skactiveml.pool.ProbCover#

class skactiveml.pool.ProbCover(n_classes=None, deltas=None, alpha=0.95, cluster_algo=<class 'sklearn.cluster._kmeans.KMeans'>, cluster_algo_dict=None, n_cluster_param_name='n_clusters', distance_func=<function pairwise_distances>, missing_label=nan, random_state=None)[source]#

Bases: SingleAnnotatorPoolQueryStrategy

Probability Coverage (ProbCover)

This class implements the Probability Coverage (ProbCover) query strategy [1], which aims at maximizing the probability coverage in a meaningful sample embedding space.

Parameters
n_classesNone or int, default=None

This parameter is used to determine the delta value. If n_classes=None, the number of classes is extracted from the given labels. If this extracted number of classes is below 2, n_classes=2 is used as a fallback.

deltasNone or array-like of shape (n_deltas,), default=None

List of deltas (ball radii) to be tested for finding the maximum value satisfying a sample coverage >= alpha. If no value in deltas satisfies this constraint, a warning is raised where the minimum delta value is used. If deltas=None, the values np.arange(0.1, 2.1, 0.1) are used.

alphafloat in (0, 1), alpha=0.95

Minimum coverage as a constraint for the delta selection.

cluster_algoClusterMixin.__class__, default=sklearn.cluster.KMeans

The cluster algorithm to be used for determining the best delta value.

cluster_algo_dictdict, default=None

The parameters passed to the clustering algorithm cluster_algo, excluding the parameter for the number of clusters.

n_cluster_param_namestring, default=”n_clusters”

The name of the parameter for the number of clusters.

distance_funccallable, default=sklearn.metrics.pairwise_distances

Takes as input X to compute the distances between each pair of samples. This function can also only return the precomputed distances of each pair in X for speedup.

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

Value to represent a missing label.

random_stateNone or int or np.random.RandomState, default=None

The random state to use.

References

1

O. Yehuda, A. Dekel, G. Hacohen, and D. Weinshall. Active Learning Through a Covering Lens. In Adv. Neural Inf. Process. Syst., 2022.

Methods

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

query(X, y[, candidates, batch_size, ...])

Determines for which candidate samples labels are to be queried.

set_params(**params)

Set the parameters of this estimator.
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.

query(X, y, candidates=None, batch_size=1, return_utilities=False, update=False)[source]#

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,)

Labels of the training data set (possibly including unlabeled ones indicated by self.missing_label).

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 the samples in (X,y).

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. The indexing refers to the samples in X.

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 labeled samples will be set to np.nan. The indexing refers to the samples in X.

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.ProbCover#

Probability Coverage (ProbCover)

Probability Coverage (ProbCover)