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sklearn 내부의 pickle lib 를 통해 모델을 저장하고 다시 로드하여 재사용할 수 있다.
pickle.dump(obj, file[, protocol])-
Write a pickled representation of obj to the open file object file. This is equivalent to
Pickler(file, protocol).dump(obj).If the protocol parameter is omitted, protocol 0 is used. If protocol is specified as a negative value or
HIGHEST_PROTOCOL, the highest protocol version will be used.Changed in version 2.3: Introduced the protocol parameter.
file must have a
write()method that accepts a single string argument. It can thus be a file object opened for writing, aStringIOobject, or any other custom object that meets this interface.
pickle.load(file)-
Read a string from the open file object file and interpret it as a pickle data stream, reconstructing and returning the original object hierarchy. This is equivalent to
Unpickler(file).load().file must have two methods, a
read()method that takes an integer argument, and areadline()method that requires no arguments. Both methods should return a string. Thus file can be a file object opened for reading, aStringIOobject, or any other custom object that meets this interface.This function automatically determines whether the data stream was written in binary mode or not.
pickle.dumps(obj[, protocol])¶-
파일 저장은 아래와 같이 joblib 를 통해 저장할 수 있다.
In the specific case of the scikit, it may be more interesting to use joblib’s replacement of pickle (joblib.dump & joblib.load), which is more efficient on big data, but can only pickle to the disk and not to a string:
Later you can load back the pickled model (possibly in another Python process) with:
출처: http://fifthstory.tistory.com/entry/sklearn-model-백업-재사용 [다섯번째 이야기]
광고 클릭에서 발생하는 수익금은 모두 웹사이트 서버의 유지 및 관리, 그리고 기술 콘텐츠 향상을 위해 쓰여집니다.
