULTlP3EXVu3dvvv/++0zLgoODiY6O5rvvvsPBwYGvvvqKmjVr5lhj2LBhODo6snnzZurWrUtqavabWevXr8+mTZu4cOECp06dYt++fXz22We4uLjkclgQEhJCuXLlsiz/7rvvaNKkCevWrWPo0KEsWbIES0tLHBwcOHnyZK5fJzu5XXk+9JqHrXlypmW25sl86DWvQOrnltSX+iZRX3sdzKuALkb/5W83ElTW+seyW2b1Alh40bCOGheH/Wz7p3Hm+ubuYN1efzvxR7Dprb+tpIIuEnTxgPphYx2YVwQzd0DJtv/vTYomNj7DY9prYF456zhU1mD7aobX7am/rTkBNr30t2266++r/6ZWnZepVe0SK9dXNe2fbwmtX1CMPYSUhJCTpzk4abuQpk+fTu3atVmyZEm27cLDwzlz5gzbtm3j6tWrvPfee3Tq1IlOnToREhKSpX2nTp04e/YsZmZm2NraUqNGDQICAvLSRS5fvoyPj0+mZSkpKRw/ftywZadFixYEBwcDUK9evQIJOHlZecpb38t2eQXru/hVWUTHsrvwsr2GOalGv/JLfalfZPV1d8CsImgOAzpIXA6xE0AXm/2yh/VLmX1Ngm5w+paVRyVtAHMPsPDV37d9DeI/g5iRDwMN+hASNxVi3gGbvtn2//jJUDDL8EeYnR8oDwNS8u9ZX1e9H5QYsHpRH6pUdvrdYgCkgpkjTV7+H0P6JnH5ZpPif/+fwfoFzdhDSMb6xSFPc3CaNm1K586d6d27N1qtlkuXLmXbbu3atdy6dYuQkBBq1aqFTqcjICAgxzk4gYGBvPfee3z66ae4ublRpUoVmjXLuu/6aVy5coXGjRtnWnb79m18fHwyvb6Fhf4tePDgAfb29nl6rTR5XXnuppSjos3dLMu1ijnveHyPmUr/V55GsUTlVp33pqzk2GlHKtmEAnAruRKgylff89N/qS/1S2R9RQsqc314sXsbrFrodyep/wUlIcuyJs170aTmJjZvr8at6LrpdaIHQamv9FtXNBf0u7IcZ6Y/blkHnBZB7EdgO1C/LGUPWL8MZmVB/Q9Yt6NCqdXU9bLFf/Lr+v5rr4O5Z3odC6/02yk7wborqB6GLO1tSNoEpb5+2MBcP4Y0mov4+ngyb4YT3XvvJCKhG8S/AqW+B3PXXLzLOStxP98SWL8wGPucmbT6VmP8C7Tu08jTFhxbW1uio6O5cOEC9+7dIyYmJtt2H3zwAbNmzSI1NZUhQ4bQsGHDTOEiIiIiU/vatWuzYsUKQkJCaN26NZUqVWLBggV56SJXrlzB19c30zJnZ2fu3LljuB8SEoKzszMABw8epFGjRnl6LcjfyjM7xJ8kbeZJ2klaG8ZfnkmDg8foc2o93yfNQttsFGfvlGfvKX0Qe6vycv73/CDSwk3/8ut5o+JqXih9EDerMB7dNF5Y/Zf6Ur/E1dc9ADMn/W3zipD6cO5f6mUwc82yzNenPG++eo1fN5zjVvRrmWs5LdSHG12sfouPwyRQPfLRmbAILBukb9VJPQv2/mA7CLR3qF87np9XvcPpoFcyzOm5od+FBg8nPM/TT3TW3gHtXQzrr6KGhO/AYWL67jSVSj8O9UHQxVHKeg/L57/EsHf2EBFbB1L/A8vmoEQ97Vv8WCXu51tC6wNGv6WlsOoXhzxtwbl+/TqlS5dm69atfPvtt3z66ac5tv3hhx/o3bs3VlZWmJubG5ZrtVpWrlzJ+PHjDcvc3d2JiIhg5cqVlC9fnmXLltG6desca1+6dAmdTketWrWyPBYWFoa7u3umZWXKlMHS0pK9e/fSokUL5syZw5AhQ7h27RrR0dFZAtHTyu/Kk3a0VE5HUTlUr0f/aS8y6pH6a26/zl+RbQz3u7hup6nzccP92FRHriZ4czXBm6BEb64mVOdKojfRmjIF2v8nkfpSv8jrp82/AbBsCim74cEbYOENlo0BrWFZWTcffljcni7d3+JB1BVgkP55Nr31R2DFTQSHKfotP9pgiHlL/7iFLzhMgIRvwayCPsykUVIf7vZKxcoyjtmfl2PIyFWE3tbqd2ml9dHq4eebmSOY+0DMKMAG7Mekhxn1YUgNhNgPH7Z1Aaf5YPcuJHyBve1PfPvdZMZ+lsTlC3/og5CSpA9zFnn7TMuoRP58S2j9NYtKG/2WlpJw9FNByVPAsbW15c6dO7Rq1Qq1Ws3BgwdxcXHBy8srU7ugoCCCgoKYNWsW586dY/PmzbRt2xaVSsXJkycJDw83tE1MTGTx4sV89NFHqNVqfv31VzZt2sTvv2ezL/qhPXv2kJiYmCXgxMbG4ujomO1zvvjiC6ZMmcLs2bN58803adSoER999BFTp07Ny1tRYCvPtvvdsj0s/HH1ryT6cCUxfZ7R0HM/4GwRTXX7IKrbBeFtH0R1+yA6ue7E2XIDAMceNGLIudUAjKq8HK37c4z4rLtJfLhIfalvYFY+PTyoLMBx2iMN9Msy1n+g+wycVRnmtTxk+Tyo7MG2v35ujeqRU2LY++vnw2Rk0xui+2JmrmLYmyMYPyOF0HsVwDzDlh+rl/T9NDyns/5fGkWBmGHg+CWU/g1Utplfw9yNJq0XZT6PjnUXff9VjmCV9y3SaUrsz7cE1zfmEGJqIeexAcfc3Jxjx47Rp08fNm7ciJmZGTExMXz++ed07tyZnj31s/k7derEokWL8PHxYdSoUYbnX79+3XD+mrp161KuXDl69OhBYmIidnZ2vP7664a21tbWjB071nB/8eLFvP7669jZPfLB8ZBWq+XBgwd88MEH2fY7Yz+Sk5Pp2rUrK1aswMvLi9WrV2dq/9133z3ubchRTR+LErdyPkgtzfGYxhyPyTj/SMHVKoLqdlfRot+KpkLHyCo/Ytn8dUZNfpHzZ2P4q0lPghOr6bf6JFYnKKEawYlVSdJl/zMojP5LfalfIPXN3QH3xzbJUl+VwxwKu7fSbz8abiBruAGw6UKT5j0z17d6IXMbq+aPH4NKBaUW6rfuPFX/rQsk1ORYv4CZan1jDyGmFHJUiqI8/UQNIxYdHU3p0qULpNaJEycYNGgQ33//M6s2ehvvyjndkbFT7nPwtDllLCMZV/Xbh1t+grE20x/NoVNUhCZXIijBm6uJ3uy834HAhJxPCVCk/Zf6Ul/qS/0SVP/yITeS/fXnbTPz9i7UkGBs9W3m5W0+7ZOkfR+vXbs2yzzaEnmphsJQUOEmo3nL44175ZwSx8HT+i06URoXPrn8Ja+c3kD9A8fpdHwrfv/NZcGNd/kvrhYetjd5s9IPVLe/CkBN+0C2NuxBXcdzAJS2jJJD2aW+1Jf6Uv8hU5gYXNznscmvPF+qQUDglVTI4VQZeVUSVk4d5lxP8uR6kid7ItsZlluqNKhIv1bO9SRPojT64Nip7C4+rT5Dfyi7uw9HZ1SlXkQ17Mt4cyXRmzvJFVAKIE+XhPdH6kt9qS/1n6a+se9OMvbdVc/MFhxjUNJWzkdpFEvUiv7IjsCEmvhdnM+tZP2ZV/dHtWJZ0ldom47gvzsueGmP85HXXJY858dfTTpxomVT1j8/ABdL/akB3K3uyaHsUl/qS32Tr2/sW1qMeUuObMEpIUrqyvm0KtbypN+0epkOZXcwj6O6fRDedsFUt79KNbtrPNA4AzDKYwU93LbS+NBhAHq5b8HOLJGrifrD2h+kZt6laOzvj9SX+lL/2a1v7FtajHVLjgScEqCkr5x5rR+vdeR0bH1Ox9bP8pyNd1/h2IPGpJ2ksI/77zRxPmF4/L7ahasJ1bma4I22rC99R9Rn7OTyHDttXWT9l/pSX+pL/YKqb+whxBhDjgScYmYsK2dB1w9MqJnpaKwh537Azeo+1e2vpp/Dxy6I/hV/w0aVCD/DaF099rNW377iGoISqnHoQYti6b/Ul/pSX+rnlrGHEGMLORJwipGxrZyFW19FuNqNcLUbB6Nbptcf78jEqRdJvH4JrZJ2JmyFdzyWsDmsJ4cetMBSpeaPhn0ITqxq2MUVlOBNSJIXGiXn68MY1/sj9aW+1DeF+sYeQowp5EjAKSbGunIWT/1yQIarLqOi5eH92JinAOBgEc/lBB+87YJ4yeUfLFRaADQ6C64nVTFcquKvyJe5nFCjGPov9aW+1Jf66Yw9hBhLyJGAUwyMfeUsCfW1WJCg1f/6RmvK8H7gbAAsVWq87ELwybCb6znH/+jitpNwtSuXE2rQu+F1pnt9whdTJ3Ps9HOUsojBwTyBOynlkauyS32pL/ULs34aYw8hxhByJOAUMWNfOUt6fY1ixZUEX64k+ML99OW2ZomG+hPeteG/FaU5clF/bZ8OZXczw2caCal26bu4MvwfrnblaYNPSX9/pL7Ul/rFV/9Rxh5CSnrIkYBThIx95TTm+kk6O0N9v8lw7PQSw2NHHzRh6pVPqW5/FW+7YF522Ue/8r8ZHn+gKcXVxOp8FPgN4Wo3XCwj0CoWPEh1LrL+S32pL/WNu35OjD2ElOSQIwGniBj7ymnK9UOTPQi955FpWRnLSKrbBT/czXUVb/tgoh+ew2dk5ZW8Wn4jDQ8eRcGMzq7bec4rhX4j6jN2cjk5lF3qS32pnyvGHkJKasiRgFMEjH3lfBbrR2lcOBrjwtGYJlke2xrehcD4GoZLT4ysvoFaFsfgZ1jmCLeblDdckT1tN1dwYlVSdNlcibqQ+i/1pb7ULxn1n5axh5CSGHIk4BQyY185pX5WF+LrcCG+jqF++Ymb+WjqRRKvB+Jjr9/aU93uKi1KH8HKTP+a5+Nq0+/0/wDoX3491xM9sw1PRdF/qS/1pX7R1M8tYw8hJS3kSMApRMa+ckr93NTXH8q+N+plw+PmpOJhexMf+yC0Stpl3xQ+8JzHtvudORrTBHNS2dBgANcTq2Ta4nMzqTKN6tuY0Psj9aX+s1U/r4w9hJSkkCMBp5AY+8op9fNfX4sFIUlVCUmqmmGpiheP/I2teTIAjhbx3EspR52Hh7KnUSvWmJXz4ciMqtS9X5UHdq24kuhTpP2X+lJf6hcPYw8hJSXkyNXEC4Gxr5xSv3DrqxVrYlKdAHiQ6sw7/y2k/fEdNDhwjL6nfmV50hfomgzj4m0nvHVHGFt1Dg2dTgHgYXOTdc8PpH6p0wDYm8dT1jICuSq71Jf6JaN+QTH2q4SXhKuQyxacAmbsK6fUL776iTo77Ko3oO+0lzNdlb2URQy6h7u4bM0TSdbZkKjVn8OnnctfzKoxiWiNM0EJ3lx5eP6eqwnVCUr0NgSpoui/1Jf6z3r9gmbsW1oy1i8OEnAKkLGvnFK/ZNaPzRBSLifUYNi5VYb7Z2KfZ0bQBKo/PJy9u9s2SlnEGR4PT3HlaqI3Ey9Pp0rtysyfpGXc5NtyKLvUl/qFUL8wmErIsRrjX6B1n4YEnAJiCiun1De++jeSq3DjTpUMSxTcrcKobh+kP6LLLlh/hfbaLnwzw4kDn09mgf1PNOAYWixo4/I3DubxXE2ozrUkLzmUXepL/XzUBzDz9jbaEFKY9YuDBJwCYCorp9Q3hfoqwtTlCFOX40D0C1nqpwS9zDH7imgfrvqvV/iFFqWPAKBVzAhNqvxwN1f6EV3Xk6qQKldll/pS/4n11ywqbdQhpCRMDC5IEnDyyZRWTqn/LNSvx9m4eobHR11YgofNTcMWn+p2QXjbB9PWZS/mKh0A/8XV5JXTGwDo4fYHd1IqcCKmUTH1X+pL/ZJd35hDiKmFHAk4+VDTx8LkVk6p/2zVT1UsuZZUjWtJ1dgZ0dGw3EqVQlW7EKrbX0WnmBuWj6v6LXsjX+JETCOa1jdnZY3BHJlRgefuV8W6dHWuJnpzL6UcclV2qf+s1jf2EGJKIUelKMrTH18qADhx4gSDBg1i7dq1NGrUqLi7I0SRUVLiQZOEysEVJSkGfhkJYZcg9l56I2tHcPeFcjXBvQZ4v4jK3bf4Oi1EEVHUGlRWhTPZ2NgpGg0qy4J/bx73fSxbcIQQT01l7QDWDvrbtk7w5noAlMQHEH4J7l3SB557l+DCNji2BrpNB3dflOhQWD8GOk5A5dkURZ0AqRpUds7FNyAhCpCEm5wVRrh5Egk4Qoh8U9k5g2cz/b+HFEWB+Ptg/vCDLTkWtOr0+4G74Ne3UEqV02/pcfcF95pQrga4+ejDlBBC5JEEHCFEoVCpVODoln6/fG14JyC9QYU60HmqfotP2CU4+hNokgwPK6U99MGn11eonCuhJMeCuRUqy7wdyi6EeLZIwBFCFAuVqze09jPcV3RaiLrxMPBcTt/VZe2ob7B/MfyzEOWza6gsrFCu/A3J8fotPi5eqMxl94AQIp0EHCFEiaAyM4eyVfX/anfJ2sDnZbB1RmVhpb//7/dwdZ/+trkVims1/S4ud1/9lp9yNaB0FVRmcsk9IZ5FEnCEEEZB5dkUPJumLxjyI4QHZZ7cfOM4nP0tvU352uC/FwDl3B/gVA5VlSZF3HMhRHGQgCOEMEoqS1uoWEf/LwMlJT59F5cqw9abrVPAuxVUaaKfAL2qP5Su/HCCs36Lj8rBtYhHIYQoLBJwhBAmRWXtAB4N9f8yen8faJL1t9UJkKqGC1v1h7I/pNiXzbyLq2pL/VwhIYTRkYAjhHgmqOxKp9+2doC3Nj88lD1cv8UnbTdXWCCc+h+kxEOnyfDSeyixYbDxPWjzof4cPppk0KXKoexClGAScIQQzyz9oezu+n/eLxqWK4oCMbfBwlq/IDEK4iOAhyd+v/wX/PwGShmPzBOb3WuAW3VUac8TQhQbCThCCPEIlUoFzpXS75erCe/9ld7ArTq0H58+1+fyX6BL1T9mZo7iUlUferp+hqp0Zf1Zm82tUZnLR64QRUXWNiGEyCWVmw+0/chwX0lVQ0RwhvP3BMK9i2Bpq2/w71LYOxfl08uoLG1Rgg9CSpx+i09pDzmUXYhCIAFHCCHySWVhpb+4aLma2Tfwag5mZvojvwD+XQKXdulvW9qhuPtmntzsXhNKldNvSRJC5IkEHCGEKGSqqs2havP0BQOWQPjlDJObA+HK33ByXXqbjOfwubgTHF1RVW5QxD0XwnhJwBFCiCKmsnEEj0b6fxkoCVEPd3MFZn7C1klQqQEMXKZvt2YYOFXINLlZZetURL0XwjhIwBEFSlFrUFkV/TWBFI0GlaVci0gYN5V9Gf2WnoxbewDe3gqpKQAo6kSIDdNfpkKdaGiiOFXIfFV2zyaoylYtwt4LUbJIwMmHQW9Hg1V4luVN6lsyb4YT/pNjOHZaU+CvW5LrXz7kRrL/GMN9M29vLN8YjuaHVeiCggq6q4b6KgfHAq8tREmhKlUu/baVHby7HUWngwe30q/GnnZx0msH9WGo/Xho+xFKQiRs/ABav4PKs5l+QjSKHMouTJ4EnAJWksNHcdTXBQWh+WFVoYWctPpWY/wLtK4QJZ3KzAzKeOj/1exgWK5oU/VXZbe21y+Ij4DIENDotwAR9A/8NASlbLUMW3wensPHxUsOZRcmQ36TC5CxhY+iql8UIUcIoacytwDXaun33X3hw3/TG5T2gNbv6bf43DkPF/4E5eEJDM2tUNyq60NPhwmoylRBSU0BM0s5lF0YHQk4BcRYw0dR1S/skCOEeDoqd1/oOMFwX1EnQvjVzLu6Qo6C+cM5bQeXwV+zUSadR2XtgHLjGKQk6Lf4yKHsogSTgFMAjD18FHb9NBJyhCh5VFZ2UKme/l92KjWAlqPSr7u1fwn8t01/28YJpVyN9F1caVdlt3cpms4L8RgScPLJ2MNHUYWbNBJyhDAuqmotoVrL9AV9voUWIzJv8Tm7GZJjDE2U8s+h8v9bf/vqPrBzQVWxTtF2XDzzJODkQ00fC6MOH0UdbtJIyBHCeKnsXfSBJ0PoURQF4sL0l6gIuwyKLv0Jf0wENx8YvFrfdt1oKOX+cItPTf3FSa3singU4lkgAScf/Ec6GG34KK5wk0ZCjhCmQ6VSQaly+n8+L2d+8I11oNV/xiipKfoAdCH9vD6oVChlqmTezeXREFWZKkU8CmFqJODkw7zl8QReN77wUdzhJo2EHCFMn6qMR/ptC2vw//vhoezXH27xuZR+kdJLu0GnhTYfQodPUJJjYdOH0HIkKs+m+uepVKjMzItvQMJoSMDJh8ArqWBVsDWflXCTRkKOEM8e/aHs3vp/dbobliupKXA/CGxK6RfEhsGdc5Acq79/7SD8+DqKa/WHFyWtob/AqZsvOFeSQ9lFJhJwShBjDzdN6uftUgkScoQQ8HALT/na6ffdqsPHx9IblCoHLd58eMbmw3B6Y/pjVvYoabu42ryvP4ePNhXMzOVQ9meUBJwSwhTCzbwZeb/Yn4QcIcSTqNx9ocs0w30lOTb9EhWG3Vw7oc37+gaHV8Jf36GMO47K1gnl1llQJ4C7rxzK/gyQgFMCmEq48Z8cw5pFpfNcR0KOECI3VDaloEoT/b8MlLQzM5erBQ36p19p/d/FcPZ3fRsH14e7uWpmumSFykaua2cqJOAUM1MKN8Zw7SohhOlL2yWl8m4F3q3SH+j6OTTs/3CLz8OtPsd+Bk2Gq7JXrItqzB797ZAjYOuEqlzNIu2/KBgScIqRsYWPoqovIUcIURhUpdz15+DxaWNYpuh0EH0zPfDoUtOfsGU8OFeCYWv1bX8fC/ZlDWdsxqUqKosCPtJEFBgJOMXEWMNHUdWXkCOEKAoqMzNw8dT/q9Up84ODVoJWDTy8SnvIUYgI0h/KDmBmgeLqneEcPr5Q6XlUzhWLdAwiexJwioGxhw+5dpUQ4lmgcvVOv21uAR/+m34oe8bJzbdOw7nN+oat/aDzVP1FTH//GJoO0Z/DR6fTn8NHjugqMhJwipixhw+5dpUQ4llmOJQ9w+HsAEpKvP6q7GkTmmPv6c/bU7uz/v71I/pz+Lj7gluN9PP4uNcARzcJPoVAAk4RMvbwIdeuEkKI7KmsHaBy/fT7ZavChDPpR3TZl4EG/fVbfgJ3wIm16U+2K51+Dp9Wo1G5eKLodHLiwnySgFNEjD185Ka+mbd3gYcQCTlCCGNkOKLLvQb0/NKwXIm/n+H8PQ8vWXFmE7QcqW9w7EeUv76D9/9BZe+Ccu8iqJP05/CxdiiOoRgdCThFoCSFj6KoX1ghREKOEMJUqBxcwds102Hshq09AC5VoWZHsCujv79/CZz6n76dc+WHu7h8M1yV3RuVpW1RDqHEk4BTyEpa+CiK+oUZQiTkCCFMVcZ5OKrqraF66/QHO4yH57pmmNwcCFf3Ga7UjspMfw4fv10AKKGnwKZUponSzxoJOIWoJIaPoqhf2CFEQo4Q4lmjcq6kPydPhkPZFa0GIkPSA4824zl8PtFftHSE/npdytYpYFs6fXJzmSomf1V2CTiFpKSGj6KqX9QhRwghnjUqc0tw89H/o0fmB/vOhVT9Z7ei08Kl3RBxLf1xS1sUt+oZzuFTAyrU0Z8M0UQ8M1O0N27cSFRUFACpqamkpqY+4Rl5V9LDR1HV1wUFkbJqJbw+BDPv3G0m1eh0T1U/LeQ8Ki4uLlevJ4QQpkRVrhaqSvX0t83MUY09Ap9dg3d3Qt950HSofn5P0L+w/XNYPRD+WQCAkpqCsulDlBv6K7kripJ5fpCRKJFbcEJDQ/nvv/+oXr061apVe2zbuLg4rK2tsbLK/nTZv/76Kzdv3iQ8PJx9+/bh6OjIsWPHqFq1KsuXLy/wvhtL+MgkYT6kBoO5J01ajaNvx1O0bT+XxCQLsB8PFlX17bShkLwZ7MdkX0dJ4b333uPrh7uRZ/53kZvHTxK1bj2jx46lHbD/8GGmnP8PBwsLZtWrSy2nUplK/Hn7DhtDbwHwnFMpPq5Zg1uJiUy7oH+OAsyu/zzmD/dVXzh5io9+XIPWzo7p06fTurV+n/XcuXPx8/Pj0KFDdOnShVWrVtGmTRsqVqzIsmXL8PPzK5j3TgghjIThUPYMh7MDKIkPIPxS+oTmmLvwXwBUe0F/IdObJ+DHwQ8PZffNcJFSX1R2eb/AcmF7bMC5desWbdu25eWXX+b77783LL99+zazZ8/mu+++y9Q+PDycsWPHotFoKF++PF999VWOwSM7H374IcHBwcTGxvLKK69QvXr1HNtu2bKFpUuXotVqmTBhAi+99FK27f75559MfY+Li2PUqFFMnjzZsEyj0fDNN98wceLEp+5rdowy3CgpYNMXzMpilTiImZ+Y0bP3XBIt5oFjCiQsBMdP9W21N8CsXM61tKGUK1cOwu5yNymJ+qVLM6l2LULiE/jxpx/p/PPP/O/PbXzzfF20isLKa9f4rv7zmUrcT0nhh6aNMVOpGHpE/9fDxtBbTKldiyr29nwTeJkrcXHULKUPRptv32a0pwcNZ33DpEmTaN26NdHR0YSFhREfH8/58+fx8vIiMTGRTZs2UaVKFVq0aFEw750QQpgAlZ0zeDZLv+/iiTIlENK22tg46ic43wuE0xsgJd7QVnF0T5/X0/xN/XMVJdOEaeX0Jtg5Ex7cBueK0HESqvqvFPq4nriLqnfv3pkCAsCVK1eoXLlylraTJ09myJAh/PrrrzRv3px169blqjMTJ05ky5YtuLu74+fn99itNz169MDT05MVK1bw0ksv5bjL6fnnn+fw4cPs3r2b4OBg1qxZw+eff06VKlUMbSwtLXFwcODkyZO56m9GRhluAFTWYOZCGcsvGOM3grf8A0jUdQUzBzBzAZV9elvtdTCvkmMptNcNP7PytrZ0qVAegGXBwbzq7KyfK1OpIh61alHJ1o7UbDZ5Dq/qhZlKxfqbobQvp98XbGFmxq3EJDQ6HZHqFKwynPxKURQq2dlRoUIFwybUP//8k44dO3LlyhW8vLyoUqUK27dvp127dpw6dYoGDRrk800TQgjTplKpDCcaVLnXQNXnO1TvBMC0YPjkNLzxK3Seqj/SK/EBHP0J1An6J5/4FWVWI5S4cH242fQ+PLgFKPr/f/tQv7yQ5WkOzuXLl/Hx8cm07O7duyQkJNCuXTsAXnnlFQ4dOpSrumXLlgVAp9MRGRlJYmJiljZRUVGcO3eOnTt3cubMGT7//HO6detG+/btOXbsWJb2/fr1Y/Pmzfj4+BAeHk7Lli3ZsWMHGk3mkFCvXr08BxyjDTcA2jBsU97iwf29LF+5g2vB/4F5jfTHVRlm2WtvPCHg3MgSStdev4GnvT21nEqhCwqif2ln3tj9F++cO88bXl7Zljn/IIb99+8zsIoHAK95VGZDaCiDDh/lRFQ0VezsDG37Vq7MuDPneO211xgyZAgAO3fupE2bNgQFBeHp6YmlpSUBAQFERETQpk2bbF9TCCHEk6lUKlTOFVH5tkXV2g/VqwtRjdmtn9/jXlPfyLkCeDbRX3l950xITclcRJOkX17I8hRwrly5gq+vb6ZlQUFBmf4yVqvV2Nrm7qRD+/fvByAyMpJhw4Zx6tSpLG22bt3KihUrWLt2LZUqVSIyMpLffvuNvXv30qRJkyztT58+zccff8zq1avZsGEDZ86coWHDhlhaWmZq9+DBA+zt7bM8/0mMOtwADeo5MXP6OJ5vtZPYBAdQOYLufnoDXYbJutpwMM8wwz56kH5ejuHxzAHnbPQDjkdFMbJaVcOyrSdP0btyRVp36cxetf4qvVPPX+CP27cBiFFrmH35Mp/XeQ6zh5s4XaytmdugPm3d3XjDyxOLDFtwtt+9S/ty7nTp0oWdO3eSkpLCf//9R/fu3Vm6dCl+fn5s3boVgL/++gtXV1deeuklwsLC8v3eCSGE0FOZmadv8an+Eqr+i/X3H9zO/gk5LS9AeQo4169fx9PTM9MyJycn7ty5Y7h/4sSJJ04QflRAQAAA5cuX588//+SFF17I0mbIkCHMnz8fgFGjRvH8889nmucTERGRqX2DBg1Yt24de/bsoUOHDtSvX58ZM2ZkqXvw4EEaNWqUq/7W9LEw6nDTpL4li2ZVYl1APU5dsAHLFqBygOQN+rk5yVvBolbOBZwWgnmGXZW6B5QurZ9wFqPWsPBqENMzBJWE1FQeqDWMcHbmw9LO7A4Lx8zbm7E1fOlcvjyKovD5f/8xrmYNyjwyd2tvWDgXY2N57eFWnTSnoqMZX7MGo0aN4tatWyQkJHD69Gn27NlD8+bNOXbsGH369OHw4cM0btyYM2fO8OKLL2b5PRFCCFEInCvmbnkBynXAUavVmJubY26e+QRBtWvX5tKlS5w/f564uDiWLVtGjx6Zj8u/dOkSFy9ezLF2WFgYiqKg0WiIiIggKiqK+Pj4LO0CAgLw9fWlfPnyWfqxbNmyTPednZ0JDw9n+fLldOrUiVu3bhmOtElz7do1oqOjs2yVehL/kQ5GHW7mzXBiuN+/+vqKBlJ2gIUXWL0AMSNAexVs+uifoA0Dc7fMReImgvZhqFVSQZU+Z/2PO3e4GhfH60eO0vPfA0w4ex4V+pCTrNVy49w5zBMSsHxjOEsjIvnrYYA5FBHJJ2fP0fPfA/Q/dJjE1FR+vn6DHXfvMateXUNYSpOqU4hR639fHjx4gJ2dHSqVCjMzM1JSUgwT3TZu3Ej37t3p06cPnTp1onbtzFcCFkIIUQg6ToJHLyFhaatfXshyfZj4tWvXst0yY25uzldffcXMmTNRq9X4+/vj4ZH5r+09e/aQmJhIrVqZtwqsWrWK06dPc/78edq2bcvdu3dp3769Iey0bt2axYsXA/otNNu2bWPOnDnExsZy7tw5QkJCsLS0JDQ0lPPnzxvqarVaFi9ezMCBAylfvjy//PILq1evZs2aNZlef9GiRUydOjW3bwXzlscTeN14w43/5BgCL4VD8lzQxYJVS7BspP9nOyDzk1R2YN098zLL5zNMQlbAJv05gz2r0LdyJWwfCaAvurnS6q+92Jqb86GvD5ofVlGzZ08qXbxI7fg4/m7zUpbn9KlUkdc9M8/9efv4SUZ5V6W/R2U6/bMfVYcOjBgxAhsbm/Q+DB4MQHJyMq+88gqWlpZYWlrKUVRCCFFEVPVfQYFiOYpKpTzm7D13795l0KBBODs7s3HjRszMzIiIiOD69euG3Tl3795l4MCB/PHHHzg6Oub4Qlqtli+//JIPPvggy1yXU6dOYW5ujouLC6VLlzb8FQ76o2SSkpKwezix9NChQ/j6+uLi4gLA0qVLWb58ueF8OB06dODbb781PFdRFMwe7hecP38+iYmJfPLJJ3l9vwD97rdBgwaB40KwqpevWo8y9jk9lw+5keyfw3lycmDm7Z3rMx7HajQ4WlgYfk9s5i3IdV+FEEIYt7Tv47Vr12aZZvLYgPO0oqOjDXMvngWFFXCMPdw0qW/JmkWlcx1wIG8hJyMJOEII8ex5XMApkEs1PEvhprCYQriZN8Mpz8/PeNmF3F7WQQghhHjUM3MtqpLMVMKN/+SYfNWRkCOEEKKgSMApZqYUbgqivoQcIYQQBUECTjEytvBRVPUl5AghhMgvCTjFxFjDR1HVl5AjhBAiPyTgFANjDx+FXT+NhBwhhBB5JQGniBl7+CiqcJNGQo4QQoi8kIBThIw9fBR1uEkjIUcIIURuScApIsYePnJTvzBCiIQcIYQQuSEBpwiUpPBRFPULK4RIyBFCCPG0JOAUspIWPoqifmGGEAk5QgghnoYEnEJUEsNHUdQv7BAiIUcIIcSTSMApJCU1fBRVfQk5QgghipMEnEJQ0sNHUdUvypAjhBBCZCQBp4AZS/goqvpFFXKEEEKIjCTgFCBjCx9FVb8oQo4QQgiRkQScAmKs4aOo6sucGSGEEEVJAk4BMPbwIdeWEkIIYWok4OSTsYcPubaUEEIIUyQBJx9q+lgYdfiQa0sJIYQwVRJw8sF/pIPRho/iCjdpJOQIIYQoTBJw8mHe8nijDB/FHW7SSMgRQghRWCTg5EPgldQCr/mshJs0EnKEEEIUBgk4JYixh5sm9S3z9DwJOUIIIQqaRXF3wJit/b40jRq5FXjdNYtKF3jNoqqvqDXYzFuQ5+dbjfHP2+tqNKgs8xawhBBCmB7ZgiMKlMqqeEKGhBshhBAZScARQgghhMmRgCOEEEIIkyMBRwghhBAmRyYZ54FarQbg0qVLxdwTIYQQ4tmV9j2c9r2ckQScPLh48SIA06dPL+aeCCGEEOLatWu0aNEi0zKVoihKMfXHaF26dImePXvyzTffUKFCheLuTqG5f/8+77//PnPnzsXV1bW4u1OoZKymScZqep6VcYKM9WloNBqCg4Pp1q0bzs7OmR6TLTh54ODgAECDBg2oVKlSMfem8Ny6dQuAOnXqmPQ4QcZqqmSspudZGSfIWJ9W8+bNs10uk4yFEEIIYXIk4AghhBDC5EjAEUIIIYTJkYCTB6VKlcLPz49SpUoVd1cK1bMyTpCxmioZq+l5VsYJMtb8kqOohBBCCGFyZAuOEEIIIUyOBBwhhBBCmBwJOAIARVHQaDQAaLVakpOTi7lHhSMhIaG4u1Cs1Gq14ecsjN+jp6c3xd9v+Z01jZ9rxu+YoiIB5xmnVqt59913GTlyJK+88grLli3j0qVLtGvXjs6dO9O9e3eOHz8OQHh4OEOGDOG1117jww8/zPbaHyXd4MGD6d69O506deLrr78GYM6cOfTp04fXXnvNcBkOUxhrSkoKHTt2pFWrVrRp04aGDRuyc+dO2rdvT6dOnejduzfXrl0DIDg4mL59+zJw4EBmzJhRzD1/ehqNhsGDBxtuT548mX79+jFkyBDDicNyGtvatWvp2bMn/fv358CBA8XS/9zIONZ//vmHvn37Mnr0aLp3705ERAQAH330Ed27d6djx4589NFHhuca81j37duXq99ZYx7r6tWreeGFF2jXrh21atVi9erVQPafW5D9Z1dJk913DOTuc3fnzp306NGD/v37s3nz5qd/cUXkyvHjx5WePXsq/fv3V5YuXVrc3cm3/fv3K0eOHFEURVGio6OVfv36Kf/73/+U9evXZ2k7cuRIZffu3YqiKMr69euVH3/8sUj7ml9JSUnK66+/nmlZQECA4ufnp+h0OiUsLEwZPny4oijGP9ZHzZs3Txk3bpwye/Zs5dChQ5ke02q1Ss+ePZVz584piqIos2fPVvbs2VMc3cy1y5cvK2PHjlUURVGWL1+uzJw5U1EURbly5Yry4Ycf5ji2M2fOKP3791dSUlKUuLg45fXXX1c0Gk2xjeNpZBzr/PnzlZSUFEVRFGXmzJnK3r17FUVRlH79+mV5nrGPNTe/s8Y+1jQxMTFKixYtlKCgoGw/txQl58+ukia775jcfO7evn1b6dKli/LgwQNFrVYrI0aMUCIiIp7qtWULTi4kJCQwadIkFi1axK+//kpwcLDRX1G8VatWNG3aFIBvvvmG4cOHc/HiRby9vTO1u3v3LgkJCbRr1w6AV155hUOHDhV5f/PjypUreHl5ZVq2YcMGxo4di0qlws3NDRcXF27fvm30Y80oMTGR7du3M3nyZC5evEi1atUyPX7q1CmqV69OnTp1AOjbt6/RjPfy5cv4+PgA8Oeff/Lee+8BUL16dWJjY3Mc2/r16/H398fKygoHBwcaNWpU4tfljGMdM2YMVlZWXL16lcDAQFq2bElkZCSOjo5ZnmfsY83N76yxjzXNL7/8wtChQ6lWrVq2n1uQ/WdXdHR0UXX7qWX3HZObz93NmzczbNgwnJycsLS0pGPHjpw4ceKpXlsCTi7s2bOHzp07U7FiRVQqFb169TKaL4InWbZsGfb29nTq1Inhw4fj7u6OWq3mwIEDhISEEBQURIMGDQzt1Wo1tra2xdjj3PPx8eHVV19Fq9WSlJTE77//TlRUFFWqVDG0UavVBAYGGv1YM9q+fTtdunTB0dGR8ePHo1KpUKvVBAQEEBkZSVBQEA0bNjS0N6bxXrlyBV9fX9RqNTY2NobrxAFYWFjkODZjHHPaWNNEREQwfvx4Zs6ciaWlJaVLl2bMmDGo1WoUReHXX38FMPqx5uZ31tjHCvq5Ktu2bWPgwIFA9p9bQLafXXZ2dkXb+VzI+B2Tm8/d/PxMJeDkgjGuPE9j9+7dnDlzhvHjxwPg4eFBhQoVsLKyQqfTsX79epycnLhz547hOSdOnMjyV1VJZ2Njw3PPPYe5uTm2trasWrUKlUpFYmIiADqdjvv37+Pm5mb0Y81o8+bNvPrqqwB4e3vj6uqKlZUVMTEx7NixAycnJ+7evWtof+LEiSxb8EqqtC8HCwsLYmNjDcvj4+PRaDQ5ju3R3+eLFy9SsWLFIu17bmX8IkxJSeGDDz5g4sSJeHh4AGBmZsbzzz+PlZUVKpWK33//ncTERKMfa25+Z419rABHjx7l+eefN4T17D63gGw/u6ytrYt+AE/h0e+Y3HzuPvqzPnny5FN/HkvAyYXs3mhj+SLIyaVLl1izZg2zZ8/G3NwcgE8++YQ7d+6g0WjYs2cPtra21K5dm0uXLnH+/Hni4uJYtmwZPXr0KObe587q1av5+++/AThy5AgJCQm8/PLLLFy4EEVRWLVqFa1atTKJsaa5f/8+Go0Gd3d3YmNjmTp1KlFRUSQlJbFv3z5sbGxo2bIlAQEBhIaGEhYWxqZNm2jbtm1xd/2phIWF4e7ujpmZGXXr1mXt2rWkpqYyZ84cevbsmePYOnbsyPz589FqtQQEBFCpUiVsbGyKeziPlTZWgClTpjBo0CAaNWpkeHzr1q1s2rQJnU7H1atXDZOsjXmsuf2dNeaxptmxY4dhNw1k/7kFZPvZVRJl9x2Tm8/djh07smTJEpKSkjhx4gTx8fFPH1oLfEaRCQsJCVG6du2qREVFKUFBQUr//v0VrVZb3N3KlzFjxigtW7ZUOnbsqHTs2FHp27evcuDAAaV3795K165dlfHjxyvR0dGKoijKuXPnlP79+yu9e/dW9u3bV7wdz4OYmBhlzJgxSseOHZWBAwcqR48eVRITE5Vx48YpnTp1UmbNmmX4eRr7WNOsX79e+f777w33AwIClG7duindunVTpk+friQnJyuKoij79u1T+vTpowwYMEA5e/ZscXU3V2JiYjJNvoyMjFTefvttpUuXLsqKFSsMy7MbW2pqqvLFF18onTt3ViZMmKAkJSUVef9zI+NYL168qNSrV8+wznbq1EnZsGGDkpKSokyZMkXp0KGD0rdvX2XHjh2Kohj3WBUld7+zxj5WRVGUzp07GyaQp7V59HNLUZQcP7tKmuy+Y2JjY3P1ubt06VKla9euip+fnxIZGfnUry2Xasil3377jR9//BEXFxemTp2Kp6dncXcpX1JTU9HpdFhZWRV3V0QhCA0NxdLSknLlyhV3VwpcQkICp06dKrF/uRakR8eanJxc4rdM5NWz/HMFOH78OI0bNy7GXhWs4vyOkYAjhBBCCJMjc3CEEEIIYXIk4AghhBDC5EjAEUIIIYTJkYAjhDA5T7qo38KFC4uoJ0KI4iIBRwhhtMLCwgwX7zt58iSHDx/mzJkzfPfdd4993pEjR55YOzU1lS1btrB3716ioqIKpL9CiKIjAUcIYbQ0Go3hxGdeXl7s2LGDr7/+mmbNmj3xeUCmsx8/6vvvv+fkyZP4+/uze/du1Go13377bcF1XghRqCyKuwNCCJFXycnJgP76Pba2tqjVai5cuGA4Dfyjdu3axfHjxzl37hwvvPACDRs2ZN68eVnaXbt2jdOnT3Pw4EHatGmDvb09//77L5cvX2b+/Pl4enoa7dmthXhWSMARQhilU6dOMX/+fAIDA4mLi6NZs2acO3eOWrVq4ebmlu1zHB0dKVeuHDqdju7duxuujfMoS0tL2rdvz4EDB3BycsLLy4spU6bg5ubGli1bWL16dSGOTAhREGQXlRDCKNWrVw9fX186derE1KlTqVu3Lp6enoSGhnLz5s1sn1OjRg3Wrl2Lq6srcXFxOQYVOzs7jh07Rps2bYiOjubVV1+lW7dujBs3jlq1alG5cuVCHJkQoiBIwBFCGKXk5GS2bdvG0aNH2bJlC2XKlKF27doMGDCAPn36oCgKWq3W0F5RFMaNG8fgwYNxd3dn6tSp7N+/nw0bNmSpvXnzZt59913Onj1L7969GTt2LO3bt+fUqVNPnN8jhCgZ5FINQgijtGTJEgIDA2nWrBl169Zlz549HDhwgPDwcCpUqEBCQgKtWrVi3LhxAHz77beoVCpat26Nv78/1apVY/To0SxdupTOnTvTv39/AG7cuMHChQvZu3cv5ubmqNVqVq9ezdy5c9FoNCxYsIDSpUsX59CFEE9B5uAIIYzSiBEjWLduHebm5lhaWtK9e3eqVq1KWFgYI0eOzNT28OHDVKlSBV9fXxYsWEBsbCwDBgygcePGNGzYkDlz5jB8+HDGjh2Li4sLHTt25PTp0wwbNoyYmBjq1q1L5cqV+eeffwgODqZRo0bFNGohxNOSLThCCKO1ZMkSXF1d6du3LwABAQEkJCTQr1+/xz6vQYMG7N69GxcXF8Oy+Ph4ABwcHIiMjGTgwIFUqFCByMhIrK2tad26Nb169WL48OF4eXkxbNgwmjVrhkqlKrwBCiHyTLbgCCGMVuPGjbG1tTXct7Ozw8LiyR9rVapUwdnZOdMyBwcHw+1x48bx1VdfUb9+fVavXo2lpSWDBg0CYNOmTXz33XdMmDCB77//nho1ahTMYIQQBUq24AghTEZcXBxqtTrTlhkhxLNJAo4QQgghTI4cJi6EEEIIkyMBRwghhBAmRwKOEEIIIUyOBBwhhBBCmBwJOEIIIYQwORJwhBBCCGFyJOAIIYQQwuT8H9+G2zbxVn1cAAAAAElFTkSuQmCC\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n 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\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# ////////////////////////////////////// 特征概述 ///////////////////////////////////// #\\n\",\n    \"worksheet = writer.get_sheet_by_name(\\\"模型变量信息\\\")\\n\",\n    \"\\n\",\n    \"start_row, start_col = 2, 2\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\\\"入模变量信息\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, feature_describe.query(\\\"变量名 in @card.features_\\\").reset_index(drop=True).reset_index().rename(columns={\\\"index\\\": \\\"序号\\\"}), (end_row + 1, start_col))\\n\",\n    \"\\n\",\n    \"# 变量分布情况\\n\",\n    \"import toad\\n\",\n    \"data_info = toad.detect(train[card.rules.keys()]).reset_index().rename(columns={\\\"index\\\": \\\"变量名称\\\", \\\"type\\\": \\\"变量类型\\\", \\\"size\\\": \\\"样本个数\\\", \\\"missing\\\": \\\"缺失值\\\", \\\"unique\\\": \\\"唯一值个数\\\"})\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"变量分布情况\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, data_info, (end_row + 1, start_col))\\n\",\n    \"\\n\",\n    \"# 变量相关性\\n\",\n    \"data_corr = woe_train[card.features_].corr()\\n\",\n    \"logistic.corr(woe_train[card.features_ + [target]], save=\\\"model_report/train_corr.png\\\", annot=False)\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"变量相关性\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/train_corr.png\\\", (end_row + 1, start_col), figsize=(700, 500))\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, data_corr.reset_index().rename(columns={\\\"index\\\": \\\"\\\"}), (end_row + 1, start_col))\\n\",\n    \"\\n\",\n    \"conditional_column = f\\\"{get_column_letter(start_col + 1)}{end_row - len(data_corr)}:{get_column_letter(end_col - 1)}{end_row - 1}\\\"\\n\",\n    \"worksheet.conditional_formatting.add(conditional_column, ColorScaleRule(start_type='num', start_value=-1.0, start_color='0000ff', mid_type='num', mid_value=0., mid_color='FFFFFF', end_type='num', end_value=1.0, end_color='0000ff'))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# 变量分箱信息\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"变量分箱信息\\\", style=\\\"header\\\")\\n\",\n    \"\\n\",\n    \"for col in logistic.feature_names_in_:\\n\",\n    \"    feature_table = feature_bin_stats(train, col, target=target, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", combiner=combiner)\\n\",\n    \"    _ = bin_plot(feature_table, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", figsize=(8, 4), save=f\\\"model_report/bin_plots/data_{col}.png\\\")\\n\",\n    \"    \\n\",\n    \"    end_row, end_col = writer.insert_pic2sheet(worksheet, f\\\"model_report/bin_plots/data_{col}.png\\\", (end_row + 1, start_col), figsize=(700, 400))\\n\",\n    \"    end_row, end_col = writer.insert_df2sheet(worksheet, feature_table, (end_row, start_col))\\n\",\n    \"\\n\",\n    \"    for c in [\\\"坏样本率\\\", \\\"LIFT值\\\"]:\\n\",\n    \"        conditional_column = get_column_letter(start_col + feature_table.columns.get_loc(c))\\n\",\n    \"        writer.add_conditional_formatting(worksheet, f'{conditional_column}{end_row - len(feature_table)}', f'{conditional_column}{end_row}')\\n\",\n    \"\\n\",\n    \"    for c in [\\\"样本占比\\\", \\\"好样本占比\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\"]:\\n\",\n    \"        conditional_column = get_column_letter(start_col + feature_table.columns.get_loc(c))\\n\",\n    \"        writer.set_number_format(worksheet, f\\\"{conditional_column}{end_row - len(feature_table)}:{conditional_column}{end_row}\\\", \\\"0.00%\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 257,\n   \"id\": \"4729b83f-1964-453d-9c9e-d675c3d9eefc\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train[\\\"score\\\"] = card.predict(train)\\n\",\n    \"oot[\\\"score\\\"] = card.predict(oot)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 258,\n   \"id\": \"fb9bd2a8-7e9f-4c8d-9593-030b08f55bd6\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# ////////////////////////////////////// 评分卡说明 ///////////////////////////////////// #\\n\",\n    \"worksheet = writer.get_sheet_by_name(\\\"评分卡结果\\\")\\n\",\n    \"\\n\",\n    \"# 评分卡刻度\\n\",\n    \"scorecard_kedu = card.scorecard_scale()\\n\",\n    \"scorecard_points = card.scorecard_points(feature_map=feature_map)\\n\",\n    \"scorecard_clip = card.score_clip(train[\\\"score\\\"], clip=25)\\n\",\n    \"\\n\",\n    \"start_row, start_col = 2, 2\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\\\"评分卡刻度\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, scorecard_kedu, (end_row + 1, start_col))\\n\",\n    \"\\n\",\n    \"# 评分卡对应分数\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"评分卡分数\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, scorecard_points, (end_row + 1, start_col), merge_column=\\\"变量名称\\\")\\n\",\n    \"\\n\",\n    \"# 评分效果\\n\",\n    \"score_table_train = feature_bin_stats(train, \\\"score\\\", desc=\\\"建模集模型评分\\\", target=target, rules=scorecard_clip)\\n\",\n    \"score_table_test = feature_bin_stats(oot, \\\"score\\\", desc=\\\"跨时间验证集模型评分\\\", target=target, rules=scorecard_clip)\\n\",\n    \"\\n\",\n    \"# sp.bin_plot(score_table_train, desc=\\\"测试集模型评分\\\", figsize=(10, 6))\\n\",\n    \"# sp.bin_plot(score_table_test, desc=\\\"测试集模型评分\\\", figsize=(10, 6))\\n\",\n    \"\\n\",\n    \"ks_plot(train[\\\"score\\\"], train[target], title=\\\"Train \\\\tDataset\\\", save=\\\"model_report/train_ksplot.png\\\")\\n\",\n    \"ks_plot(oot[\\\"score\\\"], oot[target], title=\\\"OOT \\\\tDataset\\\", save=\\\"model_report/test_ksplot.png\\\")\\n\",\n    \"\\n\",\n    \"hist_plot(train[\\\"score\\\"], train[target], save=\\\"model_report/train_scorehist.png\\\", bins=30)\\n\",\n    \"hist_plot(oot[\\\"score\\\"], oot[target], save=\\\"model_report/test_scorehist.png\\\", bins=30)\\n\",\n    \"\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"建模集评分模型效果\\\", style=\\\"header\\\")\\n\",\n    \"ks_row = end_row\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/train_ksplot.png\\\", (ks_row, start_col))\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/train_scorehist.png\\\", (ks_row, end_col))\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, score_table_train, (end_row + 1, start_col))\\n\",\n    \"\\n\",\n    \"for c in [\\\"坏样本率\\\", \\\"LIFT值\\\", \\\"分档KS值\\\"]:\\n\",\n    \"    conditional_column = get_column_letter(start_col + score_table_train.columns.get_loc(c))\\n\",\n    \"    writer.add_conditional_formatting(worksheet, f'{conditional_column}{end_row - len(score_table_train)}', f'{conditional_column}{end_row}')\\n\",\n    \"\\n\",\n    \"for c in [\\\"样本占比\\\", \\\"好样本占比\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\", \\\"分档KS值\\\"]:\\n\",\n    \"    conditional_column = get_column_letter(start_col + score_table_train.columns.get_loc(c))\\n\",\n    \"    writer.set_number_format(worksheet, f\\\"{conditional_column}{end_row - len(score_table_train)}:{conditional_column}{end_row}\\\", \\\"0.00%\\\")\\n\",\n    \"\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"跨时间验证集评分模型效果\\\", style=\\\"header\\\")\\n\",\n    \"ks_row = end_row\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/test_ksplot.png\\\", (ks_row, start_col))\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/test_scorehist.png\\\", (ks_row, end_col))\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, score_table_test, (end_row + 1, start_col))\\n\",\n    \"\\n\",\n    \"for c in [\\\"坏样本率\\\", \\\"LIFT值\\\", \\\"分档KS值\\\"]:\\n\",\n    \"    conditional_column = get_column_letter(start_col + score_table_test.columns.get_loc(c))\\n\",\n    \"    writer.add_conditional_formatting(worksheet, f'{conditional_column}{end_row - len(score_table_test)}', f'{conditional_column}{end_row}')\\n\",\n    \"\\n\",\n    \"for c in [\\\"样本占比\\\", \\\"好样本占比\\\", \\\"坏样本占比\\\", \\\"坏样本率\\\", \\\"LIFT值\\\", \\\"累积LIFT值\\\", \\\"分档KS值\\\"]:\\n\",\n    \"    conditional_column = get_column_letter(start_col + score_table_test.columns.get_loc(c))\\n\",\n    \"    writer.set_number_format(worksheet, f\\\"{conditional_column}{end_row - len(score_table_test)}:{conditional_column}{end_row}\\\", \\\"0.00%\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 259,\n   \"id\": \"d7104836-add3-482b-9311-9309ce8ec5ba\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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gxAi8vL1q0aMFXX33FtWvX6NOnD/Hx8el+pnHjxnh7e5OQkEDt2rXx8PCgZcuWFCpUCL1eT/PmzdO037x5Mw8ePGDTpk3MmTOHAQMGUKxYMby9vY2n/ADy5MlDdHQ0AK6uri88NdW2bVsOHTpEkyZNWLhwIZByrKT2d/HiRXx9fdHr9QwaNOi5/4+KoqQJzSEhIWlGkBwdHWnSpInx1N/FixeNc5F0Oh3jxo1j2rRpeHt7AxAZGUmRIkX4/vvv2bBhA5UqVWLnzp3pHgdSLpBIHT0TwpwkKAlVHTlyhO7du1O9enU+++wzTp8+jU6n49ixY4wePTrdVTWpn2g//fRT1q9fj7OzM6dPn+bmzZsMHz6cjz76KM0L7B9//MHEiRNxdHTk4MGDXL16lcePH5OQkMDjx4+NbzL/ljdvXm7dusV3331Hhw4daNeuHadPn37uxOHY2Fji4+O5evUqVapUoXr16uTNm5dr164xZcoU46fnZyUmJnL//n30ej1arZbq1auzYcMGnJyc0rwRPstgMODp6cnPP/9M69atiYyMpGzZsmk+1et0OtavX4+HhwdLly5l+/bt+Pj48PHHH6PX6wkJCeHQoUO0bt06Td/Jycl89913BAYGAilX3zk4OGT4/3bo0CE+++wzoqKiOHr0qPH0W2JiIjdv3jRelfg8vr6+REVFMWfOHLp27UqhQoXo0aMH/v7+fPLJJ3Tq1IkhQ4YY2w8fPjzNiIujoyNPnjzBxcXluRPyAwMD8fb2plGjRgwePJhbt27h5+eHl5cX5cqVIzQ0lNmzZ/PkyZM0P7d//36KFi2KwWCgePHiHD16lPbt26frv1+/fsY5W4cPH2by5MnMnDmTcuXKsXXr1jTzbCAlXGzcuNEYBPLmzcvJkyeJjIxk+PDhuLq6cvToUWP7I0eOGOfprV27Nt0oZkBAgHFic9myZY3htly5cly6dClN2ytXrnD+/HkuX74MgEajoV27dsarC1OvGIyIiCAiIoJChQqRmJhIaGjoc68mrFChQpqr/X777Tdq1qxpvP3vK9Pi4uKMoXfJkiW89tpraY69PHnypDneixYtSkxMDBUrVuSvv/4yBr/w8PDnjgQKYQ4SlISqGjZsSEhICL///jsNGjRg+PDh1KtXjy1btvDDDz8wevToNO27d++Ou7s7bm5uNG7cmGbNmlG6dGmKFClCQEAA8+bNSzP6Ubt2bZYsWULnzp3RaDR4eXkREhJC0aJF6dGjx3Pnc6TSaDQcOHCAli1b8vvvvxMQEMD27dvTvYHs37+fmJgYevfuTefOnYmPj8fHx4eAgAB+/vln40jIswYMGGA8zVGvXj0g5U1Cr9c/d1RAq9UaT5P8/vvvHD16lKVLl+Li4kJiYqLxOdvb29OnTx8WL17M559/jq+vL3Xr1qVevXo4OjpSu3ZtZsyYwb1799I8D61WS3R0NG+88QYA0dHRxMbGotPpiIiISBd8WrduzaJFi2jatClubm7ky5ePv//+mwoVKtC7d+8XhixICXQffvghbm5uODg4cO/ePTZs2EBISMhzw2tsbCwuLi788ssvnDhxglWrVrFlyxY++eQT6tWrR/v27Y2XoRcsWJDhw4cTGxvL9evX2b59O9988w0tW7bk3r17lC1bltdffz1NqLhw4QJ///03ly5dYunSpRQsWDDNKaWMfPrpp7i7uxMXF4ednd1z2/z8888EBQWxatUqY5/x8fHs2rWLadOmcevWrTQBo3Llynz88cc4Ojpy8+ZN6tevb/yeRqPh/fffN8750Wg0lClThhMnTtCgQQMePXrEzp07ATh58iTTpk2jcOHCTJo0ifDwcHQ6HVu2bDGezk69CGDNmjW88847ALi5ufHrr78a5xA9q127dmzbto3bt28TGhrKd999l+aU9+bNm/H39ycxMZHg4GCOHTtGiRIlOHnyJMePH+e9994jOjqa6OhoFEUhT548ODg4sH//fh49esTWrVupW7cuhQoVokCBAmzdupWEhATmzZtH586dX/r/IYQpyGRuoao//viDq1evsnTpUoKCgpg4cSKenp7ExMTg7u6eZt5MqtSRlWvXrrF3717WrVtHXFwcU6dO5b333qNq1arGtjExMWzatAlXV1e2bNlCmzZtWLduHQsXLmT58uU0a9aMvn37ppsPAdCyZUuKFy+ORqMhNDSUhQsXUqpUKeOcjlTFihVj7969xk+8Li4uTJ06FUh5s3reG07btm3T3Xfs2DHu3LmTbmI2gKenZ7o1osLDw42TqFOvzNJqtbi7u7Nz506qV69OcHAwrVq14uzZs+zfvx8nJyfjPI9atWoZJ0y7urrSsmVL/vzzT37//Xfu3r1L+/btefLkCQ0bNsTBwYHBgwcb1/x5/PgxGzZswMfHhy+++IK3334bf39/5s2bx4IFC2jdujX9+/enRIkS6Z4LwNtvv83XX39N/vz50Wg0dOnShS5duvDzzz8/d8HC6OhoY0D28/OjcOHCFClShPz582NnZ0dkZCReXl4A5MuXj+TkZHQ6HdWqVUOn0/HDDz9w9OhRSpUqRUBAAHZ2dgwbNgxIORV46NAhY5BZvXo1Bw8e5ODBg7Rp0+a59ScmJqLT6fjtt9+4ffs2f/75JyEhITRq1Ag7Ozvy58/Pzp07sbe356233qJevXpp1oAyGAxMnDiRXr16sWfPnjRrJhUqVIibN2/i4eGBj48PPj4+xmCr0Wiws7MzLhMB8N5777Fo0SIaNWrEunXrjGuG1a5dm+XLl1OsWDHeeecdunTpQkJCAk2aNGHBggUsWrSI0NBQRo0aRXx8vHEdqPj4eLp168aiRYuMp2ZT+fj40KtXLzp06ICLiwuLFi3C3d2db7/9lqdPnzJ06FA++OAD6tSpg6IodO3alaZNm7Jo0SKuXr2aZt7du+++y+TJk5k1axYTJ04kMjKSd955h9deew2AKVOmMGzYMBYuXIifn58EJaEeRQgVXbhwQUlMTDTeDg8PV7744gtlyJAhSosWLZSmTZsq77//vpKQkGBss2HDBuXChQvKihUrlLi4OOP9T58+VWbNmqWMGDHCeJ9Op1P0er3xdvv27Y3/joyMVPbt26ecOHEix5/Xxo0bFX9/f6VHjx7KtWvXMvUzT548UXr06KH06dMnU+1jYmKURYsWKTVq1FCuXLmS5nvXr19XunfvrsyaNStTfRkMBuX69evKpUuXlK+++ko5e/ascvv2bSUqKkrR6XRKfHy8kpSUZGyfmJioJCcnK4qS8nvs1auX8Xvh4eHKTz/9pFy8ePGFj/ntt98qFy5cUBRFUaKjo5VZs2Ypb7zxhrJ///7ntg8LC8vUc1EURdHr9crWrVuNt2NiYpQvvvhCmThxojJ58mTl1q1bxu89evQo3c/fvn1b+eijj5T27dsrgwYNUubPn6/odDrj9xcvXqwcP35cOXnypHLr1q0031MUJc0xrSiKcvfuXWXIkCHKJ598oiiKouzatUvp3Lmz0qdPH6Vx48bKZ599lqZ9VFSU8uuvvxpv37t3T+nevXu6fk0hNjZWad68ufLHH39k2ObJkydKbGys8faKFSuUOXPmGG8/fPgwzd+moqT8H2eVTqd77v+PEOakURRZmELkHhcvXqRatWomfxxFUThx4gR6vT7N/JqXSUxMJCgo6LlXTmWVXq/Hzs7uuaNyOc1cv1dzS05OJiQkhKdPn+Lr65tuNDGrDAZDujlooaGhxMbGvvT//HnLGgghTE+CkhBCCCFEBmQytxBCCCFEBiQoCSGEEEJkQIKSEEIIIUQGJCgJIYQQQmRAgpIQQgghRAYkKAkhhBBCZECCkhBCCCFEBiQoCSGEEEJkQIKSEEIIIUQGJCgJIYQQQmRAgpIQQgghRAYkKAkhhBBCZECCkhBCCCFEBiQoCSGEEEJkQILSczx9+lTtEkQWhIeH2+T/mS0+J5E9cixYF3lNsi1ZDkqJOsUUdZCYmPP96vV647+//fZbTp8+zbFjxzJsv2/fPu7evcuOHTs4ffo0kZGRmX6sxMTEF34/MDCQPXv2ZLo/S6YkJVlUv7t37+aPP/7IdPugoKBMt01ISMBgMGSnrGyT49B2mfM1KSty8rj5559/+PbbbzEYDGmeb24ir0nZY6nHoX1Wf8DJUUPf4ZGc+TNn3yyDThTM1s9FR0czb948+vXrR6VKldJ8b/r06cybN49du3axYsUKChYsSExMDCtXrqR8+fJp2h4/fpzvvvuOI0eOcPv2bfR6PQUKFMDLyyvdY+7du5eiRYtSs2ZN/P396devH2PHjmXz5s0Z1qkoCjdu3MjUc1q7di0tW7akXLlyL21769YtkpKSKFu2bKb6zgkaBwcSRo/M0s9oy5XD4d2BJG3cgCEk5LltnJetyFRfOp2OsLAw8ubNS3x8PLt378bV1RWtVkvTpk1f+vNffvklw4YNo3jx4mnuHzJkCP369aNp06asWLGCK1euEBsbS82aNRk7dqyxXWJiImfPniU8PJymTZvi5OTE2LFj2bBhQ6bq1+v1XLt2jaJFi5InT54035Pj0Pqp8Zr05Zdf0rdvX+7fv8/27duJjo7m4sWL9OrVi27duqVpa6rjJjw8nBkzZhAXF0doaKjx73L16tVotbZ98kJek/7/edjicZitn8rpkPQqNm7cyJ07dxg+fDh//fUXDx48MH4vIiICgE6dOlGvXj10Oh1btmxJ94J04cIFli1bRr169ejatSt37tzh3r17Gb7ox8fHG5Ovr68vgwYNonHjxi+s8969e1y9epUdO3bw8OHDF7bNkycPS5cu5aOPPuLSpUvpvn/48GFCQ0MBuHPnDnv37jXWZakMISEkbdyAw7sD0WbijfdF3n77bVavXs369etJSkoiISGB/Pnz4+Pjk65tQkICd+/eTXOfg4NDmuMEICoqihEjRhhf1D744ANWrlzJ+vXruXjxYpq2dnZ2PHz4kE2bNhEREYG/vz+FChXK8A/8k08+QafT8ddffzFo0CDq1q3L7Nmz033ilOPQNqjxmnTkyBEA3N3d8fLy4pdffmHu3Ll06dIlXVtTHTd58uRhzZo1vP7663z++eds2rSJt99+2+ZDEshrUipbPQ6zPKJkCvVrOWS67R9//MHMmTN54403iIiI4OHDhwQFBVG4cGH++usv/vzzT959911+/PFH/vnnHwYNGoSfnx9XrlxBURT69evH999/j6urq7FPRVEYP348586dIzExkXz58hEUFMSvv/5K8+bN0zz+pUuX2LdvH48ePWLMmDEEBARw/fp1qlev/tx6Hz9+TEBAAMePHyc6OppGjRrh5OT0wufo7OyMVqulVatW6V5AASIjI7lx4wZXrlxBr9ej0WhYt24dt2/fZubMmZn+XZrbs2HpRSNLL+Pu7s7x48fZvn07//3vfxk9ejR//PEHY8aM4c0336RBgwZUr16dVatWcezYMZydnalcuTL//PMPYWFhPHjwgF9++YVevXoxYsQIAP766y+uXbtGdHQ0Op2OiIgI7ty5w9ixY0n61ynBS5cuUaBAATw9PXn06BFlypShb9++7NmzhwkTJjBy5EhKliwJQExMDOPGjcPR0ZGaNWuyfv16pk+fztChQylRokSafuU4zLq+wzM+BdC1vTN+HVwy3f5ZXwak/7ScEbVfk65du0Z4eDgjRoxg5cqVeHh40LlzZypUqJCuVlMeNzt27CA2NpbNmzcTEBBAt27daNiwIUlJSTg4ZP41PjsSVyzL8Ht29Rtg3+C1TLd/ltPI0ZlqJ69Jtn0cqh7169dyYNlsz0y3r127NhUrVqRTp0689dZb3L59G0VRKF++PAMGDODdd98FoGDBgjx69IiAgAASExOpXLkypUuXxsvLK80LEkCNGjXQaDQAlChRAnd3d+rUqUPevHnTPX7q0OrQoUNp3rw5M2bMwNvb+7ltAfLnz8+IESNISEjAzc2NyMhIPDw8XvgcNRoNLVq0oHXr1s89GBRF4dChQ8THx+Pk5MS2bduIjY1lxowZmfgNqutVR5aSk5NZuHAh1atX5/jx4/j4+HDhwgXCwsIYMGAApUuXZsuWLXz44Ydcv36doUOHUrNmTdzd3VmyZAk//PADffr0YdWqVcYXJEiZpOjk5MSjR4/47rvvCAkJ4d69e0DK7/tZx48fJzw8nDp16nD06FESExPp27cv586d44MPPmD16tX88MMPJCQk0LNnT9atWwdAXFwcBoOBfPnysXHjRq5cuZKmXzkOrZPar0lnzpwhKiqK/v37A7B161bc3d25fv16uramPG7q1q2LTqdjwIABLF68mK5du3LixAn69evHtWvXMvvrtDrympTClo9DVUeUUkPS6GlRWfoEd/bsWbZt28akSZMoWrQoT548SZduY2JisLOzY/fu3ZQpU4atW7dSoEAB+vXr99w+k5OTSUxMZPny5eTLlw8PDw9u3LhB9erV0wzZ5cmTh3/++YctW7ZQv359AFxcXBg6dOhz+zUYDEyYMIE33niDPHnycPv2bb7++mt69uyZ7vEvXLhAaGgoW7Zs4d69e6xduxZ7e3vatm2b5g8IoGPHjrz99ttcvHiRfv368f7772f696e2VxlZ0mg0zJkzBzc3N+zs7IiNjWXfvn20a9eO6OhoGjZsSJs2bQAYNWoUO3fuJDo6mmbNmrFu3TqqV6+OXq9PNxkyOTkZjUaDo6MjHTt2pG3btowfPx5IOwE3OTmZ/fv307JlS4KCgqhatSq9evWiePHitG7dms2bNzNv3jxj+8WLF3P48GEAgoODefDgAQ4ODoSHhz83fMhxmDVZed3ITvvMUvM1qWfPnuzfv5/r169z8+ZNmjZtyokTJyhZsiQfffQR48ePp27duoDpjptbt27x888/4+bmxi+//EJUVBRhYWHMnj37pYE8J2R25Ce77V9EXpNS2PJxqFpQejYkZWXO0/79+1m4cCFTp06lePHiPH36lKSkJDZt2sSPP/7IF198gaIo3Lp1i/r161O+fHn+/PNPoqOjuXXrFgkJCRw7dowPP/wQb29vAH799Vf++9//EhERkWYuRt68eSlRogR16tQx3rdp0yYqVqzI7NmzCQ0N5ddffyU8PJz3338fnU5HhQoV0px2WLp0KQ0aNKBkyZJs2LCBgQMHsnfvXuzs7Hj77beN7TQaDePGjaNx48ZMmzaNqlWr4urqyoEDB1i6dGm6NygHBweePHmCs7MzsbGxxvvj4uJ4+PAhpUqVyvx/hgqyG5YSEhJ4/PgxCQkJlCtXjsKFCxMTE8PJkyf56aefqFOnDgUKFDC27dixIw8ePECj0TBq1CguX76MwWDAzs4ufU0GAy4uLsaJi8WKFUvX5vfff6d+/foEBwdz5swZmjZtapw4mDoZ8lmJiYkEBgZy9OhRihYtypUrV3BxcaFr166UKVMmTVs5Dq2T2q9JqaMXGo2GEydOMG/ePLp06UKHDh2oUqUKX375pfENylTHTcmSJenVqxfnzp2jadOm/Prrr1SuXJlZs2YB8MYbbxjDgq2R16QUtnwcqhKUshuSAEqXLs3q1auJjY2lUKFCtG/fnps3bzJx4kRiYmKAlBf7QYMGcebMGRwdHalRowY7d+6kXLlyvP/++zRs2DBNn82bN6d58+Zs3ryZK1euoNPpKFasGEOGDCFfvnxp2g4aNMj4R/H48WPat2/PP//889xJcwcPHsTHx4e4uDgWLlyIp6cnBQsWZOnSpSxdupTx48fzwQcfUKZMGbRaLY0aNUp3njh16PRZycnJnDhxgsDAQEaNGsWhQ4cYPHgwefPm5d69e+zcuZPJkydn6feqhn+HpczYt28fOp2OWbNmUb58ec6dO8f06dNp0aIFn332WZp1Plq3bs358+eJj4+nTZs2uLq6Uq9ePfbu3Ztu6Fqj0aDX62nSpAmurq54e3sbJyw+e167UKFCjB8/HhcXFzp27Ejjxo05efIkS5YsYd++fdSvX5+HDx8a3/AUReHYsWPMmzeP8PBwHj16RPHixUlOTk733OQ4tE5qvibp9Xq2bdvGihUrKFCgAB07dkSj0RiPbx8fH6ZPn25sb6rjJigoiL/++ouyZctSvXp1zp49y6effoqzszNxcXHPPd5thbwm2f5xmOWglJioZPtS/n97dhg8MVHByUnz0p+JiYlBr9czadIk9u3bR/78+Y2XL//7skZFUahUqRKLFy/Gz8+Pfv36MWvWLHQ6Hc2aNTO2S72UsVq1anz44YfcvXuXAgUK0L17dypVqkTPnj2NVx64u7sTGxuLp6cn7du3B8DT8/lzrJ5Nrvnz5+f48ePUrl0bgPHjx/PgwYM0/3H169fn4sWLeHt78+jRI37//XfOnz+f7s1Gr9eTlJTEpEmTcHR0ZMCAAbz99ts8ffoURVF45513Xvp7fBVKUlKmL+XPLMeRo1GSktC8ZLJdzZo16dq1q3HIt06dOtStW5fk5GSCgoL46KOPjG3/85//pPv5nTt3cvv2bXQ6XZr73d3defjwIY6Ojrz2WsrEz/DwcBRFSfOC4Ovry3fffUfp0qVxdnbmv//9L0+fPmXo0KE4Ojpy4MABPDw8WLRoEZAyIfqNN97g+PHjHDp0iCNHjmAwGIiNjcXJyYlmzZqxYkXK71KOQ+uk5muSvb09CxYsSPMYz47s/ZupjpuKFStSsWJFY/uCBQvi7OwMkG7+la2R16RccBwqViQxMVE5efKkkpiYqCiKoiQnJyv//e9/lYkTJyr/+c9/lLfeeksZPHiwEhERoSiKomzbtk3Ztm2bcuTIEWMfycnJyueff64MHz7c2M+zvv/+e+Xbb79VFEVRDAaDcuXKFeXEiRNp2hw8eFCJiYkx3p4/f/5Laz9z5oyyePHiF7Z58uSJ0qhRI+WNN95QOnTooHTo0EH5/PPPX9p3qqSkpEy3tRVxcXHKhAkTlHfffVfZunXrS9v/+uuvSs2aNZXAwMCXtj1w4IDSpk0bZdWqVWnuv3v3rtKnTx9lz549L+1Dr9cr169fV86cOaP88ssvSlBQkBIREaEkJiYqSUlJil6vf+7PyXFoHSzlNelZOp1OGTlyZIbfN9Vx86wpU6Zkuq2tyc2vSc+ypeNQoyj/Gu/L5WJiYkhOTs4w3T5PREREulMjwrxu376d7tLWF7UtXry48WoOSyTHoUiVnWMhK0xx3Jw7d844HyW3ktekrLHk41CCkhBCCCFEBlRfR0kIIYQQwlJJUBJCCCGEyIAEJSGEEEKIDEhQEkIIIYTIgAQlIYQQQogMSFASQgghhMiABCUhhBBCiAxIUBJCCCGEyIAEJSGEEEKIDEhQEkIIIYTIgAQlIYQQQogM2KtdgFDHkydP2LNnD2XLlsXBwUHtcoQQwiIkJSURGhrKW2+9Rd68edUuR1gACUq51J49e5g1a5baZQghhMXq06eP2iUICyBBKZcqU6YMANOnT8fX11flaoQQwjIEBgYya9Ys42ukEBKUrEhSUhIDBw7kyy+/NN4XExPDpEmTePz4MZ6enixcuBBPT8+X9uXo6AiAr68vdevWNVnNQghhjVJfI4WQoGRFrl+/TuHChdPct2TJEurXr0///v05ceIEq1evZvLkyWnaREdHEx0dnea+hw8fmrxeIYQQwtpJULIiQUFBVKhQwXg7MTGRs2fPMn36dAAaNWrExo0b0/3c5s2bWblypdnqFEIIIWyFBCUrEhwcTL169Yy37969S4UKFdBq/3+VB3v79P+l/fv3p2vXrmnuu3jxImPGjDFZrUIIIYQtkKBkRYKDg9NchZE3b17u3btnvH39+vXnXs7q4eGBh4dHmvvu379vsjqFEEIIWyELTlqR8PBwChUqZLydL18+HBwcOHLkCImJiSxdupRu3bqpWKEQQghhWyQoWYno6Gjc3d3T3T937lw2b95M9+7dadmypVzBJoQQQuQgOfVmJezs7Bg6dCgACQkJdOjQgS+++ILSpUuzadMmdYsTQgghbJQEJSvh5uZG06ZNAXB2dmbnzp14eXmpXJUQQghh2+TUm5WSkCSEyIzAGxHsOBxM4I0ItUsRwirJiJIQQtiowBsRTF1zHL3egL29ljnDGuPrk0/tsoSwKjKiJIQQNupi6CP0egMGBfR6AxdDH6ldkhBWR4KSEJmkJCWpXYLZKXq92iWIbDpxVodWlx97ey1aDdjba6lWtoDaZQlhdeTUmxCZpHFwIGH0yCz9jLZcORzeHUjSxg0YQkJyvCZT9++8bEWO9ylM78jxREZNjaKqrwOzJzXi0rXHVCtbQE67CZENMqIkhAkZQkJI2rgBh3cHoi1Xzur6F9bnl18TGPlRFOXL2LN6oSeVSufn7VYVJCQJkU0SlIQwMQlLwlx2/5LAmOnRVPW1Z/PyvOT1kJd4IV6V/BWJLEtMVNQuwepIWBKmtmtPPBM+jaZOdQe+WJoX9zzy8i5ETpA5SiLLnJw0VGz0IMf6q1/LgWWzPRk9LYozf+b8hOmc6j/oRMFXquPZMGOKOUWm7l9Yrm274pi5JIYm9R1ZOd8TF2eN2iUJYTPkI4dQlbWEpJwiI0sip23cnhKSWjRxZNUCCUlC5DQJSkI1uS0kpZKwJHLK6k2xzF8Rw5vNnVg+xxMnJwlJQuQ0CUpCFbk1JKWSsCRehaIo+K+NwX9tLB3fcOKzmR44OkhIEsIUJCgJs8vtISmVhCWRHYqisHBlDKs3xdG9ozMLpntgby8hSQhTkaAkzEpCUloSlkRWBWyIY8P2eN7xc2HWJHfs7CQkCWFKEpSE2dhCSLLGMCNhybZ0etOJ0UPc+HhcHrRaCUlCmJoEJWEW1h6S6tdyALDaMCNhyXaULG7PB++6odFISBLCHCQoCZOzhZC0bLYngFWHGQlL1ilJr5CcLIu8CqEWCUrCpGwlJI2eFgVYf5iRsGRdEhMVRk6JYsbCpyiKhCUh1CBBSZiMLYWkZ/u39jAjYck6xCcofDApiiO/66hc0V5OtQmhEglKwiRsNSSlsvYwI2HJssXGGRg6/gnHz+qYO8Wdd/xc1S5JiFxLgpLIcWqHGHP1b+1hRsKSZXoaY2DQmCec/zuJxTM86PaWi9olCZGrSVASOcpSQoy5+rf2MCNhybJERhnoP+oJlwL1+M/y4K03nNUuSYhcT4KSyDGWFmLM1b+1hxkJS5bhcYSBfiMiuXpNz8p5nrzRXEKSEJZAgpLIEZYaYszVv7WHGQlL6gp/mEyf4ZHcupPM54vy0ryxk9olCROJjY1VuwSRRRKUxCuz9BBjrv6tPcxIWFLHvfvJ9PngCfcfGli/NC+N6jmqXZLIgnPnztGlSxd69uzJ2rVrjfdfu3aNQYMG0b17dzZt2mS8v2/fvnTs2JG2bduycOFCFSoWWSVBSbwSawkx5urf2sOMhCXzunVHT58PIomMMrDRPy91a0pIshQPHz7kzp07ab6io6PTtImNjWXq1KkEBASwfft2QkNDCQwMBFI2L169ejUff/wxe/fuBSAhIQE3Nzd2797N/v37mThxotmfl8g6CUoi26wtxJirf2sPM8/2L0zn2k09vT94Qmy8wuYVealZ1UHtksQzxowZQ6tWrdJ8bd68OU2bQ4cO0a5dO4oVK4ZGo6FLly6cOHECgLJly3Lv3j1mzZrFBx98AEBwcDClS5c2+3MRr0aCksgWaw0x5urfVsKSMJ0f9yWQnKzw5UovqlSUkGRp/P39OXz4cJqv/v37p2kTEhJCnTp1jLd1Oh0uLinLOXz77bd07dqVwMBAfvrpJ/R6PRUqVOA///kPycnJxMfH8/3335v1OYnskaAkssWaQ4yp+09lC2FJmM7ooW58vykfFcraq12KeA5vb2+KFy+e5svDwyNNG09PT8LCwoy3z58/T7n//S2WKFGCffv2sX//fm7cuMHp06dxdnamatWq2NnZ4eLiwoYN8mHEGkhQEtlirSHGXCEplbWHJWE6Wq2GQt52apchXkHr1q3ZsmULkZGRhIaGcubMGeMIU8OGDSlcuDDFihWjUaNGREVFsWnTJv773/8CcOrUKbkCzkpIUBLZYo0hxtwhKZWEJQFw7i8d127q1S5D5CAfHx8GDhzIgAEDmDNnDvPnz0er1XL9+nXu378PwIMHD9i7dy8VK1bEz8+PH374gbZt27JixQrmz5+v8jMQmSFjvsIi2GpISvVsmEnauCHHT2uZun/xak6c1fH+xCfUru7AxmVeapcjcpCfnx9+fn5pbo8dO5affvqJq1evkpiYyODBgylbtiwAy5cvV6tUkU0SlITqbD0kpZKwlDv9ejyRkVOjKF3SnsUzPNUuR5jYxo0b8fDwoGnTpmqXInKInHoTqsotISmVnIbLXX75NYERH0VRvow9W1bmJX8+ecm1dZ6enmg0GrXLEDlI/mqFanJbSEolYSl32P1LAmOmR1PV157Ny/OS10NeboWwRvKXK1SRW0NSKglLtm3XnngmfBpNneoOfLE0L+555KVWCGslf73C7HJ7SEolYck2bdsVx5S5T2lcz5G1S/KSx01eZoWwZvIXLMxKQlJaEpZsy8btccxcEkOLJo6sXuiJi7PMVRHC2klQEmZjCyHJGsOMhCXzWL0plvkrYnizhRPL53ji6CghSQhbIEFJmIW1h6T6tVL24rLWMCNhyXQURcF/bQz+a2Pp9KYTn33qgaODhCQhbIUEJWFythCSls1OWf/GmsOMhCXTWLE+ltWb4ni7ozPzp3lgby8hSQhbIkFJJREREUyePFntMkzOVkLS6GlRgPWHGQlLOa9pAycGvuPKzEnu2NlJSBLC1khQMrH4+Hj0+vT7O+3cuZPQ0FAVKjIfWwpJz/Zv7WFGwlLOqlXNgUkj8qDVSkgSwhZJUDKxhQsXsmHDBq5evWq8Lz4+ni1btjBy5EgVKzMtWw1Jqaw9zEhYyj6DQVG7BCGEGclebyZ0+/ZtSpUqxe7duwkKCuLWrVvUrVuXyMhI3nnnHV5//XW1SzQJtUOMufq39r3bZG+4rNMlKYz/JJoqFe15r5+b2uUIIcxARpRM6OjRo+zevZtHjx4xZcoUduzYwYMHDzh27BidO3dWuzyTsJQQY67+rX3kR0aWMi8xUWHU1CgOHEnESS79FyLXkKBkQm+++SZLly5l7Nix9O7dmx9++IHatWvz1VdfMXz4cCIiItQuMUdZWogxV//WHmYkLL1cfILC+xOfcOR3HTPG52FAT1e1SxJCmIkEJRO5fv06w4cP58CBA7i4uODq6opGo+HChQvkyZOHIkWKsHDhwkz3d+7cObp06ULPnj1Zu3at8f7g4GDat29PvXr1WLBggSmeSqZYaogxV//WHmYkLGUsJtbA0HFPOHEuiblT3HnHT0KSELmJBCUT2bJlC/fv32fjxo3MmjWL6Ohozp49y4kTJ/j000+5du0aI0aMyFRfsbGxTJ06lYCAALZv305oaCiBgYEAfPvtt7Rr145Dhw5x/vz5NJPGU0VHR3Pnzp00Xw8fPsyx52rpIcZc/Vt7mJGwlN7TGAODxz7h/IUkFs/woNtbLmqXJIQwM5nMbSLTpk0jJiaGI0eOUKxYMTp06MDDhw95/PgxJUuW5M6dOxQqVChTfR06dIh27dpRrFgxALp06cKJEyfw9fUlOTmZsmXL4unpSb58+Z67FMHmzZtZuXJljj6/VNYSYszVv7VPwP53/7lZZJSBQWOfEByix3+WB280d1a7JCGECmREyUTs7OyYM2cOY8aMYebMmdjZ2VGrVi2qVKnC3bt3eeutt1i6dGmm+goJCaFOnTrG2zqdDheXlE+2/fr1Y/78+bz77rs4Ojri6+ub7uf79+/P4cOH03z5+/u/8nO0thBjrv6tfeTn2f5zq0cRBvqNiOTqNT0r53lKSBIiF5OgZEKOjo706dOHGjVqMGjQICpWrEinTp3o378/AwcOpEKFCpnqx9PTk7CwMOPt8+fPU+5/b5C//PIL1apVo1u3boSGhvLgwYN0P+/h4UHx4sXTfHl7e7/Sc7PWEGOu/m0lLOVG4Q+T6Ts8klt3kvl8UV6aN3ZSuyQhhIokKJlR2bJl6du3LzVr1gRSTqFlRuvWrdmyZQuRkZGEhoZy5swZ4wjTjh07+Oyzz3jrrbd466232LNnj4mqT8uaQ4yp+09lC2Ept7kblkyfD55w/6GBLz7LS6N6jmqXJIRQmQQlK+Dj48PAgQMZMGAAc+bMYf78+Wi1Kf91Wq2W8PBwEhMTCQ0NxcPDwyw1WWuIMVdISmXtYSk3uXVHT58PIomMMrDRPy/1aklIEpYnt+wTakkkKJnZ3r172bx5c5Z/zs/Pjx9//JENGzbg4+ODn58fp06dYsyYMXTv3p06deoQERFB+/btTVB1etYYYswdklJJWLIOa7bEEZegsHlFXmpWdVC7HJHL5eZ9Qi2NXPVmRo8fP+b7779n6dKlnDx5kqNHj2b7k8HGjRvx8PBAo9GYLRyZkq2GpFTWfjVcbjBjnDtD+rhSuqS8LAr1LVy4kCJFitCiRQvKly8P/P8+oXPnzlW5utxFRpRMLD4+nsuXL6MoCvnz52fdunUEBgZy/Phxtm7dmu1+PT090WhsYxsFWw9JqWRkybI5OWkkJAmLkLpP6IEDB1izZg1vv/02CxYs4NNPP7XpfUItlQQlE+nTpw9Lly7l8uXLTJo0iRYtWhgvyS9Tpgzjx4/HzU021cwtISmVhCXLcfFKEhGRBrXLECKd3LhPqCWToGQiq1atwsXFhVOnTvHDDz+wd+9efvrpJ7755hv27dtHUlISefLkUbtMVeW2kJRKwpL6zv6po9/IJ8xY9FTtUoRIJ7ftE2rpJCiZiIeHB7Vr18bb25uHDx/i6upK4cKFKVOmDL179+b8+fM0btxY7TJVk1tDUioJS+o5cVbH4A+fUNhby7SxufvDirA8Ob1PqHh1ckLeRG7cuMHJkydp2bIlM2fORKfT4e7uToUKFbh69Sr+/v4sW7ZM7TJVkdtDUiqZ4G1+R44nMmpqFKVL2rPRPy/588lnRWFZnt0nVKvV4uzsnG6f0PXr16tdZq4iQclEfHx8GD16NHq9nsGDB1O9enUcHFIuOf77779ZtmxZpvd6syUSktKSsGQ+v/yawIcfR1OhnD0b/POS10NCkrA8OblPqMgZ8kphYvb29tSpU8cYkvbu3cvGjRsZNWoU58+fV7k687KFkGSNp8nkNBzs/iWBMdOjqVrJgc3LJSQJy5WT+4SKnCEjSmbWvn17m1j3KKusPSTVr5USdK115Cc3jyzt2hPP1HlPqVfLgTULPXFzlZAkLFvqPqEANWrUIDQ0lE6dOlGtWjVq1qzJDz/8oG6BuYy8YgiTs4WQtGy2J4BVj/zkxpGlr76LY8rcpzSu58jaxXklJIkcd+7cObp06ULPnj1Zu3at8f5r164xaNAgunfvzqZNm17a/kWyu0+oyBnyqqGCZcuW8fDhQ7XLMAtbCUmjp0UB1h9mclNY2vR1HJ8ujqFFE0dWL/TExdk2FmgV5vHw4UPu3LmT5is6OjpNm9jYWKZOnUpAQADbt28nNDSUwMBAABRFYfXq1Xz88cfs3bv3pe2F5ZKgZGYRERGcO3cOb29vtUsxOVsKSc/2b+1hJjeEpTWbY5m3PIY3WzixfI4njo4SkkTWjBkzhlatWqX5+vc+nYcOHaJdu3YUK1YMjUZDly5dOHHiBJAyCnTv3j1mzZrFBx988NL2L5PdfULFq5OgZEZRUVEMHz6ciRMnql2KydlqSEpl7WHGlsPSyvWxLP08lk5vOvHZpx44OkhIElnn7+/P4cOH03z1798/TZuQkBDq1KljvK3T6XBxcQHg22+/pWvXrgQGBvLTTz+h1+tf2P5FUvcJ7datGydPnmT+/Pk59CxFZkhQMqHExEROnjwJpIwkDRo0iEGDBlGtWjWVKzMttUOMufq39jBjq2GpbGk7enZ1YcF0D+ztJSSJ7PH29qZ48eJpvjw8PNK08fT0JCwszHj7/PnzlPvf31KJEiXYt28f+/fv58aNG5w+ffqF7f/NVPuEiqyToGRCGo2G2bNnc+/ePYYOHcqYMWM4e/YsDx48ULs0k7GUEGOu/q09zNhiWGrX0plPJ7ij1UpIEqbVunVrtmzZQmRkJKGhoZw5c8Y4YtSwYUMKFy5MsWLFaNSoEVFRUS9sn0r2CbU8sjyACZw8eZLLly9jMBi4c+cOb7/9Nq+//joXLlxg3759BAYGUrZsWfLly8eIESPULjfHWFqIMVf/1n5pf25eOkCIV+Hj48PAgQMZMGAA+fPnZ/78+Wi1Wq5fv46LiwuFCxfmwYMH7N27l65du2bY/lmrVq3iq6++Mu4TqtPpeOuttyhSpAh6vZ7//Oc/uX6fUHOTESUTcHJyokKFCpQtW5aSJUvi5eXFr7/+SqFChWjXrh0RERGULFkSX19ftUvNMZYaYszVv7WP/FjryJJerzBpVjR7DyWoXYrIpfz8/Pjxxx/ZsGEDPj4++Pn5cefOHZYsWUKXLl3o378/gwcPpmzZss9t/2+yT6jlkRElE6hdu7bx3w8ePKB06dJcvHiRL774gjlz5gAwYMAAlarLeZYeYszVv7WP/FjbyJIuSWH8jGgO/JpIWR87tcsRAoCNGzfi4eFB06ZNs/Xzsk+o5ZGgZGKNGzfGwcGB1157jVq1ajF//nxWrVqldlk5xlpCjLn6t/Yw8+/+LVViosLoaVEcOa7jo9F5GNDDVe2ShABSJni/Ctkn1PLIqTcTK1myJEWKFAGgbt26LF26lC+//FLlqnKGtYUYc/Vv7afJnu3fEsUnKLw/8QlHjuuYMV5CkrBNsk+o5ZCgZGbFihVj1KhRapfxyqw1xJirf1sJS5YmJtbA0HFPOHk+iXlT3XnHT0KSyB3at2/Pxo0b+eabb9JdKSdMS4KSCuzsrH8+hTWHGFP3n8oWwpIliX5qYPDYJ5y/kMTiGR74dXj5Qn1CCPGqJCiZ2d27d9UuIUdYa4gxV0hKZe1hyVJERhkYMOoJlwL1LJvtSYc2zmqXJITZ5aZ9Qi2JBCUzSk5OZtiwYWqXkSOsMcSYOySlkrD0ah5FGOg3IpKr1/UEzPekTTMntUsSwuxy0z6hlkaCkomk7jIdERFBQkICiYmJ2NnZ4ejoqHJllslWQ1IqCUvZE/4wmb7DI7l1J5nPF+WlWSMJSSL3yU37hFoiCUomoNfrmThxIm+//TaTJ09m69atxpEkJyd5of83Ww9JqSQsZc3dsGT6fPCE+w8NrF+al0b15EOGyB1y6z6hlkqCkgnY29uzZs0aevXqxYULF1izZg1BQUG0aNGCK1eu0KNHDwYNGsS0adO4deuW2uWqKreEpFQSljJvjv9TnkQb2LQsL3VrSkgSuUdu3CfUksmCkyYSEBDA06dPWblyJV988QVvvvkm9evXJyAggLFjx8p5ZnJfSEpl7YtSmsvsjzx48DAZ3/IOapcihFnk1n1CLZ0EJRMZPny48d+Ojo5Ur16d+Ph46tSpw6NHj3J9UMqtISmVhKWXy5dXS768Mugtco/UfUKTkpIoWbIkiqLw66+/Mn78eNq1a8eJEydo0aIFxYsXV7vUXEWCkgkZDAYMBgMzZ84kICCAOXPmsHDhQnQ6ndqlqSq3h6RUEpbSunpNT4lidjg7adQuRQhV5LZ9Qq2FBCUTmT17NrGxscTHx3P//n3atWtHUlISb7/9NpAyWa9///707t1b5UrNS0JSWhKWUvx5MYkh457QtoUTsz/yULscIVRn6/uEWhMJSiYybdo047/nz5/PkCFD+PHHHwkKCmLBggUqVqYeWwhJ2nLlrC7MWHpYOvunjqETovDOp+WDd93ULkcIi1CyZEnjv5/dJ3Ts2LEqVpU7yQQAM5g8eTL58+dn4MCBTJgwAYPBoHZJZmftIal+rZQJxdZ6tZqlXg13/IyOwR8+obC3li8D8lK0sPVv7yOEKdjKPqHWSIKSmRUoUACtNnf92m0hJC2b7Qlg1WHG0sLSkeOJDJv4hFIl7Nka4EUhbwlJQryILewTao1y1zu2MDtbCUmjp0UB1h9mLCUs/fJrAiM/iqJ8GXu2rMxL/nzyUiTEi9jKPqHWSF6dhMnYUkh6tn9rDzNqh6XdvyQwZno0VX3t2bw8L3k95GVIiBexpX1CrZG8QplAdHQ0Bw8e5OnTp2qXohpbDUmprD3MqBWWdu2JZ8Kn0dSp4cB6/7y455GXICGeJfuEWh656s0EnJ2duXv3LkOGDMHBwYEWLVrQunXrNFcx2DK1Q4y5+rf2q9XMfTXcV9/F8eniGJrUd2TlfE9cnGW9JCGelbpP6OPHj/Hy8qJ+/focP36cjRs3yj6hKpKgZAKOjo4MGDCAAQMG8PDhQ44ePcrs2bOJiIigRYsWtG3blrJly6pdpklYSogxV//WHmbMFZY2fR3HvOUxtGjiyPLZnjg6SkgS4t9S9wn97rvvWLhwIX/88QeOjo60aNGCJ0+e0KNHD/LkyUORIkUYOnRorvnwrTYJSibm7e1N9+7d6d69O0+fPuXIkSMsW7aMBw8e0LNnT7p06aJ2iTnG0kKMufq39jBj6v7XbI5l6eexvNnCicWfeODoICFJiIzIPqGWR4KSGbm7u9OpUyc6deqETqfj8ePHapeUYyw1xJirf2sPM6bs384OOr3pxLypHtjbS0gS4kVkn1DLIzMpVeLo6EiRIkXULiNHWHqIMVf/1j4B21T9D+njxsKPJSQJkVkGgwG9Xs/MmTMJDw9n0qRJdOjQgWLFiqldWq4kI0rilVhLiDFX/9Y88vO8/nOKRiMhSYjMkH1CLY8EJZFt1hZizNW/LYWlrEpOVpi7LIb2rZ2oU10uZxYiq2SfUMsjp95UkpyczLFjx9QuI9usNcSYq39rPU327/6zQq9XmDgrmq074zlrok2JhchNZJ9QyyBBSSVHjhxhxowZapeRbdYcYkzdfypbCEuZpUtS+PDjaPb8ksiHw9wY1t8tx+sRIjfLjfuEWgr5rZvYl19+Schz3nA2btxIp06dVKgoZ1hriDFXSEpl7WEpMxITFUZNieLAr4l8NDoP7/WTkCSEsB0SlEwsLi6OAQMG8J///IfFixdz9epVDh8+jE6nS3MZqLWxxhBj7pCUypbDUnyCwvsTn3DkuI5PJrgzoIerWR9fCCFMTYKSCc2dO5dDhw5Rrlw5tmzZQpMmTViwYAFTpkxh7dq1ODg4ZLqvc+fO0aVLF3r27MnatWvTfO/y5cv4+fmxYUPOXaVkbrYaklLZYliKiTUwdNwTTp5PYt5Ud3p1dTHL4wphTXQ6XZrbsbGxGbaVfUItkwQlE5o0aRKffPIJrq6ufPbZZ+TNmxc7Ozveffddli5dmul+YmNjmTp1KgEBAWzfvp3Q0FACAwMBiImJ4YMPPmDAgAH07dv3uT8fHR3NnTt30nw9fPgwR55jTrD1kJTKlsLS0xgDg8c+4fyFJBbP8MCvg4QkkTtl9CH26NGjdO/enffff5+OHTvy6NEjAMaNG0fHjh158803GTduXJq+nt0ntG/fvmzYsIFbt26Z9fmI9GR5ABO5f/8+O3fupEiRIrz33nsMGDCAEiVK0KdPH5o2bUq/fv1YsWIFI0eOfGlfhw4dol27dsbFxrp06cKJEyfw9fVl+/btvPXWW7Rp0ybDEarNmzezcuXKHH1+OSW3hKRU1r50AEBklIFBY58QHKJn2WxP2jSTzTqF7Xn48CF37txJc5+HhwceHh7G26kfYjds2EDRokWZPHkygYGB+Pr6cuHCBb766iscHR2ZO3culy5donnz5kRERLB79+7nPmZu3ifUkklQMpHjx4+zbt06atSogZubG3Xq1MHT05OxY8fSqVMnwsPDKVOmTKb6CgkJoX79+sbbOp0OF5eUT/D79++nWLFiLFq0iPDwcBYvXmz8Xqr+/fvTtWvXNPddvHiRMWPGvNqTfEW5LSSlsuaw9CjCwLujI7lxO5mA+Z40ayQhSdim570+jhgxIs2H2xd9iE1td/XqVa5cucKECRN4/Pgx7u7umXr83LRPqKWToGQipUqVws/Pj6+//hoAPz8//P39URSFypUrc/v2bTp06JCpvjw9PQkLCzPePn/+PE2bNgVAURSWL18OwIoVK/j9999p06ZNmp//96cgSBnxUlNuDUmprDEshT9MZsCoJ4SFJ7N2cV4a1pUFJYXt8vf3p1q1amnu+/fr6Is+xAI8evSISZMm4e/vj4ODA15eXowcORKdToeDgwNff/01vXr1emkttrxPqDWQOUomUqtWLapWrUqHDh1YsWIFCQkJrF+/HmdnZ1atWsXTp085evRopvpq3bo1W7ZsITIyktDQUM6cOUOdOnUA8PLyIigoCIPBwN9//20VewHl9pCUytrmLI3/NJrwhwa++ExCkrB93t7eFC9ePM3Xv4PS8z7Elvvf31piYiJjx45lypQplCxZEgCtVkvNmjVxdHREo9Hw/fffExcXl6W6bGmfUGshI0omYmdnR7du3Xj99dfRaDQ0adKEH3/8kZYtW9K+fXtq1KjB9OnTadas2Uv78vHxYeDAgQwYMID8+fMzf/5848Jj48aNY8qUKeh0Olq2bEnlypVN/dReiYSktKxpZGnmRHeinyrUqJL5qzWFsGWtW7dmxIgRtGnThoiICM6cOWM8ZTd9+nR69+5N3bp1je337NlDYmIiXbt2JTQ0NN0cKGGZNIqiKGoXkZtERkZib2+f6fPUGfHz82PixIm89tpr2fr5c+fO0bt3b7Zt25bmDzmzKjZ6kOWfsfaQFHSiILoVy0wyQVpbrpxJJ2Bnt3/nZStyvBYhLFlWXxu/++47Nm/eTP78+fn444/x8fHhypUr9OrVi8KFCwMpm0IPGjSITp06MXv2bE6fPo2HhweDBw/mzTffNPVTEq9IRpTMzMvLi5MnT1KlSpV0w7hZsXHjxlf6eXOz9pBUv1bKKIo1jPzkVP/3Ej3xMShotZocrUUIW+Ln54efn1+a2xMnTuTUqVM4Ozunaz9z5sxXerzk5GROnDhhnKcqTE/mKKlg1apVz/0DygpPT080Gut4A7OFkLRstieAVc0pepX+/4ktTM/Lg/Ffm/HieEKI9DZu3EiDBg1e+TU+I9a+T6g1kqBkZsuXL6dZs2Y4OuaOybC2EpJGT4sCLCvMmKr/v2OKMTSoL252Ot7uKAtJCpEVOfUh1lb3CbVGEpTMaPny5dy6dYvBgwerXYpZ2FJIerZ/Swgzpur//NOSDAvqjZd9LBt8N1OimF2OP74Q4uVsdZ9QayRByYSio6ONa2TMmTOHsLAw2rdvn27vH1tkqyEplS2GpZNRpfkg+B0KO0az3ncLRZyic/xxhRAvl5P7hIpXJ0HJBJKTk4mIiECn03H9+nXGjh2LoiiMGjWKzz//nO3bt3Px4kWbvTRU7RBjrv5tKSwdc3mdkVd7UtIpgi98t1DQMSbHH08IkTk5tU+oyBly1ZsJrF+/nitXrpCUlERcXBy//PILHh4ePHjwgLi4OFatWkWhQoUoWbKkxe7Bll2WEmLM1b8lXq2W1f73zvyFiSebUdHrMatLbcHTPiFHH0MIkXk5uU+oyBkSlExg6NChxn8vX76ckiVLsmXLFgoVKsTgwYNZtWoVa9asUbFC07C0EGOu/q05LO19XJVp1xpTrcBD1q0rgfO3xU2yjpMQInNycp9QkTPk1JuJ1a5dm1KlSvH111+j0+nYvn0706ZNU7usHGepIcZc/VvjabjvH9ZgyrUu1HK/xeqS63H+dqPJ6hdCZE7qPqFnz57l119/pVChQmn2CS1ZsmSm9wkVOUOCkok1adKEWrVq4ejoyKeffkqNGjXYv3+/2mXlKEsPMebq35rC0tfhdfnkRicaeoQSUGE7rnZJJq9fCPFyOblPqMgZEpTMrGfPnrz++utql5FjrCXEmKt/awhLm++/xrxb7WieN4hl5b/FWavP0f6FENmXuk/opEmTqFmzJnPmzOHMmTO0bNmSOXPmsGHDBn766Se1y8xVJCipoEKFCmqXkCOsLcSYq39LDkuKAjcT8vGG1z8sLrsTR23yC/sXQqjD29ubAgUK4OzsTI8ePfjwww+pWrUqrq6uLFmyRO3ychUJSiJbrDXEmKt/Sw1LGg1MK7WXeWW/w0FreGn/QgjL4OXlxaVLl4iOlvXNzE2CkhkZDAYmTJigdhk5wppDjKn7T2WpYUmrAXuNkqn+hRCWIyf2CRVZJ0HJjLRaLcHBwWqXkSOsNcSYKySlUjssGRRYeac5NxPy5fhjCyHMJ7ftE2pJJCiZgF6vZ9y4cXz11Vds2bKF3377jXXr1gGQJ08elavLGdYYYswdklKpFZaSFQ2f3niLdWFNORRRKccfVwhhHrltn1BLIwtOmoC9vT3jx49nyZIl/Pzzz5QuXZrHjx9z7do1rl27xsyZM/H09MTT05M33niDokWLql2y6mw1JKUy96KUuquhTL/Wmb0R1Xiv6G8MLHI8Rx9PCGE60dHRvPfee2zfvp05c+YQExNj3CdURpTMT4KSiZw9exZFURg2bBhHjx6lVatW1KtXj3v37tG+fXscHR1xdXWlYMGCapeqOlsPSanMFZaUPu8yeVgwByNKMqrYfxlUVEKSENYgOTmZqKgoDAaDcZ/Q/PnzM2rUKMaMGcPt27epXbs2Xl5eFC9eXO1ycw0JSiZSu3ZtOnTogMFgoHHjxlSuXBlnZ2fCw8Px8vKibNmyapdoEXJLSEpl6rAUH3ydCUNCOXqvJBNr/UFvewlJQliL3LxPqCWToGQiqWnfzs6OrVu34u/vz4YNG2jbtq3KlVmO3BaSUpkqLMUn2zMm5D+cii7G9Lpn6T33dZI2BsnVa0JYidy6T6ilk6BkInPnzjUOoR49epT33nuP0NBQIiMj0Wq12NvbU7duXRo2bKh2qarIrSEpVU6HpdhkR0Zd7cEfT0sys/SPdNZcIGljiMlGroQQplW7dm3c3Nz4+uuvmTNnjs3uE2oNJCiZyEcffYRGoyE+Pp6EhATq1atHeHg4Dx48oG3btsTExFC4cGG1y1RFbg9JqXIqLEXrnRge/A6XY4syt8wPtMt/OUf7F0KYX5MmTYz//vTTT/n666/Zv3+/XPmmAglKJqLRaABwcXFh7NixlClThr59+7Jr1y5ef/117OzsVK5QHRKS0nrVMPNE78KwoN5cjS/IonI7aeUVlKP9CyEsQ8+ePW1mHT5rI+somUGZMmWAlPDUvXt3CUlWHJIsaYXtx0luDA7sS2i8N/7lvk0Xkl61fyGEZbGVfUKtjQQlYRbWHpLq13IAsJjtSBQFRl3twe3EfKwo/zVN8754pEjCkhBCZI8EJWFythCSls32BLCYvds0GhhX4iCrKnzFa57Xc7x/IYTlsKV9Qq2RzFEysXPnznHs2DEURUGj0WAwGNBoNDg4OODt7Y2fn59Nr7RqKyFp9LQovgzwMvsK2y/qv7b7bZP2L4SwDLa0T6g1khElExszZgxVq1alXr16ABw9epQ6depQtGhRVq9eTUJCgsoVmo4thaRn+1dr77YneheT9i+EUFdu2CfUGsmIkom5urrSunVrNBoN3377LQsXLsTX15eff/6ZRYsW4eHhoXaJJmGrISmVuUeWgi5EMzSoN+8VPUbPQudyvH8ZWRJCfbJPqGWSoGQiq1at4unTp0RHR/Pzzz/TtGlTfHx88PX1BaBevXo2u8+b2iHGXP2bKywFNR7AoF3ROGkSec0jc/ORstK/hCUhLIfsE2p5JCiZSI0aNYiIiODcuXOsWLGCzZs3M2vWLOP3bfUgt5QQY67+TR02/vwrnuE7n+Kez5kv6h+g2P3HOdq/hCUhLIvsE2p5ZI6SiTRu3JiOHTvSpk0b3nnnHfz9/dm6dStHjx5VuzSTsbQQY67+TTXn5/zTkgwL6o2X9imbGvxAmZE9VL/aTghhWsWLF8fOzg4HBwe2bt2Kq6srmzZtom3btrl2DT61yYiSidWuXZvp06dz7949ChQoQEBAAKdPn8bJyYk333zTeCrO2llqiDFX/zk9MnMyqjRjQnpQ1PEJaytuxTsshqSNcRZxtZ0QIvN0Ol2WrmyWfUItjwQlE6tbty4zZszg8ePHJCYmUqRIEZKTkwEoVqyYytXlDEsPMebqP6fCxtEn5RkX0p3Szo9ZU3Er+R3icrT/jEhYEiLrzp07x+zZs3F2dqZly5YMHToUSLnCecWKFXh6evLgwQM2btxIgQIFCA0NZdKkSTg6OlK5cuV0G93KPqGWR4KSGbz22mtql2Ay1hJizNX/q4aNQxG+TLrmR0WXcFZX3IanfdrlI8wdloTIrR4+fMidO3fS3Ofh4ZHmSuXY2FimTp3Khg0bKFq0KJMnTyYwMBBfX18uXLjAV199haOjI3PnzuXSpUu8/vrrjBs3jlmzZlGtWjWWLl3K4cOHadWqlbFP2SfU8sgcJZFt1hZizNV/duf87H1clYmh3ajqdo/PK25NF5Jetf/MerZ/IXKrMWPG0KpVqzRfmzdvTtPm0KFDtGvXjmLFiqHRaOjSpQsnTpwAYOTIkTg6OnL16lWuXLlC48aN+eOPPyhfvjzVqlUDoHv37sb2zyP7hFoGGVES2WKtIcZc/Wd15Of7hzX49EZH6rjfZEX5r3G1e3FN5hpZchw5Okf7FcJa+Pv7GwNNqn+vexcSEkL9+vWNt3U6HS4u/78w7KNHj5g0aRL+/v44ODgQEhJCnTp1MmwvLJMEJTMwGAxcuXKFiIgIDAYDDg4OeHp6UqxYMfLmzat2edlizSHG1P2nymyY+eZBHebebE9Dj1CWlvsWFzt9jvafXTJHSeRm3t7eFC9e/IVtPD09CQsLM94+f/48TZs2BSAxMZGxY8cyZcoUSpYsaWwfGBhobH/u3DnKZWJUeN++fYSGhjJixIjsPBXxiuTUmwmEh4dz5MgRjh07BsD777/P3r17mTFjBlu2bOH8+fP07NmTuXPnqlxp9llriDFXSEr1stNkBgWOPqlAs7zBLC//TaZDUmb7F0KYTuvWrdmyZQuRkZGEhoZy5swZ44jR9OnT6d27N3Xr1jW2b9y4MXv37uX27duEh4eza9euNPOTUu3evdv4b0VRWLduHX369DH9ExLPJUHJBAIDA5kwYQJ79+4F4PHjx0yYMAFvb28CAwNp37493t7eLFy4UOVKs88aQ4y5Q1KqF4UZrQY+K7eDJWV34KhNzvH+hRCm4+Pjw8CBAxkwYABz5sxh/vz5aLVarly5wi+//IK/vz9t27alXbt27Ny5Ew8PD6ZNm8aYMWMYM2YM06ZNw93dPV2/M2fOJCYmBkhZqXvAgAFWe/bBFsipNxPYu3cvcXFxxiFYJycnPvvsM+7fv49Go2HPnj3ExcWpXKVlsdWQlOpFp8mctVkbRcpq/0II0/Hz88PPzy/N7YkTJ3Lq1CmcnZ3TtW/WrBnNmjV7YZ++vr6MGDGCoUOHotVquXr1KlOmTEFRFADc3d2ZMmVKzj4RkSEJSiawYMECXF1diYyMBODzzz9nz549DB06lIsXL+Lu7k7//v0xGAxotTKoZ+shKZUhJATdhg1s9ejLWyW+wfP2Pznev4QlIdS1ceNGPDw8jJf5Z0fx4sUxGAysX7+e06dPM3bsWGrUqEFycjLJycmUKFEiBysWLyNByUTi4+O5du0aAMeOHSMwMJAPP/yQhIQEBg6Uy65T5ZaQBCnzkeb/txzfPNCjDOlBP6cvZdFIIWyMp6fnK/dRu3ZtGjVqRLFixfjzzz9ZtGgRo0aNsuk1+SyZBCUTef311zEYDISHh/P9999z7Ngx9u3bh5ubG7/99huJiYnUrl2biRMnql2qanJTSEpWNMy80YEfHtViQOET9H16E4eBssK2ECK9t99+G0iZxtG+fXsGDhzIwYMHqVOnDg4ODipXl/tIUDKRS5cukS9fPnbs2MGaNWvo2bMny5cvZ8KECQwbNozXXnuN2NhYtctUTW4KSXpFw/RrndkbUY33iv7G+0WPooQi25EIIZ7r9OnThISEsH79ei5fvsz169dxcHDA3l7estUgE2RMZOLEiQwePJj27duTlJREtWrV0Gg0rF+/nrt376LRaMiTJ4/aZaoiN4WkJIOWiaHd2BtRjVHFD/NBsaOkTl0w5wrbcjWcENYjOjoaV1dX6tWrx61bt2jTpg1hYWF88sknapeWK0k8NbHUJeg/+OAD8ufPD6QsW59b5aaQlGiwY1xId45FVWBCiQP0KXwmXRvZ6FYI8W9t2rQBoFGjRoSHh1O9enXKly/Phg2y/6IaZETJTFJDUk5TFMW43oals4WQlNmRmfhke0Zd7cGxqApMLfXzc0NSKhlZEkI8T6FChahevToAVatW5bPPPlO5otxJgpKZPHr0KNs/e+7cObp06ULPnj1Zu3Ztmu/t3LmTs2fP8vPPP6PT6V61TJOx9pBUv1bKBMrMhI3YZEeGX32HM9GlmVn6R/5T8I+X9i9hSQghLJMEJTOZN29etiZvx8bGMnXqVAICAti+fTuhoaHGvYL0ej07duygSZMmfPvttxnuLB0dHc2dO3fSfD18+PCVnk9W2EJIWjY75ZLfl4WNaL0Tw4J689fTEswt8wOdC1zI9ONIWBJCCMsjQckMbty4wfnz53Fzc8vyzx46dIh27dpRrFgxNBoNXbp04cSJEwAcPnyYhg0bYmdnh06nyzAobd68mVatWqX5GjNmzKs8pUyzlZA0eloU8OKw8UTvwtCgvvwTV4RF5XbSLv/lLD+ehCUhhLAsEpRMLCIigvfee48PPvggWz8fEhJi3GQRQKfT4eLiAsC3335Lx44duXnzJkWLFs3w1Fv//v05fPhwmi9/f/9s1ZMVthSSnu3/eWFDr2h4L6g3ofHe+Jf7llZeQdl+XAlLQghhOeSqNxOKiYnhgw8+4IMPPuDnn39m1apVFC5cmJo1a/L666/TqFGjl/bh6elJWFiY8fb58+dp2rQper2eP/74g6FDh5KYmEhcXBxvvvkmR44cSdeHh4cHHh4eae67f//+qz/BF7DVkJTq31eT2YeEMKDwSbzs43jN8/orP75cDSeESLVnzx4ePXrEgAED2LFjB7GxsQwYMEDtsnINGVEykbi4OMaPH8/48ePp3LkzQUFB/PrrryxbtoyqVauydOlS3nnnnZf207p1a7Zs2UJkZCShoaGcOXOGOnXqYG9vzx9//MHhw4cZPHgw8+bNe25IUoPaIcZc/f97ZKZd/ss5EpIy6j+nyciSENbh0qVLnDp1CoDff/+d06dPq1xR7iIjSiYyb948PvroI0qVKgVAhQoV2LVrF926deOtt95i3759mVpPycfHh4EDBzJgwADy58/P/PnzjRvppm66GBoaSoMGDUz3ZLLAUkKMOfpPMNjjbOUjPzKyJITlmzx5svHfy5YtU7GS3ElGlExk8uTJxpAEKQuHrV+/nlGjRnHhwgW6d+9OixYtMtWXn58fP/74Ixs2bMDHxwc/Pz/jpwuAtm3b4uPjk9NPIcssKcSYuv/r8fnpfPEDDkdWtPqRHxlZEkKIjElQMpHUK9z8/f1ZvXo1nTt3Ztu2bUycOJHVq1dTsmTJbPe9cePGNCNITZo0wdXV9ZVrfhWWFGJM3X9wXEHeDexPksGOkk4RgPWHGQlLQlim1AWFL1y4wOPHj1WuJneSoGRiY8aMwcnJiVWrVuHl5UXx4sVZtmwZP/30U7YXiPT09DSedrMElhRiTN3/P7GFGRzYFwdNMhsqbaa86/+vR2XtYUbCkhCWJSkpiSFDhgDwww8/EB0drXJFuZMEJTMYOHAgrVq1IjQ0FABHR0fGjh2Lo6OjypW9OksKMabu/8+LSQwJ6oubXSIbfTfj4xyRro21hxkJS0KoR1EUxo0bh06n49ChQ1y/ft24/dWTJ0/w9vZWucLcSYKSmTRs2JCyZcuqXUaOsqQQY+r+HV3i6Dcygjz2CWz03UJx5ycZtrX2MPNs/0II80lISCAsLAxHR0cKFizIzp076d27N4sWLcLJyUnt8nItCUoiWywpxJi6f+c8T8lX/A4a+zg8qwQR5fTy0562EpaEEObj5OTE06dP0ev1FCpUyLiYcHR0NMnJyfzyyy9cvHiRoKAgoqKi1C4315CgJLLFUkKMOfp38YjGPf9jSla/gMZRzz8ar0w9ji2EJSGE+Vy/fp2qVavSu3dvDh06xJkzZ9iwYQMajYZ79+5x8OBBDh06xMGDB9mzZ4/a5eYaso6SyBZLCTHm6D8+2gPFoMXe3oC9QaGyEpnpx5N1kIQQmVW2bFkmTJjAunXrKFOmDO7u7rRu3ZqPPvqIoUOHUrFiRYtZMy83kRElkS2WEmLM0X9CjDt3/qlMn3aV+Dj5DyqStSFvax9ZEkKYT758+Xj06BENGzbknXfe4ddff2XWrFmUL1+eO3fuqF1eriRByYz27NnDpk2bANixY4fx38JyQ1KqhBh33m5VIcshKZWEJSFEZi1atIjbt29Tt25dhg8fzn//+19q1qzJzZs31S4tV5KgZEayX8/zWXpIyikSloQQmVWiRAnq1q1Lvnz5mDhxIi4uLpna9krkPJmjZEayX096uSUkpZI5S0KIrHJ3dwd4pR0dRPbJiJJQTW4LSalkZEkIkR2BgYHExsaqXUauI0HJzEJCQlizZg3btm1TuxRV5daQlErCkhAiqxYtWmRR21flFhKUzExRFLy9vVmzZo3apagmt4ekVBKWhBCZtWPHDipWrKj6Bui5kQQlEzt58mSaSzrLly9P586defz4MQaDQcXK1CEhKS0JS0KIl9m5cyf79u3jww8/VLuUXEmCkoklJyczduxYdDqd8b7Y2Fjy5MmDVpu7fv22EJKsMcxIWBLCcmRmjtHTp08ZO3YsAJs2bWLfvn0MHTo0171nWAr5rZuQwWCgZs2aNG7cmE2bNqEoCuHh4SxbtoymTZuqXZ5ZWXtIql/LAcBqw4yEJSFM49y5c3Tp0oWePXuydu3adN9fuXIlx48fN97u27cvHTt2pG3btixcuPC5fbq7u3P27FkWLVrEtWvXWLNmDWvWrOHgwYNERESgKIrJno9IT5YHMJEHDx4wbtw4EhIScHR0RKPRcPbsWVxcXChWrBgzZsxQu0SzsYWQtGy2J4BVX9ovSwcIkbNiY2OZOnUqGzZsoGjRokyePJnAwEB8fX2NbYKDg+nUqRMACQkJuLm58eWXXz63v4CAAE6dOkVSUhIRERGsX78eb29vrly5QlhYGBMnTsTe3p5SpUrx3XffmeU5CglKJlOwYEHjH8OcOXOYOnUqAGFhYXh4eODm5qZmeWZjKyFp9LQovgzwsvowI2FJiMx5+PBhui1DPDw88PDwMN4+dOgQ7dq1o1ixYgB06dKFEydOpAlKt2/fpkSJEkBKaCpdunSGjzl8+HCGDx8OwNy5cylcuDDbtm2jQ4cOVK1albVr1z531EqYlpx6M4OrV68a5yg5OTnx/vvvEx4ernJVpmdLIenZ/q39NJmchhPi5caMGUOrVq3SfG3evDlNm5CQEOrUqWO8rdPpcHFxMd5OSEjAycnJeEl/hQoV+M9//kNycjLx8fF8//33GT5+vXr1qFKlCrt27eLEiRMcO3aMkSNH5vCzFJkhI0omFBoayk8//URYWBiDBw/G29sbBwcHoqOjGTx4MLt371a7RJOx1ZCUytpHfmRkSYgX8/f3p1q1amnue3Y0CcDT05OwsDDj7fPnz6eZf3r16lXKPfNhxNnZmapVqwLg4uLChg0b6Nq163Mfv02bNsZ/r127liVLlhAcHJyuJmF6EpRMKF++fLzxxhu0atXKOPlOo9Gg1+vR6/UqV2c6aocYc/Vv7WFGwpIQGfP29qZ48eIvbNO6dWtGjBhBmzZtiIiI4MyZM4wZM8b4/aCgICpUqGC8vWnTJkqWLEnLli05depUllbZHjdunHGvUGFeEpRMyMvLCy8vL7XLMCtLCTHm6t/aw4yEJSGyz8fHh4EDBzJgwADy58/P/Pnz01zCHxwcTKtWrYy3/fz8mDZtGgsXLjS2z4rXXnstx2oXmSdBSeQYSwsx5urf2sOMhCUhss/Pzw8/P780tydOnMhrr71G8+bNqVy5svF7Hh4eLF++XI0yxSuQydwiR1hqiDFX/9Y+AVsmeAuRMzZu3EiDBg0AaNSoEe7u7q/cp8FgYPHixa/cj8geCUpm9OOPPzJhwoR0l5xaO0sPMebq39rDjIQlIV6dp6dnjm9cq9Vq+e2333K0T5F5EpRMLDk52fjvgIAAXFxcGDRoELNmzeKvv/5Sr7AcYi0hxlz9W3uYkbAkhHr0ej0LFizg6NGj7Nu3jz/++IMffvgBINesvWeJZI6Sib3xxhscOHAAe3t7IiMjjStyT5s2jTFjxvDrr7+qW+ArsLYQY67+rX1O0bP9CyHMx97e3ri1yeXLl6lUqRK3b9/mzp07hIWFsX79ejw9PcmbNy+1a9cmX758apecK8iIkonZ29tz5coVIGVpADs7O+zs7OjRo4fKlb0aaw0x5urf2kd+UvsXQphXfHw85cuXp127djx9+pSaNWtiZ2eHt7c3hQoVwtXVFQcHBxwdHdUuNdeQESUTuXLlCnPmzOHRo0dMnDgRR0dHnj59Snh4OIUKFaJMmTJWvbGhNYcYU/efyhZGloQQ5uXo6MioUaNwdXXl2LFjVK9eHS8vL3Q6HVWqVHnhFijCNGREyUSKFy/O+vXrOX/+PPv27ePHH3+kffv2xgXDXF1djduaWCNrDTHmCkmprH1kSQhhXqmn1JydnTl9+jSFChXiwIEDtGnTxqYXKrZkMqJkIs+7JLRdu3Zs2rSJ+Ph4/vzzzzQrtlobawwx5g5Jqax9ZEkIYT7r1q0jLi4ORVHYs2cPycnJnD59mn79+mFnZ8fFixepVKkSlSpVUrvUXEOCkhkVKFAAd3d37O3tGTNmDEWKFFG7JIthqyEplYQlIURmdOrUCb1eT0JCAn///TdOTk5ER0dz6tQp6tatS0xMDD4+PmqXmatIUDKjmjVrsnr1arXLsDi2HpJSSVgSQrxMoUKFjP8eNWoUNWrUYNSoUWzZsoVevXphZ2enYnW5k8xREqrKLSEplcxZEkJkVq1atdBqtbi6ujJs2DAJSSqRoKSCwMDALO0abatyW0hKJWFJCCGshwQlFSxatCjHl7i3Nrk1JKWSsCSEyKxTp06xfv16tcvItSQomdmOHTuoWLEirq6uapeimtweklJJWBJCPM+ZM2fS3P7ss89o3769StUICUpmtHPnTvbt28eHH36odimqkZCUloQlIcS/jRgxgsTERAAuXLhA27Zt5SppFUlQMqGnT58yduxYADZt2sS+ffsYOnQoWm3u/LXbQkiyxjAjYSl3S7gTROTx70i4E6R2KSKTSpcuzfTp07lx4wZOTk64uroSEBDAypUrWblypZyGM7Pc+Y5tJu7u7pw9e5ZFixZx7do11qxZw5o1azh48CARERFWvYVJVll7SKpfywHAasOMhKXcKeF2IA82zyTy6HbCtn0iYclKlClThsePH/P+++/TrVs37t+/T548eXB1dcXR0ZFSpUqpXWKuIusomUBAQACnTp0iKSmJiIgI1q9fj7e3N1euXCEsLIyJEydib29PqVKl+O6779Qu1+RsISQtm+0JYNXrIMk6S7mDotNhCArCcPkSyp/n8E7w4n6B+yjoib95GefiFdUuUbxE6dKlGTx4MGXLlmXPnj1s2bKF2bNnW/VuDtZMgpIJDB8+nOHDhwMwd+5cChcuzLZt2+jQoQNVq1Zl7dq1rF27VuUqzcNWQtLoaVF8GeBl9WFGwpJtUp5EknzpMobLlzBcDYakJHByRlOqBE8e/AVaDRo7e1xKVVG7VJEJQ4cOBeDs2bO89dZbuLu788UXXzB37lzs7eVt29zkN25i9erVw8PDg127djFx4kQiIyMZOXKk2mWZhS2FpGf7t/YwI2HJ+ikGA8rt2yRfvoTh0iWUu3cA0OTPj13DRmirVkVbthwae3vy3gnC6eZlXEpVkdEkK3H9+nVu3brFvHnz6Nu3LxcvXsTDw0NCkkrkt25ibdq0Mf577dq1LFmyhODgYKpVq6ZiVaZnqyEplbWHGQlL1kdJTMQQFIjh8mWS/7kM0dGg0aApXRr7Tp3RVqmCplDhdGu0ORevKAHJypw4cYLIyEhKlizJ/v37adCgAXv27GH16tW8//77apeX60hQMrNx48Zx6tQptcswKbVDjLn6t/YwI2HJuhguXyJp8yZwdkZbqRJ2VaqhrVwZjZub2qWJHNa7d28Abty4wb1792jUqBF16tRh2bJlEpRUIEFJBa+99praJZiMpYQYc/Vv7WFGwpJlUQwGlFu3SL58CbsqVdE+s0u8tnJlHIaPSDmlJnt+5Qo+Pj74/O8YaNiwIQ0bNlS3oFxKlgewATqdTu0SAMsLMebq39ov7ZelA9SlJCaS/PffJH21jcSPp6FbuoTkQwcx3LiRpp3G2QW7ChUlJAlhZjKiZEYGg4HPPvuM8ePHZ+nnzp07x+zZs3F2dqZly5bGKyKOHj3KihUr8PT05MGDB2zcuJECBQqYovSXstQQY67+rX3kR0aWzEuJiDBOxDZcvQrJenBx+f9TapUqySk1ISyEBCUz0mq1/Pbbb1kKSrGxsUydOpUNGzZQtGhRJk+eTGBgIL6+vly4cIGvvvoKR0dH5s6dy6VLl2jevLnpnkAGLD3EmKt/aw8zEpZMRzEYUG7e/P+r1MLuAaApWBC7pk3RVq2GtkwZGS0SwgJJUDIBvV7PkiVLeO2114iLi6NQoULcunWLLl264JbFT4mHDh2iXbt2FCtWDIAuXbpw4sQJfH19jcsMXL16lStXrjBhwoTn9hEdHU10dHSa+x4+fJiNZ5aetYQYc/Vv7WHm3/2LV6MkJKDftTPlKrWYGNBq0ZYpi12XrmirVEVbsKDaJQohXkKCkgnY29vTtm1bFi5cyOXLl6lUqRK3b9/mzp07hIWFsX79ejw9PcmbNy+1a9cmX758GfYVEhJC/fr1jbd1Oh0uLi7G248ePWLSpEn4+/vj4ODw3D42b97MypUrc+4J/o+1hRhz9W9LYUlkjZKUhObZv0MnJwy3b6Ot6Itd1apofSuhcXVVr0AhRJZJUDKR+Ph4ypcvT8mSJbl48SI1a9bEzs4Ob29vChUqhFarxcHBAUdHxxf24+npSVhYmPH2+fPnadq0KQCJiYmMHTuWKVOmULJkyQz76N+/P127dk1z38WLFxkzZky2n5+1hhhz9W8rYclx5Ogc7dfWKAYDyo0bxlNqmnxeOL73/5dvazQaHCdNTre2kRDCekhQMhFHR0dGjRqFq6srx44do3r16nh5eaHT6ahSpQqlS5fOVD+tW7dmxIgRtGnThoiICM6cOWMMONOnT6d3797UrVv3hX14eHjg4eGR5r779+9n63mlsuYQY+r+U9lCWBLpKQnxGK4EpoSjf/6B2P+dUitbFm3l9FuESEgSwrpJUDKR2rVrG/99+vRp2rRpw+7du2nTpg16vT7T/fj4+DBw4EAGDBhA/vz5mT9/PlqtlitXrvDLL79w4cIF/P390Wg0DBo0iO7du5vi6aRjrSHGXCEplbWHJZHC8OhRyhVqly9iCA2F5GRwdUVbqbKcUhPCxklQMpF169YRFxeHoijs2bOH5ORkTp8+Tb9+/bCzs+PixYtUqlSJSpUqvbQvPz8//Pz80tyeOHEip06dwtnZ2ZRPI0PWGGLMHZJSSViyPkpycppTakp4ygisplBh7Jq3wK5KFTQ+peUqNSFyAQlKJtKpUyf0ej0JCQn8/fffODk5ER0dzalTp6hbty4xMTHGFVezauPGjXh4eNjUkL6thqRUEpasS9IX6zD8cznllFq5ctg1aoy2ahW0BbzVLk1YmIzWuUu1cuVKatWqRePGjTPVXlgeCUomUqhQIeO/R40aRY0aNRg1ahRbtmyhV69e2L3CJ1FPT8+cKNFi2HpISiVhyfIYHj7EcDUY+0aN09xv16QpdvUboPX1RfPMVaYi93j48CF37txJc9+/53u+aJ27VMHBwXTq1CnT7YXlkaBkBrVq1QLA1dWVYcOGqVyNZcktISmVhCV1KcnJKNevkXz5MoZLF1EePABAW6Ei2mdWtberkn5StshdnndV8IgRI4zr18GL17lLdfv2bUqUKJHp9sLySFASqsltISmVhCXzUuLiMFz5JyUcXfkH4uLAzg5tufLYNX0dbeUqaUKSEAD+/v5Uq1YtzX3/vnr4ZevcJSQk4OTkZJwm8bL2wjJJUBKqyK0hKZWEJdMyPHiA4dJFDJcvY7gWCgYDuOVBW7Vqyl5qvr5oVLoQQlgHb29vihcv/sI2L1rnDlJ2TSj3zEbTL2svLJMEJWF2uT0kpZKwlLMM98NIPnkSw+VLKP/bokdTpCh2rVpjV6UqmlKl0Gi1KlcpbMmL1rkDCAoKokKFCpluLyyTBCVhVhKS0pKwlHOUBw9IPnYMbfny2DVrnnJKLX9+tcsSNiyjde5SBQcH06pVq0y3F5ZJgpIwG1sISdpy5awuzNhKWFIUBeVBOIZLl0i+fAn7Nm9gV6my8fvaypVxmjtPTqkJs8ponbvXXnuN5s2bU7ly5Re2F5ZPgpIwC2sPSfVrpWx0aq1hxlrDkqLXYwgNxXD5UsoptUePANAUK5ayOvYzNPYOYP/8jaGFMJfUde4AGjVqpHI1IidIUBImZwshadnslLWrrDnMWEtYUmJjMfxzOWVV7CtXICEB7O3Rlq+AXYuW2FWugiZfPrXLFOK5bG2dOyFBSZiYrYSk0dOi+DLAy+rDjCWGJUVRUMLvG0+pKdevg6KAhwd2NWuhrVoVbYWKaJyc1C5VCJELSVASJmNLIenZ/q09zFhSWFKePkW3dAnK48cAaIoXx+6NN1OuUitRQq5SE0KoToKSMAlbDUmprD3MqBGWlJgYlNgYtIUK//+defKgLVsOTcvW2FWtgiavl8nrEEKIrJCPayLHqR1izNX/s2FD+8yicjnF2vtXFAVDWBj6g7+Q6P8ZidOmoN+5M00bjUaDQ+8+2DdpIiFJCGGRZERJ5ChLCTHm6t/aR35yun9Fn4Qh5Jmr1Iyn1Epg92Zb7KpWzYmyhRDCbCQoiRxjaSHGXP1bW5jJ6f6Vp09J/udyynYhgVcgMREcHNBWqIhd6zYpV6nlzZujNQshhLlIUBI5wlJDjLn6t/Qwk9P9G+7dw3DpIsmXL6PcvJFylZqnJ3Z16qKtUhVthQpoHB1ztEYhhFCDBCXxyiw9xJirf0sLM6bsX//9dxiCg9CUKIl923Zoq1RFU7y4cZd0IYSwFRKUxCuxlhBjrv4tKcy8av+6dWvRnzqJcuMG9j16pglB9n7d0Li6opHF9YQQNk6uehPZZm0hxlz9W/vVaoaQEBJnfopy4wb67V+RfOUfePo0TRttkSISkoQQuYIEJZEt1hpizNW/tYclEhPR5MuH44RJOH0yE83/9q4SQojcRoKSyBZrDjGm7j+VtYclJSICrcw7EkLkchKURLZYa4gxV0hKZe1hSQghcjsJSiJbrDHEmDskpZKwJIQQ1kuCkrAIthqSUpkjLOnWrUVbpSq4uuZ4/0IIkVtJUBKqs/WQlMrkc4pu3ED/4w/g6AhyRZoQQuQICUpCVbklJKUyx2kyTf78OE2cJKfhhBAiB0hQEqrJbSEplclHlkJDZc6SEELkEAlKQhW5NSSlkgneQghhHSQoCbPL7SEp1bNhRlOmjEn7l7AkhBDZI0FJmJWEpLQMISEkzpmNcuuWyfqXsCSEENknQUmYjS2EJJOEjbg4cHPDYeBANGXL5nj3EpaEECL7JCgJs7D2kFS/lgOA6cJGVBTJv/2G48BBMmdJCCEsiAQlYXK2EJKWzU5Zl8iaJ2BLWBJCiKyToCRMylZC0uhpUYD1hxkJS0IIkTUSlITJ2FJIerZ/Q0gIuvVfoK1XH9zdc/xxJSwJIYTlkKAkTMLWQpJv3ms8+WUbeq0eAOXaNfTbv4KkJMiTJ8cfX8KSEEJYBglKIsfZYkjyr70K559PYW+wR0ExttUUK4bTR1OsMszk1rCUqFNe3sjGJCbmvucsRE6xV7sAYVtsLSQBVM8fTLJDPFF5IMEhAddEV9zjUk65PbtdSNLGDRhCQnK0nmfDjDX2b4mcHDX0HR5pM8dnZgSdKJjjtQiRW8iIksgxlvom8ar9X3hcAZ3Gjli3eJLtk3HSOaX5OWsf+cmNI0u2dHwK6xIbG6t2CSKLJCiJHGHtbxIv6j/wSRk+OjUGr2a9KBBZAEe9Y7qft/YwkxvDUk6y9uNfZN+5c+fo0qULPXv2ZO3atcb7k5KSmDZtGm+//Tb9+vXjzp07AIwbN46OHTvy5ptvMm7cOLXKFlkgQUm8Mmt/k8hM/4FPyuDV2O+5ISmVtYcZCUvZYwnH58soSbkvXL3qc3748CF37txJ8xUdHZ2mTWxsLFOnTiUgIIDt27cTGhpKYGAgAJs3b8bV1ZUdO3Ywffp0li5dCkBERAS7d+/mwIEDLFmy5JVqFOYhc5TEK7GGNwlz9m/tc4r+3b94MWs5PjUODiSMHgmkbMNjyjlpltK/87IVr/Q4Y8aMSXffiBEjGDlypPH2oUOHaNeuHcWKFQOgS5cunDhxAl9fX3bv3s22bdsAKF++PNHR0Tx+/Bh3EywpIkxLRpREtlnLm4S5+7f2kZ9n+xcZk+PTNvtP5e/vz+HDh9N89e/fP02bkJAQ6tSpY7yt0+lwcXFBp9Ph7OxMnmeWDrG3t8fLy4uRI0ei0+lQFIXt27ebrH6RcyQoiWyx1jcJc/Vv7W8Wqf2L55Pj07b7B/D29qZ48eJpvjw8PNK08fT0JCwszHj7/PnzlCtXDnt7+zSn6WJiYkhKSkKr1VKzZk0cHR3RaDR8//33xMXFmaR+kXMkKIlsseY3CXNNjLX2N4vcsFRAdsjxmTv6z4zWrVuzZcsWIiMjCQ0N5cyZM9SpUwetVkv16tXZtm0ber2epUuX0rlzZ/bs2cOuXbswGAxcvXrVOMFbWDYJSiJbrPVNwtxXD+WGN4vcRI7P3NX/y/j4+DBw4EAGDBjAnDlzmD9/PlptytvqpEmT+P333+ncuTNFixalY8eOvPHGG/z999+0a9eOKVOmMGPGDFxdXc1et8gamcwtssUa3yTUusTa2id4ixRyfObO/l/Gz88PPz+/NLcnTpzIa6+9xurVq9O0dXR0ZObMmWatT7w6GVESFsFW34RS2fona1snx2fu7j8rNm7cSIMGDVStQeQsCUpWIKsLmlkbW38TSpWb3ixsibUfn/VrOWSqnbUfn5Zy/Ht6eqLRaFR7fJHz5NSbhUtd0GzDhg0ULVqUyZMnExgYiK+vb5oFza5evcrSpUufu4BZdHR0uoXS7t69C2BcHC3LdJHZ+7l/qVTBnoHd8zBweAxXgvU50qep+j93zgtdxCs+7zNn0dy7h337Dujv3UO5c/fV+jNx/47nzuVQYRYmk8evNR2fL+r/3DlN5o5dKzs+X9S/JpvHbuprok6ny8nKhBXTKIoi20pbsB9//JHr168bFz87efIkV65cYeDAgXTu3Jlt27YZ1+oYMmQI69atS9fHihUrWLlypTnLFkIIqzZ9+nT69OmjdhnCAsiIkoULCQmhfv36xtsvW9Dsefr370/Xrl3T3BcTE8Pvv/9OlSpVcHDI3NC8EELYuqSkJEJDQ3nrrbfULkVYCAlKFu55C5o1bdo0wwXNnsfDwyPdQmkAvr6+OV+wEEJYuYYNG6pdgrAgMpnbwmV1QTMhhBBC5ByZo2QFvvvuOzZv3kz+/Pn5+OOP8fHxAVJ2oZ46dSq3bt3Cz8+PQYMGqVuoEEIIYWMkKFmhZxc0E0IIIYTpSFCyQlFRUXh4eMhaHUIIIYSJSVASQgghhMiATOYWQgghhMiABCUhhBBCiAxIUBJCCCGEyIAEJSGy4MyZM2qXIES2yLErRPZIUBIiCxYsWMDTp0/VLkOILJNjV4jskaAkRBb07dsXNzc3tcsQIsvk2BUieyQoCZEFhw8fJj4+Xu0yhMgyOXaFyB4JSkJkQaVKlXBwcFC7DCGyTI5dIbJHgpIQWRAZGYms0SqskRy7QmSPrMwtxHP8+eefnD9/Hjc3N+rWrUv58uXVLkmITJFjV4icJUFJiH+ZN28e7u7uVKtWjaioKI4fP86TJ0/4+OOPKVasmNrlCZEhOXaFyHkSlIT4F71ej729fZr7rl27xqpVq1i8eLFKVQnxcnLsCpHzJCgJ8S9Pnz5l1apVXLt2DXt7e/LmzUu3bt2oXbu22qUJIYQwMwlKQvzLsGHD6N+/Pw0bNgTgxo0bfPbZZ7Ru3ZpOnTqpXJ0QQghzkqvehPiXJ0+eGEMSgI+PD4sWLWL9+vUqViVExpKTk3nvvfe4ffu28bYQImdIUBLiXwoUKMCXX36Z5r5Tp06RL18+lSoS4uVCQ0OZPHkyoaGhrF69mpMnT9KhQwcWLlzIgwcP1C5PCKslp96E+JfY2FiWLFnC2bNnqVChAvfu3cPBwYG5c+dSvHhxtcsT4rmmTJnClClT+PLLL2ncuDF///03NWrU4M6dOyxbtoyWLVsyadIktcsUwurYv7yJELmLm5sbH3/8MTqdjrt37+Lp6WkcTbp58yalSpVSuUIh0oqIiCAmJoY8efLw5ptvEh4ezl9//UXhwoVp3749T5484fHjx2qXKYRVkqAkRAYcHR0pXbp0mvsGDRrErl278PT0VKkqIdI7cuQIjo6OQMpyABUrVsTDw4PZs2dTqlQp3nrrLTw8PFSuUgjrJHOUhMiCn3/+WUKSsDjdunWjYMGCAOTJk4dVq1YxcuRI3n77bb766ivu37+vcoVCWC8ZURIiC5ycnNQuQYjnKlmyJMOGDaN27dpMmDCBP//8kz59+uDp6UlAQAAVKlRQu0QhrJJM5hbiX/r160eRIkWoUKECFStWpEKFCsZP60JYI4PBgFYrJxCEyA4JSkL8i16v5/r16wQFBREcHExwcDDh4eF4eHgwdOhQGjdurHaJQgghzESCkhCZ9PTpU+zs7HB1dVW7FCGEEGYiQUkIIWzU48ePWbt2LZcuXaJ48eIsWLBA7ZKEsDoSlIQQwsacO3eOunXrGm+fPHmS999/n7/++ku9ooSwUjK7TwghbEhycjITJ05Mc1/JkiVJTExUqSIhrJsEJSGEsAFJSUkcPXoUOzs73Nzc0myMm5SURJ48eVSsTgjrJesoCZEJV65c4eeff2b48OG4uLioXY4Q6SQmJjJ//nzKli1LkSJF6NGjBzVr1kSr1XLhwgWGDBmidolCWCWZoyREJgQFBXHw4EGioqKYOnWq2uUI8VwdOnTAx8eHv/76ixIlSjB06FAKFy5MoUKFyJ8/v9rlCWGVJCgJkQWycJ+wZD/99BOdOnXiwIEDbNu2DUVR8PX1ZdKkSdjbywkEIbJDXvGFyAIJScKSderUCYCiRYuSL18+vvzyS2rVqsXYsWOJiIhQuTohrJOMKAkhhI2JjIxkz5499O3bF0hZTyk4OJiGDRuqXJkQ1keCkhBCCCFEBuQ8ghBCCCFEBiQoCfE/BoOBmTNnylwOYXNGjBhBQkKC2mUIYZUkKAnxPwaDgf379zNp0iTu3bvHF198wcWLFxk6dCjbtm2TNxphlWJjY/n1119xdnZWuxQhrJIEJSH+x97enmbNmvHRRx+xf/9+KleuzB9//EGPHj2Ii4uja9eubNu2Te0yhXiua9euce3aNQDi4+M5ceIEADqdDgcHBzVLE8KqSVAS4n/i4uKIj4+nTJky1KpVi3z58nHr1i3s7e0ZMmQI/fr14+LFi2qXKcRzXb16lf379wMpQWnBggVAykipRqNRszQhrJqsQCbE/3z//fcYDAYAnJycAAgPD2fRokX4+vrSuHFjevXqpWaJQmSoSpUqLFiwgN9//53k5GRiY2Pp27cvSUlJsiGuEK9AgpIQ/9O7d2/u378PQGBgIBcuXODTTz9l3bp1LFmyhHHjxqlcoRAZK168OB4eHnz11VfpvteyZUvi4+Nln0IhskGCkhDPcHNzY9iwYdSuXZt3332Xs2fP0rt3bwoVKsSKFSskLAmLVrZsWSIiIsiXLx/Hjh1j7969hIeHExMTQ2RkpAQlIbJBFpwUIpPi4+NxdnaW+R7C6owaNYrBgwdTvXp1tUsRwurIiJIQmSSfxoW1qlWrlnH+nRAia2RESQghhBAiAzKiJMRLPH78mC+++IKHDx9SrFgxxo4dq3ZJQgghzERGlITIwM2bNylVqhSQshbN7t27+fTTT/njjz9UrkwIIYS5yIKTQjxHcnIyQ4YMMd7WarXUrVuX+Ph4FasSQghhbhKUhHiGoihcunQJOzs77Ozs0nzPYDDIflnC6ly5coXFixdLyBcimyQoCfGMqKgopk2bBkC5cuUYMWIEGzduZMuWLcyfPx8/Pz+VKxQia7RaLc7Oznz22WdqlyKEVZI5SkL8S5s2bXB3dyc4OJiqVavSr18/HBwcKFSokKxDI6yWwWBAq5XPxkJklfzVCPEvvXr1YufOncydO5ekpCTWrl3L3bt3JSQJqyYhSYjskb8cIf5l4MCBaLVaSpUqRenSpdm1axd6vZ6PPvpINhcVQohcRtZREiIDRYsWpVixYtjZ2TF48GBCQ0M5fvw4LVu2VLs0IZ5Lr9ezfft2jh8/jpubG2XLlqVjx46UKFFC7dKEsFoyR0kIIWzEuHHjaNiwId26dUOn03Hu3DnWrl3LkCFDaNKkidrlCWGVZERJCCFsxP379+nevTsATk5ONG7cmOrVq/Puu+9KUBIim2SOkhBC2Ah7e3uOHDmS5r5bt27h4OCgUkVCWD859SZEFqxbty7Nit1CWJKHDx8ybdo04uLiqF27Nnfv3iUoKIj58+dTpUoVtcsTwirJqTch/iUwMBBfX99099+4cYPg4GAVKhIic7y9vfn888+5f/8+ISEhtG7dmipVqqDVaomJiSFPnjxqlyiE1ZERJSGekZiYSIMGDZg/fz6rV6/Gw8ODqKgoPvnkE37//Xfatm1LhQoV1C5TiCxr2bIl33//PZ6enmqXIoRVkaAkxL907NgRd3d3qlatSocOHbh//z41a9YkMTGRkiVLql2eENly//59ChcurHYZQlgdmcwtxDMOHjyIm5sbixYt4tatW9jb2xMZGcn48ePZv3+/2uUJkW0SkoTIHhlREuIZBw4c4PTp00RGRnL69Gnat29PREQEXl5e3Lt3j/bt29OxY0e1yxTiucaPH0/JkiWpUKECFStWxMfHB41Go3ZZQlg1CUpCPEdoaCi3b9+mQYMGuLi4cOPGDVxcXJg5cyYBAQFqlyfEc927d4/g4GCCgoIIDg7mxo0b2NvbU6ZMGd5++21q166tdolCWB0JSkJkQXR0NB4eHmqXIUSmJSUlERoaire3N/nz51e7HCGsjgQlIYQQQogMyGRuIYQQQogMSFAS4l8++OCDDL/XrVs3M1YiRNbIsStEzpOVuYX4l//+97989NFHz/3ekydPzFuMEFkgx64QOU/mKAnxL3fv3s3we3Z2drIejbBYcuwKkfMkKAkhhBBCZEDmKAkhhBBCZECCkhBCCCFEBiQoCSGEEEJkQIKSEEIIIUQG/g91Diy0IIAEEwAAAABJRU5ErkJggg==\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n 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\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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376NChA/z8/FId+/r1a7i4uGDUqFGwt7dH8+bNsXPnTjx48AB9+/ZFQkJCqsc0bNgQjo6OSExMhIeHB2xtbdGiRQsUKVIEarUazZo10x974sQJDBgwAJcvX0a7du0wevRo9O7dGy9fvkwVqFhbW6dYSdS3b1/cuXMHN27cwOzZs/WnufTu3Rv+/v4oXLgw1qxZAyBl6BUcHIxSpUohIiICAwYM+OhpTx8GWSEhISnChbTuL1q0KGxtbVG3bl0oFAoIgoCjR48iKCgIL1++RLdu3eDr64vdu3cjMDAwxSqRkydPon379ql/WURERNnAAIOIiD7Z6dOn0a1bN1StWhVLlizBpUuXoFKpcPbsWYwdOzbFlQyAf7/lnTVrFjZu3Ahzc3NcunQJjx8/xsiRI/Hdd9+laKKuX78OLy8vKBQK/PnnnwgODkZERAQSExMRERHx0dNIChQogCdPnuDgwYPo0KED2rVrh0uXLsHX1zfVsXFxcUhISEBwcDAqVaqEqlWrokCBAnjw4AGmTp2KGTNmpHqMUqnEq1evoFarIZFIULVqVWzatAlmZmb6pvO/tFot7OzscPToUbRs2RJv376Fq6ur/ptu4H14sXHjRtja2sLb2xu7du2Cs7MzfvjhB6jVaoSEhODkyZNo2bJlirE1Gg0OHjyIoKAgAO+vhvKxzTSB983lkiVLEBUVBX9/f/1pJEqlEo8fP/7oKQEAEBoaiqCgIJQuXRpffvklFAoFJk+ejKdPn0Imk+Grr77Chg0b0KFDhxRzodPpsGTJEkybNg1xcXG4ePEimjdvjlmzZmH79u36MCooKAiOjo5o0KABhgwZgidPnqBr166wt7dH2bJlERoairlz56Y61ePEiRMoXrw4tFotSpYsCX9//zQb6P79++v3cfDz88OUKVMwe/ZslC1bFtu3b09xakZy3b6+vti0aRMqVKiAAgUK4MKFC3j79i1GjhwJS0tL/T4eSUlJuHbtGr755hsEBwfDx8cHMpkMBw4cwIIFC/DZZ5+lGNvV1VUfOiUTBEG/n0RoaKh+bwuZTIY2bdror/KSfPndoKAgFC5cGAqFAnFxcXj48GGaIYybm1uKvWP8/f1RvXr1FPd/eGWWv/76C9WrV0dkZGSKceLj4/Hy5UtYW1vrf78SiQRFihRJsUnrmTNnUqy+IiIi+hQMMIiI6JPVr18fISEhOHfuHOrWrYuRI0eidu3a2Lp1Kw4dOoSxY8emOL5bt26wsbGBlZUVGjZsiKZNm6JMmTIoVqwYfHx88NNPP6VYLeDh4YHFixfjs88+gyAIsLe3R0hICIoXL44ePXrAwcHho7UJgoDff/8dLVq0wLlz5+Dj44Ndu3bpG79kJ06cQGxsLPr06YPPPvsMCQkJcHZ2ho+PD44ePYq3b9+mGnvgwIGwtbUFANSuXRsAULx4cajV6jRXYEgkEqjValhbW+PcuXPw9/eHt7c3LCwsoFQq9a9ZJpOhb9++WLRoEdauXQt3d3fUqlULtWvXhkKhgIeHB2bMmIEXL16keB0SiQTR0dFo3bo1ACA6OhpxcXFQqVSIjIxMFUi0bNkSP//8Mxo3bqw/teOff/6Bm5sb+vTp89HwQ6VSYcOGDZg8eTIOHz4MLy8vTJ06FT4+Phg5ciTc3NxQvnx5ODg46EMZ4P3pCKdOnULr1q0xe/ZsXLhwAaGhoXB1dYWvry8ePnyIo0ePAgAKFy6MkSNH6pvxXbt2Yc+ePWjRogVevHgBV1dXNGnSBLdu3dKPf/PmTfzzzz+4desWvL299Q19RmbNmgUbGxvEx8enuQklABw9ehT37t3DqlWr9GMmJCTgwIEDmDZtGp48eaJv8jUaDdq1awe5XI5p06ahYcOG+OKLL9C0aVMEBATgxYsXKd4fgiDAxcUF58+f18/vh+Li4uDl5YXo6GjExsZi165daNCgATQajf73v2LFCnz11VcA3p/S9ddff6FkyZKpXkf79u2xevVqvH37FlevXsXff/+NmjVr6u9v164dduzYgadPnyI0NBQHDx6Ep6cn5s+fjx07dkCj0eDy5csIDAxEqVKlUK1aNZw6dQqhoaG4desWLl68iIoVKwJ4v1omJiYGTk5OGf4OiIiIMiPnrj1HRET51vXr1xEcHAxvb2/cu3cPXl5esLOzQ2xsLGxsbFLsy5AseSXCgwcPcOzYMaxfvx7x8fH4/vvvMXz48BSXXIyNjcXmzZthaWmJrVu3olWrVli/fj0WLlyI5cuXo2nTpujXrx/c3d1TPU+LFi1QsmRJCIKA0NBQLFy4EKVLl4a1tXWK40qUKIFjx47p91awsLDA999/DwC4cOECPDw8Uo3dtm3bVLedPXsWz549S9WEAoCdnV2KzUyB96cyJG+emXylDIlEAhsbG+zfvx9Vq1bF/fv34enpiStXruDEiRMwMzPT71tQo0YNjBo1CsD7fRdatGiBGzdu4Ny5c3j+/Dnat2+Pd+/eoX79+pDL5RgyZAjGjRsH4P2eCZs2bYKzszM2bNiA7t27Y+nSpfjpp5+wYMECtGzZEgMGDECpUqVS1CyXyzFz5kzExcVh9+7d+oYVeL9S4cP9Lz4UFxeHli1bolChQmjfvj2GDBmCESNGoGzZsgCA6dOn6491cHCARqOBSqVClSpVoFKpcOjQIfj7+6N06dLw8fGBVCrVbw754MEDnDx5Uh8wrF69Gn/++Sf+/PNPtGrVKs16lEolVCoV/vrrLzx9+hQ3btxASEgIGjRoAKlUioIFC2L//v2QyWTo2LEjateuneIqH1qtFl5eXujVqxeOHDmC4OBgAIC5uTmqVauGsmXL4sSJE3j8+DGGDRsGmUyGM2fOwMvLC3v27MHgwYPRrl07mJmZYfjw4fj5558xf/78VFfhqVq1KurUqYNWrVpBpVKhU6dO6Nu3LyZOnIjQ0FB8++23sLW1Ra1atQAAz58/x+DBg7Fly5ZUe080aNAAHh4eaNKkCezt7bF69WpIJBKsXLkSzs7O6NixI3r16oUOHTrAwsICP//8M2xsbDBp0iQMGTIEP/30EyQSCYYNG6b/vY0ePRp9+vSBhYUFZsyYof9v659//kmx1wcREdGnEnRpfUVERESUBYGBgShfvrz+m+mwsDAcPnwYly5dQkhICNRqNSpXrgxvb2994+3r64tatWrB398fgwcP1i/Zj42NxdKlS/H69WusWLECwPsl+RKJRP/teIcOHfTf1L979w4XL16EnZ1dqitffKrNmzcjKioKFy5cwE8//YQyZcpk+JioqCgMHz4ccrkc27Zty/D4uLg4rF69Gtu3b8fu3btThDCPHj3CpEmTUK1aNUybNi3DsXQ6HR4/foy4uDjcvHkT5cqV0+9fYGFhAY1Go98MFXj/Tb9MJoNEIsG7d+/wzTffYOfOnQDe/w4vXbqEMmXKpAiTMhIREYG5c+fC29s71X2nT59GkyZN9L/HSZMmYezYsWmuFADer2TYvXs3+vTpo5+r3bt34/79+5BIJPjmm2/04UpERAQKFiyY4vHPnj3DqlWr8M8//6BYsWIoV64cJkyYoF9ZknyJWolEghIlSqBo0aIpVp2oVKoUKzhevHiBmTNnokSJEpgxYwYOHjyIrVu3wsbGBg8fPsQXX3yB8ePHAwDmzZuH8uXLo1u3bjh//jz8/Pzw3XffQSaTQavVYsuWLdixYweGDh2KHj16ZHp+M+PJkyfo27cvdu3ahRIlSqR5TGRkJKytrfWvb+rUqXBzc8PAgQMBvH8fJ1/CNplOp0N4eDjs7OwytbIlNjYWz549SzNYJCIiyg4GGEREZHICAwNRpUoVgz+PTqfD+fPnoVarU1xhIiNKpRL37t1L80oWWaVWqyGVStNcxZLTcmJe4+LicO/evTRXrIhFo9EgJCQEMTExcHd3T7X6Jqu0Wm2qPU5CQ0MRFxeXI79zIiIiShsDDCIiIiIiIiIyetzEk4iIiIiIiIiMHgMMIiIiIiIiIjJ6DDCIiIiIiIiIyOgxwCAiIiIiIiIio8cAg4iIiIiIiIiMHgMMIiIiIiIiIjJ6DDCIiIiIiIiIyOgxwCAiIiIiIiIio8cAg4iIiIiIiIiMHgMMIiIiIiIiIjJ6DDCIiIiIiIiIyOgxwCAiIiIiIiIio8cAg4iIiIiIiIiMHgMMIiIiIiIiIjJ6DDCIiIiIiIiIyOgxwCAiIiIiIiIio8cAg4iIiIiIiIiMHgMMIiIiIiIiIjJ6DDCIiIiIiIiIyOgxwCAiIiIiIiIio8cAg4jyrJiYGLFLoCx6/fp1nvy95cXXRFnH94Fp4ecREZHxYYBBREZPrVbr/33v3r24dOkSzp49+9Hjjx8/jufPn2Pfvn24dOkS3r59m+nnUiqV6d4fFBSEI0eOZHo8yprDhw/j+vXrmT7+3r17mT42MTERWq02O2VlG9+LeVNufiZlRU6+Z+7cuYO9e/dCq9WmeL35CT+PsofvQyIyJAYYRPmUUqkzqnGjo6Px3Xff4e7du6numz59OgDgwIEDWLFiBRYuXIi5c+ciODg41bEBAQE4ePAgli1bhj///BP//PMPIiMj03zOY8eO4e+//wYALF26FJGRkRg2bFi6dep0Ojx69ChTr2ndunUICQnJ1LFPnjxBaGhopo7NSbqkJFHHValUePz4MaKiovDq1SscPnwYa9asSbcZ/NC2bdvw7NmzVLcPHTpUP8aKFSvwzTffYPjw4Vi2bFmK45RKJc6dO4cDBw4gLCwMUVFRGDRoUKaeG3jfyN6/fx+xsbGp7uN70bSJ8Zm0bds2AMCrV6/g7e2NWbNmoVu3bjhw4ECqYw31nnn9+jVGjBiB+fPnY9myZRgwYABGjhyZ6822GPh59O/rAPg+JCLjJBO7ACISh5mZgPINwrL0mDo15Fg21w5jp0Xh8o20G9R75wtnqx5fX188e/YMI0eOxJIlS1C8eHEULvx+rOQ/sjp37oyAgAAEBwdj69atKFKkSIoxbt68iWXLlqFly5aoUqUKvLy88OLFC7i6uqb5nAkJCfpvitzd3TF48GC0a9cu3TpfvHiB4OBg7Nu3D82aNYOjo+NHj7W2toa3tzdsbW3Rp08fVK5cOcX9fn5+cHZ2hqurK549e4Zr165h9OjRSEhIgIWFRfoTlkMEuRyJY0dn+/GSsmUh/2oQknw3QftBg2y+bEWmHt+9e3dUqFABhQsXRvfu3ZGYmIhSpUrB2dk51bGJiYmIiIhAiRIl9LfJ5XKEhYWhZMmS+tuioqIwatQoVKtWDQDwzTffQBAEaLXaVH9YS6VSvHnzBps3b0alSpWwZ88eFClSBFu2bMGAAQNS1TBz5kxMnToVd+7cwYoVK3Dt2jVUrVoVQ4YMQZMmTfTH8b1o+sT4TDp9+jT69esHGxsb2NvbY//+/fD19U3zeEO9Z6ytrbFmzRps2LABXl5eqFChAk6fPg2JJO9/58XPo/f4PiQiY8YAg4gyJTPhRVZcv34ds2fPRuvWrREZGYk3b97g3r17KFq0KP7++2/cuHEDX331FX799VfcuXMHgwcPRteuXXH37l3odDr0798fv/zyCywtLfVj6nQ6TJw4EVevXoVSqYSDgwPu3buHM2fOoFmzZime/9atWzh+/DjCw8Mxbtw4+Pj44OHDh6hatWqa9UZERMDHxwcBAQGIjo5GgwYNYGZmlu5rNDc3h0QigaenJ8qVK5fq/rdv3+LRo0e4e/cu1Go1BEHA+vXr8fTpU8yePTvrkyoCbUgIknw3pRliZIaNjQ0CAgKwa9cunDp1CmPHjsX169cxbtw4tGnTBnXr1kXVqlWxatUqnD17Fubm5qhYsSLu3LmDly9fIiwsDH/88Qd69eqFUaNGAQD+/vtvPHjwANHR0VCpVIiMjMSzZ88wfvx4JP1nZcitW7dQqFAh2NnZITw8HC4uLujXrx+OHDmCSZMmYfTo0XBycgIAxMbG4ttvv4VCoUD16tWxceNGTJ8+HcOGDUOpUqVSjMv3Ytb0G/nxpexd2puja4eUIUp6x39om499pmsQ+zPpwYMHeP36NUaNGoWVK1fC1tYWn332Gdzc3FLVasj3zL59+xAXF4ctW7bAx8cHX3zxBerXr4+kpCTI5fJMz2d2KFcs++h90jp1IatbL9PHf8hs9NhMHcfPI74Picj4McYkogzldHgBAB4eHihfvjw6d+6Mjh074unTp9DpdChXrhwGDhyIr776CgBQuHBhhIeHw8fHB0qlEhUrVkSZMmVgb2+folEAgGrVqkEQBABAqVKlYGNjg5o1a6JAgQKpnr9AgQJISEjAsGHD0KxZM8yYMQOOjo5pHgsABQsWxKhRo5CYmAgrKyu8ffsWtra26b5GQRDQvHlztGzZMs0/0nQ6HU6ePImEhASYmZlhx44diIuLw4wZMzIxg8bjwxBDUrZsph+n0WiwcOFCVK1aFQEBAXB2dsbNmzfx8uVLDBw4EGXKlMHWrVsxYcIEPHz4EMOGDUP16tVhY2ODxYsX49ChQ+jbty9WrVqlbxaA9xvUmZmZITw8HAcPHkRISAhevHgB4P2cfyggIACvX79GzZo14e/vD6VSiX79+uHq1av45ptvsHr1ahw6dAiJiYno2bMn1q9fDwCIj4+HVquFg4MDfH19U51mwPei6RH7M+ny5cuIiorSf9O+fft22NjY4OHDh6mONeR7platWlCpVBg4cCAWLVqELl264Pz58+jfvz8ePHiQ2ek0Ofw8eo/vQyIydlyBQUTpMkR4kezKlSvYsWMHJk+ejOLFi+Pdu3epvg2KjY2FVCrF4cOH4eLigu3bt6NQoULo379/mmNqNBoolUosX74cDg4OsLW1xaNHj1C1atUUS0+tra1x584dbN26FXXq1AEAWFhYfPTcXa1Wi0mTJqF169awtrbG06dPsXv3bvTs2TPV89+8eROhoaHYunUrXrx4gXXr1kEmk6Ft27Yp/rAFgE6dOqF79+4IDAxE//798fXXX2d1Go3Cf1diZIYgCJg3bx6srKwglUoRFxeH48ePo127doiOjkb9+vXRqlUrAMCYMWOwf/9+REdHo2nTpli/fj2qVq0KtVqd6pxojUYDQRCgUCjQqVMntG3bFhMnTgSQcvNFjUaDEydOoEWLFrh37x4qV66MXr16oWTJkmjZsiW2bNmCn376SX/8okWL4OfnBwC4f/8+wsLCIJfL8fr16zRDAb4XMy8rKyWyc3xmifmZ1LNnT5w4cQIPHz7E48eP0bhxY5w/fx5OTk747rvvMHHiRNSqVQuA4d4zT548wdGjR2FlZYU//vgDUVFRePnyJebOnZthSJYTMrtSIrvHp4efR+/xfUhExo4BBhF9lCHDixMnTmDhwoX4/vvvUbJkScTExCApKQmbN2/Gr7/+ig0bNkCn0+HJkyeoU6cOypUrhxs3biA6OhpPnjxBYmIizp49iwkTJujPnz1z5gxOnTqFyMhI3Lp1S/9cBQoUQKlSpVCzZk39bZs3b0b58uUxd+5chIaG4syZM3j9+jW+/vprqFQquLm5pVg67+3tjbp168LJyQmbNm3CoEGDcOzYMUilUnTv3l1/nCAI+Pbbb9GwYUNMmzYNlStXhqWlJX7//Xd4e3unahrlcjnevXsHc3NzxMXF6W+Pj4/HmzdvULp06Rydd0P6MMTIjORzyBMTE1G2bFkULVoUsbGxuHDhAn777TfUrFkThQoV0h/bqVMnhIWFQRAEjBkzBrdv34ZWq4VUKk1di1YLCwsLHDx4EKdPn05xnnqyc+fOoU6dOrh//z4uX76Mxo0bY8aMGYiPj8eDBw9QvHjxFMcrlUoEBQXB398fxYsXx927d2FhYYEuXbrAxcUlxbF8L5oesT+Tkr/tFwQB58+fx08//YTPP/8cHTp0QKVKlbBt2zZ942io94yTkxN69eqFq1evonHjxjhz5gwqVqyIOXPmAABat26tb+LzGn4evcf3IREZOwYYRJQmQ4YXAFCmTBmsXr0acXFxKFKkCNq3b4/Hjx/Dy8tLv4O6IAgYPHgwLl++DIVCgWrVqmH//v0oW7Ysvv76a9SvXz/FmM2aNUOzZs2wZcsW3L17FyqVCiVKlMDQoUPh4OCQ4tjBgwfr/1iNiIhA+/btcefOHWzZsiVVrX/++SecnZ0RHx+PhQsXws7ODoULF4a3tze8vb0xceJEfPPNN3BxcYFEIkGDBg1SnYecvAT4QxqNBufPn0dQUBDGjBmDkydPYsiQIShQoABevHiB/fv3Y8qUKTk15bkiOcRQZOKb0ePHj0OlUmHOnDkoV64crl69iunTp6N58+ZYsmQJYmJi9Me2bNkS165dQ0JCAlq1agVLS0vUrl0bx44dS7UMWxAEqNVqNGrUCJaWlnB0dNRvPvfhudNFihTBxIkTYWFhgU6dOqFhw4a4cOECFi9ejOPHj6NOnTp48+aNvhnV6XQ4e/YsfvrpJ7x+/Rrh4eEoWbIkNBpNqtfG96LpEfMzSa1WY8eOHVixYgUKFSqETp06QRAE/Xvb2dlZf+UTwHDvmXv37uHvv/+Gq6srqlatiitXrmDWrFkwNzdHfHx8mu/1vIKfR3wfEpFpYIBBlE8plbpMXTEkq0u1lUodzMyEDI+LjY2FWq3G5MmTcfz4cRQsWBBl/79/grW1dYpjdTodKlSogEWLFqFr167o378/5syZA5VKhaZNm+qPe/XqFXbt2oUqVapgwoQJeP78OQoVKoRu3bqhQoUK6NmzJxo3bgzg/WZtcXFxsLOzQ/v27QEAdnZ2adb64Tc9BQsWREBAADw8PAAAEydORFhYWIo/qOrUqYPAwEA4OjoiPDwc586dw7Vr11I1gGq1GklJSZg8eTIUCgUGDhyI7t27IyYmBjqdDr17985wHj+VLikp01cMyeq4QgYbrVWvXh1dunTRL1+uWbMmatWqBY1Gg3v37uG7777TH/vll1+mevz+/fvx9OlTqFSqFLfb2NjgzZs3UCgUqFfv/aZ/r1+/hk6nS/HHuru7Ow4ePIgyZcrA3Nwcp06dQkxMDIYNGwaFQoHff/8dtra2+PnnnwG83wizdevWCAgIwMmTJ3H69GlotVrExcXBzMwMTZs2xYoV7+eS70XTI+Znkkwmw4IFC1I8x4erYP7LUO+Z8uXLo3z58vrjCxcuDHNzcwBItb9HXsPPI74PichE6IiIcplSqdRduHBBp1QqdTqdTqfRaHSnTp3SeXl56b788ktdx44ddUOGDNFFRkbqdDqdbseOHbodO3boTp8+rR9Do9Ho1q5dqxs5cqR+nA/98ssvur179+p0Op1Oq9Xq7t69qzt//nyKY/78809dbGys/uf58+dnWPvly5d1ixYtSveYd+/e6Ro0aKBr3bq1rkOHDroOHTro1q5dm+HYyZKSkjJ9bF4SHx+vmzRpku6rr77Sbd++PcPjz5w5o6tevbouKCgow2N///13XatWrXSrVq1Kcfvz5891ffv21R05ciTDMdRqte7hw4e6y5cv6/744w/dvXv3dJGRkTqlUqlLSkrSqdXqNB/H96LxM5bPpA+pVCrd6NGjP3q/od4zH5o6dWqmj81r8vPn0Yf4PiQiYyPodP9Z60ZElAfExsZCo9F89NugtERGRqZa3k+57+nTp6kuA5jesSVLltTvsG+M+F4kIHvvg6wwxHvm6tWr+v0O8it+HmUN34dEZGgMMIiIiIiIiIjI6EkyPoSIiIiIiIiISFwMMIiIiIiIiIjI6DHAICIiIiIiIiKjl2cuo/ru3TscOXIErq6uKa6rTURERERERET/SkpKQmhoKDp27IgCBQqIXU6m5ZkA48iRI5gzZ47YZRARERERERGZjL59+4pdQqblmQDDxcUFADB9+nS4u7uLXA0RERERERGRcQoKCsKcOXP0fbSpyDMBhkKhAAC4u7vzWtFEREREREREGUjuo00FN/EkIiIiIiIiIqPHAIOIiIiIiIiIjB4DDCIiIiIiIiIyegwwiIiIiIiIiMjoMcAgIiIiIiIiIqPHAIOIiIiIiIiIjB4DDCIiIiIiIiIyegwwiIiIiIiIiMjoMcAgIiIiIiIiIqPHAIOIiIiIiIiIjB4DDCIiIiIiIiIyegwwiIiIiIiIiMjoMcAgIiIiIiIiIqPHAIOIiIiIiIiIjB4DDCIiIiIiIiIyegwwiIhyUdCjSOzzu4+gR5Fil0JEREREZFJkYhdARJRfBAZHYPjUZ7ApFAZru3uYN6Ih3J0dxC6LiIiIiMgkcAUGEVEu8fGNQ8TzYngV6oKkJC0CQ8PFLomIiIiIyGQwwCAiygX3Q9U4e9YccjMlEmNtkBhjjyquhcQui4iIDESXlJSnnoeIyBjwFBIiolxw/ooKtjYCFs+zxIRpKshi3VG+tL3YZRERkYEIcjkSx47OsfEkZctC/tUgJPlugjYkRH+7+bIVOfYcRETGjiswiIhywcCeljixuyAa1CiIySMLIPQRcPIvldhlERGRidCGhCDJdxPkXw2CpGxZscshIhIFAwwiolxiZ/v+I7dTa3M4O0mxfH0stFqdyFUREZGpYIhBRPkdAwwiolwmkwkYPcgK9x9ocOKUUuxyiIjIQAwRMjDEIKL8jAEGEZGB/H46EUf+SIROl3qVRfuWZnBzkWL5hjio1VyFQUSUFxkqZPgwxCAiyk8YYBARGUDkWy1+WBiDbfvikUZ+AYlEwOghVnj4RIMjfybmfoFERGRwhlwpkRxiEBHlJwwwiIgM4MdlMYiL02Hud7aQSIQ0j2nV1AwV3WRYuTEOSVyFQUSU5xj6dI8Pr0ZCRJQfMMAgIsph/heUOPyHEsP7W6Kcy8evVi0IAsYOtcLTF1r8cpSrMIiI8iLuWUFElHMYYBAR5aDYOC1mLIxB2TJSDO9vleHxTRsoMKK/JTyqynOhOiIiEgNDDCKinMEAg4goBy1dF4dXYVrMnWILhSLtU0c+JAgCxo+wRtkyH1+pQUREpo8hBhHRp2OAQfQfQY8isc/vPoIeRYpdCpmYG4FJ2L4/AX2+sECNKlxRQUREKTHEICL6NPzKjwhAWLgGf99Kgl9ADP44Gw2ZIgmlKgTgx68bwt3ZQezyyASoknSYNj8aRQtLMH54xqeOEBFR/vRhiJHku4kbcRIRZQEDDMqXVEk67DmUgL9vJeFGYBKev9ICAGQyHaQKCeKjHRH2JB6BoeEMMChT1m2NR8hDDdYtsoO1VfYWt10PTMKKDbFY+ZMdrCy5QI6IKK9iiEFElD38C5nyvMi3Wjx5pk5xm0wKrNgQh8s3klC5ghzfjbHG3vX22L1RhrI1b8HGIQJvnpSCma6gSFWTKQl5qMaaLXHo2NoMTRuYZXsciQCcv5KEHfsTcrA6IiIyRjydhIgo67gCg/IUjUaH4Ica3AhM0q+uePxMg5ZNFPCZX0B/nEQi4MSegrC3EyAIH260WBA/ft0Ql29FYN06CZav0aFFfW22v1GnvE+r1WHaT9GwshLw/VibTxqremU5mjVQYMOOePTqagEba77viIjyMq7EICLKGgYYZNKiorX4+/b7sOLvW0n457YacfE6AEBBewHVK8vRvbM56nooUj3WoUDazaG7swPcnR1QyUmFvt+8w4yfY7Bohu1/gg6i93YeTMCNW2osmG4DB/tPDxzGDLVC16/eYsueBIwazL00iIjyOoYYRESZxwCDTIZWq8PDJxpc/2B1RegjDQBAIgHcy8rwWVtzVK8sh0cVOUoWl3xS6FCzqgKjBlth+fo4NKqTiC7tLXLqpVAeER6pxeI1cWhUV4HP2prnyJiVysvRupkZfHfHo293CxSw5SoMIqK8jiEGEVHmGEWAoVKpoFD8+w15XFwcrKz4zWN+p9PpUgQQShXQqV8kNBqggO371RWdWpujRhU5qlSQGWTTwxH9LXHhigqzF8eiaX2zHPmGnfKOgvYC5k62QbXK8hxdoTN6sBX+9FfCd2c8xo+wzrFxiYjIeDHEICLKWK4EGFevXsXcuXNhbm6OFi1aYNiwYQAAf39/rFixAnZ2dggLC4Ovry8KFSqEb7/9Fs+fP4dKpULlypWxePHi3CiTRKTT6fD42ftLmd64pcaNwCTUrCrHjIn/7ilgYS5g+Tw7uJSWooyTNFdO6ZBKBSyaaYtbQWqGF5SKIAjo0CpnVl58yM1VhvaeZti6LwEDeljyvUdElE8wxCAiU6NSqSAIAuRyea48n8EDjLi4OHz//ffYtGkTihcvjilTpiAoKAju7u64efMmdu7cCYVCgR9//BG3bt1Cs2bNEBkZicOHDxu6NBJRQqIOgXffnwZy4//7V7x9937vCmsrAdUryeFeNvXbs2WT7F/hIbuKFpaiaGFprj8v5W+jBlvh+Ckl1m+Pw+TRn7Y5KBERmQ6GGERkjD62KOHMmTP48ccfYW5uDgsLCyxevBguLi4Gq8PgAcbJkyfRrl07lChRAgDw+eef4/z583B3d8fo0aMBAMHBwbh79y4mTZqEiIgI2Nik/8d6dHQ0oqOjU9z25s0bw7wA+mQ6nQ7PX2n/vTLIrSQEBauheb99Bco4SdG8oRlqVJajemU5ypaRQiLhhplkvB48VsOltGE/Pl1Kv9/TZceBBHzVyxKFCzFEIyLKLxhiEJExSW9Rwu3bt/HTTz+hfv36uVKLwQOMkJAQ1KlTR/+zSqWChcW/myGGh4dj8uTJWLp0KeRyOezt7TF69GioVCrI5XLs3r0bvXr1SjHmli1bsHLlSkOXTjkgOkaL9r0j8SZCCwCwtBBQpYIMQ/taokZlOapVlsPezrSWx5/8S4mqFWVsKPOpsxeVGDIhCj7z7Qy+ImjkV1Y4/Hsi1m6Nx/QJXIVBRJSfMMQgotzw5s0bPHv2LMVttra2sLW11f+c3qKEO3fuoE+fPrlWr8EDDDs7O7x8+VL/87Vr19C4cWMAgFKpxPjx4zF16lQ4OTkBACQSCapXr64//pdffsFnn30GS0tL/W0DBgxAly5dUjxPYGAgxo0bZ7gXQh/1Kkzz/jSQwCSUdZGhe6d/AypbGwnaNDeDS2kpalSWw81VBpnMdFdXhEdq8e2MKHhUlWOjdwGuFMln4uK1mLEwBi6lpWhSL/WleXNaqRJSfNHRHHt+TcDg3pYoXpShGRFRfsIQg4gMLa0eetSoUfqzJYD0FyVMnjwZgiBApVLh5MmTqFu3LgoWLGiweg0eYLRs2RKjRo1Cq1atEBkZicuXL+snafr06ejTpw9q1aqlP/7IkSNQKpXo0qULQkNDU6VBQOpECABevXpl0NdB76mSdAgKVqfYu+Ll6/erK8wUQK8uqS81mpe+OS7kIMH342wwfUEMNu6Mx9C+vFpOfrJsfRyev9Jix+oCUChyJ7z6eqAVDh5LxJotcZg92TbjBxARUZ7CEIOIDGnp0qWoUqVKitv+22untyihbNmy+tujoqJw4sQJg67IMHiA4ezsjEGDBmHgwIEoWLAg5s+fD4lEgrt37+KPP/7AzZs3sXTpUgiCgMGDB6Nz586YO3cu1q1bB1tbW8yYMSPF6gvKXeGRWvz9/7Dixq0k3LqbBKXq/X3FikhQo7IcX/WUo0YVOdzLyaCQ5/0VCd07myPgigpL18ahrocCVSvmzo67JK5/bidh694E9O5qgVrVDL/6IlmxIlL0+MwCu39JwNC+VihVgqswiIjyG4YYRGQojo6OKFmyZLrHfGxRQnR0NBYtWoRx48bBwsICZ86cQevWrQ1ab65cRrVr167o2rVrip+9vLxw8eJFmJunvgTh7Nmzc6Ms+oh30VrM847BjcAkPH3xfnWFXAZULC9Dry4WqF5FjhqV5fn2yhyCIGDOZBsE3knChB+icGiLA6ytTGsfD8oaVZIO036KRuFCEnz7de6vuhne3xJ+Z5V48ETNAIOIKJ9iiEFEYvnYogRbW1vUr18fAwYMAADUrVsXHTt2NGgtuRJg/Jevry9sbW0hCHn/23pj9jZKi39uJaGAnQTVK/+7isDaUsDft9RwLydHr65yVK8sQ+XycpiZ8feVzNZGgkWz7ND3m7eYsTAGi2by/ZyXbdgej/sPNFi90E6UsKpwISn89heEVMr3GBFRfsYQg4jE8rFFCe3atUO7du1yrQ5RAgw7OzsxnjbfCnoUiX+Cw1HAvCCiIi31+1c8fPL+OqYdW5uheuV/fycymYA/9xlu45W8wqOKHKMGWWHZ+jg0qpuILu1T7/9Bpi/0kRqrNsehvacZWjQy7FVH0sPwgojI9EjKls3xkOG/IQYRkRiSFyXkNlECDMo9QY8i8f2aAITeqICEGA2AGBSwE1Cjihxd2pujRhU5KrtzD4fsGt7fEheuqTB7cSyqV5ajjBP/k8pLtFodpi+IgaW5gO/H553NaImIKHcYaqXEhyEGEZEYxFqUwBP387jA0HCo1VrYFXmNYmWDMW5cNC4eK4Q1CwtgeH8r1KmhgKUFv9nNLqlUwKIZtlDIgQk/REOl0oldEuWg3YcScO2fJEwZY41CDsbxcRkVrcVvvyeKXQYREWVCcsgg+WCX/pySHGIQEeUnxvEXORlMFddCkMkkKFA4HAWLR6J5HQfu1ZDDijhK8eP3trhzX43Fq2PFLodyyKswDRatikOD2u9XKxmLHQcSMGlWNG7fSxK7FCIiysCHKyUMFWIQEeUnDDDyOHdnB8wb0RB921XAvBEN4e7sIHZJeZJnYzP07WaBzXsS4H9eKXY59Il0Oh1m/hwDrVaH2V7GtUFrv+4WsLMRsHx9nNilEBFRJhg6xCAiyk8YYOQD7s4O6O7pxvDCwLxGWqN8WRmmzI1GWLhG7HLoExw/pcTpABXGDrU2usuW2lhLMLiPJc6cV+FGIFdhEBGZAoYYREQ5gwEGUQ4xMxPgPdsW8Qk67P2NexSYspgYHWpXl6Nfd+O8skzfbhZwKCBg+QaeskREZCoYYhARfToGGEQ5yNVZhgObHDDyK0uxS6FP0ONzC2xdWQAymfGcOvIhK0sJhvWzwvkrSbh8QyV2OURElEkMMYiIPg0DDKIcVraMzKj2TKDskUiM+3fYq6sFHAtJsGxdHHQ6Xv2GiMhUMMQgIso+BhhERCbI3EzAiP6WuPpPEs5f4V4YRESmhCEGEVH2MMAgMqC/byVhoU8svyE3AUvWxGLNFtNazfBlZwsUKyLB0nV8jxERmRqGGEREWccAg8iALlxVYeOOePxyjJt6GrObd5Kwfns8XoZpTer0H4VCwDcDrXDzjhpnznMvDCIiU8MQg4goaxhgEBnQsH6WqOMhx5wlsXjwWC12OZSGJLUO3/8UjUIOEkz82krscrKsSwdzlCouwfL1cdBquQqDiMjUMMQgIso8BhhEBiSVClg0wxYKBfDtjGioVGwwjc3GHfG4H6rBjIk2sLE2vY9EuUzAqMFWuHNfjT/9lWKXQ0RE2cAQg4goc0zvr3UiE1PEUYqfvrfFnftqLF4dK3Y59IEHj9Xw8Y1D2xZmaNnETOxysq1Ta3OUcZJixYY4aDQMyYiITBFDDCKijDHAIMoFLRqZoV83C2zekwD/8/yW3BhotTpMnx8DCzMB08Zbi13OJ5FKBYwZYoXghxoEXOFeGEREpoohBhFR+hhgEOWSSSOtUb6sDJPnRiMsXCN2Ofnenl8TcfWfJEwebQ3HglKxy/lkbVuYYbtPATSuqxC7FCIi+gQMMYiIPo4BBlEuMTMT4D3bFomJOnjNjuaGiyJ6/UaDn31iUa+mHF07mItdTo6QSATUrqEwqauoEBFR2hhiEBGljQEGUS5ydZbh+/E2uHA1CRt2xItdTr6k0+kw8+cYaDQ6zJlsw4afiIiMEkMMIqLUGGAQ5bJuHc3RztMMy9bF4Z/bSWKXk++cOK3EqXMqjBlqDaeSMrHLMZh30VqxSyAiok/EEIOIKCUGGES5TBAEzPayQRFHCSbMiEJMLBvN3PIuWos5i2NQyV2GAV9aiF2OwSxeHYvP+kdCqeRpSkREpo4hBhHRvxhgEInA1kaCxbPs8PK1FjMWxkCnY6OZGxasiMW7aB3mTbGBTJZ3Tx1pUFuBV2Fa7Pk1QexSiIgoBzDEICJ6jwEGkUhqVJFjzBArPH2hQVw8AwxDe/xMjUPHEzG4tyUquMnFLseg6tWUo46HHGu3xiMhke8tIqK8gCEGEREDDCJRDe1riZ1r7GFtxf8UDa10SRn2rLPHyEFWYpdicIIgYOxQK4RHarHzIFdhEBElM/VT6/JyiKFLyp19wXLreYjIMPLuDnZEJkAqFSAVu4h8pGrFvL3y4kO1qinQqI4C67fHocdn5gzJiIjw/pLm5RuEpXlfnRpyLJtrh7HTonD5xqc3uffOF4akbFloQ0I+eawPfRhiJPluytGxxSTI5UgcOzpHxpKULaufn//Ov/myFTnyHEQkDv5FS2REtFrT/maIjMvYYVZ4+06Hbfu4CoOIKD05HV4kM9RKiQ9DDEotL69UIcrvGGAQGYmwcA2+HPoWZwKUYpeSZySpdfj9dGK+3SS1akU5mjdSYNOueETH8Go3RERpMVR4AcCgTXRyk05pY4hBlDcxwMgHEp/dw9uAg0h8dk/sUigddjYSJKmBKfOi8fqNRuxy8gTfXfEY8310jv9BakrGDrFCdIwOm/fEi10KEZHRMWR4ARi+ic7p01PyGoYYRHkPA4w8LvHZPbzcMRNvz+zCyx0zGWIYMTMzAUtm2SIxUQev2dHQaPLnqoGc8uipGis3xqF1MzPU9VCIXY5oKrjJ0aa5GTbvTsDbKK7CICJKZujwIhmb6Mwz9Ok2nH8i08cAI49LeHwb0kSgcHghyBIEJDy+LXZJlA5XZxm+H2+Di9eSsHEHvzHPLq1Wh+nzY6BQCJg+wVrsckQ3erAV4hN0fE8REf2focOLOjVSbhrNJjpzuGcIEWWEAUYeZ1G6ErQKQKKTwDrBGhalK4ldEmWgW0dztPc0w9L1cfj7Vv499eFTHDiSiMs3kjB5lDUKF+J1Xsq5yNChlRm2749HeCRXYRBR/pYb4cWyuXapbmeIkTHuGUJEGWGAkceZlyyPov1mQuNeBhaJZlAo7MUuiTIgCAJmedmgaGEJJsyIQkwsG86seP1GgwUrY1HHQ45unczFLsdojBpkBVUSsH5bnNilEBGJKjfCi7HTotK8nyFG+rhnCBFlhAFGPmBesjysew8DZDJoTp8SuxzKBFsbCRbPtMOrMC1+WBCTb6+ikR1zvWOhUukwZ7INBEEQuxyjUcZJhs/ammPXLwncJJaI8rXcCC/SG58hRvo4P0SUHgYY+YRgawtpnbrQXL4EXXS02OVQJtSoIseYIVY45qfEwaOJYpdjEv44k4g/zigxarAVnEvJxC7H6IwcZIUvP7OAXM5gh4jyLzHDi2Rs0tPH+SGij2GAkY9Im7cANBqoz/4ldimUSUP7WqJeTTnmLInBg8dqscsxatExWsxeHIuKbjIM6mUpdjlGqWQxKaaNt4FDAX70ExHllOzuqcEmPX2cHyJKC/+KzUckhQtDUqUKtFevQKflvgqmQCoVsPAHW5iZCZjwQzRUKp5K8jE/+8Qi8p0Wc7+zgUzGFQZERGR4n7ohKJv09HF+iOi/GGDkM/Ku3aDwmgJBwl+9qSjiKMX8aba4G6zGz6tixS7HKF26rsLe3xIxsKclKpWXZ/wAIiKiT5RTVzNhk54+zg8RfYhdbD4j2NtDsLAQuwzKouYNzdCvuwW27k3A6QCl2OUYlUSlDtPnx8CphBSjB1uJXY7JOHg0AQt9GIgREWVHTl+KlU16+jg/RJSMAQaRifAaaY1WTc1Q0J7/2X5o5cY4PH6mwezJNrAw56kjmRXySAPfXfEIfcS9VYiIsiKnw4tkbNLTx/khIoABRr6mUyp5eU4TolAIWPmTHapW5CkSH6peSY5h/SxRv5ZC7FJMypA+ljA3F7BiY5zYpRARmQxDhRfJ2KSnj/NDRAww8ints6dQzvwB2nv3xC6F6JO0bGqGb7+2FrsMk+NQQIL+3S1w3E+JoBCuwiAiyoihw4tkbNLTx/khyt8YYORTQtGigEwGjd9JsUuhbNLpdHj9RiN2GWTCBvWyhI21gOXruRcGEVF6ciu8SMYmPX2cH6L8iwFGPiXI5JA1aw7t/XvQPn0qdjmUDbMWxaLXiLeIic1/l8Tl5WRzhp2tBF/1soTfWRUC7xr+D3IiIlOU2+FFMjbp6eP8EOVPDDDyMWmDhoCZOdSn/MQuhbLh83bmeBWmxQ8LYvLVXiY6nQ5Dv32HeUtjxC4lTxjwpQUK2ApYvoF7YRAR/ZdY4UUyNunp4/wQ5T8MMPIxwcIC0oYNof37BrQR4WKXQ1lUvbIcY4da4ZifEgeOJopdTq7ZfyQRF68loayzTOxS8gRrKwkG97HEXxdUuB7IVRhERMnEDi+SsUlPH+eHKH9hgJHPyZo2BQQBmjNnxC6FsmFIH0vUqynH3CUx+eJymGHhGixcGYva1eXo3tlc7HLyjL7dLFHQXsCyddwLg4gIMJ7wIhmb9PRxfojyDwYY+ZxQwB6SmjWhuXgBujguITc1UqmAhT/YwsxMwIQZ0VAq8/apJHOXxCJRqcOcKTaQSASxy8kzLC0EDOtvhYvXknDxmkrscoiIRGVs4UUyNunp4/wQ5Q8MMAiy5p6ASgXNubNil0LZUMRRivnTbBEUrMai1Xn3G/Q//ZX4/YwSowZZoYwTTx/Jab0+t0DhQhIsWx+Xr/ZUISL6kLGGF8nYpKeP80OU9zHAIEiKF4ekYiWo//KHTsVvX01R84Zm6NfdAlv3JuB0gFLscnJcdIwWsxbHwL2cDIN6W4pdTp5kZiZgxABLXL+ZhHOX+DlARPmPIcILQzTRbNLTx/khytsYYBAAQObZEoiNhebyZbFLoWzyGmmNCuVkmDI3Gq/faMQuJ0ctWhWLiEgt5k6xgVzGU0cMpXsnC5QoKsHSdVyFQUT5i6FWXhiqif6wSafUGGIQ5V0MMAgAILi6QihdGprTftBptWKXQ9mgUAhYPMsWSqUOk2ZFQ6PJGw3o5Rsq7Pk1EQN7WKBKBbnY5eRpCoWAr7+yQmycDm8i+DlARPmDIU8bMWQTndykU9oYYhDlTQwwCAAgCAJkLVpCFx4O7c1/xC6HssnVWYZpE2xw6XoSNuyIF7ucT5ao1GH6/BiUKi7BmKHWYpeTL3RpZ46jOxxQuJBU7FKIiAzO0HteGLqJ1oaE5PiYeQlDDKK8hwEG6UmqVoVQyBFqv5NcPm7CvuhgjvaeZli2Pg43Ao1vA7KsWOUbh0dPNZg92RYW5jx1JDfIZAJkPE2HiPKB3Nqwk0105nHPECLKCAMM0hMkEkibtwAEAUhIELscyiZBEDB7sg2KFpbg25lRJntp1aDg96tIurY3R4PaCrHLISKiPMTQ4UWdGilPeWQTnTncM4SIMsIAg1KQNmgAxfhvIVjySg+mzMZaAu/Zdvh+nA3MzEzv23S1WoepP8WggK2AyWN46ohYnr7Q4LqJr+IhIvqv3Agvls21S3U7Q4yMcc8QIsoIAwxKQZBIIAim1/BSatUqyeHZ2EzsMrLlXqgaDx9rMG2CDQrY8mNKLOOmRWHKnGio1aa5ioeIKC25EV6MnRaV5v0MMdLHPUOIKCPsDIjI6FQqL8ef+wqiXQvTDGDyiq8HWuHxMw0OnUgUuxQiohyTG+FFeuMzxEgf54eI0sMAgz5K+/wZdNHRYpdBOeSvi0qT2g+jkANXA4nNs7ECld1lWLUpDqok03nvEBGlR8zwIhmb9PRxfojoYxhgUJp00dFQLfoZ6tOnxC6FcsD9UDWGTojCzz6xYpdCJkQQBIwdZoXnr7TYf5gb+xIRpSW7e2qwSU8f54eI0sIAg9Ik2NpCUr0GNAEB0PGKJCbPzVWG/l9aYNv+BJw6pxS7nDSFR2rh4xsHlYrf9BuTxnUVqFFFjjVb4k1qBQ8RUW741A1Bjb1JF/tz39jnh4hyn0zsAsh4yTw9obp+DZrzAZB5thS7HPpEk76xxpUbSfhuXjR+2+qAIo5SsUtKYZ53DP78S4l2LczgUpofTcZCEASMG2aFAaPfYfehBAzowSsUEREBOXc1kw+b9CTfTUa10aSZmYDyDcKy9diszs+984XTvN2Y54eIch9XYNBHSUqWgsStPNRnTkOn5qUUTZ1CIWDJbFsolTpMnBUNjcZ4vk0/dU6JY35KfDPQiuGFEapXU4G6HnKs3RqH+ATjed8QEYklpy/FmtdWGnB+iMhQGGBQuqSeLYHoaGiuXhW7FMoBLqVlmD7BBpevJ2HdtnixywEAxMZpMfPnGLi5SjGkL7/dN1Zjh1kh4q0OOw4Yx/uGiEgsOd2cJ8srTTrnh4gMiQEGpUtSvjyEEiWhOXUKOq1W7HIoB3TtYI4OLc2wYmMcrgeKv7Jm0ao4vInQYt53tlDIedURY1WzqgKN6ymwYUc8YuP4WUBE+ZOhmvNkpt6kc36IyNAYYFC6BEGAzNMTutevoL1zW+xyKAcIgoBZXjYoVkSCiTOiEB0jXjN69R8Vdv2SgP7dLVC1oly0OihzxgyxwrsoHbbs5ca+RJT/GLo5T2aqTTrnh4hyAwMMypCkeg3AwQFqv5Nil0I5xMZagiWz7PD6jRbTF8RAp8v9fQ2USh2mz49BiWISjB1mnevPT1lXtaIcno0V8N0Vj6horsIgovwjt5rzZKbWpHN+iCi3MMCgDAlSKWTNmkP34AG0Dx+KXQ7lkGqV5Bg71AonTimx/3Birj//6i1xePBYg9leNrC04KkjpmLMUGvExOrgu5t7YRBR/pDbzXkyU2nSOT9EpNPpEBsbmyvPxQCDMkVarz5gaQn1Ka7CyEuG9LVE/VpyzPWOQegjda49771QNdZvi8fn7czRqK5Zrj0vfTr3sjK0bWGGbfsSEBfPVRhElLeJ1ZwnM/YmnfNDlH9cvXoVn3/+OXr27Il169aluG///v24cuUKjh49CpVKZdA6jCLAMPSLpE8nmJlB2qgxtIGB0IZl73rgZHwkEgELf7CFhYWA8T9EQ6k0/KkkGo0O036Khq2NgCljeOqIKZr4tTV2rbGHlaVR/C+EiMggxG7Okxlrk875Ico/4uLi8P3338PHxwe7du1CaGgogoKCAABqtRr79u1Do0aNsHfvXkilUoPWkit/fX4srfH390e3bt3w9ddfo1OnTggPDwcAhIaGolu3bujduzfmzp2bGyVSJsiaNAWkUmhOnxK7FMpBhQtJsWCaLe6FqPHrCcOfSrJtfwJu3lFj2ngb2NuxATZFpUpI4eYqE7sMIiKDMZbmPJmxNemcH6K8482bN3j27FmKf6Kjo1Mcc/LkSbRr1w4lSpSAIAj4/PPPcf78eQCAn58f6tevD6lUCpVKZfoBRnppzc2bN7Fz505s3LgR9evXx61bt6DVavHtt99ixowZ2LlzJ6ysrODn52foMikTBBsbSOvWg+byJej+86Ym09a0gRl2rimA7p3NDfo8z15qsHRtLJo1UKB9S546QkRExsfYmvNkxtKkc36I8pZx48bB09MzxT9btmxJcUxISAhq1qyp/1mlUsHCwgIAsHfvXnTq1AmPHz9G8eLFDX52hcG/QvswrQGgT2vc3d0xevRoAEBwcDDu3r2LSZMm4fr16yhXrhyqVKkCAOjWrRs2b94MT09P/ZjR0dGpUqE3b94Y+qUQAGnzFoCBUzUSR82qCoM/x+xFMRAkAmZMtIEgcOPOvECn0yFRCViY8/dJRKbPWJvzZB826Um+m6ANCcnV5+f8EOU9S5cu1ffeyWxtbVP8bGdnh5cvX+p/vnbtGho3bgy1Wo3r169j2LBhUCqViI+PR5s2bXD69GmD1WvwACMkJAR16tTR//xhWgMA4eHhmDx5MpYuXQq5XJ5uupNsy5YtWLlypaFLpzRIHB0h+aKb2GWQiRoz1ApPX2hQvChDsLxAo9Gh94i3KF9Ohtlethk/gIjIiBmiOZeULZvjTfR/m/TcYuzhRTKGGERZ4+joiJIlS6Z7TMuWLTFq1Ci0atUKkZGRuHz5MsaNGweJRILr168DADZv3oxixYqhbdu2Bq3X4KeQpJXWlP3/si6lUonx48dj6tSpcHJySvP4q1ev6o9PNmDAAPj5+aX4Z+nSpYZ+KUT5xvXAJOw8mPOXyazsLke7FoY9TYVyj1QqoGJ5OQ4cTsTTFxqxyyEiyjZDNeeGOp3hwyY9Nxg6vKhTQ56j4/F0EqKc5ezsjEGDBmHgwIGYN28e5s+fD4nkfZQgCAIEQUBoaKi+pzckgwcYLVu2xNatW/H27VuEhobi8uXL+hUW06dPR58+fVCrVi398Q0bNsSxY8fw9OlTvH79GgcOHEhx+gjwfklLyZIlU/zj6Oho6JdClG/sOhiPud6xuHaTVwii9I0YYAmJFFjlGyd2KURE2WLI5tyQTXRyk25ouRFeLJtrl+PjMsQgylldu3bFr7/+ik2bNsHZ2Rldu3bFxYsX9fe3bdsWzs7OBq8jWwFGZGQkpkyZkqljP5bW3L17F3/88QeWLl2Ktm3bol27dti/fz9sbW0xbdo0jBs3DuPGjcO0adNgY2OTnTLJgHQaDdTnA6C5FSh2KWQAP3xrg2JFJJg4IxrRMdpPGuv5Sw20WsNfnpXEUcRRil5dLPDriUQ8fKIWuxwioiwxdHNu6CY6N06PyI3wYuy0qBwfG2CIQWRIvr6+qFu3rv7nRo0awdLS0uDPm+4eGAkJCZDL5ZDJUh62f/9+hIaGZvpJunbtiq5du6b42cvLCxcvXoS5eerl5E2bNkXTpk0zPT6JQBCg8T8DCBJIKlXmhox5jI21BEtm2aH3iLeYviAGS+fYZut3HBunRe+v36JpfQVmT+YeCXnVsH5W2PtrAlZuisPimTn/LRoRkSHk1p4Opr4nQ26EF8nj56U9Q4jyOjs7cf7mS3cFxsKFC7Fp0yYEBwfrb0tISMDWrVv1VxDJjuS0Jq3wgkyDIJFA2sITupcvoP3/ZXEpb6lWSY6xw6xw4pQS+35LzNYYS9bE4fUbLb7oaJHxwWSyCjlI0LebJY7+qUTwA67CICLjl9t7OpjySoDcCi+AvLNnCBEZzkcDjKdPn6J06dL4/fffsWbNGnTv3h0LFizArFmz0Lt3bzRp0iTbT2pnZ8dv7PMAac1agJ0dNH4nxS6FDGRIH0s0qC3HvKUxCHmYtcb02k0Vdh5MQL/uFqhWKWc35yLjM7iPJSwtBKzYyL0wiMi4ibWngymHGDkpvfnPC3uGEJFhfTTA8Pf3x+HDhxEeHo6pU6di3759CAsLw9mzZ/HZZ5/lZo1kpASZDLKmzaANvg/t0ydil0MGIJEIWDDdFpYWAsb/EAWlMnN7WahUOkyfH4PiRSQYN8zKwFWSMbC3k2BgTwv8flqJu/eN9/J6RERi7umQ30OMjMKjvLBnCBEZ1kcDjDZt2sDb2xvjx49Hnz59cOjQIXh4eGDnzp0YOXIkIiMjc7NOMlLSBg0Bc3Oo/fzELoUMpHAhKeZPs8X9UA0W+sRm6jFrtsYh9JEGM71sYGVp8IsdkZEY2MMStjYClm3gKgwiMl65uadDWvJriMH5IaKckGZn8fDhQ4wcORK///47LCwsYGlpCUEQcPPmTVhbW6NYsWJYuHBhbtdKRkiwsIC0YSNo/74BbXi42OWQgTRtYIaBPSywfX8CTv6lTPfY+6FqrNsaj85tzNCknlkuVUjGwNZGgkG9LHH6nAo373AVBhEZJzHDi2T5rUnn/BBRTkkzwNi6dStevXoFX19fzJkzB9HR0bhy5QrOnz+PWbNm4cGDBxg1alRu10pGSta0GSCRQHPmtNilkAF9+7U1KrrJ8P2P0XgVpknzGI1Gh2nzo2FtLeC7sbz8cX7Ur7sF7AsIWLaeqzCIKH/I7p4a+aVJ5/wQUU5KM8CYNm0a6tatC5VKhYIFC+LLL7+EhYUFIiIi4OTkBCsrKxQpUiS3ayUjJdjZQVqrNjQXL0AXm7lTDMj0KBQClsy2hSoJmDQrGhpN6v0wdhxIwD+31fh+nA0cCvDUkfzI2kqCoX2tcO6SClf/UYldDhGRQX3qhqB5vUnn/BBRTkuzw5BKpZg3bx7GjRuH2bNnQyqVokaNGqhUqRKeP3+Ojh07wtvbO7drJSMmbdECSEqC5txZsUshAyrjJMMP31rj8o0krN0an+K+5y818F4bhyb1FejYiqeO5Ge9u1rAsaCEqzCIKE/LqauZ5NUmnfNDRIbw0a9IFQoF+vbti2rVqmHw4MEoX748OnfujAEDBmDQoEFwc3PLzTrJyEmKFoOkUmWo//oLOhW/dc3LPm9njo6tzXD2kgpq9ftVGDqdDj8sjAEAzJxow8sk53MW5gLGD7dC47qKNFfqEBGZupy+FGtea9I5P0RkKJle4+3q6op+/fqhevXqAIDPP//cQCWRqZJ5egJxsdBcuih2KWRAgiBgzmRbbF1ZADLZ+6Dit9+VOHdJhQkjrFCimFTkCskYfNHRAsP6WUEqZZhFRHlLTjfnyfJKk875ISJD4knqlGMEF1cIpZ2hOX0aOq1W7HLIgCwtBMj/H15cuhmBmYveoXzZ96cOEBER5VWGas6TmXqTzvkhoqyKjIzElClTMn18lgKMY8eOYcuWLVkuivIHQRAg8/SELiIc2sBAscuhXBD0KBKz1v6DJK0KGrubCH76VuySiIiIDMLQzXkyU23SOT9ElJ6EhASo1epUt+/fvx+hoaGZHifTAUZERAR++eUXfPHFF7hw4QLmz5+f6Seh/ENSpSrkXw2GpHJlsUuhXBAYGg65RQycq9+AzCwOgaHhYpdERiZRqcOy9bE4E6AUuxQiomzLreY8mak16ZwfIsrIwoULsWnTJgQHB+tvS0hIwNatWzF69OhMj5NugJGQkIDbt29Dp9OhYMGCWL9+PYKCghAQEIDt27dnv3rKswSJBNLq1SFIuQ9CflDFtRBkMgmkEkAmk6CKayGxSyIjI5MCx04qsXhNLLRabuhJRKYnt5vzZKbSpHN+iCgjT58+RenSpfH7779jzZo16N69OxYsWIBZs2ahd+/eaNKkSabHSjPA6Nu3L7y9vXH79m1MnjwZzZs3x9KlSwEALi4umDhxIqysrHLkxRCR6XJ3dsC8EQ3Rt10FzBvREO7ODmKXREZGJhMwarAV7odqcOI0V2EQkWkRqzlPZuxNOueHiDLD398fhw8fRnh4OKZOnYp9+/YhLCwMZ8+exWeffZalsdIMMFatWgULCwtcvHgRhw4dwrFjx/Dbb79hz549OH78OJKSkmBtbZ0jL4byNp2O37jmde7ODuju6cbwgj6qvacZypaRYsWGOF5WlYhMhtjNeTJjbdI5P0SUWW3atIG3tzfGjx+PPn364NChQ/Dw8MDOnTsxcuRIREZGZnqsNAMMW1tbeHh4wNHREW/evIGlpSWKFi0KFxcX9OnTB9euXUPDhg1z7AVR3pR0YD/Ue3aJXQYRiUwqFTB6sBUePNbg8B+JYpdDRJQhY2nOkxlbk875IaLMevjwIUaOHInff/8dFhYWsLS0hCAIuHnzJqytrVGsWDEsXLgw0+OlGWA8evQIFy5cQMWKFTF79mwMHjwYNjY2cHNzQ3BwMJYuXYqRI0fm2IuiPEoQoLl0Cbq3vDIFUX7XupkZKpSTwWdTPJLUXIVBRMbL2JrzZMbSpHN+iCgrtm7dilevXsHX1xdz5sxBdHQ0rly5gvPnz2PWrFl48OABRo0alenx0gwwnJ2dMXbsWFSoUAFDhgzBmjVrsHbtWtjZ2eHly5dYtmwZihQpkmMvivImWbPmAAD1mdMiV0JEYpNIBIwZaoUnzzU4dIyrMIjIOBlrc55M7Cad80NEWTVt2jTUrVsXKpUKBQsWxJdffgkLCwtERETAyckJVlZWWcoW0r0KiUwmQ82aNSGXywEAx44dg6+vL8aMGYNr16592iuhPE9wcICkhgc0F85DFx8vdjlEJLLmDRWoWlGGVb5xUKm4CoOIjIshmnNDNNFiNenGHl4kY4hBZFykUinmzZuHcePGYfbs2ZBKpahRowYqVaqE58+fo2PHjvD29s70eOkGGP/Vvn17+Pr6Ys+ePahZs2aWi6f8R9bCE1AqoQk4J3YpRCQyQRAwdqgVXrzWYt/hBLHLISLSM1Rzbqgm+sMmPTcYOryoU0Oeo+MxxCAyLgqFAn379kW1atUwePBglC9fHp07d8aAAQMwaNAguLm5ZXqsLAUYRFklKVkSkvLuUP/lD53aeNN6IsodDesoULOaHKu3xCNRyVUYRCQ+Qzbnhmyik5t0Q8uN8GLZXLscH5chBpHxcnV1Rb9+/VC9enUAwOeff57px2Y5wFi2bBnevHmT1YdRPib19ASio6G5clXsUohIZMmrMN6Ea7H7F67CICJxGbo5N3QTrQ0JyfEx/ys3woux06JyfGyAIQZRXpSlACMyMhJXr16Fo6OjoeqhPEjiVh5CyZLQnDoJnVYrdjlEJLK6HgrUryXH2q1xiIvnZwIRiSO39nQw9SY6N8KL5PHz0p4hRJS+Y8eOYcuWLVl+XKYDjKioKIwcORJeXl5ZfhLK3wRBgKyFJ3RhYdDeviV2OURkBMYOtUbkOx127OcqDCLKfbm9p4MpN9G5FV4AeWfPECJKX0REBH755Rd88cUXuHDhAubPn5/px340wFAqlbhw4QKA9ysvBg8ejMGDB6NKlSqfXjHlO5LqNQAHB6j9/MQuhYiMQI0qcjSpr8CNW9wbh4hyl1h7OphyiJGT0pv/vLBnCBGlLSEhAbdv34ZOp0PBggWxfv16BAUFISAgANu3b8/0OB8NMARBwNy5c/HixQsMGzYM48aNw5UrVxAWFpYjL4DyF0Eqhax5C+gePoD2wQOxyyEiI+A92xarFuT8xm1EROkRc0+H/B5iZBQe5YU9Q4gopb59+8Lb2xu3b9/G5MmT0bx5cyxduhQA4OLigokTJ8LKyirT48n+e8OFCxdw+/ZtaLVaPHv2DN27d0eTJk1w8+ZNHD9+HEFBQXB1dYWDgwNGjRqVYy+M8j5p3XpQHz8G9amTULgME7scIhKZtRUvhEVEuS8393RIy4dNepLvpnzTVHN+iPKnVatWYefOnbh48SIOHToElUqFjh07olixYlCr1fjyyy9hbW2d6fFS/fVoZmYGNzc3uLq6wsnJCfb29jhz5gyKFCmCdu3aITIyEk5OTnB3d8/RF0Z5n2BmBmnjJtAGBkL7+pXY5RAREVE+JGZ4kSy/rcTg/BDlX7a2tvDw8ICjoyPevHkDS0tLFC1aFC4uLujTpw+uXbuGhg0bZnq8VCswPDw89P8eFhaGMmXKIDAwEBs2bMC8efMAAAMHDvz0V0L5kqxxE2hOn4L2/n1IihQVuxwiMhI37yShZDEpHOy5KoOITEt299TILysNOD9E+dujR49w4cIFtGjRArNnz4ZKpYKNjQ3c3NwQHByMpUuXYtmyZZkeL1WA8aGGDRtCLpejXr16qFGjBubPn49Vq1Z98oug/EuwsYHZzNkQsnCeExHlba/CNOgx7C0G9rDA5NE2YpdDRJRpn7ohaF5v0jk/ROTs7IyxY8dCrVZjyJAhqFq1KuTy91dq+ueff7Bs2TIUKVIk0+Ol+1WXk5MTihUrBgCoVasWvL29sW3btk8onwgML4gohaKFpejcxhw7DiQgLFwjdjlERJmSU1czyaunS3B+iOhDMpkMNWvW1IcXx44dg6+vL8aMGYNr165lepwsrdUtUaIExowZk7VKiYiIMjDyK0uoNcDarfFil0JElKGcvhRrXmvSOT9ElJH27dvD19cXe/bsQc2aNTP9uCyfbCyVSrP6EKI06bRa6CIjxS6DiIyAU0kZurY3x55fE/DiFVdhEJHxyunmPFleadI5P0RkSFkKMJ4/f26oOigfUu/eBeUyb+g0bFaICPjmq/enl63ZEidyJUREaTNUc57M1Jt0zg8RZcWyZcvw5s2bLD0m0wGGRqPBiBEjslwU0cdIqlUH3r2D9nrmz3kioryreFEpvuxsgQNHEvH0OYNNIjIuhm7Ok5lqk875IaKsiIyMxNWrV+Ho6Jilx6UZYERHR+sHTUxMhFKphFQqhUKh+PRKif5PUrEihGLFoPbzg06nE7scIjICw/tbQioFfHy5CoOIjEduNefJTK1J5/wQUVZERUVh5MiR8PLyyvJjUwUYarUaXl5e6N69O6ZMmYLt27frV16YmZl9erVE/ycIAmQtPKF7+QLaoLtil0NERqCIoxS9u1rg1xOJePBYLXY5RES53pwnM5UmnfNDRBlRKpW4cOECgPeLJAYPHozBgwejSpUqWR4rVYAhk8mwZs0a9OrVCzdv3sSaNWtw7949NG/eHHfv3kWPHj0wePBgTJs2DU+ePPn0V0P5msSjJmBXABq/k2KXQkRGYmhfK5ibCVi5kaswiEhcYjXnyYy9Sef8EFFmCIKAuXPn4sWLFxg2bBjGjRuHK1euICwsLMtjydK60cfHBzExMVi5ciU2bNiANm3aoE6dOvDx8cH48eOzfJ4K0ccIMhlkzZpB/eshaB8/hqR0abFLIiKRFXSQoG83C6zfHo8RA9Rwc03zf1VERAYldnOe7MMmPcl3E7QhIaLV8iHODxFl5MKFC7h9+za0Wi2ePXuG7t27o0mTJrh58yaOHz+OoKAguLq6wsHBAaNGjcrUmGnugTFy5EhMmTIFtWrVwjfffIMuXbrAwcEBNWvWRHh4eI6+KCJpgwaAuTnUp/zELoWIjMTgPpawshTw4/K32Od3H0GPeMllIso9xtKcJzO2lQacHyLKDDMzM7i5ucHV1RVOTk6wt7fHmTNnUKRIEbRr1w6RkZFwcnKCu7t7psf86NdaWq0WWq0Ws2fPho+PD+bNm4eFCxdCpVLlyIshSiaYW0DasBE0p/ygDX8DSSGu8CHK7wrYStCxDbD7oA437krhXPkSFoypC3dnB7FLI6I8ztia82TGstKA80NEmeXh4aH/97CwMJQpUwaBgYHYsGED5s2bBwAYOHBglsZMM8CYO3cu4uLikJCQgFevXqFdu3ZISkpC9+7dAbzfhGPAgAHo06dPNl8KUUqyps2gOXMGmtOnIen+pdjlEJERcKv0FgUvxSIh1ho6QYXA0HB9gHHoeAL8z6tQoZwM5cvJUKGcDI4FJRAEQeSqiciUGWtznkzsJp3zQ0TZ1bBhQ8jlctSrVw81atTA/PnzsWrVqiyPk2aAMW3aNP2/z58/H0OHDsWvv/6Ke/fuYcGCBdmvmugjBDs7SGvVgubSRcjatYNgbSN2SUQkspoVCqGY6z2o1VrIZBJUcS2kvy8qWoebd5NwzE+pv62gvYAK5eRw/3+g4V5OBudSUshkDDWIKGOGaM4lZcvmeBP93yY9txh7eJGMIQaRcXJyctL/e61ateDt7Y1t27Zh/PjxWRonw53RpkyZAgAYNGgQwsPDodVqIZGkuXUG0SeRtvCE5tJFqM+ehbxde7HLISKRuTs7YN6IhggMDUcV10IpTh8Z0MMSA3pYIjpGi3shatwNViMoRI2gYDW27I1H0v//tjZTAG6u/wYa7uVkqOgmh4U5Qw0i+pehmnNDNdEfNum5wdDhRZ0a8hwdjyEGkfErUaIExowZk+XHZWlr90KFCmV8EFE2SYoWhaRyFWjO/gVZC08IZmZil0REInN3dkh33wtbGwlq11Cgdg2F/rYktQ4PHmtw936SPtz4w1+Jvb8lAgC2rCiAejX/PT42TovYOB2KOPIUFKL8yJDNuSGb6OQmXTF6bI6O+1+5EV4sm2uX4+MyxCAyflKpNMuP4bXpyKjIWnhCtTzw/akkTZqKXQ4RmSC5TEB5VxnKf3D5VZ1Oh9dvtLgbrEZl95T/6zt1ToVJs6JRwE6Ae9l/V2tUcJPDpbQUcp6CQpRnGbo5N3QTnRtNeW6EF2OnRWGbj32Oj88Qg8h4PX/+HCVKlMjy4xhgkFERXFwgqVoNUCgyPpiIKJMEQUDRwlIULZw66a9RWY4fvrVGUPD71Ro7DyZA+f8LbsnlQLkyMv2+Gsnhho01T6UkMnW5taeDqTfRuRFeJI+fl/YMIaKP02g0GDFiBA4fPpzlx6YKMKKjo3Hp0iXUq1cPNjbcSJFylyAIUAweInYZRJSPlCohRZ8vLPU/q9U6PH6mwd37/99bI1gN//NKHDyaqD+mZHEJ3MvK0LebJerXYuBKZGpye08HUw4xciu8APLOniFE9K/o6GjY2toiMjISlpaWEAQBZmZmUGTzC+tUAYa5uTmeP3+OoUOHQi6Xo3nz5mjZsmWKXUOJiIjyKplMgKuzDK7OMnRs/e/tbyI07wON/wcbd4PVeBetTfHYt1FarPKN05+KUraMDAoFT0EhMiZi7elgyiFGTkpv/vPCniFE9C+1Wg0vLy9ERETA3t4ederUQUBAAHx9fWGWzf0OUwUYCoUCAwcOxMCBA/HmzRv4+/tj7ty5iIyMRPPmzdG2bVu4urp+8oshIiIyJY4FpXAsKEWTeh//H+6TZxrs+y0BCf9frCGTAq5l/j39pHzZ96eg2NvxFBQisYi5p0N+DzEyCo/ywp4hRPQvmUyGNWvW4ODBg1i4cCGuX78OhUKB5s2b4927d+jRowesra1RrFgxDBs2LFOLJtLdA8PR0RHdunVDt27dEBMTg9OnT2PZsmUICwtDz5498fnnn+fUayNKRRcTA82d25DVrSd2KUREmVKtkhzX/nTE42caBP3/0q53g9UIuKLCoeP/noJSrIgk5b4aZWUoWVwKiYSrNYgMLTf3dEhLfg0xOD9E+ZOPjw9iYmKwcuVKbNiwAW3atEGdOnXg4+OD8ePHw9HRMUvjZXoTTxsbG3Tu3BmdO3eGSqVCRERElosnygrNxQtQHzkMSclSkGRjh1oiIjFIpQJcSsvgUlqG9i3/vT08Uoug4KT3p6GEJO+toYL2/2eh9PjMHLMn26YYK0mt41VQiHKYmOFFsvzWpHN+iPKvkSNH6v9doVCgatWqSEhIQM2aNREeHm64AONDCoUCxYoVy85DiTJN2rAR1H/8DvUpPyj69Re7HCKiT1LIQYJGdc3QqO6/p6AkKnW4H/o+zHAqmfIKKUlqHeq0CUeJohL9ZV2TV2s42PMUFCJjkd09NfJLk875ISKtVgutVovZs2fDx8cH8+bNw8KFC6FSqbI8Fi+jSkZLsLSEtEFDaP7yh65DRwgODmKXRESUo8zNBFStKEfVivJU9ymVOgzoYYGgYDWu/J2Ew38o9fcVLiT59/QTt/ehRoI6CrcfRqCKayG4O/Pzkig3fOqGoHm9Sef8ENHcuXMRFxeHhIQEvHr1Cu3atUNSUhK6d+8OAFAqlRgwYAD69OmTqfEYYJBRkzVtBs1f/lD7n4G8S1exyyEiyjXWVhKMG2at//ltlPb9vhrByVdBSULAZRXUmvf3SyQaWNjp4FTxAuaPrM8Qg8jAcupqJnm1Sef8EBEATJs2Tf/v8+fPx9ChQ/Hrr7/i3r17WLBgQZbHy1aAodFocP78eTRu3Dg7DyfKNMHBARKPmtCcD4CsTVsIlpZil0REJAp7Ownq11Kgfq1/r5uuUukQ8lCNrb89x+kL0Xj3qghehjghMDScAQaRAeX0pVjzWpPO+SGitEyZMgUAMGjQIISHh0Or1UIiydppsdk6ifb06dOYMWNGdh5KlGWyFp6ASgXNubNil0JEZFQUCgEVy8sxsLstSro9hkPxF4h644iidgXFLo0oz8rp5jzZh026pGzZHBs3t3F+iCgzChUqlOXwAsggwNi2bRtC0kg4fX190blz5yw/GVF2SEqUgKRCBaj/8ocuKed3DiciMnXuzg6YN6Ihxgy1wsqFcjSuxQCDyBAM1ZwnM/UmnfNDRIaWboARHx+PgQMH4ssvv8SiRYsQHBwMPz8/qFSqFJdDITI0aQtPICYGmiuXxS6FiMgouTs7oFcbN7RswPCCyBAM3ZwnM9UmnfNDRLnhowHGjz/+iJMnT6Js2bLYunUrGjVqhAULFmDq1KlYt24d5PLUO6YTGYqknBuEUqWgOX0KOq1W7HKIiIgoH8mt5jyZqTXpnB8iSkt0dDT+/PNPxMTE5NiYHw0wJk+ejJkzZ8LS0hJLlixBgQIFIJVK8dVXX8Hb2zvHCiDKDEEQIPNsCV1YGLS3AsUuh4jI6KnVOqzdGofnLzVil0Jk0nK7OU9mKk0654eIPsbc3BzPnz/H0KFD0a9fP2zatAlPnjz5pDHTDDBevXqF1atXIygoCMOHD8e+fftw5coV