Github user yinxusen commented on a diff in the pull request:
https://github.com/apache/spark/pull/11053#discussion_r53269919
--- Diff:
examples/src/main/python/ml/estimator_transformer_param_example.py ---
@@ -0,0 +1,95 @@
+#
+# 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
+from pyspark.mllib.linalg import Vectors
+# $example on$
+from pyspark.ml.classification import LogisticRegression
+# $example off$
+from pyspark.ml.param import Param, Params
+
+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}
+ # Specify 1 Param, overwriting the original maxIter.
+ paramMap[lr.maxIter] = 30
+ # Specify multiple Params.
+ paramMap.update({lr.regParam: 0.1, lr.threshold: 0.55})
+
+ # You can combine paramMaps, which are python dictionaries.
+ # Change output column name
+ paramMap2 = {lr.probabilityCol: "myProbability"}
+ 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")
--- End diff --
no need to break the line here
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