Python中类对象的属性错误-朴素贝叶斯

问题描述

我正在尝试从头开始制作Naive Bayes分类器，以便在Python中过滤垃圾邮件。当我尝试训练课程时，我不断收到此错误：

Traceback (most recent call last):
  File "/Users/francescacape/Desktop/testnb.py",line 97,in <module>
    nb.train(traindata)
  File "/Users/francescacape/Desktop/testnb.py",line 36,in train
    self._hamdocs += 1
AttributeError: 'NLPNaiveBayes' object has no attribute '_hamdocs'

我是编程的新手，这是我建立的第一个类。我不知道为什么会这样，因为属性是在构造函数下定义的。抱歉，我的代码有点长。

from collections import defaultdict
import math
from nltk.tokenize import word_tokenize
import glob



class NLPNaiveBayes:

    def _init__(self):
        self._spamdocs = 0
        self._hamdocs = 0
        self.totaldocs = self._spamdocs + self._hamdocs
        self.spamwordcount = defaultdict(int)
        self.hamwordcount = defaultdict(int)
        self.spamwords = {}
        self.hamwords = {}
        self.totalwords = set()
        self.totalspamwords = []
        self.totalhamwords = []
        self.priorlogham = math.log(self._hamdocs / self.totaldocs)
        self.priorlogspam = math.log(self._spamdocs / self.totaldocs)

    @staticmethod
    def preprocessing(message):
        toks = list(word_tokenize(message))
        words = [word.islower() for word in toks if word.isalnum()]
        return set(words)

    def train(self,data):
        for message,cat in data:
            if is_spam:
                self._spamdocs += 1
                self.totalspamwords.append(message)
            else:
                self._hamdocs += 1
                self.totalhamwords.append(message)
            words = self.preprocessing(message)
            for word in words:
                if is_spam:
                    self.totalwords.update(word)
                    self.spamwords.update(word)
                    self.spamwordcount[word] += 1
                else:
                    self.totalwords.update(word)
                    self.hamwords.update(word)
                    self.hamwordcount[word] += 1

        for word,count in self.totalspamwords.item():
            self.spamwords[word] = math.log((int(count) + 1)) / (self._spamdocs + self.totaldocs)
        for word,count in self.totalhamwords.item():
            self.hamwords[word] = math.log((int(count) + 1)) / (self._hamdocs + self.totaldocs)


nb = NLPNaiveBayes()
nb.train(traindata)

解决方法

如果这是您代码的精确副本，那么我认为您的init方法名称不正确-应该__init__-前后两个下划线。您似乎只有一个下划线（_init__之后）。

由于名称错误，这意味着在构造实例时将不会执行该操作。