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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2016/09/11 09:02:20 UTC
[jira] [Resolved] (SPARK-16834) TrainValildationSplit and direct
evaluation produce different scores
[ https://issues.apache.org/jira/browse/SPARK-16834?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-16834.
-------------------------------
Resolution: Won't Fix
I think the answer is the same as in https://issues.apache.org/jira/browse/SPARK-16832 because it's apparently by design that these classes are deterministic by default. If you seed the split randomly it'll work as you expect.
> TrainValildationSplit and direct evaluation produce different scores
> --------------------------------------------------------------------
>
> Key: SPARK-16834
> URL: https://issues.apache.org/jira/browse/SPARK-16834
> Project: Spark
> Issue Type: Bug
> Components: ML, PySpark
> Affects Versions: 2.0.0
> Reporter: Max Moroz
>
> The two segments of code below are supposed to do the same thing: one is using TrainValidationSplit, the other performs the same evaluation manually. However, their results are statistically different (in my case, in a loop of 20, I regularly get ~19 True values).
> Unfortunately, I didn't find the bug in the source code.
> {code}
> dataset = spark.createDataFrame(
> [(Vectors.dense([0.0]), 0.0),
> (Vectors.dense([0.4]), 1.0),
> (Vectors.dense([0.5]), 0.0),
> (Vectors.dense([0.6]), 1.0),
> (Vectors.dense([1.0]), 1.0)] * 1000,
> ["features", "label"]).cache()
> paramGrid = pyspark.ml.tuning.ParamGridBuilder().build()
> # note that test is NEVER used in this code
> # I create it only to utilize randomSplit
> for i in range(20):
> train, test = dataset.randomSplit([0.8, 0.2])
> tvs = pyspark.ml.tuning.TrainValidationSplit(estimator=pyspark.ml.regression.LinearRegression(),
> estimatorParamMaps=paramGrid,
> evaluator=pyspark.ml.evaluation.RegressionEvaluator(),
> trainRatio=0.5)
> model = tvs.fit(train)
> train, val, test = dataset.randomSplit([0.4, 0.4, 0.2])
> lr=pyspark.ml.regression.LinearRegression()
> evaluator=pyspark.ml.evaluation.RegressionEvaluator()
> lrModel = lr.fit(train)
> predicted = lrModel.transform(val)
> print(model.validationMetrics[0] < evaluator.evaluate(predicted))
> {code}
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