diff options
author | Yanbo Liang <ybliang8@gmail.com> | 2015-07-31 11:56:52 -0700 |
---|---|---|
committer | Joseph K. Bradley <joseph@databricks.com> | 2015-07-31 11:56:52 -0700 |
commit | e8bdcdeabb2df139a656f86686cdb53c891b1f4b (patch) | |
tree | d6ccab74f50d58e7b18a786ce66dcd8f5fe30f60 /mllib/src/test | |
parent | 4011a947154d97a9ffb5a71f077481a12534d36b (diff) | |
download | spark-e8bdcdeabb2df139a656f86686cdb53c891b1f4b.tar.gz spark-e8bdcdeabb2df139a656f86686cdb53c891b1f4b.tar.bz2 spark-e8bdcdeabb2df139a656f86686cdb53c891b1f4b.zip |
[SPARK-6885] [ML] decision tree support predict class probabilities
Decision tree support predict class probabilities.
Implement the prediction probabilities function referred the old DecisionTree API and the [sklean API](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/tree/tree.py#L593).
I make the DecisionTreeClassificationModel inherit from ProbabilisticClassificationModel, make the predictRaw to return the raw counts vector and make raw2probabilityInPlace/predictProbability return the probabilities for each prediction.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #7694 from yanboliang/spark-6885 and squashes the following commits:
08d5b7f [Yanbo Liang] fix ImpurityStats null parameters and raw2probabilityInPlace sum = 0 issue
2174278 [Yanbo Liang] solve merge conflicts
7e90ba8 [Yanbo Liang] fix typos
33ae183 [Yanbo Liang] fix annotation
ff043d3 [Yanbo Liang] raw2probabilityInPlace should operate in-place
c32d6ce [Yanbo Liang] optimize calculateImpurityStats function again
6167fb0 [Yanbo Liang] optimize calculateImpurityStats function
fbbe2ec [Yanbo Liang] eliminate duplicated struct and code
beb1634 [Yanbo Liang] try to eliminate impurityStats for each LearningNode
99e8943 [Yanbo Liang] code optimization
5ec3323 [Yanbo Liang] implement InformationGainAndImpurityStats
227c91b [Yanbo Liang] refactor LearningNode to store ImpurityCalculator
d746ffc [Yanbo Liang] decision tree support predict class probabilities
Diffstat (limited to 'mllib/src/test')
3 files changed, 30 insertions, 4 deletions
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala index 73b4805c4c..c7bbf1ce07 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala @@ -21,12 +21,13 @@ import org.apache.spark.SparkFunSuite import org.apache.spark.ml.impl.TreeTests import org.apache.spark.ml.param.ParamsSuite import org.apache.spark.ml.tree.LeafNode -import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree, DecisionTreeSuite => OldDecisionTreeSuite} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.Row class DecisionTreeClassifierSuite extends SparkFunSuite with MLlibTestSparkContext { @@ -57,7 +58,7 @@ class DecisionTreeClassifierSuite extends SparkFunSuite with MLlibTestSparkConte test("params") { ParamsSuite.checkParams(new DecisionTreeClassifier) - val model = new DecisionTreeClassificationModel("dtc", new LeafNode(0.0, 0.0)) + val model = new DecisionTreeClassificationModel("dtc", new LeafNode(0.0, 0.0, null), 2) ParamsSuite.checkParams(model) } @@ -231,6 +232,31 @@ class DecisionTreeClassifierSuite extends SparkFunSuite with MLlibTestSparkConte compareAPIs(rdd, dt, categoricalFeatures = Map.empty[Int, Int], numClasses) } + test("predictRaw and predictProbability") { + val rdd = continuousDataPointsForMulticlassRDD + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(4) + .setMaxBins(100) + val categoricalFeatures = Map(0 -> 3) + val numClasses = 3 + + val newData: DataFrame = TreeTests.setMetadata(rdd, categoricalFeatures, numClasses) + val newTree = dt.fit(newData) + + val predictions = newTree.transform(newData) + .select(newTree.getPredictionCol, newTree.getRawPredictionCol, newTree.getProbabilityCol) + .collect() + + predictions.foreach { case Row(pred: Double, rawPred: Vector, probPred: Vector) => + assert(pred === rawPred.argmax, + s"Expected prediction $pred but calculated ${rawPred.argmax} from rawPrediction.") + val sum = rawPred.toArray.sum + assert(Vectors.dense(rawPred.toArray.map(_ / sum)) === probPred, + "probability prediction mismatch") + } + } + ///////////////////////////////////////////////////////////////////////////// // Tests of model save/load ///////////////////////////////////////////////////////////////////////////// diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala index a7bc77965f..d4b5896c12 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala @@ -58,7 +58,7 @@ class GBTClassifierSuite extends SparkFunSuite with MLlibTestSparkContext { test("params") { ParamsSuite.checkParams(new GBTClassifier) val model = new GBTClassificationModel("gbtc", - Array(new DecisionTreeRegressionModel("dtr", new LeafNode(0.0, 0.0))), + Array(new DecisionTreeRegressionModel("dtr", new LeafNode(0.0, 0.0, null))), Array(1.0)) ParamsSuite.checkParams(model) } diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala index ab711c8e4b..dbb2577c62 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala @@ -66,7 +66,7 @@ class RandomForestClassifierSuite extends SparkFunSuite with MLlibTestSparkConte test("params") { ParamsSuite.checkParams(new RandomForestClassifier) val model = new RandomForestClassificationModel("rfc", - Array(new DecisionTreeClassificationModel("dtc", new LeafNode(0.0, 0.0))), 2) + Array(new DecisionTreeClassificationModel("dtc", new LeafNode(0.0, 0.0, null), 2)), 2) ParamsSuite.checkParams(model) } |