I am running following code on Spark 1.3.0. It is from
https://spark.apache.org/docs/1.3.0/ml-guide.html
On running val model1 = lr.fit(training.toDF) I get
java.lang.UnsupportedOperationException: empty collection
what could be the reason?
----------------
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.sql.{Row, SQLContext}
val conf = new SparkConf().setAppName("SimpleParamsExample")
val sc = new SparkContext(conf)
val sqlContext = new SQLContext(sc)
import sqlContext.implicits._
// Prepare training data.
// We use LabeledPoint, which is a case class. Spark SQL can convert RDDs
of case classes
// into DataFrames, where it uses the case class metadata to infer the
schema.
val training = sc.parallelize(Seq(
LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))))
// Create a LogisticRegression instance. This instance is an Estimator.
val lr = new LogisticRegression()
// Print out the parameters, documentation, and any default values.
println("LogisticRegression parameters:\n" + lr.explainParams() + "\n")
// We may set parameters using setter methods.
lr.setMaxIter(10)
.setRegParam(0.01)
// Learn a LogisticRegression model. This uses the parameters stored in lr.
*val model1 = lr.fit(training.toDF)*
*Some more information:*
scala> training.toDF
res26: org.apache.spark.sql.DataFrame = [label: double, features: vecto]
scala> training.toDF.collect()
res27: Array[org.apache.spark.sql.Row] = Array([1.0,[0.0,1.1,0.1]],
[0.0,[2.0,1.0,-1.0]], [0.0,[2.0,1.3,1.0]], [1.0,[0.0,1.2,-0.5]])
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