Just found that you can specify number of features when loading libsvm
source:
val df = spark.read.option("numFeatures", "100").format("libsvm")
Liang-Chi Hsieh wrote
> 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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