skactiveml.pool.ProbabilisticAL#

class skactiveml.pool.ProbabilisticAL(prior=1, m_max=1, missing_label=nan, metric=None, metric_dict=None, random_state=None)[source]#

Bases: SingleAnnotatorPoolQueryStrategy

Multi-class Probabilistic Active Learning (McPAL)

This class implements the query strategy Multi-class Probabilistic Active Learning (McPAL) [1], which estimates the performance gain when labeling samples.

Parameters
priorfloat, default=1.0

Prior probabilities for the Dirichlet distribution of the samples.

m_maxint, default=1.0

Maximum number of hypothetically acquired labels.

missing_labelscalar or string or np.nan or None, default=np.nan

Value to represent a missing label.

metricstr or callable, default=None

The metric must be None or a valid kernel as defined by the function sklearn.metrics.pairwise.pairwise_kernels. The kernel is used to calculate the frequency of labels near the candidates and multiplied with the probabilities returned by clf to get a kernel frequency estimate for each class. If metric is set to None, the predict_freq function of the clf will be used instead. If this is not defined, a TypeError is raised.

metric_dictdict, default=None

Any further parameters that should be passed directly to the kernel function. If metric_dict is None and metric is ‘rbf’ metric_dict is set to {‘gamma’: ‘mean’}.

random_stateNone or int or np.random.RandomState, default=None

The random state to use.

References

1

D. Kottke, G. Krempl, D. Lang, J. Teschner, and M. Spiliopoulou. Multi-class Probabilistic Active Learning. In Eur. Conf. Artif. Intell., pages 586–594, 2016.

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, utility_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.

utility_weightarray-like, default=None

Weight for each candidate (multiplied with utilities). Usually, this is to be the density of a candidate in ProbabilisticAL. The length of utility_weight is usually n_samples, except for the case when candidates contains samples (ndim >= 2). Then the length is n_candidates.

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.ProbabilisticAL#

Parallel Utility Estimation Wrapper

Parallel Utility Estimation Wrapper

Multi-class Probabilistic Active Learning

Multi-class Probabilistic Active Learning