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__all__ = ['COMPASDataset'] |
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import numpy as np |
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import pandas as pd |
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from pkg_resources import resource_filename |
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from ethically.dataset.core import Dataset |
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COMPAS_PATH = resource_filename(__name__, |
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'compas-scores-two-years.csv') |
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class COMPASDataset(Dataset): |
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"""ProPublica Recidivism/COMPAS Dataset. |
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See :class:`~ethically.dataset.Dataset` for a description of |
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the arguments and attributes. |
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References: |
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https://github.com/propublica/compas-analysis |
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""" |
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def __init__(self): |
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super().__init__(target='is_recid', |
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sensitive_attributes=['race', 'sex'], |
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prediction=['y_pred', 'score_factor', |
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'score_text']) |
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def _load_data(self): |
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return pd.read_csv(COMPAS_PATH) |
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def _preprocess(self): |
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"""Perform the same preprocessing as the original analysis. |
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https://github.com/propublica/compas-analysis/blob/master/Compas%20Analysis.ipynb |
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""" |
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self.df = self.df[(self.df['days_b_screening_arrest'] <= 30) |
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& (self.df['days_b_screening_arrest'] >= -30) |
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& (self.df['is_recid'] != -1) |
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& (self.df['c_charge_degree'] != 'O') |
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& (self.df['score_text'] != 'N/A')] |
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self.df['c_jail_out'] = pd.to_datetime(self.df['c_jail_out']) |
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self.df['c_jail_in'] = pd.to_datetime(self.df['c_jail_in']) |
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self.df['length_of_stay'] = (self.df['c_jail_out'] |
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- self.df['c_jail_in']) |
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self.df['score_factor'] = np.where(self.df['score_text'] |
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!= 'Low', |
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'HighScore', 'LowScore') |
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self.df['y_pred'] = (self.df['score_factor'] == 'HighScore') |
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def _validate(self): |
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# pylint: disable=line-too-long |
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super()._validate() |
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assert len(self.df) == 6172, 'the number of rows should be 6172,'\ |
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' but it is {}.'.format(len(self.df)) |
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assert len(self.df.columns) == 56, 'the number of columns should be 56,'\ |
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' but it is {}.'.format(len(self.df.columns)) |
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ResponsiblyAI /
responsibly
