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import numpy as np
from sklearn.base import BaseEstimator, MetaEstimatorMixin, clone
from sklearn.utils.metaestimators import if_delegate_has_method
from sklearn.utils.validation import has_fit_parameter
from aif360.sklearn.utils import check_inputs, check_groups
class Reweighing(BaseEstimator):
"""Sample reweighing.
Reweighing is a preprocessing technique that weights the examples in each
(group, label) combination differently to ensure fairness before
classification [#kamiran12]_.
Note:
This breaks the scikit-learn API by returning new sample weights from
``fit_transform()``. See :class:`ReweighingMeta` for a workaround.
See also:
:class:`ReweighingMeta`
References:
.. [#kamiran12] `F. Kamiran and T. Calders, "Data Preprocessing
Techniques for Classification without Discrimination," Knowledge and
Information Systems, 2012.
<https://link.springer.com/article/10.1007/s10115-011-0463-8>`_
Attributes:
prot_attr_ (str or list(str)): Protected attribute(s) used for
reweighing.
groups_ (array, shape (n_groups,)): A list of group labels known to the
transformer.
classes_ (array, shape (n_classes,)): A list of class labels known to
the transformer.
reweigh_factors_ (array, shape (n_groups, n_labels)): Reweighing factors
for each combination of group and class labels used to debias
samples. Existing sample weights are multiplied by the corresponding
factor for that sample's group and class.
"""
def __init__(self, prot_attr=None):
"""
Args:
prot_attr (single label or list-like, optional): Protected
attribute(s) to use in the reweighing process. If more than one
attribute, all combinations of values (intersections) are
considered. Default is ``None`` meaning all protected attributes
from the dataset are used.
"""
self.prot_attr = prot_attr
def fit(self, X, y, sample_weight=None):
"""Only :meth:`fit_transform` is allowed for this algorithm."""
self.fit_transform(X, y, sample_weight=sample_weight)
return self
def fit_transform(self, X, y, sample_weight=None):
"""Compute the factors for reweighing the dataset and transform the
sample weights.
Args:
X (pandas.DataFrame): Training samples.
y (array-like): Training labels.
sample_weight (array-like, optional): Sample weights.
Returns:
tuple:
Samples and their weights.
* **X** -- Unchanged samples.
* **sample_weight** -- Transformed sample weights.
"""
X, y, sample_weight = check_inputs(X, y, sample_weight)
sample_weight_t = np.empty_like(sample_weight)
groups, self.prot_attr_ = check_groups(X, self.prot_attr)
# TODO: maintain categorical ordering
self.groups_ = np.unique(groups)
self.classes_ = np.unique(y)
n_groups = len(self.groups_)
n_classes = len(self.classes_)
self.reweigh_factors_ = np.full((n_groups, n_classes), np.nan)
def N_(i): return sample_weight[i].sum()
N = sample_weight.sum()
for i, g in enumerate(self.groups_):
for j, c in enumerate(self.classes_):
g_and_c = (groups == g) & (y == c)
if np.any(g_and_c):
W_gc = N_(groups == g) * N_(y == c) / (N * N_(g_and_c))
sample_weight_t[g_and_c] = W_gc * sample_weight[g_and_c]
self.reweigh_factors_[i, j] = W_gc
return X, sample_weight_t
class ReweighingMeta(BaseEstimator, MetaEstimatorMixin):
"""A meta-estimator which wraps a given estimator with a reweighing
preprocessing step.
This is necessary for use in a Pipeline, etc.
Attributes:
estimator_ (sklearn.BaseEstimator): The fitted underlying estimator.
reweigher_: The fitted underlying reweigher.
classes_ (array, shape (n_classes,)): Class labels from `estimator_`.
"""
def __init__(self, estimator, reweigher=None):
"""
Args:
estimator (sklearn.BaseEstimator): Estimator to be wrapped.
reweigher (optional): Preprocessor which returns new sample weights
from ``transform()``. If ``None``, defaults to
:class:`~aif360.sklearn.preprocessing.Reweighing`.
"""
self.reweigher = reweigher
self.estimator = estimator
@property
def _estimator_type(self):
return self.estimator._estimator_type
@property
def classes_(self):
"""Class labels from the base estimator."""
return self.estimator_.classes_
def fit(self, X, y, sample_weight=None):
"""Performs ``self.reweigher_.fit_transform(X, y, sample_weight)`` and
then ``self.estimator_.fit(X, y, sample_weight)`` using the reweighed
samples.
Args:
X (pandas.DataFrame): Training samples.
y (array-like): Training labels.
sample_weight (array-like, optional): Sample weights.
Returns:
self
"""
if not has_fit_parameter(self.estimator, 'sample_weight'):
raise TypeError("`estimator` (type: {}) does not have fit parameter"
" `sample_weight`.".format(type(self.estimator)))
if self.reweigher is None:
self.reweigher_ = Reweighing()
else:
self.reweigher_ = clone(self.reweigher)
self.estimator_ = clone(self.estimator)
X, sample_weight = self.reweigher_.fit_transform(X, y,
sample_weight=sample_weight)
self.estimator_.fit(X, y, sample_weight=sample_weight)
return self
@if_delegate_has_method('estimator_')
def predict(self, X):
"""Predict class labels for the given samples using ``self.estimator_``.
Args:
X (array-like): Test samples.
Returns:
array: Predicted class label per sample.
"""
return self.estimator_.predict(X)
@if_delegate_has_method('estimator_')
def predict_proba(self, X):
"""Probability estimates from ``self.estimator_``.
The returned estimates for all classes are ordered by the label of
classes.
Args:
X (array-like): Test samples.
Returns:
array: Returns the probability of the sample for each class in the
model, where classes are ordered as they are in ``self.classes_``.
"""
return self.estimator_.predict_proba(X)
@if_delegate_has_method('estimator_')
def predict_log_proba(self, X):
"""Log of probability estimates from ``self.estimator_``.
The returned estimates for all classes are ordered by the label of
classes.
Args:
X (array-like): Test samples.
Returns:
array: Returns the log-probability of the sample for each class in
the model, where classes are ordered as they are in
``self.classes_``.
"""
return self.estimator_.predict_log_proba(X)
@if_delegate_has_method('estimator_')
def score(self, X, y, sample_weight=None):
"""Returns the output of the estimator's score function on the given
test data and labels.
Args:
X (array-like): Test samples.
y (array-like): True labels for X.
sample_weight (array-like, optional): Sample weights.
Returns:
float: `self.estimator.score(X, y, sample_weight)`
"""
return self.estimator_.score(X, y, sample_weight=sample_weight)
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