[GitHub] spark pull request #19381: [SPARK-10884][ML] Support prediction on single in...

[smurching <gi...@git.apache.org>]

Github user smurching commented on a diff in the pull request:

    https://github.com/apache/spark/pull/19381#discussion_r148708939
  
    --- Diff: mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala ---
    @@ -165,6 +165,35 @@ class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext with Defa
           Vector, NaiveBayesModel](model, testDataset)
       }
     
    +  test("prediction on single instance") {
    +    val nPoints = 1000
    +    val piArray = Array(0.5, 0.1, 0.4).map(math.log)
    +    val thetaArray = Array(
    +      Array(0.70, 0.10, 0.10, 0.10), // label 0
    +      Array(0.10, 0.70, 0.10, 0.10), // label 1
    +      Array(0.10, 0.10, 0.70, 0.10)  // label 2
    +    ).map(_.map(math.log))
    +    val pi = Vectors.dense(piArray)
    +    val theta = new DenseMatrix(3, 4, thetaArray.flatten, true)
    +
    +    val testDataset =
    +      generateNaiveBayesInput(piArray, thetaArray, nPoints, seed, "multinomial").toDF()
    +    val nb = new NaiveBayes().setSmoothing(1.0).setModelType("multinomial")
    +    val model = nb.fit(testDataset)
    +
    +    validateModelFit(pi, theta, model)
    --- End diff --
    
    Do we need lines 184-186? They seem unrelated to what we want to test (that `predict` produces the same result as `transform` on a single instance). Similarly, I don't think we need to create `piArray`, `thetaArray`, `pi`, `theta`, etc; this test should just fit a model on a dataset and compare the fitted model's `predict` and `transform` outputs.


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