标签：res log stat svm ssi false extern weight 1.0
一、 sklearn中提供了高效的模型持久化模块joblib，将模型保存至硬盘。
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。
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]))
==========================================================================================================
joblib是sklearn的外部模块。
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]))
最后可以知道joblib在使用上比较容易，读取速度也相对pickle快。
链接：https://www.zhihu.com/question/27187105/answer/55895472
标签：res log stat svm ssi false extern weight 1.0
原文地址：https://www.cnblogs.com/USTC-ZCC/p/10106725.html
