[ { "path": "CODE_OF_CONDUCT.md", "content": "## Code of Conduct\nThis project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).\nFor more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact\nopensource-codeofconduct@amazon.com with any additional questions or comments.\n" }, { "path": "CONTRIBUTING.md", "content": "# Contributing Guidelines\n\nThank you for your interest in contributing to our project. Whether it's a bug report, new feature, correction, or additional\ndocumentation, we greatly value feedback and contributions from our community.\n\nPlease read through this document before submitting any issues or pull requests to ensure we have all the necessary\ninformation to effectively respond to your bug report or contribution.\n\n\n## Reporting Bugs/Feature Requests\n\nWe welcome you to use the GitHub issue tracker to report bugs or suggest features.\n\nWhen filing an issue, please check existing open, or recently closed, issues to make sure somebody else hasn't already\nreported the issue. Please try to include as much information as you can. Details like these are incredibly useful:\n\n* A reproducible test case or series of steps\n* The version of our code being used\n* Any modifications you've made relevant to the bug\n* Anything unusual about your environment or deployment\n\n\n## Contributing via Pull Requests\nContributions via pull requests are much appreciated. Before sending us a pull request, please ensure that:\n\n1. You are working against the latest source on the *main* branch.\n2. You check existing open, and recently merged, pull requests to make sure someone else hasn't addressed the problem already.\n3. You open an issue to discuss any significant work - we would hate for your time to be wasted.\n\nTo send us a pull request, please:\n\n1. Fork the repository.\n2. Modify the source; please focus on the specific change you are contributing. If you also reformat all the code, it will be hard for us to focus on your change.\n3. Ensure local tests pass.\n4. Commit to your fork using clear commit messages.\n5. Send us a pull request, answering any default questions in the pull request interface.\n6. Pay attention to any automated CI failures reported in the pull request, and stay involved in the conversation.\n\nGitHub provides additional document on [forking a repository](https://help.github.com/articles/fork-a-repo/) and\n[creating a pull request](https://help.github.com/articles/creating-a-pull-request/).\n\n\n## Finding contributions to work on\nLooking at the existing issues is a great way to find something to contribute on. As our projects, by default, use the default GitHub issue labels (enhancement/bug/duplicate/help wanted/invalid/question/wontfix), looking at any 'help wanted' issues is a great place to start.\n\n\n## Code of Conduct\nThis project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).\nFor more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact\nopensource-codeofconduct@amazon.com with any additional questions or comments.\n\n\n## Security issue notifications\nIf you discover a potential security issue in this project we ask that you notify AWS/Amazon Security via our [vulnerability reporting page](http://aws.amazon.com/security/vulnerability-reporting/). Please do **not** create a public github issue.\n\n\n## Licensing\n\nSee the [LICENSE](LICENSE) file for our project's licensing. We will ask you to confirm the licensing of your contribution.\n" }, { "path": "LICENSE", "content": "MIT License\n\nCopyright (c) 2021-2022 Prince Grover\nCopyright (c) 2021-2022 Zheng Li\nCopyright (c) 2022 Jianbo Liu\nCopyright (c) 2022 Jakub Zablocki\nCopyright (c) 2022 Jianbo Liu\nCopyright (c) 2022 Hao Zhou\nCopyright (c) 2022 Julia Xu\nCopyright (c) 2022 Anqi Cheng\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "README.md", "content": "# FDB: Fraud Dataset Benchmark\n\n*By [Prince Grover](groverpr), [Zheng Li](zhengli0817), [Julia Xu](SheliaXin), [Justin Tittelfitz](jtittelfitz), Anqi Cheng, [Jakub Zablocki](qbaza), Jianbo Liu, and [Hao Zhou](haozhouamzn)*\n\n\n[![made-with-python](https://img.shields.io/badge/Made%20with-Python-1f425f.svg?color=purple)](https://www.python.org/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) \n\n\nThe **Fraud Dataset Benchmark (FDB)** is a compilation of publicly available datasets relevant to **fraud detection** ([arXiv Link](https://arxiv.org/abs/2208.14417)). The FDB aims to cover a wide variety of fraud detection tasks, ranging from card not present transaction fraud, bot attacks, malicious traffic, loan risk and content moderation. The Python based data loaders from FDB provide dataset loading, standardized train-test splits and performance evaluation metrics. The goal of our work is to provide researchers working in the field of fraud and abuse detection a standardized set of benchmarking datasets and evaluation tools for their experiments. Using FDB tools we We demonstrate several applications of FDB that are of broad interest for fraud detection, including feature engineering, comparison of supervised learning algorithms, label noise removal, class-imbalance treatment and semi-supervised learning. \n\n\n## Datasets used in FDB\nBrief summary of the datasets used in FDB. Each dataset is described in detail in [data source section](#data-sources).\n\n| **#** | **Dataset name** | **Dataset key** | **Fraud category** | **#Train** | **#Test** | **Class ratio (train)** | **#Feats** | **#Cat** | **#Num** | **#Text** | **#Enrichable** |\n|-------|------------------------------------------------------------|-----------------|-------------------------------------|------------|-----------|-------------------------|------------|----------|----------|-----------|-----------------|\n| 1 | IEEE-CIS Fraud Detection | ieeecis | Card Not Present Transactions Fraud | 561,013 | 28,527 | 3.50% | 67 | 6 | 61 | 0 | 0 |\n| 2 | Credit Card Fraud Detection | ccfraud | Card Not Present Transactions Fraud | 227,845 | 56,962 | 0.18% | 28 | 0 | 28 | 0 | 0 |\n| 3 | Fraud ecommerce | fraudecom | Card Not Present Transactions Fraud | 120,889 | 30,223 | 10.60% | 6 | 2 | 3 | 0 | 1 |\n| 4 | Simulated Credit Card Transactions generated using Sparkov | sparknov | Card Not Present Transactions Fraud | 1,296,675 | 20,000 | 5.70% | 17 | 10 | 6 | 1 | 0 |\n| 5 | Twitter Bots Accounts | twitterbot | Bot Attacks | 29,950 | 7,488 | 33.10% | 16 | 6 | 6 | 4 | 0 |\n| 6 | Malicious URLs dataset | malurl | Malicious Traffic | 586,072 | 65,119 | 34.20% | 2 | 0 | 1 | 1 | 0 |\n| 7 | Fake Job Posting Prediction | fakejob | Content Moderation | 14,304 | 3,576 | 4.70% | 16 | 10 | 1 | 5 | 0 |\n| 8 | Vehicle Loan Default Prediction | vehicleloan | Credit Risk | 186,523 | 46,631 | 21.60% | 38 | 13 | 22 | 3 | 0 |\n| 9 | IP Blocklist | ipblock | Malicious Traffic | 172,000 | 43,000 | 7% | 1 | 0 | 0 | 0 | 1 |\n\n\n## Installation\n\n### Requirements\n- Kaggle account\n - **Important**: `ieeecis` dataset requires you to [**join IEEE-CIS competetion**](https://www.kaggle.com/competitions/ieee-fraud-detection/overview) from your Kaggle account, before you can call fdb API. Otherwise you will get ApiException: (403).\n- AWS account\n- Python 3.7+ \n\n- Python requirements\n```\nautogluon==0.4.2\nh2o==3.36.1.2\nboto3==1.20.21\nclick==8.0.3\nclick-plugins==1.1.1\nFaker==4.14.2\njoblib==1.0.0\nkaggle==1.5.12\nnumpy==1.19.5\npandas==1.1.2\nregex==2020.7.14\nscikit-learn==0.22.1\nscipy==1.5.4\nauto-sklearn==0.14.7\ndask==2022.8.1\n```\n\n### Step 1: Setup Kaggle CLI\nThe `FraudDatasetBenchmark` object is going to load datasets from the source (which in most of the cases is Kaggle), and then it will modify/standardize on the fly, and provide train-test splits. So, the first step is to setup Kaggle CLI in the machine being used to run Python.\n\nUse intructions from [How to Use Kaggle](https://www.kaggle.com/docs/api) guide. The steps include:\n\nRemember to download the authentication token from \"My Account\" on Kaggle, and save token at `~/.kaggle/kaggle.json` on Linux, OSX and at `C:\\Users.kaggle\\kaggle.json` on Windows. If the token is not there, an error will be raised. Hence, once you’ve downloaded the token, you should move it from your Downloads folder to this folder.\n \n \n#### Step 1.2. [Join IEEE-CIS competetion](https://www.kaggle.com/competitions/ieee-fraud-detection/overview) from your Kaggle account, before you can call `fdb.datasets` with `ieeecis`. Otherwise you will get ApiException: (403).\n \n \n### Step 2: Clone Repo\nOnce Kaggle CLI is setup and installed, clone the github repo using `git clone https://github.com/amazon-research/fraud-dataset-benchmark.git` if using HTTPS, or `git clone git@github.com:amazon-research/fraud-dataset-benchmark.git` if using SSH. \n\n### Step 3: Install\nOnce repo is cloned, from your terminal, `cd` to the repo and type `pip install .`, which will install the required classes and methods.\n\n\n## FraudDatasetBenchmark Usage\nThe usage is straightforward, where you create a `dataset` object of `FraudDatasetBenchmark` class, and extract useful goodies like train/test splits and eval_metrics. \n\n**Important note**: If you are running multiple experiments that require re-loading dataframes multiple times, default setting of downloading from Kaggle before loading into dataframe exceed the account level API limits. So, use the setting to persist the downloaded dataset and then load from the persisted data. During the first call of FraudDatasetBenchmark(), use `load_pre_downloaded=False, delete_downloaded=False` and for subsequent calls, use `load_pre_downloaded=True, delete_downloaded=False`. The default setting is \n`load_pre_downloaded=False, delete_downloaded=True`\n```\nfrom fdb.datasets import FraudDatasetBenchmark\n\n# all_keys = ['fakejob', 'vehicleloan', 'malurl', 'ieeecis', 'ccfraud', 'fraudecom', 'twitterbot', 'ipblock'] \nkey = 'ipblock'\n\nobj = FraudDatasetBenchmark(\n key=key,\n load_pre_downloaded=False, # default\n delete_downloaded=True, # default\n add_random_values_if_real_na = { \n \"EVENT_TIMESTAMP\": True, \n \"LABEL_TIMESTAMP\": True,\n \"ENTITY_ID\": True,\n \"ENTITY_TYPE\": True,\n \"ENTITY_ID\": True,\n \"EVENT_ID\": True\n } # default\n )\nprint(obj.key)\n\nprint('Train set: ')\ndisplay(obj.train.head())\nprint(len(obj.train.columns))\nprint(obj.train.shape)\n\nprint('Test set: ')\ndisplay(obj.test.head())\nprint(obj.test.shape)\n\nprint('Test scores')\ndisplay(obj.test_labels.head())\nprint(obj.test_labels['EVENT_LABEL'].value_counts())\nprint(obj.train['EVENT_LABEL'].value_counts(normalize=True))\nprint('=========')\n\n``` \nNotebook template to load dataset using FDB data-loader is available at [scripts/examples/Test_FDB_Loader.ipynb](scripts/examples/Test_FDB_Loader.ipynb)\n\n## Reproducibility\nReproducibility scripts are available at [scripts/reproducibility/](scripts/reproducibility/) in respective folders for [afd](scripts/reproducibility/afd), [autogluon](scripts/reproducibility/autogluon) and [h2o](scripts/reproducibility/h2o). Each folder also had README with steps to reproduce.\n\n\n## Benchmark Results\n\n\n\n| **Dataset key** | **AUC-ROC** | | | | |\n|:---------------:|:-----------:|:-----------:|:-------------:|:----------------:|:----------------:|\n| | **AFD OFI** | **AFD TFI** | **AutoGluon** | **H2O** | **Auto-sklearn** |\n| ccfraud | 0.985 | 0.99 | 0.99 | **0.992** | 0.988 |\n| fakejob | 0.987 | - | **0.998** | 0.99 | 0.983 |\n| fraudecom | 0.519 | **0.636** | 0.522 | 0.518 | 0.515 |\n| ieeecis | 0.938 | **0.94** | 0.855 | 0.89 | 0.932 |\n| malurl | 0.985 | - | **0.998** | Training failure | 0.5 |\n| sparknov | **0.998** | - | 0.997 | 0.997 | 0.995 |\n| twitterbot | 0.934 | - | **0.943** | 0.938 | 0.936 |\n| vehicleloan | **0.673** | - | 0.669 | 0.67 | 0.664 |\n| ipblock | **0.937** | - | 0.804 | Training failure | 0.5 |\n\n### ROC Curves\n\nThe numbers in the legend represent AUC-ROC from different models from our baseline evaluations on AutoML. \n![roc curves](images/all_fdb.png)\n\n\n## Data Sources\n\n\n1. **IEEE-CIS Fraud Detection**\n - Source URL: https://www.kaggle.com/c/ieee-fraud-detection/overview\n - Source license: https://www.kaggle.com/competitions/ieee-fraud-detection/rules\n - Variables: Anonymized product, card, address, email domain, device, transaction date information. Numeric columns with name prefixes as V, C, D and M, and meaning hidden from public.\n - Fraud category: Card Not Present Transaction Fraud\n - Provider: [Vesta Corporation](https://www.vesta.io/)\n - Release date: 2019-10-03\n - Description: Prepared by IEEE Computational Intelligence Society, this card-non-present transaction fraud dataset was launched during IEEE-CIS Fraud Detection Kaggle competition, and was provided by Vesta Corporation. The original dataset contains 393 features which are reduced to 67 features in the benchmark. Feature selection was performed based on highly voted Kaggle kernels. The fraud rate in training segment of source dataset is 3.5%. We only used training files (train transaction and train identity) containing 590,540 transactions in the benchmark, and split that into train (95%) and test (5%) segments based on time. Based on the insights from a Kaggle kernel written by the competition winner, we added UUID (called it as ENTITY_ID) that represents a fingerprint and was created using card, address, time and D1 features.\n\n2. **Credit Card Fraud Detection**\n - Source URL: https://www.kaggle.com/mlg-ulb/creditcardfraud/\n - Source license: https://opendatacommons.org/licenses/dbcl/1-0/\n - Variables: PCA transformed features, time, amount (highly imbalanced)\n - Fraud category: Card Not Present Transaction Fraud\n - Provider: [Machine Learning Group - ULB](https://mlg.ulb.ac.be/)\n - Release date: 2018-03-23\n - Description: This dataset contains anonymized credit card transactions by European cardholders in September 2013. The dataset contains 492 frauds out of 284,807 transactions over 2 days. Data only contains numerical features that are the result of a PCA transformation, plus non transformed time and amount.\n\n3. **Fraud ecommerce**\n - Source URL: https://www.kaggle.com/vbinh002/fraud-ecommerce\n - Source license: None\n - Variables: The features include sign up time, purchase time, purchase value, device id, user id, browser, and IP address. We added a new feature that measured the time difference between sign up and purchase, as the age of an account is often an important variable in fraud detection.\n - Fraud category: Card Not Present Transaction Fraud\n - Provider: [Binh Vu](https://www.kaggle.com/vbinh002) \n - Release date: 2018-12-09\n - Description: This dataset contains ~150k e-commerce transactions.\n\n4. **Simulated Credit Card Transactions generated using Sparkov**\n - Source URL: https://www.kaggle.com/kartik2112/fraud-detection\n - Source license: https://creativecommons.org/publicdomain/zero/1.0/\n - Variables: Transaction date, credit card number, merchant, category, amount, name, street, gender. All variables are synthetically generated using the Sparknov tool.\n - Fraud category: Card Not Present Transaction Fraud\n - Provider: [Kartik Shenoy](https://www.kaggle.com/kartik2112)\n - Release date: 2020-08-05\n - Description: This is a simulated credit card transaction dataset. The dataset was generated using Sparkov Data Generation tool and we modified a version of dataset created for Kaggle. It covers transactions of 1000 customers with a pool of 800 merchants over 6 months. We used both train and test segments directly from the source and randomly down sampled test segment.\n\n5. **Twitter Bots Accounts**\n - Source URL: https://www.kaggle.com/code/davidmartngutirrez/bots-accounts-eda/data?select=twitter_human_bots_dataset.csv\n - Source license: https://creativecommons.org/publicdomain/zero/1.0/\n - Variables: Features like account creation date, follower and following counts, profile description, account age, meta data about profile picture and account activity, and a label indicating whether the account is human or bot.\n - Fraud category: Bot Attacks\n - Provider: [David Martín Gutiérrez](https://www.kaggle.com/davidmartngutirrez)\n - Release date: 2020-08-20\n - Description: The dataset composes of 37,438 rows corresponding to different user accounts from Twitter.\n\n6. **Malicious URLs dataset**\n - Source URL: https://www.kaggle.com/datasets/sid321axn/malicious-urls-dataset\n - Source license: https://creativecommons.org/publicdomain/zero/1.0/\n - Variables: The Kaggle dataset is curated using five different sources, and contains url and type. Even though original dataset has multiclass label (type), we converted it into binary label.\n - Fraud category: Malicious Traffic\n - Provider: [Manu Siddhartha](https://www.kaggle.com/sid321axn) \n - Release date: 2021-07-23\n - Description: The Kaggle dataset is curated using five different sources, and contains url and type. Even though original dataset has multiclass label (type), we converted it into binary label. There is no timestamp information from the source. Therefore, we generate a dummy timestamp column for consistency.\n\n7. **Real / Fake Job Posting Prediction**\n - Source URL: https://www.kaggle.com/shivamb/real-or-fake-fake-jobposting-prediction\n - Source license: https://creativecommons.org/publicdomain/zero/1.0/\n - Variables: Title, location, department, company, salary range, requirements, description, benefits, telecommuting. Most of the variables are categorical and free form text in nature.\n - Fraud category: Content Moderation\n - Provider: [Shivam Bansal](https://www.kaggle.com/shivamb) \n - Release date: 2020-02-29\n - Description: This Kaggle dataset contains 18K job descriptions out of which about 800 are fake. The data consists of both textual information and meta-information about the jobs. The task is to train classification model to detect which job posts are fraudulent.\n\n8. **Vehicle Loan Default Prediction**\n - Source URL: https://www.kaggle.com/avikpaul4u/vehicle-loan-default-prediction\n - Source license: Unknown\n - Variables: Loanee information, loan information, credit bureau data, and history.\n - Fraud category: Credit Risk\n - Provider: [Avik Paul](https://www.kaggle.com/avikpaul4u) \n - Release date: 2019-11-12\n - Description: The task in this dataset is to determine the probability of vehicle loan default, particularly the risk of default on the first monthly installments. It contains data for 233k loans with 21.7% default rate.\n \n9. **IP Blocklist**\n - Source URL: http://cinsscore.com/list/ci-badguys.txt\n - Source license: Unknown\n - Variables: The dataset contains IP address and label telling malicious or fake. A dummy categorical variable that has no relation label is added.\n - Fraud category: Malicious Traffic\n - Provider: [CINSscore.com](http://cinsscore.com)\n - Release date: 2017-09-25\n - Description: This dataset is made up from malicious IP address from cinsscore.com. To the list of malicious IP addresses, we added randomly generated IP address using Faker labeled as benign.\n \n\n## Citation\n```\n@misc{grover2023fraud,\n title={Fraud Dataset Benchmark and Applications}, \n author={Prince Grover and Julia Xu and Justin Tittelfitz and Anqi Cheng and Zheng Li and Jakub Zablocki and Jianbo Liu and Hao Zhou},\n year={2023},\n eprint={2208.14417},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n```\n\n## License\nThis project is licensed under the MIT-0 License.\n\n\n## Acknowledgement\nWe thank creators of all datasets used in the benchmark and organizations that have helped in hosting the datasets and making them widely availabel for research purposes. \n\n\n\n\n\n" }, { "path": "scripts/examples/Test_FDB_Loader.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"sys.path.append('../../src/')\\n\",\n \"from fdb.datasets import FraudDatasetBenchmark\\n\",\n \"from fdb.kaggle_configs import KAGGLE_CONFIGS\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Notebook setups\\n\",\n \"\\n\",\n \"import os\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"from io import StringIO\\n\",\n \"\\n\",\n \"from IPython.core.display import display, HTML\\n\",\n \"from IPython.display import clear_output\\n\",\n \"display(HTML(\\\"\\\"))\\n\",\n \"pd.set_option('display.max_columns', None)\\n\",\n \"pd.set_option('display.max_colwidth', 200)\\n\",\n \"pd.set_option('display.max_rows', 500)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import shutil\\n\",\n \"\\n\",\n \"if os.path.exists('tmp'):\\n\",\n \" shutil.rmtree('tmp')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# UNCOMMENT IF YOU NEED TO UPLOAD DATA TO AN S3 BUCKET IN YOUR ACCOUNT\\n\",\n \"\\n\",\n \"# import boto3\\n\",\n \"# BUCKET=''\\n\",\n \"\\n\",\n \"# def _s3_upload(df):\\n\",\n \"# csv_memory=StringIO()\\n\",\n \"# df.to_csv(csv_memory, index=False)\\n\",\n \"# content = csv_memory.getvalue()\\n\",\n \"# s3_client.put_object(\\n\",\n \"# Body=content,\\n\",\n \"# Bucket=BUCKET,\\n\",\n \"# Key=KEY,\\n\",\n \"# ACL='bucket-owner-full-control')\\n\",\n \"# s3_client = boto3.client('s3')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# All options for keys\\n\",\n \"all_keys = ['fakejob', 'vehicleloan', 'malurl', 'ieeecis', 'ccfraud','fraudecom', 'twitterbot', 'ipblock']\\n\",\n \"# all_keys = ['ipblock']\\n\",\n \"# all_keys = ['twitterbot']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Default setting\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Default setting pulls data from the source in your system, modified the data and adds random values for columns that are missing, if add_random_values_if_real_na flags are True.\\n\",\n \"\\n\",\n \"Defalt parameters: \\n\",\n \"- load_pre_downloaded: False\\n\",\n \"- delete_downloaded: True\\n\",\n \"- add_random_values_if_real_na = ```\\n\",\n \"{\\n\",\n \"\\\"EVENT_TIMESTAMP\\\": True,\\n\",\n \"\\\"LABEL_TIMESTAMP\\\": True,\\n\",\n \"\\\"ENTITY_ID\\\": True,\\n\",\n \"\\\"ENTITY_TYPE\\\": True,\\n\",\n \"\\\"EVENT_ID\\\": True\\n\",\n \"}\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Data download location /Users/pringrov/Documents/git/fraud-dataset-benchmark/scripts/examples/tmp\\n\",\n \"fakejob\\n\",\n \"Train set: \\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
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13001301Part-Time Finance AssistantGB, LND,NaNNaNNaNSalary:£9 - £10 per hour We are currently going through an exciting period of change and a new client base, resulting in this part-time finance position being created. You will offer a flexible, a...Your role will be a varied, interesting and interactive role, and will likely to be approximately 15-20 hours per week (sometimes more) and will include: - Book-keeping via Sage Line 50 - Bank rec...Salary:£9 - £10 per hour000Part-timeNaNNaNAccountingNaN005a5dbdb-9778-4e4a-b967-7850dd483a542022-08-28T17:32:28Z2023-05-05T08:46:09Zuser
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010Customer Service Associate - Part TimeUS, AZ, PhoenixNaNNaNNovitex Enterprise Solutions, formerly Pitney Bowes Management Services, delivers innovative document and communications management solutions that help companies around the world drive business pr...The Customer Service Associate will be based in Phoenix, AZ. The right candidate will be an integral part of our talented team, supporting our continued growth.Responsibilities:Perform various Mai...Minimum Requirements:Minimum of 6 months customer service related experience requiredHigh school diploma or equivalent (GED) requiredValid Driver's License and good driving record requiredPreferre...NaN010Part-timeEntry levelHigh School or equivalentFinancial ServicesCustomer Service1743dd4b-f989-4227-8480-cbafa760b4de2022-12-31T18:14:06Zuser
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216VP of Sales - Vault DragonSG, 01, SingaporeSales120000-150000Jungle Ventures is the leading Singapore based, entrepreneur backed, venture capital firm, that funds and actively supports start-ups in scaling across Asia Pacific. We pride ourselves on leading ...About Vault Dragon Vault Dragon is Dropbox for your physical stuff - a startup that is changing the aesthetic face of Singapore by creating more space in households and offices. We also save count...Key Superpowers3-5 years of high-pressure sales experience, but if you absorb knowledge like a sponge and keep getting promoted we are flexiblePreferably mastery of both phone and field sales for ...Basic: SGD 120,000Equity negotiable for a rock starGround floor opportunity to make a difference and do things as Dean said \\\"my way\\\"Hire and train your own superhero sales team, the way you wantMa...011Full-timeExecutiveBachelor's DegreeFacilities ServicesSales298d3508-76bb-4362-9ad4-f843fa3f99fa2022-10-30T20:49:56Zuser
319Visual DesignerUS, NY, New YorkNaNNaNKettle is an independent digital agency based in New York City and the Bay Area. We’re committed to making digital do more — for both people and brands — because we believe the digital world offer...Kettle is hiring a Visual Designer!Job Location: New York, NYKettle is a growing digital agency focused on delivering outstanding products, and we’ve been working hard to find equally outstanding ...NaNNaN010NaNNaNNaNNaNNaNcad2f705-4b22-4110-bb06-b34a47c62a6d2022-05-30T19:30:26Zuser
421Marketing AssistantUS, TX, AustinNaNNaNIntelliBright was created to leverage enterprise level online business practices to generate exclusive leads on behalf of our medium and small business clients across a wide variety of verticals. ...IntelliBright is growing fast and is looking for a Marketing Assistant to join our team. Your invaluable input will help our small to midsize business clientele to achieve their greatest potential...Job RequirementsAssist in creating client online marketing campaignsConduct research on various industry niches to determine potential partnership opportunities and make decisions on which website...NaN010NaNNaNNaNNaNMarketing24c31ad9-95a9-479c-87c5-de6af06ddef62022-12-05T07:48:39Zuser
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ipEVENT_TIMESTAMPENTITY_TYPEEVENT_IDENTITY_IDdummy_cat
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\"\n ],\n \"text/plain\": [\n \" EVENT_LABEL EVENT_ID\\n\",\n \"0 1 c6bcbff7-7c2e-4780-8007-ed29cea7535b\\n\",\n \"1 1 3a1e89c3-9bd1-4f32-8b7e-69c97e2fba92\\n\",\n \"2 1 1a634e22-b87c-4981-ad66-587a82bfa6e8\\n\",\n \"3 1 dc3e7d0c-1bfd-49d6-ba7d-e9e6e325e8c3\\n\",\n \"4 1 59012ee9-8f28-48f2-a133-10cd558ed319\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0 40003\\n\",\n \"1 2997\\n\",\n \"Name: EVENT_LABEL, dtype: int64\\n\",\n \"0 0.930215\\n\",\n \"1 0.069785\\n\",\n \"Name: EVENT_LABEL, dtype: float64\\n\",\n \"========= \\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# for key, val in KAGGLE_CONFIGS.items():\\n\",\n \"for key in all_keys:\\n\",\n \" obj = FraudDatasetBenchmark(key=key, )\\n\",\n \" print(obj.key)\\n\",\n \" print('Train set: ')\\n\",\n \" display(obj.train.head())\\n\",\n \" print(len(obj.train.columns))\\n\",\n \" print(obj.train.shape)\\n\",\n \" print('Test set: ')\\n\",\n \" display(obj.test.head())\\n\",\n \" print(obj.test.shape)\\n\",\n \" print('Test scores')\\n\",\n \" display(obj.test_labels.head())\\n\",\n \" print(obj.test_labels['EVENT_LABEL'].value_counts())\\n\",\n \" print(obj.train['EVENT_LABEL'].value_counts(normalize=True))\\n\",\n \" print('=========','\\\\n')\\n\",\n \"\\n\",\n \"# KEY= f'public/official-dataset-names/{val[\\\"name\\\"]}/train.csv'\\n\",\n \"# _s3_upload(obj.train)\\n\",\n \"\\n\",\n \"\\n\",\n \"# KEY= f'public/official-dataset-names/{val[\\\"name\\\"]}/test.csv'\\n\",\n \"# _s3_upload(obj.test)\\n\",\n \"\\n\",\n \"\\n\",\n \"# KEY= f'public/official-dataset-names/{val[\\\"name\\\"]}/test_labels.csv'\\n\",\n \"# _s3_upload(obj.test_labels)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Without random values in missing columns\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Parameter settings\\n\",\n \"\\n\",\n \"- load_pre_downloaded: False\\n\",\n \"- delete_downloaded: True\\n\",\n \"- add_random_values_if_real_na = ```\\n\",\n \"{\\n\",\n \"\\\"EVENT_TIMESTAMP\\\": False,\\n\",\n \"\\\"LABEL_TIMESTAMP\\\": False,\\n\",\n \"\\\"ENTITY_ID\\\": False,\\n\",\n \"\\\"ENTITY_TYPE\\\": False,\\n\",\n \"\\\"EVENT_ID\\\": False\\n\",\n \"}\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"all_keys = ['ccfraud']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Data download location /Users/pringrov/Documents/git/fraud-dataset-benchmark/scripts/examples/tmp\\n\",\n \"ccfraud\\n\",\n \"Train set: \\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" EVENT_LABEL\\n\",\n \"227845 0\\n\",\n \"227846 0\\n\",\n \"227847 0\\n\",\n \"227848 0\\n\",\n \"227849 0\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0 56887\\n\",\n \"1 75\\n\",\n \"Name: EVENT_LABEL, dtype: int64\\n\",\n \"0 0.99817\\n\",\n \"1 0.00183\\n\",\n \"Name: EVENT_LABEL, dtype: float64\\n\",\n \"========= \\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"for key in all_keys:\\n\",\n \" obj = FraudDatasetBenchmark(key=key, \\n\",\n \" add_random_values_if_real_na = { \\\"EVENT_TIMESTAMP\\\": False, \\n\",\n \" \\\"LABEL_TIMESTAMP\\\": False,\\n\",\n \" \\\"ENTITY_ID\\\": False,\\n\",\n \" \\\"ENTITY_TYPE\\\": False,\\n\",\n \" \\\"EVENT_ID\\\": False})\\n\",\n \" print(obj.key)\\n\",\n \" print('Train set: ')\\n\",\n \" display(obj.train.head())\\n\",\n \" print(len(obj.train.columns))\\n\",\n \" print(obj.train.shape)\\n\",\n \" print('Test set: ')\\n\",\n \" display(obj.test.head())\\n\",\n \" print(obj.test.shape)\\n\",\n \" print('Test scores')\\n\",\n \" display(obj.test_labels.head())\\n\",\n \" print(obj.test_labels['EVENT_LABEL'].value_counts())\\n\",\n \" print(obj.train['EVENT_LABEL'].value_counts(normalize=True))\\n\",\n \" print('=========','\\\\n')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Persisting downloaded data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Important**: If you are running multiple experiments, download from Kaggle multiple times might exceed account level API call limits. So persisting the downloaded dataset is recommended in such scenarios\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### First download but not delete the data \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Parameter settings\\n\",\n \"\\n\",\n \"- load_pre_downloaded: False\\n\",\n \"- delete_downloaded: False\\n\",\n \"- add_random_values_if_real_na = ```\\n\",\n \"{\\n\",\n \"\\\"EVENT_TIMESTAMP\\\": False,\\n\",\n \"\\\"LABEL_TIMESTAMP\\\": False,\\n\",\n \"\\\"ENTITY_ID\\\": False,\\n\",\n \"\\\"ENTITY_TYPE\\\": True,\\n\",\n \"\\\"EVENT_ID\\\": True\\n\",\n \"}\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"all_keys = ['twitterbot']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Data download location /Users/pringrov/Documents/git/fraud-dataset-benchmark/scripts/examples/tmp\\n\",\n \"twitterbot\\n\",\n \"Train set: \\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
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20963209632013-05-27 21:22:15TrueFalseWHO is Kyle Tyrone Ferguson? Father, Husband, Friend, Nerd, Teacher, Black man... Into Superheroes, MTG, Deck building games, tv, movies, and other geekery.3237423952823True1463172686enMount Morris, MIhttp://abs.twimg.com/images/themes/theme1/bg.pnghttp://pbs.twimg.com/profile_images/1022468674169569280/7tpDfAwa_normal.jpgblerdwords11448False4.33626400user
633163312009-09-14 18:58:36FalseFalseComedian, writer, former TV news producer, drunk historian, Twitter Queen, asshole and owner of Dewey. @doinitpodcast host Bookings: smark@wmeagency.com @Cameo686643507891528False74231747enLos Angeles - Always a Texanhttp://abs.twimg.com/images/themes/theme9/bg.gifhttp://pbs.twimg.com/profile_images/772455794243571712/bGBBHx0N_normal.jpgJennyJohnsonHi518732True4.69439910user
17209172092010-06-06 16:27:08TrueFalseNaN7454657False152688783NaNAbu Dhabihttp://abs.twimg.com/images/themes/theme1/bg.pnghttp://pbs.twimg.com/profile_images/968278541/For-Twitter_normal.pngAbnerJosh161False0.04337261user
23964239642010-06-22 21:56:09FalseFalseInformation and updates from southern Saskatchewan from the hardworking journalists and photographers who make up the Global Regina newsroom.151755881991True158502985enRegina, SK Canadahttp://abs.twimg.com/images/themes/theme1/bg.pnghttp://pbs.twimg.com/profile_images/722495430097903616/dKLfuc1-_normal.jpgGlobalRegina103379True27.86537100user
30569305692009-03-10 02:26:45FalseFalseDetritus26161118405657False23544268nounknownhttp://abs.twimg.com/images/themes/theme11/bg.gifhttp://pbs.twimg.com/profile_images/1281416121212493826/HVjvkjRz_normal.jpgOfficialKat4980True1.19141800user
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20963209632013-05-27 21:22:15TrueFalseWHO is Kyle Tyrone Ferguson? Father, Husband, Friend, Nerd, Teacher, Black man... Into Superheroes, MTG, Deck building games, tv, movies, and other geekery.3237423952823True1463172686enMount Morris, MIhttp://abs.twimg.com/images/themes/theme1/bg.pnghttp://pbs.twimg.com/profile_images/1022468674169569280/7tpDfAwa_normal.jpgblerdwords11448False4.33626400user
633163312009-09-14 18:58:36FalseFalseComedian, writer, former TV news producer, drunk historian, Twitter Queen, asshole and owner of Dewey. @doinitpodcast host Bookings: smark@wmeagency.com @Cameo686643507891528False74231747enLos Angeles - Always a Texanhttp://abs.twimg.com/images/themes/theme9/bg.gifhttp://pbs.twimg.com/profile_images/772455794243571712/bGBBHx0N_normal.jpgJennyJohnsonHi518732True4.69439910user
17209172092010-06-06 16:27:08TrueFalseNaN7454657False152688783NaNAbu Dhabihttp://abs.twimg.com/images/themes/theme1/bg.pnghttp://pbs.twimg.com/profile_images/968278541/For-Twitter_normal.pngAbnerJosh161False0.04337261user
23964239642010-06-22 21:56:09FalseFalseInformation and updates from southern Saskatchewan from the hardworking journalists and photographers who make up the Global Regina newsroom.151755881991True158502985enRegina, SK Canadahttp://abs.twimg.com/images/themes/theme1/bg.pnghttp://pbs.twimg.com/profile_images/722495430097903616/dKLfuc1-_normal.jpgGlobalRegina103379True27.86537100user
30569305692009-03-10 02:26:45FalseFalseDetritus26161118405657False23544268nounknownhttp://abs.twimg.com/images/themes/theme11/bg.gifhttp://pbs.twimg.com/profile_images/1281416121212493826/HVjvkjRz_normal.jpgOfficialKat4980True1.19141800user
\\n\",\n \"
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Into Superheroes, MTG, Deck building games, tv, movies, and other geekery. \\n\",\n \"6331 Comedian, writer, former TV news producer, drunk historian, Twitter Queen, asshole and owner of Dewey. @doinitpodcast host Bookings: smark@wmeagency.com @Cameo \\n\",\n \"17209 NaN \\n\",\n \"23964 Information and updates from southern Saskatchewan from the hardworking journalists and photographers who make up the Global Regina newsroom. \\n\",\n \"30569 Detritus \\n\",\n \"\\n\",\n \" favourites_count followers_count friends_count geo_enabled EVENT_ID \\\\\\n\",\n \"20963 32374 2395 2823 True 1463172686 \\n\",\n \"6331 68664 350789 1528 False 74231747 \\n\",\n \"17209 74 54 657 False 152688783 \\n\",\n \"23964 1517 55881 991 True 158502985 \\n\",\n \"30569 2616 1118405 657 False 23544268 \\n\",\n \"\\n\",\n \" lang location \\\\\\n\",\n \"20963 en Mount Morris, MI \\n\",\n \"6331 en Los Angeles - Always a Texan \\n\",\n \"17209 NaN Abu Dhabi \\n\",\n \"23964 en Regina, SK Canada \\n\",\n \"30569 no unknown \\n\",\n \"\\n\",\n \" profile_background_image_url \\\\\\n\",\n \"20963 http://abs.twimg.com/images/themes/theme1/bg.png \\n\",\n \"6331 http://abs.twimg.com/images/themes/theme9/bg.gif \\n\",\n \"17209 http://abs.twimg.com/images/themes/theme1/bg.png \\n\",\n \"23964 http://abs.twimg.com/images/themes/theme1/bg.png \\n\",\n \"30569 http://abs.twimg.com/images/themes/theme11/bg.gif \\n\",\n \"\\n\",\n \" profile_image_url \\\\\\n\",\n \"20963 http://pbs.twimg.com/profile_images/1022468674169569280/7tpDfAwa_normal.jpg \\n\",\n \"6331 http://pbs.twimg.com/profile_images/772455794243571712/bGBBHx0N_normal.jpg \\n\",\n \"17209 http://pbs.twimg.com/profile_images/968278541/For-Twitter_normal.png \\n\",\n \"23964 http://pbs.twimg.com/profile_images/722495430097903616/dKLfuc1-_normal.jpg \\n\",\n \"30569 http://pbs.twimg.com/profile_images/1281416121212493826/HVjvkjRz_normal.jpg \\n\",\n \"\\n\",\n \" screen_name statuses_count verified average_tweets_per_day \\\\\\n\",\n \"20963 blerdwords 11448 False 4.336 \\n\",\n \"6331 JennyJohnsonHi5 18732 True 4.694 \\n\",\n \"17209 AbnerJosh 161 False 0.043 \\n\",\n \"23964 GlobalRegina 103379 True 27.865 \\n\",\n \"30569 OfficialKat 4980 True 1.191 \\n\",\n \"\\n\",\n \" account_age_days EVENT_LABEL ENTITY_TYPE \\n\",\n \"20963 2640 0 user \\n\",\n \"6331 3991 0 user \\n\",\n \"17209 3726 1 user \\n\",\n \"23964 3710 0 user \\n\",\n \"30569 4180 0 user \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"21\\n\",\n \"(29950, 21)\\n\",\n \"========= \\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"for key in all_keys:\\n\",\n \" obj = FraudDatasetBenchmark(key=key, \\n\",\n \" load_pre_downloaded=True,\\n\",\n \" delete_downloaded=False,\\n\",\n \" add_random_values_if_real_na = { \\\"EVENT_TIMESTAMP\\\": False, \\n\",\n \" \\\"LABEL_TIMESTAMP\\\": False,\\n\",\n \" \\\"ENTITY_ID\\\": False,\\n\",\n \" \\\"ENTITY_TYPE\\\": True,\\n\",\n \" \\\"EVENT_ID\\\": True})\\n\",\n \" print(obj.key)\\n\",\n \" print('Train set: ')\\n\",\n \" display(obj.train.head())\\n\",\n \" print(len(obj.train.columns))\\n\",\n \" print(obj.train.shape)\\n\",\n \" print('=========','\\\\n')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# End\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "scripts/reproducibility/afd/README.md", "content": "## Steps to reproduce AFD models\nAmazon Fraud Detector (AFD) models can be either run via AWS Console or using API calls. In this folder, we provide scripts that make API calls to create model artifacts and then to score the model on test data.\n\nHigh level steps to train and deploy model are:\n\n![afd steps](../../../images/afd_steps.png)\n\nYou can use provided scripts to replicate performance shown in the benchmark.\n\n1. Setup AWS credentials in terminal for the AWS account where you want to run AFD, and store the data. You can use environment variables as [following](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-envvars.html)\n\n\n2. Use the [template data-loader notebook](../../examples/Test_FDB_Loader.ipynb) to upload the benchmark data on S3. (AFD requires data to be saved in S3 and require an S3 path) \n\n\n3. Create AFD resources including entities, event types, and model. Update values in `IAM_ROLE`, `BUCKET`, `KEY` and `MODEL_NAME` in the `create_afd_resources.py`, then run following.\n\n```\npython create_afd_resources.py configs/{dataset-you-want-to-use}\n```\n\nYou can keep `MODEL_TYPE` as **ONLINE_FRAUD_INSIGHTS** or **TRANSACTION_FRAUD_INSIGHTS** to run corresponding models.\n\nThis will initiate automatic model training. Wait for ~1 hour for models to train. You can check status in your console.\n\n4. Create detector and use it to score on the test data. Update values in `IAM_ROLE`, `BUCKET`, `TEST_PATH`, `TEST_LABELS_PATH` and `MODEL_NAME` in the `score_afd_resources.py`, then run following.\n\n```\npython score_afd_model.py\n```\nThis will print performance metrics in terminal as well as save in S3 location you provide in the script.\n\nAfter a model training is completed, AFD console would show performance metrics like following (trained on `ieeecis` with ONLINE_FRAUD_INSIGHTS).\n\n![ieee ofi sample](../../../images/ieee_ofi_sample.png)\n\n\n\n**In order to fully deep dive into working of Amazon Fraud Detector, [here](https://d1.awsstatic.com/fraud-detector/afd-technical-guide-detecting-new-account-fraud.pdf) is the link to technical guide.**\n\n" }, { "path": "scripts/reproducibility/afd/configs/CreditCardFraudDetection.json", "content": "{\n \"dataset\": \"Credit Card Fraud Detection\",\n \"variable_mappings\": [\n {\n \"variable_name\": \"v1\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v2\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v3\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v4\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v5\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v6\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v7\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v8\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v9\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v10\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v11\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v12\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v13\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v14\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v15\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v16\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v17\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v18\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v19\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v20\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v21\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v22\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v23\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v24\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v25\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v26\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v27\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v28\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"amount\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n }\n ],\n \"label_mappings\": {\n \"FRAUD\": [\n \"1\"\n ],\n \"LEGIT\": [\n \"0\"\n ]\n }\n}" }, { "path": "scripts/reproducibility/afd/configs/FakeJobPostingPrediction.json", "content": "{\n \"dataset\": \"Fake Job Posting Prediction\", \n \"variable_mappings\": [\n {\n \"variable_name\": \"title\",\n \"variable_type\": \"FREE_FORM_TEXT\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"location\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"department\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"salary_range\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"company_profile\",\n \"variable_type\": \"FREE_FORM_TEXT\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"description\",\n \"variable_type\": \"FREE_FORM_TEXT\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"requirements\",\n \"variable_type\": \"FREE_FORM_TEXT\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"benefits\",\n \"variable_type\": \"FREE_FORM_TEXT\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"telecommuting\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"has_company_logo\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"has_questions\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"employment_type\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"required_experience\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"required_education\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"industry\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"function\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n }\n ],\n \"label_mappings\": {\n \"FRAUD\": [\n \"1\"\n ],\n \"LEGIT\": [\n \"0\"\n ]\n }\n}" }, { "path": "scripts/reproducibility/afd/configs/Fraudecommerce.json", "content": "{\n \"dataset\": \"Fraud ecommerce\",\n \"variable_mappings\": [\n {\n \"variable_name\": \"purchase_value\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"source\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"browser\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"age\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"ip_address\",\n \"variable_type\": \"IP_ADDRESS\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"time_since_signup\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n }\n ],\n \"label_mappings\": {\n \"FRAUD\": [\n \"1\"\n ],\n \"LEGIT\": [\n \"0\"\n ]\n }\n}" }, { "path": "scripts/reproducibility/afd/configs/IEEECISFraudDetection.json", "content": "{\n \"dataset\": \"IEEE-CIS Fraud Detection\",\n \"variable_mappings\": [\n {\n \"variable_name\": \"transactionamt\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"productcd\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"card1\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"card2\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"card3\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"card5\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"card6\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"addr1\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"dist1\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"p_emaildomain\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"r_emaildomain\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"c1\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"c2\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"c4\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"c5\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"c6\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"c7\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"c8\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"c9\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"c10\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"c11\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"c12\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"c13\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"c14\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v62\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v70\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v76\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v78\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v82\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v91\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v127\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v130\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v139\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v160\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v165\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v187\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v203\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v207\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v209\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v210\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v221\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v234\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v257\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v258\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v261\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v264\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v266\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v267\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v271\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v274\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v277\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v283\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v285\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v289\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v291\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"v294\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"id_01\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"id_02\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"id_05\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"id_06\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"id_09\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"id_13\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"id_17\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"id_19\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"id_20\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"devicetype\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"deviceinfo\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n }\n ],\n \"label_mappings\": {\n \"FRAUD\": [\n \"1\"\n ],\n \"LEGIT\": [\n \"0\"\n ]\n }\n}" }, { "path": "scripts/reproducibility/afd/configs/IPBlocklist.json", "content": "{\n \"dataset\": \"IP-BlockList\",\n \"variable_mappings\": [\n {\n \"variable_name\": \"ip\",\n \"variable_type\": \"IP_ADDRESS\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"dummy_cat\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n }\n ],\n \"label_mappings\": {\n \"FRAUD\": [\n \"1\"\n ],\n \"LEGIT\": [\n \"0\"\n ]\n }\n}" }, { "path": "scripts/reproducibility/afd/configs/MaliciousURL.json", "content": "{\n \"dataset\": \"Malicious URLs Dataset\",\n \"variable_mappings\": [\n {\n \"variable_name\": \"url\",\n \"variable_type\": \"FREE_FORM_TEXT\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"dummy_cat\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n }\n ],\n \"label_mappings\": {\n \"FRAUD\": [\n \"malignant\"\n ],\n \"LEGIT\": [\n \"benign\"\n ]\n }\n}" }, { "path": "scripts/reproducibility/afd/configs/SimulatedCreditCardTransactionsSparkov.json", "content": "{\n \"dataset\": \"Simulated Credit Card Transactions generated using Sparkov\",\n \"variable_mappings\": [\n {\n \"variable_name\": \"cc_num\",\n \"variable_type\": \"CARD_BIN\",\n \"data_type\": \"INTEGER\"\n },\n {\n \"variable_name\": \"category\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"amt\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"first\",\n \"variable_type\": \"BILLING_NAME\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"last\",\n \"variable_type\": \"BILLING_NAME\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"gender\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"street\",\n \"variable_type\": \"BILLING_ADDRESS_L1\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"city\",\n \"variable_type\": \"BILLING_CITY\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"state\",\n \"variable_type\": \"BILLING_STATE\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"zip\",\n \"variable_type\": \"BILLING_ZIP\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"lat\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"long\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"city_pop\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"job\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"dob\",\n \"variable_type\": \"FREE_FORM_TEXT\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"merch_lat\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"merch_long\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n }\n ],\n \"label_mappings\": {\n \"FRAUD\": [\n \"1\"\n ],\n \"LEGIT\": [\n \"0\"\n ]\n }\n}" }, { "path": "scripts/reproducibility/afd/configs/TwitterBotAccounts.json", "content": "{\n \"dataset\": \"Twitter Bots Accounts\",\n \"variable_mappings\": [\n {\n \"variable_name\": \"default_profile\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"default_profile_image\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"description\",\n \"variable_type\": \"FREE_FORM_TEXT\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"favourites_count\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"followers_count\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"friends_count\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"geo_enabled\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"lang\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"location\",\n \"variable_type\": \"FREE_FORM_TEXT\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"profile_background_image_url\",\n \"variable_type\": \"FREE_FORM_TEXT\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"profile_image_url\",\n \"variable_type\": \"FREE_FORM_TEXT\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"screen_name\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"statuses_count\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"verified\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"average_tweets_per_day\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"account_age_days\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n }\n ],\n \"label_mappings\": {\n \"FRAUD\": [\n \"bot\"\n ],\n \"LEGIT\": [\n \"human\"\n ]\n }\n}" }, { "path": "scripts/reproducibility/afd/configs/VehicleLoanDefaultPrediction.json", "content": "{\n \"dataset\": \"Vehicle Loan Default Prediction\",\n \"variable_mappings\": [\n {\n \"variable_name\": \"disbursed_amount\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"asset_cost\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"ltv\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"branch_id\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"supplier_id\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"manufacturer_id\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"current_pincode_id\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"date_of_birth\",\n \"variable_type\": \"FREE_FORM_TEXT\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"employment_type\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"state_id\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"employee_code_id\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"mobileno_avl_flag\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"aadhar_flag\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"pan_flag\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"voterid_flag\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"driving_flag\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"passport_flag\",\n \"variable_type\": \"CATEGORICAL\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"perform_cns_score\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"perform_cns_score_description\",\n \"variable_type\": \"FREE_FORM_TEXT\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"pri_no_of_accts\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"pri_active_accts\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"pri_overdue_accts\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"pri_current_balance\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"pri_sanctioned_amount\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"pri_disbursed_amount\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"sec_no_of_accts\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"sec_active_accts\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"sec_overdue_accts\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"sec_current_balance\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"sec_sanctioned_amount\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"sec_disbursed_amount\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"primary_instal_amt\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"sec_instal_amt\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"new_accts_in_last_six_months\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"delinquent_accts_in_last_six_months\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"average_acct_age\",\n \"variable_type\": \"FREE_FORM_TEXT\",\n \"data_type\": \"STRING\"\n },\n {\n \"variable_name\": \"credit_history_length\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n },\n {\n \"variable_name\": \"no_of_inquiries\",\n \"variable_type\": \"NUMERIC\",\n \"data_type\": \"FLOAT\"\n }\n ],\n \"label_mappings\": {\n \"FRAUD\": [\n \"1\"\n ],\n \"LEGIT\": [\n \"0\"\n ]\n }\n}" }, { "path": "scripts/reproducibility/afd/create_afd_resources.py", "content": "# TO BE UPDATED BY USER\nIAM_ROLE = \"\"\nBUCKET = \"\"\nKEY = \"\"\nMODEL_NAME = \"\" # lower case alphanumeric only, only _ allowed as delimiter\nMODEL_TYPE = \"ONLINE_FRAUD_INSIGHTS\" # or TRANSACTION_FRAUD_INSIGHTS\n\nimport os\nimport time\nimport json\nimport boto3\nimport click\nimport string\nimport random\nimport logging\nimport pandas as pd\n\n\nMODEL_DESC = \"Benchmarking model\"\nEVENT_DESC = \"Event for benchmarking model\"\nENTITY_TYPE = \"user\" # this is provided in the dummy data. Will need to change if using different data\nENTITY_DESC = \"Entity for benchmarking model\"\n\nBATCH_PREDICTION_JOB = DETECTOR_NAME = EVENT_TYPE = MODEL_NAME # Others are kept same as model name\n\n# boto3 connections\nclient = boto3.client('frauddetector') \ns3 = boto3.client('s3')\n\n@click.command()\n@click.argument(\"config\", type=click.Path(exists=True))\ndef afd_train_model_demo(config):\n \n #############################################\n ##### Setup #####\n with open(config, \"r\") as f:\n config_file = json.load(f)\n \n \n EVENT_VARIABLES = [variable[\"variable_name\"] for variable in config_file[\"variable_mappings\"]]\n EVENT_LABELS = [v for k,v in config_file[\"label_mappings\"].items()]\n EVENT_LABELS = [item for sublist in EVENT_LABELS for item in sublist] # flattening list of lists\n\n # Variable mappings of demo data in this use case. Important to teach this to customer\n click.echo(f'{pd.DataFrame(config_file[\"variable_mappings\"])}')\n click.echo(f'{pd.DataFrame(config_file[\"label_mappings\"])}')\n\n S3_DATA_PATH = \"s3://\" + os.path.join(BUCKET, KEY)\n \n #############################################\n ##### Create event variables and labels #####\n \n # -- create variable --\n for variable in config_file[\"variable_mappings\"]:\n \n DEFAULT_VALUE = '0.0' if variable[\"data_type\"] == \"FLOAT\" else ''\n \n try:\n resp = client.get_variables(name = variable[\"variable_name\"])\n click.echo(\"{0} exists, data type: {1}\".format(variable[\"variable_name\"], resp['variables'][0]['dataType']))\n except:\n click.echo(\"Creating variable: {0}\".format(variable[\"variable_name\"]))\n resp = client.create_variable(\n name = variable[\"variable_name\"],\n dataType = variable[\"data_type\"],\n dataSource ='EVENT',\n defaultValue = DEFAULT_VALUE, \n description = variable[\"variable_name\"],\n variableType = variable[\"variable_type\"])\n # Putting FRAUD\n for f in config_file[\"label_mappings\"][\"FRAUD\"]:\n response = client.put_label(\n name = f,\n description = \"FRAUD\")\n # Putting LEGIT\n for f in config_file[\"label_mappings\"][\"LEGIT\"]:\n response = client.put_label(\n name = f,\n description = \"LEGIT\")\n\n #############################################\n ##### Define Entity and Event Types #####\n \n # -- create entity type --\n try:\n response = client.get_entity_types(name = ENTITY_TYPE)\n click.echo(\"-- entity type exists --\")\n click.echo(response)\n except:\n response = client.put_entity_type(\n name = ENTITY_TYPE,\n description = ENTITY_DESC\n )\n click.echo(\"-- create entity type --\")\n click.echo(response)\n\n\n # -- create event type --\n try:\n response = client.get_event_types(name = EVENT_TYPE)\n click.echo(\"\\n-- event type exists --\")\n click.echo(response)\n except:\n response = client.put_event_type (\n name = EVENT_TYPE,\n eventVariables = EVENT_VARIABLES,\n labels = EVENT_LABELS,\n entityTypes = [ENTITY_TYPE])\n click.echo(\"\\n-- create event type --\")\n click.echo(response)\n\n #############################################\n ##### Batch import training file for TFI #####\n if MODEL_TYPE == \"TRANSACTION_FRAUD_INSIGHTS\":\n try:\n response = client.create_batch_import_job(\n jobId = BATCH_PREDICTION_JOB,\n inputPath = S3_DATA_PATH,\n outputPath = \"s3://\" + BUCKET,\n eventTypeName = EVENT_TYPE,\n iamRoleArn = IAM_ROLE\n ) \n except Exception:\n pass\n\n # -- wait until batch import is finished --\n print(\"--- waiting until batch import is finished \")\n stime = time.time()\n while True:\n response = client.get_batch_import_jobs(jobId=BATCH_PREDICTION_JOB)\n if 'IN_PROGRESS' in response['batchImports'][0]['status']:\n print(f\"current progress: {(time.time() - stime)/60:{3}.{3}} minutes\")\n time.sleep(60) # sleep for 1 minute \n else:\n print(\"Batch Impoort status : \" + response['batchImports'][0]['status'])\n break\n\n etime = time.time()\n print(f\"Elapsed time: {(etime - stime)/60:{3}.{3}} minutes \\n\" )\n print(response)\n\n\n #############################################\n ##### Create and train your model #####\n try:\n response = client.create_model(\n description = MODEL_DESC,\n eventTypeName = EVENT_TYPE,\n modelId = MODEL_NAME,\n modelType = MODEL_TYPE)\n click.echo(\"-- initalize model --\")\n click.echo(response)\n except Exception:\n pass\n \n # -- initalized the model, it's now ready to train --\n \n # -- first define training_data_schema for model to use --\n\n \n if MODEL_TYPE == \"TRANSACTION_FRAUD_INSIGHTS\": \n training_data_schema = {\n 'modelVariables' : EVENT_VARIABLES,\n 'labelSchema' : {\n 'labelMapper' : config_file[\"label_mappings\"],\n 'unlabeledEventsTreatment': 'IGNORE'\n }\n }\n response = client.create_model_version(\n modelId = MODEL_NAME,\n modelType = MODEL_TYPE,\n trainingDataSource = 'INGESTED_EVENTS',\n trainingDataSchema = training_data_schema,\n ingestedEventsDetail={ # This needs to be changed\n 'ingestedEventsTimeWindow': {\n 'startTime': '2020-12-10T00:00:00Z', # '2021-08-28T00:00:00Z',\n 'endTime': '2022-06-07T00:00:00Z' #'2022-05-10T00:00:00Z'\n }\n }\n )\n else:\n training_data_schema = {\n 'modelVariables' : EVENT_VARIABLES,\n 'labelSchema' : {\n 'labelMapper' : config_file[\"label_mappings\"]\n }\n }\n response = client.create_model_version(\n modelId = MODEL_NAME,\n modelType = MODEL_TYPE,\n trainingDataSource = 'EXTERNAL_EVENTS',\n trainingDataSchema = training_data_schema,\n externalEventsDetail = {\n 'dataLocation' : S3_DATA_PATH,\n 'dataAccessRoleArn': IAM_ROLE\n }\n )\n model_version = response['modelVersionNumber']\n click.echo(\"-- model training --\")\n click.echo(response)\n\n\nif __name__==\"__main__\":\n afd_train_model_demo()\n" }, { "path": "scripts/reproducibility/afd/score_afd_model.py", "content": "# TO BE UPDATED BY USER\nIAM_ROLE = \"\"\nBUCKET = \"\"\nTEST_PATH = \"\"\nTEST_LABELS_PATH = \"\"\nMODEL_NAME = \"\" # lower case alphanumeric only, only _ allowed as delimiter\nMODEL_TYPE = \"ONLINE_FRAUD_INSIGHTS\" # or TRANSACTION_FRAUD_INSIGHTS\n\nimport os\nimport ast\nimport time\nimport json\nimport boto3\nimport click\nimport string\nimport random\nimport logging\nimport numpy as np\nimport pandas as pd\nfrom sklearn.metrics import roc_curve, auc\n\n# boto3 connections\nclient = boto3.client('frauddetector') \ns3 = boto3.client('s3')\n\nBATCH_PREDICTION_JOB = DETECTOR_NAME = EVENT_TYPE = MODEL_NAME\nmodel_version = '1.0'\nDETECTOR_DESC = \"Benchmarking detector\"\n\n\ndef create_outcomes(outcomes):\n \"\"\" \n Create Fraud Detector Outcomes \n \"\"\" \n for outcome in outcomes:\n print(\"creating outcome variable: {0} \".format(outcome))\n response = client.put_outcome(name = outcome, description = outcome)\n\n\ndef create_rules(score_cuts, outcomes):\n \"\"\"\n Creating rules \n \n Arguments:\n score_cuts - list of score cuts to create rules\n outcomes - list of outcomes associated with the rules\n \n Returns:\n a rule list to used when create detector\n \"\"\"\n \n if len(score_cuts)+1 != len(outcomes):\n logging.error('Your socre cuts and outcomes are not matched.')\n \n rule_list = []\n for i in range(len(outcomes)):\n # rule expression\n if i < (len(outcomes)-1):\n rule = \"${0}_insightscore > {1}\".format(MODEL_NAME,score_cuts[i])\n else:\n rule = \"${0}_insightscore <= {1}\".format(MODEL_NAME,score_cuts[i-1])\n \n # append to rule_list (used when create detector)\n rule_id = \"rules{0}_{1}\".format(i, MODEL_NAME[:9])\n \n rule_list.append({\n \"ruleId\": rule_id, \n \"ruleVersion\" : '1',\n \"detectorId\" : DETECTOR_NAME\n })\n \n # create rules\n print(\"creating rule: {0}: IF {1} THEN {2}\".format(rule_id, rule, outcomes[i]))\n try:\n response = client.create_rule(\n ruleId = rule_id,\n detectorId = DETECTOR_NAME,\n expression = rule,\n language = 'DETECTORPL',\n outcomes = [outcomes[i]]\n )\n except:\n print(\"this rule already exists in this detector\")\n \n return rule_list\n\n\ndef ast_with_nan(x):\n try:\n return ast.literal_eval(x)\n except:\n return np.nan\n\n\ndef afd_train_model_demo():\n\n # -- activate the model version --\n try:\n response = client.update_model_version_status (\n modelId = MODEL_NAME,\n modelType = MODEL_TYPE,\n modelVersionNumber = model_version,\n status = 'ACTIVE'\n )\n print(\"-- activating model --\")\n print(response)\n except Exception:\n print(\"First train the model\")\n \n # -- wait until model is active --\n print(\"--- waiting until model status is active \")\n stime = time.time()\n while True:\n response = client.get_model_version(modelId=MODEL_NAME, modelType = MODEL_TYPE, modelVersionNumber = model_version)\n if response['status'] != 'ACTIVE':\n print(response['status'])\n print(f\"current progress: {(time.time() - stime)/60:{3}.{3}} minutes\")\n time.sleep(60) # sleep for 1 minute \n if response['status'] == 'ACTIVE':\n print(\"Model status : \" + response['status'])\n break\n \n etime = time.time()\n print(\"Elapsed time : %s\" % (etime - stime) + \" seconds \\n\" )\n print(response)\n\n # -- put detector, initalizes your detector -- \n response = client.put_detector(\n detectorId = DETECTOR_NAME, \n description = DETECTOR_DESC,\n eventTypeName = EVENT_TYPE )\n\n # -- decide what threshold and corresponding outcome you want to add -- \n # here, we create three simple rules by cutting the score at [950,750], and create three outcome ['fraud', 'investigate', 'approve'] \n # it will create 3 rules:\n # score > 950: fraud\n # score <= 750: approve\n\n score_cuts = [750] # recommended to fine tune this based on your business use case\n outcomes = ['fraud', 'approve'] # recommended to define this based on your business use case\n\n # -- create outcomes -- \n print(\" -- create outcomes --\")\n create_outcomes(outcomes)\n\n # -- create rules --\n print(\" -- create rules --\")\n rule_list = create_rules(score_cuts, outcomes)\n\n # -- create detector version --\n client.create_detector_version(\n detectorId = DETECTOR_NAME,\n rules = rule_list,\n modelVersions = [{\"modelId\": MODEL_NAME, \n \"modelType\": MODEL_TYPE,\n \"modelVersionNumber\": model_version}],\n # there are 2 options for ruleExecutionMode:\n # 'ALL_MATCHED' - return all matched rules' outcome\n # 'FIRST_MATCHED' - return first matched rule's outcome\n ruleExecutionMode = 'FIRST_MATCHED'\n )\n\n print(\"\\n -- detector created -- \")\n print(response) \n\n response = client.update_detector_version_status(\n detectorId = DETECTOR_NAME,\n detectorVersionId = '1',\n status = 'ACTIVE'\n )\n print(\"\\n -- detector activated -- \")\n print(response)\n\n # -- wait until detector is active --\n print(\"\\n --- waiting until detector status is active \")\n stime = time.time()\n while True:\n response = client.describe_detector(\n detectorId = DETECTOR_NAME,\n )\n if response['detectorVersionSummaries'][0]['status'] != 'ACTIVE':\n print(response['detectorVersionSummaries'][0]['status'])\n print(f\"current progress: {(time.time() - stime)/60:{3}.{3}} minutes\")\n time.sleep(60)\n if response['detectorVersionSummaries'][0]['status'] == 'ACTIVE':\n break\n etime = time.time()\n print(\"Elapsed time : %s\" % (etime - stime) + \" seconds \\n\" )\n print(response)\n\n # -- create detector evaluation --\n try:\n client.create_batch_prediction_job (\n jobId = BATCH_PREDICTION_JOB,\n inputPath = os.path.join('s3://', BUCKET, TEST_PATH),\n outputPath =os.path.join('s3://', BUCKET),\n eventTypeName = EVENT_TYPE,\n detectorName = DETECTOR_NAME,\n detectorVersion = '1',\n iamRoleArn = IAM_ROLE)\n except Exception as e:\n print(e)\n print(\"batch prediction job already exists\")\n\n # -- wait until batch prediction job is completed --\n print(\"\\n --- waiting until batch prediction job is completed \")\n stime = time.time()\n while True:\n response = client.get_batch_prediction_jobs(jobId=BATCH_PREDICTION_JOB)\n response = response['batchPredictions'][0]\n if (response['status'] != 'COMPLETE') and (response['status'] != 'FAILED'):\n print(f\"current progress: {(time.time() - stime)/60:{3}.{3}} minutes\")\n time.sleep(60)\n if response['status'] == 'COMPLETE':\n break\n etime = time.time()\n print(\"Elapsed time : %s\" % (etime - stime) + \" seconds \\n\" )\n print(response)\n\n # -- get batch prediction job result --\n contents = s3.list_objects_v2(Bucket=BUCKET, Prefix=os.path.join(TEST_PATH))['Contents']\n print(contents)\n S3_SCORE_PATH = sorted([c['Key'] for c in contents if c['Key'].endswith('output.csv')])[-1]\n print(S3_SCORE_PATH)\n\n # -- get test performance --\n # Predictions\n print(os.path.join('s3://', BUCKET, S3_SCORE_PATH))\n predictions = pd.read_csv(os.path.join('s3://', BUCKET, S3_SCORE_PATH))\n predictions = predictions.copy()[~predictions.MODEL_SCORES.isna()]\n\n predictions['scores'] = predictions['MODEL_SCORES'].\\\n apply(lambda x: ast_with_nan(x)).\\\n apply(lambda x: x.get(MODEL_NAME))\n\n # Labels\n labels = pd.read_csv(os.path.join('s3://', BUCKET, TEST_LABELS_PATH))\n# labels['EVENT_LABEL'] = labels['EVENT_LABEL'].map({'benign': 0, 'malignant': 1})\n predictions = predictions.merge(labels, on='EVENT_ID', how='left')\n print('Test size: ', predictions.shape)\n\n fpr, tpr, threshold = roc_curve(predictions['EVENT_LABEL'], predictions['scores'])\n test_auc = auc(fpr,tpr)\n print('AUC: ', test_auc)\n\n test_metrics = {}\n test_metrics['auc'] = test_auc\n test_metrics['fpr'] = list(fpr)\n test_metrics['tpr'] = list(tpr)\n test_metrics['threshold'] = list(threshold)\n\n # -- put test metrics in s3 --\n s3.put_object(\n Body=json.dumps(test_metrics), \n Bucket=BUCKET, \n Key='test_metrics.json') \n\n print(\"\\n -- test metrics saved -- \")\n\nif __name__ == \"__main__\":\n afd_train_model_demo()\n\n \n\n \n\n \n\n " }, { "path": "scripts/reproducibility/autogluon/README.md", "content": " - benchmark_ag.py: a script for autogluon benchmarking\n - example-ag-ieeecis.ipynb: an example notebook using benchmark_ag.py \n\nNote that autogluon is not perfectly reproducible because some underlying models are not deterministically seeded, you might see slightly different results than in the paper.\n" }, { "path": "scripts/reproducibility/autogluon/benchmark_ag.py", "content": "import pandas as pd\nimport os\nimport gc\nimport joblib\nimport datetime\n\nimport matplotlib as mpl\nfrom sklearn.metrics import roc_auc_score, roc_curve\n\nmpl.rcParams['figure.dpi'] = 150\npd.set_option('display.max_columns', 500)\npd.set_option('display.max_rows', 500)\npd.set_option('display.width', 200)\npd.set_option('display.float_format', lambda x: '%.3f' % x)\n\nimport logging\nFORMAT = \"%(levelname)s: %(name)s: %(message)s\"\nDATE_FORMAT = \"%Y-%m-%d %H:%M:%S\"\nlogging.basicConfig(level=logging.WARN, format=FORMAT, datefmt=DATE_FORMAT)\nlogger = logging.getLogger(os.path.basename(__file__))\nlogger.setLevel(logging.DEBUG)\n\nimport sys\nsys.path.append('../')\nfrom benchmark_utils import load_data, get_recall\n\nfrom autogluon.tabular import TabularPredictor\n\ndef run_ag(dataset, base_path, time_limit=3600, presets=None, hyperparameters=None, feature_metadata='infer', verbosity=2):\n gc.collect()\n features, df_train, df_test = load_data(dataset, base_path)\n\n dateTimeObj = datetime.datetime.now()\n timestampStr = dateTimeObj.strftime(\"%Y%m%d_%H%M%S\")\n \n suffix = (f\"_{presets}\" if presets is not None else \"\") \\\n + (f\"_{hyperparameters}\" if hyperparameters is not None else \"\") \\\n + (\"_feature_metadata\" if feature_metadata != 'infer' else \"\") \n folder = f\"ag-{timestampStr}\" \\\n + suffix\n\n predictor = TabularPredictor(label='EVENT_LABEL', eval_metric='roc_auc', path=f\"{base_path}/{dataset}/AutogluonModels/{folder}/\", \n verbosity=verbosity)\n predictor.fit(df_train[features + ['EVENT_LABEL'] ], \n time_limit=time_limit, presets=presets, hyperparameters=hyperparameters, feature_metadata=feature_metadata)\n\n leaderboard = predictor.leaderboard(df_test[features + ['EVENT_LABEL'] ])\n\n leaderboard_file = \"leaderboard\" \\\n + suffix \\\n + \".csv\"\n leaderboard.to_csv(f\"{base_path}/{dataset}/{leaderboard_file}\", index=False)\n \n df_pred = predictor.predict_proba(df_test[ features ], \n as_multiclass=False)\n \n auc = roc_auc_score(df_test['EVENT_LABEL'], df_pred)\n logger.info(f\"auc on test data: {auc}\")\n pos_label = predictor.positive_class\n fpr, tpr, thresholds = roc_curve(df_test['EVENT_LABEL'], df_pred, \n pos_label=pos_label)\n \n y_true = df_test['EVENT_LABEL']\n y_true = (y_true==pos_label)\n \n recall = get_recall(fpr, tpr, fpr_target=0.01)\n logger.info(f\"tpr@1%fpr on test data: {recall}\")\n \n test_metrics_ag_bq = {\n \"labels\": df_test['EVENT_LABEL'],\n \"pred_prob\": df_pred, \n \"auc\": auc,\n \"tpr@1%fpr\": recall,\n \"fpr\": fpr,\n \"tpr\": tpr,\n \"thresholds\": thresholds\n }\n metrics_file = \"test_metrics_ag\" \\\n + suffix \\\n + \".joblib\"\n joblib.dump(test_metrics_ag_bq, f\"{base_path}/{dataset}/{metrics_file}\")" }, { "path": "scripts/reproducibility/autogluon/example-ag-ieeecis.