skactiveml.pool.ExpectedModelVarianceReduction

class skactiveml.pool.ExpectedModelVarianceReduction(integration_dict=None, missing_label=nan, random_state=None)

Bases: SingleAnnotatorPoolQueryStrategy

Expected Model Variance Reduction (EMVR)

This class implements the active learning strategy “Expected Model Variance Reduction” (EMVR) [1], which tries to select the sample that minimizes the expected model variance.

Parameters

integration_dictdict, 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.

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

Value to represent a missing label.

random_stateint or np.random.RandomState or None, default=None

Random state for candidate selection.

References

1

Cohn, David A and Ghahramani, Zoubin and Jordan, Michael I. Active learning with statistical models, pages 129–145, 1996.

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, X_eval=None, batch_size=1, return_utilities=False)

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).

regProbabilisticRegressor

Predicts the output and the conditional distribution.

fit_regbool, optional (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).

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

**paramsdict

Estimator parameters.

Returns

selfestimator instance

Estimator instance.

Examples using skactiveml.pool.ExpectedModelVarianceReduction

Expected Model Variance Reduction

Expected Model Variance Reduction