skactiveml.stream.budgetmanager.FixedUncertaintyBudgetManager

class skactiveml.stream.budgetmanager.FixedUncertaintyBudgetManager(classes, w=100, budget=None)

Bases: EstimatedBudgetZliobaite

Budget Manager for Fixed Uncertainty Strategy

Budget manager which implements the budgeting for the Fixed Uncertainty Strategy [1]. If the not budget is not exhausted, a sample is queried, when the utility is higher than the specified budget and the probability of the most likely class exceeds a threshold calculated based on the budget and the number of classes. See also EstimatedBudgetZliobaite.

Parameters

classesarray-like of shape (n_classes)

Holds the label for each class.

wint, default=100

Specifies the size of the memory window. Controlls the budget in the last w steps taken.

budgetfloat, default=None

Specifies the ratio of samples which are allowed to be sampled, with 0 <= budget <= 1. If budget is None, it is replaced with the default budget 0.1.

References

1

I. Žliobaitė, A. Bifet, B. Pfahringer, and G. Holmes. Active Learning With Drifting Streaming Data. IEEE Trans. Neural Netw. Learn. Syst., 25(1):27–39, 2014

Methods

get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
query_by_utility(utilities)	Ask the budget manager which utilities are sufficient to query the corresponding labels.
set_params(**params)	Set the parameters of this estimator.
update(candidates, queried_indices)	Updates the budget manager.

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_by_utility(utilities)

Ask the budget manager which utilities are sufficient to query the corresponding labels.

Parameters

utilitiesarray-like of shape (n_samples,)

The utilities provided by the stream-based active learning strategy, which are used to determine whether querying a sample is worth it given the budgeting constraint.

Returns

queried_indicesnp.ndarray of shape (n_queried_indices,)

The indices of samples in candidates whose labels are queried, with 0 <= queried_indices <= n_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.

update(candidates, queried_indices)

Updates the budget manager.

Parameters

candidates{array-like, sparse matrix} of shape (n_samples, n_features)

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

queried_indicesnp.ndarray of shape (n_queried_indices,)

The indices of samples in candidates whose labels are queried, with 0 <= queried_indices <= n_candidates.

Returns

selfFixedUncertaintyBudgetManager

The budget manager returns itself, after it is updated.