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"7d350d0d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"%load_ext autoreload\\n\",\n \"%autoreload 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"1d6a8c41\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from IPython.core.display import display, HTML\\n\",\n \"from IPython.display import clear_output\\n\",\n \"display(HTML(\\\"\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"611127d9\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: pip install required packages if not installed already\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"321cb018\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# !pip install autogluon\\n\",\n \"import benchmark_ag\\n\",\n \"from benchmark_ag import load_data, run_ag\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1d191102\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: download data using fdb\\n\",\n \"Example: https://github.com/amazon-research/fraud-dataset-benchmark/blob/main/scripts/examples/Test_FDB_Loader.ipynb\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"33fd8a7b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# This is where datasets are stored: {BASE_PATH}/{dataset}/\\n\",\n \"BASE_PATH = \\\"/home/ec2-user/SageMaker/official-dataset-names\\\"\\n\",\n \"dataset = \\\"IEEE-CIS Fraud Detection\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c4bca656\",\n \"metadata\": {},\n \"source\": [\n \"Make sure three files are downloaded:\\n\",\n \"1. {BASE_PATH}/{dataset}/train.csv\\n\",\n \"2. {BASE_PATH}/{dataset}/test.csv\\n\",\n \"3. {BASE_PATH}/{dataset}/test_labels.csv\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"52b3fcdb\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: look at data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"b07893d7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INFO: benchmark_utils.py: IEEE-CIS Fraud Detection\\n\",\n \"INFO: benchmark_utils.py: (313060, 194)\\n\",\n \"INFO: benchmark_utils.py: (27330, 71)\\n\",\n \"INFO: benchmark_utils.py: (29527, 2)\\n\",\n \"INFO: benchmark_utils.py: (27329, 72)\\n\",\n \"INFO: benchmark_utils.py: 67\\n\",\n \"INFO: benchmark_utils.py: ['transactionamt', 'productcd', 'card1', 'card2', 'card3', 'card5', 'card6', 'addr1', 'dist1', 'p_emaildomain', 'r_emaildomain', 'c1', 'c2', 'c4', 'c5', 'c6', 'c7', 'c8', 'c9', 'c10', 'c11', 'c12', 'c13', 'c14', 'v62', 'v70', 'v76', 'v78', 'v82', 'v91', 'v127', 'v130', 'v139', 'v160', 'v165', 'v187', 'v203', 'v207', 'v209', 'v210', 'v221', 'v234', 'v257', 'v258', 'v261', 'v264', 'v266', 'v267', 'v271', 'v274', 'v277', 'v283', 'v285', 'v289', 'v291', 'v294', 'id_01', 'id_02', 'id_05', 'id_06', 'id_09', 'id_13', 'id_17', 'id_19', 'id_20', 'devicetype', 'deviceinfo']\\n\"\n ]\n }\n ],\n \"source\": [\n \"features, df_train, df_test = load_data(dataset, BASE_PATH)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"8cad73e3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" 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The function run_ag below also saves a leaderboard file (leaderboard_xxx.csv) and a test metrics file (test_metrics_xxx.joblib) into {BASE_PATH}/{dataset}/, respectively\\n\",\n \"2. AutoGluon models are saved at {BASE_PATH}/{dataset}/AutogluonModels\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"54b6b65a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INFO: benchmark_utils.py: IEEE-CIS Fraud Detection\\n\",\n \"INFO: benchmark_utils.py: (313060, 194)\\n\",\n \"INFO: benchmark_utils.py: (27330, 71)\\n\",\n \"INFO: benchmark_utils.py: (29527, 2)\\n\",\n \"INFO: benchmark_utils.py: (27329, 72)\\n\",\n \"INFO: benchmark_utils.py: 67\\n\",\n \"INFO: benchmark_utils.py: ['transactionamt', 'productcd', 'card1', 'card2', 'card3', 'card5', 'card6', 'addr1', 'dist1', 'p_emaildomain', 'r_emaildomain', 'c1', 'c2', 'c4', 'c5', 'c6', 'c7', 'c8', 'c9', 'c10', 'c11', 'c12', 'c13', 'c14', 'v62', 'v70', 'v76', 'v78', 'v82', 'v91', 'v127', 'v130', 'v139', 'v160', 'v165', 'v187', 'v203', 'v207', 'v209', 'v210', 'v221', 'v234', 'v257', 'v258', 'v261', 'v264', 'v266', 'v267', 'v271', 'v274', 'v277', 'v283', 'v285', 'v289', 'v291', 'v294', 'id_01', 'id_02', 'id_05', 'id_06', 'id_09', 'id_13', 'id_17', 'id_19', 'id_20', 'devicetype', 'deviceinfo']\\n\",\n \"INFO: autogluon.tabular.predictor.predictor: Presets specified: ['best_quality']\\n\",\n \"INFO: autogluon.tabular.learner.default_learner: Beginning AutoGluon training ... Time limit = 3600s\\n\",\n \"INFO: autogluon.tabular.learner.default_learner: AutoGluon will save models to \\\"/home/ec2-user/SageMaker/official-dataset-names/IEEE-CIS Fraud Detection/AutogluonModels/ag-20220615_135015_best_quality/\\\"\\n\",\n \"INFO: autogluon.tabular.learner.default_learner: AutoGluon Version: 0.4.2\\n\",\n \"INFO: autogluon.tabular.learner.default_learner: Python Version: 3.7.10\\n\",\n \"INFO: autogluon.tabular.learner.default_learner: Operating System: Linux\\n\",\n \"INFO: autogluon.tabular.learner.default_learner: Train Data Rows: 313060\\n\",\n \"INFO: autogluon.tabular.learner.default_learner: Train Data Columns: 67\\n\",\n \"INFO: autogluon.tabular.learner.default_learner: Label Column: EVENT_LABEL\\n\",\n \"INFO: autogluon.tabular.learner.default_learner: Preprocessing data ...\\n\",\n \"Level 25: autogluon.core.utils.utils: AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).\\n\",\n \"INFO: autogluon.core.utils.utils: \\t2 unique label values: [0, 1]\\n\",\n \"Level 25: autogluon.core.utils.utils: \\tIf 'binary' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])\\n\",\n \"INFO: autogluon.core.data.label_cleaner: Selected class <--> label mapping: class 1 = 1, class 0 = 0\\n\",\n \"INFO: autogluon.tabular.learner.default_learner: Using Feature Generators to preprocess the data ...\\n\",\n \"INFO: autogluon.features.generators.abstract: Fitting AutoMLPipelineFeatureGenerator...\\n\",\n \"INFO: autogluon.features.generators.abstract: \\tAvailable Memory: 502950.08 MB\\n\",\n \"INFO: autogluon.features.generators.abstract: \\tTrain Data (Original) Memory Usage: 248.99 MB (0.0% of available memory)\\n\",\n \"INFO: autogluon.features.generators.abstract: \\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\\n\",\n \"INFO: autogluon.features.generators.abstract: \\tStage 1 Generators:\\n\",\n \"INFO: autogluon.features.generators.abstract: \\t\\tFitting AsTypeFeatureGenerator...\\n\",\n \"INFO: autogluon.features.generators.abstract: \\tStage 2 Generators:\\n\",\n \"INFO: autogluon.features.generators.abstract: \\t\\tFitting FillNaFeatureGenerator...\\n\",\n \"INFO: autogluon.features.generators.abstract: \\tStage 3 Generators:\\n\",\n \"INFO: autogluon.features.generators.abstract: \\t\\tFitting IdentityFeatureGenerator...\\n\",\n \"INFO: autogluon.features.generators.abstract: \\t\\tFitting CategoryFeatureGenerator...\\n\",\n \"INFO: autogluon.features.generators.abstract: \\t\\t\\tFitting CategoryMemoryMinimizeFeatureGenerator...\\n\",\n \"INFO: autogluon.features.generators.abstract: \\tStage 4 Generators:\\n\",\n \"INFO: autogluon.features.generators.abstract: \\t\\tFitting DropUniqueFeatureGenerator...\\n\",\n \"INFO: autogluon.features.generators.abstract: \\tTypes of features in original data (raw dtype, special dtypes):\\n\",\n \"INFO: autogluon.common.features.feature_metadata: \\t\\t('float', []) : 61 | ['transactionamt', 'card1', 'card2', 'card3', 'card5', ...]\\n\",\n \"INFO: autogluon.common.features.feature_metadata: \\t\\t('object', []) : 6 | ['productcd', 'card6', 'p_emaildomain', 'r_emaildomain', 'devicetype', ...]\\n\",\n \"INFO: autogluon.features.generators.abstract: \\tTypes of features in processed data (raw dtype, special dtypes):\\n\",\n \"INFO: autogluon.common.features.feature_metadata: \\t\\t('category', []) : 6 | ['productcd', 'card6', 'p_emaildomain', 'r_emaildomain', 'devicetype', ...]\\n\",\n \"INFO: autogluon.common.features.feature_metadata: \\t\\t('float', []) : 61 | ['transactionamt', 'card1', 'card2', 'card3', 'card5', ...]\\n\",\n \"INFO: autogluon.features.generators.abstract: \\t2.6s = Fit runtime\\n\",\n \"INFO: autogluon.features.generators.abstract: \\t67 features in original data used to generate 67 features in processed data.\\n\",\n \"INFO: autogluon.features.generators.abstract: \\tTrain Data (Processed) Memory Usage: 154.97 MB (0.0% of available memory)\\n\",\n \"INFO: autogluon.tabular.learner.default_learner: Data preprocessing and feature engineering runtime = 2.94s ...\\n\",\n \"Level 25: autogluon.core.trainer.abstract_trainer: AutoGluon will gauge predictive performance using evaluation metric: 'roc_auc'\\n\",\n \"Level 25: autogluon.core.trainer.abstract_trainer: \\tThis metric expects predicted probabilities rather than predicted class labels, so you'll need to use predict_proba() instead of predict()\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\tTo change this, specify the eval_metric parameter of Predictor()\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: AutoGluon will fit 2 stack levels (L1 to L2) ...\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting 13 L1 models ...\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 2397.44s of the 3597.05s of remaining time.\\n\",\n \"WARNING: autogluon.core.models.ensemble.bagged_ensemble_model: \\tNot enough time to generate out-of-fold predictions for model. Estimated time required was 3589.28s compared to 3115.73s of available time.\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\tTime limit exceeded... Skipping KNeighborsUnif_BAG_L1.\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 2390.48s of the 3590.09s of remaining time.\\n\",\n \"WARNING: autogluon.core.models.ensemble.bagged_ensemble_model: \\tNot enough time to generate out-of-fold predictions for model. Estimated time required was 3399.45s compared to 3106.63s of available time.\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\tTime limit exceeded... Skipping KNeighborsDist_BAG_L1.\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 2383.77s of the 3583.38s of remaining time.\\n\",\n \"INFO: autogluon.core.models.ensemble.bagged_ensemble_model: \\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9629\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t177.58s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t36.28s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: LightGBM_BAG_L1 ... Training model for up to 2196.14s of the 3395.75s of remaining time.\\n\",\n \"INFO: autogluon.core.models.ensemble.bagged_ensemble_model: \\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.969\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t97.61s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t17.24s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: RandomForestGini_BAG_L1 ... Training model for up to 2095.39s of the 3295.0s of remaining time.\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9456\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t15.42s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t24.02s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: RandomForestEntr_BAG_L1 ... Training model for up to 2054.81s of the 3254.42s of remaining time.\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9474\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t13.73s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t26.66s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: CatBoost_BAG_L1 ... Training model for up to 2012.05s of the 3211.67s of remaining time.\\n\",\n \"INFO: autogluon.core.models.ensemble.bagged_ensemble_model: \\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9563\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t1615.45s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t3.48s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: ExtraTreesGini_BAG_L1 ... Training model for up to 394.91s of the 1594.53s of remaining time.\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9468\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t13.38s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t35.27s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: ExtraTreesEntr_BAG_L1 ... Training model for up to 344.0s of the 1543.61s of remaining time.\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9505\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t15.11s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t41.22s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 285.31s of the 1484.93s of remaining time.\\n\",\n \"INFO: autogluon.core.models.ensemble.bagged_ensemble_model: \\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9086\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t166.51s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t6.71s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: XGBoost_BAG_L1 ... Training model for up to 116.32s of the 1315.93s of remaining time.\\n\",\n \"INFO: autogluon.core.models.ensemble.bagged_ensemble_model: \\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9652\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t94.35s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t8.73s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: NeuralNetTorch_BAG_L1 ... Training model for up to 19.33s of the 1218.94s of remaining time.\\n\",\n \"INFO: autogluon.core.models.ensemble.bagged_ensemble_model: \\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\tTime limit exceeded... Skipping NeuralNetTorch_BAG_L1.\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: LightGBMLarge_BAG_L1 ... Training model for up to 3.46s of the 1203.08s of remaining time.\\n\",\n \"INFO: autogluon.core.models.ensemble.bagged_ensemble_model: \\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\tTime limit exceeded... Skipping LightGBMLarge_BAG_L1.\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Completed 1/20 k-fold bagging repeats ...\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 1197.96s of remaining time.\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9719\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t92.94s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.09s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting 11 L2 models ...\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 1104.9s of the 1104.8s of remaining time.\\n\",\n \"INFO: autogluon.core.models.ensemble.bagged_ensemble_model: \\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9747\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t21.16s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t2.79s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: LightGBM_BAG_L2 ... Training model for up to 1080.72s of the 1080.63s of remaining time.\\n\",\n \"INFO: autogluon.core.models.ensemble.bagged_ensemble_model: \\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9752\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t11.64s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t1.43s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: RandomForestGini_BAG_L2 ... Training model for up to 1067.58s of the 1067.49s of remaining time.\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9632\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t16.3s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t21.33s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: RandomForestEntr_BAG_L2 ... Training model for up to 1029.08s of the 1028.98s of remaining time.\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9649\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t14.04s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t20.67s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: CatBoost_BAG_L2 ... Training model for up to 993.29s of the 993.19s of remaining time.\\n\",\n \"INFO: autogluon.core.models.ensemble.bagged_ensemble_model: \\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9762\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t393.78s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t1.04s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: ExtraTreesGini_BAG_L2 ... Training model for up to 598.03s of the 597.92s of remaining time.\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9641\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t14.12s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t37.16s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: ExtraTreesEntr_BAG_L2 ... Training model for up to 545.02s of the 544.92s of remaining time.\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9641\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t14.5s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t37.31s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: NeuralNetFastAI_BAG_L2 ... Training model for up to 491.61s of the 491.51s of remaining time.\\n\",\n \"INFO: autogluon.core.models.ensemble.bagged_ensemble_model: \\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9736\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t289.96s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t6.78s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: XGBoost_BAG_L2 ... Training model for up to 198.91s of the 198.81s of remaining time.\\n\",\n \"INFO: autogluon.core.models.ensemble.bagged_ensemble_model: \\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9752\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t25.26s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t3.99s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: NeuralNetTorch_BAG_L2 ... Training model for up to 171.76s of the 171.67s of remaining time.\\n\",\n \"INFO: autogluon.core.models.ensemble.bagged_ensemble_model: \\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\tTime limit exceeded... Skipping NeuralNetTorch_BAG_L2.\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 148.85s of the 148.75s of remaining time.\\n\",\n \"INFO: autogluon.core.models.ensemble.bagged_ensemble_model: \\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9753\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t44.1s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t2.06s\\t = Validation runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Completed 1/20 k-fold bagging repeats ...\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the 95.08s of remaining time.\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.9767\\t = Validation score (roc_auc)\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t110.29s\\t = Training runtime\\n\",\n \"INFO: autogluon.core.trainer.abstract_trainer: \\t0.13s\\t = Validation runtime\\n\",\n \"INFO: autogluon.tabular.learner.default_learner: AutoGluon training complete, total runtime = 3615.62s ... Best model: \\\"WeightedEnsemble_L3\\\"\\n\",\n \"INFO: autogluon.tabular.predictor.predictor: TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\\\"/home/ec2-user/SageMaker/official-dataset-names/IEEE-CIS Fraud Detection/AutogluonModels/ag-20220615_135015_best_quality/\\\")\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" model score_test score_val pred_time_test pred_time_val fit_time pred_time_test_marginal pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order\\n\",\n \"0 WeightedEnsemble_L2 0.889 0.972 17.168 130.219 491.324 0.008 0.094 92.936 2 True 10\\n\",\n \"1 ExtraTreesGini_BAG_L1 0.879 0.947 1.356 35.271 13.385 1.356 35.271 13.385 1 True 6\\n\",\n \"2 RandomForestEntr_BAG_L1 0.878 0.947 0.948 26.657 13.727 0.948 26.657 13.727 1 True 4\\n\",\n \"3 ExtraTreesEntr_BAG_L1 0.878 0.950 1.526 41.223 15.112 1.526 41.223 15.112 1 True 7\\n\",\n \"4 WeightedEnsemble_L3 0.876 0.977 36.877 330.085 3131.503 0.023 0.126 110.288 3 True 21\\n\",\n \"5 RandomForestGini_BAG_L2 0.873 0.963 26.468 220.941 2225.452 0.544 21.332 16.300 2 True 13\\n\",\n \"6 RandomForestGini_BAG_L1 0.872 0.946 0.740 24.016 15.415 0.740 24.016 15.415 1 True 3\\n\",\n \"7 RandomForestEntr_BAG_L2 0.871 0.965 26.435 220.275 2223.195 0.511 20.666 14.044 2 True 14\\n\",\n \"8 ExtraTreesGini_BAG_L2 0.871 0.964 26.592 236.773 2223.269 0.669 37.164 14.118 2 True 16\\n\",\n \"9 CatBoost_BAG_L2 0.868 0.976 26.427 200.651 2602.927 0.504 1.042 393.775 2 True 15\\n\",\n \"10 ExtraTreesEntr_BAG_L2 0.868 0.964 26.599 236.924 2223.651 0.675 37.315 14.499 2 True 17\\n\",\n \"11 CatBoost_BAG_L1 0.865 0.956 1.264 3.485 1615.448 1.264 3.485 1615.448 1 True 5\\n\",\n \"12 LightGBM_BAG_L2 0.864 0.975 26.580 201.041 2220.792 0.656 1.432 11.641 2 True 12\\n\",\n \"13 XGBoost_BAG_L2 0.864 0.975 28.676 203.598 2234.414 2.753 3.989 25.263 2 True 19\\n\",\n \"14 LightGBMXT_BAG_L2 0.862 0.975 26.927 202.401 2230.314 1.004 2.792 21.163 2 True 11\\n\",\n \"15 LightGBMLarge_BAG_L2 0.860 0.975 26.780 201.669 2253.252 0.857 2.060 44.101 2 True 20\\n\",\n \"16 NeuralNetFastAI_BAG_L2 0.859 0.974 30.341 206.390 2499.116 4.417 6.781 289.964 2 True 18\\n\",\n \"17 XGBoost_BAG_L1 0.857 0.965 3.367 8.731 94.352 3.367 8.731 94.352 1 True 9\\n\",\n \"18 LightGBMXT_BAG_L1 0.853 0.963 7.588 36.276 177.582 7.588 36.276 177.582 1 True 1\\n\",\n \"19 LightGBM_BAG_L1 0.851 0.969 3.731 17.236 97.615 3.731 17.236 97.615 1 True 2\\n\",\n \"20 NeuralNetFastAI_BAG_L1 0.837 0.909 5.404 6.713 166.515 5.404 6.713 166.515 1 True 8\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INFO: benchmark_ag.py: auc on test data: 0.8761825926835967\\n\",\n \"INFO: benchmark_ag.py: tpr@1%fpr on test data: 0.4408502772643253\\n\"\n ]\n }\n ],\n \"source\": [\n \"run_ag(dataset, BASE_PATH, time_limit=3600, presets='best_quality')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0f720c57\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"conda_mxnet_latest_p37\",\n \"language\": \"python\",\n \"name\": \"conda_mxnet_latest_p37\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "scripts/reproducibility/autosklearn/README.md", "content": "## Steps to reproduce Auto-sklearn models\n\n\n1. Load and save the datasets locally using [FDB Loader](../../examples/Test_FDB_Loader.ipynb). Keep note of `{DATASET_PATH}` that contains local paths to datasets containing `train.csv`, `test.csv` and `test_labels.csv` from FDB loader.\n\n2. Run `benchmark_autosklearn.py` using following:\n```\npython3 benchmark_autosklearn.py {DATASET_PATH}\n```\n\n3. The script after running successfully will save results in the `DATASET_PATH`. The evaluation metrics on `test.csv` will be saved in `test_metrics_autosklearn.joblib`. \n\n*Note: Python 3.7+ is needed to run the used version of auto-sklearn and to reproduce the results. Similar to other auto-ml frameworks, auto-sklearn is also not perfectly reproducible because some underlying models are not deterministically seeded. However, the variations in results are within acceptable errors.