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from logging import warning
import numpy as np
import pandas as pd
from aif360.datasets import BinaryLabelDataset
class StandardDataset(BinaryLabelDataset):
"""Base class for every :obj:`BinaryLabelDataset` provided out of the box by
aif360.
It is not strictly necessary to inherit this class when adding custom
datasets but it may be useful.
This class is very loosely based on code from
https://github.com/algofairness/fairness-comparison.
"""
def __init__(self, df, label_name, favorable_classes,
protected_attribute_names, privileged_classes,
instance_weights_name='', scores_name='',
categorical_features=[], features_to_keep=[],
features_to_drop=[], na_values=[], custom_preprocessing=None,
metadata=None):
"""
Subclasses of StandardDataset should perform the following before
calling `super().__init__`:
1. Load the dataframe from a raw file.
Then, this class will go through a standard preprocessing routine which:
2. (optional) Performs some dataset-specific preprocessing (e.g.
renaming columns/values, handling missing data).
3. Drops unrequested columns (see `features_to_keep` and
`features_to_drop` for details).
4. Drops rows with NA values.
5. Creates a one-hot encoding of the categorical variables.
6. Maps protected attributes to binary privileged/unprivileged
values (1/0).
7. Maps labels to binary favorable/unfavorable labels (1/0).
Args:
df (pandas.DataFrame): DataFrame on which to perform standard
processing.
label_name: Name of the label column in `df`.
favorable_classes (list or function): Label values which are
considered favorable or a boolean function which returns `True`
if favorable. All others are unfavorable. Label values are
mapped to 1 (favorable) and 0 (unfavorable) if they are not
already binary and numerical.
protected_attribute_names (list): List of names corresponding to
protected attribute columns in `df`.
privileged_classes (list(list or function)): Each element is
a list of values which are considered privileged or a boolean
function which return `True` if privileged for the corresponding
column in `protected_attribute_names`. All others are
unprivileged. Values are mapped to 1 (privileged) and 0
(unprivileged) if they are not already numerical.
instance_weights_name (optional): Name of the instance weights
column in `df`.
categorical_features (optional, list): List of column names in the
DataFrame which are to be expanded into one-hot vectors.
features_to_keep (optional, list): Column names to keep. All others
are dropped except those present in `protected_attribute_names`,
`categorical_features`, `label_name` or `instance_weights_name`.
Defaults to all columns if not provided.
features_to_drop (optional, list): Column names to drop. *Note: this
overrides* `features_to_keep`.
na_values (optional): Additional strings to recognize as NA. See
:func:`pandas.read_csv` for details.
custom_preprocessing (function): A function object which
acts on and returns a DataFrame (f: DataFrame -> DataFrame). If
`None`, no extra preprocessing is applied.
metadata (optional): Additional metadata to append.
"""
# 2. Perform dataset-specific preprocessing
if custom_preprocessing:
df = custom_preprocessing(df)
# 3. Drop unrequested columns
features_to_keep = features_to_keep or df.columns.tolist()
keep = (set(features_to_keep) | set(protected_attribute_names)
| set(categorical_features) | set([label_name]))
if instance_weights_name:
keep |= set([instance_weights_name])
df = df[sorted(keep - set(features_to_drop), key=df.columns.get_loc)]
categorical_features = sorted(set(categorical_features) - set(features_to_drop), key=df.columns.get_loc)
# 4. Remove any rows that have missing data.
dropped = df.dropna()
count = df.shape[0] - dropped.shape[0]
if count > 0:
warning("Missing Data: {} rows removed from {}.".format(count,
type(self).__name__))
df = dropped
# 5. Create a one-hot encoding of the categorical variables.
df = pd.get_dummies(df, columns=categorical_features, prefix_sep='=')
# 6. Map protected attributes to privileged/unprivileged
privileged_protected_attributes = []
unprivileged_protected_attributes = []
for attr, vals in zip(protected_attribute_names, privileged_classes):
privileged_values = [1.]
unprivileged_values = [0.]
if callable(vals):
df[attr] = df[attr].apply(vals)
elif np.issubdtype(df[attr].dtype, np.number):
# this attribute is numeric; no remapping needed
privileged_values = vals
unprivileged_values = list(set(df[attr]).difference(vals))
else:
# find all instances which match any of the attribute values
priv = np.logical_or.reduce(np.equal.outer(vals, df[attr].to_numpy()))
df.loc[priv, attr] = privileged_values[0]
df.loc[~priv, attr] = unprivileged_values[0]
privileged_protected_attributes.append(
np.array(privileged_values, dtype=np.float64))
unprivileged_protected_attributes.append(
np.array(unprivileged_values, dtype=np.float64))
# 7. Make labels binary
favorable_label = 1.
unfavorable_label = 0.
if callable(favorable_classes):
df[label_name] = df[label_name].apply(favorable_classes)
elif np.issubdtype(df[label_name], np.number) and len(set(df[label_name])) == 2:
# labels are already binary; don't change them
favorable_label = favorable_classes[0]
unfavorable_label = set(df[label_name]).difference(favorable_classes).pop()
else:
# find all instances which match any of the favorable classes
pos = np.logical_or.reduce(np.equal.outer(favorable_classes,
df[label_name].to_numpy()))
df.loc[pos, label_name] = favorable_label
df.loc[~pos, label_name] = unfavorable_label
super(StandardDataset, self).__init__(df=df, label_names=[label_name],
protected_attribute_names=protected_attribute_names,
privileged_protected_attributes=privileged_protected_attributes,
unprivileged_protected_attributes=unprivileged_protected_attributes,
instance_weights_name=instance_weights_name,
scores_names=[scores_name] if scores_name else [],
favorable_label=favorable_label,
unfavorable_label=unfavorable_label, metadata=metadata)
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