skactiveml.stream.VariableUncertainty

class skactiveml.stream.VariableUncertainty(budget_manager=None, budget=None, random_state=None)

Bases: UncertaintyZliobaite

The VariableUncertainty (Var-Uncertainty in [1]) query strategy samples instances based on the classifiers uncertainty assessed based on the classifier’s predictions. The instance is queried when the probability of the most likely class exceeds a time-dependent threshold calculated based on the budget, the number of classes and the number of observed and acquired samples.

Parameters

budgetfloat, optional (default=None)

The budget which models the budgeting constraint used in the stream-based active learning setting.

budgetmanagerBudgetManager, optional (default=None)

The BudgetManager which models the budgeting constraint used in the stream-based active learning setting. if set to None, VariableUncertaintyBudgetManager will be used by default. The budget manager will be initialized based on the following conditions:

If only a budget is given the default budgetmanager is initialized with the given budget. If only a budgetmanager is given use the budgetmanager. If both are not given the default budgetmanager with the default budget. If both are given and the budget differs from budgetmanager.budget a warning is thrown.

random_stateint, RandomState instance, optional (default=None)

Controls the randomness of the estimator.

References

[1] Žliobaitė, I., Bifet, A., Pfahringer, B., & Holmes, G. (2014). Active

Learning With Drifting Streaming Data. IEEE Transactions on Neural Networks and Learning Systems, 25(1), 27-39.

Methods

get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
query(candidates, clf[, X, y, ...])	Ask the query strategy which instances in candidates to acquire.
set_params(**params)	Set the parameters of this estimator.
update(candidates, queried_indices[, ...])	Updates the budget manager and the count for seen and queried instances

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(candidates, clf, X=None, y=None, sample_weight=None, fit_clf=False, return_utilities=False)

Ask the query strategy which instances in candidates to acquire.

Parameters

candidates{array-like, sparse matrix} of shape

(n_samples, n_features)

The instances which may be queried. Sparse matrices are accepted only if they are supported by the base query strategy.

clfSkactivemlClassifier

Model implementing the methods fit and predict_freq.

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

(default=None)

Input samples used to fit the classifier.

yarray-like of shape (n_samples), optional (default=None)

Labels of the input samples ‘X’. There may be missing labels.

sample_weightarray-like of shape (n_samples,), optional

(default=None)

Sample weights for X, used to fit the clf.

fit_clfbool, optional (default=False)

If true, refit the classifier also requires X and y to be given.

return_utilitiesbool, optional (default=False)

If true, also return the utilities based on the query strategy. The default is False.

Returns

queried_indicesndarray of shape (n_queried_instances,)

The indices of instances in candidates which should be queried, with 0 <= n_queried_instances <= n_samples.

utilities: ndarray of shape (n_samples,), optional

The utilities based on the query strategy. Only provided if return_utilities is True.

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.

update(candidates, queried_indices, budget_manager_param_dict=None)

Updates the budget manager and the count for seen and queried instances

Parameters

candidates{array-like, sparse matrix} of shape

(n_samples, n_features)

The instances which could be queried. Sparse matrices are accepted only if they are supported by the base query strategy.

queried_indicesarray-like of shape (n_samples,)

Indicates which instances from candidates have been queried.

budget_manager_param_dictkwargs, optional (default=None)

Optional kwargs for budget manager.

Returns

selfUncertaintyZliobaite

The UncertaintyZliobaite returns itself, after it is updated.

Examples using skactiveml.stream.VariableUncertainty

Variable-Uncertainty

Variable-Uncertainty