skactiveml.stream.budgetmanager.EstimatedBudgetZliobaite

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

Bases: BudgetManager

Budget manager which checks, whether the specified budget has been exhausted already. If not, a sample is queried, when the utility is higher than the specified budget.

This budget manager calculates the estimated budget [1] spent in the last w steps and compares that to the budget. If the ratio is smaller than the specified budget, i.e., budget - u_t / w > 0, the budget manager queries a sample when its utility is higher than the budget. u is the estimate of how many true labels were queried within the last w steps. The incremental function, u_t = u_t-1 * (w-1) / w + labeling_t, is used to calculate u at time t.

Parameters

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, *args, **kwargs)	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 EstimatedBudgetZliobaite.

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.

abstract query_by_utility(utilities, *args, **kwargs)

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 EstimatedBudgetZliobaite.

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

selfEstimatedBudgetZliobaite

The EstimatedBudgetZliobaite returns itself, after it is updated.