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#
# 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.linalg import Vectors
from pyspark.mllib.regression import LabeledPoint
def parseLine(line):
parts = line.split(',')
label = float(parts[0])
features = Vectors.dense([float(x) for x in parts[1].split(' ')])
return LabeledPoint(label, features)
# $example off$
if __name__ == "__main__":
sc = SparkContext(appName="PythonNaiveBayesExample")
# $example on$
data = sc.textFile('data/mllib/sample_naive_bayes_data.txt').map(parseLine)
# Split data aproximately into training (60%) and test (40%)
training, test = data.randomSplit([0.6, 0.4], seed=0)
# 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 = 'target/tmp/myNaiveBayesModel'
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))
# $example off$
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