SingleAnnotatorPoolQueryStrategy#

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

Bases: PoolQueryStrategy

Base class for all pool-based active learning query strategies with a single annotator in scikit-activeml.

Methods

`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`query`(X, y, *args[, 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.

abstract query(X, y, *args, candidates=None, batch_size=1, return_utilities=False, **kwargs)[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).
If candidates is of shape (n_candidates, …), the candidate samples are directly given in candidates (not necessarily contained in X). This is not supported by all query strategies.

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 labeled samples 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.

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: