diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 37f2257c..d844f78e 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -15,7 +15,7 @@ jobs: fail-fast: false matrix: python: [3.8, 3.9] - os: [ubuntu-latest, macos-latest] + os: [ubuntu-latest, macos-13] uses: ./.github/workflows/build-virny.yml with: @@ -28,7 +28,7 @@ jobs: fail-fast: false matrix: python: [3.8, 3.9] - os: [ubuntu-latest, macos-latest] + os: [ubuntu-latest, macos-13] uses: ./.github/workflows/unit-tests.yml with: diff --git a/.gitignore b/.gitignore index 375238bf..53a72458 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,7 @@ notebooks .DS_Store .ipynb_checkpoints docs/examples/test.py +tests/results # Remove big files from GitHub repo virny/datasets/2018 diff --git a/MANIFEST.in b/MANIFEST.in index 4de2de82..9d553406 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,3 +1,3 @@ -include virny/datasets/*.csv -include virny/datasets/*.gz -include virny/datasets/*.zip \ No newline at end of file +include virny/datasets/data/*.csv +include virny/datasets/data/*.gz +include virny/datasets/data/*.zip \ No newline at end of file diff --git a/README.md b/README.md index 9c420e41..4f2e628c 100644 --- a/README.md +++ b/README.md @@ -32,7 +32,8 @@ **Virny** is a Python library for in-depth profiling of model performance across overall and disparity dimensions. In addition to its metric computation capabilities, the library provides an interactive tool called _VirnyView_ -to streamline responsible model selection and generate nutritional labels for ML models. +to streamline responsible model selection and generate nutritional labels for ML models. + The Virny library was developed based on three fundamental principles: 1) easy extensibility of model analysis capabilities; @@ -65,33 +66,52 @@ pip install virny * [Interactive Demo](https://huggingface.co/spaces/denys-herasymuk/virny-demo) -## 💡 Features +## 😎 Why Virny + +In contrast to existing fairness software libraries and model card generating frameworks, our system stands out in four key aspects: + +1. Virny facilitates the measurement of **all normatively important performance dimensions** (including _fairness_, _stability_, and _uncertainty_) for a set of initialized models, both overall and broken down by user-defined subgroups of interest. + +2. Virny enables data scientists to analyze performance using **multiple sensitive attributes** (including _non-binary_) and their _intersections_. + +3. Virny offers **diverse APIs for metric computation**, designed to analyze multiple models in a single execution, assessing stability and uncertainty on correct and incorrect predictions broken down by protected groups, and testing models on multiple test sets, including in-domain and out-of-domain. + +4. Virny implements streamlined flow design tailored for **responsible model selection**, reducing the complexity associated with numerous model types, performance dimensions, and data-centric and model-centric interventions. + + +## 💡 List of Features -* Entire pipeline for profiling model accuracy, stability, uncertainty, and fairness +* Profiling of all normatively important performance dimensions: accuracy, stability, uncertainty, and fairness * Ability to analyze non-binary sensitive attributes and their intersections -* Compatibility with [pre-, in-, and post-processors](https://aif360.readthedocs.io/en/latest/modules/algorithms.html#) for fairness enhancement from AIF360 * Convenient metric computation interfaces: an interface for multiple models, an interface for multiple test sets, and an interface for saving results into a user-defined database +* Interactive _VirnyView_ visualizer that profiles dataset properties related to protected groups, computes comprehensive [nutritional labels](http://sites.computer.org/debull/A19sept/p13.pdf) for individual models, compares multiple models according to multiple metrics, and guides users through model selection +* Compatibility with [pre-, in-, and post-processors](https://aif360.readthedocs.io/en/latest/modules/algorithms.html#) for fairness enhancement from AIF360 * An `error_analysis` computation mode to analyze model stability and confidence for correct and incorrect prodictions broken down by groups * Metric static and interactive visualizations * Data loaders with subsampling for popular fair-ML benchmark datasets -* User-friendly parameters input via config yaml files -* Check out [our documentation](https://dataresponsibly.github.io/Virny/) for a comprehensive overview +* User-friendly parameters input via config yaml files +Check out [our documentation](https://dataresponsibly.github.io/Virny/) for a comprehensive overview. -## 📖 Library Overview -![Virny_Architecture](https://github.com/DataResponsibly/Virny/assets/42843889/91620e0f-11ff-4093-8fb6-c88c90bff711) +## 🤗 Affiliations + +![NYU-UCU-Logos](https://user-images.githubusercontent.com/42843889/216840888-071bf184-f0e3-4a3e-94dc-c0d1c7784143.png) -The software framework decouples the process of model profiling into several stages, including **subgroup metric computation**, -**disparity metric composition**, and **metric visualization**. This separation empowers data scientists with greater control and -flexibility in employing the library, both during model development and for post-deployment monitoring. The above figure demonstrates -how the library constructs a pipeline for model analysis. Inputs to a user interface are shown in green, pipeline stages are shown in blue, -and the output of each stage is shown in purple. +## 💬 Citation -## 🤗 Affiliations +If Virny has been useful to you, and you would like to cite it in a scientific publication, please refer to the [paper](https://dl.acm.org/doi/abs/10.1145/3626246.3654738) published at SIGMOD: -![NYU-UCU-Logos](https://user-images.githubusercontent.com/42843889/216840888-071bf184-f0e3-4a3e-94dc-c0d1c7784143.png) +```bibtex +@inproceedings{herasymuk2024responsible, + title={Responsible Model Selection with Virny and VirnyView}, + author={Herasymuk, Denys and Arif Khan, Falaah and Stoyanovich, Julia}, + booktitle={Companion of the 2024 International Conference on Management of Data}, + pages={488--491}, + year={2024} +} +``` ## 📝 License diff --git a/docs/.pages b/docs/.pages index 09e594de..5af65377 100644 --- a/docs/.pages +++ b/docs/.pages @@ -1,5 +1,6 @@ nav: - introduction - - api - examples + - glossary + - api - release_notes diff --git a/docs/api/analyzers/AbstractOverallVarianceAnalyzer.md b/docs/api/analyzers/AbstractOverallVarianceAnalyzer.md index 14b39884..2a494100 100644 --- a/docs/api/analyzers/AbstractOverallVarianceAnalyzer.md +++ b/docs/api/analyzers/AbstractOverallVarianceAnalyzer.md @@ -42,6 +42,10 @@ Abstract class for an analyzer that computes overall variance metrics for subgro Number of estimators in ensemble to measure base_model stability +- **random_state** (*int*) – defaults to `None` + + [Optional] Controls the randomness of the bootstrap approach for model arbitrariness evaluation + - **with_predict_proba** (*bool*) – defaults to `True` [Optional] A flag if model can return probabilities for its predictions. If no, only metrics based on labels (not labels and probabilities) will be computed. diff --git a/docs/api/analyzers/BatchOverallVarianceAnalyzer.md b/docs/api/analyzers/BatchOverallVarianceAnalyzer.md index 5c2ecd48..155be0d9 100644 --- a/docs/api/analyzers/BatchOverallVarianceAnalyzer.md +++ b/docs/api/analyzers/BatchOverallVarianceAnalyzer.md @@ -46,6 +46,10 @@ Analyzer to compute subgroup variance metrics for batch learning models. Number of estimators in ensemble to measure base_model stability +- **random_state** (*int*) – defaults to `None` + + [Optional] Controls the randomness of the bootstrap approach for model arbitrariness evaluation + - **with_predict_proba** (*bool*) – defaults to `True` [Optional] A flag if model can return probabilities for its predictions. If no, only metrics based on labels (not labels and probabilities) will be computed. diff --git a/docs/api/analyzers/BatchOverallVarianceAnalyzerPostProcessing.md b/docs/api/analyzers/BatchOverallVarianceAnalyzerPostProcessing.md index df2128f0..c63280ba 100644 --- a/docs/api/analyzers/BatchOverallVarianceAnalyzerPostProcessing.md +++ b/docs/api/analyzers/BatchOverallVarianceAnalyzerPostProcessing.md @@ -54,6 +54,10 @@ Analyzer to compute subgroup variance metrics using the defined post-processor. Number of estimators in ensemble to measure base_model stability +- **random_state** (*int*) – defaults to `None` + + [Optional] Controls the randomness of the bootstrap approach for model arbitrariness evaluation + - **with_predict_proba** (*bool*) – defaults to `True` [Optional] A flag if model can return probabilities for its predictions. If no, only metrics based on labels (not labels and probabilities) will be computed. diff --git a/docs/api/analyzers/SubgroupVarianceAnalyzer.md b/docs/api/analyzers/SubgroupVarianceAnalyzer.md index 80f21e11..ffacbe1b 100644 --- a/docs/api/analyzers/SubgroupVarianceAnalyzer.md +++ b/docs/api/analyzers/SubgroupVarianceAnalyzer.md @@ -50,10 +50,18 @@ Analyzer to compute variance metrics for subgroups. A sensitive attribute to use for post-processing +- **random_state** (*int*) – defaults to `None` + + [Optional] Controls the randomness of the bootstrap approach for model arbitrariness evaluation + - **computation_mode** (*str*) – defaults to `None` [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. +- **with_predict_proba** (*bool*) – defaults to `True` + + [Optional] True, if models in models_config have a predict_proba method and can return probabilities for predictions, False, otherwise. Note that if it is set to False, only metrics based on labels (not labels and probabilities) will be computed. Ignored when a postprocessor is not None, and set to False in this case. + - **notebook_logs_stdout** (*bool*) – defaults to `False` [Optional] True, if this interface was execute in a Jupyter notebook, False, otherwise. diff --git a/docs/api/custom-classes/BaseFlowDataset.md b/docs/api/custom-classes/BaseFlowDataset.md index ebb98a0c..0a5ebad3 100644 --- a/docs/api/custom-classes/BaseFlowDataset.md +++ b/docs/api/custom-classes/BaseFlowDataset.md @@ -6,9 +6,9 @@ Dataset class with custom train and test splits that is used as input for metric ## Parameters -- **init_features_df** (*pandas.core.frame.DataFrame*) +- **init_sensitive_attrs_df** (*pandas.core.frame.DataFrame*) - Full train + test non-preprocessed dataset of features without the target column. It is used for creating test groups. + Full train + test non-preprocessed dataset of sensitive attributes with initial indexes. It is used for creating test groups. - **X_train_val** (*pandas.core.frame.DataFrame*) diff --git a/docs/api/datasets/BankMarketingDataset.md b/docs/api/datasets/BankMarketingDataset.md new file mode 100644 index 00000000..49513df4 --- /dev/null +++ b/docs/api/datasets/BankMarketingDataset.md @@ -0,0 +1,19 @@ +# BankMarketingDataset + +Dataset class for the Bank Marketing dataset that contains sensitive attributes among feature columns. Source: https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/bank-full.csv General description and analysis: https://arxiv.org/pdf/2110.00530.pdf (Section 3.1.5) Broad description: https://archive.ics.uci.edu/dataset/222/bank+marketing + + + +## Parameters + +- **subsample_size** (*int*) – defaults to `None` + + Subsample size to create based on the input dataset + +- **subsample_seed** (*int*) – defaults to `None` + + Seed for sampling using the sample() method from pandas + + + + diff --git a/docs/api/datasets/CardiovascularDiseaseDataset.md b/docs/api/datasets/CardiovascularDiseaseDataset.md new file mode 100644 index 00000000..04dfb296 --- /dev/null +++ b/docs/api/datasets/CardiovascularDiseaseDataset.md @@ -0,0 +1,19 @@ +# CardiovascularDiseaseDataset + +Dataset class for the Cardiovascular Disease dataset that contains sensitive attributes among feature columns. Source and broad description: https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-dataset + + + +## Parameters + +- **subsample_size** (*int*) – defaults to `None` + + Subsample size to create based on the input dataset + +- **subsample_seed** (*int*) – defaults to `None` + + Seed for sampling using the sample() method from pandas + + + + diff --git a/docs/api/datasets/CreditCardDefaultDataset.md b/docs/api/datasets/CreditCardDefaultDataset.md deleted file mode 100644 index 2fd08783..00000000 --- a/docs/api/datasets/CreditCardDefaultDataset.md +++ /dev/null @@ -1,15 +0,0 @@ -# CreditCardDefaultDataset - -Dataset class for the Credit Card Default dataset that contains sensitive attributes among feature columns. Description: https://arxiv.org/pdf/2110.00530.pdf (Section 3.1.6) - - - -## Parameters - -- **dataset_path** (*str*) – defaults to `None` - - [Optional] Path to a file with the data - - - - diff --git a/docs/api/datasets/DiabetesDataset.md b/docs/api/datasets/DiabetesDataset.md deleted file mode 100644 index bd802e5c..00000000 --- a/docs/api/datasets/DiabetesDataset.md +++ /dev/null @@ -1,23 +0,0 @@ -# DiabetesDataset - -Dataset class for the Diabetes dataset that contains sensitive attributes among feature columns. Source: https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/diabetes-clean.csv Description: https://arxiv.org/pdf/2110.00530.pdf - - - -## Parameters - -- **subsample_size** (*int*) – defaults to `None` - - Subsample size to create based on the input dataset - -- **subsample_seed** (*int*) – defaults to `None` - - Seed for sampling using the sample() method from pandas - -- **dataset_path** – defaults to `None` - - [Optional] Path to a file with the data - - - - diff --git a/docs/api/datasets/DiabetesDataset2019.md b/docs/api/datasets/DiabetesDataset2019.md new file mode 100644 index 00000000..f82f5ee2 --- /dev/null +++ b/docs/api/datasets/DiabetesDataset2019.md @@ -0,0 +1,23 @@ +# DiabetesDataset2019 + +Dataset class for the Diabetes 2019 dataset that contains sensitive attributes among feature columns. Source and broad description: https://www.kaggle.com/datasets/tigganeha4/diabetes-dataset-2019/data + + + +## Parameters + +- **subsample_size** (*int*) – defaults to `None` + + Subsample size to create based on the input dataset + +- **subsample_seed** (*int*) – defaults to `None` + + Seed for sampling using the sample() method from pandas + +- **with_nulls** (*bool*) – defaults to `True` + + Whether to keep nulls in the dataset or drop rows with any nulls. Default: True. + + + + diff --git a/docs/api/datasets/GermanCreditDataset.md b/docs/api/datasets/GermanCreditDataset.md new file mode 100644 index 00000000..c0a3ae9a --- /dev/null +++ b/docs/api/datasets/GermanCreditDataset.md @@ -0,0 +1,19 @@ +# GermanCreditDataset + +Dataset class for the German Credit dataset that contains sensitive attributes among feature columns. Source: https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/german_data_credit.csv General description and analysis: https://arxiv.org/pdf/2110.00530.pdf (Section 3.1.3) Broad description: https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data + + + +## Parameters + +- **subsample_size** (*int*) – defaults to `None` + + Subsample size to create based on the input dataset + +- **subsample_seed** (*int*) – defaults to `None` + + Seed for sampling using the sample() method from pandas + + + + diff --git a/docs/api/overview.md b/docs/api/overview.md index 8465db18..c14ccd98 100644 --- a/docs/api/overview.md +++ b/docs/api/overview.md @@ -49,10 +49,12 @@ The purpose is to provide sample datasets for functionality testing and show exa - [ACSMobilityDataset](../datasets/ACSMobilityDataset) - [ACSPublicCoverageDataset](../datasets/ACSPublicCoverageDataset) - [ACSTravelTimeDataset](../datasets/ACSTravelTimeDataset) +- [BankMarketingDataset](../datasets/BankMarketingDataset) +- [CardiovascularDiseaseDataset](../datasets/CardiovascularDiseaseDataset) - [CompasDataset](../datasets/CompasDataset) - [CompasWithoutSensitiveAttrsDataset](../datasets/CompasWithoutSensitiveAttrsDataset) -- [CreditCardDefaultDataset](../datasets/CreditCardDefaultDataset) -- [DiabetesDataset](../datasets/DiabetesDataset) +- [DiabetesDataset2019](../datasets/DiabetesDataset2019) +- [GermanCreditDataset](../datasets/GermanCreditDataset) - [LawSchoolDataset](../datasets/LawSchoolDataset) - [RicciDataset](../datasets/RicciDataset) - [StudentPerformancePortugueseDataset](../datasets/StudentPerformancePortugueseDataset) diff --git a/docs/api/preprocessing/preprocess-dataset.md b/docs/api/preprocessing/preprocess-dataset.md index a67a84bc..8e935291 100644 --- a/docs/api/preprocessing/preprocess-dataset.md +++ b/docs/api/preprocessing/preprocess-dataset.md @@ -14,6 +14,10 @@ Preprocess an input dataset using sklearn ColumnTransformer. Split the dataset o Instance of sklearn ColumnTransformer to preprocess categorical and numerical columns. +- **sensitive_attributes_dct** (*dict*) + + Dictionary of sensitive attribute names and their disadvantaged values. + - **test_set_fraction** (*float*) Fraction from 0 to 1. Used to split the input dataset on the train and test sets. diff --git a/docs/api/user-interfaces/compute-metrics-with-config.md b/docs/api/user-interfaces/compute-metrics-with-config.md index 584e3300..b85b1425 100644 --- a/docs/api/user-interfaces/compute-metrics-with-config.md +++ b/docs/api/user-interfaces/compute-metrics-with-config.md @@ -26,6 +26,10 @@ Return a dictionary where keys are model names, and values are metrics for sensi [Optional] Postprocessor object to apply to model predictions before metrics computation +- **with_predict_proba** (*bool*) – defaults to `True` + + [Optional] True, if models in models_config have a predict_proba method and can return probabilities for predictions, False, otherwise. Note that if it is set to False, only metrics based on labels (not labels and probabilities) will be computed. Ignored when a postprocessor is not None, and set to False in this case. + - **notebook_logs_stdout** (*bool*) – defaults to `False` [Optional] True, if this interface was execute in a Jupyter notebook, False, otherwise. diff --git a/docs/api/user-interfaces/compute-metrics-with-db-writer.md b/docs/api/user-interfaces/compute-metrics-with-db-writer.md index 6df16064..495673a0 100644 --- a/docs/api/user-interfaces/compute-metrics-with-db-writer.md +++ b/docs/api/user-interfaces/compute-metrics-with-db-writer.md @@ -30,6 +30,10 @@ Return a dictionary where keys are model names, and values are metrics for sensi [Optional] Postprocessor object to apply to model predictions before metrics computation +- **with_predict_proba** (*bool*) – defaults to `True` + + [Optional] True, if models in models_config have a predict_proba method and can return probabilities for predictions, False, otherwise. Note that if it is set to False, only metrics based on labels (not labels and probabilities) will be computed. Ignored when a postprocessor is not None, and set to False in this case. + - **notebook_logs_stdout** (*bool*) – defaults to `False` [Optional] True, if this interface was execute in a Jupyter notebook, False, otherwise. diff --git a/docs/api/user-interfaces/compute-metrics-with-multiple-test-sets.md b/docs/api/user-interfaces/compute-metrics-with-multiple-test-sets.md index f35eea93..dae1df80 100644 --- a/docs/api/user-interfaces/compute-metrics-with-multiple-test-sets.md +++ b/docs/api/user-interfaces/compute-metrics-with-multiple-test-sets.md @@ -30,6 +30,10 @@ Compute stability and accuracy metrics for each model in models_config based on Python function object has one argument (run_models_metrics_df) and save this metrics df to a target database +- **with_predict_proba** (*bool*) – defaults to `True` + + [Optional] True, if models in models_config have a predict_proba method and can return probabilities for predictions, False, otherwise. Note that if it is set to False, only metrics based on labels (not labels and probabilities) will be computed. Ignored when a postprocessor is not None, and set to False in this case. + - **notebook_logs_stdout** (*bool*) – defaults to `False` [Optional] True, if this interface was execute in a Jupyter notebook, False, otherwise. diff --git a/docs/api/utils/create-test-protected-groups.md b/docs/api/utils/create-test-protected-groups.md index 94e43443..a54d584b 100644 --- a/docs/api/utils/create-test-protected-groups.md +++ b/docs/api/utils/create-test-protected-groups.md @@ -10,9 +10,9 @@ Return a dictionary where keys are subgroup names, and values are X_test row ind Test feature set -- **init_features_df** (*pandas.core.frame.DataFrame*) +- **init_sensitive_attrs_df** (*pandas.core.frame.DataFrame*) - Initial full dataset without preprocessing + Initial full dataset of sensitive attributes without preprocessing - **sensitive_attributes_dct** (*dict*) diff --git a/docs/examples/.pages b/docs/examples/.pages index d367bb0e..72a1eaad 100644 --- a/docs/examples/.pages +++ b/docs/examples/.pages @@ -1,7 +1,6 @@ title: Examples 🍱 nav: - Multiple_Models_Interface_Use_Case.md - - Interactive_Web_App_Demo.md - Multiple_Models_Interface_With_DB_Writer.md - Multiple_Models_Interface_With_Error_Analysis.md - Multiple_Models_Interface_With_Multiple_Test_Sets.md diff --git a/docs/examples/Multiple_Models_Interface_Use_Case.ipynb b/docs/examples/Multiple_Models_Interface_Use_Case.ipynb index 0b76b5c1..4b5f7da3 100644 --- a/docs/examples/Multiple_Models_Interface_Use_Case.ipynb +++ b/docs/examples/Multiple_Models_Interface_Use_Case.ipynb @@ -6,8 +6,8 @@ "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:26:58.910833Z", - "start_time": "2024-01-29T12:26:58.504524Z" + "end_time": "2024-06-01T21:28:20.194046Z", + "start_time": "2024-06-01T21:28:19.904829Z" } }, "outputs": [], @@ -23,8 +23,8 @@ "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:26:58.919201Z", - "start_time": "2024-01-29T12:26:58.911753Z" + "end_time": "2024-06-01T21:28:20.202757Z", + "start_time": "2024-06-01T21:28:20.194432Z" } }, "outputs": [], @@ -41,8 +41,8 @@ "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:26:58.928488Z", - "start_time": "2024-01-29T12:26:58.920029Z" + "end_time": "2024-06-01T21:28:20.212602Z", + "start_time": "2024-06-01T21:28:20.203488Z" } }, "outputs": [ @@ -100,8 +100,8 @@ "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:27:00.458227Z", - "start_time": "2024-01-29T12:26:58.929301Z" + "end_time": "2024-06-01T21:28:22.129627Z", + "start_time": "2024-06-01T21:28:20.213909Z" } }, "outputs": [ @@ -172,8 +172,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:27:00.481521Z", - "start_time": "2024-01-29T12:27:00.460259Z" + "end_time": "2024-06-01T21:28:22.155157Z", + "start_time": "2024-06-01T21:28:22.132190Z" } }, "id": "ce359a052925eb3a" @@ -225,8 +225,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:27:00.504549Z", - "start_time": "2024-01-29T12:27:00.483417Z" + "end_time": "2024-06-01T21:28:22.179692Z", + "start_time": "2024-06-01T21:28:22.156292Z" } }, "id": "2ece07ab7e3a9acc" @@ -250,6 +250,8 @@ "\n", "* **bootstrap_fraction**: float, the fraction from a train set in the range [0.0 - 1.0] to fit models in bootstrap (usually more than 0.5).\n", "\n", + "* **random_state**: int, a seed to control the randomness of the whole model evaluation pipeline.\n", + "\n", "* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics.\n", "\n", "* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including intersectional attributes), and values are disadvantaged values for these attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify disadvantaged values for intersectional groups since they will be derived from disadvantaged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair.\n", @@ -271,6 +273,7 @@ "config_yaml_content = \"\"\"\n", "dataset_name: COMPAS_Without_Sensitive_Attributes\n", "bootstrap_fraction: 0.8\n", + "random_state: 42\n", "n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example\n", "sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None}\n", "\"\"\"\n", @@ -281,8 +284,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:27:00.526221Z", - "start_time": "2024-01-29T12:27:00.505539Z" + "end_time": "2024-06-01T21:28:22.202465Z", + "start_time": "2024-06-01T21:28:22.179915Z" } }, "id": "af22ee06f1e3eb1a" @@ -298,8 +301,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:27:00.550121Z", - "start_time": "2024-01-29T12:27:00.526867Z" + "end_time": "2024-06-01T21:28:22.223293Z", + "start_time": "2024-06-01T21:28:22.201612Z" } }, "id": "65181f72484bb92b" @@ -332,8 +335,8 @@ "id": "9e3d7bf3", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:27:00.570483Z", - "start_time": "2024-01-29T12:27:00.548836Z" + "end_time": "2024-06-01T21:28:22.246298Z", + "start_time": "2024-06-01T21:28:22.224449Z" } }, "outputs": [], @@ -380,8 +383,8 @@ "id": "6c55c6a0", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:27:00.607691Z", - "start_time": "2024-01-29T12:27:00.569891Z" + "end_time": "2024-06-01T21:28:22.279902Z", + "start_time": "2024-06-01T21:28:22.246569Z" } }, "outputs": [ @@ -396,7 +399,7 @@ } ], "source": [ - "data_loader = CompasWithoutSensitiveAttrsDataset(dataset_path=os.path.join('virny', 'datasets', 'COMPAS.csv'))\n", + "data_loader = CompasWithoutSensitiveAttrsDataset(dataset_path=os.path.join('virny', 'datasets', 'data', 'COMPAS.csv'))\n", "data_loader.X_data[data_loader.X_data.columns[:5]].head()" ] }, @@ -413,8 +416,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:27:00.633194Z", - "start_time": "2024-01-29T12:27:00.607854Z" + "end_time": "2024-06-01T21:28:22.318013Z", + "start_time": "2024-06-01T21:28:22.279503Z" } }, "id": "ebbef5eaf9dc0943" @@ -424,13 +427,17 @@ "execution_count": 12, "outputs": [], "source": [ - "base_flow_dataset = preprocess_dataset(data_loader, column_transformer, TEST_SET_FRACTION, DATASET_SPLIT_SEED)" + "base_flow_dataset = preprocess_dataset(data_loader=data_loader, \n", + " column_transformer=column_transformer,\n", + " sensitive_attributes_dct=config.sensitive_attributes_dct,\n", + " test_set_fraction=TEST_SET_FRACTION,\n", + " dataset_split_seed=DATASET_SPLIT_SEED)" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:27:00.725753Z", - "start_time": "2024-01-29T12:27:00.629020Z" + "end_time": "2024-06-01T21:28:22.338421Z", + "start_time": "2024-06-01T21:28:22.302891Z" } }, "id": "97ed4609effbf53f" @@ -453,17 +460,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "2024/01/29, 14:27:00: Tuning DecisionTreeClassifier...\n", - "2024/01/29, 14:27:02: Tuning for DecisionTreeClassifier is finished [F1 score = 0.6554846983071246, Accuracy = 0.6575048862828714]\n", + "2024/06/02, 00:28:22: Tuning DecisionTreeClassifier...\n", + "2024/06/02, 00:28:23: Tuning for DecisionTreeClassifier is finished [F1 score = 0.6554846983071246, Accuracy = 0.6575048862828714]\n", "\n", - "2024/01/29, 14:27:02: Tuning LogisticRegression...\n", - "2024/01/29, 14:27:02: Tuning for LogisticRegression is finished [F1 score = 0.6483823116804865, Accuracy = 0.6520611566087312]\n", + "2024/06/02, 00:28:23: Tuning LogisticRegression...\n", + "2024/06/02, 00:28:23: Tuning for LogisticRegression is finished [F1 score = 0.6483823116804865, Accuracy = 0.6520611566087312]\n", "\n", - "2024/01/29, 14:27:02: Tuning RandomForestClassifier...\n", - "2024/01/29, 14:27:02: Tuning for RandomForestClassifier is finished [F1 score = 0.6569271025126497, Accuracy = 0.6586904492688075]\n", + "2024/06/02, 00:28:23: Tuning RandomForestClassifier...\n", + "2024/06/02, 00:28:24: Tuning for RandomForestClassifier is finished [F1 score = 0.6569271025126497, Accuracy = 0.6586904492688075]\n", "\n", - "2024/01/29, 14:27:02: Tuning XGBClassifier...\n", - "2024/01/29, 14:27:03: Tuning for XGBClassifier is finished [F1 score = 0.6623616224585352, Accuracy = 0.6646105242187331]\n" + "2024/06/02, 00:28:24: Tuning XGBClassifier...\n", + "2024/06/02, 00:28:24: Tuning for XGBClassifier is finished [F1 score = 0.6623616224585352, Accuracy = 0.6646105242187331]\n" ] }, { @@ -483,8 +490,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:27:03.760590Z", - "start_time": "2024-01-29T12:27:00.661050Z" + "end_time": "2024-06-01T21:28:24.773655Z", + "start_time": "2024-06-01T21:28:22.332650Z" } }, "id": "782741c190a4690b" @@ -502,8 +509,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:27:03.851255Z", - "start_time": "2024-01-29T12:27:03.764496Z" + "end_time": "2024-06-01T21:28:24.815424Z", + "start_time": "2024-06-01T21:28:24.777875Z" } }, "id": "21ccc879c5c3e215" @@ -551,8 +558,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:27:03.852017Z", - "start_time": "2024-01-29T12:27:03.812481Z" + "end_time": "2024-06-01T21:28:24.840430Z", + "start_time": "2024-06-01T21:28:24.809995Z" } }, "id": "3b15f202741fa2ae" @@ -579,8 +586,8 @@ "id": "197eadaa", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:27:29.855795Z", - "start_time": "2024-01-29T12:27:03.843880Z" + "end_time": "2024-06-01T21:28:49.846374Z", + "start_time": "2024-06-01T21:28:24.839220Z" } }, "outputs": [ @@ -590,7 +597,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "b1eb8ac5750249e898009575e3105d81" + "model_id": "c5df85718510494b86a44fec1648d243" } }, "metadata": {}, @@ -602,7 +609,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "a3a719967c68434fb03a82e96e6bb2b5" + "model_id": "f2ad8926d39142cca0855d89158f5bc1" } }, "metadata": {}, @@ -614,7 +621,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "23a86c8fe1f84d1e852b35f6f50043e0" + "model_id": "88521ccb14384c879ae36e8cb3c60ffb" } }, "metadata": {}, @@ -626,7 +633,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "173809428453453fa34c524a7b28defd" + "model_id": "66f015e934fd49d7b6192687414acb45" } }, "metadata": {}, @@ -638,7 +645,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "7255818659364d73856051c780c401e6" + "model_id": "4fc2e410757a4754906314e108dd774a" } }, "metadata": {}, @@ -664,15 +671,15 @@ "id": "bea94683", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:27:29.887437Z", - "start_time": "2024-01-29T12:27:29.856473Z" + "end_time": "2024-06-01T21:28:49.875190Z", + "start_time": "2024-06-01T21:28:49.846627Z" } }, "outputs": [ { "data": { - "text/plain": " Metric overall sex_priv sex_dis race_priv \\\n0 Statistical_Bias 0.418525 0.414663 0.419489 0.414622 \n1 Mean_Prediction 0.518803 0.573717 0.505091 0.583054 \n2 Aleatoric_Uncertainty 0.868869 0.871348 0.868251 0.861129 \n3 IQR 0.085847 0.087701 0.085384 0.089732 \n4 Std 0.069670 0.074842 0.068379 0.071254 \n5 Overall_Uncertainty 0.892819 0.900199 0.890976 0.887327 \n6 Epistemic_Uncertainty 0.023950 0.028850 0.022726 0.026199 \n7 Label_Stability 0.815417 0.793934 0.820781 0.811787 \n8 Jitter 0.129962 0.144397 0.126358 0.130667 \n9 TPR 0.662420 0.520000 0.689394 0.544218 \n10 TNR 0.726496 0.794118 0.706013 0.779026 \n11 PPV 0.661017 0.582090 0.674074 0.575540 \n12 FNR 0.337580 0.480000 0.310606 0.455782 \n13 FPR 0.273504 0.205882 0.293987 0.220974 \n14 Accuracy 0.697917 0.696682 0.698225 0.695652 \n15 F1 0.661718 0.549296 0.681648 0.559441 \n16 Selection-Rate 0.446970 0.317536 0.479290 0.335749 \n17 Positive-Rate 1.002123 0.893333 1.022727 0.945578 \n18 Sample_Size 1056.000000 211.000000 845.000000 414.000000 \n\n race_dis \n0 0.421042 \n1 0.477370 \n2 0.873861 \n3 0.083342 \n4 0.068649 \n5 0.896361 \n6 0.022499 \n7 0.817757 \n8 0.129507 \n9 0.716049 \n10 0.682390 \n11 0.696697 \n12 0.283951 \n13 0.317610 \n14 0.699377 \n15 0.706240 \n16 0.518692 \n17 1.027778 \n18 642.000000 ", - "text/html": "
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Metricoverallsex_privsex_disrace_privrace_dis
0Statistical_Bias0.4185250.4146630.4194890.4146220.421042
1Mean_Prediction0.5188030.5737170.5050910.5830540.477370
2Aleatoric_Uncertainty0.8688690.8713480.8682510.8611290.873861
3IQR0.0858470.0877010.0853840.0897320.083342
4Std0.0696700.0748420.0683790.0712540.068649
5Overall_Uncertainty0.8928190.9001990.8909760.8873270.896361
6Epistemic_Uncertainty0.0239500.0288500.0227260.0261990.022499
7Label_Stability0.8154170.7939340.8207810.8117870.817757
8Jitter0.1299620.1443970.1263580.1306670.129507
9TPR0.6624200.5200000.6893940.5442180.716049
10TNR0.7264960.7941180.7060130.7790260.682390
11PPV0.6610170.5820900.6740740.5755400.696697
12FNR0.3375800.4800000.3106060.4557820.283951
13FPR0.2735040.2058820.2939870.2209740.317610
14Accuracy0.6979170.6966820.6982250.6956520.699377
15F10.6617180.5492960.6816480.5594410.706240
16Selection-Rate0.4469700.3175360.4792900.3357490.518692
17Positive-Rate1.0021230.8933331.0227270.9455781.027778
18Sample_Size1056.000000211.000000845.000000414.000000642.000000
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" + "text/plain": " Metric overall sex_priv sex_dis race_priv \\\n0 IQR 0.093218 0.092883 0.093302 0.095182 \n1 Overall_Uncertainty 0.899836 0.909407 0.897446 0.896719 \n2 Std 0.076228 0.077296 0.075962 0.075141 \n3 Mean_Prediction 0.520117 0.572049 0.507149 0.581026 \n4 Aleatoric_Uncertainty 0.869944 0.875791 0.868484 0.866015 \n5 Statistical_Bias 0.422194 0.416842 0.423530 0.418523 \n6 Epistemic_Uncertainty 0.029893 0.033616 0.028963 0.030704 \n7 Jitter 0.148098 0.159899 0.145152 0.138860 \n8 Label_Stability 0.786591 0.766825 0.791527 0.801256 \n9 TPR 0.687898 0.573333 0.709596 0.578231 \n10 TNR 0.687179 0.808824 0.650334 0.756554 \n11 PPV 0.639053 0.623188 0.641553 0.566667 \n12 FNR 0.312102 0.426667 0.290404 0.421769 \n13 FPR 0.312821 0.191176 0.349666 0.243446 \n14 Accuracy 0.687500 0.725118 0.678107 0.693237 \n15 F1 0.662577 0.597222 0.673861 0.572391 \n16 Selection-Rate 0.480114 0.327014 0.518343 0.362319 \n17 Sample_Size 1056.000000 211.000000 845.000000 414.000000 \n\n race_dis \n0 0.091952 \n1 0.901847 \n2 0.076929 \n3 0.480839 \n4 0.872477 \n5 0.424561 \n6 0.029369 \n7 0.154056 \n8 0.777134 \n9 0.737654 \n10 0.628931 \n11 0.669468 \n12 0.262346 \n13 0.371069 \n14 0.683801 \n15 0.701909 \n16 0.556075 \n17 642.000000 ", + "text/html": "
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Metricoverallsex_privsex_disrace_privrace_dis
0IQR0.0932180.0928830.0933020.0951820.091952
1Overall_Uncertainty0.8998360.9094070.8974460.8967190.901847
2Std0.0762280.0772960.0759620.0751410.076929
3Mean_Prediction0.5201170.5720490.5071490.5810260.480839
4Aleatoric_Uncertainty0.8699440.8757910.8684840.8660150.872477
5Statistical_Bias0.4221940.4168420.4235300.4185230.424561
6Epistemic_Uncertainty0.0298930.0336160.0289630.0307040.029369
7Jitter0.1480980.1598990.1451520.1388600.154056
8Label_Stability0.7865910.7668250.7915270.8012560.777134
9TPR0.6878980.5733330.7095960.5782310.737654
10TNR0.6871790.8088240.6503340.7565540.628931
11PPV0.6390530.6231880.6415530.5666670.669468
12FNR0.3121020.4266670.2904040.4217690.262346
13FPR0.3128210.1911760.3496660.2434460.371069
14Accuracy0.6875000.7251180.6781070.6932370.683801
15F10.6625770.5972220.6738610.5723910.701909
16Selection-Rate0.4801140.3270140.5183430.3623190.556075
17Sample_Size1056.000000211.000000845.000000414.000000642.000000
\n
" }, "execution_count": 17, "metadata": {}, @@ -706,8 +713,8 @@ "id": "f94a20dc", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:27:29.911770Z", - "start_time": "2024-01-29T12:27:29.883738Z" + "end_time": "2024-06-01T21:28:49.916799Z", + "start_time": "2024-06-01T21:28:49.875032Z" } }, "outputs": [], @@ -721,8 +728,8 @@ "id": "b04d06cf", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:27:29.982612Z", - "start_time": "2024-01-29T12:27:29.908326Z" + "end_time": "2024-06-01T21:28:49.943701Z", + "start_time": "2024-06-01T21:28:49.900460Z" } }, "outputs": [], @@ -744,8 +751,8 @@ "id": "be6ace22", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:27:29.986480Z", - "start_time": "2024-01-29T12:27:29.928833Z" + "end_time": "2024-06-01T21:28:49.959380Z", + "start_time": "2024-06-01T21:28:49.923586Z" } }, "outputs": [], @@ -759,8 +766,8 @@ "outputs": [ { "data": { - "text/plain": " Metric sex race sex&race \\\n0 Accuracy_Difference 0.001542 0.003725 -0.010969 \n1 Aleatoric_Uncertainty_Difference -0.003098 0.012733 0.004796 \n2 Aleatoric_Uncertainty_Ratio 0.996445 1.014786 1.005535 \n3 Epistemic_Uncertainty_Difference -0.006124 -0.003699 -0.004695 \n4 Epistemic_Uncertainty_Ratio 0.787719 0.858799 0.821512 \n.. ... ... ... ... \n71 Statistical_Parity_Difference 0.165322 0.188090 0.216400 \n72 Std_Difference -0.000450 0.002229 0.002248 \n73 Std_Ratio 0.990382 1.049497 1.049664 \n74 Equalized_Odds_TNR -0.089447 -0.099039 -0.131890 \n75 Equalized_Odds_TPR 0.175152 0.179264 0.176509 \n\n Model_Name \n0 DecisionTreeClassifier \n1 DecisionTreeClassifier \n2 DecisionTreeClassifier \n3 DecisionTreeClassifier \n4 DecisionTreeClassifier \n.. ... \n71 XGBClassifier \n72 XGBClassifier \n73 XGBClassifier \n74 XGBClassifier \n75 XGBClassifier \n\n[76 rows x 5 columns]", - "text/html": "
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Metricsexracesex&raceModel_Name
0Accuracy_Difference0.0015420.003725-0.010969DecisionTreeClassifier
1Aleatoric_Uncertainty_Difference-0.0030980.0127330.004796DecisionTreeClassifier
2Aleatoric_Uncertainty_Ratio0.9964451.0147861.005535DecisionTreeClassifier
3Epistemic_Uncertainty_Difference-0.006124-0.003699-0.004695DecisionTreeClassifier
4Epistemic_Uncertainty_Ratio0.7877190.8587990.821512DecisionTreeClassifier
..................
71Statistical_Parity_Difference0.1653220.1880900.216400XGBClassifier
72Std_Difference-0.0004500.0022290.002248XGBClassifier
73Std_Ratio0.9903821.0494971.049664XGBClassifier
74Equalized_Odds_TNR-0.089447-0.099039-0.131890XGBClassifier
75Equalized_Odds_TPR0.1751520.1792640.176509XGBClassifier
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" + "text/plain": " Metric sex race sex&race \\\n0 Accuracy_Difference -0.047012 -0.009436 -0.039300 \n1 Aleatoric_Uncertainty_Difference -0.007307 0.006463 0.000802 \n2 Aleatoric_Uncertainty_Ratio 0.991656 1.007463 1.000922 \n3 Epistemic_Uncertainty_Difference -0.004654 -0.001335 -0.003381 \n4 Epistemic_Uncertainty_Ratio 0.861563 0.956510 0.892966 \n.. ... ... ... ... \n71 Disparate_Impact 1.465176 1.537383 1.596796 \n72 Std_Difference 0.000151 0.002984 0.002995 \n73 Std_Ratio 1.003178 1.065098 1.064903 \n74 Equalized_Odds_TNR -0.076968 -0.101583 -0.123015 \n75 Equalized_Odds_TPR 0.153535 0.152053 0.155233 \n\n Model_Name \n0 DecisionTreeClassifier \n1 DecisionTreeClassifier \n2 DecisionTreeClassifier \n3 DecisionTreeClassifier \n4 DecisionTreeClassifier \n.. ... \n71 XGBClassifier \n72 XGBClassifier \n73 XGBClassifier \n74 XGBClassifier \n75 XGBClassifier \n\n[76 rows x 5 columns]", + "text/html": "
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Metricsexracesex&raceModel_Name
0Accuracy_Difference-0.047012-0.009436-0.039300DecisionTreeClassifier
1Aleatoric_Uncertainty_Difference-0.0073070.0064630.000802DecisionTreeClassifier
2Aleatoric_Uncertainty_Ratio0.9916561.0074631.000922DecisionTreeClassifier
3Epistemic_Uncertainty_Difference-0.004654-0.001335-0.003381DecisionTreeClassifier
4Epistemic_Uncertainty_Ratio0.8615630.9565100.892966DecisionTreeClassifier
..................
71Disparate_Impact1.4651761.5373831.596796XGBClassifier
72Std_Difference0.0001510.0029840.002995XGBClassifier
73Std_Ratio1.0031781.0650981.064903XGBClassifier
74Equalized_Odds_TNR-0.076968-0.101583-0.123015XGBClassifier
75Equalized_Odds_TPR0.1535350.1520530.155233XGBClassifier
\n

76 rows × 5 columns

\n
" }, "execution_count": 21, "metadata": {}, @@ -773,8 +780,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:27:29.991697Z", - "start_time": "2024-01-29T12:27:29.965232Z" + "end_time": "2024-06-01T21:28:49.983151Z", + "start_time": "2024-06-01T21:28:49.956902Z" } }, "id": "a286da0406c6401d" @@ -782,9 +789,7 @@ { "cell_type": "markdown", "id": "deb45226", - "metadata": { - "is_executing": true - }, + "metadata": {}, "source": [ "## Metric Visualization" ] @@ -803,8 +808,8 @@ "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:27:30.074595Z", - "start_time": "2024-01-29T12:27:29.991248Z" + "end_time": "2024-06-01T21:28:50.040594Z", + "start_time": "2024-06-01T21:28:49.982914Z" } }, "outputs": [], @@ -820,14 +825,14 @@ "id": "5efb1bf2", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:27:30.093255Z", - "start_time": "2024-01-29T12:27:30.026746Z" + "end_time": "2024-06-01T21:28:50.070303Z", + "start_time": "2024-06-01T21:28:50.015744Z" } }, "outputs": [ { "data": { - "text/html": "\n
\n", + "text/html": "\n
\n", "text/plain": "alt.Chart(...)" }, "execution_count": 23, @@ -848,14 +853,14 @@ "id": "0eb8528e", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:27:30.135889Z", - "start_time": "2024-01-29T12:27:30.082590Z" + "end_time": "2024-06-01T21:28:50.128106Z", + "start_time": "2024-06-01T21:28:50.069642Z" } }, "outputs": [ { "data": { - "text/html": "\n
\n", + "text/html": "\n
\n", "text/plain": "alt.Chart(...)" }, "execution_count": 24, @@ -876,15 +881,15 @@ "id": "df024aed", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:27:30.389484Z", - "start_time": "2024-01-29T12:27:30.136516Z" + "end_time": "2024-06-01T21:28:50.362860Z", + "start_time": "2024-06-01T21:28:50.123627Z" } }, "outputs": [ { "data": { - "text/plain": "
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" 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" }, "metadata": {}, "output_type": "display_data" @@ -905,7 +910,7 @@ { "data": { "text/plain": "
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}, "metadata": {}, "output_type": "display_data" @@ -931,8 +936,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:27:30.735771Z", - "start_time": "2024-01-29T12:27:30.389147Z" + "end_time": "2024-06-01T21:28:50.683383Z", + "start_time": "2024-06-01T21:28:50.362940Z" } }, "id": "4bfca852f6ef428a" @@ -943,8 +948,8 @@ "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:27:30.735913Z", - "start_time": "2024-01-29T12:27:30.727667Z" + "end_time": "2024-06-01T21:28:50.688445Z", + "start_time": "2024-06-01T21:28:50.683504Z" } }, "outputs": [], diff --git a/docs/examples/Multiple_Models_Interface_Use_Case.md b/docs/examples/Multiple_Models_Interface_Use_Case.md index 235fff3f..e8aaf36e 100644 --- a/docs/examples/Multiple_Models_Interface_Use_Case.md +++ b/docs/examples/Multiple_Models_Interface_Use_Case.md @@ -105,6 +105,8 @@ models_params_for_tuning = { * **bootstrap_fraction**: float, the fraction from a train set in the range [0.0 - 1.0] to fit models in bootstrap (usually more than 0.5). +* **random_state**: int, a seed to control the randomness of the whole model evaluation pipeline. + * **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics. * **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including intersectional attributes), and values are disadvantaged values for these attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify disadvantaged values for intersectional groups since they will be derived from disadvantaged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair. @@ -118,6 +120,7 @@ config_yaml_path = os.path.join(ROOT_DIR, 'experiment_config.yaml') config_yaml_content = """ dataset_name: COMPAS_Without_Sensitive_Attributes bootstrap_fraction: 0.8 +random_state: 42 n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None} """ @@ -182,7 +185,7 @@ class CompasWithoutSensitiveAttrsDataset(BaseDataLoader): ```python -data_loader = CompasWithoutSensitiveAttrsDataset(dataset_path=os.path.join('virny', 'datasets', 'COMPAS.csv')) +data_loader = CompasWithoutSensitiveAttrsDataset(dataset_path=os.path.join('virny', 'datasets', 'data', 'COMPAS.csv')) data_loader.X_data[data_loader.X_data.columns[:5]].head() ``` @@ -271,7 +274,11 @@ column_transformer = ColumnTransformer(transformers=[ ```python -base_flow_dataset = preprocess_dataset(data_loader, column_transformer, TEST_SET_FRACTION, DATASET_SPLIT_SEED) +base_flow_dataset = preprocess_dataset(data_loader=data_loader, + column_transformer=column_transformer, + sensitive_attributes_dct=config.sensitive_attributes_dct, + test_set_fraction=TEST_SET_FRACTION, + dataset_split_seed=DATASET_SPLIT_SEED) ``` ### Tune models and create a models config for metrics computation @@ -282,17 +289,17 @@ tuned_params_df, models_config = tune_ML_models(models_params_for_tuning, base_f tuned_params_df ``` - 2024/01/29, 14:27:00: Tuning DecisionTreeClassifier... - 2024/01/29, 14:27:02: Tuning for DecisionTreeClassifier is finished [F1 score = 0.6554846983071246, Accuracy = 0.6575048862828714] + 2024/06/02, 00:28:22: Tuning DecisionTreeClassifier... + 2024/06/02, 00:28:23: Tuning for DecisionTreeClassifier is finished [F1 score = 0.6554846983071246, Accuracy = 0.6575048862828714] - 2024/01/29, 14:27:02: Tuning LogisticRegression... - 2024/01/29, 14:27:02: Tuning for LogisticRegression is finished [F1 score = 0.6483823116804865, Accuracy = 0.6520611566087312] + 2024/06/02, 00:28:23: Tuning LogisticRegression... + 2024/06/02, 00:28:23: Tuning for LogisticRegression is finished [F1 score = 0.6483823116804865, Accuracy = 0.6520611566087312] - 2024/01/29, 14:27:02: Tuning RandomForestClassifier... - 2024/01/29, 14:27:02: Tuning for RandomForestClassifier is finished [F1 score = 0.6569271025126497, Accuracy = 0.6586904492688075] + 2024/06/02, 00:28:23: Tuning RandomForestClassifier... + 2024/06/02, 00:28:24: Tuning for RandomForestClassifier is finished [F1 score = 0.6569271025126497, Accuracy = 0.6586904492688075] - 2024/01/29, 14:27:02: Tuning XGBClassifier... - 2024/01/29, 14:27:03: Tuning for XGBClassifier is finished [F1 score = 0.6623616224585352, Accuracy = 0.6646105242187331] + 2024/06/02, 00:28:24: Tuning XGBClassifier... + 2024/06/02, 00:28:24: Tuning for XGBClassifier is finished [F1 score = 0.6623616224585352, Accuracy = 0.6646105242187331] @@ -465,168 +472,159 @@ sample_model_metrics_df[sample_model_metrics_df.columns[:6]].head(20) 0 - Statistical_Bias - 0.418525 - 0.414663 - 0.419489 - 0.414622 - 0.421042 + IQR + 0.093218 + 0.092883 + 0.093302 + 0.095182 + 0.091952 1 - Mean_Prediction - 0.518803 - 0.573717 - 0.505091 - 0.583054 - 0.477370 + Overall_Uncertainty + 0.899836 + 0.909407 + 0.897446 + 0.896719 + 0.901847 2 - Aleatoric_Uncertainty - 0.868869 - 0.871348 - 0.868251 - 0.861129 - 0.873861 + Std + 0.076228 + 0.077296 + 0.075962 + 0.075141 + 0.076929 3 - IQR - 0.085847 - 0.087701 - 0.085384 - 0.089732 - 0.083342 + Mean_Prediction + 0.520117 + 0.572049 + 0.507149 + 0.581026 + 0.480839 4 - Std - 0.069670 - 0.074842 - 0.068379 - 0.071254 - 0.068649 + Aleatoric_Uncertainty + 0.869944 + 0.875791 + 0.868484 + 0.866015 + 0.872477 5 - Overall_Uncertainty - 0.892819 - 0.900199 - 0.890976 - 0.887327 - 0.896361 + Statistical_Bias + 0.422194 + 0.416842 + 0.423530 + 0.418523 + 0.424561 6 Epistemic_Uncertainty - 0.023950 - 0.028850 - 0.022726 - 0.026199 - 0.022499 + 0.029893 + 0.033616 + 0.028963 + 0.030704 + 0.029369 7 - Label_Stability - 0.815417 - 0.793934 - 0.820781 - 0.811787 - 0.817757 + Jitter + 0.148098 + 0.159899 + 0.145152 + 0.138860 + 0.154056 8 - Jitter - 0.129962 - 0.144397 - 0.126358 - 0.130667 - 0.129507 + Label_Stability + 0.786591 + 0.766825 + 0.791527 + 0.801256 + 0.777134 9 TPR - 0.662420 - 0.520000 - 0.689394 - 0.544218 - 0.716049 + 0.687898 + 0.573333 + 0.709596 + 0.578231 + 0.737654 10 TNR - 0.726496 - 0.794118 - 0.706013 - 0.779026 - 0.682390 + 0.687179 + 0.808824 + 0.650334 + 0.756554 + 0.628931 11 PPV - 0.661017 - 0.582090 - 0.674074 - 0.575540 - 0.696697 + 0.639053 + 0.623188 + 0.641553 + 0.566667 + 0.669468 12 FNR - 0.337580 - 0.480000 - 0.310606 - 0.455782 - 0.283951 + 0.312102 + 0.426667 + 0.290404 + 0.421769 + 0.262346 13 FPR - 0.273504 - 0.205882 - 0.293987 - 0.220974 - 0.317610 + 0.312821 + 0.191176 + 0.349666 + 0.243446 + 0.371069 14 Accuracy - 0.697917 - 0.696682 - 0.698225 - 0.695652 - 0.699377 + 0.687500 + 0.725118 + 0.678107 + 0.693237 + 0.683801 15 F1 - 0.661718 - 0.549296 - 0.681648 - 0.559441 - 0.706240 + 0.662577 + 0.597222 + 0.673861 + 0.572391 + 0.701909 16 Selection-Rate - 0.446970 - 0.317536 - 0.479290 - 0.335749 - 0.518692 + 0.480114 + 0.327014 + 0.518343 + 0.362319 + 0.556075 17 - Positive-Rate - 1.002123 - 0.893333 - 1.022727 - 0.945578 - 1.027778 - - - 18 Sample_Size 1056.000000 211.000000 @@ -698,41 +696,41 @@ models_composed_metrics_df 0 Accuracy_Difference - 0.001542 - 0.003725 - -0.010969 + -0.047012 + -0.009436 + -0.039300 DecisionTreeClassifier 1 Aleatoric_Uncertainty_Difference - -0.003098 - 0.012733 - 0.004796 + -0.007307 + 0.006463 + 0.000802 DecisionTreeClassifier 2 Aleatoric_Uncertainty_Ratio - 0.996445 - 1.014786 - 1.005535 + 0.991656 + 1.007463 + 1.000922 DecisionTreeClassifier 3 Epistemic_Uncertainty_Difference - -0.006124 - -0.003699 - -0.004695 + -0.004654 + -0.001335 + -0.003381 DecisionTreeClassifier 4 Epistemic_Uncertainty_Ratio - 0.787719 - 0.858799 - 0.821512 + 0.861563 + 0.956510 + 0.892966 DecisionTreeClassifier @@ -745,42 +743,42 @@ models_composed_metrics_df 71 - Statistical_Parity_Difference - 0.165322 - 0.188090 - 0.216400 + Disparate_Impact + 1.465176 + 1.537383 + 1.596796 XGBClassifier 72 Std_Difference - -0.000450 - 0.002229 - 0.002248 + 0.000151 + 0.002984 + 0.002995 XGBClassifier 73 Std_Ratio - 0.990382 - 1.049497 - 1.049664 + 1.003178 + 1.065098 + 1.064903 XGBClassifier 74 Equalized_Odds_TNR - -0.089447 - -0.099039 - -0.131890 + -0.076968 + -0.101583 + -0.123015 XGBClassifier 75 Equalized_Odds_TPR - 0.175152 - 0.179264 - 0.176509 + 0.153535 + 0.152053 + 0.155233 XGBClassifier @@ -813,13 +811,13 @@ visualizer.create_overall_metrics_bar_char( -
+
@@ -882,13 +880,13 @@ visualizer.create_overall_metrics_bar_char( -
+
diff --git a/docs/examples/Multiple_Models_Interface_Use_Case_files/Multiple_Models_Interface_Use_Case_43_0.png b/docs/examples/Multiple_Models_Interface_Use_Case_files/Multiple_Models_Interface_Use_Case_43_0.png index a3b1292b..ed4e7ff4 100644 Binary files a/docs/examples/Multiple_Models_Interface_Use_Case_files/Multiple_Models_Interface_Use_Case_43_0.png and b/docs/examples/Multiple_Models_Interface_Use_Case_files/Multiple_Models_Interface_Use_Case_43_0.png differ diff --git a/docs/examples/Multiple_Models_Interface_Use_Case_files/Multiple_Models_Interface_Use_Case_44_0.png b/docs/examples/Multiple_Models_Interface_Use_Case_files/Multiple_Models_Interface_Use_Case_44_0.png index 44d80f4f..a9cdd098 100644 Binary files a/docs/examples/Multiple_Models_Interface_Use_Case_files/Multiple_Models_Interface_Use_Case_44_0.png and b/docs/examples/Multiple_Models_Interface_Use_Case_files/Multiple_Models_Interface_Use_Case_44_0.png differ diff --git a/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb b/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb index ab1a5fe7..21fb6e82 100644 --- a/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_DB_Writer.ipynb @@ -105,7 +105,7 @@ "from virny.custom_classes.metrics_visualizer import MetricsVisualizer\n", "from virny.custom_classes.metrics_composer import MetricsComposer\n", "from virny.preprocessing.basic_preprocessing import preprocess_dataset\n", - "from virny.datasets.data_loaders import CompasWithoutSensitiveAttrsDataset" + "from virny.datasets import CompasWithoutSensitiveAttrsDataset" ] }, { @@ -149,16 +149,89 @@ }, { "cell_type": "markdown", - "id": "4b21d920", - "metadata": {}, "source": [ - "### Create a Dataset class" + "### Create a config object" + ], + "metadata": { + "collapsed": false + }, + "id": "d22f31eb" + }, + { + "cell_type": "markdown", + "source": [ + "`compute_metrics_with_db_writer` interface requires that your **yaml file** includes the following parameters:\n", + "\n", + "* **dataset_name**: str, a name of your dataset; it will be used to name files with metrics.\n", + "\n", + "* **bootstrap_fraction**: float, the fraction from a train set in the range [0.0 - 1.0] to fit models in bootstrap (usually more than 0.5).\n", + "\n", + "* **random_state**: int, a seed to control the randomness of the whole model evaluation pipeline.\n", + "\n", + "* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics.\n", + "\n", + "* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including intersectional attributes), and values are disadvantaged values for these attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify disadvantaged values for intersectional groups since they will be derived from disadvantaged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair.\n", + "\n", + "Note that disadvantaged value in a sensitive attribute dictionary must be **the same as in the original dataset**. For example, when distinct values of the _sex_ column in the original dataset are 'F' and 'M', and after pre-processing they became 0 and 1 respectively, you still need to set a disadvantaged value as 'F' or 'M' in the sensitive attribute dictionary.\n" + ], + "metadata": { + "collapsed": false + }, + "id": "2ab8dda3" + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [], + "source": [ + "ROOT_DIR = os.getcwd()\n", + "config_yaml_path = os.path.join(ROOT_DIR, 'experiment_config.yaml')\n", + "config_yaml_content = \\\n", + "\"\"\"dataset_name: COMPAS_Without_Sensitive_Attributes\n", + "bootstrap_fraction: 0.8\n", + "random_state: 42\n", + "n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example\n", + "sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None}\n", + "\"\"\"\n", + "\n", + "with open(config_yaml_path, 'w', encoding='utf-8') as f:\n", + " f.write(config_yaml_content)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2024-01-29T12:31:37.286356Z" + } + }, + "id": "79dcac74" + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "abc8bd6f", + "metadata": { + "ExecuteTime": { + "end_time": "2024-01-29T12:31:37.333453Z", + "start_time": "2024-01-29T12:31:37.310610Z" + } + }, + "outputs": [], + "source": [ + "config = create_config_obj(config_yaml_path=config_yaml_path)" ] }, { "cell_type": "markdown", - "id": "57b42644", - "metadata": {}, + "source": [ + "### Create a Dataset class" + ], + "metadata": { + "collapsed": false + }, + "id": "4b21d920" + }, + { + "cell_type": "markdown", "source": [ "Based on the BaseDataset class, your **dataset class** should include the following attributes:\n", "\n", @@ -167,18 +240,15 @@ "* **Optional attributes**: X_data, y_data, columns_with_nulls\n", "\n", "For more details, please refer to the library documentation." - ] + ], + "metadata": { + "collapsed": false + }, + "id": "57b42644" }, { "cell_type": "code", "execution_count": 5, - "id": "30a74059", - "metadata": { - "ExecuteTime": { - "end_time": "2024-01-29T12:31:37.235984Z", - "start_time": "2024-01-29T12:31:37.204682Z" - } - }, "outputs": [ { "data": { @@ -193,7 +263,14 @@ "source": [ "data_loader = CompasWithoutSensitiveAttrsDataset()\n", "data_loader.X_data[data_loader.X_data.columns[:5]].head()" - ] + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2024-01-29T12:31:37.204682Z" + } + }, + "id": "30a74059" }, { "cell_type": "code", @@ -208,7 +285,6 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:31:37.258724Z", "start_time": "2024-01-29T12:31:37.236370Z" } }, @@ -219,83 +295,20 @@ "execution_count": 7, "outputs": [], "source": [ - "base_flow_dataset = preprocess_dataset(data_loader, column_transformer, TEST_SET_FRACTION, DATASET_SPLIT_SEED)" + "base_flow_dataset = preprocess_dataset(data_loader=data_loader,\n", + " column_transformer=column_transformer,\n", + " sensitive_attributes_dct=config.sensitive_attributes_dct,\n", + " test_set_fraction=TEST_SET_FRACTION,\n", + " dataset_split_seed=DATASET_SPLIT_SEED)" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:31:37.300586Z", "start_time": "2024-01-29T12:31:37.257924Z" } }, "id": "6cd3c2f8ad510bb2" }, - { - "cell_type": "markdown", - "id": "d22f31eb", - "metadata": {}, - "source": [ - "### Create a config object" - ] - }, - { - "cell_type": "markdown", - "id": "2ab8dda3", - "metadata": {}, - "source": [ - "`compute_metrics_with_db_writer` interface requires that your **yaml file** includes the following parameters:\n", - "\n", - "* **dataset_name**: str, a name of your dataset; it will be used to name files with metrics.\n", - "\n", - "* **bootstrap_fraction**: float, the fraction from a train set in the range [0.0 - 1.0] to fit models in bootstrap (usually more than 0.5).\n", - "\n", - "* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics.\n", - "\n", - "* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including intersectional attributes), and values are disadvantaged values for these attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify disadvantaged values for intersectional groups since they will be derived from disadvantaged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair.\n", - "\n", - "Note that disadvantaged value in a sensitive attribute dictionary must be **the same as in the original dataset**. For example, when distinct values of the _sex_ column in the original dataset are 'F' and 'M', and after pre-processing they became 0 and 1 respectively, you still need to set a disadvantaged value as 'F' or 'M' in the sensitive attribute dictionary.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "79dcac74", - "metadata": { - "ExecuteTime": { - "end_time": "2024-01-29T12:31:37.311984Z", - "start_time": "2024-01-29T12:31:37.286356Z" - } - }, - "outputs": [], - "source": [ - "ROOT_DIR = os.getcwd()\n", - "config_yaml_path = os.path.join(ROOT_DIR, 'experiment_config.yaml')\n", - "config_yaml_content = \\\n", - "\"\"\"dataset_name: COMPAS_Without_Sensitive_Attributes\n", - "bootstrap_fraction: 0.8\n", - "n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example\n", - "sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None}\n", - "\"\"\"\n", - "\n", - "with open(config_yaml_path, 'w', encoding='utf-8') as f:\n", - " f.write(config_yaml_content)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "abc8bd6f", - "metadata": { - "ExecuteTime": { - "end_time": "2024-01-29T12:31:37.333453Z", - "start_time": "2024-01-29T12:31:37.310610Z" - } - }, - "outputs": [], - "source": [ - "config = create_config_obj(config_yaml_path=config_yaml_path)" - ] - }, { "cell_type": "markdown", "id": "f04867aa", diff --git a/docs/examples/Multiple_Models_Interface_With_DB_Writer.md b/docs/examples/Multiple_Models_Interface_With_DB_Writer.md index ae494783..dcc47316 100644 --- a/docs/examples/Multiple_Models_Interface_With_DB_Writer.md +++ b/docs/examples/Multiple_Models_Interface_With_DB_Writer.md @@ -31,7 +31,7 @@ from virny.utils.custom_initializers import create_config_obj, create_models_met from virny.custom_classes.metrics_visualizer import MetricsVisualizer from virny.custom_classes.metrics_composer import MetricsComposer from virny.preprocessing.basic_preprocessing import preprocess_dataset -from virny.datasets.data_loaders import CompasWithoutSensitiveAttrsDataset +from virny.datasets import CompasWithoutSensitiveAttrsDataset ``` @@ -51,6 +51,44 @@ TEST_SET_FRACTION = 0.2 DATASET_SPLIT_SEED = 42 ``` +### Create a config object + +`compute_metrics_with_db_writer` interface requires that your **yaml file** includes the following parameters: + +* **dataset_name**: str, a name of your dataset; it will be used to name files with metrics. + +* **bootstrap_fraction**: float, the fraction from a train set in the range [0.0 - 1.0] to fit models in bootstrap (usually more than 0.5). + +* **random_state**: int, a seed to control the randomness of the whole model evaluation pipeline. + +* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics. + +* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including intersectional attributes), and values are disadvantaged values for these attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify disadvantaged values for intersectional groups since they will be derived from disadvantaged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair. + +Note that disadvantaged value in a sensitive attribute dictionary must be **the same as in the original dataset**. For example, when distinct values of the _sex_ column in the original dataset are 'F' and 'M', and after pre-processing they became 0 and 1 respectively, you still need to set a disadvantaged value as 'F' or 'M' in the sensitive attribute dictionary. + + + +```python +ROOT_DIR = os.getcwd() +config_yaml_path = os.path.join(ROOT_DIR, 'experiment_config.yaml') +config_yaml_content = \ +"""dataset_name: COMPAS_Without_Sensitive_Attributes +bootstrap_fraction: 0.8 +random_state: 42 +n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example +sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None} +""" + +with open(config_yaml_path, 'w', encoding='utf-8') as f: + f.write(config_yaml_content) +``` + + +```python +config = create_config_obj(config_yaml_path=config_yaml_path) +``` + ### Create a Dataset class Based on the BaseDataset class, your **dataset class** should include the following attributes: @@ -152,42 +190,11 @@ column_transformer = ColumnTransformer(transformers=[ ```python -base_flow_dataset = preprocess_dataset(data_loader, column_transformer, TEST_SET_FRACTION, DATASET_SPLIT_SEED) -``` - -### Create a config object - -`compute_metrics_with_db_writer` interface requires that your **yaml file** includes the following parameters: - -* **dataset_name**: str, a name of your dataset; it will be used to name files with metrics. - -* **bootstrap_fraction**: float, the fraction from a train set in the range [0.0 - 1.0] to fit models in bootstrap (usually more than 0.5). - -* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics. - -* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including intersectional attributes), and values are disadvantaged values for these attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify disadvantaged values for intersectional groups since they will be derived from disadvantaged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair. - -Note that disadvantaged value in a sensitive attribute dictionary must be **the same as in the original dataset**. For example, when distinct values of the _sex_ column in the original dataset are 'F' and 'M', and after pre-processing they became 0 and 1 respectively, you still need to set a disadvantaged value as 'F' or 'M' in the sensitive attribute dictionary. - - - -```python -ROOT_DIR = os.getcwd() -config_yaml_path = os.path.join(ROOT_DIR, 'experiment_config.yaml') -config_yaml_content = \ -"""dataset_name: COMPAS_Without_Sensitive_Attributes -bootstrap_fraction: 0.8 -n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example -sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None} -""" - -with open(config_yaml_path, 'w', encoding='utf-8') as f: - f.write(config_yaml_content) -``` - - -```python -config = create_config_obj(config_yaml_path=config_yaml_path) +base_flow_dataset = preprocess_dataset(data_loader=data_loader, + column_transformer=column_transformer, + sensitive_attributes_dct=config.sensitive_attributes_dct, + test_set_fraction=TEST_SET_FRACTION, + dataset_split_seed=DATASET_SPLIT_SEED) ``` ### Create a models config diff --git a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb index dce10f40..e987615d 100644 --- a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.ipynb @@ -6,8 +6,8 @@ "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:36:38.625283Z", - "start_time": "2024-01-29T12:36:38.294860Z" + "end_time": "2024-06-01T21:32:40.514680Z", + "start_time": "2024-06-01T21:32:40.211761Z" } }, "outputs": [], @@ -23,8 +23,8 @@ "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:36:38.635040Z", - "start_time": "2024-01-29T12:36:38.625844Z" + "end_time": "2024-06-01T21:32:40.525555Z", + "start_time": "2024-06-01T21:32:40.515836Z" } }, "outputs": [], @@ -41,8 +41,8 @@ "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:36:38.645228Z", - "start_time": "2024-01-29T12:36:38.635229Z" + "end_time": "2024-06-01T21:32:40.538209Z", + "start_time": "2024-06-01T21:32:40.526255Z" } }, "outputs": [ @@ -102,8 +102,8 @@ "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:36:40.012141Z", - "start_time": "2024-01-29T12:36:38.644902Z" + "end_time": "2024-06-01T21:32:42.636150Z", + "start_time": "2024-06-01T21:32:40.536406Z" } }, "outputs": [ @@ -131,7 +131,7 @@ "\n", "from virny.utils.custom_initializers import create_config_obj, read_model_metric_dfs, create_models_config_from_tuned_params_df\n", "from virny.user_interfaces.multiple_models_api import compute_metrics_with_config\n", - "from virny.datasets.data_loaders import CompasWithoutSensitiveAttrsDataset\n", + "from virny.datasets import CompasWithoutSensitiveAttrsDataset\n", "from virny.preprocessing.basic_preprocessing import preprocess_dataset\n", "from virny.custom_classes.metrics_visualizer import MetricsVisualizer\n", "from virny.custom_classes.metrics_composer import MetricsComposer" @@ -171,8 +171,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:36:40.034931Z", - "start_time": "2024-01-29T12:36:40.013129Z" + "end_time": "2024-06-01T21:32:42.662239Z", + "start_time": "2024-06-01T21:32:42.639658Z" } }, "id": "76d98eaabfcfc9c0" @@ -196,6 +196,8 @@ "\n", "* **bootstrap_fraction**: float, the fraction from a train set in the range [0.0 - 1.0] to fit models in bootstrap (usually more than 0.5).\n", "\n", + "* **random_state**: int, a seed to control the randomness of the whole model evaluation pipeline.\n", + "\n", "* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics.\n", "\n", "* **computation_mode**: str, 'default' or 'error_analysis'. Name of the computation mode. When a default computation mode measures metrics for sex_priv and sex_dis, an `error_analysis` mode measures metrics for (sex_priv, sex_priv_correct, sex_priv_incorrect) and (sex_dis, sex_dis_correct, sex_dis_incorrect). Therefore, a user can analyze how a model is certain about its incorrect predictions.\n", @@ -219,6 +221,7 @@ "config_yaml_content = \"\"\"\n", "dataset_name: COMPAS_Without_Sensitive_Attributes\n", "bootstrap_fraction: 0.8\n", + "random_state: 42\n", "n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example\n", "computation_mode: error_analysis\n", "sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None}\n", @@ -230,8 +233,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:36:40.057943Z", - "start_time": "2024-01-29T12:36:40.035274Z" + "end_time": "2024-06-01T21:32:42.688222Z", + "start_time": "2024-06-01T21:32:42.663180Z" } }, "id": "efc95fa248b9f135" @@ -247,8 +250,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:36:40.077837Z", - "start_time": "2024-01-29T12:36:40.056639Z" + "end_time": "2024-06-01T21:32:42.710745Z", + "start_time": "2024-06-01T21:32:42.686442Z" } }, "id": "f3a59ca9319a774d" @@ -281,8 +284,8 @@ "id": "6c55c6a0", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:36:40.108697Z", - "start_time": "2024-01-29T12:36:40.078057Z" + "end_time": "2024-06-01T21:32:42.747758Z", + "start_time": "2024-06-01T21:32:42.711432Z" } }, "outputs": [ @@ -314,8 +317,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:36:40.132862Z", - "start_time": "2024-01-29T12:36:40.108973Z" + "end_time": "2024-06-01T21:32:42.785294Z", + "start_time": "2024-06-01T21:32:42.746359Z" } }, "id": "8ee9e8a8c10245bf" @@ -325,13 +328,17 @@ "execution_count": 10, "outputs": [], "source": [ - "base_flow_dataset = preprocess_dataset(data_loader, column_transformer, TEST_SET_FRACTION, DATASET_SPLIT_SEED)" + "base_flow_dataset = preprocess_dataset(data_loader=data_loader,\n", + " column_transformer=column_transformer,\n", + " sensitive_attributes_dct=config.sensitive_attributes_dct,\n", + " test_set_fraction=TEST_SET_FRACTION,\n", + " dataset_split_seed=DATASET_SPLIT_SEED)" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:36:40.208062Z", - "start_time": "2024-01-29T12:36:40.130095Z" + "end_time": "2024-06-01T21:32:42.808641Z", + "start_time": "2024-06-01T21:32:42.770185Z" } }, "id": "6dba3327ebe01279" @@ -382,8 +389,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:36:40.208317Z", - "start_time": "2024-01-29T12:36:40.159406Z" + "end_time": "2024-06-01T21:32:42.825775Z", + "start_time": "2024-06-01T21:32:42.802098Z" } }, "id": "8c6061673bb72efa" @@ -410,8 +417,8 @@ "id": "197eadaa", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:37:05.846663Z", - "start_time": "2024-01-29T12:36:40.180812Z" + "end_time": "2024-06-01T21:33:09.582891Z", + "start_time": "2024-06-01T21:32:42.826018Z" } }, "outputs": [ @@ -421,7 +428,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "5dcb2e69c1184c90bab858a5e0528f73" + "model_id": "b161be95803d4bcd8fbc48b11fb75d75" } }, "metadata": {}, @@ -433,7 +440,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "70e601539ca042cd84e5eb19fc6699ea" + "model_id": "f663bc0b121d49b0a050b22a944e3265" } }, "metadata": {}, @@ -445,7 +452,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "152e6eb8a3c34811ac171e0b854e7bef" + "model_id": "2873756e9fc243b19ecea6500256c527" } }, "metadata": {}, @@ -457,7 +464,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "80cddf14ebfe4c7dace3330f3b63dd4c" + "model_id": "50cfec2b48504570a33c6a54903839ef" } }, "metadata": {}, @@ -469,7 +476,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "b58fd966079f4db2969068fc242dd7c3" + "model_id": "60fda77987fc4b669c37b633f26852ca" } }, "metadata": {}, @@ -494,15 +501,15 @@ "id": "bea94683", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:37:05.875960Z", - "start_time": "2024-01-29T12:37:05.846292Z" + "end_time": "2024-06-01T21:33:09.610270Z", + "start_time": "2024-06-01T21:33:09.583455Z" } }, "outputs": [ { "data": { - "text/plain": " Metric overall sex_priv sex_priv_correct \\\n0 Mean_Prediction 0.521995 0.577845 0.601574 \n1 Std 0.072253 0.075557 0.068902 \n2 Overall_Uncertainty 0.887763 0.896446 0.875769 \n3 Aleatoric_Uncertainty 0.860720 0.866208 0.850550 \n4 IQR 0.082323 0.084506 0.074594 \n5 Statistical_Bias 0.416585 0.411295 0.320042 \n6 Epistemic_Uncertainty 0.027043 0.030239 0.025219 \n7 Jitter 0.110380 0.130717 0.099423 \n8 Label_Stability 0.859508 0.825403 0.871448 \n9 TPR 0.651805 0.466667 1.000000 \n10 TNR 0.733333 0.808824 1.000000 \n11 PPV 0.663067 0.573770 1.000000 \n12 FNR 0.348195 0.533333 0.000000 \n13 FPR 0.266667 0.191176 0.000000 \n14 Accuracy 0.696970 0.687204 1.000000 \n15 F1 0.657388 0.514706 1.000000 \n16 Selection-Rate 0.438447 0.289100 0.241379 \n17 Positive-Rate 0.983015 0.813333 1.000000 \n18 Sample_Size 1056.000000 211.000000 145.000000 \n\n sex_priv_incorrect \n0 0.525711 \n1 0.090176 \n2 0.941874 \n3 0.900606 \n4 0.106282 \n5 0.611776 \n6 0.041268 \n7 0.199468 \n8 0.724242 \n9 0.000000 \n10 0.000000 \n11 0.000000 \n12 1.000000 \n13 1.000000 \n14 0.000000 \n15 0.000000 \n16 0.393939 \n17 0.650000 \n18 66.000000 ", - 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Metricoverallsex_privsex_priv_correctsex_priv_incorrect
0Mean_Prediction0.5219950.5778450.6015740.525711
1Std0.0722530.0755570.0689020.090176
2Overall_Uncertainty0.8877630.8964460.8757690.941874
3Aleatoric_Uncertainty0.8607200.8662080.8505500.900606
4IQR0.0823230.0845060.0745940.106282
5Statistical_Bias0.4165850.4112950.3200420.611776
6Epistemic_Uncertainty0.0270430.0302390.0252190.041268
7Jitter0.1103800.1307170.0994230.199468
8Label_Stability0.8595080.8254030.8714480.724242
9TPR0.6518050.4666671.0000000.000000
10TNR0.7333330.8088241.0000000.000000
11PPV0.6630670.5737701.0000000.000000
12FNR0.3481950.5333330.0000001.000000
13FPR0.2666670.1911760.0000001.000000
14Accuracy0.6969700.6872041.0000000.000000
15F10.6573880.5147061.0000000.000000
16Selection-Rate0.4384470.2891000.2413790.393939
17Positive-Rate0.9830150.8133331.0000000.650000
18Sample_Size1056.000000211.000000145.00000066.000000
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" + "text/plain": " Metric overall sex_priv sex_priv_correct \\\n0 Statistical_Bias 0.416691 0.413261 0.324033 \n1 Overall_Uncertainty 0.887649 0.898580 0.880975 \n2 Aleatoric_Uncertainty 0.859615 0.866990 0.852746 \n3 IQR 0.087474 0.088773 0.081936 \n4 Std 0.073404 0.076654 0.071201 \n5 Mean_Prediction 0.519733 0.575657 0.597694 \n6 Epistemic_Uncertainty 0.028034 0.031589 0.028229 \n7 Jitter 0.108416 0.130465 0.102774 \n8 Label_Stability 0.862917 0.827488 0.866939 \n9 TPR 0.656051 0.493333 1.000000 \n10 TNR 0.733333 0.808824 1.000000 \n11 PPV 0.664516 0.587302 1.000000 \n12 FNR 0.343949 0.506667 0.000000 \n13 FPR 0.266667 0.191176 0.000000 \n14 Accuracy 0.698864 0.696682 1.000000 \n15 F1 0.660256 0.536232 1.000000 \n16 Selection-Rate 0.440341 0.298578 0.251701 \n17 Sample_Size 1056.000000 211.000000 147.000000 \n\n sex_priv_incorrect \n0 0.618208 \n1 0.939015 \n2 0.899708 \n3 0.104479 \n4 0.089178 \n5 0.525040 \n6 0.039308 \n7 0.194069 \n8 0.736875 \n9 0.000000 \n10 0.000000 \n11 0.000000 \n12 1.000000 \n13 1.000000 \n14 0.000000 \n15 0.000000 \n16 0.406250 \n17 64.000000 ", + "text/html": "
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Metricoverallsex_privsex_priv_correctsex_priv_incorrect
0Statistical_Bias0.4166910.4132610.3240330.618208
1Overall_Uncertainty0.8876490.8985800.8809750.939015
2Aleatoric_Uncertainty0.8596150.8669900.8527460.899708
3IQR0.0874740.0887730.0819360.104479
4Std0.0734040.0766540.0712010.089178
5Mean_Prediction0.5197330.5756570.5976940.525040
6Epistemic_Uncertainty0.0280340.0315890.0282290.039308
7Jitter0.1084160.1304650.1027740.194069
8Label_Stability0.8629170.8274880.8669390.736875
9TPR0.6560510.4933331.0000000.000000
10TNR0.7333330.8088241.0000000.000000
11PPV0.6645160.5873021.0000000.000000
12FNR0.3439490.5066670.0000001.000000
13FPR0.2666670.1911760.0000001.000000
14Accuracy0.6988640.6966821.0000000.000000
15F10.6602560.5362321.0000000.000000
16Selection-Rate0.4403410.2985780.2517010.406250
17Sample_Size1056.000000211.000000147.00000064.000000
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" }, "execution_count": 13, "metadata": {}, @@ -536,8 +543,8 @@ "id": "f94a20dc", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:37:05.901573Z", - "start_time": "2024-01-29T12:37:05.874076Z" + "end_time": "2024-06-01T21:33:09.656225Z", + "start_time": "2024-06-01T21:33:09.612140Z" } }, "outputs": [], @@ -551,8 +558,8 @@ "id": "b04d06cf", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:37:05.947991Z", - "start_time": "2024-01-29T12:37:05.900013Z" + "end_time": "2024-06-01T21:33:09.687694Z", + "start_time": "2024-06-01T21:33:09.640611Z" } }, "outputs": [], @@ -574,8 +581,8 @@ "id": "be6ace22", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:37:06.007772Z", - "start_time": "2024-01-29T12:37:05.921757Z" + "end_time": "2024-06-01T21:33:09.735472Z", + "start_time": "2024-06-01T21:33:09.663580Z" } }, "outputs": [], @@ -605,8 +612,8 @@ "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:37:06.008158Z", - "start_time": "2024-01-29T12:37:05.950960Z" + "end_time": "2024-06-01T21:33:09.735943Z", + "start_time": "2024-06-01T21:33:09.698636Z" } }, "outputs": [], @@ -622,14 +629,14 @@ "id": "5efb1bf2", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:37:06.048668Z", - "start_time": "2024-01-29T12:37:05.983729Z" + "end_time": "2024-06-01T21:33:09.785875Z", + "start_time": "2024-06-01T21:33:09.732766Z" } }, "outputs": [ { "data": { - "text/html": "\n
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\n", "text/plain": "alt.Chart(...)" }, "execution_count": 18, @@ -650,14 +657,14 @@ "id": "0eb8528e", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:37:06.085762Z", - "start_time": "2024-01-29T12:37:06.036898Z" + "end_time": "2024-06-01T21:33:09.841654Z", + "start_time": "2024-06-01T21:33:09.786357Z" } }, "outputs": [ { "data": { - "text/html": "\n
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\n", "text/plain": "alt.Chart(...)" }, "execution_count": 19, @@ -678,15 +685,15 @@ "id": "df024aed", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:37:06.386898Z", - "start_time": "2024-01-29T12:37:06.085600Z" + "end_time": "2024-06-01T21:33:10.083977Z", + "start_time": "2024-06-01T21:33:09.842542Z" } }, "outputs": [ { "data": { - "text/plain": "
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" }, "metadata": {}, "output_type": "display_data" @@ -706,15 +713,15 @@ "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:37:06.722358Z", - "start_time": "2024-01-29T12:37:06.387444Z" + "end_time": "2024-06-01T21:33:10.551403Z", + "start_time": "2024-06-01T21:33:10.084830Z" } }, "outputs": [ { "data": { "text/plain": "
", - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -746,8 +753,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:37:06.722527Z", - "start_time": "2024-01-29T12:37:06.720821Z" + "end_time": "2024-06-01T21:33:10.551684Z", + "start_time": "2024-06-01T21:33:10.526374Z" } }, "id": "a6ebe5c2fae387cb" diff --git a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.md b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.md index b44b25cb..402aab08 100644 --- a/docs/examples/Multiple_Models_Interface_With_Error_Analysis.md +++ b/docs/examples/Multiple_Models_Interface_With_Error_Analysis.md @@ -30,7 +30,7 @@ from sklearn.preprocessing import StandardScaler from virny.utils.custom_initializers import create_config_obj, read_model_metric_dfs, create_models_config_from_tuned_params_df from virny.user_interfaces.multiple_models_api import compute_metrics_with_config -from virny.datasets.data_loaders import CompasWithoutSensitiveAttrsDataset +from virny.datasets import CompasWithoutSensitiveAttrsDataset from virny.preprocessing.basic_preprocessing import preprocess_dataset from virny.custom_classes.metrics_visualizer import MetricsVisualizer from virny.custom_classes.metrics_composer import MetricsComposer @@ -62,6 +62,8 @@ TEST_SET_FRACTION = 0.2 * **bootstrap_fraction**: float, the fraction from a train set in the range [0.0 - 1.0] to fit models in bootstrap (usually more than 0.5). +* **random_state**: int, a seed to control the randomness of the whole model evaluation pipeline. + * **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics. * **computation_mode**: str, 'default' or 'error_analysis'. Name of the computation mode. When a default computation mode measures metrics for sex_priv and sex_dis, an `error_analysis` mode measures metrics for (sex_priv, sex_priv_correct, sex_priv_incorrect) and (sex_dis, sex_dis_correct, sex_dis_incorrect). Therefore, a user can analyze how a model is certain about its incorrect predictions. @@ -77,6 +79,7 @@ config_yaml_path = os.path.join(ROOT_DIR, 'experiment_config.yaml') config_yaml_content = """ dataset_name: COMPAS_Without_Sensitive_Attributes bootstrap_fraction: 0.8 +random_state: 42 n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example computation_mode: error_analysis sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None} @@ -193,7 +196,11 @@ column_transformer = ColumnTransformer(transformers=[ ```python -base_flow_dataset = preprocess_dataset(data_loader, column_transformer, TEST_SET_FRACTION, DATASET_SPLIT_SEED) +base_flow_dataset = preprocess_dataset(data_loader=data_loader, + column_transformer=column_transformer, + sensitive_attributes_dct=config.sensitive_attributes_dct, + test_set_fraction=TEST_SET_FRACTION, + dataset_split_seed=DATASET_SPLIT_SEED) ``` ### Create a models config for metrics computation @@ -289,81 +296,81 @@ sample_model_metrics_df[sample_model_metrics_df.columns[:5]].head(20) 0 - Mean_Prediction - 0.521995 - 0.577845 - 0.601574 - 0.525711 + Statistical_Bias + 0.416691 + 0.413261 + 0.324033 + 0.618208 1 - Std - 0.072253 - 0.075557 - 0.068902 - 0.090176 + Overall_Uncertainty + 0.887649 + 0.898580 + 0.880975 + 0.939015 2 - Overall_Uncertainty - 0.887763 - 0.896446 - 0.875769 - 0.941874 + Aleatoric_Uncertainty + 0.859615 + 0.866990 + 0.852746 + 0.899708 3 - Aleatoric_Uncertainty - 0.860720 - 0.866208 - 0.850550 - 0.900606 + IQR + 0.087474 + 0.088773 + 0.081936 + 0.104479 4 - IQR - 0.082323 - 0.084506 - 0.074594 - 0.106282 + Std + 0.073404 + 0.076654 + 0.071201 + 0.089178 5 - Statistical_Bias - 0.416585 - 0.411295 - 0.320042 - 0.611776 + Mean_Prediction + 0.519733 + 0.575657 + 0.597694 + 0.525040 6 Epistemic_Uncertainty - 0.027043 - 0.030239 - 0.025219 - 0.041268 + 0.028034 + 0.031589 + 0.028229 + 0.039308 7 Jitter - 0.110380 - 0.130717 - 0.099423 - 0.199468 + 0.108416 + 0.130465 + 0.102774 + 0.194069 8 Label_Stability - 0.859508 - 0.825403 - 0.871448 - 0.724242 + 0.862917 + 0.827488 + 0.866939 + 0.736875 9 TPR - 0.651805 - 0.466667 + 0.656051 + 0.493333 1.000000 0.000000 @@ -378,16 +385,16 @@ sample_model_metrics_df[sample_model_metrics_df.columns[:5]].head(20) 11 PPV - 0.663067 - 0.573770 + 0.664516 + 0.587302 1.000000 0.000000 12 FNR - 0.348195 - 0.533333 + 0.343949 + 0.506667 0.000000 1.000000 @@ -402,42 +409,34 @@ sample_model_metrics_df[sample_model_metrics_df.columns[:5]].head(20) 14 Accuracy - 0.696970 - 0.687204 + 0.698864 + 0.696682 1.000000 0.000000 15 F1 - 0.657388 - 0.514706 + 0.660256 + 0.536232 1.000000 0.000000 16 Selection-Rate - 0.438447 - 0.289100 - 0.241379 - 0.393939 + 0.440341 + 0.298578 + 0.251701 + 0.406250 17 - Positive-Rate - 0.983015 - 0.813333 - 1.000000 - 0.650000 - - - 18 Sample_Size 1056.000000 211.000000 - 145.000000 - 66.000000 + 147.000000 + 64.000000 @@ -489,13 +488,13 @@ visualizer.create_overall_metrics_bar_char( -
+
@@ -558,13 +557,13 @@ visualizer.create_overall_metrics_bar_char( -
+
diff --git a/docs/examples/Multiple_Models_Interface_With_Error_Analysis_files/Multiple_Models_Interface_With_Error_Analysis_38_0.png b/docs/examples/Multiple_Models_Interface_With_Error_Analysis_files/Multiple_Models_Interface_With_Error_Analysis_38_0.png index 37234dd4..93fc83b0 100644 Binary files a/docs/examples/Multiple_Models_Interface_With_Error_Analysis_files/Multiple_Models_Interface_With_Error_Analysis_38_0.png and b/docs/examples/Multiple_Models_Interface_With_Error_Analysis_files/Multiple_Models_Interface_With_Error_Analysis_38_0.png differ diff --git a/docs/examples/Multiple_Models_Interface_With_Error_Analysis_files/Multiple_Models_Interface_With_Error_Analysis_39_0.png b/docs/examples/Multiple_Models_Interface_With_Error_Analysis_files/Multiple_Models_Interface_With_Error_Analysis_39_0.png index 97774823..5ad01dd0 100644 Binary files a/docs/examples/Multiple_Models_Interface_With_Error_Analysis_files/Multiple_Models_Interface_With_Error_Analysis_39_0.png and b/docs/examples/Multiple_Models_Interface_With_Error_Analysis_files/Multiple_Models_Interface_With_Error_Analysis_39_0.png differ diff --git a/docs/examples/Multiple_Models_Interface_With_Inprocessor.ipynb b/docs/examples/Multiple_Models_Interface_With_Inprocessor.ipynb index d3d5224b..c7e553ab 100644 --- a/docs/examples/Multiple_Models_Interface_With_Inprocessor.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Inprocessor.ipynb @@ -6,8 +6,8 @@ "id": "248cbed8", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:44:15.669598Z", - "start_time": "2024-01-29T12:44:15.216009Z" + "end_time": "2024-06-01T21:33:30.937709Z", + "start_time": "2024-06-01T21:33:30.565281Z" } }, "outputs": [], @@ -23,8 +23,8 @@ "id": "7ec6cd08", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:44:15.679615Z", - "start_time": "2024-01-29T12:44:15.670057Z" + "end_time": "2024-06-01T21:33:30.946427Z", + "start_time": "2024-06-01T21:33:30.938578Z" } }, "outputs": [], @@ -41,8 +41,8 @@ "id": "b8cb69f2", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:44:15.689276Z", - "start_time": "2024-01-29T12:44:15.679903Z" + "end_time": "2024-06-01T21:33:30.956352Z", + "start_time": "2024-06-01T21:33:30.946839Z" } }, "outputs": [ @@ -100,8 +100,8 @@ "id": "7a9241de", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:44:16.959157Z", - "start_time": "2024-01-29T12:44:15.690014Z" + "end_time": "2024-06-01T21:33:32.962108Z", + "start_time": "2024-06-01T21:33:30.957091Z" } }, "outputs": [ @@ -166,8 +166,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:44:16.985255Z", - "start_time": "2024-01-29T12:44:16.959855Z" + "end_time": "2024-06-01T21:33:32.986634Z", + "start_time": "2024-06-01T21:33:32.962998Z" } }, "id": "ce359a052925eb3a" @@ -191,6 +191,10 @@ "\n", "* **bootstrap_fraction**: float, the fraction from a train set in the range [0.0 - 1.0] to fit models in bootstrap (usually more than 0.5).\n", "\n", + "* **random_state**: int, a seed to control the randomness of the whole model evaluation pipeline.\n", + "\n", + "* **computation_mode**: str, 'default' or 'error_analysis'. Name of the computation mode. When a default computation mode measures metrics for sex_priv and sex_dis, an `error_analysis` mode measures metrics for (sex_priv, sex_priv_correct, sex_priv_incorrect) and (sex_dis, sex_dis_correct, sex_dis_incorrect). Therefore, a user can analyze how a model is certain about its incorrect predictions.\n", + "\n", "* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics.\n", "\n", "* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including intersectional attributes), and values are disadvantaged values for these attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify disadvantaged values for intersectional groups since they will be derived from disadvantaged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair." @@ -210,9 +214,10 @@ "config_yaml_content = \"\"\"\n", "dataset_name: Law_School\n", "bootstrap_fraction: 0.8\n", + "random_state: 42\n", "computation_mode: error_analysis\n", "n_estimators: 30 # Better to input the higher number of estimators than 100; this is only for this use case example\n", - "sensitive_attributes_dct: {'male': '0.0', 'race': 'Non-White', 'male&race': None}\n", + "sensitive_attributes_dct: {'male': '0', 'race': 'Non-White', 'male&race': None}\n", "\"\"\"\n", "\n", "with open(config_yaml_path, 'w', encoding='utf-8') as f:\n", @@ -221,8 +226,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:44:17.009928Z", - "start_time": "2024-01-29T12:44:16.983539Z" + "end_time": "2024-06-01T21:33:33.013254Z", + "start_time": "2024-06-01T21:33:32.987966Z" } }, "id": "af22ee06f1e3eb1a" @@ -238,8 +243,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:44:17.028644Z", - "start_time": "2024-01-29T12:44:17.006813Z" + "end_time": "2024-06-01T21:33:33.037761Z", + "start_time": "2024-06-01T21:33:33.013366Z" } }, "id": "65181f72484bb92b" @@ -258,8 +263,8 @@ "id": "6c55c6a0", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:44:17.092758Z", - "start_time": "2024-01-29T12:44:17.028946Z" + "end_time": "2024-06-01T21:33:33.104224Z", + "start_time": "2024-06-01T21:33:33.037836Z" } }, "outputs": [ @@ -274,7 +279,7 @@ } ], "source": [ - "from virny.datasets.data_loaders import LawSchoolDataset\n", + "from virny.datasets import LawSchoolDataset\n", "\n", "data_loader = LawSchoolDataset()\n", "data_loader.X_data[data_loader.X_data.columns[:5]].head()" @@ -293,8 +298,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:44:17.110916Z", - "start_time": "2024-01-29T12:44:17.091799Z" + "end_time": "2024-06-01T21:33:33.144708Z", + "start_time": "2024-06-01T21:33:33.105113Z" } }, "id": "ebbef5eaf9dc0943" @@ -307,15 +312,19 @@ "# Create a binary race column for in-processing since aif360 inprocessors use a sensitive attribute during their learning.\n", "data_loader.X_data['race_binary'] = data_loader.X_data['race'].apply(lambda x: 1 if x == 'White' else 0)\n", "\n", - "base_flow_dataset = preprocess_dataset(data_loader, column_transformer, TEST_SET_FRACTION, DATASET_SPLIT_SEED)\n", + "base_flow_dataset = preprocess_dataset(data_loader=data_loader,\n", + " column_transformer=column_transformer,\n", + " sensitive_attributes_dct=config.sensitive_attributes_dct,\n", + " test_set_fraction=TEST_SET_FRACTION,\n", + " dataset_split_seed=DATASET_SPLIT_SEED)\n", "base_flow_dataset.X_train_val['race_binary'] = data_loader.X_data.loc[base_flow_dataset.X_train_val.index, 'race_binary']\n", "base_flow_dataset.X_test['race_binary'] = data_loader.X_data.loc[base_flow_dataset.X_test.index, 'race_binary']" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:44:17.169548Z", - "start_time": "2024-01-29T12:44:17.111451Z" + "end_time": "2024-06-01T21:33:33.183437Z", + "start_time": "2024-06-01T21:33:33.128358Z" } }, "id": "97ed4609effbf53f" @@ -336,10 +345,15 @@ "To use inprocessors from AIF360 together with Virny, we need to create a wrapper to use it as a basic model in the _models_config_.\n", "\n", "A wrapper should include the following methods:\n", + "\n", "* **fit(self, X, y)**: fits an inprocessor based on X and y pandas dataframes. Returns self.\n", + "\n", "* **predict_proba(self, X)**: predicts using the fitted inprocessor based on X features pandas dataframe. Returns probabilities for **ZERO** class. These probabilities will be used by Virny in the downstream metric computation.\n", + "\n", "* **predict(self, X)**: predicts using the fitted inprocessor based on X features pandas dataframe. Returns labels for each sample.\n", + "\n", "* **__copy__(self)** and **__deepcopy__(self, memo)**: methods, which will be used during copy.copy(inprocessor_wrapper) and copy.deepcopy(inprocessor_wrapper). Return a new instance of inprocessor's wrapper.\n", + "\n", "* **get_params(self)**: returns parameters of the wrapper. Alignment with sklearn API." ], "metadata": { @@ -374,9 +388,31 @@ "\n", " \"\"\"\n", "\n", - " def __init__(self, inprocessor, sensitive_attr_for_intervention):\n", + " def __init__(self, estimator, sensitive_attr_for_intervention):\n", " self.sensitive_attr_for_intervention = sensitive_attr_for_intervention\n", - " self.inprocessor = inprocessor\n", + " self.estimator = estimator\n", + " self.inprocessor = ExponentiatedGradientReduction(estimator=self.estimator,\n", + " constraints='DemographicParity',\n", + " drop_prot_attr=True)\n", + "\n", + " def __copy__(self):\n", + " new_estimator = copy.copy(self.estimator)\n", + " return ExpGradientReductionWrapper(estimator=new_estimator,\n", + " sensitive_attr_for_intervention=self.sensitive_attr_for_intervention)\n", + "\n", + " def __deepcopy__(self, memo):\n", + " new_estimator = copy.deepcopy(self.estimator)\n", + " return ExpGradientReductionWrapper(estimator=new_estimator,\n", + " sensitive_attr_for_intervention=self.sensitive_attr_for_intervention)\n", + "\n", + " def get_params(self):\n", + " return {'sensitive_attr_for_intervention': self.sensitive_attr_for_intervention}\n", + " \n", + " def set_params(self, random_state):\n", + " self.estimator.set_params(random_state=random_state)\n", + " self.inprocessor = ExponentiatedGradientReduction(estimator=self.estimator,\n", + " constraints='DemographicParity',\n", + " drop_prot_attr=True)\n", "\n", " def fit(self, X, y):\n", " train_binary_dataset = construct_binary_label_dataset_from_df(X_sample=X,\n", @@ -407,26 +443,13 @@ " target_column='target',\n", " sensitive_attribute=self.sensitive_attr_for_intervention)\n", " test_dataset_pred = self.inprocessor.predict(test_binary_dataset)\n", - " return test_dataset_pred.labels\n", - "\n", - " def __copy__(self):\n", - " new_inprocessor = copy.copy(self.inprocessor)\n", - " return ExpGradientReductionWrapper(inprocessor=new_inprocessor,\n", - " sensitive_attr_for_intervention=self.sensitive_attr_for_intervention)\n", - "\n", - " def __deepcopy__(self, memo):\n", - " new_inprocessor = copy.deepcopy(self.inprocessor)\n", - " return ExpGradientReductionWrapper(inprocessor=new_inprocessor,\n", - " sensitive_attr_for_intervention=self.sensitive_attr_for_intervention)\n", - "\n", - " def get_params(self):\n", - " return {'sensitive_attr_for_intervention': self.sensitive_attr_for_intervention}\n" + " return test_dataset_pred.labels\n" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:44:29.014997Z", - "start_time": "2024-01-29T12:44:17.171342Z" + "end_time": "2024-06-01T21:33:45.201967Z", + "start_time": "2024-06-01T21:33:33.185198Z" } }, "id": "4535191384245578" @@ -440,22 +463,18 @@ "# Note that in the above wrapper, 1 is used as a favorable label, and 0 is used as an unfavorable label.\n", "sensitive_attr_for_intervention = 'race_binary'\n", "\n", - "# Define an inprocessor\n", + "# Define an estimator\n", "estimator = LogisticRegression(solver='lbfgs', max_iter=1000)\n", - "inprocessor = ExponentiatedGradientReduction(estimator=estimator,\n", - " constraints='DemographicParity',\n", - " drop_prot_attr=True)\n", - "\n", "models_config = {\n", - " 'ExponentiatedGradientReduction': ExpGradientReductionWrapper(inprocessor=inprocessor, \n", + " 'ExponentiatedGradientReduction': ExpGradientReductionWrapper(estimator=estimator,\n", " sensitive_attr_for_intervention=sensitive_attr_for_intervention)\n", "}" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:44:29.055048Z", - "start_time": "2024-01-29T12:44:29.017052Z" + "end_time": "2024-06-01T21:33:45.241046Z", + "start_time": "2024-06-01T21:33:45.201718Z" } }, "id": "bd1ee45da7516769" @@ -482,8 +501,8 @@ "id": "197eadaa", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:45:31.882569Z", - "start_time": "2024-01-29T12:44:29.055317Z" + "end_time": "2024-06-01T21:35:11.092550Z", + "start_time": "2024-06-01T21:33:45.240130Z" } }, "outputs": [ @@ -493,7 +512,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "6f9b8110d29a44729fd00bc1865603f8" + "model_id": "a98934f403fd46c68235a0b22e113cfe" } }, "metadata": {}, @@ -505,7 +524,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "840eebfe1f0c4d0fb8c2257d4c65a3c7" + "model_id": "c33b280794fb4e299167634e4514c58d" } }, "metadata": {}, @@ -534,15 +553,15 @@ "id": "bea94683", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:45:31.883698Z", - "start_time": "2024-01-29T12:45:31.315538Z" + "end_time": "2024-06-01T21:35:11.141796Z", + "start_time": "2024-06-01T21:35:11.096365Z" } }, "outputs": [ { "data": { - "text/plain": " Metric overall male_priv male_priv_correct \\\n0 Mean_Prediction 0.023388 0.020879 0.014551 \n1 Overall_Uncertainty 0.022109 0.019718 0.013374 \n2 Aleatoric_Uncertainty 0.008804 0.007275 0.005080 \n3 Statistical_Bias 0.098350 0.089518 0.004390 \n4 IQR 0.010310 0.009667 0.007016 \n5 Std 0.009641 0.008739 0.005772 \n6 Epistemic_Uncertainty 0.013305 0.012443 0.008294 \n7 Label_Stability 0.987516 0.988232 0.991728 \n8 Jitter 0.009299 0.008656 0.006096 \n9 TPR 0.990612 0.991163 1.000000 \n10 TNR 0.138889 0.133028 1.000000 \n11 PPV 0.908487 0.918534 1.000000 \n12 FNR 0.009388 0.008837 0.000000 \n13 FPR 0.861111 0.866972 0.000000 \n14 Accuracy 0.902163 0.912162 1.000000 \n15 F1 0.947774 0.953468 1.000000 \n16 Selection-Rate 0.977163 0.979730 0.986574 \n17 Positive-Rate 1.090397 1.079070 1.000000 \n18 Sample_Size 4160.000000 2368.000000 2160.000000 \n\n male_priv_incorrect male_dis \n0 0.086595 0.026703 \n1 0.085599 0.025269 \n2 0.030067 0.010825 \n3 0.973543 0.110021 \n4 0.037194 0.011161 \n5 0.039553 0.010832 \n6 0.055532 0.014444 \n7 0.951923 0.986570 \n8 0.035234 0.010149 \n9 0.000000 0.989861 \n10 0.000000 0.144860 \n11 0.000000 0.895129 \n12 1.000000 0.010139 \n13 1.000000 0.855140 \n14 0.000000 0.888951 \n15 0.000000 0.940114 \n16 0.908654 0.973772 \n17 9.947368 1.105830 \n18 208.000000 1792.000000 ", - 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Metricoverallmale_privmale_priv_correctmale_priv_incorrectmale_dis
