I would like to create a class that uses sklearn transformation methods. I found this article and I am using it as an example.

sklearn

from sklearn import preprocessing
from sklearn.base import TransformerMixin

def minmax(dataframe):
minmax_transformer = preprocessing.MinMaxScaler()
return minmax_tranformer


class FunctionFeaturizer(TransformerMixin):
def __init__(self, scaler):
self.scaler = scaler

def fit(self, X, y=None):
return self

def transform(self, X):
fv = self.scaler(X)
return fv

if __name__=="__main__":
scaling = FunctionFeaturizer(minmax)
df = pd.DataFrame({'feature': np.arange(10)})
df_scaled = scaling.fit(df).transform(df)
print(df_scaled)


The output is StandardScaler(copy=True, with_mean=True, with_std=True) which is actually the result of the preprocessing.StandardScaler().fit(df) if I use it out of the class.

StandardScaler(copy=True, with_mean=True, with_std=True)

preprocessing.StandardScaler().fit(df)

What I am expecting is:

array([[0. ],
[0.11111111],
[0.22222222],
[0.33333333],
[0.44444444],
[0.55555556],
[0.66666667],
[0.77777778],
[0.88888889],
[1. ]])


I am feeling that I am mixing few things here but I do not know what.

Update
I did some modifications:

def minmax():
return preprocessing.MinMaxScaler()

class FunctionFeaturizer(TransformerMixin):
def __init__(self, scaler):
self.scaler = scaler

def fit(self, X, y=None):
return self

def fit_transform(self, X):
self.scaler.fit(X)
return self.scaler.transform(X)

if __name__=="__main__":
scaling = FunctionFeaturizer(minmax)
df = pd.DataFrame({'feature': np.arange(10)})
df_scaled = scaling.fit_transform(df)
print(df_scaled)


But now I am receiving the following error:

Traceback (most recent call last):
File "C:/my_file.py", line 33, in <module>
test_scale = scaling.fit_transform(df)
File "C:/my_file.py", line 26, in fit_transform
self.scaler.fit(X)
AttributeError: 'function' object has no attribute 'fit'


fit()

scaler().transform()

transform()

transform()

self.scaler(X)

minmax(X)

minmax

X

dataframe

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