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)

Bases: SingleAnnotatorPoolQueryStrategy

Active Learning with Cost Embedding (ALCE).

Cost sensitive multi-class algorithm. Assume each class has at least one sample in the labeled pool. This implementation is based on libact.

Parameters

classes: array-like of shape(n_classes,)

base_regressorsklearn regressor, optional (default=None)

cost_matrix: array-like of shape (n_classes, n_classes),

optional (default=None)

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 variant.

missing_label: str or numeric, optional (default=MISSING_LABEL)

Specifies the symbol that represents a missing label.

random_stateint or np.random.RandomState, optional

(default=None)

Random state for annotator selection.

embed_dimint, optional (default=None)

If is None, embed_dim = n_classes.

mds_paramsdict, optional (default=None)

For further information, see https://scikit-learn.org/stable/modules/generated/sklearn.manifold.MDS.html

nn_paramsdict, optional (default=None)

For further information, see https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html

References

[1] Kuan-Hao, and Hsuan-Tien Lin. “A Novel Uncertainty Sampling Algorithm

for Cost-sensitive Multiclass Active Learning”, In Proceedings of the IEEE International Conference on Data Mining (ICDM), 2016

Methods

get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
query(X, y[, sample_weight, candidates, ...])	Query the next instance to be labeled.
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)

Query the next instance to be labeled.

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,), optional

(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), 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, 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 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

**paramsdict

Estimator parameters.

Returns

selfestimator instance

Estimator instance.

Examples using skactiveml.pool.CostEmbeddingAL

Active Learning with Cost Embedding

Active Learning with Cost Embedding