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;
}
/**
* 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;
}