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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 11:59:56 GMT
Anyone help?

> 在 2017年3月13日,19:38,jinhong lu <lujinhong2@gmail.com> 写道:
> 
> 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
> 

Thanks,
lujinhong


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