用户警告：您的 stop_words 可能与您的预处理不一致

Question

我正在关注this 教程，使用以下代码制作聊天机器人。

import nltk
import numpy as np
import random
import string
import bs4 as bs
import urllib.request
import re

# Creating the Corpus
raw_html = urllib.request.urlopen('https://en.wikipedia.org/wiki/Tennis')
raw_html = raw_html.read()
article_html = bs.BeautifulSoup(raw_html, 'lxml')
article_paragraphs = article_html.find_all('p')

article_text = ''

for para in article_paragraphs:
    article_text += para.text

article_text = article_text.lower()

# Text Preprocessing
article_text = re.sub(r'\[[0-9]*\]', ' ', article_text)
article_text = re.sub(r'\s+', ' ', article_text)
article_sentences = nltk.sent_tokenize(article_text)
article_words = nltk.word_tokenize(article_text)

wnlemmatizer = nltk.stem.WordNetLemmatizer()

# Helper Function
def perform_lemmatization(tokens):
    return [wnlemmatizer.lemmatize(token) for token in tokens]

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

def get_processed_text(document):
    return perform_lemmatization(nltk.word_tokenize(document.lower().translate(punctuation_removal)))

# Responding to Greetings
greeting_inputs = ("hey", "good morning", "good evening", "morning", "evening", "hi", "whatsup")
greeting_responses = ["hey", "hey hows you?", "*nods*", "hello, how you doing", "hello", "Welcome, I am good and you"]

def generate_greeting_response(greeting):
    for token in greeting.split():
        if token.lower() in greeting_inputs:
            return random.choice(greeting_responses)
        else:
            return 'Try again'

# Responding to User Queries
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

def generate_response(user_input):
    tennisrobo_response = ''
    article_sentences.append(user_input)

    word_vectorizer = TfidfVectorizer(tokenizer=get_processed_text, stop_words='english')
    all_word_vectors = word_vectorizer.fit_transform(article_sentences)
    similar_vector_values = cosine_similarity(all_word_vectors[-1], all_word_vectors)
    similar_sentence_number = similar_vector_values.argsort()[0][-2]

    matched_vector = similar_vector_values.flatten()
    matched_vector.sort()
    vector_matched = matched_vector[-2]

    if vector_matched == 0:
        tennisrobo_response = tennisrobo_response + "I am sorry, I could not understand you"
        return tennisrobo_response
    else:
        tennisrobo_response = tennisrobo_response + article_sentences[similar_sentence_number]
        return tennisrobo_response
print(generate_response('tennis'))

运行代码，出现如下错误：

UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.
  warnings.warn('Your stop_words may be inconsistent with '

在谷歌搜索后，我得到了链接到 this 的答案，说我的停用词和分词器之间可能存在不一致。但是，我对 python 和 NLTK 很陌生，找不到不一致的地方。

导致此错误的代码部分在哪里？

Answer 1

代码运行没有问题，请注意你得到的不是错误，而是警告。请注意，您可以使用

来禁止所有警告

import warnings
warnings.filterwarnings("ignore")

由于您使用的是自定义预处理器/标记器，因此出现警告。请参阅调用perform_lemmatization(nltk.word_tokenize(document.lower().translate(punctuation_removal))) 的get_processed_text 方法。 如果您删除词形还原，您将不会看到警告：

def get_processed_text(document):
    return nltk.word_tokenize(document.lower().translate(punctuation_removal))