コード例 #1
0
 private int getNoOfClasses(MLModel mlModel) {
   if (mlModel.getEncodings() == null) {
     return -1;
   }
   int responseIndex = mlModel.getEncodings().size() - 1;
   return mlModel.getEncodings().get(responseIndex) != null
       ? mlModel.getEncodings().get(responseIndex).size()
       : -1;
 }
コード例 #2
0
  /**
   * This method builds a decision tree model
   *
   * @param sparkContext JavaSparkContext initialized with the application
   * @param modelID Model ID
   * @param trainingData Training data as a JavaRDD of LabeledPoints
   * @param testingData Testing data as a JavaRDD of LabeledPoints
   * @param workflow Machine learning workflow
   * @param mlModel Deployable machine learning model
   * @throws MLModelBuilderException
   */
  private ModelSummary buildDecisionTreeModel(
      JavaSparkContext sparkContext,
      long modelID,
      JavaRDD<LabeledPoint> trainingData,
      JavaRDD<LabeledPoint> testingData,
      Workflow workflow,
      MLModel mlModel,
      SortedMap<Integer, String> includedFeatures,
      Map<Integer, Integer> categoricalFeatureInfo)
      throws MLModelBuilderException {
    try {
      Map<String, String> hyperParameters = workflow.getHyperParameters();
      DecisionTree decisionTree = new DecisionTree();
      DecisionTreeModel decisionTreeModel =
          decisionTree.train(
              trainingData,
              getNoOfClasses(mlModel),
              categoricalFeatureInfo,
              hyperParameters.get(MLConstants.IMPURITY),
              Integer.parseInt(hyperParameters.get(MLConstants.MAX_DEPTH)),
              Integer.parseInt(hyperParameters.get(MLConstants.MAX_BINS)));

      // remove from cache
      trainingData.unpersist();
      // add test data to cache
      testingData.cache();

      JavaPairRDD<Double, Double> predictionsAndLabels =
          decisionTree.test(decisionTreeModel, testingData).cache();
      ClassClassificationAndRegressionModelSummary classClassificationAndRegressionModelSummary =
          SparkModelUtils.getClassClassificationModelSummary(
              sparkContext, testingData, predictionsAndLabels);

      // remove from cache
      testingData.unpersist();

      mlModel.setModel(new MLDecisionTreeModel(decisionTreeModel));

      classClassificationAndRegressionModelSummary.setFeatures(
          includedFeatures.values().toArray(new String[0]));
      classClassificationAndRegressionModelSummary.setAlgorithm(
          SUPERVISED_ALGORITHM.DECISION_TREE.toString());

      MulticlassMetrics multiclassMetrics =
          getMulticlassMetrics(sparkContext, predictionsAndLabels);

      predictionsAndLabels.unpersist();

      classClassificationAndRegressionModelSummary.setMulticlassConfusionMatrix(
          getMulticlassConfusionMatrix(multiclassMetrics, mlModel));
      Double modelAccuracy = getModelAccuracy(multiclassMetrics);
      classClassificationAndRegressionModelSummary.setModelAccuracy(modelAccuracy);
      classClassificationAndRegressionModelSummary.setDatasetVersion(workflow.getDatasetVersion());

      return classClassificationAndRegressionModelSummary;
    } catch (Exception e) {
      throw new MLModelBuilderException(
          "An error occurred while building decision tree model: " + e.getMessage(), e);
    }
  }
コード例 #3
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  /**
   * This method applies a stacked autoencoders model to a given dataset and make predictions
   *
   * @param ctxt JavaSparkContext
   * @param deeplearningModel Stacked Autoencoders model
   * @param test Testing dataset as a JavaRDD of labeled points
   * @return
   */
  public JavaPairRDD<Double, Double> test(
      JavaSparkContext ctxt,
      final DeepLearningModel deeplearningModel,
      JavaRDD<LabeledPoint> test,
      MLModel mlModel)
      throws MLModelBuilderException {

    Scope.enter();

    if (deeplearningModel == null) {
      throw new MLModelBuilderException("DeeplearningModel is Null");
    }

    int numberOfFeatures = mlModel.getFeatures().size();
    List<Feature> features = mlModel.getFeatures();
    String[] names = new String[numberOfFeatures + 1];
    for (int i = 0; i < numberOfFeatures; i++) {
      names[i] = features.get(i).getName();
    }
    names[numberOfFeatures] = mlModel.getResponseVariable();

    Frame testData = DeeplearningModelUtils.javaRDDToFrame(names, test);
    Frame testDataWithoutLabels = testData.subframe(0, testData.numCols() - 1);
    int numRows = (int) testDataWithoutLabels.numRows();
    Vec predictionsVector = deeplearningModel.score(testDataWithoutLabels).vec(0);
    double[] predictionValues = new double[numRows];
    for (int i = 0; i < numRows; i++) {
      predictionValues[i] = predictionsVector.at(i);
    }
    Vec labelsVector = testData.vec(testData.numCols() - 1);
    double[] labels = new double[numRows];
    for (int i = 0; i < numRows; i++) {
      labels[i] = labelsVector.at(i);
    }

    Scope.exit();

    ArrayList<Tuple2<Double, Double>> tupleList = new ArrayList<Tuple2<Double, Double>>();
    for (int i = 0; i < labels.length; i++) {
      tupleList.add(new Tuple2<Double, Double>(predictionValues[i], labels[i]));
    }

    return ctxt.parallelizePairs(tupleList);
  }
コード例 #4
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  /**
   * This method builds a naive bayes model
   *
   * @param sparkContext JavaSparkContext initialized with the application
   * @param modelID Model ID
   * @param trainingData Training data as a JavaRDD of LabeledPoints
   * @param testingData Testing data as a JavaRDD of LabeledPoints
   * @param workflow Machine learning workflow
   * @param mlModel Deployable machine learning model
   * @throws MLModelBuilderException
   */
  private ModelSummary buildNaiveBayesModel(
      JavaSparkContext sparkContext,
      long modelID,
      JavaRDD<LabeledPoint> trainingData,
      JavaRDD<LabeledPoint> testingData,
      Workflow workflow,
      MLModel mlModel,
      SortedMap<Integer, String> includedFeatures)
      throws MLModelBuilderException {
    try {
      Map<String, String> hyperParameters = workflow.getHyperParameters();
      NaiveBayesClassifier naiveBayesClassifier = new NaiveBayesClassifier();
      NaiveBayesModel naiveBayesModel =
          naiveBayesClassifier.train(
              trainingData, Double.parseDouble(hyperParameters.get(MLConstants.LAMBDA)));

      // remove from cache
      trainingData.unpersist();
      // add test data to cache
      testingData.cache();

      JavaPairRDD<Double, Double> predictionsAndLabels =
          naiveBayesClassifier.test(naiveBayesModel, testingData).cache();
      ClassClassificationAndRegressionModelSummary classClassificationAndRegressionModelSummary =
          SparkModelUtils.getClassClassificationModelSummary(
              sparkContext, testingData, predictionsAndLabels);

      // remove from cache
      testingData.unpersist();

      mlModel.setModel(new MLClassificationModel(naiveBayesModel));

      classClassificationAndRegressionModelSummary.setFeatures(
          includedFeatures.values().toArray(new String[0]));
      classClassificationAndRegressionModelSummary.setAlgorithm(
          SUPERVISED_ALGORITHM.NAIVE_BAYES.toString());

      MulticlassMetrics multiclassMetrics =
          getMulticlassMetrics(sparkContext, predictionsAndLabels);

      predictionsAndLabels.unpersist();

      classClassificationAndRegressionModelSummary.setMulticlassConfusionMatrix(
          getMulticlassConfusionMatrix(multiclassMetrics, mlModel));
      Double modelAccuracy = getModelAccuracy(multiclassMetrics);
      classClassificationAndRegressionModelSummary.setModelAccuracy(modelAccuracy);
      classClassificationAndRegressionModelSummary.setDatasetVersion(workflow.getDatasetVersion());

      return classClassificationAndRegressionModelSummary;
    } catch (Exception e) {
      throw new MLModelBuilderException(
          "An error occurred while building naive bayes model: " + e.getMessage(), e);
    }
  }
コード例 #5
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  /**
   * This method returns multiclass confusion matrix for a given multiclass metric object
   *
   * @param multiclassMetrics Multiclass metric object
   */
  private MulticlassConfusionMatrix getMulticlassConfusionMatrix(
      MulticlassMetrics multiclassMetrics, MLModel mlModel) {
    MulticlassConfusionMatrix multiclassConfusionMatrix = new MulticlassConfusionMatrix();
    if (multiclassMetrics != null) {
      int size = multiclassMetrics.confusionMatrix().numCols();
      double[] matrixArray = multiclassMetrics.confusionMatrix().toArray();
      double[][] matrix = new double[size][size];
      // set values of matrix into a 2D array
      for (int i = 0; i < size; i++) {
        for (int j = 0; j < size; j++) {
          matrix[i][j] = matrixArray[(j * size) + i];
        }
      }
      multiclassConfusionMatrix.setMatrix(matrix);

      List<Map<String, Integer>> encodings = mlModel.getEncodings();
      // decode only if encodings are available
      if (encodings != null) {
        // last index is response variable encoding
        Map<String, Integer> encodingMap = encodings.get(encodings.size() - 1);
        List<String> decodedLabels = new ArrayList<String>();
        for (double label : multiclassMetrics.labels()) {
          Integer labelInt = (int) label;
          String decodedLabel = MLUtils.getKeyByValue(encodingMap, labelInt);
          if (decodedLabel != null) {
            decodedLabels.add(decodedLabel);
          } else {
            continue;
          }
        }
        multiclassConfusionMatrix.setLabels(decodedLabels);
      } else {
        List<String> labelList = toStringList(multiclassMetrics.labels());
        multiclassConfusionMatrix.setLabels(labelList);
      }

      multiclassConfusionMatrix.setSize(size);
    }
    return multiclassConfusionMatrix;
  }
コード例 #6
0
  /**
   * This method builds a lasso regression model
   *
   * @param sparkContext JavaSparkContext initialized with the application
   * @param modelID Model ID
   * @param trainingData Training data as a JavaRDD of LabeledPoints
   * @param testingData Testing data as a JavaRDD of LabeledPoints
   * @param workflow Machine learning workflow
   * @param mlModel Deployable machine learning model
   * @throws MLModelBuilderException
   */
  private ModelSummary buildLassoRegressionModel(
      JavaSparkContext sparkContext,
      long modelID,
      JavaRDD<LabeledPoint> trainingData,
      JavaRDD<LabeledPoint> testingData,
      Workflow workflow,
      MLModel mlModel,
      SortedMap<Integer, String> includedFeatures)
      throws MLModelBuilderException {
    try {
      LassoRegression lassoRegression = new LassoRegression();
      Map<String, String> hyperParameters = workflow.getHyperParameters();
      LassoModel lassoModel =
          lassoRegression.train(
              trainingData,
              Integer.parseInt(hyperParameters.get(MLConstants.ITERATIONS)),
              Double.parseDouble(hyperParameters.get(MLConstants.LEARNING_RATE)),
              Double.parseDouble(hyperParameters.get(MLConstants.REGULARIZATION_PARAMETER)),
              Double.parseDouble(hyperParameters.get(MLConstants.SGD_DATA_FRACTION)));

      // remove from cache
      trainingData.unpersist();
      // add test data to cache
      testingData.cache();

      Vector weights = lassoModel.weights();
      if (!isValidWeights(weights)) {
        throw new MLModelBuilderException(
            "Weights of the model generated are null or infinity. [Weights] "
                + vectorToString(weights));
      }
      JavaRDD<Tuple2<Double, Double>> predictionsAndLabels =
          lassoRegression.test(lassoModel, testingData).cache();
      ClassClassificationAndRegressionModelSummary regressionModelSummary =
          SparkModelUtils.generateRegressionModelSummary(
              sparkContext, testingData, predictionsAndLabels);

      // remove from cache
      testingData.unpersist();

      mlModel.setModel(new MLGeneralizedLinearModel(lassoModel));

      List<FeatureImportance> featureWeights =
          getFeatureWeights(includedFeatures, lassoModel.weights().toArray());
      regressionModelSummary.setFeatures(includedFeatures.values().toArray(new String[0]));
      regressionModelSummary.setAlgorithm(SUPERVISED_ALGORITHM.LASSO_REGRESSION.toString());
      regressionModelSummary.setFeatureImportance(featureWeights);

      RegressionMetrics regressionMetrics =
          getRegressionMetrics(sparkContext, predictionsAndLabels);

      predictionsAndLabels.unpersist();

      Double meanSquaredError = regressionMetrics.meanSquaredError();
      regressionModelSummary.setMeanSquaredError(meanSquaredError);
      regressionModelSummary.setDatasetVersion(workflow.getDatasetVersion());

      return regressionModelSummary;
    } catch (Exception e) {
      throw new MLModelBuilderException(
          "An error occurred while building lasso regression model: " + e.getMessage(), e);
    }
  }
コード例 #7
0
  /**
   * This method builds a support vector machine (SVM) model
   *
   * @param sparkContext JavaSparkContext initialized with the application
   * @param modelID Model ID
   * @param trainingData Training data as a JavaRDD of LabeledPoints
   * @param testingData Testing data as a JavaRDD of LabeledPoints
   * @param workflow Machine learning workflow
   * @param mlModel Deployable machine learning model
   * @throws MLModelBuilderException
   */
  private ModelSummary buildSVMModel(
      JavaSparkContext sparkContext,
      long modelID,
      JavaRDD<LabeledPoint> trainingData,
      JavaRDD<LabeledPoint> testingData,
      Workflow workflow,
      MLModel mlModel,
      SortedMap<Integer, String> includedFeatures)
      throws MLModelBuilderException {

    if (getNoOfClasses(mlModel) > 2) {
      throw new MLModelBuilderException(
          "A binary classification algorithm cannot have more than "
              + "two distinct values in response variable.");
    }

    try {
      SVM svm = new SVM();
      Map<String, String> hyperParameters = workflow.getHyperParameters();
      SVMModel svmModel =
          svm.train(
              trainingData,
              Integer.parseInt(hyperParameters.get(MLConstants.ITERATIONS)),
              hyperParameters.get(MLConstants.REGULARIZATION_TYPE),
              Double.parseDouble(hyperParameters.get(MLConstants.REGULARIZATION_PARAMETER)),
              Double.parseDouble(hyperParameters.get(MLConstants.LEARNING_RATE)),
              Double.parseDouble(hyperParameters.get(MLConstants.SGD_DATA_FRACTION)));

      // remove from cache
      trainingData.unpersist();
      // add test data to cache
      testingData.cache();

      Vector weights = svmModel.weights();
      if (!isValidWeights(weights)) {
        throw new MLModelBuilderException(
            "Weights of the model generated are null or infinity. [Weights] "
                + vectorToString(weights));
      }

      // getting scores and labels without clearing threshold to get confusion matrix
      JavaRDD<Tuple2<Object, Object>> scoresAndLabelsThresholded = svm.test(svmModel, testingData);
      MulticlassMetrics multiclassMetrics =
          new MulticlassMetrics(JavaRDD.toRDD(scoresAndLabelsThresholded));
      MulticlassConfusionMatrix multiclassConfusionMatrix =
          getMulticlassConfusionMatrix(multiclassMetrics, mlModel);

      svmModel.clearThreshold();
      JavaRDD<Tuple2<Object, Object>> scoresAndLabels = svm.test(svmModel, testingData);
      ProbabilisticClassificationModelSummary probabilisticClassificationModelSummary =
          SparkModelUtils.generateProbabilisticClassificationModelSummary(
              sparkContext, testingData, scoresAndLabels);

      // remove from cache
      testingData.unpersist();

      mlModel.setModel(new MLClassificationModel(svmModel));

      List<FeatureImportance> featureWeights =
          getFeatureWeights(includedFeatures, svmModel.weights().toArray());
      probabilisticClassificationModelSummary.setFeatures(
          includedFeatures.values().toArray(new String[0]));
      probabilisticClassificationModelSummary.setFeatureImportance(featureWeights);
      probabilisticClassificationModelSummary.setAlgorithm(SUPERVISED_ALGORITHM.SVM.toString());

      probabilisticClassificationModelSummary.setMulticlassConfusionMatrix(
          multiclassConfusionMatrix);
      Double modelAccuracy = getModelAccuracy(multiclassMetrics);
      probabilisticClassificationModelSummary.setModelAccuracy(modelAccuracy);
      probabilisticClassificationModelSummary.setDatasetVersion(workflow.getDatasetVersion());

      return probabilisticClassificationModelSummary;
    } catch (Exception e) {
      throw new MLModelBuilderException(
          "An error occurred while building SVM model: " + e.getMessage(), e);
    }
  }
コード例 #8
0
  /** Build a supervised model. */
  public MLModel build() throws MLModelBuilderException {
    MLModelConfigurationContext context = getContext();
    JavaSparkContext sparkContext = null;
    DatabaseService databaseService = MLCoreServiceValueHolder.getInstance().getDatabaseService();
    MLModel mlModel = new MLModel();
    try {
      sparkContext = context.getSparkContext();
      Workflow workflow = context.getFacts();
      long modelId = context.getModelId();

      // Verify validity of response variable
      String typeOfResponseVariable =
          getTypeOfResponseVariable(workflow.getResponseVariable(), workflow.getFeatures());

      if (typeOfResponseVariable == null) {
        throw new MLModelBuilderException(
            "Type of response variable cannot be null for supervised learning " + "algorithms.");
      }

      // Stops model building if a categorical attribute is used with numerical prediction
      if (workflow.getAlgorithmClass().equals(AlgorithmType.NUMERICAL_PREDICTION.getValue())
          && typeOfResponseVariable.equals(FeatureType.CATEGORICAL)) {
        throw new MLModelBuilderException(
            "Categorical attribute "
                + workflow.getResponseVariable()
                + " cannot be used as the response variable of the Numerical Prediction algorithm: "
                + workflow.getAlgorithmName());
      }

      // generate train and test datasets by converting tokens to labeled points
      int responseIndex = context.getResponseIndex();
      SortedMap<Integer, String> includedFeatures =
          MLUtils.getIncludedFeaturesAfterReordering(
              workflow, context.getNewToOldIndicesList(), responseIndex);

      // gets the pre-processed dataset
      JavaRDD<LabeledPoint> labeledPoints = preProcess().cache();

      JavaRDD<LabeledPoint>[] dataSplit =
          labeledPoints.randomSplit(
              new double[] {workflow.getTrainDataFraction(), 1 - workflow.getTrainDataFraction()},
              MLConstants.RANDOM_SEED);

      // remove from cache
      labeledPoints.unpersist();

      JavaRDD<LabeledPoint> trainingData = dataSplit[0].cache();
      JavaRDD<LabeledPoint> testingData = dataSplit[1];
      // create a deployable MLModel object
      mlModel.setAlgorithmName(workflow.getAlgorithmName());
      mlModel.setAlgorithmClass(workflow.getAlgorithmClass());
      mlModel.setFeatures(workflow.getIncludedFeatures());
      mlModel.setResponseVariable(workflow.getResponseVariable());
      mlModel.setEncodings(context.getEncodings());
      mlModel.setNewToOldIndicesList(context.getNewToOldIndicesList());
      mlModel.setResponseIndex(responseIndex);

      ModelSummary summaryModel = null;
      Map<Integer, Integer> categoricalFeatureInfo;

      // build a machine learning model according to user selected algorithm
      SUPERVISED_ALGORITHM supervisedAlgorithm =
          SUPERVISED_ALGORITHM.valueOf(workflow.getAlgorithmName());
      switch (supervisedAlgorithm) {
        case LOGISTIC_REGRESSION:
          summaryModel =
              buildLogisticRegressionModel(
                  sparkContext,
                  modelId,
                  trainingData,
                  testingData,
                  workflow,
                  mlModel,
                  includedFeatures,
                  true);
          break;
        case LOGISTIC_REGRESSION_LBFGS:
          summaryModel =
              buildLogisticRegressionModel(
                  sparkContext,
                  modelId,
                  trainingData,
                  testingData,
                  workflow,
                  mlModel,
                  includedFeatures,
                  false);
          break;
        case DECISION_TREE:
          categoricalFeatureInfo = getCategoricalFeatureInfo(context.getEncodings());
          summaryModel =
              buildDecisionTreeModel(
                  sparkContext,
                  modelId,
                  trainingData,
                  testingData,
                  workflow,
                  mlModel,
                  includedFeatures,
                  categoricalFeatureInfo);
          break;
        case RANDOM_FOREST:
          categoricalFeatureInfo = getCategoricalFeatureInfo(context.getEncodings());
          summaryModel =
              buildRandomForestTreeModel(
                  sparkContext,
                  modelId,
                  trainingData,
                  testingData,
                  workflow,
                  mlModel,
                  includedFeatures,
                  categoricalFeatureInfo);
          break;
        case SVM:
          summaryModel =
              buildSVMModel(
                  sparkContext,
                  modelId,
                  trainingData,
                  testingData,
                  workflow,
                  mlModel,
                  includedFeatures);
          break;
        case NAIVE_BAYES:
          summaryModel =
              buildNaiveBayesModel(
                  sparkContext,
                  modelId,
                  trainingData,
                  testingData,
                  workflow,
                  mlModel,
                  includedFeatures);
          break;
        case LINEAR_REGRESSION:
          summaryModel =
              buildLinearRegressionModel(
                  sparkContext,
                  modelId,
                  trainingData,
                  testingData,
                  workflow,
                  mlModel,
                  includedFeatures);
          break;
        case RIDGE_REGRESSION:
          summaryModel =
              buildRidgeRegressionModel(
                  sparkContext,
                  modelId,
                  trainingData,
                  testingData,
                  workflow,
                  mlModel,
                  includedFeatures);
          break;
        case LASSO_REGRESSION:
          summaryModel =
              buildLassoRegressionModel(
                  sparkContext,
                  modelId,
                  trainingData,
                  testingData,
                  workflow,
                  mlModel,
                  includedFeatures);
          break;
        default:
          throw new AlgorithmNameException("Incorrect algorithm name");
      }

      // persist model summary
      databaseService.updateModelSummary(modelId, summaryModel);
      return mlModel;
    } catch (Exception e) {
      throw new MLModelBuilderException(
          "An error occurred while building supervised machine learning model: " + e.getMessage(),
          e);
    }
  }
コード例 #9
0
  /**
   * This method trains a stacked autoencoder
   *
   * @param trainData Training dataset as a JavaRDD
   * @param batchSize Size of a training mini-batch
   * @param layerSizes Number of neurons for each layer
   * @param epochs Number of epochs to train
   * @param responseColumn Name of the response column
   * @param modelName Name of the model
   * @return DeepLearningModel
   */
  public DeepLearningModel train(
      JavaRDD<LabeledPoint> trainData,
      int batchSize,
      int[] layerSizes,
      String activationType,
      int epochs,
      String responseColumn,
      String modelName,
      MLModel mlModel,
      long modelID) {
    // build stacked autoencoder by training the model with training data

    double trainingFraction = 1;
    try {
      Scope.enter();
      if (trainData != null) {

        int numberOfFeatures = mlModel.getFeatures().size();
        List<Feature> features = mlModel.getFeatures();
        String[] names = new String[numberOfFeatures + 1];
        for (int i = 0; i < numberOfFeatures; i++) {
          names[i] = features.get(i).getName();
        }
        names[numberOfFeatures] = mlModel.getResponseVariable();

