Repository: fidelity/mab2rec Branch: main Commit: 1cd7ebfd5fa7 Files: 74 Total size: 1.2 MB Directory structure: gitextract_avjheom_/ ├── .github/ │ └── workflows/ │ └── ci.yml ├── .gitignore ├── CHANGELOG.txt ├── CODEOWNERS ├── CONTRIBUTING.md ├── LICENSE ├── README.md ├── docs/ │ ├── .buildinfo │ ├── .nojekyll │ ├── _sources/ │ │ ├── api.rst.txt │ │ ├── contributing.rst.txt │ │ ├── examples.rst.txt │ │ ├── index.rst.txt │ │ ├── installation.rst.txt │ │ └── quick.rst.txt │ ├── _static/ │ │ ├── basic.css │ │ ├── css/ │ │ │ ├── badge_only.css │ │ │ └── theme.css │ │ ├── doctools.js │ │ ├── documentation_options.js │ │ ├── jquery-3.5.1.js │ │ ├── jquery.js │ │ ├── js/ │ │ │ ├── badge_only.js │ │ │ └── theme.js │ │ ├── language_data.js │ │ ├── pygments.css │ │ ├── searchtools.js │ │ ├── underscore-1.13.1.js │ │ └── underscore.js │ ├── api.html │ ├── contributing.html │ ├── examples.html │ ├── genindex.html │ ├── index.html │ ├── installation.html │ ├── objects.inv │ ├── py-modindex.html │ ├── quick.html │ ├── search.html │ └── searchindex.js ├── docsrc/ │ ├── Makefile │ ├── api.rst │ ├── conf.py │ ├── contributing.rst │ ├── examples.rst │ ├── index.rst │ ├── installation.rst │ ├── make.bat │ ├── quick.rst │ └── requirements.txt ├── mab2rec/ │ ├── __init__.py │ ├── _version.py │ ├── pipeline.py │ ├── rec.py │ ├── utils.py │ └── visualization.py ├── requirements.txt ├── scripts/ │ └── data_prep/ │ ├── concat_files.sh │ ├── concat_first_two_columns.sh │ ├── insert_header.sh │ ├── remove_columns.sh │ ├── remove_duplicate_lines.sh │ ├── remove_empty_lines.sh │ ├── remove_header.sh │ ├── rename_header.sh │ └── sort_except_header.sh ├── setup.py └── tests/ ├── __init__.py ├── run_all.py ├── test_base.py ├── test_invalid.py ├── test_pipeline.py ├── test_rec.py └── test_visualization.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/workflows/ci.yml ================================================ name: ci on: push: branches: - main pull_request: branches: - main schedule: - cron: '00 12 * * 1' # Runs every Monday at 8:00 AM EST jobs: Test: runs-on: ${{ matrix.os }} strategy: matrix: python-version: ['3.8', '3.9', '3.10'] os: [ubuntu-latest, macos-latest, windows-latest] fail-fast: false steps: - uses: actions/checkout@v2 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v2 with: python-version: ${{ matrix.python-version }} - name: Check shell: bash run: | python3 -m pip install --upgrade pip pip install -e . python3 -m unittest discover -v tests python3 setup.py install ================================================ FILE: .gitignore ================================================ # Pycharm cache files .cache/* .idea/* .pytest_cache/* tests/__pycache__/* mab2rec/__pycache__/* dist/* # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ *.swp # Jupyter Notebook .ipynb_checkpoints #*.ipynb # IPython profile_default/ ipython_config.py # Compiled python modules. *.pyc # Python egg metadata, regenerated from source files by setuptools. /*.egg-info # Dev and data folders on working branch dev/* data/* notebooks/* # Metadata .DS_Store # Build folder build/* eggs/ .eggs/ # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ .pytest_cache/ # Spyder project settings .spyderproject .spyproject # Documentation docsrc/_build/ ================================================ FILE: CHANGELOG.txt ================================================ ========= CHANGELOG ========= ------------------------------------------------------------------------------- September, 03, 2024 1.3.1 ------------------------------------------------------------------------------- Minor: - Updated requirements to use mabwiser>=2.7.4 to reflect change from np.Inf to np.inf in mabwiser. - Fixed default KMeans n_init parameters in tests instead of using 'auto' used in scikit-learn>=1.4 ------------------------------------------------------------------------------- February, 22, 2024 1.3.0 ------------------------------------------------------------------------------- Major: - Added optional `apply_sigmoid` argument to recommend() method, to control whether sigmoid transformation is applied to scores or not. Minor: - Fixed bug when recommending single context. ------------------------------------------------------------------------------- February, 05, 2024 1.2.1 ------------------------------------------------------------------------------- Minor: - Replaced NoReturn type hinting with None - thank you @SaraEkmanSVT ------------------------------------------------------------------------------- August, 16, 2023 1.2.0 ------------------------------------------------------------------------------- Major: - Removed spock-config dependency and train/test scripts using Spock - Updated tests and documentation to reflect Python 3.8+ support ------------------------------------------------------------------------------- February, 23, 2023 1.1.0 ------------------------------------------------------------------------------- Major: - Updated requirements to use mabwiser>=2.7 to benefit from enhancements, including vectorized predict for Linear policies and tracking of arm status. - Fixed tests due to changes in random seeding for Linear policies. Minor: - Added Diversity metrics to available MAB evaluation metrics. ------------------------------------------------------------------------------- August, 16, 2022 1.0.3 ------------------------------------------------------------------------------- Minor: - Fixed bug with inconsistency between scored and eligible items. ------------------------------------------------------------------------------- July, 5, 2022 1.0.2 ------------------------------------------------------------------------------- Minor: - Fixed bug with arguments not being passed correctly to load_response_data. ------------------------------------------------------------------------------- May, 12, 2022 1.0.1 ------------------------------------------------------------------------------- Minor: - Add encodings generation to Seq2Pat section in feature_engineering notebook. - Explicitly claim Mab2Rec requires Python 3.7+ for Installation in README. - Library source scripts are not updated, thus the pypi version is not changed. ------------------------------------------------------------------------------- April, 15, 2022 1.0.1 ------------------------------------------------------------------------------- Minor: - Fix missing top-k recommendations when top messages are excluded - thanks @nateewall! ------------------------------------------------------------------------------- March, 18, 2022 1.0.0 ------------------------------------------------------------------------------- - Initial public release. ================================================ FILE: CODEOWNERS ================================================ # These owners will be the default owners for everything in the repo. * @bkleyn @skadio ================================================ FILE: CONTRIBUTING.md ================================================ # Contributing to Mab2Rec Thank you for contributing to Mab2Rec! This guide will help you get started and know what to expect. All contributions and project spaces are subject to our [Code of Conduct](https://github.com/fidelity/.github/blob/main/CODE_OF_CONDUCT.md). We welcome all types of contributions, including: * Code contributions * Bug reports * Responsibly disclosed security concerns * Documentation fixes * Feature requests and user stories (although we can't guarantee we'll get to all requests, it's helpful to know where we can improve) If you end up using our library in a project, give us a star on GitHub! Please note that we periodically fork upstream repos to stage contributions from Fidelity. We do not accept contributions against these forked repos, and request you make contributions against upstream projects directly. If you have any questions, please contact [opensource@fmr.com](mailto:opensource@fmr.com). ## How to report a bug Please [open an issue](https://github.com/fidelity/mab2rec/issues) **unless** you are making a significant security disclosure. When reporting a bug, please start from a fresh pull of the default branch and document how you encountered the issue. Reports with insufficient detail and which we can't reproduce may be closed without action. While bugs can be frustrating, we ask participants to contribute positively and professionally to the discourse. While we commit to take the contents of the report seriously, abusive behavior be will not be tolerated. ## How to disclose security concerns responsibly Please follow the instructions in our [security policy](https://github.com/fidelity/.github/blob/main/SECURITY.md) (also visible in the Security tab on the project's repo). ## How to contribute documentation fixes Minor documentation fixes can be submitted directly as a pull request without filing an issue in advance. More significant changes (e.g., refactoring to support a new documentation format, major reorganizations of content, etc.) should first be discussed in an issue to ensure everyone's time is used effectively. When opening a PR or issue with a documentation change, please add a `documentation` label. ## How to request features or submit a user story To request a feature please open an issue and tag it as `feature enhancement`. If you already have an implementation, please [link the pull request to the issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword). Please include as much information and context as you can. Understanding how the feature solves a specific problem will help us prioritize the request. Please understand that we will not be able to provide an implementation timeline on all requests, although requests that include an implementation are more likely to land sooner. If you won't do the work yourself, please also add a `good first issue` or `help wanted` label. These are special issue tags which are intended to help new and existing contributors get involved in a meaningful and accessible way. * `good first issue` - Small changes that are suitable for a beginner * `help wanted` - More involved changes This will help match your request with others who are looking for a way to get involved. ## Code contributions Code contributions are welcome in all of our projects as long as you follow a few rules: * With any piece of code, please adhere to PEP-8 standards. * If you're fixing an issue with an existing piece of code, please make sure all the tests pass, and there is no change in functionality. * If you want to add a new feature, please open up an issue first. * When adding a new feature, make sure you have relevant test coverage. * Any changes to the public API should conform to the current standards, be properly documented, typed, and be intuitive. * Your contribution must be received under the project's open source license. * You must have permission to make the contribution. We strongly recommend including a Signed-off-by line to indicate your adherence to the [Developer Certificate of Origin](https://developercertificate.org/). * All code contributions must be made via PR, and all checks must pass before merging. While not strictly necessary, we encourage you to open an issue prior to your pull request to let the project know to expect your code. This helps the team plan for the next release and may result in your feature being a higher priority, and also decreases the likelihood of two independent contributions that do the same thing. ## Documentation contributions * Make sure you follow the standards set by the rest of the repo. * Be concise, but do not omit details. Verbose documentation is preferred to incomplete documentation. ## Getting started (and helping others find their footing) Anyone may open an issue and apply a `good first issue` or `help wanted` label for others to work on. We only ask that when someone else picks up your issue and decides to work on it that you be responsive to their questions. ## Getting help If you have other questions about this project, please [open an issue](https://github.com/fidelity/mab2rec/issues). To reach the Fidelity OSPO directly, please email [opensource@fmr.com](mailto:opensource@fmr.com). ================================================ FILE: LICENSE ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. 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See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: README.md ================================================ [![ci](https://github.com/fidelity/mab2rec/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/fidelity/mab2rec/actions/workflows/ci.yml) [![PyPI version fury.io](https://badge.fury.io/py/mab2rec.svg)](https://pypi.python.org/pypi/mab2rec/) [![PyPI license](https://img.shields.io/pypi/l/mab2rec.svg)](https://pypi.python.org/pypi/mab2rec/) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) [![Downloads](https://static.pepy.tech/personalized-badge/mab2rec?period=total&units=international_system&left_color=grey&right_color=orange&left_text=Downloads)](https://pepy.tech/project/mab2rec) # Mab2Rec: Multi-Armed Bandits Recommender Mab2Rec ([AAAI'24](https://ojs.aaai.org/index.php/AAAI/article/view/30341)) is a Python library for building bandit-based recommendation algorithms. It supports **context-free**, **parametric** and **non-parametric** **contextual** bandit models powered by [MABWiser](https://github.com/fidelity/mabwiser) and fairness and recommenders evaluations powered by [Jurity](https://github.com/fidelity/jurity). The library is designed with rapid experimentation in mind, follows the [PEP-8 standards](https://www.python.org/dev/peps/pep-0008/), and is tested heavily. Mab2Rec is built on top of several other open-source software developed at the AI Center at Fidelity: * [MABWiser](https://github.com/fidelity/mabwiser) to create multi-armed bandit recommendation algorithms ([Bridge@AAAI'24](http://osullivan.ucc.ie/CPML2024/papers/06.pdf), [TMLR'22](https://openreview.net/pdf?id=sX9d3gfwtE), [IJAIT'21](https://www.worldscientific.com/doi/abs/10.1142/S0218213021500214), [ICTAI'19](https://ieeexplore.ieee.org/document/8995418)). * [TextWiser](https://github.com/fidelity/textwiser) to create item representations via text featurization ([AAAI'21](https://ojs.aaai.org/index.php/AAAI/article/view/17814)). * [Selective](https://github.com/fidelity/selective) to create user representations via feature selection ([AMAI'24](https://link.springer.com/article/10.1007/s10472-024-09941-x), [CPAIOR'21](https://link.springer.com/chapter/10.1007/978-3-030-78230-6_27), [DSO@IJCAI'21](https://arxiv.org/abs/2112.03105)). * [Seq2Pat](https://github.com/fidelity/seq2pat) to create user representations via sequential pattern mining ([AI Magazine'23](https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12081), [AAAI'22](https://ojs.aaai.org/index.php/AAAI/article/view/21542), [Bridge@AAAI'23](http://osullivan.ucc.ie/CPML2023/submissions/09.pdf), [Frontiers'22](https://www.frontiersin.org/articles/10.3389/frai.2022.868085/full), [KDF@AAAI'22](https://arxiv.org/abs/2201.09178), [CMU Blog Post](https://www.cmu.edu/tepper/news/stories/2023/may/fidelity-ai.html)) * [Jurity](https://github.com/fidelity/jurity) to evaluate recommendations including fairness metrics ([ACM'24](https://dl.acm.org/doi/10.1145/3700145), [LION'23](https://link.springer.com/chapter/10.1007/978-3-031-44505-7_29), [CIKM'22](https://ceur-ws.org/Vol-3318/short6.pdf), [ICMLA'21](https://ieeexplore.ieee.org/abstract/document/9680169)). An introduction to **content- and context-aware** recommender systems and an overview of the building blocks of the library is presented at [AAAI 2024](https://underline.io/lecture/91479-building-higher-order-abstractions-from-the-components-of-recommender-systems) and [All Things Open 2021](https://www.youtube.com/watch?v=54d_YUalvOA). There is a corresponding [blogpost](https://2022.allthingsopen.org/introducing-mab2rec-a-multi-armed-bandit-recommender-library/) to serve as a starting point for practioners to build and deploy bandit-based recommenders using Mab2Rec. Documentation is available at [fidelity.github.io/mab2rec](https://fidelity.github.io/mab2rec). ## Usage Patterns Mab2Rec supports prototyping with a **single** bandit algorithm or benchmarking with **multiple** bandit algorithms. If you are new user, the best place to start is to experiment with multiple bandits using the [tutorial notebooks](notebooks). ## Quick Start ### Single Recommender ```python # Example of how to train an singler recommender to generate top-4 recommendations # Import from mab2rec import BanditRecommender, LearningPolicy from mab2rec.pipeline import train, score # LinGreedy recommender to select top-4 items with 10% random exploration rec = BanditRecommender(LearningPolicy.LinGreedy(epsilon=0.1), top_k=4) # Train on (user, item, response) interactions in train data using user features train(rec, data='data/data_train.csv', user_features='data/features_user.csv') # Score recommendations for users in test data. The output df holds # user_id, item_id, score columns for every test user for top-k items df = score(rec, data='data/data_test.csv', user_features='data/features_user.csv') ``` ### Multiple Recommenders ```python # Example of how to benchmark multiple recommenders to generate top-4 recommendations from mab2rec import BanditRecommender, LearningPolicy from mab2rec.pipeline import benchmark from jurity.recommenders import BinaryRecoMetrics, RankingRecoMetrics # Recommenders (many more available) recommenders = {"Random": BanditRecommender(LearningPolicy.Random()), "Popularity": BanditRecommender(LearningPolicy.Popularity()), "LinGreedy": BanditRecommender(LearningPolicy.LinGreedy(epsilon=0.1))} # Column names for the response, user, and item id columns metric_params = {'click_column': 'score', 'user_id_column': 'user_id', 'item_id_column':'item_id'} # Performance metrics for benchmarking (many more available) metrics = [] for top_k in [3, 5, 10]: metrics.append(BinaryRecoMetrics.CTR(**metric_params, k=top_k)) metrics.append(RankingRecoMetrics.NDCG(**metric_params, k=top_k)) # Benchmarking with a collection of recommenders and metrics # This returns two dictionaries; # reco_to_results: recommendations for each algorithm on cross-validation data # reco_to_metrics: evaluation metrics for each algorithm reco_to_results, reco_to_metrics = benchmark(recommenders, metrics=metrics, train_data="data/data_train.csv", cv=5, user_features="data/features_user.csv") ``` ## Usage Examples We provide extensive tutorials in the [notebooks](notebooks) folder with guidelines on building recommenders, performing model selection, and evaluating performance. 1. [Data Overview:](https://github.com/fidelity/mab2rec/tree/master/notebooks/1_data_overview.ipynb) Overview of data required to train recommender. 2. [Feature Engineering:](https://github.com/fidelity/mab2rec/tree/master/notebooks/2_feature_engineering.ipynb) Creating user and item features from structured, unstructured, and sequential data. 3. [Model Selection:](https://github.com/fidelity/mab2rec/tree/master/notebooks/3_model_selection.ipynb) Model selection by benchmarking recommenders using cross-validation. 4. [Evaluation:](https://github.com/fidelity/mab2rec/tree/master/notebooks/4_evaluation.ipynb) Benchmarking of selected recommenders and baselines on test data with detailed evaluation. 5. [Advanced:](https://github.com/fidelity/mab2rec/tree/master/notebooks/5_advanced.ipynb) Demonstration of advanced functionality such as persistency, eligibility, item availability, and memory efficiency. ## Installation Mab2Rec requires **Python 3.8+** and can be installed from PyPI using ``pip install mab2rec`` or by building from source as shown in [installation instructions](https://fidelity.github.io/mab2rec/installation.html). ## Citation If you use Mab2Rec in a publication, please cite it as: ```bibtex @inproceedings{DBLP:conf/aaai/KadiogluK24, author = {Serdar Kadioglu and Bernard Kleynhans}, title = {Building Higher-Order Abstractions from the Components of Recommender Systems}, booktitle = {Thirty-Eighth {AAAI} Conference on Artificial Intelligence, {AAAI} 2024, Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence, {IAAI} 2024, Fourteenth Symposium on Educational Advances in Artificial Intelligence, {EAAI} 2014, February 20-27, 2024, Vancouver, Canada}, pages = {22998--23004}, publisher = {{AAAI} Press}, year = {2024}, url = {https://doi.org/10.1609/aaai.v38i21.30341}, doi = {10.1609/AAAI.V38I21.30341} } ``` ## Support Please submit bug reports and feature requests as [Issues](https://github.com/fidelity/mab2rec/issues). ## License Mab2Rec is licensed under the [Apache License 2.0](LICENSE).
================================================ FILE: docs/.buildinfo ================================================ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. config: 8217e7f6830b05122d0f67cec6a6b522 tags: 645f666f9bcd5a90fca523b33c5a78b7 ================================================ FILE: docs/.nojekyll ================================================ ================================================ FILE: docs/_sources/api.rst.txt ================================================ .. _Mab2Rec API: Mab2Rec Public API ================== .. automodule:: mab2rec :members: :undoc-members: :show-inheritance: BanditRecommender ^^^^^^^^^^^^^^^^^ .. autoclass:: mab2rec.BanditRecommender :members: :undoc-members: :show-inheritance: LearningPolicy ^^^^^^^^^^^^^^ .. autoclass:: mab2rec.LearningPolicy :members: :undoc-members: NeighborhoodPolicy ^^^^^^^^^^^^^^^^^^ .. autoclass:: mab2rec.NeighborhoodPolicy :members: :undoc-members: Pipeline ^^^^^^^^ .. automodule:: mab2rec.pipeline :members: :undoc-members: Visualization ^^^^^^^^^^^^^ .. automodule:: mab2rec.visualization :members: :undoc-members: ================================================ FILE: docs/_sources/contributing.rst.txt ================================================ .. _contributing: Contributing ============ We welcome contributions of all from everybody, and we will make an effort to respond to any questions and requests. Code is not the only way to make a contribution! If you end up using our library in a project, give us a star on GitHub! Code Contributions ^^^^^^^^^^^^^^^^^^ - With any piece of code, please adhere to PEP-8 standards. - If you're fixing an issue with an existing piece of code, please make sure all the tests pass, and there is no change in functionality. - If you want to add a new feature, please open up an issue first. - When adding a new feature, make sure you have relevant test coverage. - Any changes to the public API should conform to the current standards, be properly documented, typed, and be intuitive. Documentation Contributions ^^^^^^^^^^^^^^^^^^^^^^^^^^^ - Make sure you follow the standards set by the rest of the repo. - Be concise, but do not omit details. Verbose documentation is preferred to incomplete documentation. ================================================ FILE: docs/_sources/examples.rst.txt ================================================ .. _examples: Usage Examples ============== We provide extensive tutorials in Jupyter notebooks under the :repo:`notebooks ` folder for guidelines on building recommenders, performing model selection, and evaluating performance: - :repo:`Data Overview ` provides an overview of data required to train recommender. - :repo:`Feature Engineering ` gives an overview of methods to create user and item features from structured, unstructured, and sequential data. - :repo:`Model Selection ` shows to do model selection by benchmarking recommenders using cross-validation. - :repo:`Evaluation ` benchmarks selected recommenders and baselines on test data with detailed evaluations. - :repo:`Advanced ` demonstrates some advanced functionality. ================================================ FILE: docs/_sources/index.rst.txt ================================================ Mab2Rec: Multi-Armed Bandits Recommender ======================================== Mab2Rec is a Python library for building bandit-based recommendation algorithms. It supports **context-free**, **parametric** and **non-parametric** **contextual** bandit models powered by `MABWiser `_ and fairness and recommenders evaluations powered by `Jurity `_. It supports `all bandit policies available in MABWiser `_. The library is designed with rapid experimentation in mind, follows the `PEP-8 standards `_ and is tested heavily. Mab2Rec and several of the open-source software it is built on is developed by the Artificial Intelligence Center at Fidelity Investments, including: * `MABWiser `_ to create multi-armed bandit recommendation algorithms (`IJAIT'21 `_, `ICTAI'19 `_). * `TextWiser `_ to create item representations via text featurization (`AAAI'21 `_). * `Selective `_ to create user representations via feature selection. * `Seq2Pat `_ to enhance users representations via sequential pattern mining (`AAAI'22 `_). * `Jurity `_ to evaluate recommendations including fairness metrics (`ICMLA'21 `_). An introduction to **content- and context-aware** recommender systems and an overview of the building blocks of the library is `presented at All Things Open 2021 `_. .. include:: quick.rst Source Code =========== The source code is hosted on :repo:`GitHub <>`. .. sidebar:: Contents .. toctree:: :maxdepth: 2 installation quick examples contributing api Indices and tables ================== * :ref:`genindex` * :ref:`modindex` ================================================ FILE: docs/_sources/installation.rst.txt ================================================ .. _installation: Installation ============ .. admonition:: Installation Options There are two options to install the library: 1. Install from PyPI using the prebuilt wheel package (``pip install mab2rec``) 2. Build from the source code Requirements ------------ The library requires Python **3.7+**. The ``requirements.txt`` lists the necessary packages. Source Code ----------- You can build a wheel package on your platform from scratch using the source code: .. code-block:: python git clone https://github.com/fidelity/mab2rec.git cd mab2rec pip install setuptools wheel # if wheel is not installed python setup.py sdist bdist_wheel pip install dist/mab2rec-X.X.X-py3-none-any.whl Test Your Setup --------------- To confirm that cloning was successful, run the tests included in the project. All tests should pass. .. code-block:: python git clone https://github.com/fidelity/mab2rec.git cd mab2rec python -m unittest discover tests Upgrade the Library ------------------- To upgrade to the latest version of the library, run ``pip install --upgrade mab2rec``. If you installed from the source code: .. code-block:: python git pull origin master python setup.py sdist bdist_wheel pip install --upgrade --no-cache-dir dist/mab2rec-X.X.X-py3-none-any.whl ================================================ FILE: docs/_sources/quick.rst.txt ================================================ .. _quick: Quick Start =========== Individual Recommender ---------------------- .. code-block:: python # Example of how to train an individual recommender to generate top-4 recommendations # Import from mab2rec import BanditRecommender, LearningPolicy from mab2rec.pipeline import train, score # LinGreedy recommender to select top-4 items with 10% random exploration rec = BanditRecommender(LearningPolicy.LinGreedy(epsilon=0.1), top_k=4) # Train on (user, item, response) interactions in train data using user features train(rec, data='data/data_train.csv', user_features='data/features_user.csv') # Score recommendations for users in test data. The output df holds # user_id, item_id, score columns for every test user for top-k items df = score(rec, data='data/data_test.csv', user_features='data/features_user.csv') Multiple Recommenders --------------------- .. code-block:: python # Example of how to benchmark multiple bandit algorithms to generate top-4 recommendations from mab2rec import BanditRecommender, LearningPolicy from mab2rec.pipeline import benchmark from jurity.recommenders import BinaryRecoMetrics, RankingRecoMetrics # Recommenders (many more available) recommenders = {"Random": BanditRecommender(LearningPolicy.Random()), "Popularity": BanditRecommender(LearningPolicy.Popularity()), "LinGreedy": BanditRecommender(LearningPolicy.LinGreedy(epsilon=0.1))} # Column names for the response, user, and item id columns metric_params = {'click_column': 'score', 'user_id_column': 'user_id', 'item_id_column':'item_id'} # Performance metrics for benchmarking (many more available) metrics = [] for top_k in [3, 5, 10]: metrics.append(BinaryRecoMetrics.CTR(**metric_params, k=top_k)) metrics.append(RankingRecoMetrics.NDCG(**metric_params, k=top_k)) # Benchmarking with a collection of recommenders and metrics # This returns two dictionaries; # reco_to_results: recommendations for each algorithm on cross-validation data # reco_to_metrics: evaluation metrics for each algorithm reco_to_results, reco_to_metrics = benchmark(recommenders, metrics=metrics, train_data="data/data_train.csv", cv=5, user_features="data/features_user.csv") ================================================ FILE: docs/_static/basic.css ================================================ /* * basic.css * ~~~~~~~~~ * * Sphinx stylesheet -- basic theme. * * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ /* -- main layout ----------------------------------------------------------- */ div.clearer { clear: both; } div.section::after { display: block; content: ''; clear: left; } 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* * Sphinx JavaScript utilities for all documentation. * * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ /** * select a different prefix for underscore */ $u = _.noConflict(); /** * make the code below compatible with browsers without * an installed firebug like debugger if (!window.console || !console.firebug) { var names = ["log", "debug", "info", "warn", "error", "assert", "dir", "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", "profile", "profileEnd"]; window.console = {}; for (var i = 0; i < names.length; ++i) window.console[names[i]] = function() {}; } */ /** * small helper function to urldecode strings * * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL */ jQuery.urldecode = function(x) { if (!x) { return x } return decodeURIComponent(x.replace(/\+/g, ' ')); }; /** * small helper function to urlencode strings */ jQuery.urlencode = encodeURIComponent; /** * This function returns the parsed url parameters of the * current request. Multiple values per key are supported, * it will always return arrays of strings for the value parts. */ jQuery.getQueryParameters = function(s) { if (typeof s === 'undefined') s = document.location.search; var parts = s.substr(s.indexOf('?') + 1).split('&'); var result = {}; for (var i = 0; i < parts.length; i++) { var tmp = parts[i].split('=', 2); var key = jQuery.urldecode(tmp[0]); var value = jQuery.urldecode(tmp[1]); if (key in result) result[key].push(value); else result[key] = [value]; } return result; }; /** * highlight a given string on a jquery object by wrapping it in * span elements with the given class name. */ jQuery.fn.highlightText = function(text, className) { function highlight(node, addItems) { if (node.nodeType === 3) { var val = node.nodeValue; var pos = val.toLowerCase().indexOf(text); if (pos >= 0 && !jQuery(node.parentNode).hasClass(className) && !jQuery(node.parentNode).hasClass("nohighlight")) { var span; var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); if (isInSVG) { span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); } else { span = document.createElement("span"); span.className = className; } span.appendChild(document.createTextNode(val.substr(pos, text.length))); node.parentNode.insertBefore(span, node.parentNode.insertBefore( document.createTextNode(val.substr(pos + text.length)), node.nextSibling)); node.nodeValue = val.substr(0, pos); if (isInSVG) { var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); var bbox = node.parentElement.getBBox(); rect.x.baseVal.value = bbox.x; rect.y.baseVal.value = bbox.y; rect.width.baseVal.value = bbox.width; rect.height.baseVal.value = bbox.height; rect.setAttribute('class', className); addItems.push({ "parent": node.parentNode, "target": rect}); } } } else if (!jQuery(node).is("button, select, textarea")) { jQuery.each(node.childNodes, function() { highlight(this, addItems); }); } } var addItems = []; var result = this.each(function() { highlight(this, addItems); }); for (var i = 0; i < addItems.length; ++i) { jQuery(addItems[i].parent).before(addItems[i].target); } return result; }; /* * backward compatibility for jQuery.browser * This will be supported until firefox bug is fixed. */ if (!jQuery.browser) { jQuery.uaMatch = function(ua) { ua = ua.toLowerCase(); var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || /(webkit)[ \/]([\w.]+)/.exec(ua) || /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || /(msie) ([\w.]+)/.exec(ua) || ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || []; return { browser: match[ 1 ] || "", version: match[ 2 ] || "0" }; }; jQuery.browser = {}; jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; } /** * Small JavaScript module for the documentation. */ var Documentation = { init : function() { this.fixFirefoxAnchorBug(); this.highlightSearchWords(); this.initIndexTable(); if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { this.initOnKeyListeners(); } }, /** * i18n support */ TRANSLATIONS : {}, PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, LOCALE : 'unknown', // gettext and ngettext don't access this so that the functions // can safely bound to a different name (_ = Documentation.gettext) gettext : function(string) { var translated = Documentation.TRANSLATIONS[string]; if (typeof translated === 'undefined') return string; return (typeof translated === 'string') ? translated : translated[0]; }, ngettext : function(singular, plural, n) { var translated = Documentation.TRANSLATIONS[singular]; if (typeof translated === 'undefined') return (n == 1) ? singular : plural; return translated[Documentation.PLURALEXPR(n)]; }, addTranslations : function(catalog) { for (var key in catalog.messages) this.TRANSLATIONS[key] = catalog.messages[key]; this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); this.LOCALE = catalog.locale; }, /** * add context elements like header anchor links */ addContextElements : function() { $('div[id] > :header:first').each(function() { $('\u00B6'). attr('href', '#' + this.id). attr('title', _('Permalink to this headline')). appendTo(this); }); $('dt[id]').each(function() { $('\u00B6'). attr('href', '#' + this.id). attr('title', _('Permalink to this definition')). appendTo(this); }); }, /** * workaround a firefox stupidity * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 */ fixFirefoxAnchorBug : function() { if (document.location.hash && $.browser.mozilla) window.setTimeout(function() { document.location.href += ''; }, 10); }, /** * highlight the search words provided in the url in the text */ highlightSearchWords : function() { var params = $.getQueryParameters(); var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; if (terms.length) { var body = $('div.body'); if (!body.length) { body = $('body'); } window.setTimeout(function() { $.each(terms, function() { body.highlightText(this.toLowerCase(), 'highlighted'); }); }, 10); $('

' + _('Hide Search Matches') + '

