python - 在 NLTK 中保存朴素贝叶斯训练分类器

Question

对于如何保存经过训练的分类器，我有些困惑。例如，每次我想使用它时重新训练一个分类器显然真的很糟糕而且很慢，我如何保存它并在需要时再次加载它？代码如下，提前感谢您的帮助。我正在使用带有 NLTK 朴素贝叶斯分类器的 Python。

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()