9O3bFyNGjMCjR4+wYsWKT3pioqySVK0GoWAhqP38xC6FiMjohYVrsWZrPKbMi4ZWqxO7HCKTJFZznszYm3TODxGlR6FQYODAgdi9ezeWLFkCW1tbzJ07F926dYOPjw9CQ0OzPGaaAUZAQADWr1+PX3/9FWvWrEHNmjVhZ2eH8ePHY/bs2Xj9+jVcXFw++QURZYUglULavDl0jx5C++CB2OUQERm14kWl+H6cNS5fT8KWPQlil0NkcsRuzpMZa5PO+SGirHB0dES3bt2wbt06+Pr6olSpUli2bBl69uyJQ4cOZXqcNAOM0qVLo2vXrrhy5QrOnDmDIkWKYOnSpdDpdKhYsSKcnJzQoUOHnHotRJkmrVsPsLKC2u+k2KUQERm9LzqYw7OxAkvWxiL4gVrscohMhrE058mMrUnn/BDRp7CxsUHnzp2xfPlybN26FXXr1s30Y9MMMGrUqIHKlSujQ4cOWLFiBRITE7Fx40aYm5tj1apViImJgb+/f469AKLMEhQKyLt1h6x5c7FLISIyeoIgYM5kW9hYCZg0KxqqJJ5KQpQRY2vOkxlLk875Icqfrl69is8//xw9e/bEunXr9Lc/ePAAgwcPRrdu3bB58+Ysj6tQKFCsWLFMH59mgCGVSvHFF19g8uTJqF69OubNm4fLly+jRYsWmDdvHjZt2oTffvsty8UR5QSpR01IypYTuwwiIpNQ0EGCOVNscTdYDZ+NcWKXQ2TUjLU5TyZ2k875Icqf4uLi8P3338PHxwe7du1CaGgogoKCAAA6nQ6rV6/GDz/8gGPHjhm8lo9ehQR4f55KoUKFYG5ujh49emDChAmoXLkyLC0tsXjxYoMXR0RERJ/Os7EZvuhojnXb43E90PiaDiJjYIjm3BBNtFhNurGHF8kYYhBlzZs3b/Ds2bMU/0RHR6c45uTJk2jXrh1KlCgBQRDw+eef4/z58wAAV1dXvHjxAnPmzME333xj8HrTDTD+y97eHrdu3Ur1gnJaXBy/ISIiIspJU8dao3hRCbxmRSEuXit2OURGxVDNuaGa6A+b9Nxg6PCiTg15jo7HEIMo88aNGwdPT88U/2zZsiXFMSEhIahZs6b+Z5VKBQsLCwDA3r170aVLFwQFBeG3336DWp21Pbc0Gg3Onj2b6eOzFGAAwKpVq2Bubp6lx3zsfJlkK1euREBAgP7nfv36oVOnTmjbti0WLlyY1RIpn9A+ewbVxvXQJXJ3fSKijFhbSbBgmi2evdRiwYpYscshMhqGbM4N2UQnN+mGlhvhxbK5djk+LkMMosxZunQp/Pz8UvwzYMCAFMfY2dnh5cuX+p+vXbuGsv//76pUqVI4fvw4Tpw4gUePHuHSpUtZev7Tp09jxowZmT4+SwHG8uXL0bRpUygUikw/Jr3zZZLdv38fpUqVAgAkJibCysoKhw8fxokTJ+Dl5ZWVEik/0WqhvXkTmv8vXyIiovTVqq7A4N6W2PNrIk4HKMUuh0h0hm7ODd1Ea0NCcnzM/8qN8GLstKgcHxtgiEGUGY6OjihZsmSKf2xtbVMc07JlS2zduhVv375FaGgoLl++rF+RUb9+fRQtWhQlSpRAgwYNEBWV9n/P27ZtQ0gan1m+vr7o3LlzpuvNdICxfPlyPHnyBEOGDMn04ED658ske/r0qT7AuH//PsqUKZPumNHR0anO03nz5k2W6iLTJ3FygqRcOajPnIEui0uViIjyq7FDrVC+rAzTfopB5FueSkL5V27t6WDqTXRuhBfJ4+elPUOI8hJnZ2cMGjQIAwcOxLx58zB//nxIJBI8fPgQr169AgCEhYXh2LFjKF++fJpjxMfHY+DAgfjyyy+xaNEiBAcHw8/PDyqVCiNHjsx0LbKP3REdHY3hw4dj165dmDdvHmJjY9G+fXuoVKosrcAICQlBnTp19D9/eL4M8H7FhZmZGQRBAAC4ubnhyy+/hEajgUqlwokTJ9ClS5cUY27ZsgUrV67MdA2Ud0k9W0K7ZjW0169BWifz1w8mIsqvFAoBC3+wRbfBkZjxcwyWz7PV/z+YKL/I7T0dPmyik3w35crKiZySW+EFAIPNT27vGUKUF3Xt2hVdu3ZN8fP48ePx22+/ITg4GEqlEkOGDIGrq2uqx/7444+4ceMGypYtizVr1uDvv//GggULEBgYiBMnTkAuz/w+OKlWYGg0GkRGRkKlUuHhw4cYP348dDodxowZg7Vr12LXrl0IDAzEs2fPMvUE6Z0vAwDBwcEpfjY3N0flypUhlUphYWGBTZtSn9s3YMCAVOfpLF26NNMvmvIOiXsFCMWKQ+3nB51OJ3Y5REQmwb2sDOOGWeFPfyXu3OcKNspfxNrTgSsB3ktv/vPCniFE+YWvry8aNWqEn3/+GYcOHcLx48fRu3fvNI+dPHkyZs6cCUtLSyxZsgQFChSAVCrFV199BW9v7yw9b6oVGBs3bsTdu3eRlJSE+Ph4/PHHH7C1tUVYWBji4+OxatUqFClSBE5OTplaBdGyZUuMGjUKrVq1QmRkJC5fvoxx48bp77937x7c3Nz0P2/evBlOTk5o0aIFLl68mOYVSWxtbVOdl5O8dIXyF0EQIPP0RNL2bdDeuQNppUpil0REZBK+6mmJejUVqFQ+Z3f/JzJ2ubWnwzYf+1T3m/JKjJyQUXhk6PnJb/NNZEh2dpnbfPfVq1fYv38/ihUrhuHDh2PgwIEoVaoU+vbti8aNG6N///5YsWIFRo8enanxUgUYw4YN0//78uXL4eTkhK1bt6JIkSIYMmQIVq1ahTVr1mTyZaU8X6ZgwYL682WS3b9/H56envqfu3btimnTpmHhwoX644nSI/GoCRw5AvWpkwwwiIgySSoVUNmd4QXlP7m5p0Na8muIwfkhyp8CAgKwfv16VKtWDVZWVqhZsybs7Owwfvx4dO7cGa9fv4aLi0umx0t3E08PDw+ULl0au3fvhkqlwq5duzBt2rQsF921a1f8+uuv2LRpE5ydndG1a1dcvHgRANCsWTNUrFhRf6ytrS2WL1+OEydOYMeOHSn2zyBKiyCVQtasGXQhIdA+fiR2OURERGTExAwvkuW300k4P0T5V+nSpdG1a1dcuXIFZ86cQZEiRbB06VLodDpUrFgRTk5O6NChQ6bHSzfAaNSoEWrUqAGFQoFZs2ahWrVqOHHixCe/CF9fX9St+37DxQYNGsDGxuaTx6T8TVq/AWBhAfUpP7FLISIyOTqdDgeOJOB+KPfDIMqq7O6pkV+adM4PUf5Wo0YNVK5cGR06dMCKFSuQmJiIjRs3wtzcHKtWrUJMTAz8/f0zPV6mL6MKAD179kSTJk2yXPR/2dnZccdzylGCuTmkDRtB+88/0PKSukREWRITq8Pi1bHwmh0NVRI3RCbKrE/dEDSvN+mcHyKSSqX44osvMHnyZFSvXh3z5s3D5cuX0aJFC8ybNw+bNm3Cb7/9lunxshRgAEix4SaRMZE1aQpIpNCcPiV2KUREJsXWRoK539nibrAaKzem3jybiFLLqauZ5NUmnfNDRB9ydHREoUKFYG5ujh49emDChAmoXLkyLC0tsXjx4kyPk+UAg8hYCXZ2kNapDc3lS9DFxIhdDhGRSWnRyAzdOplj/fZ4XA/M+T0CiPKSnL4Ua15r0jk/RJQRe3t73Lp1C9HR0Vl6XKYDDK1Wi0mTJmW5MKLcJG3eAkhKgvrsX2KXQkRkcr4bY43iRSXwmhWFuHit2OUQGaWcbs6T5ZUmnfNDRJm1atUqmJubZ+kxmQ4wJBIJ7t+/n+WiiHKTpEhRSKpUgebSJei0/OObiCgrrK0kWDDdFs9eajF/eazY5RAZHUM158lMvUnn/BBRZi1fvhxNmzaFQqHI0uNSBRhqtRrffvstdu7cia1bt+Kvv/7C+vXrAQDW1tY5Uy2RAcm6fAEzr8kQJDxDiogoq2pVU2BIH0vs/S0RpwOUYpdDZDQM3ZwnM9UmnfNDRJm1fPlyPHnyBEOGDMnyY2WpbpDJMHHiRCxevBhHjx5FmTJlEBERgQcPHuDBgweYPXs27OzsYGdnh9atW6N48eI58iKIcoqkYEGxSyAiMmljhljhr4sqTPspBoe3yeFgz0CY8rfcas6TfdikJ/lugjYkxODP+Sk4P0SUnujoaAwfPhy7du3CvHnzEBsbi/bt20OlUn36CgwAuHLlCnQ6HUaMGAFzc3N4enqiTp06cHNzQ/v27dG8eXM0atQIhQsXzpEXRERERMZDoRDw8wxbRMVo8cPCGOh0vLQq5V+53ZwnM5WVBpwfIvoYjUaDyMhIqFQqPHz4EOPHj4dOp8OYMWOwdu1a7Nq1C4GBgXj27Fmmx0y1AgMAPDw80KFDB2i1WjRs2BAVK1aEubk5Xr9+DXt7e7i6uubYiyIyJF1SEiCTQRAEsUshIjIp5V1lGD/cGgtXxuLQ8UR0aW8hdklEuU6s5jyZsa804PwQUXo2btyIu3fvIikpCfHx8fjjjz9ga2uLsLAwxMfHY9WqVShSpAicnJywcuXKTI2Z5gqMkiVLQiqVQi6XY/v27bC0tMTmzZvRtm1bSKXSHH1RRIaiff4MylkzoOXms0RE2TKwhwVqV5djzpJYPH+pEbscolwldnOezFhXGnB+iCgjw4YNg7e3N1auXIkhQ4Zg3rx5KFasGIoUKYJ58+ahRo0a+O233zIdXgAfWYHx448/IioqClqtFv7+/hg+fDhCQ0Px9u1bSCQSyGQy1KpVC/Xr18+xF0eU04TCRQBBgObUSUjLlxe7HCIikyOVCpg/3Rad+0ViytxobFlRABIJV7RR3mcszXkyY1tpwPkhoqzy8PCAlZUVdu/ejXnz5mHXrl2YNm1alsdJcwXGd999hwULFmD27Nlo1KgRGjZsCGtra4SFhaF69epwdnZG0aJFP/lFEBmSIJdD1qQptEFB0D7P/HlVRET0r5LFpPh+nDUu30jC2YsqscshMjhja86TGctKA84PEWVHo0aNUKNGDSgUCsyaNQvVqlXDiRMnsjxOmgFG8n4BFhYWGD9+PPr3749ffvkFtWvXRpMmTdCpUyeUKVPm014BUS6QNmwEmJlB7ecndilERCarawdz7FhdAE0bmIldCpFBGWtznkzsJp3zQ0Q5pWfPnmjSpEmWH5fhddFcXFwAvA81unXrxj0wyKQIlpaQ1m8A7Y3r0EVGil0OEZFJEgQBtapl7TJnRKbGEM25IZposZp0Yw8vkjHEIDIdbm5uWX4ML+xOeZ6sWXMAgPrMaZErISIiImNkqObcUE30h016bjB0eFGnhjxHx2OIQZR3McCgPE+wt4ekZk1oLpyHLi5O7HKIiEzeXxeVuBFovN/AEmWFIZtzQzbRyU26oeVGeLFsrl2Oj8sQg8h4abVaTJo0KVuPTfMqJMmuXr2Ks2fPQqfTQRAEaLVaCIIAuVwOR0dHdO3aFQoFl5SS8ZO1aAnVlSvQBJyDrHUbscshIjJZqiQdZi+KAQAc2uIAayt+F0Kmy9DNuaGvjpEbV9vIjfBi7LQobPOxz/HxeXUSIuMkkUhw//797D02vTvHjRuHypUro3bt2gAAf39/1KxZE8WLF8fq1auRmJiYrSclym2S4sUhqVARan9/6JL4rSERUXYp5O8vrfrspRYLVsSKXQ5RtuXWng6mvhIgN8KL5PHz0p4hRASo1Wp8++232LlzJ7Zu3Yq//voL69evBwBYW1tna8x0V2BYWlqiZcuWEAQBe/fuxcKFC+Hu7o6jR4/i559/hq2tbbaelEgMUk9PaFeugObKZcgaNBS7HCIik1WrmgJD+1pi3bZ4tGhshuYNeXUSMi25vaeDKa8EyK3wAoBBV6rk5p4hRPSeTCbDxIkTsXjxYhw9ehRlypRBREQEHjx4gAcPHmD27Nmws7ODnZ0dWrdujeLFi2c4ZporMFatWoUFCxYgOjoaR48eRVRUFJydneHu7g4AqF27NurUqZOzr47IwCRly0Eo5QTNKT/otFqxyyEiMmmjh1jBvZwM036KQeRbfqaS6RBrTweuBHgvvfnPC3uGEFFKV65cgU6nw4gRI2Bubg5PT0/UqVMHbm5uaN++PZo3b45GjRqhcOHCmRovzQCjWrVqqFixIkqVKoUVK1ZgyJAh6NChg/7+zA5OZEwEQYDMsyV0b95AG3hT7HKIiEyaQi7g5x9sERWjxQ8LY6DT6cQuiShTcmtPh7Tk9xAjo/DI0PNjSitfiPIKDw8PLFy4EN988w2mTp2KadOm4bPPPkP9+vVhb2+PqlWromzZspDJ0j05RC/NAKNhw4bo1KkTWrVqhd69e2Pp0qXYvn07/P39c/TFEOU2SbVqEAoWgtrPj39sExF9IjdXGcYPt8af/kocOs59scg05OaeDmnJryEG54cofypZsiSkUinkcjm2b98OS0tLbN68GW3btoVUKs3yeOnGHB4eHpg+fTpevHiBQoUKwcfHB5cuXYKZmRnatGmjP6WEyFQIEgmkzZtDc+E8EB8PWFmJXRIRkUkb2MMCZwKUmLMkFrVrKFCyWNb/GCHKTWKGF8lMeU+M7OD8EOVfP/74I6KioqDVauHv74/hw4cjNDQUb9++hUQigUwmQ61atVC/fv1MjZdugFGrVi3MmDEDERERUCqVKFasGDQaDQCgRIkSn/5qiEQgbdAQ0kaNIQiC2KUQEZk8qVTA/Gm26NQvEt/NjcaWFQUgkfDzlfKP7O6pkV+adM4PUf723XffQRAEJCQkIDExEbVr18br168RFhaGtm3bIjY2FkWLFs30eBmeaFKvXr1PKpjI2AjZWKpEREQfV6KYFNPGW+O7eTHYvDsBg3pbil0SUa741A1B83qTzvkhouQvjS0sLDB+/Hi4uLigX79+OHDgAJo0aZLl00jS3AODiIiIKCu6tDdHyyYKLFkbi/uharHLITK4nLqaSV7d84HzQ0T/5eLiAuB9qNGtW7ds7YHBAIPyNe2rl9BFR4tdBhGRyRMEAXMm28LWWsCk2dFQqbhRMuVdOX0p1rzWpHN+iMhQMgwwtFotbt++jbNnz8Lf3x/nz5/H7du38e7du1woj8hwdDExUC2YD/XpU2KXQkSUJzjYSzBvqi2CgtW8KgnlWTndnCfLK00654eIMnL8+HGsXLkyW49NFWC8fv0ap0+fxtmzZwEAX3/9NY4dO4YZM2Zg69atuHbtGnr27Ikff/zx06omEplgYwNJterQnA+ALiFB7HKIiPKE5g3NsHl5AXTrZC52KUQ5zlDNeTJTb9I5P0SUlsOHD+v/XafTYf369ejbt2+2xkoVYAQFBWHSpEk4duwYACAiIgKTJk2Co6MjgoKC0L59ezg6OmLhwoXZLJ/IeMg8PYHERGjOB4hdChFRnlG/loJXIqE8x9DNeTJTbdI5P0T0MbNnz0ZsbCwA4MqVKxg4cCAKFCiQrbFSXYXk2LFjiI+PR+PGjQEAZmZmWLJkCV69egVBEHDkyBHEx8dnv3oiIyIp5QRJOTeo/c9A2rQZBFmGF+YhIiKifCa3mvNkpnb1Dc4PEaXH3d0do0aNwrBhwyCRSBAcHIypU6dCp3u/X5aNjQ2mTp2aqbFSrcBYsGABevTogbdv3wIA1q5di+LFi2PYsGFo1KgRbGxsMGDAAGi12hx8SUTikXq2BKKioLl2VexSiIjynFtBSbhwVSV2GUTZltvNeTJTWWnA+SGijJQsWRJFihTBxo0bMWTIEBQoUAAtWrRAs2bN0LhxY3Tq1CnTY6X5dXNCQgIePHgAADh79iyCgoIwYcIEJCYmYtCgQTnzKoiMhMTdHULx4tCc8oO0dh0IEl6ch4goJ+h0Osz8OQYvXmlwZHtBONjz85VMi1jNeTJjX2nA+SGizPDw8ECDBg1QokQJ3LhxAz///DPGjBmDevXqZXmsNP+SaNKkCWrUqIHXr1/jl19+wZ49e9CqVSvs2LEDAwYMQM+ePbkHBuUZgiBA1qIldK9eQXv3jtjlEBHlGYIg4MeptoiO1WH6gmj9UtGMKJW5cwnW3HoeMk1iN+fJjHWlAeeHiDKre/fuKFGiBI4dO4YaNWpg0KBB+PPPP5GUlPXPjjRXYNy6dQsODg7Yt28f1qxZg549e2L58uWYNGkSRowYgXr16iEuLu6TXwiRsZB4eABHDkPt5wdppcpil0NElGe4ucowYbg1FqyMxS/HEtG1g0WGjzEzE1C+QdgnPW9mmqt75wt/0nNQ3mUszXkyY1tpwPkhoqy4dOkSQkJCsHHjRty+fRsPHz6EXC6HLBv7D6a5AsPLywtDhgxB+/btkZSUhCpVqkAQBGzcuBHPnz+HIAiwtrb+5BdCZCwEqRSy5s2hCw2B9tEjscshIspTBva0QJ0acsz1jsWzlxqDP5+xNVdkWoz1/WMsKw04P0SUVdHR0bC0tETt2rXx5MkTtGrVCi9fvsTMmTOzPFa6J6O6uLjAzMwM33zzDYoUKQKFQoFu3bplt24ioyat3wCwsID6lJ/YpRAR5SkSiYD502wBAN/NjYZWa7hTN4y1uSLTYOzvH7GbdM4PEWVHq1at0KVLF0yYMAFDhw5Fly5d8MMPPyAmJibLY2VqN62CBQtmeWAiUyOYmUHaqDG0//yNdye2I/HZPbFLIiLKM0r8r737Do+iWsMA/s7W9BBa6AQIHREBEUHwKqggIkiRLkiRIlWaKIgFFUWkF+lNQCmioqAiiCjSUUBqgvRQE9KTbXP/iBsTSCa7yc7OzOb9PY/PJWEz+/HeM5M5386cKa3HpNeCcOCoFSvWp8ryHmqfXJG6yTF+5JhEKzVJ18r+xSYGkXqFh4ejbt26AIA6derg008/dXsbLjUwbt++7faGibTIHlkWIkQIO/YgdukH9zUx7KdPwX7uHBwXL8Jx/TrE2FiIyckQbVaXF6cjIiqs2rf2w1OPm/HpZ0k4E23z6La1MrkidZJr/Mg1ic46SfcGufevRg8ZPbo9NjGIfJdLq2Z8+OGHePfddxEYGCh3PUSKSr1zAbbAZASmBMCYpkPqxb/hV6565t9blywGclstV6cDzGbAZIZgNgEmM2A2QzCZALMZxp69IBj/+wXtuHwZjsuXIJjMgNkEmEwQ/v15mE3/ft8MGI0QBEHufzoRkewEQcC744LRtpcV495NwIbFYTCZCn58Y/OCCkLO8SPnwpLOSbpp2AiPbvde3mhezJoS6vHtcmFPIt+UZwPjwoULOHz4MJsXVCj4V6yNmNANSAxKgqA3oHTF2tn+3jRsBERLOpBuASzpENMtQHr6v39OBywZX4sW5/ctEJOSgNjYjAZHFo6Tf8P2/Xd5FyUIgClrg8MEfdPHYHisWbaXWb/5OqPZ8W/DxPm/GQ2VjJ/L/n0TBJ1LF2EREXlM0TAdpkwIxqCx8ZizNBmjBxdsUXA2L6gg5B4/ck+ivTEp90bzYsTEeKyeF+bx7bOJQeR7JBsYsbGxGDhwIIYMGeKteogU5VeuOkr3eBupF/+Gf8Xa2a6+AABdxYoeey/9E09C/0jjexofGc2RjP/9twGSQ2NEMPtl25bocMD+6+7crw7JjdEImM0wv/d+tmaG/chh2E+cyN7syKEhIpjNsMTHIDX5BvwrP3BfXkREOXmiqRkvPu+HJZ+n4PEmJjR80JSv7bB5QQXhrfGj9Um0N5oXzu3rIiNlu1LFmT8RaVuuDYykpCQMGTIEQ4YMwXfffYf58+ejVKlSqFevHpo3b44mTZp4s04ir/ErV90rE3HBeVWFJ7al08Hvk08hOhwZzY7MxkfuDZHMxojNdt+VGGJCAsSLF+DIciUJJNb40BssuF58M0r1mswmBhG55PXhQfjjkAXj30vA1yuLIijQvSvC2LyggvD2mg5abmJ4q3kBQPbbbby1ZggRSdu6dStu376NPn36YMOGDUhOTkafPn1c+tkcGxgpKSkYM2YMxowZg4YNG+LTTz/F7t27cePGDRw8eBAzZszA3LlzsXbtWk/+O4iogASdDvDzA/z8CtQYMfzvCRj+90Tm16IoZlzdke22GQsSj+xA6pE9CE0MRlhsEFLPH2cDg4hcEhigw0dvhaDnkLtYsT4VQ/u5fqsqmxdUEEqt6aDlJoYnSeXvC2uGEFHeTpw4gQsXLqBPnz747bffYLFYXG5g5Phxx4cffogJEyagYcOGAIBq1aph06ZNCA8Px3PPPYeSJUtiwIABHvsHEJG6CYIAwWSCEBQMXbFi0JUpA11EBMyNmiE12IK7IfEwW8zwj7qjdKlEpCEN6pqw6JNQvNIrwOWfYfOCCspbazrkpLA/HSOv/VfufApr04hIbV5//XUsXLgQADBr1iwsWLDA5Z/NsYHx+uuvo2KWe/2bNGmCpUuXYvjw4Th27Bg6deqEJ554IqcfJaJCxLlmiF+rF4BnnoCpbQelSyIijWnW2Ozyk0jYvCBP8OaaDjkprE0M5kNEnpDjLSTOJ47MnDkTZrMZXbp0Qfv27ZGcnIz3338fY8aM8WqRRKRe3lozhIgKNzYvyFOUbF44FbbbSZgPETklJSUhKCgIx44dQ9myZVGsWDG3fl5yxayRI0fCbDZj/vz5CAsLQ7ly5TBr1ix88803sFgsBSqciIiIKKvL1+zYvTf9vu+zeUFqlt/xWViuNGA+RORktVozl6LYsmULEhIS3N5Gnkt+9+3bFy1atEB0dDQAwGQyYdSoUTCZ8vfIMyLyfaLDAesX62E/eEDpUohIQ96fkYhRbyXgSow983tsXpCaFXR8+voknfkQFW6iKGL06NGwWCzYsWMH/vnnn8wrLu7evYsSJUq4vU2Xnln26KOPokqVKm5vnIgKKYcd4q1bsK79HPa/TyhdDRFpxKTXggEAr7+X8YkMmxekZp4an746SWc+RJSWloaYmBiYTCaULFkSGzduRI8ePTBt2jSYzeZ8bdO9h64TEblAMBhh7D8AQtmyGfeu/nsFFxGRlLKl9Zj0WhAO/mlF+TI62Z8WQZRfnm6u+doknfkQEQCYzWYkJibCZrMhPDwcFy9eRJkyZZCQkAC73Y4ff/wRx48fx5kzZxAfn/PTm+7FBgYRyULw84Np0GAIYWGwLP4MjqtXlS6JiDSgfWs/PPW4GddvOjBg9F1ZF1wkyg+5rgzylUk68yEip3/++Qd16tRBjx49sGPHDhw4cADLli2DIAi4du0afvrpJ+zYsQM//fQTtm7d6tI22cAgItkIQcEwDX4VMJlhWTgfjtu3lS6JiFROEAS8Oy7jVpITp20e337WyRWRu+S+rUnrk3TmQ0RZValSBWPHjkX9+vVRuXJlBAcHo2XLlti5cyeefvpp9OnTB6NGjcLQoUPRo0cPl7bJBgYRyUooWhSmIUMAmw3W+fMg5mO1YSIqXIqG6WD1fO+Ca2pQgXhr/Gh1ks58iCgnRYsWxe3bt/Hoo4+ie/fu+OWXX/Dee++hatWquHLlitvbc7mBsXXrVqxYsQIAsGHDhsw/ExHlRVeqNEwDB0NMTIBlwTyIKSlKl0REhQybF1QQ3h4/WpukMx8ikjJt2jRcvnwZDRs2xKuvvoqdO3eiXr16uHjxotvbcrmBceLECezbtw8A8Ntvv2H//v1uvxkRFV66iAgY+/WHeOMGLEsWQbRYlC6JiAoJNi+oIJQaP1qZpDMfInJF+fLl0bBhQxQtWhTjxo2Dv78/OnXq5PZ2XG5gvP7661i4cCEAYNasWViwYIHbb0ZEhZu+Rk0Ye/aCeP48rCuXQ7TblS6JiHyc1ORKtHpvsiXaZLgnhmSndPNL7ZN05kNE+REcnLHWVYUKFdz+WYOniyEikqKv3wBiSgpsG76Edd1aGLv3gKDjcjxE5Hl5Ta4EoxFpI4YBAHSRkTC+3Dfj0c9RUR6vxW/WHI9vk+Sl9OTcKeskXa7xmR/Mh4gK6vTp0yhfvjwCAwNd/hm3Zg1RUVFYuHAhPv/8c7eLIyJyMjzWDIZn28Bx8ABsX2+BKIpKl0REPsbdyRU/yaWs1DI5d1Lb+GQ+ROQJ06ZNgyAIbv2MWw0MURRRokSJzFtJiIjyS//0M9A3fxywWgE2MIjIg/I7ueIkiAD1Tc6d1DI+mQ8RecKGDRtQvXp1BAQEuPVzkg2MP/74I9ujTapWrYp27drhzp07cDgc+auUiAiAIAgwvNABhs4v8hYSIvKYgk6uOAkq3NQ6OXdSenwyHyLyhI0bN2Lbtm147bXX3P5ZyVmD3W7HqFGjYMnytIDk5GQEBQVBxwkHERWQoNO5fdkYEVFuPDW54iQoZ95a9NSbi6tmJcfkXI7xo9T4VHvzwon7L5H6JCYmYtSoUQCAFStWYNu2bXjllVfy1VPIdRFPh8OBevXqoWnTplixYgUGDBiAmzdv4rPPPkOzZs3yXz0RUS4cN28CCfHQRVZVuhQi0hhPT664MOD9si56Csi38KkSC57KNTmXa/xkHZ/eIHfzotFDRo9uj/svkfdZLBaYTKYc/y44OBgHDx7EtGnTkJiYiIULF2LAgAGIj4/Hww8/jLCwMJc/1MyxgXHz5k2MHj0aaWlpMJlMEAQBBw8ehL+/P8qWLYvJkyfn/19GRJQL25dfwHHpIkxDh0OXj8cqEVHhJNfkipMgab6Sj5yTcznzceZvGjbCo9u9lzeaF7OmhHp8u74yPonU4tChQ5gyZQr8/Pzw5JNP4pVXXgEA7N69G3PmzEFoaChu3ryJ5cuXo3jx4gCAefPmYd++fbBarYiNjcXSpUtRokQJnDp1CjExMRg3bhwMBgMqVqyIzZs3u1RHjtdslCxZEqtXr8aGDRtQq1YtrFmzBosXL8aECRMwdOhQhISEeCgGIqL/GHu9BCEwEJaFC+C4cUPpcohIA+rUMMg6ueLl6NK0no/ck3O58/HGpNwbzYsRE+M9vm1A++OTSC2Sk5Px5ptvYt68eVi3bh2io6Nx+vRpAMCxY8ewdu1aLF26FI8++ihOnDiR+XOvvvoqVq9ejfXr16Nnz54YN24cTCYT2rRpg5kzZ+KRRx7B4cOHXW5eAC48heTcuXOZa2CYzWYMHjwYNzixICIZCKGhMA5+FRAEWObPg3g3TumSiEjFjAYgLV3EsDfuynpPPidB0rSaj7fWdNBqPk7eaF44t+9La4YQacWtW7dw5cqVbP8lJCRke82OHTvQunVrlC1bFoIgoH379ti7dy8AYNiwYTCZTDh37hxOnTqFpk2b5vg+Dz/8MGrXro1NmzZh79692LNnD4YNG5bja6Xk2sCIjo7GjBkzEBMTg/79+2P06NH4+OOPkZCQgP79+7v9RkRErtCVLAnT4MFAWios8+dDTE5WuiQiUqFGDxnx4ZshiPrHjkN/2WR/P06CpGktH2+v6aC1fLLyVvMCgKxXqnhzzRAiLRk5ciRatGiR7b+VK1dme01UVBQaNGiQ+bXFYoG/v3/m17dv38b48ePx/vvvw2jMeU2bp556Co888giKFCmCRYsWweFw4OzZs27Xm+sinkWLFsXTTz+NFi1aQBRFABmPPbTZbLDZ5D9RIKLCS1euPEwDXoFlwXxYPlsA06vDIJjNSpdFRCoh92XnueE99dK0ko9SazpoJR+5SeXvC2uGEGnNzJkz8cADD2T73r1LRoSGhiImJibz68OHD2c+2CM9PR2jRo3CG2+8gQpurGE3evRo7Nu3z+16c70CIywsDLVr10bdunXx4IMP4sEHH0TdunVRv359NGrUyO03IiJyhy6yKox9XoZ46RKsS5dAtKn3kW1E5D1KP8pRy5+ke4MW8lFyTQct5COnvPZfX1gzhEhrSpQogXLlymX7794GRsuWLbFq1SrExcUhOjoaBw4cyLwiY9KkSejRowcaNmzo9ns3btzY7Z9x/8Gr+XDo0CG0b98eXbt2xaJFi+77+7lz5+L33393+fVEVDjoH6gLQ9fucJw5Deua1RAdDqVLIiIFKd28cCrsk9C8qD0fb67pkBO15yMX5kOkXREREejbty/69OmD999/H1OnToVOp8OpU6fw448/YubMmWjVqhVat26NjRs3ylqL7A0MqRVLnc6ePYvy5cu7/HoiKjwMjRvD0K49HEePwrZxQ+YtbURUuKileeHESZY0NeejZPPCSc35yIH5EGlfhw4d8PXXX2PZsmWIiIhAhw4dEB8fj3379mH79u3Yvn07tm3bhk6dOuW5LYfDgU8++SRfdbjcwPj6668xduxYXLlyxa03kFqx1Ony5cuZDQxXXp+QkHDfSqm3bt1yqy4i0g7Dky2gb9ES9t9/g23b90qXQ0ReprbmhRMnWdIKSz75HZ/MR1phyYdIq5YvX45HHnkEfn5+bv+sTqfDr7/+mq/3zXURTwCw2+3Q6/UAgHnz5qFx48bo168fHnvsMbRt2xb16tXL8w2ioqKyrZlx74qlaWlpMJvNEATBpdcDwMqVKzF37ty8/3VE5DMMbZ8HkpNh/2E7hMBAGB7/n9IlEZEXqLV54cSFGaX5ej4FHZ/MR5qv50OkZaGh9y9WfC+bzYbp06ejcePGSElJQXh4OC5duoT27dsjMDAwX+8r2cB4+umn8cMPP8BgMCAuLg6TJ08GAEycOBEjR47EL7/8kucbSK1YCgDnzp1DZJaual6vB4DevXvjhRdeyPa948ePY+TIkXnWQ0TaJAgCDC92gZicDNu276Fv+DCEfB74iEgb1N68cOIkS5qv5uOp8cl8pPlqPkSFgcFgQKtWrfDxxx/j77//Rs2aNXH58mVcuXIFMTExWLp0KUJDQ1GkSBHUr18fRYsWzXObkreQGAwGnDp1CkDG5EGv10Ov16NLly4uFy21YikAnDlzBtWqVXP59UDGY13uXSm1RIkSLtdERNok6PUw9u4D06jX2Lwg8nHeeNSlJ/Fyd2m+lo+nxyfzkeZr+RAVJqmpqahatSpat26NxMRE1KtXD3q9HiVKlEB4eDgCAgJgNBphMplc2l6OV2CcOnUK77//Pm7fvo1x48bBZDIhMTERN27cQHh4OCpXruzyQnpZVywtVqxY5oqlTmfPnkWLFi1cfj0RFW6C0QghvJTSZRCRzLzxqEtP4yfF0nwlH7maa8xHmq/kQ1TYmEwmDB8+HAEBAdizZw/q1q2LsLAwWCwW1K5dG5UqVXJrezl2BsqVK4elS5fi8OHD2LZtG77++ms8++yz2LdvHwAgICAAFovF5TfJacVS57b+97//oVatWpKvJyKSIiYlKV0CEXmYNx51KQd+UixN6/nIfWUQ85Gm9XyICiPnrSF+fn7Yv38/wsPD8cMPP+Cpp56CzWZze3s5XoERHBx83/dat26NFStWIDU1FUePHs1224e7li9fjpCQEABAkyZN8r0dIiLbjp9g2/0LTCNHQVesuNLlEJGHeOtRl7rISI9/kstPiqVpNR9vrcnCfKRpNR+iwmrx4sVISUmBKIrYunUr7HY79u/fj5deegl6vR7Hjx9HzZo1UbNmTZe25/K9GcWLF0dwcDAMBgNGjhyJlStX5vsfERoamvnUESKigtDVrgPYbLDOnwcxIUHpcohIpXKbXMn1SS4/KZamtXy8vaAs85GmtXyICrPnn38enTp1Qtu2bVGzZk2YzWYkJCRg3759SE9Px82bN5Gamury9lxuYNSrVw8LFixAp06dULp06XwVT0TkabrSpWEaOAhiQgIsC+dDdOMASESFQ8MHDej0nF+Okys5J0FZJ1l0P61MQpV6Gg7zkaaVfIgKu/DwcJQtWxZVqlTB8OHDMW7cOPz444+oUaMGunXrhkGDBqF+/foub4+rYxKR5ukiKsHYtz/E69dhWbwIohtr9BCRb2v0kBF1axkx7r3EHCdXck+CnNvXutMXYrHh57M4A88ufqr2SajSj/JlPtLUng8RZffQQw9Bp9MhICAAgwYNgl6vd3sbbjcwTp8+jeTkZLffiIhITvqaNWHs2Qvi+WhYVy6HaLcrXRIRKcw5ufrrb+mJlTeaGFpwJ9aBH3al4cuvU7FoVTI+npeENz5IwEvDb6Hb4FjMXJSIdwwNCk0TQ+nJuRPzkabWfIhIHjku4ill2rRpmDNnjhy1EBEViL5+A4jJybBt3ADr+rUwdusBgY9hJiqUsk6uDh/Le5VzrS8MmJ4u4m6CA/EJWf433oG7CQ7cjRcRn+DA3QTn/2Z8b84HoahXx5i5jbPnbRj+5n9rCZlMQJEQHQS9DQ6bDrcvl0dqUhCONkhG9fj9Hq1fbbfbqGVy7qS28cl8iKgg9u3bh7///hv9+vVz+2fdamBs2LAB1atXR0BAgNtvRETkDYZmzYGkJNi2b4MtIBCG9i9w0WCiQia/kys1TIIsVhEJiTk3H4IDBbzYzj/b60dOiscvv6cjNS33bRqNQJFQHYoECygSqkOFsno8UFOH4KDsx8YHahrw9cowFAnVITREB3+/jL8/fSEWby48hFtXSuDGPxFY/fdTaNogHeVv/OnRf7szf9OwER7drrvUNjl3UsP4BJgPEbnvwIEDaNSoUebXn376KWbNmpWvbbncwNi4cSO2bduGRYsW5euNiIi8Rd+qNcTkZNh/2QUhKAiGp55WuiQi8pKCTq48NQmyiQISbP7w11ngr//vCpBbd+xYszE1y5US/zUp4hJEpKSIuW6zRlXDfQ2Mh+sZUbpkRsMho/EgoEiIDkVChMzv+fvBpUZuUKAONaref9VajYiieH9QUxyPvg3rmq8w65/n0P2XtpjWMAiNk39zI5W8KT3pVOvk3EnpSTrzIaL8GDp0KPbs2QOz2Yxjx46hVatW+X4wSK4NjMTERLz11luYMWMGVqxYgT179uCVV16BjpdjE5HKCYIAQ4eOGbeTbP0WCAyEoUlTpcsiIpm5M7nS6a34csdZVEcoqiM+29/dOwmKP30ZsbZAxNv8EW/zx12bPxJs/oi3//fnzP+1Z/xvot0PADC18ma0LvZ35raTkkUsWp2C0H8bDGEhAkoW16FaFUNm8yE0REDYv1dBhIZkXDVRJERAYMD9TYgeHb1zVWyNiKKoEVEUad/MQaMa1zDiYi8M/vUJjK0Xgm667+ELF7rJMTnXRUZ6fBJ97/j0FrU3L5zYxCBSn0qVKmHSpEkYMmQIzGYzAgICMG/ePIhiRtPe39/f5dtJcm1gBAcH4+DBg5g2bRoSExOxcOFCDBgwAPHx8Xj44YcRFhbGy7KJSLUEnQ7GHj1hTU2B7csvIAQGQv9gPaXLIiKZuDO5MpjSYLP4YcZnyTAb2+Axyy0YbQLu2gJg1NnxbqVvs02CPh74N775p/J92xEgIlifhiKGFIQY0hBmTEZFvzsoYkhBqCENoYYU1AyMyfYzEeX1+PvXEtDptHsOVc58F6uqfIaJN7vj46MN0PCZm6h255DSZRWIXJNzuSbR3l4zRO7mRaOHjHm/yA1sYhCpS+XKlXHz5k0MHjwYly9fRv/+/REWFga73Q6bzYaKFSu6vK37Ghjz5s3Dvn37YLVaERsbi6VLl6JEiRI4deoUYmJiMG7cOBgMBlSsWBGbN2/26D+MiMiTBIMBxpf7wTJ/HqwrV0IYWRS6ChWULouIPMzdyVVQsTtITwlE7NWyAIANqIJgfRpCDKkoY7qb+TrnJKjzq73ReONuhNy5gNB/mxNFDCkI0qdDL+R+y0dOBEHwiasVAvUWTC+1An9VfxQPvNYB1uV3NTtJlHNyLuck2ltrhnijeTFrimefbAOwiUGkJpUqVUL//v1RpUoVbN26FatWrcKUKVNQrVo1t7d1XwPj1VdfxauvvgoA+OCDD1CqVCl8/vnnaNOmDerUqYNFixZxHQwi0gzBbIbplYGw7fwZQpkySpdDRB6Wn8lVWmIIytY8CdFmgkmXjsnCQdQS7ub4WkdUFOphJR5+1zkJuubB6gsuPV2E2axMR0QnAA8l/AHr8luanSTKPTmXexLtjby90bwYMTEeq+eFeXz7bGIQAIhWKwSjZ6/yUcN7ackrr7wCADh48CCee+45BAcHY8mSJfjggw9gMLj3YFTJVz/88MMICQnBpk2bMG7cOMTFxWHYsGH5r5yISAFCYCCMbZ9Xugwi8rD8Tj7TkoJx9VQtTBxvR7VvVt23Bsa91DwJMpsFVG9yM8e/8/Tk/Mzekjl+P2s+X0/+GeKN69nW/VArb63poObx4wpvNC+c2/elNUNIPQSjEWkj8j+H1UVGurz/+s2ak+/38WX//PMPLl26hA8//BC9evXC8ePHERIS4nbzAgAkV+R86qmn8Mgjj6BIkSJYtGgRHA4Hzp49m+/CiYiIiDyhoJPPtKRgdG5RLc/mhVPWSZAuMtLt9/M2by+46IiKgmXZMnyd/hheP98Bc648AYd7d9d4lbfXdNDa+MnKW80LALLl4+01Q8i3aHn/VYu9e/fi+PHjqFChArZv345y5crht99+w4IFC9zelluPFBk9ejTKli3r9psQEamJeDcOlqVLICYnK10KEeWDUk9D0MpJrFL5iNFRmBu5Dp1b67Ek5jGMinoRSXaT197fVUqt6aCV8SM3qfzlzMeZP1F+cP8tmB49emDo0KF44403MHjwYAwdOhSTJ0/G7t273d6W289Ebdy4sdtvQkSkJuKdO3Cc/BuWzxZCTE9XuhwicoPSj3JU+0ms0vno/zmLSaFrMPFVI/bEV8VLp17G5TTPr21QEN5a0yEnah8/cstrfMqdj9Zu3yF1Kez7rydERESgSZMmAIBHH30U69evd3sbbjcwiIi0TlclEsbefSBeugjrsiUQbTalSyIiFyg9OXdS60msWvIRo6PQJXYVlkz1w217KHqc7Iv9CRGK1XMvb67pkBO1jh+5MR/yBRyfynO5geFwOPDJJ5/IWQsRkdfo6z4IQ9fucJw+Deua1RAdDqVLIiIJapmcO6ntJNbbazrkxREVhYZ/rsSGRSEoHmjF4DM9sO5GQ4gqWBdDyeaFk9rGj9yYD/kSjk9ludzA0Ol0+PXXX+WshYjIqwyNG8PwfDs4jh6BbdNGiGo4syai+6iteeGklpNYpdZ0yIsjKgqlf1iBL1aGo1nZa5h6qTXevdAGVodvXQCc3/zVMn7kxnzIF3F8Kue+3yA2mw0fffQRdu/ejW3btuHIkSPYsmULACAwMNDb9RERycrQoiX0LVrC/tse2LZ9r3Q5RHQPtTYvnNRwEqvkmg55cURFwfzlcsxfGokBtU9g8+36GHCmF+5YAzxcqTIKOj7VMH7kxHxILeR+ug3Hp/fc18AwGAxo1aoVFi1ahAkTJmDatGn45JNPMHfuXMTExGDp0qXYuHEjduzYgdjYWCVqJiLyKEPb56F/pDHsP2yH7Vf3V0MmInmovXnhpPRJrNJrOuTFERUF+8rlGD3jYXzc5HecTC6NHif74XRKuAer9T5P5uOLkyDmQ2rijUf0cnx6R47X8KWmpqJq1apo3bo1EhMTUa9ePej1epQoUQLh4eEICAiA0WiEyaS+R2MREblLEAQYunSF7oG6sG3aCPuhQ0qXRFToqW1Nh7woeRKr5uaFkzOf599uiZVP/wyHKKD3qZdxJLG8B6r1Prny8ZVJEPMhtfHGI3qNL/f1+LbpfoacvmkymTB8+HAEBARgz549qFu3LsLCwmCxWFC7dm1UqlTJ23USEclK0Oth7N0HloXzYf18NRDgD32t2kqXRVRoqXFNh7xkPYm1Ll+m2Uc2ytU8cuZTb3RfrPdfi7m/V0SNgOse2763yJ0Px0/OfCUfUobc48e5fdOwER7dLt0vxysw6tevj6JFi8LPzw/79+9HeHg4fvjhBzz11FOw8XGDROSjBKMRpgGvQChTBtZlS+H457zSJREVWmpd0yEvWv+kWO4rX5z5lB7SHW+3PIMAvXpvDcqJt/Lh+MmZ1vMhZck9fthU844cGxiLFy/GrFmzMHPmTGzduhXvvPMOFixYgGPHjuHPP//E5s2bcerUKW/XSkQkO8HPH6ZBQyAUKQLLZwvhuHZN6ZKICiVv3RbBe6L/4601R3LL5441EFfSi8j2vgWldD5qx3xICzh+tC/HBsbzzz+PTp06oW3btqhZsybMZjMSEhKwb98+pKen4+bNm0hNTfV2rUREXiEEB8M4+FUIgUEQ4+8qXQ4ReUBukysu7JbB2wum5pTP5H+eQ4+T/bD/iEX293eXGvJRM+ZDWsLxo205NjDCw8NRtmxZVKlSBcOHD8e4cePw448/okaNGujWrRsGDRqE+vXre7tWIiKv0RUrBtMbb0Jfs5bSpRBRAUlNrriwm3JPe7l3EjG2wk8IM6Sg74i7WLs5xWt15EUt+agV8yEt4vjRrhwbGFk99NBD0Ol0CAgIwKBBg6DX671RFxGR4gQe74g0L6/JlTfuibYuX+bx7XqK0o+qzZp/pTpFsbrmMjz2iAnvfJKEydMSYbGKXq8pKzXlo8ZJFvMhLeP40aY8GxhERJTBfuIERIv6Lm0mopy5OrkqrAu75WfyKfeVKqE1ymP+R6EY0DMA679KRd8RdxEb5/D4e7pC6cm5k1onWcyHfAHHj/awgUFE5ALHjRuwLlkE68oVEO12pcshojy4O7kqbCex+Z18euN2G71ewJghQZg2OQTHTlrRqV8sTkd59yl4apmcO6ltfDIf8iUcP9rCBgYRkQt04eEwdOgIx4njsK1fB1FU9rJmIspdfidXheUktiCTT2/ebvP8M35YMz8MVhvQbWAcfvwlzePvmRO1Tc6d1DI+mQ/5Io4f7WADg4jIRYbmj0PfqjXsB/bD9s3XSpdDRDko6OTK109i1Z7Pvbfb1K1lxKZlYahaWY9hbyRg7tJkOBzyNZDVOjl3Unp8Mh/yZRw/2sAGBhGRGwytWkPfrBnsO3+G7ecdSpdDRFl4anLlqyexWs2nZHE9Vs8NwwvP+mHO0mSMnJiA5BTPr4shx+Tclx7Rq/bmhZOv7r++KD1dfVezcvyon0HpAoiItEQQBBg6dIKYnJxxFUZAIAyPPqp0WaRy6ekizGbBZ95HjTw9ucp6Emtdvky1C3G6Suv5mM0CPnwzGNWrGPDxvCRcGGTH/I9CUa60Z54WJdfkXK58vP2IXrmbF40eMnp0e762//oqs1lA9SY3C7ydvMbnmb0l3doex4+6sYFBROQmQaeDsUcvWFNSYftiHYSAAOgffFDpskjFCnqS5urkwd2TNF8h1+TKV05ifSUfQRDwcrcARFbSY9y7CYi5bvdIA0POybmc+TjzNw0b4dHt3ssbzYtZU0I9vl1f2X9Jmq8c38h1vIWEiCgfBIMBxr79IFSsCOvKFbCfO6t0SeSjtHLZtlLkzkfrlxP7Yj7NGpvx86biePghU4G3pfV8vDGp8kbzYsTEeI9vG9D+/kvStL7/Uv6wgUFElE+C2QzTK4MglCgB6+JFcFy+pHRJ5GPYvJDmrXy0ehLry/kE+Ge/VUoURdjt7t1P78v5eJI3mhfO7fvSmiEkL+6/hRcbGEREBSAEBsI0eAgQGAjLggVw3LihdEnkI9i8kObtfLR2ElvY8vl8UypeHnEXsXddW9zT22s6KJ1PQXireQFA9kf0emvNEJJXYTu+UXZsYBARFZBQpAhMg18FBMCyYB7Eu3FKl0Qax+aFNKXy0cpJbGHMJzhIh6MnrOjcPxZno22Sr1VqTQetjB+5SeUv9+021uXLPL5d8q7CeHyj7NjAICLyAF3JkjANGgKkpML23XdKl0MaJnVyJkCEHvd/whzpfxPPhR/A3e1rkHbljLdKVYTSzR21n8QW1nzatfLD5/PDkG4BugyMw45f03N9rZJrOqh9/Mgtr/HpC2uGkHwK6/GNsuNTSIiIPERXvjxMw4ZBKBmudCkkAzE9HbDZAKsVos0G2Kz/fm275+uM/834XsZ/+qaPZdvWw8EX8Hzxv2ASbDDpbDAJdph1NoQFO1C5jIjrk9MxyWaD+UEbjP/+nUmwwahz4IubDfDBxWezbW9ZjRUINaQD24CkX38BXhkFv4jaXkzHO5Q+eXVS6+r0hT2furWM2LQ0DK++Ho9XX4/HiAGBGNwnAIKQfb0Mb67pkBO1jh+5MR8qiMJ+fKP/sIFBRORBuvIVlC7Bp6RdPInUf07Av0w1mEtWhGh1NgX+bRL82yyAzQZdjZoQTP89lcBx5TIcJ05kaSQ4f0b6a1it0D1UH8bn22WrJX3Ku0BCQr7+Hfr6DbJ9XdyYiAbBl2Bx6JEuGmB16GEKNKJSlQAciwJu3g2GxWGARdT/+78GWBx6WEQDTiSXuW/721IroEXZ/QiwmhGUHAjHstVwDB4BXenS+apXjdRy8uqktpNYb6/pkBel8gkvocea+WGYNDUBsxYn4+x5Gz54IyTbop9KNi+c1DZ+5MZ8qCB4/Kes2MAgIpKRmJ4OGI0QdIX3jj1RFIH0dCAlBWJKMsSUFCAlBUJwCHRVqmR7rWXZUoi3bma8JjkZsFrhDwDYg9wvCM9gnvwOULRo5teOK1dg2/Y9oNMBBkPGf0YjBIMBMBizfM8Awd8PMAT/93UOV9EYWrXOaHBkbuO/bcJggODcptFwz9dGwGQCcCtzW9tiH8C22Acyv270kBGz3gnFoHyenH11vREaReyF1T8R6f42FE3zh2X6NBiebwd9s+b3fQKtNWo7eXVSy0msUms65EWpfPzMAj5+KwTVI1PwyfxkXLwch3lTQ1GmlF6W98tv/moZP3JjPlQQPP7TvdjAICKSiWixwDJ3NnQVK8LQsbPmJ5Gi3Q4IQrZmjOP2LThOngSSMxoTYmoKkJzyb5Piv2YFHPev26CrXx+mexoYMBohFCsGXbnySLt7DalXT0IUHBAFAQHVH0ZgjUb/NR+MhuzNiJCQbJvSP9wI+ocbQdB7ZtJiuOc2EE/xxMnZ6buVMWHfSKx48xr8K9aGOaQ0rGs/h23TRjhOnYSxWw8I9+SjFWo9eXVSw0mst9Z0WD0vzO2fVyofQRDQv0cgqlYy4LXJCejULxazPwhFwwdNef+wGwo6PtUwfuTEfDwv7coZpF78G/4Va8OvXHWly5GVJ4//ushIj48fjk9lsIFBRCQTwWSCLrIq7Dt/BgKDYGz9bN4/5AWixZLRXMjWaEjN1nDI2oDIuBoiBUhPg2n8BAhl/ruFQbx6DbZNGzO+8PeH4O8PBARCCAgAwopAFxCY8f3AQCAgAMK//yEgEEJI8H21mXq9lPln+5UzuP35UYh2GwS9AUWeeAp6N07WPNW4kJMnT85O362MsKaNM782DhwE+55fYft6C9I/mgpj9x7Q19bWuhhqb144KX0Sq/SaDnlRMp/Hm5jx5eIwDBkfjz7D7mLy2PuPO/nlC/nIifl4XtqVM4j5/G2INhvu6g0o3fNtn21iePr4L9f4uXd8kvzYwCAikpHh+XZAcjLs27dBCAyEofnjHtmu6HAAaWkQU5L/u+IhNQVicnJmA0JMSYaueAkYnmmV7Wcts2dBvHwp5w3r9ZmNBgQEQAgtAqF0mczGgxAQkO3lupo1YX7/w4wmhYcbBn7lqqN0j7d99pMmuSfngiDA0Pxx6KpWg3XlClgXLYSjWXMYnm+Xba0QtVLbmg55UXKSpebmhZOS+VSJMODLxWEYNSkeEz9M9Mg2fSkfOTAfeaRe/BuizYaw+IxbulIvnPC5342APMd/OcdP1vFJ8mMDg4hIRoIgwNClK8SUZNg2b4ItPQGpAdbMCblos953y4UuohKE4P8+JXRcvQrbN1uyXx2RmgqIYu5vbDYD/gE5/pXhySchpqVBCAgEAgMg+P/bmAgMBEwmt251EUymf9d3kIdfueo8OSsgXenSMI0eA9vWb2H/ZRcc587C+FJv6MqWk/V9C0qNazrkxVcmWXKNTyXzCQ3RYdH0Ipg2Lwkrvkgt0LZ8MR9PYj7y8a9YG3cNBliMVoQmhQDRsRCbipq/RTUrrY4f5/ZNw0Z4dLt0PzYwiIhkJuj1ML7UB+mzp0Pc+gMMBhvsjh1I05sBq+2+1xtfGZT9Un9BgJiamnEbRomS0Dmvjsh6O0ZgAOCf5coJQ+6H93ufiEHeJWfzQrRaIRjvv7JAMBphfKEDdDVqwrp2DSyfLYT5rckZC43m971sNslxVlBqXdMhL1qfZMndXFMyH4NBwIQRwQVqYPhyPp7AfORhP3Mausiq2a5MxPk44NCfsJf7GYYWLZUu0SO0Pn4Ky3hUGhsYREReIJhMSG1QBeKtKAgOAaJBhLFCBPyrPpjllo2MRoRQokS2n9WVKQPza2MUqpw8SfbbRoxGpI0YBiBjwTKpk7T00a8V6L38Zs0p0M/nxVu3RXBht/9468og5iON+UjTaj75Zdu1E7YtX8HQrj0MT7bIvDJRfNQBq2MlbN98DSEkBPqHGyldaoFw/JCrCu9z/YiIvMy/6oOIL5qCuLB4xBdNgbFdOxiefgaGx5pBX78B9DVqQFe+AgQ/f6VLzVN6usTtKxp+Lzl5e02HrCdpushIj7+f1uSWv1z5aC1/by+YynykMR9pWssnP0RRhPXbb2Db8hV09epB37x5tr8XdDoYe/SErmpVWNd+Dvvp0wpVWnAcP+QOXoFBROQlvrQopdksoHqTm5lfy3nycWZvSY9uTylKrOnAT5oySI1PLuym3NNetDI+mY805uN5ot0O2xfrYd+/D/qmj8HQqXPmI8xPX4jF8ejbeKBKcdSIKApjvwGwzJ4J67IlEIaNgK58eYWrd49SCzb78vjxdbwCg4jIi/zKVUdY0w6abl7cSyuPulSaN9Z0yElh/6Qpr/Epdz7O7auV0vuv2scn85HGfDxPtFhgXb40o3nRqjUMnV/M1rwYP3s/Vn9/Cm8u/B2nL8RC8PeHadAQICAAls8WwHH7tsL/AvcouWCzL46fwoANDCIiyjetPepSSUo+6rKwnqSpJR+1frKXn/23MN1uo/Tk3In5SFNrPvkhpqbCsnABHCdOwNCxE4ytn818wsiNW3ZMnZOMMwfqIf5WcdhsDhyPzmhWCKGhGU0Mux3WhfMhJnnmccHeoFRz38mXxk9hwQYGERHlizeaF3I86tJXuJt/YTtJYz7S8rv/emPNEDUQBODpx82KT86d1DY+1dK8cFJbPvkhJiTAMmcWxAv/wPhSbxiaPw4AuBpjx+RpiWjR6Q727zcjtEQsAoKTYDDo8ECV4gCAk2esOHm3GEwDBkK8exeWRZ9BTE9X8p/jMiWb+06+MH4KEzYwiIgoX7zRvMjrk5PCKr+Th8JyksZ8pBVk8lkYbrep/4ABDeoaMWVmkiom505qGZ9qa144qSWf/HDcvgXLzBkQb92C8ZWB0NdvgItXbHjjgwQ8/eIdbPo2FR2e9cOPXxbDyhml0bdDJbw/qClqRBQFAMxdloyOfePQc1oYTjzUHeKlS7CuWA7Rblf4X+Z9PP77PjYwiIgoX7zRvFDTybFaFDQfXz9JYz7S1J6P0rfbNHrIiHlTi0AQ1Pn0I6XHp9qPz0rnkx+OK1cymhdpqTANHYYLflUx9p14tOoai60/pqHbC/74aUMxvDs+BOXL6FEjoig6t6iW2bwAgI8mhWDC8CDcuGVHj3kVMPfOs3Cc/Bspa9ZBFNU5luWg9uMbeQYbGERElC9sXnifp/Lx1ZM05iON+UjLms/BP20e2aYvrRmileOzlsanI+ocLHNmAXoDrrYfhlGLw9CmRyx+2p2OPl388fOmYpj4WjBKh+sltxMcpEOfrgH48YtimPdhKP4KeQSfXW2Gpdv88e4niTh/0TPjWc14fCs82MAgIiJV0MrJsVI8nY+vnaQxH2nMR5pcxx9fWTNEaws2a2F8Om7ehGXBfKT7h+Kd1H54dqQRe/ZZMLBXAHZuKo7xw4JRoph04+Jeer2Alo+bsXpeGJ7+4AXE1HkKG7amoXW3WAwYfRe/7U/3ySsyeHwrXNjAICIixbF5IU2ufApykqamk2A15qMmzEeanMcfX1gzRKsLNqt9fAolSuD8A8/hqZ97YsdfARjWLxC7NhfDqEFBKBpW8ClazWpGfDgxBL98VRzD+gfi5Bkb+o2Kx3M9Y7F+SypS09RzDC8IHt8KHzYwiIhIUWxeSJM7n/yepDkf7ac0teajFsxHmtbz8caaIVpesFnN41MQBER2fxJDXi2JXZuLYWi/QISGeH5qVryoDkP7BmLnrCQsHBYLk1HA5I8T8b/2t3HgqMXj7+dNWt9/KX/YwCAiIsWweSHNW/lo9SSN+UhjPtKYj2u8uWCzL60ZkpUoivhtXyq2rLmc7fsmk4A+XQMQFCjvlEx0OIBNX+DR4yuwcaoFn88vgiYPm1CtikHW95UT99/Ciw0MIiJSBJsX0rydT0FO0my//wbHhQvyFJYLLeWjBOYjzdtrOmgtn6y8uWCzr6wZ4iSKInb+lo5uA27jxtxVaLp/Nqyxd71aAwAIOh2MffsCgg62zxagQaVUzHgvFEXuueJj83ep2PpjGqw2dd9ewuObMg4dOoT27duja9euWLRo0X1/P3fuXPz++++y18EGBhEReR2bF9KUyic/J2mi1Qr7zp9hmTUDth+2Z3zSJzMt5aME5iNNqTUdtJKP3KTy94U1QwDA4RCxfWca2veJw2uv38Kr+rVoU+wEQto8BUOY59f7cIWueAmYBg6CmJQEy2cLIaal3veajVvTMPrtBLToeAefrUpGXLz8x3N38fimjOTkZLz55puYN28e1q1bh+joaJw+fTrba86ePYvy5cvLXgsbGERE5FVsXkhTOh93T9IEoxGm0WOhq/cQbN9/B8ucWXDcuSNbfVrLx9uYT97kzOfhegbJNR20kI+c8hqfWl8zxGYT8e2PaWjbKxYjJibAYEnGtifX4WH/aBi6doO51dOKrh+kq1ABxr79IcZcg3XpEoi27I9XXTOvCD6bFooqEXp8ujAZj7e7jUlTE3DuvDoew8rjmzxu3bqFK1euZPsvISEh22t27NiB1q1bo2zZshAEAe3bt8fevXuzveby5ctsYBARkW9R+uRD7dSSj9tNjIAAmHr3gbHnSxCvXoPl46mwHzrk8bq0mo+3MB/XyJmPXi/g1dfvSm5f7fnIxdXxqcV8rDYRm7am4tnusRjzdgIEAPNed+DzB1YhLPkajH37wfBoE6XLBADoa9aEsVt3OM6ehXXt59mumtPpBPyvqRnLZ4Xh29VF0a6VH77enobnesbi5RFx+OX3dDgcytxewuObfEaOHIkWLVpk+2/lypXZXhMVFYUGDRpkfm2xWODv75/5dVpaGsxms1cadGxgEBGRV6jl5EOt1JZPfk7S9A8/DNP48RBKl4Z19UpYVq+CmHr/Zcr54Qv5yMnbazrkRW35ZCVXPuOHBmH/YSuOHLfBFJCEDT+fxRnkfLuAmvORg7vjUyv5WCwi1m9JxTNd7uCNDxIRGCBgzgch+PoTKx47PB+4exfGQUOgr/ug0qVmo2/0CAzPtYXj8CHYvv0mx9dUq2LAe6+H4JctxfHaoEBE/WPHwLHxaN0tFms2piA5xXu3l/D4L6+ZM2fi559/zvZf7969s70mNDQUMTExmV8fPnwYkVn+7efOncv2tZzYwCAiItmp7eRDbdSaT35O0nTFisM0bAQMrZ+F48hhWD6eCsf58wWqw5fykYNSazrkRS35yM2Zz67f05Hx2bQDtnQ/zFyUgAlpzXBaLNxNjPyOTzXnI4oiVm9IQcvOdzD540QUL6rDZ9NCsXl5GFpWug7rnJmAzQbT8OHQV62qdLk50rd8CvpmzWDf+TNsv/yS6+uKFtFh4EuB2Lm5GD59JwShIQLe+zQJj7e/g6mzE3H5ml3WOnn8l1+JEiVQrly5bP+FhIRke03Lli2xatUqxMXFITo6GgcOHMh2RcaZM2dQrVo1r9TLBgYREclKrScfaqH2fPJzkibo9TC0ag3T8JGAIMAyeyas338H0e7+ia4v5uNp3mhe5LamQ17UkI+cchqfISVvwhyQjNuXKyD6+EPo/1d/vHuhDXbFVUOK3XeeTuKKgu6/as1HEATs/sOC8mX0WDazCL5YFIb/NTXDceYMLHPnQPDzg2nkKOjKyb8eQH4JggBDh07Q1X0Qti2bYT96RPL1RoOANk/54cvFRfHFojA0b2zCqg2pePrFOzgbLc8aGZ48/vvqI3q9JSIiAn379kWfPn3w/vvvY+rUqdDp/mslnD17FtWrV/dKLapsYCQnJytdAhEReYDaJ59KkyMfNZ2k6SpVgmnceOgaPgz7D9thmT0Tjtu3Xf55rYwfpU9ivdG8KMj2lc5HLrnlY0kJRNmapxHZ8BDKVj6LWkHXsP1ObYyM6oLmR8fgjfPtsm2nsOXjLjXkk5zigChmX/th1pQQfL4gDE0bmSAIAuxHDsO6aCGE4sVhGjEKuhIlFKnVHYJOB2OvlyBUqgTr6tWwnzvn0s/Vq2PEp++GYuemYhj3ahCqVtZ7vDZPH/+98YheX9p/c9KhQwd8/fXXWLZsGSIiItChQwfs27cPAPC///0PtWrV8kodXmlg5PbMWKvViokTJ6Jz58546aWXcOXKFQDA6NGj0bZtWzzzzDMYPXq0N0okIiIP08rkUyly5aO2kzTBzx+mnr1g7N0H4o0bsHz8EewHD+T5c2pb0yEvSp7Eqrl54eRrJ/lS+aQlBePKyVro0y4SM4ptx6LIddj90CdYVH01upY8iLLmu/dt78yxBKQsXV4o8skPpcbP3QQHZi9Jwv9euIN9h7P/OwID/ptG2fb8CuuqlRAiImAaNgJCqDKPSs0PwWSCacBACCWKw7p0MRzXrrn8s6VK6vFyt4D7Fm58fUoC5i5Lxp3Y/K2TIcfx3xuP6DW+3Nfj21az5cuX45FHHgEANGnSBMHBwV55X9kbGFLPjF25ciUCAgKwYcMGTJo0CTNmzAAAxMbG4ttvv8UPP/yA6dOny10iERF5GJsX0uTMR60nafr6DWAe9zqEcmXhuH49z9ercU2HvPjKJF2u8VmY8klLCkbnFtVQHRm33hh1DjwScgFjKuzAq2V3Z3ttukOPnqdeRvON7TGs3xlsLfsS4srVlv3fIRdfGD+xcQ5MX5CEJzvcwbxlKXikvgnFi+Y8bbLt2gnbxg3Q1a4D06AhEAICZK1NDkJAAEyDhgAmc8aTScT8P2nEYhURd9eBOUuS8fgLtzFhSgJOnXV9HGh1/Di3X5iEhoYq8lhg2RsYUs+M/fbbbzF8+HAAQNWqVZGQkIA7d+7k2b1JSEi471m1t27dkvufQkRELmDzQprc+aj5JE0oWhSmocNheLZNnq9V65oOedH6JF3r41Nustz2BREfVt6Cp8NO4s/rRTDhEyue2NIBPc8PxsKrzXAyuRQUenKl27Q+fm7etmPq7EQ82fE2Fq9JweOPmvDN6qKY+2EoqlY25Pgzupo1oX/8fzD27QfBZPJ4Td4ihIXBNHgIjC/3LdCk1GQU8NknRbBtXVG8+Lw/tu1MQ/s+cej1ahx27E6H3Z77YNb6+HFERXl8m3S/nPdED4qKikKjRo0yv3Y+M9ZiscDPzw9BQUH/FWMwICwsDMOGDYPFYoHRaMT69evRrVu3bNtcuXIl5s6dK3fpRETkJjYvpHkrn6wnadblyzx+UlWQ7Qm6+z87cVy+BKFMWQj6/+6j9tZtEbrISFnykTN/ufjK+JSLbLd96Rx4MuwMngw7A1EETqeUwm/6R7DH+iAWnn4cC679D8WNiXgsNArNQqPwaOh5BOotHnt/T9Hy+Im5YcfiNSnY8G0q7Hag7dN+GPhSACpXzHuqpCtVGroOHQtcgxroSpf22LYqVzTgrdHBGDEgEBu3puHzjSl4dUI8ypXRoVenAHR8zg/BQf/9PtDy+CHvkv0KjNyeGWswGJCQkJD5/aSkJFitVuh0OtSrVw8mU8aCOF999RVSUlKybbN37973Pat25syZcv9TiIhIApsX0ry9poNWPukWkxJhmTMblpmfwnHzpmzvk1v+alszRCne3n+ZT84EAagZeB0D/L7GmofW4vcv/fF+4z9QP+gSfo6ridHRndH86Bi8cqYH1lxvhKvp6lhrQavj5/JVOyZNTcBTne/gy69T0e4ZP2xfVxQfTQrJtXkhpqdDdORvbQetEUWxwP/W0BAd+nUPwI9fFsPs90MQXkKPD2cnoXn7O5gyIxGAdscPKUP2BkZuz4zV6XSoW7cuPv/8c9hsNsyYMQPt2rXD1q1bsWnTJjgcDpw7dy5zYc+sQkJC7ntWbQkNrPRLROSr2LzImxJrOmjhJE0ICoaxew+It2/DMu0j2P74o0D3X+dEanyqdc0Qb1Jq/9XC+ASUzSf4q+Xo+O6TmN7qGHbVm44l1VehR/h+3LIEY9rlZ7DtTh2v1ZMbLY6f8xdteH1KAp7pegdbtqeh8/P++PHLYpgyIQQVyuV+1YWYmAjLnFmwffN1QctXPdHhgG3t57Bt2uiRY7LBIOCZJ/ywdkEYNi0Lw1PNzVj/VSqAjNtO+oyI8+qCzVo5/tD9ZG9gSD0zdvz48fjtt9/Qrl07lClTBm3btsXTTz+Nv/76C61bt8Ybb7yByZMnI0CDi+EQERUWbF64Rqk1HbRwkqav9xDM41+HrmJF2NavhXX5MoToUz2y7bzGp5rXDPEGpfdftY9PNeVjrlYZD4dcxGvlf8ZXDyzEd3Xn4IUSf2Z7/ZUYO4a9EY+NW1Nx645d9vrUlI+r4+f7n9PQpkcstv2chl6d/LFjQzFMHhOMMqWkHwXquHMHllkzIF6/Dl3Vqp4oX9UEnQ4ICoL9tz2w//SjR7ddp4YRH78Vgp2bi6FvN3/sO2LBmSjPj9e8FmxW+/GHcib7GhhAxjNjO3TokO3rcePGoXHjxliwYEG215pMJrz77rveKIuIiApIa4+6VJKSj7rUwj2/QpEwGIcMhX3XTti+24qNdaIx8Xw7HEislO9tqiUfNeYN5G//9caaIWqh9OTcKbfxWS6Hx7FeuWbHX39b8eMv6QCA2jUM+F8TE/7XxIw6NQzQ6Tz3xAC155ObRxuaMKBnAHq/GIBiuTxZ5L73uHYNloXzAYsFpiFDoatc2ROlq56h7fMQ4+Nh+24rEBoKwyONPbr9ksX1OHHaBpvNo5sFkH18rp4XluvrtPD7kbKT/QqMnGR9ZiwREWmTN5oXcjzq0le4m78WPmkSdDoYWrSEadRrSLGb8Fn1NRhZbgcMgvufzPliPp6U3/23sNxuo5bJuZOr47NxAxN2bymGr1eGYdTAQJiMAhasSEHn/nFo9vwdTJiSgO0705CYVLB1DbSSz7GkMtgRWyPba8NCdXhtUJDrzYvz52GZPRMAYBo+stA0L4CMY7Kxew/oqlWHbf062E/+7fH3ULK571TYjv9ap0gDQ6lnxhIRked4o3kh16MutS6/kwetnKTpyldAt5P9selWfbxc+g+srrkMEX63Xf55X8+noAoy+SwMt9uobXLu5Or4FAQBNaoaMah3INZ/Foa93xXHtMkhaNzAiJ/3pGPExAQ0bn0bLw2Nw7K1KYi+YHNrjQMt5HMk5FEMPNMDvU71w7yr/0N+l3Cw/30ClvlzIQQFwTRiFHRlyni2aA0QDAYY+/WDULpMxhUKFy8qXZIkHv99nyINDCIi0j5vNC/UdHKsFgXNRysnaakOE6ZcbIMR515EaVM81tdajE4lDgOQnokUlnzyS+35KH35ttqPP/nJPyxUh+ef8cP0d0Kx97vi+HxBEbzcLQBxdx34aG4Snu0ei6c638Gps3n/e9WcjygCew/b0aPHRbz8c0ucs5TGa+V+wue1liI/n5vaD+yHdcliCKVKZTQvihXzfNEaIfj5wzRoMISgIFgWLYTj1i2lS8qR2o9v5BlsYBARUb6weeF9nspHSydpv9ytjs5/D8SfSRUwKeJ7zIj8EkUMKTm+tjDm4w7mI02O44/aHtFrMAho+KAJY4YE4ds1xbBzUzFMHhOEyEoGlCuTfRHLmBt23Lj13+1baj0+iyLw691IvHTqZQw82xOX7/rh9fqH8fOGMLzc7DYC9O7Xatu1E9bP10AXGQnT0OEQgoNlqFxbhJAQGAcNAUQR1oXzISYmKl1SNjy+FR5sYBARkSqo9eRYLTydj5ZO0m5ZgzH4bHdMu/QUHguNxsAyv973msKcjyuYjzS5jj9qXzOkbGk9uncIwMJpRRAclH1asHRtCpq3uwMAKBOuQ/8eARj+5l3VLNjsEIGf46qj28n+GHauG25bgzCx4nf4ru5cdNN/D/3a5W7nL4oirN98DduWr6CrVw/GgYMg+Pm5XZuv0oWHw/TKQIjx8bB8thBierrSJQEAQoMFdG3v77Hx6WvHN1/DBgYRESmOzQtpcuWjpZM0EQLW3GiM7if7Ye6VJ7L9HfORxnykyXn80fKaId1e8MfowYEIDhRw45YDr4yJx8E/Pf+4CHcXbLaLArbfqYXOfw/Ea1EvItluwjsR3+CbB+ahc8kjMOkyrhpxd3yKdjts69bC/vMO6Js+BmPvlyEYfOdJWJ6ii6gEY5+XIV65DOvyZRDt8j+uV0rZ0joYDMBrkxM8Oj595fjmi9jAICIiRbF5IU3ufLR2knYuNRzJDnPm140eMmLW24HMJxccP9K0no+ca4ZUiTDglV6B+PHLYniwtkGW93BnwWaHCHx7uy46nBiE8ec7wiEK+KDyV/jqgQVoX+IvGHX3P1nF1fxFmxXW5Uth378P+latYej8IgQdp0m50dd5AIYXu8Bx6iRs69e5tQisJzV6yIiNS4qiUgV9rq/xC0rEhp/P4gzcf6qZ1o9vvop7JhERKcaVyYNo9U5Tw1vv4w5vNXe0epLW6CEjZk3QI/m9KYi48gfyWuAzvzSdD8dPrpiPa0ZMjMeR4/JdeZE1f8mnqwBYff0RmAUbPqmyEZvqLESbYidgEKT3e5fy1xsgBATC0LETjK2f5dMSXWBo0hT6Vq1hP7Aftu+/8/r7Zx0/h/7KeXz6BSWiXK2TWPHNObyrr88mho+Qp51KRESUB1cnD4LRiLQRwzzynrrISBhf7pvxKLh7Prn0mzXHI+/hKd6+MiXrSVpO+aiNM5+JE6/ihaRieDNiG5oVicLkf9oi1hbo8ffTaj4cPzmTO59713TQWj5ZeXPB5qz52EQhW3NCEID51deimCHZ7aeK5LVmiCAIMHTrzsaFmwytWgPx8UB6OkRR9Fp+ru6//iEJsKabIOhE2PQCTgphqC66/3h2Le+/vohXYBARkdcpdduIVj5JYT7Ssubz81Ezhp7rig8vtsIjIeexsc5naBoqz8mlFvPh+LmfN5oXOa3poJV85PZwPUOu+VuXL4O168tYY2uD1n8NR1RqiWx/X9zofvPC6d41Q+697YHNC/cJggDDi11geKGD6poXAJCaEAKD0Qo//3QYIKKWGJfv9+X+qx5sYBARkVcpveaF2k9CmI+0nPMRsP7mw+h+sj/uWAMxv9o6jK+wHWZBe2saFBTHT9680bzIbU0HLeQjtzPRdjzX8062/P2CErHm+7P4cE8FtHzxLqYdrY+IsFTYRc9OVZyfnDuuXIblk4/huHPbo9svjASdTpXNCwBISwrG1dM10bN1TbxlP4LqcP/qi6y4/6oDGxhEROQ1Sk+unNR6EsJ8pOWVT1RqSfQ42Q+rrz+C7uEH8XmtpYj0v+HxOrSaj7eoNR8nbzQvpLav9nzkUv8BA957PRhGI3AnLsutITobzAHJeH9qENZeaYry5htY1fInrFoTgZp1gz1eh/3cOVjmzIaYlAzYlH2Chi9yXL8Ox/XrHt9ufo9vaUnB6NyiWoGbF06Fdf9VEzYwiIgApKd7bwVtb76XmhRkciXnIwjVchKilsmnk1bzsYgGfHL5aQw+0w1hxhSsrbUU3cP3Q/DwAp9azacg23eH2vLJSsnmhZOa85FDo4eMmDe1CL79IQ13YrPvizq9A/E3w+EfnICKdf7CCzV+xYPx+2TLx7pwPoTQIjCPGgVdeLhHt13YiQ4HrMuXwrJwPsR4zzQMAP5+pOy4iCcREQCzWUD1Jjfz/fPu/HI9s7dkvt9Hqwp68iHXwll5LezmLWo7OXNSy8Jl+clnb0IkOp0YiLcjvsX4Cj/isdBovPVPW9y2eu4TXS3nk5/tu0st+cgtv/kznwxGczoiHvwTRpMdBocdtewZ6xTIlY9QrhxMrwyCEOj5xX4LO0Gng7HXS7DMngXLwvkwDR8Jwd+/QNvk70e6F6/AICIqILX+clULT+Qj5ycd9y7s5m1qHz9Kf9JUkHzibIEYEdUFUy60RoPgi9hQexEeL3LGo/UpnQ+g7JoOeVFDPnIq6P7LfKTXKZAjH9OQoWxeyEhXrjyMfftDvH4d1qWLIdryf1zy5O/HwnAlZ2HBBgYRUQGoffKpNE/lI/dJgi99cu5LJ2meyUfAhlsN0fXv/rhpCcbsql+iS8mDqFHkPOJ+3wyLwVLgOpU+iVV6TYe8KJ2PXJiPNHfykVqnwNP5CGZzgbdB0vQ1asDYvScc587BumYNRIfD7W14+vej3B+C+Nr+q2ZsYBAR5RObF9I8nY+vnSTINX585STN0/n8k1YCPU/1xaJrj+GS3R8fPzoTcbvX4XbYbc03MdTcvHDi/iuN+UjztXwKA/3DD8PwfDs4jh6B7estbv2sHL8fvXElp9K3oxYWbGAQEeVDQX65ilbvNDu89T45kWty7isnsXI2v3zlJE2OfKyiAfOuPoEmtWJg1NsBMeNTQdFDC3xyfEpjPtKYjzRfyacw0T/ZAvrmj8P+yy7Ydv7s0s9odfwofTtqYcJFPImI3FTQX66C0Yi0EcMkX6OLjCzwwlB+s+bk6+cKSu4rU7S+cJbW83Fu3zRshEe3ey85b4to36c+4jZ+C6MegOiAwe650yGOT2nMRxrzkab1fAobQRBgeKEDxIR42L7eAiE0FPoGDXN9vdbHD8ejd/AKDCIiN3jrthGtftLEfKT5Sj7eOEmT87aI0XNLYdwfIxH2eDeUCKwHvaj36PtwfEpjPtKYjzSt5lNYCTodjD1fghAZCevna2A/k/NCyhw/5Co2MIiIXOTtNS+09kuW+UjzxqMus9JaPnK7N//TdysjrGkHBA0Y6RNrhhQU919pzEca8yEpgtEIU78BEEqUhHXpEjiuXMn29xw/5A42MIiIXKDUgp1a+SXLfPLmjUdd3ktL+chJanzKfaVK+kdToatZC47z5yGmpXn8PTyB+6805iON+ZArhIAAmAYPBvz9YPlsARx37gDwfnPfieNHu9jAICLKg9JPG1H7L1nm4xpvPOoyJ1rJRy55jU/Z80lIgO3bb2CZNQPp48ci/b13YFm6BLbt22A/fgxibCxE0TOLiOYH919pzEca8yF3CEXCYBo0BLBaYV21EqIoKtLcd+L40SY2MIiIJCh9cuak1l+ySn1yci+15pOVko+61EI+clBTPsYBr8DwbBsI5cpBvHYNtu3bYF2yGOnvTEb6hPFInz0LjsuXZHnv3ORn/y1Mt9vw+C+N+VB+6EqXhumVQTB26wZBEBRr7jtx/GgPGxhERLlQy8mZk9p+yXqjeSH1ycm91JaP3NzNn/lIkzsffZ0HYHimFUwv94N50lswfzQNppGvwdC5C/QP1QfsNsBkyl7ThQuwrFoJ2887YD91CmJCgsfqye/+6wuP6HUFj//SmA8VhK5yZehKlQagbHPfieNHW9jAICLKgdpOzpzU9EvWG82LvD45uZea8pFTfscn85HmzXwEsxm6SpVgeOwxGLt0hXnUaOjCS2V7jRgfD0d0NGzffA3rwvlIn/Qm0ia+AcuCebB+vQX2QwfhiImBHg633rsgxze51wyxLl/m8e26i8d/acyH5OGZW+m0cPyngmEDg8hHpad7755qb76XN6j15MxJLb9kvdG8yM/21ZKPXJiPNF/KR//gg/B7512YP5gK49BhMLzQAfqatSAmJcG+ezesq1fBMvUD/NFgKtbVWox3Ir5B95IH0CD4IoL0OS8YqvZ8vPGIXik8/ktjPuRpAkSMKf8jXim9p8DbUvvxjTzDoHQBRCQPs1lA9SY37/u+HCcfZ/aW9Mh21EAtazrkJesvWevyZYqc9KuxeeGkhnzkwHyk+Wo+QmAg9FWrAVWrZX5PtNsh3rgB8epVrJh2DtUDbqBZkSi0L/EXAGDapaew5kbjzNfr4UCrh5Iw8d0In8vHU+Q4/usiIz2ez735e4vamxdOvjo+fZUIINSQil6l9uOmNQRbbtfL13Z89fhP9+MVGESFiFZOPpSitjUd8uJrnxR4On/mI435SFN7PoJeD12ZMtA//DBmXGmJQWd74Mk/X8OTR0dh8Jlu2BlXI9vr2z50Fx8YZkH3zngMSV2ENyp+j04lDuOBwCvw11ncfn+15+MuuY7/vrJmiFaa+06+Nj59m4B3LjyH3+OrYFLEVjQLPef2Fgrb8b+wYwODqJBg8yJvalzTIS++8ktWrvHJfKQxH2lazOeOLQh7EyJxzVIk83uNHjJi/IQyWGlvh803H4Rd1OHZoicwKeJ7rKm1HHvrf4Sv68zDx1U2on/pPWgeehbhpnhkvSe9RpHziPt9MyyG/5odWswnJ3L+fvSFNUO01tx38pXxWRjYRD3GRHXEmZRSmFZlE+oEXnX5Z3n8L3x4CwlRIcDmhWvUuqZDXrR+uSPzkcZ8pDEfadnzqQug7r9/I6KMKR7VAm6gesANVA+4jloB1/FM0VOZP3vX5o+zKeE4mhqOp2ptRdxuOxDmQPG44jDZMp6Y4lv5aG/8eCNvbzX3V88L8/j2tT4+C5MUhxlDz3bFqporMKfqevQ+1QeX0otJ/ozW91/KH16BQeTjtHbZp5K02Lxw0uonBcxHGvORxnykSecj4JqlCH65Wx2fXWuO16JexHPHh6LJ4XHofaoPPrjYCjtia8BfZ0HN4MswCjZAzHjaSbopPduWfDMfz9FqPk7ebO7LfbuNFvMvTGJtQRhythsAYEG1tShqSMr1tdx/Cy82MIh8mFYv+/QVUvnzJM37VwYxH2nMR1phySfZYcafSeXxxc2H8d7F59DzVD/Mu/oErKIBEDJOG83p5vt+zhEVhfSPP4LuwQchlC/vsX+HXLzd3Nfa+MnKm819X1kzhPLvUnoxDDvXFcWMyZhXbR0CdOn3vYbH/8KNDQwiH6bFNR18RV6/XAv7SZpStzVp5SSE+UhjPtI8nc/pu5UxYd9IhD3eLeP2Ebsp5xfGx8O2aRPEy5cBQSjw+8pFqea+VsaP3KTyz/z9FRTk8ff11pohVHAnkstiTHRHVAu4gemRG2EQ7Jl/x+M/sYFB5MO0uqaD1rmSjy8s7JZfSo8ftZ+EMB9pzEeaXPmcvlsZYU07ZK59IUUID4f+0SaAn5/H3t+TlGzuq338yC2v8enMxzRmLIRKlTz+/lzDQDt+i6+K9y48hyah5zE5YisAkcd/AsAGBpFPY/PC+1zNR+5fgmo9SVPL+FHrSQjzkcZ8pKklH/HGDYg3b8A86S0IVaq4//MOhwxV/Ufp5r5ax4/cpPLJ+pQbR1QUbGtWw9R/QKHKh+635XY9zLvyOJ4vfgwfNtitiuNbYd1/1YQNDCJymVpOjtXK3XwK2y/BgoyfwrBmiNr2L+YjrbDl4+6CzZmfpPft53Y+6ePHIn32TFg3b4L90EE4blz3aFNDDc19tY0fueWWT5A+DU+XPIwFD36G+J1f4HbY7cwmRmHKh3K3KKYZdjsa4lndHmyZ9COP/8QGhtLS08W8X6Sh9yHfpbbJg9rkN5/C8kuwoOPH19cMUev+pZbxyXykqXXB5vzmo3+kMWB3wL73d1hXr4Llg/eR/vp4pM+eBetXm2E/dAiOGzdkv1LDVTz+50ZEmCEZnR+6ibnd/8HWSd/jiTvfYmbkF/ii9iLseWgafq8/DdMqfo+iKUEISM245cj5lBvfz4dc0eghEx6e3B1/OmogIPYKAHXMaTg+lWNQuoDCzmwWUL3JTdlPPs7sLenxbVLhodbJg1oUNB9ff864J8aPnPlkflI8bIRHt+sqte9fSo9P5pM3b63psHpemNs/n598jJ06AwBEux3ijRtwXL4E8fIlOC5fhv3332C3Zvw7hZIlYX5zkts1eVJhPv7r4IADAoD/FmxtFnoOXUseRGlzPMqY4uGv/zeTL4EX9UBScRNi0kMRYymCPxPL45olFIIhHZ1rfAudOR0QAbPlv6fcaDkfKris+9fRP1+AVdQj63hzhy4yUrbzB45P72IDQwXUdtknUVZqnzwozVP5+OovQa3ko1TecuxfvnSSppXjj9L7r9JrOuQlv/kIej2EMmWgK1MGeKQxgH+bGtevw3H5MmC7vybLiuVAYiKE8uWhq1ABQrnyEIoXh6Dz/EXHSucjN9FuhxgXh4eDL6C0KR6lzXdR1hSP0uZ4lDbFo5QpHq2ODcdta3Dmz/jprAgzpuCf1OI4b66Kx1uVwuLv/LD7VBCuWUKRaPdDThPQfUkVseLNa9Bv/uG+hWLVmk9O0tNFmM3yP4HHW++jpPv3r4JNW+X+EMS5fZIfGxgqoMbLPokA7UwelOLpfLR0kuYK5iNNrv3LV07StNbcV3J8qrl54eSpfAS9HkLZstCVLZvz3xcvDkfsHTj2/Aq7zZbxTX9/6MqVg1C+AnTly6OcORBX0sOQ309yAfXmk9/3dty8AfHOHYhxcRBjYyHGxQLx8YAoYkmNf18nAretwbhmCcXx5DL4Ma4mHGL2xtBPcbXwU1ytjHzeDcWrLuaT8ZSbxkj7cleuNWrh+O+8sjovBR0/vn5ltSv5lDbdhV3U4aY1xKVteuNKTqVvRy0s2MBQAbVe9kmFG5sX0uTKRysnaXlhPtLk3L985SRNi819jk9p3sjH+FxbAG0zrh6Iifn39pPLcFy+BMfu3bDbbfiuLpBg88PJ5NI4lVIKJ5NL4++UMriaXgSuNDW0ko+YmprZjMj43zgIgYEwtHwq2+us334D8cI/gE4HoUgRCEWLQahWDUJYUQhFi+LlyTpcs4TihiUEVjHvqYNW8lEKz6+kuZKPDg7Mr7YWdlGHl0/3+ffKHmneuJJTydtRCxM2MFRAC5+cUOHC8SNN7ny0fpLGfKRpPR9vnaRptbnP8SnNW/kIej2EcuWgK1cOeDTje6LNBvF6DN4c8jdqBcagZmAMeoQfgElnR7pDjyZHxsMm6jO34a+zINVhRNamhhrzsf95NLNBkbVhgdTU7C80GqGrXv2+nzd27wHBZAJCQ3O81eZAYt5XFDipMR814fmVNJcfRQwdPrzYGvOrrcXMyC8w+GwPWFxorvnq7aiFDRsYPogHRyoIjh9p3spHqydpzEear+Tjjby91dz3pTVDCspXxmduBIMBQrny2HzbjM23M75nEOyI9L+Jsua72ZoXADCh4jb8r8hZnEwujZMppWEpWR59XquO4RPv4sBRm2x1OqKiYFm2FMaOnWD9fA3Eq1clX595S5fZD0LRjKsmdJUrZ15BIRQtCiGsKBAcDEG4/woTXXi4R+r29fFTUDy/kuZuPgcSK2HiP+3wUZWv8H7lLRgX3RGiC1dQaXX80H/YwPAxPDh6hmi1QjB6Z/FTb75XXjh+pHk7H639kmU+0ry9poPW8pFbbvn7ypohBVVY91+bqMfplNI4nVL6vr/bfbcarA49agfGoHepfTAIe4FZwDT442S10pmNjZPJpRFjCUVB1tS4lxgdDcvHHwHIWNdDvH0719eaxo6HULQo4O+fY4PCGwrr+HEVz6+k5Tef7bF1UMKYhDEVfsIt64/4+NLTcGU/1Nr4oezYwPAhPDh6jmA0Im3EMI9tTxcZmetB0m/WHI+9T0Fw/EhTKh+t/JJlPnlTYk0HLeUjJ6nx6StrhhQE99+c/RxXEz/H1czIZ2IAPnrnHMQrl1EzMAa1A66hd6k/YNQ5AACx1gCcSimNk8mlcDK5DG4LOsT9/hv0Bst9T9Vwh1C6NIwv9YZt08Zc89GVK5fv7XsCx480nl9JK2g+q280RklTIl4qtQ83LcFYcb3JfdvPiVbGD93P88+UIkXw4KhuWQ+SushIpcu5D8ePNKXz4fiRpvZ8nLyxpkNOtJKPXPIan3Ln49y+WnH/lZaZzzsp+PJoSWy41QDvXngOXU6+giZHxqPHyb6YcqE1frlbDcWMSXi59F7MqLoB7z08D3G71+F22G1YDJZ8v78YEwPbpo3qz4fjJ0dK56MFnsjn08stse1ObYwq/zPaFDuW+f28FmxW+/ihnLGB4QN4cNQGtR4kOX6kqSWfwjp+XH3UpVrzyUrJBZu1kI8c1JKPWj/Zy8/+K2eTR23jM698LKIBJ5LLYsOthnjnQlt0+fsVNDk8Hkvja0GvtwJixtUZ6ab0AtWh1Xy8hflomyfyESFg0j/P40BCBN6J+BaNQ87n2dx3Uuv4odyxgaFxPDjKpzCcpHH8SFNbPoVt/Lj7qEu15SM3d/NnPtKYj2vkvlJFLbfb5DefdNGIH28+lPG4USHjNNtsMRe4HrWNT/5+lKa2fNTMU/lYRQNGRXXG+bTimFVtA2YPSeDx30exgaFhhfHgmJ4ueu29vHGSpuRBsjCOH3eoNR+1jB/AO2s65PXJyb3UlI+c8js+mY805pO3wnC7TUGP/6fvVsaEfSMR9ng3FI8rXqA1MLJSy/jk70dparkysTBKsvthseklmIoEwvLZQlz92/VHAKtl/FDe2MDQKLX+8pCb2SygepObef7X69U4xMY50OvVOJdef+9/gHdO0pT6pKmwjh9XqT0ftfyS9UbzIj/bV0s+cmE+0piPNLXno/TtNp46/p++WxlhTTt4rHnhpPT45O/HvKnpysTCptFDRrw7pTwm3+kJ2G1YUH0tihhSXP55NYwfyhsbGBqk9l8eSvNUPr66sJsc40e0emcceuN9tPLJiRp+yaqxeeGkhnzkwHykMR9pzEeaHMd/X7qSUyvnn0qPT7VdmVhYZM3nm6NFMOxcV4SbEjC3+hrE/bbJ5cV0lR4/lDc+RlVjtPLLQymezkfuRyx5+5MmucaPYDTCMmeWLP+erI+gNQ0b4fHtZ6W2NR3y4muPANPa/uttzEca85HGfKTJdfz3lUf0aqW576Tk+FRzc99X5ZTPX0nlMet6U/SssQVxv/4FhDlcvqXL145vvoZXYGgID17S5MrHVzqxco8fX7jdRo1rOuSF41Ma85HGfKQxH2nMJ2++cCWn1pr7ThyfhYNUPja/FDhMafl6IpCvjB9fxAaGRhT04OWtS/y9/V5Och/ctX4Q88YvP184SVPrmg554fiUxnykMR9pzEca85HmC2uGaLG578Tx6dvyyufYnWoFeiKQ1sePr+ItJBrgiYOXYDQibcSwPF+X9XL9/P5S9Js1J18/l1/eOrhr9XIyb1326Qu322ixeeHE8SmN+UhjPtKYjzTmI02r+Th5s7mvi4yU9XYbNTzlxlVsXkhzJR/nE4FWvHkN+s0/5GtRXa3vv76IV2ConLcPXlrrNDIfad6+7FNr+chNKn9fWtgtv7j/SmM+0piPNOYjzdtrOmgtn6y82dz3hdtRPYHNC2nu5OOJJwJpef/1RWxgqJhSBy+t7KTMJ29KXPappXzklNf4LOwnadx/pTEfacxHGvORptSaDlrJR25S+fvC7agFxeaFNB7fiA0MlVL64KX2nZT5uEapNR20ko9cXBmfhfkkjfuvNOYjjflIYz55U3JNBy3kIyelzx/Ufvm/0vuv2imdT2Hff9WCDQwVUnrndFLrTqqWR3mpNZ+slFzTQQv5yEEt+aj1JI3HN2nMRxrzkcZ8XKP0gs1qz0cuzEeaO/uvry/OnxMe35R36NAhtG/fHl27dsWiRYsyv2+1WjFx4kR07twZL730Eq5cuSJrHVzEU2XUsnM6qXHhGjU9ykuN+cjJ3fHJfKQxH9cVhoXdePyXxnykqaW576S2fLJSw4LNas5HDsxHmrv5CEYjLHNmyZqPc2F/ISjY49t2F4//yktOTsabb76JZcuWoUyZMnj99ddx+vRp1KhRAytXrkRAQAA2bNiAc+fOYcaMGZg+fbpstfAKDBVR287ppLZOo9oe5aW2fOSS3/HJfKQxH9f4+pohPP5LYz7SlFrTIS9qyUduPP5LYz7S1JqPWm5H5fFffrdu3cKVK1ey/ZeQkJDtNTt27EDr1q1RtmxZCIKA9u3bY+/evQCAb7/9FsOHDwcAVK1a9b6f9TQ2MFRCrTunk5p2UjV8cnIvNeUjB+YjjflI88TxzZdP0nj8l8Z88qbkmg55UUM+cuLxXxrzkab2fJS+ssCTx38+/S13I0eORIsWLbL9t3LlymyviYqKQoMGDTK/tlgs8Pf3h8VigZ+fH4KCgjL/zmCQ9yYPNjBUQG2XfebGV3bSe3kqf+YjjflIYz7SfPUkTY7jvy+dpKm9eeGk9P6r9JoOeVE6H7kwH2nMRxrzkebp4783ruTUav4zZ87Ezz//nO2/3r17Z3tNaGgoYmJiMr8+fPgwIiMjYTAYsl1xkZSUBKvM66awgaECarzsMze+sJNm5emDI/ORxnykMR9pzMc1vnKSppXmvpOS41PNzQsn7r/SmI805iON+eTNG1dyKn07an6VKFEC5cqVy/ZfSEhItte0bNkSq1atQlxcHKKjo3HgwAE0aNAAOp0OdevWxeeffw6bzYYZM2agXbt2stbLBoYKqPWyz9z4ykFSrpNj5iON+UhjPtKYT9585SRNS819J45PacxHGvORxnykMR9phWXNELlERESgb9++6NOnD95//31MnToVOl1GK2H8+PH47bff0K5dO5QpUwZt27aVtRY2MFRAC5+c3EvrB0nmI435SGM+0piPNK3n462TNK019504PqUxH2nMRxrzkcZ8pPnq7aje0qFDB3z99ddYtmwZIiIi0KFDB+zbtw9FixbFggUL8N1336Ffv36y18EGhg/y1j3DWj1IMh9pzEca85HGfKT5Sj7eOEnzVnPfF2638RRfGZ9yYT7SmI805iON+WjP8uXL8cgjj3j9fdnA8DFSOz9P0ry/IBzzkcZ8pDEfaczn/u1npbV85JZb/r6yZkhBcf+VxnykMR9pzEca89Gm0NBQCILg9ff1SgPj0KFDaN++Pbp27YpFixZlft9qtWLixIno3LkzXnrpJVy5cgUAEB0djU6dOqF79+6YMmWKN0r0CXnt/IX9JE2p1eyZjzTmI435SNNKPoAyazpoKR85SY1PX1kzpCC4/0pjPtKYjzTmI02pBZu1kg/dT/YGRnJyMt58803MmzcP69atQ3R0NE6fPg0AWLlyJQICArBhwwZMmjQJM2bMgMPhwOjRozF58mSsXbsWgYGB+Pnnn+UuU/Nc2fkL80ma0o/iU/tBkvlIYz7SmI9rlFrTQSv5yCWv8ekra4bkF/dfacxHGvORxnzypuSCzVrIh+4newNjx44daN26NcqWLQtBENC+fXvs3bsXAPDtt99i+PDhAICqVasiISEBR44cQdWqVfHAAw8AADp16pT5esqZqwfHwnqSpvQvDye1HiSZjzTmI00tj7pUaz5ZKblgsxbykYNa8lHrwm752X8L05WcPP5LYz7SmI9rlF6wWe350P0Mcr9BVFQUGjVqlPm1xWKBv78/LBYL/Pz8EBQU9F8xBgOioqLQoEGD+16fVUJCAhISErJ97+rVqwCQeXWHplji8v2jNasZ0LdTEPq+moRTZ225vu7QoTBYYuOAAwchXLsGw7NtYLt2DeKVq/l+7xwdOAjToUOe3ea93MjL1XzudehQGKwBAbLkc2/+suaVR1b5zedemeOroPIYn94eW57K5175zsvN/VfuvGpGJMqST+b2/83/0CHBtbwKeHxT07HLFTmNT8mx5eHjv9rzcmX/zZaXzL8flTzW5yQ/x7dDh8Ig1ntItvMHZ/6CCsaWJ47/HvvdCEiOTyX2RTl+PxYoLzf2X2/kJdf5g5PbWRXg+CZ3Xgf2e/Z3I3D/+MwzLw8e/2UfXx7knDdbLBaFK3GPIIqiKOcbLFmyBCEhIXjxxRcBAJ9++imaNWuGBg0aoE2bNti2bRsAICkpCSNHjkTHjh1x+vRpjBo1CgDwxRdfwGw2o3379pnbnDNnDubOnStn2UREREREREQ+bdKkSejZs6fSZbhM9gbGhQsXMHToUKxevRqxsbF48803sXbtWuh0OowfPx5169ZFly5d8OGHH6JevXp4/PHH0bFjRyxbtgwmkwnDhg3D0qVLERwcnLnNnK7ASEpKwm+//YbatWvDaHTtkmMiIiIiIiKiwsZqtSI6OhrPPfccihQponQ5LpO9gQEAmzdvxsqVK1GsWDG89dZbiIiIAIDMhsalS5fQoUMH9OvXDwCwe/duzJ49GyaTCRMmTEDdunXlLpGIiIiIiIiIVMwrDYx7dejQAePGjUPjxo29/dZEREREREREpEGKNDDi4+MREhICQRC8/dZEREREREREpEGKNDCIiIiIiIiIiNyhU7oAIiIiIiIiIqK8sIFBRERERERERKrHBgYRERERERERqR4bGERERERERESkegalCyB5xMTE4M8//0RgYCDq1auHkJAQpUtSrfT0dJw5cwaBgYGoXLkyn46TB+blvtjYWAQGBsJsNitdChFRnq5fv4558+bhn3/+gSAIqFOnDnr06IFy5copXZoqMS8ideG5qm/jU0h80Ny5cxETE4PatWsjPj4e+/btQ8WKFTF27FgEBwcrXZ6qrFu3Dr/++isqV66M+Ph4nD17Fk8++ST69esHo9GodHmqc29eZ86cQYsWLZhXLnbv3o1Vq1YhJCQE8fHxsFgs6NKlC9q2bat0aap06tQpzJ49G4mJibDZbChZsiT69OmD+vXrK12aKjnzSkpKgs1mQ4kSJZhXLji23LN48WK0bt0a5cqVg91ux4EDB7BkyRI8+eST6NGjh9Llqc69eR08eBCLFy9mXrngscs9PH65h+f2vo8NDB8UFxeHsLCwbN/bt28fVqxYgYULFypUlTpFRUUhMjIy82tRFLF582b89NNPzCoHOeX11Vdf4ccff2ReOfj111/RvHnzzK9TUlKwdOlSXLhwAdOnT1ewMnXatGkT2rRpAz8/PwDA5cuXMW/ePISFhWH8+PEKV6c+zMt1zMozvvvuO7Rp00bpMlTJ4XDg9OnT8Pf3R9myZWEymZhXLrg/uod5uYfnqr6PDQwfZLPZsH79evz2228IDAxElSpV0LZtW5QvX17p0lQvLS0t88/OXxSU3a5du/D7778jICAAVapUQYsWLWAwGJiXG2JiYlC6dGmly1Cl2NhYHD58GP7+/qhSpQpKly6NP//8E/Xq1VO6NM1gXjnj2HLd9u3b0apVKwAZ42n58uUICAjA8OHDeezKwd69e7FgwQI0atQI6enpOHnyJMqVK4eJEyfCZDIpXZ5mcH/MHY9fBZeWlsZzVR/BRTx90Pjx4+Hn54cFCxbggw8+wIMPPoiJEyfit99+U7o01Rk7dmzmn9evX4/evXujZcuWzCoX77//Po4dO4bevXujXbt2SE9PR//+/REVFaV0aar0ySefZP55y5YtaNu2LVq2bImrV68qWJV6ffPNN3j99deRkJCAy5cvY/r06Rg0aBAqVqyodGmqtGrVqsw/79q1C506dUKvXr0QEBCgYFXqxLHlnnnz5mX+2WQyYcSIEahQoQI+/PBDBatSr9mzZ2PhwoUYNmwYxowZg2XLlqFx48Z49913lS5NlXjscg+PX57B5oUPEcnndO/e/b7vJSQkiB07dlSgGnVr1arVfd87e/as+OKLLypQjfr16NHjvu9dv349xzFHoti6devMP6empoqiKIp//PGH2LNnT6VKUrUuXbqIVqs12/cOHjwoDhs2TKGK1O3ZZ5/N/PP169fF9PR08auvvhL79eunYFXqxLHlnjZt2mT+2W63i6Ioig6HQ3zuueeUKknV2rVrd9/4stvt2fZR+g+PXe7h8cs1DodDXLJkiZiSkqJ0KSQzXoHhgwwGA3bt2pXte5cuXeLCNTnIemmn1WoFAERGRiI+Pl6pklTNeWlsVjabDenp6QpVpG4Gw38PenI+gaRx48a4ffu2UiWpWmpqarbbuACgTp06+OeffxSqSN10uv9+hYeHh8NkMqF9+/aIiYlRsCp14thyXUpKCm7duoXZs2fj+++/x5UrVwAA58+fR2BgoMLVqVPnzp3Rq1cv7Nu3D1arFZcuXcLYsWPRrFkzpUtTJR673MPjl2tsNhvmzp2LiRMnIjY2FuvXr8c///yDd999F7t371a6PPIgPkbVB33yySeYOHEili1bhvr16+Pq1as4c+YMpk6dqnRpqpKSkoIyZcqgS5cuCA4OxvDhw1G3bl189dVXaNSokdLlqdLUqVMxZswYVKtWDQ0aNMDVq1exfft2TJw4UenSVCclJQVGoxG9e/dGREQEunTpglq1amHXrl2oVq2a0uWp0pAhQ9C1a1e8/PLLaNCgAa5du4YFCxbg+eefV7o01UlJSUFCQgLGjx+PKlWq4Mknn0RkZCSOHj2KUqVKKV2e6gwZMgTdunVDnz59OLbyEBAQgB9//BF///03Tp48iYYNGwIA1q5dy1sictGjRw888MAD+PLLLzFz5kwUKVIETz/9NNq3b690aarDY5f7ePxyjdFoRKtWrfD8889j+/btKFGiBH799VdUrVoVu3btwpw5c/Dqq6/iiSeeULpUKiAu4unDrl27hlOnTiE8PBy1atXK1vGm7GJjYxEWFgZBEPDdd9/hmWeeyfbpOf1HFEUcOXIEUVFRKFKkCJo0acLH80pwPou8Ro0aMJlMWLlyJTp06MDMchETE4MtW7Zkjq+nn34ajzzyiNJlqdbFixdx8uRJNG3aFCEhIZg5cya6deuG8PBwpUtTHY4t1x0/fhzVq1fnApRucC5w7e/vj8jISLRo0QJBQUFKl6VaPHa5h8evvNlsNowfPx7Tp0/HL7/8gurVq2Pjxo1o2LAhHn30UaxevRq7d+/GkiVLlC6VCogNDB906tQpTJ8+HcWLF0d6ejpu376NZs2a4ZVXXlG6NNWJjY3F8ePH8dhjj0Gv12d+f/PmzejQoYOClanThQsXEBERASAju23btiEgIABt2rThiW4OVq5ciebNm6NSpUpKl6IJNpsN69aty3zKTWRkJJ+glIfk5OT7Lus/ffo0atSooVBF6ieKIqKjo+Hn54dy5copXY4q1ahRA1WqVMHUqVPxwAMPKF2O6r3//vsICgpChw4dYLFYcPjwYWzevBlvvvkm88sFj135w+NX7tatW4dff/0VCxYsQGxsLM6cOYNZs2bBaDRiwYIFOH/+PGrVqsUPKH0AP5L3QVOmTMGHH36IqVOnYsaMGVi1ahX0en22JyJQhj59+mDv3r14+eWXM+/zBYClS5cqWJV6vfbaa5l/3rdvH/R6PX744QeOrVx8/PHH6NWrF5YsWYKUlBSly1G98ePHw9/fHwsWLMCHH37IJyjlYdCgQejduzemTJmSuYYPkP3pSpShd+/emX9ev3495s+fj969e+OLL75QsCr1ioiIwFtvvYWZM2eid+/e+O6775CcnKx0Wap16tQpjBgxAuXLl0eVKlXw4osvYtasWbx1Nxc8drmHxy/XdOzYMfMW3XXr1uHAgQOYNWsWypcvj8mTJ6Ny5cpsXvgINjB8UGpqKkqUKJH5tSAI6Nev330LexZ2VqsVJUuWxIQJEzBp0iQMHz4cR44cAQA4HA6Fq1OnrItItWzZEl27dsWMGTOwb98+BatSrzJlymDbtm2w2+3o2rUr3nrrLezZsyfbCRv95/r16+jUqRMEQYDZbEbTpk0xd+5czJw5U+nSVMdqtSIpKQkbN25EnTp10LdvX8TGxgLIuJKFsrtx40bmnzt37oxPP/0Ua9aswZdffqlgVeqVnJyMRx55BEuXLsWUKVNw/vx59OjRA/3798eJEyeULk91uMC163jsch+PX64xmUywWCwYNGgQDAYDmjdvjsOHD6Nnz56YNGkSPvvsM6VLJA9hG8oHNWvWDMOHD8fYsWNRvnx5pKWlYc6cObyM8R5GoxHx8fGw2WyoWrUqFi1ahFGjRqFXr14QBEHp8lTHZrMhNTUV+/btQ2RkJARBQLFixZCWlgbeiZaztLQ0BAcHY+DAgRg4cCD279+P7du346OPPsLbb7+duTgeZXA+QSnrAlt8glLOjEZj5n7Xvn17lC1bFoMGDcInn3zC41cOdDodbDYbDAYDrFYrDAYDihUrxqsKcpH12FS+fHkMGzYMw4YNw6VLl7hGQQ64wLXreOxyH49frhs/fny2rx966KHMP/fp0weiKHKc+QCugeGjtm/fjg0bNiA2NhaCIOCJJ57AgAED4Ofnp3RpqrJ7924cP34cQ4cOBQBYLBZMnDgRP/30E/bv3891HbKwWCzYvXs3Tpw4gdOnT2PcuHGoUqUK3nrrLTRp0gStWrVSukTVWblyZbZLP0narVu3MHHiRKSkpNz3BKXatWsrXZ7qzJs3D4GBgejTpw+AjGbPhAkTcO7cOezZsyfz0b2FXWpqKqZPn44zZ84gOTkZ7777LurUqYMlS5YgKSkJI0eOVLpE8gFZF7gODQ1F06ZNuVhzLnjsch2PX0T3YwODKAdcSMp1SUlJXGmdPOr69euZK63zCUrS4uLiEBYWlvl1cnIytmzZgh49eihYlTbs37+fq/i7IDo6GuvXr0e5cuXQpUsXfhCSgw8++AAtW7bkI9jdwGNXwfD4RYUZzwp9UNYFFbds2YK2bduiZcuWOHTokIJVqdOqVasy/7xr1y506tQJvXr14oQpFznlNXjwYJw9e1bBqtQrt/HFvKSVKlUKpUuXxtdff43Vq1dnW3uFsnNOAKKjo/H+++9j48aN6Nixo8JVqZszq9OnT3Ns5WLo0KFITU0FkHF7xFNPPYW0tDQuspiLL7/8EpMmTcI777yTbUFwytngwYOxe/fubN8LDAxk8yIXgwcPxpYtW7J9j80L18XGxmLFihWYMWMG1q5dq3Q55AGcpfmgnTt3Zv65VatW+PbbbzFlyhTMmjVLwarUKesKzrVq1cLatWvRsWNHfPzxxwpWpV7Myz3Myz2cNLmHebmOWblHp9Nh9uzZSElJQZUqVdCoUSMMHDgQV69eVbo0VSpevDi+//571K9fH2+88Qb69++P9evX4+bNm0qXpkqHDx/Gpk2b8Morr+DgwYNKl6N6zCt/EhMTAQBFixbFM888g8DAQC4K7iPYwPBBWR8R5LyPsHHjxrh9+7ZSJalW1istwsPDYTKZ0L59e8TExChYlXoxL/cwL/dw0uQe5uU6ZuWet99+G+fPn0e/fv3w559/IioqCp9//jmKFy+udGmqZLFYoNfr0bZtW6xatQqTJ09GUlISRo8ezatfcxAUFITVq1dj0KBB2LRpEzp06IDp06fjr7/+Uro0VWJe7rPb7ejevXvm16VLl8azzz6b2dQgbeNTSHxMSkoKjEYjevfujYiICHTp0gW1atXCrl27Mp+NTBlSUlKQkJCA8ePHo0qVKnjyyScRGRmJo0ePolSpUkqXpzrMyz3My31vv/02JkyYgH79+kEURURFRWH//v2cNOWCebmOWbmnaNGi+Oyzz3Ds2DFs374dS5cuRaVKlTBt2jSlS1OlN998M9vX5cuXR//+/dG/f3+FKlI356Pq69evj/r16yMlJQW7d+/GmjVrYLPZ0KBBA4UrVBfm5Z6YmBiULl0aFovlvr/j4vy+gYt4+qj09HScOXMGNWrUgMlkwsqVK9GhQweuiJ2Dixcv4uTJk2jatClCQkIwc+ZMdOvWjY+KywXzcg/zcp9z0hQXF4dKlSqhS5cuCA0NVbos1WJermNWRMr7888/Ua9ePaXL0Azm5brY2FgMGTIE69evx6BBg1C+fHk8/PDD0Ov1+P3333H79m3Mnj1b6TKpgNjAICIiIiIiIs174oknUKRIEVy8eBHVqlVDu3btkJ6ejvDwcDz11FPZbrUnbeIaGERERERERKR5rVq1wsaNGzFq1CjExcVh7dq1KFeuHFq3bs3mhY9gA4OIiIiIJMXGxuLXX39VugzNSEtLQ3R0tNJlEBU648ePh16vR506dVC/fn2sXbsWe/fuxfTp08EbD3wDGxiFxKlTp/DJJ59kPkaOcnf58mWsW7cuc9Ekksa83MO83MNJk3uYl+uYlXtOnDiBJUuWYP78+UqXogm//PILBg4ciPXr1ytdiiZwf3QP88pbeHg4jEYjgoOD8dZbb6FJkybYvn270mWRB7CBUUjodDr4+fnh008/VboU1bt58yaOHTvG1dZdxLzcw7zcw0mTe5iX65iVe5o3b45Vq1ahbdu2SpeiCa1atcKOHTtQo0YNpUvRBO6P7mFeeStTpgzeeeedzK8fffRRtG7dWsGKyFO4iGch43A4oNOxb+WKxMREPrXFDczLPczLPZcvX0b58uWVLkMzmJfrmBWRenB/dA/zosKIDQwiIiIiIiLyWcuXL0ePHj1gMpmULoUKiA0MIiIiIsrmzJkz+OOPP+Dv748qVaqgYcOGSpdERJQvFosFP/7yUgAAEhJJREFUDRs2xJ9//skr0X0AnyVDhd6uXbvw+++/IyAgAFWqVEGLFi0QFBSkdFmakJaWBgDw8/NTuBL14vgiOXGSSXJYsmQJoqOj0a5dO1gsFuzZsweffvoppk6digoVKihdnuqsWrUKL730EoCMY/68efPg7++PSZMmoVq1agpXp048dpEckpOTIYoigoKC4HA4cOvWLYSHhyM5ORl6vZ7NCx/B/xepUHv//fdx7Ngx9O7dG+3atUN6ejr69++PEydOKF2aKo0dOzbzz+vXr0fv3r3RsmVL7NixQ8Gq1Ivjyz2ffPJJ5p+3bNmCtm3bomXLljh06JCCVanXkiVLsGLFCtSoUQOlS5fGnj170L17d1y6dEnp0lSHY8s9O3fuxIcffojGjRujefPmGDVqFN57771sC+LRf7744ovMP9eqVQtr165Fx44d8fHHHytYlXrx2OUeHr9c99NPP+HLL78EkPGklqznrYIgKFUWeZpIPsNut4vvvPOOeOfOHaVL0YwePXrc973r16+L3bt3V6Aa9WvVqtV93zt79qz44osvKlCN+nF8uad169aZf05NTRVFURT/+OMPsWfPnkqVpGrdunW773tRUVFi3759FahG3Ti23NO+fXsxJiYm2/fi4uLEdu3aKVOQyj333HM5fv/ZZ5/1ciXawGOXe3j8ct3p06fFbt26iW+99ZY4YsQIsW7duuLYsWPFQYMGibVr11a6PPIQXoHhQxwOB7Zv347x48fj2rVrWLJkCY4fP45XXnkFn3/+eebl/vSf9PR0nDx5Mtv3bDYb0tPTFapI3bIufGS1WgEAkZGRiI+PV6okVeP4co/B8N9djWazGQDQuHFj3L59W6mSVC01NRXXr1/P9r1ixYrhzp07ClWkXhxb7nnzzTfRp08fzJ49G7///js2bNiAXr16oU+fPkqXpjopKSlISEjA+PHjsWjRIkRFRQEAjh49ilKlSilcnTrx2OUeHr9cV61aNdy6dQu9evXC6NGj8f3332P06NF4++23UaRIEVgsFqVLJA/gGhg+xGAw4PHHH8eAAQOwfft21KpVC0eOHEGXLl1w/vx5vPDCC+jZsyd69OihdKmqMXXqVIwZMwbVqlVDgwYNcPXqVWzfvh0TJ05UujTVSUlJQZkyZdClSxcEBwdj+PDhqFu3Lr766is0atRI6fJUiePLdSkpKTAajejduzciIiLQpUsX1KpVC7t27eI95LlwTjKfffZZNGjQANeuXcOqVavQr18/pUtTFY4t9zVs2BCbN2/Gjh07sH//fhQpUgRz585FxYoVlS5NdQICArB7925cvHgRJ0+eRMmSJQEAu3fvxgcffKBwderEY5frePxyjyAIqFy5MkqXLo3AwEAcPnwYP/74I6Kjo5GcnIz4+HiUKFFC6TKpgPgUEh+SkpKCN954AzNnzsTRo0fh7++PDRs2oHnz5nj88cexbt06/PXXX5g6darSpaqKKIo4cuQIoqOjERoaiiZNmiA4OFjpslQtNjYWYWFhEAQB3333HZ555plsnxDQf5zjKyoqCkWKFOH4ykN6ejrOnDmDGjVqwGQyYeXKlejQoQMzy0VKSgp27NiRefxq0aIFJ5m5yGlsdezYkYvq5iAxMTFzn0tLS8P+/fvh7+/PZnUuduzYgfr166No0aJKl6IZPHa5h8cv112+fBnh4eEwmUxITU2FzWaDn58fXn31Vbz22muoUaOG0iVSAfEWEh/y1VdfweFwAPjvErMbN25g2rRpuHHjBpo2bcrmxT2uXbuGMWPGYNGiRfjll1+wadMm/Prrr0qXpVqxsbHYvXs3QkNDMxdDatOmDb755huFK1OnCxcuQBAENGjQAE899RRu376NHTt28BLGXKxcuRLXrl1D3bp1M29X6t27N5sXuUhMTMScOXOwdetWnDt3DtHR0bwEOxcWiwVXrly5b2z9+eefyhamUv3798/889atW3Ho0CF89NFHmDdvnoJVqdfQoUPRrl07bN++XelSNIHHLvfw+OWe8uXLZ+bk7++P4OBgGI1GVKpUCXFxcQpXR57AKzB8zPTp0zF69Ghs3rwZx44dw7Bhw7B48WLExsZi9OjRCA8PV7pEVenTpw8mTpyIyMhIABmPX5oxYwbKli2Ll19+WeHq1Of555/Ho48+ilOnTuGDDz5AuXLlAGQ0Mb777juFq1OfDh06YPPmzQCA77//HgkJCfjll19QoUIFvPHGGwpXpz61a9dGWFgY+vTpg+7duyMgIEDpklRt0KBB6N27Nx599FEAGQ2zTz/9FC1atEC7du0Urk5dunXrhmLFikGn0+G9995DaGgoAOC5557D1q1bFa5OfVq3bo1t27YByJg8mUwm3L17Fy+//DK++uorhatTn2eeeQaLFy/GkiVL8Ndff6FNmzZo2bIlKleurHRpqsRjl3t4/PKM1NRUGI1GXjHsA3gFho8JDAzEoEGDcPv2bbz88ss4ePAgevTogSlTpmDNmjVKl6c6KSkpmc0LICO/CRMmZE466T9WqxUlS5bEhAkTMGnSJAwfPhxHjhwBgMwrfyi7rAvntmzZEl27dsWMGTOwb98+BatSrzJlymDbtm2w2+3o2rUr3nrrLezZsydzwVjK7u7du5kTAACIiIjAtGnTsGzZMgWrUh+r1Qo/Pz/MnTsX3bp1Q79+/XDhwgUAgN1uV7Y4FRJFEXa7HVeuXAHw33HMZDJxAeJcJCcno0KFCnj33Xexdu1aFC9eHFOnTkW7du1w+PBhpctTHR67XMfjl+f4+/uzeeEj2MDwMYMGDcLChQvxyiuvoGLFimjVqlXmpVRDhgwBL7jJrnr16pg6dSqSk5Mzv/fFF1+gfPnyClalTkajEfHx8bDZbKhatSoWLVqEGTNm4Mcff+SztXNgs9mQmpqKffv24fbt20hMTASQMRngfpiztLQ0BAcHY+DAgfjmm2/Qpk0b7Ny5Ey+88AKfd5+D4sWLY/Xq1dm+t2/fPt6Hfw+j0YiUlBQAwKOPPoqPPvoI48ePx59//sljVw7S09PRvXt3zJw5Ex06dMC1a9cAAHPnzuUi4Llo27Zt5p8DAwPRoUMHLFq0COvXr0eDBg0UrEydeOxyHY9fRPfjLSRUqNlsNqxcuRLffvst/Pz8kJCQgAcffBDjxo1DWFiY0uWpzu7du3H8+HEMHToUQMalxRMnTsRPP/2E/fv3Z3vMamFnsViwe/dunDhxAqdPn8a4ceNQpUoVvPXWW2jSpAlatWqldImqs3LlSvTu3VvpMjQjOTkZ06dPx8GDB1GtWjVcu3YNRqMx2+1dlGHjxo2IiYnBsGHDAABxcXEYO3Ysjhw5gj/++CNz3SjK3T///INKlSopXQb5AB673LNhwwZcv3498/h19+5djBkzhscvKrTYwCD6l8VigdFoZEc7H06fPs1VnV2UlJTEVcPJoywWC65evYrQ0FB+gilBFMVsx3e73Y5ff/0VTzzxhIJVqd+hQ4ewbNkylCtXDgMHDkSxYsWULknVmJfreOxyHY9fBRMbG4sTJ06gefPmSpdCHsBbSAqBO3fu4KOPPsKYMWMwY8YMpctRlVWrVmX++ffff0fnzp3Rq1cvnD17VsGq1CtrXrt27UKnTp3Qq1cv6HQ8lOQkp7wGDx7M8ZWL3MYX85JmMplw584dTJw4ER988AFX88+F8+T/0KFDGDJkCD766CPUrVtX4arUqVOnTkhISAAAfPbZZ3jzzTfxwAMPYOzYsQpXpk7Myz1t2rTB3LlzYTAYUKlSJTYv8tCmTRvMmzcv23pjer2ezQs3nDhxAkuWLMH8+fOVLoU8gLMOH3bx4kUAQLFixTB27Fg0a9bsvnsOC7svvvgi88+1atXC2rVr0bFjR3z88ccKVqVezMs9zMs9zMs9nDS5jlm5p1atWnjnnXcQHx+PyMhIlC1bFm3btuUjCHPBvNwTHx+PK1euoFOnTtiyZQsfLZ4H5lVwzZs3x6pVq7KtV0PaxQaGj7Lb7RgwYEDm1zqdDg0bNkRqaqqCValP1isHwsPDYTKZ0L59e8TExChYlXoxL/cwL/cwL/dw0uQ6ZuWe119/HaGhoejcuTN+++037N69Gx9//DEfC5oL5uUeg8GAqVOnYu7cubh8+TI6d+6M1157Dd9//z3PU3PAvDyHi/T7Bj5LxseIooi///4bderUgV6vz/Z3DocDfn5+ClWmPikpKUhISMD48eNRpUoVPPnkk4iMjMTRo0dRqlQppctTHeblHublHublvtdffx2ffPIJOnfuDLPZjMaNG2P//v2cNOWAWbknICAAb731Fm7evImffvoJx48fR61atTBy5EilS1Ml5uUeZ7O6TJkyGDZsGIYNG4Zjx45h586dqFSpEmrWrKlwherCvIiy4yKePubu3bvo06cPtmzZgmHDhkEURTRo0AB6vR779+9HqVKlMGnSJKXLVJWLFy/i5MmTaNq0KUJCQjBz5kx069YN4eHhSpemSszLPczLPczLfc5JU1xcHCIiIvD000/ziUC5YFZEyrt27RrKlCmjdBmawbzcd+bMGfzxxx/w9/dHlSpV0LBhQ6VLIg9iA8MHPfXUUwgODsbZs2dRp04dvPTSSzAajQgPD+eCZURERCRpyJAhuS5217FjR2zatMnLFakb8yJSjyVLliA6Ohrt2rWDxWLB4cOHcfDgQUydOhUVKlRQujzyAK6B4YO6deuGjRs34oMPPoDVasWiRYtw9epVNi9yMGTIkFz/rmPHjl6sRBuYl3uYl3uYl3uYl+uYlXt27tyJCRMm5Pjf3bt3lS5PdZiXe7g/uod5uWfnzp348MMP0bhxYzRv3hyjRo3Ce++9h3feeUfp0shDuAaGD+rbty8AoGLFiqhUqRI++ugjLF++HBMmTMDbb78Ns9mscIXq4TzpyAlPOu7HvNzDvNzDvNzDvFzHrNzz888/5/p3966vRczLXdwf3cO83JOamorr169nWz+rWLFifMS4D+EtJD7s1q1bWLNmDUaNGgUAiI6OxsWLF/Hkk08qXJl6XL16Nde/0+v1XDzwHszLPczLPczLPczLdcyKSD24P7qHebnn0KFDmDhxIp599lk0aNAA165dw6pVq9CvXz+0b99e6fLIA9jAICIiIiIiIp+QkpKCHTt2ICoqCkWKFEGLFi1QsWJFpcsiD2EDg4iIiIiIiDQvMTERwcHBAIC0tDTs378f/v7+aNSokcKVkadwEU8iIiIiIiLSvP79+2f+eevWrTh06BA++ugjzJs3T8GqyJPYwChkFi9erHQJREREREREHpeQkJD55+effx6jR4/G0qVLsWPHDgWrIk9iA8MHnT59OsfvX7hwAWfPnvVyNURERERERPISRRF2ux1XrlwBkHELCQCYTCakp6crWRp5EBsYPiY9PR1du3bF9u3b0a5dO/Tq1QvPP/88jhw5gm+++QYDBgxQukQiIiIiIiKPSk9PR/fu3TFz5kx06NAB165dAwDMnTsXPXr0ULg68hQu4umD2rZti+DgYNSpUwdt2rTB9evXUa9ePaSnp6NChQpKl0dEREREROQV//zzDypVqqR0GeQhvALDx/z0008IDAzEtGnTcOnSJRgMBsTFxWHMmDHYvn270uURERERERHJok2bNpg7dy6yfkbP5oVvYQPDxzgcDtSqVQuffPIJjh07hq+++goHDhxAtWrVcPToUXz77bdKl0hERERERORx8fHxuHLlCjp27IgtW7bAYrEoXRJ5GG8h8VHR0dG4fPkyHnnkEfj7++PChQvw9/fHu+++y8cIERERERGRz/nf//6HX375BdeuXcOmTZuwY8cOVKlSBS1btsQTTzwBf39/pUukAmIDo5BJSEhASEiI0mUQERERERF51JNPPomdO3dm+96xY8ewc+dOPPPMM6hZs6ZClZGnsIFBREREREREmnft2jWUKVNG6TJIRmxgEBEREREREZHqcRFPHzRkyJBc/65jx45erISIiIiIiMg7OA/yfQalCyDP27lzJyZMmJDj3929e9e7xRAREREREXkB50G+j7eQ+KCrV6/m+nd6vR6lSpXyYjVERERERETy4zzI97GBQURERERERESqxzUwiIiIiIiIiEj12MAgIiIiIiIiItVjA4OIiIiIiIiIVI8NDCIiIiIiIiJSPTYwiIiIiIiIiEj1/g/FlOYpxCPAQgAAAABJRU5ErkJggg==\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# ////////////////////////////////////// 模型稳定性 ///////////////////////////////////// #\\n\",\n    \"worksheet = writer.get_sheet_by_name(\\\"模型稳定性\\\")\\n\",\n    \"start_row, start_col = 2, 2\\n\",\n    \"\\n\",\n    \"# 变量 CSI 表\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\\\"入模变量稳定性指标 (Characteristic Stability Index, CSI): 建模集 vs 跨时间验证集\\\", style=\\\"header\\\")\\n\",\n    \"\\n\",\n    \"for col in card._feature_names:\\n\",\n    \"    feature_table_train = feature_bin_stats(train, col, target=target, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", combiner=combiner)\\n\",\n    \"    feature_table_test = feature_bin_stats(oot, col, target=target, desc=feature_map.get(col, \\\"\\\") or \\\"逻辑回归入模变量\\\", combiner=combiner)\\n\",\n    \"    train_test_csi_table = csi_plot(feature_table_train, feature_table_test, card[col], result=True, plot=True, max_len=35, figsize=(6, 6), labels=[\\\"建模集\\\", \\\"跨时间验证集\\\"], save=f\\\"model_report/csi_{col}.png\\\")\\n\",\n    \"    \\n\",\n    \"    end_row, end_col = writer.insert_pic2sheet(worksheet, f\\\"model_report/csi_{col}.png\\\", (end_row, start_col), figsize=(600, 400))\\n\",\n    \"    end_row, end_col = writer.insert_df2sheet(worksheet, train_test_csi_table, (end_row + 1, start_col))\\n\",\n    \"    \\n\",\n    \"    conditional_column = get_column_letter(start_col + train_test_csi_table.columns.get_loc(\\\"分档CSI值\\\"))\\n\",\n    \"    writer.add_conditional_formatting(worksheet, f'{conditional_column}{end_row-len(train_test_csi_table)}', f'{conditional_column}{end_row}')\\n\",\n    \"\\n\",\n    \"    for c in [\\\"建模集样本占比\\\", \\\"建模集坏样本率\\\", \\\"跨时间验证集样本占比\\\", \\\"跨时间验证集坏样本率\\\"]:\\n\",\n    \"        conditional_column = get_column_letter(start_col + train_test_csi_table.columns.get_loc(c))\\n\",\n    \"        writer.set_number_format(worksheet, f\\\"{conditional_column}{end_row - len(train_test_csi_table)}:{conditional_column}{end_row}\\\", \\\"0.00%\\\")\\n\",\n    \"\\n\",\n    \"# 评分分布稳定性\\n\",\n    \"train_test_score_psi = psi_plot(score_table_train, score_table_test, labels=[\\\"建模集\\\", \\\"跨时间验证集\\\"], save=\\\"model_report/train_test_psiplot.png\\\", result=True)\\n\",\n    \"\\n\",\n    \"end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\\\"模型评分稳定性指标 (Population Stability Index, PSI): 建模集 vs 跨时间验证集\\\", style=\\\"header\\\")\\n\",\n    \"end_row, end_col = writer.insert_pic2sheet(worksheet, \\\"model_report/train_test_psiplot.png\\\", (end_row, start_col), figsize=(1000, 400))\\n\",\n    \"end_row, end_col = writer.insert_df2sheet(worksheet, train_test_score_psi, (end_row + 1, start_col))\\n\",\n    \"\\n\",\n    \"conditional_column = get_column_letter(start_col + train_test_score_psi.columns.get_loc(\\\"分档PSI值\\\"))\\n\",\n    \"writer.add_conditional_formatting(worksheet, f'{conditional_column}{end_row-len(train_test_score_psi)}', f'{conditional_column}{end_row}')\\n\",\n    \"\\n\",\n    \"for c in [\\\"建模集样本占比\\\", \\\"建模集坏样本率\\\", \\\"跨时间验证集样本占比\\\", \\\"跨时间验证集坏样本率\\\"]:\\n\",\n    \"    conditional_column = get_column_letter(start_col + train_test_score_psi.columns.get_loc(c))\\n\",\n    \"    writer.set_number_format(worksheet, f\\\"{conditional_column}{end_row - len(train_test_score_psi)}:{conditional_column}{end_row}\\\", \\\"0.00%\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 260,\n   \"id\": \"6d41410b-56c0-4e97-a17e-6470d6d1d5c2\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"writer.save(\\\"model_report/评分卡模型报告.xlsx\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"8fd0c465-a54a-449e-bf40-2b48faec6d53\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.8\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "examples/特征选择.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"> https://github.com/YC-Coder-Chen/feature-engineering-handbook\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"toc\": true\n   },\n   \"source\": [\n    \"
Table of Contents
\\n\",\n    \"
\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Reference**\\n\",\n    \"- https://scikit-learn.org/stable/modules/feature_selection.html\\n\",\n    \"- https://machinelearningmastery.com/an-introduction-to-feature-selection/\\n\",\n    \"- https://dcor.readthedocs.io/en/latest/functions/dcor.independence.distance_covariance_test.html\\n\",\n    \"- https://en.wikipedia.org/wiki/Distance_correlation\\n\",\n    \"- https://stats.stackexchange.com/questions/56881/whats-the-relationship-between-r2-and-f-test\\n\",\n    \"- https://en.wikipedia.org/wiki/Mutual_information\\n\",\n    \"- https://libguides.library.kent.edu/SPSS/ChiSquare\\n\",\n    \"- https://chrisalbon.com/machine_learning/feature_selection/anova_f-value_for_feature_selection/\\n\",\n    \"- https://online.stat.psu.edu/stat414/node/218/\\n\",\n    \"- http://featureselection.asu.edu/algorithms.php\\n\",\n    \"- https://en.wikipedia.org/wiki/Minimum_redundancy_feature_selection\\n\",\n    \"- Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis & Machine Intelligence, (8), 1226-1238.\\n\",\n    \"- Hall, M. A., & Smith, L. A. (1999, May). Feature selection for machine learning: comparing a correlation-based filter approach to the wrapper. In FLAIRS conference (Vol. 1999, pp. 235-239).\\n\",\n    \"- Yu, L., & Liu, H. (2003). Feature selection for high-dimensional data: A fast correlation-based filter solution. In Proceedings of the 20th international conference on machine learning (ICML-03) (pp. 856-863).\\n\",\n    \"- Zhao, Z., & Liu, H. (2007, June). Spectral feature selection for supervised and unsupervised learning. In Proceedings of the 24th international conference on Machine learning (pp. 1151-1157). ACM.\\n\",\n    \"- Robnik-Šikonja, M., & Kononenko, I. (2003). Theoretical and empirical analysis of ReliefF and RReliefF. Machine learning, 53(1-2), 23-69.\\n\",\n    \"- https://machinelearningmastery.com/an-introduction-to-feature-selection/\\n\",\n    \"- http://rasbt.github.io/mlxtend/user_guide/feature_selection/SequentialFeatureSelector/\\n\",\n    \"- https://cloud.tencent.com/developer/article/1087796\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Feature Selection 特征选择\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"数据预处理后，我们生成了大量的新变量（比如独热编码生成了大量仅包含0或1的变量）。但实际上，部分新生成的变量可能是多余：一方面它们本身不一定包含有用的信息，故无法提高模型性能；另一方面过这些多余变量在构建模型时会消耗大量内存和计算能力。因此，我们应该进行特征选择并选择特征子集进行建模。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Filter Methods 过滤法\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"过滤法通过使用一些统计量或假设检验结果为每个变量打分。得分较高的功能往往更加重要，因此应被包含在子集中。以下为一个简单的基于过滤法的机器学习工作流（以最简单的训练-验证-测试这种数据集划分方法为例）。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"![image](https://itlubber.art/upload/过滤法工作流.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Univariate Filter Methods 单变量特征过滤\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"单变量过滤方法依据单变量统计量或统计检验选择最佳特征。其仅仅考虑单个变量与目标变量的关系（方差选择法仅基于单个变量）。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Variance Threshold 方差选择法\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"方差选择法删除变量方差低于某个阈值的所有特征。例如，我们应删除方差为零的特征（所有观测点中具有相同值的特征），因为该特征无法解释目标变量的任何变化。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:26:35.507588Z\",\n     \"start_time\": \"2020-03-30T03:26:34.556950Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from sklearn.feature_selection import VarianceThreshold\\n\",\n    \"\\n\",\n    \"# 合成一些数据集用于演示\\n\",\n    \"train_set = np.array([[1,2,3],[1,4,7],[1,4,9]]) # 可见第一个变量方差为0\\n\",\n    \"# array([[1, 2, 3],\\n\",\n    \"#        [1, 4, 7],\\n\",\n    \"#        [1, 4, 9]])\\n\",\n    \"\\n\",\n    \"test_set = np.array([[3,2,3],[1,2,7]]) # 故意将第二个变量方差设为0\\n\",\n    \"# array([[3, 2, 3],\\n\",\n    \"#        [1, 2, 7]])\\n\",\n    \"\\n\",\n    \"selector = VarianceThreshold()\\n\",\n    \"selector.fit(train_set) # 在训练集上训练\\n\",\n    \"transformed_train = selector.transform(train_set) # 转换训练集\\n\",\n    \"# 下面为返回结果，可见第一个变量已被删除\\n\",\n    \"# array([[2, 3],\\n\",\n    \"#        [4, 7],\\n\",\n    \"#        [4, 9]])\\n\",\n    \"\\n\",\n    \"transformed_test = selector.transform(test_set) # 转换测试集\\n\",\n    \"# 下面为返回结果，可见第一个变量已被删除\\n\",\n    \"# array([[2, 3],\\n\",\n    \"#        [2, 7]])\\n\",\n    \"# 虽然测试集中第二个变量的方差也为0\\n\",\n    \"# 但是我们的选择是基于训练集，所以我们依然删除第一个变量\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Pearson Correlation (regression problem)  皮尔森相关系数 (回归问题)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"皮尔森相关系数一般用于衡量两个**连续**变量之间的线性相关性，也可以用于衡量二元变量与目标变量的相关性。故可以将类别变量利用独热编码转换为多个二元变量之后利用皮尔森相关系数进行筛选。\\n\",\n    \"\\n\",\n    \"公式：\\n\",\n    \"$r = \\\\frac{\\\\sum_{i=1}^{n}(X_i-\\\\bar{X})(Y_i-\\\\bar{Y})}{\\\\sqrt{(X_i-\\\\bar{X})^2}\\\\sqrt{(Y_i-\\\\bar{Y})^2}}$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:26:35.549368Z\",\n     \"start_time\": \"2020-03-30T03:26:35.509833Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from scipy.stats import pearsonr\\n\",\n    \"from sklearn.feature_selection import SelectKBest\\n\",\n    \"\\n\",\n    \"# 直接载入数据集\\n\",\n    \"from sklearn.datasets import fetch_california_housing\\n\",\n    \"dataset = fetch_california_housing()\\n\",\n    \"X, y = dataset.data, dataset.target # 利用 california_housing 数据集来演示\\n\",\n    \"# 此数据集中，X，y均为连续变量，故此满足使用皮尔森相关系数的条件\\n\",\n    \"\\n\",\n    \"# 选择前15000个观测点作为训练集\\n\",\n    \"# 剩下的作为测试集\\n\",\n    \"train_set = X[0:15000,:]\\n\",\n    \"test_set = X[15000:,]\\n\",\n    \"train_y = y[0:15000]\\n\",\n    \"\\n\",\n    \"# sklearn 中没有直接的函数可以使用\\n\",\n    \"# 此处将用 scipy.stats.pearsonr函数来实现基于皮尔森相关系数的特征过滤\\n\",\n    \"# 注意 scipy.stats.pearsonr 计算的是两个变量之间的相关系数\\n\",\n    \"# 因sklearn SelectKBest需要，我们将基于scipy.stats.pearsonr 重写允许多特征同时输入的函数 udf_pearsonr\\n\",\n    \"\\n\",\n    \"def udf_pearsonr(X, y):\\n\",\n    \"    # 将会分别计算每一个变量与目标变量的关系\\n\",\n    \"    result = np.array([pearsonr(x, y) for x in X.T]) # 包含(皮尔森相关系数, p值) 的列表\\n\",\n    \"    return np.absolute(result[:,0]), result[:,1] \\n\",\n    \"\\n\",\n    \"# SelectKBest 将会基于一个判别函数自动选择得分高的变量\\n\",\n    \"# 这里的判别函数为皮尔森相关系数\\n\",\n    \"selector = SelectKBest(udf_pearsonr, k=2) # k => 我们想要选择的变量数\\n\",\n    \"selector.fit(train_set, train_y) # 在训练集上训练\\n\",\n    \"transformed_train = selector.transform(train_set) # 转换训练集\\n\",\n    \"transformed_train.shape #(15000, 2), 其选择了第一个及第七个变量 \\n\",\n    \"assert np.array_equal(transformed_train, train_set[:,[0,6]]) \\n\",\n    \"\\n\",\n    \"transformed_test = selector.transform(test_set) # 转换测试集\\n\",\n    \"assert np.array_equal(transformed_test, test_set[:,[0,6]]);\\n\",\n    \"# 可见对于测试集，其依然选择了第一个及第七个变量 \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:26:35.560082Z\",\n     \"start_time\": \"2020-03-30T03:26:35.551499Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"第1个变量和目标的皮尔森相关系数的绝对值为0.7, p-值为0.0\\n\",\n      \"第2个变量和目标的皮尔森相关系数的绝对值为0.07, p-值为0.0\\n\",\n      \"第3个变量和目标的皮尔森相关系数的绝对值为0.14, p-值为0.0\\n\",\n      \"第4个变量和目标的皮尔森相关系数的绝对值为0.04, p-值为0.0\\n\",\n      \"第5个变量和目标的皮尔森相关系数的绝对值为0.02, p-值为0.011\\n\",\n      \"第6个变量和目标的皮尔森相关系数的绝对值为0.05, p-值为0.0\\n\",\n      \"第7个变量和目标的皮尔森相关系数的绝对值为0.23, p-值为0.0\\n\",\n      \"第8个变量和目标的皮尔森相关系数的绝对值为0.08, p-值为0.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 验算一下我们的结果\\n\",\n    \"for idx in range(train_set.shape[1]):\\n\",\n    \"    pea_score, p_value = pearsonr(train_set[:,idx], train_y)\\n\",\n    \"    print(f\\\"第{idx + 1}个变量和目标的皮尔森相关系数的绝对值为{round(np.abs(pea_score),2)}, p-值为{round(p_value,3)}\\\")\\n\",\n    \"# 应选择第一个及第七个变量 \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Distance Correlation (regression problem) 距离相关系数 (回归问题)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"与皮尔森相关系数类似，距离相关系数也一般被用于衡量两个连续变量之间的相关性。但与皮尔森相关系数不同的是，距离相关系数还衡量了两个变量之间的非线性关联。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"公式:  \\n\",\n    \"  \\n\",\n    \"首先，计算(n x n)距离矩阵dX。dX中的每一个元素为$dX_{ij}$ 。类似的，我们也可以计算距离矩阵dY，其中dY中的每个元素为$dY_{ij}$。$dX_{ij}$是为观测点i与观测点j之间的距离:     \\n\",\n    \"  \\n\",\n    \"$dX_{ij} = \\\\left \\\\| X_i - X_j \\\\right \\\\|$  \\n\",\n    \"$dY_{ij} = \\\\left \\\\| Y_i - Y_j \\\\right \\\\|$\\n\",\n    \"   \\n\",\n    \"其次，我们计算如下双中心距离并更新距离矩阵。其中，$\\\\bar{X_i}$为距离矩阵dX的第i行平均值，$\\\\bar{X_j}$为距离矩阵dX的第j列的平均值， $\\\\sum_i^N \\\\sum_j^N dX_{ij}$ 为全局平均值： \\n\",\n    \"  \\n\",\n    \"$dX_{ij} = dX_{ij} - \\\\bar{X_i} - \\\\bar{X_j} + \\\\frac{1}{N^2} \\\\sum_i^N \\\\sum_j^N dX_{ij}$  \\n\",\n    \"$dY_{ij} = dY_{ij} - \\\\bar{Y_i} - \\\\bar{Y_j} + \\\\frac{1}{N^2} \\\\sum_i^N \\\\sum_j^N dY_{ij}$   \\n\",\n    \"  \\n\",\n    \"随后，我们便可以算出样本距离协方差及距离方差：  \\n\",\n    \"  \\n\",\n    \"$dCov^2 (X, Y) = \\\\frac{1}{N^2} \\\\sum_{i}^{N} \\\\sum_{j}^{N} dX_{ij}dY_{ij}$  \\n\",\n    \"$dVar^2(X) = Cov^2_D (X, X)$  \\n\",\n    \"  \\n\",\n    \"最后，距离相关系数$dCor(X,Y)$ 便为如下： \\n\",\n    \"  \\n\",\n    \"$dCor(X,Y) = \\\\frac{dCov_D(X, Y)}{\\\\sqrt{dVar^2(X)}\\\\sqrt{dVar^2(Y)} }$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:34:59.679022Z\",\n     \"start_time\": \"2020-03-30T03:26:35.561654Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from dcor import distance_correlation\\n\",\n    \"from dcor.independence import distance_covariance_test\\n\",\n    \"from sklearn.feature_selection import SelectKBest\\n\",\n    \"\\n\",\n    \"# 直接载入数据集\\n\",\n    \"from sklearn.datasets import fetch_california_housing\\n\",\n    \"dataset = fetch_california_housing()\\n\",\n    \"X, y = dataset.data, dataset.target # 利用 california_housing 数据集来演示\\n\",\n    \"# 此数据集中，X，y均为连续变量，故此满足使用距离相关系数的条件\\n\",\n    \"\\n\",\n    \"# 选择前15000个观测点作为训练集\\n\",\n    \"# 剩下的作为测试集\\n\",\n    \"train_set = X[0:15000,:]\\n\",\n    \"test_set = X[15000:,]\\n\",\n    \"train_y = y[0:15000]\\n\",\n    \"\\n\",\n    \"# sklearn 中没有直接的函数可以使用\\n\",\n    \"# 此处将用 dcor.distance_correlation函数来实现基于距离相关系数的特征过滤\\n\",\n    \"# 注意 dcor.distance_correlation 计算的是两个变量之间的相关系数\\n\",\n    \"# 因sklearn SelectKBest需要，我们将基于dcor.distance_correlation 重写允许多特征同时输入的函数 udf_dcorr\\n\",\n    \"\\n\",\n    \"def udf_dcorr(X, y):\\n\",\n    \"    # 将会分别计算每一个变量与目标变量的关系\\n\",\n    \"    result = np.array([[distance_correlation(x, y), \\n\",\n    \"                        distance_covariance_test(x,y)[0]]for x in X.T]) # 包含(距离相关系数, p值) 的列表\\n\",\n    \"    return result[:,0], result[:,1]\\n\",\n    \"\\n\",\n    \"# SelectKBest 将会基于一个判别函数自动选择得分高的变量\\n\",\n    \"# 这里的判别函数为距离相关系数\\n\",\n    \"selector = SelectKBest(udf_dcorr, k=2) # k => 我们想要选择的变量数\\n\",\n    \"selector.fit(train_set, train_y) # 在训练集上训练\\n\",\n    \"transformed_train = selector.transform(train_set) # 转换训练集\\n\",\n    \"transformed_train.shape #(15000, 2), 其选择了第一个及第三个变量 \\n\",\n    \"assert np.array_equal(transformed_train, train_set[:,[0,2]])\\n\",\n    \"\\n\",\n    \"transformed_test = selector.transform(test_set) # 转换测试集\\n\",\n    \"assert np.array_equal(transformed_test, test_set[:,[0,2]]);\\n\",\n    \"# 可见对于测试集，其依然选择了第一个及第三个变量 \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:47.881323Z\",\n     \"start_time\": \"2020-03-30T03:34:59.700756Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"第1个变量和目标的距离相关系数为0.66, p-值为1.0\\n\",\n      \"第2个变量和目标的距离相关系数为0.07, p-值为1.0\\n\",\n      \"第3个变量和目标的距离相关系数为0.31, p-值为1.0\\n\",\n      \"第4个变量和目标的距离相关系数为0.12, p-值为1.0\\n\",\n      \"第5个变量和目标的距离相关系数为0.08, p-值为1.0\\n\",\n      \"第6个变量和目标的距离相关系数为0.29, p-值为1.0\\n\",\n      \"第7个变量和目标的距离相关系数为0.25, p-值为1.0\\n\",\n      \"第8个变量和目标的距离相关系数为0.19, p-值为1.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 验算一下我们的结果\\n\",\n    \"for idx in range(train_set.shape[1]):\\n\",\n    \"    d_score = distance_correlation(train_set[:,idx], train_y)\\n\",\n    \"    p_value = distance_covariance_test(train_set[:,idx], train_y)[0]\\n\",\n    \"    print(f\\\"第{idx + 1}个变量和目标的距离相关系数为{round(d_score,2)}, p-值为{round(p_value,3)}\\\")\\n\",\n    \"# 应选择第一个及第三个变量 \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-11T04:22:20.392522Z\",\n     \"start_time\": \"2020-03-11T04:22:20.377296Z\"\n    }\n   },\n   \"source\": [\n    \"#### F-Score (regression problem) F-统计量 (回归问题)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"F统计量（F-Score）用于检验线性回归模型的整体显著性。在sklearn中，其将对每一个变量分别建立一个一元的线性回归模型，然后分别报告每一个对应模型的F统计量。F-统计量的零假设是该线性模型系数不显著，在一元模型中，该统计量能够反映各变量与目标变量之间的线性关系。因此，我们应该选择具有较高F统计量的特征（更有可能拒绝原假设）。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"公式：  \\n\",\n    \"  \\n\",\n    \"$F = \\\\frac{(SST - SSR)/(p - 1)}{SSR/(n - p)} =  \\\\frac{SST - SSR}{SSR/(n - 2)} =  \\\\frac{R^2}{(1 - R^2)(n - 2)} = \\\\frac{\\\\rho ^2}{(1 - \\\\rho ^2)(n - 2)}$  \\n\",\n    \" \\n\",\n    \"其中:  \\n\",\n    \"\\n\",\n    \"$SST = \\\\sum_{i=1}^{n}(y_i - \\\\overline{y}) ^2$  \\n\",\n    \"  \\n\",\n    \"$\\\\overline{y} = \\\\frac{1}{n} \\\\sum_{i=1}^{n}y_i$  \\n\",\n    \"  \\n\",\n    \"$SSR = \\\\sum_{i=1}^{n}(\\\\widehat{y}_i - \\\\overline{y})^2$  \\n\",\n    \"  \\n\",\n    \"$\\\\widehat{y}_i$ 为模型预测值\\n\",\n    \"  \\n\",\n    \"  \\n\",\n    \"SST为总平方和，SSR为回归平方和，p为线性回归自变量数（包括常数项，故在上述的一元线性模型中，p=2），$\\\\rho$为自变量与因变量的线性相关系数，n为总观测数。因上述线性模型为一元线性模型，故可证$\\\\rho^2 = R^2$。 \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:48.061253Z\",\n     \"start_time\": \"2020-03-30T03:43:47.898644Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from sklearn.feature_selection import f_regression\\n\",\n    \"from sklearn.feature_selection import SelectKBest\\n\",\n    \"\\n\",\n    \"# 直接载入数据集\\n\",\n    \"from sklearn.datasets import fetch_california_housing\\n\",\n    \"dataset = fetch_california_housing()\\n\",\n    \"X, y = dataset.data, dataset.target # 利用 california_housing 数据集来演示\\n\",\n    \"# 此数据集中，X，y均为连续变量，故此满足使用F统计量的条件\\n\",\n    \"\\n\",\n    \"# 选择前15000个观测点作为训练集\\n\",\n    \"# 剩下的作为测试集\\n\",\n    \"train_set = X[0:15000,:]\\n\",\n    \"test_set = X[15000:,]\\n\",\n    \"train_y = y[0:15000]\\n\",\n    \"\\n\",\n    \"# sklearn 中直接提供了函数用于计算F统计量\\n\",\n    \"# SelectKBest 将会基于一个判别函数自动选择得分高的变量\\n\",\n    \"# 这里的判别函数为F统计量\\n\",\n    \"selector = SelectKBest(f_regression, k=2) # k => 我们想要选择的变量数\\n\",\n    \"selector.fit(train_set, train_y) # 在训练集上训练\\n\",\n    \"transformed_train = selector.transform(train_set) # 转换训练集\\n\",\n    \"transformed_train.shape #(15000, 2), 其选择了第一个及第七个变量 \\n\",\n    \"assert np.array_equal(transformed_train, train_set[:,[0,6]]) \\n\",\n    \"\\n\",\n    \"transformed_test = selector.transform(test_set) # 转换测试集\\n\",\n    \"assert np.array_equal(transformed_test, test_set[:,[0,6]]);\\n\",\n    \"# 可见对于测试集，其依然选择了第一个及第七个变量 \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:48.078788Z\",\n     \"start_time\": \"2020-03-30T03:43:48.064038Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"第1个变量的F统计量为14111.79, p-值为0.0\\n\",\n      \"第2个变量的F统计量为71.99, p-值为0.0\\n\",\n      \"第3个变量的F统计量为317.04, p-值为0.0\\n\",\n      \"第4个变量的F统计量为23.93, p-值为0.0\\n\",\n      \"第5个变量的F统计量为6.54, p-值为0.011\\n\",\n      \"第6个变量的F统计量为35.93, p-值为0.0\\n\",\n      \"第7个变量的F统计量为846.61, p-值为0.0\\n\",\n      \"第8个变量的F统计量为98.06, p-值为0.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 验算一下我们的结果\\n\",\n    \"for idx in range(train_set.shape[1]):\\n\",\n    \"    score, p_value = f_regression(train_set[:,idx].reshape(-1,1), train_y)\\n\",\n    \"    print(f\\\"第{idx + 1}个变量的F统计量为{round(score[0],2)}, p-值为{round(p_value[0],3)}\\\")\\n\",\n    \"# 故应选择第一个及第七个变量 \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Mutual Information (regression problem) 互信息 (回归问题)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"互信息（Mutual Information）衡量变量间的相互依赖性。其本质为熵差，即$H(X) - H(X|Y)，即知道另一个变量信息后混乱的降低程度$。当且仅当两个随机变量独立时MI等于零。MI值越高，两变量之间的相关性则越强。与Pearson相关和F统计量相比，它还捕获了非线性关系。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"公式:  \\n\",\n    \"  \\n\",\n    \"- 若两个变量均为离散变量:  \\n\",\n    \"  \\n\",\n    \"    $I(x, y) = H(Y) - H(Y|X) = \\\\sum_{x\\\\in \\\\mathit{X}}  \\\\sum_{x\\\\in \\\\mathit{Y}} \\\\textit{p}_{(X,Y)}(x,y) \\\\textrm{log}(\\\\frac{\\\\textit{p}_{(X,Y)}(x,y)}{\\\\textit{p}_{X}(x)\\\\textit{p}_{Y}(y)})$  \\n\",\n    \"\\n\",\n    \"    $\\\\textit{p}_{(X,Y)}(x,y)$ 为x和y的联合概率质量函数 (PMF)， $\\\\textit{p}_{X}(x)$则为x的联合概率质量函数 (PMF)。  \\n\",\n    \"  \\n\",\n    \"- 若两个变量均为连续变量:  \\n\",\n    \"\\n\",\n    \"    $I(X, Y) = H(Y) - H(Y|X) = \\\\int_X \\\\int_Y  \\\\textit{p}_{(X,Y)}(x,y) \\\\textrm{log}(\\\\frac{\\\\textit{p}_{(X,Y)}(x,y)}{\\\\textit{p}_{X}(x)\\\\textit{p}_{Y}(y)}) \\\\, \\\\, dx dy$  \\n\",\n    \"    \\n\",\n    \"    $\\\\textit{p}_{(X,Y)}(x,y)$ 为x和y的联合概率密度函数 (PDF)，$\\\\textit{p}_{X}(x)$则为x的概率密度函数 (PDF)。连续变量情形下，在实际操作中，往往先对数据离散化分桶，然后逐个桶进行计算。\\n\",\n    \"  \\n\",\n    \"\\n\",\n    \"但是实际上，一种极有可能的情况是，x和y中的一个可能是离散变量，而另一个是连续变量。因此在sklearn中，它基于[1]和[2]中提出的基于k最临近算法的熵估计非参数方法。   \\n\",\n    \"   \\n\",\n    \"[1] A. Kraskov, H. Stogbauer and P. Grassberger, “Estimating mutual information”. Phys. Rev. E 69, 2004.  \\n\",\n    \"[2] B. C. Ross “Mutual Information between Discrete and Continuous Data Sets”. PLoS ONE 9(2), 2014. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:50.738206Z\",\n     \"start_time\": \"2020-03-30T03:43:48.090754Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from sklearn.feature_selection import mutual_info_regression\\n\",\n    \"from sklearn.feature_selection import SelectKBest\\n\",\n    \"\\n\",\n    \"# 直接载入数据集\\n\",\n    \"from sklearn.datasets import fetch_california_housing\\n\",\n    \"dataset = fetch_california_housing()\\n\",\n    \"X, y = dataset.data, dataset.target # 利用 california_housing 数据集来演示\\n\",\n    \"# 此数据集中，X，y均为连续变量，故此满足使用MI的条件\\n\",\n    \"\\n\",\n    \"# 选择前15000个观测点作为训练集\\n\",\n    \"# 剩下的作为测试集\\n\",\n    \"train_set = X[0:15000,:].astype(float)\\n\",\n    \"test_set = X[15000:,].astype(float)\\n\",\n    \"train_y = y[0:15000].astype(float)\\n\",\n    \"\\n\",\n    \"# KNN中的临近数是一个非常重要的参数\\n\",\n    \"# 故我们重写了一个新的MI计算函数更好的来控制这一参数\\n\",\n    \"def udf_MI(X, y):\\n\",\n    \"    result = mutual_info_regression(X, y, n_neighbors = 5) # 用户可以输入想要的临近数\\n\",\n    \"    return result\\n\",\n    \"\\n\",\n    \"# SelectKBest 将会基于一个判别函数自动选择得分高的变量\\n\",\n    \"# 这里的判别函数为F统计量\\n\",\n    \"selector = SelectKBest(udf_MI, k=2) # k => 我们想要选择的变量数\\n\",\n    \"selector.fit(train_set, train_y) # 在训练集上训练\\n\",\n    \"transformed_train = selector.transform(train_set) # 转换训练集\\n\",\n    \"transformed_train.shape #(15000, 2), 其选择了第一个及第八个变量\\n\",\n    \"assert np.array_equal(transformed_train, train_set[:,[0,7]])\\n\",\n    \"\\n\",\n    \"transformed_test = selector.transform(test_set) # 转换测试集\\n\",\n    \"assert np.array_equal(transformed_test, test_set[:,[0,7]]);\\n\",\n    \"# 可见对于测试集，其依然选择了第一个及第八个变量\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:53.172117Z\",\n     \"start_time\": \"2020-03-30T03:43:50.742937Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"第1个变量与因变量的互信息为0.38\\n\",\n      \"第2个变量与因变量的互信息为0.03\\n\",\n      \"第3个变量与因变量的互信息为0.1\\n\",\n      \"第4个变量与因变量的互信息为0.03\\n\",\n      \"第5个变量与因变量的互信息为0.02\\n\",\n      \"第6个变量与因变量的互信息为0.09\\n\",\n      \"第7个变量与因变量的互信息为0.37\\n\",\n      \"第8个变量与因变量的互信息为0.46\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 验算上述结果\\n\",\n    \"for idx in range(train_set.shape[1]):\\n\",\n    \"    score = mutual_info_regression(train_set[:,idx].reshape(-1,1), train_y, n_neighbors = 5)\\n\",\n    \"    print(f\\\"第{idx + 1}个变量与因变量的互信息为{round(score[0],2)}\\\")\\n\",\n    \"# 故应选择第一个及第八个变量\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Chi-squared Statistics (classification problem) 卡方统计量 (分类问题)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"卡方统计量主要用于衡量两个类别特征之间的相关性。sklearn提供了chi2函数用于计算卡方统计量。其输入的特征变量必须为布尔值或频率（故对于类别变量应考虑独热编码）。卡方统计量的零假设为两个变量是独立的，因为卡方统计量值越高，则两个类别变量的相关性越强。因此，我们应该选择具有较高卡方统计量的特征。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"公式:  \\n\",\n    \"  \\n\",\n    \"$\\\\chi^2 = \\\\sum_{i=1}^{r} \\\\sum_{j=1}^{c} \\\\frac{(O_{i,j} - E_{i,j})^2}{E_{i,j}} = n \\\\sum_{i,j} p_ip_j(\\\\frac{\\\\frac{O_{i,j}}{n} - p_i p_j}{p_i p_j})^2$  \\n\",\n    \"  \\n\",\n    \"其中，$O_{i,j}$为在变量X上具有i-th类别值且在变量Y上具有j-th类别值的实际观测点计数。$E_{i,j}$ 为利用概率估计的应在在变量X上具有i-th类别值且在变量Y上具有j-th类别值的观测点数量。n为总观测数，$p_i$为在变量X上具有i-th类别值的概率，$p_j$为在变量Y上具有j-th类别值的概率。  \\n\",\n    \"  \\n\",\n    \"**值得注意的是，通过解析源代码，我们发现在sklearn中利用chi2计算出来的卡方统计量并不是统计意义上的卡方统计量**。当输入变量为布尔变量时，chi2计算值为该布尔变量为True时候的卡方统计量（我们将会在下文举例说明）。这样的优势是，独热编码生成的所有布尔值变量的chi2值之和将等于原始变量统计意义上的卡方统计量。  \\n\",\n    \"  \\n\",\n    \"举个简单的例子，假设一个变量I有0，1，2两种可能的值，则独特编码后一共会产生3个新的布尔值变量。这三个布尔值变量的chi2计算出来的值之和，将等于变量I与因变量直接计算得出的统计意义上的卡方统计量。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### 解析sklearn中chi2的计算\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:53.264829Z\",\n     \"start_time\": \"2020-03-30T03:43:53.173619Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"  \\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"  \\n\",\n       \"
TypeOutput
0J0
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2J0
3B2
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6C0
7C0
8C1
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\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"   Type  Output\\n\",\n       \"0     J       0\\n\",\n       \"1     J       1\\n\",\n       \"2     J       0\\n\",\n       \"3     B       2\\n\",\n       \"4     B       0\\n\",\n       \"5     B       1\\n\",\n       \"6     C       0\\n\",\n       \"7     C       0\\n\",\n       \"8     C       1\\n\",\n       \"9     C       2\\n\",\n       \"10    C       2\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 首先，随机生成一个数据集\\n\",\n    \"import pandas as pd\\n\",\n    \"sample_dict = {'Type': ['J','J','J',\\n\",\n    \"                        'B','B','B',\\n\",\n    \"                        'C','C','C','C','C'], \\n\",\n    \"               'Output': [0, 1, 0, \\n\",\n    \"                          2, 0, 1,  \\n\",\n    \"                          0, 0, 1, 2, 2,]}\\n\",\n    \"sample_raw = pd.DataFrame(sample_dict)\\n\",\n    \"sample_raw #原始数据，Output是我们的目标变量，Type为类别变量\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:53.297338Z\",\n     \"start_time\": \"2020-03-30T03:43:53.266661Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(array([0.17777778, 0.42666667, 1.15555556]),\\n\",\n       \" array([0.91494723, 0.8078868 , 0.56114397]))\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 下面利用独热编码生成布尔变量，并利用sklearn计算每一个布尔变量的chi2值\\n\",\n    \"sample = pd.get_dummies(sample_raw)\\n\",\n    \"from sklearn.feature_selection import chi2\\n\",\n    \"chi2(sample.values[:,[1,2,3]],sample.values[:,[0]])\\n\",\n    \"# 第一行为每一个布尔变量的chi2值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:53.314068Z\",\n     \"start_time\": \"2020-03-30T03:43:53.298829Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"
\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"  \\n\",\n       \"  \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"    \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"      \\n\",\n       \"    \\n\",\n       \"  \\n\",\n       \"
TypeOutputCount
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\\n\",\n       \"
\"\n      ],\n      \"text/plain\": [\n       \"  Type  Output  Count\\n\",\n       \"0    B       0      1\\n\",\n       \"1    B       1      1\\n\",\n       \"2    B       2      1\\n\",\n       \"3    C       0      2\\n\",\n       \"4    C       1      1\\n\",\n       \"5    C       2      2\\n\",\n       \"6    J       0      2\\n\",\n       \"7    J       1      1\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 下面直接计算原始变量Type与output统计学意义上的卡方统计量\\n\",\n    \"# 首先，先统计每一个类别下出现的观测数，用于创建列联表\\n\",\n    \"obs_df = sample_raw.groupby(['Type','Output']).size().reset_index()\\n\",\n    \"obs_df.columns = ['Type','Output','Count']\\n\",\n    \"obs_df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"即列联表（contingency table）为：  \\n\",\n    \"\\n\",\n    \"| Type/Output | 0 | 1 | 2 |\\n\",\n    \"|------|------|------|------|\\n\",\n    \"|  B  | 1 | 1 | 1 |\\n\",\n    \"|  C  | 2 | 1 | 2 |\\n\",\n    \"|  J  | 2 | 1 | 0 |\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:53.321849Z\",\n     \"start_time\": \"2020-03-30T03:43:53.315439Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(1.7600000000000002,\\n\",\n       \" 0.779791873961373,\\n\",\n       \" 4,\\n\",\n       \" array([[1.36363636, 0.81818182, 0.81818182],\\n\",\n       \"        [2.27272727, 1.36363636, 1.36363636],\\n\",\n       \"        [1.36363636, 0.81818182, 0.81818182]]))\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from scipy.stats import chi2_contingency\\n\",\n    \"obs = np.array([[1, 1, 1], [2, 1, 2],[2, 1, 0]])\\n\",\n    \"chi2_contingency(obs) # 第一个值即为变量Type与output统计学意义上的卡方统计量\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:53.330007Z\",\n     \"start_time\": \"2020-03-30T03:43:53.323857Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 而chi2函数算出来的布尔值之和为即为原始变量的统计意义上的卡方统计量\\n\",\n    \"chi2(sample.values[:,[1,2,3]],sample.values[:,[0]])[0].sum() == chi2_contingency(obs)[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:53.336779Z\",\n     \"start_time\": \"2020-03-30T03:43:53.331453Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Power_divergenceResult(statistic=0.17777777777777778, pvalue=0.9149472287300311)\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 那么sklearn中的chi2是如何计算的呢？\\n\",\n    \"# 不妨以第一个生成的布尔值为例，即Type为B\\n\",\n    \"# chi2出来的值为0.17777778\\n\",\n    \"# 而这与利用scipy以下代码计算出的计算一致\\n\",\n    \"from scipy.stats import chisquare\\n\",\n    \"f_exp = np.array([5/11, 3/11, 3/11]) * 3 # 预期频数为 output的先验概率 * Type为B 观测数\\n\",\n    \"chisquare([1,1,1], f_exp=f_exp) # [1,1,1] 即Type为B 的观测实际频数\\n\",\n    \"# 即sklearn 中的chi2 仅考虑了Type为B情形下的列连表\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### 如何利用sklearn 来进行特征选择\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:53.350699Z\",\n     \"start_time\": \"2020-03-30T03:43:53.338216Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from sklearn.feature_selection import chi2\\n\",\n    \"from sklearn.feature_selection import SelectKBest\\n\",\n    \"\\n\",\n    \"# 直接载入数据集\\n\",\n    \"from sklearn.datasets import load_iris # 利用iris数据作为演示数据集\\n\",\n    \"iris = load_iris()\\n\",\n    \"X, y = iris.data, iris.target\\n\",\n    \"# 此数据集中，X为连续变量，y为类别变量\\n\",\n    \"# 不满足chi2的使用条件\\n\",\n    \"\\n\",\n    \"# 将连续变量变为布尔值变量以满足chi2使用条件\\n\",\n    \"# 不妨利用其是否大于均值来生成布尔值（仅作为演示用）\\n\",\n    \"X = X > X.mean(0)\\n\",\n    \"\\n\",\n    \"# iris 数据集使用前需要被打乱顺序\\n\",\n    \"np.random.seed(1234)\\n\",\n    \"idx = np.random.permutation(len(X))\\n\",\n    \"X = X[idx]\\n\",\n    \"y = y[idx]\\n\",\n    \"\\n\",\n    \"# 选择前100个观测点作为训练集\\n\",\n    \"# 剩下的作为测试集\\n\",\n    \"train_set = X[0:100,:]\\n\",\n    \"test_set = X[100:,]\\n\",\n    \"train_y = y[0:100]\\n\",\n    \"\\n\",\n    \"# sklearn 中直接提供了函数用于计算卡方统计量\\n\",\n    \"# SelectKBest 将会基于一个判别函数自动选择得分高的变量\\n\",\n    \"# 这里的判别函数为F统计量\\n\",\n    \"selector = SelectKBest(chi2, k=2) # k => 我们想要选择的变量数\\n\",\n    \"selector.fit(train_set, train_y) # 在训练集上训练\\n\",\n    \"transformed_train = selector.transform(train_set) # 转换训练集\\n\",\n    \"transformed_train.shape #(100, 2), 其选择了第三个及第四个变量 \\n\",\n    \"assert np.array_equal(transformed_train, train_set[:,[2,3]]) \\n\",\n    \"\\n\",\n    \"transformed_test = selector.transform(test_set) # 转换测试集\\n\",\n    \"assert np.array_equal(transformed_test, test_set[:,[2,3]]);\\n\",\n    \"# 可见对于测试集，其依然选择了第三个及第四个变量 \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:53.358715Z\",\n     \"start_time\": \"2020-03-30T03:43:53.351973Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"第1个变量与因变量的卡方统计量为29.69，p值为0.0\\n\",\n      \"第2个变量与因变量的卡方统计量为19.42，p值为0.0\\n\",\n      \"第3个变量与因变量的卡方统计量为31.97，p值为0.0\\n\",\n      \"第4个变量与因变量的卡方统计量为31.71，p值为0.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 验证上述结果\\n\",\n    \"for idx in range(train_set.shape[1]):\\n\",\n    \"    score, p_value = chi2(train_set[:,idx].reshape(-1,1), train_y)\\n\",\n    \"    print(f\\\"第{idx + 1}个变量与因变量的卡方统计量为{round(score[0],2)}，p值为{round(p_value[0],3)}\\\")\\n\",\n    \"# 故应选择第三个及第四个变量 \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### F-Score (classification problem) F-统计量 (分类问题)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"在分类机器学习问题中，若变量特征为类别特征，则我们可以使用独热编码配合上述chi2方法选择最重要的特征。但若特征为连续变量，则我们可以使用ANOVA-F值。ANOVA F统计量的零假设是若按目标变量（类别）分组，则连续变量的总体均值是相同的。故我们应选择具有高ANOVA-F统计量的连续变量，因为这些连续变量与目标变量的关联性强。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"公式：  \\n\",\n    \"  \\n\",\n    \"$F = \\\\frac{MSB}{MSE} = \\\\frac{ \\\\frac{SS(between)}{m-1}}{ \\\\frac{SS(error)}{n-m}}$   \\n\",\n    \"  \\n\",\n    \"其中，SS(between)为组间的平方和，即组均值和总体均值之间的平方和。 SS(error)是组内的平方和，即数据与组均值之间的平方和。 m是目标变量的总类别数，n是观测数。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:53.368868Z\",\n     \"start_time\": \"2020-03-30T03:43:53.360087Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from sklearn.feature_selection import f_classif\\n\",\n    \"from sklearn.feature_selection import SelectKBest\\n\",\n    \"\\n\",\n    \"# 直接载入数据集\\n\",\n    \"from sklearn.datasets import load_iris # 利用iris数据作为演示数据集\\n\",\n    \"iris = load_iris()\\n\",\n    \"X, y = iris.data, iris.target\\n\",\n    \"# 此数据集中，X为连续变量，y为类别变量\\n\",\n    \"# 满足ANOVA-F的使用条件\\n\",\n    \"\\n\",\n    \"# iris 数据集使用前需要被打乱顺序\\n\",\n    \"np.random.seed(1234)\\n\",\n    \"idx = np.random.permutation(len(X))\\n\",\n    \"X = X[idx]\\n\",\n    \"y = y[idx]\\n\",\n    \"\\n\",\n    \"# 选择前100个观测点作为训练集\\n\",\n    \"# 剩下的作为测试集\\n\",\n    \"train_set = X[0:100,:]\\n\",\n    \"test_set = X[100:,]\\n\",\n    \"train_y = y[0:100]\\n\",\n    \"\\n\",\n    \"# sklearn 中直接提供了函数用于计算ANOVA-F\\n\",\n    \"# SelectKBest 将会基于一个判别函数自动选择得分高的变量\\n\",\n    \"# 这里的判别函数为F统计量\\n\",\n    \"selector = SelectKBest(f_classif, k=2) # k => 我们想要选择的变量数\\n\",\n    \"selector.fit(train_set, train_y) # 在训练集上训练\\n\",\n    \"transformed_train = selector.transform(train_set) # 转换训练集\\n\",\n    \"transformed_train.shape #(100, 2), 其选择了第三个及第四个变量 \\n\",\n    \"assert np.array_equal(transformed_train, train_set[:,[2,3]]) \\n\",\n    \"\\n\",\n    \"transformed_test = selector.transform(test_set) # 转换测试集\\n\",\n    \"assert np.array_equal(transformed_test, test_set[:,[2,3]]);\\n\",\n    \"# 可见对于测试集，其依然选择了第三个及第四个变量 \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:53.375786Z\",\n     \"start_time\": \"2020-03-30T03:43:53.370057Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"第1个变量与因变量的ANOVA-F统计量为91.39，p值为0.0\\n\",\n      \"第2个变量与因变量的ANOVA-F统计量为33.18，p值为0.0\\n\",\n      \"第3个变量与因变量的ANOVA-F统计量为733.94，p值为0.0\\n\",\n      \"第4个变量与因变量的ANOVA-F统计量为608.95，p值为0.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 验证上述结果\\n\",\n    \"for idx in range(train_set.shape[1]):\\n\",\n    \"    score, p_value = f_classif(train_set[:,idx].reshape(-1,1), train_y)\\n\",\n    \"    print(f\\\"第{idx + 1}个变量与因变量的ANOVA-F统计量为{round(score[0],2)}，p值为{round(p_value[0],3)}\\\")\\n\",\n    \"# 故应选择第三个及第四个变量 \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Mutual Information (classification problem) 互信息 (分类问题)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"【与1.1.1.5一样】互信息（Mutual Information）衡量变量间的相互依赖性。其本质为熵差，即$H(X) - H(X|Y)，即知道另一个变量信息后混乱的降低程度$。当且仅当两个随机变量独立时MI等于零。MI值越高，两变量之间的相关性则越强。与Pearson相关和F统计量相比，它还捕获了非线性关系。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"公式:  \\n\",\n    \"  \\n\",\n    \"- 若两个变量均为离散变量:  \\n\",\n    \"  \\n\",\n    \"    $I(x, y) = H(Y) - H(Y|X) = \\\\sum_{x\\\\in \\\\mathit{X}}  \\\\sum_{x\\\\in \\\\mathit{Y}} \\\\textit{p}_{(X,Y)}(x,y) \\\\textrm{log}(\\\\frac{\\\\textit{p}_{(X,Y)}(x,y)}{\\\\textit{p}_{X}(x)\\\\textit{p}_{Y}(y)})$  \\n\",\n    \"\\n\",\n    \"    $\\\\textit{p}_{(X,Y)}(x,y)$ 为x和y的联合概率质量函数 (PMF)， $\\\\textit{p}_{X}(x)$则为x的的联合概率质量函数 (PMF)。  \\n\",\n    \"  \\n\",\n    \"- 若两个变量均为连续变量:  \\n\",\n    \"\\n\",\n    \"    $I(X, Y) = H(Y) - H(Y|X) = \\\\int_X \\\\int_Y  \\\\textit{p}_{(X,Y)}(x,y) \\\\textrm{log}(\\\\frac{\\\\textit{p}_{(X,Y)}(x,y)}{\\\\textit{p}_{X}(x)\\\\textit{p}_{Y}(y)}) \\\\, \\\\, dx dy$  \\n\",\n    \"    \\n\",\n    \"    $\\\\textit{p}_{(X,Y)}(x,y)$ 为x和y的联合概率密度函数 (PDF)，$\\\\textit{p}_{X}(x)$则为x的的联合概率密度函数 (PDF)。连续变量情形下，在实际操作中，往往先对数据离散化分桶，然后逐个桶进行计算。\\n\",\n    \"  \\n\",\n    \"\\n\",\n    \"但是实际上，一种极有可能的情况是，x和y中的一个可能是离散变量，而另一个是连续变量。因此在sklearn中，它基于[1]和[2]中提出的基于k最临近算法的熵估计非参数方法。   \\n\",\n    \"   \\n\",\n    \"[1] A. Kraskov, H. Stogbauer and P. Grassberger, “Estimating mutual information”. Phys. Rev. E 69, 2004.  \\n\",\n    \"[2] B. C. Ross “Mutual Information between Discrete and Continuous Data Sets”. PLoS ONE 9(2), 2014. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:53.397199Z\",\n     \"start_time\": \"2020-03-30T03:43:53.377347Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from sklearn.feature_selection import mutual_info_classif\\n\",\n    \"from sklearn.feature_selection import SelectKBest\\n\",\n    \"\\n\",\n    \"# 直接载入数据集\\n\",\n    \"from sklearn.datasets import load_iris # 利用iris数据作为演示数据集\\n\",\n    \"iris = load_iris()\\n\",\n    \"X, y = iris.data, iris.target\\n\",\n    \"# 此数据集中，X为连续变量，y为类别变量\\n\",\n    \"# 满足MI的使用条件\\n\",\n    \"\\n\",\n    \"# iris 数据集使用前需要被打乱顺序\\n\",\n    \"np.random.seed(1234)\\n\",\n    \"idx = np.random.permutation(len(X))\\n\",\n    \"X = X[idx]\\n\",\n    \"y = y[idx]\\n\",\n    \"\\n\",\n    \"# 选择前100个观测点作为训练集\\n\",\n    \"# 剩下的作为测试集\\n\",\n    \"train_set = X[0:100,:]\\n\",\n    \"test_set = X[100:,]\\n\",\n    \"train_y = y[0:100]\\n\",\n    \"\\n\",\n    \"# KNN中的临近数是一个非常重要的参数\\n\",\n    \"# 故我们重写了一个新的MI计算函数更好的来控制这一参数\\n\",\n    \"def udf_MI(X, y):\\n\",\n    \"    result = mutual_info_classif(X, y, n_neighbors = 5) # 用户可以输入想要的临近数\\n\",\n    \"    return result\\n\",\n    \"\\n\",\n    \"# SelectKBest 将会基于一个判别函数自动选择得分高的变量\\n\",\n    \"# 这里的判别函数为F统计量\\n\",\n    \"selector = SelectKBest(udf_MI, k=2) # k => 我们想要选择的变量数\\n\",\n    \"selector.fit(train_set, train_y) # 在训练集上训练\\n\",\n    \"transformed_train = selector.transform(train_set) # 转换训练集\\n\",\n    \"transformed_train.shape #(100, 2), 其选择了第三个及第四个变量 \\n\",\n    \"assert np.array_equal(transformed_train, train_set[:,[2,3]]) \\n\",\n    \"\\n\",\n    \"transformed_test = selector.transform(test_set) # 转换测试集\\n\",\n    \"assert np.array_equal(transformed_test, test_set[:,[2,3]]);\\n\",\n    \"# 可见对于测试集，其依然选择了第三个及第四个变量 \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:53.414839Z\",\n     \"start_time\": \"2020-03-30T03:43:53.398716Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"第1个变量与因变量的互信息为0.56\\n\",\n      \"第2个变量与因变量的互信息为0.28\\n\",\n      \"第3个变量与因变量的互信息为0.99\\n\",\n      \"第4个变量与因变量的互信息为1.02\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 验算上述结果\\n\",\n    \"for idx in range(train_set.shape[1]):\\n\",\n    \"    score = mutual_info_classif(train_set[:,idx].reshape(-1,1), train_y, n_neighbors = 5)\\n\",\n    \"    print(f\\\"第{idx + 1}个变量与因变量的互信息为{round(score[0],2)}\\\")\\n\",\n    \"# 故应选择第三个及第四个变量\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Multivariate Filter Methods 多元特征过滤\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"单变量特征过滤仅考虑了每一变量与目标变量之间的关系，而忽视了变量间的相关性。多元变量过滤则解决了这一问题，其考虑了变量之间的相互关系，基于整个特征空间选择最佳特征。因此多元特征过滤在删除冗余变量方面表现更好。这里利用亚利桑那州立大学开发的[skfeature](https://github.com/jundongl/scikit-feature)模块来进行多元特征过滤。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Max-Relevance Min-Redundancy (mRMR) 最大相关最小冗余\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"最大相关最小冗余试图寻找一个与目标变量有较高相关性（例如：MI）的变量子集，同时这个子集中的变量还应具有较低的相互关联性。通过解析源代码，我们发现，skfeature中最大相关最小冗余方法仅适用于分类问题中的离散特征，因为它计算过程中使用的是计算离散情形下的互信息 (MI)的公式。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"公式：  \\n\",\n    \"假设数据集共包含m个特征，则基于mRMR公式，其中第n个特征的重要性可被表示为：\\n\",\n    \"\\n\",\n    \"$f^{mRMR}(X_i) = I(Y, X_i) - \\\\frac{1}{|S|}\\\\sum_{X_s \\\\in S} I(X_s, X_i)$  \\n\",\n    \"  \\n\",\n    \"$I(Y, X_i)$为变量$X_i$与目标变量Y的互信息。$\\\\frac{1}{|S|}\\\\sum_{X_s \\\\in S} I(X_s, X_i)$为变量$X_i$与现有变量子集中所有变量的互信息的平均值。      \\n\",\n    \"  \\n\",\n    \"mRMR其实是一个逐步（step-wise）的方法，在mRMR特征选择过程的每一步中，具有最高特征重要性$f^{mRMR}(X_i)$的变量$X_i, (X_i \\\\notin  S)$将会被加入子集中，直至子集中的变量数达到用户要求。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:53.437413Z\",\n     \"start_time\": \"2020-03-30T03:43:53.416458Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from skfeature.function.information_theoretical_based import MRMR\\n\",\n    \"from sklearn.datasets import load_iris  # 利用iris数据作为演示数据集\\n\",\n    \"\\n\",\n    \"# 载入数据集\\n\",\n    \"iris = load_iris()\\n\",\n    \"X, y = iris.data, iris.target\\n\",\n    \"\\n\",\n    \"# iris 数据集使用前需要被打乱顺序\\n\",\n    \"np.random.seed(1234)\\n\",\n    \"idx = np.random.permutation(len(X))\\n\",\n    \"X = X[idx]\\n\",\n    \"y = y[idx]\\n\",\n    \"\\n\",\n    \"# 选择前100个观测点作为训练集\\n\",\n    \"# 剩下的50个观测点作为测试集\\n\",\n    \"# 由于skfeature中的mRMR仅适用于离散变量\\n\",\n    \"# 因此我们通过将float转换为int而把所有连续变量转换为离散变量\\n\",\n    \"# 此转换仅用于演示目的\\n\",\n    \"\\n\",\n    \"train_set = X[0:100,:].astype(int)\\n\",\n    \"test_set = X[100:,].astype(int)\\n\",\n    \"train_y = y[0:100].astype(int)\\n\",\n    \"\\n\",\n    \"feature_index,_,_ = MRMR.mrmr(train_set, train_y, n_selected_features=2) # 在训练集上训练\\n\",\n    \"transformed_train = train_set[:,feature_index] # 转换训练集\\n\",\n    \"assert np.array_equal(transformed_train, train_set[:,[2,3]])  # 其选择了第三个及第四个变量 \\n\",\n    \"\\n\",\n    \"transformed_test = test_set[:,feature_index] # 转换测试集\\n\",\n    \"assert np.array_equal(transformed_test, test_set[:,[2,3]]) # 其选择了第三个及第四个变量 \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Correlation-based Feature Selection (CFS) 基于相关性的特征选择\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"与mRMR类似，基于相关性的特征选择（CFS）也基于一个类似的假设：一个好的特征子集应包含与目标高度相关且彼此不相关的特征。 通过解析源代码，我们发现，skfeature中CFS的实现也仅适用于分类问题中的离散特征。因为其使用的是离散情形下的对称不确定性（symmetrical uncertainty）作为变量间相关性的衡量标准。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"公式：  \\n\",\n    \"  \\n\",\n    \"$Merit_S =\\\\frac{ \\\\sum_{i=1}^{k} SU(X_i, y)}{\\\\sqrt{k + \\\\sum_{i=1}^{k} \\\\sum_{j=1}^{k} SU(X_i, X_j) }}, \\\\, \\\\, \\\\, X_i \\\\in S^*$  \\n\",\n    \"  \\n\",\n    \"$SU(X, Y) = 2 * \\\\frac{H(X) + H(Y) - H(X|Y)}{H(X) + H(Y)}$  \\n\",\n    \"  \\n\",\n    \"$S$为特征子集. 我们需要寻找最大化$Merit_S$的最优子集$S^*$。  \\n\",\n    \"$SU(X_i, y)$为离散变量$X_i$与目标变量$y$间的对称不确定性（SU）。     \\n\",\n    \"$SU(X_i, X_j)$为离散变量$X_i$与离散变量$X_j$间的对称不确定性（SU）。  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:53.461564Z\",\n     \"start_time\": \"2020-03-30T03:43:53.443040Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from skfeature.function.statistical_based import CFS\\n\",\n    \"from sklearn.datasets import load_iris  # 利用iris数据作为演示数据集\\n\",\n    \"\\n\",\n    \"# 载入数据集\\n\",\n    \"iris = load_iris()\\n\",\n    \"X, y = iris.data, iris.target\\n\",\n    \"\\n\",\n    \"# iris 数据集使用前需要被打乱顺序\\n\",\n    \"np.random.seed(1234)\\n\",\n    \"idx = np.random.permutation(len(X))\\n\",\n    \"X = X[idx]\\n\",\n    \"y = y[idx]\\n\",\n    \"\\n\",\n    \"# 选择前100个观测点作为训练集\\n\",\n    \"# 剩下的50个观测点作为测试集\\n\",\n    \"# 由于skfeature中的CFS仅适用于离散变量\\n\",\n    \"# 因此我们通过将float转换为int而把所有连续变量转换为离散变量\\n\",\n    \"# 此转换仅用于演示目的\\n\",\n    \"\\n\",\n    \"train_set = X[0:100,:].astype(int)\\n\",\n    \"test_set = X[100:,].astype(int)\\n\",\n    \"train_y = y[0:100].astype(int)\\n\",\n    \"\\n\",\n    \"num_feature = 2 # 从原数据集中选择两个变量\\n\",\n    \"\\n\",\n    \"feature_index = CFS.cfs(train_set, train_y) # 在训练集上训练\\n\",\n    \"transformed_train = train_set[:,feature_index[0:num_feature]] # 转换训练集\\n\",\n    \"assert np.array_equal(transformed_train, train_set[:,[3,2]])  # 其选择了第三个及第四个变量 \\n\",\n    \"\\n\",\n    \"transformed_test = test_set[:,feature_index[0:num_feature]] # 转换测试集\\n\",\n    \"assert np.array_equal(transformed_test, test_set[:,[3,2]]) # 其选择了第三个及第四个变量 \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Fast Correlation-based Filter (FCBF) 基于相关性的快速特征选择\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"相比于CFS，FCBS能够更加高效的筛选变量。其同样为逐步（step-wise）的方法，具体步骤与mRMR非常类似，但FCBS使用对称不确定性（SU）衡量变量间的关联性。FCBF首先剔除与目标变量具有较低SU值的变量，并对剩下的变量按与目标变量的SU值从最高到最低排序，然后逐一删除冗余特征。与mRMR，CFS相似，在skfeature中实现的FCBF仅适用于具有离散变量的分类问题。