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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) | |