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import os | |
import pandas as pd | |
from aif360.datasets import RegressionDataset | |
try: | |
import tempeh.configurations as tc | |
except ImportError as error: | |
from logging import warning | |
warning("{}: LawSchoolGPADataset will be unavailable. To install, run:\n" | |
"pip install 'aif360[LawSchoolGPA]'".format(error)) | |
class LawSchoolGPADataset(RegressionDataset): | |
"""Law School GPA dataset. | |
See https://github.com/microsoft/tempeh for details. | |
""" | |
def __init__(self, dep_var_name='zfygpa', | |
protected_attribute_names=['race'], | |
privileged_classes=[['white']], | |
instance_weights_name=None, | |
categorical_features=[], | |
na_values=[], custom_preprocessing=None, | |
metadata=None): | |
"""See :obj:`RegressionDataset` for a description of the arguments.""" | |
dataset = tc.datasets["lawschool_gpa"]() | |
X_train,X_test = dataset.get_X(format=pd.DataFrame) | |
y_train, y_test = dataset.get_y(format=pd.Series) | |
A_train, A_test = dataset.get_sensitive_features(name='race', | |
format=pd.Series) | |
all_train = pd.concat([X_train, y_train, A_train], axis=1) | |
all_test = pd.concat([X_test, y_test, A_test], axis=1) | |
df = pd.concat([all_train, all_test], axis=0) | |
super(LawSchoolGPADataset, self).__init__(df=df, | |
dep_var_name=dep_var_name, | |
protected_attribute_names=protected_attribute_names, | |
privileged_classes=privileged_classes, | |
instance_weights_name=instance_weights_name, | |
categorical_features=categorical_features, | |
na_values=na_values, | |
custom_preprocessing=custom_preprocessing, metadata=metadata) | |