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.
  #
  
  """
  An example demonstrating aft survival regression.
  Run with:
    bin/spark-submit examples/src/main/python/ml/aft_survival_regression.py
  """
  from __future__ import print_function
  
  # $example on$
  from pyspark.ml.regression import AFTSurvivalRegression
  from pyspark.ml.linalg import Vectors
  # $example off$
  from pyspark.sql import SparkSession
  
  if __name__ == "__main__":
      spark = SparkSession \
          .builder \
          .appName("AFTSurvivalRegressionExample") \
          .getOrCreate()
  
      # $example on$
      training = spark.createDataFrame([
          (1.218, 1.0, Vectors.dense(1.560, -0.605)),
          (2.949, 0.0, Vectors.dense(0.346, 2.158)),
          (3.627, 0.0, Vectors.dense(1.380, 0.231)),
          (0.273, 1.0, Vectors.dense(0.520, 1.151)),
          (4.199, 0.0, Vectors.dense(0.795, -0.226))], ["label", "censor", "features"])
      quantileProbabilities = [0.3, 0.6]
      aft = AFTSurvivalRegression(quantileProbabilities=quantileProbabilities,
                                  quantilesCol="quantiles")
  
      model = aft.fit(training)
  
      # Print the coefficients, intercept and scale parameter for AFT survival regression
      print("Coefficients: " + str(model.coefficients))
      print("Intercept: " + str(model.intercept))
      print("Scale: " + str(model.scale))
      model.transform(training).show(truncate=False)
      # $example off$
  
      spark.stop()
  