') .appendTo($('#searchbox')); } }, /** * init the domain index toggle buttons */ initIndexTable : function() { var togglers = $('img.toggler').click(function() { var src = $(this).attr('src'); var idnum = $(this).attr('id').substr(7); $('tr.cg-' + idnum).toggle(); if (src.substr(-9) === 'minus.png') $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); else $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); }).css('display', ''); if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { togglers.click(); } }, /** * helper function to hide the search marks again */ hideSearchWords : function() { $('#searchbox .highlight-link').fadeOut(300); $('span.highlighted').removeClass('highlighted'); var url = new URL(window.location); url.searchParams.delete('highlight'); window.history.replaceState({}, '', url); }, /** * make the url absolute */ makeURL : function(relativeURL) { return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; }, /** * get the current relative url */ getCurrentURL : function() { var path = document.location.pathname; var parts = path.split(/\//); $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { if (this === '..') parts.pop(); }); var url = parts.join('/'); return path.substring(url.lastIndexOf('/') + 1, path.length - 1); }, initOnKeyListeners: function() { $(document).keydown(function(event) { var activeElementType = document.activeElement.tagName; // don't navigate when in search box, textarea, dropdown or button if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT' && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) { switch (event.keyCode) { case 37: // left var prevHref = $('link[rel="prev"]').prop('href'); if (prevHref) { window.location.href = prevHref; return false; } break; case 39: // right var nextHref = $('link[rel="next"]').prop('href'); if (nextHref) { window.location.href = nextHref; return false; } break; } } }); } }; // quick alias for translations _ = Documentation.gettext; $(document).ready(function() { Documentation.init(); }); ================================================ FILE: docs/_static/documentation_options.js ================================================ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), VERSION: '1.2.0', LANGUAGE: 'None', COLLAPSE_INDEX: false, BUILDER: 'html', FILE_SUFFIX: '.html', LINK_SUFFIX: '.html', HAS_SOURCE: true, SOURCELINK_SUFFIX: '.txt', NAVIGATION_WITH_KEYS: false }; ================================================ FILE: docs/_static/jquery-3.5.1.js ================================================ /*! * jQuery JavaScript Library v3.5.1 * https://jquery.com/ * * Includes Sizzle.js * https://sizzlejs.com/ * * Copyright JS Foundation and other contributors * Released under the MIT license * https://jquery.org/license * * Date: 2020-05-04T22:49Z */ ( function( global, factory ) { "use strict"; if ( typeof module === "object" && typeof module.exports === "object" ) { // For CommonJS and CommonJS-like environments where a proper `window` // is present, execute the factory and get jQuery. // For environments that do not have a `window` with a `document` // (such as Node.js), expose a factory as module.exports. // This accentuates the need for the creation of a real `window`. // e.g. var jQuery = require("jquery")(window); // See ticket #14549 for more info. module.exports = global.document ? factory( global, true ) : function( w ) { if ( !w.document ) { throw new Error( "jQuery requires a window with a document" ); } return factory( w ); }; } else { factory( global ); } // Pass this if window is not defined yet } )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { // Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 // throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode // arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common // enough that all such attempts are guarded in a try block. "use strict"; var arr = []; var getProto = Object.getPrototypeOf; var slice = arr.slice; var flat = arr.flat ? function( array ) { return arr.flat.call( array ); } : function( array ) { return arr.concat.apply( [], array ); }; var push = arr.push; var indexOf = arr.indexOf; var class2type = {}; var toString = class2type.toString; var hasOwn = class2type.hasOwnProperty; var fnToString = hasOwn.toString; var ObjectFunctionString = fnToString.call( Object ); var support = {}; var isFunction = function isFunction( obj ) { // Support: Chrome <=57, Firefox <=52 // In some browsers, typeof returns "function" for HTML elements // (i.e., `typeof document.createElement( "object" ) === "function"`). // We don't want to classify *any* DOM node as a function. return typeof obj === "function" && typeof obj.nodeType !== "number"; }; var isWindow = function isWindow( obj ) { return obj != null && obj === obj.window; }; var document = window.document; var preservedScriptAttributes = { type: true, src: true, nonce: true, noModule: true }; function DOMEval( code, node, doc ) { doc = doc || document; var i, val, script = doc.createElement( "script" ); script.text = code; if ( node ) { for ( i in preservedScriptAttributes ) { // Support: Firefox 64+, Edge 18+ // Some browsers don't support the "nonce" property on scripts. // On the other hand, just using `getAttribute` is not enough as // the `nonce` attribute is reset to an empty string whenever it // becomes browsing-context connected. // See https://github.com/whatwg/html/issues/2369 // See https://html.spec.whatwg.org/#nonce-attributes // The `node.getAttribute` check was added for the sake of // `jQuery.globalEval` so that it can fake a nonce-containing node // via an object. val = node[ i ] || node.getAttribute && node.getAttribute( i ); if ( val ) { script.setAttribute( i, val ); } } } doc.head.appendChild( script ).parentNode.removeChild( script ); } function toType( obj ) { if ( obj == null ) { return obj + ""; } // Support: Android <=2.3 only (functionish RegExp) return typeof obj === "object" || typeof obj === "function" ? class2type[ toString.call( obj ) ] || "object" : typeof obj; } /* global Symbol */ // Defining this global in .eslintrc.json would create a danger of using the global // unguarded in another place, it seems safer to define global only for this module var version = "3.5.1", // Define a local copy of jQuery jQuery = function( selector, context ) { // The jQuery object is actually just the init constructor 'enhanced' // Need init if jQuery is called (just allow error to be thrown if not included) return new jQuery.fn.init( selector, context ); }; jQuery.fn = jQuery.prototype = { // The current version of jQuery being used jquery: version, constructor: jQuery, // The default length of a jQuery object is 0 length: 0, toArray: function() { return slice.call( this ); }, // Get the Nth element in the matched element set OR // Get the whole matched element set as a clean array get: function( num ) { // Return all the elements in a clean array if ( num == null ) { return slice.call( this ); } // Return just the one element from the set return num < 0 ? this[ num + this.length ] : this[ num ]; }, // Take an array of elements and push it onto the stack // (returning the new matched element set) pushStack: function( elems ) { // Build a new jQuery matched element set var ret = jQuery.merge( this.constructor(), elems ); // Add the old object onto the stack (as a reference) ret.prevObject = this; // Return the newly-formed element set return ret; }, // Execute a callback for every element in the matched set. each: function( callback ) { return jQuery.each( this, callback ); }, map: function( callback ) { return this.pushStack( jQuery.map( this, function( elem, i ) { return callback.call( elem, i, elem ); } ) ); }, slice: function() { return this.pushStack( slice.apply( this, arguments ) ); }, first: function() { return this.eq( 0 ); }, last: function() { return this.eq( -1 ); }, even: function() { return this.pushStack( jQuery.grep( this, function( _elem, i ) { return ( i + 1 ) % 2; } ) ); }, odd: function() { return this.pushStack( jQuery.grep( this, function( _elem, i ) { return i % 2; } ) ); }, eq: function( i ) { var len = this.length, j = +i + ( i < 0 ? len : 0 ); return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); }, end: function() { return this.prevObject || this.constructor(); }, // For internal use only. // Behaves like an Array's method, not like a jQuery method. push: push, sort: arr.sort, splice: arr.splice }; jQuery.extend = jQuery.fn.extend = function() { var options, name, src, copy, copyIsArray, clone, target = arguments[ 0 ] || {}, i = 1, length = arguments.length, deep = false; // Handle a deep copy situation if ( typeof target === "boolean" ) { deep = target; // Skip the boolean and the target target = arguments[ i ] || {}; i++; } // Handle case when target is a string or something (possible in deep copy) if ( typeof target !== "object" && !isFunction( target ) ) { target = {}; } // Extend jQuery itself if only one argument is passed if ( i === length ) { target = this; i--; } for ( ; i < length; i++ ) { // Only deal with non-null/undefined values if ( ( options = arguments[ i ] ) != null ) { // Extend the base object for ( name in options ) { copy = options[ name ]; // Prevent Object.prototype pollution // Prevent never-ending loop if ( name === "__proto__" || target === copy ) { continue; } // Recurse if we're merging plain objects or arrays if ( deep && copy && ( jQuery.isPlainObject( copy ) || ( copyIsArray = Array.isArray( copy ) ) ) ) { src = target[ name ]; // Ensure proper type for the source value if ( copyIsArray && !Array.isArray( src ) ) { clone = []; } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { clone = {}; } else { clone = src; } copyIsArray = false; // Never move original objects, clone them target[ name ] = jQuery.extend( deep, clone, copy ); // Don't bring in undefined values } else if ( copy !== undefined ) { target[ name ] = copy; } } } } // Return the modified object return target; }; jQuery.extend( { // Unique for each copy of jQuery on the page expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), // Assume jQuery is ready without the ready module isReady: true, error: function( msg ) { throw new Error( msg ); }, noop: function() {}, isPlainObject: function( obj ) { var proto, Ctor; // Detect obvious negatives // Use toString instead of jQuery.type to catch host objects if ( !obj || toString.call( obj ) !== "[object Object]" ) { return false; } proto = getProto( obj ); // Objects with no prototype (e.g., `Object.create( null )`) are plain if ( !proto ) { return true; } // Objects with prototype are plain iff they were constructed by a global Object function Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; }, isEmptyObject: function( obj ) { var name; for ( name in obj ) { return false; } return true; }, // Evaluates a script in a provided context; falls back to the global one // if not specified. globalEval: function( code, options, doc ) { DOMEval( code, { nonce: options && options.nonce }, doc ); }, each: function( obj, callback ) { var length, i = 0; if ( isArrayLike( obj ) ) { length = obj.length; for ( ; i < length; i++ ) { if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { break; } } } else { for ( i in obj ) { if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { break; } } } return obj; }, // results is for internal usage only makeArray: function( arr, results ) { var ret = results || []; if ( arr != null ) { if ( isArrayLike( Object( arr ) ) ) { jQuery.merge( ret, typeof arr === "string" ? [ arr ] : arr ); } else { push.call( ret, arr ); } } return ret; }, inArray: function( elem, arr, i ) { return arr == null ? -1 : indexOf.call( arr, elem, i ); }, // Support: Android <=4.0 only, PhantomJS 1 only // push.apply(_, arraylike) throws on ancient WebKit merge: function( first, second ) { var len = +second.length, j = 0, i = first.length; for ( ; j < len; j++ ) { first[ i++ ] = second[ j ]; } first.length = i; return first; }, grep: function( elems, callback, invert ) { var callbackInverse, matches = [], i = 0, length = elems.length, callbackExpect = !invert; // Go through the array, only saving the items // that pass the validator function for ( ; i < length; i++ ) { callbackInverse = !callback( elems[ i ], i ); if ( callbackInverse !== callbackExpect ) { matches.push( elems[ i ] ); } } return matches; }, // arg is for internal usage only map: function( elems, callback, arg ) { var length, value, i = 0, ret = []; // Go through the array, translating each of the items to their new values if ( isArrayLike( elems ) ) { length = elems.length; for ( ; i < length; i++ ) { value = callback( elems[ i ], i, arg ); if ( value != null ) { ret.push( value ); } } // Go through every key on the object, } else { for ( i in elems ) { value = callback( elems[ i ], i, arg ); if ( value != null ) { ret.push( value ); } } } // Flatten any nested arrays return flat( ret ); }, // A global GUID counter for objects guid: 1, // jQuery.support is not used in Core but other projects attach their // properties to it so it needs to exist. support: support } ); if ( typeof Symbol === "function" ) { jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; } // Populate the class2type map jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), function( _i, name ) { class2type[ "[object " + name + "]" ] = name.toLowerCase(); } ); function isArrayLike( obj ) { // Support: real iOS 8.2 only (not reproducible in simulator) // `in` check used to prevent JIT error (gh-2145) // hasOwn isn't used here due to false negatives // regarding Nodelist length in IE var length = !!obj && "length" in obj && obj.length, type = toType( obj ); if ( isFunction( obj ) || isWindow( obj ) ) { return false; } return type === "array" || length === 0 || typeof length === "number" && length > 0 && ( length - 1 ) in obj; } var Sizzle = /*! * Sizzle CSS Selector Engine v2.3.5 * https://sizzlejs.com/ * * Copyright JS Foundation and other contributors * Released under the MIT license * https://js.foundation/ * * Date: 2020-03-14 */ ( function( window ) { var i, support, Expr, getText, isXML, tokenize, compile, select, outermostContext, sortInput, hasDuplicate, // Local document vars setDocument, document, docElem, documentIsHTML, rbuggyQSA, rbuggyMatches, matches, contains, // Instance-specific data expando = "sizzle" + 1 * new Date(), preferredDoc = window.document, dirruns = 0, done = 0, classCache = createCache(), tokenCache = createCache(), compilerCache = createCache(), nonnativeSelectorCache = createCache(), sortOrder = function( a, b ) { if ( a === b ) { hasDuplicate = true; } return 0; }, // Instance methods hasOwn = ( {} ).hasOwnProperty, arr = [], pop = arr.pop, pushNative = arr.push, push = arr.push, slice = arr.slice, // Use a stripped-down indexOf as it's faster than native // https://jsperf.com/thor-indexof-vs-for/5 indexOf = function( list, elem ) { var i = 0, len = list.length; for ( ; i < len; i++ ) { if ( list[ i ] === elem ) { return i; } } return -1; }, booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + "ismap|loop|multiple|open|readonly|required|scoped", // Regular expressions // http://www.w3.org/TR/css3-selectors/#whitespace whitespace = "[\\x20\\t\\r\\n\\f]", // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + // Operator (capture 2) "*([*^$|!~]?=)" + whitespace + // "Attribute values must be CSS identifiers [capture 5] // or strings [capture 3 or capture 4]" "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace + "*\\]", pseudos = ":(" + identifier + ")(?:\\((" + // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: // 1. quoted (capture 3; capture 4 or capture 5) "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + // 2. simple (capture 6) "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + // 3. anything else (capture 2) ".*" + ")\\)|)", // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter rwhitespace = new RegExp( whitespace + "+", "g" ), rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ), rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ), rdescend = new RegExp( whitespace + "|>" ), rpseudo = new RegExp( pseudos ), ridentifier = new RegExp( "^" + identifier + "$" ), matchExpr = { "ID": new RegExp( "^#(" + identifier + ")" ), "CLASS": new RegExp( "^\\.(" + identifier + ")" ), "TAG": new RegExp( "^(" + identifier + "|[*])" ), "ATTR": new RegExp( "^" + attributes ), "PSEUDO": new RegExp( "^" + pseudos ), "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), // For use in libraries implementing .is() // We use this for POS matching in `select` "needsContext": new RegExp( "^" + whitespace + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) }, rhtml = /HTML$/i, rinputs = /^(?:input|select|textarea|button)$/i, rheader = /^h\d$/i, rnative = /^[^{]+\{\s*\[native \w/, // Easily-parseable/retrievable ID or TAG or CLASS selectors rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, rsibling = /[+~]/, // CSS escapes // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), funescape = function( escape, nonHex ) { var high = "0x" + escape.slice( 1 ) - 0x10000; return nonHex ? // Strip the backslash prefix from a non-hex escape sequence nonHex : // Replace a hexadecimal escape sequence with the encoded Unicode code point // Support: IE <=11+ // For values outside the Basic Multilingual Plane (BMP), manually construct a // surrogate pair high < 0 ? String.fromCharCode( high + 0x10000 ) : String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); }, // CSS string/identifier serialization // https://drafts.csswg.org/cssom/#common-serializing-idioms rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, fcssescape = function( ch, asCodePoint ) { if ( asCodePoint ) { // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER if ( ch === "\0" ) { return "\uFFFD"; } // Control characters and (dependent upon position) numbers get escaped as code points return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; } // Other potentially-special ASCII characters get backslash-escaped return "\\" + ch; }, // Used for iframes // See setDocument() // Removing the function wrapper causes a "Permission Denied" // error in IE unloadHandler = function() { setDocument(); }, inDisabledFieldset = addCombinator( function( elem ) { return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; }, { dir: "parentNode", next: "legend" } ); // Optimize for push.apply( _, NodeList ) try { push.apply( ( arr = slice.call( preferredDoc.childNodes ) ), preferredDoc.childNodes ); // Support: Android<4.0 // Detect silently failing push.apply // eslint-disable-next-line no-unused-expressions arr[ preferredDoc.childNodes.length ].nodeType; } catch ( e ) { push = { apply: arr.length ? // Leverage slice if possible function( target, els ) { pushNative.apply( target, slice.call( els ) ); } : // Support: IE<9 // Otherwise append directly function( target, els ) { var j = target.length, i = 0; // Can't trust NodeList.length while ( ( target[ j++ ] = els[ i++ ] ) ) {} target.length = j - 1; } }; } function Sizzle( selector, context, results, seed ) { var m, i, elem, nid, match, groups, newSelector, newContext = context && context.ownerDocument, // nodeType defaults to 9, since context defaults to document nodeType = context ? context.nodeType : 9; results = results || []; // Return early from calls with invalid selector or context if ( typeof selector !== "string" || !selector || nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { return results; } // Try to shortcut find operations (as opposed to filters) in HTML documents if ( !seed ) { setDocument( context ); context = context || document; if ( documentIsHTML ) { // If the selector is sufficiently simple, try using a "get*By*" DOM method // (excepting DocumentFragment context, where the methods don't exist) if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { // ID selector if ( ( m = match[ 1 ] ) ) { // Document context if ( nodeType === 9 ) { if ( ( elem = context.getElementById( m ) ) ) { // Support: IE, Opera, Webkit // TODO: identify versions // getElementById can match elements by name instead of ID if ( elem.id === m ) { results.push( elem ); return results; } } else { return results; } // Element context } else { // Support: IE, Opera, Webkit // TODO: identify versions // getElementById can match elements by name instead of ID if ( newContext && ( elem = newContext.getElementById( m ) ) && contains( context, elem ) && elem.id === m ) { results.push( elem ); return results; } } // Type selector } else if ( match[ 2 ] ) { push.apply( results, context.getElementsByTagName( selector ) ); return results; // Class selector } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && context.getElementsByClassName ) { push.apply( results, context.getElementsByClassName( m ) ); return results; } } // Take advantage of querySelectorAll if ( support.qsa && !nonnativeSelectorCache[ selector + " " ] && ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && // Support: IE 8 only // Exclude object elements ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { newSelector = selector; newContext = context; // qSA considers elements outside a scoping root when evaluating child or // descendant combinators, which is not what we want. // In such cases, we work around the behavior by prefixing every selector in the // list with an ID selector referencing the scope context. // The technique has to be used as well when a leading combinator is used // as such selectors are not recognized by querySelectorAll. // Thanks to Andrew Dupont for this technique. if ( nodeType === 1 && ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { // Expand context for sibling selectors newContext = rsibling.test( selector ) && testContext( context.parentNode ) || context; // We can use :scope instead of the ID hack if the browser // supports it & if we're not changing the context. if ( newContext !== context || !support.scope ) { // Capture the context ID, setting it first if necessary if ( ( nid = context.getAttribute( "id" ) ) ) { nid = nid.replace( rcssescape, fcssescape ); } else { context.setAttribute( "id", ( nid = expando ) ); } } // Prefix every selector in the list groups = tokenize( selector ); i = groups.length; while ( i-- ) { groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + toSelector( groups[ i ] ); } newSelector = groups.join( "," ); } try { push.apply( results, newContext.querySelectorAll( newSelector ) ); return results; } catch ( qsaError ) { nonnativeSelectorCache( selector, true ); } finally { if ( nid === expando ) { context.removeAttribute( "id" ); } } } } } // All others return select( selector.replace( rtrim, "$1" ), context, results, seed ); } /** * Create key-value caches of limited size * @returns {function(string, object)} Returns the Object data after storing it on itself with * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) * deleting the oldest entry */ function createCache() { var keys = []; function cache( key, value ) { // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) if ( keys.push( key + " " ) > Expr.cacheLength ) { // Only keep the most recent entries delete cache[ keys.shift() ]; } return ( cache[ key + " " ] = value ); } return cache; } /** * Mark a function for special use by Sizzle * @param {Function} fn The function to mark */ function markFunction( fn ) { fn[ expando ] = true; return fn; } /** * Support testing using an element * @param {Function} fn Passed the created element and returns a boolean result */ function assert( fn ) { var el = document.createElement( "fieldset" ); try { return !!fn( el ); } catch ( e ) { return false; } finally { // Remove from its parent by default if ( el.parentNode ) { el.parentNode.removeChild( el ); } // release memory in IE el = null; } } /** * Adds the same handler for all of the specified attrs * @param {String} attrs Pipe-separated list of attributes * @param {Function} handler The method that will be applied */ function addHandle( attrs, handler ) { var arr = attrs.split( "|" ), i = arr.length; while ( i-- ) { Expr.attrHandle[ arr[ i ] ] = handler; } } /** * Checks document order of two siblings * @param {Element} a * @param {Element} b * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b */ function siblingCheck( a, b ) { var cur = b && a, diff = cur && a.nodeType === 1 && b.nodeType === 1 && a.sourceIndex - b.sourceIndex; // Use IE sourceIndex if available on both nodes if ( diff ) { return diff; } // Check if b follows a if ( cur ) { while ( ( cur = cur.nextSibling ) ) { if ( cur === b ) { return -1; } } } return a ? 1 : -1; } /** * Returns a function to use in pseudos for input types * @param {String} type */ function createInputPseudo( type ) { return function( elem ) { var name = elem.nodeName.toLowerCase(); return name === "input" && elem.type === type; }; } /** * Returns a function to use in pseudos for buttons * @param {String} type */ function createButtonPseudo( type ) { return function( elem ) { var name = elem.nodeName.toLowerCase(); return ( name === "input" || name === "button" ) && elem.type === type; }; } /** * Returns a function to use in pseudos for :enabled/:disabled * @param {Boolean} disabled true for :disabled; false for :enabled */ function createDisabledPseudo( disabled ) { // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable return function( elem ) { // Only certain elements can match :enabled or :disabled // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled if ( "form" in elem ) { // Check for inherited disabledness on relevant non-disabled elements: // * listed form-associated elements in a disabled fieldset // https://html.spec.whatwg.org/multipage/forms.html#category-listed // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled // * option elements in a disabled optgroup // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled // All such elements have a "form" property. if ( elem.parentNode && elem.disabled === false ) { // Option elements defer to a parent optgroup if present if ( "label" in elem ) { if ( "label" in elem.parentNode ) { return elem.parentNode.disabled === disabled; } else { return elem.disabled === disabled; } } // Support: IE 6 - 11 // Use the isDisabled shortcut property to check for disabled fieldset ancestors return elem.isDisabled === disabled || // Where there is no isDisabled, check manually /* jshint -W018 */ elem.isDisabled !== !disabled && inDisabledFieldset( elem ) === disabled; } return elem.disabled === disabled; // Try to winnow out elements that can't be disabled before trusting the disabled property. // Some victims get caught in our net (label, legend, menu, track), but it shouldn't // even exist on them, let alone have a boolean value. } else if ( "label" in elem ) { return elem.disabled === disabled; } // Remaining elements are neither :enabled nor :disabled return false; }; } /** * Returns a function to use in pseudos for positionals * @param {Function} fn */ function createPositionalPseudo( fn ) { return markFunction( function( argument ) { argument = +argument; return markFunction( function( seed, matches ) { var j, matchIndexes = fn( [], seed.length, argument ), i = matchIndexes.length; // Match elements found at the specified indexes while ( i-- ) { if ( seed[ ( j = matchIndexes[ i ] ) ] ) { seed[ j ] = !( matches[ j ] = seed[ j ] ); } } } ); } ); } /** * Checks a node for validity as a Sizzle context * @param {Element|Object=} context * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value */ function testContext( context ) { return context && typeof context.getElementsByTagName !== "undefined" && context; } // Expose support vars for convenience support = Sizzle.support = {}; /** * Detects XML nodes * @param {Element|Object} elem An element or a document * @returns {Boolean} True iff elem is a non-HTML XML node */ isXML = Sizzle.isXML = function( elem ) { var namespace = elem.namespaceURI, docElem = ( elem.ownerDocument || elem ).documentElement; // Support: IE <=8 // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes // https://bugs.jquery.com/ticket/4833 return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); }; /** * Sets document-related variables once based on the current document * @param {Element|Object} [doc] An element or document object to use to set the document * @returns {Object} Returns the current document */ setDocument = Sizzle.setDocument = function( node ) { var hasCompare, subWindow, doc = node ? node.ownerDocument || node : preferredDoc; // Return early if doc is invalid or already selected // Support: IE 11+, Edge 17 - 18+ // IE/Edge sometimes throw a "Permission denied" error when strict-comparing // two documents; shallow comparisons work. // eslint-disable-next-line eqeqeq if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { return document; } // Update global variables document = doc; docElem = document.documentElement; documentIsHTML = !isXML( document ); // Support: IE 9 - 11+, Edge 12 - 18+ // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) // Support: IE 11+, Edge 17 - 18+ // IE/Edge sometimes throw a "Permission denied" error when strict-comparing // two documents; shallow comparisons work. // eslint-disable-next-line eqeqeq if ( preferredDoc != document && ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { // Support: IE 11, Edge if ( subWindow.addEventListener ) { subWindow.addEventListener( "unload", unloadHandler, false ); // Support: IE 9 - 10 only } else if ( subWindow.attachEvent ) { subWindow.attachEvent( "onunload", unloadHandler ); } } // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, // Safari 4 - 5 only, Opera <=11.6 - 12.x only // IE/Edge & older browsers don't support the :scope pseudo-class. // Support: Safari 6.0 only // Safari 6.0 supports :scope but it's an alias of :root there. support.scope = assert( function( el ) { docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); return typeof el.querySelectorAll !== "undefined" && !el.querySelectorAll( ":scope fieldset div" ).length; } ); /* Attributes ---------------------------------------------------------------------- */ // Support: IE<8 // Verify that getAttribute really returns attributes and not properties // (excepting IE8 booleans) support.attributes = assert( function( el ) { el.className = "i"; return !el.getAttribute( "className" ); } ); /* getElement(s)By* ---------------------------------------------------------------------- */ // Check if getElementsByTagName("*") returns only elements support.getElementsByTagName = assert( function( el ) { el.appendChild( document.createComment( "" ) ); return !el.getElementsByTagName( "*" ).length; } ); // Support: IE<9 support.getElementsByClassName = rnative.test( document.getElementsByClassName ); // Support: IE<10 // Check if getElementById returns elements by name // The broken getElementById methods don't pick up programmatically-set names, // so use a roundabout getElementsByName test support.getById = assert( function( el ) { docElem.appendChild( el ).id = expando; return !document.getElementsByName || !document.getElementsByName( expando ).length; } ); // ID filter and find if ( support.getById ) { Expr.filter[ "ID" ] = function( id ) { var attrId = id.replace( runescape, funescape ); return function( elem ) { return elem.getAttribute( "id" ) === attrId; }; }; Expr.find[ "ID" ] = function( id, context ) { if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { var elem = context.getElementById( id ); return elem ? [ elem ] : []; } }; } else { Expr.filter[ "ID" ] = function( id ) { var attrId = id.replace( runescape, funescape ); return function( elem ) { var node = typeof elem.getAttributeNode !== "undefined" && elem.getAttributeNode( "id" ); return node && node.value === attrId; }; }; // Support: IE 6 - 7 only // getElementById is not reliable as a find shortcut Expr.find[ "ID" ] = function( id, context ) { if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { var node, i, elems, elem = context.getElementById( id ); if ( elem ) { // Verify the id attribute node = elem.getAttributeNode( "id" ); if ( node && node.value === id ) { return [ elem ]; } // Fall back on getElementsByName elems = context.getElementsByName( id ); i = 0; while ( ( elem = elems[ i++ ] ) ) { node = elem.getAttributeNode( "id" ); if ( node && node.value === id ) { return [ elem ]; } } } return []; } }; } // Tag Expr.find[ "TAG" ] = support.getElementsByTagName ? function( tag, context ) { if ( typeof context.getElementsByTagName !== "undefined" ) { return context.getElementsByTagName( tag ); // DocumentFragment nodes don't have gEBTN } else if ( support.qsa ) { return context.querySelectorAll( tag ); } } : function( tag, context ) { var elem, tmp = [], i = 0, // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too results = context.getElementsByTagName( tag ); // Filter out possible comments if ( tag === "*" ) { while ( ( elem = results[ i++ ] ) ) { if ( elem.nodeType === 1 ) { tmp.push( elem ); } } return tmp; } return results; }; // Class Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { return context.getElementsByClassName( className ); } }; /* QSA/matchesSelector ---------------------------------------------------------------------- */ // QSA and matchesSelector support // matchesSelector(:active) reports false when true (IE9/Opera 11.5) rbuggyMatches = []; // qSa(:focus) reports false when true (Chrome 21) // We allow this because of a bug in IE8/9 that throws an error // whenever `document.activeElement` is accessed on an iframe // So, we allow :focus to pass through QSA all the time to avoid the IE error // See https://bugs.jquery.com/ticket/13378 rbuggyQSA = []; if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { // Build QSA regex // Regex strategy adopted from Diego Perini assert( function( el ) { var input; // Select is set to empty string on purpose // This is to test IE's treatment of not explicitly // setting a boolean content attribute, // since its presence should be enough // https://bugs.jquery.com/ticket/12359 docElem.appendChild( el ).innerHTML = "" + ""; // Support: IE8, Opera 11-12.16 // Nothing should be selected when empty strings follow ^= or $= or *= // The test attribute must be unknown in Opera but "safe" for WinRT // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); } // Support: IE8 // Boolean attributes and "value" are not treated correctly if ( !el.querySelectorAll( "[selected]" ).length ) { rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); } // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { rbuggyQSA.push( "~=" ); } // Support: IE 11+, Edge 15 - 18+ // IE 11/Edge don't find elements on a `[name='']` query in some cases. // Adding a temporary attribute to the document before the selection works // around the issue. // Interestingly, IE 10 & older don't seem to have the issue. input = document.createElement( "input" ); input.setAttribute( "name", "" ); el.appendChild( input ); if ( !el.querySelectorAll( "[name='']" ).length ) { rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + whitespace + "*(?:''|\"\")" ); } // Webkit/Opera - :checked should return selected option elements // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked // IE8 throws error here and will not see later tests if ( !el.querySelectorAll( ":checked" ).length ) { rbuggyQSA.push( ":checked" ); } // Support: Safari 8+, iOS 8+ // https://bugs.webkit.org/show_bug.cgi?id=136851 // In-page `selector#id sibling-combinator selector` fails if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { rbuggyQSA.push( ".#.+[+~]" ); } // Support: Firefox <=3.6 - 5 only // Old Firefox doesn't throw on a badly-escaped identifier. el.querySelectorAll( "\\\f" ); rbuggyQSA.push( "[\\r\\n\\f]" ); } ); assert( function( el ) { el.innerHTML = "" + ""; // Support: Windows 8 Native Apps // The type and name attributes are restricted during .innerHTML assignment var input = document.createElement( "input" ); input.setAttribute( "type", "hidden" ); el.appendChild( input ).setAttribute( "name", "D" ); // Support: IE8 // Enforce case-sensitivity of name attribute if ( el.querySelectorAll( "[name=d]" ).length ) { rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); } // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) // IE8 throws error here and will not see later tests if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { rbuggyQSA.push( ":enabled", ":disabled" ); } // Support: IE9-11+ // IE's :disabled selector does not pick up the children of disabled fieldsets docElem.appendChild( el ).disabled = true; if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { rbuggyQSA.push( ":enabled", ":disabled" ); } // Support: Opera 10 - 11 only // Opera 10-11 does not throw on post-comma invalid pseudos el.querySelectorAll( "*,:x" ); rbuggyQSA.push( ",.*:" ); } ); } if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || docElem.webkitMatchesSelector || docElem.mozMatchesSelector || docElem.oMatchesSelector || docElem.msMatchesSelector ) ) ) ) { assert( function( el ) { // Check to see if it's possible to do matchesSelector // on a disconnected node (IE 9) support.disconnectedMatch = matches.call( el, "*" ); // This should fail with an exception // Gecko does not error, returns false instead matches.call( el, "[s!='']:x" ); rbuggyMatches.push( "!=", pseudos ); } ); } rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); /* Contains ---------------------------------------------------------------------- */ hasCompare = rnative.test( docElem.compareDocumentPosition ); // Element contains another // Purposefully self-exclusive // As in, an element does not contain itself contains = hasCompare || rnative.test( docElem.contains ) ? function( a, b ) { var adown = a.nodeType === 9 ? a.documentElement : a, bup = b && b.parentNode; return a === bup || !!( bup && bup.nodeType === 1 && ( adown.contains ? adown.contains( bup ) : a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 ) ); } : function( a, b ) { if ( b ) { while ( ( b = b.parentNode ) ) { if ( b === a ) { return true; } } } return false; }; /* Sorting ---------------------------------------------------------------------- */ // Document order sorting sortOrder = hasCompare ? function( a, b ) { // Flag for duplicate removal if ( a === b ) { hasDuplicate = true; return 0; } // Sort on method existence if only one input has compareDocumentPosition var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; if ( compare ) { return compare; } // Calculate position if both inputs belong to the same document // Support: IE 11+, Edge 17 - 18+ // IE/Edge sometimes throw a "Permission denied" error when strict-comparing // two documents; shallow comparisons work. // eslint-disable-next-line eqeqeq compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? a.compareDocumentPosition( b ) : // Otherwise we know they are disconnected 1; // Disconnected nodes if ( compare & 1 || ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { // Choose the first element that is related to our preferred document // Support: IE 11+, Edge 17 - 18+ // IE/Edge sometimes throw a "Permission denied" error when strict-comparing // two documents; shallow comparisons work. // eslint-disable-next-line eqeqeq if ( a == document || a.ownerDocument == preferredDoc && contains( preferredDoc, a ) ) { return -1; } // Support: IE 11+, Edge 17 - 18+ // IE/Edge sometimes throw a "Permission denied" error when strict-comparing // two documents; shallow comparisons work. // eslint-disable-next-line eqeqeq if ( b == document || b.ownerDocument == preferredDoc && contains( preferredDoc, b ) ) { return 1; } // Maintain original order return sortInput ? ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : 0; } return compare & 4 ? -1 : 1; } : function( a, b ) { // Exit early if the nodes are identical if ( a === b ) { hasDuplicate = true; return 0; } var cur, i = 0, aup = a.parentNode, bup = b.parentNode, ap = [ a ], bp = [ b ]; // Parentless nodes are either documents or disconnected if ( !aup || !bup ) { // Support: IE 11+, Edge 17 - 18+ // IE/Edge sometimes throw a "Permission denied" error when strict-comparing // two documents; shallow comparisons work. /* eslint-disable eqeqeq */ return a == document ? -1 : b == document ? 1 : /* eslint-enable eqeqeq */ aup ? -1 : bup ? 1 : sortInput ? ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : 0; // If the nodes are siblings, we can do a quick check } else if ( aup === bup ) { return siblingCheck( a, b ); } // Otherwise we need full lists of their ancestors for comparison cur = a; while ( ( cur = cur.parentNode ) ) { ap.unshift( cur ); } cur = b; while ( ( cur = cur.parentNode ) ) { bp.unshift( cur ); } // Walk down the tree looking for a discrepancy while ( ap[ i ] === bp[ i ] ) { i++; } return i ? // Do a sibling check if the nodes have a common ancestor siblingCheck( ap[ i ], bp[ i ] ) : // Otherwise nodes in our document sort first // Support: IE 11+, Edge 17 - 18+ // IE/Edge sometimes throw a "Permission denied" error when strict-comparing // two documents; shallow comparisons work. /* eslint-disable eqeqeq */ ap[ i ] == preferredDoc ? -1 : bp[ i ] == preferredDoc ? 1 : /* eslint-enable eqeqeq */ 0; }; return document; }; Sizzle.matches = function( expr, elements ) { return Sizzle( expr, null, null, elements ); }; Sizzle.matchesSelector = function( elem, expr ) { setDocument( elem ); if ( support.matchesSelector && documentIsHTML && !nonnativeSelectorCache[ expr + " " ] && ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { try { var ret = matches.call( elem, expr ); // IE 9's matchesSelector returns false on disconnected nodes if ( ret || support.disconnectedMatch || // As well, disconnected nodes are said to be in a document // fragment in IE 9 elem.document && elem.document.nodeType !== 11 ) { return ret; } } catch ( e ) { nonnativeSelectorCache( expr, true ); } } return Sizzle( expr, document, null, [ elem ] ).length > 0; }; Sizzle.contains = function( context, elem ) { // Set document vars if needed // Support: IE 11+, Edge 17 - 18+ // IE/Edge sometimes throw a "Permission denied" error when strict-comparing // two documents; shallow comparisons work. // eslint-disable-next-line eqeqeq if ( ( context.ownerDocument || context ) != document ) { setDocument( context ); } return contains( context, elem ); }; Sizzle.attr = function( elem, name ) { // Set document vars if needed // Support: IE 11+, Edge 17 - 18+ // IE/Edge sometimes throw a "Permission denied" error when strict-comparing // two documents; shallow comparisons work. // eslint-disable-next-line eqeqeq if ( ( elem.ownerDocument || elem ) != document ) { setDocument( elem ); } var fn = Expr.attrHandle[ name.toLowerCase() ], // Don't get fooled by Object.prototype properties (jQuery #13807) val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? fn( elem, name, !documentIsHTML ) : undefined; return val !== undefined ? val : support.attributes || !documentIsHTML ? elem.getAttribute( name ) : ( val = elem.getAttributeNode( name ) ) && val.specified ? val.value : null; }; Sizzle.escape = function( sel ) { return ( sel + "" ).replace( rcssescape, fcssescape ); }; Sizzle.error = function( msg ) { throw new Error( "Syntax error, unrecognized expression: " + msg ); }; /** * Document sorting and removing duplicates * @param {ArrayLike} results */ Sizzle.uniqueSort = function( results ) { var elem, duplicates = [], j = 0, i = 0; // Unless we *know* we can detect duplicates, assume their presence hasDuplicate = !support.detectDuplicates; sortInput = !support.sortStable && results.slice( 0 ); results.sort( sortOrder ); if ( hasDuplicate ) { while ( ( elem = results[ i++ ] ) ) { if ( elem === results[ i ] ) { j = duplicates.push( i ); } } while ( j-- ) { results.splice( duplicates[ j ], 1 ); } } // Clear input after sorting to release objects // See https://github.com/jquery/sizzle/pull/225 sortInput = null; return results; }; /** * Utility function for retrieving the text value of an array of DOM nodes * @param {Array|Element} elem */ getText = Sizzle.getText = function( elem ) { var node, ret = "", i = 0, nodeType = elem.nodeType; if ( !nodeType ) { // If no nodeType, this is expected to be an array while ( ( node = elem[ i++ ] ) ) { // Do not traverse comment nodes ret += getText( node ); } } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { // Use textContent for elements // innerText usage removed for consistency of new lines (jQuery #11153) if ( typeof elem.textContent === "string" ) { return elem.textContent; } else { // Traverse its children for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { ret += getText( elem ); } } } else if ( nodeType === 3 || nodeType === 4 ) { return elem.nodeValue; } // Do not include comment or processing instruction nodes return ret; }; Expr = Sizzle.selectors = { // Can be adjusted by the user cacheLength: 50, createPseudo: markFunction, match: matchExpr, attrHandle: {}, find: {}, relative: { ">": { dir: "parentNode", first: true }, " ": { dir: "parentNode" }, "+": { dir: "previousSibling", first: true }, "~": { dir: "previousSibling" } }, preFilter: { "ATTR": function( match ) { match[ 1 ] = match[ 1 ].replace( runescape, funescape ); // Move the given value to match[3] whether quoted or unquoted match[ 3 ] = ( match[ 3 ] || match[ 4 ] || match[ 5 ] || "" ).replace( runescape, funescape ); if ( match[ 2 ] === "~=" ) { match[ 3 ] = " " + match[ 3 ] + " "; } return match.slice( 0, 4 ); }, "CHILD": function( match ) { /* matches from matchExpr["CHILD"] 1 type (only|nth|...) 2 what (child|of-type) 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) 4 xn-component of xn+y argument ([+-]?\d*n|) 5 sign of xn-component 6 x of xn-component 7 sign of y-component 8 y of y-component */ match[ 1 ] = match[ 1 ].toLowerCase(); if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { // nth-* requires argument if ( !match[ 3 ] ) { Sizzle.error( match[ 0 ] ); } // numeric x and y parameters for Expr.filter.CHILD // remember that false/true cast respectively to 0/1 match[ 4 ] = +( match[ 4 ] ? match[ 5 ] + ( match[ 6 ] || 1 ) : 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); // other types prohibit arguments } else if ( match[ 3 ] ) { Sizzle.error( match[ 0 ] ); } return match; }, "PSEUDO": function( match ) { var excess, unquoted = !match[ 6 ] && match[ 2 ]; if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { return null; } // Accept quoted arguments as-is if ( match[ 3 ] ) { match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; // Strip excess characters from unquoted arguments } else if ( unquoted && rpseudo.test( unquoted ) && // Get excess from tokenize (recursively) ( excess = tokenize( unquoted, true ) ) && // advance to the next closing parenthesis ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { // excess is a negative index match[ 0 ] = match[ 0 ].slice( 0, excess ); match[ 2 ] = unquoted.slice( 0, excess ); } // Return only captures needed by the pseudo filter method (type and argument) return match.slice( 0, 3 ); } }, filter: { "TAG": function( nodeNameSelector ) { var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); return nodeNameSelector === "*" ? function() { return true; } : function( elem ) { return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; }; }, "CLASS": function( className ) { var pattern = classCache[ className + " " ]; return pattern || ( pattern = new RegExp( "(^|" + whitespace + ")" + className + "(" + whitespace + "|$)" ) ) && classCache( className, function( elem ) { return pattern.test( typeof elem.className === "string" && elem.className || typeof elem.getAttribute !== "undefined" && elem.getAttribute( "class" ) || "" ); } ); }, "ATTR": function( name, operator, check ) { return function( elem ) { var result = Sizzle.attr( elem, name ); if ( result == null ) { return operator === "!="; } if ( !operator ) { return true; } result += ""; /* eslint-disable max-len */ return operator === "=" ? result === check : operator === "!=" ? result !== check : operator === "^=" ? check && result.indexOf( check ) === 0 : operator === "*=" ? check && result.indexOf( check ) > -1 : operator === "$=" ? check && result.slice( -check.length ) === check : operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : false; /* eslint-enable max-len */ }; }, "CHILD": function( type, what, _argument, first, last ) { var simple = type.slice( 0, 3 ) !== "nth", forward = type.slice( -4 ) !== "last", ofType = what === "of-type"; return first === 1 && last === 0 ? // Shortcut for :nth-*(n) function( elem ) { return !!elem.parentNode; } : function( elem, _context, xml ) { var cache, uniqueCache, outerCache, node, nodeIndex, start, dir = simple !== forward ? "nextSibling" : "previousSibling", parent = elem.parentNode, name = ofType && elem.nodeName.toLowerCase(), useCache = !xml && !ofType, diff = false; if ( parent ) { // :(first|last|only)-(child|of-type) if ( simple ) { while ( dir ) { node = elem; while ( ( node = node[ dir ] ) ) { if ( ofType ? node.nodeName.toLowerCase() === name : node.nodeType === 1 ) { return false; } } // Reverse direction for :only-* (if we haven't yet done so) start = dir = type === "only" && !start && "nextSibling"; } return true; } start = [ forward ? parent.firstChild : parent.lastChild ]; // non-xml :nth-child(...) stores cache data on `parent` if ( forward && useCache ) { // Seek `elem` from a previously-cached index // ...in a gzip-friendly way node = parent; outerCache = node[ expando ] || ( node[ expando ] = {} ); // Support: IE <9 only // Defend against cloned attroperties (jQuery gh-1709) uniqueCache = outerCache[ node.uniqueID ] || ( outerCache[ node.uniqueID ] = {} ); cache = uniqueCache[ type ] || []; nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; diff = nodeIndex && cache[ 2 ]; node = nodeIndex && parent.childNodes[ nodeIndex ]; while ( ( node = ++nodeIndex && node && node[ dir ] || // Fallback to seeking `elem` from the start ( diff = nodeIndex = 0 ) || start.pop() ) ) { // When found, cache indexes on `parent` and break if ( node.nodeType === 1 && ++diff && node === elem ) { uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; break; } } } else { // Use previously-cached element index if available if ( useCache ) { // ...in a gzip-friendly way node = elem; outerCache = node[ expando ] || ( node[ expando ] = {} ); // Support: IE <9 only // Defend against cloned attroperties (jQuery gh-1709) uniqueCache = outerCache[ node.uniqueID ] || ( outerCache[ node.uniqueID ] = {} ); cache = uniqueCache[ type ] || []; nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; diff = nodeIndex; } // xml :nth-child(...) // or :nth-last-child(...) or :nth(-last)?-of-type(...) if ( diff === false ) { // Use the same loop as above to seek `elem` from the start while ( ( node = ++nodeIndex && node && node[ dir ] || ( diff = nodeIndex = 0 ) || start.pop() ) ) { if ( ( ofType ? node.nodeName.toLowerCase() === name : node.nodeType === 1 ) && ++diff ) { // Cache the index of each encountered element if ( useCache ) { outerCache = node[ expando ] || ( node[ expando ] = {} ); // Support: IE <9 only // Defend against cloned attroperties (jQuery gh-1709) uniqueCache = outerCache[ node.uniqueID ] || ( outerCache[ node.uniqueID ] = {} ); uniqueCache[ type ] = [ dirruns, diff ]; } if ( node === elem ) { break; } } } } } // Incorporate the offset, then check against cycle size diff -= last; return diff === first || ( diff % first === 0 && diff / first >= 0 ); } }; }, "PSEUDO": function( pseudo, argument ) { // pseudo-class names are case-insensitive // http://www.w3.org/TR/selectors/#pseudo-classes // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters // Remember that setFilters inherits from pseudos var args, fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || Sizzle.error( "unsupported pseudo: " + pseudo ); // The user may use createPseudo to indicate that // arguments are needed to create the filter function // just as Sizzle does if ( fn[ expando ] ) { return fn( argument ); } // But maintain support for old signatures if ( fn.length > 1 ) { args = [ pseudo, pseudo, "", argument ]; return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? markFunction( function( seed, matches ) { var idx, matched = fn( seed, argument ), i = matched.length; while ( i-- ) { idx = indexOf( seed, matched[ i ] ); seed[ idx ] = !( matches[ idx ] = matched[ i ] ); } } ) : function( elem ) { return fn( elem, 0, args ); }; } return fn; } }, pseudos: { // Potentially complex pseudos "not": markFunction( function( selector ) { // Trim the selector passed to compile // to avoid treating leading and trailing // spaces as combinators var input = [], results = [], matcher = compile( selector.replace( rtrim, "$1" ) ); return matcher[ expando ] ? markFunction( function( seed, matches, _context, xml ) { var elem, unmatched = matcher( seed, null, xml, [] ), i = seed.length; // Match elements unmatched by `matcher` while ( i-- ) { if ( ( elem = unmatched[ i ] ) ) { seed[ i ] = !( matches[ i ] = elem ); } } } ) : function( elem, _context, xml ) { input[ 0 ] = elem; matcher( input, null, xml, results ); // Don't keep the element (issue #299) input[ 0 ] = null; return !results.pop(); }; } ), "has": markFunction( function( selector ) { return function( elem ) { return Sizzle( selector, elem ).length > 0; }; } ), "contains": markFunction( function( text ) { text = text.replace( runescape, funescape ); return function( elem ) { return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; }; } ), // "Whether an element is represented by a :lang() selector // is based solely on the element's language value // being equal to the identifier C, // or beginning with the identifier C immediately followed by "-". // The matching of C against the element's language value is performed case-insensitively. // The identifier C does not have to be a valid language name." // http://www.w3.org/TR/selectors/#lang-pseudo "lang": markFunction( function( lang ) { // lang value must be a valid identifier if ( !ridentifier.test( lang || "" ) ) { Sizzle.error( "unsupported lang: " + lang ); } lang = lang.replace( runescape, funescape ).toLowerCase(); return function( elem ) { var elemLang; do { if ( ( elemLang = documentIsHTML ? elem.lang : elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { elemLang = elemLang.toLowerCase(); return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; } } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); return false; }; } ), // Miscellaneous "target": function( elem ) { var hash = window.location && window.location.hash; return hash && hash.slice( 1 ) === elem.id; }, "root": function( elem ) { return elem === docElem; }, "focus": function( elem ) { return elem === document.activeElement && ( !document.hasFocus || document.hasFocus() ) && !!( elem.type || elem.href || ~elem.tabIndex ); }, // Boolean properties "enabled": createDisabledPseudo( false ), "disabled": createDisabledPseudo( true ), "checked": function( elem ) { // In CSS3, :checked should return both checked and selected elements // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked var nodeName = elem.nodeName.toLowerCase(); return ( nodeName === "input" && !!elem.checked ) || ( nodeName === "option" && !!elem.selected ); }, "selected": function( elem ) { // Accessing this property makes selected-by-default // options in Safari work properly if ( elem.parentNode ) { // eslint-disable-next-line no-unused-expressions elem.parentNode.selectedIndex; } return elem.selected === true; }, // Contents "empty": function( elem ) { // http://www.w3.org/TR/selectors/#empty-pseudo // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), // but not by others (comment: 8; processing instruction: 7; etc.) // nodeType < 6 works because attributes (2) do not appear as children for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { if ( elem.nodeType < 6 ) { return false; } } return true; }, "parent": function( elem ) { return !Expr.pseudos[ "empty" ]( elem ); }, // Element/input types "header": function( elem ) { return rheader.test( elem.nodeName ); }, "input": function( elem ) { return rinputs.test( elem.nodeName ); }, "button": function( elem ) { var name = elem.nodeName.toLowerCase(); return name === "input" && elem.type === "button" || name === "button"; }, "text": function( elem ) { var attr; return elem.nodeName.toLowerCase() === "input" && elem.type === "text" && // Support: IE<8 // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" ( ( attr = elem.getAttribute( "type" ) ) == null || attr.toLowerCase() === "text" ); }, // Position-in-collection "first": createPositionalPseudo( function() { return [ 0 ]; } ), "last": createPositionalPseudo( function( _matchIndexes, length ) { return [ length - 1 ]; } ), "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { return [ argument < 0 ? argument + length : argument ]; } ), "even": createPositionalPseudo( function( matchIndexes, length ) { var i = 0; for ( ; i < length; i += 2 ) { matchIndexes.push( i ); } return matchIndexes; } ), "odd": createPositionalPseudo( function( matchIndexes, length ) { var i = 1; for ( ; i < length; i += 2 ) { matchIndexes.push( i ); } return matchIndexes; } ), "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { var i = argument < 0 ? argument + length : argument > length ? length : argument; for ( ; --i >= 0; ) { matchIndexes.push( i ); } return matchIndexes; } ), "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { var i = argument < 0 ? argument + length : argument; for ( ; ++i < length; ) { matchIndexes.push( i ); } return matchIndexes; } ) } }; Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; // Add button/input type pseudos for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { Expr.pseudos[ i ] = createInputPseudo( i ); } for ( i in { submit: true, reset: true } ) { Expr.pseudos[ i ] = createButtonPseudo( i ); } // Easy API for creating new setFilters function setFilters() {} setFilters.prototype = Expr.filters = Expr.pseudos; Expr.setFilters = new setFilters(); tokenize = Sizzle.tokenize = function( selector, parseOnly ) { var matched, match, tokens, type, soFar, groups, preFilters, cached = tokenCache[ selector + " " ]; if ( cached ) { return parseOnly ? 0 : cached.slice( 0 ); } soFar = selector; groups = []; preFilters = Expr.preFilter; while ( soFar ) { // Comma and first run if ( !matched || ( match = rcomma.exec( soFar ) ) ) { if ( match ) { // Don't consume trailing commas as valid soFar = soFar.slice( match[ 0 ].length ) || soFar; } groups.push( ( tokens = [] ) ); } matched = false; // Combinators if ( ( match = rcombinators.exec( soFar ) ) ) { matched = match.shift(); tokens.push( { value: matched, // Cast descendant combinators to space type: match[ 0 ].replace( rtrim, " " ) } ); soFar = soFar.slice( matched.length ); } // Filters for ( type in Expr.filter ) { if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || ( match = preFilters[ type ]( match ) ) ) ) { matched = match.shift(); tokens.push( { value: matched, type: type, matches: match } ); soFar = soFar.slice( matched.length ); } } if ( !matched ) { break; } } // Return the length of the invalid excess // if we're just parsing // Otherwise, throw an error or return tokens return parseOnly ? soFar.length : soFar ? Sizzle.error( selector ) : // Cache the tokens tokenCache( selector, groups ).slice( 0 ); }; function toSelector( tokens ) { var i = 0, len = tokens.length, selector = ""; for ( ; i < len; i++ ) { selector += tokens[ i ].value; } return selector; } function addCombinator( matcher, combinator, base ) { var dir = combinator.dir, skip = combinator.next, key = skip || dir, checkNonElements = base && key === "parentNode", doneName = done++; return combinator.first ? // Check against closest ancestor/preceding element function( elem, context, xml ) { while ( ( elem = elem[ dir ] ) ) { if ( elem.nodeType === 1 || checkNonElements ) { return matcher( elem, context, xml ); } } return false; } : // Check against all ancestor/preceding elements function( elem, context, xml ) { var oldCache, uniqueCache, outerCache, newCache = [ dirruns, doneName ]; // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching if ( xml ) { while ( ( elem = elem[ dir ] ) ) { if ( elem.nodeType === 1 || checkNonElements ) { if ( matcher( elem, context, xml ) ) { return true; } } } } else { while ( ( elem = elem[ dir ] ) ) { if ( elem.nodeType === 1 || checkNonElements ) { outerCache = elem[ expando ] || ( elem[ expando ] = {} ); // Support: IE <9 only // Defend against cloned attroperties (jQuery gh-1709) uniqueCache = outerCache[ elem.uniqueID ] || ( outerCache[ elem.uniqueID ] = {} ); if ( skip && skip === elem.nodeName.toLowerCase() ) { elem = elem[ dir ] || elem; } else if ( ( oldCache = uniqueCache[ key ] ) && oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { // Assign to newCache so results back-propagate to previous elements return ( newCache[ 2 ] = oldCache[ 2 ] ); } else { // Reuse newcache so results back-propagate to previous elements uniqueCache[ key ] = newCache; // A match means we're done; a fail means we have to keep checking if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { return true; } } } } } return false; }; } function elementMatcher( matchers ) { return matchers.length > 1 ? function( elem, context, xml ) { var i = matchers.length; while ( i-- ) { if ( !matchers[ i ]( elem, context, xml ) ) { return false; } } return true; } : matchers[ 0 ]; } function multipleContexts( selector, contexts, results ) { var i = 0, len = contexts.length; for ( ; i < len; i++ ) { Sizzle( selector, contexts[ i ], results ); } return results; } function condense( unmatched, map, filter, context, xml ) { var elem, newUnmatched = [], i = 0, len = unmatched.length, mapped = map != null; for ( ; i < len; i++ ) { if ( ( elem = unmatched[ i ] ) ) { if ( !filter || filter( elem, context, xml ) ) { newUnmatched.push( elem ); if ( mapped ) { map.push( i ); } } } } return newUnmatched; } function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { if ( postFilter && !postFilter[ expando ] ) { postFilter = setMatcher( postFilter ); } if ( postFinder && !postFinder[ expando ] ) { postFinder = setMatcher( postFinder, postSelector ); } return markFunction( function( seed, results, context, xml ) { var temp, i, elem, preMap = [], postMap = [], preexisting = results.length, // Get initial elements from seed or context elems = seed || multipleContexts( selector || "*", context.nodeType ? [ context ] : context, [] ), // Prefilter to get matcher input, preserving a map for seed-results synchronization matcherIn = preFilter && ( seed || !selector ) ? condense( elems, preMap, preFilter, context, xml ) : elems, matcherOut = matcher ? // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, postFinder || ( seed ? preFilter : preexisting || postFilter ) ? // ...intermediate processing is necessary [] : // ...otherwise use results directly results : matcherIn; // Find primary matches if ( matcher ) { matcher( matcherIn, matcherOut, context, xml ); } // Apply postFilter if ( postFilter ) { temp = condense( matcherOut, postMap ); postFilter( temp, [], context, xml ); // Un-match failing elements by moving them back to matcherIn i = temp.length; while ( i-- ) { if ( ( elem = temp[ i ] ) ) { matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); } } } if ( seed ) { if ( postFinder || preFilter ) { if ( postFinder ) { // Get the final matcherOut by condensing this intermediate into postFinder contexts temp = []; i = matcherOut.length; while ( i-- ) { if ( ( elem = matcherOut[ i ] ) ) { // Restore matcherIn since elem is not yet a final match temp.push( ( matcherIn[ i ] = elem ) ); } } postFinder( null, ( matcherOut = [] ), temp, xml ); } // Move matched elements from seed to results to keep them synchronized i = matcherOut.length; while ( i-- ) { if ( ( elem = matcherOut[ i ] ) && ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { seed[ temp ] = !( results[ temp ] = elem ); } } } // Add elements to results, through postFinder if defined } else { matcherOut = condense( matcherOut === results ? matcherOut.splice( preexisting, matcherOut.length ) : matcherOut ); if ( postFinder ) { postFinder( null, results, matcherOut, xml ); } else { push.apply( results, matcherOut ); } } } ); } function matcherFromTokens( tokens ) { var checkContext, matcher, j, len = tokens.length, leadingRelative = Expr.relative[ tokens[ 0 ].type ], implicitRelative = leadingRelative || Expr.relative[ " " ], i = leadingRelative ? 1 : 0, // The foundational matcher ensures that elements are reachable from top-level context(s) matchContext = addCombinator( function( elem ) { return elem === checkContext; }, implicitRelative, true ), matchAnyContext = addCombinator( function( elem ) { return indexOf( checkContext, elem ) > -1; }, implicitRelative, true ), matchers = [ function( elem, context, xml ) { var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( ( checkContext = context ).nodeType ? matchContext( elem, context, xml ) : matchAnyContext( elem, context, xml ) ); // Avoid hanging onto element (issue #299) checkContext = null; return ret; } ]; for ( ; i < len; i++ ) { if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; } else { matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); // Return special upon seeing a positional matcher if ( matcher[ expando ] ) { // Find the next relative operator (if any) for proper handling j = ++i; for ( ; j < len; j++ ) { if ( Expr.relative[ tokens[ j ].type ] ) { break; } } return setMatcher( i > 1 && elementMatcher( matchers ), i > 1 && toSelector( // If the preceding token was a descendant combinator, insert an implicit any-element `*` tokens .slice( 0, i - 1 ) .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) ).replace( rtrim, "$1" ), matcher, i < j && matcherFromTokens( tokens.slice( i, j ) ), j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), j < len && toSelector( tokens ) ); } matchers.push( matcher ); } } return elementMatcher( matchers ); } function matcherFromGroupMatchers( elementMatchers, setMatchers ) { var bySet = setMatchers.length > 0, byElement = elementMatchers.length > 0, superMatcher = function( seed, context, xml, results, outermost ) { var elem, j, matcher, matchedCount = 0, i = "0", unmatched = seed && [], setMatched = [], contextBackup = outermostContext, // We must always have either seed elements or outermost context elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), // Use integer dirruns iff this is the outermost matcher dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), len = elems.length; if ( outermost ) { // Support: IE 11+, Edge 17 - 18+ // IE/Edge sometimes throw a "Permission denied" error when strict-comparing // two documents; shallow comparisons work. // eslint-disable-next-line eqeqeq outermostContext = context == document || context || outermost; } // Add elements passing elementMatchers directly to results // Support: IE<9, Safari // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { if ( byElement && elem ) { j = 0; // Support: IE 11+, Edge 17 - 18+ // IE/Edge sometimes throw a "Permission denied" error when strict-comparing // two documents; shallow comparisons work. // eslint-disable-next-line eqeqeq if ( !context && elem.ownerDocument != document ) { setDocument( elem ); xml = !documentIsHTML; } while ( ( matcher = elementMatchers[ j++ ] ) ) { if ( matcher( elem, context || document, xml ) ) { results.push( elem ); break; } } if ( outermost ) { dirruns = dirrunsUnique; } } // Track unmatched elements for set filters if ( bySet ) { // They will have gone through all possible matchers if ( ( elem = !matcher && elem ) ) { matchedCount--; } // Lengthen the array for every element, matched or not if ( seed ) { unmatched.push( elem ); } } } // `i` is now the count of elements visited above, and adding it to `matchedCount` // makes the latter nonnegative. matchedCount += i; // Apply set filters to unmatched elements // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` // equals `i`), unless we didn't visit _any_ elements in the above loop because we have // no element matchers and no seed. // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that // case, which will result in a "00" `matchedCount` that differs from `i` but is also // numerically zero. if ( bySet && i !== matchedCount ) { j = 0; while ( ( matcher = setMatchers[ j++ ] ) ) { matcher( unmatched, setMatched, context, xml ); } if ( seed ) { // Reintegrate element matches to eliminate the need for sorting if ( matchedCount > 0 ) { while ( i-- ) { if ( !( unmatched[ i ] || setMatched[ i ] ) ) { setMatched[ i ] = pop.call( results ); } } } // Discard index placeholder values to get only actual matches setMatched = condense( setMatched ); } // Add matches to results push.apply( results, setMatched ); // Seedless set matches succeeding multiple successful matchers stipulate sorting if ( outermost && !seed && setMatched.length > 0 && ( matchedCount + setMatchers.length ) > 1 ) { Sizzle.uniqueSort( results ); } } // Override manipulation of globals by nested matchers if ( outermost ) { dirruns = dirrunsUnique; outermostContext = contextBackup; } return unmatched; }; return bySet ? markFunction( superMatcher ) : superMatcher; } compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { var i, setMatchers = [], elementMatchers = [], cached = compilerCache[ selector + " " ]; if ( !cached ) { // Generate a function of recursive functions that can be used to check each element if ( !match ) { match = tokenize( selector ); } i = match.length; while ( i-- ) { cached = matcherFromTokens( match[ i ] ); if ( cached[ expando ] ) { setMatchers.push( cached ); } else { elementMatchers.push( cached ); } } // Cache the compiled function cached = compilerCache( selector, matcherFromGroupMatchers( elementMatchers, setMatchers ) ); // Save selector and tokenization cached.selector = selector; } return cached; }; /** * A low-level selection function that works with Sizzle's compiled * selector functions * @param {String|Function} selector A selector or a pre-compiled * selector function built with Sizzle.compile * @param {Element} context * @param {Array} [results] * @param {Array} [seed] A set of elements to match against */ select = Sizzle.select = function( selector, context, results, seed ) { var i, tokens, token, type, find, compiled = typeof selector === "function" && selector, match = !seed && tokenize( ( selector = compiled.selector || selector ) ); results = results || []; // Try to minimize operations if there is only one selector in the list and no seed // (the latter of which guarantees us context) if ( match.length === 1 ) { // Reduce context if the leading compound selector is an ID tokens = match[ 0 ] = match[ 0 ].slice( 0 ); if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { context = ( Expr.find[ "ID" ]( token.matches[ 0 ] .replace( runescape, funescape ), context ) || [] )[ 0 ]; if ( !context ) { return results; // Precompiled matchers will still verify ancestry, so step up a level } else if ( compiled ) { context = context.parentNode; } selector = selector.slice( tokens.shift().value.length ); } // Fetch a seed set for right-to-left matching i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; while ( i-- ) { token = tokens[ i ]; // Abort if we hit a combinator if ( Expr.relative[ ( type = token.type ) ] ) { break; } if ( ( find = Expr.find[ type ] ) ) { // Search, expanding context for leading sibling combinators if ( ( seed = find( token.matches[ 0 ].replace( runescape, funescape ), rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || context ) ) ) { // If seed is empty or no tokens remain, we can return early tokens.splice( i, 1 ); selector = seed.length && toSelector( tokens ); if ( !selector ) { push.apply( results, seed ); return results; } break; } } } } // Compile and execute a filtering function if one is not provided // Provide `match` to avoid retokenization if we modified the selector above ( compiled || compile( selector, match ) )( seed, context, !documentIsHTML, results, !context || rsibling.test( selector ) && testContext( context.parentNode ) || context ); return results; }; // One-time assignments // Sort stability support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; // Support: Chrome 14-35+ // Always assume duplicates if they aren't passed to the comparison function support.detectDuplicates = !!hasDuplicate; // Initialize against the default document setDocument(); // Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) // Detached nodes confoundingly follow *each other* support.sortDetached = assert( function( el ) { // Should return 1, but returns 4 (following) return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; } ); // Support: IE<8 // Prevent attribute/property "interpolation" // https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx if ( !assert( function( el ) { el.innerHTML = ""; return el.firstChild.getAttribute( "href" ) === "#"; } ) ) { addHandle( "type|href|height|width", function( elem, name, isXML ) { if ( !isXML ) { return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); } } ); } // Support: IE<9 // Use defaultValue in place of getAttribute("value") if ( !support.attributes || !assert( function( el ) { el.innerHTML = ""; el.firstChild.setAttribute( "value", "" ); return el.firstChild.getAttribute( "value" ) === ""; } ) ) { addHandle( "value", function( elem, _name, isXML ) { if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { return elem.defaultValue; } } ); } // Support: IE<9 // Use getAttributeNode to fetch booleans when getAttribute lies if ( !assert( function( el ) { return el.getAttribute( "disabled" ) == null; } ) ) { addHandle( booleans, function( elem, name, isXML ) { var val; if ( !isXML ) { return elem[ name ] === true ? name.toLowerCase() : ( val = elem.getAttributeNode( name ) ) && val.specified ? val.value : null; } } ); } return Sizzle; } )( window ); jQuery.find = Sizzle; jQuery.expr = Sizzle.selectors; // Deprecated jQuery.expr[ ":" ] = jQuery.expr.pseudos; jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; jQuery.text = Sizzle.getText; jQuery.isXMLDoc = Sizzle.isXML; jQuery.contains = Sizzle.contains; jQuery.escapeSelector = Sizzle.escape; var dir = function( elem, dir, until ) { var matched = [], truncate = until !== undefined; while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { if ( elem.nodeType === 1 ) { if ( truncate && jQuery( elem ).is( until ) ) { break; } matched.push( elem ); } } return matched; }; var siblings = function( n, elem ) { var matched = []; for ( ; n; n = n.nextSibling ) { if ( n.nodeType === 1 && n !== elem ) { matched.push( n ); } } return matched; }; var rneedsContext = jQuery.expr.match.needsContext; function nodeName( elem, name ) { return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); }; var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); // Implement the identical functionality for filter and not function winnow( elements, qualifier, not ) { if ( isFunction( qualifier ) ) { return jQuery.grep( elements, function( elem, i ) { return !!qualifier.call( elem, i, elem ) !== not; } ); } // Single element if ( qualifier.nodeType ) { return jQuery.grep( elements, function( elem ) { return ( elem === qualifier ) !== not; } ); } // Arraylike of elements (jQuery, arguments, Array) if ( typeof qualifier !== "string" ) { return jQuery.grep( elements, function( elem ) { return ( indexOf.call( qualifier, elem ) > -1 ) !== not; } ); } // Filtered directly for both simple and complex selectors return jQuery.filter( qualifier, elements, not ); } jQuery.filter = function( expr, elems, not ) { var elem = elems[ 0 ]; if ( not ) { expr = ":not(" + expr + ")"; } if ( elems.length === 1 && elem.nodeType === 1 ) { return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; } return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { return elem.nodeType === 1; } ) ); }; jQuery.fn.extend( { find: function( selector ) { var i, ret, len = this.length, self = this; if ( typeof selector !