问题
I am using sklearn
's Pipeline
and FunctionTransformer
with a custom function
from sklearn.externals import joblib
from sklearn.preprocessing import FunctionTransformer
from sklearn.pipeline import Pipeline
This is my code:
def f(x):
return x*2
pipe = Pipeline([("times_2", FunctionTransformer(f))])
joblib.dump(pipe, "pipe.joblib")
del pipe
del f
pipe = joblib.load("pipe.joblib") # Causes an exception
And I get this error:
AttributeError: module '__ main__' has no attribute 'f'
How can this be resolved ?
Note that this issue occurs also in pickle
回答1:
I was able to hack a solution using the marshal
module (in addition to pickle
) and override the magic methods getstate
and setstate
used by pickle
.
import marshal
from types import FunctionType
from sklearn.base import BaseEstimator, TransformerMixin
class MyFunctionTransformer(BaseEstimator, TransformerMixin):
def __init__(self, f):
self.func = f
def __call__(self, X):
return self.func(X)
def __getstate__(self):
self.func_name = self.func.__name__
self.func_code = marshal.dumps(self.func.__code__)
del self.func
return self.__dict__
def __setstate__(self, d):
d["func"] = FunctionType(marshal.loads(d["func_code"]), globals(), d["func_name"])
del d["func_name"]
del d["func_code"]
self.__dict__ = d
def fit(self, X, y=None):
return self
def transform(self, X):
return self.func(X)
Now, if we use MyFunctionTransformer
instead of FunctionTransformer
, the code works as expected:
from sklearn.externals import joblib
from sklearn.pipeline import Pipeline
@MyFunctionTransformer
def my_transform(x):
return x*2
pipe = Pipeline([("times_2", my_transform)])
joblib.dump(pipe, "pipe.joblib")
del pipe
del my_transform
pipe = joblib.load("pipe.joblib")
The way this works, is by deleting the function f
from the pickle, and instead marshaling
its code, and its name.
dill also looks like a good alternative to marshaling
来源:https://stackoverflow.com/questions/54012769/saving-an-sklearn-functiontransformer-with-the-function-it-wraps