[jira] [Updated] (SPARK-21268) Move center calculations to a distributed map in KMeans
[ https://issues.apache.org/jira/browse/SPARK-21268?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Guillaume Dardelet updated SPARK-21268: --- Description: As I was monitoring the perfomance of my algorithm with SparkUI, I noticed that their was a "collectAsMap" operation that was done hundreds of time at every iteration of Kmeans: https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala#L295 It would work just as well by performing the following "foreach" on the RDD, and would slightly improve perfomance. Edit: Per Sean Owen recommendations, scal() and VectorWithNorm creation should be computed in a distributed map before the collectAsMap. was: As I was monitoring the perfomance of my algorithm with SparkUI, I noticed that their was a "collectAsMap" operation that was done hundreds of time at every iteration of Kmeans: https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala#L295 It would work just as well by performing the following "foreach" on the RDD, and would slightly improve perfomance. Edit: > Move center calculations to a distributed map in KMeans > --- > > Key: SPARK-21268 > URL: https://issues.apache.org/jira/browse/SPARK-21268 > Project: Spark > Issue Type: Improvement > Components: MLlib >Affects Versions: 2.0.0, 2.0.1, 2.0.2, 2.1.0, 2.1.1 >Reporter: Guillaume Dardelet >Priority: Trivial > Labels: beginner, easyfix, newbie > Original Estimate: 1h > Remaining Estimate: 1h > > As I was monitoring the perfomance of my algorithm with SparkUI, I noticed > that their was a "collectAsMap" operation that was done hundreds of time at > every iteration of Kmeans: > https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala#L295 > It would work just as well by performing the following "foreach" on the RDD, > and would slightly improve perfomance. > Edit: > Per Sean Owen recommendations, scal() and VectorWithNorm creation should be > computed in a distributed map before the collectAsMap. -- 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] [Updated] (SPARK-21268) Move center calculations to a distributed map in KMeans
[ https://issues.apache.org/jira/browse/SPARK-21268?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Guillaume Dardelet updated SPARK-21268: --- Summary: Move center calculations to a distributed map in KMeans (was: Move some calculations to a distributed map in KMeans) > Move center calculations to a distributed map in KMeans > --- > > Key: SPARK-21268 > URL: https://issues.apache.org/jira/browse/SPARK-21268 > Project: Spark > Issue Type: Improvement > Components: MLlib >Affects Versions: 2.0.0, 2.0.1, 2.0.2, 2.1.0, 2.1.1 >Reporter: Guillaume Dardelet >Priority: Trivial > Labels: beginner, easyfix, newbie > Original Estimate: 1h > Remaining Estimate: 1h > > As I was monitoring the perfomance of my algorithm with SparkUI, I noticed > that their was a "collectAsMap" operation that was done hundreds of time at > every iteration of Kmeans: > https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala#L295 > It would work just as well by performing the following "foreach" on the RDD, > and would slightly improve perfomance. > Edit: -- 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] [Updated] (SPARK-21268) Move some calculations to a distributed map in KMeans
[ https://issues.apache.org/jira/browse/SPARK-21268?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Guillaume Dardelet updated SPARK-21268: --- Description: As I was monitoring the perfomance of my algorithm with SparkUI, I noticed that their was a "collectAsMap" operation that was done hundreds of time at every iteration of Kmeans: https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala#L295 It would work just as well by performing the following "foreach" on the RDD, and would slightly improve perfomance. Edit: was: As I was monitoring the perfomance of my algorithm with SparkUI, I noticed that their was a "collectAsMap" operation that was done hundreds of time at every iteration of Kmeans: https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala#L295 It would work just as well by performing the following "foreach" on the RDD, and would slightly improve perfomance. Thoughts ? > Move some calculations to a distributed map in KMeans > - > > Key: SPARK-21268 > URL: https://issues.apache.org/jira/browse/SPARK-21268 > Project: Spark > Issue Type: Improvement > Components: MLlib >Affects Versions: 2.0.0, 2.0.1, 2.0.2, 2.1.0, 2.1.1 >Reporter: Guillaume Dardelet >Priority: Trivial > Labels: beginner, easyfix, newbie > Original Estimate: 1h > Remaining Estimate: 1h > > As I was monitoring the perfomance of my algorithm with SparkUI, I noticed > that their was a "collectAsMap" operation that was done hundreds of time at > every iteration of Kmeans: > https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala#L295 > It would work just as well by performing the following "foreach" on the RDD, > and would slightly improve perfomance. > Edit: -- 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] [Updated] (SPARK-21268) Move some calculations to a distributed map in KMeans