== "string" ) { return this.pushStack( jQuery( selector ).filter( function() { for ( i = 0; i < len; i++ ) { if ( jQuery.contains( self[ i ], this ) ) { return true; } } } ) ); } ret = this.pushStack( [] ); for ( i = 0; i < len; i++ ) { jQuery.find( selector, self[ i ], ret ); } return len > 1 ? jQuery.uniqueSort( ret ) : ret; }, filter: function( selector ) { return this.pushStack( winnow( this, selector || [], false ) ); }, not: function( selector ) { return this.pushStack( winnow( this, selector || [], true ) ); }, is: function( selector ) { return !!winnow( this, // If this is a positional/relative selector, check membership in the returned set // so $("p:first").is("p:last") won't return true for a doc with two "p". typeof selector === "string" && rneedsContext.test( selector ) ? jQuery( selector ) : selector || [], false ).length; } } ); // Initialize a jQuery object // A central reference to the root jQuery(document) var rootjQuery, // A simple way to check for HTML strings // Prioritize #id over to avoid XSS via location.hash (#9521) // Strict HTML recognition (#11290: must start with <) // Shortcut simple #id case for speed rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, init = jQuery.fn.init = function( selector, context, root ) { var match, elem; // HANDLE: $(""), $(null), $(undefined), $(false) if ( !selector ) { return this; } // Method init() accepts an alternate rootjQuery // so migrate can support jQuery.sub (gh-2101) root = root || rootjQuery; // Handle HTML strings if ( typeof selector === "string" ) { if ( selector[ 0 ] === "<" && selector[ selector.length - 1 ] === ">" && selector.length >= 3 ) { // Assume that strings that start and end with <> are HTML and skip the regex check match = [ null, selector, null ]; } else { match = rquickExpr.exec( selector ); } // Match html or make sure no context is specified for #id if ( match && ( match[ 1 ] || !context ) ) { // HANDLE: $(html) -> $(array) if ( match[ 1 ] ) { context = context instanceof jQuery ? context[ 0 ] : context; // Option to run scripts is true for back-compat // Intentionally let the error be thrown if parseHTML is not present jQuery.merge( this, jQuery.parseHTML( match[ 1 ], context && context.nodeType ? context.ownerDocument || context : document, true ) ); // HANDLE: $(html, props) if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { for ( match in context ) { // Properties of context are called as methods if possible if ( isFunction( this[ match ] ) ) { this[ match ]( context[ match ] ); // ...and otherwise set as attributes } else { this.attr( match, context[ match ] ); } } } return this; // HANDLE: $(#id) } else { elem = document.getElementById( match[ 2 ] ); if ( elem ) { // Inject the element directly into the jQuery object this[ 0 ] = elem; this.length = 1; } return this; } // HANDLE: $(expr, $(...)) } else if ( !context || context.jquery ) { return ( context || root ).find( selector ); // HANDLE: $(expr, context) // (which is just equivalent to: $(context).find(expr) } else { return this.constructor( context ).find( selector ); } // HANDLE: $(DOMElement) } else if ( selector.nodeType ) { this[ 0 ] = selector; this.length = 1; return this; // HANDLE: $(function) // Shortcut for document ready } else if ( isFunction( selector ) ) { return root.ready !== undefined ? root.ready( selector ) : // Execute immediately if ready is not present selector( jQuery ); } return jQuery.makeArray( selector, this ); }; // Give the init function the jQuery prototype for later instantiation init.prototype = jQuery.fn; // Initialize central reference rootjQuery = jQuery( document ); var rparentsprev = /^(?:parents|prev(?:Until|All))/, // Methods guaranteed to produce a unique set when starting from a unique set guaranteedUnique = { children: true, contents: true, next: true, prev: true }; jQuery.fn.extend( { has: function( target ) { var targets = jQuery( target, this ), l = targets.length; return this.filter( function() { var i = 0; for ( ; i < l; i++ ) { if ( jQuery.contains( this, targets[ i ] ) ) { return true; } } } ); }, closest: function( selectors, context ) { var cur, i = 0, l = this.length, matched = [], targets = typeof selectors !== "string" && jQuery( selectors ); // Positional selectors never match, since there's no _selection_ context if ( !rneedsContext.test( selectors ) ) { for ( ; i < l; i++ ) { for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { // Always skip document fragments if ( cur.nodeType < 11 && ( targets ? targets.index( cur ) > -1 : // Don't pass non-elements to Sizzle cur.nodeType === 1 && jQuery.find.matchesSelector( cur, selectors ) ) ) { matched.push( cur ); break; } } } } return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); }, // Determine the position of an element within the set index: function( elem ) { // No argument, return index in parent if ( !elem ) { return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; } // Index in selector if ( typeof elem === "string" ) { return indexOf.call( jQuery( elem ), this[ 0 ] ); } // Locate the position of the desired element return indexOf.call( this, // If it receives a jQuery object, the first element is used elem.jquery ? elem[ 0 ] : elem ); }, add: function( selector, context ) { return this.pushStack( jQuery.uniqueSort( jQuery.merge( this.get(), jQuery( selector, context ) ) ) ); }, addBack: function( selector ) { return this.add( selector == null ? this.prevObject : this.prevObject.filter( selector ) ); } } ); function sibling( cur, dir ) { while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} return cur; } jQuery.each( { parent: function( elem ) { var parent = elem.parentNode; return parent && parent.nodeType !== 11 ? parent : null; }, parents: function( elem ) { return dir( elem, "parentNode" ); }, parentsUntil: function( elem, _i, until ) { return dir( elem, "parentNode", until ); }, next: function( elem ) { return sibling( elem, "nextSibling" ); }, prev: function( elem ) { return sibling( elem, "previousSibling" ); }, nextAll: function( elem ) { return dir( elem, "nextSibling" ); }, prevAll: function( elem ) { return dir( elem, "previousSibling" ); }, nextUntil: function( elem, _i, until ) { return dir( elem, "nextSibling", until ); }, prevUntil: function( elem, _i, until ) { return dir( elem, "previousSibling", until ); }, siblings: function( elem ) { return siblings( ( elem.parentNode || {} ).firstChild, elem ); }, children: function( elem ) { return siblings( elem.firstChild ); }, contents: function( elem ) { if ( elem.contentDocument != null && // Support: IE 11+ // elements with no `data` attribute has an object // `contentDocument` with a `null` prototype. getProto( elem.contentDocument ) ) { return elem.contentDocument; } // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only // Treat the template element as a regular one in browsers that // don't support it. if ( nodeName( elem, "template" ) ) { elem = elem.content || elem; } return jQuery.merge( [], elem.childNodes ); } }, function( name, fn ) { jQuery.fn[ name ] = function( until, selector ) { var matched = jQuery.map( this, fn, until ); if ( name.slice( -5 ) !== "Until" ) { selector = until; } if ( selector && typeof selector === "string" ) { matched = jQuery.filter( selector, matched ); } if ( this.length > 1 ) { // Remove duplicates if ( !guaranteedUnique[ name ] ) { jQuery.uniqueSort( matched ); } // Reverse order for parents* and prev-derivatives if ( rparentsprev.test( name ) ) { matched.reverse(); } } return this.pushStack( matched ); }; } ); var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); // Convert String-formatted options into Object-formatted ones function createOptions( options ) { var object = {}; jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { object[ flag ] = true; } ); return object; } /* * Create a callback list using the following parameters: * * options: an optional list of space-separated options that will change how * the callback list behaves or a more traditional option object * * By default a callback list will act like an event callback list and can be * "fired" multiple times. * * Possible options: * * once: will ensure the callback list can only be fired once (like a Deferred) * * memory: will keep track of previous values and will call any callback added * after the list has been fired right away with the latest "memorized" * values (like a Deferred) * * unique: will ensure a callback can only be added once (no duplicate in the list) * * stopOnFalse: interrupt callings when a callback returns false * */ jQuery.Callbacks = function( options ) { // Convert options from String-formatted to Object-formatted if needed // (we check in cache first) options = typeof options === "string" ? createOptions( options ) : jQuery.extend( {}, options ); var // Flag to know if list is currently firing firing, // Last fire value for non-forgettable lists memory, // Flag to know if list was already fired fired, // Flag to prevent firing locked, // Actual callback list list = [], // Queue of execution data for repeatable lists queue = [], // Index of currently firing callback (modified by add/remove as needed) firingIndex = -1, // Fire callbacks fire = function() { // Enforce single-firing locked = locked || options.once; // Execute callbacks for all pending executions, // respecting firingIndex overrides and runtime changes fired = firing = true; for ( ; queue.length; firingIndex = -1 ) { memory = queue.shift(); while ( ++firingIndex < list.length ) { // Run callback and check for early termination if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && options.stopOnFalse ) { // Jump to end and forget the data so .add doesn't re-fire firingIndex = list.length; memory = false; } } } // Forget the data if we're done with it if ( !options.memory ) { memory = false; } firing = false; // Clean up if we're done firing for good if ( locked ) { // Keep an empty list if we have data for future add calls if ( memory ) { list = []; // Otherwise, this object is spent } else { list = ""; } } }, // Actual Callbacks object self = { // Add a callback or a collection of callbacks to the list add: function() { if ( list ) { // If we have memory from a past run, we should fire after adding if ( memory && !firing ) { firingIndex = list.length - 1; queue.push( memory ); } ( function add( args ) { jQuery.each( args, function( _, arg ) { if ( isFunction( arg ) ) { if ( !options.unique || !self.has( arg ) ) { list.push( arg ); } } else if ( arg && arg.length && toType( arg ) !== "string" ) { // Inspect recursively add( arg ); } } ); } )( arguments ); if ( memory && !firing ) { fire(); } } return this; }, // Remove a callback from the list remove: function() { jQuery.each( arguments, function( _, arg ) { var index; while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { list.splice( index, 1 ); // Handle firing indexes if ( index <= firingIndex ) { firingIndex--; } } } ); return this; }, // Check if a given callback is in the list. // If no argument is given, return whether or not list has callbacks attached. has: function( fn ) { return fn ? jQuery.inArray( fn, list ) > -1 : list.length > 0; }, // Remove all callbacks from the list empty: function() { if ( list ) { list = []; } return this; }, // Disable .fire and .add // Abort any current/pending executions // Clear all callbacks and values disable: function() { locked = queue = []; list = memory = ""; return this; }, disabled: function() { return !list; }, // Disable .fire // Also disable .add unless we have memory (since it would have no effect) // Abort any pending executions lock: function() { locked = queue = []; if ( !memory && !firing ) { list = memory = ""; } return this; }, locked: function() { return !!locked; }, // Call all callbacks with the given context and arguments fireWith: function( context, args ) { if ( !locked ) { args = args || []; args = [ context, args.slice ? args.slice() : args ]; queue.push( args ); if ( !firing ) { fire(); } } return this; }, // Call all the callbacks with the given arguments fire: function() { self.fireWith( this, arguments ); return this; }, // To know if the callbacks have already been called at least once fired: function() { return !!fired; } }; return self; }; function Identity( v ) { return v; } function Thrower( ex ) { throw ex; } function adoptValue( value, resolve, reject, noValue ) { var method; try { // Check for promise aspect first to privilege synchronous behavior if ( value && isFunction( ( method = value.promise ) ) ) { method.call( value ).done( resolve ).fail( reject ); // Other thenables } else if ( value && isFunction( ( method = value.then ) ) ) { method.call( value, resolve, reject ); // Other non-thenables } else { // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: // * false: [ value ].slice( 0 ) => resolve( value ) // * true: [ value ].slice( 1 ) => resolve() resolve.apply( undefined, [ value ].slice( noValue ) ); } // For Promises/A+, convert exceptions into rejections // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in // Deferred#then to conditionally suppress rejection. } catch ( value ) { // Support: Android 4.0 only // Strict mode functions invoked without .call/.apply get global-object context reject.apply( undefined, [ value ] ); } } jQuery.extend( { Deferred: function( func ) { var tuples = [ // action, add listener, callbacks, // ... .then handlers, argument index, [final state] [ "notify", "progress", jQuery.Callbacks( "memory" ), jQuery.Callbacks( "memory" ), 2 ], [ "resolve", "done", jQuery.Callbacks( "once memory" ), jQuery.Callbacks( "once memory" ), 0, "resolved" ], [ "reject", "fail", jQuery.Callbacks( "once memory" ), jQuery.Callbacks( "once memory" ), 1, "rejected" ] ], state = "pending", promise = { state: function() { return state; }, always: function() { deferred.done( arguments ).fail( arguments ); return this; }, "catch": function( fn ) { return promise.then( null, fn ); }, // Keep pipe for back-compat pipe: function( /* fnDone, fnFail, fnProgress */ ) { var fns = arguments; return jQuery.Deferred( function( newDefer ) { jQuery.each( tuples, function( _i, tuple ) { // Map tuples (progress, done, fail) to arguments (done, fail, progress) var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; // deferred.progress(function() { bind to newDefer or newDefer.notify }) // deferred.done(function() { bind to newDefer or newDefer.resolve }) // deferred.fail(function() { bind to newDefer or newDefer.reject }) deferred[ tuple[ 1 ] ]( function() { var returned = fn && fn.apply( this, arguments ); if ( returned && isFunction( returned.promise ) ) { returned.promise() .progress( newDefer.notify ) .done( newDefer.resolve ) .fail( newDefer.reject ); } else { newDefer[ tuple[ 0 ] + "With" ]( this, fn ? [ returned ] : arguments ); } } ); } ); fns = null; } ).promise(); }, then: function( onFulfilled, onRejected, onProgress ) { var maxDepth = 0; function resolve( depth, deferred, handler, special ) { return function() { var that = this, args = arguments, mightThrow = function() { var returned, then; // Support: Promises/A+ section 2.3.3.3.3 // https://promisesaplus.com/#point-59 // Ignore double-resolution attempts if ( depth < maxDepth ) { return; } returned = handler.apply( that, args ); // Support: Promises/A+ section 2.3.1 // https://promisesaplus.com/#point-48 if ( returned === deferred.promise() ) { throw new TypeError( "Thenable self-resolution" ); } // Support: Promises/A+ sections 2.3.3.1, 3.5 // https://promisesaplus.com/#point-54 // https://promisesaplus.com/#point-75 // Retrieve `then` only once then = returned && // Support: Promises/A+ section 2.3.4 // https://promisesaplus.com/#point-64 // Only check objects and functions for thenability ( typeof returned === "object" || typeof returned === "function" ) && returned.then; // Handle a returned thenable if ( isFunction( then ) ) { // Special processors (notify) just wait for resolution if ( special ) { then.call( returned, resolve( maxDepth, deferred, Identity, special ), resolve( maxDepth, deferred, Thrower, special ) ); // Normal processors (resolve) also hook into progress } else { // ...and disregard older resolution values maxDepth++; then.call( returned, resolve( maxDepth, deferred, Identity, special ), resolve( maxDepth, deferred, Thrower, special ), resolve( maxDepth, deferred, Identity, deferred.notifyWith ) ); } // Handle all other returned values } else { // Only substitute handlers pass on context // and multiple values (non-spec behavior) if ( handler !== Identity ) { that = undefined; args = [ returned ]; } // Process the value(s) // Default process is resolve ( special || deferred.resolveWith )( that, args ); } }, // Only normal processors (resolve) catch and reject exceptions process = special ? mightThrow : function() { try { mightThrow(); } catch ( e ) { if ( jQuery.Deferred.exceptionHook ) { jQuery.Deferred.exceptionHook( e, process.stackTrace ); } // Support: Promises/A+ section 2.3.3.3.4.1 // https://promisesaplus.com/#point-61 // Ignore post-resolution exceptions if ( depth + 1 >= maxDepth ) { // Only substitute handlers pass on context // and multiple values (non-spec behavior) if ( handler !== Thrower ) { that = undefined; args = [ e ]; } deferred.rejectWith( that, args ); } } }; // Support: Promises/A+ section 2.3.3.3.1 // https://promisesaplus.com/#point-57 // Re-resolve promises immediately to dodge false rejection from // subsequent errors if ( depth ) { process(); } else { // Call an optional hook to record the stack, in case of exception // since it's otherwise lost when execution goes async if ( jQuery.Deferred.getStackHook ) { process.stackTrace = jQuery.Deferred.getStackHook(); } window.setTimeout( process ); } }; } return jQuery.Deferred( function( newDefer ) { // progress_handlers.add( ... ) tuples[ 0 ][ 3 ].add( resolve( 0, newDefer, isFunction( onProgress ) ? onProgress : Identity, newDefer.notifyWith ) ); // fulfilled_handlers.add( ... ) tuples[ 1 ][ 3 ].add( resolve( 0, newDefer, isFunction( onFulfilled ) ? onFulfilled : Identity ) ); // rejected_handlers.add( ... ) tuples[ 2 ][ 3 ].add( resolve( 0, newDefer, isFunction( onRejected ) ? onRejected : Thrower ) ); } ).promise(); }, // Get a promise for this deferred // If obj is provided, the promise aspect is added to the object promise: function( obj ) { return obj != null ? jQuery.extend( obj, promise ) : promise; } }, deferred = {}; // Add list-specific methods jQuery.each( tuples, function( i, tuple ) { var list = tuple[ 2 ], stateString = tuple[ 5 ]; // promise.progress = list.add // promise.done = list.add // promise.fail = list.add promise[ tuple[ 1 ] ] = list.add; // Handle state if ( stateString ) { list.add( function() { // state = "resolved" (i.e., fulfilled) // state = "rejected" state = stateString; }, // rejected_callbacks.disable // fulfilled_callbacks.disable tuples[ 3 - i ][ 2 ].disable, // rejected_handlers.disable // fulfilled_handlers.disable tuples[ 3 - i ][ 3 ].disable, // progress_callbacks.lock tuples[ 0 ][ 2 ].lock, // progress_handlers.lock tuples[ 0 ][ 3 ].lock ); } // progress_handlers.fire // fulfilled_handlers.fire // rejected_handlers.fire list.add( tuple[ 3 ].fire ); // deferred.notify = function() { deferred.notifyWith(...) } // deferred.resolve = function() { deferred.resolveWith(...) } // deferred.reject = function() { deferred.rejectWith(...) } deferred[ tuple[ 0 ] ] = function() { deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); return this; }; // deferred.notifyWith = list.fireWith // deferred.resolveWith = list.fireWith // deferred.rejectWith = list.fireWith deferred[ tuple[ 0 ] + "With" ] = list.fireWith; } ); // Make the deferred a promise promise.promise( deferred ); // Call given func if any if ( func ) { func.call( deferred, deferred ); } // All done! return deferred; }, // Deferred helper when: function( singleValue ) { var // count of uncompleted subordinates remaining = arguments.length, // count of unprocessed arguments i = remaining, // subordinate fulfillment data resolveContexts = Array( i ), resolveValues = slice.call( arguments ), // the master Deferred master = jQuery.Deferred(), // subordinate callback factory updateFunc = function( i ) { return function( value ) { resolveContexts[ i ] = this; resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; if ( !( --remaining ) ) { master.resolveWith( resolveContexts, resolveValues ); } }; }; // Single- and empty arguments are adopted like Promise.resolve if ( remaining <= 1 ) { adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject, !remaining ); // Use .then() to unwrap secondary thenables (cf. gh-3000) if ( master.state() === "pending" || isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { return master.then(); } } // Multiple arguments are aggregated like Promise.all array elements while ( i-- ) { adoptValue( resolveValues[ i ], updateFunc( i ), master.reject ); } return master.promise(); } } ); // These usually indicate a programmer mistake during development, // warn about them ASAP rather than swallowing them by default. var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; jQuery.Deferred.exceptionHook = function( error, stack ) { // Support: IE 8 - 9 only // Console exists when dev tools are open, which can happen at any time if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); } }; jQuery.readyException = function( error ) { window.setTimeout( function() { throw error; } ); }; // The deferred used on DOM ready var readyList = jQuery.Deferred(); jQuery.fn.ready = function( fn ) { readyList .then( fn ) // Wrap jQuery.readyException in a function so that the lookup // happens at the time of error handling instead of callback // registration. .catch( function( error ) { jQuery.readyException( error ); } ); return this; }; jQuery.extend( { // Is the DOM ready to be used? Set to true once it occurs. isReady: false, // A counter to track how many items to wait for before // the ready event fires. See #6781 readyWait: 1, // Handle when the DOM is ready ready: function( wait ) { // Abort if there are pending holds or we're already ready if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { return; } // Remember that the DOM is ready jQuery.isReady = true; // If a normal DOM Ready event fired, decrement, and wait if need be if ( wait !== true && --jQuery.readyWait > 0 ) { return; } // If there are functions bound, to execute readyList.resolveWith( document, [ jQuery ] ); } } ); jQuery.ready.then = readyList.then; // The ready event handler and self cleanup method function completed() { document.removeEventListener( "DOMContentLoaded", completed ); window.removeEventListener( "load", completed ); jQuery.ready(); } // Catch cases where $(document).ready() is called // after the browser event has already occurred. // Support: IE <=9 - 10 only // Older IE sometimes signals "interactive" too soon if ( document.readyState === "complete" || ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { // Handle it asynchronously to allow scripts the opportunity to delay ready window.setTimeout( jQuery.ready ); } else { // Use the handy event callback document.addEventListener( "DOMContentLoaded", completed ); // A fallback to window.onload, that will always work window.addEventListener( "load", completed ); } // Multifunctional method to get and set values of a collection // The value/s can optionally be executed if it's a function var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { var i = 0, len = elems.length, bulk = key == null; // Sets many values if ( toType( key ) === "object" ) { chainable = true; for ( i in key ) { access( elems, fn, i, key[ i ], true, emptyGet, raw ); } // Sets one value } else if ( value !== undefined ) { chainable = true; if ( !isFunction( value ) ) { raw = true; } if ( bulk ) { // Bulk operations run against the entire set if ( raw ) { fn.call( elems, value ); fn = null; // ...except when executing function values } else { bulk = fn; fn = function( elem, _key, value ) { return bulk.call( jQuery( elem ), value ); }; } } if ( fn ) { for ( ; i < len; i++ ) { fn( elems[ i ], key, raw ? value : value.call( elems[ i ], i, fn( elems[ i ], key ) ) ); } } } if ( chainable ) { return elems; } // Gets if ( bulk ) { return fn.call( elems ); } return len ? fn( elems[ 0 ], key ) : emptyGet; }; // Matches dashed string for camelizing var rmsPrefix = /^-ms-/, rdashAlpha = /-([a-z])/g; // Used by camelCase as callback to replace() function fcamelCase( _all, letter ) { return letter.toUpperCase(); } // Convert dashed to camelCase; used by the css and data modules // Support: IE <=9 - 11, Edge 12 - 15 // Microsoft forgot to hump their vendor prefix (#9572) function camelCase( string ) { return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); } var acceptData = function( owner ) { // Accepts only: // - Node // - Node.ELEMENT_NODE // - Node.DOCUMENT_NODE // - Object // - Any return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); }; function Data() { this.expando = jQuery.expando + Data.uid++; } Data.uid = 1; Data.prototype = { cache: function( owner ) { // Check if the owner object already has a cache var value = owner[ this.expando ]; // If not, create one if ( !value ) { value = {}; // We can accept data for non-element nodes in modern browsers, // but we should not, see #8335. // Always return an empty object. if ( acceptData( owner ) ) { // If it is a node unlikely to be stringify-ed or looped over // use plain assignment if ( owner.nodeType ) { owner[ this.expando ] = value; // Otherwise secure it in a non-enumerable property // configurable must be true to allow the property to be // deleted when data is removed } else { Object.defineProperty( owner, this.expando, { value: value, configurable: true } ); } } } return value; }, set: function( owner, data, value ) { var prop, cache = this.cache( owner ); // Handle: [ owner, key, value ] args // Always use camelCase key (gh-2257) if ( typeof data === "string" ) { cache[ camelCase( data ) ] = value; // Handle: [ owner, { properties } ] args } else { // Copy the properties one-by-one to the cache object for ( prop in data ) { cache[ camelCase( prop ) ] = data[ prop ]; } } return cache; }, get: function( owner, key ) { return key === undefined ? this.cache( owner ) : // Always use camelCase key (gh-2257) owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; }, access: function( owner, key, value ) { // In cases where either: // // 1. No key was specified // 2. A string key was specified, but no value provided // // Take the "read" path and allow the get method to determine // which value to return, respectively either: // // 1. The entire cache object // 2. The data stored at the key // if ( key === undefined || ( ( key && typeof key === "string" ) && value === undefined ) ) { return this.get( owner, key ); } // When the key is not a string, or both a key and value // are specified, set or extend (existing objects) with either: // // 1. An object of properties // 2. A key and value // this.set( owner, key, value ); // Since the "set" path can have two possible entry points // return the expected data based on which path was taken[*] return value !== undefined ? value : key; }, remove: function( owner, key ) { var i, cache = owner[ this.expando ]; if ( cache === undefined ) { return; } if ( key !== undefined ) { // Support array or space separated string of keys if ( Array.isArray( key ) ) { // If key is an array of keys... // We always set camelCase keys, so remove that. key = key.map( camelCase ); } else { key = camelCase( key ); // If a key with the spaces exists, use it. // Otherwise, create an array by matching non-whitespace key = key in cache ? [ key ] : ( key.match( rnothtmlwhite ) || [] ); } i = key.length; while ( i-- ) { delete cache[ key[ i ] ]; } } // Remove the expando if there's no more data if ( key === undefined || jQuery.isEmptyObject( cache ) ) { // Support: Chrome <=35 - 45 // Webkit & Blink performance suffers when deleting properties // from DOM nodes, so set to undefined instead // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) if ( owner.nodeType ) { owner[ this.expando ] = undefined; } else { delete owner[ this.expando ]; } } }, hasData: function( owner ) { var cache = owner[ this.expando ]; return cache !== undefined && !jQuery.isEmptyObject( cache ); } }; var dataPriv = new Data(); var dataUser = new Data(); // Implementation Summary // // 1. Enforce API surface and semantic compatibility with 1.9.x branch // 2. Improve the module's maintainability by reducing the storage // paths to a single mechanism. // 3. Use the same single mechanism to support "private" and "user" data. // 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) // 5. Avoid exposing implementation details on user objects (eg. expando properties) // 6. Provide a clear path for implementation upgrade to WeakMap in 2014 var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, rmultiDash = /[A-Z]/g; function getData( data ) { if ( data === "true" ) { return true; } if ( data === "false" ) { return false; } if ( data === "null" ) { return null; } // Only convert to a number if it doesn't change the string if ( data === +data + "" ) { return +data; } if ( rbrace.test( data ) ) { return JSON.parse( data ); } return data; } function dataAttr( elem, key, data ) { var name; // If nothing was found internally, try to fetch any // data from the HTML5 data-* attribute if ( data === undefined && elem.nodeType === 1 ) { name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); data = elem.getAttribute( name ); if ( typeof data === "string" ) { try { data = getData( data ); } catch ( e ) {} // Make sure we set the data so it isn't changed later dataUser.set( elem, key, data ); } else { data = undefined; } } return data; } jQuery.extend( { hasData: function( elem ) { return dataUser.hasData( elem ) || dataPriv.hasData( elem ); }, data: function( elem, name, data ) { return dataUser.access( elem, name, data ); }, removeData: function( elem, name ) { dataUser.remove( elem, name ); }, // TODO: Now that all calls to _data and _removeData have been replaced // with direct calls to dataPriv methods, these can be deprecated. _data: function( elem, name, data ) { return dataPriv.access( elem, name, data ); }, _removeData: function( elem, name ) { dataPriv.remove( elem, name ); } } ); jQuery.fn.extend( { data: function( key, value ) { var i, name, data, elem = this[ 0 ], attrs = elem && elem.attributes; // Gets all values if ( key === undefined ) { if ( this.length ) { data = dataUser.get( elem ); if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { i = attrs.length; while ( i-- ) { // Support: IE 11 only // The attrs elements can be null (#14894) if ( attrs[ i ] ) { name = attrs[ i ].name; if ( name.indexOf( "data-" ) === 0 ) { name = camelCase( name.slice( 5 ) ); dataAttr( elem, name, data[ name ] ); } } } dataPriv.set( elem, "hasDataAttrs", true ); } } return data; } // Sets multiple values if ( typeof key === "object" ) { return this.each( function() { dataUser.set( this, key ); } ); } return access( this, function( value ) { var data; // The calling jQuery object (element matches) is not empty // (and therefore has an element appears at this[ 0 ]) and the // `value` parameter was not undefined. An empty jQuery object // will result in `undefined` for elem = this[ 0 ] which will // throw an exception if an attempt to read a data cache is made. if ( elem && value === undefined ) { // Attempt to get data from the cache // The key will always be camelCased in Data data = dataUser.get( elem, key ); if ( data !== undefined ) { return data; } // Attempt to "discover" the data in // HTML5 custom data-* attrs data = dataAttr( elem, key ); if ( data !== undefined ) { return data; } // We tried really hard, but the data doesn't exist. return; } // Set the data... this.each( function() { // We always store the camelCased key dataUser.set( this, key, value ); } ); }, null, value, arguments.length > 1, null, true ); }, removeData: function( key ) { return this.each( function() { dataUser.remove( this, key ); } ); } } ); jQuery.extend( { queue: function( elem, type, data ) { var queue; if ( elem ) { type = ( type || "fx" ) + "queue"; queue = dataPriv.get( elem, type ); // Speed up dequeue by getting out quickly if this is just a lookup if ( data ) { if ( !queue || Array.isArray( data ) ) { queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); } else { queue.push( data ); } } return queue || []; } }, dequeue: function( elem, type ) { type = type || "fx"; var queue = jQuery.queue( elem, type ), startLength = queue.length, fn = queue.shift(), hooks = jQuery._queueHooks( elem, type ), next = function() { jQuery.dequeue( elem, type ); }; // If the fx queue is dequeued, always remove the progress sentinel if ( fn === "inprogress" ) { fn = queue.shift(); startLength--; } if ( fn ) { // Add a progress sentinel to prevent the fx queue from being // automatically dequeued if ( type === "fx" ) { queue.unshift( "inprogress" ); } // Clear up the last queue stop function delete hooks.stop; fn.call( elem, next, hooks ); } if ( !startLength && hooks ) { hooks.empty.fire(); } }, // Not public - generate a queueHooks object, or return the current one _queueHooks: function( elem, type ) { var key = type + "queueHooks"; return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { empty: jQuery.Callbacks( "once memory" ).add( function() { dataPriv.remove( elem, [ type + "queue", key ] ); } ) } ); } } ); jQuery.fn.extend( { queue: function( type, data ) { var setter = 2; if ( typeof type !== "string" ) { data = type; type = "fx"; setter--; } if ( arguments.length < setter ) { return jQuery.queue( this[ 0 ], type ); } return data === undefined ? this : this.each( function() { var queue = jQuery.queue( this, type, data ); // Ensure a hooks for this queue jQuery._queueHooks( this, type ); if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { jQuery.dequeue( this, type ); } } ); }, dequeue: function( type ) { return this.each( function() { jQuery.dequeue( this, type ); } ); }, clearQueue: function( type ) { return this.queue( type || "fx", [] ); }, // Get a promise resolved when queues of a certain type // are emptied (fx is the type by default) promise: function( type, obj ) { var tmp, count = 1, defer = jQuery.Deferred(), elements = this, i = this.length, resolve = function() { if ( !( --count ) ) { defer.resolveWith( elements, [ elements ] ); } }; if ( typeof type !== "string" ) { obj = type; type = undefined; } type = type || "fx"; while ( i-- ) { tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); if ( tmp && tmp.empty ) { count++; tmp.empty.add( resolve ); } } resolve(); return defer.promise( obj ); } } ); var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; var documentElement = document.documentElement; var isAttached = function( elem ) { return jQuery.contains( elem.ownerDocument, elem ); }, composed = { composed: true }; // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only // Check attachment across shadow DOM boundaries when possible (gh-3504) // Support: iOS 10.0-10.2 only // Early iOS 10 versions support `attachShadow` but not `getRootNode`, // leading to errors. We need to check for `getRootNode`. if ( documentElement.getRootNode ) { isAttached = function( elem ) { return jQuery.contains( elem.ownerDocument, elem ) || elem.getRootNode( composed ) === elem.ownerDocument; }; } var isHiddenWithinTree = function( elem, el ) { // isHiddenWithinTree might be called from jQuery#filter function; // in that case, element will be second argument elem = el || elem; // Inline style trumps all return elem.style.display === "none" || elem.style.display === "" && // Otherwise, check computed style // Support: Firefox <=43 - 45 // Disconnected elements can have computed display: none, so first confirm that elem is // in the document. isAttached( elem ) && jQuery.css( elem, "display" ) === "none"; }; function adjustCSS( elem, prop, valueParts, tween ) { var adjusted, scale, maxIterations = 20, currentValue = tween ? function() { return tween.cur(); } : function() { return jQuery.css( elem, prop, "" ); }, initial = currentValue(), unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), // Starting value computation is required for potential unit mismatches initialInUnit = elem.nodeType && ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && rcssNum.exec( jQuery.css( elem, prop ) ); if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { // Support: Firefox <=54 // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) initial = initial / 2; // Trust units reported by jQuery.css unit = unit || initialInUnit[ 3 ]; // Iteratively approximate from a nonzero starting point initialInUnit = +initial || 1; while ( maxIterations-- ) { // Evaluate and update our best guess (doubling guesses that zero out). // Finish if the scale equals or crosses 1 (making the old*new product non-positive). jQuery.style( elem, prop, initialInUnit + unit ); if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { maxIterations = 0; } initialInUnit = initialInUnit / scale; } initialInUnit = initialInUnit * 2; jQuery.style( elem, prop, initialInUnit + unit ); // Make sure we update the tween properties later on valueParts = valueParts || []; } if ( valueParts ) { initialInUnit = +initialInUnit || +initial || 0; // Apply relative offset (+=/-=) if specified adjusted = valueParts[ 1 ] ? initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : +valueParts[ 2 ]; if ( tween ) { tween.unit = unit; tween.start = initialInUnit; tween.end = adjusted; } } return adjusted; } var defaultDisplayMap = {}; function getDefaultDisplay( elem ) { var temp, doc = elem.ownerDocument, nodeName = elem.nodeName, display = defaultDisplayMap[ nodeName ]; if ( display ) { return display; } temp = doc.body.appendChild( doc.createElement( nodeName ) ); display = jQuery.css( temp, "display" ); temp.parentNode.removeChild( temp ); if ( display === "none" ) { display = "block"; } defaultDisplayMap[ nodeName ] = display; return display; } function showHide( elements, show ) { var display, elem, values = [], index = 0, length = elements.length; // Determine new display value for elements that need to change for ( ; index < length; index++ ) { elem = elements[ index ]; if ( !elem.style ) { continue; } display = elem.style.display; if ( show ) { // Since we force visibility upon cascade-hidden elements, an immediate (and slow) // check is required in this first loop unless we have a nonempty display value (either // inline or about-to-be-restored) if ( display === "none" ) { values[ index ] = dataPriv.get( elem, "display" ) || null; if ( !values[ index ] ) { elem.style.display = ""; } } if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { values[ index ] = getDefaultDisplay( elem ); } } else { if ( display !== "none" ) { values[ index ] = "none"; // Remember what we're overwriting dataPriv.set( elem, "display", display ); } } } // Set the display of the elements in a second loop to avoid constant reflow for ( index = 0; index < length; index++ ) { if ( values[ index ] != null ) { elements[ index ].style.display = values[ index ]; } } return elements; } jQuery.fn.extend( { show: function() { return showHide( this, true ); }, hide: function() { return showHide( this ); }, toggle: function( state ) { if ( typeof state === "boolean" ) { return state ? this.show() : this.hide(); } return this.each( function() { if ( isHiddenWithinTree( this ) ) { jQuery( this ).show(); } else { jQuery( this ).hide(); } } ); } } ); var rcheckableType = ( /^(?:checkbox|radio)$/i ); var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); ( function() { var fragment = document.createDocumentFragment(), div = fragment.appendChild( document.createElement( "div" ) ), input = document.createElement( "input" ); // Support: Android 4.0 - 4.3 only // Check state lost if the name is set (#11217) // Support: Windows Web Apps (WWA) // `name` and `type` must use .setAttribute for WWA (#14901) input.setAttribute( "type", "radio" ); input.setAttribute( "checked", "checked" ); input.setAttribute( "name", "t" ); div.appendChild( input ); // Support: Android <=4.1 only // Older WebKit doesn't clone checked state correctly in fragments support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; // Support: IE <=11 only // Make sure textarea (and checkbox) defaultValue is properly cloned div.innerHTML = ""; support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; // Support: IE <=9 only // IE <=9 replaces "; support.option = !!div.lastChild; } )(); // We have to close these tags to support XHTML (#13200) var wrapMap = { // XHTML parsers do not magically insert elements in the // same way that tag soup parsers do. So we cannot shorten // this by omitting or other required elements. thead: [ 1, "", "
" ], col: [ 2, "", "
" ], tr: [ 2, "", "
" ], td: [ 3, "", "
" ], _default: [ 0, "", "" ] }; wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; wrapMap.th = wrapMap.td; // Support: IE <=9 only if ( !support.option ) { wrapMap.optgroup = wrapMap.option = [ 1, "" ]; } function getAll( context, tag ) { // Support: IE <=9 - 11 only // Use typeof to avoid zero-argument method invocation on host objects (#15151) var ret; if ( typeof context.getElementsByTagName !== "undefined" ) { ret = context.getElementsByTagName( tag || "*" ); } else if ( typeof context.querySelectorAll !== "undefined" ) { ret = context.querySelectorAll( tag || "*" ); } else { ret = []; } if ( tag === undefined || tag && nodeName( context, tag ) ) { return jQuery.merge( [ context ], ret ); } return ret; } // Mark scripts as having already been evaluated function setGlobalEval( elems, refElements ) { var i = 0, l = elems.length; for ( ; i < l; i++ ) { dataPriv.set( elems[ i ], "globalEval", !refElements || dataPriv.get( refElements[ i ], "globalEval" ) ); } } var rhtml = /<|&#?\w+;/; function buildFragment( elems, context, scripts, selection, ignored ) { var elem, tmp, tag, wrap, attached, j, fragment = context.createDocumentFragment(), nodes = [], i = 0, l = elems.length; for ( ; i < l; i++ ) { elem = elems[ i ]; if ( elem || elem === 0 ) { // Add nodes directly if ( toType( elem ) === "object" ) { // Support: Android <=4.0 only, PhantomJS 1 only // push.apply(_, arraylike) throws on ancient WebKit jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); // Convert non-html into a text node } else if ( !rhtml.test( elem ) ) { nodes.push( context.createTextNode( elem ) ); // Convert html into DOM nodes } else { tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); // Deserialize a standard representation tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); wrap = wrapMap[ tag ] || wrapMap._default; tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; // Descend through wrappers to the right content j = wrap[ 0 ]; while ( j-- ) { tmp = tmp.lastChild; } // Support: Android <=4.0 only, PhantomJS 1 only // push.apply(_, arraylike) throws on ancient WebKit jQuery.merge( nodes, tmp.childNodes ); // Remember the top-level container tmp = fragment.firstChild; // Ensure the created nodes are orphaned (#12392) tmp.textContent = ""; } } } // Remove wrapper from fragment fragment.textContent = ""; i = 0; while ( ( elem = nodes[ i++ ] ) ) { // Skip elements already in the context collection (trac-4087) if ( selection && jQuery.inArray( elem, selection ) > -1 ) { if ( ignored ) { ignored.push( elem ); } continue; } attached = isAttached( elem ); // Append to fragment tmp = getAll( fragment.appendChild( elem ), "script" ); // Preserve script evaluation history if ( attached ) { setGlobalEval( tmp ); } // Capture executables if ( scripts ) { j = 0; while ( ( elem = tmp[ j++ ] ) ) { if ( rscriptType.test( elem.type || "" ) ) { scripts.push( elem ); } } } } return fragment; } var rkeyEvent = /^key/, rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/, rtypenamespace = /^([^.]*)(?:\.(.+)|)/; function returnTrue() { return true; } function returnFalse() { return false; } // Support: IE <=9 - 11+ // focus() and blur() are asynchronous, except when they are no-op. // So expect focus to be synchronous when the element is already active, // and blur to be synchronous when the element is not already active. // (focus and blur are always synchronous in other supported browsers, // this just defines when we can count on it). function expectSync( elem, type ) { return ( elem === safeActiveElement() ) === ( type === "focus" ); } // Support: IE <=9 only // Accessing document.activeElement can throw unexpectedly // https://bugs.jquery.com/ticket/13393 function safeActiveElement() { try { return document.activeElement; } catch ( err ) { } } function on( elem, types, selector, data, fn, one ) { var origFn, type; // Types can be a map of types/handlers if ( typeof types === "object" ) { // ( types-Object, selector, data ) if ( typeof selector !== "string" ) { // ( types-Object, data ) data = data || selector; selector = undefined; } for ( type in types ) { on( elem, type, selector, data, types[ type ], one ); } return elem; } if ( data == null && fn == null ) { // ( types, fn ) fn = selector; data = selector = undefined; } else if ( fn == null ) { if ( typeof selector === "string" ) { // ( types, selector, fn ) fn = data; data = undefined; } else { // ( types, data, fn ) fn = data; data = selector; selector = undefined; } } if ( fn === false ) { fn = returnFalse; } else if ( !fn ) { return elem; } if ( one === 1 ) { origFn = fn; fn = function( event ) { // Can use an empty set, since event contains the info jQuery().off( event ); return origFn.apply( this, arguments ); }; // Use same guid so caller can remove using origFn fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); } return elem.each( function() { jQuery.event.add( this, types, fn, data, selector ); } ); } /* * Helper functions for managing events -- not part of the public interface. * Props to Dean Edwards' addEvent library for many of the ideas. */ jQuery.event = { global: {}, add: function( elem, types, handler, data, selector ) { var handleObjIn, eventHandle, tmp, events, t, handleObj, special, handlers, type, namespaces, origType, elemData = dataPriv.get( elem ); // Only attach events to objects that accept data if ( !acceptData( elem ) ) { return; } // Caller can pass in an object of custom data in lieu of the handler if ( handler.handler ) { handleObjIn = handler; handler = handleObjIn.handler; selector = handleObjIn.selector; } // Ensure that invalid selectors throw exceptions at attach time // Evaluate against documentElement in case elem is a non-element node (e.g., document) if ( selector ) { jQuery.find.matchesSelector( documentElement, selector ); } // Make sure that the handler has a unique ID, used to find/remove it later if ( !handler.guid ) { handler.guid = jQuery.guid++; } // Init the element's event structure and main handler, if this is the first if ( !( events = elemData.events ) ) { events = elemData.events = Object.create( null ); } if ( !( eventHandle = elemData.handle ) ) { eventHandle = elemData.handle = function( e ) { // Discard the second event of a jQuery.event.trigger() and // when an event is called after a page has unloaded return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? jQuery.event.dispatch.apply( elem, arguments ) : undefined; }; } // Handle multiple events separated by a space types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; t = types.length; while ( t-- ) { tmp = rtypenamespace.exec( types[ t ] ) || []; type = origType = tmp[ 1 ]; namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); // There *must* be a type, no attaching namespace-only handlers if ( !type ) { continue; } // If event changes its type, use the special event handlers for the changed type special = jQuery.event.special[ type ] || {}; // If selector defined, determine special event api type, otherwise given type type = ( selector ? special.delegateType : special.bindType ) || type; // Update special based on newly reset type special = jQuery.event.special[ type ] || {}; // handleObj is passed to all event handlers handleObj = jQuery.extend( { type: type, origType: origType, data: data, handler: handler, guid: handler.guid, selector: selector, needsContext: selector && jQuery.expr.match.needsContext.test( selector ), namespace: namespaces.join( "." ) }, handleObjIn ); // Init the event handler queue if we're the first if ( !( handlers = events[ type ] ) ) { handlers = events[ type ] = []; handlers.delegateCount = 0; // Only use addEventListener if the special events handler returns false if ( !special.setup || special.setup.call( elem, data, namespaces, eventHandle ) === false ) { if ( elem.addEventListener ) { elem.addEventListener( type, eventHandle ); } } } if ( special.add ) { special.add.call( elem, handleObj ); if ( !handleObj.handler.guid ) { handleObj.handler.guid = handler.guid; } } // Add to the element's handler list, delegates in front if ( selector ) { handlers.splice( handlers.delegateCount++, 0, handleObj ); } else { handlers.push( handleObj ); } // Keep track of which events have ever been used, for event optimization jQuery.event.global[ type ] = true; } }, // Detach an event or set of events from an element remove: function( elem, types, handler, selector, mappedTypes ) { var j, origCount, tmp, events, t, handleObj, special, handlers, type, namespaces, origType, elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); if ( !elemData || !( events = elemData.events ) ) { return; } // Once for each type.namespace in types; type may be omitted types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; t = types.length; while ( t-- ) { tmp = rtypenamespace.exec( types[ t ] ) || []; type = origType = tmp[ 1 ]; namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); // Unbind all events (on this namespace, if provided) for the element if ( !type ) { for ( type in events ) { jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); } continue; } special = jQuery.event.special[ type ] || {}; type = ( selector ? special.delegateType : special.bindType ) || type; handlers = events[ type ] || []; tmp = tmp[ 2 ] && new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); // Remove matching events origCount = j = handlers.length; while ( j-- ) { handleObj = handlers[ j ]; if ( ( mappedTypes || origType === handleObj.origType ) && ( !handler || handler.guid === handleObj.guid ) && ( !tmp || tmp.test( handleObj.namespace ) ) && ( !selector || selector === handleObj.selector || selector === "**" && handleObj.selector ) ) { handlers.splice( j, 1 ); if ( handleObj.selector ) { handlers.delegateCount--; } if ( special.remove ) { special.remove.call( elem, handleObj ); } } } // Remove generic event handler if we removed something and no more handlers exist // (avoids potential for endless recursion during removal of special event handlers) if ( origCount && !handlers.length ) { if ( !special.teardown || special.teardown.call( elem, namespaces, elemData.handle ) === false ) { jQuery.removeEvent( elem, type, elemData.handle ); } delete events[ type ]; } } // Remove data and the expando if it's no longer used if ( jQuery.isEmptyObject( events ) ) { dataPriv.remove( elem, "handle events" ); } }, dispatch: function( nativeEvent ) { var i, j, ret, matched, handleObj, handlerQueue, args = new Array( arguments.length ), // Make a writable jQuery.Event from the native event object event = jQuery.event.fix( nativeEvent ), handlers = ( dataPriv.get( this, "events" ) || Object.create( null ) )[ event.type ] || [], special = jQuery.event.special[ event.type ] || {}; // Use the fix-ed jQuery.Event rather than the (read-only) native event args[ 0 ] = event; for ( i = 1; i < arguments.length; i++ ) { args[ i ] = arguments[ i ]; } event.delegateTarget = this; // Call the preDispatch hook for the mapped type, and let it bail if desired if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { return; } // Determine handlers handlerQueue = jQuery.event.handlers.call( this, event, handlers ); // Run delegates first; they may want to stop propagation beneath us i = 0; while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { event.currentTarget = matched.elem; j = 0; while ( ( handleObj = matched.handlers[ j++ ] ) && !event.isImmediatePropagationStopped() ) { // If the event is namespaced, then each handler is only invoked if it is // specially universal or its namespaces are a superset of the event's. if ( !event.rnamespace || handleObj.namespace === false || event.rnamespace.test( handleObj.namespace ) ) { event.handleObj = handleObj; event.data = handleObj.data; ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || handleObj.handler ).apply( matched.elem, args ); if ( ret !== undefined ) { if ( ( event.result = ret ) === false ) { event.preventDefault(); event.stopPropagation(); } } } } } // Call the postDispatch hook for the mapped type if ( special.postDispatch ) { special.postDispatch.call( this, event ); } return event.result; }, handlers: function( event, handlers ) { var i, handleObj, sel, matchedHandlers, matchedSelectors, handlerQueue = [], delegateCount = handlers.delegateCount, cur = event.target; // Find delegate handlers if ( delegateCount && // Support: IE <=9 // Black-hole SVG instance trees (trac-13180) cur.nodeType && // Support: Firefox <=42 // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click // Support: IE 11 only // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) !( event.type === "click" && event.button >= 1 ) ) { for ( ; cur !== this; cur = cur.parentNode || this ) { // Don't check non-elements (#13208) // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { matchedHandlers = []; matchedSelectors = {}; for ( i = 0; i < delegateCount; i++ ) { handleObj = handlers[ i ]; // Don't conflict with Object.prototype properties (#13203) sel = handleObj.selector + " "; if ( matchedSelectors[ sel ] === undefined ) { matchedSelectors[ sel ] = handleObj.needsContext ? jQuery( sel, this ).index( cur ) > -1 : jQuery.find( sel, this, null, [ cur ] ).length; } if ( matchedSelectors[ sel ] ) { matchedHandlers.push( handleObj ); } } if ( matchedHandlers.length ) { handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); } } } } // Add the remaining (directly-bound) handlers cur = this; if ( delegateCount < handlers.length ) { handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); } return handlerQueue; }, addProp: function( name, hook ) { Object.defineProperty( jQuery.Event.prototype, name, { enumerable: true, configurable: true, get: isFunction( hook ) ? function() { if ( this.originalEvent ) { return hook( this.originalEvent ); } } : function() { if ( this.originalEvent ) { return this.originalEvent[ name ]; } }, set: function( value ) { Object.defineProperty( this, name, { enumerable: true, configurable: true, writable: true, value: value } ); } } ); }, fix: function( originalEvent ) { return originalEvent[ jQuery.expando ] ? originalEvent : new jQuery.Event( originalEvent ); }, special: { load: { // Prevent triggered image.load events from bubbling to window.load noBubble: true }, click: { // Utilize native event to ensure correct state for checkable inputs setup: function( data ) { // For mutual compressibility with _default, replace `this` access with a local var. // `|| data` is dead code meant only to preserve the variable through minification. var el = this || data; // Claim the first handler if ( rcheckableType.test( el.type ) && el.click && nodeName( el, "input" ) ) { // dataPriv.set( el, "click", ... ) leverageNative( el, "click", returnTrue ); } // Return false to allow normal processing in the caller return false; }, trigger: function( data ) { // For mutual compressibility with _default, replace `this` access with a local var. // `|| data` is dead code meant only to preserve the variable through minification. var el = this || data; // Force setup before triggering a click if ( rcheckableType.test( el.type ) && el.click && nodeName( el, "input" ) ) { leverageNative( el, "click" ); } // Return non-false to allow normal event-path propagation return true; }, // For cross-browser consistency, suppress native .click() on links // Also prevent it if we're currently inside a leveraged native-event stack _default: function( event ) { var target = event.target; return rcheckableType.test( target.type ) && target.click && nodeName( target, "input" ) && dataPriv.get( target, "click" ) || nodeName( target, "a" ); } }, beforeunload: { postDispatch: function( event ) { // Support: Firefox 20+ // Firefox doesn't alert if the returnValue field is not set. if ( event.result !== undefined && event.originalEvent ) { event.originalEvent.returnValue = event.result; } } } } }; // Ensure the presence of an event listener that handles manually-triggered // synthetic events by interrupting progress until reinvoked in response to // *native* events that it fires directly, ensuring that state changes have // already occurred before other listeners are invoked. function leverageNative( el, type, expectSync ) { // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add if ( !expectSync ) { if ( dataPriv.get( el, type ) === undefined ) { jQuery.event.add( el, type, returnTrue ); } return; } // Register the controller as a special universal handler for all event namespaces dataPriv.set( el, type, false ); jQuery.event.add( el, type, { namespace: false, handler: function( event ) { var notAsync, result, saved = dataPriv.get( this, type ); if ( ( event.isTrigger & 1 ) && this[ type ] ) { // Interrupt processing of the outer synthetic .trigger()ed event // Saved data should be false in such cases, but might be a leftover capture object // from an async native handler (gh-4350) if ( !saved.length ) { // Store arguments for use when handling the inner native event // There will always be at least one argument (an event object), so this array // will not be confused with a leftover capture object. saved = slice.call( arguments ); dataPriv.set( this, type, saved ); // Trigger the native event and capture its result // Support: IE <=9 - 11+ // focus() and blur() are asynchronous notAsync = expectSync( this, type ); this[ type ](); result = dataPriv.get( this, type ); if ( saved !== result || notAsync ) { dataPriv.set( this, type, false ); } else { result = {}; } if ( saved !== result ) { // Cancel the outer synthetic event event.stopImmediatePropagation(); event.preventDefault(); return result.value; } // If this is an inner synthetic event for an event with a bubbling surrogate // (focus or blur), assume that the surrogate already propagated from triggering the // native event and prevent that from happening again here. // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the // bubbling surrogate propagates *after* the non-bubbling base), but that seems // less bad than duplication. } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { event.stopPropagation(); } // If this is a native event triggered above, everything is now in order // Fire an inner synthetic event with the original arguments } else if ( saved.length ) { // ...and capture the result dataPriv.set( this, type, { value: jQuery.event.trigger( // Support: IE <=9 - 11+ // Extend with the prototype to reset the above stopImmediatePropagation() jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), saved.slice( 1 ), this ) } ); // Abort handling of the native event event.stopImmediatePropagation(); } } } ); } jQuery.removeEvent = function( elem, type, handle ) { // This "if" is needed for plain objects if ( elem.removeEventListener ) { elem.removeEventListener( type, handle ); } }; jQuery.Event = function( src, props ) { // Allow instantiation without the 'new' keyword if ( !( this instanceof jQuery.Event ) ) { return new jQuery.Event( src, props ); } // Event object if ( src && src.type ) { this.originalEvent = src; this.type = src.type; // Events bubbling up the document may have been marked as prevented // by a handler lower down the tree; reflect the correct value. this.isDefaultPrevented = src.defaultPrevented || src.defaultPrevented === undefined && // Support: Android <=2.3 only src.returnValue === false ? returnTrue : returnFalse; // Create target properties // Support: Safari <=6 - 7 only // Target should not be a text node (#504, #13143) this.target = ( src.target && src.target.nodeType === 3 ) ? src.target.parentNode : src.target; this.currentTarget = src.currentTarget; this.relatedTarget = src.relatedTarget; // Event type } else { this.type = src; } // Put explicitly provided properties onto the event object if ( props ) { jQuery.extend( this, props ); } // Create a timestamp if incoming event doesn't have one this.timeStamp = src && src.timeStamp || Date.now(); // Mark it as fixed this[ jQuery.expando ] = true; }; // jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding // https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html jQuery.Event.prototype = { constructor: jQuery.Event, isDefaultPrevented: returnFalse, isPropagationStopped: returnFalse, isImmediatePropagationStopped: returnFalse, isSimulated: false, preventDefault: function() { var e = this.originalEvent; this.isDefaultPrevented = returnTrue; if ( e && !this.isSimulated ) { e.preventDefault(); } }, stopPropagation: function() { var e = this.originalEvent; this.isPropagationStopped = returnTrue; if ( e && !this.isSimulated ) { e.stopPropagation(); } }, stopImmediatePropagation: function() { var e = this.originalEvent; this.isImmediatePropagationStopped = returnTrue; if ( e && !this.isSimulated ) { e.stopImmediatePropagation(); } this.stopPropagation(); } }; // Includes all common event props including KeyEvent and MouseEvent specific props jQuery.each( { altKey: true, bubbles: true, cancelable: true, changedTouches: true, ctrlKey: true, detail: true, eventPhase: true, metaKey: true, pageX: true, pageY: true, shiftKey: true, view: true, "char": true, code: true, charCode: true, key: true, keyCode: true, button: true, buttons: true, clientX: true, clientY: true, offsetX: true, offsetY: true, pointerId: true, pointerType: true, screenX: true, screenY: true, targetTouches: true, toElement: true, touches: true, which: function( event ) { var button = event.button; // Add which for key events if ( event.which == null && rkeyEvent.test( event.type ) ) { return event.charCode != null ? event.charCode : event.keyCode; } // Add which for click: 1 === left; 2 === middle; 3 === right if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) { if ( button & 1 ) { return 1; } if ( button & 2 ) { return 3; } if ( button & 4 ) { return 2; } return 0; } return event.which; } }, jQuery.event.addProp ); jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { jQuery.event.special[ type ] = { // Utilize native event if possible so blur/focus sequence is correct setup: function() { // Claim the first handler // dataPriv.set( this, "focus", ... ) // dataPriv.set( this, "blur", ... ) leverageNative( this, type, expectSync ); // Return false to allow normal processing in the caller return false; }, trigger: function() { // Force setup before trigger leverageNative( this, type ); // Return non-false to allow normal event-path propagation return true; }, delegateType: delegateType }; } ); // Create mouseenter/leave events using mouseover/out and event-time checks // so that event delegation works in jQuery. // Do the same for pointerenter/pointerleave and pointerover/pointerout // // Support: Safari 7 only // Safari sends mouseenter too often; see: // https://bugs.chromium.org/p/chromium/issues/detail?id=470258 // for the description of the bug (it existed in older Chrome versions as well). jQuery.each( { mouseenter: "mouseover", mouseleave: "mouseout", pointerenter: "pointerover", pointerleave: "pointerout" }, function( orig, fix ) { jQuery.event.special[ orig ] = { delegateType: fix, bindType: fix, handle: function( event ) { var ret, target = this, related = event.relatedTarget, handleObj = event.handleObj; // For mouseenter/leave call the handler if related is outside the target. // NB: No relatedTarget if the mouse left/entered the browser window if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { event.type = handleObj.origType; ret = handleObj.handler.apply( this, arguments ); event.type = fix; } return ret; } }; } ); jQuery.fn.extend( { on: function( types, selector, data, fn ) { return on( this, types, selector, data, fn ); }, one: function( types, selector, data, fn ) { return on( this, types, selector, data, fn, 1 ); }, off: function( types, selector, fn ) { var handleObj, type; if ( types && types.preventDefault && types.handleObj ) { // ( event ) dispatched jQuery.Event handleObj = types.handleObj; jQuery( types.delegateTarget ).off( handleObj.namespace ? handleObj.origType + "." + handleObj.namespace : handleObj.origType, handleObj.selector, handleObj.handler ); return this; } if ( typeof types === "object" ) { // ( types-object [, selector] ) for ( type in types ) { this.off( type, selector, types[ type ] ); } return this; } if ( selector === false || typeof selector === "function" ) { // ( types [, fn] ) fn = selector; selector = undefined; } if ( fn === false ) { fn = returnFalse; } return this.each( function() { jQuery.event.remove( this, types, fn, selector ); } ); } } ); var // Support: IE <=10 - 11, Edge 12 - 13 only // In IE/Edge using regex groups here causes severe slowdowns. // See https://connect.microsoft.com/IE/feedback/details/1736512/ rnoInnerhtml = /\s*$/g; // Prefer a tbody over its parent table for containing new rows function manipulationTarget( elem, content ) { if ( nodeName( elem, "table" ) && nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { return jQuery( elem ).children( "tbody" )[ 0 ] || elem; } return elem; } // Replace/restore the type attribute of script elements for safe DOM manipulation function disableScript( elem ) { elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; return elem; } function restoreScript( elem ) { if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { elem.type = elem.type.slice( 5 ); } else { elem.removeAttribute( "type" ); } return elem; } function cloneCopyEvent( src, dest ) { var i, l, type, pdataOld, udataOld, udataCur, events; if ( dest.nodeType !== 1 ) { return; } // 1. Copy private data: events, handlers, etc. if ( dataPriv.hasData( src ) ) { pdataOld = dataPriv.get( src ); events = pdataOld.events; if ( events ) { dataPriv.remove( dest, "handle events" ); for ( type in events ) { for ( i = 0, l = events[ type ].length; i < l; i++ ) { jQuery.event.add( dest, type, events[ type ][ i ] ); } } } } // 2. Copy user data if ( dataUser.hasData( src ) ) { udataOld = dataUser.access( src ); udataCur = jQuery.extend( {}, udataOld ); dataUser.set( dest, udataCur ); } } // Fix IE bugs, see support tests function fixInput( src, dest ) { var nodeName = dest.nodeName.toLowerCase(); // Fails to persist the checked state of a cloned checkbox or radio button. if ( nodeName === "input" && rcheckableType.test( src.type ) ) { dest.checked = src.checked; // Fails to return the selected option to the default selected state when cloning options } else if ( nodeName === "input" || nodeName === "textarea" ) { dest.defaultValue = src.defaultValue; } } function domManip( collection, args, callback, ignored ) { // Flatten any nested arrays args = flat( args ); var fragment, first, scripts, hasScripts, node, doc, i = 0, l = collection.length, iNoClone = l - 1, value = args[ 0 ], valueIsFunction = isFunction( value ); // We can't cloneNode fragments that contain checked, in WebKit if ( valueIsFunction || ( l > 1 && typeof value === "string" && !support.checkClone && rchecked.test( value ) ) ) { return collection.each( function( index ) { var self = collection.eq( index ); if ( valueIsFunction ) { args[ 0 ] = value.call( this, index, self.html() ); } domManip( self, args, callback, ignored ); } ); } if ( l ) { fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); first = fragment.firstChild; if ( fragment.childNodes.length === 1 ) { fragment = first; } // Require either new content or an interest in ignored elements to invoke the callback if ( first || ignored ) { scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); hasScripts = scripts.length; // Use the original fragment for the last item // instead of the first because it can end up // being emptied incorrectly in certain situations (#8070). for ( ; i < l; i++ ) { node = fragment; if ( i !== iNoClone ) { node = jQuery.clone( node, true, true ); // Keep references to cloned scripts for later restoration if ( hasScripts ) { // Support: Android <=4.0 only, PhantomJS 1 only // push.apply(_, arraylike) throws on ancient WebKit jQuery.merge( scripts, getAll( node, "script" ) ); } } callback.call( collection[ i ], node, i ); } if ( hasScripts ) { doc = scripts[ scripts.length - 1 ].ownerDocument; // Reenable scripts jQuery.map( scripts, restoreScript ); // Evaluate executable scripts on first document insertion for ( i = 0; i < hasScripts; i++ ) { node = scripts[ i ]; if ( rscriptType.test( node.type || "" ) && !dataPriv.access( node, "globalEval" ) && jQuery.contains( doc, node ) ) { if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { // Optional AJAX dependency, but won't run scripts if not present if ( jQuery._evalUrl && !node.noModule ) { jQuery._evalUrl( node.src, { nonce: node.nonce || node.getAttribute( "nonce" ) }, doc ); } } else { DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); } } } } } } return collection; } function remove( elem, selector, keepData ) { var node, nodes = selector ? jQuery.filter( selector, elem ) : elem, i = 0; for ( ; ( node = nodes[ i ] ) != null; i++ ) { if ( !keepData && node.nodeType === 1 ) { jQuery.cleanData( getAll( node ) ); } if ( node.parentNode ) { if ( keepData && isAttached( node ) ) { setGlobalEval( getAll( node, "script" ) ); } node.parentNode.removeChild( node ); } } return elem; } jQuery.extend( { htmlPrefilter: function( html ) { return html; }, clone: function( elem, dataAndEvents, deepDataAndEvents ) { var i, l, srcElements, destElements, clone = elem.cloneNode( true ), inPage = isAttached( elem ); // Fix IE cloning issues if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && !jQuery.isXMLDoc( elem ) ) { // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 destElements = getAll( clone ); srcElements = getAll( elem ); for ( i = 0, l = srcElements.length; i < l; i++ ) { fixInput( srcElements[ i ], destElements[ i ] ); } } // Copy the events from the original to the clone if ( dataAndEvents ) { if ( deepDataAndEvents ) { srcElements = srcElements || getAll( elem ); destElements = destElements || getAll( clone ); for ( i = 0, l = srcElements.length; i < l; i++ ) { cloneCopyEvent( srcElements[ i ], destElements[ i ] ); } } else { cloneCopyEvent( elem, clone ); } } // Preserve script evaluation history destElements = getAll( clone, "script" ); if ( destElements.length > 0 ) { setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); } // Return the cloned set return clone; }, cleanData: function( elems ) { var data, elem, type, special = jQuery.event.special, i = 0; for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { if ( acceptData( elem ) ) { if ( ( data = elem[ dataPriv.expando ] ) ) { if ( data.events ) { for ( type in data.events ) { if ( special[ type ] ) { jQuery.event.remove( elem, type ); // This is a shortcut to avoid jQuery.event.remove's overhead } else { jQuery.removeEvent( elem, type, data.handle ); } } } // Support: Chrome <=35 - 45+ // Assign undefined instead of using delete, see Data#remove elem[ dataPriv.expando ] = undefined; } if ( elem[ dataUser.expando ] ) { // Support: Chrome <=35 - 45+ // Assign undefined instead of using delete, see Data#remove elem[ dataUser.expando ] = undefined; } } } } } ); jQuery.fn.extend( { detach: function( selector ) { return remove( this, selector, true ); }, remove: function( selector ) { return remove( this, selector ); }, text: function( value ) { return access( this, function( value ) { return value === undefined ? jQuery.text( this ) : this.empty().each( function() { if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { this.textContent = value; } } ); }, null, value, arguments.length ); }, append: function() { return domManip( this, arguments, function( elem ) { if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { var target = manipulationTarget( this, elem ); target.appendChild( elem ); } } ); }, prepend: function() { return domManip( this, arguments, function( elem ) { if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { var target = manipulationTarget( this, elem ); target.insertBefore( elem, target.firstChild ); } } ); }, before: function() { return domManip( this, arguments, function( elem ) { if ( this.parentNode ) { this.parentNode.insertBefore( elem, this ); } } ); }, after: function() { return domManip( this, arguments, function( elem ) { if ( this.parentNode ) { this.parentNode.insertBefore( elem, this.nextSibling ); } } ); }, empty: function() { var elem, i = 0; for ( ; ( elem = this[ i ] ) != null; i++ ) { if ( elem.nodeType === 1 ) { // Prevent memory leaks jQuery.cleanData( getAll( elem, false ) ); // Remove any remaining nodes elem.textContent = ""; } } return this; }, clone: function( dataAndEvents, deepDataAndEvents ) { dataAndEvents = dataAndEvents == null ? false : dataAndEvents; deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; return this.map( function() { return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); } ); }, html: function( value ) { return access( this, function( value ) { var elem = this[ 0 ] || {}, i = 0, l = this.length; if ( value === undefined && elem.nodeType === 1 ) { return elem.innerHTML; } // See if we can take a shortcut and just use innerHTML if ( typeof value === "string" && !rnoInnerhtml.test( value ) && !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { value = jQuery.htmlPrefilter( value ); try { for ( ; i < l; i++ ) { elem = this[ i ] || {}; // Remove element nodes and prevent memory leaks if ( elem.nodeType === 1 ) { jQuery.cleanData( getAll( elem, false ) ); elem.innerHTML = value; } } elem = 0; // If using innerHTML throws an exception, use the fallback method } catch ( e ) {} } if ( elem ) { this.empty().append( value ); } }, null, value, arguments.length ); }, replaceWith: function() { var ignored = []; // Make the changes, replacing each non-ignored context element with the new content return domManip( this, arguments, function( elem ) { var parent = this.parentNode; if ( jQuery.inArray( this, ignored ) < 0 ) { jQuery.cleanData( getAll( this ) ); if ( parent ) { parent.replaceChild( elem, this ); } } // Force callback invocation }, ignored ); } } ); jQuery.each( { appendTo: "append", prependTo: "prepend", insertBefore: "before", insertAfter: "after", replaceAll: "replaceWith" }, function( name, original ) { jQuery.fn[ name ] = function( selector ) { var elems, ret = [], insert = jQuery( selector ), last = insert.length - 1, i = 0; for ( ; i <= last; i++ ) { elems = i === last ? this : this.clone( true ); jQuery( insert[ i ] )[ original ]( elems ); // Support: Android <=4.0 only, PhantomJS 1 only // .get() because push.apply(_, arraylike) throws on ancient WebKit push.apply( ret, elems.get() ); } return this.pushStack( ret ); }; } ); var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); var getStyles = function( elem ) { // Support: IE <=11 only, Firefox <=30 (#15098, #14150) // IE throws on elements created in popups // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" var view = elem.ownerDocument.defaultView; if ( !view || !view.opener ) { view = window; } return view.getComputedStyle( elem ); }; var swap = function( elem, options, callback ) { var ret, name, old = {}; // Remember the old values, and insert the new ones for ( name in options ) { old[ name ] = elem.style[ name ]; elem.style[ name ] = options[ name ]; } ret = callback.call( elem ); // Revert the old values for ( name in options ) { elem.style[ name ] = old[ name ]; } return ret; }; var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); ( function() { // Executing both pixelPosition & boxSizingReliable tests require only one layout // so they're executed at the same time to save the second computation. function computeStyleTests() { // This is a singleton, we need to execute it only once if ( !div ) { return; } container.style.cssText = "position:absolute;left:-11111px;width:60px;" + "margin-top:1px;padding:0;border:0"; div.style.cssText = "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + "margin:auto;border:1px;padding:1px;" + "width:60%;top:1%"; documentElement.appendChild( container ).appendChild( div ); var divStyle = window.getComputedStyle( div ); pixelPositionVal = divStyle.top !