EvaluationExample.java

public class EvaluationExample {

	public static void main(String... args) {
		// where are the trained model and the corresponding normalizers stored ?
		String modelPath = "data" + File.separator + "models" + File.separator;
		String normalizerPath = "data" + File.separator + "normalizers" + File.separator;

		String modelName = "train.model";

		// define the paths to the train features, test features and test labels
		// train test split can be 80/20
		String trainFeaturePath = "data" + File.separator + "trainTestSplit" + File.separator +
				"train" + File.separator + "features" + File.separator;

		String testFeaturePath = "data" + File.separator + "trainTestSplit" + File.separator +
				"test" + File.separator + "features" + File.separator;
		String testLabelPath = "data" + File.separator + "trainTestSplit" + File.separator +
				"test" + File.separator + "labels" + File.separator;

		// there are 31 files/examples used for training
		Integer maxFieldId = 30;

		// setup helper for serialization
		SerializationUtils serializationHelper = new SerializationUtils(modelPath, modelName, normalizerPath);

		try {
			MultiLayerNetwork net = serializationHelper.loadNetwork();
			NormalizerStandardize normalizer = serializationHelper.loadNormalizer();

			Map<String, List<String>> trainFeaturesMap = extractRows(trainFeaturePath, 0, maxFieldId);
			Map<String, List<String>> testFeaturesMap = extractRows(testFeaturePath, 0, maxFieldId);
			Map<String, List<String>> testLabelsMap = extractRows(testLabelPath, 0, maxFieldId);


			for (String fileName : trainFeaturesMap.keySet()) {
				// reset model for new file evaluation
				net.rnnClearPreviousState();

				List<String> trainFeatures = trainFeaturesMap.get(fileName);
				List<String> testFeatures = testFeaturesMap.get(fileName);
				List<String> testLabels = testLabelsMap.get(fileName);

				// init train history
				for (String trainFeature : trainFeatures) {
					INDArray featureArray = createArray( Integer.parseInt(trainFeature) );

					if (normalizer != null) {
						normalizer.transform(featureArray);
					}

					// init with value
					INDArray initOutput = net.rnnTimeStep(featureArray);
				}


				INDArray rnnOutput = null;
				Double predicted = null;
				// evaluate on test set
				for (int testIndex = 0; testIndex < testFeatures.size(); testIndex++) {
					String inputValue = testFeatures.get(testIndex);

					INDArray featureArray = createArray( Integer.parseInt(inputValue) );


					if (normalizer != null) {
						normalizer.transform(featureArray);
					}

					rnnOutput = net.rnnTimeStep(featureArray);
					normalizer.revertLabels(rnnOutput);

					// extract double value out of the output, check expected vs predicted difference (RMSE, MAPE, etc.)
					Integer expected = Integer.parseInt( testLabels.get(testIndex) );
					// ...
				}

			}

			// Successfully tested the NN model
		} catch (Exception e) {
			// log error
		}
	}


	/**
	 * Extracts row values from the provided data
	 * @param path Path to the files to read
	 * @return a map for each file containing a list of the values (1 value per row)
	 */
	public static Map<String, List<String>> extractRows(String path, Integer minFileId, Integer maxFileId) {
		Map<String, List<String>> fileMap = new HashMap<>();

		for (int i = minFileId; i <= maxFileId; i++) {
			List<String> list = new ArrayList<>();

			String fileName = i + ".csv";
			Path filePath = Paths.get(path, fileName);
			try (BufferedReader br = Files.newBufferedReader(filePath)) {
				//br returns as stream and convert it into a List
				list = br.lines().collect(Collectors.toList());
			} catch (IOException e) {
				e.printStackTrace();
			}

			fileMap.put(fileName, list);
		}

		return fileMap;
	}

	/**
	 * Creates an INDArray using an integer value
	 * @return INDArray to be used for RNN nets in dl4j
	 */
	public static INDArray createArray(Integer value) {
		// number of time steps used for testing the next value
		INDArray data = Nd4j.ones(1, 1, 1);

		double[] independent = new double[1];
		independent[0] = value;

		INDArray ind = Nd4j.create(independent, new int[]{1, 1, 1});
		data.putScalar(0, 0, 0, ind.getDouble(0));

		return data;
	}

}