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 <hamidizad...@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 <hamidizad...@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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