如何存储经过训练的分类器？

Question

分类器被训练来预测新闻类时，URL被送入它的

目前：对于每一个输入，我训练分类，然后返回输出，因此我失去了训练的分类

预期：一旦我训练分类器，我应该能够从存储器调用此分类每当需要

任何光到该将高度赞赏。

PS：在业余NLP

Answer 1

是that你想要什么？从docs

例如：

>>> from sklearn import svm 
>>> from sklearn import datasets 
>>> clf = svm.SVC() 
>>> iris = datasets.load_iris() 
>>> X, y = iris.data, iris.target 
>>> clf.fit(X, y) 
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, 
    decision_function_shape=None, degree=3, gamma='auto', kernel='rbf', 
    max_iter=-1, probability=False, random_state=None, shrinking=True, 
    tol=0.001, verbose=False) 

>>> import pickle 
>>> s = pickle.dumps(clf) # <-- save/dump classifier to disk 
>>> clf2 = pickle.loads(s) # <-- read/load saved classifier from disk to a new variable 
>>> clf2.predict(X[0:1])  # <-- use loaded (from disk) classifier 
array([0]) 
>>> y[0] 
0 

Answer 2

您可以使用味酸/的cPickle转储/加载模型。

import pickle 

#Some COde 
model_path = "classifier.model" 
if not os.path.exists(model_path): 
    #Some Code 
    classifier = # Some Classifer 
    pickle.dump(classifier, open(model_path, "wb")) 
else: 
    classifier = pickle.load(open(model_path, "rb")) 

#Some Code 

欲了解更多信息：Pickle/cPickle