naiveBayesSpamClassifier.py

import os
import io


from pandas import DataFrame
from sklearn. feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
 
def readFile(path):
    for root, dirname, filenames in os.walk(path):
        for filename in filenames:
            path=os.path.join(root,filename)
            inBody=False
            lines=[]
            f=io.open(path,"r", encoding='latin1')
            for line in f:
                if inBody:
                    lines.append(line)
                elif line=='\n':
                    inBody=True
            f.close()
            message='\n'.join(lines)
            yield path,message
            
def dataFrameFromDirectory(path, classification):
    rows=[]
    index=[]
    for filename, message in readFile(path):
        rows.append({"message":message, 'class':classification})
        index.append(filename)
    return DataFrame(rows,index=index)
data= DataFrame({'message':[], 'class':[]})

data= data.append(dataFrameFromDirectory('emails/spam','spam'))
data= data.append(dataFrameFromDirectory('emails/ham','ham'))
print(data.head())

vectorizer= CountVectorizer()
#token each work into a numbers
counts= vectorizer.fit_transform(data['message'].values)


#perform the  the Naive Bayers.
classifier= MultinomialNB() 
target= data['class'].values
classifier.fit(counts, target)


test= ['Low selling viagra now!!', "Hi Samuel, how about a hackthon tomorrow?"]

test_counts= vectorizer.transform(test)



#test the model to see if it can if identify the first message as spam and the second one
#as ham.
predictions= classifier.predict(test_counts)
print(predictions)