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"公式：  \\n\",\n    \"  \\n\",\n    \"$SU(X, Y) = 2 * \\\\frac{H(X) + H(Y) - H(X|Y)}{H(X) + H(Y)}$   \\n\",\n    \"  \\n\",\n    \"步骤：  \\n\",\n    \"  \\n\",\n    \"1)计算每个特征变量$X_i$与目标变量$y$之间的相关性$SU(X_i,y)$  \\n\",\n    \"2)仅保留$SU(X_i,y)$大于一定阈值$\\\\sigma$的特征变量，组成候选列表$S_{list}$  \\n\",\n    \"3)按照$SU(X_i,y)$值的大小对$S_{list}$中的变量从大到小排序  \\n\",\n    \"4)按顺序依次计算每一个特征$X_i$与候选列表$S_{list}$中顺序靠后的每一个特征$X_j$的相关SU值$SU(X_i,X_j)$  \\n\",\n    \"5)若$SU(X_i,X_j)$大于$SU(X_j,y)$，则从候选列表$S_{list}$ 中删除$X_j$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:53.474859Z\",\n     \"start_time\": \"2020-03-30T03:43:53.463397Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from skfeature.function.information_theoretical_based import FCBF\\n\",\n    \"from sklearn.datasets import load_iris  # 利用iris数据作为演示数据集\\n\",\n    \"\\n\",\n    \"# 载入数据集\\n\",\n    \"iris = load_iris()\\n\",\n    \"X, y = iris.data, iris.target\\n\",\n    \"\\n\",\n    \"# iris 数据集使用前需要被打乱顺序\\n\",\n    \"np.random.seed(1234)\\n\",\n    \"idx = np.random.permutation(len(X))\\n\",\n    \"X = X[idx]\\n\",\n    \"y = y[idx]\\n\",\n    \"\\n\",\n    \"# 选择前100个观测点作为训练集\\n\",\n    \"# 剩下的50个观测点作为测试集\\n\",\n    \"# 由于skfeature中的FCFS仅适用于离散变量\\n\",\n    \"# 因此我们通过将float转换为int而把所有连续变量转换为离散变量\\n\",\n    \"# 此转换仅用于演示目的\\n\",\n    \"\\n\",\n    \"train_set = X[0:100,:].astype(int)\\n\",\n    \"test_set = X[100:,].astype(int)\\n\",\n    \"train_y = y[0:100].astype(int)\\n\",\n    \"\\n\",\n    \"num_feature = 2 # 从原数据集中选择两个变量\\n\",\n    \"\\n\",\n    \"feature_index = FCBF.fcbf(train_set, train_y, n_selected_features = num_feature)[0] # 在训练集上训练\\n\",\n    \"transformed_train = train_set[:,feature_index[0:num_feature]] # 转换训练集\\n\",\n    \"assert np.array_equal(transformed_train, train_set[:,[3, 2]])  # 其选择了第三个及第四个变量 \\n\",\n    \"# 这是由于其他变量目标变量𝑦之间的相关性过低造成的\\n\",\n    \"\\n\",\n    \"transformed_test = test_set[:,feature_index[0:num_feature]] # 转换测试集\\n\",\n    \"assert np.array_equal(transformed_test, test_set[:,[3, 2]]) # # 其选择了第三个及第四个变量 \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### ReliefF\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"ReliefF方法是一种基于Relief方法的特征加权算法。在Relief方法中，其根据特征与目标变量的相关性强弱（二分类）给变量分配权重，并删除权重低于特定阈值的特征。其将相关性定义为变量区分邻近观测点的能力。\\n\",\n    \"\\n\",\n    \"具体来说，在每一步中，Relief方法都会从训练集中随机选择一个观测点S，然后找到具有相同目标标签的S的最近邻观测点，称为NearHit。它还将找到具有不同目标标签的S的最近邻观测点，称为NearMiss。然后根据以下规则更新每个功能的权重：\\n\",\n    \"\\n\",\n    \"1）若观测点S在某变量上与NearHit的距离大于与NearMiss的距离，则该变量的权重将增加，因为变量有助于区分最邻近情形下的目标标签。2）相反，若观测点S在某变量上与NearHit的距离小于与NearMiss的距离，则该变量的权重会降低。\\n\",\n    \"\\n\",\n    \"将上述过程重复m次，最后我们会获得每个特征变量的平均权重。特征变量的权重越大，则特征的分类能力越强，越应该被留在最终的特征子集中。\\n\",\n    \"\\n\",\n    \"在ReliefF中，其修改了权重更新的方式，因此ReliefF方法可以被应用于多类分类问题。另外，它随机采样K个最近的观测点而不是一个。\\n\",\n    \"\\n\",\n    \"在skfeature中实现的ReliefF可用于分类问题中的连续特征或二元类别特征，因其使用的是L1范数来衡量差异。针对非二元特征，我们可以先将其独热编码，再使用ReliefF方法。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"公式：  \\n\",\n    \"\\n\",\n    \"Formula:  \\n\",\n    \"  \\n\",\n    \"$W(X_i) = W(X_i) - \\\\frac{\\\\sum_{i=1}^{k} diff(X_i, S, H_j) }{m*k} + \\\\frac{\\\\sum_{C\\\\notin class(S)} [\\\\frac{p(C)}{1-P(class(S))} \\\\sum_{i=1}^{k} diff(X_i, S, M_j(C)) ]}{m*k}$  \\n\",\n    \"\\n\",\n    \"$diff(X_i, R_1, R_2) = \\\\left\\\\{\\\\begin{matrix}\\n\",\n    \"\\\\frac{|R_1(X_i) - R_2(X_i)|}{max(X_i)- min(X_i)} & 若X_i\\\\ 为连续变量\\\\\\\\ \\n\",\n    \" 0 & 若X_i为类别变量且\\\\ R_1(X_i) =R_2(X_i)\\\\\\\\ \\n\",\n    \" 1 & 若X_i为类别变量且 \\\\ R_1(X_i) \\\\neq R_2(X_i)\\n\",\n    \"\\\\end{matrix}\\\\right.$ \\n\",\n    \"  \\n\",\n    \"$R_1$ and $R_2$ 为任意两个观测点。$X_i$为某一特征变量. S为我们选择的观测点. $H_j$为第j个NearHit，$M_j$为第j个NearMiss. C为与我们所选的观测点不同的其他目标类别标签。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:53.510692Z\",\n     \"start_time\": \"2020-03-30T03:43:53.476404Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from skfeature.function.similarity_based import reliefF\\n\",\n    \"from sklearn.datasets import load_iris  # 利用iris数据作为演示数据集\\n\",\n    \"\\n\",\n    \"# 载入数据集\\n\",\n    \"iris = load_iris()\\n\",\n    \"X, y = iris.data, iris.target\\n\",\n    \"\\n\",\n    \"# iris 数据集使用前需要被打乱顺序\\n\",\n    \"np.random.seed(1234)\\n\",\n    \"idx = np.random.permutation(len(X))\\n\",\n    \"X = X[idx]\\n\",\n    \"y = y[idx]\\n\",\n    \"\\n\",\n    \"# 选择前100个观测点作为训练集\\n\",\n    \"# 剩下的50个观测点作为测试集\\n\",\n    \"# skfeature中的reliefF直接适用于连续变量\\n\",\n    \"\\n\",\n    \"train_set = X[0:100,:]\\n\",\n    \"test_set = X[100:,]\\n\",\n    \"train_y = y[0:100]\\n\",\n    \"\\n\",\n    \"num_feature = 2 # 从原数据集中选择两个变量\\n\",\n    \"\\n\",\n    \"score = reliefF.reliefF(train_set, train_y) # 计算每一个变量的权重\\n\",\n    \"feature_index = reliefF.feature_ranking(score) # 依据权重选择变量\\n\",\n    \"transformed_train = train_set[:,feature_index[0:num_feature]] # 转换训练集\\n\",\n    \"assert np.array_equal(transformed_train, train_set[:,[2, 3]])  # 其选择了第三个及第四个变量 \\n\",\n    \"\\n\",\n    \"transformed_test = test_set[:,feature_index[0:num_feature]] # 转换测试集\\n\",\n    \"assert np.array_equal(transformed_test, test_set[:,[2, 3]]) # 其选择了第三个及第四个变量 \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Spectral Feature Selection (SPEC) 基于谱图的特征选择\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"基于谱图的特征选择(SPEC)方法是基于谱图理论的无监督方法。其首先建立变量相似度集合S，并建立其图表示。然后，其根据构造图的频谱评估特征。由于在skfeature中实现的SPEC方基于RBF（高斯）内核建立相似集，因此其可用于分类问题中的连续特征或二元类别特征。针对非二元特征，我们可以先将其独热编码，再使用ReliefF方法。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:53.539878Z\",\n     \"start_time\": \"2020-03-30T03:43:53.512125Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from skfeature.function.similarity_based import SPEC\\n\",\n    \"from sklearn.datasets import load_iris  # 利用iris数据作为演示数据集\\n\",\n    \"\\n\",\n    \"# 载入数据集\\n\",\n    \"iris = load_iris()\\n\",\n    \"X, y = iris.data, iris.target\\n\",\n    \"\\n\",\n    \"# iris 数据集使用前需要被打乱顺序\\n\",\n    \"np.random.seed(1234)\\n\",\n    \"idx = np.random.permutation(len(X))\\n\",\n    \"X = X[idx]\\n\",\n    \"y = y[idx]\\n\",\n    \"\\n\",\n    \"# 选择前100个观测点作为训练集\\n\",\n    \"# 剩下的50个观测点作为测试集\\n\",\n    \"# skfeature中的SEPC方法直接适用于连续变量\\n\",\n    \"\\n\",\n    \"train_set = X[0:100,:]\\n\",\n    \"test_set = X[100:,]\\n\",\n    \"train_y = y[0:100]\\n\",\n    \"\\n\",\n    \"num_feature = 2 # 从原数据集中选择两个变量\\n\",\n    \"\\n\",\n    \"score = SPEC.spec(train_set) # 计算每一个变量的得分\\n\",\n    \"feature_index = SPEC.feature_ranking(score) #依据变量得分选择变量\\n\",\n    \"transformed_train = train_set[:,feature_index[0:num_feature]] # 转换训练集\\n\",\n    \"assert np.array_equal(transformed_train, train_set[:,[1, 0]])  # 其选择了第一个及第二个变量 \\n\",\n    \"\\n\",\n    \"transformed_test = test_set[:,feature_index[0:num_feature]] # 转换测试集\\n\",\n    \"assert np.array_equal(transformed_test, test_set[:,[1, 0]]) # 其选择了第一个及第二个变量 \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Wrapper Methods 封装方法\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"封装方法将特征选择问题视作搜索问题，即其目标为从特征子集集合中搜索出一个最佳的子集，而这一子集在模型中表现最佳。在每一步中，其在特征子集上训练模型，然后对其进行评估，并在下一步继续调整特征子集,重新训练评估，直到找到最佳子集或达到最大迭代次数为止。穷尽搜索在封装方法中为NP-Hard，故人们提出了一些方法来降低封装方法所需要的迭代次数，以便可以在有限的时间内达到一个较好的效果。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"![image](https://itlubber.art/upload/封装法工作流.png)|\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Deterministic Algorithms 确定性算法\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"在不考虑模型随机性的情况下，给定相同的数据输入，确定性算法将始终输出相同的最优特征子集。顺序向前选择（SFS），顺序向后选择（SBS）均为确定性算法。\\n\",\n    \"\\n\",\n    \"顺序向前选择（SFS）方法将从最优单变量模型开始，然后在迭代中，其会在上一步变量子集的基础上，以穷举的方法在现有变量子集中增加一个新变量，使得新增一个变量后的变量子集可以获得最大的模型表现提升。迭代将持续直到所选变量的数量满足要求为止。\\n\",\n    \"\\n\",\n    \"顺序向后选择（SBS）则从适合一个包含所有变量的模型开始，然后在迭代中，其会在上一步变量子集的基础上，以穷举的方法在现有变量子集中删除一个对模型负影响最低的变量，直到所选特征的数量满足要求为止。\\n\",\n    \"\\n\",\n    \"但是顺序向前选择（SFS）方法和顺序向后选择（SBS）均为逐步（step-wise）的方法，都可能会陷入局部最优状态。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Recursive Feature Elimination (SBS) 递归式特征消除\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"在sklearn中，它仅实现递归特征消除（SBS）方法。其提供了两个函数来实现这一方法，一个是RFE，另一个是RFECV。与RFE函数相比，REFCV使用交叉验证的结果来选择最优的特征数量，而在RFE中，要选择的特征数量由用户预定义。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:43:56.695018Z\",\n     \"start_time\": \"2020-03-30T03:43:53.541618Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# RFE函数 演示\\n\",\n    \"import numpy as np\\n\",\n    \"from sklearn.feature_selection import RFE\\n\",\n    \"\\n\",\n    \"# 直接载入数据集\\n\",\n    \"from sklearn.datasets import fetch_california_housing\\n\",\n    \"dataset = fetch_california_housing()\\n\",\n    \"X, y = dataset.data, dataset.target # 利用 california_housing 数据集来演示\\n\",\n    \"\\n\",\n    \"# 选择前15000个观测点作为训练集\\n\",\n    \"# 剩下的作为测试集\\n\",\n    \"train_set = X[0:15000,:]\\n\",\n    \"test_set = X[15000:,]\\n\",\n    \"train_y = y[0:15000]\\n\",\n    \"\\n\",\n    \"# 选择用于衡量子集表现的有监督的机器学习模型\\n\",\n    \"from sklearn.ensemble import ExtraTreesRegressor # 使用ExtraTrees 模型作为示范\\n\",\n    \"clf = ExtraTreesRegressor(n_estimators=25)\\n\",\n    \"selector = RFE(estimator = clf, n_features_to_select = 4, step = 1) \\n\",\n    \"# 与RFECV不同，此处RFE函数需要用户定义选择的变量数量，此处设置为选择4个最好的变量，每一步我们仅删除一个变量\\n\",\n    \"\\n\",\n    \"selector = selector.fit(train_set, train_y) # 在训练集上训练\\n\",\n    \"\\n\",\n    \"transformed_train = train_set[:,selector.support_]  # 转换训练集\\n\",\n    \"assert np.array_equal(transformed_train, train_set[:,[0,5,6,7]]) # 选择了第一个，第六个，第七个及第八个变量\\n\",\n    \"\\n\",\n    \"transformed_test = test_set[:,selector.support_] # 转换训练集\\n\",\n    \"assert np.array_equal(transformed_test, test_set[:,[0,5,6,7]]) # 选择了第一个，第六个，第七个及第八个变量\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:44:14.363261Z\",\n     \"start_time\": \"2020-03-30T03:43:56.696872Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# RFECV 函数 演示\\n\",\n    \"import numpy as np\\n\",\n    \"from sklearn.feature_selection import RFECV\\n\",\n    \"\\n\",\n    \"# 直接载入数据集\\n\",\n    \"from sklearn.datasets import fetch_california_housing\\n\",\n    \"dataset = fetch_california_housing()\\n\",\n    \"X, y = dataset.data, dataset.target # 利用 california_housing 数据集来演示\\n\",\n    \"\\n\",\n    \"# 选择前15000个观测点作为训练集\\n\",\n    \"# 剩下的作为测试集\\n\",\n    \"train_set = X[0:15000,:]\\n\",\n    \"test_set = X[15000:,]\\n\",\n    \"train_y = y[0:15000]\\n\",\n    \"\\n\",\n    \"# 选择用于衡量子集表现的有监督的机器学习模型\\n\",\n    \"from sklearn.ensemble import ExtraTreesRegressor # 使用ExtraTrees 模型作为示范\\n\",\n    \"clf = ExtraTreesRegressor(n_estimators=25)\\n\",\n    \"selector = RFECV(estimator = clf, step = 1, cv = 5) # 使用5折交叉验证\\n\",\n    \"# 每一步我们仅删除一个变量\\n\",\n    \"selector = selector.fit(train_set, train_y)\\n\",\n    \"\\n\",\n    \"transformed_train = train_set[:,selector.support_]  # 转换训练集\\n\",\n    \"assert np.array_equal(transformed_train, train_set) # 选择了所有的变量\\n\",\n    \"\\n\",\n    \"transformed_test = test_set[:,selector.support_] # 转换训练集\\n\",\n    \"assert np.array_equal(transformed_test, test_set) # 选择了所有的变量\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Randomized Algorithms 随机方法\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"与确定性算法相比，随机方法在搜索最佳特征子集时引入了一定程度的随机性。因此，在相同数据输入的情形下，它可能会输出不同的最优特征子集结果，但此方法中的随机性将有助于避免模型陷入局部最优结果。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Simulated Annealing (SA) 基于模拟退火特征选择\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"模拟退火是一种随机最优化方法，近年来被引入到特征选择领域。在每一步中，我们将根据当前的最优特征子集随机选择一个特征子集。若新的特征子集效果更好，那么我们将采用它并更新当前最优特征子集。若新特征子集的表现不佳，我们仍会以一定的概率接受它，这个接受概率取决于当前的状态（温度）。\\n\",\n    \"\\n\",\n    \"以一定的概率接受变现不佳的特征子集对于模拟退火算法至关重要，因为这有助于算法避免陷入局部最优状态。随着迭代的进行，模拟退火算法可收敛为良好且稳定的最终结果。\\n\",\n    \"\\n\",\n    \"由于未发现能较好实现SA算法的函数，因此我编写了一个[python脚本](../SA.py)来实现SA算法，以供您参考。其能够很好地兼容sklearn中的模型，支持分类及回归问题。它还提供了内置交叉验证方法。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-28T15:49:02.243080Z\",\n     \"start_time\": \"2020-03-28T15:49:02.236948Z\"\n    }\n   },\n   \"source\": [\n    \"公式:  \\n\",\n    \"  \\n\",\n    \"在每一步中，接受表现不佳的特征子集的概率为:  \\n\",\n    \"$Prob = exp( - \\\\frac{loss_{n} - loss_{o}}{k * Cur\\\\_{Temperature}})$  \\n\",\n    \"  \\n\",\n    \"Prob为接受表现不佳的特征子集的概率，$loss_n$为新特征子集的损失（loss），$loss_o$为新特征子集创建前的最优（最低）损失（loss），$Cur\\\\_{Temperature}$为当前的温度。\\n\",\n    \"  \\n\",\n    \"模拟退火的伪代码为:  \\n\",\n    \"\\n\",\n    \"![image](https://itlubber.art/upload/基于模拟退火特征选择.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"回归问题演示\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:45:25.268299Z\",\n     \"start_time\": \"2020-03-30T03:44:14.364883Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import sys \\n\",\n    \"sys.path.append(\\\"..\\\") \\n\",\n    \"from SA import Simulated_Annealing # 导入我们撰写的模块\\n\",\n    \"\\n\",\n    \"# 直接载入数据集\\n\",\n    \"from sklearn.datasets import fetch_california_housing\\n\",\n    \"dataset = fetch_california_housing()\\n\",\n    \"X, y = dataset.data, dataset.target # 利用 california_housing 数据集来演示\\n\",\n    \"\\n\",\n    \"# 选择前15000个观测点作为训练集\\n\",\n    \"# 剩下的作为测试集\\n\",\n    \"train_set = X[0:15000,:]\\n\",\n    \"test_set = X[15000:,]\\n\",\n    \"train_y = y[0:15000]\\n\",\n    \"\\n\",\n    \"# 选择用于衡量子集表现的有监督的机器学习模型\\n\",\n    \"from sklearn.ensemble import ExtraTreesRegressor # 使用ExtraTrees 模型作为示范\\n\",\n    \"\\n\",\n    \"# 选择模拟退火中评价特征子集的的损失函数\\n\",\n    \"from sklearn.metrics import mean_squared_error # 回归问题我们使用MSE\\n\",\n    \"\\n\",\n    \"clf = ExtraTreesRegressor(n_estimators=25)\\n\",\n    \"selector = Simulated_Annealing(loss_func = mean_squared_error, estimator = clf, \\n\",\n    \"                               init_temp = 0.2, min_temp = 0.005, iteration = 10, alpha = 0.9)\\n\",\n    \"# 在训练集中训练\\n\",\n    \"# SA.py中有具体每个参数的含义，此处不赘述\\n\",\n    \"\\n\",\n    \"selector.fit(X_train = train_set, y_train = train_y, cv = 5) # 使用5折交叉验证\\n\",\n    \"\\n\",\n    \"transformed_train = selector.transform(train_set) # 转换训练集\\n\",\n    \"transformed_test = selector.transform(test_set)  # 转换测试集\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:45:25.272088Z\",\n     \"start_time\": \"2020-03-30T03:45:25.269721Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"selector.best_sol # 返回最优特征的索引\\n\",\n    \"selector.best_loss; # 返回最优特征子集对应的损失\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"分类问题演示\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:45:25.567132Z\",\n     \"start_time\": \"2020-03-30T03:45:25.273497Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import sys \\n\",\n    \"sys.path.append(\\\"..\\\") \\n\",\n    \"import numpy as np\\n\",\n    \"import random\\n\",\n    \"from SA import Simulated_Annealing # 导入我们撰写的模块\\n\",\n    \"\\n\",\n    \"from sklearn.datasets import load_iris  # 利用iris数据作为演示数据集\\n\",\n    \"\\n\",\n    \"# 载入数据集\\n\",\n    \"iris = load_iris()\\n\",\n    \"X, y = iris.data, iris.target\\n\",\n    \"\\n\",\n    \"# iris 数据集使用前需要被打乱顺序\\n\",\n    \"np.random.seed(1234)\\n\",\n    \"idx = np.random.permutation(len(X))\\n\",\n    \"X = X[idx]\\n\",\n    \"y = y[idx]\\n\",\n    \"\\n\",\n    \"# 选择前100个观测点作为训练集\\n\",\n    \"# 剩下的前20个观测点作为验证集，剩下的30个观测作为测试集\\n\",\n    \"train_set = X[0:100,:]\\n\",\n    \"val_set = X[100:120,:]\\n\",\n    \"test_set = X[120:,:]\\n\",\n    \"\\n\",\n    \"train_y = y[0:100]\\n\",\n    \"val_y = y[100:120]\\n\",\n    \"test_y = y[120:]\\n\",\n    \"\\n\",\n    \"# 重制随机种子 \\n\",\n    \"# 随机方法需要随机性的存在\\n\",\n    \"random.seed()\\n\",\n    \"np.random.seed()\\n\",\n    \"\\n\",\n    \"# 选择用于衡量子集表现的有监督的机器学习模型\\n\",\n    \"from sklearn.ensemble import ExtraTreesClassifier # we use extratree as predictive model\\n\",\n    \"\\n\",\n    \"# 选择模拟退火中评价特征子集的的损失函数\\n\",\n    \"from sklearn.metrics import log_loss # 回归问题中，我们使用交叉熵损失函数\\n\",\n    \"\\n\",\n    \"clf = ExtraTreesClassifier(n_estimators=25)\\n\",\n    \"selector = Simulated_Annealing(loss_func = log_loss, estimator = clf, \\n\",\n    \"                               init_temp = 0.2, min_temp = 0.005, iteration = 10, \\n\",\n    \"                               alpha = 0.9, predict_type = 'predict_proba')\\n\",\n    \"# 在训练集中训练\\n\",\n    \"# SA.py中有具体每个参数的含义，此处不赘述\\n\",\n    \"\\n\",\n    \"selector.fit(X_train = train_set, y_train = train_y, X_val = val_set, \\n\",\n    \"             y_val = val_y, stop_point = 15) \\n\",\n    \"# 此函数允许用户导入自己定义的验证集，此处尝试一下\\n\",\n    \"\\n\",\n    \"transformed_train = selector.transform(train_set)  # 转换训练集\\n\",\n    \"transformed_test = selector.transform(test_set)  # 转换测试集\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:45:25.571342Z\",\n     \"start_time\": \"2020-03-30T03:45:25.568710Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"selector.best_sol # 返回最优特征的索引\\n\",\n    \"selector.best_loss; # 返回最优特征子集对应的损失\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Genetic Algorithm (GA) 基于基因算法特征选择\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"遗传算法是一种基于进化生物学概念的最优化搜索算法。它借鉴了自然界中的进化过程，并通过允许个体候选解通过“交叉”和“变异”来进化得到更优的候选解及种群。其还结合了自然界中的竞争理念，即仅允许最合适或最优的几个候选解“生存”下来并“繁殖”其后代。经过种群及个体候选解的持续迭代，基因算法（GA）会收敛到优化解决方案。\\n\",\n    \"\\n\",\n    \"与模拟退火类似，我也编写了一个[python脚本](../GA.py)来实现GA算法，以供您参考。它提供了两种算法，包括“one-max”和“ NSGA2”。 “one-max”为传统的单目标GA算法，“NSGA2”则为一个多目标GA算法。在特征选择中，“one-max”的目标是减少模拟在验证集上的损失，而“NSGA2”的目标一是减少损失，二是同时要最小化特征子集中特征的数量。\\n\",\n    \"  \\n\",\n    \"此python脚本能够很好地兼容sklearn中的模型，支持分类及回归问题。它还提供了内置交叉验证方法。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"基因算法的伪代码如下：  \\n\",\n    \"\\n\",\n    \"![image](https://itlubber.art/upload/基于基因算法特征选择.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"回归问题演示\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:47:34.026251Z\",\n     \"start_time\": \"2020-03-30T03:45:25.573202Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|██████████| 10/10 [01:31<00:00,  9.11s/it]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import sys \\n\",\n    \"sys.path.append(\\\"..\\\") \\n\",\n    \"from GA import Genetic_Algorithm # 导入我们撰写的模块\\n\",\n    \"\\n\",\n    \"# 直接载入数据集\\n\",\n    \"from sklearn.datasets import fetch_california_housing\\n\",\n    \"dataset = fetch_california_housing()\\n\",\n    \"X, y = dataset.data, dataset.target # 利用 california_housing 数据集来演示\\n\",\n    \"\\n\",\n    \"# 选择前15000个观测点作为训练集\\n\",\n    \"# 剩下的作为测试集\\n\",\n    \"train_set = X[0:15000,:]\\n\",\n    \"test_set = X[15000:,]\\n\",\n    \"train_y = y[0:15000]\\n\",\n    \"\\n\",\n    \"# 选择用于衡量子集表现的有监督的机器学习模型\\n\",\n    \"from sklearn.ensemble import ExtraTreesRegressor # 使用ExtraTrees 模型作为示范\\n\",\n    \"\\n\",\n    \"# 选择模拟退火中评价特征子集的的损失函数\\n\",\n    \"from sklearn.metrics import mean_squared_error # 回归问题我们使用MSE\\n\",\n    \"\\n\",\n    \"clf = ExtraTreesRegressor(n_estimators=25)\\n\",\n    \"selector = Genetic_Algorithm(loss_func = mean_squared_error, estimator = clf, \\n\",\n    \"                             n_gen = 10, n_pop = 20, algorithm = 'NSGA2')\\n\",\n    \"# 在训练集中训练\\n\",\n    \"# GA.py中有具体每个参数的含义，此处不赘述\\n\",\n    \"\\n\",\n    \"selector.fit(X_train = train_set, y_train = train_y, cv = 5) # 使用5折交叉验证\\n\",\n    \"\\n\",\n    \"transformed_train = selector.transform(train_set) # 转换训练集\\n\",\n    \"transformed_test = selector.transform(test_set)  # 转换测试集\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:47:34.030792Z\",\n     \"start_time\": \"2020-03-30T03:47:34.028121Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"selector.best_sol # 返回最优特征的索引\\n\",\n    \"selector.best_loss; # 返回最优特征子集对应的损失\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"分类问题演示\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:47:34.316372Z\",\n     \"start_time\": \"2020-03-30T03:47:34.032578Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|██████████| 15/15 [00:00<00:00, 128.87it/s]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import sys \\n\",\n    \"sys.path.append(\\\"..\\\") \\n\",\n    \"import numpy as np\\n\",\n    \"import random\\n\",\n    \"from GA import Genetic_Algorithm # 导入我们撰写的模块\\n\",\n    \"\\n\",\n    \"from sklearn.datasets import load_iris  # 利用iris数据作为演示数据集\\n\",\n    \"\\n\",\n    \"# 载入数据集\\n\",\n    \"iris = load_iris()\\n\",\n    \"X, y = iris.data, iris.target\\n\",\n    \"\\n\",\n    \"# iris 数据集使用前需要被打乱顺序\\n\",\n    \"np.random.seed(1234)\\n\",\n    \"idx = np.random.permutation(len(X))\\n\",\n    \"X = X[idx]\\n\",\n    \"y = y[idx]\\n\",\n    \"\\n\",\n    \"# 选择前100个观测点作为训练集\\n\",\n    \"# 剩下的前20个观测点作为验证集，剩下的30个观测作为测试集\\n\",\n    \"train_set = X[0:100,:]\\n\",\n    \"val_set = X[100:120,:]\\n\",\n    \"test_set = X[120:,:]\\n\",\n    \"\\n\",\n    \"train_y = y[0:100]\\n\",\n    \"val_y = y[100:120]\\n\",\n    \"test_y = y[120:]\\n\",\n    \"\\n\",\n    \"# 重制随机种子 \\n\",\n    \"# 随机方法需要随机性的存在\\n\",\n    \"random.seed()\\n\",\n    \"np.random.seed()\\n\",\n    \"\\n\",\n    \"# 选择用于衡量子集表现的有监督的机器学习模型\\n\",\n    \"from sklearn.ensemble import ExtraTreesClassifier # we use extratree as predictive model\\n\",\n    \"\\n\",\n    \"# 选择模拟退火中评价特征子集的的损失函数\\n\",\n    \"from sklearn.metrics import log_loss # 回归问题中，我们使用交叉熵损失函数\\n\",\n    \"\\n\",\n    \"clf = ExtraTreesClassifier(n_estimators=25)\\n\",\n    \"selector = Genetic_Algorithm(loss_func = log_loss, estimator = clf, \\n\",\n    \"                             n_gen = 15, n_pop = 10, predict_type = 'predict_proba')\\n\",\n    \"# 在训练集中训练\\n\",\n    \"# GA.py中有具体每个参数的含义，此处不赘述\\n\",\n    \"\\n\",\n    \"selector.fit(X_train = train_set, y_train = train_y, X_val = val_set, \\n\",\n    \"             y_val = val_y, stop_point = 15) \\n\",\n    \"# 此函数允许用户导入自己定义的验证集，此处尝试一下\\n\",\n    \"\\n\",\n    \"transformed_train = selector.transform(train_set)  # 转换训练集\\n\",\n    \"transformed_test = selector.transform(test_set)  # 转换测试集\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:47:34.320140Z\",\n     \"start_time\": \"2020-03-30T03:47:34.317774Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"selector.best_sol # 返回最优特征的索引\\n\",\n    \"selector.best_loss; # 返回最优特征子集对应的损失\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Embedded Methods 嵌入方法\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"过滤法的特征选择过程与后续的机器学习模型无关，因此过滤法可能导致较差的模型性能。\\n\",\n    \"\\n\",\n    \"封装方法利用预定义的有监督的机器学习模型来选择最佳功能。但是，由于它们需要在大量可能的特征子集上多次训练模型，因此尽管它们通常会导致更好的性能，但它们同时也需要较长的处理时间。\\n\",\n    \"\\n\",\n    \"嵌入式方法将特征选择过程嵌入到机器学习模型中，即利用机器学习来为每一个特征打分。嵌入式方法在创建模型时即完成了对特征子集的选择。因此，与过滤法相比，它们往往具有更好的性能。与封装方法相比，它们节省了大量的处理时间和计算能力。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**三种方法的一个简单对比**.  \\n\",\n    \"   \\n\",\n    \"|方面 | 过滤法 | 封装法\\t| 嵌入法\\n\",\n    \"|--|--|--|--|\\n\",\n    \"|是否需模型参与| 否 | 是 | 是 |\\n\",\n    \"|是否需要交叉验证 |\\t可能(可利用交叉验证选择保留的特征数目) | 是 | 可能(可利用交叉验证选择保留的特征数目)\\n\",\n    \"|处理时间 |\\t短 | 长 | 中等\\n\",\n    \"|对参与模型的限制|\\t无 | 无 | 是 (嵌入法使用的模型为线性模型或树类模型)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"![image](https://itlubber.art/upload/嵌入法工作流.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 基于正则化模型的方法\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"许多机器学习模型在其损失函数中引入了正则项（L1正则或L2正则），以防止过拟合问题。线性模型（例如线性向量支持机，逻辑回归，线性回归）中的L1正则项能够有效地将某些特征的特征系数缩小为零，从而实现解的稀疏。因此，基于带正则项线性模型的特征系数，我们可以为特征打分。系数越高，往往该特征在线性模型中越重要。\\n\",\n    \"\\n\",\n    \"我们可以使用sklearn SelectFromModel函数删除特征系数低或为零的特征。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Lasso Regression (Linear Regression with L1 Norm) 套索回归\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:47:34.517758Z\",\n     \"start_time\": \"2020-03-30T03:47:34.321877Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 0.346,  0.003, -0.   , -0.   , -0.   , -0.   , -0.033,  0.   ])\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from sklearn.feature_selection import SelectFromModel\\n\",\n    \"from sklearn.linear_model import Lasso # 我们也可以使用带L2正则项的岭回归\\n\",\n    \"\\n\",\n    \"# 直接载入数据集\\n\",\n    \"from sklearn.datasets import fetch_california_housing\\n\",\n    \"dataset = fetch_california_housing()\\n\",\n    \"X, y = dataset.data, dataset.target # 利用 california_housing 数据集来演示\\n\",\n    \"\\n\",\n    \"# 选择前15000个观测点作为训练集\\n\",\n    \"# 剩下的作为测试集\\n\",\n    \"train_set = X[0:15000,:]\\n\",\n    \"test_set = X[15000:,]\\n\",\n    \"train_y = y[0:15000]\\n\",\n    \"\\n\",\n    \"clf = Lasso(normalize=True, alpha = 0.001)  \\n\",\n    \"# 在进行线性回归前，我们需要先对变量进行缩放操作，否则回归系数大小无法比较\\n\",\n    \"# alpha控制正则效果的大小，alpha越大，正则效果越强\\n\",\n    \"\\n\",\n    \"clf.fit(train_set, train_y) # 在训练集上训练\\n\",\n    \"np.round(clf.coef_ ,3) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:47:34.524942Z\",\n     \"start_time\": \"2020-03-30T03:47:34.519609Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"selector = SelectFromModel(clf, prefit=True, threshold=1e-5)\\n\",\n    \"# 阈值被设置为1e-5，因此绝对系数低于1e-5的特征将被删除\\n\",\n    \"# 我们还可以设置max_features参数以选择最重要的前几个特征\\n\",\n    \"\\n\",\n    \"transformed_train = selector.transform(train_set) # 转换训练集\\n\",\n    \"transformed_test = selector.transform(test_set) #转换测试集\\n\",\n    \"\\n\",\n    \"assert np.array_equal(transformed_train, train_set[:,[0,1,6]]) \\n\",\n    \"# 选择第一个，第二个及第七个变量\\n\",\n    \"assert np.array_equal(transformed_test, test_set[:,[0,1,6]]) \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Logistic Regression (with L1 Norm) 逻辑回归\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:47:34.538253Z\",\n     \"start_time\": \"2020-03-30T03:47:34.526597Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0.   ,  1.   , -3.452, -0.159],\\n\",\n       \"       [ 0.   , -1.201,  0.053,  0.   ],\\n\",\n       \"       [ 0.   ,  0.   ,  1.331,  3.27 ]])\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from sklearn.feature_selection import SelectFromModel\\n\",\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"from sklearn.datasets import load_iris  # 利用iris数据作为演示数据集\\n\",\n    \"\\n\",\n    \"# 载入数据集\\n\",\n    \"iris = load_iris()\\n\",\n    \"X, y = iris.data, iris.target\\n\",\n    \"\\n\",\n    \"# iris 数据集使用前需要被打乱顺序\\n\",\n    \"np.random.seed(1234)\\n\",\n    \"idx = np.random.permutation(len(X))\\n\",\n    \"X = X[idx]\\n\",\n    \"y = y[idx]\\n\",\n    \"\\n\",\n    \"# 选择前100个观测点作为训练集\\n\",\n    \"# 剩下的50个观测点作为测试集\\n\",\n    \"train_set = X[0:100,:]\\n\",\n    \"test_set = X[100:,]\\n\",\n    \"train_y = y[0:100]\\n\",\n    \"\\n\",\n    \"# 在进行逻辑回归前，我们需要先对变量进行缩放操作，否则回归系数大小无法比较\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"model = StandardScaler()\\n\",\n    \"model.fit(train_set) \\n\",\n    \"standardized_train = model.transform(train_set)\\n\",\n    \"standardized_test = model.transform(test_set)\\n\",\n    \"\\n\",\n    \"clf = LogisticRegression(penalty='l1', C = 0.7, \\n\",\n    \"                         random_state=1234, solver='liblinear') \\n\",\n    \"# 我们也可以将正则项设置为 'l2'\\n\",\n    \"# C控制正则效果的大小，C越大，正则效果越弱\\n\",\n    \"\\n\",\n    \"clf.fit(standardized_train, train_y)\\n\",\n    \"np.round(clf.coef_,3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:47:34.544196Z\",\n     \"start_time\": \"2020-03-30T03:47:34.539789Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"selector = SelectFromModel(clf, prefit=True, threshold=1e-5)\\n\",\n    \"# 阈值被设置为1e-5，因此绝对系数低于1e-5的特征将被删除\\n\",\n    \"# 我们还可以设置max_features参数以选择最重要的前几个特征\\n\",\n    \"\\n\",\n    \"transformed_train = selector.transform(train_set) # 转换训练集\\n\",\n    \"transformed_test = selector.transform(test_set) #转换测试集\\n\",\n    \"\\n\",\n    \"assert np.array_equal(transformed_train, train_set[:,[1,2,3]]) \\n\",\n    \"# 选择第2个, 第3个及第4个变量\\n\",\n    \"assert np.array_equal(transformed_test, test_set[:,[1,2,3]]) \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### LinearSVR/ LinearSVC 线性向量支持机\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:47:34.568504Z\",\n     \"start_time\": \"2020-03-30T03:47:34.545714Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 0.254,  0.026,  0.026, -0.017,  0.032, -0.01 , -0.1  , -0.037])\"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# LinearSVC 用于分类问题\\n\",\n    \"# LinearSVR 用于回归问题\\n\",\n    \"# 这里以LinearSVR为例\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"from sklearn.feature_selection import SelectFromModel\\n\",\n    \"from sklearn.svm import LinearSVR\\n\",\n    \"\\n\",\n    \"# 直接载入数据集\\n\",\n    \"from sklearn.datasets import fetch_california_housing\\n\",\n    \"dataset = fetch_california_housing()\\n\",\n    \"X, y = dataset.data, dataset.target # 利用 california_housing 数据集来演示\\n\",\n    \"\\n\",\n    \"# 选择前15000个观测点作为训练集\\n\",\n    \"# 剩下的作为测试集\\n\",\n    \"train_set = X[0:15000,:]\\n\",\n    \"test_set = X[15000:,]\\n\",\n    \"train_y = y[0:15000]\\n\",\n    \"\\n\",\n    \"# 在进行逻辑回归前，我们需要先对变量进行缩放操作，否则回归系数大小无法比较\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"model = StandardScaler()\\n\",\n    \"model.fit(train_set) \\n\",\n    \"standardized_train = model.transform(train_set)\\n\",\n    \"standardized_test = model.transform(test_set)\\n\",\n    \"\\n\",\n    \"clf = LinearSVR(C = 0.0001, random_state = 123) \\n\",\n    \"# C控制正则效果的大小，C越大，正则效果越弱\\n\",\n    \"\\n\",\n    \"clf.fit(standardized_train, train_y)\\n\",\n    \"np.round(clf.coef_,3) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:47:34.576429Z\",\n     \"start_time\": \"2020-03-30T03:47:34.570070Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"selector = SelectFromModel(clf, prefit=True, threshold=1e-2)\\n\",\n    \"# 阈值被设置为1e-2，因此绝对系数低于1e-2的特征将被删除\\n\",\n    \"# 我们还可以设置max_features参数以选择最重要的前几个特征\\n\",\n    \"\\n\",\n    \"transformed_train = selector.transform(train_set) # 转换训练集\\n\",\n    \"transformed_test = selector.transform(test_set) #转换测试集\\n\",\n    \"\\n\",\n    \"assert np.array_equal(transformed_train, train_set[:,[0,1,2,3,4,6,7]]) \\n\",\n    \"# 仅第6个变量被删去\\n\",\n    \"assert np.array_equal(transformed_test, test_set[:,[0,1,2,3,4,6,7]]) \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Tree Based Methods 基于树模型的方法\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"机器学习的一大分支便是基于树的机器学习模型，例如随机森林，AdaBoost，Xgboost等。您可以在我的朋友和我撰写的一系列博客中找到有关这些基于树的机器学习模型的更多介绍[此处](https://github.com/YC-Coder-Chen/Tree-Math)。\\n\",\n    \"\\n\",\n    \"这些非参的树状模型在建立的过程中记录了每一个变量如何在树节点的分叉中逐步降低模型损失，并可以根据上述记录分析每个特征的特征重要性。而我们可以基于这特征重要性删去一些不重要的变量。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:47:38.272357Z\",\n     \"start_time\": \"2020-03-30T03:47:34.577834Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0.52 , 0.045, 0.031, 0.026, 0.027, 0.139, 0.106, 0.107])\"\n      ]\n     },\n     \"execution_count\": 43,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 我们以随机森林为例\\n\",\n    \"import numpy as np\\n\",\n    \"from sklearn.feature_selection import SelectFromModel\\n\",\n    \"from sklearn.ensemble import RandomForestRegressor\\n\",\n    \"\\n\",\n    \"# 直接载入数据集\\n\",\n    \"from sklearn.datasets import fetch_california_housing\\n\",\n    \"dataset = fetch_california_housing()\\n\",\n    \"X, y = dataset.data, dataset.target # 利用 california_housing 数据集来演示\\n\",\n    \"\\n\",\n    \"# 选择前15000个观测点作为训练集\\n\",\n    \"# 剩下的作为测试集\\n\",\n    \"train_set = X[0:15000,:]\\n\",\n    \"test_set = X[15000:,]\\n\",\n    \"train_y = y[0:15000]\\n\",\n    \"\\n\",\n    \"# 在树类机器学习模型中，我们无需缩放变量操作\\n\",\n    \"\\n\",\n    \"clf = RandomForestRegressor(n_estimators = 50, random_state = 123)\\n\",\n    \"\\n\",\n    \"clf.fit(train_set, train_y)\\n\",\n    \"np.round(clf.feature_importances_, 3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:47:38.541659Z\",\n     \"start_time\": \"2020-03-30T03:47:38.274508Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 可视化特征重要性\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.rcParams['font.sans-serif']=['SimHei']\\n\",\n    \"%matplotlib inline\\n\",\n    \"importances = clf.feature_importances_\\n\",\n    \"indices = np.argsort(importances)\\n\",\n    \"plt.figure(figsize=(12,12))\\n\",\n    \"plt.title('特征重要性')\\n\",\n    \"plt.barh(range(len(indices)), importances[indices], color='seagreen', align='center')\\n\",\n    \"plt.yticks(range(len(indices)),np.array(dataset.feature_names)[indices])\\n\",\n    \"plt.xlabel('特征相对重要性');\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-03-30T03:47:38.568452Z\",\n     \"start_time\": \"2020-03-30T03:47:38.543519Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"selector = SelectFromModel(clf, prefit=True, threshold='median')\\n\",\n    \"# 阈值被设定为'median', 即以特征重要性的中位数作为阈值，大约为0.076\\n\",\n    \"# 我们还可以设置max_features参数以选择最重要的前几个特征\\n\",\n    \"\\n\",\n    \"transformed_train = selector.transform(train_set)\\n\",\n    \"transformed_test = selector.transform(test_set)\\n\",\n    \"assert np.array_equal(transformed_train, train_set[:,[0,5,6,7]]) \\n\",\n    \"# 选择来第1个，第6个, 第7个及第8个特征\\n\",\n    \"assert np.array_equal(transformed_test, test_set[:,[0,5,6,7]]) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.13\"\n  },\n  \"toc\": {\n   \"base_numbering\": 1,\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": false,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": true,\n   \"toc_position\": {\n    \"height\": \"720px\",\n    \"left\": \"20.8789px\",\n    \"top\": \"180px\",\n    \"width\": \"360px\"\n   },\n   \"toc_section_display\": true,\n   \"toc_window_display\": true\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "requirements.txt",
    "content": "Cython\nwget\nnumpy>1.23.1,<1.24.0\npandas\nmatplotlib\nseaborn>=0.10.0\nscipy>=1.6.0\nstatsmodels<0.14,>=0.13.2\nscikit-learn>=1.3.1\ntoad\nscorecardpy\nortools<9.8.0,>=9.7.0\nropwr>=0.4.0\noptbinning\nCairoSVG>=2.7.0\ngraphviz>=0.20.1\ndtreeviz>=2.2.1\nreportlab\nsvglib\ncategory-encoders>=2.6.0\nsklearn_pandas\nsklearn2pmml\npypmml\njoblib>=0.12\nsix>=1.15.0\nopenpyxl>=3.0.10\nsweetviz\nnumexpr\ncvxpy>=1.4.1\nprotobuf>=5.27.0\ndill\n"
  },
  {
    "path": "scorecardpipeline/__init__.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\n@Time    : 2023/2/15 17:55\n@Author  : itlubber\n@Site    : itlubber.art\n\"\"\"\nfrom toad.metrics import KS, AUC, F1, PSI\nfrom openpyxl.formatting.rule import ColorScaleRule\nfrom openpyxl.utils import get_column_letter, column_index_from_string\nfrom sklearn.pipeline import Pipeline, FeatureUnion, make_pipeline, make_union\n\nfrom .logger import init_logger\nfrom .utils import *\nfrom .processing import FeatureSelection, FeatureImportanceSelector, StepwiseSelection, Combiner, WOETransformer, feature_bin_stats, feature_efficiency_analysis\nfrom .model import ITLubberLogisticRegression, ScoreCard\nfrom .excel_writer import ExcelWriter, dataframe2excel\nfrom .auto_eda import auto_eda_sweetviz\nfrom .auto_report import auto_data_testing_report\nfrom .rule import Rule, ruleset_report, sawpin_badrate_prediction_by_score, bin_table_badrate_prediction\nfrom .rule_extraction import DecisionTreeRuleExtractor\nfrom .feature_engineering import NumExprDerive\nfrom .feature_selection import RFE, RFECV, SelectKBest, SelectFromModel, GenericUnivariateSelect, TypeSelector, RegexSelector, ModeSelector, NullSelector, InformationValueSelector, LiftSelector, VarianceSelector, VIFSelector, CorrSelector, PSISelector, NullImportanceSelector, TargetPermutationSelector, ExhaustiveSelector\nfrom .scorecard import StandardScoreTransformer, NPRoundStandardScoreTransformer, RoundStandardScoreTransformer, BoxCoxScoreTransformer\n\n\n__version__ = \"0.1.38.09\"\n__all__ = (\n    \"__version__\"\n    , \"FeatureSelection\", \"FeatureImportanceSelector\", \"StepwiseSelection\", \"Combiner\", \"WOETransformer\"\n    , \"ITLubberLogisticRegression\", \"ScoreCard\", \"Rule\", \"DecisionTreeRuleExtractor\", \"ruleset_report\", \"sawpin_badrate_prediction_by_score\", \"bin_table_badrate_prediction\"\n    , \"Pipeline\", \"KS\", \"AUC\", \"PSI\", \"F1\", \"FeatureUnion\", \"make_pipeline\", \"make_union\"\n    , \"init_logger\", \"init_setting\", \"load_pickle\", \"save_pickle\", \"germancredit\"\n    , \"ColorScaleRule\", \"get_column_letter\", \"column_index_from_string\", \"seed_everything\"\n    , \"feature_bins\", \"feature_bin_stats\", \"feature_efficiency_analysis\", \"extract_feature_bin\", \"inverse_feature_bins\", \"sample_lift_transformer\", \"feature_describe\", \"groupby_feature_describe\"\n    , \"bin_plot\", \"corr_plot\", \"ks_plot\", \"hist_plot\", \"psi_plot\", \"csi_plot\", \"dataframe_plot\", \"distribution_plot\"\n    , \"ExcelWriter\", \"dataframe2excel\", \"auto_eda_sweetviz\", \"auto_data_testing_report\"\n    , \"RFE\", \"RFECV\", \"SelectKBest\", \"SelectFromModel\", \"GenericUnivariateSelect\", \"NumExprDerive\"\n    , \"StandardScoreTransformer\", \"NPRoundStandardScoreTransformer\", \"RoundStandardScoreTransformer\", \"BoxCoxScoreTransformer\"\n    , \"TypeSelector\", \"RegexSelector\", \"ModeSelector\", \"NullSelector\", \"InformationValueSelector\", \"LiftSelector\"\n    , \"VarianceSelector\", \"VIFSelector\", \"CorrSelector\", \"PSISelector\", \"NullImportanceSelector\", \"TargetPermutationSelector\", \"ExhaustiveSelector\"\n)\n"
  },
  {
    "path": "scorecardpipeline/auto_eda.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\n@Time    : 2023/12/29 11:17\n@Author  : itlubber\n@Site    : itlubber.art\n\"\"\"\n\nimport os\nimport re\nimport time\nimport traceback\nimport pandas as pd\nfrom datetime import datetime, timedelta\n\nimport sweetviz as sv\n\n\ndef auto_eda_sweetviz(all_data, target=None, save=\"model_report/auto_eda.html\", pairwise=True, labels=None, exclude=None, num_features=None, cat_features=None, text_features=None):\n    \"\"\"对数据量和特征个数较少的数据集进行自动 EDA 产出分析报告文档\n\n    :param all_data: 需要 EDA 的数据集\n    :param target: 目标变量，仅支持整数型或布尔型的目标变量\n    :param labels: 当传入 target 时为需要对比的数据集名称 [true, false]，当不传入 target 时为数据集名字\n    :param save: 报告存储路径，后缀使用 .html\n    :param pairwise: 是否需要显示特征之间的分布情况\n    :param exclude: 需要排除的特征名称\n    :param num_features: 需要强制转为数值型的特征名称\n    :param cat_features: 需要强制转为类别型的特征名称\n    :param text_features: 需要强制转为文本的特征名称\n    \"\"\"\n    # 配置文件初始化\n    sv.config_parser.read_dict({\"Layout\": {\"show_logo\": 0}})\n\n    # 设置跳过某些列\n    feature_config = sv.FeatureConfig(skip=exclude, force_num=num_features, force_cat=cat_features, force_text=text_features)\n\n    if save:\n        if os.path.dirname(save) != \"\" and not os.path.exists(os.path.dirname(save)):\n            os.makedirs(os.path.dirname(save), exist_ok=True)\n\n    if target is None:\n        report_all = sv.analyze(\n            [all_data, labels] if labels else all_data\n            , pairwise_analysis=\"on\" if pairwise else \"off\"\n            , feat_cfg=feature_config\n        )\n        report_all.show_html(save, open_browser=False)\n    else:\n        # 自动 eda 两个数据集，根据数据集中某个字段区分\n        report = sv.compare_intra(\n            all_data\n            , all_data[target] == 1\n            , labels if labels else [\"坏客户\", \"好客户\"]\n            , feat_cfg=feature_config\n            , pairwise_analysis=\"on\"\n        )\n        report.show_html(save, open_browser=False)\n\n\nif __name__ == '__main__':\n    import numpy as np\n    from scorecardpipeline import germancredit\n\n    # 加载数据集\n    data = germancredit()\n\n    # 设置目标变量名称 & 映射目标变量值域为 {0, 1}\n    target = \"creditability\"\n    data[target] = data[target].map({\"good\": 0, \"bad\": 1})\n\n    # 随机替换 20% 的数据为 np.nan\n    for col in data.columns.drop(target):\n        for i in range(len(data)):\n            if np.random.rand() > 0.8:\n                data[col].loc[i] = np.nan\n\n    # 自动 eda 并保存文件\n    auto_eda_sweetviz(data, target=target)\n"
  },
  {
    "path": "scorecardpipeline/auto_report.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\n@Time    : 2023/12/29 11:17\n@Author  : itlubber\n@Site    : itlubber.art\n\"\"\"\nimport traceback\nimport warnings\n\nwarnings.filterwarnings(\"ignore\")\n\nimport os\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nfrom openpyxl.worksheet.worksheet import Worksheet\n\nfrom .utils import *\nfrom .processing import *\nfrom .excel_writer import ExcelWriter, dataframe2excel\n\n\ndef auto_data_testing_report(data: pd.DataFrame, features=None, target=\"target\", overdue=None, dpd=None, date=None, data_summary_comment=\"\", freq=\"M\", excel_writer=None, sheet=\"分析报告\", start_col=2, start_row=2, dropna=False, writer_params={}, bin_params={}, feature_map={}, corr=False, pictures=[\"bin\", \"ks\", \"hist\"], suffix=\"\"):\n    \"\"\"自动数据测试报告，用于三方数据评估或自有评分效果评估\n\n    :param suffix: 用于避免未保存excel时，同名图片被覆盖的图片后缀名称\n    :param corr: 是否需要评估数值类变量之间的相关性，默认为 False，设置为 True 后会输出变量相关性图和表\n    :param pictures: 需要包含的图片，支持 [\"ks\", \"hist\", \"bin\"]\n    :param data: 需要评估的数据集，需要包含目标变量\n    :param features: 需要进行分析的特征名称，支持单个字符串传入或列表传入\n    :param target: 目标变量名称\n    :param overdue: 逾期天数字段名称, 当传入 overdue 时，会忽略 target 参数\n    :param dpd: 逾期定义方式，逾期天数 > DPD 为 1，其他为 0，仅 overdue 字段起作用时有用\n    :param date: 日期列，通常为借款人申请日期或放款日期，可选字段，传入的情况下，结合字段 freq 参数输出不同时间粒度下的好坏客户分布情况\n    :param freq: 结合 date 日期使用，输出需要统计的粒度，默认 M，即按月统计\n    :param data_summary_comment: 数据样本概况中需要填入的备注信息，例如 \"去除了历史最大逾期天数[0, dpd]内的灰客户\" 等\n    :param excel_writer: 需要保存的excel文件名称或写入器\n    :param sheet: 需要保存的 sheet 名称，可传入已有的 worksheet 或 文字信息\n    :param start_col: 开始列\n    :param start_row: 开始行\n    :param dropna: 在分析字段是是否剔除缺失值或指定值，默认 False\n    :param writer_params: excel写入器初始化参数，仅在 excel_writer 为字符串时有效\n    :param bin_params: 统计分箱的参数，支持 `feature_bin_stats` 方法的参数\n    :param feature_map: 特征字典，增加文档可读性使用，默认 {}\n\n    **参考样例**\n\n    >>> import numpy as np\n    >>> from scorecardpipeline import *\n    >>>\n    >>> # 加载数据集，标签转换为 0 和 1\n    >>> target = \"creditability\"\n    >>> data = germancredit()\n    >>> data[target] = data[target].map({\"good\": 0, \"bad\": 1})\n    >>> data[\"MOB1\"] = [np.random.randint(0, 30) for i in range(len(data))]\n    >>> features = data.columns.drop([target, \"MOB1\"]).tolist()\n    >>>\n    >>> # 测试报告输出\n    >>> auto_data_testing_report(data\n    >>>                          , features=features\n    >>>                          , target=target\n    >>>                          , date=None # 传入日期列名，会按 freq 统计不同时间维度好坏样本的分布情况\n    >>>                          , freq=\"M\"\n    >>>                          , data_summary_comment=\"三方数据测试报告样例，支持同时评估多个不同标签定义下的数据有效性\"\n    >>>                          , excel_writer=\"三方数据测试报告.xlsx\"\n    >>>                          , sheet=\"分析报告\"\n    >>>                          , start_col=2\n    >>>                          , start_row=2\n    >>>                          , dropna=False\n    >>>                          , writer_params={}\n    >>>                          , overdue=[\"MOB1\"]\n    >>>                          , dpd=[15, 7, 3]\n    >>>                          , bin_params={\"method\": \"dt\", \"min_bin_size\": 0.05, \"max_n_bins\": 10, \"return_cols\": [\"坏样本数\", \"坏样本占比\", \"坏样本率\", \"LIFT值\", \"坏账改善\", \"累积LIFT值\", \"分档KS值\"]} # feature_bin_stats 函数的相关参数\n    >>>                          , pictures=['bin', 'ks', 'hist'] # 类别型变量不支持 ks 和 hist\n    >>>                          , corr=True\n    >>>                          )\n    \"\"\"\n    init_setting()\n\n    data = data.copy()\n\n    if not isinstance(features, (list, tuple)):\n        features = [features]\n\n    if overdue and not isinstance(overdue, list):\n        overdue = [overdue]\n\n    if dpd and not isinstance(dpd, list):\n        dpd = [dpd]\n\n    if overdue:\n        target = f\"{overdue[0]} {dpd[0]}+\"\n        data[target] = (data[overdue[0]] > dpd[0]).astype(int)\n\n    if isinstance(excel_writer, ExcelWriter):\n        writer = excel_writer\n    else:\n        writer = ExcelWriter(**writer_params)\n\n    worksheet = writer.get_sheet_by_name(sheet)\n\n    if bin_params and \"del_grey\" in bin_params and bin_params.get(\"del_grey\"):\n        merge_columns = [\"指标名称\", \"指标含义\", \"分箱\"]\n    else:\n        merge_columns = [\"指标名称\", \"指标含义\", \"分箱\", \"样本总数\", \"样本占比\"]\n\n    return_cols = []\n    if bin_params:\n        if \"return_cols\" in bin_params and bin_params.get(\"return_cols\"):\n            return_cols = bin_params.pop(\"return_cols\")\n            if not isinstance(return_cols, (list, np.ndarray)):\n                return_cols = [return_cols]\n            return_cols = list(set(return_cols) - set(merge_columns))\n        else:\n            return_cols = []\n\n    max_columns_len = len(merge_columns) + len(return_cols) * len(overdue) * len(dpd) if overdue and len(overdue) > 0 else len(merge_columns) + len(return_cols)\n\n    end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\"数据有效性分析报告\", style=\"header_middle\", end_space=(start_row, start_col + max_columns_len - 1))\n\n    if date is not None and date in data.columns:\n        if data[date].dtype.name in [\"str\", \"object\"]:\n            start_date = pd.to_datetime(data[date]).min().strftime(\"%Y-%m-%d\")\n            end_date = pd.to_datetime(data[date]).max().strftime(\"%Y-%m-%d\")\n        else:\n            start_date = data[date].min().strftime(\"%Y-%m-%d\")\n            end_date = data[date].max().strftime(\"%Y-%m-%d\")\n\n        dataset_summary = pd.DataFrame(\n            [\n                [\"回溯数据集\", start_date, end_date, len(data), data[target].sum(), data[target].sum() / len(data), data_summary_comment]\n            ],\n            columns=[\"数据集\", \"开始时间\", \"结束时间\", \"样本总数\", \"坏客户数\", \"坏客户占比\", \"备注\"],\n        )\n        end_row, end_col = dataframe2excel(dataset_summary, writer, worksheet, percent_cols=[\"样本占比\", \"坏客户占比\"], start_row=end_row + 2, title=\"样本总体分布情况\")\n\n        distribution = distribution_plot(data, date=date, freq=freq, target=target, save=f\"model_report/sample_time_distribution{suffix}.png\", result=True)\n        end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"样本时间分布情况\", style=\"header\", end_space=(end_row + 2, start_col + len(distribution.columns) - 1))\n        end_row, end_col = writer.insert_pic2sheet(worksheet, f\"model_report/sample_time_distribution{suffix}.png\", (end_row + 1, start_col), figsize=(720, 370))\n        end_row, end_col = dataframe2excel(distribution, writer, worksheet, percent_cols=[\"样本占比\", \"好样本占比\", \"坏样本占比\", \"坏样本率\"], condition_cols=[\"坏样本率\"], start_row=end_row)\n        end_row += 2\n    else:\n        dataset_summary = pd.DataFrame(\n            [\n                [\"回溯数据集\", len(data), data[target].sum(), data[target].sum() / len(data), data_summary_comment]\n            ],\n            columns=[\"数据集\", \"样本总数\", \"坏客户数\", \"坏客户占比\", \"备注\"],\n        )\n        end_row, end_col = dataframe2excel(dataset_summary, writer, worksheet, percent_cols=[\"样本占比\", \"坏客户占比\"], start_row=end_row + 2, title=\"样本总体分布情况\")\n        end_row += 2\n\n    # 变量相关性\n    if corr:\n        temp = data[features].select_dtypes(include=\"number\")\n        corr_plot(temp, save=f\"model_report/auto_report_corr_plot{suffix}.png\", annot=True if len(temp.columns) <= 10 else False, fontsize=14 if len(temp.columns) <= 10 else 12)\n        end_row, end_col = dataframe2excel(temp.corr(), writer, worksheet, color_cols=list(temp.columns), start_row=end_row, figures=[f\"model_report/auto_report_corr_plot{suffix}.png\"], title=\"数值类变量相关性\", figsize=(min(60 * len(temp.columns), 1080), min(55 * len(temp.columns), 950)), index=True, custom_cols=list(temp.columns), custom_format=\"0.00\")\n        end_row += 2\n\n    end_row, end_col = writer.insert_value2sheet(worksheet, (end_row, start_col), value=\"数值类特征 OR 评分效果评估\", style=\"header_middle\", end_space=(end_row, start_col + max_columns_len - 1))\n    features_iter = tqdm(features)\n    for col in features_iter:\n        features_iter.set_postfix(feature=feature_map.get(col, col))\n        try:\n            if overdue is None:\n                temp = data[[col, target]]\n            else:\n                temp = data[list(set([col, target] + overdue))]\n            \n            if isinstance(dropna, bool) and dropna is True:\n                temp = temp.dropna(subset=col).reset_index(drop=True)\n            elif isinstance(dropna, (float, int, str)):\n                temp = temp[temp[col] != dropna].reset_index(drop=True)\n\n            score_table_train = feature_bin_stats(temp, col, overdue=overdue, dpd=dpd, desc=f\"{feature_map.get(col, col)}\", target=target, **bin_params)\n            if pictures and len(pictures) > 0:\n                if \"bin\" in pictures:\n                    if score_table_train.columns.nlevels > 1:\n                        _ = score_table_train[[\"分箱详情\", target]]\n                        _.columns = [c[-1] for c in _.columns]\n                    else:\n                        _ = score_table_train.copy()\n\n                    bin_plot(_, desc=f\"{feature_map.get(col, col)}\", figsize=(10, 5), anchor=0.935, save=f\"model_report/feature_bins_plot_{col}{suffix}.png\")\n\n                if temp[col].dtypes.name not in ['object', 'str', 'category']:\n                    if \"ks\" in pictures:\n                        _ = temp.dropna().reset_index(drop=True)\n                        has_ks = len(_) > 0 and _[col].nunique() > 1 and _[target].nunique() > 1\n                        if has_ks:\n                            ks_plot(_[col], _[target], figsize=(10, 5), title=f\"{feature_map.get(col, col)}\", save=f\"model_report/feature_ks_plot_{col}{suffix}.png\")\n                    if \"hist\" in pictures:\n                        _ = temp.dropna().reset_index(drop=True)\n                        if len(_) > 0:\n                            hist_plot(_[col], y_true=_[target], figsize=(10, 6), desc=f\"{feature_map.get(col, col)} 好客户 VS 坏客户\", bins=30, anchor=1.11, fontsize=14, labels={0: \"好客户\", 1: \"坏客户\"}, save=f\"model_report/feature_hist_plot_{col}{suffix}.png\")\n\n            if (len(temp) < len(data)) and (isinstance(dropna, bool) and dropna is True) or (isinstance(dropna, (float, int, str))):\n                end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=f\"数据字段: {feature_map.get(col, col)} (缺失率: {round((1 - len(temp) / len(data)) * 100, 2)}%)\", style=\"header\", end_space=(end_row + 2, start_col + max_columns_len - 1))\n            else:\n                end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=f\"数据字段: {feature_map.get(col, col)}\", style=\"header\", end_space=(end_row + 2, start_col + max_columns_len - 1))\n\n            if pictures and len(pictures) > 0:\n                ks_row = end_row + 1\n                if \"bin\" in pictures:\n                    end_row, end_col = writer.insert_pic2sheet(worksheet, f\"model_report/feature_bins_plot_{col}{suffix}.png\", (ks_row, start_col), figsize=(600, 350))\n                if temp[col].dtypes.name not in ['object', 'str', 'category'] and temp[col].isnull().sum() != len(temp):\n                    if \"ks\" in pictures and has_ks:\n                        end_row, end_col = writer.insert_pic2sheet(worksheet, f\"model_report/feature_ks_plot_{col}{suffix}.png\", (ks_row, end_col - 1), figsize=(600, 350))\n                    if \"hist\" in pictures:\n                        end_row, end_col = writer.insert_pic2sheet(worksheet, f\"model_report/feature_hist_plot_{col}{suffix}.png\", (ks_row, end_col - 1), figsize=(600, 350))\n            if return_cols:\n                if score_table_train.columns.nlevels > 1 and not isinstance(merge_columns[0], tuple):\n                    merge_columns = [(\"分箱详情\", c) for c in merge_columns]\n\n                end_row, end_col = dataframe2excel(score_table_train[merge_columns + [c for c in score_table_train.columns if (isinstance(c, (tuple, list)) and c[-1] in return_cols) or (not isinstance(c, (tuple, list)) and c in return_cols) or (isinstance(return_cols[0], (tuple, list)) and isinstance(c, (tuple, list)) and c in return_cols)]], writer, worksheet, percent_cols=[\"样本占比\", \"好样本占比\", \"坏样本占比\", \"坏样本率\", \"LIFT值\", \"坏账改善\", \"累积LIFT值\", \"累积坏账改善\"], condition_cols=[\"坏样本率\", \"LIFT值\"], merge_column=[\"指标名称\", \"指标含义\"], merge=True, fill=True, start_row=end_row)\n            else:\n                end_row, end_col = dataframe2excel(score_table_train, writer, worksheet, percent_cols=[\"样本占比\", \"好样本占比\", \"坏样本占比\", \"坏样本率\", \"LIFT值\", \"坏账改善\", \"累积LIFT值\", \"累积坏账改善\"], condition_cols=[\"坏样本率\", \"LIFT值\"], merge_column=[\"指标名称\", \"指标含义\"], merge=True, fill=True, start_row=end_row)\n        except:\n            print(f\"数据字段 {col} 分析时发生异常，请排查数据中是否存在异常:\\n{traceback.format_exc()}\")\n\n    if not isinstance(excel_writer, ExcelWriter) and not isinstance(sheet, Worksheet):\n        writer.save(excel_writer)\n\n    return end_row, end_col\n"
  },
  {
    "path": "scorecardpipeline/excel_writer.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\n@Time    : 2023/05/14 16:23\n@Author  : itlubber\n@Site    : itlubber.art\n\"\"\"\nimport sys\nimport warnings\n\nwarnings.filterwarnings(\"ignore\")\n\nimport re\nimport os\nimport copy\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom openpyxl.cell.cell import Cell\nfrom openpyxl.drawing.image import Image\nfrom openpyxl import load_workbook, Workbook\nfrom openpyxl.worksheet.worksheet import Worksheet\nfrom openpyxl.worksheet.hyperlink import Hyperlink\nfrom openpyxl.utils.dataframe import dataframe_to_rows\nfrom openpyxl.formatting.rule import DataBarRule, ColorScaleRule\nfrom openpyxl.utils import get_column_letter, column_index_from_string\nfrom openpyxl.styles import NamedStyle, Border, Side, Alignment, PatternFill, Font\n\n\nclass ExcelWriter:\n\n    def __init__(self, style_excel=None, style_sheet_name=\"初始化\", mode=\"replace\", fontsize=10, font='楷体', theme_color='2639E9', opacity=0.85, system=None):\n        \"\"\"excel 写入方法\n\n        :param style_excel: 样式模版文件，默认安装包路径下的 template.xlsx ，如果路径调整需要进行相应的调整\n        :param style_sheet_name: 模版文件内初始样式sheet名称，默认即可\n        :param mode: 写入模式，默认 replace，可选 replace、append，当选择 append 模式时会将已存在的excel中的内容复制到新的文件中\n        :param fontsize: 插入excel文件中内容的字体大小，默认 10\n        :param font: 插入excel文件中内容的字体，默认 楷体\n        :param theme_color: 主题色，默认 2639E9，注意不包含 #\n        :param system: excel报告适配的系统，默认 mac，可选 windows、linux，设置为 windows 时会重新适配 picture 的大小\n        :param opacity: 写入dataframe时使用颜色填充主题色的透明度设置，默认 0.85\n        \"\"\"\n        self.system = system\n\n        if self.system is None:\n            self.system = \"mac\" if sys.platform == \"darwin\" else \"windows\"\n\n        self.english_width = 0.12\n        self.chinese_width = 0.21\n        self.mode = mode\n        self.font = font\n        self.opacity = opacity\n        self.fontsize = fontsize\n        self.theme_color = theme_color\n        self.workbook = load_workbook(style_excel or os.path.join(os.path.dirname(os.path.abspath(__file__)), 'template.xlsx'))\n        self.style_sheet = self.workbook[style_sheet_name]\n\n        self.name_styles = []\n        self.init_style(font, fontsize, theme_color)\n        for style in self.name_styles:\n            if style.name not in self.workbook.style_names:\n                self.workbook.add_named_style(style)\n\n    def add_conditional_formatting(self, worksheet, start_space, end_space, condition_color=None):\n        \"\"\"\n        设置条件格式\n\n        :param worksheet: 当前选择设置条件格式的sheet\n        :param start_space: 开始单元格位置\n        :param end_space: 结束单元格位置\n        :param condition_color: 条件格式主题色\n        \"\"\"\n        worksheet.conditional_formatting.add(f'{start_space}:{end_space}', DataBarRule(start_type='min', end_type='max', color=condition_color or self.theme_color))\n\n    @staticmethod\n    def set_column_width(worksheet, column, width):\n        \"\"\"\n        调整excel列宽\n\n        :param worksheet: 当前选择调整列宽的sheet\n        :param column: 列，可以直接输入 index 或者 字母\n        :param width: 设置列的宽度\n        \"\"\"\n        worksheet.column_dimensions[column if isinstance(column, str) else get_column_letter(column)] = width\n\n    @staticmethod\n    def set_number_format(worksheet, space, _format):\n        \"\"\"\n        设置数值显示格式\n\n        :param worksheet: 当前选择调整数值显示格式的sheet\n        :param space: 单元格范围\n        :param _format: 显示格式，参考 openpyxl\n        \"\"\"\n        cells = worksheet[space]\n        if isinstance(cells, Cell):\n            cells = [cells]\n\n        for cell in cells:\n            if isinstance(cell, tuple):\n                for c in cell:\n                    c.number_format = _format\n            else:\n                cell.number_format = _format\n\n    def set_freeze_panes(self, worksheet, space):\n        \"\"\"\n        设置数值显示格式\n\n        :param worksheet: 当前选择调整数值显示格式的sheet\n        :param space: 单元格范围\n        \"\"\"\n        if not isinstance(worksheet, Worksheet):\n            worksheet = self.get_sheet_by_name(worksheet)\n\n        if isinstance(space, (tuple, list)):\n            space = self.get_cell_space(space)\n\n        worksheet.freeze_panes = space\n\n    def get_sheet_by_name(self, name):\n        \"\"\"\n        获取sheet名称为name的工作簿，如果不存在，则从初始模版文件中拷贝一个名称为name的sheet\n\n        :param name: 需要获取的工作簿名称\n        \"\"\"\n        if name not in self.workbook.sheetnames:\n            worksheet = self.workbook.copy_worksheet(self.style_sheet)\n            worksheet.title = name\n        else:\n            worksheet = self.workbook[name]\n\n        return worksheet\n\n    def move_sheet(self, worksheet, offset: int = 0, index: int = None):\n        \"\"\"移动 sheet 位置\n\n        :param worksheet: 需要移动的sheet，支持输入字符串和Worksheet\n        :param offset: 需要移动的相对位置，默认 0，在传入 index 时参数不生效\n        :param index: 需要移动到的位置索引，超出移动到最后\n        \"\"\"\n        total_sheets = len(self.workbook.sheetnames)\n        if index:\n            offset = -(total_sheets - 1) + index\n\n            if offset >= total_sheets:\n                offset = total_sheets - 1\n\n        self.workbook.move_sheet(worksheet, offset=offset)\n\n    def insert_hyperlink2sheet(self, worksheet, insert_space, hyperlink=None, file=None, sheet=None, target_space=None):\n        \"\"\"向sheet中的某个单元格插入超链接\n\n        :param worksheet: 需要插入超链接的sheet\n        :param insert_space: 超链接插入的单元格位置，可以是 \"B2\" 或者 (2, 2) 任意一种形式，首个单元格从 (1, 1) 开始\n        :param hyperlink: 超链接的地址, 与 target_space 参数互斥，优先 hyperlink，格式参考: [f\"file://{文件地址} - #{表名}!{单元格位置}\", f\"#{表名}!{单元格位置}\", f\"{单元格位置}\"], 其中 单元格位置 为类似 \"B2\" 的格式\n        :param file: 超链接的文件路径，默认 None，即当前excel文件，传入 hyperlink 参数时无效，传入file时确保sheet参数已传，否则默认为当前sheet\n        :param sheet: 超链接的sheet名称，默认 None，即当前sheet，传入 hyperlink 参数时无效\n        :param target_space: 超链接的单元格位置，默认 None，支持 \"B2\" 或者 (2, 2) 任意一种形式，传入 hyperlink 参数时无效\n        \"\"\"\n        if isinstance(insert_space, str):\n            start_col = re.findall('\\D+', insert_space)[0]\n            start_row = int(re.findall(\"\\d+\", insert_space)[0])\n        else:\n            start_col = get_column_letter(insert_space[1])\n            start_row = insert_space[0]\n\n        cell = worksheet[f\"{start_col}{start_row}\"]\n\n        if hyperlink is None:\n            if target_space is None:\n                raise ValueError(\"hyperlink 和 target_space 二选一，必须选择传入一个\")\n\n            if sheet is None:\n                sheet = worksheet.title\n\n            if isinstance(target_space, str):\n                target_col = re.findall('\\D+', target_space)[0]\n                target_row = int(re.findall(\"\\d+\", target_space)[0])\n            else:\n                target_col = get_column_letter(target_space[1])\n                target_row = target_space[0]\n\n            if file:\n                hyperlink = f\"file://{file} - #{sheet}!{target_col}{target_row}\"\n            else:\n                hyperlink = f\"#{sheet}!{target_col}{target_row}\"\n\n        cell.hyperlink = Hyperlink(ref=f\"{start_col}{start_row}\", location=hyperlink, display=f\"{cell.value}\")\n\n    def insert_value2sheet(self, worksheet, insert_space, value=\"\", style=\"content\", auto_width=False, end_space=None, align: dict=None, max_col_width=50):\n        \"\"\"\n        向sheet中的某个单元格插入某种样式的内容\n\n        :param worksheet: 需要插入内容的sheet\n        :param insert_space: 内容插入的单元格位置，可以是 \"B2\" 或者 (2, 2) 任意一种形式\n        :param value: 需要插入的内容\n        :param style: 渲染的样式，参考 init_style 中初始设置的样式\n        :param end_space: 如果需要合并单元格，传入需要截止的单元格位置信息，可以是 \"B2\" 或者 (2, 2) 任意一种形式\n        :param auto_width: 是否开启自动调整列宽\n        :param align: 文本排列方式, 参考: Alignment\n        :param max_col_width: 单元格列最大宽度，默认 50\n\n        :return: 返回插入元素最后一列之后、最后一行之后的位置\n        \"\"\"\n        if isinstance(insert_space, str):\n            start_col = re.findall('\\D+', insert_space)[0]\n            start_row = int(re.findall(\"\\d+\", insert_space)[0])\n        else:\n            start_col = get_column_letter(insert_space[1])\n            start_row = insert_space[0]\n\n        cell = worksheet[f\"{start_col}{start_row}\"]\n        cell.style = style\n\n        if align:\n            _align = {\"horizontal\": \"center\", \"vertical\": \"center\"}\n            _align.update(align)\n            cell.alignment = Alignment(**_align)\n\n        if end_space is not None:\n            if isinstance(end_space, str):\n                end_col = re.findall('\\D+', end_space)[0]\n                end_row = int(re.findall(\"\\d+\", end_space)[0])\n            else:\n                end_col = get_column_letter(end_space[1])\n                end_row = end_space[0]\n\n            worksheet.merge_cells(f\"{start_col}{start_row}:{end_col}{end_row}\")\n\n        worksheet[f\"{start_col}{start_row}\"] = value\n\n        if auto_width:\n            # original_styles = [worksheet[f\"{start_col}{i}\"].fill.copy() for i in range(1, worksheet.max_row + 1)]\n            curr_width = worksheet.column_dimensions[start_col].width\n            auto_width = min(max([(self.check_contain_chinese(value)[1] * self.english_width + self.check_contain_chinese(value)[2] * self.chinese_width) * self.fontsize, 10, curr_width]), max_col_width)\n            worksheet.column_dimensions[start_col].width = auto_width\n\n            # for i in range(worksheet.max_row):\n            #     worksheet[f\"{start_col}{i + 1}\"].fill = original_styles[i]\n\n        if end_space is not None:\n            return end_row + 1, column_index_from_string(end_col) + 1\n        else:\n            return start_row + 1, column_index_from_string(start_col) + 1\n\n    def insert_pic2sheet(self, worksheet, fig, insert_space, figsize=(600, 250)):\n        \"\"\"\n        向excel中插入图片内容\n\n        :param worksheet: 需要插入内容的sheet\n        :param fig: 需要插入的图片路径\n        :param insert_space: 插入图片的起始单元格\n        :param figsize: 图片大小设置\n        :return: 返回插入元素最后一列之后、最后一行之后的位置\n        \"\"\"\n        if isinstance(insert_space, str):\n            start_row = int(re.findall(\"\\d+\", insert_space)[0])\n            start_col = re.findall('\\D+', insert_space)[0]\n        else:\n            start_row, start_col = insert_space\n            start_col = get_column_letter(start_col)\n\n        image = Image(fig)\n        image.width, image.height = figsize\n        worksheet.add_image(image, f\"{start_col}{start_row}\")\n\n        return start_row + int(figsize[1] / (16.0 if self.system != 'mac' else 17.5)), column_index_from_string(start_col) + 8\n\n    def insert_rows(self, worksheet, row, row_index, col_index, merge_rows=None, style=\"\", auto_width=False, style_only=False, multi_levels=False):\n        \"\"\"\n        向excel中插入一行数据，insert_df2sheet 依赖本方法\n\n        :param worksheet: 需要插入内容的sheet\n        :param row: 数据内容\n        :param row_index: 插入数据的行索引，用来判断使用哪种边框样式\n        :param col_index: 插入数据的列索引，用来判断使用哪种边框样式\n        :param merge_rows: 需要合并单元的行索引\n        :param style: 插入数据的excel风格\n        :param auto_width: 是否自动调整列宽，自动调整列宽会导致该列样式模版发生变化，非内容列默认填充的白色失效\n        :param style_only: 是否使用填充样式\n        :param multi_levels: 是否多层索引或多层级列\n        \"\"\"\n        curr_col = column_index_from_string(col_index)\n\n        if multi_levels and style == \"header\":\n            row = pd.Series(row).ffill().to_list()\n            item, start, length = self.calc_continuous_cnt(row)\n            while start is not None:\n                if start + length < len(row):\n                    if start == 0:\n                        self.insert_value2sheet(worksheet, f'{get_column_letter(curr_col + start)}{row_index}', self.astype_insertvalue(item), style=f\"{style}_left\" if style else \"left\", auto_width=auto_width, end_space=f'{get_column_letter(curr_col + start + length - 1)}{row_index}')\n                    else:\n                        self.insert_value2sheet(worksheet, f'{get_column_letter(curr_col + start)}{row_index}', self.astype_insertvalue(item), style=f\"{style}_middle\" if style else \"middle\", auto_width=auto_width, end_space=f'{get_column_letter(curr_col + start + length - 1)}{row_index}')\n                else:\n                    self.insert_value2sheet(worksheet, f'{get_column_letter(curr_col + start)}{row_index}', self.astype_insertvalue(item), style=f\"{style}_right\" if style else \"right\", auto_width=auto_width, end_space=f'{get_column_letter(curr_col + start + length - 1)}{row_index}')\n\n                item, start, length = self.calc_continuous_cnt(row, start + length)\n        else:\n            for j, v in enumerate(row):\n                if merge_rows is not None and row_index + 1 not in merge_rows:\n                    if j == 0:\n                        self.insert_value2sheet(worksheet, f'{get_column_letter(curr_col + j)}{row_index}', self.astype_insertvalue(v), style=\"merge_left\", auto_width=auto_width)\n                    elif j == len(row) - 1:\n                        self.insert_value2sheet(worksheet, f'{get_column_letter(curr_col + j)}{row_index}', self.astype_insertvalue(v), style=\"merge_right\", auto_width=auto_width)\n                    else:\n                        self.insert_value2sheet(worksheet, f'{get_column_letter(curr_col + j)}{row_index}', self.astype_insertvalue(v), style=\"merge_middle\", auto_width=auto_width)\n                elif style_only or len(row) <= 1:\n                    self.insert_value2sheet(worksheet, f'{get_column_letter(curr_col + j)}{row_index}', self.astype_insertvalue(v), style=style or \"middle\", auto_width=auto_width)\n                else:\n                    if j == 0:\n                        self.insert_value2sheet(worksheet, f'{get_column_letter(curr_col + j)}{row_index}', self.astype_insertvalue(v), style=f\"{style}_left\" if style else \"left\", auto_width=auto_width)\n                    elif j == len(row) - 1:\n                        self.insert_value2sheet(worksheet, f'{get_column_letter(curr_col + j)}{row_index}', self.astype_insertvalue(v), style=f\"{style}_right\" if style else \"right\", auto_width=auto_width)\n                    else:\n                        self.insert_value2sheet(worksheet, f'{get_column_letter(curr_col + j)}{row_index}', self.astype_insertvalue(v), style=f\"{style}_middle\" if style else \"middle\", auto_width=auto_width)\n\n    def insert_df2sheet(self, worksheet, data, insert_space, merge_column=None, header=True, index=False, auto_width=False, fill=False, merge=False, merge_index=True):\n        \"\"\"\n        向excel文件中插入指定样式的dataframe数据\n\n        :param worksheet: 需要插入内容的sheet\n        :param data: 需要插入的dataframe\n        :param insert_space: 插入内容的起始单元格位置\n        :param merge_column: 需要分组显示的列，index或者列名，需要提前排好序，从 0.1.33 开始 ExcleWriter 不会自动处理顺序\n        :param header: 是否存储dataframe的header，暂不支持多级表头，从 0.1.30 开始支持多层表头和多层索引\n        :param index: 是否存储dataframe的index\n        :param merge_index: 当存储dataframe的index时，index中同一层级连续相同值是否合并，默认 True，即合并\n        :param auto_width: 是否自动调整列宽，自动调整列宽会导致该列样式模版发生变化，非内容列默认填充的白色失效\n        :param fill: 是否使用颜色填充而非边框，当 fill 为 True 时，merge_column 失效\n        :param merge: 是否合并单元格，配合 merge_column 一起使用，当前版本仅在 merge_column 只有一列时有效\n        :return: 返回插入元素最后一列之后、最后一行之后的位置\n        \"\"\"\n        df = data.copy()\n\n        if isinstance(insert_space, str):\n            start_row = int(re.findall(\"\\d+\", insert_space)[0])\n            start_col = re.findall('\\D+', insert_space)[0]\n        else:\n            start_row, start_col = insert_space\n            start_col = get_column_letter(start_col)\n\n        def get_merge_rows(values, start_row):\n            _rows = []\n            item, start, length = self.calc_continuous_cnt(values)\n            while start is not None:\n                _rows.append(start + start_row)\n                item, start, length = self.calc_continuous_cnt(values, start + length)\n\n            _rows.append(len(values) + start_row)\n            return _rows\n\n        if index and merge_index:\n            merge_index_cols = {i: get_column_letter(column_index_from_string(start_col) + i) for i in range(df.index.nlevels)}\n            merge_index_rows = {i: get_merge_rows(df.index.get_level_values(i).tolist(), start_row + df.columns.nlevels if header else start_row) for i in range(df.index.nlevels)}\n        else:\n            merge_index_cols = None\n            merge_index_rows = None\n\n        if merge_column:\n            if not isinstance(merge_column, (list, np.ndarray)):\n                merge_column = [merge_column]\n\n            if isinstance(merge_column[0], (int, float)) and (merge_column[0] not in df.columns):\n                merge_column = [df.columns.tolist()[col] if col not in df.columns else col for col in merge_column]\n\n            # 从 0.1.33 开始不修改需要保存的原始数据\n            # if df[merge_column].values.tolist() != df[merge_column].sort_values(merge_column).values.tolist():\n            #     df = df.sort_values(merge_column)\n\n            if index:\n                # merge_cols = [get_column_letter(df.columns.get_loc(col) + column_index_from_string(start_col) + df.index.nlevels) for col in merge_column]\n                merge_cols = {col: get_column_letter(df.columns.get_loc(col) + column_index_from_string(start_col) + df.index.nlevels) for col in merge_column}\n            else:\n                # merge_cols = [get_column_letter(df.columns.get_loc(col) + column_index_from_string(start_col)) for col in merge_column]\n                merge_cols = {col: get_column_letter(df.columns.get_loc(col) + column_index_from_string(start_col)) for col in merge_column}\n\n            if header:\n                # merge_rows = list(np.cumsum(df.groupby(merge_column)[merge_column].count().values[:, 0]) + start_row + df.columns.nlevels)\n                merge_rows = {col: get_merge_rows(df[col].tolist(), start_row + df.columns.nlevels) for col in merge_column}\n            else:\n                # merge_rows = list(np.cumsum(df.groupby(merge_column)[merge_column].count().values[:, 0]) + start_row)\n                merge_rows = {col: get_merge_rows(df[col].tolist(), start_row) for col in merge_column}\n        else:\n            merge_cols = None\n            merge_rows = None\n\n        def _iter_rows(df, header=True, index=True):\n            columns = df.columns.tolist()\n            indexs = df.index.tolist()\n            for i, row in enumerate(dataframe_to_rows(df, header=header, index=False)):\n                if header:\n                    if i < df.columns.nlevels:\n                        if index:\n                            if df.columns.nlevels > 1:\n                                if i == df.columns.nlevels - 1:\n                                    yield list(df.index.names) + [c[i] for c in columns]\n                                else:\n                                    yield [None] * df.index.nlevels + [c[i] for c in columns]\n                            else:\n                                yield list(df.index.names) + columns\n                        else:\n                            if df.columns.nlevels > 1 and i < df.columns.nlevels:\n                                yield [c[i] for c in columns]\n                            else:\n                                yield columns\n                    else:\n                        if index:\n                            if df.index.nlevels > 1:\n                                yield list(indexs[int(i - df.columns.nlevels)]) + row\n                            else:\n                                yield [indexs[int(i - df.columns.nlevels)]] + row\n                        else:\n                            yield row\n                else:\n                    if index:\n                        if df.index.nlevels > 1:\n                            yield list(indexs[i]) + row\n                        else:\n                            yield [indexs[i]] + row\n                    else:\n                        yield row\n\n        for i, row in enumerate(_iter_rows(df, header=header, index=index)):\n            if fill:\n                if header and i < df.columns.nlevels:\n                    self.insert_rows(worksheet, row, start_row + i, start_col, style=\"header\", auto_width=auto_width, multi_levels=True if df.columns.nlevels > 1 else False)\n                elif i == 0:\n                    self.insert_rows(worksheet, row, start_row + i, start_col, style=\"middle_even_first\", auto_width=auto_width, style_only=True)\n                else:\n                    if df.columns.nlevels % 2 == 1:\n                        if i % 2 == 1:\n                            style = \"middle_odd_last\" if (header and i == len(df) + df.columns.nlevels - 1) or (not header and i + 1 == len(df)) else \"middle_odd\"\n                        else:\n                            style = \"middle_even_last\" if (header and i == len(df) + df.columns.nlevels - 1) or (not header and i + 1 == len(df)) else \"middle_even\"\n                    else:\n                        if i % 2 == 1:\n                            style = \"middle_even_last\" if (header and i == len(df) + df.columns.nlevels - 1) or (not header and i + 1 == len(df)) else \"middle_even\"\n                        else:\n                            style = \"middle_odd_last\" if (header and i == len(df) + df.columns.nlevels - 1) or (not header and i + 1 == len(df)) else \"middle_odd\"\n                    self.insert_rows(worksheet, row, start_row + i, start_col, style=style, auto_width=auto_width, style_only=True)\n            else:\n                if header and i < df.columns.nlevels:\n                    self.insert_rows(worksheet, row, start_row + i, start_col, style=\"header\", auto_width=auto_width, multi_levels=True if df.columns.nlevels > 1 else False)\n                elif i == 0:\n                    self.insert_rows(worksheet, row, start_row + i, start_col, style=\"first\", auto_width=auto_width)\n                elif (header and i == len(df) + df.columns.nlevels - 1) or (not header and i + 1 == len(df)):\n                    self.insert_rows(worksheet, row, start_row + i, start_col, style=\"last\", auto_width=auto_width)\n                else:\n                    # self.insert_rows(worksheet, row, start_row + i, start_col, auto_width=auto_width, merge_rows=merge_rows)\n                    if merge_rows and len(merge_rows) > 0:\n                        self.insert_rows(worksheet, row, start_row + i, start_col, auto_width=auto_width, merge_rows=sorted(set(_row for _rows in merge_rows.values() for _row in _rows)))\n                    else:\n                        self.insert_rows(worksheet, row, start_row + i, start_col, auto_width=auto_width)\n\n        # 合并索引单元格\n        if index and merge_index and merge_index_rows and len(merge_index_rows) > 0:\n            for col in merge_index_cols.keys():\n                merge_col = merge_index_cols[col]\n                merge_row = merge_index_rows[col]\n\n                for s, e in zip(merge_row[:-1], merge_row[1:]):\n                    if e - s > 1:\n                        self.merge_cells(worksheet, f\"{merge_col}{s}\", f\"{merge_col}{e - 1}\")\n\n        # 合并列单元格\n        if merge and merge_column and merge_cols and len(merge_cols) > 0:\n            for col in merge_cols.keys():\n                merge_col = merge_cols[col]\n                merge_row = merge_rows[col]\n\n                for s, e in zip(merge_row[:-1], merge_row[1:]):\n                    if e - s > 1:\n                        self.merge_cells(worksheet, f\"{merge_col}{s}\", f\"{merge_col}{e - 1}\")\n\n        end_row = start_row + len(data) + df.columns.nlevels if header else start_row + len(data)\n\n        return end_row, column_index_from_string(start_col) + len(data.columns)\n\n    def merge_cells(self, worksheet, start, end):\n        \"\"\"合并同一列单元格并保证样式相应合并\n\n        :param worksheet: 需要合并单元格的sheet\n        :param start: 合并单元格开始的位置\n        :param end: 合并单元格结束的位置\n        :return:\n        \"\"\"\n        if isinstance(start, str):\n            start_col, start_row = self.get_cell_space(start)\n        elif isinstance(start, (tuple, list)):\n            start_col, start_row = start[0], start[1]\n        else:\n            raise TypeError(\"仅支持二元组或字符串\")\n\n        if isinstance(end, str):\n            end_col, end_row = self.get_cell_space(end)\n        elif isinstance(end, (tuple, list)):\n            end_col, end_row = end[0], end[1]\n        else:\n            raise TypeError(\"仅支持二元组或字符串\")\n\n        # 判断是否单列合并\n        if end_col - start_col != 0:\n            raise ValueError(\"仅支持单列合并单元格\")\n\n        # 暂存单元格样式\n        cell_style = worksheet[f\"{get_column_letter(start_col)}{start_row}\"].style\n        top_border_style = worksheet[f\"{get_column_letter(start_col)}{start_row}\"].border.top\n        border_style = worksheet[f\"{get_column_letter(start_col)}{end_row}\"].border\n        border_style = Border(\n            top=Side(style=top_border_style.style, color=top_border_style.color) if top_border_style else None,\n            left=Side(style=border_style.left.style, color=border_style.left.color) if border_style.left else None,\n            right=Side(style=border_style.right.style, color=border_style.right.color) if border_style.right else None,\n            bottom=Side(style=border_style.bottom.style, color=border_style.bottom.color) if border_style.bottom else None,\n        )\n\n        # 将单元格样式应用到需要合并的单元格\n        merged_cell = worksheet[f\"{get_column_letter(start_col)}{start_row}\"]\n        merged_cell.style = copy.deepcopy(cell_style)\n        merged_cell.border = border_style\n\n        # 合并单元格\n        worksheet.merge_cells(f\"{get_column_letter(start_col)}{start_row}:{get_column_letter(start_col)}{end_row}\")\n\n    @staticmethod\n    def check_contain_chinese(check_str):\n        \"\"\"\n        检查字符串中是否包含中文\n\n        :param check_str: 需要检查的字符串\n        :return: 返回每个字符是否是中文 list，英文字符个数，中文字符个数\n        \"\"\"\n        out = []\n        for ch in str(check_str).encode('utf-8').decode('utf-8'):\n            if u'\\u4e00' <= ch <= u'\\u9fff':\n                out.append(True)\n            else:\n                out.append(False)\n        return out, len(out) - sum(out), sum(out)\n\n    @staticmethod\n    def astype_insertvalue(value, decimal_point=4):\n        \"\"\"\n        格式化需要存储excel的内容，如果是浮点型，按照设置的精度进行保存，如果是类别型或其他特殊类型，转字符串存储，如果非以上两种，直接原始值存储\n\n        :param value: 需要插入 excel 的内容\n        :param decimal_point: 如果是浮点型，需要保留的精度，默认小数点后4位数\n        :return: 格式化后存入excel的内容\n        \"\"\"\n        if re.search('tuple|list|set|numpy.ndarray|Categorical|numpy.dtype|Interval', str(type(value))):\n            return str(value)\n        elif re.search('float', str(type(value))):\n            return round(float(value), decimal_point)\n        else:\n            return value\n\n    @staticmethod\n    def calc_continuous_cnt(list_, index_=0):\n        \"\"\"\n        根据传入的 list ，计算 list 中某个 index 开始，连续出现该元素的个数\n\n        :param list_: 需要检索的 list\n        :param index_: 元素索引\n\n        :return: 元素值，索引值，连续出现的个数\n\n        **参考样例**\n\n        >>> calc_continuous_cnt = ExcelWriter.calc_continuous_cnt\n        >>> list_ = ['A','A','A','A','B','C','C','D','D','D']\n        >>> calc_continuous_cnt(list_, 0)\n        ('A', 0, 4)\n        >>> calc_continuous_cnt(list_, 4)\n        ('B', 4, 1)\n        >>> calc_continuous_cnt(list_, 6)\n        ('C', 6, 1)\n        \"\"\"\n        if index_ >= len(list_):\n            return None, None, None\n\n        else:\n            cnt, str_ = 0, list_[index_]\n            for i in range(index_, len(list_), 1):\n                if list_[i] == str_:\n                    cnt = cnt + 1\n                else:\n                    break\n            return str_, index_, cnt\n\n    @staticmethod\n    def itlubber_border(border, color, white=False):\n        \"\"\"\n        itlubber 的边框样式生成器\n\n        :param border: 边框样式，如果输入长度为 3，则生成 [左，右，下]，如果长度为4，则生成 [左，右，下，上]\n        :param color: 边框颜色\n        :param white: 是否显示白色边框\n\n        :return: Border\n        \"\"\"\n        if len(border) == 3:\n            return Border(\n                left=Side(border_style=None if not white and color[0] == \"FFFFFF\" else border[0], color=None if not white and color[0] == \"FFFFFF\" else color[0]),\n                right=Side(border_style=None if not white and color[1] == \"FFFFFF\" else border[1], color=None if not white and color[1] == \"FFFFFF\" else color[1]),\n                bottom=Side(border_style=border[2], color=color[2]),\n            )\n        else:\n            return Border(\n                left=Side(border_style=None if not white and color[0] == \"FFFFFF\" else border[0], color=None if not white and color[0] == \"FFFFFF\" else color[0]),\n                right=Side(border_style=None if not white and color[1] == \"FFFFFF\" else border[1], color=None if not white and color[1] == \"FFFFFF\" else color[1]),\n                bottom=Side(border_style=border[2], color=color[2]),\n                top=Side(border_style=border[3], color=color[3]),\n            )\n\n    @staticmethod\n    def get_cell_space(space):\n        \"\"\"\n        根据传入的不同格式的位置，转换为另一种形式的excel单元格定位\n\n        :param space: 传入的excel单元格定位，支持两种格式，B1 或 (2, 2)\n\n        :return: 返回单元格定位，tuple / str\n\n        **参考样例**\n\n        >>> get_cell_space = ExcelWriter.get_cell_space\n        >>> get_cell_space(\"B3\")\n        (2, 3)\n        >>> get_cell_space((2, 2))\n        'B2'\n        \"\"\"\n        if isinstance(space, str):\n            start_row = int(re.findall(\"\\d+\", space)[0])\n            start_col = re.findall('\\D+', space)[0]\n            return column_index_from_string(start_col), start_row\n        else:\n            start_row = space[0]\n            if isinstance(space[1], int):\n                start_col = get_column_letter(space[1])\n            else:\n                start_col = space[1]\n            return f\"{start_col}{start_row}\"\n\n    @staticmethod\n    def calculate_rgba_color(hex_color, opacity, prefix=\"#\"):\n        \"\"\"\n        根据某个颜色计算某个透明度对应的颜色\n\n        :param hex_color: hex格式的颜色值\n        :param opacity: 透明度，[0, 1] 之间的数值\n        :param prefix: 返回颜色的前缀\n        :return: 对应某个透明度的颜色\n        \"\"\"\n        rgb_color = tuple(int(hex_color.lstrip('#')[i:i + 2], 16) for i in (0, 2, 4))\n        rgba_color = tuple(int((1 - opacity) * c + opacity * 255) for c in rgb_color)\n\n        return prefix + '{:02X}{:02X}{:02X}'.format(*rgba_color)\n\n    def init_style(self, font, fontsize, theme_color):\n        \"\"\"\n        初始化单元格样式\n\n        :param font: 字体名称\n        :param fontsize: 字体大小\n        :param theme_color: 主题颜色\n        \"\"\"\n        header_style, header_left_style, header_middle_style, header_right_style = NamedStyle(name=\"header\"), NamedStyle(name=\"header_left\"), NamedStyle(name=\"header_middle\"), NamedStyle(name=\"header_right\")\n        last_style, last_left_style, last_middle_style, last_right_style = NamedStyle(name=\"last\"), NamedStyle(name=\"last_left\"), NamedStyle(name=\"last_middle\"), NamedStyle(name=\"last_right\")\n        content_style, left_style, middle_style, right_style = NamedStyle(name=\"content\"), NamedStyle(name=\"left\"), NamedStyle(name=\"middle\"), NamedStyle(name=\"right\")\n        merge_style, merge_left_style, merge_middle_style, merge_right_style = NamedStyle(name=\"merge\"), NamedStyle(name=\"merge_left\"), NamedStyle(name=\"merge_middle\"), NamedStyle(name=\"merge_right\")\n        first_style, first_left_style, first_middle_style, first_right_style = NamedStyle(name=\"first\"), NamedStyle(name=\"first_left\"), NamedStyle(name=\"first_middle\"), NamedStyle(name=\"first_right\")\n\n        header_font = Font(size=fontsize, name=font, color=\"FFFFFF\", bold=True)\n        header_fill = PatternFill(fill_type=\"solid\", start_color=theme_color)\n        alignment = Alignment(horizontal='center', vertical='center', wrap_text=False)\n        content_fill = PatternFill(fill_type=\"solid\", start_color=\"FFFFFF\")\n        content_font = Font(size=fontsize, name=font, color=\"000000\")\n        even_fill = PatternFill(fill_type=\"solid\", start_color=self.calculate_rgba_color(self.theme_color, self.opacity, prefix=\"\"))\n\n        header_style.font, header_left_style.font, header_middle_style.font, header_right_style.font = header_font, header_font, header_font, header_font\n        header_style.fill, header_left_style.fill, header_middle_style.fill, header_right_style.fill = header_fill, header_fill, header_fill, header_fill\n        header_style.alignment, header_left_style.alignment, header_middle_style.alignment, header_right_style.alignment = Alignment(horizontal='left', vertical='center', wrap_text=True), alignment, alignment, alignment\n\n        header_style.border = self.itlubber_border([\"medium\", \"medium\", \"medium\", \"medium\"], [theme_color, theme_color, theme_color, theme_color], white=True)\n        header_left_style.border = self.itlubber_border([\"medium\", \"thin\", \"medium\", \"medium\"], [theme_color, \"FFFFFF\", theme_color, theme_color], white=True)\n        header_middle_style.border = self.itlubber_border([\"thin\", \"thin\", \"medium\", \"medium\"], [\"FFFFFF\", \"FFFFFF\", theme_color, theme_color], white=True)\n        header_right_style.border = self.itlubber_border([\"thin\", \"medium\", \"medium\", \"medium\"], [\"FFFFFF\", theme_color, theme_color, theme_color], white=True)\n\n        last_style.font, last_left_style.font, last_middle_style.font, last_right_style.font = content_font, content_font, content_font, content_font\n        last_style.fill, last_left_style.fill, last_middle_style.fill, last_right_style.fill = content_fill, content_fill, content_fill, content_fill\n        last_style.alignment, last_left_style.alignment, last_middle_style.alignment, last_right_style.alignment = alignment, alignment, alignment, alignment\n\n        last_style.border = self.itlubber_border([\"medium\", \"medium\", \"medium\"], [theme_color, theme_color, theme_color])\n        last_left_style.border = self.itlubber_border([\"medium\", \"thin\", \"medium\"], [theme_color, \"FFFFFF\", theme_color])\n        last_middle_style.border = self.itlubber_border([\"thin\", \"thin\", \"medium\"], [\"FFFFFF\", \"FFFFFF\", theme_color])\n        last_right_style.border = self.itlubber_border([\"thin\", \"medium\", \"medium\"], [\"FFFFFF\", theme_color, theme_color])\n\n        content_style.font, left_style.font, middle_style.font, right_style.font = content_font, content_font, content_font, content_font\n        content_style.fill, left_style.fill, middle_style.fill, right_style.fill = content_fill, content_fill, content_fill, content_fill\n        content_style.alignment, left_style.alignment, middle_style.alignment, right_style.alignment = alignment, alignment, alignment, alignment\n\n        content_style.border = self.itlubber_border([\"medium\", \"medium\", \"thin\"], [theme_color, theme_color, theme_color])\n        left_style.border = self.itlubber_border([\"medium\", \"thin\", \"thin\"], [theme_color, \"FFFFFF\", theme_color])\n        middle_style.border = self.itlubber_border([\"thin\", \"medium\", \"thin\"], [\"FFFFFF\", \"FFFFFF\", theme_color])\n        right_style.border = self.itlubber_border([\"thin\", \"medium\", \"thin\"], [\"FFFFFF\", theme_color, theme_color])\n\n        merge_style.font, merge_left_style.font, merge_middle_style.font, merge_right_style.font = content_font, content_font, content_font, content_font\n        merge_style.fill, merge_left_style.fill, merge_middle_style.fill, merge_right_style.fill = content_fill, content_fill, content_fill, content_fill\n        merge_style.alignment, merge_left_style.alignment, merge_middle_style.alignment, merge_right_style.alignment = alignment, alignment, alignment, alignment\n\n        merge_style.border = self.itlubber_border([\"medium\", \"medium\", \"thin\"], [\"FFFFFF\", \"FFFFFF\", \"FFFFFF\"])\n        merge_left_style.border = self.itlubber_border([\"medium\", \"thin\", \"thin\"], [theme_color, \"FFFFFF\", \"FFFFFF\"])\n        merge_middle_style.border = self.itlubber_border([\"thin\", \"medium\", \"thin\"], [\"FFFFFF\", \"FFFFFF\", \"FFFFFF\"])\n        merge_right_style.border = self.itlubber_border([\"thin\", \"medium\", \"thin\"], [\"FFFFFF\", theme_color, \"FFFFFF\"])\n\n        first_style.font, first_left_style.font, first_middle_style.font, first_right_style.font = content_font, content_font, content_font, content_font\n        first_style.fill, first_left_style.fill, first_middle_style.fill, first_right_style.fill = content_fill, content_fill, content_fill, content_fill\n        first_style.alignment, first_left_style.alignment, first_middle_style.alignment, first_right_style.alignment = alignment, alignment, alignment, alignment\n\n        first_style.border = self.itlubber_border([\"medium\", \"medium\", \"thin\", \"medium\"], [theme_color, theme_color, theme_color, theme_color])\n        first_left_style.border = self.itlubber_border([\"medium\", \"thin\", \"thin\", \"medium\"], [theme_color, \"FFFFFF\", theme_color, theme_color])\n        first_middle_style.border = self.itlubber_border([\"thin\", \"thin\", \"thin\", \"medium\"], [\"FFFFFF\", \"FFFFFF\", theme_color, theme_color])\n        first_right_style.border = self.itlubber_border([\"thin\", \"medium\", \"thin\", \"medium\"], [\"FFFFFF\", theme_color, theme_color, theme_color])\n\n        middle_odd_style, middle_odd_first_style, middle_odd_last_style = NamedStyle(name=\"middle_odd\"), NamedStyle(name=\"middle_odd_first\"), NamedStyle(name=\"middle_odd_last\")\n        middle_even_style, middle_even_first_style, middle_even_last_style = NamedStyle(name=\"middle_even\"), NamedStyle(name=\"middle_even_first\"), NamedStyle(name=\"middle_even_last\")\n\n        middle_odd_style.font, middle_odd_first_style.font, middle_odd_last_style.font, middle_even_style.font, middle_even_first_style.font, middle_even_last_style.font = content_font, content_font, content_font, content_font, content_font, content_font\n        middle_odd_style.alignment, middle_odd_first_style.alignment, middle_odd_last_style.alignment, middle_even_style.alignment, middle_even_first_style.alignment, middle_even_last_style.alignment = alignment, alignment, alignment, alignment, alignment, alignment\n        middle_odd_style.fill, middle_odd_first_style.fill, middle_odd_last_style.fill, middle_even_style.fill, middle_even_first_style.fill, middle_even_last_style.fill = content_fill, content_fill, content_fill, even_fill, even_fill, even_fill\n\n        middle_odd_first_style.border = Border(top=Side(border_style=\"medium\", color=self.theme_color))\n        middle_odd_last_style.border = Border(bottom=Side(border_style=\"medium\", color=self.theme_color))\n        middle_even_first_style.border = Border(top=Side(border_style=\"medium\", color=self.theme_color))\n        middle_even_last_style.border = Border(bottom=Side(border_style=\"medium\", color=self.theme_color))\n        middle_even_style.border = Border(bottom=Side(border_style=\"thin\", color=\"FFFFFF\"))\n        middle_odd_style.border = Border(bottom=Side(border_style=\"thin\", color=\"FFFFFF\"))\n\n        self.name_styles.extend([\n            header_style, header_left_style, header_middle_style, header_right_style,\n            last_style, last_left_style, last_middle_style, last_right_style,\n            content_style, left_style, middle_style, right_style,\n            merge_style, merge_left_style, merge_middle_style, merge_right_style,\n            first_style, first_left_style, first_middle_style, first_right_style,\n            middle_odd_style, middle_even_first_style, middle_odd_last_style, middle_even_style, middle_odd_first_style, middle_even_last_style,\n        ])\n\n    def save(self, filename, close=True):\n        \"\"\"\n        保存excel文件\n\n        :param filename: 需要保存 excel 文件的路径\n        :param close: 是否需要释放 writer\n        \"\"\"\n        if self.style_sheet.title in self.workbook.sheetnames:\n            self.workbook.remove(self.style_sheet)\n\n        if os.path.exists(filename) and self.mode == \"append\":\n            _workbook = load_workbook(filename)\n\n            for _sheet_name in _workbook.sheetnames:\n                if _sheet_name not in self.workbook.sheetnames:\n                    _worksheet = self.get_sheet_by_name(_sheet_name)\n\n                    for i, row in enumerate(_workbook[_sheet_name].iter_rows()):\n                        for j, cell in enumerate(row):\n                            _worksheet.cell(row=i + 1, column=j + 1).value = cell.value\n                            _worksheet.cell(row=i + 1, column=j + 1).style = cell.style\n\n                            if i == _workbook[_sheet_name].max_row - 1:\n                                _worksheet.column_dimensions[get_column_letter(j + 1)].width = _workbook[_sheet_name].column_dimensions[get_column_letter(j + 1)].width\n\n                        # self.workbook.move_sheet(_worksheet, offset=-len(self.workbook.sheetnames) + 1)\n\n            _workbook.close()\n\n        if os.path.dirname(filename) != \"\" and not os.path.exists(os.path.dirname(filename)):\n            os.makedirs(os.path.dirname(filename), exist_ok=True)\n\n        self.workbook.save(filename)\n\n        if close:\n            self.workbook.close()\n\n\ndef dataframe2excel(data, excel_writer, sheet_name=None, title=None, header=True, theme_color=\"2639E9\", condition_color=None, fill=True, percent_cols=None, condition_cols=None, custom_cols=None, custom_format=\"#,##0\", color_cols=None, percent_rows=None, condition_rows=None, custom_rows=None, color_rows=None, start_col=2, start_row=2, mode=\"replace\", figures=None, figsize=(600, 350), writer_params={}, **kwargs):\n    \"\"\"\n    向excel文件中插入指定样式的dataframe数据\n\n    :param data: 需要保存的dataframe数据，index默认不保存，如果需要保存先 .reset_index().rename(columns={\"index\": \"索引名称\"}) 再保存，有部分索引 reset_index 之后是 0 而非 index，根据实际情况进行修改\n    :param excel_writer: 需要保存到的 excel 文件路径或者 ExcelWriter\n    :param sheet_name: 需要插入内容的sheet，如果是 Worksheet，则直接向 Worksheet 插入数据\n    :param title: 是否在dataframe之前的位置插入一个标题\n    :param figures: 需要数据表与标题之间插入的图片，支持一次性传入多张图片的路径，会根据传入顺序依次插入\n    :param figsize: 插入图像的大小，为了统一排版，目前仅支持设置一个图片大小，默认: (600, 350) (长度, 高度)\n    :param header: 是否存储dataframe的header，暂不支持多级表头\n    :param theme_color: 主题色\n    :param condition_color: 条件格式主题颜色，不传默认为 theme_color\n    :param fill: 是否使用单元个颜色填充样式还是使用边框样式\n    :param percent_cols: 需要显示为百分数的列，仅修改显示格式，不更改数值\n    :param condition_cols: 需要显示条件格式的列（无边框渐变数据条）\n    :param color_cols: 需要显示为条件格式颜色填充的列（单元格填充渐变色）\n    :param custom_cols: 需要显示自定义格式的列，与 custom_format 参数搭配使用\n    :param custom_format: 显示的自定义格式，与 custom_cols 参数搭配使用，默认 #,##0 ，即显示为有分隔符的整数\n    :param start_col: 在excel中的开始列数，默认 2，即第二列开始\n    :param start_row: 在excel中的开始行数，默认 2，即第二行开始，如果 title 有值的话，会从 start_row + 2 行开始插入dataframe数据\n    :param mode: excel写入的模式，可选 append 和 replace ，默认 replace ，选择 append 时会在已有的excel文件中增加内容，不覆盖原有内容\n    :param writer_params: 透传至 ExcelWriter 内的参数\n    :param **kwargs: 其他参数，透传至 insert_df2sheet 方法，例如 传入 auto_width=True 会根据内容自动调整列宽\n\n    :return: 返回插入元素最后一列之后、最后一行之后的位置\n\n    **参考样例**\n\n    >>> writer = ExcelWriter(theme_color='3f1dba')\n    >>> worksheet = writer.get_sheet_by_name(\"模型报告\")\n    >>> end_row, end_col = writer.insert_value2sheet(worksheet, \"B2\", value=\"模型报告\", style=\"header\")\n    >>> end_row, end_col = writer.insert_value2sheet(worksheet, \"B4\", value=\"模型报告\", style=\"header\", end_space=\"D4\")\n    >>> end_row, end_col = writer.insert_value2sheet(worksheet, \"B6\", value=\"当前模型主要为评分卡模型\", style=\"header_middle\", auto_width=True)\n    >>> # 单层索引保存样例\n    >>> sample = pd.DataFrame(np.concatenate([np.random.random_sample((10, 10)) * 40, np.random.randint(0, 3, (10, 2))], axis=1), columns=[f\"B{i}\" for i in range(10)] + [\"target\", \"type\"])\n    >>> end_row, end_col = writer.insert_df2sheet(worksheet, sample, (end_row + 2, column_index_from_string(\"B\")))\n    >>> end_row, end_col = writer.insert_df2sheet(worksheet, sample, (end_row + 2, column_index_from_string(\"B\")), fill=True)\n    >>> end_row, end_col = writer.insert_df2sheet(worksheet, sample, (end_row + 2, column_index_from_string(\"B\")), fill=True, header=False, index=True)\n    >>> end_row, end_col = writer.insert_df2sheet(worksheet, sample, (end_row + 2, column_index_from_string(\"B\")), merge_column=\"target\")\n    >>> end_row, end_col = writer.insert_df2sheet(worksheet, sample.set_index(\"type\"), (end_row + 2, column_index_from_string(\"B\")), merge_column=\"target\", index=True, fill=True)\n    >>> end_row, end_col = writer.insert_df2sheet(worksheet, sample, (end_row + 2, column_index_from_string(\"B\")), merge_column=[\"target\", \"type\"])\n    >>> end_row, end_col = writer.insert_df2sheet(worksheet, sample, (end_row + 2, column_index_from_string(\"B\")), merge_column=[10, 11])\n    >>> end_row, end_col = dataframe2excel(sample, writer, theme_color='3f1dba', sheet_name=\"模型报告\", start_row=end_row + 2, percent_cols=[\"B2\", \"B6\"], condition_cols=[\"B3\", \"B9\"], color_cols=[\"B4\"])\n    >>> end_row, end_col = dataframe2excel(sample, writer, theme_color='3f1dba', sheet_name=\"模型报告\", start_row=end_row + 2, percent_cols=[\"B2\", \"B6\"], condition_cols=[\"B3\", \"B9\"], color_cols=[\"B4\"], title=\"测试样例\")\n    >>> end_row, end_col = dataframe2excel(sample, writer, theme_color='3f1dba', sheet_name=\"模型报告\", start_row=end_row + 2, percent_cols=[\"B2\", \"B6\"], condition_cols=[\"B3\", \"B9\"], color_cols=[\"B4\"], title=\"测试样例\", figures=[\"../examples/model_report/auto_report_corr_plot.png\"])\n    >>> # 多层索引保存样例\n    >>> multi_sample = pd.DataFrame(np.random.randint(0, 150, size=(8, 12)), columns=pd.MultiIndex.from_product([['模拟考', '正式考'], ['数学', '语文', '英语', '物理', '化学', '生物']]), index=pd.MultiIndex.from_product([['期中', '期末'], ['雷军', '李斌'], ['测试一', '测试二']]))\n    >>> multi_sample.index.names = [\"考试类型\", \"姓名\", \"测试\"]\n    >>> end_row, end_col = dataframe2excel(multi_sample, writer, theme_color='3f1dba', sheet_name=\"模型报告\", start_row=end_row + 2, title=\"测试样例\", index=True, header=False)\n    >>> end_row, end_col = dataframe2excel(multi_sample, writer, theme_color='3f1dba', sheet_name=\"模型报告\", start_row=end_row + 2, title=\"测试样例\", index=True)\n    >>> end_row, end_col = dataframe2excel(multi_sample, writer, theme_color='3f1dba', sheet_name=\"模型报告\", start_row=end_row + 2, title=\"测试样例\", index=True, fill=False)\n    >>> end_row, end_col = dataframe2excel(multi_sample.reset_index(names=multi_sample.index.names, col_level=-1), writer, theme_color='3f1dba', sheet_name=\"模型报告\", start_row=end_row + 2, title=\"测试样例\", index=False, fill=False, merge_column=[('', '考试类型'), ('', '姓名')])\n    >>> end_row, end_col = dataframe2excel(multi_sample.reset_index(names=multi_sample.index.names, col_level=-1), writer, theme_color='3f1dba', sheet_name=\"模型报告\", start_row=end_row + 2, title=\"测试样例\", index=False, fill=False, merge_column=[('', '考试类型')], merge=True)\n    >>> end_row, end_col = dataframe2excel(multi_sample.reset_index(names=multi_sample.index.names, col_level=-1), writer, theme_color='3f1dba', sheet_name=\"模型报告\", start_row=end_row + 2, title=\"测试样例\", index=True, fill=True, merge_column=[('', '考试类型')], merge=True)\n    >>> writer.save(\"./测试样例.xlsx\")\n    \"\"\"\n    if isinstance(excel_writer, ExcelWriter):\n        writer = excel_writer\n    else:\n        writer = ExcelWriter(theme_color=theme_color, mode=mode, **writer_params)\n\n        # if os.path.exists(excel_writer) and mode == \"append\":\n        #     workbook = load_workbook(excel_writer)\n\n        #     for _sheet_name in workbook.sheetnames:\n        #         _worksheet = writer.get_sheet_by_name(_sheet_name or \"Sheet1\")\n\n        #         for i, row in enumerate(workbook[_sheet_name].iter_rows()):\n        #             for j, cell in enumerate(row):\n        #                 _worksheet.cell(row=i + 1, column=j + 1, value=cell.value)\n\n        #     workbook.close()\n\n    if isinstance(sheet_name, Worksheet):\n        worksheet = sheet_name\n    else:\n        worksheet = writer.get_sheet_by_name(sheet_name or \"Sheet1\")\n\n    if title:\n        col_width = len(data.columns) + data.index.nlevels if kwargs.get(\"index\", False) else len(data.columns)\n        start_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=title, style=\"header\", end_space=(start_row, start_col + col_width - 1))\n        start_row += 1\n\n    if figures is not None:\n        if isinstance(figures, str):\n            figures = [figures]\n\n        pic_row = start_row\n        for i, pic in enumerate(figures):\n            if i == 0:\n                start_row, end_col = writer.insert_pic2sheet(worksheet, pic, (pic_row, start_col), figsize=figsize)\n            else:\n                start_row, end_col = writer.insert_pic2sheet(worksheet, pic, (pic_row, end_col - 1), figsize=figsize)\n\n    if \"merge_column\" in kwargs and kwargs[\"merge_column\"]:\n        if not isinstance(kwargs[\"merge_column\"][0], (tuple, list)):\n            kwargs[\"merge_column\"] = [c for c in data.columns if (isinstance(c, tuple) and c[-1] in kwargs[\"merge_column\"]) or (not isinstance(c, tuple) and c in kwargs[\"merge_column\"])]\n\n    end_row, end_col = writer.insert_df2sheet(worksheet, data, (start_row, start_col), fill=fill, header=header, **kwargs)\n\n    if percent_cols:\n        if not isinstance(percent_cols[0], (tuple, list)):\n            percent_cols = [c for c in data.columns if (isinstance(c, tuple) and c[-1] in percent_cols) or (not isinstance(c, tuple) and c in percent_cols)]\n        for c in [c for c in percent_cols if c in data.columns]:\n            conditional_column = get_column_letter(start_col + data.columns.get_loc(c) + data.index.nlevels if kwargs.get(\"index\", False) else start_col + data.columns.get_loc(c))\n            writer.set_number_format(worksheet, f\"{conditional_column}{end_row - len(data)}:{conditional_column}{end_row - 1}\", \"0.00%\")\n\n    if custom_cols:\n        if not isinstance(custom_cols[0], (tuple, list)):\n            custom_cols = [c for c in data.columns if (isinstance(c, tuple) and c[-1] in custom_cols) or (not isinstance(c, tuple) and c in custom_cols)]\n        for c in [c for c in custom_cols if c in data.columns]:\n            conditional_column = get_column_letter(start_col + data.columns.get_loc(c) + data.index.nlevels if kwargs.get(\"index\", False) else start_col + data.columns.get_loc(c))\n            writer.set_number_format(worksheet, f\"{conditional_column}{end_row - len(data)}:{conditional_column}{end_row - 1}\", custom_format)\n\n    if condition_cols:\n        if not isinstance(condition_cols[0], (tuple, list)):\n            condition_cols = [c for c in data.columns if (isinstance(c, tuple) and c[-1] in condition_cols) or (not isinstance(c, tuple) and c in condition_cols)]\n        for c in [c for c in condition_cols if c in data.columns]:\n            conditional_column = get_column_letter(start_col + data.columns.get_loc(c) + data.index.nlevels if kwargs.get(\"index\", False) else start_col + data.columns.get_loc(c))\n            writer.add_conditional_formatting(worksheet, f'{conditional_column}{end_row - len(data)}', f'{conditional_column}{end_row - 1}', condition_color=condition_color or theme_color)\n\n    if color_cols:\n        if not isinstance(color_cols[0], (tuple, list)):\n            color_cols = [c for c in data.columns if (isinstance(c, tuple) and c[-1] in color_cols) or (not isinstance(c, tuple) and c in color_cols)]\n        for c in [c for c in color_cols if c in data.columns]:\n            try:\n                rule = ColorScaleRule(start_type='num', start_value=data[c].min(), start_color=condition_color or theme_color, mid_type='num', mid_value=0., mid_color='FFFFFF', end_type='num', end_value=data[c].max(), end_color=condition_color or theme_color)\n                conditional_column = get_column_letter(start_col + data.columns.get_loc(c) + data.index.nlevels if kwargs.get(\"index\", False) else start_col + data.columns.get_loc(c))\n                worksheet.conditional_formatting.add(f\"{conditional_column}{end_row - len(data)}:{conditional_column}{end_row - 1}\", rule)\n            except:\n                import traceback\n                traceback.print_exc()\n\n    if percent_rows:\n        if not isinstance(percent_rows[0], (tuple, list)):\n            percent_rows = [c for c in data.index if (isinstance(c, tuple) and c[-1] in percent_rows) or (not isinstance(c, tuple) and c in percent_rows)]\n        for c in [c for c in percent_rows if c in data.index]:\n            insert_row = data.index.get_loc(c).start if data.index.nlevels > 1 and not isinstance(data.index.get_loc(c), (int, float)) else data.index.get_loc(c)\n            index_row = start_row + insert_row + data.columns.nlevels if kwargs.get(\"header\", True) else start_row + insert_row\n            index_col = start_col + data.index.nlevels if kwargs.get(\"index\", False) else start_col\n            writer.set_number_format(worksheet, f\"{get_column_letter(index_col)}{index_row}:{get_column_letter(index_col + len(data.columns))}{index_row}\", \"0.00%\")\n\n    if custom_rows:\n        if not isinstance(custom_rows[0], (tuple, list)):\n            custom_rows = [c for c in data.index if (isinstance(c, tuple) and c[-1] in custom_rows) or (not isinstance(c, tuple) and c in custom_rows)]\n        for c in [c for c in custom_rows if c in data.index]:\n            insert_row = data.index.get_loc(c).start if data.index.nlevels > 1 and not isinstance(data.index.get_loc(c), (int, float)) else data.index.get_loc(c)\n            index_row = start_row + insert_row + data.columns.nlevels if kwargs.get(\"header\", True) else start_row + insert_row\n            index_col = start_col + data.index.nlevels if kwargs.get(\"index\", False) else start_col\n            writer.set_number_format(worksheet, f\"{get_column_letter(index_col)}{index_row}:{get_column_letter(index_col + len(data.columns))}{index_row}\", custom_format)\n\n    if condition_rows:\n        if not isinstance(condition_rows[0], (tuple, list)):\n            condition_rows = [c for c in data.index if (isinstance(c, tuple) and c[-1] in condition_rows) or (not isinstance(c, tuple) and c in condition_rows)]\n        for c in [c for c in condition_rows if c in data.index]:\n            insert_row = data.index.get_loc(c).start if data.index.nlevels > 1 and not isinstance(data.index.get_loc(c), (int, float)) else data.index.get_loc(c)\n            index_row = start_row + insert_row + data.columns.nlevels if kwargs.get(\"header\", True) else start_row + insert_row\n            index_col = start_col + data.index.nlevels if kwargs.get(\"index\", False) else start_col\n            writer.add_conditional_formatting(worksheet, f'{get_column_letter(index_col)}{index_row}', f'{get_column_letter(index_col + len(data.columns))}{index_row}', condition_color=condition_color or theme_color)\n\n    if color_rows:\n        if not isinstance(color_rows[0], (tuple, list)):\n            color_rows = [c for c in data.index if (isinstance(c, tuple) and c[-1] in color_rows) or (not isinstance(c, tuple) and c in color_rows)]\n        for c in [c for c in color_rows if c in data.index]:\n            try:\n                insert_row = data.index.get_loc(c).start if data.index.nlevels > 1 and not isinstance(data.index.get_loc(c), (int, float)) else data.index.get_loc(c)\n                rule = ColorScaleRule(start_type='num', start_value=data.loc[c].min(), start_color=condition_color or theme_color, mid_type='num', mid_value=0., mid_color='FFFFFF', end_type='num', end_value=data.loc[c].max(), end_color=condition_color or theme_color)\n                index_row = start_row + insert_row + data.columns.nlevels if kwargs.get(\"header\", True) else start_row + insert_row\n                index_col = start_col + data.index.nlevels if kwargs.get(\"index\", False) else start_col\n                worksheet.conditional_formatting.add(f\"{get_column_letter(index_col)}{index_row}:{get_column_letter(index_col + len(data.columns))}{index_row}\", rule)\n            except:\n                import traceback\n                traceback.print_exc()\n\n    if not isinstance(excel_writer, ExcelWriter) and not isinstance(sheet_name, Worksheet):\n        writer.save(excel_writer)\n\n    return end_row, end_col\n\n\nif __name__ == \"__main__\":\n    end_row = 0\n\n    writer = ExcelWriter(theme_color='3f1dba')\n    worksheet = writer.get_sheet_by_name(\"模型报告\")\n\n    sample = pd.DataFrame(np.concatenate([np.random.random_sample((10, 10)) * 40, np.random.randint(0, 3, (10, 2))], axis=1), columns=[f\"B{i}\" for i in range(10)] + [\"target\", \"type\"])\n    end_row, end_col = writer.insert_df2sheet(worksheet, sample, (end_row + 2, column_index_from_string(\"B\")), fill=True, header=False, index=True)\n    writer.insert_hyperlink2sheet(worksheet, \"B2\", hyperlink=\"D5\")\n    end_row, end_col = writer.insert_df2sheet(worksheet, sample.set_index(\"type\"), (end_row + 2, column_index_from_string(\"B\")), merge_column=[\"target\"], index=True, fill=True, merge=True)\n    end_row, end_col = writer.insert_df2sheet(worksheet, sample, (end_row + 2, column_index_from_string(\"B\")), merge_column=[\"target\", \"type\"], index=True, fill=False, merge=True)\n    end_row, end_col = writer.insert_df2sheet(worksheet, sample.sort_values([\"target\", \"type\"]), (end_row + 2, column_index_from_string(\"B\")), merge_column=[\"target\"], index=True, fill=False, merge=True)\n    end_row, end_col = writer.insert_df2sheet(worksheet, sample, (end_row + 2, column_index_from_string(\"B\")))\n\n    multi_sample = pd.DataFrame(np.random.randint(0, 150, size=(8, 12)), columns=pd.MultiIndex.from_product([['模拟考', '正式考'], ['数学', '语文', '英语', '物理', '化学', '生物']]), index=pd.MultiIndex.from_product([['期中', '期末'], ['雷军', '李斌'], ['测试一', '测试二']]))\n    multi_sample.index.names = [\"考试类型\", \"姓名\", \"测试\"]\n    end_row, end_col = dataframe2excel(multi_sample, writer, theme_color='3f1dba', sheet_name=\"模型报告\", start_row=end_row + 2, title=\"测试样例\", index=True, header=False, fill=False, merge_index=True)\n\n    data = pd.read_pickle(\"/Users/lubberit/Downloads/black_list.pkl\")\n    data.index.names = (\"指标名称\", \"命中情况\")\n    end_row, end_col = dataframe2excel(data, writer, sheet_name=\"模型报告\", start_row=end_row + 2, title=None, index=True, fill=False, theme_color='3f1dba')\n    data = data.reset_index(names=[(\"\", \"\"), (\"渠道\", \"时间\")]).sort_values([(\"\", \"\"), (\"渠道\", \"时间\")]).reset_index(drop=True)\n    end_row, end_col = dataframe2excel(data, writer, sheet_name=\"模型报告\", start_row=end_row + 2, title=None, index=False, fill=True, merge_column=[(\"\", \"\")], merge=True, theme_color='3f1dba')\n    end_row, end_col = dataframe2excel(data, writer, sheet_name=\"模型报告\", start_row=end_row + 2, index=False, fill=False, merge_column=[(\"\", \"\")], merge=True, auto_width=False, theme_color='3f1dba')\n\n    for color_rows in data[data[(\"渠道\", \"时间\")] == \"命中率\"].index:\n        rule = ColorScaleRule(start_type='num', start_value=0, start_color='3f1dba', end_type='num', end_value=data.iloc[color_rows, 2:].max(), end_color='c04d9c')\n        worksheet.conditional_formatting.add(f\"{get_column_letter(2 + 2)}{end_row - len(data) + color_rows}:{get_column_letter(2 + len(data.columns))}{end_row - len(data) + color_rows}\", rule)\n        writer.set_number_format(worksheet, f\"{get_column_letter(2 + 2)}{end_row - len(data) + color_rows}:{get_column_letter(2 + len(data.columns))}{end_row - len(data) + color_rows}\", \"0.00%\")\n\n    end_row, end_col = dataframe2excel(data.set_index([('', ''), ('渠道', '时间')]), writer, sheet_name=\"模型报告\", condition_color=\"F76E6C\", color_rows=[\"命中率\"], percent_rows=[\"命中率\"], start_row=end_row + 2, index=True, fill=False, auto_width=False, theme_color='3f1dba')\n\n    writer.set_freeze_panes(\"模型报告\", \"B1\")\n\n    writer.save(\"测试样例.xlsx\")\n"
