ai.libs.jaicore.ml.tsc.classifier.trees

Class TimeSeriesBagOfFeaturesLearningAlgorithm

java.lang.Object

ai.libs.jaicore.basic.algorithm.AAlgorithm<TimeSeriesDataset,C>

ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSCLearningAlgorithm<java.lang.Integer,TimeSeriesBagOfFeaturesClassifier>

ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm

All Implemented Interfaces:

ai.libs.jaicore.basic.algorithm.IAlgorithm<TimeSeriesDataset,TimeSeriesBagOfFeaturesClassifier>, ai.libs.jaicore.basic.Cancelable, ai.libs.jaicore.basic.ILoggingCustomizable, java.lang.Iterable<ai.libs.jaicore.basic.algorithm.events.AlgorithmEvent>, java.util.concurrent.Callable<TimeSeriesBagOfFeaturesClassifier>, java.util.Iterator<ai.libs.jaicore.basic.algorithm.events.AlgorithmEvent>

public class TimeSeriesBagOfFeaturesLearningAlgorithm
extends ASimplifiedTSCLearningAlgorithm<java.lang.Integer,TimeSeriesBagOfFeaturesClassifier>

Algorithm to train a Time Series Bag-of-Features (TSBF) classifier as described in Baydogan, Mustafa & Runger, George & Tuv, Eugene. (2013). A Bag-of-Features Framework to Classify Time Series. IEEE Transactions on Pattern Analysis and Machine Intelligence. 35. 2796-802. 10.1109/TPAMI.2013.72.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static interface	TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig

Field Summary

Fields

Modifier and Type	Field and Description
static boolean	USE_BIAS_CORRECTION Indicator whether Bessel's correction should in feature generation.

Constructor Summary

Constructors

Constructor and Description
TimeSeriesBagOfFeaturesLearningAlgorithm(TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig config, TimeSeriesBagOfFeaturesClassifier classifier, TimeSeriesDataset data) Constructor for a TSBF training algorithm.

Method Summary

Modifier and Type	Method and Description
TimeSeriesBagOfFeaturesClassifier	call() Training procedure construction a Time Series Bag-of-Features (TSBF) classifier using the given input data.
static int[][]	discretizeProbs(int numBins, double[][] probs) Function discretizing probabilities into bins.
static ai.libs.jaicore.basic.sets.Pair<int[][][],int[][]>	formHistogramsAndRelativeFreqs(int[][] discretizedProbs, int[] targets, int numInstances, int numClasses, int numBins) Function calculating the histograms as described in the paper's section 2.2 ("Codebook and Learning").
static double[][][][]	generateFeatures(double[][] data, int[][] subsequences, int[][][] intervals) Function generating the features for the internal probability measurement model based on the given subseries and their corresponding intervals.
static double[][]	generateHistogramInstances(int[][][] histograms, int[][] relativeFreqsOfClasses) Generates a matrix consisting of the histogram values for each instance out of the given histograms and the relative frequencies of classes for each instance.
ai.libs.jaicore.basic.sets.Pair<int[][],int[][][]>	generateSubsequencesAndIntervals(int r, int d, int lMin, int T) Method randomly determining the subsequences and their intervals to be used for feature generation of the instances.
TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig	getConfig()
static double[][]	measureOOBProbabilitiesUsingCV(double[][] subSeqValueMatrix, int[] targetMatrix, int numProbInstances, int numFolds, int numClasses, weka.classifiers.trees.RandomForest rf) Function measuring the out-of-bag (OOB) probabilities using a cross validation with numFolds many folds.
ai.libs.jaicore.basic.algorithm.events.AlgorithmEvent	nextWithException()
void	registerListener(java.lang.Object listener)

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

getClassifier

Methods inherited from class ai.libs.jaicore.basic.algorithm.AAlgorithm

activate, announceTimeoutDetected, avoidReinterruptionOnShutdownOnCurrentThread, cancel, checkAndConductTermination, checkTermination, computeTimeoutAware, getActivationTime, getId, getInput, getLoggerName, getNumCPUs, getRemainingTimeToDeadline, getState, getTimeout, getTimeoutPrecautionOffset, hasNext, hasThreadBeenInterruptedDuringShutdown, interruptThreadAsPartOfShutdown, isCanceled, isShutdownInitialized, isStopCriterionSatisfied, isTimeouted, iterator, next, post, registerActiveThread, resolveShutdownInterruptOnCurrentThread, setConfig, setLoggerName, setMaxNumThreads, setNumCPUs, setState, setTimeout, setTimeout, setTimeoutPrecautionOffset, shutdown, terminate, unregisterActiveThread, unregisterThreadAndShutdown

Methods inherited from class java.lang.Object

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

Methods inherited from interface java.lang.Iterable

forEach, spliterator

Methods inherited from interface java.util.Iterator

forEachRemaining, remove

Field Detail

USE_BIAS_CORRECTION

public static final boolean USE_BIAS_CORRECTION

Indicator whether Bessel's correction should in feature generation.

See Also:

Constant Field Values

Constructor Detail

TimeSeriesBagOfFeaturesLearningAlgorithm

public TimeSeriesBagOfFeaturesLearningAlgorithm(TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig config,
                                                TimeSeriesBagOfFeaturesClassifier classifier,
                                                TimeSeriesDataset data)

Constructor for a TSBF training algorithm.

Method Detail

call

public TimeSeriesBagOfFeaturesClassifier call()
                                       throws ai.libs.jaicore.basic.algorithm.exceptions.AlgorithmException

Training procedure construction a Time Series Bag-of-Features (TSBF) classifier using the given input data.