*\n" }, { "path": "scripts/reproducibility/autosklearn/benchmark_autosklearn.py", "content": "\nimport json\nimport joblib\nimport datetime\nimport numpy as np\nimport pandas as pd\nimport os, sys, shutil\n\nfrom autosklearn.metrics import roc_auc, log_loss\nfrom autosklearn.classification import AutoSklearnClassifier\n\nfrom sklearn.metrics import roc_auc_score, roc_curve\nfrom pandas.api.types import is_numeric_dtype, is_string_dtype\n\nimport logging\nFORMAT = \"%(levelname)s: %(name)s: %(message)s\"\nDATE_FORMAT = \"%Y-%m-%d %H:%M:%S\"\nlogging.basicConfig(level=logging.WARN, format=FORMAT, datefmt=DATE_FORMAT)\nlogger = logging.getLogger(os.path.basename(__file__))\nlogger.setLevel(logging.DEBUG)\n\nlogging_config = {\n 'version': 1,\n 'disable_existing_loggers': False,\n 'formatters': {\n 'simple': {\n 'format': '%(levelname)-8s %(name)-15s %(message)s'\n }\n },\n 'handlers':{\n 'console_handler': {\n 'class': 'logging.StreamHandler',\n 'formatter': 'simple'\n },\n 'file_handler': {\n 'class':'logging.FileHandler',\n 'mode': 'a',\n 'encoding': 'utf-8',\n 'filename':'main.log',\n 'formatter': 'simple'\n },\n 'spec_handler':{\n 'class':'logging.FileHandler',\n 'filename':'dummy_autosklearn.log',\n 'formatter': 'simple'\n },\n 'distributed_logfile':{\n 'filename':'distributed.log',\n 'class': 'logging.FileHandler',\n 'formatter': 'simple',\n 'level': 'DEBUG'\n }\n },\n 'loggers': {\n '': {\n 'level': 'INFO',\n 'handlers':['file_handler', 'console_handler']\n },\n 'autosklearn': {\n 'level': 'INFO',\n 'propagate': False,\n 'handlers': ['spec_handler']\n },\n 'smac': {\n 'level': 'INFO',\n 'propagate': False,\n 'handlers': ['spec_handler']\n },\n 'EnsembleBuilder': {\n 'level': 'INFO',\n 'propagate': False,\n 'handlers': ['spec_handler']\n },\n },\n}\n\ndef load_data(dataset_path):\n logger.info(dataset_path)\n \n df_train = pd.read_csv(f\"{dataset_path}/train.csv\", lineterminator='\\n')\n logger.info(df_train.shape)\n \n df_test = pd.read_csv(f\"{dataset_path}/test.csv\")\n logger.info(df_test.shape)\n \n df_test_labels = pd.read_csv(f\"{dataset_path}/test_labels.csv\")\n logger.info(df_test_labels.shape)\n \n df_test = df_test.merge(df_test_labels, how=\"inner\", on=\"EVENT_ID\")\n logger.info(df_test.shape)\n \n \n features_to_exclude = (\"EVENT_LABEL\", \"EVENT_TIMESTAMP\", \"LABEL_TIMESTAMP\", \"ENTITY_TYPE\", \"ENTITY_ID\", \"EVENT_ID\")\n features = [x for x in df_test.columns if x not in features_to_exclude ]\n logger.info(len(features))\n logger.info(features)\n \n return features, df_train, df_test\n\n\ndef get_recall(fpr, tpr, fpr_target=0.01): \n return np.interp(fpr_target, fpr, tpr)\n\n\ndef run_autosklearn(dataset_path):\n \n features, df_train, df_test = load_data(dataset_path)\n\n dateTimeObj = datetime.datetime.now()\n timestampStr = dateTimeObj.strftime(\"%Y%m%d_%H%M%S\")\n \n numeric_features = [f for f in features if is_numeric_dtype(df_train[f])]\n categorical_features = [f for f in features if f not in numeric_features]\n logger.info(f'categorical: {categorical_features}')\n logger.info(f'numeric: {numeric_features}')\n \n labels = sorted(df_train['EVENT_LABEL'].unique())\n df_train['EVENT_LABEL'].replace({labels[0]: 0, labels[1]: 1}, inplace=True)\n df_test['EVENT_LABEL'].replace({labels[0]: 0, labels[1]: 1}, inplace=True)\n \n for df in [df_train, df_test]:\n df[categorical_features] = df[categorical_features].fillna('')\n df[categorical_features] = df[categorical_features].astype('category')\n \n out_dir = f\"{dataset_path}/AutoSklearnModels/\"\n if os.path.exists(out_dir):\n shutil.rmtree(out_dir)\n \n automl = AutoSklearnClassifier(\n metric=roc_auc,\n scoring_functions=[roc_auc, log_loss],\n tmp_folder=out_dir, # for debugging\n delete_tmp_folder_after_terminate=False,\n logging_config=logging_config,\n n_jobs=-1,\n memory_limit=None\n )\n \n assert len(categorical_features) + len(numeric_features) == len(features)\n \n logger.info('Fitting')\n automl.fit(df_train[features], df_train['EVENT_LABEL'])\n joblib.dump(automl, f\"{dataset_path}/automl.joblib\")\n \n cv = pd.DataFrame(automl.cv_results_)\n cv.to_csv(f\"{dataset_path}/cv_results_autosklearn.csv\", index=False)\n \n df_pred = automl.predict_proba(df_test[features])[:,1]\n \n auc_score = roc_auc_score(df_test['EVENT_LABEL'], df_pred)\n logger.info(f\"auc on test data: {auc_score}\")\n \n fpr, tpr, thresholds = roc_curve(df_test['EVENT_LABEL'], df_pred)\n \n recall = get_recall(fpr, tpr, fpr_target=0.01)\n logger.info(f\"tpr@1%fpr on test data: {recall}\")\n \n test_metrics = {\n \"labels\": df_test['EVENT_LABEL'],\n \"pred_prob\": df_pred, \n \"auc\": auc_score,\n \"tpr@1%fpr\": recall,\n \"fpr\": fpr,\n \"tpr\": tpr,\n \"thresholds\": thresholds\n }\n joblib.dump(test_metrics, f\"{dataset_path}/test_metrics_autosklearn.joblib\")\n \nif __name__ == \"__main__\":\n args = sys.argv\n logger.info(args)\n run_autosklearn(args[1])\n " }, { "path": "scripts/reproducibility/benchmark_utils.py", "content": "import numpy as np\nimport pandas as pd\nimport os\n\nimport matplotlib as mpl\n\nmpl.rcParams['figure.dpi'] = 150\npd.set_option('display.max_columns', 500)\npd.set_option('display.max_rows', 500)\npd.set_option('display.width', 200)\npd.set_option('display.float_format', lambda x: '%.3f' % x)\n\nimport logging\nFORMAT = \"%(levelname)s: %(name)s: %(message)s\"\nDATE_FORMAT = \"%Y-%m-%d %H:%M:%S\"\nlogging.basicConfig(level=logging.WARN, format=FORMAT, datefmt=DATE_FORMAT)\nlogger = logging.getLogger(os.path.basename(__file__))\nlogger.setLevel(logging.DEBUG)\n\n\n\ndef load_data(dataset, base_path):\n logger.info(dataset)\n \n df_train = pd.read_csv(f\"{base_path}/{dataset}/train.csv\", lineterminator='\\n')\n logger.info(df_train.shape)\n \n df_test = pd.read_csv(f\"{base_path}/{dataset}/test.csv\")\n logger.info(df_test.shape)\n \n df_test_labels = pd.read_csv(f\"{base_path}/{dataset}/test_labels.csv\")\n logger.info(df_test_labels.shape)\n \n df_test = df_test.merge(df_test_labels, how=\"inner\", on=\"EVENT_ID\")\n logger.info(df_test.shape)\n \n \n features_to_exclude = (\"EVENT_LABEL\", \"EVENT_TIMESTAMP\", \"LABEL_TIMESTAMP\", \"ENTITY_TYPE\", \"ENTITY_ID\", \"EVENT_ID\")\n features = [x for x in df_test.columns if x not in features_to_exclude ]\n logger.info(len(features))\n logger.info(features)\n \n return features, df_train, df_test\n\ndef get_recall(fpr, tpr, fpr_target=0.01): \n return np.interp(fpr_target, fpr, tpr)" }, { "path": "scripts/reproducibility/h2o/README.md", "content": "- benchmark_h2o.py: a script for h2o benchmarking\n- example-h2o-ieeecis.ipynb: an example notebook using benchmark_h2o.py\n\nNote that h2o is not perfectly reproducible because some underlying models are not deterministically seeded, you might see slightly different results than in the paper.\n" }, { "path": "scripts/reproducibility/h2o/benchmark_h2o.py", "content": "import pandas as pd\nimport os\nimport gc\nimport joblib\n\nimport matplotlib as mpl\nfrom sklearn.metrics import roc_auc_score, roc_curve\n\nmpl.rcParams['figure.dpi'] = 150\npd.set_option('display.max_columns', 500)\npd.set_option('display.max_rows', 500)\npd.set_option('display.width', 200)\npd.set_option('display.float_format', lambda x: '%.3f' % x)\n\nimport logging\nFORMAT = \"%(levelname)s: %(name)s: %(message)s\"\nDATE_FORMAT = \"%Y-%m-%d %H:%M:%S\"\nlogging.basicConfig(level=logging.WARN, format=FORMAT, datefmt=DATE_FORMAT)\nlogger = logging.getLogger(os.path.basename(__file__))\nlogger.setLevel(logging.DEBUG)\n\nimport sys\nsys.path.append('../')\nfrom benchmark_utils import load_data, get_recall\n\nimport h2o\nfrom h2o.automl import H2OAutoML\n \ndef run_h2o(dataset, base_path, connect_url=None, time_limit=None, include_algos=None, exclude_algos=None, verbosity=\"info\", seed=10):\n if connect_url is not None:\n _ = h2o.connect(url=connect_url, https=True, verbose=True)\n h2o.cluster().show_status(True)\n else:\n h2o.init()\n \n gc.collect()\n features, df_train, df_test = load_data(dataset, base_path)\n \n df_train_h2o = h2o.H2OFrame(df_train)\n feature_types_h2o = {k:df_train_h2o.types[k] for k in df_train_h2o.types if k in features}\n # force test schema the same as train schema, otherwise predict will throw errors\n df_test_h2o = h2o.H2OFrame(df_test, column_types=feature_types_h2o)\n \n df_train_h2o['EVENT_LABEL'] = df_train_h2o['EVENT_LABEL'].asfactor()\n df_test_h2o['EVENT_LABEL'] = df_test_h2o['EVENT_LABEL'].asfactor()\n \n aml = H2OAutoML(max_runtime_secs = time_limit, seed = seed,\n include_algos=include_algos,\n exclude_algos=exclude_algos,\n export_checkpoints_dir=f\"{base_path}/{dataset}/H2OModels/\",\n verbosity=verbosity)\n \n # use validation error in the leaderboard to avoid leakage when calling aml.predict\n aml.train(x = features, \n y = 'EVENT_LABEL', \n training_frame = df_train_h2o, \n )\n \n lb = aml.leaderboard\n # lb.head(rows=lb.nrows)\n \n h2o.h2o.download_csv(lb, f\"{base_path}/{dataset}/leaderboard_h2o.csv\")\n \n lb_2 = h2o.automl.get_leaderboard(aml, extra_columns = \"ALL\")\n h2o.h2o.download_csv(lb_2, f\"{base_path}/{dataset}/leaderboard_h2o_full.csv\")\n # Get training timing info\n info = aml.training_info\n joblib.dump(info, f\"{base_path}/{dataset}/training_info.joblib\")\n \n df_pred_h2o = aml.predict(df_test_h2o[features])\n pos_label = df_test_h2o['EVENT_LABEL'].levels()[0][-1] # levels are ordered alphabetically\n\n pos_label2 = 'p'+pos_label if pos_label=='1' else pos_label\n df_pred_h2o = (h2o.as_list(df_pred_h2o[pos_label2]))[pos_label2]\n\n auc = roc_auc_score(df_test['EVENT_LABEL'], df_pred_h2o)\n logger.info(f\"auc on test data: {auc}\")\n \n fpr, tpr, thresholds = roc_curve(df_test['EVENT_LABEL'].astype(str), df_pred_h2o, \n pos_label=pos_label)\n \n y_true = df_test['EVENT_LABEL']\n y_true = (y_true.astype(str)==pos_label)\n \n recall = get_recall(fpr, tpr, fpr_target=0.01)\n logger.info(f\"tpr@1%fpr on test data: {recall}\")\n\n test_metrics_h2o = {\n \"pos_label\": pos_label,\n \"labels\": df_test['EVENT_LABEL'],\n \"pred_prob\": df_pred_h2o, \n \"auc\": auc,\n \"tpr@1%fpr\": recall,\n \"fpr\": fpr,\n \"tpr\": tpr,\n \"thresholds\": thresholds\n }\n joblib.dump(test_metrics_h2o, f\"{base_path}/{dataset}/test_metrics_h2o.joblib\")\n \n h2o.cluster().shutdown(prompt=False)" }, { "path": "scripts/reproducibility/h2o/example-h2o-ieeecis.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"afc2eecf\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"%load_ext autoreload\\n\",\n \"%autoreload 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"f00a81aa\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from IPython.core.display import display, HTML\\n\",\n \"from IPython.display import clear_output\\n\",\n \"display(HTML(\\\"\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"11759d10\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import warnings\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2baa2261\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: pip install required packages if not installed already\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"1efdc80c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# !pip install h2o\\n\",\n \"import benchmark_h2o\\n\",\n \"from benchmark_h2o import load_data, run_h2o\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6b1e0a24\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: download data using fdb\\n\",\n \"Example: https://github.com/amazon-research/fraud-dataset-benchmark/blob/main/scripts/examples/Test_FDB_Loader.ipynb\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"0a34e883\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# This is where datasets are stored: {BASE_PATH}/{dataset}/\\n\",\n \"BASE_PATH = \\\"/home/ec2-user/SageMaker/official-dataset-names\\\"\\n\",\n \"dataset = \\\"IEEE-CIS Fraud Detection\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8aed893e\",\n \"metadata\": {},\n \"source\": [\n \"Make sure three files are downloaded:\\n\",\n \"1. {BASE_PATH}/{dataset}/train.csv\\n\",\n \"2. {BASE_PATH}/{dataset}/test.csv\\n\",\n \"3. {BASE_PATH}/{dataset}/test_labels.csv\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b02d77e0\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: look at data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"9dfd0df9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INFO: benchmark_utils.py: IEEE-CIS Fraud Detection\\n\",\n \"INFO: benchmark_utils.py: (313060, 194)\\n\",\n \"INFO: benchmark_utils.py: (27330, 71)\\n\",\n \"INFO: benchmark_utils.py: (29527, 2)\\n\",\n \"INFO: benchmark_utils.py: (27329, 72)\\n\",\n \"INFO: benchmark_utils.py: 67\\n\",\n \"INFO: benchmark_utils.py: ['transactionamt', 'productcd', 'card1', 'card2', 'card3', 'card5', 'card6', 'addr1', 'dist1', 'p_emaildomain', 'r_emaildomain', 'c1', 'c2', 'c4', 'c5', 'c6', 'c7', 'c8', 'c9', 'c10', 'c11', 'c12', 'c13', 'c14', 'v62', 'v70', 'v76', 'v78', 'v82', 'v91', 'v127', 'v130', 'v139', 'v160', 'v165', 'v187', 'v203', 'v207', 'v209', 'v210', 'v221', 'v234', 'v257', 'v258', 'v261', 'v264', 'v266', 'v267', 'v271', 'v274', 'v277', 'v283', 'v285', 'v289', 'v291', 'v294', 'id_01', 'id_02', 'id_05', 'id_06', 'id_09', 'id_13', 'id_17', 'id_19', 'id_20', 'devicetype', 'deviceinfo']\\n\"\n ]\n }\n ],\n \"source\": [\n \"features, df_train, df_test = load_data(dataset, BASE_PATH)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"eebaa1d5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" 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The function run_h2o below also saves a leaderboard file (leaderboard_xxx.csv) and a test metrics file (test_metrics_xxx.joblib) into {BASE_PATH}/{dataset}/, respectively\\n\",\n \"2. H2O models are saved at {BASE_PATH}/{dataset}/H2OModels\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"f851663a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# H2OStartupError: Your java is not supported: java version \\\"1.7.0_261\\\"; OpenJDK Runtime Environment (amzn-2.6.22.1.84.amzn1-x86_64 u261-b02); OpenJDK 64-Bit Server VM (build 24.261-b02, mixed mode)\\n\",\n \"# If you see this error above, you may need to run the following instructions:\\n\",\n \"\\n\",\n \"# !sudo yum install -y java-1.8.0-openjdk.x86_64\\n\",\n \"# !sudo /usr/sbin/alternatives --set java /usr/lib/jvm/jre-1.8.0-openjdk.x86_64/bin/java\\n\",\n \"# !sudo /usr/sbin/alternatives --set javac /usr/lib/jvm/jre-1.8.0-openjdk.x86_64/bin/javac\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d259b767\",\n \"metadata\": {},\n \"source\": [\n \"https://swiftotter.com/technical/amazon-aws-jenkins-2-60-1-java-8-update#/\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"63ec8755\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"openjdk version \\\"1.8.0_312\\\"\\n\",\n \"OpenJDK Runtime Environment (build 1.8.0_312-b07)\\n\",\n \"OpenJDK 64-Bit Server VM (build 25.312-b07, mixed mode)\\n\"\n ]\n }\n ],\n \"source\": [\n \"!java -version\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"9dc0646d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'3.36.1.2'\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import h2o\\n\",\n \"h2o.__version__\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"7c060159\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.\\n\",\n \"Attempting to start a local H2O server...\\n\",\n \" Java Version: openjdk version \\\"1.8.0_312\\\"; OpenJDK Runtime Environment (build 1.8.0_312-b07); OpenJDK 64-Bit Server VM (build 25.312-b07, mixed mode)\\n\",\n \" Starting server from /home/ec2-user/anaconda3/envs/mxnet_latest_p37/lib/python3.7/site-packages/h2o/backend/bin/h2o.jar\\n\",\n \" Ice root: /tmp/tmpag6zcv5a\\n\",\n \" JVM stdout: /tmp/tmpag6zcv5a/h2o_ec2_user_started_from_python.out\\n\",\n \" JVM stderr: /tmp/tmpag6zcv5a/h2o_ec2_user_started_from_python.err\\n\",\n \" Server is running at http://127.0.0.1:54321\\n\",\n \"Connecting to H2O server at http://127.0.0.1:54321 ... successful.\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
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H2O_cluster_uptime:02 secs
H2O_cluster_timezone:UTC
H2O_data_parsing_timezone:UTC
H2O_cluster_version:3.36.1.2
H2O_cluster_version_age:20 days
H2O_cluster_name:H2O_from_python_ec2_user_t9z3ig
H2O_cluster_total_nodes:1
H2O_cluster_free_memory:26.64 Gb
H2O_cluster_total_cores:64
H2O_cluster_allowed_cores:64
H2O_cluster_status:locked, healthy
H2O_connection_url:http://127.0.0.1:54321
H2O_connection_proxy:{\\\"http\\\": null, \\\"https\\\": null}
H2O_internal_security:False
Python_version:3.7.10 final
\"\n ],\n \"text/plain\": [\n \"-------------------------- -------------------------------\\n\",\n \"H2O_cluster_uptime: 02 secs\\n\",\n \"H2O_cluster_timezone: UTC\\n\",\n \"H2O_data_parsing_timezone: UTC\\n\",\n \"H2O_cluster_version: 3.36.1.2\\n\",\n \"H2O_cluster_version_age: 20 days\\n\",\n \"H2O_cluster_name: H2O_from_python_ec2_user_t9z3ig\\n\",\n \"H2O_cluster_total_nodes: 1\\n\",\n \"H2O_cluster_free_memory: 26.64 Gb\\n\",\n \"H2O_cluster_total_cores: 64\\n\",\n \"H2O_cluster_allowed_cores: 64\\n\",\n \"H2O_cluster_status: locked, healthy\\n\",\n \"H2O_connection_url: http://127.0.0.1:54321\\n\",\n \"H2O_connection_proxy: {\\\"http\\\": null, \\\"https\\\": null}\\n\",\n \"H2O_internal_security: False\\n\",\n \"Python_version: 3.7.10 final\\n\",\n \"-------------------------- -------------------------------\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INFO: benchmark_utils.py: IEEE-CIS Fraud Detection\\n\",\n \"INFO: benchmark_utils.py: (313060, 194)\\n\",\n \"INFO: benchmark_utils.py: (27330, 71)\\n\",\n \"INFO: benchmark_utils.py: (29527, 2)\\n\",\n \"INFO: benchmark_utils.py: (27329, 72)\\n\",\n \"INFO: benchmark_utils.py: 67\\n\",\n \"INFO: benchmark_utils.py: ['transactionamt', 'productcd', 'card1', 'card2', 'card3', 'card5', 'card6', 'addr1', 'dist1', 'p_emaildomain', 'r_emaildomain', 'c1', 'c2', 'c4', 'c5', 'c6', 'c7', 'c8', 'c9', 'c10', 'c11', 'c12', 'c13', 'c14', 'v62', 'v70', 'v76', 'v78', 'v82', 'v91', 'v127', 'v130', 'v139', 'v160', 'v165', 'v187', 'v203', 'v207', 'v209', 'v210', 'v221', 'v234', 'v257', 'v258', 'v261', 'v264', 'v266', 'v267', 'v271', 'v274', 'v277', 'v283', 'v285', 'v289', 'v291', 'v294', 'id_01', 'id_02', 'id_05', 'id_06', 'id_09', 'id_13', 'id_17', 'id_19', 'id_20', 'devicetype', 'deviceinfo']\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\\n\",\n \"Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\\n\",\n \"AutoML progress: |\\n\",\n \"17:56:29.266: Project: AutoML_1_20220615_175629\\n\",\n \"17:56:29.267: 5-fold cross-validation will be used.\\n\",\n \"17:56:29.270: Setting stopping tolerance adaptively based on the training frame: 0.0017872537194430643\\n\",\n \"17:56:29.271: Build control seed: 10\\n\",\n \"17:56:29.274: training frame: Frame key: AutoML_1_20220615_175629_training_py_11_sid_9519 cols: 194 rows: 313060 chunks: 68 size: 137888486 checksum: -8673498857111412012\\n\",\n \"17:56:29.274: validation frame: NULL\\n\",\n \"17:56:29.274: leaderboard frame: NULL\\n\",\n \"17:56:29.275: blending frame: NULL\\n\",\n \"17:56:29.275: response column: EVENT_LABEL\\n\",\n \"17:56:29.275: fold column: null\\n\",\n \"17:56:29.275: weights column: null\\n\",\n \"17:56:29.289: Loading execution steps: [{XGBoost : [def_2 (1g, 10w), def_1 (2g, 10w), def_3 (3g, 10w), grid_1 (4g, 90w), lr_search (6g, 30w)]}, {GLM : [def_1 (1g, 10w)]}, {DRF : [def_1 (2g, 10w), XRT (3g, 10w)]}, {GBM : [def_5 (1g, 10w), def_2 (2g, 10w), def_3 (2g, 10w), def_4 (2g, 10w), def_1 (3g, 10w), grid_1 (4g, 60w), lr_annealing (6g, 10w)]}, {DeepLearning : [def_1 (3g, 10w), grid_1 (4g, 30w), grid_2 (5g, 30w), grid_3 (5g, 30w)]}, {completion : [resume_best_grids (10g, 60w)]}, {StackedEnsemble : [best_of_family_1 (1g, 5w), best_of_family_2 (2g, 5w), best_of_family_3 (3g, 5w), best_of_family_4 (4g, 5w), best_of_family_5 (5g, 5w), all_2 (2g, 10w), all_3 (3g, 10w), all_4 (4g, 10w), all_5 (5g, 10w), monotonic (6g, 10w), best_of_family_gbm (6g, 10w), all_gbm (7g, 10w), best_of_family_xglm (8g, 10w), all_xglm (8g, 10w), best_of_family (10g, 10w), best_N (10g, 10w)]}]\\n\",\n \"17:56:29.317: AutoML job created: 2022.06.15 17:56:29.241\\n\",\n \"17:56:29.318: AutoML build started: 2022.06.15 17:56:29.317\\n\",\n \"17:56:29.402: AutoML: starting XGBoost_1_AutoML_1_20220615_175629 model training\\n\",\n \"\\n\",\n \"████████████████\\n\",\n \"18:10:12.338: New leader: XGBoost_1_AutoML_1_20220615_175629, auc: 0.9547716353973954\\n\",\n \"18:10:12.344: AutoML: starting GLM_1_AutoML_1_20220615_175629 model training\\n\",\n \"\\n\",\n \"██\\n\",\n \"18:12:16.9: AutoML: starting GBM_1_AutoML_1_20220615_175629 model training\\n\",\n \"\\n\",\n \"███\\n\",\n \"18:15:33.456: New leader: GBM_1_AutoML_1_20220615_175629, auc: 0.9552574031901246\\n\",\n \"18:15:33.476: AutoML: starting StackedEnsemble_BestOfFamily_1_AutoML_1_20220615_175629 model training\\n\",\n \"\\n\",\n \"\\n\",\n \"18:15:37.718: New leader: StackedEnsemble_BestOfFamily_1_AutoML_1_20220615_175629, auc: 0.9629154248601777\\n\",\n \"18:15:37.722: AutoML: starting XGBoost_2_AutoML_1_20220615_175629 model training\\n\",\n \"\\n\",\n \"███████\\n\",\n \"18:22:03.32: AutoML: starting DRF_1_AutoML_1_20220615_175629 model training\\n\",\n \"\\n\",\n \"█\\n\",\n \"18:23:30.486: AutoML: starting GBM_2_AutoML_1_20220615_175629 model training\\n\",\n \"\\n\",\n \"██\\n\",\n \"18:25:25.304: AutoML: starting GBM_3_AutoML_1_20220615_175629 model training\\n\",\n \"\\n\",\n \"██\\n\",\n \"18:27:17.200: AutoML: starting GBM_4_AutoML_1_20220615_175629 model training\\n\",\n \"\\n\",\n \"██\\n\",\n \"18:29:30.748: AutoML: starting StackedEnsemble_BestOfFamily_2_AutoML_1_20220615_175629 model training\\n\",\n \"\\n\",\n \"\\n\",\n \"18:29:34.124: New leader: StackedEnsemble_BestOfFamily_2_AutoML_1_20220615_175629, auc: 0.9629166327028895\\n\",\n \"18:29:34.130: AutoML: starting StackedEnsemble_AllModels_1_AutoML_1_20220615_175629 model training\\n\",\n \"\\n\",\n \"\\n\",\n \"18:29:37.696: New leader: StackedEnsemble_AllModels_1_AutoML_1_20220615_175629, auc: 0.9641251732621091\\n\",\n \"18:29:37.699: AutoML: starting XGBoost_3_AutoML_1_20220615_175629 model training\\n\",\n \"\\n\",\n \"████\\n\",\n \"18:33:45.401: AutoML: starting XRT_1_AutoML_1_20220615_175629 model training\\n\",\n \"\\n\",\n \"██\\n\",\n \"18:35:01.402: AutoML: starting GBM_5_AutoML_1_20220615_175629 model training\\n\",\n \"\\n\",\n \"██\\n\",\n \"18:37:04.114: AutoML: starting DeepLearning_1_AutoML_1_20220615_175629 model training\\n\",\n \"\\n\",\n \"████████\\n\",\n \"18:45:23.506: AutoML: starting StackedEnsemble_BestOfFamily_3_AutoML_1_20220615_175629 model training\\n\",\n \"\\n\",\n \"\\n\",\n \"18:45:26.972: AutoML: starting StackedEnsemble_AllModels_2_AutoML_1_20220615_175629 model training\\n\",\n \"\\n\",\n \"\\n\",\n \"18:45:30.973: AutoML: starting XGBoost_grid_1_AutoML_1_20220615_175629 hyperparameter search\\n\",\n \"\\n\",\n \"██████\\n\",\n \"18:50:41.172: AutoML: starting GBM_grid_1_AutoML_1_20220615_175629 hyperparameter search\\n\",\n \"\\n\",\n \"███\\n\",\n \"18:54:01.180: AutoML: starting DeepLearning_grid_1_AutoML_1_20220615_175629 hyperparameter search\\n\",\n \"\\n\",\n \"███| (done) 100%\\n\",\n \"\\n\",\n \"18:56:30.999: Actual modeling steps: [{XGBoost : [def_2 (1g, 10w)]}, {GLM : [def_1 (1g, 10w)]}, {GBM : [def_5 (1g, 10w)]}, {StackedEnsemble : [best_of_family_1 (1g, 5w)]}, {XGBoost : [def_1 (2g, 10w)]}, {DRF : [def_1 (2g, 10w)]}, {GBM : [def_2 (2g, 10w), def_3 (2g, 10w), def_4 (2g, 10w)]}, {StackedEnsemble : [best_of_family_2 (2g, 5w), all_2 (2g, 10w)]}, {XGBoost : [def_3 (3g, 10w)]}, {DRF : [XRT (3g, 10w)]}, {GBM : [def_1 (3g, 10w)]}, {DeepLearning : [def_1 (3g, 10w)]}, {StackedEnsemble : [best_of_family_3 (3g, 5w), all_3 (3g, 10w)]}, {XGBoost : [grid_1 (4g, 90w)]}, {GBM : [grid_1 (4g, 60w)]}, {DeepLearning : [grid_1 (4g, 30w)]}]\\n\",\n \"18:56:30.999: AutoML build stopped: 2022.06.15 18:56:30.999\\n\",\n \"18:56:30.999: AutoML build done: built 16 models\\n\",\n \"18:56:30.999: AutoML duration: 1:00:01.682\\n\",\n \"\\n\",\n \"stackedensemble prediction progress: |███████████████████████████████████████████| (done) 100%\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INFO: benchmark_h2o.py: auc on test data: 0.8816474193300993\\n\",\n \"INFO: benchmark_h2o.py: tpr@1%fpr on test data: 0.43807763401109057\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"H2O session _sid_9519 closed.\\n\"\n ]\n }\n ],\n \"source\": [\n \"run_h2o(dataset, BASE_PATH, time_limit=3600)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b0a81d69\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"70efd958\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"conda_mxnet_latest_p37\",\n \"language\": \"python\",\n \"name\": \"conda_mxnet_latest_p37\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "scripts/reproducibility/label-noise/benchmark_experiments.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c77e5eb5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#! pip install humanize\\n\",\n \"#! pip install catboost\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f8bd366d\",\n \"metadata\": {},\n \"source\": [\n \"# Label noise\\n\",\n \"\\n\",\n \"\\n\",\n \"## Problem statement \\n\",\n \"Have some binary classification task, traditionally assume data of the form X,y\\n\",\n \"\\n\",\n \"In reality, some of the labels may be incorrect, distinguish\\n\",\n \"```\\n\",\n \"y - true label\\n\",\n \"y* - observed, possibly incorrect label\\n\",\n \"```\\n\",\n \"\\n\",\n \"This can obviously effect model training, validation. Would also effect benchmarking process (comparing performance on noisy data doesn't tell you about performance on actual data).