在NLTK中保存朴素贝叶斯训练分类器

Question

关于如何保存经过训练的分类器，我有些困惑。就像在其中一样，每次我想使用分类器时都要对其进行重新训练显然很糟糕而且很慢，如何保存它并在需要时再次加载它？代码如下，在此先感谢您的帮助。我正在将Python与NLTK朴素贝叶斯分类器一起使用。

classifier = nltk.NaiveBayesClassifier.train(training_set)
# look inside the classifier train method in the source code of the NLTK library

def train(labeled_featuresets, estimator=nltk.probability.ELEProbDist):
    # Create the P(label) distribution
    label_probdist = estimator(label_freqdist)
    # Create the P(fval|label, fname) distribution
    feature_probdist = {}
    return NaiveBayesClassifier(label_probdist, feature_probdist)

Answer 1

保存：

import pickle
f = open('my_classifier.pickle', 'wb')
pickle.dump(classifier, f)
f.close()

稍后加载：

import pickle
f = open('my_classifier.pickle', 'rb')
classifier = pickle.load(f)
f.close()