PoolQueryStrategy

class skactiveml.base.PoolQueryStrategy(missing_label=nan, random_state=None)

Bases: QueryStrategy

Base class for all pool-based active learning query strategies in scikit-activeml.

Parameters:

missing_labelscalar or string or np.nan or None, default=np.nan

Value to represent a missing label.

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(*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.PoolQueryStrategy

Batch Active Learning by Diverse Gradient Embedding (BADGE)

Batch Active Learning by Diverse Gradient Embedding (BADGE)

Batch Bayesian Active Learning by Disagreement (BatchBALD)

Batch Bayesian Active Learning by Disagreement (BatchBALD)

Clustering Uncertainty-weighted Embeddings (CLUE)

Clustering Uncertainty-weighted Embeddings (CLUE)

Contrastive Active Learning (CAL)

Contrastive Active Learning (CAL)

Core Set

Core Set

Active Learning with Cost Embedding (ALCE)

Active Learning with Cost Embedding (ALCE)

Discriminative Active Learning (DAL)

Discriminative Active Learning (DAL)

Dropout Query (DropQuery)

Dropout Query (DropQuery)

Epistemic Uncertainty Sampling (EpisUS)

Epistemic Uncertainty Sampling (EpisUS)

Fast Active Learning by Contrastive UNcertainty (FALCUN)

Fast Active Learning by Contrastive UNcertainty (FALCUN)

Batch Density-Diversity-Distribution-Distance Sampling (4DS)

Batch Density-Diversity-Distribution-Distance Sampling (4DS)

Density-Diversity-Distribution-Distance Sampling (4DS)

Density-Diversity-Distribution-Distance Sampling (4DS)

Bayesian Active Learning by Disagreement (BALD)

Bayesian Active Learning by Disagreement (BALD)

MaxHerding

MaxHerding

Monte-Carlo Expected Error Reduction (EER) with Log-Loss

Monte-Carlo Expected Error Reduction (EER) with Log-Loss

Monte-Carlo Expected Error Reduction (EER) with Misclassification-Loss

Monte-Carlo Expected Error Reduction (EER) with Misclassification-Loss

Parallel Utility Estimation Wrapper

Parallel Utility Estimation Wrapper

Probability Coverage (ProbCover)

Probability Coverage (ProbCover)

Multi-class Probabilistic Active Learning (McPAL)

Multi-class Probabilistic Active Learning (McPAL)

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

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

Query-by-Committee (QBC) with Variation Ratios

Query-by-Committee (QBC) with Variation Ratios

Query-by-Committee (QBC) with Vote Entropy

Query-by-Committee (QBC) with Vote Entropy

Querying Informative and Representative Examples (QUIRE)

Querying Informative and Representative Examples (QUIRE)

Random Sampling

Random Sampling

Sub-sampling Wrapper

Sub-sampling Wrapper

Typical Clustering (TypiClust)

Typical Clustering (TypiClust)

Density-weighted Uncertainty Sampling (DWUS)

Density-weighted Uncertainty Sampling (DWUS)

Dual Strategy for Active Learning

Dual Strategy for Active Learning

Uncertainty Sampling (US) with Entropy

Uncertainty Sampling (US) with Entropy

Uncertainty Sampling (US) with Least-Confidence

Uncertainty Sampling (US) with Least-Confidence

Uncertainty Sampling (US) with Margin

Uncertainty Sampling (US) with Margin

Uncertainty Sampling with Expected Average Precision (USAP)

Uncertainty Sampling with Expected Average Precision (USAP)

Value of Information (VOI)

Value of Information (VOI)

Value of Information (VOI) on Labeled Samples

Value of Information (VOI) on Labeled Samples

Value of Information (VOI) on Unlabeled Samples

Value of Information (VOI) on Unlabeled Samples

Expected Model Change Maximization

Expected Model Change Maximization

Expected Model Output Change

Expected Model Output Change

Expected Model Variance Reduction

Expected Model Variance Reduction

Greedy Sampling on the Target Space (GSy)

Greedy Sampling on the Target Space (GSy)

Improved Greedy Sampling (GSi)

Improved Greedy Sampling (GSi)

Greedy Sampling on the Feature Space (GSx)

Greedy Sampling on the Feature Space (GSx)

Regression based Kullback Leibler Divergence Maximization

Regression based Kullback Leibler Divergence Maximization

Query-by-Committee (QBC) with Empirical Variance

Query-by-Committee (QBC) with Empirical Variance

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 Random Selection

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

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

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

Interval Estimation Threshold

Interval Estimation Threshold

Core Set + Greedy Selection

Core Set + Greedy Selection

Random Sampling

Random Sampling