This is the code implementation for our paper submitted to ICML 2023:

ReMasker: Imputing Tabular Data with Masked Autoencoding

1. Require environment of python>=3.9

2. pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116

3. pip install timm

4. pip install hyperimpute

Modify the corresponding configuration in the config file or command-line arguments.

Example:

Path of datasets: --path (./)

You can run this basic command to get the imputation results of ReMasker on iris dataset:

python plugin_mae.py --dataset iris

import numpy as np
import pandas as pd
from utils import get_args_parser
from remasker_impute import ReMasker

X_raw = np.arange(50).reshape(10, 5) * 1.0
X = pd.DataFrame(X_raw, columns=['0', '1', '2', '3', '4'])
X.iat[3,0] = np.nan

imputer = ReMasker()

imputed = imputer.fit_transform(X)
print(imputed[3,0])