1. 儲存變數

1.1 pickle(泡菜)

  pickle庫可以指定多個變數儲存在.pickle檔案中，如果需要儲存的變數不是很多，這種方法可以採用。

import pickle

# obj0, obj1, obj2 are created here...
obj0, obj1, obj2 = [1, 2], [2, 3], [3, 4]
# Saving the objects:
# 通過傳遞protocol = -1到dump()來減少檔案大小
with open('test.pickle', 'wb') as f:  # Python 3: open(..., 'wb')
 

    pickle.dump([obj0, obj1, obj2], f)
f.close()

# Getting back the objects:
with open('test.pickle', 'rb') as f:  # Python 3: open(..., 'rb')
    x0, x1, x2 = pickle.load(f)
print(x0)
f.close()

[1, 2]

1.2 cPickle

  cPickle的速度更快，其餘和cPickle基無差別。在python3裡面，cPickle變成_pickle。具體如下：

Docstring:   Optimized C implementation for the Python pickle module.
 

import  _pickle as cpickle

# obj0, obj1, obj2 are created here...
obj0, obj1, obj2 = [1, 2], [2, 3], [3, 4]
# Saving the objects:
# 通過傳遞protocol = -1到dump()來減少檔案大小
with open('test.pickle', 'wb') as f:  # Python 3: open(..., 'wb')
    cpickle.dump([obj0, obj1, obj2], f)
f.close()

del x0, x1, x2 
# Getting back the objects:
 

with open('test.pickle', 'rb') as f:  # Python 3: open(..., 'rb')
    x0, x1, x2 = cpickle.load(f)
print(x0)
f.close()

[1, 2]

1.3 shelve

  似乎不支援內建函式等其他的物件，也不是很智慧嘛～這樣到不如直接用pickle。參考：http://www.php.cn/python-tutorials-410803.html

import shelve

T='Hiya'
val=[1,2,3]

filename='test'
my_shelf = shelve.open(filename,'n') # 'n' for new

for key in dir():
    try:
        my_shelf[key] = globals()[key]
    except:
        #
        # __builtins__, my_shelf, and imported modules can not be shelved.
        #
        print('ERROR shelving: {0}'.format(key))
my_shelf.close()

del val,T

my_shelf = shelve.open(filename)
for key in my_shelf:
    globals()[key]=my_shelf[key]
my_shelf.close()

print(T)
# Hiya
print(val)
# [1, 2, 3]

1.4 dill

  pycharm裡可以儲存檔案，but在jupyter裡報錯，不知道是個啥原因啊～

dump_session(filename='/tmp/session.pkl', main=None, byref=False)
    pickle the current state of __main__ to a file

import dill
# 儲存檔案
filename = 'globalsave.pkl'
dill.dump_session(filename)

dill.load_session(filename)

2. 儲存模型檔案

2.1 .model檔案

2.1.1 訓練模型

import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn import cross_validation, metrics
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# -- 載入鳶尾花資料集
iris_dataset = load_iris()
# -- 資料處理&訓練集、測試集切分
rawdata = pd.DataFrame(iris_dataset['data'], columns=['x0', 'x1', 'x2', 'x3'])
rawlabel = pd.DataFrame(iris_dataset['target'], columns=['label'])
dt_model = DecisionTreeClassifier()

train_X, test_X, train_y, test_y = train_test_split(rawdata,
                                                    rawlabel, test_size=0.3, random_state=0)
dt_model.fit(X=train_X, y=train_y)


print(metrics.classification_report(train_y,
                                    dt_model.predict(X=train_X)))

print(metrics.classification_report(test_y,
                                    dt_model.predict(X=test_X)))

2.1.2 儲存&呼叫模型檔案。

from sklearn.externals import joblib

# 模型儲存
joblib.dump(dt_model, './Code/dt_model.model')
# 模型載入
dt_model_load = joblib.load('./Code/dt_model.model')

print(metrics.classification_report(test_y,
                                    dt_model_load.predict(X=test_X)))

2.2 pickle檔案

  發現pickle也可以用，不知道是否效能方面存在一定的問題～

import pickle
with open('dt_model.pickle', 'wb') as f:
    pickle.dump(dt_model, f)
f.close()

import pickle
with open('dt_model.pickle', 'rb') as f:  # Python 3: open(..., 'rb')
    x = pickle.load(f)

2.3 pmml檔案

  sklearn訓練的模型可以儲存為pmml檔案，似乎可以用java直接呼叫～後面用到再說，其餘不贅。

                                    2018-09-29 於南京 紫東創業園