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Sep 19, 2019
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update chapter 7 source code #21

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159 changes: 90 additions & 69 deletions ...classification/sourceCode/cookbookapp/src/main/java/UciSequenceClassificationExample.java
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,9 @@
import org.slf4j.LoggerFactory;

import java.io.File;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.net.MalformedURLException;
import java.net.URL;
import java.util.ArrayList;
import java.util.Collections;
Expand All @@ -35,100 +38,118 @@
public class UciSequenceClassificationExample {

//Replace with your file system location where you want to store feature/labels from data after extraction.
static String trainfeatureDir = "D:/train/features/";
static String trainlabelDir = "D:/train/labels/";
static String testlabelDir = "D:/test/labels/";
static String testfeatureDir = "D:/test/features/";
static String trainfeatureDir = "{PATH-TO-TRAIN-FEATURES}"; //eg: C:/train/features/
static String trainlabelDir = "{PATH-TO-TRAIN-LABELS}"; //eg: C:/train/labels/
static String testlabelDir = "{PATH-TO-TEST-LABELS}"; //eg: C:/test/labels/
static String testfeatureDir = "{PATH-TO-TEST-FEATURES}"; //eg: C:/test/features/


private static Logger log = LoggerFactory.getLogger(UciSequenceClassificationExample.class);

public static void main(String[] args) throws Exception {

downloadUCIData();

SequenceRecordReader trainFeaturesSequenceReader = new CSVSequenceRecordReader();
trainFeaturesSequenceReader.initialize(new NumberedFileInputSplit(new File(trainfeatureDir).getAbsolutePath()+"/%d.csv",0,449));
SequenceRecordReader trainLabelsSequenceReader = new CSVSequenceRecordReader();
trainLabelsSequenceReader.initialize(new NumberedFileInputSplit(new File(trainlabelDir).getAbsolutePath()+"/%d.csv",0,449));

SequenceRecordReader testFeaturesSequenceReader = new CSVSequenceRecordReader();
testFeaturesSequenceReader.initialize(new NumberedFileInputSplit(new File(testfeatureDir).getAbsolutePath()+"/%d.csv",0,149));
SequenceRecordReader testLabelsSequenceReader = new CSVSequenceRecordReader();
testLabelsSequenceReader.initialize(new NumberedFileInputSplit(new File(testlabelDir).getAbsolutePath()+"/%d.csv",0,149));

int batchSize = 10;
int numOfClasses = 6;

DataSetIterator trainIterator = new SequenceRecordReaderDataSetIterator(trainFeaturesSequenceReader,trainLabelsSequenceReader,batchSize,numOfClasses,false, SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END);
DataSetIterator testIterator = new SequenceRecordReaderDataSetIterator(testFeaturesSequenceReader,testLabelsSequenceReader,batchSize,numOfClasses,false, SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END);

DataNormalization normalization = new NormalizerStandardize();
normalization.fit(trainIterator);
trainIterator.setPreProcessor(normalization);
testIterator.setPreProcessor(normalization);

ComputationGraphConfiguration configuration = new NeuralNetConfiguration.Builder()
.seed(123)
.weightInit(WeightInit.XAVIER)
.updater(new Nadam())
.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue)
.gradientNormalizationThreshold(0.5)
.graphBuilder()
.addInputs("trainFeatures")
.setOutputs("predictSequence")
.addLayer("L1",new LSTM.Builder().activation(Activation.TANH).nIn(1).nOut(10).build(),"trainFeatures")
.addLayer("predictSequence",new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation. SOFTMAX).nIn(10).nOut(numOfClasses).build(),"L1")
.build();
ComputationGraph model = new ComputationGraph(configuration);
model.init();
log.info("Starting training...");

//Print the score (loss function value) every 20 iterations
model.setListeners(new ScoreIterationListener(20), new EvaluativeListener(testIterator, 1, InvocationType.EPOCH_END));

int nEpochs = 100;
model.fit(trainIterator,nEpochs);

log.info("Evaluating...");
Evaluation evaluation = model.evaluate(testIterator);
System.out.println(evaluation.stats());
try {
downloadUCIData();

SequenceRecordReader trainFeaturesSequenceReader = new CSVSequenceRecordReader();
trainFeaturesSequenceReader.initialize(new NumberedFileInputSplit(new File(trainfeatureDir).getAbsolutePath() + "/%d.csv", 0, 449));
SequenceRecordReader trainLabelsSequenceReader = new CSVSequenceRecordReader();
trainLabelsSequenceReader.initialize(new NumberedFileInputSplit(new File(trainlabelDir).getAbsolutePath() + "/%d.csv", 0, 449));

