[scikit-learn] Imblearn: SMOTENC

[S Hamidizade]

Thanks. The code is provided here:
https://github.com/scikit-learn-contrib/imbalanced-learn/issues/537

Best regards,

On Thu, Jan 24, 2019 at 7:15 PM Guillaume Lemaître <g.lemaitre58 at gmail.com>
wrote:

> You should open a ticket on imbalanced-learn GitHub issue. This is easier
> to post a reproducible example and for us to test it.
> From the error message, I can understand that you have 161 features and
> require a feature above the index 160.
>
>
>
> On Thu, 24 Jan 2019 at 16:19, S Hamidizade <hamidizade.s at gmail.com> wrote:
>
>> Thanks. Unfortunately, now the error is:
>> ValueError: Some of the categorical indices are out of range. Indices
>> should be between 0 and 160.
>> Best regards,
>>
>> On Sun, Jan 20, 2019 at 8:31 PM S Hamidizade <hamidizade.s at gmail.com>
>> wrote:
>>
>>> Dear Scikit-learners
>>> Hi.
>>>
>>> I would greatly appreciate if you could let me know how to use
>>> SMOTENC.  I wrote:
>>>
>>> num_indices1 = list(X.iloc[:,np.r_[0:94,95,97,100:123]].columns.values)
>>> cat_indices1 = list(X.iloc[:,np.r_[94,96,98,99,123:160]].columns.values)
>>> print(len(num_indices1))
>>> print(len(cat_indices1))
>>>
>>> pipeline=Pipeline(steps= [
>>>     # Categorical features
>>>     ('feature_processing', FeatureUnion(transformer_list = [
>>>             ('categorical', MultiColumn(cat_indices1)),
>>>
>>>             #numeric
>>>             ('numeric', Pipeline(steps = [
>>>                 ('select', MultiColumn(num_indices1)),
>>>                 ('scale', StandardScaler())
>>>                         ]))
>>>         ])),
>>>     ('clf', rg)
>>>     ]
>>> )
>>>
>>> Therefore, as it is indicated I have 5 categorical features. Really,
>>> indices 123 to 160 are related to one categorical feature with 37 possible
>>> values which is converted into 37 columns using get_dummies.
>>>  Sorry, I think SMOTENC should be inserted before the classifier ('clf',
>>> reg) but I don't know how to define "categorical_features" in SMOTENC.
>>> Besides, could you please let me know where to use imblearn.pipeline?
>>>
>>> Thanks in advance.
>>> Best regards,
>>>
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>
>
> --
> Guillaume Lemaitre
> INRIA Saclay - Parietal team
> Center for Data Science Paris-Saclay
> https://glemaitre.github.io/
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