BiasReweighingClassifier#

class empulse.models.BiasReweighingClassifier(estimator, *, strategy='statistical parity', transform_feature=None)[source]#

Classifier which reweighs instances during training to remove bias against a subgroup.

Read more in the User Guide.

Parameters:
estimatorEstimator instance

Base estimator which is used for fitting and predicting. Base estimator must accept sample_weight as an argument in its fit method.

strategy{‘statistical parity’, ‘demographic parity’} or Callable, default=’statistical parity’

Determines how the sample weights are computed. Sample weights are passed to the estimator’s fit method.

  • 'statistical_parity' or 'demographic parity': probability of positive predictions are equal between subgroups of sensitive feature.

  • Callable: function which computes the sample weights based on the target and sensitive feature. Callable accepts two arguments: y_true and sensitive_feature and returns the sample weights. Sample weights are a numpy array where each represents the weight given to that respective instance. Sample weights should be normalized to fall between 0 and 1.

transform_featureOptional[Callable], default=None

Function which transforms sensitive feature before computing sample weights.

References

[1]

Rahman, S., Janssens, B., & Bogaert, M. (2025). Profit-driven pre-processing in B2B customer churn modeling using fairness techniques. Journal of Business Research, 189, 115159. doi:10.1016/j.jbusres.2024.115159

Examples

  1. Using the BiasReweighingClassifier with a logistic regression model:

import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
from empulse.models import BiasReweighingClassifier

X, y = make_classification()
high_clv = np.random.randint(0, 2, size=X.shape[0])

model = BiasReweighingClassifier(estimator=LogisticRegression())
model.fit(X, y, sensitive_feature=high_clv)

  1. Converting a continuous attribute to a binary attribute:

import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
from empulse.models import BiasReweighingClassifier

X, y = make_classification()
clv = np.random.rand(X.shape[0]) * 100

model = BiasReweighingClassifier(
    estimator=LogisticRegression(),
    transform_feature=lambda clv: (clv > np.quantile(clv, 0.8)).astype(int)
)
model.fit(X, y, sensitive_feature=clv)

  1. Using a custom strategy function:

import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
from empulse.models import BiasReweighingClassifier

X, y = make_classification()
high_clv = np.random.randint(0, 2, size=X.shape[0])

# Simple strategy to double the weight for the sensitive feature
def strategy(y_true, sensitive_feature):
    sample_weights = np.ones(len(sensitive_feature))
    sample_weights[np.where(sensitive_feature == 0)] = 0.5
    return sample_weights

model = BiasReweighingClassifier(
    estimator=LogisticRegression(),
    strategy=strategy
)
model.fit(X, y, sensitive_feature=high_clv)

  1. Passing the sensitive feature in a cross-validation grid search:

import numpy as np
from sklearn import config_context
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from empulse.models import BiasReweighingClassifier

with config_context(enable_metadata_routing=True):
    X, y = make_classification()
    high_clv = np.random.randint(0, 2, size=X.shape[0])

    param_grid = {'model__estimator__C': [0.1, 1, 10]}
    pipeline = Pipeline([
        ('scaler', StandardScaler()),
        ('model', BiasReweighingClassifier(LogisticRegression()).set_fit_request(sensitive_feature=True))
    ])
    search = GridSearchCV(pipeline, param_grid)
    search.fit(X, y, sensitive_feature=high_clv)

  1. Passing the sensitive feature through metadata routing in a cross-validation grid search:

import numpy as np
from sklearn import config_context
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from empulse.models import BiasReweighingClassifier

with config_context(enable_metadata_routing=True):
    X, y = make_classification()
    high_clv = np.random.randint(0, 2, size=X.shape[0])

    param_grid = {'model__estimator__C': [0.1, 1, 10]}
    pipeline = Pipeline([
        ('scaler', StandardScaler()),
        ('model', BiasReweighingClassifier(LogisticRegression()).set_fit_request(sensitive_feature=True))
    ])
    search = GridSearchCV(pipeline, param_grid)
    search.fit(X, y, sensitive_feature=high_clv)
fit(X, y, *, sensitive_feature=None, **fit_params)[source]#

Fit the estimator and reweigh the instances according to the strategy.

Parameters:
X2D array-like, shape=(n_samples, n_features)
y1D array-like, shape=(n_samples,)
sensitive_feature1D array-like, shape=(n_samples,), default = None

Sensitive attribute used to determine the sample weights.

fit_paramsdict

Additional parameters passed to the estimator’s fit method.

Returns:
selfBiasReweighingClassifier
get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

predict(X)[source]#

Predict class labels for X.

Parameters:
X2D array-like, shape=(n_samples, n_dim)

Features to predict.

Returns:
y_pred1D numpy.ndarray, shape=(n_samples,)

Predicted class labels.

predict_proba(X)[source]#

Predict class probabilities for X.

Parameters:
X2D array-like, shape=(n_samples, n_dim)

Features to predict.

Returns:
y_pred2D numpy.ndarray, shape=(n_samples, n_classes)

Predicted class probabilities.

score(X, y, sample_weight=None)#

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:
Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:
scorefloat

Mean accuracy of self.predict(X) w.r.t. y.

set_fit_request(*, sensitive_feature='$UNCHANGED$')#

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
sensitive_featurestr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sensitive_feature parameter in fit.

Returns:
selfobject

The updated object.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

set_score_request(*, sample_weight='$UNCHANGED$')#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:
selfobject

The updated object.