        Frame frame = DeeplearningModelUtils.javaRDDToFrame(names, trainData);

        // H2O uses default C<x> for column header
        // String classifColName = "C" + frame.numCols();
        String classifColName = mlModel.getResponseVariable();

        // Convert response to categorical (digits 1 to <num of columns>)
        int ci = frame.find(classifColName);
        Scope.track(frame.replace(ci, frame.vecs()[ci].toEnum())._key);

        // Splitting train file to train, validation and test
        // Using FrameSplitter (instead of SuffleSplitFrame) gives a weird exception
        // barrier onExCompletion for hex.deeplearning.DeepLearning$DeepLearningDriver@78ec854
        double[] ratios = new double[] {trainingFraction, 1 - trainingFraction};
        @SuppressWarnings("unchecked")
        Frame[] splits =
            ShuffleSplitFrame.shuffleSplitFrame(
                frame, generateNumKeys(frame._key, ratios.length), ratios, 123456789);

        Frame trainFrame = splits[0];
        Frame vframe = splits[1];

        if (log.isDebugEnabled()) {
          log.debug("Creating Deeplearning parameters");
        }

        DeepLearningParameters deeplearningParameters = new DeepLearningParameters();

        // convert model name
        String dlModelName = modelName.replace('.', '_').replace('-', '_');

        // populate model parameters
        deeplearningParameters._model_id = Key.make(dlModelName + "_dl");
        deeplearningParameters._train = trainFrame._key;
        deeplearningParameters._valid = vframe._key;
        deeplearningParameters._response_column = classifColName; // last column is the response
        // This is causin all the predictions to be 0.0
        // p._autoencoder = true;
        deeplearningParameters._activation = getActivationType(activationType);
        deeplearningParameters._hidden = layerSizes;
        deeplearningParameters._train_samples_per_iteration = batchSize;
        deeplearningParameters._input_dropout_ratio = 0.2;
        deeplearningParameters._l1 = 1e-5;
        deeplearningParameters._max_w2 = 10;
        deeplearningParameters._epochs = epochs;

        // speed up training
        deeplearningParameters._adaptive_rate =
            true; // disable adaptive per-weight learning rate -> default
        // settings for learning rate and momentum are probably
        // not ideal (slow convergence)
        deeplearningParameters._replicate_training_data =
            true; // avoid extra communication cost upfront, got
        // enough data on each node for load balancing
        deeplearningParameters._overwrite_with_best_model =
            true; // no need to keep the best model around
        deeplearningParameters._diagnostics =
            false; // no need to compute statistics during training
        deeplearningParameters._classification_stop = -1;
        deeplearningParameters._score_interval =
            60; // score and print progress report (only) every 20 seconds
        deeplearningParameters._score_training_samples =
            batchSize / 10; // only score on a small sample of the
        // training set -> don't want to spend
        // too much time scoring (note: there
        // will be at least 1 row per chunk)

        DKV.put(trainFrame);
        DKV.put(vframe);

        deeplearning = new DeepLearning(deeplearningParameters);

        if (log.isDebugEnabled()) {
          log.debug("Start training deeplearning model ....");
        }

        try {
          dlModel = deeplearning.trainModel().get();
          if (log.isDebugEnabled()) {
            log.debug("Successfully finished Training deeplearning model.");
          }

        } catch (RuntimeException ex) {
          log.error("Error in training Stacked Autoencoder classifier model", ex);
        }
      } else {
        log.error("Train file not found!");
      }
    } catch (RuntimeException ex) {
      log.error("Failed to train the deeplearning model [id] " + modelID + ". " + ex.getMessage());
    } finally {
      Scope.exit();
    }

    return dlModel;
  }