BiasResamplingClassifier#

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

Classifier which resamples 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.

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

Determines how the group weights are computed. Group weights determine how much to over or undersample each combination of target and sensitive feature. For example, a weight of 2 for the pair (y_true == 1, sensitive_feature == 0) means that the resampled dataset should have twice as many instances with y_true == 1 and sensitive_feature == 0 compared to the original dataset.

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

  • Callable: function which computes the group weights based on the target and sensitive feature. Callable accepts two arguments: y_true and sensitive_feature and returns the group weights. Group weights are a 2x2 matrix where the rows represent the target variable and the columns represent the sensitive feature. The element at position (i, j) is the weight for the pair (y_true == i, sensitive_feature == j).

transform_featureOptional[Callable], default=None

Function which transforms sensitive feature before resampling the training data.

Attributes:
classes_numpy.ndarray, shape=(n_classes,)

Unique classes in the target.

estimator_Estimator instance

Fitted base estimator.

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 BiasResamplingClassifier 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 BiasResamplingClassifier

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

model = BiasResamplingClassifier(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 BiasResamplingClassifier

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

model = BiasResamplingClassifier(
    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 BiasResamplingClassifier

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):
    return np.array([
        [1, 2],
        [1, 2]
    ])

model = BiasResamplingClassifier(
    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 BiasResamplingClassifier

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', BiasResamplingClassifier(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 BiasResamplingClassifier

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', BiasResamplingClassifier(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 resample the instances according to the strategy.

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

Training data.

y1D array-like, shape=(n_samples,)

Target values.

sensitive_feature1D array-like, shape=(n_samples,), default = None

Sensitive feature used to determine the group weights.

fit_paramsdict

Additional parameters passed to the estimator’s fit method.

Returns:
selfBiasResamplingClassifier
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)
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)
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.