  },
  {
    "path": "scorecardpipeline/feature_engineering.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\n@Time    : 2024/5/10 10:28\n@Author  : itlubber\n@Site    : itlubber.art\n\"\"\"\nimport numpy as np\nimport numexpr as ne\nfrom pandas import DataFrame\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\n\nclass NumExprDerive(BaseEstimator, TransformerMixin):\n    \"\"\"Derive features by expressions.\n\n    **参考样例**\n\n    >>> import pandas as pd\n    >>> from scorecardpipeline.feature_engineering import NumExprDerive\n    >>> X = pd.DataFrame({\"f0\": [2, 1.0, 3], \"f1\": [np.inf, 2, 3], \"f2\": [2, 3, 4], \"f3\": [2.1, 1.4, -6.2]})\n    >>> fd = NumExprDerive(derivings=[(\"f4\", \"where(f1>1, 0, 1)\"), (\"f5\"、\"f1+f2\"), (\"f6\", \"sin(f1)\"), (\"f7\", \"abs(f3)\"))\n    >>> fd.fit_transform(X)\n    \"\"\"\n    def __init__(self, derivings=None):\n        \"\"\"\n        :param derivings: list, default=None. Each entry is a (name, expr) pair representing a deriving rule.\n        \"\"\"\n        self.derivings = derivings\n\n    def fit(self, X, y=None):\n        self._check_keywords()\n        self._validate_data(X, dtype=None, ensure_2d=True, force_all_finite=False)\n        return self\n\n    def _check_keywords(self):\n        derivings = self.derivings\n        if derivings is None:\n            raise ValueError(\"Deriving rules should not be empty!\")\n        if not isinstance(derivings, list):\n            raise ValueError(\"Deriving rules should be a list!\")\n        for i, entry in enumerate(derivings):\n            if not isinstance(entry, tuple):\n                raise ValueError(\"The {}-th deriving rule should be a tuple!\".format(i))\n            if len(entry) != 2:\n                raise ValueError(\"The f}-th deriving rule is not a two-element (drived_name, expression) tuple!\".format(i))\n            name, expr = entry\n            if not isinstance(name, str) or not isinstance(expr, str):\n                raise ValueError(\"The {}-th deriving rule is not a two-string tuple!\".format(i))\n\n    @staticmethod\n    def _get_context(X, feature_names=None):\n        if feature_names is not None:\n            return {name: X[:, i] for i, name in enumerate(feature_names)}\n        return {\"f%d\" % i: X[:, i] for i in range(X.shape[1])}\n\n    def _transform_frame(self, X):\n        feature_names = X.columns.tolist()\n        self.features_names = feature_names\n        index = X.index\n        X = self._validate_data(X, dtype=\"numeric\", ensure_2d=True, force_all_finite=False)\n        context = self._get_context(X, feature_names=feature_names)\n        n_derived = len(self.derivings)\n        X_derived = np.empty((X.shape[0], n_derived), dtype=np.float64)\n        derived_names = []\n        for i, (name, expr) in enumerate(self.derivings):\n            derived_names.append(name)\n            X_derived[:, i] = ne.evaluate(expr, local_dict=context)\n        data = np.hstack((X, X_derived))\n        columns = feature_names + derived_names\n        return DataFrame(data=data, columns=columns, index=index)\n\n    def _transform_ndarray(self, X):\n        X = self._validate_data(X, dtype=\"numeric\", ensure_2d=True, force_all_finite=False)\n        context = self._get_context(X, feature_names=None)\n        n_derived = len(self.derivings)\n        X_derived = np.empty((X.shape[0], n_derived), dtype=np.float64)\n        derived_names = []\n        for i, (name, expr) in enumerate(self.derivings):\n            derived_names.append(name)\n            X_derived[:, i] = ne.evaluate(expr, local_dict=context)\n        return np.hstack((X, X_derived))\n\n    def transform(self, X):\n        if isinstance(X, DataFrame):\n            return self._transform_frame(X)\n        return self._transform_ndarray(X)\n\n    def _more_tags(self):\n        return {\n            \"X_types\": [\"2darray\"],\n            \"allow_nan\": True,\n        }\n"
  },
  {
    "path": "scorecardpipeline/feature_selection.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\n@Time    : 2024/5/8 14:06\n@Author  : itlubber\n@Site    : itlubber.art\n\"\"\"\n\nimport operator\nimport sys\nimport types\nfrom copy import deepcopy\nfrom functools import reduce\nfrom itertools import chain, combinations\nfrom functools import partial\nfrom abc import ABCMeta, abstractmethod\n\nimport math\nimport numpy as np\nimport pandas as pd\nfrom joblib import Parallel, delayed\n\nfrom scipy.stats import sem\nfrom scipy.stats._continuous_distns import t\nfrom sklearn.metrics import check_scoring, get_scorer\nfrom sklearn.model_selection._validation import cross_val_score, _score\nfrom sklearn.utils._encode import _unique\nfrom sklearn.utils._mask import _get_mask\nfrom sklearn.model_selection import check_cv\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.utils import _safe_indexing, check_X_y\nfrom sklearn.model_selection import StratifiedKFold, GroupKFold\nfrom sklearn.utils.sparsefuncs import mean_variance_axis, min_max_axis\nfrom sklearn.utils.validation import check_is_fitted, check_array, indexable, column_or_1d\nfrom sklearn.base import BaseEstimator, TransformerMixin, clone, is_classifier, MetaEstimatorMixin\nfrom sklearn.feature_selection import RFECV, RFE, SelectFromModel, SelectKBest, GenericUnivariateSelect\nfrom sklearn.feature_selection._from_model import _calculate_threshold, _get_feature_importances\n# from statsmodels.stats.outliers_influence import variance_inflation_factor\n\nfrom .processing import Combiner\n\n\nclass SelectorMixin(BaseEstimator, TransformerMixin):\n\n    def __init__(self):\n        self.select_columns = None\n        self.scores_ = None\n        self.dropped = None\n        self.n_features_in_ = None\n\n    def transform(self, x):\n        check_is_fitted(self, \"select_columns\")\n        return x[[col for col in self.select_columns if col in x.columns]]\n    \n    def __call__(self, *args, **kwargs):\n        self.fit(*args, **kwargs)\n        return self.select_columns\n\n    def fit(self, x, y=None):\n        pass\n\n\nclass TypeSelector(SelectorMixin):\n\n    def __init__(self, dtype_include=None, dtype_exclude=None, exclude=None):\n        super().__init__()\n        self.dtype_include = dtype_include\n        self.dtype_exclude = dtype_exclude\n        self.exclude = exclude\n\n    def fit(self, x: pd.DataFrame, y=None, **fit_params):\n        if not hasattr(x, 'iloc'):\n            raise ValueError(\"make_column_selector can only be applied to pandas dataframes\")\n        \n        self.n_features_in_ = x.shape[1]\n        \n        if self.exclude:\n            if not isinstance(self.exclude, (list, tuple, np.ndarray)):\n                self.exclude = [self.exclude]\n\n            x = x.drop(columns=[c for c in self.exclude if c in x.columns])\n\n        if self.dtype_include is not None or self.dtype_exclude is not None:\n            cols = x.select_dtypes(include=self.dtype_include, exclude=self.dtype_exclude).columns\n        else:\n            cols = x.columns\n        \n        self.scores_ = x.dtypes\n        self.select_columns = list(set(cols.tolist()))\n        if self.exclude:\n            self.select_columns = list(set(self.select_columns + self.exclude))\n        \n        self.dropped = pd.DataFrame([(col, f\"data type or name not match\") for col in x.columns if col not in self.select_columns], columns=[\"variable\", \"rm_reason\"])\n        return self\n\n\nclass RegexSelector(SelectorMixin):\n    def __init__(self, pattern=None, exclude=None):\n        super().__init__()\n        self.pattern = pattern\n        self.exclude = exclude\n\n        if self.pattern is None:\n            raise ValueError(\"pattern must be a regular expression.\")\n\n    def fit(self, x: pd.DataFrame, y=None, **fit_params):\n        if not hasattr(x, 'iloc'):\n            raise ValueError(\"make_column_selector can only be applied to pandas dataframes\")\n\n        self.n_features_in_ = x.shape[1]\n\n        if self.exclude:\n            if not isinstance(self.exclude, (list, tuple, np.ndarray)):\n                self.exclude = [self.exclude]\n\n            x = x.drop(columns=[c for c in self.exclude if c in x.columns])\n\n        self.scores_ = x.columns.str.contains(self.pattern, regex=True).astype(int)\n        self.select_columns = list(set(x.columns[self.scores_ == 1].tolist()))\n        if self.exclude:\n            self.select_columns = list(set(self.select_columns + self.exclude))\n\n        self.dropped = pd.DataFrame([(col, f\"feature name not match {self.pattern}\") for col in x.columns if col not in self.select_columns], columns=[\"variable\", \"rm_reason\"])\n        return self\n\n\ndef value_ratio(x, value):\n    if isinstance(x, pd.DataFrame):\n        return np.mean(_get_mask(x.values, value), axis=0)\n\n    return np.mean(_get_mask(x, value), axis=0)\n\n\ndef mode_ratio(x, dropna=True):\n    if isinstance(x, (list, np.ndarray)):\n        x = pd.Series(x)\n\n    summary = x.value_counts(dropna=dropna)\n    return (summary.index[0], summary.iloc[0] / sum(summary)) if len(summary) > 0 else (np.nan, 1.0)\n\n\nclass NullSelector(SelectorMixin):\n\n    def __init__(self, threshold=0.95, missing_values=np.nan, exclude=None, **kwargs):\n        super().__init__()\n        self.exclude = exclude\n        self.threshold = threshold\n        self.missing_values = missing_values\n        self.dropped = None\n        self.select_columns = None\n        self.scores_ = None\n        self.n_features_in_ = None\n        self.kwargs = kwargs\n\n    def fit(self, x: pd.DataFrame, y=None):\n        self.n_features_in_ = x.shape[1]\n\n        if self.exclude:\n            if not isinstance(self.exclude, (list, tuple, np.ndarray)):\n                self.exclude = [self.exclude]\n\n            x = x.drop(columns=[c for c in self.exclude if c in x.columns])\n\n        self.scores_ = pd.Series(value_ratio(x, self.missing_values), index=x.columns)\n        self.threshold = _calculate_threshold(self, self.scores_, self.threshold)\n        self.select_columns = list(set((self.scores_[self.scores_ < self.threshold]).index.tolist()))\n        if self.exclude:\n            self.select_columns = list(set(self.select_columns + self.exclude))\n\n        self.dropped = pd.DataFrame([(col, f\"nan ratio >= {self.threshold}\") for col in x.columns if col not in self.select_columns], columns=[\"variable\", \"rm_reason\"])\n        return self\n\n\nclass ModeSelector(SelectorMixin):\n\n    def __init__(self, threshold=0.95, exclude=None, dropna=True, n_jobs=None, **kwargs):\n        super().__init__()\n        self.dropna = dropna\n        self.exclude = exclude\n        self.threshold = threshold\n        self.dropped = None\n        self.select_columns = None\n        self.scores_ = None\n        self.n_features_in_ = None\n        self.kwargs = kwargs\n        self.n_jobs = n_jobs\n\n    def fit(self, x: pd.DataFrame, y=None):\n        self.n_features_in_ = x.shape[1]\n\n        if self.exclude:\n            if not isinstance(self.exclude, (list, tuple, np.ndarray)):\n                self.exclude = [self.exclude]\n\n            x = x.drop(columns=[c for c in self.exclude if c in x.columns])\n\n        self.scores_ = pd.DataFrame(Parallel(n_jobs=self.n_jobs)(delayed(mode_ratio)(x[c], self.dropna) for c in x.columns), columns=[\"Mode\", \"Ratio\"], index=x.columns)\n        self.threshold = _calculate_threshold(self, self.scores_, self.threshold)\n        self.select_columns = list(set((self.scores_[self.scores_[\"Ratio\"] < self.threshold]).index.tolist()))\n        if self.exclude:\n            self.select_columns = list(set(self.select_columns + self.exclude))\n\n        self.dropped = pd.DataFrame([(col, f\"mode ratio >= {self.threshold}\") for col in x.columns if col not in self.select_columns], columns=[\"variable\", \"rm_reason\"])\n        return self\n\n\nclass CardinalitySelector(SelectorMixin):\n    \"\"\"Feature selection via categorical feature's cardinality.\n\n    **参考样例**\n\n    >>> import pandas as pd\n    >>> from scorecardpipeline.feature_selection import CardinalitySelector\n    >>> x = pd.DataFrame({\"f2\": [\"F\", \"м\", \"F\"], \"f3\": [\"M1\", \"M2\", \"м3\"]})\n    >>> cs = CardinalitySelector(threshold=2)\n    >>> cs.fit_transform(x)\n    \"\"\"\n    def __init__(self, threshold=10, exclude=None, dropna=True):\n        super().__init__()\n        self.exclude = exclude\n        self.threshold = threshold\n        self.dropna = dropna\n\n    def fit(self, x, y=None, **fit_params):\n        self.n_features_in_ = x.shape[1]\n\n        if self.exclude:\n            if not isinstance(self.exclude, (list, tuple, np.ndarray)):\n                self.exclude = [self.exclude]\n\n        self.scores_ = pd.Series(x.nunique(axis=0, dropna=self.dropna).values, index=x.columns)\n        self.threshold = _calculate_threshold(self, self.scores_, self.threshold)\n        self.select_columns = list(set((self.scores_[self.scores_ < self.threshold]).index.tolist()))\n\n        if self.exclude:\n            self.select_columns = list(set(self.select_columns + self.exclude))\n\n        self.dropped = pd.DataFrame([(col, f\"cardinality >= {self.threshold}\") for col in x.columns if col not in self.select_columns], columns=[\"variable\", \"rm_reason\"])\n        return self\n\n\ndef IV(x, y, regularization=1.0):\n    uniques = np.unique(x)\n    n_cats = len(uniques)\n\n    if n_cats <= 1:\n        return 0.0\n\n    event_mask = y == 1\n    nonevent_mask = y != 1\n    event_tot = np.count_nonzero(event_mask) + 2 * regularization\n    nonevent_tot = np.count_nonzero(nonevent_mask) + 2 * regularization\n\n    event_rates = np.zeros(n_cats, dtype=np.float64)\n    nonevent_rates = np.zeros(n_cats, dtype=np.float64)\n    for i, cat in enumerate(uniques):\n        mask = x == cat\n        event_rates[i] = np.count_nonzero(mask & event_mask) + regularization\n        nonevent_rates[i] = np.count_nonzero(mask & nonevent_mask) + regularization\n\n    # Ignore unique values. This helps to prevent overfitting on id-like columns.\n    bad_pos = (event_rates + nonevent_rates) == (2 * regularization + 1)\n    event_rates /= event_tot\n    nonevent_rates /= nonevent_tot\n    ivs = (event_rates - nonevent_rates) * np.log(event_rates / nonevent_rates)\n    ivs[bad_pos] = 0.\n    return np.sum(ivs).item()\n\n\ndef _IV(x, y, regularization=1.0, n_jobs=None):\n    x = check_array(x, dtype=None, force_all_finite=True, ensure_2d=True)\n    le = LabelEncoder()\n    y = le.fit_transform(y)\n    if len(le.classes_) != 2:\n        raise ValueError(\"Only support binary label for computing information value!\")\n    _, n_features = x.shape\n    iv_values = Parallel(n_jobs=n_jobs)(delayed(IV)(x[:, i], y, regularization=regularization) for i in range(n_features))\n    return np.asarray(iv_values, dtype=np.float64)\n\n\nclass InformationValueSelector(SelectorMixin):\n\n    def __init__(self, threshold=0.02, target=\"target\", regularization=1.0, methods=None, n_jobs=None, combiner=None, **kwargs):\n        super().__init__()\n        self.dropped = None\n        self.select_columns = None\n        self.scores_ = None\n        self.n_features_in_ = None\n        self.combiner = combiner\n        self.threshold = threshold\n        self.target = target\n        self.regularization = regularization\n        self.n_jobs = n_jobs\n        self.methods = methods\n        self.kwargs = kwargs\n\n    def fit(self, x: pd.DataFrame, y=None):\n        if y is None:\n            if self.target not in x.columns:\n                raise ValueError(f\"需要传入 y 或者 x 中包含 {self.target}.\")\n            y = x[self.target]\n            x = x.drop(columns=self.target)\n\n        self.n_features_in_ = x.shape[1]\n\n        if self.combiner:\n            xt = self.combiner.transform(x)\n        elif self.methods:\n            temp = x.copy()\n            temp[self.target] = y\n            self.combiner = Combiner(target=self.target, method=self.methods, n_jobs=self.n_jobs, **self.kwargs)\n            self.combiner.fit(temp)\n            xt = self.combiner.transform(x)\n        else:\n            xt = x.copy()\n\n        self.scores_ = pd.Series(_IV(xt, y, regularization=self.regularization, n_jobs=self.n_jobs), index=xt.columns)\n        self.threshold = _calculate_threshold(self, self.scores_, self.threshold)\n        self.select_columns = list(set((self.scores_[self.scores_ >= self.threshold]).index.tolist() + [self.target]))\n        self.dropped = pd.DataFrame([(col, f\"IV <= {self.threshold}\") for col in xt.columns if col not in self.select_columns], columns=[\"variable\", \"rm_reason\"])\n        return self\n\n\ndef LIFT(y_pred, y_true):\n    \"\"\"Calculate lift according to label data.\n\n    **参考样例**\n\n    >>> import numpy as np\n    >>> y_true = np.array([0, 1, 1, 0, 1, 1, 0, 1, 1])\n    >>> y_pred = np.array([1, 0, 1, 0, 1, 1, 1, 1, 1])\n    >>> LIFT(y_true, y_pred) # (5 / 7) / (6 / 9)\n    1.0714285714285716\n    \"\"\"\n    if len(np.unique(y_pred)) <= 1:\n        return 1.0\n\n    _y_true = column_or_1d(y_true)\n    base_bad_rate = np.average(y_true)\n\n    score = []\n    for v in np.unique(y_pred):\n        if pd.isnull(v):\n            _y_pred = column_or_1d(y_pred.isnull())\n        else:\n            _y_pred = column_or_1d(y_pred == v)\n        hit_bad_rate = np.count_nonzero((_y_true == 1) & (_y_pred == 1)) / np.count_nonzero(_y_pred)\n        score.append(hit_bad_rate / base_bad_rate)\n\n    return np.nanmax(score)\n\n\nclass LiftSelector(SelectorMixin):\n    \"\"\"Feature selection via lift score.\n\n    **属性字段**\n\n    :param threshold_: float. The threshold value used for feature selection.\n    :param scores_ : array-like of shape (n_features,). Lift scores of features.\n    :param select_columns : array-like\n    :param dropped : DataFrame\n    \"\"\"\n    def __init__(self, target=\"target\", threshold=3.0, n_jobs=None, methods=None, combiner=None, **kwargs):\n        \"\"\"\n        :param target: target\n        :param threshold: float or str (default=3.0). Feature which has a lift score greater than `threshold` will be kept.\n        :param n_jobs: int or None, (default=None). Number of parallel.\n        :param combiner: Combiner\n        :param methods: Combiner's methods\n        \"\"\"\n        super().__init__()\n        self.threshold = threshold\n        self.n_jobs = n_jobs\n        self.target = target\n        self.methods = methods\n        self.combiner = combiner\n        self.kwargs = kwargs\n\n    def fit(self, x: pd.DataFrame, y=None, **fit_params):\n        if y is None:\n            if self.target not in x.columns:\n                raise ValueError(f\"需要传入 y 或者 x 中包含 {self.target}.\")\n            y = x[self.target]\n            x = x.drop(columns=self.target)\n\n        self.n_features_in_ = x.shape[1]\n\n        if self.combiner:\n            xt = self.combiner.transform(x)\n        elif self.methods:\n            temp = x.copy()\n            temp[self.target] = y\n            self.combiner = Combiner(target=self.target, method=self.methods, n_jobs=self.n_jobs, **self.kwargs)\n            self.combiner.fit(temp)\n            xt = self.combiner.transform(x)\n        else:\n            xt = x.copy()\n\n        # _lift = {}\n        # for c in tqdm(xt.columns):\n        #     _lift[c] = LIFT(xt[c], y)\n        # self.scores_ = pd.Series(_lift)\n        \n        self.scores_ = pd.Series(Parallel(n_jobs=self.n_jobs)(delayed(LIFT)(xt[c], y) for c in xt.columns), index=xt.columns)\n        self.threshold = _calculate_threshold(self, self.scores_, self.threshold)\n        self.select_columns = list(set((self.scores_[self.scores_ >= self.threshold]).index.tolist() + [self.target]))\n        self.dropped = pd.DataFrame([(col, f\"LIFT < {self.threshold}\") for col in xt.columns if col not in self.select_columns], columns=[\"variable\", \"rm_reason\"])\n        return self\n\n\nclass VarianceSelector(SelectorMixin):\n    \"\"\"Feature selector that removes all low-variance features.\"\"\"\n\n    def __init__(self, threshold=0.0, exclude=None):\n        super().__init__()\n        self.threshold = threshold\n        if exclude is not None:\n            self.exclude = exclude if isinstance(exclude, (list, np.ndarray)) else [exclude]\n        else:\n            self.exclude = []\n\n    def fit(self, x, y=None):\n        self.n_features_in_ = x.shape[1]\n        \n        if hasattr(x, \"toarray\"):  # sparse matrix\n            _, scores = mean_variance_axis(x, axis=0)\n            if self.threshold == 0:\n                mins, maxes = min_max_axis(x, axis=0)\n                peak_to_peaks = maxes - mins\n        else:\n            scores = np.nanvar(x, axis=0)\n            if self.threshold == 0:\n                peak_to_peaks = np.ptp(x, axis=0)\n\n        if self.threshold == 0:\n            # Use peak-to-peak to avoid numeric precision issues for constant features\n            compare_arr = np.array([scores, peak_to_peaks])\n            scores = np.nanmin(compare_arr, axis=0)\n\n        if np.all(~np.isfinite(scores) | (scores <= self.threshold)):\n            msg = \"No feature in x meets the variance threshold {0:.5f}\"\n            if x.shape[0] == 1:\n                msg += \" (x contains only one sample)\"\n            raise ValueError(msg.format(self.threshold))\n\n        self.scores_ = pd.Series(scores, index=x.columns)\n        self.threshold = _calculate_threshold(self, self.scores_, self.threshold)\n        self.select_columns = list(set((self.scores_[self.scores_ > self.threshold]).index.tolist() + self.exclude))\n        self.dropped = pd.DataFrame([(col, f\"Variance <= {self.threshold}\") for col in x.columns if col not in self.select_columns], columns=[\"variable\", \"rm_reason\"])\n        \n        return self\n\n\ndef VIF(x, n_jobs=None, missing=-1):\n    columns = x.columns\n    x = x.fillna(missing).values\n    lr = partial(lambda x, y: LinearRegression(fit_intercept=False).fit(x, y).predict(x))\n    y_pred = Parallel(n_jobs=n_jobs)(delayed(lr)(x[:, np.arange(x.shape[1]) != i], x[:, i]) for i in range(x.shape[1]))\n    vif = [np.sum(x[:, i] ** 2) / np.sum((y_pred[i] - x[:, i]) ** 2) for i in range(x.shape[1])]\n\n    return pd.Series(vif, index=columns)\n\n\nclass VIFSelector(SelectorMixin):\n\n    def __init__(self, threshold=4.0, exclude=None, missing=-1, n_jobs=None):\n        \"\"\"VIF越高，多重共线性的影响越严重, 在金融风险中我们使用经验法则:若VIF>4，则我们认为存在多重共线性, 计算比较消耗资源, 如果数据维度较大的情况下, 尽量不要使用\n\n        :param exclude: 数据集中需要强制保留的变量\n        :param threshold: 阈值, VIF 大于 threshold 即剔除该特征\n        :param missing: 缺失值默认填充 -1\n        :param n_jobs: 线程数\n        \"\"\"\n        super().__init__()\n        self.threshold = threshold\n        self.missing = missing\n        self.n_jobs = n_jobs\n        if exclude is not None:\n            self.exclude = exclude if isinstance(exclude, (list, np.ndarray)) else [exclude]\n        else:\n            self.exclude = []\n\n    def fit(self, x: pd.DataFrame, y=None):\n        if self.exclude:\n            x = x.drop(columns=self.exclude)\n        \n        self.n_features_in_ = x.shape[1]\n        \n        # vif = partial(variance_inflation_factor, np.matrix(x.fillna(self.missing)))\n        # self.scores_ = pd.Series(Parallel(n_jobs=None)(delayed(vif)(i) for i in range(x.shape[1])), index=x.columns)\n        self.scores_ = VIF(x, missing=self.missing, n_jobs=self.n_jobs)\n        \n        self.threshold = _calculate_threshold(self, self.scores_, self.threshold)\n        self.select_columns = list(set((self.scores_[self.scores_ < self.threshold]).index.tolist() + self.exclude))\n        self.dropped = pd.DataFrame([(col, f\"VIF >= {self.threshold}\") for col in x.columns if col not in self.select_columns], columns=[\"variable\", \"rm_reason\"])\n\n        return self\n\n\nclass CorrSelector(SelectorMixin):\n    def __init__(self, threshold=0.7, method=\"pearson\", weights=None, exclude=None, **kwargs):\n        super().__init__()\n        self.threshold = threshold\n        self.method = method\n        self.weights = weights\n        if exclude is not None:\n            self.exclude = exclude if isinstance(exclude, (list, np.ndarray)) else [exclude]\n        else:\n            self.exclude = []\n        self.kwargs = kwargs\n\n    def fit(self, x: pd.DataFrame, y=None):\n        if self.exclude:\n            x = x.drop(columns=self.exclude)\n\n        self.n_features_in_ = x.shape[1]\n        \n        _weight = pd.Series(np.zeros(self.n_features_in_), index=x.columns)\n        \n        if self.weights is not None:\n            if isinstance(self.weights, pd.Series):\n                _weight_columns = list(set(self.weights.index) & set(x.columns))\n                _weight.loc[_weight_columns] = self.weights[_weight_columns]\n            else:\n                _weight = pd.Series(self.weights, index=x.columns)\n\n        self.weights = _weight\n        x = x[sorted(x.columns, key=lambda c: self.weights.loc[c], reverse=True)]\n\n        corr = x.corr(method=self.method, **self.kwargs)\n        self.scores_ = corr\n        self.threshold = _calculate_threshold(self, self.scores_, self.threshold)\n\n        # corr_matrix = self.scores_.values\n        # mask = np.full(self.n_features_in_, True, dtype=bool)\n        # for i in range(self.n_features_in_):\n        #     if not mask[i]:\n        #         continue\n        #     for j in range(i + 1, self.n_features_in_):\n        #         if not mask[j]:\n        #             continue\n        #         if abs(corr_matrix[i, j]) < self.threshold:\n        #             continue\n        #         mask[j] = False\n        #\n        # self.select_columns = list(set([c for i, c in enumerate(x.columns) if mask[i]] + self.exclude))\n\n        drops = []\n        ix, cn = np.where(np.triu(corr.values, 1) > self.threshold)\n        weights = self.weights.values\n\n        if len(ix):\n            graph = np.hstack([ix.reshape((-1, 1)), cn.reshape((-1, 1))])\n            uni, counts = np.unique(graph, return_counts=True)\n\n            while True:\n                nodes = uni[np.argwhere(counts == np.amax(counts))].flatten()\n                n = nodes[np.argsort(weights[nodes])[0]]\n\n                i, c = np.where(graph == n)\n                pairs = graph[(i, 1 - c)]\n\n                if weights[pairs].sum() > weights[n]:\n                    dro = [n]\n                else:\n                    dro = pairs.tolist()\n\n                drops += dro\n\n                di, _ = np.where(np.isin(graph, dro))\n                graph = np.delete(graph, di, axis=0)\n\n                if len(graph) <= 0:\n                    break\n\n                uni, counts = np.unique(graph, return_counts=True)\n\n        self.dropped = pd.DataFrame([(col, f\"corr > {self.threshold}\") for col in corr.index[drops].values], columns=[\"variable\", \"rm_reason\"])\n        self.select_columns = list(set([c for c in x.columns if c not in corr.index[drops].values] + self.exclude))\n\n        return self\n\n\ndef _psi_score(expected, actual):\n    n_expected = len(expected)\n    n_actual = len(actual)\n\n    psi = []\n    for value in _unique(expected):\n        expected_cnt = np.count_nonzero(expected == value)\n        actual_cnt = np.count_nonzero(actual == value)\n        expected_cnt = expected_cnt if expected_cnt else 1.\n        actual_cnt = actual_cnt if actual_cnt else 1.\n        expected_rate = expected_cnt / n_expected\n        actual_rate = actual_cnt / n_actual\n        psi.append((actual_rate - expected_rate) * np.log(actual_rate / expected_rate))\n    \n    return sum(psi)\n\n\ndef PSI(train, test, n_jobs=None, verbose=0, pre_dispatch='2*n_jobs'):\n    parallel = Parallel(n_jobs=n_jobs, verbose=verbose, pre_dispatch=pre_dispatch)\n    scores = parallel(delayed(_psi_score)(train[:, i], test[:,i]) for i in range(len(train.columns)))\n    return scores\n\n\nclass PSISelector(SelectorMixin):\n\n    def __init__(self, threshold=0.1, cv=None, method=None, exclude=None, n_jobs=None, verbose=0, pre_dispatch='2*n_jobs', **kwargs):\n        super().__init__()\n        self.threshold = threshold\n        self.cv = cv\n        self.method = method\n        self.n_jobs = n_jobs\n        self.verbose = verbose\n        self.pre_dispatch = pre_dispatch\n        if exclude is not None:\n            self.exclude = exclude if isinstance(exclude, (list, np.ndarray)) else [exclude]\n        else:\n            self.exclude = []\n        self.kwargs = kwargs\n\n    def fit(self, x: pd.DataFrame, y=None, groups=None):\n        if self.method is not None:\n            temp = x.copy()\n            if y is not None:\n                if self.kwargs and \"target\" in self.kwargs and self.kwargs[\"target\"] not in temp.columns:\n                    temp[self.kwargs[\"target\"]] = y\n                elif \"target\" not in temp.columns:\n                    temp[\"target\"] = y\n\n            self.combiner = Combiner(method=self.method, n_jobs=self.n_jobs, **self.kwargs).fit(temp)\n            x = self.combiner.transform(x)\n\n        if self.exclude:\n            x = x.drop(columns=self.exclude)\n\n        self.n_features_in_ = x.shape[1]\n        x, groups = indexable(x, groups)\n        cv = check_cv(self.cv)\n        n_jobs = self.n_jobs\n        verbose = self.verbose\n        pre_dispatch = self.pre_dispatch\n\n        cv_scores = []\n        for train, test in cv.split(x, y, groups):\n            scores = PSI(_safe_indexing(x, train), _safe_indexing(x, test), n_jobs=n_jobs, verbose=verbose, pre_dispatch=pre_dispatch)\n            cv_scores.append(scores)\n\n        self.scores_ = pd.Series(np.mean(cv_scores, axis=0), index=x.columns)\n        self.threshold = _calculate_threshold(self, self.scores_, self.threshold)\n        self.select_columns = list(set((self.scores_[self.scores_ >= self.threshold]).index.tolist() + self.exclude))\n        self.dropped = pd.DataFrame([(col, f\"PSI >= {self.threshold}\") for col in x.columns if col not in self.select_columns], columns=[\"variable\", \"rm_reason\"])\n\n        return self\n\n\nclass NullImportanceSelector(SelectorMixin):\n    \n    def __init__(self, estimator, target=\"target\", threshold=1.0, norm_order=1, importance_getter='auto', cv=3, n_runs=5, **kwargs):\n        super().__init__()\n        self.estimator = estimator\n        self.threshold = threshold\n        self.norm_order = norm_order\n        self.importance_getter = importance_getter\n        self.cv = cv\n        self.n_runs = n_runs\n        self.target = target\n    \n    @staticmethod\n    def _feature_score_v0(actual_importances, null_importances):\n        return actual_importances.mean(axis=1) / null_importances.mean(axis=1)\n    \n    @staticmethod\n    def _feature_score_v1(actual_importances, null_importances):\n        # 未进行特征shuffle的特征重要性除以shuffle以后的0.75分位数作为score\n        actual_importance = actual_importances.mean()\n        return np.log(1e-10 + actual_importance / (1. + np.percentile(null_importances, 75)))\n    \n    @staticmethod\n    def _feature_score_v2(actual_importances, null_importances):\n        # shuffle之后特征重要性低于实际target对应特征的重要性0.25分位数的次数百分比\n        return np.count_nonzero(null_importances < np.percentile(actual_importances, 25)) / null_importances.shape[0]\n\n    def fit(self, x: pd.DataFrame, y=None):\n        if self.target in x.columns:\n            y = x[self.target]\n            x = x.drop(columns=self.target)\n\n        cv = check_cv(self.cv, y, classifier=is_classifier(self.estimator))\n        \n        n_splits = cv.get_n_splits()\n        n_runs = self.n_runs\n        getter = self.importance_getter\n        norm_order = self.norm_order\n        \n        # 计算shuffle之后的特征重要性\n        estimator = deepcopy(self.estimator)\n        n_samples, n_features = x.shape\n        null_importances = np.zeros((n_features, n_splits * n_runs))\n        idx = np.arange(n_samples)\n        for run in range(n_runs):\n            np.random.shuffle(idx)\n            y_shuffled = y[idx]\n\n            for fold_, (train_idx, valid_idx) in enumerate(cv.split(y_shuffled, y_shuffled)):\n                estimator.fit(x.loc[train_idx], y_shuffled.loc[train_idx])\n                null_importance = _get_feature_importances(estimator, getter, transform_func=None, norm_order=norm_order)\n                null_importances[:, n_splits * run + fold_] = null_importance\n        \n        # 计算未shuffle的特征重要性\n        estimator = clone(self.estimator)\n        actual_importances = np.zeros((n_features, n_splits * n_runs))\n        for run in range(n_runs):\n            np.random.shuffle(idx)\n            y_shuffled = y[idx]\n            x_shuffled = x[idx]\n            \n            for fold_, (train_idx, valid_idx) in enumerate(cv.split(y_shuffled, y_shuffled)):\n                estimator.fit(x_shuffled.loc[train_idx], y_shuffled.loc[train_idx])\n                actual_importance = _get_feature_importances(estimator, getter, transform_func=None, norm_order=norm_order)\n                actual_importances[:, n_splits * run + fold_] = actual_importance\n\n        self.null_importances = null_importances\n        self.actual_importances_ = actual_importances\n        \n        scores = np.zeros(n_features)\n        for i in range(n_features):\n            scores[i] = self._feature_score_v2(actual_importances[i, :], null_importances[i, :])\n\n        self.scores_ = pd.Series(scores, index=x.columns)\n        self.threshold = _calculate_threshold(self.estimator, scores, self.threshold)\n        \n        if self.threshold > 1.0:\n            self.select_columns = list(set(self.scores_.sort_values(ascending=False).iloc[:math.floor(self.threshold)].index.tolist() + [self.target]))\n            self.dropped = pd.DataFrame([(col, f\"nullimportance not top {self.threshold}\") for col in x.columns if col not in self.select_columns], columns=[\"variable\", \"rm_reason\"])\n        else:\n            self.select_columns = list(set((self.scores_[self.scores_ > self.threshold]).index.tolist() + [self.target]))\n            self.dropped = pd.DataFrame([(col, f\"nullimportance <= {self.threshold}\") for col in x.columns if col not in self.select_columns], columns=[\"variable\", \"rm_reason\"])\n\n\nclass TargetPermutationSelector(NullImportanceSelector):\n    \n    def __init__(self, estimator, target=\"target\", threshold=1.0, norm_order=1, importance_getter='auto', cv=3, n_runs=5, **kwargs):\n        super().__init__(estimator, target=target, threshold=threshold, norm_order=norm_order, importance_getter=importance_getter, cv=cv, n_runs=n_runs, **kwargs)\n\n\nclass ExhaustiveSelector(SelectorMixin, MetaEstimatorMixin):\n    \"\"\"Exhaustive Feature Selection for Classification and Regression.\n\n    **属性字段**\n\n    :param subset_info_: list of dicts. A list of dictionary with the following keys: 'support_mask', mask array of the selected features 'cv_scores', cross validate scores\n    :param support_mask_: array-like of booleans. Array of final chosen features\n    :param best_idx_: array-like, shape = [n_predictions]. Feature Indices of the selected feature subsets.\n    :param best_score_: float. Cross validation average score of the selected subset.\n    :param best_feature_indices_: array-like, shape = (n_features,), Feature indices of the selected feature subsets.\n\n    **参考样例**\n    \n    >>> from sklearn.neighbors import KNeighborsClassifier\n    >>> from sklearn.datasets import load_iris\n    >>> from scorecardpipeline.feature_selection import ExhaustiveSelector\n    >>> X, y = load_iris(return_X_y=True, as_frame=True)\n    >>> knn = KNeighborsClassifier(n_neighbors=3)\n    >>> efs = ExhaustiveSelector(knn, min_features=1, max_features=4, cv=3)\n    >>> efs.fit(X, y)\n    ExhaustiveFeatureSelector(estimator=KNeighborsClassifier(n_neighbors=3), max_features=4)\n    >>> efs.best_score_\n    0.9733333333333333\n    >>> efs.best_idx_\n    12\n    \"\"\"\n    def __init__(self, estimator, min_features=1, max_features=1, scoring=\"accuracy\", cv=3, verbose=0, n_jobs=None, pre_dispatch='2*n_jobs'):\n        \"\"\"\n        :param estimator: scikit-learn classifier or regressor\n        :param min_features: int (default: 1). Minimum number of features to select\n        :param max_features: int (default: 1). Maximum number of features to select\n        :param verbose: bool (default: True). Prints progress as the number of epochs to stdout.\n        :param scoring: str, (default='_passthrough_scorer'). Scoring metric in faccuracy, f1, precision, recall, roc_auc) for classifiers, {'mean_absolute_error', 'mean_squared_error', 'median_absolute_error', 'r2'} for regressors, or a callable object or function with signature ``scorer(estimator, X, y)``.\n        :param cv: int (default: 5). Scikit-learn cross-validation generator or `int`, If estimator is a classifier (or y consists of integer class labels), stratified k-fold is performed, and regular k-fold cross-validation otherwise. No cross-validation if cv is None, False, or 0.\n        :param n_jobs: int (default: 1). The number of CPUs to use for evaluating different feature subsets in parallel. -1 means 'all CPUs'.\n        :param pre_dispatch: int, or string (default: '2*n_jobs'). Controls the number of jobs that get dispatched during parallel execution if `n_jobs > 1` or `n_jobs=-1`.\n        \"\"\"\n        super().__init__()\n        self.estimator = estimator\n        self.min_features = min_features\n        self.max_features = max_features\n        self.scoring = scoring\n        self.cv = cv\n        self.verbose = verbose\n        self.n_jobs = n_jobs\n        self.pre_dispatch = pre_dispatch\n    \n    def _validate_params(self, x, y):\n        check_X_y(x, y, estimator=self.estimator)\n        _, n_features = x.shape\n        if not isinstance(self.min_features, int) or (self.max_features > n_features or self.max_features < 1):\n            raise AttributeError(\"max_features must be smaller than %d and larger than 0\" % (n_features + 1))\n        if not isinstance(self.min_features, int) or (self.min_features > n_features or self.min_features < 1):\n            raise AttributeError(\"min_features must be smaller than %d and larger than 0\" % (n_features + 1))\n        \n        if self.max_features < self.min_features:\n            raise AttributeError(\"min_features must be less equal than max_features\")\n        return x, y\n    \n    @staticmethod\n    def _calc_score(estimator, x, y, indices, groups=None, scoring=None, cv=None, **fit_params):\n        _, n_features = x.shape\n        mask = np.in1d(np.arange(n_features), indices)\n        x = x[:, mask]\n        \n        if cv is None:\n            try:\n                estimator.fit(x, y, **fit_params)\n            except:\n                scores = np.nan\n            else:\n                scores = _score(estimator, x, y, scoring)\n            \n            scores = np.asarray([scores], dtype=np.float64)\n        else:\n            scores = cross_val_score(estimator, x, y, groups=groups, cv=cv, scoring=scoring, n_jobs=None, pre_dispatch='2*n_jobs', error_score=np.nan, fit_params=fit_params)\n        \n        return mask, scores\n\n    @staticmethod\n    def ncr(n, r):\n        \"\"\"Return the number of combinations of length r from n items.\n\n        :param n: int, Total number of items\n        :param r: int, Number of items to select from n\n        :return: Number of combinations, integer\n        \"\"\"\n        r = min(r, n - r)\n        if r == 0:\n            return 1\n        numerator = reduce(operator.mul, range(n, n - r, -1))\n        denominator = reduce(operator.mul, range(1, r + 1))\n        return numerator // denominator\n\n    @staticmethod\n    def _calc_confidence(scores, confidence=0.95):\n        std_err = sem(scores)\n        bound = std_err * t._ppf((1 + confidence) / 2.0, len(scores))\n        return bound, std_err\n\n    def fit(self, X, y, groups=None, **fit_params):\n        \"\"\"Perform feature selection and learn model from training data.\n\n        :param X: array-like of shape (n_samples, n_features)\n        :param y: array-like of shape (n_samples, ), Target values.\n        :param groups: array-like of shape (n_samples,), Group labels for the samples used while splitting the dataset into train/test set. Passed to the fit method of the cross-validator.\n        :param fit_params: dict, Parameters to pass to the fit method of classifier\n        :return: ExhaustiveFeatureSelector\n        \"\"\"\n        X, y = self._validate_params(X, y)\n        _, n_features = X.shape\n        min_features, max_features = self.min_features, self.max_features\n        candidates = chain.from_iterable(combinations(range(n_features), r=i) for i in range(min_features, max_features + 1))\n        # chain has no __len__ method\n        n_combinations = sum(self.ncr(n=n_features, r=i) for i in range(min_features, max_features + 1))\n\n        estimator = self.estimator\n        scoring = check_scoring(estimator, self.scoring)\n        cv = self.cv\n        n_jobs = self.n_jobs\n        pre_dispatch = self.pre_dispatch\n        parallel = Parallel(n_jobs=n_jobs, pre_dispatch=pre_dispatch)\n        work = enumerate(parallel(delayed(self._calc_score)(clone(estimator), X, y, c, groups=groups, scoring=scoring, cv=cv, **fit_params) for c in candidates))\n        \n        subset_info = []\n        append_subset_info = subset_info.append\n        try:\n            for iteration, (mask, cv_scores) in work:\n                avg_score = np.nanmean(cv_scores).item()\n                append_subset_info({\"support_mask\": mask, \"cv_scores\": cv_scores, \"avg_score\": avg_score})\n                if self.verbose:\n                    print(\"Feature set: %d/%d, avg score: %.3f\" % (iteration + 1, n_combinations, avg_score))\n        except KeyboardInterrupt:\n            print(\"Stopping early due to keyboard interrupt...\")\n        finally:\n            max_score = float(\"-inf\")\n            best_idx, best_info = -1, {}\n            for i, info in enumerate(subset_info):\n                if info[\"avg_score\"] > max_score:\n                    max_score = info[\"avg_score\"]\n                    best_idx, best_info = i, info\n            score = max_score\n            mask = best_info[\"support_mask\"]\n            self.subset_info_ = subset_info\n            self.support_mask_ = mask\n            self.best_idx_ = best_idx\n            self.best_score_ = score\n            self.best_feature_indices_ = np.where(mask)[0]\n            return self\n    \n    def _get_support_mask(self):\n        check_is_fitted(self, \"support_mask_\")\n        return self.support_mask_\n\n\nclass BorutaSelector(SelectorMixin):\n\n    def __init__(self):\n        # 对原始特征进行复制一份，并且将其按行进行随机打乱，称为Shadow Feature。将Shadow Feature与原始特征Real Feature进行横向拼接在一起，使用某种模型（随机森林、GBDT）进行计算特征重要性。将Shadow Feature中重要性最高的值为基准，删除Real Feature中重要性低于其的特征。多重复几个迭代。（一般来说随机生成的特征效果不如原始的，因此可以以Shadow Feature的特征重要性作为基准来判断Real Feature的好坏）\n        super().__init__()\n\n\nclass MICSelector(SelectorMixin):\n    pass\n\n\nclass FeatureImportanceSelector(SelectorMixin):\n    pass\n\n\nclass StabilitySelector(SelectorMixin):\n    pass\n\n\nclass REFSelector(SelectorMixin):\n    pass\n\n\nclass SequentialFeatureSelector(SelectorMixin):\n    pass\n\n\n# class SelectFromModel(SelectorMixin):\n#     pass\n"
  },
  {
    "path": "scorecardpipeline/financial.py",
    "content": "\"\"\"Some simple financial calculations.\n\npatterned after spreadsheet computations.\n\nThere is some complexity in each function\nso that the functions behave like ufuncs with\nbroadcasting and being able to be called with scalars\nor arrays (or other sequences).\n\nFunctions support the :class:`decimal.Decimal` type unless\notherwise stated.\n\"\"\"\n\nfrom decimal import Decimal\n\nimport numba as nb\nimport numpy as np\n\n__all__ = ['fv', 'pmt', 'nper', 'ipmt', 'ppmt', 'pv', 'rate',\n        'irr', 'npv', 'mirr',\n        'NoRealSolutionError', 'IterationsExceededError']\n\n_when_to_num = {'end': 0, 'begin': 1,\n                'e': 0, 'b': 1,\n                0: 0, 1: 1,\n                'beginning': 1,\n                'start': 1,\n                'finish': 0}\n\n\nclass NoRealSolutionError(Exception):\n    \"\"\"No real solution to the problem.\"\"\"\n\n\nclass IterationsExceededError(Exception):\n    \"\"\"Maximum number of iterations reached.\"\"\"\n\n\ndef _convert_when(when):\n    # Test to see if when has already been converted to ndarray\n    # This will happen if one function calls another, for example ppmt\n    if isinstance(when, np.ndarray):\n        return when\n    try:\n        return _when_to_num[when]\n    except (KeyError, TypeError):\n        return [_when_to_num[x] for x in when]\n\n\ndef _return_ufunc_like(array):\n    try:\n        # If size of array is one, return scalar\n        return array.item()\n    except ValueError:\n        # Otherwise, return entire array\n        return array\n\n\ndef _is_object_array(array):\n    return array.dtype == np.dtype(\"O\")\n\n\ndef _use_decimal_dtype(*arrays):\n    return any(_is_object_array(array) for array in arrays)\n\n\ndef _to_decimal_array_1d(array):\n    return np.array([Decimal(x) for x in array.tolist()])\n\n\ndef _to_decimal_array_2d(array):\n    decimals = [Decimal(x) for row in array.tolist() for x in row]\n    return np.array(decimals).reshape(array.shape)\n\n\ndef _get_output_array_shape(*arrays):\n    return tuple(array.shape[0] for array in arrays)\n\n\ndef fv(rate, nper, pmt, pv, when='end'):\n    \"\"\"Compute the future value.\n\n    Given:\n    * a present value, `pv`\n    * an interest `rate` compounded once per period, of which\n    there are\n    * `nper` total\n    * a (fixed) payment, `pmt`, paid either\n    * at the beginning (`when` = {'begin', 1}) or the end\n    (`when` = {'end', 0}) of each period\n\n    Return:\n    the value at the end of the `nper` periods\n\n    Parameters\n    ----------\n    rate : scalar or array_like of shape(M, )\n        Rate of interest as decimal (not per cent) per period\n    nper : scalar or array_like of shape(M, )\n        Number of compounding periods\n    pmt : scalar or array_like of shape(M, )\n        Payment\n    pv : scalar or array_like of shape(M, )\n        Present value\n    when : {{'begin', 1}, {'end', 0}}, {string, int}, optional\n        When payments are due ('begin' (1) or 'end' (0)).\n        Defaults to {'end', 0}.\n\n    Returns\n    -------\n    out : ndarray\n        Future values.  If all input is scalar, returns a scalar float.  If\n        any input is array_like, returns future values for each input element.\n        If multiple inputs are array_like, they all must have the same shape.\n\n    Notes\n    -----\n    The future value is computed by solving the equation::\n\n    fv +\n    pv*(1+rate)**nper +\n    pmt*(1 + rate*when)/rate*((1 + rate)**nper - 1) == 0\n\n    or, when ``rate == 0``::\n\n    fv + pv + pmt * nper == 0\n\n    References\n    ----------\n    .. [WRW] Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May).\n    Open Document Format for Office Applications (OpenDocument)v1.2,\n    Part 2: Recalculated Formula (OpenFormula) Format - Annotated Version,\n    Pre-Draft 12. Organization for the Advancement of Structured Information\n    Standards (OASIS). Billerica, MA, USA. [ODT Document].\n    Available:\n    http://www.oasis-open.org/committees/documents.php?wg_abbrev=office-formula\n    OpenDocument-formula-20090508.odt\n\n    Examples\n    --------\n    >>> import numpy as np\n    >>> import numpy_financial as npf\n\n    What is the future value after 10 years of saving $100 now, with\n    an additional monthly savings of $100.  Assume the interest rate is\n    5% (annually) compounded monthly?\n\n    >>> npf.fv(0.05/12, 10*12, -100, -100)\n    15692.92889433575\n\n    By convention, the negative sign represents cash flow out (i.e. money not\n    available today).  Thus, saving $100 a month at 5% annual interest leads\n    to $15,692.93 available to spend in 10 years.\n\n    If any input is array_like, returns an array of equal shape.  Let's\n    compare different interest rates from the example above.\n\n    >>> a = np.array((0.05, 0.06, 0.07))/12\n    >>> npf.fv(a, 10*12, -100, -100)\n    array([15692.92889434, 16569.87435405, 17509.44688102])\n\n    \"\"\"\n    when = _convert_when(when)\n    rate, nper, pmt, pv, when = np.broadcast_arrays(rate, nper, pmt, pv, when)\n\n    fv_array = np.empty_like(rate)\n    zero = rate == 0\n    nonzero = ~zero\n\n    fv_array[zero] = -(pv[zero] + pmt[zero] * nper[zero])\n\n    rate_nonzero = rate[nonzero]\n    temp = (1 + rate_nonzero) ** nper[nonzero]\n    fv_array[nonzero] = (\n            - pv[nonzero] * temp\n            - pmt[nonzero] * (1 + rate_nonzero * when[nonzero]) / rate_nonzero\n            * (temp - 1)\n    )\n\n    if np.ndim(fv_array) == 0:\n        # Follow the ufunc convention of returning scalars for scalar\n        # and 0d array inputs.\n        return fv_array.item(0)\n    return fv_array\n\n\ndef pmt(rate, nper, pv, fv=0, when='end'):\n    \"\"\"Compute the payment against loan principal plus interest.\n\n    Given:\n    * a present value, `pv` (e.g., an amount borrowed)\n    * a future value, `fv` (e.g., 0)\n    * an interest `rate` compounded once per period, of which\n    there are\n    * `nper` total\n    * and (optional) specification of whether payment is made\n    at the beginning (`when` = {'begin', 1}) or the end\n    (`when` = {'end', 0}) of each period\n\n    Return:\n    the (fixed) periodic payment.\n\n    Parameters\n    ----------\n    rate : array_like\n        Rate of interest (per period)\n    nper : array_like\n        Number of compounding periods\n    pv : array_like\n        Present value\n    fv : array_like,  optional\n        Future value (default = 0)\n    when : {{'begin', 1}, {'end', 0}}, {string, int}\n        When payments are due ('begin' (1) or 'end' (0))\n\n    Returns\n    -------\n    out : ndarray\n        Payment against loan plus interest.  If all input is scalar, returns a\n        scalar float.  If any input is array_like, returns payment for each\n        input element. If multiple inputs are array_like, they all must have\n        the same shape.\n\n    Notes\n    -----\n    The payment is computed by solving the equation::\n\n    fv +\n    pv*(1 + rate)**nper +\n    pmt*(1 + rate*when)/rate*((1 + rate)**nper - 1) == 0\n\n    or, when ``rate == 0``::\n\n    fv + pv + pmt * nper == 0\n\n    for ``pmt``.\n\n    Note that computing a monthly mortgage payment is only\n    one use for this function.  For example, pmt returns the\n    periodic deposit one must make to achieve a specified\n    future balance given an initial deposit, a fixed,\n    periodically compounded interest rate, and the total\n    number of periods.\n\n    References\n    ----------\n    .. [WRW] Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May).\n    Open Document Format for Office Applications (OpenDocument)v1.2,\n    Part 2: Recalculated Formula (OpenFormula) Format - Annotated Version,\n    Pre-Draft 12. Organization for the Advancement of Structured Information\n    Standards (OASIS). Billerica, MA, USA. [ODT Document].\n    Available:\n    http://www.oasis-open.org/committees/documents.php\n    ?wg_abbrev=office-formulaOpenDocument-formula-20090508.odt\n\n    Examples\n    --------\n    >>> import numpy_financial as npf\n\n    What is the monthly payment needed to pay off a $200,000 loan in 15\n    years at an annual interest rate of 7.5%?\n\n    >>> npf.pmt(0.075/12, 12*15, 200000)\n    -1854.0247200054619\n\n    In order to pay-off (i.e., have a future-value of 0) the $200,000 obtained\n    today, a monthly payment of $1,854.02 would be required.  Note that this\n    example illustrates usage of `fv` having a default value of 0.\n\n    \"\"\"\n    when = _convert_when(when)\n    (rate, nper, pv, fv, when) = map(np.array, [rate, nper, pv, fv, when])\n    temp = (1 + rate) ** nper\n    mask = (rate == 0)\n    masked_rate = np.where(mask, 1, rate)\n    fact = np.where(mask != 0, nper,\n                    (1 + masked_rate * when) * (temp - 1) / masked_rate)\n    return -(fv + pv * temp) / fact\n\n\ndef nper(rate, pmt, pv, fv=0, when='end'):\n    \"\"\"Compute the number of periodic payments.\n\n    :class:`decimal.Decimal` type is not supported.\n\n    Parameters\n    ----------\n    rate : array_like\n        Rate of interest (per period)\n    pmt : array_like\n        Payment\n    pv : array_like\n        Present value\n    fv : array_like, optional\n        Future value\n    when : {{'begin', 1}, {'end', 0}}, {string, int}, optional\n        When payments are due ('begin' (1) or 'end' (0))\n\n    Notes\n    -----\n    The number of periods ``nper`` is computed by solving the equation::\n\n    fv + pv*(1+rate)**nper + pmt*(1+rate*when)/rate*((1+rate)**nper-1) = 0\n\n    but if ``rate = 0`` then::\n\n    fv + pv + pmt*nper = 0\n\n    Examples\n    --------\n    >>> import numpy as np\n    >>> import numpy_financial as npf\n\n    If you only had $150/month to pay towards the loan, how long would it take\n    to pay-off a loan of $8,000 at 7% annual interest?\n\n    >>> print(np.round(npf.nper(0.07/12, -150, 8000), 5))\n    64.07335\n\n    So, over 64 months would be required to pay off the loan.\n\n    The same analysis could be done with several different interest rates\n    and/or payments and/or total amounts to produce an entire table.\n\n    >>> npf.nper(*(np.ogrid[0.07/12: 0.08/12: 0.01/12,\n    ...                     -150   : -99    : 50    ,\n    ...                     8000   : 9001   : 1000]))\n    array([[[ 64.07334877,  74.06368256],\n            [108.07548412, 127.99022654]],\n    \n        [[ 66.12443902,  76.87897353],\n            [114.70165583, 137.90124779]]])\n    \"\"\"\n    when = _convert_when(when)\n    rate, pmt, pv, fv, when = np.broadcast_arrays(rate, pmt, pv, fv, when)\n    nper_array = np.empty_like(rate, dtype=np.float64)\n\n    zero = rate == 0\n    nonzero = ~zero\n\n    with np.errstate(divide='ignore'):\n        # Infinite numbers of payments are okay, so ignore the\n        # potential divide by zero.\n        nper_array[zero] = -(fv[zero] + pv[zero]) / pmt[zero]\n\n    nonzero_rate = rate[nonzero]\n    z = pmt[nonzero] * (1 + nonzero_rate * when[nonzero]) / nonzero_rate\n    nper_array[nonzero] = (\n            np.log((-fv[nonzero] + z) / (pv[nonzero] + z))\n            / np.log(1 + nonzero_rate)\n    )\n\n    return nper_array\n\n\ndef _value_like(arr, value):\n    entry = arr.item(0)\n    if isinstance(entry, Decimal):\n        return Decimal(value)\n    return np.array(value, dtype=arr.dtype).item(0)\n\n\ndef ipmt(rate, per, nper, pv, fv=0, when='end'):\n    \"\"\"Compute the interest portion of a payment.\n\n    Parameters\n    ----------\n    rate : scalar or array_like of shape(M, )\n        Rate of interest as decimal (not per cent) per period\n    per : scalar or array_like of shape(M, )\n        Interest paid against the loan changes during the life or the loan.\n        The `per` is the payment period to calculate the interest amount.\n    nper : scalar or array_like of shape(M, )\n        Number of compounding periods\n    pv : scalar or array_like of shape(M, )\n        Present value\n    fv : scalar or array_like of shape(M, ), optional\n        Future value\n    when : {{'begin', 1}, {'end', 0}}, {string, int}, optional\n        When payments are due ('begin' (1) or 'end' (0)).\n        Defaults to {'end', 0}.\n\n    Returns\n    -------\n    out : ndarray\n        Interest portion of payment.  If all input is scalar, returns a scalar\n        float.  If any input is array_like, returns interest payment for each\n        input element. If multiple inputs are array_like, they all must have\n        the same shape.\n\n    See Also\n    --------\n    ppmt, pmt, pv\n\n    Notes\n    -----\n    The total payment is made up of payment against principal plus interest.\n\n    ``pmt = ppmt + ipmt``\n\n    Examples\n    --------\n    >>> import numpy as np\n    >>> import numpy_financial as npf\n\n    What is the amortization schedule for a 1 year loan of $2500 at\n    8.24% interest per year compounded monthly?\n\n    >>> principal = 2500.00\n\n    The 'per' variable represents the periods of the loan.  Remember that\n    financial equations start the period count at 1!\n\n    >>> per = np.arange(1*12) + 1\n    >>> ipmt = npf.ipmt(0.0824/12, per, 1*12, principal)\n    >>> ppmt = npf.ppmt(0.0824/12, per, 1*12, principal)\n\n    Each element of the sum of the 'ipmt' and 'ppmt' arrays should equal\n    'pmt'.\n\n    >>> pmt = npf.pmt(0.0824/12, 1*12, principal)\n    >>> np.allclose(ipmt + ppmt, pmt)\n    True\n\n    >>> fmt = '{0:2d} {1:8.2f} {2:8.2f} {3:8.2f}'\n    >>> for payment in per:\n    ...     index = payment - 1\n    ...     principal = principal + ppmt[index]\n    ...     print(fmt.format(payment, ppmt[index], ipmt[index], principal))\n    1  -200.58   -17.17  2299.42\n    2  -201.96   -15.79  2097.46\n    3  -203.35   -14.40  1894.11\n    4  -204.74   -13.01  1689.37\n    5  -206.15   -11.60  1483.22\n    6  -207.56   -10.18  1275.66\n    7  -208.99    -8.76  1066.67\n    8  -210.42    -7.32   856.25\n    9  -211.87    -5.88   644.38\n    10  -213.32    -4.42   431.05\n    11  -214.79    -2.96   216.26\n    12  -216.26    -1.49    -0.00\n\n    >>> interestpd = np.sum(ipmt)\n    >>> np.round(interestpd, 2)\n    -112.98\n\n    \"\"\"\n    when = _convert_when(when)\n    rate, per, nper, pv, fv, when = np.broadcast_arrays(rate, per, nper,\n                                                        pv, fv, when)\n\n    total_pmt = pmt(rate, nper, pv, fv, when)\n    ipmt_array = np.array(_rbl(rate, per, total_pmt, pv, when) * rate)\n\n    # Payments start at the first period, so payments before that\n    # don't make any sense.\n    ipmt_array[per < 1] = _value_like(ipmt_array, np.nan)\n    # If payments occur at the beginning of a period and this is the\n    # first period, then no interest has accrued.\n    per1_and_begin = (when == 1) & (per == 1)\n    ipmt_array[per1_and_begin] = _value_like(ipmt_array, 0)\n    # If paying at the beginning we need to discount by one period.\n    per_gt_1_and_begin = (when == 1) & (per > 1)\n    ipmt_array[per_gt_1_and_begin] = (\n            ipmt_array[per_gt_1_and_begin] / (1 + rate[per_gt_1_and_begin])\n    )\n\n    if np.ndim(ipmt_array) == 0:\n        # Follow the ufunc convention of returning scalars for scalar\n        # and 0d array inputs.\n        return ipmt_array.item(0)\n    return ipmt_array\n\n\ndef _rbl(rate, per, pmt, pv, when):\n    \"\"\"Remaining balance on loan.\n\n    This function is here to simply have a different name for the 'fv'\n    function to not interfere with the 'fv' keyword argument within the 'ipmt'\n    function.  It is the 'remaining balance on loan' which might be useful as\n    it's own function, but is easily calculated with the 'fv' function.\n    \"\"\"\n    return fv(rate, (per - 1), pmt, pv, when)\n\n\ndef ppmt(rate, per, nper, pv, fv=0, when='end'):\n    \"\"\"Compute the payment against loan principle.\n\n    Parameters\n    ----------\n    rate : array_like\n        Rate of interest (per period)\n    per : array_like, int\n        Amount paid against the loan changes.  The `per` is the period of\n        interest.\n    nper : array_like\n        Number of compounding periods\n    pv : array_like\n        Present value\n    fv : array_like, optional\n        Future value\n    when : {{'begin', 1}, {'end', 0}}, {string, int}\n        When payments are due ('begin' (1) or 'end' (0))\n\n    See Also\n    --------\n    pmt, pv, ipmt\n\n    \"\"\"\n    total = pmt(rate, nper, pv, fv, when)\n    return total - ipmt(rate, per, nper, pv, fv, when)\n\n\ndef pv(rate, nper, pmt, fv=0, when='end'):\n    \"\"\"Compute the present value.\n\n    Given:\n    * a future value, `fv`\n    * an interest `rate` compounded once per period, of which\n    there are\n    * `nper` total\n    * a (fixed) payment, `pmt`, paid either\n    * at the beginning (`when` = {'begin', 1}) or the end\n    (`when` = {'end', 0}) of each period\n\n    Return:\n    the value now\n\n    Parameters\n    ----------\n    rate : array_like\n        Rate of interest (per period)\n    nper : array_like\n        Number of compounding periods\n    pmt : array_like\n        Payment\n    fv : array_like, optional\n        Future value\n    when : {{'begin', 1}, {'end', 0}}, {string, int}, optional\n        When payments are due ('begin' (1) or 'end' (0))\n\n    Returns\n    -------\n    out : ndarray, float\n        Present value of a series of payments or investments.\n\n    Notes\n    -----\n    The present value is computed by solving the equation::\n\n    fv +\n    pv*(1 + rate)**nper +\n    pmt*(1 + rate*when)/rate*((1 + rate)**nper - 1) = 0\n\n    or, when ``rate = 0``::\n\n    fv + pv + pmt * nper = 0\n\n    for `pv`, which is then returned.\n\n    References\n    ----------\n    .. [WRW] Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May).\n    Open Document Format for Office Applications (OpenDocument)v1.2,\n    Part 2: Recalculated Formula (OpenFormula) Format - Annotated Version,\n    Pre-Draft 12. Organization for the Advancement of Structured Information\n    Standards (OASIS). Billerica, MA, USA. [ODT Document].\n    Available:\n    http://www.oasis-open.org/committees/documents.php?wg_abbrev=office-formula\n    OpenDocument-formula-20090508.odt\n\n    Examples\n    --------\n    >>> import numpy as np\n    >>> import numpy_financial as npf\n\n    What is the present value (e.g., the initial investment)\n    of an investment that needs to total $15692.93\n    after 10 years of saving $100 every month?  Assume the\n    interest rate is 5% (annually) compounded monthly.\n\n    >>> npf.pv(0.05/12, 10*12, -100, 15692.93)\n    -100.00067131625819\n\n    By convention, the negative sign represents cash flow out\n    (i.e., money not available today).  Thus, to end up with\n    $15,692.93 in 10 years saving $100 a month at 5% annual\n    interest, one's initial deposit should also be $100.\n\n    If any input is array_like, ``pv`` returns an array of equal shape.\n    Let's compare different interest rates in the example above:\n\n    >>> a = np.array((0.05, 0.04, 0.03))/12\n    >>> npf.pv(a, 10*12, -100, 15692.93)\n    array([ -100.00067132,  -649.26771385, -1273.78633713])\n\n    So, to end up with the same $15692.93 under the same $100 per month\n    \"savings plan,\" for annual interest rates of 4% and 3%, one would\n    need initial investments of $649.27 and $1273.79, respectively.\n\n    \"\"\"\n    when = _convert_when(when)\n    (rate, nper, pmt, fv, when) = map(np.asarray, [rate, nper, pmt, fv, when])\n    temp = (1 + rate) ** nper\n    fact = np.where(rate == 0, nper, (1 + rate * when) * (temp - 1) / rate)\n    return -(fv + pmt * fact) / temp\n\n\n# Computed with Sage\n#  (y + (r + 1)^n*x + p*((r + 1)^n - 1)*(r*w + 1)/r)/(n*(r + 1)^(n - 1)*x -\n#  p*((r + 1)^n - 1)*(r*w + 1)/r^2 + n*p*(r + 1)^(n - 1)*(r*w + 1)/r +\n#  p*((r + 1)^n - 1)*w/r)\n\n\ndef _g_div_gp(r, n, p, x, y, w):\n    # Evaluate g(r_n)/g'(r_n), where g =\n    # fv + pv*(1+rate)**nper + pmt*(1+rate*when)/rate * ((1+rate)**nper - 1)\n    t1 = (r + 1) ** n\n    t2 = (r + 1) ** (n - 1)\n    g = y + t1 * x + p * (t1 - 1) * (r * w + 1) / r\n    gp = (n * t2 * x\n        - p * (t1 - 1) * (r * w + 1) / (r ** 2)\n        + n * p * t2 * (r * w + 1) / r\n        + p * (t1 - 1) * w / r)\n    return g / gp\n\n\n# Use Newton's iteration until the change is less than 1e-6\n#  for all values or a maximum of 100 iterations is reached.\n#  Newton's rule is\n#  r_{n+1} = r_{n} - g(r_n)/g'(r_n)\n#     where\n#  g(r) is the formula\n#  g'(r) is the derivative with respect to r.\ndef rate(\n        nper,\n        pmt,\n        pv,\n        fv,\n        when='end',\n        guess=None,\n        tol=None,\n        maxiter=100,\n        *,\n        raise_exceptions=False):\n    \"\"\"Compute the rate of interest per period.\n\n    Parameters\n    ----------\n    nper : array_like\n        Number of compounding periods\n    pmt : array_like\n        Payment\n    pv : array_like\n        Present value\n    fv : array_like\n        Future value\n    when : {{'begin', 1}, {'end', 0}}, {string, int}, optional\n        When payments are due ('begin' (1) or 'end' (0))\n    guess : Number, optional\n        Starting guess for solving the rate of interest, default 0.1\n    tol : Number, optional\n        Required tolerance for the solution, default 1e-6\n    maxiter : int, optional\n        Maximum iterations in finding the solution\n    raise_exceptions: bool, optional\n        Flag to raise an exception when at least one of the rates\n        cannot be computed due to having reached the maximum number of\n        iterations (IterationsExceededException). Set to False as default,\n        thus returning NaNs for those rates.\n\n    Notes\n    -----\n    The rate of interest is computed by iteratively solving the\n    (non-linear) equation::\n\n    fv + pv*(1+rate)**nper + pmt*(1+rate*when)/rate * ((1+rate)**nper - 1) = 0\n\n    for ``rate``.\n\n    References\n    ----------\n    Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May). Open Document\n    Format for Office Applications (OpenDocument)v1.2, Part 2: Recalculated\n    Formula (OpenFormula) Format - Annotated Version, Pre-Draft 12.\n    Organization for the Advancement of Structured Information Standards\n    (OASIS). Billerica, MA, USA. [ODT Document]. Available:\n    http://www.oasis-open.org/committees/documents.php?wg_abbrev=office-formula\n    OpenDocument-formula-20090508.odt\n\n    \"\"\"\n    when = _convert_when(when)\n    default_type = Decimal if isinstance(pmt, Decimal) else float\n\n    # Handle casting defaults to Decimal if/when pmt is a Decimal and\n    # guess and/or tol are not given default values\n    if guess is None:\n        guess = default_type('0.1')\n\n    if tol is None:\n        tol = default_type('1e-6')\n\n    (nper, pmt, pv, fv, when) = map(np.asarray, [nper, pmt, pv, fv, when])\n\n    rn = guess\n    iterator = 0\n    close = False\n    while (iterator < maxiter) and not np.all(close):\n        rnp1 = rn - _g_div_gp(rn, nper, pmt, pv, fv, when)\n        diff = abs(rnp1 - rn)\n        close = diff < tol\n        iterator += 1\n        rn = rnp1\n\n    if not np.all(close):\n        if np.isscalar(rn):\n            if raise_exceptions:\n                raise IterationsExceededError('Maximum number of iterations exceeded.')\n            return default_type(np.nan)\n        else:\n            # Return nan's in array of the same shape as rn\n            # where the solution is not close to tol.\n            if raise_exceptions:\n                raise IterationsExceededError(f'Maximum iterations exceeded in '\n                                            f'{len(close) - close.sum()} rate(s).')\n            rn[~close] = np.nan\n    return rn\n\n\ndef irr(values, *, guess=None, tol=1e-12, maxiter=100, raise_exceptions=False):\n    r\"\"\"Return the Internal Rate of Return (IRR).\n\n    This is the \"average\" periodically compounded rate of return\n    that gives a net present value of 0.0; for a more complete explanation,\n    see Notes below.\n\n    :class:`decimal.Decimal` type is not supported.\n\n    Parameters\n    ----------\n    values : array_like, shape(N,)\n        Input cash flows per time period.  By convention, net \"deposits\"\n        are negative and net \"withdrawals\" are positive.  Thus, for\n        example, at least the first element of `values`, which represents\n        the initial investment, will typically be negative.\n    guess : float, optional\n        Initial guess of the IRR for the iterative solver. If no guess is\n        given an heuristic is used to estimate the guess through the ratio of\n        positive to negative cash lows\n    tol : float, optional\n        Required tolerance to accept solution. Default is 1e-12.\n    maxiter : int, optional\n        Maximum iterations to perform in finding a solution. Default is 100.\n    raise_exceptions: bool, optional\n        Flag to raise an exception when the irr cannot be computed due to\n        either having all cashflows of the same sign (NoRealSolutionException) or\n        having reached the maximum number of iterations (IterationsExceededException).\n        Set to False as default, thus returning NaNs in the two previous\n        cases.\n\n    Returns\n    -------\n    out : float\n        Internal Rate of Return for periodic input values.\n\n    Notes\n    -----\n    The IRR is perhaps best understood through an example (illustrated\n    using np.irr in the Examples section below). Suppose one invests 100\n    units and then makes the following withdrawals at regular (fixed)\n    intervals: 39, 59, 55, 20.  Assuming the ending value is 0, one's 100\n    unit investment yields 173 units; however, due to the combination of\n    compounding and the periodic withdrawals, the \"average\" rate of return\n    is neither simply 0.73/4 nor (1.73)^0.25-1.  Rather, it is the solution\n    (for :math:`r`) of the equation:\n\n    .. math:: -100 + \\\\frac{39}{1+r} + \\\\frac{59}{(1+r)^2}\n    + \\\\frac{55}{(1+r)^3} + \\\\frac{20}{(1+r)^4} = 0\n\n    In general, for `values` :math:`= [v_0, v_1, ... v_M]`,\n    irr is the solution of the equation: [G]_\n\n    .. math:: \\\\sum_{t=0}^M{\\\\frac{v_t}{(1+irr)^{t}}} = 0\n\n    References\n    ----------\n    .. [G] L. J. Gitman, \"Principles of Managerial Finance, Brief,\" 3rd ed.,\n    Addison-Wesley, 2003, pg. 348.\n\n    Examples\n    --------\n    >>> import numpy_financial as npf\n\n    >>> round(npf.irr([-100, 39, 59, 55, 20]), 5)\n    0.28095\n    >>> round(npf.irr([-100, 0, 0, 74]), 5)\n    -0.0955\n    >>> round(npf.irr([-100, 100, 0, -7]), 5)\n    -0.0833\n    >>> round(npf.irr([-100, 100, 0, 7]), 5)\n    0.06206\n    >>> round(npf.irr([-5, 10.5, 1, -8, 1]), 5)\n    0.0886\n\n    \"\"\"\n    values = np.atleast_1d(values)\n    if values.ndim != 1:\n        raise ValueError(\"Cashflows must be a rank-1 array\")\n\n    # If all values are of the same sign no solution exists\n    # we don't perform any further calculations and exit early\n    same_sign = np.all(values > 0) if values[0] > 0 else np.all(values < 0)\n    if same_sign:\n        if raise_exceptions:\n            raise NoRealSolutionError('No real solution exists for IRR since all '\n                                    'cashflows are of the same sign.')\n        return np.nan\n\n    # If no value is passed for `guess`, then make a heuristic estimate\n    if guess is None:\n        positive_cashflow = values > 0\n        inflow = values.sum(where=positive_cashflow)\n        outflow = -values.sum(where=~positive_cashflow)\n        guess = inflow / outflow - 1\n\n    # We aim to solve eirr such that NPV is exactly zero. This can be framed as\n    # simply finding the closest root of a polynomial to a given initial guess\n    # as follows:\n    #           V0           V1           V2           V3\n    # NPV = ---------- + ---------- + ---------- + ---------- + ... = 0\n    #       (1+eirr)^0   (1+eirr)^1   (1+eirr)^2   (1+eirr)^3\n    #\n    # by letting g = (1+eirr), we substitute to get\n    #\n    # NPV = V0 * 1/g^0   + V1 * 1/g^1   +  V2 * 1/x^2  +  V3 * 1/g^3  + ... = 0\n    #\n    # Multiplying by g^N this becomes\n    #\n    # V0 * g^N   + V1 * g^{N-1}   +  V2 * g^{N-2}  +  V3 * g^{N-3}  + ... = 0\n    #\n    # which we solve using Newton-Raphson and then reverse out the solution\n    # as eirr = g - 1 (if we are close enough to a solution)\n    npv_ = np.polynomial.Polynomial(values[::-1])\n    d_npv = npv_.deriv()\n    g = 1 + guess\n\n    for _ in range(maxiter):\n        delta = npv_(g) / d_npv(g)\n        if abs(delta) < tol:\n            return g - 1\n        g -= delta\n\n    if raise_exceptions:\n        raise IterationsExceededError('Maximum number of iterations exceeded.')\n\n    return np.nan\n\n\n@nb.njit(parallel=True)\ndef _npv_native(rates, values, out):\n    for i in nb.prange(rates.shape[0]):\n        for j in nb.prange(values.shape[0]):\n            acc = 0.0\n            for t in range(values.shape[1]):\n                acc += values[j, t] / ((1.0 + rates[i]) ** t)\n            out[i, j] = acc\n\n\n# We require ``forceobj=True`` here to support decimal.Decimal types\n@nb.jit(forceobj=True)\ndef _npv_decimal(rates, values, out):\n    for i in range(rates.shape[0]):\n        for j in range(values.shape[0]):\n            acc = Decimal(\"0.0\")\n            for t in range(values.shape[1]):\n                acc += values[j, t] / ((Decimal(\"1.0\") + rates[i]) ** t)\n            out[i, j] = acc\n\n\ndef npv(rate, values):\n    r\"\"\"Return the NPV (Net Present Value) of a cash flow series.\n\n    Parameters\n    ----------\n    rate : scalar or array_like shape(K, )\n        The discount rate.\n    values : array_like, shape(M, ) or shape(M, N)\n        The values of the time series of cash flows.  The (fixed) time\n        interval between cash flow \"events\" must be the same as that for\n        which `rate` is given (i.e., if `rate` is per year, then precisely\n        a year is understood to elapse between each cash flow event).  By\n        convention, investments or \"deposits\" are negative, income or\n        \"withdrawals\" are positive; `values` must begin with the initial\n        investment, thus `values[0]` will typically be negative.\n\n    Returns\n    -------\n    out : float or array shape(K, M)\n        The NPV of the input cash flow series `values` at the discount\n        `rate`. `out` follows the ufunc convention of returning scalars\n        instead of single element arrays.\n\n    Warnings\n    --------\n    ``npv`` considers a series of cashflows starting in the present (t = 0).\n    NPV can also be defined with a series of future cashflows, paid at the\n    end, rather than the start, of each period. If future cashflows are used,\n    the first cashflow `values[0]` must be zeroed and added to the net\n    present value of the future cashflows. This is demonstrated in the\n    examples.\n\n    Notes\n    -----\n    Returns the result of: [G]_\n\n    .. math :: \\\\sum_{t=0}^{M-1}{\\\\frac{values_t}{(1+rate)^{t}}}\n\n    References\n    ----------\n    .. [G] L. J. Gitman, \"Principles of Managerial Finance, Brief,\" 3rd ed.,\n    Addison-Wesley, 2003, pg. 346.\n\n    Examples\n    --------\n    >>> import numpy as np\n    >>> import numpy_financial as npf\n\n    Consider a potential project with an initial investment of $40 000 and\n    projected cashflows of $5 000, $8 000, $12 000 and $30 000 at the end of\n    each period discounted at a rate of 8% per period. To find the project's\n    net present value:\n\n    >>> rate, cashflows = 0.08, [-40_000, 5_000, 8_000, 12_000, 30_000]\n    >>> np.round(npf.npv(rate, cashflows), 5)\n    3065.22267\n\n    It may be preferable to split the projected cashflow into an initial\n    investment and expected future cashflows. In this case, the value of\n    the initial cashflow is zero and the initial investment is later added\n    to the future cashflows net present value:\n\n    >>> initial_cashflow = cashflows[0]\n    >>> cashflows[0] = 0\n    >>> np.round(npf.npv(rate, cashflows) + initial_cashflow, 5)\n    3065.22267\n\n    The NPV calculation may be applied to several ``rates`` and ``cashflows``\n    simulatneously. This produces an array of shape\n    ``(len(rates), len(cashflows))``.\n\n    >>> rates = [0.00, 0.05, 0.10]\n    >>> cashflows = [[-4_000, 500, 800], [-5_000, 600, 900]]\n    >>> npf.npv(rates, cashflows).round(2)\n    array([[-2700.  , -3500.  ],\n        [-2798.19, -3612.24],\n        [-2884.3 , -3710.74]])\n\n    The NPV calculation also supports `decimal.Decimal` types, for example\n    if using Decimal ``rates``:\n\n    >>> rates = [Decimal(\"0.00\"), Decimal(\"0.05\"), Decimal(\"0.10\")]\n    >>> cashflows = [[-4_000, 500, 800], [-5_000, 600, 900]]\n    >>> npf.npv(rates, cashflows)\n    array([[Decimal('-2700.0'), Decimal('-3500.0')],\n        [Decimal('-2798.185941043083900226757370'),\n            Decimal('-3612.244897959183673469387756')],\n        [Decimal('-2884.297520661157024793388430'),\n            Decimal('-3710.743801652892561983471074')]], dtype=object)\n\n    This also works for Decimal cashflows.\n\n    \"\"\"\n    rates = np.atleast_1d(rate)\n    values = np.atleast_2d(values)\n\n    if rates.ndim != 1:\n        msg = \"invalid shape for rates. Rate must be either a scalar or 1d array\"\n        raise ValueError(msg)\n\n    if values.ndim != 2:\n        msg = \"invalid shape for values. Values must be either a 1d or 2d array\"\n        raise ValueError(msg)\n\n    dtype = Decimal if _use_decimal_dtype(rates, values) else np.float64\n\n    if dtype == Decimal:\n        rates = _to_decimal_array_1d(rates)\n        values = _to_decimal_array_2d(values)\n\n    shape = _get_output_array_shape(rates, values)\n    out = np.empty(shape=shape, dtype=dtype)\n\n    if dtype == Decimal:\n        _npv_decimal(rates, values, out)\n    else:\n        _npv_native(rates, values, out)\n\n    return _return_ufunc_like(out)\n\n\ndef mirr(values, finance_rate, reinvest_rate, *, raise_exceptions=False):\n    r\"\"\"\n    Return the Modified Internal Rate of Return (MIRR).\n\n    MIRR is a financial metric that takes into account both the cost of\n    the investment and the return on reinvested cash flows. It is useful\n    for evaluating the profitability of an investment with multiple cash\n    inflows and outflows.\n\n    Parameters\n    ----------\n    values : array_like\n        Cash flows, where the first value is considered a sunk cost at time zero.\n        It must contain at least one positive and one negative value.\n    finance_rate : scalar\n        Interest rate paid on the cash flows.\n    reinvest_rate : scalar\n        Interest rate received on the cash flows upon reinvestment.\n    raise_exceptions: bool, optional\n        Flag to raise an exception when the MIRR cannot be computed due to\n        having all cash flows of the same sign (NoRealSolutionException).\n        Set to False as default,thus returning NaNs in the previous case.\n\n    Returns\n    -------\n    out : float\n        Modified internal rate of return\n\n    Notes\n    -----\n    The MIRR formula is as follows:\n\n    .. math::\n\n        MIRR = \n        \\\\left( \\\\frac{{FV_{positive}}}{{PV_{negative}}} \\\\right)^{\\\\frac{{1}}{{n-1}}}\n        * (1+r) - 1\n\n    where:\n        - \\(FV_{positive}\\) is the future value of positive cash flows,\n        - \\(PV_{negative}\\) is the present value of negative cash flows,\n        - \\(n\\) is the number of periods.\n        - \\(r\\) is the reinvestment rate.\n\n    Examples\n    --------\n    >>> import numpy_financial as npf\n\n    Consider a project with an initial investment of -$100 \n    and projected cash flows of $50, -$60, and $70 at the end of each period. \n    The project has a finance rate of 10% and a reinvestment rate of 12%.\n\n    >>> npf.mirr([-100, 50, -60, 70], 0.10, 0.12)\n    -0.03909366594356467\n\n    Now, let's consider the scenario where all cash flows are negative.\n\n    >>> npf.mirr([-100, -50, -60, -70], 0.10, 0.12)\n    nan\n\n    Finally, let's explore the situation where all cash flows are positive, \n    and the `raise_exceptions` parameter is set to True.\n\n    >>> npf.mirr([\n    ...    100, 50, 60, 70], \n    ...    0.10, 0.12, \n    ...    raise_exceptions=True\n    ... ) #doctest: +NORMALIZE_WHITESPACE\n    Traceback (most recent call last):\n        ...\n    numpy_financial._financial.NoRealSolutionError: \n    No real solution exists for MIRR since  all cashflows are of the same sign.\n    \"\"\"\n    values = np.asarray(values)\n    n = values.size\n\n    # Without this explicit cast the 1/(n - 1) computation below\n    # becomes a float, which causes TypeError when using Decimal\n    # values.\n    if isinstance(finance_rate, Decimal):\n        n = Decimal(n)\n\n    pos = values > 0\n    neg = values < 0\n    if not (pos.any() and neg.any()):\n        if raise_exceptions:\n            raise NoRealSolutionError('No real solution exists for MIRR since'\n                                    ' all cashflows are of the same sign.')\n        return np.nan\n    numer = np.abs(npv(reinvest_rate, values * pos))\n    denom = np.abs(npv(finance_rate, values * neg))\n    return (numer / denom) ** (1 / (n - 1)) * (1 + reinvest_rate) - 1\n"
  },
  {
    "path": "scorecardpipeline/germancredit.csv",
