[SPARK-13012][DOCUMENTATION] Replace example code in ml-guide.md using include_example

Replaced example code in ml-guide.md using include_example

Author: Devaraj K <devaraj@apache.org>

Closes #11053 from devaraj-kavali/SPARK-13012.

2 files changed, 151 insertions, 0 deletions

diff --git a/examples/src/main/python/ml/estimator_transformer_param_example.py b/examples/src/main/python/ml/estimator_transformer_param_example.py
new file mode 100644
index 0000000000..9a8993dac4
--- /dev/null
+++ b/examples/src/main/python/ml/estimator_transformer_param_example.py
@@ -0,0 +1,87 @@
+#
+# 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.
+#
+
+"""
+Estimator Transformer Param Example.
+"""
+from pyspark import SparkContext, SQLContext
+# $example on$
+from pyspark.mllib.linalg import Vectors
+from pyspark.ml.classification import LogisticRegression
+# $example off$
+
+if __name__ == "__main__":
+
+    sc = SparkContext(appName="EstimatorTransformerParamExample")
+    sqlContext = SQLContext(sc)
+
+    # $example on$
+    # Prepare training data from a list of (label, features) tuples.
+    training = sqlContext.createDataFrame([
+        (1.0, Vectors.dense([0.0, 1.1, 0.1])),
+        (0.0, Vectors.dense([2.0, 1.0, -1.0])),
+        (0.0, Vectors.dense([2.0, 1.3, 1.0])),
+        (1.0, Vectors.dense([0.0, 1.2, -0.5]))], ["label", "features"])
+
+    # Create a LogisticRegression instance. This instance is an Estimator.
+    lr = LogisticRegression(maxIter=10, regParam=0.01)
+    # Print out the parameters, documentation, and any default values.
+    print "LogisticRegression parameters:\n" + lr.explainParams() + "\n"
+
+    # Learn a LogisticRegression model. This uses the parameters stored in lr.
+    model1 = lr.fit(training)
+
+    # Since model1 is a Model (i.e., a transformer produced by an Estimator),
+    # we can view the parameters it used during fit().
+    # This prints the parameter (name: value) pairs, where names are unique IDs for this
+    # LogisticRegression instance.
+    print "Model 1 was fit using parameters: "
+    print model1.extractParamMap()
+
+    # We may alternatively specify parameters using a Python dictionary as a paramMap
+    paramMap = {lr.maxIter: 20}
+    paramMap[lr.maxIter] = 30  # Specify 1 Param, overwriting the original maxIter.
+    paramMap.update({lr.regParam: 0.1, lr.threshold: 0.55})  # Specify multiple Params.
+
+    # You can combine paramMaps, which are python dictionaries.
+    paramMap2 = {lr.probabilityCol: "myProbability"}  # Change output column name
+    paramMapCombined = paramMap.copy()
+    paramMapCombined.update(paramMap2)
+
+    # Now learn a new model using the paramMapCombined parameters.
+    # paramMapCombined overrides all parameters set earlier via lr.set* methods.
+    model2 = lr.fit(training, paramMapCombined)
+    print "Model 2 was fit using parameters: "
+    print model2.extractParamMap()
+
+    # Prepare test data
+    test = sqlContext.createDataFrame([
+        (1.0, Vectors.dense([-1.0, 1.5, 1.3])),
+        (0.0, Vectors.dense([3.0, 2.0, -0.1])),
+        (1.0, Vectors.dense([0.0, 2.2, -1.5]))], ["label", "features"])
+
+    # Make predictions on test data using the Transformer.transform() method.
+    # LogisticRegression.transform will only use the 'features' column.
+    # Note that model2.transform() outputs a "myProbability" column instead of the usual
+    # 'probability' column since we renamed the lr.probabilityCol parameter previously.
+    prediction = model2.transform(test)
+    selected = prediction.select("features", "label", "myProbability", "prediction")
+    for row in selected.collect():
+        print row
+    # $example off$
+
+    sc.stop()
diff --git a/examples/src/main/python/ml/pipeline_example.py b/examples/src/main/python/ml/pipeline_example.py
new file mode 100644
index 0000000000..3288568f0c
--- /dev/null
+++ b/examples/src/main/python/ml/pipeline_example.py
@@ -0,0 +1,64 @@
+#
+# 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.
+#
+
+"""
+Pipeline Example.
+"""
+from pyspark import SparkContext, SQLContext
+# $example on$
+from pyspark.ml import Pipeline
+from pyspark.ml.classification import LogisticRegression
+from pyspark.ml.feature import HashingTF, Tokenizer
+# $example off$
+
+if __name__ == "__main__":
+
+    sc = SparkContext(appName="PipelineExample")
+    sqlContext = SQLContext(sc)
+
+    # $example on$
+    # Prepare training documents from a list of (id, text, label) tuples.
+    training = sqlContext.createDataFrame([
+        (0L, "a b c d e spark", 1.0),
+        (1L, "b d", 0.0),
+        (2L, "spark f g h", 1.0),
+        (3L, "hadoop mapreduce", 0.0)], ["id", "text", "label"])
+
+    # Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
+    tokenizer = Tokenizer(inputCol="text", outputCol="words")
+    hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
+    lr = LogisticRegression(maxIter=10, regParam=0.01)
+    pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
+
+    # Fit the pipeline to training documents.
+    model = pipeline.fit(training)
+
+    # Prepare test documents, which are unlabeled (id, text) tuples.
+    test = sqlContext.createDataFrame([
+        (4L, "spark i j k"),
+        (5L, "l m n"),
+        (6L, "mapreduce spark"),
+        (7L, "apache hadoop")], ["id", "text"])
+
+    # Make predictions on test documents and print columns of interest.
+    prediction = model.transform(test)
+    selected = prediction.select("id", "text", "prediction")
+    for row in selected.collect():
+        print(row)
+    # $example off$
+
+    sc.stop()