== "1%"; // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 // Some styles come back with percentage values, even though they shouldn't div.style.right = "60%"; pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; // Support: IE 9 - 11 only // Detect misreporting of content dimensions for box-sizing:border-box elements boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; // Support: IE 9 only // Detect overflow:scroll screwiness (gh-3699) // Support: Chrome <=64 // Don't get tricked when zoom affects offsetWidth (gh-4029) div.style.position = "absolute"; scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; documentElement.removeChild( container ); // Nullify the div so it wouldn't be stored in the memory and // it will also be a sign that checks already performed div = null; } function roundPixelMeasures( measure ) { return Math.round( parseFloat( measure ) ); } var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, reliableTrDimensionsVal, reliableMarginLeftVal, container = document.createElement( "div" ), div = document.createElement( "div" ); // Finish early in limited (non-browser) environments if ( !div.style ) { return; } // Support: IE <=9 - 11 only // Style of cloned element affects source element cloned (#8908) div.style.backgroundClip = "content-box"; div.cloneNode( true ).style.backgroundClip = ""; support.clearCloneStyle = div.style.backgroundClip === "content-box"; jQuery.extend( support, { boxSizingReliable: function() { computeStyleTests(); return boxSizingReliableVal; }, pixelBoxStyles: function() { computeStyleTests(); return pixelBoxStylesVal; }, pixelPosition: function() { computeStyleTests(); return pixelPositionVal; }, reliableMarginLeft: function() { computeStyleTests(); return reliableMarginLeftVal; }, scrollboxSize: function() { computeStyleTests(); return scrollboxSizeVal; }, // Support: IE 9 - 11+, Edge 15 - 18+ // IE/Edge misreport `getComputedStyle` of table rows with width/height // set in CSS while `offset*` properties report correct values. // Behavior in IE 9 is more subtle than in newer versions & it passes // some versions of this test; make sure not to make it pass there! reliableTrDimensions: function() { var table, tr, trChild, trStyle; if ( reliableTrDimensionsVal == null ) { table = document.createElement( "table" ); tr = document.createElement( "tr" ); trChild = document.createElement( "div" ); table.style.cssText = "position:absolute;left:-11111px"; tr.style.height = "1px"; trChild.style.height = "9px"; documentElement .appendChild( table ) .appendChild( tr ) .appendChild( trChild ); trStyle = window.getComputedStyle( tr ); reliableTrDimensionsVal = parseInt( trStyle.height ) > 3; documentElement.removeChild( table ); } return reliableTrDimensionsVal; } } ); } )(); function curCSS( elem, name, computed ) { var width, minWidth, maxWidth, ret, // Support: Firefox 51+ // Retrieving style before computed somehow // fixes an issue with getting wrong values // on detached elements style = elem.style; computed = computed || getStyles( elem ); // getPropertyValue is needed for: // .css('filter') (IE 9 only, #12537) // .css('--customProperty) (#3144) if ( computed ) { ret = computed.getPropertyValue( name ) || computed[ name ]; if ( ret === "" && !isAttached( elem ) ) { ret = jQuery.style( elem, name ); } // A tribute to the "awesome hack by Dean Edwards" // Android Browser returns percentage for some values, // but width seems to be reliably pixels. // This is against the CSSOM draft spec: // https://drafts.csswg.org/cssom/#resolved-values if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { // Remember the original values width = style.width; minWidth = style.minWidth; maxWidth = style.maxWidth; // Put in the new values to get a computed value out style.minWidth = style.maxWidth = style.width = ret; ret = computed.width; // Revert the changed values style.width = width; style.minWidth = minWidth; style.maxWidth = maxWidth; } } return ret !== undefined ? // Support: IE <=9 - 11 only // IE returns zIndex value as an integer. ret + "" : ret; } function addGetHookIf( conditionFn, hookFn ) { // Define the hook, we'll check on the first run if it's really needed. return { get: function() { if ( conditionFn() ) { // Hook not needed (or it's not possible to use it due // to missing dependency), remove it. delete this.get; return; } // Hook needed; redefine it so that the support test is not executed again. return ( this.get = hookFn ).apply( this, arguments ); } }; } var cssPrefixes = [ "Webkit", "Moz", "ms" ], emptyStyle = document.createElement( "div" ).style, vendorProps = {}; // Return a vendor-prefixed property or undefined function vendorPropName( name ) { // Check for vendor prefixed names var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), i = cssPrefixes.length; while ( i-- ) { name = cssPrefixes[ i ] + capName; if ( name in emptyStyle ) { return name; } } } // Return a potentially-mapped jQuery.cssProps or vendor prefixed property function finalPropName( name ) { var final = jQuery.cssProps[ name ] || vendorProps[ name ]; if ( final ) { return final; } if ( name in emptyStyle ) { return name; } return vendorProps[ name ] = vendorPropName( name ) || name; } var // Swappable if display is none or starts with table // except "table", "table-cell", or "table-caption" // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display rdisplayswap = /^(none|table(?!-c[ea]).+)/, rcustomProp = /^--/, cssShow = { position: "absolute", visibility: "hidden", display: "block" }, cssNormalTransform = { letterSpacing: "0", fontWeight: "400" }; function setPositiveNumber( _elem, value, subtract ) { // Any relative (+/-) values have already been // normalized at this point var matches = rcssNum.exec( value ); return matches ? // Guard against undefined "subtract", e.g., when used as in cssHooks Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : value; } function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { var i = dimension === "width" ? 1 : 0, extra = 0, delta = 0; // Adjustment may not be necessary if ( box === ( isBorderBox ? "border" : "content" ) ) { return 0; } for ( ; i < 4; i += 2 ) { // Both box models exclude margin if ( box === "margin" ) { delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); } // If we get here with a content-box, we're seeking "padding" or "border" or "margin" if ( !isBorderBox ) { // Add padding delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); // For "border" or "margin", add border if ( box !== "padding" ) { delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); // But still keep track of it otherwise } else { extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); } // If we get here with a border-box (content + padding + border), we're seeking "content" or // "padding" or "margin" } else { // For "content", subtract padding if ( box === "content" ) { delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); } // For "content" or "padding", subtract border if ( box !== "margin" ) { delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); } } } // Account for positive content-box scroll gutter when requested by providing computedVal if ( !isBorderBox && computedVal >= 0 ) { // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border // Assuming integer scroll gutter, subtract the rest and round down delta += Math.max( 0, Math.ceil( elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - computedVal - delta - extra - 0.5 // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter // Use an explicit zero to avoid NaN (gh-3964) ) ) || 0; } return delta; } function getWidthOrHeight( elem, dimension, extra ) { // Start with computed style var styles = getStyles( elem ), // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). // Fake content-box until we know it's needed to know the true value. boxSizingNeeded = !support.boxSizingReliable() || extra, isBorderBox = boxSizingNeeded && jQuery.css( elem, "boxSizing", false, styles ) === "border-box", valueIsBorderBox = isBorderBox, val = curCSS( elem, dimension, styles ), offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); // Support: Firefox <=54 // Return a confounding non-pixel value or feign ignorance, as appropriate. if ( rnumnonpx.test( val ) ) { if ( !extra ) { return val; } val = "auto"; } // Support: IE 9 - 11 only // Use offsetWidth/offsetHeight for when box sizing is unreliable. // In those cases, the computed value can be trusted to be border-box. if ( ( !support.boxSizingReliable() && isBorderBox || // Support: IE 10 - 11+, Edge 15 - 18+ // IE/Edge misreport `getComputedStyle` of table rows with width/height // set in CSS while `offset*` properties report correct values. // Interestingly, in some cases IE 9 doesn't suffer from this issue. !support.reliableTrDimensions() && nodeName( elem, "tr" ) || // Fall back to offsetWidth/offsetHeight when value is "auto" // This happens for inline elements with no explicit setting (gh-3571) val === "auto" || // Support: Android <=4.1 - 4.3 only // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && // Make sure the element is visible & connected elem.getClientRects().length ) { isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; // Where available, offsetWidth/offsetHeight approximate border box dimensions. // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the // retrieved value as a content box dimension. valueIsBorderBox = offsetProp in elem; if ( valueIsBorderBox ) { val = elem[ offsetProp ]; } } // Normalize "" and auto val = parseFloat( val ) || 0; // Adjust for the element's box model return ( val + boxModelAdjustment( elem, dimension, extra || ( isBorderBox ? "border" : "content" ), valueIsBorderBox, styles, // Provide the current computed size to request scroll gutter calculation (gh-3589) val ) ) + "px"; } jQuery.extend( { // Add in style property hooks for overriding the default // behavior of getting and setting a style property cssHooks: { opacity: { get: function( elem, computed ) { if ( computed ) { // We should always get a number back from opacity var ret = curCSS( elem, "opacity" ); return ret === "" ? "1" : ret; } } } }, // Don't automatically add "px" to these possibly-unitless properties cssNumber: { "animationIterationCount": true, "columnCount": true, "fillOpacity": true, "flexGrow": true, "flexShrink": true, "fontWeight": true, "gridArea": true, "gridColumn": true, "gridColumnEnd": true, "gridColumnStart": true, "gridRow": true, "gridRowEnd": true, "gridRowStart": true, "lineHeight": true, "opacity": true, "order": true, "orphans": true, "widows": true, "zIndex": true, "zoom": true }, // Add in properties whose names you wish to fix before // setting or getting the value cssProps: {}, // Get and set the style property on a DOM Node style: function( elem, name, value, extra ) { // Don't set styles on text and comment nodes if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { return; } // Make sure that we're working with the right name var ret, type, hooks, origName = camelCase( name ), isCustomProp = rcustomProp.test( name ), style = elem.style; // Make sure that we're working with the right name. We don't // want to query the value if it is a CSS custom property // since they are user-defined. if ( !isCustomProp ) { name = finalPropName( origName ); } // Gets hook for the prefixed version, then unprefixed version hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; // Check if we're setting a value if ( value !== undefined ) { type = typeof value; // Convert "+=" or "-=" to relative numbers (#7345) if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { value = adjustCSS( elem, name, ret ); // Fixes bug #9237 type = "number"; } // Make sure that null and NaN values aren't set (#7116) if ( value == null || value !== value ) { return; } // If a number was passed in, add the unit (except for certain CSS properties) // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append // "px" to a few hardcoded values. if ( type === "number" && !isCustomProp ) { value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); } // background-* props affect original clone's values if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { style[ name ] = "inherit"; } // If a hook was provided, use that value, otherwise just set the specified value if ( !hooks || !( "set" in hooks ) || ( value = hooks.set( elem, value, extra ) ) !== undefined ) { if ( isCustomProp ) { style.setProperty( name, value ); } else { style[ name ] = value; } } } else { // If a hook was provided get the non-computed value from there if ( hooks && "get" in hooks && ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { return ret; } // Otherwise just get the value from the style object return style[ name ]; } }, css: function( elem, name, extra, styles ) { var val, num, hooks, origName = camelCase( name ), isCustomProp = rcustomProp.test( name ); // Make sure that we're working with the right name. We don't // want to modify the value if it is a CSS custom property // since they are user-defined. if ( !isCustomProp ) { name = finalPropName( origName ); } // Try prefixed name followed by the unprefixed name hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; // If a hook was provided get the computed value from there if ( hooks && "get" in hooks ) { val = hooks.get( elem, true, extra ); } // Otherwise, if a way to get the computed value exists, use that if ( val === undefined ) { val = curCSS( elem, name, styles ); } // Convert "normal" to computed value if ( val === "normal" && name in cssNormalTransform ) { val = cssNormalTransform[ name ]; } // Make numeric if forced or a qualifier was provided and val looks numeric if ( extra === "" || extra ) { num = parseFloat( val ); return extra === true || isFinite( num ) ? num || 0 : val; } return val; } } ); jQuery.each( [ "height", "width" ], function( _i, dimension ) { jQuery.cssHooks[ dimension ] = { get: function( elem, computed, extra ) { if ( computed ) { // Certain elements can have dimension info if we invisibly show them // but it must have a current display style that would benefit return rdisplayswap.test( jQuery.css( elem, "display" ) ) && // Support: Safari 8+ // Table columns in Safari have non-zero offsetWidth & zero // getBoundingClientRect().width unless display is changed. // Support: IE <=11 only // Running getBoundingClientRect on a disconnected node // in IE throws an error. ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? swap( elem, cssShow, function() { return getWidthOrHeight( elem, dimension, extra ); } ) : getWidthOrHeight( elem, dimension, extra ); } }, set: function( elem, value, extra ) { var matches, styles = getStyles( elem ), // Only read styles.position if the test has a chance to fail // to avoid forcing a reflow. scrollboxSizeBuggy = !support.scrollboxSize() && styles.position === "absolute", // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) boxSizingNeeded = scrollboxSizeBuggy || extra, isBorderBox = boxSizingNeeded && jQuery.css( elem, "boxSizing", false, styles ) === "border-box", subtract = extra ? boxModelAdjustment( elem, dimension, extra, isBorderBox, styles ) : 0; // Account for unreliable border-box dimensions by comparing offset* to computed and // faking a content-box to get border and padding (gh-3699) if ( isBorderBox && scrollboxSizeBuggy ) { subtract -= Math.ceil( elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - parseFloat( styles[ dimension ] ) - boxModelAdjustment( elem, dimension, "border", false, styles ) - 0.5 ); } // Convert to pixels if value adjustment is needed if ( subtract && ( matches = rcssNum.exec( value ) ) && ( matches[ 3 ] || "px" ) !== "px" ) { elem.style[ dimension ] = value; value = jQuery.css( elem, dimension ); } return setPositiveNumber( elem, value, subtract ); } }; } ); jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, function( elem, computed ) { if ( computed ) { return ( parseFloat( curCSS( elem, "marginLeft" ) ) || elem.getBoundingClientRect().left - swap( elem, { marginLeft: 0 }, function() { return elem.getBoundingClientRect().left; } ) ) + "px"; } } ); // These hooks are used by animate to expand properties jQuery.each( { margin: "", padding: "", border: "Width" }, function( prefix, suffix ) { jQuery.cssHooks[ prefix + suffix ] = { expand: function( value ) { var i = 0, expanded = {}, // Assumes a single number if not a string parts = typeof value === "string" ? value.split( " " ) : [ value ]; for ( ; i < 4; i++ ) { expanded[ prefix + cssExpand[ i ] + suffix ] = parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; } return expanded; } }; if ( prefix !== "margin" ) { jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; } } ); jQuery.fn.extend( { css: function( name, value ) { return access( this, function( elem, name, value ) { var styles, len, map = {}, i = 0; if ( Array.isArray( name ) ) { styles = getStyles( elem ); len = name.length; for ( ; i < len; i++ ) { map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); } return map; } return value !== undefined ? jQuery.style( elem, name, value ) : jQuery.css( elem, name ); }, name, value, arguments.length > 1 ); } } ); function Tween( elem, options, prop, end, easing ) { return new Tween.prototype.init( elem, options, prop, end, easing ); } jQuery.Tween = Tween; Tween.prototype = { constructor: Tween, init: function( elem, options, prop, end, easing, unit ) { this.elem = elem; this.prop = prop; this.easing = easing || jQuery.easing._default; this.options = options; this.start = this.now = this.cur(); this.end = end; this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); }, cur: function() { var hooks = Tween.propHooks[ this.prop ]; return hooks && hooks.get ? hooks.get( this ) : Tween.propHooks._default.get( this ); }, run: function( percent ) { var eased, hooks = Tween.propHooks[ this.prop ]; if ( this.options.duration ) { this.pos = eased = jQuery.easing[ this.easing ]( percent, this.options.duration * percent, 0, 1, this.options.duration ); } else { this.pos = eased = percent; } this.now = ( this.end - this.start ) * eased + this.start; if ( this.options.step ) { this.options.step.call( this.elem, this.now, this ); } if ( hooks && hooks.set ) { hooks.set( this ); } else { Tween.propHooks._default.set( this ); } return this; } }; Tween.prototype.init.prototype = Tween.prototype; Tween.propHooks = { _default: { get: function( tween ) { var result; // Use a property on the element directly when it is not a DOM element, // or when there is no matching style property that exists. if ( tween.elem.nodeType !== 1 || tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { return tween.elem[ tween.prop ]; } // Passing an empty string as a 3rd parameter to .css will automatically // attempt a parseFloat and fallback to a string if the parse fails. // Simple values such as "10px" are parsed to Float; // complex values such as "rotate(1rad)" are returned as-is. result = jQuery.css( tween.elem, tween.prop, "" ); // Empty strings, null, undefined and "auto" are converted to 0. return !result || result === "auto" ? 0 : result; }, set: function( tween ) { // Use step hook for back compat. // Use cssHook if its there. // Use .style if available and use plain properties where available. if ( jQuery.fx.step[ tween.prop ] ) { jQuery.fx.step[ tween.prop ]( tween ); } else if ( tween.elem.nodeType === 1 && ( jQuery.cssHooks[ tween.prop ] || tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); } else { tween.elem[ tween.prop ] = tween.now; } } } }; // Support: IE <=9 only // Panic based approach to setting things on disconnected nodes Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { set: function( tween ) { if ( tween.elem.nodeType && tween.elem.parentNode ) { tween.elem[ tween.prop ] = tween.now; } } }; jQuery.easing = { linear: function( p ) { return p; }, swing: function( p ) { return 0.5 - Math.cos( p * Math.PI ) / 2; }, _default: "swing" }; jQuery.fx = Tween.prototype.init; // Back compat <1.8 extension point jQuery.fx.step = {}; var fxNow, inProgress, rfxtypes = /^(?:toggle|show|hide)$/, rrun = /queueHooks$/; function schedule() { if ( inProgress ) { if ( document.hidden === false && window.requestAnimationFrame ) { window.requestAnimationFrame( schedule ); } else { window.setTimeout( schedule, jQuery.fx.interval ); } jQuery.fx.tick(); } } // Animations created synchronously will run synchronously function createFxNow() { window.setTimeout( function() { fxNow = undefined; } ); return ( fxNow = Date.now() ); } // Generate parameters to create a standard animation function genFx( type, includeWidth ) { var which, i = 0, attrs = { height: type }; // If we include width, step value is 1 to do all cssExpand values, // otherwise step value is 2 to skip over Left and Right includeWidth = includeWidth ? 1 : 0; for ( ; i < 4; i += 2 - includeWidth ) { which = cssExpand[ i ]; attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; } if ( includeWidth ) { attrs.opacity = attrs.width = type; } return attrs; } function createTween( value, prop, animation ) { var tween, collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), index = 0, length = collection.length; for ( ; index < length; index++ ) { if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { // We're done with this property return tween; } } } function defaultPrefilter( elem, props, opts ) { var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, isBox = "width" in props || "height" in props, anim = this, orig = {}, style = elem.style, hidden = elem.nodeType && isHiddenWithinTree( elem ), dataShow = dataPriv.get( elem, "fxshow" ); // Queue-skipping animations hijack the fx hooks if ( !opts.queue ) { hooks = jQuery._queueHooks( elem, "fx" ); if ( hooks.unqueued == null ) { hooks.unqueued = 0; oldfire = hooks.empty.fire; hooks.empty.fire = function() { if ( !hooks.unqueued ) { oldfire(); } }; } hooks.unqueued++; anim.always( function() { // Ensure the complete handler is called before this completes anim.always( function() { hooks.unqueued--; if ( !jQuery.queue( elem, "fx" ).length ) { hooks.empty.fire(); } } ); } ); } // Detect show/hide animations for ( prop in props ) { value = props[ prop ]; if ( rfxtypes.test( value ) ) { delete props[ prop ]; toggle = toggle || value === "toggle"; if ( value === ( hidden ? "hide" : "show" ) ) { // Pretend to be hidden if this is a "show" and // there is still data from a stopped show/hide if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { hidden = true; // Ignore all other no-op show/hide data } else { continue; } } orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); } } // Bail out if this is a no-op like .hide().hide() propTween = !jQuery.isEmptyObject( props ); if ( !propTween && jQuery.isEmptyObject( orig ) ) { return; } // Restrict "overflow" and "display" styles during box animations if ( isBox && elem.nodeType === 1 ) { // Support: IE <=9 - 11, Edge 12 - 15 // Record all 3 overflow attributes because IE does not infer the shorthand // from identically-valued overflowX and overflowY and Edge just mirrors // the overflowX value there. opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; // Identify a display type, preferring old show/hide data over the CSS cascade restoreDisplay = dataShow && dataShow.display; if ( restoreDisplay == null ) { restoreDisplay = dataPriv.get( elem, "display" ); } display = jQuery.css( elem, "display" ); if ( display === "none" ) { if ( restoreDisplay ) { display = restoreDisplay; } else { // Get nonempty value(s) by temporarily forcing visibility showHide( [ elem ], true ); restoreDisplay = elem.style.display || restoreDisplay; display = jQuery.css( elem, "display" ); showHide( [ elem ] ); } } // Animate inline elements as inline-block if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { if ( jQuery.css( elem, "float" ) === "none" ) { // Restore the original display value at the end of pure show/hide animations if ( !propTween ) { anim.done( function() { style.display = restoreDisplay; } ); if ( restoreDisplay == null ) { display = style.display; restoreDisplay = display === "none" ? "" : display; } } style.display = "inline-block"; } } } if ( opts.overflow ) { style.overflow = "hidden"; anim.always( function() { style.overflow = opts.overflow[ 0 ]; style.overflowX = opts.overflow[ 1 ]; style.overflowY = opts.overflow[ 2 ]; } ); } // Implement show/hide animations propTween = false; for ( prop in orig ) { // General show/hide setup for this element animation if ( !propTween ) { if ( dataShow ) { if ( "hidden" in dataShow ) { hidden = dataShow.hidden; } } else { dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); } // Store hidden/visible for toggle so `.stop().toggle()` "reverses" if ( toggle ) { dataShow.hidden = !hidden; } // Show elements before animating them if ( hidden ) { showHide( [ elem ], true ); } /* eslint-disable no-loop-func */ anim.done( function() { /* eslint-enable no-loop-func */ // The final step of a "hide" animation is actually hiding the element if ( !hidden ) { showHide( [ elem ] ); } dataPriv.remove( elem, "fxshow" ); for ( prop in orig ) { jQuery.style( elem, prop, orig[ prop ] ); } } ); } // Per-property setup propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); if ( !( prop in dataShow ) ) { dataShow[ prop ] = propTween.start; if ( hidden ) { propTween.end = propTween.start; propTween.start = 0; } } } } function propFilter( props, specialEasing ) { var index, name, easing, value, hooks; // camelCase, specialEasing and expand cssHook pass for ( index in props ) { name = camelCase( index ); easing = specialEasing[ name ]; value = props[ index ]; if ( Array.isArray( value ) ) { easing = value[ 1 ]; value = props[ index ] = value[ 0 ]; } if ( index !== name ) { props[ name ] = value; delete props[ index ]; } hooks = jQuery.cssHooks[ name ]; if ( hooks && "expand" in hooks ) { value = hooks.expand( value ); delete props[ name ]; // Not quite $.extend, this won't overwrite existing keys. // Reusing 'index' because we have the correct "name" for ( index in value ) { if ( !( index in props ) ) { props[ index ] = value[ index ]; specialEasing[ index ] = easing; } } } else { specialEasing[ name ] = easing; } } } function Animation( elem, properties, options ) { var result, stopped, index = 0, length = Animation.prefilters.length, deferred = jQuery.Deferred().always( function() { // Don't match elem in the :animated selector delete tick.elem; } ), tick = function() { if ( stopped ) { return false; } var currentTime = fxNow || createFxNow(), remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), // Support: Android 2.3 only // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) temp = remaining / animation.duration || 0, percent = 1 - temp, index = 0, length = animation.tweens.length; for ( ; index < length; index++ ) { animation.tweens[ index ].run( percent ); } deferred.notifyWith( elem, [ animation, percent, remaining ] ); // If there's more to do, yield if ( percent < 1 && length ) { return remaining; } // If this was an empty animation, synthesize a final progress notification if ( !length ) { deferred.notifyWith( elem, [ animation, 1, 0 ] ); } // Resolve the animation and report its conclusion deferred.resolveWith( elem, [ animation ] ); return false; }, animation = deferred.promise( { elem: elem, props: jQuery.extend( {}, properties ), opts: jQuery.extend( true, { specialEasing: {}, easing: jQuery.easing._default }, options ), originalProperties: properties, originalOptions: options, startTime: fxNow || createFxNow(), duration: options.duration, tweens: [], createTween: function( prop, end ) { var tween = jQuery.Tween( elem, animation.opts, prop, end, animation.opts.specialEasing[ prop ] || animation.opts.easing ); animation.tweens.push( tween ); return tween; }, stop: function( gotoEnd ) { var index = 0, // If we are going to the end, we want to run all the tweens // otherwise we skip this part length = gotoEnd ? animation.tweens.length : 0; if ( stopped ) { return this; } stopped = true; for ( ; index < length; index++ ) { animation.tweens[ index ].run( 1 ); } // Resolve when we played the last frame; otherwise, reject if ( gotoEnd ) { deferred.notifyWith( elem, [ animation, 1, 0 ] ); deferred.resolveWith( elem, [ animation, gotoEnd ] ); } else { deferred.rejectWith( elem, [ animation, gotoEnd ] ); } return this; } } ), props = animation.props; propFilter( props, animation.opts.specialEasing ); for ( ; index < length; index++ ) { result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); if ( result ) { if ( isFunction( result.stop ) ) { jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = result.stop.bind( result ); } return result; } } jQuery.map( props, createTween, animation ); if ( isFunction( animation.opts.start ) ) { animation.opts.start.call( elem, animation ); } // Attach callbacks from options animation .progress( animation.opts.progress ) .done( animation.opts.done, animation.opts.complete ) .fail( animation.opts.fail ) .always( animation.opts.always ); jQuery.fx.timer( jQuery.extend( tick, { elem: elem, anim: animation, queue: animation.opts.queue } ) ); return animation; } jQuery.Animation = jQuery.extend( Animation, { tweeners: { "*": [ function( prop, value ) { var tween = this.createTween( prop, value ); adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); return tween; } ] }, tweener: function( props, callback ) { if ( isFunction( props ) ) { callback = props; props = [ "*" ]; } else { props = props.match( rnothtmlwhite ); } var prop, index = 0, length = props.length; for ( ; index < length; index++ ) { prop = props[ index ]; Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; Animation.tweeners[ prop ].unshift( callback ); } }, prefilters: [ defaultPrefilter ], prefilter: function( callback, prepend ) { if ( prepend ) { Animation.prefilters.unshift( callback ); } else { Animation.prefilters.push( callback ); } } } ); jQuery.speed = function( speed, easing, fn ) { var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { complete: fn || !fn && easing || isFunction( speed ) && speed, duration: speed, easing: fn && easing || easing && !isFunction( easing ) && easing }; // Go to the end state if fx are off if ( jQuery.fx.off ) { opt.duration = 0; } else { if ( typeof opt.duration !== "number" ) { if ( opt.duration in jQuery.fx.speeds ) { opt.duration = jQuery.fx.speeds[ opt.duration ]; } else { opt.duration = jQuery.fx.speeds._default; } } } // Normalize opt.queue - true/undefined/null -> "fx" if ( opt.queue == null || opt.queue === true ) { opt.queue = "fx"; } // Queueing opt.old = opt.complete; opt.complete = function() { if ( isFunction( opt.old ) ) { opt.old.call( this ); } if ( opt.queue ) { jQuery.dequeue( this, opt.queue ); } }; return opt; }; jQuery.fn.extend( { fadeTo: function( speed, to, easing, callback ) { // Show any hidden elements after setting opacity to 0 return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() // Animate to the value specified .end().animate( { opacity: to }, speed, easing, callback ); }, animate: function( prop, speed, easing, callback ) { var empty = jQuery.isEmptyObject( prop ), optall = jQuery.speed( speed, easing, callback ), doAnimation = function() { // Operate on a copy of prop so per-property easing won't be lost var anim = Animation( this, jQuery.extend( {}, prop ), optall ); // Empty animations, or finishing resolves immediately if ( empty || dataPriv.get( this, "finish" ) ) { anim.stop( true ); } }; doAnimation.finish = doAnimation; return empty || optall.queue === false ? this.each( doAnimation ) : this.queue( optall.queue, doAnimation ); }, stop: function( type, clearQueue, gotoEnd ) { var stopQueue = function( hooks ) { var stop = hooks.stop; delete hooks.stop; stop( gotoEnd ); }; if ( typeof type !== "string" ) { gotoEnd = clearQueue; clearQueue = type; type = undefined; } if ( clearQueue ) { this.queue( type || "fx", [] ); } return this.each( function() { var dequeue = true, index = type != null && type + "queueHooks", timers = jQuery.timers, data = dataPriv.get( this ); if ( index ) { if ( data[ index ] && data[ index ].stop ) { stopQueue( data[ index ] ); } } else { for ( index in data ) { if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { stopQueue( data[ index ] ); } } } for ( index = timers.length; index--; ) { if ( timers[ index ].elem === this && ( type == null || timers[ index ].queue === type ) ) { timers[ index ].anim.stop( gotoEnd ); dequeue = false; timers.splice( index, 1 ); } } // Start the next in the queue if the last step wasn't forced. // Timers currently will call their complete callbacks, which // will dequeue but only if they were gotoEnd. if ( dequeue || !gotoEnd ) { jQuery.dequeue( this, type ); } } ); }, finish: function( type ) { if ( type !== false ) { type = type || "fx"; } return this.each( function() { var index, data = dataPriv.get( this ), queue = data[ type + "queue" ], hooks = data[ type + "queueHooks" ], timers = jQuery.timers, length = queue ? queue.length : 0; // Enable finishing flag on private data data.finish = true; // Empty the queue first jQuery.queue( this, type, [] ); if ( hooks && hooks.stop ) { hooks.stop.call( this, true ); } // Look for any active animations, and finish them for ( index = timers.length; index--; ) { if ( timers[ index ].elem === this && timers[ index ].queue === type ) { timers[ index ].anim.stop( true ); timers.splice( index, 1 ); } } // Look for any animations in the old queue and finish them for ( index = 0; index < length; index++ ) { if ( queue[ index ] && queue[ index ].finish ) { queue[ index ].finish.call( this ); } } // Turn off finishing flag delete data.finish; } ); } } ); jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { var cssFn = jQuery.fn[ name ]; jQuery.fn[ name ] = function( speed, easing, callback ) { return speed == null || typeof speed === "boolean" ? cssFn.apply( this, arguments ) : this.animate( genFx( name, true ), speed, easing, callback ); }; } ); // Generate shortcuts for custom animations jQuery.each( { slideDown: genFx( "show" ), slideUp: genFx( "hide" ), slideToggle: genFx( "toggle" ), fadeIn: { opacity: "show" }, fadeOut: { opacity: "hide" }, fadeToggle: { opacity: "toggle" } }, function( name, props ) { jQuery.fn[ name ] = function( speed, easing, callback ) { return this.animate( props, speed, easing, callback ); }; } ); jQuery.timers = []; jQuery.fx.tick = function() { var timer, i = 0, timers = jQuery.timers; fxNow = Date.now(); for ( ; i < timers.length; i++ ) { timer = timers[ i ]; // Run the timer and safely remove it when done (allowing for external removal) if ( !timer() && timers[ i ] === timer ) { timers.splice( i--, 1 ); } } if ( !timers.length ) { jQuery.fx.stop(); } fxNow = undefined; }; jQuery.fx.timer = function( timer ) { jQuery.timers.push( timer ); jQuery.fx.start(); }; jQuery.fx.interval = 13; jQuery.fx.start = function() { if ( inProgress ) { return; } inProgress = true; schedule(); }; jQuery.fx.stop = function() { inProgress = null; }; jQuery.fx.speeds = { slow: 600, fast: 200, // Default speed _default: 400 }; // Based off of the plugin by Clint Helfers, with permission. // https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ jQuery.fn.delay = function( time, type ) { time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; type = type || "fx"; return this.queue( type, function( next, hooks ) { var timeout = window.setTimeout( next, time ); hooks.stop = function() { window.clearTimeout( timeout ); }; } ); }; ( function() { var input = document.createElement( "input" ), select = document.createElement( "select" ), opt = select.appendChild( document.createElement( "option" ) ); input.type = "checkbox"; // Support: Android <=4.3 only // Default value for a checkbox should be "on" support.checkOn = input.value !== ""; // Support: IE <=11 only // Must access selectedIndex to make default options select support.optSelected = opt.selected; // Support: IE <=11 only // An input loses its value after becoming a radio input = document.createElement( "input" ); input.value = "t"; input.type = "radio"; support.radioValue = input.value === "t"; } )(); var boolHook, attrHandle = jQuery.expr.attrHandle; jQuery.fn.extend( { attr: function( name, value ) { return access( this, jQuery.attr, name, value, arguments.length > 1 ); }, removeAttr: function( name ) { return this.each( function() { jQuery.removeAttr( this, name ); } ); } } ); jQuery.extend( { attr: function( elem, name, value ) { var ret, hooks, nType = elem.nodeType; // Don't get/set attributes on text, comment and attribute nodes if ( nType === 3 || nType === 8 || nType === 2 ) { return; } // Fallback to prop when attributes are not supported if ( typeof elem.getAttribute === "undefined" ) { return jQuery.prop( elem, name, value ); } // Attribute hooks are determined by the lowercase version // Grab necessary hook if one is defined if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { hooks = jQuery.attrHooks[ name.toLowerCase() ] || ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); } if ( value !== undefined ) { if ( value === null ) { jQuery.removeAttr( elem, name ); return; } if ( hooks && "set" in hooks && ( ret = hooks.set( elem, value, name ) ) !== undefined ) { return ret; } elem.setAttribute( name, value + "" ); return value; } if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { return ret; } ret = jQuery.find.attr( elem, name ); // Non-existent attributes return null, we normalize to undefined return ret == null ? undefined : ret; }, attrHooks: { type: { set: function( elem, value ) { if ( !support.radioValue && value === "radio" && nodeName( elem, "input" ) ) { var val = elem.value; elem.setAttribute( "type", value ); if ( val ) { elem.value = val; } return value; } } } }, removeAttr: function( elem, value ) { var name, i = 0, // Attribute names can contain non-HTML whitespace characters // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 attrNames = value && value.match( rnothtmlwhite ); if ( attrNames && elem.nodeType === 1 ) { while ( ( name = attrNames[ i++ ] ) ) { elem.removeAttribute( name ); } } } } ); // Hooks for boolean attributes boolHook = { set: function( elem, value, name ) { if ( value === false ) { // Remove boolean attributes when set to false jQuery.removeAttr( elem, name ); } else { elem.setAttribute( name, name ); } return name; } }; jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { var getter = attrHandle[ name ] || jQuery.find.attr; attrHandle[ name ] = function( elem, name, isXML ) { var ret, handle, lowercaseName = name.toLowerCase(); if ( !isXML ) { // Avoid an infinite loop by temporarily removing this function from the getter handle = attrHandle[ lowercaseName ]; attrHandle[ lowercaseName ] = ret; ret = getter( elem, name, isXML ) != null ? lowercaseName : null; attrHandle[ lowercaseName ] = handle; } return ret; }; } ); var rfocusable = /^(?:input|select|textarea|button)$/i, rclickable = /^(?:a|area)$/i; jQuery.fn.extend( { prop: function( name, value ) { return access( this, jQuery.prop, name, value, arguments.length > 1 ); }, removeProp: function( name ) { return this.each( function() { delete this[ jQuery.propFix[ name ] || name ]; } ); } } ); jQuery.extend( { prop: function( elem, name, value ) { var ret, hooks, nType = elem.nodeType; // Don't get/set properties on text, comment and attribute nodes if ( nType === 3 || nType === 8 || nType === 2 ) { return; } if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { // Fix name and attach hooks name = jQuery.propFix[ name ] || name; hooks = jQuery.propHooks[ name ]; } if ( value !== undefined ) { if ( hooks && "set" in hooks && ( ret = hooks.set( elem, value, name ) ) !== undefined ) { return ret; } return ( elem[ name ] = value ); } if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { return ret; } return elem[ name ]; }, propHooks: { tabIndex: { get: function( elem ) { // Support: IE <=9 - 11 only // elem.tabIndex doesn't always return the // correct value when it hasn't been explicitly set // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ // Use proper attribute retrieval(#12072) var tabindex = jQuery.find.attr( elem, "tabindex" ); if ( tabindex ) { return parseInt( tabindex, 10 ); } if ( rfocusable.test( elem.nodeName ) || rclickable.test( elem.nodeName ) && elem.href ) { return 0; } return -1; } } }, propFix: { "for": "htmlFor", "class": "className" } } ); // Support: IE <=11 only // Accessing the selectedIndex property // forces the browser to respect setting selected // on the option // The getter ensures a default option is selected // when in an optgroup // eslint rule "no-unused-expressions" is disabled for this code // since it considers such accessions noop if ( !support.optSelected ) { jQuery.propHooks.selected = { get: function( elem ) { /* eslint no-unused-expressions: "off" */ var parent = elem.parentNode; if ( parent && parent.parentNode ) { parent.parentNode.selectedIndex; } return null; }, set: function( elem ) { /* eslint no-unused-expressions: "off" */ var parent = elem.parentNode; if ( parent ) { parent.selectedIndex; if ( parent.parentNode ) { parent.parentNode.selectedIndex; } } } }; } jQuery.each( [ "tabIndex", "readOnly", "maxLength", "cellSpacing", "cellPadding", "rowSpan", "colSpan", "useMap", "frameBorder", "contentEditable" ], function() { jQuery.propFix[ this.toLowerCase() ] = this; } ); // Strip and collapse whitespace according to HTML spec // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace function stripAndCollapse( value ) { var tokens = value.match( rnothtmlwhite ) || []; return tokens.join( " " ); } function getClass( elem ) { return elem.getAttribute && elem.getAttribute( "class" ) || ""; } function classesToArray( value ) { if ( Array.isArray( value ) ) { return value; } if ( typeof value === "string" ) { return value.match( rnothtmlwhite ) || []; } return []; } jQuery.fn.extend( { addClass: function( value ) { var classes, elem, cur, curValue, clazz, j, finalValue, i = 0; if ( isFunction( value ) ) { return this.each( function( j ) { jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); } ); } classes = classesToArray( value ); if ( classes.length ) { while ( ( elem = this[ i++ ] ) ) { curValue = getClass( elem ); cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); if ( cur ) { j = 0; while ( ( clazz = classes[ j++ ] ) ) { if ( cur.indexOf( " " + clazz + " " ) < 0 ) { cur += clazz + " "; } } // Only assign if different to avoid unneeded rendering. finalValue = stripAndCollapse( cur ); if ( curValue !== finalValue ) { elem.setAttribute( "class", finalValue ); } } } } return this; }, removeClass: function( value ) { var classes, elem, cur, curValue, clazz, j, finalValue, i = 0; if ( isFunction( value ) ) { return this.each( function( j ) { jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); } ); } if ( !arguments.length ) { return this.attr( "class", "" ); } classes = classesToArray( value ); if ( classes.length ) { while ( ( elem = this[ i++ ] ) ) { curValue = getClass( elem ); // This expression is here for better compressibility (see addClass) cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); if ( cur ) { j = 0; while ( ( clazz = classes[ j++ ] ) ) { // Remove *all* instances while ( cur.indexOf( " " + clazz + " " ) > -1 ) { cur = cur.replace( " " + clazz + " ", " " ); } } // Only assign if different to avoid unneeded rendering. finalValue = stripAndCollapse( cur ); if ( curValue !== finalValue ) { elem.setAttribute( "class", finalValue ); } } } } return this; }, toggleClass: function( value, stateVal ) { var type = typeof value, isValidValue = type === "string" || Array.isArray( value ); if ( typeof stateVal === "boolean" && isValidValue ) { return stateVal ? this.addClass( value ) : this.removeClass( value ); } if ( isFunction( value ) ) { return this.each( function( i ) { jQuery( this ).toggleClass( value.call( this, i, getClass( this ), stateVal ), stateVal ); } ); } return this.each( function() { var className, i, self, classNames; if ( isValidValue ) { // Toggle individual class names i = 0; self = jQuery( this ); classNames = classesToArray( value ); while ( ( className = classNames[ i++ ] ) ) { // Check each className given, space separated list if ( self.hasClass( className ) ) { self.removeClass( className ); } else { self.addClass( className ); } } // Toggle whole class name } else if ( value === undefined || type === "boolean" ) { className = getClass( this ); if ( className ) { // Store className if set dataPriv.set( this, "__className__", className ); } // If the element has a class name or if we're passed `false`, // then remove the whole classname (if there was one, the above saved it). // Otherwise bring back whatever was previously saved (if anything), // falling back to the empty string if nothing was stored. if ( this.setAttribute ) { this.setAttribute( "class", className || value === false ? "" : dataPriv.get( this, "__className__" ) || "" ); } } } ); }, hasClass: function( selector ) { var className, elem, i = 0; className = " " + selector + " "; while ( ( elem = this[ i++ ] ) ) { if ( elem.nodeType === 1 && ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { return true; } } return false; } } ); var rreturn = /\r/g; jQuery.fn.extend( { val: function( value ) { var hooks, ret, valueIsFunction, elem = this[ 0 ]; if ( !arguments.length ) { if ( elem ) { hooks = jQuery.valHooks[ elem.type ] || jQuery.valHooks[ elem.nodeName.toLowerCase() ]; if ( hooks && "get" in hooks && ( ret = hooks.get( elem, "value" ) ) !== undefined ) { return ret; } ret = elem.value; // Handle most common string cases if ( typeof ret === "string" ) { return ret.replace( rreturn, "" ); } // Handle cases where value is null/undef or number return ret == null ? "" : ret; } return; } valueIsFunction = isFunction( value ); return this.each( function( i ) { var val; if ( this.nodeType !== 1 ) { return; } if ( valueIsFunction ) { val = value.call( this, i, jQuery( this ).val() ); } else { val = value; } // Treat null/undefined as ""; convert numbers to string if ( val == null ) { val = ""; } else if ( typeof val === "number" ) { val += ""; } else if ( Array.isArray( val ) ) { val = jQuery.map( val, function( value ) { return value == null ? "" : value + ""; } ); } hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; // If set returns undefined, fall back to normal setting if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { this.value = val; } } ); } } ); jQuery.extend( { valHooks: { option: { get: function( elem ) { var val = jQuery.find.attr( elem, "value" ); return val != null ? val : // Support: IE <=10 - 11 only // option.text throws exceptions (#14686, #14858) // Strip and collapse whitespace // https://html.spec.whatwg.org/#strip-and-collapse-whitespace stripAndCollapse( jQuery.text( elem ) ); } }, select: { get: function( elem ) { var value, option, i, options = elem.options, index = elem.selectedIndex, one = elem.type === "select-one", values = one ? null : [], max = one ? index + 1 : options.length; if ( index < 0 ) { i = max; } else { i = one ? index : 0; } // Loop through all the selected options for ( ; i < max; i++ ) { option = options[ i ]; // Support: IE <=9 only // IE8-9 doesn't update selected after form reset (#2551) if ( ( option.selected || i === index ) && // Don't return options that are disabled or in a disabled optgroup !option.disabled && ( !option.parentNode.disabled || !nodeName( option.parentNode, "optgroup" ) ) ) { // Get the specific value for the option value = jQuery( option ).val(); // We don't need an array for one selects if ( one ) { return value; } // Multi-Selects return an array values.push( value ); } } return values; }, set: function( elem, value ) { var optionSet, option, options = elem.options, values = jQuery.makeArray( value ), i = options.length; while ( i-- ) { option = options[ i ]; /* eslint-disable no-cond-assign */ if ( option.selected = jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 ) { optionSet = true; } /* eslint-enable no-cond-assign */ } // Force browsers to behave consistently when non-matching value is set if ( !optionSet ) { elem.selectedIndex = -1; } return values; } } } } ); // Radios and checkboxes getter/setter jQuery.each( [ "radio", "checkbox" ], function() { jQuery.valHooks[ this ] = { set: function( elem, value ) { if ( Array.isArray( value ) ) { return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); } } }; if ( !support.checkOn ) { jQuery.valHooks[ this ].get = function( elem ) { return elem.getAttribute( "value" ) === null ? "on" : elem.value; }; } } ); // Return jQuery for attributes-only inclusion support.focusin = "onfocusin" in window; var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, stopPropagationCallback = function( e ) { e.stopPropagation(); }; jQuery.extend( jQuery.event, { trigger: function( event, data, elem, onlyHandlers ) { var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, eventPath = [ elem || document ], type = hasOwn.call( event, "type" ) ? event.type : event, namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; cur = lastElement = tmp = elem = elem || document; // Don't do events on text and comment nodes if ( elem.nodeType === 3 || elem.nodeType === 8 ) { return; } // focus/blur morphs to focusin/out; ensure we're not firing them right now if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { return; } if ( type.indexOf( "." ) > -1 ) { // Namespaced trigger; create a regexp to match event type in handle() namespaces = type.split( "." ); type = namespaces.shift(); namespaces.sort(); } ontype = type.indexOf( ":" ) < 0 && "on" + type; // Caller can pass in a jQuery.Event object, Object, or just an event type string event = event[ jQuery.expando ] ? event : new jQuery.Event( type, typeof event === "object" && event ); // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) event.isTrigger = onlyHandlers ? 2 : 3; event.namespace = namespaces.join( "." ); event.rnamespace = event.namespace ? new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : null; // Clean up the event in case it is being reused event.result = undefined; if ( !event.target ) { event.target = elem; } // Clone any incoming data and prepend the event, creating the handler arg list data = data == null ? [ event ] : jQuery.makeArray( data, [ event ] ); // Allow special events to draw outside the lines special = jQuery.event.special[ type ] || {}; if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { return; } // Determine event propagation path in advance, per W3C events spec (#9951) // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { bubbleType = special.delegateType || type; if ( !rfocusMorph.test( bubbleType + type ) ) { cur = cur.parentNode; } for ( ; cur; cur = cur.parentNode ) { eventPath.push( cur ); tmp = cur; } // Only add window if we got to document (e.g., not plain obj or detached DOM) if ( tmp === ( elem.ownerDocument || document ) ) { eventPath.push( tmp.defaultView || tmp.parentWindow || window ); } } // Fire handlers on the event path i = 0; while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { lastElement = cur; event.type = i > 1 ? bubbleType : special.bindType || type; // jQuery handler handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] && dataPriv.get( cur, "handle" ); if ( handle ) { handle.apply( cur, data ); } // Native handler handle = ontype && cur[ ontype ]; if ( handle && handle.apply && acceptData( cur ) ) { event.result = handle.apply( cur, data ); if ( event.result === false ) { event.preventDefault(); } } } event.type = type; // If nobody prevented the default action, do it now if ( !onlyHandlers && !event.isDefaultPrevented() ) { if ( ( !special._default || special._default.apply( eventPath.pop(), data ) === false ) && acceptData( elem ) ) { // Call a native DOM method on the target with the same name as the event. // Don't do default actions on window, that's where global variables be (#6170) if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { // Don't re-trigger an onFOO event when we call its FOO() method tmp = elem[ ontype ]; if ( tmp ) { elem[ ontype ] = null; } // Prevent re-triggering of the same event, since we already bubbled it above jQuery.event.triggered = type; if ( event.isPropagationStopped() ) { lastElement.addEventListener( type, stopPropagationCallback ); } elem[ type ](); if ( event.isPropagationStopped() ) { lastElement.removeEventListener( type, stopPropagationCallback ); } jQuery.event.triggered = undefined; if ( tmp ) { elem[ ontype ] = tmp; } } } } return event.result; }, // Piggyback on a donor event to simulate a different one // Used only for `focus(in | out)` events simulate: function( type, elem, event ) { var e = jQuery.extend( new jQuery.Event(), event, { type: type, isSimulated: true } ); jQuery.event.trigger( e, null, elem ); } } ); jQuery.fn.extend( { trigger: function( type, data ) { return this.each( function() { jQuery.event.trigger( type, data, this ); } ); }, triggerHandler: function( type, data ) { var elem = this[ 0 ]; if ( elem ) { return jQuery.event.trigger( type, data, elem, true ); } } } ); // Support: Firefox <=44 // Firefox doesn't have focus(in | out) events // Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 // // Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 // focus(in | out) events fire after focus & blur events, // which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order // Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 if ( !support.focusin ) { jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { // Attach a single capturing handler on the document while someone wants focusin/focusout var handler = function( event ) { jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); }; jQuery.event.special[ fix ] = { setup: function() { // Handle: regular nodes (via `this.ownerDocument`), window // (via `this.document`) & document (via `this`). var doc = this.ownerDocument || this.document || this, attaches = dataPriv.access( doc, fix ); if ( !attaches ) { doc.addEventListener( orig, handler, true ); } dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); }, teardown: function() { var doc = this.ownerDocument || this.document || this, attaches = dataPriv.access( doc, fix ) - 1; if ( !attaches ) { doc.removeEventListener( orig, handler, true ); dataPriv.remove( doc, fix ); } else { dataPriv.access( doc, fix, attaches ); } } }; } ); } var location = window.location; var nonce = { guid: Date.now() }; var rquery = ( /\?/ ); // Cross-browser xml parsing jQuery.parseXML = function( data ) { var xml; if ( !data || typeof data !== "string" ) { return null; } // Support: IE 9 - 11 only // IE throws on parseFromString with invalid input. try { xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); } catch ( e ) { xml = undefined; } if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) { jQuery.error( "Invalid XML: " + data ); } return xml; }; var rbracket = /\[\]$/, rCRLF = /\r?\n/g, rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, rsubmittable = /^(?:input|select|textarea|keygen)/i; function buildParams( prefix, obj, traditional, add ) { var name; if ( Array.isArray( obj ) ) { // Serialize array item. jQuery.each( obj, function( i, v ) { if ( traditional || rbracket.test( prefix ) ) { // Treat each array item as a scalar. add( prefix, v ); } else { // Item is non-scalar (array or object), encode its numeric index. buildParams( prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", v, traditional, add ); } } ); } else if ( !traditional && toType( obj ) === "object" ) { // Serialize object item. for ( name in obj ) { buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); } } else { // Serialize scalar item. add( prefix, obj ); } } // Serialize an array of form elements or a set of // key/values into a query string jQuery.param = function( a, traditional ) { var prefix, s = [], add = function( key, valueOrFunction ) { // If value is a function, invoke it and use its return value var value = isFunction( valueOrFunction ) ? valueOrFunction() : valueOrFunction; s[ s.length ] = encodeURIComponent( key ) + "=" + encodeURIComponent( value == null ? "" : value ); }; if ( a == null ) { return ""; } // If an array was passed in, assume that it is an array of form elements. if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { // Serialize the form elements jQuery.each( a, function() { add( this.name, this.value ); } ); } else { // If traditional, encode the "old" way (the way 1.3.2 or older // did it), otherwise encode params recursively. for ( prefix in a ) { buildParams( prefix, a[ prefix ], traditional, add ); } } // Return the resulting serialization return s.join( "&" ); }; jQuery.fn.extend( { serialize: function() { return jQuery.param( this.serializeArray() ); }, serializeArray: function() { return this.map( function() { // Can add propHook for "elements" to filter or add form elements var elements = jQuery.prop( this, "elements" ); return elements ? jQuery.makeArray( elements ) : this; } ) .filter( function() { var type = this.type; // Use .is( ":disabled" ) so that fieldset[disabled] works return this.name && !jQuery( this ).is( ":disabled" ) && rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && ( this.checked || !rcheckableType.test( type ) ); } ) .map( function( _i, elem ) { var val = jQuery( this ).val(); if ( val == null ) { return null; } if ( Array.isArray( val ) ) { return jQuery.map( val, function( val ) { return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; } ); } return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; } ).get(); } } ); var r20 = /%20/g, rhash = /#.*$/, rantiCache = /([?&])_=[^&]*/, rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, // #7653, #8125, #8152: local protocol detection rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, rnoContent = /^(?:GET|HEAD)$/, rprotocol = /^\/\//, /* Prefilters * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) * 2) These are called: * - BEFORE asking for a transport * - AFTER param serialization (s.data is a string if s.processData is true) * 3) key is the dataType * 4) the catchall symbol "*" can be used * 5) execution will start with transport dataType and THEN continue down to "*" if needed */ prefilters = {}, /* Transports bindings * 1) key is the dataType * 2) the catchall symbol "*" can be used * 3) selection will start with transport dataType and THEN go to "*" if needed */ transports = {}, // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression allTypes = "*/".concat( "*" ), // Anchor tag for parsing the document origin originAnchor = document.createElement( "a" ); originAnchor.href = location.href; // Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport function addToPrefiltersOrTransports( structure ) { // dataTypeExpression is optional and defaults to "*" return function( dataTypeExpression, func ) { if ( typeof dataTypeExpression !== "string" ) { func = dataTypeExpression; dataTypeExpression = "*"; } var dataType, i = 0, dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; if ( isFunction( func ) ) { // For each dataType in the dataTypeExpression while ( ( dataType = dataTypes[ i++ ] ) ) { // Prepend if requested if ( dataType[ 0 ] === "+" ) { dataType = dataType.slice( 1 ) || "*"; ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); // Otherwise append } else { ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); } } } }; } // Base inspection function for prefilters and transports function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { var inspected = {}, seekingTransport = ( structure === transports ); function inspect( dataType ) { var selected; inspected[ dataType ] = true; jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); if ( typeof dataTypeOrTransport === "string" && !seekingTransport && !inspected[ dataTypeOrTransport ] ) { options.dataTypes.unshift( dataTypeOrTransport ); inspect( dataTypeOrTransport ); return false; } else if ( seekingTransport ) { return !( selected = dataTypeOrTransport ); } } ); return selected; } return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); } // A special extend for ajax options // that takes "flat" options (not to be deep extended) // Fixes #9887 function ajaxExtend( target, src ) { var key, deep, flatOptions = jQuery.ajaxSettings.flatOptions || {}; for ( key in src ) { if ( src[ key ] !== undefined ) { ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; } } if ( deep ) { jQuery.extend( true, target, deep ); } return target; } /* Handles responses to an ajax request: * - finds the right dataType (mediates between content-type and expected dataType) * - returns the corresponding response */ function ajaxHandleResponses( s, jqXHR, responses ) { var ct, type, finalDataType, firstDataType, contents = s.contents, dataTypes = s.dataTypes; // Remove auto dataType and get content-type in the process while ( dataTypes[ 0 ] === "*" ) { dataTypes.shift(); if ( ct === undefined ) { ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); } } // Check if we're dealing with a known content-type if ( ct ) { for ( type in contents ) { if ( contents[ type ] && contents[ type ].test( ct ) ) { dataTypes.unshift( type ); break; } } } // Check to see if we have a response for the expected dataType if ( dataTypes[ 0 ] in responses ) { finalDataType = dataTypes[ 0 ]; } else { // Try convertible dataTypes for ( type in responses ) { if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { finalDataType = type; break; } if ( !firstDataType ) { firstDataType = type; } } // Or just use first one finalDataType = finalDataType || firstDataType; } // If we found a dataType // We add the dataType to the list if needed // and return the corresponding response if ( finalDataType ) { if ( finalDataType !== dataTypes[ 0 ] ) { dataTypes.unshift( finalDataType ); } return responses[ finalDataType ]; } } /* Chain conversions given the request and the original response * Also sets the responseXXX fields on the jqXHR instance */ function ajaxConvert( s, response, jqXHR, isSuccess ) { var conv2, current, conv, tmp, prev, converters = {}, // Work with a copy of dataTypes in case we need to modify it for conversion dataTypes = s.dataTypes.slice(); // Create converters map with lowercased keys if ( dataTypes[ 1 ] ) { for ( conv in s.converters ) { converters[ conv.toLowerCase() ] = s.converters[ conv ]; } } current = dataTypes.shift(); // Convert to each sequential dataType while ( current ) { if ( s.responseFields[ current ] ) { jqXHR[ s.responseFields[ current ] ] = response; } // Apply the dataFilter if provided if ( !prev && isSuccess && s.dataFilter ) { response = s.dataFilter( response, s.dataType ); } prev = current; current = dataTypes.shift(); if ( current ) { // There's only work to do if current dataType is non-auto if ( current === "*" ) { current = prev; // Convert response if prev dataType is non-auto and differs from current } else if ( prev !== "*" && prev !== current ) { // Seek a direct converter conv = converters[ prev + " " + current ] || converters[ "* " + current ]; // If none found, seek a pair if ( !conv ) { for ( conv2 in converters ) { // If conv2 outputs current tmp = conv2.split( " " ); if ( tmp[ 1 ] === current ) { // If prev can be converted to accepted input conv = converters[ prev + " " + tmp[ 0 ] ] || converters[ "* " + tmp[ 0 ] ]; if ( conv ) { // Condense equivalence converters if ( conv === true ) { conv = converters[ conv2 ]; // Otherwise, insert the intermediate dataType } else if ( converters[ conv2 ] !== true ) { current = tmp[ 0 ]; dataTypes.unshift( tmp[ 1 ] ); } break; } } } } // Apply converter (if not an equivalence) if ( conv !== true ) { // Unless errors are allowed to bubble, catch and return them if ( conv && s.throws ) { response = conv( response ); } else { try { response = conv( response ); } catch ( e ) { return { state: "parsererror", error: conv ? e : "No conversion from " + prev + " to " + current }; } } } } } } return { state: "success", data: response }; } jQuery.extend( { // Counter for holding the number of active queries active: 0, // Last-Modified header cache for next request lastModified: {}, etag: {}, ajaxSettings: { url: location.href, type: "GET", isLocal: rlocalProtocol.test( location.protocol ), global: true, processData: true, async: true, contentType: "application/x-www-form-urlencoded; charset=UTF-8", /* timeout: 0, data: null, dataType: null, username: null, password: null, cache: null, throws: false, traditional: false, headers: {}, */ accepts: { "*": allTypes, text: "text/plain", html: "text/html", xml: "application/xml, text/xml", json: "application/json, text/javascript" }, contents: { xml: /\bxml\b/, html: /\bhtml/, json: /\bjson\b/ }, responseFields: { xml: "responseXML", text: "responseText", json: "responseJSON" }, // Data converters // Keys separate source (or catchall "*") and destination types with a single space converters: { // Convert anything to text "* text": String, // Text to html (true = no transformation) "text html": true, // Evaluate text as a json expression "text json": JSON.parse, // Parse text as xml "text xml": jQuery.parseXML }, // For options that shouldn't be deep extended: // you can add your own custom options here if // and when you create one that shouldn't be // deep extended (see ajaxExtend) flatOptions: { url: true, context: true } }, // Creates a full fledged settings object into target // with both ajaxSettings and settings fields. // If target is omitted, writes into ajaxSettings. ajaxSetup: function( target, settings ) { return settings ? // Building a settings object ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : // Extending ajaxSettings ajaxExtend( jQuery.ajaxSettings, target ); }, ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), ajaxTransport: addToPrefiltersOrTransports( transports ), // Main method ajax: function( url, options ) { // If url is an object, simulate pre-1.5 signature if ( typeof url === "object" ) { options = url; url = undefined; } // Force options to be an object options = options || {}; var transport, // URL without anti-cache param cacheURL, // Response headers responseHeadersString, responseHeaders, // timeout handle timeoutTimer, // Url cleanup var urlAnchor, // Request state (becomes false upon send and true upon completion) completed, // To know if global events are to be dispatched fireGlobals, // Loop variable i, // uncached part of the url uncached, // Create the final options object s = jQuery.ajaxSetup( {}, options ), // Callbacks context callbackContext = s.context || s, // Context for global events is callbackContext if it is a DOM node or jQuery collection globalEventContext = s.context && ( callbackContext.nodeType || callbackContext.jquery ) ? jQuery( callbackContext ) : jQuery.event, // Deferreds deferred = jQuery.Deferred(), completeDeferred = jQuery.Callbacks( "once memory" ), // Status-dependent callbacks statusCode = s.statusCode || {}, // Headers (they are sent all at once) requestHeaders = {}, requestHeadersNames = {}, // Default abort message strAbort = "canceled", // Fake xhr jqXHR = { readyState: 0, // Builds headers hashtable if needed getResponseHeader: function( key ) { var match; if ( completed ) { if ( !responseHeaders ) { responseHeaders = {}; while ( ( match = rheaders.exec( responseHeadersString ) ) ) { responseHeaders[ match[ 1 ].toLowerCase() + " " ] = ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) .concat( match[ 2 ] ); } } match = responseHeaders[ key.toLowerCase() + " " ]; } return match == null ? null : match.join( ", " ); }, // Raw string getAllResponseHeaders: function() { return completed ? responseHeadersString : null; }, // Caches the header setRequestHeader: function( name, value ) { if ( completed == null ) { name = requestHeadersNames[ name.toLowerCase() ] = requestHeadersNames[ name.toLowerCase() ] || name; requestHeaders[ name ] = value; } return this; }, // Overrides response content-type header overrideMimeType: function( type ) { if ( completed == null ) { s.mimeType = type; } return this; }, // Status-dependent callbacks statusCode: function( map ) { var code; if ( map ) { if ( completed ) { // Execute the appropriate callbacks jqXHR.always( map[ jqXHR.status ] ); } else { // Lazy-add the new callbacks in a way that preserves old ones for ( code in map ) { statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; } } } return this; }, // Cancel the request abort: function( statusText ) { var finalText = statusText || strAbort; if ( transport ) { transport.abort( finalText ); } done( 0, finalText ); return this; } }; // Attach deferreds deferred.promise( jqXHR ); // Add protocol if not provided (prefilters might expect it) // Handle falsy url in the settings object (#10093: consistency with old signature) // We also use the url parameter if available s.url = ( ( url || s.url || location.href ) + "" ) .replace( rprotocol, location.protocol + "//" ); // Alias method option to type as per ticket #12004 s.type = options.method || options.type || s.method || s.type; // Extract dataTypes list s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; // A cross-domain request is in order when the origin doesn't match the current origin. if ( s.crossDomain == null ) { urlAnchor = document.createElement( "a" ); // Support: IE <=8 - 11, Edge 12 - 15 // IE throws exception on accessing the href property if url is malformed, // e.g. http://example.com:80x/ try { urlAnchor.href = s.url; // Support: IE <=8 - 11 only // Anchor's host property isn't correctly set when s.url is relative urlAnchor.href = urlAnchor.href; s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== urlAnchor.protocol + "//" + urlAnchor.host; } catch ( e ) { // If there is an error parsing the URL, assume it is crossDomain, // it can be rejected by the transport if it is invalid s.crossDomain = true; } } // Convert data if not already a string if ( s.data && s.processData && typeof s.data !== "string" ) { s.data = jQuery.param( s.data, s.traditional ); } // Apply prefilters inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); // If request was aborted inside a prefilter, stop there if ( completed ) { return jqXHR; } // We can fire global events as of now if asked to // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) fireGlobals = jQuery.event && s.global; // Watch for a new set of requests if ( fireGlobals && jQuery.active++ === 0 ) { jQuery.event.trigger( "ajaxStart" ); } // Uppercase the type s.type = s.type.toUpperCase(); // Determine if request has content s.hasContent = !rnoContent.test( s.type ); // Save the URL in case we're toying with the If-Modified-Since // and/or If-None-Match header later on // Remove hash to simplify url manipulation cacheURL = s.url.replace( rhash, "" ); // More options handling for requests with no content if ( !s.hasContent ) { // Remember the hash so we can put it back uncached = s.url.slice( cacheURL.length ); // If data is available and should be processed, append data to url if ( s.data && ( s.processData || typeof s.data === "string" ) ) { cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; // #9682: remove data so that it's not used in an eventual retry delete s.data; } // Add or update anti-cache param if needed if ( s.cache === false ) { cacheURL = cacheURL.replace( rantiCache, "$1" ); uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + uncached; } // Put hash and anti-cache on the URL that will be requested (gh-1732) s.url = cacheURL + uncached; // Change '%20' to '+' if this is encoded form body content (gh-2658) } else if ( s.data && s.processData && ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { s.data = s.data.replace( r20, "+" ); } // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. if ( s.ifModified ) { if ( jQuery.lastModified[ cacheURL ] ) { jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); } if ( jQuery.etag[ cacheURL ] ) { jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); } } // Set the correct header, if data is being sent if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { jqXHR.setRequestHeader( "Content-Type", s.contentType ); } // Set the Accepts header for the server, depending on the dataType jqXHR.setRequestHeader( "Accept", s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? s.accepts[ s.dataTypes[ 0 ] ] + ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : s.accepts[ "*" ] ); // Check for headers option for ( i in s.headers ) { jqXHR.setRequestHeader( i, s.headers[ i ] ); } // Allow custom headers/mimetypes and early abort if ( s.beforeSend && ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { // Abort if not done already and return return jqXHR.abort(); } // Aborting is no longer a cancellation strAbort = "abort"; // Install callbacks on deferreds completeDeferred.add( s.complete ); jqXHR.done( s.success ); jqXHR.fail( s.error ); // Get transport transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); // If no transport, we auto-abort if ( !transport ) { done( -1, "No Transport" ); } else { jqXHR.readyState = 1; // Send global event if ( fireGlobals ) { globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); } // If request was aborted inside ajaxSend, stop there if ( completed ) { return jqXHR; } // Timeout if ( s.async && s.timeout > 0 ) { timeoutTimer = window.setTimeout( function() { jqXHR.abort( "timeout" ); }, s.timeout ); } try { completed = false; transport.send( requestHeaders, done ); } catch ( e ) { // Rethrow post-completion exceptions if ( completed ) { throw e; } // Propagate others as results done( -1, e ); } } // Callback for when everything is done function done( status, nativeStatusText, responses, headers ) { var isSuccess, success, error, response, modified, statusText = nativeStatusText; // Ignore repeat invocations if ( completed ) { return; } completed = true; // Clear timeout if it exists if ( timeoutTimer ) { window.clearTimeout( timeoutTimer ); } // Dereference transport for early garbage collection // (no matter how long the jqXHR object will be used) transport = undefined; // Cache response headers responseHeadersString = headers || ""; // Set readyState jqXHR.readyState = status > 0 ? 4 : 0; // Determine if successful isSuccess = status >= 200 && status < 300 || status === 304; // Get response data if ( responses ) { response = ajaxHandleResponses( s, jqXHR, responses ); } // Use a noop converter for missing script if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) { s.converters[ "text script" ] = function() {}; } // Convert no matter what (that way responseXXX fields are always set) response = ajaxConvert( s, response, jqXHR, isSuccess ); // If successful, handle type chaining if ( isSuccess ) { // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. if ( s.ifModified ) { modified = jqXHR.getResponseHeader( "Last-Modified" ); if ( modified ) { jQuery.lastModified[ cacheURL ] = modified; } modified = jqXHR.getResponseHeader( "etag" ); if ( modified ) { jQuery.etag[ cacheURL ] = modified; } } // if no content if ( status === 204 || s.type === "HEAD" ) { statusText = "nocontent"; // if not modified } else if ( status === 304 ) { statusText = "notmodified"; // If we have data, let's convert it } else { statusText = response.state; success = response.data; error = response.error; isSuccess = !error; } } else { // Extract error from statusText and normalize for non-aborts error = statusText; if ( status || !statusText ) { statusText = "error"; if ( status < 0 ) { status = 0; } } } // Set data for the fake xhr object jqXHR.status = status; jqXHR.statusText = ( nativeStatusText || statusText ) + ""; // Success/Error if ( isSuccess ) { deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); } else { deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); } // Status-dependent callbacks jqXHR.statusCode( statusCode ); statusCode = undefined; if ( fireGlobals ) { globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", [ jqXHR, s, isSuccess ? success : error ] ); } // Complete completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); if ( fireGlobals ) { globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); // Handle the global AJAX counter if ( !( --jQuery.active ) ) { jQuery.event.trigger( "ajaxStop" ); } } } return jqXHR; }, getJSON: function( url, data, callback ) { return jQuery.get( url, data, callback, "json" ); }, getScript: function( url, callback ) { return jQuery.get( url, undefined, callback, "script" ); } } ); jQuery.each( [ "get", "post" ], function( _i, method ) { jQuery[ method ] = function( url, data, callback, type ) { // Shift arguments if data argument was omitted if ( isFunction( data ) ) { type = type || callback; callback = data; data = undefined; } // The url can be an options object (which then must have .url) return jQuery.ajax( jQuery.extend( { url: url, type: method, dataType: type, data: data, success: callback }, jQuery.isPlainObject( url ) && url ) ); }; } ); jQuery.ajaxPrefilter( function( s ) { var i; for ( i in s.headers ) { if ( i.toLowerCase() === "content-type" ) { s.contentType = s.headers[ i ] || ""; } } } ); jQuery._evalUrl = function( url, options, doc ) { return jQuery.ajax( { url: url, // Make this explicit, since user can override this through ajaxSetup (#11264) type: "GET", dataType: "script", cache: true, async: false, global: false, // Only evaluate the response if it is successful (gh-4126) // dataFilter is not invoked for failure responses, so using it instead // of the default converter is kludgy but it works. converters: { "text script": function() {} }, dataFilter: function( response ) { jQuery.globalEval( response, options, doc ); } } ); }; jQuery.fn.extend( { wrapAll: function( html ) { var wrap; if ( this[ 0 ] ) { if ( isFunction( html ) ) { html = html.call( this[ 0 ] ); } // The elements to wrap the target around wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); if ( this[ 0 ].parentNode ) { wrap.insertBefore( this[ 0 ] ); } wrap.map( function() { var elem = this; while ( elem.firstElementChild ) { elem = elem.firstElementChild; } return elem; } ).append( this ); } return this; }, wrapInner: function( html ) { if ( isFunction( html ) ) { return this.each( function( i ) { jQuery( this ).wrapInner( html.call( this, i ) ); } ); } return this.each( function() { var self = jQuery( this ), contents = self.contents(); if ( contents.length ) { contents.wrapAll( html ); } else { self.append( html ); } } ); }, wrap: function( html ) { var htmlIsFunction = isFunction( html ); return this.each( function( i ) { jQuery( this ).wrapAll( htmlIsFunction ? 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Mab2Rec Public API

