skactiveml.pool.Clue#

class skactiveml.pool.Clue(missing_label=nan, random_state=None, cluster_algo=<class 'sklearn.cluster._kmeans.KMeans'>, cluster_algo_dict=None, n_cluster_param_name='n_clusters', method='entropy', clf_embedding_flag_name=None)[source]#

Bases: SingleAnnotatorPoolQueryStrategy

Clustering Uncertainty-weighted Embeddings (CLUE)

This class implements the Clustering Uncertainty-weighted Embeddings (CLUE) query strategy [1], which considers both diversity and uncertainty of the samples.

Parameters
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.

cluster_algoClusterMixin.__class__, default=KMeans

The cluster algorithm to be used. It must implement a fit_transform method, which takes samples X and sample_weight as inputs, e.g., sklearn.clustering.KMeans and sklearn.clustering.MiniBatchKMeans.

cluster_algo_dictdict, default=None

The parameters passed to the clustering algorithm cluster_algo, excluding the parameter for the number of clusters.

n_cluster_param_namestring, default=”n_clusters”

The name of the parameter for the number of clusters.

method‘least_confident’ or ‘margin_sampling’ or ‘entropy’, default=”entropy”

  • method=’least_confident’ queries the sample whose maximal posterior probability is minimal.

  • method=’margin_sampling’ queries the sample whose posterior probability gap between the most and the second most probable class label is minimal.

  • method=’entropy’ queries the sample whose posterior’s have the maximal entropy.

clf_embedding_flag_namestr or None, default=None

Name of the flag, which is passed to the predict_proba method for getting the (learned) sample representations.

  • If clf_embedding_flag_name=None and predict_proba returns only one output, the input samples X are used.

  • If predict_proba returns two outputs or clf_embedding_name is not None, (proba, embeddings) are expected as outputs.

References

1

Prabhu, Viraj, Arjun Chandrasekaran, Kate Saenko, and Judy Hoffman, “Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings”, ICCV, 2022.

Methods

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

query(X, y, clf[, fit_clf, sample_weight, ...])

Query the next samples 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, clf, fit_clf=True, sample_weight=None, candidates=None, batch_size=1, return_utilities=False)[source]#

Query the next samples to be labeled

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.)

clfskactiveml.base.SkactivemlClassifier

Classifier implementing the methods fit and predict_proba.

fit_clfbool, default=True

Defines whether the classifier clf should be fitted on X, y, and sample_weight.

sample_weight: array-like of shape (n_samples,), default=None

Weights of training samples in X.

candidatesNone or array-like of shape (n_candidates, ) of type int, 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).

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 queried, e.g., query_indices[0] indicates the first selected sample. The indexing refers to the samples in X.

utilitiesnumpy.ndarray of shape (batch_size, n_samples)

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. The indexing refers to the samples in X.

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.Clue#

Clustering Uncertainty-weighted Embeddings (CLUE)

Clustering Uncertainty-weighted Embeddings (CLUE)