模型的保存和加载

1、方法一：pickle

from sklearn import svm
from sklearn import datasets

iris = datasets.load_iris()
x, y = iris.data, iris.target

model = svm.SVC()
model.fit(x, y)

import pickle
# save
 with open('model.pickle', 'wb') as f:
    pickle.dump(model, f)
# restore
with open('model.pickle', 'rb') as f:
   clf2 = pickle.load(f)
   print(clf2.predict(x[0:1]))

 

2、方法二：joblib

from sklearn import svm
from sklearn import datasets

iris = datasets.load_iris()
x, y = iris.data, iris.target

model = svm.SVC()
model.fit(x, y)

from sklearn.externals import joblib
# Save
joblib.dump(model, 'model.pkl')
# restore
clf3 = joblib.load('model.pkl')
print(clf3.predict(x[0:1]))

# joblib保存为二进制
from sklearn.externals import joblib
joblib.dump(model, 'model.m')
clf4 = joblib.load('model.m')
print(clf4.predict(x[0:1]))