在特定文件上测试 NLTK 分类器 [英] Testing the NLTK classifier on specific file
问题描述
以下代码运行朴素贝叶斯电影评论分类器.该代码生成了一份信息量最大的功能列表.
The following code run Naive Bayes movie review classifier. The code generate a list of the most informative features.
注意: **movie review** 文件夹在 nltk 中.
from itertools import chain
from nltk.corpus import stopwords
from nltk.probability import FreqDist
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import movie_reviews
stop = stopwords.words('english')
documents = [([w for w in movie_reviews.words(i) if w.lower() not in stop and w.lower() not in string.punctuation], i.split('/')[0]) for i in movie_reviews.fileids()]
word_features = FreqDist(chain(*[i for i,j in documents]))
word_features = word_features.keys()[:100]
numtrain = int(len(documents) * 90 / 100)
train_set = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in documents[:numtrain]]
test_set = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in documents[numtrain:]]
classifier = NaiveBayesClassifier.train(train_set)
print nltk.classify.accuracy(classifier, test_set)
classifier.show_most_informative_features(5)
如何在特定文件上测试分类器?
如果我的问题有歧义或错误，请告诉我.
Please let me know if my question is ambiguous or wrong.
推荐答案
首先，仔细阅读这些答案，它们包含您需要的部分答案，并简要说明分类器的作用以及它在 NLTK 中的工作原理:
First, read these answers carefully, they contain parts of the answers you require and also briefly explains what the classifier does and how it works in NLTK:
在注释数据上测试分类器
现在回答你的问题.我们假设您的问题是这个问题的后续:在NLTK中使用我自己的语料库而不是movie_reviews语料库进行分类
Now to answer your question. We assume that your question is a follow-up of this question: Using my own corpus instead of movie_reviews corpus for Classification in NLTK
如果您的测试文本的结构与 movie_review 语料库的结构相同，那么您可以像读取训练数据一样简单地读取测试数据:
If your test text is structured the same way as the movie_review corpus, then you can simply read the test data as you would for the training data:
以防万一代码的解释不清楚，这里有一个演练:
Just in case the explanation of the code is unclear, here's a walkthrough:
traindir = '/home/alvas/my_movie_reviews'
mr = CategorizedPlaintextCorpusReader(traindir, r'(?!.).*.txt', cat_pattern=r'(neg|pos)/.*', encoding='ascii')
上面两行是读取一个目录my_movie_reviews，结构如下:
The two lines above is to read a directory my_movie_reviews with such a structure:
my_movie_reviews
pos
123.txt
234.txt
eg
456.txt
789.txt
README
然后下一行提取带有 pos/neg 标记的文档，该标记是目录结构的一部分.
Then the next line extracts documents with its pos/neg tag that's part of the directory structure.
documents = [([w for w in mr.words(i) if w.lower() not in stop and w not in string.punctuation], i.split('/')[0]) for i in mr.fileids()]
以下是对上述行的解释:
Here's the explanation for the above line:
# This extracts the pos/neg tag
labels = [i for i.split('/')[0]) for i in mr.fileids()]
# Reads the words from the corpus through the CategorizedPlaintextCorpusReader object
words = [w for w in mr.words(i)]
# Removes the stopwords
words = [w for w in mr.words(i) if w.lower() not in stop]
# Removes the punctuation
words = [w for w in mr.words(i) w not in string.punctuation]
# Removes the stopwords and punctuations
words = [w for w in mr.words(i) if w.lower() not in stop and w not in string.punctuation]
# Removes the stopwords and punctuations and put them in a tuple with the pos/neg labels
documents = [([w for w in mr.words(i) if w.lower() not in stop and w not in string.punctuation], i.split('/')[0]) for i in mr.fileids()]
读取测试数据时应该应用相同的过程！！！
现在进入特征处理:
以下几行是分类器的额外前 100 个特征:
The following lines extra top 100 features for the classifier:
# Extract the words features and put them into FreqDist
# object which records the no. of times each unique word occurs
word_features = FreqDist(chain(*[i for i,j in documents]))
# Cuts the FreqDist to the top 100 words in terms of their counts.
word_features = word_features.keys()[:100]
接下来将文档处理为可分类的格式:
Next to processing the documents into classify-able format:
# Splits the training data into training size and testing size
numtrain = int(len(documents) * 90 / 100)
# Process the documents for training data
train_set = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in documents[:numtrain]]
# Process the documents for testing data
test_set = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in documents[numtrain:]]
现在解释train_set和`test_set的长列表推导式:
Now to explain that long list comprehension for train_set and `test_set:
# Take the first `numtrain` no. of documents
# as training documents
train_docs = documents[:numtrain]
# Takes the rest of the documents as test documents.
test_docs = documents[numtrain:]
# These extract the feature sets for the classifier
# please look at the full explanation on https://stackoverflow.com/questions/20827741/nltk-naivebayesclassifier-training-for-sentiment-analysis/
train_set = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in train_docs]
测试文档中的特征提取也需要像上面一样处理文档！！！
这里是如何读取测试数据:
So here's how you can read the test data:
stop = stopwords.words('english')
