LabelCardinalityInconsistency

class skactiveml.pool.LabelCardinalityInconsistency(missing_label=nan, random_state=None, target_type='auto')

Bases: SingleAnnotatorPoolQueryStrategy

Label Cardinality Inconsistency (LCI)

This class implements the query strategy Label Cardinality Inconsistency (LCI) [1] that selects samples based on the difference in label cardinality between the labeled pool and the predicted number of positive labels in the unlabeled pool. This strategy is multi-label-only: y must be two-dimensional and each row must be either fully labeled or fully unlabeled.

Parameters:

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

Value to represent a missing label.

random_stateint or RandomState instance or None, default=None

Controls the randomness of the estimator.

target_type“auto” or “multi-label”, default=”auto”

Declared target type. A fitted classifier’s target specification is authoritative when available. This strategy supports only multi-label classification with a single annotator.

References

[1]

R. Wang and S. Ye (2019). Multi-Label Active Learning Driven by Uncertainty and Inconsistency. In 2019 International Conference on Machine Learning and Cybernetics.

Methods

query(X, y, clf[, fit_clf, sample_weight, ...])	Determines for which candidate samples labels are to be queried.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
set_params(**params)	Set the parameters of this estimator.

LabelCardinalityInconsistency.query(X, y, clf, fit_clf=True, sample_weight=None, candidates=None, batch_size=1, return_utilities=False)

Determines for which candidate samples labels are to be queried.

Parameters:

Xarray-like of shape (n_samples, n_features)

Training data set, usually complete, i.e., including the labeled and unlabeled samples.

yarray-like of shape (n_samples, n_outputs)

Labels of the training data set (possibly including unlabeled rows indicated by self.missing_label). Each row must either contain only observed labels or only missing_label values, i.e., no mixing within a row. This strategy supports multi-label data only.

clfskactiveml.base.SkactivemlClassifier

Classifier implementing the methods fit and predict.

fit_clfbool, default=True

Defines whether the classifier clf should be fitted on X, y, and sample_weight.

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

Weights of training samples in X. One weight per sample is supported. Per-target weights are forwarded to clf.fit without additional validation and require estimator support.

candidatesNone or array-like of shape (n_candidates), dtype=int or array-like of shape (n_candidates, n_features), default=None

• If candidates is None, the unlabeled samples from (X, y) are considered as candidates.

• If candidates is of shape (n_candidates,) and of type int, candidates is considered as the indices of samples in (X, y).

• If candidates is of shape (n_candidates, n_features), the candidates are directly given in candidates.

A given candidates is authoritative, i.e., an index array is taken as given, such that labeled samples remain candidates, e.g., to relabel them or to recompute their utilities.

batch_sizeint, default=1

The number of samples to be selected in one AL cycle.

return_utilitiesbool, default=False

If True, also return the utilities based on the query strategy.

Returns:

query_indicesnumpy.ndarray of shape (batch_size,)

The query indices indicate for which candidate sample a label is to be queried, e.g., query_indices[0] indicates the first selected sample.

• If candidates is None or of shape (n_candidates,), the indexing refers to the samples in X.

• If candidates is of shape (n_candidates, n_features), the indexing refers to the samples in candidates.

utilitiesnumpy.ndarray of shape (batch_size, n_samples) or numpy.ndarray of shape (batch_size, n_candidates)

The utilities of samples after each selected sample of the batch, e.g., utilities[0] indicates the utilities used for selecting the first sample (with index query_indices[0]) of the batch. Utilities for samples that are no candidates will be set to np.nan.

• If candidates is None or of shape (n_candidates,), the indexing refers to the samples in X.

• If candidates is of shape (n_candidates, n_features), the indexing refers to the samples in candidates.

Notes

An exhausted candidate pool, i.e., a fully labeled (X, y) queried with candidates=None or an empty candidates, is a valid acquisition state. It is answered with an empty batch of batch_size zero and a warning naming the exhaustion.

LabelCardinalityInconsistency.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.

LabelCardinalityInconsistency.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.

LabelCardinalityInconsistency.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.

Examples using skactiveml.pool.LabelCardinalityInconsistency

Label Cardinality Inconsistency (LCI)

Label Cardinality Inconsistency (LCI)