skactiveml.pool.FourDs

class skactiveml.pool.FourDs(lmbda=None, missing_label=nan, random_state=None)

Bases: SingleAnnotatorPoolQueryStrategy

Implementation of the pool-based query strategy 4DS for training a MixtureModelClassifier [1].

Parameters

lmbdafloat between 0 and 1, optional

(default=min((batch_size-1)*0.05, 0.5))

For the selection of more than one sample within each query round, 4DS uses a diversity measure to avoid the selection of redundant samples whose influence is regulated by the weighting factor ‘lmbda’.

missing_labelscalar or string or np.nan or None, optional

(default=MISSING_LABEL)

Value to represent a missing label.

random_stateint or np.random.RandomState, optional (default=None)

The random state to use.

References

———

[1] Reitmaier, T., & Sick, B. (2013). Let us know your decision: Pool-based

active training of a generative classifier with the selection strategy 4DS. Information Sciences, 230, 106-131.

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, ...])	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, clf, fit_clf=True, sample_weight=None, candidates=None, return_utilities=False, batch_size=1)

Determines for which candidate samples labels are to be queried.

Parameters

X: array-like of shape (n_samples, n_features)

Training data set, usually complete, i.e. including the labeled and unlabeled samples.

y: array-like of shape (n_samples)

Labels of the training data set (possibly including unlabeled ones indicated by self.MISSING_LABEL.

clfskactiveml.classifier.MixtureModelClassifier

GMM-based classifier to be trained.

fit_clfbool, optional (default=True)

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

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.FourDs

Density-Diversity-Distribution-Distance Sampling

Density-Diversity-Distribution-Distance Sampling

Batch Density-Diversity-Distribution-Distance Sampling

Batch Density-Diversity-Distribution-Distance Sampling