# Reads the training data.
traindir = '/home/alvas/my_movie_reviews'
mr = CategorizedPlaintextCorpusReader(traindir, r'(?!.).*.txt', cat_pattern=r'(neg|pos)/.*', encoding='ascii')
# Converts training data into tuples of [(words,label), ...]
documents = [([w for w in mr.words(i) if w.lower() not in stop and w not in string.punctuation], i.split('/')[0]) for i in mr.fileids()]
# Now do the same for the testing data.
testdir = '/home/alvas/test_reviews'
mr_test = CategorizedPlaintextCorpusReader(testdir, r'(?!.).*.txt', cat_pattern=r'(neg|pos)/.*', encoding='ascii')
# Converts testing data into tuples of [(words,label), ...]
test_documents = [([w for w in mr_test.words(i) if w.lower() not in stop and w not in string.punctuation], i.split('/')[0]) for i in mr_test.fileids()]
然后继续上述处理步骤，只需按照@yvespeirsman 的回答执行此操作即可获取测试文档的标签:
Then continue with the processing steps described above, and simply do this to get the label for the test document as @yvespeirsman answered:
#### FOR TRAINING DATA ####
stop = stopwords.words('english')
# Reads the training data.
traindir = '/home/alvas/my_movie_reviews'
mr = CategorizedPlaintextCorpusReader(traindir, r'(?!.).*.txt', cat_pattern=r'(neg|pos)/.*', encoding='ascii')
# Converts training data into tuples of [(words,label), ...]
documents = [([w for w in mr.words(i) if w.lower() not in stop and w not in string.punctuation], i.split('/')[0]) for i in mr.fileids()]
# Extract training features.
word_features = FreqDist(chain(*[i for i,j in documents]))
word_features = word_features.keys()[:100]
# Assuming that you're using full data set
# since your test set is different.
train_set = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in documents]
#### TRAINS THE TAGGER ####
# Train the tagger
classifier = NaiveBayesClassifier.train(train_set)
#### FOR TESTING DATA ####
# Now do the same reading and processing for the testing data.
testdir = '/home/alvas/test_reviews'
mr_test = CategorizedPlaintextCorpusReader(testdir, r'(?!.).*.txt', cat_pattern=r'(neg|pos)/.*', encoding='ascii')
# Converts testing data into tuples of [(words,label), ...]
test_documents = [([w for w in mr_test.words(i) if w.lower() not in stop and w not in string.punctuation], i.split('/')[0]) for i in mr_test.fileids()]
# Reads test data into features:
test_set = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in test_documents]
#### Evaluate the classifier ####
for doc, gold_label in test_set:
tagged_label = classifier.classify(doc)
if tagged_label == gold_label:
print("Woohoo, correct")
else:
print("Boohoo, wrong")
如果以上代码和解释对您来说没有意义，那么您必须在继续之前阅读本教程:http://www.nltk.org/howto/classify.html
If the above code and explanation makes no sense to you, then you MUST read this tutorial before proceeding: http://www.nltk.org/howto/classify.html
现在假设您的测试数据中没有注释，即您的 test.txt 不在像 movie_review 那样的目录结构中，而只是一个纯文本文件:
Now let's say you have no annotation in your test data, i.e. your test.txt is not in the directory structure like the movie_review and just a plain textfile:
est_movie_reviews
1.txt
2.txt
然后将其读入分类语料库是没有意义的，您可以简单地阅读并标记文档，即:
Then there's no point in reading it into a categorized corpus, you can simply do read and tag the documents, i.e.:
for infile in os.listdir(`test_movie_reviews):
for line in open(infile, 'r'):
tagged_label = classifier.classify(doc)
但是你不能在没有注释的情况下评估结果，所以如果if-else，你不能检查标签，而且你需要标记你的文本 如果您没有使用 CategorizedPlaintextCorpusReader.
BUT you CANNOT evaluate the results without annotation, so you can't check the tag if the if-else, also you need to tokenize your text if you're not using the CategorizedPlaintextCorpusReader.
如果你只想标记一个纯文本文件 test.txt:
If you just want to tag a plaintext file test.txt:
import string
from itertools import chain
from nltk.corpus import stopwords
from nltk.probability import FreqDist
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import movie_reviews
from nltk import word_tokenize
stop = stopwords.words('english')
# Extracts the documents.
documents = [([w for w in movie_reviews.words(i) if w.lower() not in stop and w.lower() not in string.punctuation], i.split('/')[0]) for i in movie_reviews.fileids()]
# Extract the features.
word_features = FreqDist(chain(*[i for i,j in documents]))
word_features = word_features.keys()[:100]
# Converts documents to features.
train_set = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in documents]
# Train the classifier.
classifier = NaiveBayesClassifier.train(train_set)
# Tag the test file.
with open('test.txt', 'r') as fin:
for test_sentence in fin:
# Tokenize the line.
doc = word_tokenize(test_sentence.lower())
featurized_doc = {i:(i in doc) for i in word_features}
tagged_label = classifier.classify(featurized_doc)
print(tagged_label)
再次强调，请不要只是复制和粘贴解决方案，而要尝试了解其工作原理和方式.
Once again, please don't just copy and paste the solution and try to understand why and how it works.
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