skactiveml.base.QueryStrategy

class skactiveml.base.QueryStrategy(random_state=None)

Bases: ABC, BaseEstimator

Base class for all query strategies in scikit-activeml.

Parameters

random_stateint or RandomState instance, optional (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(*args, **kwargs)	Determines the query for active learning based on input arguments.
set_params(**params)	Set the parameters of this estimator.

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(*args, **kwargs)

Determines the query for active learning based on input arguments.

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.

Examples using skactiveml.base.QueryStrategy

Core Set

Core Set

Random Sampling

Random Sampling

Active Learning with Cost Embedding

Active Learning with Cost Embedding

Clustering Uncertainty-weighted Embeddings (CLUE)

Clustering Uncertainty-weighted Embeddings (CLUE)

Contrastive Active Learning (CAL)

Contrastive Active Learning (CAL)

Batch Active Learning by Diverse Gradient Embedding (BADGE)

Batch Active Learning by Diverse Gradient Embedding (BADGE)

Typical Clustering (TypiClust)

Typical Clustering (TypiClust)

Uncertainty Sampling with Entropy

Uncertainty Sampling with Entropy

Epistemic Uncertainty Sampling

Epistemic Uncertainty Sampling

Querying Informative and Representative Examples

Querying Informative and Representative Examples

Uncertainty Sampling with Margin

Uncertainty Sampling with Margin

Uncertainty Sampling with Least-Confidence

Uncertainty Sampling with Least-Confidence

Probability Coverage (ProbCover)

Probability Coverage (ProbCover)

Expected Average Precision

Expected Average Precision

Density-Diversity-Distribution-Distance Sampling

Density-Diversity-Distribution-Distance Sampling

Monte-Carlo EER with Log-Loss

Monte-Carlo EER with Log-Loss

Batch Density-Diversity-Distribution-Distance Sampling

Batch Density-Diversity-Distribution-Distance Sampling

Monte-Carlo EER with Misclassification-Loss

Monte-Carlo EER with Misclassification-Loss

Sub-sampling Wrapper

Sub-sampling Wrapper

Discriminative Active Learning

Discriminative Active Learning

Parallel Utility Estimation Wrapper

Parallel Utility Estimation Wrapper

Multi-class Probabilistic Active Learning

Multi-class Probabilistic Active Learning

Query-by-Committee (QBC) with Vote Entropy

Query-by-Committee (QBC) with Vote Entropy

Query-by-Committee (QBC) with Variation Ratios

Query-by-Committee (QBC) with Variation Ratios

Query-by-Committee (QBC) with Kullback-Leibler Divergence

Query-by-Committee (QBC) with Kullback-Leibler Divergence

Value of Information

Value of Information

Value of Information on Labeled Samples

Value of Information on Labeled Samples

Value of Information on Unlabeled Samples

Value of Information on Unlabeled Samples

Batch Bayesian Active Learning by Disagreement (BatchBALD)

Batch Bayesian Active Learning by Disagreement (BatchBALD)

Bayesian Active Learning by Disagreement (BALD)

Bayesian Active Learning by Disagreement (BALD)

Density-weighted Uncertainty Sampling

Density-weighted Uncertainty Sampling

Greedy Sampling on the Feature Space (GSx)

Greedy Sampling on the Feature Space (GSx)

Greedy Sampling on the Target Space (GSy)

Greedy Sampling on the Target Space (GSy)

Improved Greedy Sampling (GSi)

Improved Greedy Sampling (GSi)

Expected Model Variance Reduction

Expected Model Variance Reduction

Expected Model Change

Expected Model Change

Regression Tree Based Active Learning (RT-AL) with Random Selection

Regression Tree Based Active Learning (RT-AL) with Random Selection

Regression Tree Based Active Learning (RT-AL) with Diversity Selection

Regression Tree Based Active Learning (RT-AL) with Diversity Selection

Regression Tree Based Active Learning (RT-AL) with Representativity Selection

Regression Tree Based Active Learning (RT-AL) with Representativity Selection

Query-by-Committee (QBC) with Empirical Variance

Query-by-Committee (QBC) with Empirical Variance

Expected Model Output Change

Expected Model Output Change

Regression based Kullback Leibler Divergence Maximization

Regression based Kullback Leibler Divergence Maximization

Dual strategy for Active Learning

Dual strategy for Active Learning

Split

Split

Periodic Sampling

Periodic Sampling

Stream Random Sampling

Stream Random Sampling

Fixed-Uncertainty

Fixed-Uncertainty

Variable-Uncertainty

Variable-Uncertainty

Density Based Active Learning for Data Streams

Density Based Active Learning for Data Streams

Randomized-Variable-Uncertainty

Randomized-Variable-Uncertainty

Cognitive Dual-Query Strategy with Random Sampling

Cognitive Dual-Query Strategy with Random Sampling

Cognitive Dual-Query Strategy with Fixed-Uncertainty

Cognitive Dual-Query Strategy with Fixed-Uncertainty

Cognitive Dual-Query Strategy with Variable-Uncertainty

Cognitive Dual-Query Strategy with Variable-Uncertainty

Cognitive Dual-Query Strategy with Randomized-Variable-Uncertainty

Cognitive Dual-Query Strategy with Randomized-Variable-Uncertainty

Probabilistic Active Learning in Datastreams

Probabilistic Active Learning in Datastreams