Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6443#discussion_r31296391
--- Diff: examples/src/main/python/ml/simple_params_example.py ---
@@ -0,0 +1,92 @@
+#
+# 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 pprint
+import sys
+
+from pyspark import SparkContext
+from pyspark.ml.classification import LogisticRegression
+from pyspark.mllib.linalg import DenseVector
+from pyspark.mllib.regression import LabeledPoint
+from pyspark.sql import SQLContext
+
+"""
+A simple example demonstrating ways to specify parameters for Estimators
and Transformers.
+Run with:
+ bin/spark-submit examples/src/main/python/ml/simple_params_example.py
+"""
+
+if __name__ == "__main__":
+ if len(sys.argv) > 1:
+ print("Usage: random_forest_example", file=sys.stderr)
+ exit(1)
+ sc = SparkContext(appName="PythonSimpleParamsExample")
+ sqlContext = SQLContext(sc)
+
+ # prepare training data.
+ training = sc.parallelize([
+ LabeledPoint(1.0, DenseVector([0.0, 1.1, 0.1])),
+ LabeledPoint(0.0, DenseVector([2.0, 1.0, -1.0])),
+ LabeledPoint(0.0, DenseVector([2.0, 1.3, 1.0])),
+ LabeledPoint(1.0, DenseVector([0.0, 1.2, -0.5]))]).toDF()
+
+ # Create a LogisticRegression instance. This instance is an Estimator.
+ lr = LogisticRegression()
+ # Print out the parameters, documentation, and any default values.
+ print("LogisticRegression parameters:\n" + lr.explainParams() + "\n")
+
+ # We may set parameters using setter methods.
+ lr.setMaxIter(10) \
+ .setRegParam(0.01)
+
+ # 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:\n")
+ pprint.pprint(model1.extractParamMap())
+
+ # We may alternatively specify parameters using a parameter map,
+ # either overriding the default parameters or specifying new values.
--- End diff --
Line 69 is confusing and seems superfluous. Remove?
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