[jira] [Assigned] (SPARK-21690) one-pass imputer

2017-08-28 Thread Apache Spark (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-21690?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Apache Spark reassigned SPARK-21690:


Assignee: Apache Spark  (was: zhengruifeng)

> one-pass imputer
> 
>
> Key: SPARK-21690
> URL: https://issues.apache.org/jira/browse/SPARK-21690
> Project: Spark
>  Issue Type: Improvement
>  Components: ML
>Affects Versions: 2.2.1
>Reporter: zhengruifeng
>Assignee: Apache Spark
>
> {code}
> val surrogates = $(inputCols).map { inputCol =>
>   val ic = col(inputCol)
>   val filtered = dataset.select(ic.cast(DoubleType))
> .filter(ic.isNotNull && ic =!= $(missingValue) && !ic.isNaN)
>   if(filtered.take(1).length == 0) {
> throw new SparkException(s"surrogate cannot be computed. " +
>   s"All the values in $inputCol are Null, Nan or 
> missingValue(${$(missingValue)})")
>   }
>   val surrogate = $(strategy) match {
> case Imputer.mean => filtered.select(avg(inputCol)).as[Double].first()
> case Imputer.median => filtered.stat.approxQuantile(inputCol, 
> Array(0.5), 0.001).head
>   }
>   surrogate
> }
> {code}
> Current impl of {{Imputer}} process one column after after another. In this 
> place, we should parallelize the processing in a more efficient way.



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[jira] [Assigned] (SPARK-21690) one-pass imputer

2017-08-28 Thread Apache Spark (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-21690?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Apache Spark reassigned SPARK-21690:


Assignee: zhengruifeng  (was: Apache Spark)

> one-pass imputer
> 
>
> Key: SPARK-21690
> URL: https://issues.apache.org/jira/browse/SPARK-21690
> Project: Spark
>  Issue Type: Improvement
>  Components: ML
>Affects Versions: 2.2.1
>Reporter: zhengruifeng
>Assignee: zhengruifeng
>
> {code}
> val surrogates = $(inputCols).map { inputCol =>
>   val ic = col(inputCol)
>   val filtered = dataset.select(ic.cast(DoubleType))
> .filter(ic.isNotNull && ic =!= $(missingValue) && !ic.isNaN)
>   if(filtered.take(1).length == 0) {
> throw new SparkException(s"surrogate cannot be computed. " +
>   s"All the values in $inputCol are Null, Nan or 
> missingValue(${$(missingValue)})")
>   }
>   val surrogate = $(strategy) match {
> case Imputer.mean => filtered.select(avg(inputCol)).as[Double].first()
> case Imputer.median => filtered.stat.approxQuantile(inputCol, 
> Array(0.5), 0.001).head
>   }
>   surrogate
> }
> {code}
> Current impl of {{Imputer}} process one column after after another. In this 
> place, we should parallelize the processing in a more efficient way.



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[jira] [Assigned] (SPARK-21690) one-pass imputer

2017-08-28 Thread Apache Spark (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-21690?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Apache Spark reassigned SPARK-21690:


Assignee: zhengruifeng  (was: Apache Spark)

> one-pass imputer
> 
>
> Key: SPARK-21690
> URL: https://issues.apache.org/jira/browse/SPARK-21690
> Project: Spark
>  Issue Type: Improvement
>  Components: ML
>Affects Versions: 2.2.1
>Reporter: zhengruifeng
>Assignee: zhengruifeng
>
> {code}
> val surrogates = $(inputCols).map { inputCol =>
>   val ic = col(inputCol)
>   val filtered = dataset.select(ic.cast(DoubleType))
> .filter(ic.isNotNull && ic =!= $(missingValue) && !ic.isNaN)
>   if(filtered.take(1).length == 0) {
> throw new SparkException(s"surrogate cannot be computed. " +
>   s"All the values in $inputCol are Null, Nan or 
> missingValue(${$(missingValue)})")
>   }
>   val surrogate = $(strategy) match {
> case Imputer.mean => filtered.select(avg(inputCol)).as[Double].first()
> case Imputer.median => filtered.stat.approxQuantile(inputCol, 
> Array(0.5), 0.001).head
>   }
>   surrogate
> }
> {code}
> Current impl of {{Imputer}} process one column after after another. In this 
> place, we should parallelize the processing in a more efficient way.



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[jira] [Assigned] (SPARK-21690) one-pass imputer

2017-08-28 Thread Apache Spark (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-21690?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Apache Spark reassigned SPARK-21690:


Assignee: Apache Spark  (was: zhengruifeng)

> one-pass imputer
> 
>
> Key: SPARK-21690
> URL: https://issues.apache.org/jira/browse/SPARK-21690
> Project: Spark
>  Issue Type: Improvement
>  Components: ML
>Affects Versions: 2.2.1
>Reporter: zhengruifeng
>Assignee: Apache Spark
>
> {code}
> val surrogates = $(inputCols).map { inputCol =>
>   val ic = col(inputCol)
>   val filtered = dataset.select(ic.cast(DoubleType))
> .filter(ic.isNotNull && ic =!= $(missingValue) && !ic.isNaN)
>   if(filtered.take(1).length == 0) {
> throw new SparkException(s"surrogate cannot be computed. " +
>   s"All the values in $inputCol are Null, Nan or 
> missingValue(${$(missingValue)})")
>   }
>   val surrogate = $(strategy) match {
> case Imputer.mean => filtered.select(avg(inputCol)).as[Double].first()
> case Imputer.median => filtered.stat.approxQuantile(inputCol, 
> Array(0.5), 0.001).head
>   }
>   surrogate
> }
> {code}
> Current impl of {{Imputer}} process one column after after another. In this 
> place, we should parallelize the processing in a more efficient way.



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[jira] [Assigned] (SPARK-21690) one-pass imputer

2017-08-17 Thread Yanbo Liang (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-21690?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Yanbo Liang reassigned SPARK-21690:
---

Assignee: zhengruifeng

> one-pass imputer
> 
>
> Key: SPARK-21690
> URL: https://issues.apache.org/jira/browse/SPARK-21690
> Project: Spark
>  Issue Type: Improvement
>  Components: ML
>Affects Versions: 2.2.1
>Reporter: zhengruifeng
>Assignee: zhengruifeng
>
> {code}
> val surrogates = $(inputCols).map { inputCol =>
>   val ic = col(inputCol)
>   val filtered = dataset.select(ic.cast(DoubleType))
> .filter(ic.isNotNull && ic =!= $(missingValue) && !ic.isNaN)
>   if(filtered.take(1).length == 0) {
> throw new SparkException(s"surrogate cannot be computed. " +
>   s"All the values in $inputCol are Null, Nan or 
> missingValue(${$(missingValue)})")
>   }
>   val surrogate = $(strategy) match {
> case Imputer.mean => filtered.select(avg(inputCol)).as[Double].first()
> case Imputer.median => filtered.stat.approxQuantile(inputCol, 
> Array(0.5), 0.001).head
>   }
>   surrogate
> }
> {code}
> Current impl of {{Imputer}} process one column after after another. In this 
> place, we should parallelize the processing in a more efficient way.



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