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I'm trying to create a custom transformation but haven't been successful. The sample code provided works fine but when I define my own function with the same transformation it fails. See below for the exact snippets.
Works:
from sklearn import preprocessing
import numpy as np
# Applies log transformation to the feature
processor = preprocessing.FunctionTransformer(np.log1p)
Does not work:
from sklearn import preprocessing
import numpy as np
def CustomT(X):
return np.log1p(X)
# Applies log transformation to the feature
processor = preprocessing.FunctionTransformer(CustomT)
Error (more detailed logs available if required):
Failed to train : <type 'exceptions.TypeError'> : expected string or Unicode object, NoneType found
I used a pared down dataset to generate this log: https://we.tl/t-YWJABcMO5s
Hello @Alex_Combessie ,
Is it possible to do a Robustscaler as custom preprocessing ? If yes how possible is it possible to fit_transform the same model to all the features ?
Thanks in advance
Sure, there you go:
Note: this custom processor should be applied to each feature.