BanditRecommender

class mab2rec.BanditRecommender(learning_policy: Union[mabwiser.mab.LearningPolicy.EpsilonGreedy, mabwiser.mab.LearningPolicy.Popularity, mabwiser.mab.LearningPolicy.Random, mabwiser.mab.LearningPolicy.Softmax, mabwiser.mab.LearningPolicy.ThompsonSampling, mabwiser.mab.LearningPolicy.UCB1, mabwiser.mab.LearningPolicy.LinGreedy, mabwiser.mab.LearningPolicy.LinTS, mabwiser.mab.LearningPolicy.LinUCB], neighborhood_policy: Union[None, mabwiser.mab.NeighborhoodPolicy.LSHNearest, mabwiser.mab.NeighborhoodPolicy.Clusters, mabwiser.mab.NeighborhoodPolicy.KNearest, mabwiser.mab.NeighborhoodPolicy.Radius, mabwiser.mab.NeighborhoodPolicy.TreeBandit] = None, top_k: int = 10, seed: int = 12345, n_jobs: int = 1, backend: Optional[str] = None)

Bases: object

Mab2Rec: Multi-Armed Bandit Recommender

Mab2Rec is a library to support prototyping and building of bandit-based recommendation algorithms. It is powered by MABWiser which supports context-free, parametric and non-parametric contextual bandit models.

learning_policy

The learning policy.

Type

MABWiser LearningPolicy

neighborhood_policy

The neighborhood policy.

Type

MABWiser NeighborhoodPolicy

top_k

The number of items to recommend.

Type

int, default=10

seed

The random seed to initialize the internal random number generator.

Type

int, Constants.default_seed

n_jobs

This is used to specify how many concurrent processes/threads should be used for parallelized routines. Default value is set to 1. If set to -1, all CPUs are used. If set to -2, all CPUs but one are used, and so on.

Type

int

backend

Specify a parallelization backend implementation supported in the joblib library. Supported options are: - “loky” used by default, can induce some communication and memory overhead when exchanging input and

output data with the worker Python processes.

  • “multiprocessing” previous process-based backend based on multiprocessing.Pool. Less robust than loky.

  • “threading” is a very low-overhead backend but it suffers from the Python Global Interpreter Lock if the called function relies a lot on Python objects.

Default value is None. In this case the default backend selected by joblib will be used.

Type

str, optional

mab

The multi-armed bandit.

Type

MAB

Examples

>>> from mab2rec import BanditRecommender, LearningPolicy
>>> decisions = ['Arm1', 'Arm1', 'Arm3', 'Arm1', 'Arm2', 'Arm3']
>>> rewards = [0, 1, 1, 0, 1, 0]
>>> rec = BanditRecommender(LearningPolicy.EpsilonGreedy(epsilon=0.25), top_k=2)
>>> rec.fit(decisions, rewards)
>>> rec.recommend()
['Arm2', 'Arm1']
>>> rec.add_arm('Arm4')
>>> rec.partial_fit(['Arm4'], [1])
>>> rec.recommend()[0]
['Arm2', 'Arm4']

>>> from mab2rec import BanditRecommender, LearningPolicy, NeighborhoodPolicy
>>> decisions = ['Arm1', 'Arm1', 'Arm3', 'Arm1', 'Arm2', 'Arm3']
>>> rewards = [0, 1, 1, 0, 1, 0]
>>> contexts = [[0, 0, 0], [1, 0, 1], [0, 1, 1], [0, 0, 0], [1, 1, 1], [0, 1, 0]]
>>> rec = BanditRecommender(LearningPolicy.EpsilonGreedy(), NeighborhoodPolicy.KNearest(k=3), top_k=2)
>>> rec.fit(decisions, rewards, contexts)
>>> rec.recommend([[1, 1, 0], [1, 1, 1], [0, 1, 0]])
[['Arm2', 'Arm3'], ['Arm3', 'Arm2'], ['Arm3', 'Arm2']]

>>> from mab2rec import BanditRecommender, LearningPolicy
>>> decisions = ['Arm1', 'Arm1', 'Arm3', 'Arm1', 'Arm2', 'Arm3']
>>> rewards = [0, 1, 1, 0, 1, 0]
>>> contexts = [[0, 0, 0], [1, 0, 1], [0, 1, 1], [0, 0, 0], [1, 1, 1], [0, 1, 0]]
>>> rec = BanditRecommender(LearningPolicy.LinGreedy(epsilon=0.1), top_k=2)
>>> rec.fit(decisions, rewards, contexts)
>>> rec.recommend([[1, 1, 0], [1, 1, 1], [0, 1, 0]])
[['Arm2', 'Arm1'], ['Arm2', 'Arm1'], ['Arm2', 'Arm3']]
>>> arm_to_features = {'Arm1': [0, 1], 'Arm2': [0, 0], 'Arm3': [0, 0], 'Arm4': [0, 1]}
>>> rec.add_arm('Arm4')
>>> rec.warm_start(arm_to_features, distance_quantile=0.75)
>>> rec.recommend([[1, 1, 0], [1, 1, 1], [0, 1, 0]])
[['Arm2', 'Arm4'], ['Arm2', 'Arm4'], ['Arm2', 'Arm3']]
add_arm(arm: Arm, binarizer=None) → None

Adds an _arm_ to the list of arms.

Incorporates the arm into the learning and neighborhood policies with no training data.

Parameters

  • arm (Arm) – The new arm to be added.

  • binarizer (Callable, default=None) – The new binarizer function for Thompson Sampling.

Return type

Returns nothing.

fit(decisions: Union[List[Arm], numpy.ndarray, pandas.core.series.Series], rewards: Union[List[Union[int, float]], numpy.ndarray, pandas.core.series.Series], contexts: Union[None, List[List[Union[int, float]]], numpy.ndarray, pandas.core.series.Series, pandas.core.frame.DataFrame] = None) → None

Fits the recommender the given decisions, their corresponding rewards and contexts, if any. If the recommender arms has not been initialized using the set_arms, the recommender arms will be set to the list of arms in decisions.

Validates arguments and raises exceptions in case there are violations.

This function makes the following assumptions:

  • each decision corresponds to an arm of the bandit.

  • there are no None, Nan, or Infinity values in the contexts.

Parameters

  • decisions (Union[List[Arm], np.ndarray, pd.Series]) – The decisions that are made.

  • rewards (Union[List[Num], np.ndarray, pd.Series]) – The rewards that are received corresponding to the decisions.

  • contexts (Union[None, List[List[Num]], np.ndarray, pd.Series, pd.DataFrame], default=None) – The context under which each decision is made.

Return type

Returns nothing.

partial_fit(decisions: Union[List[Arm], numpy.ndarray, pandas.core.series.Series], rewards: Union[List[Union[int, float]], numpy.ndarray, pandas.core.series.Series], contexts: Union[None, List[List[Union[int, float]]], numpy.ndarray, pandas.core.series.Series, pandas.core.frame.DataFrame] = None) → None

Updates the recommender with the given decisions, their corresponding rewards and contexts, if any.

Validates arguments and raises exceptions in case there are violations.

This function makes the following assumptions:

  • each decision corresponds to an arm of the bandit.

  • there are no None, Nan, or Infinity values in the contexts.

Parameters

  • decisions (Union[List[Arm], np.ndarray, pd.Series]) – The decisions that are made.

  • rewards (Union[List[Num], np.ndarray, pd.Series]) – The rewards that are received corresponding to the decisions.

  • contexts (Union[None, List[List[Num]], np.ndarray, pd.Series, pd.DataFrame], default=None) – The context under which each decision is made.

Return type

Returns nothing.

predict(contexts: Union[None, List[List[Union[int, float]]], numpy.ndarray, pandas.core.series.Series, pandas.core.frame.DataFrame] = None) → Union[Arm, List[Arm]]

Returns the “best” arm (or arms list if multiple contexts are given) based on the expected reward.

The definition of the best depends on the specified learning policy. Contextual learning policies and neighborhood policies require contexts data in training. In testing, they return the best arm given new context(s).

Parameters

contexts (Union[None, List[List[Num]], np.ndarray, pd.Series, pd.DataFrame], default=None) – The context under which each decision is made. If contexts is not None for context-free bandits, the predictions returned will be a list of the same length as contexts.

Return type

The recommended arm or recommended arms list.

predict_expectations(contexts: Union[None, List[List[Union[int, float]]], numpy.ndarray, pandas.core.series.Series, pandas.core.frame.DataFrame] = None) → Union[Dict[Arm, Union[int, float]], List[Dict[Arm, Union[int, float]]]]

Returns a dictionary of arms (key) to their expected rewards (value).

Contextual learning policies and neighborhood policies require contexts data for expected rewards.

Parameters

contexts (Union[None, List[Num], List[List[Num]], np.ndarray, pd.Series, pd.DataFrame], default=None) – The context for the expected rewards. If contexts is not None for context-free bandits, the predicted expectations returned will be a list of the same length as contexts.

Return type

The dictionary of arms (key) to their expected rewards (value), or a list of such dictionaries.

recommend(contexts: Union[None, List[List[Union[int, float]]], numpy.ndarray, pandas.core.series.Series, pandas.core.frame.DataFrame] = None, excluded_arms: Optional[List[List[Arm]]] = None, return_scores: bool = False) → Union[List[Arm], Tuple[List[Arm], List[Union[int, float]]], List[List[Arm]], Tuple[List[List[Arm]], List[List[Union[int, float]]]]]

Generate _top-k_ recommendations based on the expected reward.

Recommend up to k arms with the highest predicted expectations. For contextual bandits, only items not included in the excluded arms can be recommended.

Parameters

  • contexts (np.ndarray, default=None) – The context under which each decision is made. If contexts is not None for context-free bandits, the recommendations returned will be a list of the same length as contexts.

  • excluded_arms (List[List[Arm]], default=None) – List of list of arms to exclude from recommended arms.

  • return_scores (bool, default=False) – Return score for each recommended item.

Return type

List of tuples of the form ([arm_1, arm_2, …, arm_k], [score_1, score_2, …, score_k])

remove_arm(arm: Arm) → None

Removes an _arm_ from the list of arms.

Parameters

arm (Arm) – The existing arm to be removed.

Return type

Returns nothing.

set_arms(arms: List[Arm], binarizer=None) → None

Initializes the recommender and sets the recommender with given list of arms. Existing arms not in the given list of arms are removed and new arms are incorporated into the learning and neighborhood policies with no training data. If the recommender has already been initialized it will not be re-initialized.

Parameters

  • arms (List[Arm]) – The new arm to be added.

  • binarizer (Callable, default=None) – The new binarizer function for Thompson Sampling.

Return type

Returns nothing.

warm_start(arm_to_features: Dict[Arm, List[Union[int, float]]], distance_quantile: Optional[float] = None) → None

Warm-start untrained (cold) arms of the multi-armed bandit.

Validates arguments and raises exceptions in case there are violations.

Parameters

  • arm_to_features (Dict[Arm, List[Num]]) – Numeric representation for each arm.

  • distance_quantile (float, default=None) – Value between 0 and 1 used to determine if an item can be warm started or not using closest item. All cold items will be warm started if 1 and none will be warm started if 0.

Return type

Returns nothing.

LearningPolicy

class mab2rec.LearningPolicy
class EpsilonGreedy(epsilon: Union[int, float] = 0.1)

Epsilon Greedy Learning Policy.

This policy selects the arm with the highest expected reward with probability 1 - \(\epsilon\), and with probability \(\epsilon\) it selects an arm at random for exploration.

epsilon

The probability of selecting a random arm for exploration. Integer or float. Must be between 0 and 1. Default value is 0.1.

Type

Num

Example

>>> from mabwiser.mab import MAB, LearningPolicy
>>> arms = ['Arm1', 'Arm2']
>>> decisions = ['Arm1', 'Arm1', 'Arm2', 'Arm1']
>>> rewards = [20, 17, 25, 9]
>>> mab = MAB(arms, LearningPolicy.EpsilonGreedy(epsilon=0.25), seed=123456)
>>> mab.fit(decisions, rewards)
>>> mab.predict()
'Arm1'
epsilon: Union[int, float]

Alias for field number 0

class LinGreedy(epsilon: Union[int, float] = 0.1, l2_lambda: Union[int, float] = 1.0, scale: bool = False)

LinGreedy Learning Policy.

This policy trains a ridge regression for each arm. Then, given a given context, it predicts a regression value. This policy selects the arm with the highest regression value with probability 1 - \(\epsilon\), and with probability \(\epsilon\) it selects an arm at random for exploration.

epsilon

The probability of selecting a random arm for exploration. Integer or float. Must be between 0 and 1. Default value is 0.1.

Type

Num

l2_lambda

The regularization strength. Integer or float. Cannot be negative. Default value is 1.0.

Type

Num

scale

Whether to scale features to have zero mean and unit variance. Uses StandardScaler in sklearn.preprocessing. Default value is False.

Type

bool

Example

>>> from mabwiser.mab import MAB, LearningPolicy
>>> list_of_arms = ['Arm1', 'Arm2']
>>> decisions = ['Arm1', 'Arm1', 'Arm2', 'Arm1']
>>> rewards = [20, 17, 25, 9]
>>> contexts = [[0, 1, 2, 3], [1, 2, 3, 0], [2, 3, 1, 0], [3, 2, 1, 0]]
>>> mab = MAB(list_of_arms, LearningPolicy.LinGreedy(epsilon=0.5))
>>> mab.fit(decisions, rewards, contexts)
>>> mab.predict([[3, 2, 0, 1]])
'Arm2'
epsilon: Union[int, float]

Alias for field number 0

l2_lambda: Union[int, float]

Alias for field number 1

scale: bool

Alias for field number 2

class LinTS(alpha: Union[int, float] = 1.0, l2_lambda: Union[int, float] = 1.0, scale: bool = False)

LinTS Learning Policy

For each arm LinTS trains a ridge regression and creates a multivariate normal distribution for the coefficients using the calculated coefficients as the mean and the covariance as:

\[\alpha^{2} (x_i^{T}x_i + \lambda * I_d)^{-1}\]

The normal distribution is randomly sampled to obtain expected coefficients for the ridge regression for each prediction.

\(\alpha\) is a factor used to adjust how conservative the estimate is. Higher \(\alpha\) values promote more exploration.

The multivariate normal distribution uses Cholesky decomposition to guarantee deterministic behavior. This method requires that the covariance is a positive definite matrix. To ensure this is the case, alpha and l2_lambda are required to be greater than zero.

alpha

The multiplier to determine the degree of exploration. Integer or float. Must be greater than zero. Default value is 1.0.

Type

Num

l2_lambda

The regularization strength. Integer or float. Must be greater than zero. Default value is 1.0.

Type

Num

scale

Whether to scale features to have zero mean and unit variance. Uses StandardScaler in sklearn.preprocessing. Default value is False.

Type

bool

Example

>>> from mabwiser.mab import MAB, LearningPolicy
>>> list_of_arms = ['Arm1', 'Arm2']
>>> decisions = ['Arm1', 'Arm1', 'Arm2', 'Arm1']
>>> rewards = [20, 17, 25, 9]
>>> contexts = [[0, 1, 2, 3], [1, 2, 3, 0], [2, 3, 1, 0], [3, 2, 1, 0]]
>>> mab = MAB(list_of_arms, LearningPolicy.LinTS(alpha=0.25))
>>> mab.fit(decisions, rewards, contexts)
>>> mab.predict([[3, 2, 0, 1]])
'Arm2'
alpha: Union[int, float]

Alias for field number 0

l2_lambda: Union[int, float]

Alias for field number 1

scale: bool

Alias for field number 2

class LinUCB(alpha: Union[int, float] = 1.0, l2_lambda: Union[int, float] = 1.0, scale: bool = False)

LinUCB Learning Policy.

This policy trains a ridge regression for each arm. Then, given a given context, it predicts a regression value and calculates the upper confidence bound of that prediction. The arm with the highest highest upper bound is selected.

The UCB for each arm is calculated as:

\[UCB = x_i \beta + \alpha \sqrt{(x_i^{T}x_i + \lambda * I_d)^{-1}x_i}\]

Where \(\beta\) is the matrix of the ridge regression coefficients, \(\lambda\) is the regularization strength, and I_d is a dxd identity matrix where d is the number of features in the context data.

\(\alpha\) is a factor used to adjust how conservative the estimate is. Higher \(\alpha\) values promote more exploration.

alpha

The parameter to control the exploration. Integer or float. Cannot be negative. Default value is 1.0.

Type

Num

l2_lambda

The regularization strength. Integer or float. Cannot be negative. Default value is 1.0.

Type

Num

scale

Whether to scale features to have zero mean and unit variance. Uses StandardScaler in sklearn.preprocessing. Default value is False.

Type

bool

Example

>>> from mabwiser.mab import MAB, LearningPolicy
>>> list_of_arms = ['Arm1', 'Arm2']
>>> decisions = ['Arm1', 'Arm1', 'Arm2', 'Arm1']
>>> rewards = [20, 17, 25, 9]
>>> contexts = [[0, 1, 2, 3], [1, 2, 3, 0], [2, 3, 1, 0], [3, 2, 1, 0]]
>>> mab = MAB(list_of_arms, LearningPolicy.LinUCB(alpha=1.25))
>>> mab.fit(decisions, rewards, contexts)
>>> mab.predict([[3, 2, 0, 1]])
'Arm2'
alpha: Union[int, float]

Alias for field number 0

l2_lambda: Union[int, float]

Alias for field number 1

scale: bool

Alias for field number 2

class Popularity

Randomized Popularity Learning Policy.

Returns a randomized popular arm for each prediction. The probability of selection for each arm is weighted by their mean reward. It assumes that the rewards are non-negative.

The probability of selection is calculated as:

\[P(arm) = \frac{ \mu_i } { \Sigma{ \mu } }\]

where \(\mu_i\) is the mean reward for that arm.

Example

>>> from mabwiser.mab import MAB, LearningPolicy
>>> list_of_arms = ['Arm1', 'Arm2']
>>> decisions = ['Arm1', 'Arm1', 'Arm2', 'Arm1']
>>> rewards = [20, 17, 25, 9]
>>> mab = MAB(list_of_arms, LearningPolicy.Popularity())
>>> mab.fit(decisions, rewards)
>>> mab.predict()
'Arm1'
class Random

Random Learning Policy.

Returns a random arm for each prediction. The probability of selection for each arm is uniformly at random.

Example

>>> from mabwiser.mab import MAB, LearningPolicy
>>> list_of_arms = ['Arm1', 'Arm2']
>>> decisions = ['Arm1', 'Arm1', 'Arm2', 'Arm1']
>>> rewards = [20, 17, 25, 9]
>>> mab = MAB(list_of_arms, LearningPolicy.Random())
>>> mab.fit(decisions, rewards)
>>> mab.predict()
'Arm2'
class Softmax(tau: Union[int, float] = 1)

Softmax Learning Policy.

This policy selects each arm with a probability proportionate to its average reward. The average reward is calculated as a logistic function with each probability as:

\[P(arm) = \frac{ e ^ \frac{\mu_i - \max{\mu}}{ \tau } } { \Sigma{e ^ \frac{\mu - \max{\mu}}{ \tau }} }\]

where \(\mu_i\) is the mean reward for that arm and \(\tau\) is the “temperature” to determine the degree of exploration.

tau

The temperature to control the exploration. Integer or float. Must be greater than zero. Default value is 1.

Type

Num

Example

>>> from mabwiser.mab import MAB, LearningPolicy
>>> list_of_arms = ['Arm1', 'Arm2']
>>> decisions = ['Arm1', 'Arm1', 'Arm2', 'Arm1']
>>> rewards = [20, 17, 25, 9]
>>> mab = MAB(list_of_arms, LearningPolicy.Softmax(tau=1))
>>> mab.fit(decisions, rewards)
>>> mab.predict()
'Arm2'
tau: Union[int, float]

Alias for field number 0

class ThompsonSampling(binarizer: Optional[Callable] = None)

Thompson Sampling Learning Policy.

This policy creates a beta distribution for each arm and then randomly samples from these distributions. The arm with the highest sample value is selected.

Notice that rewards must be binary to create beta distributions. If rewards are not binary, see the binarizer function.

binarizer

If rewards are not binary, a binarizer function is required. Given an arm decision and its corresponding reward, the binarizer function returns True/False or 0/1 to denote whether the decision counts as a success, i.e., True/1 based on the reward or False/0 otherwise.

The function signature of the binarizer is:

binarize(arm: Arm, reward: Num) -> True/False or 0/1

Type

Callable

Example

>>> from mabwiser.mab import MAB, LearningPolicy
>>> list_of_arms = ['Arm1', 'Arm2']
>>> decisions = ['Arm1', 'Arm1', 'Arm2', 'Arm1']
>>> rewards = [1, 1, 1, 0]
>>> mab = MAB(list_of_arms, LearningPolicy.ThompsonSampling())
>>> mab.fit(decisions, rewards)
>>> mab.predict()
'Arm2'

>>> from mabwiser.mab import MAB, LearningPolicy
>>> list_of_arms = ['Arm1', 'Arm2']
>>> arm_to_threshold = {'Arm1':10, 'Arm2':10}
>>> decisions = ['Arm1', 'Arm1', 'Arm2', 'Arm1']
>>> rewards = [10, 20, 15, 7]
>>> def binarize(arm, reward): return reward > arm_to_threshold[arm]
>>> mab = MAB(list_of_arms, LearningPolicy.ThompsonSampling(binarizer=binarize))
>>> mab.fit(decisions, rewards)
>>> mab.predict()
'Arm2'
binarizer: Callable

Alias for field number 0

class UCB1(alpha: Union[int, float] = 1)

Upper Confidence Bound1 Learning Policy.

This policy calculates an upper confidence bound for the mean reward of each arm. It greedily selects the arm with the highest upper confidence bound.

The UCB for each arm is calculated as:

\[UCB = \mu_i + \alpha \times \sqrt[]{\frac{2 \times log(N)}{n_i}}\]

Where \(\mu_i\) is the mean for that arm, \(N\) is the total number of trials, and \(n_i\) is the number of times the arm has been selected.

\(\alpha\) is a factor used to adjust how conservative the estimate is. Higher \(\alpha\) values promote more exploration.

alpha

The parameter to control the exploration. Integer of float. Cannot be negative. Default value is 1.

Type

Num

Example

>>> from mabwiser.mab import MAB, LearningPolicy
>>> list_of_arms = ['Arm1', 'Arm2']
>>> decisions = ['Arm1', 'Arm1', 'Arm2', 'Arm1']
>>> rewards = [20, 17, 25, 9]
>>> mab = MAB(list_of_arms, LearningPolicy.UCB1(alpha=1.25))
>>> mab.fit(decisions, rewards)
>>> mab.predict()
'Arm2'
alpha: Union[int, float]

Alias for field number 0

NeighborhoodPolicy

class mab2rec.NeighborhoodPolicy
class Clusters(n_clusters: Union[int, float] = 2, is_minibatch: bool = False)

Clusters Neighborhood Policy.

Clusters is a k-means clustering approach that uses the observations from the closest cluster with a learning policy. Supports KMeans and MiniBatchKMeans.

n_clusters

The number of clusters. Integer. Must be at least 2. Default value is 2.

Type

Num

is_minibatch

Boolean flag to use MiniBatchKMeans or not. Default value is False.

Type

bool

Example

>>> from mabwiser.mab import MAB, LearningPolicy, NeighborhoodPolicy
>>> list_of_arms = [1, 2, 3, 4]
>>> decisions = [1, 1, 1, 2, 2, 3, 3, 3, 3, 3]
>>> rewards = [0, 1, 1, 0, 0, 0, 0, 1, 1, 1]
>>> contexts = [[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0],[0, 2, 2, 3, 5], [1, 3, 1, 1, 1],                             [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]]
>>> mab = MAB(list_of_arms, LearningPolicy.EpsilonGreedy(epsilon=0), NeighborhoodPolicy.Clusters(3))
>>> mab.fit(decisions, rewards, contexts)
>>> mab.predict([[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]])
[3, 1]
is_minibatch: bool

Alias for field number 1

n_clusters: Union[int, float]

Alias for field number 0

class KNearest(k: int = 1, metric: str = 'euclidean')

KNearest Neighborhood Policy.

KNearest is a nearest neighbors approach that selects the k-nearest observations to be used with a learning policy.

k

The number of neighbors to select. Integer value. Must be greater than zero. Default value is 1.

Type

int

metric

The metric used to calculate distance. Accepts any of the metrics supported by scipy.spatial.distance.cdist. Default value is Euclidean distance.

Type

str

Example

>>> from mabwiser.mab import MAB, LearningPolicy, NeighborhoodPolicy
>>> list_of_arms = [1, 2, 3, 4]
>>> decisions = [1, 1, 1, 2, 2, 3, 3, 3, 3, 3]
>>> rewards = [0, 1, 1, 0, 0, 0, 0, 1, 1, 1]
>>> contexts = [[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0],[0, 2, 2, 3, 5], [1, 3, 1, 1, 1],                             [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]]
>>> mab = MAB(list_of_arms, LearningPolicy.EpsilonGreedy(epsilon=0),                           NeighborhoodPolicy.KNearest(2, "euclidean"))
>>> mab.fit(decisions, rewards, contexts)
>>> mab.predict([[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]])
[1, 1]
k: int

Alias for field number 0

metric: str

Alias for field number 1

class LSHNearest(n_dimensions: int = 5, n_tables: int = 3, no_nhood_prob_of_arm: Optional[List] = None)

Locality-Sensitive Hashing Approximate Nearest Neighbors Policy.

LSHNearest is a nearest neighbors approach that uses locality sensitive hashing with a simhash to select observations to be used with a learning policy.

For the simhash, contexts are projected onto a hyperplane of n_context_cols x n_dimensions and each column of the hyperplane is evaluated for its sign, giving an ordered array of binary values. This is converted to a base 10 integer used as the hash code to assign the context to a hash table. This process is repeated for a specified number of hash tables, where each has a unique, randomly-generated hyperplane. To select the neighbors for a context, the hash code is calculated for each hash table and any contexts with the same hashes are selected as the neighbors.

As with the radius or k value for other nearest neighbors algorithms, selecting the best number of dimensions and tables requires tuning. For the dimensions, a good starting point is to use the log of the square root of the number of rows in the training data. This will give you sqrt(n_rows) number of hashes.

The number of dimensions and number of tables have inverse effects from each other on the number of empty neighborhoods and average neighborhood size. Increasing the dimensionality decreases the number of collisions, which increases the precision of the approximate neighborhood but also potentially increases the number of empty neighborhoods. Increasing the number of hash tables increases the likelihood of capturing neighbors the other random hyperplanes miss and increases the average neighborhood size. It should be noted that the fit operation is O(2**n_dimensions).

n_dimensions

The number of dimensions to use for the hyperplane. Integer value. Must be greater than zero. Default value is 5.

Type

int

n_tables

The number of hash tables. Integer value. Must be greater than zero. Default value is 3.

Type

int

no_nhood_prob_of_arm

The probabilities associated with each arm. Used to select random arm if context has no neighbors. If not given, a uniform random distribution over all arms is assumed. The probabilities should sum up to 1.

Type

None or List

Example

>>> from mabwiser.mab import MAB, LearningPolicy, NeighborhoodPolicy
>>> list_of_arms = [1, 2, 3, 4]
>>> decisions = [1, 1, 1, 2, 2, 3, 3, 3, 3, 3]
>>> rewards = [0, 1, 1, 0, 0, 0, 0, 1, 1, 1]
>>> contexts = [[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0],[0, 2, 2, 3, 5], [1, 3, 1, 1, 1],                             [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]]
>>> mab = MAB(list_of_arms, LearningPolicy.EpsilonGreedy(epsilon=0),                           NeighborhoodPolicy.LSHNearest(5, 3))
>>> mab.fit(decisions, rewards, contexts)
>>> mab.predict([[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]])
[3, 1]
n_dimensions: int

Alias for field number 0

n_tables: int

Alias for field number 1

no_nhood_prob_of_arm: Optional[List]

Alias for field number 2

class Radius(radius: Union[int, float] = 0.05, metric: str = 'euclidean', no_nhood_prob_of_arm: Optional[List] = None)

Radius Neighborhood Policy.

Radius is a nearest neighborhood approach that selects the observations within a given radius to be used with a learning policy.

radius

The maximum distance within which to select observations. Integer or Float. Must be greater than zero. Default value is 1.

Type

Num

metric

The metric used to calculate distance. Accepts any of the metrics supported by scipy.spatial.distance.cdist. Default value is Euclidean distance.

Type

str

no_nhood_prob_of_arm

The probabilities associated with each arm. Used to select random arm if context has no neighbors. If not given, a uniform random distribution over all arms is assumed. The probabilities should sum up to 1.

Type

None or List

Example

>>> from mabwiser.mab import MAB, LearningPolicy, NeighborhoodPolicy
>>> list_of_arms = [1, 2, 3, 4]
>>> decisions = [1, 1, 1, 2, 2, 3, 3, 3, 3, 3]
>>> rewards = [0, 1, 1, 0, 0, 0, 0, 1, 1, 1]
>>> contexts = [[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0],[0, 2, 2, 3, 5], [1, 3, 1, 1, 1],                             [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]]
>>> mab = MAB(list_of_arms, LearningPolicy.EpsilonGreedy(epsilon=0),                           NeighborhoodPolicy.Radius(2, "euclidean"))
>>> mab.fit(decisions, rewards, contexts)
>>> mab.predict([[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]])
[3, 1]
metric: str

Alias for field number 1

no_nhood_prob_of_arm: Optional[List]

Alias for field number 2

radius: Union[int, float]

Alias for field number 0

class TreeBandit(tree_parameters: Dict = {})

TreeBandit Neighborhood Policy.

This policy fits a decision tree for each arm using context history. It uses the leaves of these trees to partition the context space into regions and keeps a list of rewards for each leaf. To predict, it receives a context vector and goes to the corresponding leaf at each arm’s tree and applies the given context-free MAB learning policy to predict expectations and choose an arm.

The TreeBandit neighborhood policy is compatible with the following context-free learning policies only: EpsilonGreedy, ThompsonSampling and UCB1.

The TreeBandit neighborhood policy is a modified version of the TreeHeuristic algorithm presented in: Adam N. Elmachtoub, Ryan McNellis, Sechan Oh, Marek Petrik A Practical Method for Solving Contextual Bandit Problems Using Decision Trees, UAI 2017

tree_parameters

Parameters of the decision tree. The keys must match the parameters of sklearn.tree.DecisionTreeRegressor. When a parameter is not given, the default parameters from sklearn.tree.DecisionTreeRegressor will be chosen. Default value is an empty dictionary.

Type

Dict, **kwarg

Example

>>> from mabwiser.mab import MAB, LearningPolicy, NeighborhoodPolicy
>>> list_of_arms = ['Arm1', 'Arm2']
>>> decisions = ['Arm1', 'Arm1', 'Arm2', 'Arm1']
>>> rewards = [20, 17, 25, 9]
>>> contexts = [[0, 1, 2, 3], [1, 2, 3, 0], [2, 3, 1, 0], [3, 2, 1, 0]]
>>> mab = MAB(list_of_arms, LearningPolicy.EpsilonGreedy(epsilon=0), NeighborhoodPolicy.TreeBandit())
>>> mab.fit(decisions, rewards, contexts)
>>> mab.predict([[3, 2, 0, 1]])
'Arm2'
tree_parameters: Dict

Alias for field number 0

Pipeline

mab2rec.pipeline.benchmark(recommenders: Dict[str, mab2rec.rec.BanditRecommender], metrics: List[Union[jurity.recommenders.BinaryRecoMetrics, jurity.recommenders.RankingRecoMetrics]], train_data: Union[str, pandas.core.frame.DataFrame], test_data: Optional[Union[str, pandas.core.frame.DataFrame]] = None, cv: Optional[int] = None, user_features: Optional[Union[str, pandas.core.frame.DataFrame]] = None, user_features_list: Optional[Union[str, List[str]]] = None, user_features_dtypes: Optional[Union[str, Dict]] = None, item_features: Optional[Union[str, pandas.core.frame.DataFrame]] = None, item_list: Optional[Union[str, List[Arm]]] = None, item_eligibility: Optional[Union[str, pandas.core.frame.DataFrame]] = None, warm_start: bool = False, warm_start_distance: Optional[float] = None, user_id_col: str = 'user_id', item_id_col: str = 'item_id', response_col: str = 'response', batch_size: int = 100000, verbose: bool = False) → Union[Tuple[Dict[str, pandas.core.frame.DataFrame], Dict[str, Dict[str, float]]], Tuple[List[Dict[str, pandas.core.frame.DataFrame]], List[Dict[str, Dict[str, float]]]]]

Benchmark Recommenders.

Benchmark a given set of recommender algorithms by training, scoring and evaluating each algorithm If using cross-validation (cv) it benchmarks the algorithms on cv-many folds from the train data, otherwise it trains on the train data and evaluates on the test data.

Parameters

  • recommenders (Dict[str, BanditRecommender]) – The recommender algorithms to be benchmarked. Dictionary with names (key) and recommender algorithms (value).

  • metrics (List[Union[BinaryRecoMetrics, RankingRecoMetrics]]) – List of metrics used to evaluate recommendations.

  • train_data (Union[str, pd.DataFrame]) – Training data used to train recommenders. Data should have a row for each training sample (user_id, item_id, response). Column names should be consistent with user_id_col, item_id_col and response_col arguments. CSV format with file header or Data Frame.

  • test_data (Union[str, pd.DataFrame]) – Test data used to generate recommendations. Data should have a row for each training sample (user_id, item_id, response). Column names should be consistent with user_id_col, item_id_col and response_col arguments. CSV format with file header or Data Frame.

  • cv (int, default=None) – Number of folds in the train data to use for cross-fold validation. A grouped K-fold iterator is used to ensure that the same user is not contained in different folds. Test data must be None when using cv.

  • user_features (Union[str, pd.DataFrame], default=None) – User features containing features for each user_id. Each row should include user_id and list of features (user_id, u_1, u_2, …, u_p). CSV format with file header or Data Frame.

  • user_features_list (Union[str, List[str]], default=None) – List of user features to use. Must be a subset of features in (u_1, u_2, … u_p). If None, all the features in user_features are used. CSV format with file header or List.

  • user_features_dtypes (Union[str, Dict], default=None) – Data type for each user feature. Maps each user feature name to valid data type. If none, no data type casting is done upon load and data types or inferred by Pandas library. JSON format or Dictionary.

  • item_features (Union[str, pd.DataFrame], default=None) – Item features file containing features for each item_id. Each row should include item_id and list of features (item_id, i_1, i_2, …. i_q). CSV format with file header or Data Frame.

  • item_list (Union[str, List[Arm]], default=None) – List of items to train. If None, all the items in data are used. CSV format with file header or List.

  • item_eligibility (Union[str, pd.DataFrame], default=None) – Items each user is eligible for. Used to generate excluded_arms lists. If None, all the items can be evaluated for recommendation for each user. CSV format with file header or Data Frame.

  • warm_start (bool, default=False) – Whether to warm start untrained (cold) arms after training or not.

  • warm_start_distance (float, default=None) – Warm start distance quantile. Value between 0 and 1 used to determine if an item can be warm started or not using closest item. All cold items will be warm started if 1 and none will be warm started if 0. Must be specified if warm_start=True.

  • user_id_col (str, default=Constants.user_id) – User id column name.

  • item_id_col (str, default=Constants.item_id) – Item id column name.

  • response_col (str, default=Constants.response) – Response column name.

  • batch_size (str, default=100000) – Batch size used for chunking data.

  • verbose (bool, default=False) – Whether to print progress status or not.

Returns

  • Tuple with recommendations and evaluation metrics for each algorithm.

  • The tuple values are lists of dictionaries if cross-validation is used, representing the results on each fold,

  • and individual dictionaries otherwise.

mab2rec.pipeline.score(recommender: Union[str, mab2rec.rec.BanditRecommender], data: Union[str, pandas.core.frame.DataFrame], user_features: Optional[Union[str, pandas.core.frame.DataFrame]] = None, user_features_list: Optional[Union[str, List[str]]] = None, user_features_dtypes: Optional[Union[str, Dict]] = None, item_features: Optional[Union[str, pandas.core.frame.DataFrame]] = None, item_list: Optional[Union[str, List[Arm]]] = None, item_eligibility: Optional[Union[str, pandas.core.frame.DataFrame]] = None, warm_start: bool = False, warm_start_distance: Optional[float] = None, user_id_col: str = 'user_id', item_id_col: str = 'item_id', response_col: str = 'response', batch_size: int = 100000, save_file: Optional[Union[str, bool]] = None) → pandas.core.frame.DataFrame

Score Recommender.

Generates top-k recommendations for users in given data.

Parameters

  • recommender (Union[str, BanditRecommender]) – The recommender algorithm to be scored. Could be an instantiated BanditRecommender or file path of serialized recommender in pickle file.

  • data (Union[str, pd.DataFrame]) – Training data. Data should have a row for each training sample (user_id, item_id, response). Column names should be consistent with user_id_col, item_id_col and response_col arguments. CSV format with file header or Data Frame.

  • user_features (Union[str, pd.DataFrame], default=None) – User features containing features for each user_id. Each row should include user_id and list of features (user_id, u_1, u_2, …, u_p). CSV format with file header or Data Frame.

  • user_features_list (Union[str, List[str]], default=None) – List of user features to use. Must be a subset of features in (u_1, u_2, … u_p). If None, all the features in user_features are used. CSV format with file header or List.

  • user_features_dtypes (Union[str, Dict], default=None) – Data type for each user feature. Maps each user feature name to valid data type. If none, no data type casting is done upon load and data types or inferred by Pandas library. JSON format or Dictionary.

  • item_features (Union[str, pd.DataFrame], default=None) – Item features file containing features for each item_id. Each row should include item_id and list of features (item_id, i_1, i_2, …. i_q). CSV format with file header or Data Frame.

  • item_list (Union[str, List[Arm]], default=None) – List of items to train. If None, all the items in data are used. CSV format with file header or List.

  • item_eligibility (Union[str, pd.DataFrame], default=None) – Items each user is eligible for. Used to generate excluded_arms lists. If None, all the items can be evaluated for recommendation for each user. CSV format with file header or Data Frame.

  • warm_start (bool, default=False) – Whether to warm start untrained (cold) arms after training or not.

  • warm_start_distance (float, default=None) – Warm start distance quantile. Value between 0 and 1 used to determine if an item can be warm started or not using closest item. All cold items will be warm started if 1 and none will be warm started if 0. Must be specified if warm_start=True.

  • user_id_col (str, default=Constants.user_id) – User id column name.

  • item_id_col (str, default=Constants.item_id) – Item id column name.

  • response_col (str, default=Constants.response) – Response column name.

  • batch_size (str, default=100000) – Batch size used for chunking data.

  • save_file (str, default=None) – File name to save recommender pickle. If None, recommender is not saved to file.

Return type

Scored recommendations.

mab2rec.pipeline.train(recommender: mab2rec.rec.BanditRecommender, data: Union[str, pandas.core.frame.DataFrame], user_features: Optional[Union[str, pandas.core.frame.DataFrame]] = None, user_features_list: Optional[Union[str, List[str]]] = None, user_features_dtypes: Optional[Union[str, Dict]] = None, item_features: Optional[Union[str, pandas.core.frame.DataFrame]] = None, item_list: Optional[Union[str, List[Arm]]] = None, item_eligibility: Optional[Union[str, pandas.core.frame.DataFrame]] = None, warm_start: bool = False, warm_start_distance: Optional[float] = None, user_id_col: str = 'user_id', item_id_col: str = 'item_id', response_col: str = 'response', batch_size: int = 100000, save_file: Optional[Union[str, bool]] = None) → None

Trains Recommender.

Parameters

  • recommender (BanditRecommender) – The recommender algorithm to be trained. The recommender object is updated in-place.

  • data (Union[str, pd.DataFrame]) – Training data. Data should have a row for each training sample (user_id, item_id, response). Column names should be consistent with user_id_col, item_id_col and response_col arguments. CSV format with file header or Data Frame.

  • user_features (Union[str, pd.DataFrame], default=None) – User features containing features for each user_id. Each row should include user_id and list of features (user_id, u_1, u_2, …, u_p). CSV format with file header or Data Frame.

  • user_features_list (Union[str, List[str]], default=None) – List of user features to use. Must be a subset of features in (u_1, u_2, … u_p). If None, all the features in user_features are used. CSV format with file header or List.

  • user_features_dtypes (Union[str, Dict], default=None) – Data type for each user feature. Maps each user feature name to valid data type. If none, no data type casting is done upon load and data types or inferred by Pandas library. JSON format or Dictionary.