    "content": "status_of_existing_checking_account,duration_in_month,credit_history,purpose,credit_amount,savings_account_and_bonds,present_employment_since,installment_rate_in_percentage_of_disposable_income,personal_status_and_sex,other_debtors_or_guarantors,present_residence_since,property,age_in_years,other_installment_plans,housing,number_of_existing_credits_at_this_bank,job,number_of_people_being_liable_to_provide_maintenance_for,telephone,foreign_worker,creditability\r\n... < 0 DM,6,critical account/ other credits existing (not at this bank),radio/television,1169,unknown/ no savings account,... >= 7 years,4,male : divorced/separated,none,4,real estate,67,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,48,existing credits paid back duly till now,radio/television,5951,... < 100 DM,1 <= ... < 4 years,2,male : divorced/separated,none,2,real estate,22,none,own,1,skilled employee / official,1,none,yes,bad\r\nno checking account,12,critical account/ other credits existing (not at this bank),education,2096,... < 100 DM,4 <= ... < 7 years,2,male : divorced/separated,none,3,real estate,49,none,own,1,unskilled - resident,2,none,yes,good\r\n... < 0 DM,42,existing credits paid back duly till now,furniture/equipment,7882,... < 100 DM,4 <= ... < 7 years,2,male : divorced/separated,guarantor,4,building society savings agreement/ life insurance,45,none,for free,1,skilled employee / official,2,none,yes,good\r\n... < 0 DM,24,delay in paying off in the past,car (new),4870,... < 100 DM,1 <= ... < 4 years,3,male : divorced/separated,none,4,unknown / no property,53,none,for free,2,skilled employee / official,2,none,yes,bad\r\nno checking account,36,existing credits paid back duly till now,education,9055,unknown/ no savings account,1 <= ... < 4 years,2,male : divorced/separated,none,4,unknown / no property,35,none,for free,1,unskilled - resident,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,24,existing credits paid back duly till now,furniture/equipment,2835,500 <= ... < 1000 DM,... >= 7 years,3,male : divorced/separated,none,4,building society savings agreement/ life insurance,53,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,36,existing credits paid back duly till now,car (used),6948,... < 100 DM,1 <= ... < 4 years,2,male : divorced/separated,none,2,\"car or other, not in attribute Savings account/bonds\",35,none,rent,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,existing credits paid back duly till now,radio/television,3059,... >= 1000 DM,4 <= ... < 7 years,2,male : divorced/separated,none,4,real estate,61,none,own,1,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,30,critical account/ other credits existing (not at this bank),car (new),5234,... < 100 DM,unemployed,4,male : divorced/separated,none,2,\"car or other, not in attribute Savings account/bonds\",28,none,own,2,management/ self-employed/ highly qualified employee/ officer,1,none,yes,bad\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,car (new),1295,... < 100 DM,... < 1 year,3,male : divorced/separated,none,1,\"car or other, not in attribute Savings account/bonds\",25,none,rent,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,48,existing credits paid back duly till now,business,4308,... < 100 DM,... < 1 year,3,male : divorced/separated,none,4,building society savings agreement/ life insurance,24,none,rent,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,radio/television,1567,... < 100 DM,1 <= ... < 4 years,1,male : divorced/separated,none,1,\"car or other, not in attribute Savings account/bonds\",22,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,24,critical account/ other credits existing (not at this bank),car (new),1199,... < 100 DM,... >= 7 years,4,male : divorced/separated,none,4,\"car or other, not in attribute Savings account/bonds\",60,none,own,2,unskilled - resident,1,none,yes,bad\r\n... < 0 DM,15,existing credits paid back duly till now,car (new),1403,... < 100 DM,1 <= ... < 4 years,2,male : divorced/separated,none,4,\"car or other, not in attribute Savings account/bonds\",28,none,rent,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,24,existing credits paid back duly till now,radio/television,1282,100 <= ... < 500 DM,1 <= ... < 4 years,4,male : divorced/separated,none,2,\"car or other, not in attribute Savings account/bonds\",32,none,own,1,unskilled - resident,1,none,yes,bad\r\nno checking account,24,critical account/ other credits existing (not at this bank),radio/television,2424,unknown/ no savings account,... >= 7 years,4,male : divorced/separated,none,4,building society savings agreement/ life insurance,53,none,own,2,skilled employee / official,1,none,yes,good\r\n... < 0 DM,30,no credits taken/ all credits paid back duly,business,8072,unknown/ no savings account,... < 1 year,2,male : divorced/separated,none,3,\"car or other, not in attribute Savings account/bonds\",25,bank,own,3,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,24,existing credits paid back duly till now,car (used),12579,... < 100 DM,... >= 7 years,4,male : divorced/separated,none,2,unknown / no property,44,none,for free,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,24,existing credits paid back duly till now,radio/television,3430,500 <= ... < 1000 DM,... >= 7 years,3,male : divorced/separated,none,2,\"car or other, not in attribute Savings account/bonds\",31,none,own,1,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,9,critical account/ other credits existing (not at this bank),car (new),2134,... < 100 DM,1 <= ... < 4 years,4,male : divorced/separated,none,4,\"car or other, not in attribute Savings account/bonds\",48,none,own,3,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,6,existing credits paid back duly till now,radio/television,2647,500 <= ... < 1000 DM,1 <= ... < 4 years,2,male : divorced/separated,none,3,real estate,44,none,rent,1,skilled employee / official,2,none,yes,good\r\n... < 0 DM,10,critical account/ other credits existing (not at this bank),car (new),2241,... < 100 DM,... < 1 year,1,male : divorced/separated,none,3,real estate,48,none,rent,2,unskilled - resident,2,none,no,good\r\n0 <= ... < 200 DM,12,critical account/ other credits existing (not at this bank),car (used),1804,100 <= ... < 500 DM,... < 1 year,3,male : divorced/separated,none,4,building society savings agreement/ life insurance,44,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,10,critical account/ other credits existing (not at this bank),furniture/equipment,2069,unknown/ no savings account,1 <= ... < 4 years,2,male : divorced/separated,none,1,\"car or other, not in attribute Savings account/bonds\",26,none,own,2,skilled employee / official,1,none,no,good\r\n... < 0 DM,6,existing credits paid back duly till now,furniture/equipment,1374,... < 100 DM,1 <= ... < 4 years,1,male : divorced/separated,none,2,real estate,36,bank,own,1,unskilled - resident,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,6,no credits taken/ all credits paid back duly,radio/television,426,... < 100 DM,... >= 7 years,4,male : divorced/separated,none,4,\"car or other, not in attribute Savings account/bonds\",39,none,own,1,unskilled - resident,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,12,all credits at this bank paid back duly,radio/television,409,... >= 1000 DM,1 <= ... < 4 years,3,male : divorced/separated,none,3,real estate,42,none,rent,2,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,7,existing credits paid back duly till now,radio/television,2415,... < 100 DM,1 <= ... < 4 years,3,male : divorced/separated,guarantor,2,real estate,34,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,60,delay in paying off in the past,business,6836,... < 100 DM,... >= 7 years,3,male : divorced/separated,none,4,unknown / no property,63,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,18,existing credits paid back duly till now,business,1913,... >= 1000 DM,... < 1 year,3,male : divorced/separated,none,3,real estate,36,bank,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,24,existing credits paid back duly till now,furniture/equipment,4020,... < 100 DM,1 <= ... < 4 years,2,male : divorced/separated,none,2,\"car or other, not in attribute Savings account/bonds\",27,stores,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,18,existing credits paid back duly till now,car (new),5866,100 <= ... < 500 DM,1 <= ... < 4 years,2,male : divorced/separated,none,2,\"car or other, not in attribute Savings account/bonds\",30,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),business,1264,unknown/ no savings account,... >= 7 years,4,male : divorced/separated,none,4,unknown / no property,57,none,rent,1,unskilled - resident,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,12,existing credits paid back duly till now,furniture/equipment,1474,... < 100 DM,... < 1 year,4,male : divorced/separated,none,1,building society savings agreement/ life insurance,33,bank,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,45,critical account/ other credits existing (not at this bank),radio/television,4746,... < 100 DM,... < 1 year,4,male : divorced/separated,none,2,building society savings agreement/ life insurance,25,none,own,2,unskilled - resident,1,none,yes,bad\r\nno checking account,48,critical account/ other credits existing (not at this bank),education,6110,... < 100 DM,1 <= ... < 4 years,1,male : divorced/separated,none,3,unknown / no property,31,bank,for free,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,18,existing credits paid back duly till now,radio/television,2100,... < 100 DM,1 <= ... < 4 years,4,male : divorced/separated,co-applicant,2,real estate,37,stores,own,1,skilled employee / official,1,none,yes,bad\r\n... >= 200 DM / salary assignments for at least 1 year,10,existing credits paid back duly till now,domestic appliances,1225,... < 100 DM,1 <= ... < 4 years,2,male : divorced/separated,none,2,\"car or other, not in attribute Savings account/bonds\",37,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,9,existing credits paid back duly till now,radio/television,458,... < 100 DM,1 <= ... < 4 years,4,male : divorced/separated,none,3,real estate,24,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,30,existing credits paid back duly till now,radio/television,2333,500 <= ... < 1000 DM,... >= 7 years,4,male : divorced/separated,none,2,\"car or other, not in attribute Savings account/bonds\",30,bank,own,1,management/ self-employed/ highly qualified employee/ officer,1,none,yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,radio/television,1158,500 <= ... < 1000 DM,1 <= ... < 4 years,3,male : divorced/separated,none,1,\"car or other, not in attribute Savings account/bonds\",26,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,18,delay in paying off in the past,repairs,6204,... < 100 DM,1 <= ... < 4 years,2,male : divorced/separated,none,4,real estate,44,none,own,1,unskilled - resident,2,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,30,critical account/ other credits existing (not at this bank),car (used),6187,100 <= ... < 500 DM,4 <= ... < 7 years,1,male : divorced/separated,none,4,\"car or other, not in attribute Savings account/bonds\",24,none,rent,2,skilled employee / official,1,none,yes,good\r\n... < 0 DM,48,critical account/ other credits existing (not at this bank),car (used),6143,... < 100 DM,... >= 7 years,4,male : divorced/separated,none,4,unknown / no property,58,stores,for free,2,unskilled - resident,1,none,yes,bad\r\nno checking account,11,critical account/ other credits existing (not at this bank),car (new),1393,... < 100 DM,... < 1 year,4,male : divorced/separated,none,4,\"car or other, not in attribute Savings account/bonds\",35,none,own,2,management/ self-employed/ highly qualified employee/ officer,1,none,yes,good\r\nno checking account,36,existing credits paid back duly till now,radio/television,2299,500 <= ... < 1000 DM,... >= 7 years,4,male : divorced/separated,none,4,\"car or other, not in attribute Savings account/bonds\",39,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,6,existing credits paid back duly till now,car (used),1352,500 <= ... < 1000 DM,unemployed,1,male : divorced/separated,none,2,building society savings agreement/ life insurance,23,none,rent,1,unemployed/ unskilled - non-resident,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,11,critical account/ other credits existing (not at this bank),car (new),7228,... < 100 DM,1 <= ... < 4 years,1,male : divorced/separated,none,4,building society savings agreement/ life insurance,39,none,own,2,unskilled - resident,1,none,yes,good\r\nno checking account,12,existing credits paid back duly till now,radio/television,2073,100 <= ... < 500 DM,1 <= ... < 4 years,4,male : divorced/separated,co-applicant,2,real estate,28,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,24,delay in paying off in the past,furniture/equipment,2333,unknown/ no savings account,... < 1 year,4,female : divorced/separated/married,none,2,building society savings agreement/ life insurance,29,bank,own,1,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,27,delay in paying off in the past,car (used),5965,... < 100 DM,... >= 7 years,1,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",30,none,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,existing credits paid back duly till now,radio/television,1262,... < 100 DM,1 <= ... < 4 years,3,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",25,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,18,existing credits paid back duly till now,car (used),3378,unknown/ no savings account,1 <= ... < 4 years,2,female : divorced/separated/married,none,1,building society savings agreement/ life insurance,31,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,36,delay in paying off in the past,car (new),2225,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,4,unknown / no property,57,bank,for free,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,6,all credits at this bank paid back duly,car (new),783,unknown/ no savings account,1 <= ... < 4 years,1,female : divorced/separated/married,guarantor,2,real estate,26,stores,own,1,unskilled - resident,2,none,yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,radio/television,6468,unknown/ no savings account,unemployed,2,female : divorced/separated/married,none,1,unknown / no property,52,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,36,critical account/ other credits existing (not at this bank),radio/television,9566,... < 100 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",31,stores,own,2,skilled employee / official,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,18,existing credits paid back duly till now,car (new),1961,... < 100 DM,... >= 7 years,3,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",23,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,none,yes,good\r\n... < 0 DM,36,critical account/ other credits existing (not at this bank),furniture/equipment,6229,... < 100 DM,... < 1 year,4,female : divorced/separated/married,co-applicant,4,unknown / no property,23,none,rent,2,unskilled - resident,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,9,existing credits paid back duly till now,business,1391,... < 100 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,1,real estate,27,bank,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,15,critical account/ other credits existing (not at this bank),radio/television,1537,unknown/ no savings account,... >= 7 years,4,female : divorced/separated/married,guarantor,4,real estate,50,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,36,no credits taken/ all credits paid back duly,business,1953,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,4,unknown / no property,61,none,for free,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,48,no credits taken/ all credits paid back duly,business,14421,... < 100 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",25,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,24,existing credits paid back duly till now,radio/television,3181,... < 100 DM,... < 1 year,4,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,26,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,27,existing credits paid back duly till now,repairs,5190,unknown/ no savings account,... >= 7 years,4,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,48,none,own,4,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,existing credits paid back duly till now,radio/television,2171,... < 100 DM,... < 1 year,2,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",29,bank,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,car (new),1007,... >= 1000 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,1,real estate,22,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,36,existing credits paid back duly till now,education,1819,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,4,unknown / no property,37,stores,for free,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,36,existing credits paid back duly till now,radio/television,2394,unknown/ no savings account,1 <= ... < 4 years,4,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",25,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,36,existing credits paid back duly till now,car (used),8133,... < 100 DM,1 <= ... < 4 years,1,female : divorced/separated/married,none,2,building society savings agreement/ life insurance,30,bank,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,7,critical account/ other credits existing (not at this bank),radio/television,730,unknown/ no savings account,... >= 7 years,4,female : divorced/separated/married,none,2,building society savings agreement/ life insurance,46,none,rent,2,unskilled - resident,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,8,critical account/ other credits existing (not at this bank),others,1164,... < 100 DM,... >= 7 years,3,female : divorced/separated/married,none,4,unknown / no property,51,bank,for free,2,management/ self-employed/ highly qualified employee/ officer,2,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,42,critical account/ other credits existing (not at this bank),business,5954,... < 100 DM,4 <= ... < 7 years,2,female : divorced/separated/married,none,1,real estate,41,bank,own,2,unskilled - resident,1,none,yes,good\r\n... < 0 DM,36,existing credits paid back duly till now,education,1977,unknown/ no savings account,... >= 7 years,4,female : divorced/separated/married,none,4,unknown / no property,40,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,12,critical account/ other credits existing (not at this bank),car (used),1526,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,4,unknown / no property,66,none,for free,2,management/ self-employed/ highly qualified employee/ officer,1,none,yes,good\r\n... < 0 DM,42,existing credits paid back duly till now,radio/television,3965,... < 100 DM,... < 1 year,4,female : divorced/separated/married,none,3,\"car or other, not in attribute Savings account/bonds\",34,none,own,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,11,delay in paying off in the past,radio/television,4771,... < 100 DM,4 <= ... < 7 years,2,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,51,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,54,no credits taken/ all credits paid back duly,car (used),9436,unknown/ no savings account,1 <= ... < 4 years,2,female : divorced/separated/married,none,2,building society savings agreement/ life insurance,39,none,own,1,unskilled - resident,2,none,yes,good\r\n0 <= ... < 200 DM,30,existing credits paid back duly till now,furniture/equipment,3832,... < 100 DM,... < 1 year,2,female : divorced/separated/married,none,1,building society savings agreement/ life insurance,22,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,24,existing credits paid back duly till now,radio/television,5943,unknown/ no savings account,... < 1 year,1,female : divorced/separated/married,none,1,\"car or other, not in attribute Savings account/bonds\",44,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,15,existing credits paid back duly till now,radio/television,1213,500 <= ... < 1000 DM,... >= 7 years,4,female : divorced/separated/married,none,3,building society savings agreement/ life insurance,47,stores,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,18,existing credits paid back duly till now,business,1568,100 <= ... < 500 DM,1 <= ... < 4 years,3,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,24,none,rent,1,unskilled - resident,1,none,yes,good\r\n... < 0 DM,24,existing credits paid back duly till now,others,1755,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,guarantor,4,real estate,58,none,own,1,unskilled - resident,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,10,existing credits paid back duly till now,radio/television,2315,... < 100 DM,... >= 7 years,3,female : divorced/separated/married,none,4,real estate,52,none,own,1,unskilled - resident,1,none,yes,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),business,1412,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,guarantor,2,real estate,29,none,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,18,critical account/ other credits existing (not at this bank),furniture/equipment,1295,... < 100 DM,... < 1 year,4,female : divorced/separated/married,none,1,building society savings agreement/ life insurance,27,none,own,2,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,36,existing credits paid back duly till now,education,12612,100 <= ... < 500 DM,1 <= ... < 4 years,1,female : divorced/separated/married,none,4,unknown / no property,47,none,for free,1,skilled employee / official,2,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,18,existing credits paid back duly till now,car (new),2249,100 <= ... < 500 DM,4 <= ... < 7 years,4,female : divorced/separated/married,none,3,\"car or other, not in attribute Savings account/bonds\",30,none,own,1,management/ self-employed/ highly qualified employee/ officer,2,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,12,no credits taken/ all credits paid back duly,repairs,1108,... < 100 DM,4 <= ... < 7 years,4,female : divorced/separated/married,none,3,real estate,28,none,own,2,skilled employee / official,1,none,yes,bad\r\nno checking account,12,critical account/ other credits existing (not at this bank),radio/television,618,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,4,real estate,56,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,12,critical account/ other credits existing (not at this bank),car (used),1409,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,3,real estate,54,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),radio/television,797,unknown/ no savings account,... >= 7 years,4,female : divorced/separated/married,none,3,building society savings agreement/ life insurance,33,bank,own,1,unskilled - resident,2,none,yes,bad\r\n... >= 200 DM / salary assignments for at least 1 year,24,critical account/ other credits existing (not at this bank),furniture/equipment,3617,unknown/ no savings account,... >= 7 years,4,female : divorced/separated/married,co-applicant,4,unknown / no property,20,none,rent,2,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,car (new),1318,... >= 1000 DM,... >= 7 years,4,female : divorced/separated/married,none,4,real estate,54,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,54,no credits taken/ all credits paid back duly,business,15945,... < 100 DM,... < 1 year,3,female : divorced/separated/married,none,4,unknown / no property,58,none,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,12,critical account/ other credits existing (not at this bank),education,2012,unknown/ no savings account,4 <= ... < 7 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",61,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,18,existing credits paid back duly till now,business,2622,100 <= ... < 500 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",34,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,36,critical account/ other credits existing (not at this bank),radio/television,2337,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,4,real estate,36,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,20,delay in paying off in the past,car (used),7057,unknown/ no savings account,4 <= ... < 7 years,3,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,36,bank,rent,2,management/ self-employed/ highly qualified employee/ officer,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,24,existing credits paid back duly till now,car (new),1469,100 <= ... < 500 DM,... >= 7 years,4,female : divorced/separated/married,none,4,real estate,41,none,rent,1,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,36,existing credits paid back duly till now,radio/television,2323,... < 100 DM,4 <= ... < 7 years,4,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",24,none,rent,1,skilled employee / official,1,none,yes,good\r\nno checking account,6,delay in paying off in the past,radio/television,932,... < 100 DM,1 <= ... < 4 years,3,female : divorced/separated/married,none,2,real estate,24,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,9,critical account/ other credits existing (not at this bank),furniture/equipment,1919,... < 100 DM,4 <= ... < 7 years,4,female : divorced/separated/married,none,3,\"car or other, not in attribute Savings account/bonds\",35,none,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,existing credits paid back duly till now,car (used),2445,unknown/ no savings account,... < 1 year,2,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",26,none,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,24,critical account/ other credits existing (not at this bank),others,11938,... < 100 DM,1 <= ... < 4 years,2,female : divorced/separated/married,co-applicant,3,\"car or other, not in attribute Savings account/bonds\",39,none,own,2,management/ self-employed/ highly qualified employee/ officer,2,\"yes, registered under the customers name\",yes,bad\r\nno checking account,18,all credits at this bank paid back duly,car (new),6458,... < 100 DM,... >= 7 years,2,female : divorced/separated/married,none,4,unknown / no property,39,bank,own,2,management/ self-employed/ highly qualified employee/ officer,2,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,car (new),6078,... < 100 DM,4 <= ... < 7 years,2,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",32,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,24,existing credits paid back duly till now,furniture/equipment,7721,unknown/ no savings account,... < 1 year,1,female : divorced/separated/married,none,2,building society savings agreement/ life insurance,30,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",no,good\r\n0 <= ... < 200 DM,14,existing credits paid back duly till now,business,1410,500 <= ... < 1000 DM,... >= 7 years,1,female : divorced/separated/married,none,2,real estate,35,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,6,delay in paying off in the past,business,1449,100 <= ... < 500 DM,... >= 7 years,1,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",31,bank,own,2,skilled employee / official,2,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,15,existing credits paid back duly till now,education,392,... < 100 DM,... < 1 year,4,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,23,none,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,18,existing credits paid back duly till now,car (new),6260,... < 100 DM,4 <= ... < 7 years,3,female : divorced/separated/married,none,3,real estate,28,none,rent,1,unskilled - resident,1,none,yes,good\r\nno checking account,36,critical account/ other credits existing (not at this bank),car (new),7855,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,real estate,25,stores,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,12,existing credits paid back duly till now,radio/television,1680,500 <= ... < 1000 DM,... >= 7 years,3,female : divorced/separated/married,none,1,real estate,35,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,48,critical account/ other credits existing (not at this bank),radio/television,3578,unknown/ no savings account,... >= 7 years,4,female : divorced/separated/married,none,1,real estate,47,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,42,existing credits paid back duly till now,radio/television,7174,unknown/ no savings account,4 <= ... < 7 years,4,female : divorced/separated/married,none,3,\"car or other, not in attribute Savings account/bonds\",30,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,10,critical account/ other credits existing (not at this bank),furniture/equipment,2132,unknown/ no savings account,... < 1 year,2,female : divorced/separated/married,co-applicant,3,real estate,27,none,rent,2,skilled employee / official,1,none,no,good\r\n... < 0 DM,33,critical account/ other credits existing (not at this bank),furniture/equipment,4281,500 <= ... < 1000 DM,1 <= ... < 4 years,1,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",23,none,own,2,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,12,critical account/ other credits existing (not at this bank),car (new),2366,500 <= ... < 1000 DM,4 <= ... < 7 years,3,female : divorced/separated/married,none,3,\"car or other, not in attribute Savings account/bonds\",36,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,21,existing credits paid back duly till now,radio/television,1835,... < 100 DM,1 <= ... < 4 years,3,female : divorced/separated/married,none,2,real estate,25,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,24,critical account/ other credits existing (not at this bank),car (used),3868,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",41,none,rent,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,existing credits paid back duly till now,furniture/equipment,1768,... < 100 DM,1 <= ... < 4 years,3,female : divorced/separated/married,none,2,real estate,24,none,rent,1,unskilled - resident,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,10,critical account/ other credits existing (not at this bank),car (new),781,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,4,unknown / no property,63,none,for free,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,18,existing credits paid back duly till now,furniture/equipment,1924,unknown/ no savings account,... < 1 year,4,female : divorced/separated/married,none,3,real estate,27,none,rent,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,12,critical account/ other credits existing (not at this bank),car (new),2121,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,building society savings agreement/ life insurance,30,none,own,2,skilled employee / official,1,none,yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,radio/television,701,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,real estate,40,none,own,1,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,repairs,639,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",30,none,own,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,12,critical account/ other credits existing (not at this bank),car (used),1860,... < 100 DM,unemployed,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",34,none,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,12,critical account/ other credits existing (not at this bank),car (new),3499,... < 100 DM,1 <= ... < 4 years,3,female : divorced/separated/married,co-applicant,2,real estate,29,none,own,2,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,48,existing credits paid back duly till now,car (new),8487,unknown/ no savings account,4 <= ... < 7 years,1,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",24,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,36,delay in paying off in the past,education,6887,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,3,building society savings agreement/ life insurance,29,stores,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,15,existing credits paid back duly till now,furniture/equipment,2708,... < 100 DM,... < 1 year,2,female : divorced/separated/married,none,3,building society savings agreement/ life insurance,27,bank,own,2,unskilled - resident,1,none,yes,good\r\nno checking account,18,existing credits paid back duly till now,furniture/equipment,1984,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,4,unknown / no property,47,bank,for free,2,skilled employee / official,1,none,yes,good\r\nno checking account,60,existing credits paid back duly till now,radio/television,10144,100 <= ... < 500 DM,4 <= ... < 7 years,2,female : divorced/separated/married,none,4,real estate,21,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),radio/television,1240,unknown/ no savings account,... >= 7 years,4,female : divorced/separated/married,none,2,real estate,38,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,27,delay in paying off in the past,car (used),8613,... >= 1000 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",27,none,own,2,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,radio/television,766,500 <= ... < 1000 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,3,real estate,66,none,own,1,unskilled - resident,1,none,yes,bad\r\n0 <= ... < 200 DM,15,critical account/ other credits existing (not at this bank),radio/television,2728,unknown/ no savings account,4 <= ... < 7 years,4,female : divorced/separated/married,guarantor,2,real estate,35,bank,own,3,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,12,existing credits paid back duly till now,radio/television,1881,... < 100 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",44,none,rent,1,unskilled - resident,1,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,6,existing credits paid back duly till now,car (new),709,... >= 1000 DM,... < 1 year,2,female : divorced/separated/married,none,2,real estate,27,none,own,1,unemployed/ unskilled - non-resident,1,none,no,good\r\n0 <= ... < 200 DM,36,existing credits paid back duly till now,radio/television,4795,... < 100 DM,... < 1 year,4,female : divorced/separated/married,none,1,unknown / no property,30,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,27,existing credits paid back duly till now,radio/television,3416,... < 100 DM,1 <= ... < 4 years,3,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",27,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,none,yes,good\r\n... < 0 DM,18,existing credits paid back duly till now,furniture/equipment,2462,... < 100 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",22,none,own,1,skilled employee / official,1,none,yes,bad\r\nno checking account,21,critical account/ other credits existing (not at this bank),furniture/equipment,2288,... < 100 DM,... < 1 year,4,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,23,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,48,all credits at this bank paid back duly,business,3566,100 <= ... < 500 DM,4 <= ... < 7 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",30,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,6,critical account/ other credits existing (not at this bank),car (new),860,... < 100 DM,... >= 7 years,1,female : divorced/separated/married,none,4,unknown / no property,39,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),car (new),682,100 <= ... < 500 DM,4 <= ... < 7 years,4,female : divorced/separated/married,none,3,\"car or other, not in attribute Savings account/bonds\",51,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,36,critical account/ other credits existing (not at this bank),furniture/equipment,5371,... < 100 DM,1 <= ... < 4 years,3,female : divorced/separated/married,guarantor,2,building society savings agreement/ life insurance,28,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,18,critical account/ other credits existing (not at this bank),radio/television,1582,... >= 1000 DM,... >= 7 years,4,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",46,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,6,existing credits paid back duly till now,radio/television,1346,100 <= ... < 500 DM,... >= 7 years,2,female : divorced/separated/married,none,4,unknown / no property,42,bank,for free,1,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,10,existing credits paid back duly till now,radio/television,1924,... < 100 DM,1 <= ... < 4 years,1,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,38,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",no,good\r\n... >= 200 DM / salary assignments for at least 1 year,36,existing credits paid back duly till now,radio/television,5848,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,1,\"car or other, not in attribute Savings account/bonds\",24,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,24,critical account/ other credits existing (not at this bank),car (used),7758,... >= 1000 DM,... >= 7 years,2,female : divorced/separated/married,none,4,unknown / no property,29,none,rent,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,24,delay in paying off in the past,business,6967,100 <= ... < 500 DM,4 <= ... < 7 years,4,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",36,none,rent,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,furniture/equipment,1282,... < 100 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",20,none,rent,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,9,critical account/ other credits existing (not at this bank),repairs,1288,100 <= ... < 500 DM,... >= 7 years,3,female : divorced/separated/married,guarantor,4,real estate,48,none,own,2,skilled employee / official,2,none,no,good\r\n... < 0 DM,12,all credits at this bank paid back duly,retraining,339,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,1,\"car or other, not in attribute Savings account/bonds\",45,bank,own,1,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,24,existing credits paid back duly till now,car (new),3512,100 <= ... < 500 DM,4 <= ... < 7 years,2,female : divorced/separated/married,none,3,\"car or other, not in attribute Savings account/bonds\",38,bank,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,6,critical account/ other credits existing (not at this bank),radio/television,1898,unknown/ no savings account,1 <= ... < 4 years,1,female : divorced/separated/married,none,2,real estate,34,none,own,2,unskilled - resident,2,none,yes,good\r\nno checking account,24,critical account/ other credits existing (not at this bank),radio/television,2872,100 <= ... < 500 DM,... >= 7 years,3,female : divorced/separated/married,none,4,real estate,36,none,own,1,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,18,critical account/ other credits existing (not at this bank),car (new),1055,... < 100 DM,... < 1 year,4,female : divorced/separated/married,none,1,building society savings agreement/ life insurance,30,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,15,existing credits paid back duly till now,domestic appliances,1262,500 <= ... < 1000 DM,4 <= ... < 7 years,4,female : divorced/separated/married,none,3,building society savings agreement/ life insurance,36,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,10,existing credits paid back duly till now,car (new),7308,... < 100 DM,unemployed,2,female : divorced/separated/married,none,4,unknown / no property,70,bank,for free,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,36,existing credits paid back duly till now,car (new),909,500 <= ... < 1000 DM,... >= 7 years,4,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,36,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,6,existing credits paid back duly till now,furniture/equipment,2978,500 <= ... < 1000 DM,1 <= ... < 4 years,1,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",32,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,18,existing credits paid back duly till now,furniture/equipment,1131,... < 100 DM,unemployed,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",33,none,own,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,11,existing credits paid back duly till now,furniture/equipment,1577,... >= 1000 DM,... < 1 year,4,female : divorced/separated/married,none,1,real estate,20,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,24,existing credits paid back duly till now,furniture/equipment,3972,... < 100 DM,4 <= ... < 7 years,2,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,25,none,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,24,critical account/ other credits existing (not at this bank),business,1935,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,4,real estate,31,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,15,no credits taken/ all credits paid back duly,car (new),950,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,3,\"car or other, not in attribute Savings account/bonds\",33,none,rent,2,skilled employee / official,2,none,yes,bad\r\nno checking account,12,existing credits paid back duly till now,furniture/equipment,763,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,1,real estate,26,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,24,delay in paying off in the past,furniture/equipment,2064,... < 100 DM,unemployed,3,female : divorced/separated/married,none,2,building society savings agreement/ life insurance,34,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,8,existing credits paid back duly till now,radio/television,1414,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,guarantor,2,real estate,33,none,own,1,skilled employee / official,1,none,no,good\r\n... < 0 DM,21,delay in paying off in the past,education,3414,... < 100 DM,... < 1 year,2,female : divorced/separated/married,none,1,building society savings agreement/ life insurance,26,none,own,2,skilled employee / official,1,none,yes,bad\r\nno checking account,30,all credits at this bank paid back duly,car (used),7485,unknown/ no savings account,unemployed,4,female : divorced/separated/married,none,1,real estate,53,bank,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,12,existing credits paid back duly till now,furniture/equipment,2577,... < 100 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,1,\"car or other, not in attribute Savings account/bonds\",42,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,6,critical account/ other credits existing (not at this bank),radio/television,338,500 <= ... < 1000 DM,... >= 7 years,4,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",52,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,12,existing credits paid back duly till now,radio/television,1963,... < 100 DM,4 <= ... < 7 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",31,none,rent,2,management/ self-employed/ highly qualified employee/ officer,2,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,21,critical account/ other credits existing (not at this bank),car (new),571,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,4,real estate,65,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,36,delay in paying off in the past,business,9572,... < 100 DM,... < 1 year,1,female : divorced/separated/married,none,1,\"car or other, not in attribute Savings account/bonds\",28,none,own,2,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,36,delay in paying off in the past,business,4455,... < 100 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,2,real estate,30,stores,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,21,all credits at this bank paid back duly,car (new),1647,unknown/ no savings account,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,building society savings agreement/ life insurance,40,none,own,2,unskilled - resident,2,none,yes,bad\r\nno checking account,24,critical account/ other credits existing (not at this bank),furniture/equipment,3777,... >= 1000 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,4,real estate,50,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,18,critical account/ other credits existing (not at this bank),car (new),884,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",36,bank,own,1,skilled employee / official,2,\"yes, registered under the customers name\",yes,bad\r\nno checking account,15,critical account/ other credits existing (not at this bank),radio/television,1360,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,building society savings agreement/ life insurance,31,none,own,2,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,9,all credits at this bank paid back duly,car (used),5129,... < 100 DM,... >= 7 years,2,female : divorced/separated/married,none,4,unknown / no property,74,bank,for free,1,management/ self-employed/ highly qualified employee/ officer,2,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,16,critical account/ other credits existing (not at this bank),car (new),1175,... < 100 DM,unemployed,2,female : divorced/separated/married,none,3,\"car or other, not in attribute Savings account/bonds\",68,none,for free,3,unemployed/ unskilled - non-resident,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,radio/television,674,100 <= ... < 500 DM,4 <= ... < 7 years,4,female : divorced/separated/married,none,1,building society savings agreement/ life insurance,20,none,own,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,18,no credits taken/ all credits paid back duly,furniture/equipment,3244,... < 100 DM,1 <= ... < 4 years,1,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",33,bank,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,24,existing credits paid back duly till now,business,4591,... >= 1000 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,3,building society savings agreement/ life insurance,54,none,own,3,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,48,no credits taken/ all credits paid back duly,business,3844,100 <= ... < 500 DM,4 <= ... < 7 years,4,female : divorced/separated/married,none,4,unknown / no property,34,none,for free,1,unskilled - resident,2,none,yes,bad\r\n0 <= ... < 200 DM,27,existing credits paid back duly till now,business,3915,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",36,none,own,1,skilled employee / official,2,\"yes, registered under the customers name\",yes,bad\r\nno checking account,6,existing credits paid back duly till now,radio/television,2108,... < 100 DM,4 <= ... < 7 years,2,female : divorced/separated/married,none,2,real estate,29,none,rent,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,45,existing credits paid back duly till now,radio/television,3031,100 <= ... < 500 DM,1 <= ... < 4 years,4,female : divorced/separated/married,guarantor,4,building society savings agreement/ life insurance,21,none,rent,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,9,critical account/ other credits existing (not at this bank),education,1501,... < 100 DM,... >= 7 years,2,female : divorced/separated/married,none,3,\"car or other, not in attribute Savings account/bonds\",34,none,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,6,critical account/ other credits existing (not at this bank),radio/television,1382,... < 100 DM,1 <= ... < 4 years,1,female : divorced/separated/married,none,1,\"car or other, not in attribute Savings account/bonds\",28,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,furniture/equipment,951,100 <= ... < 500 DM,... < 1 year,4,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",27,bank,rent,4,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,24,existing credits paid back duly till now,car (used),2760,unknown/ no savings account,... >= 7 years,4,female : divorced/separated/married,none,4,unknown / no property,36,bank,for free,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,18,delay in paying off in the past,furniture/equipment,4297,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,3,unknown / no property,40,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,9,critical account/ other credits existing (not at this bank),education,936,500 <= ... < 1000 DM,... >= 7 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",52,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,car (new),1168,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,3,real estate,27,none,own,1,unskilled - resident,1,none,yes,good\r\nno checking account,27,delay in paying off in the past,business,5117,... < 100 DM,4 <= ... < 7 years,3,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",26,none,own,2,skilled employee / official,1,none,yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,retraining,902,... < 100 DM,4 <= ... < 7 years,4,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,21,none,rent,1,skilled employee / official,1,none,yes,bad\r\nno checking account,12,critical account/ other credits existing (not at this bank),car (new),1495,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,1,real estate,38,none,own,2,unskilled - resident,2,none,yes,good\r\n... < 0 DM,30,critical account/ other credits existing (not at this bank),car (used),10623,... < 100 DM,... >= 7 years,3,female : divorced/separated/married,none,4,unknown / no property,38,none,for free,3,management/ self-employed/ highly qualified employee/ officer,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),furniture/equipment,1935,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,4,real estate,43,none,own,3,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,12,critical account/ other credits existing (not at this bank),domestic appliances,1424,... < 100 DM,4 <= ... < 7 years,4,female : divorced/separated/married,none,3,building society savings agreement/ life insurance,26,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,24,existing credits paid back duly till now,business,6568,... < 100 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",21,stores,own,1,unskilled - resident,1,none,yes,good\r\nno checking account,12,existing credits paid back duly till now,car (used),1413,... >= 1000 DM,4 <= ... < 7 years,3,female : divorced/separated/married,none,2,building society savings agreement/ life insurance,55,none,own,1,skilled employee / official,1,none,no,good\r\nno checking account,9,critical account/ other credits existing (not at this bank),radio/television,3074,unknown/ no savings account,1 <= ... < 4 years,1,female : divorced/separated/married,none,2,real estate,33,none,own,2,skilled employee / official,2,none,yes,good\r\nno checking account,36,existing credits paid back duly till now,radio/television,3835,unknown/ no savings account,... >= 7 years,2,female : divorced/separated/married,none,4,real estate,45,none,own,1,unskilled - resident,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,27,no credits taken/ all credits paid back duly,business,5293,... < 100 DM,unemployed,2,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,50,stores,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n... >= 200 DM / salary assignments for at least 1 year,30,delay in paying off in the past,business,1908,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,4,real estate,66,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,36,critical account/ other credits existing (not at this bank),radio/television,3342,unknown/ no savings account,... >= 7 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",51,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,6,critical account/ other credits existing (not at this bank),retraining,932,unknown/ no savings account,4 <= ... < 7 years,1,female : divorced/separated/married,none,3,building society savings agreement/ life insurance,39,none,own,2,unskilled - resident,1,none,yes,good\r\n... < 0 DM,18,no credits taken/ all credits paid back duly,business,3104,... < 100 DM,4 <= ... < 7 years,3,female : divorced/separated/married,none,1,building society savings agreement/ life insurance,31,bank,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,36,existing credits paid back duly till now,radio/television,3913,... < 100 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,2,real estate,23,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,24,existing credits paid back duly till now,furniture/equipment,3021,... < 100 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,2,real estate,24,none,rent,1,unskilled - resident,1,none,yes,good\r\nno checking account,10,existing credits paid back duly till now,car (new),1364,... < 100 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",64,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,radio/television,625,... < 100 DM,... < 1 year,4,female : divorced/separated/married,guarantor,1,real estate,26,bank,own,1,unskilled - resident,1,none,yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,education,1200,unknown/ no savings account,1 <= ... < 4 years,4,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,23,bank,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,existing credits paid back duly till now,radio/television,707,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,real estate,30,bank,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,24,delay in paying off in the past,business,2978,unknown/ no savings account,1 <= ... < 4 years,4,female : divorced/separated/married,none,4,real estate,32,none,own,2,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,15,existing credits paid back duly till now,car (used),4657,... < 100 DM,1 <= ... < 4 years,3,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",30,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,36,no credits taken/ all credits paid back duly,repairs,2613,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",27,none,own,2,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,48,existing credits paid back duly till now,radio/television,10961,... >= 1000 DM,4 <= ... < 7 years,1,female : divorced/separated/married,co-applicant,2,unknown / no property,27,bank,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,12,existing credits paid back duly till now,furniture/equipment,7865,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,4,unknown / no property,53,none,for free,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,9,existing credits paid back duly till now,radio/television,1478,... < 100 DM,4 <= ... < 7 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",22,none,own,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,24,existing credits paid back duly till now,furniture/equipment,3149,... < 100 DM,... < 1 year,4,female : divorced/separated/married,none,1,unknown / no property,22,bank,for free,1,skilled employee / official,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,36,existing credits paid back duly till now,radio/television,4210,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",26,none,own,1,skilled employee / official,1,none,yes,bad\r\nno checking account,9,existing credits paid back duly till now,car (new),2507,500 <= ... < 1000 DM,... >= 7 years,2,female : divorced/separated/married,none,4,unknown / no property,51,none,for free,1,unskilled - resident,1,none,yes,good\r\nno checking account,12,existing credits paid back duly till now,radio/television,2141,100 <= ... < 500 DM,4 <= ... < 7 years,3,female : divorced/separated/married,none,1,unknown / no property,35,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,18,existing credits paid back duly till now,radio/television,866,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,guarantor,2,real estate,25,none,own,1,unskilled - resident,1,none,yes,good\r\nno checking account,4,critical account/ other credits existing (not at this bank),radio/television,1544,... < 100 DM,4 <= ... < 7 years,2,female : divorced/separated/married,none,1,real estate,42,none,own,3,unskilled - resident,2,none,yes,good\r\n... < 0 DM,24,existing credits paid back duly till now,radio/television,1823,... < 100 DM,unemployed,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",30,stores,own,1,management/ self-employed/ highly qualified employee/ officer,2,none,yes,bad\r\n0 <= ... < 200 DM,6,existing credits paid back duly till now,car (new),14555,unknown/ no savings account,unemployed,1,female : divorced/separated/married,none,2,building society savings agreement/ life insurance,23,none,own,1,unemployed/ unskilled - non-resident,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,21,existing credits paid back duly till now,business,2767,100 <= ... < 500 DM,... >= 7 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",61,bank,rent,2,unskilled - resident,1,none,yes,bad\r\nno checking account,12,critical account/ other credits existing (not at this bank),radio/television,1291,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,building society savings agreement/ life insurance,35,none,own,2,skilled employee / official,1,none,yes,good\r\n... < 0 DM,30,existing credits paid back duly till now,radio/television,2522,... < 100 DM,... >= 7 years,1,female : divorced/separated/married,guarantor,3,building society savings agreement/ life insurance,39,none,own,1,skilled employee / official,2,none,yes,good\r\n... < 0 DM,24,existing credits paid back duly till now,car (new),915,unknown/ no savings account,... >= 7 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",29,bank,own,1,skilled employee / official,1,none,yes,bad\r\nno checking account,6,existing credits paid back duly till now,radio/television,1595,... < 100 DM,4 <= ... < 7 years,3,female : divorced/separated/married,none,2,building society savings agreement/ life insurance,51,none,own,1,skilled employee / official,2,none,yes,good\r\n... < 0 DM,48,no credits taken/ all credits paid back duly,car (used),4605,... < 100 DM,... >= 7 years,3,female : divorced/separated/married,none,4,unknown / no property,24,none,for free,2,skilled employee / official,2,none,yes,bad\r\nno checking account,12,critical account/ other credits existing (not at this bank),business,1185,... < 100 DM,1 <= ... < 4 years,3,female : divorced/separated/married,none,2,real estate,27,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,12,all credits at this bank paid back duly,retraining,3447,500 <= ... < 1000 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,3,real estate,35,none,own,1,unskilled - resident,2,none,yes,good\r\nno checking account,24,existing credits paid back duly till now,business,1258,... < 100 DM,4 <= ... < 7 years,4,female : divorced/separated/married,none,1,real estate,25,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),radio/television,717,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,4,real estate,52,none,own,3,skilled employee / official,1,none,yes,good\r\nno checking account,6,no credits taken/ all credits paid back duly,car (new),1204,100 <= ... < 500 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,1,unknown / no property,35,bank,rent,1,skilled employee / official,1,none,no,good\r\n... >= 200 DM / salary assignments for at least 1 year,24,existing credits paid back duly till now,furniture/equipment,1925,... < 100 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,2,real estate,26,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,18,existing credits paid back duly till now,radio/television,433,... < 100 DM,unemployed,3,female : divorced/separated/married,co-applicant,4,real estate,22,none,rent,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,6,critical account/ other credits existing (not at this bank),car (new),666,... >= 1000 DM,4 <= ... < 7 years,3,female : divorced/separated/married,none,4,real estate,39,none,own,2,unskilled - resident,1,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,12,existing credits paid back duly till now,furniture/equipment,2251,... < 100 DM,1 <= ... < 4 years,1,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",46,none,own,1,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,30,existing credits paid back duly till now,car (new),2150,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,guarantor,2,unknown / no property,24,bank,own,1,skilled employee / official,1,none,yes,bad\r\nno checking account,24,delay in paying off in the past,furniture/equipment,4151,100 <= ... < 500 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,3,building society savings agreement/ life insurance,35,none,own,2,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,9,existing credits paid back duly till now,furniture/equipment,2030,unknown/ no savings account,4 <= ... < 7 years,2,female : divorced/separated/married,none,1,\"car or other, not in attribute Savings account/bonds\",24,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,60,delay in paying off in the past,radio/television,7418,unknown/ no savings account,1 <= ... < 4 years,1,female : divorced/separated/married,none,1,real estate,27,none,own,1,unskilled - resident,1,none,yes,good\r\nno checking account,24,critical account/ other credits existing (not at this bank),radio/television,2684,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,real estate,35,none,own,2,unskilled - resident,1,none,yes,good\r\n... < 0 DM,12,all credits at this bank paid back duly,radio/television,2149,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,1,unknown / no property,29,none,for free,1,skilled employee / official,1,none,yes,bad\r\nno checking account,15,existing credits paid back duly till now,car (used),3812,100 <= ... < 500 DM,... < 1 year,1,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",23,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,11,critical account/ other credits existing (not at this bank),radio/television,1154,100 <= ... < 500 DM,unemployed,4,female : divorced/separated/married,none,4,real estate,57,none,own,3,unskilled - resident,1,none,yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,furniture/equipment,1657,... < 100 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,2,real estate,27,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,24,existing credits paid back duly till now,radio/television,1603,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",55,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,18,critical account/ other credits existing (not at this bank),car (new),5302,... < 100 DM,... >= 7 years,2,female : divorced/separated/married,none,4,unknown / no property,36,none,for free,3,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),education,2748,... < 100 DM,... >= 7 years,2,female : divorced/separated/married,none,4,unknown / no property,57,bank,for free,3,unskilled - resident,1,none,yes,good\r\nno checking account,10,critical account/ other credits existing (not at this bank),car (new),1231,... < 100 DM,... >= 7 years,3,female : divorced/separated/married,none,4,real estate,32,none,own,2,unskilled - resident,2,none,no,good\r\n0 <= ... < 200 DM,15,existing credits paid back duly till now,radio/television,802,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,3,\"car or other, not in attribute Savings account/bonds\",37,none,own,1,skilled employee / official,2,none,yes,bad\r\nno checking account,36,critical account/ other credits existing (not at this bank),business,6304,unknown/ no savings account,... >= 7 years,4,female : divorced/separated/married,none,4,real estate,36,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,24,existing credits paid back duly till now,radio/television,1533,... < 100 DM,... < 1 year,4,female : divorced/separated/married,none,3,\"car or other, not in attribute Savings account/bonds\",38,stores,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,14,existing credits paid back duly till now,car (new),8978,... < 100 DM,... >= 7 years,1,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,45,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",no,bad\r\nno checking account,24,existing credits paid back duly till now,radio/television,999,unknown/ no savings account,... >= 7 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",25,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,18,existing credits paid back duly till now,car (new),2662,unknown/ no savings account,4 <= ... < 7 years,4,female : divorced/separated/married,none,3,building society savings agreement/ life insurance,32,none,own,1,skilled employee / official,1,none,no,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),furniture/equipment,1402,500 <= ... < 1000 DM,4 <= ... < 7 years,3,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",37,none,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,48,all credits at this bank paid back duly,car (new),12169,unknown/ no savings account,unemployed,4,female : divorced/separated/married,co-applicant,4,unknown / no property,36,none,for free,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,48,existing credits paid back duly till now,radio/television,3060,... < 100 DM,4 <= ... < 7 years,4,female : divorced/separated/married,none,4,real estate,28,none,own,2,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,30,existing credits paid back duly till now,repairs,11998,... < 100 DM,... < 1 year,1,female : divorced/separated/married,none,1,unknown / no property,34,none,own,1,unskilled - resident,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,9,existing credits paid back duly till now,radio/television,2697,... < 100 DM,1 <= ... < 4 years,1,female : divorced/separated/married,none,2,real estate,32,none,own,1,skilled employee / official,2,none,yes,good\r\nno checking account,18,critical account/ other credits existing (not at this bank),radio/television,2404,... < 100 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",26,none,own,2,skilled employee / official,1,none,yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,furniture/equipment,1262,unknown/ no savings account,... >= 7 years,2,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,49,none,own,1,unskilled - resident,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,6,existing credits paid back duly till now,furniture/equipment,4611,... < 100 DM,... < 1 year,1,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,32,none,own,1,skilled employee / official,1,none,yes,bad\r\nno checking account,24,existing credits paid back duly till now,radio/television,1901,100 <= ... < 500 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",29,none,rent,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,15,critical account/ other credits existing (not at this bank),car (used),3368,... >= 1000 DM,... >= 7 years,3,female : divorced/separated/married,none,4,unknown / no property,23,none,rent,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,existing credits paid back duly till now,furniture/equipment,1574,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,real estate,50,none,own,1,skilled employee / official,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,18,all credits at this bank paid back duly,radio/television,1445,unknown/ no savings account,4 <= ... < 7 years,4,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",49,bank,own,1,unskilled - resident,1,none,yes,good\r\nno checking account,15,critical account/ other credits existing (not at this bank),furniture/equipment,1520,unknown/ no savings account,... >= 7 years,4,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,63,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,24,critical account/ other credits existing (not at this bank),car (new),3878,100 <= ... < 500 DM,... < 1 year,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",37,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,47,existing credits paid back duly till now,car (new),10722,... < 100 DM,... < 1 year,1,female : divorced/separated/married,none,1,real estate,35,none,own,1,unskilled - resident,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,48,existing credits paid back duly till now,car (used),4788,... < 100 DM,4 <= ... < 7 years,4,female : divorced/separated/married,none,3,building society savings agreement/ life insurance,26,none,own,1,skilled employee / official,2,none,yes,good\r\n0 <= ... < 200 DM,48,delay in paying off in the past,others,7582,100 <= ... < 500 DM,unemployed,2,female : divorced/separated/married,none,4,unknown / no property,31,none,for free,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,radio/television,1092,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,guarantor,4,real estate,49,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,24,delay in paying off in the past,radio/television,1024,... < 100 DM,... < 1 year,4,female : divorced/separated/married,none,4,real estate,48,stores,own,1,skilled employee / official,1,none,yes,bad\r\nno checking account,12,existing credits paid back duly till now,business,1076,... < 100 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,2,real estate,26,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",no,good\r\n0 <= ... < 200 DM,36,existing credits paid back duly till now,car (used),9398,... < 100 DM,... < 1 year,1,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",28,none,rent,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,24,critical account/ other credits existing (not at this bank),car (used),6419,... < 100 DM,... >= 7 years,2,female : divorced/separated/married,none,4,unknown / no property,44,none,for free,2,management/ self-employed/ highly qualified employee/ officer,2,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,42,critical account/ other credits existing (not at this bank),car (used),4796,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,4,unknown / no property,56,none,for free,1,skilled employee / official,1,none,yes,good\r\nno checking account,48,critical account/ other credits existing (not at this bank),business,7629,unknown/ no savings account,... >= 7 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",46,bank,own,2,management/ self-employed/ highly qualified employee/ officer,2,none,yes,good\r\n0 <= ... < 200 DM,48,existing credits paid back duly till now,furniture/equipment,9960,... < 100 DM,... < 1 year,1,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",26,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,12,existing credits paid back duly till now,car (used),4675,unknown/ no savings account,... < 1 year,1,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",20,none,rent,1,skilled employee / official,1,none,yes,good\r\nno checking account,10,existing credits paid back duly till now,car (new),1287,unknown/ no savings account,... >= 7 years,4,female : divorced/separated/married,co-applicant,2,building society savings agreement/ life insurance,45,none,own,1,unskilled - resident,1,none,no,good\r\nno checking account,18,existing credits paid back duly till now,furniture/equipment,2515,... < 100 DM,1 <= ... < 4 years,3,female : divorced/separated/married,none,4,real estate,43,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,21,critical account/ other credits existing (not at this bank),furniture/equipment,2745,... >= 1000 DM,4 <= ... < 7 years,3,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",32,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,6,existing credits paid back duly till now,car (new),672,... < 100 DM,unemployed,1,female : divorced/separated/married,none,4,real estate,54,none,own,1,unemployed/ unskilled - non-resident,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,36,no credits taken/ all credits paid back duly,radio/television,3804,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,1,\"car or other, not in attribute Savings account/bonds\",42,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n... >= 200 DM / salary assignments for at least 1 year,24,critical account/ other credits existing (not at this bank),car (new),1344,unknown/ no savings account,4 <= ... < 7 years,4,female : divorced/separated/married,none,2,real estate,37,bank,own,2,unskilled - resident,2,none,yes,bad\r\n... < 0 DM,10,critical account/ other credits existing (not at this bank),car (new),1038,... < 100 DM,4 <= ... < 7 years,4,female : divorced/separated/married,co-applicant,3,building society savings agreement/ life insurance,49,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,48,critical account/ other credits existing (not at this bank),car (new),10127,500 <= ... < 1000 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,2,unknown / no property,44,bank,for free,1,skilled employee / official,1,none,yes,bad\r\nno checking account,6,existing credits paid back duly till now,furniture/equipment,1543,... >= 1000 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,real estate,33,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,30,existing credits paid back duly till now,car (used),4811,unknown/ no savings account,4 <= ... < 7 years,2,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,24,stores,rent,1,unskilled - resident,1,none,yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,radio/television,727,100 <= ... < 500 DM,... < 1 year,4,female : divorced/separated/married,none,3,unknown / no property,33,none,own,1,unskilled - resident,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,8,existing credits paid back duly till now,furniture/equipment,1237,... < 100 DM,1 <= ... < 4 years,3,female : divorced/separated/married,none,4,real estate,24,none,own,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,9,existing credits paid back duly till now,car (new),276,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,4,real estate,22,none,rent,1,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,48,existing credits paid back duly till now,others,5381,unknown/ no savings account,unemployed,3,female : divorced/separated/married,none,4,unknown / no property,40,bank,for free,1,unemployed/ unskilled - non-resident,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,24,existing credits paid back duly till now,furniture/equipment,5511,100 <= ... < 500 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,1,\"car or other, not in attribute Savings account/bonds\",25,stores,own,1,skilled employee / official,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,24,existing credits paid back duly till now,furniture/equipment,3749,... < 100 DM,... < 1 year,2,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",26,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,car (new),685,... < 100 DM,4 <= ... < 7 years,2,female : divorced/separated/married,none,3,\"car or other, not in attribute Savings account/bonds\",25,bank,own,1,unskilled - resident,1,none,yes,bad\r\n... >= 200 DM / salary assignments for at least 1 year,4,existing credits paid back duly till now,car (new),1494,unknown/ no savings account,... < 1 year,1,female : divorced/separated/married,none,2,real estate,29,none,own,1,unskilled - resident,2,none,no,good\r\n... < 0 DM,36,all credits at this bank paid back duly,furniture/equipment,2746,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",31,bank,own,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,12,existing credits paid back duly till now,furniture/equipment,708,... < 100 DM,1 <= ... < 4 years,2,female : divorced/separated/married,guarantor,3,building society savings agreement/ life insurance,38,none,own,1,unskilled - resident,2,none,yes,good\r\n0 <= ... < 200 DM,24,existing credits paid back duly till now,furniture/equipment,4351,unknown/ no savings account,1 <= ... < 4 years,1,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,48,none,own,1,unskilled - resident,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),education,701,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",32,none,own,2,skilled employee / official,1,none,yes,good\r\n... < 0 DM,15,delay in paying off in the past,furniture/equipment,3643,... < 100 DM,... >= 7 years,1,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,27,none,own,2,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,30,critical account/ other credits existing (not at this bank),car (new),4249,... < 100 DM,unemployed,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",28,none,own,2,management/ self-employed/ highly qualified employee/ officer,1,none,yes,bad\r\n... < 0 DM,24,existing credits paid back duly till now,radio/television,1938,... < 100 DM,... < 1 year,4,female : divorced/separated/married,none,3,building society savings agreement/ life insurance,32,none,own,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,24,existing credits paid back duly till now,car (used),2910,... < 100 DM,4 <= ... < 7 years,2,female : divorced/separated/married,none,1,unknown / no property,34,none,for free,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,18,existing credits paid back duly till now,furniture/equipment,2659,... >= 1000 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",28,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,18,critical account/ other credits existing (not at this bank),car (new),1028,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,3,real estate,36,none,own,2,skilled employee / official,1,none,yes,good\r\n... < 0 DM,8,critical account/ other credits existing (not at this bank),car (new),3398,... < 100 DM,4 <= ... < 7 years,1,female : divorced/separated/married,none,4,real estate,39,none,own,2,unskilled - resident,1,none,no,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),furniture/equipment,5801,unknown/ no savings account,... >= 7 years,2,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,49,none,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,24,existing credits paid back duly till now,car (new),1525,... >= 1000 DM,4 <= ... < 7 years,4,female : divorced/separated/married,none,3,\"car or other, not in attribute Savings account/bonds\",34,none,own,1,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,36,existing credits paid back duly till now,radio/television,4473,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",31,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,6,existing credits paid back duly till now,radio/television,1068,... < 100 DM,... >= 7 years,4,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",28,none,own,1,skilled employee / official,2,none,yes,good\r\n... < 0 DM,24,critical account/ other credits existing (not at this bank),car (used),6615,... < 100 DM,unemployed,2,female : divorced/separated/married,none,4,unknown / no property,75,none,for free,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,18,critical account/ other credits existing (not at this bank),education,1864,100 <= ... < 500 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,real estate,30,none,own,2,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,60,existing credits paid back duly till now,car (new),7408,100 <= ... < 500 DM,... < 1 year,4,female : divorced/separated/married,none,2,building society savings agreement/ life insurance,24,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,none,yes,bad\r\nno checking account,48,critical account/ other credits existing (not at this bank),car (used),11590,100 <= ... < 500 DM,1 <= ... < 4 years,2,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",24,bank,rent,2,unskilled - resident,1,none,yes,bad\r\n... < 0 DM,24,no credits taken/ all credits paid back duly,furniture/equipment,4110,... < 100 DM,... >= 7 years,3,female : divorced/separated/married,none,4,unknown / no property,23,bank,rent,2,skilled employee / official,2,none,yes,bad\r\n... < 0 DM,6,critical account/ other credits existing (not at this bank),furniture/equipment,3384,... < 100 DM,1 <= ... < 4 years,1,female : divorced/separated/married,none,4,real estate,44,none,rent,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,13,existing credits paid back duly till now,radio/television,2101,... < 100 DM,... < 1 year,2,female : divorced/separated/married,guarantor,4,building society savings agreement/ life insurance,23,none,own,1,unskilled - resident,1,none,yes,good\r\n... < 0 DM,15,existing credits paid back duly till now,domestic appliances,1275,unknown/ no savings account,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",24,none,rent,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,24,existing credits paid back duly till now,furniture/equipment,4169,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,4,building society savings agreement/ life insurance,28,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,10,existing credits paid back duly till now,furniture/equipment,1521,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",31,none,own,1,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,24,critical account/ other credits existing (not at this bank),education,5743,... < 100 DM,... < 1 year,2,female : divorced/separated/married,none,4,unknown / no property,24,none,for free,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,21,existing credits paid back duly till now,furniture/equipment,3599,... < 100 DM,4 <= ... < 7 years,1,female : divorced/separated/married,none,4,\"car or other, not in attribute Savings account/bonds\",26,none,rent,1,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,18,existing credits paid back duly till now,radio/television,3213,500 <= ... < 1000 DM,... < 1 year,1,female : divorced/separated/married,none,3,real estate,25,none,rent,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,18,existing credits paid back duly till now,business,4439,... < 100 DM,... >= 7 years,1,female : divorced/separated/married,co-applicant,1,real estate,33,bank,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,10,existing credits paid back duly till now,car (new),3949,... < 100 DM,... < 1 year,1,female : divorced/separated/married,guarantor,1,building society savings agreement/ life insurance,37,none,own,1,unskilled - resident,2,none,yes,good\r\nno checking account,15,critical account/ other credits existing (not at this bank),radio/television,1459,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",43,none,own,1,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,13,critical account/ other credits existing (not at this bank),radio/television,882,... < 100 DM,... < 1 year,4,female : divorced/separated/married,guarantor,4,real estate,23,none,own,2,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,24,existing credits paid back duly till now,radio/television,3758,500 <= ... < 1000 DM,unemployed,1,female : divorced/separated/married,none,4,unknown / no property,23,none,rent,1,unemployed/ unskilled - non-resident,1,none,yes,good\r\nno checking account,6,delay in paying off in the past,business,1743,100 <= ... < 500 DM,1 <= ... < 4 years,1,female : divorced/separated/married,none,2,real estate,34,none,own,2,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,9,critical account/ other credits existing (not at this bank),education,1136,... >= 1000 DM,... >= 7 years,4,female : divorced/separated/married,none,3,unknown / no property,32,none,for free,2,skilled employee / official,2,none,yes,bad\r\nno checking account,9,existing credits paid back duly till now,domestic appliances,1236,... < 100 DM,... < 1 year,1,female : divorced/separated/married,none,4,real estate,23,none,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,9,existing credits paid back duly till now,furniture/equipment,959,... < 100 DM,1 <= ... < 4 years,1,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",29,none,own,1,skilled employee / official,1,none,no,bad\r\nno checking account,18,critical account/ other credits existing (not at this bank),car (used),3229,unknown/ no savings account,unemployed,2,female : divorced/separated/married,none,4,unknown / no property,38,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,12,no credits taken/ all credits paid back duly,radio/television,6199,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,building society savings agreement/ life insurance,28,none,rent,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,10,existing credits paid back duly till now,education,727,500 <= ... < 1000 DM,... >= 7 years,4,female : divorced/separated/married,none,4,unknown / no property,46,none,for free,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,24,existing credits paid back duly till now,car (new),1246,... < 100 DM,... < 1 year,4,female : divorced/separated/married,none,2,real estate,23,stores,own,1,unskilled - resident,1,none,yes,bad\r\nno checking account,12,critical account/ other credits existing (not at this bank),radio/television,2331,unknown/ no savings account,... >= 7 years,1,female : divorced/separated/married,co-applicant,4,real estate,49,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,36,delay in paying off in the past,radio/television,4463,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,\"car or other, not in attribute Savings account/bonds\",26,none,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,12,existing credits paid back duly till now,radio/television,776,... < 100 DM,1 <= ... < 4 years,4,female : divorced/separated/married,none,2,real estate,28,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,30,existing credits paid back duly till now,furniture/equipment,2406,... < 100 DM,4 <= ... < 7 years,4,female : divorced/separated/married,none,4,real estate,23,none,rent,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,18,existing credits paid back duly till now,education,1239,unknown/ no savings account,1 <= ... < 4 years,4,male : single,none,4,unknown / no property,61,none,for free,1,skilled employee / official,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,12,existing credits paid back duly till now,radio/television,3399,unknown/ no savings account,... >= 7 years,2,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",37,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,12,delay in paying off in the past,car (new),2247,... < 100 DM,1 <= ... < 4 years,2,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",36,stores,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,6,existing credits paid back duly till now,furniture/equipment,1766,... < 100 DM,1 <= ... < 4 years,1,male : single,none,2,building society savings agreement/ life insurance,21,none,rent,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,18,existing credits paid back duly till now,furniture/equipment,2473,... < 100 DM,unemployed,4,male : single,none,1,\"car or other, not in attribute Savings account/bonds\",25,none,own,1,unemployed/ unskilled - non-resident,1,none,yes,bad\r\nno checking account,12,existing credits paid back duly till now,business,1542,... < 100 DM,4 <= ... < 7 years,2,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",36,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,18,critical account/ other credits existing (not at this bank),car (used),3850,... < 100 DM,4 <= ... < 7 years,3,male : single,none,1,\"car or other, not in attribute Savings account/bonds\",27,none,own,2,skilled employee / official,1,none,yes,good\r\n... < 0 DM,18,existing credits paid back duly till now,furniture/equipment,3650,... < 100 DM,... < 1 year,1,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",22,none,rent,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,36,existing credits paid back duly till now,furniture/equipment,3446,... < 100 DM,... >= 7 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",42,none,own,1,skilled employee / official,2,none,yes,bad\r\n0 <= ... < 200 DM,18,existing credits paid back duly till now,furniture/equipment,3001,... < 100 DM,4 <= ... < 7 years,2,male : single,none,4,real estate,40,none,rent,1,skilled employee / official,1,none,yes,good\r\nno checking account,36,existing credits paid back duly till now,car (new),3079,unknown/ no savings account,1 <= ... < 4 years,4,male : single,none,4,real estate,36,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,18,critical account/ other credits existing (not at this bank),radio/television,6070,... < 100 DM,... >= 7 years,3,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",33,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,10,critical account/ other credits existing (not at this bank),furniture/equipment,2146,... < 100 DM,... < 1 year,1,male : single,none,3,real estate,23,none,rent,2,skilled employee / official,1,none,yes,good\r\nno checking account,60,critical account/ other credits existing (not at this bank),car (new),13756,unknown/ no savings account,... >= 7 years,2,male : single,none,4,unknown / no property,63,bank,for free,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,60,all credits at this bank paid back duly,others,14782,100 <= ... < 500 DM,... >= 7 years,3,male : single,none,4,unknown / no property,60,bank,for free,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,48,all credits at this bank paid back duly,business,7685,... < 100 DM,4 <= ... < 7 years,2,male : single,guarantor,4,\"car or other, not in attribute Savings account/bonds\",37,none,rent,1,skilled employee / official,1,none,yes,bad\r\nno checking account,18,delay in paying off in the past,radio/television,2320,... < 100 DM,unemployed,2,male : single,none,3,real estate,34,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,7,delay in paying off in the past,radio/television,846,unknown/ no savings account,... >= 7 years,3,male : single,none,4,unknown / no property,36,none,for free,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,36,existing credits paid back duly till now,car (new),14318,... < 100 DM,... >= 7 years,4,male : single,none,2,unknown / no property,57,none,for free,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,6,critical account/ other credits existing (not at this bank),car (new),362,100 <= ... < 500 DM,1 <= ... < 4 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",52,none,own,2,unskilled - resident,1,none,yes,good\r\n... < 0 DM,20,existing credits paid back duly till now,furniture/equipment,2212,unknown/ no savings account,4 <= ... < 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",39,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,18,existing credits paid back duly till now,car (used),12976,... < 100 DM,unemployed,3,male : single,none,4,unknown / no property,38,none,for free,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,22,existing credits paid back duly till now,car (new),1283,unknown/ no savings account,4 <= ... < 7 years,4,male : single,none,4,building society savings agreement/ life insurance,25,none,rent,1,skilled employee / official,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,12,existing credits paid back duly till now,car (new),1330,... < 100 DM,... < 1 year,4,male : single,none,1,real estate,26,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,30,delay in paying off in the past,business,4272,100 <= ... < 500 DM,1 <= ... < 4 years,2,male : single,none,2,building society savings agreement/ life insurance,26,none,own,2,unskilled - resident,1,none,yes,good\r\nno checking account,18,critical account/ other credits existing (not at this bank),radio/television,2238,... < 100 DM,1 <= ... < 4 years,2,male : single,none,1,\"car or other, not in attribute Savings account/bonds\",25,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,18,existing credits paid back duly till now,radio/television,1126,unknown/ no savings account,... < 1 year,4,male : single,none,2,real estate,21,none,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,18,critical account/ other credits existing (not at this bank),furniture/equipment,7374,... < 100 DM,unemployed,4,male : single,none,4,building society savings agreement/ life insurance,40,stores,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,15,critical account/ other credits existing (not at this bank),business,2326,500 <= ... < 1000 DM,1 <= ... < 4 years,2,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",27,bank,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,9,existing credits paid back duly till now,business,1449,... < 100 DM,4 <= ... < 7 years,3,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",27,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,18,existing credits paid back duly till now,car (new),1820,... < 100 DM,1 <= ... < 4 years,2,male : single,none,2,building society savings agreement/ life insurance,30,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,furniture/equipment,983,... >= 1000 DM,... < 1 year,1,male : single,none,4,real estate,19,none,rent,1,unskilled - resident,1,none,yes,good\r\n... < 0 DM,36,existing credits paid back duly till now,car (new),3249,... < 100 DM,4 <= ... < 7 years,2,male : single,none,4,unknown / no property,39,bank,for free,1,management/ self-employed/ highly qualified employee/ officer,2,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,6,critical account/ other credits existing (not at this bank),radio/television,1957,... < 100 DM,4 <= ... < 7 years,1,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",31,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,9,critical account/ other credits existing (not at this bank),furniture/equipment,2406,... < 100 DM,unemployed,2,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",31,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,none,yes,good\r\n0 <= ... < 200 DM,39,delay in paying off in the past,education,11760,100 <= ... < 500 DM,4 <= ... < 7 years,2,male : single,none,3,unknown / no property,32,none,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,furniture/equipment,2578,... < 100 DM,unemployed,3,male : single,none,4,unknown / no property,55,none,for free,1,management/ self-employed/ highly qualified employee/ officer,1,none,yes,good\r\n... < 0 DM,36,critical account/ other credits existing (not at this bank),furniture/equipment,2348,... < 100 DM,1 <= ... < 4 years,3,male : single,none,2,building society savings agreement/ life insurance,46,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,car (new),1223,... < 100 DM,... >= 7 years,1,male : single,none,1,real estate,46,none,rent,2,skilled employee / official,1,none,yes,bad\r\nno checking account,24,critical account/ other credits existing (not at this bank),radio/television,1516,... >= 1000 DM,1 <= ... < 4 years,4,male : single,none,1,real estate,43,none,own,2,unskilled - resident,1,none,yes,good\r\nno checking account,18,existing credits paid back duly till now,radio/television,1473,... < 100 DM,... < 1 year,3,male : single,none,4,real estate,39,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,18,critical account/ other credits existing (not at this bank),business,1887,unknown/ no savings account,1 <= ... < 4 years,4,male : single,none,4,real estate,28,bank,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,24,delay in paying off in the past,business,8648,... < 100 DM,... < 1 year,2,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",27,bank,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,14,delay in paying off in the past,car (new),802,... < 100 DM,1 <= ... < 4 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",27,none,own,2,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,18,delay in paying off in the past,car (new),2899,unknown/ no savings account,... >= 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",43,none,own,1,skilled employee / official,2,none,yes,good\r\n0 <= ... < 200 DM,24,existing credits paid back duly till now,radio/television,2039,... < 100 DM,... < 1 year,1,male : single,none,1,building society savings agreement/ life insurance,22,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,24,critical account/ other credits existing (not at this bank),car (used),2197,unknown/ no savings account,4 <= ... < 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",43,none,own,2,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,15,existing credits paid back duly till now,radio/television,1053,... < 100 DM,... < 1 year,4,male : single,none,2,real estate,27,none,own,1,skilled employee / official,1,none,no,good\r\nno checking account,24,existing credits paid back duly till now,radio/television,3235,500 <= ... < 1000 DM,... >= 7 years,3,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",26,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,12,critical account/ other credits existing (not at this bank),car (new),939,500 <= ... < 1000 DM,4 <= ... < 7 years,4,male : single,none,2,real estate,28,none,own,3,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,24,existing credits paid back duly till now,radio/television,1967,... < 100 DM,... >= 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",20,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,33,critical account/ other credits existing (not at this bank),car (used),7253,... < 100 DM,4 <= ... < 7 years,3,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",35,none,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),business,2292,... < 100 DM,unemployed,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",42,stores,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,10,existing credits paid back duly till now,car (new),1597,500 <= ... < 1000 DM,1 <= ... < 4 years,3,male : single,none,2,unknown / no property,40,none,rent,1,unskilled - resident,2,none,no,good\r\n... < 0 DM,24,existing credits paid back duly till now,car (new),1381,unknown/ no savings account,1 <= ... < 4 years,4,male : single,none,2,building society savings agreement/ life insurance,35,none,own,1,skilled employee / official,1,none,yes,bad\r\nno checking account,36,critical account/ other credits existing (not at this bank),car (used),5842,... < 100 DM,... >= 7 years,2,male : single,none,2,building society savings agreement/ life insurance,35,none,own,2,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,car (new),2579,... < 100 DM,... < 1 year,4,male : single,none,1,real estate,33,none,own,1,unskilled - resident,2,none,yes,bad\r\n... < 0 DM,18,delay in paying off in the past,education,8471,unknown/ no savings account,1 <= ... < 4 years,1,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",23,none,rent,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,21,existing credits paid back duly till now,car (new),2782,500 <= ... < 1000 DM,4 <= ... < 7 years,1,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",31,bank,own,1,management/ self-employed/ highly qualified employee/ officer,1,none,yes,good\r\n0 <= ... < 200 DM,18,existing credits paid back duly till now,car (new),1042,unknown/ no savings account,1 <= ... < 4 years,4,male : single,none,2,building society savings agreement/ life insurance,33,none,own,1,skilled employee / official,1,none,yes,bad\r\nno checking account,15,existing credits paid back duly till now,car (new),3186,... >= 1000 DM,4 <= ... < 7 years,2,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",20,none,rent,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,car (used),2028,unknown/ no savings account,1 <= ... < 4 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",30,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,12,critical account/ other credits existing (not at this bank),car (new),958,... < 100 DM,4 <= ... < 7 years,2,male : single,none,3,real estate,47,none,own,2,unskilled - resident,2,none,yes,good\r\nno checking account,21,delay in paying off in the past,furniture/equipment,1591,100 <= ... < 500 DM,4 <= ... < 7 years,4,male : single,none,3,real estate,34,none,own,2,management/ self-employed/ highly qualified employee/ officer,1,none,yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,furniture/equipment,2762,unknown/ no savings account,... >= 7 years,1,male : single,none,2,building society savings agreement/ life insurance,25,bank,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,18,existing credits paid back duly till now,car (used),2779,... < 100 DM,1 <= ... < 4 years,1,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",21,none,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,28,critical account/ other credits existing (not at this bank),radio/television,2743,... < 100 DM,... >= 7 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",29,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,18,critical account/ other credits existing (not at this bank),radio/television,1149,... >= 1000 DM,1 <= ... < 4 years,4,male : single,none,3,real estate,46,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,9,existing credits paid back duly till now,furniture/equipment,1313,... < 100 DM,... >= 7 years,1,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",20,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,18,critical account/ other credits existing (not at this bank),repairs,1190,... < 100 DM,unemployed,2,male : single,none,4,unknown / no property,55,none,for free,3,unemployed/ unskilled - non-resident,2,none,yes,bad\r\nno checking account,5,existing credits paid back duly till now,business,3448,... < 100 DM,4 <= ... < 7 years,1,male : single,none,4,real estate,74,none,own,1,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,24,existing credits paid back duly till now,others,11328,... < 100 DM,1 <= ... < 4 years,2,male : single,co-applicant,3,\"car or other, not in attribute Savings account/bonds\",29,bank,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,6,critical account/ other credits existing (not at this bank),furniture/equipment,1872,... < 100 DM,unemployed,4,male : single,none,4,unknown / no property,36,none,for free,3,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,24,critical account/ other credits existing (not at this bank),repairs,2058,... < 100 DM,1 <= ... < 4 years,4,male : single,none,2,real estate,33,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,9,existing credits paid back duly till now,furniture/equipment,2136,... < 100 DM,1 <= ... < 4 years,3,male : single,none,2,real estate,25,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,radio/television,1484,unknown/ no savings account,1 <= ... < 4 years,2,male : single,none,1,real estate,25,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,6,existing credits paid back duly till now,repairs,660,500 <= ... < 1000 DM,4 <= ... < 7 years,2,male : single,none,4,real estate,23,none,rent,1,unskilled - resident,1,none,yes,good\r\nno checking account,24,critical account/ other credits existing (not at this bank),car (new),1287,... >= 1000 DM,... >= 7 years,4,male : single,none,4,real estate,37,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,42,critical account/ other credits existing (not at this bank),repairs,3394,... < 100 DM,unemployed,4,male : single,co-applicant,4,\"car or other, not in attribute Savings account/bonds\",65,none,own,2,unemployed/ unskilled - non-resident,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,12,all credits at this bank paid back duly,business,609,... < 100 DM,... < 1 year,4,male : single,none,1,real estate,26,none,own,1,unemployed/ unskilled - non-resident,1,none,yes,bad\r\nno checking account,12,existing credits paid back duly till now,car (new),1884,... < 100 DM,... >= 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",39,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,furniture/equipment,1620,... < 100 DM,1 <= ... < 4 years,2,male : single,co-applicant,3,building society savings agreement/ life insurance,30,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,20,delay in paying off in the past,others,2629,... < 100 DM,1 <= ... < 4 years,2,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",29,bank,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,existing credits paid back duly till now,education,719,... < 100 DM,... >= 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",41,bank,own,1,unskilled - resident,2,none,yes,bad\r\n0 <= ... < 200 DM,48,critical account/ other credits existing (not at this bank),furniture/equipment,5096,... < 100 DM,1 <= ... < 4 years,2,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",30,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,9,critical account/ other credits existing (not at this bank),education,1244,unknown/ no savings account,... >= 7 years,4,male : single,none,4,building society savings agreement/ life insurance,41,none,rent,2,unskilled - resident,1,none,yes,good\r\n... < 0 DM,36,existing credits paid back duly till now,car (new),1842,... < 100 DM,... < 1 year,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",34,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,7,existing credits paid back duly till now,radio/television,2576,... < 100 DM,1 <= ... < 4 years,2,male : single,guarantor,2,real estate,35,none,own,1,skilled employee / official,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,12,existing credits paid back duly till now,furniture/equipment,1424,unknown/ no savings account,... >= 7 years,3,male : single,none,4,real estate,55,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,15,delay in paying off in the past,repairs,1512,... >= 1000 DM,1 <= ... < 4 years,3,male : single,none,3,building society savings agreement/ life insurance,61,stores,own,2,skilled employee / official,1,none,yes,bad\r\nno checking account,36,critical account/ other credits existing (not at this bank),car (used),11054,unknown/ no savings account,1 <= ... < 4 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",30,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,6,existing credits paid back duly till now,radio/television,518,... < 100 DM,1 <= ... < 4 years,3,male : single,none,1,real estate,29,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,12,no credits taken/ all credits paid back duly,furniture/equipment,2759,... < 100 DM,... >= 7 years,2,male : single,none,4,building society savings agreement/ life insurance,34,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,24,existing credits paid back duly till now,car (used),2670,... < 100 DM,... >= 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",35,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,24,existing credits paid back duly till now,car (new),4817,... < 100 DM,4 <= ... < 7 years,2,male : single,co-applicant,3,building society savings agreement/ life insurance,31,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,24,existing credits paid back duly till now,car (used),2679,... < 100 DM,... < 1 year,4,male : single,none,1,unknown / no property,29,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,11,critical account/ other credits existing (not at this bank),car (new),3905,... < 100 DM,1 <= ... < 4 years,2,male : single,none,2,real estate,36,none,rent,2,skilled employee / official,2,none,yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,car (used),3386,... < 100 DM,... >= 7 years,3,male : single,none,4,unknown / no property,35,none,for free,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,6,existing credits paid back duly till now,domestic appliances,343,... < 100 DM,... < 1 year,4,male : single,none,1,real estate,27,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,18,existing credits paid back duly till now,radio/television,4594,... < 100 DM,... < 1 year,3,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",32,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,36,existing credits paid back duly till now,furniture/equipment,3620,... < 100 DM,1 <= ... < 4 years,1,male : single,guarantor,2,building society savings agreement/ life insurance,37,none,own,1,skilled employee / official,2,none,yes,good\r\n... < 0 DM,15,existing credits paid back duly till now,car (new),1721,... < 100 DM,... < 1 year,2,male : single,none,3,real estate,36,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,furniture/equipment,3017,... < 100 DM,... < 1 year,3,male : single,none,1,real estate,34,none,rent,1,management/ self-employed/ highly qualified employee/ officer,1,none,yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,retraining,754,unknown/ no savings account,... >= 7 years,4,male : single,none,4,building society savings agreement/ life insurance,38,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,18,existing credits paid back duly till now,business,1950,... < 100 DM,4 <= ... < 7 years,4,male : single,none,1,\"car or other, not in attribute Savings account/bonds\",34,stores,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,24,existing credits paid back duly till now,car (used),2924,... < 100 DM,1 <= ... < 4 years,3,male : single,guarantor,4,unknown / no property,63,bank,own,1,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,24,delay in paying off in the past,radio/television,1659,... < 100 DM,... < 1 year,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",29,none,rent,1,unskilled - resident,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,48,delay in paying off in the past,radio/television,7238,unknown/ no savings account,... >= 7 years,3,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",32,bank,own,2,skilled employee / official,2,none,yes,good\r\nno checking account,33,delay in paying off in the past,business,2764,... < 100 DM,1 <= ... < 4 years,2,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",26,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,24,delay in paying off in the past,car (used),4679,... < 100 DM,4 <= ... < 7 years,3,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",35,none,own,2,unskilled - resident,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,24,existing credits paid back duly till now,radio/television,3092,100 <= ... < 500 DM,... < 1 year,3,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",22,none,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,6,existing credits paid back duly till now,education,448,... < 100 DM,... < 1 year,4,male : single,none,4,building society savings agreement/ life insurance,23,none,own,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,9,existing credits paid back duly till now,car (new),654,... < 100 DM,1 <= ... < 4 years,4,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",28,none,own,1,unskilled - resident,1,none,yes,bad\r\nno checking account,6,existing credits paid back duly till now,retraining,1238,unknown/ no savings account,unemployed,4,male : single,none,4,building society savings agreement/ life insurance,36,none,own,1,management/ self-employed/ highly qualified employee/ officer,2,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,18,critical account/ other credits existing (not at this bank),radio/television,1245,... < 100 DM,1 <= ... < 4 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",33,none,own,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,18,no credits taken/ all credits paid back duly,furniture/equipment,3114,... < 100 DM,... < 1 year,1,male : single,none,4,building society savings agreement/ life insurance,26,none,rent,1,skilled employee / official,1,none,yes,bad\r\nno checking account,39,existing credits paid back duly till now,car (used),2569,500 <= ... < 1000 DM,1 <= ... < 4 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",24,none,own,1,skilled employee / official,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,24,existing credits paid back duly till now,radio/television,5152,... < 100 DM,4 <= ... < 7 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",25,bank,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,business,1037,100 <= ... < 500 DM,4 <= ... < 7 years,3,male : single,none,4,real estate,39,none,own,1,unskilled - resident,1,none,yes,good\r\n... < 0 DM,15,critical account/ other credits existing (not at this bank),furniture/equipment,1478,... < 100 DM,... >= 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",44,none,own,2,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,12,critical account/ other credits existing (not at this bank),radio/television,3573,... < 100 DM,1 <= ... < 4 years,1,male : single,none,1,real estate,23,none,own,1,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,24,existing credits paid back duly till now,car (new),1201,... < 100 DM,... < 1 year,4,male : single,none,1,building society savings agreement/ life insurance,26,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,30,existing credits