[ https://issues.apache.org/jira/browse/SPARK-21268?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Guillaume Dardelet updated SPARK-21268: --- Summary: Move some calculations to a distributed map in KMeans (was: Redundant collectAsMap in KMeans) > Move some calculations to a distributed map in KMeans > - > > Key: SPARK-21268 > URL: https://issues.apache.org/jira/browse/SPARK-21268 > Project: Spark > Issue Type: Improvement > Components: MLlib >Affects Versions: 2.0.0, 2.0.1, 2.0.2, 2.1.0, 2.1.1 >Reporter: Guillaume Dardelet >Priority: Trivial > Labels: beginner, easyfix, newbie > Original Estimate: 1h > Remaining Estimate: 1h > > As I was monitoring the perfomance of my algorithm with SparkUI, I noticed > that their was a "collectAsMap" operation that was done hundreds of time at > every iteration of Kmeans: > https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala#L295 > It would work just as well by performing the following "foreach" on the RDD, > and would slightly improve perfomance. > Thoughts ? -- 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] [Commented] (SPARK-21268) Redundant collectAsMap in KMeans
[ https://issues.apache.org/jira/browse/SPARK-21268?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16070177#comment-16070177 ] Guillaume Dardelet commented on SPARK-21268: Ok I understand why it is useful now thank you. As for performing the scale and newCenter creation, should I create a new issue or reference this one in the pull request ? > Redundant collectAsMap in KMeans > > > Key: SPARK-21268 > URL: https://issues.apache.org/jira/browse/SPARK-21268 > Project: Spark > Issue Type: Improvement > Components: MLlib >Affects Versions: 2.0.0, 2.0.1, 2.0.2, 2.1.0, 2.1.1 >Reporter: Guillaume Dardelet >Priority: Trivial > Labels: beginner, easyfix, newbie > Original Estimate: 1h > Remaining Estimate: 1h > > As I was monitoring the perfomance of my algorithm with SparkUI, I noticed > that their was a "collectAsMap" operation that was done hundreds of time at > every iteration of Kmeans: > https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala#L295 > It would work just as well by performing the following "foreach" on the RDD, > and would slightly improve perfomance. > Thoughts ? -- 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] [Created] (SPARK-21268) Redundant collectAsMap in KMeans
Guillaume Dardelet created SPARK-21268: -- Summary: Redundant collectAsMap in KMeans Key: SPARK-21268 URL: https://issues.apache.org/jira/browse/SPARK-21268 Project: Spark Issue Type: Improvement Components: MLlib Affects Versions: 2.1.1, 2.1.0, 2.0.2, 2.0.1, 2.0.0 Reporter: Guillaume Dardelet Priority: Trivial As I was monitoring the perfomance of my algorithm with SparkUI, I noticed that their was a "collectAsMap" operation that was done hundreds of time at every iteration of Kmeans: https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala#L295 It would work just as well by performing the following "foreach" on the RDD, and would slightly improve perfomance. Thoughts ? -- 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] [Comment Edited] (SPARK-12606) Scala/Java compatibility issue Re: how to extend java transformer from Scala UnaryTransformer ?
[
https://issues.apache.org/jira/browse/SPARK-12606?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15955191#comment-15955191
]
Guillaume Dardelet edited comment on SPARK-12606 at 6/1/17 9:55 AM:
I had the same issue in Scala and I solved it by overloading the constructor so
that it initialises the UID.
The error comes from the initialisation of the parameter "inputCol".
You get "null__inputCol" because when the parameter was initialised, your class
didn't have a uid.
Therefore, instead of
{code}
class Lemmatizer extends UnaryTransformer[String, String, Lemmatizer] {
override val uid: String = Identifiable.randomUID("lemmatizer")
protected def createTransformFunc: String => String = ???
protected def outputDataType: DataType = StringType
}
{code}
Do this:
{code}
class Lemmatizer(override val uid: String) extends UnaryTransformer[String,
String, Lemmatizer] {
def this() = this( Identifiable.randomUID("lemmatizer") )
protected def createTransformFunc: String => String = ???
protected def outputDataType: DataType = StringType
}
{code}
was (Author: panoramix):
I had the same issue in Scala and I solved it by overloading the constructor so
that it initialises the UID.
The error comes from the initialisation of the parameter "inputCol".
