skactiveml.pool.DiscriminativeAL

class skactiveml.pool.DiscriminativeAL(greedy_selection=False, missing_label=nan, random_state=None)

Bases: SingleAnnotatorPoolQueryStrategy

Discriminative Active Learning.

This class implement the “Discriminative Active Learning” (DAL) strategy. Its idea is to solve a binary classification task to choose samples for labeling such that the labeled set and the unlabeled pool are indistinguishable.

Parameters

greedy_selectionbool, optional (default=False)

This parameter is only relevant for batch_size>1. If greedy_selection=False the classifying discriminator is refitted after each sample selection within a batch. Otherwise, the discriminator is kept fixed.

missing_labelscalar or string or np.nan or None, optional

(default=np.nan)

Value to represent a missing label.

random_stateNone or int or np.random.RandomState, optional

(default=None)

The random state to use.

References

[1] Gissin D, Shalev-Shwartz S. “Discriminative active learning.”

arXiv:1907.06347. 2019.

Methods

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

discriminatorskactiveml.base.SkactivemlClassifier

Model implementing the methods fit and predict_proba. The parameters classes and missing_label will be internally redefined.

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).

batch_sizeint, optional (default=1)

The number of samples to be selected in one AL cycle.

return_utilitiesbool, optional (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 index of 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

**paramsdict

Estimator parameters.

Returns

selfestimator instance

Estimator instance.

Examples using skactiveml.pool.DiscriminativeAL

Discriminative Active Learning

Discriminative Active Learning