Question

昨天，我试图完成有关文本矢量化的Udacity的第11课。我遍历了代码，一切似乎都正常工作-我收到一些电子邮件，打开它们，删除一些签名词，然后将每封电子邮件的词干词返回到列表中。

这里是循环1：

for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
    for path in from_person:
        ### only look at first 200 emails when developing
        ### once everything is working, remove this line to run over full dataset
#        temp_counter += 1
    if temp_counter < 200:
        path = os.path.join('/xxx', path[:-1])
        email = open(path, "r")

        ### use parseOutText to extract the text from the opened email

        email_stemmed = parseOutText(email)

        ### use str.replace() to remove any instances of the words
        ### ["sara", "shackleton", "chris", "germani"]

        email_stemmed.replace("sara","")
        email_stemmed.replace("shackleton","")
        email_stemmed.replace("chris","")
        email_stemmed.replace("germani","")

    ### append the text to word_data

    word_data.append(email_stemmed.replace('\n', ' ').strip())

    ### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
        if from_person == "sara":
            from_data.append(0)
        elif from_person == "chris":
            from_data.append(1)

    email.close()

这里是循环2：

for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
    for path in from_person:
        ### only look at first 200 emails when developing
        ### once everything is working, remove this line to run over full dataset
#        temp_counter += 1
        if temp_counter < 200:
            path = os.path.join('/xxx', path[:-1])
            email = open(path, "r")

            ### use parseOutText to extract the text from the opened email
            stemmed_email = parseOutText(email)

            ### use str.replace() to remove any instances of the words
            ### ["sara", "shackleton", "chris", "germani"]
            signature_words = ["sara", "shackleton", "chris", "germani"]
            for each_word in signature_words:
                stemmed_email = stemmed_email.replace(each_word, '')         #careful here, dont use another variable, I did and broke my head to solve it

            ### append the text to word_data
            word_data.append(stemmed_email)

            ### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
            if name == "sara":
                from_data.append(0)
            else: # its chris
                from_data.append(1)


            email.close()

代码的下一部分按预期工作：

print("emails processed")
from_sara.close()
from_chris.close()

pickle.dump( word_data, open("/xxx/your_word_data.pkl", "wb") )
pickle.dump( from_data, open("xxx/your_email_authors.pkl", "wb") )


print("Answer to Lesson 11 quiz 19: ")
print(word_data[152])


### in Part 4, do TfIdf vectorization here

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction import stop_words
print("SKLearn has this many Stop Words: ")
print(len(stop_words.ENGLISH_STOP_WORDS))

vectorizer = TfidfVectorizer(stop_words="english", lowercase=True)
vectorizer.fit_transform(word_data)

feature_names = vectorizer.get_feature_names()

print('Number of different words: ')
print(len(feature_names))

但是当我用循环1计算单词的总数时，我得到了错误的结果。在循环2中进行操作时，我得到了正确的结果。

我一直在看这段代码太久了，我无法发现差异-在循环1中我做错了什么？

根据记录，我一直得到的错误答案是38825。正确答案应该是38757。

非常感谢您的帮助，亲切的陌生人！

Answer 1

这些行什么也没做：

email_stemmed.replace("sara","")
email_stemmed.replace("shackleton","")
email_stemmed.replace("chris","")
email_stemmed.replace("germani","")

replace返回一个新字符串，并且不修改email_stemmed。相反，您应该将返回值设置为email_stemmed：

email_stemmed = email_stemmed.replace("sara", "")

依此类推。

第二个循环确实在for循环中设置了返回值：

for each_word in signature_words:
    stemmed_email = stemmed_email.replace(each_word, '')

上面的代码段并不相同，因为email_stemmed的正确使用使第一个代码段replace的末尾完全不变，而第二个代码段的末尾{{ 1}}实际上已经删除了每个单词。

看起来它们应该执行相同操作的两个python循环，但是输出不同的结果？

1 个答案: