from sklearn.externals import joblib

#lr是一个LogisticRegression模型

joblib.dump(lr, 'lr.model')

lr = joblib.load('lr.model')

>>> 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)

>>> clf2 = pickle.loads(s)

>>> clf2.predict(X[0:1])

array([0])

>>> y[0]

0

或者 ：

>>> from sklearn.externals import joblib

>>> joblib.dump(clf, 'filename.pkl')

>>> clf = joblib.load('filename.pkl') 

两种保存Model的模块pickle与joblib。

使用 pickle 保存

首先简单建立与训练一个SVCModel。

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)

==========================================================================================================

使用pickle来保存与读取训练好的Model。 (若忘记什么是pickle，可以回顾13.8 pickle 保存数据视频。)

import pickle #pickle模块

#保存Model(注:save文件夹要预先建立，否则会报错)

with open('save/clf.pickle', 'wb') as f:

    pickle.dump(clf, f)

#读取Model

with open('save/clf.pickle', 'rb') as f:

    clf2 = pickle.load(f)

    #测试读取后的Model

    print(clf2.predict(X[0:1]))

==========================================================================================================

from sklearn.externals import joblib #jbolib模块

#保存Model(注:save文件夹要预先建立，否则会报错)

joblib.dump(clf, 'save/clf.pkl')

#读取Model

clf3 = joblib.load('save/clf.pkl')

#测试读取后的Model

print(clf3.predict(X[0:1]))

# [0]