skactiveml.pool.QueryByCommittee#

class skactiveml.pool.QueryByCommittee(method='KL_divergence', eps=1e-07, sample_predictions_method_name=None, sample_predictions_dict=None, missing_label=nan, random_state=None)[source]#

Bases: SingleAnnotatorPoolQueryStrategy

Query-by-Committee (QBC)

The Query-by-Committee (QBC) strategy uses an ensemble of estimators to identify on which samples many estimators disagree.

Parameters

method“KL_divergence” or “vote_entropy” or “variation_ratios, default=’KL_divergence’

The method to calculate the disagreement in the case of classification. ‘KL_divergence’, ‘vote_entropy’, and ‘variation_ratios’ are possible. In the case of regression, this parameter is ignored and the empirical variance is used.

epsfloat > 0, default=1e-7

Minimum probability threshold to compute log-probabilities (only relevant for method=’KL_divergence’).

sample_predictions_method_namestr, default=None

Certain estimators may offer methods enabling to construct a committee by sampling predictions of committee members. This parameter is to indicate the name of such a method.

If sample_predictions_method_name=None no sampling is performed.

If sample_predictions_method_name is not None and in the case of classification, the method is expected to take samples of the shape (n_samples, *) as input and to output probabilities of the shape (n_members, n_samples, n_classes), e.g., sample_proba in skactiveml.base.ClassFrequencyEstimator.

If sample_predictions_method_name is not None and in the case of regression, the method is expected to take samples of the shape (n_samples, *) as input and to output numerical values of the shape (n_members, n_samples), e.g., sample_y in sklearn.gaussian_process.GaussianProcessRegressor.

sample_predictions_dictdict, default=None

Parameters (excluding the samples) that are passed to the method with the name sample_predictions_method_name.

This parameter must be None, if sample_predictions_method_name is None.

Otherwise, it may be used to define the number of sampled members, e.g., by defining n_samples as parameter to the method sample_proba of skactiveml.base.ClassFrequencyEstimator or sample_y of sklearn.gaussian_process.GaussianProcessRegressor.

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

The random state to use.

References

1: H.S. Seung, M. Opper, and H. Sompolinsky. Query by committee. In ACM Workshop on Computational Learning Theory, pages 287-294, 1992.
2: N. Abe and H. Mamitsuka. Query Learning Strategies Using Boosting and Bagging. In International Conference on Machine Learning, pages 1-9, 1998.
3: Burbidge, Robert and Rowland, Jem J and King, Ross D. Active Learning for Regression Based on Query by Committee. In International Conference on Intelligent Data Engineering and Automated Learning, pages 209-218, 2007.
4: Beluch, W. H., Genewein, T., Nürnberger, A., and Köhler, J. M. The Power of Ensembles for Active Learning in Image Classification. In Conference on Computer Vision and Pattern Recognition, pages 9368-9377, 2018

Methods

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

ensemblearray-like of SkactivemlClassifier or array-like of SkactivemlRegressor or SkactivemlClassifier or SkactivemlRegressor

If ensemble is a SkactivemlClassifier or a SkactivemlRegressor and has n_estimators plus estimators_ after fitting as attributes, its estimators will be used as committee.
If ensemble is array-like, each element of this list must be SkactivemlClassifier or a SkactivemlRegressor and will be used as committee member.
If ensemble is a SkactivemlClassifier or a SkactivemlRegressor and implements a method with the name sample_predictions_method_name, this method is used to sample predictions of committee members.

fit_ensemblebool, default=True

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

sample_weight: array-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 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