ai.libs.jaicore.ml.tsc.classifier.shapelets

Class ShapeletTransformTSClassifier

java.lang.Object

ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier<java.lang.Integer>

ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier

public class ShapeletTransformTSClassifier
extends ASimplifiedTSClassifier<java.lang.Integer>

Class for a ShapeletTransform classifier as described in Jason Lines, Luke M. Davis, Jon Hills, and Anthony Bagnall. 2012. A shapelet transform for time series classification. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '12). ACM, New York, NY, USA, 289-297. The classifier model is built of shapelets which are used for the transformation of instances to the new feature space built by the shapelets as dimensions. The feature values are the minimum distances of a time series to the feature dimension's shapelet. An ensemble classifier trained on the derived representation is then used for prediction. This classifier only supports univariate time series prediction.

Field Summary

Fields inherited from class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier

classMapper, trained

Constructor Summary

Constructors

Constructor and Description
ShapeletTransformTSClassifier(int k, int seed) Constructs an Shapelet Transform classifier using k shapelets, k/2 clusters of the shapelets after shapelet extraction and the FStat quality measure.
ShapeletTransformTSClassifier(int k, int numClusters, IQualityMeasure qm, int seed, boolean clusterShapelets) Constructs an Shapelet Transform classifier using k shapelets, k/2 clusters of the shapelets after shapelet extraction (if clusterShapelets is true and the quality measure function qm.
ShapeletTransformTSClassifier(int k, int numClusters, IQualityMeasure qm, int seed, boolean clusterShapelets, int minShapeletLength, int maxShapeletLength, boolean useHIVECOTEEnsemble, int numFolds) Constructs an Shapelet Transform classifier using k shapelets, k/2 clusters of the shapelets after shapelet extraction (if clusterShapelets is true and the quality measure function qm.
ShapeletTransformTSClassifier(int k, IQualityMeasure qm, int seed, boolean clusterShapelets) Constructs an Shapelet Transform classifier using k shapelets, k/2 clusters of the shapelets after shapelet extraction (if clusterShapelets is true and the quality measure function qm.

Method Summary

Modifier and Type	Method and Description
ShapeletTransformLearningAlgorithm	getLearningAlgorithm(TimeSeriesDataset dataset)
AMinimumDistanceSearchStrategy	getMinDistanceSearchStrategy() Getter for minDistanceSearchStrategy.
java.util.List<Shapelet>	getShapelets() Getter for shapelets.
java.lang.Integer	predict(double[] univInstance) Performs a prediction based on the given univariate double[] instance representation and returns the result.
java.lang.Integer	predict(java.util.List<double[]> multivInstance) Performs a prediction based on the given multivariate list of double[] instance representation and returns the result.
java.util.List<java.lang.Integer>	predict(TimeSeriesDataset dataset) Performs predictions based on the given instances in the given dataset.
void	setClassifier(weka.classifiers.Classifier classifier) Setter for classifier.
void	setShapelets(java.util.List<Shapelet> shapelets) Setter for shapelets.

Methods inherited from class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier

getClassMapper, isTrained, setClassMapper, train

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

ShapeletTransformTSClassifier

public ShapeletTransformTSClassifier(int k,
                                     int seed)

Constructs an Shapelet Transform classifier using k shapelets, k/2 clusters of the shapelets after shapelet extraction and the FStat quality measure.

Parameters:

k - Number of shapelets searched for and used as shapelet clustering input

seed - Seed for randomized operations

ShapeletTransformTSClassifier

public ShapeletTransformTSClassifier(int k,
                                     IQualityMeasure qm,
                                     int seed,
                                     boolean clusterShapelets)

Constructs an Shapelet Transform classifier using k shapelets, k/2 clusters of the shapelets after shapelet extraction (if clusterShapelets is true and the quality measure function qm.

Parameters:

k - Number of shapelets searched for and used as shapelet clustering input if enabled

qm - Quality measure function to be used to assess shapelets

seed - See for randomized operations

clusterShapelets - Indicator whether shapelet clustering should be used after extracting the best k shapelets

ShapeletTransformTSClassifier

public ShapeletTransformTSClassifier(int k,
                                     int numClusters,
                                     IQualityMeasure qm,
                                     int seed,
                                     boolean clusterShapelets)

Constructs an Shapelet Transform classifier using k shapelets, k/2 clusters of the shapelets after shapelet extraction (if clusterShapelets is true and the quality measure function qm.

