skactiveml.pool.FourDs#

class skactiveml.pool.FourDs(lmbda=None, missing_label=nan, random_state=None)[source]#

Bases: SingleAnnotatorPoolQueryStrategy

4DS

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

Parameters
lmbdafloat between 0 and 1, 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, default=np.nan

Value to represent a missing label.

random_stateint or np.random.RandomState, default=None

The random state to use.

References

1

T. Reitmaier and B. Sick. Let us know your decision: Pool-based active training of a generative classifier with the selection strategy 4DS. Inf. Sci., 230:106–131, 2013.

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, return_utilities=False, batch_size=1)[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.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,), 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).

  • If candidates is of shape (n_candidates, *), candidates is considered as the candidate 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.

  • 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)

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, the indexing refers to the samples in X.

  • If candidates is of shape (n_candidates,) and of type int, utilities refers to the samples in X.

  • If candidates is of shape (n_candidates, *), utilities refers to the indexing 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#

Batch Density-Diversity-Distribution-Distance Sampling

Batch Density-Diversity-Distribution-Distance Sampling

Density-Diversity-Distribution-Distance Sampling

Density-Diversity-Distribution-Distance Sampling