org.apache.spark.ml.evaluation.BinaryClassificationEvaluator Java Examples

private Evaluator getEvaluator(TrainingSettings trainingSettings, Predictor predictor) {

        Evaluator evaluator = null;
        if(predictor instanceof RandomForestClassifier
               || predictor instanceof GBTClassifier
               || predictor instanceof DecisionTreeClassifier) {

            evaluator = new MulticlassClassificationEvaluator()
                    .setLabelCol("indexedLabel")
                    .setPredictionCol("prediction")
                    .setMetricName(trainingSettings.getMetricName());
        }

        if(predictor instanceof LogisticRegression) {
            evaluator = new BinaryClassificationEvaluator().setMetricName(trainingSettings.getMetricName());
        }

        return evaluator;
    }
 

public static void main(String[] args) {
  SparkSession spark = SparkSession
    .builder()
    .appName("JavaModelSelectionViaCrossValidationExample")
    .getOrCreate();

  // $example on$
  // Prepare training documents, which are labeled.
  Dataset<Row> training = spark.createDataFrame(Arrays.asList(
    new JavaLabeledDocument(0L, "a b c d e spark", 1.0),
    new JavaLabeledDocument(1L, "b d", 0.0),
    new JavaLabeledDocument(2L,"spark f g h", 1.0),
    new JavaLabeledDocument(3L, "hadoop mapreduce", 0.0),
    new JavaLabeledDocument(4L, "b spark who", 1.0),
    new JavaLabeledDocument(5L, "g d a y", 0.0),
    new JavaLabeledDocument(6L, "spark fly", 1.0),
    new JavaLabeledDocument(7L, "was mapreduce", 0.0),
    new JavaLabeledDocument(8L, "e spark program", 1.0),
    new JavaLabeledDocument(9L, "a e c l", 0.0),
    new JavaLabeledDocument(10L, "spark compile", 1.0),
    new JavaLabeledDocument(11L, "hadoop software", 0.0)
  ), JavaLabeledDocument.class);

  // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
  Tokenizer tokenizer = new Tokenizer()
    .setInputCol("text")
    .setOutputCol("words");
  HashingTF hashingTF = new HashingTF()
    .setNumFeatures(1000)
    .setInputCol(tokenizer.getOutputCol())
    .setOutputCol("features");
  LogisticRegression lr = new LogisticRegression()
    .setMaxIter(10)
    .setRegParam(0.01);
  Pipeline pipeline = new Pipeline()
    .setStages(new PipelineStage[] {tokenizer, hashingTF, lr});

  // We use a ParamGridBuilder to construct a grid of parameters to search over.
  // With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
  // this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.
  ParamMap[] paramGrid = new ParamGridBuilder()
    .addGrid(hashingTF.numFeatures(), new int[] {10, 100, 1000})
    .addGrid(lr.regParam(), new double[] {0.1, 0.01})
    .build();

  // We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
  // This will allow us to jointly choose parameters for all Pipeline stages.
  // A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
  // Note that the evaluator here is a BinaryClassificationEvaluator and its default metric
  // is areaUnderROC.
  CrossValidator cv = new CrossValidator()
    .setEstimator(pipeline)
    .setEvaluator(new BinaryClassificationEvaluator())
    .setEstimatorParamMaps(paramGrid).setNumFolds(2);  // Use 3+ in practice

  // Run cross-validation, and choose the best set of parameters.
  CrossValidatorModel cvModel = cv.fit(training);

  // Prepare test documents, which are unlabeled.
  Dataset<Row> test = spark.createDataFrame(Arrays.asList(
    new JavaDocument(4L, "spark i j k"),
    new JavaDocument(5L, "l m n"),
    new JavaDocument(6L, "mapreduce spark"),
    new JavaDocument(7L, "apache hadoop")
  ), JavaDocument.class);

  // Make predictions on test documents. cvModel uses the best model found (lrModel).
  Dataset<Row> predictions = cvModel.transform(test);
  for (Row r : predictions.select("id", "text", "probability", "prediction").collectAsList()) {
    System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2)
      + ", prediction=" + r.get(3));
  }
  // $example off$

  spark.stop();
}