`bqlearn.irbl`.IRBL¶

class bqlearn.irbl.IRBL(base_estimator, final_estimator)[source]¶

A Reweighted Classifier for Biquality Learning.

An IRBL [1] classifier is a is a meta-algorithm that uses the covariate shift trick to reweight untrusted examples from two classifiers learned on the trusted and untrusted dataset.

Parameters:

base_estimatorobject, optional (default=None): The base estimator from which the IRBLClassifier is built. Support for probability prediction is required.
final_estimatorobject, optional (default=None): The final estimator from which the IRBLClassifier is built. Support for sample weighting is required.

Attributes:

final_estimator_classifier: The final fitted estimator.
sample_weight_ndarray, shape (n_samples,): The weights of the examples computed during fit().
classes_ndarray of shape (n_classes,): The classes labels.
n_classes_int: The number of classes.

References

[1]

Nodet, V. Lemaire, A. Bondu, A. Cornuéjols, “Importance Reweighting for Biquality Learning”, IJCNN, 2021.

Methods

`decision_function`(X)	Call decision function of the final_estimator.
`fit`(X, y[, sample_quality])	Fit the reweighted model.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict the classes of X.
`predict_log_proba`(X)	Predict log probability for each possible outcome.
`predict_proba`(X)	Predict probability for each possible outcome.
`score`(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
`set_fit_request`(*[, sample_quality])	Request metadata passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.

decision_function(X)[source]¶

Call decision function of the final_estimator.

Parameters:

Xarray-like, shape (n_samples, n_features): The input samples.

Returns:

yndarray, shape (n_samples,): The predicted classes.

fit(X, y, sample_quality=None)[source]¶

Fit the reweighted model.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.
yarray-like of shape (n_samples,): The target values (class labels in classification, real numbers in regression).
sample_qualityarray-like, shape (n_samples,): Sample qualities.

Returns:

selfobject

get_metadata_routing()[source]¶

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)[source]¶

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 the classes of X.

Parameters:

Xarray-like, shape (n_samples, n_features): The input samples.

Returns:

yndarray, shape (n_samples,): The predicted classes.

predict_log_proba(X)[source]¶

Predict log probability for each possible outcome.

Parameters:

Xarray-like, shape (n_samples, n_features): The input samples.

Returns:

log_parray, shape (n_samples, n_classes): Array with log prediction probabilities.

predict_proba(X)[source]¶

Predict probability for each possible outcome.

Parameters:

Xarray-like, shape (n_samples, n_features): The input samples.

Returns:

parray, shape (n_samples, n_classes): The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

score(X, y, sample_weight=None)[source]¶

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(*, sample_quality: bool | None | str = '$UNCHANGED$') → IRBL[source]¶

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.

New 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.Pipeline. Otherwise it has no effect.

Parameters:

sample_qualitystr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_quality parameter in fit.

Returns:

selfobject: The updated object.

set_params(**params)[source]¶

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: bool | None | str = '$UNCHANGED$') → IRBL[source]¶

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.

New 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.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.

bqlearn.irbl.IRBL¶

`bqlearn.irbl`.IRBL¶