flink-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From tillrohrmann <...@git.apache.org>
Subject [GitHub] flink pull request: [FLINK-1723] [ml] [WIP] Add cross validation f...
Date Mon, 20 Jul 2015 07:45:52 GMT
Github user tillrohrmann commented on a diff in the pull request:

    https://github.com/apache/flink/pull/891#discussion_r34973140
  
    --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/evaluation/CrossValidation.scala
---
    @@ -0,0 +1,97 @@
    +/*
    + * Licensed to the Apache Software Foundation (ASF) under one
    + * or more contributor license agreements.  See the NOTICE file
    + * distributed with this work for additional information
    + * regarding copyright ownership.  The ASF licenses this file
    + * to you 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 org.apache.flink.ml.evaluation
    +
    +import org.apache.flink.api.scala._
    +import org.apache.flink.ml.RichDataSet
    +import java.util.Random
    +
    +import org.apache.flink.ml.pipeline.{EvaluateDataSetOperation, FitOperation, Predictor}
    +
    +object CrossValidation {
    +  def crossValScore[P <: Predictor[P], T](
    +      predictor: P,
    +      data: DataSet[T],
    +      scorerOption: Option[Scorer] = None,
    +      cv: FoldGenerator = KFold(),
    +      seed: Long = new Random().nextLong())(implicit fitOperation: FitOperation[P, T],
    +      evaluateDataSetOperation: EvaluateDataSetOperation[P, T, Double]): Array[DataSet[Double]]
= {
    +    val folds = cv.folds(data, 1)
    +
    +    val scores = folds.map {
    +      case (training: DataSet[T], testing: DataSet[T]) =>
    +        predictor.fit(training)
    +        if (scorerOption.isEmpty) {
    +          predictor.score(testing)
    +        } else {
    +          val s = scorerOption.get
    +          s.evaluate(testing, predictor)
    +        }
    +    }
    +    // TODO: Undecided on the return type: Array[DS[Double]] or DS[Double] i.e. reduce->union?
    +    // Or: Return mean and std?
    +    scores//.reduce((right: DataSet[Double], left: DataSet[Double]) => left.union(right)).mean()
    +  }
    +}
    +
    +abstract class FoldGenerator {
    +
    +  /** Takes a DataSet as input and creates splits (folds) of the data into
    +    * (training, testing) pairs.
    +    *
    +    * @param input The DataSet that will be split into folds
    +    * @param seed Seed for replicable splitting of the data
    +    * @tparam T The type of the DataSet
    +    * @return An Array containing K (training, testing) tuples, where training and testing
are
    +    *         DataSets
    +    */
    +  def folds[T](
    +      input: DataSet[T],
    +      seed: Long = new Random().nextLong()): Array[(DataSet[T], DataSet[T])]
    +}
    +
    +class KFold(numFolds: Int) extends FoldGenerator{
    +
    +  /** Takes a DataSet as input and creates K splits (folds) of the data into non-overlapping
    +    * (training, testing) pairs.
    +    *
    +    * Code based on Apache Spark implementation
    +    * @param input The DataSet that will be split into folds
    +    * @param seed Seed for replicable splitting of the data
    +    * @tparam T The type of the DataSet
    +    * @return An Array containing K (training, testing) tuples, where training and testing
are
    +    *         DataSets
    +    */
    +  override def folds[T](
    +      input: DataSet[T],
    +      seed: Long = new Random().nextLong()): Array[(DataSet[T], DataSet[T])] = {
    +    val numFoldsF = numFolds.toFloat
    +    (1 to numFolds).map { fold =>
    +      val lb = (fold - 1) / numFoldsF
    +      val ub = fold / numFoldsF
    +      val validation = input.sampleBounded(lb, ub, complement = false, seed = seed)
    +      val training = input.sampleBounded(lb, ub, complement = true, seed = seed)
    +      (training, validation)
    --- End diff --
    
    Given that `input` is deterministic, this code will produce mutually exclusive validation
and training data sets.


---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastructure@apache.org or file a JIRA ticket
with INFRA.
---

Mime
View raw message