Java Code Examples for org.apache.spark.api.java.JavaRDD#wrapRDD()

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Example 1
Source File: Evaluation.java    From oryx with Apache License 2.0 6 votes vote down vote up 
/**
 * Computes root mean squared error of {@link Rating#rating()} versus predicted value.
 */
static double rmse(MatrixFactorizationModel mfModel, JavaRDD<Rating> testData) {
  JavaPairRDD<Tuple2<Integer,Integer>,Double> testUserProductValues =
      testData.mapToPair(rating -> new Tuple2<>(new Tuple2<>(rating.user(), rating.product()), rating.rating()));
  @SuppressWarnings("unchecked")
  RDD<Tuple2<Object,Object>> testUserProducts =
      (RDD<Tuple2<Object,Object>>) (RDD<?>) testUserProductValues.keys().rdd();
  JavaRDD<Rating> predictions = testData.wrapRDD(mfModel.predict(testUserProducts));
  double mse = predictions.mapToPair(
      rating -> new Tuple2<>(new Tuple2<>(rating.user(), rating.product()), rating.rating())
  ).join(testUserProductValues).values().mapToDouble(valuePrediction -> {
    double diff = valuePrediction._1() - valuePrediction._2();
    return diff * diff;
  }).mean();
  return Math.sqrt(mse);
}
Example 2
Source File: MLUpdate.java    From oryx with Apache License 2.0 5 votes vote down vote up 
/**
 * Default implementation which randomly splits new data into train/test sets.
 * This handles the case where {@link #getTestFraction()} is not 0 or 1.
 *
 * @param newData data that has arrived in the current input batch
 * @return a {@link Pair} of train, test {@link RDD}s.
 */
protected Pair<JavaRDD<M>,JavaRDD<M>> splitNewDataToTrainTest(JavaRDD<M> newData) {
  RDD<M>[] testTrainRDDs = newData.rdd().randomSplit(
      new double[]{1.0 - testFraction, testFraction},
      RandomManager.getRandom().nextLong());
  return new Pair<>(newData.wrapRDD(testTrainRDDs[0]),
                    newData.wrapRDD(testTrainRDDs[1]));
}