[jira] [Assigned] (SPARK-21690) one-pass imputer
[ 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-21690) one-pass imputer
[ 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-21690) one-pass imputer
[ 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-21690) one-pass imputer
[ 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-21690) one-pass imputer
[ 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org