You get "null__inputCol" because when the parameter was initialised, your class
didn't have a uid.
Therefore, instead of
{code}
class Lemmatizer extends UnaryTransformer[String, String, Lemmatizer] {
override val uid: String = Identifiable.randomUID("lemmatizer")
protected def createTransformFunc: String) => String = ???
protected def outputDataType: DataType = StringType
}
{code}
Do this:
{code}
class Lemmatizer(override val uid: String) extends UnaryTransformer[String,
String, Lemmatizer] {
def this() = this( Identifiable.randomUID("lemmatizer") )
protected def createTransformFunc: String) => String = ???
protected def outputDataType: DataType = StringType
}
{code}
> Scala/Java compatibility issue Re: how to extend java transformer from Scala
> UnaryTransformer ?
> ---
>
> Key: SPARK-12606
> URL: https://issues.apache.org/jira/browse/SPARK-12606
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.5.2
> Environment: Java 8, Mac OS, Spark-1.5.2
>Reporter: Andrew Davidson
> Labels: transformers
>
> Hi Andy,
> I suspect that you hit the Scala/Java compatibility issue, I can also
> reproduce this issue, so could you file a JIRA to track this issue?
> Yanbo
> 2016-01-02 3:38 GMT+08:00 Andy Davidson :
> I am trying to write a trivial transformer I use use in my pipeline. I am
> using java and spark 1.5.2. It was suggested that I use the Tokenize.scala
> class as an example. This should be very easy how ever I do not understand
> Scala, I am having trouble debugging the following exception.
> Any help would be greatly appreciated.
> Happy New Year
> Andy
> java.lang.IllegalArgumentException: requirement failed: Param null__inputCol
> does not belong to Stemmer_2f3aa96d-7919-4eaa-ad54-f7c620b92d1c.
> at scala.Predef$.require(Predef.scala:233)
> at org.apache.spark.ml.param.Params$class.shouldOwn(params.scala:557)
> at org.apache.spark.ml.param.Params$class.set(params.scala:436)
> at org.apache.spark.ml.PipelineStage.set(Pipeline.scala:37)
> at org.apache.spark.ml.param.Params$class.set(params.scala:422)
> at org.apache.spark.ml.PipelineStage.set(Pipeline.scala:37)
> at
> org.apache.spark.ml.UnaryTransformer.setInputCol(Transformer.scala:83)
> at com.pws.xxx.ml.StemmerTest.test(StemmerTest.java:30)
> public class StemmerTest extends AbstractSparkTest {
> @Test
> public void test() {
> Stemmer stemmer = new Stemmer()
> .setInputCol("raw”) //line 30
> .setOutputCol("filtered");
> }
> }
> /**
> * @ see
> spark-1.5.1/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
> * @ see
> https://chimpler.wordpress.com/2014/06/11/classifiying-documents-using-naive-bayes-on-apache-spark-mllib/
> * @ see
> http://www.tonytruong.net/movie-rating-prediction-with-apache-spark-and-hortonworks/
> *
> * @author andrewdavidson
> *
> */
> public class Stemmer extends UnaryTransformer, List,
> Stemmer> implements Serializable{
> static Logger logger = LoggerFactory.getLogger(Stemmer.class);
> private static final long serialVersionUID = 1L;
> private static final ArrayType inputType =
> DataTypes.createArrayType(DataTypes.StringType, true);
> private final String uid = Stemmer.class.getSimpleName() + "_" +
> UUID.randomUUID().toString();
> @Override
> public
[jira] [Comment Edited] (SPARK-12606) Scala/Java compatibility issue Re: how to extend java transformer from Scala UnaryTransformer ?
[
https://issues.apache.org/jira/browse/SPARK-12606?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15955191#comment-15955191
]
Guillaume Dardelet edited comment on SPARK-12606 at 4/4/17 2:27 PM:
I had the same issue in Scala and I solved it by overloading the constructor so
that it initialises the UID.
The error comes from the initialisation of the parameter "inputCol".
You get "null__inputCol" because when the parameter was initialised, your class
didn't have a uid.
Therefore, instead of
{code:scala}
class Lemmatizer extends UnaryTransformer[String, String, Lemmatizer] {
override val uid: String = Identifiable.randomUID("lemmatizer")
protected def createTransformFunc: String) => String = ???
protected def outputDataType: DataType = StringType
}
{code}
Do this:
class Lemmatizer(override val uid: String) extends UnaryTransformer[String,
String, Lemmatizer] {
def this() = this( Identifiable.randomUID("lemmatizer") )
protected def createTransformFunc: String) => String = ???
protected def outputDataType: DataType = StringType
}
was (Author: panoramix):
I had the same issue in Scala and I solved it by overloading the constructor so
that it initialises the UID.
