skactiveml.pool.DropQuery#

class skactiveml.pool.DropQuery(dropout_rate=0.75, n_dropout_samples=5, cluster_algo=<class 'sklearn.cluster._kmeans.KMeans'>, cluster_algo_dict=None, n_cluster_param_name='n_clusters', clf_embedding_flag_name=None, missing_label=nan, random_state=None)[source]#

Bases: SingleAnnotatorPoolQueryStrategy

Dropout Query (DropQuery)

This class implements the query strategy Dropout Query (DropQuery) [1] that incorporates both uncertainty and sample diversity into every selected batch. For this purpose, samples are filtered according to a disagreement-based measure via dropout such that only the samples with a disagreement above a threshold are clustered for selecting the samples nearest to the respective clusters.

Parameters
dropout_ratefloat, default=0.75

Dropout rate used to generate samples.

n_dropout_samplesint, default=3

Number of dropout samples.

cluster_algoClusterMixin.__class__, default=KMeans

The cluster algorithm to be used. It must implement a fit_transform method, which takes samples X 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.

clf_embedding_flag_namestr or None, default=None

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

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

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

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

S. R. Gupte, J. Aklilu, J. J. Nirschl, and S. Yeung-Levy, “Revisiting Active Learning in the Era of Vision Foundation Models.” Trans. Mach. Learn., 2024.

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.

fit_clfbool, default=True

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

sample_weightarray-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 be 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.DropQuery#

Dropout Query (DropQuery)

Dropout Query (DropQuery)