skactiveml.pool.Quire

class skactiveml.pool.Quire(classes, lmbda=1.0, metric='rbf', metric_dict=None, missing_label=nan, random_state=None)

Bases: SingleAnnotatorPoolQueryStrategy

Implementation of the AL strategy “QUerying Informative and Representative Examples” (QUIRE for short) [1].

Parameters

classes: array-like, shape (n_classes)

Array of class labels.

lmbdafloat, default=1.0

Controls informativeness (high) and representativeness (low). Values must be greater than 0.

metricstr or callable, default=’rbf’

The metric must a be a valid kernel defined by the function sklearn.metrics.pairwise.pairwise_kernels or ‘precomputed’.

metric_dictdict, default=None

Any further parameters are passed directly to the metric function.

missing_labelscalar or string or np.nan or None, 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] Huang, S. J., Jin, R., & Zhou, Z. H. (2010). Active learning by

querying informative and representative examples. Advances in neural information processing systems, 23.

Methods

get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
query(X, y[, candidates, batch_size, ...])	Determines for which candidate samples labels are to be queried.
set_params(**params)	Set the parameters of this estimator.

Attributes

METRICS

METRICS = ['additive_chi2', 'chi2', 'cosine', 'linear', 'poly', 'polynomial', 'rbf', 'laplacian', 'sigmoid', 'precomputed']

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, candidates=None, batch_size=1, return_utilities=False)

Determines for which candidate samples labels are to be queried.

Parameters

Xarray-like of shape (n_samples, n_features) or shape

(n_samples, n_samples) if metric == ‘precomputed’

Training data set, including the labeled and unlabeled samples.

yarray-like of shape (n_samples)

Labels of the training data set, including unlabeled ones indicated by self.missing_label.

candidatesNone or array-like of shape (n_candidates), dtype=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). The option candidates with shape (n_candidates, n_features) is not supported.

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. If candidates is None or of shape (n_candidates), the indexing refers to samples in X.

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.

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.Quire

Querying Informative and Representative Examples

Querying Informative and Representative Examples