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 --------------------------------------------------------------------- To unsubscribe e-mail: dev-unsubscr...@spark.apache.org