1 files changed, 54 insertions, 0 deletions
diff --git a/examples/src/main/python/mllib/logistic_regression_with_lbfgs_example.py b/examples/src/main/python/mllib/logistic_regression_with_lbfgs_example.py
new file mode 100644
index 0000000000..e030b74ba6
--- /dev/null
+++ b/examples/src/main/python/mllib/logistic_regression_with_lbfgs_example.py
@@ -0,0 +1,54 @@
+#
+# 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.
+#
+
+"""
+Logistic Regression With LBFGS Example.
+"""
+from __future__ import print_function
+
+from pyspark import SparkContext
+# $example on$
+from pyspark.mllib.classification import LogisticRegressionWithLBFGS, LogisticRegressionModel
+from pyspark.mllib.regression import LabeledPoint
+# $example off$
+
+if __name__ == "__main__":
+
+    sc = SparkContext(appName="PythonLogisticRegressionWithLBFGSExample")
+
+    # $example on$
+    # Load and parse the data
+    def parsePoint(line):
+        values = [float(x) for x in line.split(' ')]
+        return LabeledPoint(values[0], values[1:])
+
+    data = sc.textFile("data/mllib/sample_svm_data.txt")
+    parsedData = data.map(parsePoint)
+
+    # Build the model
+    model = LogisticRegressionWithLBFGS.train(parsedData)
+
+    # Evaluating the model on training data
+    labelsAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features)))
+    trainErr = labelsAndPreds.filter(lambda (v, p): v != p).count() / float(parsedData.count())
+    print("Training Error = " + str(trainErr))
+
+    # Save and load model
+    model.save(sc, "target/tmp/pythonLogisticRegressionWithLBFGSModel")
+    sameModel = LogisticRegressionModel.load(sc,
+                                             "target/tmp/pythonLogisticRegressionWithLBFGSModel")
+    # $example off$