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From Cheng Lian <lian.cs....@gmail.com>
Subject Re: Saving a mllib model in Spark SQL
Date Tue, 20 Jan 2015 21:06:39 GMT
Yeah, as Michael said, I forgot that UDT is not a public API. Xiangrui's 
suggestion makes more sense.

Cheng

On 1/20/15 12:49 PM, Xiangrui Meng wrote:
> You can save the cluster centers as a SchemaRDD of two columns (id:
> Int, center: Array[Double]). When you load it back, you can construct
> the k-means model from its cluster centers. -Xiangrui
>
> On Tue, Jan 20, 2015 at 11:55 AM, Cheng Lian <lian.cs.zju@gmail.com> wrote:
>> This is because KMeanModel is neither a built-in type nor a user defined
>> type recognized by Spark SQL. I think you can write your own UDT version of
>> KMeansModel in this case. You may refer to o.a.s.mllib.linalg.Vector and
>> o.a.s.mllib.linalg.VectorUDT as an example.
>>
>> Cheng
>>
>> On 1/20/15 5:34 AM, Divyansh Jain wrote:
>>
>> Hey people,
>>
>> I have run into some issues regarding saving the k-means mllib model in
>> Spark SQL by converting to a schema RDD. This is what I am doing:
>>
>> case class Model(id: String, model:
>> org.apache.spark.mllib.clustering.KMeansModel)
>>     import sqlContext.createSchemaRDD
>>     val rowRdd = sc.makeRDD(Seq("id", model)).map(p => Model("id", model))
>>
>> This is the error that I get :
>>
>> scala.MatchError: org.apache.spark.mllib.classification.ClassificationModel
>> (of class scala.reflect.internal.Types$TypeRef$anon$6)
>>   at
>> org.apache.spark.sql.catalyst.ScalaReflection$.schemaFor(ScalaReflection.scala:53)
>>   at
>> org.apache.spark.sql.catalyst.ScalaReflection$anonfun$schemaFor$1.apply(ScalaReflection.scala:64)
>>   at
>> org.apache.spark.sql.catalyst.ScalaReflection$anonfun$schemaFor$1.apply(ScalaReflection.scala:62)
>>   at
>> scala.collection.TraversableLike$anonfun$map$1.apply(TraversableLike.scala:244)
>>   at
>> scala.collection.TraversableLike$anonfun$map$1.apply(TraversableLike.scala:244)
>>   at scala.collection.immutable.List.foreach(List.scala:318)
>>   at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
>>   at scala.collection.AbstractTraversable.map(Traversable.scala:105)
>>   at
>> org.apache.spark.sql.catalyst.ScalaReflection$.schemaFor(ScalaReflection.scala:62)
>>   at
>> org.apache.spark.sql.catalyst.ScalaReflection$.schemaFor(ScalaReflection.scala:50)
>>   at
>> org.apache.spark.sql.catalyst.ScalaReflection$.attributesFor(ScalaReflection.scala:44)
>>   at
>> org.apache.spark.sql.execution.ExistingRdd$.fromProductRdd(basicOperators.scala:229)
>>   at org.apache.spark.sql.SQLContext.createSchemaRDD(SQLContext.scala:94)
>>
>> Any help would be appreciated. Thanks!
>>
>>
>>
>>
>>
>>
>>
>> --
>> View this message in context:
>> http://apache-spark-user-list.1001560.n3.nabble.com/Saving-a-mllib-model-in-Spark-SQL-tp21264.html
>> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>>
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