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View Code? Open in Web Editor NEWExperimental implementations of several (over/under)-sampling techniques not yet available in the imbalanced-learn library.
License: BSD 3-Clause "New" or "Revised" License
Experimental implementations of several (over/under)-sampling techniques not yet available in the imbalanced-learn library.
License: BSD 3-Clause "New" or "Revised" License
All occurrences of the ._bit_generator
attribute in np.random.Generator
should be changed to bit_generator
since numpy made this attribute public since 1.17. See: scikit-learn/scikit-learn#20669 (comment)
Allow X
to be a sparse array data type.
This is per NEP29 recommendations. It is also going to allow support for the new np.random.Generator
interface which has faster random number generation and has more functionality that the old np.random.RandomState
used by sklearn's check_random_state
function.
Given that tSNE is the bottleneck. To speed things up we can allow a pre-trained TSNE embedding object (one that implements the transform
method) to be passed into the constructor or the transform
method. Either
loras = LORAS(..., pretrained_tsne=None)
or
loras.transform(X, y, pretrained_tsne=None)
Something like this: https://imbalanced-learn.org/stable/over_sampling.html#a-practical-guide
Add some typing information to make arguments a little easier to read (maybe). I tend to prefer stub files (e.g. see here) over in-place typing since there is less distracting lines of code. However it means the file count doubles because every python source is accompanied by a .pyi
file.
When I use LORAS to resample the data, I receive an error stating that it cannot import name delayed even when I use pip to install delayed.
Another new clustering-based oversampling algorithm that uses some Gaussian Mixture models can be found in [1]. would be interesting to see how it compared to ProWRAS
.
AMDO is outlined in [1] and is optimized for multi-class imbalanced datasets. The MDO algorithm seems a bit easier to implement so maybe that could be attempted first.
[1] Outlines an oversampling technique using Optimal Transport. Seems interesting enough to try out.
[1] https://www.aaai.org/ojs/index.php/AAAI/article/view/4503/4381
Currently, scikit-learn's implementation is used. One of the following would likely perform better in terms of runtime:
The current lines that would need to be changed are:
Lines 112 to 115 in b896367
Line 149 in b896367
This paper [1] presents ProWRAS, which is supposedly an improvement over LoRAS that also incorperates elements of the Proximity Weighted Synthetic oversampling (ProWSyn) algorithm.
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