/**
* 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);
}
}
/**
* 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);
}
}
/**
* 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);
}
}