The error comes from the initialisation of the parameter "inputCol".
You get "null__inputCol" because when the parameter was initialised, your class
didn't have a uid.
Therefore, instead of
class Lemmatizer extends UnaryTransformer[String, String, Lemmatizer] {
override val uid: String = Identifiable.randomUID("lemmatizer")
protected def createTransformFunc: String) => String = ???
protected def outputDataType: DataType = StringType
}
Do this:
class Lemmatizer(override val uid: String) extends UnaryTransformer[String,
String, Lemmatizer] {
def this() = this( Identifiable.randomUID("lemmatizer") )
protected def createTransformFunc: String) => String = ???
protected def outputDataType: DataType = StringType
}
> Scala/Java compatibility issue Re: how to extend java transformer from Scala
> UnaryTransformer ?
> ---
>
> Key: SPARK-12606
> URL: https://issues.apache.org/jira/browse/SPARK-12606
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.5.2
> Environment: Java 8, Mac OS, Spark-1.5.2
>Reporter: Andrew Davidson
> Labels: transformers
>
> Hi Andy,
> I suspect that you hit the Scala/Java compatibility issue, I can also
> reproduce this issue, so could you file a JIRA to track this issue?
> Yanbo
> 2016-01-02 3:38 GMT+08:00 Andy Davidson :
> I am trying to write a trivial transformer I use use in my pipeline. I am
> using java and spark 1.5.2. It was suggested that I use the Tokenize.scala
> class as an example. This should be very easy how ever I do not understand
> Scala, I am having trouble debugging the following exception.
> Any help would be greatly appreciated.
> Happy New Year
> Andy
> java.lang.IllegalArgumentException: requirement failed: Param null__inputCol
> does not belong to Stemmer_2f3aa96d-7919-4eaa-ad54-f7c620b92d1c.
> at scala.Predef$.require(Predef.scala:233)
> at org.apache.spark.ml.param.Params$class.shouldOwn(params.scala:557)
> at org.apache.spark.ml.param.Params$class.set(params.scala:436)
> at org.apache.spark.ml.PipelineStage.set(Pipeline.scala:37)
> at org.apache.spark.ml.param.Params$class.set(params.scala:422)
> at org.apache.spark.ml.PipelineStage.set(Pipeline.scala:37)
> at
> org.apache.spark.ml.UnaryTransformer.setInputCol(Transformer.scala:83)
> at com.pws.xxx.ml.StemmerTest.test(StemmerTest.java:30)
> public class StemmerTest extends AbstractSparkTest {
> @Test
> public void test() {
> Stemmer stemmer = new Stemmer()
> .setInputCol("raw”) //line 30
> .setOutputCol("filtered");
> }
> }
> /**
> * @ see
> spark-1.5.1/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
> * @ see
> https://chimpler.wordpress.com/2014/06/11/classifiying-documents-using-naive-bayes-on-apache-spark-mllib/
> * @ see
> http://www.tonytruong.net/movie-rating-prediction-with-apache-spark-and-hortonworks/
> *
> * @author andrewdavidson
> *
> */
> public class Stemmer extends UnaryTransformer, List,
> Stemmer> implements Serializable{
> static Logger logger = LoggerFactory.getLogger(Stemmer.class);
> private static final long serialVersionUID = 1L;
> private static final ArrayType inputType =
> DataTypes.createArrayType(DataTypes.StringType, true);
> private final String uid = Stemmer.class.getSimpleName() + "_" +
> UUID.randomUUID().toString();
> @Override
> public String uid() {
> return u
[jira] [Comment Edited] (SPARK-12606) Scala/Java compatibility issue Re: how to extend java transformer from Scala UnaryTransformer ?
[
https://issues.apache.org/jira/browse/SPARK-12606?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15955191#comment-15955191
]
Guillaume Dardelet edited comment on SPARK-12606 at 4/4/17 2:27 PM:
I had the same issue in Scala and I solved it by overloading the constructor so
that it initialises the UID.
The error comes from the initialisation of the parameter "inputCol".
You get "null__inputCol" because when the parameter was initialised, your class
didn't have a uid.
Therefore, instead of
{code}
class Lemmatizer extends UnaryTransformer[String, String, Lemmatizer] {
override val uid: String = Identifiable.randomUID("lemmatizer")
protected def createTransformFunc: String) => String = ???
protected def outputDataType: DataType = StringType
}
{code}
Do this:
{code}
class Lemmatizer(override val uid: String) extends UnaryTransformer[String,
String, Lemmatizer] {
def this() = this( Identifiable.randomUID("lemmatizer") )
protected def createTransformFunc: String) => String = ???
protected def outputDataType: DataType = StringType
}
{code}
was (Author: panoramix):
I had the same issue in Scala and I solved it by overloading the constructor so
that it initialises the UID.
The error comes from the initialisation of the parameter "inputCol".
You get "null__inputCol" because when the parameter was initialised, your class
didn't have a uid.
Therefore, instead of
{code:scala}
class Lemmatizer extends UnaryTransformer[String, String, Lemmatizer] {
override val uid: String = Identifiable.randomUID("lemmatizer")
protected def createTransformFunc: String) => String = ???
protected def outputDataType: DataType = StringType
}
{code}
Do this:
class Lemmatizer(override val uid: String) extends UnaryTransformer[String,
String, Lemmatizer] {
def this() = this( Identifiable.randomUID("lemmatizer") )
protected def createTransformFunc: String) => String = ???
protected def outputDataType: DataType = StringType
}
> Scala/Java compatibility issue Re: how to extend java transformer from Scala
> UnaryTransformer ?
> ---
>
> Key: SPARK-12606
> URL: https://issues.apache.org/jira/browse/SPARK-12606
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.5.2
> Environment: Java 8, Mac OS, Spark-1.5.2
>Reporter: Andrew Davidson
> Labels: transformers
>
> Hi Andy,
> I suspect that you hit the Scala/Java compatibility issue, I can also
> reproduce this issue, so could you file a JIRA to track this issue?
> Yanbo
> 2016-01-02 3:38 GMT+08:00 Andy Davidson :
> I am trying to write a trivial transformer I use use in my pipeline. I am
> using java and spark 1.5.2. It was suggested that I use the Tokenize.scala
> class as an example. This should be very easy how ever I do not understand
> Scala, I am having trouble debugging the following exception.
> Any help would be greatly appreciated.
> Happy New Year
> Andy
> java.lang.IllegalArgumentException: requirement failed: Param null__inputCol
> does not belong to Stemmer_2f3aa96d-7919-4eaa-ad54-f7c620b92d1c.
> at scala.Predef$.require(Predef.scala:233)
> at org.apache.spark.ml.param.Params$class.shouldOwn(params.scala:557)
> at org.apache.spark.ml.param.Params$class.set(params.scala:436)
> at org.apache.spark.ml.PipelineStage.set(Pipeline.scala:37)
> at org.apache.spark.ml.param.Params$class.set(params.scala:422)
> at org.apache.spark.ml.PipelineStage.set(Pipeline.scala:37)
> at
> org.apache.spark.ml.UnaryTransformer.setInputCol(Transformer.scala:83)
> at com.pws.xxx.ml.StemmerTest.test(StemmerTest.java:30)
> public class StemmerTest extends AbstractSparkTest {
> @Test
> public void test() {
> Stemmer stemmer = new Stemmer()
> .setInputCol("raw”) //line 30
> .setOutputCol("filtered");
> }
> }
> /**
> * @ see
> spark-1.5.1/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
> * @ see
> https://chimpler.wordpress.com/2014/06/11/classifiying-documents-using-naive-bayes-on-apache-spark-mllib/
> * @ see
> http://www.tonytruong.net/movie-rating-prediction-with-apache-spark-and-hortonworks/
> *
> * @author andrewdavidson
> *
> */
> public class Stemmer extends UnaryTransformer, List,
> Stemmer> implements Serializable{
> static Logger logger = LoggerFactory.getLogger(Stemmer.class);
> private static final long serialVersionUID = 1L;
> private static final ArrayType inputType =
> DataTypes.createArrayType(DataTypes.StringType, true);
> private final String uid = Stemmer.class.getSimpleName() + "_" +
> UUID.randomUUID().toString();
> @Override
> public Strin
[jira] [Commented] (SPARK-12606) Scala/Java compatibility issue Re: how to extend java transformer from Scala UnaryTransformer ?
