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Refactor NN code #111

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merged 16 commits into from
Jun 4, 2019
Merged

Refactor NN code #111

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5cc6602
Move NN iterators into own package
michelole May 29, 2019
d524666
Refactor code around new interface `InputRepresentation`
michelole Jun 3, 2019
65ba2a4
Rename iterators and classifiers for better readability
michelole Jun 3, 2019
2bc8a92
Extract architecture code into own interface
michelole Jun 3, 2019
94f1b42
Pull-up `saveAndLoad` test method
michelole Jun 3, 2019
6ee98d9
DRY `loadFeaturesForNarrative`
michelole Jun 3, 2019
af562fd
Use `f` ordering for tensors
michelole Jun 3, 2019
623f97c
DRY Use `getUnits` wherever possible
michelole Jun 4, 2019
0436427
Use `Math.max` where possible
michelole Jun 4, 2019
23eb491
Make `getNext` code paths consistent
michelole Jun 4, 2019
2d5121f
Pull up `getNext` method
michelole Jun 4, 2019
1dcc61b
DRY `getLongestSequenceSize` method
michelole Jun 4, 2019
f2999ce
Refactor `fillBinaryMultiHotVector` as a pure method
michelole Jun 4, 2019
bd1ab8c
Refactor `fillBinaryMultiHotVector` as a pure method
michelole Jun 4, 2019
5d707fa
Remove unused attributes and document empty methods
michelole Jun 4, 2019
f075134
Remove unnecessary comparison
michelole Jun 4, 2019
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4 changes: 2 additions & 2 deletions src/main/java/at/medunigraz/imi/bst/n2c2/classifier/factory/NNClassifierFactory.java
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Original file line number Diff line number Diff line change
Expand Up @@ -2,11 +2,11 @@

import at.medunigraz.imi.bst.n2c2.classifier.Classifier;
import at.medunigraz.imi.bst.n2c2.model.Criterion;
import at.medunigraz.imi.bst.n2c2.nn.LSTMClassifier;
import at.medunigraz.imi.bst.n2c2.nn.LSTMEmbeddingsClassifier;

public class NNClassifierFactory implements ClassifierFactory {

private static final Classifier classifier = new LSTMClassifier();
private static final Classifier classifier = new LSTMEmbeddingsClassifier();

@Override
public Classifier getClassifier(Criterion criterion) {
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225 changes: 0 additions & 225 deletions src/main/java/at/medunigraz/imi/bst/n2c2/nn/BILSTMC3GClassifier.java
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40 changes: 18 additions & 22 deletions src/main/java/at/medunigraz/imi/bst/n2c2/nn/BaseNNClassifier.java
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
import at.medunigraz.imi.bst.n2c2.model.Criterion;
import at.medunigraz.imi.bst.n2c2.model.Eligibility;
import at.medunigraz.imi.bst.n2c2.model.Patient;
import at.medunigraz.imi.bst.n2c2.nn.iterator.BaseNNIterator;
import at.medunigraz.imi.bst.n2c2.util.DatasetUtil;
import org.apache.commons.io.FileUtils;
import org.apache.logging.log4j.LogManager;
Expand All @@ -19,7 +20,6 @@
import org.deeplearning4j.util.ModelSerializer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.indexing.NDArrayIndex;

import java.io.*;
Expand All @@ -35,18 +35,13 @@ public abstract class BaseNNClassifier extends PatientBasedClassifier {
// size of mini-batch for training
protected static final int BATCH_SIZE = 10;

// specifies time series length
protected int truncateLength = 64;

public int vectorSize;

// training data
protected List<Patient> patientExamples;

// multi layer network
protected MultiLayerNetwork net;

public DataSetIterator fullSetIterator;
public BaseNNIterator fullSetIterator;

/**
* Training for binary multi label classifcation.
Expand Down Expand Up @@ -135,7 +130,7 @@ protected void saveModel(int epoch) {
props.setProperty(getModelName() + ".bestModelEpoch", new Integer(epoch).toString());

// TODO truncateLength does not change each epoch, this could be persisted in saveParams()
props.setProperty(getModelName() + ".truncateLength", new Integer(truncateLength).toString());
props.setProperty(getModelName() + ".truncateLength", new Integer(fullSetIterator.getTruncateLength()).toString());

File f = new File(root, getModelName() + ".properties");
OutputStream out = new FileOutputStream(f);
Expand All @@ -149,6 +144,8 @@ protected void saveModel(int epoch) {
} catch (IOException e) {
e.printStackTrace();
}

fullSetIterator.save(root);
}

protected abstract String getModelName();
Expand All @@ -162,7 +159,7 @@ protected void saveModel(int epoch) {
public Map<Criterion, Double> predict(Patient p) {
String patientNarrative = p.getText();

INDArray features = loadFeaturesForNarrative(patientNarrative, this.truncateLength);
INDArray features = fullSetIterator.loadFeaturesForNarrative(patientNarrative, fullSetIterator.getTruncateLength());
INDArray networkOutput = net.output(features);

int timeSeriesLength = networkOutput.size(2);
Expand Down Expand Up @@ -203,7 +200,11 @@ public Eligibility predict(Patient p, Criterion c) {
@Override
public void train(List<Patient> examples) {
if (isTrained(examples)) {
initializeNetworkFromFile(getModelPath(examples));
try {
initializeNetworkFromFile(getModelPath(examples));
} catch (IOException e) {
throw new RuntimeException(e);
}
}
else {
this.patientExamples = examples;
Expand All @@ -212,7 +213,7 @@ public void train(List<Patient> examples) {
// initializeMonitoring();

LOG.info("Minibatchsize :\t" + BATCH_SIZE);
LOG.info("Truncate length:\t" + truncateLength);
LOG.info("Truncate length:\t" + fullSetIterator.getTruncateLength());

trainFullSetBML();
}
Expand All @@ -236,19 +237,14 @@ public boolean isTrained(List<Patient> patients) {
return new File(getModelPath(patients), getModelName() + ".properties").exists();
}

protected abstract INDArray loadFeaturesForNarrative(String reviewContents, int maxLength);
public void initializeNetworkFromFile(String pathToModel) throws IOException {
Properties prop = loadProperties(pathToModel);
final int bestEpoch = Integer.parseInt(prop.getProperty(getModelName() + ".bestModelEpoch"));

public void initializeNetworkFromFile(String pathToModel) {
try {
Properties prop = loadProperties(pathToModel);
final int bestEpoch = Integer.parseInt(prop.getProperty(getModelName() + ".bestModelEpoch"));
this.truncateLength = Integer.parseInt(prop.getProperty(getModelName() + ".truncateLength"));
File networkFile = new File(pathToModel, getModelName() + "_" + bestEpoch + ".zip");
this.net = ModelSerializer.restoreMultiLayerNetwork(networkFile);

File networkFile = new File(pathToModel, getModelName() + "_" + bestEpoch + ".zip");
this.net = ModelSerializer.restoreMultiLayerNetwork(networkFile);
} catch (IOException e) {
e.printStackTrace();
}
fullSetIterator.load(new File(pathToModel));
}

/**
Expand Down
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