Deep unsupervised feature selection by discarding nuisance and correlated features

Updates:

• The pypi package was broken, now it is fixed.

Requirements:

• torch >= 1.9
• scikit-learn >= 0.24
• omegaconf >= 2.0.6
• scipy >= 1.6.0
• matplotlib

How to use:

Install the package from pypi: pip install lscae

Prepare your dataset by applying Standard Scaler on it

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
dataset = scaler.fit_transform(dataset)

Then load it as torch.utils.data.Dataset and run feature selection Please see an example here

import lscae
import torch
from omegaconf import OmegaConf

# define you cfg parameters
cfg = OmegaConf.create({"input_dim": 100})

# define you dataset (Torch based)

dataset = torch.utils.data.Dataset(...)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=cfg.batch_size, shuffle=True, drop_last=True)
lscae.Lscae(kwargs=cfg).select_features(dataloader)

If you use this code, please cite the publication:

@article{shaham2022deep,
  title={Deep unsupervised feature selection by discarding nuisance and correlated features},
  author={Shaham, Uri and Lindenbaum, Ofir and Svirsky, Jonathan and Kluger, Yuval},
  journal={Neural Networks},
  year={2022},
  publisher={Elsevier}
}