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[ENH] Keep the index of the samples after undersampling #724

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qiiiibeau opened this issue Jun 11, 2020 · 2 comments
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[ENH] Keep the index of the samples after undersampling #724

qiiiibeau opened this issue Jun 11, 2020 · 2 comments

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@qiiiibeau
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qiiiibeau commented Jun 11, 2020 •
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Hello, I'm undersampling some imbalanced data with each sample a unique name as index. I don't want to lose the samples' index after undersampling because I'm doing a graph - based task where each sample represent a node, I need to know where it is located in the graph.

To be more illustrative, my data is a dataframe looks like:

feat_1 feat_2 feat_3 label
Thomas 0.5 2.2 3.0 1
Kelly 0.63 1.5 1.4 0
Peter 0.9 1.1 3.4 1
George 0.2 2.1 4 1
... ... ... ... ...

The current version of imblearn undersampling methods e.g. RandomUnderSampler().fit_resample() returns me a dataframe with index [0: length of selected samples] such as

feat_1 feat_2 feat_3 label
0 0.5 2.2 3.0 1
1 0.2 2.1 4 1

where all the original index are lost. I need it to be like:

feat_1 feat_2 feat_3 label
Thomas 0.5 2.2 3.0 1
George 0.2 2.1 4 1

This improvement would help a lot for graph-based imbalanced learning and maybe also in other cases.

Thank you.
@glemaitre
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We might want to add support for this feature for samplers having a fitted attribute sample_indices_ after fit.
Otherwise, the index is meaningless. However, it makes the behaviour different from one sampler to another while a user can easily reassign an index which would be less surprising:

df_res, y_res = sampler.fit_resample(df, y)
df_res.index = df.index[sampler.sample_indices_]

@chkoar do you have any thought on this?

@glemaitre glemaitre mentioned this issue Feb 15, 2021
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@glemaitre
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This feature was added for the RandomUnderSampler and RandomOverSampler.

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@glemaitre @qiiiibeau