API Reference

This is an overview of the API.

skactiveml.base

The skactiveml.base package implements the base classes for skactiveml.

Classes

base.QueryStrategy	Base class for all query strategies in scikit-activeml.
base.SingleAnnotatorPoolQueryStrategy	Base class for all pool-based active learning query strategies with a single annotator in scikit-activeml.
base.MultiAnnotatorPoolQueryStrategy	Base class for all pool-based active learning query strategies with multiple annotators in scikit-activeml.
base.BudgetManager	Base class for all budget managers for stream-based active learning to model budgeting constraints.
base.SingleAnnotatorStreamQueryStrategy	Base class for all stream-based active learning query strategies.
base.SkactivemlClassifier	Skactiveml Classifier
base.ClassFrequencyEstimator	Class Frequency Estimator
base.AnnotatorModelMixin	Annotator Model
base.SkactivemlRegressor	Skactiveml Regressor
base.ProbabilisticRegressor	Base class for scikit-activeml probabilistic regressors.

skactiveml.pool

The skactiveml.pool package implements query strategies for pool-based active learning.

Classes

pool.RandomSampling	Random Sampling (RS)
pool.ProbabilisticAL	Multi-class Probabilistic Active Learning (McPAL)
pool.UncertaintySampling	Uncertainty Sampling (US)
pool.EpistemicUncertaintySampling	Epistemic Uncertainty Sampling (EpisUS)
pool.MonteCarloEER	Monte Carlo Expected Error Reduction
pool.ValueOfInformationEER	Value of Information (VOI)
pool.QueryByCommittee	Query-by-Committee (QBC)
pool.Quire	QUerying Informative and Representative Examples (QUIRE)
pool.FourDs	4DS
pool.CostEmbeddingAL	Active Learning with Cost Embedding (ALCE)
pool.ExpectedModelChangeMaximization	Expected Model Change (EMC)
pool.ExpectedModelOutputChange	Regression based Expected Model Output Change (EMOC)
pool.ExpectedModelVarianceReduction	Expected Model Variance Reduction (EMVR)
pool.KLDivergenceMaximization	Regression based Kullback-Leibler Divergence Maximization
pool.GreedySamplingX	Greedy Sampling in the Feature Space (GSx)
pool.GreedySamplingTarget	Greedy Sampling in the Target Space (GSi or GSy)
pool.DiscriminativeAL	Discriminative Active Learning (DAL)
pool.BatchBALD	Batch Bayesian Active Learning by Disagreement (BatchBALD)
pool.Clue	Clustering Uncertainty-weighted Embeddings (CLUE)
pool.DropQuery	Dropout Query (DropQuery)
pool.CoreSet	Core Set
pool.TypiClust	Typical Clustering (TypiClust)
pool.Badge	Batch Active Learning by Diverse Gradient Embedding (BADGE)
pool.ProbCover	Probability Coverage (ProbCover)
pool.ContrastiveAL	Contrastive Active Learning (ContrastiveAL)
pool.GreedyBALD	Greedy Bayesian Active Learning by Disagreement (GreedyBALD)
pool.RegressionTreeBasedAL	Regression Tree-based Active Learning (RT-AL)
pool.SubSamplingWrapper	Sub-sampling Wrapper
pool.ParallelUtilityEstimationWrapper	Parallel Utility Estimation Wrapper
pool.Falcun	Fast Active Learning by Contrastive UNcertainty (FALCUN)

Functions

pool.cost_reduction	Calculate the expected cost reduction.
pool.uncertainty_scores	Computes uncertainty scores.
pool.expected_average_precision	Calculate the expected average precision [R24358fbc63f7-1].
pool.average_kl_divergence	Calculates the average Kullback-Leibler (KL) divergence for measuring the level of disagreement in QueryByCommittee.
pool.vote_entropy	Calculates the vote entropy for measuring the level of disagreement in QueryByCommittee.
pool.variation_ratios	Calculates the variation ratios for measuring the level of disagreement in QueryByCommittee.
pool.batch_bald	BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
pool.k_greedy_center	An active learning method that greedily forms a batch to minimize the maximum distance to a cluster center among all unlabeled datapoints.

skactiveml.pool.multiannotator

The skactiveml.pool.multiannotator package implements multi annotator pool-based active learning for multiple annotators.

Classes

pool.multiannotator.IntervalEstimationThreshold	Interval Estimation Threshold (IEThresh)
pool.multiannotator.IntervalEstimationAnnotModel	IELearning
pool.multiannotator.SingleAnnotatorWrapper	Single Annotator Wrapper

skactiveml.pool.utils

Classes

pool.utils.IndexClassifierWrapper

Classifier to simplify retraining classifiers in an active learning scenario.

skactiveml.stream

The skactiveml.stream package implements query strategies for stream-based active learning.

