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From thvasilo <...@git.apache.org>
Subject [GitHub] flink pull request: [FLINK-2297] [ml] Add threshold setting for SV...
Date Wed, 01 Jul 2015 09:47:40 GMT
Github user thvasilo commented on a diff in the pull request:

    https://github.com/apache/flink/pull/874#discussion_r33663520
  
    --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/classification/SVM.scala
---
    @@ -242,8 +275,21 @@ object SVM{
             }
           }
     
    -      override def predict(value: T, model: DenseVector): Double = {
    -        value.asBreeze dot model.asBreeze
    +      override def predict(value: T, model: DenseVector, predictParameters: ParameterMap):
    +        Double = {
    +        val thresholdOption = predictParameters.get(Threshold)
    +
    +        val rawValue = value.asBreeze dot model.asBreeze
    +        // If the Threshold option has been reset, we will get back a Some(None) thresholdOption
    +        // causing the exception when we try to get the value. In that case we just return
the
    +        // raw value
    +        try {
    +          val thresOptionValue = thresholdOption.get
    +          if (rawValue > thresOptionValue) 1.0 else -1.0
    +        }
    +        catch {
    +          case e: java.lang.ClassCastException => rawValue
    +        }
    --- End diff --
    
    This relates to the previous discussion:
    
    I do believe we want this turned on by default, when you train a binary classifier you
expect that `predict` will return binary labels, not the decision function values.
    
    So if we have `None` as default, the user could write:
    
    ```scala
    val svm = SVM().
          setBlocks(env.getParallelism)
    
    svm.fit(train)
    val eval = svm.evaluate(test)
    ```
    
    and the eval output would not make sense, but if he wrote
    
    ```scala
    val svm = SVM().
          setBlocks(env.getParallelism).
          setThreshold(0.0)
    
    svm.fit(train)
    val eval = svm.evaluate(test)
    ```
    
    it would.


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