我是编程的新手，所以请耐心等待，因为我上周开始学习python。我愿意发布您需要的更多信息，但请记住，我是一个n00b。
我的问题：
我正在使用带有python 2.7的Visual Studio Code的MACOSX Sierra并运行到YUGE数据处理时间（即5分钟以上，接近10分钟以上，以及此特定代码30分钟以上）
有什么建议吗？我真的无法在任何地方找到解决方案。
运行这些进程时，我的CPU活动监视器稳定在98％，我不知道这是正常的，也不知道该做些什么来加快速度。
警告：
在简单的编码中，我的处理时间并不算太糟糕，但是当算法被引入时，事情就会陷入困境并且令人沮丧。
下面是我正在使用的编码似乎运行正常，除了疯狂的处理时间，最后包含输出：
import nltk
import random
from nltk.corpus import movie_reviews
from nltk.classify.scikitlearn import SklearnClassifier
import pickle
from sklearn.naive_bayes import MultinomialNB, GaussianNB, BernoulliNB
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.svm import SVC, LinearSVC, NuSVC
from nltk.classify import ClassifierI
from statistics import mode
class VoteClassifier(ClassifierI):
def __init__(self, *classifiers):
self._classifiers = classifiers
def classify(self, features):
votes = []
for c in self._classifiers:
v = c.classify(features)
votes.append(v)
return mode(votes)
def confidence(self, features):
votes = []
for c in self._classifiers:
v = c.classify(features)
votes.append(v)
choice_votes = votes.count(mode(votes))
conf = choice_votes / len(votes)
return conf
documents = [(list(movie_reviews.words(fileid)), category)
for category in movie_reviews.categories()
for fileid in movie_reviews.fileids(category)]
random.shuffle(documents)
all_words = []
for w in movie_reviews.words():
all_words.append(w.lower())
all_words = nltk.FreqDist(all_words)
word_features = list(all_words.keys())[:3000]
def find_features(document):
words = set(document)
features = {}
for w in word_features:
features[w] = (w in words)
return features
# print((find_features(movie_reviews.words('neg/cv000_29416.txt'))))
featuresets = [(find_features(rev), category) for (rev, category) in documents]
training_set = featuresets[:1900]
testing_set = featuresets[:1900:]
# classifier = nltk.NaiveBayesClassifier.train(training_set)
classifier_f = open("naivebayes.pickle", "rb")
classifier = pickle.load(classifier_f)
classifier_f.close()
print("Original Naive Bayes Algo accuracy percent:", (nltk.classify.accuracy(classifier, testing_set))*100)
classifier.show_most_informative_features(15)
# save_classifier = open("naivebayes.pickle", "wb")
# pickle.dump(classifier, save_classifier)
# save_classifier.close()
MNB_classifier = SklearnClassifier(MultinomialNB())
MNB_classifier.train(training_set)
print("MNB_classifier accuracy percent:", (nltk.classify.accuracy(MNB_classifier, testing_set))*100)
# GaussianNB_classifier = SklearnClassifier(GaussianNB())
# GaussianNB_classifier.train(training_set)
# print("GaussianNB_classifier accuracy percent:", (nltk.classify.accuracy(GaussianNB_classifier, testing_set))*100)
BernoulliNB_classifier = SklearnClassifier(BernoulliNB())
BernoulliNB_classifier.train(training_set)
print("BernoulliNB_classifier accuracy percent:", (nltk.classify.accuracy(BernoulliNB_classifier, testing_set))*100)
LogisticRegression_classifier = SklearnClassifier(LogisticRegression())
LogisticRegression_classifier.train(training_set)
print("LogisticRegression_classifier accuracy percent:", (nltk.classify.accuracy(LogisticRegression_classifier, testing_set))*100)
SGDClassifier_classifier = SklearnClassifier(SGDClassifier())
SGDClassifier_classifier.train(training_set)
print("SGDClassifier_classifier accuracy percent:", (nltk.classify.accuracy(SGDClassifier_classifier, testing_set))*100)
