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

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

Author: Kazuki Taniguchi <kazuki.t.1018@gmail.com>

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

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

1 files changed, 76 insertions, 0 deletions

diff --git a/examples/src/main/python/mllib/gradient_boosted_trees.py b/examples/src/main/python/mllib/gradient_boosted_trees.py
new file mode 100644
index 0000000000..e647773ad9
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+++ b/examples/src/main/python/mllib/gradient_boosted_trees.py
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+#
+# 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()