aizatrosli / tabulartorch

beyond decision tree

tabulartorch

Evaluating SOTA library/implementation on tabular data such as:

• widedeep
• tabzilla

Evaluation is based on:

• confusion matrix
• pytorch with GPU support
• memory usage (must fit in 24GB vmem GPU)

Installation

• Install latest package from RAPIS.ai with pytorch support.

How to Run

Widedeep

• Install widedeep libarary

  pip install pytorch-widedeep

Tabzilla

• Clone the repo
• Install dependencies

  pip instal openml

• Download sample dataset from openml.

  cd tabzilla
  python tabzilla_data_preprocessing.py --dataset_name openml__california__361089

• Run test experiment with GPU config.

  python ./tabzilla_experiment.py --experiment_config ./tabzilla_experiment_config_gpu.yml --model_name SAINT --dataset_dir ./datasets/openml__california__361089/ 

Enviroment

• Ubuntu 20.04 WSL
• Intel i7 12700k
• 32 GB RAM
• Nvidia RTX3090 24GB