1 files changed, 60 insertions, 0 deletions
diff --git a/examples/src/main/python/mllib/correlations.py b/examples/src/main/python/mllib/correlations.py
new file mode 100755
index 0000000000..6b16a56e44
--- /dev/null
+++ b/examples/src/main/python/mllib/correlations.py
@@ -0,0 +1,60 @@
+#
+# 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.
+#
+
+"""
+Correlations using MLlib.
+"""
+
+import sys
+
+from pyspark import SparkContext
+from pyspark.mllib.regression import LabeledPoint
+from pyspark.mllib.stat import Statistics
+from pyspark.mllib.util import MLUtils
+
+
+if __name__ == "__main__":
+    if len(sys.argv) not in [1,2]:
+        print >> sys.stderr, "Usage: correlations (<file>)"
+        exit(-1)
+    sc = SparkContext(appName="PythonCorrelations")
+    if len(sys.argv) == 2:
+        filepath = sys.argv[1]
+    else:
+        filepath = 'data/mllib/sample_linear_regression_data.txt'
+    corrType = 'pearson'
+
+    points = MLUtils.loadLibSVMFile(sc, filepath)\
+        .map(lambda lp: LabeledPoint(lp.label, lp.features.toArray()))
+
+    print
+    print 'Summary of data file: ' + filepath
+    print '%d data points' % points.count()
+
+    # Statistics (correlations)
+    print
+    print 'Correlation (%s) between label and each feature' % corrType
+    print 'Feature\tCorrelation'
+    numFeatures = points.take(1)[0].features.size
+    labelRDD = points.map(lambda lp: lp.label)
+    for i in range(numFeatures):
+        featureRDD = points.map(lambda lp: lp.features[i])
+        corr = Statistics.corr(labelRDD, featureRDD, corrType)
+        print '%d\t%g' % (i, corr)
+    print
+
+    sc.stop()