对停用词进行标记，生成的标记 ['ha', 'le', 'u', 'wa'] 不在 stop_words 中

我正在使用 Python 制作一个聊天机器人。 代码：

import nltk
import numpy as np
import random
import string 
f=open('/home/hostbooks/ML/stewy/speech/chatbot.txt','r',errors = 'ignore')
raw=f.read()
raw=raw.lower()# converts to lowercase

sent_tokens = nltk.sent_tokenize(raw)# converts to list of sentences 
word_tokens = nltk.word_tokenize(raw)# converts to list of words

lemmer = nltk.stem.WordNetLemmatizer()    

def LemTokens(tokens):
    return [lemmer.lemmatize(token) for token in tokens]

remove_punct_dict = dict((ord(punct), None) for punct in string.punctuation)

def LemNormalize(text):
    return LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict)))

GREETING_INPUTS = ("hello", "hi", "greetings", "sup", "what's up","hey","hii")
GREETING_RESPONSES = ["hi", "hey", "*nods*", "hi there", "hello", "I am glad! You are talking to me"]


def greeting(sentence):
    for word in sentence.split():
        if word.lower() in GREETING_INPUTS:
            return random.choice(GREETING_RESPONSES)

from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.metrics.pairwise import cosine_similarity

def response(user_response):
    robo_response=''
    sent_tokens.append(user_response)    

    TfidfVec = TfidfVectorizer(tokenizer=LemNormalize, stop_words='english')
    tfidf = TfidfVec.fit_transform(sent_tokens)
    vals = cosine_similarity(tfidf[-1], tfidf)
    idx=vals.argsort()[0][-2]
    flat = vals.flatten()
    flat.sort()
    req_tfidf = flat[-2]    

    if(req_tfidf==0):
        robo_response=robo_response+"I am sorry! I don't understand you"
        return robo_response
    else:
        robo_response = robo_response+sent_tokens[idx]
        return robo_response

flag=True
print("ROBO: My name is Robo. I will answer your queries about Chatbots. If you want to exit, type Bye!")

while(flag==True):
    user_response = input()
    user_response=user_response.lower()
    if(user_response!='bye'):
        if(user_response=='thanks' or user_response=='thank you' ):
            flag=False
            print("ROBO: You are welcome..")
        else:
            if(greeting(user_response)!=None):
                print("ROBO: "+greeting(user_response))
            else:
                print("ROBO: ",end="")
                print(response(user_response))
                sent_tokens.remove(user_response)
    else:
        flag=False
        print("ROBO: Bye! take care..")

它运行良好，但每次对话都会出现此错误：

/home/hostbooks/django1/myproject/lib/python3.6/site-packages/sklearn/feature_extraction/text.py:300: UserWarning: Your stop_words may be inconsistent with your preprocessing. 

Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.

以下是 CMD 的一些对话：

ROBO：聊天机器人是一种通过听觉或文本方法进行对话的软件。

印度是什么

    /home/hostbooks/django1/myproject/lib/python3.6/site-packages/sklearn/feature_extraction/text.py:300: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words. 'stop_words.' % sorted(inconsistent))

ROBO：印度的野生动物传统上在印度文化中被视为宽容，它们在这些森林和其他受保护的栖息地中得到了支持。

什么是聊天机器人

    /home/hostbooks/django1/myproject/lib/python3.6/site-packages/sklearn/feature_extraction/text.py:300: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words. 'stop_words.' % sorted(inconsistent))

ROBO：聊天机器人是一种通过听觉或文本方法进行对话的软件。

原因是你使用了自定义的tokenizer并使用默认值stop_words='english'因此，在提取特征时，会检查特征之间是否存在不一致stop_words and tokenizer

如果你更深入地研究代码sklearn/feature_extraction/text.py您会发现此代码片段执行一致性检查：

def _check_stop_words_consistency(self, stop_words, preprocess, tokenize):
    """Check if stop words are consistent

    Returns
    -------
    is_consistent : True if stop words are consistent with the preprocessor
                    and tokenizer, False if they are not, None if the check
                    was previously performed, "error" if it could not be
                    performed (e.g. because of the use of a custom
                    preprocessor / tokenizer)
    """
    if id(self.stop_words) == getattr(self, '_stop_words_id', None):
        # Stop words are were previously validated
        return None

    # NB: stop_words is validated, unlike self.stop_words
    try:
        inconsistent = set()
        for w in stop_words or ():
            tokens = list(tokenize(preprocess(w)))
            for token in tokens:
                if token not in stop_words:
                    inconsistent.add(token)
        self._stop_words_id = id(self.stop_words)

        if inconsistent:
            warnings.warn('Your stop_words may be inconsistent with '
                          'your preprocessing. Tokenizing the stop '
                          'words generated tokens %r not in '
                          'stop_words.' % sorted(inconsistent))

正如您所看到的，如果发现不一致，它会发出警告。

希望能帮助到你。