skactiveml.base.MultiAnnotatorPoolQueryStrategy#

class skactiveml.base.MultiAnnotatorPoolQueryStrategy(missing_label=nan, random_state=None)[source]#

Bases: PoolQueryStrategy

Base class for all pool-based active learning query strategies with multiple annotators in scikit-activeml.

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

Value to represent a missing label.

random_stateint or RandomState instance, default=None

Controls the randomness of the estimator.

Methods

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

query(X, y, *args[, candidates, annotators, ...])

Determines which candidate sample is to be annotated by which annotator.

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.

abstract query(X, y, *args, candidates=None, annotators=None, batch_size=1, return_utilities=False, **kwargs)[source]#

Determines which candidate sample is to be annotated by which annotator.

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_annotators)

Labels of the training data set for each annotator (possibly including unlabeled ones indicated by self.MISSING_LABEL), meaning that y[i, j] contains the label annotated by annotator i for sample j.

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

See parameter annotators.

annotatorsNone or array-like of shape (n_avl_annotators), dtype=int or array-like of shape (n_candidates, n_annotators), default=None

  • If candidate samples and annotators are not specified, i.e., candidates=None, annotators=None the unlabeled target values, y, are the candidates annotator-sample-pairs.

  • If candidate samples and available annotators are specified: The annotator-sample-pairs, for which the sample is a candidate sample and the annotator is an available annotator are considered as candidate annotator-sample-pairs.

  • If candidates is None, all samples of X are considered as candidate samples. In this case n_candidates equals len(X).

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

  • If candidates is of shape (n_candidates, n_features), the sample candidates are directly given in candidates (not necessarily contained in X). This is not supported by all query strategies.

  • If annotators is None, all annotators are considered as available annotators.

  • If annotators is of shape (n_avl_annotators), and of type int, annotators is considered as the indices of the available annotators.

  • If annotators is a boolean array of shape (n_candidates, n_annotators) the annotator-sample-pairs, for which the sample is a candidate sample and the boolean matrix has entry True are considered as candidate annotator-sample pairs.

batch_sizeint or str, default=1

The number of annotators-sample pairs to be selected in one AL cycle. If adaptive=True, batch_size=’adaptive’ is allowed.

return_utilitiesbool, default=False

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

Returns
query_indicesnp.ndarray of shape (batch_size, 2)

The query_indices indicate which candidate sample pairs are to be queried is, i.e., which candidate sample is to be annotated by which annotator, e.g., query_indices[:, 0] indicates the selected candidate samples and query_indices[:, 1] indicates the respectively selected annotators.

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

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

utilities: numpy.ndarray of shape (batch_size, n_samples, n_annotators) or numpy.ndarray of shape (batch_size, n_candidates, n_annotators)

The utilities of all candidate samples w.r.t. to the available annotators after each selected sample of the batch, e.g., utilities[0, :, j] indicates the utilities used for selecting the first sample-annotator-pair (with indices query_indices[0]).

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

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

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.