/**
* This method gets model accuracy from given multi-class metrics
*
* @param multiclassMetrics multi-class metrics object
*/
private Double getModelAccuracy(MulticlassMetrics multiclassMetrics) {
DecimalFormat decimalFormat = new DecimalFormat(MLConstants.DECIMAL_FORMAT);
Double modelAccuracy = 0.0;
int confusionMatrixSize = multiclassMetrics.confusionMatrix().numCols();
int confusionMatrixDiagonal = 0;
long totalPopulation = arraySum(multiclassMetrics.confusionMatrix().toArray());
for (int i = 0; i < confusionMatrixSize; i++) {
int diagonalValueIndex = multiclassMetrics.confusionMatrix().index(i, i);
confusionMatrixDiagonal += multiclassMetrics.confusionMatrix().toArray()[diagonalValueIndex];
}
if (totalPopulation > 0) {
modelAccuracy = (double) confusionMatrixDiagonal / totalPopulation;
}
return Double.parseDouble(decimalFormat.format(modelAccuracy * 100));
}
/**
* 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;
}
public static void main(String[] args) {
// parse the arguments
Params params = parse(args);
SparkConf conf = new SparkConf().setAppName("JavaOneVsRestExample");
JavaSparkContext jsc = new JavaSparkContext(conf);
SQLContext jsql = new SQLContext(jsc);
// configure the base classifier
LogisticRegression classifier =
new LogisticRegression()
.setMaxIter(params.maxIter)
.setTol(params.tol)
.setFitIntercept(params.fitIntercept);
if (params.regParam != null) {
classifier.setRegParam(params.regParam);
}
if (params.elasticNetParam != null) {
classifier.setElasticNetParam(params.elasticNetParam);
}
// instantiate the One Vs Rest Classifier
OneVsRest ovr = new OneVsRest().setClassifier(classifier);
String input = params.input;
RDD<LabeledPoint> inputData = MLUtils.loadLibSVMFile(jsc.sc(), input);
RDD<LabeledPoint> train;
RDD<LabeledPoint> test;
// compute the train/ test split: if testInput is not provided use part of input
String testInput = params.testInput;
if (testInput != null) {
train = inputData;
// compute the number of features in the training set.
int numFeatures = inputData.first().features().size();
test = MLUtils.loadLibSVMFile(jsc.sc(), testInput, numFeatures);
} else {
double f = params.fracTest;
RDD<LabeledPoint>[] tmp = inputData.randomSplit(new double[] {1 - f, f}, 12345);
train = tmp[0];
test = tmp[1];
}
// train the multiclass model
DataFrame trainingDataFrame = jsql.createDataFrame(train, LabeledPoint.class);
OneVsRestModel ovrModel = ovr.fit(trainingDataFrame.cache());
// score the model on test data
DataFrame testDataFrame = jsql.createDataFrame(test, LabeledPoint.class);
DataFrame predictions = ovrModel.transform(testDataFrame.cache()).select("prediction", "label");
// obtain metrics
MulticlassMetrics metrics = new MulticlassMetrics(predictions);
StructField predictionColSchema = predictions.schema().apply("prediction");
Integer numClasses = (Integer) MetadataUtils.getNumClasses(predictionColSchema).get();
// compute the false positive rate per label
StringBuilder results = new StringBuilder();
results.append("label\tfpr\n");
for (int label = 0; label < numClasses; label++) {
results.append(label);
results.append("\t");
results.append(metrics.falsePositiveRate((double) label));
results.append("\n");
}
Matrix confusionMatrix = metrics.confusionMatrix();
// output the Confusion Matrix
System.out.println("Confusion Matrix");
System.out.println(confusionMatrix);
System.out.println();
System.out.println(results);
jsc.stop();
}