paid back duly till now,furniture/equipment,3622,... >= 1000 DM,... >= 7 years,4,male : single,none,4,building society savings agreement/ life insurance,57,none,rent,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,15,delay in paying off in the past,furniture/equipment,960,... >= 1000 DM,4 <= ... < 7 years,3,male : single,none,2,building society savings agreement/ life insurance,30,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),car (new),1163,500 <= ... < 1000 DM,1 <= ... < 4 years,4,male : single,none,4,real estate,44,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,6,delay in paying off in the past,car (new),1209,... < 100 DM,unemployed,4,male : single,none,4,building society savings agreement/ life insurance,47,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,12,existing credits paid back duly till now,radio/television,3077,... < 100 DM,1 <= ... < 4 years,2,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",52,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,24,existing credits paid back duly till now,car (new),3757,... < 100 DM,... >= 7 years,4,male : single,co-applicant,4,unknown / no property,62,none,for free,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,10,existing credits paid back duly till now,car (new),1418,100 <= ... < 500 DM,1 <= ... < 4 years,3,male : single,none,2,real estate,35,none,rent,1,unskilled - resident,1,none,no,good\r\nno checking account,6,existing credits paid back duly till now,car (new),3518,... < 100 DM,1 <= ... < 4 years,2,male : single,guarantor,3,building society savings agreement/ life insurance,26,none,rent,1,skilled employee / official,1,none,yes,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),radio/television,1934,... < 100 DM,... >= 7 years,2,male : single,none,2,unknown / no property,26,none,own,2,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,27,no credits taken/ all credits paid back duly,business,8318,... < 100 DM,... >= 7 years,2,male : single,none,4,unknown / no property,42,none,for free,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,6,critical account/ other credits existing (not at this bank),radio/television,1237,100 <= ... < 500 DM,1 <= ... < 4 years,1,male : single,none,1,building society savings agreement/ life insurance,27,none,own,2,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,6,existing credits paid back duly till now,radio/television,368,unknown/ no savings account,... >= 7 years,4,male : single,none,4,building society savings agreement/ life insurance,38,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,12,critical account/ other credits existing (not at this bank),car (new),2122,... < 100 DM,1 <= ... < 4 years,3,male : single,none,2,real estate,39,none,rent,2,unskilled - resident,2,none,no,good\r\n... < 0 DM,24,existing credits paid back duly till now,furniture/equipment,2996,unknown/ no savings account,1 <= ... < 4 years,2,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",20,none,own,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,36,existing credits paid back duly till now,furniture/equipment,9034,100 <= ... < 500 DM,... < 1 year,4,male : single,co-applicant,1,unknown / no property,29,none,rent,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,24,critical account/ other credits existing (not at this bank),furniture/equipment,1585,... < 100 DM,4 <= ... < 7 years,4,male : single,none,3,building society savings agreement/ life insurance,40,none,own,2,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,18,existing credits paid back duly till now,radio/television,1301,... < 100 DM,... >= 7 years,4,male : single,guarantor,2,real estate,32,none,own,1,unskilled - resident,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,6,critical account/ other credits existing (not at this bank),car (new),1323,100 <= ... < 500 DM,... >= 7 years,2,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",28,none,own,2,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,24,existing credits paid back duly till now,car (new),3123,... < 100 DM,... < 1 year,4,male : single,none,1,building society savings agreement/ life insurance,27,none,own,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,36,existing credits paid back duly till now,car (used),5493,... < 100 DM,... >= 7 years,2,male : single,none,4,unknown / no property,42,none,for free,1,skilled employee / official,2,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,9,existing credits paid back duly till now,radio/television,1126,100 <= ... < 500 DM,... >= 7 years,2,male : single,none,4,real estate,49,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,24,critical account/ other credits existing (not at this bank),radio/television,1216,100 <= ... < 500 DM,... < 1 year,4,male : single,none,4,unknown / no property,38,bank,own,2,skilled employee / official,2,none,yes,bad\r\n... < 0 DM,24,existing credits paid back duly till now,car (new),1207,... < 100 DM,... < 1 year,4,male : single,none,4,building society savings agreement/ life insurance,24,none,rent,1,skilled employee / official,1,none,yes,bad\r\nno checking account,10,existing credits paid back duly till now,car (new),1309,unknown/ no savings account,1 <= ... < 4 years,4,male : single,guarantor,4,building society savings agreement/ life insurance,27,none,own,1,unskilled - resident,1,none,yes,bad\r\n... >= 200 DM / salary assignments for at least 1 year,15,critical account/ other credits existing (not at this bank),car (used),2360,500 <= ... < 1000 DM,1 <= ... < 4 years,2,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",36,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,15,all credits at this bank paid back duly,car (new),6850,100 <= ... < 500 DM,unemployed,1,male : single,none,2,building society savings agreement/ life insurance,34,none,own,1,management/ self-employed/ highly qualified employee/ officer,2,\"yes, registered under the customers name\",yes,bad\r\nno checking account,24,existing credits paid back duly till now,radio/television,1413,... < 100 DM,1 <= ... < 4 years,4,male : single,none,2,building society savings agreement/ life insurance,28,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,39,existing credits paid back duly till now,car (used),8588,100 <= ... < 500 DM,... >= 7 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",45,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,car (new),759,... < 100 DM,4 <= ... < 7 years,4,male : single,none,2,real estate,26,none,own,1,skilled employee / official,1,none,yes,bad\r\nno checking account,36,existing credits paid back duly till now,car (used),4686,... < 100 DM,1 <= ... < 4 years,2,male : single,none,2,unknown / no property,32,none,for free,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,15,existing credits paid back duly till now,business,2687,... < 100 DM,4 <= ... < 7 years,2,male : single,none,4,building society savings agreement/ life insurance,26,none,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,12,delay in paying off in the past,radio/television,585,... < 100 DM,1 <= ... < 4 years,4,male : single,co-applicant,4,real estate,20,none,rent,2,skilled employee / official,1,none,yes,good\r\nno checking account,24,existing credits paid back duly till now,car (new),2255,unknown/ no savings account,... < 1 year,4,male : single,none,1,building society savings agreement/ life insurance,54,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,6,critical account/ other credits existing (not at this bank),car (new),609,... < 100 DM,4 <= ... < 7 years,4,male : single,none,3,building society savings agreement/ life insurance,37,none,own,2,skilled employee / official,1,none,no,good\r\n... < 0 DM,6,critical account/ other credits existing (not at this bank),car (new),1361,... < 100 DM,... < 1 year,2,male : single,none,4,real estate,40,none,own,1,unskilled - resident,2,none,no,good\r\nno checking account,36,critical account/ other credits existing (not at this bank),furniture/equipment,7127,... < 100 DM,... < 1 year,2,male : single,none,4,building society savings agreement/ life insurance,23,none,rent,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,6,existing credits paid back duly till now,car (new),1203,100 <= ... < 500 DM,... >= 7 years,3,male : single,none,2,building society savings agreement/ life insurance,43,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,6,critical account/ other credits existing (not at this bank),radio/television,700,unknown/ no savings account,... >= 7 years,4,male : single,none,4,unknown / no property,36,none,for free,2,skilled employee / official,1,none,yes,good\r\nno checking account,24,critical account/ other credits existing (not at this bank),repairs,5507,... < 100 DM,... >= 7 years,3,male : single,none,4,unknown / no property,44,none,for free,2,skilled employee / official,1,none,yes,good\r\n... < 0 DM,18,existing credits paid back duly till now,radio/television,3190,... < 100 DM,1 <= ... < 4 years,2,male : single,none,2,real estate,24,none,own,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,48,no credits taken/ all credits paid back duly,furniture/equipment,7119,... < 100 DM,1 <= ... < 4 years,3,male : single,none,4,unknown / no property,53,none,for free,2,skilled employee / official,2,none,yes,bad\r\nno checking account,24,existing credits paid back duly till now,car (used),3488,100 <= ... < 500 DM,4 <= ... < 7 years,3,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",23,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,18,existing credits paid back duly till now,radio/television,1113,... < 100 DM,1 <= ... < 4 years,4,male : single,guarantor,4,real estate,26,none,own,1,unskilled - resident,2,none,yes,good\r\n0 <= ... < 200 DM,26,existing credits paid back duly till now,car (used),7966,... < 100 DM,... < 1 year,2,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",30,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,15,critical account/ other credits existing (not at this bank),education,1532,100 <= ... < 500 DM,1 <= ... < 4 years,4,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",31,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,4,critical account/ other credits existing (not at this bank),radio/television,1503,... < 100 DM,4 <= ... < 7 years,2,male : single,none,1,real estate,42,none,own,2,unskilled - resident,2,none,yes,good\r\n... < 0 DM,36,existing credits paid back duly till now,radio/television,2302,... < 100 DM,1 <= ... < 4 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",31,none,rent,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,6,existing credits paid back duly till now,car (new),662,... < 100 DM,... < 1 year,3,male : single,none,4,real estate,41,none,own,1,unskilled - resident,2,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,36,existing credits paid back duly till now,education,2273,... < 100 DM,4 <= ... < 7 years,3,male : single,none,1,\"car or other, not in attribute Savings account/bonds\",32,none,own,2,skilled employee / official,2,none,yes,good\r\n0 <= ... < 200 DM,15,existing credits paid back duly till now,car (new),2631,100 <= ... < 500 DM,1 <= ... < 4 years,2,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",28,none,rent,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,12,delay in paying off in the past,car (used),1503,... < 100 DM,1 <= ... < 4 years,4,male : single,none,4,real estate,41,none,rent,1,skilled employee / official,1,none,yes,good\r\nno checking account,24,existing credits paid back duly till now,radio/television,1311,100 <= ... < 500 DM,4 <= ... < 7 years,4,male : single,none,3,building society savings agreement/ life insurance,26,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,24,existing credits paid back duly till now,radio/television,3105,unknown/ no savings account,... < 1 year,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",25,none,own,2,skilled employee / official,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,21,critical account/ other credits existing (not at this bank),education,2319,... < 100 DM,... < 1 year,2,male : single,none,1,\"car or other, not in attribute Savings account/bonds\",33,none,rent,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,6,existing credits paid back duly till now,car (new),1374,unknown/ no savings account,unemployed,4,male : single,none,3,building society savings agreement/ life insurance,75,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,18,critical account/ other credits existing (not at this bank),furniture/equipment,3612,... < 100 DM,... >= 7 years,3,male : single,none,4,building society savings agreement/ life insurance,37,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,48,existing credits paid back duly till now,car (new),7763,... < 100 DM,... >= 7 years,4,male : single,none,4,unknown / no property,42,bank,for free,1,management/ self-employed/ highly qualified employee/ officer,1,none,yes,bad\r\n... >= 200 DM / salary assignments for at least 1 year,18,existing credits paid back duly till now,furniture/equipment,3049,... < 100 DM,... < 1 year,1,male : single,none,1,building society savings agreement/ life insurance,45,stores,own,1,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,radio/television,1534,... < 100 DM,... < 1 year,1,male : single,none,1,real estate,23,none,rent,1,skilled employee / official,1,none,yes,bad\r\nno checking account,24,delay in paying off in the past,car (new),2032,... < 100 DM,... >= 7 years,4,male : single,none,4,unknown / no property,60,none,for free,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,30,existing credits paid back duly till now,furniture/equipment,6350,unknown/ no savings account,... >= 7 years,4,male : single,none,4,building society savings agreement/ life insurance,31,none,own,1,skilled employee / official,1,none,yes,bad\r\n... >= 200 DM / salary assignments for at least 1 year,18,existing credits paid back duly till now,furniture/equipment,2864,... < 100 DM,1 <= ... < 4 years,2,male : single,none,1,real estate,34,none,own,1,unskilled - resident,2,none,yes,bad\r\nno checking account,12,critical account/ other credits existing (not at this bank),car (new),1255,... < 100 DM,... >= 7 years,4,male : single,none,4,real estate,61,none,own,2,unskilled - resident,1,none,yes,good\r\n... < 0 DM,24,delay in paying off in the past,car (new),1333,... < 100 DM,unemployed,4,male : single,none,2,real estate,43,none,for free,2,skilled employee / official,2,none,yes,bad\r\nno checking account,24,critical account/ other credits existing (not at this bank),car (new),2022,... < 100 DM,1 <= ... < 4 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",37,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,24,existing credits paid back duly till now,radio/television,1552,... < 100 DM,4 <= ... < 7 years,3,male : single,none,1,\"car or other, not in attribute Savings account/bonds\",32,bank,own,1,skilled employee / official,2,none,yes,good\r\n... < 0 DM,12,all credits at this bank paid back duly,radio/television,626,... < 100 DM,1 <= ... < 4 years,4,male : single,none,4,real estate,24,bank,own,1,unskilled - resident,1,none,yes,bad\r\nno checking account,48,critical account/ other credits existing (not at this bank),car (used),8858,unknown/ no savings account,4 <= ... < 7 years,2,male : single,none,1,unknown / no property,35,none,for free,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),repairs,996,unknown/ no savings account,4 <= ... < 7 years,4,male : single,none,4,real estate,23,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,6,all credits at this bank paid back duly,radio/television,1750,500 <= ... < 1000 DM,... >= 7 years,2,male : single,none,4,building society savings agreement/ life insurance,45,bank,own,1,unskilled - resident,2,none,yes,good\r\n... < 0 DM,48,existing credits paid back duly till now,radio/television,6999,... < 100 DM,4 <= ... < 7 years,1,male : single,guarantor,1,real estate,34,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,12,critical account/ other credits existing (not at this bank),car (new),1995,100 <= ... < 500 DM,... < 1 year,4,male : single,none,1,\"car or other, not in attribute Savings account/bonds\",27,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,9,existing credits paid back duly till now,education,1199,... < 100 DM,4 <= ... < 7 years,4,male : single,none,4,building society savings agreement/ life insurance,67,none,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,radio/television,1331,... < 100 DM,... < 1 year,2,male : single,none,1,\"car or other, not in attribute Savings account/bonds\",22,stores,own,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,18,no credits taken/ all credits paid back duly,car (new),2278,100 <= ... < 500 DM,... < 1 year,3,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",28,none,own,2,skilled employee / official,1,none,yes,bad\r\nno checking account,21,no credits taken/ all credits paid back duly,car (new),5003,unknown/ no savings account,1 <= ... < 4 years,1,male : single,none,4,building society savings agreement/ life insurance,29,bank,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,24,all credits at this bank paid back duly,furniture/equipment,3552,... < 100 DM,4 <= ... < 7 years,3,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",27,bank,own,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,18,critical account/ other credits existing (not at this bank),furniture/equipment,1928,... < 100 DM,... < 1 year,2,male : single,none,2,real estate,31,none,own,2,unskilled - resident,1,none,yes,bad\r\n... < 0 DM,24,existing credits paid back duly till now,car (used),2964,unknown/ no savings account,... >= 7 years,4,male : single,none,4,unknown / no property,49,bank,for free,1,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,24,all credits at this bank paid back duly,radio/television,1546,... < 100 DM,4 <= ... < 7 years,4,male : single,guarantor,4,\"car or other, not in attribute Savings account/bonds\",24,bank,rent,1,unskilled - resident,1,none,yes,bad\r\n... >= 200 DM / salary assignments for at least 1 year,6,delay in paying off in the past,radio/television,683,... < 100 DM,... < 1 year,2,male : single,none,1,building society savings agreement/ life insurance,29,bank,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,36,existing credits paid back duly till now,car (new),12389,unknown/ no savings account,1 <= ... < 4 years,1,male : single,none,4,unknown / no property,37,none,for free,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,24,delay in paying off in the past,business,4712,unknown/ no savings account,1 <= ... < 4 years,4,male : single,none,2,building society savings agreement/ life insurance,37,bank,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,24,delay in paying off in the past,radio/television,1553,100 <= ... < 500 DM,4 <= ... < 7 years,3,male : single,none,2,building society savings agreement/ life insurance,23,none,rent,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,car (new),1372,... < 100 DM,4 <= ... < 7 years,2,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",36,none,own,1,skilled employee / official,1,none,yes,bad\r\nno checking account,24,critical account/ other credits existing (not at this bank),radio/television,2578,... >= 1000 DM,... >= 7 years,2,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",34,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,48,existing credits paid back duly till now,radio/television,3979,unknown/ no savings account,4 <= ... < 7 years,4,male : single,none,1,\"car or other, not in attribute Savings account/bonds\",41,none,own,2,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,48,existing credits paid back duly till now,radio/television,6758,... < 100 DM,1 <= ... < 4 years,3,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",31,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,24,existing credits paid back duly till now,furniture/equipment,3234,... < 100 DM,... < 1 year,4,male : single,none,4,real estate,23,none,rent,1,unskilled - resident,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,30,critical account/ other credits existing (not at this bank),radio/television,5954,... < 100 DM,4 <= ... < 7 years,3,male : single,co-applicant,2,\"car or other, not in attribute Savings account/bonds\",38,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,24,existing credits paid back duly till now,car (used),5433,unknown/ no savings account,unemployed,2,male : single,none,4,building society savings agreement/ life insurance,26,none,rent,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,15,existing credits paid back duly till now,business,806,... < 100 DM,1 <= ... < 4 years,4,male : single,none,4,building society savings agreement/ life insurance,22,none,own,1,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,9,existing credits paid back duly till now,radio/television,1082,... < 100 DM,... >= 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",27,none,own,2,unskilled - resident,1,none,yes,good\r\nno checking account,15,critical account/ other credits existing (not at this bank),furniture/equipment,2788,... < 100 DM,4 <= ... < 7 years,2,male : single,co-applicant,3,\"car or other, not in attribute Savings account/bonds\",24,bank,own,2,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,radio/television,2930,... < 100 DM,4 <= ... < 7 years,2,male : single,none,1,real estate,27,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,24,critical account/ other credits existing (not at this bank),education,1927,unknown/ no savings account,1 <= ... < 4 years,3,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",33,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,36,critical account/ other credits existing (not at this bank),car (new),2820,... < 100 DM,... < 1 year,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",27,none,own,2,skilled employee / official,1,none,yes,bad\r\nno checking account,24,existing credits paid back duly till now,retraining,937,... < 100 DM,... < 1 year,4,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",27,none,own,2,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,18,critical account/ other credits existing (not at this bank),car (new),1056,... < 100 DM,... >= 7 years,3,male : single,guarantor,3,real estate,30,bank,own,2,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,12,critical account/ other credits existing (not at this bank),car (new),3124,... < 100 DM,... < 1 year,1,male : single,none,3,real estate,49,bank,own,2,unskilled - resident,2,none,yes,good\r\nno checking account,9,existing credits paid back duly till now,furniture/equipment,1388,... < 100 DM,1 <= ... < 4 years,4,male : single,none,2,real estate,26,none,rent,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,36,existing credits paid back duly till now,repairs,2384,... < 100 DM,... < 1 year,4,male : single,none,1,unknown / no property,33,none,rent,1,unskilled - resident,1,none,yes,bad\r\nno checking account,12,existing credits paid back duly till now,car (new),2133,unknown/ no savings account,... >= 7 years,4,male : single,none,4,unknown / no property,52,none,for free,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,18,existing credits paid back duly till now,furniture/equipment,2039,... < 100 DM,1 <= ... < 4 years,1,male : single,none,4,real estate,20,bank,rent,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,9,critical account/ other credits existing (not at this bank),car (new),2799,... < 100 DM,1 <= ... < 4 years,2,male : single,none,2,real estate,36,none,rent,2,skilled employee / official,2,none,yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,furniture/equipment,1289,... < 100 DM,1 <= ... < 4 years,4,male : single,guarantor,1,building society savings agreement/ life insurance,21,none,own,1,unskilled - resident,1,none,yes,good\r\n... < 0 DM,18,existing credits paid back duly till now,domestic appliances,1217,... < 100 DM,1 <= ... < 4 years,4,male : single,none,3,real estate,47,none,own,1,unskilled - resident,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,12,critical account/ other credits existing (not at this bank),furniture/equipment,2246,... < 100 DM,... >= 7 years,3,male : single,none,3,building society savings agreement/ life insurance,60,none,own,2,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,12,critical account/ other credits existing (not at this bank),radio/television,385,... < 100 DM,4 <= ... < 7 years,4,male : single,none,3,real estate,58,none,own,4,unskilled - resident,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,24,delay in paying off in the past,car (new),1965,unknown/ no savings account,1 <= ... < 4 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",42,none,rent,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,21,existing credits paid back duly till now,business,1572,... >= 1000 DM,... >= 7 years,4,male : single,none,4,real estate,36,bank,own,1,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,24,existing credits paid back duly till now,car (new),2718,... < 100 DM,1 <= ... < 4 years,3,male : single,none,4,building society savings agreement/ life insurance,20,none,rent,1,unskilled - resident,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,24,all credits at this bank paid back duly,others,1358,unknown/ no savings account,... >= 7 years,4,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",40,stores,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,6,all credits at this bank paid back duly,car (new),931,100 <= ... < 500 DM,... < 1 year,1,male : single,none,1,building society savings agreement/ life insurance,32,stores,own,1,unskilled - resident,1,none,yes,bad\r\n... < 0 DM,24,existing credits paid back duly till now,car (new),1442,... < 100 DM,4 <= ... < 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",23,none,rent,2,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,24,no credits taken/ all credits paid back duly,business,4241,... < 100 DM,1 <= ... < 4 years,1,male : single,none,4,real estate,36,none,own,3,unskilled - resident,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,18,critical account/ other credits existing (not at this bank),car (new),2775,... < 100 DM,4 <= ... < 7 years,2,male : single,none,2,building society savings agreement/ life insurance,31,bank,own,2,skilled employee / official,1,none,yes,bad\r\nno checking account,24,delay in paying off in the past,business,3863,... < 100 DM,1 <= ... < 4 years,1,male : single,none,2,unknown / no property,32,none,for free,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,7,existing credits paid back duly till now,radio/television,2329,... < 100 DM,... < 1 year,1,male : single,guarantor,1,real estate,45,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,9,existing credits paid back duly till now,furniture/equipment,918,... < 100 DM,1 <= ... < 4 years,4,male : single,none,1,building society savings agreement/ life insurance,30,none,own,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,24,all credits at this bank paid back duly,education,1837,... < 100 DM,4 <= ... < 7 years,4,male : single,none,4,unknown / no property,34,bank,for free,1,unskilled - resident,1,none,yes,bad\r\nno checking account,36,existing credits paid back duly till now,furniture/equipment,3349,... < 100 DM,1 <= ... < 4 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",28,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\n... >= 200 DM / salary assignments for at least 1 year,10,existing credits paid back duly till now,furniture/equipment,1275,... < 100 DM,... < 1 year,4,male : single,none,2,building society savings agreement/ life insurance,23,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,24,all credits at this bank paid back duly,furniture/equipment,2828,500 <= ... < 1000 DM,1 <= ... < 4 years,4,male : single,none,4,real estate,22,stores,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,24,critical account/ other credits existing (not at this bank),business,4526,... < 100 DM,1 <= ... < 4 years,3,male : single,none,2,real estate,74,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,36,existing credits paid back duly till now,radio/television,2671,100 <= ... < 500 DM,1 <= ... < 4 years,4,male : single,co-applicant,4,unknown / no property,50,none,for free,1,skilled employee / official,1,none,yes,bad\r\nno checking account,18,existing credits paid back duly till now,radio/television,2051,... < 100 DM,... < 1 year,4,male : single,none,1,real estate,33,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,15,existing credits paid back duly till now,car (used),1300,unknown/ no savings account,... >= 7 years,4,male : single,none,4,unknown / no property,45,bank,for free,1,skilled employee / official,2,none,yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,domestic appliances,741,100 <= ... < 500 DM,unemployed,4,male : single,none,3,building society savings agreement/ life insurance,22,none,own,1,skilled employee / official,1,none,yes,bad\r\n... >= 200 DM / salary assignments for at least 1 year,10,existing credits paid back duly till now,car (new),1240,100 <= ... < 500 DM,... >= 7 years,1,male : single,none,4,unknown / no property,48,none,for free,1,unskilled - resident,2,none,yes,bad\r\n... < 0 DM,21,existing credits paid back duly till now,radio/television,3357,... >= 1000 DM,... < 1 year,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",29,bank,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,24,all credits at this bank paid back duly,car (used),3632,... < 100 DM,1 <= ... < 4 years,1,male : single,guarantor,4,\"car or other, not in attribute Savings account/bonds\",22,bank,rent,1,skilled employee / official,1,none,no,good\r\nno checking account,18,delay in paying off in the past,furniture/equipment,1808,... < 100 DM,4 <= ... < 7 years,4,male : single,none,1,real estate,22,none,own,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,48,no credits taken/ all credits paid back duly,business,12204,unknown/ no savings account,1 <= ... < 4 years,2,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",48,bank,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,60,delay in paying off in the past,radio/television,9157,unknown/ no savings account,1 <= ... < 4 years,2,male : single,none,2,unknown / no property,27,none,for free,1,management/ self-employed/ highly qualified employee/ officer,1,none,yes,good\r\n... < 0 DM,6,critical account/ other credits existing (not at this bank),car (new),3676,... < 100 DM,1 <= ... < 4 years,1,male : single,none,3,real estate,37,none,rent,3,skilled employee / official,2,none,yes,good\r\n0 <= ... < 200 DM,30,existing credits paid back duly till now,furniture/equipment,3441,100 <= ... < 500 DM,1 <= ... < 4 years,2,male : single,co-applicant,4,\"car or other, not in attribute Savings account/bonds\",21,none,rent,1,skilled employee / official,1,none,yes,bad\r\nno checking account,12,existing credits paid back duly till now,car (new),640,... < 100 DM,1 <= ... < 4 years,4,male : single,none,2,real estate,49,none,own,1,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,21,critical account/ other credits existing (not at this bank),business,3652,... < 100 DM,4 <= ... < 7 years,2,male : single,none,3,building society savings agreement/ life insurance,27,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,18,critical account/ other credits existing (not at this bank),car (new),1530,... < 100 DM,1 <= ... < 4 years,3,male : single,none,2,building society savings agreement/ life insurance,32,bank,own,2,skilled employee / official,1,none,yes,bad\r\nno checking account,48,existing credits paid back duly till now,business,3914,unknown/ no savings account,1 <= ... < 4 years,4,male : single,none,2,real estate,38,bank,own,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,12,existing credits paid back duly till now,furniture/equipment,1858,... < 100 DM,... < 1 year,4,male : single,none,1,\"car or other, not in attribute Savings account/bonds\",22,none,rent,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,18,existing credits paid back duly till now,radio/television,2600,... < 100 DM,1 <= ... < 4 years,4,male : single,none,4,unknown / no property,65,none,for free,2,skilled employee / official,1,none,yes,bad\r\nno checking account,15,existing credits paid back duly till now,radio/television,1979,unknown/ no savings account,... >= 7 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",35,none,own,1,skilled employee / official,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,6,existing credits paid back duly till now,furniture/equipment,2116,... < 100 DM,1 <= ... < 4 years,2,male : single,none,2,real estate,41,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,9,all credits at this bank paid back duly,car (new),1437,100 <= ... < 500 DM,4 <= ... < 7 years,2,male : single,none,3,unknown / no property,29,none,own,1,skilled employee / official,1,none,yes,bad\r\nno checking account,42,critical account/ other credits existing (not at this bank),furniture/equipment,4042,500 <= ... < 1000 DM,1 <= ... < 4 years,4,male : single,none,4,real estate,36,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,9,existing credits paid back duly till now,education,3832,unknown/ no savings account,... >= 7 years,1,male : single,none,4,real estate,64,none,own,1,unskilled - resident,1,none,yes,good\r\n... < 0 DM,24,existing credits paid back duly till now,radio/television,3660,... < 100 DM,1 <= ... < 4 years,2,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",28,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,18,all credits at this bank paid back duly,furniture/equipment,1553,... < 100 DM,1 <= ... < 4 years,4,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",44,bank,own,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,15,existing credits paid back duly till now,radio/television,1444,unknown/ no savings account,... < 1 year,4,male : single,none,1,building society savings agreement/ life insurance,23,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,9,existing credits paid back duly till now,furniture/equipment,1980,... < 100 DM,... < 1 year,2,male : single,co-applicant,2,\"car or other, not in attribute Savings account/bonds\",19,none,rent,2,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,24,existing credits paid back duly till now,car (new),1355,... < 100 DM,... < 1 year,3,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",25,none,own,1,unskilled - resident,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,12,existing credits paid back duly till now,education,1393,... < 100 DM,... >= 7 years,4,male : single,none,4,building society savings agreement/ life insurance,47,bank,own,3,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,24,existing credits paid back duly till now,radio/television,1376,500 <= ... < 1000 DM,4 <= ... < 7 years,4,male : single,none,1,\"car or other, not in attribute Savings account/bonds\",28,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,60,delay in paying off in the past,radio/television,15653,... < 100 DM,4 <= ... < 7 years,2,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",21,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,existing credits paid back duly till now,radio/television,1493,... < 100 DM,... < 1 year,4,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",34,none,own,1,skilled employee / official,2,none,yes,good\r\n... < 0 DM,42,delay in paying off in the past,radio/television,4370,... < 100 DM,4 <= ... < 7 years,3,male : single,none,2,building society savings agreement/ life insurance,26,bank,own,2,skilled employee / official,2,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,18,existing credits paid back duly till now,education,750,... < 100 DM,unemployed,4,male : single,none,1,real estate,27,none,own,1,unemployed/ unskilled - non-resident,1,none,yes,bad\r\n0 <= ... < 200 DM,15,existing credits paid back duly till now,repairs,1308,... < 100 DM,... >= 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",38,none,own,2,unskilled - resident,1,none,yes,good\r\nno checking account,15,existing credits paid back duly till now,education,4623,100 <= ... < 500 DM,1 <= ... < 4 years,3,male : single,none,2,building society savings agreement/ life insurance,40,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,24,critical account/ other credits existing (not at this bank),radio/television,1851,... < 100 DM,4 <= ... < 7 years,4,male : single,guarantor,2,\"car or other, not in attribute Savings account/bonds\",33,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,18,critical account/ other credits existing (not at this bank),radio/television,1880,... < 100 DM,4 <= ... < 7 years,4,male : single,none,1,building society savings agreement/ life insurance,32,none,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,36,delay in paying off in the past,business,7980,unknown/ no savings account,... < 1 year,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",27,none,rent,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,30,no credits taken/ all credits paid back duly,furniture/equipment,4583,... < 100 DM,1 <= ... < 4 years,2,male : single,guarantor,2,real estate,32,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,12,existing credits paid back duly till now,car (new),1386,500 <= ... < 1000 DM,1 <= ... < 4 years,2,male : single,none,2,building society savings agreement/ life insurance,26,none,own,1,skilled employee / official,1,none,yes,bad\r\n... >= 200 DM / salary assignments for at least 1 year,24,existing credits paid back duly till now,car (new),947,... < 100 DM,4 <= ... < 7 years,4,male : single,none,3,unknown / no property,38,bank,for free,1,skilled employee / official,2,none,yes,bad\r\n... < 0 DM,12,existing credits paid back duly till now,education,684,... < 100 DM,1 <= ... < 4 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",40,none,rent,1,unskilled - resident,2,none,yes,bad\r\n... < 0 DM,48,existing credits paid back duly till now,education,7476,... < 100 DM,4 <= ... < 7 years,4,male : single,none,1,unknown / no property,50,none,for free,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,furniture/equipment,1922,... < 100 DM,1 <= ... < 4 years,4,male : single,none,2,building society savings agreement/ life insurance,37,none,own,1,unskilled - resident,1,none,yes,bad\r\n... < 0 DM,24,existing credits paid back duly till now,car (new),2303,... < 100 DM,... >= 7 years,4,male : single,co-applicant,1,real estate,45,none,own,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,36,delay in paying off in the past,car (new),8086,100 <= ... < 500 DM,... >= 7 years,2,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",42,none,own,4,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,24,critical account/ other credits existing (not at this bank),car (used),2346,... < 100 DM,4 <= ... < 7 years,4,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",35,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,14,existing credits paid back duly till now,car (new),3973,... < 100 DM,unemployed,1,male : single,none,4,unknown / no property,22,none,for free,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,car (new),888,... < 100 DM,... >= 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",41,bank,own,1,unskilled - resident,2,none,yes,bad\r\nno checking account,48,existing credits paid back duly till now,radio/television,10222,unknown/ no savings account,4 <= ... < 7 years,4,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",37,stores,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,30,no credits taken/ all credits paid back duly,business,4221,... < 100 DM,1 <= ... < 4 years,2,male : single,none,1,\"car or other, not in attribute Savings account/bonds\",28,none,own,2,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,18,critical account/ other credits existing (not at this bank),furniture/equipment,6361,... < 100 DM,... >= 7 years,2,male : single,none,1,unknown / no property,41,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,12,existing credits paid back duly till now,radio/television,1297,... < 100 DM,1 <= ... < 4 years,3,male : single,none,4,real estate,23,none,rent,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,car (new),900,unknown/ no savings account,1 <= ... < 4 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",23,none,own,1,skilled employee / official,1,none,yes,bad\r\nno checking account,21,existing credits paid back duly till now,furniture/equipment,2241,... < 100 DM,... >= 7 years,4,male : single,none,2,real estate,50,none,own,2,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,6,delay in paying off in the past,furniture/equipment,1050,... < 100 DM,unemployed,4,male : single,none,1,building society savings agreement/ life insurance,35,stores,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,6,critical account/ other credits existing (not at this bank),education,1047,... < 100 DM,1 <= ... < 4 years,2,male : single,none,4,building society savings agreement/ life insurance,50,none,own,1,unskilled - resident,1,none,yes,good\r\nno checking account,24,critical account/ other credits existing (not at this bank),others,6314,... < 100 DM,unemployed,4,male : single,co-applicant,2,unknown / no property,27,bank,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,30,all credits at this bank paid back duly,furniture/equipment,3496,... >= 1000 DM,1 <= ... < 4 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",34,stores,own,1,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,48,all credits at this bank paid back duly,business,3609,... < 100 DM,1 <= ... < 4 years,1,male : single,none,1,real estate,27,stores,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,12,critical account/ other credits existing (not at this bank),car (new),4843,... < 100 DM,... >= 7 years,3,male : single,co-applicant,4,building society savings agreement/ life insurance,43,none,rent,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n... >= 200 DM / salary assignments for at least 1 year,30,critical account/ other credits existing (not at this bank),radio/television,3017,... < 100 DM,... >= 7 years,4,male : single,none,4,building society savings agreement/ life insurance,47,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,24,critical account/ other credits existing (not at this bank),business,4139,100 <= ... < 500 DM,1 <= ... < 4 years,3,male : single,none,3,building society savings agreement/ life insurance,27,none,own,2,unskilled - resident,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,36,existing credits paid back duly till now,business,5742,100 <= ... < 500 DM,4 <= ... < 7 years,2,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",31,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,60,existing credits paid back duly till now,car (new),10366,... < 100 DM,... >= 7 years,2,male : single,none,4,building society savings agreement/ life insurance,42,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,6,critical account/ other credits existing (not at this bank),car (new),2080,500 <= ... < 1000 DM,1 <= ... < 4 years,1,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",24,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,21,delay in paying off in the past,business,2580,500 <= ... < 1000 DM,... < 1 year,4,male : single,none,2,real estate,41,bank,own,1,unskilled - resident,2,none,yes,bad\r\nno checking account,30,critical account/ other credits existing (not at this bank),radio/television,4530,... < 100 DM,4 <= ... < 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",26,none,rent,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,24,critical account/ other credits existing (not at this bank),furniture/equipment,5150,... < 100 DM,... >= 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",33,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,72,existing credits paid back duly till now,radio/television,5595,100 <= ... < 500 DM,1 <= ... < 4 years,2,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",24,none,own,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,24,existing credits paid back duly till now,radio/television,2384,... < 100 DM,... >= 7 years,4,male : single,none,4,real estate,64,bank,rent,1,unskilled - resident,1,none,yes,good\r\nno checking account,18,existing credits paid back duly till now,radio/television,1453,... < 100 DM,... < 1 year,3,male : single,none,1,real estate,26,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,6,existing credits paid back duly till now,education,1538,... < 100 DM,... < 1 year,1,male : single,none,2,unknown / no property,56,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,12,existing credits paid back duly till now,radio/television,2279,unknown/ no savings account,1 <= ... < 4 years,4,male : single,none,4,unknown / no property,37,none,for free,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,15,delay in paying off in the past,radio/television,1478,... < 100 DM,1 <= ... < 4 years,4,male : single,none,3,real estate,33,bank,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,24,critical account/ other credits existing (not at this bank),radio/television,5103,... < 100 DM,... < 1 year,3,male : single,none,3,unknown / no property,47,none,for free,3,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,36,delay in paying off in the past,business,9857,100 <= ... < 500 DM,4 <= ... < 7 years,1,male : single,none,3,building society savings agreement/ life insurance,31,none,own,2,unskilled - resident,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,60,existing credits paid back duly till now,car (new),6527,unknown/ no savings account,1 <= ... < 4 years,4,male : single,none,4,unknown / no property,34,none,for free,1,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,10,critical account/ other credits existing (not at this bank),radio/television,1347,unknown/ no savings account,4 <= ... < 7 years,4,male : single,none,2,building society savings agreement/ life insurance,27,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,36,delay in paying off in the past,car (new),2862,100 <= ... < 500 DM,... >= 7 years,4,male : single,none,3,unknown / no property,30,none,for free,1,skilled employee / official,1,none,yes,good\r\nno checking account,9,existing credits paid back duly till now,radio/television,2753,100 <= ... < 500 DM,... >= 7 years,3,male : single,co-applicant,4,\"car or other, not in attribute Savings account/bonds\",35,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,car (new),3651,... >= 1000 DM,1 <= ... < 4 years,1,male : single,none,3,building society savings agreement/ life insurance,31,none,own,1,skilled employee / official,2,none,yes,good\r\n... < 0 DM,15,critical account/ other credits existing (not at this bank),furniture/equipment,975,... < 100 DM,1 <= ... < 4 years,2,male : single,none,3,building society savings agreement/ life insurance,25,none,own,2,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,15,existing credits paid back duly till now,repairs,2631,100 <= ... < 500 DM,1 <= ... < 4 years,3,male : single,none,2,real estate,25,none,own,1,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,24,existing credits paid back duly till now,radio/television,2896,100 <= ... < 500 DM,... < 1 year,2,male : single,none,1,\"car or other, not in attribute Savings account/bonds\",29,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,6,critical account/ other credits existing (not at this bank),car (new),4716,unknown/ no savings account,... < 1 year,1,male : single,none,3,real estate,44,none,own,2,unskilled - resident,2,none,yes,good\r\nno checking account,24,existing credits paid back duly till now,radio/television,2284,... < 100 DM,4 <= ... < 7 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",28,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,6,existing credits paid back duly till now,car (used),1236,500 <= ... < 1000 DM,1 <= ... < 4 years,2,male : single,none,4,building society savings agreement/ life insurance,50,none,rent,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,radio/television,1103,... < 100 DM,4 <= ... < 7 years,4,male : single,guarantor,3,real estate,29,none,own,2,skilled employee / official,1,none,no,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),car (new),926,... < 100 DM,unemployed,1,male : single,none,2,building society savings agreement/ life insurance,38,none,own,1,unemployed/ unskilled - non-resident,1,none,yes,good\r\nno checking account,18,critical account/ other credits existing (not at this bank),radio/television,1800,... < 100 DM,1 <= ... < 4 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",24,none,own,2,skilled employee / official,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,15,existing credits paid back duly till now,education,1905,... < 100 DM,... >= 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",40,none,rent,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,existing credits paid back duly till now,furniture/equipment,1123,500 <= ... < 1000 DM,1 <= ... < 4 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",29,none,rent,1,unskilled - resident,1,none,yes,bad\r\n... < 0 DM,48,critical account/ other credits existing (not at this bank),car (used),6331,... < 100 DM,... >= 7 years,4,male : single,none,4,unknown / no property,46,none,for free,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n... >= 200 DM / salary assignments for at least 1 year,24,existing credits paid back duly till now,radio/television,1377,100 <= ... < 500 DM,... >= 7 years,4,male : single,none,2,unknown / no property,47,none,for free,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,30,delay in paying off in the past,business,2503,100 <= ... < 500 DM,... >= 7 years,4,male : single,none,2,building society savings agreement/ life insurance,41,stores,own,2,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,27,existing credits paid back duly till now,business,2528,... < 100 DM,... < 1 year,4,male : single,none,1,building society savings agreement/ life insurance,32,none,own,1,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,15,existing credits paid back duly till now,car (new),5324,500 <= ... < 1000 DM,... >= 7 years,1,male : single,none,4,unknown / no property,35,none,for free,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,48,existing credits paid back duly till now,car (new),6560,100 <= ... < 500 DM,4 <= ... < 7 years,3,male : single,none,2,building society savings agreement/ life insurance,24,none,own,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,12,no credits taken/ all credits paid back duly,furniture/equipment,2969,... < 100 DM,... < 1 year,4,male : single,none,3,building society savings agreement/ life insurance,25,none,rent,2,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,9,existing credits paid back duly till now,radio/television,1206,... < 100 DM,... >= 7 years,4,male : single,none,4,real estate,25,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,9,existing credits paid back duly till now,radio/television,2118,... < 100 DM,1 <= ... < 4 years,2,male : single,none,2,real estate,37,none,own,1,unskilled - resident,2,none,yes,good\r\nno checking account,18,critical account/ other credits existing (not at this bank),radio/television,629,500 <= ... < 1000 DM,... >= 7 years,4,male : single,none,3,building society savings agreement/ life insurance,32,bank,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,6,all credits at this bank paid back duly,education,1198,... < 100 DM,... >= 7 years,4,male : single,none,4,unknown / no property,35,none,for free,1,skilled employee / official,1,none,yes,bad\r\nno checking account,21,existing credits paid back duly till now,car (used),2476,unknown/ no savings account,... >= 7 years,4,male : single,none,4,real estate,46,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,9,critical account/ other credits existing (not at this bank),radio/television,1138,... < 100 DM,1 <= ... < 4 years,4,male : single,none,4,real estate,25,none,own,2,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,60,existing credits paid back duly till now,car (new),14027,... < 100 DM,4 <= ... < 7 years,4,male : single,none,2,unknown / no property,27,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,30,critical account/ other credits existing (not at this bank),car (used),7596,unknown/ no savings account,... >= 7 years,1,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",63,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,30,critical account/ other credits existing (not at this bank),radio/television,3077,unknown/ no savings account,... >= 7 years,3,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",40,none,own,2,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,18,existing credits paid back duly till now,radio/television,1505,... < 100 DM,1 <= ... < 4 years,4,male : single,none,2,unknown / no property,32,none,for free,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,24,critical account/ other credits existing (not at this bank),radio/television,3148,unknown/ no savings account,1 <= ... < 4 years,3,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",31,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,20,no credits taken/ all credits paid back duly,car (used),6148,100 <= ... < 500 DM,... >= 7 years,3,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",31,bank,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,9,no credits taken/ all credits paid back duly,radio/television,1337,... < 100 DM,... < 1 year,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",34,none,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,6,all credits at this bank paid back duly,education,433,... >= 1000 DM,... < 1 year,4,male : single,none,2,building society savings agreement/ life insurance,24,bank,rent,1,skilled employee / official,2,none,yes,bad\r\n... < 0 DM,12,existing credits paid back duly till now,car (new),1228,... < 100 DM,1 <= ... < 4 years,4,male : single,none,2,real estate,24,none,own,1,unskilled - resident,1,none,yes,bad\r\n0 <= ... < 200 DM,9,existing credits paid back duly till now,radio/television,790,500 <= ... < 1000 DM,1 <= ... < 4 years,4,male : single,none,3,real estate,66,none,own,1,unskilled - resident,1,none,yes,good\r\nno checking account,27,existing credits paid back duly till now,car (new),2570,... < 100 DM,1 <= ... < 4 years,3,male : single,none,3,real estate,21,none,rent,1,skilled employee / official,1,none,yes,bad\r\nno checking account,6,critical account/ other credits existing (not at this bank),car (new),250,... >= 1000 DM,1 <= ... < 4 years,2,male : single,none,2,real estate,41,bank,own,2,unskilled - resident,1,none,yes,good\r\nno checking account,15,critical account/ other credits existing (not at this bank),radio/television,1316,500 <= ... < 1000 DM,1 <= ... < 4 years,2,male : single,none,2,building society savings agreement/ life insurance,47,none,own,2,unskilled - resident,1,none,yes,good\r\n... < 0 DM,18,existing credits paid back duly till now,radio/television,1882,... < 100 DM,1 <= ... < 4 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",25,bank,rent,2,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,48,all credits at this bank paid back duly,business,6416,... < 100 DM,... >= 7 years,4,male : single,none,3,unknown / no property,59,none,rent,1,skilled employee / official,1,none,yes,bad\r\n... >= 200 DM / salary assignments for at least 1 year,24,critical account/ other credits existing (not at this bank),business,1275,... >= 1000 DM,1 <= ... < 4 years,2,male : single,none,4,real estate,36,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,24,delay in paying off in the past,radio/television,6403,... < 100 DM,... < 1 year,1,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",33,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,24,existing credits paid back duly till now,radio/television,1987,... < 100 DM,1 <= ... < 4 years,2,male : single,none,4,real estate,21,none,rent,1,unskilled - resident,2,none,yes,bad\r\n0 <= ... < 200 DM,8,existing credits paid back duly till now,radio/television,760,... < 100 DM,4 <= ... < 7 years,4,male : single,guarantor,2,real estate,44,none,own,1,unskilled - resident,1,none,yes,good\r\nno checking account,24,existing credits paid back duly till now,car (used),2603,... >= 1000 DM,1 <= ... < 4 years,2,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",28,none,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,4,critical account/ other credits existing (not at this bank),car (new),3380,... < 100 DM,4 <= ... < 7 years,1,male : single,none,1,real estate,37,none,own,1,skilled employee / official,2,none,yes,good\r\n0 <= ... < 200 DM,36,all credits at this bank paid back duly,domestic appliances,3990,unknown/ no savings account,... < 1 year,3,male : single,none,2,unknown / no property,29,bank,own,1,unemployed/ unskilled - non-resident,1,none,yes,good\r\n0 <= ... < 200 DM,24,existing credits paid back duly till now,car (used),11560,... < 100 DM,1 <= ... < 4 years,1,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",23,none,rent,2,management/ self-employed/ highly qualified employee/ officer,1,none,yes,bad\r\n... < 0 DM,18,existing credits paid back duly till now,car (new),4380,100 <= ... < 500 DM,1 <= ... < 4 years,3,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",35,none,own,1,unskilled - resident,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,6,critical account/ other credits existing (not at this bank),car (new),6761,... < 100 DM,4 <= ... < 7 years,1,male : single,none,3,unknown / no property,45,none,own,2,management/ self-employed/ highly qualified employee/ officer,2,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,30,no credits taken/ all credits paid back duly,business,4280,100 <= ... < 500 DM,1 <= ... < 4 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",26,none,rent,2,unskilled - resident,1,none,yes,bad\r\n... < 0 DM,24,all credits at this bank paid back duly,car (new),2325,100 <= ... < 500 DM,4 <= ... < 7 years,2,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",32,bank,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,10,all credits at this bank paid back duly,radio/television,1048,... < 100 DM,1 <= ... < 4 years,4,male : single,none,4,real estate,23,stores,own,1,unskilled - resident,1,none,yes,good\r\nno checking account,21,existing credits paid back duly till now,radio/television,3160,unknown/ no savings account,... >= 7 years,4,male : single,none,3,building society savings agreement/ life insurance,41,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,24,all credits at this bank paid back duly,furniture/equipment,2483,500 <= ... < 1000 DM,1 <= ... < 4 years,4,male : single,none,4,real estate,22,stores,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,39,critical account/ other credits existing (not at this bank),furniture/equipment,14179,unknown/ no savings account,4 <= ... < 7 years,4,male : single,none,4,building society savings agreement/ life insurance,30,none,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,13,critical account/ other credits existing (not at this bank),business,1797,... < 100 DM,... < 1 year,3,male : single,none,1,building society savings agreement/ life insurance,28,bank,own,2,unskilled - resident,1,none,yes,good\r\n... < 0 DM,15,existing credits paid back duly till now,car (new),2511,... < 100 DM,unemployed,1,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",23,none,rent,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,car (new),1274,... < 100 DM,... < 1 year,3,male : single,none,1,real estate,37,none,own,1,unskilled - resident,1,none,yes,bad\r\nno checking account,21,existing credits paid back duly till now,car (used),5248,unknown/ no savings account,1 <= ... < 4 years,1,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",26,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,15,existing credits paid back duly till now,car (used),3029,... < 100 DM,4 <= ... < 7 years,2,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",33,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,6,existing credits paid back duly till now,furniture/equipment,428,... < 100 DM,... >= 7 years,2,male : single,none,1,building society savings agreement/ life insurance,49,bank,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,18,existing credits paid back duly till now,car (new),976,... < 100 DM,... < 1 year,1,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",23,none,own,1,unskilled - resident,1,none,yes,bad\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,business,841,100 <= ... < 500 DM,4 <= ... < 7 years,2,male : single,none,4,real estate,23,none,rent,1,unskilled - resident,1,none,yes,good\r\nno checking account,30,critical account/ other credits existing (not at this bank),radio/television,5771,... < 100 DM,4 <= ... < 7 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",25,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,12,delay in paying off in the past,repairs,1555,... >= 1000 DM,... >= 7 years,4,male : single,none,4,unknown / no property,55,none,for free,2,skilled employee / official,2,none,yes,bad\r\n... < 0 DM,24,existing credits paid back duly till now,car (new),1285,unknown/ no savings account,4 <= ... < 7 years,4,male : single,none,4,unknown / no property,32,none,rent,1,skilled employee / official,1,none,yes,bad\r\n... >= 200 DM / salary assignments for at least 1 year,6,critical account/ other credits existing (not at this bank),car (new),1299,... < 100 DM,1 <= ... < 4 years,1,male : single,none,1,real estate,74,none,own,3,unemployed/ unskilled - non-resident,2,none,no,good\r\n... >= 200 DM / salary assignments for at least 1 year,15,critical account/ other credits existing (not at this bank),radio/television,1271,unknown/ no savings account,1 <= ... < 4 years,3,male : single,none,4,unknown / no property,39,none,for free,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,24,existing credits paid back duly till now,car (new),1393,... < 100 DM,1 <= ... < 4 years,2,male : single,guarantor,2,real estate,31,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,12,critical account/ other credits existing (not at this bank),car (new),691,... < 100 DM,... >= 7 years,4,male : single,none,3,building society savings agreement/ life insurance,35,none,own,2,skilled employee / official,1,none,yes,bad\r\nno checking account,15,critical account/ other credits existing (not at this bank),car (new),5045,unknown/ no savings account,... >= 7 years,1,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",59,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,18,critical account/ other credits existing (not at this bank),furniture/equipment,2124,... < 100 DM,1 <= ... < 4 years,4,male : single,none,4,real estate,24,none,rent,2,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,12,existing credits paid back duly till now,radio/television,2214,... < 100 DM,1 <= ... < 4 years,4,male : single,none,3,building society savings agreement/ life insurance,24,none,own,1,unskilled - resident,1,none,yes,good\r\nno checking account,21,critical account/ other credits existing (not at this bank),car (new),12680,unknown/ no savings account,... >= 7 years,4,male : single,none,4,unknown / no property,30,none,for free,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,24,critical account/ other credits existing (not at this bank),car (new),2463,100 <= ... < 500 DM,4 <= ... < 7 years,4,male : single,none,3,building society savings agreement/ life insurance,27,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,radio/television,1155,... < 100 DM,... >= 7 years,3,male : single,guarantor,3,real estate,40,bank,own,2,unskilled - resident,1,none,yes,good\r\n... < 0 DM,30,existing credits paid back duly till now,furniture/equipment,3108,... < 100 DM,... < 1 year,2,male : single,none,4,building society savings agreement/ life insurance,31,none,own,1,unskilled - resident,1,none,yes,bad\r\nno checking account,10,existing credits paid back duly till now,car (used),2901,unknown/ no savings account,... < 1 year,1,male : single,none,4,real estate,31,none,rent,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,12,critical account/ other credits existing (not at this bank),furniture/equipment,3617,... < 100 DM,... >= 7 years,1,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",28,none,rent,3,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),radio/television,1655,... < 100 DM,... >= 7 years,2,male : single,none,4,real estate,63,none,own,2,unskilled - resident,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,24,existing credits paid back duly till now,car (used),2812,unknown/ no savings account,... >= 7 years,2,male : single,none,4,real estate,26,none,rent,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,36,critical account/ other credits existing (not at this bank),education,8065,... < 100 DM,1 <= ... < 4 years,3,male : single,none,2,unknown / no property,25,none,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,21,critical account/ other credits existing (not at this bank),car (used),3275,... < 100 DM,... >= 7 years,1,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",36,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,24,critical account/ other credits existing (not at this bank),radio/television,2223,100 <= ... < 500 DM,... >= 7 years,4,male : single,none,4,building society savings agreement/ life insurance,52,bank,own,2,skilled employee / official,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,12,critical account/ other credits existing (not at this bank),car (new),1480,500 <= ... < 1000 DM,unemployed,2,male : single,none,4,unknown / no property,66,bank,for free,3,unemployed/ unskilled - non-resident,1,none,yes,good\r\n... < 0 DM,24,existing credits paid back duly till now,car (new),1371,unknown/ no savings account,1 <= ... < 4 years,4,male : single,none,4,real estate,25,none,rent,1,skilled employee / official,1,none,yes,bad\r\nno checking account,36,critical account/ other credits existing (not at this bank),car (new),3535,... < 100 DM,4 <= ... < 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",37,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,18,existing credits paid back duly till now,radio/television,3509,... < 100 DM,4 <= ... < 7 years,4,male : single,guarantor,1,real estate,25,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,36,critical account/ other credits existing (not at this bank),car (used),5711,... >= 1000 DM,... >= 7 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",38,none,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,18,existing credits paid back duly till now,repairs,3872,... < 100 DM,unemployed,2,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",67,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,39,critical account/ other credits existing (not at this bank),radio/television,4933,... < 100 DM,4 <= ... < 7 years,2,male : single,guarantor,2,real estate,25,none,own,2,skilled employee / official,1,none,yes,bad\r\nno checking account,24,critical account/ other credits existing (not at this bank),car (new),1940,... >= 1000 DM,... >= 7 years,4,male : single,none,4,real estate,60,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,12,no credits taken/ all credits paid back duly,retraining,1410,... < 100 DM,1 <= ... < 4 years,2,male : single,none,2,real estate,31,none,own,1,unskilled - resident,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,car (new),836,100 <= ... < 500 DM,... < 1 year,4,male : single,none,2,building society savings agreement/ life insurance,23,bank,own,1,unskilled - resident,1,none,yes,bad\r\n0 <= ... < 200 DM,20,existing credits paid back duly till now,car (used),6468,unknown/ no savings account,unemployed,1,male : single,none,4,real estate,60,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,18,existing credits paid back duly till now,business,1941,... >= 1000 DM,1 <= ... < 4 years,4,male : single,none,2,building society savings agreement/ life insurance,35,none,own,1,unskilled - resident,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,22,existing credits paid back duly till now,radio/television,2675,500 <= ... < 1000 DM,... >= 7 years,3,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",40,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,48,critical account/ other credits existing (not at this bank),car (used),2751,unknown/ no savings account,... >= 7 years,4,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",38,none,own,2,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,48,delay in paying off in the past,education,6224,... < 100 DM,... >= 7 years,4,male : single,none,4,unknown / no property,50,none,for free,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,40,critical account/ other credits existing (not at this bank),education,5998,... < 100 DM,1 <= ... < 4 years,4,male : single,none,3,unknown / no property,27,bank,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,21,existing credits paid back duly till now,business,1188,... < 100 DM,... >= 7 years,2,male : single,none,4,building society savings agreement/ life insurance,39,none,own,1,skilled employee / official,2,none,yes,bad\r\nno checking account,24,existing credits paid back duly till now,car (used),6313,unknown/ no savings account,... >= 7 years,3,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",41,none,own,1,management/ self-employed/ highly qualified employee/ officer,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,6,critical account/ other credits existing (not at this bank),furniture/equipment,1221,unknown/ no savings account,1 <= ... < 4 years,1,male : single,none,2,building society savings agreement/ life insurance,27,none,own,2,skilled employee / official,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,24,existing credits paid back duly till now,furniture/equipment,2892,... < 100 DM,... >= 7 years,3,male : single,none,4,unknown / no property,51,none,for free,1,skilled employee / official,1,none,yes,good\r\nno checking account,24,existing credits paid back duly till now,furniture/equipment,3062,500 <= ... < 1000 DM,... >= 7 years,4,male : single,none,3,unknown / no property,32,none,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,9,existing credits paid back duly till now,furniture/equipment,2301,100 <= ... < 500 DM,... < 1 year,2,male : single,none,4,building society savings agreement/ life insurance,22,none,rent,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,18,existing credits paid back duly till now,car (used),7511,unknown/ no savings account,... >= 7 years,1,male : single,none,4,building society savings agreement/ life insurance,51,none,for free,1,skilled employee / official,2,\"yes, registered under the customers name\",yes,bad\r\nno checking account,12,critical account/ other credits existing (not at this bank),furniture/equipment,1258,... < 100 DM,... < 1 year,2,male : single,none,4,building society savings agreement/ life insurance,22,none,rent,2,unskilled - resident,1,none,yes,good\r\nno checking account,24,delay in paying off in the past,car (new),717,unknown/ no savings account,... >= 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",54,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,9,existing credits paid back duly till now,car (new),1549,unknown/ no savings account,... < 1 year,4,male : single,none,2,real estate,35,none,own,1,unemployed/ unskilled - non-resident,1,none,yes,good\r\nno checking account,24,critical account/ other credits existing (not at this bank),education,1597,... < 100 DM,... >= 7 years,4,male : single,none,4,unknown / no property,54,none,for free,2,skilled employee / official,2,none,yes,good\r\n0 <= ... < 200 DM,18,critical account/ other credits existing (not at this bank),radio/television,1795,... < 100 DM,... >= 7 years,3,male : single,guarantor,4,real estate,48,bank,rent,2,unskilled - resident,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,20,critical account/ other credits existing (not at this bank),furniture/equipment,4272,... < 100 DM,... >= 7 years,1,male : single,none,4,building society savings agreement/ life insurance,24,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),radio/television,976,unknown/ no savings account,... >= 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",35,none,own,2,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,car (new),7472,unknown/ no savings account,unemployed,1,male : single,none,2,real estate,24,none,rent,1,unemployed/ unskilled - non-resident,1,none,yes,good\r\n... < 0 DM,36,existing credits paid back duly till now,car (new),9271,... < 100 DM,4 <= ... < 7 years,2,male : single,none,1,\"car or other, not in attribute Savings account/bonds\",24,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,6,existing credits paid back duly till now,radio/television,590,... < 100 DM,... < 1 year,3,male : single,none,3,real estate,26,none,own,1,unskilled - resident,1,none,no,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),radio/television,930,unknown/ no savings account,... >= 7 years,4,male : single,none,4,real estate,65,none,own,4,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,42,all credits at this bank paid back duly,car (used),9283,... < 100 DM,unemployed,1,male : single,none,2,unknown / no property,55,bank,for free,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,15,no credits taken/ all credits paid back duly,car (new),1778,... < 100 DM,... < 1 year,2,male : single,none,1,real estate,26,none,rent,2,unemployed/ unskilled - non-resident,1,none,yes,bad\r\n0 <= ... < 200 DM,8,existing credits paid back duly till now,business,907,... < 100 DM,... < 1 year,3,male : single,none,2,real estate,26,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,6,existing credits paid back duly till now,radio/television,484,... < 100 DM,4 <= ... < 7 years,3,male : single,guarantor,3,real estate,28,bank,own,1,unskilled - resident,1,none,yes,good\r\n... < 0 DM,36,critical account/ other credits existing (not at this bank),car (used),9629,... < 100 DM,4 <= ... < 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",24,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,48,existing credits paid back duly till now,domestic appliances,3051,... < 100 DM,1 <= ... < 4 years,3,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",54,none,own,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,48,existing credits paid back duly till now,car (new),3931,... < 100 DM,4 <= ... < 7 years,4,male : single,none,4,unknown / no property,46,none,for free,1,skilled employee / official,2,none,yes,bad\r\n0 <= ... < 200 DM,36,delay in paying off in the past,car (new),7432,... < 100 DM,1 <= ... < 4 years,2,male : single,none,2,building society savings agreement/ life insurance,54,none,rent,1,skilled employee / official,1,none,yes,good\r\nno checking account,6,existing credits paid back duly till now,domestic appliances,1338,500 <= ... < 1000 DM,1 <= ... < 4 years,1,male : single,none,4,real estate,62,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,6,critical account/ other credits existing (not at this bank),radio/television,1554,... < 100 DM,4 <= ... < 7 years,1,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",24,none,rent,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,36,existing credits paid back duly till now,others,15857,... < 100 DM,unemployed,2,male : single,co-applicant,3,\"car or other, not in attribute Savings account/bonds\",43,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,none,yes,good\r\n... < 0 DM,18,existing credits paid back duly till now,radio/television,1345,... < 100 DM,1 <= ... < 4 years,4,male : single,none,3,real estate,26,bank,own,1,skilled employee / official,1,none,yes,bad\r\nno checking account,12,existing credits paid back duly till now,car (new),1101,... < 100 DM,1 <= ... < 4 years,3,male : single,none,2,real estate,27,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,12,existing credits paid back duly till now,radio/television,3016,... < 100 DM,1 <= ... < 4 years,3,male : single,none,1,\"car or other, not in attribute Savings account/bonds\",24,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,36,existing credits paid back duly till now,furniture/equipment,2712,... < 100 DM,... >= 7 years,2,male : single,none,2,building society savings agreement/ life insurance,41,bank,own,1,skilled employee / official,2,none,yes,bad\r\n... < 0 DM,8,critical account/ other credits existing (not at this bank),car (new),731,... < 100 DM,... >= 7 years,4,male : single,none,4,real estate,47,none,own,2,unskilled - resident,1,none,yes,good\r\nno checking account,18,critical account/ other credits existing (not at this bank),furniture/equipment,3780,... < 100 DM,... < 1 year,3,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",35,none,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,21,critical account/ other credits existing (not at this bank),car (new),1602,... < 100 DM,... >= 7 years,4,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",30,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,18,critical account/ other credits existing (not at this bank),car (new),3966,... < 100 DM,... >= 7 years,1,male : single,none,4,real estate,33,bank,rent,3,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,18,no credits taken/ all credits paid back duly,business,4165,... < 100 DM,1 <= ... < 4 years,2,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",36,stores,own,2,skilled employee / official,2,none,yes,bad\r\n... < 0 DM,36,existing credits paid back duly till now,car (used),8335,unknown/ no savings account,... >= 7 years,3,male : single,none,4,unknown / no property,47,none,for free,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,48,delay in paying off in the past,business,6681,unknown/ no savings account,1 <= ... < 4 years,4,male : single,none,4,unknown / no property,38,none,for free,1,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,24,delay in paying off in the past,business,2375,500 <= ... < 1000 DM,1 <= ... < 4 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",44,none,own,2,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,18,existing credits paid back duly till now,car (new),1216,... < 100 DM,... < 1 year,4,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",23,none,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,45,no credits taken/ all credits paid back duly,business,11816,... < 100 DM,... >= 7 years,2,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",29,none,rent,2,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,24,existing credits paid back duly till now,radio/television,5084,unknown/ no savings account,... >= 7 years,2,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",42,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,15,existing credits paid back duly till now,radio/television,2327,... < 100 DM,... < 1 year,2,male : single,none,3,real estate,25,none,own,1,unskilled - resident,1,none,yes,bad\r\n... < 0 DM,12,no credits taken/ all credits paid back duly,car (new),1082,... < 100 DM,1 <= ... < 4 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",48,bank,own,2,skilled employee / official,1,none,yes,bad\r\nno checking account,12,existing credits paid back duly till now,radio/television,886,unknown/ no savings account,1 <= ... < 4 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",21,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,4,existing credits paid back duly till now,furniture/equipment,601,... < 100 DM,... < 1 year,1,male : single,none,3,real estate,23,none,rent,1,unskilled - resident,2,none,yes,good\r\n... < 0 DM,24,critical account/ other credits existing (not at this bank),car (used),2957,... < 100 DM,... >= 7 years,4,male : single,none,4,building society savings agreement/ life insurance,63,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,24,critical account/ other credits existing (not at this bank),radio/television,2611,... < 100 DM,... >= 7 years,4,male : single,co-applicant,3,real estate,46,none,own,2,skilled employee / official,1,none,yes,good\r\n... < 0 DM,36,existing credits paid back duly till now,furniture/equipment,5179,... < 100 DM,4 <= ... < 7 years,4,male : single,none,2,building society savings agreement/ life insurance,29,none,own,1,skilled employee / official,1,none,yes,bad\r\nno checking account,21,delay in paying off in the past,car (used),2993,... < 100 DM,1 <= ... < 4 years,3,male : single,none,2,real estate,28,stores,own,2,unskilled - resident,1,none,yes,good\r\nno checking account,18,existing credits paid back duly till now,repairs,1943,... < 100 DM,... < 1 year,4,male : single,none,4,real estate,23,none,own,1,skilled employee / official,1,none,yes,bad\r\nno checking account,24,all credits at this bank paid back duly,business,1559,... < 100 DM,4 <= ... < 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",50,bank,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,18,existing credits paid back duly till now,furniture/equipment,3422,... < 100 DM,... >= 7 years,4,male : single,none,4,building society savings agreement/ life insurance,47,bank,own,3,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,21,existing credits paid back duly till now,furniture/equipment,3976,unknown/ no savings account,4 <= ... < 7 years,2,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",35,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,18,existing credits paid back duly till now,car (new),6761,unknown/ no savings account,1 <= ... < 4 years,2,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",68,none,rent,2,skilled employee / official,1,none,yes,bad\r\nno checking account,24,existing credits paid back duly till now,car (new),1249,... < 100 DM,... < 1 year,4,male : single,none,2,real estate,28,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,9,existing credits paid back duly till now,radio/television,1364,... < 100 DM,4 <= ... < 7 years,3,male : single,none,4,real estate,59,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,radio/television,709,... < 100 DM,... >= 7 years,4,male : single,none,4,real estate,57,stores,own,1,unskilled - resident,1,none,yes,bad\r\n... < 0 DM,20,critical account/ other credits existing (not at this bank),car (new),2235,... < 100 DM,1 <= ... < 4 years,4,male : single,guarantor,2,building society savings agreement/ life insurance,33,bank,rent,2,skilled employee / official,1,none,no,bad\r\nno checking account,24,critical account/ other credits existing (not at this bank),car (used),4042,unknown/ no savings account,4 <= ... < 7 years,3,male : single,none,4,building society savings agreement/ life insurance,43,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,15,critical account/ other credits existing (not at this bank),radio/television,1471,... < 100 DM,1 <= ... < 4 years,4,male : single,none,4,unknown / no property,35,none,for free,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,18,all credits at this bank paid back duly,car (new),1442,... < 100 DM,4 <= ... < 7 years,4,male : single,none,4,unknown / no property,32,none,for free,2,unskilled - resident,2,none,yes,bad\r\nno checking account,36,delay in paying off in the past,car (new),10875,... < 100 DM,... >= 7 years,2,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",45,none,own,2,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,24,existing credits paid back duly till now,car (new),1474,100 <= ... < 500 DM,... < 1 year,4,male : single,none,3,real estate,33,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,10,existing credits paid back duly till now,retraining,894,unknown/ no savings account,4 <= ... < 7 years,4,male : single,none,3,building society savings agreement/ life insurance,40,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,15,critical account/ other credits existing (not at this bank),furniture/equipment,3343,... < 100 DM,1 <= ... < 4 years,4,male : single,none,2,unknown / no property,28,none,for free,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,15,existing credits paid back duly till now,car (new),3959,... < 100 DM,1 <= ... < 4 years,3,male : single,none,2,building society savings agreement/ life insurance,29,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,9,existing credits paid back duly till now,car (new),3577,100 <= ... < 500 DM,1 <= ... < 4 years,1,male : single,guarantor,2,real estate,26,none,rent,1,skilled employee / official,2,none,no,good\r\nno checking account,24,critical account/ other credits existing (not at this bank),car (used),5804,... >= 1000 DM,1 <= ... < 4 years,4,male : single,none,2,real estate,27,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,18,delay in paying off in the past,business,2169,... < 100 DM,1 <= ... < 4 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",28,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,24,existing credits paid back duly till now,radio/television,2439,... < 100 DM,... < 1 year,4,male : single,none,4,real estate,35,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,27,critical account/ other credits existing (not at this bank),furniture/equipment,4526,... >= 1000 DM,... < 1 year,4,male : single,none,2,real estate,32,stores,own,2,unskilled - resident,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,10,existing credits paid back duly till now,furniture/equipment,2210,... < 100 DM,1 <= ... < 4 years,2,male : single,none,2,real estate,25,bank,rent,1,unskilled - resident,1,none,yes,bad\r\nno checking account,15,existing credits paid back duly till now,furniture/equipment,2221,500 <= ... < 1000 DM,1 <= ... < 4 years,2,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",20,none,rent,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,18,existing credits paid back duly till now,radio/television,2389,... < 100 DM,... < 1 year,4,male : single,none,1,\"car or other, not in attribute Savings account/bonds\",27,stores,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),furniture/equipment,3331,... < 100 DM,... >= 7 years,2,male : single,none,4,building society savings agreement/ life insurance,42,stores,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,36,existing credits paid back duly till now,business,7409,unknown/ no savings account,... >= 7 years,3,male : single,none,2,building society savings agreement/ life insurance,37,none,own,2,skilled employee / official,1,none,yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,furniture/equipment,652,... < 100 DM,... >= 7 years,4,male : single,none,4,building society savings agreement/ life insurance,24,none,rent,1,skilled employee / official,1,none,yes,good\r\nno checking account,36,delay in paying off in the past,furniture/equipment,7678,500 <= ... < 1000 DM,4 <= ... < 7 years,2,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",40,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,6,critical account/ other credits existing (not at this bank),car (new),1343,... < 100 DM,... >= 7 years,1,male : single,none,4,real estate,46,none,own,2,skilled employee / official,2,none,no,good\r\n... < 0 DM,24,critical account/ other credits existing (not at this bank),business,1382,100 <= ... < 500 DM,4 <= ... < 7 years,4,male : single,none,1,real estate,26,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,15,existing credits paid back duly till now,domestic appliances,874,unknown/ no savings account,... < 1 year,4,male : single,none,1,real estate,24,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,furniture/equipment,3590,... < 100 DM,1 <= ... < 4 years,2,male : single,co-applicant,2,building society savings agreement/ life insurance,29,none,own,1,unskilled - resident,2,none,yes,good\r\n0 <= ... < 200 DM,11,critical account/ other credits existing (not at this bank),car (new),1322,... >= 1000 DM,1 <= ... < 4 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",40,none,own,2,skilled employee / official,1,none,yes,good\r\n... < 0 DM,18,all credits at this bank paid back duly,radio/television,1940,... < 100 DM,... < 1 year,3,male : single,co-applicant,4,unknown / no property,36,bank,for free,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,36,existing credits paid back duly till now,radio/television,3595,... < 100 DM,... >= 7 years,4,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",28,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,9,existing credits paid back duly till now,car (new),1422,... < 100 DM,... < 1 year,3,male : single,none,2,unknown / no property,27,none,for free,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,30,critical account/ other credits existing (not at this bank),radio/television,6742,unknown/ no savings account,4 <= ... < 7 years,2,male : single,none,3,building society savings agreement/ life insurance,36,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,24,existing credits paid back duly till now,car (used),7814,... < 100 DM,4 <= ... < 7 years,3,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",38,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,24,existing credits paid back duly till now,car (used),9277,unknown/ no savings account,1 <= ... < 4 years,2,male : single,none,4,unknown / no property,48,none,for free,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,30,critical account/ other credits existing (not at this bank),car (new),2181,unknown/ no savings account,... >= 7 years,4,male : single,none,4,real estate,36,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,18,critical account/ other credits existing (not at this bank),radio/television,1098,... < 100 DM,unemployed,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",65,none,own,2,unemployed/ unskilled - non-resident,1,none,yes,good\r\n0 <= ... < 200 DM,24,existing credits paid back duly till now,furniture/equipment,4057,... < 100 DM,4 <= ... < 7 years,3,male : single,none,3,\"car or other, not in attribute Savings account/bonds\",43,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,12,existing credits paid back duly till now,education,795,... < 100 DM,... < 1 year,4,male : single,none,4,building society savings agreement/ life insurance,53,none,own,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,24,critical account/ other credits existing (not at this bank),business,2825,unknown/ no savings account,4 <= ... < 7 years,4,male : single,none,3,unknown / no property,34,none,own,2,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,48,existing credits paid back duly till now,business,15672,... < 100 DM,1 <= ... < 4 years,2,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",23,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,36,critical account/ other credits existing (not at this bank),car (new),6614,... < 100 DM,... >= 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",34,none,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,28,all credits at this bank paid back duly,car (used),7824,unknown/ no savings account,... < 1 year,3,male : single,guarantor,4,real estate,40,bank,rent,2,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,27,critical account/ other credits existing (not at this bank),business,2442,... < 100 DM,... >= 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",43,stores,own,4,management/ self-employed/ highly qualified employee/ officer,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,15,critical account/ other credits existing (not at this bank),radio/television,1829,... < 100 DM,... >= 7 years,4,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",46,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,12,critical account/ other credits existing (not at this bank),car (new),2171,... < 100 DM,1 <= ... < 4 years,4,male : single,none,4,building society savings agreement/ life insurance,38,bank,own,2,unskilled - resident,1,none,no,good\r\n0 <= ... < 200 DM,36,critical account/ other credits existing (not at this bank),car (used),5800,... < 100 DM,1 <= ... < 4 years,3,male : single,none,4,\"car or other, not in attribute Savings account/bonds\",34,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,18,critical account/ other credits existing (not at this bank),radio/television,1169,unknown/ no savings account,1 <= ... < 4 years,4,male : single,none,3,building society savings agreement/ life insurance,29,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,36,delay in paying off in the past,car (used),8947,unknown/ no savings account,4 <= ... < 7 years,3,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",31,stores,own,1,management/ self-employed/ highly qualified employee/ officer,2,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,21,existing credits paid back duly till now,radio/television,2606,... < 100 DM,... < 1 year,4,male : single,none,4,building society savings agreement/ life insurance,28,none,rent,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),furniture/equipment,1592,... >= 1000 DM,4 <= ... < 7 years,3,male : single,none,2,building society savings agreement/ life insurance,35,none,own,1,skilled employee / official,1,none,no,good\r\nno checking account,15,existing credits paid back duly till now,furniture/equipment,2186,unknown/ no savings account,4 <= ... < 7 years,1,male : single,none,4,real estate,33,bank,rent,1,unskilled - resident,1,none,yes,good\r\n... < 0 DM,18,existing credits paid back duly till now,furniture/equipment,4153,... < 100 DM,1 <= ... < 4 years,2,male : single,co-applicant,3,\"car or other, not in attribute Savings account/bonds\",42,none,own,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,16,critical account/ other credits existing (not at this bank),car (new),2625,... < 100 DM,... >= 7 years,2,male : single,guarantor,4,building society savings agreement/ life insurance,43,bank,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,20,critical account/ other credits existing (not at this bank),car (new),3485,unknown/ no savings account,... < 1 year,2,male : single,none,4,real estate,44,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,36,critical account/ other credits existing (not at this bank),car (used),10477,unknown/ no savings account,... >= 7 years,2,male : single,none,4,unknown / no property,42,none,for free,2,skilled employee / official,1,none,yes,good\r\nno checking account,15,existing credits paid back duly till now,radio/television,1386,unknown/ no savings account,1 <= ... < 4 years,4,male : single,none,2,real estate,40,none,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,24,existing credits paid back duly till now,radio/television,1278,... < 100 DM,... >= 7 years,4,male : single,none,1,real estate,36,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,radio/television,1107,... < 100 DM,1 <= ... < 4 years,2,male : single,none,2,real estate,20,none,rent,1,management/ self-employed/ highly qualified employee/ officer,2,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,21,existing credits paid back duly till now,car (new),3763,unknown/ no savings account,4 <= ... < 7 years,2,male : single,co-applicant,2,real estate,24,none,own,1,unskilled - resident,1,none,no,good\r\n0 <= ... < 200 DM,36,existing credits paid back duly till now,education,3711,unknown/ no savings account,1 <= ... < 4 years,2,male : single,none,2,\"car or other, not in attribute Savings account/bonds\",27,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,15,delay in paying off in the past,car (used),3594,... < 100 DM,... < 1 year,1,male : married/widowed,none,2,building society savings agreement/ life insurance,46,none,own,2,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,9,existing credits paid back duly till now,car (new),3195,unknown/ no savings account,1 <= ... < 4 years,1,male : married/widowed,none,2,real estate,33,none,own,1,unskilled - resident,1,none,yes,good\r\nno checking account,36,delay in paying off in the past,radio/television,4454,... < 100 DM,1 <= ... < 4 years,4,male : married/widowed,none,4,real estate,34,none,own,2,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,24,critical account/ other credits existing (not at this bank),furniture/equipment,4736,... < 100 DM,... < 1 year,2,male : married/widowed,none,4,\"car or other, not in attribute Savings account/bonds\",25,bank,own,1,unskilled - resident,1,none,yes,bad\r\n0 <= ... < 200 DM,30,existing credits paid back duly till now,radio/television,2991,unknown/ no savings account,... >= 7 years,2,male : married/widowed,none,4,\"car or other, not in attribute Savings account/bonds\",25,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,11,existing credits paid back duly till now,business,2142,... >= 1000 DM,... >= 7 years,1,male : married/widowed,none,2,real estate,28,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,24,all credits at this bank paid back duly,business,3161,... < 100 DM,1 <= ... < 4 years,4,male : married/widowed,none,2,building society savings agreement/ life insurance,31,none,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,48,no credits taken/ all credits paid back duly,others,18424,... < 100 DM,1 <= ... < 4 years,1,male : married/widowed,none,2,building society savings agreement/ life insurance,32,bank,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",no,bad\r\nno checking account,10,existing credits paid back duly till now,car (used),2848,100 <= ... < 500 DM,1 <= ... < 4 years,1,male : married/widowed,co-applicant,2,real estate,32,none,own,1,skilled employee / official,2,none,yes,good\r\n... < 0 DM,6,existing credits paid back duly till now,car (new),14896,... < 100 DM,... >= 7 years,1,male : married/widowed,none,4,unknown / no property,68,bank,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,24,existing credits paid back duly till now,furniture/equipment,2359,100 <= ... < 500 DM,unemployed,1,male : married/widowed,none,1,building society savings agreement/ life insurance,33,none,own,1,skilled employee / official,1,none,yes,bad\r\n... < 0 DM,24,existing credits paid back duly till now,furniture/equipment,3345,... < 100 DM,... >= 7 years,4,male : married/widowed,none,2,building society savings agreement/ life insurance,39,none,rent,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,18,critical account/ other credits existing (not at this bank),furniture/equipment,1817,... < 100 DM,1 <= ... < 4 years,4,male : married/widowed,none,2,unknown / no property,28,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,48,delay in paying off in the past,radio/television,12749,500 <= ... < 1000 DM,4 <= ... < 7 years,4,male : married/widowed,none,1,\"car or other, not in attribute Savings account/bonds\",37,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,9,existing credits paid back duly till now,radio/television,1366,... < 100 DM,... < 1 year,3,male : married/widowed,none,4,building society savings agreement/ life insurance,22,none,rent,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,12,existing credits paid back duly till now,car (new),2002,... < 100 DM,4 <= ... < 7 years,3,male : married/widowed,none,4,building society savings agreement/ life insurance,30,none,rent,1,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,24,all credits at this bank paid back duly,furniture/equipment,6872,... < 100 DM,... < 1 year,2,male : married/widowed,none,1,building society savings agreement/ life insurance,55,bank,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,12,all credits at this bank paid back duly,car (new),697,... < 100 DM,... < 1 year,4,male : married/widowed,none,2,\"car or other, not in attribute Savings account/bonds\",46,bank,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,18,critical account/ other credits existing (not at this bank),furniture/equipment,1049,... < 100 DM,... < 1 year,4,male : married/widowed,none,4,building society savings agreement/ life insurance,21,none,rent,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,48,existing credits paid back duly till now,car (used),10297,... < 100 DM,4 <= ... < 7 years,4,male : married/widowed,none,4,unknown / no property,39,stores,for free,3,skilled employee / official,2,\"yes, registered under the customers name\",yes,bad\r\nno checking account,30,existing credits paid back duly till now,radio/television,1867,unknown/ no savings account,... >= 7 years,4,male : married/widowed,none,4,\"car or other, not in attribute Savings account/bonds\",58,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,12,delay in paying off in the past,car (new),1344,... < 100 DM,1 <= ... < 4 years,4,male : married/widowed,none,2,real estate,43,none,own,2,unskilled - resident,2,none,yes,good\r\n... < 0 DM,24,existing credits paid back duly till now,furniture/equipment,1747,... < 100 DM,... < 1 year,4,male : married/widowed,co-applicant,1,building society savings agreement/ life insurance,24,none,own,1,unskilled - resident,1,none,no,good\r\n0 <= ... < 200 DM,9,existing credits paid back duly till now,radio/television,1670,... < 100 DM,... < 1 year,4,male : married/widowed,none,2,\"car or other, not in attribute Savings account/bonds\",22,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\nno checking account,9,critical account/ other credits existing (not at this bank),car (new),1224,... < 100 DM,1 <= ... < 4 years,3,male : married/widowed,none,1,real estate,30,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),radio/television,522,500 <= ... < 1000 DM,... >= 7 