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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)
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