[
https://issues.apache.org/jira/browse/SPARK-12606?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15955191#comment-15955191
]
Guillaume Dardelet commented on SPARK-12606:
I had the same issue in Scala and I solved it by overloading the constructor so
that it initialises the UID.
The error comes from the initialisation of the parameter "inputCol".
You get "null__inputCol" because when the parameter was initialised, your class
didn't have a uid.
Therefore, instead of
class Lemmatizer extends UnaryTransformer[String, String, Lemmatizer] {
override val uid: String = Identifiable.randomUID("lemmatizer")
protected def createTransformFunc: String) => String = ???
protected def outputDataType: DataType = StringType
}
Do this:
class Lemmatizer(override val uid: String) extends UnaryTransformer[String,
String, Lemmatizer] {
def this() = this( Identifiable.randomUID("lemmatizer") )
protected def createTransformFunc: String) => String = ???
protected def outputDataType: DataType = StringType
}
> Scala/Java compatibility issue Re: how to extend java transformer from Scala
> UnaryTransformer ?
> ---
>
> Key: SPARK-12606
> URL: https://issues.apache.org/jira/browse/SPARK-12606
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.5.2
> Environment: Java 8, Mac OS, Spark-1.5.2
>Reporter: Andrew Davidson
> Labels: transformers
>
> Hi Andy,
> I suspect that you hit the Scala/Java compatibility issue, I can also
> reproduce this issue, so could you file a JIRA to track this issue?
> Yanbo
> 2016-01-02 3:38 GMT+08:00 Andy Davidson :
> I am trying to write a trivial transformer I use use in my pipeline. I am
> using java and spark 1.5.2. It was suggested that I use the Tokenize.scala
> class as an example. This should be very easy how ever I do not understand
> Scala, I am having trouble debugging the following exception.
> Any help would be greatly appreciated.
> Happy New Year
> Andy
> java.lang.IllegalArgumentException: requirement failed: Param null__inputCol
> does not belong to Stemmer_2f3aa96d-7919-4eaa-ad54-f7c620b92d1c.
> at scala.Predef$.require(Predef.scala:233)
> at org.apache.spark.ml.param.Params$class.shouldOwn(params.scala:557)
> at org.apache.spark.ml.param.Params$class.set(params.scala:436)
> at org.apache.spark.ml.PipelineStage.set(Pipeline.scala:37)
> at org.apache.spark.ml.param.Params$class.set(params.scala:422)
> at org.apache.spark.ml.PipelineStage.set(Pipeline.scala:37)
> at
> org.apache.spark.ml.UnaryTransformer.setInputCol(Transformer.scala:83)
> at com.pws.xxx.ml.StemmerTest.test(StemmerTest.java:30)
> public class StemmerTest extends AbstractSparkTest {
> @Test
> public void test() {
> Stemmer stemmer = new Stemmer()
> .setInputCol("raw”) //line 30
> .setOutputCol("filtered");
> }
> }
> /**
> * @ see
> spark-1.5.1/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
> * @ see
> https://chimpler.wordpress.com/2014/06/11/classifiying-documents-using-naive-bayes-on-apache-spark-mllib/
> * @ see
> http://www.tonytruong.net/movie-rating-prediction-with-apache-spark-and-hortonworks/
> *
> * @author andrewdavidson
> *
> */
> public class Stemmer extends UnaryTransformer, List,
> Stemmer> implements Serializable{
> static Logger logger = LoggerFactory.getLogger(Stemmer.class);
> private static final long serialVersionUID = 1L;
> private static final ArrayType inputType =
> DataTypes.createArrayType(DataTypes.StringType, true);
> private final String uid = Stemmer.class.getSimpleName() + "_" +
> UUID.randomUUID().toString();
> @Override
> public String uid() {
> return uid;
> }
> /*
>override protected def validateInputType(inputType: DataType): Unit = {
> require(inputType == StringType, s"Input type must be string type but got
> $inputType.")
> }
> */
> @Override
> public void validateInputType(DataType inputTypeArg) {
> String msg = "inputType must be " + inputType.simpleString() + " but
> got " + inputTypeArg.simpleString();
> assert (inputType.equals(inputTypeArg)) : msg;
> }
>
> @Override
> public Function1, List> createTransformFunc() {
> //
> http://stackoverflow.com/questions/6545066/using-scala-from-java-passing-functions-as-parameters
> Function1, List> f = new
> AbstractFunction1, List>() {
> public List apply(List words) {
> for(String word : words) {
> logger.error("AEDWIP input word: {}", word);
> }
> return wor
