skactiveml.pool.CostEmbeddingAL#

class skactiveml.pool.CostEmbeddingAL(classes, base_regressor=None, cost_matrix=None, embed_dim=None, mds_params=None, nn_params=None, missing_label=nan, random_state=None)[source]#

Bases: SingleAnnotatorPoolQueryStrategy

Active Learning with Cost Embedding (ALCE)

The Active Learning with Cost Embeddings (ALCE) [1] query strategy uses a cost-sensitive uncertainty measure based on a distance measure embedding space reflecting a given cost matrix.

This implementation is based on libact [2].

Parameters
classes: array-like of shape (n_classes,)

List of all classes that can occur.

base_regressorsklearn.base.RegressorMixin, default=None

An sklearn regression model implementing the methods fit and predict.

cost_matrix: array-like of shape (n_classes, n_classes), default=None

Cost matrix with cost_matrix[i,j] defining the cost of predicting class j for a sample with the actual class i.

embed_dimint, optional (default=None)

If it is None, embed_dim=n_classes is used.

mds_paramsdict, default=None

Parameters passed to sklearn.manifold.MDS.

nn_paramsdict, default=None

Parameters passed to sklearn.neighbors.NearestNeighbors.

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

Value to represent a missing label.

random_stateint or np.random.RandomState, default=None

Random state for annotator selection.

References

1

K.-H. Huang and H.-T. Lin. A Novel Uncertainty Sampling Algorithm for Cost-Sensitive Multiclass Active Learning. In IEEE Int. Conf. Data Min., pages 925–930, 2016.

2

Y.-Y. Yang, S.-C. Lee, Y.-A. Chung, T.-E. Wu, S.-A. Chen, and H.-T. Lin. libact: Pool-based Active Learning in Python. arXiv:1710.00379, 2017.

Methods

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

query(X, y[, sample_weight, 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, 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).

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 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.CostEmbeddingAL#

Active Learning with Cost Embedding

Active Learning with Cost Embedding