Classes

stream.StreamRandomSampling	Random Sampling for Data Streams.
stream.PeriodicSampling	Periodic Sampling for Data Streams
stream.FixedUncertainty	Fixed Uncertainty Strategy
stream.VariableUncertainty	Variable Uncertainty Strategy
stream.Split	The Split query strategy (Split Strategy in [R79653b04d29c-1]) queries labels based on the classifiers uncertainty assessed based on the classifier's predictions.
stream.StreamProbabilisticAL	Probabilistic Active Learning in Datastreams
stream.RandomVariableUncertainty	The RandomVariableUncertainty (Uncertainty Strategy With Randomization in [R7113787876b0-1]) query strategy samples samples based on the classifier's uncertainty assessed based on the classifier's predictions.
stream.StreamDensityBasedAL	The StreamDensityBasedAL [Rddab91d1dd87-1] query strategy is an extension to the uncertainty based query strategies proposed by Žliobaitė et al. [Rddab91d1dd87-2].
stream.CognitiveDualQueryStrategy	This class is the base for the CognitiveDualQueryStrategy query strategy proposed in [R1e46791e62c8-1].
stream.CognitiveDualQueryStrategyRan	This class implements the CognitiveDualQueryStrategy [Rffbcc2a90939-1] strategy with Random Sampling.
stream.CognitiveDualQueryStrategyRanVarUn	This class implements the CognitiveDualQueryStrategy [R291d9e1f2fe6-1] strategy with RandomVariableUncertainty.
stream.CognitiveDualQueryStrategyVarUn	This class implements the CognitiveDualQueryStrategy [Rb64f38603851-1] strategy with VariableUncertainty.
stream.CognitiveDualQueryStrategyFixUn	This class implements the CognitiveDualQueryStrategy [R68dceac87beb-1] strategy with FixedUncertainty.

skactiveml.stream.budgetmanager

The skactiveml.stream.budgetmanager package implements budget managers, which are capable of modeling the budget constraints in stream-based active learning settings.

Classes

stream.budgetmanager.EstimatedBudgetZliobaite	Budget manager which checks, whether the specified budget has been exhausted already.
stream.budgetmanager.FixedUncertaintyBudgetManager	Budget Manager for Fixed Uncertainty Strategy
stream.budgetmanager.VariableUncertaintyBudgetManager	Budget Manager for Variable Uncertainty Strategy
stream.budgetmanager.SplitBudgetManager	Budget Manager for Split Strategy
stream.budgetmanager.BalancedIncrementalQuantileFilter	Balanced Incremental Quantile Filter (BIQF)
stream.budgetmanager.RandomVariableUncertaintyBudgetManager	Budget Manager for Uncertainty Strategy With Randomization
stream.budgetmanager.DensityBasedSplitBudgetManager	Budget Manager for DBALStream
stream.budgetmanager.RandomBudgetManager	Budget manager which checks, whether the specified budget has been exhausted already.

skactiveml.classifier

The skactiveml.classifier module.

Classes

classifier.ParzenWindowClassifier	Parzen Window Classifier (PWC)
classifier.MixtureModelClassifier	Classifier based on a Mixture Model (CMM)
classifier.SklearnClassifier	Sklearn Classifier
classifier.SlidingWindowClassifier	Sliding Window Classifier

skactiveml.classifier.multiannotator

Classes

classifier.multiannotator.AnnotatorLogisticRegression	Logistic Regression for Crowds
classifier.multiannotator.AnnotatorEnsembleClassifier	Ensemble of Annotator-wise Classifier

skactiveml.visualization

The skactiveml.visualization module includes various tools for visualization.

Functions

visualization.plot_utilities	Plot the utility for the given single-annotator query strategy.
visualization.plot_decision_boundary	Plot the decision boundary of the given classifier.
visualization.plot_contour_for_samples	Plot the utility for the given query strategy.
visualization.plot_annotator_utilities	Plot the utility for the given query strategy.
visualization.plot_stream_training_data	Plot the utility for the given query strategy.
visualization.plot_stream_decision_boundary	Plot the decision boundary of the given classifier.
visualization.mesh	Function to get samples of a mesh grid as well as x-mesh and y-mesh with given resolution in the specified bounds.

skactiveml.utils

The skactiveml.utils module includes various utilities.

Classes

utils.ExtLabelEncoder

Encode class labels with value between 0 and classes-1 and uses -1 for unlabeled samples.

Functions

utils.rand_argmax	Returns index of maximum value.
utils.rand_argmin	Returns index of minimum value.
utils.compute_vote_vectors	Counts number of votes per class label for each sample.
utils.majority_vote	Assigns a label to each sample based on weighted voting.
utils.is_unlabeled	Creates a boolean mask indicating missing labels.
utils.is_labeled	Creates a boolean mask indicating present labels.
utils.check_classes	Check whether class labels are uniformly strings or numbers.
utils.check_missing_label	Check whether a missing label is compatible to a given target type.
utils.check_cost_matrix	Check whether cost matrix has shape (n_classes, n_classes).
utils.check_scalar	Validate scalar parameters type and value.
utils.check_classifier_params	Check whether the parameters are compatible to each other (only if classes is not None).
utils.check_X_y	Input validation for standard estimators.
utils.check_random_state	Check validity of the given random state.
utils.call_func	Calls a function with the given parameters given in kwargs, if they exist as parameters in f_callable.
utils.match_signature	A decorator that matches the signature to a given method from a reference and hides it when the reference object does not have the wrapped function.
utils.check_class_prior	Check if the class_prior is a valid prior.
utils.ext_confusion_matrix	Compute confusion matrix [R3479f72bc894-1] to evaluate the accuracy of a classification.
utils.labeled_indices	Return an array of indices indicating present labels.
utils.unlabeled_indices	Return an array of indices indicating missing labels.
utils.check_type	Check if obj is one of the given types.
utils.check_bound	Validates bound and returns the bound of X if bound is None.
utils.check_equal_missing_label	Check whether two missing label values are equal to each other.
utils.check_budget_manager	Validate if budget_manager is a budget manager class and create a copy budget_manager_.
utils.check_indices	Check if indices fit to array.
utils.simple_batch	Generates a batch by selecting the highest values in the utilities.
utils.check_n_features	Validate and update the number of features for an estimator based on the input data.
utils._check_callable	Checks if func is a callable and if the number of free parameters is correct.

skactiveml.exceptions

Classes

exceptions.MappingError

Exception class to raise if a strategy needs a mapping between samples and candidates which is not available.