\\n\",\n \"\\n\",\n \"## Types of noise\\n\",\n \"\\n\",\n \"Can be completely independent:\\n\",\n \"`p(y* != y | x, y) = p(y* != y)`\\n\",\n \"\\n\",\n \"class-dependent, depends on y:\\n\",\n \"`p(y* != y | x, y) = p(y* != y | y)`\\n\",\n \"\\n\",\n \"feature-dependent, depends on x:\\n\",\n \"`p(y* != y | x, y) = p(y* != y | x, y)`\\n\",\n \"\\n\",\n \"In fraud modeling, higher likelihood of `(y*, y) = (0, 1)` than reverse.\\n\",\n \"(missed fraud, label maturity, intentional data poisoning, etc.)\\n\",\n \"\\n\",\n \"\\\"feature-dependent\\\" is probably most realistic in fraud but fewer removal techniques and also harder to synthetically generate. We will work with \\\"boundary conditional\\\" noise, probability of being mislabeled is weighted by distance from some decision boundary (score from model trained on clean data), implemented in scikit-clean.\\n\",\n \"\\n\",\n \"## Literature/packages\\n\",\n \"\\n\",\n \"Many methods in the literature to address this; can build loss functions that are robust to noise, can try to identify and filter (remove) or clean (flip label) examples identified as noisy.\\n\",\n \"\\n\",\n \"Some packages including CleanLab and scikit-clean. Can also hand-code an ensemble method. Most of these are model-agnostic.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9b172deb\",\n \"metadata\": {},\n \"source\": [\n \"## CleanLab\\n\",\n \"\\n\",\n \"well-established, state of the art, open source package with some theoretical guarantees\\n\",\n \"\\n\",\n \"score all examples with y* = 1, determine average score t_1\\n\",\n \"now score all examples with y* = 0. Any that score above t_1 are marked as noise\\n\",\n \"\\n\",\n \"can wrap any (sklearn-compatible) model with this process. \\n\",\n \"\\n\",\n \"## scikit-clean \\n\",\n \"\\n\",\n \"library of several different approaches including filtering as well as noise generation. Is similarly designed to be model-agnostic but doesn't always do a great job (doesn't handle unencoded categorical features well). Some of its methods can also be *very* slow relative to others\\n\",\n \"\\n\",\n \"## micro-models\\n\",\n \"\\n\",\n \"slice up training data, train a model on each slice, let models vote on whether to remove data. Can use majority (more than half of models \\\"misclassify\\\" example), consensus (all models misclassify) or any other threshold.\\n\",\n \"\\n\",\n \"## experiment design\\n\",\n \"\\n\",\n \"take 7 of the datasets - [‘ieeecis’, ‘ccfraud’, ‘fraudecom’, ‘sparknov’, ‘fakejob’, ‘vehicleloan’,‘twitterbot’]\\n\",\n \"* drop IP and malurl dataset as they are difficult to work with \\\"out of the box\\\"\\n\",\n \"* use numerical and categorical features, target-encode categorical features (drop text and enrichable features)\\n\",\n \"\\n\",\n \"add boundary-conditional noise `n` to training data (flipping both classes).\\n\",\n \"\\n\",\n \"values: `n in [0, 0.1, 0.2, 0.3, 0.4, 0.5]`\\n\",\n \" \\n\",\n \"target encoding is done after noise is added\\n\",\n \" \\n\",\n \"Catboost used as base classifier in all cases (with default settings)\\n\",\n \"\\n\",\n \"compare following methods for cleaning training data\\n\",\n \"* baseline (no cleaning done)\\n\",\n \"* CleanLab\\n\",\n \"* scikit-clean MCS \\n\",\n \"* micro-model majority voting (hand-built)\\n\",\n \"* micro-model consensus voting (hand-built)\\n\",\n \"\\n\",\n \"measure AUC on (clean) test data\\n\",\n \"\\n\",\n \"repeat process 5 times for each experiment (start with clean data, add random noise, filter noise back out, train classifier, etc.), compute mean and std. dev of AUC for each\\n\",\n \"\\n\",\n \"CleanLab usually winds up being the best, but not uniformly. Baseline is sometimes the best for zero noise (as expected), and sometimes MCS or micro-model majority will come out ahead\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"846f161f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# basic imports\\n\",\n \"import os\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import warnings\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"import humanize\\n\",\n \"import pickle\\n\",\n \"\\n\",\n \"# basics from sklearn\\n\",\n \"from sklearn.metrics import roc_auc_score\\n\",\n \"from category_encoders.target_encoder import TargetEncoder\\n\",\n \"\\n\",\n \"# noise generation\\n\",\n \"from skclean.simulate_noise import flip_labels_cc, BCNoise\\n\",\n \"\\n\",\n \"# base classifiers\\n\",\n \"from catboost import CatBoostClassifier\\n\",\n \"\\n\",\n \"# cleaning methods/helpers\\n\",\n \"from cleanlab.classification import CleanLearning\\n\",\n \"from micro_models import MicroModelCleaner\\n\",\n \"from skclean.pipeline import Pipeline\\n\",\n \"from skclean.handlers import Filter\\n\",\n \"from skclean.detectors import MCS\\n\",\n \"\\n\",\n \"# dataset loader\\n\",\n \"from load_fdb_datasets import prepare_noisy_dataset, dataset_stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"85117ba5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# wrapper definitions for the various types of cleaning methods we will use. \\n\",\n \"# Each one wraps a model_class (in our case catboost, but could use xgboost, etc.)\\n\",\n \"# resulting model_class can then take noisy data in its .fit() method and clean before training\\n\",\n \"\\n\",\n \"def baseline_model(model_class, params):\\n\",\n \" return model_class(**params)\\n\",\n \"\\n\",\n \"def cleanlab_model(model_class, params, pulearning=False):\\n\",\n \" if pulearning:\\n\",\n \" return CleanLearning(model_class(**params), pulearning=pulearning)\\n\",\n \" else:\\n\",\n \" return CleanLearning(model_class(**params))\\n\",\n \" \\n\",\n \"def micromodels(model_class, pulearning, num_clfs, threshold, params):\\n\",\n \" return MicroModelCleaner(model_class, pulearning=pulearning, num_clfs=num_clfs, threshold=threshold, **params)\\n\",\n \"\\n\",\n \"def skclean_MCS(model_class, params):\\n\",\n \" skclean_pipeline = Pipeline([\\n\",\n \" ('detector',MCS(classifier=model_class(**params))),\\n\",\n \" ('handler',Filter(model_class(**params)))\\n\",\n \" ])\\n\",\n \" return skclean_pipeline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"bd6bcd08\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# some high-level parameters, \\n\",\n \"# the number of runs for each experiment (determine mean/std. dev)\\n\",\n \"num_samples = 5 \\n\",\n \"# whether to use target encoding on categorical features\\n\",\n \"target_encoding = True\\n\",\n \"# whether to save intermediate results to disk (in case of failure etc.)\\n\",\n \"save_results = True\\n\",\n \"\\n\",\n \"# we will be creating a lot of classifiers, let's use the same parameters for each\\n\",\n \"model_config_dict = {\\n\",\n \" 'catboost': {\\n\",\n \" 'model_class': CatBoostClassifier,\\n\",\n \" 'default_params': {\\n\",\n \" 'verbose': False,\\n\",\n \" 'iterations': 100\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"\\n\",\n \"# all of our experiments will use catboost and boundary-consistent noise\\n\",\n \"base_model_type = 'catboost'\\n\",\n \"noise_type = 'boundary-consistent'\\n\",\n \"model_class = model_config_dict[base_model_type]['model_class']\\n\",\n \"\\n\",\n \"# the set of experimental parameters, we will iterate over all these datasets\\n\",\n \"keys = ['ieeecis', 'sparknov', 'ccfraud', 'fraudecom', 'fakejob', 'vehicleloan', 'twitterbot']\\n\",\n \"# all these cleaning methods\\n\",\n \"clf_types = ['baseline', 'skclean_MCS', 'cleanlab', 'micromodels_majority', 'micromodels_consensus']\\n\",\n \"# all these noise levels\\n\",\n \"noise_amounts = [0, 0.1, 0.2, 0.3, 0.4, 0.5]\\n\",\n \"# and we will let cleaning methods know that noise can happen for either class\\n\",\n \"pulearning = None\\n\",\n \"\\n\",\n \"# a little bit of setup for saving intermediate results to disk\\n\",\n \"if save_results:\\n\",\n \" results_file_path = './results'\\n\",\n \" results_file_name = '{}_noise_benchmark_results.pkl'\\n\",\n \" try:\\n\",\n \" os.mkdir(results_file_path)\\n\",\n \" except OSError as error:\\n\",\n \" print(error) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ef2e3bd8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# initialize results dict, we will index results by dataset/noise_amount/cleaning_method\\n\",\n \"results = {}\\n\",\n \"\\n\",\n \"# main experimental loop \\n\",\n \"for key in keys:\\n\",\n \" # check to see if we have already run this experiment and saved to disk\\n\",\n \" full_result_path = os.path.join(results_file_path,results_file_name.format(key))\\n\",\n \" if os.path.exists(full_result_path) and save_results:\\n\",\n \" with open(full_result_path, 'rb') as results_file:\\n\",\n \" results[key] = pickle.load(results_file)\\n\",\n \" # otherwise start from scratch\\n\",\n \" else:\\n\",\n \" # initialize sub-results\\n\",\n \" results[key] = {}\\n\",\n \" model_params = model_config_dict[base_model_type]['default_params']\\n\",\n \" \\n\",\n \" for noise_amount in noise_amounts:\\n\",\n \" print(f\\\"\\\\n =={key}_{noise_amount}== \\\\n\\\")\\n\",\n \" \\n\",\n \" # initialize sub-sub-results\\n\",\n \" results[key][noise_amount] = {}\\n\",\n \"\\n\",\n \" # these are the cleaning classifiers we will use\\n\",\n \" clfs = {\\n\",\n \" 'baseline': baseline_model(model_class, model_params),\\n\",\n \" 'skclean_MCS': skclean_MCS(model_class, model_params),\\n\",\n \" 'cleanlab': cleanlab_model(model_class, model_params, pulearning),\\n\",\n \" 'micromodels_majority': micromodels(model_class, pulearning=pulearning,\\n\",\n \" num_clfs=8, threshold=0.5, params=model_params),\\n\",\n \" 'micromodels_consensus': micromodels(model_class, pulearning=pulearning,\\n\",\n \" num_clfs=8, threshold=1, params=model_params),\\n\",\n \"\\n\",\n \" }\\n\",\n \" print('generating datasets')\\n\",\n \" # preparing a dataset has some overhead, we want to do this five times for each dataset/noise level\\n\",\n \" # we will save a little bit of time by doing this in advance and using same set of five\\n\",\n \" # for each cleaning method\\n\",\n \" datasets = [prepare_noisy_dataset(key, noise_type, noise_amount, split=1, target_encoding=target_encoding) \\n\",\n \" for i in range(num_samples)]\\n\",\n \" \\n\",\n \" # now for each cleaning method, train a \\\"clean\\\" model on noisy training data, then determine\\n\",\n \" # auc on clean test data and record the results. Do this five times for each cleaning method\\n\",\n \" # to determine mean/std. dev\\n\",\n \" for clf_type in clfs:\\n\",\n \" print(f\\\"testing {clf_type}\\\")\\n\",\n \" auc = []\\n\",\n \" try:\\n\",\n \" for i in range(num_samples):\\n\",\n \" # grab the dataset we need for this run and extract metadata and subsets\\n\",\n \" dataset = datasets[i]\\n\",\n \" features, cat_features, label = dataset['features'], dataset['cat_features'], dataset['label']\\n\",\n \" train, test = dataset['train'], dataset['test']\\n\",\n \" X_tr, y_tr = train[features], train[label].values.reshape(-1)\\n\",\n \" X_ts, y_ts = test[features], test[label].values.reshape(-1)\\n\",\n \" clf = clfs[clf_type]\\n\",\n \" # fit the \\\"clean\\\" classifier on noisy training data\\n\",\n \" clf.fit(X_tr, y_tr)\\n\",\n \" # make predictions on clean test data and calculate AUC\\n\",\n \" y_pred = clf.predict_proba(X_ts)[:, 1]\\n\",\n \" auc.append(roc_auc_score(y_ts, y_pred))\\n\",\n \" print(f\\\"{clf_type} auc: {auc}\\\", end=\\\"\\\\r\\\", flush=True)\\n\",\n \" # store mean/std. dev for this run in the results dict\\n\",\n \" results[key][noise_amount][clf_type] = (np.mean(auc), np.std(auc), auc)\\n\",\n \" print('\\\\n{} auc: {:.2f} ± {:.4f}\\\\n'.format(clf_type,\\n\",\n \" *results[key][noise_amount][clf_type][:2]))\\n\",\n \" # if this run failed for some reason, handle it gracefully\\n\",\n \" except Exception as e:\\n\",\n \" results[key][noise_amount][clf_type] = (0, 0, [0] * num_samples)\\n\",\n \" print(e)\\n\",\n \" \\n\",\n \" # if we are saving intermediate results to disk, do so now\\n\",\n \" if save_results:\\n\",\n \" with open(full_result_path, 'wb') as results_file:\\n\",\n \" pickle.dump(results[key], results_file)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7a8a4509\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# a couple of helper functions to analyze/summarize results\\n\",\n \"\\n\",\n \"def highlight_max(s, props=''):\\n\",\n \" return np.where(s == np.nanmax(s.values), props, '')\\n\",\n \"\\n\",\n \"def record_places(places, scores):\\n\",\n \" scores = {k: v for k, v in sorted(scores.items(), key=lambda item: item[1], reverse=True)}\\n\",\n \" last_score, last_stddev, last_placement = (2, 0, 1)\\n\",\n \" for i, clf in enumerate(scores.keys()): \\n\",\n \" if scores[clf][0] + scores[clf][1] >= last_score:\\n\",\n \" placement = last_placement \\n\",\n \" else:\\n\",\n \" placement = i+1\\n\",\n \" last_score, last_stddev = scores[clf] \\n\",\n \" last_placement = i+1\\n\",\n \" places[clf][placement] += 1 \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7fa49c8e\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# create dataframe of results for each experiment, also process results into dict for keeping track of \\n\",\n \"# 1st/2nd/etc. place, as well as a dict for plotting later\\n\",\n \"\\n\",\n \"places = {clf:{p:0 for p in range(1,len(clf_types)+1)} for clf in clf_types}\\n\",\n \"plots = {key:{clf:[[],[]] for clf in clf_types} for key in keys}\\n\",\n \" \\n\",\n \"for key in results.keys():\\n\",\n \" print(f\\\"\\\\n =={key}==\\\\n\\\")\\n\",\n \" rows = pd.Index([clf_type for clf_type in clf_types])\\n\",\n \" columns = pd.MultiIndex.from_product([noise_amounts, ['mean','std_dev']], names=['type 2 noise', 'auc'])\\n\",\n \" df = pd.DataFrame(index=rows, columns=columns)\\n\",\n \" \\n\",\n \" for noise_amount in noise_amounts:\\n\",\n \" scores = {}\\n\",\n \" for clf_type in clf_types:\\n\",\n \" auc = results[key][noise_amount][clf_type] \\n\",\n \" df.loc[clf_type, (noise_amount, 'mean')] = auc[0] \\n\",\n \" df.loc[clf_type, (noise_amount, 'std_dev')] = auc[1]\\n\",\n \" scores[clf_type] = (auc[0], auc[1])\\n\",\n \"\\n\",\n \" plots[key][clf_type][0].append(noise_amount)\\n\",\n \" plots[key][clf_type][1].append(auc[0])\\n\",\n \" record_places(places, scores)\\n\",\n \" display(df.style.set_caption(f\\\"{key}\\\")\\n\",\n \" .format({(n,'mean'): \\\"{:.2f}\\\" for n in noise_amounts})\\n\",\n \" .format({(n,'std_dev'): \\\"{:.4f}\\\" for n in noise_amounts})\\n\",\n \" .apply(highlight_max, props='font-weight:bold;background-color:lightblue', axis=0,\\n\",\n \" subset=[[n,'mean'] for n in noise_amounts]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8cb8dbd8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# produce \\\"race results\\\" (i.e. how many first place, second place, etc. finishes)\\n\",\n \"\\n\",\n \"race_results = pd.DataFrame.from_dict(places).rename(index=lambda x : humanize.ordinal(x))\\n\",\n \"race_results['totals'] = race_results.sum(axis=1)\\n\",\n \"display(race_results)\\n\",\n \"print(race_results.to_latex())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"602877ee\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# finally, we can plot the results of individual experiments\\n\",\n \"\\n\",\n \"colors = ['black','purple','green','red','orange']\\n\",\n \"linestyles = ['-','--',':']\\n\",\n \"ylims = {\\n\",\n \" 'boundary-consistent': {\\n\",\n \" 'ieeecis':[0.5,0.9],\\n\",\n \" 'sparknov':[0.5,1],\\n\",\n \" 'ccfraud':[0.25,1],\\n\",\n \" 'fraudecom':[0.48,0.52],\\n\",\n \" 'fakejob':[0.5,1],\\n\",\n \" 'vehicleloan':[0.57,0.66],\\n\",\n \" 'twitterbot':[0.7,0.95]\\n\",\n \" },\\n\",\n \" 'class-conditional': {\\n\",\n \" 'ieeecis':[0.7,0.9],\\n\",\n \" 'sparknov':[0.7,1],\\n\",\n \" 'ccfraud':[0.8,1],\\n\",\n \" 'fraudecom':[0.48,0.52],\\n\",\n \" 'fakejob':[0.7,1],\\n\",\n \" 'vehicleloan':[0.5,0.7],\\n\",\n \" 'twitterbot':[0.8,0.95]\\n\",\n \" }\\n\",\n \"}\\n\",\n \"\\n\",\n \"x_labels = {\\n\",\n \" 'boundary-consistent':'Boundary-Consistent Noise Level',\\n\",\n \" 'class-conditional':'Class-Conditional Type 2 Noise Level'\\n\",\n \"}\\n\",\n \"\\n\",\n \"legends = {\\n\",\n \" 'boundary-consistent':'Cleaning Method',\\n\",\n \" 'class-conditional':'Type 1 Noise, Cleaning Method'\\n\",\n \"}\\n\",\n \"def fix_failures(x):\\n\",\n \" if x == 0:\\n\",\n \" return None\\n\",\n \" else:\\n\",\n \" return x\\n\",\n \"\\n\",\n \"def labels(noise_type, noise_amount, clf_type):\\n\",\n \" if noise_type == 'boundary-consistent':\\n\",\n \" return '{}'.format(clf_type)\\n\",\n \" elif noise_type == 'class-conditional':\\n\",\n \" return '{}, {}'.format(noise_amount, clf_type)\\n\",\n \"\\n\",\n \"for key in results.keys():\\n\",\n \" plt.figure(figsize=(10,10))\\n\",\n \" \\n\",\n \" for c, clf_type in enumerate(clf_types):\\n\",\n \" a = plots[key][clf_type]\\n\",\n \" plt.plot(a[0],[fix_failures(c) for c in a[1]],\\n\",\n \" label=labels(noise_type, noise_amount, clf_type),\\n\",\n \" color=colors[c],\\n\",\n \" linestyle=linestyles[0])\\n\",\n \" plt.title(key)\\n\",\n \" plt.xlabel(x_labels[noise_type])\\n\",\n \" plt.ylabel('Test AUC')\\n\",\n \" plt.ylim(ylims[noise_type][key])\\n\",\n \" plt.legend(title=legends[noise_type])\\n\",\n \" plt.savefig(f\\\"./figures/label_noise_{key}.png\\\")\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b891c49a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"conda_python3\",\n \"language\": \"python\",\n \"name\": \"conda_python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "scripts/reproducibility/label-noise/feature_dict.py", "content": "feature_dict = {\n 'ieeecis': {\n 'transactionamt': 'numeric',\n 'productcd': 'categorical',\n 'card1': 'numeric',\n 'card2': 'numeric',\n 'card3': 'numeric',\n 'card5': 'numeric',\n 'card6': 'categorical',\n 'addr1': 'numeric',\n 'dist1': 'numeric',\n 'p_emaildomain': 'categorical',\n 'r_emaildomain': 'categorical',\n 'c1': 'numeric',\n 'c2': 'numeric',\n 'c4': 'numeric',\n 'c5': 'numeric',\n 'c6': 'numeric',\n 'c7': 'numeric',\n 'c8': 'numeric',\n 'c9': 'numeric',\n 'c10': 'numeric',\n 'c11': 'numeric',\n 'c12': 'numeric',\n 'c13': 'numeric',\n 'c14': 'numeric',\n 'v62': 'numeric',\n 'v70': 'numeric',\n 'v76': 'numeric',\n 'v78': 'numeric',\n 'v82': 'numeric',\n 'v91': 'numeric',\n 'v127': 'numeric',\n 'v130': 'numeric',\n 'v139': 'numeric',\n 'v160': 'numeric',\n 'v165': 'numeric',\n 'v187': 'numeric',\n 'v203': 'numeric',\n 'v207': 'numeric',\n 'v209': 'numeric',\n 'v210': 'numeric',\n 'v221': 'numeric',\n 'v234': 'numeric',\n 'v257': 'numeric',\n 'v258': 'numeric',\n 'v261': 'numeric',\n 'v264': 'numeric',\n 'v266': 'numeric',\n 'v267': 'numeric',\n 'v271': 'numeric',\n 'v274': 'numeric',\n 'v277': 'numeric',\n 'v283': 'numeric',\n 'v285': 'numeric',\n 'v289': 'numeric',\n 'v291': 'numeric',\n 'v294': 'numeric',\n 'id_01': 'numeric',\n 'id_02': 'numeric',\n 'id_05': 'numeric',\n 'id_06': 'numeric',\n 'id_09': 'numeric',\n 'id_13': 'numeric',\n 'id_17': 'numeric',\n 'id_19': 'numeric',\n 'id_20': 'numeric',\n 'devicetype': 'categorical',\n 'deviceinfo': 'categorical'\n },\n 'ccfraud': {\n 'v1': 'numeric',\n 'v2': 'numeric',\n 'v3': 'numeric',\n 'v4': 'numeric',\n 'v5': 'numeric',\n 'v6': 'numeric',\n 'v7': 'numeric',\n 'v8': 'numeric',\n 'v9': 'numeric',\n 'v10': 'numeric',\n 'v11': 'numeric',\n 'v12': 'numeric',\n 'v13': 'numeric',\n 'v14': 'numeric',\n 'v15': 'numeric',\n 'v16': 'numeric',\n 'v17': 'numeric',\n 'v18': 'numeric',\n 'v19': 'numeric',\n 'v20': 'numeric',\n 'v21': 'numeric',\n 'v22': 'numeric',\n 'v23': 'numeric',\n 'v24': 'numeric',\n 'v25': 'numeric',\n 'v26': 'numeric',\n 'v27': 'numeric',\n 'v28': 'numeric',\n 'amount': 'numeric'\n },\n 'fraudecom': {\n 'purchase_value': 'numeric',\n 'source': 'categorical',\n 'browser': 'categorical',\n 'age': 'numeric',\n 'ip_address': 'enrichable',\n 'time_since_signup': 'numeric'\n },\n 'sparknov': {\n 'cc_num': 'categorical',\n 'category': 'categorical',\n 'amt': 'numeric',\n 'first': 'categorical',\n 'last': 'categorical',\n 'gender': 'categorical',\n 'street': 'categorical',\n 'city': 'categorical',\n 'state': 'categorical',\n 'zip': 'categorical',\n 'lat': 'numeric',\n 'long': 'numeric',\n 'city_pop': 'numeric',\n 'job': 'categorical',\n 'dob': 'text',\n 'merch_lat': 'numeric',\n 'merch_long': 'numeric'\n },\n 'twitterbot': {\n 'created_at' : 'text',\n 'default_profile': 'categorical',\n 'default_profile_image': 'categorical',\n 'description': 'text',\n 'favourites_count': 'numeric',\n 'followers_count': 'numeric',\n 'friends_count': 'numeric',\n 'geo_enabled': 'categorical',\n 'lang': 'categorical',\n 'location': 'categorical',\n 'profile_background_image_url': 'text',\n 'profile_image_url': 'text',\n 'screen_name': 'text',\n 'statuses_count': 'numeric',\n 'verified': 'categorical',\n 'average_tweets_per_day': 'numeric',\n 'account_age_days': 'numeric'\n },\n 'fakejob': {\n 'title': 'categorical',\n 'location': 'categorical',\n 'department': 'categorical',\n 'salary_range': 'text',\n 'company_profile': 'text',\n 'description': 'text',\n 'requirements': 'text',\n 'benefits': 'text',\n 'telecommuting': 'categorical',\n 'has_company_logo': 'categorical',\n 'has_questions': 'categorical',\n 'employment_type': 'categorical',\n 'required_experience': 'categorical',\n 'required_education': 'categorical',\n 'industry': 'categorical',\n 'function': 'categorical'\n },\n 'vehicleloan': {\n 'disbursed_amount': 'numeric',\n 'asset_cost': 'numeric',\n 'ltv': 'numeric',\n 'branch_id': 'categorical',\n 'supplier_id': 'categorical',\n 'manufacturer_id': 'categorical',\n 'current_pincode_id': 'categorical',\n 'date_of_birth': 'text',\n 'employment_type': 'categorical',\n 'state_id': 'categorical',\n 'employee_code_id': 'categorical',\n 'mobileno_avl_flag': 'categorical',\n 'aadhar_flag': 'categorical',\n 'pan_flag': 'categorical',\n 'voterid_flag': 'categorical',\n 'driving_flag': 'categorical',\n 'passport_flag': 'categorical',\n 'perform_cns_score': 'numeric',\n 'perform_cns_score_description': 'categorical',\n 'pri_no_of_accts': 'numeric',\n 'pri_active_accts': 'numeric',\n 'pri_overdue_accts': 'numeric',\n 'pri_current_balance': 'numeric',\n 'pri_sanctioned_amount': 'numeric',\n 'pri_disbursed_amount': 'numeric',\n 'sec_no_of_accts': 'numeric',\n 'sec_active_accts': 'numeric',\n 'sec_overdue_accts': 'numeric',\n 'sec_current_balance': 'numeric',\n 'sec_sanctioned_amount': 'numeric',\n 'sec_disbursed_amount': 'numeric',\n 'primary_instal_amt': 'numeric',\n 'sec_instal_amt': 'numeric',\n 'new_accts_in_last_six_months': 'numeric',\n 'delinquent_accts_in_last_six_months': 'numeric',\n 'average_acct_age': 'text',\n 'credit_history_length': 'text',\n 'no_of_inquiries': 'numeric'\n }\n}" }, { "path": "scripts/reproducibility/label-noise/load_fdb_datasets.py", "content": "import os\nimport re\nimport json\nimport pandas as pd\nimport numpy as np\nimport warnings\nfrom datetime import datetime\n\nfrom category_encoders.target_encoder import TargetEncoder\nfrom skclean.simulate_noise import flip_labels_cc, BCNoise\n\nfrom fdb.datasets import FraudDatasetBenchmark\n\nimport feature_dict\n\nDATASET_PATH = './data/dataset.csv'\nMETADATA_PATH = './data/feature_metadata.json'\nFD = feature_dict.feature_dict\n\ndef noise_amount(df):\n return df[df.noise == 1].shape[0]\n\ndef noise_rate(df):\n if df.shape[0] > 0:\n return noise_amount(df)/df.shape[0]\n else:\n return None\n\ndef type_1_noise_amount(df):\n # examples with true label 0, mislabeled as 1\n # here 'df.label' is the observed label, not the true one\n return df[(df.label==1) & (df.noise == 1)].shape[0]\n\ndef type_2_noise_amount(df):\n # examples with true label 1, mislabeled as 0\n # here 'df.label' is the observed label, not the true one\n return df[(df.label==0) & (df.noise == 1)].shape[0]\n\ndef actual_legit_amount(df):\n return df[(df.label == 0) | (df.noise == 1)].shape[0]\n\ndef observed_legit_amount(df):\n return df[df.label == 0].shape[0]\n\ndef actual_fraud_amount(df):\n return df[((df.label == 1) & (df.noise == 0)) | ((df.label == 0) & (df.noise == 1))].shape[0]\n\ndef observed_fraud_amount(df):\n return df[df.label == 1].shape[0]\n\ndef actual_fraud_rate(df):\n if df.shape[0] > 0:\n return actual_fraud_amount(df)/df.shape[0]\n else:\n return None\n\ndef observed_fraud_rate(df):\n if df.shape[0] > 0:\n return observed_fraud_amount(df)/df.shape[0]\n else:\n return None\n\ndef type_1_noise_rate(df):\n if df.shape[0] > 0:\n return type_1_noise_amount(df)/actual_legit_amount(df)\n else:\n return None\n\ndef type_2_noise_rate(df):\n if df.shape[0] > 0:\n return type_2_noise_amount(df)/actual_fraud_amount(df)\n else:\n return None\n\ndef prepare_data_fdb(key, drop_text_enr_features=True): \n \"\"\"\n main function, gets datasets from FDB and then does some preprocessing/cleaning so they are suitable\n for modeling, returns data and metadata\n \n inputs: \n key - the FDB dataset to load\n drop_text_enr_features - whether we want to drop text/enrichable features\n this returns\n df - full pandas dataframe containing features, labels and metadata\n this includes training and test data, with a 'dataset' column to indicate which\n all of these datasets have a timestamp column (even if it is \"fake\") and by default\n data will be sorted by this column. All test > train w.r.t. this timestamp\n \n features - list of feature names\n cat_features - list of categorical feature names (subset of features)\n label - name of label column\n record_id - name of unique id column\n \"\"\"\n \n obj = FraudDatasetBenchmark(key=key)\n \n print(obj.key)\n \n # extract training and testing data (and test labels) from the return object\n # sort training data by event timestamp\n train_df = obj.train.sort_values(by='EVENT_TIMESTAMP',ignore_index=True)\n test_df = obj.test.reset_index(drop=True)\n test_labels = obj.test_labels.reset_index(drop=True)\n\n # define metadata and label column names\n metadata = ['EVENT_LABEL', 'EVENT_TIMESTAMP', 'ENTITY_ID', 'ENTITY_TYPE', 'EVENT_ID',\n 'label', 'LABEL_TIMESTAMP', 'noise', 'dataset']\n label = ['label']\n \n # we maintain a feature dictionary in another file, this helps us determine which are categorical, numerical, etc.