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TomekLinks fit_sample taking long time #567

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atulec04 opened this issue May 8, 2019 · 3 comments
Closed

TomekLinks fit_sample taking long time #567

atulec04 opened this issue May 8, 2019 · 3 comments

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@atulec04
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@atulec04 atulec04 commented May 8, 2019

I am working on a text classification problem. I am using TomekLinks class of imblearn module to resample my data.But after calling fit_sample(X,y) method of TomekLinks class program does nothing even if i wait for 30 mins. My data set is 1800000 records long(text data).Here is the code snippet

from imblearn.under_sampling import TomekLinks

tl = TomekLinks(return_indices=True, ratio='majority',random_state=42)
X_tl, y_tl = tl.fit_sample(train_x,y_binary)

Can anyone help as why it is taking such a long time? and how to hanle this situation

@hayesall
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@hayesall hayesall commented May 8, 2019

"Tomek Links" is a fairly expensive algorithm since it has to compute pairwise distances between all examples. Even before taking the dimensionality of your text data into account, it will have to compute something on the order of(1.8 * 10^6)^2 values.

From page 4 of "A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data": "As finding Tomek links is computationally demanding, it would be computationally cheaper if it was performed on a reduced data set."

Maybe you could sample from your data set (while preserving the underlying distribution) or try some other dimensionality reduction techniques first?

@atulec04
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@atulec04 atulec04 commented May 8, 2019

Ok

@nikunjsonule
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@nikunjsonule nikunjsonule commented Apr 16, 2022

"Tomek Links" is a fairly expensive algorithm since it has to compute pairwise distances between all examples. Even before taking the dimensionality of your text data into account, it will have to compute something on the order of(1.8 * 10^6)^2 values.

From page 4 of "A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data": "As finding Tomek links is computationally demanding, it would be computationally cheaper if it was performed on a reduced data set."

Maybe you could sample from your data set (while preserving the underlying distribution) or try some other dimensionality reduction techniques first?

Yes totally agree

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