在机器学习过程中，一般用来训练模型的过程比较长，所以我们一般会将训练的模型进行保存（持久化），然后进行评估，预测等等，这样便可以节省大量的时间。
在模型持久化过程中，我们使用scikit-learn提供的joblib.dump()方法，但是在使用过程中会出现很多问题。如我们使用如下语句：
- joblib.dump(clf,'../../data/model/randomforest.pkl')
此语句将产生大量的模型文件，如下图所示
然后，我们再使用joblib.load(‘../../data/model/randomforest.pkl’)进行加载，会出现如下错误：
- Traceback (most recent call last):
- File "E:\workspace\forest\com\baihe\RandomForest_losing.py", line 65, in <module>
- clf = joblib.load('../../data/model/randomforest.pkl')
- File "D:\Program Files\python27\lib\site-packages\sklearn\externals\joblib\numpy_pickle.py", line 425, in load
- obj = unpickler.load()
- File "D:\Program Files\python27\lib\pickle.py", line 858, in load
- dispatch[key](self)
- File "D:\Program Files\python27\lib\site-packages\sklearn\externals\joblib\numpy_pickle.py", line 285, in load_build
- Unpickler.load_build(self)
- File "D:\Program Files\python27\lib\pickle.py", line 1217, in load_build
- setstate(state)
- File "_tree.pyx", line 2280, in sklearn.tree._tree.Tree.__setstate__ (sklearn\tree\_tree.c:18350)
- ValueError: Did not recognise loaded array layout
正确使用joblib的方法是：设置dump中的compress参数，当设置参数时，模型持久化便会压缩成一个文件。源码中对compress参数的描述如下：
- compress: integer for 0 to 9, optional
- Optional compression level for the data. 0 is no compression.
- Higher means more compression, but also slower read and
- write times. Using a value of 3 is often a good compromise.
- See the notes for more details.
以下是我们进行模型持久化的正确操作语句：
-
- joblib.dump(clf,'../../data/model/randomforest.pkl',compress=3)
-
- clf = joblib.load('../../data/model/randomforest.pkl')