CoreSet

class skactiveml.pool.CoreSet(metric='euclidean', metric_dict=None, missing_label=nan, random_state=None)

Bases: SingleAnnotatorPoolQueryStrategy

Core Set

This class implements core-set based query strategies, i.e., the standard greedy algorithm for the k-center problem [1]. CoreSet applies a k-center (farthest-first) selection in an embedding space, seeded by the labeled set, to minimize the maximum distance of unlabeled samples to the labeled/selected samples. It is a pure diversity criterion without explicit consideration of prediction uncertainty.

Parameters:

metricstr or callable, default=’euclidean’

The metric must comply with the function sklearn.metrics.pairwise_distances_argmin_min.

metric_dictdict, default=None

Any further parameters are passed directly to the function sklearn.metrics.pairwise_distances_argmin_min as metric_kwargs.

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]

O. Sener and S. Savarese. Active Learning for Convolutional Neural Networks: A Core-Set Approach. In Int. Conf. Learn. Represent., 2018.

Methods

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

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.CoreSet

Core Set

Core Set

Core Set + Greedy Selection

Core Set + Greedy Selection