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From dbtsai <...@git.apache.org>
Subject [GitHub] spark pull request: [Spark-12732][ML] bug fix in linear regression...
Date Sun, 31 Jan 2016 03:35:48 GMT
Github user dbtsai commented on a diff in the pull request:

    https://github.com/apache/spark/pull/10702#discussion_r51354776
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala ---
    @@ -219,33 +219,44 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override
val uid: String
         }
     
         val yMean = ySummarizer.mean(0)
    -    val yStd = math.sqrt(ySummarizer.variance(0))
    -
    -    // If the yStd is zero, then the intercept is yMean with zero coefficient;
    -    // as a result, training is not needed.
    -    if (yStd == 0.0) {
    -      logWarning(s"The standard deviation of the label is zero, so the coefficients will
be " +
    -        s"zeros and the intercept will be the mean of the label; as a result, " +
    -        s"training is not needed.")
    -      if (handlePersistence) instances.unpersist()
    -      val coefficients = Vectors.sparse(numFeatures, Seq())
    -      val intercept = yMean
    -
    -      val model = new LinearRegressionModel(uid, coefficients, intercept)
    -      // Handle possible missing or invalid prediction columns
    -      val (summaryModel, predictionColName) = model.findSummaryModelAndPredictionCol()
    -
    -      val trainingSummary = new LinearRegressionTrainingSummary(
    -        summaryModel.transform(dataset),
    -        predictionColName,
    -        $(labelCol),
    -        model,
    -        Array(0D),
    -        $(featuresCol),
    -        Array(0D))
    -      return copyValues(model.setSummary(trainingSummary))
    +    val rawYStd = math.sqrt(ySummarizer.variance(0))
    +    if (rawYStd == 0.0) {
    +      if ($(fitIntercept) || yMean==0.0) {
    +        // If the rawYStd is zero and fitIntercept=true, then the intercept is yMean
with
    +        // zero coefficient; as a result, training is not needed.
    +        // Also, if yMean==0 and rawYStd==0, all the coefficients are zero regardless
of
    +        // the fitIntercept
    +        logWarning(s"The standard deviation of the label is zero, so the coefficients
will be " +
    --- End diff --
    
    Maybe you want to update the warning message for the second situation as well.


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