[
https://issues.apache.org/jira/browse/SPARK-16831?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Joseph K. Bradley updated SPARK-16831:
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Summary: PySpark CrossValidator reports incorrect avgMetrics (was:
CrossValidator reports incorrect avgMetrics)
> PySpark CrossValidator reports incorrect avgMetrics
> ---------------------------------------------------
>
> Key: SPARK-16831
> URL: https://issues.apache.org/jira/browse/SPARK-16831
> Project: Spark
> Issue Type: Bug
> Components: ML, PySpark
> Affects Versions: 2.0.0
> Reporter: Max Moroz
> Assignee: Max Moroz
> Fix For: 2.0.1, 2.1.0
>
>
> The avgMetrics are summed up across all folds instead of being averaged. This
> is an easy fix in CrossValidator._fit() function:
> {code}metrics[j]+=metric{code} should be
> {code}metrics[j]+=metric/nFolds{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()
> 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}
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