Hi Chris,
I forgot to mention that this pipeline I have used within the GridSearchCV.
I have done what you suggested early but didn't work, it said:
'GridSearchCV' object has no attribute 'named_steps'.
I somehow figured out now
Thanks for your help though.
Best regards,
Mitali Katoch
On Thu, May 6, 2021, 12:47 Chris Aridas <ch...@aridas.eu> wrote:
> Hi,
>
> Assuming that you have trained your pipeline, the following piece of code
> should work.
>
>
> pipeline.named_steps["feature_sel"].transform(X)
>
> Best,
> Chris
>
> On Thu, May 6, 2021 at 12:52 PM mitali katoch <mitalikat...@gmail.com>
> wrote:
>
>> Dear Scikit team,
>>
>> I am working with FeatureUnion in the pipeline and best parameters are as
>> follows:
>> Pipeline(steps=[('feature_sel',
>> FeatureUnion(transformer_list= [ ('selectk',
>> SelectKBest(k=500)),
>> ('sel_fromModel',
>>
>> SelectFromModel(estimator=LogisticRegression(C=1,
>>
>> penalty='l1',
>>
>> solver='liblinear'),
>>
>> max_features=100))]
>> )),
>> ('sampler', SMOTE(k_neighbors=2, random_state=10)),
>> ('model', SVC(random_state=10))]
>> )
>>
>> I would like to extract those SelectKBest(k=500) and max_features=100
>> from the pipeline.
>>
>> Could you please confirm whether it is possible to do it, If yes, could
>> you share the solution, I would highly appreciate that.
>>
>> Thanks in advance.
>>
>> Best Regards,
>> Mitali Katoch
>>
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