\n feature_dict = FD[key]\n raw_features = feature_dict.keys()\n num_features = [f for f in raw_features if feature_dict[f] == 'numeric']\n cat_features = [f for f in raw_features if feature_dict[f] == 'categorical']\n txt_features = [f for f in raw_features if feature_dict[f] == 'text']\n enr_features = [f for f in raw_features if feature_dict[f] == 'enrichable']\n \n # add / rename labels\n train_df.rename({'EVENT_LABEL':'label'}, axis=1, inplace=True)\n test_df['label'] = test_labels['EVENT_LABEL']\n if key == 'twitterbot':\n train_df.loc[train_df.label == 'bot', 'label'] = 1\n test_df.loc[test_df.label == 'bot', 'label'] = 1\n train_df.loc[train_df.label == 'human', 'label'] = 0\n test_df.loc[test_df.label == 'human', 'label'] = 0\n\n # put train / test into single dataframe, create a 'dataset' column to keep track\n train_df['dataset'] = 'train' \n test_df['dataset'] = 'test'\n\n # create noise column - we won't generate any noise now but it may be useful to have (can also be ignored)\n train_df['noise'] = 0\n test_df['noise'] = 0\n\n # concatenate train/test into single dataframe \n # (remember we have 'dataset' column to separate them again if needed)\n df = pd.concat([train_df, test_df], axis=0, ignore_index=True)\n \n # there are a few date columns that are timestamps, we convert those to epoch\n # the new values are put into new columns, those column names are added to the numerical features\n if key == 'twitterbot':\n df['eng_created_at'] = df['created_at'].apply(lambda x : datetime.strptime(x, '%Y-%m-%d %H:%M:%S').timestamp())\n num_features.append('eng_created_at')\n if key == 'sparknov':\n df['eng_dob'] = df['dob'].apply(lambda x : datetime.strptime(x, '%Y-%m-%d').timestamp())\n num_features.append('eng_dob')\n \n # fakejob has a salary range column, e.g. \"10000 - 20000\" that can be converted into two numerical columns\n if key == 'fakejob':\n def convert(x):\n r = re.search(r\"([0-9]*)-([0-9]*)\",str(x))\n try:\n m, M = r.group(1), r.group(2)\n if m == '' or M == '':\n m, M = 0,0\n except:\n m, M = 0,0\n return m,M\n\n df['salary_min'], df['salary_max'] = zip(*df['salary_range'].map(convert))\n num_features = num_features + ['salary_min','salary_max']\n \n # vehicleloan has a timestamp column that we convert to epoch\n # it also has \"account age\" and \"credit history\" length cols \n # in form \"Xyrs Ymon\" that can be converted to numeric\n if key == 'vehicleloan':\n df['eng_dob'] = df['date_of_birth'].apply(lambda x : datetime.strptime(x, '%d-%m-%Y').timestamp())\n \n def convert(x):\n r = re.search(r\"([0-9]*)yrs ([0-9]*)mon\", x)\n try:\n age = 12*float(r.group(1)) + float(r.group(2))\n except:\n age = 0\n return age\n \n df['eng_average_acct_age'] = df['average_acct_age'].apply(convert)\n df['eng_credit_history_length'] = df['credit_history_length'].apply(convert)\n num_features = num_features + ['eng_dob','eng_average_acct_age','eng_credit_history_length']\n \n # by default we will drop any remaining text or enrichable (IP address) features as we won't use them\n # but you can pass in False for this if they are of interest\n if drop_text_enr_features:\n df.drop(txt_features + enr_features, axis=1, inplace=True)\n features = num_features + cat_features\n \n # cast all numeric features to float just in case they aren't\n for feature in num_features:\n df[feature] = df[feature].astype('float64')\n df[feature].fillna(0, inplace=True)\n \n # cast all categorical features to str in case they aren't\n for feature in cat_features:\n df[feature] = df[feature].astype(str) \n df[feature].fillna('', inplace=True)\n \n # rename the timestamp column\n df.rename({'EVENT_TIMESTAMP':'creation_date'}, axis=1, inplace=True) \n \n # cast the label to int just to be sure\n df['label'] = df['label'].astype('int')\n \n # name of unique id column will always be EVENT_ID\n record_id = 'EVENT_ID'\n \n if drop_text_enr_features:\n return df, features, cat_features, label, record_id\n else:\n return df, features, cat_features, txt_features, enr_features, label, record_id\n\n\ndef add_noise(df, noise_type, noise_amount, *, time_index=None, features=None, cat_features=None, label=None):\n\n if noise_type not in ['random', 'time-dependent', 'boundary-consistent']:\n raise(Exception('Invalid Noise Type'))\n \n # if we want time-dependent noise it will be useful to convert timestamps into epoch\n def convert_to_millis(x):\n try:\n m = datetime.strptime(x, '%Y-%m-%dT%H:%M:%SZ').timestamp()\n except:\n m = datetime.strptime(x, '%Y-%m-%d %H:%M:%S').timestamp()\n return m\n\n # random noise can be class-conditional in both directions (other types of noise cannot)\n # if noise_amount is passed in as [r,s] we can flip labels in both directions: \n # r is percent of 0s flipped to 1s\n # s is percent of 1s flipped to 0s\n # for random noise, if noise_amount is a single number, assume it is s, and that r=0 \n # (i.e. class-conditional noise where only 1s get flipped to 0s)\n if isinstance(noise_amount, tuple) or isinstance(noise_amount, list): \n if noise_type != 'random':\n raise(Exception('For time-dependent and boundary-consistent noise,'\n 'only a single value is allowed for noise_amount'))\n r = noise_amount[0]\n s = noise_amount[1]\n else:\n r = 0\n s = noise_amount\n \n # we will add noise to a *copy* of the dataframe\n df_copy = df.copy()\n \n if noise_type == 'time-dependent':\n df_copy['event_millis'] = df_copy[time_index].apply(convert_to_millis)\n df_copy['event_millis'] = df_copy['event_millis'] - df_copy['event_millis'].min() \n mislabel = df_copy[(df_copy.noise == 0) \n & (df_copy.label == 1)].sample(frac = s, \n weights=df_copy['event_millis']).index\n df_copy.loc[mislabel,'noise'] = 1\n df_copy.loc[mislabel,'label'] = 0\n else:\n if noise_type == 'boundary-consistent':\n from catboost import CatBoostClassifier\n warnings.filterwarnings(\"ignore\", category=FutureWarning)\n target_encoder = TargetEncoder(cols=cat_features)\n reshaped_y = df_copy[label].values.reshape(df_copy[label].shape[0],)\n X = target_encoder.fit_transform(df_copy[features], reshaped_y)\n clf = CatBoostClassifier(verbose=False)\n clf.fit(X, reshaped_y)\n _, noisy_labels = BCNoise(clf, noise_level=s).simulate_noise(X, reshaped_y)\n else: \n lcm = np.array([[1-r,r],[s,1-s]])\n noisy_labels = flip_labels_cc(df_copy.label,lcm)\n\n idx = (df_copy.label != noisy_labels)\n df_copy.loc[idx,'noise'] = 1\n df_copy['label'] = noisy_labels\n \n return df_copy\n\n\ndef train_valid_split(df, split=0.7, shuffle=True, sort_key='creation_date'):\n if shuffle:\n df = df.sample(frac=1).reset_index(drop=True)\n else:\n df = df.sort_values(by=sort_key, ignore_index=True)\n train_idx = int(round(split*df.shape[0]))\n train = df[:train_idx].reset_index(drop=True)\n valid = df[train_idx:].reset_index(drop=True)\n \n return train, valid\n \n\ndef prepare_noisy_dataset(key, noise_type, noise_amount, split=0.7, shuffle=True, \n sort_key='creation_date', target_encoding=False):\n \"\"\"\n this function can be used to fetch datasets from FDB, \n starts by calling prepare_data_fdb and then adding noise\n \n input: \n key - name of FDB dataset\n noise_type - what type of noise to add\n noise_amount - how much noise to add\n split - training/validation split\n shuffle - whether or not to shuffle or sort before doing train/valid split\n sort_key - key to use to sort for train/valid split as well as weight for time-dependent noise\n \"\"\"\n \n # start by getting clean dataset\n \n df, features, cat_features, label, record_id = prepare_data_fdb(key)\n\n if noise_type == 'boundary-consistent':\n train_and_valid = add_noise(df[df.dataset == 'train'], noise_type, noise_amount, \n time_index=sort_key, features=features, cat_features=cat_features, label=label)\n else:\n train_and_valid = add_noise(df[df.dataset == 'train'], noise_type, noise_amount, time_index=sort_key)\n \n train, valid = train_valid_split(train_and_valid, split, shuffle=shuffle, sort_key=sort_key)\n test = df[df.dataset == 'test'].reset_index(drop=True)\n \n train = train[features + ['noise'] + label]\n valid = valid[features + ['noise'] + label]\n test = test[features + ['noise'] + label]\n \n if target_encoding:\n warnings.filterwarnings(\"ignore\", category=FutureWarning)\n target_encoder = TargetEncoder(cols=cat_features)\n reshaped_y = train[label].values.reshape(train[label].shape[0],)\n train.loc[:, features] = target_encoder.fit_transform(train[features], reshaped_y)\n valid.loc[:, features] = target_encoder.transform(valid[features])\n test.loc[:, features] = target_encoder.transform(test[features])\n cat_features = None\n \n dataset = {\n 'description': f\"{key} dataset with noise type: {noise_type}, noise amount: {noise_amount} \",\n 'features':features,\n 'cat_features':cat_features,\n 'label':label,\n 'record_id':record_id,\n 'train':train,\n 'valid':valid,\n 'test':test, \n 'noise':(noise_rate(train), noise_rate(valid), noise_rate(test)),\n 'fraud_level':(actual_fraud_rate(train), actual_fraud_rate(valid), actual_fraud_rate(test)),\n 'observed_fraud_level':(observed_fraud_rate(train),observed_fraud_rate(valid),observed_fraud_rate(test)),\n 'type_1_noise_rate':(type_1_noise_rate(train),type_1_noise_rate(valid),type_1_noise_rate(test)),\n 'type_2_noise_rate':(type_2_noise_rate(train),type_2_noise_rate(valid),type_2_noise_rate(test))\n }\n \n return dataset\n\n\ndef dataset_stats(dataset):\n noise = dataset['noise']\n fraud_level = dataset['fraud_level']\n observed_fraud_level = dataset['observed_fraud_level']\n type_1_noise_rate = dataset['type_1_noise_rate']\n type_2_noise_rate = dataset['type_2_noise_rate']\n stats = list(zip(['train','valid','test'],noise,type_1_noise_rate,type_2_noise_rate,fraud_level,observed_fraud_level))\n print(dataset['description'])\n for stat in stats:\n print('{} - total noise rate: {:.3f}, type 1 noise rate: {:.3f}, type 2 noise rate: {:.3f},\\n'\n '(actual) fraud rate: {:.3f}, observed fraud rate: {:.3f}'.format(*stat))\n \n" }, { "path": "scripts/reproducibility/label-noise/micro_models.py", "content": "import logging\nimport pandas as pd\nimport numpy as np\n\n\nclass MicroModelError(Exception):\n \"\"\"\n basic exception type for micro-model specific errors\n \"\"\"\n def __init__(self, error_message):\n logging.error(error_message)\n\n\nclass MicroModel:\n \"\"\"\n Basic wrapper for the model to be used in ensemble noise removal, ModelClass can be anything that implements\n fit and predict_proba. Mainly used by MicroModelEnsemble, user is probably not calling this directly\n \"\"\"\n\n def __init__(self, ModelClass, *args, **kwargs):\n \"\"\"\n initialization of the class, ModelClass should be a *class* not an object\n e.g. CatBoostClassifier, not CatBoostClassifier()\n \"\"\"\n self.clf = ModelClass(*args, **kwargs)\n self.thresh = None\n\n def set_thresh(self, thresh):\n # can set a threshold to be used in model predictions\n self.thresh = thresh\n\n def fit(self, x, y, *args, **kwargs):\n # pass-through method to call model.fit()\n self.clf.fit(x, y.values.ravel(), *args, **kwargs)\n\n def predict_proba(self, x, *args, **kwargs):\n # pass-through method to call model.predict_proba()\n if 'predict_proba' in dir(self.clf):\n return self.clf.predict_proba(x, *args, **kwargs)\n else:\n raise (MicroModelError('ModelClass must implement predict_proba'))\n\n def predict(self, x):\n # make predictions, using either defined threshold (if set) or default value of 0.5\n if self.thresh is not None:\n t = self.thresh\n else:\n t = 0.5\n scores = self.predict_proba(x)[:, 1]\n preds = [int(s > t) for s in scores]\n return scores, preds\n\n\nclass MicroModelEnsemble:\n \"\"\"\n Ensemble of micro-models used to remove noise\n \"\"\"\n\n def __init__(self, ModelClass, num_clfs=16, score_type='preds_avg', *args, **kwargs):\n \"\"\"\n initialization of the class, ModelClass should be a *class* not an object\n e.g. CatBoostClassifier, not CatBoostClassifier()\n params:\n ModelClass - base class to use, needs to implement fit and predict_proba\n num_clfs - number of classifiers to use in cleaning ensemble\n score_type - means of computing anomaly score from micro-model scores\n args/kwargs - any other parameters to pass to model constructor, e.g. cat_features or iterations for CatBoost\n \"\"\"\n self.score_type = score_type\n \n if type(num_clfs) is not int or num_clfs <= 0:\n raise (MicroModelError('num_clfs must be a positive integer'))\n self.ModelClass = ModelClass\n\n # one classifier that will be trained over entire dataset\n self.big_clf = MicroModel(ModelClass=ModelClass, *args, **kwargs)\n\n # micro-models to later be trained over slices\n self.num_clfs = num_clfs\n self.clfs = []\n for i in range(num_clfs):\n self.clfs.append(MicroModel(ModelClass=ModelClass, *args, **kwargs))\n self.thresholds = {}\n\n def fit(self, x, y, *args, **kwargs):\n # assumption that data is already shuffled or sorted (by date or other appropriate key)\n # according to the usecase\n\n if not isinstance(y, pd.DataFrame):\n y = pd.DataFrame(y)\n\n # fit one classifier on all the data\n self.big_clf.fit(x, y, *args, **kwargs)\n\n # now fit individual models on slices of data\n stride = round(x.shape[0] / self.num_clfs)\n for i, clf in enumerate(self.clfs):\n idx = slice(i * stride, min((i + 1) * stride, x.shape[0]))\n x_i = x.iloc[idx, :]\n y_i = y.iloc[idx, :]\n clf.fit(x_i, y_i, *args, **kwargs)\n\n def predict_proba(self, x, *args, **kwargs):\n # output is the mean of the (binary) predictions of all models in the ensemble\n # e.g. the percentage of models that voted on the example\n results = pd.DataFrame(index=np.arange(x.shape[0]))\n if self.score_type == 'preds_avg':\n for i, clf in enumerate(self.clfs):\n _, results[i] = clf.predict(x, *args, **kwargs)\n elif self.score_type == 'score_avg':\n for i, clf in enumerate(self.clfs):\n results[i] = clf.predict_proba(x, *args, **kwargs)[:, 1]\n\n scores = results.mean(axis=1, numeric_only=True)\n return scores\n\n def predict(self, x, threshold=0.5, *args, **kwargs):\n # compare output of predict_proba to a threshold in order to make a binary prediction, default is 0.5\n scores = self.predict_proba(x)\n preds = np.array([int(s >= threshold) for s in scores])\n return scores, preds\n\n def filter_noise(self, x, y, pulearning=True, threshold=0.5):\n # compare ensemble predictions to observed labels and return the examples that are NOT considered noise\n # i.e. this is noise REMOVAL\n # pu_learning=True means a class-conditional assumption is being made,\n # there no examples of true 0s mislabeled as 1s\n scores, susp = self.predict(x, threshold)\n if pulearning:\n conf = ((y == 1) | ((y == 0) & (susp == 0)))\n else:\n conf = (((y == 1) & (scores > 1 - threshold)) | ((y == 0) & (scores < threshold)))\n\n return x[conf].reset_index(drop=True), y[conf]\n\n def clean_noise(self, x, y, pulearning=True, threshold=0.5):\n # compare ensemble predictions to observed labels and return all examples with corrected labels\n # i.e. this is noise CLEANING\n # pu_learning=True means a class-conditional assumption is being made,\n # there no examples of true 0s mislabeled as 1s\n x = x.copy()\n y = y.copy()\n _, susp = self.predict(x, threshold)\n # flip all the probable 1s to actual 1s\n probable_1 = (y == 0) & (susp == 1)\n y[probable_1] = 1\n if not pulearning:\n # if there are both types of noise, flip probable 0s to actual 0s\n probable_0 = (y == 1) & (susp == 0)\n y[probable_0] = 0\n\n return x, y\n\n\nclass MicroModelCleaner:\n \"\"\"\n This class performs the entire model training process end-to-end - given a dataset it will first train an ensemble\n then remove noise, then train a final model on the clean data\n \"\"\"\n\n def __init__(self, ModelClass, strategy='filter', pulearning=True, num_clfs=16, threshold=0.5, *args, **kwargs):\n \"\"\"\n initialization of the class, ModelClass should be a *class* not an object\n e.g. CatBoostClassifier, not CatBoostClassifier()\n params:\n ModelClass - base class to use, needs to implement fit and predict_proba\n strategy - whether to remove noise ('filter') or flip labels ('clean')\n pulearning - class-conditional assumption, if True assume there is no true 0's mislabeled as 1's\n num_clfs - number of classifiers to use in cleaning ensemble\n threshold - percentage of classifiers that have to vote to remove noise (0.5 is majority voting)\n args/kwargs - any other parameters to pass to model constructor, e.g. cat_features or iterations for CatBoost\n \"\"\"\n self.detector = MicroModelEnsemble(ModelClass, num_clfs, *args, **kwargs)\n self.clf = ModelClass(*args, **kwargs)\n if strategy.lower() not in ['filter', 'clean']:\n raise (MicroModelError('strategy must be filter or clean'))\n self.strategy = strategy.lower()\n self.pulearning = pulearning\n self.threshold = threshold\n\n def fit(self, x, y, *args, **kwargs):\n # first train the Ensemble to deal with the noise\n self.detector.fit(x, y, *args, **kwargs)\n if self.strategy == 'filter':\n x_clean, y_clean = self.detector.filter_noise(x, y, self.pulearning, self.threshold)\n else:\n x_clean, y_clean = self.detector.clean_noise(x, y, self.pulearning, self.threshold)\n\n # then train final model on clean data\n self.clf.fit(x_clean, y_clean, *args, **kwargs)\n\n def predict(self, x, *args, **kwargs):\n return self.clf.predict(x, *args, **kwargs)\n\n def predict_proba(self, x, *args, **kwargs):\n return self.clf.predict_proba(x, *args, **kwargs)\n\n" }, { "path": "setup.py", "content": "import os\nfrom glob import glob\n\nfrom setuptools import find_packages, setup\n\n\nsetup(\n name='fraud_dataset_benchmark',\n version='1.0',\n \n # declare your packages\n packages=find_packages(where='src', exclude=('test',)),\n package_dir={'': 'src'},\n include_package_data=True,\n data_files=[('.',[\n 'src/fdb/versioned_datasets/ipblock/20220607.zip',\n ])],\n\n # Enable build-time format checking\n check_format=False,\n\n # Enable type checking\n test_mypy=False,\n\n # Enable linting at build time\n test_flake8=False,\n\n # exclude_package_data={\n # '': glob('fdb/*/__pycache__', recursive=True),\n # }\n)\n" }, { "path": "src/__init__.py", "content": "" }, { "path": "src/fdb/__init__.py", "content": "" }, { "path": "src/fdb/datasets.py", "content": "from abc import abstractmethod, ABC\nfrom fdb.preprocessing import *\nfrom fdb.preprocessing_objects import load_data\nfrom sklearn.metrics import roc_auc_score, roc_curve, auc\n\nclass FraudDatasetBenchmark(ABC):\n def __init__(\n self, \n key, \n load_pre_downloaded=False,\n delete_downloaded=True,\n add_random_values_if_real_na = {\n \"EVENT_TIMESTAMP\": True,\n \"LABEL_TIMESTAMP\": True,\n \"ENTITY_ID\": True,\n \"ENTITY_TYPE\": True,\n \"EVENT_ID\": True\n }):\n self.key = key\n self.obj = load_data(self.key, load_pre_downloaded, delete_downloaded, add_random_values_if_real_na)\n \n @property\n def train(self):\n return self.obj.train\n\n @property\n def test(self):\n return self.obj.test\n\n @property\n def test_labels(self):\n return self.obj.test_labels\n\n def eval(self, y_pred):\n \n \"\"\"\n Method to evaluate predictions against the test set\n \"\"\"\n roc_score = roc_auc_score(self.test_labels['EVENT_LABEL'], y_pred)\n fpr, tpr, thres = roc_curve(self.test_labels['EVENT_LABEL'], y_pred)\n tpr_1fpr = np.interp(0.01, fpr, tpr)\n metrics = {'roc_score': roc_score, 'tpr_1fpr': tpr_1fpr}\n return metrics\n\n\n" }, { "path": "src/fdb/kaggle_configs.py", "content": "KAGGLE_CONFIGS = {\n\n \"fakejob\":\n {\n \"owner\": \"shivamb\",\n \"dataset\": \"real-or-fake-fake-jobposting-prediction\",\n \"filename\": 'fake_job_postings.csv',\n \"name\": \"Real / Fake Job Posting Prediction\",\n \"type\": \"datasets\",\n \"version\": 1\n }, \n\n \"vehicleloan\":\n {\n \"owner\": \"avikpaul4u\",\n \"dataset\": \"vehicle-loan-default-prediction\",\n \"filename\": 'train.csv',\n \"name\": \"Vehicle Loan Default Prediction\",\n \"type\": \"datasets\",\n \"version\": 4\n },\n\n \"malurl\":\n {\n \"owner\": \"sid321axn\",\n \"dataset\": \"malicious-urls-dataset\",\n \"filename\": 'malicious_phish.csv',\n \"name\": \"Malicious URLs Dataset\",\n \"type\": \"datasets\",\n \"version\": 1\n },\n\n \"ieeecis\": \n {\n \"owner\": \"ieee-fraud-detection\",\n \"name\": \"IEEE-CIS Fraud Detection\",\n \"type\": \"competitions\",\n },\n\n \"ccfraud\": \n {\n \"owner\": \"mlg-ulb\",\n \"dataset\": \"creditcardfraud\",\n \"filename\": 'creditcard.csv',\n \"name\": \"Credit Card Fraud Detection\",\n \"type\": \"datasets\",\n \"version\": 3\n },\n\n \"fraudecom\": \n {\n \"owner\": \"vbinh002\",\n \"dataset\": \"fraud-ecommerce\",\n \"filename\": 'Fraud_Data.csv',\n \"name\": \"Fraud ecommerce\",\n \"type\": \"datasets\",\n \"version\": 1\n },\n\n \"sparknov\":\n {\n \"owner\": \"kartik2112\",\n \"dataset\": \"fraud-detection\",\n \"name\": \"Simulated Credit Card Transactions generated using Sparkov\",\n \"type\": \"datasets\",\n \"version\": 1\n },\n\n \"twitterbot\":\n {\n \"owner\": \"davidmartngutirrez\",\n \"dataset\": \"twitter-bots-accounts\",\n \"filename\": \"twitter_human_bots_dataset.csv\",\n \"name\": \"Twitter Bots Accounts\",\n \"type\": \"datasets\",\n \"version\": 2\n }\n}" }, { "path": "src/fdb/preprocessing.py", "content": "\n\nimport os\nimport re\nimport shutil\nimport kaggle\nimport pkgutil\nimport requests\nimport zipfile\nimport numpy as np\nfrom abc import ABC\nimport pandas as pd\nimport socket, struct\nfrom faker import Faker\nfrom zipfile import ZipFile\nfrom datetime import datetime\nfrom datetime import timedelta\nfrom io import StringIO, BytesIO\nfrom dateutil.relativedelta import relativedelta\n\nfrom fdb.kaggle_configs import KAGGLE_CONFIGS\n\nfake = Faker(['en_US'])\n\n\n# Naming convention for the meta data columns in standardized datasets\n_EVENT_TIMESTAMP = 'EVENT_TIMESTAMP' # timestamp column\n_ENTITY_TYPE = 'ENTITY_TYPE' # afd specific requirement\n_EVENT_LABEL = 'EVENT_LABEL' # label column\n_EVENT_ID = 'EVENT_ID' # transaction/event id \n_ENTITY_ID = 'ENTITY_ID' # represents user/account id\n_LABEL_TIMESTAMP = 'LABEL_TIMESTAMP' # added in a cases where entity id is meaninful\n\n# Kaggle config related strings\n_OWNER = 'owner'\n_COMPETITIONS = 'competitions'\n_TYPE = 'type'\n_FILENAME = 'filename'\n_DATASETS = 'datasets'\n_DATASET = 'dataset'\n_VERSION = 'version'\n\n# Some fixed parameters\n_RANDOM_STATE = 1\n_CWD = os.getcwd()\n_DOWNLOAD_LOCATION = os.path.join(_CWD, 'tmp')\n_TIMESTAMP_FORMAT = '%Y-%m-%dT%H:%M:%SZ'\n_DEFAULT_LABEL_TIMESTAMP = datetime.now().strftime(_TIMESTAMP_FORMAT)\n\n\nclass BasePreProcessor(ABC):\n def __init__(\n self, \n key = None, \n train_percentage = 0.8,\n timestamp_col = None, \n label_col = None, \n label_timestamp_col = None,\n event_id_col = None,\n entity_id_col = None,\n features_to_drop = [],\n load_pre_downloaded = False,\n delete_downloaded = True,\n add_random_values_if_real_na = {\n \"EVENT_TIMESTAMP\": True,\n \"LABEL_TIMESTAMP\": True,\n \"ENTITY_ID\": True,\n \"ENTITY_TYPE\": True,\n \"EVENT_ID\": True\n }\n ):\n \n self.key = key \n self.train_percentage = train_percentage\n self.features_to_drop = features_to_drop\n self.delete_downloaded = delete_downloaded\n \n self._timestamp_col = timestamp_col\n self._label_col = label_col\n self._label_timestamp_col = label_timestamp_col\n self._event_id_col = event_id_col\n self._entity_id_col = entity_id_col\n self._add_random_values_if_real_na = add_random_values_if_real_na\n \n # Simply get all required objects at the time of object creation\n if KAGGLE_CONFIGS.get(self.key) and not load_pre_downloaded:\n self.download_kaggle_data() # download the data when an object is created\n self.load_data()\n self.preprocess()\n self.train_test_split()\n\n\n def _download_kaggle_data_from_competetions(self):\n file_name = KAGGLE_CONFIGS[self.key][_OWNER]\n kaggle.api.competition_download_files(\n competition = KAGGLE_CONFIGS[self.key][_OWNER],\n path = _DOWNLOAD_LOCATION\n )\n return file_name\n\n def _download_kaggle_data_from_datasets_with_given_filename(self):\n file_name = KAGGLE_CONFIGS[self.key][_FILENAME]\n response = kaggle.api.datasets_download_file(\n owner_slug = KAGGLE_CONFIGS[self.key][_OWNER],\n dataset_slug = KAGGLE_CONFIGS[self.key][_DATASET],\n file_name = file_name,\n dataset_version_number=KAGGLE_CONFIGS[self.key][_VERSION],\n _preload_content = False,\n )\n with open(os.path.join(_DOWNLOAD_LOCATION, file_name + '.zip'), 'wb') as f:\n f.write(response.data)\n return file_name\n\n def _download_kaggle_data_from_datasets_containing_single_file(self):\n file_name = KAGGLE_CONFIGS[self.key][_DATASET]\n kaggle.api.dataset_download_files(\n dataset = os.path.join(KAGGLE_CONFIGS[self.key][_OWNER], KAGGLE_CONFIGS[self.key][_DATASET]),\n path = _DOWNLOAD_LOCATION\n )\n return file_name\n\n def download_kaggle_data(self):\n \"\"\"\n Download and extract the data from Kaggle. Puts the data in tmp directory within current directory.