朴素贝叶斯法(2) 之 恶意留言过滤

携程笔试的时候碰到了这个题目,当时其实没多想。贝叶斯这个路子怕也太过气了吧… 携程也真是…

回顾思路

  • 计算先验概率
  • 计算条件概率
  • 不同类别概率估计

原始数据集

代码

加载数据集

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import numpy as np

def loadDataSet():
postingList=[['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'],
['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'],
['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'],
['stop', 'posting', 'stupid', 'worthless', 'garbage'],
['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'],
['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']]
classVec = [0,1,0,1,0,1] #1 is abusive, 0 not
return postingList,classVec

这里类别为两类,1-恶意留言;0-非恶意留言。

vocab

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def getVocabList(dataSet):
vocab = {}
vocab_reverse = {}
index = 0
for line in dataSet:
for word in line:
if word not in vocab:
vocab[word] = index
vocab_reverse[index] = word
index += 1
return vocab,vocab_reverse

先验概率与条件概率

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def native_bayes(vocab,postingList,classVec):
# 先验概率
label = [0,1]
label_num = len(label)
vocab_len = len(vocab)

prior_probability = np.ones(label_num) # 初始化先验概率
conditional_probability = np.ones((label_num,vocab_len)) # 初始化条件概率
postingList_ids = [[vocab[word] for word in line]for line in postingList]
# 默认N为2,
p_n = np.array([2,2])

for i in range(len(postingList_ids)):
for word in postingList_ids[i]:
conditional_probability[classVec[i]][word]+=1
p_n[classVec[i]] += 1

# 条件概率
conditional_probability[0] /= p_n[0]
conditional_probability[1] /= p_n[1]

# 先验概率
all_N = sum(p_n)
p_n = p_n/all_N
return p_n,conditional_probability

argmax 判断

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def judge(testEntry):
postingList,classVec = loadDataSet()
vocab,vocab_reverse = getVocabList(postingList)
p_n,conditional_probability = native_bayes(vocab,postingList,classVec)
Ans_p = p_n

testEntry_ids = [vocab[word] for word in testEntry]
for num in testEntry_ids:
Ans_p[0] *= conditional_probability[0][num]
Ans_p[1] *= conditional_probability[1][num]
return np.argmax(Ans_p)

调用

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judge(testEntry = ['stupid', 'garbage'])

输出 1,和我们预期的一样。

朴素贝叶斯法(2) 之 恶意留言过滤

https://iii.run/archives/9c1c700671a6.html

作者

mmmwhy

发布于

2018-09-17

更新于

2021-05-30

许可协议