As the libsvm format can't specify number of features, and looks like
NaiveBayes doesn't have such parameter, if your training/testing data is
sparse, the number of features inferred from the data files can be
inconsistent.
We may need to fix this.
Before a fixing going into NaiveBayes, currently a workaround is to align
the number of features between training and testing data before fitting the
model.
jinhong lu wrote
> 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@
> > 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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Liang-Chi Hsieh | @viirya
Spark Technology Center
http://www.spark.tc/
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