\n \"\"\"\n\n if not os.path.exists(_DOWNLOAD_LOCATION):\n os.mkdir(_DOWNLOAD_LOCATION)\n\n print('Data download location', _DOWNLOAD_LOCATION)\n \n \n if KAGGLE_CONFIGS[self.key][_TYPE] == _COMPETITIONS:\n file_name = self._download_kaggle_data_from_competetions()\n \n elif KAGGLE_CONFIGS[self.key][_TYPE] == _DATASETS:\n # If filename is given, download single file,\n # Else download all files.\n if KAGGLE_CONFIGS[self.key].get(_FILENAME):\n file_name = self._download_kaggle_data_from_datasets_with_given_filename()\n else:\n file_name = self._download_kaggle_data_from_datasets_containing_single_file()\n \n else:\n raise ValueError('Type should be among competetions or datasets in config')\n \n with zipfile.ZipFile(os.path.join(_DOWNLOAD_LOCATION, file_name + '.zip'), 'r') as zip_ref:\n zip_ref.extractall(_DOWNLOAD_LOCATION)\n\n def load_data(self):\n self.df = pd.read_csv(os.path.join(_DOWNLOAD_LOCATION, KAGGLE_CONFIGS[self.key]['filename']), dtype='object')\n # delete downloaded data after loading in memory\n if self.delete_downloaded: shutil.rmtree(_DOWNLOAD_LOCATION)\n\n @property\n def timestamp_col(self):\n return self._timestamp_col # If timestamp not available, will create fake timestamps\n\n @property\n def label_col(self):\n if self._label_col is None:\n raise ValueError('Label column not specified')\n else:\n return self._label_col\n\n @property\n def event_id_col(self):\n return self._event_id_col # If event id not available, will create fake event ids\n \n @property\n def entity_id_col(self):\n return self._entity_id_col\n\n def standardize_timestamp_col(self):\n if self.timestamp_col is not None:\n self.df[_EVENT_TIMESTAMP] = pd.to_datetime(self.df[self.timestamp_col]).apply(lambda x: x.strftime(_TIMESTAMP_FORMAT))\n self.df.drop(self.timestamp_col, axis=1, inplace=True)\n elif self.timestamp_col is None and self._add_random_values_if_real_na[_EVENT_TIMESTAMP]:\n self.df[_EVENT_TIMESTAMP] = self.df[_EVENT_LABEL].apply(\n lambda x: fake.date_time_between(\n start_date='-1y', # think about making it to fixed date. vs from now?\n end_date='now',\n tzinfo=None).strftime(_TIMESTAMP_FORMAT))\n \n if self._label_timestamp_col is None and self._add_random_values_if_real_na[_LABEL_TIMESTAMP]:\n self.df[_LABEL_TIMESTAMP] = _DEFAULT_LABEL_TIMESTAMP # most recent date \n elif self._label_timestamp_col is not None:\n self.df[_LABEL_TIMESTAMP] = pd.to_datetime(self.df[self._label_timestamp_col]).apply(lambda x: x.strftime(_TIMESTAMP_FORMAT))\n self.df.drop(self._label_timestamp_col, axis=1, inplace=True)\n\n def standardize_label_col(self):\n self.df.rename({self.label_col: _EVENT_LABEL}, axis=1, inplace=True)\n self.df[_EVENT_LABEL] = self.df[_EVENT_LABEL].astype(int)\n\n def standardize_event_id_col(self):\n if self.event_id_col is not None:\n self.df.rename({self.event_id_col: _EVENT_ID}, axis=1, inplace=True)\n self.df[_EVENT_ID] = self.df[_EVENT_ID].astype(str)\n elif self.event_id_col is None and self._add_random_values_if_real_na[_EVENT_ID]: # add fake one if not exist\n self.df[_EVENT_ID] = self.df[_EVENT_LABEL].apply(\n lambda x: fake.uuid4())\n\n \n def standardize_entity_id_col(self):\n if self.entity_id_col is not None:\n self.df.rename({self.entity_id_col: _ENTITY_ID}, axis=1, inplace=True)\n elif self.entity_id_col is None and self._add_random_values_if_real_na[_ENTITY_ID]: # add fake one if not exist\n self.df[_ENTITY_ID] = self.df[_EVENT_LABEL].apply(\n lambda x: fake.uuid4())\n\n def rename_features(self):\n rename_map = {} # default is empty map that won't rename any columns\n self.df.rename(rename_map, axis=1, inplace=True)\n\n def subset_features(self):\n features_to_select = self.df.columns.tolist()\n self.df = self.df[features_to_select] # all by default\n \n def drop_features(self):\n self.df.drop(self.features_to_drop, axis=1, inplace=True)\n\n def add_meta_data(self):\n if self._add_random_values_if_real_na[_ENTITY_TYPE]: \n self.df[_ENTITY_TYPE] = 'user'\n\n def sort_by_timestamp(self):\n self.df.sort_values(by=_EVENT_TIMESTAMP, ascending=True, inplace=True)\n\n def lower_case_col_names(self):\n self.df.columns = [s.lower() for s in self.df.columns]\n \n def preprocess(self):\n self.lower_case_col_names()\n self.standardize_label_col()\n self.standardize_event_id_col()\n self.standardize_entity_id_col()\n self.standardize_timestamp_col()\n self.add_meta_data()\n self.rename_features()\n self.subset_features()\n self.drop_features()\n if self.timestamp_col:\n self.sort_by_timestamp()\n\n def train_test_split(self):\n \"\"\"\n Default setting is out of time with 80%-20% into training and testing respectively\n \"\"\"\n if self.timestamp_col: \n split_pt = int(self.df.shape[0]*self.train_percentage)\n self.train = self.df.copy().iloc[:split_pt, :]\n self.test = self.df.copy().iloc[split_pt:, :]\n else: # random if no timestamp col available\n self.train = self.df.sample(frac=self.train_percentage, random_state=_RANDOM_STATE)\n self.test = self.df.copy()[~self.df.index.isin(self.train.index)]\n self.test.reset_index(drop=True, inplace=True)\n \n self.test_labels = self.test[[_EVENT_LABEL]]\n if self.event_id_col is None and self._add_random_values_if_real_na[_EVENT_ID]:\n self.test_labels[_EVENT_ID] = self.test[_EVENT_ID]\n self.test.drop([_EVENT_LABEL, _LABEL_TIMESTAMP], axis=1, inplace=True, errors=\"ignore\")\n\n\nclass FakejobPreProcessor(BasePreProcessor):\n def __init__(self, **kw):\n super(FakejobPreProcessor, self).__init__(**kw)\n\n\nclass VehicleloanPreProcessor(BasePreProcessor):\n def __init__(self, **kw):\n super(VehicleloanPreProcessor, self).__init__(**kw)\n\n\nclass MalurlPreProcessor(BasePreProcessor):\n \"\"\"\n This one originally multiple classes for manignant. \n We will combine all malignant one class to keep benchmark binary for now\n \n \"\"\"\n def __init__(self, **kw):\n super(MalurlPreProcessor, self).__init__(**kw)\n\n def standardize_label_col(self):\n self.df.rename({self.label_col: _EVENT_LABEL}, axis=1, inplace=True)\n binary_mapper = {\n 'defacement': 1,\n 'phishing': 1,\n 'malware': 1,\n 'benign': 0\n }\n \n self.df[_EVENT_LABEL] = self.df[_EVENT_LABEL].map(binary_mapper)\n\n def add_dummy_col(self):\n self.df['dummy_cat'] = self.df[_EVENT_LABEL].apply(lambda x: fake.uuid4())\n\n def preprocess(self):\n super(MalurlPreProcessor, self).preprocess()\n self.add_dummy_col()\n\nclass IEEEPreProcessor(BasePreProcessor):\n \"\"\"\n Some pre-processing was done using kaggle kernels below. \n\n References:\n Data Source: https://www.kaggle.com/c/ieee-fraud-detection/data\n\n Some processing from: https://www.kaggle.com/cdeotte/xgb-fraud-with-magic-0-9600\n Feature selection to reduce to 100: https://www.kaggle.com/code/pavelvpster/ieee-fraud-feature-selection-rfecv/notebook\n\n \"\"\"\n def __init__(self, **kw):\n super(IEEEPreProcessor, self).__init__(**kw)\n\n @staticmethod\n def _dtypes_cols():\n\n # FIRST 53 COLUMNS\n cols = ['TransactionID', 'TransactionDT', 'TransactionAmt',\n 'ProductCD', 'card1', 'card2', 'card3', 'card4', 'card5', 'card6',\n 'addr1', 'addr2', 'dist1', 'dist2', 'P_emaildomain', 'R_emaildomain',\n 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10', 'C11',\n 'C12', 'C13', 'C14', 'D1', 'D2', 'D3', 'D4', 'D5', 'D6', 'D7', 'D8',\n 'D9', 'D10', 'D11', 'D12', 'D13', 'D14', 'D15', 'M1', 'M2', 'M3', 'M4',\n 'M5', 'M6', 'M7', 'M8', 'M9']\n\n # V COLUMNS TO LOAD DECIDED BY CORRELATION EDA\n # https://www.kaggle.com/cdeotte/eda-for-columns-v-and-id\n v = [1, 3, 4, 6, 8, 11]\n v += [13, 14, 17, 20, 23, 26, 27, 30]\n v += [36, 37, 40, 41, 44, 47, 48]\n v += [54, 56, 59, 62, 65, 67, 68, 70]\n v += [76, 78, 80, 82, 86, 88, 89, 91]\n\n #v += [96, 98, 99, 104] #relates to groups, no NAN \n v += [107, 108, 111, 115, 117, 120, 121, 123] # maybe group, no NAN\n v += [124, 127, 129, 130, 136] # relates to groups, no NAN\n\n # LOTS OF NAN BELOW\n v += [138, 139, 142, 147, 156, 162] #b1\n v += [165, 160, 166] #b1\n v += [178, 176, 173, 182] #b2\n v += [187, 203, 205, 207, 215] #b2\n v += [169, 171, 175, 180, 185, 188, 198, 210, 209] #b2\n v += [218, 223, 224, 226, 228, 229, 235] #b3\n v += [240, 258, 257, 253, 252, 260, 261] #b3\n v += [264, 266, 267, 274, 277] #b3\n v += [220, 221, 234, 238, 250, 271] #b3\n\n v += [294, 284, 285, 286, 291, 297] # relates to grous, no NAN\n v += [303, 305, 307, 309, 310, 320] # relates to groups, no NAN\n v += [281, 283, 289, 296, 301, 314] # relates to groups, no NAN\n\n # COLUMNS WITH STRINGS\n str_type = ['ProductCD', 'card4', 'card6', 'P_emaildomain', 'R_emaildomain','M1', 'M2', 'M3', 'M4','M5',\n 'M6', 'M7', 'M8', 'M9', 'id_12', 'id_15', 'id_16', 'id_23', 'id_27', 'id_28', 'id_29', 'id_30', \n 'id_31', 'id_33', 'id_34', 'id_35', 'id_36', 'id_37', 'id_38', 'DeviceType', 'DeviceInfo']\n str_type += ['id-12', 'id-15', 'id-16', 'id-23', 'id-27', 'id-28', 'id-29', 'id-30', \n 'id-31', 'id-33', 'id-34', 'id-35', 'id-36', 'id-37', 'id-38']\n\n\n cols += ['V'+str(x) for x in v]\n dtypes = {}\n for c in cols+['id_0'+str(x) for x in range(1,10)]+['id_'+str(x) for x in range(10,34)]+\\\n ['id-0'+str(x) for x in range(1,10)]+['id-'+str(x) for x in range(10,34)]:\n dtypes[c] = 'float32'\n for c in str_type: dtypes[c] = 'category'\n\n return dtypes, cols\n\n\n def load_data(self):\n \"\"\"\n Hard coded file names for this dataset as it contains multiple files to be combined\n \"\"\"\n\n dtypes, cols = IEEEPreProcessor._dtypes_cols()\n\n self.df = pd.read_csv(\n os.path.join(_DOWNLOAD_LOCATION,\n 'train_transaction.csv'), \n index_col='TransactionID',\n dtype=dtypes, \n usecols=cols+['isFraud'])\n\n self.df_id = pd.read_csv(\n os.path.join(_DOWNLOAD_LOCATION, \n 'train_identity.csv'),\n index_col='TransactionID', \n dtype=dtypes)\n self.df = self.df.merge(self.df_id, how='left', left_index=True, right_index=True)\n\n # delete downloaded data after loading in memory\n if self.delete_downloaded: shutil.rmtree(_DOWNLOAD_LOCATION)\n\n def normalization(self):\n # NORMALIZE D COLUMNS\n for i in range(1,16):\n if i in [1,2,3,5,9]: continue\n self.df['d'+str(i)] = self.df['d'+str(i)] - self.df[self.timestamp_col]/np.float32(24*60*60)\n\n def standardize_entity_id_col(self):\n def _encode_CB(col1, col2, df):\n nm = col1+'_'+col2\n df[nm] = df[col1].astype(str)+'_'+df[col2].astype(str)\n \n _encode_CB('card1', 'addr1', self.df)\n self.df['day'] = self.df[self.timestamp_col] / (24*60*60)\n self.df[_ENTITY_ID] = self.df['card1_addr1'].astype(str) + '_' + np.floor(self.df['day'] - self.df['d1']).astype(str)\n\n @staticmethod\n def _add_seconds(x):\n init_time = '2021-01-01T00:00:00Z'\n dt_format = _TIMESTAMP_FORMAT\n init_time = datetime.strptime(init_time, dt_format) # start date from last 18 months\n final_time = init_time + timedelta(seconds=x)\n return final_time.strftime(_TIMESTAMP_FORMAT) \n\n def standardize_timestamp_col(self):\n self.df[_EVENT_TIMESTAMP] = self.df[self.timestamp_col].apply(lambda x: IEEEPreProcessor._add_seconds(x)) \n self.df.drop(self.timestamp_col, axis=1, inplace=True)\n if self._add_random_values_if_real_na[\"LABEL_TIMESTAMP\"]:\n self.df[_LABEL_TIMESTAMP] = _DEFAULT_LABEL_TIMESTAMP # most recent date \n\n def subset_features(self):\n features_to_select = \\\n ['transactionamt', 'productcd', 'card1', 'card2', 'card3', 'card5', 'card6', 'addr1', 'dist1',\n 'p_emaildomain', 'r_emaildomain', 'c1', 'c2', 'c4', 'c5', 'c6', 'c7', 'c8', 'c9', 'c10', 'c11',\n 'c12', 'c13', 'c14', 'v62', 'v70', 'v76', 'v78', 'v82', 'v91', 'v127', 'v130', 'v139', 'v160',\n 'v165', 'v187', 'v203', 'v207', 'v209', 'v210', 'v221', 'v234', 'v257', 'v258', 'v261', 'v264',\n 'v266', 'v267', 'v271', 'v274', 'v277', 'v283', 'v285', 'v289', 'v291', 'v294', 'id_01', 'id_02',\n 'id_05', 'id_06', 'id_09', 'id_13', 'id_17', 'id_19', 'id_20', 'devicetype', 'deviceinfo',\n 'EVENT_TIMESTAMP', 'ENTITY_ID', 'ENTITY_TYPE', 'EVENT_ID', 'EVENT_LABEL', 'LABEL_TIMESTAMP']\n self.df = self.df.loc[:, self.df.columns.isin(features_to_select)]\n\n def preprocess(self):\n self.lower_case_col_names()\n self.normalization() # normalize D columns\n self.standardize_label_col()\n self.standardize_event_id_col()\n self.standardize_entity_id_col()\n self.standardize_timestamp_col()\n self.add_meta_data()\n self.rename_features()\n self.subset_features() \n if self.timestamp_col:\n self.sort_by_timestamp()\n\n\nclass CCFraudPreProcessor(BasePreProcessor):\n def __init__(self, **kw):\n super(CCFraudPreProcessor, self).__init__(**kw)\n\n @staticmethod\n def _add_minutes(x):\n dt_format = _TIMESTAMP_FORMAT\n init_time = datetime.strptime('2021-09-01T00:00:00Z', dt_format) # chose randomly but in last 18 months\n final_time = init_time + timedelta(minutes=x)\n return final_time.strftime(_TIMESTAMP_FORMAT) \n\n def standardize_timestamp_col(self):\n self.df[_EVENT_TIMESTAMP] = self.df[self.timestamp_col].astype(float).apply(lambda x: CCFraudPreProcessor._add_minutes(x)) \n self.df.drop(self.timestamp_col, axis=1, inplace=True)\n if self._add_random_values_if_real_na[_LABEL_TIMESTAMP]:\n self.df[_LABEL_TIMESTAMP] = _DEFAULT_LABEL_TIMESTAMP # most recent date \n \nclass FraudecomPreProcessor(BasePreProcessor):\n def __init__(self, ip_address_col, signup_time_col, **kw):\n self.ip_address_col = ip_address_col\n self.signup_time_col = signup_time_col\n super(FraudecomPreProcessor, self).__init__(**kw)\n\n @staticmethod\n def _add_years(init_time):\n dt_format = '%Y-%m-%d %H:%M:%S'\n init_time = datetime.strptime(init_time, dt_format)\n final_time = init_time + relativedelta(years=6) # move to more recent time range\n return final_time.strftime(_TIMESTAMP_FORMAT) \n\n\n def standardize_timestamp_col(self):\n\n self.df[_EVENT_TIMESTAMP] = self.df[self.timestamp_col].apply(lambda x: FraudecomPreProcessor._add_years(x)) \n self.df.drop(self.timestamp_col, axis=1, inplace=True)\n\n # Also add _LABEL_TIMESTAMP to allow training of this dataset with TFI\n if self._add_random_values_if_real_na[_LABEL_TIMESTAMP]:\n self.df[_LABEL_TIMESTAMP] = _DEFAULT_LABEL_TIMESTAMP # most recent date \n\n def process_ip(self):\n \"\"\"\n This dataset has ip address as a feature, but needs to be converted into standard IPV4.\n \"\"\"\n self.df[self.ip_address_col] = self.df[self.ip_address_col].astype(float).astype(int).\\\n apply(lambda x: socket.inet_ntoa(struct.pack('!L', x)))\n\n def create_time_since_signup(self):\n self.df['time_since_signup'] = (\n pd.to_datetime(self.df[self.timestamp_col]) -\\\n pd.to_datetime(self.df[self.signup_time_col])).dt.seconds\n\n def preprocess(self):\n self.lower_case_col_names()\n self.standardize_label_col()\n self.standardize_event_id_col()\n self.standardize_entity_id_col()\n self.create_time_since_signup() # One manually engineered feature\n self.standardize_timestamp_col()\n self.add_meta_data()\n self.process_ip() # This extra step added\n self.rename_features()\n self.drop_features() # Replace select with drop\n if self.timestamp_col:\n self.sort_by_timestamp()\n\n\nclass SparknovPreProcessor(BasePreProcessor):\n def __init__(self, **kw):\n super(SparknovPreProcessor, self).__init__(**kw)\n \n def load_data(self):\n \"\"\"\n Hard coded file names for this dataset as it contains multiple files to be combined\n \"\"\"\n\n df_train = pd.read_csv(os.path.join(_DOWNLOAD_LOCATION,'fraudTrain.csv'))\n df_train['seg'] = 'train'\n\n df_test = pd.read_csv(os.path.join(_DOWNLOAD_LOCATION,'fraudTest.csv'))\n df_test['seg'] = 'test'\n\n self.df = pd.concat([df_train, df_test], ignore_index=True)\n\n # delete downloaded data after loading in memory\n if self.delete_downloaded: shutil.rmtree(_DOWNLOAD_LOCATION)\n\n @staticmethod\n def _add_months(x):\n _TIMESTAMP_FORMAT_SPARKNOV = '%Y-%m-%d %H:%M:%S'\n\n x = datetime.strptime(x, _TIMESTAMP_FORMAT_SPARKNOV) \n final_time = x + relativedelta(months=20) # chosen to move dates close to now()\n return final_time.strftime(_TIMESTAMP_FORMAT) \n\n def standardize_timestamp_col(self):\n\n self.df[_EVENT_TIMESTAMP] = self.df[self.timestamp_col].apply(lambda x: SparknovPreProcessor._add_months(x)) \n self.df.drop(self.timestamp_col, axis=1, inplace=True)\n self.df[_LABEL_TIMESTAMP] = _DEFAULT_LABEL_TIMESTAMP # most recent date \n\n def standardize_entity_id_col(self):\n\n self.df.rename({self.entity_id_col: _ENTITY_ID}, axis=1, inplace=True)\n self.df[_ENTITY_ID] = self.df[_ENTITY_ID].\\\n str.lower().\\\n apply(lambda x: re.sub(r'[^A-Za-z0-9]+', '_', x))\n \n def train_test_split(self):\n self.train = self.df.copy()[self.df['seg'] == 'train']\n self.train.reset_index(drop=True, inplace=True)\n self.train.drop(['seg'], axis=1, inplace=True)\n \n self.test = self.df.copy()[self.df['seg'] == 'test']\n self.test.reset_index(drop=True, inplace=True)\n self.test.drop(['seg'], axis=1, inplace=True)\n self.test = self.test.sample(n=20000, random_state=1)\n \n self.test_labels = self.test[[_EVENT_LABEL]]\n if self.event_id_col is None and self._add_random_values_if_real_na[_EVENT_ID]:\n self.test_labels[_EVENT_ID] = self.test[_EVENT_ID]\n self.test.drop([_EVENT_LABEL, _LABEL_TIMESTAMP], axis=1, inplace=True, errors=\"ignore\")\n\n\nclass TwitterbotPreProcessor(BasePreProcessor):\n def __init__(self, **kw):\n super(TwitterbotPreProcessor, self).__init__(**kw)\n\n def standardize_label_col(self):\n self.df.rename({self.label_col: _EVENT_LABEL}, axis=1, inplace=True)\n binary_mapper = {\n 'bot': 1,\n 'human': 0\n }\n \n self.df[_EVENT_LABEL] = self.df[_EVENT_LABEL].map(binary_mapper)\n\n\nclass IPBlocklistPreProcessor(BasePreProcessor):\n \"\"\"\n The dataset source is http://cinsscore.com/list/ci-badguys.txt. \n In order to download/access the latest version of this dataset, a sign-in/sign-up to is not required\n\n Since this dataset is not version controlled from the source, we added the version of dataset we used for experiments\n discussed in the paper. The versioned dataset is as of 2022-06-07. \n The code is set to pick the fixed version. If the user is interested to use the latest version,\n 'version' argument will need to be turned off (i.e. set to None) \n \"\"\"\n def __init__(self, version, **kw):\n self.version = version # string or None. If string, picks one from versioned_datasets, else creates one from source \n super(IPBlocklistPreProcessor, self).__init__(**kw)\n \n def load_data(self):\n if self.version is None:\n # load malicious IPs from the source\n _URL = 'http://cinsscore.com/list/ci-badguys.txt' # contains confirmed malicious IPs\n _N_BENIGN = 200000\n \n res = requests.get(_URL)\n ip_mal = pd.read_csv(StringIO(res.text), sep='\\n', names=['ip'], header=None)\n ip_mal['is_ip_malign'] = 1\n \n # add fake IPs as benign\n ip_ben = pd.DataFrame({\n 'ip': [fake.ipv4() for i in range(_N_BENIGN)], \n 'is_ip_malign': 0\n })\n \n self.df = pd.concat([ip_mal, ip_ben], axis=0, ignore_index=True)\n else:\n\n _VERSIONED_DATA_PATH = f'versioned_datasets/{self.key}/{self.version}.zip'\n data = pkgutil.get_data(__name__, _VERSIONED_DATA_PATH)\n with zipfile.ZipFile(BytesIO(data)) as f:\n self.train = pd.read_csv(f.open('train.csv'))\n self.test = pd.read_csv(f.open('test.csv'))\n self.test_labels = pd.read_csv(f.open('test_labels.csv'))\n\n def add_dummy_col(self):\n self.df['dummy_cat'] = self.df[_EVENT_LABEL].apply(lambda x: fake.uuid4())\n \n def train_test_split(self):\n if self.version is None:\n super(IPBlocklistPreProcessor, self).train_test_split()\n \n def preprocess(self):\n if self.version is None:\n super(IPBlocklistPreProcessor, self).preprocess()\n self.add_dummy_col() \n" }, { "path": "src/fdb/preprocessing_objects.py", "content": "from fdb.preprocessing import *\n\n\ndef load_data(key, load_pre_downloaded, delete_downloaded, add_random_values_if_real_na):\n common_kw = {\n \"key\": key,\n \"load_pre_downloaded\": load_pre_downloaded,\n \"delete_downloaded\": delete_downloaded,\n \"add_random_values_if_real_na\": add_random_values_if_real_na\n }\n\n if key == 'fakejob':\n obj = FakejobPreProcessor(\n train_percentage = 0.8,\n timestamp_col = None, \n label_col = 'fraudulent', \n event_id_col = 'job_id',\n **common_kw\n )\n \n elif key == 'vehicleloan':\n obj = VehicleloanPreProcessor(\n train_percentage = 0.8,\n timestamp_col = None, \n label_col = 'loan_default', \n event_id_col = 'uniqueid',\n features_to_drop = ['disbursal_date'],\n **common_kw\n )\n\n elif key == 'malurl':\n obj = MalurlPreProcessor(\n train_percentage = 0.9,\n timestamp_col = None,\n label_col = 'type',\n event_id_col = None,\n **common_kw\n )\n\n elif key == 'ieeecis':\n obj = IEEEPreProcessor(\n train_percentage = 0.95,\n timestamp_col = 'transactiondt',\n label_col = 'isfraud',\n event_id_col = None,\n entity_id_col = None, # manually created in code\n **common_kw\n )\n\n elif key == 'ccfraud':\n obj = CCFraudPreProcessor(\n train_percentage = 0.8,\n timestamp_col = 'time',\n label_col = 'class',\n event_id_col = None,\n **common_kw\n )\n\n elif key == 'fraudecom':\n obj = FraudecomPreProcessor(\n train_percentage = 0.8,\n timestamp_col = 'purchase_time',\n signup_time_col = 'signup_time',\n label_col = 'class',\n event_id_col = 'user_id',\n entity_id_col = 'device_id',\n ip_address_col = 'ip_address',\n features_to_drop = ['signup_time', 'sex'],\n **common_kw\n )\n\n elif key == 'sparknov':\n obj = SparknovPreProcessor(\n timestamp_col = 'trans_date_trans_time',\n label_col = 'is_fraud',\n event_id_col = 'trans_num',\n entity_id_col = 'merchant',\n features_to_drop = ['unix_time', 'unnamed: 0'],\n **common_kw\n )\n\n elif key == 'twitterbot':\n obj = TwitterbotPreProcessor(\n train_percentage = 0.8,\n timestamp_col = None,\n label_col = 'account_type',\n event_id_col = 'id',\n **common_kw\n )\n\n elif key == 'ipblock':\n obj = IPBlocklistPreProcessor(\n label_col = 'is_ip_malign',\n version = '20220607',\n **common_kw\n )\n\n else:\n raise ValueError('Invalid key')\n\n return obj" }, { "path": "src/fdb/versioned_datasets/__init__.py", "content": "" }, { "path": "src/fdb/versioned_datasets/ipblock/__init__.py", "content": "" } ]