0Mean_Prediction0.0233880.0208790.0145510.0865950.026703
1Overall_Uncertainty0.0221090.0197180.0133740.0855990.025269
2Aleatoric_Uncertainty0.0088040.0072750.0050800.0300670.010825
3Statistical_Bias0.0983500.0895180.0043900.9735430.110021
4IQR0.0103100.0096670.0070160.0371940.011161
5Std0.0096410.0087390.0057720.0395530.010832
6Epistemic_Uncertainty0.0133050.0124430.0082940.0555320.014444
7Label_Stability0.9875160.9882320.9917280.9519230.986570
8Jitter0.0092990.0086560.0060960.0352340.010149
9TPR0.9906120.9911631.0000000.0000000.989861
10TNR0.1388890.1330281.0000000.0000000.144860
11PPV0.9084870.9185341.0000000.0000000.895129
12FNR0.0093880.0088370.0000001.0000000.010139
13FPR0.8611110.8669720.0000001.0000000.855140
14Accuracy0.9021630.9121621.0000000.0000000.888951
15F10.9477740.9534681.0000000.0000000.940114
16Selection-Rate0.9771630.9797300.9865740.9086540.973772
17Positive-Rate1.0903971.0790701.0000009.9473681.105830
18Sample_Size4160.0000002368.0000002160.000000208.0000001792.000000
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" + "text/plain": " Metric overall male_priv male_priv_correct \\\n0 Aleatoric_Uncertainty 0.005905 0.004883 0.003296 \n1 IQR 0.010355 0.009922 0.008073 \n2 Mean_Prediction 0.024633 0.021842 0.015440 \n3 Overall_Uncertainty 0.020169 0.018285 0.012946 \n4 Statistical_Bias 0.098458 0.089847 0.004210 \n5 Std 0.009615 0.008868 0.006229 \n6 Epistemic_Uncertainty 0.014264 0.013402 0.009650 \n7 Label_Stability 0.989087 0.989696 0.992222 \n8 Jitter 0.008198 0.007553 0.005556 \n9 TPR 0.990612 0.991163 1.000000 \n10 TNR 0.141204 0.133028 1.000000 \n11 PPV 0.908711 0.918534 1.000000 \n12 FNR 0.009388 0.008837 0.000000 \n13 FPR 0.858796 0.866972 0.000000 \n14 Accuracy 0.902404 0.912162 1.000000 \n15 F1 0.947895 0.953468 1.000000 \n16 Selection-Rate 0.976923 0.979730 0.986574 \n17 Sample_Size 4160.000000 2368.000000 2160.000000 \n\n male_priv_incorrect male_dis \n0 0.021364 0.007256 \n1 0.029115 0.010926 \n2 0.088320 0.028322 \n3 0.073729 0.022659 \n4 0.979146 0.109838 \n5 0.036276 0.010603 \n6 0.052365 0.015403 \n7 0.963462 0.988281 \n8 0.028294 0.009050 \n9 0.000000 0.989861 \n10 0.000000 0.149533 \n11 0.000000 0.895642 \n12 1.000000 0.010139 \n13 1.000000 0.850467 \n14 0.000000 0.889509 \n15 0.000000 0.940397 \n16 0.908654 0.973214 \n17 208.000000 1792.000000 ", + "text/html": "
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Metricoverallmale_privmale_priv_correctmale_priv_incorrectmale_dis
0Aleatoric_Uncertainty0.0059050.0048830.0032960.0213640.007256
1IQR0.0103550.0099220.0080730.0291150.010926
2Mean_Prediction0.0246330.0218420.0154400.0883200.028322
3Overall_Uncertainty0.0201690.0182850.0129460.0737290.022659
4Statistical_Bias0.0984580.0898470.0042100.9791460.109838
5Std0.0096150.0088680.0062290.0362760.010603
6Epistemic_Uncertainty0.0142640.0134020.0096500.0523650.015403
7Label_Stability0.9890870.9896960.9922220.9634620.988281
8Jitter0.0081980.0075530.0055560.0282940.009050
9TPR0.9906120.9911631.0000000.0000000.989861
10TNR0.1412040.1330281.0000000.0000000.149533
11PPV0.9087110.9185341.0000000.0000000.895642
12FNR0.0093880.0088370.0000001.0000000.010139
13FPR0.8587960.8669720.0000001.0000000.850467
14Accuracy0.9024040.9121621.0000000.0000000.889509
15F10.9478950.9534681.0000000.0000000.940397
16Selection-Rate0.9769230.9797300.9865740.9086540.973214
17Sample_Size4160.0000002368.0000002160.000000208.0000001792.000000
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" }, "execution_count": 14, "metadata": {}, @@ -576,8 +595,8 @@ "id": "f94a20dc", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:45:31.883793Z", - "start_time": "2024-01-29T12:45:31.363730Z" + "end_time": "2024-06-01T21:35:11.255086Z", + "start_time": "2024-06-01T21:35:11.142133Z" } }, "outputs": [], @@ -591,8 +610,8 @@ "id": "b04d06cf", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:45:31.883861Z", - "start_time": "2024-01-29T12:45:31.401036Z" + "end_time": "2024-06-01T21:35:11.255329Z", + "start_time": "2024-06-01T21:35:11.179995Z" } }, "outputs": [], @@ -614,8 +633,8 @@ "id": "be6ace22", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:45:31.883916Z", - "start_time": "2024-01-29T12:45:31.434780Z" + "end_time": "2024-06-01T21:35:11.278187Z", + "start_time": "2024-06-01T21:35:11.219033Z" } }, "outputs": [], @@ -629,8 +648,8 @@ "outputs": [ { "data": { - "text/plain": " Metric male race male&race \\\n0 Accuracy_Difference -0.023211 -0.180454 -0.160337 \n1 Aleatoric_Uncertainty_Difference 0.003550 0.030174 0.032971 \n2 Aleatoric_Uncertainty_Ratio 1.487968 8.174811 6.327559 \n3 Epistemic_Uncertainty_Difference 0.002002 0.010810 0.015361 \n4 Epistemic_Uncertainty_Ratio 1.160858 1.927304 2.270925 \n5 Equalized_Odds_FNR 0.001302 0.001559 0.003110 \n6 Equalized_Odds_FPR -0.011832 0.082345 0.057266 \n7 IQR_Difference 0.001494 0.018301 0.021933 \n8 Jitter_Difference 0.001493 0.015634 0.017956 \n9 Label_Stability_Ratio 0.998318 0.979427 0.976888 \n10 Label_Stability_Difference -0.001662 -0.020380 -0.022865 \n11 Overall_Uncertainty_Difference 0.005552 0.040984 0.048332 \n12 Overall_Uncertainty_Ratio 1.281547 3.583619 3.644668 \n13 Disparate_Impact 1.024800 1.248497 1.215937 \n14 Statistical_Parity_Difference -0.005957 -0.010275 -0.011401 \n15 Std_Difference 0.002092 0.011726 0.015143 \n16 Std_Ratio 1.239415 2.493035 2.794353 \n17 Equalized_Odds_TNR 0.011832 -0.082345 -0.057266 \n18 Equalized_Odds_TPR -0.001302 -0.001559 -0.003110 \n\n Model_Name \n0 ExponentiatedGradientReduction \n1 ExponentiatedGradientReduction \n2 ExponentiatedGradientReduction \n3 ExponentiatedGradientReduction \n4 ExponentiatedGradientReduction \n5 ExponentiatedGradientReduction \n6 ExponentiatedGradientReduction \n7 ExponentiatedGradientReduction \n8 ExponentiatedGradientReduction \n9 ExponentiatedGradientReduction \n10 ExponentiatedGradientReduction \n11 ExponentiatedGradientReduction \n12 ExponentiatedGradientReduction \n13 ExponentiatedGradientReduction \n14 ExponentiatedGradientReduction \n15 ExponentiatedGradientReduction \n16 ExponentiatedGradientReduction \n17 ExponentiatedGradientReduction \n18 ExponentiatedGradientReduction ", - "text/html": "
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Metricmaleracemale&raceModel_Name
0Accuracy_Difference-0.023211-0.180454-0.160337ExponentiatedGradientReduction
1Aleatoric_Uncertainty_Difference0.0035500.0301740.032971ExponentiatedGradientReduction
2Aleatoric_Uncertainty_Ratio1.4879688.1748116.327559ExponentiatedGradientReduction
3Epistemic_Uncertainty_Difference0.0020020.0108100.015361ExponentiatedGradientReduction
4Epistemic_Uncertainty_Ratio1.1608581.9273042.270925ExponentiatedGradientReduction
5Equalized_Odds_FNR0.0013020.0015590.003110ExponentiatedGradientReduction
6Equalized_Odds_FPR-0.0118320.0823450.057266ExponentiatedGradientReduction
7IQR_Difference0.0014940.0183010.021933ExponentiatedGradientReduction
8Jitter_Difference0.0014930.0156340.017956ExponentiatedGradientReduction
9Label_Stability_Ratio0.9983180.9794270.976888ExponentiatedGradientReduction
10Label_Stability_Difference-0.001662-0.020380-0.022865ExponentiatedGradientReduction
11Overall_Uncertainty_Difference0.0055520.0409840.048332ExponentiatedGradientReduction
12Overall_Uncertainty_Ratio1.2815473.5836193.644668ExponentiatedGradientReduction
13Disparate_Impact1.0248001.2484971.215937ExponentiatedGradientReduction
14Statistical_Parity_Difference-0.005957-0.010275-0.011401ExponentiatedGradientReduction
15Std_Difference0.0020920.0117260.015143ExponentiatedGradientReduction
16Std_Ratio1.2394152.4930352.794353ExponentiatedGradientReduction
17Equalized_Odds_TNR0.011832-0.082345-0.057266ExponentiatedGradientReduction
18Equalized_Odds_TPR-0.001302-0.001559-0.003110ExponentiatedGradientReduction
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" + "text/plain": " Metric male race male&race \\\n0 Accuracy_Difference -0.022653 -0.178877 -0.157307 \n1 Aleatoric_Uncertainty_Difference 0.002373 0.018372 0.021097 \n2 Aleatoric_Uncertainty_Ratio 1.485922 6.916304 5.985519 \n3 Epistemic_Uncertainty_Difference 0.002001 0.009870 0.014769 \n4 Epistemic_Uncertainty_Ratio 1.149317 1.773535 2.128039 \n5 Equalized_Odds_FNR 0.001302 0.001559 0.003110 \n6 Equalized_Odds_FPR -0.016505 0.076428 0.045638 \n7 IQR_Difference 0.001005 0.010219 0.012572 \n8 Jitter_Difference 0.001498 0.009698 0.013220 \n9 Label_Stability_Ratio 0.998571 0.988954 0.984495 \n10 Label_Stability_Difference -0.001415 -0.010944 -0.015355 \n11 Overall_Uncertainty_Difference 0.004374 0.028242 0.035866 \n12 Overall_Uncertainty_Ratio 1.239210 2.780147 3.070293 \n13 Statistical_Parity_Difference -0.006515 -0.011852 -0.014432 \n14 Disparate_Impact 0.993350 0.987890 0.985245 \n15 Std_Difference 0.001734 0.009583 0.013289 \n16 Std_Ratio 1.195550 2.175074 2.552202 \n17 Equalized_Odds_TNR 0.016505 -0.076428 -0.045638 \n18 Equalized_Odds_TPR -0.001302 -0.001559 -0.003110 \n\n Model_Name \n0 ExponentiatedGradientReduction \n1 ExponentiatedGradientReduction \n2 ExponentiatedGradientReduction \n3 ExponentiatedGradientReduction \n4 ExponentiatedGradientReduction \n5 ExponentiatedGradientReduction \n6 ExponentiatedGradientReduction \n7 ExponentiatedGradientReduction \n8 ExponentiatedGradientReduction \n9 ExponentiatedGradientReduction \n10 ExponentiatedGradientReduction \n11 ExponentiatedGradientReduction \n12 ExponentiatedGradientReduction \n13 ExponentiatedGradientReduction \n14 ExponentiatedGradientReduction \n15 ExponentiatedGradientReduction \n16 ExponentiatedGradientReduction \n17 ExponentiatedGradientReduction \n18 ExponentiatedGradientReduction ", + "text/html": "
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Metricmaleracemale&raceModel_Name
0Accuracy_Difference-0.022653-0.178877-0.157307ExponentiatedGradientReduction
1Aleatoric_Uncertainty_Difference0.0023730.0183720.021097ExponentiatedGradientReduction
2Aleatoric_Uncertainty_Ratio1.4859226.9163045.985519ExponentiatedGradientReduction
3Epistemic_Uncertainty_Difference0.0020010.0098700.014769ExponentiatedGradientReduction
4Epistemic_Uncertainty_Ratio1.1493171.7735352.128039ExponentiatedGradientReduction
5Equalized_Odds_FNR0.0013020.0015590.003110ExponentiatedGradientReduction
6Equalized_Odds_FPR-0.0165050.0764280.045638ExponentiatedGradientReduction
7IQR_Difference0.0010050.0102190.012572ExponentiatedGradientReduction
8Jitter_Difference0.0014980.0096980.013220ExponentiatedGradientReduction
9Label_Stability_Ratio0.9985710.9889540.984495ExponentiatedGradientReduction
10Label_Stability_Difference-0.001415-0.010944-0.015355ExponentiatedGradientReduction
11Overall_Uncertainty_Difference0.0043740.0282420.035866ExponentiatedGradientReduction
12Overall_Uncertainty_Ratio1.2392102.7801473.070293ExponentiatedGradientReduction
13Statistical_Parity_Difference-0.006515-0.011852-0.014432ExponentiatedGradientReduction
14Disparate_Impact0.9933500.9878900.985245ExponentiatedGradientReduction
15Std_Difference0.0017340.0095830.013289ExponentiatedGradientReduction
16Std_Ratio1.1955502.1750742.552202ExponentiatedGradientReduction
17Equalized_Odds_TNR0.016505-0.076428-0.045638ExponentiatedGradientReduction
18Equalized_Odds_TPR-0.001302-0.001559-0.003110ExponentiatedGradientReduction
\n
" }, "execution_count": 18, "metadata": {}, @@ -643,8 +662,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:45:31.884361Z", - "start_time": "2024-01-29T12:45:31.471867Z" + "end_time": "2024-06-01T21:35:11.303850Z", + "start_time": "2024-06-01T21:35:11.262112Z" } }, "id": "a286da0406c6401d" @@ -671,8 +690,8 @@ "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:45:31.884430Z", - "start_time": "2024-01-29T12:45:31.509522Z" + "end_time": "2024-06-01T21:35:11.364635Z", + "start_time": "2024-06-01T21:35:11.304469Z" } }, "outputs": [], @@ -688,14 +707,14 @@ "id": "5efb1bf2", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:45:31.884716Z", - "start_time": "2024-01-29T12:45:31.548828Z" + "end_time": "2024-06-01T21:35:11.415554Z", + "start_time": "2024-06-01T21:35:11.351668Z" } }, "outputs": [ { "data": { - "text/html": "\n
\n", + "text/html": "\n
\n", "text/plain": "alt.Chart(...)" }, "execution_count": 20, @@ -712,21 +731,21 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "id": "0eb8528e", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:46:36.876749Z", - "start_time": "2024-01-29T12:46:36.777546Z" + "end_time": "2024-06-01T21:35:11.482682Z", + "start_time": "2024-06-01T21:35:11.415968Z" } }, "outputs": [ { "data": { - "text/html": "\n
\n", + "text/html": "\n
\n", "text/plain": "alt.Chart(...)" }, - "execution_count": 22, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -746,8 +765,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:45:31.885027Z", - "start_time": "2024-01-29T12:45:31.669893Z" + "end_time": "2024-06-01T21:35:11.482823Z", + "start_time": "2024-06-01T21:35:11.480109Z" } }, "id": "713fe55ec5bf095c" diff --git a/docs/examples/Multiple_Models_Interface_With_Inprocessor.md b/docs/examples/Multiple_Models_Interface_With_Inprocessor.md index 0a080ad5..eeb2228f 100644 --- a/docs/examples/Multiple_Models_Interface_With_Inprocessor.md +++ b/docs/examples/Multiple_Models_Interface_With_Inprocessor.md @@ -57,6 +57,10 @@ TEST_SET_FRACTION = 0.2 * **bootstrap_fraction**: float, the fraction from a train set in the range [0.0 - 1.0] to fit models in bootstrap (usually more than 0.5). +* **random_state**: int, a seed to control the randomness of the whole model evaluation pipeline. + +* **computation_mode**: str, 'default' or 'error_analysis'. Name of the computation mode. When a default computation mode measures metrics for sex_priv and sex_dis, an `error_analysis` mode measures metrics for (sex_priv, sex_priv_correct, sex_priv_incorrect) and (sex_dis, sex_dis_correct, sex_dis_incorrect). Therefore, a user can analyze how a model is certain about its incorrect predictions. + * **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics. * **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including intersectional attributes), and values are disadvantaged values for these attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify disadvantaged values for intersectional groups since they will be derived from disadvantaged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair. @@ -68,9 +72,10 @@ config_yaml_path = os.path.join(ROOT_DIR, 'experiment_config.yaml') config_yaml_content = """ dataset_name: Law_School bootstrap_fraction: 0.8 +random_state: 42 computation_mode: error_analysis n_estimators: 30 # Better to input the higher number of estimators than 100; this is only for this use case example -sensitive_attributes_dct: {'male': '0.0', 'race': 'Non-White', 'male&race': None} +sensitive_attributes_dct: {'male': '0', 'race': 'Non-White', 'male&race': None} """ with open(config_yaml_path, 'w', encoding='utf-8') as f: @@ -87,7 +92,7 @@ SAVE_RESULTS_DIR_PATH = os.path.join(ROOT_DIR, 'results', f'{config.dataset_name ```python -from virny.datasets.data_loaders import LawSchoolDataset +from virny.datasets import LawSchoolDataset data_loader = LawSchoolDataset() data_loader.X_data[data_loader.X_data.columns[:5]].head() @@ -181,7 +186,11 @@ column_transformer = ColumnTransformer(transformers=[ # Create a binary race column for in-processing since aif360 inprocessors use a sensitive attribute during their learning. data_loader.X_data['race_binary'] = data_loader.X_data['race'].apply(lambda x: 1 if x == 'White' else 0) -base_flow_dataset = preprocess_dataset(data_loader, column_transformer, TEST_SET_FRACTION, DATASET_SPLIT_SEED) +base_flow_dataset = preprocess_dataset(data_loader=data_loader, + column_transformer=column_transformer, + sensitive_attributes_dct=config.sensitive_attributes_dct, + test_set_fraction=TEST_SET_FRACTION, + dataset_split_seed=DATASET_SPLIT_SEED) base_flow_dataset.X_train_val['race_binary'] = data_loader.X_data.loc[base_flow_dataset.X_train_val.index, 'race_binary'] base_flow_dataset.X_test['race_binary'] = data_loader.X_data.loc[base_flow_dataset.X_test.index, 'race_binary'] ``` @@ -226,9 +235,31 @@ class ExpGradientReductionWrapper(BaseInprocessingWrapper): """ - def __init__(self, inprocessor, sensitive_attr_for_intervention): + def __init__(self, estimator, sensitive_attr_for_intervention): self.sensitive_attr_for_intervention = sensitive_attr_for_intervention - self.inprocessor = inprocessor + self.estimator = estimator + self.inprocessor = ExponentiatedGradientReduction(estimator=self.estimator, + constraints='DemographicParity', + drop_prot_attr=True) + + def __copy__(self): + new_estimator = copy.copy(self.estimator) + return ExpGradientReductionWrapper(estimator=new_estimator, + sensitive_attr_for_intervention=self.sensitive_attr_for_intervention) + + def __deepcopy__(self, memo): + new_estimator = copy.deepcopy(self.estimator) + return ExpGradientReductionWrapper(estimator=new_estimator, + sensitive_attr_for_intervention=self.sensitive_attr_for_intervention) + + def get_params(self): + return {'sensitive_attr_for_intervention': self.sensitive_attr_for_intervention} + + def set_params(self, random_state): + self.estimator.set_params(random_state=random_state) + self.inprocessor = ExponentiatedGradientReduction(estimator=self.estimator, + constraints='DemographicParity', + drop_prot_attr=True) def fit(self, X, y): train_binary_dataset = construct_binary_label_dataset_from_df(X_sample=X, @@ -261,19 +292,6 @@ class ExpGradientReductionWrapper(BaseInprocessingWrapper): test_dataset_pred = self.inprocessor.predict(test_binary_dataset) return test_dataset_pred.labels - def __copy__(self): - new_inprocessor = copy.copy(self.inprocessor) - return ExpGradientReductionWrapper(inprocessor=new_inprocessor, - sensitive_attr_for_intervention=self.sensitive_attr_for_intervention) - - def __deepcopy__(self, memo): - new_inprocessor = copy.deepcopy(self.inprocessor) - return ExpGradientReductionWrapper(inprocessor=new_inprocessor, - sensitive_attr_for_intervention=self.sensitive_attr_for_intervention) - - def get_params(self): - return {'sensitive_attr_for_intervention': self.sensitive_attr_for_intervention} - ``` @@ -282,14 +300,10 @@ class ExpGradientReductionWrapper(BaseInprocessingWrapper): # Note that in the above wrapper, 1 is used as a favorable label, and 0 is used as an unfavorable label. sensitive_attr_for_intervention = 'race_binary' -# Define an inprocessor +# Define an estimator estimator = LogisticRegression(solver='lbfgs', max_iter=1000) -inprocessor = ExponentiatedGradientReduction(estimator=estimator, - constraints='DemographicParity', - drop_prot_attr=True) - models_config = { - 'ExponentiatedGradientReduction': ExpGradientReductionWrapper(inprocessor=inprocessor, + 'ExponentiatedGradientReduction': ExpGradientReductionWrapper(estimator=estimator, sensitive_attr_for_intervention=sensitive_attr_for_intervention) } ``` @@ -355,84 +369,84 @@ sample_model_metrics_df[sample_model_metrics_df.columns[:6]].head(20) 0 - Mean_Prediction - 0.023388 - 0.020879 - 0.014551 - 0.086595 - 0.026703 + Aleatoric_Uncertainty + 0.005905 + 0.004883 + 0.003296 + 0.021364 + 0.007256 1 - Overall_Uncertainty - 0.022109 - 0.019718 - 0.013374 - 0.085599 - 0.025269 + IQR + 0.010355 + 0.009922 + 0.008073 + 0.029115 + 0.010926 2 - Aleatoric_Uncertainty - 0.008804 - 0.007275 - 0.005080 - 0.030067 - 0.010825 + Mean_Prediction + 0.024633 + 0.021842 + 0.015440 + 0.088320 + 0.028322 3 - Statistical_Bias - 0.098350 - 0.089518 - 0.004390 - 0.973543 - 0.110021 + Overall_Uncertainty + 0.020169 + 0.018285 + 0.012946 + 0.073729 + 0.022659 4 - IQR - 0.010310 - 0.009667 - 0.007016 - 0.037194 - 0.011161 + Statistical_Bias + 0.098458 + 0.089847 + 0.004210 + 0.979146 + 0.109838 5 Std - 0.009641 - 0.008739 - 0.005772 - 0.039553 - 0.010832 + 0.009615 + 0.008868 + 0.006229 + 0.036276 + 0.010603 6 Epistemic_Uncertainty - 0.013305 - 0.012443 - 0.008294 - 0.055532 - 0.014444 + 0.014264 + 0.013402 + 0.009650 + 0.052365 + 0.015403 7 Label_Stability - 0.987516 - 0.988232 - 0.991728 - 0.951923 - 0.986570 + 0.989087 + 0.989696 + 0.992222 + 0.963462 + 0.988281 8 Jitter - 0.009299 - 0.008656 - 0.006096 - 0.035234 - 0.010149 + 0.008198 + 0.007553 + 0.005556 + 0.028294 + 0.009050 9 @@ -446,20 +460,20 @@ sample_model_metrics_df[sample_model_metrics_df.columns[:6]].head(20) 10 TNR - 0.138889 + 0.141204 0.133028 1.000000 0.000000 - 0.144860 + 0.149533 11 PPV - 0.908487 + 0.908711 0.918534 1.000000 0.000000 - 0.895129 + 0.895642 12 @@ -473,50 +487,41 @@ sample_model_metrics_df[sample_model_metrics_df.columns[:6]].head(20) 13 FPR - 0.861111 + 0.858796 0.866972 0.000000 1.000000 - 0.855140 + 0.850467 14 Accuracy - 0.902163 + 0.902404 0.912162 1.000000 0.000000 - 0.888951 + 0.889509 15 F1 - 0.947774 + 0.947895 0.953468 1.000000 0.000000 - 0.940114 + 0.940397 16 Selection-Rate - 0.977163 + 0.976923 0.979730 0.986574 0.908654 - 0.973772 + 0.973214 17 - Positive-Rate - 1.090397 - 1.079070 - 1.000000 - 9.947368 - 1.105830 - - - 18 Sample_Size 4160.000000 2368.000000 @@ -588,41 +593,41 @@ models_composed_metrics_df 0 Accuracy_Difference - -0.023211 - -0.180454 - -0.160337 + -0.022653 + -0.178877 + -0.157307 ExponentiatedGradientReduction 1 Aleatoric_Uncertainty_Difference - 0.003550 - 0.030174 - 0.032971 + 0.002373 + 0.018372 + 0.021097 ExponentiatedGradientReduction 2 Aleatoric_Uncertainty_Ratio - 1.487968 - 8.174811 - 6.327559 + 1.485922 + 6.916304 + 5.985519 ExponentiatedGradientReduction 3 Epistemic_Uncertainty_Difference - 0.002002 - 0.010810 - 0.015361 + 0.002001 + 0.009870 + 0.014769 ExponentiatedGradientReduction 4 Epistemic_Uncertainty_Ratio - 1.160858 - 1.927304 - 2.270925 + 1.149317 + 1.773535 + 2.128039 ExponentiatedGradientReduction @@ -636,97 +641,97 @@ models_composed_metrics_df 6 Equalized_Odds_FPR - -0.011832 - 0.082345 - 0.057266 + -0.016505 + 0.076428 + 0.045638 ExponentiatedGradientReduction 7 IQR_Difference - 0.001494 - 0.018301 - 0.021933 + 0.001005 + 0.010219 + 0.012572 ExponentiatedGradientReduction 8 Jitter_Difference - 0.001493 - 0.015634 - 0.017956 + 0.001498 + 0.009698 + 0.013220 ExponentiatedGradientReduction 9 Label_Stability_Ratio - 0.998318 - 0.979427 - 0.976888 + 0.998571 + 0.988954 + 0.984495 ExponentiatedGradientReduction 10 Label_Stability_Difference - -0.001662 - -0.020380 - -0.022865 + -0.001415 + -0.010944 + -0.015355 ExponentiatedGradientReduction 11 Overall_Uncertainty_Difference - 0.005552 - 0.040984 - 0.048332 + 0.004374 + 0.028242 + 0.035866 ExponentiatedGradientReduction 12 Overall_Uncertainty_Ratio - 1.281547 - 3.583619 - 3.644668 + 1.239210 + 2.780147 + 3.070293 ExponentiatedGradientReduction 13 - Disparate_Impact - 1.024800 - 1.248497 - 1.215937 + Statistical_Parity_Difference + -0.006515 + -0.011852 + -0.014432 ExponentiatedGradientReduction 14 - Statistical_Parity_Difference - -0.005957 - -0.010275 - -0.011401 + Disparate_Impact + 0.993350 + 0.987890 + 0.985245 ExponentiatedGradientReduction 15 Std_Difference - 0.002092 - 0.011726 - 0.015143 + 0.001734 + 0.009583 + 0.013289 ExponentiatedGradientReduction 16 Std_Ratio - 1.239415 - 2.493035 - 2.794353 + 1.195550 + 2.175074 + 2.552202 ExponentiatedGradientReduction 17 Equalized_Odds_TNR - 0.011832 - -0.082345 - -0.057266 + 0.016505 + -0.076428 + -0.045638 ExponentiatedGradientReduction @@ -766,13 +771,13 @@ visualizer.create_overall_metrics_bar_char( -
+
@@ -835,13 +840,13 @@ visualizer.create_overall_metrics_bar_char( -
+
diff --git a/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.ipynb b/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.ipynb index 0663eb0e..3c1e710e 100644 --- a/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.ipynb +++ b/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.ipynb @@ -48,7 +48,7 @@ "id": "8b3e85da", "metadata": {}, "source": [ - "In this example, we are going to conduct a deep performance profiling for 2 models. For that, we will use `compute_metrics_with_multiple_test_sets` interface that will run metric computation for multiple models and test each model using multiple test sets." + "In this example, we are going to conduct a deep performance profiling for 2 models under multiple test sets. It turns out particularly effective in case we want to test models on different test sets, but to avoid an overhead with retraining a bootstrap with 50-200 estimators. For that, we will use `compute_metrics_with_multiple_test_sets` interface that will run metric computation for multiple models and test each model using multiple test sets." ] }, { @@ -93,7 +93,7 @@ "from virny.user_interfaces.multiple_models_with_multiple_test_sets_api import compute_metrics_with_multiple_test_sets\n", "from virny.utils.custom_initializers import create_config_obj, create_models_metrics_dct_from_database_df\n", "from virny.preprocessing.basic_preprocessing import preprocess_dataset\n", - "from virny.datasets.data_loaders import CompasWithoutSensitiveAttrsDataset" + "from virny.datasets import CompasWithoutSensitiveAttrsDataset" ] }, { @@ -137,36 +137,105 @@ }, { "cell_type": "markdown", - "id": "4b21d920", + "id": "d22f31eb", "metadata": {}, "source": [ - "### Create a Dataset class" + "### Create a config object" ] }, { "cell_type": "markdown", - "id": "57b42644", + "id": "2ab8dda3", "metadata": {}, "source": [ - "Based on the BaseDataset class, your **dataset class** should include the following attributes:\n", + "`compute_metrics_with_multiple_test_sets` interface requires that your **yaml file** includes the following parameters:\n", "\n", - "* **Obligatory attributes**: dataset, target, features, numerical_columns, categorical_columns\n", + "* **dataset_name**: str, a name of your dataset; it will be used to name files with metrics.\n", "\n", - "* **Optional attributes**: X_data, y_data, columns_with_nulls\n", + "* **bootstrap_fraction**: float, the fraction from a train set in the range [0.0 - 1.0] to fit models in bootstrap (usually more than 0.5).\n", "\n", - "For more details, please refer to the library documentation." + "* **random_state**: int, a seed to control the randomness of the whole model evaluation pipeline.\n", + "\n", + "* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics.\n", + "\n", + "* **computation_mode**: str, 'default' or 'error_analysis'. Name of the computation mode. When a default computation mode measures metrics for sex_priv and sex_dis, an `error_analysis` mode measures metrics for (sex_priv, sex_priv_correct, sex_priv_incorrect) and (sex_dis, sex_dis_correct, sex_dis_incorrect). Therefore, a user can analyze how a model is certain about its incorrect predictions.\n", + "\n", + "* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including intersectional attributes), and values are disadvantaged values for these attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify disadvantaged values for intersectional groups since they will be derived from disadvantaged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair.\n", + "\n", + "Note that disadvantaged value in a sensitive attribute dictionary must be **the same as in the original dataset**. For example, when distinct values of the _sex_ column in the original dataset are 'F' and 'M', and after pre-processing they became 0 and 1 respectively, you still need to set a disadvantaged value as 'F' or 'M' in the sensitive attribute dictionary.\n" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "30a74059", + "execution_count": 8, + "id": "79dcac74", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T12:52:44.320407Z", - "start_time": "2024-01-29T12:52:44.286801Z" + "end_time": "2024-01-29T12:52:44.392671Z", + "start_time": "2024-01-29T12:52:44.367898Z" + } + }, + "outputs": [], + "source": [ + "ROOT_DIR = os.getcwd()\n", + "config_yaml_path = os.path.join(ROOT_DIR, 'experiment_config.yaml')\n", + "config_yaml_content = \\\n", + "\"\"\"dataset_name: COMPAS_Without_Sensitive_Attributes\n", + "bootstrap_fraction: 0.8\n", + "random_state: 42\n", + "n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example\n", + "computation_mode: error_analysis\n", + "sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None}\n", + "\"\"\"\n", + "\n", + "with open(config_yaml_path, 'w', encoding='utf-8') as f:\n", + " f.write(config_yaml_content)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "abc8bd6f", + "metadata": { + "ExecuteTime": { + "end_time": "2024-01-29T12:52:44.415396Z", + "start_time": "2024-01-29T12:52:44.391594Z" } }, + "outputs": [], + "source": [ + "config = create_config_obj(config_yaml_path=config_yaml_path)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Create a Dataset class" + ], + "metadata": { + "collapsed": false + }, + "id": "4b21d920" + }, + { + "cell_type": "markdown", + "source": [ + "Based on the BaseDataset class, your **dataset class** should include the following attributes:\n", + "\n", + "* **Obligatory attributes**: dataset, target, features, numerical_columns, categorical_columns\n", + "\n", + "* **Optional attributes**: X_data, y_data, columns_with_nulls\n", + "\n", + "For more details, please refer to the library documentation." + ], + "metadata": { + "collapsed": false + }, + "id": "57b42644" + }, + { + "cell_type": "code", + "execution_count": 5, "outputs": [ { "data": { @@ -181,7 +250,14 @@ "source": [ "data_loader = CompasWithoutSensitiveAttrsDataset()\n", "data_loader.X_data[data_loader.X_data.columns[:5]].head()" - ] + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2024-01-29T12:52:44.286801Z" + } + }, + "id": "30a74059" }, { "cell_type": "code", @@ -196,7 +272,6 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:52:44.338769Z", "start_time": "2024-01-29T12:52:44.317039Z" } }, @@ -207,84 +282,20 @@ "execution_count": 7, "outputs": [], "source": [ - "base_flow_dataset = preprocess_dataset(data_loader, column_transformer, TEST_SET_FRACTION, DATASET_SPLIT_SEED)" + "base_flow_dataset = preprocess_dataset(data_loader=data_loader,\n", + " column_transformer=column_transformer,\n", + " sensitive_attributes_dct=config.sensitive_attributes_dct,\n", + " test_set_fraction=TEST_SET_FRACTION,\n", + " dataset_split_seed=DATASET_SPLIT_SEED)" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T12:52:44.378705Z", "start_time": "2024-01-29T12:52:44.337332Z" } }, "id": "3737429ccf1869c" }, - { - "cell_type": "markdown", - "id": "d22f31eb", - "metadata": {}, - "source": [ - "### Create a config object" - ] - }, - { - "cell_type": "markdown", - "id": "2ab8dda3", - "metadata": {}, - "source": [ - "`compute_metrics_with_multiple_test_sets` interface requires that your **yaml file** includes the following parameters:\n", - "\n", - "* **dataset_name**: str, a name of your dataset; it will be used to name files with metrics.\n", - "\n", - "* **bootstrap_fraction**: float, the fraction from a train set in the range [0.0 - 1.0] to fit models in bootstrap (usually more than 0.5).\n", - "\n", - "* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics.\n", - "\n", - "* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including intersectional attributes), and values are disadvantaged values for these attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify disadvantaged values for intersectional groups since they will be derived from disadvantaged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair.\n", - "\n", - "Note that disadvantaged value in a sensitive attribute dictionary must be **the same as in the original dataset**. For example, when distinct values of the _sex_ column in the original dataset are 'F' and 'M', and after pre-processing they became 0 and 1 respectively, you still need to set a disadvantaged value as 'F' or 'M' in the sensitive attribute dictionary.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "79dcac74", - "metadata": { - "ExecuteTime": { - "end_time": "2024-01-29T12:52:44.392671Z", - "start_time": "2024-01-29T12:52:44.367898Z" - } - }, - "outputs": [], - "source": [ - "ROOT_DIR = os.getcwd()\n", - "config_yaml_path = os.path.join(ROOT_DIR, 'experiment_config.yaml')\n", - "config_yaml_content = \\\n", - "\"\"\"dataset_name: COMPAS_Without_Sensitive_Attributes\n", - "bootstrap_fraction: 0.8\n", - "n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example\n", - "computation_mode: error_analysis\n", - "sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None}\n", - "\"\"\"\n", - "\n", - "with open(config_yaml_path, 'w', encoding='utf-8') as f:\n", - " f.write(config_yaml_content)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "abc8bd6f", - "metadata": { - "ExecuteTime": { - "end_time": "2024-01-29T12:52:44.415396Z", - "start_time": "2024-01-29T12:52:44.391594Z" - } - }, - "outputs": [], - "source": [ - "config = create_config_obj(config_yaml_path=config_yaml_path)" - ] - }, { "cell_type": "markdown", "id": "f04867aa", @@ -425,7 +436,7 @@ } ], "source": [ - "extra_test_sets_lst = [(base_flow_dataset.X_test, base_flow_dataset.y_test, base_flow_dataset.init_features_df)]\n", + "extra_test_sets_lst = [(base_flow_dataset.X_test, base_flow_dataset.y_test, base_flow_dataset.init_sensitive_attrs_df)]\n", "compute_metrics_with_multiple_test_sets(dataset=base_flow_dataset,\n", " extra_test_sets_lst=extra_test_sets_lst,\n", " config=config,\n", diff --git a/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.md b/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.md index 72dec4de..4f76ec00 100644 --- a/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.md +++ b/docs/examples/Multiple_Models_Interface_With_Multiple_Test_Sets.md @@ -1,6 +1,7 @@ # Multiple Models Interface For Multiple Test Sets -In this example, we are going to conduct a deep performance profiling for 2 models. For that, we will use `compute_metrics_with_multiple_test_sets` interface that will run metric computation for multiple models and test each model using multiple test sets. +In this example, we are going to conduct a deep performance profiling for 2 models under multiple test sets. It turns out particularly effective in case we want to test models on different test sets, but to avoid an overhead with retraining a bootstrap with 50-200 estimators. +For that, we will use `compute_metrics_with_multiple_test_sets` interface that will run metric computation for multiple models and test each model using multiple test sets. ## Import dependencies @@ -19,7 +20,7 @@ from sklearn.preprocessing import StandardScaler from virny.user_interfaces.multiple_models_with_multiple_test_sets_api import compute_metrics_with_multiple_test_sets from virny.utils.custom_initializers import create_config_obj, create_models_metrics_dct_from_database_df from virny.preprocessing.basic_preprocessing import preprocess_dataset -from virny.datasets.data_loaders import CompasWithoutSensitiveAttrsDataset +from virny.datasets import CompasWithoutSensitiveAttrsDataset ``` @@ -39,6 +40,47 @@ TEST_SET_FRACTION = 0.2 DATASET_SPLIT_SEED = 42 ``` +### Create a config object + +`compute_metrics_with_multiple_test_sets` interface requires that your **yaml file** includes the following parameters: + +* **dataset_name**: str, a name of your dataset; it will be used to name files with metrics. + +* **bootstrap_fraction**: float, the fraction from a train set in the range [0.0 - 1.0] to fit models in bootstrap (usually more than 0.5). + +* **random_state**: int, a seed to control the randomness of the whole model evaluation pipeline. + +* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics. + +* **computation_mode**: str, 'default' or 'error_analysis'. Name of the computation mode. When a default computation mode measures metrics for sex_priv and sex_dis, an `error_analysis` mode measures metrics for (sex_priv, sex_priv_correct, sex_priv_incorrect) and (sex_dis, sex_dis_correct, sex_dis_incorrect). Therefore, a user can analyze how a model is certain about its incorrect predictions. + +* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including intersectional attributes), and values are disadvantaged values for these attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify disadvantaged values for intersectional groups since they will be derived from disadvantaged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair. + +Note that disadvantaged value in a sensitive attribute dictionary must be **the same as in the original dataset**. For example, when distinct values of the _sex_ column in the original dataset are 'F' and 'M', and after pre-processing they became 0 and 1 respectively, you still need to set a disadvantaged value as 'F' or 'M' in the sensitive attribute dictionary. + + + +```python +ROOT_DIR = os.getcwd() +config_yaml_path = os.path.join(ROOT_DIR, 'experiment_config.yaml') +config_yaml_content = \ +"""dataset_name: COMPAS_Without_Sensitive_Attributes +bootstrap_fraction: 0.8 +random_state: 42 +n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example +computation_mode: error_analysis +sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None} +""" + +with open(config_yaml_path, 'w', encoding='utf-8') as f: + f.write(config_yaml_content) +``` + + +```python +config = create_config_obj(config_yaml_path=config_yaml_path) +``` + ### Create a Dataset class Based on the BaseDataset class, your **dataset class** should include the following attributes: @@ -140,43 +182,11 @@ column_transformer = ColumnTransformer(transformers=[ ```python -base_flow_dataset = preprocess_dataset(data_loader, column_transformer, TEST_SET_FRACTION, DATASET_SPLIT_SEED) -``` - -### Create a config object - -`compute_metrics_with_multiple_test_sets` interface requires that your **yaml file** includes the following parameters: - -* **dataset_name**: str, a name of your dataset; it will be used to name files with metrics. - -* **bootstrap_fraction**: float, the fraction from a train set in the range [0.0 - 1.0] to fit models in bootstrap (usually more than 0.5). - -* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics. - -* **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including intersectional attributes), and values are disadvantaged values for these attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify disadvantaged values for intersectional groups since they will be derived from disadvantaged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair. - -Note that disadvantaged value in a sensitive attribute dictionary must be **the same as in the original dataset**. For example, when distinct values of the _sex_ column in the original dataset are 'F' and 'M', and after pre-processing they became 0 and 1 respectively, you still need to set a disadvantaged value as 'F' or 'M' in the sensitive attribute dictionary. - - - -```python -ROOT_DIR = os.getcwd() -config_yaml_path = os.path.join(ROOT_DIR, 'experiment_config.yaml') -config_yaml_content = \ -"""dataset_name: COMPAS_Without_Sensitive_Attributes -bootstrap_fraction: 0.8 -n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example -computation_mode: error_analysis -sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None} -""" - -with open(config_yaml_path, 'w', encoding='utf-8') as f: - f.write(config_yaml_content) -``` - - -```python -config = create_config_obj(config_yaml_path=config_yaml_path) +base_flow_dataset = preprocess_dataset(data_loader=data_loader, + column_transformer=column_transformer, + sensitive_attributes_dct=config.sensitive_attributes_dct, + test_set_fraction=TEST_SET_FRACTION, + dataset_split_seed=DATASET_SPLIT_SEED) ``` ### Create a models config @@ -238,7 +248,7 @@ print('Current session uuid: ', custom_table_fields_dct['session_uuid']) ```python -extra_test_sets_lst = [(base_flow_dataset.X_test, base_flow_dataset.y_test, base_flow_dataset.init_features_df)] +extra_test_sets_lst = [(base_flow_dataset.X_test, base_flow_dataset.y_test, base_flow_dataset.init_sensitive_attrs_df)] compute_metrics_with_multiple_test_sets(dataset=base_flow_dataset, extra_test_sets_lst=extra_test_sets_lst, config=config, @@ -247,6 +257,12 @@ compute_metrics_with_multiple_test_sets(dataset=base_flow_dataset, db_writer_func=db_writer_func) ``` + Analyze multiple models: 0%| | 0/2 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Metricoverallmale_privmale_priv_correctmale_priv_incorrectmale_dis
0Jitter0.0444280.0410030.0358460.0931840.048953
1Label_Stability0.9497500.9543580.9619860.8771830.943661
2TPR0.9946350.9948841.0000000.0000000.994297
3TNR0.0787040.0733941.0000000.0000000.084112
4PPV0.9030690.9137121.0000000.0000000.888952
5FNR0.0053650.0051160.0000001.0000000.005703
6FPR0.9212960.9266060.0000001.0000000.915888
7Accuracy0.8995190.9100511.0000000.0000000.885603
8F10.9466430.9525721.0000000.0000000.938678
9Selection-Rate0.9870190.9885980.9925750.9483570.984933
10Positive-Rate1.1013951.0888371.00000018.3636361.118504
11Sample_Size4160.0000002368.0000002155.000000213.0000001792.000000
\n" + "text/plain": " Metric overall male_priv male_priv_correct \\\n0 Jitter 0.044141 0.040939 0.035644 \n1 Label_Stability 0.949913 0.953970 0.961893 \n2 TPR 0.994903 0.994884 1.000000 \n3 TNR 0.078704 0.073394 1.000000 \n4 PPV 0.903092 0.913712 1.000000 \n5 FNR 0.005097 0.005116 0.000000 \n6 FPR 0.921296 0.926606 0.000000 \n7 Accuracy 0.899760 0.910051 1.000000 \n8 F1 0.946777 0.952572 1.000000 \n9 Selection-Rate 0.987260 0.988598 0.992575 \n10 Sample_Size 4160.000000 2368.000000 2155.000000 \n\n male_priv_incorrect male_dis \n0 0.094502 0.048374 \n1 0.873803 0.944554 \n2 0.000000 0.994930 \n3 0.000000 0.084112 \n4 0.000000 0.889015 \n5 1.000000 0.005070 \n6 1.000000 0.915888 \n7 0.000000 0.886161 \n8 0.000000 0.938995 \n9 0.948357 0.985491 \n10 213.000000 1792.000000 ", + "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
Metricoverallmale_privmale_priv_correctmale_priv_incorrectmale_dis
0Jitter0.0441410.0409390.0356440.0945020.048374
1Label_Stability0.9499130.9539700.9618930.8738030.944554
2TPR0.9949030.9948841.0000000.0000000.994930
3TNR0.0787040.0733941.0000000.0000000.084112
4PPV0.9030920.9137121.0000000.0000000.889015
5FNR0.0050970.0051160.0000001.0000000.005070
6FPR0.9212960.9266060.0000001.0000000.915888
7Accuracy0.8997600.9100511.0000000.0000000.886161
8F10.9467770.9525721.0000000.0000000.938995
9Selection-Rate0.9872600.9885980.9925750.9483570.985491
10Sample_Size4160.0000002368.0000002155.000000213.0000001792.000000
\n
" }, "execution_count": 17, "metadata": {}, @@ -641,8 +642,8 @@ "id": "f94a20dc", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T13:02:11.333996Z", - "start_time": "2024-01-29T13:02:11.285158Z" + "end_time": "2024-06-01T21:36:58.085792Z", + "start_time": "2024-06-01T21:36:57.992507Z" } }, "outputs": [], @@ -656,8 +657,8 @@ "id": "b04d06cf", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T13:02:11.372240Z", - "start_time": "2024-01-29T13:02:11.335361Z" + "end_time": "2024-06-01T21:36:58.129262Z", + "start_time": "2024-06-01T21:36:58.036103Z" } }, "outputs": [], @@ -679,8 +680,8 @@ "id": "be6ace22", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T13:02:11.412123Z", - "start_time": "2024-01-29T13:02:11.372363Z" + "end_time": "2024-06-01T21:36:58.132644Z", + "start_time": "2024-06-01T21:36:58.076545Z" } }, "outputs": [], @@ -694,8 +695,8 @@ "outputs": [ { "data": { - "text/plain": " Metric male race male&race \\\n0 Accuracy_Difference -0.024448 -0.195943 -0.173922 \n1 Equalized_Odds_FNR 0.000587 -0.006129 -0.005741 \n2 Equalized_Odds_FPR -0.010718 0.129278 0.098266 \n3 Jitter_Difference 0.007950 0.033568 0.050556 \n4 Label_Stability_Ratio 0.988791 0.944396 0.920733 \n5 Label_Stability_Difference -0.010697 -0.053262 -0.075760 \n6 Disparate_Impact 1.027247 1.281367 1.247873 \n7 Statistical_Parity_Difference -0.003665 0.015315 0.014099 \n8 Equalized_Odds_TNR 0.010718 -0.129278 -0.098266 \n9 Equalized_Odds_TPR -0.000587 0.006129 0.005741 \n10 Accuracy_Difference -0.012594 -0.157805 -0.114099 \n11 Equalized_Odds_FNR 0.001093 0.014145 0.014464 \n12 Equalized_Odds_FPR -0.109191 -0.081198 -0.180871 \n13 Jitter_Difference 0.011503 0.093584 0.104403 \n14 Label_Stability_Ratio 0.980684 0.846239 0.824561 \n15 Label_Stability_Difference -0.018378 -0.148557 -0.167866 \n16 Disparate_Impact 1.013982 1.194074 1.135934 \n17 Statistical_Parity_Difference -0.016334 -0.052917 -0.075504 \n18 Equalized_Odds_TNR 0.109191 0.081198 0.180871 \n19 Equalized_Odds_TPR -0.001093 -0.014145 -0.014464 \n\n Model_Name \n0 LogisticRegression \n1 LogisticRegression \n2 LogisticRegression \n3 LogisticRegression \n4 LogisticRegression \n5 LogisticRegression \n6 LogisticRegression \n7 LogisticRegression \n8 LogisticRegression \n9 LogisticRegression \n10 RandomForestClassifier \n11 RandomForestClassifier \n12 RandomForestClassifier \n13 RandomForestClassifier \n14 RandomForestClassifier \n15 RandomForestClassifier \n16 RandomForestClassifier \n17 RandomForestClassifier \n18 RandomForestClassifier \n19 RandomForestClassifier ", - "text/html": "
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Metricmaleracemale&raceModel_Name
0Accuracy_Difference-0.024448-0.195943-0.173922LogisticRegression
1Equalized_Odds_FNR0.000587-0.006129-0.005741LogisticRegression
2Equalized_Odds_FPR-0.0107180.1292780.098266LogisticRegression
3Jitter_Difference0.0079500.0335680.050556LogisticRegression
4Label_Stability_Ratio0.9887910.9443960.920733LogisticRegression
5Label_Stability_Difference-0.010697-0.053262-0.075760LogisticRegression
6Disparate_Impact1.0272471.2813671.247873LogisticRegression
7Statistical_Parity_Difference-0.0036650.0153150.014099LogisticRegression
8Equalized_Odds_TNR0.010718-0.129278-0.098266LogisticRegression
9Equalized_Odds_TPR-0.0005870.0061290.005741LogisticRegression
10Accuracy_Difference-0.012594-0.157805-0.114099RandomForestClassifier
11Equalized_Odds_FNR0.0010930.0141450.014464RandomForestClassifier
12Equalized_Odds_FPR-0.109191-0.081198-0.180871RandomForestClassifier
13Jitter_Difference0.0115030.0935840.104403RandomForestClassifier
14Label_Stability_Ratio0.9806840.8462390.824561RandomForestClassifier
15Label_Stability_Difference-0.018378-0.148557-0.167866RandomForestClassifier
16Disparate_Impact1.0139821.1940741.135934RandomForestClassifier
17Statistical_Parity_Difference-0.016334-0.052917-0.075504RandomForestClassifier
18Equalized_Odds_TNR0.1091910.0811980.180871RandomForestClassifier
19Equalized_Odds_TPR-0.001093-0.014145-0.014464RandomForestClassifier
\n
" + "text/plain": " Metric male race male&race \\\n0 Accuracy_Difference -0.023890 -0.196227 -0.174183 \n1 Equalized_Odds_FNR -0.000047 -0.005823 -0.005454 \n2 Equalized_Odds_FPR -0.010718 0.129278 0.098266 \n3 Jitter_Difference 0.007435 0.034351 0.049795 \n4 Label_Stability_Ratio 0.990130 0.943974 0.924259 \n5 Label_Stability_Difference -0.009416 -0.053678 -0.072383 \n6 Statistical_Parity_Difference -0.003107 0.015031 0.013838 \n7 Disparate_Impact 0.996857 1.015261 1.014032 \n8 Equalized_Odds_TNR 0.010718 -0.129278 -0.098266 \n9 Equalized_Odds_TPR 0.000047 0.005823 0.005454 \n10 Accuracy_Difference -0.020693 -0.158407 -0.134267 \n11 Equalized_Odds_FNR 0.004134 0.020908 0.029136 \n12 Equalized_Odds_FPR -0.058218 -0.104439 -0.140207 \n13 Jitter_Difference 0.009800 0.093877 0.101423 \n14 Label_Stability_Ratio 0.981678 0.844858 0.825698 \n15 Label_Stability_Difference -0.017405 -0.149755 -0.166575 \n16 Statistical_Parity_Difference -0.013514 -0.061529 -0.076446 \n17 Disparate_Impact 0.986242 0.937586 0.922193 \n18 Equalized_Odds_TNR 0.058218 0.104439 0.140207 \n19 Equalized_Odds_TPR -0.004134 -0.020908 -0.029136 \n\n Model_Name \n0 LogisticRegression \n1 LogisticRegression \n2 LogisticRegression \n3 LogisticRegression \n4 LogisticRegression \n5 LogisticRegression \n6 LogisticRegression \n7 LogisticRegression \n8 LogisticRegression \n9 LogisticRegression \n10 RandomForestClassifier \n11 RandomForestClassifier \n12 RandomForestClassifier \n13 RandomForestClassifier \n14 RandomForestClassifier \n15 RandomForestClassifier \n16 RandomForestClassifier \n17 RandomForestClassifier \n18 RandomForestClassifier \n19 RandomForestClassifier ", + "text/html": "
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Metricmaleracemale&raceModel_Name
0Accuracy_Difference-0.023890-0.196227-0.174183LogisticRegression
1Equalized_Odds_FNR-0.000047-0.005823-0.005454LogisticRegression
2Equalized_Odds_FPR-0.0107180.1292780.098266LogisticRegression
3Jitter_Difference0.0074350.0343510.049795LogisticRegression
4Label_Stability_Ratio0.9901300.9439740.924259LogisticRegression
5Label_Stability_Difference-0.009416-0.053678-0.072383LogisticRegression
6Statistical_Parity_Difference-0.0031070.0150310.013838LogisticRegression
7Disparate_Impact0.9968571.0152611.014032LogisticRegression
8Equalized_Odds_TNR0.010718-0.129278-0.098266LogisticRegression
9Equalized_Odds_TPR0.0000470.0058230.005454LogisticRegression
10Accuracy_Difference-0.020693-0.158407-0.134267RandomForestClassifier
11Equalized_Odds_FNR0.0041340.0209080.029136RandomForestClassifier
12Equalized_Odds_FPR-0.058218-0.104439-0.140207RandomForestClassifier
13Jitter_Difference0.0098000.0938770.101423RandomForestClassifier
14Label_Stability_Ratio0.9816780.8448580.825698RandomForestClassifier
15Label_Stability_Difference-0.017405-0.149755-0.166575RandomForestClassifier
16Statistical_Parity_Difference-0.013514-0.061529-0.076446RandomForestClassifier
17Disparate_Impact0.9862420.9375860.922193RandomForestClassifier
18Equalized_Odds_TNR0.0582180.1044390.140207RandomForestClassifier
19Equalized_Odds_TPR-0.004134-0.020908-0.029136RandomForestClassifier
\n
" }, "execution_count": 21, "metadata": {}, @@ -708,8 +709,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T13:02:11.448850Z", - "start_time": "2024-01-29T13:02:11.412374Z" + "end_time": "2024-06-01T21:36:58.167706Z", + "start_time": "2024-06-01T21:36:58.123737Z" } }, "id": "a286da0406c6401d" @@ -736,8 +737,8 @@ "id": "435b9d98", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T13:02:11.491006Z", - "start_time": "2024-01-29T13:02:11.449534Z" + "end_time": "2024-06-01T21:36:58.227052Z", + "start_time": "2024-06-01T21:36:58.167Z" } }, "outputs": [], @@ -753,14 +754,14 @@ "id": "5efb1bf2", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T13:02:11.554246Z", - "start_time": "2024-01-29T13:02:11.491173Z" + "end_time": "2024-06-01T21:36:58.278703Z", + "start_time": "2024-06-01T21:36:58.212297Z" } }, "outputs": [ { "data": { - "text/html": "\n
\n", + "text/html": "\n
\n", "text/plain": "alt.Chart(...)" }, "execution_count": 23, @@ -781,14 +782,14 @@ "id": "0eb8528e", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T13:02:11.614847Z", - "start_time": "2024-01-29T13:02:11.554711Z" + "end_time": "2024-06-01T21:36:58.351296Z", + "start_time": "2024-06-01T21:36:58.278923Z" } }, "outputs": [ { "data": { - "text/html": "\n
\n", + "text/html": "\n
\n", "text/plain": "alt.Chart(...)" }, "execution_count": 24, @@ -810,7 +811,7 @@ { "data": { "text/plain": "
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" }, "metadata": {}, "output_type": "display_data" @@ -826,8 +827,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-29T13:02:11.796199Z", - "start_time": "2024-01-29T13:02:11.614732Z" + "end_time": "2024-06-01T21:36:58.531441Z", + "start_time": "2024-06-01T21:36:58.348051Z" } }, "id": "eeb7c1e88b43163b" @@ -838,15 +839,15 @@ "id": "df024aed", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T13:02:12.044591Z", - "start_time": "2024-01-29T13:02:11.796813Z" + "end_time": "2024-06-01T21:36:58.785143Z", + "start_time": "2024-06-01T21:36:58.532044Z" } }, "outputs": [ { "data": { "text/plain": "
", - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -875,8 +876,8 @@ "id": "2326c129", "metadata": { "ExecuteTime": { - "end_time": "2024-01-29T13:02:12.045968Z", - "start_time": "2024-01-29T13:02:12.044534Z" + "end_time": "2024-06-01T21:36:58.791239Z", + "start_time": "2024-06-01T21:36:58.785485Z" } }, "outputs": [], diff --git a/docs/examples/Multiple_Models_Interface_With_Postprocessor.md b/docs/examples/Multiple_Models_Interface_With_Postprocessor.md index a4afedcb..691e41f7 100644 --- a/docs/examples/Multiple_Models_Interface_With_Postprocessor.md +++ b/docs/examples/Multiple_Models_Interface_With_Postprocessor.md @@ -84,10 +84,12 @@ models_params_for_tuning = { * **bootstrap_fraction**: float, the fraction from a train set in the range [0.0 - 1.0] to fit models in bootstrap (usually more than 0.5). -* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics. - * **computation_mode**: str, 'default' or 'error_analysis'. Name of the computation mode. When a default computation mode measures metrics for sex_priv and sex_dis, an `error_analysis` mode measures metrics for (sex_priv, sex_priv_correct, sex_priv_incorrect) and (sex_dis, sex_dis_correct, sex_dis_incorrect). Therefore, a user can analyze how a model is certain about its incorrect predictions. +* **random_state**: int, a seed to control the randomness of the whole model evaluation pipeline. + +* **n_estimators**: int, the number of estimators for bootstrap to compute subgroup stability metrics. + * **sensitive_attributes_dct**: dict, a dictionary where keys are sensitive attribute names (including intersectional attributes), and values are disadvantaged values for these attributes. Intersectional attributes must include '&' between sensitive attributes. You do not need to specify disadvantaged values for intersectional groups since they will be derived from disadvantaged values in sensitive_attributes_dct for each separate sensitive attribute in this intersectional pair. * **postprocessing_sensitive_attribute**: str, a name of a sensitive attribute to use for postprocessing. @@ -102,8 +104,9 @@ config_yaml_content = """ dataset_name: Law_School bootstrap_fraction: 0.8 computation_mode: error_analysis +random_state: 42 n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example -sensitive_attributes_dct: {'male': '0.0', 'race': 'Non-White', 'male&race': None} +sensitive_attributes_dct: {'male': '0', 'race': 'Non-White', 'male&race': None} postprocessing_sensitive_attribute: 'race_binary' """ @@ -121,7 +124,7 @@ SAVE_RESULTS_DIR_PATH = os.path.join(ROOT_DIR, 'results', f'{config.dataset_name ```python -from virny.datasets.data_loaders import LawSchoolDataset +from virny.datasets import LawSchoolDataset data_loader = LawSchoolDataset() data_loader.X_data[data_loader.X_data.columns[:5]].head() @@ -215,7 +218,11 @@ column_transformer = ColumnTransformer(transformers=[ # Create a binary race column for postprocessing since aif360 postprocessors can postprocess a dataset only based on binary columns. data_loader.X_data['race_binary'] = data_loader.X_data['race'].apply(lambda x: 1 if x == 'White' else 0) -base_flow_dataset = preprocess_dataset(data_loader, column_transformer, TEST_SET_FRACTION, DATASET_SPLIT_SEED) +base_flow_dataset = preprocess_dataset(data_loader=data_loader, + column_transformer=column_transformer, + sensitive_attributes_dct=config.sensitive_attributes_dct, + test_set_fraction=TEST_SET_FRACTION, + dataset_split_seed=DATASET_SPLIT_SEED) base_flow_dataset.X_train_val['race_binary'] = data_loader.X_data.loc[base_flow_dataset.X_train_val.index, 'race_binary'] base_flow_dataset.X_test['race_binary'] = data_loader.X_data.loc[base_flow_dataset.X_test.index, 'race_binary'] ``` @@ -240,11 +247,11 @@ tuned_params_df, models_config = tune_ML_models(models_params_for_tuning, base_f tuned_params_df ``` - 2024/01/29, 15:01:08: Tuning LogisticRegression... - 2024/01/29, 15:01:10: Tuning for LogisticRegression is finished [F1 score = 0.6563618630035558, Accuracy = 0.8987258083904316] + 2024/06/02, 00:35:52: Tuning LogisticRegression... + 2024/06/02, 00:35:54: Tuning for LogisticRegression is finished [F1 score = 0.6563618630035558, Accuracy = 0.8987258083904316] - 2024/01/29, 15:01:10: Tuning RandomForestClassifier... - 2024/01/29, 15:01:12: Tuning for RandomForestClassifier is finished [F1 score = 0.6538551003755212, Accuracy = 0.8980646712345234] + 2024/06/02, 00:35:54: Tuning RandomForestClassifier... + 2024/06/02, 00:35:56: Tuning for RandomForestClassifier is finished [F1 score = 0.6538551003755212, Accuracy = 0.8980646712345234] @@ -392,29 +399,29 @@ sample_model_metrics_df[sample_model_metrics_df.columns[:6]].head(20) 0 Jitter - 0.044428 - 0.041003 - 0.035846 - 0.093184 - 0.048953 + 0.044141 + 0.040939 + 0.035644 + 0.094502 + 0.048374 1 Label_Stability - 0.949750 - 0.954358 - 0.961986 - 0.877183 - 0.943661 + 0.949913 + 0.953970 + 0.961893 + 0.873803 + 0.944554 2 TPR - 0.994635 + 0.994903 0.994884 1.000000 0.000000 - 0.994297 + 0.994930 3 @@ -428,20 +435,20 @@ sample_model_metrics_df[sample_model_metrics_df.columns[:6]].head(20) 4 PPV - 0.903069 + 0.903092 0.913712 1.000000 0.000000 - 0.888952 + 0.889015 5 FNR - 0.005365 + 0.005097 0.005116 0.000000 1.000000 - 0.005703 + 0.005070 6 @@ -455,41 +462,32 @@ sample_model_metrics_df[sample_model_metrics_df.columns[:6]].head(20) 7 Accuracy - 0.899519 + 0.899760 0.910051 1.000000 0.000000 - 0.885603 + 0.886161 8 F1 - 0.946643 + 0.946777 0.952572 1.000000 0.000000 - 0.938678 + 0.938995 9 Selection-Rate - 0.987019 + 0.987260 0.988598 0.992575 0.948357 - 0.984933 + 0.985491 10 - Positive-Rate - 1.101395 - 1.088837 - 1.000000 - 18.363636 - 1.118504 - - - 11 Sample_Size 4160.000000 2368.000000 @@ -561,17 +559,17 @@ models_composed_metrics_df 0 Accuracy_Difference - -0.024448 - -0.195943 - -0.173922 + -0.023890 + -0.196227 + -0.174183 LogisticRegression 1 Equalized_Odds_FNR - 0.000587 - -0.006129 - -0.005741 + -0.000047 + -0.005823 + -0.005454 LogisticRegression @@ -585,41 +583,41 @@ models_composed_metrics_df 3 Jitter_Difference - 0.007950 - 0.033568 - 0.050556 + 0.007435 + 0.034351 + 0.049795 LogisticRegression 4 Label_Stability_Ratio - 0.988791 - 0.944396 - 0.920733 + 0.990130 + 0.943974 + 0.924259 LogisticRegression 5 Label_Stability_Difference - -0.010697 - -0.053262 - -0.075760 + -0.009416 + -0.053678 + -0.072383 LogisticRegression 6 - Disparate_Impact - 1.027247 - 1.281367 - 1.247873 + Statistical_Parity_Difference + -0.003107 + 0.015031 + 0.013838 LogisticRegression 7 - Statistical_Parity_Difference - -0.003665 - 0.015315 - 0.014099 + Disparate_Impact + 0.996857 + 1.015261 + 1.014032 LogisticRegression @@ -633,89 +631,89 @@ models_composed_metrics_df 9 Equalized_Odds_TPR - -0.000587 - 0.006129 - 0.005741 + 0.000047 + 0.005823 + 0.005454 LogisticRegression 10 Accuracy_Difference - -0.012594 - -0.157805 - -0.114099 + -0.020693 + -0.158407 + -0.134267 RandomForestClassifier 11 Equalized_Odds_FNR - 0.001093 - 0.014145 - 0.014464 + 0.004134 + 0.020908 + 0.029136 RandomForestClassifier 12 Equalized_Odds_FPR - -0.109191 - -0.081198 - -0.180871 + -0.058218 + -0.104439 + -0.140207 RandomForestClassifier 13 Jitter_Difference - 0.011503 - 0.093584 - 0.104403 + 0.009800 + 0.093877 + 0.101423 RandomForestClassifier 14 Label_Stability_Ratio - 0.980684 - 0.846239 - 0.824561 + 0.981678 + 0.844858 + 0.825698 RandomForestClassifier 15 Label_Stability_Difference - -0.018378 - -0.148557 - -0.167866 + -0.017405 + -0.149755 + -0.166575 RandomForestClassifier 16 - Disparate_Impact - 1.013982 - 1.194074 - 1.135934 + Statistical_Parity_Difference + -0.013514 + -0.061529 + -0.076446 RandomForestClassifier 17 - Statistical_Parity_Difference - -0.016334 - -0.052917 - -0.075504 + Disparate_Impact + 0.986242 + 0.937586 + 0.922193 RandomForestClassifier 18 Equalized_Odds_TNR - 0.109191 - 0.081198 - 0.180871 + 0.058218 + 0.104439 + 0.140207 RandomForestClassifier 19 Equalized_Odds_TPR - -0.001093 - -0.014145 - -0.014464 + -0.004134 + -0.020908 + -0.029136 RandomForestClassifier @@ -747,13 +745,13 @@ visualizer.create_overall_metrics_bar_char( -
+
@@ -816,13 +814,13 @@ visualizer.create_overall_metrics_bar_char( -
+
diff --git a/docs/examples/Multiple_Models_Interface_With_Postprocessor_files/Multiple_Models_Interface_With_Postprocessor_42_0.png b/docs/examples/Multiple_Models_Interface_With_Postprocessor_files/Multiple_Models_Interface_With_Postprocessor_42_0.png index 394d81b8..42a0fe64 100644 Binary files a/docs/examples/Multiple_Models_Interface_With_Postprocessor_files/Multiple_Models_Interface_With_Postprocessor_42_0.png and b/docs/examples/Multiple_Models_Interface_With_Postprocessor_files/Multiple_Models_Interface_With_Postprocessor_42_0.png differ diff --git a/docs/examples/Multiple_Models_Interface_With_Postprocessor_files/Multiple_Models_Interface_With_Postprocessor_43_0.png b/docs/examples/Multiple_Models_Interface_With_Postprocessor_files/Multiple_Models_Interface_With_Postprocessor_43_0.png index c15cf69d..c669a2d4 100644 Binary files a/docs/examples/Multiple_Models_Interface_With_Postprocessor_files/Multiple_Models_Interface_With_Postprocessor_43_0.png and b/docs/examples/Multiple_Models_Interface_With_Postprocessor_files/Multiple_Models_Interface_With_Postprocessor_43_0.png differ diff --git a/docs/examples/experiment_config.yaml b/docs/examples/experiment_config.yaml index 4853f145..d2c62434 100644 --- a/docs/examples/experiment_config.yaml +++ b/docs/examples/experiment_config.yaml @@ -1,7 +1,6 @@ - -dataset_name: Law_School +dataset_name: COMPAS_Without_Sensitive_Attributes bootstrap_fraction: 0.8 -computation_mode: error_analysis +random_state: 42 n_estimators: 50 # Better to input the higher number of estimators than 100; this is only for this use case example -sensitive_attributes_dct: {'male': '0.0', 'race': 'Non-White', 'male&race': None} -postprocessing_sensitive_attribute: 'race_binary' +computation_mode: error_analysis +sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': None} diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__094751/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20240423__094754.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__094751/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20240423__094754.csv new file mode 100644 index 00000000..8201be37 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__094751/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20240423__094754.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins +Std,0.07831872826975794,0.08270283474467377,0.07722399872395057,0.07633860801278426,0.07959562824855407,0.0777233028362307,0.07890965992642839,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",,0.03919155 +Overall_Uncertainty,0.897979858475757,0.9068245579655727,0.8957713003783001,0.8929531670810873,0.9012213697489553,0.8981146362442641,0.8978460978979558,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",,0.03919155 +IQR,0.09600120769075234,0.09556038091729,0.09611128396199561,0.09673013434958595,0.09553115218178487,0.09575296570554365,0.09624757615154438,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",,0.03919155 +Statistical_Bias,0.4203911998830184,0.41514364445235247,0.42170153620949236,0.4159811138513608,0.423235087137078,0.4145390991826763,0.42619913378562213,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",,0.03919155 +Mean_Prediction,0.520125169443092,0.5742731200591116,0.5066042018928195,0.5813075114931838,0.48067113578462145,0.5729452226437408,0.4677037581533915,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",,0.03919155 +Aleatoric_Uncertainty,0.8672832959266745,0.8699087963784233,0.8666276975890189,0.8625668789150667,0.8703247237192068,0.8662884942991466,0.8682705896173911,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",,0.03919155 +Epistemic_Uncertainty,0.030696562549082484,0.036915761587149376,0.029143602789281187,0.030386288166020625,0.03089664602974851,0.03182614194511757,0.029575508280564655,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",,0.03919155 +Label_Stability,0.8006060606060607,0.7738388625592417,0.8072899408284023,0.8169082125603865,0.7900934579439253,0.8085931558935361,0.7926792452830189,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",,0.03919155 +Jitter,0.13941249226963523,0.15619692426733756,0.13522135007849315,0.1286069210292817,0.14638057091995663,0.13427019476992352,0.14451597997689652,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",,0.03919155 +TPR,0.692144373673036,0.5866666666666667,0.7121212121212122,0.5850340136054422,0.7407407407407407,0.6063829787234043,0.7491166077738516,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",,0.03919155 +TNR,0.6888888888888889,0.8088235294117647,0.6525612472160356,0.7602996254681648,0.6289308176100629,0.7692307692307693,0.5789473684210527,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",,0.03919155 +PPV,0.6417322834645669,0.6285714285714286,0.6438356164383562,0.5733333333333334,0.6703910614525139,0.59375,0.6708860759493671,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",,0.03919155 +FNR,0.3078556263269639,0.41333333333333333,0.2878787878787879,0.41496598639455784,0.25925925925925924,0.39361702127659576,0.2508833922261484,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",,0.03919155 +FPR,0.3111111111111111,0.19117647058823528,0.34743875278396436,0.2397003745318352,0.3710691823899371,0.23076923076923078,0.42105263157894735,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",,0.03919155 +Accuracy,0.6903409090909091,0.7298578199052133,0.6804733727810651,0.6980676328502415,0.6853582554517134,0.7110266159695817,0.6698113207547169,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",,0.03919155 +F1,0.6659856996935649,0.6068965517241379,0.6762589928057554,0.5791245791245792,0.7038123167155426,0.6,0.7078464106844741,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",,0.03919155 +Selection-Rate,0.4810606060606061,0.33175355450236965,0.5183431952662721,0.36231884057971014,0.557632398753894,0.3650190114068441,0.5962264150943396,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",,0.03919155 +Sample_Size,1056.0,211.0,845.0,414.0,642.0,526.0,530.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",,0.03919155 diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__094751/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20240423__094754.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__094751/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20240423__094754.csv new file mode 100644 index 00000000..828c5565 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__094751/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20240423__094754.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins +Std,0.022984138990112094,0.022396281257716817,0.023130929500804885,0.022620085748038525,0.02321890229574832,0.022698829985893405,0.023267294718827245,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",,0.05894328333333333 +Overall_Uncertainty,0.9101177401132338,0.9181250079877493,0.9081182921587689,0.9153425475108262,0.9067484717914216,0.9174779553884139,0.9028130736325832,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",,0.05894328333333333 +IQR,0.03126343646963143,0.030994458600156056,0.031330601357748934,0.031284979743127925,0.031249544078311244,0.0313496167594397,0.03117790659710471,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",,0.05894328333333333 +Statistical_Bias,0.43412595063153514,0.43365158572846624,0.4342444015126564,0.4338711958920065,0.4342902317252497,0.43262869253163727,0.4356119086703016,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",,0.05894328333333333 +Mean_Prediction,0.5208930584643658,0.5691625290609926,0.5088399717236698,0.5855003760849228,0.4792303956997077,0.5742545362185733,0.46793430884415255,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",,0.05894328333333333 +Aleatoric_Uncertainty,0.9071640689196158,0.9156536633729426,0.9050441820206194,0.9128228982517558,0.9035149172942172,0.9149052954109018,0.8994812667792074,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",,0.05894328333333333 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a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__094751/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20240423__094754.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__094751/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20240423__094754.csv new file mode 100644 index 00000000..a7f764f7 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__094751/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20240423__094754.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins +Std,0.06812437832894026,0.07420981699330095,0.06660481908849043,0.06910096129787284,0.06749461921813327,0.06881025896327372,0.06744367415222441,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 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0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",,0.16169821666666667 +PPV,0.673728813559322,0.6164383561643836,0.6842105263157895,0.6222222222222222,0.6943620178041543,0.6201117318435754,0.7064846416382252,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",,0.16169821666666667 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'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",,0.16169821666666667 +Selection-Rate,0.44696969696969696,0.3459715639810427,0.47218934911242605,0.32608695652173914,0.5249221183800623,0.3403041825095057,0.5528301886792453,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",,0.16169821666666667 +Sample_Size,1056.0,211.0,845.0,414.0,642.0,526.0,530.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",,0.16169821666666667 diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__094751/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20240423__094754.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__094751/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20240423__094754.csv new file mode 100644 index 00000000..1349e4e9 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__094751/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20240423__094754.csv @@ -0,0 +1,19 @@ 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None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",,0.19517400000000001 +PPV,0.6709677419354839,0.5970149253731343,0.6834170854271356,0.5985401459854015,0.7012195121951219,0.6045197740112994,0.7118055555555556,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",,0.19517400000000001 +FNR,0.3375796178343949,0.4666666666666667,0.31313131313131315,0.4421768707482993,0.29012345679012347,0.4308510638297872,0.2756183745583039,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",,0.19517400000000001 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None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",,0.19517400000000001 +Accuracy,0.7045454545454546,0.7061611374407583,0.7041420118343196,0.7101449275362319,0.7009345794392523,0.7129277566539924,0.6962264150943396,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",,0.19517400000000001 +F1,0.6666666666666666,0.5633802816901409,0.6851385390428212,0.5774647887323944,0.7055214723926381,0.5863013698630137,0.7180385288966725,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",,0.19517400000000001 +Selection-Rate,0.4403409090909091,0.3175355450236967,0.4710059171597633,0.3309178743961353,0.5109034267912772,0.3365019011406844,0.5433962264150943,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",,0.19517400000000001 +Sample_Size,1056.0,211.0,845.0,414.0,642.0,526.0,530.0,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 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a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__094936/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20240423__094939.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__094936/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20240423__094939.csv new file mode 100644 index 00000000..7b7fe062 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__094936/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20240423__094939.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins +Std,0.01992587657705821,0.019280893090605333,0.02008693162515473,0.019566339542272174,0.02015772756210715,0.019592206265551082,0.0202570286220634,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': 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b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__094936/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20240423__094939.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins +Std,0.0681768750684563,0.07410919517066657,0.0666955501671943,0.06956097795236235,0.06728432274145144,0.06939797533317382,0.06696499065479322,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",,0.1641656 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'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",,0.1641656 +Overall_Uncertainty,0.8598973555141145,0.8793256731264536,0.8550460241339919,0.850095509458468,0.866218172129438,0.8602427966449459,0.8595545214861574,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",,0.1641656 +Statistical_Bias,0.40495240300513624,0.3972081100751739,0.4068861850267008,0.39429863323947517,0.4118225909848616,0.39727835011312235,0.4125685385168332,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 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+Epistemic_Uncertainty,0.024606425367989848,0.030758662798752168,0.023070186198888232,0.02640787695323421,0.023444741635449184,0.026379384208881196,0.022846847348539256,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",,0.1641656 +Jitter,0.1161123995052567,0.13693393945255852,0.11091317473735056,0.11740905057675252,0.11527624133765658,0.11788313804609302,0.1143550250288798,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",,0.1641656 +Label_Stability,0.8342045454545455,0.7971563981042653,0.8434556213017752,0.8289855072463768,0.837570093457944,0.8299619771863118,0.8384150943396227,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",,0.1641656 +TPR,0.6666666666666666,0.5733333333333334,0.6843434343434344,0.5578231292517006,0.7160493827160493,0.574468085106383,0.7279151943462897,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 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+PPV,0.6781857451403888,0.6417910447761194,0.6843434343434344,0.6259541984732825,0.6987951807228916,0.627906976744186,0.7079037800687286,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",,0.1641656 +FNR,0.3333333333333333,0.4266666666666667,0.31565656565656564,0.4421768707482993,0.2839506172839506,0.425531914893617,0.27208480565371024,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",,0.1641656 +FPR,0.2547008547008547,0.17647058823529413,0.27839643652561247,0.18352059925093633,0.31446540880503143,0.1893491124260355,0.3441295546558704,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",,0.1641656 +Accuracy,0.7102272727272727,0.7345971563981043,0.7041420118343196,0.7246376811594203,0.7009345794392523,0.7262357414448669,0.6943396226415094,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",,0.1641656 +F1,0.6723768736616702,0.6056338028169014,0.6843434343434344,0.5899280575539568,0.7073170731707317,0.6,0.7177700348432056,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",,0.1641656 +Selection-Rate,0.4384469696969697,0.3175355450236967,0.4686390532544379,0.3164251207729469,0.5171339563862928,0.3269961977186312,0.5490566037735849,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",,0.1641656 +Sample_Size,1056.0,211.0,845.0,414.0,642.0,526.0,530.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",,0.1641656 diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__094936/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20240423__094939.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__094936/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20240423__094939.csv new file mode 100644 index 00000000..f86361f4 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__094936/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20240423__094939.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins +Std,0.046325407922267914,0.046940695494413376,0.04617176949977875,0.044825151562690735,0.047292862087488174,0.045148320496082306,0.04749361425638199,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",,0.1905441 +Mean_Prediction,0.5262044072151184,0.5827558636665344,0.5120832920074463,0.5937976837158203,0.4826161861419678,0.5844709873199463,0.46837756037712097,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",,0.1905441 +IQR,0.06136917078754667,0.0625071104668893,0.061085022536255196,0.059548282320948616,0.06254338858376411,0.06010420563783029,0.06262458903047274,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",,0.1905441 +Overall_Uncertainty,0.8778870840379044,0.8923500767512017,0.8742756148515071,0.870981912683954,0.8823399515465267,0.8786765391791946,0.8771035870486239,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",,0.1905441 +Statistical_Bias,0.4125250543848696,0.4101223391944198,0.41312502231999965,0.40693444553492725,0.4161302133628698,0.40791138428698925,0.4171039043310678,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",,0.1905441 +Aleatoric_Uncertainty,0.8692153692245483,0.8836120367050171,0.8656203746795654,0.8627690672874451,0.8733721971511841,0.8704155087471008,0.8680241703987122,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",,0.1905441 +Epistemic_Uncertainty,0.008671714813356046,0.008738040046184614,0.008655240171941636,0.008212845396508905,0.008967754395342586,0.008261030432093808,0.009079416649911742,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': 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'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",,0.1905441 +Selection-Rate,0.4375,0.3175355450236967,0.46745562130177515,0.32367149758454106,0.5109034267912772,0.33079847908745247,0.5433962264150943,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",,0.1905441 +Sample_Size,1056.0,211.0,845.0,414.0,642.0,526.0,530.0,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 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+Sample_Size,1056.0,211.0,845.0,414.0,642.0,526.0,530.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",42,0.038877133333333334 diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__095334/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20240423__095337.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__095334/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20240423__095337.csv new file mode 100644 index 00000000..ddf51788 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__095334/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20240423__095337.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins +Overall_Uncertainty,0.9113463914637511,0.9197978513777793,0.9092360269171713,0.9163233844447729,0.9081369287002884,0.918766773125671,0.9039820126822984,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.054440216666666666 +Statistical_Bias,0.4349363283173577,0.4344937955506501,0.43504683058218047,0.43433306791288717,0.4353253467090255,0.4333038826946261,0.43655645359576667,LogisticRegression,"{'C': 1, 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'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.054440216666666666 +Aleatoric_Uncertainty,0.9086231626304789,0.917298554899211,0.9064568812473993,0.9138828188161628,0.9052314217256925,0.9162462105769467,0.9010576471213435,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.054440216666666666 +IQR,0.027530551760038283,0.026885306150093848,0.02769167226145636,0.02712688294929466,0.027790861553882305,0.027419161561839045,0.02764110127749639,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.054440216666666666 +Epistemic_Uncertainty,0.00272322883327214,0.0024992964785682803,0.0027791456697719985,0.0024405656286100585,0.002905506974595906,0.0025205625487243477,0.002924365560954878,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.054440216666666666 +Jitter,0.04155303030303035,0.05982783634780927,0.036989735539186115,0.03993295869072265,0.04259774938012583,0.04558857763637791,0.037547939930689306,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.054440216666666666 +Label_Stability,0.9435984848484847,0.9143127962085309,0.9509112426035503,0.9457004830917874,0.9422429906542056,0.9371102661596958,0.9500377358490566,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.054440216666666666 +TPR,0.6263269639065817,0.48,0.6540404040404041,0.4421768707482993,0.7098765432098766,0.48404255319148937,0.7208480565371025,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 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False}",42,0.054440216666666666 +FNR,0.37367303609341823,0.52,0.34595959595959597,0.5578231292517006,0.29012345679012347,0.5159574468085106,0.2791519434628975,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.054440216666666666 +FPR,0.26837606837606837,0.19117647058823528,0.29175946547884185,0.18352059925093633,0.33962264150943394,0.1893491124260355,0.3765182186234818,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.054440216666666666 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a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__095334/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20240423__095337.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__095334/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20240423__095337.csv new file mode 100644 index 00000000..0d6e96a3 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240423__095334/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20240423__095337.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins +Overall_Uncertainty,0.8621636502678327,0.8803766224447757,0.8576157956769036,0.8516827419756987,0.8689223668300499,0.8614131047232362,0.862908531317753,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16313286666666665 +Statistical_Bias,0.40555632087192,0.39860214012279876,0.4072928086092745,0.39507258366072,0.4123168616903574,0.3978565745929863,0.4131979558581825,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16313286666666665 +Std,0.06986874362680465,0.07569031436019155,0.06841507330166306,0.07076511810069319,0.0692907077511195,0.07041891005278857,0.06932272940026211,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16313286666666665 +Mean_Prediction,0.5223580732536224,0.5772518864256917,0.508650860733733,0.595097466806707,0.4754513615231286,0.5840777726169991,0.4611041829420449,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 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+Accuracy,0.6875,0.7251184834123223,0.6781065088757396,0.6932367149758454,0.6838006230529595,0.7072243346007605,0.6679245283018868,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",42,0.037709650000000004 +F1,0.6625766871165644,0.5972222222222222,0.6738609112709832,0.5723905723905723,0.7019089574155654,0.5947368421052631,0.705685618729097,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}",42,0.037709650000000004 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a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__212604/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20240601__212607.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__212604/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20240601__212607.csv new file mode 100644 index 00000000..7503f821 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__212604/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20240601__212607.