Source code for

__all__ = ['AdultDataset']

import numpy as np
import pandas as pd
from pkg_resources import resource_filename

from ethically.dataset.core import Dataset

ADULT_TRAIN_PATH = resource_filename(__name__,
ADULT_TEST_PATH = resource_filename(__name__,

COLUMN_NAMES = ['age', 'workclass', 'fnlwgt', 'education',
                'education-num', 'marital_status', 'occupation',
                'relationship', 'race', 'sex', 'capital_gain',
                'capital_loss', 'hours_per_week', 'native_country',

[docs]class AdultDataset(Dataset): """Adult Dataset. See :class:`~ethically.dataset.Dataset` for a description of the arguments and attributes. References: """ def __init__(self): super().__init__(target='income_per_year', sensitive_attributes=['sex', 'race']) def _load_data(self): train_df = pd.read_csv(ADULT_TRAIN_PATH, names=COLUMN_NAMES, skipinitialspace=True, header=None, index_col=False, na_values='?') test_df = pd.read_csv(ADULT_TEST_PATH, names=COLUMN_NAMES, skipinitialspace=True, header=0, index_col=False, na_values='?') train_df['dataset'] = 'train' test_df['dataset'] = 'test' return pd.concat([train_df, test_df], ignore_index=True) def _preprocess(self): """Perform the same preprocessing as the dataset doc file.""" self.df = self.df.dropna() self.df = self.df.drop(['fnlwgt'], axis=1) self.df['income_per_year'] = (self.df['income_per_year'] .str .replace('.', '')) def _validate(self): # pylint: disable=line-too-long super()._validate() assert len(self.df) == 45222, 'the number of rows should be 45222,'\ ' but it is {}.'.format(len(self.df)) assert len(self.df.columns) == 15, 'the number of columns should be 15,'\ ' but it is {}.'.format(len(self.df.columns)) train_df = self.df[self.df['dataset'] == 'train'] test_df = self.df[self.df['dataset'] == 'test'] assert len(train_df) == 30162, 'the number of train rows should be 30162,'\ ' but it is {}.'.format(len(train_df)) assert len(test_df) == 15060, 'the number of train rows should be 15060,'\ ' but it is {}.'.format(len(test_df))