skactiveml.base.BudgetManager

class skactiveml.base.BudgetManager(budget=None)

Bases: ABC, BaseEstimator

Base class for all budget managers for stream-based active learning in scikit-activeml to model budgeting constraints.

Parameters

budgetfloat (default=None)

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

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 instance.
set_params(**params)	Set the parameters of this estimator.
update(candidates, queried_indices, *args, ...)	Updates the BudgetManager.

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 instance.

Parameters

utilitiesndarray of shape (n_samples,)

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

Returns

queried_indicesndarray of shape (n_queried_instances,)

The indices of instances represented by utilities which should be queried, with 0 <= n_queried_instances <= n_samples.

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.

abstract update(candidates, queried_indices, *args, **kwargs)

Updates the BudgetManager.

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.

queried_indicesarray-like

Indicates which instances from candidates have been queried.

Returns

selfBudgetManager

The BudgetManager returns itself, after it is updated.