Parameters:

k - Number of shapelets searched for and used as shapelet clustering input if enabled

numClusters - Number of clusters into which the shapelets are clustered

qm - Quality measure function to be used to assess shapelets

seed - See for randomized operations

clusterShapelets - Indicator whether shapelet clustering should be used after extracting the best k shapelets

ShapeletTransformTSClassifier

public ShapeletTransformTSClassifier(int k,
                                     int numClusters,
                                     IQualityMeasure qm,
                                     int seed,
                                     boolean clusterShapelets,
                                     int minShapeletLength,
                                     int maxShapeletLength,
                                     boolean useHIVECOTEEnsemble,
                                     int numFolds)

Constructs an Shapelet Transform classifier using k shapelets, k/2 clusters of the shapelets after shapelet extraction (if clusterShapelets is true and the quality measure function qm. minShapeletLength and maxShapeletLength specify the shapelet length borders, while useHIVECOTEEnsemble defines whether the HIVE COTE ensemble or the CAWPE ensemble should be used.

Parameters:

k - Number of shapelets searched for and used as shapelet clustering input if enabled

qm - Quality measure function to be used to assess shapelets

seed - See for randomized operations

clusterShapelets - Indicator whether shapelet clustering should be used after extracting the best k shapelets

minShapeletLength - The minimal length of the shapelets

maxShapeletLength - The maximal length of the shapelets

useHIVECOTEEnsemble - Indicator whether the HIVE COTE ensemble should be used (CAWPE otherwise)

timeout - The timeout used for the training

numFolds - See ShapeletTransformLearningAlgorithm#numFolds

Method Detail

getShapelets

public java.util.List<Shapelet> getShapelets()

Getter for shapelets.

Returns:

The actual list of shapelets used for the transformation

setShapelets

public void setShapelets(java.util.List<Shapelet> shapelets)

Setter for shapelets.

Parameters:

shapelets - The new list of shapelets to be set

setClassifier

public void setClassifier(weka.classifiers.Classifier classifier)

Setter for classifier.

Parameters:

classifier - The classifier to be set

predict

public java.lang.Integer predict(double[] univInstance)
                          throws PredictionException

Performs a prediction based on the given univariate double[] instance representation and returns the result.

Specified by:

predict in class ASimplifiedTSClassifier<java.lang.Integer>

Parameters:

univInstance - Univariate instance given by a double vector of time series values used for the prediction

Returns:

Returns the result of the prediction

Throws:

PredictionException - If something fails during the prediction process.

predict

public java.lang.Integer predict(java.util.List<double[]> multivInstance)
                          throws PredictionException

Performs a prediction based on the given multivariate list of double[] instance representation and returns the result.

Specified by:

predict in class ASimplifiedTSClassifier<java.lang.Integer>

Parameters:

multivInstance - Multivariate instance given by a list of multiple double[] time series used for the prediction

Returns:

Returns the result of the prediction

Throws:

PredictionException - If something fails during the prediction process.

predict

public java.util.List<java.lang.Integer> predict(TimeSeriesDataset dataset)
                                          throws PredictionException

Performs predictions based on the given instances in the given dataset.

Specified by:

predict in class ASimplifiedTSClassifier<java.lang.Integer>

Parameters:

dataset - The TimeSeriesDataset for which predictions should be made.

Returns:

Returns the result of the predictions

Throws:

PredictionException - If something fails during the prediction process

getMinDistanceSearchStrategy

public AMinimumDistanceSearchStrategy getMinDistanceSearchStrategy()

Getter for minDistanceSearchStrategy.

Returns:

the minDistanceSearchStrategy

getLearningAlgorithm

public ShapeletTransformLearningAlgorithm getLearningAlgorithm(TimeSeriesDataset dataset)

Specified by:

getLearningAlgorithm in class ASimplifiedTSClassifier<java.lang.Integer>