[SPARK-5094][MLlib] Add Python API for Gradient Boosted Trees

This PR is implementing the Gradient Boosted Trees for Python API.

Author: Kazuki Taniguchi <[email protected]>

Closes apache#3951 from kazk1018/gbt_for_py and squashes the following commits:

620d247 [Kazuki Taniguchi] [SPARK-5094][MLlib] Add Python API for Gradient Boosted Trees

Author: Kazuki Taniguchi

examples/src/main/python/mllib/gradient_boosted_trees.py

@@ -0,0 +1,76 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+Gradient boosted Trees classification and regression using MLlib.
+"""
+
+import sys
+
+from pyspark.context import SparkContext
+from pyspark.mllib.tree import GradientBoostedTrees
+from pyspark.mllib.util import MLUtils
+
+
+def testClassification(trainingData, testData):
+    # Train a GradientBoostedTrees model.
+    #  Empty categoricalFeaturesInfo indicates all features are continuous.
+    model = GradientBoostedTrees.trainClassifier(trainingData, categoricalFeaturesInfo={},
+                                                 numIterations=30, maxDepth=4)
+    # Evaluate model on test instances and compute test error
+    predictions = model.predict(testData.map(lambda x: x.features))
+    labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions)
+    testErr = labelsAndPredictions.filter(lambda (v, p): v != p).count() \
+        / float(testData.count())
+    print('Test Error = ' + str(testErr))
+    print('Learned classification ensemble model:')
+    print(model.toDebugString())
+
+
+def testRegression(trainingData, testData):
+    # Train a GradientBoostedTrees model.
+    #  Empty categoricalFeaturesInfo indicates all features are continuous.
+    model = GradientBoostedTrees.trainRegressor(trainingData, categoricalFeaturesInfo={},
+                                                numIterations=30, maxDepth=4)
+    # Evaluate model on test instances and compute test error
+    predictions = model.predict(testData.map(lambda x: x.features))
+    labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions)
+    testMSE = labelsAndPredictions.map(lambda (v, p): (v - p) * (v - p)).sum() \
+        / float(testData.count())
+    print('Test Mean Squared Error = ' + str(testMSE))
+    print('Learned regression ensemble model:')
+    print(model.toDebugString())
+
+
+if __name__ == "__main__":
+    if len(sys.argv) > 1:
+        print >> sys.stderr, "Usage: gradient_boosted_trees"
+        exit(1)
+    sc = SparkContext(appName="PythonGradientBoostedTrees")
+
+    # Load and parse the data file into an RDD of LabeledPoint.
+    data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt')
+    # Split the data into training and test sets (30% held out for testing)
+    (trainingData, testData) = data.randomSplit([0.7, 0.3])
+
+    print('\nRunning example of classification using GradientBoostedTrees\n')
+    testClassification(trainingData, testData)
+
+    print('\nRunning example of regression using GradientBoostedTrees\n')
+    testRegression(trainingData, testData)
+
+    sc.stop()

mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala

@@ -41,10 +41,11 @@ import org.apache.spark.mllib.regression._
 import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, Statistics}
 import org.apache.spark.mllib.stat.correlation.CorrelationNames
 import org.apache.spark.mllib.stat.test.ChiSqTestResult
-import org.apache.spark.mllib.tree.{RandomForest, DecisionTree}
-import org.apache.spark.mllib.tree.configuration.{Algo, Strategy}
+import org.apache.spark.mllib.tree.{GradientBoostedTrees, RandomForest, DecisionTree}
+import org.apache.spark.mllib.tree.configuration.{BoostingStrategy, Algo, Strategy}
 import org.apache.spark.mllib.tree.impurity._
-import org.apache.spark.mllib.tree.model.{RandomForestModel, DecisionTreeModel}
+import org.apache.spark.mllib.tree.loss.Losses
+import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel, RandomForestModel, DecisionTreeModel}
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
@@ -532,6 +533,35 @@ class PythonMLLibAPI extends Serializable {
     }
   }
 
+  /**
+   * Java stub for Python mllib GradientBoostedTrees.train().
+   * This stub returns a handle to the Java object instead of the content of the Java object.
+   * Extra care needs to be taken in the Python code to ensure it gets freed on exit;
+   * see the Py4J documentation.
+   */
+  def trainGradientBoostedTreesModel(
+      data: JavaRDD[LabeledPoint],
+      algoStr: String,
+      categoricalFeaturesInfo: JMap[Int, Int],
+      lossStr: String,
+      numIterations: Int,
+      learningRate: Double,
+      maxDepth: Int): GradientBoostedTreesModel = {
+    val boostingStrategy = BoostingStrategy.defaultParams(algoStr)
+    boostingStrategy.setLoss(Losses.fromString(lossStr))
+    boostingStrategy.setNumIterations(numIterations)
+    boostingStrategy.setLearningRate(learningRate)
+    boostingStrategy.treeStrategy.setMaxDepth(maxDepth)
+    boostingStrategy.treeStrategy.categoricalFeaturesInfo = categoricalFeaturesInfo.asScala.toMap
+
+    val cached = data.rdd.persist(StorageLevel.MEMORY_AND_DISK)
+    try {
+      GradientBoostedTrees.train(cached, boostingStrategy)
+    } finally {
+      cached.unpersist(blocking = false)
+    }
+  }
+
   /**
    * Java stub for mllib Statistics.colStats(X: RDD[Vector]).
    * TODO figure out return type.

