spamclassifier.py

#Importing Libraries

import os
import io
import numpy
from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB


#Function to read the messages leaving the header from each of the files and adding it
#to the list for classification

def readFiles(path):
    for root, dirnames, 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


#Function to append the message and their particular classification

def dataFrameFromDirectory(path, classification):
    rows = []
    index = []
    for filename, message in readFiles(path):
        rows.append({'message': message, 'class': classification})
        index.append(filename)

    return DataFrame(rows, index=index)


#Main to call functions
data = DataFrame({'message': [], 'class': []})

data = data.append(dataFrameFromDirectory('G:\spam classifier/emails/spam', 'spam'))
data = data.append(dataFrameFromDirectory('G:\spam classifier/emails/ham', 'ham'))




#Reading Data Frame
data.head()



#Training data using MultinomialNB classifier
vectorizer = CountVectorizer()
counts = vectorizer.fit_transform(data['message'].values)

classifier = MultinomialNB()
targets = data['class'].values
classifier.fit(counts, targets)


#Predicting the Spam Emails

examples = ['Free Viagra now!!!', "Hi Bob, how about a game of golf tomorrow?"]
example_counts = vectorizer.transform(examples)
predictions = classifier.predict(example_counts)
predictions