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Posted to issues@spark.apache.org by "Max Moroz (JIRA)" <ji...@apache.org> on 2016/08/01 08:43:20 UTC
[jira] [Updated] (SPARK-16831) CrossValidator reports incorrect
avgMetrics
[ https://issues.apache.org/jira/browse/SPARK-16831?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Max Moroz updated SPARK-16831:
------------------------------
Summary: CrossValidator reports incorrect avgMetrics (was: CrossValidator and TrainValidationSplit don't report correct avgMetrics)
> 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
>
> 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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