/**
* 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);
}
}
/**
* Displays the tree, starting with the given root node.
*
* @param root the Node that is the root of the tree to be displayed
* @param offset the String
*/
public static void displayTree(Node root, String offset) {
if (root.children.size() == 0) {
DecisionTree.appendText("\n" + offset + " THEN (" + root.label + ") (Leaf node)");
return;
} else {
Enumeration enum1 = root.children.elements();
Enumeration enum2 = root.linkLabels.elements();
DecisionTree.appendText("\n" + offset + " " + root.label + " (Interior node)");
while (enum1.hasMoreElements()) {
DecisionTree.appendText("\n" + offset + " IF (" + (String) enum2.nextElement() + ")");
displayTree((Node) enum1.nextElement(), offset + " ");
}
}
}
public static void main(String args[]) {
DecisionTree decisionTree = makeOutlookTree();
Map<String, String> case1 = new HashMap<String, String>();
case1.put("Outlook", "Overcast");
case1.put("Temperature", "Hot");
case1.put("Humidity", "High");
case1.put("Wind", "Strong");
try {
System.out.println(decisionTree.classify(case1));
} catch (UnknownDecisionException e) {
System.out.println("?");
}
}
