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....@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!
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