Thanks for the response Yanbo. Here is the source (it uses the
sample_libsvm_data.txt file used in the
mlliv examples).
-Raj
————— IOTest.scala -------------
import org.apache.spark.{SparkConf,SparkContext}
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.DataFrame
object IOTest {
val InputFile = "/tmp/sample_libsvm_data.txt"
val OutputDir ="/tmp/out"
val sconf = new SparkConf().setAppName("test").setMaster("local[*]")
val sqlc = new SQLContext( new SparkContext( sconf ))
val df = sqlc.read.format("libsvm").load( InputFile )
df.show; df.printSchema
df.write.format("json").mode("overwrite").save( OutputDir )
val data = sqlc.read.format("json").load( OutputDir )
data.show; data.printSchema
def main( args: Array[String]):Unit = {}
}
-----------------------
On Feb 26, 2016, at 12:47 AM, Yanbo Liang
<yblia...@gmail.com<mailto:yblia...@gmail.com>> wrote:
Hi Raj,
Could you share your code which can help others to diagnose this issue? Which
version did you use?
I can not reproduce this problem in my environment.
Thanks
Yanbo
2016-02-26 10:49 GMT+08:00 raj.kumar
<raj.ku...@hooklogic.com<mailto:raj.ku...@hooklogic.com>>:
Hi,
I am using mllib. I use the ml vectorization tools to create the vectorized
input dataframe for
the ml/mllib machine-learning models with schema:
root
|-- label: double (nullable = true)
|-- features: vector (nullable = true)
To avoid repeated vectorization, I am trying to save and load this dataframe
using
df.write.format("json").mode("overwrite").save( url )
val data = Spark.sqlc.read.format("json").load( url )
However when I load the dataframe, the newly loaded dataframe has the
following schema:
root
|-- features: struct (nullable = true)
| |-- indices: array (nullable = true)
| | |-- element: long (containsNull = true)
| |-- size: long (nullable = true)
| |-- type: long (nullable = true)
| |-- values: array (nullable = true)
| | |-- element: double (containsNull = true)
|-- label: double (nullable = true)
which the machine-learning models do not recognize.
Is there a way I can save and load this dataframe without the schema
changing.
I assume it has to do with the fact that Vector is not a basic type.
thanks
-Raj
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