bqlearn.plugin.PluginCorrection

class bqlearn.plugin.PluginCorrection(estimator, *, prefit=False, transition_matrix='iterative', quantile=0.97, n_iter=100, noise_free_prior=0.0)

A Noise Corrected Plug-in Classifier.

PluginCorrection [1] learns a classifier on noisy data and uses a noise transition matrix \(\mathbf{T}\) at prediction time (plug-in) to correct the classifier.

\[\forall x \in \mathcal{X}, f_T(x)=(\mathbf{T}^{t})^{-1}\cdot f_U(x)\]

Parameters:

estimatorestimator object

An estimator object implementing fit and predict_proba.

prefitbool, default=False

Whether a prefit model is expected to be passed into the constructor directly or not. If True, estimator must be a fitted estimator. If False, estimator is fitted by calling fit.

transition_matrix{‘iterative’, ‘anchor’, ‘gold’, ‘confusion’} or array-like of shape (n_classes, n_classes), default=’iterative’

Algorithm to estimate the transition matrix. ‘gold’ and ‘confusion’ are only available on biquality data.

quantilefloat, default=0.97

Quantile used to select the anchor points. Only used when transition_matrix=’anchor’ or transition_matrix=’iterative’.

n_iterint, default=100

Number of iteratives to compute the transition matrix. Only used when transition_matrix=’iterative’.

noise_free_priorfloat, default=0.0

Factor for the convex combination between the estimated transition_matrix and the identity matrix to lower the condition number of the estimated transition matrix. It’s equivalent to take a more conservative noise-free prior.

random_stateint or RandomState, default=None

Controls the random seed given at base_estimator. Pass an int for reproducible output across multiple function calls.

Attributes:

estimator_classifier

The fitted estimator.

transition_matrix_: ndarray of shape (n_classes, n_classes)

Estimated transition matrix between untrusted and untrusted labels.

classes_ndarray of shape (n_classes,)

The classes labels.

n_classes_int

The number of classes.

n_features_in_int

Number of features seen during fit. Only defined if the underlying estimator exposes such an attribute when fit.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Only defined if the underlying estimator exposes such an attribute when fit.

References

[1]

13. Zhang, J. Lee, and S. Agarwal. “Learning from noisy labels with no change to the training process.”, ICML, 2021.

Methods

decision_function(X)	Noise-corrected plug-in of predicted probabilites from estimator.
fit(X, y[, sample_quality])	Fit the noise corrected plug-in classification 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.
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)

Noise-corrected plug-in of predicted probabilites from estimator.

For binary classification, the score is the margin between the two classes.

Parameters:

Xarray-like, shape (n_samples, n_features)

The input samples.

Returns:

yndarray, shape (n_samples, n_classes)

The predicted classes.

fit(X, y, sample_quality=None)

Fit the noise corrected plug-in classification model.

Parameters:

Xarray-like of shape (n_samples, n_features)

The samples.

yarray-like of shape (n_samples,)

The targets.

sample_qualityarray-like, shape (n_samples,)

Per-sample qualities.

Returns:

selfobject

Returns self.

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)

Predict the classes of X.

Parameters:

Xarray-like, shape (n_samples, n_features)

The input samples.

Returns:

yndarray, shape (n_samples,)

The predicted classes.

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(*, sample_quality: bool | None | str = '$UNCHANGED$') → PluginCorrection

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)

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 = '$UNCHANGED$') → PluginCorrection

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.