Dear Sir,
I have made such a transformer, below given is an example that generates 3
new columns, from existing 2 columns of a numpy array , first column is for
element wise addition, second is for element wise multiplication and third
is for element wise division .
>>> import numpy as np
>>> from sklearn.preprocessing import FunctionTransformer
>>> def col_add(x):
x1 = x[:, 0] + x[:, 1]
x2 = x[:, 0] * x[:, 1]
x3 = x[:, 0] / x[:, 1]
return np.c_[x, x1, x2, x3]
>>> col_adder = FunctionTransformer(col_add)
>>> arr = np.array([[2, 7], [4, 9], [3, 5]])
>>> arr
array([[2, 7],
[4, 9],
[3, 5]])
>>> col_adder.transform(arr) # will add 3 columns
array([[ 2. , 7. , 9. , 14. , 0.28571429],
[ 4. , 9. , 13. , 36. , 0.44444444],
[ 3. , 5. , 8. , 15. , 0.6 ]])
>>>
So in this way a function transformer can be used to add new features
generated from existing columns ?
On Fri, Jun 18, 2021 at 4:15 PM Manprit Singh <manpritsingh...@gmail.com>
wrote:
> Dear sir ,
>
> Just need to know if I can use a function transformer to generate new
> columns in the data set .
>
> Just see the below written pipeline
>
> num_pipeline = Pipeline([('imputer', SimpleImputer(strategy="median")),
> ('attribs_adder', column_adder),
> ('std_scaler', StandardScaler()),
> ])
> This pipeline is for numerical attributes in the dataset, firstly it will
> treat all mising values in the data set using SimpleImputer , then i have
> made a function to add three more columns in the existing data, i have made
> a function transformer with this function and then StandardScaler .
>
> The columns being added are generated from existing columns (by element
> wise division of two columns) . So Using a function transformer is ok ?
>
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