1 files changed, 14 insertions, 15 deletions
diff --git a/examples/src/main/python/mllib/decision_tree_runner.py b/examples/src/main/python/mllib/decision_tree_runner.py
index fccabd841b..513ed8fd51 100755
--- a/examples/src/main/python/mllib/decision_tree_runner.py
+++ b/examples/src/main/python/mllib/decision_tree_runner.py
@@ -20,6 +20,7 @@ Decision tree classification and regression using MLlib.
 
 This example requires NumPy (http://www.numpy.org/).
 """
+from __future__ import print_function
 
 import numpy
 import os
@@ -83,18 +84,17 @@ def reindexClassLabels(data):
     numClasses = len(classCounts)
     # origToNewLabels: class --> index in 0,...,numClasses-1
     if (numClasses < 2):
-        print >> sys.stderr, \
-            "Dataset for classification should have at least 2 classes." + \
-            " The given dataset had only %d classes." % numClasses
+        print("Dataset for classification should have at least 2 classes."
+              " The given dataset had only %d classes." % numClasses, file=sys.stderr)
         exit(1)
     origToNewLabels = dict([(sortedClasses[i], i) for i in range(0, numClasses)])
 
-    print "numClasses = %d" % numClasses
-    print "Per-class example fractions, counts:"
-    print "Class\tFrac\tCount"
+    print("numClasses = %d" % numClasses)
+    print("Per-class example fractions, counts:")
+    print("Class\tFrac\tCount")
     for c in sortedClasses:
         frac = classCounts[c] / (numExamples + 0.0)
-        print "%g\t%g\t%d" % (c, frac, classCounts[c])
+        print("%g\t%g\t%d" % (c, frac, classCounts[c]))
 
     if (sortedClasses[0] == 0 and sortedClasses[-1] == numClasses - 1):
         return (data, origToNewLabels)
@@ -105,8 +105,7 @@ def reindexClassLabels(data):
 
 
 def usage():
-    print >> sys.stderr, \
-        "Usage: decision_tree_runner [libsvm format data filepath]"
+    print("Usage: decision_tree_runner [libsvm format data filepath]", file=sys.stderr)
     exit(1)
 
 
@@ -133,13 +132,13 @@ if __name__ == "__main__":
     model = DecisionTree.trainClassifier(reindexedData, numClasses=numClasses,
                                          categoricalFeaturesInfo=categoricalFeaturesInfo)
     # Print learned tree and stats.
-    print "Trained DecisionTree for classification:"
-    print "  Model numNodes: %d" % model.numNodes()
-    print "  Model depth: %d" % model.depth()
-    print "  Training accuracy: %g" % getAccuracy(model, reindexedData)
+    print("Trained DecisionTree for classification:")
+    print("  Model numNodes: %d" % model.numNodes())
+    print("  Model depth: %d" % model.depth())
+    print("  Training accuracy: %g" % getAccuracy(model, reindexedData))
     if model.numNodes() < 20:
-        print model.toDebugString()
+        print(model.toDebugString())
     else:
-        print model
+        print(model)
 
     sc.stop()