ClassFrequencyEstimator#

class skactiveml.base.ClassFrequencyEstimator(class_prior=0, classes=None, missing_label=nan, cost_matrix=None, random_state=None, target_type='auto')[source]#

Bases: SkactivemlClassifier

Class Frequency Estimator

Extends scikit-activeml classifiers to estimators that are able to estimate class frequencies for given samples (by calling predict_freq).

Parameters:

classesarray-like of shape (n_classes,) or a list of array-like of shape (2,), default=None: Holds the label for each class. Nested binary vocabularies describe one vocabulary per output for multi-label classification. 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.
class_priorfloat or array-like of shape (n_classes,) or (n_outputs, 2), default=0: Prior observations of the class frequency estimates. If class_prior is an array for single-output classification, the entry class_prior[i] indicates the non-negative prior number of samples belonging to class classes_[i]. For multi-label classification, an array must contain one binary prior per output. If class_prior is a float, it indicates the non-negative prior number of samples per class for every output.
random_stateint or np.RandomState or None, default=None: Determines random number for predict method. Pass an int for reproducible results across multiple method calls.
target_type“auto” or “single-output” or “multi-label” or “multi-output”, default=”auto”: Declared target type. Concrete estimators reject resolved specifications outside their exact capabilities.

Attributes:

class_prior_np.ndarray of shape (n_classes,) or (n_outputs, 2): Validated prior observations. The two-dimensional representation is used only for multi-label targets and follows each output’s canonical binary class vocabulary.

Methods

`fit`(X, y[, sample_weight])	Fit the model using X as training data and y as class labels.
`predict`(X, **kwargs)	Return class label predictions for the test samples X.
`predict_freq`(X, **kwargs)	Return class frequency estimates for the test samples X.
`predict_proba`(X, **kwargs)	Return probability estimates for the test data X.
`sample_proba`(X[, n_samples, random_state])	Samples probability vectors from Dirichlet distributions whose parameters alphas are defined as the sum of the frequency estimates returned by predict_freq and the class_prior.
`score`(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`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.

abstract ClassFrequencyEstimator.fit(X, y, sample_weight=None)#

Fit the model using X as training data and y as class labels.

Parameters:

Xarray-like of shape (n_samples, …): The samples X whose shape depends on the respective classifier.
yarray-like of shape (n_samples,) or (n_samples, n_outputs) or (n_samples, n_annotators): Labels of the training data set (possibly including unlabeled ones indicated by missing_label). For multioutput problems, a row y[i] must either contain only observed labels or only missing_label values, i.e., no mixing within a row. For multi-annotator classification, a row can contain labeled and unlabeled entries, where y[i, j] indicates the potential class label for sample X[i] from annotator j.
sample_weightarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None: It contains the weights of the training samples. For two- dimensional targets, either one weight per sample or one weight per target entry can be provided.

Returns:

self: skactiveml.base.SkactivemlClassifier: The skactiveml.base.SkactivemlClassifier object fitted on the training data.

ClassFrequencyEstimator.predict(X, **kwargs)#

Return class label predictions for the test samples X.

Parameters:

Xarray-like of shape (n_samples, …): Input samples.

Returns:

ynumpy.ndarray of shape (n_samples,): Predicted class labels of the test samples X.

abstract ClassFrequencyEstimator.predict_freq(X, **kwargs)[source]#

Return class frequency estimates for the test samples X.

Parameters:

X: array-like of shape (n_samples, n_features): Test samples whose class frequencies are to be estimated.

Returns:

F: array-like of shape (n_samples, n_classes) or (n_samples, n_outputs, 2): The class frequency estimates of the test samples X. For multi-label targets, the final axis follows each output’s canonical binary class vocabulary.

ClassFrequencyEstimator.predict_proba(X, **kwargs)[source]#

Return probability estimates for the test data X.

Parameters:

Xarray-like of shape (n_samples, n_features): Input samples.

Returns:

Parray-like of shape (n_samples, n_classes) or (n_samples, n_outputs): The class probabilities of the test samples. For multi-label targets, each entry is the probability of the second class in the corresponding canonical binary class vocabulary. An output with zero estimated frequencies and zero prior has probability 0.5.

ClassFrequencyEstimator.sample_proba(X, n_samples=10, random_state=None)[source]#

Samples probability vectors from Dirichlet distributions whose parameters alphas are defined as the sum of the frequency estimates returned by predict_freq and the class_prior.

Parameters:

Xarray-like of shape (n_test_samples, n_features): Test samples for which n_samples probability vectors are to be sampled.
n_samplesint, default=10: Number of probability vectors to sample for each X[i].
random_stateint or numpy.random.RandomState or None, default=None: Ensure reproducibility when sampling probability vectors from the Dirichlet distributions.

Returns:

Parray-like of shape (n_samples, n_test_samples, n_classes) or (n_samples, n_test_samples, n_outputs, 2): There are n_samples class probability vectors for each test sample in X. For multi-label targets, the final axis follows each output’s canonical binary class vocabulary.

Raises:

ValueError: If any class has zero frequency observations after adding the prior. Set a positive class_prior to make every Dirichlet parameter positive.

ClassFrequencyEstimator.score(X, y, sample_weight=None)#

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

Parameters:

Xarray-like of shape (n_samples, …): Test samples.
yarray-like of shape (n_samples,): True class labels of the test samples X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights of the test sample X.

Returns:

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

ClassFrequencyEstimator.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.

ClassFrequencyEstimator.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.

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

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.

ClassFrequencyEstimator.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.

ClassFrequencyEstimator.set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → ClassFrequencyEstimator#

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

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: