Python_Data_Science_第10课

#Bayesian Methods to create Anti-spammer
We can construct P(Spam | Word) for every (meaningful) word we encounter
during training.
Then multiply these together when analyzing a new mail to get the probability of it being spam.
Assumes the presence of different words are independent of each other - one reason this is called "Naive Bayes"

理论就是:  不考虑词和词之间的关系，简单的将每个词贡献的'spam‘值算出来，最后根据所有的这些词贡献出的'spam'值来分析新的邮件。

下面则是代码
首先是使用pandas读入数据，然后使用scikit-learn 来build 一个spam classifier, 最后使用这个spam classifier 来predict两个字符串到底应该归类spam 或者ham.

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Author: hezhb
# Created Time: Tue 01 May 2018 11:49:35 AM CST

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

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


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)


PATH='./hands-on/emails/'


data = DataFrame({'message':[], 'class':[]})
data = data.append(dataFrameFromDirectory(PATH+'spam', 'spam'))
data = data.append(dataFrameFromDirectory(PATH+'ham', 'ham'))
#print(data.head())

"""
Now we will use CountVectorizer to split up each message into its list of words
and throw that into a MultinomialNB classifier, call fit() and we've got
a trained spam filter ready to go.
"""

vectorizer = CountVectorizer(encoding='latin1')
counts = vectorizer.fit_transform(data['message'].values)
classifier = MultinomialNB()
targets = data['class'].values
classifier.fit(counts, targets)

#Now can try this classifier out
examples = ['Free viagra Now', 'Hi Bob, how about a game of golf tommorrow.']
example_counts = vectorizer.transform(examples)
predictions = classifier.predict(example_counts)
print(predictions)