如何將訓練有素的樸素貝葉斯分類器保存到磁盤并用于預測數據？我有來自scikit-learn網站的以下示例程序：from sklearn import datasetsiris = datasets.load_iris()from sklearn.naive_bayes import GaussianNBgnb = GaussianNB()y_pred = gnb.fit(iris.data, iris.target).predict(iris.data)print "Number of mislabeled points : %d" % (iris.target != y_pred).sum()
3 回答
繁花如伊
TA貢獻2012條經驗 獲得超12個贊
分類器只是可以像其他任何東西一樣被腌制和傾倒的對象。繼續您的示例：
import cPickle
# save the classifier
with open('my_dumped_classifier.pkl', 'wb') as fid:
cPickle.dump(gnb, fid)
# load it again
with open('my_dumped_classifier.pkl', 'rb') as fid:
gnb_loaded = cPickle.load(fid)
鴻蒙傳說
TA貢獻1865條經驗 獲得超7個贊
您還可以使用joblib.dump和joblib.load，它們在處理數字數組方面比默認的python pickler效率更高。
Joblib包含在scikit-learn中：
>>> from sklearn.externals import joblib
>>> from sklearn.datasets import load_digits
>>> from sklearn.linear_model import SGDClassifier
>>> digits = load_digits()
>>> clf = SGDClassifier().fit(digits.data, digits.target)
>>> clf.score(digits.data, digits.target) # evaluate training error
0.9526989426822482
>>> filename = '/tmp/digits_classifier.joblib.pkl'
>>> _ = joblib.dump(clf, filename, compress=9)
>>> clf2 = joblib.load(filename)
>>> clf2
SGDClassifier(alpha=0.0001, class_weight=None, epsilon=0.1, eta0=0.0,
fit_intercept=True, learning_rate='optimal', loss='hinge', n_iter=5,
n_jobs=1, penalty='l2', power_t=0.5, rho=0.85, seed=0,
shuffle=False, verbose=0, warm_start=False)
>>> clf2.score(digits.data, digits.target)
0.9526989426822482
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