[scikit-learn] Imblearn: SMOTENC
g.lemaitre58 at gmail.com
Thu Jan 24 10:43:04 EST 2019
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>
>> Dear Scikit-learners
>> 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)
>> pipeline=Pipeline(steps= [
>> # Categorical features
>> ('feature_processing', FeatureUnion(transformer_list = [
>> ('categorical', MultiColumn(cat_indices1)),
>> ('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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INRIA Saclay - Parietal team
Center for Data Science Paris-Saclay
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