bqlearn.baseline.BiqualityBaseline

class bqlearn.baseline.BiqualityBaseline(estimator, baseline='no_correction')[source]

A Biquality Baseline.

A BaselineBiqualityClassifier lift usual scikit-learn classifiers to train on biquality data as a baseline algortihm.

Parameters:
estimatorobject, optional (default=None)

The base estimator from which the BaselineBiqualityClassifier is built.

baseline{‘trusted_only’, ‘untrusted_only’, ‘no_correction’, ‘semi_supervised’},

default=’no_correction’

Attributes:
estimator_classifier

The final fitted estimator.

classes_ndarray of shape (n_classes,)

The classes labels.

n_classes_int

The number of classes.

Methods

decision_function(X)

Parameters:

fit(X, y[, sample_quality])

Fit the baseline model.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

predict(X)

Parameters:

predict_proba(X)

Parameters:

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]
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_.

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

Fit the baseline 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()[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]
Parameters:
Xarray-like, shape (n_samples, n_features)

The input samples.

Returns:
yndarray, shape (n_samples,)

The predicted classes.

predict_proba(X)[source]
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 = '$UNCHANGED$') → BiqualityBaseline[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 = '$UNCHANGED$') → BiqualityBaseline[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.