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.
  #
  
  """
  NaiveBayes Example.
  
  Usage:
    `spark-submit --master local[4] examples/src/main/python/mllib/naive_bayes_example.py`
  """
  
  from __future__ import print_function
  
  import shutil
  
  from pyspark import SparkContext
  # $example on$
  from pyspark.mllib.classification import NaiveBayes, NaiveBayesModel
  from pyspark.mllib.util import MLUtils
  from pyspark.sql import SparkSession
  
  
  # $example off$
  
  if __name__ == "__main__":
  
      spark = SparkSession.builder.master("local").appName("PythonNaiveBayesExample").config("spark.sql.warehouse.dir", "D:/work/python/spark_naive_bayes/spark").getOrCreate()
  
      sc = spark.sparkContext
  
      # $example on$
      # Load and parse the data file.
      data = MLUtils.loadLibSVMFile(sc, "D:\work\python\spark_naive_bayes\spark\data\mllib\sample_libsvm_data.txt")
  
      # Split data approximately into training (60%) and test (40%)
      training, test = data.randomSplit([0.6, 0.4])
  
      # Train a naive Bayes model.
      model = NaiveBayes.train(training, 1.0)
  
      # Make prediction and test accuracy.
      predictionAndLabel = test.map(lambda p: (model.predict(p.features), p.label))
      accuracy = 1.0 * predictionAndLabel.filter(lambda (x, v): x == v).count() / test.count()
      print('model accuracy {}'.format(accuracy))
  
      # Save and load model
      output_dir = 'output/'
      shutil.rmtree(output_dir, ignore_errors=True)
      model.save(sc, output_dir)
      sameModel = NaiveBayesModel.load(sc, output_dir)
      predictionAndLabel = test.map(lambda p: (sameModel.predict(p.features), p.label))
      accuracy = 1.0 * predictionAndLabel.filter(lambda (x, v): x == v).count() / test.count()
      print('sameModel accuracy {}'.format(accuracy))
  
      from pyspark.mllib.linalg import SparseVector
      testsparsevector = SparseVector(692, [5, 6], [5.0, 6.0])
  
      print(sameModel.predict(testsparsevector))
  
      # $example off$
      sc.stop()