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From jinhong lu <lujinho...@gmail.com>
Subject Re: how to construct parameter for model.transform() from datafile
Date Mon, 13 Mar 2017 11:38:35 GMT
After train the mode, I got the result look like this:


	scala>  predictionResult.show()
	+-----+--------------------+--------------------+--------------------+----------+
	|label|            features|       rawPrediction|         probability|prediction|
	+-----+--------------------+--------------------+--------------------+----------+
	|  0.0|(144109,[100],[2.0])|[-12.246737725034...|[0.96061209556737...|       0.0|
	|  0.0|(144109,[100],[2.0])|[-12.246737725034...|[0.96061209556737...|       0.0|
	|  0.0|(144109,[100],[24...|[-146.81612388602...|[9.73704654529197...|       1.0|

And then, I transform() the data by these code:

	import org.apache.spark.ml.linalg.Vectors
	import org.apache.spark.ml.linalg.Vector
	import scala.collection.mutable

	   def lineToVector(line:String ):Vector={
	    val seq = new mutable.Queue[(Int,Double)]
	    val content = line.split(" ");
	    for( s <- content){
	      val index = s.split(":")(0).toInt
	      val value = s.split(":")(1).toDouble
	       seq += ((index,value))
	    }
	    return Vectors.sparse(144109, seq)
	  }

	 val df = sc.sequenceFile[org.apache.hadoop.io.LongWritable, org.apache.hadoop.io.Text]("/data/gamein/gameall_sdc/wh/gameall.db/edt_udid_label_format/ds=20170312/001006_0").map(line=>line._2).map(line
=> (line.toString.split("\t")(0),lineToVector(line.toString.split("\t")(1)))).toDF("udid",
"features")
	 val predictionResult = model.transform(df)
	 predictionResult.show()


But I got the error look like this:

 Caused by: java.lang.IllegalArgumentException: requirement failed: You may not write an element
to index 804201 because the declared size of your vector is 144109
  at scala.Predef$.require(Predef.scala:224)
  at org.apache.spark.ml.linalg.Vectors$.sparse(Vectors.scala:219)
  at lineToVector(<console>:55)
  at $anonfun$4.apply(<console>:50)
  at $anonfun$4.apply(<console>:50)
  at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
  at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
  at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
  at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(generated.java:84)
  at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
  at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:246)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:240)
  at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803)
  at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803)

So I change    

 	return Vectors.sparse(144109, seq)

to 

	return Vectors.sparse(804202, seq)

Another error occurs:

	Caused by: java.lang.IllegalArgumentException: requirement failed: The columns of A don't
match the number of elements of x. A: 144109, x: 804202
	  at scala.Predef$.require(Predef.scala:224)
	  at org.apache.spark.ml.linalg.BLAS$.gemv(BLAS.scala:521)
	  at org.apache.spark.ml.linalg.Matrix$class.multiply(Matrices.scala:110)
	  at org.apache.spark.ml.linalg.DenseMatrix.multiply(Matrices.scala:176)

what should I do?
> 在 2017年3月13日,16:31,jinhong lu <lujinhong2@gmail.com> 写道:
> 
> Hi, all:
> 
> I got these training data:
> 
> 	0 31607:17
> 	0 111905:36
> 	0 109:3 506:41 1509:1 2106:4 5309:1 7209:5 8406:1 27108:1 27709:1 30209:8 36109:20 41408:1
42309:1 46509:1 47709:5 57809:1 58009:1 58709:2 112109:4 123305:48 142509:1
> 	0 407:14 2905:2 5209:2 6509:2 6909:2 14509:2 18507:10
> 	0 604:3 3505:9 6401:3 6503:2 6505:3 7809:8 10509:3 12109:3 15207:19 31607:19
> 	0 19109:7 29705:4 123305:32
> 	0 15309:1 43005:1 108509:1
> 	1 604:1 6401:1 6503:1 15207:4 31607:40
> 	0 1807:19
> 	0 301:14 501:1 1502:14 2507:12 123305:4
> 	0 607:14 19109:460 123305:448
> 	0 5406:14 7209:4 10509:3 19109:6 24706:10 26106:4 31409:1 123305:48 128209:1
> 	1 1606:1 2306:3 3905:19 4408:3 4506:8 8707:3 19109:50 24809:1 26509:2 27709:2 56509:8
122705:62 123305:31 124005:2
> 
> And then I train the model by spark:
> 
> 	import org.apache.spark.ml.classification.NaiveBayes
> 	import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
> 	import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
> 	import org.apache.spark.sql.SparkSession
> 
> 	val spark = SparkSession.builder.appName("NaiveBayesExample").getOrCreate()
> 	val data = spark.read.format("libsvm").load("/tmp/ljhn1829/aplus/training_data3")
> 	val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3), seed = 1234L)
> 	//val model = new NaiveBayes().fit(trainingData)
> 	val model = new NaiveBayes().setThresholds(Array(10.0,1.0)).fit(trainingData)
> 	val predictions = model.transform(testData)
> 	predictions.show()
> 
> 
> OK, I have got my model by the cole above, but how can I use this model to predict the
classfication of other data like these:
> 
> 	ID1	509:2 5102:4 25909:1 31709:4 121905:19
> 	ID2	800201:1
> 	ID3	116005:4
> 	ID4	800201:1
> 	ID5	19109:1  21708:1 23208:1 49809:1 88609:1
> 	ID6	800201:1
> 	ID7	43505:7 106405:7
> 
> I know I can use the transform() method, but how to contrust the parameter for transform()
method?
> 
> 
> 
> 
> 
> Thanks,
> lujinhong
> 

Thanks,
lujinhong


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