[jira] [Commented] (SPARK-24712) TrainValidationSplit ignores label column name and forces to be "label"

["Marco Gaido (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-24712?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16529746#comment-16529746 ] 

Marco Gaido commented on SPARK-24712:
-------------------------------------

The problem is that you have not set the label on the evaluator you are passing to {{TrainValidationSplit}}. Please set it there and it will work. I am closing this, feel free to reopen if you face a problem.

> TrainValidationSplit ignores label column name and forces to be "label"
> -----------------------------------------------------------------------
>
>                 Key: SPARK-24712
>                 URL: https://issues.apache.org/jira/browse/SPARK-24712
>             Project: Spark
>          Issue Type: Bug
>          Components: ML
>    Affects Versions: 2.2.0
>            Reporter: Pablo J. Villacorta
>            Priority: Major
>
> When a TrainValidationSplit is fit on a Pipeline containing a ML model, the labelCol property of the model is ignored, and the call to fit() will fail unless the labelCol equals "label". As an example, the following pyspark code only works when the variable labelColumn is set to "label"
> {code:java}
> from pyspark.sql.functions import rand, randn
> from pyspark.ml.regression import LinearRegression
> labelColumn = "target"  # CHANGE THIS TO "label" AND THE CODE WORKS
> df = spark.range(0, 10).select(rand(seed=10).alias("uniform"), randn(seed=27).alias(labelColumn))
> vectorAssembler = VectorAssembler().setInputCols(["uniform"]).setOutputCol("features")
> lr = LinearRegression().setFeaturesCol("features").setLabelCol(labelColumn)
> mypipeline = Pipeline(stages = [vectorAssembler, lr])
> paramGrid = ParamGridBuilder()\
> .addGrid(lr.regParam, [0.01, 0.1])\
> .build()
> trainValidationSplit = TrainValidationSplit()\
> .setEstimator(mypipeline)\
> .setEvaluator(RegressionEvaluator())\
> .setEstimatorParamMaps(paramGrid)\
> .setTrainRatio(0.8)
> trainValidationSplit.fit(df)  # FAIL UNLESS labelColumn IS SET TO "label"
> {code}



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