  • item_features (Union[str, pd.DataFrame], default=None) – Item features file containing features for each item_id. Each row should include item_id and list of features (item_id, i_1, i_2, …. i_q). CSV format with file header or Data Frame.

  • item_list (Union[str, List[Arm]], default=None) – List of items to train. If None, all the items in data are used. CSV format with file header or List.

  • item_eligibility (Union[str, pd.DataFrame], default=None) – Items each user is eligible for. Not used during training. CSV format with file header or Data Frame.

  • warm_start (bool, default=False) – Whether to warm start untrained (cold) arms after training or not.

  • warm_start_distance (float, default=None) – Warm start distance quantile. Value between 0 and 1 used to determine if an item can be warm started or not using closest item. All cold items will be warm started if 1 and none will be warm started if 0. Must be specified if warm_start=True.

  • user_id_col (str, default=Constants.user_id) – User id column name.

  • item_id_col (str, default=Constants.item_id) – Item id column name.

  • response_col (str, default=Constants.response) – Response column name.

  • batch_size (str, default=100000) – Batch size used for chunking data.

  • save_file (Union[str, bool], default=None) – File name to save recommender pickle. If None, recommender is not saved to file.

Return type

Returns nothing.

Visualization

mab2rec.visualization.plot_inter_diversity_at_k(recommendation_results: Union[Dict[str, pandas.core.frame.DataFrame], List[Dict[str, pandas.core.frame.DataFrame]]], k_list: List[int], user_id_col: str = 'user_id', item_id_col: str = 'item_id', score_col: str = 'score', sample_size: Optional[float] = None, seed: int = 12345, num_runs: int = 10, n_jobs: int = 1, working_memory: Optional[int] = None, **kwargs)

Plots recommendation metric values (y-axis) for different values of k (x-axis) for each of the benchmark algorithms.

Parameters

  • recommendation_results (Union[Dict[str, pd.DataFrame], List[Dict[str, pd.DataFrame]]]) – Dictionary or list of dictionaries with recommendation results returned by benchmark function.

  • k_list (List[int]) – List of top-k values to evaluate.

  • user_id_col (str, default=Constants.user_id) – User id column name.

  • item_id_col (str, default=Constants.item_id) – Item id column name.

  • score_col (str, default=Constants.score) – Recommendation score column name.

  • sample_size (float, default=None) – Proportion of users to randomly sample for evaluation. If None, no sampling is performed.

  • seed (int, default=Constants.default_seed) – The seed used to create random state.

  • num_runs (int) – num_runs is used to report the approximation of Inter-List Diversity over multiple runs on smaller samples of users, default=10, for a speed-up on evaluations. The sampling size is defined by user_sample_size. The final result is averaged over the multiple runs.

  • n_jobs (int) – Number of jobs to use for computation in parallel, leveraged by sklearn.metrics.pairwise_distances_chunked. -1 means using all processors. Default=1.

  • working_memory (Union[int, None]) – Maximum memory for temporary distance matrix chunks, leveraged by sklearn.metrics.pairwise_distances_chunked. When None (default), the value of sklearn.get_config()[‘working_memory’], i.e. 1024M, is used.

  • **kwargs – Other parameters passed to sns.catplot.

Returns

ax – The plot with metric values.

Return type

matplotlib.axes.Axes

mab2rec.visualization.plot_intra_diversity_at_k(recommendation_results: Union[Dict[str, pandas.core.frame.DataFrame], List[Dict[str, pandas.core.frame.DataFrame]]], item_features: pandas.core.frame.DataFrame, k_list: List[int], user_id_col: str = 'user_id', item_id_col: str = 'item_id', score_col: str = 'score', sample_size: Optional[float] = None, seed: int = 12345, n_jobs: int = 1, num_runs: int = 10, **kwargs)

Plots recommendation metric values (y-axis) for different values of k (x-axis) for each of the benchmark algorithms.

Parameters

  • recommendation_results (Union[Dict[str, pd.DataFrame], List[Dict[str, pd.DataFrame]]]) – Dictionary or list of dictionaries with recommendation results returned by benchmark function.

  • item_features (pd.DataFrame) – Data frame with features for each item_id.

  • k_list (List[int]) – List of top-k values to evaluate.

  • user_id_col (str, default=Constants.user_id) – User id column name.

  • item_id_col (str, default=Constants.item_id) – Item id column name.

  • score_col (str, default=Constants.score) – Recommendation score column name.

  • sample_size (float, default=None) – Proportion of users to randomly sample for evaluation. If None, no sampling is performed.

  • seed (int, default=Constants.default_seed) – The seed used to create random state.

  • num_runs (int) – num_runs is used to report the approximation of Intra-List Diversity over multiple runs on smaller samples of users, default=10, for a speed-up on evaluations. The sampling size is defined by user_sample_size. The final result is averaged over the multiple runs.

  • n_jobs (int) – Number of jobs to use for computation in parallel, leveraged by sklearn.metrics.pairwise_distances. -1 means using all processors. Default=1.

  • **kwargs – Other parameters passed to sns.catplot.

Returns

ax – The plot with metric values.

Return type

matplotlib.axes.Axes

mab2rec.visualization.plot_metrics_at_k(metric_results: Union[Dict[str, Dict[str, float]], List[Dict[str, Dict[str, float]]]], **kwargs)

Plots recommendation metric values (y-axis) for different values of k (x-axis) for each of the benchmark algorithms.

Parameters

  • metric_results (Union[Dict[str, Dict[str, float]], List[Dict[str, Dict[str, float]]]]) – Nested-dictionary or list of dictionaries with evaluation results returned by benchmark function.

  • **kwargs – Other parameters passed to sns.catplot.

Returns

ax – The plot with metric values.

Return type

matplotlib.axes.Axes

mab2rec.visualization.plot_num_items_per_recommendation(recommendation_results: Union[Dict[str, pandas.core.frame.DataFrame], List[Dict[str, pandas.core.frame.DataFrame]]], actual_results: pandas.core.frame.DataFrame, normalize: bool = False, user_id_col: str = 'user_id', **kwargs)

Plots recommendation counts (y-axis) versus actual counts or average responses (x-axis) for each item.

Parameters

  • recommendation_results (Union[Dict[str, pd.DataFrame], List[Dict[str, pd.DataFrame]]]) – Dictionary or list of dictionaries with recommendation results returned by benchmark function.

  • actual_results (pd.DataFrame) – Test data frame used to generate recommendations. Data should have a row for each sample (user_id, item_id, response).

  • normalize (bool, default=False) – Whether to normalize the number of items to be proportions such that they add to 1.

  • user_id_col (str) – User id column name. Default value is set to Constants.user_id

  • **kwargs – Other parameters passed to sns.catplot.

Returns

ax – The plot with counts or proportions for different number of items per recommendation.

Return type

matplotlib.axes.Axes

mab2rec.visualization.plot_personalization_heatmap(recommendation_results: Union[Dict[str, pandas.core.frame.DataFrame], List[Dict[str, pandas.core.frame.DataFrame]]], user_to_cluster: Dict[Union[int, str], int], k: int, user_id_col: str = 'user_id', item_id_col: str = 'item_id', figsize: Optional[Tuple[int, int]] = None, **kwargs)

Plot heatmaps to visualize level of personalization, by calculating the distribution of recommendations by item within different user clusters.

Parameters

  • recommendation_results (Union[Dict[str, pd.DataFrame], List[Dict[str, pd.DataFrame]]]) – Dictionary or list of dictionaries with recommendation results returned by benchmark function.

  • user_to_cluster (Dict[Union[int, str], int]) – Mapping from user_id to cluster. Clusters could be derived from clustering algorithm such as KMeans or defined based on specific user features (e.g. age bands)

  • k (int) – Top-k recommendations to evaluate.

  • user_id_col (str, default=Constants.user_id) – User id column name.

  • item_id_col (str, default=Constants.item_id) – Item id column name.

  • figsize (Tuple[int, int], default=None) – Figure size of heatmap set using plt.figure()

  • **kwargs – Other parameters passed to sns.catplot.

Returns

ax – The plot with counts or proportions for different number of items per recommendation.

Return type

matplotlib.axes.Axes

Plots recommendation counts (y-axis) versus actual counts or average responses (x-axis) for each item.

Parameters

  • recommendation_results (Union[Dict[str, pd.DataFrame], List[Dict[str, pd.DataFrame]]]) – Dictionary or list of dictionaries with recommendation results returned by benchmark function.

  • actual_results (pd.DataFrame) – Test data frame used to generate recommendations. Data should have a row for each sample (user_id, item_id, response).

  • k (int) – Top-k recommendations to evaluate.

  • average_response (bool, default=False) – Whether to plot the average response/reward or not.

  • user_id_col (str, default=Constants.user_id) – User id column name.

  • item_id_col (str, default=Constants.item_id) – Item id column name.

  • response_col (str, default=Constants.response) – Response column name.

  • **kwargs – Other parameters passed to sns.relplot.

Returns

ax – The plot with recommended counts.

Return type

matplotlib.axes.Axes

Plots recommendation counts (y-axis) for different items (x-axis) for each of the benchmark algorithms. Only the top_n_items with the most recommendations for each algorithm are shown.

Parameters

  • recommendation_results (Union[Dict[str, pd.DataFrame], List[Dict[str, pd.DataFrame]]]) – Dictionary or list of dictionaries with recommendation results returned by benchmark function.

  • k (int) – Top-k recommendations to evaluate.

  • top_n_items (int, default=None) – Top-n number of items based on number of recommendations to plot.

  • normalize (bool, default=False) – Whether to normalize the counts per item to be proportions such that they add to 1.

  • user_id_col (str, default=Constants.user_id) – User id column name.

  • item_id_col (str, default=Constants.item_id) – Item id column name.

  • **kwargs – Other parameters passed to sns.catplot.

Returns

ax – The plot with recommended counts by item.

Return type

matplotlib.axes.Axes

================================================ FILE: docs/contributing.html ================================================ Contributing — Mab2Rec 1.2.0 documentation

Contributing

We welcome contributions of all from everybody, and we will make an effort to respond to any questions and requests. Code is not the only way to make a contribution!

If you end up using our library in a project, give us a star on GitHub!

Code Contributions

  • With any piece of code, please adhere to PEP-8 standards.

  • If you’re fixing an issue with an existing piece of code, please make sure all the tests pass, and there is no change in functionality.

  • If you want to add a new feature, please open up an issue first.

  • When adding a new feature, make sure you have relevant test coverage.

  • Any changes to the public API should conform to the current standards, be properly documented, typed, and be intuitive.

Documentation Contributions

  • Make sure you follow the standards set by the rest of the repo.

  • Be concise, but do not omit details. Verbose documentation is preferred to incomplete documentation.

================================================ FILE: docs/examples.html ================================================ Usage Examples — Mab2Rec 1.2.0 documentation

Usage Examples

We provide extensive tutorials in Jupyter notebooks under the notebooks folder for guidelines on building recommenders, performing model selection, and evaluating performance:

  • Data Overview provides an overview of data required to train recommender.

  • Feature Engineering gives an overview of methods to create user and item features from structured, unstructured, and sequential data.

  • Model Selection shows to do model selection by benchmarking recommenders using cross-validation.

  • Evaluation benchmarks selected recommenders and baselines on test data with detailed evaluations.

  • Advanced demonstrates some advanced functionality.

================================================ FILE: docs/genindex.html ================================================ Index — Mab2Rec 1.2.0 documentation
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================================================ FILE: docs/index.html ================================================ Mab2Rec: Multi-Armed Bandits Recommender — Mab2Rec 1.2.0 documentation

Mab2Rec: Multi-Armed Bandits Recommender

Mab2Rec is a Python library for building bandit-based recommendation algorithms. It supports context-free, parametric and non-parametric contextual bandit models powered by MABWiser and fairness and recommenders evaluations powered by Jurity. It supports all bandit policies available in MABWiser. The library is designed with rapid experimentation in mind, follows the PEP-8 standards and is tested heavily.

Mab2Rec and several of the open-source software it is built on is developed by the Artificial Intelligence Center at Fidelity Investments, including:

An introduction to content- and context-aware recommender systems and an overview of the building blocks of the library is presented at All Things Open 2021.

Quick Start

Individual Recommender

# Example of how to train an individual recommender to generate top-4 recommendations

# Import
from mab2rec import BanditRecommender, LearningPolicy
from mab2rec.pipeline import train, score

# LinGreedy recommender to select top-4 items with 10% random exploration
rec = BanditRecommender(LearningPolicy.LinGreedy(epsilon=0.1), top_k=4)

# Train on (user, item, response) interactions in train data using user features
train(rec, data='data/data_train.csv',
      user_features='data/features_user.csv')

# Score recommendations for users in test data. The output df holds
# user_id, item_id, score columns for every test user for top-k items
df = score(rec, data='data/data_test.csv',
           user_features='data/features_user.csv')

Multiple Recommenders

# Example of how to benchmark multiple bandit algorithms to generate top-4 recommendations

from mab2rec import BanditRecommender, LearningPolicy
from mab2rec.pipeline import benchmark
from jurity.recommenders import BinaryRecoMetrics, RankingRecoMetrics

# Recommenders (many more available)
recommenders = {"Random": BanditRecommender(LearningPolicy.Random()),
                "Popularity": BanditRecommender(LearningPolicy.Popularity()),
                "LinGreedy": BanditRecommender(LearningPolicy.LinGreedy(epsilon=0.1))}

# Column names for the response, user, and item id columns
metric_params = {'click_column': 'score', 'user_id_column': 'user_id', 'item_id_column':'item_id'}

# Performance metrics for benchmarking (many more available)
metrics = []
for top_k in [3, 5, 10]:
    metrics.append(BinaryRecoMetrics.CTR(**metric_params, k=top_k))
    metrics.append(RankingRecoMetrics.NDCG(**metric_params, k=top_k))

# Benchmarking with a collection of recommenders and metrics
# This returns two dictionaries;
# reco_to_results: recommendations for each algorithm on cross-validation data
# reco_to_metrics: evaluation metrics for each algorithm
reco_to_results, reco_to_metrics = benchmark(recommenders,
                                             metrics=metrics,
                                             train_data="data/data_train.csv",
                                             cv=5,
                                             user_features="data/features_user.csv")

Source Code

The source code is hosted on GitHub.

Indices and tables

================================================ FILE: docs/installation.html ================================================ Installation — Mab2Rec 1.2.0 documentation

Installation

Installation Options

There are two options to install the library:

  1. Install from PyPI using the prebuilt wheel package (pip install mab2rec)

  2. Build from the source code

Requirements

The library requires Python 3.7+. The requirements.txt lists the necessary packages.

Source Code

You can build a wheel package on your platform from scratch using the source code:

git clone https://github.com/fidelity/mab2rec.git
cd mab2rec
pip install setuptools wheel # if wheel is not installed
python setup.py sdist bdist_wheel
pip install dist/mab2rec-X.X.X-py3-none-any.whl

Test Your Setup

To confirm that cloning was successful, run the tests included in the project.

All tests should pass.

git clone https://github.com/fidelity/mab2rec.git
cd mab2rec
python -m unittest discover tests

Upgrade the Library

To upgrade to the latest version of the library, run pip install --upgrade mab2rec.

If you installed from the source code:

git pull origin master
python setup.py sdist bdist_wheel
pip install --upgrade --no-cache-dir dist/mab2rec-X.X.X-py3-none-any.whl
================================================ FILE: docs/py-modindex.html ================================================ Python Module Index — Mab2Rec 1.2.0 documentation
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  • Python Module Index

Python Module Index

m
 
m
mab2rec
    mab2rec.pipeline
    mab2rec.visualization
================================================ FILE: docs/quick.html ================================================ Quick Start — Mab2Rec 1.2.0 documentation

Quick Start

Individual Recommender

# Example of how to train an individual recommender to generate top-4 recommendations

# Import
from mab2rec import BanditRecommender, LearningPolicy
from mab2rec.pipeline import train, score

# LinGreedy recommender to select top-4 items with 10% random exploration
rec = BanditRecommender(LearningPolicy.LinGreedy(epsilon=0.1), top_k=4)

# Train on (user, item, response) interactions in train data using user features
train(rec, data='data/data_train.csv',
      user_features='data/features_user.csv')

# Score recommendations for users in test data. The output df holds
# user_id, item_id, score columns for every test user for top-k items
df = score(rec, data='data/data_test.csv',
           user_features='data/features_user.csv')

Multiple Recommenders

# Example of how to benchmark multiple bandit algorithms to generate top-4 recommendations

from mab2rec import BanditRecommender, LearningPolicy
from mab2rec.pipeline import benchmark
from jurity.recommenders import BinaryRecoMetrics, RankingRecoMetrics

# Recommenders (many more available)
recommenders = {"Random": BanditRecommender(LearningPolicy.Random()),
                "Popularity": BanditRecommender(LearningPolicy.Popularity()),
                "LinGreedy": BanditRecommender(LearningPolicy.LinGreedy(epsilon=0.1))}

# Column names for the response, user, and item id columns
metric_params = {'click_column': 'score', 'user_id_column': 'user_id', 'item_id_column':'item_id'}

# Performance metrics for benchmarking (many more available)
metrics = []
for top_k in [3, 5, 10]:
    metrics.append(BinaryRecoMetrics.CTR(**metric_params, k=top_k))
    metrics.append(RankingRecoMetrics.NDCG(**metric_params, k=top_k))

