[jira] [Commented] (FLINK-1979) Implement Loss Functions

["ASF GitHub Bot (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/FLINK-1979?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15309038#comment-15309038 ] 

ASF GitHub Bot commented on FLINK-1979:
---------------------------------------

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

    https://github.com/apache/flink/pull/1985#discussion_r65291353
  
    --- Diff: flink-libraries/flink-ml/src/main/scala/org/apache/flink/ml/optimization/PartialLossFunction.scala ---
    @@ -47,21 +47,106 @@ object SquaredLoss extends PartialLossFunction {
     
       /** Calculates the loss depending on the label and the prediction
         *
    -    * @param prediction
    -    * @param label
    -    * @return
    +    * @param prediction The predicted value
    +    * @param label The true value
    +    * @return The loss
         */
       override def loss(prediction: Double, label: Double): Double = {
         0.5 * (prediction - label) * (prediction - label)
       }
     
       /** Calculates the derivative of the [[PartialLossFunction]]
         *
    -    * @param prediction
    -    * @param label
    -    * @return
    +    * @param prediction The predicted value
    +    * @param label The true value
    +    * @return The derivative of the loss function
         */
       override def derivative(prediction: Double, label: Double): Double = {
         (prediction - label)
       }
     }
    +
    +/** Logistic loss function which can be used with the [[GenericLossFunction]]
    +  *
    +  *
    +  * The [[LogisticLoss]] function implements `log(1 + -exp(prediction*label))`
    +  * for binary classification with label in {-1, 1}
    +  */
    +object LogisticLoss extends PartialLossFunction {
    +
    +  /** Calculates the loss depending on the label and the prediction
    +    *
    +    * @param prediction The predicted value
    +    * @param label The true value
    +    * @return The loss
    +    */
    +  override def loss(prediction: Double, label: Double): Double = {
    +    val z = prediction * label
    +
    +    // based on implementation in scikit-learn
    +    // approximately equal and saves the computation of the log
    +    if (z > 18) {
    +      return math.exp(-z)
    +    }
    +    else if (z < -18) {
    +      return -z
    +    }
    +
    +    math.log(1 + math.exp(-z))
    --- End diff --
    
    Using `return` is not recommended in Scala. Could you change this like following?
    
    ```scala
    if (z > 18) {
      math.exp(-z)
    } else if (z < -18) {
      -z
    } else {
      math.log(1 + math.exp(-z))
    }
    ```


> Implement Loss Functions
> ------------------------
>
>                 Key: FLINK-1979
>                 URL: https://issues.apache.org/jira/browse/FLINK-1979
>             Project: Flink
>          Issue Type: Improvement
>          Components: Machine Learning Library
>            Reporter: Johannes Günther
>            Assignee: Johannes Günther
>            Priority: Minor
>              Labels: ML
>
> For convex optimization problems, optimizer methods like SGD rely on a pluggable implementation of a loss function and its first derivative.



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