【4.4.1】模型的保存与调用

当训练好一个模型以后，希望其能在以后的数据分析中被调用，常用的有pickle和joblib两种方式来保存模型。

一、Pickle来保存模型

代码：

# Save Model Using Pickle
import pandas
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
import pickle
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = pandas.read_csv(url, names=names)
array = dataframe.values
X = array[:,0:8]
Y = array[:,8]
test_size = 0.33
seed = 7
X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size=test_size, random_state=seed)
# Fit the model on 33%
model = LogisticRegression()
model.fit(X_train, Y_train)
# save the model to disk
filename = 'finalized_model.sav'
pickle.dump(model, open(filename, 'wb'))
 
# some time later...
 
# load the model from disk
loaded_model = pickle.load(open(filename, 'rb'))

result = loaded_model.score(X_test, Y_test)
print(result)

result2 = loaded_model.predict(X_test)
print result2

结果：

0.7559055118110236
[0. 1. 1. 0. 0. 0. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.
 1. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 1. 0. 1. 1.
 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0.
 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1.
 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1.
 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.
 0. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.
 1. 1. 0. 0. 1. 0. 0. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0.
 1. 1. 0. 0. 0. 1. 1. 0. 0. 1. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 1. 1. 0.
 1. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 1. 1.]

二、joblib保存模型

代码：

# Save Model Using joblib
import pandas
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.externals import joblib
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = pandas.read_csv(url, names=names)
array = dataframe.values
X = array[:,0:8]
Y = array[:,8]
test_size = 0.33
seed = 7
X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size=test_size, random_state=seed)
# Fit the model on 33%
model = LogisticRegression()
model.fit(X_train, Y_train)


# 保存模型
filename = 'finalized_model.sav'
joblib.dump(model, filename)
 
# some time later...
 
# 调用模型
loaded_model = joblib.load(filename)
result = loaded_model.score(X_test, Y_test)
print(result)

结果：

0.7559055118110236

三、讨论

在以后调用之前创建的模型的时候，主要注意以下几点：

1. python 和scikit-learn版本需要跟之前一致，不一致可能会导致一些报错
2. 不要unpickle不确定的数据
3. 为了避免以后模型有变，训练模型的数据最好不要删。

The pickle API for serializing standard Python objects. The joblib API for efficiently serializing Python objects with NumPy arrays.

参考资料

• http://scikit-learn.org/stable/modules/model_persistence.html
• https://machinelearningmastery.com/save-load-machine-learning-models-python-scikit-learn/