Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1727#discussion_r15725100
--- Diff: examples/src/main/python/mllib/tree.py ---
@@ -0,0 +1,92 @@
+#
+# 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.
+#
+
+"""
+Decision tree classification and regression using MLlib.
+"""
+
+import sys, numpy
+
+from operator import add
+
+from pyspark import SparkContext
+from pyspark.mllib.regression import LabeledPoint
+from pyspark.mllib.tree import DecisionTree
+
+
+# Parse a line of text into an MLlib LabeledPoint object
+def parsePoint(line):
+ values = [float(s) for s in line.split(',')]
+ if values[0] == -1: # Convert -1 labels to 0 for MLlib
+ values[0] = 0
+ return LabeledPoint(values[0], values[1:])
+
+# Return accuracy of DecisionTreeModel on the given RDD[LabeledPoint].
+def getAccuracy(dtModel, data):
+ seqOp = (lambda acc, x: acc + (x[0] == x[1]))
+ predictions = dtModel.predict(data)
+ truth = data.map(lambda p: p.label)
+ trainCorrect = predictions.zip(truth).aggregate(0, seqOp, add)
+ return trainCorrect / (0.0 + data.count())
+
+# Return mean squared error (MSE) of DecisionTreeModel on the given
RDD[LabeledPoint].
+def getMSE(dtModel, data):
+ seqOp = (lambda acc, x: acc + numpy.square(x[0] - x[1]))
+ predictions = dtModel.predict(data)
+ truth = data.map(lambda p: p.label)
+ trainMSE = predictions.zip(truth).aggregate(0, seqOp, add)
+ return trainMSE / (0.0 + data.count())
+
+# Return a new LabeledPoint with the label and feature 0 swapped.
+def swapLabelAndFeature0(labeledPoint):
+ newLabel = labeledPoint.label
+ newFeatures = labeledPoint.features
+ (newLabel, newFeatures[0]) = (newFeatures[0], newLabel)
+ return LabeledPoint(newLabel, newFeatures)
+
+
+if __name__ == "__main__":
+ if len(sys.argv) != 1:
+ print >> sys.stderr, "Usage: logistic_regression"
+ exit(-1)
+ sc = SparkContext(appName="PythonDT")
+
+ # Load data.
+ dataPath = 'data/mllib/sample_tree_data.csv'
--- End diff --
Shall we make this configurable?
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