Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/4087#discussion_r26169291
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala
---
@@ -85,19 +90,87 @@ class NaiveBayesSuite extends FunSuite with
MLlibTestSparkContext {
assert(numOfPredictions < input.length / 5)
}
- test("Naive Bayes") {
- val nPoints = 10000
+ def validateModelFit(
+ piData: Array[Double],
+ thetaData: Array[Array[Double]],
+ model: NaiveBayesModel) = {
+ def closeFit(d1: Double, d2: Double, precision: Double): Boolean = {
+ (d1 - d2).abs <= precision
+ }
+ val modelIndex = (0 until piData.length).zip(model.labels.map(_.toInt))
+ for (i <- modelIndex) {
+ assert(closeFit(math.exp(piData(i._2)), math.exp(model.pi(i._1)),
0.05))
+ }
+ for (i <- modelIndex) {
+ for (j <- 0 until thetaData(i._2).length) {
+ assert(closeFit(math.exp(thetaData(i._2)(j)),
math.exp(model.theta(i._1)(j)), 0.05))
+ }
+ }
+ }
- val pi = NaiveBayesSuite.smallPi
- val theta = NaiveBayesSuite.smallTheta
+ test("Naive Bayes Multinomial") {
+ val nPoints = 1000
+ val pi = Array(0.5, 0.1, 0.4).map(math.log)
+ val theta = 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 testData = NaiveBayesSuite.generateNaiveBayesInput(
+ pi,
--- End diff --
For function calls, you can put multiple parameters in 1 line. (here and
below)
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