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import os
import pandas as pd
from aif360.datasets import StandardDataset
class BankDataset(StandardDataset):
"""Bank marketing Dataset.
See :file:`aif360/data/raw/bank/README.md`.
"""
def __init__(self, label_name='y', favorable_classes=['yes'],
protected_attribute_names=['age'],
privileged_classes=[lambda x: x >= 25],
instance_weights_name=None,
categorical_features=['job', 'marital', 'education', 'default',
'housing', 'loan', 'contact', 'month', 'day_of_week',
'poutcome'],
features_to_keep=[], features_to_drop=[],
na_values=["unknown"], custom_preprocessing=None,
metadata=None):
"""See :obj:`StandardDataset` for a description of the arguments.
By default, this code converts the 'age' attribute to a binary value
where privileged is `age >= 25` and unprivileged is `age < 25` as in
:obj:`GermanDataset`.
"""
filepath = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'..', 'data', 'raw', 'bank', 'bank-additional-full.csv')
try:
df = pd.read_csv(filepath, sep=';', 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://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank-additional.zip")
print("\nunzip it and place the files, as-is, in the folder:")
print("\n\t{}\n".format(os.path.abspath(os.path.join(
os.path.abspath(__file__), '..', '..', 'data', 'raw', 'bank'))))
import sys
sys.exit(1)
super(BankDataset, 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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