skactiveml.pool.DiscriminativeAL#

class skactiveml.pool.DiscriminativeAL(greedy_selection=False, missing_label=nan, random_state=None)[source]#

Bases: SingleAnnotatorPoolQueryStrategy

Discriminative Active Learning (DAL)

This class implements the “Discriminative Active Learning” (DAL) [1] 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, 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.

  • If greedy_selection=True, the discriminator is kept fixed.

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

Value to represent a missing label.

random_stateNone or int or np.random.RandomState, default=None

The random state to use.

References

1

D. Gissin and S. Shalev-Shwartz. 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)[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).

discriminatorskactiveml.base.SkactivemlClassifier

Classification model implementing the methods fit and predict_proba. It will be used to solve the binary classification problem of separating labeled and unlabeled samples. 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), 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.DiscriminativeAL#

Discriminative Active Learning

Discriminative Active Learning