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
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