AnnotatorEnsembleClassifier

class skactiveml.classifier.multiannotator.AnnotatorEnsembleClassifier(estimators, voting='hard', classes=None, missing_label=nan, cost_matrix=None, random_state=None)

Bases: MetaEstimatorMixin, SkactivemlClassifier

Ensemble of Annotator-wise Classifier

This strategy consists of fitting one classifier per annotator.

Parameters:

estimatorslist of (str, SkactivemlClassifier) tuples

The ensemble of estimators to use in the ensemble. Each element of the list is defined as a tuple of string (i.e., name of the estimator) and an estimator instance.

voting‘hard’ or ‘soft’, default=’hard’

If ‘hard’, uses predicted class labels for majority rule voting. Else if ‘soft’, predicts the class label based on the argmax of the sums of the predicted probabilities, which is recommended for an ensemble of well-calibrated classifiers.

classesarray-like of shape (n_classes,), default=None

Holds the label for each class. If none, the classes are determined during the fit.

missing_labelscalar or str or np.nan or None, default=np.nan

Value to represent a missing label.

cost_matrixarray-like of shape (n_classes, n_classes)

Cost matrix with cost_matrix[i,j] indicating cost of predicting class classes[j] for a sample of class classes[i]. Can be only set, if classes is not none.

random_stateint or RandomState instance or None, default=None

Determines random number for predict method. Pass an int for reproducible results across multiple method calls.

Attributes:

classes_np.ndarray of shape (n_classes,)

Holds the label for each class after fitting.

cost_matrix_np.ndarray of shape (classes, classes)

Cost matrix with cost_matrix_[i,j] indicating cost of predicting class classes_[j] for a sample of class classes_[i].

estimators_list of estimators

The elements of the estimators parameter, having been fitted on the training data.

Methods

fit(X, y[, sample_weight])	Fit the model using X as samples and y as class labels.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
predict(X, **kwargs)	Return class label predictions for the test samples X.
predict_proba(X)	Return probability estimates for the test data X.
score(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
set_fit_request(*[, sample_weight])	Configure whether metadata should be requested to be passed to the fit method.
set_params(**params)	Set the parameters of this estimator.
set_score_request(*[, sample_weight])	Configure whether metadata should be requested to be passed to the score method.

fit(X, y, sample_weight=None)

Fit the model using X as samples and y as class labels.

Parameters:

Xarray-like of shape (n_samples, …)

The feature matrix representing the samples.

yarray-like of shape (n_samples, n_annotators)

It contains the class labels of the training samples, where missing labels are represented via missing_label. Specifically, label y[n, m] refers to the label of sample X[n] from annotator m.

sample_weightarray-like of shape (n_samples, n_annotators)

It contains the weights of the training samples’ class labels. It must have the same shape as y.

Returns:

self: skactiveml.classifier.multiannotator.AnnotatorEnsembleClassifier,

The AnnotatorEnsembleClassifier object fitted on the training data.

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, **kwargs)

Return class label predictions for the test samples X.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input samples.

Returns:

ynumpy.ndarray of shape (n_samples,)

Predicted class labels of the test samples X.

predict_proba(X)

Return probability estimates for the test data X.

Parameters:

Xarray-like of shape (n_samples, …)

Test samples.

Returns:

Pnp.ndarray of shape (n_samples, classes)

The class probabilities of the test samples. Classes are ordered according to the attribute self.classes_.

score(X, y, sample_weight=None)

Return the mean accuracy on the given test data and labels.

Parameters:

Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:

scorefloat

Mean accuracy of self.predict(X) regarding y.

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → AnnotatorEnsembleClassifier

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight 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 = '$UNCHANGED$') → AnnotatorEnsembleClassifier

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

Added in version 1.3.

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.