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins +Overall_Uncertainty,0.9113463914637511,0.9197978513777793,0.9092360269171713,0.9163233844447729,0.9081369287002884,0.918766773125671,0.9039820126822984,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 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0.0001, 'verbose': 0, 'warm_start': False}",42,0.055517366666666665 +TPR,0.6263269639065817,0.48,0.6540404040404041,0.4421768707482993,0.7098765432098766,0.48404255319148937,0.7208480565371025,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.055517366666666665 +TNR,0.7316239316239316,0.8088235294117647,0.7082405345211581,0.8164794007490637,0.660377358490566,0.8106508875739645,0.6234817813765182,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.055517366666666665 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+FPR,0.26837606837606837,0.19117647058823528,0.29175946547884185,0.18352059925093633,0.33962264150943394,0.1893491124260355,0.3765182186234818,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.055517366666666665 +Accuracy,0.6846590909090909,0.6919431279620853,0.6828402366863905,0.6835748792270532,0.6853582554517134,0.6939163498098859,0.6754716981132075,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.055517366666666665 +F1,0.6392199349945829,0.5255474452554745,0.6590330788804071,0.49808429118773945,0.6948640483383686,0.5306122448979592,0.7034482758620689,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.055517366666666665 +Selection-Rate,0.42803030303030304,0.2938388625592417,0.46153846153846156,0.2753623188405797,0.5264797507788161,0.2946768060836502,0.560377358490566,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.055517366666666665 +Sample_Size,1056.0,211.0,845.0,414.0,642.0,526.0,530.0,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.055517366666666665 diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__212604/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20240601__212607.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__212604/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20240601__212607.csv new file mode 100644 index 00000000..90583502 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__212604/Metrics_COMPAS_Without_Sensitive_Attributes_RandomForestClassifier_50_Estimators_20240601__212607.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins +Overall_Uncertainty,0.8621636502678327,0.8803766224447757,0.8576157956769036,0.8516827419756987,0.8689223668300499,0.8614131047232362,0.862908531317753,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16101063333333335 +Mean_Prediction,0.5223580732536224,0.5772518864256917,0.508650860733733,0.595097466806707,0.4754513615231286,0.5840777726169991,0.4611041829420449,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16101063333333335 +IQR,0.09124162229148948,0.10084392452988764,0.08884388764971195,0.09338211705424111,0.08986130323887394,0.09254294805407841,0.08995011785541067,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16101063333333335 +Std,0.06986874362680465,0.07569031436019155,0.06841507330166306,0.07076511810069319,0.0692907077511195,0.07041891005278857,0.06932272940026211,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16101063333333335 +Aleatoric_Uncertainty,0.8369293613974009,0.8491088226125131,0.8338880994845148,0.8247828216002686,0.8447621767805984,0.8349342332813541,0.8389094319427607,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16101063333333335 +Statistical_Bias,0.40555632087192,0.39860214012279876,0.4072928086092745,0.39507258366072,0.4123168616903574,0.3978565745929863,0.4131979558581825,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16101063333333335 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'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16101063333333335 +Jitter,0.11241110080395789,0.13552567946609934,0.10663929477116273,0.11512964606132318,0.11065802021743266,0.11488787149840932,0.10995302271852146,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16101063333333335 +TPR,0.6772823779193206,0.6,0.6919191919191919,0.564625850340136,0.7283950617283951,0.5851063829787234,0.7385159010600707,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 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100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16101063333333335 +FPR,0.26324786324786326,0.20588235294117646,0.2806236080178174,0.1947565543071161,0.32075471698113206,0.20118343195266272,0.3481781376518219,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16101063333333335 +Accuracy,0.7102272727272727,0.7251184834123223,0.7065088757396449,0.7198067632850241,0.7040498442367601,0.7224334600760456,0.6981132075471698,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16101063333333335 +F1,0.6758474576271186,0.6081081081081081,0.6884422110552764,0.5886524822695035,0.7129909365558912,0.6010928961748634,0.7231833910034602,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16101063333333335 +Selection-Rate,0.4479166666666667,0.3459715639810427,0.47337278106508873,0.32608695652173914,0.5264797507788161,0.33840304182509506,0.5566037735849056,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16101063333333335 +Sample_Size,1056.0,211.0,845.0,414.0,642.0,526.0,530.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16101063333333335 diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__212604/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20240601__212607.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__212604/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20240601__212607.csv new file mode 100644 index 00000000..15280fa6 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__212604/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20240601__212607.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins 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None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",42,0.17575463333333333 +IQR,0.06311619525888201,0.06303178932147004,0.06313727177106417,0.06049261641675147,0.06480803582062974,0.061164910745711384,0.06505275309648154,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",42,0.17575463333333333 +Std,0.047656238079071045,0.04753536731004715,0.04768642410635948,0.04584195464849472,0.0488261952996254,0.04615282639861107,0.04914829879999161,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 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'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",42,0.17575463333333333 +Statistical_Bias,0.4138804492046774,0.4111096437720326,0.4145723307979177,0.40894096200736824,0.4170657259954656,0.4091476917181858,0.4185774877667427,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",42,0.17575463333333333 +Epistemic_Uncertainty,0.009076709264506766,0.008969632336327416,0.009103314910850147,0.008480800941153444,0.009460943144404821,0.008552872382228571,0.009596401251248277,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': 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'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",42,0.17575463333333333 +Jitter,0.06837662337662333,0.09858593674436626,0.0608332327013646,0.07585724144730366,0.0635526734058108,0.07694886319546851,0.05986907970735486,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",42,0.17575463333333333 +TPR,0.6624203821656051,0.5333333333333333,0.6868686868686869,0.5578231292517006,0.7098765432098766,0.5691489361702128,0.7243816254416962,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 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None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",42,0.17575463333333333 +PPV,0.6680942184154176,0.5882352941176471,0.681704260651629,0.5942028985507246,0.6990881458966566,0.5977653631284916,0.7118055555555556,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",42,0.17575463333333333 +FNR,0.3375796178343949,0.4666666666666667,0.31313131313131315,0.4421768707482993,0.29012345679012347,0.4308510638297872,0.2756183745583039,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",42,0.17575463333333333 +FPR,0.26495726495726496,0.20588235294117646,0.2828507795100223,0.20973782771535582,0.3113207547169811,0.21301775147928995,0.3360323886639676,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': 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a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__212822/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20240601__212824.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__212822/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20240601__212824.csv new file mode 100644 index 00000000..fb2a7659 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__212822/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20240601__212824.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins +IQR,0.027530551760038283,0.026885306150093848,0.02769167226145636,0.02712688294929466,0.027790861553882305,0.027419161561839045,0.02764110127749639,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.04898075 +Overall_Uncertainty,0.9113463914637511,0.9197978513777793,0.9092360269171713,0.9163233844447729,0.9081369287002884,0.918766773125671,0.9039820126822984,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.04898075 +Std,0.022001522231657432,0.02202236209638192,0.02199631843111676,0.022079539514525234,0.02195121202120997,0.02218723058537651,0.021817215450419248,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 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42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.04898075 +Statistical_Bias,0.4349363283173577,0.4344937955506501,0.43504683058218047,0.43433306791288717,0.4353253467090255,0.4333038826946261,0.43655645359576667,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.04898075 +Epistemic_Uncertainty,0.00272322883327214,0.0024992964785682803,0.0027791456697719985,0.0024405656286100585,0.002905506974595906,0.0025205625487243477,0.002924365560954878,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 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None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16407118333333334 +Jitter,0.11241110080395789,0.13552567946609934,0.10663929477116273,0.11512964606132318,0.11065802021743266,0.11488787149840932,0.10995302271852146,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16407118333333334 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None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16407118333333334 +FNR,0.3227176220806794,0.4,0.30808080808080807,0.43537414965986393,0.2716049382716049,0.4148936170212766,0.26148409893992935,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16407118333333334 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'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16407118333333334 +Sample_Size,1056.0,211.0,845.0,414.0,642.0,526.0,530.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.16407118333333334 diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__212822/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20240601__212824.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__212822/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20240601__212824.csv new file mode 100644 index 00000000..fee52565 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__212822/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20240601__212824.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_dis,race_priv,race_dis,sex&race_priv,sex&race_dis,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins +IQR,0.06311619525888201,0.06303178932147004,0.06313727177106417,0.06049261641675147,0.06480803582062974,0.061164910745711384,0.06505275309648154,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 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'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}",42,0.16591475000000003 diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__213242/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20240601__213242.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__213242/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20240601__213242.csv new file mode 100644 index 00000000..e3611059 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__213242/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_50_Estimators_20240601__213242.csv @@ -0,0 +1,19 @@ 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'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",42,0.06431680000000001 +FNR,0.34394904458598724,0.5066666666666667,0.0,1.0,0.31313131313131315,0.0,1.0,0.48299319727891155,0.0,1.0,0.2808641975308642,0.0,1.0,0.4521276595744681,0.0,1.0,0.27208480565371024,0.0,1.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",42,0.06431680000000001 +FPR,0.26666666666666666,0.19117647058823528,0.0,1.0,0.289532293986637,0.0,1.0,0.20973782771535582,0.0,1.0,0.31446540880503143,0.0,1.0,0.20710059171597633,0.0,1.0,0.3481781376518219,0.0,1.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",42,0.06431680000000001 +Accuracy,0.6988636363636364,0.6966824644549763,1.0,0.0,0.6994082840236686,1.0,0.0,0.6932367149758454,1.0,0.0,0.7024922118380063,1.0,0.0,0.7053231939163498,1.0,0.0,0.6924528301886792,1.0,0.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",42,0.06431680000000001 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0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",42,0.06431680000000001 +Sample_Size,1056.0,211.0,147.0,64.0,845.0,591.0,254.0,414.0,287.0,127.0,642.0,451.0,191.0,526.0,371.0,155.0,530.0,367.0,163.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",42,0.06431680000000001 diff --git a/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__213242/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20240601__213242.csv b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__213242/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20240601__213242.csv new file mode 100644 index 00000000..b7ab7a30 --- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__213242/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20240601__213242.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_priv_correct,sex_priv_incorrect,sex_dis,sex_dis_correct,sex_dis_incorrect,race_priv,race_priv_correct,race_priv_incorrect,race_dis,race_dis_correct,race_dis_incorrect,sex&race_priv,sex&race_priv_correct,sex&race_priv_incorrect,sex&race_dis,sex&race_dis_correct,sex&race_dis_incorrect,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins 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+Aleatoric_Uncertainty,0.9123450427125207,0.9206250776414039,0.9087794673951712,0.9472321406560185,0.910277483694776,0.9011401638416987,0.929949996960543,0.9177452762926515,0.909632087577234,0.9352722412274845,0.908862649095427,0.8982131727090862,0.9320595283528033,0.9198895585007832,0.911255981277903,0.9394625751861952,0.9048574666660564,0.8939420212567616,0.9275768239714491,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.07079771666666666 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+Std,0.02159601509099498,0.02160183879099128,0.020988968179904716,0.022978440471278012,0.021594560888984066,0.021467455990035054,0.021868215839333252,0.02164960109403705,0.020406011260539973,0.0243361348564773,0.02156145963108934,0.02199138698587159,0.02062498420483097,0.021757533210174453,0.020903418727786727,0.023693879707513094,0.02143571597648855,0.021847384427022786,0.020578871178283574,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.07079771666666666 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--- /dev/null +++ b/docs/examples/results/COMPAS_Without_Sensitive_Attributes_Metrics_20240601__213242/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20240601__213242.csv @@ -0,0 +1,19 @@ +Metric,overall,sex_priv,sex_priv_correct,sex_priv_incorrect,sex_dis,sex_dis_correct,sex_dis_incorrect,race_priv,race_priv_correct,race_priv_incorrect,race_dis,race_dis_correct,race_dis_incorrect,sex&race_priv,sex&race_priv_correct,sex&race_priv_incorrect,sex&race_dis,sex&race_dis_correct,sex&race_dis_incorrect,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins +Statistical_Bias,0.4076823270703299,0.40689025602140133,0.3221053810741164,0.6253190863940675,0.407880110492015,0.30014967709553964,0.6628278293665424,0.4011794889462743,0.30171857191168744,0.6597778732362001,0.41187574604752464,0.30656615909714025,0.6532777222876366,0.40332170165791936,0.30787774893994396,0.6517374690060747,0.4120100421022694,0.30124421180556704,0.6592069560571051,XGBClassifier,"{'objective': 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'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.30198443333333336 +TNR,0.0763888888888889,0.06880733944954129,1.0,0.0,0.08411214953271028,1.0,0.0,0.12547528517110265,1.0,0.0,0.0,,0.0,0.0953757225433526,1.0,0.0,0.0,,0.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.30198443333333336 +PPV,0.9028724440116845,0.9133219470538002,1.0,0.0,0.8890147225368064,1.0,0.0,0.9337938975244675,1.0,0.0,0.7334384858044164,1.0,0.0,0.91715193223928,1.0,0.0,0.7393939393939394,1.0,0.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 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'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.30198443333333336 +Accuracy,0.8995192307692308,0.9096283783783784,1.0,0.0,0.8861607142857143,1.0,0.0,0.9293817356778219,1.0,0.0,0.7334384858044164,1.0,0.0,0.9133159268929504,1.0,0.0,0.7393939393939394,1.0,0.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.30198443333333336 +F1,0.9466564573762124,0.9523597506678539,1.0,0.0,0.9389952153110048,1.0,0.0,0.9630399287516699,1.0,0.0,0.8462238398544131,1.0,0.0,0.9542825667860093,1.0,0.0,0.8501742160278746,1.0,0.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.30198443333333336 +Selection-Rate,0.9875,0.9890202702702703,0.9930362116991643,0.9485981308411215,0.9854910714285714,0.9886649874055415,0.9607843137254902,0.9852524106636416,0.9899298138541349,0.9236947791164659,1.0,1.0,1.0,0.9864229765013055,0.9905660377358491,0.9427710843373494,1.0,1.0,1.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.30198443333333336 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'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.29245715 diff --git a/docs/examples/results/Law_School_Metrics_20240423__091846/Metrics_Law_School_RandomForestClassifier_50_Estimators_20240423__091850.csv b/docs/examples/results/Law_School_Metrics_20240423__091846/Metrics_Law_School_RandomForestClassifier_50_Estimators_20240423__091850.csv new file mode 100644 index 00000000..52abb070 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20240423__091846/Metrics_Law_School_RandomForestClassifier_50_Estimators_20240423__091850.csv @@ -0,0 +1,12 @@ 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'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.7223406 +PPV,0.9084870848708487,0.9165950128976784,1.0,0.0,0.8976423231742381,1.0,0.0,0.9321253954558527,1.0,0.0,0.7687074829931972,1.0,0.0,0.9184378320935175,1.0,0.0,0.7840531561461794,1.0,0.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.7223406 +FNR,0.009388412017167383,0.008372093023255815,0.0,1.0,0.010773130544993664,0.0,1.0,0.006742261722341404,0.0,1.0,0.02795698924731183,0.0,1.0,0.007749712973593571,0.0,1.0,0.03278688524590164,0.0,1.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.7223406 +FPR,0.8611111111111112,0.8899082568807339,0.0,1.0,0.8317757009345794,0.0,1.0,0.8973384030418251,0.0,1.0,0.8047337278106509,0.0,1.0,0.8872832369942196,0.0,1.0,0.7558139534883721,0.0,1.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.7223406 +Accuracy,0.9021634615384615,0.910472972972973,1.0,0.0,0.8911830357142857,1.0,0.0,0.926829268292683,1.0,0.0,0.7649842271293376,1.0,0.0,0.9127937336814621,1.0,0.0,0.7787878787878788,1.0,0.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.7223406 +F1,0.9477736430129603,0.9526362823949955,1.0,0.0,0.9412119384986434,1.0,0.0,0.9617210682492582,1.0,0.0,0.8584995251661919,1.0,0.0,0.9539183222958058,1.0,0.0,0.8660550458715597,1.0,0.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.7223406 +Selection-Rate,0.9771634615384616,0.9822635135135135,0.9888682745825603,0.9150943396226415,0.9704241071428571,0.9774577332498434,0.9128205128205128,0.9861032331253545,0.9917380660954712,0.9147286821705426,0.9274447949526814,0.931958762886598,0.912751677852349,0.9827676240208877,0.9888443935926774,0.9191616766467066,0.9121212121212121,0.9182879377431906,0.8904109589041096,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.7223406 +Sample_Size,4160.0,2368.0,2156.0,212.0,1792.0,1597.0,195.0,3526.0,3268.0,258.0,634.0,485.0,149.0,3830.0,3496.0,334.0,330.0,257.0,73.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.7223406 diff --git a/docs/examples/results/Law_School_Metrics_20240423__093819/Metrics_Law_School_ExponentiatedGradientReduction_30_Estimators_20240423__093821.csv b/docs/examples/results/Law_School_Metrics_20240423__093819/Metrics_Law_School_ExponentiatedGradientReduction_30_Estimators_20240423__093821.csv new file mode 100644 index 00000000..35378f10 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20240423__093819/Metrics_Law_School_ExponentiatedGradientReduction_30_Estimators_20240423__093821.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_priv_correct,male_priv_incorrect,male_dis,male_dis_correct,male_dis_incorrect,race_priv,race_priv_correct,race_priv_incorrect,race_dis,race_dis_correct,race_dis_incorrect,male&race_priv,male&race_priv_correct,male&race_priv_incorrect,male&race_dis,male&race_dis_correct,male&race_dis_incorrect,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins +Std,0.00961542953427722,0.00886838407735902,0.006229151033667494,0.03627580414646332,0.010602596745204843,0.005570702648559963,0.05111188558385099,0.008154987056425726,0.005328218511346041,0.0455184841966322,0.017737701106681592,0.010228499946079926,0.04036035776773471,0.008561268780545379,0.005557576778481297,0.04084635091316056,0.021850083130619504,0.011443668890911632,0.05437012762970661,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5360479333333332 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'race_binary'},42,1.5360479333333332 +Aleatoric_Uncertainty,0.005905253454270866,0.004883117237352727,0.0032960511709477064,0.021364187926943338,0.007255933455198404,0.0036038997472324524,0.03665664926579297,0.0031053028589085765,0.0020106752907160593,0.017573807570743543,0.02147721843731098,0.013178767065899861,0.04647761623979004,0.0042316827441307835,0.0026668121999192567,0.02105164098620806,0.02532881654468454,0.014078307111274002,0.060486658524092476,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5360479333333332 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'race_binary'},42,1.5360479333333332 +Epistemic_Uncertainty,0.014263888061297685,0.013401861994890064,0.009649880563715426,0.05236474608785743,0.015402993934764896,0.008075962675953594,0.07438929608903366,0.012759655219653242,0.008434872401965823,0.06992351843086032,0.02262970036356629,0.012746459641704585,0.05240452684208637,0.013092335193312302,0.008619043325217974,0.06117336189822804,0.027861001650340122,0.014062794846126287,0.07098039791350834,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5360479333333332 +Label_Stability,0.9890865384615386,0.9896959459459459,0.9922222222222221,0.9634615384615385,0.98828125,0.9939355918025932,0.9427609427609427,0.9907543959160521,0.9935936546674802,0.9532258064516128,0.9798107255520505,0.9885154061624649,0.9535864978902953,0.9903046127067016,0.9933409436834095,0.9576687116564419,0.974949494949495,0.9874666666666667,0.9358333333333334,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5360479333333332 +Jitter,0.008197944297082226,0.007552811432121774,0.005555555555555551,0.028293545534924854,0.009050441297208537,0.004642409033877038,0.044537327295948115,0.006719867519445048,0.004547909083896119,0.035428253615128004,0.016418289278073928,0.009436878199555661,0.037450894805761616,0.007149245220731671,0.004801081194562532,0.03238840702348209,0.020369209334726596,0.010308045977011484,0.05181034482758617,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5360479333333332 +TPR,0.9906115879828327,0.9911627906976744,1.0,0.0,0.9898605830164765,1.0,0.0,0.9908060067422617,1.0,0.0,0.989247311827957,1.0,0.0,0.9908151549942594,1.0,0.0,0.9877049180327869,1.0,0.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5360479333333332 +TNR,0.1412037037037037,0.13302752293577982,1.0,0.0,0.14953271028037382,1.0,0.0,0.17110266159695817,1.0,0.0,0.09467455621301775,1.0,0.0,0.15028901734104047,1.0,0.0,0.10465116279069768,1.0,0.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5360479333333332 +PPV,0.9087106299212598,0.9185344827586207,1.0,0.0,0.8956422018348624,1.0,0.0,0.9368299043755434,1.0,0.0,0.7504078303425775,1.0,0.0,0.9215162840363054,1.0,0.0,0.7578616352201258,1.0,0.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5360479333333332 +FNR,0.009388412017167383,0.008837209302325582,0.0,1.0,0.010139416983523447,0.0,1.0,0.009193993257738278,0.0,1.0,0.010752688172043012,0.0,1.0,0.009184845005740528,0.0,1.0,0.012295081967213115,0.0,1.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5360479333333332 +FPR,0.8587962962962963,0.8669724770642202,0.0,1.0,0.8504672897196262,0.0,1.0,0.8288973384030418,0.0,1.0,0.9053254437869822,0.0,1.0,0.8497109826589595,0.0,1.0,0.8953488372093024,0.0,1.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5360479333333332 +Accuracy,0.9024038461538462,0.9121621621621622,1.0,0.0,0.8895089285714286,1.0,0.0,0.9296653431650596,1.0,0.0,0.750788643533123,1.0,0.0,0.9148825065274151,1.0,0.0,0.7575757575757576,1.0,0.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5360479333333332 +F1,0.9478952772073922,0.9534675615212528,1.0,0.0,0.9403973509933775,1.0,0.0,0.9630622579684242,1.0,0.0,0.8534322820037106,1.0,0.0,0.9549100968188106,1.0,0.0,0.8576512455516014,1.0,0.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5360479333333332 +Selection-Rate,0.9769230769230769,0.9797297297297297,0.986574074074074,0.9086538461538461,0.9732142857142857,0.9799247176913425,0.9191919191919192,0.9787294384571753,0.9862721171446004,0.8790322580645161,0.9668769716088328,0.9663865546218487,0.9683544303797469,0.9780678851174934,0.9851598173515982,0.901840490797546,0.9636363636363636,0.964,0.9625,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5360479333333332 +Sample_Size,4160.0,2368.0,2160.0,208.0,1792.0,1594.0,198.0,3526.0,3278.0,248.0,634.0,476.0,158.0,3830.0,3504.0,326.0,330.0,250.0,80.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5360479333333332 diff --git a/docs/examples/results/Law_School_Metrics_20240423__094322/Metrics_Law_School_ExponentiatedGradientReduction_30_Estimators_20240423__094324.csv b/docs/examples/results/Law_School_Metrics_20240423__094322/Metrics_Law_School_ExponentiatedGradientReduction_30_Estimators_20240423__094324.csv new file mode 100644 index 00000000..7be8a44d --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20240423__094322/Metrics_Law_School_ExponentiatedGradientReduction_30_Estimators_20240423__094324.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_priv_correct,male_priv_incorrect,male_dis,male_dis_correct,male_dis_incorrect,race_priv,race_priv_correct,race_priv_incorrect,race_dis,race_dis_correct,race_dis_incorrect,male&race_priv,male&race_priv_correct,male&race_priv_incorrect,male&race_dis,male&race_dis_correct,male&race_dis_incorrect,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins +IQR,0.010354520128125464,0.009921698904014458,0.008073449653730618,0.029115056503115848,0.01092646245998644,0.005680893019413271,0.053155945734095676,0.008797110899311676,0.006598295886871602,0.03786048029761233,0.019016073662506256,0.01022012774755837,0.0455152524948809,0.009357235389569403,0.0068477668440104205,0.03633017337619111,0.021929067245306418,0.009997678814361526,0.059214656092009196,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5821633833333335 +Mean_Prediction,0.024633128054093834,0.021841896627271318,0.015440315002686522,0.08831985965180576,0.028321541010966438,0.020068350049003404,0.09476389653303245,0.022746787320570932,0.015406140232573586,0.11977356616918097,0.03512403882128903,0.031173745171298537,0.04702492348834901,0.023373170358129913,0.01630878110397611,0.09930451988743945,0.03925627343452361,0.032776245582328105,0.059506360472634565,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5821633833333335 +Aleatoric_Uncertainty,0.005905253454270866,0.004883117237352727,0.0032960511709477064,0.021364187926943338,0.007255933455198404,0.0036038997472324524,0.03665664926579297,0.0031053028589085765,0.0020106752907160593,0.017573807570743543,0.02147721843731098,0.013178767065899861,0.04647761623979004,0.0042316827441307835,0.0026668121999192567,0.02105164098620806,0.02532881654468454,0.014078307111274002,0.060486658524092476,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5821633833333335 +Overall_Uncertainty,0.02016914151556855,0.01828497923224279,0.012945931734663133,0.07372893401480077,0.0226589273899633,0.011679862423186046,0.11104594535482663,0.01586495807856182,0.010445547692681882,0.08749732600160387,0.04410691880087727,0.025925226707604446,0.09888214308187641,0.017324017937443086,0.01128585552513723,0.0822250028844361,0.05318981819502466,0.02814110195740029,0.13146705643760082,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5821633833333335 +Statistical_Bias,0.09845814659649249,0.08984659238260745,0.004210380698012953,0.9791457137226275,0.10983770037912634,0.0033171171757175313,0.9673821934409126,0.07169236453284282,0.003403908917848952,0.9743115480245766,0.24731642349937702,0.006772885825825331,0.9719918914272921,0.08637384069279111,0.0035991232174785726,0.9760750984642491,0.23870933329702665,0.007082317327027217,0.9625437582032749,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5821633833333335 +Std,0.00961542953427722,0.00886838407735902,0.006229151033667494,0.03627580414646332,0.010602596745204843,0.005570702648559963,0.05111188558385099,0.008154987056425726,0.005328218511346041,0.0455184841966322,0.017737701106681592,0.010228499946079926,0.04036035776773471,0.008561268780545379,0.005557576778481297,0.04084635091316056,0.021850083130619504,0.011443668890911632,0.05437012762970661,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5821633833333335 +Epistemic_Uncertainty,0.014263888061297685,0.013401861994890064,0.009649880563715426,0.05236474608785743,0.015402993934764896,0.008075962675953594,0.07438929608903366,0.012759655219653242,0.008434872401965823,0.06992351843086032,0.02262970036356629,0.012746459641704585,0.05240452684208637,0.013092335193312302,0.008619043325217974,0.06117336189822804,0.027861001650340122,0.014062794846126287,0.07098039791350834,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5821633833333335 +Jitter,0.008197944297082226,0.007552811432121774,0.005555555555555551,0.028293545534924854,0.009050441297208537,0.004642409033877038,0.044537327295948115,0.006719867519445048,0.004547909083896119,0.035428253615128004,0.016418289278073928,0.009436878199555661,0.037450894805761616,0.007149245220731671,0.004801081194562532,0.03238840702348209,0.020369209334726596,0.010308045977011484,0.05181034482758617,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5821633833333335 +Label_Stability,0.9890865384615386,0.9896959459459459,0.9922222222222221,0.9634615384615385,0.98828125,0.9939355918025932,0.9427609427609427,0.9907543959160521,0.9935936546674802,0.9532258064516128,0.9798107255520505,0.9885154061624649,0.9535864978902953,0.9903046127067016,0.9933409436834095,0.9576687116564419,0.974949494949495,0.9874666666666667,0.9358333333333334,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5821633833333335 +TPR,0.9906115879828327,0.9911627906976744,1.0,0.0,0.9898605830164765,1.0,0.0,0.9908060067422617,1.0,0.0,0.989247311827957,1.0,0.0,0.9908151549942594,1.0,0.0,0.9877049180327869,1.0,0.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5821633833333335 +TNR,0.1412037037037037,0.13302752293577982,1.0,0.0,0.14953271028037382,1.0,0.0,0.17110266159695817,1.0,0.0,0.09467455621301775,1.0,0.0,0.15028901734104047,1.0,0.0,0.10465116279069768,1.0,0.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5821633833333335 +PPV,0.9087106299212598,0.9185344827586207,1.0,0.0,0.8956422018348624,1.0,0.0,0.9368299043755434,1.0,0.0,0.7504078303425775,1.0,0.0,0.9215162840363054,1.0,0.0,0.7578616352201258,1.0,0.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5821633833333335 +FNR,0.009388412017167383,0.008837209302325582,0.0,1.0,0.010139416983523447,0.0,1.0,0.009193993257738278,0.0,1.0,0.010752688172043012,0.0,1.0,0.009184845005740528,0.0,1.0,0.012295081967213115,0.0,1.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5821633833333335 +FPR,0.8587962962962963,0.8669724770642202,0.0,1.0,0.8504672897196262,0.0,1.0,0.8288973384030418,0.0,1.0,0.9053254437869822,0.0,1.0,0.8497109826589595,0.0,1.0,0.8953488372093024,0.0,1.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5821633833333335 +Accuracy,0.9024038461538462,0.9121621621621622,1.0,0.0,0.8895089285714286,1.0,0.0,0.9296653431650596,1.0,0.0,0.750788643533123,1.0,0.0,0.9148825065274151,1.0,0.0,0.7575757575757576,1.0,0.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5821633833333335 +F1,0.9478952772073922,0.9534675615212528,1.0,0.0,0.9403973509933775,1.0,0.0,0.9630622579684242,1.0,0.0,0.8534322820037106,1.0,0.0,0.9549100968188106,1.0,0.0,0.8576512455516014,1.0,0.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5821633833333335 +Selection-Rate,0.9769230769230769,0.9797297297297297,0.986574074074074,0.9086538461538461,0.9732142857142857,0.9799247176913425,0.9191919191919192,0.9787294384571753,0.9862721171446004,0.8790322580645161,0.9668769716088328,0.9663865546218487,0.9683544303797469,0.9780678851174934,0.9851598173515982,0.901840490797546,0.9636363636363636,0.964,0.9625,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5821633833333335 +Sample_Size,4160.0,2368.0,2160.0,208.0,1792.0,1594.0,198.0,3526.0,3278.0,248.0,634.0,476.0,158.0,3830.0,3504.0,326.0,330.0,250.0,80.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.5821633833333335 diff --git a/docs/examples/results/Law_School_Metrics_20240601__213333/Metrics_Law_School_ExponentiatedGradientReduction_30_Estimators_20240601__213345.csv b/docs/examples/results/Law_School_Metrics_20240601__213333/Metrics_Law_School_ExponentiatedGradientReduction_30_Estimators_20240601__213345.csv new file mode 100644 index 00000000..fc3d0031 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20240601__213333/Metrics_Law_School_ExponentiatedGradientReduction_30_Estimators_20240601__213345.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_priv_correct,male_priv_incorrect,male_dis,male_dis_correct,male_dis_incorrect,race_priv,race_priv_correct,race_priv_incorrect,race_dis,race_dis_correct,race_dis_incorrect,male&race_priv,male&race_priv_correct,male&race_priv_incorrect,male&race_dis,male&race_dis_correct,male&race_dis_incorrect,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins +Aleatoric_Uncertainty,0.005905253454270866,0.004883117237352727,0.0032960511709477064,0.021364187926943338,0.007255933455198404,0.0036038997472324524,0.03665664926579297,0.0031053028589085765,0.0020106752907160593,0.017573807570743543,0.02147721843731098,0.013178767065899861,0.04647761623979004,0.0042316827441307835,0.0026668121999192567,0.02105164098620806,0.02532881654468454,0.014078307111274002,0.060486658524092476,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.4298639 +IQR,0.010354520128125464,0.009921698904014458,0.008073449653730618,0.029115056503115848,0.01092646245998644,0.005680893019413271,0.053155945734095676,0.008797110899311676,0.006598295886871602,0.03786048029761233,0.019016073662506256,0.01022012774755837,0.0455152524948809,0.009357235389569403,0.0068477668440104205,0.03633017337619111,0.021929067245306418,0.009997678814361526,0.059214656092009196,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.4298639 +Mean_Prediction,0.024633128054093834,0.021841896627271318,0.015440315002686522,0.08831985965180576,0.028321541010966438,0.020068350049003404,0.09476389653303245,0.022746787320570932,0.015406140232573586,0.11977356616918097,0.03512403882128903,0.031173745171298537,0.04702492348834901,0.023373170358129913,0.01630878110397611,0.09930451988743945,0.03925627343452361,0.032776245582328105,0.059506360472634565,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.4298639 +Overall_Uncertainty,0.02016914151556855,0.01828497923224279,0.012945931734663133,0.07372893401480077,0.0226589273899633,0.011679862423186046,0.11104594535482663,0.01586495807856182,0.010445547692681882,0.08749732600160387,0.04410691880087727,0.025925226707604446,0.09888214308187641,0.017324017937443086,0.01128585552513723,0.0822250028844361,0.05318981819502466,0.02814110195740029,0.13146705643760082,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.4298639 +Statistical_Bias,0.09845814659649249,0.08984659238260745,0.004210380698012953,0.9791457137226275,0.10983770037912634,0.0033171171757175313,0.9673821934409126,0.07169236453284282,0.003403908917848952,0.9743115480245766,0.24731642349937702,0.006772885825825331,0.9719918914272921,0.08637384069279111,0.0035991232174785726,0.9760750984642491,0.23870933329702665,0.007082317327027217,0.9625437582032749,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.4298639 +Std,0.00961542953427722,0.00886838407735902,0.006229151033667494,0.03627580414646332,0.010602596745204843,0.005570702648559963,0.05111188558385099,0.008154987056425726,0.005328218511346041,0.0455184841966322,0.017737701106681592,0.010228499946079926,0.04036035776773471,0.008561268780545379,0.005557576778481297,0.04084635091316056,0.021850083130619504,0.011443668890911632,0.05437012762970661,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.4298639 +Epistemic_Uncertainty,0.014263888061297685,0.013401861994890064,0.009649880563715426,0.05236474608785743,0.015402993934764896,0.008075962675953594,0.07438929608903366,0.012759655219653242,0.008434872401965823,0.06992351843086032,0.02262970036356629,0.012746459641704585,0.05240452684208637,0.013092335193312302,0.008619043325217974,0.06117336189822804,0.027861001650340122,0.014062794846126287,0.07098039791350834,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.4298639 +Label_Stability,0.9890865384615386,0.9896959459459459,0.9922222222222221,0.9634615384615385,0.98828125,0.9939355918025932,0.9427609427609427,0.9907543959160521,0.9935936546674802,0.9532258064516128,0.9798107255520505,0.9885154061624649,0.9535864978902953,0.9903046127067016,0.9933409436834095,0.9576687116564419,0.974949494949495,0.9874666666666667,0.9358333333333334,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.4298639 +Jitter,0.008197944297082226,0.007552811432121774,0.005555555555555551,0.028293545534924854,0.009050441297208537,0.004642409033877038,0.044537327295948115,0.006719867519445048,0.004547909083896119,0.035428253615128004,0.016418289278073928,0.009436878199555661,0.037450894805761616,0.007149245220731671,0.004801081194562532,0.03238840702348209,0.020369209334726596,0.010308045977011484,0.05181034482758617,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.4298639 +TPR,0.9906115879828327,0.9911627906976744,1.0,0.0,0.9898605830164765,1.0,0.0,0.9908060067422617,1.0,0.0,0.989247311827957,1.0,0.0,0.9908151549942594,1.0,0.0,0.9877049180327869,1.0,0.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.4298639 +TNR,0.1412037037037037,0.13302752293577982,1.0,0.0,0.14953271028037382,1.0,0.0,0.17110266159695817,1.0,0.0,0.09467455621301775,1.0,0.0,0.15028901734104047,1.0,0.0,0.10465116279069768,1.0,0.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.4298639 +PPV,0.9087106299212598,0.9185344827586207,1.0,0.0,0.8956422018348624,1.0,0.0,0.9368299043755434,1.0,0.0,0.7504078303425775,1.0,0.0,0.9215162840363054,1.0,0.0,0.7578616352201258,1.0,0.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.4298639 +FNR,0.009388412017167383,0.008837209302325582,0.0,1.0,0.010139416983523447,0.0,1.0,0.009193993257738278,0.0,1.0,0.010752688172043012,0.0,1.0,0.009184845005740528,0.0,1.0,0.012295081967213115,0.0,1.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.4298639 +FPR,0.8587962962962963,0.8669724770642202,0.0,1.0,0.8504672897196262,0.0,1.0,0.8288973384030418,0.0,1.0,0.9053254437869822,0.0,1.0,0.8497109826589595,0.0,1.0,0.8953488372093024,0.0,1.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.4298639 +Accuracy,0.9024038461538462,0.9121621621621622,1.0,0.0,0.8895089285714286,1.0,0.0,0.9296653431650596,1.0,0.0,0.750788643533123,1.0,0.0,0.9148825065274151,1.0,0.0,0.7575757575757576,1.0,0.0,ExponentiatedGradientReduction,{'sensitive_attr_for_intervention': 'race_binary'},42,1.4298639 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a/docs/examples/results/Law_School_Metrics_20240601__213552/Metrics_Law_School_LogisticRegression_50_Estimators_20240601__213556.csv b/docs/examples/results/Law_School_Metrics_20240601__213552/Metrics_Law_School_LogisticRegression_50_Estimators_20240601__213556.csv new file mode 100644 index 00000000..aaebfab3 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20240601__213552/Metrics_Law_School_LogisticRegression_50_Estimators_20240601__213556.csv @@ -0,0 +1,12 @@ +Metric,overall,male_priv,male_priv_correct,male_priv_incorrect,male_dis,male_dis_correct,male_dis_incorrect,race_priv,race_priv_correct,race_priv_incorrect,race_dis,race_dis_correct,race_dis_incorrect,male&race_priv,male&race_priv_correct,male&race_priv_incorrect,male&race_dis,male&race_dis_correct,male&race_dis_incorrect,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins 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'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.30004178333333337 +TNR,0.0787037037037037,0.07339449541284404,1.0,0.0,0.08411214953271028,1.0,0.0,0.12927756653992395,1.0,0.0,0.0,,0.0,0.09826589595375723,1.0,0.0,0.0,,0.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.30004178333333337 +PPV,0.9030922814706599,0.9137120888509184,1.0,0.0,0.8890147225368064,1.0,0.0,0.9340627699395335,1.0,0.0,0.7334384858044164,1.0,0.0,0.9173947577442415,1.0,0.0,0.7393939393939394,1.0,0.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.30004178333333337 +FNR,0.00509656652360515,0.0051162790697674414,0.0,1.0,0.005069708491761723,0.0,1.0,0.0058228623965675755,0.0,1.0,0.0,0.0,,0.0054535017221584384,0.0,1.0,0.0,0.0,,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.30004178333333337 +FPR,0.9212962962962963,0.926605504587156,0.0,1.0,0.9158878504672897,0.0,1.0,0.870722433460076,0.0,1.0,1.0,,1.0,0.9017341040462428,0.0,1.0,1.0,,1.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.30004178333333337 +Accuracy,0.8997596153846154,0.9100506756756757,1.0,0.0,0.8861607142857143,1.0,0.0,0.9296653431650596,1.0,0.0,0.7334384858044164,1.0,0.0,0.9135770234986945,1.0,0.0,0.7393939393939394,1.0,0.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.30004178333333337 +F1,0.9467772814294831,0.9525718102872411,1.0,0.0,0.9389952153110048,1.0,0.0,0.9631828978622328,1.0,0.0,0.8462238398544131,1.0,0.0,0.9544139925630079,1.0,0.0,0.8501742160278746,1.0,0.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.30004178333333337 +Selection-Rate,0.9872596153846154,0.988597972972973,0.9925754060324826,0.9483568075117371,0.9854910714285714,0.9886649874055415,0.9607843137254902,0.9849688031764039,0.9896278218425869,0.9233870967741935,1.0,1.0,1.0,0.9861618798955614,0.9902829379822806,0.9425981873111783,1.0,1.0,1.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.30004178333333337 +Sample_Size,4160.0,2368.0,2155.0,213.0,1792.0,1588.0,204.0,3526.0,3278.0,248.0,634.0,465.0,169.0,3830.0,3499.0,331.0,330.0,244.0,86.0,LogisticRegression,"{'C': 100, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",42,0.30004178333333337 diff --git a/docs/examples/results/Law_School_Metrics_20240601__213552/Metrics_Law_School_RandomForestClassifier_50_Estimators_20240601__213556.csv b/docs/examples/results/Law_School_Metrics_20240601__213552/Metrics_Law_School_RandomForestClassifier_50_Estimators_20240601__213556.csv new file mode 100644 index 00000000..32624189 --- /dev/null +++ b/docs/examples/results/Law_School_Metrics_20240601__213552/Metrics_Law_School_RandomForestClassifier_50_Estimators_20240601__213556.csv @@ -0,0 +1,12 @@ +Metric,overall,male_priv,male_priv_correct,male_priv_incorrect,male_dis,male_dis_correct,male_dis_incorrect,race_priv,race_priv_correct,race_priv_incorrect,race_dis,race_dis_correct,race_dis_incorrect,male&race_priv,male&race_priv_correct,male&race_priv_incorrect,male&race_dis,male&race_dis_correct,male&race_dis_incorrect,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins +Jitter,0.04164815541601258,0.03742657198014336,0.026669818993399014,0.14796501457725938,0.04722667638483967,0.03176410413789578,0.1731361932528105,0.027340919351291255,0.020098074124048182,0.11870144196674748,0.12121805188952538,0.08745086535640957,0.23308621407746874,0.0336026003090531,0.02379275530629749,0.13662070233498827,0.13502535559678447,0.09745414118955077,0.2672966172770463,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.7193573666666667 +Label_Stability,0.9424519230769232,0.9499493243243243,0.9661353104726599,0.7836190476190477,0.9325446428571429,0.9568922305764411,0.7342857142857143,0.9652750992626207,0.9764921946740128,0.8237837837837838,0.8155205047318611,0.8663655030800822,0.6470748299319729,0.9556657963446475,0.9704661138118387,0.8002402402402403,0.7890909090909092,0.8498054474708171,0.5753424657534246,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.7193573666666667 +TPR,0.990343347639485,0.9920930232558139,1.0,0.0,0.9879594423320659,1.0,0.0,0.992951271835734,1.0,0.0,0.9720430107526882,1.0,0.0,0.9922502870264064,1.0,0.0,0.9631147540983607,1.0,0.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.7193573666666667 +TNR,0.14351851851851852,0.11467889908256881,1.0,0.0,0.17289719626168223,1.0,0.0,0.10266159695817491,1.0,0.0,0.20710059171597633,1.0,0.0,0.11560693641618497,1.0,0.0,0.2558139534883721,1.0,0.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.7193573666666667 +PPV,0.9089118660758247,0.9170249355116079,1.0,0.0,0.8980414746543779,1.0,0.0,0.9321058688147296,1.0,0.0,0.7713310580204779,1.0,0.0,0.9186819027371778,1.0,0.0,0.7859531772575251,1.0,0.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.7193573666666667 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None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.7193573666666667 +Accuracy,0.9024038461538462,0.9113175675675675,1.0,0.0,0.890625,1.0,0.0,0.9265456608054453,1.0,0.0,0.7681388012618297,1.0,0.0,0.9130548302872062,1.0,0.0,0.7787878787878788,1.0,0.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.7193573666666667 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+Selection-Rate,0.9764423076923077,0.9822635135135135,0.9884151992585728,0.919047619047619,0.96875,0.9768170426065163,0.9030612244897959,0.9858196256381169,0.9917355371900827,0.9111969111969112,0.9242902208201893,0.9281314168377823,0.9115646258503401,0.9825065274151435,0.9885616242493566,0.918918918918919,0.906060606060606,0.914396887159533,0.8767123287671232,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.7193573666666667 +Sample_Size,4160.0,2368.0,2158.0,210.0,1792.0,1596.0,196.0,3526.0,3267.0,259.0,634.0,487.0,147.0,3830.0,3497.0,333.0,330.0,257.0,73.0,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}",42,0.7193573666666667 diff --git a/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20240423__094754.csv b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20240423__094754.csv new file mode 100644 index 00000000..aa620af4 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20240423__094754.csv @@ -0,0 +1,5 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +COMPAS_Without_Sensitive_Attributes,DecisionTreeClassifier,0.6555,0.6575,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" +COMPAS_Without_Sensitive_Attributes,LogisticRegression,0.6484,0.6521,"{'C': 1, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +COMPAS_Without_Sensitive_Attributes,RandomForestClassifier,0.6569,0.6587,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 100}" +COMPAS_Without_Sensitive_Attributes,XGBClassifier,0.6624,0.6646,"{'lambda': 100, 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}" diff --git a/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20240423__094939.csv b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20240423__094939.csv new file mode 100644 index 00000000..aa620af4 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20240423__094939.csv @@ -0,0 +1,5 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +COMPAS_Without_Sensitive_Attributes,DecisionTreeClassifier,0.6555,0.6575,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" +COMPAS_Without_Sensitive_Attributes,LogisticRegression,0.6484,0.6521,"{'C': 1, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +COMPAS_Without_Sensitive_Attributes,RandomForestClassifier,0.6569,0.6587,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 100}" +COMPAS_Without_Sensitive_Attributes,XGBClassifier,0.6624,0.6646,"{'lambda': 100, 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}" diff --git a/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20240423__095337.csv b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20240423__095337.csv new file mode 100644 index 00000000..aa620af4 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20240423__095337.csv @@ -0,0 +1,5 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +COMPAS_Without_Sensitive_Attributes,DecisionTreeClassifier,0.6555,0.6575,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" +COMPAS_Without_Sensitive_Attributes,LogisticRegression,0.6484,0.6521,"{'C': 1, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +COMPAS_Without_Sensitive_Attributes,RandomForestClassifier,0.6569,0.6587,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 100}" +COMPAS_Without_Sensitive_Attributes,XGBClassifier,0.6624,0.6646,"{'lambda': 100, 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}" diff --git a/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20240601__212607.csv b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20240601__212607.csv new file mode 100644 index 00000000..aa620af4 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20240601__212607.csv @@ -0,0 +1,5 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +COMPAS_Without_Sensitive_Attributes,DecisionTreeClassifier,0.6555,0.6575,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" +COMPAS_Without_Sensitive_Attributes,LogisticRegression,0.6484,0.6521,"{'C': 1, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +COMPAS_Without_Sensitive_Attributes,RandomForestClassifier,0.6569,0.6587,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 100}" +COMPAS_Without_Sensitive_Attributes,XGBClassifier,0.6624,0.6646,"{'lambda': 100, 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}" diff --git a/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20240601__212824.csv b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20240601__212824.csv new file mode 100644 index 00000000..aa620af4 --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_COMPAS_Without_Sensitive_Attributes_20240601__212824.csv @@ -0,0 +1,5 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +COMPAS_Without_Sensitive_Attributes,DecisionTreeClassifier,0.6555,0.6575,"{'criterion': 'gini', 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_split': 0.1}" +COMPAS_Without_Sensitive_Attributes,LogisticRegression,0.6484,0.6521,"{'C': 1, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +COMPAS_Without_Sensitive_Attributes,RandomForestClassifier,0.6569,0.6587,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 100}" +COMPAS_Without_Sensitive_Attributes,XGBClassifier,0.6624,0.6646,"{'lambda': 100, 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}" diff --git a/docs/examples/results/models_tuning/tuning_results_Law_School_20240423__091518.csv b/docs/examples/results/models_tuning/tuning_results_Law_School_20240423__091518.csv new file mode 100644 index 00000000..96487f1d --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_Law_School_20240423__091518.csv @@ -0,0 +1,3 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +Law_School,LogisticRegression,0.6564,0.8987,"{'C': 100, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +Law_School,RandomForestClassifier,0.6539,0.8981,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 50}" diff --git a/docs/examples/results/models_tuning/tuning_results_Law_School_20240423__091850.csv b/docs/examples/results/models_tuning/tuning_results_Law_School_20240423__091850.csv new file mode 100644 index 00000000..96487f1d --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_Law_School_20240423__091850.csv @@ -0,0 +1,3 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +Law_School,LogisticRegression,0.6564,0.8987,"{'C': 100, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +Law_School,RandomForestClassifier,0.6539,0.8981,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 50}" diff --git a/docs/examples/results/models_tuning/tuning_results_Law_School_20240601__213556.csv b/docs/examples/results/models_tuning/tuning_results_Law_School_20240601__213556.csv new file mode 100644 index 00000000..96487f1d --- /dev/null +++ b/docs/examples/results/models_tuning/tuning_results_Law_School_20240601__213556.csv @@ -0,0 +1,3 @@ +Dataset_Name,Model_Name,F1_Score,Accuracy_Score,Model_Best_Params +Law_School,LogisticRegression,0.6564,0.8987,"{'C': 100, 'max_iter': 250, 'penalty': 'l2', 'solver': 'newton-cg'}" +Law_School,RandomForestClassifier,0.6539,0.8981,"{'max_depth': 10, 'max_features': 0.6, 'min_samples_leaf': 1, 'n_estimators': 50}" diff --git a/docs/glossary/.pages b/docs/glossary/.pages new file mode 100644 index 00000000..67230191 --- /dev/null +++ b/docs/glossary/.pages @@ -0,0 +1,5 @@ +title: Glossary 📖 +nav: + - bootstrap_approach.md + - overall_performance_dimensions.md + - disparity_performance_dimensions.md diff --git a/docs/glossary/bootstrap_approach.md b/docs/glossary/bootstrap_approach.md new file mode 100644 index 00000000..a318ae73 --- /dev/null +++ b/docs/glossary/bootstrap_approach.md @@ -0,0 +1,32 @@ +# Measuring the Model Arbitrariness + +Measuring only model accuracy is insufficient for reliable model development since an unstable or uncertain model can also lead +to severe consequences in the production environment. Unfortunately, leading approaches to uncertainty quantification are +tied to deep learning architectures and use Bayesian neural networks with methods like Monte Carlo Dropout[^1][^2]. +However, deep learning models are not state-of-the-art on tabular datasets and are still outperformed by ensemble and +tree-based models[^3]. Hence, model-agnostic uncertainty quantification is a major gap. +We take a first step in this direction and propose a data-centric approach by bootstrapping over the training set[^4]. + +
+ data-centric-stability +
Figure 1. Data-centric evaluaton of model arbitrariness.
+
+ +Let $D(X,Y)$ be a (training) dataset of features and targets. Let $h(D)$ be the trained model, fit on the full dataset $D$, +whose arbitrariness we are interested in quantifying. We construct multiple training sets $D_i$ by sampling with replacement +from the given training set $D$, and then fitting estimators $h_i(D_i)$ (with the same architecture and hyper-parameters as $h(D)$) +on these bootstrapped training sets to construct an $\textit{approximating ensemble}$. At inference time, we use the approximating ensemble +to construct a $\textit{predictive distribution}$ of class labels, with probabilities, for each test sample $x$ according to each model $h_i$. +We will then use this predictive distribution to compute different measures of variation/disagreement/arbitrariness between +the predictions of members of the approximating ensemble, and quantify the expected instability of a model trained on the dataset. + + +**References** + +[^1]: Yarin Gal and Zoubin Ghahramani. "Dropout as a bayesian approximation: Representing model uncertainty in deep learning." international conference on machine learning. PMLR, 2016. + +[^2]: Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A Simple Way to Prevent Neural NetworksfromOverfitting.JournalofMachineLearningResearch15,56(2014),1929–1958. + +[^3]: Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, and Gjergji Kasneci. 2022. Deep neural networks and tabular data: A survey. IEEE Transactions on Neural Networks and Learning Systems (2022). + +[^4]: Bradley Efron and Robert J Tibshirani. 1994. An introduction to the bootstrap. CRC press. diff --git a/docs/glossary/disparity_performance_dimensions.md b/docs/glossary/disparity_performance_dimensions.md new file mode 100644 index 00000000..a23132b3 --- /dev/null +++ b/docs/glossary/disparity_performance_dimensions.md @@ -0,0 +1,147 @@ +# Disparity Performance Dimensions + +This page contains short descriptions and mathematical definitions for each disparity metric implemented in Virny. + + +## Notation + +Let $Y$ be the true outcome, $X$ be the covariates or features, and $A$ be a set of sensitive attributes. +To start, we restrict our analysis to the binary classification of group membership, letting $A=0$ denote the disadvantaged group +and $A=1$ denote the privileged group. We are interested in constructing a classifier $\hat{Y} = f(X,A)$ that predicts $Y$ using +an appropriate loss function. In fair-ML, we apply additional constraints on the interaction between $\hat{Y}$, $Y$, +and $A$ to ensure that the classifier $\hat{Y}$ does not discriminate on the basis of sensitive attributes $A$. +Various fairness definitions are formalized as distinct constraints, and when the fairness constraint is not satisfied, +it is commonly known as the measure of model unfairness, which we will discuss next. + +We will now rewrite influential fairness measures by expressing them as the difference or ratio between different +$\textit{base measures}$ on the disadvantage ($\textit{dis}$) and privileged ($\textit{priv}$) groups: $\Delta f = f_{dis} - f_{priv}$, $\mathcal{Q} f = f_{dis} / f_{priv}$. + + +## Error Disparity + +### Equalized Odds + +Hardt, Price, and Srebro[^1] state that a predictor $\hat{Y}$ satisfies $\textit{equalized odds}$ with respect +to sensitive attribute $A$ and outcome $Y$, if $\hat{Y}$ and $A$ are independent conditional on $Y$. In our framework, +we focus on the following expression of equalized odds: + +$$ +P(\hat{Y}=\hat{y}|A=0,Y=y)=P(\hat{Y}=\hat{y}|A=1,Y=y), y \in \{0,1\} +$$ + +For $\hat{Y} = 1$ (the predicted positive outcome) and $Y = 0$ (the true negative outcome), this fairness constraint +requires parity in false positive rates (FPR) across the groups $A = 0$ and $A = 1$. For $\hat{Y} = 0$ (the predicted negative outcome) +and $Y = 1$ (the true positive outcome), the constraint requires parity in false negative rates (FNR). A violation of this constraint +(e.g., the disparity in FPR and FNR across groups) is reported as a measure of model unfairness. +In our library, we refer to FPR and FNR as the $\textit{base measures}$, and we say that the fairness criterion of +Equalized Odds is $\textit{composed}$ as the difference between these $\textit{base measures}$ computed +for the disadvantaged group ($A=0$, which we call $\textit{dis}$) and for the privileged group ($A=1$, which we call $\textit{priv}$), respectively. + +Equalized Odds Violation (False Positive): + +$$ +\Delta\text{FPR} = P(\hat{Y}=1|A=0,Y=0)- P(\hat{Y}=1|A=1,Y=0) +$$ + +Equalized Odds Violation (False Negative): + +$$ +\Delta\text{FNR} = P(\hat{Y}=0|A=0,Y=1)- P(\hat{Y}=0|A=1,Y=1) +$$ + + +### Accuracy Difference + +Accuracy Difference is a fairness notion that requires equal accuracy across groups. + +$$ +\Delta(\text{Accuracy}) = P(\hat{Y}=Y|A=0) - P(\hat{Y}=Y|A=1) +$$ + + +## Representation Disparity + +### Disparate Impact + +Inspired by the 4/5th's rule in legal doctrines, Disparate Impact[^2][^3] has been formulated as a fairness metric: + +$$ +\text{Disparate Impact} = \mathcal{Q}(\text{Selection Rate}) = \frac{P(\hat{Y}=1|A=0)}{P(\hat{Y}=1|A=1)} +$$ + +$P(\hat{Y}=1)$ is simply the Selection Rate of the classifier, and so the measure of Disparate Impact is composed +as the ratio of the Selection Rate on the $\textit{dis}$ and $\textit{priv}$ groups, respectively. + + +### Statistical Parity Difference + +Statistical Parity[^4][^5][^6][^7] is the fairness notion that asks if comparable proportions of samples from +each protected group receive the positive outcome: + +$$ +P(\hat{Y}=1|A=0) = P(\hat{Y}=1|A=1) +$$ + +Statistical Parity Difference (SPD) is a popular fairness measure composed simply as the difference between +the classifier's Selection Rate on $\textit{dis}$ and $\textit{priv}$ groups, respectively. + +$$ +\text{Statistical Parity Difference} = \Delta(\text{Selection Rate}) = P(\hat{Y}=1|A=0) - P(\hat{Y}=1|A=1) +$$ + + +## Stability Disparity + +### Label Stability Difference + +Label Stability Difference measures the equality (or lack thereof) of _label stability_ across groups. +In practice, this metric is implemented as a difference between the averaged metric value for $\textit{dis}$ and $\textit{priv}$ groups: + +$$ +\Delta U_{h, \text{stability}}(D) = \mathbb{E}_{x \in D_\text{dis}}[U_{\text{stability}}(x)] - \mathbb{E}_{x^* \in D_\text{priv}}[U_{\text{stability}}(x^*)] +$$ + +where $D$ is a test set and $x$ and $x^*$ are $dis$ and $priv$ samples in $D$. + + +## Uncertainty Disparity + +### Epistemic Uncertainty Difference + +Epistemic Uncertainty Difference measures the equality (or lack thereof) of _epistemic (model) uncertainty_ across groups. +In practice, this metric is implemented as a difference between the averaged metric value for $\textit{dis}$ and $\textit{priv}$ groups: + +$$ +\Delta U_{h, \text{epistemic}}(D) = \mathbb{E}_{x \in D_\text{dis}}[U_{\text{epistemic}}(x)] - \mathbb{E}_{x^* \in D_\text{priv}}[U_{\text{epistemic}}(x^*)] +$$ + +where $D$ is a test set and $x$ and $x^*$ are $dis$ and $priv$ samples in $D$. + + +### Aleatoric Uncertainty Difference + +Aleatoric Uncertainty Difference measures the equality (or lack thereof) of _aleatoric (data) uncertainty_ across groups. +In practice, this metric is implemented as a difference between the averaged metric value for $\textit{dis}$ and $\textit{priv}$ groups: + +$$ +\Delta U_{h, \text{aleatoric}}(D) = \mathbb{E}_{x \in D_\text{dis}}[U_{\text{aleatoric}}(x)] - \mathbb{E}_{x^* \in D_\text{priv}}[U_{\text{aleatoric}}(x^*)] +$$ + +where $D$ is a test set and $x$ and $x^*$ are $dis$ and $priv$ samples in $D$. + + +**References** + +[^1]: Moritz Hardt, Eric Price, and Nati Srebro. “Equality of Opportunity in Supervised Learning”. In: Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain. Ed. by Daniel D. Lee et al. 2016, pp. 3315–3323. + +[^2]: Alexandra Chouldechova. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data 5, 2 (2017), 153–163. 2016 + +[^3]: Feldman, Michael, et al. "Certifying and removing disparate impact." proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. 2015. + +[^4]: Toon Calders and Sicco Verwer. Three naive bayes approaches for discrimination-free classification. Data Mining and Knowledge Discovery, 21(2):277–292, 2010. + +[^5]: Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. Certifying and removing disparate impact. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’15, pages 259–268, New York, NY, USA, 2015. ACM. + +[^6]: Faisal Kamiran and Toon Calders. Classifying without discriminating. 2009 2nd International Conference on Computer, Control and Communication, IC4 2009, 2009. + +[^7]: Toshihiro Kamishima, Shotaro Akaho, and Jun Sakuma. Fairness-aware learning through regularization approach. Proceedings - IEEE International Conference on Data Mining, ICDM, pages 643–650, 2011. diff --git a/docs/glossary/overall_performance_dimensions.md b/docs/glossary/overall_performance_dimensions.md new file mode 100644 index 00000000..1c388aac --- /dev/null +++ b/docs/glossary/overall_performance_dimensions.md @@ -0,0 +1,127 @@ +# Overall Performance Dimensions + +This page contains short descriptions and mathematical definitions for each overall metric implemented in Virny. + + +## Correctness + +### Accuracy + +Accuracy[^1] is the fraction of correct predictions. If $\hat{Y}_i$ is the predicted value of the $i$-th sample and $Y_i$ +is the corresponding true value, then the fraction of correct predictions over $n_{samples}$ is defined as: + +$$ +\text{Accuracy } (Y, \hat{Y}) = \frac{1}{n_{samples}} \sum_{i=1}^{n_{samples}} \mathbf{1}(\hat{Y}_i = Y_i) +$$ + + +### F1 Score + +The F1 score[^2] can be interpreted as a harmonic mean of the precision and recall, where an F1 score reaches +its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal. +The formula for the F1 score is: + +$$ +F_1 = \frac{2}{\text{ recall }^{-1} + \text{ precision }^{-1}} = 2 \frac{\text{ precision } \cdot \text{ recall }}{\text{ precision } + \text{ recall }} = \frac{2 \mathrm{TP}}{2 \mathrm{TP}+\mathrm{FP}+\mathrm{FN}} +$$ + +where $TP$ is the number of true positives, $FN$ is the number of false negatives, and $FP$ is the number of false positives. +F1 is by default calculated as 0.0 when there are no true positives, false negatives, or false positives. + + +## Representation + +### Selection Rate + +Selection Rate (or Base Rate) means the fraction of data points in each class classified as 1. The formula for the Selection Rate is: + +$$ +\text{Selection Rate} = P(\hat{Y} = 1) = \frac{TP + FP}{TP + FP + TN + FN} +$$ + + +## Stability + +### Label Stability + + +Label Stability[^3] is defined as the normalized absolute difference between the number of times +a sample is classified as positive or negative: + +$$ +U_{h, \text{stability}}(x) = \frac{1}{m} (\sum_{j=1}^m \mathbf{1}[h_{D_j}(x)>=0.5] - \sum_{i=1}^m \mathbf{1}[h_{D_i}(x)<0.5]) +$$ + +where $x$ is an unseen test sample, and $h_{D_j}(x)$ is the predicted probability of the positive class of the $j^{\text{th}}$ model in the ensemble that has $m$ estimators. + +Thus, Label Stability is a metric used to evaluate the level of disagreement between estimators in the ensemble. +When the absolute difference is large, the label is more stable. On the other hand, if the difference is exactly zero, +the estimator is said to be "highly unstable", meaning that a test sample has an equal probability of being classified +as positive or negative by the ensemble. + + +### Jitter + +Jitter[^4] is another measure of the disagreement between estimators in the ensemble, for each individual test example. +It reuses a notion of $\textit{Churn}$[^5] to define a "$\textit{pairwise jitter}$": + +$$ +J_{i, j}\left(p_\theta\right)=\text{Churn}_{i, j}\left(p_\theta\right)=\frac{\left|p_{\theta i}(x) \neq p_{\theta j}(x)\right|_{x \in X}}{|X|} +$$ + +where $x$ is an unseen test sample, and $p_{\theta i}(x)$, $p_{\theta j}(x)$ are the prediction labels of the $i^{\text{th}}$ and $j^{\text{th}}$ estimator in the ensemble for $x$, respectively. + +To obtain the overall measure of disagreement across all models in the ensemble, we need to calculate +the average of $\textit{pairwise jitters}$ for all model pairs. This broader definition is referred to as $\textit{jitter}$: + +$$ +J\left(p_\theta\right)=\frac{\sum_{\forall i, j \in N} J_{i, j}\left(p_\theta\right)}{N \cdot(N-1) \cdot \frac{1}{2}} \text{, where } i + +Epistemic (model) uncertainty captures arbitrariness in outcomes due to uncertainty over the "true" model parameters. If we knew +the "best" model-type or hypothesis class to model each of our estimators in the bootstrap ($h_D$s) with, then, for different training datasets $D$, +we would fit the exact same model, and thereby have zero predictive variance. Following Tahir et al.[^6], we define +the epistemic uncertainty as a measure of predictive variance: + +$$ +U_{h, \text{epistemic}}(x) = \text{Var}_j[h_{D_j}(x)] +$$ + +where $h_{D_j}(x)$ is the predicted probability of the positive class. + + +### Aleatoric Uncertainty + + +The source of aleatoric uncertainty is the inherent stochasticity present in the data that, in general, cannot be reduced/mitigated. +An intuitive way to think about aleatoric uncertainty is through an information-theoretic perspective: if there simply is not enough +(target-specific) information in a given data point, then even the "best" model will not be able to make a confident prediction for it. +Following Tahir et al.[^6], we define the aleatoric uncertainty as the expected entropy for the prediction: + +$$ +U_{h, \text{aleatoric}}(x) = \mathbb{E}_j[h_{D_j}(x)log(h_{D_j}(x)) + (1-h_{D_j}(x))log(1-h_{D_j}(x))] +$$ + +where $h_{D_j}(x)$ and 1- $h_{D_j}(x)$ are the predicted probabilities of the positive and negative class respectively. + + +**References** + +[^1]: Accuracy Score. Metrics and scoring: quantifying the quality of predictions. [Link](https://scikit-learn.org/stable/modules/model_evaluation.html#accuracy-score) + +[^2]: F1 score, scikit-learn documentation. [Link](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score) + +[^3]: Michael C. Darling and David J. Stracuzzi. “Toward Uncertainty Quantification for Supervised Classification”. In: 2018. + +[^4]: Huiting Liu et al. “Model Stability with Continuous Data Updates”. In: arXiv preprint arXiv:2201.05692 (2022). + +[^5]: Mahdi Milani Fard et al. “Launch and iterate: Reducing prediction churn”. In: Advances in Neural Information Processing Systems 29 (2016). + +[^6]: Anique Tahir, Lu Cheng, and Huan Liu. 2023. Fairness through Aleatoric Uncertainty. In Proceedings of the 32nd ACM International Conference on +Information and Knowledge Management (CIKM ’23). Association for Computing Machinery, New York, NY, USA, 2372–2381. diff --git a/docs/index.md b/docs/index.md index 0a011ef0..5a857539 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1 +1 @@ -Redirect to introduction/introduction.md +Redirect to introduction/welcome_to_virny.md \ No newline at end of file diff --git a/docs/introduction/.pages b/docs/introduction/.pages index 9e117fc8..2096682e 100644 --- a/docs/introduction/.pages +++ b/docs/introduction/.pages @@ -1,3 +1,5 @@ title: Introduction 🚀 nav: - welcome_to_virny.md + - virny_overview.md + - virnyview_overview.md diff --git a/docs/introduction/virny_overview.md b/docs/introduction/virny_overview.md new file mode 100644 index 00000000..7c3dee9f --- /dev/null +++ b/docs/introduction/virny_overview.md @@ -0,0 +1,11 @@ +# Virny Overview + +![Virny_Architecture](https://github.com/DataResponsibly/Virny/assets/42843889/91620e0f-11ff-4093-8fb6-c88c90bff711) + +The software framework decouples the process of model profiling into several stages, including **subgroup metric computation**, +**disparity metric composition**, and **metric visualization**. This separation empowers data scientists with greater control and +flexibility in employing the library, both during model development and for post-deployment monitoring. The above figure demonstrates +how the library constructs a pipeline for model analysis. Inputs to a user interface are shown in green, pipeline stages are shown in blue, +and the output of each stage is shown in purple. + +See more details in [our SIGMOD demo paper](https://dl.acm.org/doi/abs/10.1145/3626246.3654738). diff --git a/docs/examples/Interactive_Web_App_Demo.md b/docs/introduction/virnyview_overview.md similarity index 94% rename from docs/examples/Interactive_Web_App_Demo.md rename to docs/introduction/virnyview_overview.md index 970a1b70..b774c9e9 100644 --- a/docs/examples/Interactive_Web_App_Demo.md +++ b/docs/introduction/virnyview_overview.md @@ -4,7 +4,7 @@ * [Demonstrative Jupyter notebooks for the Virny capabilities](https://huggingface.co/spaces/denys-herasymuk/virny-demo/tree/main/notebooks) -## Application overview +## Application Overview _VirnyView_ serves as a visualization component within the Virny model profiling library, empowering data scientists _to engage in responsible model selection_ and _to generate a nutritional label for their model_. Users can declaratively @@ -14,7 +14,7 @@ Subsequently, the tool utilizes the output from Virny to construct an interactiv This application allows users to scrutinize dataset properties related to protected groups, compare models across diverse performance dimensions, and generate a comprehensive nutritional label for the most optimal model. -![UI view of the second screen in the interactive web app](./Interactive_Web_App_Demo_files/UI_View.png) +![UI view of the second screen in the interactive web app](./virnyview_overview_files/UI_View.png) *Figure 1. A sample UI view of the second screen in the interactive web app.* The application comprises six screens, each featuring user input options and a corresponding visualization @@ -23,7 +23,7 @@ overall metrics, and disparity metrics across various model dimensions. This sel Refer to Figure 1 for a visual representation of the sample web interface showcasing these screens. -## User flow description +## User Flow Description Our tool is developed with a user-friendly flow design specifically tailored to guide users through a responsible model selection process. This streamlined flow reduces the complexity associated with numerous model types and pre-, in-, and post-processing techniques. @@ -33,7 +33,7 @@ concerning multiple protected groups and performance dimensions. The user flow comprises six steps, as illustrated in Figures 2 and 3, and can be outlined as follows. -![Steps 1-4 in the user flow for responsible model selection](./Interactive_Web_App_Demo_files/Step1-4.png) +![Steps 1-4 in the user flow for responsible model selection](./virnyview_overview_files/Step1-4.png) *Figure 2. Steps 1-4 in the user flow for responsible model selection.* _**Step 1**: Analyze demographic composition of the dataset._ @@ -69,7 +69,7 @@ Subsequently, users can choose one or two models for a detailed breakdown of per and generate a comprehensive nutritional label in the later steps. -![Steps 5-6 in the user flow for responsible model selection](./Interactive_Web_App_Demo_files/Step5-6.png) +![Steps 5-6 in the user flow for responsible model selection](./virnyview_overview_files/Step5-6.png) *Figure 3. Steps 5-6 in the user flow for responsible model selection.* _**Step 5**: Generate a nutritional label for the selected model._ @@ -85,7 +85,7 @@ communicates the model's performance across diverse dimensions. _**Step 6**: Summarize the performance of a particular model across different dimensions and protected groups._ Finally, in the sixth step, users select a particular model and specify a desirable range for overall and disparity metrics -encompassing accuracy, stability, uncertainty, and positive rate dimensions. This allows for a clear indication of whether a specific model +encompassing accuracy, stability, uncertainty, and selection rate dimensions. This allows for a clear indication of whether a specific model meets these defined constraints. The matrix adopts a color-coded scheme, where green signifies compliance with the constraints, and red signals non-compliance. This final colored matrix serves as a model performance summary and can be incorporated into a model card, similarly to the visualization generated in the fifth step. diff --git a/docs/examples/Interactive_Web_App_Demo_files/Step1-4.png b/docs/introduction/virnyview_overview_files/Step1-4.png similarity index 100% rename from docs/examples/Interactive_Web_App_Demo_files/Step1-4.png rename to docs/introduction/virnyview_overview_files/Step1-4.png diff --git a/docs/examples/Interactive_Web_App_Demo_files/Step5-6.png b/docs/introduction/virnyview_overview_files/Step5-6.png similarity index 100% rename from docs/examples/Interactive_Web_App_Demo_files/Step5-6.png rename to docs/introduction/virnyview_overview_files/Step5-6.png diff --git a/docs/examples/Interactive_Web_App_Demo_files/UI_View.png b/docs/introduction/virnyview_overview_files/UI_View.png similarity index 100% rename from docs/examples/Interactive_Web_App_Demo_files/UI_View.png rename to docs/introduction/virnyview_overview_files/UI_View.png diff --git a/docs/introduction/welcome_to_virny.md b/docs/introduction/welcome_to_virny.md index 27fcfd17..deea2665 100644 --- a/docs/introduction/welcome_to_virny.md +++ b/docs/introduction/welcome_to_virny.md @@ -5,6 +5,7 @@ **Virny** is a Python library for in-depth profiling of model performance across overall and disparity dimensions. In addition to its metric computation capabilities, the library provides an interactive tool called _VirnyView_ to streamline responsible model selection and generate nutritional labels for ML models. + The Virny library was developed based on three fundamental principles: 1) easy extensibility of model analysis capabilities; @@ -37,33 +38,52 @@ pip install virny * [Interactive Demo](https://huggingface.co/spaces/denys-herasymuk/virny-demo) -## 💡 Features +## 😎 Why Virny + +In contrast to existing fairness software libraries and model card generating frameworks, our system stands out in four key aspects: + +1. Virny facilitates the measurement of **all normatively important performance dimensions** (including _fairness_, _stability_, and _uncertainty_) for a set of initialized models, both overall and broken down by user-defined subgroups of interest. + +2. Virny enables data scientists to analyze performance using **multiple sensitive attributes** (including _non-binary_) and their _intersections_. + +3. Virny offers **diverse APIs for metric computation**, designed to analyze multiple models in a single execution, assessing stability and uncertainty on correct and incorrect predictions broken down by protected groups, and testing models on multiple test sets, including in-domain and out-of-domain. + +4. Virny implements streamlined flow design tailored for **responsible model selection**, reducing the complexity associated with numerous model types, performance dimensions, and data-centric and model-centric interventions. + + +## 💡 List of Features -* Entire pipeline for profiling model accuracy, stability, uncertainty, and fairness +* Profiling of all normatively important performance dimensions: accuracy, stability, uncertainty, and fairness * Ability to analyze non-binary sensitive attributes and their intersections -* Compatibility with [pre-, in-, and post-processors](https://aif360.readthedocs.io/en/latest/modules/algorithms.html#) for fairness enhancement from AIF360 * Convenient metric computation interfaces: an interface for multiple models, an interface for multiple test sets, and an interface for saving results into a user-defined database +* Interactive _VirnyView_ visualizer that profiles dataset properties related to protected groups, computes comprehensive [nutritional labels](http://sites.computer.org/debull/A19sept/p13.pdf) for individual models, compares multiple models according to multiple metrics, and guides users through model selection +* Compatibility with [pre-, in-, and post-processors](https://aif360.readthedocs.io/en/latest/modules/algorithms.html#) for fairness enhancement from AIF360 * An `error_analysis` computation mode to analyze model stability and confidence for correct and incorrect prodictions broken down by groups * Metric static and interactive visualizations * Data loaders with subsampling for popular fair-ML benchmark datasets * User-friendly parameters input via config yaml files -* Check out [our documentation](https://dataresponsibly.github.io/Virny/) for a comprehensive overview +Check out [our documentation](https://dataresponsibly.github.io/Virny/) for a comprehensive overview. -## 📖 Library Overview -![Virny_Architecture](https://github.com/DataResponsibly/Virny/assets/42843889/91620e0f-11ff-4093-8fb6-c88c90bff711) +## 🤗 Affiliations + +![NYU-UCU-Logos](https://user-images.githubusercontent.com/42843889/216840888-071bf184-f0e3-4a3e-94dc-c0d1c7784143.png) -The software framework decouples the process of model profiling into several stages, including **subgroup metric computation**, -**disparity metric composition**, and **metric visualization**. This separation empowers data scientists with greater control and -flexibility in employing the library, both during model development and for post-deployment monitoring. The above figure demonstrates -how the library constructs a pipeline for model analysis. Inputs to a user interface are shown in green, pipeline stages are shown in blue, -and the output of each stage is shown in purple. +## 💬 Citation -## 🤗 Affiliations +If Virny has been useful to you, and you would like to cite it in a scientific publication, please refer to the [paper](https://dl.acm.org/doi/abs/10.1145/3626246.3654738) published at SIGMOD: -![NYU-UCU-Logos](https://user-images.githubusercontent.com/42843889/216840888-071bf184-f0e3-4a3e-94dc-c0d1c7784143.png) +```bibtex +@inproceedings{herasymuk2024responsible, + title={Responsible Model Selection with Virny and VirnyView}, + author={Herasymuk, Denys and Arif Khan, Falaah and Stoyanovich, Julia}, + booktitle={Companion of the 2024 International Conference on Management of Data}, + pages={488--491}, + year={2024} +} +``` ## 📝 License diff --git a/docs/javascripts/config.js b/docs/javascripts/mathjax.js similarity index 78% rename from docs/javascripts/config.js rename to docs/javascripts/mathjax.js index 06dbf38b..7e48906a 100644 --- a/docs/javascripts/config.js +++ b/docs/javascripts/mathjax.js @@ -12,5 +12,8 @@ window.MathJax = { }; document$.subscribe(() => { + MathJax.startup.output.clearCache() + MathJax.typesetClear() + MathJax.texReset() MathJax.typesetPromise() }) diff --git a/docs/release_notes/.pages b/docs/release_notes/.pages index 5efc8cc6..8f5829a8 100644 --- a/docs/release_notes/.pages +++ b/docs/release_notes/.pages @@ -1,5 +1,6 @@ title: Release Notes 📒 nav: + - 0.5.0.md - 0.4.0.md - 0.3.0.md - 0.2.0.md diff --git a/docs/release_notes/0.1.0.md b/docs/release_notes/0.1.0.md index 868321ba..f116ef19 100644 --- a/docs/release_notes/0.1.0.md +++ b/docs/release_notes/0.1.0.md @@ -47,7 +47,7 @@ * Entropy * Jitter * Label Stability - * **9 subgroup error metrics** + * **8 subgroup error metrics** * TPR * TNR * PPV @@ -56,7 +56,6 @@ * Accuracy * F1 * Selection-Rate - * Positive-Rate * **5 group variance metrics** * Label Stability Ratio * IQR Difference diff --git a/docs/release_notes/0.2.0.md b/docs/release_notes/0.2.0.md index e6221b1f..f854abf5 100644 --- a/docs/release_notes/0.2.0.md +++ b/docs/release_notes/0.2.0.md @@ -17,19 +17,19 @@ database. In such a way, a model comparison on different test sets is faster and more accurate. -## 🗃 Datasets +## 🗃 New Benchmark Fair-ML Datasets * Added 5 new data loaders for all tasks in the folktables benchmark - * **ACSIncomeDataset**. Dataset class for the income task from the folktables dataset. + * **ACSIncomeDataset**. A data loader for the income task from the folktables dataset. Target: binary classification, predict if a person has an annual income > $50,000. - * **ACSEmploymentDataset**. Dataset class for the employment task from the folktables dataset. + * **ACSEmploymentDataset**. A data loader for the employment task from the folktables dataset. Target: binary classification, predict if a person is employed. - * **ACSMobilityDataset**. Dataset class for the mobility task from the folktables dataset. + * **ACSMobilityDataset**. A data loader for the mobility task from the folktables dataset. Target: binary classification, predict whether a young adult moved addresses in the last year. - * **ACSPublicCoverageDataset**. Dataset class for the public coverage task from the folktables dataset. + * **ACSPublicCoverageDataset**. A data loader for the public coverage task from the folktables dataset. Target: binary classification, predict whether a low-income individual, not eligible for Medicare, has coverage from public health insurance. - * **ACSTravelTimeDataset**. Dataset class for the travel time task from the folktables dataset. + * **ACSTravelTimeDataset**. A data loader for the travel time task from the folktables dataset. Target: binary classification, predict whether a working adult has a travel time to work of greater than 20 minutes. diff --git a/docs/release_notes/0.3.0.md b/docs/release_notes/0.3.0.md index 9ea9fc98..72222a11 100644 --- a/docs/release_notes/0.3.0.md +++ b/docs/release_notes/0.3.0.md @@ -21,18 +21,15 @@ sensitive_attributes_dct: {'sex': 1, 'race': 'African-American', 'sex&race': Non ``` -## 🗃 New Fairness Datasets +## 🗃 New Benchmark Fair-ML Datasets -* **LawSchoolDataset**. A dataset class for the Law School dataset that contains sensitive attributes among feature columns. - * Target: predict whether a candidate would pass the bar exam or predict a student’s first-year average grade (FYA). +* **LawSchoolDataset**. A data loader for the Law School dataset that contains sensitive attributes among feature columns. + * Target: binary classify whether a candidate would pass the bar exam or predict a student’s first-year average grade (FYA). * Source: [https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/law_school_clean.csv](https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/law_school_clean.csv). * Broader description: [https://arxiv.org/pdf/2110.00530.pdf](https://arxiv.org/pdf/2110.00530.pdf). -* **DiabetesDataset**. A dataset class for the Diabetes dataset that contains sensitive attributes among feature columns. - * Target: predict whether a patient will readmit within 30 days. - * Source: [https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/diabetes-clean.csv](https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/diabetes-clean.csv). - * Broader description: [https://arxiv.org/pdf/2110.00530.pdf](https://arxiv.org/pdf/2110.00530.pdf). -* **RicciDataset**. A dataset class for the Ricci dataset that contains sensitive attributes among feature columns. - * Target: predict whether an individual obtains a promotion based on the exam results. + +* **RicciDataset**. A data loader for the Ricci dataset that contains sensitive attributes among feature columns. + * Target: binary classify whether an individual obtains a promotion based on the exam results. * Source: [https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/ricci_race.csv](https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/ricci_race.csv). * Broader description: [https://arxiv.org/pdf/2110.00530.pdf](https://arxiv.org/pdf/2110.00530.pdf). diff --git a/docs/release_notes/0.4.0.md b/docs/release_notes/0.4.0.md index 4ae4b335..0a49e887 100644 --- a/docs/release_notes/0.4.0.md +++ b/docs/release_notes/0.4.0.md @@ -35,14 +35,9 @@ * **Improved definition of disparity metrics**. Now, all disparity metrics and their expressions are defined in the [Metric Composer](https://github.com/DataResponsibly/Virny/blob/main/virny/custom_classes/metrics_composer.py). -## 🗃 New Benchmark Fair-ML Datasets - -* **CreditCardDefaultDataset**. A data loader for the Taiwan Credit Card Default dataset that contains sensitive attributes among feature columns. - * Target: predict whether a customer will face the default situation in the next month or not. - * Source: [https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/credit-card-clients.csv](https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/credit-card-clients.csv). - * Broader description: [https://arxiv.org/pdf/2110.00530.pdf](https://arxiv.org/pdf/2110.00530.pdf) (Section 3.1.6). +## 🗃 New Benchmark Fair-ML Dataset * **StudentPerformancePortugueseDataset**. A data loader for the Student Performance dataset for the Portuguese subject that contains sensitive attributes among feature columns. - * Target: the initial regression task was to predict the final year grade of the students. To get a binary classification task, we used a preprocessed dataset from [this repo](https://github.com/tailequy/fairness_dataset). The target label is derived from the attribute _G3_ (representing the final grade), where _target_ = {Low, High}, corresponding to _G3_ = {<10, ≥10}. + * Target: The initial regression task was to predict the final year grade of the students. To get a binary classification task, we used a preprocessed dataset from [this repo](https://github.com/tailequy/fairness_dataset). The target label is derived from the attribute _G3_ (representing the final grade), where _target_ = {Low, High}, corresponding to _G3_ = {<10, ≥10}. * Source: [https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/student_por_new.csv](https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/student_por_new.csv). * Broader description: [https://arxiv.org/pdf/2110.00530.pdf](https://arxiv.org/pdf/2110.00530.pdf) (Section 3.4.1). diff --git a/docs/release_notes/0.5.0.md b/docs/release_notes/0.5.0.md new file mode 100644 index 00000000..f8c72303 --- /dev/null +++ b/docs/release_notes/0.5.0.md @@ -0,0 +1,59 @@ +# 0.5.0 - 2024-06-02 + +- [PyPI](https://pypi.org/project/virny/) +- [GitHub](https://github.com/DataResponsibly/Virny/releases/tag/0.5.0) + + +## 📝 SIGMOD Demo Paper + +* Virny demonstration paper was accepted and published at SIGMOD! 🎉🥳 Explore [our work](https://dl.acm.org/doi/abs/10.1145/3626246.3654738) in the ACM digital library. + +```bibtex +@inproceedings{herasymuk2024responsible, + title={Responsible Model Selection with Virny and VirnyView}, + author={Herasymuk, Denys and Arif Khan, Falaah and Stoyanovich, Julia}, + booktitle={Companion of the 2024 International Conference on Management of Data}, + pages={488--491}, + year={2024} +} +``` + + +## 📖 Glossary + +* Our documentation was extended with a new "Glossary" section that provides: + * Explanation of our approach for measuring model stability and uncertainty + * Detailed description of the overall and disparity metrics computed by Virny + + +## ⚙️ More Features for Experimental Studies + +* Metric computation interfaces were extended with a new optional parameter - `with_predict_proba`. +If set to `False`, Virny computes metrics only based on prediction labels, NOT prediction probabilities. Specifically, it can be useful in the case when a model cannot provide probabilities for its predictions. +Default: `True`. + +* Metric computation interfaces were enabled to measure the computation runtime in minutes for each model. +It can be particularly useful for a large experimental studies with thousands of pipelines to estimate their runtimes and to benchmark models between each other. + + +## 🗃 New Benchmark Fair-ML Datasets + +* **DiabetesDataset2019**. A data loader for the Diabetes 2019 dataset that contains sensitive attributes among feature columns. + * Target: Binary classify whether a person has a diabetes disease or not. + * Source and broad description: [https://www.kaggle.com/datasets/tigganeha4/diabetes-dataset-2019/data](https://www.kaggle.com/datasets/tigganeha4/diabetes-dataset-2019/data). + +* **GermanCreditDataset**. A data loader for the German Credit dataset that contains sensitive attributes among feature columns. + * Target: Binary classify people described by a set of attributes as good or bad credit risks. + * Source: [https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/german_data_credit.csv](https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/german_data_credit.csv). + * General description and analysis: [https://arxiv.org/pdf/2110.00530.pdf](https://arxiv.org/pdf/2110.00530.pdf) (Section 3.1.3). + * Broad description: [https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data](https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data). + +* **BankMarketingDataset**. A data loader for the Bank Marketing dataset that contains sensitive attributes among feature columns. + * Target: The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit. + * Source: [https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/bank-full.csv](https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/bank-full.csv). + * General description and analysis: [https://arxiv.org/pdf/2110.00530.pdf](https://arxiv.org/pdf/2110.00530.pdf) (Section 3.1.5). + * Broad description: [https://archive.ics.uci.edu/dataset/222/bank+marketing](https://archive.ics.uci.edu/dataset/222/bank+marketing). + +* **CardiovascularDiseaseDataset**. A data loader for the Cardiovascular Disease dataset that contains sensitive attributes among feature columns. + * Target: Binary classify whether a person has a cardiovascular disease or not. + * Source and broad description: [https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-dataset](https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-dataset). diff --git a/mkdocs.yml b/mkdocs.yml index 07aba3a9..a5be9888 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -59,7 +59,7 @@ plugins: index.md: introduction/welcome_to_virny.md extra_javascript: - - javascripts/config.js + - javascripts/mathjax.js - https://polyfill.io/v3/polyfill.min.js?features=es6 - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js diff --git a/requirements.txt b/requirements.txt index 22206002..5094fa86 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,6 +2,7 @@ wheel~=0.38.4 twine~=4.0.2 aif360~=0.5.0 numpy==1.23.5 +fairlearn~=0.9.0 matplotlib~=3.6.2 pandas~=1.5.2 altair~=4.2.0 diff --git a/tests/__init__.py b/tests/__init__.py index 04162dfa..18206444 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -1,5 +1,6 @@ import os import pytest +import numpy as np import pandas as pd from munch import DefaultMunch @@ -25,6 +26,56 @@ def get_root_dir(): return root_dir +def compare_metric_dfs_v2(df1, df2): + # Check shape + if not df1.shape == df2.shape: + return False + + # Check column names + if not sorted(df1.columns.tolist()) == sorted(df2.columns.tolist()): + return False + + # Check values + df1 = df1.sort_values(by='Metric').reset_index(drop=True) + df2 = df2.sort_values(by='Metric').reset_index(drop=True) + + if not df1.equals(df2): + return False + + return True + + +def compare_metric_dfs_with_tolerance(df1, df2, tolerance=1e-6): + # Check shape + if not df1.shape == df2.shape: + return False + + # Check column names + if not sorted(df1.columns.tolist()) == sorted(df2.columns.tolist()): + return False + + # Check values + df1 = df1.sort_values(by='Metric').reset_index(drop=True) + df2 = df2.sort_values(by='Metric').reset_index(drop=True) + + categorical_cols = ['Metric', 'Model_Name', 'Model_Params'] + numerical_cols = [col for col in df1.columns if col not in categorical_cols] + + # Compare numerical columns with tolerance + close_numerical = np.isclose(df1[numerical_cols], df2[numerical_cols], atol=tolerance).all(axis=0) + + # Compare categorical columns directly + equal_categorical = (df1[categorical_cols] == df2[categorical_cols]).all() + + # Combine both results + overall_equal = close_numerical.all() and equal_categorical.all() + + if not overall_equal: + return False + + return True + + def compare_metric_dfs(expected_composed_metrics_df, actual_composed_metrics_df, model_name, metrics_lst, groups, alpha=0.000_001): for metric_name in metrics_lst: @@ -90,7 +141,7 @@ def models_config(): @pytest.fixture(scope='package') def compas_dataset_class(): - dataset_path = os.path.join(ROOT_DIR, 'virny', 'datasets', 'COMPAS.csv') + dataset_path = os.path.join(ROOT_DIR, 'virny', 'datasets', 'data', 'COMPAS.csv') df = pd.read_csv(dataset_path) int_columns = ['recidivism', 'age', 'age_cat_25 - 45', 'age_cat_Greater than 45', @@ -111,7 +162,7 @@ def compas_dataset_class(): @pytest.fixture(scope='package') def compas_without_sensitive_attrs_dataset_class(): - dataset_path = os.path.join(ROOT_DIR, 'virny', 'datasets', 'COMPAS.csv') + dataset_path = os.path.join(ROOT_DIR, 'virny', 'datasets', 'data', 'COMPAS.csv') df = pd.read_csv(dataset_path) int_columns = ['recidivism', 'age', 'age_cat_25 - 45', 'age_cat_Greater than 45', diff --git a/tests/analyzers/test_subgroup_error_analyzer.py b/tests/analyzers/test_subgroup_error_analyzer.py index d0f612f5..5f279bbc 100644 --- a/tests/analyzers/test_subgroup_error_analyzer.py +++ b/tests/analyzers/test_subgroup_error_analyzer.py @@ -28,7 +28,6 @@ def test_overall_accuracy_metrics_computation(): expected_ACCURACY = (TP+TN)/(TP+TN+FP+FN) expected_F1 = (2*TP)/(2*TP+FP+FN) expected_SELECTION_RATE = (TP+FP)/(TP+FP+TN+FN) - expected_POSITIVE_RATE = (TP+FP)/(TP+FN) assert abs(prediction_metrics[TPR] - expected_TPR) < alpha assert abs(prediction_metrics[TNR] - expected_TNR) < alpha @@ -38,4 +37,3 @@ def test_overall_accuracy_metrics_computation(): assert abs(prediction_metrics[ACCURACY] - expected_ACCURACY) < alpha assert abs(prediction_metrics[F1] - expected_F1) < alpha assert abs(prediction_metrics[SELECTION_RATE] - expected_SELECTION_RATE) < alpha - assert abs(prediction_metrics[POSITIVE_RATE] - expected_POSITIVE_RATE) < alpha diff --git a/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20230202__094821/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_100_Estimators_20230202__094825.csv b/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20230202__094821/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_100_Estimators_20230202__094825.csv index 335c50da..17e1cff1 100644 --- a/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20230202__094821/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_100_Estimators_20230202__094825.csv +++ b/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20230202__094821/Metrics_COMPAS_Without_Sensitive_Attributes_DecisionTreeClassifier_100_Estimators_20230202__094825.csv @@ -14,4 +14,3 @@ FPR,0.27240773286467485,0.24806201550387597,0.27954545454545454,0.20522388059701 Accuracy,0.6799242424242424,0.6834170854271356,0.6791131855309218,0.6697892271662763,0.6868044515103339,0.6477272727272727,0.6814671814671814,101,DecisionTreeClassifier,Run_1 F1,0.642706131078224,0.5531914893617021,0.6583850931677019,0.5087108013937283,0.701062215477997,0.41509433962264153,0.7110332749562172,101,DecisionTreeClassifier,Run_1 Selection-Rate,0.4346590909090909,0.35678391959798994,0.45274212368728123,0.2997658079625293,0.5262321144674086,0.3181818181818182,0.555984555984556,101,DecisionTreeClassifier,Run_1 -Positive-Rate,0.9425051334702259,1.0142857142857142,0.9304556354916067,0.8050314465408805,1.0091463414634145,1.12,1.017667844522968,101,DecisionTreeClassifier,Run_1 diff --git a/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20230202__094821/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_100_Estimators_20230202__094825.csv b/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20230202__094821/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_100_Estimators_20230202__094825.csv index d9733d41..ded979c0 100644 --- a/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20230202__094821/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_100_Estimators_20230202__094825.csv +++ b/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20230202__094821/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_100_Estimators_20230202__094825.csv @@ -14,4 +14,3 @@ FPR,0.2513464991023339,0.24031007751937986,0.2546728971962617,0.1450381679389313 Accuracy,0.6714015151515151,0.6635514018691588,0.6733966745843231,0.672093023255814,0.670926517571885,0.6767676767676768,0.675146771037182,102,LogisticRegression,Run_1 F1,0.6272824919441461,0.55,0.6433203631647212,0.4797047970479705,0.6878787878787879,0.42857142857142855,0.7014388489208633,102,LogisticRegression,Run_1 Selection-Rate,0.4090909090909091,0.35046728971962615,0.42399049881235157,0.23953488372093024,0.5255591054313099,0.21212121212121213,0.538160469667319,102,LogisticRegression,Run_1 -Positive-Rate,0.8657314629258517,0.8823529411764706,0.8623188405797102,0.6130952380952381,0.9939577039274925,0.6,0.9786476868327402,102,LogisticRegression,Run_1 diff --git a/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20231021__205806/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231021__205809.csv b/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20231021__205806/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231021__205809.csv index a0da3ee6..f79a3a3a 100644 --- a/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20231021__205806/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231021__205809.csv +++ b/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20231021__205806/Metrics_COMPAS_Without_Sensitive_Attributes_LogisticRegression_50_Estimators_20231021__205809.csv @@ -16,5 +16,4 @@ FPR,0.26666666666666666,0.19117647058823528,0.289532293986637,0.1835205992509363 Accuracy,0.6837121212121212,0.6919431279620853,0.6816568047337278,0.6859903381642513,0.6822429906542056,0.6958174904942965,0.6716981132075471,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" F1,0.6369565217391304,0.5255474452554745,0.6564495530012772,0.5038167938931297,0.6899696048632219,0.5348837209302325,0.6979166666666666,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" Selection-Rate,0.4251893939393939,0.2938388625592417,0.45798816568047335,0.2777777777777778,0.5202492211838006,0.2965779467680608,0.5528301886792453,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" -Positive-Rate,0.9532908704883227,0.8266666666666667,0.9772727272727273,0.782312925170068,1.0308641975308641,0.8297872340425532,1.0353356890459364,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" Sample_Size,1056.0,,,,,,,LogisticRegression,"{'C': 1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}" diff --git a/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20231021__205806/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231021__205809.csv b/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20231021__205806/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231021__205809.csv index 4143bd2f..0baebec7 100644 --- a/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20231021__205806/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231021__205809.csv +++ b/tests/files_for_tests/COMPAS_Without_Sensitive_Attributes_Metrics_20231021__205806/Metrics_COMPAS_Without_Sensitive_Attributes_XGBClassifier_50_Estimators_20231021__205809.csv @@ -16,5 +16,4 @@ FPR,0.26153846153846155,0.19852941176470587,0.2806236080178174,0.205992509363295 Accuracy,0.7026515151515151,0.6966824644549763,0.7041420118343196,0.7053140096618358,0.7009345794392523,0.7091254752851711,0.6962264150943396,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" F1,0.6638115631691649,0.5428571428571428,0.6851385390428212,0.5673758865248227,0.7055214723926381,0.5785123966942148,0.7180385288966725,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" Selection-Rate,0.4384469696969697,0.3080568720379147,0.4710059171597633,0.32608695652173914,0.5109034267912772,0.33269961977186313,0.5433962264150943,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" -Positive-Rate,0.9830148619957537,0.8666666666666667,1.005050505050505,0.9183673469387755,1.0123456790123457,0.9308510638297872,1.017667844522968,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" Sample_Size,1056.0,,,,,,,XGBClassifier,"{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': 0, 'lambda': 100}" diff --git a/tests/files_for_tests/COMPAS_use_case/COMPAS_RF_expected_metrics.csv b/tests/files_for_tests/COMPAS_use_case/COMPAS_RF_expected_metrics.csv index 25ead2b1..84955917 100644 --- a/tests/files_for_tests/COMPAS_use_case/COMPAS_RF_expected_metrics.csv +++ b/tests/files_for_tests/COMPAS_use_case/COMPAS_RF_expected_metrics.csv @@ -16,5 +16,4 @@ FPR,0.261538,0.198529,0.280624,0.187266,0.323899,0.195266,0.352227,RandomForestC Accuracy,0.710227,0.734597,0.704142,0.724638,0.700935,0.726236,0.694340,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." F1,0.675159,0.621622,0.685139,0.592857,0.709970,0.604396,0.719723,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." Selection-Rate,0.446023,0.345972,0.471006,0.321256,0.526480,0.334601,0.556604,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." -Positive-Rate,1.000000,0.973333,1.005051,0.904762,1.043210,0.936170,1.042403,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." Sample_Size,1056.000000,NaN,NaN,NaN,NaN,NaN,NaN,RandomForestClassifier,"{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 10, 'max_features': 0.6, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'm..." diff --git a/tests/files_for_tests/composed_metrics/Multiple_Models_Interface_Use_Case.csv b/tests/files_for_tests/composed_metrics/Multiple_Models_Interface_Use_Case.csv index 36314b34..3cd89de9 100644 --- a/tests/files_for_tests/composed_metrics/Multiple_Models_Interface_Use_Case.csv +++ b/tests/files_for_tests/composed_metrics/Multiple_Models_Interface_Use_Case.csv @@ -10,7 +10,6 @@ Label_Stability_Ratio,1.020156,0.996574,0.997767,DecisionTreeClassifier Overall_Uncertainty_Difference,-0.007746,0.012562,0.003668,DecisionTreeClassifier Overall_Uncertainty_Ratio,0.991382,1.014195,1.004118,DecisionTreeClassifier Statistical_Parity_Difference,0.196061,0.116213,0.100951,DecisionTreeClassifier -Disparate_Impact,1.237170,1.127488,1.108450,DecisionTreeClassifier Std_Difference,-0.002647,0.000628,-0.000949,DecisionTreeClassifier Std_Ratio,0.963159,1.009053,0.986485,DecisionTreeClassifier Equalized_Odds_TNR,-0.095457,-0.100382,-0.132202,DecisionTreeClassifier @@ -26,7 +25,7 @@ Label_Stability_Ratio,1.026811,0.989255,1.003616,LogisticRegression Overall_Uncertainty_Difference,-0.010267,-0.008368,-0.014665,LogisticRegression Overall_Uncertainty_Ratio,0.988816,0.990853,0.984011,LogisticRegression Statistical_Parity_Difference,0.1641493031212316,0.24247144340602278,0.25625224191118445,LogisticRegression -Disparate_Impact,1.182185,1.317713,1.247712,LogisticRegression +Disparate_Impact,1.558637,1.872897,1.864030,LogisticRegression Std_Difference,0.000235,0.000197,-0.000059,LogisticRegression Std_Ratio,1.010249,1.008557,0.997473,LogisticRegression Equalized_Odds_TNR,-0.098356,-0.152957,-0.183121,LogisticRegression @@ -42,7 +41,6 @@ Label_Stability_Ratio,1.089016,1.017421,1.025583,RandomForestClassifier Overall_Uncertainty_Difference,-0.025571,0.016989,0.000197,RandomForestClassifier Overall_Uncertainty_Ratio,0.970915,1.020024,1.000230,RandomForestClassifier Statistical_Parity_Difference,0.060909,0.145251,0.102699,RandomForestClassifier -Disparate_Impact,1.064341,1.161756,1.109701,RandomForestClassifier Std_Difference,-0.005710,-0.000903,-0.000312,RandomForestClassifier Std_Ratio,0.923399,0.987202,0.995557,RandomForestClassifier Equalized_Odds_TNR,-0.079867,-0.126599,-0.145905,RandomForestClassifier @@ -58,7 +56,7 @@ Label_Stability_Ratio,1.070995,1.015429,1.029570,XGBClassifier Overall_Uncertainty_Difference,-0.018830,0.008251,-0.003674,XGBClassifier Overall_Uncertainty_Ratio,0.978951,1.009435,0.995831,XGBClassifier Statistical_Parity_Difference,0.1629490451218486,0.18481647026953812,0.21069660664323125,XGBClassifier -Disparate_Impact,1.159674,1.102332,1.093266,XGBClassifier +Disparate_Impact,1.528958,1.566771,1.633294,XGBClassifier Std_Difference,0.000692,0.002415,0.002737,XGBClassifier Std_Ratio,1.015140,1.053930,1.060978,XGBClassifier Equalized_Odds_TNR,-0.082094,-0.102184,-0.128932,XGBClassifier diff --git a/tests/files_for_tests/configs/law_school_config.yaml b/tests/files_for_tests/configs/law_school_config.yaml new file mode 100644 index 00000000..69737b2a --- /dev/null +++ b/tests/files_for_tests/configs/law_school_config.yaml @@ -0,0 +1,4 @@ +dataset_name: law_school +bootstrap_fraction: 0.8 +n_estimators: 50 +sensitive_attributes_dct: {'male': '0', 'race': 'Non-White', 'male & race': None} \ No newline at end of file diff --git a/tests/files_for_tests/law_school_dataset_20k/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240423__010106.csv b/tests/files_for_tests/law_school_dataset_20k/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240423__010106.csv new file mode 100644 index 00000000..37b7e613 --- /dev/null +++ b/tests/files_for_tests/law_school_dataset_20k/Metrics_law_school_DecisionTreeClassifier_50_Estimators_20240423__010106.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins +Statistical_Bias,0.14871459501539216,0.13922538692994388,0.1612539056997345,0.12087562594490028,0.3035414166913454,0.13498160237084172,0.3081005399506289,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",100,0.13008081666666665 +Std,0.041588229003281706,0.04014981475236609,0.04348899069199162,0.035707999117036614,0.07429121098892864,0.03859272201556965,0.0763542646485458,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",100,0.13008081666666665 +IQR,0.053505982148091664,0.05084500766436651,0.05702226985872847,0.044968952602027774,0.10098479315664254,0.049091273172528614,0.1047433620765962,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",100,0.13008081666666665 +Overall_Uncertainty,0.37102753012884837,0.35860883223592943,0.3874379523444912,0.3189445800260836,0.6606875964732465,0.3439739430925971,0.6850131008829163,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",100,0.13008081666666665 +Mean_Prediction,0.10730163589001154,0.10127383420207314,0.11526694526335872,0.08243941922626423,0.2455732067991173,0.0941339892140465,0.26012614125045447,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",100,0.13008081666666665 +Aleatoric_Uncertainty,0.35318189936459926,0.3413540927984995,0.368811500898374,0.3031037696893525,0.6316921284417605,0.3271516798716977,0.6552902043882751,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",100,0.13008081666666665 +Epistemic_Uncertainty,0.01784563076424911,0.017254739437429945,0.018626451446117187,0.015840810336731126,0.028995468031486005,0.016822263220899414,0.029722896494641216,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",100,0.13008081666666665 +Jitter,0.03490286499215079,0.027672021511307245,0.044457908163265336,0.013944459235764642,0.15146333612309273,0.023045132413278664,0.1725244279529997,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",100,0.13008081666666665 +Label_Stability,0.9493557692307693,0.9616385135135135,0.9331249999999999,0.9821100397050483,0.767192429022082,0.9684804177545692,0.7273939393939394,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",100,0.13008081666666665 +TPR,0.9959763948497854,0.9986046511627907,0.9923954372623575,1.0,0.967741935483871,0.9991389207807119,0.9508196721311475,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",100,0.13008081666666665 +TNR,0.1111111111111111,0.06880733944954129,0.1542056074766355,0.0,0.28402366863905326,0.04335260115606936,0.38372093023255816,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",100,0.13008081666666665 +PPV,0.9062728825970222,0.9136170212765957,0.8963938179736691,0.9254112308564946,0.7880910683012259,0.9131689401888772,0.8140350877192982,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",100,0.13008081666666665 +FNR,0.004023605150214592,0.0013953488372093023,0.0076045627376425855,0.0,0.03225806451612903,0.0008610792192881745,0.04918032786885246,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",100,0.13008081666666665 +FPR,0.8888888888888888,0.9311926605504587,0.8457943925233645,1.0,0.7159763313609467,0.9566473988439307,0.6162790697674418,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",100,0.13008081666666665 +Accuracy,0.9040865384615384,0.9130067567567568,0.8922991071428571,0.9254112308564946,0.7854889589905363,0.9127937336814621,0.803030303030303,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",100,0.13008081666666665 +F1,0.9490095846645368,0.9542222222222222,0.9419548872180451,0.9612608631609957,0.8687258687258688,0.9542214912280702,0.8771266540642723,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",100,0.13008081666666665 +Selection-Rate,0.9848557692307692,0.9923986486486487,0.9748883928571429,1.0,0.9006309148264984,0.9953002610966057,0.8636363636363636,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",100,0.13008081666666665 +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,DecisionTreeClassifier,"{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 20, 'max_features': 0.6, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}",100,0.13008081666666665 diff --git a/tests/files_for_tests/law_school_dataset_20k/Metrics_law_school_LogisticRegression_50_Estimators_20240423__010106.csv b/tests/files_for_tests/law_school_dataset_20k/Metrics_law_school_LogisticRegression_50_Estimators_20240423__010106.csv new file mode 100644 index 00000000..c5a529f6 --- /dev/null +++ b/tests/files_for_tests/law_school_dataset_20k/Metrics_law_school_LogisticRegression_50_Estimators_20240423__010106.csv @@ -0,0 +1,19 @@ +Metric,overall,male_priv,male_dis,race_priv,race_dis,male&race_priv,male&race_dis,Model_Name,Model_Params,Virny_Random_State,Runtime_in_Mins +Statistical_Bias,0.13884987532256288,0.12866752312911567,0.15230512643533242,0.11307241003120876,0.28221161446659226,0.12620186366973807,0.28564346511140876,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",100,0.18614128333333332 +Std,0.009171479431908339,0.008607050335232607,0.009917332166801268,0.007286748976075047,0.019653434616873924,0.008239377682865036,0.01998950882232001,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",100,0.18614128333333332 +IQR,0.012256803023512921,0.011445377171906256,0.013329044327421729,0.009739371815749175,0.026257532421896154,0.010976514379596776,0.027115910618054847,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",100,0.18614128333333332 +Overall_Uncertainty,0.342781514255694,0.3232951664439918,0.36853133100687174,0.2930493994499396,0.6193673767242902,0.3160946056979695,0.6525113923650412,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",100,0.18614128333333332 +Mean_Prediction,0.10451562164193534,0.09389099362298176,0.11855530866698118,0.07598923254313926,0.2631655395636309,0.0885187447414513,0.2901763444566442,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",100,0.18614128333333332 +Aleatoric_Uncertainty,0.34187256444643516,0.32243319534477255,0.36756030218791796,0.2923502561201371,0.6172915852012101,0.3152813861742673,0.6504913910597774,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",100,0.18614128333333332 +Epistemic_Uncertainty,0.0009089498092588189,0.0008619710992192609,0.0009710288189537786,0.0006991433298024763,0.0020757915230801283,0.0008132195237021689,0.0020200013052638077,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",100,0.18614128333333332 +Jitter,0.008828885400313994,0.007884376723662437,0.010076986151603502,0.005247780337319282,0.02874525204403535,0.006921084883039359,0.030970933828076638,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",100,0.18614128333333332 +Label_Stability,0.9874423076923078,0.9889695945945947,0.9854241071428572,0.9924333522404991,0.9596845425867508,0.9902872062663186,0.9544242424242425,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",100,0.18614128333333332 +TPR,0.9849785407725322,0.9897674418604652,0.9784537389100126,0.9947900704872816,0.9161290322580645,0.9913892078071183,0.8934426229508197,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",100,0.18614128333333332 +TNR,0.24074074074074073,0.17889908256880735,0.3037383177570093,0.11787072243346007,0.4319526627218935,0.16473988439306358,0.5465116279069767,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",100,0.18614128333333332 +PPV,0.918,0.9224100563502384,0.9119905493207324,0.9332949971247844,0.8160919540229885,0.9227892065188351,0.8482490272373541,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",100,0.18614128333333332 +FNR,0.015021459227467811,0.010232558139534883,0.021546261089987327,0.005209929512718358,0.08387096774193549,0.008610792192881744,0.10655737704918032,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",100,0.18614128333333332 +FPR,0.7592592592592593,0.8211009174311926,0.6962616822429907,0.8821292775665399,0.5680473372781065,0.8352601156069365,0.45348837209302323,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",100,0.18614128333333332 +Accuracy,0.9076923076923077,0.9151182432432432,0.8978794642857143,0.9293817356778219,0.7870662460567823,0.9167101827676241,0.803030303030303,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",100,0.18614128333333332 +F1,0.9503105590062112,0.9549024007179717,0.9440538061754815,0.9630618602581219,0.8632218844984803,0.9558599695585996,0.8702594810379242,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",100,0.18614128333333332 +Selection-Rate,0.9615384615384616,0.9742398648648649,0.9447544642857143,0.9863868406125922,0.8233438485804416,0.9772845953002611,0.7787878787878788,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",100,0.18614128333333332 +Sample_Size,4160.0,2368.0,1792.0,3526.0,634.0,3830.0,330.0,LogisticRegression,"{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 250, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}",100,0.18614128333333332 diff --git a/tests/user_interfaces/conftest.py b/tests/user_interfaces/conftest.py new file mode 100644 index 00000000..fe2028b6 --- /dev/null +++ b/tests/user_interfaces/conftest.py @@ -0,0 +1,76 @@ +import pytest +import pathlib + +from sklearn.linear_model import LogisticRegression +from sklearn.tree import DecisionTreeClassifier +from sklearn.compose import ColumnTransformer +from sklearn.preprocessing import OneHotEncoder, StandardScaler + +from virny.preprocessing.basic_preprocessing import preprocess_dataset +from virny.utils.custom_initializers import create_config_obj +from virny.datasets import LawSchoolDataset + + +@pytest.fixture(scope="function") +def common_seed(): + return 42 + + +@pytest.fixture(scope="function") +def law_school_dataset_1k_params(common_seed): + # Define init variables + dataset_split_seed = 42 + test_set_fraction = 0.2 + data_loader = LawSchoolDataset(subsample_size=1_000, subsample_seed=common_seed) + config = create_config_obj(config_yaml_path=str(pathlib.Path(__file__).parent.parent.joinpath('files_for_tests', 'configs', 'law_school_config.yaml'))) + models_config = { + 'LogisticRegression': LogisticRegression(penalty='l2', + C=0.1, + max_iter=250), + } + save_results_dir_path = str(pathlib.Path(__file__).parent.parent.joinpath('results', 'law_school_dataset_1k')) + + # Preprocess the dataset + column_transformer = ColumnTransformer(transformers=[ + ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns), + ('numerical_features', StandardScaler(), data_loader.numerical_columns), + ]) + base_flow_dataset = preprocess_dataset(data_loader=data_loader, + column_transformer=column_transformer, + sensitive_attributes_dct=config.sensitive_attributes_dct, + test_set_fraction=test_set_fraction, + dataset_split_seed=dataset_split_seed) + + return base_flow_dataset, config, models_config, save_results_dir_path + + +@pytest.fixture(scope="function") +def law_school_dataset_20k_params(common_seed): + # Define init variables + dataset_split_seed = 42 + test_set_fraction = 0.2 + data_loader = LawSchoolDataset() + config = create_config_obj(config_yaml_path=str(pathlib.Path(__file__).parent.parent.joinpath('files_for_tests', 'configs', 'law_school_config.yaml'))) + models_config = { + 'LogisticRegression': LogisticRegression(penalty='l2', + C=0.1, + max_iter=250), + 'DecisionTreeClassifier': DecisionTreeClassifier(criterion='gini', + max_depth=20, + max_features=0.6, + min_samples_split=0.1), + } + save_results_dir_path = str(pathlib.Path(__file__).parent.parent.joinpath('results', 'law_school_dataset_20k')) + + # Preprocess the dataset + column_transformer = ColumnTransformer(transformers=[ + ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns), + ('numerical_features', StandardScaler(), data_loader.numerical_columns), + ]) + base_flow_dataset = preprocess_dataset(data_loader=data_loader, + column_transformer=column_transformer, + sensitive_attributes_dct=config.sensitive_attributes_dct, + test_set_fraction=test_set_fraction, + dataset_split_seed=dataset_split_seed) + + return base_flow_dataset, config, models_config, save_results_dir_path diff --git a/tests/user_interfaces/test_compute_model_metrics.py b/tests/user_interfaces/test_compute_model_metrics.py index a9d1add4..f2494c70 100644 --- a/tests/user_interfaces/test_compute_model_metrics.py +++ b/tests/user_interfaces/test_compute_model_metrics.py @@ -11,7 +11,7 @@ from virny.analyzers.subgroup_variance_calculator import SubgroupVarianceCalculator from virny.analyzers.subgroup_error_analyzer import SubgroupErrorAnalyzer from virny.utils.stability_utils import count_prediction_metrics -from virny.datasets.data_loaders import CompasWithoutSensitiveAttrsDataset +from virny.datasets import CompasWithoutSensitiveAttrsDataset from virny.preprocessing.basic_preprocessing import preprocess_dataset @@ -26,8 +26,12 @@ def test_subgroup_variance_and_error_analyzers(COMPAS_y_test, COMPAS_RF_bootstra ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns), ('numerical_features', StandardScaler(), data_loader.numerical_columns), ]) - base_flow_dataset = preprocess_dataset(data_loader, column_transformer, test_set_fraction, dataset_split_seed) - test_protected_groups = create_test_protected_groups(base_flow_dataset.X_test, base_flow_dataset.init_features_df, + base_flow_dataset = preprocess_dataset(data_loader=data_loader, + column_transformer=column_transformer, + sensitive_attributes_dct=sensitive_attributes_dct, + test_set_fraction=test_set_fraction, + dataset_split_seed=dataset_split_seed) + test_protected_groups = create_test_protected_groups(base_flow_dataset.X_test, base_flow_dataset.init_sensitive_attrs_df, sensitive_attributes_dct) y_preds, prediction_metrics = count_prediction_metrics(COMPAS_y_test, COMPAS_RF_bootstrap_predictions) @@ -76,8 +80,7 @@ def test_subgroup_variance_and_error_analyzers(COMPAS_y_test, COMPAS_RF_bootstra FPR, F1, ACCURACY, - SELECTION_RATE, - POSITIVE_RATE]) + SELECTION_RATE]) # Check stability metrics compare_metric_dfs(expected_composed_metrics_df=COMPAS_RF_expected_metrics, actual_composed_metrics_df=metrics_df, diff --git a/tests/user_interfaces/test_multiple_models_api.py b/tests/user_interfaces/test_multiple_models_api.py new file mode 100644 index 00000000..c5fda9f5 --- /dev/null +++ b/tests/user_interfaces/test_multiple_models_api.py @@ -0,0 +1,90 @@ +import copy +import pathlib + +from virny.utils.custom_initializers import read_model_metric_dfs +from virny.user_interfaces import compute_metrics_with_config + +from tests import compare_metric_dfs_v2, compare_metric_dfs_with_tolerance + + +def test_compute_metrics_with_config_same_seed(law_school_dataset_1k_params): + base_flow_dataset, config, models_config, save_results_dir_path = law_school_dataset_1k_params + + config.random_state = 100 + metrics_dct1 = compute_metrics_with_config(dataset=base_flow_dataset, + config=config, + models_config=copy.deepcopy(models_config), + save_results_dir_path=save_results_dir_path) + metrics_dct2 = compute_metrics_with_config(dataset=base_flow_dataset, + config=config, + models_config=copy.deepcopy(models_config), + save_results_dir_path=save_results_dir_path) + + # Drop technical columns + metrics_dct1['LogisticRegression'] = metrics_dct1['LogisticRegression'].drop('Runtime_in_Mins', axis=1) + metrics_dct2['LogisticRegression'] = metrics_dct2['LogisticRegression'].drop('Runtime_in_Mins', axis=1) + + assert compare_metric_dfs_v2(metrics_dct1['LogisticRegression'], metrics_dct2['LogisticRegression']) + + +def test_compute_metrics_with_config_diff_seeds(law_school_dataset_1k_params): + base_flow_dataset, config, models_config, save_results_dir_path = law_school_dataset_1k_params + + config.random_state = 100 + metrics_dct1 = compute_metrics_with_config(dataset=base_flow_dataset, + config=config, + models_config=copy.deepcopy(models_config), + save_results_dir_path=save_results_dir_path) + config.random_state = 200 + metrics_dct2 = compute_metrics_with_config(dataset=base_flow_dataset, + config=config, + models_config=copy.deepcopy(models_config), + save_results_dir_path=save_results_dir_path) + + # Drop technical columns + metrics_dct1['LogisticRegression'] = metrics_dct1['LogisticRegression'].drop('Runtime_in_Mins', axis=1) + metrics_dct2['LogisticRegression'] = metrics_dct2['LogisticRegression'].drop('Runtime_in_Mins', axis=1) + + assert not compare_metric_dfs_v2(metrics_dct1['LogisticRegression'], metrics_dct2['LogisticRegression']) + + +def test_compute_metrics_with_config_none_seeds(law_school_dataset_1k_params): + base_flow_dataset, config, models_config, save_results_dir_path = law_school_dataset_1k_params + + metrics_dct1 = compute_metrics_with_config(dataset=base_flow_dataset, + config=config, + models_config=copy.deepcopy(models_config), + save_results_dir_path=save_results_dir_path) + metrics_dct2 = compute_metrics_with_config(dataset=base_flow_dataset, + config=config, + models_config=copy.deepcopy(models_config), + save_results_dir_path=save_results_dir_path) + + # Drop technical columns + metrics_dct1['LogisticRegression'] = metrics_dct1['LogisticRegression'].drop('Runtime_in_Mins', axis=1) + metrics_dct2['LogisticRegression'] = metrics_dct2['LogisticRegression'].drop('Runtime_in_Mins', axis=1) + + assert not compare_metric_dfs_v2(metrics_dct1['LogisticRegression'], metrics_dct2['LogisticRegression']) + + +def test_compute_metrics_with_config_should_equal_prev_release_results(law_school_dataset_20k_params): + base_flow_dataset, config, models_config, save_results_dir_path = law_school_dataset_20k_params + + config.random_state = 100 + metrics_dct = compute_metrics_with_config(dataset=base_flow_dataset, + config=config, + models_config=copy.deepcopy(models_config), + save_results_dir_path=save_results_dir_path) + + metrics_path = str(pathlib.Path(__file__).parent.parent.joinpath('files_for_tests', 'law_school_dataset_20k')) + expected_metrics_dct = read_model_metric_dfs(metrics_path, model_names=['LogisticRegression', 'DecisionTreeClassifier']) + + # Drop technical columns + metrics_dct['LogisticRegression'] = metrics_dct['LogisticRegression'].drop('Runtime_in_Mins', axis=1) + metrics_dct['DecisionTreeClassifier'] = metrics_dct['DecisionTreeClassifier'].drop('Runtime_in_Mins', axis=1) + + expected_metrics_dct['LogisticRegression'] = expected_metrics_dct['LogisticRegression'].drop('Runtime_in_Mins', axis=1) + expected_metrics_dct['DecisionTreeClassifier'] = expected_metrics_dct['DecisionTreeClassifier'].drop('Runtime_in_Mins', axis=1) + + assert compare_metric_dfs_with_tolerance(expected_metrics_dct['LogisticRegression'], metrics_dct['LogisticRegression']) + assert compare_metric_dfs_with_tolerance(expected_metrics_dct['DecisionTreeClassifier'], metrics_dct['DecisionTreeClassifier']) diff --git a/tests/utils/test_common_helpers.py b/tests/utils/test_common_helpers.py index 624fa239..d8ca49a6 100644 --- a/tests/utils/test_common_helpers.py +++ b/tests/utils/test_common_helpers.py @@ -82,6 +82,6 @@ def test_confusion_matrix_metrics(): actual_metrics = confusion_matrix_metrics(y_true, y_preds) - required_fields = ['TPR', 'TNR', 'PPV', 'FNR', 'FPR', 'Accuracy', 'F1', 'Selection-Rate', 'Positive-Rate'] + required_fields = ['TPR', 'TNR', 'PPV', 'FNR', 'FPR', 'Accuracy', 'F1', 'Selection-Rate'] for field in required_fields: assert field in actual_metrics.keys() diff --git a/tests/utils/test_stability_utils.py b/tests/utils/test_stability_utils.py index 3a9c8951..44cc362c 100644 --- a/tests/utils/test_stability_utils.py +++ b/tests/utils/test_stability_utils.py @@ -70,6 +70,7 @@ def test_generate_bootstrap_true1(compas_without_sensitive_attrs_dataset_class, ]) base_flow_ds = preprocess_dataset(compas_without_sensitive_attrs_dataset_class, column_transformer, + sensitive_attributes_dct={'sex': 1, 'race': 'African-American', 'sex&race': None}, test_set_fraction=0.2, dataset_split_seed=42) boostrap_size = int(config_params.bootstrap_fraction * base_flow_ds.X_train_val.shape[0]) diff --git a/virny/__version__.py b/virny/__version__.py index b87975ee..1d212215 100644 --- a/virny/__version__.py +++ b/virny/__version__.py @@ -1,3 +1,3 @@ -VERSION = (0, 4, 0) +VERSION = (0, 5, 0) __version__ = ".".join(map(str, VERSION)) diff --git a/virny/analyzers/abstract_overall_variance_analyzer.py b/virny/analyzers/abstract_overall_variance_analyzer.py index 110de40d..342ad955 100644 --- a/virny/analyzers/abstract_overall_variance_analyzer.py +++ b/virny/analyzers/abstract_overall_variance_analyzer.py @@ -35,6 +35,8 @@ class AbstractOverallVarianceAnalyzer(metaclass=ABCMeta): Name of dataset, used for correct results naming n_estimators Number of estimators in ensemble to measure base_model stability + random_state + [Optional] Controls the randomness of the bootstrap approach for model arbitrariness evaluation with_predict_proba [Optional] A flag if model can return probabilities for its predictions. If no, only metrics based on labels (not labels and probabilities) will be computed. @@ -49,7 +51,7 @@ class AbstractOverallVarianceAnalyzer(metaclass=ABCMeta): def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, X_train: pd.DataFrame, y_train: pd.DataFrame, X_test: pd.DataFrame, y_test: pd.DataFrame, - dataset_name: str, n_estimators: int, with_predict_proba: bool = True, + dataset_name: str, n_estimators: int, random_state: int = None, with_predict_proba: bool = True, notebook_logs_stdout: bool = False, verbose: int = 0): self.base_model = base_model self.base_model_name = base_model_name @@ -57,9 +59,10 @@ def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, self.dataset_name = dataset_name self.n_estimators = n_estimators self.models_lst = [deepcopy(base_model) for _ in range(n_estimators)] + self.random_state = random_state + self.with_predict_proba = with_predict_proba self.models_predictions = None self.prediction_metrics = None - self.with_predict_proba = with_predict_proba self._notebook_logs_stdout = notebook_logs_stdout self._verbose = verbose @@ -145,11 +148,29 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b # Train and test each estimator in models_predictions for idx in cycle_range: classifier = self.models_lst[idx] + + # If True, fit the classifier. Otherwise, use already fitted classifier. if with_fit: - X_sample, y_sample = generate_bootstrap(self.X_train, self.y_train, boostrap_size, with_replacement) + classifier_random_state = self.random_state + idx + 1 if self.random_state is not None else None + X_sample, y_sample = generate_bootstrap(features=self.X_train, + labels=self.y_train, + boostrap_size=boostrap_size, + with_replacement=with_replacement, + random_state=classifier_random_state) + if 'random_state' in classifier.get_params(): + classifier.set_params(random_state=classifier_random_state) classifier = self._fit_model(classifier, X_sample, y_sample) - models_predictions[idx] = self._batch_predict_proba(classifier, self.X_test) + + # Use a predict_proba method if the classifier supports it. + # Note that model predictions do not preserve X_test indexes. + # Indexes of the predictions will be aligned with X_test later in the pipeline. + if self.with_predict_proba: + models_predictions[idx] = self._batch_predict_proba(classifier, self.X_test) + else: + models_predictions[idx] = self._batch_predict(classifier, self.X_test) + self.models_lst[idx] = classifier + # Force garbage collection to avoid out of memory error if with_fit and ((idx + 1) % 10 == 0 or (idx + 1) == self.n_estimators): gc.collect() diff --git a/virny/analyzers/abstract_subgroup_analyzer.py b/virny/analyzers/abstract_subgroup_analyzer.py index 9ed295bc..85ecaffb 100644 --- a/virny/analyzers/abstract_subgroup_analyzer.py +++ b/virny/analyzers/abstract_subgroup_analyzer.py @@ -5,8 +5,7 @@ from datetime import datetime, timezone from abc import ABCMeta, abstractmethod -from virny.configs.constants import (ComputationMode, TPR, TNR, PPV, FPR, FNR, ACCURACY, F1, - SELECTION_RATE, POSITIVE_RATE) +from virny.configs.constants import ComputationMode, TPR, TNR, PPV, FPR, FNR, ACCURACY, F1, SELECTION_RATE class AbstractSubgroupAnalyzer(metaclass=ABCMeta): @@ -94,7 +93,6 @@ def _partition_and_compute_metrics_for_error_analysis(self, y_preds, models_pred ACCURACY: None, F1: None, SELECTION_RATE: None, - POSITIVE_RATE: None, } else: metrics_dct = self._compute_metrics(self.y_test[partition_indexes], y_preds[partition_indexes]) diff --git a/virny/analyzers/batch_overall_variance_analyzer.py b/virny/analyzers/batch_overall_variance_analyzer.py index 3525de6d..a0646ac0 100644 --- a/virny/analyzers/batch_overall_variance_analyzer.py +++ b/virny/analyzers/batch_overall_variance_analyzer.py @@ -30,6 +30,8 @@ class BatchOverallVarianceAnalyzer(AbstractOverallVarianceAnalyzer): Name of dataset, used for correct results naming n_estimators Number of estimators in ensemble to measure base_model stability + random_state + [Optional] Controls the randomness of the bootstrap approach for model arbitrariness evaluation with_predict_proba [Optional] A flag if model can return probabilities for its predictions. If no, only metrics based on labels (not labels and probabilities) will be computed. @@ -43,7 +45,7 @@ class BatchOverallVarianceAnalyzer(AbstractOverallVarianceAnalyzer): """ def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, X_train: pd.DataFrame, y_train: pd.DataFrame, X_test: pd.DataFrame, y_test: pd.DataFrame, - target_column: str, dataset_name: str, n_estimators: int, + target_column: str, dataset_name: str, n_estimators: int, random_state: int = None, with_predict_proba: bool = True, notebook_logs_stdout: bool = False, verbose: int = 0): super().__init__(base_model=base_model, base_model_name=base_model_name, @@ -54,6 +56,7 @@ def __init__(self, base_model, base_model_name: str, bootstrap_fraction: float, y_test=y_test, dataset_name=dataset_name, n_estimators=n_estimators, + random_state=random_state, with_predict_proba=with_predict_proba, notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) diff --git a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py index 4c70d289..fe2d518e 100644 --- a/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py +++ b/virny/analyzers/batch_overall_variance_analyzer_postprocessing.py @@ -39,6 +39,8 @@ class BatchOverallVarianceAnalyzerPostProcessing(BatchOverallVarianceAnalyzer): Name of dataset, used for correct results naming n_estimators Number of estimators in ensemble to measure base_model stability + random_state + [Optional] Controls the randomness of the bootstrap approach for model arbitrariness evaluation with_predict_proba [Optional] A flag if model can return probabilities for its predictions. If no, only metrics based on labels (not labels and probabilities) will be computed. @@ -53,7 +55,7 @@ class BatchOverallVarianceAnalyzerPostProcessing(BatchOverallVarianceAnalyzer): def __init__(self, postprocessor, sensitive_attribute: str, base_model, base_model_name: str, bootstrap_fraction: float, X_train: pd.DataFrame, y_train: pd.DataFrame, X_test: pd.DataFrame, y_test: pd.DataFrame, - target_column: str, dataset_name: str, n_estimators: int, + target_column: str, dataset_name: str, n_estimators: int, random_state: int = None, with_predict_proba: bool = True, notebook_logs_stdout: bool = False, verbose: int = 0): if sensitive_attribute is None: raise ValueError('Sensitive attribute for postprocessing is not defined. ' @@ -69,6 +71,7 @@ def __init__(self, postprocessor, sensitive_attribute: str, target_column=target_column, dataset_name=dataset_name, n_estimators=n_estimators, + random_state=random_state, with_predict_proba=with_predict_proba, notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) @@ -117,7 +120,13 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b for idx in cycle_range: classifier = self.models_lst[idx] if with_fit: - X_sample, y_sample = generate_bootstrap(self.X_train, self.y_train, boostrap_size, with_replacement) + classifier_random_state = self.random_state + idx + 1 if self.random_state is not None else None + X_sample, y_sample = generate_bootstrap(features=self.X_train, + labels=self.y_train, + boostrap_size=boostrap_size, + with_replacement=with_replacement, + random_state=classifier_random_state) + classifier.set_params(random_state=classifier_random_state) classifier = self._fit_model(classifier, X_sample, y_sample) # Force garbage collection to avoid out of memory error @@ -128,7 +137,9 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b train_binary_label_dataset_sample_pred = predict_on_binary_label_dataset(classifier, train_binary_label_dataset_sample) test_binary_label_dataset_pred = predict_on_binary_label_dataset(classifier, self.test_binary_label_dataset) postprocessor_fitted = self.postprocessor.fit(train_binary_label_dataset_sample, train_binary_label_dataset_sample_pred) - + + # Note that model predictions do not preserve X_test indexes. + # Indexes of the predictions will be aligned with X_test later in the pipeline. models_predictions[idx] = postprocessor_fitted.predict(test_binary_label_dataset_pred).labels.ravel() self.models_lst[idx] = classifier diff --git a/virny/analyzers/subgroup_variance_analyzer.py b/virny/analyzers/subgroup_variance_analyzer.py index 0b94aa39..a429d5bd 100644 --- a/virny/analyzers/subgroup_variance_analyzer.py +++ b/virny/analyzers/subgroup_variance_analyzer.py @@ -1,6 +1,6 @@ import pandas as pd -from virny.configs.constants import ModelSetting, ComputationMode +from virny.configs.constants import ModelSetting from virny.custom_classes.base_dataset import BaseFlowDataset from virny.analyzers.subgroup_variance_calculator import SubgroupVarianceCalculator from virny.analyzers.batch_overall_variance_analyzer import BatchOverallVarianceAnalyzer @@ -37,8 +37,14 @@ class SubgroupVarianceAnalyzer: One of postprocessors from aif360 (https://aif360.readthedocs.io/en/stable/modules/algorithms.html#module-aif360.algorithms.postprocessing) postprocessing_sensitive_attribute A sensitive attribute to use for post-processing + random_state + [Optional] Controls the randomness of the bootstrap approach for model arbitrariness evaluation computation_mode [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. + with_predict_proba + [Optional] True, if models in models_config have a predict_proba method and can return probabilities for predictions, + False, otherwise. Note that if it is set to False, only metrics based on labels (not labels and probabilities) will be computed. + Ignored when a postprocessor is not None, and set to False in this case. notebook_logs_stdout [Optional] True, if this interface was execute in a Jupyter notebook, False, otherwise. @@ -50,8 +56,10 @@ class SubgroupVarianceAnalyzer: def __init__(self, model_setting: ModelSetting, n_estimators: int, base_model, base_model_name: str, bootstrap_fraction: float, dataset: BaseFlowDataset, dataset_name: str, sensitive_attributes_dct: dict, test_protected_groups: dict, postprocessor=None, - postprocessing_sensitive_attribute : str = None, computation_mode: str = None, - notebook_logs_stdout: bool = False, verbose: int = 0): + postprocessing_sensitive_attribute : str = None, random_state: int = None, computation_mode: str = None, + with_predict_proba: bool = True, notebook_logs_stdout: bool = False, verbose: int = 0): + + with_predict_proba = False if postprocessor is not None else with_predict_proba if model_setting == ModelSetting.BATCH: if postprocessor is not None: print('Enabled a postprocessing mode') @@ -67,7 +75,8 @@ def __init__(self, model_setting: ModelSetting, n_estimators: int, base_model, b dataset_name=dataset_name, target_column=dataset.target, n_estimators=n_estimators, - with_predict_proba=False, + random_state=random_state, + with_predict_proba=with_predict_proba, notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) else: @@ -81,6 +90,8 @@ def __init__(self, model_setting: ModelSetting, n_estimators: int, base_model, b dataset_name=dataset_name, target_column=dataset.target, n_estimators=n_estimators, + random_state=random_state, + with_predict_proba=with_predict_proba, notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) else: @@ -91,8 +102,6 @@ def __init__(self, model_setting: ModelSetting, n_estimators: int, base_model, b self.base_model_name = overall_variance_analyzer.base_model_name self.__overall_variance_analyzer = overall_variance_analyzer - - with_predict_proba = False if postprocessor is not None else True self.__subgroup_variance_calculator = SubgroupVarianceCalculator(X_test=dataset.X_test, y_test=dataset.y_test, sensitive_attributes_dct=sensitive_attributes_dct, diff --git a/virny/configs/constants.py b/virny/configs/constants.py index 8e0e0f91..f237771a 100644 --- a/virny/configs/constants.py +++ b/virny/configs/constants.py @@ -24,7 +24,6 @@ class ComputationMode(Enum): F1 = 'F1' ACCURACY = 'Accuracy' SELECTION_RATE = 'Selection-Rate' -POSITIVE_RATE = 'Positive-Rate' # Stability metrics STD = 'Std' diff --git a/virny/custom_classes/base_dataset.py b/virny/custom_classes/base_dataset.py index 888ef3ca..081eb15c 100644 --- a/virny/custom_classes/base_dataset.py +++ b/virny/custom_classes/base_dataset.py @@ -8,8 +8,8 @@ class BaseFlowDataset: Parameters ---------- - init_features_df - Full train + test non-preprocessed dataset of features without the target column. + init_sensitive_attrs_df + Full train + test non-preprocessed dataset of sensitive attributes with initial indexes. It is used for creating test groups. X_train_val Train dataframe of features @@ -28,15 +28,29 @@ class BaseFlowDataset: """ - def __init__(self, init_features_df: pd.DataFrame, X_train_val: pd.DataFrame, X_test: pd.DataFrame, - y_train_val: pd.DataFrame, y_test: pd.DataFrame, - target: str, numerical_columns: list, categorical_columns: list): + def __init__(self, init_sensitive_attrs_df: pd.DataFrame, X_train_val: pd.DataFrame, X_test: pd.DataFrame, + y_train_val: pd.DataFrame, y_test: pd.DataFrame, target: str, + numerical_columns: list, categorical_columns: list): # Validate input sets - if not isinstance(init_features_df, pd.DataFrame) or not isinstance(X_train_val, pd.DataFrame) \ + if not isinstance(init_sensitive_attrs_df, pd.DataFrame) or not isinstance(X_train_val, pd.DataFrame) \ or not isinstance(X_test, pd.DataFrame): raise ValueError("Input feature sets must be in a pd.DataFrame format") - self.init_features_df = init_features_df + assert X_test.index.isin(init_sensitive_attrs_df.index).all(), \ + ("Not all indexes of X_test are present in init_sensitive_attrs_df. " + "It is important to correctly compute metrics for protected groups in the test set.") + assert y_test.index.isin(init_sensitive_attrs_df.index).all(), \ + ("Not all indexes of y_test are present in init_sensitive_attrs_df. " + "It is important to correctly compute metrics for protected groups in the test set.") + + assert X_train_val.index.equals(y_train_val.index) is True, \ + "Indexes of X_train_val and y_train_val are different" + assert X_test.index.equals(y_test.index) is True, \ + ("Indexes of X_test and y_test should be the same to correctly compute metrics " + "for protected groups in the test set") + + # Define parameters + self.init_sensitive_attrs_df = init_sensitive_attrs_df self.X_train_val = X_train_val self.X_test = X_test self.y_train_val = y_train_val @@ -46,4 +60,3 @@ def __init__(self, init_features_df: pd.DataFrame, X_train_val: pd.DataFrame, X_ self.categorical_columns = categorical_columns self.features = numerical_columns + categorical_columns self.target = target - self.columns_with_nulls = self.init_features_df.columns[self.init_features_df.isna().any().to_list()].to_list() diff --git a/virny/custom_classes/base_inprocessing_wrapper.py b/virny/custom_classes/base_inprocessing_wrapper.py index 0719c3bb..4e49d69a 100644 --- a/virny/custom_classes/base_inprocessing_wrapper.py +++ b/virny/custom_classes/base_inprocessing_wrapper.py @@ -13,14 +13,23 @@ def fit(self, X, y): @abc.abstractmethod def predict_proba(self, X): - """Predicts using the fitted inprocessor based on X features pandas dataframe. Returns probabilities - for **ZERO** class. These probabilities will be used by Virny in the downstream metric computation.""" + """ + Predicts using the fitted inprocessor based on X features pandas dataframe. Returns probabilities + for **ZERO** class. These probabilities will be used by Virny in the downstream metric computation. + + If the inprocessor does not support prediction probabilities, the implementation of this method can be skipped, + and with_predict_proba = False should be set in the metric computation interface. + + Indexes from the returned array of predictions should start from zero. + """ raise NotImplementedError @abc.abstractmethod def predict(self, X): - """Predicts using the fitted inprocessor based on X features pandas dataframe. Returns labels for each - sample.""" + """ + Predicts using the fitted inprocessor based on X features pandas dataframe. Returns labels for each + sample. Indexes from the returned array of predictions should start from zero. + """ raise NotImplementedError @abc.abstractmethod @@ -35,3 +44,8 @@ def __deepcopy__(self, memo): def get_params(self): """Returns parameters of the wrapper. Alignment with sklearn API.""" raise NotImplementedError + + @abc.abstractmethod + def set_params(self, random_state: int): + """Set a random state of the inprocessor.""" + raise NotImplementedError diff --git a/virny/custom_classes/metrics_composer.py b/virny/custom_classes/metrics_composer.py index 7787a650..7c5e42b8 100644 --- a/virny/custom_classes/metrics_composer.py +++ b/virny/custom_classes/metrics_composer.py @@ -34,16 +34,16 @@ def __init__(self, models_metrics_dct: dict, sensitive_attributes_dct: dict): FPR: [(EQUALIZED_ODDS_FPR, self._difference_operation)], FNR: [(EQUALIZED_ODDS_FNR, self._difference_operation)], ACCURACY: [(ACCURACY_DIFFERENCE, self._difference_operation)], - POSITIVE_RATE: [(DISPARATE_IMPACT, self._ratio_operation)], - SELECTION_RATE: [(STATISTICAL_PARITY_DIFFERENCE, self._difference_operation)], + SELECTION_RATE: [(STATISTICAL_PARITY_DIFFERENCE, self._difference_operation), + (DISPARATE_IMPACT, self._ratio_operation)], # Stability disparity metrics LABEL_STABILITY: [(LABEL_STABILITY_RATIO, self._ratio_operation), (LABEL_STABILITY_DIFFERENCE, self._difference_operation)], JITTER: [(JITTER_DIFFERENCE, self._difference_operation)], IQR: [(IQR_DIFFERENCE, self._difference_operation)], + # Uncertainty disparity metrics STD: [(STD_DIFFERENCE, self._difference_operation), (STD_RATIO, self._ratio_operation)], - # Uncertainty disparity metrics OVERALL_UNCERTAINTY: [(OVERALL_UNCERTAINTY_DIFFERENCE, self._difference_operation), (OVERALL_UNCERTAINTY_RATIO, self._ratio_operation)], ALEATORIC_UNCERTAINTY: [(ALEATORIC_UNCERTAINTY_DIFFERENCE, self._difference_operation), diff --git a/virny/custom_classes/metrics_interactive_visualizer.py b/virny/custom_classes/metrics_interactive_visualizer.py index a6a18dde..90124a1b 100644 --- a/virny/custom_classes/metrics_interactive_visualizer.py +++ b/virny/custom_classes/metrics_interactive_visualizer.py @@ -55,7 +55,7 @@ def __init__(self, X_data: pd.DataFrame, y_data: pd.DataFrame, model_metrics, se self.max_groups = 8 # Metric names - self.all_accuracy_metrics = [STATISTICAL_BIAS, TPR, TNR, PPV, FNR, FPR, F1, ACCURACY, POSITIVE_RATE] + self.all_accuracy_metrics = [STATISTICAL_BIAS, TPR, TNR, PPV, FNR, FPR, F1, ACCURACY] self.all_stability_metrics = [STD, IQR, JITTER, LABEL_STABILITY] self.all_uncertainty_metrics = [ALEATORIC_UNCERTAINTY, OVERALL_UNCERTAINTY, EPISTEMIC_UNCERTAINTY] self.all_error_disparity_metrics = [EQUALIZED_ODDS_TPR, EQUALIZED_ODDS_TNR, EQUALIZED_ODDS_FPR, EQUALIZED_ODDS_FNR, DISPARATE_IMPACT, STATISTICAL_PARITY_DIFFERENCE, ACCURACY_DIFFERENCE] @@ -367,7 +367,7 @@ def create_web_app(self, start_app=True): """) with gr.Row(): accuracy_metric_vw4 = gr.Dropdown( - sorted([metric for metric in self.all_accuracy_metrics if metric not in (POSITIVE_RATE, SELECTION_RATE)]), + sorted([metric for metric in self.all_accuracy_metrics if metric != SELECTION_RATE]), value=ACCURACY, multiselect=False, label="Correctness Metric", scale=2 ) @@ -391,7 +391,7 @@ def create_web_app(self, start_app=True): subgroup_uncertainty_max_val_vw4 = gr.Text(value="1.0", label="Max value", scale=1) with gr.Row(): positive_rate_metric_vw4 = gr.Dropdown( - [POSITIVE_RATE, SELECTION_RATE], + [SELECTION_RATE], value=SELECTION_RATE, multiselect=False, label="Representation Metric", scale=2 ) diff --git a/virny/custom_classes/metrics_visualizer.py b/virny/custom_classes/metrics_visualizer.py index 0b1bff8e..75a531c0 100644 --- a/virny/custom_classes/metrics_visualizer.py +++ b/virny/custom_classes/metrics_visualizer.py @@ -36,7 +36,7 @@ def __init__(self, models_metrics_dct: dict, models_composed_metrics_df: pd.Data self.sensitive_attributes_dct = sensitive_attributes_dct # Metric names - self.all_accuracy_metrics = [STATISTICAL_BIAS, TPR, TNR, PPV, FNR, FPR, F1, ACCURACY, POSITIVE_RATE] + self.all_accuracy_metrics = [STATISTICAL_BIAS, TPR, TNR, PPV, FNR, FPR, F1, ACCURACY] self.all_stability_metrics = [STD, IQR, JITTER, LABEL_STABILITY] self.all_uncertainty_metrics = [ALEATORIC_UNCERTAINTY, OVERALL_UNCERTAINTY, EPISTEMIC_UNCERTAINTY] self.all_error_disparity_metrics = [EQUALIZED_ODDS_TPR, EQUALIZED_ODDS_TNR, EQUALIZED_ODDS_FPR, EQUALIZED_ODDS_FNR, DISPARATE_IMPACT, STATISTICAL_PARITY_DIFFERENCE, ACCURACY_DIFFERENCE] diff --git a/virny/datasets/__init__.py b/virny/datasets/__init__.py index 15353e4a..348740e4 100644 --- a/virny/datasets/__init__.py +++ b/virny/datasets/__init__.py @@ -2,19 +2,24 @@ This module contains sample datasets and data loaders. The purpose is to provide sample datasets for functionality testing and show examples of data loaders (aka dataset classes). """ -from .data_loaders import CompasWithoutSensitiveAttrsDataset, DiabetesDataset, CompasDataset, \ - ACSIncomeDataset, ACSEmploymentDataset, ACSMobilityDataset, ACSTravelTimeDataset, ACSPublicCoverageDataset, \ - RicciDataset, LawSchoolDataset, CreditCardDefaultDataset, StudentPerformancePortugueseDataset +from .finance import GermanCreditDataset, BankMarketingDataset +from .compas import CompasWithoutSensitiveAttrsDataset, CompasDataset +from .healthcare import CardiovascularDiseaseDataset, DiabetesDataset2019, RicciDataset +from .education import LawSchoolDataset, StudentPerformancePortugueseDataset +from .folktables import (ACSIncomeDataset, ACSEmploymentDataset, ACSMobilityDataset, ACSTravelTimeDataset, + ACSPublicCoverageDataset) __all__ = [ + "GermanCreditDataset", + "BankMarketingDataset", "CompasWithoutSensitiveAttrsDataset", "CompasDataset", - "CreditCardDefaultDataset", - "DiabetesDataset", + "CardiovascularDiseaseDataset", + "DiabetesDataset2019", "RicciDataset", - "StudentPerformancePortugueseDataset", "LawSchoolDataset", + "StudentPerformancePortugueseDataset", "ACSIncomeDataset", "ACSEmploymentDataset", "ACSMobilityDataset", diff --git a/virny/datasets/compas.py b/virny/datasets/compas.py new file mode 100644 index 00000000..aa569ce3 --- /dev/null +++ b/virny/datasets/compas.py @@ -0,0 +1,93 @@ +import pathlib +import pandas as pd + +from virny.datasets.base import BaseDataLoader + + +class CompasDataset(BaseDataLoader): + """ + Dataset class for the COMPAS dataset that contains sensitive attributes among feature columns. + + Parameters + ---------- + subsample_size + Subsample size to create based on the input dataset + subsample_seed + Seed for sampling using the sample() method from pandas + dataset_path + [Optional] Path to a file with the data + + """ + def __init__(self, subsample_size: int = None, subsample_seed: int = None, dataset_path=None): + if dataset_path is None: + filename = 'COMPAS.csv' + dataset_path = pathlib.Path(__file__).parent.joinpath('data').joinpath(filename) + + df = pd.read_csv(dataset_path) + if subsample_size: + df = df.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ + else df.sample(subsample_size) + df = df.reset_index(drop=True) + + int_columns = ['recidivism', 'age', 'age_cat_25 - 45', 'age_cat_Greater than 45', + 'age_cat_Less than 25', 'c_charge_degree_F', 'c_charge_degree_M', 'sex'] + int_columns_dct = {col: "int" for col in int_columns} + df = df.astype(int_columns_dct) + + target = 'recidivism' + numerical_columns = ['age', 'juv_fel_count', 'juv_misd_count', 'juv_other_count', 'priors_count'] + categorical_columns = ['race', 'age_cat_25 - 45', 'age_cat_Greater than 45', + 'age_cat_Less than 25', 'c_charge_degree_F', 'c_charge_degree_M', 'sex'] + + super().__init__( + full_df=df, + target=target, + numerical_columns=numerical_columns, + categorical_columns=categorical_columns, + ) + + +class CompasWithoutSensitiveAttrsDataset(BaseDataLoader): + """ + Dataset class for the COMPAS dataset that does not contain sensitive attributes among feature columns + to test blind classifiers + + Parameters + ---------- + subsample_size + Subsample size to create based on the input dataset + subsample_seed + Seed for sampling using the sample() method from pandas + dataset_path + [Optional] Path to a file with the data + + """ + def __init__(self, subsample_size: int = None, subsample_seed: int = None, dataset_path=None): + if dataset_path is None: + filename = 'COMPAS.csv' + dataset_path = pathlib.Path(__file__).parent.joinpath('data').joinpath(filename) + + df = pd.read_csv(dataset_path) + if subsample_size: + df = df.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ + else df.sample(subsample_size) + df = df.reset_index(drop=True) + + # Initial data types transformation + int_columns = ['recidivism', 'age', 'age_cat_25 - 45', 'age_cat_Greater than 45', + 'age_cat_Less than 25', 'c_charge_degree_F', 'c_charge_degree_M', 'sex'] + int_columns_dct = {col: "int" for col in int_columns} + df = df.astype(int_columns_dct) + + # Define params + target = 'recidivism' + numerical_columns = ['juv_fel_count', 'juv_misd_count', 'juv_other_count','priors_count'] + categorical_columns = ['age_cat_25 - 45', 'age_cat_Greater than 45','age_cat_Less than 25', + 'c_charge_degree_F', 'c_charge_degree_M'] + + super().__init__( + full_df=df, + target=target, + numerical_columns=numerical_columns, + categorical_columns=categorical_columns + ) diff --git a/virny/datasets/credit_card_default_clean.csv b/virny/datasets/credit_card_default_clean.csv deleted file mode 100644 index 475e1433..00000000 --- a/virny/datasets/credit_card_default_clean.csv +++ /dev/null @@ -1,29947 +0,0 @@ -,limit_bal,sex,education,marriage,age,pay_0,pay_2,pay_3,pay_4,pay_5,pay_6,bill_amt1,bill_amt2,bill_amt3,bill_amt4,bill_amt5,bill_amt6,pay_amt1,pay_amt2,pay_amt3,pay_amt4,pay_amt5,pay_amt6,default_payment -0,20000,female,2,1,24,2,2,-1,-1,-2,-2,3913,3102,689,0,0,0,0,689,0,0,0,0,1 -1,120000,female,2,2,26,-1,2,0,0,0,2,2682,1725,2682,3272,3455,3261,0,1000,1000,1000,0,2000,1 -2,90000,female,2,2,34,0,0,0,0,0,0,29239,14027,13559,14331,14948,15549,1518,1500,1000,1000,1000,5000,0 -3,50000,female,2,1,37,0,0,0,0,0,0,46990,48233,49291,28314,28959,29547,2000,2019,1200,1100,1069,1000,0 -4,50000,male,2,1,57,-1,0,-1,0,0,0,8617,5670,35835,20940,19146,19131,2000,36681,10000,9000,689,679,0 -5,50000,male,1,2,37,0,0,0,0,0,0,64400,57069,57608,19394,19619,20024,2500,1815,657,1000,1000,800,0 -6,500000,male,1,2,29,0,0,0,0,0,0,367965,412023,445007,542653,483003,473944,55000,40000,38000,20239,13750,13770,0 -7,100000,female,2,2,23,0,-1,-1,0,0,-1,11876,380,601,221,-159,567,380,601,0,581,1687,1542,0 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+30,management,single,tertiary,no,536,no,no,cellular,15,oct,244,1,105,1,success,yes +41,management,married,tertiary,no,3872,no,no,cellular,15,oct,231,1,-1,0,unknown,no +19,student,single,primary,no,103,no,no,cellular,15,oct,96,2,97,2,success,no +54,blue-collar,married,primary,no,1311,no,no,telephone,15,oct,485,3,184,3,failure,yes +26,admin.,single,secondary,no,766,no,no,cellular,15,oct,457,2,-1,0,unknown,yes +62,housemaid,married,tertiary,no,1528,no,no,cellular,15,oct,134,2,184,1,success,yes +37,services,married,secondary,no,1960,yes,yes,cellular,15,oct,246,5,-1,0,unknown,no +43,services,married,primary,no,2557,yes,no,telephone,15,oct,166,3,183,5,success,yes +74,retired,married,primary,no,279,no,no,telephone,15,oct,227,1,-1,0,unknown,no +40,unemployed,single,tertiary,no,79,no,no,cellular,15,oct,570,1,135,1,failure,yes +37,management,married,tertiary,no,413,no,no,cellular,16,oct,138,2,-1,0,unknown,no +57,management,married,tertiary,no,1318,no,no,cellular,16,oct,115,1,185,2,failure,yes +75,retired,married,secondary,no,3771,no,no,telephone,16,oct,186,1,183,1,failure,yes +77,retired,married,tertiary,no,0,no,no,cellular,16,oct,847,1,60,1,success,yes +31,management,married,tertiary,no,6138,no,no,cellular,16,oct,154,2,119,5,failure,yes +61,technician,married,primary,no,6384,no,no,cellular,16,oct,55,2,-1,0,unknown,no +69,retired,married,secondary,no,0,no,no,cellular,16,oct,1580,2,-1,0,unknown,no +22,services,single,secondary,no,129,no,no,cellular,16,oct,258,1,-1,0,unknown,yes +50,management,married,tertiary,no,469,no,no,cellular,16,oct,170,1,183,1,success,yes +27,management,married,tertiary,no,893,no,no,cellular,16,oct,154,2,-1,0,unknown,yes +50,admin.,single,secondary,no,3702,no,no,cellular,16,oct,109,5,-1,0,unknown,no +72,retired,married,secondary,no,2850,no,no,cellular,16,oct,131,2,241,1,success,no +31,admin.,single,secondary,no,523,no,yes,cellular,16,oct,141,1,126,4,failure,no +51,admin.,married,secondary,no,423,yes,yes,cellular,16,oct,95,1,-1,0,unknown,no +65,retired,married,primary,no,493,no,no,cellular,16,oct,622,1,182,1,failure,no +34,services,single,tertiary,no,3119,no,no,cellular,16,oct,306,1,-1,0,unknown,yes +33,management,single,tertiary,no,8514,no,no,cellular,16,oct,165,1,57,1,failure,no +73,retired,married,unknown,no,519,no,no,telephone,16,oct,434,1,57,1,failure,yes +35,admin.,married,tertiary,no,690,no,no,cellular,16,oct,108,1,-1,0,unknown,no +40,technician,married,secondary,no,2266,no,no,cellular,16,oct,447,1,147,5,failure,yes +37,services,married,secondary,no,4017,no,no,cellular,16,oct,156,1,-1,0,unknown,yes +44,technician,single,tertiary,no,643,no,no,cellular,16,oct,324,1,143,3,failure,yes +40,technician,married,secondary,no,2338,no,no,cellular,16,oct,717,2,-1,0,unknown,yes +45,blue-collar,single,secondary,no,0,yes,no,cellular,16,oct,283,1,165,3,failure,no +61,management,married,tertiary,no,0,no,no,cellular,16,oct,136,2,-1,0,unknown,yes +70,retired,married,secondary,no,3175,no,no,cellular,19,oct,115,2,-1,0,unknown,no +63,retired,married,secondary,no,588,no,no,cellular,19,oct,143,2,-1,0,unknown,no +62,retired,married,primary,no,1515,yes,no,cellular,19,oct,131,1,187,2,failure,no +33,unknown,single,secondary,no,9,no,no,cellular,19,oct,238,1,-1,0,unknown,no +47,blue-collar,married,secondary,no,820,no,no,cellular,19,oct,98,1,-1,0,unknown,yes +62,blue-collar,married,primary,no,1381,no,no,cellular,19,oct,1020,1,-1,0,unknown,no +39,management,single,tertiary,no,1648,no,no,cellular,19,oct,123,1,-1,0,unknown,no +46,blue-collar,married,tertiary,no,3745,no,no,cellular,19,oct,82,1,-1,0,unknown,no +26,management,single,tertiary,no,1177,no,no,cellular,19,oct,116,1,-1,0,unknown,no +37,management,single,tertiary,no,254,no,no,cellular,19,oct,120,1,-1,0,unknown,no +26,technician,single,secondary,no,334,no,no,cellular,19,oct,217,1,-1,0,unknown,no +80,management,married,primary,no,6483,no,no,telephone,19,oct,123,1,-1,0,unknown,no +30,admin.,single,secondary,no,648,no,no,cellular,19,oct,54,1,-1,0,unknown,no +72,retired,married,primary,no,806,no,no,cellular,19,oct,169,1,-1,0,unknown,no +46,housemaid,single,primary,no,10889,no,no,cellular,19,oct,167,3,-1,0,unknown,no +47,services,single,unknown,no,1784,no,no,cellular,19,oct,385,2,-1,0,unknown,yes +59,unemployed,married,secondary,no,561,no,no,telephone,19,oct,323,2,-1,0,unknown,no +30,unemployed,married,primary,no,1787,no,no,cellular,19,oct,79,1,-1,0,unknown,no +56,management,single,unknown,no,111,no,no,cellular,19,oct,79,1,186,1,failure,no +30,admin.,single,tertiary,no,261,no,no,cellular,19,oct,233,1,137,20,failure,no +54,management,married,unknown,no,831,yes,yes,telephone,19,oct,208,3,109,3,failure,no +57,unknown,married,primary,no,8,no,no,telephone,19,oct,270,2,-1,0,unknown,no +28,admin.,married,secondary,no,172,yes,no,cellular,19,oct,123,2,-1,0,unknown,no +60,management,married,secondary,no,0,no,no,telephone,19,oct,1745,3,62,3,failure,no +30,student,single,tertiary,no,7529,no,no,cellular,19,oct,328,1,-1,0,unknown,yes +63,retired,married,primary,no,12067,no,no,telephone,20,oct,2187,1,-1,0,unknown,no +61,admin.,married,secondary,no,1541,no,no,cellular,20,oct,59,1,104,3,failure,no +31,unemployed,single,secondary,no,8304,no,no,cellular,20,oct,164,1,183,1,failure,no +39,management,married,tertiary,no,1802,no,no,cellular,20,oct,92,1,336,1,failure,no +22,student,single,primary,no,3472,no,no,cellular,20,oct,332,1,183,3,failure,yes +39,technician,married,tertiary,no,206,no,no,cellular,20,oct,148,2,195,2,success,yes +37,management,single,tertiary,no,0,no,no,cellular,20,oct,107,1,258,3,other,no +72,retired,married,primary,no,1388,no,no,telephone,20,oct,513,1,-1,0,unknown,yes +77,retired,married,unknown,no,1807,no,no,telephone,20,oct,373,1,-1,0,unknown,yes +36,technician,married,secondary,no,480,no,no,cellular,20,oct,213,1,-1,0,unknown,yes +43,technician,married,secondary,no,69,yes,yes,cellular,20,oct,304,1,337,1,failure,no +65,retired,married,secondary,no,571,yes,no,cellular,20,oct,349,1,183,2,success,yes +54,technician,married,secondary,no,3994,no,yes,cellular,20,oct,595,2,187,2,success,yes +32,blue-collar,married,primary,no,400,no,no,cellular,20,oct,76,2,-1,0,unknown,no +25,student,single,secondary,no,3165,no,no,telephone,20,oct,251,2,-1,0,unknown,no +46,admin.,single,secondary,no,1693,yes,no,cellular,20,oct,427,1,-1,0,unknown,yes +28,management,single,tertiary,no,80,no,no,cellular,20,oct,676,2,146,2,failure,yes +61,management,married,tertiary,no,267,no,no,cellular,21,oct,192,3,184,1,success,yes +53,housemaid,married,primary,no,2581,no,no,cellular,21,oct,828,1,189,1,failure,yes +69,retired,married,tertiary,no,473,no,no,cellular,21,oct,110,1,106,2,failure,no +27,student,single,tertiary,no,1134,no,no,cellular,21,oct,825,1,183,4,success,yes +74,retired,married,tertiary,no,4912,no,no,telephone,21,oct,129,3,-1,0,unknown,no +67,retired,married,tertiary,no,3810,no,no,cellular,21,oct,1206,1,-1,0,unknown,no +33,management,single,tertiary,no,6807,no,no,cellular,21,oct,512,2,184,1,success,yes +27,student,single,tertiary,no,24025,no,no,cellular,21,oct,99,1,-1,0,unknown,no +57,management,married,tertiary,no,375,no,no,cellular,21,oct,135,1,-1,0,unknown,yes +33,self-employed,single,tertiary,no,658,yes,no,cellular,21,oct,307,1,208,1,success,yes +51,technician,married,secondary,no,371,yes,yes,cellular,21,oct,287,1,-1,0,unknown,yes +64,management,married,tertiary,no,4688,no,no,cellular,21,oct,200,1,-1,0,unknown,yes +35,technician,single,secondary,no,70,yes,no,cellular,21,oct,197,2,188,2,other,no +19,student,single,secondary,no,329,no,no,cellular,22,oct,252,2,-1,0,unknown,yes +33,admin.,married,tertiary,no,79,yes,no,cellular,22,oct,249,2,335,2,failure,yes +31,blue-collar,single,unknown,no,6281,no,no,cellular,22,oct,257,1,128,3,other,yes +92,retired,married,unknown,no,775,no,no,cellular,22,oct,313,3,-1,0,unknown,yes +35,technician,married,secondary,no,2779,no,no,cellular,22,oct,118,2,-1,0,unknown,yes +30,technician,single,tertiary,no,411,no,no,cellular,22,oct,127,3,149,2,failure,yes +68,retired,single,tertiary,no,2144,no,no,cellular,22,oct,277,1,184,2,success,yes +41,technician,single,tertiary,no,431,no,no,cellular,22,oct,133,1,107,2,success,yes +35,management,single,tertiary,no,1750,yes,no,cellular,22,oct,422,1,175,4,success,no +32,management,single,tertiary,no,643,no,no,cellular,22,oct,287,1,-1,0,unknown,no +29,self-employed,married,secondary,no,123,no,yes,cellular,22,oct,247,1,184,3,success,yes +70,retired,married,secondary,no,2064,no,no,cellular,22,oct,334,1,-1,0,unknown,no +54,admin.,married,secondary,no,0,no,no,cellular,22,oct,391,2,-1,0,unknown,no +27,blue-collar,single,secondary,no,3792,no,no,cellular,22,oct,504,1,183,2,success,yes +78,retired,married,unknown,no,4807,no,no,telephone,22,oct,1002,4,-1,0,unknown,yes +82,retired,married,primary,no,8603,no,no,cellular,22,oct,306,3,107,1,success,yes +29,technician,single,tertiary,no,2178,no,no,cellular,22,oct,260,2,139,1,other,yes +55,services,married,secondary,no,0,no,no,cellular,22,oct,102,1,106,1,failure,no +76,retired,married,tertiary,no,2991,no,no,cellular,22,oct,344,1,183,1,success,yes +69,management,married,secondary,no,4,no,no,cellular,23,oct,114,4,-1,0,unknown,no +38,management,married,tertiary,no,4174,no,no,cellular,23,oct,336,2,184,5,failure,yes +60,technician,married,secondary,no,10925,no,no,cellular,23,oct,270,3,185,1,success,yes +43,blue-collar,married,secondary,no,14646,no,no,cellular,23,oct,210,1,-1,0,unknown,yes +61,retired,married,secondary,no,86,no,no,cellular,23,oct,149,1,-1,0,unknown,yes +43,technician,single,unknown,no,11115,no,no,cellular,23,oct,476,1,74,2,success,yes +73,retired,married,primary,no,796,no,no,cellular,23,oct,427,1,182,2,failure,yes +39,technician,married,secondary,no,0,yes,no,cellular,23,oct,622,1,184,1,success,no +57,management,married,primary,no,6900,no,no,cellular,23,oct,255,4,185,2,failure,yes +33,management,married,tertiary,no,5878,no,no,cellular,23,oct,438,1,430,2,failure,yes +77,management,married,unknown,no,1780,yes,no,cellular,23,oct,221,2,183,3,success,yes +45,technician,married,secondary,no,1673,no,no,cellular,23,oct,76,1,-1,0,unknown,no +35,technician,single,secondary,no,183,no,no,cellular,23,oct,281,1,52,4,failure,yes +37,admin.,single,secondary,no,0,no,no,cellular,23,oct,93,1,-1,0,unknown,no +59,retired,married,primary,no,3648,no,no,telephone,23,oct,257,2,126,3,success,yes +39,management,single,tertiary,no,416,no,no,cellular,23,oct,56,4,-1,0,unknown,no +76,retired,married,secondary,no,4112,no,no,telephone,23,oct,282,2,-1,0,unknown,yes +40,admin.,married,tertiary,no,1108,no,no,cellular,26,oct,61,2,76,3,failure,no +42,management,married,tertiary,no,2463,no,no,cellular,26,oct,183,2,-1,0,unknown,no +40,admin.,married,secondary,no,0,no,no,cellular,26,oct,87,2,200,2,failure,no +70,unknown,married,tertiary,no,353,no,no,cellular,26,oct,110,2,-1,0,unknown,no +59,retired,married,tertiary,no,1425,no,no,cellular,26,oct,151,2,77,3,failure,no +52,admin.,married,secondary,no,722,no,no,cellular,26,oct,99,2,185,3,failure,no +46,admin.,married,secondary,no,0,yes,no,cellular,26,oct,87,1,185,2,other,no +70,retired,married,secondary,no,2815,no,no,cellular,26,oct,67,1,-1,0,unknown,no +73,retired,married,primary,no,542,no,no,cellular,26,oct,355,1,180,5,failure,yes +50,technician,single,secondary,no,3897,no,no,cellular,26,oct,298,1,-1,0,unknown,no +81,retired,married,primary,no,243,no,no,cellular,27,oct,131,1,-1,0,unknown,yes +55,admin.,married,secondary,no,865,yes,no,cellular,27,oct,96,1,111,3,failure,no +52,technician,married,secondary,no,1373,no,no,cellular,27,oct,157,1,-1,0,unknown,no +41,unknown,single,unknown,no,942,no,no,cellular,27,oct,143,1,188,1,success,yes +58,blue-collar,married,secondary,no,3027,yes,no,cellular,27,oct,120,1,186,1,success,no +57,technician,married,secondary,no,254,no,no,cellular,27,oct,207,1,-1,0,unknown,yes +46,management,single,tertiary,no,311,yes,yes,cellular,27,oct,96,1,158,1,failure,no +28,technician,single,unknown,no,0,yes,no,cellular,27,oct,96,1,267,1,failure,yes +38,blue-collar,single,secondary,no,0,no,no,cellular,27,oct,297,1,75,3,failure,yes +30,management,single,tertiary,no,398,no,no,cellular,27,oct,102,1,180,3,success,yes +52,services,married,secondary,no,992,yes,no,cellular,27,oct,424,1,515,1,failure,yes +22,student,single,secondary,no,23,no,no,cellular,27,oct,137,1,-1,0,unknown,yes +53,unknown,married,primary,no,732,no,no,cellular,27,oct,759,2,-1,0,unknown,yes +60,admin.,married,unknown,no,4629,yes,no,cellular,27,oct,624,1,-1,0,unknown,yes +66,retired,married,primary,no,747,no,no,telephone,27,oct,515,1,-1,0,unknown,no +70,retired,married,secondary,no,616,no,no,cellular,27,oct,89,1,-1,0,unknown,no +55,technician,married,secondary,no,0,yes,yes,cellular,27,oct,88,1,-1,0,unknown,no +54,admin.,married,unknown,no,140,no,no,telephone,27,oct,521,2,-1,0,unknown,no +59,housemaid,married,secondary,no,0,no,no,telephone,27,oct,376,1,81,3,failure,no +80,retired,married,secondary,no,87,no,no,telephone,27,oct,219,2,-1,0,unknown,no +56,management,married,secondary,no,0,no,no,cellular,27,oct,298,1,111,8,other,yes +33,management,single,tertiary,no,373,no,yes,cellular,27,oct,259,1,-1,0,unknown,no +71,technician,married,tertiary,no,383,no,no,cellular,27,oct,327,2,183,3,success,no +76,retired,married,unknown,no,992,no,no,cellular,27,oct,139,2,-1,0,unknown,no +44,management,single,tertiary,no,5063,no,no,cellular,27,oct,418,2,-1,0,unknown,yes +65,retired,married,secondary,no,1624,no,no,cellular,27,oct,189,2,-1,0,unknown,no +34,admin.,single,tertiary,no,18,yes,yes,cellular,27,oct,114,1,180,1,success,no +60,housemaid,married,primary,no,182,no,no,cellular,27,oct,83,2,76,3,failure,no +29,services,single,tertiary,no,1788,no,no,cellular,27,oct,353,1,-1,0,unknown,yes +52,services,married,secondary,no,659,no,no,cellular,27,oct,113,1,-1,0,unknown,yes +36,student,single,tertiary,no,129,no,no,cellular,27,oct,149,1,-1,0,unknown,yes +33,management,single,tertiary,no,3170,no,no,cellular,28,oct,246,2,-1,0,unknown,no +63,retired,married,tertiary,no,937,no,no,cellular,28,oct,219,2,-1,0,unknown,no +30,management,single,tertiary,no,16935,no,no,cellular,28,oct,108,1,-1,0,unknown,no +29,technician,married,secondary,no,1599,yes,no,cellular,28,oct,95,1,112,2,failure,no +61,retired,married,secondary,no,89,no,no,cellular,28,oct,154,1,181,2,failure,no +50,management,married,tertiary,no,3463,no,yes,cellular,28,oct,96,1,-1,0,unknown,yes +65,admin.,married,secondary,no,11240,yes,no,telephone,28,oct,149,1,-1,0,unknown,no +26,student,single,unknown,no,603,no,no,cellular,28,oct,73,1,-1,0,unknown,no +67,retired,married,primary,no,2,no,no,cellular,28,oct,1118,1,-1,0,unknown,no +68,retired,married,primary,no,7945,no,no,cellular,28,oct,210,1,-1,0,unknown,no +74,retired,married,tertiary,no,1742,no,no,cellular,28,oct,1092,1,-1,0,unknown,no +75,self-employed,married,unknown,no,4984,no,no,telephone,28,oct,509,1,-1,0,unknown,yes +27,management,single,tertiary,no,390,no,no,cellular,28,oct,190,1,82,1,success,no +65,retired,married,primary,no,1004,no,no,cellular,28,oct,371,1,-1,0,unknown,yes +64,retired,married,secondary,no,4787,no,no,cellular,28,oct,501,1,-1,0,unknown,yes +38,technician,single,tertiary,no,1727,no,no,telephone,28,oct,182,2,-1,0,unknown,no +42,blue-collar,married,secondary,no,3213,no,no,telephone,28,oct,172,1,-1,0,unknown,no +30,admin.,single,secondary,no,914,yes,no,cellular,28,oct,524,3,272,1,failure,yes +57,housemaid,married,tertiary,no,2326,no,no,cellular,28,oct,207,1,194,1,success,yes +31,management,married,tertiary,no,81,yes,no,cellular,29,oct,164,2,-1,0,unknown,yes +52,blue-collar,married,secondary,no,2230,no,no,cellular,29,oct,423,3,-1,0,unknown,yes +25,management,single,tertiary,no,667,no,no,cellular,29,oct,408,2,-1,0,unknown,no +36,management,married,tertiary,no,203,no,no,cellular,29,oct,139,1,-1,0,unknown,yes +55,management,married,tertiary,no,13578,no,no,cellular,29,oct,247,2,-1,0,unknown,no +64,management,married,tertiary,no,3951,no,no,cellular,29,oct,164,1,-1,0,unknown,yes +54,technician,married,unknown,no,240,yes,no,cellular,29,oct,142,1,-1,0,unknown,yes +26,admin.,single,secondary,no,338,no,no,cellular,29,oct,209,1,188,2,unknown,yes +51,self-employed,single,secondary,no,0,no,no,cellular,29,oct,1707,2,-1,0,unknown,yes +72,retired,married,secondary,no,0,no,no,cellular,29,oct,189,3,-1,0,unknown,yes +30,management,single,tertiary,no,0,no,no,cellular,29,oct,437,2,-1,0,unknown,no +30,management,married,tertiary,no,944,no,no,cellular,29,oct,251,2,-1,0,unknown,yes +61,blue-collar,married,secondary,no,272,no,no,cellular,29,oct,318,1,269,1,failure,no +60,blue-collar,married,primary,no,5,no,no,cellular,29,oct,401,1,-1,0,unknown,yes +30,management,single,tertiary,no,697,no,no,cellular,29,oct,215,3,80,5,success,yes +39,management,married,tertiary,no,53,yes,no,cellular,29,oct,679,1,-1,0,unknown,no 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+99858;19475;2;171;65.0;120;80;1;1;1;1;1;0 +99859;21006;1;167;86.0;150;90;1;2;0;0;1;1 +99860;18833;1;161;72.0;140;95;3;1;0;0;1;1 +99861;16002;1;155;48.0;90;70;1;1;0;0;1;0 +99862;18748;2;186;101.0;110;80;3;1;0;0;1;0 +99863;20548;2;169;94.0;120;80;1;1;0;0;1;0 +99864;21883;2;178;102.0;150;100;1;2;0;1;0;1 +99866;20660;1;169;74.0;110;70;1;1;0;0;1;0 +99867;18467;1;158;70.0;120;80;1;1;0;0;1;1 +99868;20359;2;160;75.0;140;90;3;1;0;0;0;1 +99871;17312;1;159;45.0;110;70;2;2;0;0;1;0 +99872;19510;1;166;93.0;150;90;3;3;0;0;1;1 +99877;19718;1;169;97.0;120;80;1;1;0;0;1;0 +99878;15334;1;165;62.0;100;60;1;1;0;0;1;0 +99879;19784;1;153;65.0;150;90;1;1;0;0;0;1 +99880;17320;2;172;94.0;120;80;1;1;0;0;1;0 +99882;23281;1;164;98.0;140;90;2;1;0;1;1;1 +99883;19829;1;159;74.0;140;89;1;2;0;0;1;0 +99884;19438;2;175;80.0;120;80;1;1;0;0;1;1 +99890;14420;1;160;55.0;140;90;1;1;0;0;1;0 +99891;19834;1;164;85.0;165;110;3;3;0;0;1;1 +99894;16688;1;157;60.0;140;100;1;1;0;0;1;1 +99896;22609;2;166;64.0;120;80;1;3;0;0;1;1 +99897;15243;2;171;67.0;110;60;1;1;0;0;1;0 +99899;20253;1;159;66.0;110;80;1;1;0;0;1;1 +99901;15843;1;143;70.0;120;90;2;1;0;0;1;1 +99904;18428;2;168;64.0;110;70;1;1;0;0;1;0 +99906;23413;2;171;70.0;140;90;1;1;0;0;1;0 +99908;14489;1;160;53.0;90;60;1;1;0;0;1;0 +99909;22608;1;149;61.0;130;90;2;3;0;0;1;0 +99910;20962;2;174;83.0;120;80;3;1;0;0;1;1 +99911;21307;1;168;78.0;140;80;3;1;0;0;1;1 +99915;16854;1;166;83.0;120;80;1;1;0;0;1;0 +99916;19614;1;162;74.0;120;80;1;1;0;0;1;1 +99919;22570;2;160;55.0;140;90;2;1;0;0;1;1 +99922;23464;2;170;97.0;130;90;2;1;0;0;1;1 +99923;21397;1;150;71.0;110;80;1;1;0;0;1;0 +99925;21333;1;164;115.0;130;80;1;1;0;0;1;1 +99930;23305;2;172;113.0;110;80;1;1;0;0;0;1 +99931;20388;1;164;90.0;130;80;1;1;0;0;1;1 +99932;22119;1;167;68.0;167;85;3;1;0;0;1;1 +99933;19137;2;167;74.0;130;80;1;1;1;1;1;0 +99934;15254;1;158;55.0;110;70;1;1;0;0;1;0 +99935;19727;1;172;75.0;120;80;1;1;0;0;1;1 +99936;17373;2;173;69.0;110;70;1;1;1;1;1;1 +99938;15151;1;170;60.0;120;80;2;1;0;0;1;0 +99939;21726;1;164;88.0;140;90;1;1;0;0;1;0 +99940;21453;1;158;75.0;120;80;3;3;0;0;1;0 +99941;15536;2;166;90.0;120;80;1;1;1;0;1;0 +99943;15239;2;169;67.0;160;90;1;1;0;0;1;1 +99945;19069;1;163;64.0;130;70;1;1;0;0;1;1 +99947;20587;2;169;79.0;140;90;1;1;0;0;0;1 +99948;16598;1;161;50.0;110;75;1;1;0;0;1;0 +99949;21151;1;178;69.0;130;90;1;1;0;0;1;1 +99950;19538;2;155;76.0;130;90;1;1;0;0;1;0 +99951;22491;2;170;72.0;120;80;1;1;0;0;0;1 +99952;15450;2;165;66.0;120;80;1;1;0;0;0;0 +99954;20327;1;156;67.0;140;80;2;1;0;0;0;0 +99955;21416;2;168;63.0;140;1000;1;1;0;0;1;1 +99956;16093;2;157;61.0;110;90;1;1;0;0;1;0 +99957;20529;2;175;78.0;120;80;1;1;0;0;0;0 +99958;22572;2;173;103.0;140;80;3;1;1;1;0;1 +99959;20136;2;177;80.0;130;80;1;1;0;0;1;0 +99960;17065;1;165;76.0;140;90;1;1;0;0;1;1 +99961;22142;2;175;72.0;130;80;1;1;0;0;1;0 +99962;18226;1;168;75.0;120;80;1;1;0;0;1;0 +99963;21264;2;182;100.0;120;80;1;1;0;0;1;1 +99964;21699;1;163;65.0;120;80;2;2;0;0;1;0 +99965;16843;1;168;75.0;120;79;1;1;0;0;1;0 +99967;18800;1;163;78.0;90;60;1;1;0;0;1;1 +99969;22423;1;163;74.0;160;100;2;2;0;0;1;1 +99971;17727;2;167;69.0;110;80;1;1;0;1;0;0 +99972;17500;2;182;110.0;130;90;2;2;0;0;1;1 +99973;18941;1;153;86.0;130;90;1;2;0;0;1;1 +99974;19652;1;165;72.0;120;80;1;1;0;0;1;0 +99975;17926;2;168;80.0;120;80;1;1;0;0;1;1 +99977;18194;1;156;102.0;130;80;1;1;0;1;0;1 +99978;18198;2;180;78.0;120;80;1;1;0;0;1;0 +99979;18852;1;151;49.0;120;80;1;1;0;0;1;0 +99981;21978;1;160;59.0;110;70;1;1;0;0;1;0 +99985;21013;1;157;83.0;120;70;1;1;0;0;1;1 +99986;15094;1;168;72.0;110;70;1;1;0;0;1;1 +99988;20609;1;159;72.0;130;90;2;2;0;0;1;0 +99990;18792;1;161;56.0;170;90;1;1;0;0;1;1 +99991;19699;1;172;70.0;130;90;1;1;0;0;1;1 +99992;21074;1;165;80.0;150;80;1;1;0;0;1;1 +99993;19240;2;168;76.0;120;80;1;1;1;0;1;0 +99995;22601;1;158;126.0;140;90;2;2;0;0;1;1 +99996;19066;2;183;105.0;180;90;3;1;0;1;0;1 +99998;22431;1;163;72.0;135;80;1;2;0;0;0;1 +99999;20540;1;170;72.0;120;80;2;1;0;0;1;0 diff --git a/virny/datasets/data/diabetes_dataset__2019.csv b/virny/datasets/data/diabetes_dataset__2019.csv new file mode 100644 index 00000000..0d83dcb5 --- /dev/null +++ b/virny/datasets/data/diabetes_dataset__2019.csv @@ -0,0 +1,953 @@ +Age,Gender,Family_Diabetes,highBP,PhysicallyActive,BMI,Smoking,Alcohol,Sleep,SoundSleep,RegularMedicine,JunkFood,Stress,BPLevel,Pregancies,Pdiabetes,UriationFreq,Diabetic +50-59,Male,no,yes,one hr or more,39,no,no,8,6,no,occasionally,sometimes,high,0,0,not much,no +50-59,Male,no,yes,less than half an hr,28,no,no,8,6,yes,very often,sometimes,normal,0,0,not much,no +40-49,Male,no,no,one hr or more,24,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Male,no,no,one hr or more,23,no,no,8,6,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Male,no,no,less than half an hr,27,no,no,8,8,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Male,no,yes,none,21,no,yes,10,10,no,occasionally,sometimes,high,0,0,not much,yes +less than 40,Male,no,no,one hr or more,24,no,no,8,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,20,no,no,7,7,yes,occasionally,sometimes,low,0,0,not much,no +40-49,Male,yes,no,one hr or more,23,no,no,7,7,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,more than half an hr,20,no,no,8,8,o,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,none,20,no,no,7,7,no,occasionally,not at all,normal,0,0,not much,no +40-49,Male,no,no,less than half an hr,26,yes,no,8,7,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,no,no,less than half an hr,21,no,no,6,6,no,occasionally,sometimes,normal,1,0,not much,no +less than 40,Female,no,no,one hr or more,22,no,no,8,7,no,occasionally,very often,normal,2,0,not much,no +less than 40,Male,no,no,one hr or more,15,no,no,7,7,no,occasionally,very often,normal,0,0,not much,no +40-49,Female,yes,no,none,34,no,no,6,6,no,occasionally,sometimes,normal,3,0,not much,no +50-59,Male,yes,no,more than half an hr,22,no,no,7,6,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,no,no,more than half an hr,24,no,no,6,6,no,occasionally,sometimes,normal,2,0,quite often, no +40-49,Male,yes,no,one hr or more,24,no,no,11,11,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,one hr or more,22,no,no,8,6,no,often,not at all,normal,0,0,not much,no +less than 40,Male,yes,no,one hr or more,30,no,yes,9,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,one hr or more,24,no,no,4,4,yes,often,always,normal,3,0,quite often,yes +less than 40,Female,yes,no,one hr or more,24,no,no,4,4,yes,often,always,normal,3,0,not much,yes +less than 40,Female,no,no,one hr or more,20,no,no,9,9,no,occasionally,sometimes,normal,2,0,not much,no +less than 40,Male,yes,no,more than half an hr,24,yes,no,8,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,no,no,more than half an hr,21,no,no,7,7,no,often,not at all,normal,0,0,not much,no +less than 40,Female,no,no,one hr or more,20,no,no,6,6,no,very often,not at all,normal,0,0,not much,no +50-59,Male,no,no,one hr or more,27,no,no,7,7,no,very often,not at all,normal,0,0,not much,no +40-49,Male,yes,no,none,29,no,no,6,6,no,occasionally,not at all,normal,0,0,not much,no +60 or older,Female,no,no,none,18,no,no,6,6,yes,occasionally,sometimes,normal,3,0,quite often,yes +less than 40,Female,no,no,one hr or more,32,no,no,8,8,yes,occasionally,sometimes,normal,3,0,not much,no +40-49,Female,no,no,one hr or more,21,no,no,7,7,no,occasionally,sometimes,normal,3,0,quite