Rename iterators and classifiers for better readability

src/main/java/at/medunigraz/imi/bst/n2c2/classifier/factory/NNClassifierFactory.java

@@ -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) {

src/main/java/at/medunigraz/imi/bst/n2c2/nn/BILSTMC3GClassifier.java → src/main/java/at/medunigraz/imi/bst/n2c2/nn/BiLSTMCharacterTrigramClassifier.java

@@ -5,7 +5,7 @@
 
 import at.medunigraz.imi.bst.n2c2.model.Criterion;
 import at.medunigraz.imi.bst.n2c2.nn.input.CharacterTrigram;
-import at.medunigraz.imi.bst.n2c2.nn.iterator.NGramIterator;
+import at.medunigraz.imi.bst.n2c2.nn.iterator.SentenceIterator;
 import org.apache.logging.log4j.LogManager;
 import org.apache.logging.log4j.Logger;
 import org.deeplearning4j.nn.conf.GradientNormalization;
@@ -32,7 +32,7 @@
  * @author Markus
  *
  */
-public class BILSTMC3GClassifier extends BaseNNClassifier {
+public class BiLSTMCharacterTrigramClassifier extends BaseNNClassifier {
 
 	private static final Logger LOG = LogManager.getLogger();
 
@@ -49,7 +49,7 @@ public void initializeNetworkFromFile(String pathToModel) {
 		try {
 			prop = loadProperties(pathToModel);
 			final int truncateLength = Integer.parseInt(prop.getProperty(getModelName() + ".truncateLength"));
-			fullSetIterator = new NGramIterator(new CharacterTrigram(), truncateLength, BATCH_SIZE);
+			fullSetIterator = new SentenceIterator(new CharacterTrigram(), truncateLength, BATCH_SIZE);
 		} catch (IOException e) {
 			throw new RuntimeException(e);
 		}
@@ -73,7 +73,7 @@ protected void initializeNetworkBinaryMultiLabelDeep() {
 		Nd4j.getMemoryManager().setAutoGcWindow(10000);
 		// Nd4j.getMemoryManager().togglePeriodicGc(false);
 
-		fullSetIterator = new NGramIterator(patientExamples, new CharacterTrigram(NGramIterator.createPatientLines(patientExamples)), BATCH_SIZE);
+		fullSetIterator = new SentenceIterator(patientExamples, new CharacterTrigram(SentenceIterator.createPatientLines(patientExamples)), BATCH_SIZE);
 
 		int nOutFF = 150;
 		int lstmLayerSize = 128;

src/main/java/at/medunigraz/imi/bst/n2c2/nn/LSTMClassifier.java → src/main/java/at/medunigraz/imi/bst/n2c2/nn/LSTMEmbeddingsClassifier.java

@@ -5,7 +5,7 @@
 import java.util.Properties;
 
 import at.medunigraz.imi.bst.n2c2.nn.input.WordEmbedding;
-import at.medunigraz.imi.bst.n2c2.nn.iterator.N2c2PatientIteratorBML;
+import at.medunigraz.imi.bst.n2c2.nn.iterator.TokenIterator;
 import org.apache.logging.log4j.LogManager;
 import org.apache.logging.log4j.Logger;
 import org.deeplearning4j.nn.conf.GradientNormalization;
@@ -22,18 +22,16 @@
 import org.nd4j.linalg.learning.config.Adam;
 import org.nd4j.linalg.lossfunctions.LossFunctions;
 
-import at.medunigraz.imi.bst.n2c2.model.Patient;
-
 /**
  * LSTM classifier for n2c2 task 2018 refactored from dl4j examples.
  *
  * @author Markus
  *
  */
-public class LSTMClassifier extends BaseNNClassifier {
+public class LSTMEmbeddingsClassifier extends BaseNNClassifier {
 
 	// location of precalculated vectors
-	private static final File PRETRAINED_VECTORS = new File(LSTMClassifier.class.getClassLoader().getResource("vectors.vec").getFile());
+	private static final File PRETRAINED_VECTORS = new File(LSTMEmbeddingsClassifier.class.getClassLoader().getResource("vectors.vec").getFile());
 
 	// logging
 	private static final Logger LOG = LogManager.getLogger();
@@ -47,7 +45,7 @@ private void initializeNetworkBinaryMultiLabelDebug() {
 
 		Nd4j.getMemoryManager().setAutoGcWindow(10000); // https://deeplearning4j.org/workspaces
 
-		fullSetIterator = new N2c2PatientIteratorBML(patientExamples, new WordEmbedding(PRETRAINED_VECTORS), BATCH_SIZE);
+		fullSetIterator = new TokenIterator(patientExamples, new WordEmbedding(PRETRAINED_VECTORS), BATCH_SIZE);
 
 		// Set up network configuration
 		MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(0)
@@ -81,7 +79,7 @@ public void initializeNetworkFromFile(String pathToModel) {
 		try {
 			prop = loadProperties(pathToModel);
 			final int truncateLength = Integer.parseInt(prop.getProperty(getModelName() + ".truncateLength"));
-			fullSetIterator = new N2c2PatientIteratorBML(new WordEmbedding(PRETRAINED_VECTORS), truncateLength, BATCH_SIZE);
+			fullSetIterator = new TokenIterator(new WordEmbedding(PRETRAINED_VECTORS), truncateLength, BATCH_SIZE);
 		} catch (IOException e) {
 			throw new RuntimeException(e);
 		}

src/main/java/at/medunigraz/imi/bst/n2c2/nn/iterator/NGramIterator.java → src/main/java/at/medunigraz/imi/bst/n2c2/nn/iterator/SentenceIterator.java

@@ -16,11 +16,11 @@
 import at.medunigraz.imi.bst.n2c2.model.Patient;
 
 /**
- * A character 3-gram DataSetIterator.
+ * A sentence iterator.
  * 
  * @author Markus
  */
-public class NGramIterator extends BaseNNIterator {
+public class SentenceIterator extends BaseNNIterator {
 
 	private static final long serialVersionUID = 1L;
 
@@ -34,7 +34,7 @@ public class NGramIterator extends BaseNNIterator {
 	 * @param batchSize
 	 *            Minibatch size.
 	 */
-	public NGramIterator(List<Patient> patients, InputRepresentation inputRepresentation, int batchSize) {
+	public SentenceIterator(List<Patient> patients, InputRepresentation inputRepresentation, int batchSize) {
 		super(inputRepresentation);
 
 		this.patients = patients;
@@ -50,7 +50,7 @@ public NGramIterator(List<Patient> patients, InputRepresentation inputRepresenta
 	 * @param truncateLength
 	 * @param batchSize
 	 */
-	public NGramIterator(InputRepresentation inputRepresentation, int truncateLength, int batchSize) {
+	public SentenceIterator(InputRepresentation inputRepresentation, int truncateLength, int batchSize) {
 		super(inputRepresentation);
 
 		this.truncateLength = truncateLength;

src/main/java/at/medunigraz/imi/bst/n2c2/nn/iterator/N2c2PatientIteratorBML.java → src/main/java/at/medunigraz/imi/bst/n2c2/nn/iterator/TokenIterator.java

@@ -22,7 +22,7 @@
  * @author Markus
  *
  */
-public class N2c2PatientIteratorBML extends BaseNNIterator {
+public class TokenIterator extends BaseNNIterator {
 	private static final Logger LOG = LogManager.getLogger();
 
 	private static final long serialVersionUID = 1L;
@@ -37,7 +37,7 @@ public class N2c2PatientIteratorBML extends BaseNNIterator {
 	 * @param batchSize
 	 *            Mini batch size use for processing.
 	 */
-	public N2c2PatientIteratorBML(List<Patient> patients, InputRepresentation inputRepresentation, int batchSize) {
+	public TokenIterator(List<Patient> patients, InputRepresentation inputRepresentation, int batchSize) {
 		super(inputRepresentation);
 
 		this.patients = patients;
@@ -55,7 +55,7 @@ public N2c2PatientIteratorBML(List<Patient> patients, InputRepresentation inputR
 	 * @param truncateLength
 	 * @param batchSize
 	 */
-	public N2c2PatientIteratorBML(InputRepresentation inputRepresentation, int truncateLength, int batchSize) {
+	public TokenIterator(InputRepresentation inputRepresentation, int truncateLength, int batchSize) {
 		super(inputRepresentation);
 
 		this.truncateLength = truncateLength;

src/test/java/at/medunigraz/imi/bst/n2c2/nn/BaseNNClassifierTest.java

@@ -16,7 +16,7 @@
 
 public abstract class BaseNNClassifierTest {
 
-    protected static final File SAMPLE = new File(BILSTMC3GClassifierTest.class.getResource("/gold-standard/sample.xml").getPath());
+    protected static final File SAMPLE = new File(BaseNNClassifierTest.class.getResource("/gold-standard/sample.xml").getPath());
 
     protected BaseNNClassifier classifier;
 

src/test/java/at/medunigraz/imi/bst/n2c2/nn/BILSTMC3GClassifierTest.java → src/test/java/at/medunigraz/imi/bst/n2c2/nn/BiLSTMCharacterTrigramClassifierTest.java

@@ -13,10 +13,10 @@
 import at.medunigraz.imi.bst.n2c2.dao.PatientDAO;
 import at.medunigraz.imi.bst.n2c2.model.Patient;
 
-public class BILSTMC3GClassifierTest extends BaseNNClassifierTest {
+public class BiLSTMCharacterTrigramClassifierTest extends BaseNNClassifierTest {
 
-	public BILSTMC3GClassifierTest() {
-		this.classifier = new BILSTMC3GClassifier();
+	public BiLSTMCharacterTrigramClassifierTest() {
+		this.classifier = new BiLSTMCharacterTrigramClassifier();
 	}
 
 	@Test
@@ -27,13 +27,13 @@ public void saveAndLoad() throws IOException, SAXException {
 		train.add(p);
 
 		// We first train on some examples...
-		BILSTMC3GClassifier trainClassifier = new BILSTMC3GClassifier();
+		BiLSTMCharacterTrigramClassifier trainClassifier = new BiLSTMCharacterTrigramClassifier();
 		trainClassifier.deleteModelDir(train);	// Delete any previously trained models, to ensure training is tested
 		trainClassifier.train(train);			// This should persist models
 		assertTrue(trainClassifier.isTrained(train));
 
 		// ... and then try to load the model on a new instance.
-		BILSTMC3GClassifier testClassifier = new BILSTMC3GClassifier();
+		BiLSTMCharacterTrigramClassifier testClassifier = new BiLSTMCharacterTrigramClassifier();
 		assertTrue(testClassifier.isTrained(train));
 		// TODO use Mockito to call train() and ensure trainFullSetBMC is NOT called.
 		testClassifier.initializeNetworkFromFile(BaseNNClassifier.getModelPath(train));

src/test/java/at/medunigraz/imi/bst/n2c2/nn/LSTMEmbeddingsClassifierTest.java

@@ -0,0 +1,8 @@
+package at.medunigraz.imi.bst.n2c2.nn;
+
+public class LSTMEmbeddingsClassifierTest extends BaseNNClassifierTest {
+
+    public LSTMEmbeddingsClassifierTest() {
+        this.classifier = new LSTMEmbeddingsClassifier();
+    }
+}