srowen commented on a change in pull request #26413: [SPARK-16872][ML][PYSPARK]
Impl Gaussian Naive Bayes Classifier
URL: https://github.com/apache/spark/pull/26413#discussion_r344249771
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File path:
mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
##########
@@ -204,7 +224,80 @@ class NaiveBayes @Since("1.5.0") (
val pi = Vectors.dense(piArray)
val theta = new DenseMatrix(numLabels, numFeatures, thetaArray, true)
- new NaiveBayesModel(uid, pi, theta).setOldLabels(labelArray)
+ new NaiveBayesModel(uid, pi.compressed, theta.compressed, null)
+ .setOldLabels(labelArray)
+ }
+
+ private def trainGaussianImpl(
+ dataset: Dataset[_],
+ instr: Instrumentation): NaiveBayesModel = {
+ val spark = dataset.sparkSession
+ import spark.implicits._
+
+ val w = if (isDefined(weightCol) && $(weightCol).nonEmpty) {
+ col($(weightCol)).cast(DoubleType)
+ } else {
+ lit(1.0)
+ }
+
+ // Aggregates mean vector and square-sum vector per label.
+ // TODO: Summarizer directly returns square-sum vector.
+ val aggregated = dataset.groupBy(col($(labelCol)))
+ .agg(sum(w).as("weightSum"), Summarizer.metrics("mean", "normL2")
+ .summary(col($(featuresCol)), w).as("summary"))
+ .select($(labelCol), "weightSum", "summary.mean", "summary.normL2")
+ .as[(Double, Double, Vector, Vector)]
+ .map { case (label, weightSum, mean, normL2) =>
+ (label, weightSum, mean, Vectors.dense(normL2.toArray.map(v => v * v)))
+ }.collect().sortBy(_._1)
+
+ val numFeatures = aggregated.head._3.size
+ instr.logNumFeatures(numFeatures)
+
+ val numLabels = aggregated.length
+ instr.logNumClasses(numLabels)
+
+ val numInstances = aggregated.map(_._2).sum
+
+ // If the ratio of data variance between dimensions is too small, it
+ // will cause numerical errors. To address this, we artificially
+ // boost the variance by epsilon, a small fraction of the standard
+ // deviation of the largest dimension.
+ // Refer to scikit-learn's implementation
+ //
[https://github.com/scikit-learn/scikit-learn/blob/0.21.X/sklearn/naive_bayes.py#L348]
+ // and discussion [https://github.com/scikit-learn/scikit-learn/pull/5349]
for detail.
+ val epsilon = Iterator.range(0, numFeatures).map { j =>
+ val globalSum = aggregated.map(t => t._3(j) * t._2).sum
Review comment:
It doesn't matter much, but in a few cases around here it might be clearer
to use `case (_, x, y, _)` or whatever to name the variables in the expression
rather than `._2` etc
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