FIX make sure the decision function of weak learner is symmetric (#37)

core/src/main/scala/org/apache/spark/ml/classification/BoostingClassifier.scala

@@ -367,7 +367,15 @@ class BoostingClassificationModel(
  val res = Array.fill(numClasses)(0.0)
  var i = 0
  while (i < numModels) {
- res(models(i).predict(features).toInt) += weights(i)
+ val prediction = models(i).predict(features).toInt
+ val weight = weights(i)
+ var c = 0
+ while (c < numClasses) {
+ if (prediction == c) {
+ res(c) += weight
+ } else { res(c) -= 1.0 / (numClasses - 1) * weight }
+ c += 1
+ }
  i += 1
  }
  Vectors.dense(res)

core/src/test/scala/org/apache/spark/ml/classification/BoostingClassifierSuite.scala

@@ -25,6 +25,7 @@ import org.scalatest.funsuite.AnyFunSuite
 import org.scalatest.matchers.should.Matchers._
 
 import scala.collection.mutable.ListBuffer
+import org.apache.spark.ml.linalg.DenseVector
 
 class BoostingClassifierSuite extends AnyFunSuite with BeforeAndAfterAll {
 
@@ -81,7 +82,7 @@ class BoostingClassifierSuite extends AnyFunSuite with BeforeAndAfterAll {
  bcModel.models.take(i))
  metrics += mce.evaluate(model.transform(test))
  })
- 
+
  assert(
  metrics.toList
  .sliding(2)
@@ -122,6 +123,36 @@ class BoostingClassifierSuite extends AnyFunSuite with BeforeAndAfterAll {
  mce.evaluate(bcrModel.transform(test)) === mce.evaluate(bcdModel.transform(test)) +- 0.02)
  }
 
+ List("discrete", "real").foreach { algorithm =>
+ test(
+ f"$algorithm boosting decision function respects the symmetric constraint for weak learners") {
+ val data =
+ spark.read
+ .format("libsvm")
+ .load("../data/letter/letter.svm")
+ .withColumn("label", col("label") - lit(1))
+ .cache()
+ data.count()
+ val numClasses = data.select("label").distinct().count()
+
+ val lr = new DecisionTreeClassifier().setMaxDepth(1)
+ val boost = new BoostingClassifier()
+ .setBaseLearner(lr)
+ .setNumBaseLearners(10)
+ .setAlgorithm(algorithm)
+ val boostModel = boost.fit(data)
+
+ val predictions = boostModel
+ .transform(data)
+ .select("rawPrediction")
+ .distinct()
+ .collect()
+ .map(_.getAs[DenseVector](0).toArray)
+
+ predictions.foreach(raw => assert(raw.sum === 0.0 +- 1e-6))
+ }
+ }
+
  test("read/write") {
  val data =
  spark.read

docs/bagging.md

@@ -28,7 +28,7 @@ new BaggingClassifier()
  .setBaseLearner(new DecisionTreeClassifier()) //Base learner used by the meta-estimator.
  .setNumBaseLearners(10) //Number of base learners.
  .setSubsampleRatio(0.8) //Ratio sampling of examples.
- .setReplacement(true) //Exemples drawn with replacement or not.
+ .setReplacement(true) //Samples drawn with replacement or not.
  .setSubspaceRatio(0.8) //Ratio sampling of features.
  .setVotingStrategy("soft") //Soft or Hard majority vote.
  .setParallelism(4) //Number of base learners trained simultaneously.
@@ -37,7 +37,7 @@ new BaggingRegressor()
  .setBaseLearner(new DecisionTreeRegressor()) //Base learner used by the meta-estimator.
  .setNumBaseLearners(10) //Number of base learners.
  .setSubsampleRatio(0.8) //Sampling ratio of examples.
- .setReplacement(true) //Exemples drawn with replacement or not.
+ .setReplacement(true) //Samples drawn with replacement or not.
  .setSubspaceRatio(0.8) //Sampling ratio of features.
  .setParallelism(4) //Number of base learners trained simultaneously.
 ```