skactiveml.pool.UncertaintySampling#

class skactiveml.pool.UncertaintySampling(method='least_confident', cost_matrix=None, missing_label=nan, random_state=None)[source]#

Bases: SingleAnnotatorPoolQueryStrategy

Uncertainty Sampling.

This class implement various uncertainty based query strategies, i.e., the standard uncertainty measures [1], cost-sensitive ones [2], and one optimizing expected average precision [3].

Parameters

methodstring, default=’least_confident’: The method to calculate the uncertainty, entropy, least_confident, margin_sampling, and expected_average_precision are possible.
cost_matrixarray-like of shape (n_classes, n_classes): Cost matrix with cost_matrix[i,j] defining the cost of predicting class j for a sample with the actual class i. Only supported for least_confident and margin_sampling variant.
missing_labelscalar or string or np.nan or None, default=np.nan: Value to represent a missing label.
random_stateint or np.random.RandomState: The random state to use.

References

[1] Settles, Burr. Active learning literature survey.: University of Wisconsin-Madison Department of Computer Sciences, 2009.
[2] Chen, Po-Lung, and Hsuan-Tien Lin. “Active learning for multiclass: cost-sensitive classification using probabilistic models.” 2013 Conference on Technologies and Applications of Artificial Intelligence. IEEE, 2013.
[3] Wang, Hanmo, et al. “Uncertainty sampling for action recognition: via maximizing expected average precision.” IJCAI International Joint Conference on Artificial Intelligence. 2018.

Methods

`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`query`(X, y, clf[, fit_clf, 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, clf, fit_clf=True, sample_weight=None, utility_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.
clfskactiveml.base.SkactivemlClassifier: Model implementing the methods fit and predict_proba.
fit_clfbool, optional (default=True): Defines whether the classifier should be fitted on X, y, and sample_weight.
sample_weight: array-like of shape (n_samples), optional (default=None): Weights of training samples in X.
utility_weight: array-like, optional (default=None): Weight for each candidate (multiplied with utilities). Usually, this is to be the density of a candidate. The length of utility_weight is usually n_samples, except for the case when candidates contains samples (ndim >= 2). Then the length is n_candidates.
candidatesNone or array-like of shape (n_candidates), dtype=int or: array-like of shape (n_candidates, n_features), optional (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, n_features), the candidates 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 samples in X. If candidates is of shape (n_candidates, n_features), the indexing refers to 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 samples in X. If candidates is of shape (n_candidates, n_features), the indexing refers to 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