python/pyspark/mllib/tests.py

@@ -169,7 +169,7 @@ def test_kmeans_deterministic(self):
 
     def test_classification(self):
         from pyspark.mllib.classification import LogisticRegressionWithSGD, SVMWithSGD, NaiveBayes
-        from pyspark.mllib.tree import DecisionTree
+        from pyspark.mllib.tree import DecisionTree, RandomForest, GradientBoostedTrees
         data = [
             LabeledPoint(0.0, [1, 0, 0]),
             LabeledPoint(1.0, [0, 1, 1]),
@@ -198,18 +198,31 @@ def test_classification(self):
         self.assertTrue(nb_model.predict(features[3]) > 0)
 
         categoricalFeaturesInfo = {0: 3}  # feature 0 has 3 categories
-        dt_model = \
-            DecisionTree.trainClassifier(rdd, numClasses=2,
-                                         categoricalFeaturesInfo=categoricalFeaturesInfo)
+        dt_model = DecisionTree.trainClassifier(
+            rdd, numClasses=2, categoricalFeaturesInfo=categoricalFeaturesInfo)
         self.assertTrue(dt_model.predict(features[0]) <= 0)
         self.assertTrue(dt_model.predict(features[1]) > 0)
         self.assertTrue(dt_model.predict(features[2]) <= 0)
         self.assertTrue(dt_model.predict(features[3]) > 0)
 
+        rf_model = RandomForest.trainClassifier(
+            rdd, numClasses=2, categoricalFeaturesInfo=categoricalFeaturesInfo, numTrees=100)
+        self.assertTrue(rf_model.predict(features[0]) <= 0)
+        self.assertTrue(rf_model.predict(features[1]) > 0)
+        self.assertTrue(rf_model.predict(features[2]) <= 0)
+        self.assertTrue(rf_model.predict(features[3]) > 0)
+
+        gbt_model = GradientBoostedTrees.trainClassifier(
+            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo)
+        self.assertTrue(gbt_model.predict(features[0]) <= 0)
+        self.assertTrue(gbt_model.predict(features[1]) > 0)
+        self.assertTrue(gbt_model.predict(features[2]) <= 0)
+        self.assertTrue(gbt_model.predict(features[3]) > 0)
+
     def test_regression(self):
         from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \
             RidgeRegressionWithSGD
-        from pyspark.mllib.tree import DecisionTree
+        from pyspark.mllib.tree import DecisionTree, RandomForest, GradientBoostedTrees
         data = [
             LabeledPoint(-1.0, [0, -1]),
             LabeledPoint(1.0, [0, 1]),
@@ -238,13 +251,27 @@ def test_regression(self):
         self.assertTrue(rr_model.predict(features[3]) > 0)
 
         categoricalFeaturesInfo = {0: 2}  # feature 0 has 2 categories
-        dt_model = \
-            DecisionTree.trainRegressor(rdd, categoricalFeaturesInfo=categoricalFeaturesInfo)
+        dt_model = DecisionTree.trainRegressor(
+            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo)
         self.assertTrue(dt_model.predict(features[0]) <= 0)
         self.assertTrue(dt_model.predict(features[1]) > 0)
         self.assertTrue(dt_model.predict(features[2]) <= 0)
         self.assertTrue(dt_model.predict(features[3]) > 0)
 
+        rf_model = RandomForest.trainRegressor(
+            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numTrees=100)
+        self.assertTrue(rf_model.predict(features[0]) <= 0)
+        self.assertTrue(rf_model.predict(features[1]) > 0)
+        self.assertTrue(rf_model.predict(features[2]) <= 0)
+        self.assertTrue(rf_model.predict(features[3]) > 0)
+
+        gbt_model = GradientBoostedTrees.trainRegressor(
+            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo)
+        self.assertTrue(gbt_model.predict(features[0]) <= 0)
+        self.assertTrue(gbt_model.predict(features[1]) > 0)
+        self.assertTrue(gbt_model.predict(features[2]) <= 0)
+        self.assertTrue(gbt_model.predict(features[3]) > 0)
+
 
 class StatTests(PySparkTestCase):
     # SPARK-4023