text: #
  # 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.
  """
  
  # $example on$
  from pyspark.ml.linalg import Vectors
  from pyspark.ml.classification import LogisticRegression
  # $example off$
  from pyspark.sql import SparkSession
  
  if __name__ == "__main__":
      spark = SparkSession\
          .builder\
          .appName("EstimatorTransformerParamExample")\
          .getOrCreate()
  
      # $example on$
      # Prepare training data from a list of (label, features) tuples.
      training = spark.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 = spark.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
      # $example off$
  
      spark.stop()
  