Question

如果标题混乱，我们很抱歉;让我解释一下。

所以，我已经编写了一个程序，使用来自sklearn的nltk和工具按主题对电子邮件进行分类。

以下是代码：

#Extract Emails
tech = extract_message("C:\\Users\\Cody\\Documents\\Emails\\tech.html")
gary = extract_message("C:\\Users\\Cody\\Documents\\Emails\\gary.html")
gary2 = extract_message("C:\\Users\\Cody\\Documents\\Emails\\gary2.html")
jesus = extract_message("C:\\Users\\Cody\\Documents\\Emails\\Jesus.html")
jesus2 = extract_message("C:\\Users\\Cody\\Documents\\Emails\\jesus2.html")
hockey = extract_message("C:\\Users\\Cody\\Documents\\Emails\\hockey.html")
hockey2 = extract_message("C:\\Users\\Cody\\Documents\\Emails\\hockey2.html")
shop = extract_message("C:\\Users\\Cody\\Documents\\Emails\\shop.html")

#Build dictionary of features
count_vect = CountVectorizer()
x_train_counts = count_vect.fit_transform(news.data)

#Downscaling
tfidf_transformer = TfidfTransformer()
x_train_tfidf = tfidf_transformer.fit_transform(x_train_counts)
tf_transformer = TfidfTransformer(use_idf=False).fit(x_train_counts)
x_train_tf = tf_transformer.transform(x_train_counts)

#Train classifier
clf = MultinomialNB().fit(x_train_tfidf, news.target)

#List of the extracted emails
docs_new = [gary, gary2, jesus, jesus2, shop, tech, hockey, hockey2]

#Extract feautures from emails
x_new_counts = count_vect.transform(docs_new)
x_new_tfidf = tfidf_transformer.transform(x_new_counts)

#Predict the categories for each email
predicted = clf.predict(x_new_tfidf)

现在，我希望根据预测的标签将每个变量存储在适当的列表中。我想我可以做到这一点：

#Store Files in a category
hockey_emails = []
computer_emails = []
politics_emails = []
tech_emails = []
religion_emails = []
forsale_emails = []

#Print out results and store each email in the appropritate category list
for doc, category in zip(docs_new, predicted):
  print('%r ---> %s' % (doc, news.target_names[category]))
   if(news.target_names[category] == 'comp.sys.ibm.pc.hardware'):
        computer_emails.append(doc)
   if(news.target_names[category] == 'rec.sport.hockey'):
        hockey_emails.append(doc)
   if(news.target_names[category] == 'talk.politics.misc'):
       politics_emails.append(doc)
   if(news.target_names[category] == 'soc.religion.christian'):
       religion_emails.append(doc)
   if(news.target_names[category] == 'misc.forsale'):
       forsale_emails.append(doc)
   if(news.target_names[category] == 'comp.sys.ibm.pc.hardware'):
       computer_emails.append(doc)

如果我打印出这些列表中的一个，我的输出就是说，例如曲棍球，显示存储在变量中的内容而不是变量本身。

我想要这个：

print(hockey_emails)

output: ['hockey', 'hockey2']

但我得到了这个：

 output: ['View View online click here Hi Thanks for signing up as a EA SPORTS NHL insider You ll now receive all of the latest and greatest news and info at this e mail address as you ve requested EA com If you need technical assistance please contact EA Help Privacy Policy Our Certified Online Privacy Policy gives you confidence whenever you play EA games To view our complete Privacy and Cookie Policy go to privacy ea com or write to Privacy Policy Administrator Electronic Arts Inc Redwood Shores Parkway Redwood City CA Electronic Arts Inc All Rights Reserved Privacy Policy User Agreement Legal ActionsMark as UnreadMark as ReadMark as SpamStarClear StarArchive Previous Next ', 'View News From The Hockey Writers The Editor s Choice stories from The Hockey Writers View this email in your browser edition Recap Stars Steamroll Predators By Matt Pryor on Dec am As the old Mary Chapin Carpenter song goes Sometimes you re the windshield Sometimes you re the bug It hasn t happened very often this season but the Dallas Stars had a windshield Continue Reading A Review of Years in Blue and White Damien Cox One on One By Anthony Fusco on Dec pm The Toronto Maple Leafs are one of the most storied and iconic franchises in the entire National Hockey League They have a century of history that spans all the way back to the early s When you have an Continue Reading Bruins Will Not Miss Beleskey By Kyle Benson on Dec am On Monday it was announced that Matt Beleskey will miss the next six weeks due to a knee injury he sustained over the weekend in a game against the Buffalo Sabres Six weeks is a long stint to be without a potential top Continue Reading Recent Articles Galchenyuk Injury Costly for CanadiensFacing Off Picking Team Canada for World JuniorsAre Johnson s Nomadic Days Over Share Tweet Forward Latest News Prospects Anaheim Ducks Arizona Coyotes Boston Bruins Buffalo Sabres Calgary Flames Carolina Hurricanes Chicago Blackhawks Colorado Avalanche Columbus Blue Jackets Dallas Stars Detroit Red Wings Edmonton Oilers Florida Panthers Los Angeles Kings Minnesota Wild Montreal Canadiens Nashville Predators New Jersey Devils New York Islanders New York Rangers Philadelphia Flyers Pittsburgh Penguins Ottawa Senators San Jose Sharks St Louis Blues Tampa Bay Lightning Toronto Maple Leafs Vancouver Canucks Washington Capitals Winnipeg Jets Copyright The Hockey Writers All rights reserved You are receiving this email because you opted in at The Hockey Writers or one of our Network Sites Our mailing address is The Hockey Writers Victoria Ave St Lambert QC J R R CanadaAdd us to your address book unsubscribe from this list update subscription preferences ActionsMark as UnreadMark as ReadMark as SpamStarClear StarArchive Previous Next ']

我认为这很简单，但我坐在这里挠挠脑袋。这甚至可能吗？我应该使用其他东西而不是列表吗？这可能很简单，我只是在消隐。

Answer 1

你必须自己跟踪这些名字，Python不会为你做这些。

println()

Answer 2

不要这样做。使用字典来保存您的电子邮件集合，当您想知道什么是字符时，您可以打印字典键。

docs_new = dict()
docs_new["tech"] = extract_message("C:\\Users\\Cody\\Documents\\Emails\\tech.html")
docs_new["gary"] = extract_message("C:\\Users\\Cody\\Documents\\Emails\\gary.html")
etc.

当你遍历字典时，你会看到键。

for doc, category in zip(docs_new, predicted):
    print('%s ---> %s' % (doc, news.target_names[category]))

（更多字典基础：要迭代dict值，请将上面的docs_new替换为docs_new.values();或使用docs_new.items()作为键和值。）

想要在列表中存储变量名称，而不是变量的内容

2 个答案: