python 保存对象文件

之前用 R 语言一直感觉 .Rdata 格式的文件很好用，可以把每次执行的中间文件保存便于下次调用，刚熟悉 Python 还没接触这块知识，所以有时候做项目不太顺手，索性上网搜了下，整理如下：

模型存档

#############
# joblib 库
from sklearn.linear_model import LogisticRegression
from sklearn.externals import joblib
# 模型保存
lr_model = LogisticRegression()
joblib.dump(lr_model, 'xx.model')
# 模型载入
lr_model = joblib.load('xx.model')

#############
# pickle 库
# 模型保存
import pickle
with open('lr_model.pickle', 'wb') as fp:
    pickle.dump(lr_model, fp)
# 模型载入
with open('lr_model.pickle', 'rb') as fp:
    lr_model = pickle.load(fp) 

　　命令行参数解析

import argparse

# 定义参数解析
def build_arg_parser():
    parser = argparse.ArgumentParser(description='Trains the classifier')
    parser.add_argument("--feature-map-file", dest="feature_map_file", required=True,
                        help="Input pickle file containing the feature map")
    parser.add_argument("--model-file", dest="model_file", required=False,
                        help="Output file where the trained model will be stored")
    return parser

args = build_arg_parser().parse_args()
feature_map_file = args.feature_map_file
model_file = args.model_file

　　

对象保存

#############
# pickle 库
import pickle
x, y = 1, range(10)
with open('xx.pickle', 'wb') as fp:
    # 通过传递protocol = -1到dump()来减少文件大小
    pickle.dump([x, y], fp)
# 对象载入
with open('xx.pickle', 'rb') as fp:
    x, y = pickle.load(fp)
print(x)

#############
# _pickle 库
# 对象保存
import  _pickle as cpickle
x, y = 1, range(10)
with open('xx.pickle', 'wb') as fp:
    # 通过传递protocol = -1到dump()来减少文件大小
    cpickle.dump([x, y], fp)
# 对象载入
del x, y
with open('xx.pickle', 'rb') as fp:
    x, y = cpickle.load(fp)
print(x)

############
# dill 库
import dill
# 文件保存
filename = 'globalsave.pkl'
dill.dump_session(filename)
# 文件载入
dill.load_session(filename)

############
# 其他库如：pmml，shelve