/**
* A utility method to generate class classification model summary
*
* @param predictionsAndLabels Predictions and actual labels
* @return Class classification model summary
*/
public static ClassClassificationAndRegressionModelSummary getClassClassificationModelSummary(
JavaSparkContext sparkContext,
JavaRDD<LabeledPoint> testingData,
JavaPairRDD<Double, Double> predictionsAndLabels) {
ClassClassificationAndRegressionModelSummary classClassificationModelSummary =
new ClassClassificationAndRegressionModelSummary();
// store predictions and actuals
List<PredictedVsActual> predictedVsActuals = new ArrayList<PredictedVsActual>();
for (Tuple2<Double, Double> scoreAndLabel : predictionsAndLabels.collect()) {
PredictedVsActual predictedVsActual = new PredictedVsActual();
predictedVsActual.setPredicted(scoreAndLabel._1());
predictedVsActual.setActual(scoreAndLabel._2());
predictedVsActuals.add(predictedVsActual);
}
// create a list of feature values
List<double[]> features = new ArrayList<double[]>();
for (LabeledPoint labeledPoint : testingData.collect()) {
if (labeledPoint != null && labeledPoint.features() != null) {
double[] rowFeatures = labeledPoint.features().toArray();
features.add(rowFeatures);
}
}
// create a list of feature values with predicted vs. actuals
List<TestResultDataPoint> testResultDataPoints = new ArrayList<TestResultDataPoint>();
for (int i = 0; i < features.size(); i++) {
TestResultDataPoint testResultDataPoint = new TestResultDataPoint();
testResultDataPoint.setPredictedVsActual(predictedVsActuals.get(i));
testResultDataPoint.setFeatureValues(features.get(i));
testResultDataPoints.add(testResultDataPoint);
}
// covert List to JavaRDD
JavaRDD<TestResultDataPoint> testResultDataPointsJavaRDD =
sparkContext.parallelize(testResultDataPoints);
// collect RDD as a sampled list
List<TestResultDataPoint> testResultDataPointsSample;
if (testResultDataPointsJavaRDD.count()
> MLCoreServiceValueHolder.getInstance().getSummaryStatSettings().getSampleSize()) {
testResultDataPointsSample =
testResultDataPointsJavaRDD.takeSample(
true,
MLCoreServiceValueHolder.getInstance().getSummaryStatSettings().getSampleSize());
} else {
testResultDataPointsSample = testResultDataPointsJavaRDD.collect();
}
classClassificationModelSummary.setTestResultDataPointsSample(testResultDataPointsSample);
classClassificationModelSummary.setPredictedVsActuals(predictedVsActuals);
// calculate test error
double error =
1.0
* predictionsAndLabels
.filter(
new Function<Tuple2<Double, Double>, Boolean>() {
private static final long serialVersionUID = -3063364114286182333L;
@Override
public Boolean call(Tuple2<Double, Double> pl) {
return !pl._1().equals(pl._2());
}
})
.count()
/ predictionsAndLabels.count();
classClassificationModelSummary.setError(error);
return classClassificationModelSummary;
}
/**
* A utility method to generate regression model summary
*
* @param predictionsAndLabels Tuple2 containing predicted and actual values
* @return Regression model summary
*/
public static ClassClassificationAndRegressionModelSummary generateRegressionModelSummary(
JavaSparkContext sparkContext,
JavaRDD<LabeledPoint> testingData,
JavaRDD<Tuple2<Double, Double>> predictionsAndLabels) {
ClassClassificationAndRegressionModelSummary regressionModelSummary =
new ClassClassificationAndRegressionModelSummary();
// store predictions and actuals
List<PredictedVsActual> predictedVsActuals = new ArrayList<PredictedVsActual>();
DecimalFormat decimalFormat = new DecimalFormat(MLConstants.DECIMAL_FORMAT);
for (Tuple2<Double, Double> scoreAndLabel : predictionsAndLabels.collect()) {
PredictedVsActual predictedVsActual = new PredictedVsActual();
predictedVsActual.setPredicted(Double.parseDouble(decimalFormat.format(scoreAndLabel._1())));
predictedVsActual.setActual(Double.parseDouble(decimalFormat.format(scoreAndLabel._2())));
predictedVsActuals.add(predictedVsActual);
}
// create a list of feature values
List<double[]> features = new ArrayList<double[]>();
for (LabeledPoint labeledPoint : testingData.collect()) {
if (labeledPoint != null && labeledPoint.features() != null) {
double[] rowFeatures = labeledPoint.features().toArray();
features.add(rowFeatures);
}
}
// create a list of feature values with predicted vs. actuals
List<TestResultDataPoint> testResultDataPoints = new ArrayList<TestResultDataPoint>();
for (int i = 0; i < features.size(); i++) {
TestResultDataPoint testResultDataPoint = new TestResultDataPoint();
testResultDataPoint.setPredictedVsActual(predictedVsActuals.get(i));
testResultDataPoint.setFeatureValues(features.get(i));
testResultDataPoints.add(testResultDataPoint);
}
// covert List to JavaRDD
JavaRDD<TestResultDataPoint> testResultDataPointsJavaRDD =
sparkContext.parallelize(testResultDataPoints);
// collect RDD as a sampled list
List<TestResultDataPoint> testResultDataPointsSample;
if (testResultDataPointsJavaRDD.count()
> MLCoreServiceValueHolder.getInstance().getSummaryStatSettings().getSampleSize()) {
testResultDataPointsSample =
testResultDataPointsJavaRDD.takeSample(
true,
MLCoreServiceValueHolder.getInstance().getSummaryStatSettings().getSampleSize());
} else {
testResultDataPointsSample = testResultDataPointsJavaRDD.collect();
}
regressionModelSummary.setTestResultDataPointsSample(testResultDataPointsSample);
regressionModelSummary.setPredictedVsActuals(predictedVsActuals);
// calculate mean squared error (MSE)
double meanSquaredError =
new JavaDoubleRDD(
predictionsAndLabels
.map(
new Function<Tuple2<Double, Double>, Object>() {
private static final long serialVersionUID = -162193633199074816L;
public Object call(Tuple2<Double, Double> pair) {
return Math.pow(pair._1() - pair._2(), 2.0);
}
})
.rdd())
.mean();
regressionModelSummary.setError(meanSquaredError);
return regressionModelSummary;
}