text: #
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  # The ASF licenses this file to You under the Apache License, Version 2.0
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  """
  Decision Tree Regression Example.
  """
  from __future__ import print_function
  
  # $example on$
  from pyspark.ml import Pipeline
  from pyspark.ml.regression import DecisionTreeRegressor
  from pyspark.ml.feature import VectorIndexer
  from pyspark.ml.evaluation import RegressionEvaluator
  # $example off$
  from pyspark.sql import SparkSession
  
  if __name__ == "__main__":
      spark = SparkSession\
          .builder\
          .appName("DecisionTreeRegressionExample")\
          .getOrCreate()
  
      # $example on$
      # Load the data stored in LIBSVM format as a DataFrame.
      data = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
  
      # Automatically identify categorical features, and index them.
      # We specify maxCategories so features with > 4 distinct values are treated as continuous.
      featureIndexer =\
          VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
  
      # Split the data into training and test sets (30% held out for testing)
      (trainingData, testData) = data.randomSplit([0.7, 0.3])
  
      # Train a DecisionTree model.
      dt = DecisionTreeRegressor(featuresCol="indexedFeatures")
  
      # Chain indexer and tree in a Pipeline
      pipeline = Pipeline(stages=[featureIndexer, dt])
  
      # Train model.  This also runs the indexer.
      model = pipeline.fit(trainingData)
  
      # Make predictions.
      predictions = model.transform(testData)
  
      # Select example rows to display.
      predictions.select("prediction", "label", "features").show(5)
  
      # Select (prediction, true label) and compute test error
      evaluator = RegressionEvaluator(
          labelCol="label", predictionCol="prediction", metricName="rmse")
      rmse = evaluator.evaluate(predictions)
      print("Root Mean Squared Error (RMSE) on test data = %g" % rmse)
  
      treeModel = model.stages[1]
      # summary only
      print(treeModel)
      # $example off$
  
      spark.stop()
  