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 <lujinho...@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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