# Benchmarking with a collection of recommenders and metrics
# This returns two dictionaries;
# reco_to_results: recommendations for each algorithm on cross-validation data
# reco_to_metrics: evaluation metrics for each algorithm
reco_to_results, reco_to_metrics = benchmark(recommenders,
                                             metrics=metrics,
                                             train_data="data/data_train.csv",
                                             cv=5,
                                             user_features="data/features_user.csv")
================================================ FILE: docs/search.html ================================================ Search — Mab2Rec 1.2.0 documentation
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Public API","Contributing","Usage Examples","Mab2Rec: Multi-Armed Bandits Recommender","Installation","Quick Start"],titleterms:{"public":0,api:0,arm:3,bandit:3,banditrecommend:0,code:[1,3,4],content:3,contribut:1,document:1,exampl:2,indic:3,individu:[3,5],instal:4,learningpolici:0,librari:4,mab2rec:[0,3],multi:3,multipl:[3,5],neighborhoodpolici:0,option:4,pipelin:0,quick:[3,5],recommend:[3,5],requir:4,setup:4,sourc:[3,4],start:[3,5],tabl:3,test:4,upgrad:4,usag:2,visual:0,your:4}}) ================================================ FILE: docsrc/Makefile ================================================ # Minimal makefile for Sphinx documentation # # You can set these variables from the command line, and also # from the environment for the first two. SPHINXOPTS ?= SPHINXBUILD ?= sphinx-build SOURCEDIR = . BUILDDIR = _build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help Makefile github: @make html @cp -a _build/html/. ../docs # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) ================================================ FILE: docsrc/api.rst ================================================ .. _Mab2Rec API: Mab2Rec Public API ================== .. automodule:: mab2rec :members: :undoc-members: :show-inheritance: BanditRecommender ^^^^^^^^^^^^^^^^^ .. autoclass:: mab2rec.BanditRecommender :members: :undoc-members: :show-inheritance: LearningPolicy ^^^^^^^^^^^^^^ .. autoclass:: mab2rec.LearningPolicy :members: :undoc-members: NeighborhoodPolicy ^^^^^^^^^^^^^^^^^^ .. autoclass:: mab2rec.NeighborhoodPolicy :members: :undoc-members: Pipeline ^^^^^^^^ .. automodule:: mab2rec.pipeline :members: :undoc-members: Visualization ^^^^^^^^^^^^^ .. automodule:: mab2rec.visualization :members: :undoc-members: ================================================ FILE: docsrc/conf.py ================================================ # Configuration file for the Sphinx documentation builder. # # This file only contains a selection of the most common options. For a full # list see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # import os import sys sys.path.insert(0, os.path.abspath('.')) sys.path.append(os.path.join(os.path.dirname(__name__), '..')) with open(os.path.join('..', 'mab2rec', '_version.py')) as fp: exec(fp.read()) # -- Project information ----------------------------------------------------- project = 'Mab2Rec' copyright = __copyright__ author = __author__ # The short X.Y version version = '' # The full version, including alpha/beta/rc tags release = __version__ # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.napoleon', 'sphinx.ext.mathjax', 'sphinx.ext.extlinks', ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = '.rst' # The master toctree document. master_doc = 'index' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] # The name of the Pygments (syntax highlighting) style to use. pygments_style = None # Common links extlinks = {'repo': ('https://github.com/fidelity/mab2rec%s', None), 'docs': ('https://fidelity.github.io/mab2rec%s', None)} # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = 'sphinx_rtd_theme' # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # -- Options for HTMLHelp output --------------------------------------------- # Output file base name for HTML help builder. htmlhelp_basename = 'Mab2Recdoc' # -- Options for LaTeX output ------------------------------------------------ latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'Mab2Rec.tex', 'Mab2Rec Documentation', 'FMR LLC', 'manual'), ] # -- Options for manual page output ------------------------------------------ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'mab2rec', 'Mab2Rec Documentation', [author], 1) ] # -- Options for Texinfo output ---------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'Mab2Rec', 'Mab2Rec Documentation', author, 'FMR LLC', 'One line description of project.', 'Miscellaneous'), ] # -- Options for Epub output ------------------------------------------------- # Bibliographic Dublin Core info. epub_title = project # The unique identifier of the text. This can be a ISBN number # or the project homepage. # # epub_identifier = '' # A unique identification for the text. # # epub_uid = '' # A list of files that should not be packed into the epub file. epub_exclude_files = ['search.html'] # -- Extension configuration ------------------------------------------------- ================================================ FILE: docsrc/contributing.rst ================================================ .. _contributing: Contributing ============ We welcome contributions of all from everybody, and we will make an effort to respond to any questions and requests. Code is not the only way to make a contribution! If you end up using our library in a project, give us a star on GitHub! Code Contributions ^^^^^^^^^^^^^^^^^^ - With any piece of code, please adhere to PEP-8 standards. - If you're fixing an issue with an existing piece of code, please make sure all the tests pass, and there is no change in functionality. - If you want to add a new feature, please open up an issue first. - When adding a new feature, make sure you have relevant test coverage. - Any changes to the public API should conform to the current standards, be properly documented, typed, and be intuitive. Documentation Contributions ^^^^^^^^^^^^^^^^^^^^^^^^^^^ - Make sure you follow the standards set by the rest of the repo. - Be concise, but do not omit details. Verbose documentation is preferred to incomplete documentation. ================================================ FILE: docsrc/examples.rst ================================================ .. _examples: Usage Examples ============== We provide extensive tutorials in Jupyter notebooks under the :repo:`notebooks ` folder for guidelines on building recommenders, performing model selection, and evaluating performance: - :repo:`Data Overview ` provides an overview of data required to train recommender. - :repo:`Feature Engineering ` gives an overview of methods to create user and item features from structured, unstructured, and sequential data. - :repo:`Model Selection ` shows to do model selection by benchmarking recommenders using cross-validation. - :repo:`Evaluation ` benchmarks selected recommenders and baselines on test data with detailed evaluations. - :repo:`Advanced ` demonstrates some advanced functionality. ================================================ FILE: docsrc/index.rst ================================================ Mab2Rec: Multi-Armed Bandits Recommender ======================================== Mab2Rec is a Python library for building bandit-based recommendation algorithms. It supports **context-free**, **parametric** and **non-parametric** **contextual** bandit models powered by `MABWiser `_ and fairness and recommenders evaluations powered by `Jurity `_. It supports `all bandit policies available in MABWiser `_. The library is designed with rapid experimentation in mind, follows the `PEP-8 standards `_ and is tested heavily. Mab2Rec and several of the open-source software it is built on is developed by the Artificial Intelligence Center at Fidelity Investments, including: * `MABWiser `_ to create multi-armed bandit recommendation algorithms (`IJAIT'21 `_, `ICTAI'19 `_). * `TextWiser `_ to create item representations via text featurization (`AAAI'21 `_). * `Selective `_ to create user representations via feature selection. * `Seq2Pat `_ to enhance users representations via sequential pattern mining (`AAAI'22 `_). * `Jurity `_ to evaluate recommendations including fairness metrics (`ICMLA'21 `_). An introduction to **content- and context-aware** recommender systems and an overview of the building blocks of the library is `presented at All Things Open 2021 `_. .. include:: quick.rst Source Code =========== The source code is hosted on :repo:`GitHub <>`. .. sidebar:: Contents .. toctree:: :maxdepth: 2 installation quick examples contributing api Indices and tables ================== * :ref:`genindex` * :ref:`modindex` ================================================ FILE: docsrc/installation.rst ================================================ .. _installation: Installation ============ .. admonition:: Installation Options There are two options to install the library: 1. Install from PyPI using the prebuilt wheel package (``pip install mab2rec``) 2. Build from the source code Requirements ------------ The library requires Python **3.7+**. The ``requirements.txt`` lists the necessary packages. Source Code ----------- You can build a wheel package on your platform from scratch using the source code: .. code-block:: python git clone https://github.com/fidelity/mab2rec.git cd mab2rec pip install setuptools wheel # if wheel is not installed python setup.py sdist bdist_wheel pip install dist/mab2rec-X.X.X-py3-none-any.whl Test Your Setup --------------- To confirm that cloning was successful, run the tests included in the project. All tests should pass. .. code-block:: python git clone https://github.com/fidelity/mab2rec.git cd mab2rec python -m unittest discover tests Upgrade the Library ------------------- To upgrade to the latest version of the library, run ``pip install --upgrade mab2rec``. If you installed from the source code: .. code-block:: python git pull origin master python setup.py sdist bdist_wheel pip install --upgrade --no-cache-dir dist/mab2rec-X.X.X-py3-none-any.whl ================================================ FILE: docsrc/make.bat ================================================ @ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set SOURCEDIR=. set BUILDDIR=_build if "%1" == "" goto help %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( echo. echo.The 'sphinx-build' command was not found. Make sure you have Sphinx echo.installed, then set the SPHINXBUILD environment variable to point echo.to the full path of the 'sphinx-build' executable. Alternatively you echo.may add the Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.http://sphinx-doc.org/ exit /b 1 ) %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% goto end :help %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% :end popd ================================================ FILE: docsrc/quick.rst ================================================ .. _quick: Quick Start =========== Individual Recommender ---------------------- .. code-block:: python # Example of how to train an individual recommender to generate top-4 recommendations # Import from mab2rec import BanditRecommender, LearningPolicy from mab2rec.pipeline import train, score # LinGreedy recommender to select top-4 items with 10% random exploration rec = BanditRecommender(LearningPolicy.LinGreedy(epsilon=0.1), top_k=4) # Train on (user, item, response) interactions in train data using user features train(rec, data='data/data_train.csv', user_features='data/features_user.csv') # Score recommendations for users in test data. The output df holds # user_id, item_id, score columns for every test user for top-k items df = score(rec, data='data/data_test.csv', user_features='data/features_user.csv') Multiple Recommenders --------------------- .. code-block:: python # Example of how to benchmark multiple bandit algorithms to generate top-4 recommendations from mab2rec import BanditRecommender, LearningPolicy from mab2rec.pipeline import benchmark from jurity.recommenders import BinaryRecoMetrics, RankingRecoMetrics # Recommenders (many more available) recommenders = {"Random": BanditRecommender(LearningPolicy.Random()), "Popularity": BanditRecommender(LearningPolicy.Popularity()), "LinGreedy": BanditRecommender(LearningPolicy.LinGreedy(epsilon=0.1))} # Column names for the response, user, and item id columns metric_params = {'click_column': 'score', 'user_id_column': 'user_id', 'item_id_column':'item_id'} # Performance metrics for benchmarking (many more available) metrics = [] for top_k in [3, 5, 10]: metrics.append(BinaryRecoMetrics.CTR(**metric_params, k=top_k)) metrics.append(RankingRecoMetrics.NDCG(**metric_params, k=top_k)) # Benchmarking with a collection of recommenders and metrics # This returns two dictionaries; # reco_to_results: recommendations for each algorithm on cross-validation data # reco_to_metrics: evaluation metrics for each algorithm reco_to_results, reco_to_metrics = benchmark(recommenders, metrics=metrics, train_data="data/data_train.csv", cv=5, user_features="data/features_user.csv") ================================================ FILE: docsrc/requirements.txt ================================================ sphinx sphinx_rtd_theme ================================================ FILE: mab2rec/__init__.py ================================================ # -*- coding: utf-8 -*- # Copyright FMR LLC # SPDX-License-Identifier: Apache-2.0 from mabwiser.mab import LearningPolicy, NeighborhoodPolicy from .rec import BanditRecommender ================================================ FILE: mab2rec/_version.py ================================================ # -*- coding: utf-8 -*- # Copyright FMR LLC # SPDX-License-Identifier: Apache-2.0 __author__ = "FMR LLC" __email__ = "opensource@fmr.com" __version__ = "1.3.1" __copyright__ = "Copyright (C), FMR LLC" ================================================ FILE: mab2rec/pipeline.py ================================================ # -*- coding: utf-8 -*- # Copyright FMR LLC # SPDX-License-Identifier: Apache-2.0 import os from copy import deepcopy from time import time from typing import Dict, List, Tuple, Union import numpy as np import pandas as pd from jurity.recommenders import CombinedMetrics, BinaryRecoMetrics, RankingRecoMetrics, DiversityRecoMetrics from sklearn.model_selection import GroupKFold from mabwiser.utils import check_true, Arm from mab2rec.rec import BanditRecommender from mab2rec.utils import Constants from mab2rec.utils import explode_recommendations, load_data, load_response_data, merge_user_features from mab2rec.utils import load_pickle, save_pickle def train(recommender: BanditRecommender, data: Union[str, pd.DataFrame], user_features: Union[str, pd.DataFrame] = None, user_features_list: Union[str, List[str]] = None, user_features_dtypes: Union[str, Dict] = None, item_features: Union[str, pd.DataFrame] = None, item_list: Union[str, List[Arm]] = None, item_eligibility: Union[str, pd.DataFrame] = None, warm_start: bool = False, warm_start_distance: float = None, user_id_col: str = Constants.user_id, item_id_col: str = Constants.item_id, response_col: str = Constants.response, batch_size: int = 100000, save_file: Union[str, bool] = None) -> None: """ Trains Recommender. Parameters ---------- recommender : BanditRecommender The recommender algorithm to be trained. The recommender object is updated in-place. data : Union[str, pd.DataFrame] Training data. Data should have a row for each training sample (user_id, item_id, response). Column names should be consistent with user_id_col, item_id_col and response_col arguments. CSV format with file header or Data Frame. user_features : Union[str, pd.DataFrame], default=None User features containing features for each user_id. Each row should include user_id and list of features (user_id, u_1, u_2, ..., u_p). CSV format with file header or Data Frame. user_features_list : Union[str, List[str]], default=None List of user features to use. Must be a subset of features in (u_1, u_2, ... u_p). If None, all the features in user_features are used. CSV format with file header or List. user_features_dtypes: Union[str, Dict], default=None Data type for each user feature. Maps each user feature name to valid data type. If none, no data type casting is done upon load and data types or inferred by Pandas library. JSON format or Dictionary. item_features : Union[str, pd.DataFrame], default=None Item features file containing features for each item_id. Each row should include item_id and list of features (item_id, i_1, i_2, .... i_q). CSV format with file header or Data Frame. item_list : Union[str, List[Arm]], default=None List of items to train. If None, all the items in data are used. CSV format with file header or List. item_eligibility: Union[str, pd.DataFrame], default=None Items each user is eligible for. Not used during training. CSV format with file header or Data Frame. warm_start : bool, default=False Whether to warm start untrained (cold) arms after training or not. warm_start_distance : float, default=None Warm start distance quantile. Value between 0 and 1 used to determine if an item can be warm started or not using closest item. All cold items will be warm started if 1 and none will be warm started if 0. Must be specified if warm_start=True. user_id_col : str, default=Constants.user_id User id column name. item_id_col : str, default=Constants.item_id Item id column name. response_col : str, default=Constants.response Response column name. batch_size : str, default=100000 Batch size used for chunking data. save_file : Union[str, bool], default=None File name to save recommender pickle. If None, recommender is not saved to file. Returns ------- Returns nothing. """ _validate_recommender(recommender) _validate_common_args(data, user_features, user_features_list, user_features_dtypes, item_features, item_list, item_eligibility, warm_start, warm_start_distance, user_id_col, item_id_col, response_col, batch_size) _validate_save(save_file) # Import data train_data_df, item_list, user_features_df, \ item_to_features, _ = load_data(data=data, user_features=user_features, user_features_list=user_features_list, user_features_dtypes=user_features_dtypes, item_features=item_features, item_list=item_list, item_eligibility=item_eligibility, user_id_col=user_id_col, item_id_col=item_id_col, response_col=response_col) # Initialize and set arms of recommender recommender.set_arms(item_list) # Loop through the data in batches and fit recommender num_batches = max(1, len(train_data_df) // batch_size) for df in np.array_split(train_data_df, num_batches): if recommender.mab.is_contextual: check_true(user_features_df is not None, ValueError("User features are required for contextual bandits.")) feature_cols = [c for c in user_features_df.columns if c != user_id_col] df = merge_user_features(pd.DataFrame(df), user_features_df, user_id_col) if recommender.mab._is_initial_fit: recommender.partial_fit(df[item_id_col], df[response_col], df[feature_cols]) else: recommender.fit(df[item_id_col], df[response_col], df[feature_cols]) else: if recommender.mab._is_initial_fit: recommender.partial_fit(df[item_id_col], df[response_col]) else: recommender.fit(df[item_id_col], df[response_col]) # Warm start if warm_start: recommender.warm_start(item_to_features, warm_start_distance) # Save file if save_file is not None: if isinstance(save_file, str): os.makedirs(os.path.dirname(save_file), exist_ok=True) save_pickle(recommender, save_file) elif save_file: save_pickle(recommender, "recommender.pkl") def score(recommender: Union[str, BanditRecommender], data: Union[str, pd.DataFrame], user_features: Union[str, pd.DataFrame] = None, user_features_list: Union[str, List[str]] = None, user_features_dtypes: Union[str, Dict] = None, item_features: Union[str, pd.DataFrame] = None, item_list: Union[str, List[Arm]] = None, item_eligibility: Union[str, pd.DataFrame] = None, warm_start: bool = False, warm_start_distance: float = None, user_id_col: str = Constants.user_id, item_id_col: str = Constants.item_id, response_col: str = Constants.response, batch_size: int = 100000, save_file: Union[str, bool] = None) -> pd.DataFrame: """ Score Recommender. Generates top-k recommendations for users in given data. Parameters ---------- recommender : Union[str, BanditRecommender] The recommender algorithm to be scored. Could be an instantiated BanditRecommender or file path of serialized recommender in pickle file. data : Union[str, pd.DataFrame] Training data. Data should have a row for each training sample (user_id, item_id, response). Column names should be consistent with user_id_col, item_id_col and response_col arguments. CSV format with file header or Data Frame. user_features : Union[str, pd.DataFrame], default=None User features containing features for each user_id. Each row should include user_id and list of features (user_id, u_1, u_2, ..., u_p). CSV format with file header or Data Frame. user_features_list : Union[str, List[str]], default=None List of user features to use. Must be a subset of features in (u_1, u_2, ... u_p). If None, all the features in user_features are used. CSV format with file header or List. user_features_dtypes: Union[str, Dict], default=None Data type for each user feature. Maps each user feature name to valid data type. If none, no data type casting is done upon load and data types or inferred by Pandas library. JSON format or Dictionary. item_features : Union[str, pd.DataFrame], default=None Item features file containing features for each item_id. Each row should include item_id and list of features (item_id, i_1, i_2, .... i_q). CSV format with file header or Data Frame. item_list : Union[str, List[Arm]], default=None List of items to train. If None, all the items in data are used. CSV format with file header or List. item_eligibility: Union[str, pd.DataFrame], default=None Items each user is eligible for. Used to generate excluded_arms lists. If None, all the items can be evaluated for recommendation for each user. CSV format with file header or Data Frame. warm_start : bool, default=False Whether to warm start untrained (cold) arms after training or not. warm_start_distance : float, default=None Warm start distance quantile. Value between 0 and 1 used to determine if an item can be warm started or not using closest item. All cold items will be warm started if 1 and none will be warm started if 0. Must be specified if warm_start=True. user_id_col : str, default=Constants.user_id User id column name. item_id_col : str, default=Constants.item_id Item id column name. response_col : str, default=Constants.response Response column name. batch_size : str, default=100000 Batch size used for chunking data. save_file : str, default=None File name to save recommender pickle. If None, recommender is not saved to file. Returns ------- Scored recommendations. """ # Load recommender if isinstance(recommender, str): recommender = load_pickle(recommender) _validate_recommender(recommender, is_fit=True) _validate_common_args(data, user_features, user_features_list, user_features_dtypes, item_features, item_list, item_eligibility, warm_start, warm_start_distance, user_id_col, item_id_col, response_col, batch_size) _validate_save(save_file) # Import data item_list_out = recommender.mab.arms if item_list is None else item_list test_data_df, item_list_out, user_features_df, \ item_to_features, excluded_df = load_data(data=data, user_features=user_features, user_features_list=user_features_list, user_features_dtypes=user_features_dtypes, item_features=item_features, item_list=item_list_out, item_eligibility=item_eligibility, user_id_col=user_id_col, item_id_col=item_id_col, response_col=response_col) # Set arms to recommender if item_list is not None: recommender.set_arms(item_list_out) # Warm start if warm_start: recommender.warm_start(item_to_features, warm_start_distance) # Loop through users in batches and get recommendations users = test_data_df[user_id_col].unique().tolist() recommendations = [] scores = [] num_batches = max(1, len(users) // batch_size) for users_of_batch in np.array_split(users, num_batches): # Data frame of users to score df = pd.DataFrame({user_id_col: users_of_batch}) # Merge user features and get feature column names if user_features_df is not None: df = merge_user_features(df, user_features_df, user_id_col) feature_cols = [c for c in user_features_df.columns if c != user_id_col] contexts = df[feature_cols].fillna(0) else: contexts = [[]] * len(df) # Merge excluded item list if item_eligibility is not None: df = df.merge(excluded_df, how='left', on=user_id_col) excluded_arms_batch = df[item_id_col].tolist() else: excluded_arms_batch = None # Get recommendations recs_of_batch, scores_of_batch = recommender.recommend(contexts, excluded_arms_batch, return_scores=True) recommendations += recs_of_batch scores += scores_of_batch # Convert recommendations to data frame df = pd.DataFrame(users, columns=[user_id_col]) df[item_id_col] = recommendations df[Constants.score] = scores df = explode_recommendations(df, user_id_col, [item_id_col, Constants.score]) # Save to csv if save_file is not None: if isinstance(save_file, str): df.to_csv(save_file, index=False) elif save_file: df.to_csv("results.csv", index=False) return df def benchmark(recommenders: Dict[str, BanditRecommender], metrics: List[Union[BinaryRecoMetrics, RankingRecoMetrics]], train_data: Union[str, pd.DataFrame], test_data: Union[str, pd.DataFrame] = None, cv: int = None, user_features: Union[str, pd.DataFrame] = None, user_features_list: Union[str, List[str]] = None, user_features_dtypes: Union[str, Dict] = None, item_features: Union[str, pd.DataFrame] = None, item_list: Union[str, List[Arm]] = None, item_eligibility: Union[str, pd.DataFrame] = None, warm_start: bool = False, warm_start_distance: float = None, user_id_col: str = Constants.user_id, item_id_col: str = Constants.item_id, response_col: str = Constants.response, batch_size: int = 100000, verbose: bool = False) -> Union[Tuple[Dict[str, pd.DataFrame], Dict[str, Dict[str, float]]], Tuple[List[Dict[str, pd.DataFrame]], List[Dict[str, Dict[str, float]]]]]: """ Benchmark Recommenders. Benchmark a given set of recommender algorithms by training, scoring and evaluating each algorithm If using cross-validation (cv) it benchmarks the algorithms on cv-many folds from the train data, otherwise it trains on the train data and evaluates on the test data. Parameters ---------- recommenders : Dict[str, BanditRecommender] The recommender algorithms to be benchmarked. Dictionary with names (key) and recommender algorithms (value). metrics : List[Union[BinaryRecoMetrics, RankingRecoMetrics]] List of metrics used to evaluate recommendations. train_data : Union[str, pd.DataFrame] Training data used to train recommenders. Data should have a row for each training sample (user_id, item_id, response). Column names should be consistent with user_id_col, item_id_col and response_col arguments. CSV format with file header or Data Frame. test_data : Union[str, pd.DataFrame] Test data used to generate recommendations. Data should have a row for each training sample (user_id, item_id, response). Column names should be consistent with user_id_col, item_id_col and response_col arguments. CSV format with file header or Data Frame. cv : int, default=None Number of folds in the train data to use for cross-fold validation. A grouped K-fold iterator is used to ensure that the same user is not contained in different folds. Test data must be None when using cv. user_features : Union[str, pd.DataFrame], default=None User features containing features for each user_id. Each row should include user_id and list of features (user_id, u_1, u_2, ..., u_p). CSV format with file header or Data Frame. user_features_list : Union[str, List[str]], default=None List of user features to use. Must be a subset of features in (u_1, u_2, ... u_p). If None, all the features in user_features are used. CSV format with file header or List. user_features_dtypes: Union[str, Dict], default=None Data type for each user feature. Maps each user feature name to valid data type. If none, no data type casting is done upon load and data types or inferred by Pandas library. JSON format or Dictionary. item_features : Union[str, pd.DataFrame], default=None Item features file containing features for each item_id. Each row should include item_id and list of features (item_id, i_1, i_2, .... i_q). CSV format with file header or Data Frame. item_list : Union[str, List[Arm]], default=None List of items to train. If None, all the items in data are used. CSV format with file header or List. item_eligibility: Union[str, pd.DataFrame], default=None Items each user is eligible for. Used to generate excluded_arms lists. If None, all the items can be evaluated for recommendation for each user. CSV format with file header or Data Frame. warm_start : bool, default=False Whether to warm start untrained (cold) arms after training or not. warm_start_distance : float, default=None Warm start distance quantile. Value between 0 and 1 used to determine if an item can be warm started or not using closest item. All cold items will be warm started if 1 and none will be warm started if 0. Must be specified if warm_start=True. user_id_col : str, default=Constants.user_id User id column name. item_id_col : str, default=Constants.item_id Item id column name. response_col : str, default=Constants.response Response column name. batch_size : str, default=100000 Batch size used for chunking data. verbose : bool, default=False Whether to print progress status or not. Returns ------- Tuple with recommendations and evaluation metrics for each algorithm. The tuple values are lists of dictionaries if cross-validation is used, representing the results on each fold, and individual dictionaries otherwise. """ _validate_recommender(recommenders) _validate_common_args(train_data, user_features, user_features_list, user_features_dtypes, item_features, item_list, item_eligibility, warm_start, warm_start_distance, user_id_col, item_id_col, response_col, batch_size) _validate_bench(recommenders, metrics, train_data, test_data, cv) # Convert input arguments to dictionary args = locals() args.pop('cv') if cv is None: return _bench(**args) else: # Read data if isinstance(train_data, str): df = pd.read_csv(train_data) else: df = pd.DataFrame(train_data) # Initialize lists to store recommendation results and metrics for each fold recommendations_list = [] metrics_list = [] # Split data into cv folds and run benchmark group_kfold = GroupKFold(n_splits=cv) i = 1 for train_index, test_index in group_kfold.split(df, groups=df[user_id_col]): if verbose: print(f'CV Fold = {i} \n') # Set train/test data frames args['train_data'] = df.iloc[train_index, :] args['test_data'] = df.iloc[test_index, :] # Run benchmark recommendations, metrics = _bench(**args) # Append recommendations_list.append(recommendations) metrics_list.append(metrics) i += 1 return recommendations_list, metrics_list def _bench(recommenders: Dict[str, BanditRecommender], metrics: List[Union[BinaryRecoMetrics, RankingRecoMetrics]], train_data: Union[str, pd.DataFrame], test_data: Union[str, pd.DataFrame], user_features: Union[str, pd.DataFrame] = None, user_features_list: Union[str, List[str]] = None, user_features_dtypes: Union[str, Dict] = None, item_features: Union[str, pd.DataFrame] = None, item_list: Union[str, List[str]] = None, item_eligibility: Union[str, pd.DataFrame] = None, warm_start: bool = False, warm_start_distance: float = None, user_id_col: str = Constants.user_id, item_id_col: str = Constants.item_id, response_col: str = Constants.response, batch_size: int = 100000, verbose: bool = False) -> Tuple[Dict[str, pd.DataFrame], Dict[str, Dict[str, float]]]: # Import data train_data_df, item_list, user_features_df, \ item_to_features, _ = load_data(data=train_data, user_features=user_features, user_features_list=user_features_list, user_features_dtypes=user_features_dtypes, item_features=item_features, item_list=item_list, item_eligibility=item_eligibility, user_id_col=user_id_col, item_id_col=item_id_col, response_col=response_col) test_data_df = load_response_data(test_data, user_id_col, item_id_col, response_col) recommendations = dict() rec_metrics = dict() for name, recommender in recommenders.items(): if verbose: print(f'>>> {name}') print('Running...') # Copy recommender rec = deepcopy(recommender) # Train t0 = time() train( recommender=rec, data=train_data_df, user_features=user_features_df, item_features=item_features, item_list=item_list, warm_start=warm_start, warm_start_distance=warm_start_distance, user_id_col=user_id_col, item_id_col=item_id_col, response_col=response_col, batch_size=batch_size, save_file=None) # Score recommendations[name] = score( recommender=rec, data=test_data_df, user_features=user_features_df, item_features=item_features, item_list=item_list, item_eligibility=item_eligibility, warm_start=warm_start, warm_start_distance=warm_start_distance, user_id_col=user_id_col, item_id_col=item_id_col, response_col=response_col, batch_size=batch_size, save_file=None) # Evaluate cm = CombinedMetrics(*metrics) rec_metrics[name] = cm.get_score(test_data_df.rename(columns={response_col: Constants.score}), recommendations[name]) if verbose: print(f"Done: {(time() - t0) / 60:.2f} minutes \n") return recommendations, rec_metrics def _validate_common_args(data, user_features, user_features_list, user_features_dtypes, item_features, item_list, item_eligibility, warm_start, warm_start_distance, user_id_col, item_id_col, response_col, batch_size): # Train/test data check_true(data is not None, ValueError("Data input cannot be none.")) check_true(isinstance(data, (str, pd.DataFrame)), TypeError("Data should be string of filepath or data frame.")) # User features if user_features is not None: check_true(isinstance(user_features, (str, pd.DataFrame)), TypeError("User features should be string of filepath or data frame.")) if user_features_list is not None: check_true(user_features is not None, ValueError("User features should be given if user list is specified.")) check_true(isinstance(user_features_list, (str, list)), TypeError("User features list should be string of filepath or list.")) if user_features_dtypes is not None: check_true(user_features is not None, ValueError("User features should be given if user dtypes is specified.")) check_true(isinstance(user_features_dtypes, (str, dict)), TypeError("User features dtypes should be string of filepath or dictionary.")) # Item features if item_features is not None: check_true(isinstance(item_features, (str, pd.DataFrame)), TypeError("Item features should be string of filepath or data frame.")) if item_list is not None: check_true(isinstance(item_list, (str, list)), TypeError("Item list should be string of filepath or list.")) if item_eligibility is not None: check_true(isinstance(item_eligibility, (str, pd.DataFrame)), TypeError("Item eligibility should be string of filepath or data frame.")) # Warm start check_true(isinstance(warm_start, bool), TypeError("Warm start flag should be boolean.")) if warm_start: check_true(warm_start_distance is not None, ValueError("Warm start distance cannot be none.")) check_true(isinstance(warm_start_distance, float), TypeError("Warm start distance should be a float.")) check_true(isinstance(warm_start_distance, float), TypeError("Warm start distance should be a float.")) check_true(0 <= warm_start_distance <= 1, ValueError("Warm start distance should be between 0 and 1.")) check_true(item_features is not None, ValueError("Item features are required to warm start arms.")) else: check_true(warm_start_distance is None, ValueError("Warm start distance should be none if warm start false.")) # IDs check_true(isinstance(user_id_col, str), TypeError("User id should be a string.")) check_true(isinstance(item_id_col, str), TypeError("Item id should be a string.")) check_true(isinstance(response_col, str), TypeError("Response column should be a string.")) # Batch size check_true(isinstance(batch_size, int), TypeError("Batch size should be an integer.")) check_true(batch_size > 0, ValueError("Batch size should be positive.")) def _validate_recommender(recommender, is_fit=False): if not isinstance(recommender, dict): recommender_dict = {"": recommender} else: recommender_dict = recommender for rec in recommender_dict.values(): check_true(isinstance(rec, BanditRecommender), TypeError("Recommender should be a BanditRecommender instance.")) if is_fit: check_true(rec.mab is not None, ValueError("Recommender has not been initialized.")) check_true(rec.mab._is_initial_fit, ValueError("Recommender has not been fit.")) def _validate_save(save_file): if save_file is not None: check_true(isinstance(save_file, (bool, str)), TypeError("Save file should be boolean or a string filepath.")) def _validate_bench(recommenders, metrics, train_data, test_data, cv): # Recommenders check_true(recommenders is not None, ValueError("Recommenders cannot be none.")) check_true(isinstance(recommenders, dict), TypeError("Recommenders should be given as a dictionary.")) # Metrics check_true(isinstance(metrics, list), TypeError("Metrics should be given as a list.")) for v in metrics: check_true(isinstance(v, (BinaryRecoMetrics.AUC, BinaryRecoMetrics.CTR, RankingRecoMetrics.Precision, RankingRecoMetrics.Recall, RankingRecoMetrics.NDCG, RankingRecoMetrics.MAP, DiversityRecoMetrics.InterListDiversity, DiversityRecoMetrics.IntraListDiversity)), TypeError("Evaluation metric values must be BinaryRecoMetrics, RankingRecoMetrics, " "or DiversityRecoMetrics instances.")) # Train/test data check_true(train_data is not None, ValueError("Train data cannot be none.")) check_true(isinstance(train_data, (str, pd.DataFrame)), TypeError("Train data should be string of filepath or data frame.")) if test_data is not None: check_true(isinstance(test_data, (str, pd.DataFrame)), TypeError("Test data should be string of filepath or data frame.")) # CV if cv is not None: check_true(isinstance(cv, int), TypeError("Cross-validation (cv) must be an integer.")) check_true(test_data is None, ValueError("Test data must be None when using Cross-validation (cv).")) ================================================ FILE: mab2rec/rec.py ================================================ # -*- coding: utf-8 -*- # Copyright FMR LLC # SPDX-License-Identifier: Apache-2.0 from typing import Dict, List, Tuple, Union import numpy as np import pandas as pd from mabwiser.mab import MAB, LearningPolicy, NeighborhoodPolicy from mabwiser.utils import Arm, Num, check_true from scipy.special import expit from mab2rec.utils import Constants from mab2rec._version import __author__, __email__, __version__, __copyright__ __author__ = __author__ __email__ = __email__ __version__ = __version__ __copyright__ = __copyright__ class BanditRecommender: """**Mab2Rec: Multi-Armed Bandit Recommender** Mab2Rec is a library to support prototyping and building of bandit-based recommendation algorithms. It is powered by MABWiser which supports **context-free**, **parametric** and **non-parametric** **contextual** bandit models. Attributes ---------- learning_policy : MABWiser LearningPolicy The learning policy. neighborhood_policy : MABWiser NeighborhoodPolicy The neighborhood policy. top_k : int, default=10 The number of items to recommend. seed : int, Constants.default_seed The random seed to initialize the internal random number generator. n_jobs : int This is used to specify how many concurrent processes/threads should be used for parallelized routines. Default value is set to 1. If set to -1, all CPUs are used. If set to -2, all CPUs but one are used, and so on. backend : str, optional Specify a parallelization backend implementation supported in the joblib library. Supported options are: - “loky” used by default, can induce some communication and memory overhead when exchanging input and output data with the worker Python processes. - “multiprocessing” previous process-based backend based on multiprocessing.Pool. Less robust than loky. - “threading” is a very low-overhead backend but it suffers from the Python Global Interpreter Lock if the called function relies a lot on Python objects. Default value is None. In this case the default backend selected by joblib will be used. mab : MAB The multi-armed bandit. Examples -------- >>> from mab2rec import BanditRecommender, LearningPolicy >>> decisions = ['Arm1', 'Arm1', 'Arm3', 'Arm1', 'Arm2', 'Arm3'] >>> rewards = [0, 1, 1, 0, 1, 0] >>> rec = BanditRecommender(LearningPolicy.EpsilonGreedy(epsilon=0.25), top_k=2) >>> rec.fit(decisions, rewards) >>> rec.recommend() ['Arm2', 'Arm1'] >>> rec.add_arm('Arm4') >>> rec.partial_fit(['Arm4'], [1]) >>> rec.recommend()[0] ['Arm2', 'Arm4'] >>> from mab2rec import BanditRecommender, LearningPolicy, NeighborhoodPolicy >>> decisions = ['Arm1', 'Arm1', 'Arm3', 'Arm1', 'Arm2', 'Arm3'] >>> rewards = [0, 1, 1, 0, 1, 0] >>> contexts = [[0, 0, 0], [1, 0, 1], [0, 1, 1], [0, 0, 0], [1, 1, 1], [0, 1, 0]] >>> rec = BanditRecommender(LearningPolicy.EpsilonGreedy(), NeighborhoodPolicy.KNearest(k=3), top_k=2) >>> rec.fit(decisions, rewards, contexts) >>> rec.recommend([[1, 1, 0], [1, 1, 1], [0, 1, 0]]) [['Arm2', 'Arm3'], ['Arm3', 'Arm2'], ['Arm3', 'Arm2']] >>> from mab2rec import BanditRecommender, LearningPolicy >>> decisions = ['Arm1', 'Arm1', 'Arm3', 'Arm1', 'Arm2', 'Arm3'] >>> rewards = [0, 1, 1, 0, 1, 0] >>> contexts = [[0, 0, 0], [1, 0, 1], [0, 1, 1], [0, 0, 0], [1, 1, 1], [0, 1, 0]] >>> rec = BanditRecommender(LearningPolicy.LinGreedy(epsilon=0.1), top_k=2) >>> rec.fit(decisions, rewards, contexts) >>> rec.recommend([[1, 1, 0], [1, 1, 1], [0, 1, 0]]) [['Arm2', 'Arm1'], ['Arm2', 'Arm1'], ['Arm2', 'Arm3']] >>> arm_to_features = {'Arm1': [0, 1], 'Arm2': [0, 0], 'Arm3': [0, 0], 'Arm4': [0, 1]} >>> rec.add_arm('Arm4') >>> rec.warm_start(arm_to_features, distance_quantile=0.75) >>> rec.recommend([[1, 1, 0], [1, 1, 1], [0, 1, 0]]) [['Arm2', 'Arm4'], ['Arm2', 'Arm4'], ['Arm2', 'Arm3']] """ def __init__(self, learning_policy: Union[LearningPolicy.EpsilonGreedy, LearningPolicy.Popularity, LearningPolicy.Random, LearningPolicy.Softmax, LearningPolicy.ThompsonSampling, LearningPolicy.UCB1, LearningPolicy.LinGreedy, LearningPolicy.LinTS, LearningPolicy.LinUCB], neighborhood_policy: Union[None, NeighborhoodPolicy.LSHNearest, NeighborhoodPolicy.Clusters, NeighborhoodPolicy.KNearest, NeighborhoodPolicy.Radius, NeighborhoodPolicy.TreeBandit] = None, top_k: int = 10, seed: int = Constants.default_seed, n_jobs: int = 1, backend: str = None): """Initializes bandit recommender with the given arguments. Validates the arguments and raises exception in case there are violations. Parameters ---------- learning_policy : LearningPolicy The learning policy. neighborhood_policy : NeighborhoodPolicy, default=None The context policy. top_k : int, default=10 The number of items to recommend. seed : numbers.Rational, default=Constants.default_seed The random seed to initialize the random number generator. Default value is set to Constants.default_seed.value top_k : int, default=10 The number of items to recommend. n_jobs : int, default=1 This is used to specify how many concurrent processes/threads should be used for parallelized routines. If set to -1, all CPUs are used. If set to -2, all CPUs but one are used, and so on. backend : str, default=None Specify a parallelization backend implementation supported in the joblib library. Supported options are: - “loky” used by default, can induce some communication and memory overhead when exchanging input and output data with the worker Python processes. - “multiprocessing” previous process-based backend based on multiprocessing.Pool. Less robust than loky. - “threading” is a very low-overhead backend but it suffers from the Python Global Interpreter Lock if the called function relies a lot on Python objects. Default value is None. In this case the default backend selected by joblib will be used. """ # Set given arguments self.learning_policy = learning_policy self.neighborhood_policy = neighborhood_policy self.top_k = top_k self.seed = seed self.n_jobs = n_jobs self.backend = backend # Validate that MAB can be instantiated with given arguments self.mab = None self._validate_mab_args() def _init(self, arms: List[Union[Arm]]) -> None: """Initializes recommender with given list of arms. Parameters ---------- arms : List[Union[Arm]] The list of all of the arms available for decisions. Arms can be integers, strings, etc. Returns ------- Returns nothing """ self.mab = MAB(arms, self.learning_policy, self.neighborhood_policy, self.seed, self.n_jobs, self.backend) def add_arm(self, arm: Arm, binarizer=None) -> None: """Adds an _arm_ to the list of arms. Incorporates the arm into the learning and neighborhood policies with no training data. Parameters ---------- arm : Arm The new arm to be added. binarizer : Callable, default=None The new binarizer function for Thompson Sampling. Returns ------- Returns nothing. """ if self.mab is None: self._init([arm]) else: self.mab.add_arm(arm, binarizer) def fit(self, decisions: Union[List[Arm], np.ndarray, pd.Series], rewards: Union[List[Num], np.ndarray, pd.Series], contexts: Union[None, List[List[Num]], np.ndarray, pd.Series, pd.DataFrame] = None) -> None: """Fits the recommender the given *decisions*, their corresponding *rewards* and *contexts*, if any. If the recommender arms has not been initialized using the `set_arms`, the recommender arms will be set to the list of arms in *decisions*. Validates arguments and raises exceptions in case there are violations. This function makes the following assumptions: - each decision corresponds to an arm of the bandit. - there are no ``None``, ``Nan``, or ``Infinity`` values in the contexts. Parameters ---------- decisions : Union[List[Arm], np.ndarray, pd.Series] The decisions that are made. rewards : Union[List[Num], np.ndarray, pd.Series] The rewards that are received corresponding to the decisions. contexts : Union[None, List[List[Num]], np.ndarray, pd.Series, pd.DataFrame], default=None The context under which each decision is made. Returns ------- Returns nothing. """ if self.mab is None: self._init(np.unique(decisions).tolist()) self.mab.fit(decisions, rewards, contexts) def partial_fit(self, decisions: Union[List[Arm], np.ndarray, pd.Series], rewards: Union[List[Num], np.ndarray, pd.Series], contexts: Union[None, List[List[Num]], np.ndarray, pd.Series, pd.DataFrame] = None) -> None: """Updates the recommender with the given *decisions*, their corresponding *rewards* and *contexts*, if any. Validates arguments and raises exceptions in case there are violations. This function makes the following assumptions: - each decision corresponds to an arm of the bandit. - there are no ``None``, ``Nan``, or ``Infinity`` values in the contexts. Parameters ---------- decisions : Union[List[Arm], np.ndarray, pd.Series] The decisions that are made. rewards : Union[List[Num], np.ndarray, pd.Series] The rewards that are received corresponding to the decisions. contexts : Union[None, List[List[Num]], np.ndarray, pd.Series, pd.DataFrame], default=None The context under which each decision is made. Returns ------- Returns nothing. """ self._validate_mab(is_fit=True) self.mab.partial_fit(decisions, rewards, contexts) def predict(self, contexts: Union[None, List[List[Num]], np.ndarray, pd.Series, pd.DataFrame] = None) -> Union[Arm, List[Arm]]: """Returns the "best" arm (or arms list if multiple contexts are given) based on the expected reward. The definition of the *best* depends on the specified learning policy. Contextual learning policies and neighborhood policies require contexts data in training. In testing, they return the best arm given new context(s). Parameters ---------- contexts : Union[None, List[List[Num]], np.ndarray, pd.Series, pd.DataFrame], default=None The context under which each decision is made. If contexts is not ``None`` for context-free bandits, the predictions returned will be a list of the same length as contexts. Returns ------- The recommended arm or recommended arms list. """ self._validate_mab(is_fit=True) return self.mab.predict(contexts) def predict_expectations(self, contexts: Union[None, List[List[Num]], np.ndarray, pd.Series, pd.DataFrame] = None) \ -> Union[Dict[Arm, Num], List[Dict[Arm, Num]]]: """Returns a dictionary of arms (key) to their expected rewards (value). Contextual learning policies and neighborhood policies require contexts data for expected rewards. Parameters ---------- contexts : Union[None, List[Num], List[List[Num]], np.ndarray, pd.Series, pd.DataFrame], default=None The context for the expected rewards. If contexts is not ``None`` for context-free bandits, the predicted expectations returned will be a list of the same length as contexts. Returns ------- The dictionary of arms (key) to their expected rewards (value), or a list of such dictionaries. """ self._validate_mab(is_fit=True) return self.mab.predict_expectations(contexts) def recommend(self, contexts: Union[None, List[List[Num]], np.ndarray, pd.Series, pd.DataFrame] = None, excluded_arms: List[List[Arm]] = None, return_scores: bool = False, apply_sigmoid: bool = True) \ -> Union[Union[List[Arm], Tuple[List[Arm], List[Num]], Union[List[List[Arm]], Tuple[List[List[Arm]], List[List[Num]]]]]]: """Generate _top-k_ recommendations based on the expected reward. Recommend up to k arms with the highest predicted expectations. For contextual bandits, only items not included in the excluded arms can be recommended. Parameters ---------- contexts : np.ndarray, default=None The context under which each decision is made. If contexts is not ``None`` for context-free bandits, the recommendations returned will be a list of the same length as contexts. excluded_arms : List[List[Arm]], default=None List of list of arms to exclude from recommended arms. return_scores : bool, default=False Return score for each recommended item. apply_sigmoid : bool, default=True Whether to apply sigmoid transformation to scores before ranking. Returns ------- List of tuples of the form ([arm_1, arm_2, ..., arm_k], [score_1, score_2, ..., score_k]) """ self._validate_mab(is_fit=True) self._validate_get_rec(contexts, excluded_arms) # Get predicted expectations num_contexts = len(contexts) if contexts is not None else 1 if num_contexts == 1: expectations = [self.mab.predict_expectations(contexts)] else: expectations = self.mab.predict_expectations(contexts) # Take sigmoid of expectations so that values are between 0 and 1 if apply_sigmoid: expectations = expit(pd.DataFrame(expectations)[self.mab.arms].values) else: expectations = pd.DataFrame(expectations)[self.mab.arms].values # Create an exclusion mask, where exclusion_mask[context_ind][arm_ind] denotes if the arm with the # index arm_ind was excluded for context with the index context_ind. # The value will be True if it is excluded and those arms will not be returned as part of the results. arm_to_index = {arm: arm_ind for arm_ind, arm in enumerate(self.mab.arms)} exclude_mask = np.zeros((num_contexts, len(self.mab.arms)), dtype=bool) if excluded_arms is not None: for context_ind, excluded in enumerate(excluded_arms): exclude_mask[context_ind][[arm_to_index[arm] for arm in excluded if arm in arm_to_index]] = True # Set excluded item scores to -1, so they automatically get placed lower in best results expectations[exclude_mask] = -1. # Get best `top_k` results by sorting the expectations arm_inds = np.flip(np.argsort(expectations)[:, -self.top_k:], axis=1) # Get the list of top_k recommended items and corresponding expectations for each context recommendations = [[]] * num_contexts scores = [[]] * num_contexts for context_ind in range(num_contexts): recommendations[context_ind] = [self.mab.arms[arm_ind] for arm_ind in arm_inds[context_ind] if not exclude_mask[context_ind, arm_ind]] if return_scores: scores[context_ind] = [expectations[context_ind, arm_ind] for arm_ind in arm_inds[context_ind] if not exclude_mask[context_ind, arm_ind]] # Return recommendations and scores if return_scores: if num_contexts > 1: return recommendations, scores else: return recommendations[0], scores[0] else: if num_contexts > 1: return recommendations else: return recommendations[0] def remove_arm(self, arm: Arm) -> None: """Removes an _arm_ from the list of arms. Parameters ---------- arm : Arm The existing arm to be removed. Returns ------- Returns nothing. """ self._validate_mab() self.mab.remove_arm(arm) def set_arms(self, arms: List[Arm], binarizer=None) -> None: """Initializes the recommender and sets the recommender with given list of arms. Existing arms not in the given list of arms are removed and new arms are incorporated into the learning and neighborhood policies with no training data. If the recommender has already been initialized it will not be re-initialized. Parameters ---------- arms : List[Arm] The new arm to be added. binarizer : Callable, default=None The new binarizer function for Thompson Sampling. Returns ------- Returns nothing. """ # Initialize mab if self.mab is None: self._init(arms) # Remove arms arms_to_remove = [] for existing_arm in self.mab.arms: if existing_arm not in arms: arms_to_remove.append(existing_arm) for arm in arms_to_remove: self.remove_arm(arm) # Add arms for new_arm in arms: if new_arm not in self.mab.arms: self.add_arm(new_arm, binarizer) def warm_start(self, arm_to_features: Dict[Arm, List[Num]], distance_quantile: float = None) -> None: """Warm-start untrained (cold) arms of the multi-armed bandit. Validates arguments and raises exceptions in case there are violations. Parameters ---------- arm_to_features : Dict[Arm, List[Num]] Numeric representation for each arm. distance_quantile : float, default=None Value between 0 and 1 used to determine if an item can be warm started or not using closest item. All cold items will be warm started if 1 and none will be warm started if 0. Returns ------- Returns nothing. """ self._validate_mab(is_fit=True) self.mab.warm_start(arm_to_features, distance_quantile) def _validate_mab_args(self): _ = MAB([1], self.learning_policy, self.neighborhood_policy, self.seed, self.n_jobs, self.backend) check_true(isinstance(self.top_k, int), ValueError("Top k should be an integer.")) check_true(self.top_k > 0, ValueError("Top k should be positive.")) def _validate_mab(self, is_fit=False): check_true(self.mab is not None, ValueError("Recommender has not been initialized.")) if is_fit: check_true(self.mab._is_initial_fit, ValueError("Recommender has not been fit.")) @staticmethod def _validate_get_rec(contexts, excluded_arms): if excluded_arms is not None: check_true(contexts is not None, ValueError("Excluded arms should either be None, or a list of exclusion lists per context.")) check_true(len(excluded_arms) == len(contexts), ValueError("Excluded arms should either be None, or a list of exclusion lists per context.")) ================================================ FILE: mab2rec/utils.py ================================================ # -*- coding: utf-8 -*- # Copyright FMR LLC # SPDX-License-Identifier: Apache-2.0 import json import pickle from typing import Dict, List, NamedTuple, Union import numpy as np import pandas as pd from jurity.recommenders import BinaryRecoMetrics, RankingRecoMetrics from mabwiser.utils import Arm, Num from mabwiser.utils import check_true class Constants(NamedTuple): """ Constant values used by the modules. """ default_seed = 12345 user_id = 'user_id' item_id = 'item_id' response = 'response' score = 'score' def explode_recommendations(df: pd.DataFrame, unique_col: str, explode_cols: List[str]): """Replicates the explode functionality in pandas 0.25 and later. Assumes that there are two levels in the dataframe. The unique column is the first level, and it contains de-duplicated values. The columns in explode_cols is the second level, where each of these columns contain a list of values. Each column in explode_cols is assumed to contain a list of same length. The output is the normalized dataframe where the lists are split into individual rows. """ # First, remove anything with 0 length lens = df[explode_cols[0]].str.len() df = df[lens != 0].reset_index(drop=True) # Calculate lengths of the second level list of values lens = df[explode_cols[0]].str.len() # Repeat the unique column to get the same number of values as the second level unique_vals = np.repeat(df[unique_col], lens.values) cols = {unique_col: unique_vals.values} # Concatenate all second level values to get a flattened list cols.update({key: np.concatenate(df[key]) for key in explode_cols}) return pd.DataFrame(cols) def concat_recommendations_list(recommendation_results_list: List[Dict[str, pd.DataFrame]]) -> Dict[str, pd.DataFrame]: """ Concatenates recommendation results split across multiple data frames into a single dataframe. Parameters ---------- recommendation_results_list: List[Dict[str, pd.DataFrame]] List of dictionaries returned by benchmark function. Returns ------- Dictionary with recommendations by algorithm. """ recommendation_results_concat = dict() for recommendation_results in recommendation_results_list: for name, df in recommendation_results.items(): if name not in recommendation_results_concat: recommendation_results_concat[name] = df else: recommendation_results_concat[name] = pd.concat((recommendation_results_concat[name], df)) return recommendation_results_concat def default_metrics(top_k_values=None, user_id_col: str = Constants.user_id, item_id_col: str = Constants.item_id): metric_params = {'click_column': Constants.score, 'user_id_column': user_id_col, 'item_id_column': item_id_col} metrics = [] for k in top_k_values: metrics.append(BinaryRecoMetrics.AUC(**metric_params, k=k)) metrics.append(BinaryRecoMetrics.CTR(**metric_params, k=k)) metrics.append(RankingRecoMetrics.Precision(**metric_params, k=k)) metrics.append(RankingRecoMetrics.Recall(**metric_params, k=k)) metrics.append(RankingRecoMetrics.NDCG(**metric_params, k=k)) metrics.append(RankingRecoMetrics.MAP(**metric_params, k=k)) return metrics def load_data(data: Union[str, pd.DataFrame], user_features: Union[str, pd.DataFrame] = None, user_features_list: Union[str, List[str]] = None, user_features_dtypes: Union[str, Dict] = None, item_features: Union[str, pd.DataFrame] = None, item_list: Union[str, List[str]] = None, item_eligibility: Union[str, pd.DataFrame] = None, user_id_col: str = Constants.user_id, item_id_col: str = Constants.item_id, response_col: str = Constants.response): """ Import data. Parameters ---------- data: Union[str, pd.DataFrame] Data should have a row for each sample (user_id, item_id, response). Column names should be consistent with user_id_col, item_id_col and response_col arguments. CSV format with file header or Data Frame. user_features: Union[str, pd.DataFrame] User features containing features for each user_id. Each row should include user_id and list of features (user_id, u_1, u_2, ..., u_p). CSV format with file header or Data Frame. user_features_list: Union[str, List[str]] List of user features to use. Must be a subset of features in (u_1, u_2, ... u_p). If None, all the features in user_features are used. CSV format with file header or List. user_features_dtypes: Union[str, Dict] User features data types file with mappings of features to their dtypes upon loading. Data should have a key, value pair for user feature, e.g., {"feature_1": "float32"} The keys should be consistent with `user_features` file. user_id_col : str, default=Constants.user_id User id column name. item_features: Union[str, pd.DataFrame] Item features file containing features for each item_id. Each row should include item_id and list of features (item_id, i_1, i_2, .... i_q). CSV format with file header or Data Frame. item_eligibility: Union[str, pd.DataFrame], default=None Items each user is eligible for. Used to generate excluded_arms lists. If None, all the items can be evaluated for recommendation for each user. CSV format with file header or Data Frame. item_list: List[Arm] List of items. item_id_col : str, default=Constants.item_id Item id column name. response_col : str, default=Constants.response Response column name. Returns ------- Data frame with response data. """ # Response data data_df = load_response_data(data, user_id_col, item_id_col, response_col) # Item list item_list = load_items(data_df, item_list, item_id_col) # User features if user_features is not None: user_features_df = load_user_features(user_features, user_features_list, user_features_dtypes, user_id_col) else: user_features_df = None # Item features if item_features is not None: item_to_features = load_item_features(item_features, item_list, item_id_col) else: item_to_features = None # Item eligibility if item_eligibility is not None: excluded_df = load_excluded_items(item_eligibility, item_list, user_id_col, item_id_col) else: excluded_df = None return data_df, item_list, user_features_df, item_to_features, excluded_df def load_response_data(data: Union[str, pd.DataFrame], user_id_col: str = Constants.user_id, item_id_col: str = Constants.item_id, response_col: str = Constants.response) -> pd.DataFrame: """ Import response data. Parameters ---------- data: Union[str, pd.DataFrame] Data should have a row for each sample (user_id, item_id, response). Column names should be consistent with user_id_col, item_id_col and response_col arguments. CSV format with file header or Data Frame. user_id_col : str, default=Constants.user_id User id column name. item_id_col : str, default=Constants.item_id Item id column name. response_col : str, default=Constants.response Response column name. Returns ------- Data frame with response data. """ df = load_data_frame(data) check_true(user_id_col in df.columns, ValueError(f"{user_id_col} not in data file.")) check_true(item_id_col in df.columns, ValueError(f"{item_id_col} not in data file.")) check_true(response_col in df.columns, ValueError(f"{response_col} not in data file.")) return df[[user_id_col, item_id_col, response_col]].astype({response_col: int}) def load_items(data_df: pd.DataFrame, item_list: Union[str, List[str]] = None, item_id_col: str = Constants.item_id): """ Import item list. Parameters ---------- data_df: pd.DataFrame Data frame with response data. Data should have a row for each sample (user_id, item_id, response). item_list: Union[str, List[str]], default=None List of items. If None, the list of items in data_df are returned. CSV format with file header or List. item_id_col : str, default=Constants.item_id Item id column name. Returns ------- List of items. """ if item_list is None: check_true(item_id_col in data_df.columns, ValueError(f"{item_id_col} not in data file.")) return data_df[item_id_col].unique().tolist() else: return load_list(item_list) def load_item_features(item_features: Union[str, pd.DataFrame], item_list: List[Arm], item_id_col: str = Constants.item_id) -> Dict[Arm, List[Num]]: """ Import item features. Parameters ---------- item_features: Union[str, pd.DataFrame] Item features file containing features for each item_id. Each row should include item_id and list of features (item_id, i_1, i_2, .... i_q). CSV format with file header or Data Frame. item_list: List[Arm] List of items. item_id_col: str Item id column name. Returns ------- Dictionary mapping item features to each item. """ df = load_data_frame(item_features) check_true(item_id_col in df.columns, ValueError(f"{item_id_col} not in item features.")) check_true(len(df) == df[item_id_col].nunique(), ValueError(f"Duplicate item ids in item features.")) # Convert from data frame to dictionary item_to_features = df.set_index(item_id_col).T.to_dict("list") # Drop features for items not in item list item_to_features = {item: features for item, features in item_to_features.items() if item in item_list} # Raise error if features are missing for item in item list for item in item_list: if item not in item_to_features: raise ValueError(f"{item} not found in item features.") return item_to_features def load_user_features(user_features: Union[str, pd.DataFrame], user_features_list: Union[str, List[str]] = None, user_features_dtypes: Union[str, Dict] = None, user_id_col: str = Constants.user_id) -> pd.DataFrame: """ Import user features. Parameters ---------- user_features: Union[str, pd.DataFrame] User features containing features for each user_id. Each row should include user_id and list of features (user_id, u_1, u_2, ..., u_p). CSV format with file header or Data Frame. user_features_list: Union[str, List[str]] List of user features to use. Must be a subset of features in (u_1, u_2, ... u_p). If None, all the features in user_features are used. CSV format with file header or List. user_features_dtypes: Union[str, Dict] User features data types file with mappings of features to their dtypes upon loading. Data should have a key, value pair for user feature, e.g., {"feature_1": "float32"} The keys should be consistent with `user_features` file. user_id_col: str User id column name. Returns ------- Data frame with user features. """ # User features data types data_types = None if user_features_dtypes is not None: if isinstance(user_features_dtypes, str): with open(user_features_dtypes, 'r') as f: data_types = json.load(f) else: data_types = user_features_dtypes # Load data if isinstance(user_features, str): df = pd.read_csv(user_features, dtype=data_types) else: df = pd.DataFrame(user_features) check_true(user_id_col in df.columns, ValueError(f"{user_id_col} not in user features.")) check_true(len(df) == df[user_id_col].nunique(), ValueError(f"Duplicate user ids in user features.")) # Subset if user_features_list is not None: keep = [user_id_col] + load_list(user_features_list) return df[keep] else: return df def load_data_frame(data: Union[str, pd.DataFrame]) -> pd.DataFrame: """ Load file as data frame. Parameters ---------- data: Union[str, pd.DataFrame] CSV file with header if string input, otherwise Data Frame. Returns ------- Data frame with user features. """ if isinstance(data, str): return pd.read_csv(data) elif isinstance(data, pd.DataFrame): return data else: raise TypeError("Data must be string of filepath or data frame.") def load_excluded_items(item_eligibility: Union[str, pd.DataFrame], item_list: List[Arm], user_id_col: str = Constants.user_id, item_id_col: str = Constants.item_id) -> pd.DataFrame: """Convert eligibility data to excluded_arms list for each user_id. Parameters ---------- item_eligibility: Union[str, pd.DataFrame], default=None Items each user is eligible for. Used to generate excluded_arms lists. If None, all the items can be evaluated for recommendation for each user. CSV format with file header or Data Frame. item_list: List The list of all arms. user_id_col: str User id column name. item_id_col: str Item id column name. Returns ------- DataFrame with user id and list of list of arms to exclude from recommended arms. """ # Load data df = load_data_frame(item_eligibility).copy() check_true(user_id_col in df.columns, ValueError(user_id_col + ' missing from eligibility_data.')) check_true(item_id_col in df.columns, ValueError(item_id_col + ' missing from eligibility_data.')) # Create list of excluded items for each user. df['excluded_arms'] = df.apply(lambda x: get_exclusion_list(item_list, x[item_id_col]), axis=1) df.drop(item_id_col, axis=1, inplace=True) df.columns = [user_id_col, item_id_col] return df def load_pickle(pickle_file: str): """ Returns the loaded pickle object. """ with open(pickle_file, 'rb') as infile: return pickle.load(infile) def load_list(data: Union[str, List]) -> List: """ Load file as list. Parameters ---------- data: Union[str, pd.DataFrame] CSV file with header if string input, otherwise List. Returns ------- Data frame with user features. """ if isinstance(data, str): return pd.read_csv(data).iloc[:, 0].tolist() elif isinstance(data, list): return data else: raise TypeError("Data must be string of filepath or list.") def get_exclusion_list(arms, eligible_list): return list(set(arms).difference(set(eval(eligible_list)))) def print_interaction_stats(df: pd.DataFrame, user_id_col: str = Constants.user_id, item_id_col: str = Constants.item_id, response_col: str = Constants.response) -> None: """ Print number of rows, number of users, number of items in interaction data. Parameters ---------- df: pd.DataFrame Interaction data frame with (user_id, item_id, response) in each row. user_id_col: str User id column name. item_id_col: str Item id column name. response_col: str Response column name. Returns ------- Returns nothing. """ print(f"Number of rows: {len(df):,}") print(f"Number of users: {df[user_id_col].nunique():,}") print(f"Number of items: {df[item_id_col].nunique():,}") print(f"Mean response rate: {df[response_col].mean():.4f}\n") def merge_user_features(responses_df: pd.DataFrame, user_features_df: pd.DataFrame, user_id_col: str = Constants.user_id) -> pd.DataFrame: """ Merge responses and user features. Parameters ---------- responses_df : pd.DataFrame Responses. user_features_df : pd.DataFrame User features. user_id_col: str User id column name. Returns ------- Data frame with merged responses and user features. """ # Subset features to only include users in response data and then merge df = user_features_df[user_features_df[user_id_col].isin(responses_df[user_id_col].unique())] return responses_df.merge(df, on=user_id_col, how="left") def save_json(obj, json_file) -> None: """ Save obj as json file. """ with open(json_file, 'w') as f: json.dump(obj, f) def save_pickle(obj, pickle_file) -> None: """ Save serializable object as pickle file. """ with open(pickle_file, 'wb') as fp: pickle.dump(obj, fp) ================================================ FILE: mab2rec/visualization.py ================================================ # -*- coding: utf-8 -*- # Copyright FMR LLC # SPDX-License-Identifier: Apache-2.0 from typing import Dict, List, Tuple, Union import matplotlib.pyplot as plt import pandas as pd import seaborn as sns from jurity.recommenders import DiversityRecoMetrics from mab2rec.utils import Constants from mab2rec.utils import concat_recommendations_list def plot_metrics_at_k(metric_results: Union[Dict[str, Dict[str, float]], List[Dict[str, Dict[str, float]]]], **kwargs): """ Plots recommendation metric values (y-axis) for different values of k (x-axis) for each of the benchmark algorithms. Parameters ---------- metric_results : Union[Dict[str, Dict[str, float]], List[Dict[str, Dict[str, float]]]] Nested-dictionary or list of dictionaries with evaluation results returned by benchmark function. **kwargs Other parameters passed to ``sns.catplot``. Returns ------- ax : matplotlib.axes.Axes The plot with metric values. """ if not isinstance(metric_results, list): metric_results_list = [metric_results] else: metric_results_list = metric_results # Format data for plot out = [] for metric_results in metric_results_list: for algo_name, results in metric_results.items(): for metric_name, value in results.items(): d = {'algorithm': algo_name, 'metric_name': metric_name.split('@')[0], 'k': int(metric_name.split('@')[1]), 'value': value} out.append(d) df = pd.DataFrame(out) # Plot ax = sns.catplot(x='k', y='value', col='metric_name', hue='algorithm', data=df, kind='point', sharey=False, **kwargs) return ax def plot_inter_diversity_at_k(recommendation_results: Union[Dict[str, pd.DataFrame], List[Dict[str, pd.DataFrame]]], k_list: List[int], user_id_col: str = Constants.user_id, item_id_col: str = Constants.item_id, score_col: str = Constants.score, sample_size: float = None, seed: int = Constants.default_seed, num_runs: int = 10, n_jobs: int = 1, working_memory: int = None, **kwargs): """ Plots recommendation metric values (y-axis) for different values of k (x-axis) for each of the benchmark algorithms. Parameters ---------- recommendation_results : Union[Dict[str, pd.DataFrame], List[Dict[str, pd.DataFrame]]] Dictionary or list of dictionaries with recommendation results returned by benchmark function. k_list : List[int] List of top-k values to evaluate. user_id_col : str, default=Constants.user_id User id column name. item_id_col : str, default=Constants.item_id Item id column name. score_col: str, default=Constants.score Recommendation score column name. sample_size: float, default=None Proportion of users to randomly sample for evaluation. If None, no sampling is performed. seed : int, default=Constants.default_seed The seed used to create random state. num_runs: int num_runs is used to report the approximation of Inter-List Diversity over multiple runs on smaller samples of users, default=10, for a speed-up on evaluations. The sampling size is defined by user_sample_size. The final result is averaged over the multiple runs. n_jobs: int Number of jobs to use for computation in parallel, leveraged by sklearn.metrics.pairwise_distances_chunked. -1 means using all processors. Default=1. working_memory: Union[int, None] Maximum memory for temporary distance matrix chunks, leveraged by sklearn.metrics.pairwise_distances_chunked. When None (default), the value of sklearn.get_config()['working_memory'], i.e. 1024M, is used. **kwargs Other parameters passed to ``sns.catplot``. Returns ------- ax : matplotlib.axes.Axes The plot with metric values. """ if not isinstance(recommendation_results, list): recommendation_results_list = [recommendation_results] else: recommendation_results_list = recommendation_results # Calculate metrics out = [] for recommendation_results in recommendation_results_list: for algo_name, rec_df in recommendation_results.items(): for k in k_list: metric = DiversityRecoMetrics.InterListDiversity(click_column=score_col, k=k, user_id_column=user_id_col, item_id_column=item_id_col, user_sample_size=sample_size, seed=seed, num_runs=num_runs, n_jobs=n_jobs, working_memory=working_memory) inter_list_diversity = metric.get_score(rec_df, rec_df) d = {'algorithm': algo_name, 'metric_name': 'Inter-list Diversity', 'k': k, 'value': inter_list_diversity} out.append(d) df = pd.DataFrame(out) # Plot ax = sns.catplot(x='k', y='value', col='metric_name', hue='algorithm', data=df, kind='point', sharey=False, **kwargs) return ax def plot_intra_diversity_at_k(recommendation_results: Union[Dict[str, pd.DataFrame], List[Dict[str, pd.DataFrame]]], item_features: pd.DataFrame, k_list: List[int], user_id_col: str = Constants.user_id, item_id_col: str = Constants.item_id, score_col: str = Constants.score, sample_size: float = None, seed: int = Constants.default_seed, n_jobs: int = 1, num_runs: int = 10, **kwargs): """ Plots recommendation metric values (y-axis) for different values of k (x-axis) for each of the benchmark algorithms. Parameters ---------- recommendation_results : Union[Dict[str, pd.DataFrame], List[Dict[str, pd.DataFrame]]] Dictionary or list of dictionaries with recommendation results returned by benchmark function. item_features : pd.DataFrame Data frame with features for each item_id. k_list : List[int] List of top-k values to evaluate. user_id_col : str, default=Constants.user_id User id column name. item_id_col : str, default=Constants.item_id Item id column name. score_col: str, default=Constants.score Recommendation score column name. sample_size: float, default=None Proportion of users to randomly sample for evaluation. If None, no sampling is performed. seed : int, default=Constants.default_seed The seed used to create random state. num_runs: int num_runs is used to report the approximation of Intra-List Diversity over multiple runs on smaller samples of users, default=10, for a speed-up on evaluations. The sampling size is defined by user_sample_size. The final result is averaged over the multiple runs. n_jobs: int Number of jobs to use for computation in parallel, leveraged by sklearn.metrics.pairwise_distances. -1 means using all processors. Default=1. **kwargs Other parameters passed to ``sns.catplot``. Returns ------- ax : matplotlib.axes.Axes The plot with metric values. """ if not isinstance(recommendation_results, list): recommendation_results_list = [recommendation_results] else: recommendation_results_list = recommendation_results # Calculate metrics out = [] for recommendation_results in recommendation_results_list: for algo_name, rec_df in recommendation_results.items(): for k in k_list: metric = DiversityRecoMetrics.IntraListDiversity(item_features, click_column=score_col, k=k, user_id_column=user_id_col, item_id_column=item_id_col, user_sample_size=sample_size, seed=seed, num_runs=num_runs, n_jobs=n_jobs) intra_list_diversity = metric.get_score(rec_df, rec_df) d = {'algorithm': algo_name, 'metric_name': 'Intra-list Diversity', 'k': k, 'value': intra_list_diversity} out.append(d) df = pd.DataFrame(out) # Plot ax = sns.catplot(x='k', y='value', col='metric_name', hue='algorithm', data=df, kind='point', sharey=False, **kwargs) return ax def plot_recommended_counts(recommendation_results: Union[Dict[str, pd.DataFrame], List[Dict[str, pd.DataFrame]]], actual_results: pd.DataFrame, k: int, average_response: bool = False, user_id_col: str = Constants.user_id, item_id_col: str = Constants.item_id, response_col: str = Constants.response, **kwargs): """ Plots recommendation counts (y-axis) versus actual counts or average responses (x-axis) for each item. Parameters ---------- recommendation_results : Union[Dict[str, pd.DataFrame], List[Dict[str, pd.DataFrame]]] Dictionary or list of dictionaries with recommendation results returned by benchmark function. actual_results : pd.DataFrame Test data frame used to generate recommendations. Data should have a row for each sample (user_id, item_id, response). k : int Top-k recommendations to evaluate. average_response : bool, default=False Whether to plot the average response/reward or not. user_id_col : str, default=Constants.user_id User id column name. item_id_col : str, default=Constants.item_id Item id column name. response_col : str, default=Constants.response Response column name. **kwargs Other parameters passed to ``sns.relplot``. Returns ------- ax : matplotlib.axes.Axes The plot with recommended counts. """ # Concatenate recommendation results from different cv-folds if isinstance(recommendation_results, list): recommendation_results = concat_recommendations_list(recommendation_results) # Actual if average_response: actual_counts = actual_results.groupby(item_id_col)[response_col].mean() else: actual_counts = actual_results.groupby(item_id_col).size() # Recommended out = [] for algo_name, rec_df in recommendation_results.items(): rec_sorted_df = rec_df.sort_values(Constants.score, ascending=False).groupby(user_id_col).head(k) rec_counts = rec_sorted_df.groupby(item_id_col).size() for item_id in rec_counts.index: if item_id in actual_counts.index: a_value = actual_counts[item_id] else: a_value = 0 out.append({'algorithm': algo_name, 'item_id': item_id, 'actual': a_value, 'recommended': rec_counts[item_id]}) df = pd.DataFrame(out) # Plot g = sns.relplot(x='actual', y='recommended', col='algorithm', data=df, **kwargs) if not average_response: xy_max = max(df['actual'].max(), df['recommended'].max()) for ax in g.axes.flat: ax.plot([0, xy_max], [0, xy_max], color="darkred", linestyle="--", alpha=0.5) return g def plot_recommended_counts_by_item(recommendation_results: Union[Dict[str, pd.DataFrame], List[Dict[str, pd.DataFrame]]], k: int, top_n_items: int = None, normalize: bool = False, user_id_col: str = Constants.user_id, item_id_col: str = Constants.item_id, **kwargs): """ Plots recommendation counts (y-axis) for different items (x-axis) for each of the benchmark algorithms. Only the top_n_items with the most recommendations for each algorithm are shown. Parameters ---------- recommendation_results : Union[Dict[str, pd.DataFrame], List[Dict[str, pd.DataFrame]]] Dictionary or list of dictionaries with recommendation results returned by benchmark function. k : int Top-k recommendations to evaluate. top_n_items : int, default=None Top-n number of items based on number of recommendations to plot. normalize : bool, default=False Whether to normalize the counts per item to be proportions such that they add to 1. user_id_col : str, default=Constants.user_id User id column name. item_id_col : str, default=Constants.item_id Item id column name. **kwargs Other parameters passed to ``sns.catplot``. Returns ------- ax : matplotlib.axes.Axes The plot with recommended counts by item. """ # Concatenate recommendation results from different cv-folds if isinstance(recommendation_results, list): recommendation_results = concat_recommendations_list(recommendation_results) # Calculate metrics out = [] for algo_name, rec_df in recommendation_results.items(): rec_sorted_df = rec_df.sort_values(Constants.score, ascending=False).groupby(user_id_col).head(k) rec_counts = rec_sorted_df[item_id_col].value_counts(normalize=normalize) rank = 0 for item_id, value in rec_counts.items(): out.append({'algorithm': algo_name, 'k': k, 'item_id': item_id, 'rank': rank, 'value': value}) rank += 1 df = pd.DataFrame(out) if top_n_items is not None: df = df[df['rank'] < top_n_items] df.drop(columns='rank', inplace=True) ax = sns.catplot(x='item_id', y='value', col='algorithm', data=df, kind='bar', color='grey', **kwargs) ax.set_xticklabels([]) return ax def plot_num_items_per_recommendation(recommendation_results: Union[Dict[str, pd.DataFrame], List[Dict[str, pd.DataFrame]]], actual_results: pd.DataFrame, normalize: bool = False, user_id_col: str = Constants.user_id, **kwargs): """ Plots recommendation counts (y-axis) versus actual counts or average responses (x-axis) for each item. Parameters ---------- recommendation_results : Union[Dict[str, pd.DataFrame], List[Dict[str, pd.DataFrame]]] Dictionary or list of dictionaries with recommendation results returned by benchmark function. actual_results : pd.DataFrame Test data frame used to generate recommendations. Data should have a row for each sample (user_id, item_id, response). normalize : bool, default=False Whether to normalize the number of items to be proportions such that they add to 1. user_id_col: str User id column name. Default value is set to Constants.user_id **kwargs Other parameters passed to ``sns.catplot``. Returns ------- ax : matplotlib.axes.Axes The plot with counts or proportions for different number of items per recommendation. """ # Concatenate recommendation results from different cv-folds if isinstance(recommendation_results, list): recommendation_results = concat_recommendations_list(recommendation_results) # Distinct users in actual results users_df = pd.DataFrame(actual_results[user_id_col].unique(), columns=[user_id_col]) out = [] for algo_name, rec_df in recommendation_results.items(): # Merge recommendations for each user df = users_df.merge(rec_df, on=user_id_col, how='left') # Calculate distribution of number of items per recommendation users_per_num_item = pd.value_counts(df.groupby(user_id_col).size(), normalize=normalize) for num_items, value in users_per_num_item.items(): out.append({'algorithm': algo_name, 'k': num_items, 'value': value}) df = pd.DataFrame(out) # Plot ax = sns.catplot(x='k', y='value', col='algorithm', data=df, kind='bar', color='grey', **kwargs) return ax def plot_personalization_heatmap(recommendation_results: Union[Dict[str, pd.DataFrame], List[Dict[str, pd.DataFrame]]], user_to_cluster: Dict[Union[int, str], int], k: int, user_id_col: str = Constants.user_id, item_id_col: str = Constants.item_id, figsize: Tuple[int, int] = None, **kwargs): """ Plot heatmaps to visualize level of personalization, by calculating the distribution of recommendations by item within different user clusters. Parameters ---------- recommendation_results : Union[Dict[str, pd.DataFrame], List[Dict[str, pd.DataFrame]]] Dictionary or list of dictionaries with recommendation results returned by benchmark function. user_to_cluster : Dict[Union[int, str], int] Mapping from user_id to cluster. Clusters could be derived from clustering algorithm such as KMeans or defined based on specific user features (e.g. age bands) k : int Top-k recommendations to evaluate. user_id_col : str, default=Constants.user_id User id column name. item_id_col : str, default=Constants.item_id Item id column name. figsize: Tuple[int, int], default=None Figure size of heatmap set using plt.figure() **kwargs Other parameters passed to ``sns.catplot``. Returns ------- ax : matplotlib.axes.Axes The plot with counts or proportions for different number of items per recommendation. """ # Concatenate recommendation results from different cv-folds if isinstance(recommendation_results, list): recommendation_results = concat_recommendations_list(recommendation_results) axes = dict() for algo_name, rec_df in recommendation_results.items(): rec_sorted_df = rec_df.sort_values(Constants.score, ascending=False).groupby(user_id_col).head(k) rec_sorted_df['cluster'] = rec_sorted_df[user_id_col].map(user_to_cluster) # Calculate percentage of recommendations by item within each cluster df = rec_sorted_df.groupby(['cluster', item_id_col]).size() df = df.groupby(level=0, group_keys=False).apply(lambda x: x / float(x.sum())).reset_index() df = df.pivot(index=item_id_col, columns='cluster').fillna(0) df.columns = df.columns.droplevel() df.sort_index(inplace=True) # Plot if figsize is not None: plt.figure(figsize=figsize) ax = sns.heatmap(df, **kwargs) ax.set_title(algo_name) ax.set_yticklabels([]) plt.show() axes[algo_name] = ax return axes ================================================ FILE: requirements.txt ================================================ numpy>=1.20.2 pandas>=1.1.0 scikit-learn>=0.24.0 scipy matplotlib seaborn>=0.1.0 mabwiser>=2.7.4 jurity>=1.3.2 ================================================ FILE: scripts/data_prep/concat_files.sh ================================================ #!