Throws:

ai.libs.jaicore.basic.algorithm.exceptions.AlgorithmException

generateSubsequencesAndIntervals

public ai.libs.jaicore.basic.sets.Pair<int[][],int[][][]> generateSubsequencesAndIntervals(int r,
                                                                                           int d,
                                                                                           int lMin,
                                                                                           int T)

Method randomly determining the subsequences and their intervals to be used for feature generation of the instances. As a result, a pair of each subsequence's start and end index and the intervals' start and end indices is returned.

Parameters:

r - The number of possible intervals in a time series

d - The number of intervals for each subsequence

lMin - The minimum subsequence length

T - The length of the time series

Returns:

a pair of each subsequence's start and end index and the intervals' start and end indices

generateFeatures

public static double[][][][] generateFeatures(double[][] data,
                                              int[][] subsequences,
                                              int[][][] intervals)

Function generating the features for the internal probability measurement model based on the given subseries and their corresponding intervals. The features are built using the TimeSeriesFeature implementation. As a result, a tensor consisting of the generated features for each interval in each subsequence for each instance is returned (4 dimensions).

Parameters:

data - The data used for feature generation

subsequences - The subsequences used for feature generation (the start and end [exclusive] index is stored for each subsequence)

intervals - The intervals of each subsequence used for the feature generation (the start and end [exclusive] index is stored for each interval)

Returns:

Returns a tensor consisting of the generated features for each interval in each subsequence for each instance

generateHistogramInstances

public static double[][] generateHistogramInstances(int[][][] histograms,
                                                    int[][] relativeFreqsOfClasses)

Generates a matrix consisting of the histogram values for each instance out of the given histograms and the relative frequencies of classes for each instance. The histogram values for each instance, class and bin are concatenated. Furthermore, the relative frequencies are also added to the instance's features.

Parameters:

histograms - The histograms for each instance (number of instances x number of classes - 1 x number of bins)

relativeFreqsOfClasses - The relative frequencies of the classes for each instance (previously extracted from each subseries instance per origin instance; dimensionality is number of instances x number of classes)

Returns:

Returns a matrix storing the features for each instance (number of instances x number of features)

measureOOBProbabilitiesUsingCV

public static double[][] measureOOBProbabilitiesUsingCV(double[][] subSeqValueMatrix,
                                                        int[] targetMatrix,
                                                        int numProbInstances,
                                                        int numFolds,
                                                        int numClasses,
                                                        weka.classifiers.trees.RandomForest rf)
                                                 throws TrainingException

Function measuring the out-of-bag (OOB) probabilities using a cross validation with numFolds many folds. For each fold, the data given by subSeqValueMatrix is split into a training and test set. The test set's probabilities are then derived by a trained Random Forest classifier.

Parameters:

subSeqValueMatrix - Input data used to derive the OOB probabilities

targetMatrix - The target values of the input data

numProbInstances - Number of instances for which the probabilities should be derived

numFolds - Number of folds used for the measurement

numClasses - Number of total classes

rf - Random Forest classifier which is retrained in each fold

Returns:

Returns a matrix storing the probability for each input instance given by subSeqValueMatrix

Throws:

TrainingException - Thrown when the classifier rf could not be trained in any fold

formHistogramsAndRelativeFreqs

public static ai.libs.jaicore.basic.sets.Pair<int[][][],int[][]> formHistogramsAndRelativeFreqs(int[][] discretizedProbs,
                                                                                                int[] targets,
                                                                                                int numInstances,
                                                                                                int numClasses,
                                                                                                int numBins)

Function calculating the histograms as described in the paper's section 2.2 ("Codebook and Learning"). All probabilities rows belonging to one instance are aggregated by evaluating the discretized probabilities discretizedProbs. Furthermore, the relative frequencies of the classes are collected. As the result, a pair of the generated histograms for all instances and the corresponding normalized relative class frequencies is returned.

Parameters:

discretizedProbs - The discretized (binned) probabilities of all instance's subseries rows (the number of rows must be divisible by the number of total instances)

targets - The targets corresponding to the discretized probabilities

numInstances - The total number of instances (must be <= the number of rows in discretizedProbs

numClasses - The total number of classes

numBins - The number of bins using within the discretization

Returns:

Returns a pair of the histograms per instance (numInstances in total) and the corresponding relative frequencies (normalized)

discretizeProbs

public static int[][] discretizeProbs(int numBins,
                                      double[][] probs)

Function discretizing probabilities into bins. The bins are determined by steps of 1 / numBins. The result is a matrix with the same dimensionality as probs storing the identifier of the corresponding bins.

Parameters:

numBins - Number of bins, determines the probability steps for each bin

probs - Matrix storing the probabilities of each row for each class (columns)

Returns:

Returns a matrix sharing the dimensionality of probs with the discrete bin identifier

registerListener

public void registerListener(java.lang.Object listener)

Specified by:

registerListener in interface ai.libs.jaicore.basic.algorithm.IAlgorithm<TimeSeriesDataset,TimeSeriesBagOfFeaturesClassifier>

Overrides:

registerListener in class ai.libs.jaicore.basic.algorithm.AAlgorithm<TimeSeriesDataset,TimeSeriesBagOfFeaturesClassifier>

nextWithException

public ai.libs.jaicore.basic.algorithm.events.AlgorithmEvent nextWithException()

getConfig

public TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig getConfig()

Specified by:

getConfig in interface ai.libs.jaicore.basic.algorithm.IAlgorithm<TimeSeriesDataset,TimeSeriesBagOfFeaturesClassifier>

Overrides:

getConfig in class ai.libs.jaicore.basic.algorithm.AAlgorithm<TimeSeriesDataset,TimeSeriesBagOfFeaturesClassifier>