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From dbt...@apache.org
Subject spark git commit: [SPARK-8551] [ML] Elastic net python code example
Date Tue, 30 Jun 2015 06:50:40 GMT
Repository: spark
Updated Branches:
  refs/heads/master 12671dd5e -> 545245741


[SPARK-8551] [ML] Elastic net python code example

Author: Shuo Xiang <shuoxiangpub@gmail.com>

Closes #6946 from coderxiang/en-java-code-example and squashes the following commits:

7a4bdf8 [Shuo Xiang] address comments
cddb02b [Shuo Xiang] add elastic net python example code
f4fa534 [Shuo Xiang] Merge remote-tracking branch 'upstream/master'
6ad4865 [Shuo Xiang] Merge remote-tracking branch 'upstream/master'
180b496 [Shuo Xiang] Merge remote-tracking branch 'upstream/master'
aa0717d [Shuo Xiang] Merge remote-tracking branch 'upstream/master'
5f109b4 [Shuo Xiang] Merge remote-tracking branch 'upstream/master'
c5c5bfe [Shuo Xiang] Merge remote-tracking branch 'upstream/master'
98804c9 [Shuo Xiang] fix bug in topBykey and update test


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/54524574
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/54524574
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/54524574

Branch: refs/heads/master
Commit: 5452457410ffe881773f2f2cdcdc752467b19720
Parents: 12671dd
Author: Shuo Xiang <shuoxiangpub@gmail.com>
Authored: Mon Jun 29 23:50:34 2015 -0700
Committer: DB Tsai <dbt@netflix.com>
Committed: Mon Jun 29 23:50:34 2015 -0700

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 .../src/main/python/ml/logistic_regression.py   | 67 ++++++++++++++++++++
 1 file changed, 67 insertions(+)
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http://git-wip-us.apache.org/repos/asf/spark/blob/54524574/examples/src/main/python/ml/logistic_regression.py
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diff --git a/examples/src/main/python/ml/logistic_regression.py b/examples/src/main/python/ml/logistic_regression.py
new file mode 100644
index 0000000..55afe1b
--- /dev/null
+++ b/examples/src/main/python/ml/logistic_regression.py
@@ -0,0 +1,67 @@
+#
+# 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.
+#
+
+from __future__ import print_function
+
+import sys
+
+from pyspark import SparkContext
+from pyspark.ml.classification import LogisticRegression
+from pyspark.mllib.evaluation import MulticlassMetrics
+from pyspark.ml.feature import StringIndexer
+from pyspark.mllib.util import MLUtils
+from pyspark.sql import SQLContext
+
+"""
+A simple example demonstrating a logistic regression with elastic net regularization Pipeline.
+Run with:
+  bin/spark-submit examples/src/main/python/ml/logistic_regression.py
+"""
+
+if __name__ == "__main__":
+
+    if len(sys.argv) > 1:
+        print("Usage: logistic_regression", file=sys.stderr)
+        exit(-1)
+
+    sc = SparkContext(appName="PythonLogisticRegressionExample")
+    sqlContext = SQLContext(sc)
+
+    # Load and parse the data file into a dataframe.
+    df = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+    # Map labels into an indexed column of labels in [0, numLabels)
+    stringIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel")
+    si_model = stringIndexer.fit(df)
+    td = si_model.transform(df)
+    [training, test] = td.randomSplit([0.7, 0.3])
+
+    lr = LogisticRegression(maxIter=100, regParam=0.3).setLabelCol("indexedLabel")
+    lr.setElasticNetParam(0.8)
+
+    # Fit the model
+    lrModel = lr.fit(training)
+
+    predictionAndLabels = lrModel.transform(test).select("prediction", "indexedLabel") \
+        .map(lambda x: (x.prediction, x.indexedLabel))
+
+    metrics = MulticlassMetrics(predictionAndLabels)
+    print("weighted f-measure %.3f" % metrics.weightedFMeasure())
+    print("precision %s" % metrics.precision())
+    print("recall %s" % metrics.recall())
+
+    sc.stop()


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