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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-2157) Create evaluation framework for ML library
Date Thu, 02 Jul 2015 10:37:04 GMT

    [ https://issues.apache.org/jira/browse/FLINK-2157?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14611780#comment-14611780
] 

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

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

    https://github.com/apache/flink/pull/871#discussion_r33765751
  
    --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/classification/Classifier.scala
---
    @@ -0,0 +1,43 @@
    +/*
    + * 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.classification
    +
    +import org.apache.flink.api.scala._
    +import org.apache.flink.ml.evaluation.ClassificationScores
    +import org.apache.flink.ml.pipeline.Predictor
    +
    +/** Trait that classification algorithms should implement
    +  *
    +  * @tparam Self Type of the implementing class
    +  */
    +trait Classifier[Self] extends Predictor[Self]{
    +  that: Self =>
    +
    +  override def calculateScore[Prediction](input: DataSet[(Prediction, Prediction)])
    +    : DataSet[Double] = {
    +    val tpi = input.getType()
    +    if (tpi == createTypeInformation[(Double, Double)]) {
    +      val doubleInput = input.asInstanceOf[DataSet[(Double, Double)]]
    +      ClassificationScores.accuracyScore.evaluate(doubleInput)
    --- End diff --
    
    Wondering if we are happy with this way of doing type checking.


> 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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