RVAE for Mixed Type Features (Tabular Data)

Code for paper: "Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data" AISTATS 2020. Check it here https://arxiv.org/abs/1907.06671. Please consider citing us if you use our code.

Instalation

• Please install ./setup.py in folder ./src in order to use core_models package.

• Use Pytorch 1.3.1 at least

Usage

Data folder

• Clean is found (or inserted) in data_simple:

+ e.g. ./data_simple/Wine/Wine.csv + the folder contains both clean data, and then after nosing, several noisy replicas given by

noising_process.py

in separate folders.

Output folder

• Current scripts generate folder ./outputsexperimentsi/{dataset}/{noisetype}/{corruptionlevel}runj/{Model_Name}/
• Therein results for outlier detection metrics (cell and row), and repair of cells are presented. The latter only if algorithm provides this.

Simple Work Flow

Noising a dataset:

• Go to ./src/datasetprepsimple/ to run noising of datasets in data folder:

+ Open noising_process.py + Edit script definitions: dataset; noise type; corruption level; + Run

noising_process.py

Running a model:

• Go to ./src/scripts/ to run a specific model (choose from scripts therein):

+ Make sure you pick correct hyper-parameters (see paper), and turn

--cuda-on

for GPU. For instance: +

runRVAECVI.sh

, for our main algorithm. +

runVAEl2.sh

, for VAE-L2 baseline. +

run_CondPred.sh

, for NN-based Conditional Predictor (pseudo-likelihood). +

run_baselines.sh

, for assorted baselines in paper.

License

MIT