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From tillrohrmann <...@git.apache.org>
Subject [GitHub] flink pull request: [FLINK-2157] [ml] [WIP] Create evaluation fram...
Date Wed, 08 Jul 2015 16:20:21 GMT
Github user tillrohrmann commented on a diff in the pull request:

    --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/pipeline/Predictor.scala
    @@ -72,12 +74,36 @@ trait Predictor[Self] extends Estimator[Self] with WithParameters
       def evaluate[Testing, PredictionValue](
           testing: DataSet[Testing],
    -      evaluateParameters: ParameterMap = ParameterMap.Empty)(implicit
    -      evaluator: EvaluateDataSetOperation[Self, Testing, PredictionValue])
    +      evaluateParameters: ParameterMap = ParameterMap.Empty)
    +      (implicit evaluator: EvaluateDataSetOperation[Self, Testing, PredictionValue])
         : DataSet[(PredictionValue, PredictionValue)] = {
         evaluator.evaluateDataSet(this, evaluateParameters, testing)
    +  /** Calculates a numerical score for the [[Predictor]]
    +    *
    +    * By convention, higher scores are considered better, so even if a loss is used as
a performance
    +    * measure, it will be negated, so that that higher is better.
    +    * @param testing The evaluation DataSet, that contains the features and the true
    +    * @param evaluateOperation An EvaluateDataSetOperation that produces Double results
    +    * @tparam Testing The type of the features and true value, for example [[LabeledVector]]
    +    * @return A DataSet containing one Double that indicates the score of the predictor
    +    */
    +  def score[Testing](testing: DataSet[Testing])
    --- End diff --
    I think it is fine, if we restrict the `Predictor` to only predict `Double`. However,
as long as we don't do it, the `score` function should be as general as all the other methods
as well.

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