[jira] [Updated] (SPARK-16831) CrossValidator reports incorrect avgMetrics

2016-08-11 Thread Sean Owen (JIRA) 

 [ 
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}



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[jira] [Updated] (SPARK-16831) CrossValidator reports incorrect avgMetrics

2016-08-03 Thread Sean Owen (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-16831?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Sean Owen updated SPARK-16831:
--
Assignee: Max Moroz

> 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: 1.6.3, 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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[jira] [Updated] (SPARK-16831) CrossValidator reports incorrect avgMetrics

2016-08-01 Thread Max Moroz (JIRA) 

 [ 
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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