bqlearn.unbiased.LossCorrection

class bqlearn.unbiased.LossCorrection(estimator, *, transition_matrix='anchor', quantile=0.97, n_iter=100, noise_free_prior=0, n_jobs=None)

A Classifier corrected with the method of unbiased estimators [1].

It construts a surrogate loss \(\tilde{L}\) from the loss of interest \(L\) such that \(\mathbb{E}_{\tilde{y}}[\tilde{L}(f(x),\tilde{y})] = L(f(x),y)\).

\[\tilde{L}(f(x),y) = \frac{(1-\mathbb{P}(\tilde{Y}= y|Y\neq ))L(f(x), y) - \mathbb{P}(\tilde{Y}\neq y | Y =y ) L(f(x), -y) } {1 - \mathbb{P}(\tilde{Y}= y| Y\neq y ) - \mathbb{P}(\tilde{Y}\neq |Y =y)}\]

It does support multiclass classification thanks to a One versus Rest approach.

Parameters:

estimatorobject, optional (default=None)

The estimator which will be corrected to handle label noise. Support for negative sample weighting is required. Support for probability prediction for certain methods of transition matrix estimation.

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

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.

n_jobsint, default=None

The number of jobs to use for the computation: the n_classes one-vs-rest problems are computed in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.

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]

14. Natarajan, I. S. Dhillon, P. Ravikumar, and A. Tewari, “Learning with Noisy Labels”, NeurIPS, 2013.

Methods

decision_function(X)	Call predict of the regressor estimator.
fit(X, y[, sample_quality])	Fit the noisy transition matrix and the corrected classifier.
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_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)

Call predict of the regressor estimator.

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_params)

Fit the noisy transition matrix and the corrected classifier.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

Training data.

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

Target values.

Returns:

selfobject

Returns the instance itself.

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.

predict_proba(X)

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)

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 = '$UNCHANGED$') → LossCorrection

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 = '$UNCHANGED$') → LossCorrection

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.