skactiveml.base.SingleAnnotatorStreamQueryStrategy#

class skactiveml.base.SingleAnnotatorStreamQueryStrategy(budget, random_state=None)[source]#

Bases: QueryStrategy

Base class for all stream-based active learning query strategies.

Parameters

budgetfloat: Specifies the ratio of labels which are allowed to be queried, with 0 <= budget <= 1.
random_stateint or RandomState instance or None, default=None: Controls the randomness of the estimator.

Methods

`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`query`(candidates, *args[, return_utilities])	Determines for which candidate samples labels are to be queried.
`set_params`(**params)	Set the parameters of this estimator.
`update`(candidates, queried_indices, *args[, ...])	Updates the budget manager and the count for seen and queried labels.

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(candidates, *args, return_utilities=False, **kwargs)[source]#

Determines for which candidate samples labels are to be queried.

The query startegy determines the most useful samples in candidates, which can be acquired within the budgeting constraint specified by budget. Please note that, this method does not change the internal state of the query strategy. To adapt the query strategy to the selected candidates, use update(…).

Parameters

candidates{array-like, sparse matrix} of shape (n_candidates, n_features): The samples which may be queried. Sparse matrices are accepted only if they are supported by the base query strategy.
return_utilitiesbool, default=False: If True, also return the utilities based on the query strategy.

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.
utilities: np.ndarray of shape (n_candidates,),: 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.

abstract update(candidates, queried_indices, *args, budget_manager_param_dict=None, **kwargs)[source]#

Updates the budget manager and the count for seen and queried labels. This function should be used in conjunction with the query function.

Parameters

candidates{array-like, sparse matrix} of shape (n_candidates, 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.
budget_manager_param_dictdict, default=None: Optional kwargs for budget_manager.

Returns