Hello Spark fans,
I am trying to use the IDF model available in the spark mllib to create an
tf-idf representation of a n RDD[Vectors]. Below i have attached my MWE
I get the following error
"java.lang.IndexOutOfBoundsException: 7 not in [-4,4)
at breeze.linalg.DenseVector.apply$mcI$sp(DenseVector.scala:70)
at breeze.linalg.DenseVector.apply(DenseVector.scala:69)
at
org.apache.spark.mllib.feature.IDF$DocumentFrequencyAggregator.add(IDF.scala:81)
"
Any ideas?
Regards,
Shivani
import org.apache.spark.mllib.feature.VectorTransformer
import com.box.analytics.ml.dms.vector.{SparkSparseVector,SparkDenseVector}
import org.apache.spark.mllib.linalg.{DenseVector => SDV, SparseVector =>
SSV}
import org.apache.spark.mllib.linalg.{Vector => SparkVector}
import org.apache.spark.mllib.linalg.distributed.{IndexedRow,
IndexedRowMatrix}
import org.apache.spark.mllib.feature._
val doc1s = new IndexedRow(1L, new SSV(4, Array(1, 3, 5, 7),Array(1.0,
1.0, 0.0, 5.0)))
val doc2s = new IndexedRow(2L, new SSV(4, Array(1, 2, 4, 13),
Array(0.0, 1.0, 2.0, 0.0)))
val doc3s = new IndexedRow(3L, new SSV(4, Array(10, 14, 20,
21),Array(2.0, 0.0, 2.0, 1.0)))
val doc4s = new IndexedRow(4L, new SSV(4, Array(3, 7, 13,
20),Array(2.0, 0.0, 2.0, 1.0)))
val indata = sc.parallelize(List(doc1s,doc2s,doc3s,doc4s)).map(e=>e.vector)
(new IDF()).fit(indata).idf
--
Software Engineer
Analytics Engineering Team@ Box
Mountain View, CA
