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• spark-gp-master
  • src
    • main
      • scala
        • org
          • apache
            • spark
              • ml
                • classification
                  • examples
                    • MNIST.scala
                    • Iris.scala
                  • GaussianProcessClassifier.scala
                • commons
                  • kernel
                    • Kernel.scala
                    • RBFKernel.scala
                    • ScalarTimesKernel.scala
                    • package.scala
                    • ARDRBFKernel.scala
                    • SumOfKernels.scala
                  • util
                    • Integrator.scala
                    • Scaling.scala
                    • logDetAndInv.scala
                    • DiffFunctionMemoized.scala
                  • ProjectedGaussianProcessHelper.scala
                  • ActiveSetProvider.scala
                  • GaussianProcessParams.scala
                  • GaussianProcessCommons.scala
                • regression
                  • examples
                    • GPExample.scala
                    • Airfoil.scala
                    • Synthetics.scala
                  • GaussianProcessRegression.scala
                  • benchmark
                    • PerformanceBenchmark.scala
    • test
      • scala
        • org
          • apache
            • spark
              • ml
                • commons
                  • kernel
                    • RBFKernelTest.scala
                    • ARDRBFKernelTest.scala
                  • util
                    • IntegratorTest.scala
  • LICENSE
  • data
    • iris.csv
    • airfoil.csv
  • README.md
  • build.sbt

package org.apache.spark.ml.regression.examples

import org.apache.spark.ml.evaluation.RegressionEvaluator
import org.apache.spark.ml.regression.GaussianProcessRegression
import org.apache.spark.ml.tuning.{CrossValidator, ParamGridBuilder}
import org.apache.spark.sql.{DataFrame, SparkSession}

trait GPExample {
  def name : String

  val spark = SparkSession.builder().appName(name).master("local[4]").getOrCreate()

  /*
   * It takes `gp`, runs 10-fold cross-validation on `instances` and returns the rmse
   * if it's below `expectedRMSE`. Exception is generated otherwise.
   */
  def cv(gp: GaussianProcessRegression, instances: DataFrame, expectedRMSE: Double) = {
    val cv = new CrossValidator()
      .setEstimator(gp)
      .setEvaluator(new RegressionEvaluator())
      .setEstimatorParamMaps(new ParamGridBuilder().build())
      .setNumFolds(10)

    val rmse = cv.fit(instances).avgMetrics.head
    println("RMSE: " + rmse)
    assert(rmse < expectedRMSE)
  }
}