skactiveml.pool.KLDivergenceMaximization#

class skactiveml.pool.KLDivergenceMaximization(integration_dict_target_val=None, integration_dict_cross_entropy=None, missing_label=nan, random_state=None)[source]#

Bases: SingleAnnotatorPoolQueryStrategy

Regression based Kullback-Leibler Divergence Maximization

This class implements a query [1], which selects those samples that maximize the expected Kullback-Leibler divergence, where it is assumed that the target probabilities for different samples are independent.

Parameters
integration_dict_target_valdict, default=None

Dictionary for integration arguments, i.e. integration method etc., used for calculating the expected y value for the candidate samples. For details see method skactiveml.pool.utils._conditional_expect.

integration_dict_cross_entropydict, default=None

Dictionary for integration arguments, i.e. integration method etc., used for calculating the cross entropy between the updated conditional estimator by the X_cand value and the old conditional estimator. For details see method conditional_expect.

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

Random state for candidate selection.

References

1

D. Elreedy, A. F. Atiya, and S. I. Shaheen. A Novel Active Learning Regression Framework for Balancing the Exploration-Exploitation Trade-Off. Entropy, 21(7):651, 2019.

Methods

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

query(X, y, reg[, fit_reg, sample_weight, ...])

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, reg, fit_reg=True, sample_weight=None, candidates=None, batch_size=1, return_utilities=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).

regskactiveml.base.ProbabilisticRegressor

Predicts the entropy and the cross entropy and the potential y-values for the candidate samples.

fit_regbool, default=True

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

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

Weights of training samples in X.

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)

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, the indexing refers to the samples in X.

  • If candidates is of shape (n_candidates,) and of type int, utilities refers to the samples in X.

  • If candidates is of shape (n_candidates, *), utilities refers to the indexing 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.KLDivergenceMaximization#

Regression based Kullback Leibler Divergence Maximization

Regression based Kullback Leibler Divergence Maximization