often,no +60 or older,Male,no,yes,none,26,no,no,4,4,yes,occasionally,not at all,high,0,0,not much,yes +60 or older,Female,yes,no,more than half an hr,31,no,no,8,7,yes,occasionally,not at all,normal,3,0,not much,no +60 or older,Female,yes,no,none,26,no,no,7,7,no,occasionally,sometimes,normal,3,0,not much,no +60 or older,Male,yes,no,none,21,no,no,8,8,yes,occasionally,not at all,normal,0,0,not much,yes +40-49,Female,yes,no,more than half an hr,28,no,no,8,6,yes,occasionally,always,normal,2,0,not much,yes +50-59,Male,no,yes,one hr or more,24,no,no,8,8,yes,occasionally,sometimes,high,0,0,not much,yes +40-49,Male,no,yes,more than half an hr,27,no,no,8,8,yes,occasionally,not at all,high,0,0,not much,yes +40-49,Male,no,no,more than half an hr,21,no,no,6,6,yes,occasionally,sometimes,normal,0,0,not much,yes +60 or older,Male,no,no,none,27,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,no +60 or older,Male,no,no,none,24,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,36,no,no,6,4,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,less than half an hr,21,no,no,6,6,no,occasionally,not at all,normal,0,0,not much,no +40-49,Male,yes,no,less than half an hr,22,no,no,6,6,yes,occasionally,sometimes,normal,0,0,not much,no +40-49,Male,yes,no,less than half an hr,21,no,no,6,6,no,occasionally,sometimes,low,0,0,not much,no +50-59,Male,yes,no,one hr or more,38,no,yes,8,6,yes,occasionally,very often,normal,0,0,not much,yes +50-59,Female,yes,yes,more than half an hr,40,no,no,9,5,yes,very often,sometimes,high,2,yes,not much,yes +60 or older,Male,yes,no,none,35,no,no,5,5,no,occasionally,sometimes,high,0,0,quite often,yes +60 or older,Female,yes,yes,none,24,no,no,4,4,yes,occasionally,always,high,3,0,quite often,yes +60 or older,Male,no,no,none,18,no,no,5,5,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Male,no,no,more than half an hr,23,no,no,7,4,no,often,sometimes,normal,2,0,not much,no +60 or older,Male,no,yes,less than half an hr,19,no,yes,6,6,yes,often,sometimes,high,1,0,not much,no +less than 40,Male,no,no,more than half an hr,26,yes,yes,8,6,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,more than half an hr,30,yes,yes,5,5,yes,often,not at all,normal,1,0,not much,no +60 or older,Female,no,yes,less than half an hr,27,no,no,6,6,yes,occasionally,always,high,0,0,quite often,yes +less than 40,Male,no,no,none,18,yes,yes,7,6,no,occasionally,very often,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,29,yes,yes,6,5,yes,often,very often,normal,0,0,not much,no +50-59,Male,no,yes,less than half an hr,19,no,yes,6,6,yes,occasionally,very often,high,0,0,not much,no +50-59,Female,yes,no,none,21,no,no,8,8,yes,occasionally,very often,normal,0,0,quite often,yes +less than 40,Male,no,no,none,24,no,no,10,10,no,occasionally,not at all,normal,0,0,not much,no +60 or older,Male,no,no,none,31,no,no,6,6,no,occasionally,very often,normal,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,22,no,no,8,8,no,occasionally,very often,normal,0,0,not much,no +less than 40,Male,yes,no,none,19,no,yes,8,8,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Male,no,no,one hr or more,27,no,no,6,4,no,occasionally,very often,normal,0,0,not much,no +40-49,Male,yes,yes,less than half an hr,26,no,no,10,6,yes,occasionally,very often,high,0,0,quite often,yes +50-59,Female,yes,no,none,21,no,no,8,5,yes,occasionally,sometimes,normal,0,0,not much,yes +60 or older,Male,yes,no,one hr or more,26,no,no,6,5,yes,occasionally,not at all,normal,0,0,not much,yes +50-59,Male,yes,no,one hr or more,23,yes,yes,5,4,yes,occasionally,very often,normal,0,0,not much,yes +less than 40,Male,yes,yes,one hr or more,26,yes,yes,5,4,yes,often,not at all,high,0,0,not much,yes +60 or older,Female,yes,yes,less than half an hr,18,no,no,4,3,yes,occasionally,sometimes,high,0,0,not much,yes +60 or older,Female,yes,yes,less than half an hr,19,no,no,9,9,yes,occasionally,always,high,4,0,quite often,yes +50-59,Male,yes,no,none,24,no,yes,7,7,no,occasionally,always,normal,0,0,not much,no +60 or older,Female,yes,yes,less than half an hr,33,no,no,6,5,yes,occasionally,sometimes,high,2,0,not much,yes +50-59,Male,yes,yes,none,31,no,yes,7,6,yes,occasionally,always,high,0,0,not much,yes +40-49,Female,no,no,none,23,no,no,7,7,no,often,very often,normal,1,0,not much,no +50-59,Male,yes,no,less than half an hr,23,no,no,7,7,yes,occasionally,sometimes,normal,0,0,not much,yes +50-59,Female,no,yes,none,20,no,no,7,4,yes,occasionally,sometimes,high,1,0,not much,yes +50-59,Male,no,no,more than half an hr,28,no,yes,7,4,no,occasionally,sometimes,high,0,0,not much,no +60 or older,Male,yes,yes,less than half an hr,26,no,yes,6,6,yes,occasionally,not at all,high,0,0,not much,no +less than 40,Male,no,no,one hr or more,24,no,no,7,7,no,often,sometimes,normal,0,0,not much,no +60 or older,Male,no,no,more than half an hr,24,no,no,7,7,no,occasionally,sometimes,normal,0,0,quite often,yes +less than 40,Female,no,no,less than half an hr,17,no,no,4,4,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,28,yes,yes,4,4,no,occasionally,very often,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,23,yes,no,6,3,no,occasionally,very often,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,24,no,no,6,6,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Female,no,no,less than half an hr,19,no,no,7,3,yes,occasionally,sometimes,normal,1,0,not much,no +60 or older,Male,no,yes,less than half an hr,15,no,no,4,4,yes,occasionally,very often,high,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,22,no,no,6,2,no,often,not at all,normal,0,0,quite often,no +less than 40,Male,yes,no,more than half an hr,23,no,no,7,3,no,occasionally,sometimes,high,0,0,not much,no +less than 40,Male,no,no,one hr or more,18,no,no,8,4,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,21,no,no,5,5,no,very often,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,one hr or more,21,no,no,7,6,yes,often,very often,normal,0,0,not much,no +less than 40,Male,yes,no,one hr or more,21,no,no,9,6,no,very often,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,one hr or more,22,no,no,5,2,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,one hr or more,25,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Male,no,yes,less than half an hr,20,yes,yes,6,6,no,occasionally,very often,high,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,24,no,no,8,5,no,often,not at all,normal,0,0,not much,no +less than 40,Female,no,no,less than half an hr,21,no,no,8,8,no,occasionally,sometimes,normal,0,0,quite often,no +50-59,Male,no,no,less than half an hr,26,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Female,no,no,less than half an hr,27,no,no,8,6,no,occasionally,sometimes,low,1,0,not much,no +less than 40,Female,no,no,one hr or more,26,no,no,8,6,no,occasionally,sometimes,low,0,0,not much,no +less than 40,Male,no,yes,less than half an hr,24,no,no,8,8,no,occasionally,very often,normal,0,0,not much,no +less than 40,Female,no,no,less than half an hr,21,no,no,8,8,no,occasionally,not at all,normal,3,yes,not much,yes +40-49,Female,no,no,less than half an hr,22,no,no,8,8,no,occasionally,not at all,normal,3,yes,not much,no +60 or older,Male,no,no,more than half an hr,23,no,yes,7,6,yes,occasionally,very often,high,0,0,quite often,yes +60 or older,Male,no,no,less than half an hr,28,no,no,8,8,yes,occasionally,not at all,normal,0,0,quite often,yes +60 or older,Male,yes,yes,less than half an hr,32,no,no,8,8,no,often,very often,high,0,0,not much,yes +40-49,Female,yes,no,less than half an hr,33,no,no,6,6 ,no,very often,very often,normal,2,yes,not much,yes +less than 40,Female,yes,no,one hr or more,24,no,no,8,8,no,often,sometimes,normal,0,0,not much,no +50-59,Female,yes,no,less than half an hr,28,no,no,7,4,yes,occasionally,very often,high,0,0,quite often,yes +50-59,Male,no,no,less than half an hr,30,no,no,7,2,no,often,always,normal,0,0,not much,no +50-59,Male,yes,yes,one hr or more,30,yes,yes,7,7,yes,often,always,high,0,0,quite often,yes +less than 40,Female,yes,no,more than half an hr,,no,no,7,6,no,occasionally,not at all,normal,0,0,not much, +less than 40,Female,no,no,more than half an hr,27,no,no,9,6,no,always,very often,high,0,0,not much,no +less than 40,Male,no,no,one hr or more,22,yes,no,9,2,no,always,always,high,0,yes,quite often,no +less than 40,Female,no,no,more than half an hr,,no,no,5,4,no,often,very often,normal,0,0,not much,no +less than 40,Female,yes,no,more than half an hr,18,no,no,7,3,yes,often,very often,normal,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,22,no,no,7,5,no,often,very often,normal,0,0,not much,no +60 or older,Male,yes,yes,more than half an hr,21,no,yes,8,4,yes,occasionally,sometimes,normal,0,0,quite often,no +60 or older,Female,no,yes,one hr or more,26,no,no,8,6,yes,occasionally,sometimes,normal,0,yes,not much,yes +50-59,Female,yes,yes,none,,no,no,6,3,yes,occasionally,always,high,2,0,not much,yes +less than 40,Male,no,yes,more than half an hr,,no,no,6,6,no,occasionally,sometimes,normal,0,0,quite often,no +50-59,Female,no,yes,less than half an hr,31,no,no,7,4,yes,occasionally,very often,high,0,0,quite often,no +50-59,Male,no,yes,less than half an hr,33,no,yes,5,5,yes,occasionally,sometimes,high,0,0,not much,no +less than 40,Female,no,yes,one hr or more,30,no,no,7,3,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,more than half an hr,23,no,yes,6,6,no,often,very often,normal,0,0,not much,no +less than 40,Female,yes,no,one hr or more,29,no,no,6,6,no,often,sometimes,normal,2,0,quite often,no +40-49,Female,yes,no,more than half an hr,17,no,no,7,7,no,very often,sometimes,normal,3,0,not much,no +50-59,Female,yes,no,less than half an hr,24,no,no,8,5,yes,occasionally,sometimes,normal,2,0,not much,no +60 or older,Male,yes,no,more than half an hr,26,no,yes,8,3,yes,occasionally,sometimes,normal,0,0,quite often,yes +less than 40,Female,yes,no,one hr or more,27,no,yes,8,6,no,very often,very often,normal,0,0,not much,no +40-49,Male,yes,yes,more than half an hr,21,no,no,6,4,yes,occasionally,sometimes,normal,0,0,not much,yes +40-49,Female,yes,yes,less than half an hr,26,no,no,7,4,no,occasionally,very often,high,0,0,quite often,no +less than 40,Male,yes,no,one hr or more,20,no,no,7,6,no,occasionally,not at all,normal,0,,not much,no +less than 40,Male,no,no,more than half an hr,22,no,no,6,6,no,occasionally,sometimes,Low,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,21,yes,no,8,8,no,occasionally,very often,normal,,0,not much,no +less than 40,Female,yes,no,one hr or more,26,no,no,7,5,no,often,sometimes,Low,,no,not much,no +less than 40,Female,yes,no,none,21,no,yes,9,8,no,occasionally,sometimes,normal,,0,not much,no +less than 40,Female,yes,no,none,21,no,yes,8,8,no,occasionally,sometimes,normal,,0,not much,no +less than 40,Female,yes,no,more than half an hr,24,no,no,8,8,yes,often,sometimes,normal,,0,quite often,no +less than 40,Male,no,no,more than half an hr,24,yes,yes,7,6,no,often,sometimes,normal,,0,not much,no +less than 40,Male,no,no,less than half an hr,26,no,yes,6,4,no,occasionally,very often,normal,,0,not much,no +50-59,Male,yes,no,less than half an hr,32,no,no,8,4,yes,occasionally,sometimes,normal,,0,not much,no +40-49,Male,yes,no,more than half an hr,34,no,no,7,5,no,often,sometimes,normal,,0,quite often,no +less than 40,Male,no,no,more than half an hr,30,no,no,7,4,no,occasionally,sometimes,normal,,0,not much,no +50-59,Female,no,yes,more than half an hr,42,no,no,7,3,yes,occasionally,always,high,,0,not much,no +less than 40,Male,yes,no,less than half an hr,33,no,no,8,4,no,occasionally,sometimes,normal,,0,not much,no +less than 40,Male,no,no,one hr or more,23,no,no,7,5,no,occasionally,sometimes,normal,,0,not much,no +50-59,Male,yes,yes,more than half an hr,27,no,yes,7,5,no,occasionally,sometimes,high,,0,not much,no +40-49,Female,no,no,one hr or more,25,no,no,5,4,no,occasionally,sometimes,normal,,0,not much,no +less than 40,Female,no,no,more than half an hr,28,no,no,6,5,no,occasionally,sometimes,normal,,0,not much,no +40-49,Female,no,no,less than half an hr,28,no,no,6,4,no,occasionally,sometimes,normal,,0,not much,no +40-49,Female,no,no,less than half an hr,33,no,no,7,5,no,occasionally,sometimes,normal,,0,not much,no +50-59,Female,no,no,less than half an hr,33,no,no,7,5,yes,occasionally,sometimes,normal,,0,quite often,no +50-59,Female,no,no,more than half an hr,28,no,no,7,3,no,occasionally,sometimes,normal,,0,quite often,no +less than 40,Female,no,no,less than half an hr,28,no,no,6,3,no,occasionally,not at all,normal,,0,quite often,no +40-49,Female,yes,yes,more than half an hr,36,no,no,7,6,yes,occasionally,sometimes,normal,,0,quite often,no +less than 40,Female,no,no,more than half an hr,33,no,no,8,4,no,occasionally,sometimes,normal,,0,quite often,no +less than 40,Female,no,no,less than half an hr,28,no,no,6,5,no,occasionally,sometimes,normal,,0,not much,no +40-49,Male,yes,no,more than half an hr,28,no,no,7,5,no,occasionally,sometimes,normal,,0,quite often,no +less than 40,Male,no,no,more than half an hr,33,no,no,8,4,no,occasionally,sometimes,normal,,0,not much,no +40-49,Female,yes,no,less than half an hr,38,no,no,7,4,no,occasionally,not at all,normal,,0,quite often,no +50-59,Female,no,yes,more than half an hr,33,no,no,6,4,yes,occasionally,sometimes,high,,0,quite often,no +40-49,Male,no,no,less than half an hr,38,no,no,5,4,yes,occasionally,sometimes,normal,,0,quite often,no +40-49,Female,yes,no,less than half an hr,36,no,no,7,5,no,occasionally,sometimes,normal,,0,quite often,no +less than 40,Female,yes,no,less than half an hr,33,no,no,6,4,no,occasionally,very often,normal,,0,quite often,no +less than 40,Female,no,no,more than half an hr,33,no,no,7,4,no,often,not at all,normal,,0,quite often,no +40-49,Female,no,no,less than half an hr,38,no,no,6,4,no,occasionally,sometimes,normal,,0,quite often,no +40-49,Female,yes,no,more than half an hr,36,no,no,7,3,yes,occasionally,sometimes,normal,,0,quite often,no +less than 40,Female,no,no,less than half an hr,33,no,no,7,4,no,occasionally,sometimes,normal,,0,quite often,no +50-59,Male,no,no,less than half an hr,38,no,no,6,3,yes,occasionally,sometimes,normal,,0,quite often,no +60 or older,Female,yes,no,less than half an hr,38,no,no,5,2,yes,occasionally,very often,high,,0,quite often,yes +60 or older,Male,no,yes,less than half an hr,33,no,no,6,2,yes,occasionally,very often,high,,0,quite often,yes +less than 40,Female,no,no,less than half an hr,33,no,no,8,3,no,occasionally,sometimes,normal,,0,quite often,no +less than 40,Female,yes,yes,more than half an hr,38,no,no,8,4,yes,occasionally,sometimes,normal,,0,quite often,no +less than 40,Male,yes,no,less than half an hr,32,no,no,6,4,no,often,sometimes,normal,,0,quite often,no +less than 40,Male,no,no,more than half an hr,33,no,no,8,3,no,often,sometimes,normal,,0,quite often,no +40-49,Female,yes,no,none,22,no,no,10,10,yes,occasionally,always,normal,2,0,not much,no +40-49,Male,yes,no,none,24,no,no,8,8,no,occasionally,sometimes,normal,0,0,not much,yes +60 or older,Female,yes,yes,more than half an hr,23,no,no,9,9,yes,occasionally,always,high,3,0,quite often,yes +60 or older,Female,yes,yes,less than half an hr,27,no,no,7,7,yes,occasionally,sometimes,high,3,0,quite often,yes +60 or older,Male,yes,yes,more than half an hr,24,no,yes,7,7,yes,occasionally,always,high,0,0,not much,yes +less than 40,Female,yes,no,none,18,no,no,8,8,yes,often,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,none,20,no,no,9,9,no,often,sometimes,normal,0,0,not much,no +less than 40,Female,no,no,more than half an hr,19,no,no,7,7,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,one hr or more,28,yes,yes,7,7,no,often,not at all,normal,0,0,not much,yes +less than 40,Female,no,no,less than half an hr,19,no,no,8,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,more than half an hr,25,yes,yes,7,5,no,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,25,no,yes,7,7,no,often,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,24,yes,yes,10,7,no,always,always,normal,0,0,not much,no +less than 40,Male,yes,yes,more than half an hr,27,no,no,7,5,no,occasionally,sometimes,High,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,19,yes,yes,7,5,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Male,no,no,none,25,yes,yes,10,10,no,often,very often,normal,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,28,no,no,8,8,yes,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,yes,no,one hr or more,25,no,no,5,5,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,24,no,no,6,5,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,less than half an hr,21,no,no,8,7,no,often,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,more than half an hr,23,no,no,9,8,no,often,sometimes,normal,0,0,not much,no +less than 40,Male,no,yes,less than half an hr,28,yes,yes,8,4,no,always,always,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,20,no,no,7,4,no,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,18,no,no,6,1,no,always,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,one hr or more,25,no,no,6,6,no,always,always,normal,0,0,not much,no +less than 40,Male,yes,yes,less than half an hr,28,no,no,6,2,no,occasionally,not at all,high,0,0,quite often,no +less than 40,Male,yes,no,less than half an hr,27,yes,yes,6,6,no,always,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,20,no,no,8,6,no,always,not at all,normal,0,0,not much,no +less than 40,Male,yes,no,less than half an hr,20,no,no,7,4,no,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,yes,one hr or more,17,no,no,8,0,no,occasionally,not at all,normal ,0,0,not much,no +less than 40,Male,no,yes,less than half an hr,26,no,no,6,4,no,very often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,21,no,no,7,4,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,yes,no,less than half an hr,29,no,no,8,7,no,often,sometimes,Low,0,0,not much,no +less than 40,Male,yes,yes,more than half an hr,29,no,yes,6,4,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,yes,no,more than half an hr,24,yes,yes,8,8,no,occasionally,sometimes,High,0,0,not much,no +less than 40,Male,yes,no,less than half an hr,23,no,no,5,5,no,always,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,less than half an hr,23,no,no,5,5,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Female,yes,no,more than half an hr,29,no,no,6,3,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,no,no,none,19,no,no,9,5,no,very often,sometimes,normal,2,0,not much,no +less than 40,Male,no,no,less than half an hr,23,yes,yes,8,6,no,very often,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,one hr or more,17,no,no,6,6,no,often,sometimes,normal,0,0,not much,no +50-59,Female,yes,no,one hr or more,23,no,no,6,5,no,occasionally,sometimes,high,2,0,not much,no +40-49,Male,yes,no,less than half an hr,24,no,no,7,5,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,less than half an hr,25,no,no,8,4,yes,occasionally,sometimes,normal,0,0,not much,no +50-59,Female,yes,no,less than half an hr,35,no,no,8,5,yes,often,very often,normal,2,0,quite often,yes +40-49,Female,yes,yes,more than half an hr,33,no,no,7,2,yes,occasionally,sometimes,High,0,0,quite often,yes +40-49,Female,yes,no,one hr or more,34,no,no,6,2,yes,always,very often,normal,0,0,quite often,yes +50-59,Male,yes,yes,more than half an hr,36,yes,no,6,4,yes,often,very often,high,0,0,not much,yes +60 or older,Male,no,no,less than half an hr,30,no,yes,6,2,yes,often,always,High,0,0,not much,yes +less than 40,Male,yes,no,one hr or more,28,yes,yes,5,3,yes,always,always,normal,0,0,quite often,yes +40-49,Male,yes,yes,less than half an hr,35,yes,yes,5,2,yes,very often,sometimes,high,0,0,not much,yes +40-49,Male,no,no,one hr or more,24,no,yes,6,5,no,occasionally,not at all,normal,0,0,not much,no +50-59,Female,no,yes,one hr or more,22,no,no,8,5,yes,occasionally,always,normal,3,0,not much,yes +less than 40,Male,no,no,less than half an hr,30,no,yes,8,8,yes,occasionally,sometimes,normal,0,0,not much,yes +40-49,Male,yes,yes,one hr or more,22,no,no,7,6,yes,occasionally,sometimes,High,0,0,not much,yes +less than 40,Male,yes,no,more than half an hr,22,no,no,8,6,no,often,sometimes,normal,0,0,not much,yes +60 or older,Male,yes,yes,more than half an hr,27,no,no,6,5,yes,occasionally,sometimes,high,0,0,quite often,yes +60 or older,Male,no,yes,none,23,no,no,6,5,yes,occasionally,sometimes,high,0,0,not much,no +60 or older,Male,no,yes,less than half an hr,27,no,yes,6,5,yes,occasionally,very often,high,0,0,not much,no +60 or older,Female,yes,yes,one hr or more,29,no,no,7,4,yes,occasionally,sometimes,high,2,0,quite often,yes +50-59,Male,no,yes,one hr or more,39,no,no,8,6,no,occasionally,sometimes,high,0,0,not much,no +50-59,Male,yes,yes,less than half an hr,28,no,no,8,6,yes,very often,sometimes,normal,0,0,not much,yes +40-49,Male,no,no,one hr or more,24,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Male,no,no,one hr or more,23,no,no,8,6,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Male,yes,no,less than half an hr,27,no,no,8,8,no,occasionally,sometimes,normal,0,0,not much,yes +40-49,Male,yes,yes,none,21,no,yes,10,10,no,occasionally,sometimes,high,0,0,not much,yes +less than 40,Male,no,no,one hr or more,24,no,no,8,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,20,no,no,7,7,yes,occasionally,sometimes,low,0,0,not much,no +40-49,Male,yes,no,one hr or more,23,no,no,7,7,no,occasionally,sometimes,normal,0,0,not much,yes +less than 40,Male,no,no,more than half an hr,20,no,no,8,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,none,20,no,no,7,7,no,occasionally,not at all,normal,0,0,not much,no +40-49,Male,no,no,less than half an hr,26,yes,no,8,7,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,no,no,less than half an hr,21,no,no,6,6,no,occasionally,sometimes,normal,1,0,not much,no +less than 40,Female,no,no,one hr or more,22,no,no,8,7,no,occasionally,very often,normal,2,0,not much,no +less than 40,Male,no,no,one hr or more,15,no,no,7,7,no,occasionally,very often,normal,0,0,not much,no +40-49,Female,yes,no,none,34,no,no,6,6,no,occasionally,sometimes,normal,3,0,not much,no +50-59,Male,yes,no,more than half an hr,22,no,no,7,6,no,occasionally,sometimes,normal,0,0,not much,yes +less than 40,Female,no,no,more than half an hr,24,no,no,6,6,no,occasionally,sometimes,normal,2,0,quite often,no +40-49,Male,yes,no,one hr or more,24,no,no,11,11,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,one hr or more,22,no,no,8,6,no,often,not at all,normal,0,0,not much,no +less than 40,Male,yes,no,one hr or more,30,no,yes,9,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,one hr or more,24,no,no,4,4,yes,often,always,normal,3,0,quite often,yes +less than 40,Female,yes,no,one hr or more,24,no,no,4,4,yes,often,always,normal,3,0,not much,yes +less than 40,Female,no,no,one hr or more,20,no,no,9,9,no,occasionally,sometimes,normal,2,0,not much,no +less than 40,Male,yes,no,more than half an hr,24,yes,no,8,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,no,no,more than half an hr,21,no,no,7,7,no,often,not at all,normal,0,0,not much,no +less than 40,Female,no,no,one hr or more,20,no,no,6,6,no,very often,not at all,normal,0,0,not much,no +50-59,Male,no,no,one hr or more,27,no,no,7,7,no,very often,not at all,normal,0,0,not much,yes +40-49,Male,yes,no,none,29,no,no,6,6,no,occasionally,not at all,normal,0,0,not much,no +60 or older,Female,no,no,none,18,no,no,6,6,yes,occasionally,sometimes,normal,3,0,quite often,yes +less than 40,Female,no,no,one hr or more,32,no,no,8,8,yes,occasionally,sometimes,normal,3,0,not much,no +40-49,Female,no,no,one hr or more,21,no,no,7,7,no,occasionally,sometimes,normal,3,0,quite often,no +60 or older,Male,no,yes,none,26,no,no,4,4,yes,occasionally,not at all,high,0,0,not much,yes +60 or older,Female,yes,no,more than half an hr,31,no,no,8,7,yes,occasionally,not at all,normal,3,0,not much,yes +60 or older,Female,yes,no,none,26,no,no,7,7,no,occasionally,sometimes,normal,3,0,not much,yes +60 or older,Male,yes,no,none,21,no,no,8,8,yes,occasionally,not at all,normal,0,0,not much,yes +40-49,Female,yes,no,more than half an hr,28,no,no,8,6,yes,occasionally,always,normal,2,0,not much,yes +50-59,Male,no,yes,one hr or more,24,no,no,8,8,yes,occasionally,sometimes,high,0,0,not much,yes +40-49,Male,no,yes,more than half an hr,27,no,no,8,8,yes,occasionally,not at all,high,0,0,not much,yes +40-49,Male,no,no,more than half an hr,21,no,no,6,6,yes,occasionally,sometimes,normal,0,0,not much,yes +60 or older,Male,no,no,none,27,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,no +60 or older,Male,no,no,none,24,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,yes +less than 40,Male,yes,no,more than half an hr,36,no,no,6,4,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,less than half an hr,21,no,no,6,6,no,occasionally,not at all,normal,0,0,not much,no +40-49,Male,yes,no,less than half an hr,22,no,no,6,6,yes,occasionally,sometimes,normal,0,0,not much,no +40-49,Male,yes,no,less than half an hr,21,no,no,6,6,no,occasionally,sometimes,low,0,0,not much,no +50-59,Male,yes,no,one hr or more,38,no,yes,8,6,yes,occasionally,very often,normal,0,0,not much,yes +50-59,Female,yes,yes,more than half an hr,40,no,no,9,5,yes,very often,sometimes,high,2,yes,not much,yes +60 or older,Male,yes,no,none,35,no,no,5,5,no,occasionally,sometimes,high,0,0,quite often,yes +60 or older,Female,yes,yes,none,24,no,no,4,4,yes,occasionally,always,high,3,0,quite often,yes +60 or older,Male,no,no,none,18,no,no,5,5,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Male,no,no,more than half an hr,23,no,no,7,4,no,often,sometimes,normal,2,0,not much,no +60 or older,Male,no,yes,less than half an hr,19,no,yes,6,6,yes,often,sometimes,high,1,0,not much,yes +less than 40,Male,no,no,more than half an hr,26,yes,yes,8,6,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,more than half an hr,30,yes,yes,5,5,yes,often,not at all,normal,1,0,not much,no +60 or older,Female,no,yes,less than half an hr,27,no,no,6,6,yes,occasionally,always,high,0,0,quite often,yes +less than 40,Male,no,no,none,18,yes,yes,7,6,no,occasionally,very often,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,29,yes,yes,6,5,yes,often,very often,normal,0,0,not much,no +50-59,Male,no,yes,less than half an hr,19,no,yes,6,6,yes,occasionally,very often,high,0,0,not much,yes +50-59,Female,yes,no,none,21,no,no,8,8,yes,occasionally,very often,normal,0,0,quite often,yes +less than 40,Male,no,no,none,24,no,no,10,10,no,occasionally,not at all,normal,0,0,not much,no +60 or older,Male,no,no,none,31,no,no,6,6,no,occasionally,very often,normal,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,22,no,no,8,8,no,occasionally,very often,normal,0,0,not much,no +less than 40,Male,yes,no,none,19,no,yes,8,8,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Male,no,no,one hr or more,27,no,no,6,4,no,occasionally,very often,normal,0,0,not much,no +40-49,Male,yes,yes,less than half an hr,26,no,no,10,6,yes,occasionally,very often,high,0,0,quite often,yes +50-59,Female,yes,no,none,21,no,no,8,5,yes,occasionally,sometimes,normal,0,0,not much,yes +60 or older,Male,yes,no,one hr or more,26,no,no,6,5,yes,occasionally,not at all,normal,0,0,not much,yes +50-59,Male,yes,no,one hr or more,23,yes,yes,5,4,yes,occasionally,very often,normal,0,0,not much,yes +less than 40,Male,yes,yes,one hr or more,26,yes,yes,5,4,yes,often,not at all,high,0,0,not much,yes +60 or older,Female,yes,yes,less than half an hr,18,no,no,4,3,yes,occasionally,sometimes,high,0,0,not much,yes +60 or older,Female,yes,yes,less than half an hr,19,no,no,9,9,yes,occasionally,always,high,4,0,quite often,yes +50-59,Male,yes,no,none,24,no,yes,7,7,no,occasionally,always,normal,0,0,not much,no +60 or older,Female,yes,yes,less than half an hr,33,no,no,6,5,yes,occasionally,sometimes,high,2,0,not much,yes +50-59,Male,yes,yes,none,31,no,yes,7,6,yes,occasionally,always,high,0,0,not much,yes +40-49,Female,no,no,none,23,no,no,7,7,no,often,very often,normal,1,0,not much,no +50-59,Male,yes,no,less than half an hr,23,no,no,7,7,yes,occasionally,sometimes,normal,0,0,not much,yes +50-59,Female,no,yes,none,20,no,no,7,4,yes,occasionally,sometimes,high,1,0,not much,yes +50-59,Male,no,no,more than half an hr,28,no,yes,7,4,no,occasionally,sometimes,high,0,0,not much,yes +60 or older,Male,yes,yes,less than half an hr,26,no,yes,6,6,yes,occasionally,not at all,high,0,0,not much,yes +less than 40,Male,no,no,one hr or more,24,no,no,7,7,no,often,sometimes,normal,0,0,not much,no +60 or older,Male,no,no,more than half an hr,24,no,no,7,7,no,occasionally,sometimes,normal,0,0,quite often,yes +less than 40,Female,no,no,less than half an hr,17,no,no,4,4,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,28,yes,yes,4,4,no,occasionally,very often,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,23,yes,no,6,3,no,occasionally,very often,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,24,no,no,6,6,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Female,no,no,less than half an hr,19,no,no,7,3,yes,occasionally,sometimes,normal,1,0,not much,no +60 or older,Male,no,yes,less than half an hr,15,no,no,4,4,yes,occasionally,very often,high,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,22,no,no,6,2,no,often,not at all,normal,0,0,quite often,no +less than 40,Male,yes,no,more than half an hr,23,no,no,7,3,no,occasionally,sometimes,high,0,0,not much,no +less than 40,Male,no,no,one hr or more,18,no,no,8,4,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,yes,no,more than half an hr,21,no,no,5,5,no,very often,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,one hr or more,21,no,no,7,6,yes,often,very often,normal,0,0,not much,no +less than 40,Male,yes,no,one hr or more,21,no,no,9,6,no,very often,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,one hr or more,22,no,no,5,2,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,one hr or more,25,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Male,no,yes,less than half an hr,20,yes,yes,6,6,no,occasionally,very often,high,0,0,quite often,yes +less than 40,Male,no,no,more than half an hr,24,no,no,8,5,no,often,not at all,normal,0,0,not much,no +less than 40,Female,no,no,less than half an hr,21,no,no,8,8,no,occasionally,sometimes,normal,0,0,quite often,no +50-59,Male,no,no,less than half an hr,26,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Female,no,no,less than half an hr,27,no,no,8,6,no,occasionally,sometimes,low,1,0,not much,no +less than 40,Female,no,no,one hr or more,26,no,no,8,6,no,occasionally,sometimes,low,0,0,not much,no +less than 40,Male,no,yes,less than half an hr,24,no,no,8,8,no,occasionally,very often,normal,0,0,not much,no +less than 40,Female,no,no,less than half an hr,21,no,no,8,8,no,occasionally,not at all,normal,3,yes,not much,yes +40-49,Female,no,no,less than half an hr,22,no,no,8,8,no,occasionally,not at all,normal,3,0,not much,no +60 or older,Male,no,no,more than half an hr,23,no,yes,7,6,yes,occasionally,very often,high,0,0,quite often,yes +60 or older,Male,no,no,less than half an hr,28,no,no,8,8,yes,occasionally,not at all,normal,0,0,quite often,yes +60 or older,Male,yes,yes,less than half an hr,32,no,no,8,8,no,often,very often,high,0,0,not much,yes +40-49,Female,yes,no,less than half an hr,33,no,no,6,6,no,very often,very often,normal,2,yes,not much,yes +less than 40,Female,yes,no,one hr or more,24,no,no,8,8,no,often,sometimes,normal,0,0,not much,no +50-59,Female,yes,no,less than half an hr,28,no,no,7,4,yes,occasionally,very often,high,0,0,quite often,yes +50-59,Male,no,no,less than half an hr,30,no,no,7,2,no,often,always,normal,0,0,not much,no +50-59,Male,yes,yes,one hr or more,30,yes,yes,7,7,yes,often,always,high,0,0,quite often,yes +less than 40,Female,yes,no,more than half an hr,27,no,no,7,6,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Female,no,no,more than half an hr,27,no,no,9,6,no,always,very often,high,0,0,not much,no +less than 40,Male,no,no,one hr or more,22,yes,no,9,2,no,always,always,high,0,0,quite often,no +less than 40,Female,no,no,more than half an hr,23,no,no,5,4,no,often,very often,normal,0,0,not much,no +less than 40,Female,yes,no,more than half an hr,18,no,no,7,3,yes,often,very often,normal,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,22,no,no,7,5,no,often,very often,normal,0,0,not much,no +60 or older,Male,yes,yes,more than half an hr,21,no,yes,8,4,yes,occasionally,sometimes,normal,0,0,quite often,no +60 or older,Female,no,yes,one hr or more,26,no,no,8,6,yes,occasionally,sometimes,normal,0,0,not much,yes +50-59,Female,yes,yes,none,28,no,no,6,3,yes,occasionally,always,high,2,0,not much,yes +less than 40,Male,no,yes,more than half an hr,26,no,no,6,6,no,occasionally,sometimes,normal,0,0,quite often,no +50-59,Female,no,yes,less than half an hr,31,no,no,7,4,yes,occasionally,very often,high,0,0,quite often,no +50-59,Male,no,yes,less than half an hr,33,no,yes,5,5,yes,occasionally,sometimes,high,0,0,not much,no +less than 40,Female,no,yes,one hr or more,30,no,no,7,3,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,more than half an hr,23,no,yes,6,6,no,often,very often,normal,0,0,not much,no +less than 40,Female,yes,no,one hr or more,29,no,no,6,6,no,often,sometimes,normal,2,0,quite often,no +40-49,Female,yes,no,more than half an hr,17,no,no,7,7,no,very often,sometimes,normal,3,0,not much,no +50-59,Female,yes,no,less than half an hr,24,no,no,8,5,yes,occasionally,sometimes,normal,2,0,not much,no +60 or older,Male,yes,no,more than half an hr,26,no,yes,8,3,yes,occasionally,sometimes,normal,0,0,quite often,yes +less than 40,Female,yes,no,one hr or more,27,no,yes,8,6,no,very often,very often,normal,0,0,not much,no +40-49,Male,yes,yes,more than half an hr,21,no,no,6,4,yes,occasionally,sometimes,normal,0,0,not much,yes +40-49,Female,yes,yes,less than half an hr,26,no,no,7,4,no,occasionally,very often,high,0,0,quite often,no +less than 40,Male,yes,no,one hr or more,20,no,no,7,6,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Male,no,no,more than half an hr,22,no,no,6,6,no,occasionally,sometimes,low,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,21,yes,no,8,8,no,occasionally,very often,normal,0,0,not much,no +less than 40,Female,yes,no,one hr or more,26,no,no,7,5,no,often,sometimes,low,0,0,not much,no +less than 40,Female,yes,no,none,21,no,yes,9,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,none,21,no,yes,8,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,more than half an hr,24,no,no,8,8,yes,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,24,yes,yes,7,6,no,often,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,26,no,yes,6,4,no,occasionally,very often,normal,0,0,not much,no +50-59,Male,yes,no,less than half an hr,32,no,no,8,4,yes,occasionally,sometimes,normal,0,0,not much,yes +40-49,Male,yes,no,more than half an hr,34,no,no,7,5,no,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,30,no,no,7,4,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Female,no,yes,more than half an hr,42,no,no,7,3,yes,occasionally,always,high,0,0,not much,no +less than 40,Male,yes,no,less than half an hr,33,no,no,8,4,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,one hr or more,23,no,no,7,5,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Male,yes,yes,more than half an hr,27,no,yes,7,5,no,occasionally,sometimes,high,0,0,not much,no +40-49,Female,no,no,one hr or more,25,no,no,5,4,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,no,no,more than half an hr,28,no,no,6,5,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Female,no,no,less than half an hr,28,no,no,6,4,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Female,no,no,less than half an hr,33,no,no,7,5,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Female,no,no,less than half an hr,33,no,no,7,5,yes,occasionally,sometimes,normal,0,0,quite often,no +50-59,Female,no,no,more than half an hr,28,no,no,7,3,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,no,no,less than half an hr,28,no,no,6,3,no,occasionally,not at all,normal,0,0,quite often,no +40-49,Female,yes,yes,more than half an hr,36,no,no,7,6,yes,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,no,no,more than half an hr,33,no,no,8,4,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,no,no,less than half an hr,28,no,no,6,5,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Male,yes,no,more than half an hr,28,no,no,7,5,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,33,no,no,8,4,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Female,yes,no,less than half an hr,38,no,no,7,4,no,occasionally,not at all,normal,0,0,quite often,no +50-59,Female,no,yes,more than half an hr,33,no,no,6,4,yes,occasionally,sometimes,high,0,0,quite often,yes +40-49,Male,no,no,less than half an hr,38,no,no,5,4,yes,occasionally,sometimes,normal,0,0,quite often,no +40-49,Female,yes,no,less than half an hr,36,no,no,7,5,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,yes,no,less than half an hr,33,no,no,6,4,no,occasionally,very often,normal,0,0,quite often,no +less than 40,Female,no,no,more than half an hr,33,no,no,7,4,no,often,not at all,normal,0,0,quite often,no +40-49,Female,no,no,less than half an hr,38,no,no,6,4,no,occasionally,sometimes,normal,0,0,quite often,no +40-49,Female,yes,no,more than half an hr,36,no,no,7,3,yes,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,no,no,less than half an hr,33,no,no,7,4,no,occasionally,sometimes,normal,0,0,quite often,no +50-59,Male,no,no,less than half an hr,38,no,no,6,3,yes,occasionally,sometimes,normal,0,0,quite often,no +60 or older,Female,yes,no,less than half an hr,38,no,no,5,2,yes,occasionally,very often,high,0,0,quite often,yes +60 or older,Male,no,yes,less than half an hr,33,no,no,6,2,yes,occasionally,very often,high,0,0,quite often,yes +less than 40,Female,no,no,less than half an hr,33,no,no,8,3,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,yes,yes,more than half an hr,38,no,no,8,4,yes,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,yes,no,less than half an hr,32,no,no,6,4,no,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,33,no,no,8,3,no,often,sometimes,normal,0,0,quite often,no +40-49,Female,yes,no,none,22,no,no,10,10,yes,occasionally,always,normal,2,0,not much,no +40-49,Male,yes,no,none,24,no,no,8,8,no,occasionally,sometimes,normal,0,0,not much,yes +60 or older,Female,yes,yes,more than half an hr,23,no,no,9,9,yes,occasionally,always,high,3,0,quite often,yes +60 or older,Female,yes,yes,less than half an hr,27,no,no,7,7,yes,occasionally,sometimes,high,3,0,quite often,yes +60 or older,Male,yes,yes,more than half an hr,24,no,yes,7,7,yes,occasionally,always,high,0,0,not much,yes +less than 40,Female,yes,no,none,18,no,no,8,8,yes,often,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,none,20,no,no,9,9,no,often,sometimes,normal,0,0,not much,no +less than 40,Female,no,no,more than half an hr,19,no,no,7,7,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,one hr or more,28,yes,yes,7,7,no,often,not at all,normal,0,0,not much,yes +less than 40,Female,no,no,less than half an hr,19,no,no,8,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,more than half an hr,25,yes,yes,7,5,no,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,25,no,yes,7,7,no,often,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,24,yes,yes,10,7,no,always,always,normal,0,0,not much,no +less than 40,Male,yes,yes,more than half an hr,27,no,no,7,5,no,occasionally,sometimes,high,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,19,yes,yes,7,5,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Male,no,no,none,25,yes,yes,10,10,no,often,very often,normal,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,28,no,no,8,8,yes,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,yes,no,one hr or more,25,no,no,5,5,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,24,no,no,6,5,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,less than half an hr,21,no,no,8,7,no,often,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,more than half an hr,23,no,no,9,8,no,often,sometimes,normal,0,0,not much,no +less than 40,Male,no,yes,less than half an hr,28,yes,yes,8,4,no,always,always,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,20,no,no,7,4,no,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,18,no,no,6,1,no,always,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,one hr or more,25,no,no,6,6,no,always,always,normal,0,0,not much,no +less than 40,Male,yes,yes,less than half an hr,28,no,no,6,2,no,occasionally,not at all,high,0,0,quite often,no +less than 40,Male,yes,no,less than half an hr,27,yes,yes,6,6,no,always,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,20,no,no,8,6,no,always,not at all,normal,0,0,not much,no +less than 40,Male,yes,no,less than half an hr,20,no,no,7,4,no,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,yes,one hr or more,17,no,no,8,0,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Male,no,yes,less than half an hr,26,no,no,6,4,no,very often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,21,no,no,7,4,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,yes,no,less than half an hr,29,no,no,8,7,no,often,sometimes,low,0,0,not much,no +less than 40,Male,yes,yes,more than half an hr,29,no,yes,6,4,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,yes,no,more than half an hr,24,yes,yes,8,8,no,occasionally,sometimes,high,0,0,not much,no +less than 40,Male,yes,no,less than half an hr,23,no,no,5,5,no,always,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,less than half an hr,23,no,no,5,5,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Female,yes,no,more than half an hr,29,no,no,6,3,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,no,no,none,19,no,no,9,5,no,very often,sometimes,normal,2,0,not much,no +less than 40,Male,no,no,less than half an hr,23,yes,yes,8,6,no,very often,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,one hr or more,17,no,no,6,6,no,often,sometimes,normal,0,0,not much,no +50-59,Female,yes,no,one hr or more,23,no,no,6,5,no,occasionally,sometimes,high,2,0,not much,no +40-49,Male,yes,no,less than half an hr,24,no,no,7,5,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,less than half an hr,25,no,no,8,4,yes,occasionally,sometimes,normal,0,0,not much,no +50-59,Female,yes,no,less than half an hr,35,no,no,8,5,yes,often,very often,normal,2,0,quite often,yes +40-49,Female,yes,yes,more than half an hr,33,no,no,7,2,yes,occasionally,sometimes,high,0,0,quite often,yes +40-49,Female,yes,no,one hr or more,34,no,no,6,2,yes,always,very often,normal,0,0,quite often,yes +50-59,Male,yes,yes,more than half an hr,36,yes,no,6,4,yes,often,very often,high,0,0,not much,yes +60 or older,Male,no,no,less than half an hr,30,no,yes,6,2,yes,often,always,high,0,0,not much,yes +less than 40,Male,yes,no,one hr or more,28,yes,yes,5,3,yes,always,always,normal,0,0,quite often,yes +40-49,Male,yes,yes,less than half an hr,35,yes,yes,5,2,yes,very often,sometimes,high,0,0,not much,yes +40-49,Male,no,no,one hr or more,24,no,yes,6,5,no,occasionally,not at all,normal,0,0,not much,no +50-59,Female,no,yes,one hr or more,22,no,no,8,5,yes,occasionally,always,normal,3,0,not much,yes +less than 40,Male,no,no,less than half an hr,30,no,yes,8,8,yes,occasionally,sometimes,normal,0,0,not much,yes +40-49,Male,yes,yes,one hr or more,22,no,no,7,6,yes,occasionally,sometimes,high,0,0,not much,yes +less than 40,Male,yes,no,more than half an hr,22,no,no,8,6,no,often,sometimes,normal,0,0,not much,yes +60 or older,Male,yes,yes,more than half an hr,27,no,no,6,5,yes,occasionally,sometimes,high,0,0,quite often,yes +60 or older,Male,no,yes,none,23,no,no,6,5,yes,occasionally,sometimes,high,0,0,not much,yes +60 or older,Male,no,yes,less than half an hr,27,no,yes,6,5,yes,occasionally,very often,high,0,0,not much,yes +60 or older,Female,yes,yes,one hr or more,29,no,no,7,4,yes,occasionally,sometimes,high,2,0,quite often,yes +50-59,Male,no,yes,one hr or more,39,no,no,8,6,no,occasionally,sometimes,high,0,0,not much,no +50-59,Male,no,yes,less than half an hr,28,no,no,8,6,yes,very often,sometimes,normal,0,0,not much,no +40-49,Male,no,no,one hr or more,24,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Male,no,no,one hr or more,23,no,no,8,6,no,occasionally,sometimes,normal,0,0,not much,yes +40-49,Male,no,no,less than half an hr,27,no,no,8,8,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Male,no,yes,none,21,no,yes,10,10,no,occasionally,sometimes,high,0,0,not much,yes +less than 40,Male,no,no,one hr or more,24,no,no,8,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,20,no,no,7,7,yes,occasionally,sometimes,low,0,0,not much,no +40-49,Male,yes,no,one hr or more,23,no,no,7,7,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,more than half an hr,20,no,no,8,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,none,20,no,no,7,7,no,occasionally,not at all,normal,0,0,not much,no +40-49,Male,no,no,less than half an hr,26,yes,no,8,7,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,no,no,less than half an hr,21,no,no,6,6,no,occasionally,sometimes,normal,1,0,not much,no +less than 40,Female,no,no,one hr or more,22,no,no,8,7,no,occasionally,very often,normal,2,0,not much,no +less than 40,Male,no,no,one hr or more,15,no,no,7,7,no,occasionally,very often,normal,0,0,not much,no +40-49,Female,yes,no,none,34,no,no,6,6,no,occasionally,sometimes,normal,3,0,not much,no +50-59,Male,yes,no,more than half an hr,22,no,no,7,6,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,no,no,more than half an hr,24,no,no,6,6,no,occasionally,sometimes,normal,2,0,quite often,no +40-49,Male,yes,no,one hr or more,24,no,no,11,11,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,one hr or more,22,no,no,8,6,no,often,not at all,normal,0,0,not much,no +less than 40,Male,yes,no,one hr or more,30,no,yes,9,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,one hr or more,24,no,no,4,4,yes,often,always,normal,3,0,quite often,yes +less than 40,Female,yes,no,one hr or more,24,no,no,4,4,yes,often,always,normal,3,0,not much,yes +less than 40,Female,no,no,one hr or more,20,no,no,9,9,no,occasionally,sometimes,normal,2,0,not much,no +less than 40,Male,yes,no,more than half an hr,24,yes,no,8,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,no,no,more than half an hr,21,no,no,7,7,no,often,not at all,normal,0,0,not much,no +less than 40,Female,no,no,one hr or more,20,no,no,6,6,no,very often,not at all,normal,0,0,not much,no +50-59,Male,no,no,one hr or more,27,no,no,7,7,no,very often,not at all,normal,0,0,not much,no +40-49,Male,yes,no,none,29,no,no,6,6,no,occasionally,not at all,normal,0,0,not much,no +60 or older,Female,no,no,none,18,no,no,6,6,yes,occasionally,sometimes,normal,3,0,quite often,yes +less than 40,Female,no,no,one hr or more,32,no,no,8,8,yes,occasionally,sometimes,normal,3,0,not much,no +40-49,Female,no,no,one hr or more,21,no,no,7,7,no,occasionally,sometimes,normal,3,0,quite often,no +60 or older,Male,no,yes,none,26,no,no,4,4,yes,occasionally,not at all,high,0,0,not much,yes +60 or older,Female,yes,no,more than half an hr,31,no,no,8,7,yes,occasionally,not at all,normal,3,0,not much,yes +60 or older,Female,yes,no,none,26,no,no,7,7,no,occasionally,sometimes,normal,3,0,not much,no +60 or older,Male,yes,no,none,21,no,no,8,8,yes,occasionally,not at all,normal,0,0,not much,yes +40-49,Female,yes,no,more than half an hr,28,no,no,8,6,yes,occasionally,always,normal,2,0,not much,yes +50-59,Male,no,yes,one hr or more,24,no,no,8,8,yes,occasionally,sometimes,high,0,0,not much,yes +40-49,Male,no,yes,more than half an hr,27,no,no,8,8,yes,occasionally,not at all,high,0,0,not much,yes +40-49,Male,no,no,more than half an hr,21,no,no,6,6,yes,occasionally,sometimes,normal,0,0,not much,yes +60 or older,Male,no,no,none,27,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,no +60 or older,Male,no,no,none,24,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,yes +less than 40,Male,yes,no,more than half an hr,36,no,no,6,4,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,less than half an hr,21,no,no,6,6,no,occasionally,not at all,normal,0,0,not much,no +40-49,Male,yes,no,less than half an hr,22,no,no,6,6,yes,occasionally,sometimes,normal,0,0,not much,no +40-49,Male,yes,no,less than half an hr,21,no,no,6,6,no,occasionally,sometimes,low,0,0,not much,no +50-59,Male,yes,no,one hr or more,38,no,yes,8,6,yes,occasionally,very often,normal,0,0,not much,yes +50-59,Female,yes,yes,more than half an hr,40,no,no,9,5,yes,very often,sometimes,high,2,yes,not much,yes +60 or older,Male,yes,no,none,35,no,no,5,5,no,occasionally,sometimes,high,0,0,quite often,yes +60 or older,Female,yes,yes,none,24,no,no,4,4,yes,occasionally,always,high,3,0,quite often,yes +60 or older,Male,no,no,none,18,no,no,5,5,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Male,no,no,more than half an hr,23,no,no,7,4,no,often,sometimes,normal,2,0,not much,no +60 or older,Male,no,yes,less than half an hr,19,no,yes,6,6,yes,often,sometimes,high,1,0,not much,no +less than 40,Male,no,no,more than half an hr,26,yes,yes,8,6,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,more than half an hr,30,yes,yes,5,5,yes,often,not at all,normal,1,0,not much,no +60 or older,Female,no,yes,less than half an hr,27,no,no,6,6,yes,occasionally,always,high,0,0,quite often,yes +less than 40,Male,no,no,none,18,yes,yes,7,6,no,occasionally,very often,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,29,yes,yes,6,5,yes,often,very often,normal,0,0,not much,no +50-59,Male,no,yes,less than half an hr,19,no,yes,6,6,yes,occasionally,very often,high,0,0,not much,no +50-59,Female,yes,no,none,21,no,no,8,8,yes,occasionally,very often,normal,0,0,quite often,yes +less than 40,Male,no,no,none,24,no,no,10,10,no,occasionally,not at all,normal,0,0,not much,no +60 or older,Male,no,no,none,31,no,no,6,6,no,occasionally,very often,normal,0,0,not much,yes +less than 40,Male,yes,no,more than half an hr,22,no,no,8,8,no,occasionally,very often,normal,0,0,not much,no +less than 40,Male,yes,no,none,19,no,yes,8,8,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Male,no,no,one hr or more,27,no,no,6,4,no,occasionally,very often,normal,0,0,not much,no +40-49,Male,yes,yes,less than half an hr,26,no,no,10,6,yes,occasionally,very often,high,0,0,quite often,yes +50-59,Female,yes,no,none,21,no,no,8,5,yes,occasionally,sometimes,normal,0,0,not much,yes +60 or older,Male,yes,no,one hr or more,26,no,no,6,5,yes,occasionally,not at all,normal,0,0,not much,yes +50-59,Male,yes,no,one hr or more,23,yes,yes,5,4,yes,occasionally,very often,normal,0,0,not much,yes +less than 40,Male,yes,yes,one hr or more,26,yes,yes,5,4,yes,often,not at all,high,0,0,not much,yes +60 or older,Female,yes,yes,less than half an hr,18,no,no,4,3,yes,occasionally,sometimes,high,0,0,not much,yes +60 or older,Female,yes,yes,less than half an hr,19,no,no,9,9,yes,occasionally,always,high,4,0,quite often,yes +50-59,Male,yes,no,none,24,no,yes,7,7,no,occasionally,always,normal,0,0,not much,no +60 or older,Female,yes,yes,less than half an hr,33,no,no,6,5,yes,occasionally,sometimes,high,2,0,not much,yes +50-59,Male,yes,yes,none,31,no,yes,7,6,yes,occasionally,always,high,0,0,not much,yes +40-49,Female,no,no,none,23,no,no,7,7,no,often,very often,normal,1,0,not much,no +50-59,Male,yes,no,less than half an hr,23,no,no,7,7,yes,occasionally,sometimes,normal,0,0,not much,yes +50-59,Female,no,yes,none,20,no,no,7,4,yes,occasionally,sometimes,high,1,0,not much,yes +50-59,Male,no,no,more than half an hr,28,no,yes,7,4,no,occasionally,sometimes,high,0,0,not much,no +60 or older,Male,yes,yes,less than half an hr,26,no,yes,6,6,yes,occasionally,not at all,high,0,0,not much,no +less than 40,Male,no,no,one hr or more,24,no,no,7,7,no,often,sometimes,normal,0,0,not much,no +60 or older,Male,no,no,more than half an hr,24,no,no,7,7,no,occasionally,sometimes,normal,0,0,quite often,yes +less than 40,Female,no,no,less than half an hr,17,no,no,4,4,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,28,yes,yes,4,4,no,occasionally,very often,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,23,yes,no,6,3,no,occasionally,very often,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,24,no,no,6,6,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Female,no,no,less than half an hr,19,no,no,7,3,yes,occasionally,sometimes,normal,1,0,not much,no +60 or older,Male,no,yes,less than half an hr,15,no,no,4,4,yes,occasionally,very often,high,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,22,no,no,6,2,no,often,not at all,normal,0,0,quite often,no +less than 40,Male,yes,no,more than half an hr,23,no,no,7,3,no,occasionally,sometimes,high,0,0,not much,no +less than 40,Male,no,no,one hr or more,18,no,no,8,4,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,21,no,no,5,5,no,very often,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,one hr or more,21,no,no,7,6,yes,often,very often,normal,0,0,not much,no +less than 40,Male,yes,no,one hr or more,21,no,no,9,6,no,very often,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,one hr or more,22,no,no,5,2,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,one hr or more,25,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Male,no,yes,less than half an hr,20,yes,yes,6,6,no,occasionally,very often,high,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,24,no,no,8,5,no,often,not at all,normal,0,0,not much,no +less than 40,Female,no,no,less than half an hr,21,no,no,8,8,no,occasionally,sometimes,normal,0,0,quite often,no +50-59,Male,no,no,less than half an hr,26,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Female,no,no,less than half an hr,27,no,no,8,6,no,occasionally,sometimes,low,1,0,not much,no +less than 40,Female,no,no,one hr or more,26,no,no,8,6,no,occasionally,sometimes,low,0,0,not much,no +less than 40,Male,no,yes,less than half an hr,24,no,no,8,8,no,occasionally,very often,normal,0,0,not much,no +less than 40,Female,no,no,less than half an hr,21,no,no,8,8,no,occasionally,not at all,normal,3,0,not much,yes +40-49,Female,no,no,less than half an hr,22,no,no,8,8,no,occasionally,not at all,normal,3,0,not much,no +60 or older,Male,no,no,more than half an hr,23,no,yes,7,6,yes,occasionally,very often,high,0,0,quite often,yes +60 or older,Male,no,no,less than half an hr,28,no,no,8,8,yes,occasionally,not at all,normal,0,0,quite often,yes +60 or older,Male,yes,yes,less than half an hr,32,no,no,8,8,no,often,very often,high,0,0,not much,yes +40-49,Female,yes,no,less than half an hr,33,no,no,6,6,no,very often,very often,normal,2,yes,not much,yes +less than 40,Female,yes,no,one hr or more,24,no,no,8,8,no,often,sometimes,normal,0,0,not much,no +50-59,Female,yes,no,less than half an hr,28,no,no,7,4,yes,occasionally,very often,high,0,0,quite often,yes +50-59,Male,no,no,less than half an hr,30,no,no,7,2,no,often,always,normal,0,0,not much,no +50-59,Male,yes,yes,one hr or more,30,yes,yes,7,7,yes,often,always,high,0,0,quite often,yes +less than 40,Female,yes,no,more than half an hr,27,no,no,7,6,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Female,no,no,more than half an hr,27,no,no,9,6,no,always,very often,high,0,0,not much,no +less than 40,Male,no,no,one hr or more,22,yes,no,9,2,no,always,always,high,0,0,quite often,no +less than 40,Female,no,no,more than half an hr,23,no,no,5,4,no,often,very often,normal,0,0,not much,no +less than 40,Female,yes,no,more than half an hr,18,no,no,7,3,yes,often,very often,normal,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,22,no,no,7,5,no,often,very often,normal,0,0,not much,no +60 or older,Male,yes,yes,more than half an hr,21,no,yes,8,4,yes,occasionally,sometimes,normal,0,0,quite often,yes +60 or older,Female,no,yes,one hr or more,26,no,no,8,6,yes,occasionally,sometimes,normal,0,0,not much,yes +50-59,Female,yes,yes,none,28,no,no,6,3,yes,occasionally,always,high,2,0,not much,yes +less than 40,Male,no,yes,more than half an hr,26,no,no,6,6,no,occasionally,sometimes,normal,0,0,quite often,no +50-59,Female,no,yes,less than half an hr,31,no,no,7,4,yes,occasionally,very often,high,0,0,quite often,no +50-59,Male,no,yes,less than half an hr,33,no,yes,5,5,yes,occasionally,sometimes,high,0,0,not much,no +less than 40,Female,no,yes,one hr or more,30,no,no,7,3,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,more than half an hr,23,no,yes,6,6,no,often,very often,normal,0,0,not much,no +less than 40,Female,yes,no,one hr or more,29,no,no,6,6,no,often,sometimes,normal,2,0,quite often,no +40-49,Female,yes,no,more than half an hr,17,no,no,7,7,no,very often,sometimes,normal,3,0,not much,no +50-59,Female,yes,no,less than half an hr,24,no,no,8,5,yes,occasionally,sometimes,normal,2,0,not much,no +60 or older,Male,yes,no,more than half an hr,26,no,yes,8,3,yes,occasionally,sometimes,normal,0,0,quite often,yes +less than 40,Female,yes,no,one hr or more,27,no,yes,8,6,no,very often,very often,normal,0,0,not much,no +40-49,Male,yes,yes,more than half an hr,21,no,no,6,4,yes,occasionally,sometimes,normal,0,0,not much,yes +40-49,Female,yes,yes,less than half an hr,26,no,no,7,4,no,occasionally,very often,high,0,0,quite often,no +less than 40,Male,yes,no,one hr or more,20,no,no,7,6,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Male,no,no,more than half an hr,22,no,no,6,6,no,occasionally,sometimes,low,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,21,yes,no,8,8,no,occasionally,very often,normal,0,0,not much,no +less than 40,Female,yes,no,one hr or more,26,no,no,7,5,no,often,sometimes,low,0,0,not much,no +less than 40,Female,yes,no,none,21,no,yes,9,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,none,21,no,yes,8,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,more than half an hr,24,no,no,8,8,yes,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,24,yes,yes,7,6,no,often,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,26,no,yes,6,4,no,occasionally,very often,normal,0,0,not much,no +50-59,Male,yes,no,less than half an hr,32,no,no,8,4,yes,occasionally,sometimes,normal,0,0,not much,no +40-49,Male,yes,no,more than half an hr,34,no,no,7,5,no,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,30,no,no,7,4,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Female,no,yes,more than half an hr,42,no,no,7,3,yes,occasionally,always,high,0,0,not much,no +less than 40,Male,yes,no,less than half an hr,33,no,no,8,4,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,one hr or more,23,no,no,7,5,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Male,yes,yes,more than half an hr,27,no,yes,7,5,no,occasionally,sometimes,high,0,0,not much,no +40-49,Female,no,no,one hr or more,25,no,no,5,4,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,no,no,more than half an hr,28,no,no,6,5,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Female,no,no,less than half an hr,28,no,no,6,4,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Female,no,no,less than half an hr,33,no,no,7,5,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Female,no,no,less than half an hr,33,no,no,7,5,yes,occasionally,sometimes,normal,0,0,quite often,yes +50-59,Female,no,no,more than half an hr,28,no,no,7,3,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,no,no,less than half an hr,28,no,no,6,3,no,occasionally,not at all,normal,0,0,quite often,no +40-49,Female,yes,yes,more than half an hr,36,no,no,7,6,yes,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,no,no,more than half an hr,33,no,no,8,4,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,no,no,less than half an hr,28,no,no,6,5,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Male,yes,no,more than half an hr,28,no,no,7,5,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,33,no,no,8,4,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Female,yes,no,less than half an hr,38,no,no,7,4,no,occasionally,not at all,normal,0,0,quite often,no +50-59,Female,no,yes,more than half an hr,33,no,no,6,4,yes,occasionally,sometimes,high,0,0,quite often,no +40-49,Male,no,no,less than half an hr,38,no,no,5,4,yes,occasionally,sometimes,normal,0,0,quite often,no +40-49,Female,yes,no,less than half an hr,36,no,no,7,5,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,yes,no,less than half an hr,33,no,no,6,4,no,occasionally,very often,normal,0,0,quite often,no +less than 40,Female,no,no,more than half an hr,33,no,no,7,4,no,often,not at all,normal,0,0,quite often,no +40-49,Female,no,no,less than half an hr,38,no,no,6,4,no,occasionally,sometimes,normal,0,0,quite often,no +40-49,Female,yes,no,more than half an hr,36,no,no,7,3,yes,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,no,no,less than half an hr,33,no,no,7,4,no,occasionally,sometimes,normal,0,0,quite often,no +50-59,Male,no,no,less than half an hr,38,no,no,6,3,yes,occasionally,sometimes,normal,0,0,quite often,no +60 or older,Female,yes,no,less than half an hr,38,no,no,5,2,yes,occasionally,very often,high,0,0,quite often,yes +60 or older,Male,no,yes,less than half an hr,33,no,no,6,2,yes,occasionally,very often,high,0,0,quite often,yes +less than 40,Female,no,no,less than half an hr,33,no,no,8,3,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,yes,yes,more than half an hr,38,no,no,8,4,yes,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,yes,no,less than half an hr,32,no,no,6,4,no,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,33,no,no,8,3,no,often,sometimes,normal,0,0,quite often,no +40-49,Female,yes,no,none,22,no,no,10,10,yes,occasionally,always,normal,2,0,not much,no +40-49,Male,yes,no,none,24,no,no,8,8,no,occasionally,sometimes,normal,0,0,not much,yes +60 or older,Female,yes,yes,more than half an hr,23,no,no,9,9,yes,occasionally,always,high,3,0,quite often,yes +60 or older,Female,yes,yes,less than half an hr,27,no,no,7,7,yes,occasionally,sometimes,high,3,0,quite often,yes +60 or older,Male,yes,yes,more than half an hr,24,no,yes,7,7,yes,occasionally,always,high,0,0,not much,yes +less than 40,Female,yes,no,none,18,no,no,8,8,yes,often,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,none,20,no,no,9,9,no,often,sometimes,normal,0,0,not much,no +less than 40,Female,no,no,more than half an hr,19,no,no,7,7,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,one hr or more,28,yes,yes,7,7,no,often,not at all,normal,0,0,not much,yes +less than 40,Female,no,no,less than half an hr,19,no,no,8,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,more than half an hr,25,yes,yes,7,5,no,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,25,no,yes,7,7,no,often,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,24,yes,yes,10,7,no,always,always,normal,0,0,not much,no +less than 40,Male,yes,yes,more than half an hr,27,no,no,7,5,no,occasionally,sometimes,high,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,19,yes,yes,7,5,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Male,no,no,none,25,yes,yes,10,10,no,often,very often,normal,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,28,no,no,8,8,yes,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,yes,no,one hr or more,25,no,no,5,5,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,24,no,no,6,5,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,less than half an hr,21,no,no,8,7,no,often,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,more than half an hr,23,no,no,9,8,no,often,sometimes,normal,0,0,not much,no +less than 40,Male,no,yes,less than half an hr,28,yes,yes,8,4,no,always,always,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,20,no,no,7,4,no,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,18,no,no,6,1,no,always,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,one hr or more,25,no,no,6,6,no,always,always,normal,0,0,not much,no +less than 40,Male,yes,yes,less than half an hr,28,no,no,6,2,no,occasionally,not at all,high,0,0,quite often,no +less than 40,Male,yes,no,less than half an hr,27,yes,yes,6,6,no,always,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,20,no,no,8,6,no,always,not at all,normal,0,0,not much,no +less than 40,Male,yes,no,less than half an hr,20,no,no,7,4,no,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,yes,one hr or more,17,no,no,8,0,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Male,no,yes,less than half an hr,26,no,no,6,4,no,very often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,21,no,no,7,4,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,yes,no,less than half an hr,29,no,no,8,7,no,often,sometimes,low,0,0,not much,no +less than 40,Male,yes,yes,more than half an hr,29,no,yes,6,4,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,yes,no,more than half an hr,24,yes,yes,8,8,no,occasionally,sometimes,high,0,0,not much,no +less than 40,Male,yes,no,less than half an hr,23,no,no,5,5,no,always,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,less than half an hr,23,no,no,5,5,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Female,yes,no,more than half an hr,29,no,no,6,3,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,no,no,none,19,no,no,9,5,no,very often,sometimes,normal,2,0,not much,no +less than 40,Male,no,no,less than half an hr,23,yes,yes,8,6,no,very often,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,one hr or more,17,no,no,6,6,no,often,sometimes,normal,0,0,not much,no +50-59,Female,yes,no,one hr or more,23,no,no,6,5,no,occasionally,sometimes,high,2,0,not much,no +40-49,Male,yes,no,less than half an hr,24,no,no,7,5,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,less than half an hr,25,no,no,8,4,yes,occasionally,sometimes,normal,0,0,not much,no +50-59,Female,yes,no,less than half an hr,35,no,no,8,5,yes,often,very often,normal,2,0,quite often,yes +40-49,Female,yes,yes,more than half an hr,33,no,no,7,2,yes,occasionally,sometimes,high,0,0,quite often,yes +40-49,Female,yes,no,one hr or more,34,no,no,6,2,yes,always,very often,normal,0,0,quite often,yes +50-59,Male,yes,yes,more than half an hr,36,yes,no,6,4,yes,often,very often,high,0,0,not much,yes +60 or older,Male,no,no,less than half an hr,30,no,yes,6,2,yes,often,always,high,0,0,not much,yes +less than 40,Male,yes,no,one hr or more,28,yes,yes,5,3,yes,always,always,normal,0,0,quite often,yes +40-49,Male,yes,yes,less than half an hr,35,yes,yes,5,2,yes,very often,sometimes,high,0,0,not much,yes +40-49,Male,no,no,one hr or more,24,no,yes,6,5,no,occasionally,not at all,normal,0,0,not much,no +50-59,Female,no,yes,one hr or more,22,no,no,8,5,yes,occasionally,always,normal,3,0,not much,yes +less than 40,Male,no,no,less than half an hr,30,no,yes,8,8,yes,occasionally,sometimes,normal,0,0,not much,yes +40-49,Male,yes,yes,one hr or more,22,no,no,7,6,yes,occasionally,sometimes,high,0,0,not much,yes +less than 40,Male,yes,no,more than half an hr,22,no,no,8,6,no,often,sometimes,normal,0,0,not much,yes +60 or older,Male,yes,yes,more than half an hr,27,no,no,6,5,yes,occasionally,sometimes,high,0,0,quite often,yes +60 or older,Male,no,yes,none,23,no,no,6,5,yes,occasionally,sometimes,high,0,0,not much,no +60 or older,Male,no,yes,less than half an hr,27,no,yes,6,5,yes,occasionally,very often,high,0,0,not much,yes +60 or older,Female,yes,yes,one hr or more,29,no,no,7,4,yes,occasionally,sometimes,high,2,0,quite often,yes +50-59,Male,no,yes,one hr or more,39,no,no,8,6,no,occasionally,sometimes,high,0,0,not much,no +50-59,Male,no,yes,less than half an hr,28,no,no,8,6,yes,very often,sometimes,normal,0,0,not much,no +40-49,Male,no,no,one hr or more,24,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Male,no,no,one hr or more,23,no,no,8,6,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Male,no,no,less than half an hr,27,no,no,8,8,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Male,no,yes,none,21,no,yes,10,10,no,occasionally,sometimes,high,0,0,not much,yes +less than 40,Male,no,no,one hr or more,24,no,no,8,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,20,no,no,7,7,yes,occasionally,sometimes,low,0,0,not much,no +40-49,Male,yes,no,one hr or more,23,no,no,7,7,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,more than half an hr,20,no,no,8,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,none,20,no,no,7,7,no,occasionally,not at all,normal,0,0,not much,no +40-49,Male,no,no,less than half an hr,26,yes,no,8,7,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,no,no,less than half an hr,21,no,no,6,6,no,occasionally,sometimes,normal,1,0,not much,no +less than 40,Female,no,no,one hr or more,22,no,no,8,7,no,occasionally,very often,normal,2,0,not much,no +less than 40,Male,no,no,one hr or more,15,no,no,7,7,no,occasionally,very often,normal,0,0,not much,no +40-49,Female,yes,no,none,34,no,no,6,6,no,occasionally,sometimes,normal,3,0,not much,no +50-59,Male,yes,no,more than half an hr,22,no,no,7,6,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,no,no,more than half an hr,24,no,no,6,6,no,occasionally,sometimes,normal,2,0,quite often,no +40-49,Male,yes,no,one hr or more,24,no,no,11,11,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,one hr or more,22,no,no,8,6,no,often,not at all,normal,0,0,not much,no +less than 40,Male,yes,no,one hr or more,30,no,yes,9,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,one hr or more,24,no,no,4,4,yes,often,always,normal,3,0,quite often,yes +less than 40,Female,yes,no,one hr or more,24,no,no,4,4,yes,often,always,normal,3,0,not much,yes +less than 40,Female,no,no,one hr or more,20,no,no,9,9,no,occasionally,sometimes,normal,2,0,not much,no +less than 40,Male,yes,no,more than half an hr,24,yes,no,8,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,no,no,more than half an hr,21,no,no,7,7,no,often,not at all,normal,0,0,not much,no +less than 40,Female,no,no,one hr or more,20,no,no,6,6,no,very often,not at all,normal,0,0,not much,no +50-59,Male,yes,no,one hr or more,27,no,no,7,7,no,very often,not at all,normal,0,0,not much,yes +40-49,Male,yes,no,none,29,no,no,6,6,no,occasionally,not at all,normal,0,0,not much,no +60 or older,Female,no,no,none,18,no,no,6,6,yes,occasionally,sometimes,normal,3,0,quite often,yes +less than 40,Female,no,no,one hr or more,32,no,no,8,8,yes,occasionally,sometimes,normal,3,0,not much,no +40-49,Female,no,no,one hr or more,21,no,no,7,7,no,occasionally,sometimes,normal,3,0,quite often,no +60 or older,Male,no,yes,none,26,no,no,4,4,yes,occasionally,not at all,high,0,0,not much,yes +60 or older,Female,yes,no,more than half an hr,31,no,no,8,7,yes,occasionally,not at all,normal,3,0,not much,yes +60 or older,Female,yes,no,none,26,no,no,7,7,no,occasionally,sometimes,normal,3,0,not much,yes +60 or older,Male,yes,no,none,21,no,no,8,8,yes,occasionally,not at all,normal,0,0,not much,yes +40-49,Female,yes,no,more than half an hr,28,no,no,8,6,yes,occasionally,always,normal,2,0,not much,yes +50-59,Male,no,yes,one hr or more,24,no,no,8,8,yes,occasionally,sometimes,high,0,0,not much,yes +40-49,Male,no,yes,more than half an hr,27,no,no,8,8,yes,occasionally,not at all,high,0,0,not much,yes +40-49,Male,no,no,more than half an hr,21,no,no,6,6,yes,occasionally,sometimes,normal,0,0,not much,yes +60 or older,Male,no,no,none,27,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,no +60 or older,Male,no,no,none,24,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,yes +less than 40,Male,yes,no,more than half an hr,36,no,no,6,4,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,less than half an hr,21,no,no,6,6,no,occasionally,not at all,normal,0,0,not much,no +40-49,Male,yes,no,less than half an hr,22,no,no,6,6,yes,occasionally,sometimes,normal,0,0,not much,no +40-49,Male,yes,no,less than half an hr,21,no,no,6,6,no,occasionally,sometimes,low,0,0,not much,no +50-59,Male,yes,no,one hr or more,38,no,yes,8,6,yes,occasionally,very often,normal,0,0,not much,yes +50-59,Female,yes,yes,more than half an hr,40,no,no,9,5,yes,very often,sometimes,high,2,yes,not much,yes +60 or older,Male,yes,no,none,35,no,no,5,5,no,occasionally,sometimes,high,0,0,quite often,yes +60 or older,Female,yes,yes,none,24,no,no,4,4,yes,occasionally,always,high,3,0,quite often,yes +60 or older,Male,no,no,none,18,no,no,5,5,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Male,no,no,more than half an hr,23,no,no,7,4,no,often,sometimes,normal,2,0,not much,no +60 or older,Male,no,yes,less than half an hr,19,no,yes,6,6,yes,often,sometimes,high,1,0,not much,yes +less than 40,Male,no,no,more than half an hr,26,yes,yes,8,6,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,more than half an hr,30,yes,yes,5,5,yes,often,not at all,normal,1,0,not much,no +60 or older,Female,no,yes,less than half an hr,27,no,no,6,6,yes,occasionally,always,high,0,0,quite often,yes +less than 40,Male,no,no,none,18,yes,yes,7,6,no,occasionally,very often,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,29,yes,yes,6,5,yes,often,very often,normal,0,0,not much,no +50-59,Male,no,yes,less than half an hr,19,no,yes,6,6,yes,occasionally,very often,high,0,0,not much,no +50-59,Female,yes,no,none,21,no,no,8,8,yes,occasionally,very often,normal,0,0,quite often,yes +less than 40,Male,no,no,none,24,no,no,10,10,no,occasionally,not at all,normal,0,0,not much,no +60 or older,Male,no,no,none,31,no,no,6,6,no,occasionally,very often,normal,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,22,no,no,8,8,no,occasionally,very often,normal,0,0,not much,no +less than 40,Male,yes,no,none,19,no,yes,8,8,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Male,no,no,one hr or more,27,no,no,6,4,no,occasionally,very often,normal,0,0,not much,no +40-49,Male,yes,yes,less than half an hr,26,no,no,10,6,yes,occasionally,very often,high,0,0,quite often,yes +50-59,Female,yes,no,none,21,no,no,8,5,yes,occasionally,sometimes,normal,0,0,not much,yes +60 or older,Male,yes,no,one hr or more,26,no,no,6,5,yes,occasionally,not at all,normal,0,0,not much,yes +50-59,Male,yes,no,one hr or more,23,yes,yes,5,4,yes,occasionally,very often,normal,0,0,not much,yes +less than 40,Male,yes,yes,one hr or more,26,yes,yes,5,4,yes,often,not at all,high,0,0,not much,yes +60 or older,Female,yes,yes,less than half an hr,18,no,no,4,3,yes,occasionally,sometimes,high,0,0,not much,yes +60 or older,Female,yes,yes,less than half an hr,19,no,no,9,9,yes,occasionally,always,high,4,0,quite often,yes +50-59,Male,yes,no,none,24,no,yes,7,7,no,occasionally,always,normal,0,0,not much,no +60 or older,Female,yes,yes,less than half an hr,33,no,no,6,5,yes,occasionally,sometimes,high,2,0,not much,yes +50-59,Male,yes,yes,none,31,no,yes,7,6,yes,occasionally,always,high,0,0,not much,yes +40-49,Female,no,no,none,23,no,no,7,7,no,often,very often,normal,1,0,not much,no +50-59,Male,yes,no,less than half an hr,23,no,no,7,7,yes,occasionally,sometimes,normal,0,0,not much,yes +50-59,Female,no,yes,none,20,no,no,7,4,yes,occasionally,sometimes,high,1,0,not much,yes +50-59,Male,no,no,more than half an hr,28,no,yes,7,4,no,occasionally,sometimes,high,0,0,not much,no +60 or older,Male,yes,yes,less than half an hr,26,no,yes,6,6,yes,occasionally,not at all,high,0,0,not much,no +less than 40,Male,no,no,one hr or more,24,no,no,7,7,no,often,sometimes,normal,0,0,not much,no +60 or older,Male,no,no,more than half an hr,24,no,no,7,7,no,occasionally,sometimes,normal,0,0,quite often,yes +less than 40,Female,no,no,less than half an hr,17,no,no,4,4,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,28,yes,yes,4,4,no,occasionally,very often,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,23,yes,no,6,3,no,occasionally,very often,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,24,no,no,6,6,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Female,no,no,less than half an hr,19,no,no,7,3,yes,occasionally,sometimes,normal,1,0,not much,no +60 or older,Male,no,yes,less than half an hr,15,no,no,4,4,yes,occasionally,very often,high,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,22,no,no,6,2,no,often,not at all,normal,0,0,quite often,no +less than 40,Male,yes,no,more than half an hr,23,no,no,7,3,no,occasionally,sometimes,high,0,0,not much,no +less than 40,Male,no,no,one hr or more,18,no,no,8,4,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,21,no,no,5,5,no,very often,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,one hr or more,21,no,no,7,6,yes,often,very often,normal,0,0,not much,no +less than 40,Male,yes,no,one hr or more,21,no,no,9,6,no,very often,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,one hr or more,22,no,no,5,2,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,one hr or more,25,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Male,no,yes,less than half an hr,20,yes,yes,6,6,no,occasionally,very often,high,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,24,no,no,8,5,no,often,not at all,normal,0,0,not much,no +less than 40,Female,no,no,less than half an hr,21,no,no,8,8,no,occasionally,sometimes,normal,0,0,quite often,no +50-59,Male,no,no,less than half an hr,26,no,no,6,6,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Female,no,no,less than half an hr,27,no,no,8,6,no,occasionally,sometimes,low,1,0,not much,no +less than 40,Female,no,no,one hr or more,26,no,no,8,6,no,occasionally,sometimes,low,0,0,not much,no +less than 40,Male,no,yes,less than half an hr,24,no,no,8,8,no,occasionally,very often,normal,0,0,not much,no +less than 40,Female,no,no,less than half an hr,21,no,no,8,8,no,occasionally,not at all,normal,3,yes,not much,yes +40-49,Female,no,no,less than half an hr,22,no,no,8,8,no,occasionally,not at all,normal,3,0,not much,no +60 or older,Male,no,no,more than half an hr,23,no,yes,7,6,yes,occasionally,very often,high,0,0,quite often,yes +60 or older,Male,no,no,less than half an hr,28,no,no,8,8,yes,occasionally,not at all,normal,0,0,quite often,yes +60 or older,Male,yes,yes,less than half an hr,32,no,no,8,8,no,often,very often,high,0,0,not much,yes +40-49,Female,yes,no,less than half an hr,33,no,no,6,6,no,very often,very often,normal,2,yes,not much,yes +less than 40,Female,yes,no,one hr or more,24,no,no,8,8,no,often,sometimes,normal,0,0,not much,no +50-59,Female,yes,no,less than half an hr,28,no,no,7,4,yes,occasionally,very often,high,0,0,quite often,yes +50-59,Male,no,no,less than half an hr,30,no,no,7,2,no,often,always,normal,0,0,not much,no +50-59,Male,yes,yes,one hr or more,30,yes,yes,7,7,yes,often,always,high,0,0,quite often,yes +less than 40,Female,yes,no,more than half an hr,27,no,no,7,6,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Female,no,no,more than half an hr,27,no,no,9,6,no,always,very often,high,0,0,not much,no +less than 40,Male,no,no,one hr or more,22,yes,no,9,2,no,always,always,high,0,0,quite often,no +less than 40,Female,no,no,more than half an hr,23,no,no,5,4,no,often,very often,normal,0,0,not much,no +less than 40,Female,yes,no,more than half an hr,18,no,no,7,3,yes,often,very often,normal,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,22,no,no,7,5,no,often,very often,normal,0,0,not much,no +60 or older,Male,yes,yes,more than half an hr,21,no,yes,8,4,yes,occasionally,sometimes,normal,0,0,quite often,no +60 or older,Female,no,yes,one hr or more,26,no,no,8,6,yes,occasionally,sometimes,normal,0,0,not much,yes +50-59,Female,yes,yes,none,28,no,no,6,3,yes,occasionally,always,high,2,0,not much,yes +less than 40,Male,no,yes,more than half an hr,26,no,no,6,6,no,occasionally,sometimes,normal,0,0,quite often,no +50-59,Female,no,yes,less than half an hr,31,no,no,7,4,yes,occasionally,very often,high,0,0,quite often,no +50-59,Male,no,yes,less than half an hr,33,no,yes,5,5,yes,occasionally,sometimes,high,0,0,not much,no +less than 40,Female,no,yes,one hr or more,30,no,no,7,3,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,more than half an hr,23,no,yes,6,6,no,often,very often,normal,0,0,not much,no +less than 40,Female,yes,no,one hr or more,29,no,no,6,6,no,often,sometimes,normal,2,0,quite often,no +40-49,Female,yes,no,more than half an hr,17,no,no,7,7,no,very often,sometimes,normal,3,0,not much,no +50-59,Female,yes,no,less than half an hr,24,no,no,8,5,yes,occasionally,sometimes,normal,2,0,not much,no +60 or older,Male,yes,no,more than half an hr,26,no,yes,8,3,yes,occasionally,sometimes,normal,0,0,quite often,yes +less than 40,Female,yes,no,one hr or more,27,no,yes,8,6,no,very often,very often,normal,0,0,not much,no +40-49,Male,yes,yes,more than half an hr,21,no,no,6,4,yes,occasionally,sometimes,normal,0,0,not much,yes +40-49,Female,yes,yes,less than half an hr,26,no,no,7,4,no,occasionally,very often,high,0,0,quite often,no +less than 40,Male,yes,no,one hr or more,20,no,no,7,6,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Male,no,no,more than half an hr,23,no,no,6,6,no,occasionally,sometimes,low,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,21,yes,no,8,8,no,occasionally,very often,normal,0,0,not much,no +less than 40,Female,yes,no,one hr or more,26,no,no,7,5,no,often,sometimes,low,0,0,not much,no +less than 40,Female,yes,no,none,21,no,yes,9,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,none,21,no,yes,8,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,more than half an hr,24,no,no,8,8,yes,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,24,yes,yes,7,6,no,often,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,26,no,yes,6,4,no,occasionally,very often,normal,0,0,not much,no +50-59,Male,yes,no,less than half an hr,32,no,no,8,4,yes,occasionally,sometimes,normal,0,0,not much,no +40-49,Male,yes,no,more than half an hr,34,no,no,7,5,no,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,30,no,no,7,4,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Female,no,yes,more than half an hr,45,no,no,7,3,yes,occasionally,always,high,0,0,not much,yes +less than 40,Male,yes,no,less than half an hr,33,no,no,8,4,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,one hr or more,23,no,no,7,5,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Male,yes,yes,more than half an hr,27,no,yes,7,5,no,occasionally,sometimes,high,0,0,not much,no +40-49,Female,no,no,one hr or more,25,no,no,5,4,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,no,no,more than half an hr,28,no,no,6,5,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Female,no,no,less than half an hr,28,no,no,6,4,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Female,no,no,less than half an hr,33,no,no,7,5,no,occasionally,sometimes,normal,0,0,not much,no +50-59,Female,no,no,less than half an hr,33,no,no,7,5,yes,occasionally,sometimes,normal,0,0,quite often,no +50-59,Female,no,no,more than half an hr,28,no,no,7,3,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,no,no,less than half an hr,28,no,no,6,3,no,occasionally,not at all,normal,0,0,quite often,no +40-49,Female,yes,yes,more than half an hr,36,no,no,7,6,yes,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,no,no,more than half an hr,33,no,no,8,4,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,no,no,less than half an hr,28,no,no,6,5,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Male,yes,no,more than half an hr,28,no,no,7,5,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,33,no,no,8,4,no,occasionally,sometimes,normal,0,0,not much,no +40-49,Female,yes,no,less than half an hr,38,no,no,7,4,no,occasionally,not at all,normal,0,0,quite often,no +50-59,Female,no,yes,more than half an hr,33,no,no,6,4,yes,occasionally,sometimes,high,0,0,quite often,no +40-49,Male,no,no,less than half an hr,38,no,no,5,4,yes,occasionally,sometimes,normal,0,0,quite often,no +40-49,Female,yes,no,less than half an hr,36,no,no,7,5,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,yes,no,less than half an hr,33,no,no,6,4,no,occasionally,very often,normal,0,0,quite often,no +less than 40,Female,no,no,more than half an hr,33,no,no,7,4,no,often,not at all,normal,0,0,quite often,no +40-49,Female,no,no,less than half an hr,38,no,no,6,4,no,occasionally,sometimes,normal,0,0,quite often,no +40-49,Female,yes,no,more than half an hr,36,no,no,7,3,yes,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,no,no,less than half an hr,33,no,no,7,4,no,occasionally,sometimes,normal,0,0,quite often,no +50-59,Male,no,no,less than half an hr,38,no,no,6,3,yes,occasionally,sometimes,normal,0,0,quite often,no +60 or older,Female,yes,no,less than half an hr,38,no,no,5,2,yes,occasionally,very often,high,0,0,quite often,yes +60 or older,Male,no,yes,less than half an hr,33,no,no,6,2,yes,occasionally,very often,high,0,0,quite often,yes +less than 40,Female,no,no,less than half an hr,33,no,no,8,3,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,yes,yes,more than half an hr,38,no,no,8,4,yes,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,yes,no,less than half an hr,32,no,no,6,4,no,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,33,no,no,8,3,no,often,sometimes,normal,0,0,quite often,no +40-49,Female,yes,no,none,22,no,no,10,10,yes,occasionally,always,normal,2,0,not much,no +40-49,Male,yes,no,none,25,no,no,8,8,no,occasionally,sometimes,normal,0,0,not much,yes +60 or older,Female,yes,yes,more than half an hr,23,no,no,9,9,yes,occasionally,always,high,3,0,quite often,yes +60 or older,Female,yes,yes,less than half an hr,27,no,no,7,7,yes,occasionally,sometimes,high,3,0,quite often,yes +60 or older,Male,yes,yes,more than half an hr,24,no,yes,7,7,yes,occasionally,always,high,0,0,not much,yes +less than 40,Female,yes,no,none,18,no,no,8,8,yes,often,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,none,20,no,no,9,9,no,often,sometimes,normal,0,0,not much,no +less than 40,Female,no,no,more than half an hr,19,no,no,7,7,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,one hr or more,28,yes,yes,7,7,no,often,not at all,normal,0,0,not much,yes +less than 40,Female,no,no,less than half an hr,19,no,no,8,8,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,more than half an hr,25,yes,yes,7,5,no,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,25,no,yes,7,7,no,often,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,24,yes,yes,10,7,no,always,always,normal,0,0,not much,no +less than 40,Male,yes,yes,more than half an hr,27,no,no,7,5,no,occasionally,sometimes,high,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,19,yes,yes,7,5,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Male,no,no,none,25,yes,yes,10,10,no,often,very often,normal,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,28,no,no,8,8,yes,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,yes,no,one hr or more,25,no,no,5,5,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,more than half an hr,24,no,no,6,5,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,less than half an hr,21,no,no,8,7,no,often,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,more than half an hr,23,no,no,9,8,no,often,sometimes,normal,0,0,not much,no +less than 40,Male,no,yes,less than half an hr,28,yes,yes,8,4,no,always,always,normal,0,0,not much,no +less than 40,Male,no,no,less than half an hr,20,no,no,7,4,no,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,18,no,no,6,1,no,always,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,one hr or more,25,no,no,6,6,no,always,always,normal,0,0,not much,no +less than 40,Male,yes,yes,less than half an hr,28,no,no,6,2,no,occasionally,not at all,high,0,0,quite often,no +less than 40,Male,yes,no,less than half an hr,27,yes,yes,6,6,no,always,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,less than half an hr,20,no,no,8,6,no,always,not at all,normal,0,0,not much,no +less than 40,Male,yes,no,less than half an hr,20,no,no,7,4,no,often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,yes,one hr or more,17,no,no,8,0,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Male,no,yes,less than half an hr,29,no,no,6,4,no,very often,sometimes,normal,0,0,quite often,no +less than 40,Male,no,no,more than half an hr,21,no,no,7,4,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,yes,no,less than half an hr,29,no,no,8,7,no,often,sometimes,low,0,0,not much,no +less than 40,Male,yes,yes,more than half an hr,29,no,yes,6,4,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Male,yes,no,more than half an hr,24,yes,yes,8,8,no,occasionally,sometimes,high,0,0,not much,no +less than 40,Male,yes,no,less than half an hr,23,no,no,5,5,no,always,sometimes,normal,0,0,not much,no +less than 40,Male,yes,no,less than half an hr,23,no,no,5,5,no,occasionally,not at all,normal,0,0,not much,no +less than 40,Female,yes,no,more than half an hr,29,no,no,6,3,no,occasionally,sometimes,normal,0,0,quite often,no +less than 40,Female,no,no,none,19,no,no,9,5,no,very often,sometimes,normal,2,0,not much,no +less than 40,Male,no,no,less than half an hr,23,yes,yes,8,6,no,very often,sometimes,normal,0,0,not much,no +less than 40,Male,no,no,one hr or more,17,no,no,6,6,no,often,sometimes,normal,0,0,not much,no +50-59,Female,yes,no,one hr or more,23,no,no,6,5,no,occasionally,sometimes,high,2,0,not much,no +40-49,Male,yes,no,less than half an hr,24,no,no,7,5,no,occasionally,sometimes,normal,0,0,not much,no +less than 40,Female,yes,no,less than half an hr,25,no,no,8,4,yes,occasionally,sometimes,normal,0,0,not much,no +50-59,Female,yes,no,less than half an hr,35,no,no,8,5,yes,often,very often,normal,2,0,quite often,yes +40-49,Female,yes,yes,more than half an hr,33,no,no,7,2,yes,occasionally,sometimes,high,0,0,quite often,yes +40-49,Female,yes,no,one hr or more,34,no,no,6,2,yes,always,very often,normal,0,0,quite often,yes +50-59,Male,yes,yes,more than half an hr,36,yes,no,6,4,yes,often,very often,high,0,0,not much,yes +60 or older,Male,no,no,less than half an hr,30,no,yes,6,2,yes,often,always,high,0,0,not much,yes +less than 40,Male,yes,no,one hr or more,28,yes,yes,5,3,yes,always,always,normal,0,0,quite often,yes +40-49,Male,yes,yes,less than half an hr,35,yes,yes,5,2,yes,very often,sometimes,high,0,0,not much,yes +40-49,Male,no,no,one hr or more,24,no,yes,6,5,no,occasionally,not at all,normal,0,0,not much,no +50-59,Female,no,yes,one hr or more,22,no,no,8,5,yes,occasionally,always,normal,3,0,not much,yes +less than 40,Male,no,no,less than half an hr,30,no,yes,8,8,yes,occasionally,sometimes,normal,0,0,not much,yes +40-49,Male,yes,yes,one hr or more,22,no,no,7,6,yes,occasionally,sometimes,high,0,0,not much,yes +less than 40,Male,yes,no,more than half an hr,25,no,no,8,6,no,often,sometimes,normal,0,0,not much,yes +60 or older,Male,yes,yes,more than half an hr,27,no,no,6,5,yes,occasionally,sometimes,high,0,0,quite often,yes +60 or older,Male,no,yes,none,23,no,no,6,5,yes,occasionally,sometimes,high,0,0,not much,no +60 or older,Male,no,yes,less than half an hr,27,no,yes,6,5,yes,occasionally,very often,high,0,0,not much,no +60 or older,Female,yes,yes,one hr or more,30,no,no,7,4,yes,occasionally,sometimes,high,2,0,quite often,yes diff --git a/virny/datasets/data/german_data_credit.csv b/virny/datasets/data/german_data_credit.csv new file mode 100644 index 00000000..8d7a7a3d --- /dev/null +++ b/virny/datasets/data/german_data_credit.csv @@ -0,0 +1,1001 @@ +checking-account,duration,credit-history,purpose,credit-amount,savings-account,employment-since,installment-rate,other-debtors,residence-since,property,age,other-installment,housing,existing-credits,job,number-people-provide-maintenance-for,telephone,foreign-worker,sex,marital-status,class-label +<0 DM,6,critical account,radio/television,1169,no savings account,>=7 years,4,none,4,real estate,67,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,48,existing credits paid back duly till now,radio/television,5951,<100 DM,1<= < 4 years,2,none,2,real estate,22,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +no account,12,critical account,education,2096,<100 DM,4<= <7 years,2,none,3,real estate,49,none,own,1,unskilled - resident,2,none,yes,male,divorced/separated,1 +<0 DM,42,existing credits paid back duly till now,furniture/equipment,7882,<100 DM,4<= <7 years,2,guarantor,4,savings agreement/life insurance,45,none,for free,1,skilled employee / official,2,none,yes,male,divorced/separated,1 +<0 DM,24,delay in paying off,car (new),4870,<100 DM,1<= < 4 years,3,none,4,unknown / no property,53,none,for free,2,skilled employee / official,2,none,yes,male,divorced/separated,0 +no account,36,existing credits paid back duly till now,education,9055,no savings account,1<= < 4 years,2,none,4,unknown / no property,35,none,for free,1,unskilled - resident,2,yes,yes,male,divorced/separated,1 +no account,24,existing credits paid back duly till now,furniture/equipment,2835,500 <= < 1000 DM,>=7 years,3,none,4,savings agreement/life insurance,53,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,36,existing credits paid back duly till now,car (used),6948,<100 DM,1<= < 4 years,2,none,2,car or other,35,none,rent,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +no account,12,existing credits paid back duly till now,radio/television,3059,>= 1000 DM,4<= <7 years,2,none,4,real estate,61,none,own,1,unskilled - resident,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,30,critical account,car (new),5234,<100 DM,unemployed,4,none,2,car or other,28,none,own,2,management/ highly qualified employee,1,none,yes,male,married/widowed,0 +0 <= <200 DM,12,existing credits paid back duly till now,car (new),1295,<100 DM,<1 years,3,none,1,car or other,25,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +<0 DM,48,existing credits paid back duly till now,business,4308,<100 DM,<1 years,3,none,4,savings agreement/life insurance,24,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,12,existing credits paid back duly till now,radio/television,1567,<100 DM,1<= < 4 years,1,none,1,car or other,22,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +<0 DM,24,critical account,car (new),1199,<100 DM,>=7 years,4,none,4,car or other,60,none,own,2,unskilled - resident,1,none,yes,male,divorced/separated,0 +<0 DM,15,existing credits paid back duly till now,car (new),1403,<100 DM,1<= < 4 years,2,none,4,car or other,28,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,24,existing credits paid back duly till now,radio/television,1282,100 <= <500 DM,1<= < 4 years,4,none,2,car or other,32,none,own,1,unskilled - resident,1,none,yes,female,divorced/separated,0 +no account,24,critical account,radio/television,2424,no savings account,>=7 years,4,none,4,savings agreement/life insurance,53,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,30,no credits taken,business,8072,no savings account,<1 years,2,none,3,car or other,25,bank,own,3,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,24,existing credits paid back duly till now,car (used),12579,<100 DM,>=7 years,4,none,2,unknown / no property,44,none,for free,1,management/ highly qualified employee,1,yes,yes,female,divorced/separated,0 +no account,24,existing credits paid back duly till now,radio/television,3430,500 <= < 1000 DM,>=7 years,3,none,2,car or other,31,none,own,1,skilled employee / official,2,yes,yes,male,divorced/separated,1 +no account,9,critical account,car (new),2134,<100 DM,1<= < 4 years,4,none,4,car or other,48,none,own,3,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,6,existing credits paid back duly till now,radio/television,2647,500 <= < 1000 DM,1<= < 4 years,2,none,3,real estate,44,none,rent,1,skilled employee / official,2,none,yes,male,divorced/separated,1 +<0 DM,10,critical account,car (new),2241,<100 DM,<1 years,1,none,3,real estate,48,none,rent,2,unskilled - resident,2,none,no,male,divorced/separated,1 +0 <= <200 DM,12,critical account,car (used),1804,100 <= <500 DM,<1 years,3,none,4,savings agreement/life insurance,44,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,10,critical account,furniture/equipment,2069,no savings account,1<= < 4 years,2,none,1,car or other,26,none,own,2,skilled employee / official,1,none,no,male,married/widowed,1 +<0 DM,6,existing credits paid back duly till now,furniture/equipment,1374,<100 DM,1<= < 4 years,1,none,2,real estate,36,bank,own,1,unskilled - resident,1,yes,yes,male,divorced/separated,1 +no account,6,no credits taken,radio/television,426,<100 DM,>=7 years,4,none,4,car or other,39,none,own,1,unskilled - resident,1,none,yes,male,married/widowed,1 +>= 200 DM ,12,all credits at this bank paid back duly,radio/television,409,>= 1000 DM,1<= < 4 years,3,none,3,real estate,42,none,rent,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,7,existing credits paid back duly till now,radio/television,2415,<100 DM,1<= < 4 years,3,guarantor,2,real estate,34,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,60,delay in paying off,business,6836,<100 DM,>=7 years,3,none,4,unknown / no property,63,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,0 +0 <= <200 DM,18,existing credits paid back duly till now,business,1913,>= 1000 DM,<1 years,3,none,3,real estate,36,bank,own,1,skilled employee / official,1,yes,yes,male,married/widowed,1 +<0 DM,24,existing credits paid back duly till now,furniture/equipment,4020,<100 DM,1<= < 4 years,2,none,2,car or other,27,store,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,18,existing credits paid back duly till now,car (new),5866,100 <= <500 DM,1<= < 4 years,2,none,2,car or other,30,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,12,critical account,business,1264,no savings account,>=7 years,4,none,4,unknown / no property,57,none,rent,1,unskilled - resident,1,none,yes,male,divorced/separated,1 +>= 200 DM ,12,existing credits paid back duly till now,furniture/equipment,1474,<100 DM,<1 years,4,none,1,savings agreement/life insurance,33,bank,own,1,management/ highly qualified employee,1,yes,yes,female,divorced/separated,1 +0 <= <200 DM,45,critical account,radio/television,4746,<100 DM,<1 years,4,none,2,savings agreement/life insurance,25,none,own,2,unskilled - resident,1,none,yes,male,divorced/separated,0 +no account,48,critical account,education,6110,<100 DM,1<= < 4 years,1,none,3,unknown / no property,31,bank,for free,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +>= 200 DM ,18,existing credits paid back duly till now,radio/television,2100,<100 DM,1<= < 4 years,4,co-applicant,2,real estate,37,store,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +>= 200 DM ,10,existing credits paid back duly till now,domestic appliances,1225,<100 DM,1<= < 4 years,2,none,2,car or other,37,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,9,existing credits paid back duly till now,radio/television,458,<100 DM,1<= < 4 years,4,none,3,real estate,24,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,30,existing credits paid back duly till now,radio/television,2333,500 <= < 1000 DM,>=7 years,4,none,2,car or other,30,bank,own,1,management/ highly qualified employee,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,radio/television,1158,500 <= < 1000 DM,1<= < 4 years,3,none,1,car or other,26,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,18,delay in paying off,repairs,6204,<100 DM,1<= < 4 years,2,none,4,real estate,44,none,own,1,unskilled - resident,2,yes,yes,male,divorced/separated,1 +<0 DM,30,critical account,car (used),6187,100 <= <500 DM,4<= <7 years,1,none,4,car or other,24,none,rent,2,skilled employee / official,1,none,yes,male,married/widowed,1 +<0 DM,48,critical account,car (used),6143,<100 DM,>=7 years,4,none,4,unknown / no property,58,store,for free,2,unskilled - resident,1,none,yes,female,divorced/separated,0 +no account,11,critical account,car (new),1393,<100 DM,<1 years,4,none,4,car or other,35,none,own,2,management/ highly qualified employee,1,none,yes,female,divorced/separated,1 +no account,36,existing credits paid back duly till now,radio/television,2299,500 <= < 1000 DM,>=7 years,4,none,4,car or other,39,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,6,existing credits paid back duly till now,car (used),1352,500 <= < 1000 DM,unemployed,1,none,2,savings agreement/life insurance,23,none,rent,1,unemployed/ unskilled - non-resident,1,yes,yes,female,divorced/separated,1 +no account,11,critical account,car (new),7228,<100 DM,1<= < 4 years,1,none,4,savings agreement/life insurance,39,none,own,2,unskilled - resident,1,none,yes,male,divorced/separated,1 +no account,12,existing credits paid back duly till now,radio/television,2073,100 <= <500 DM,1<= < 4 years,4,co-applicant,2,real estate,28,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,24,delay in paying off,furniture/equipment,2333,no savings account,<1 years,4,none,2,savings agreement/life insurance,29,bank,own,1,unskilled - resident,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,27,delay in paying off,car (used),5965,<100 DM,>=7 years,1,none,2,car or other,30,none,own,2,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +no account,12,existing credits paid back duly till now,radio/television,1262,<100 DM,1<= < 4 years,3,none,2,car or other,25,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,18,existing credits paid back duly till now,car (used),3378,no savings account,1<= < 4 years,2,none,1,savings agreement/life insurance,31,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,36,delay in paying off,car (new),2225,<100 DM,>=7 years,4,none,4,unknown / no property,57,bank,for free,2,skilled employee / official,1,yes,yes,male,divorced/separated,0 +no account,6,all credits at this bank paid back duly,car (new),783,no savings account,1<= < 4 years,1,guarantor,2,real estate,26,store,own,1,unskilled - resident,2,none,yes,male,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,radio/television,6468,no savings account,unemployed,2,none,1,unknown / no property,52,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +no account,36,critical account,radio/television,9566,<100 DM,1<= < 4 years,2,none,2,car or other,31,store,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +>= 200 DM ,18,existing credits paid back duly till now,car (new),1961,<100 DM,>=7 years,3,none,2,car or other,23,none,own,1,management/ highly qualified employee,1,none,yes,female,divorced/separated,1 +<0 DM,36,critical account,furniture/equipment,6229,<100 DM,<1 years,4,co-applicant,4,unknown / no property,23,none,rent,2,unskilled - resident,1,yes,yes,female,divorced/separated,0 +0 <= <200 DM,9,existing credits paid back duly till now,business,1391,<100 DM,1<= < 4 years,2,none,1,real estate,27,bank,own,1,skilled employee / official,1,yes,yes,male,married/widowed,1 +0 <= <200 DM,15,critical account,radio/television,1537,no savings account,>=7 years,4,guarantor,4,real estate,50,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,36,no credits taken,business,1953,<100 DM,>=7 years,4,none,4,unknown / no property,61,none,for free,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +0 <= <200 DM,48,no credits taken,business,14421,<100 DM,1<= < 4 years,2,none,2,car or other,25,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,0 +no account,24,existing credits paid back duly till now,radio/television,3181,<100 DM,<1 years,4,none,4,savings agreement/life insurance,26,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +no account,27,existing credits paid back duly till now,repairs,5190,no savings account,>=7 years,4,none,4,savings agreement/life insurance,48,none,own,4,skilled employee / official,2,yes,yes,male,divorced/separated,1 +no account,12,existing credits paid back duly till now,radio/television,2171,<100 DM,<1 years,2,none,2,car or other,29,bank,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,car (new),1007,>= 1000 DM,1<= < 4 years,4,none,1,real estate,22,none,own,1,skilled employee / official,1,none,yes,male,married/widowed,1 +no account,36,existing credits paid back duly till now,education,1819,<100 DM,1<= < 4 years,4,none,4,unknown / no property,37,store,for free,1,skilled employee / official,1,yes,yes,male,divorced/separated,0 +no account,36,existing credits paid back duly till now,radio/television,2394,no savings account,1<= < 4 years,4,none,4,car or other,25,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,36,existing credits paid back duly till now,car (used),8133,<100 DM,1<= < 4 years,1,none,2,savings agreement/life insurance,30,bank,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,7,critical account,radio/television,730,no savings account,>=7 years,4,none,2,savings agreement/life insurance,46,none,rent,2,unskilled - resident,1,yes,yes,male,divorced/separated,1 +<0 DM,8,critical account,others,1164,<100 DM,>=7 years,3,none,4,unknown / no property,51,bank,for free,2,management/ highly qualified employee,2,yes,yes,male,divorced/separated,1 +0 <= <200 DM,42,critical account,business,5954,<100 DM,4<= <7 years,2,none,1,real estate,41,bank,own,2,unskilled - resident,1,none,yes,female,divorced/separated,1 +<0 DM,36,existing credits paid back duly till now,education,1977,no savings account,>=7 years,4,none,4,unknown / no property,40,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +<0 DM,12,critical account,car (used),1526,<100 DM,>=7 years,4,none,4,unknown / no property,66,none,for free,2,management/ highly qualified employee,1,none,yes,male,divorced/separated,1 +<0 DM,42,existing credits paid back duly till now,radio/television,3965,<100 DM,<1 years,4,none,3,car or other,34,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +0 <= <200 DM,11,delay in paying off,radio/television,4771,<100 DM,4<= <7 years,2,none,4,savings agreement/life insurance,51,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,54,no credits taken,car (used),9436,no savings account,1<= < 4 years,2,none,2,savings agreement/life insurance,39,none,own,1,unskilled - resident,2,none,yes,male,divorced/separated,1 +0 <= <200 DM,30,existing credits paid back duly till now,furniture/equipment,3832,<100 DM,<1 years,2,none,1,savings agreement/life insurance,22,none,own,1,skilled employee / official,1,none,yes,male,married/widowed,1 +no account,24,existing credits paid back duly till now,radio/television,5943,no savings account,<1 years,1,none,1,car or other,44,none,own,2,skilled employee / official,1,yes,yes,female,divorced/separated,0 +no account,15,existing credits paid back duly till now,radio/television,1213,500 <= < 1000 DM,>=7 years,4,none,3,savings agreement/life insurance,47,store,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,18,existing credits paid back duly till now,business,1568,100 <= <500 DM,1<= < 4 years,3,none,4,savings agreement/life insurance,24,none,rent,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +<0 DM,24,existing credits paid back duly till now,others,1755,<100 DM,>=7 years,4,guarantor,4,real estate,58,none,own,1,unskilled - resident,1,yes,yes,female,divorced/separated,1 +<0 DM,10,existing credits paid back duly till now,radio/television,2315,<100 DM,>=7 years,3,none,4,real estate,52,none,own,1,unskilled - resident,1,none,yes,male,divorced/separated,1 +no account,12,critical account,business,1412,<100 DM,1<= < 4 years,4,guarantor,2,real estate,29,none,own,2,management/ highly qualified employee,1,yes,yes,female,divorced/separated,1 +0 <= <200 DM,18,critical account,furniture/equipment,1295,<100 DM,<1 years,4,none,1,savings agreement/life insurance,27,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,36,existing credits paid back duly till now,education,12612,100 <= <500 DM,1<= < 4 years,1,none,4,unknown / no property,47,none,for free,1,skilled employee / official,2,yes,yes,male,divorced/separated,0 +<0 DM,18,existing credits paid back duly till now,car (new),2249,100 <= <500 DM,4<= <7 years,4,none,3,car or other,30,none,own,1,management/ highly qualified employee,2,yes,yes,male,divorced/separated,1 +<0 DM,12,no credits taken,repairs,1108,<100 DM,4<= <7 years,4,none,3,real estate,28,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,12,critical account,radio/television,618,<100 DM,>=7 years,4,none,4,real estate,56,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,12,critical account,car (used),1409,<100 DM,>=7 years,4,none,3,real estate,54,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,12,critical account,radio/television,797,no savings account,>=7 years,4,none,3,savings agreement/life insurance,33,bank,own,1,unskilled - resident,2,none,yes,female,divorced/separated,0 +>= 200 DM ,24,critical account,furniture/equipment,3617,no savings account,>=7 years,4,co-applicant,4,unknown / no property,20,none,rent,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,car (new),1318,>= 1000 DM,>=7 years,4,none,4,real estate,54,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,54,no credits taken,business,15945,<100 DM,<1 years,3,none,4,unknown / no property,58,none,rent,1,skilled employee / official,1,yes,yes,male,divorced/separated,0 +no account,12,critical account,education,2012,no savings account,4<= <7 years,4,none,2,car or other,61,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,18,existing credits paid back duly till now,business,2622,100 <= <500 DM,1<= < 4 years,4,none,4,car or other,34,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,36,critical account,radio/television,2337,<100 DM,>=7 years,4,none,4,real estate,36,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,20,delay in paying off,car (used),7057,no savings account,4<= <7 years,3,none,4,savings agreement/life insurance,36,bank,rent,2,management/ highly qualified employee,2,yes,yes,male,divorced/separated,1 +no account,24,existing credits paid back duly till now,car (new),1469,100 <= <500 DM,>=7 years,4,none,4,real estate,41,none,rent,1,unskilled - resident,1,none,yes,male,married/widowed,1 +0 <= <200 DM,36,existing credits paid back duly till now,radio/television,2323,<100 DM,4<= <7 years,4,none,4,car or other,24,none,rent,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,6,delay in paying off,radio/television,932,<100 DM,1<= < 4 years,3,none,2,real estate,24,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,9,critical account,furniture/equipment,1919,<100 DM,4<= <7 years,4,none,3,car or other,35,none,rent,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,12,existing credits paid back duly till now,car (used),2445,no savings account,<1 years,2,none,4,car or other,26,none,rent,1,skilled employee / official,1,yes,yes,male,married/widowed,1 +0 <= <200 DM,24,critical account,others,11938,<100 DM,1<= < 4 years,2,co-applicant,3,car or other,39,none,own,2,management/ highly qualified employee,2,yes,yes,male,divorced/separated,0 +no account,18,all credits at this bank paid back duly,car (new),6458,<100 DM,>=7 years,2,none,4,unknown / no property,39,bank,own,2,management/ highly qualified employee,2,yes,yes,male,divorced/separated,0 +0 <= <200 DM,12,existing credits paid back duly till now,car (new),6078,<100 DM,4<= <7 years,2,none,2,car or other,32,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,24,existing credits paid back duly till now,furniture/equipment,7721,no savings account,<1 years,1,none,2,savings agreement/life insurance,30,none,own,1,skilled employee / official,1,yes,no,female,divorced/separated,1 +0 <= <200 DM,14,existing credits paid back duly till now,business,1410,500 <= < 1000 DM,>=7 years,1,none,2,real estate,35,none,own,1,skilled employee / official,1,yes,yes,male,married/widowed,1 +0 <= <200 DM,6,delay in paying off,business,1449,100 <= <500 DM,>=7 years,1,none,2,car or other,31,bank,own,2,skilled employee / official,2,none,yes,male,divorced/separated,1 +>= 200 DM ,15,existing credits paid back duly till now,education,392,<100 DM,<1 years,4,none,4,savings agreement/life insurance,23,none,rent,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +0 <= <200 DM,18,existing credits paid back duly till now,car (new),6260,<100 DM,4<= <7 years,3,none,3,real estate,28,none,rent,1,unskilled - resident,1,none,yes,male,divorced/separated,1 +no account,36,critical account,car (new),7855,<100 DM,1<= < 4 years,4,none,2,real estate,25,store,own,2,skilled employee / official,1,yes,yes,female,divorced/separated,0 +<0 DM,12,existing credits paid back duly till now,radio/television,1680,500 <= < 1000 DM,>=7 years,3,none,1,real estate,35,none,own,1,skilled employee / official,1,none,yes,male,married/widowed,1 +no account,48,critical account,radio/television,3578,no savings account,>=7 years,4,none,1,real estate,47,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,42,existing credits paid back duly till now,radio/television,7174,no savings account,4<= <7 years,4,none,3,car or other,30,none,own,1,management/ highly qualified employee,1,yes,yes,female,divorced/separated,0 +<0 DM,10,critical account,furniture/equipment,2132,no savings account,<1 years,2,co-applicant,3,real estate,27,none,rent,2,skilled employee / official,1,none,no,female,divorced/separated,1 +<0 DM,33,critical account,furniture/equipment,4281,500 <= < 1000 DM,1<= < 4 years,1,none,4,car or other,23,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,12,critical account,car (new),2366,500 <= < 1000 DM,4<= <7 years,3,none,3,car or other,36,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +<0 DM,21,existing credits paid back duly till now,radio/television,1835,<100 DM,1<= < 4 years,3,none,2,real estate,25,none,own,2,skilled employee / official,1,yes,yes,female,divorced/separated,0 +no account,24,critical account,car (used),3868,<100 DM,>=7 years,4,none,2,car or other,41,none,rent,2,management/ highly qualified employee,1,yes,yes,female,divorced/separated,1 +no account,12,existing credits paid back duly till now,furniture/equipment,1768,<100 DM,1<= < 4 years,3,none,2,real estate,24,none,rent,1,unskilled - resident,1,none,yes,male,divorced/separated,1 +>= 200 DM ,10,critical account,car (new),781,<100 DM,>=7 years,4,none,4,unknown / no property,63,none,for free,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,18,existing credits paid back duly till now,furniture/equipment,1924,no savings account,<1 years,4,none,3,real estate,27,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +<0 DM,12,critical account,car (new),2121,<100 DM,1<= < 4 years,4,none,2,savings agreement/life insurance,30,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,radio/television,701,<100 DM,1<= < 4 years,4,none,2,real estate,40,none,own,1,unskilled - resident,1,none,yes,male,married/widowed,1 +0 <= <200 DM,12,existing credits paid back duly till now,repairs,639,<100 DM,1<= < 4 years,4,none,2,car or other,30,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +0 <= <200 DM,12,critical account,car (used),1860,<100 DM,unemployed,4,none,2,car or other,34,none,own,2,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +<0 DM,12,critical account,car (new),3499,<100 DM,1<= < 4 years,3,co-applicant,2,real estate,29,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,48,existing credits paid back duly till now,car (new),8487,no savings account,4<= <7 years,1,none,2,car or other,24,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,36,delay in paying off,education,6887,<100 DM,1<= < 4 years,4,none,3,savings agreement/life insurance,29,store,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,0 +no account,15,existing credits paid back duly till now,furniture/equipment,2708,<100 DM,<1 years,2,none,3,savings agreement/life insurance,27,bank,own,2,unskilled - resident,1,none,yes,male,divorced/separated,1 +no account,18,existing credits paid back duly till now,furniture/equipment,1984,<100 DM,1<= < 4 years,4,none,4,unknown / no property,47,bank,for free,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,60,existing credits paid back duly till now,radio/television,10144,100 <= <500 DM,4<= <7 years,2,none,4,real estate,21,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +no account,12,critical account,radio/television,1240,no savings account,>=7 years,4,none,2,real estate,38,none,own,2,skilled employee / official,1,yes,yes,female,divorced/separated,1 +no account,27,delay in paying off,car (used),8613,>= 1000 DM,1<= < 4 years,2,none,2,car or other,27,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,radio/television,766,500 <= < 1000 DM,1<= < 4 years,4,none,3,real estate,66,none,own,1,unskilled - resident,1,none,yes,male,divorced/separated,0 +0 <= <200 DM,15,critical account,radio/television,2728,no savings account,4<= <7 years,4,guarantor,2,real estate,35,bank,own,3,skilled employee / official,1,yes,yes,male,divorced/separated,1 +>= 200 DM ,12,existing credits paid back duly till now,radio/television,1881,<100 DM,1<= < 4 years,2,none,2,car or other,44,none,rent,1,unskilled - resident,1,yes,yes,female,divorced/separated,1 +>= 200 DM ,6,existing credits paid back duly till now,car (new),709,>= 1000 DM,<1 years,2,none,2,real estate,27,none,own,1,unemployed/ unskilled - non-resident,1,none,no,male,married/widowed,1 +0 <= <200 DM,36,existing credits paid back duly till now,radio/television,4795,<100 DM,<1 years,4,none,1,unknown / no property,30,none,own,1,management/ highly qualified employee,1,yes,yes,female,divorced/separated,1 +<0 DM,27,existing credits paid back duly till now,radio/television,3416,<100 DM,1<= < 4 years,3,none,2,car or other,27,none,own,1,management/ highly qualified employee,1,none,yes,male,divorced/separated,1 +<0 DM,18,existing credits paid back duly till now,furniture/equipment,2462,<100 DM,1<= < 4 years,2,none,2,car or other,22,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,21,critical account,furniture/equipment,2288,<100 DM,<1 years,4,none,4,savings agreement/life insurance,23,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +0 <= <200 DM,48,all credits at this bank paid back duly,business,3566,100 <= <500 DM,4<= <7 years,4,none,2,car or other,30,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,6,critical account,car (new),860,<100 DM,>=7 years,1,none,4,unknown / no property,39,none,own,2,skilled employee / official,1,yes,yes,female,divorced/separated,1 +no account,12,critical account,car (new),682,100 <= <500 DM,4<= <7 years,4,none,3,car or other,51,none,own,2,skilled employee / official,1,yes,yes,female,divorced/separated,1 +<0 DM,36,critical account,furniture/equipment,5371,<100 DM,1<= < 4 years,3,guarantor,2,savings agreement/life insurance,28,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,18,critical account,radio/television,1582,>= 1000 DM,>=7 years,4,none,4,car or other,46,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,6,existing credits paid back duly till now,radio/television,1346,100 <= <500 DM,>=7 years,2,none,4,unknown / no property,42,bank,for free,1,skilled employee / official,2,yes,yes,male,divorced/separated,1 +no account,10,existing credits paid back duly till now,radio/television,1924,<100 DM,1<= < 4 years,1,none,4,savings agreement/life insurance,38,none,own,1,skilled employee / official,1,yes,no,male,divorced/separated,1 +>= 200 DM ,36,existing credits paid back duly till now,radio/television,5848,<100 DM,1<= < 4 years,4,none,1,car or other,24,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,24,critical account,car (used),7758,>= 1000 DM,>=7 years,2,none,4,unknown / no property,29,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,24,delay in paying off,business,6967,100 <= <500 DM,4<= <7 years,4,none,4,car or other,36,none,rent,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,furniture/equipment,1282,<100 DM,1<= < 4 years,2,none,4,car or other,20,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +<0 DM,9,critical account,repairs,1288,100 <= <500 DM,>=7 years,3,guarantor,4,real estate,48,none,own,2,skilled employee / official,2,none,no,male,divorced/separated,1 +<0 DM,12,all credits at this bank paid back duly,retraining,339,<100 DM,>=7 years,4,none,1,car or other,45,bank,own,1,unskilled - resident,1,none,yes,male,married/widowed,1 +0 <= <200 DM,24,existing credits paid back duly till now,car (new),3512,100 <= <500 DM,4<= <7 years,2,none,3,car or other,38,bank,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,6,critical account,radio/television,1898,no savings account,1<= < 4 years,1,none,2,real estate,34,none,own,2,unskilled - resident,2,none,yes,male,divorced/separated,1 +no account,24,critical account,radio/television,2872,100 <= <500 DM,>=7 years,3,none,4,real estate,36,none,own,1,skilled employee / official,2,yes,yes,male,divorced/separated,1 +no account,18,critical account,car (new),1055,<100 DM,<1 years,4,none,1,savings agreement/life insurance,30,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,15,existing credits paid back duly till now,domestic appliances,1262,500 <= < 1000 DM,4<= <7 years,4,none,3,savings agreement/life insurance,36,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,10,existing credits paid back duly till now,car (new),7308,<100 DM,unemployed,2,none,4,unknown / no property,70,bank,for free,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +no account,36,existing credits paid back duly till now,car (new),909,500 <= < 1000 DM,>=7 years,4,none,4,savings agreement/life insurance,36,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,6,existing credits paid back duly till now,furniture/equipment,2978,500 <= < 1000 DM,1<= < 4 years,1,none,2,car or other,32,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,18,existing credits paid back duly till now,furniture/equipment,1131,<100 DM,unemployed,4,none,2,car or other,33,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,11,existing credits paid back duly till now,furniture/equipment,1577,>= 1000 DM,<1 years,4,none,1,real estate,20,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,24,existing credits paid back duly till now,furniture/equipment,3972,<100 DM,4<= <7 years,2,none,4,savings agreement/life insurance,25,none,rent,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +0 <= <200 DM,24,critical account,business,1935,<100 DM,>=7 years,4,none,4,real estate,31,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,0 +<0 DM,15,no credits taken,car (new),950,<100 DM,>=7 years,4,none,3,car or other,33,none,rent,2,skilled employee / official,2,none,yes,male,divorced/separated,0 +no account,12,existing credits paid back duly till now,furniture/equipment,763,<100 DM,1<= < 4 years,4,none,1,real estate,26,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +0 <= <200 DM,24,delay in paying off,furniture/equipment,2064,<100 DM,unemployed,3,none,2,savings agreement/life insurance,34,none,own,1,management/ highly qualified employee,1,yes,yes,female,divorced/separated,0 +0 <= <200 DM,8,existing credits paid back duly till now,radio/television,1414,<100 DM,1<= < 4 years,4,guarantor,2,real estate,33,none,own,1,skilled employee / official,1,none,no,male,divorced/separated,1 +<0 DM,21,delay in paying off,education,3414,<100 DM,<1 years,2,none,1,savings agreement/life insurance,26,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,30,all credits at this bank paid back duly,car (used),7485,no savings account,unemployed,4,none,1,real estate,53,bank,own,1,management/ highly qualified employee,1,yes,yes,female,divorced/separated,0 +<0 DM,12,existing credits paid back duly till now,furniture/equipment,2577,<100 DM,1<= < 4 years,2,none,1,car or other,42,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,6,critical account,radio/television,338,500 <= < 1000 DM,>=7 years,4,none,4,car or other,52,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,12,existing credits paid back duly till now,radio/television,1963,<100 DM,4<= <7 years,4,none,2,car or other,31,none,rent,2,management/ highly qualified employee,2,yes,yes,male,divorced/separated,1 +<0 DM,21,critical account,car (new),571,<100 DM,>=7 years,4,none,4,real estate,65,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,36,delay in paying off,business,9572,<100 DM,<1 years,1,none,1,car or other,28,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,0 +0 <= <200 DM,36,delay in paying off,business,4455,<100 DM,1<= < 4 years,2,none,2,real estate,30,store,own,2,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +<0 DM,21,all credits at this bank paid back duly,car (new),1647,no savings account,1<= < 4 years,4,none,2,savings agreement/life insurance,40,none,own,2,unskilled - resident,2,none,yes,male,divorced/separated,0 +no account,24,critical account,furniture/equipment,3777,>= 1000 DM,1<= < 4 years,4,none,4,real estate,50,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,18,critical account,car (new),884,<100 DM,>=7 years,4,none,4,car or other,36,bank,own,1,skilled employee / official,2,yes,yes,male,divorced/separated,0 +no account,15,critical account,radio/television,1360,<100 DM,1<= < 4 years,4,none,2,savings agreement/life insurance,31,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,9,all credits at this bank paid back duly,car (used),5129,<100 DM,>=7 years,2,none,4,unknown / no property,74,bank,for free,1,management/ highly qualified employee,2,yes,yes,female,divorced/separated,0 +0 <= <200 DM,16,critical account,car (new),1175,<100 DM,unemployed,2,none,3,car or other,68,none,for free,3,unemployed/ unskilled - non-resident,1,yes,yes,male,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,radio/television,674,100 <= <500 DM,4<= <7 years,4,none,1,savings agreement/life insurance,20,none,own,1,skilled employee / official,1,none,yes,male,married/widowed,0 +0 <= <200 DM,18,no credits taken,furniture/equipment,3244,<100 DM,1<= < 4 years,1,none,4,car or other,33,bank,own,2,skilled employee / official,1,yes,yes,female,divorced/separated,1 +no account,24,existing credits paid back duly till now,business,4591,>= 1000 DM,1<= < 4 years,2,none,3,savings agreement/life insurance,54,none,own,3,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +0 <= <200 DM,48,no credits taken,business,3844,100 <= <500 DM,4<= <7 years,4,none,4,unknown / no property,34,none,for free,1,unskilled - resident,2,none,yes,male,divorced/separated,0 +0 <= <200 DM,27,existing credits paid back duly till now,business,3915,<100 DM,1<= < 4 years,4,none,2,car or other,36,none,own,1,skilled employee / official,2,yes,yes,male,divorced/separated,0 +no account,6,existing credits paid back duly till now,radio/television,2108,<100 DM,4<= <7 years,2,none,2,real estate,29,none,rent,1,skilled employee / official,1,none,yes,male,married/widowed,1 +0 <= <200 DM,45,existing credits paid back duly till now,radio/television,3031,100 <= <500 DM,1<= < 4 years,4,guarantor,4,savings agreement/life insurance,21,none,rent,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +0 <= <200 DM,9,critical account,education,1501,<100 DM,>=7 years,2,none,3,car or other,34,none,own,2,management/ highly qualified employee,1,yes,yes,female,divorced/separated,0 +no account,6,critical account,radio/television,1382,<100 DM,1<= < 4 years,1,none,1,car or other,28,none,own,2,skilled employee / official,1,yes,yes,female,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,furniture/equipment,951,100 <= <500 DM,<1 years,4,none,4,car or other,27,bank,rent,4,skilled employee / official,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,24,existing credits paid back duly till now,car (used),2760,no savings account,>=7 years,4,none,4,unknown / no property,36,bank,for free,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,18,delay in paying off,furniture/equipment,4297,<100 DM,>=7 years,4,none,3,unknown / no property,40,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +no account,9,critical account,education,936,500 <= < 1000 DM,>=7 years,4,none,2,car or other,52,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,car (new),1168,<100 DM,1<= < 4 years,4,none,3,real estate,27,none,own,1,unskilled - resident,1,none,yes,male,married/widowed,1 +no account,27,delay in paying off,business,5117,<100 DM,4<= <7 years,3,none,4,car or other,26,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,retraining,902,<100 DM,4<= <7 years,4,none,4,savings agreement/life insurance,21,none,rent,1,skilled employee / official,1,none,yes,male,married/widowed,0 +no account,12,critical account,car (new),1495,<100 DM,>=7 years,4,none,1,real estate,38,none,own,2,unskilled - resident,2,none,yes,male,divorced/separated,1 +<0 DM,30,critical account,car (used),10623,<100 DM,>=7 years,3,none,4,unknown / no property,38,none,for free,3,management/ highly qualified employee,2,yes,yes,male,divorced/separated,1 +no account,12,critical account,furniture/equipment,1935,<100 DM,>=7 years,4,none,4,real estate,43,none,own,3,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,12,critical account,domestic appliances,1424,<100 DM,4<= <7 years,4,none,3,savings agreement/life insurance,26,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,24,existing credits paid back duly till now,business,6568,<100 DM,1<= < 4 years,2,none,2,car or other,21,store,own,1,unskilled - resident,1,none,yes,male,married/widowed,1 +no account,12,existing credits paid back duly till now,car (used),1413,>= 1000 DM,4<= <7 years,3,none,2,savings agreement/life insurance,55,none,own,1,skilled employee / official,1,none,no,male,divorced/separated,1 +no account,9,critical account,radio/television,3074,no savings account,1<= < 4 years,1,none,2,real estate,33,none,own,2,skilled employee / official,2,none,yes,male,divorced/separated,1 +no account,36,existing credits paid back duly till now,radio/television,3835,no savings account,>=7 years,2,none,4,real estate,45,none,own,1,unskilled - resident,1,yes,yes,female,divorced/separated,1 +<0 DM,27,no credits taken,business,5293,<100 DM,unemployed,2,none,4,savings agreement/life insurance,50,store,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,0 +>= 200 DM ,30,delay in paying off,business,1908,<100 DM,>=7 years,4,none,4,real estate,66,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +no account,36,critical account,radio/television,3342,no savings account,>=7 years,4,none,2,car or other,51,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,6,critical account,retraining,932,no savings account,4<= <7 years,1,none,3,savings agreement/life insurance,39,none,own,2,unskilled - resident,1,none,yes,female,divorced/separated,1 +<0 DM,18,no credits taken,business,3104,<100 DM,4<= <7 years,3,none,1,savings agreement/life insurance,31,bank,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +>= 200 DM ,36,existing credits paid back duly till now,radio/television,3913,<100 DM,1<= < 4 years,2,none,2,real estate,23,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,24,existing credits paid back duly till now,furniture/equipment,3021,<100 DM,1<= < 4 years,2,none,2,real estate,24,none,rent,1,unskilled - resident,1,none,yes,male,divorced/separated,1 +no account,10,existing credits paid back duly till now,car (new),1364,<100 DM,1<= < 4 years,2,none,4,car or other,64,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,radio/television,625,<100 DM,<1 years,4,guarantor,1,real estate,26,bank,own,1,unskilled - resident,1,none,yes,male,married/widowed,1 +<0 DM,12,existing credits paid back duly till now,education,1200,no savings account,1<= < 4 years,4,none,4,savings agreement/life insurance,23,bank,rent,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +no account,12,existing credits paid back duly till now,radio/television,707,<100 DM,1<= < 4 years,4,none,2,real estate,30,bank,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,24,delay in paying off,business,2978,no savings account,1<= < 4 years,4,none,4,real estate,32,none,own,2,skilled employee / official,2,yes,yes,male,divorced/separated,1 +no account,15,existing credits paid back duly till now,car (used),4657,<100 DM,1<= < 4 years,3,none,2,car or other,30,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,36,no credits taken,repairs,2613,<100 DM,1<= < 4 years,4,none,2,car or other,27,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,48,existing credits paid back duly till now,radio/television,10961,>= 1000 DM,4<= <7 years,1,co-applicant,2,unknown / no property,27,bank,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,0 +<0 DM,12,existing credits paid back duly till now,furniture/equipment,7865,<100 DM,>=7 years,4,none,4,unknown / no property,53,none,for free,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +no account,9,existing credits paid back duly till now,radio/television,1478,<100 DM,4<= <7 years,4,none,2,car or other,22,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +<0 DM,24,existing credits paid back duly till now,furniture/equipment,3149,<100 DM,<1 years,4,none,1,unknown / no property,22,bank,for free,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +>= 200 DM ,36,existing credits paid back duly till now,radio/television,4210,<100 DM,1<= < 4 years,4,none,2,car or other,26,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,9,existing credits paid back duly till now,car (new),2507,500 <= < 1000 DM,>=7 years,2,none,4,unknown / no property,51,none,for free,1,unskilled - resident,1,none,yes,male,divorced/separated,1 +no account,12,existing credits paid back duly till now,radio/television,2141,100 <= <500 DM,4<= <7 years,3,none,1,unknown / no property,35,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,18,existing credits paid back duly till now,radio/television,866,<100 DM,1<= < 4 years,4,guarantor,2,real estate,25,none,own,1,unskilled - resident,1,none,yes,male,married/widowed,1 +no account,4,critical account,radio/television,1544,<100 DM,4<= <7 years,2,none,1,real estate,42,none,own,3,unskilled - resident,2,none,yes,male,divorced/separated,1 +<0 DM,24,existing credits paid back duly till now,radio/television,1823,<100 DM,unemployed,4,none,2,car or other,30,store,own,1,management/ highly qualified employee,2,none,yes,male,divorced/separated,0 +0 <= <200 DM,6,existing credits paid back duly till now,car (new),14555,no savings account,unemployed,1,none,2,savings agreement/life insurance,23,none,own,1,unemployed/ unskilled - non-resident,1,yes,yes,male,divorced/separated,0 +0 <= <200 DM,21,existing credits paid back duly till now,business,2767,100 <= <500 DM,>=7 years,4,none,2,car or other,61,bank,rent,2,unskilled - resident,1,none,yes,male,divorced/separated,0 +no account,12,critical account,radio/television,1291,<100 DM,1<= < 4 years,4,none,2,savings agreement/life insurance,35,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,30,existing credits paid back duly till now,radio/television,2522,<100 DM,>=7 years,1,guarantor,3,savings agreement/life insurance,39,none,own,1,skilled employee / official,2,none,yes,male,divorced/separated,1 +<0 DM,24,existing credits paid back duly till now,car (new),915,no savings account,>=7 years,4,none,2,car or other,29,bank,own,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +no account,6,existing credits paid back duly till now,radio/television,1595,<100 DM,4<= <7 years,3,none,2,savings agreement/life insurance,51,none,own,1,skilled employee / official,2,none,yes,male,divorced/separated,1 +<0 DM,48,no credits taken,car (used),4605,<100 DM,>=7 years,3,none,4,unknown / no property,24,none,for free,2,skilled employee / official,2,none,yes,male,divorced/separated,0 +no account,12,critical account,business,1185,<100 DM,1<= < 4 years,3,none,2,real estate,27,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,12,all credits at this bank paid back duly,retraining,3447,500 <= < 1000 DM,1<= < 4 years,4,none,3,real estate,35,none,own,1,unskilled - resident,2,none,yes,female,divorced/separated,1 +no account,24,existing credits paid back duly till now,business,1258,<100 DM,4<= <7 years,4,none,1,real estate,25,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,12,critical account,radio/television,717,<100 DM,>=7 years,4,none,4,real estate,52,none,own,3,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,6,no credits taken,car (new),1204,100 <= <500 DM,1<= < 4 years,4,none,1,unknown / no property,35,bank,rent,1,skilled employee / official,1,none,no,male,divorced/separated,1 +>= 200 DM ,24,existing credits paid back duly till now,furniture/equipment,1925,<100 DM,1<= < 4 years,2,none,2,real estate,26,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,18,existing credits paid back duly till now,radio/television,433,<100 DM,unemployed,3,co-applicant,4,real estate,22,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +<0 DM,6,critical account,car (new),666,>= 1000 DM,4<= <7 years,3,none,4,real estate,39,none,own,2,unskilled - resident,1,yes,yes,female,divorced/separated,1 +>= 200 DM ,12,existing credits paid back duly till now,furniture/equipment,2251,<100 DM,1<= < 4 years,1,none,2,car or other,46,none,own,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,30,existing credits paid back duly till now,car (new),2150,<100 DM,1<= < 4 years,4,guarantor,2,unknown / no property,24,bank,own,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +no account,24,delay in paying off,furniture/equipment,4151,100 <= <500 DM,1<= < 4 years,2,none,3,savings agreement/life insurance,35,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,9,existing credits paid back duly till now,furniture/equipment,2030,no savings account,4<= <7 years,2,none,1,car or other,24,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,60,delay in paying off,radio/television,7418,no savings account,1<= < 4 years,1,none,1,real estate,27,none,own,1,unskilled - resident,1,none,yes,male,divorced/separated,1 +no account,24,critical account,radio/television,2684,<100 DM,1<= < 4 years,4,none,2,real estate,35,none,own,2,unskilled - resident,1,none,yes,male,divorced/separated,1 +<0 DM,12,all credits at this bank paid back duly,radio/television,2149,<100 DM,1<= < 4 years,4,none,1,unknown / no property,29,none,for free,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,15,existing credits paid back duly till now,car (used),3812,100 <= <500 DM,<1 years,1,none,4,car or other,23,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +no account,11,critical account,radio/television,1154,100 <= <500 DM,unemployed,4,none,4,real estate,57,none,own,3,unskilled - resident,1,none,yes,female,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,furniture/equipment,1657,<100 DM,1<= < 4 years,2,none,2,real estate,27,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,24,existing credits paid back duly till now,radio/television,1603,<100 DM,>=7 years,4,none,4,car or other,55,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,18,critical account,car (new),5302,<100 DM,>=7 years,2,none,4,unknown / no property,36,none,for free,3,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +no account,12,critical account,education,2748,<100 DM,>=7 years,2,none,4,unknown / no property,57,bank,for free,3,unskilled - resident,1,none,yes,female,divorced/separated,1 +no account,10,critical account,car (new),1231,<100 DM,>=7 years,3,none,4,real estate,32,none,own,2,unskilled - resident,2,none,no,male,divorced/separated,1 +0 <= <200 DM,15,existing credits paid back duly till now,radio/television,802,<100 DM,>=7 years,4,none,3,car or other,37,none,own,1,skilled employee / official,2,none,yes,male,divorced/separated,0 +no account,36,critical account,business,6304,no savings account,>=7 years,4,none,4,real estate,36,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,24,existing credits paid back duly till now,radio/television,1533,<100 DM,<1 years,4,none,3,car or other,38,store,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +<0 DM,14,existing credits paid back duly till now,car (new),8978,<100 DM,>=7 years,1,none,4,savings agreement/life insurance,45,none,own,1,management/ highly qualified employee,1,yes,no,male,divorced/separated,0 +no account,24,existing credits paid back duly till now,radio/television,999,no savings account,>=7 years,4,none,2,car or other,25,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,18,existing credits paid back duly till now,car (new),2662,no savings account,4<= <7 years,4,none,3,savings agreement/life insurance,32,none,own,1,skilled employee / official,1,none,no,male,divorced/separated,1 +no account,12,critical account,furniture/equipment,1402,500 <= < 1000 DM,4<= <7 years,3,none,4,car or other,37,none,rent,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +0 <= <200 DM,48,all credits at this bank paid back duly,car (new),12169,no savings account,unemployed,4,co-applicant,4,unknown / no property,36,none,for free,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,48,existing credits paid back duly till now,radio/television,3060,<100 DM,4<= <7 years,4,none,4,real estate,28,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,0 +<0 DM,30,existing credits paid back duly till now,repairs,11998,<100 DM,<1 years,1,none,1,unknown / no property,34,none,own,1,unskilled - resident,1,yes,yes,male,divorced/separated,0 +no account,9,existing credits paid back duly till now,radio/television,2697,<100 DM,1<= < 4 years,1,none,2,real estate,32,none,own,1,skilled employee / official,2,none,yes,male,divorced/separated,1 +no account,18,critical account,radio/television,2404,<100 DM,1<= < 4 years,2,none,2,car or other,26,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,furniture/equipment,1262,no savings account,>=7 years,2,none,4,savings agreement/life insurance,49,none,own,1,unskilled - resident,1,yes,yes,male,divorced/separated,1 +no account,6,existing credits paid back duly till now,furniture/equipment,4611,<100 DM,<1 years,1,none,4,savings agreement/life insurance,32,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +no account,24,existing credits paid back duly till now,radio/television,1901,100 <= <500 DM,1<= < 4 years,4,none,4,car or other,29,none,rent,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +no account,15,critical account,car (used),3368,>= 1000 DM,>=7 years,3,none,4,unknown / no property,23,none,rent,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,12,existing credits paid back duly till now,furniture/equipment,1574,<100 DM,1<= < 4 years,4,none,2,real estate,50,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +>= 200 DM ,18,all credits at this bank paid back duly,radio/television,1445,no savings account,4<= <7 years,4,none,4,car or other,49,bank,own,1,unskilled - resident,1,none,yes,male,divorced/separated,1 +no account,15,critical account,furniture/equipment,1520,no savings account,>=7 years,4,none,4,savings agreement/life insurance,63,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,24,critical account,car (new),3878,100 <= <500 DM,<1 years,4,none,2,car or other,37,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,47,existing credits paid back duly till now,car (new),10722,<100 DM,<1 years,1,none,1,real estate,35,none,own,1,unskilled - resident,1,yes,yes,female,divorced/separated,1 +<0 DM,48,existing credits paid back duly till now,car (used),4788,<100 DM,4<= <7 years,4,none,3,savings agreement/life insurance,26,none,own,1,skilled employee / official,2,none,yes,male,divorced/separated,1 +0 <= <200 DM,48,delay in paying off,others,7582,100 <= <500 DM,unemployed,2,none,4,unknown / no property,31,none,for free,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,radio/television,1092,<100 DM,1<= < 4 years,4,guarantor,4,real estate,49,none,own,2,skilled employee / official,1,yes,yes,female,divorced/separated,1 +<0 DM,24,delay in paying off,radio/television,1024,<100 DM,<1 years,4,none,4,real estate,48,store,own,1,skilled employee / official,1,none,yes,male,married/widowed,0 +no account,12,existing credits paid back duly till now,business,1076,<100 DM,1<= < 4 years,2,none,2,real estate,26,none,own,1,skilled employee / official,1,yes,no,male,married/widowed,1 +0 <= <200 DM,36,existing credits paid back duly till now,car (used),9398,<100 DM,<1 years,1,none,4,car or other,28,none,rent,1,management/ highly qualified employee,1,yes,yes,male,married/widowed,0 +<0 DM,24,critical account,car (used),6419,<100 DM,>=7 years,2,none,4,unknown / no property,44,none,for free,2,management/ highly qualified employee,2,yes,yes,female,divorced/separated,1 +>= 200 DM ,42,critical account,car (used),4796,<100 DM,>=7 years,4,none,4,unknown / no property,56,none,for free,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,48,critical account,business,7629,no savings account,>=7 years,4,none,2,car or other,46,bank,own,2,management/ highly qualified employee,2,none,yes,male,divorced/separated,1 +0 <= <200 DM,48,existing credits paid back duly till now,furniture/equipment,9960,<100 DM,<1 years,1,none,2,car or other,26,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,0 +no account,12,existing credits paid back duly till now,car (used),4675,no savings account,<1 years,1,none,4,car or other,20,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,10,existing credits paid back duly till now,car (new),1287,no savings account,>=7 years,4,co-applicant,2,savings agreement/life insurance,45,none,own,1,unskilled - resident,1,none,no,male,divorced/separated,1 +no account,18,existing credits paid back duly till now,furniture/equipment,2515,<100 DM,1<= < 4 years,3,none,4,real estate,43,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,21,critical account,furniture/equipment,2745,>= 1000 DM,4<= <7 years,3,none,2,car or other,32,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,6,existing credits paid back duly till now,car (new),672,<100 DM,unemployed,1,none,4,real estate,54,none,own,1,unemployed/ unskilled - non-resident,1,yes,yes,female,divorced/separated,1 +0 <= <200 DM,36,no credits taken,radio/television,3804,<100 DM,1<= < 4 years,4,none,1,car or other,42,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,0 +>= 200 DM ,24,critical account,car (new),1344,no savings account,4<= <7 years,4,none,2,real estate,37,bank,own,2,unskilled - resident,2,none,yes,male,divorced/separated,0 +<0 DM,10,critical account,car (new),1038,<100 DM,4<= <7 years,4,co-applicant,3,savings agreement/life insurance,49,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,48,critical account,car (new),10127,500 <= < 1000 DM,1<= < 4 years,2,none,2,unknown / no property,44,bank,for free,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,6,existing credits paid back duly till now,furniture/equipment,1543,>= 1000 DM,1<= < 4 years,4,none,2,real estate,33,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,30,existing credits paid back duly till now,car (used),4811,no savings account,4<= <7 years,2,none,4,savings agreement/life insurance,24,store,rent,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,radio/television,727,100 <= <500 DM,<1 years,4,none,3,unknown / no property,33,none,own,1,unskilled - resident,1,yes,yes,male,married/widowed,0 +0 <= <200 DM,8,existing credits paid back duly till now,furniture/equipment,1237,<100 DM,1<= < 4 years,3,none,4,real estate,24,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,9,existing credits paid back duly till now,car (new),276,<100 DM,1<= < 4 years,4,none,4,real estate,22,none,rent,1,unskilled - resident,1,none,yes,male,married/widowed,1 +0 <= <200 DM,48,existing credits paid back duly till now,others,5381,no savings account,unemployed,3,none,4,unknown / no property,40,bank,for free,1,unemployed/ unskilled - non-resident,1,yes,yes,male,divorced/separated,1 +no account,24,existing credits paid back duly till now,furniture/equipment,5511,100 <= <500 DM,1<= < 4 years,4,none,1,car or other,25,store,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +>= 200 DM ,24,existing credits paid back duly till now,furniture/equipment,3749,<100 DM,<1 years,2,none,4,car or other,26,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,car (new),685,<100 DM,4<= <7 years,2,none,3,car or other,25,bank,own,1,unskilled - resident,1,none,yes,male,married/widowed,0 +>= 200 DM ,4,existing credits paid back duly till now,car (new),1494,no savings account,<1 years,1,none,2,real estate,29,none,own,1,unskilled - resident,2,none,no,male,divorced/separated,1 +<0 DM,36,all credits at this bank paid back duly,furniture/equipment,2746,<100 DM,>=7 years,4,none,4,car or other,31,bank,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +<0 DM,12,existing credits paid back duly till now,furniture/equipment,708,<100 DM,1<= < 4 years,2,guarantor,3,savings agreement/life insurance,38,none,own,1,unskilled - resident,2,none,yes,male,divorced/separated,1 +0 <= <200 DM,24,existing credits paid back duly till now,furniture/equipment,4351,no savings account,1<= < 4 years,1,none,4,savings agreement/life insurance,48,none,own,1,unskilled - resident,1,yes,yes,female,divorced/separated,1 +no account,12,critical account,education,701,<100 DM,1<= < 4 years,4,none,2,car or other,32,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,15,delay in paying off,furniture/equipment,3643,<100 DM,>=7 years,1,none,4,savings agreement/life insurance,27,none,own,2,unskilled - resident,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,30,critical account,car (new),4249,<100 DM,unemployed,4,none,2,car or other,28,none,own,2,management/ highly qualified employee,1,none,yes,male,married/widowed,0 +<0 DM,24,existing credits paid back duly till now,radio/television,1938,<100 DM,<1 years,4,none,3,savings agreement/life insurance,32,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +<0 DM,24,existing credits paid back duly till now,car (used),2910,<100 DM,4<= <7 years,2,none,1,unknown / no property,34,none,for free,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +<0 DM,18,existing credits paid back duly till now,furniture/equipment,2659,>= 1000 DM,1<= < 4 years,4,none,2,car or other,28,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,18,critical account,car (new),1028,<100 DM,1<= < 4 years,4,none,3,real estate,36,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,8,critical account,car (new),3398,<100 DM,4<= <7 years,1,none,4,real estate,39,none,own,2,unskilled - resident,1,none,no,male,divorced/separated,1 +no account,12,critical account,furniture/equipment,5801,no savings account,>=7 years,2,none,4,savings agreement/life insurance,49,none,rent,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,24,existing credits paid back duly till now,car (new),1525,>= 1000 DM,4<= <7 years,4,none,3,car or other,34,none,own,1,skilled employee / official,2,yes,yes,female,divorced/separated,1 +>= 200 DM ,36,existing credits paid back duly till now,radio/television,4473,<100 DM,>=7 years,4,none,2,car or other,31,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,6,existing credits paid back duly till now,radio/television,1068,<100 DM,>=7 years,4,none,4,car or other,28,none,own,1,skilled employee / official,2,none,yes,male,divorced/separated,1 +<0 DM,24,critical account,car (used),6615,<100 DM,unemployed,2,none,4,unknown / no property,75,none,for free,2,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +no account,18,critical account,education,1864,100 <= <500 DM,1<= < 4 years,4,none,2,real estate,30,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,60,existing credits paid back duly till now,car (new),7408,100 <= <500 DM,<1 years,4,none,2,savings agreement/life insurance,24,none,own,1,management/ highly qualified employee,1,none,yes,female,divorced/separated,0 +no account,48,critical account,car (used),11590,100 <= <500 DM,1<= < 4 years,2,none,4,car or other,24,bank,rent,2,unskilled - resident,1,none,yes,female,divorced/separated,0 +<0 DM,24,no credits taken,furniture/equipment,4110,<100 DM,>=7 years,3,none,4,unknown / no property,23,bank,rent,2,skilled employee / official,2,none,yes,male,divorced/separated,0 +<0 DM,6,critical account,furniture/equipment,3384,<100 DM,1<= < 4 years,1,none,4,real estate,44,none,rent,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +0 <= <200 DM,13,existing credits paid back duly till now,radio/television,2101,<100 DM,<1 years,2,guarantor,4,savings agreement/life insurance,23,none,own,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +<0 DM,15,existing credits paid back duly till now,domestic appliances,1275,no savings account,1<= < 4 years,4,none,2,car or other,24,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +<0 DM,24,existing credits paid back duly till now,furniture/equipment,4169,<100 DM,1<= < 4 years,4,none,4,savings agreement/life insurance,28,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,10,existing credits paid back duly till now,furniture/equipment,1521,<100 DM,1<= < 4 years,4,none,2,car or other,31,none,own,1,unskilled - resident,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,24,critical account,education,5743,<100 DM,<1 years,2,none,4,unknown / no property,24,none,for free,2,skilled employee / official,1,yes,yes,female,divorced/separated,1 +<0 DM,21,existing credits paid back duly till now,furniture/equipment,3599,<100 DM,4<= <7 years,1,none,4,car or other,26,none,rent,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,18,existing credits paid back duly till now,radio/television,3213,500 <= < 1000 DM,<1 years,1,none,3,real estate,25,none,rent,1,skilled employee / official,1,none,yes,male,married/widowed,1 +0 <= <200 DM,18,existing credits paid back duly till now,business,4439,<100 DM,>=7 years,1,co-applicant,1,real estate,33,bank,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +>= 200 DM ,10,existing credits paid back duly till now,car (new),3949,<100 DM,<1 years,1,guarantor,1,savings agreement/life insurance,37,none,own,1,unskilled - resident,2,none,yes,male,divorced/separated,1 +no account,15,critical account,radio/television,1459,<100 DM,1<= < 4 years,4,none,2,car or other,43,none,own,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,13,critical account,radio/television,882,<100 DM,<1 years,4,guarantor,4,real estate,23,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,24,existing credits paid back duly till now,radio/television,3758,500 <= < 1000 DM,unemployed,1,none,4,unknown / no property,23,none,rent,1,unemployed/ unskilled - non-resident,1,none,yes,female,divorced/separated,1 +no account,6,delay in paying off,business,1743,100 <= <500 DM,1<= < 4 years,1,none,2,real estate,34,none,own,2,unskilled - resident,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,9,critical account,education,1136,>= 1000 DM,>=7 years,4,none,3,unknown / no property,32,none,for free,2,skilled employee / official,2,none,yes,male,divorced/separated,0 +no account,9,existing credits paid back duly till now,domestic appliances,1236,<100 DM,<1 years,1,none,4,real estate,23,none,rent,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +0 <= <200 DM,9,existing credits paid back duly till now,furniture/equipment,959,<100 DM,1<= < 4 years,1,none,2,car or other,29,none,own,1,skilled employee / official,1,none,no,female,divorced/separated,0 +no account,18,critical account,car (used),3229,no savings account,unemployed,2,none,4,unknown / no property,38,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +<0 DM,12,no credits taken,radio/television,6199,<100 DM,1<= < 4 years,4,none,2,savings agreement/life insurance,28,none,rent,2,skilled employee / official,1,yes,yes,male,divorced/separated,0 +no account,10,existing credits paid back duly till now,education,727,500 <= < 1000 DM,>=7 years,4,none,4,unknown / no property,46,none,for free,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,24,existing credits paid back duly till now,car (new),1246,<100 DM,<1 years,4,none,2,real estate,23,store,own,1,unskilled - resident,1,none,yes,male,divorced/separated,0 +no account,12,critical account,radio/television,2331,no savings account,>=7 years,1,co-applicant,4,real estate,49,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,36,delay in paying off,radio/television,4463,<100 DM,1<= < 4 years,4,none,2,car or other,26,none,own,2,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +no account,12,existing credits paid back duly till now,radio/television,776,<100 DM,1<= < 4 years,4,none,2,real estate,28,none,own,1,skilled employee / official,1,none,yes,male,married/widowed,1 +<0 DM,30,existing credits paid back duly till now,furniture/equipment,2406,<100 DM,4<= <7 years,4,none,4,real estate,23,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,18,existing credits paid back duly till now,education,1239,no savings account,1<= < 4 years,4,none,4,unknown / no property,61,none,for free,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +>= 200 DM ,12,existing credits paid back duly till now,radio/television,3399,no savings account,>=7 years,2,none,3,car or other,37,none,own,1,management/ highly qualified employee,1,none,yes,male,divorced/separated,1 +>= 200 DM ,12,delay in paying off,car (new),2247,<100 DM,1<= < 4 years,2,none,2,car or other,36,store,own,2,skilled employee / official,1,yes,yes,female,divorced/separated,1 +no account,6,existing credits paid back duly till now,furniture/equipment,1766,<100 DM,1<= < 4 years,1,none,2,savings agreement/life insurance,21,none,rent,1,skilled employee / official,1,none,yes,male,married/widowed,1 +<0 DM,18,existing credits paid back duly till now,furniture/equipment,2473,<100 DM,unemployed,4,none,1,car or other,25,none,own,1,unemployed/ unskilled - non-resident,1,none,yes,male,divorced/separated,0 +no account,12,existing credits paid back duly till now,business,1542,<100 DM,4<= <7 years,2,none,4,car or other,36,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,18,critical account,car (used),3850,<100 DM,4<= <7 years,3,none,1,car or other,27,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,18,existing credits paid back duly till now,furniture/equipment,3650,<100 DM,<1 years,1,none,4,car or other,22,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,36,existing credits paid back duly till now,furniture/equipment,3446,<100 DM,>=7 years,4,none,2,car or other,42,none,own,1,skilled employee / official,2,none,yes,male,divorced/separated,0 +0 <= <200 DM,18,existing credits paid back duly till now,furniture/equipment,3001,<100 DM,4<= <7 years,2,none,4,real estate,40,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,36,existing credits paid back duly till now,car (new),3079,no savings account,1<= < 4 years,4,none,4,real estate,36,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,18,critical account,radio/television,6070,<100 DM,>=7 years,3,none,4,car or other,33,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,10,critical account,furniture/equipment,2146,<100 DM,<1 years,1,none,3,real estate,23,none,rent,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,60,critical account,car (new),13756,no savings account,>=7 years,2,none,4,unknown / no property,63,bank,for free,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,60,all credits at this bank paid back duly,others,14782,100 <= <500 DM,>=7 years,3,none,4,unknown / no property,60,bank,for free,2,management/ highly qualified employee,1,yes,yes,female,divorced/separated,0 +<0 DM,48,all credits at this bank paid back duly,business,7685,<100 DM,4<= <7 years,2,guarantor,4,car or other,37,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +no account,18,delay in paying off,radio/television,2320,<100 DM,unemployed,2,none,3,real estate,34,none,own,2,skilled employee / official,1,none,yes,male,married/widowed,1 +no account,7,delay in paying off,radio/television,846,no savings account,>=7 years,3,none,4,unknown / no property,36,none,for free,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,36,existing credits paid back duly till now,car (new),14318,<100 DM,>=7 years,4,none,2,unknown / no property,57,none,for free,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +no account,6,critical account,car (new),362,100 <= <500 DM,1<= < 4 years,4,none,4,car or other,52,none,own,2,unskilled - resident,1,none,yes,female,divorced/separated,1 +<0 DM,20,existing credits paid back duly till now,furniture/equipment,2212,no savings account,4<= <7 years,4,none,4,car or other,39,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,18,existing credits paid back duly till now,car (used),12976,<100 DM,unemployed,3,none,4,unknown / no property,38,none,for free,1,management/ highly qualified employee,1,yes,yes,female,divorced/separated,0 +no account,22,existing credits paid back duly till now,car (new),1283,no savings account,4<= <7 years,4,none,4,savings agreement/life insurance,25,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +>= 200 DM ,12,existing credits paid back duly till now,car (new),1330,<100 DM,<1 years,4,none,1,real estate,26,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,30,delay in paying off,business,4272,100 <= <500 DM,1<= < 4 years,2,none,2,savings agreement/life insurance,26,none,own,2,unskilled - resident,1,none,yes,male,divorced/separated,1 +no account,18,critical account,radio/television,2238,<100 DM,1<= < 4 years,2,none,1,car or other,25,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,18,existing credits paid back duly till now,radio/television,1126,no savings account,<1 years,4,none,2,real estate,21,none,rent,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +0 <= <200 DM,18,critical account,furniture/equipment,7374,<100 DM,unemployed,4,none,4,savings agreement/life insurance,40,store,own,2,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,15,critical account,business,2326,500 <= < 1000 DM,1<= < 4 years,2,none,4,car or other,27,bank,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,9,existing credits paid back duly till now,business,1449,<100 DM,4<= <7 years,3,none,2,car or other,27,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,18,existing credits paid back duly till now,car (new),1820,<100 DM,1<= < 4 years,2,none,2,savings agreement/life insurance,30,none,own,1,management/ highly qualified employee,1,yes,yes,male,married/widowed,1 +0 <= <200 DM,12,existing credits paid back duly till now,furniture/equipment,983,>= 1000 DM,<1 years,1,none,4,real estate,19,none,rent,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +<0 DM,36,existing credits paid back duly till now,car (new),3249,<100 DM,4<= <7 years,2,none,4,unknown / no property,39,bank,for free,1,management/ highly qualified employee,2,yes,yes,male,divorced/separated,1 +<0 DM,6,critical account,radio/television,1957,<100 DM,4<= <7 years,1,none,4,car or other,31,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,9,critical account,furniture/equipment,2406,<100 DM,unemployed,2,none,3,car or other,31,none,own,1,management/ highly qualified employee,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,39,delay in paying off,education,11760,100 <= <500 DM,4<= <7 years,2,none,3,unknown / no property,32,none,rent,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,furniture/equipment,2578,<100 DM,unemployed,3,none,4,unknown / no property,55,none,for free,1,management/ highly qualified employee,1,none,yes,female,divorced/separated,1 +<0 DM,36,critical account,furniture/equipment,2348,<100 DM,1<= < 4 years,3,none,2,savings agreement/life insurance,46,none,own,2,skilled employee / official,1,yes,yes,male,married/widowed,1 +0 <= <200 DM,12,existing credits paid back duly till now,car (new),1223,<100 DM,>=7 years,1,none,1,real estate,46,none,rent,2,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,24,critical account,radio/television,1516,>= 1000 DM,1<= < 4 years,4,none,1,real estate,43,none,own,2,unskilled - resident,1,none,yes,female,divorced/separated,1 +no account,18,existing credits paid back duly till now,radio/television,1473,<100 DM,<1 years,3,none,4,real estate,39,none,own,1,skilled employee / official,1,yes,yes,male,married/widowed,1 +0 <= <200 DM,18,critical account,business,1887,no savings account,1<= < 4 years,4,none,4,real estate,28,bank,own,2,skilled employee / official,1,none,yes,male,married/widowed,1 +no account,24,delay in paying off,business,8648,<100 DM,<1 years,2,none,2,car or other,27,bank,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,0 +no account,14,delay in paying off,car (new),802,<100 DM,1<= < 4 years,4,none,2,car or other,27,none,own,2,unskilled - resident,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,18,delay in paying off,car (new),2899,no savings account,>=7 years,4,none,4,car or other,43,none,own,1,skilled employee / official,2,none,yes,male,divorced/separated,1 +0 <= <200 DM,24,existing credits paid back duly till now,radio/television,2039,<100 DM,<1 years,1,none,1,savings agreement/life insurance,22,none,own,1,skilled employee / official,1,yes,yes,male,married/widowed,0 +no account,24,critical account,car (used),2197,no savings account,4<= <7 years,4,none,4,car or other,43,none,own,2,skilled employee / official,2,yes,yes,male,divorced/separated,1 +<0 DM,15,existing credits paid back duly till now,radio/television,1053,<100 DM,<1 years,4,none,2,real estate,27,none,own,1,skilled employee / official,1,none,no,male,married/widowed,1 +no account,24,existing credits paid back duly till now,radio/television,3235,500 <= < 1000 DM,>=7 years,3,none,2,car or other,26,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +>= 200 DM ,12,critical account,car (new),939,500 <= < 1000 DM,4<= <7 years,4,none,2,real estate,28,none,own,3,skilled employee / official,1,yes,yes,male,married/widowed,0 +0 <= <200 DM,24,existing credits paid back duly till now,radio/television,1967,<100 DM,>=7 years,4,none,4,car or other,20,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +no account,33,critical account,car (used),7253,<100 DM,4<= <7 years,3,none,2,car or other,35,none,own,2,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +no account,12,critical account,business,2292,<100 DM,unemployed,4,none,2,car or other,42,store,own,2,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +no account,10,existing credits paid back duly till now,car (new),1597,500 <= < 1000 DM,1<= < 4 years,3,none,2,unknown / no property,40,none,rent,1,unskilled - resident,2,none,no,male,divorced/separated,1 +<0 DM,24,existing credits paid back duly till now,car (new),1381,no savings account,1<= < 4 years,4,none,2,savings agreement/life insurance,35,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +no account,36,critical account,car (used),5842,<100 DM,>=7 years,2,none,2,savings agreement/life insurance,35,none,own,2,skilled employee / official,2,yes,yes,male,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,car (new),2579,<100 DM,<1 years,4,none,1,real estate,33,none,own,1,unskilled - resident,2,none,yes,male,divorced/separated,0 +<0 DM,18,delay in paying off,education,8471,no savings account,1<= < 4 years,1,none,2,car or other,23,none,rent,2,skilled employee / official,1,yes,yes,female,divorced/separated,1 +no account,21,existing credits paid back duly till now,car (new),2782,500 <= < 1000 DM,4<= <7 years,1,none,2,car or other,31,bank,own,1,management/ highly qualified employee,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,18,existing credits paid back duly till now,car (new),1042,no savings account,1<= < 4 years,4,none,2,savings agreement/life insurance,33,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +no account,15,existing credits paid back duly till now,car (new),3186,>= 1000 DM,4<= <7 years,2,none,3,car or other,20,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,car (used),2028,no savings account,1<= < 4 years,4,none,2,car or other,30,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,12,critical account,car (new),958,<100 DM,4<= <7 years,2,none,3,real estate,47,none,own,2,unskilled - resident,2,none,yes,male,divorced/separated,1 +no account,21,delay in paying off,furniture/equipment,1591,100 <= <500 DM,4<= <7 years,4,none,3,real estate,34,none,own,2,management/ highly qualified employee,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,furniture/equipment,2762,no savings account,>=7 years,1,none,2,savings agreement/life insurance,25,bank,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,0 +0 <= <200 DM,18,existing credits paid back duly till now,car (used),2779,<100 DM,1<= < 4 years,1,none,3,car or other,21,none,rent,1,skilled employee / official,1,yes,yes,male,married/widowed,1 +no account,28,critical account,radio/television,2743,<100 DM,>=7 years,4,none,2,car or other,29,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,18,critical account,radio/television,1149,>= 1000 DM,1<= < 4 years,4,none,3,real estate,46,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,9,existing credits paid back duly till now,furniture/equipment,1313,<100 DM,>=7 years,1,none,4,car or other,20,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,18,critical account,repairs,1190,<100 DM,unemployed,2,none,4,unknown / no property,55,none,for free,3,unemployed/ unskilled - non-resident,2,none,yes,female,divorced/separated,0 +no account,5,existing credits paid back duly till now,business,3448,<100 DM,4<= <7 years,1,none,4,real estate,74,none,own,1,unskilled - resident,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,24,existing credits paid back duly till now,others,11328,<100 DM,1<= < 4 years,2,co-applicant,3,car or other,29,bank,own,2,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +<0 DM,6,critical account,furniture/equipment,1872,<100 DM,unemployed,4,none,4,unknown / no property,36,none,for free,3,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +no account,24,critical account,repairs,2058,<100 DM,1<= < 4 years,4,none,2,real estate,33,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,9,existing credits paid back duly till now,furniture/equipment,2136,<100 DM,1<= < 4 years,3,none,2,real estate,25,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,radio/television,1484,no savings account,1<= < 4 years,2,none,1,real estate,25,none,own,1,skilled employee / official,1,yes,yes,male,married/widowed,0 +no account,6,existing credits paid back duly till now,repairs,660,500 <= < 1000 DM,4<= <7 years,2,none,4,real estate,23,none,rent,1,unskilled - resident,1,none,yes,male,married/widowed,1 +no account,24,critical account,car (new),1287,>= 1000 DM,>=7 years,4,none,4,real estate,37,none,own,2,skilled employee / official,1,yes,yes,female,divorced/separated,1 +<0 DM,42,critical account,repairs,3394,<100 DM,unemployed,4,co-applicant,4,car or other,65,none,own,2,unemployed/ unskilled - non-resident,1,none,yes,male,divorced/separated,1 +>= 200 DM ,12,all credits at this bank paid back duly,business,609,<100 DM,<1 years,4,none,1,real estate,26,none,own,1,unemployed/ unskilled - non-resident,1,none,yes,female,divorced/separated,0 +no account,12,existing credits paid back duly till now,car (new),1884,<100 DM,>=7 years,4,none,4,car or other,39,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,furniture/equipment,1620,<100 DM,1<= < 4 years,2,co-applicant,3,savings agreement/life insurance,30,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,20,delay in paying off,others,2629,<100 DM,1<= < 4 years,2,none,3,car or other,29,bank,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,12,existing credits paid back duly till now,education,719,<100 DM,>=7 years,4,none,4,car or other,41,bank,own,1,unskilled - resident,2,none,yes,male,divorced/separated,0 +0 <= <200 DM,48,critical account,furniture/equipment,5096,<100 DM,1<= < 4 years,2,none,3,car or other,30,none,own,1,management/ highly qualified employee,1,yes,yes,female,divorced/separated,0 +no account,9,critical account,education,1244,no savings account,>=7 years,4,none,4,savings agreement/life insurance,41,none,rent,2,unskilled - resident,1,none,yes,female,divorced/separated,1 +<0 DM,36,existing credits paid back duly till now,car (new),1842,<100 DM,<1 years,4,none,4,car or other,34,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,0 +0 <= <200 DM,7,existing credits paid back duly till now,radio/television,2576,<100 DM,1<= < 4 years,2,guarantor,2,real estate,35,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +>= 200 DM ,12,existing credits paid back duly till now,furniture/equipment,1424,no savings account,>=7 years,3,none,4,real estate,55,none,own,1,management/ highly qualified employee,1,yes,yes,female,divorced/separated,1 +0 <= <200 DM,15,delay in paying off,repairs,1512,>= 1000 DM,1<= < 4 years,3,none,3,savings agreement/life insurance,61,store,own,2,skilled employee / official,1,none,yes,male,married/widowed,0 +no account,36,critical account,car (used),11054,no savings account,1<= < 4 years,4,none,2,car or other,30,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +no account,6,existing credits paid back duly till now,radio/television,518,<100 DM,1<= < 4 years,3,none,1,real estate,29,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,12,no credits taken,furniture/equipment,2759,<100 DM,>=7 years,2,none,4,savings agreement/life insurance,34,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,24,existing credits paid back duly till now,car (used),2670,<100 DM,>=7 years,4,none,4,car or other,35,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +<0 DM,24,existing credits paid back duly till now,car (new),4817,<100 DM,4<= <7 years,2,co-applicant,3,savings agreement/life insurance,31,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,0 +no account,24,existing credits paid back duly till now,car (used),2679,<100 DM,<1 years,4,none,1,unknown / no property,29,none,own,1,management/ highly qualified employee,1,yes,yes,female,divorced/separated,1 +<0 DM,11,critical account,car (new),3905,<100 DM,1<= < 4 years,2,none,2,real estate,36,none,rent,2,skilled employee / official,2,none,yes,male,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,car (used),3386,<100 DM,>=7 years,3,none,4,unknown / no property,35,none,for free,1,skilled employee / official,1,yes,yes,male,divorced/separated,0 +<0 DM,6,existing credits paid back duly till now,domestic appliances,343,<100 DM,<1 years,4,none,1,real estate,27,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,18,existing credits paid back duly till now,radio/television,4594,<100 DM,<1 years,3,none,2,car or other,32,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,36,existing credits paid back duly till now,furniture/equipment,3620,<100 DM,1<= < 4 years,1,guarantor,2,savings agreement/life insurance,37,none,own,1,skilled employee / official,2,none,yes,male,divorced/separated,1 +<0 DM,15,existing credits paid back duly till now,car (new),1721,<100 DM,<1 years,2,none,3,real estate,36,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,furniture/equipment,3017,<100 DM,<1 years,3,none,1,real estate,34,none,rent,1,management/ highly qualified employee,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,retraining,754,no savings account,>=7 years,4,none,4,savings agreement/life insurance,38,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,18,existing credits paid back duly till now,business,1950,<100 DM,4<= <7 years,4,none,1,car or other,34,store,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,24,existing credits paid back duly till now,car (used),2924,<100 DM,1<= < 4 years,3,guarantor,4,unknown / no property,63,bank,own,1,skilled employee / official,2,yes,yes,male,divorced/separated,1 +<0 DM,24,delay in paying off,radio/television,1659,<100 DM,<1 years,4,none,2,car or other,29,none,rent,1,unskilled - resident,1,yes,yes,female,divorced/separated,0 +no account,48,delay in paying off,radio/television,7238,no savings account,>=7 years,3,none,3,car or other,32,bank,own,2,skilled employee / official,2,none,yes,male,divorced/separated,1 +no account,33,delay in paying off,business,2764,<100 DM,1<= < 4 years,2,none,2,car or other,26,none,own,2,skilled employee / official,1,yes,yes,female,divorced/separated,1 +no account,24,delay in paying off,car (used),4679,<100 DM,4<= <7 years,3,none,3,car or other,35,none,own,2,unskilled - resident,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,24,existing credits paid back duly till now,radio/television,3092,100 <= <500 DM,<1 years,3,none,2,car or other,22,none,rent,1,skilled employee / official,1,yes,yes,male,married/widowed,0 +<0 DM,6,existing credits paid back duly till now,education,448,<100 DM,<1 years,4,none,4,savings agreement/life insurance,23,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +<0 DM,9,existing credits paid back duly till now,car (new),654,<100 DM,1<= < 4 years,4,none,3,car or other,28,none,own,1,unskilled - resident,1,none,yes,male,divorced/separated,0 +no account,6,existing credits paid back duly till now,retraining,1238,no savings account,unemployed,4,none,4,savings agreement/life insurance,36,none,own,1,management/ highly qualified employee,2,yes,yes,male,divorced/separated,1 +0 <= <200 DM,18,critical account,radio/television,1245,<100 DM,1<= < 4 years,4,none,2,car or other,33,none,own,1,skilled employee / official,1,none,yes,male,married/widowed,0 +<0 DM,18,no credits taken,furniture/equipment,3114,<100 DM,<1 years,1,none,4,savings agreement/life insurance,26,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +no account,39,existing credits paid back duly till now,car (used),2569,500 <= < 1000 DM,1<= < 4 years,4,none,4,car or other,24,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +>= 200 DM ,24,existing credits paid back duly till now,radio/television,5152,<100 DM,4<= <7 years,4,none,2,car or other,25,bank,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,business,1037,100 <= <500 DM,4<= <7 years,3,none,4,real estate,39,none,own,1,unskilled - resident,1,none,yes,male,divorced/separated,1 +<0 DM,15,critical account,furniture/equipment,1478,<100 DM,>=7 years,4,none,4,car or other,44,none,own,2,skilled employee / official,2,yes,yes,male,divorced/separated,1 +0 <= <200 DM,12,critical account,radio/television,3573,<100 DM,1<= < 4 years,1,none,1,real estate,23,none,own,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,24,existing credits paid back duly till now,car (new),1201,<100 DM,<1 years,4,none,1,savings agreement/life insurance,26,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,30,existing credits paid back duly till now,furniture/equipment,3622,>= 1000 DM,>=7 years,4,none,4,savings agreement/life insurance,57,none,rent,2,skilled employee / official,1,yes,yes,female,divorced/separated,1 +no account,15,delay in paying off,furniture/equipment,960,>= 1000 DM,4<= <7 years,3,none,2,savings agreement/life insurance,30,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,12,critical account,car (new),1163,500 <= < 1000 DM,1<= < 4 years,4,none,4,real estate,44,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,6,delay in paying off,car (new),1209,<100 DM,unemployed,4,none,4,savings agreement/life insurance,47,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +no account,12,existing credits paid back duly till now,radio/television,3077,<100 DM,1<= < 4 years,2,none,4,car or other,52,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,24,existing credits paid back duly till now,car (new),3757,<100 DM,>=7 years,4,co-applicant,4,unknown / no property,62,none,for free,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +no account,10,existing credits paid back duly till now,car (new),1418,100 <= <500 DM,1<= < 4 years,3,none,2,real estate,35,none,rent,1,unskilled - resident,1,none,no,male,divorced/separated,1 +no account,6,existing credits paid back duly till now,car (new),3518,<100 DM,1<= < 4 years,2,guarantor,3,savings agreement/life insurance,26,none,rent,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,12,critical account,radio/television,1934,<100 DM,>=7 years,2,none,2,unknown / no property,26,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,27,no credits taken,business,8318,<100 DM,>=7 years,2,none,4,unknown / no property,42,none,for free,2,management/ highly qualified employee,1,yes,yes,female,divorced/separated,0 +no account,6,critical account,radio/television,1237,100 <= <500 DM,1<= < 4 years,1,none,1,savings agreement/life insurance,27,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,6,existing credits paid back duly till now,radio/television,368,no savings account,>=7 years,4,none,4,savings agreement/life insurance,38,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,12,critical account,car (new),2122,<100 DM,1<= < 4 years,3,none,2,real estate,39,none,rent,2,unskilled - resident,2,none,no,male,divorced/separated,1 +<0 DM,24,existing credits paid back duly till now,furniture/equipment,2996,no savings account,1<= < 4 years,2,none,4,car or other,20,none,own,1,skilled employee / official,1,none,yes,male,married/widowed,0 +0 <= <200 DM,36,existing credits paid back duly till now,furniture/equipment,9034,100 <= <500 DM,<1 years,4,co-applicant,1,unknown / no property,29,none,rent,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +no account,24,critical account,furniture/equipment,1585,<100 DM,4<= <7 years,4,none,3,savings agreement/life insurance,40,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,18,existing credits paid back duly till now,radio/television,1301,<100 DM,>=7 years,4,guarantor,2,real estate,32,none,own,1,unskilled - resident,1,none,yes,male,married/widowed,1 +>= 200 DM ,6,critical account,car (new),1323,100 <= <500 DM,>=7 years,2,none,4,car or other,28,none,own,2,skilled employee / official,2,yes,yes,male,divorced/separated,1 +<0 DM,24,existing credits paid back duly till now,car (new),3123,<100 DM,<1 years,4,none,1,savings agreement/life insurance,27,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +<0 DM,36,existing credits paid back duly till now,car (used),5493,<100 DM,>=7 years,2,none,4,unknown / no property,42,none,for free,1,skilled employee / official,2,none,yes,male,divorced/separated,1 +>= 200 DM ,9,existing credits paid back duly till now,radio/television,1126,100 <= <500 DM,>=7 years,2,none,4,real estate,49,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,24,critical account,radio/television,1216,100 <= <500 DM,<1 years,4,none,4,unknown / no property,38,bank,own,2,skilled employee / official,2,none,yes,male,divorced/separated,0 +<0 DM,24,existing credits paid back duly till now,car (new),1207,<100 DM,<1 years,4,none,4,savings agreement/life insurance,24,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +no account,10,existing credits paid back duly till now,car (new),1309,no savings account,1<= < 4 years,4,guarantor,4,savings agreement/life insurance,27,none,own,1,unskilled - resident,1,none,yes,male,divorced/separated,0 +>= 200 DM ,15,critical account,car (used),2360,500 <= < 1000 DM,1<= < 4 years,2,none,2,car or other,36,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,15,all credits at this bank paid back duly,car (new),6850,100 <= <500 DM,unemployed,1,none,2,savings agreement/life insurance,34,none,own,1,management/ highly qualified employee,2,yes,yes,male,divorced/separated,0 +no account,24,existing credits paid back duly till now,radio/television,1413,<100 DM,1<= < 4 years,4,none,2,savings agreement/life insurance,28,none,own,1,skilled employee / official,1,none,yes,male,married/widowed,1 +no account,39,existing credits paid back duly till now,car (used),8588,100 <= <500 DM,>=7 years,4,none,2,car or other,45,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,car (new),759,<100 DM,4<= <7 years,4,none,2,real estate,26,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,36,existing credits paid back duly till now,car (used),4686,<100 DM,1<= < 4 years,2,none,2,unknown / no property,32,none,for free,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +>= 200 DM ,15,existing credits paid back duly till now,business,2687,<100 DM,4<= <7 years,2,none,4,savings agreement/life insurance,26,none,rent,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,12,delay in paying off,radio/television,585,<100 DM,1<= < 4 years,4,co-applicant,4,real estate,20,none,rent,2,skilled employee / official,1,none,yes,male,married/widowed,1 +no account,24,existing credits paid back duly till now,car (new),2255,no savings account,<1 years,4,none,1,savings agreement/life insurance,54,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,6,critical account,car (new),609,<100 DM,4<= <7 years,4,none,3,savings agreement/life insurance,37,none,own,2,skilled employee / official,1,none,no,female,divorced/separated,1 +<0 DM,6,critical account,car (new),1361,<100 DM,<1 years,2,none,4,real estate,40,none,own,1,unskilled - resident,2,none,no,male,divorced/separated,1 +no account,36,critical account,furniture/equipment,7127,<100 DM,<1 years,2,none,4,savings agreement/life insurance,23,none,rent,2,skilled employee / official,1,yes,yes,female,divorced/separated,0 +<0 DM,6,existing credits paid back duly till now,car (new),1203,100 <= <500 DM,>=7 years,3,none,2,savings agreement/life insurance,43,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,6,critical account,radio/television,700,no savings account,>=7 years,4,none,4,unknown / no property,36,none,for free,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,24,critical account,repairs,5507,<100 DM,>=7 years,3,none,4,unknown / no property,44,none,for free,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,18,existing credits paid back duly till now,radio/television,3190,<100 DM,1<= < 4 years,2,none,2,real estate,24,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +<0 DM,48,no credits taken,furniture/equipment,7119,<100 DM,1<= < 4 years,3,none,4,unknown / no property,53,none,for free,2,skilled employee / official,2,none,yes,male,divorced/separated,0 +no account,24,existing credits paid back duly till now,car (used),3488,100 <= <500 DM,4<= <7 years,3,none,4,car or other,23,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,18,existing credits paid back duly till now,radio/television,1113,<100 DM,1<= < 4 years,4,guarantor,4,real estate,26,none,own,1,unskilled - resident,2,none,yes,female,divorced/separated,1 +0 <= <200 DM,26,existing credits paid back duly till now,car (used),7966,<100 DM,<1 years,2,none,3,car or other,30,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,15,critical account,education,1532,100 <= <500 DM,1<= < 4 years,4,none,3,car or other,31,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,4,critical account,radio/television,1503,<100 DM,4<= <7 years,2,none,1,real estate,42,none,own,2,unskilled - resident,2,none,yes,male,divorced/separated,1 +<0 DM,36,existing credits paid back duly till now,radio/television,2302,<100 DM,1<= < 4 years,4,none,4,car or other,31,none,rent,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +<0 DM,6,existing credits paid back duly till now,car (new),662,<100 DM,<1 years,3,none,4,real estate,41,none,own,1,unskilled - resident,2,yes,yes,male,divorced/separated,1 +0 <= <200 DM,36,existing credits paid back duly till now,education,2273,<100 DM,4<= <7 years,3,none,1,car or other,32,none,own,2,skilled employee / official,2,none,yes,male,divorced/separated,1 +0 <= <200 DM,15,existing credits paid back duly till now,car (new),2631,100 <= <500 DM,1<= < 4 years,2,none,4,car or other,28,none,rent,2,skilled employee / official,1,yes,yes,female,divorced/separated,0 +no account,12,delay in paying off,car (used),1503,<100 DM,1<= < 4 years,4,none,4,real estate,41,none,rent,1,skilled employee / official,1,none,yes,male,married/widowed,1 +no account,24,existing credits paid back duly till now,radio/television,1311,100 <= <500 DM,4<= <7 years,4,none,3,savings agreement/life insurance,26,none,own,1,skilled employee / official,1,yes,yes,male,married/widowed,1 +no account,24,existing credits paid back duly till now,radio/television,3105,no savings account,<1 years,4,none,2,car or other,25,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +>= 200 DM ,21,critical account,education,2319,<100 DM,<1 years,2,none,1,car or other,33,none,rent,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +<0 DM,6,existing credits paid back duly till now,car (new),1374,no savings account,unemployed,4,none,3,savings agreement/life insurance,75,none,own,1,management/ highly qualified employee,1,yes,yes,female,divorced/separated,1 +0 <= <200 DM,18,critical account,furniture/equipment,3612,<100 DM,>=7 years,3,none,4,savings agreement/life insurance,37,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +<0 DM,48,existing credits paid back duly till now,car (new),7763,<100 DM,>=7 years,4,none,4,unknown / no property,42,bank,for free,1,management/ highly qualified employee,1,none,yes,male,divorced/separated,0 +>= 200 DM ,18,existing credits paid back duly till now,furniture/equipment,3049,<100 DM,<1 years,1,none,1,savings agreement/life insurance,45,store,own,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,radio/television,1534,<100 DM,<1 years,1,none,1,real estate,23,none,rent,1,skilled employee / official,1,none,yes,male,married/widowed,0 +no account,24,delay in paying off,car (new),2032,<100 DM,>=7 years,4,none,4,unknown / no property,60,none,for free,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,30,existing credits paid back duly till now,furniture/equipment,6350,no savings account,>=7 years,4,none,4,savings agreement/life insurance,31,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +>= 200 DM ,18,existing credits paid back duly till now,furniture/equipment,2864,<100 DM,1<= < 4 years,2,none,1,real estate,34,none,own,1,unskilled - resident,2,none,yes,male,divorced/separated,0 +no account,12,critical account,car (new),1255,<100 DM,>=7 years,4,none,4,real estate,61,none,own,2,unskilled - resident,1,none,yes,male,divorced/separated,1 +<0 DM,24,delay in paying off,car (new),1333,<100 DM,unemployed,4,none,2,real estate,43,none,for free,2,skilled employee / official,2,none,yes,male,divorced/separated,0 +no account,24,critical account,car (new),2022,<100 DM,1<= < 4 years,4,none,4,car or other,37,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +no account,24,existing credits paid back duly till now,radio/television,1552,<100 DM,4<= <7 years,3,none,1,car or other,32,bank,own,1,skilled employee / official,2,none,yes,male,divorced/separated,1 +<0 DM,12,all credits at this bank paid back duly,radio/television,626,<100 DM,1<= < 4 years,4,none,4,real estate,24,bank,own,1,unskilled - resident,1,none,yes,female,divorced/separated,0 +no account,48,critical account,car (used),8858,no savings account,4<= <7 years,2,none,1,unknown / no property,35,none,for free,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,12,critical account,repairs,996,no savings account,4<= <7 years,4,none,4,real estate,23,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,6,all credits at this bank paid back duly,radio/television,1750,500 <= < 1000 DM,>=7 years,2,none,4,savings agreement/life insurance,45,bank,own,1,unskilled - resident,2,none,yes,male,divorced/separated,1 +<0 DM,48,existing credits paid back duly till now,radio/television,6999,<100 DM,4<= <7 years,1,guarantor,1,real estate,34,none,own,2,skilled employee / official,1,yes,yes,male,married/widowed,0 +0 <= <200 DM,12,critical account,car (new),1995,100 <= <500 DM,<1 years,4,none,1,car or other,27,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,9,existing credits paid back duly till now,education,1199,<100 DM,4<= <7 years,4,none,4,savings agreement/life insurance,67,none,own,2,management/ highly qualified employee,1,yes,yes,female,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,radio/television,1331,<100 DM,<1 years,2,none,1,car or other,22,store,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +0 <= <200 DM,18,no credits taken,car (new),2278,100 <= <500 DM,<1 years,3,none,3,car or other,28,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,0 +no account,21,no credits taken,car (new),5003,no savings account,1<= < 4 years,1,none,4,savings agreement/life insurance,29,bank,own,2,skilled employee / official,1,yes,yes,female,divorced/separated,0 +<0 DM,24,all credits at this bank paid back duly,furniture/equipment,3552,<100 DM,4<= <7 years,3,none,4,car or other,27,bank,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +0 <= <200 DM,18,critical account,furniture/equipment,1928,<100 DM,<1 years,2,none,2,real estate,31,none,own,2,unskilled - resident,1,none,yes,male,divorced/separated,0 +<0 DM,24,existing credits paid back duly till now,car (used),2964,no savings account,>=7 years,4,none,4,unknown / no property,49,bank,for free,1,skilled employee / official,2,yes,yes,male,divorced/separated,1 +<0 DM,24,all credits at this bank paid back duly,radio/television,1546,<100 DM,4<= <7 years,4,guarantor,4,car or other,24,bank,rent,1,unskilled - resident,1,none,yes,male,divorced/separated,0 +>= 200 DM ,6,delay in paying off,radio/television,683,<100 DM,<1 years,2,none,1,savings agreement/life insurance,29,bank,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,36,existing credits paid back duly till now,car (new),12389,no savings account,1<= < 4 years,1,none,4,unknown / no property,37,none,for free,1,skilled employee / official,1,yes,yes,male,divorced/separated,0 +0 <= <200 DM,24,delay in paying off,business,4712,no savings account,1<= < 4 years,4,none,2,savings agreement/life insurance,37,bank,own,2,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,24,delay in paying off,radio/television,1553,100 <= <500 DM,4<= <7 years,3,none,2,savings agreement/life insurance,23,none,rent,2,skilled employee / official,1,yes,yes,female,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,car (new),1372,<100 DM,4<= <7 years,2,none,3,car or other,36,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,24,critical account,radio/television,2578,>= 1000 DM,>=7 years,2,none,2,car or other,34,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,48,existing credits paid back duly till now,radio/television,3979,no savings account,4<= <7 years,4,none,1,car or other,41,none,own,2,skilled employee / official,2,yes,yes,male,divorced/separated,1 +<0 DM,48,existing credits paid back duly till now,radio/television,6758,<100 DM,1<= < 4 years,3,none,2,car or other,31,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,0 +<0 DM,24,existing credits paid back duly till now,furniture/equipment,3234,<100 DM,<1 years,4,none,4,real estate,23,none,rent,1,unskilled - resident,1,yes,yes,female,divorced/separated,0 +no account,30,critical account,radio/television,5954,<100 DM,4<= <7 years,3,co-applicant,2,car or other,38,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,24,existing credits paid back duly till now,car (used),5433,no savings account,unemployed,2,none,4,savings agreement/life insurance,26,none,rent,1,management/ highly qualified employee,1,yes,yes,female,divorced/separated,1 +<0 DM,15,existing credits paid back duly till now,business,806,<100 DM,1<= < 4 years,4,none,4,savings agreement/life insurance,22,none,own,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,9,existing credits paid back duly till now,radio/television,1082,<100 DM,>=7 years,4,none,4,car or other,27,none,own,2,unskilled - resident,1,none,yes,male,divorced/separated,1 +no account,15,critical account,furniture/equipment,2788,<100 DM,4<= <7 years,2,co-applicant,3,car or other,24,bank,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,radio/television,2930,<100 DM,4<= <7 years,2,none,1,real estate,27,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,24,critical account,education,1927,no savings account,1<= < 4 years,3,none,2,car or other,33,none,own,2,skilled employee / official,1,yes,yes,female,divorced/separated,1 +0 <= <200 DM,36,critical account,car (new),2820,<100 DM,<1 years,4,none,4,car or other,27,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,24,existing credits paid back duly till now,retraining,937,<100 DM,<1 years,4,none,3,car or other,27,none,own,2,unskilled - resident,1,none,yes,male,married/widowed,1 +0 <= <200 DM,18,critical account,car (new),1056,<100 DM,>=7 years,3,guarantor,3,real estate,30,bank,own,2,skilled employee / official,1,none,yes,male,divorced/separated,0 +0 <= <200 DM,12,critical account,car (new),3124,<100 DM,<1 years,1,none,3,real estate,49,bank,own,2,unskilled - resident,2,none,yes,male,divorced/separated,1 +no account,9,existing credits paid back duly till now,furniture/equipment,1388,<100 DM,1<= < 4 years,4,none,2,real estate,26,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,36,existing credits paid back duly till now,repairs,2384,<100 DM,<1 years,4,none,1,unknown / no property,33,none,rent,1,unskilled - resident,1,none,yes,male,divorced/separated,0 +no account,12,existing credits paid back duly till now,car (new),2133,no savings account,>=7 years,4,none,4,unknown / no property,52,none,for free,1,management/ highly qualified employee,1,yes,yes,female,divorced/separated,1 +<0 DM,18,existing credits paid back duly till now,furniture/equipment,2039,<100 DM,1<= < 4 years,1,none,4,real estate,20,bank,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +<0 DM,9,critical account,car (new),2799,<100 DM,1<= < 4 years,2,none,2,real estate,36,none,rent,2,skilled employee / official,2,none,yes,male,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,furniture/equipment,1289,<100 DM,1<= < 4 years,4,guarantor,1,savings agreement/life insurance,21,none,own,1,unskilled - resident,1,none,yes,male,divorced/separated,1 +<0 DM,18,existing credits paid back duly till now,domestic appliances,1217,<100 DM,1<= < 4 years,4,none,3,real estate,47,none,own,1,unskilled - resident,1,yes,yes,male,married/widowed,0 +<0 DM,12,critical account,furniture/equipment,2246,<100 DM,>=7 years,3,none,3,savings agreement/life insurance,60,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,0 +<0 DM,12,critical account,radio/television,385,<100 DM,4<= <7 years,4,none,3,real estate,58,none,own,4,unskilled - resident,1,yes,yes,female,divorced/separated,1 +0 <= <200 DM,24,delay in paying off,car (new),1965,no savings account,1<= < 4 years,4,none,4,car or other,42,none,rent,2,skilled employee / official,1,yes,yes,female,divorced/separated,1 +no account,21,existing credits paid back duly till now,business,1572,>= 1000 DM,>=7 years,4,none,4,real estate,36,bank,own,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,24,existing credits paid back duly till now,car (new),2718,<100 DM,1<= < 4 years,3,none,4,savings agreement/life insurance,20,none,rent,1,unskilled - resident,1,yes,yes,female,divorced/separated,0 +<0 DM,24,all credits at this bank paid back duly,others,1358,no savings account,>=7 years,4,none,3,car or other,40,store,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +0 <= <200 DM,6,all credits at this bank paid back duly,car (new),931,100 <= <500 DM,<1 years,1,none,1,savings agreement/life insurance,32,store,own,1,unskilled - resident,1,none,yes,female,divorced/separated,0 +<0 DM,24,existing credits paid back duly till now,car (new),1442,<100 DM,4<= <7 years,4,none,4,car or other,23,none,rent,2,skilled employee / official,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,24,no credits taken,business,4241,<100 DM,1<= < 4 years,1,none,4,real estate,36,none,own,3,unskilled - resident,1,yes,yes,male,divorced/separated,0 +no account,18,critical account,car (new),2775,<100 DM,4<= <7 years,2,none,2,savings agreement/life insurance,31,bank,own,2,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,24,delay in paying off,business,3863,<100 DM,1<= < 4 years,1,none,2,unknown / no property,32,none,for free,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,7,existing credits paid back duly till now,radio/television,2329,<100 DM,<1 years,1,guarantor,1,real estate,45,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,9,existing credits paid back duly till now,furniture/equipment,918,<100 DM,1<= < 4 years,4,none,1,savings agreement/life insurance,30,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,24,all credits at this bank paid back duly,education,1837,<100 DM,4<= <7 years,4,none,4,unknown / no property,34,bank,for free,1,unskilled - resident,1,none,yes,female,divorced/separated,0 +no account,36,existing credits paid back duly till now,furniture/equipment,3349,<100 DM,1<= < 4 years,4,none,2,car or other,28,none,own,1,management/ highly qualified employee,1,yes,yes,female,divorced/separated,0 +>= 200 DM ,10,existing credits paid back duly till now,furniture/equipment,1275,<100 DM,<1 years,4,none,2,savings agreement/life insurance,23,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,24,all credits at this bank paid back duly,furniture/equipment,2828,500 <= < 1000 DM,1<= < 4 years,4,none,4,real estate,22,store,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,24,critical account,business,4526,<100 DM,1<= < 4 years,3,none,2,real estate,74,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,36,existing credits paid back duly till now,radio/television,2671,100 <= <500 DM,1<= < 4 years,4,co-applicant,4,unknown / no property,50,none,for free,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +no account,18,existing credits paid back duly till now,radio/television,2051,<100 DM,<1 years,4,none,1,real estate,33,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,15,existing credits paid back duly till now,car (used),1300,no savings account,>=7 years,4,none,4,unknown / no property,45,bank,for free,1,skilled employee / official,2,none,yes,male,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,domestic appliances,741,100 <= <500 DM,unemployed,4,none,3,savings agreement/life insurance,22,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +>= 200 DM ,10,existing credits paid back duly till now,car (new),1240,100 <= <500 DM,>=7 years,1,none,4,unknown / no property,48,none,for free,1,unskilled - resident,2,none,yes,female,divorced/separated,0 +<0 DM,21,existing credits paid back duly till now,radio/television,3357,>= 1000 DM,<1 years,4,none,2,car or other,29,bank,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,24,all credits at this bank paid back duly,car (used),3632,<100 DM,1<= < 4 years,1,guarantor,4,car or other,22,bank,rent,1,skilled employee / official,1,none,no,female,divorced/separated,1 +no account,18,delay in paying off,furniture/equipment,1808,<100 DM,4<= <7 years,4,none,1,real estate,22,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,48,no credits taken,business,12204,no savings account,1<= < 4 years,2,none,2,car or other,48,bank,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,60,delay in paying off,radio/television,9157,no savings account,1<= < 4 years,2,none,2,unknown / no property,27,none,for free,1,management/ highly qualified employee,1,none,yes,male,divorced/separated,1 +<0 DM,6,critical account,car (new),3676,<100 DM,1<= < 4 years,1,none,3,real estate,37,none,rent,3,skilled employee / official,2,none,yes,male,divorced/separated,1 +0 <= <200 DM,30,existing credits paid back duly till now,furniture/equipment,3441,100 <= <500 DM,1<= < 4 years,2,co-applicant,4,car or other,21,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +no account,12,existing credits paid back duly till now,car (new),640,<100 DM,1<= < 4 years,4,none,2,real estate,49,none,own,1,unskilled - resident,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,21,critical account,business,3652,<100 DM,4<= <7 years,2,none,3,savings agreement/life insurance,27,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,18,critical account,car (new),1530,<100 DM,1<= < 4 years,3,none,2,savings agreement/life insurance,32,bank,own,2,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,48,existing credits paid back duly till now,business,3914,no savings account,1<= < 4 years,4,none,2,real estate,38,bank,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +<0 DM,12,existing credits paid back duly till now,furniture/equipment,1858,<100 DM,<1 years,4,none,1,car or other,22,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,18,existing credits paid back duly till now,radio/television,2600,<100 DM,1<= < 4 years,4,none,4,unknown / no property,65,none,for free,2,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,15,existing credits paid back duly till now,radio/television,1979,no savings account,>=7 years,4,none,2,car or other,35,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +>= 200 DM ,6,existing credits paid back duly till now,furniture/equipment,2116,<100 DM,1<= < 4 years,2,none,2,real estate,41,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,9,all credits at this bank paid back duly,car (new),1437,100 <= <500 DM,4<= <7 years,2,none,3,unknown / no property,29,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,42,critical account,furniture/equipment,4042,500 <= < 1000 DM,1<= < 4 years,4,none,4,real estate,36,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,9,existing credits paid back duly till now,education,3832,no savings account,>=7 years,1,none,4,real estate,64,none,own,1,unskilled - resident,1,none,yes,male,divorced/separated,1 +<0 DM,24,existing credits paid back duly till now,radio/television,3660,<100 DM,1<= < 4 years,2,none,4,car or other,28,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,18,all credits at this bank paid back duly,furniture/equipment,1553,<100 DM,1<= < 4 years,4,none,3,car or other,44,bank,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +0 <= <200 DM,15,existing credits paid back duly till now,radio/television,1444,no savings account,<1 years,4,none,1,savings agreement/life insurance,23,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,9,existing credits paid back duly till now,furniture/equipment,1980,<100 DM,<1 years,2,co-applicant,2,car or other,19,none,rent,2,skilled employee / official,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,24,existing credits paid back duly till now,car (new),1355,<100 DM,<1 years,3,none,4,car or other,25,none,own,1,unskilled - resident,1,yes,yes,female,divorced/separated,0 +no account,12,existing credits paid back duly till now,education,1393,<100 DM,>=7 years,4,none,4,savings agreement/life insurance,47,bank,own,3,skilled employee / official,2,yes,yes,male,divorced/separated,1 +no account,24,existing credits paid back duly till now,radio/television,1376,500 <= < 1000 DM,4<= <7 years,4,none,1,car or other,28,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,60,delay in paying off,radio/television,15653,<100 DM,4<= <7 years,2,none,4,car or other,21,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,12,existing credits paid back duly till now,radio/television,1493,<100 DM,<1 years,4,none,3,car or other,34,none,own,1,skilled employee / official,2,none,yes,female,divorced/separated,1 +<0 DM,42,delay in paying off,radio/television,4370,<100 DM,4<= <7 years,3,none,2,savings agreement/life insurance,26,bank,own,2,skilled employee / official,2,yes,yes,male,divorced/separated,0 +<0 DM,18,existing credits paid back duly till now,education,750,<100 DM,unemployed,4,none,1,real estate,27,none,own,1,unemployed/ unskilled - non-resident,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,15,existing credits paid back duly till now,repairs,1308,<100 DM,>=7 years,4,none,4,car or other,38,none,own,2,unskilled - resident,1,none,yes,male,divorced/separated,1 +no account,15,existing credits paid back duly till now,education,4623,100 <= <500 DM,1<= < 4 years,3,none,2,savings agreement/life insurance,40,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +no account,24,critical account,radio/television,1851,<100 DM,4<= <7 years,4,guarantor,2,car or other,33,none,own,2,skilled employee / official,1,yes,yes,male,married/widowed,1 +<0 DM,18,critical account,radio/television,1880,<100 DM,4<= <7 years,4,none,1,savings agreement/life insurance,32,none,own,2,management/ highly qualified employee,1,yes,yes,male,married/widowed,1 +no account,36,delay in paying off,business,7980,no savings account,<1 years,4,none,4,car or other,27,none,rent,2,skilled employee / official,1,yes,yes,male,divorced/separated,0 +<0 DM,30,no credits taken,furniture/equipment,4583,<100 DM,1<= < 4 years,2,guarantor,2,real estate,32,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,12,existing credits paid back duly till now,car (new),1386,500 <= < 1000 DM,1<= < 4 years,2,none,2,savings agreement/life insurance,26,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +>= 200 DM ,24,existing credits paid back duly till now,car (new),947,<100 DM,4<= <7 years,4,none,3,unknown / no property,38,bank,for free,1,skilled employee / official,2,none,yes,male,divorced/separated,0 +<0 DM,12,existing credits paid back duly till now,education,684,<100 DM,1<= < 4 years,4,none,4,car or other,40,none,rent,1,unskilled - resident,2,none,yes,male,divorced/separated,0 +<0 DM,48,existing credits paid back duly till now,education,7476,<100 DM,4<= <7 years,4,none,1,unknown / no property,50,none,for free,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,furniture/equipment,1922,<100 DM,1<= < 4 years,4,none,2,savings agreement/life insurance,37,none,own,1,unskilled - resident,1,none,yes,male,divorced/separated,0 +<0 DM,24,existing credits paid back duly till now,car (new),2303,<100 DM,>=7 years,4,co-applicant,1,real estate,45,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +0 <= <200 DM,36,delay in paying off,car (new),8086,100 <= <500 DM,>=7 years,2,none,4,car or other,42,none,own,4,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +no account,24,critical account,car (used),2346,<100 DM,4<= <7 years,4,none,3,car or other,35,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,14,existing credits paid back duly till now,car (new),3973,<100 DM,unemployed,1,none,4,unknown / no property,22,none,for free,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,car (new),888,<100 DM,>=7 years,4,none,4,car or other,41,bank,own,1,unskilled - resident,2,none,yes,male,divorced/separated,0 +no account,48,existing credits paid back duly till now,radio/television,10222,no savings account,4<= <7 years,4,none,3,car or other,37,store,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,30,no credits taken,business,4221,<100 DM,1<= < 4 years,2,none,1,car or other,28,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,18,critical account,furniture/equipment,6361,<100 DM,>=7 years,2,none,1,unknown / no property,41,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +>= 200 DM ,12,existing credits paid back duly till now,radio/television,1297,<100 DM,1<= < 4 years,3,none,4,real estate,23,none,rent,1,skilled employee / official,1,none,yes,male,married/widowed,1 +<0 DM,12,existing credits paid back duly till now,car (new),900,no savings account,1<= < 4 years,4,none,2,car or other,23,none,own,1,skilled employee / official,1,none,yes,male,married/widowed,0 +no account,21,existing credits paid back duly till now,furniture/equipment,2241,<100 DM,>=7 years,4,none,2,real estate,50,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,6,delay in paying off,furniture/equipment,1050,<100 DM,unemployed,4,none,1,savings agreement/life insurance,35,store,own,2,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +>= 200 DM ,6,critical account,education,1047,<100 DM,1<= < 4 years,2,none,4,savings agreement/life insurance,50,none,own,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +no account,24,critical account,others,6314,<100 DM,unemployed,4,co-applicant,2,unknown / no property,27,bank,own,2,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,30,all credits at this bank paid back duly,furniture/equipment,3496,>= 1000 DM,1<= < 4 years,4,none,2,car or other,34,store,own,1,skilled employee / official,2,yes,yes,male,divorced/separated,1 +no account,48,all credits at this bank paid back duly,business,3609,<100 DM,1<= < 4 years,1,none,1,real estate,27,store,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,12,critical account,car (new),4843,<100 DM,>=7 years,3,co-applicant,4,savings agreement/life insurance,43,none,rent,2,skilled employee / official,1,yes,yes,male,divorced/separated,0 +>= 200 DM ,30,critical account,radio/television,3017,<100 DM,>=7 years,4,none,4,savings agreement/life insurance,47,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,24,critical account,business,4139,100 <= <500 DM,1<= < 4 years,3,none,3,savings agreement/life insurance,27,none,own,2,unskilled - resident,1,yes,yes,male,divorced/separated,1 +no account,36,existing credits paid back duly till now,business,5742,100 <= <500 DM,4<= <7 years,2,none,2,car or other,31,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,60,existing credits paid back duly till now,car (new),10366,<100 DM,>=7 years,2,none,4,savings agreement/life insurance,42,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +no account,6,critical account,car (new),2080,500 <= < 1000 DM,1<= < 4 years,1,none,2,car or other,24,none,own,1,skilled employee / official,1,none,yes,male,married/widowed,1 +no account,21,delay in paying off,business,2580,500 <= < 1000 DM,<1 years,4,none,2,real estate,41,bank,own,1,unskilled - resident,2,none,yes,male,divorced/separated,0 +no account,30,critical account,radio/television,4530,<100 DM,4<= <7 years,4,none,4,car or other,26,none,rent,1,management/ highly qualified employee,1,yes,yes,female,divorced/separated,1 +no account,24,critical account,furniture/equipment,5150,<100 DM,>=7 years,4,none,4,car or other,33,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,72,existing credits paid back duly till now,radio/television,5595,100 <= <500 DM,1<= < 4 years,2,none,2,car or other,24,none,own,1,skilled employee / official,1,none,yes,male,married/widowed,0 +<0 DM,24,existing credits paid back duly till now,radio/television,2384,<100 DM,>=7 years,4,none,4,real estate,64,bank,rent,1,unskilled - resident,1,none,yes,male,divorced/separated,1 +no account,18,existing credits paid back duly till now,radio/television,1453,<100 DM,<1 years,3,none,1,real estate,26,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,6,existing credits paid back duly till now,education,1538,<100 DM,<1 years,1,none,2,unknown / no property,56,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,12,existing credits paid back duly till now,radio/television,2279,no savings account,1<= < 4 years,4,none,4,unknown / no property,37,none,for free,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,15,delay in paying off,radio/television,1478,<100 DM,1<= < 4 years,4,none,3,real estate,33,bank,own,2,skilled employee / official,1,none,yes,male,married/widowed,1 +no account,24,critical account,radio/television,5103,<100 DM,<1 years,3,none,3,unknown / no property,47,none,for free,3,skilled employee / official,1,yes,yes,male,married/widowed,1 +0 <= <200 DM,36,delay in paying off,business,9857,100 <= <500 DM,4<= <7 years,1,none,3,savings agreement/life insurance,31,none,own,2,unskilled - resident,2,yes,yes,male,divorced/separated,1 +no account,60,existing credits paid back duly till now,car (new),6527,no savings account,1<= < 4 years,4,none,4,unknown / no property,34,none,for free,1,skilled employee / official,2,yes,yes,male,divorced/separated,1 +>= 200 DM ,10,critical account,radio/television,1347,no savings account,4<= <7 years,4,none,2,savings agreement/life insurance,27,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,36,delay in paying off,car (new),2862,100 <= <500 DM,>=7 years,4,none,3,unknown / no property,30,none,for free,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,9,existing credits paid back duly till now,radio/television,2753,100 <= <500 DM,>=7 years,3,co-applicant,4,car or other,35,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,car (new),3651,>= 1000 DM,1<= < 4 years,1,none,3,savings agreement/life insurance,31,none,own,1,skilled employee / official,2,none,yes,male,divorced/separated,1 +<0 DM,15,critical account,furniture/equipment,975,<100 DM,1<= < 4 years,2,none,3,savings agreement/life insurance,25,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,15,existing credits paid back duly till now,repairs,2631,100 <= <500 DM,1<= < 4 years,3,none,2,real estate,25,none,own,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,24,existing credits paid back duly till now,radio/television,2896,100 <= <500 DM,<1 years,2,none,1,car or other,29,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,6,critical account,car (new),4716,no savings account,<1 years,1,none,3,real estate,44,none,own,2,unskilled - resident,2,none,yes,male,divorced/separated,1 +no account,24,existing credits paid back duly till now,radio/television,2284,<100 DM,4<= <7 years,4,none,2,car or other,28,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,6,existing credits paid back duly till now,car (used),1236,500 <= < 1000 DM,1<= < 4 years,2,none,4,savings agreement/life insurance,50,none,rent,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,radio/television,1103,<100 DM,4<= <7 years,4,guarantor,3,real estate,29,none,own,2,skilled employee / official,1,none,no,male,divorced/separated,1 +no account,12,critical account,car (new),926,<100 DM,unemployed,1,none,2,savings agreement/life insurance,38,none,own,1,unemployed/ unskilled - non-resident,1,none,yes,female,divorced/separated,1 +no account,18,critical account,radio/television,1800,<100 DM,1<= < 4 years,4,none,2,car or other,24,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +>= 200 DM ,15,existing credits paid back duly till now,education,1905,<100 DM,>=7 years,4,none,4,car or other,40,none,rent,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +no account,12,existing credits paid back duly till now,furniture/equipment,1123,500 <= < 1000 DM,1<= < 4 years,4,none,4,car or other,29,none,rent,1,unskilled - resident,1,none,yes,female,divorced/separated,0 +<0 DM,48,critical account,car (used),6331,<100 DM,>=7 years,4,none,4,unknown / no property,46,none,for free,2,skilled employee / official,1,yes,yes,male,divorced/separated,0 +>= 200 DM ,24,existing credits paid back duly till now,radio/television,1377,100 <= <500 DM,>=7 years,4,none,2,unknown / no property,47,none,for free,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +0 <= <200 DM,30,delay in paying off,business,2503,100 <= <500 DM,>=7 years,4,none,2,savings agreement/life insurance,41,store,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,27,existing credits paid back duly till now,business,2528,<100 DM,<1 years,4,none,1,savings agreement/life insurance,32,none,own,1,skilled employee / official,2,yes,yes,female,divorced/separated,1 +no account,15,existing credits paid back duly till now,car (new),5324,500 <= < 1000 DM,>=7 years,1,none,4,unknown / no property,35,none,for free,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,48,existing credits paid back duly till now,car (new),6560,100 <= <500 DM,4<= <7 years,3,none,2,savings agreement/life insurance,24,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +0 <= <200 DM,12,no credits taken,furniture/equipment,2969,<100 DM,<1 years,4,none,3,savings agreement/life insurance,25,none,rent,2,skilled employee / official,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,9,existing credits paid back duly till now,radio/television,1206,<100 DM,>=7 years,4,none,4,real estate,25,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,9,existing credits paid back duly till now,radio/television,2118,<100 DM,1<= < 4 years,2,none,2,real estate,37,none,own,1,unskilled - resident,2,none,yes,male,divorced/separated,1 +no account,18,critical account,radio/television,629,500 <= < 1000 DM,>=7 years,4,none,3,savings agreement/life insurance,32,bank,own,2,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +<0 DM,6,all credits at this bank paid back duly,education,1198,<100 DM,>=7 years,4,none,4,unknown / no property,35,none,for free,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +no account,21,existing credits paid back duly till now,car (used),2476,no savings account,>=7 years,4,none,4,real estate,46,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +<0 DM,9,critical account,radio/television,1138,<100 DM,1<= < 4 years,4,none,4,real estate,25,none,own,2,unskilled - resident,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,60,existing credits paid back duly till now,car (new),14027,<100 DM,4<= <7 years,4,none,2,unknown / no property,27,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +no account,30,critical account,car (used),7596,no savings account,>=7 years,1,none,4,car or other,63,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,30,critical account,radio/television,3077,no savings account,>=7 years,3,none,2,car or other,40,none,own,2,skilled employee / official,2,yes,yes,male,divorced/separated,1 +no account,18,existing credits paid back duly till now,radio/television,1505,<100 DM,1<= < 4 years,4,none,2,unknown / no property,32,none,for free,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +>= 200 DM ,24,critical account,radio/television,3148,no savings account,1<= < 4 years,3,none,2,car or other,31,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,20,no credits taken,car (used),6148,100 <= <500 DM,>=7 years,3,none,4,car or other,31,bank,own,2,skilled employee / official,1,yes,yes,male,married/widowed,1 +>= 200 DM ,9,no credits taken,radio/television,1337,<100 DM,<1 years,4,none,2,car or other,34,none,own,2,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +0 <= <200 DM,6,all credits at this bank paid back duly,education,433,>= 1000 DM,<1 years,4,none,2,savings agreement/life insurance,24,bank,rent,1,skilled employee / official,2,none,yes,female,divorced/separated,0 +<0 DM,12,existing credits paid back duly till now,car (new),1228,<100 DM,1<= < 4 years,4,none,2,real estate,24,none,own,1,unskilled - resident,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,9,existing credits paid back duly till now,radio/television,790,500 <= < 1000 DM,1<= < 4 years,4,none,3,real estate,66,none,own,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +no account,27,existing credits paid back duly till now,car (new),2570,<100 DM,1<= < 4 years,3,none,3,real estate,21,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +no account,6,critical account,car (new),250,>= 1000 DM,1<= < 4 years,2,none,2,real estate,41,bank,own,2,unskilled - resident,1,none,yes,female,divorced/separated,1 +no account,15,critical account,radio/television,1316,500 <= < 1000 DM,1<= < 4 years,2,none,2,savings agreement/life insurance,47,none,own,2,unskilled - resident,1,none,yes,male,married/widowed,1 +<0 DM,18,existing credits paid back duly till now,radio/television,1882,<100 DM,1<= < 4 years,4,none,4,car or other,25,bank,rent,2,skilled employee / official,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,48,all credits at this bank paid back duly,business,6416,<100 DM,>=7 years,4,none,3,unknown / no property,59,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +>= 200 DM ,24,critical account,business,1275,>= 1000 DM,1<= < 4 years,2,none,4,real estate,36,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,24,delay in paying off,radio/television,6403,<100 DM,<1 years,1,none,2,car or other,33,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,24,existing credits paid back duly till now,radio/television,1987,<100 DM,1<= < 4 years,2,none,4,real estate,21,none,rent,1,unskilled - resident,2,none,yes,male,divorced/separated,0 +0 <= <200 DM,8,existing credits paid back duly till now,radio/television,760,<100 DM,4<= <7 years,4,guarantor,2,real estate,44,none,own,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +no account,24,existing credits paid back duly till now,car (used),2603,>= 1000 DM,1<= < 4 years,2,none,4,car or other,28,none,rent,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +no account,4,critical account,car (new),3380,<100 DM,4<= <7 years,1,none,1,real estate,37,none,own,1,skilled employee / official,2,none,yes,female,divorced/separated,1 +0 <= <200 DM,36,all credits at this bank paid back duly,domestic appliances,3990,no savings account,<1 years,3,none,2,unknown / no property,29,bank,own,1,unemployed/ unskilled - non-resident,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,24,existing credits paid back duly till now,car (used),11560,<100 DM,1<= < 4 years,1,none,4,car or other,23,none,rent,2,management/ highly qualified employee,1,none,yes,female,divorced/separated,0 +<0 DM,18,existing credits paid back duly till now,car (new),4380,100 <= <500 DM,1<= < 4 years,3,none,4,car or other,35,none,own,1,unskilled - resident,2,yes,yes,male,divorced/separated,1 +no account,6,critical account,car (new),6761,<100 DM,4<= <7 years,1,none,3,unknown / no property,45,none,own,2,management/ highly qualified employee,2,yes,yes,male,divorced/separated,1 +0 <= <200 DM,30,no credits taken,business,4280,100 <= <500 DM,1<= < 4 years,4,none,4,car or other,26,none,rent,2,unskilled - resident,1,none,yes,female,divorced/separated,0 +<0 DM,24,all credits at this bank paid back duly,car (new),2325,100 <= <500 DM,4<= <7 years,2,none,3,car or other,32,bank,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,10,all credits at this bank paid back duly,radio/television,1048,<100 DM,1<= < 4 years,4,none,4,real estate,23,store,own,1,unskilled - resident,1,none,yes,male,divorced/separated,1 +no account,21,existing credits paid back duly till now,radio/television,3160,no savings account,>=7 years,4,none,3,savings agreement/life insurance,41,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,24,all credits at this bank paid back duly,furniture/equipment,2483,500 <= < 1000 DM,1<= < 4 years,4,none,4,real estate,22,store,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,39,critical account,furniture/equipment,14179,no savings account,4<= <7 years,4,none,4,savings agreement/life insurance,30,none,own,2,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +<0 DM,13,critical account,business,1797,<100 DM,<1 years,3,none,1,savings agreement/life insurance,28,bank,own,2,unskilled - resident,1,none,yes,male,divorced/separated,1 +<0 DM,15,existing credits paid back duly till now,car (new),2511,<100 DM,unemployed,1,none,4,car or other,23,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,car (new),1274,<100 DM,<1 years,3,none,1,real estate,37,none,own,1,unskilled - resident,1,none,yes,female,divorced/separated,0 +no account,21,existing credits paid back duly till now,car (used),5248,no savings account,1<= < 4 years,1,none,3,car or other,26,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,15,existing credits paid back duly till now,car (used),3029,<100 DM,4<= <7 years,2,none,2,car or other,33,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,6,existing credits paid back duly till now,furniture/equipment,428,<100 DM,>=7 years,2,none,1,savings agreement/life insurance,49,bank,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +<0 DM,18,existing credits paid back duly till now,car (new),976,<100 DM,<1 years,1,none,2,car or other,23,none,own,1,unskilled - resident,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,12,existing credits paid back duly till now,business,841,100 <= <500 DM,4<= <7 years,2,none,4,real estate,23,none,rent,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +no account,30,critical account,radio/television,5771,<100 DM,4<= <7 years,4,none,2,car or other,25,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,12,delay in paying off,repairs,1555,>= 1000 DM,>=7 years,4,none,4,unknown / no property,55,none,for free,2,skilled employee / official,2,none,yes,male,divorced/separated,0 +<0 DM,24,existing credits paid back duly till now,car (new),1285,no savings account,4<= <7 years,4,none,4,unknown / no property,32,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +>= 200 DM ,6,critical account,car (new),1299,<100 DM,1<= < 4 years,1,none,1,real estate,74,none,own,3,unemployed/ unskilled - non-resident,2,none,no,male,divorced/separated,1 +>= 200 DM ,15,critical account,radio/television,1271,no savings account,1<= < 4 years,3,none,4,unknown / no property,39,none,for free,2,skilled employee / official,1,yes,yes,male,divorced/separated,0 +no account,24,existing credits paid back duly till now,car (new),1393,<100 DM,1<= < 4 years,2,guarantor,2,real estate,31,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,12,critical account,car (new),691,<100 DM,>=7 years,4,none,3,savings agreement/life insurance,35,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,15,critical account,car (new),5045,no savings account,>=7 years,1,none,4,car or other,59,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +<0 DM,18,critical account,furniture/equipment,2124,<100 DM,1<= < 4 years,4,none,4,real estate,24,none,rent,2,skilled employee / official,1,none,yes,female,divorced/separated,0 +<0 DM,12,existing credits paid back duly till now,radio/television,2214,<100 DM,1<= < 4 years,4,none,3,savings agreement/life insurance,24,none,own,1,unskilled - resident,1,none,yes,male,divorced/separated,1 +no account,21,critical account,car (new),12680,no savings account,>=7 years,4,none,4,unknown / no property,30,none,for free,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +no account,24,critical account,car (new),2463,100 <= <500 DM,4<= <7 years,4,none,3,savings agreement/life insurance,27,none,own,2,skilled employee / official,1,yes,yes,male,married/widowed,1 +0 <= <200 DM,12,existing credits paid back duly till now,radio/television,1155,<100 DM,>=7 years,3,guarantor,3,real estate,40,bank,own,2,unskilled - resident,1,none,yes,male,married/widowed,1 +<0 DM,30,existing credits paid back duly till now,furniture/equipment,3108,<100 DM,<1 years,2,none,4,savings agreement/life insurance,31,none,own,1,unskilled - resident,1,none,yes,male,divorced/separated,0 +no account,10,existing credits paid back duly till now,car (used),2901,no savings account,<1 years,1,none,4,real estate,31,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,12,critical account,furniture/equipment,3617,<100 DM,>=7 years,1,none,4,car or other,28,none,rent,3,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,12,critical account,radio/television,1655,<100 DM,>=7 years,2,none,4,real estate,63,none,own,2,unskilled - resident,1,yes,yes,male,divorced/separated,1 +<0 DM,24,existing credits paid back duly till now,car (used),2812,no savings account,>=7 years,2,none,4,real estate,26,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,36,critical account,education,8065,<100 DM,1<= < 4 years,3,none,2,unknown / no property,25,none,own,2,management/ highly qualified employee,1,yes,yes,female,divorced/separated,0 +no account,21,critical account,car (used),3275,<100 DM,>=7 years,1,none,4,car or other,36,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +no account,24,critical account,radio/television,2223,100 <= <500 DM,>=7 years,4,none,4,savings agreement/life insurance,52,bank,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +>= 200 DM ,12,critical account,car (new),1480,500 <= < 1000 DM,unemployed,2,none,4,unknown / no property,66,bank,for free,3,unemployed/ unskilled - non-resident,1,none,yes,male,divorced/separated,1 +<0 DM,24,existing credits paid back duly till now,car (new),1371,no savings account,1<= < 4 years,4,none,4,real estate,25,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +no account,36,critical account,car (new),3535,<100 DM,4<= <7 years,4,none,4,car or other,37,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,18,existing credits paid back duly till now,radio/television,3509,<100 DM,4<= <7 years,4,guarantor,1,real estate,25,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,36,critical account,car (used),5711,>= 1000 DM,>=7 years,4,none,2,car or other,38,none,own,2,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,18,existing credits paid back duly till now,repairs,3872,<100 DM,unemployed,2,none,4,car or other,67,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +0 <= <200 DM,39,critical account,radio/television,4933,<100 DM,4<= <7 years,2,guarantor,2,real estate,25,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,24,critical account,car (new),1940,>= 1000 DM,>=7 years,4,none,4,real estate,60,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,12,no credits taken,retraining,1410,<100 DM,1<= < 4 years,2,none,2,real estate,31,none,own,1,unskilled - resident,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,car (new),836,100 <= <500 DM,<1 years,4,none,2,savings agreement/life insurance,23,bank,own,1,unskilled - resident,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,20,existing credits paid back duly till now,car (used),6468,no savings account,unemployed,1,none,4,real estate,60,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,18,existing credits paid back duly till now,business,1941,>= 1000 DM,1<= < 4 years,4,none,2,savings agreement/life insurance,35,none,own,1,unskilled - resident,1,yes,yes,male,divorced/separated,1 +no account,22,existing credits paid back duly till now,radio/television,2675,500 <= < 1000 DM,>=7 years,3,none,4,car or other,40,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,48,critical account,car (used),2751,no savings account,>=7 years,4,none,3,car or other,38,none,own,2,skilled employee / official,2,yes,yes,male,divorced/separated,1 +0 <= <200 DM,48,delay in paying off,education,6224,<100 DM,>=7 years,4,none,4,unknown / no property,50,none,for free,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +<0 DM,40,critical account,education,5998,<100 DM,1<= < 4 years,4,none,3,unknown / no property,27,bank,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,0 +0 <= <200 DM,21,existing credits paid back duly till now,business,1188,<100 DM,>=7 years,2,none,4,savings agreement/life insurance,39,none,own,1,skilled employee / official,2,none,yes,female,divorced/separated,0 +no account,24,existing credits paid back duly till now,car (used),6313,no savings account,>=7 years,3,none,4,car or other,41,none,own,1,management/ highly qualified employee,2,yes,yes,male,divorced/separated,1 +no account,6,critical account,furniture/equipment,1221,no savings account,1<= < 4 years,1,none,2,savings agreement/life insurance,27,none,own,2,skilled employee / official,1,none,yes,male,married/widowed,1 +>= 200 DM ,24,existing credits paid back duly till now,furniture/equipment,2892,<100 DM,>=7 years,3,none,4,unknown / no property,51,none,for free,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,24,existing credits paid back duly till now,furniture/equipment,3062,500 <= < 1000 DM,>=7 years,4,none,3,unknown / no property,32,none,rent,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,9,existing credits paid back duly till now,furniture/equipment,2301,100 <= <500 DM,<1 years,2,none,4,savings agreement/life insurance,22,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,18,existing credits paid back duly till now,car (used),7511,no savings account,>=7 years,1,none,4,savings agreement/life insurance,51,none,for free,1,skilled employee / official,2,yes,yes,male,divorced/separated,0 +no account,12,critical account,furniture/equipment,1258,<100 DM,<1 years,2,none,4,savings agreement/life insurance,22,none,rent,2,unskilled - resident,1,none,yes,female,divorced/separated,1 +no account,24,delay in paying off,car (new),717,no savings account,>=7 years,4,none,4,car or other,54,none,own,2,skilled employee / official,1,yes,yes,male,married/widowed,1 +0 <= <200 DM,9,existing credits paid back duly till now,car (new),1549,no savings account,<1 years,4,none,2,real estate,35,none,own,1,unemployed/ unskilled - non-resident,1,none,yes,male,divorced/separated,1 +no account,24,critical account,education,1597,<100 DM,>=7 years,4,none,4,unknown / no property,54,none,for free,2,skilled employee / official,2,none,yes,male,divorced/separated,1 +0 <= <200 DM,18,critical account,radio/television,1795,<100 DM,>=7 years,3,guarantor,4,real estate,48,bank,rent,2,unskilled - resident,1,yes,yes,female,divorced/separated,1 +<0 DM,20,critical account,furniture/equipment,4272,<100 DM,>=7 years,1,none,4,savings agreement/life insurance,24,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,12,critical account,radio/television,976,no savings account,>=7 years,4,none,4,car or other,35,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,12,existing credits paid back duly till now,car (new),7472,no savings account,unemployed,1,none,2,real estate,24,none,rent,1,unemployed/ unskilled - non-resident,1,none,yes,female,divorced/separated,1 +<0 DM,36,existing credits paid back duly till now,car (new),9271,<100 DM,4<= <7 years,2,none,1,car or other,24,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,0 +0 <= <200 DM,6,existing credits paid back duly till now,radio/television,590,<100 DM,<1 years,3,none,3,real estate,26,none,own,1,unskilled - resident,1,none,no,male,married/widowed,1 +no account,12,critical account,radio/television,930,no savings account,>=7 years,4,none,4,real estate,65,none,own,4,skilled employee / official,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,42,all credits at this bank paid back duly,car (used),9283,<100 DM,unemployed,1,none,2,unknown / no property,55,bank,for free,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,15,no credits taken,car (new),1778,<100 DM,<1 years,2,none,1,real estate,26,none,rent,2,unemployed/ unskilled - non-resident,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,8,existing credits paid back duly till now,business,907,<100 DM,<1 years,3,none,2,real estate,26,none,own,1,skilled employee / official,1,yes,yes,male,married/widowed,1 +0 <= <200 DM,6,existing credits paid back duly till now,radio/television,484,<100 DM,4<= <7 years,3,guarantor,3,real estate,28,bank,own,1,unskilled - resident,1,none,yes,male,married/widowed,1 +<0 DM,36,critical account,car (used),9629,<100 DM,4<= <7 years,4,none,4,car or other,24,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,0 +<0 DM,48,existing credits paid back duly till now,domestic appliances,3051,<100 DM,1<= < 4 years,3,none,4,car or other,54,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +<0 DM,48,existing credits paid back duly till now,car (new),3931,<100 DM,4<= <7 years,4,none,4,unknown / no property,46,none,for free,1,skilled employee / official,2,none,yes,male,divorced/separated,0 +0 <= <200 DM,36,delay in paying off,car (new),7432,<100 DM,1<= < 4 years,2,none,2,savings agreement/life insurance,54,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,6,existing credits paid back duly till now,domestic appliances,1338,500 <= < 1000 DM,1<= < 4 years,1,none,4,real estate,62,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,6,critical account,radio/television,1554,<100 DM,4<= <7 years,1,none,2,car or other,24,none,rent,2,skilled employee / official,1,yes,yes,female,divorced/separated,1 +<0 DM,36,existing credits paid back duly till now,others,15857,<100 DM,unemployed,2,co-applicant,3,car or other,43,none,own,1,management/ highly qualified employee,1,none,yes,male,divorced/separated,1 +<0 DM,18,existing credits paid back duly till now,radio/television,1345,<100 DM,1<= < 4 years,4,none,3,real estate,26,bank,own,1,skilled employee / official,1,none,yes,male,married/widowed,0 +no account,12,existing credits paid back duly till now,car (new),1101,<100 DM,1<= < 4 years,3,none,2,real estate,27,none,own,2,skilled employee / official,1,yes,yes,male,married/widowed,1 +>= 200 DM ,12,existing credits paid back duly till now,radio/television,3016,<100 DM,1<= < 4 years,3,none,1,car or other,24,none,own,1,skilled employee / official,1,none,yes,male,married/widowed,1 +<0 DM,36,existing credits paid back duly till now,furniture/equipment,2712,<100 DM,>=7 years,2,none,2,savings agreement/life insurance,41,bank,own,1,skilled employee / official,2,none,yes,male,divorced/separated,0 +<0 DM,8,critical account,car (new),731,<100 DM,>=7 years,4,none,4,real estate,47,none,own,2,unskilled - resident,1,none,yes,male,divorced/separated,1 +no account,18,critical account,furniture/equipment,3780,<100 DM,<1 years,3,none,2,car or other,35,none,own,2,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +<0 DM,21,critical account,car (new),1602,<100 DM,>=7 years,4,none,3,car or other,30,none,own,2,skilled employee / official,1,yes,yes,male,married/widowed,1 +<0 DM,18,critical account,car (new),3966,<100 DM,>=7 years,1,none,4,real estate,33,bank,rent,3,skilled employee / official,1,yes,yes,female,divorced/separated,0 +no account,18,no credits taken,business,4165,<100 DM,1<= < 4 years,2,none,2,car or other,36,store,own,2,skilled employee / official,2,none,yes,male,divorced/separated,0 +<0 DM,36,existing credits paid back duly till now,car (used),8335,no savings account,>=7 years,3,none,4,unknown / no property,47,none,for free,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +0 <= <200 DM,48,delay in paying off,business,6681,no savings account,1<= < 4 years,4,none,4,unknown / no property,38,none,for free,1,skilled employee / official,2,yes,yes,male,divorced/separated,1 +no account,24,delay in paying off,business,2375,500 <= < 1000 DM,1<= < 4 years,4,none,2,car or other,44,none,own,2,skilled employee / official,2,yes,yes,male,divorced/separated,1 +<0 DM,18,existing credits paid back duly till now,car (new),1216,<100 DM,<1 years,4,none,3,car or other,23,none,rent,1,skilled employee / official,1,yes,yes,female,divorced/separated,0 +<0 DM,45,no credits taken,business,11816,<100 DM,>=7 years,2,none,4,car or other,29,none,rent,2,skilled employee / official,1,none,yes,male,divorced/separated,0 +0 <= <200 DM,24,existing credits paid back duly till now,radio/television,5084,no savings account,>=7 years,2,none,4,car or other,42,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +>= 200 DM ,15,existing credits paid back duly till now,radio/television,2327,<100 DM,<1 years,2,none,3,real estate,25,none,own,1,unskilled - resident,1,none,yes,female,divorced/separated,0 +<0 DM,12,no credits taken,car (new),1082,<100 DM,1<= < 4 years,4,none,4,car or other,48,bank,own,2,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,12,existing credits paid back duly till now,radio/television,886,no savings account,1<= < 4 years,4,none,2,car or other,21,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,4,existing credits paid back duly till now,furniture/equipment,601,<100 DM,<1 years,1,none,3,real estate,23,none,rent,1,unskilled - resident,2,none,yes,female,divorced/separated,1 +<0 DM,24,critical account,car (used),2957,<100 DM,>=7 years,4,none,4,savings agreement/life insurance,63,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,24,critical account,radio/television,2611,<100 DM,>=7 years,4,co-applicant,3,real estate,46,none,own,2,skilled employee / official,1,none,yes,male,married/widowed,1 +<0 DM,36,existing credits paid back duly till now,furniture/equipment,5179,<100 DM,4<= <7 years,4,none,2,savings agreement/life insurance,29,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,21,delay in paying off,car (used),2993,<100 DM,1<= < 4 years,3,none,2,real estate,28,store,own,2,unskilled - resident,1,none,yes,male,divorced/separated,1 +no account,18,existing credits paid back duly till now,repairs,1943,<100 DM,<1 years,4,none,4,real estate,23,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +no account,24,all credits at this bank paid back duly,business,1559,<100 DM,4<= <7 years,4,none,4,car or other,50,bank,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,18,existing credits paid back duly till now,furniture/equipment,3422,<100 DM,>=7 years,4,none,4,savings agreement/life insurance,47,bank,own,3,skilled employee / official,2,yes,yes,male,divorced/separated,1 +0 <= <200 DM,21,existing credits paid back duly till now,furniture/equipment,3976,no savings account,4<= <7 years,2,none,3,car or other,35,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,18,existing credits paid back duly till now,car (new),6761,no savings account,1<= < 4 years,2,none,4,car or other,68,none,rent,2,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,24,existing credits paid back duly till now,car (new),1249,<100 DM,<1 years,4,none,2,real estate,28,none,own,1,skilled employee / official,1,none,yes,male,married/widowed,1 +<0 DM,9,existing credits paid back duly till now,radio/television,1364,<100 DM,4<= <7 years,3,none,4,real estate,59,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,radio/television,709,<100 DM,>=7 years,4,none,4,real estate,57,store,own,1,unskilled - resident,1,none,yes,male,divorced/separated,0 +<0 DM,20,critical account,car (new),2235,<100 DM,1<= < 4 years,4,guarantor,2,savings agreement/life insurance,33,bank,rent,2,skilled employee / official,1,none,no,male,married/widowed,0 +no account,24,critical account,car (used),4042,no savings account,4<= <7 years,3,none,4,savings agreement/life insurance,43,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,15,critical account,radio/television,1471,<100 DM,1<= < 4 years,4,none,4,unknown / no property,35,none,for free,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,18,all credits at this bank paid back duly,car (new),1442,<100 DM,4<= <7 years,4,none,4,unknown / no property,32,none,for free,2,unskilled - resident,2,none,yes,male,divorced/separated,0 +no account,36,delay in paying off,car (new),10875,<100 DM,>=7 years,2,none,2,car or other,45,none,own,2,skilled employee / official,2,yes,yes,male,divorced/separated,1 +no account,24,existing credits paid back duly till now,car (new),1474,100 <= <500 DM,<1 years,4,none,3,real estate,33,none,own,1,skilled employee / official,1,yes,yes,male,married/widowed,1 +no account,10,existing credits paid back duly till now,retraining,894,no savings account,4<= <7 years,4,none,3,savings agreement/life insurance,40,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +no account,15,critical account,furniture/equipment,3343,<100 DM,1<= < 4 years,4,none,2,unknown / no property,28,none,for free,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,15,existing credits paid back duly till now,car (new),3959,<100 DM,1<= < 4 years,3,none,2,savings agreement/life insurance,29,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,0 +no account,9,existing credits paid back duly till now,car (new),3577,100 <= <500 DM,1<= < 4 years,1,guarantor,2,real estate,26,none,rent,1,skilled employee / official,2,none,no,male,divorced/separated,1 +no account,24,critical account,car (used),5804,>= 1000 DM,1<= < 4 years,4,none,2,real estate,27,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,18,delay in paying off,business,2169,<100 DM,1<= < 4 years,4,none,2,car or other,28,none,own,1,skilled employee / official,1,yes,yes,male,married/widowed,0 +<0 DM,24,existing credits paid back duly till now,radio/television,2439,<100 DM,<1 years,4,none,4,real estate,35,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,0 +no account,27,critical account,furniture/equipment,4526,>= 1000 DM,<1 years,4,none,2,real estate,32,store,own,2,unskilled - resident,2,yes,yes,male,divorced/separated,1 +no account,10,existing credits paid back duly till now,furniture/equipment,2210,<100 DM,1<= < 4 years,2,none,2,real estate,25,bank,rent,1,unskilled - resident,1,none,yes,male,divorced/separated,0 +no account,15,existing credits paid back duly till now,furniture/equipment,2221,500 <= < 1000 DM,1<= < 4 years,2,none,4,car or other,20,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,18,existing credits paid back duly till now,radio/television,2389,<100 DM,<1 years,4,none,1,car or other,27,store,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,12,critical account,furniture/equipment,3331,<100 DM,>=7 years,2,none,4,savings agreement/life insurance,42,store,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,36,existing credits paid back duly till now,business,7409,no savings account,>=7 years,3,none,2,savings agreement/life insurance,37,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,furniture/equipment,652,<100 DM,>=7 years,4,none,4,savings agreement/life insurance,24,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,36,delay in paying off,furniture/equipment,7678,500 <= < 1000 DM,4<= <7 years,2,none,4,car or other,40,none,own,2,skilled employee / official,1,yes,yes,female,divorced/separated,1 +>= 200 DM ,6,critical account,car (new),1343,<100 DM,>=7 years,1,none,4,real estate,46,none,own,2,skilled employee / official,2,none,no,male,divorced/separated,1 +<0 DM,24,critical account,business,1382,100 <= <500 DM,4<= <7 years,4,none,1,real estate,26,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,15,existing credits paid back duly till now,domestic appliances,874,no savings account,<1 years,4,none,1,real estate,24,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,furniture/equipment,3590,<100 DM,1<= < 4 years,2,co-applicant,2,savings agreement/life insurance,29,none,own,1,unskilled - resident,2,none,yes,male,divorced/separated,1 +0 <= <200 DM,11,critical account,car (new),1322,>= 1000 DM,1<= < 4 years,4,none,4,car or other,40,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,18,all credits at this bank paid back duly,radio/television,1940,<100 DM,<1 years,3,co-applicant,4,unknown / no property,36,bank,for free,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +no account,36,existing credits paid back duly till now,radio/television,3595,<100 DM,>=7 years,4,none,2,car or other,28,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,9,existing credits paid back duly till now,car (new),1422,<100 DM,<1 years,3,none,2,unknown / no property,27,none,for free,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +no account,30,critical account,radio/television,6742,no savings account,4<= <7 years,2,none,3,savings agreement/life insurance,36,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,24,existing credits paid back duly till now,car (used),7814,<100 DM,4<= <7 years,3,none,3,car or other,38,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +no account,24,existing credits paid back duly till now,car (used),9277,no savings account,1<= < 4 years,2,none,4,unknown / no property,48,none,for free,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,30,critical account,car (new),2181,no savings account,>=7 years,4,none,4,real estate,36,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,18,critical account,radio/television,1098,<100 DM,unemployed,4,none,4,car or other,65,none,own,2,unemployed/ unskilled - non-resident,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,24,existing credits paid back duly till now,furniture/equipment,4057,<100 DM,4<= <7 years,3,none,3,car or other,43,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,0 +<0 DM,12,existing credits paid back duly till now,education,795,<100 DM,<1 years,4,none,4,savings agreement/life insurance,53,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,24,critical account,business,2825,no savings account,4<= <7 years,4,none,3,unknown / no property,34,none,own,2,skilled employee / official,2,yes,yes,male,divorced/separated,1 +0 <= <200 DM,48,existing credits paid back duly till now,business,15672,<100 DM,1<= < 4 years,2,none,2,car or other,23,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,0 +no account,36,critical account,car (new),6614,<100 DM,>=7 years,4,none,4,car or other,34,none,own,2,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +no account,28,all credits at this bank paid back duly,car (used),7824,no savings account,<1 years,3,guarantor,4,real estate,40,bank,rent,2,skilled employee / official,2,yes,yes,male,divorced/separated,1 +<0 DM,27,critical account,business,2442,<100 DM,>=7 years,4,none,4,car or other,43,store,own,4,management/ highly qualified employee,2,yes,yes,male,divorced/separated,1 +no account,15,critical account,radio/television,1829,<100 DM,>=7 years,4,none,4,car or other,46,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,12,critical account,car (new),2171,<100 DM,1<= < 4 years,4,none,4,savings agreement/life insurance,38,bank,own,2,unskilled - resident,1,none,no,male,divorced/separated,1 +0 <= <200 DM,36,critical account,car (used),5800,<100 DM,1<= < 4 years,3,none,4,car or other,34,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,18,critical account,radio/television,1169,no savings account,1<= < 4 years,4,none,3,savings agreement/life insurance,29,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,36,delay in paying off,car (used),8947,no savings account,4<= <7 years,3,none,2,car or other,31,store,own,1,management/ highly qualified employee,2,yes,yes,male,divorced/separated,1 +<0 DM,21,existing credits paid back duly till now,radio/television,2606,<100 DM,<1 years,4,none,4,savings agreement/life insurance,28,none,rent,1,management/ highly qualified employee,1,yes,yes,female,divorced/separated,1 +no account,12,critical account,furniture/equipment,1592,>= 1000 DM,4<= <7 years,3,none,2,savings agreement/life insurance,35,none,own,1,skilled employee / official,1,none,no,female,divorced/separated,1 +no account,15,existing credits paid back duly till now,furniture/equipment,2186,no savings account,4<= <7 years,1,none,4,real estate,33,bank,rent,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +<0 DM,18,existing credits paid back duly till now,furniture/equipment,4153,<100 DM,1<= < 4 years,2,co-applicant,3,car or other,42,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +<0 DM,16,critical account,car (new),2625,<100 DM,>=7 years,2,guarantor,4,savings agreement/life insurance,43,bank,rent,1,skilled employee / official,1,yes,yes,male,divorced/separated,0 +no account,20,critical account,car (new),3485,no savings account,<1 years,2,none,4,real estate,44,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,36,critical account,car (used),10477,no savings account,>=7 years,2,none,4,unknown / no property,42,none,for free,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,15,existing credits paid back duly till now,radio/television,1386,no savings account,1<= < 4 years,4,none,2,real estate,40,none,rent,1,skilled employee / official,1,yes,yes,male,married/widowed,1 +no account,24,existing credits paid back duly till now,radio/television,1278,<100 DM,>=7 years,4,none,1,real estate,36,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,radio/television,1107,<100 DM,1<= < 4 years,2,none,2,real estate,20,none,rent,1,management/ highly qualified employee,2,yes,yes,male,divorced/separated,1 +<0 DM,21,existing credits paid back duly till now,car (new),3763,no savings account,4<= <7 years,2,co-applicant,2,real estate,24,none,own,1,unskilled - resident,1,none,no,male,divorced/separated,1 +0 <= <200 DM,36,existing credits paid back duly till now,education,3711,no savings account,1<= < 4 years,2,none,2,car or other,27,none,own,1,skilled employee / official,1,none,yes,male,married/widowed,1 +no account,15,delay in paying off,car (used),3594,<100 DM,<1 years,1,none,2,savings agreement/life insurance,46,none,own,2,unskilled - resident,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,9,existing credits paid back duly till now,car (new),3195,no savings account,1<= < 4 years,1,none,2,real estate,33,none,own,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +no account,36,delay in paying off,radio/television,4454,<100 DM,1<= < 4 years,4,none,4,real estate,34,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,24,critical account,furniture/equipment,4736,<100 DM,<1 years,2,none,4,car or other,25,bank,own,1,unskilled - resident,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,30,existing credits paid back duly till now,radio/television,2991,no savings account,>=7 years,2,none,4,car or other,25,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,11,existing credits paid back duly till now,business,2142,>= 1000 DM,>=7 years,1,none,2,real estate,28,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,24,all credits at this bank paid back duly,business,3161,<100 DM,1<= < 4 years,4,none,2,savings agreement/life insurance,31,none,rent,1,skilled employee / official,1,yes,yes,male,divorced/separated,0 +0 <= <200 DM,48,no credits taken,others,18424,<100 DM,1<= < 4 years,1,none,2,savings agreement/life insurance,32,bank,own,1,management/ highly qualified employee,1,yes,no,female,divorced/separated,0 +no account,10,existing credits paid back duly till now,car (used),2848,100 <= <500 DM,1<= < 4 years,1,co-applicant,2,real estate,32,none,own,1,skilled employee / official,2,none,yes,male,divorced/separated,1 +<0 DM,6,existing credits paid back duly till now,car (new),14896,<100 DM,>=7 years,1,none,4,unknown / no property,68,bank,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +<0 DM,24,existing credits paid back duly till now,furniture/equipment,2359,100 <= <500 DM,unemployed,1,none,1,savings agreement/life insurance,33,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +<0 DM,24,existing credits paid back duly till now,furniture/equipment,3345,<100 DM,>=7 years,4,none,2,savings agreement/life insurance,39,none,rent,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +no account,18,critical account,furniture/equipment,1817,<100 DM,1<= < 4 years,4,none,2,unknown / no property,28,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +no account,48,delay in paying off,radio/television,12749,500 <= < 1000 DM,4<= <7 years,4,none,1,car or other,37,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +<0 DM,9,existing credits paid back duly till now,radio/television,1366,<100 DM,<1 years,3,none,4,savings agreement/life insurance,22,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,12,existing credits paid back duly till now,car (new),2002,<100 DM,4<= <7 years,3,none,4,savings agreement/life insurance,30,none,rent,1,skilled employee / official,2,yes,yes,male,divorced/separated,1 +<0 DM,24,all credits at this bank paid back duly,furniture/equipment,6872,<100 DM,<1 years,2,none,1,savings agreement/life insurance,55,bank,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,0 +<0 DM,12,all credits at this bank paid back duly,car (new),697,<100 DM,<1 years,4,none,2,car or other,46,bank,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,0 +<0 DM,18,critical account,furniture/equipment,1049,<100 DM,<1 years,4,none,4,savings agreement/life insurance,21,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,48,existing credits paid back duly till now,car (used),10297,<100 DM,4<= <7 years,4,none,4,unknown / no property,39,store,for free,3,skilled employee / official,2,yes,yes,male,divorced/separated,0 +no account,30,existing credits paid back duly till now,radio/television,1867,no savings account,>=7 years,4,none,4,car or other,58,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +<0 DM,12,delay in paying off,car (new),1344,<100 DM,1<= < 4 years,4,none,2,real estate,43,none,own,2,unskilled - resident,2,none,yes,male,divorced/separated,1 +<0 DM,24,existing credits paid back duly till now,furniture/equipment,1747,<100 DM,<1 years,4,co-applicant,1,savings agreement/life insurance,24,none,own,1,unskilled - resident,1,none,no,male,divorced/separated,1 +0 <= <200 DM,9,existing credits paid back duly till now,radio/television,1670,<100 DM,<1 years,4,none,2,car or other,22,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,0 +no account,9,critical account,car (new),1224,<100 DM,1<= < 4 years,3,none,1,real estate,30,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,12,critical account,radio/television,522,500 <= < 1000 DM,>=7 years,4,none,4,savings agreement/life insurance,42,none,own,2,skilled employee / official,2,yes,yes,male,divorced/separated,1 +<0 DM,12,existing credits paid back duly till now,radio/television,1498,<100 DM,1<= < 4 years,4,none,1,car or other,23,bank,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,30,delay in paying off,radio/television,1919,100 <= <500 DM,<1 years,4,none,3,unknown / no property,30,store,own,2,management/ highly qualified employee,1,none,yes,male,divorced/separated,0 +>= 200 DM ,9,existing credits paid back duly till now,radio/television,745,<100 DM,1<= < 4 years,3,none,2,real estate,28,none,own,1,unskilled - resident,1,none,yes,female,divorced/separated,0 +0 <= <200 DM,6,existing credits paid back duly till now,radio/television,2063,<100 DM,<1 years,4,none,3,car or other,30,none,rent,1,management/ highly qualified employee,1,yes,yes,male,married/widowed,1 +0 <= <200 DM,60,existing credits paid back duly till now,education,6288,<100 DM,1<= < 4 years,4,none,4,unknown / no property,42,none,for free,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,24,critical account,car (used),6842,no savings account,1<= < 4 years,2,none,4,savings agreement/life insurance,46,none,own,2,management/ highly qualified employee,2,yes,yes,male,divorced/separated,1 +no account,12,existing credits paid back duly till now,car (new),3527,no savings account,<1 years,2,none,3,savings agreement/life insurance,45,none,own,1,management/ highly qualified employee,2,yes,yes,male,divorced/separated,1 +no account,10,existing credits paid back duly till now,car (new),1546,<100 DM,1<= < 4 years,3,none,2,real estate,31,none,own,1,unskilled - resident,2,none,no,male,divorced/separated,1 +no account,24,existing credits paid back duly till now,furniture/equipment,929,no savings account,4<= <7 years,4,none,2,car or other,31,store,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,4,critical account,car (new),1455,<100 DM,4<= <7 years,2,none,1,real estate,42,none,own,3,unskilled - resident,2,none,yes,male,divorced/separated,1 +<0 DM,15,existing credits paid back duly till now,furniture/equipment,1845,<100 DM,<1 years,4,guarantor,1,savings agreement/life insurance,46,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,48,no credits taken,car (new),8358,500 <= < 1000 DM,<1 years,1,none,1,car or other,30,none,own,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,24,all credits at this bank paid back duly,furniture/equipment,3349,500 <= < 1000 DM,<1 years,4,none,4,unknown / no property,30,none,for free,1,skilled employee / official,2,yes,yes,male,divorced/separated,0 +no account,12,existing credits paid back duly till now,car (new),2859,no savings account,unemployed,4,none,4,unknown / no property,38,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +no account,18,existing credits paid back duly till now,furniture/equipment,1533,<100 DM,<1 years,4,co-applicant,1,savings agreement/life insurance,43,none,own,1,unskilled - resident,2,none,yes,male,married/widowed,0 +no account,24,existing credits paid back duly till now,radio/television,3621,100 <= <500 DM,>=7 years,2,none,4,car or other,31,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,0 +0 <= <200 DM,18,critical account,business,3590,<100 DM,unemployed,3,none,3,car or other,40,none,own,3,unemployed/ unskilled - non-resident,2,yes,yes,male,married/widowed,1 +<0 DM,36,delay in paying off,business,2145,<100 DM,4<= <7 years,2,none,1,car or other,24,none,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,0 +0 <= <200 DM,24,existing credits paid back duly till now,car (used),4113,500 <= < 1000 DM,<1 years,3,none,4,car or other,28,none,rent,1,skilled employee / official,1,none,yes,female,divorced/separated,0 +no account,36,existing credits paid back duly till now,furniture/equipment,10974,<100 DM,unemployed,4,none,2,car or other,26,none,own,2,management/ highly qualified employee,1,yes,yes,female,divorced/separated,0 +<0 DM,12,existing credits paid back duly till now,car (new),1893,<100 DM,1<= < 4 years,4,guarantor,4,savings agreement/life insurance,29,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +<0 DM,24,critical account,radio/television,1231,>= 1000 DM,>=7 years,4,none,4,savings agreement/life insurance,57,none,rent,2,management/ highly qualified employee,1,yes,yes,female,divorced/separated,1 +>= 200 DM ,30,critical account,radio/television,3656,no savings account,>=7 years,4,none,4,savings agreement/life insurance,49,store,own,2,unskilled - resident,1,none,yes,male,divorced/separated,1 +0 <= <200 DM,9,critical account,radio/television,1154,<100 DM,>=7 years,2,none,4,real estate,37,none,own,3,unskilled - resident,1,none,yes,male,divorced/separated,1 +<0 DM,28,existing credits paid back duly till now,car (new),4006,<100 DM,1<= < 4 years,3,none,2,car or other,45,none,own,1,unskilled - resident,1,none,yes,male,divorced/separated,0 +0 <= <200 DM,24,existing credits paid back duly till now,furniture/equipment,3069,100 <= <500 DM,>=7 years,4,none,4,unknown / no property,30,none,for free,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,6,critical account,radio/television,1740,<100 DM,>=7 years,2,none,2,real estate,30,none,rent,2,skilled employee / official,1,none,yes,male,married/widowed,1 +0 <= <200 DM,21,delay in paying off,car (new),2353,<100 DM,1<= < 4 years,1,none,4,savings agreement/life insurance,47,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,15,existing credits paid back duly till now,car (new),3556,no savings account,1<= < 4 years,3,none,2,unknown / no property,29,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,24,existing credits paid back duly till now,radio/television,2397,500 <= < 1000 DM,>=7 years,3,none,2,car or other,35,bank,own,2,skilled employee / official,1,yes,yes,male,divorced/separated,0 +0 <= <200 DM,6,existing credits paid back duly till now,repairs,454,<100 DM,<1 years,3,none,1,savings agreement/life insurance,22,none,own,1,unskilled - resident,1,none,yes,male,married/widowed,1 +0 <= <200 DM,30,existing credits paid back duly till now,radio/television,1715,no savings account,1<= < 4 years,4,none,1,car or other,26,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,27,critical account,radio/television,2520,500 <= < 1000 DM,1<= < 4 years,4,none,2,savings agreement/life insurance,23,none,own,2,unskilled - resident,1,none,yes,male,divorced/separated,0 +no account,15,existing credits paid back duly till now,radio/television,3568,<100 DM,>=7 years,4,none,2,car or other,54,bank,rent,1,management/ highly qualified employee,1,yes,yes,female,divorced/separated,1 +no account,42,existing credits paid back duly till now,radio/television,7166,no savings account,4<= <7 years,2,none,4,savings agreement/life insurance,29,none,rent,1,skilled employee / official,1,yes,yes,male,married/widowed,1 +<0 DM,11,critical account,car (new),3939,<100 DM,1<= < 4 years,1,none,2,real estate,40,none,own,2,unskilled - resident,2,none,yes,male,divorced/separated,1 +0 <= <200 DM,15,existing credits paid back duly till now,repairs,1514,100 <= <500 DM,1<= < 4 years,4,guarantor,2,real estate,22,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,24,existing credits paid back duly till now,car (new),7393,<100 DM,1<= < 4 years,1,none,4,savings agreement/life insurance,43,none,own,1,unskilled - resident,2,none,yes,male,divorced/separated,1 +<0 DM,24,all credits at this bank paid back duly,car (new),1193,<100 DM,unemployed,1,co-applicant,4,unknown / no property,29,none,rent,2,unemployed/ unskilled - non-resident,1,none,yes,female,divorced/separated,0 +<0 DM,60,existing credits paid back duly till now,business,7297,<100 DM,>=7 years,4,co-applicant,4,unknown / no property,36,none,rent,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,30,critical account,radio/television,2831,<100 DM,1<= < 4 years,4,none,2,car or other,33,none,own,1,skilled employee / official,1,yes,yes,female,divorced/separated,1 +>= 200 DM ,24,existing credits paid back duly till now,radio/television,1258,500 <= < 1000 DM,1<= < 4 years,3,none,3,car or other,57,none,own,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,6,existing credits paid back duly till now,radio/television,753,<100 DM,1<= < 4 years,2,guarantor,3,real estate,64,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +0 <= <200 DM,18,delay in paying off,business,2427,no savings account,>=7 years,4,none,2,savings agreement/life insurance,42,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,24,delay in paying off,car (new),2538,<100 DM,>=7 years,4,none,4,car or other,47,none,own,2,unskilled - resident,2,none,yes,male,divorced/separated,0 +0 <= <200 DM,15,all credits at this bank paid back duly,car (new),1264,100 <= <500 DM,1<= < 4 years,2,none,2,savings agreement/life insurance,25,none,rent,1,skilled employee / official,1,none,yes,male,married/widowed,0 +0 <= <200 DM,30,critical account,furniture/equipment,8386,<100 DM,4<= <7 years,2,none,2,savings agreement/life insurance,49,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,0 +no account,48,existing credits paid back duly till now,business,4844,<100 DM,unemployed,3,none,2,car or other,33,bank,rent,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,0 +>= 200 DM ,21,existing credits paid back duly till now,car (new),2923,100 <= <500 DM,1<= < 4 years,1,none,1,car or other,28,bank,own,1,management/ highly qualified employee,1,yes,yes,female,divorced/separated,1 +<0 DM,36,existing credits paid back duly till now,car (used),8229,<100 DM,1<= < 4 years,2,none,2,savings agreement/life insurance,26,none,own,1,skilled employee / official,2,none,yes,male,divorced/separated,0 +no account,24,critical account,furniture/equipment,2028,<100 DM,4<= <7 years,2,none,2,savings agreement/life insurance,30,none,own,2,unskilled - resident,1,none,yes,male,divorced/separated,1 +<0 DM,15,critical account,furniture/equipment,1433,<100 DM,1<= < 4 years,4,none,3,savings agreement/life insurance,25,none,rent,2,skilled employee / official,1,none,yes,female,divorced/separated,1 +>= 200 DM ,42,no credits taken,business,6289,<100 DM,<1 years,2,none,1,savings agreement/life insurance,33,none,own,2,skilled employee / official,1,none,yes,male,divorced/separated,1 +no account,13,existing credits paid back duly till now,radio/television,1409,100 <= <500 DM,unemployed,2,none,4,real estate,64,none,own,1,skilled employee / official,1,none,yes,female,divorced/separated,1 +<0 DM,24,existing credits paid back duly till now,car (used),6579,<100 DM,unemployed,4,none,2,unknown / no property,29,none,for free,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +0 <= <200 DM,24,critical account,radio/television,1743,<100 DM,>=7 years,4,none,2,savings agreement/life insurance,48,none,own,2,unskilled - resident,1,none,yes,male,divorced/separated,1 +no account,12,critical account,education,3565,no savings account,<1 years,2,none,1,savings agreement/life insurance,37,none,own,2,unskilled - resident,2,none,yes,male,divorced/separated,1 +no account,15,all credits at this bank paid back duly,radio/television,1569,100 <= <500 DM,>=7 years,4,none,4,car or other,34,bank,own,1,unskilled - resident,2,none,yes,male,divorced/separated,1 +<0 DM,18,existing credits paid back duly till now,radio/television,1936,no savings account,4<= <7 years,2,none,4,car or other,23,none,rent,2,unskilled - resident,1,none,yes,male,married/widowed,1 +<0 DM,36,existing credits paid back duly till now,furniture/equipment,3959,<100 DM,unemployed,4,none,3,savings agreement/life insurance,30,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +no account,12,existing credits paid back duly till now,car (new),2390,no savings account,>=7 years,4,none,3,car or other,50,none,own,1,skilled employee / official,1,yes,yes,male,divorced/separated,1 +no account,12,existing credits paid back duly till now,furniture/equipment,1736,<100 DM,4<= <7 years,3,none,4,real estate,31,none,own,1,unskilled - resident,1,none,yes,female,divorced/separated,1 +<0 DM,30,existing credits paid back duly till now,car (used),3857,<100 DM,1<= < 4 years,4,none,4,savings agreement/life insurance,40,none,own,1,management/ highly qualified employee,1,yes,yes,male,divorced/separated,1 +no account,12,existing credits paid back duly till now,radio/television,804,<100 DM,>=7 years,4,none,4,car or other,38,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 +<0 DM,45,existing credits paid back duly till now,radio/television,1845,<100 DM,1<= < 4 years,4,none,4,unknown / no property,23,none,for free,1,skilled employee / official,1,yes,yes,male,divorced/separated,0 +0 <= <200 DM,45,critical account,car (used),4576,100 <= <500 DM,unemployed,3,none,4,car or other,27,none,own,1,skilled employee / official,1,none,yes,male,divorced/separated,1 diff --git a/virny/datasets/law_school_clean.csv b/virny/datasets/data/law_school_clean.csv similarity index 100% rename from virny/datasets/law_school_clean.csv rename to virny/datasets/data/law_school_clean.csv diff --git a/virny/datasets/ricci_race.csv b/virny/datasets/data/ricci_race.csv similarity index 100% rename from virny/datasets/ricci_race.csv rename to virny/datasets/data/ricci_race.csv diff --git a/virny/datasets/student_por_new.csv b/virny/datasets/data/student_por_new.csv similarity index 100% rename from virny/datasets/student_por_new.csv rename to virny/datasets/data/student_por_new.csv diff --git a/virny/datasets/data_loaders.py b/virny/datasets/data_loaders.py deleted file mode 100644 index 0b78b3ed..00000000 --- a/virny/datasets/data_loaders.py +++ /dev/null @@ -1,779 +0,0 @@ -import pathlib -import pandas as pd -import numpy as np - -from sklearn import preprocessing -from folktables import ACSDataSource, ACSEmployment, ACSIncome, ACSTravelTime, ACSPublicCoverage, ACSMobility, \ - employment_filter, adult_filter, public_coverage_filter -from virny.datasets.base import BaseDataLoader - - -class CreditCardDefaultDataset(BaseDataLoader): - """ - Dataset class for the Credit Card Default dataset that contains sensitive attributes among feature columns. - Description: https://arxiv.org/pdf/2110.00530.pdf (Section 3.1.6) - - Parameters - ---------- - dataset_path - [Optional] Path to a file with the data - - """ - def __init__(self, dataset_path: str = None): - if dataset_path is None: - filename = 'credit_card_default_clean.csv' - dataset_path = pathlib.Path(__file__).parent.joinpath(filename) - - df = pd.read_csv(dataset_path) - target = 'default_payment' - numerical_columns = [ - "limit_bal", "age", - "bill_amt1", "bill_amt2", "bill_amt3", - "bill_amt4", "bill_amt5", "bill_amt6", - "pay_amt1", "pay_amt2", "pay_amt3", - "pay_amt4", "pay_amt5", "pay_amt6" - ] - categorical_columns = [ - "sex", "education", "marriage", - "pay_0", "pay_2", "pay_3", - "pay_4", "pay_5", "pay_6" - ] - - super().