bqlearn.baseline.BiqualityBaseline

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

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_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_params(**params)	Set the parameters of this estimator.

decision_function(X)

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)

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_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)

Parameters:

Xarray-like, shape (n_samples, n_features)

The input samples.

Returns:

yndarray, shape (n_samples,)

The predicted classes.

predict_proba(X)

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_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.