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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-2157) Create evaluation framework for ML library
Date Wed, 08 Jul 2015 15:31:04 GMT

    [ https://issues.apache.org/jira/browse/FLINK-2157?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14618774#comment-14618774
] 

ASF GitHub Bot commented on FLINK-2157:
---------------------------------------

Github user tillrohrmann commented on a diff in the pull request:

    https://github.com/apache/flink/pull/871#discussion_r34161323
  
    --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/evaluation/Score.scala
---
    @@ -0,0 +1,140 @@
    +/*
    + * 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.common.typeinfo.TypeInformation
    +import org.apache.flink.api.scala._
    +import org.apache.flink.ml._
    +
    +import scala.reflect.ClassTag
    +
    +/**
    + * Evaluation score
    + *
    + * Takes a whole data set and then computes the evaluation score on them (obviously,
again encoded
    + * in a DataSet)
    + *
    + * @tparam PredictionType output type
    + */
    +trait Score[PredictionType] {
    +  def evaluate(trueAndPredicted: DataSet[(PredictionType, PredictionType)]): DataSet[Double]
    +}
    +
    +/** Traits to allow us to determine at runtime if a Score is a loss (lower is better)
or a
    +  * performance score (higher is better)
    +  */
    +trait Loss
    +
    +trait PerformanceScore
    +
    +/**
    + * Metrics expressible as a mean of a function taking output pairs as input
    + *
    + * @param scoringFct function to apply to all elements
    + * @tparam PredictionType output type
    + */
    +abstract class MeanScore[PredictionType: TypeInformation: ClassTag](
    +    scoringFct: (PredictionType, PredictionType) => Double)
    +    (implicit yyt: TypeInformation[(PredictionType, PredictionType)])
    +  extends Score[PredictionType] with Serializable {
    +  def evaluate(trueAndPredicted: DataSet[(PredictionType, PredictionType)]): DataSet[Double]
= {
    +    trueAndPredicted.map(yy => scoringFct(yy._1, yy._2)).mean()
    +  }
    +}
    +
    +object RegressionScores {
    +  /**
    +   * Squared loss function
    +   *
    +   * returns (y1 - y2)'
    +   *
    +   * @return a Loss object
    +   */
    +  def squaredLoss = new MeanScore[Double]((y1,y2) => (y1 - y2) * (y1 - y2)) with Loss
    +
    +  /**
    +   * Zero One Loss Function also usable for score information
    +   *
    +   * returns 1 if sign of outputs differ and 0 if the signs are equal
    +   *
    +   * @return a Loss object
    +   */
    +  def zeroOneSignumLoss = new MeanScore[Double]({ (y1, y2) =>
    +    val sy1 = scala.math.signum(y1)
    +    val sy2 = scala.math.signum(y2)
    +    if (sy1 == sy2) 0 else 1
    +  }) with Loss
    +
    +  /** Calculates the coefficient of determination, $R^2^$
    +    *
    +    * $R^2^$ indicates how well the data fit the a calculated model
    +    * Reference: [[http://en.wikipedia.org/wiki/Coefficient_of_determination]]
    +    */
    +  def r2Score = new Score[Double] with PerformanceScore {
    +    override def evaluate(trueAndPredicted: DataSet[(Double, Double)]): DataSet[Double]
= {
    +      val onlyTrue = trueAndPredicted.map(truthPrediction => truthPrediction._1)
    +      val meanTruth = onlyTrue.mean()
    +
    +      val ssRes = trueAndPredicted
    +        .map(tp => (tp._1 - tp._2) * (tp._1 - tp._2)).reduce(_ + _)
    +      val ssTot = onlyTrue
    +        .mapWithBcVariable(meanTruth) {
    +          case (truth: Double, meanTruth: Double) => (truth - meanTruth) * (truth
- meanTruth)
    +        }.reduce(_ + _)
    +
    +      val r2 = ssRes
    +        .mapWithBcVariable(ssTot) {
    +          case (ssRes: Double, ssTot: Double) =>
    +          // We avoid dividing by 0 and just assign 0.0
    +          if (ssTot == 0.0) {
    +            0.0
    +          }
    +          else {
    +            1 - (ssRes / ssTot)
    +          }
    +      }
    +      r2
    +    }
    +  }
    +}
    +
    +object ClassificationScores {
    +  /** Calculates the fraction of correct predictions
    +    *
    +    */
    +  def accuracyScore =
    +    new MeanScore[Double]((y1, y2) => if (y1 == y2) 1 else 0) with PerformanceScore
    +
    +  /**
    +   * Zero One Loss Function
    +   *
    +   * returns 1 if outputs differ and 0 if they are equal
    +   *
    +   * @tparam T output type
    +   * @return a Loss object
    +   */
    +  def zeroOneLoss[T: TypeInformation: ClassTag] = {
    +    // TODO: If T == Double, == comparison could be problematic
    --- End diff --
    
    Yes, this is a good idea.


> Create evaluation framework for ML library
> ------------------------------------------
>
>                 Key: FLINK-2157
>                 URL: https://issues.apache.org/jira/browse/FLINK-2157
>             Project: Flink
>          Issue Type: New Feature
>          Components: Machine Learning Library
>            Reporter: Till Rohrmann
>            Assignee: Theodore Vasiloudis
>              Labels: ML
>             Fix For: 0.10
>
>
> Currently, FlinkML lacks means to evaluate the performance of trained models. It would
be great to add some {{Evaluators}} which can calculate some score based on the information
about true and predicted labels. This could also be used for the cross validation to choose
the right hyper parameters.
> Possible scores could be F score [1], zero-one-loss score, etc.
> Resources
> [1] [http://en.wikipedia.org/wiki/F1_score]



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