__init__( - full_df=df, - target=target, - numerical_columns=numerical_columns, - categorical_columns=categorical_columns, - ) - - -class CreditDataset(BaseDataLoader): - """ - Dataset class for the Credit dataset that contains sensitive attributes among feature columns. - Source: https://www.kaggle.com/competitions/GiveMeSomeCredit/overview - - Parameters - ---------- - subsample_size - Subsample size to create based on the input dataset - subsample_seed - Seed for sampling using the sample() method from pandas - - """ - def __init__(self, subsample_size: int = None, subsample_seed: int = None): - filename = 'givemesomecredit.csv' - dataset_path = pathlib.Path(__file__).parent.joinpath(filename) - - df = pd.read_csv(dataset_path, index_col=0) - if subsample_size: - df = df.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ - else df.sample(subsample_size) - df = df.reset_index(drop=True) - - target = 'SeriousDlqin2yrs' - numerical_columns = ['RevolvingUtilizationOfUnsecuredLines', 'age', 'NumberOfTime30-59DaysPastDueNotWorse', - 'DebtRatio', 'MonthlyIncome', 'NumberOfOpenCreditLinesAndLoans', - 'NumberOfTimes90DaysLate', 'NumberRealEstateLoansOrLines'] - categorical_columns = [] - - super().__init__( - full_df=df, - target=target, - numerical_columns=numerical_columns, - categorical_columns=categorical_columns, - ) - - -class StudentPerformancePortugueseDataset(BaseDataLoader): - """ - Dataset class for the Student Performance Portuguese dataset that contains sensitive attributes among feature columns. - Source: https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/student_por_new.csv - Description: https://arxiv.org/pdf/2110.00530.pdf (Section 3.4.1) - - Parameters - ---------- - subsample_size - Subsample size to create based on the input dataset - subsample_seed - Seed for sampling using the sample() method from pandas - - """ - def __init__(self, subsample_size: int = None, subsample_seed: int = None): - filename = 'student_por_new.csv' - dataset_path = pathlib.Path(__file__).parent.joinpath(filename) - df = pd.read_csv(dataset_path) - - if subsample_size: - df = df.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ - else df.sample(subsample_size) - df = df.reset_index(drop=True) - - # Preprocessing - df['class'] = [1 if v == "High" else 0 for v in df['class']] - - target = 'class' - numerical_columns = ['age', 'Medu', 'Fedu', 'traveltime', 'studytime', 'failures', 'famrel', 'freetime', - 'goout', 'Dalc', 'Walc', 'health', 'absences', 'G1', 'G2'] - categorical_columns = [column for column in df.columns if column not in numerical_columns + [target]] - - super().__init__( - full_df=df, - target=target, - numerical_columns=numerical_columns, - categorical_columns=categorical_columns, - ) - - -class LawSchoolDataset(BaseDataLoader): - """ - Dataset class for the Law School dataset that contains sensitive attributes among feature columns. - Source: https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/law_school_clean.csv - Description: https://arxiv.org/pdf/2110.00530.pdf - - Parameters - ---------- - subsample_size - Subsample size to create based on the input dataset - subsample_seed - Seed for sampling using the sample() method from pandas - dataset_path - [Optional] Path to a file with the data - - """ - def __init__(self, subsample_size: int = None, subsample_seed: int = None, dataset_path=None): - if dataset_path is None: - filename = 'law_school_clean.csv' - dataset_path = pathlib.Path(__file__).parent.joinpath(filename) - - df = pd.read_csv(dataset_path) - if subsample_size: - df = df.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ - else df.sample(subsample_size) - df = df.reset_index(drop=True) - - # Cast columns - columns_to_cast = ['fulltime', 'fam_inc', 'male', 'tier'] - columns_to_cast_dct = {col: "str" for col in columns_to_cast} - df = df.astype(columns_to_cast_dct) - - target = 'pass_bar' - numerical_columns = ['decile1b', 'decile3', 'lsat', 'ugpa', 'zfygpa', 'zgpa'] - categorical_columns = ['fulltime', 'fam_inc', 'male', 'tier', 'race'] - - super().__init__( - full_df=df, - target=target, - numerical_columns=numerical_columns, - categorical_columns=categorical_columns, - ) - - -class DiabetesDataset(BaseDataLoader): - """ - Dataset class for the Diabetes dataset that contains sensitive attributes among feature columns. - Source: https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/diabetes-clean.csv - Description: https://arxiv.org/pdf/2110.00530.pdf - - Parameters - ---------- - subsample_size - Subsample size to create based on the input dataset - subsample_seed - Seed for sampling using the sample() method from pandas - dataset_path - [Optional] Path to a file with the data - - """ - def __init__(self, subsample_size: int = None, subsample_seed: int = None, dataset_path=None): - if dataset_path is None: - filename = 'diabetes-clean.csv' - dataset_path = pathlib.Path(__file__).parent.joinpath(filename) - - df = pd.read_csv(dataset_path) - if subsample_size: - df = df.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ - else df.sample(subsample_size) - df = df.reset_index(drop=True) - - # Cast columns - columns_to_cast = ['admission_type_id', 'discharge_disposition_id', 'admission_source_id'] - columns_to_cast_dct = {col: "str" for col in columns_to_cast} - df = df.astype(columns_to_cast_dct) - df = df.drop(['encounter_id', 'patient_nbr'], axis=1) - - # Encode labels - le = preprocessing.LabelEncoder() - for i in ['diag_1', 'diag_2', 'diag_3']: - df[i] = le.fit_transform(df[i]) - - target = 'readmitted' - df[target] = df[target].replace(['<30'], 1) - df[target] = df[target].replace(['>30'], 0) - numerical_columns = ['number_diagnoses', 'time_in_hospital', 'num_lab_procedures', 'num_procedures', - 'num_medications', 'number_outpatient', 'number_emergency', 'number_inpatient'] - categorical_columns = [ - 'race', 'gender', 'age', 'admission_type_id', 'discharge_disposition_id', 'admission_source_id', - 'diag_1', 'diag_2', 'diag_3', 'max_glu_serum', 'A1Cresult', 'metformin', 'repaglinide', 'nateglinide', - 'chlorpropamide', 'glimepiride', 'acetohexamide', 'glipizide', 'glyburide', 'tolbutamide', 'pioglitazone', - 'rosiglitazone', 'acarbose', 'miglitol', 'troglitazone', 'tolazamide', 'examide', 'citoglipton', - 'insulin', 'glyburide-metformin', 'glipizide-metformin', 'glimepiride-pioglitazone', - 'metformin-rosiglitazone', 'metformin-pioglitazone', 'change', 'diabetesMed' - ] - - super().__init__( - full_df=df, - target=target, - numerical_columns=numerical_columns, - categorical_columns=categorical_columns, - ) - - -class RicciDataset(BaseDataLoader): - """ - Dataset class for the Ricci dataset that contains sensitive attributes among feature columns. - Source: https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/ricci_race.csv - Description: https://arxiv.org/pdf/2110.00530.pdf - - Parameters - ---------- - dataset_path - [Optional] Path to a file with the data - - """ - def __init__(self, dataset_path=None): - if dataset_path is None: - filename = 'ricci_race.csv' - dataset_path = pathlib.Path(__file__).parent.joinpath(filename) - - df = pd.read_csv(dataset_path) - - target = 'Promoted' - df[target] = df[target].replace([-1], 0) - numerical_columns = ['Oral', 'Written', 'Combine'] - categorical_columns = ['Position', 'Race'] - - super().__init__( - full_df=df, - target=target, - numerical_columns=numerical_columns, - categorical_columns=categorical_columns, - ) - - -class CompasDataset(BaseDataLoader): - """ - Dataset class for the COMPAS dataset that contains sensitive attributes among feature columns. - - Parameters - ---------- - subsample_size - Subsample size to create based on the input dataset - subsample_seed - Seed for sampling using the sample() method from pandas - dataset_path - [Optional] Path to a file with the data - - """ - def __init__(self, subsample_size: int = None, subsample_seed: int = None, dataset_path=None): - if dataset_path is None: - filename = 'COMPAS.csv' - dataset_path = pathlib.Path(__file__).parent.joinpath(filename) - - df = pd.read_csv(dataset_path) - if subsample_size: - df = df.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ - else df.sample(subsample_size) - df = df.reset_index(drop=True) - - int_columns = ['recidivism', 'age', 'age_cat_25 - 45', 'age_cat_Greater than 45', - 'age_cat_Less than 25', 'c_charge_degree_F', 'c_charge_degree_M', 'sex'] - int_columns_dct = {col: "int" for col in int_columns} - df = df.astype(int_columns_dct) - - target = 'recidivism' - numerical_columns = ['age', 'juv_fel_count', 'juv_misd_count', 'juv_other_count', 'priors_count'] - categorical_columns = ['race', 'age_cat_25 - 45', 'age_cat_Greater than 45', - 'age_cat_Less than 25', 'c_charge_degree_F', 'c_charge_degree_M', 'sex'] - - super().__init__( - full_df=df, - target=target, - numerical_columns=numerical_columns, - categorical_columns=categorical_columns, - ) - - -class CompasWithoutSensitiveAttrsDataset(BaseDataLoader): - """ - Dataset class for the COMPAS dataset that does not contain sensitive attributes among feature columns - to test blind classifiers - - Parameters - ---------- - subsample_size - Subsample size to create based on the input dataset - subsample_seed - Seed for sampling using the sample() method from pandas - dataset_path - [Optional] Path to a file with the data - - """ - def __init__(self, subsample_size: int = None, subsample_seed: int = None, dataset_path=None): - if dataset_path is None: - filename = 'COMPAS.csv' - dataset_path = pathlib.Path(__file__).parent.joinpath(filename) - - df = pd.read_csv(dataset_path) - if subsample_size: - df = df.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ - else df.sample(subsample_size) - df = df.reset_index(drop=True) - - # Initial data types transformation - int_columns = ['recidivism', 'age', 'age_cat_25 - 45', 'age_cat_Greater than 45', - 'age_cat_Less than 25', 'c_charge_degree_F', 'c_charge_degree_M', 'sex'] - int_columns_dct = {col: "int" for col in int_columns} - df = df.astype(int_columns_dct) - - # Define params - target = 'recidivism' - numerical_columns = ['juv_fel_count', 'juv_misd_count', 'juv_other_count','priors_count'] - categorical_columns = ['age_cat_25 - 45', 'age_cat_Greater than 45','age_cat_Less than 25', - 'c_charge_degree_F', 'c_charge_degree_M'] - - super().__init__( - full_df=df, - target=target, - numerical_columns=numerical_columns, - categorical_columns=categorical_columns - ) - - -class ACSIncomeDataset(BaseDataLoader): - """ - Dataset class for the income task from the folktables dataset. - Target: binary classification, predict if a person has an annual income > $50,000. - Source of the dataset: https://github.com/socialfoundations/folktables - - Parameters - ---------- - state - State in the US for which to get the data. All states in the US are available. - year - Year for which to get the data. Five different years of data collection are available: 2014–2018 inclusive. - root_dir - Path to the root directory where to store the extracted dataset or where it is stored. - with_nulls - Whether to keep nulls in the dataset or replace them on the new categorical class. Default: False. - with_filter - Whether to use a folktables filter for this task. Default: True. - optimize - Whether to optimize the dataset size by downcasting categorical columns. Default: True. - subsample_size - Subsample size to create based on the input dataset. - subsample_seed - Seed for sampling using the sample() method from pandas. - - """ - def __init__(self, state, year, root_dir=None, with_nulls=False, with_filter=True, - optimize=True, subsample_size: int = None, subsample_seed: int = None): - data_dir = pathlib.Path(__file__).parent if root_dir is None else root_dir - data_source = ACSDataSource( - survey_year=year, - horizon='1-Year', - survey='person', - root_dir=data_dir - ) - acs_data = data_source.get_data(states=state, download=True) - if with_filter: - acs_data = adult_filter(acs_data) - if subsample_size: - acs_data = acs_data.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ - else acs_data.sample(subsample_size) - acs_data = acs_data.reset_index(drop=True) - - features = ACSIncome.features - target = ACSIncome.target - categorical_columns = ['SCHL', 'COW', 'MAR', 'OCCP', 'POBP', 'RELP', 'SEX', 'RAC1P'] - numerical_columns = ['AGEP', 'WKHP'] - - if with_nulls: - X_data = acs_data[features] - else: - X_data = acs_data[features].apply(lambda x: np.nan_to_num(x, -1)) - - if optimize: - X_data = optimize_data_loading(X_data, categorical_columns) - - optimized_X_data = X_data[categorical_columns].astype('str') - for col in numerical_columns: - optimized_X_data[col] = X_data[col] - y_data = acs_data[target].apply(lambda x: int(x > 50_000)) - - columns_with_nulls = optimized_X_data.columns[optimized_X_data.isna().any().to_list()].to_list() - - super().__init__( - full_df=optimized_X_data, - target=target, - numerical_columns=numerical_columns, - categorical_columns=categorical_columns, - X_data=optimized_X_data, - y_data=y_data, - columns_with_nulls=columns_with_nulls, - ) - - def update_X_data(self, X_data): - """ - To save simulated nulls - """ - self.X_data = X_data - self.columns_with_nulls = self.X_data.columns[self.X_data.isna().any().to_list()].to_list() - - -class ACSEmploymentDataset(BaseDataLoader): - """ - Dataset class for the employment task from the folktables dataset. - Target: binary classification, predict if a person is employed. - Source of the dataset: https://github.com/socialfoundations/folktables - - Parameters - ---------- - state - State in the US for which to get the data. All states in the US are available. - year - Year for which to get the data. Five different years of data collection are available: 2014–2018 inclusive. - root_dir - Path to the root directory where to store the extracted dataset or where it is stored. - with_nulls - Whether to keep nulls in the dataset or replace them on the new categorical class. Default: False. - with_filter - Whether to use a folktables filter for this task. Default: True. - optimize - Whether to optimize the dataset size by downcasting categorical columns. Default: True. - subsample_size - Subsample size to create based on the input dataset. - subsample_seed - Seed for sampling using the sample() method from pandas. - - """ - def __init__(self, state, year, root_dir=None, with_nulls=False, with_filter=True, - optimize=True, subsample_size: int = None, subsample_seed: int = None): - data_dir = pathlib.Path(__file__).parent if root_dir is None else root_dir - data_source = ACSDataSource( - survey_year=year, - horizon='1-Year', - survey='person', - root_dir=data_dir - ) - acs_data = data_source.get_data(states=state, download=True) - if with_filter: - acs_data = employment_filter(acs_data) - if subsample_size: - acs_data = acs_data.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ - else acs_data.sample(subsample_size) - acs_data = acs_data.reset_index(drop=True) - - features = ACSEmployment.features - target = ACSEmployment.target - categorical_columns = ['MAR', 'MIL', 'ESP', 'MIG', 'DREM', 'NATIVITY', 'DIS', 'DEAR', 'DEYE', 'SEX', 'RAC1P', 'RELP', 'CIT', 'ANC','SCHL'] - numerical_columns = ['AGEP'] - - if with_nulls is True: - X_data = acs_data[features] - else: - X_data = acs_data[features].apply(lambda x: np.nan_to_num(x, -1)) - - if optimize: - X_data = optimize_data_loading(X_data, categorical_columns) - - optimized_X_data = X_data[categorical_columns].astype('str') - for col in numerical_columns: - optimized_X_data[col] = X_data[col] - y_data = acs_data[target].apply(lambda x: int(x == 1)) - - columns_with_nulls = optimized_X_data.columns[optimized_X_data.isna().any().to_list()].to_list() - - super().__init__( - full_df=optimized_X_data, - target=target, - numerical_columns=numerical_columns, - categorical_columns=categorical_columns, - X_data=optimized_X_data, - y_data=y_data, - columns_with_nulls=columns_with_nulls, - ) - - def update_X_data(self, X_data): - """ - To save simulated nulls - """ - self.X_data = X_data - self.columns_with_nulls = self.X_data.columns[self.X_data.isna().any().to_list()].to_list() - - -class ACSMobilityDataset(BaseDataLoader): - """ - Dataset class for the mobility task from the folktables dataset. - Target: binary classification, predict whether a young adult moved addresses in the last year. - Source of the dataset: https://github.com/socialfoundations/folktables - - Parameters - ---------- - state - State in the US for which to get the data. All states in the US are available. - year - Year for which to get the data. Five different years of data collection are available: 2014–2018 inclusive. - root_dir - Path to the root directory where to store the extracted dataset or where it is stored. - with_nulls - Whether to keep nulls in the dataset or replace them on the new categorical class. Default: False. - - """ - def __init__(self, state, year, root_dir=None, with_nulls=False): - data_dir = pathlib.Path(__file__).parent if root_dir is None else root_dir - data_source = ACSDataSource( - survey_year=year, - horizon='1-Year', - survey='person', - root_dir=data_dir - ) - acs_data = data_source.get_data(states=state, download=True) - features = ACSMobility.features - target = ACSMobility.target - categorical_columns = ['MAR','SEX','DIS','ESP','CIT','MIL','ANC','NATIVITY','RELP','DEAR','DEYE','DREM','RAC1P','GCL','COW','ESR'] - numerical_columns = ['AGEP', 'SCHL', 'PINCP', 'WKHP', 'JWMNP'] - - if with_nulls: - X_data = acs_data[features] - else: - X_data = acs_data[features].apply(lambda x: np.nan_to_num(x, -1)) - - filtered_X_data = X_data[categorical_columns].astype('str') - for col in numerical_columns: - filtered_X_data[col] = X_data[col] - - y_data = acs_data[target].apply(lambda x: int(x == 1)) - - columns_with_nulls = filtered_X_data.columns[filtered_X_data.isna().any().to_list()].to_list() - - super().__init__( - full_df=acs_data, - target=target, - numerical_columns=numerical_columns, - categorical_columns=categorical_columns, - X_data=filtered_X_data, - y_data=y_data, - columns_with_nulls=columns_with_nulls, - ) - - def update_X_data(self, X_data): - """ - To save simulated nulls - """ - self.X_data = X_data - self.columns_with_nulls = self.X_data.columns[self.X_data.isna().any().to_list()].to_list() - - -class ACSPublicCoverageDataset(BaseDataLoader): - """ - Dataset class for the public coverage task from the folktables dataset. - Target: binary classification, predict whether a low-income individual, not eligible for Medicare, - has coverage from public health insurance. - Source of the dataset: https://github.com/socialfoundations/folktables - - Parameters - ---------- - state - State in the US for which to get the data. All states in the US are available. - year - Year for which to get the data. Five different years of data collection are available: 2014–2018 inclusive. - root_dir - Path to the root directory where to store the extracted dataset or where it is stored. - with_nulls - Whether to keep nulls in the dataset or replace them on the new categorical class. Default: False. - with_filter - Whether to use a folktables filter for this task. Default: True. - optimize - Whether to optimize the dataset size by downcasting categorical columns. Default: True. - subsample_size - Subsample size to create based on the input dataset. - subsample_seed - Seed for sampling using the sample() method from pandas. - - """ - def __init__(self, state, year, root_dir=None, with_nulls=False, with_filter=True, - optimize=True, subsample_size: int = None, subsample_seed: int = None): - data_dir = pathlib.Path(__file__).parent if root_dir is None else root_dir - data_source = ACSDataSource( - survey_year=year, - horizon='1-Year', - survey='person', - root_dir=data_dir - ) - acs_data = data_source.get_data(states=state, download=True) - if with_filter: - acs_data = public_coverage_filter(acs_data) - if subsample_size: - acs_data = acs_data.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ - else acs_data.sample(subsample_size) - acs_data = acs_data.reset_index(drop=True) - - features = ACSPublicCoverage.features - target = ACSPublicCoverage.target - categorical_columns = ['SCHL','MAR','SEX','DIS','ESP','CIT','MIG','MIL','ANC','NATIVITY','DEAR','DEYE','DREM','ESR','ST','FER','RAC1P'] - numerical_columns = ['AGEP', 'PINCP'] - - if with_nulls is True: - X_data = acs_data[features] - else: - X_data = acs_data[features].apply(lambda x: np.nan_to_num(x, -1)) - - if optimize: - X_data = optimize_data_loading(X_data, categorical_columns) - - optimized_X_data = X_data[categorical_columns].astype('str') - for col in numerical_columns: - optimized_X_data[col] = X_data[col] - y_data = acs_data[target].apply(lambda x: int(x == 1)) - - columns_with_nulls = optimized_X_data.columns[optimized_X_data.isna().any().to_list()].to_list() - - super().__init__( - full_df=optimized_X_data, - target=target, - numerical_columns=numerical_columns, - categorical_columns=categorical_columns, - X_data=optimized_X_data, - y_data=y_data, - columns_with_nulls=columns_with_nulls, - ) - - def update_X_data(self, X_data): - """ - To save simulated nulls - """ - self.X_data = X_data - self.columns_with_nulls = self.X_data.columns[self.X_data.isna().any().to_list()].to_list() - - -class ACSTravelTimeDataset(BaseDataLoader): - """ - Dataset class for the travel time task from the folktables dataset. - Target: binary classification, predict whether a working adult has a travel time to work of greater than 20 minutes. - Source of the dataset: https://github.com/socialfoundations/folktables - - Parameters - ---------- - state - State in the US for which to get the data. All states in the US are available. - year - Year for which to get the data. Five different years of data collection are available: 2014–2018 inclusive. - root_dir - Path to the root directory where to store the extracted dataset or where it is stored. - with_nulls - Whether to keep nulls in the dataset or replace them on the new categorical class. Default: False. - - """ - def __init__(self, state, year, root_dir=None, with_nulls=False): - data_dir = pathlib.Path(__file__).parent if root_dir is None else root_dir - data_source = ACSDataSource( - survey_year=year, - horizon='1-Year', - survey='person', - root_dir=data_dir - ) - acs_data = data_source.get_data(states=state, download=True) - features = ACSTravelTime.features - target = ACSTravelTime.target - categorical_columns = ['MAR','SEX','DIS','ESP','MIG','RELP','RAC1P','PUMA','ST','CIT','OCCP','POWPUMA','POVPIP'] - numerical_columns = ['AGEP', 'SCHL'] - - if with_nulls: - X_data = acs_data[features] - else: - X_data = acs_data[features].apply(lambda x: np.nan_to_num(x, -1)) - - filtered_X_data = X_data[categorical_columns].astype('str') - for col in numerical_columns: - filtered_X_data[col] = X_data[col] - - y_data = acs_data[target].apply(lambda x: int(x > 20)) - columns_with_nulls = filtered_X_data.columns[filtered_X_data.isna().any().to_list()].to_list() - - super().__init__( - full_df=acs_data, - target=target, - numerical_columns=numerical_columns, - categorical_columns=categorical_columns, - X_data=filtered_X_data, - y_data=y_data, - columns_with_nulls=columns_with_nulls, - ) - - def update_X_data(self, X_data): - """ - To save simulated nulls - """ - self.X_data = X_data - self.columns_with_nulls = self.X_data.columns[self.X_data.isna().any().to_list()].to_list() - - -class ACSDataset_from_demodq(BaseDataLoader): - """ Following https://github.com/schelterlabs/demographic-data-quality """ - def __init__(self, state, year, root_dir=None, with_nulls=False, optimize=True): - """ - Loading task data: instead of using the task wrapper, we subsample the acs_data dataframe on the task features - We do this to retain the nulls as task wrappers handle nulls by imputing as a special category - Alternatively, we could have altered the configuration from here: - https://github.com/zykls/folktables/blob/main/folktables/acs.py - """ - data_dir = pathlib.Path(__file__).parent if root_dir is None else root_dir - data_source = ACSDataSource( - survey_year=year, - horizon='1-Year', - survey='person', - root_dir=data_dir - ) - acs_data = data_source.get_data(states=state, download=True) - features = ['AGEP', 'COW', 'SCHL', 'MAR', 'OCCP', 'POBP', 'RELP', 'WKHP', 'SEX', 'RAC1P'] - target = 'PINCP' - categorical_columns = ['COW', 'SCHL', 'MAR', 'OCCP', 'POBP', 'RELP', 'SEX', 'RAC1P'] - numerical_columns = ['AGEP', 'WKHP'] - - if with_nulls: - X_data = acs_data[features] - else: - X_data = acs_data[features].apply(lambda x: np.nan_to_num(x, -1)) - - if optimize: - X_data = optimize_data_loading(X_data, categorical_columns) - - filtered_X_data = X_data - y_data = acs_data[target].apply(lambda x: x >= 50000).astype(int) - columns_with_nulls = filtered_X_data.columns[filtered_X_data.isna().any().to_list()].to_list() - - super().__init__( - full_df=acs_data, - target=target, - numerical_columns=numerical_columns, - categorical_columns=categorical_columns, - X_data=filtered_X_data, - y_data=y_data, - columns_with_nulls=columns_with_nulls, - ) - - def update_X_data(self, X_data): - """ - To save simulated nulls - """ - self.X_data = X_data - self.columns_with_nulls = self.X_data.columns[self.X_data.isna().any().to_list()].to_list() - - -def optimize_data_loading(data, categorical): - """ - Optimizing the dataset size by downcasting categorical columns - """ - for column in categorical: - data[column] = pd.to_numeric(data[column], downcast='integer') - return data diff --git a/virny/datasets/education.py b/virny/datasets/education.py new file mode 100644 index 00000000..3ca6913c --- /dev/null +++ b/virny/datasets/education.py @@ -0,0 +1,92 @@ +import pathlib +import pandas as pd + +from virny.datasets.base import BaseDataLoader + + +class StudentPerformancePortugueseDataset(BaseDataLoader): + """ + Dataset class for the Student Performance Portuguese dataset that contains sensitive attributes among feature columns. + Source: https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/student_por_new.csv + Description: https://arxiv.org/pdf/2110.00530.pdf (Section 3.4.1) + + Parameters + ---------- + subsample_size + Subsample size to create based on the input dataset + subsample_seed + Seed for sampling using the sample() method from pandas + + """ + def __init__(self, subsample_size: int = None, subsample_seed: int = None): + filename = 'student_por_new.csv' + dataset_path = pathlib.Path(__file__).parent.joinpath('data').joinpath(filename) + df = pd.read_csv(dataset_path) + + if subsample_size: + df = df.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ + else df.sample(subsample_size) + df = df.reset_index(drop=True) + + # Preprocessing + df['class'] = [1 if v == "High" else 0 for v in df['class']] + + target = 'class' + numerical_columns = ['age', 'Medu', 'Fedu', 'traveltime', 'studytime', 'failures', 'famrel', 'freetime', + 'goout', 'Dalc', 'Walc', 'health', 'absences', 'G1', 'G2'] + categorical_columns = [column for column in df.columns if column not in numerical_columns + [target]] + + super().__init__( + full_df=df, + target=target, + numerical_columns=numerical_columns, + categorical_columns=categorical_columns, + ) + + +class LawSchoolDataset(BaseDataLoader): + """ + Dataset class for the Law School dataset that contains sensitive attributes among feature columns. + Source: https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/law_school_clean.csv + Description: https://arxiv.org/pdf/2110.00530.pdf + + Parameters + ---------- + subsample_size + Subsample size to create based on the input dataset + subsample_seed + Seed for sampling using the sample() method from pandas + dataset_path + [Optional] Path to a file with the data + + """ + def __init__(self, subsample_size: int = None, subsample_seed: int = None, dataset_path=None): + if dataset_path is None: + filename = 'law_school_clean.csv' + dataset_path = pathlib.Path(__file__).parent.joinpath('data').joinpath(filename) + + df = pd.read_csv(dataset_path) + if subsample_size: + df = df.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ + else df.sample(subsample_size) + df = df.reset_index(drop=True) + + # Cast columns + columns_to_cast = ['fulltime', 'fam_inc', 'male', 'tier'] + int_columns_to_cast_dct = {col: "int" for col in columns_to_cast} + df = df.astype(int_columns_to_cast_dct) + str_columns_to_cast_dct = {col: "str" for col in columns_to_cast} + df = df.astype(str_columns_to_cast_dct) + + target = 'pass_bar' + df = df.astype({target: int}) + + numerical_columns = ['decile1b', 'decile3', 'lsat', 'ugpa', 'zfygpa', 'zgpa'] + categorical_columns = ['fulltime', 'fam_inc', 'male', 'tier', 'race'] + + super().__init__( + full_df=df, + target=target, + numerical_columns=numerical_columns, + categorical_columns=categorical_columns + ) diff --git a/virny/datasets/finance.py b/virny/datasets/finance.py new file mode 100644 index 00000000..3581531b --- /dev/null +++ b/virny/datasets/finance.py @@ -0,0 +1,126 @@ +import pathlib + +import numpy as np +import pandas as pd + +from virny.datasets.base import BaseDataLoader + + +class GermanCreditDataset(BaseDataLoader): + """ + Dataset class for the German Credit dataset that contains sensitive attributes among feature columns. + Source: https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/german_data_credit.csv + General description and analysis: https://arxiv.org/pdf/2110.00530.pdf (Section 3.1.3) + Broad description: https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data + + Parameters + ---------- + subsample_size + Subsample size to create based on the input dataset + subsample_seed + Seed for sampling using the sample() method from pandas + + """ + def __init__(self, subsample_size: int = None, subsample_seed: int = None): + filename = 'german_data_credit.csv' + dataset_path = pathlib.Path(__file__).parent.joinpath('data').joinpath(filename) + df = pd.read_csv(dataset_path) + + if subsample_size: + df = df.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ + else df.sample(subsample_size) + df = df.reset_index(drop=True) + + # Shortening long categorical values + df = df.replace({ + 'credit-history': { + 'critical account': '1', + 'existing credits paid back duly till now': '2', + 'delay in paying off': '3', + 'no credits taken': '4', + 'all credits at this bank paid back duly': '5' + }, + 'property': { + 'real estate': '1', + 'savings agreement/life insurance': '2', + 'unknown / no property': '3', + 'car or other': '4' + }, + 'job': { + 'skilled employee / official': '1', + 'unskilled - resident': '2', + 'management/ highly qualified employee': '3', + 'unemployed/ unskilled - non-resident': '4' + } + }) + + target = 'class-label' + numerical_columns = ['duration', 'credit-amount', 'installment-rate', 'residence-since', + 'age', 'existing-credits', 'number-people-provide-maintenance-for'] + categorical_columns = [column for column in df.columns if column not in numerical_columns + [target]] + + super().__init__( + full_df=df, + target=target, + numerical_columns=numerical_columns, + categorical_columns=categorical_columns, + ) + + +class BankMarketingDataset(BaseDataLoader): + """ + Dataset class for the Bank Marketing dataset that contains sensitive attributes among feature columns. + Source: https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/bank-full.csv + General description and analysis: https://arxiv.org/pdf/2110.00530.pdf (Section 3.1.5) + Broad description: https://archive.ics.uci.edu/dataset/222/bank+marketing + + Parameters + ---------- + subsample_size + Subsample size to create based on the input dataset + subsample_seed + Seed for sampling using the sample() method from pandas + + """ + def __init__(self, subsample_size: int = None, subsample_seed: int = None): + filename = 'bank-full.csv' + dataset_path = pathlib.Path(__file__).parent.joinpath('data').joinpath(filename) + df = pd.read_csv(dataset_path) + + if subsample_size: + df = df.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ + else df.sample(subsample_size) + df = df.reset_index(drop=True) + + # 'duration' attribute highly affects the output target (e.g., if duration=0 then y='no'). + # Yet, the duration is not known before a call is performed. + # Also, after the end of the call y is obviously known. + # Source: https://archive.ics.uci.edu/dataset/222/bank+marketing + df = df.drop(columns=['duration']) + + # Remove columns with a great percentage of 'unknown' values + df = df.drop(columns=['contact', 'poutcome']) + + # Replace 'unknown' marker with NaN + for col in df.columns: + if 'unknown' in df[col].unique(): + df[col] = df[col].replace({'unknown': np.nan}) + + # Target preprocessing + target = 'y' + df = df.replace({ + target: { + 'no': 0, + 'yes': 1, + }, + }) + + numerical_columns = ['age', 'balance', 'day', 'campaign', 'pdays', 'previous'] + categorical_columns = [column for column in df.columns if column not in numerical_columns + [target]] + + super().__init__( + full_df=df, + target=target, + numerical_columns=numerical_columns, + categorical_columns=categorical_columns, + ) diff --git a/virny/datasets/folktables.py b/virny/datasets/folktables.py new file mode 100644 index 00000000..e9d45b7c --- /dev/null +++ b/virny/datasets/folktables.py @@ -0,0 +1,393 @@ +import pathlib +import pandas as pd +import numpy as np + +from folktables import ACSDataSource, ACSEmployment, ACSIncome, ACSTravelTime, ACSPublicCoverage, ACSMobility, \ + employment_filter, adult_filter, public_coverage_filter + +from virny.datasets.base import BaseDataLoader + + +def optimize_data_loading(data, categorical): + """ + Optimizing the dataset size by downcasting categorical columns + """ + for column in categorical: + data[column] = pd.to_numeric(data[column], downcast='integer') + return data + + +class ACSIncomeDataset(BaseDataLoader): + """ + Dataset class for the income task from the folktables dataset. + Target: binary classification, predict if a person has an annual income > $50,000. + Source of the dataset: https://github.com/socialfoundations/folktables + + Parameters + ---------- + state + State in the US for which to get the data. All states in the US are available. + year + Year for which to get the data. Five different years of data collection are available: 2014–2018 inclusive. + root_dir + Path to the root directory where to store the extracted dataset or where it is stored. + with_nulls + Whether to keep nulls in the dataset or replace them on the new categorical class. Default: False. + with_filter + Whether to use a folktables filter for this task. Default: True. + optimize + Whether to optimize the dataset size by downcasting categorical columns. Default: True. + subsample_size + Subsample size to create based on the input dataset. + subsample_seed + Seed for sampling using the sample() method from pandas. + + """ + def __init__(self, state, year, root_dir=None, with_nulls=False, with_filter=True, + optimize=True, subsample_size: int = None, subsample_seed: int = None): + data_dir = pathlib.Path(__file__).parent if root_dir is None else root_dir + data_source = ACSDataSource( + survey_year=year, + horizon='1-Year', + survey='person', + root_dir=data_dir + ) + acs_data = data_source.get_data(states=state, download=True) + if with_filter: + acs_data = adult_filter(acs_data) + if subsample_size: + acs_data = acs_data.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ + else acs_data.sample(subsample_size) + acs_data = acs_data.reset_index(drop=True) + + features = ACSIncome.features + target = ACSIncome.target + categorical_columns = ['SCHL', 'COW', 'MAR', 'OCCP', 'POBP', 'RELP', 'SEX', 'RAC1P'] + numerical_columns = ['AGEP', 'WKHP'] + + if with_nulls: + X_data = acs_data[features] + else: + X_data = acs_data[features].apply(lambda x: np.nan_to_num(x, -1)) + + if optimize: + X_data = optimize_data_loading(X_data, categorical_columns) + + optimized_X_data = X_data[categorical_columns].astype('str') + for col in numerical_columns: + optimized_X_data[col] = X_data[col] + + y_data = acs_data[target].apply(lambda x: int(x > 50_000)) + columns_with_nulls = optimized_X_data.columns[optimized_X_data.isna().any().to_list()].to_list() + full_df = pd.concat([optimized_X_data, y_data], axis=1) + + super().__init__( + full_df=full_df, + target=target, + numerical_columns=numerical_columns, + categorical_columns=categorical_columns, + X_data=optimized_X_data, + y_data=y_data, + columns_with_nulls=columns_with_nulls, + ) + + def update_X_data(self, X_data): + """ + To save simulated nulls + """ + self.X_data = X_data + self.columns_with_nulls = self.X_data.columns[self.X_data.isna().any().to_list()].to_list() + + +class ACSEmploymentDataset(BaseDataLoader): + """ + Dataset class for the employment task from the folktables dataset. + Target: binary classification, predict if a person is employed. + Source of the dataset: https://github.com/socialfoundations/folktables + + Parameters + ---------- + state + State in the US for which to get the data. All states in the US are available. + year + Year for which to get the data. Five different years of data collection are available: 2014–2018 inclusive. + root_dir + Path to the root directory where to store the extracted dataset or where it is stored. + with_nulls + Whether to keep nulls in the dataset or replace them on the new categorical class. Default: False. + with_filter + Whether to use a folktables filter for this task. Default: True. + optimize + Whether to optimize the dataset size by downcasting categorical columns. Default: True. + subsample_size + Subsample size to create based on the input dataset. + subsample_seed + Seed for sampling using the sample() method from pandas. + + """ + def __init__(self, state, year, root_dir=None, with_nulls=False, with_filter=True, + optimize=True, subsample_size: int = None, subsample_seed: int = None): + data_dir = pathlib.Path(__file__).parent if root_dir is None else root_dir + data_source = ACSDataSource( + survey_year=year, + horizon='1-Year', + survey='person', + root_dir=data_dir + ) + acs_data = data_source.get_data(states=state, download=True) + if with_filter: + acs_data = employment_filter(acs_data) + if subsample_size: + acs_data = acs_data.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ + else acs_data.sample(subsample_size) + acs_data = acs_data.reset_index(drop=True) + + features = ACSEmployment.features + target = ACSEmployment.target + categorical_columns = ['MAR', 'MIL', 'ESP', 'MIG', 'DREM', 'NATIVITY', 'DIS', 'DEAR', 'DEYE', 'SEX', 'RAC1P', + 'RELP', 'CIT', 'ANC', 'SCHL'] + numerical_columns = ['AGEP'] + + if with_nulls is True: + X_data = acs_data[features] + else: + X_data = acs_data[features].apply(lambda x: np.nan_to_num(x, -1)) + + if optimize: + X_data = optimize_data_loading(X_data, categorical_columns) + + optimized_X_data = X_data[categorical_columns].astype('str') + for col in numerical_columns: + optimized_X_data[col] = X_data[col] + + y_data = acs_data[target].apply(lambda x: int(x == 1)) + columns_with_nulls = optimized_X_data.columns[optimized_X_data.isna().any().to_list()].to_list() + full_df = pd.concat([optimized_X_data, y_data], axis=1) + + super().__init__( + full_df=full_df, + target=target, + numerical_columns=numerical_columns, + categorical_columns=categorical_columns, + X_data=optimized_X_data, + y_data=y_data, + columns_with_nulls=columns_with_nulls, + ) + + def update_X_data(self, X_data): + """ + To save simulated nulls + """ + self.X_data = X_data + self.columns_with_nulls = self.X_data.columns[self.X_data.isna().any().to_list()].to_list() + + +class ACSMobilityDataset(BaseDataLoader): + """ + Dataset class for the mobility task from the folktables dataset. + Target: binary classification, predict whether a young adult moved addresses in the last year. + Source of the dataset: https://github.com/socialfoundations/folktables + + Parameters + ---------- + state + State in the US for which to get the data. All states in the US are available. + year + Year for which to get the data. Five different years of data collection are available: 2014–2018 inclusive. + root_dir + Path to the root directory where to store the extracted dataset or where it is stored. + with_nulls + Whether to keep nulls in the dataset or replace them on the new categorical class. Default: False. + + """ + def __init__(self, state, year, root_dir=None, with_nulls=False): + data_dir = pathlib.Path(__file__).parent if root_dir is None else root_dir + data_source = ACSDataSource( + survey_year=year, + horizon='1-Year', + survey='person', + root_dir=data_dir + ) + acs_data = data_source.get_data(states=state, download=True) + features = ACSMobility.features + target = ACSMobility.target + categorical_columns = ['MAR', 'SEX', 'DIS', 'ESP', 'CIT', 'MIL', 'ANC', 'NATIVITY', 'RELP', 'DEAR', 'DEYE', + 'DREM', 'RAC1P', 'GCL', 'COW', 'ESR', 'SCHL'] + numerical_columns = ['AGEP', 'PINCP', 'WKHP', 'JWMNP'] + + if with_nulls: + X_data = acs_data[features] + else: + X_data = acs_data[features].apply(lambda x: np.nan_to_num(x, -1)) + + filtered_X_data = X_data[categorical_columns].astype('str') + for col in numerical_columns: + filtered_X_data[col] = X_data[col] + + y_data = acs_data[target].apply(lambda x: int(x == 1)) + columns_with_nulls = filtered_X_data.columns[filtered_X_data.isna().any().to_list()].to_list() + full_df = pd.concat([filtered_X_data, y_data], axis=1) + + super().__init__( + full_df=full_df, + target=target, + numerical_columns=numerical_columns, + categorical_columns=categorical_columns, + X_data=filtered_X_data, + y_data=y_data, + columns_with_nulls=columns_with_nulls, + ) + + def update_X_data(self, X_data): + """ + To save simulated nulls + """ + self.X_data = X_data + self.columns_with_nulls = self.X_data.columns[self.X_data.isna().any().to_list()].to_list() + + +class ACSPublicCoverageDataset(BaseDataLoader): + """ + Dataset class for the public coverage task from the folktables dataset. + Target: binary classification, predict whether a low-income individual, not eligible for Medicare, + has coverage from public health insurance. + Source of the dataset: https://github.com/socialfoundations/folktables + + Parameters + ---------- + state + State in the US for which to get the data. All states in the US are available. + year + Year for which to get the data. Five different years of data collection are available: 2014–2018 inclusive. + root_dir + Path to the root directory where to store the extracted dataset or where it is stored. + with_nulls + Whether to keep nulls in the dataset or replace them on the new categorical class. Default: False. + with_filter + Whether to use a folktables filter for this task. Default: True. + optimize + Whether to optimize the dataset size by downcasting categorical columns. Default: True. + subsample_size + Subsample size to create based on the input dataset. + subsample_seed + Seed for sampling using the sample() method from pandas. + + """ + def __init__(self, state, year, root_dir=None, with_nulls=False, with_filter=True, + optimize=True, subsample_size: int = None, subsample_seed: int = None): + data_dir = pathlib.Path(__file__).parent if root_dir is None else root_dir + data_source = ACSDataSource( + survey_year=year, + horizon='1-Year', + survey='person', + root_dir=data_dir + ) + acs_data = data_source.get_data(states=state, download=True) + if with_filter: + acs_data = public_coverage_filter(acs_data) + if subsample_size: + acs_data = acs_data.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ + else acs_data.sample(subsample_size) + acs_data = acs_data.reset_index(drop=True) + + features = ACSPublicCoverage.features + target = ACSPublicCoverage.target + categorical_columns = ['SCHL', 'MAR', 'SEX', 'DIS', 'ESP', 'CIT', 'MIG', 'MIL', 'ANC', 'NATIVITY', 'DEAR', + 'DEYE', 'DREM', 'ESR', 'ST', 'FER', 'RAC1P'] + numerical_columns = ['AGEP', 'PINCP'] + + if with_nulls is True: + X_data = acs_data[features] + else: + X_data = acs_data[features].apply(lambda x: np.nan_to_num(x, -1)) + + if optimize: + X_data = optimize_data_loading(X_data, categorical_columns) + + optimized_X_data = X_data[categorical_columns].astype('str') + for col in numerical_columns: + optimized_X_data[col] = X_data[col] + + y_data = acs_data[target].apply(lambda x: int(x == 1)) + columns_with_nulls = optimized_X_data.columns[optimized_X_data.isna().any().to_list()].to_list() + full_df = pd.concat([optimized_X_data, y_data], axis=1) + + super().__init__( + full_df=full_df, + target=target, + numerical_columns=numerical_columns, + categorical_columns=categorical_columns, + X_data=optimized_X_data, + y_data=y_data, + columns_with_nulls=columns_with_nulls, + ) + + def update_X_data(self, X_data): + """ + To save simulated nulls + """ + self.X_data = X_data + self.columns_with_nulls = self.X_data.columns[self.X_data.isna().any().to_list()].to_list() + + +class ACSTravelTimeDataset(BaseDataLoader): + """ + Dataset class for the travel time task from the folktables dataset. + Target: binary classification, predict whether a working adult has a travel time to work of greater than 20 minutes. + Source of the dataset: https://github.com/socialfoundations/folktables + + Parameters + ---------- + state + State in the US for which to get the data. All states in the US are available. + year + Year for which to get the data. Five different years of data collection are available: 2014–2018 inclusive. + root_dir + Path to the root directory where to store the extracted dataset or where it is stored. + with_nulls + Whether to keep nulls in the dataset or replace them on the new categorical class. Default: False. + + """ + def __init__(self, state, year, root_dir=None, with_nulls=False): + data_dir = pathlib.Path(__file__).parent if root_dir is None else root_dir + data_source = ACSDataSource( + survey_year=year, + horizon='1-Year', + survey='person', + root_dir=data_dir + ) + acs_data = data_source.get_data(states=state, download=True) + features = ACSTravelTime.features + target = ACSTravelTime.target + categorical_columns = ['MAR', 'SEX', 'DIS', 'ESP', 'MIG', 'RELP', 'RAC1P', 'PUMA', 'ST', 'CIT', 'OCCP', 'POWPUMA', 'POVPIP'] + numerical_columns = ['AGEP', 'SCHL'] + + if with_nulls: + X_data = acs_data[features] + else: + X_data = acs_data[features].apply(lambda x: np.nan_to_num(x, -1)) + + filtered_X_data = X_data[categorical_columns].astype('str') + for col in numerical_columns: + filtered_X_data[col] = X_data[col] + + y_data = acs_data[target].apply(lambda x: int(x > 20)) + columns_with_nulls = filtered_X_data.columns[filtered_X_data.isna().any().to_list()].to_list() + full_df = pd.concat([filtered_X_data, y_data], axis=1) + + super().__init__( + full_df=full_df, + target=target, + numerical_columns=numerical_columns, + categorical_columns=categorical_columns, + X_data=filtered_X_data, + y_data=y_data, + columns_with_nulls=columns_with_nulls, + ) + + def update_X_data(self, X_data): + """ + To save simulated nulls + """ + self.X_data = X_data + self.columns_with_nulls = self.X_data.columns[self.X_data.isna().any().to_list()].to_list() diff --git a/virny/datasets/givemesomecredit.csv b/virny/datasets/givemesomecredit.csv deleted file mode 100644 index 6e670f83..00000000 --- a/virny/datasets/givemesomecredit.csv +++ /dev/null @@ -1,150001 +0,0 @@ -,SeriousDlqin2yrs,RevolvingUtilizationOfUnsecuredLines,age,NumberOfTime30-59DaysPastDueNotWorse,DebtRatio,MonthlyIncome,NumberOfOpenCreditLinesAndLoans,NumberOfTimes90DaysLate,NumberRealEstateLoansOrLines,NumberOfTime60-89DaysPastDueNotWorse,NumberOfDependents -1,1,0.766126609,45,2,0.802982129,9120,13,0,6,0,2 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CardiovascularDiseaseDataset(BaseDataLoader): + """ + Dataset class for the Cardiovascular Disease dataset that contains sensitive attributes among feature columns. + Source and broad description: https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-dataset + + Parameters + ---------- + subsample_size + Subsample size to create based on the input dataset + subsample_seed + Seed for sampling using the sample() method from pandas + + """ + def __init__(self, subsample_size: int = None, subsample_seed: int = None): + filename = 'cardio.csv' + dataset_path = pathlib.Path(__file__).parent.joinpath('data').joinpath(filename) + df = pd.read_csv(dataset_path, delimiter=';') + + if subsample_size: + df = df.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ + else df.sample(subsample_size) + df = df.reset_index(drop=True) + + # Preprocessing + df = df.drop(['id'], axis=1) + df['age'] = (df['age'] / 365).astype(int) + + columns_to_cast = ['gender', 'cholesterol', 'gluc', 'smoke', 'alco', 'active'] + columns_to_cast_dct = {col: str for col in columns_to_cast} + df = df.astype(columns_to_cast_dct) + + target = 'cardio' + numerical_columns = ['age', 'height', 'weight', 'ap_hi', 'ap_lo'] + categorical_columns = [column for column in df.columns if column not in numerical_columns + [target]] + + super().__init__( + full_df=df, + target=target, + numerical_columns=numerical_columns, + categorical_columns=categorical_columns, + ) + + +class DiabetesDataset2019(BaseDataLoader): + """ + Dataset class for the Diabetes 2019 dataset that contains sensitive attributes among feature columns. + Source and broad description: https://www.kaggle.com/datasets/tigganeha4/diabetes-dataset-2019/data + + Parameters + ---------- + subsample_size + Subsample size to create based on the input dataset + subsample_seed + Seed for sampling using the sample() method from pandas + with_nulls + Whether to keep nulls in the dataset or drop rows with any nulls. Default: True. + + """ + def __init__(self, subsample_size: int = None, subsample_seed: int = None, with_nulls: bool = True): + filename = 'diabetes_dataset__2019.csv' + dataset_path = pathlib.Path(__file__).parent.joinpath('data').joinpath(filename) + df = pd.read_csv(dataset_path) + + if subsample_size: + df = df.sample(subsample_size, random_state=subsample_seed) if subsample_seed is not None \ + else df.sample(subsample_size) + df = df.reset_index(drop=True) + if not with_nulls: + df = df.dropna(how='any',axis=0) + df = df.reset_index(drop=True) + + # Preprocess features + df = df.rename(columns={'Pregancies': 'Pregnancies'}) + df['RegularMedicine'].replace('o', 'no', inplace=True) + df['BPLevel'] = df['BPLevel'].str.strip().str.lower() + + # Preprocess a target + df['Diabetic'] = df['Diabetic'].str.strip() + df['Diabetic'].replace('no', 0, inplace=True) + df['Diabetic'].replace('yes', 1, inplace=True) + + target = 'Diabetic' + numerical_columns = ['BMI', 'Sleep', 'SoundSleep', 'Pregnancies'] + categorical_columns = [column for column in df.columns if column not in numerical_columns + [target]] + + super().__init__( + full_df=df, + target=target, + numerical_columns=numerical_columns, + categorical_columns=categorical_columns, + ) + + +class RicciDataset(BaseDataLoader): + """ + Dataset class for the Ricci dataset that contains sensitive attributes among feature columns. + Source: https://github.com/tailequy/fairness_dataset/blob/main/experiments/data/ricci_race.csv + Description: https://arxiv.org/pdf/2110.00530.pdf + + Parameters + ---------- + dataset_path + [Optional] Path to a file with the data + + """ + def __init__(self, dataset_path=None): + if dataset_path is None: + filename = 'ricci_race.csv' + dataset_path = pathlib.Path(__file__).parent.joinpath('data').joinpath(filename) + + df = pd.read_csv(dataset_path) + + target = 'Promoted' + df[target] = df[target].replace([-1], 0) + numerical_columns = ['Oral', 'Written', 'Combine'] + categorical_columns = ['Position', 'Race'] + + super().__init__( + full_df=df, + target=target, + numerical_columns=numerical_columns, + categorical_columns=categorical_columns, + ) diff --git a/virny/metrics/accuracy_metrics.py b/virny/metrics/accuracy_metrics.py index b2b6a463..287f2ced 100644 --- a/virny/metrics/accuracy_metrics.py +++ b/virny/metrics/accuracy_metrics.py @@ -82,6 +82,5 @@ def confusion_matrix_metrics(y_true, y_preds): metrics[ACCURACY] = (TP+TN)/(TP+TN+FP+FN) metrics[F1] = (2*TP)/(2*TP+FP+FN) metrics[SELECTION_RATE] = (TP+FP)/(TP+FP+TN+FN) - metrics[POSITIVE_RATE] = (TP+FP)/(TP+FN) return metrics diff --git a/virny/preprocessing/basic_preprocessing.py b/virny/preprocessing/basic_preprocessing.py index 6496cec9..05aa7cf6 100644 --- a/virny/preprocessing/basic_preprocessing.py +++ b/virny/preprocessing/basic_preprocessing.py @@ -3,11 +3,12 @@ from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split -from virny.datasets.data_loaders import BaseDataLoader +from virny.configs.constants import INTERSECTION_SIGN +from virny.datasets.base import BaseDataLoader from virny.custom_classes.base_dataset import BaseFlowDataset -def preprocess_dataset(data_loader: BaseDataLoader, column_transformer: ColumnTransformer, +def preprocess_dataset(data_loader: BaseDataLoader, column_transformer: ColumnTransformer, sensitive_attributes_dct: dict, test_set_fraction: float, dataset_split_seed: int) -> BaseFlowDataset: """ Preprocess an input dataset using sklearn ColumnTransformer. Split the dataset on train and test using test_set_fraction. @@ -19,6 +20,8 @@ def preprocess_dataset(data_loader: BaseDataLoader, column_transformer: ColumnTr Instance of BaseDataLoader that contains a target, numerical, and categorical columns. column_transformer Instance of sklearn ColumnTransformer to preprocess categorical and numerical columns. + sensitive_attributes_dct + Dictionary of sensitive attribute names and their disadvantaged values. test_set_fraction Fraction from 0 to 1. Used to split the input dataset on the train and test sets. dataset_split_seed @@ -39,8 +42,9 @@ def preprocess_dataset(data_loader: BaseDataLoader, column_transformer: ColumnTr column_transformer = column_transformer.set_output(transform="pandas") # Set transformer output to a pandas df X_train_features = column_transformer.fit_transform(X_train_val) X_test_features = column_transformer.transform(X_test) + sensitive_attrs = [attr for attr in sensitive_attributes_dct.keys() if INTERSECTION_SIGN not in attr] - return BaseFlowDataset(init_features_df=data_loader.full_df.drop(data_loader.target, axis=1, errors='ignore'), + return BaseFlowDataset(init_sensitive_attrs_df=data_loader.full_df[sensitive_attrs], X_train_val=X_train_features, X_test=X_test_features, y_train_val=y_train_val, diff --git a/virny/user_interfaces/multiple_models_api.py b/virny/user_interfaces/multiple_models_api.py index 464ade89..ba1b45ff 100644 --- a/virny/user_interfaces/multiple_models_api.py +++ b/virny/user_interfaces/multiple_models_api.py @@ -13,7 +13,7 @@ def compute_metrics_with_config(dataset: BaseFlowDataset, config, models_config: dict, - save_results_dir_path: str, postprocessor=None, + save_results_dir_path: str, postprocessor=None, with_predict_proba: bool = True, notebook_logs_stdout: bool = False, verbose: int = 0) -> dict: """ Compute stability and accuracy metrics for each model in models_config. Arguments are defined as an input config object. @@ -33,6 +33,10 @@ def compute_metrics_with_config(dataset: BaseFlowDataset, config, models_config: Location where to save result files with metrics postprocessor [Optional] Postprocessor object to apply to model predictions before metrics computation + with_predict_proba + [Optional] True, if models in models_config have a predict_proba method and can return probabilities for predictions, + False, otherwise. Note that if it is set to False, only metrics based on labels (not labels and probabilities) will be computed. + Ignored when a postprocessor is not None, and set to False in this case. notebook_logs_stdout [Optional] True, if this interface was execute in a Jupyter notebook, False, otherwise. @@ -48,11 +52,6 @@ def compute_metrics_with_config(dataset: BaseFlowDataset, config, models_config: start_datetime = datetime.now(timezone.utc) os.makedirs(save_results_dir_path, exist_ok=True) - # Check if a type of postprocessing_sensitive_attribute is not NoneType. - # In other words, check if postprocessing_sensitive_attribute is defined in a config yaml. - postprocessing_sensitive_attribute = config.postprocessing_sensitive_attribute \ - if type(config.postprocessing_sensitive_attribute) != type(None) else None - model_metrics_dct = dict() models_metrics_dct = run_metrics_computation(dataset=dataset, bootstrap_fraction=config.bootstrap_fraction, @@ -60,11 +59,13 @@ def compute_metrics_with_config(dataset: BaseFlowDataset, config, models_config: models_config=models_config, n_estimators=config.n_estimators, sensitive_attributes_dct=config.sensitive_attributes_dct, + random_state=config.random_state, model_setting=config.model_setting, computation_mode=config.computation_mode, postprocessor=postprocessor, - postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, + postprocessing_sensitive_attribute=config.postprocessing_sensitive_attribute, save_results=False, + with_predict_proba=with_predict_proba, notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) @@ -81,10 +82,12 @@ def compute_metrics_with_config(dataset: BaseFlowDataset, config, models_config: def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, dataset_name: str, models_config: dict, n_estimators: int, sensitive_attributes_dct: dict, - model_setting: str = ModelSetting.BATCH.value, computation_mode: str = None, - postprocessor=None, postprocessing_sensitive_attribute: str = None, + random_state: int = None, model_setting: str = ModelSetting.BATCH.value, + computation_mode: str = None, postprocessor=None, + postprocessing_sensitive_attribute: str = None, save_results: bool = True, save_results_dir_path: str = None, - notebook_logs_stdout: bool = False, verbose: int = 0) -> dict: + with_predict_proba: bool = True, notebook_logs_stdout: bool = False, + verbose: int = 0) -> dict: """ Compute stability and accuracy metrics for each model in models_config. Save results in `save_results_dir_path` folder. @@ -106,6 +109,8 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, sensitive_attributes_dct A dictionary where keys are sensitive attribute names (including attributes intersections), and values are privilege values for these attributes + random_state + [Optional] Controls the randomness of the bootstrap approach for model arbitrariness evaluation model_setting [Optional] Currently, only batch models are supported. Default: 'batch'. computation_mode @@ -118,6 +123,10 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, [Optional] If to save result metrics in a file save_results_dir_path [Optional] Location where to save result files with metrics + with_predict_proba + [Optional] True, if models in models_config have a predict_proba method and can return probabilities for predictions, + False, otherwise. Note that if it is set to False, only metrics based on labels (not labels and probabilities) will be computed. + Ignored when a postprocessor is not None, and set to False in this case. notebook_logs_stdout [Optional] True, if this interface was execute in a Jupyter notebook, False, otherwise. @@ -144,11 +153,13 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, print('#' * 30, f' [Model {model_idx + 1} / {num_models}] Analyze {model_name} ', '#' * 30) try: base_model = models_config[model_name] + computation_start_date_time = datetime.now() model_metrics_df = compute_one_model_metrics(base_model=base_model, n_estimators=n_estimators, dataset=dataset, bootstrap_fraction=bootstrap_fraction, sensitive_attributes_dct=sensitive_attributes_dct, + random_state=random_state, model_setting=model_setting, computation_mode=computation_mode, dataset_name=dataset_name, @@ -157,8 +168,13 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, save_results=save_results, save_results_dir_path=save_results_dir_path, + with_predict_proba=with_predict_proba, notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) + computation_end_date_time = datetime.now() + computation_runtime = (computation_end_date_time - computation_start_date_time).total_seconds() / 60.0 + model_metrics_df['Runtime_in_Mins'] = computation_runtime + models_metrics_dct[model_name] = model_metrics_df except Exception as err: @@ -173,9 +189,10 @@ def run_metrics_computation(dataset: BaseFlowDataset, bootstrap_fraction: float, def compute_one_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDataset, bootstrap_fraction: float, sensitive_attributes_dct: dict, dataset_name: str, base_model_name: str, - postprocessor=None, postprocessing_sensitive_attribute: str = None, - model_setting: str = ModelSetting.BATCH.value, computation_mode: str = None, save_results: bool = True, - save_results_dir_path: str = None, notebook_logs_stdout: bool = False, verbose: int = 0): + random_state: int = None, model_setting: str = ModelSetting.BATCH.value, + computation_mode: str = None, postprocessor=None, postprocessing_sensitive_attribute: str = None, + save_results: bool = True, save_results_dir_path: str = None, with_predict_proba: bool = True, + notebook_logs_stdout: bool = False, verbose: int = 0): """ Compute subgroup metrics for the base model. Save results in `save_results_dir_path` folder. @@ -199,18 +216,24 @@ def compute_one_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDa Dataset name to name a result file with metrics base_model_name Model name to name a result file with metrics + random_state + [Optional] Controls the randomness of the bootstrap approach for model arbitrariness evaluation + model_setting + [Optional] Currently, only batch models are supported. Default: 'batch'. + computation_mode + [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. postprocessor [Optional] Postprocessor object to apply to model predictions before metrics computation postprocessing_sensitive_attribute [Optional] Sensitive attribute name to apply postprocessor only to this attribute predictions save_results [Optional] If to save result metrics in a file - model_setting - [Optional] Currently, only batch models are supported. Default: 'batch'. - computation_mode - [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. save_results_dir_path [Optional] Location where to save result files with metrics + with_predict_proba + [Optional] True, if models in models_config have a predict_proba method and can return probabilities for predictions, + False, otherwise. Note that if it is set to False, only metrics based on labels (not labels and probabilities) will be computed. + Ignored when a postprocessor is not None, and set to False in this case. notebook_logs_stdout [Optional] True, if this interface was execute in a Jupyter notebook, False, otherwise. @@ -221,7 +244,7 @@ def compute_one_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDa """ model_setting = ModelSetting.BATCH if model_setting is None else ModelSetting[model_setting.upper()] - test_protected_groups = create_test_protected_groups(dataset.X_test, dataset.init_features_df, sensitive_attributes_dct) + test_protected_groups = create_test_protected_groups(dataset.X_test, dataset.init_sensitive_attrs_df, sensitive_attributes_dct) if verbose >= 2: print('\nProtected groups splits:') for g in test_protected_groups.keys(): @@ -237,9 +260,11 @@ def compute_one_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDa dataset_name=dataset_name, sensitive_attributes_dct=sensitive_attributes_dct, test_protected_groups=test_protected_groups, + random_state=random_state, computation_mode=computation_mode, postprocessor=postprocessor, postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, + with_predict_proba=with_predict_proba, notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) y_preds, variance_metrics_df = subgroup_variance_analyzer.compute_metrics(save_results=False, @@ -264,6 +289,7 @@ def compute_one_model_metrics(base_model, n_estimators: int, dataset: BaseFlowDa metrics_df = metrics_df.rename(columns={"index": "Metric"}) metrics_df['Model_Name'] = base_model_name metrics_df['Model_Params'] = str(base_model.get_params()) + metrics_df['Virny_Random_State'] = random_state if save_results: # Save metrics diff --git a/virny/user_interfaces/multiple_models_with_db_writer_api.py b/virny/user_interfaces/multiple_models_with_db_writer_api.py index d3f50ba2..c1ab02fb 100644 --- a/virny/user_interfaces/multiple_models_with_db_writer_api.py +++ b/virny/user_interfaces/multiple_models_with_db_writer_api.py @@ -6,8 +6,9 @@ def compute_metrics_with_db_writer(dataset: BaseFlowDataset, config, models_config: dict, - custom_tbl_fields_dct: dict, db_writer_func, - postprocessor=None, notebook_logs_stdout: bool = False, verbose: int = 0) -> dict: + custom_tbl_fields_dct: dict, db_writer_func, postprocessor=None, + with_predict_proba: bool = True, notebook_logs_stdout: bool = False, + verbose: int = 0) -> dict: """ Compute stability and accuracy metrics for each model in models_config. Arguments are defined as an input config object. Save results to a database after each run appending fields and value from custom_tbl_fields_dct and using db_writer_func. @@ -28,6 +29,10 @@ def compute_metrics_with_db_writer(dataset: BaseFlowDataset, config, models_conf Python function object has one argument (run_models_metrics_df) and save this metrics df to a target database postprocessor [Optional] Postprocessor object to apply to model predictions before metrics computation + with_predict_proba + [Optional] True, if models in models_config have a predict_proba method and can return probabilities for predictions, + False, otherwise. Note that if it is set to False, only metrics based on labels (not labels and probabilities) will be computed. + Ignored when a postprocessor is not None, and set to False in this case. notebook_logs_stdout [Optional] True, if this interface was execute in a Jupyter notebook, False, otherwise. @@ -40,11 +45,6 @@ def compute_metrics_with_db_writer(dataset: BaseFlowDataset, config, models_conf if notebook_logs_stdout: verbose = 0 - # Check if a type of postprocessing_sensitive_attribute is not NoneType. - # In other words, check if postprocessing_sensitive_attribute is defined in a config yaml. - postprocessing_sensitive_attribute = config.postprocessing_sensitive_attribute \ - if type(config.postprocessing_sensitive_attribute) != type(None) else None - multiple_models_metrics_dct = dict() run_models_metrics_df = pd.DataFrame() models_metrics_dct = run_metrics_computation(dataset=dataset, @@ -53,11 +53,13 @@ def compute_metrics_with_db_writer(dataset: BaseFlowDataset, config, models_conf models_config=models_config, n_estimators=config.n_estimators, sensitive_attributes_dct=config.sensitive_attributes_dct, + random_state=config.random_state, model_setting=config.model_setting, computation_mode=config.computation_mode, postprocessor=postprocessor, - postprocessing_sensitive_attribute=postprocessing_sensitive_attribute, + postprocessing_sensitive_attribute=config.postprocessing_sensitive_attribute, save_results=False, + with_predict_proba=with_predict_proba, notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) diff --git a/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py b/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py index 4025f8f9..f9765c00 100644 --- a/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py +++ b/virny/user_interfaces/multiple_models_with_multiple_test_sets_api.py @@ -12,7 +12,8 @@ def compute_metrics_with_multiple_test_sets(dataset: BaseFlowDataset, extra_test_sets_lst, config, models_config: dict, custom_tbl_fields_dct: dict, - db_writer_func, notebook_logs_stdout: bool = False, verbose: int = 0): + db_writer_func, with_predict_proba: bool = True, + notebook_logs_stdout: bool = False, verbose: int = 0): """ Compute stability and accuracy metrics for each model in models_config based on dataset.X_test and each extra test set in extra_test_sets_lst. Arguments are defined as an input config object. Save results to a database after each run @@ -35,6 +36,10 @@ def compute_metrics_with_multiple_test_sets(dataset: BaseFlowDataset, extra_test Dictionary where keys are column names and values to add to inserted metrics during saving results to a database db_writer_func Python function object has one argument (run_models_metrics_df) and save this metrics df to a target database + with_predict_proba + [Optional] True, if models in models_config have a predict_proba method and can return probabilities for predictions, + False, otherwise. Note that if it is set to False, only metrics based on labels (not labels and probabilities) will be computed. + Ignored when a postprocessor is not None, and set to False in this case. notebook_logs_stdout [Optional] True, if this interface was execute in a Jupyter notebook, False, otherwise. @@ -54,8 +59,10 @@ def compute_metrics_with_multiple_test_sets(dataset: BaseFlowDataset, extra_test models_config=models_config, n_estimators=config.n_estimators, sensitive_attributes_dct=config.sensitive_attributes_dct, + random_state=config.random_state, model_setting=config.model_setting, computation_mode=config.computation_mode, + with_predict_proba=with_predict_proba, notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) # Concatenate current run metrics with previous results and @@ -90,9 +97,10 @@ def compute_metrics_with_multiple_test_sets(dataset: BaseFlowDataset, extra_test def run_metrics_computation_with_multiple_test_sets(dataset: BaseFlowDataset, bootstrap_fraction: float, dataset_name: str, extra_test_sets_lst: list, models_config: dict, n_estimators: int, - sensitive_attributes_dct: dict, model_setting: str = ModelSetting.BATCH.value, - computation_mode: str = None, notebook_logs_stdout: bool = False, - verbose: int = 0) -> dict: + sensitive_attributes_dct: dict, random_state: int = None, + model_setting: str = ModelSetting.BATCH.value, + computation_mode: str = None, with_predict_proba: bool = True, + notebook_logs_stdout: bool = False, verbose: int = 0) -> dict: """ Compute stability and accuracy metrics for each model in models_config based on dataset.X_test and each extra test set in extra_test_sets_lst. Save results in `save_results_dir_path` folder. @@ -116,10 +124,16 @@ def run_metrics_computation_with_multiple_test_sets(dataset: BaseFlowDataset, bo sensitive_attributes_dct A dictionary where keys are sensitive attribute names (including attributes intersections), and values are privilege values for these attributes + random_state + [Optional] Controls the randomness of the bootstrap approach for model arbitrariness evaluation model_setting Currently, only batch models are supported. Default: 'batch'. computation_mode [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. + with_predict_proba + [Optional] True, if models in models_config have a predict_proba method and can return probabilities for predictions, + False, otherwise. Note that if it is set to False, only metrics based on labels (not labels and probabilities) will be computed. + Ignored when a postprocessor is not None, and set to False in this case. notebook_logs_stdout [Optional] True, if this interface was execute in a Jupyter notebook, False, otherwise. @@ -145,18 +159,26 @@ def run_metrics_computation_with_multiple_test_sets(dataset: BaseFlowDataset, bo print('#' * 30, f' [Model {model_idx + 1} / {num_models}] Analyze {model_name} ', '#' * 30) try: base_model = models_config[model_name] + computation_start_date_time = datetime.now() model_metrics_dfs_lst = compute_one_model_metrics_with_multiple_test_sets(base_model=base_model, n_estimators=n_estimators, dataset=dataset, extra_test_sets_lst=extra_test_sets_lst, bootstrap_fraction=bootstrap_fraction, sensitive_attributes_dct=sensitive_attributes_dct, + random_state=random_state, model_setting=model_setting, computation_mode=computation_mode, dataset_name=dataset_name, base_model_name=model_name, + with_predict_proba=with_predict_proba, notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) + computation_end_date_time = datetime.now() + computation_runtime = (computation_end_date_time - computation_start_date_time).total_seconds() / 60.0 + for model_metrics_df in model_metrics_dfs_lst: + model_metrics_df['Runtime_in_Mins'] = computation_runtime + models_metrics_dct[model_name] = model_metrics_dfs_lst except Exception as err: print('#' * 20, f'ERROR with {model_name}', '#' * 20) @@ -171,10 +193,10 @@ def run_metrics_computation_with_multiple_test_sets(dataset: BaseFlowDataset, bo def compute_one_model_metrics_with_multiple_test_sets(base_model, n_estimators: int, dataset: BaseFlowDataset, extra_test_sets_lst: list, bootstrap_fraction: float, sensitive_attributes_dct: dict, - dataset_name: str, base_model_name: str, + dataset_name: str, base_model_name: str, random_state: int = None, model_setting: str = ModelSetting.BATCH.value, - computation_mode: str = None, notebook_logs_stdout: bool = False, - verbose: int = 0): + computation_mode: str = None, with_predict_proba: bool = True, + notebook_logs_stdout: bool = False, verbose: int = 0): """ Compute subgroup metrics for the base model based on dataset.X_test and each extra test set in extra_test_sets_lst. Save results in `save_results_dir_path` folder. @@ -190,7 +212,7 @@ def compute_one_model_metrics_with_multiple_test_sets(base_model, n_estimators: dataset BaseFlowDataset object that contains all needed attributes like target, features, numerical_columns etc. extra_test_sets_lst - List of extra test sets like [(X_test1, y_test1, init_features_df1), (X_test2, y_test2, init_features_df2), ...] + List of extra test sets like [(X_test1, y_test1, init_sensitive_attrs_df1), (X_test2, y_test2, init_sensitive_attrs_df2), ...] to compute metrics. bootstrap_fraction Fraction of a train set in range [0.0 - 1.0] to fit models in bootstrap @@ -201,10 +223,16 @@ def compute_one_model_metrics_with_multiple_test_sets(base_model, n_estimators: Dataset name to name a result file with metrics base_model_name Model name to name a result file with metrics + random_state + [Optional] Controls the randomness of the bootstrap approach for model arbitrariness evaluation model_setting - Currently, only batch models are supported. Default: 'batch'. + [Optional] Currently, only batch models are supported. Default: 'batch'. computation_mode [Optional] A non-default mode for metrics computation. Should be included in the ComputationMode enum. + with_predict_proba + [Optional] True, if models in models_config have a predict_proba method and can return probabilities for predictions, + False, otherwise. Note that if it is set to False, only metrics based on labels (not labels and probabilities) will be computed. + Ignored when a postprocessor is not None, and set to False in this case. notebook_logs_stdout [Optional] True, if this interface was execute in a Jupyter notebook, False, otherwise. @@ -223,14 +251,16 @@ def compute_one_model_metrics_with_multiple_test_sets(base_model, n_estimators: dataset_name=dataset_name, sensitive_attributes_dct=sensitive_attributes_dct, test_protected_groups=dict(), # stub for this attribute + random_state=random_state, computation_mode=computation_mode, + with_predict_proba=with_predict_proba, notebook_logs_stdout=notebook_logs_stdout, verbose=verbose) - test_sets_lst = [(dataset.X_test, dataset.y_test, dataset.init_features_df)] + extra_test_sets_lst + test_sets_lst = [(dataset.X_test, dataset.y_test, dataset.init_sensitive_attrs_df)] + extra_test_sets_lst all_test_sets_metrics_lst = [] - for set_idx, (new_X_test, new_y_test, cur_init_features_df) in enumerate(test_sets_lst): - new_test_protected_groups = create_test_protected_groups(new_X_test, cur_init_features_df, sensitive_attributes_dct) + for set_idx, (new_X_test, new_y_test, cur_init_sensitive_attrs_df) in enumerate(test_sets_lst): + new_test_protected_groups = create_test_protected_groups(new_X_test, cur_init_sensitive_attrs_df, sensitive_attributes_dct) if verbose >= 2: print(f'\nProtected groups splits for test set index #{set_idx}:') for g in new_test_protected_groups.keys(): @@ -264,6 +294,7 @@ def compute_one_model_metrics_with_multiple_test_sets(base_model, n_estimators: metrics_df = metrics_df.rename(columns={"index": "Metric"}) metrics_df['Model_Name'] = base_model_name metrics_df['Model_Params'] = str(base_model.get_params()) + metrics_df['Virny_Random_State'] = random_state all_test_sets_metrics_lst.append(metrics_df) diff --git a/virny/utils/common_helpers.py b/virny/utils/common_helpers.py index 87a456a8..6c44d255 100644 --- a/virny/utils/common_helpers.py +++ b/virny/utils/common_helpers.py @@ -23,7 +23,9 @@ def validate_config(config_obj): Object with parameters defined in a yaml file """ - # ================== Required parameters ================== + # ============================================================================================================ + # Required parameters + # ============================================================================================================ if not isinstance(config_obj.dataset_name, str): raise ValueError('dataset_name must be string') @@ -54,7 +56,9 @@ def validate_config(config_obj): raise ValueError('Intersectional attributes in sensitive_attributes_dct must contain ' 'single sensitive attributes that also exist in sensitive_attributes_dct') - # ================== Optional parameters ================== + # ============================================================================================================ + # Optional parameters + # ============================================================================================================ if config_obj.model_setting is not None \ and not isinstance(config_obj.model_setting, str) \ and config_obj.model_setting not in ModelSetting: @@ -67,6 +71,14 @@ def validate_config(config_obj): raise ValueError('computation_mode must be a string that is included in the ComputationMode enum. ' 'Refer to this function documentation for more details!') + # ============================================================================================================ + # Default parameters + # ============================================================================================================ + if 'postprocessing_sensitive_attribute' not in config_obj: + config_obj.postprocessing_sensitive_attribute = None + if 'random_state' not in config_obj: + config_obj.random_state = None + return True @@ -84,15 +96,6 @@ def str_to_float(str_var: str, var_name: str): raise ValueError(f"{var_name} must be a float number with a '.' separator.") -def reset_model_seed(model, new_seed, verbose): - if 'random_state' in model.get_params(): - model.set_params(random_state=new_seed) - if verbose >= 1: - print('Model seed: ', model.get_params().get('random_state', None)) - - return model - - def save_metrics_to_file(metrics_df, result_filename, save_dir_path): os.makedirs(save_dir_path, exist_ok=True) diff --git a/virny/utils/data_viz_utils.py b/virny/utils/data_viz_utils.py index b5d97133..8a762d89 100644 --- a/virny/utils/data_viz_utils.py +++ b/virny/utils/data_viz_utils.py @@ -9,6 +9,7 @@ from virny.configs.constants import * from virny.utils.common_helpers import check_substring_in_list +from virny.utils.protected_groups_partitioning import create_test_protected_groups def rank_with_tolerance(pd_series: pd.Series, tolerance: float = 0.001): @@ -101,7 +102,7 @@ def create_subgroup_sorted_matrix_by_rank(model_metrics_matrix, tolerance) -> np models_distances_matrix = model_metrics_matrix.copy(deep=True).T metric_names = models_distances_matrix.columns for metric_name in metric_names: - if check_substring_in_list(metric_name, ['TPR', 'TNR', 'PPV', 'Accuracy', 'F1', 'Label_Stability', 'Positive-Rate']): + if check_substring_in_list(metric_name, ['TPR', 'TNR', 'PPV', 'Accuracy', 'F1', 'Label_Stability']): # Cast a metric to a case when the closer value to one is the better models_distances_matrix[metric_name] = 1 - models_distances_matrix[metric_name] models_distances_matrix[metric_name] = models_distances_matrix[metric_name].abs() @@ -115,6 +116,39 @@ def create_subgroup_sorted_matrix_by_rank(model_metrics_matrix, tolerance) -> np return sorted_matrix_by_rank +def create_dataset_stats_bar_chart(X_data, y_data, sensitive_attrs_dct): + # Partition on protected groups + protected_groups = create_test_protected_groups(X_data, X_data, sensitive_attrs_dct) + + # Create a df with group proportions and group base rates + subgroup_proportions_dct = compute_proportions(protected_groups, X_data) + subgroup_base_rates_dct = compute_base_rates(protected_groups, y_data) + subgroup_relative_base_rates_dct = dict() + for subgroup in subgroup_proportions_dct.keys(): + pct = subgroup_base_rates_dct[subgroup]['percentage'] * subgroup_proportions_dct[subgroup]['percentage'] + subgroup_relative_base_rates_dct[subgroup] = {'percentage': pct, 'num_rows': subgroup_base_rates_dct[subgroup]['num_rows']} + + stats_df = pd.DataFrame(columns=['Subgroup', 'Percentage', 'Num_Rows', 'Statistics_Type']) + for subgroup in subgroup_proportions_dct.keys(): + stats_df.loc[len(stats_df.index)] = [subgroup, subgroup_proportions_dct[subgroup]['percentage'], + subgroup_proportions_dct[subgroup]['num_rows'], 'Proportion'] + stats_df.loc[len(stats_df.index)] = [subgroup, subgroup_relative_base_rates_dct[subgroup]['percentage'], + subgroup_relative_base_rates_dct[subgroup]['num_rows'], 'Base_Rate'] + + # Create a row facet bar chart + facet_sort_by_lst = ['overall'] + [grp for grp in stats_df.Subgroup.unique() if grp.lower() != 'overall'] + col_facet_bar_chart = create_col_facet_bar_chart(stats_df, + x_col='Statistics_Type', + y_col='Num_Rows', + facet_column_name='Subgroup', + text_labels_column='Percentage', + x_sort_by_lst=['Proportion', 'Base_Rate'], + facet_sort_by_lst=facet_sort_by_lst, + color_legend_title='Statistics Type', + facet_title='') + return col_facet_bar_chart + + def create_col_facet_bar_chart(df, x_col, y_col, facet_column_name, text_labels_column, x_sort_by_lst=Undefined, facet_sort_by_lst=Undefined, color_legend_title=Undefined, facet_title=Undefined): num_facets = len(df[facet_column_name].unique()) @@ -393,7 +427,6 @@ def create_bar_plot_for_model_selection(all_subgroup_metrics_per_model_dct: dict PPV: 'C1', ACCURACY: 'C1', F1: 'C1', - POSITIVE_RATE: 'C1', # C2 EQUALIZED_ODDS_TPR: 'C2', EQUALIZED_ODDS_FPR: 'C2', diff --git a/virny/utils/protected_groups_partitioning.py b/virny/utils/protected_groups_partitioning.py index e13a20b6..c0684062 100644 --- a/virny/utils/protected_groups_partitioning.py +++ b/virny/utils/protected_groups_partitioning.py @@ -52,7 +52,7 @@ def check_sensitive_attrs_in_columns(df_columns, sensitive_attributes_dct): return True -def create_test_protected_groups(X_test: pd.DataFrame, init_features_df: pd.DataFrame, sensitive_attributes_dct: dict): +def create_test_protected_groups(X_test: pd.DataFrame, init_sensitive_attrs_df: pd.DataFrame, sensitive_attributes_dct: dict): """ Create protected groups based on a test feature set. Use a disadvantaged group as a reference group. @@ -62,8 +62,8 @@ def create_test_protected_groups(X_test: pd.DataFrame, init_features_df: pd.Data ---------- X_test Test feature set - init_features_df - Initial full dataset without preprocessing + init_sensitive_attrs_df + Initial full dataset of sensitive attributes without preprocessing sensitive_attributes_dct A dictionary where keys are sensitive attribute names (including attributes intersections), and values are disadvantaged values for these attributes @@ -74,12 +74,12 @@ def create_test_protected_groups(X_test: pd.DataFrame, init_features_df: pd.Data # Check spelling of sensitive attributes attrs_with_errors = [] for attr in plain_sensitive_attributes: - if attr not in init_features_df.columns: + if attr not in init_sensitive_attrs_df.columns: attrs_with_errors.append(attr) if len(attrs_with_errors) > 0: raise ValueError(f"At least one of sensitive attributes is not in dataset columns. Check spelling of {attrs_with_errors} attributes.") - X_test_with_sensitive_attrs = init_features_df[plain_sensitive_attributes].loc[X_test.index] + X_test_with_sensitive_attrs = init_sensitive_attrs_df[plain_sensitive_attributes].loc[X_test.index] groups = dict() for attr in sensitive_attributes_dct.keys(): attr = attr.strip() diff --git a/virny/utils/stability_utils.py b/virny/utils/stability_utils.py index 2ad46179..c0612d4c 100644 --- a/virny/utils/stability_utils.py +++ b/virny/utils/stability_utils.py @@ -83,8 +83,12 @@ def count_prediction_metrics(y_true, uq_results, with_predict_proba: bool = True return y_preds, metrics_dct -def generate_bootstrap(features, labels, boostrap_size, with_replacement=True): - bootstrap_index = np.random.choice(features.shape[0], size=boostrap_size, replace=with_replacement) +def generate_bootstrap(features, labels, boostrap_size, with_replacement=True, random_state=None): + # Create a local random state + rng = np.random.RandomState(random_state) + + # Generate bootstrapped indexes + bootstrap_index = rng.choice(features.shape[0], size=boostrap_size, replace=with_replacement) bootstrap_features = pd.DataFrame(features).iloc[bootstrap_index] bootstrap_labels = pd.DataFrame(labels).iloc[bootstrap_index] if len(bootstrap_features) == boostrap_size: