e2/src/main/scala/io/prediction/e2/evaluation/CrossValidation.scala

/** Copyright 2015 TappingStone, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
  * You may obtain a copy of the License at
  *
  *     http://www.apache.org/licenses/LICENSE-2.0
  *
  * Unless required by applicable law or agreed to in writing, software
  * distributed under the License is distributed on an "AS IS" BASIS,
  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
package io.prediction.e2.evaluation

import scala.reflect.ClassTag
import org.apache.spark.rdd.RDD

object CommonHelperFunctions {

  /**
   * Split a data set into evalK folds for crossvalidation.
   * Apply to data sets supplied to evaluation.
   *
   * @tparam D Data point class.
   * @tparam TD Training data class.
   * @tparam EI Evaluation Info class.
   * @tparam Q Input query class.
   * @tparam A Actual value class.
   */

  def splitData[D: ClassTag, TD, EI, Q, A](

     evalK: Int,
     dataset: RDD[D],
     evaluatorInfo: EI,
     trainingDataCreator: RDD[D] => TD,
     queryCreator: D => Q,
     actualCreator: D => A): Seq[(TD, EI, RDD[(Q, A)])] = {

    val indexedPoints = dataset.zipWithIndex

    def selectPoint(foldIdx: Int, pt: D, idx: Long, k: Int, isTraining: Boolean): Option[D] = {
      if ((idx % k == foldIdx) ^ isTraining) Some(pt)
      else None
    }

    (0 until evalK).map { foldIdx =>
      val trainingPoints = indexedPoints.flatMap { case(pt, idx) =>
        selectPoint(foldIdx, pt, idx, evalK, true)
      }
      val testingPoints = indexedPoints.flatMap { case(pt, idx) =>
        selectPoint(foldIdx, pt, idx, evalK, false)
      }

      (
        trainingDataCreator(trainingPoints),
        evaluatorInfo,
        testingPoints.map { d => (queryCreator(d), actualCreator(d)) }
      )
    }
  }
}