/bin/bash E_BADARGS=65 if [ $# -eq 1 ] then echo "Usage: `basename $0` ... " echo "Description:" echo -e "\t Take multiple input files" echo -e "\t Concatenate the header from first file and concatenate files" echo -e "\t Return concatenated output file" echo -e "\t Assumption: Each file matching pattern has same header and structure" echo -e "\t Example run: ./`basename $0` responses_train.csv responses_test.csv > responses.csv" exit $E_BADARGS fi input=$1 output=$2 echo ">>> START $input" echo -e "\t Concatenate files" files=($input) { zcat files[1] | head -1 && \ find $input -exec sh -c "zcat -q -c {} | tail -n +2 -" \; } | gzip - > $output echo "<<< FINISH $output" ================================================ FILE: scripts/data_prep/concat_first_two_columns.sh ================================================ #!/bin/bash E_BADARGS=65 if [ $# -ne 2 ] then echo "Usage: `basename $0` " echo "Description:" echo -e "\t Take an input csv file" echo -e "\t Concatenate the first two columns using an underscore "\_" in between" echo -e "\t Return the output csv" echo -e "\t For example \"ip, event_date\" becomes \"ip_event_date\ which serves as user_id" echo -e "\t Assumption: csv is comma (,) separated" echo -e "\t Example run: ./`basename $0` train.csv train_user_id.csv" exit $E_BADARGS fi input=$1 output=$2 echo ">>> START $input" echo -e "\t Concatenate first two columns" # Replace the first occurence of "," in every row # Effectively, this ends up concataneting first two columns of a csv sed 's/,/_/' $input > $output # More programmatically # awk -F, '{print $1 "_" $2}' $input > $output echo "<<< FINISH $output" ================================================ FILE: scripts/data_prep/insert_header.sh ================================================ #!/bin/bash E_BADARGS=65 if [ $# -ne 3 ] then echo "Usage: `basename $0` " echo "Description:" echo -e "\t Take an input csv file" echo -e "\t Insert the given header to the first line" echo -e "\t Return the output csv" echo -e "\t For example \"ip,event_date,tcm_id,response\" can be inserted to the first row" echo -e "\t Example run: ./`basename $0` headless.csv ip,event_date,content_id,response" exit $E_BADARGS fi input=$1 output=$2 header=$3 echo ">>> START $input" echo -e "\t Insert header $header" sed "1i $header" $input > $output echo "<<< FINISH $output" ================================================ FILE: scripts/data_prep/remove_columns.sh ================================================ #!/bin/bash E_BADARGS=65 if [ $# -lt 3 ] then echo "Usage: `basename $0` [column_index_2] [column_index_3]" echo "Description:" echo -e "\t Take an input csv file and at least 1 column index to drop" echo -e "\t Drop the given column" echo -e "\t Return the filtered output csv file" echo -e "\t Optionally, you can drop multiple columns, 2 or 3 columns" echo -e "\t Assumption: csv is comma (,) separated" echo -e "\t Assumption: indexing starts from 1" echo -e "\t Example run: ./`basename $0` train.csv train_without_ip_event_date.csv 1 2 (e.g., 1=ip and 2=event_date)" exit $E_BADARGS fi input=$1 output=$2 echo ">>> START $input" if [ $# -eq 3 ] then column1=$3 # Sort the data echo -e "\t Drop column $column1" cut --complement -d , -f $column1 $input > $output fi # Sort based on two columns if [ $# -eq 4 ] then column1=$3 column2=$4 # Sort the data echo -e "\t Drop columns: $column1 and $column2" cut --complement -d , -f $column1,$column2 $input > $output fi # Sort based on three columns if [ $# -eq 5 ] then column1=$3 column2=$4 column3=$5 # Sort the data echo -e "\t Drop columns: $column1 and $column2 and $column3" cut --complement -d , -f $column1,$column2,$column3 $input > $output fi echo "<<< FINISH $output" ================================================ FILE: scripts/data_prep/remove_duplicate_lines.sh ================================================ #!/bin/bash E_BADARGS=65 if [ $# -lt 2 ] then echo "Usage: `basename $0` [column_index_1] [column_index_2] [column_index_3]" echo "Description:" echo -e "\t Take an input csv file" echo -e "\t Removes duplicate lines. The entire line is treated as a string." echo -e "\t Return the output csv" echo -e "\t Optionally, it can drop lines based on duplicate columns." echo -e "\t Columns are treated as a string, and lines are dropped for repeated column values" echo -e "\t NOTICE: When column option is used, sorting is applied so the row order might change!" echo -e "\t NOTICE: That means, if you have a header, the header line will change its position!!!" echo -e "\t One can specificy 1, 2, or 3 columns" echo -e "\t Example run: ./`basename $0` train.csv train_no_duplicate.csv" exit $E_BADARGS fi input=$1 output=$2 echo ">>> START $input" echo -e "\t Remove duplicates." if [ $# -eq 2 ] then echo -e "\t Remove duplicates lines. The entire line is treated as a string." uniq $input > $output fi if [ $# -eq 3 ] then column1=$3 # Sort the data echo -e "\t Drop duplicates in column $column1" sort -u -t, -k$column1,$column1 $input > $output fi # Remove duplicates based on two columns if [ $# -eq 4 ] then column1=$3 column2=$4 echo -e "\t Drop duplicates in columns: $column1 and $column2" sort -u -t, -k$column1,$column1 -k$column2,$column2 $input > $output fi # Remove duplicates based on three columns if [ $# -eq 5 ] then column1=$3 column2=$4 column3=$5 echo -e "\t Drop duplicates in columns: $column1 and $column2 and $column3" sort -u -t, -k$column1,$column1 -k$column2,$column2 -k$column3,$column3 $input > $output fi echo "<<< FINISH $output" ================================================ FILE: scripts/data_prep/remove_empty_lines.sh ================================================ #!/bin/bash E_BADARGS=65 if [ $# -ne 2 ] then echo "Usage: `basename $0` " echo "Description:" echo -e "\t Take an input csv file" echo -e "\t Remove the empty lines" echo -e "\t Return the output csv" echo -e "\t Example run: ./`basename $0` train.csv train_no_empty.csv" exit $E_BADARGS fi input=$1 output=$2 echo ">>> START $input" echo -e "\t Remove empty lines" sed "/^$/d" $input > $output echo "<<< FINISH $output" ================================================ FILE: scripts/data_prep/remove_header.sh ================================================ #!/bin/bash E_BADARGS=65 if [ $# -ne 2 ] then echo "Usage: `basename $0` " echo "Description:" echo -e "\t Take an input csv file" echo -e "\t Remove the first line/header" echo -e "\t Return the output csv" echo -e "\t Example run: ./`basename $0` input.csv headless.csv" exit $E_BADARGS fi input=$1 output=$2 echo ">>> START $input" echo -e "\t Remove header" sed "1d" $input > $output echo "<<< FINISH $output" ================================================ FILE: scripts/data_prep/rename_header.sh ================================================ #!/bin/bash E_BADARGS=65 if [ $# -ne 3 ] then echo "Usage: `basename $0` " echo "Description:" echo -e "\t Take an input csv file" echo -e "\t Replace its header with the given header" echo -e "\t Return the output csv" echo -e "\t For example \"ip_event_date,tcm_id,response\" becomes \"user_id,content_id,response\"" echo -e "\t This is useful as mab2rec input" echo -e "\t Example run: ./`basename $0` train.csv train_mab2rec.csv user_id,content_id,response" exit $E_BADARGS fi input=$1 output=$2 header=$3 echo ">>> START $input" echo -e "\t Replace the header with $header" sed "1c $header" $input > $output echo "<<< FINISH $output" ================================================ FILE: scripts/data_prep/sort_except_header.sh ================================================ #!/bin/bash E_BADARGS=65 if [ $# -lt 3 ] then echo "Usage: `basename $0` [column_index_2] [column_index_3]" echo "Description:" echo -e "\t Take an input csv file and at least 1 column index to sort" echo -e "\t Sort the input based on the given column index" echo -e "\t Return the sorted output csv file" echo -e "\t Optionally, you can sort using multiple columns, 2 or 3 columns" echo -e "\t Assumption: indexing starts from 1" echo -e "\t Assumption: input csv has a header row" echo -e "\t Assumption: sorting is string sort, not numerical! For example: 1 and 10 will come before 2" echo -e "\t Sorting respects the header row. Header is not part of sorting" echo -e "\t Example run: ./`basename $0` train.csv train_sorted.csv 1 2 (e.g., 1=ip and 2=event_date)" exit $E_BADARGS fi input=$1 output=$2 echo ">>> START $input" echo -e "\t Separate the header from the rest" # Header of the data head -n 1 $input > $input\.header # Rest of the data sed '1d' $input > $input\.headless # Sort based on a single column if [ $# -eq 3 ] then column1=$3 # Sort the data echo -e "\t Sort based on column $column1" sort -t ',' -k$column1,$column1 -o $input\.sorted $input\.headless fi # Sort based on two columns if [ $# -eq 4 ] then column1=$3 column2=$4 # Sort the data echo -e "\t Sort based on columns: $column1 and $column2" sort -t ',' -k$column1,$column1 -k$column2,$column2 -o $input\.sorted $input\.headless fi # Sort based on three columns if [ $# -eq 5 ] then column1=$3 column2=$4 column3=$5 # Sort the data echo -e "\t Sort based on columns: $column1 and $column2 and $column3" sort -t ',' -k$column1,$column1 -k$column2,$column2 -k$column3,$column3 -o $input\.sorted $input\.headless fi # It is possible to do this in one line # But the above is more explicit # (head -n 2 input.csv && tail -n +3 input.csv | sort) > output.csv # Reunite sorted data with the header echo -e "\t Combine the header and sorted data together" cat $input\.header $input\.sorted > $output # Remove temp files echo -e "\t Remove temporary files" rm $input\.header $input\.headless $input\.sorted echo "<<< FINISH $output" ================================================ FILE: setup.py ================================================ # -*- coding: utf-8 -*- # Copyright FMR LLC # SPDX-License-Identifier: Apache-2.0 import os import setuptools with open("README.md", "r") as fh: long_description = fh.read() with open("requirements.txt") as fh: required = fh.read().splitlines() with open(os.path.join('mab2rec', '_version.py')) as fp: exec(fp.read()) setuptools.setup( name="mab2rec", description="Mab2Rec: Multi-Armed Bandits Recommender", long_description=long_description, long_description_content_type="text/markdown", version=__version__, author=__author__, url="", packages=setuptools.find_packages(exclude=["*.tests", "*.tests.*", "tests.*", "tests"]), install_requires=required, python_requires=">=3.8", classifiers=[ "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3.8", "Operating System :: OS Independent", ], project_urls={ "Source": "https://github.com/fidelity/mab2rec" } ) ================================================ FILE: tests/__init__.py ================================================ # -*- coding: utf-8 -*- # Copyright FMR LLC # SPDX-License-Identifier: Apache-2.0 ================================================ FILE: tests/run_all.py ================================================ # -*- coding: utf-8 -*- # Copyright FMR LLC # SPDX-License-Identifier: Apache-2.0 import unittest # Test Directory start_dir = '.' # Test Loader loader = unittest.TestLoader() # Test Suite suite = loader.discover(start_dir) # Test Runner runner = unittest.TextTestRunner() # Run the Suite runner.run(suite) ================================================ FILE: tests/test_base.py ================================================ # -*- coding: utf-8 -*- # Copyright FMR LLC # SPDX-License-Identifier: Apache-2.0 import unittest from typing import Dict, List, Union, Optional import pandas as pd import numpy as np from mabwiser.utils import Arm, Num from mab2rec import BanditRecommender, LearningPolicy, NeighborhoodPolicy class BaseTest(unittest.TestCase): # A list of valid learning policies lps = [LearningPolicy.EpsilonGreedy(), LearningPolicy.EpsilonGreedy(epsilon=0), LearningPolicy.EpsilonGreedy(epsilon=0.0), LearningPolicy.EpsilonGreedy(epsilon=0.5), LearningPolicy.EpsilonGreedy(epsilon=1), LearningPolicy.EpsilonGreedy(epsilon=1.0), LearningPolicy.Popularity(), LearningPolicy.Random(), LearningPolicy.Softmax(), LearningPolicy.Softmax(tau=0.1), LearningPolicy.Softmax(tau=0.5), LearningPolicy.Softmax(tau=1), LearningPolicy.Softmax(tau=1.0), LearningPolicy.Softmax(tau=5.0), LearningPolicy.ThompsonSampling(), LearningPolicy.UCB1(), LearningPolicy.UCB1(alpha=0), LearningPolicy.UCB1(alpha=0.0), LearningPolicy.UCB1(alpha=0.5), LearningPolicy.UCB1(alpha=1), LearningPolicy.UCB1(alpha=1.0), LearningPolicy.UCB1(alpha=5)] para_lps = [LearningPolicy.LinGreedy(epsilon=0, l2_lambda=1), LearningPolicy.LinGreedy(epsilon=0.5, l2_lambda=1), LearningPolicy.LinGreedy(epsilon=1, l2_lambda=1), LearningPolicy.LinGreedy(epsilon=0, l2_lambda=0.5), LearningPolicy.LinGreedy(epsilon=0.5, l2_lambda=0.5), LearningPolicy.LinGreedy(epsilon=1, l2_lambda=0.5), LearningPolicy.LinTS(alpha=0.00001, l2_lambda=1), LearningPolicy.LinTS(alpha=0.5, l2_lambda=1), LearningPolicy.LinTS(alpha=1, l2_lambda=1), LearningPolicy.LinTS(alpha=0.00001, l2_lambda=0.5), LearningPolicy.LinTS(alpha=0.5, l2_lambda=0.5), LearningPolicy.LinTS(alpha=1, l2_lambda=0.5), LearningPolicy.LinUCB(alpha=0, l2_lambda=1), LearningPolicy.LinUCB(alpha=0.5, l2_lambda=1), LearningPolicy.LinUCB(alpha=1, l2_lambda=1), LearningPolicy.LinUCB(alpha=0, l2_lambda=0.5), LearningPolicy.LinUCB(alpha=0.5, l2_lambda=0.5), LearningPolicy.LinUCB(alpha=1, l2_lambda=0.5)] # A list of valid context policies nps = [NeighborhoodPolicy.LSHNearest(), NeighborhoodPolicy.LSHNearest(n_dimensions=1), NeighborhoodPolicy.KNearest(), NeighborhoodPolicy.KNearest(k=3), NeighborhoodPolicy.Radius(), NeighborhoodPolicy.TreeBandit(), NeighborhoodPolicy.Clusters(), NeighborhoodPolicy.Clusters(n_clusters=3), NeighborhoodPolicy.Clusters(is_minibatch=True), NeighborhoodPolicy.Clusters(n_clusters=3, is_minibatch=True)] @staticmethod def predict(arms: List[Arm], decisions: Union[List, np.ndarray, pd.Series], rewards: Union[List, np.ndarray, pd.Series], learning_policy: Union[LearningPolicy.EpsilonGreedy, LearningPolicy.Popularity, LearningPolicy.Random, LearningPolicy.Softmax, LearningPolicy.ThompsonSampling, LearningPolicy.UCB1, LearningPolicy.LinGreedy, LearningPolicy.LinTS, LearningPolicy.LinUCB], neighborhood_policy: Union[None, NeighborhoodPolicy.Clusters, NeighborhoodPolicy.KNearest, NeighborhoodPolicy.LSHNearest, NeighborhoodPolicy.Radius, NeighborhoodPolicy.TreeBandit] = None, context_history: Union[None, List[Num], List[List[Num]], np.ndarray, pd.DataFrame, pd.Series] = None, contexts: Union[None, List[Num], List[List[Num]], np.ndarray, pd.DataFrame, pd.Series] = None, apply_sigmoid: bool = True, excluded_arms: List[List[Arm]] = None, warm_start: bool = False, arm_to_features: Dict[Arm, List[Num]] = None, top_k: Optional[int] = 5, seed: Optional[int] = 123456, n_jobs: Optional[int] = 1, backend: Optional[str] = None): """Sets up a Bandit Recommender and runs the given configuration. """ # Model rec = BanditRecommender(learning_policy, neighborhood_policy, top_k, seed, n_jobs, backend) # Initialize and train rec._init(arms) rec.fit(decisions, rewards, context_history) # Warm-start if warm_start: rec.warm_start(arm_to_features, distance_quantile=0.5) # Run recommendations = rec.recommend(contexts, excluded_arms, return_scores=True, apply_sigmoid=apply_sigmoid) return recommendations, rec def assertListAlmostEqual(self, list1, list2): """ Asserts that floating values in the given lists (almost) equals to each other """ if not isinstance(list1, list): list1 = list(list1) if not isinstance(list2, list): list2 = list(list2) self.assertEqual(len(list1), len(list2)) for index, val in enumerate(list1): self.assertAlmostEqual(val, list2[index]) @staticmethod def is_compatible(lp, np): # Special case for TreeBandit lp/np compatibility if isinstance(np, NeighborhoodPolicy.TreeBandit): return np._is_compatible(lp) return True ================================================ FILE: tests/test_invalid.py ================================================ # -*- coding: utf-8 -*- # Copyright FMR LLC # SPDX-License-Identifier: Apache-2.0 import os import unittest import pandas as pd from mab2rec import BanditRecommender, LearningPolicy, NeighborhoodPolicy from mab2rec.pipeline import train, score, benchmark from mab2rec.utils import Constants, default_metrics, load_item_features, load_data_frame, load_list TEST_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = TEST_DIR + os.sep + ".." + os.sep # Data files train_data = os.path.join(ROOT_DIR, "data", "data_train.csv") test_data = os.path.join(ROOT_DIR, "data", "data_test.csv") user_features = os.path.join(ROOT_DIR, "data", "features_user.csv") item_features = os.path.join(ROOT_DIR, "data", "features_item.csv") # Evaluation metrics metrics = default_metrics([3, 5, 10]) class InvalidTest(unittest.TestCase): # ===================== # BanditRecommender # ===================== def test_invalid_learning_policy(self): with self.assertRaises(TypeError): BanditRecommender(NeighborhoodPolicy.Radius(radius=12)) def test_invalid_neighborhood_policy(self): with self.assertRaises(TypeError): BanditRecommender(LearningPolicy.EpsilonGreedy(), LearningPolicy.Softmax()) def test_invalid_init_arms_int(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) rec._init(1) def test_invalid_init_arms_tuple(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) rec._init(1, 2) def test_invalid_add_arm_value(self): with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) rec._init([1, 2]) rec.add_arm(1) def test_invalid_remove_arm_value(self): with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) rec._init([1, 2]) rec.remove_arm(3) def test_invalid_remove_arm_no_init(self): with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) rec.remove_arm(3) def test_invalid_set_arms(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) rec.set_arms(None) def test_invalid_partial_fit(self): with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) rec.partial_fit([1, 1, 2, 2], [0, 1, 1, 1]) def test_invalid_partial_fit_with_init(self): with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) rec._init([1, 2]) rec.partial_fit([1, 1, 2, 2], [0, 1, 1, 1]) def test_invalid_predict_not_fit(self): with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) rec._init([1, 2]) _ = rec.predict() def test_invalid_predict_expectations_not_fit(self): with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) rec._init([1, 2]) _ = rec.predict_expectations() def test_invalid_recommend_not_fit(self): with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy(), top_k=2) rec._init([1, 2]) _ = rec.recommend() def test_invalid_recommend_no_contexts(self): with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.LinUCB(), top_k=2) rec.fit([1, 1, 2, 2], [0, 1, 1, 1], [[0, 1, 2], [3, 1, 2], [0, 3, 1], [2, 1, 1]]) _ = rec.recommend() def test_invalid_recommend_excluded_arms(self): with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.LinUCB(), top_k=2) rec.fit([1, 1, 2, 2], [0, 1, 1, 1], [[0, 1, 2], [3, 1, 2], [0, 3, 1], [2, 1, 1]]) _ = rec.recommend(excluded_arms=[[1], [1]]) def test_invalid_recommend_excluded_arms_dim(self): with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.LinUCB(), top_k=2) rec.fit([1, 1, 2, 2], [0, 1, 1, 1], [[0, 1, 2], [3, 1, 2], [0, 3, 1], [2, 1, 1]]) _ = rec.recommend(contexts=[[0, 1, 2]], excluded_arms=[[1], [1]]) def test_invalid_warm_start_not_fit(self): with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.LinUCB()) rec._init([1, 2, 3]) rec.warm_start(arm_to_features={1: [0.5, 0.5], 2: [1, 0.5], 3: [1, 0]}, distance_quantile=0.5) def test_invalid_warm_start_missing_arm(self): with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) rec._init([1, 2, 3]) rec.fit([1, 1, 2, 2, 3], [0, 1, 1, 1, 0]) rec.warm_start(arm_to_features={1: [0.5, 0.5], 2: [1, 0.5]}, distance_quantile=0.5) def test_invalid_warm_start_unknown_arm(self): with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) rec._init([1, 2, 3]) rec.fit([1, 1, 2, 2, 3], [0, 1, 1, 1, 0]) rec.warm_start(arm_to_features={1: [0.5, 0.5], 2: [1, 0.5], 3: [1, 0], 4: [0, 1]}, distance_quantile=0.5) def test_invalid_warm_start_distance(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) rec._init([1, 2, 3]) rec.fit([1, 1, 2, 2, 3], [0, 1, 1, 1, 0]) rec.warm_start(arm_to_features={1: [0.5, 0.5], 2: [1, 0.5], 3: [1, 0]}, distance_quantile=50) # ===================== # Train # ===================== def test_train_invalid_recommender(self): with self.assertRaises(TypeError): rec = LearningPolicy.LinUCB() train(rec, train_data, user_features) def test_train_invalid_data(self): data = pd.read_csv(train_data) with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) train(rec, data.values) with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) train(rec, data.rename(columns={Constants.user_id: "user"})) with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) train(rec, data.rename(columns={Constants.item_id: "item"})) with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) train(rec, data.rename(columns={Constants.item_id: "click"})) def test_train_invalid_user_features(self): user_features_df = pd.read_csv(user_features) with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features_df.values) with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features_df.rename(columns={Constants.user_id: "user"})) def test_train_invalid_user_features_list(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features, user_features_list=pd.Series(["u1", "u2"])) def test_train_invalid_user_features_dtypes(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features, user_features_dtypes=["int8", "int8"]) def test_train_invalid_item_features(self): item_features_df = pd.read_csv(item_features) with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features, item_features=item_features_df.values) with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features, item_features=list(item_features_df)) def test_train_invalid_item_list(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features, item_list=pd.Series(["235", "313", "433"])) def test_train_invalid_item_eligibility(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features, item_eligibility=[[234], [456]]) def test_train_invalid_warm_start(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features, warm_start=1) def test_train_invalid_warm_start_distance(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features, warm_start=True, warm_start_distance=50) def test_train_invalid_user_id_col(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features, user_id_col=0) def test_train_invalid_item_id_col(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features, item_id_col=1) def test_train_invalid_response_col(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features, item_id_col=2) def test_train_invalid_batch_size(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features, batch_size="1000") with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features, batch_size=-50) def test_train_invalid_save_file(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features, save_file=1) # ===================== # Score # ===================== def test_score_invalid_recommender(self): with self.assertRaises(TypeError): rec = LearningPolicy.LinUCB() score(rec, train_data, user_features) with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.LinUCB()) score(rec, train_data, user_features) def test_score_invalid_data(self): data = pd.read_csv(train_data) with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) train(rec, data) score(rec, data.values) with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) train(rec, data) score(rec, data.rename(columns={Constants.user_id: "user"})) with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) train(rec, data) score(rec, data.rename(columns={Constants.item_id: "item"})) with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) train(rec, data) score(rec, data.rename(columns={Constants.item_id: "click"})) def test_score_invalid_user_features(self): user_features_df = pd.read_csv(user_features) with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features_df) score(rec, train_data, user_features_df.values) with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features_df) score(rec, train_data, user_features_df.rename(columns={Constants.user_id: "user"})) def test_score_invalid_user_features_list(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features) score(rec, train_data, user_features, user_features_list=pd.Series(["u1", "u2"])) def test_score_invalid_user_features_dtypes(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features) score(rec, train_data, user_features, user_features_dtypes=["int8", "int8"]) def test_score_invalid_item_features(self): item_features_df = pd.read_csv(item_features) with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features) score(rec, train_data, user_features, item_features=item_features_df.values) with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features) score(rec, train_data, user_features, item_features=list(item_features_df)) def test_score_invalid_item_list(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features) score(rec, train_data, user_features, item_list=pd.Series(["235", "313", "433"])) def test_score_invalid_item_eligibility(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features) score(rec, train_data, user_features, item_eligibility=[[234], [456]]) def test_score_invalid_warm_start(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features) score(rec, train_data, user_features, warm_start=1) def test_score_invalid_warm_start_distance(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features) score(rec, train_data, user_features, warm_start=True, warm_start_distance=50) def test_score_invalid_user_id_col(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features) score(rec, train_data, user_features, user_id_col=0) def test_score_invalid_item_id_col(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features) score(rec, train_data, user_features, item_id_col=1) def test_score_invalid_response_col(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features) score(rec, train_data, user_features, item_id_col=2) def test_score_invalid_batch_size(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features) score(rec, train_data, user_features, batch_size="1000") with self.assertRaises(ValueError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features) score(rec, train_data, user_features, batch_size=-50) def test_score_invalid_save_file(self): with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data, user_features) score(rec, train_data, user_features, save_file=1) # ===================== # Benchmark # ===================== def test_benchmark_invalid_recommender(self): with self.assertRaises(TypeError): rec = LearningPolicy.LinUCB() benchmark(rec, metrics, train_data, test_data, user_features=user_features) with self.assertRaises(TypeError): rec = BanditRecommender(LearningPolicy.LinUCB()) benchmark(rec, metrics, train_data, test_data, user_features=user_features) with self.assertRaises(TypeError): rec = {"LinUCB": LearningPolicy.LinUCB()} benchmark(rec, metrics, train_data, test_data, user_features=user_features) def test_benchmark_invalid_data(self): train_data_df = pd.read_csv(train_data) test_data_df = pd.read_csv(test_data) rec = {"LinUCB": BanditRecommender(LearningPolicy.LinUCB()), "Random": BanditRecommender(LearningPolicy.Random())} with self.assertRaises(TypeError): benchmark(rec, metrics, train_data_df.values, test_data, user_features=user_features) with self.assertRaises(TypeError): benchmark(rec, metrics, train_data, test_data_df.values, user_features=user_features) with self.assertRaises(ValueError): benchmark(rec, metrics, train_data, test_data, cv=5, user_features=user_features) with self.assertRaises(ValueError): benchmark(rec, metrics, train_data_df.rename(columns={Constants.user_id: "user"}), test_data, cv=5, user_features=user_features) with self.assertRaises(ValueError): benchmark(rec, metrics, train_data_df.rename(columns={Constants.item_id: "item"}), test_data, cv=5, user_features=user_features) with self.assertRaises(ValueError): benchmark(rec, metrics, train_data_df.rename(columns={Constants.response: "click"}), test_data, cv=5, user_features=user_features) def test_benchmark_invalid_user_features(self): user_features_df = pd.read_csv(user_features) rec = {"LinUCB": BanditRecommender(LearningPolicy.LinUCB()), "Random": BanditRecommender(LearningPolicy.Random())} with self.assertRaises(TypeError): benchmark(rec, metrics, train_data, test_data, user_features=user_features_df.values) with self.assertRaises(ValueError): benchmark(rec, metrics, train_data, test_data, user_features=user_features_df.rename(columns={Constants.user_id: "user"})) def test_benchmark_invalid_user_features_list(self): rec = {"LinUCB": BanditRecommender(LearningPolicy.LinUCB()), "Random": BanditRecommender(LearningPolicy.Random())} with self.assertRaises(TypeError): benchmark(rec, metrics, train_data, test_data, user_features=user_features, user_features_list=pd.Series(["u1", "u2"])) with self.assertRaises(ValueError): benchmark(rec, metrics, train_data, test_data, user_features_list=pd.Series(["u1", "u2"])) def test_benchmark_invalid_user_features_dtypes(self): rec = {"LinUCB": BanditRecommender(LearningPolicy.LinUCB()), "Random": BanditRecommender(LearningPolicy.Random())} with self.assertRaises(TypeError): benchmark(rec, metrics, train_data, test_data, user_features=user_features, user_features_dtypes=["int8", "int8"]) with self.assertRaises(ValueError): benchmark(rec, metrics, train_data, test_data, user_features_dtypes=["int8", "int8"]) def test_benchmark_invalid_item_features(self): item_features_df = pd.read_csv(item_features) rec = {"LinUCB": BanditRecommender(LearningPolicy.LinUCB()), "Random": BanditRecommender(LearningPolicy.Random())} with self.assertRaises(TypeError): benchmark(rec, metrics, train_data, test_data, user_features=user_features, item_features=item_features_df.values) with self.assertRaises(TypeError): benchmark(rec, metrics, train_data, test_data, user_features=user_features, item_features=list(item_features_df)) def test_benchmark_invalid_item_list(self): rec = {"LinUCB": BanditRecommender(LearningPolicy.LinUCB()), "Random": BanditRecommender(LearningPolicy.Random())} with self.assertRaises(TypeError): benchmark(rec, metrics, train_data, test_data, user_features=user_features, item_list=pd.Series(["235", "313", "433"])) def test_benchmark_invalid_item_eligibility(self): rec = {"LinUCB": BanditRecommender(LearningPolicy.LinUCB()), "Random": BanditRecommender(LearningPolicy.Random())} with self.assertRaises(TypeError): benchmark(rec, metrics, train_data, test_data, user_features=user_features, item_eligibility=[[234], [456]]) def test_benchmark_invalid_warm_start(self): rec = {"LinUCB": BanditRecommender(LearningPolicy.LinUCB()), "Random": BanditRecommender(LearningPolicy.Random())} with self.assertRaises(TypeError): benchmark(rec, metrics, train_data, test_data, user_features=user_features, warm_start=1) def test_benchmark_invalid_warm_start_distance(self): rec = {"LinUCB": BanditRecommender(LearningPolicy.LinUCB()), "Random": BanditRecommender(LearningPolicy.Random())} with self.assertRaises(ValueError): benchmark(rec, metrics, train_data, test_data, user_features=user_features, warm_start_distance=50) def test_benchmark_invalid_warm_start_distance_value(self): rec = {"LinUCB": BanditRecommender(LearningPolicy.LinUCB()), "Random": BanditRecommender(LearningPolicy.Random())} with self.assertRaises(TypeError): benchmark(rec, metrics, train_data, test_data, user_features=user_features, warm_start=True, warm_start_distance=50) def test_benchmark_invalid_user_id_col(self): rec = {"LinUCB": BanditRecommender(LearningPolicy.LinUCB()), "Random": BanditRecommender(LearningPolicy.Random())} with self.assertRaises(TypeError): benchmark(rec, metrics, train_data, test_data, user_features=user_features, user_id_col=0) def test_benchmark_invalid_item_id_col(self): rec = {"LinUCB": BanditRecommender(LearningPolicy.LinUCB()), "Random": BanditRecommender(LearningPolicy.Random())} with self.assertRaises(TypeError): benchmark(rec, metrics, train_data, test_data, user_features=user_features, item_id_col=1) def test_benchmark_invalid_response_col(self): rec = {"LinUCB": BanditRecommender(LearningPolicy.LinUCB()), "Random": BanditRecommender(LearningPolicy.Random())} with self.assertRaises(TypeError): benchmark(rec, metrics, train_data, test_data, user_features=user_features, response_col=2) def test_benchmark_invalid_batch_size(self): rec = {"LinUCB": BanditRecommender(LearningPolicy.LinUCB()), "Random": BanditRecommender(LearningPolicy.Random())} with self.assertRaises(ValueError): benchmark(rec, metrics, train_data, test_data, user_features=user_features, batch_size=-2) def test_benchmark_invalid_save_file(self): rec = {"LinUCB": BanditRecommender(LearningPolicy.LinUCB()), "Random": BanditRecommender(LearningPolicy.Random())} with self.assertRaises(TypeError): benchmark(rec, metrics, train_data, test_data, user_features=user_features, output_dir=True) # ===================== # Utils # ===================== def test_utils_invalid_load_item_features(self): item_features_df = pd.read_csv(item_features) item_list = item_features_df[Constants.item_id].tolist() item_list.append("new_id") with self.assertRaises(ValueError): load_item_features(item_features_df, item_list) def test_utils_invalid_load_data_frame(self): item_features_df = pd.read_csv(item_features) with self.assertRaises(TypeError): load_data_frame(item_features_df.values) def test_utils_invalid_load_list(self): with self.assertRaises(TypeError): load_list((1, 2, 3)) ================================================ FILE: tests/test_pipeline.py ================================================ # -*- coding: utf-8 -*- # Copyright FMR LLC # SPDX-License-Identifier: Apache-2.0 from copy import deepcopy import json import pickle import os import tempfile import unittest import pandas as pd from mabwiser.linear import _Linear from jurity.recommenders import DiversityRecoMetrics from mab2rec import BanditRecommender, LearningPolicy, NeighborhoodPolicy from mab2rec.pipeline import train, score, benchmark from mab2rec.utils import Constants, default_metrics, concat_recommendations_list from tests.test_base import BaseTest TEST_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = TEST_DIR + os.sep + ".." + os.sep # Data files train_data = os.path.join(ROOT_DIR, "data", "data_train.csv") test_data = os.path.join(ROOT_DIR, "data", "data_test.csv") user_features = os.path.join(ROOT_DIR, "data", "features_user.csv") item_features = os.path.join(ROOT_DIR, "data", "features_item.csv") item_eligibility = os.path.join(ROOT_DIR, "data", "extended", "data_eligibility.csv") user_features_dtypes = os.path.join(ROOT_DIR, "data", "extended", "features_user_dtypes.json") # Import train_data_df = pd.read_csv(train_data) test_data_df = pd.read_csv(test_data) user_features_df = pd.read_csv(user_features) item_features_df = pd.read_csv(item_features) class TrainTest(BaseTest): def test_learning_policies_train(self): for lp in self.lps + self.para_lps: rec = BanditRecommender(lp) train(rec, train_data, user_features) train(rec, train_data_df, user_features_df) def test_neighborhood_policies_train(self): for cp in self.nps: rec = BanditRecommender(self.lps[0], cp) train(rec, train_data_df, user_features_df) def test_lingreedy_train(self): rec = BanditRecommender(LearningPolicy.LinGreedy()) train(rec, train_data_df, user_features_df) self.assertTrue(isinstance(rec.mab._imp, _Linear)) self.assertEqual(len(rec.mab._imp.arm_to_model), 201) self.assertAlmostEqual(rec.mab._imp.arm_to_model[427].beta[0], 0.19919595422109415) self.assertAlmostEqual(rec.mab._imp.arm_to_model[173].beta[0], -0.23409821140400933) def test_train_twice(self): # First train rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data_df, user_features_df) # Second train rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data_df, user_features_df) self.assertEqual(rec.mab._is_initial_fit, rec.mab._is_initial_fit) self.assertEqual(rec.mab.learning_policy, rec.mab.learning_policy) def test_learning_policies_warm_start(self): item_list = train_data_df[Constants.item_id].unique().tolist() train_item_list = [118, 125, 132, 173, 250, 275, 423, 427, 591, 751] # Assume only 10 items in train data train_df = train_data_df[train_data_df[Constants.item_id].isin(train_item_list)] for lp in self.lps + self.para_lps: rec = BanditRecommender(lp) train(rec, train_df, user_features_df, item_list=item_list, item_features=item_features_df, warm_start=True, warm_start_distance=0.75) self.assertEqual(rec.mab.arms, item_list) def test_neighborhood_policies_warm_start(self): item_list = train_data_df[Constants.item_id].unique().tolist() train_item_list = [118, 125, 132, 173, 250, 275, 423, 427, 591, 751] # Assume only 10 items in train data train_df = train_data_df[train_data_df[Constants.item_id].isin(train_item_list)] for cp in self.nps: rec = BanditRecommender(self.lps[0], cp) train(rec, train_df, user_features_df, item_list=item_list, item_features=item_features_df, warm_start=True, warm_start_distance=0.75) self.assertEqual(rec.mab.arms, item_list) def test_warm_start_twice(self): item_list = train_data_df[Constants.item_id].unique().tolist() train_item_list = [118, 125, 132, 173, 250, 275, 423, 427, 591, 751] # Assume only 10 items in train data train_df = train_data_df[train_data_df[Constants.item_id].isin(train_item_list)] # First warm start rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_df, user_features_df, item_list=item_list, item_features=item_features_df, warm_start=True, warm_start_distance=0.75) self.assertEqual(rec.mab.arms, item_list) # Second warm start rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_df, user_features_df, item_list=item_list, item_features=item_features_df, warm_start=True, warm_start_distance=0.75) self.assertEqual(rec.mab.arms, item_list) def test_warm_start_input_change(self): item_list = train_data_df[Constants.item_id].unique().tolist() train_item_list = [118, 125, 132, 173, 250, 275, 423, 427, 591, 751] # Assume only 10 items in train data train_df = train_data_df[train_data_df[Constants.item_id].isin(train_item_list)] # Copy inputs train_df_copy = train_df.copy() user_features_df_copy = user_features_df.copy() item_list_copy = item_list.copy() item_features_df_copy = item_features_df.copy() # Train and warm start rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_df, user_features_df, item_list=item_list, item_features=item_features_df, warm_start=True, warm_start_distance=0.75) # Compare after self.assertTrue(train_df.equals(train_df_copy)) self.assertTrue(user_features_df.equals(user_features_df_copy)) self.assertTrue(item_features_df.equals(item_features_df_copy)) self.assertEqual(item_list, item_list_copy) def test_user_features_list(self): user_features_list = ["u1", "u2", "u3", "u4", "u5"] # Only use these user features rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data_df, user_features_df, user_features_list=user_features_list) self.assertEqual(len(rec.mab._imp.arm_to_model[118].beta), len(user_features_list)) def test_user_features_list_csv(self): with tempfile.TemporaryDirectory() as tmp_dir: user_features_list = ["u1", "u2", "u3", "u4", "u5"] # Only use these user features user_features_list_csv = os.path.join(tmp_dir, "user_features_list.csv") pd.DataFrame(user_features_list).to_csv(user_features_list_csv, index=False) rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data_df, user_features_df, user_features_list=user_features_list_csv) self.assertEqual(len(rec.mab._imp.arm_to_model[118].beta), len(user_features_list)) def test_user_features_data_types(self): with open(user_features_dtypes, 'r') as f: data_types = json.load(f) rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data_df, user_features_df, user_features_dtypes=data_types) self.assertTrue(rec.mab._is_initial_fit) def test_user_features_data_types_json(self): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data_df, user_features_df, user_features_dtypes=user_features_dtypes) self.assertTrue(rec.mab._is_initial_fit) def test_user_id_col_change(self): train_data_df_update = train_data_df.rename(columns={"user_id": "uid"}) user_features_df_update = user_features_df.rename(columns={"user_id": "uid"}) rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data_df_update, user_features_df_update, user_id_col="uid") self.assertTrue(rec.mab._is_initial_fit) def test_item_id_col_change(self): train_data_df_update = train_data_df.rename(columns={"item_id": "iid"}) item_features_df_update = item_features_df.rename(columns={"item_id": "iid"}) rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data_df_update, user_features_df, item_features=item_features_df_update, item_id_col="iid") self.assertTrue(rec.mab._is_initial_fit) def test_response_col_change(self): train_data_df_update = train_data_df.rename(columns={"response": "ind"}) rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data_df_update, user_features_df, response_col="ind") self.assertTrue(rec.mab._is_initial_fit) def test_batch_size_small(self): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data_df, user_features_df, batch_size=1000) self.assertTrue(rec.mab._is_initial_fit) rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) train(rec, train_data_df, batch_size=1000) self.assertTrue(rec.mab._is_initial_fit) def test_batch_size_large(self): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data_df, user_features_df, batch_size=10000000) self.assertTrue(rec.mab._is_initial_fit) def test_save_file(self): # Specified file name with tempfile.TemporaryDirectory() as tmp_dir: rec_pickle = os.path.join(tmp_dir, "rec.pkl") rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data_df, user_features_df, save_file=rec_pickle) self.assertTrue(os.path.exists(rec_pickle)) with open(rec_pickle, 'rb') as f: rec = pickle.load(f) self.assertTrue(isinstance(rec, BanditRecommender)) self.assertTrue(rec.mab._is_initial_fit) def test_save_file_true(self): default_name = "recommender.pkl" rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data_df, user_features_df, save_file=True) self.assertTrue(os.path.exists(default_name)) with open(default_name, 'rb') as f: rec = pickle.load(f) self.assertTrue(isinstance(rec, BanditRecommender)) self.assertTrue(rec.mab._is_initial_fit) os.remove(default_name) class ScoreTest(BaseTest): def test_learning_policies_score(self): for lp in self.lps + self.para_lps: # Train rec = BanditRecommender(lp) train(rec, train_data_df, user_features=user_features_df) # Score w Warm Start df = score(rec, test_data_df.sample(100), user_features=user_features_df, item_list=test_data_df[Constants.item_id].tolist(), item_features=item_features_df, warm_start=True, warm_start_distance=0.75) self.assertEqual(df.shape[1], 3) self.assertEqual(df.ndim, 2) def test_neighborhood_policies_score(self): cp_prev = None for cp in self.nps: if cp_prev is not None and type(cp) == type(cp_prev): cp_prev = deepcopy(cp) continue # Train rec = BanditRecommender(self.lps[0], cp) train(rec, train_data_df, user_features_df) # Score w Warm Start df = score(rec, test_data_df.sample(10), user_features_df, item_list=test_data_df[Constants.item_id].tolist(), item_features=item_features_df, warm_start=True, warm_start_distance=0.75) self.assertEqual(df.shape[1], 3) self.assertEqual(df.ndim, 2) cp_prev = deepcopy(cp) def test_lingreedy_score(self): rec = BanditRecommender(LearningPolicy.LinGreedy(l2_lambda=1000)) train(rec, train_data_df, user_features_df) df = score(rec, test_data_df, user_features_df) score_dict = df.head().to_dict() self.assertDictEqual(score_dict['user_id'], {0: 259, 1: 259, 2: 259, 3: 259, 4: 259}) self.assertDictEqual(score_dict['item_id'], {0: 50, 1: 127, 2: 313, 3: 56, 4: 174},) self.assertDictEqual(score_dict['score'], {0: 0.5481800084403381, 1: 0.533300457736932, 2: 0.5318001465799647, 3: 0.5313858071529368, 4: 0.531052100344854},) def test_learning_policy_no_features(self): rec = BanditRecommender(LearningPolicy.EpsilonGreedy()) train(rec, train_data_df) df = score(rec, test_data_df) self.assertEqual(df.shape[1], 3) self.assertEqual(df.ndim, 2) def test_load_from_pickle(self): with tempfile.TemporaryDirectory() as tmp_dir: rec_pickle = os.path.join(tmp_dir, "rec.pkl") rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data_df, user_features_df, save_file=rec_pickle) df = score(rec_pickle, test_data_df, user_features_df) self.assertEqual(df.shape[1], 3) self.assertEqual(df.ndim, 2) def test_eligible_items(self): item_eligibility_df = pd.read_csv(item_eligibility) # Train rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data_df, user_features_df) # Score only eligible items df = score(rec, test_data_df, user_features_df, item_eligibility=item_eligibility_df) self.assertEqual(df.shape[1], 3) self.assertEqual(df.ndim, 2) def test_eligible_items_twice(self): item_eligibility_df = pd.read_csv(item_eligibility) # Train rec = BanditRecommender(LearningPolicy.Random()) train(rec, data=train_data_df) # Make recommendations that satisfy eligibility criteria df_first = score(rec, data=test_data_df, item_eligibility=item_eligibility_df) # Train again rec = BanditRecommender(LearningPolicy.Random()) train(rec, data=train_data_df) # Make recommendations that satisfy eligibility criteria df_second = score(rec, data=test_data_df, item_eligibility=item_eligibility_df) self.assertTrue(df_first.equals(df_second)) def test_batch_size_small(self): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data_df, user_features_df) df = score(rec, test_data_df, user_features_df, batch_size=1000) self.assertEqual(df.shape[1], 3) self.assertEqual(df.ndim, 2) def test_batch_size_large(self): rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data_df, user_features_df) df = score(rec, test_data_df, user_features_df, batch_size=10000000) self.assertEqual(df.shape[1], 3) self.assertEqual(df.ndim, 2) def test_save_file(self): with tempfile.TemporaryDirectory() as tmp_dir: results_csv = os.path.join(tmp_dir, "results.csv") rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data_df, user_features_df) score(rec, train_data_df, user_features_df, save_file=results_csv) df = pd.read_csv(results_csv) self.assertTrue(os.path.exists(results_csv)) self.assertTrue(isinstance(df, pd.DataFrame)) self.assertEqual(df.shape[1], 3) self.assertEqual(df.ndim, 2) def test_save_file_true(self): default_name = "results.csv" rec = BanditRecommender(LearningPolicy.LinUCB()) train(rec, train_data_df, user_features_df) score(rec, train_data_df, user_features_df, save_file=True) self.assertTrue(os.path.exists(default_name)) os.remove(default_name) class BenchmarkTest(BaseTest): recommenders = { "Random": BanditRecommender(LearningPolicy.Random()), "LinUCB": BanditRecommender(LearningPolicy.LinUCB(alpha=1.5)), "ClustersTS": BanditRecommender(LearningPolicy.ThompsonSampling(), NeighborhoodPolicy.Clusters(n_clusters=10)) } metrics = default_metrics(top_k_values=[3, 5, 10]) def test_benchmark(self): recommenders = deepcopy(self.recommenders) recommendations, rec_metrics = benchmark(recommenders, self.metrics, train_data, test_data, user_features=user_features_df) self.assertEqual(recommendations.keys(), self.recommenders.keys()) self.assertEqual(rec_metrics.keys(), self.recommenders.keys()) self.assertTrue(isinstance(recommendations["Random"], pd.DataFrame)) self.assertEqual(recommendations["Random"].shape[1], 3) self.assertEqual(recommendations["Random"].ndim, 2) self.assertAlmostEqual(rec_metrics["Random"]["AUC(score)@3"], 0.5154761904761904) self.assertAlmostEqual(rec_metrics["Random"]["CTR(score)@3"], 0.2112676056338028) self.assertAlmostEqual(rec_metrics["Random"]["Precision@3"], 0.04950495049504949) self.assertAlmostEqual(rec_metrics["Random"]["Recall@3"], 0.00774496333427291) self.assertAlmostEqual(rec_metrics["Random"]["NDCG@3"], 0.014186541542204544) self.assertAlmostEqual(rec_metrics["Random"]["MAP@3"], 0.0286028602860286) for rec in recommenders.values(): self.assertTrue(rec.mab is None) @unittest.skip("operating system differences") def test_benchmark_cv(self): recommenders = deepcopy(self.recommenders) recommendations, rec_metrics = benchmark(recommenders, self.metrics, train_data, cv=3, user_features=user_features_df) self.assertEqual(len(rec_metrics), 3) self.assertAlmostEqual(rec_metrics[0]["Random"]["AUC(score)@3"], 0.5679790026246719) self.assertAlmostEqual(rec_metrics[0]["Random"]["CTR(score)@3"], 0.2616279069767442) self.assertAlmostEqual(rec_metrics[0]["Random"]["Precision@3"], 0.05208333333333333) self.assertAlmostEqual(rec_metrics[0]["Random"]["Recall@3"], 0.016161228957922595) self.assertAlmostEqual(rec_metrics[0]["Random"]["NDCG@3"], 0.023100096565199995) self.assertAlmostEqual(rec_metrics[0]["Random"]["MAP@3"], 0.0398341049382716) self.assertEqual(len(recommendations), 3) self.assertTrue(isinstance(recommendations[0]["Random"], pd.DataFrame)) self.assertEqual(recommendations[0]["Random"].shape[1], 3) self.assertEqual(recommendations[0]["Random"].ndim, 2) recommendations = concat_recommendations_list(recommendations) self.assertTrue(isinstance(recommendations["Random"], pd.DataFrame)) self.assertEqual(recommendations["Random"][Constants.user_id].nunique(), train_data_df[Constants.user_id].nunique()) self.assertEqual(recommendations["Random"].shape[1], 3) for rec in recommenders.values(): self.assertTrue(rec.mab is None) def test_benchmark_diversity_metrics(self): recommenders = deepcopy(self.recommenders) metrics = [] metric_params = {'click_column': Constants.score, 'user_id_column': Constants.user_id, 'item_id_column': Constants.item_id} for k in [3, 5]: metrics.append(DiversityRecoMetrics.InterListDiversity(**metric_params, k=k, user_sample_size=100)) metrics.append(DiversityRecoMetrics.IntraListDiversity(**metric_params, k=k, user_sample_size=100, item_features=item_features_df)) recommendations, rec_metrics = benchmark(recommenders, metrics, train_data, test_data, user_features=user_features_df) self.assertEqual(recommendations.keys(), self.recommenders.keys()) self.assertEqual(rec_metrics.keys(), self.recommenders.keys()) self.assertAlmostEqual(rec_metrics["Random"]["Inter-List Diversity@3"], 0.9856228956228957) self.assertAlmostEqual(rec_metrics["Random"]["Inter-List Diversity@5"], 0.9749818181818182) self.assertAlmostEqual(rec_metrics["Random"]["Intra-List Diversity@3"], 0.7602157694547105) self.assertAlmostEqual(rec_metrics["Random"]["Intra-List Diversity@5"], 0.7547351779782561) ================================================ FILE: tests/test_rec.py ================================================ # -*- coding: utf-8 -*- # Copyright FMR LLC # SPDX-License-Identifier: Apache-2.0 from mab2rec import BanditRecommender, LearningPolicy, NeighborhoodPolicy from tests.test_base import BaseTest class BanditRecommenderTest(BaseTest): def test_init(self): rec = BanditRecommender(LearningPolicy.UCB1()) rec._init([1, 2, 3]) self.assertTrue(rec.mab is not None) def test_learning_policies(self): for lp in self.lps: self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], top_k=2, seed=123456) def test_parametric_learning_policies(self): for lp in self.para_lps: self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456) def test_neighborhood_policies(self): for cp in self.nps: for lp in self.lps: if not self.is_compatible(lp, cp): continue self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, neighborhood_policy=cp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456) for lp in self.para_lps: if not self.is_compatible(lp, cp): continue self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, neighborhood_policy=cp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456) def test_learning_policies_predict(self): for lp in self.lps: _, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, top_k=2, seed=123456) rec.predict() rec.predict_expectations() def test_parametric_learning_policies_predict(self): for lp in self.para_lps: _, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456) rec.predict([[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [1, 2, 1, 1, 3]]) rec.predict_expectations([[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [1, 2, 1, 1, 3]]) def test_neighborhood_policies_predict(self): for cp in self.nps: for lp in self.lps: if not self.is_compatible(lp, cp): continue _, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, neighborhood_policy=cp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456) rec.predict([[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [1, 2, 1, 1, 3]]) rec.predict_expectations([[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [1, 2, 1, 1, 3]]) for lp in self.para_lps: if not self.is_compatible(lp, cp): continue _, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, neighborhood_policy=cp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456) rec.predict([[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [1, 2, 1, 1, 3]]) rec.predict_expectations([[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [1, 2, 1, 1, 3]]) def test_learning_policies_no_sigmoid(self): for lp in self.lps: self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], apply_sigmoid=False, top_k=2, seed=123456) def test_parametric_learning_policies_no_sigmoid(self): for lp in self.para_lps: self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], apply_sigmoid=False, top_k=2, seed=123456) def test_neighborhood_policies_recommend_no_sigmoid(self): for cp in self.nps: for lp in self.lps: if not self.is_compatible(lp, cp): continue self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, neighborhood_policy=cp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], apply_sigmoid=False, top_k=2, seed=123456) for lp in self.para_lps: if not self.is_compatible(lp, cp): continue self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, neighborhood_policy=cp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], apply_sigmoid=False, top_k=2, seed=123456) def test_learning_policies_partial_fit(self): for lp in self.lps: _, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, top_k=2, seed=123456) rec.partial_fit(decisions=[1, 1, 2], rewards=[0, 1, 0]) def test_parametric_learning_policies_partial_fit(self): for lp in self.para_lps: _, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456) rec.partial_fit(decisions=[1, 1, 2], rewards=[0, 1, 0], contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [1, 2, 1, 1, 3]]) def test_neighborhood_policies_partial_fit(self): for cp in self.nps: for lp in self.lps: if not self.is_compatible(lp, cp): continue _, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, neighborhood_policy=cp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456) rec.partial_fit(decisions=[1, 1, 2], rewards=[0, 1, 0], contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [1, 2, 1, 1, 3]]) for lp in self.para_lps: if not self.is_compatible(lp, cp): continue _, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, neighborhood_policy=cp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456) rec.partial_fit(decisions=[1, 1, 2], rewards=[0, 1, 0], contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [1, 2, 1, 1, 3]]) def test_learning_policies_warm_start(self): for lp in self.lps: self.predict(arms=[1, 2, 3, 4], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], top_k=2, seed=123456, warm_start=True, arm_to_features={1: [0.5, 1], 2: [0, 1], 3: [0, 1], 4: [0.5, 1]}) def test_parametric_learning_policies_warm_start(self): for lp in self.para_lps: self.predict(arms=[1, 2, 3, 4], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456, warm_start=True, arm_to_features={1: [0.5, 1], 2: [0, 1], 3: [0, 1], 4: [0.5, 1]}) def test_neighborhood_policies_warm_start(self): for cp in self.nps: for lp in self.lps: if not self.is_compatible(lp, cp): continue self.predict(arms=[1, 2, 3, 4], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, neighborhood_policy=cp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456, warm_start=True, arm_to_features={1: [0.5, 1], 2: [0, 1], 3: [0, 1], 4: [0.5, 1]}) for lp in self.para_lps: if not self.is_compatible(lp, cp): continue self.predict(arms=[1, 2, 3, 4], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, neighborhood_policy=cp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456, warm_start=True, arm_to_features={1: [0.5, 1], 2: [0, 1], 3: [0, 1], 4: [0.5, 1]}) def test_learning_policies_add_arm(self): for lp in self.lps: rec = BanditRecommender(lp) rec._init(arms=[1, 2, 3]) rec.add_arm(4) self.assertEqual(len(rec.mab.arms), 4) self.assertTrue(4 in rec.mab.arms) self.assertTrue(4 in rec.mab._imp.arm_to_expectation) for lp in self.lps: rec = BanditRecommender(lp) rec.add_arm(4) self.assertEqual(len(rec.mab.arms), 1) self.assertTrue(4 in rec.mab.arms) self.assertTrue(4 in rec.mab._imp.arm_to_expectation) def test_parametric_learning_policies_add_arm(self): for lp in self.para_lps: rec = BanditRecommender(lp) rec._init(arms=[1, 2, 3]) rec.add_arm(4) self.assertEqual(len(rec.mab.arms), 4) self.assertTrue(4 in rec.mab.arms) self.assertTrue(4 in rec.mab._imp.arm_to_expectation) self.assertTrue(4 in rec.mab._imp.arm_to_model) for lp in self.para_lps: rec = BanditRecommender(lp) rec.add_arm(4) self.assertEqual(len(rec.mab.arms), 1) self.assertTrue(4 in rec.mab.arms) self.assertTrue(4 in rec.mab._imp.arm_to_expectation) self.assertTrue(4 in rec.mab._imp.arm_to_model) def test_neighborhood_policies_add_arm(self): for cp in self.nps: for lp in self.lps: if not self.is_compatible(lp, cp): continue rec = BanditRecommender(lp) rec._init(arms=[1, 2, 3]) rec.add_arm(4) self.assertEqual(len(rec.mab.arms), 4) self.assertTrue(4 in rec.mab.arms) self.assertTrue(4 in rec.mab._imp.arm_to_expectation) for cp in self.nps: for lp in self.lps: if not self.is_compatible(lp, cp): continue rec = BanditRecommender(lp) rec.add_arm(4) self.assertEqual(len(rec.mab.arms), 1) self.assertTrue(4 in rec.mab.arms) self.assertTrue(4 in rec.mab._imp.arm_to_expectation) def test_learning_policies_remove_arm(self): for lp in self.lps: rec = BanditRecommender(lp) rec._init(arms=[1, 2, 3]) rec.remove_arm(3) self.assertTrue(3 not in rec.mab.arms) self.assertTrue(3 not in rec.mab._imp.arm_to_expectation) def test_parametric_learning_policies_remove_arm(self): for lp in self.para_lps: rec = BanditRecommender(lp) rec._init(arms=[1, 2, 3]) rec.remove_arm(3) self.assertTrue(3 not in rec.mab.arms) self.assertTrue(3 not in rec.mab._imp.arm_to_expectation) self.assertTrue(3 not in rec.mab._imp.arm_to_model) def test_neighborhood_policies_remove_arm(self): for cp in self.nps: for lp in self.lps: if not self.is_compatible(lp, cp): continue rec = BanditRecommender(lp) rec._init(arms=[1, 2, 3]) rec.remove_arm(3) self.assertTrue(3 not in rec.mab.arms) self.assertTrue(3 not in rec.mab._imp.arm_to_expectation) def test_learning_policies_set_arms(self): for lp in self.lps: _, rec = self.predict(arms=[1, 2, 3, 4], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], top_k=2, seed=123456) rec.set_arms([2, 5]) self.assertEqual(rec.mab.arms, [2, 5]) self.assertEqual(rec.mab._imp.arms, [2, 5]) def test_parametric_learning_policies_set_arms(self): for lp in self.para_lps: _, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456) rec.set_arms([2, 5]) self.assertEqual(rec.mab.arms, [2, 5]) self.assertEqual(rec.mab._imp.arms, [2, 5]) def test_neighborhood_policies_set_arms(self): for cp in self.nps: for lp in self.lps: if not self.is_compatible(lp, cp): continue _, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, neighborhood_policy=cp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456) rec.set_arms([2, 5]) self.assertEqual(rec.mab.arms, [2, 5]) self.assertEqual(rec.mab._imp.arms, [2, 5]) for lp in self.para_lps: if not self.is_compatible(lp, cp): continue _, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=lp, neighborhood_policy=cp, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456) rec.set_arms([2, 5]) self.assertEqual(rec.mab.arms, [2, 5]) self.assertEqual(rec.mab._imp.arms, [2, 5]) def test_recommend_random(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.Random(), neighborhood_policy=None, top_k=2, seed=123456) self.assertEqual(results[0], [1, 2]) self.assertListAlmostEqual(results[1], [0.6539649643958056, 0.5950330918093963]) # No scores results = rec.recommend(return_scores=False) self.assertEqual(results, [1, 2]) def test_recommend_random_w_empty_context(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.Random(), contexts=[[]] * 5, neighborhood_policy=None, top_k=2, seed=123456) self.assertEqual(results[0], [[1, 2], [1, 2], [3, 2], [1, 3], [2, 3]]) def test_recommend_popularity(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.Popularity(), neighborhood_policy=None, top_k=2, seed=123456) self.assertEqual(results[0], [3, 1]) self.assertListAlmostEqual(results[1], [0.6871905288086486, 0.5530453076346826]) # No scores results = rec.recommend(return_scores=False) self.assertEqual(results, [1, 3]) def test_recommend_popularity_w_empty_context(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.Popularity(), contexts=[[]] * 5, neighborhood_policy=None, top_k=2, seed=123456) self.assertEqual(results[0], [[3, 1], [1, 3], [3, 1], [3, 1], [3, 1]]) def test_recommend_greedy(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.EpsilonGreedy(epsilon=0), neighborhood_policy=None, top_k=2, seed=123456) self.assertEqual(results[0], [1, 3]) self.assertListAlmostEqual(results[1], [0.6607563687658172, 0.6456563062257954]) # No scores results = rec.recommend(return_scores=False) self.assertEqual(results, [1, 3]) def test_recommend_greedy_eps(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.EpsilonGreedy(epsilon=0.5), neighborhood_policy=None, top_k=2, seed=123456) self.assertEqual(results[0], [1, 3]) self.assertListAlmostEqual(results[1], [0.6607563687658172, 0.6456563062257954]) # No scores results = rec.recommend(return_scores=False) self.assertEqual(results, [2, 3]) def test_recommend_greedy_w_empty_context(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.EpsilonGreedy(epsilon=0.5), contexts=[[]] * 5, neighborhood_policy=None, top_k=2, seed=123456) self.assertEqual(results[0], [[1, 3], [1, 2], [3, 1], [1, 3], [1, 3]]) def test_recommend_softmax(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.Softmax(), neighborhood_policy=None, top_k=2, seed=123456) self.assertEqual(results[0], [3, 1]) self.assertListAlmostEqual(results[1], [0.6927815920738961, 0.5465755461646858]) # No scores results = rec.recommend(return_scores=False) self.assertEqual(results, [2, 3]) def test_recommend_softmax_w_empty_context(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.Softmax(), contexts=[[]] * 5, neighborhood_policy=None, top_k=2, seed=123456) self.assertEqual(results[0], [[3, 1], [2, 3], [3, 2], [3, 2], [3, 1]]) def test_recommend_ucb(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.UCB1(), neighborhood_policy=None, top_k=2, seed=123456) self.assertEqual(results[0], [1, 3]) self.assertListAlmostEqual(results[1], [0.8705286139301878, 0.8263110445099603]) # No scores results = rec.recommend(return_scores=False) self.assertEqual(results, [1, 3]) def test_recommend_ucb_alpha(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.UCB1(alpha=10), neighborhood_policy=None, top_k=2, seed=123456) self.assertEqual(results[0], [2, 1]) self.assertListAlmostEqual(results[1], [0.9999997430210213, 0.9999978636449579]) # No scores results = rec.recommend(return_scores=False) self.assertEqual(results, [2, 1]) def test_recommend_ucb_w_empty_context(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.UCB1(), contexts=[[]] * 5, neighborhood_policy=None, top_k=2, seed=123456) self.assertEqual(results[0], [[1, 3], [1, 3], [1, 3], [1, 3], [1, 3]]) def test_recommend_ts(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.ThompsonSampling(), neighborhood_policy=None, top_k=2, seed=123456) self.assertEqual(results[0], [1, 2]) self.assertListAlmostEqual(results[1], [0.6378726378265008, 0.5875821922391576]) # No scores results = rec.recommend(return_scores=False) self.assertEqual(results, [3, 1]) def test_recommend_ts_w_empty_context(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.ThompsonSampling(), contexts=[[]] * 5, neighborhood_policy=None, top_k=2, seed=123456) self.assertEqual(results[0], [[1, 3], [3, 1], [1, 3], [3, 2], [1, 2]]) def test_recommend_lin_greedy(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.LinGreedy(epsilon=0.5), neighborhood_policy=None, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456) self.assertEqual(results[0], [[3, 1], [2, 3]]) self.assertListAlmostEqual(results[1][0], [0.6504125435586658, 0.5240639631785098]) self.assertListAlmostEqual(results[1][1], [0.7221703184615302, 0.7121913829791641]) # No scores results = rec.recommend([[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], return_scores=False) self.assertEqual(results, [[2, 3], [2, 1]]) def test_recommend_lin_ucb(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.LinUCB(alpha=1.25), neighborhood_policy=None, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456) self.assertEqual(results[0], [[3, 2], [1, 3]]) self.assertListAlmostEqual(results[1][0], [0.8355754378823774, 0.8103388262282213]) self.assertListAlmostEqual(results[1][1], [0.8510415343853225, 0.8454457789037026]) # No scores results = rec.recommend([[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], return_scores=False) self.assertEqual(results, [[3, 2], [1, 3]]) def test_recommend_lin_ts(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.LinTS(), neighborhood_policy=None, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456) self.assertEqual(results[0], [[3, 2], [1, 3]]) self.assertListAlmostEqual(results[1][0], [0.6393327956234724, 0.6113795857188596]) self.assertListAlmostEqual(results[1][1], [0.9231640190086698, 0.9093145340785204]) # No scores results = rec.recommend([[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], return_scores=False) self.assertEqual(results, [[3, 1], [1, 3]]) def test_recommend_clusters_ts(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.ThompsonSampling(), neighborhood_policy=NeighborhoodPolicy.Clusters(), contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456) self.assertEqual(results[0], [[2, 1], [1, 3]]) self.assertListAlmostEqual(results[1][0], [0.6470729583134509, 0.6239486262002204]) self.assertListAlmostEqual(results[1][1], [0.7257397617770284, 0.6902019029795886]) # No scores results = rec.recommend([[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], return_scores=False) self.assertEqual(results, [[2, 3], [1, 3]]) def test_recommend_radius_ts(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.ThompsonSampling(), neighborhood_policy=NeighborhoodPolicy.Radius(radius=5), contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456) self.assertEqual(results[0], [[1, 3], [3, 2]]) self.assertListAlmostEqual(results[1][0], [0.6853064650518793, 0.5794087793326232]) self.assertListAlmostEqual(results[1][1], [0.6171485591737581, 0.6039485772665535]) # No scores results = rec.recommend([[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], return_scores=False) self.assertEqual(results, [[3, 1], [2, 1]]) def test_recommend_knn_ts(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.ThompsonSampling(), neighborhood_policy=NeighborhoodPolicy.KNearest(k=2), contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], top_k=2, seed=123456) self.assertEqual(results[0], [[2, 1], [1, 3]]) self.assertListAlmostEqual(results[1][0], [0.6470729583134509, 0.6239486262002204]) self.assertListAlmostEqual(results[1][1], [0.7257397617770284, 0.7239071840518659]) # No scores results = rec.recommend([[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], return_scores=False) self.assertEqual(results, [[3, 2], [1, 3]]) def test_recommend_lin_ucb_excluded(self): results, rec = self.predict(arms=[1, 2, 3], decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3], rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1], learning_policy=LearningPolicy.LinUCB(alpha=1.25), neighborhood_policy=None, contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]], context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0], [0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0], [0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3], [0, 2, 1, 0, 0]], excluded_arms=[[3], [3]], top_k=2, seed=123456) self.assertEqual(results[0], [[2, 1], [1, 2]]) self.assertListAlmostEqual(results[1][0], [0.8103388262282213, 0.7882460646490305]) def test_recommend_exclusion_replace(self): results, rec = self.predict(arms=[1, 2, 3, 4, 5], decisions=[1, 2, 3, 4, 5], rewards=[0.9, 0.8, 0.7, 0.6, 0.5], learning_policy=LearningPolicy.EpsilonGreedy(epsilon=0.), neighborhood_policy=None, contexts=[[1, 2], [1, 2]], excluded_arms=[[], [1]], top_k=4) self.assertListEqual(results[0][0], [1, 2, 3, 4]) self.assertListEqual(results[0][1], [2, 3, 4, 5]) ================================================ FILE: tests/test_visualization.py ================================================ # -*- coding: utf-8 -*- # Copyright FMR LLC # SPDX-License-Identifier: Apache-2.0 from unittest.mock import patch import os import matplotlib.pyplot as plt import pandas as pd import seaborn as sns from sklearn.cluster import KMeans from mab2rec import BanditRecommender, LearningPolicy, NeighborhoodPolicy from mab2rec.pipeline import benchmark from mab2rec.visualization import (plot_inter_diversity_at_k, plot_intra_diversity_at_k, plot_metrics_at_k, plot_num_items_per_recommendation, plot_recommended_counts, plot_recommended_counts_by_item, plot_personalization_heatmap) from mab2rec.utils import default_metrics, print_interaction_stats from tests.test_base import BaseTest TEST_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = TEST_DIR + os.sep + ".." + os.sep # Data files train_data = os.path.join(ROOT_DIR, "data", "data_train.csv") test_data = os.path.join(ROOT_DIR, "data", "data_test.csv") user_features = os.path.join(ROOT_DIR, "data", "features_user.csv") item_features = os.path.join(ROOT_DIR, "data", "features_item.csv") item_eligibility = os.path.join(ROOT_DIR, "data", "extended", "data_eligibility.csv") user_features_dtypes = os.path.join(ROOT_DIR, "data", "extended", "features_user_dtypes.json") # Import train_data_df = pd.read_csv(train_data) test_data_df = pd.read_csv(test_data) user_features_df = pd.read_csv(user_features) item_features_df = pd.read_csv(item_features) class VisualizationTest(BaseTest): recommenders = { "Random": BanditRecommender(LearningPolicy.Random()), "LinUCB": BanditRecommender(LearningPolicy.LinUCB(alpha=1.5)), "ClustersTS": BanditRecommender(LearningPolicy.ThompsonSampling(), NeighborhoodPolicy.Clusters(n_clusters=10)) } metrics = default_metrics(top_k_values=[3, 5, 10]) recommendations, rec_metrics = benchmark(recommenders, metrics, train_data, test_data, user_features=user_features_df) recommendations_cv, rec_metrics_cv = benchmark(recommenders, metrics, train_data, cv=3, user_features=user_features_df) @patch("mab2rec.visualization.plt.show") def test_plot_metrics_at_k(self, mock_show): plot_metrics_at_k(self.rec_metrics) plot_metrics_at_k(self.rec_metrics_cv) plt.close() @patch("mab2rec.visualization.plt.show") def test_plot_inter_diversity_at_k(self, mock_show): plot_inter_diversity_at_k(self.recommendations, k_list=[3, 5, 10]) plot_inter_diversity_at_k(self.recommendations_cv, k_list=[3, 5, 10]) plt.close() @patch("mab2rec.visualization.plt.show") def test_plot_intra_diversity_at_k(self, mock_show): plot_intra_diversity_at_k(self.recommendations, item_features_df, k_list=[3, 5, 10]) plot_intra_diversity_at_k(self.recommendations_cv, item_features_df, k_list=[3, 5, 10]) plt.close() @patch("mab2rec.visualization.plt.show") def test_plot_recommended_counts(self, mock_show): plot_recommended_counts(self.recommendations, test_data_df, k=3, alpha=0.7, average_response=False) plot_recommended_counts(self.recommendations, test_data_df, k=3, alpha=0.7, average_response=True) plot_recommended_counts(self.recommendations_cv, test_data_df, k=3, alpha=0.7, average_response=False) plt.close() @patch("mab2rec.visualization.plt.show") def test_plot_recommended_counts_by_item(self, mock_show): plot_recommended_counts_by_item(self.recommendations, k=3, top_n_items=15, normalize=False) plot_recommended_counts_by_item(self.recommendations, k=3, top_n_items=15, normalize=True) plot_recommended_counts_by_item(self.recommendations_cv, k=3, top_n_items=15, normalize=False) plt.close() @patch("mab2rec.visualization.plt.show") def test_plot_num_items_per_recommendation(self, mock_show): plot_num_items_per_recommendation(self.recommendations, test_data_df, normalize=False) plot_num_items_per_recommendation(self.recommendations, test_data_df, normalize=True) plot_num_items_per_recommendation(self.recommendations_cv, test_data_df, normalize=False) plt.close() @patch("mab2rec.visualization.plt.show") def test_plot_personalization_heatmap(self, mock_show): # Create clusters based on user features X = user_features_df.iloc[:, 1:] kmeans = KMeans(n_clusters=5, n_init=10, random_state=1652) kmeans.fit(X) user_clusters = dict(zip(user_features_df['user_id'], kmeans.labels_)) cmap = sns.diverging_palette(220, 10, as_cmap=True) plot_personalization_heatmap(self.recommendations, user_clusters, k=3, cmap=cmap, vmax=0.2, figsize=(5, 5)) plot_personalization_heatmap(self.recommendations_cv, user_clusters, k=3, cmap=cmap, vmax=0.2, figsize=(5, 5)) plt.close() @patch("mab2rec.utils.print") def test_print_interaction_stats(self, mock_show): print_interaction_stats(train_data_df)