SequenceRecordReader testFeaturesSequenceReader = new CSVSequenceRecordReader();
testFeaturesSequenceReader.initialize(new NumberedFileInputSplit(new File(testfeatureDir).getAbsolutePath() + "/%d.csv", 0, 149));
SequenceRecordReader testLabelsSequenceReader = new CSVSequenceRecordReader();
testLabelsSequenceReader.initialize(new NumberedFileInputSplit(new File(testlabelDir).getAbsolutePath() + "/%d.csv", 0, 149));

int batchSize = 10;
int numOfClasses = 6;

DataSetIterator trainIterator = new SequenceRecordReaderDataSetIterator(trainFeaturesSequenceReader, trainLabelsSequenceReader, batchSize, numOfClasses, false, SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END);
DataSetIterator testIterator = new SequenceRecordReaderDataSetIterator(testFeaturesSequenceReader, testLabelsSequenceReader, batchSize, numOfClasses, false, SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END);

DataNormalization normalization = new NormalizerStandardize();
normalization.fit(trainIterator);
trainIterator.setPreProcessor(normalization);
testIterator.setPreProcessor(normalization);

ComputationGraphConfiguration configuration = new NeuralNetConfiguration.Builder()
.seed(123)
.weightInit(WeightInit.XAVIER)
.updater(new Nadam())
.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue)
.gradientNormalizationThreshold(0.5)
.graphBuilder()
.addInputs("trainFeatures")
.setOutputs("predictSequence")
.addLayer("L1", new LSTM.Builder().activation(Activation.TANH).nIn(1).nOut(10).build(), "trainFeatures")
.addLayer("predictSequence", new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(10).nOut(numOfClasses).build(), "L1")
.build();
ComputationGraph model = new ComputationGraph(configuration);
model.init();
log.info("Starting training...");

//Print the score (loss function value) every 20 iterations
model.setListeners(new ScoreIterationListener(20), new EvaluativeListener(testIterator, 1, InvocationType.EPOCH_END));

int nEpochs = 100;
model.fit(trainIterator, nEpochs);

log.info("Evaluating...");
Evaluation evaluation = model.evaluate(testIterator);
System.out.println(evaluation.stats());
} catch (FileNotFoundException e) {
System.out.println("Please provide proper file path to store TRAIN/TEST features/labels. Files will be downloaded to specified directories automatically");
} catch (Exception e) {
e.printStackTrace();
}

}

private static void downloadUCIData() throws Exception {
private static void downloadUCIData() throws IOException {


if (trainfeatureDir.equals("{PATH-TO-TRAIN-FEATURES}") ||
trainlabelDir.equals("{PATH-TO-TRAIN-LABELS}") ||
testfeatureDir.equals("{PATH-TO-TEST-FEATURES}") ||
testlabelDir.equals("{PATH-TO-TEST-LABELS}")) {
throw new FileNotFoundException();
}

final String url = "https://archive.ics.uci.edu/ml/machine-learning-databases/synthetic_control-mld/synthetic_control.data";
final String data = IOUtils.toString(new URL(url),"utf-8");
final String data = IOUtils.toString(new URL(url), "utf-8");
final String[] sequences = data.split("\n");
final List<Pair<String,Integer>> contentAndLabels = new ArrayList<>();
final List<Pair<String, Integer>> contentAndLabels = new ArrayList<>();
int lineCount = 0;
for(String sequence : sequences) {
for (String sequence : sequences) {
//Labels: first 100 examples (lines) are label 0, second 100 examples are label 1, and so on
sequence = sequence.replaceAll(" +","\n");
sequence = sequence.replaceAll(" +", "\n");
contentAndLabels.add(new Pair<>(sequence, lineCount++ / 100));
}
Collections.shuffle(contentAndLabels,new Random(12345));
Collections.shuffle(contentAndLabels, new Random(12345));


int trainCount=0;
int testCount=0;
int trainCount = 0;
int testCount = 0;
final int traintestSplit = 450;
File featureFile;
File labelFile;
for(Pair<String,Integer> sequencePair : contentAndLabels) {
if(trainCount<traintestSplit) {
featureFile = new File(trainfeatureDir+trainCount+".csv");
labelFile = new File(trainlabelDir+trainCount+".csv");

for (Pair<String, Integer> sequencePair : contentAndLabels) {
if (trainCount < traintestSplit) {
featureFile = new File(trainfeatureDir + trainCount + ".csv");
labelFile = new File(trainlabelDir + trainCount + ".csv");
trainCount++;
} else {
featureFile = new File(testfeatureDir+testCount+".csv");
labelFile = new File(testlabelDir+testCount+".csv");
featureFile = new File(testfeatureDir + testCount + ".csv");
labelFile = new File(testlabelDir + testCount + ".csv");
testCount++;
}

FileUtils.writeStringToFile(featureFile,sequencePair.getFirst(),"utf-8");
FileUtils.writeStringToFile(labelFile,sequencePair.getSecond().toString(),"utf-8");
FileUtils.writeStringToFile(featureFile, sequencePair.getFirst(), "utf-8");
FileUtils.writeStringToFile(labelFile, sequencePair.getSecond().toString(), "utf-8");
}

}

}