# SVC_classifier = SklearnClassifier(SVC())
# SVC_classifier.train(training_set)
# print("SVC_classifier accuracy percent:", (nltk.classify.accuracy(SVC_classifier, testing_set))*100)
LinearSVC_classifier = SklearnClassifier(LinearSVC())
LinearSVC_classifier.train(training_set)
print("LinearSVC_classifier accuracy percent:", (nltk.classify.accuracy(LinearSVC_classifier, testing_set))*100)
NuSVC_classifier = SklearnClassifier(NuSVC())
NuSVC_classifier.train(training_set)
print("NuSVC_classifier accuracy percent:", (nltk.classify.accuracy(NuSVC_classifier, testing_set))*100)
voted_classifier = VoteClassifier(classifier, MNB_classifier, BernoulliNB_classifier, LogisticRegression_classifier, SGDClassifier_classifier, LinearSVC_classifier, NuSVC_classifier)
print("voted_classifier accuracy percent:", (nltk.classify.accuracy(voted_classifier, testing_set))*100)
print("Classication:", voted_classifier.classify(testing_set[0][0]), "Confidence %:", voted_classifier.confidence(testing_set[0][0])*100)
print("Classication:", voted_classifier.classify(testing_set[1][0]), "Confidence %:", voted_classifier.confidence(testing_set[1][0])*100)
print("Classication:", voted_classifier.classify(testing_set[2][0]), "Confidence %:", voted_classifier.confidence(testing_set[2][0])*100)
print("Classication:", voted_classifier.classify(testing_set[3][0]), "Confidence %:", voted_classifier.confidence(testing_set[3][0])*100)
print("Classication:", voted_classifier.classify(testing_set[4][0]), "Confidence %:", voted_classifier.confidence(testing_set[4][0])*100)
print("Classication:", voted_classifier.classify(testing_set[5][0]), "Confidence %:", voted_classifier.confidence(testing_set[5][0])*100)
('Original Naive Bayes Algo accuracy percent:', 87.31578947368422)
Most Informative Features
insulting = True neg : pos = 11.0 : 1.0
sans = True neg : pos = 9.0 : 1.0
refreshingly = True pos : neg = 8.4 : 1.0
wasting = True neg : pos = 8.3 : 1.0
mediocrity = True neg : pos = 7.7 : 1.0
dismissed = True pos : neg = 7.0 : 1.0
customs = True pos : neg = 6.3 : 1.0
fabric = True pos : neg = 6.3 : 1.0
overwhelmed = True pos : neg = 6.3 : 1.0
bruckheimer = True neg : pos = 6.3 : 1.0
wires = True neg : pos = 6.3 : 1.0
uplifting = True pos : neg = 6.2 : 1.0
ugh = True neg : pos = 5.8 : 1.0
stinks = True neg : pos = 5.8 : 1.0
lang = True pos : neg = 5.7 : 1.0
('MNB_classifier accuracy percent:', 89.21052631578948)
('BernoulliNB_classifier accuracy percent:', 86.42105263157895)
('LogisticRegression_classifier accuracy percent:', 94.47368421052632)
('SGDClassifier_classifier accuracy percent:', 85.73684210526315)
('LinearSVC_classifier accuracy percent:', 99.52631578947368)
('NuSVC_classifier accuracy percent:', 91.52631578947368)
('voted_classifier accuracy percent:', 93.36842105263158)
('Classication:', u'pos', 'Confidence %:', 100)
('Classication:', u'pos', 'Confidence %:', 0)
('Classication:', u'neg', 'Confidence %:', 0)
('Classication:', u'neg', 'Confidence %:', 100)
('Classication:', u'neg', 'Confidence %:', 100)
('Classication:', u'neg', 'Confidence %:', 100)
答案 0 :(得分：0)
我不确定是否有问题。电影评论语料库并不是那么大，但是训练分类器需要很长时间......而你训练其中的七个，有三千个特征。如果您开始使用较大的数据集，如果需要整晚训练一个分类器，请不要感到惊讶。
我建议您将训练脚本与测试脚本分开（您需要挑选所有训练过的模型），和/或在适当的时间打印带有时间戳的消息，以查看哪些分类器正在吞噬您的时间。 （另外：考虑从您的功能列表中删除常见的＆＃34;停用词＆＃34;喜欢＆＃34;＆＃34;，＆＃34; a＆＃34;，＆＃34;。＆＃34;等等。）