[ https://issues.apache.org/jira/browse/SPARK-16831?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen updated SPARK-16831: ------------------------------ Fix Version/s: (was: 1.6.3) > 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} -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org