skactiveml.stream.FixedUncertainty

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

Bases: UncertaintyZliobaite

The FixedUncertainty (Fixed-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 threshold calculated based on the budget and the number of classes.

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, FixedUncertaintyBudgetManager will be used by default. The budget manager will be initialized based on the following conditions:

If only a budget is given the default budget manager is initialized with the given budget. If only a budget manager is given use the budget manager. If both are not given the default budget manager with the default budget. If both are given and the budget differs from budget manager.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.FixedUncertainty

Fixed-Uncertainty

Fixed-Uncertainty