[Case Study]NaiveBayes using Python

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1. """
   
2. NaiveBayes Example.
   
3. """
   
4. from __future__ import print_function
   
5. 
   
6. from pyspark import SparkContext
   
7. # $example on$
   
8. from pyspark.mllib.classification import NaiveBayes, NaiveBayesModel
   
9. from pyspark.mllib.linalg import Vectors
   
10. from pyspark.mllib.regression import LabeledPoint
    
11. 
    
12. 
    
13. def parseLine(line):
    
14.     parts = line.split(',')
    
15.     label = float(parts[0])
    
16.     features = Vectors.dense([float(x) for x in parts[1].split(' ')])
    
17.     return LabeledPoint(label, features)
    
18. # $example off$
    
19. 
    
20. if __name__ == "__main__":
    
21. 
    
22.     sc = SparkContext(appName="PythonNaiveBayesExample")
    
23. 
    
24.     # $example on$
    
25.     data = sc.textFile('data/mllib/sample_naive_bayes_data.txt').map(parseLine)
    
26. 
    
27.     # Split data aproximately into training (60%) and test (40%)
    
28.     training, test = data.randomSplit([0.6, 0.4], seed=0)
    
29. 
    
30.     # Train a naive Bayes model.
    
31.     model = NaiveBayes.train(training, 1.0)
    
32. 
    
33.     # Make prediction and test accuracy.
    
34.     predictionAndLabel = test.map(lambda p: (model.predict(p.features), p.label))
    
35.     accuracy = 1.0 * predictionAndLabel.filter(lambda (x, v): x == v).count() / test.count()
    
36. 
    
37.     # Save and load model
    
38.     model.save(sc, "target/tmp/myNaiveBayesModel")
    
39.     sameModel = NaiveBayesModel.load(sc, "target/tmp/myNaiveBayesModel")
    
40.     # $example off$

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关键词：Case study python naive study Using features example return future import