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| import os | |
| import pandas as pd | |
| from aif360.datasets import StandardDataset | |
| default_mappings = { | |
| 'label_maps': [{1.0: 'Did recid.', 0.0: 'No recid.'}], | |
| 'protected_attribute_maps': [{0.0: 'Male', 1.0: 'Female'}, | |
| {1.0: 'Caucasian', 0.0: 'Not Caucasian'}] | |
| } | |
| def default_preprocessing(df): | |
| """Perform the same preprocessing as the original analysis: | |
| https://github.com/propublica/compas-analysis/blob/master/Compas%20Analysis.ipynb | |
| """ | |
| return df[(df.days_b_screening_arrest <= 30) | |
| & (df.days_b_screening_arrest >= -30) | |
| & (df.is_recid != -1) | |
| & (df.c_charge_degree != 'O') | |
| & (df.score_text != 'N/A')] | |
| class CompasDataset(StandardDataset): | |
| """ProPublica COMPAS Dataset. | |
| See :file:`aif360/data/raw/compas/README.md`. | |
| """ | |
| def __init__(self, label_name='two_year_recid', favorable_classes=[0], | |
| protected_attribute_names=['sex', 'race'], | |
| privileged_classes=[['Female'], ['Caucasian']], | |
| instance_weights_name=None, | |
| categorical_features=['age_cat', 'c_charge_degree', | |
| 'c_charge_desc'], | |
| features_to_keep=['sex', 'age', 'age_cat', 'race', | |
| 'juv_fel_count', 'juv_misd_count', 'juv_other_count', | |
| 'priors_count', 'c_charge_degree', 'c_charge_desc', | |
| 'two_year_recid'], | |
| features_to_drop=[], na_values=[], | |
| custom_preprocessing=default_preprocessing, | |
| metadata=default_mappings): | |
| """See :obj:`StandardDataset` for a description of the arguments. | |
| Note: The label value 0 in this case is considered favorable (no | |
| recidivism). | |
| Examples: | |
| In some cases, it may be useful to keep track of a mapping from | |
| `float -> str` for protected attributes and/or labels. If our use | |
| case differs from the default, we can modify the mapping stored in | |
| `metadata`: | |
| >>> label_map = {1.0: 'Did recid.', 0.0: 'No recid.'} | |
| >>> protected_attribute_maps = [{1.0: 'Male', 0.0: 'Female'}] | |
| >>> cd = CompasDataset(protected_attribute_names=['sex'], | |
| ... privileged_classes=[['Male']], metadata={'label_map': label_map, | |
| ... 'protected_attribute_maps': protected_attribute_maps}) | |
| Now this information will stay attached to the dataset and can be | |
| used for more descriptive visualizations. | |
| """ | |
| filepath = os.path.join(os.path.dirname(os.path.abspath(__file__)), | |
| '..', 'data', 'raw', 'compas', 'compas-scores-two-years.csv') | |
| try: | |
| df = pd.read_csv(filepath, index_col='id', na_values=na_values) | |
| except IOError as err: | |
| print("IOError: {}".format(err)) | |
| print("To use this class, please download the following file:") | |
| print("\n\thttps://raw.githubusercontent.com/propublica/compas-analysis/master/compas-scores-two-years.csv") | |
| print("\nand place it, as-is, in the folder:") | |
| print("\n\t{}\n".format(os.path.abspath(os.path.join( | |
| os.path.abspath(__file__), '..', '..', 'data', 'raw', 'compas')))) | |
| import sys | |
| sys.exit(1) | |
| super(CompasDataset, self).__init__(df=df, label_name=label_name, | |
| favorable_classes=favorable_classes, | |
| protected_attribute_names=protected_attribute_names, | |
| privileged_classes=privileged_classes, | |
| instance_weights_name=instance_weights_name, | |
| categorical_features=categorical_features, | |
| features_to_keep=features_to_keep, | |
| features_to_drop=features_to_drop, na_values=na_values, | |
| custom_preprocessing=custom_preprocessing, metadata=metadata) | |