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From srowen <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-4362] [MLLIB] Make prediction probabili...
Date Tue, 30 Jun 2015 16:38:15 GMT
Github user srowen commented on the pull request:

    https://github.com/apache/spark/pull/6761#issuecomment-117248710
  
    Here is roughly the code to compute expected class probabilities for a given piece of
input:
    
    ```
      def expectedMultinomialProbabilities(model: NaiveBayesModel, testData: Vector) = {
        val piVector = new BDV(model.pi)
        // model.labels is row-major; treat it as col-major representation of transpose, and
transpose:
        val thetaMatrix = new BDM(model.theta(0).length, model.theta.length, model.theta.flatten).t
        val logClassProbs = piVector + (thetaMatrix * testData.toBreeze)
        logClassProbs.toArray.map(math.exp)
      }
    
      def expectedBernoulliProbabilities(model: NaiveBayesModel, testData: Vector) = {
        val piVector = new BDV(model.pi)
        val thetaMatrix = new BDM(model.theta(0).length, model.theta.length, model.theta.flatten).t
        val negThetaMatrix = new BDM(model.theta(0).length, model.theta.length, model.theta.flatten.map(v
=> math.log(1.0 - math.exp(v)))).t
        val testBreeze = testData.toBreeze
        val negTestBreeze = new BDV(Array.fill(testBreeze.size)(1.0)) - testBreeze
        val logClassProbs = piVector + (thetaMatrix * testBreeze) + (negThetaMatrix * negTestBreeze)
        logClassProbs.toArray.map(math.exp)
      }
    ```
    
    I intentionally approached it differently from the implementation in `NaiveBayesModel`;
this is probably more straightforward but less performant, but that's the point of a cross-check
in a test I suppose.
    I haven't tested it; maybe I can test my test tomorrow. It should at least compile and
be 90+% there.


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