Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/16441#discussion_r95492631
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala ---
@@ -241,19 +261,42 @@ class GBTClassificationModel private[ml](
}
override protected def predict(features: Vector): Double = {
- // TODO: When we add a generic Boosting class, handle transform there?
SPARK-7129
- // Classifies by thresholding sum of weighted tree predictions
- val treePredictions =
_trees.map(_.rootNode.predictImpl(features).prediction)
- val prediction = blas.ddot(numTrees, treePredictions, 1, _treeWeights,
1)
- if (prediction > 0.0) 1.0 else 0.0
+ // If thresholds defined, use predictRaw to get probabilities,
otherwise use optimization
+ if (isDefined(thresholds)) {
+ super.predict(features)
+ } else {
+ val prediction: Double = margin(features)
+ if (prediction > 0.0) 1.0 else 0.0
+ }
+ }
+
+ override protected def predictRaw(features: Vector): Vector = {
+ val prediction: Double = margin(features)
+ Vectors.dense(Array(-prediction, prediction))
+ }
+
+ override protected def raw2probabilityInPlace(rawPrediction: Vector):
Vector = {
+ rawPrediction match {
+ // The probability can be calculated for positive result:
+ // p+(x) = 1 / (1 + e^(-2 * F(x)))
+ // and negative result:
+ // p-(x) = 1 / (1 + e^(2 * F(x)))
+ case dv: DenseVector =>
+ dv.values(0) = getOldLossType.computeProbability(dv.values(0))
--- End diff --
You mean that if someone takes a model and changes the loss type via
`set(lossType, "other")` that the probability function should change? I don't
think it makes sense to change the probability function for a model, since the
probability is chosen to be optimal for a specific loss, but it's a good point.
What do you think?
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