skactiveml.pool.SubSamplingWrapper#

class skactiveml.pool.SubSamplingWrapper(query_strategy=None, max_candidates=0.1, exclude_non_subsample=False, missing_label=nan, random_state=None)[source]#

Bases: SingleAnnotatorPoolQueryStrategy

Sub-sampling Wrapper

This class implements a wrapper for single-annotator pool-based strategies that randomly sub-samples a set of candidates before computing their utilities.

Parameters
query_strategyskactiveml.base.SingleAnnotatorPoolQueryStrategy

The strategy used for computing the utilities of the candidate sub-sample.

max_candidatesint or float, default=0.1

Determines the number of candidates. If max_candidates is an integer, max_candidates is the maximum number of candidates whose utilities are computed. If max_candidates is a float, max_candidates is the fraction of the original number of candidates.

exclude_non_subsamplebool, default=False

  • If True, unlabeled candidates in X and y are excluded which are not part of the subsample. If candidates is an array-like of shape (n_candidates, n_features), all unlabeled data will be removed from X and y.

  • If False, X and y stay the same.

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

Value to represent a missing label.

random_stateint or np.random.RandomState, default=None

The random state to use.

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, **query_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.

**query_kwargsdict-like

Further keyword arguments are passed to the query method of the query_strategy object.

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
**paramsdict

Estimator parameters.

Returns
selfestimator instance

Estimator instance.

Examples using skactiveml.pool.SubSamplingWrapper#

Sub-sampling Wrapper

Sub-sampling Wrapper