years,4,male : married/widowed,none,4,building society savings agreement/ life insurance,42,none,own,2,skilled employee / official,2,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,12,existing credits paid back duly till now,radio/television,1498,... < 100 DM,1 <= ... < 4 years,4,male : married/widowed,none,1,\"car or other, not in attribute Savings account/bonds\",23,bank,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,30,delay in paying off in the past,radio/television,1919,100 <= ... < 500 DM,... < 1 year,4,male : married/widowed,none,3,unknown / no property,30,stores,own,2,management/ self-employed/ highly qualified employee/ officer,1,none,yes,bad\r\n... >= 200 DM / salary assignments for at least 1 year,9,existing credits paid back duly till now,radio/television,745,... < 100 DM,1 <= ... < 4 years,3,male : married/widowed,none,2,real estate,28,none,own,1,unskilled - resident,1,none,yes,bad\r\n0 <= ... < 200 DM,6,existing credits paid back duly till now,radio/television,2063,... < 100 DM,... < 1 year,4,male : married/widowed,none,3,\"car or other, not in attribute Savings account/bonds\",30,none,rent,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,60,existing credits paid back duly till now,education,6288,... < 100 DM,1 <= ... < 4 years,4,male : married/widowed,none,4,unknown / no property,42,none,for free,1,skilled employee / official,1,none,yes,bad\r\nno checking account,24,critical account/ other credits existing (not at this bank),car (used),6842,unknown/ no savings account,1 <= ... < 4 years,2,male : married/widowed,none,4,building society savings agreement/ life insurance,46,none,own,2,management/ self-employed/ highly qualified employee/ officer,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,existing credits paid back duly till now,car (new),3527,unknown/ no savings account,... < 1 year,2,male : married/widowed,none,3,building society savings agreement/ life insurance,45,none,own,1,management/ self-employed/ highly qualified employee/ officer,2,\"yes, registered under the customers name\",yes,good\r\nno checking account,10,existing credits paid back duly till now,car (new),1546,... < 100 DM,1 <= ... < 4 years,3,male : married/widowed,none,2,real estate,31,none,own,1,unskilled - resident,2,none,no,good\r\nno checking account,24,existing credits paid back duly till now,furniture/equipment,929,unknown/ no savings account,4 <= ... < 7 years,4,male : married/widowed,none,2,\"car or other, not in attribute Savings account/bonds\",31,stores,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,4,critical account/ other credits existing (not at this bank),car (new),1455,... < 100 DM,4 <= ... < 7 years,2,male : married/widowed,none,1,real estate,42,none,own,3,unskilled - resident,2,none,yes,good\r\n... < 0 DM,15,existing credits paid back duly till now,furniture/equipment,1845,... < 100 DM,... < 1 year,4,male : married/widowed,guarantor,1,building society savings agreement/ life insurance,46,none,rent,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,48,no credits taken/ all credits paid back duly,car (new),8358,500 <= ... < 1000 DM,... < 1 year,1,male : married/widowed,none,1,\"car or other, not in attribute Savings account/bonds\",30,none,own,2,skilled employee / official,1,none,yes,good\r\n... < 0 DM,24,all credits at this bank paid back duly,furniture/equipment,3349,500 <= ... < 1000 DM,... < 1 year,4,male : married/widowed,none,4,unknown / no property,30,none,for free,1,skilled employee / official,2,\"yes, registered under the customers name\",yes,bad\r\nno checking account,12,existing credits paid back duly till now,car (new),2859,unknown/ no savings account,unemployed,4,male : married/widowed,none,4,unknown / no property,38,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,18,existing credits paid back duly till now,furniture/equipment,1533,... < 100 DM,... < 1 year,4,male : married/widowed,co-applicant,1,building society savings agreement/ life insurance,43,none,own,1,unskilled - resident,2,none,yes,bad\r\nno checking account,24,existing credits paid back duly till now,radio/television,3621,100 <= ... < 500 DM,... >= 7 years,2,male : married/widowed,none,4,\"car or other, not in attribute Savings account/bonds\",31,none,own,2,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,18,critical account/ other credits existing (not at this bank),business,3590,... < 100 DM,unemployed,3,male : married/widowed,none,3,\"car or other, not in attribute Savings account/bonds\",40,none,own,3,unemployed/ unskilled - non-resident,2,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,36,delay in paying off in the past,business,2145,... < 100 DM,4 <= ... < 7 years,2,male : married/widowed,none,1,\"car or other, not in attribute Savings account/bonds\",24,none,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,24,existing credits paid back duly till now,car (used),4113,500 <= ... < 1000 DM,... < 1 year,3,male : married/widowed,none,4,\"car or other, not in attribute Savings account/bonds\",28,none,rent,1,skilled employee / official,1,none,yes,bad\r\nno checking account,36,existing credits paid back duly till now,furniture/equipment,10974,... < 100 DM,unemployed,4,male : married/widowed,none,2,\"car or other, not in attribute Savings account/bonds\",26,none,own,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\n... < 0 DM,12,existing credits paid back duly till now,car (new),1893,... < 100 DM,1 <= ... < 4 years,4,male : married/widowed,guarantor,4,building society savings agreement/ life insurance,29,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,24,critical account/ other credits existing (not at this bank),radio/television,1231,... >= 1000 DM,... >= 7 years,4,male : married/widowed,none,4,building society savings agreement/ life insurance,57,none,rent,2,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,30,critical account/ other credits existing (not at this bank),radio/television,3656,unknown/ no savings account,... >= 7 years,4,male : married/widowed,none,4,building society savings agreement/ life insurance,49,stores,own,2,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,9,critical account/ other credits existing (not at this bank),radio/television,1154,... < 100 DM,... >= 7 years,2,male : married/widowed,none,4,real estate,37,none,own,3,unskilled - resident,1,none,yes,good\r\n... < 0 DM,28,existing credits paid back duly till now,car (new),4006,... < 100 DM,1 <= ... < 4 years,3,male : married/widowed,none,2,\"car or other, not in attribute Savings account/bonds\",45,none,own,1,unskilled - resident,1,none,yes,bad\r\n0 <= ... < 200 DM,24,existing credits paid back duly till now,furniture/equipment,3069,100 <= ... < 500 DM,... >= 7 years,4,male : married/widowed,none,4,unknown / no property,30,none,for free,1,skilled employee / official,1,none,yes,good\r\nno checking account,6,critical account/ other credits existing (not at this bank),radio/television,1740,... < 100 DM,... >= 7 years,2,male : married/widowed,none,2,real estate,30,none,rent,2,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,21,delay in paying off in the past,car (new),2353,... < 100 DM,1 <= ... < 4 years,1,male : married/widowed,none,4,building society savings agreement/ life insurance,47,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,15,existing credits paid back duly till now,car (new),3556,unknown/ no savings account,1 <= ... < 4 years,3,male : married/widowed,none,2,unknown / no property,29,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,24,existing credits paid back duly till now,radio/television,2397,500 <= ... < 1000 DM,... >= 7 years,3,male : married/widowed,none,2,\"car or other, not in attribute Savings account/bonds\",35,bank,own,2,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,6,existing credits paid back duly till now,repairs,454,... < 100 DM,... < 1 year,3,male : married/widowed,none,1,building society savings agreement/ life insurance,22,none,own,1,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,30,existing credits paid back duly till now,radio/television,1715,unknown/ no savings account,1 <= ... < 4 years,4,male : married/widowed,none,1,\"car or other, not in attribute Savings account/bonds\",26,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,27,critical account/ other credits existing (not at this bank),radio/television,2520,500 <= ... < 1000 DM,1 <= ... < 4 years,4,male : married/widowed,none,2,building society savings agreement/ life insurance,23,none,own,2,unskilled - resident,1,none,yes,bad\r\nno checking account,15,existing credits paid back duly till now,radio/television,3568,... < 100 DM,... >= 7 years,4,male : married/widowed,none,2,\"car or other, not in attribute Savings account/bonds\",54,bank,rent,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,42,existing credits paid back duly till now,radio/television,7166,unknown/ no savings account,4 <= ... < 7 years,2,male : married/widowed,none,4,building society savings agreement/ life insurance,29,none,rent,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,11,critical account/ other credits existing (not at this bank),car (new),3939,... < 100 DM,1 <= ... < 4 years,1,male : married/widowed,none,2,real estate,40,none,own,2,unskilled - resident,2,none,yes,good\r\n0 <= ... < 200 DM,15,existing credits paid back duly till now,repairs,1514,100 <= ... < 500 DM,1 <= ... < 4 years,4,male : married/widowed,guarantor,2,real estate,22,none,own,1,skilled employee / official,1,none,yes,good\r\nno checking account,24,existing credits paid back duly till now,car (new),7393,... < 100 DM,1 <= ... < 4 years,1,male : married/widowed,none,4,building society savings agreement/ life insurance,43,none,own,1,unskilled - resident,2,none,yes,good\r\n... < 0 DM,24,all credits at this bank paid back duly,car (new),1193,... < 100 DM,unemployed,1,male : married/widowed,co-applicant,4,unknown / no property,29,none,rent,2,unemployed/ unskilled - non-resident,1,none,yes,bad\r\n... < 0 DM,60,existing credits paid back duly till now,business,7297,... < 100 DM,... >= 7 years,4,male : married/widowed,co-applicant,4,unknown / no property,36,none,rent,1,skilled employee / official,1,none,yes,bad\r\nno checking account,30,critical account/ other credits existing (not at this bank),radio/television,2831,... < 100 DM,1 <= ... < 4 years,4,male : married/widowed,none,2,\"car or other, not in attribute Savings account/bonds\",33,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,24,existing credits paid back duly till now,radio/television,1258,500 <= ... < 1000 DM,1 <= ... < 4 years,3,male : married/widowed,none,3,\"car or other, not in attribute Savings account/bonds\",57,none,own,1,unskilled - resident,1,none,yes,good\r\n0 <= ... < 200 DM,6,existing credits paid back duly till now,radio/television,753,... < 100 DM,1 <= ... < 4 years,2,male : married/widowed,guarantor,3,real estate,64,none,own,1,skilled employee / official,1,none,yes,good\r\n0 <= ... < 200 DM,18,delay in paying off in the past,business,2427,unknown/ no savings account,... >= 7 years,4,male : married/widowed,none,2,building society savings agreement/ life insurance,42,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,24,delay in paying off in the past,car (new),2538,... < 100 DM,... >= 7 years,4,male : married/widowed,none,4,\"car or other, not in attribute Savings account/bonds\",47,none,own,2,unskilled - resident,2,none,yes,bad\r\n0 <= ... < 200 DM,15,all credits at this bank paid back duly,car (new),1264,100 <= ... < 500 DM,1 <= ... < 4 years,2,male : married/widowed,none,2,building society savings agreement/ life insurance,25,none,rent,1,skilled employee / official,1,none,yes,bad\r\n0 <= ... < 200 DM,30,critical account/ other credits existing (not at this bank),furniture/equipment,8386,... < 100 DM,4 <= ... < 7 years,2,male : married/widowed,none,2,building society savings agreement/ life insurance,49,none,own,1,skilled employee / official,1,none,yes,bad\r\nno checking account,48,existing credits paid back duly till now,business,4844,... < 100 DM,unemployed,3,male : married/widowed,none,2,\"car or other, not in attribute Savings account/bonds\",33,bank,rent,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,bad\r\n... >= 200 DM / salary assignments for at least 1 year,21,existing credits paid back duly till now,car (new),2923,100 <= ... < 500 DM,1 <= ... < 4 years,1,male : married/widowed,none,1,\"car or other, not in attribute Savings account/bonds\",28,bank,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n... < 0 DM,36,existing credits paid back duly till now,car (used),8229,... < 100 DM,1 <= ... < 4 years,2,male : married/widowed,none,2,building society savings agreement/ life insurance,26,none,own,1,skilled employee / official,2,none,yes,bad\r\nno checking account,24,critical account/ other credits existing (not at this bank),furniture/equipment,2028,... < 100 DM,4 <= ... < 7 years,2,male : married/widowed,none,2,building society savings agreement/ life insurance,30,none,own,2,unskilled - resident,1,none,yes,good\r\n... < 0 DM,15,critical account/ other credits existing (not at this bank),furniture/equipment,1433,... < 100 DM,1 <= ... < 4 years,4,male : married/widowed,none,3,building society savings agreement/ life insurance,25,none,rent,2,skilled employee / official,1,none,yes,good\r\n... >= 200 DM / salary assignments for at least 1 year,42,no credits taken/ all credits paid back duly,business,6289,... < 100 DM,... < 1 year,2,male : married/widowed,none,1,building society savings agreement/ life insurance,33,none,own,2,skilled employee / official,1,none,yes,good\r\nno checking account,13,existing credits paid back duly till now,radio/television,1409,100 <= ... < 500 DM,unemployed,2,male : married/widowed,none,4,real estate,64,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,24,existing credits paid back duly till now,car (used),6579,... < 100 DM,unemployed,4,male : married/widowed,none,2,unknown / no property,29,none,for free,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\n0 <= ... < 200 DM,24,critical account/ other credits existing (not at this bank),radio/television,1743,... < 100 DM,... >= 7 years,4,male : married/widowed,none,2,building society savings agreement/ life insurance,48,none,own,2,unskilled - resident,1,none,yes,good\r\nno checking account,12,critical account/ other credits existing (not at this bank),education,3565,unknown/ no savings account,... < 1 year,2,male : married/widowed,none,1,building society savings agreement/ life insurance,37,none,own,2,unskilled - resident,2,none,yes,good\r\nno checking account,15,all credits at this bank paid back duly,radio/television,1569,100 <= ... < 500 DM,... >= 7 years,4,male : married/widowed,none,4,\"car or other, not in attribute Savings account/bonds\",34,bank,own,1,unskilled - resident,2,none,yes,good\r\n... < 0 DM,18,existing credits paid back duly till now,radio/television,1936,unknown/ no savings account,4 <= ... < 7 years,2,male : married/widowed,none,4,\"car or other, not in attribute Savings account/bonds\",23,none,rent,2,unskilled - resident,1,none,yes,good\r\n... < 0 DM,36,existing credits paid back duly till now,furniture/equipment,3959,... < 100 DM,unemployed,4,male : married/widowed,none,3,building society savings agreement/ life insurance,30,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,existing credits paid back duly till now,car (new),2390,unknown/ no savings account,... >= 7 years,4,male : married/widowed,none,3,\"car or other, not in attribute Savings account/bonds\",50,none,own,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,existing credits paid back duly till now,furniture/equipment,1736,... < 100 DM,4 <= ... < 7 years,3,male : married/widowed,none,4,real estate,31,none,own,1,unskilled - resident,1,none,yes,good\r\n... < 0 DM,30,existing credits paid back duly till now,car (used),3857,... < 100 DM,1 <= ... < 4 years,4,male : married/widowed,none,4,building society savings agreement/ life insurance,40,none,own,1,management/ self-employed/ highly qualified employee/ officer,1,\"yes, registered under the customers name\",yes,good\r\nno checking account,12,existing credits paid back duly till now,radio/television,804,... < 100 DM,... >= 7 years,4,male : married/widowed,none,4,\"car or other, not in attribute Savings account/bonds\",38,none,own,1,skilled employee / official,1,none,yes,good\r\n... < 0 DM,45,existing credits paid back duly till now,radio/television,1845,... < 100 DM,1 <= ... < 4 years,4,male : married/widowed,none,4,unknown / no property,23,none,for free,1,skilled employee / official,1,\"yes, registered under the customers name\",yes,bad\r\n0 <= ... < 200 DM,45,critical account/ other credits existing (not at this bank),car (used),4576,100 <= ... < 500 DM,unemployed,3,male : married/widowed,none,4,\"car or other, not in attribute Savings account/bonds\",27,none,own,1,skilled employee / official,1,none,yes,good\r\n"
  },
  {
    "path": "scorecardpipeline/logger.py",
    "content": "import os\nimport sys\nfrom logging.handlers import RotatingFileHandler, TimedRotatingFileHandler\nfrom logging import getLogger, StreamHandler, Formatter, DEBUG, INFO, ERROR\n\n\ndef init_logger(filename=None, stream=True, fmt=\"[ %(asctime)s ][ %(levelname)s ][ %(filename)s:%(funcName)s:%(lineno)d ] %(message)s\", datefmt=None):\n    \"\"\"\n    初始化日志\n\n    :param filename: 日志文件存储地址，如果不传不记录日志到文件中，默认为 None\n    :param stream: 是否显示在终端中，默认 True\n    :param fmt: 日志格式，参考：https://docs.python.org/3/library/logging.html#formatter-objects\n    :param datefmt: 日期格式\n    :return: logging.Logger\n    \"\"\"\n    logger = getLogger(\"scorecardpipeline\")\n    logger.setLevel(DEBUG)\n    formatter = Formatter(fmt, datefmt=datefmt)\n\n    if filename:\n        if os.path.dirname(filename) != \"\" and not os.path.exists(os.path.dirname(filename)):\n            try:\n                os.makedirs(os.path.dirname(filename))\n            except Exception as error:\n                print(f'错误 >> 创建日志目录失败,清手动创建目录文件位置,运行 sudo mkdir -p {os.path.dirname(filename)}')\n                print('错误 >> 报错信息 : {}'.format(error))\n\n        # fh = TimedRotatingFileHandler(filename=filename, when='D', backupCount=30, encoding=\"utf-8\")\n        fh = RotatingFileHandler(filename=filename, mode='a', maxBytes=10*1024**2, backupCount=0, encoding=\"utf-8\")\n        fh.setLevel(INFO)\n        fh.setFormatter(formatter)\n        logger.addHandler(fh)\n        fh.close()\n    \n    if stream:\n        ch = StreamHandler(sys.stdout)\n        ch.setLevel(INFO)\n        ch.setFormatter(formatter)\n        logger.addHandler(ch)\n        ch.close()\n\n    return logger\n\n\nif __name__ == '__main__':\n    logger = init_logger()\n    logger.info(type(logger))\n    logger.info(\"import scorecardpipeline as sp\")\n"
  },
  {
    "path": "scorecardpipeline/model.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\n@Time    : 2023/05/21 16:23\n@Author  : itlubber\n@Site    : itlubber.art\n\"\"\"\n\nimport os\nimport math\nimport numpy as np\nimport pandas as pd\nimport scorecardpy as sc\nimport matplotlib.pyplot as plt\nimport toad\nimport scipy\nfrom statsmodels.stats.outliers_influence import variance_inflation_factor\nfrom sklearn.metrics import classification_report\nfrom sklearn.utils._array_api import get_namespace\nfrom sklearn.utils.extmath import safe_sparse_dot\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.utils.validation import check_is_fitted\nfrom sklearn.base import BaseEstimator, TransformerMixin, ClassifierMixin\n\nfrom .utils import *\nfrom .processing import *\n\n\nclass ITLubberLogisticRegression(LogisticRegression):\n\n    def __init__(self, target=\"target\", penalty=\"l2\", calculate_stats=True, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver=\"lbfgs\", max_iter=100, multi_class=\"auto\", verbose=0, warm_start=False, n_jobs=None, l1_ratio=None, ):\n        \"\"\"ITLubberLogisticRegression，继承 sklearn.linear_model.LogisticRegression 方法，增加了统计性描述相关的内容输出，核心实现逻辑参考：https://github.com/ing-bank/skorecard/blob/main/skorecard/linear_model/linear_model.py#L11\n\n        :param target: 数据集中标签名称，默认 target\n        :param calculate_stats: 是否在训练模型时记录模型统计信息，默认 True，可以通过 summary 方法输出相关统计信息\n        :param tol: 停止求解的标准，float类型，默认为1e-4\n        :param C: 正则化系数λ的倒数，float类型，默认为1.0，必须是正浮点型数，值越小惩罚越大\n        :param fit_intercept: 是否存在截距或偏差，bool类型，默认为True\n        :param class_weight: 类型权重参数，默认 None，支持传入 dict or balanced，当设置 balanced 时，权重计算方式：n_samples / (n_classes * np.bincount(y))\n        :param solver: 求解器设置，默认 lbfgs。对于小型数据集来说，选择 liblinear 更好；对于大型数据集来说，saga 或者 sag 会更快一些。对于多类问题我们只能使用 newton-cg、sag、saga、lbfgs。对于正则化来说，newton-cg、lbfgs 和 sag 只能用于L2正则化(因为这些优化算法都需要损失函数的一阶或者二阶连续导数， 因此无法用于没有连续导数的L1正则化)；而 liblinear，saga 则可处理L1正则化。newton-cg 是牛顿家族中的共轭梯度法，lbfgs 是一种拟牛顿法，sag 则是随机平均梯度下降法，saga 是随机优化算法，liblinear 是坐标轴下降法。\n        :param penalty: 惩罚项，默认 l2，可选 l1、l2，solver 为 newton-cg、sag 和 lbfgs 时只支持L2，L1假设的是模型的参数满足拉普拉斯分布，L2假设的模型参数满足高斯分布\n        :param intercept_scaling: 仅在 solver 选择 liblinear 并且 fit_intercept 设置为 True 的时候才有用\n        :param dual: 对偶或原始方法，bool类型，默认为False，对偶方法只用在求解线性多核(liblinear)的L2惩罚项上。当样本数量>样本特征的时候，dual通常设置为False\n        :param random_state: 随机数种子，int类型，可选参数，默认为无，仅在 solver 为 sag 和 liblinear 时有用\n        :param max_iter: 算法收敛最大迭代次数，int类型，默认 100。只在 solver 为 newton-cg、sag 和 lbfgs 时有用\n        :param multi_class: 分类方法参数选择，默认 auto，可选 ovr、multinomial，如果分类问题是二分类问题，那么这两个参数的效果是一样的，主要体现在多分类问题上\n        :param verbose: 日志级别，当 solver 为 liblinear、lbfgs 时设置为任意正数显示详细计算过程\n        :param warm_start: 热启动参数，bool类型，表示是否使用上次的模型结果作为初始化，默认为 False\n        :param n_jobs: 并行运算数量，默认为1，如果设置为-1，则表示将电脑的cpu全部用上\n        :param l1_ratio: 弹性网络参数，其中0 <= l1_ratio <=1，仅当 penalty 为 elasticnet 时有效\n\n        **参考样例**\n\n        >>> feature_pipeline = Pipeline([\n        >>>     (\"preprocessing_select\", FeatureSelection(target=target, engine=\"scorecardpy\")),\n        >>>     (\"combiner\", Combiner(target=target, min_samples=0.2)),\n        >>>     (\"transform\", WOETransformer(target=target)),\n        >>>     (\"processing_select\", FeatureSelection(target=target, engine=\"scorecardpy\")),\n        >>>     (\"stepwise\", StepwiseSelection(target=target)),\n        >>>     # (\"logistic\", LogisticClassifier(target=target)),\n        >>>     (\"logistic\", ITLubberLogisticRegression(target=target)),\n        >>> ])\n        >>> feature_pipeline.fit(train)\n        >>> summary = feature_pipeline.named_steps['logistic'].summary()\n        >>> summary\n                                                            Coef.  Std.Err       z  P>|z|  [ 0.025  0.975 ]    VIF\n        const                                               -0.8511   0.0991 -8.5920 0.0000  -1.0452  -0.6569 1.0600\n        credit_history                                       0.8594   0.1912  4.4954 0.0000   0.4847   1.2341 1.0794\n        age_in_years                                         0.6176   0.2936  2.1032 0.0354   0.0421   1.1932 1.0955\n        savings_account_and_bonds                            0.8842   0.2408  3.6717 0.0002   0.4122   1.3563 1.0331\n        credit_amount                                        0.7027   0.2530  2.7771 0.0055   0.2068   1.1987 1.1587\n        status_of_existing_checking_account                  0.6891   0.1607  4.2870 0.0000   0.3740   1.0042 1.0842\n        personal_status_and_sex                              0.8785   0.5051  1.7391 0.0820  -0.1116   1.8685 1.0113\n        purpose                                              1.1370   0.2328  4.8844 0.0000   0.6807   1.5932 1.0282\n        present_employment_since                             0.7746   0.3247  2.3855 0.0171   0.1382   1.4110 1.0891\n        installment_rate_in_percentage_of_disposable_income  1.3785   0.3434  4.0144 0.0001   0.7055   2.0515 1.0300\n        duration_in_month                                    0.9310   0.1986  4.6876 0.0000   0.5417   1.3202 1.1636\n        other_installment_plans                              0.8521   0.3459  2.4637 0.0138   0.1742   1.5301 1.0117\n        housing                                              0.8251   0.4346  1.8983 0.0577  -0.0268   1.6770 1.0205\n        \"\"\"\n        super().__init__(penalty=penalty, dual=dual, tol=tol, C=C, fit_intercept=fit_intercept, intercept_scaling=intercept_scaling, class_weight=class_weight, random_state=random_state, solver=solver, max_iter=max_iter, multi_class=multi_class, verbose=verbose, warm_start=warm_start, n_jobs=n_jobs, l1_ratio=l1_ratio, )\n        self.target = target\n        self.calculate_stats = calculate_stats\n\n    def fit(self, x, sample_weight=None, **kwargs):\n        \"\"\"逻辑回归训练方法\n\n        :param x: 训练数据集，需包含目标变量\n        :param sample_weight: 样本权重，参考：https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression.fit\n        :param kwargs: 其他逻辑回归模型训练参数\n        :return: ITLubberLogisticRegression，训练完成的逻辑回归模型\n        \"\"\"\n        y = x[self.target]\n        x = x.drop(columns=[self.target])\n\n        if not self.calculate_stats:\n            return super().fit(x, y, sample_weight=sample_weight, **kwargs)\n\n        x = self.convert_sparse_matrix(x)\n\n        if isinstance(x, pd.DataFrame):\n            self.names_ = [\"const\"] + [f for f in x.columns]\n        else:\n            self.names_ = [\"const\"] + [f\"x{i}\" for i in range(x.shape[1])]\n\n        lr = super().fit(x, y, sample_weight=sample_weight, **kwargs)\n\n        predProbs = self.predict_proba(x)\n\n        # Design matrix -- add column of 1's at the beginning of your x matrix\n        if lr.fit_intercept:\n            x_design = np.hstack([np.ones((x.shape[0], 1)), x])\n        else:\n            x_design = x\n\n        self.vif = [variance_inflation_factor(np.matrix(x_design), i) for i in range(x_design.shape[-1])]\n        p = np.product(predProbs, axis=1)\n        self.cov_matrix_ = np.linalg.inv((x_design * p[..., np.newaxis]).T @ x_design)\n        std_err = np.sqrt(np.diag(self.cov_matrix_)).reshape(1, -1)\n\n        # In case fit_intercept is set to True, then in the std_error array\n        # Index 0 corresponds to the intercept, from index 1 onwards it relates to the coefficients\n        # If fit intercept is False, then all the values are related to the coefficients\n        if lr.fit_intercept:\n\n            self.std_err_intercept_ = std_err[:, 0]\n            self.std_err_coef_ = std_err[:, 1:][0]\n\n            self.z_intercept_ = self.intercept_ / self.std_err_intercept_\n\n            # Get p-values under the gaussian assumption\n            self.p_val_intercept_ = scipy.stats.norm.sf(abs(self.z_intercept_)) * 2\n\n        else:\n            self.std_err_intercept_ = np.array([np.nan])\n            self.std_err_coef_ = std_err[0]\n\n            self.z_intercept_ = np.array([np.nan])\n\n            # Get p-values under the gaussian assumption\n            self.p_val_intercept_ = np.array([np.nan])\n\n        self.z_coef_ = self.coef_ / self.std_err_coef_\n        self.p_val_coef_ = scipy.stats.norm.sf(abs(self.z_coef_)) * 2\n\n        return self\n\n    def decision_function(self, x):\n        \"\"\"决策函数\n\n        :param x: 需要预测的数据集，可以包含目标变量，会根据列名进行判断，如果包含会删除相关特征\n        :return: np.ndarray，预测结果\n        \"\"\"\n        check_is_fitted(self)\n\n        if isinstance(x, pd.DataFrame) and self.target in x.columns:\n            x = x.drop(columns=self.target)\n\n        xp, _ = get_namespace(x)\n        x = self._validate_data(x, accept_sparse=\"csr\", reset=False)\n        scores = safe_sparse_dot(x, self.coef_.T, dense_output=True) + self.intercept_\n        return xp.reshape(scores, (-1,)) if scores.shape[1] == 1 else scores\n\n    def corr(self, data, save=None, annot=True):\n        \"\"\"数据集的特征相关性图\n\n        :param data: 需要画特征相关性图的数据集\n        :param save: 图片保存的地址，如果传入路径中有文件夹不存在，会新建相关文件夹，默认 None\n        :param annot: 是否在图中显示相关性的数值，默认 True\n        \"\"\"\n        corr_plot(data.drop(columns=[self.target]), save=save, annot=annot)\n\n    def report(self, data):\n        \"\"\"逻辑回归模型报告\n\n        :param data: 需要评估的数据集\n        :return: pd.DataFrame，模型报告，包含准确率、F1等指标，参考：https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html\n        \"\"\"\n        report_dict = classification_report(data[self.target], self.predict(data.drop(columns=self.target)), output_dict=True, target_names=[\"好客户\", \"坏客户\"])\n        accuracy = report_dict.pop(\"accuracy\")\n        _report = pd.DataFrame(report_dict).T.reset_index().rename(columns={\"index\": \"desc\"})\n        _report.loc[len(_report)] = ['accuracy', '', '', accuracy, len(data)]\n        return _report\n\n    def summary(self):\n        \"\"\"\n        :return: pd.DataFrame，逻辑回归模型统计信息\n\n        - `Coef.`: 逻辑回归入模特征系数\n        - `Std.Err`: 标准误差\n        - `z`: Z检验统计量\n        - `P>|z|`: P值\n        - `[ 0.025`: 置信区间下界\n        - `0.975 ]`: 置信区间上界\n        - `VIF`: 膨胀方差因子\n\n        **参考样例**\n\n        >>> summary = logistic.summary()\n        >>> summary\n                                                            Coef.  Std.Err       z  P>|z|  [ 0.025  0.975 ]    VIF\n        const                                               -0.8511   0.0991 -8.5920 0.0000  -1.0452  -0.6569 1.0600\n        credit_history                                       0.8594   0.1912  4.4954 0.0000   0.4847   1.2341 1.0794\n        age_in_years                                         0.6176   0.2936  2.1032 0.0354   0.0421   1.1932 1.0955\n        savings_account_and_bonds                            0.8842   0.2408  3.6717 0.0002   0.4122   1.3563 1.0331\n        credit_amount                                        0.7027   0.2530  2.7771 0.0055   0.2068   1.1987 1.1587\n        status_of_existing_checking_account                  0.6891   0.1607  4.2870 0.0000   0.3740   1.0042 1.0842\n        personal_status_and_sex                              0.8785   0.5051  1.7391 0.0820  -0.1116   1.8685 1.0113\n        purpose                                              1.1370   0.2328  4.8844 0.0000   0.6807   1.5932 1.0282\n        present_employment_since                             0.7746   0.3247  2.3855 0.0171   0.1382   1.4110 1.0891\n        installment_rate_in_percentage_of_disposable_income  1.3785   0.3434  4.0144 0.0001   0.7055   2.0515 1.0300\n        duration_in_month                                    0.9310   0.1986  4.6876 0.0000   0.5417   1.3202 1.1636\n        other_installment_plans                              0.8521   0.3459  2.4637 0.0138   0.1742   1.5301 1.0117\n        housing                                              0.8251   0.4346  1.8983 0.0577  -0.0268   1.6770 1.0205\n        \"\"\"\n        check_is_fitted(self)\n\n        if not hasattr(self, \"std_err_coef_\"):\n            msg = \"Summary statistics were not calculated on .fit(). Options to fix:\\n\"\n            msg += \"\\t- Re-fit using .fit(X, y, calculate_stats=True)\\n\"\n            msg += \"\\t- Re-inititialize using LogisticRegression(calculate_stats=True)\"\n            raise AssertionError(msg)\n\n        data = {\n            \"Coef.\": (self.intercept_.tolist() + self.coef_.tolist()[0]),\n            \"Std.Err\": (self.std_err_intercept_.tolist() + self.std_err_coef_.tolist()),\n            \"z\": (self.z_intercept_.tolist() + self.z_coef_.tolist()[0]),\n            \"P>|z|\": (self.p_val_intercept_.tolist() + self.p_val_coef_.tolist()[0]),\n        }\n\n        stats = pd.DataFrame(data, index=self.names_)\n        stats[\"[ 0.025\"] = stats[\"Coef.\"] - 1.96 * stats[\"Std.Err\"]\n        stats[\"0.975 ]\"] = stats[\"Coef.\"] + 1.96 * stats[\"Std.Err\"]\n\n        stats[\"VIF\"] = self.vif\n\n        return stats\n\n    def summary2(self, feature_map={}):\n        \"\"\"summary 的基础上，支持传入数据字典，输出带有特征释义的统计信息表\n\n        :param feature_map: 数据字典，默认 {}\n\n        :return: pd.DataFrame，逻辑回归模型统计信息\n        \"\"\"\n        stats = self.summary().reset_index().rename(columns={\"index\": \"Features\"})\n\n        if feature_map is not None and len(feature_map) > 0:\n            stats.insert(loc=1, column=\"Describe\", value=[feature_map.get(c, \"\") for c in stats[\"Features\"]])\n\n        return stats\n\n    @staticmethod\n    def convert_sparse_matrix(x):\n        \"\"\"稀疏特征优化\"\"\"\n        if scipy.sparse.issparse(x):\n            return x.toarray()\n        else:\n            return x\n\n    def plot_weights(self, save=None, figsize=(15, 8), fontsize=14, color=[\"#2639E9\", \"#F76E6C\", \"#FE7715\"]):\n        \"\"\"逻辑回归模型系数误差图\n\n        :param save: 图片保存的地址，如果传入路径中有文件夹不存在，会新建相关文件夹，默认 None\n        :param figsize: 图片大小，默认 (15, 8)\n        :param fontsize: 字体大小，默认 14\n        :param color: 图片主题颜色，默认即可\n\n        :return: Figure\n        \"\"\"\n        summary = self.summary()\n\n        x = summary[\"Coef.\"]\n        y = summary.index\n        lower_error = summary[\"Coef.\"] - summary[\"[ 0.025\"]\n        upper_error = summary[\"0.975 ]\"] - summary[\"Coef.\"]\n\n        fig, ax = plt.subplots(1, 1, figsize=figsize)\n        ax.errorbar(x, y, xerr=[lower_error, upper_error], fmt=\"o\", ecolor=color[0], elinewidth=2, capthick=2, capsize=4, ms=6, mfc=color[0], mec=color[0])\n        # ax.tick_params(axis='x', labelrotation=0, grid_color=\"#FFFFFF\", labelsize=fontsize)\n        # ax.tick_params(axis='y', labelrotation=0, grid_color=\"#FFFFFF\", labelsize=fontsize)\n        ax.axvline(0, color=color[0], linestyle='--', ymax=len(y), alpha=0.5)\n        ax.spines['top'].set_color(color[0])\n        ax.spines['bottom'].set_color(color[0])\n        ax.spines['right'].set_color(color[0])\n        ax.spines['left'].set_color(color[0])\n        ax.spines['top'].set_visible(False)\n        ax.spines['right'].set_visible(False)\n\n        ax.set_title(\"Regression Meta Analysis - Weight Plot\\n\", fontsize=fontsize, fontweight=\"bold\")\n        ax.set_xlabel(\"Weight Estimates\", fontsize=fontsize, weight=\"bold\")\n        ax.set_ylabel(\"Variable\", fontsize=fontsize, weight=\"bold\")\n\n        if save:\n            if os.path.dirname(save) != \"\" and not os.path.exists(os.path.dirname(save)):\n                os.makedirs(os.path.dirname(save), exist_ok=True)\n\n            plt.savefig(save, dpi=240, format=\"png\", bbox_inches=\"tight\")\n\n        return fig\n\n\nclass ScoreCard(toad.ScoreCard, TransformerMixin):\n\n    def __init__(self, target=\"target\", pdo=60, rate=2, base_odds=35, base_score=750, combiner={}, transer=None, pretrain_lr=None, pipeline=None, **kwargs):\n        \"\"\"评分卡模型\n\n        :param target: 数据集中标签名称，默认 target\n        :param pdo: odds 每增加 rate 倍时减少 pdo 分，默认 60\n        :param rate: 倍率\n        :param base_odds: 基础 odds，通常根据业务经验设置的基础比率（违约概率/正常概率），估算方法：（1-样本坏客户占比）/坏客户占比，默认 35，即 35:1 => 0.972 => 坏样本率 2.8%\n        :param base_score: 基础 odds 对应的分数，默认 750\n        :param combiner: 分箱转换器，传入 pipeline 时可以为None\n        :param transer: woe转换器，传入 pipeline 时可以为None\n        :param pretrain_lr: 预训练好的逻辑回归模型，可以不传\n        :param pipeline: 训练好的 pipeline，必须包含 Combiner 和 WOETransformer\n        :param kwargs: 其他相关参数，具体参考 toad.ScoreCard\n        \"\"\"\n        if pipeline:\n            combiner = self.class_steps(pipeline, Combiner)[0]\n            transer = self.class_steps(pipeline, WOETransformer)[0]\n\n            if self.class_steps(pipeline, (ITLubberLogisticRegression, LogisticRegression)):\n                pretrain_lr = self.class_steps(pipeline, (ITLubberLogisticRegression, LogisticRegression))[0]\n\n        super().__init__(\n            combiner=combiner.combiner if isinstance(combiner, Combiner) else combiner, transer=transer.transformer if isinstance(transer, WOETransformer) else transer,\n            pdo=pdo, rate=rate, base_odds=base_odds, base_score=base_score, **kwargs\n        )\n\n        self.target = target\n        self.pipeline = pipeline\n        self.pretrain_lr = pretrain_lr\n\n    def fit(self, x):\n        \"\"\"评分卡模型训练方法\n\n        :param x: 转换为 WOE 后的训练数据，需包含目标变量\n\n        :return: ScoreCard，训练好的评分卡模型\n        \"\"\"\n        y = x[self.target]\n\n        if self.pretrain_lr:\n            x = x[self.pretrain_lr.feature_names_in_]\n        else:\n            x = x.drop(columns=[self.target])\n\n        self._feature_names = x.columns.tolist()\n\n        for f in self.features_:\n            if f not in self.transer:\n                raise Exception('column \\'{f}\\' is not in transer'.format(f=f))\n\n        if self.pretrain_lr:\n            self.model = self.pretrain_lr\n        else:\n            self.model.fit(x, y)\n\n        self.rules = self._generate_rules()\n\n        sub_score = self.woe_to_score(x)\n        self.base_effect = pd.Series(np.median(sub_score, axis=0), index=self.features_)\n\n        return self\n\n    def transform(self, x):\n        \"\"\"评分转换方法\n\n        :param x: 需要预测模型评分的原始数据，非 woe 转换后的数据\n\n        :return: 预测的评分分数\n        \"\"\"\n        return self.predict(x)\n\n    def _check_rules(self, combiner, transer):\n        \"\"\"评分卡特征分箱校验方法\n\n        :param combiner: 特征分箱器\n        :param transer: WOE转换器\n        :return: bool，是否通过检验\n        \"\"\"\n        for col in self.features_:\n            if col not in combiner:\n                raise Exception('column \\'{col}\\' is not in combiner'.format(col=col))\n\n            if col not in transer:\n                raise Exception('column \\'{col}\\' is not in transer'.format(col=col))\n\n            l_c = len(combiner[col])\n            l_t = len(transer[col]['woe'])\n\n            if l_c == 0:\n                continue\n\n            if np.issubdtype(combiner[col].dtype, np.number):\n                if l_c != l_t - 1:\n                    if np.isnan(combiner[col]).sum() > 0:\n                        combiner.update({col: combiner[col][:-1]})\n                    else:\n                        raise Exception('column \\'{col}\\' is not matched, assert {l_t} bins but given {l_c}'.format(col=col, l_t=l_t, l_c=l_c + 1))\n            else:\n                if l_c != l_t:\n                    if sum([sum([1 for b in r if b in (\"nan\", \"None\")]) for r in combiner[col]]) > 0:\n                        combiner.update({col: [[np.nan if b == \"nan\" else (None if b == \"None\" else b)  for b in r] for r in combiner[col]]})\n                        self._check_rules(combiner, transer)\n                    else:\n                        raise Exception('column \\'{col}\\' is not matched, assert {l_t} bins but given {l_c}'.format(col=col, l_t=l_t, l_c=l_c))\n\n        return True\n\n    @staticmethod\n    def score_clip(score, clip=50):\n        \"\"\"传入评分分数，根据评分分布情况，返回评分等距分箱规则\n\n        :param score: 评分数据\n        :param clip: 区间间隔\n\n        :return: list，评分分箱规则\n        \"\"\"\n        clip_start = max(math.ceil(score.min() / clip) * clip, math.ceil(score.quantile(0.01) / clip) * clip)\n        clip_end = min(math.ceil(score.max() / clip) * clip, math.ceil(score.quantile(0.99) / clip) * clip)\n        return [i for i in range(clip_start, clip_end, clip)]\n\n    def scorecard_scale(self):\n        \"\"\"输出评分卡基准信息，包含 base_odds、base_score、rate、pdo、A、B\n\n        :return: pd.DataFrame，评分卡基准信息\n        \"\"\"\n        scorecard_kedu = pd.DataFrame(\n            [\n                [\"base_odds\", self.base_odds, \"根据业务经验设置的基础比率（违约概率/正常概率），估算方法：（1-样本坏客户占比）/坏客户占比\"],\n                [\"base_score\", self.base_score, \"基础ODDS对应的分数\"],\n                [\"rate\", self.rate, \"设置分数的倍率\"],\n                [\"pdo\", self.pdo, \"表示分数增长PDO时，ODDS值增长到RATE倍\"],\n                [\"B\", self.factor, \"补偿值，计算方式：pdo / ln(rate)\"],\n                [\"A\", self.offset, \"刻度，计算方式：base_score - B * ln(base_odds)\"],\n            ],\n            columns=[\"刻度项\", \"刻度值\", \"备注\"],\n        )\n        return scorecard_kedu\n\n    @classmethod\n    def format_bins(self, bins, index=False, ellipsis=None, decimal=4):\n        \"\"\"分箱转换为标签\n\n        :param bins: 分箱\n        :param index: 是否需要索引\n        :param ellipsis: 字符显示最大长度\n\n        :return: ndarray: 分箱标签\n        \"\"\"\n        if len(bins) == 0:\n            return [\"全部样本\"]\n\n        if isinstance(bins, list): bins = np.array(bins)\n        EMPTYBINS = len(bins) if not isinstance(bins[0], (set, list, np.ndarray)) else -1\n\n        l = []\n        if not isinstance(bins[0], (set, list, np.ndarray)):\n            has_empty = len(bins) > 0 and pd.isnull(bins[-1])\n            if has_empty: bins = bins[:-1]\n            sp_l = [\"负无穷\"] + [round_float(b, decimal=decimal) for b in bins] + [\"正无穷\"]\n            for i in range(len(sp_l) - 1): l.append('[' + str(sp_l[i]) + ' , ' + str(sp_l[i + 1]) + ')')\n            if has_empty: l.append('缺失值')\n        else:\n            for keys in bins:\n                keys_update = set()\n                for key in keys:\n                    if pd.isnull(key) or key == \"nan\":\n                        keys_update.add(\"缺失值\")\n                    elif key.strip() == \"\":\n                        keys_update.add(\"空字符串\")\n                    else:\n                        keys_update.add(key)\n                label = ','.join(keys_update)\n\n                if ellipsis is not None:\n                    label = label[:ellipsis] + '..' if len(label) > ellipsis else label\n\n                l.append(label)\n\n        if index:\n            l = [\"{:02}.{}\".format(i if b != '缺失值' else EMPTYBINS, b) for i, b in enumerate(l)]\n\n        return np.array(l)\n\n    def scorecard_points(self, feature_map={}):\n        \"\"\"输出评分卡分箱信息及其对应的分数\n\n        :param feature_map: 数据字典，默认 {}，传入入模特征的数据字典，输出信息中将增加一列 变量含义\n        :return: pd.DataFrame，评分卡分箱信息\n        \"\"\"\n        card_points = self.export(to_frame=True).rename(columns={\"name\": \"变量名称\", \"value\": \"变量分箱\", \"score\": \"对应分数\"})\n\n        if feature_map is not None and len(feature_map) > 0:\n            card_points.insert(loc=1, column=\"变量含义\", value=[feature_map.get(c, \"\") for c in card_points[\"变量名称\"]])\n\n        return card_points\n\n    def scorecard2pmml(self, pmml: str = 'scorecard.pmml', debug: bool = False):\n        \"\"\"转换评分卡模型为本地 PMML 文件，使用本功能需要提前在环境中安装 jdk 1.8+ 以及 sklearn2pmml 库\n\n        :param pmml: 保存 PMML 模型文件的路径\n        :param debug: bool，是否开启调试模式，默认 False，当设置为 True 时，会返回评分卡 pipeline，同时显示转换细节\n\n        :return: sklearn.pipeline.Pipeline，当设置 debug 为 True 时，返回评分卡 pipeline\n        \"\"\"\n        from sklearn_pandas import DataFrameMapper\n        from sklearn.linear_model import LinearRegression\n        from sklearn2pmml import sklearn2pmml, PMMLPipeline\n        from sklearn2pmml.preprocessing import LookupTransformer, ExpressionTransformer\n\n        mapper = []\n        samples = {}\n        for var, rule in self.rules.items():\n            end_string = ''\n            expression_string = ''\n            total_bins = len(rule['scores'])\n            if isinstance(rule['bins'][0], (np.ndarray, list)):\n                default_value = 0.\n                mapping = {}\n                for bins, score in zip(rule['bins'], rule['scores'].tolist()):\n                    for _bin in bins:\n                        if pd.isnull(_bin) or _bin == 'nan':\n                            default_value = float(score)\n                        else:\n                            mapping[_bin] = float(score)\n\n                mapper.append((\n                    [var],\n                    LookupTransformer(mapping=mapping, default_value=default_value),\n                ))\n                samples[var] = [list(mapping.keys())[i] for i in np.random.randint(0, len(mapping), 20)]\n            else:\n                has_empty = len(rule['bins']) > 0 and pd.isnull(rule['bins'][-1])\n\n                if has_empty:\n                    score_empty = rule['scores'][-1]\n                    total_bins -= 1\n                    bin_scores = rule['scores'][:-1]\n                    bin_vars = rule['bins'][:-1]\n                    expression_string += f'{score_empty} if pandas.isnull(X[0]) '\n                else:\n                    bin_scores = rule['scores']\n                    bin_vars = rule['bins']\n\n                for i in range(len(bin_scores)):\n                    if i == 0:\n                        _expression_string = f'{bin_scores[i]}'\n                    elif i == total_bins - 1:\n                        _expression_string += f' if X[0] < {bin_vars[i - 1]} else {bin_scores[i]}'\n                    else:\n                        _expression_string += f' if X[0] < {bin_vars[i - 1]} else ({bin_scores[i]} '\n                        end_string += ')'\n\n                _expression_string += end_string\n\n                if has_empty:\n                    expression_string += f'else ({_expression_string})' if _expression_string.count('else') > 0 else _expression_string\n                else:\n                    expression_string += _expression_string\n\n                mapper.append((\n                    [var],\n                    ExpressionTransformer(expression_string),\n                ))\n                samples[var] = np.random.random(20) * 100\n\n        scorecard_mapper = DataFrameMapper(mapper, df_out=True)\n\n        pipeline = PMMLPipeline([\n            ('preprocessing', scorecard_mapper),\n            ('scorecard', LinearRegression(fit_intercept=False)),\n        ])\n\n        pipeline.fit(pd.DataFrame(samples), pd.Series(np.random.randint(0, 2, 20), name='score'))\n\n        pipeline.named_steps['scorecard'].coef_ = np.ones(len(scorecard_mapper.features))\n\n        try:\n            sklearn2pmml(pipeline, pmml, with_repr=True, debug=debug)\n        except:\n            import traceback\n            print(traceback.format_exc())\n            return pipeline\n\n        if debug:\n            return pipeline\n\n    @staticmethod\n    def KS_bucket(y_pred, y_true, bucket=10, method=\"quantile\"):\n        \"\"\"用于评估评分卡排序性的方法\n\n        :param y_pred: 模型预测结果，传入评分卡预测的评分或LR预测的概率\n        :param y_true: 样本好坏标签\n        :param bucket: 分箱数量，默认 10\n        :param method: 分箱方法，支持 chi、dt、quantile、step、kmeans，默认 quantile\n\n        :return: 评分卡分箱后的统计信息，推荐直接使用 feature_bin_stats 方法\n        \"\"\"\n        return toad.metrics.KS_bucket(y_pred, y_true, bucket=bucket, method=method)\n\n    @staticmethod\n    def KS(y_pred, y_true):\n        \"\"\"计算 KS 指标\n\n        :param y_pred: 模型预测结果，传入评分卡预测的评分或LR预测的概率\n        :param y_true: 样本好坏标签\n\n        :return: float，KS 指标\n        \"\"\"\n        return toad.metrics.KS(y_pred, y_true)\n\n    @staticmethod\n    def AUC(y_pred, y_true):\n        \"\"\"计算 AUC 指标\n\n        :param y_pred: 模型预测结果，传入评分卡预测的评分或LR预测的概率\n        :param y_true: 样本好坏标签\n\n        :return: float，AUC 指标\n        \"\"\"\n        return toad.metrics.AUC(y_pred, y_true)\n\n    @staticmethod\n    def perf_eva(y_pred, y_true, title=\"\", plot_type=[\"ks\", \"roc\"], save=None, figsize=(14, 6)):\n        \"\"\"评分卡效果评估方法\n\n        :param y_pred: 模型预测结果，传入评分卡预测的评分或LR预测的概率\n        :param y_true: 样本好坏标签\n        :param title: 图像标题\n        :param plot_type: 画图的类型，可选 ks、auc、lift、pr\n        :param save: 图片保存的地址，如果传入路径中有文件夹不存在，会新建相关文件夹，默认 None\n        :param figsize: 图像尺寸大小，传入一个tuple，默认 （14， 6）\n\n        :return: dict，包含 ks、auc、gini、figure\n        \"\"\"\n        # plt.figure(figsize=figsize)\n        rt = sc.perf_eva(y_true, y_pred, title=title, plot_type=plot_type, show_plot=True)\n\n        if save:\n            if os.path.dirname(save) != \"\" and not os.path.exists(os.path.dirname(save)):\n                os.makedirs(os.path.dirname(save))\n\n            rt[\"pic\"].savefig(save, dpi=240, format=\"png\", bbox_inches=\"tight\")\n\n        return rt\n\n    @staticmethod\n    def ks_plot(score, y_true, title=\"\", fontsize=14, figsize=(16, 8), save=None, **kwargs):\n        \"\"\"数值特征 KS曲线 & ROC曲线\n        \n        :param score: 数值特征，通常为评分卡分数\n        :param y_true: 标签值\n        :param title: 图像标题\n        :param fontsize: 字体大小，默认 14\n        :param figsize: 图像大小，默认 (16, 8)\n        :param save: 图片保存的地址，如果传入路径中有文件夹不存在，会新建相关文件夹，默认 None\n        :param kwargs: 其他参数，参考：scorecardpipeline.utils.hist_plot\n        \"\"\"\n        ks_plot(score, y_true, title=title, fontsize=fontsize, figsize=figsize, save=save, **kwargs)\n\n    @staticmethod\n    def PSI(y_pred_train, y_pred_oot):\n        \"\"\"计算两个数据集评分或预测结果的 PSI\n\n        :param y_pred_train: 基准数据集的数值特征，通常为评分卡分数\n        :param y_pred_oot: 对照数据集的数值特征\n        :return: float，PSI 指标值\n        \"\"\"\n        return toad.metrics.PSI(y_pred_train, y_pred_oot)\n\n    @staticmethod\n    def perf_psi(y_pred_train, y_pred_oot, y_true_train, y_true_oot, keys=[\"train\", \"test\"], x_limits=None, x_tick_break=50, show_plot=True, return_distr_dat=False):\n        \"\"\"scorecardpy 的 perf_psi 方法，基于两个数据集的画 PSI 图\n\n        :param y_pred_train: 基准数据集的数值特征，通常为评分卡分数\n        :param y_pred_oot: 对照数据集的数值特征\n        :param y_true_train: 基准数据集的真实标签\n        :param y_true_oot: 基准数据集的真实标签\n        :param keys: 基准数据集和对照数据集的名称\n        :param x_limits: x 轴的区间，默认为 None\n        :param x_tick_break: 评分区间步长\n        :param show_plot: 是否显示图像，默认 True\n        :param return_distr_dat: 是否返回分布数据\n\n        :return: dict，PSI 指标 & 图片\n        \"\"\"\n        return sc.perf_psi(\n            score={keys[0]: y_pred_train, keys[1]: y_pred_oot},\n            label={keys[0]: y_true_train, keys[1]: y_true_oot},\n            x_limits=x_limits,\n            x_tick_break=x_tick_break,\n            show_plot=show_plot,\n            return_distr_dat=return_distr_dat,\n        )\n\n    @staticmethod\n    def score_hist(score, y_true, figsize=(15, 10), bins=20, save=None, **kwargs):\n        \"\"\"数值特征分布图\n\n        :param score: 数值特征，通常为评分卡分数\n        :param y_true: 标签值\n        :param figsize: 图像大小，默认 (15, 10)\n        :param bins: 分箱数量大小，默认 30\n        :param save: 图片保存的地址，如果传入路径中有文件夹不存在，会新建相关文件夹，默认 None\n        :param kwargs: scorecardpipeline.utils.hist_plot 方法的其他参数\n        \"\"\"\n        hist_plot(score, y_true, figsize=figsize, bins=bins, save=save, **kwargs)\n\n    def _format_rule(self, rule, decimal=4, **kwargs):\n        \"\"\"分箱区间精度调整\n\n        :param rule: 分箱信息\n        :param decimal: 精度\n        :return: dict，评分卡分箱及分数\n        \"\"\"\n        bins = self.format_bins(rule['bins'])\n        scores = np.around(rule['scores'], decimals=decimal).tolist()\n\n        return dict(zip(bins, scores))\n\n    @staticmethod\n    def class_steps(pipeline, query):\n        \"\"\"根据 query 查询 pipeline 中对应的 step\n\n        :param pipeline: sklearn.pipeline.Pipeline，训练后的数据预处理 pipeline\n        :param query: 需要查询的类，可以从 pipeline 中查找 WOETransformer 和 Combiner\n\n        :return: list，对应的组件\n        \"\"\"\n        return [v for k, v in pipeline.named_steps.items() if isinstance(v, query)]\n\n    def feature_bin_stats(self, data, feature, rules={}, method='step', max_n_bins=10, desc=\"评分卡分数\", ks=False, **kwargs):\n        \"\"\"评估评分卡排序性的方法，可以输出各分数区间的各项指标\n\n        :param data: 需要查看的数据集\n        :param feature: 数值性特征名称，通常为预测的概率或评分卡分数\n        :param rules: 自定义的区间划分规则\n        :param method: 分箱方法\n        :param max_n_bins: 最大分箱数\n        :param desc: 特征描述\n        :param ks: 是否统计 KS 指标并输出相关统计信息\n        :param kwargs: Combiner.feature_bin_stats 方法的其他参数\n\n        :return: pd.DataFrame，评分各区间的统计信息\n        \"\"\"\n        return Combiner.feature_bin_stats(data, feature, target=self.target, method=method, max_n_bins=max_n_bins, desc=desc, ks=ks, **kwargs)\n"
  },
  {
    "path": "scorecardpipeline/processing.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\n@Time    : 2023/05/21 16:23\n@Author  : itlubber\n@Site    : itlubber.art\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom copy import deepcopy\nfrom functools import partial\nfrom concurrent.futures import ProcessPoolExecutor\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\nimport toad\nimport scorecardpy as sc\nfrom joblib import Parallel, delayed\nfrom optbinning import OptimalBinning\nfrom toad.plot import proportion_plot, badrate_plot\n\nfrom .utils import *\n\n\ndef drop_identical(frame, threshold=0.95, return_drop=False, exclude=None, target=None):\n    \"\"\"\n    剔除数据集中单一值占比过高的特征\n\n    :param frame: 需要进行特征单一值占比过高筛选的数据集\n    :param threshold: 单一值占比阈值，超过阈值剔除特征\n    :param return_drop: 是否返回特征剔除信息，默认 False\n    :param exclude: 是否排除某些特征，不进行单一值占比筛选，默认为 None\n    :param target: 数据集中的目标变量列名，默认为 None，即数据集中不包含 target\n\n    :return:\n        + 筛选后的数据集: pd.DataFrame，剔除单一值占比过高特征的数据集\n        + 剔除的特征: list / np.ndarray，当 return_drop 设置为 True 时，返回被剔除的单一值占比过高的特征列表\n    \"\"\"\n    cols = frame.columns.copy()\n\n    if target:\n        cols = cols.drop(target)\n\n    if exclude:\n        cols = cols.drop(exclude)\n\n    if threshold <= 1:\n        threshold = len(frame) * threshold\n\n    drop_list = []\n    for col in cols:\n        n = frame[col].value_counts().max()\n\n        if n > threshold:\n            drop_list.append(col)\n\n    if return_drop:\n        return frame.drop(columns=drop_list), np.array(drop_list)\n\n    return frame.drop(columns=drop_list)\n\n\ndef drop_corr(frame, target=None, threshold=0.7, by='IV', return_drop=False, exclude=None):\n    \"\"\"\n    剔除数据集中特征相关性过高的特征\n\n    :param frame: 需要进行特征相关性过高筛选的数据集\n    :param target: 数据集中的目标变量列名，默认为 None\n    :param threshold: 相关性阈值，超过阈值剔除特征\n    :param by: 剔除指标的依据，两个特征相关性超过阈值时，保留指标更大的特征，默认根据 IV 进行判断\n    :param return_drop: 是否返回特征剔除信息，默认 False\n    :param exclude: 是否排除某些特征，不进行单一值占比筛选，默认为 None\n\n    :return:\n        + 筛选后的数据集: pd.DataFrame，剔除特征相关性过高的数据集\n        + 剔除的特征: list，当 return_drop 设置为 True 时，返回被剔除的相关性过高的特征列表\n    \"\"\"\n    if not isinstance(by, (str, pd.Series)):\n        by = pd.Series(by, index=frame.columns)\n\n    cols = frame.columns.copy()\n\n    if exclude is not None:\n        exclude = exclude if isinstance(exclude, (list, np.ndarray)) else [exclude]\n        cols = cols.drop(exclude)\n\n    f, t = toad.utils.split_target(frame[cols], target)\n    corr = f.select_dtypes(\"number\").corr().abs()\n\n    drops = []\n    ix, cn = np.where(np.triu(corr.values, 1) > threshold)\n\n    if len(ix):\n        graph = np.hstack([ix.reshape((-1, 1)), cn.reshape((-1, 1))])\n        uni, counts = np.unique(graph, return_counts=True)\n        weights = np.zeros(len(corr.index))\n\n        if isinstance(by, pd.Series):\n            weights = by[corr.index].values\n        elif by.upper() == 'IV':\n            for ix in uni:\n                weights[ix] = toad.IV(frame[corr.index[ix]], target=t)\n\n        while True:\n            nodes = uni[np.argwhere(counts == np.amax(counts))].flatten()\n            n = nodes[np.argsort(weights[nodes])[0]]\n            i, c = np.where(graph == n)\n            pairs = graph[(i, 1 - c)]\n\n            if weights[pairs].sum() > weights[n]:\n                dro = [n]\n            else:\n                dro = pairs.tolist()\n            drops += dro\n\n            di, _ = np.where(np.isin(graph, dro))\n            graph = np.delete(graph, di, axis=0)\n\n            if len(graph) <= 0:\n                break\n\n            uni, counts = np.unique(graph, return_counts=True)\n\n    drop_list = corr.index[drops].values\n    \n    if return_drop:\n        return frame.drop(columns=drop_list), drop_list\n\n    return frame.drop(columns=drop_list)\n\n\ndef select(frame, target='target', empty=0.95, iv=0.02, corr=0.7, identical=0.95, return_drop=False, exclude=None):\n    \"\"\"\n    根据缺失率、IV指标、相关性、单一值占比等进行特征筛选，返回特征剔除后的数据集和剔除的特征信息\n\n    :param frame: 需要进行特征筛选的数据集\n    :param target: 数据集中的目标变量列名，默认为 target\n    :param empty: 缺失率阈值，超过阈值剔除特征\n    :param iv: IV 阈值，低于阈值剔除特征\n    :param corr: 相关性阈值，超过阈值剔除 IV 较小的特征\n    :param identical: 单一值占比阈值，超过阈值剔除特征\n    :param return_drop: 是否返回剔除特征信息，默认 False\n    :param exclude: 是否排除某些特征，不进行特征筛选，默认为 None\n\n    :return:\n        + 筛选后的数据集: pd.DataFrame，特征筛选后的数据集\n        + 剔除的特征信息: dict，当 return_drop 设置为 True 时，返回被剔除特征信息\n    \"\"\"\n    empty_drop = iv_drop = corr_drop = identical_drop = iv_list = None\n\n    if empty:\n        frame, empty_drop = toad.selection.drop_empty(frame, threshold=empty, return_drop=True, exclude=exclude)\n    \n    if iv:\n        frame, iv_drop, iv_list = toad.selection.drop_iv(frame, target=target, threshold=iv, return_drop=True, return_iv=True, exclude=exclude)\n\n    if corr:\n        weights = iv_list if iv else 'IV'\n        frame, corr_drop = drop_corr(frame, target=target, threshold=corr, by=weights, return_drop=True, exclude=exclude)\n\n    if identical:\n        frame, identical_drop = drop_identical(frame, threshold=identical, return_drop=True, exclude=exclude, target=target)\n\n    if return_drop:\n        drop_info = {\n            'empty': empty_drop,\n            'iv': iv_drop,\n            'corr': corr_drop,\n            'identical': identical_drop,\n        }\n        return frame, drop_info\n\n    return frame\n\n\nclass FeatureSelection(TransformerMixin, BaseEstimator):\n\n    def __init__(self, target=\"target\", empty=0.95, iv=0.02, corr=0.7, exclude=None, return_drop=True, identical=0.95, remove=None, engine=\"scorecardpy\", target_rm=False):\n        \"\"\"特征筛选方法\n\n        :param target: 数据集中标签名称，默认 target\n        :param empty: 空值率，默认 0.95, 即空值占比超过 95% 的特征会被剔除\n        :param iv: IV值，默认 0.02，即iv值小于 0.02 时特征会被剔除\n        :param corr: 相关性，默认 0.7，即特征之间相关性大于 0.7 时会剔除iv较小的特征\n        :param identical: 单一值占比，默认 0.95，即当特征的某个值占比超过 95% 时，特征会被剔除\n        :param exclude: 是否需要强制保留某些特征\n        :param return_drop: 是否返回删除信息，默认 True，即默认返回删除特征信息\n        :param remove: 引擎使用 scorecardpy 时，可以传入需要强制删除的变量\n        :param engine: 特征筛选使用的引擎，可选 \"toad\", \"scorecardpy\" 两种，默认 scorecardpy\n        :param target_rm: 是否剔除标签，默认 False，即不剔除\n        \"\"\"\n        self.engine = engine\n        self.target = target\n        self.empty = empty\n        self.identical = identical\n        self.iv = iv\n        self.corr = corr\n        self.exclude = exclude\n        self.remove = remove\n        self.return_drop = return_drop\n        self.target_rm = target_rm\n        self.select_columns = None\n        self.dropped = None\n\n    def fit(self, x, y=None):\n        \"\"\"训练特征筛选方法\n\n        :param x: 数据集，需要包含目标变量\n\n        :return: 训练后的 FeatureSelection\n        \"\"\"\n        if self.engine == \"toad\":\n            selected = select(x, target=self.target, empty=self.empty, identical=self.identical, iv=self.iv, corr=self.corr, exclude=self.exclude, return_drop=self.return_drop)\n        else:\n            selected = sc.var_filter(x, y=self.target, iv_limit=self.iv, missing_limit=self.empty, identical_limit=self.identical, var_rm=self.remove, var_kp=self.exclude, return_rm_reason=self.return_drop)\n\n        if self.return_drop and isinstance(selected, dict):\n            self.dropped = selected[\"rm\"]\n            self.select_columns = list(selected[\"dt\"].columns)\n        elif self.return_drop and isinstance(selected, (tuple, list)):\n            self.dropped = pd.DataFrame([(feature, reason) for reason, features in selected[1].items() for feature in features], columns=[\"variable\", \"rm_reason\"])\n            self.select_columns = list(selected[0].columns)\n        else:\n            self.select_columns = list(selected.columns)\n\n        if self.target_rm and self.target in self.select_columns:\n            self.select_columns.remove(self.target)\n\n        return self\n\n    def transform(self, x, y=None):\n        \"\"\"特征筛选转换器\n\n        :param x: 需要进行特征筛选的数据集\n\n        :return: pd.DataFrame，特征筛选后的数据集\n        \"\"\"\n        return x[[col for col in self.select_columns if col in x.columns]]\n\n\nclass StepwiseSelection(TransformerMixin, BaseEstimator):\n\n    def __init__(self, target=\"target\", estimator=\"ols\", direction=\"both\", criterion=\"aic\", max_iter=None, return_drop=True, exclude=None, intercept=True, p_value_enter=0.2, p_remove=0.01, p_enter=0.01, target_rm=False):\n        \"\"\"逐步回归特征筛选方法\n\n        :param target: 数据集中标签名称，默认 target\n        :param estimator: 预估器，默认 ols，可选 \"ols\", \"lr\", \"lasso\", \"ridge\"，通常默认即可\n        :param direction: 逐步回归方向，默认both，可选 \"forward\", \"backward\", \"both\"，通常默认即可\n        :param criterion: 评价指标，默认 aic，可选 \"aic\", \"bic\", \"ks\", \"auc\"，通常默认即可\n        :param max_iter: 最大迭代次数，sklearn中使用的参数，默认为 None\n        :param return_drop: 是否返回特征剔除信息，默认 True\n        :param exclude: 强制保留的某些特征\n        :param intercept: 是否包含截距，默认为 True\n        :param p_value_enter: 特征进入的 p 值，用于前向筛选时决定特征是否进入模型\n        :param p_remove: 特征剔除的 p 值，用于后向剔除时决定特征是否要剔除\n        :param p_enter: 特征 p 值，用于判断双向逐步回归是否剔除或者准入特征\n        :param target_rm: 是否剔除数据集中的标签，默认为 False，即剔除数据集中的标签\n        \"\"\"\n        self.target = target\n        self.intercept = intercept\n        self.p_value_enter = p_value_enter\n        self.p_remove = p_remove\n        self.p_enter = p_enter\n        self.estimator = estimator\n        self.direction = direction\n        self.criterion = criterion\n        self.max_iter = max_iter\n        self.return_drop = return_drop\n        self.target_rm = target_rm\n        self.exclude = exclude\n        self.select_columns = None\n        self.dropped = None\n\n    def fit(self, x, y=None):\n        \"\"\"训练逐步回归特征筛选方法\n\n        :param x: 数据集，需要包含目标变量\n\n        :return: 训练后的 StepwiseSelection\n        \"\"\"\n        selected = toad.selection.stepwise(x, target=self.target, estimator=self.estimator, direction=self.direction, criterion=self.criterion, exclude=self.exclude, intercept=self.intercept, p_value_enter=self.p_value_enter,\n                                           p_remove=self.p_remove, p_enter=self.p_enter, return_drop=self.return_drop)\n        if self.return_drop:\n            self.dropped = pd.DataFrame([(col, \"stepwise\") for col in selected[1]], columns=[\"variable\", \"rm_reason\"])\n            selected = selected[0]\n\n        self.select_columns = list(selected.columns)\n\n        if self.target_rm and self.target in self.select_columns:\n            self.select_columns.remove(self.target)\n\n        return self\n\n    def transform(self, x, y=None):\n        \"\"\"逐步回归特征筛选转换器\n\n        :param x: 需要进行特征筛选的数据集\n\n        :return: pd.DataFrame，特征筛选后的数据集\n        \"\"\"\n        return x[[col for col in self.select_columns if col in x.columns]]\n\n\nclass FeatureImportanceSelector(BaseEstimator, TransformerMixin):\n\n    def __init__(self, top_k=126, target=\"target\", selector=\"catboost\", params=None, max_iv=None):\n        \"\"\"基于特征重要性的特征筛选方法\n\n        :param top_k: 依据特征重要性进行排序，筛选最重要的 top_k 个特征\n        :param target: 数据集中标签名称，默认 target\n        :param selector: 特征选择器，目前只支持 catboost ，可以支持数据集中包含字符串的数据\n        :param params: selector 的参数，不传使用默认参数\n        :param max_iv: 是否需要删除 IV 过高的特征，建议设置为 1.0\n        \"\"\"\n        self.target = target\n        self.top_k = top_k\n        self.max_iv = max_iv\n        self.selector = selector\n        self.params = params\n        self.feature_names_ = None\n        self.high_iv_feature_names_ = None\n        self.low_importance_feature_names_ = None\n        self.select_columns = None\n        self.dropped = None\n\n    def fit(self, x, y=None):\n        \"\"\"特征重要性筛选器训练\n\n        :param x: 数据集，需要包含目标变量\n\n        :return: 训练后的 FeatureImportanceSelector\n        \"\"\"\n        x = x.copy()\n\n        if self.max_iv is not None:\n            self.high_iv_feature_names_ = list(toad.quality(x, target=self.target, cpu_cores=-1, iv_only=True).query(f\"iv > {self.max_iv}\").index)\n            x = x[[c for c in x.columns if c not in self.high_iv_feature_names_]]\n\n        X = x.drop(columns=self.target)\n        Y = x[self.target]\n\n        self.feature_names_ = list(X.columns)\n        cat_features_index = [i for i in range(len(self.feature_names_)) if self.feature_names_[i] not in X.select_dtypes(\"number\").columns]\n\n        if self.selector == \"catboost\":\n            self.catboost_selector(x=X, y=Y, cat_features=cat_features_index)\n        else:\n            pass\n\n        return self\n\n    def transform(self, x, y=None):\n        \"\"\"特征重要性筛选器转换方法\n\n        :param x: 需要进行特征筛选的数据集\n\n        :return: pd.DataFrame，特征筛选后的数据集\n        \"\"\"\n        return x[self.select_columns + [self.target]]\n\n    def catboost_selector(self, x, y, cat_features=None):\n        \"\"\"基于 `CatBoost` 的特征重要性筛选器\n\n        :param x: 需要进行特征重要性筛选的数据集，不包含目标变量\n        :param y: 数据集中对应的目标变量值\n        :param cat_features: 类别型特征的索引\n        \"\"\"\n        from catboost import Pool, cv, metrics, CatBoostClassifier\n\n        cat_data = Pool(data=x, label=y, cat_features=cat_features)\n\n        if self.params is None:\n            self.params = {\n                \"iterations\": 256,\n                \"objective\": \"CrossEntropy\",\n                \"eval_metric\": \"AUC\",\n                \"learning_rate\": 1e-2,\n                \"colsample_bylevel\": 0.1,\n                \"depth\": 4,\n                \"boosting_type\": \"Ordered\",\n                \"bootstrap_type\": \"Bernoulli\",\n                \"subsample\": 0.8,\n                \"random_seed\": 1024,\n                \"early_stopping_rounds\": 10,\n                \"verbose\": 0,\n            }\n\n        cat_model = CatBoostClassifier(**self.params)\n        cat_model.fit(cat_data, eval_set=[cat_data])\n\n        self.select_columns = [name for score, name in sorted(zip(cat_model.feature_importances_, cat_model.feature_names_), reverse=True)][:self.top_k]\n        self.low_importance_feature_names_ = [c for c in x.columns if c not in self.select_columns]\n\n\nclass Combiner(TransformerMixin, BaseEstimator):\n\n    def __init__(self, target=\"target\", method='chi', empty_separate=True, min_n_bins=2, max_n_bins=None, max_n_prebins=20, min_prebin_size=0.02, min_bin_size=0.05, max_bin_size=None, gamma=0.01, monotonic_trend=\"auto_asc_desc\", adj_rules={}, n_jobs=1, **kwargs):\n        \"\"\"特征分箱封装方法\n\n        :param target: 数据集中标签名称，默认 target\n        :param method: 特征分箱方法，可选 \"chi\", \"dt\", \"quantile\", \"step\", \"kmeans\", \"cart\", \"mdlp\", \"uniform\", 参考 toad.Combiner: https://github.com/amphibian-dev/toad/blob/master/toad/transform.py#L178-L355 & optbinning.OptimalBinning: https://gnpalencia.org/optbinning/\n        :param empty_separate: 是否空值单独一箱, 默认 True\n        :param min_n_bins: 最小分箱数，默认 2，即最小拆分2箱\n        :param max_n_bins: 最大分箱数，默认 None，即不限制拆分箱数，推荐设置 3 ～ 5，不宜过多，偶尔使用 optbinning 时不起效\n        :param max_n_prebins: 使用 optbinning 时预分箱数量\n        :param min_prebin_size: 使用 optbinning 时预分箱叶子结点（或者每箱）样本占比，默认 2%\n        :param min_bin_size: 使用 optbinning 正式分箱叶子结点（或者每箱）最小样本占比，默认 5%\n        :param max_bin_size: 使用 optbinning 正式分箱叶子结点（或者每箱）最大样本占比，默认 None\n        :param gamma: 使用 optbinning 分箱时限制过拟合的正则化参数，值越大惩罚越多，默认 0.01\n        :param monotonic_trend: 使用 optbinning 正式分箱时的坏率策略，默认 auto，可选 \"auto\", \"auto_heuristic\", \"auto_asc_desc\", \"ascending\", \"descending\", \"convex\", \"concave\", \"peak\", \"valley\", \"peak_heuristic\", \"valley_heuristic\"\n        :param adj_rules: 自定义分箱规则，toad.Combiner 能够接收的形式\n        :param n_jobs: 使用多进程加速的worker数量，默认单进程\n        \"\"\"\n        self.combiner = toad.transform.Combiner()\n        self.method = method\n        self.empty_separate = empty_separate\n        self.target = target\n        self.max_n_bins = max_n_bins\n        self.min_n_bins = min_n_bins\n        self.min_bin_size = min_bin_size\n        self.max_bin_size = max_bin_size\n        self.max_n_prebins = max_n_prebins\n        self.min_prebin_size = min_prebin_size\n        self.gamma = gamma\n        self.monotonic_trend = monotonic_trend\n        self.adj_rules = adj_rules\n        self.n_jobs = n_jobs\n        self.kwargs = kwargs\n\n    def update(self, rules):\n        \"\"\"更新 Combiner 中特征的分箱规则\n\n        :param rules: dict，需要更新规则，格式如下：{特征名称: 分箱规则}\n        \"\"\"\n        self.combiner.update(rules)\n\n        # 检查规则内容\n        for feature in rules.keys():\n            self.check_rules(feature=feature)\n\n    @staticmethod\n    def optbinning_bins(feature, data=None, target=\"target\", min_n_bins=2, max_n_bins=3, max_n_prebins=10, min_prebin_size=0.02, min_bin_size=0.05, max_bin_size=None, gamma=0.01, monotonic_trend=\"auto_asc_desc\", **kwargs):\n        \"\"\"基于 optbinning.OptimalBinning 的特征分箱方法，使用 optbinning.OptimalBinning 分箱失败时，使用 toad.transform.Combiner 的卡方分箱处理\n\n        :param feature: 需要进行分箱的特征名称\n        :param data: 训练数据集\n        :param target: 数据集中标签名称，默认 target\n        :param min_n_bins: 最小分箱数，默认 2，即最小拆分2箱\n        :param max_n_bins: 最大分箱数，默认 None，即不限制拆分箱数，推荐设置 3 ～ 5，不宜过多，偶尔不起效\n        :param max_n_prebins: 使用 optbinning 时预分箱数量\n        :param min_prebin_size: 使用 optbinning 时预分箱叶子结点（或者每箱）样本占比，默认 2%\n        :param min_bin_size: 使用 optbinning 正式分箱叶子结点（或者每箱）最小样本占比，默认 5%\n        :param max_bin_size: 使用 optbinning 正式分箱叶子结点（或者每箱）最大样本占比，默认 None\n        :param gamma: 使用 optbinning 分箱时限制过拟合的正则化参数，值越大惩罚越多，默认 0.01\n        :param monotonic_trend: 使用 optbinning 正式分箱时的坏率策略，默认 auto，可选 \"auto\", \"auto_heuristic\", \"auto_asc_desc\", \"ascending\", \"descending\", \"convex\", \"concave\", \"peak\", \"valley\", \"peak_heuristic\", \"valley_heuristic\"\n        \"\"\"\n        data = data[[feature, target]].copy()\n        if data[feature].dropna().nunique() <= min_n_bins:\n            splits = []\n            for v in data[feature].dropna().unique():\n                splits.append(v)\n\n            if str(data[feature].dtypes) in [\"object\", \"string\", \"category\"]:\n                rule = {feature: [[s] for s in splits]}\n                rule[feature].append([[np.nan]])\n            else:\n                rule = {feature: sorted(splits) + [np.nan]}\n        else:\n            try:\n                y = data[target]\n                if str(data[feature].dtypes) in [\"object\", \"string\", \"category\"]:\n                    dtype = \"categorical\"\n                    x = data[feature].astype(\"category\").values\n                else:\n                    dtype = \"numerical\"\n                    x = data[feature].values\n\n                _combiner = OptimalBinning(feature, dtype=dtype, min_n_bins=min_n_bins, max_n_bins=max_n_bins, max_n_prebins=max_n_prebins, min_prebin_size=min_prebin_size, min_bin_size=min_bin_size, max_bin_size=max_bin_size, monotonic_trend=monotonic_trend, gamma=gamma, **kwargs).fit(x, y)\n                if _combiner.status == \"OPTIMAL\":\n                    rule = {feature: [s.tolist() if isinstance(s, np.ndarray) else s for s in _combiner.splits] + [[None] if dtype == \"categorical\" else np.nan]}\n                else:\n                    raise Exception(\"optimalBinning error\")\n\n            except Exception as e:\n                _combiner = toad.transform.Combiner()\n                _combiner.fit(data[[feature, target]].dropna(), target, method=\"chi\", min_samples=min_bin_size, n_bins=max_n_bins, empty_separate=False)\n                rule = {feature: [s.tolist() if isinstance(s, np.ndarray) else s for s in _combiner.export()[feature]] + [[None] if dtype == \"categorical\" else np.nan]}\n\n        return rule\n\n    def fit(self, x: pd.DataFrame, y=None):\n        \"\"\"特征分箱训练\n\n        :param x: 需要分箱的数据集，需要包含目标变量\n\n        :return: Combiner，训练完成的分箱器\n        \"\"\"\n        x = x.copy()\n\n        # 处理数据集中分类变量包含 np.nan，toad 分箱后被转为 'nan' 字符串的问题\n        cat_cols = list(x.drop(columns=self.target).select_dtypes(exclude=\"number\").columns)\n        # x[cat_cols] = x[cat_cols].replace(np.nan, None)\n\n        if self.method in [\"cart\", \"mdlp\", \"uniform\"]:\n            feature_optbinning_bins = partial(self.optbinning_bins, data=x, target=self.target, min_n_bins=self.min_n_bins, max_n_bins=self.max_n_bins, max_n_prebins=self.max_n_prebins, min_prebin_size=self.min_prebin_size, min_bin_size=self.min_bin_size, max_bin_size=self.max_bin_size, gamma=self.gamma, monotonic_trend=self.monotonic_trend, **self.kwargs)\n            if self.n_jobs is not None:\n                rules = Parallel(n_jobs=self.n_jobs)(delayed(feature_optbinning_bins)(feature) for feature in x.columns.drop(self.target))\n                [self.combiner.update(r) for r in rules]\n                # with ProcessPoolExecutor(max_workers=self.n_jobs) as executor:\n                #     [executor.submit(feature_optbinning_bins(feature)) for feature in x.columns.drop(self.target)]\n            else:\n                for feature in x.drop(columns=[self.target]):\n                    rule = feature_optbinning_bins(feature)\n                    self.combiner.update(rule)\n        else:\n            if self.method in [\"step\", \"quantile\"]:\n                self.combiner.fit(x, y=self.target, method=self.method, n_bins=self.max_n_bins, empty_separate=self.empty_separate, **self.kwargs)\n            else:\n                self.combiner.fit(x, y=self.target, method=self.method, min_samples=self.min_bin_size, n_bins=self.max_n_bins, empty_separate=self.empty_separate, **self.kwargs)\n\n        if self.adj_rules is not None and len(self.adj_rules) > 0:\n            self.update(self.adj_rules)\n\n        # 检查类别变量空值是否被转为字符串，如果转为了字符串，强制转回空值，同时检查分箱顺序并调整为正确顺序\n        self.check_rules()\n\n        return self\n\n    def check_rules(self, feature=None):\n        \"\"\"检查类别变量空值是否被转为字符串，如果转为了字符串，强制转回空值，同时检查分箱顺序并调整为正确顺序\"\"\"\n        for col in self.combiner.rules.keys():\n            if feature is not None and col != feature:\n                continue\n\n            _rule = self.combiner[col]\n\n            if len(_rule) > 0 and not np.issubdtype(_rule.dtype, np.number) and isinstance(_rule[0], (list, tuple)):\n                if sum([sum([1 for b in r if b in (\"nan\", \"None\")]) for r in _rule]) > 0:\n                    _rule = [[np.nan if b == \"nan\" else (None if b == \"None\" else b) for b in r] for r in _rule]\n                    if [np.nan] in _rule:\n                        _rule.remove([np.nan])\n                        _rule.append([np.nan])\n                    if [None] in _rule:\n                        _rule.remove([None])\n                        _rule.append([None])\n\n                    self.combiner.update({col: _rule})\n\n    def transform(self, x, y=None, labels=False):\n        \"\"\"特征分箱转换方法\n\n        :param x: 需要进行分箱转换的数据集\n        :param labels: 进行分箱转换时是否转换为分箱信息，默认 False，即转换为分箱索引\n\n        :return: pd.DataFrame，分箱转换后的数据集\n        \"\"\"\n        return self.combiner.transform(x, labels=labels)\n\n    def export(self, to_json=None):\n        \"\"\"特征分箱器导出 json 保存\n\n        :param to_json: json 文件的路径\n\n        :return: dict，特征分箱信息\n        \"\"\"\n        return self.combiner.export(to_json=to_json)\n\n    def load(self, from_json):\n        \"\"\"特征分箱器加载离线保存的 json 文件\n\n        :param from_json: json 文件的路径\n\n        :return: Combiner，特征分箱器\n        \"\"\"\n        self.combiner.load(from_json)\n        return self\n\n    @classmethod\n    def feature_bin_stats(cls, data, feature, target=\"target\", rules=None, method='step', desc=\"\", combiner=None, ks=True, max_n_bins=None, min_bin_size=None, max_bin_size=None, greater_is_better=\"auto\", amount=None, margins=False, empty_separate=True, return_cols=None, return_rules=False, verbose=0, **kwargs):\n        \"\"\"特征分箱统计表，汇总统计特征每个分箱的各项指标信息\n\n        :param data: 需要查看分箱统计表的数据集\n        :param feature: 需要查看的分箱统计表的特征名称\n        :param target: 数据集中标签名称，默认 target\n        :param rules: 根据自定义的规则查看特征分箱统计表，支持 list（单个特征分箱规则） 或 dict（多个特征分箱规则） 格式传入\n        :param combiner: 提前训练好的特征分箱器，优先级小于 rules\n        :param method: 特征分箱方法，当传入 rules 或 combiner 时失效，可选 \"chi\", \"dt\", \"quantile\", \"step\", \"kmeans\", \"cart\", \"mdlp\", \"uniform\", 参考 toad.Combiner: https://github.com/amphibian-dev/toad/blob/master/toad/transform.py#L178-L355 & optbinning.OptimalBinning: https://gnpalencia.org/optbinning/\n        :param desc: 特征描述信息，大部分时候用于传入特征对应的中文名称或者释义\n        :param ks: 是否统计 KS 信息\n        :param max_n_bins: 最大分箱数，默认 None，即不限制拆分箱数，推荐设置 3 ～ 5，不宜过多，偶尔使用 optbinning 时不起效\n        :param min_bin_size: 使用 optbinning 正式分箱叶子结点（或者每箱）最小样本占比，默认 5%\n        :param max_bin_size: 使用 optbinning 正式分箱叶子结点（或者每箱）最大样本占比，默认 None\n        :param empty_separate: 是否空值单独一箱, 默认 False，推荐设置为 True\n        :param return_cols: list，指定返回部分特征分箱统计表的列，默认 None\n        :param return_rules: 是否返回特征分箱信息，默认 False\n        :param greater_is_better: 是否越大越好，默认 \"\"auto\", 根据最后两箱的 lift 指标自动推断是否越大越好, 可选 True、False、auto\n        :param amount: 默认为空, 支持传入数值字段（通常为放款金额）, 在分析逾期率时，输出对应的分析结果\n        :param margins: 分箱表是否输出合计, 默认为 False, 即不输出\n        :param kwargs: scorecardpipeline.processing.Combiner 的其他参数\n\n        :return:\n            + 特征分箱统计表: pd.DataFrame\n            + 特征分箱信息: list，当参数 return_rules 为 True 时返回\n        \"\"\"\n        if combiner is None:\n            if method not in [\"chi\", \"dt\", \"quantile\", \"step\", \"kmeans\", \"cart\", \"mdlp\", \"uniform\"]:\n                raise 'method is the one of [\"chi\", \"dt\", \"quantile\", \"step\", \"kmeans\", \"cart\", \"mdlp\", \"uniform\"]'\n\n            _combiner = cls(\n                target=target\n                , method=method\n                , empty_separate=empty_separate\n                , min_n_bins=2\n                , max_n_bins=max_n_bins\n                , min_bin_size=min_bin_size\n                , max_bin_size=max_bin_size\n                , **kwargs\n            )\n            _combiner.fit(data[[feature, target]])\n\n        else:\n            _combiner = deepcopy(combiner)\n\n        if rules is not None and len(rules) > 0:\n            if isinstance(rules, (list, np.ndarray)):\n                _combiner.update({feature: rules})\n            else:\n                _combiner.update(rules)\n\n        feature_bin_dict = feature_bins(_combiner[feature])\n\n        if amount:\n            temp = data.reset_index(drop=True)\n            df_bin = temp[[amount]].join(_combiner.transform(temp[[feature, target]], labels=False))\n            table = df_bin[[feature, target, amount]].groupby([feature, target])[amount].sum().unstack()\n        else:\n            df_bin = _combiner.transform(data[[feature, target]], labels=False)\n            # table = df_bin[[feature, target]].groupby([feature, target]).agg(len).unstack()\n            table = df_bin[[feature, target]].groupby([feature, target])[feature].count().unstack()\n\n        table.columns.name = None\n\n        table = table.rename(columns={0: '好样本数', 1: '坏样本数'}).fillna(0)\n        if \"好样本数\" not in table.columns:\n            table[\"好样本数\"] = 0\n        if \"坏样本数\" not in table.columns:\n            table[\"坏样本数\"] = 0\n\n        table[\"指标名称\"] = feature\n        table[\"指标含义\"] = desc\n        table = table.reset_index().rename(columns={feature: \"分箱\"})\n\n        table['样本总数'] = table['好样本数'] + table['坏样本数']\n        table['样本占比'] = table['样本总数'] / table['样本总数'].sum()\n        table['好样本占比'] = table['好样本数'] / table['好样本数'].sum()\n        table['坏样本占比'] = table['坏样本数'] / table['坏样本数'].sum()\n        table['坏样本率'] = table['坏样本数'] / table['样本总数']\n\n        table = table.fillna(0.)\n\n        table['分档WOE值'] = table.apply(lambda x: np.log(x['好样本占比'] / (x['坏样本占比'] + 1e-6)), axis=1)\n        table['分档IV值'] = table.apply(lambda x: (x['好样本占比'] - x['坏样本占比']) * np.log(x['好样本占比'] / (x['坏样本占比'] + 1e-6)), axis=1)\n\n        table = table.replace(np.inf, 0).replace(-np.inf, 0)\n\n        bad_rate = table[\"坏样本数\"].sum() / table[\"样本总数\"].sum()\n\n        table[\"LIFT值\"] = table['坏样本率'] / bad_rate\n        table[\"坏账改善\"] = (bad_rate - (table[\"坏样本数\"].sum() - table[\"坏样本数\"]) / (table[\"样本总数\"].sum() - table[\"样本总数\"])) / bad_rate\n\n        def reverse_series(series):\n            return series.reindex(series.index[::-1])\n\n        if greater_is_better == \"auto\":\n            if table[table[\"分箱\"].map(feature_bin_dict) != \"缺失值\"][\"LIFT值\"].iloc[-1] > table[\"LIFT值\"].iloc[0]:\n                table[\"累积LIFT值\"] = (reverse_series(table['坏样本数']).cumsum() / reverse_series(table['样本总数']).cumsum()) / bad_rate\n                table[\"累积坏账改善\"] = (bad_rate - ((table[\"坏样本数\"].sum() - reverse_series(table['坏样本数']).cumsum()) / (table[\"样本总数\"].sum() - reverse_series(table['样本总数']).cumsum())).fillna(0.0)) / bad_rate\n                if ks:\n                    table = table.sort_values(\"分箱\")\n                    table[\"累积好样本数\"] = reverse_series(table[\"好样本数\"]).cumsum()\n                    table[\"累积坏样本数\"] = reverse_series(table[\"坏样本数\"]).cumsum()\n                    table[\"分档KS值\"] = table[\"累积坏样本数\"] / table['坏样本数'].sum() - table[\"累积好样本数\"] / table['好样本数'].sum()\n            else:\n                table[\"累积LIFT值\"] = (table['坏样本数'].cumsum() / table['样本总数'].cumsum()) / bad_rate\n                table[\"累积坏账改善\"] = (bad_rate - ((table[\"坏样本数\"].sum() - table['坏样本数'].cumsum()) / (table[\"样本总数\"].sum() - table['样本总数'].cumsum())).fillna(0.0)) / bad_rate\n                if ks:\n                    table = table.sort_values(\"分箱\")\n                    table[\"累积好样本数\"] = table[\"好样本数\"].cumsum()\n                    table[\"累积坏样本数\"] = table[\"坏样本数\"].cumsum()\n                    table[\"分档KS值\"] = table[\"累积坏样本数\"] / table['坏样本数'].sum() - table[\"累积好样本数\"] / table['好样本数'].sum()\n        elif greater_is_better is False:\n            table[\"累积LIFT值\"] = (reverse_series(table['坏样本数']).cumsum() / reverse_series(table['样本总数']).cumsum()) / bad_rate\n            table[\"累积坏账改善\"] = (bad_rate - ((table[\"坏样本数\"].sum() - reverse_series(table['坏样本数']).cumsum()) / (table[\"样本总数\"].sum() - reverse_series(table['样本总数']).cumsum())).fillna(0.0)) / bad_rate\n            if ks:\n                table = table.sort_values(\"分箱\")\n                table[\"累积好样本数\"] = reverse_series(table[\"好样本数\"]).cumsum()\n                table[\"累积坏样本数\"] = reverse_series(table[\"坏样本数\"]).cumsum()\n                table[\"分档KS值\"] = table[\"累积坏样本数\"] / table['坏样本数'].sum() - table[\"累积好样本数\"] / table['好样本数'].sum()\n        else:\n            table[\"累积LIFT值\"] = (table['坏样本数'].cumsum() / table['样本总数'].cumsum()) / bad_rate\n            table[\"累积坏账改善\"] = (bad_rate - ((table[\"坏样本数\"].sum() - table['坏样本数'].cumsum()) / (table[\"样本总数\"].sum() - table['样本总数'].cumsum())).fillna(0.0)) / bad_rate\n            if ks:\n                table = table.sort_values(\"分箱\")\n                table[\"累积好样本数\"] = table[\"好样本数\"].cumsum()\n                table[\"累积坏样本数\"] = table[\"坏样本数\"].cumsum()\n                table[\"分档KS值\"] = table[\"累积坏样本数\"] / table['坏样本数'].sum() - table[\"累积好样本数\"] / table['好样本数'].sum()\n\n        table[\"分箱\"] = table[\"分箱\"].map(feature_bin_dict)\n        table = table.set_index(['指标名称', '指标含义', '分箱']).reindex([(feature, desc, b) for b in feature_bin_dict.values()]).fillna(0).reset_index()\n        table['指标IV值'] = table['分档IV值'].sum()\n\n        if margins:\n            table = pd.concat([table, pd.DataFrame([{\"指标名称\": feature, \"指标含义\": desc, \"分箱\": \"合计\", \"好样本数\": table[\"好样本数\"].sum(), \"坏样本数\": table[\"坏样本数\"].sum(), \"样本总数\": table[\"样本总数\"].sum(), \"样本占比\": 1.0, \"好样本占比\": 1.0, \"坏样本占比\": 1.0, \"坏样本率\": bad_rate, '分档IV值':  table['分档IV值'].sum(), '指标IV值': table['分档IV值'].sum(), \"LIFT值\": 1.0, \"坏账改善\": 1.0, \"累积LIFT值\": 1.0, \"累积坏账改善\": 1.0, \"累积好样本数\": table[\"好样本数\"].sum(), \"累积坏样本数\": table[\"坏样本数\"].sum(), \"分档KS值\": table[\"分档KS值\"].max()}])])\n\n        if return_cols:\n            table = table[[c for c in return_cols if c in table.columns]]\n        elif ks:\n            table = table[['指标名称', \"指标含义\", '分箱', '样本总数', '样本占比', '好样本数', '好样本占比', '坏样本数', '坏样本占比', '坏样本率', '分档WOE值', '分档IV值', '指标IV值', 'LIFT值', '坏账改善', '累积LIFT值', '累积坏账改善', '累积好样本数', '累积坏样本数', '分档KS值']]\n        else:\n            table = table[['指标名称', \"指标含义\", '分箱', '样本总数', '样本占比', '好样本数', '好样本占比', '坏样本数', '坏样本占比', '坏样本率', '分档WOE值', '分档IV值', '指标IV值', 'LIFT值', '坏账改善', '累积LIFT值', '累积坏账改善']]\n\n        if return_rules:\n            return table, list(_combiner[feature])\n        else:\n            return table\n\n    def bin_plot(self, data, x, rule={}, desc=\"\", result=False, save=None, **kwargs):\n        \"\"\"特征分箱图\n\n        :param data: 需要查看分箱图的数据集\n        :param x: 需要查看的分箱图的特征名称\n        :param rule: 自定义的特征分箱规则，不会修改已训练好的特征分箱信息\n        :param desc: 特征描述信息，大部分时候用于传入特征对应的中文名称或者释义\n        :param result: 是否返回特征分箱统计表，默认 False\n        :param save: 图片保存的地址，如果传入路径中有文件夹不存在，会新建相关文件夹，默认 None\n        :param kwargs: scorecardpipeline.utils.bin_plot 方法其他的参数，参考：http://localhost:63342/scorecardpipeline/docs/build/html/scorecardpipeline.html#scorecardpipeline.utils.bin_plot\n        :return: pd.DataFrame，特征分箱统计表，当 result 参数为 True 时返回\n        \"\"\"\n        feature_table = self.feature_bin_stats(data, x, target=self.target, rules=rule, desc=desc, combiner=self.combiner, ks=True)\n        bin_plot(feature_table, desc=desc, save=save, **kwargs)\n\n        if result:\n            return feature_table\n\n    def proportion_plot(self, data, x, transform=False, labels=False, keys=None):\n        \"\"\"数据集中特征的分布情况\n\n        :param data: 需要查看样本分布的数据集\n        :param x: 需要查看样本分布的特征名称\n        :param transform: 是否进行分箱转换，默认 False，当特征为数值型变量时推荐转换分箱后在查看数据分布\n        :param labels: 进行分箱转换时是否转换为分箱信息，默认 False，即转换为分箱索引\n        :param keys: 根据某个 key 划分数据集查看数据分布情况，默认 None\n        \"\"\"\n        if transform:\n            x = self.combiner.transform(x, labels=labels)\n\n        proportion_plot(x, keys=keys)\n\n    def corr_plot(self, data, transform=False, figure_size=(20, 15), save=None):\n        \"\"\"特征相关图\n\n        :param data: 需要查看特征相关性的数据集\n        :param transform: 是否进行分箱转换，默认 False\n        :param figure_size: 图像大小，默认 (20, 15)\n        :param save: 图片保存的地址，如果传入路径中有文件夹不存在，会新建相关文件夹，默认 None\n        \"\"\"\n        if transform:\n            data = self.combiner.transform(data, labels=False)\n\n        corr_plot(data, figure_size=figure_size, save=save)\n\n    def badrate_plot(self, data, date_column, feature, labels=True):\n        \"\"\"查看不同时间段的分箱是否平稳，线敞口随时间变化而增大为优，代表了特征在更新的时间区分度更强。线之前没有交叉为优，代表分箱稳定\n\n        :param data: 需要查看分箱平稳情况的数据集，需包含时间列\n        :param feature: 需要查看分箱平稳性的特征名称\n        :param date_column: 数据集中的日期列名称\n        :param labels: 进行分箱转换时是否转换为分箱信息，默认 True，即转换为分箱\n        \"\"\"\n        badrate_plot(self.combiner.transform(data[[date_column, feature, self.target]], labels=labels), target=self.target, x=date_column, by=feature)\n\n    @property\n    def rules(self):\n        \"\"\"dict，特征分箱明细信息\"\"\"\n        return self.combiner._rules\n\n    @rules.setter\n    def rules(self, value):\n        \"\"\"设置分箱信息\n\n        :param value: 特征分箱\n        \"\"\"\n        self.combiner._rules = value\n\n    def __len__(self):\n        \"\"\"返回特征分箱器特征的个数\n\n        :return: int，分箱器中特征的个数\n        \"\"\"\n        return len(self.combiner._rules.keys())\n\n    def __contains__(self, key):\n        \"\"\"查看某个特征是否在分箱器中\n\n        :param key: 特征名称\n        :return: bool，是否在分箱器中\n        \"\"\"\n        return key in self.combiner._rules\n\n    def __getitem__(self, key):\n        \"\"\"获取某个特征的分箱信息\n\n        :param key: 特征名称\n        :return: list，特征分箱信息\n        \"\"\"\n        return self.combiner._rules[key]\n\n    def __setitem__(self, key, value):\n        \"\"\"设置某个特征的分箱信息\n\n        :param key: 特征名称\n        :param value: 分箱规则\n        \"\"\"\n        self.combiner._rules[key] = value\n\n    def __iter__(self):\n        \"\"\"分箱规则的迭代器\n\n        :return: iter，迭代器\n        \"\"\"\n        return iter(self.combiner._rules)\n\n\ndef feature_bin_stats(data, feature, target=\"target\", overdue=None, dpd=None, rules=None, method='step', desc=\"\", combiner=None, ks=True, max_n_bins=None, min_bin_size=None, max_bin_size=None, greater_is_better=\"auto\", amount=None, margins=False, empty_separate=True, return_cols=None, return_rules=False, del_grey=False, verbose=0, **kwargs):\n    \"\"\"特征分箱统计表，汇总统计特征每个分箱的各项指标信息\n\n    :param data: 需要查看分箱统计表的数据集\n    :param feature: 需要查看的分箱统计表的特征名称\n    :param target: 数据集中标签名称，默认 target\n    :param overdue: 逾期天数字段名称, 当传入 overdue 时，会忽略 target 参数\n    :param dpd: 逾期定义方式，逾期天数 > DPD 为 1，其他为 0，仅 overdue 字段起作用时有用\n    :param del_grey: 是否删除逾期天数 (0, dpd] 的数据，仅 overdue 字段起作用时有用\n    :param rules: 根据自定义的规则查看特征分箱统计表，支持 list（单个特征分箱规则） 或 dict（多个特征分箱规则） 格式传入\n    :param combiner: 提前训练好的特征分箱器，优先级小于 rules\n    :param method: 特征分箱方法，当传入 rules 或 combiner 时失效，可选 \"chi\", \"dt\", \"quantile\", \"step\", \"kmeans\", \"cart\", \"mdlp\", \"uniform\", 参考 toad.Combiner: https://github.com/amphibian-dev/toad/blob/master/toad/transform.py#L178-L355 & optbinning.OptimalBinning: https://gnpalencia.org/optbinning/\n    :param desc: 特征描述信息，大部分时候用于传入特征对应的中文名称或者释义\n    :param ks: 是否统计 KS 信息\n    :param max_n_bins: 最大分箱数，默认 None，即不限制拆分箱数，推荐设置 3 ～ 5，不宜过多，偶尔使用 optbinning 时不起效\n    :param min_bin_size: 使用 optbinning 正式分箱叶子结点（或者每箱）最小样本占比，默认 5%\n    :param max_bin_size: 使用 optbinning 正式分箱叶子结点（或者每箱）最大样本占比，默认 None\n    :param empty_separate: 是否空值单独一箱, 默认 False，推荐设置为 True\n    :param return_cols: list，指定返回部分特征分箱统计表的列，默认 None\n    :param return_rules: 是否返回特征分箱信息，默认 False\n    :param greater_is_better: 是否越大越好，默认 \"\"auto\", 根据最后两箱的 lift 指标自动推断是否越大越好, 可选 True、False、auto\n    :param amount: 默认为空, 支持传入数值字段（通常为放款金额）, 在分析逾期率时，输出对应的分析结果\n    :param margins: 分箱表是否输出合计, 默认为 False, 即不输出\n    :param kwargs: scorecardpipeline.processing.Combiner 的其他参数\n\n    :return:\n        + 特征分箱统计表: pd.DataFrame\n        + 特征分箱信息: list，当参数 return_rules 为 True 时返回\n\n    \"\"\"\n    if overdue and dpd is None:\n        raise ValueError(\"传入 overdue 参数时必须同时传入 dpd\")\n\n    drop_empty = True if not empty_separate and data[feature].isnull().sum() <= 0 else False\n\n    if overdue is None:\n        table, rule = Combiner.feature_bin_stats(data, feature, target=target, rules=rules, method=method, desc=desc, combiner=combiner, ks=ks, max_n_bins=max_n_bins, min_bin_size=min_bin_size, max_bin_size=max_bin_size, greater_is_better=greater_is_better, amount=amount, margins=margins, empty_separate=empty_separate, return_cols=return_cols, return_rules=True, verbose=verbose, **kwargs)\n\n        if drop_empty:\n            table = table.iloc[:-1]\n            rule = rule[:-1]\n\n        if return_rules:\n            return table, rule\n        else:\n            return table\n\n    if not isinstance(overdue, list):\n        overdue = [overdue]\n\n    if not isinstance(dpd, list):\n        dpd = [dpd]\n\n    amount_feature = [amount] if amount is not None else []\n\n    if isinstance(del_grey, bool) and del_grey:\n        merge_columns = [\"指标名称\", \"指标含义\", \"分箱\"]\n    else:\n        merge_columns = [\"指标名称\", \"指标含义\", \"分箱\", \"样本总数\", \"样本占比\"]\n\n    table = pd.DataFrame()\n    for i, col in enumerate(overdue):\n        for j, d in enumerate(dpd):\n            target = f\"{col} {d}+\"\n            _datasets = data[[feature] + amount_feature + overdue].copy()\n            _datasets[target] = (_datasets[col] > d).astype(int)\n\n            if isinstance(del_grey, bool) and del_grey:\n                _datasets = _datasets.query(f\"({col} > {d}) | ({col} == 0)\").reset_index(drop=True)\n\n            if i == 0 and j == 0:\n                if combiner is None:\n                    if rules is not None and len(rules) > 0:\n                        if isinstance(rules, (list, np.ndarray)):\n                            rules = {feature: rules}\n\n                    combiner = Combiner(target=target, adj_rules=rules, method=method, empty_separate=empty_separate, min_n_bins=2, max_n_bins=max_n_bins, min_bin_size=min_bin_size, max_bin_size=max_bin_size, **kwargs)\n                    combiner.fit(_datasets)\n\n                table, rule = Combiner.feature_bin_stats(_datasets, feature, target=target, method=method, desc=desc, combiner=combiner, ks=ks, max_n_bins=max_n_bins, min_bin_size=min_bin_size, max_bin_size=max_bin_size, greater_is_better=greater_is_better, amount=amount, margins=margins, empty_separate=empty_separate, return_rules=True, verbose=verbose, **kwargs)\n                table.columns = pd.MultiIndex.from_tuples([(\"分箱详情\", c) if c in merge_columns else (target, c) for c in table.columns])\n            else:\n                _table = Combiner.feature_bin_stats(_datasets, feature, target=target, method=method, desc=desc, combiner=combiner, ks=ks, max_n_bins=max_n_bins, min_bin_size=min_bin_size, max_bin_size=max_bin_size, greater_is_better=greater_is_better, amount=amount, margins=margins, empty_separate=empty_separate, verbose=verbose, **kwargs)\n                _table.columns = pd.MultiIndex.from_tuples([(\"分箱详情\", c) if c in merge_columns else (target, c) for c in _table.columns])\n\n                table = table.merge(_table, on=[(\"分箱详情\", c) for c in merge_columns])\n\n    if drop_empty:\n        table = table.iloc[:-1]\n        rule = rule[:-1]\n\n    if return_cols is not None:\n        if not isinstance(return_cols, list):\n            return_cols = [return_cols]\n\n        table = table[[c for c in table.columns if (isinstance(c, tuple) and c[-1] in return_cols + merge_columns) or (not isinstance(c, tuple) and c in return_cols + merge_columns)]]\n\n    if return_rules:\n        return (table, rule)\n    else:\n        return table\n\n\nclass WOETransformer(TransformerMixin, BaseEstimator):\n\n    def __init__(self, target=\"target\", exclude=None):\n        \"\"\"WOE转换器\n\n        :param target: 数据集中标签名称，默认 target\n        :param exclude: 不需要转换 woe 的列\n        \"\"\"\n        self.target = target\n        self.exclude = exclude if isinstance(exclude, list) else [exclude] if exclude else []\n        self.transformer = toad.transform.WOETransformer()\n\n    def fit(self, x, y=None):\n        \"\"\"WOE转换器训练\n\n        :param x: Combiner 转换后的数据（label 为 False），需要包含目标变量\n        :return: WOETransformer，训练完成的WOE转换器\n        \"\"\"\n        self.transformer.fit(x.drop(columns=self.exclude + [self.target]), x[self.target])\n        return self\n\n    def transform(self, x, y=None):\n        \"\"\"特征WOE转换方法\n\n        :param x: 需要进行WOE转换的数据集\n\n        :return: pd.DataFrame，WOE转换后的数据集\n        \"\"\"\n        return self.transformer.transform(x)\n    \n    def export(self, to_json=None):\n        \"\"\"特征分箱器导出 json 保存\n\n        :param to_json: json 文件的路径\n\n        :return: dict，特征分箱信息\n        \"\"\"\n        return self.transformer.export(to_json=to_json)\n\n    def load(self, from_json):\n        \"\"\"特征分箱器加载离线保存的 json 文件\n\n        :param from_json: json 文件的路径\n\n        :return: Combiner，特征分箱器\n        \"\"\"\n        self.transformer.load(from_json)\n        return self\n\n    @property\n    def rules(self):\n        \"\"\"dict，特征 WOE 明细信息\"\"\"\n        return self.transformer._rules\n\n    @rules.setter\n    def rules(self, value):\n        self.transformer._rules = value\n\n    def __len__(self):\n        return len(self.transformer._rules.keys())\n\n    def __contains__(self, key):\n        return key in self.transformer._rules\n\n    def __getitem__(self, key):\n        return self.transformer._rules[key]\n\n    def __setitem__(self, key, value):\n        self.transformer._rules[key] = value\n\n    def __iter__(self):\n        return iter(self.transformer._rules)\n\n\ndef feature_efficiency_analysis(data, feature, overdue=[\"MOB1\"], dpd=[7, 3, 0], greater_is_better=\"auto\", verbose=True, ks=False, **kwargs):\n    auto_feature_tables = feature_bin_stats(\n        data\n        , feature\n        , min_bin_size=0.01\n        , overdue=overdue, dpd=dpd\n        , method=\"mdlp\"\n        , max_n_bins=10\n        , del_grey=False\n        , desc=f\"样本数 {len(data)} 坏样本率 {round((data[overdue[0]] > dpd[0]).mean(), 4)}\"\n        , greater_is_better=greater_is_better\n        , return_cols=[\"坏样本数\", \"坏样本率\", \"LIFT值\", \"累积LIFT值\", \"分档KS值\"]\n        , **kwargs\n    )\n\n    quantile_feature_tables = feature_bin_stats(\n        data\n        , feature, rules=data[feature].quantile([0.01, 0.02, 0.03, 0.05, 0.1, 0.2, 0.4, 0.6, 0.8] if greater_is_better else [0.1, 0.3, 0.5, 0.7, 0.9, 0.95, 0.97, 0.98, 0.99]).unique().tolist() + [np.nan]\n        , method=\"mdlp\", overdue=overdue, dpd=dpd, max_n_bins=10, greater_is_better=greater_is_better, min_bin_size=0.01, return_cols=[\"坏样本数\", \"坏样本率\", \"LIFT值\", \"累积LIFT值\", \"分档KS值\"]\n        , **kwargs\n    )\n\n    if ks:\n        ks_plot(data.dropna(subset=[feature])[feature], (data.dropna(subset=[feature])[overdue[0]] > dpd[0]).astype(int), figsize=(10, 6))\n\n    if verbose:\n        from IPython.display import display\n        display(auto_feature_tables)\n        display(quantile_feature_tables)\n    else:\n        return auto_feature_tables, quantile_feature_tables\n"
  },
  {
    "path": "scorecardpipeline/rule.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\n@Time    : 2024/2/26 12:00\n@Author  : itlubber\n@Site    : itlubber.art\n\"\"\"\nimport ast\nimport numpy as np\nimport numexpr as ne\nfrom enum import Enum\nfrom functools import reduce\nfrom typing import List\n\nimport pandas as pd\nfrom pandas import DataFrame\nfrom sklearn.utils import check_array\nfrom sklearn.metrics import f1_score, recall_score, accuracy_score, precision_score\n\nfrom .processing import feature_bin_stats, Combiner\nfrom .excel_writer import dataframe2excel\n\n\ndef _get_context(X, feature_names):\n    return {name: X[:, i] for i, name in enumerate(feature_names)}\n\n\ndef _apply_expr_on_array(expr, X, feature_names):\n    ctx = _get_context(X, feature_names)\n    return ne.evaluate(expr, local_dict=ctx)\n\n\ndef get_columns_from_query(query_str):\n    \"\"\"获取pandas query语句使用的列\n\n    :param query_str: pandas query 支持的查询语句\n    :return: query 语句使用的列名\n    \"\"\"\n    tree = ast.parse(query_str, mode='eval')\n    columns = set()\n\n    def visit_node(node):\n        if isinstance(node, ast.Attribute):\n            # 对于属性访问，递归访问其值部分\n            visit_node(node.value)\n        elif isinstance(node, ast.Name) and not isinstance(node.ctx, ast.Load):\n            pass  # 跳过非变量名\n        elif isinstance(node, ast.Name) and node.id not in {'and', 'or', 'not'}:\n            columns.add(node.id)\n        elif isinstance(node, ast.Call):\n            # 对于函数调用，访问其函数部分\n            visit_node(node.func)\n\n    for node in ast.walk(tree):\n        visit_node(node)\n\n    return sorted(columns)\n\n\nclass RuleState(str, Enum):\n    INITIALIZED = \"initialized\"\n    APPLIED = \"applied\"\n\n\nclass RuleStateError(RuntimeError):\n    pass\n\n\nclass RuleUnAppliedError(RuleStateError):\n    pass\n\n\n# 中級操作符\nop_dict = {\"GT\": \">\", \"LT\": \"<\", \"EQ\": \"==\", \"ADD\": \"+\", \"GE\": \">=\", \"LE\": \"<=\", \"SUBTRACT\": \"-\", \"MULTIPLY\": \"*\", \"DIVIDE\": \"/\", \"OR\": \"|\", \"AND\": \"&\"}\n# 数据类型 int, float-->float, 目前不支持 str\nvalue_type_dict = {\"int\": float, \"float\": float, \"string\": str, \"bool\": bool}\n# if_part, then_part, else_part\npart_dict = [\"if\", \"then\", \"else\"]\n\n\n# max_index: 数据的列数     feature_list: 列的名称\ndef json2expr(data, max_index, feature_list):\n    if data.keys()._contains_(\"operator\"):\n        op = data.get(\"operator\")\n        params = data.get(\"params\")\n        if op == \"FEATURE_INDEX\":  # 取变量，一个值{判断变量，索引是否正常}\n            feature = params[0].get(\"feature\")\n            if params[0].get(\"index\") >= max_index:  # json中的索引异常: index >= 数据列数\n                raise ValueError(\"index error\")\n            if feature not in feature_list:  # 变量异常:变量名不在数据的列名中\n                raise ValueError(\"{} do not belong to the data \".format(feature))\n            return feature\n        elif op in op_dict:  # 两个值，递归\n            value_list = [json2expr(params[0], max_index, feature_list), json2expr(params[1], max_index, feature_list)]\n            return \"(\" + str(value_list[0]) + op_dict[op] + str(value_list[1]) + \")\"\n        else:  # op 不在op_dict报错\n            raise TypeError(\"The operator: {} is invalid\".format(op))\n\n    if data.keys().__contains__(\"value\"):\n        value_type = data.get(\"value_type\")\n        value = data[\"value\"]\n        # 对取到值的类型做转换， 不在类型字典中的值报错\n        if not value_type_dict.get(value_type):\n            raise ValueError(\"Data type error!\")\n        return value_type_dict.get(value_type)(value)\n\n\nclass Rule:\n    def __init__(self, expr):  # expr 既可以传递字符串，也可以传递dict\n        \"\"\"规则集\n\n        :param expr: 类似 DataFrame 的 query 方法传参方式即可，目前仅支持数值型变量规则\n\n        **参考样例**\n\n        >>> from scorecardpipeline import *\n        >>> target = \"creditability\"\n        >>> data = germancredit()\n        >>> data[target] = data[target].map({\"good\": 0, \"bad\": 1})\n        >>> data = data.select_dtypes(\"number\") # 暂不支持字符型规则\n        >>> rule1 = Rule(\"duration_in_month < 10\")\n        >>> rule2 = Rule(\"credit_amount < 500\")\n        >>> rule1.report(data, target=target)\n        >>> rule2.report(data, target=target)\n        >>> (rule1 | rule2).report(data, target=target)\n        >>> (rule1 & rule2).report(data, target=target)\n        \"\"\"\n        self._state = RuleState.INITIALIZED\n        self.expr = expr\n        self.feature_names_in_ = get_columns_from_query(self.expr)\n\n    def __str__(self):\n        return f\"Rule({repr(self.expr)})\"\n\n    def __repr__(self):\n        return f\"Rule({repr(self.expr)})\"\n\n    def predict(self, X: DataFrame, part=\"\"):  # dict预测对应part_dict 、字符串表达式对应\"、\"其他情况报错\n        if not isinstance(X, DataFrame):\n            raise ValueError(\"Rule can only predict on DataFrame.\")\n        \n        check_array(X, dtype=None, ensure_2d=True, force_all_finite=\"allow-nan\")\n        result = X.eval(self.expr)\n        \n        # feature_names = X.columns.values.tolist()  # 取数据的列名\n        # X = X.select_dtypes(\"number\") # 仅支持数值型变量\n        # X = check_array(X, dtype=None, ensure_2d=True, force_all_finite=\"allow-nan\")\n        # if isinstance(self.expr, dict):  # dict部分\n        #     if part not in part_dict:\n        #         raise TypeError(\"Part : {} not in ['if','then','else']\".format(part))\n        #     if not self.expr[part]:  # 没有返回值的情况[]\n        #         return list()\n        #     dict2expr = json2expr(self.expr[part], X.shape[1], feature_names)\n        #     if not isinstance(dict2expr, str):  # 返回Value (类型已经做过转换),对其扩充 --> [value] * Len(X)\n        #         result = [dict2expr] * len(X)\n        #     else:  # 表达式在进行计算\n        #         result = ne.evaluate(dict2expr, local_dict={name: X[:, i] for i, name in enumerate(feature_names)})\n        #         result = result.tolist()\n        #         if not isinstance(result, list):  # result 只有一个数值时，对其扩充 --> [value] * len(X)\n        #             result = [result] * len(X)\n        # elif isinstance(self.expr, str):  # 字符串表达式部分\n        #     if part != \"\":\n        #         raise TypeError('The part of the expression must be \"\"')\n        #     result = ne.evaluate(self.expr, local_dict={name: X[:, i] for i, name in enumerate(feature_names)})\n        # else:\n        #     raise TypeError(\"Rule currently only supports dict and expression\")\n\n        self.result_ = result\n\n        return result\n\n    def report(self, datasets: pd.DataFrame, target=\"target\", overdue=None, dpd=None, del_grey=False, desc=\"\", filter_cols=None, prior_rules=None, amount=None, **kwargs) -> pd.DataFrame:\n        \"\"\"规则效果报告表格输出\n\n        :param datasets: 数据集，需要包含 目标变量 或 逾期天数，当不包含目标变量时，会通过逾期天数计算目标变量，同时需要传入逾期定义的DPD天数\n        :param target: 目标变量名称，默认 target\n        :param desc: 规则相关的描述，会出现在返回的表格当中\n        :param filter_cols: 指定返回的字段列表，默认不传\n        :param prior_rules: 先验规则，可以传入先验规则先筛选数据后再评估规则效果\n        :param overdue: 逾期天数字段名称\n        :param dpd: 逾期定义方式，逾期天数 > DPD 为 1，其他为 0，仅 overdue 字段起作用时有用\n        :param del_grey: 是否删除逾期天数 (0, dpd] 的数据，仅 overdue 字段起作用时有用\n        :param amount: 默认为空, 支持传入数值字段（通常为放款金额）, 在分析逾期率时，输出对应的分析结果\n\n        :return: pd.DataFrame，规则效果评估表\n        \"\"\"\n        return_cols = ['指标名称', \"指标含义\", '分箱', '样本总数', '样本占比', '好样本数', '好样本占比', '坏样本数', '坏样本占比', '坏样本率', 'LIFT值', '坏账改善']\n        if desc is None or desc == \"\" and \"指标含义\" in return_cols:\n            return_cols.remove(\"指标含义\")\n\n        rule_expr = self.expr\n\n        def _report_one_rule(data, target, desc='', prior_rules=None):\n            if prior_rules:\n                prior_tables = prior_rules.report(data, target=target, desc=desc, prior_rules=None)\n                prior_tables[\"规则分类\"] = \"先验规则\"\n                temp = data[~prior_rules.predict(data)]\n                if amount is not None:\n                    rule_result = pd.DataFrame({rule_expr: np.where(self.predict(temp), \"命中\", \"未命中\"), amount: temp[amount], \"target\": temp[target].tolist()})\n                else:\n                    rule_result = pd.DataFrame({rule_expr: np.where(self.predict(temp), \"命中\", \"未命中\"), \"target\": temp[target].tolist()})\n            else:\n                prior_tables = pd.DataFrame(columns=return_cols)\n                if amount is not None:\n                    rule_result = pd.DataFrame({rule_expr: np.where(self.predict(data), \"命中\", \"未命中\"), amount: data[amount], \"target\": data[target].tolist()})\n                else:\n                    rule_result = pd.DataFrame({rule_expr: np.where(self.predict(data), \"命中\", \"未命中\"), \"target\": data[target].tolist()})\n\n            combiner = Combiner(target=target)\n            combiner.load({rule_expr: [[\"命中\"], [\"未命中\"]]})\n            table = feature_bin_stats(rule_result, rule_expr, combiner=combiner, desc=desc, return_cols=return_cols, amount=amount, **kwargs)\n            table[\"风险拒绝比\"] = table[\"坏账改善\"] / table[\"样本占比\"]\n\n            # 准确率、精确率、召回率、F1分数\n            metrics = pd.DataFrame({\n                \"分箱\": [\"命中\", \"未命中\"],\n                \"准确率\": [accuracy_score(rule_result[\"target\"], rule_result[rule_expr].map({\"命中\": 1, \"未命中\": 0})), accuracy_score(rule_result[\"target\"], rule_result[rule_expr].map({\"命中\": 0, \"未命中\": 1}))],\n                \"精确率\": [precision_score(rule_result[\"target\"], rule_result[rule_expr].map({\"命中\": 1, \"未命中\": 0})), precision_score(rule_result[\"target\"], rule_result[rule_expr].map({\"命中\": 0, \"未命中\": 1}))],\n                \"召回率\": [recall_score(rule_result[\"target\"], rule_result[rule_expr].map({\"命中\": 1, \"未命中\": 0})), recall_score(rule_result[\"target\"], rule_result[rule_expr].map({\"命中\": 0, \"未命中\": 1}))],\n                \"F1分数\": [f1_score(rule_result[\"target\"], rule_result[rule_expr].map({\"命中\": 1, \"未命中\": 0})), f1_score(rule_result[\"target\"], rule_result[rule_expr].map({\"命中\": 0, \"未命中\": 1}))],\n            })\n            table = table.merge(metrics, on=\"分箱\", how=\"left\")\n\n            if prior_rules:\n                # prior_tables.insert(loc=0, column=\"规则分类\", value=[\"先验规则\"] * len(prior_tables))\n                table.insert(loc=0, column=\"规则分类\", value=[\"验证规则\"] * len(table))\n                table = pd.concat([prior_tables, table]) #.set_index([\"规则分类\"])\n            else:\n                table.insert(loc=0, column=\"规则分类\", value=[\"验证规则\"] * len(table))\n\n            return table\n\n        if isinstance(del_grey, bool) and del_grey:\n            merge_columns = [\"规则分类\", \"指标名称\", \"分箱\"]\n        else:\n            merge_columns = [\"规则分类\", \"指标名称\", \"分箱\", \"样本总数\", \"样本占比\"]\n\n        if overdue is not None:\n            if not isinstance(overdue, list):\n                overdue = [overdue]\n\n            if not isinstance(dpd, list):\n                dpd = [dpd]\n\n            for i, col in enumerate(overdue):\n                for j, d in enumerate(dpd):\n                    _datasets = datasets.copy()\n                    _datasets[f\"{col}_{d}\"] = (_datasets[col] > d).astype(int)\n\n                    if isinstance(del_grey, bool) and del_grey:\n                        _datasets = _datasets.query(f\"({col} > {d}) | ({col} == 0)\").reset_index(drop=True)\n\n                    if \"指标含义\" in return_cols:\n                        merge_columns.insert(0, \"指标含义\")\n\n                    if i == 0 and j == 0:\n                        table = _report_one_rule(_datasets, f\"{col}_{d}\", desc=desc, prior_rules=prior_rules) #.rename(columns={\"坏账改善\": f\"{col} {d}+改善\"})\n                        table.columns = pd.MultiIndex.from_tuples([(\"规则详情\", c) if c in merge_columns else (f\"{col} DPD{d}+\", c) for c in table.columns])\n                    else:\n                        _table = _report_one_rule(_datasets, f\"{col}_{d}\", desc=desc, prior_rules=prior_rules) #.rename(columns={\"坏账改善\": f\"{col} {d}+改善\"})\n                        _table.columns = pd.MultiIndex.from_tuples([(\"规则详情\", c) if c in merge_columns else (f\"{col} DPD{d}+\", c) for c in _table.columns])\n\n                        # table = table.merge(_table[[\"规则分类\", \"分箱\", f\"{col} {d}+改善\"]], on=[\"规则分类\", \"分箱\"])\n                        table = table.merge(_table, on=[(\"规则详情\", c) for c in merge_columns])\n        else:\n            _datasets = datasets.copy()\n            table = _report_one_rule(_datasets, target, desc=desc, prior_rules=prior_rules)\n\n        if filter_cols:\n            if not isinstance(filter_cols, list):\n                filter_cols = [filter_cols]\n            return table[[c for c in table.columns if (isinstance(c, tuple) and c[-1] in filter_cols + merge_columns) or (not isinstance(c, tuple) and c in filter_cols + merge_columns)]]\n\n        return table\n\n    def result(self):\n        if self._state != RuleState.APPLIED:\n            raise RuleUnAppliedError(\"Invoke `predict` to make a rule applied.\")\n        return self.result_\n\n    def __eq__(self, other):\n        if not isinstance(other, Rule):\n            raise TypeError(f\"Input should be of type Rule, got {type(other)} instead.\")\n        if self._state != other._state:\n            raise RuleStateError(f\"Input rule should be of the same state.\")\n        res = self.expr == other.expr\n        if self._state == RuleState.INITIALIZED:\n            return res\n        return res and np.all(self.result() == other.result())\n\n    # rule combinations\n    def __or__(self, other):\n        if not isinstance(other, Rule):\n            raise TypeError(f\"Input should be of type Rule, got {type(other)} instead.\")\n        if self._state != other._state:\n            raise RuleStateError(f\"Input rule should be of the same state.\")\n        if isinstance(self.expr, str):\n            r = Rule(f\"({self.expr}) | ({other.expr})\")\n            if self._state == RuleState.INITIALIZED:\n                return r\n            r.result_ = np.logical_or(self.result(), other.result())\n            r._state = RuleState.APPLIED\n            return r\n        elif isinstance(self.expr, dict):\n            self.new_dict = {}  # 汇总成新的json\n            self.new_dict[\"name\"] = str(self.expr.get(\"name\")) + str(other.expr.get(\"name\"))\n            self.new_dict[\"description\"] = str(self.expr.get(\"description\")) + \" || \" + str(other.expr.get(\"description\"))\n            self.new_dict[\"output\"] = self.expr.get(\"output\")\n\n            # if_part\n            if_dict = {}\n            if_dict[\"value_type\"] = \"bool\"\n            if_dict[\"operator\"] = \"OR\"\n            if_dict[\"params\"] = list()\n            if_dict[\"params\"].append(self.expr.get(\"if\"))\n            if_dict[\"params\"].append(other.expr.get(\"if\"))\n            self.new_dict[\"if\"] = if_dict\n\n            # then_part\n            then_part = {}\n            if not self.expr.get(\"then\") and not other.expr.get(\"then\"):  # 两条规则的then都为空\n                then_part = {}\n            elif not self.expr.get(\"then\"):  # 一条规则的then存在\n                then_part = other.expr.get(\"then\")\n            elif not other.expr.get(\"then\"):\n                then_part = self.expr.get(\"then\")\n            else:  # 两条规则的then都存在\n                if self.expr.get(\"then\").get(\"value_type\") != other.expr.get(\"then\").get(\"value_type\"):\n                    raise TypeError(\"两个规则then_part类型要一致\")\n                if self.expr.get(\"then\").get(\"value_type\") != \"bool\":\n                    raise TypeError(\"两个规则之间or运算, 类型需要设置为bool类型\")\n                then_part[\"value_type\"] = \"bool\"\n                then_part[\"operator\"] = \"OR\"\n                then_part[\"params\"] = list()\n                then_part[\"params\"].append(self.expr.get(\"then\"))\n                then_part[\"params\"].append(other.expr.get(\"then\"))\n            self.new_dict[\"then\"] = then_part\n\n        # else_part\n        else_part = {}  # self.else或者other.else存在为空的情况\n        if not self.expr.get(\"else\") and not other.expr.get(\"else\"):\n            else_part = {}\n        elif not self.expr.get(\"else\"):  # 一条规则的then存在\n            else_part = other.expr.get(\"else\")\n        elif not other.expr.get(\"else\"):\n            else_part = self.expr.get(\"else\")\n        else:\n            if self.expr.get(\"then\").get(\"value_type\") != other.expr.get(\"then\").get(\"value_type\"):\n                raise TypeError(\"两个规则else part类型要一致\")\n            if self.expr.get(\"then\").get(\"value_type\") != \"bool\":\n                raise TypeError(\"两个规则之间or运算, 类型需要设置为bool类型\")\n            else_part[\"value_type\"] = \"bool\"\n            else_part[\"operator\"] = \"OR\"\n            else_part[\"params\"] = list()\n            else_part[\"params\"].append(self.expr.get(\"else\"))\n            else_part[\"params\"].append(other.expr.get(\"else\"))\n        self.new_dict[\"else\"] = else_part\n\n        return Rule(self.new_dict)\n\n    def __and__(self, other):\n        if not isinstance(other, Rule):\n            raise TypeError(f\"Input should be of type Rule, got {type(other)} instead.\")\n        if self._state != other._state:\n            raise RuleStateError(f\"Input rule should be of the same state.\")\n        if isinstance(self.expr, str):  # 表达式\n            r = Rule(f\"({self.expr}) & ({other.expr})\")\n            if self._state == RuleState.INITIALIZED:\n                return r\n            r.result_ = np.logical_and(self.result(), other.result())\n            r._state = RuleState.APPLIED\n            return r\n        elif isinstance(self.expr, dict):  # dict\n            self.new_dict = {}  # 汇总成新的json\n            self.new_dict[\"name\"] = str(self.expr.get(\"name\")) + str(other.expr.get(\"name\"))\n            self.new_dict[\"description\"] = str(self.expr.get(\"description\")) + \" && \" + str(other.expr.get(\"description\"))\n            self.new_dict[\"output\"] = self.expr.get(\"output\")\n\n            # if_part\n            if_dict = {}\n            if_dict[\"value_type\"] = \"bool\"\n            if_dict[\"operator\"] = \"AND\"\n            if_dict[\"params\"] = list()\n            if_dict[\"params\"].append(self.expr.get(\"if\"))\n            if_dict[\"params\"].append(other.expr.get(\"if\"))\n            self.new_dict[\"if\"] = if_dict\n\n            # then_part\n            then_part = {}\n            if not self.expr.get(\"then\") and not other.expr.get(\"then\"):  # 两条规则的then都为空\n                then_part = {}\n            elif not self.expr.get(\"then\"):  # 一条规则的then存在\n                then_part = other.expr.get(\"then\")\n            elif not other.expr.get(\"then\"):\n                then_part = self.expr.get(\"then\")\n            else:  # 两条规则的then都存在\n                if self.expr[\"then\"].get(\"value_type\") != other.expr[\"then\"].get(\"value_type\"):\n                    raise TypeError(\"两个规则then_part类型要一致\")\n                if self.expr.get(\"then\").get(\"value_type\") != \"bool\":\n                    raise TypeError(\"两个规则之间and运算, 类型需要设置为bool类型\")\n                then_part[\"value_type\"] = \"bool\"\n                then_part[\"operator\"] = \"AND\"\n                then_part[\"params\"] = list()\n                then_part[\"params\"].append(self.expr.get(\"then\"))\n                then_part[\"params\"].append(other.expr.get(\"then\"))\n            self.new_dict[\"then\"] = then_part\n\n            # else_part\n            else_part = {}  # self.else 或者other.else 存在为空的情况\n            if not self.expr.get(\"else\") and not other.expr.get(\"else\"):\n                else_part = {}\n            elif not self.expr.get(\"else\"):  # 一条规则的then存在\n                else_part = other.expr.get(\"else\")\n            elif not other.expr.get(\"else\"):\n                else_part = self.expr.get(\"else\")\n            else:\n                if self.expr.get(\"else\").get(\"value_type\") != other.expr.get(\"else\").get(\"value_type\"):\n                    raise TypeError(\"两个规则else_part类型要一致\")\n                if self.expr.get(\"then\").get(\"value_type\") != \"bool\":\n                    raise TypeError(\"两个规则之间and运算, 类型需要设置为bool类型\")\n                else_part[\"value_type\"] = \"bool\"\n                else_part[\"operator\"] = \"AND\"\n                else_part[\"params\"] = list()\n                else_part[\"params\"].append(self.expr.get(\"else\"))\n                else_part[\"params\"].append(other.expr.get(\"else\"))\n            self.new_dict[\"else\"] = else_part\n\n            return Rule(self.new_dict)\n\n    def __xor__(self, other):\n        if not isinstance(other, Rule):\n            raise TypeError(f\"Input should be of type Rule, got {type(other)} instead.\")\n        if self._state != other._state:\n            raise RuleStateError(f\"Input rule should be of the same state.\")\n        r = Rule(f\"({self.expr}) ^ ({other.expr})\")\n        if self._state == RuleState.INITIALIZED:\n            return r\n        r.result_ = np.logical_xor(self.result(), other.result())\n        r._state = RuleState.APPLIED\n        return r\n\n    def __mul__(self, other):\n        return self.__or__(other)\n\n    def __invert__(self):\n        r = Rule(f\"~({self.expr})\")\n        if self._state == RuleState.INITIALIZED:\n            return r\n        r.result_ = np.logical_not(self.result())\n        r._state = RuleState.APPLIED\n        return r\n\n    @staticmethod\n    def save(report, excel_writer, sheet_name=None, merge_column=None, percent_cols=None, condition_cols=None, custom_cols=None, custom_format=\"#,##0\", color_cols=None, start_col=2, start_row=2, **kwargs):\n        \"\"\"保存规则结果至excel中，参数与 https://scorecardpipeline.itlubber.art/scorecardpipeline.html#scorecardpipeline.dataframe2excel 一致\n        \"\"\"\n        if merge_column:\n            merge_column = [c for c in report.columns if (isinstance(c, tuple) and c[-1] in merge_column) or (not isinstance(c, tuple) and c in merge_column)]\n\n        if percent_cols:\n            percent_cols = [c for c in report.columns if (isinstance(c, tuple) and c[-1] in percent_cols) or (not isinstance(c, tuple) and c in percent_cols)]\n\n        if condition_cols:\n            condition_cols = [c for c in report.columns if (isinstance(c, tuple) and c[-1] in condition_cols) or (not isinstance(c, tuple) and c in condition_cols)]\n        \n        if custom_cols:\n            custom_cols = [c for c in report.columns if (isinstance(c, tuple) and c[-1] in custom_cols) or (not isinstance(c, tuple) and c in custom_cols)]\n        \n        if color_cols:\n            color_cols = [c for c in report.columns if (isinstance(c, tuple) and c[-1] in color_cols) or (not isinstance(c, tuple) and c in color_cols)]\n        \n        end_row, end_col = dataframe2excel(report, excel_writer, sheet_name=sheet_name, merge_column=merge_column, percent_cols=percent_cols, condition_cols=condition_cols, custom_cols=custom_cols, custom_format=custom_format, color_cols=color_cols, start_col=start_col, start_row=start_row, **kwargs)\n        return end_row, end_col\n\n\ndef ruleset_report(datasets: pd.DataFrame, rules: List[Rule], target=\"target\", overdue=None, dpd=None, filter_cols=None, **kwargs) -> pd.DataFrame:\n    datasets = datasets.copy()\n\n    feature_names_missing = set([f for rule in rules for f in rule.feature_names_in_]) - set(datasets.columns)\n    if len(feature_names_missing) > 0:\n        raise ValueError(f\"数据集字段缺少以下字段: {feature_names_missing}\")\n\n    report = pd.DataFrame()\n\n    table_total = reduce(lambda r1, r2: r1 | r2, rules).report(datasets, target=target, overdue=overdue, dpd=dpd, filter_cols=filter_cols, margins=True, **kwargs)\n    table_total[(\"规则详情\", \"分箱\")] = [\"汇总\", \"剩余样本\", \"原始样本\"]\n    table_total = table_total.drop(columns=[(\"规则详情\", \"规则分类\"), (\"规则详情\", \"指标名称\")])\n\n    report = pd.concat([report, table_total.loc[table_total[(\"规则详情\", \"分箱\")] == \"原始样本\", :]])\n\n    for rule in rules:\n        table = rule.report(datasets, target=target, overdue=overdue, dpd=dpd, filter_cols=filter_cols, margins=False, **kwargs)\n        table[(\"规则详情\", \"分箱\")] = [rule.expr, \"剩余样本\"]\n        table = table.drop(columns=[(\"规则详情\", \"规则分类\"), (\"规则详情\", \"指标名称\")])\n\n        report = pd.concat([report, table])\n\n        datasets = datasets[~rule.predict(datasets)]\n\n    report = pd.concat([report, table_total.loc[table_total[(\"规则详情\", \"分箱\")] == \"汇总\", :]]).reset_index(drop=True)\n\n    return report\n\n\ndef bin_table_badrate_prediction(group, amount=None):\n    if amount is None:\n        return pd.Series(dict(\n            样本总数=len(group),\n            坏样本数=group[\"BAD_RATE\"].sum(),\n            坏样本率=group[\"BAD_RATE\"].mean(),\n        ))\n    else:\n        return pd.Series(dict(\n            样本总数=group[amount].sum(),\n            坏样本数=(group[\"BAD_RATE\"] * group[amount]).sum(),\n            坏样本率=(group[\"BAD_RATE\"] * group[amount]).sum() / group[amount].sum(),\n        ))\n\n\ndef sawpin_badrate_prediction_by_score(base: pd.DataFrame, test: pd.DataFrame, swap_in_ruleset, feature, target=\"target\", overdue=None, dpd=None, rules={}, method=\"quantile\", max_n_bins=10, amount=None, **kwargs):\n    \"\"\"\n    基于 base 上 feature 的分箱，计算 test 的逾期数据\n\n    :param base: pd.DataFrame, 有表现的数据集，用于建立分箱规则和计算坏样本率\n    :param test: pd.DataFrame, 测试数据集（包含置入样本），需要预测风险\n    :param swap_in_ruleset: list 置入规则列表\n    :param feature: str 特征名称\n    :param target: str 目标变量名称\n    :param overdue: list or str 逾期字段名称\n    :param dpd: list or int 逾期天数阈值\n    :param rules: dict 分箱规则\n    :param method: str 分箱方法\n    :param max_n_bins: int 最大分箱数\n    :param amount: 金额字段名称\n\n    **参考样例**\n\n    >>> # 使用示例\n    >>> swap_in_ruleset = [Rule(\"(当前履约机构数 < 22) & (自营资质分 >= 0) & (自营资质分 < 555)\")]\n    >>> # 单一target分析\n    >>> swap_in_data, swap_in_data_amount = sawpin_badrate_prediction_by_score(\n    >>>     swap_data, swap_data, swap_in_ruleset, \"自营资质分\", \n    >>>     target=\"target\", amount=\"放款金额\"\n    >>> )\n    >>> # 多逾期标签分析\n    >>> overdue_columns = [\"MOB1\"]  # 根据实际数据调整\n    >>> dpd_thresholds = [7, 3, 0]  # 逾期天数阈值\n    >>> swap_in_data_multi, swap_in_data_amount_multi = sawpin_badrate_prediction_by_score(\n    >>>     swap_data, swap_data, swap_in_ruleset, \"自营资质分\",\n    >>>     overdue=overdue_columns, dpd=dpd_thresholds, amount=\"放款金额\"\n    >>> )\n    >>> print(\"单一标签分析结果:\")\n    >>> print(swap_in_data)\n    >>> print(\"多标签分析结果:\")\n    >>> print(swap_in_data_multi)\n    \"\"\"\n    test = test.copy()\n\n    if isinstance(swap_in_ruleset, list):\n        rule_swap_in = reduce(lambda r1, r2: r1 | r2, swap_in_ruleset)\n        test[\"SWAPIN\"] = rule_swap_in.predict(test).astype(int)\n    else:\n        rule_swap_in = swap_in_ruleset\n        test[\"SWAPIN\"] = rule_swap_in.predict(test).astype(int)\n\n    # 如果没有指定overdue和dpd，使用单一target进行分析\n    if overdue is None or dpd is None:\n        base_table, rules = feature_bin_stats(base, feature, target=target, overdue=overdue, dpd=dpd, rules=rules, method=method, max_n_bins=max_n_bins, return_rules=True, **kwargs)\n\n        combiner = Combiner().load({feature: rules})\n        test[\"BINS\"] = combiner.transform(test[feature])\n        test[\"BAD_RATE\"] = test[\"BINS\"].map(dict(zip(base_table.index, base_table[\"坏样本率\"])))\n        swap_in_data = test.groupby(\"SWAPIN\").apply(lambda x: bin_table_badrate_prediction(x)).sort_index(ascending=False)\n        swap_in_data.index = [rule_swap_in.expr, \"剩余样本\"]\n        swap_in_data.index.name = \"规则详情\"\n        swap_in_data.loc[\"合计\", :] = pd.Series(dict(\n                样本总数=len(test),\n                坏样本数=test[\"BAD_RATE\"].sum(),\n                坏样本率=test[\"BAD_RATE\"].mean(),\n            ))\n        swap_in_data = swap_in_data.assign(\n            样本占比=lambda x: x[\"样本总数\"] / swap_in_data.loc[\"合计\", \"样本总数\"],\n            LIFT值=lambda x: x[\"坏样本率\"]  / swap_in_data.loc[\"合计\", \"坏样本率\"],\n            坏账改善=lambda x: (swap_in_data.loc[\"合计\", \"坏样本率\"] - x[\"坏样本率\"]) / swap_in_data.loc[\"合计\", \"坏样本率\"],\n            风险拒绝比=lambda x: x[\"坏账改善\"] / x[\"样本占比\"],\n        )\n\n        if amount is not None:\n            swap_in_data_amount = test.groupby(\"SWAPIN\").apply(lambda x: bin_table_badrate_prediction(x, amount=amount)).sort_index(ascending=False)\n            swap_in_data_amount.index = [rule_swap_in.expr, \"剩余样本\"]\n            swap_in_data_amount.index.name = \"规则详情\"\n            swap_in_data_amount.loc[\"合计\", :] = pd.Series(dict(\n                    样本总数=test[amount].sum(),\n                    坏样本数=(test[amount] * test[\"BAD_RATE\"]).sum(),\n                    坏样本率=(test[amount] * test[\"BAD_RATE\"]).sum() / test[amount].sum(),\n                ))\n            swap_in_data_amount = swap_in_data_amount.assign(\n                样本占比=lambda x: x[\"样本总数\"] / swap_in_data_amount.loc[\"合计\", \"样本总数\"],\n                LIFT值=lambda x: x[\"坏样本率\"]  / swap_in_data_amount.loc[\"合计\", \"坏样本率\"],\n                坏账改善=lambda x: (swap_in_data_amount.loc[\"合计\", \"坏样本率\"] - x[\"坏样本率\"]) / swap_in_data_amount.loc[\"合计\", \"坏样本率\"],\n                风险拒绝比=lambda x: x[\"坏账改善\"] / x[\"样本占比\"],\n            )\n        else:\n            swap_in_data_amount = swap_in_data.copy()\n\n        return swap_in_data, swap_in_data_amount\n\n    else:\n        # 处理多个逾期标签的情况\n        merge_columns = [\"样本总数\", \"样本占比\"]\n        swap_in_data_final, swap_in_data_amount_final = None, None\n\n        if not isinstance(overdue, list):\n            overdue = [overdue]\n\n        if not isinstance(dpd, list):\n            dpd = [dpd]\n\n        amount_feature = [amount] if amount is not None else []\n\n        # 遍历所有逾期标签组合\n        for i, col in enumerate(overdue):\n            for j, d in enumerate(dpd):\n                _target = f\"{col} {d}+\"\n\n                # 在base数据上创建新的目标变量\n                _datasets = base[[feature] + amount_feature + [col]].copy()\n                _datasets[_target] = (_datasets[col] > d).astype(int)\n\n                # 递归调用处理当前逾期标签\n                _swap_in_data, _swap_in_data_amount = sawpin_badrate_prediction_by_score(_datasets, test, swap_in_ruleset, feature, target=_target, overdue=None, dpd=None, rules=rules, amount=amount, **kwargs)\n\n                # 重命名列名为多级索引，确保规则详情相关字段在最前面\n                _swap_in_data.columns = pd.MultiIndex.from_tuples(\n                    [(\"规则详情\", c) if c in merge_columns else (_target, c) for c in _swap_in_data.columns]\n                )\n                _swap_in_data_amount.columns = pd.MultiIndex.from_tuples(\n                    [(\"规则详情\", c) if c in merge_columns else (_target, c) for c in _swap_in_data_amount.columns]\n                )\n\n                # 合并结果\n                if swap_in_data_final is None:\n                    swap_in_data_final = _swap_in_data\n                    swap_in_data_amount_final = _swap_in_data_amount\n                else:\n                    # 确保索引一致后合并\n                    swap_in_data_final = pd.concat([swap_in_data_final, _swap_in_data.drop(columns=[(\"规则详情\", c) for c in merge_columns])], axis=1)\n                    swap_in_data_amount_final = pd.concat([swap_in_data_amount_final, _swap_in_data_amount.drop(columns=[(\"规则详情\", c) for c in merge_columns])], axis=1)\n\n        # 重新排列列的顺序，确保规则详情相关字段在最前面\n        def reorder_columns(df):\n            # 提取规则详情相关的列\n            rule_columns = [col for col in df.columns if col[0] == \"规则详情\"]\n            # 提取其他列\n            other_columns = [col for col in df.columns if col[0] != \"规则详情\"]\n            # 重新组合：规则详情列在前，其他列在后\n            reordered_columns = rule_columns + other_columns\n            return df[reordered_columns]\n\n        if swap_in_data_final is not None:\n            swap_in_data_final = reorder_columns(swap_in_data_final)\n            swap_in_data_amount_final = reorder_columns(swap_in_data_amount_final)\n\n        return swap_in_data_final, swap_in_data_amount_final\n"
  },
  {
    "path": "scorecardpipeline/rule_extraction.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\n@Time    : 2024/2/29 13:29\n@Author  : itlubber\n@Site    : itlubber.art\n\"\"\"\nimport warnings\nimport os\nimport re\nimport graphviz\nimport dtreeviz\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom matplotlib import font_manager\nfrom IPython.display import display\nfrom openpyxl.worksheet.worksheet import Worksheet\n\nimport category_encoders as ce\nfrom optbinning import OptimalBinning\nfrom sklearn.tree import DecisionTreeClassifier\n\nfrom .rule import Rule\nfrom .utils import init_setting\nfrom .excel_writer import ExcelWriter, dataframe2excel\n\n\nclass DecisionTreeRuleExtractor:\n    def __init__(self, target=\"target\", labels=[\"positive\", \"negative\"], feature_map={}, nan=-1., max_iter=128, writer=None, seed=None, theme_color=\"2639E9\", decimal=4):\n        \"\"\"决策树自动规则挖掘工具包\n\n        :param target: 数据集中好坏样本标签列名称，默认 target\n        :param labels: 好坏样本标签名称，传入一个长度为2的列表，第0个元素为好样本标签，第1个元素为坏样本标签，默认 [\"positive\", \"negative\"]\n        :param feature_map: 变量名称及其含义，在后续输出报告和策略信息时增加可读性，默认 {}\n        :param nan: 在决策树策略挖掘时，默认空值填充的值，默认 -1\n        :param max_iter: 最多支持在数据集上训练多少颗树模型，每次生成一棵树后，会剔除特征重要性最高的特征后，再生成树，默认 128\n        :param writer: 在之前程序运行时生成的 ExcelWriter，可以支持传入一个已有的writer，后续所有内容将保存至该workbook中，默认 None\n        :param seed: 随机种子，保证结果可复现使用，默认为 None\n        :param theme_color: 主题色，默认 2639E9 克莱因蓝，可设置位其他颜色\n        :param decimal: 精度，决策树分裂节点阈值的精度范围，默认 4，即保留4位小数\n        \"\"\"\n        self.decimal = decimal\n        self.seed = seed\n        self.nan = nan\n        self.target = target\n        self.labels = labels\n        self.theme_color = theme_color\n        self.feature_map = feature_map\n        self.decision_trees = []\n        self.max_iter = max_iter\n        self.target_enc = None\n        self.feature_names = None\n        self.dt_rules = pd.DataFrame()\n        self.end_row = 2\n        self.start_col = 2\n        self.describe_columns = [\"组合策略\", \"命中数\", \"命中率\", \"好样本数\", \"好样本占比\", \"坏样本数\", \"坏样本占比\", \"坏样本率\", \"LIFT值\", \"坏账改善\", \"准确率\", \"精确率\", \"召回率\", \"F1分数\", \"样本整体坏率\"]\n\n        init_setting()\n\n        if writer:\n            self.writer = writer\n        else:\n            self.writer = ExcelWriter(theme_color=self.theme_color)\n\n    def encode_cat_features(self, X, y):\n        cat_features = list(set(X.select_dtypes(include=[object, pd.CategoricalDtype]).columns))\n        cat_features_index = [i for i, f in enumerate(X.columns) if f in cat_features]\n\n        if len(cat_features) > 0:\n            if self.target_enc is None:\n                self.target_enc = ce.TargetEncoder(cols=cat_features)\n                self.target_enc.fit(X[cat_features], y)\n                self.target_enc.target_mapping = {}\n                X_TE = X.join(self.target_enc.transform(X[cat_features]).add_suffix('_target'))\n                for col in cat_features:\n                    mapping = X_TE[[col, f\"{col}_target\"]].drop_duplicates()\n                    self.target_enc.target_mapping[col] = dict(zip(mapping[col], mapping[f\"{col}_target\"]))\n            else:\n                X_TE = X.join(self.target_enc.transform(X[cat_features]).add_suffix('_target'))\n\n            X_TE = X_TE.drop(columns=cat_features)\n            return X_TE.rename(columns={f\"{c}_target\": c for c in cat_features})\n        else:\n            return X\n\n    def get_dt_rules(self, tree):\n        rules = dict()\n\n        def recurse(node=0, parent=None):  # 搜每个节点的规则\n            if node == 0 or tree.tree_.children_left[node] != -1:  # 非叶子节点,搜索每个节点的规则\n                name = tree.feature_names_in_[tree.tree_.feature[node]]\n                threshold = np.round(tree.tree_.threshold[node], self.decimal)\n                if parent:\n                    recurse(tree.tree_.children_left[node], parent & Rule(\"{} <= {}\".format(name, threshold)))\n                    recurse(tree.tree_.children_right[node], parent & Rule(\"{} > {}\".format(name, threshold)))\n                else:\n                    recurse(tree.tree_.children_left[node], Rule(\"{} <= {}\".format(name, threshold)))\n                    recurse(tree.tree_.children_right[node], Rule(\"{} > {}\".format(name, threshold)))\n            else:\n                rules[node] = parent\n\n        recurse()\n\n        return list(rules.values())\n\n    def select_dt_rules(self, decision_tree, x, y, lift=0., max_samples=1., save=None, verbose=False, drop=False):\n        rules = self.get_dt_rules(decision_tree)\n\n        rules_reports = pd.DataFrame()\n        for rule in rules:\n            rules_reports = pd.concat([rules_reports, rule.report(x.join(y), target=y.name).query(\"分箱 == '命中'\")])\n\n        rules_reports = rules_reports.rename(columns={\"指标名称\": \"组合策略\", \"样本总数\": \"命中数\", \"样本占比\": \"命中率\"}).drop(columns=[\"分箱\"])\n        rules_reports[\"样本整体坏率\"] = round(y.mean(), self.decimal)\n        rules_reports = rules_reports.query(f\"LIFT值 >= {lift} & 命中率 <= {max_samples}\").reset_index(drop=True)\n\n        if len(rules_reports) > 0:\n            try:\n                if verbose:\n                    if self.feature_map is not None and len(self.feature_map) > 0:\n                        display(rules_reports.replace(self.feature_map, regex=True))\n                    else:\n                        display(rules_reports)\n\n                viz_model = dtreeviz.model(decision_tree,\n                                           X_train=x,\n                                           y_train=y,\n                                           feature_names=decision_tree.feature_names_in_,\n                                           target_name=self.target,\n                                           class_names=self.labels,\n                                           )\n\n                # font_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'matplot_chinese.ttf')\n                # font_manager.fontManager.addfont(font_path)\n                # plt.rcParams['font.family'] = font_manager.FontProperties(fname=font_path).get_name()\n                # plt.rcParams['axes.unicode_minus'] = False\n\n                decision_tree_viz = viz_model.view(\n                    scale=1.5,\n                    orientation='LR',\n                    colors={\n                        \"classes\": [None, None, [\"#2639E9\", \"#F76E6C\"], [\"#2639E9\", \"#F76E6C\", \"#FE7715\", \"#FFFFFF\"]],\n                        \"arrow\": \"#2639E9\",\n                        'text_wedge': \"#F76E6C\",\n                        \"pie\": \"#2639E9\",\n                        \"tile_alpha\": 1,\n                        \"legend_edge\": \"#FFFFFF\",\n                    },\n                    ticks_fontsize=10,\n                    label_fontsize=10,\n                    fontname=plt.rcParams['font.family'],\n                )\n\n                if verbose:\n                    display(decision_tree_viz)\n                if save:\n                    if os.path.dirname(save) and not os.path.exists(os.path.dirname(save)):\n                        os.makedirs(os.path.dirname(save))\n\n                    try:\n                        decision_tree_viz.save(\"combine_rules_cache.svg\")\n                    except graphviz.backend.execute.ExecutableNotFound:\n                        print(\"请确保您已安装 graphviz 程序并且正确配置了 PATH 路径。可参考: https://stackoverflow.com/questions/35064304/runtimeerror-make-sure-the-graphviz-executables-are-on-your-systems-path-aft\")\n\n                    try:\n                        import cairosvg\n                        cairosvg.svg2png(url=\"combine_rules_cache.svg\", write_to=save, dpi=240)\n                    except:\n                        from reportlab.graphics import renderPDF\n                        from svglib.svglib import svg2rlg\n                        drawing = svg2rlg(\"combine_rules_cache.svg\")\n                        renderPDF.drawToFile(drawing, save, dpi=240, fmt=\"PNG\")\n\n            except AttributeError:\n                print(\"请检查 dtreeviz、graphviz 等依赖库是否正确安装\")\n            except:\n                print(\"请检查 dtreeviz、graphviz 等依赖库是否正确安装\")\n\n        if os.path.isfile(\"combine_rules_cache.svg\"):\n            os.remove(\"combine_rules_cache.svg\")\n\n        if os.path.isfile(\"combine_rules_cache\"):\n            os.remove(\"combine_rules_cache\")\n\n        if drop:\n            if len(rules_reports) > 0:\n                return rules_reports, decision_tree.feature_names_in_[list(decision_tree.feature_importances_).index(max(decision_tree.feature_importances_))], len(rules_reports)\n            else:\n                return rules_reports, decision_tree.feature_names_in_[list(decision_tree.feature_importances_).index(min(decision_tree.feature_importances_))], len(rules_reports)\n        else:\n            return rules_reports, len(rules_reports)\n\n    def query_dt_rules(self, x, y, parsed_rules=None):\n        if isinstance(parsed_rules, pd.DataFrame):\n            parsed_rules = [Rule(r) for r in parsed_rules[\"组合策略\"].unique()]\n\n        rules_reports = pd.DataFrame()\n        for rule in parsed_rules:\n            rules_reports = pd.concat([rules_reports, rule.report(x.join(y), target=y.name).query(\"分箱 == '命中'\")])\n\n        rules_reports = rules_reports.rename(columns={\"指标名称\": \"组合策略\", \"样本总数\": \"命中数\", \"样本占比\": \"命中率\"}).drop(columns=[\"分箱\"])\n        rules_reports[\"样本整体坏率\"] = round(y.mean(), self.decimal)\n\n        return rules_reports\n\n    def insert_dt_rules(self, parsed_rules, end_row, start_col, save=None, sheet=None, figsize=(500, 350)):\n        if isinstance(sheet, Worksheet):\n            worksheet = sheet\n        else:\n            worksheet = self.writer.get_sheet_by_name(sheet or \"决策树组合策略挖掘\")\n\n        end_row, end_col = dataframe2excel(parsed_rules, self.writer, sheet_name=worksheet, start_row=end_row + 1, start_col=start_col, percent_cols=['好样本占比', '坏样本占比', '命中率', '坏样本率', '样本整体坏率', 'LIFT值', '坏账改善', '准确率', '精确率', '召回率', 'F1分数'], condition_cols=[\"坏样本率\", \"LIFT值\"])\n\n        if save is not None and os.path.isfile(save):\n            end_row, end_col = self.writer.insert_pic2sheet(worksheet, save, (end_row + 1, start_col), figsize=figsize)\n\n        return end_row, end_col\n\n    def fit(self, x, y=None, max_depth=2, lift=0., max_samples=1., min_score=None, verbose=False, *args, **kwargs):\n        \"\"\"组合策略挖掘\n\n        :param x: 包含标签的数据集\n        :param max_depth: 决策树最大深度，即最多组合的特征个数，默认 2\n        :param lift: 组合策略最小的lift值，默认 0.，即全部组合策略\n        :param max_samples: 每条组合策略的最大样本占比，默认 1.0，即全部组合策略\n        :param min_score: 决策树拟合时最小的auc，如果不满足则停止后续生成决策树\n        :param verbose: 是否调试模式，仅在 jupyter 环境有效\n        :param kwargs: DecisionTreeClassifier 参数，参考 https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\n        \"\"\"\n        worksheet = self.writer.get_sheet_by_name(\"策略详情\")\n\n        y = x[self.target]\n        X_TE = self.encode_cat_features(x.drop(columns=[self.target]), y)\n        X_TE = X_TE.fillna(self.nan)\n\n        self.feature_names = list(X_TE.columns)\n\n        for i in range(self.max_iter):\n            decision_tree = DecisionTreeClassifier(max_depth=max_depth, *args, **kwargs)\n            decision_tree = decision_tree.fit(X_TE, y)\n\n            if (min_score is not None and decision_tree.score(X_TE, y) < min_score) or len(X_TE.columns) < max_depth:\n                break\n\n            try:\n                parsed_rules, remove, total_rules = self.select_dt_rules(decision_tree, X_TE, y, lift=lift, max_samples=max_samples, verbose=verbose, save=f\"model_report/auto_mining_rules/combiner_rules_{i}.png\", drop=True)\n\n                if len(parsed_rules) > 0:\n                    self.dt_rules = pd.concat([self.dt_rules, parsed_rules]).reset_index(drop=True)\n\n                    if self.writer is not None:\n                        if self.feature_map is not None and len(self.feature_map) > 0:\n                            parsed_rules[\"组合策略\"] = parsed_rules[\"组合策略\"].replace(self.feature_map, regex=True)\n                        self.end_row, _ = self.insert_dt_rules(parsed_rules, self.end_row, self.start_col, save=f\"model_report/auto_mining_rules/combiner_rules_{i}.png\", figsize=(500, 100 * total_rules), sheet=worksheet)\n\n                X_TE = X_TE.drop(columns=remove)\n                self.decision_trees.append(decision_tree)\n            except:\n                import traceback\n                traceback.print_exc()\n\n        if len(self.dt_rules) <= 0:\n            print(f\"未挖掘到有效策略, 可以考虑适当调整预设的筛选参数, 降低 lift / 提高 max_samples, 当前筛选标准为: 提取 lift >= {lift} 且 max_samples <= {max_samples} 的策略\")\n\n        return self\n\n    def transform(self, x, y=None):\n        y = x[self.target]\n        X_TE = self.encode_cat_features(x.drop(columns=[self.target]), y)\n        X_TE = X_TE.fillna(self.nan)\n        if self.dt_rules is not None and len(self.dt_rules) > 0:\n            parsed_rules = self.query_dt_rules(X_TE, y, parsed_rules=self.dt_rules)\n            if self.feature_map is not None and len(self.feature_map) > 0:\n                parsed_rules[\"组合策略\"] = parsed_rules[\"组合策略\"].replace(self.feature_map, regex=True)\n            return parsed_rules\n        else:\n            return pd.DataFrame(columns=self.describe_columns)\n\n    def report(self, valid=None, sheet=\"组合策略汇总\", save=None):\n        \"\"\"组合策略插入excel文档\n\n        :param valid: 验证数据集\n        :param sheet: 保存组合策略的表格sheet名称\n        :param save: 保存报告的文件路径\n\n        :return: 返回每个数据集组合策略命中情况\n        \"\"\"\n        worksheet = self.writer.get_sheet_by_name(sheet or \"决策树组合策略挖掘\")\n\n        if sheet:\n            self.writer.workbook.move_sheet(sheet, -1)\n\n        parsed_rules_train = self.dt_rules.copy()\n\n        if self.feature_map is not None and len(self.feature_map) > 0:\n            parsed_rules_train[\"组合策略\"] = parsed_rules_train[\"组合策略\"].replace(self.feature_map, regex=True)\n\n        self.end_row, _ = self.writer.insert_value2sheet(worksheet, (2 if sheet else self.end_row + 2, self.start_col), value=\"组合策略: 训练集\", style=\"header_middle\", end_space=(2 if sheet else self.end_row + 2, self.start_col + len(parsed_rules_train.columns) - 1))\n        self.end_row, _ = self.insert_dt_rules(parsed_rules_train, self.end_row, self.start_col, sheet=worksheet)\n        outputs = (parsed_rules_train,)\n\n        if valid is not None:\n            if isinstance(valid, pd.DataFrame) and len(valid) > 0:\n                parsed_rules_val = self.transform(valid)\n                self.end_row, _ = self.writer.insert_value2sheet(worksheet, (self.end_row + 2, self.start_col), value=\"组合策略: 验证集\", style=\"header_middle\", end_space=(self.end_row + 2, self.start_col + len(parsed_rules_val.columns) - 1))\n                self.end_row, _ = self.insert_dt_rules(parsed_rules_val, self.end_row, self.start_col, sheet=worksheet)\n                outputs = outputs + (parsed_rules_val,)\n\n            elif isinstance(valid, (list, tuple)):\n                for i, dataset in enumerate(valid):\n                    if isinstance(dataset, pd.DataFrame) and len(dataset) > 0:\n                        parsed_rules_val = self.transform(dataset)\n                        self.end_row, _ = self.writer.insert_value2sheet(worksheet, (self.end_row + 2, self.start_col), value=f\"组合策略: 验证集 {i + 1}\", style=\"header_middle\", end_space=(self.end_row + 2, self.start_col + len(parsed_rules_val.columns) - 1))\n                        self.end_row, _ = self.insert_dt_rules(parsed_rules_val, self.end_row, self.start_col, sheet=worksheet)\n                        outputs = outputs + (parsed_rules_val,)\n\n            elif isinstance(valid, dict):\n                for k, dataset in valid.items():\n                    if isinstance(dataset, pd.DataFrame) and len(dataset) > 0:\n                        parsed_rules_val = self.transform(dataset)\n                        self.end_row, _ = self.writer.insert_value2sheet(worksheet, (self.end_row + 2, self.start_col), value=f\"组合策略: {k}\", style=\"header_middle\", end_space=(self.end_row + 2, self.start_col + len(parsed_rules_val.columns) - 1))\n                        self.end_row, _ = self.insert_dt_rules(parsed_rules_val, self.end_row, self.start_col, sheet=worksheet)\n                        outputs = outputs + (parsed_rules_val,)\n\n        if save:\n            self.writer.save(save)\n\n        return outputs\n"
  },
  {
    "path": "scorecardpipeline/scorecard.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\n@Time    : 2024/4/15 16:52\n@Author  : itlubber\n@Site    : itlubber.art\n\"\"\"\nimport math\nfrom abc import abstractmethod\nimport numpy as np\nimport pandas as pd\nfrom pandas import DataFrame\nfrom scipy import stats\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.utils import check_array\nfrom sklearn.utils.validation import check_is_fitted\n\n\nclass BaseScoreTransformer(BaseEstimator, TransformerMixin):\n    def __init__(self, down_lmt=300, up_lmt=1000, greater_is_better=True, cutoff=None):\n        self.down_lmt = down_lmt\n        self.up_lmt = up_lmt\n        self.greater_is_better = greater_is_better\n        self.cutoff = cutoff\n\n    @abstractmethod\n    def predict(self, x):\n        pass\n\n    @staticmethod\n    def score_clip(score, clip=50):\n        \"\"\"传入评分分数，根据评分分布情况，返回评分等距分箱规则\n\n        :param score: 评分数据\n        :param clip: 区间间隔\n\n        :return: list，评分分箱规则\n        \"\"\"\n        clip_start = max(math.ceil(score.min() / clip) * clip, math.ceil(score.quantile(0.01) / clip) * clip)\n        clip_end = min(math.ceil(score.max() / clip) * clip, math.ceil(score.quantile(0.99) / clip) * clip)\n        return [i for i in range(clip_start, clip_end, clip)]\n\n\nclass StandardScoreTransformer(BaseScoreTransformer):\n    \"\"\"Stretch the predicted probability to a normal distributed score.\"\"\"\n\n    def __init__(self, base_score=660, pdo=75, rate=2, bad_rate=0.15, down_lmt=300, up_lmt=1000, greater_is_better=True, cutoff=None):\n        super().__init__(down_lmt=down_lmt, up_lmt=up_lmt, greater_is_better=greater_is_better, cutoff=cutoff)\n        self.base_score = base_score\n        self.pdo = pdo\n        self.rate = rate\n        self.bad_rate = bad_rate\n\n    def fit(self, X, y=None, **fit_params):\n        self._validate_data(X, reset=True, accept_sparse=False, dtype=\"numeric\", copy=False, force_all_finite=True)\n\n        base_score, down_lmt, up_lmt = self.base_score, self.down_lmt, self.up_lmt\n        if not down_lmt <= base_score <= up_lmt:\n            raise ValueError(\"base_score should be greater than {} and less than {}!\".format(down_lmt, up_lmt))\n\n        bad_rate = self.bad_rate\n        if not 0.0 <= bad_rate <= 1.0:\n            raise ValueError(\"bad rate should be greater than e and less than 1!\")\n\n        base_odds = bad_rate / (1. - bad_rate)\n        if self.greater_is_better:\n            B = self.pdo / np.log(self.rate)\n        else:\n            B = -self.pdo / np.log(self.rate)\n\n        A = base_score + B + np.log(base_odds)\n\n        self.A_ = A\n        self.B_ = B\n        self.base_odds = base_odds\n        return self\n\n    def scorecard_scale(self):\n        \"\"\"输出评分卡基准信息，包含 base_odds、base_score、rate、pdo、A、B\n\n        :return: pd.DataFrame，评分卡基准信息\n        \"\"\"\n        scorecard_kedu = pd.DataFrame(\n            [\n                [\"base_odds\", self.base_odds, \"根据业务经验设置的基础比率（违约概率/正常概率），估算方法：坏客户占比 / (1 - 样本坏客户占比)\"],\n                [\"base_score\", self.base_score, \"基础ODDS对应的分数\"],\n                [\"rate\", self.rate, \"设置分数的倍率\"],\n                [\"pdo\", self.pdo, \"表示分数增长PDO时，ODDS值增长到RATE倍\"],\n                [\"B\", self.A_, \"补偿值，计算方式：pdo / ln(rate)\"],\n                [\"A\", self.B_, \"刻度，计算方式：base_score - B * ln(base_odds)\"],\n            ],\n            columns=[\"刻度项\", \"刻度值\", \"备注\"],\n        )\n        return scorecard_kedu\n\n    def _transform(self, X):\n        check_is_fitted(self, [\"A_\", \"B_\"])\n        Xt = self._validate_data(X, reset=False, accept_sparse=False, dtype=\"numeric\", copy=True, force_all_finite=True)\n        # if not np.all((0 <= Xt) & (Xt <= 1)):\n        #     raise ValueError (\"Input should be probabilities between 0 and 1.\")\n        A, B = self.A_, self.B_\n        down_lmt, up_lmt = self.down_lmt, self.up_lmt\n        points = A - B * np.log(Xt / (1.0 - Xt))\n        points = np.clip(points, down_lmt, up_lmt)\n        return points\n\n    def transform(self, X):\n        data = self._transform(X)\n        if isinstance(X, DataFrame):\n            columns = X.columns\n            index = X.index\n            return DataFrame(data=data, columns=columns, index=index)\n        return data\n\n    def predict(self, X):\n        scores = np.ravel(self._transform(X))\n        if self.cutoff is None:\n            cutoff = self._transform([[0.5]])[0][0]\n        elif not self.down_lmt < self.cutoff < self.up_lmt:\n            raise ValueError(\"Cutoff point should be within down_lmt and up_lmt!\")\n        else:\n            cutoff = self.cutoff\n\n        if self.greater_is_better:\n            return (scores < cutoff).astype(np.int)\n        else:\n            return (scores > cutoff).astype(np.int)\n\n    def _inverse_transform(self, X):\n        check_is_fitted(self, [\"A_\", \"B_\"])\n        Xt = check_array(X, accept_sparse=False, dtype=\"numeric\", copy=True, force_all_finite=True)\n        down_lmt, up_lmt = self.down_lmt, self.up_lmt\n        if not np.all(np.logical_and((down_lmt <= Xt), (Xt <= up_lmt))):\n            raise ValueError(\"Input should be points between {} and {}\".format(down_lmt, up_lmt))\n        A, B = self.A_, self.B_\n        probs = 1.0 - 1.0 / (np.exp((A - Xt) / B) + 1.0)\n        return probs\n\n    def inverse_transform(self, X):\n        data = self._inverse_transform(X)\n        if isinstance(X, DataFrame):\n            columns = X.columns\n            index = X.index\n            return DataFrame(data=data, columns=columns, index=index)\n        return data\n\n    def _more_tags(self):\n        return {\n            \"allow_nan\": False,\n        }\n\n\nclass NPRoundStandardScoreTransformer(StandardScoreTransformer):\n\n    def __init__(self, base_score=660, pdo=75, bad_rate=0.15, down_lmt=300, up_lmt=1000, round_decimals=0, greater_is_better=True, cutoff=None):\n        self.round_decimals = round_decimals\n        super(NPRoundStandardScoreTransformer, self).__init__(base_score=base_score, pdo=pdo, bad_rate=bad_rate, down_lmt=down_lmt, up_lmt=up_lmt,\n                                                              greater_is_better=greater_is_better, cutoff=cutoff)\n\n    def _transform(self, X):\n        points = super()._transform(X)\n        decimals = self.round_decimals\n        points = np.round(points, decimals=decimals)\n        return points\n\n\nclass RoundStandardScoreTransformer(StandardScoreTransformer):\n    \"\"\"Stretch the predicted probability to a normal distributed score.\"\"\"\n\n    def __init__(self, base_score=660, pdo=75, bad_rate=0.15, down_lmt=300, up_lmt=1000, round_decimals=0, greater_is_better=True, cutoff=None):\n        self.round_decimals = round_decimals\n        super(RoundStandardScoreTransformer, self).__init__(base_score=base_score, pdo=pdo, bad_rate=bad_rate, down_lmt=down_lmt, up_lmt=up_lmt,\n                                                            greater_is_better=greater_is_better, cutoff=cutoff)\n\n    def _transform(self, X):\n        points = super()._transform(X)\n        decimals = self.round_decimals\n        points = np.array([[round(x[0], decimals)] for x in points])\n        return points\n\n\nclass BoxCoxScoreTransformer(BaseScoreTransformer):\n    def __init__(self, down_lmt=300, up_lmt=1000, greater_is_better=True, cutoff=None):\n        super().__init__(down_lmt=down_lmt, up_lmt=up_lmt, greater_is_better=greater_is_better, cutoff=cutoff)\n\n    @staticmethod\n    def _box_cox_optimize(x):\n        \"\"\"Find and return optimal lambda parameter of the Box-Cox transform by MLE, for observed data x.\n\n        We here use scipy builtins which uses the brent optimizer.\n        \"\"\"\n        # the computation of Lambda is influenced by NaNs so we need to get rid of them\n        _, lmbda = stats.boxcox(x, lmbda=None)\n        return lmbda\n\n    def fit(self, X, y=None, **fit_params):\n        X = check_array(X, accept_sparse=False, dtype=\"numeric\", copy=True, force_all_finite=True)\n        if np.min(X) <= 0 or np.max(X) >= 1:\n            raise ValueError(\"The Box-Cox score transformation can only be applied to strictly positive probabilities\")\n        if self.greater_is_better:\n            self.lambdas_ = np.array([self._box_cox_optimize(1.0 - col) for col in X.T])\n        else:\n            self.lambdas_ = np.array([self._box_cox_optimize(col) for col in X.T])\n        for i, lmbda in enumerate(self.lambdas_):\n            X[:, i] = stats.boxcox(X[:, i], lmbda)\n        self.scaler_ = MinMaxScaler(feature_range=(self.down_lmt, self.up_lmt)).fit(X)\n        return self\n\n    def _transform(self, X):\n        check_is_fitted(self, [\"lambdas_\", \"scaler_\"])\n        X = check_array(X, accept_sparse=False, dtype=\"numeric\", copy=True, force_all_finite=True)\n        if np.min(X) < 0 or np.max(X) > 1:\n            raise ValueError(\"The Box-Cox score transformation can only be applied to strictly positive probabilities\")\n        if self.greater_is_better:\n            X = 1.0 - X\n        for i, lmbda in enumerate(self.lambdas_):\n            X[:, i] = stats.boxcox(X[:, i], lmbda)\n        return self.scaler_.transform(X)\n\n    def transform(self, X):\n        data = self._transform(X)\n        if isinstance(X, DataFrame):\n            columns = X.columns\n            index = X.index\n            return DataFrame(data=data, index=index, columns=columns)\n        return data\n\n    def predict(self, X):\n        scores = np.ravel(self._transform(X))\n        if self.cutoff is None:\n            lmbda = self.lambdas_[0]\n            if lmbda != 0:\n                p = (0.5 ** lmbda - 1) / lmbda\n            else:\n                p = np.log(0.5)\n            scaler = self.scaler_\n            p *= scaler.scale_\n            p += scaler.min_\n            if scaler.clip:\n                if p < scaler.feature_range[0]:\n                    p = scaler.feature_range[0]\n                elif p > scaler.feature_range[1]:\n                    p = scaler.feature_range[1]\n            cutoff = p\n        elif not self.down_lmt < self.cutoff < self.up_lmt:\n            raise ValueError(\"Cutoff point should be within 'down_lmt' and 'up_lmt'!\")\n        else:\n            cutoff = self.cutoff\n        if self.greater_is_better:\n            return (scores < cutoff).astype(np.int)\n        else:\n            return (scores > cutoff).astype(np.int)\n\n    def _inverse_transform(self, X):\n        check_is_fitted(self, [\"lambdas_\", \"scaler_\"])\n        X = check_array(X, accept_sparse=False, dtype=\"numeric\", copy=True, force_all_finite=True)\n        if np.min(X) < self.down_lmt or np.max(X) > self.up_lmt:\n            raise ValueError(\"The Box-Cox score inverse transformation can only be applied to strictly bounded scores\")\n        X_inv = self.scaler_.inverse_transform(X)\n        for i, lmbda in enumerate(self.lambdas_):\n            X_inv[:, i] = self._box_cox_inverse_tranform(X_inv[:, i], lmbda)\n        if self.greater_is_better:\n            X_inv = 1.0 - X_inv\n        return X_inv\n\n    def inverse_transform(self, X):\n        data = self._inverse_transform(X)\n        if isinstance(X, DataFrame):\n            columns = X.columns\n            index = X.index\n            return DataFrame(data=data, index=index, columns=columns)\n        return data\n\n    @staticmethod\n    def _box_cox_inverse_tranform(x, lmbda):\n        \"\"\"Return inverse-transformed input x following Box-Cox inverse transform with parameter lambda\"\"\"\n        if lmbda == 0:\n            x_inv = np.exp(x)\n        else:\n            x_inv = (x * lmbda + 1) ** (1 / lmbda)\n\n        return x_inv\n\n\nif __name__ == '__main__':\n    import sys\n\n    sys.path.append(\"../\")\n    from scorecardpipeline import *\n\n    import h2o\n    h2o.init()\n\n    test_select = h2o.H2OFrame(load_pickle(\"/Users/lubberit/Desktop/workspace/scorecardpipeline/examples/model_report/h2o_model/test_select.pkl\"))\n\n    model_path = '/Users/lubberit/Desktop/workspace/scorecardpipeline/examples/model_report/h2o_model/StackedEnsemble_BestOfFamily_1_AutoML_1_20240415_162619'\n    best_model = h2o.load_model(model_path)\n\n    # score_transform = StandardScoreTransformer(base_score=400, pdo=50, bad_rate=test_select[\"target\"].mean()[0], greater_is_better=True)\n    score_transform = BoxCoxScoreTransformer(greater_is_better=False)\n    y_pred = best_model.predict(test_select).as_data_frame()[[\"p1\"]]\n    score_transform.fit(y_pred)\n\n    print(best_model.predict(test_select))\n    score = score_transform.transform(y_pred)\n    print(score)\n    print(score_transform.inverse_transform(score))\n    # print(score_transform.scorecard_scale())\n"
  },
  {
    "path": "scorecardpipeline/utils.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\n@Time    : 2023/05/21 16:23\n@Author  : itlubber\n@Site    : itlubber.art\n\"\"\"\nimport warnings\n\nwarnings.filterwarnings(\"ignore\")\n\nimport os\nimport re\nimport six\nimport pickle\nimport random\nimport joblib\nimport warnings\nimport numpy as np\nimport pandas as pd\nfrom functools import partial\nimport matplotlib.pyplot as plt\nfrom matplotlib import font_manager\nfrom matplotlib.ticker import PercentFormatter, FuncFormatter\n\nimport toad\nimport seaborn as sns\nfrom joblib import Parallel, delayed\nfrom optbinning import OptimalBinning\nfrom sklearn.metrics import roc_curve, auc, roc_auc_score\n\nfrom .logger import init_logger\n\n\ndef seed_everything(seed: int, freeze_torch=False):\n    \"\"\"\n    固定当前环境随机种子，以保证后续实验可重复\n\n    :param seed: 随机种子\n    :param freeze_torch: 是否固定 pytorch 的随机种子\n    \"\"\"\n    random.seed(seed)\n    os.environ['PYTHONHASHSEED'] = str(seed)\n    np.random.seed(seed)\n\n    if freeze_torch:\n        import torch\n        torch.manual_seed(seed)\n        torch.cuda.manual_seed(seed)\n        torch.backends.cudnn.deterministic = True\n        torch.backends.cudnn.benchmark = True\n\n\ndef init_setting(font_path=None, seed=None, freeze_torch=False, logger=False, **kwargs):\n    \"\"\"\n    初始化环境配置，去除警告信息、修改 pandas 默认配置、固定随机种子、日志记录\n\n    :param seed: 随机种子，默认为 None\n    :param freeze_torch: 是否固定 pytorch 环境\n    :param font_path: 画图时图像使用的字体，支持系统字体名称、本地字体文件路径，默认为 scorecardppeline 提供的中文字体\n    :param logger: 是否需要初始化日志器，默认为 False ，当参数为 True 时返回 logger\n    :param kwargs: 日志初始化传入的相关参数\n\n    :return: 当 logger 为 True 时返回 logging.Logger\n    \"\"\"\n    warnings.filterwarnings(\"ignore\")\n\n    pd.options.display.float_format = '{:.4f}'.format\n    pd.set_option(\"display.max_colwidth\", 300)\n    pd.set_option('expand_frame_repr', False)\n\n    if \"seaborn-ticks\" in plt.style.available:\n        plt.style.use('seaborn-ticks')\n    else:\n        plt.style.use('seaborn-v0_8-ticks')\n\n    if font_path is not None and font_path.lower() in [font.fname.lower() for font in font_manager.fontManager.ttflist]:\n        plt.rcParams['font.family'] = font_path\n    else:\n        font_path = font_path or os.path.join(os.path.dirname(os.path.abspath(__file__)), 'matplot_chinese.ttf')\n\n        if not os.path.isfile(font_path):\n            import wget\n            font_path = wget.download(\n                \"https://itlubber.art/upload/matplot_chinese.ttf\"\n                , os.path.join(os.path.dirname(os.path.abspath(__file__)), 'matplot_chinese.ttf')\n            )\n\n        font_manager.fontManager.addfont(font_path)\n        plt.rcParams['font.family'] = font_manager.FontProperties(fname=font_path).get_name()\n\n    plt.rcParams['axes.unicode_minus'] = False\n\n    if seed:\n        seed_everything(seed, freeze_torch=freeze_torch)\n\n    if logger:\n        return init_logger(**kwargs)\n\n\ndef load_pickle(file, engine=\"joblib\"):\n    \"\"\"\n    导入 pickle 文件\n\n    :param file: pickle 文件路径\n\n    :return: pickle 文件的内容\n    \"\"\"\n    if engine == \"joblib\":\n        return joblib.load(file)\n    elif engine == \"dill\":\n        import dill\n        with open(file, \"rb\") as f:\n            return dill.load(f)\n    elif engine == \"pickle\":\n        with open(file, \"rb\") as f:\n            return pickle.load(f)\n    else:\n        raise ValueError(f\"engine 目前只支持 [joblib, dill, pickle], 不支持 {engine}\")\n\n\ndef save_pickle(obj, file, engine=\"joblib\"):\n    \"\"\"\n    保持数据至 pickle 文件\n\n    :param obj: 需要保存的数据\n    :param file: 文件路径\n    \"\"\"\n    if engine == \"joblib\":\n        return joblib.dump(obj, file)\n    elif engine == \"dill\":\n        import dill\n        with open(file, \"wb\") as f:\n            return dill.dump(obj, f)\n    elif engine == \"pickle\":\n        with open(file, \"wb\") as f:\n            return pickle.dump(obj, f)\n    else:\n        raise ValueError(f\"engine 目前只支持 [joblib, dill, pickle], 不支持 {engine}\")\n\n\ndef feature_describe(data, feature=None, percentiles=None, missing=None, cardinality=None):\n    if feature and feature not in data.columns:\n        raise ValueError(f\"feature {feature} must in columns.\")\n\n    if cardinality and cardinality < 1:\n        raise ValueError(f\"cardinality must grater 1\")\n\n    if percentiles is None:\n        percentiles = [0.01, 0.02, 0.03, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.97, 0.98, 0.99]\n\n    if feature:\n        series = data[feature]\n    else:\n        series = data.copy()\n\n    if missing:\n        series = series.replace(missing, np.nan)\n\n    if (cardinality and series.nunique() <= cardinality) or not pd.api.types.is_numeric_dtype(series):\n        describe = {\n            \"样本数\": len(series),\n            \"非空数\": len(series) - series.isnull().sum(),\n            \"查得率\": 1 - series.isnull().mean(),\n        }\n        describe.update((series.replace(np.nan, '缺失值').value_counts(dropna=False) / len(series)).to_dict())\n        return pd.Series(describe, name=feature)\n    else:\n        describe = {\n            \"样本数\": len(series),\n            \"非空数\": len(series) - series.isnull().sum(),\n            \"查得率\": 1 - series.isnull().mean(),\n            \"最小值\": series.min(),\n            \"平均值\": series.mean(),\n            \"最大值\": series.max(),\n            # \"众数\": series.mode()[0],\n        }\n        quantile = series.quantile(percentiles)\n        quantile.index = [f\"{int(i * 100)}%\" for i in percentiles]\n        describe.update(quantile.to_dict())\n        return pd.Series(describe, name=feature).reindex(['样本数', '非空数', '查得率', '最小值', '平均值'] + [f\"{int(i * 100)}%\" for i in percentiles] + ['最大值'])\n\n\ndef groupby_feature_describe(data, by=None, n_jobs=-1, **kwargs):\n    if not isinstance(by, (tuple, list, np.ndarray)):\n        by = [by]\n\n    describe = pd.DataFrame()\n\n    def __feature_describe(group, _p, f, **kwargs):\n        temp = feature_describe(group[f], **kwargs)\n        temp.index = pd.MultiIndex.from_product([[f], temp.index])\n        temp = pd.DataFrame(temp, columns=[_p])\n        return temp\n\n    def _feature_describe(group, _p, _by=None, **kwargs):\n        if len(_p) <= 1:\n            _p = _p[0]\n\n        __describe = partial(lambda f: __feature_describe(group, _p, f, **kwargs))\n        return _p, pd.concat(Parallel(n_jobs=n_jobs)(delayed(__describe)(f) for f in group.columns if f not in _by))[_p]\n\n    for info in Parallel(n_jobs=n_jobs)(delayed(_feature_describe)(group, p, _by=by, **kwargs) for p, group in data.groupby(by=by)):\n        describe[info[0]] = info[1]\n\n    if len(by) > 1:\n        describe.columns = pd.MultiIndex.from_tuples(describe.columns)\n\n    describe.index.names = [\"特征名称\", \"统计指标\"]\n\n    return describe\n\n\ndef germancredit():\n    \"\"\"\n    加载德国信贷数据集 German Credit Data\n\n    数据来源：https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data\n\n    :return: pd.DataFrame\n    \"\"\"\n    from pandas.api.types import CategoricalDtype\n\n    data = pd.read_csv(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'germancredit.csv'))\n\n    cate_levels = {\n        \"status_of_existing_checking_account\": ['... < 0 DM', '0 <= ... < 200 DM', '... >= 200 DM / salary assignments for at least 1 year', 'no checking account'],\n        \"credit_history\": [\"no credits taken/ all credits paid back duly\", \"all credits at this bank paid back duly\", \"existing credits paid back duly till now\", \"delay in paying off in the past\", \"critical account/ other credits existing (not at this bank)\"],\n        \"savings_account_and_bonds\": [\"... < 100 DM\", \"100 <= ... < 500 DM\", \"500 <= ... < 1000 DM\", \"... >= 1000 DM\", \"unknown/ no savings account\"],\n        \"present_employment_since\": [\"unemployed\", \"... < 1 year\", \"1 <= ... < 4 years\", \"4 <= ... < 7 years\", \"... >= 7 years\"],\n        \"personal_status_and_sex\": [\"male : divorced/separated\", \"female : divorced/separated/married\", \"male : single\", \"male : married/widowed\", \"female : single\"],\n        \"other_debtors_or_guarantors\": [\"none\", \"co-applicant\", \"guarantor\"],\n        \"property\": [\"real estate\", \"building society savings agreement/ life insurance\", \"car or other, not in attribute Savings account/bonds\", \"unknown / no property\"],\n        \"other_installment_plans\": [\"bank\", \"stores\", \"none\"],\n        \"housing\": [\"rent\", \"own\", \"for free\"],\n        \"job\": [\"unemployed/ unskilled - non-resident\", \"unskilled - resident\", \"skilled employee / official\", \"management/ self-employed/ highly qualified employee/ officer\"],\n        \"telephone\": [\"none\", \"yes, registered under the customers name\"],\n        \"foreign_worker\": [\"yes\", \"no\"]}\n\n    def cate_type(levels):\n        return CategoricalDtype(categories=levels, ordered=True)\n\n    for i in cate_levels.keys():\n        data[i] = data[i].astype(cate_type(cate_levels[i]))\n\n    return data\n\n\ndef round_float(num, decimal=4):\n    \"\"\"\n    调整数值分箱的上下界小数点精度，如未超出精度保持原样输出\n\n    :param num: 分箱的上界或者下界\n    :param decimal: 小数点保留的精度\n\n    :return: 精度调整后的数值\n    \"\"\"\n    if ~pd.isnull(num) and isinstance(num, float):\n        return float(str(num).split(\".\")[0] + \".\" + str(num).split(\".\")[1][:decimal])\n    else:\n        return num\n\n\ndef feature_bins(bins, decimal=4):\n    \"\"\"\n    根据 Combiner 的规则生成分箱区间，并生成区间对应的索引\n\n    :param bins: Combiner 的规则\n    :param decimal: 区间上下界需要保留的精度，默认小数点后4位\n\n    :return: dict ，key 为区间的索引，value 为区间\n    \"\"\"\n    if len(bins) == 0:\n        return {0: \"全部样本\"}\n    if isinstance(bins, list): bins = np.array(bins)\n    EMPTYBINS = len(bins) if not isinstance(bins[0], (set, list, np.ndarray)) else -1\n\n    l = []\n    if not isinstance(bins[0], (set, list, np.ndarray)):\n        has_empty = len(bins) > 0 and pd.isnull(bins[-1])\n        if has_empty: bins = bins[:-1]\n        sp_l = [\"负无穷\"] + [round_float(b, decimal=decimal) for b in bins] + [\"正无穷\"]\n        for i in range(len(sp_l) - 1): l.append('[' + str(sp_l[i]) + ' , ' + str(sp_l[i + 1]) + ')')\n        if has_empty: l.append('缺失值')\n    else:\n        for keys in bins:\n            keys_update = set()\n            for key in keys:\n                if pd.isnull(key) or (isinstance(key, str) and key == \"nan\"):\n                    keys_update.add(\"缺失值\")\n                elif isinstance(key, str) and key.strip() == \"\":\n                    keys_update.add(\"空字符串\")\n                else:\n                    keys_update.add(key)\n            label = ','.join(keys_update)\n            l.append(label)\n\n    return {i if b != \"缺失值\" else EMPTYBINS: b for i, b in enumerate(l)}\n\n\ndef extract_feature_bin(bin_var):\n    \"\"\"\n    根据单个区间提取的分箱的上下界\n\n    :param bin_var: 区间字符串\n\n    :return: list or tuple\n    \"\"\"\n    pattern = re.compile(r\"^(\\[|\\()(-inf|负无穷|[-+]?\\d+(\\.\\d+)?)(\\s*,\\s*|\\s*~\\s*)(inf|正无穷|[-+]?\\d+(\\.\\d+)?)?(\\]|\\))$\")\n    match = pattern.match(bin_var)\n\n    if match:\n        start = -np.inf if match.group(2) in [\"-inf\", \"负无穷\"] else float(match.group(2))\n        end = np.inf if match.group(5) in [\"inf\", \"正无穷\"] else float(match.group(5))\n        return start, end\n    else:\n        return [np.nan if b == \"缺失值\" else (\"\" if b == \"空字符串\" else b) for b in bin_var.split(\"%,%\" if \"%,%\" in bin_var else \",\")]\n\n\ndef inverse_feature_bins(feature_table, bin_col=\"分箱\"):\n    \"\"\"\n    根据变量分箱表得到 Combiner 的规则\n\n    :param feature_table: 变量分箱表\n    :param bin_col: 变量分箱表中分箱对应的列名，默认 分箱\n\n    :return: list\n    \"\"\"\n    if isinstance(feature_table, pd.DataFrame):\n        bin_vars = feature_table[bin_col].tolist()\n    elif isinstance(feature_table, pd.Series):\n        bin_vars = feature_table.tolist()\n    else:\n        bin_vars = feature_table\n\n    has_empty = (\"缺失值\" in bin_vars) | (np.nan in bin_vars)\n\n    bin_vars = [b for b in bin_vars if b not in [\"缺失值\", np.nan]]\n    extract_bin_vars = [extract_feature_bin(bin_var) for bin_var in bin_vars]\n\n    if isinstance(extract_bin_vars[0], tuple):\n        inverse_bin_vars = sorted({s for b in extract_bin_vars for s in b})[1:-1]\n        inverse_bin_vars += [np.nan] if has_empty else []\n    else:\n        inverse_bin_vars = extract_bin_vars\n        inverse_bin_vars += [[np.nan]] if has_empty else []\n\n    return inverse_bin_vars\n\n\ndef bin_plot(feature_table, desc=\"\", figsize=(10, 6), colors=[\"#2639E9\", \"#F76E6C\", \"#FE7715\"], save=None, anchor=0.935, max_len=35, fontdict={\"color\": \"#000000\"}, hatch=True, ending=\"分箱图\"):\n    \"\"\"简单策略挖掘：特征分箱图\n\n    :param feature_table: 特征分箱的统计信息表，由 feature_bin_stats 运行得到\n    :param desc: 特征中文含义或者其他相关信息\n    :param figsize: 图像尺寸大小，传入一个tuple，默认 （10， 6）\n    :param colors: 图片主题颜色，默认即可\n    :param save: 图片保存的地址，如果传入路径中有文件夹不存在，会新建相关文件夹，默认 None\n    :param anchor: 图例在图中的位置，通常 0.95 左右，根据图片标题与图例之间的空隙自行调整即可\n    :param max_len: 分箱显示的最大长度，防止分类变量分箱过多文本过长导致图像显示区域很小，默认最长 35 个字符\n    :param fontdict: 柱状图上的文字内容格式设置，参考 https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.text.html\n    :param hatch: 柱状图是否显示斜杠，默认显示\n    :param ending: 分箱图标题显示的后缀，标题格式为: f'{desc}{ending}'\n    \n    :return: Figure\n    \"\"\"\n    feature_table = feature_table.copy()\n\n    feature_table[\"分箱\"] = feature_table[\"分箱\"].apply(lambda x: x if not pd.isnull(x) and re.match(\"^\\[.*\\)$\", x) else (str(x)[:max_len] + \"..\" if len(str(x)) > max_len else str(x)))\n\n    fig, ax1 = plt.subplots(figsize=figsize)\n    ax1.barh(feature_table['分箱'], feature_table['好样本数'], color=colors[0], label='好样本', hatch=\"/\" if hatch else None)\n    ax1.barh(feature_table['分箱'], feature_table['坏样本数'], left=feature_table['好样本数'], color=colors[1], label='坏样本', hatch=\"\\\\\" if hatch else None)\n    ax1.set_xlabel('样本数')\n\n    ax2 = ax1.twiny()\n    ax2.plot(feature_table['坏样本率'], feature_table['分箱'], colors[2], label='坏样本率', linestyle='-.')\n    ax2.set_xlabel('坏样本率: 坏样本数 / 样本总数')\n    ax2.set_xlim(xmin=0.)\n\n    for i, rate in enumerate(feature_table['坏样本率']):\n        ax2.scatter(rate, i, color=colors[2])\n\n    if fontdict and fontdict.get(\"color\"):\n        for i, v in feature_table[['样本总数', '好样本数', '坏样本数', '坏样本率']].iterrows():\n            ax1.text(v['样本总数'] / 2, i + len(feature_table) / 60, f\"{int(v['好样本数'])}:{int(v['坏样本数'])}:{v['坏样本率']:.2%}\", fontdict=fontdict)\n\n    ax1.invert_yaxis()\n    ax2.xaxis.set_major_formatter(PercentFormatter(1, decimals=0, is_latex=True))\n    # ax2.xaxis.set_major_formatter(FuncFormatter(lambda x,_:'{}%'.format(round(x * 100, 4))))\n\n    fig.suptitle(f'{desc}{ending}\\n\\n')\n\n    handles1, labels1 = ax1.get_legend_handles_labels()\n    handles2, labels2 = ax2.get_legend_handles_labels()\n    fig.legend(handles1 + handles2, labels1 + labels2, loc='upper center', ncol=len(labels1 + labels2), bbox_to_anchor=(0.5, anchor), frameon=False)\n\n    plt.tight_layout()\n\n    if save:\n        if os.path.dirname(save) != \"\" and not os.path.exists(os.path.dirname(save)):\n            os.makedirs(os.path.dirname(save), exist_ok=True)\n\n        fig.savefig(save, dpi=240, format=\"png\", bbox_inches=\"tight\")\n\n    return fig\n\n\ndef corr_plot(data, figure_size=(16, 8), fontsize=16, mask=False, save=None, annot=True, max_len=35, linewidths=0.1, fmt='.2f', step=2 * 5 + 1, linecolor='white', **kwargs):\n    \"\"\"\n    特征相关图\n\n    :param data: 原始数据\n    :param figure_size: 图片大小，默认 (16, 8)\n    :param fontsize: 字体大小，默认 16\n    :param mask: 是否只显示下三角部分内容，默认 False\n    :param save: 图片保存的地址，如果传入路径中有文件夹不存在，会新建相关文件夹，默认 None\n    :param annot: 是否在图中显示相关性的数值，默认 True\n    :param max_len: 特征显示的最大长度，防止特征名称过长导致图像区域非常小，默认 35，可以传 None 表示不限制\n    :param fmt: 数值显示格式，当 annot 为 True 时该参数生效，默认显示两位小数点\n    :param step: 色阶的步数，以 0 为中心，默认 2（以0为中心对称） * 5（划分五个色阶） + 1（0一档单独显示）= 11\n    :param linewidths: 相关图之间的线条宽度，默认 0.1 ，如果设置为 None 则不现实线条\n    :param linecolor: 线的颜色，当 linewidths 大于 0 时生效，默认为 white\n    :param kwargs: sns.heatmap 函数其他参数，参考：https://seaborn.pydata.org/generated/seaborn.heatmap.html\n    :return: Figure\n    \"\"\"\n    if max_len is None:\n        corr = data.corr()\n    else:\n        corr = data.rename(columns={c: c if len(str(c)) <= max_len else f\"{str(c)[:max_len]}...\" for c in data.columns}).corr()\n\n    corr_mask = np.zeros_like(corr, dtype=bool)\n    corr_mask[np.triu_indices_from(corr_mask)] = True\n\n    fig, ax = plt.subplots(figsize=figure_size)\n    map_plot = sns.heatmap(corr\n                           , cmap=sns.diverging_palette(267, 267, n=step, s=100, l=40)\n                           , vmax=1\n                           , vmin=-1\n                           , center=0\n                           , square=True\n                           , linewidths=linewidths\n                           , annot=annot\n                           , fmt=fmt\n                           , linecolor=linecolor\n                           , robust=True\n                           , cbar=True\n                           , ax=ax\n                           , mask=corr_mask if mask else None\n                           , **kwargs\n                           )\n\n    map_plot.tick_params(axis='x', labelrotation=270, labelsize=fontsize)\n    map_plot.tick_params(axis='y', labelrotation=0, labelsize=fontsize)\n\n    if save:\n        if os.path.dirname(save) != \"\" and not os.path.exists(os.path.dirname(save)):\n            os.makedirs(os.path.dirname(save), exist_ok=True)\n\n        fig.savefig(save, dpi=240, format=\"png\", bbox_inches=\"tight\")\n\n    return fig\n\n\ndef ks_plot(score, target, title=\"\", fontsize=14, figsize=(16, 8), save=None, colors=[\"#2639E9\", \"#F76E6C\", \"#FE7715\"], anchor=0.945):\n    \"\"\"\n    数值特征 KS曲线 & ROC曲线\n\n    :param score: 数值特征，通常为评分卡分数\n    :param target: 标签值\n    :param title: 图像标题\n    :param fontsize: 字体大小，默认 14\n    :param figsize: 图像大小，默认 (16, 8)\n    :param save: 图片保存的地址，如果传入路径中有文件夹不存在，会新建相关文件夹，默认 None\n    :param colors: 图片主题颜色，默认即可\n    :param anchor: 图例显示的位置，默认 0.945，根据实际显示情况进行调整即可，0.95 附近小范围调整\n    :return: Figure\n    \"\"\"\n    auc_value = roc_auc_score(target, score)\n\n    if auc_value < 0.5:\n        warnings.warn('评分AUC指标小于50%, 推断数据值越大, 正样本率越高, 将数据值转为负数后进行绘图')\n        score = -score\n        auc_value = 1 - auc_value\n\n    # if np.mean(score) < 0 or np.mean(score) > 1:\n    #     warnings.warn('Since the average of pred is not in [0,1], it is treated as credit score but not probability.')\n    #     score = -score\n\n    df = pd.DataFrame({'label': target, 'pred': score})\n\n    def n0(x):\n        return sum(x == 0)\n\n    def n1(x):\n        return sum(x == 1)\n\n    df_ks = df.sort_values('pred', ascending=False).reset_index(drop=True) \\\n        .assign(group=lambda x: np.ceil((x.index + 1) / (len(x.index) / len(df.index)))) \\\n        .groupby('group')['label'].agg([n0, n1]) \\\n        .reset_index().rename(columns={'n0': 'good', 'n1': 'bad'}) \\\n        .assign(\n        group=lambda x: (x.index + 1) / len(x.index),\n        cumgood=lambda x: np.cumsum(x.good) / sum(x.good),\n        cumbad=lambda x: np.cumsum(x.bad) / sum(x.bad)\n    ).assign(ks=lambda x: abs(x.cumbad - x.cumgood))\n\n    fig, ax = plt.subplots(1, 2, figsize=figsize)\n\n    # KS曲线\n    dfks = df_ks.loc[lambda x: x.ks == max(x.ks)].sort_values('group').iloc[0]\n\n    ax[0].plot(df_ks.group, df_ks.ks, color=colors[0], label=\"KS曲线\")\n    ax[0].plot(df_ks.group, df_ks.cumgood, color=colors[1], label=\"累积好客户占比\")\n    ax[0].plot(df_ks.group, df_ks.cumbad, color=colors[2], label=\"累积坏客户占比\")\n    ax[0].fill_between(df_ks.group, df_ks.cumbad, df_ks.cumgood, color=colors[0], alpha=0.25)\n\n    ax[0].plot([dfks['group'], dfks['group']], [0, dfks['ks']], 'r--')\n    ax[0].text(dfks['group'], dfks['ks'], f\"KS: {round(dfks['ks'], 4)} at: {dfks.group:.2%}\", horizontalalignment='center', fontsize=fontsize)\n\n    ax[0].spines['top'].set_color(colors[0])\n    ax[0].spines['bottom'].set_color(colors[0])\n    ax[0].spines['right'].set_color(colors[0])\n    ax[0].spines['left'].set_color(colors[0])\n    ax[0].set_xlabel('% of Population', fontsize=fontsize)\n    ax[0].set_ylabel('% of Total Bad / Good', fontsize=fontsize)\n\n    ax[0].set_xlim((0, 1))\n    ax[0].set_ylim((0, 1))\n\n    handles1, labels1 = ax[0].get_legend_handles_labels()\n\n    # ax[0].legend(loc='upper center', ncol=len(labels1), bbox_to_anchor=(0.5, 1.1), frameon=False)\n\n    # ROC 曲线\n    fpr, tpr, thresholds = roc_curve(target, score)\n\n    ax[1].plot(fpr, tpr, color=colors[0], label=\"ROC Curve\")\n    ax[1].stackplot(fpr, tpr, color=colors[0], alpha=0.25)\n    ax[1].plot([0, 1], [0, 1], color=colors[1], lw=2, linestyle=':')\n    # ax[1].tick_params(axis='x', labelrotation=0, grid_color=\"#FFFFFF\", labelsize=fontsize)\n    # ax[1].tick_params(axis='y', labelrotation=0, grid_color=\"#FFFFFF\", labelsize=fontsize)\n    ax[1].text(0.5, 0.5, f\"AUC: {auc_value:.4f}\", fontsize=fontsize, horizontalalignment=\"center\", transform=ax[1].transAxes)\n\n    ax[1].spines['top'].set_color(colors[0])\n    ax[1].spines['bottom'].set_color(colors[0])\n    ax[1].spines['right'].set_color(colors[0])\n    ax[1].spines['left'].set_color(colors[0])\n    ax[1].set_xlabel(\"False Positive Rate\", fontsize=fontsize)\n    ax[1].set_ylabel('True Positive Rate', fontsize=fontsize)\n\n    ax[1].set_xlim((0, 1))\n    ax[1].set_ylim((0, 1))\n\n    ax[1].yaxis.tick_right()\n    ax[1].yaxis.set_label_position(\"right\")\n\n    handles2, labels2 = ax[1].get_legend_handles_labels()\n\n    if title: title += \" \"\n    fig.suptitle(f\"{title}K-S & ROC CURVE\\n\", fontsize=fontsize, fontweight=\"bold\")\n\n    fig.legend(handles1 + handles2, labels1 + labels2, loc='upper center', ncol=len(labels1 + labels2), bbox_to_anchor=(0.5, anchor), frameon=False)\n\n    plt.tight_layout()\n\n    if save:\n        if os.path.dirname(save) != \"\" and not os.path.exists(os.path.dirname(save)):\n            os.makedirs(os.path.dirname(save), exist_ok=True)\n\n        plt.savefig(save, dpi=240, format=\"png\", bbox_inches=\"tight\")\n\n    return fig\n\n\ndef hist_plot(score, y_true=None, figsize=(15, 10), bins=30, save=None, labels=[\"好样本\", \"坏样本\"], desc=\"\", anchor=1.11, fontsize=14, kde=False, **kwargs):\n    \"\"\"\n    数值特征分布图\n\n    :param score: 数值特征，通常为评分卡分数\n    :param y_true: 标签值\n    :param figsize: 图像大小，默认 (15, 10)\n    :param bins: 分箱数量大小，默认 30\n    :param save: 图片保存的地址，如果传入路径中有文件夹不存在，会新建相关文件夹，默认 None\n    :param labels: 字典或列表，图例显示的分类名称，默认 [\"好样本\", \"坏样本\"]，按照目标变量顺序对应即可，从0开始\n    :param anchor: 图例显示的位置，默认 1.1，根据实际显示情况进行调整即可，1.1 附近小范围调整\n    :param fontsize: 字体大小，默认 14\n    :param kwargs: sns.histplot 函数其他参数，参考：https://seaborn.pydata.org/generated/seaborn.histplot.html\n\n    :return: Figure\n    \"\"\"\n    target_unique = 1 if y_true is None else len(np.unique(y_true))\n\n    if y_true is not None:\n        if isinstance(labels, dict):\n            y_true = y_true.map(labels)\n            hue_order = list(labels.values())\n        else:\n            y_true = y_true.map({i: v for i, v in enumerate(labels)})\n            hue_order = labels\n    else:\n        y_true = None\n        hue_order = None\n\n    fig, ax = plt.subplots(1, 1, figsize=figsize)\n    palette = sns.diverging_palette(340, 267, n=target_unique, s=100, l=40)\n\n    sns.histplot(\n        x=score, hue=y_true, element=\"step\", stat=\"probability\", bins=bins, common_bins=True, common_norm=True, palette=palette, hue_order=hue_order[::-1], ax=ax, kde=kde, **kwargs\n    )\n\n    # sns.despine()\n\n    ax.spines['top'].set_color(\"#2639E9\")\n    ax.spines['bottom'].set_color(\"#2639E9\")\n    ax.spines['right'].set_color(\"#2639E9\")\n    ax.spines['left'].set_color(\"#2639E9\")\n\n    ax.set_xlabel(\"值域范围\", fontsize=fontsize)\n    ax.set_ylabel(\"样本占比\", fontsize=fontsize)\n\n    ax.yaxis.set_major_formatter(PercentFormatter(1))\n\n    ax.set_title(f\"{desc + ' ' if desc else '特征'}分布情况\\n\\n\", fontsize=fontsize)\n\n    if y_true is not None:\n        ax.legend([t for t in hue_order for _ in range(2)] if kde else hue_order, loc='upper center', ncol=target_unique * 2 if kde else target_unique, bbox_to_anchor=(0.5, anchor), frameon=False, fontsize=fontsize)\n\n    fig.tight_layout()\n\n    if save:\n        if os.path.dirname(save) != \"\" and not os.path.exists(os.path.dirname(save)):\n            os.makedirs(os.path.dirname(save), exist_ok=True)\n\n        plt.savefig(save, dpi=240, format=\"png\", bbox_inches=\"tight\")\n\n    return fig\n\n\ndef psi_plot(expected, actual, labels=[\"预期\", \"实际\"], desc=\"\", save=None, colors=[\"#2639E9\", \"#F76E6C\", \"#FE7715\"], figsize=(15, 8), anchor=0.94, width=0.35, result=False, plot=True, max_len=None, hatch=True):\n    \"\"\"\n    特征 PSI 图\n\n    :param expected: 期望分布情况，传入需要验证的特征分箱表\n    :param actual: 实际分布情况，传入需要参照的特征分箱表\n    :param labels: 期望分布和实际分布的名称，默认 [\"预期\", \"实际\"]\n    :param desc: 标题前缀显示的名称，默认为空，推荐传入特征名称或评分卡名字\n    :param save: 图片保存的地址，如果传入路径中有文件夹不存在，会新建相关文件夹，默认 None\n    :param colors: 图片主题颜色，默认即可\n    :param figsize: 图像大小，默认 (15, 8)\n    :param anchor: 图例显示的位置，默认 0.94，根据实际显示情况进行调整即可，0.94 附近小范围调整\n    :param width: 预期分布与实际分布柱状图之间的间隔，默认 0.35\n    :param result: 是否返回 PSI 统计表，默认 False\n    :param plot: 是否画 PSI图，默认 True\n    :param max_len: 特征显示的最大长度，防止特征名称过长导致图像区域非常小，默认 None 表示不限制\n    :param hatch: 是否显示柱状图上的斜线，默认为 True\n\n    :return: 当 result 为 True 时，返回 pd.DataFrame\n    \"\"\"\n    expected = expected.rename(columns={\"样本总数\": f\"{labels[0]}样本数\", \"样本占比\": f\"{labels[0]}样本占比\", \"坏样本率\": f\"{labels[0]}坏样本率\"})\n    actual = actual.rename(columns={\"样本总数\": f\"{labels[1]}样本数\", \"样本占比\": f\"{labels[1]}样本占比\", \"坏样本率\": f\"{labels[1]}坏样本率\"})\n    df_psi = expected.merge(actual, on=\"分箱\", how=\"outer\").replace(np.nan, 0)\n    df_psi[f\"{labels[1]}% - {labels[0]}%\"] = df_psi[f\"{labels[1]}样本占比\"] - df_psi[f\"{labels[0]}样本占比\"]\n    df_psi[f\"ln({labels[1]}% / {labels[0]}%)\"] = np.log(df_psi[f\"{labels[1]}样本占比\"] / df_psi[f\"{labels[0]}样本占比\"])\n    df_psi[\"分档PSI值\"] = (df_psi[f\"{labels[1]}% - {labels[0]}%\"] * df_psi[f\"ln({labels[1]}% / {labels[0]}%)\"])\n    df_psi = df_psi.fillna(0).replace(np.inf, 0).replace(-np.inf, 0)\n    df_psi[\"总体PSI值\"] = df_psi[\"分档PSI值\"].sum()\n    df_psi[\"指标名称\"] = desc\n\n    if plot:\n        x = df_psi['分箱'].apply(lambda l: l if max_len is None or len(str(l)) < max_len else f\"{str(l)[:max_len]}...\")\n        x_indexes = np.arange(len(x))\n        fig, ax1 = plt.subplots(figsize=figsize)\n\n        ax1.bar(x_indexes - width / 2, df_psi[f'{labels[0]}样本占比'], width, label=f'{labels[0]}样本占比', color=colors[0], hatch=\"/\" if hatch else None)\n        ax1.bar(x_indexes + width / 2, df_psi[f'{labels[1]}样本占比'], width, label=f'{labels[1]}样本占比', color=colors[1], hatch=\"\\\\\" if hatch else None)\n\n        ax1.set_ylabel('样本占比: 分箱内样本数 / 样本总数')\n        ax1.set_xticks(x_indexes)\n        ax1.set_xticklabels(x)\n        ax1.tick_params(axis='x', labelrotation=90)\n\n        ax2 = ax1.twinx()\n        ax2.plot(x, df_psi[f\"{labels[0]}坏样本率\"], color=colors[0], label=f\"{labels[0]}坏样本率\", linestyle=(5, (10, 3)))\n        ax2.plot(x, df_psi[f\"{labels[1]}坏样本率\"], color=colors[1], label=f\"{labels[1]}坏样本率\", linestyle=(5, (10, 3)))\n\n        ax2.scatter(x, df_psi[f\"{labels[0]}坏样本率\"], marker=\".\")\n        ax2.scatter(x, df_psi[f\"{labels[1]}坏样本率\"], marker=\".\")\n\n        ax2.set_ylabel('坏样本率: 坏样本数 / 样本总数')\n\n        fig.suptitle(f\"{desc + ' ' if desc else ''}{labels[0]} vs {labels[1]} 群体稳定性指数(PSI): {df_psi['分档PSI值'].sum():.4f}\\n\\n\")\n\n        handles1, labels1 = ax1.get_legend_handles_labels()\n        handles2, labels2 = ax2.get_legend_handles_labels()\n        fig.legend(handles1 + handles2, labels1 + labels2, loc='upper center', ncol=len(labels1 + labels2), bbox_to_anchor=(0.5, anchor), frameon=False)\n\n        fig.tight_layout()\n\n        if save:\n            if os.path.dirname(save) != \"\" and not os.path.exists(os.path.dirname(save)):\n                os.makedirs(os.path.dirname(save), exist_ok=True)\n\n            fig.savefig(save, dpi=240, format=\"png\", bbox_inches=\"tight\")\n\n    if result:\n        return df_psi[[\"指标名称\", \"分箱\", f\"{labels[0]}样本数\", f\"{labels[0]}样本占比\", f\"{labels[0]}坏样本率\", f\"{labels[1]}样本数\", f\"{labels[1]}样本占比\", f\"{labels[1]}坏样本率\", f\"{labels[1]}% - {labels[0]}%\", f\"ln({labels[1]}% / {labels[0]}%)\", \"分档PSI值\", \"总体PSI值\"]]\n\n\ndef csi_plot(expected, actual, score_bins, labels=[\"预期\", \"实际\"], desc=\"\", save=None, colors=[\"#2639E9\", \"#F76E6C\", \"#FE7715\"], figsize=(15, 8), anchor=0.94, width=0.35, result=False, plot=True, max_len=None, hatch=True):\n    \"\"\"\n    特征 CSI 图\n\n    :param expected: 期望分布情况，传入需要验证的特征分箱表\n    :param actual: 实际分布情况，传入需要参照的特征分箱表\n    :param score_bins: 逻辑回归模型评分表\n    :param labels: 期望分布和实际分布的名称，默认 [\"预期\", \"实际\"]\n    :param desc: 标题前缀显示的名称，默认为空，推荐传入特征名称或评分卡名字\n    :param save: 图片保存的地址，如果传入路径中有文件夹不存在，会新建相关文件夹，默认 None\n    :param colors: 图片主题颜色，默认即可\n    :param figsize: 图像大小，默认 (15, 8)\n    :param anchor: 图例显示的位置，默认 0.94，根据实际显示情况进行调整即可，0.94 附近小范围调整\n    :param width: 预期分布与实际分布柱状图之间的间隔，默认 0.35\n    :param result: 是否返回 CSI 统计表，默认 False\n    :param plot: 是否画 CSI图，默认 True\n    :param max_len: 特征显示的最大长度，防止特征名称过长导致图像区域非常小，默认 None 表示不限制\n    :param hatch: 是否显示柱状图上的斜线，默认为 True\n    :return: 当 result 为 True 时，返回 pd.DataFrame\n    \"\"\"\n    expected = expected.rename(columns={\"样本总数\": f\"{labels[0]}样本数\", \"样本占比\": f\"{labels[0]}样本占比\", \"坏样本率\": f\"{labels[0]}坏样本率\"})\n    actual = actual.rename(columns={\"样本总数\": f\"{labels[1]}样本数\", \"样本占比\": f\"{labels[1]}样本占比\", \"坏样本率\": f\"{labels[1]}坏样本率\"})\n    df_csi = expected.merge(actual, on=\"分箱\", how=\"outer\").replace(np.nan, 0)\n    df_csi[f\"{labels[1]}% - {labels[0]}%\"] = df_csi[f\"{labels[1]}样本占比\"] - df_csi[f\"{labels[0]}样本占比\"]\n    df_csi = df_csi.merge(pd.DataFrame({\"分箱\": feature_bins(score_bins[\"bins\"]).values(), \"对应分数\": score_bins[\"scores\"]}), on=\"分箱\", how=\"left\").replace(np.nan, 0)\n    df_csi[\"分档CSI值\"] = (df_csi[f\"{labels[1]}% - {labels[0]}%\"] * df_csi[\"对应分数\"])\n    df_csi = df_csi.fillna(0).replace(np.inf, 0).replace(-np.inf, 0)\n    df_csi[\"总体CSI值\"] = df_csi[\"分档CSI值\"].sum()\n    df_csi[\"指标名称\"] = desc\n\n    if plot:\n        x = df_csi['分箱'].apply(lambda l: str(l) if pd.isnull(l) or len(str(l)) < max_len else f\"{str(l)[:max_len]}...\")\n        x_indexes = np.arange(len(x))\n        fig, ax1 = plt.subplots(figsize=figsize)\n\n        ax1.bar(x_indexes - width / 2, df_csi[f'{labels[0]}样本占比'], width, label=f'{labels[0]}样本占比', color=colors[0], hatch=\"/\" if hatch else None)\n        ax1.bar(x_indexes + width / 2, df_csi[f'{labels[1]}样本占比'], width, label=f'{labels[1]}样本占比', color=colors[1], hatch=\"\\\\\" if hatch else None)\n\n        ax1.set_ylabel('样本占比: 分箱内样本数 / 样本总数')\n        ax1.set_xticks(x_indexes)\n        ax1.set_xticklabels(x)\n        ax1.tick_params(axis='x', labelrotation=90)\n\n        ax2 = ax1.twinx()\n        ax2.plot(x, df_csi[f\"{labels[0]}坏样本率\"], color=colors[0], label=f\"{labels[0]}坏样本率\", linestyle=(5, (10, 3)))\n        ax2.plot(x, df_csi[f\"{labels[1]}坏样本率\"], color=colors[1], label=f\"{labels[1]}坏样本率\", linestyle=(5, (10, 3)))\n\n        ax2.scatter(x, df_csi[f\"{labels[0]}坏样本率\"], marker=\".\")\n        ax2.scatter(x, df_csi[f\"{labels[1]}坏样本率\"], marker=\".\")\n\n        ax2.set_ylabel('坏样本率: 坏样本数 / 样本总数')\n\n        fig.suptitle(f\"{desc + ' ' if desc else ''}{labels[0]} vs {labels[1]} 特征稳定性指标(CSI): {df_csi['分档CSI值'].sum():.4f}\\n\\n\")\n\n        handles1, labels1 = ax1.get_legend_handles_labels()\n        handles2, labels2 = ax2.get_legend_handles_labels()\n        fig.legend(handles1 + handles2, labels1 + labels2, loc='upper center', ncol=len(labels1 + labels2), bbox_to_anchor=(0.5, anchor), frameon=False)\n\n        fig.tight_layout()\n\n        if save:\n            if os.path.dirname(save) != \"\" and not os.path.exists(os.path.dirname(save)):\n                os.makedirs(os.path.dirname(save), exist_ok=True)\n\n            fig.savefig(save, dpi=240, format=\"png\", bbox_inches=\"tight\")\n\n    if result:\n        return df_csi[[\"指标名称\", \"分箱\", f\"{labels[0]}样本数\", f\"{labels[0]}样本占比\", f\"{labels[0]}坏样本率\", f\"{labels[1]}样本数\", f\"{labels[1]}样本占比\", f\"{labels[1]}坏样本率\", f\"{labels[1]}% - {labels[0]}%\", \"对应分数\", \"分档CSI值\", \"总体CSI值\"]]\n\n\ndef dataframe_plot(df, row_height=0.4, font_size=14, header_color='#2639E9', row_colors=['#dae3f3', 'w'], edge_color='w', bbox=[0, 0, 1, 1], header_columns=0, ax=None, save=None, **kwargs):\n    \"\"\"\n    将 dataframe 转换为图片，推荐行和列都不多的数据集使用该方法\n\n    :param df: 需要画图的 dataframe 数据\n    :param row_height: 行高，默认 0.4\n    :param font_size: 字体大小，默认 14\n    :param header_color: 标题颜色，默认 #2639E9\n    :param row_colors: 行颜色，默认 ['#dae3f3', 'w']，交替使用两种颜色\n    :param edge_color: 表格边框颜色，默认白色\n    :param bbox: 边的显示情况，[左，右，上，下]，即仅显示上下两条边框\n    :param header_columns: 标题行数，默认仅有一个标题行，即 0\n    :param ax: 如果需要在某张画布的子图中显示，那么传入对应的 ax 即可\n    :param save: 图片保存的地址，如果传入路径中有文件夹不存在，会新建相关文件夹，默认 None\n    :param kwargs: plt.table 相关的参数，参考：https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.table.html\n\n    :return: Figure\n    \"\"\"\n    data = df.copy()\n    for col in data.select_dtypes('datetime'):\n        data[col] = data[col].dt.strftime(\"%Y-%m-%d\")\n\n    for col in data.select_dtypes('float'):\n        data[col] = data[col].apply(lambda x: np.nan if pd.isnull(x) else round(x, 4))\n\n    cols_width = [max(data[col].apply(lambda x: len(str(x).encode())).max(), len(str(col).encode())) / 8. for col in data.columns]\n\n    if ax is None:\n        size = (sum(cols_width), (len(data) + 1) * row_height)\n        fig, ax = plt.subplots(figsize=size)\n        ax.axis('off')\n\n    mpl_table = ax.table(cellText=data.values, colWidths=cols_width, bbox=bbox, colLabels=data.columns, **kwargs)\n\n    mpl_table.auto_set_font_size(False)\n    mpl_table.set_fontsize(font_size)\n\n    for k, cell in six.iteritems(mpl_table._cells):\n        cell.set_edgecolor(edge_color)\n        if k[0] == 0 or k[1] < header_columns:\n            cell.set_text_props(weight='bold', color='w')\n            cell.set_facecolor(header_color)\n        else:\n            cell.set_facecolor(row_colors[k[0] % len(row_colors)])\n\n    fig.tight_layout()\n\n    if save:\n        if os.path.dirname(save) != \"\" and not os.path.exists(os.path.dirname(save)):\n            os.makedirs(os.path.dirname(save))\n\n        fig.savefig(save, dpi=240, format=\"png\", bbox_inches=\"tight\")\n\n    return fig\n\n\ndef distribution_plot(data, date=\"date\", target=\"target\", save=None, figsize=(10, 6), colors=[\"#2639E9\", \"#F76E6C\", \"#FE7715\"], freq=\"M\", anchor=0.94, result=False, hatch=True):\n    \"\"\"\n    样本时间分布图\n\n    :param data: 数据集\n    :param date: 日期列名称，如果格式非日期，会尝试自动转为日期格式，默认 date，替换为数据中对应的日期列（如申请时间、授信时间、放款时间等）\n    :param target: 数据集中标签列的名称，默认 target\n    :param save: 图片保存的地址，如果传入路径中有文件夹不存在，会新建相关文件夹，默认 None\n    :param figsize: 图像大小，默认 (10, 6)\n    :param colors: 图片主题颜色，默认即可\n    :param freq: 汇总统计的日期格式，按年、季度、月、周、日等统计，参考：https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects\n    :param anchor: 图例显示的位置，默认 0.94，根据实际显示情况进行调整即可，0.94 附近小范围调整\n    :param result: 是否返回分布表，默认 False\n    :param hatch: 是否显示柱状图上的斜线，默认为 True\n    :return:\n    \"\"\"\n    df = data.copy()\n\n    if 'time' not in str(df[date].dtype):\n        df[date] = pd.to_datetime(df[date])\n\n    temp = df.set_index(date).assign(\n        好样本=lambda x: (x[target] == 0).astype(int),\n        坏样本=lambda x: (x[target] == 1).astype(int),\n    ).resample(freq).agg({\"好样本\": sum, \"坏样本\": sum})\n\n    temp.index = [i.strftime(\"%Y-%m-%d\") for i in temp.index]\n\n    fig, ax1 = plt.subplots(1, 1, figsize=figsize)\n    temp.plot(kind='bar', stacked=True, ax=ax1, color=colors[:2], hatch=\"/\" if hatch else None, legend=False)\n    ax1.tick_params(axis='x', labelrotation=-90)\n    ax1.set(xlabel=None)\n    ax1.set_ylabel('样本数')\n    ax1.set_title('不同时点数据集样本分布情况\\n\\n')\n\n    ax2 = plt.twinx()\n    (temp[\"坏样本\"] / temp.sum(axis=1)).plot(ax=ax2, color=colors[-1], style=\"--\", linewidth=2, label=\"坏样本率\")\n    # sns.despine()\n\n    handles1, labels1 = ax1.get_legend_handles_labels()\n    handles2, labels2 = ax2.get_legend_handles_labels()\n    fig.legend(handles1 + handles2, labels1 + labels2, loc='upper center', ncol=len(labels1 + labels2), bbox_to_anchor=(0.5, anchor), frameon=False)\n\n    fig.tight_layout()\n\n    if save:\n        if os.path.dirname(save) != \"\" and not os.path.exists(os.path.dirname(save)):\n            os.makedirs(os.path.dirname(save))\n\n        fig.savefig(save, dpi=240, format=\"png\", bbox_inches=\"tight\")\n\n    if result:\n        temp = temp.reset_index().rename(columns={date: \"日期\", \"index\": \"日期\", 0: \"好样本\", 1: \"坏样本\"})\n        temp[\"样本总数\"] = temp[\"坏样本\"] + temp[\"好样本\"]\n        temp[\"样本占比\"] = temp[\"样本总数\"] / temp[\"样本总数\"].sum()\n        temp[\"好样本占比\"] = temp[\"好样本\"] / temp[\"好样本\"].sum()\n        temp[\"坏样本占比\"] = temp[\"坏样本\"] / temp[\"坏样本\"].sum()\n        temp[\"坏样本率\"] = temp[\"坏样本\"] / temp[\"样本总数\"]\n\n        return temp[[\"日期\", \"样本总数\", \"样本占比\", \"好样本\", \"好样本占比\", \"坏样本\", \"坏样本占比\", \"坏样本率\"]]\n\n\ndef sample_lift_transformer(df, rule, target='target', sample_rate=0.7):\n    \"\"\"采取好坏样本 sample_rate:1 的抽样方式时，计算抽样样本和原始样本上的 lift 指标\n\n    :param df: 原始数据，需全部为数值型变量\n    :param rule: Rule\n    :param target: 目标变量名称\n    :param sample_rate: 好样本采样比例\n\n    :return:\n        lift_sam: float, 抽样样本上拒绝人群的lift\n        lift_ori: float, 原始样本上拒绝人群的lift\n    \"\"\"\n    rj_df = df[rule.predict(df)]\n    ps_df = df[~rule.predict(df)]\n\n    # 拒绝样本好坏样本数\n    rj = len(rj_df)\n    bad_rj = rj_df[target].sum()\n    good_rj = rj - bad_rj\n\n    # 通过样本好坏样本数\n    ps = len(ps_df)\n    bad_ps = ps_df[target].sum()\n    good_ps = ps - bad_ps\n\n    # 抽样样本上的lift\n    lift_sam = (bad_rj / rj) / ((bad_rj + bad_ps) / (rj + ps))\n\n    # 原始样本上的lift\n    lift_ori = bad_rj / (bad_rj + bad_ps) * (1 + (sample_rate * bad_ps + good_ps) / (sample_rate * bad_rj + good_rj))\n\n    return lift_sam, lift_ori\n\n\ndef tasks_executor(tasks, n_jobs=-1, pool=\"thread\"):\n    \"\"\"多进程或多线程任务执行\n\n    :param tasks: 任务\n    :param n_jobs: 线城池或进程池数量\n    :param pool: 类型，默认 thread 线城池,\n    \"\"\"\n    if len(tasks) <= 0:\n        raise ValueError(\"执行任务数必须大于0\")\n\n    if pool == \"joblib\":\n        pass\n\n    from concurrent.futures import wait, ALL_COMPLETED\n    if pool == \"thread\":\n        from concurrent.futures import ThreadPoolExecutor\n        executor = ThreadPoolExecutor(max_workers=n_jobs if n_jobs > 0 else 1)\n    elif pool == \"process\":\n        from concurrent.futures import ProcessPoolExecutor\n        executor = ProcessPoolExecutor(max_workers=n_jobs if n_jobs > 0 else 1)\n\n    _tasks = []\n    for task in tasks:\n        executor.submit(task)\n\n    wait(_tasks, return_when=ALL_COMPLETED)\n\n    return [t.result() for t in _tasks]\n\n\ndef monotonic_bad_rate_binning(df, feature, target, target_rates, greater_is_better=True):\n    \"\"\"根据目标违约率寻找最佳分箱切点，并确保逾期率单调\n\n    :param df: 包含特征和目标的DataFrame\n    :param feature: 要分箱的特征列名\n    :param target: 目标变量列名\n    :param target_rates: 目标违约率列表(从高到低或从低到高取决于greater_is_better)\n    :param greater_is_better: 评分越高是否越好(违约率越低)\n\n    :return: 分箱切点列表(已排序且唯一)\n    \"\"\"\n    df = pd.DataFrame({\"score\": df[feature], \"target\": df[target]}).dropna()\n\n    # 根据评分方向决定排序方式\n    ascending_order = not greater_is_better\n    df = df.sort_values(\"score\", ascending=ascending_order)\n\n    # 初始化变量\n    cutpoints = []\n    remaining_df = df.copy()\n    last_bad_rate = None\n\n    for rate in target_rates[:-1]:  # 最后一个箱处理剩余部分\n        if len(remaining_df) == 0:\n            break\n\n        # 使用二分法寻找满足目标违约率的分割点\n        low = remaining_df[\"score\"].min()\n        high = remaining_df[\"score\"].max()\n        best_cut = None\n\n        for _ in range(100):\n            if low >= high:\n                break\n\n            mid = (low + high) / 2\n\n            # 根据评分方向决定分箱逻辑\n            if greater_is_better:\n                temp_df = remaining_df[remaining_df[\"score\"] >= mid]\n            else:\n                temp_df = remaining_df[remaining_df[\"score\"] <= mid]\n\n            if len(temp_df) == 0:\n                break\n\n            temp_bad_rate = temp_df[\"target\"].mean()\n\n            if abs(temp_bad_rate - rate) < 0.001:  # 足够接近目标\n                best_cut = mid\n                break\n            elif temp_bad_rate > rate:\n                if greater_is_better:\n                    low = mid + 1  # 需要更高的分数（更低的违约率）\n                else:\n                    high = mid - 1  # 需要更低的分数（更高的违约率）\n            else:\n                if greater_is_better:\n                    high = mid - 1  # 需要更低的分数（更高的违约率）\n                else:\n                    low = mid + 1  # 需要更高的分数（更低的违约率）\n\n        if best_cut is None:\n            best_cut = (low + high) / 2\n\n        # 根据评分方向获取当前箱\n        if greater_is_better:\n            current_bin = remaining_df[remaining_df[\"score\"] >= best_cut]\n            remaining_df = remaining_df[remaining_df[\"score\"] < best_cut]\n        else:\n            current_bin = remaining_df[remaining_df[\"score\"] <= best_cut]\n            remaining_df = remaining_df[remaining_df[\"score\"] > best_cut]\n\n        # 计算当前箱的坏样本率\n        current_bad_rate = current_bin[\"target\"].mean()\n\n        # 检查单调性\n        if last_bad_rate is not None:\n            if (greater_is_better and current_bad_rate >= last_bad_rate) or (not greater_is_better and current_bad_rate <= last_bad_rate):\n                # 不满足单调性，跳过当前切点\n                continue\n\n        # 满足条件，记录切点\n        cutpoints.append(best_cut)\n        last_bad_rate = current_bad_rate\n\n    # 确保切点唯一且正确排序\n    cutpoints = sorted(list(set(cutpoints)), reverse=not greater_is_better)\n\n    # 后处理：合并不满足单调性的箱\n    while True:\n        # 创建分箱\n        bins = [-np.inf] + cutpoints + [np.inf] if len(cutpoints) > 0 else [-np.inf, np.inf]\n        try:\n            bin_labels = pd.cut(df[\"score\"], bins=bins)\n            bin_stats = df.groupby(bin_labels)[\"target\"].agg([\"count\", \"mean\"])\n            bin_stats.columns = [\"样本数\", \"坏样本率\"]\n\n            # 检查单调性\n            bad_rates = bin_stats[\"坏样本率\"].values\n            monotonic = True\n\n            for i in range(1, len(bad_rates)):\n                if (greater_is_better and bad_rates[i] >= bad_rates[i - 1]) or (not greater_is_better and bad_rates[i] <= bad_rates[i - 1]):\n                    monotonic = False\n                    break\n\n            if monotonic or len(cutpoints) <= 1:\n                break\n\n            # 找到需要合并的切点\n            merge_index = i - 1 if greater_is_better else i\n            if merge_index < len(cutpoints):\n                # 移除中间的切点\n                cutpoints.pop(merge_index)\n        except ValueError:\n            # 如果分箱边界不是单调的，调整排序\n            cutpoints = sorted(cutpoints)\n            continue\n\n    return cutpoints\n"
  },
  {
    "path": "setup.py",
    "content": "import os\nimport re\nfrom setuptools import setup, find_packages, Extension\n\nNAME = 'scorecardpipeline'\n\n\ndef get_version():\n    with open(f\"{NAME}/__init__.py\", \"r\", encoding=\"utf8\") as f:\n        return re.search(r'__version__ = \"(.*?)\"', f.read()).group(1)\n\n\ndef get_requirements(stage=None):\n    file_name = 'requirements'\n\n    if stage is not None:\n        file_name = f\"{file_name}-{stage}\"\n\n    requirements = []\n    with open(f\"{file_name}.txt\", 'r') as f:\n        for line in f:\n            line = line.strip()\n            if not line or line.startswith('-'):\n                continue\n\n            requirements.append(line)\n\n    return requirements\n\n\nsetup(\n    name=NAME,\n    version=get_version(),\n    description='评分卡pipeline建模包，封装toad、scorecardpy、optbinning等评分卡建模相关组件，API风格与sklearn高度一致，自持自定义模型报告输出',\n    long_description=open('README.md', encoding='utf-8').read(),\n    long_description_content_type='text/markdown',\n    url='https://github.com/itlubber/scorecardpipeline',\n    author='itlubber',\n    author_email='itlubber@qq.com',\n    packages=find_packages(),\n    include_package_data=True,\n    python_requires='>=3.6',\n    install_requires=get_requirements(),\n    license='MIT',\n    classifiers=[\n        'Operating System :: POSIX',\n        'Operating System :: Microsoft :: Windows',\n        'Operating System :: MacOS :: MacOS X',\n        'Programming Language :: Python :: 3.6',\n        'Programming Language :: Python :: 3.7',\n        'Programming Language :: Python :: 3.8',\n        'Programming Language :: Python :: 3.9',\n        'Programming Language :: Python :: 3.10',\n        'Programming Language :: Python :: 3.11',\n        'Programming Language :: Python :: 3.12',\n    ],\n)\n"
  }
]