API Reference

This is the API documentation for scikit-multiflow.

Core

The skmultiflow.core module covers core elements of scikit-multiflow.

core.base.BaseEstimator	Base Estimator class for compatibility with scikit-learn.
core.BaseSKMObject	Base class for most objects in scikit-multiflow
core.ClassifierMixin	Mixin class for all classifiers in scikit-multiflow.
core.RegressorMixin	Mixin class for all regression estimators in scikit-multiflow.
core.MetaEstimatorMixin	Mixin class for all meta estimators in scikit-multiflow.
core.MultiOutputMixin	Mixin to mark estimators that support multioutput.
core.Pipeline	[Experimental] Holds a set of sequential operation (transforms), followed by a single estimator.

Data

The skmultiflow.data module contains data stream methods including methods for batch-to-stream conversion and generators.

data.base_stream.Stream	Base Stream class.
data.DataStream	Creates a stream from a data source.
data.FileStream	Creates a stream from a file source.
data.ConceptDriftStream	Generates a stream with concept drift.
data.TemporalDataStream	Create a temporal stream from a data source.

Stream Generators

data.AGRAWALGenerator	Agrawal stream generator.
data.AnomalySineGenerator	Simulate a stream with anomalies in sine waves
data.HyperplaneGenerator	Hyperplane stream generator.
data.LEDGenerator	LED stream generator.
data.LEDGeneratorDrift	LED stream generator with concept drift.
data.MIXEDGenerator	Mixed data stream generator.
data.RandomRBFGenerator	Random Radial Basis Function stream generator.
data.RandomRBFGeneratorDrift	Random Radial Basis Function stream generator with concept drift.
data.RandomTreeGenerator	Random Tree stream generator.
data.SEAGenerator	SEA stream generator.
data.SineGenerator	Sine stream generator.
data.STAGGERGenerator	STAGGER concepts stream generator.
data.WaveformGenerator	Waveform stream generator.
data.MultilabelGenerator	Creates a multi-label stream.
data.RegressionGenerator	Creates a regression stream.

Learning methods

Anomaly detection methods

The skmultiflow.anomaly_detection module includes anomaly detection methods.

anomaly_detection.HalfSpaceTrees

Half–Space Trees.

Bayes methods

The skmultiflow.bayes module includes Bayes learning methods.

bayes.NaiveBayes

Naive Bayes classifier.

Lazy learning methods

The skmultiflow.lazy module includes lazy learning methods in which generalization of the training data is delayed until a query is received, this is, on-demand.

lazy.KNNClassifier	k-Nearest Neighbors classifier.
lazy.KNNADWINClassifier	K-Nearest Neighbors classifier with ADWIN change detector.
lazy.SAMKNNClassifier	Self Adjusting Memory coupled with the kNN classifier.
lazy.KNNRegressor	k-Nearest Neighbors regressor.

Ensemble methods

The skmultiflow.meta module includes meta learning methods.

meta.AccuracyWeightedEnsembleClassifier	Accuracy Weighted Ensemble classifier
meta.AdaptiveRandomForestClassifier	Adaptive Random Forest classifier.
meta.AdaptiveRandomForestRegressor	Adaptive Random Forest regressor.
meta.AdditiveExpertEnsembleClassifier	Additive Expert ensemble classifier.
meta.BatchIncrementalClassifier	Batch Incremental ensemble classifier.
meta.ClassifierChain	Classifier Chains for multi-label learning.
meta.ProbabilisticClassifierChain	Probabilistic Classifier Chains for multi-label learning.
meta.MonteCarloClassifierChain	Monte Carlo Sampling Classifier Chains for multi-label learning.
meta.DynamicWeightedMajorityClassifier	Dynamic Weighted Majority ensemble classifier.
meta.LearnPPNSEClassifier	Learn++.NSE ensemble classifier.
meta.LearnPPClassifier	Learn++ ensemble classifier.
meta.LeveragingBaggingClassifier	Leveraging Bagging ensemble classifier.
meta.MultiOutputLearner	Multi-Output Learner for multi-target classification or regression.
meta.OnlineAdaC2Classifier	Online AdaC2 ensemble classifier.
meta.OnlineBoostingClassifier	Online Boosting ensemble classifier.
meta.OnlineCSB2Classifier	Online CSB2 ensemble classifier.
meta.OnlineRUSBoostClassifier	Online RUSBoost ensemble classifier.
meta.OnlineSMOTEBaggingClassifier	Online SMOTEBagging ensemble classifier.
meta.OnlineUnderOverBaggingClassifier	Online Under-Over-Bagging ensemble classifier.
meta.OzaBaggingClassifier	Oza Bagging ensemble classifier.
meta.OzaBaggingADWINClassifier	Oza Bagging ensemble classifier with ADWIN change detector.
meta.RegressorChain	Regressor Chains for multi-output learning.
meta.StreamingRandomPatchesClassifier	Streaming Random Patches ensemble classifier.

Neural Networks

The skmultiflow.neural_networks module includes learning methods based on Neural Networks.

neural_networks.PerceptronMask

Mask for sklearn.linear_model.Perceptron.

Prototype based methods

The skmultiflow.prototype module includes prototype-based learning methods.

prototype.RobustSoftLearningVectorQuantization

Robust Soft Learning Vector Quantization for Streaming and Non-Streaming Data.

Rules based methods

The skmultiflow.rules module includes rule-based learning methods.

rules.VeryFastDecisionRulesClassifier

Very Fast Decision Rules classifier.

Trees based methods

The skmultiflow.trees module includes learning methods based on trees.

trees.HoeffdingTreeClassifier	Hoeffding Tree or Very Fast Decision Tree classifier.
trees.HoeffdingAdaptiveTreeClassifier	Hoeffding Adaptive Tree classifier.
trees.ExtremelyFastDecisionTreeClassifier	Extremely Fast Decision Tree classifier.
trees.LabelCombinationHoeffdingTreeClassifier	Label Combination Hoeffding Tree for multi-label classification.
trees.HoeffdingTreeRegressor	Hoeffding Tree regressor.
trees.HoeffdingAdaptiveTreeRegressor	Hoeffding Adaptive Tree regressor.
trees.iSOUPTreeRegressor	Incremental Structured Output Prediction Tree (iSOUP-Tree) for multi-target regression.
trees.StackedSingleTargetHoeffdingTreeRegressor	Stacked Single-target Hoeffding Tree regressor.

Drift Detection

The skmultiflow.drift_detection module includes methods for Concept Drift Detection.

drift_detection.ADWIN	Adaptive Windowing method for concept drift detection.
drift_detection.DDM	Drift Detection Method.
drift_detection.EDDM	Early Drift Detection Method.
drift_detection.HDDM_A	Drift Detection Method based on Hoeffding’s bounds with moving average-test.
drift_detection.HDDM_W	Drift Detection Method based on Hoeffding’s bounds with moving weighted average-test.
drift_detection.KSWIN	Kolmogorov-Smirnov Windowing method for concept drift detection.
drift_detection.PageHinkley	Page-Hinkley method for concept drift detection.

Evaluation

The skmultiflow.evaluation module includes evaluation methods for stream learning.

evaluation.EvaluateHoldout	The holdout evaluation method or periodic holdout evaluation method.
evaluation.EvaluatePrequential	The prequential evaluation method or interleaved test-then-train method.
evaluation.EvaluatePrequentialDelayed	The prequential evaluation delayed method.

Transform

The skmultiflow.transform module covers methods that perform data transformations.

transform.MissingValuesCleaner	Fill missing values with some defined value.
transform.OneHotToCategorical	Transform one-hot encoded data into categorical feature(s).
transform.WindowedMinmaxScaler	Transform features by scaling each feature to a given range.
transform.WindowedStandardScaler	Standardize features by removing the mean and scaling to unit variance.

Misc

Utilities

core.clone

Constructs a new estimator with the same parameters.