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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2016/08/30 10:36:20 UTC
[jira] [Resolved] (SPARK-16832) CrossValidator and
TrainValidationSplit are not random without seed
[ https://issues.apache.org/jira/browse/SPARK-16832?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-16832.
-------------------------------
Resolution: Won't Fix
Per [~mengxr] I think this is WontFix, and the least confusing thing to do for my follow on change is make a new JIRA: https://issues.apache.org/jira/browse/SPARK-17311
> CrossValidator and TrainValidationSplit are not random without seed
> -------------------------------------------------------------------
>
> Key: SPARK-16832
> URL: https://issues.apache.org/jira/browse/SPARK-16832
> Project: Spark
> Issue Type: Bug
> Components: ML, PySpark
> Affects Versions: 2.0.0
> Reporter: Max Moroz
> Priority: Minor
>
> Repeatedly running CrossValidator or TrainValidationSplit without an explicit seed parameter does not change results. It is supposed to be seeded with a random seed, but it seems to be instead seeded with some constant. (If seed is explicitly provided, the two classes behave as expected.)
> {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()
> tvs = pyspark.ml.tuning.TrainValidationSplit(estimator=pyspark.ml.regression.LinearRegression(),
> estimatorParamMaps=paramGrid,
> evaluator=pyspark.ml.evaluation.RegressionEvaluator(),
> trainRatio=0.8)
> model = tvs.fit(train)
> print(model.validationMetrics)
> for folds in (3, 5, 10):
> cv = pyspark.ml.tuning.CrossValidator(estimator=pyspark.ml.regression.LinearRegression(),
> estimatorParamMaps=paramGrid,
> evaluator=pyspark.ml.evaluation.RegressionEvaluator(),
> numFolds=folds
> )
> cvModel = cv.fit(dataset)
> print(folds, cvModel.avgMetrics)
> {code}
> This code produces identical results upon repeated calls.
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