Revisiting Deep Learning Models for Tabular Data (NeurIPS 2021)

Important

Check out the new tabular DL model: TabM

📜 arXiv   📦 Python package   📚 Other tabular DL projects

This is the official implementation of the paper "Revisiting Deep Learning Models for Tabular Data".

TL;DR

In one sentence: MLP-like models are still good baselines, and FT-Transformer is a new powerful adaptation of the Transformer architecture for tabular data problems.

The paper focuses on architectures for tabular data problems. The results:

• A simple tuned MLP is still a good baseline: it performs on par with or even better than most of sophisticated architectures.
• ResNet (an MLP-like model with skip connections and batch normalizations) further highlights this point: MLP-like models are good baselines for tabular deep learning, and prior work does not outperform them.
• FT-Transformer is a new architecture which changes this status quo:
  • on benchmarks, it demonstrated the best average performance among deep models (including the aforementioned MLP-like baselines);
  • on the datasets where GBDT (gradient-booosted decision trees) dominates over DL models, FT-Transformer reduces (not completely) the gap between GBDT and DL.
  • FT-Transformer is slower than MLP-like models

Python package

The Python package in the package/ directory is the recommended way to use the paper in practice and for future work.

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The rest of the document:

• Metrics & Hyperparameters
• How to reproduce the reported results
• How to cite

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How to explore metrics and hyperparameters

The output/ directory contains numerious results and (tuned) hyperparameters for various models and datasets used in the paper.

Metrics

For example, let's explore the metrics for the MLP model. First, let's load the reports (the stats.json files):

import json
from pathlib import Path

import pandas as pd

df = pd.json_normalize([
    json.loads(x.read_text())
    for x in Path('output').glob('*/mlp/tuned/*/stats.json')
])

Now, for each dataset, let's compute the test score averaged over all random seeds:

print(df.groupby('dataset')['metrics.test.score'].mean().round(3))

The output exactly matches Table 2 from the paper:

dataset
adult                 0.852
aloi                  0.954
california_housing   -0.499
covtype               0.962
epsilon               0.898
helena                0.383
higgs_small           0.723
jannis                0.719
microsoft            -0.747
yahoo                -0.757
year                 -8.853
Name: metrics.test.score, dtype: float64

Hyperparameters

The above approach can also be used to explore hyperparameters to get intuition on typical hyperparameter values for different algorithms. For example, this is how one can compute the median tuned learning rate for the MLP model:

Note

For some algorithms (e.g. MLP), more recent projects offer more results that can be explored in a similar way. For example, see this paper on TabR.

Warning

Use this approach with caution. When studying hyperparameter values:

1. Beware of outliers.
2. Take a look at raw unaggregated values to get intuition on typical values.
3. For a high-level overview, plot the distribution and/or compute multiple quantiles.

print(df[df['config.seed'] == 0]['config.training.lr'].quantile(0.5))
# Output: 0.0002161505605899536

How to reproduce the results

Note

This section is long. Use the "Outline" feature on GitHub on in your text editor to get an overview of this section.

Code overview

The code is organized as follows:

• bin:
  • training code for all the models
  • ensemble.py performs ensembling
  • tune.py performs hyperparameter tuning
  • code for the section "When FT-Transformer is better than ResNet?" of the paper:
    • analysis_gbdt_vs_nn.py runs the experiments
    • create_synthetic_data_plots.py builds plots
• lib contains common tools used by programs in bin
• output contains configuration files (inputs for programs in bin) and results (metrics, tuned configurations, etc.)
• package contains the Python package for this paper

Setup the environment

PyTorch environment

Install conda

export PROJECT_DIR=<ABSOLUTE path to the repository root>
# example: export PROJECT_DIR=/home/myusername/repositories/revisiting-models
git clone https://github.com/yandex-research/tabular-dl-revisiting-models $PROJECT_DIR
cd $PROJECT_DIR

conda create -n revisiting-models python=3.8.8
conda activate revisiting-models

conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=10.1.243 numpy=1.19.2 -c pytorch -y
conda install cudnn=7.6.5 -c anaconda -y
pip install -r requirements.txt
conda install nodejs -y
jupyter labextension install @jupyter-widgets/jupyterlab-manager

# if the following commands do not succeed, update conda
conda env config vars set PYTHONPATH=${PYTHONPATH}:${PROJECT_DIR}
conda env config vars set PROJECT_DIR=${PROJECT_DIR}
conda env config vars set LD_LIBRARY_PATH=${CONDA_PREFIX}/lib:${LD_LIBRARY_PATH}
conda env config vars set CUDA_HOME=${CONDA_PREFIX}
conda env config vars set CUDA_ROOT=${CONDA_PREFIX}

conda deactivate
conda activate revisiting-models

TensorFlow environment

This environment is needed only for experimenting with TabNet. For all other cases use the PyTorch environment.

The instructions are the same as for the PyTorch environment (including installation of PyTorch!), but:

• python=3.7.10
• cudatoolkit=10.0
• right before pip install -r requirements.txt do the following:
  • pip install tensorflow-gpu==1.14
  • comment out tensorboard in requirements.txt

Data

LICENSE: by downloading our dataset you accept licenses of all its components. We do not impose any new restrictions in addition to those licenses. You can find the list of sources in the section "References" of our paper.

1. Download the data: wget https://www.dropbox.com/s/o53umyg6mn3zhxy/data.tar.gz?dl=1 -O revisiting_models_data.tar.gz
2. Move the archive to the root of the repository: mv revisiting_models_data.tar.gz $PROJECT_DIR
3. Go to the root of the repository: cd $PROJECT_DIR
4. Unpack the archive: tar -xvf revisiting_models_data.tar.gz

Tutorial

This section only provides specific commands with few comments. After completing the tutorial, we recommend checking the next section for better understanding of how to work with the repository. It will also help to better understand the tutorial.

In this tutorial, we will reproduce the results for MLP on the California Housing dataset. We will cover:

• tuning
• evaluation
• ensembling
• comparing models with each other

Note that the chances to get exactly the same results are rather low, however, they should not differ much from ours. Before running anything, go to the root of the repository and explicitly set CUDA_VISIBLE_DEVICES (if you plan to use GPU):

cd $PROJECT_DIR
export CUDA_VISIBLE_DEVICES=0

Check the environment

Before we start, let's check that the environment is configured successfully. The following commands should train one MLP on the California Housing dataset:

mkdir draft
cp output/california_housing/mlp/tuned/0.toml draft/check_environment.toml
python bin/mlp.py draft/check_environment.toml

The result should be in the directory draft/check_environment. For now, the content of the result is not important.

Tuning

Our config for tuning MLP on the California Housing dataset is located at output/california_housing/mlp/tuning/0.toml. In order to reproduce the tuning, copy our config and run your tuning:

# you can choose any other name instead of "reproduced.toml"; it is better to keep this
# name while completing the tutorial
cp output/california_housing/mlp/tuning/0.toml output/california_housing/mlp/tuning/reproduced.toml
# let's reduce the number of tuning iterations to make tuning fast (and ineffective)
python -c "
from pathlib import Path
p = Path('output/california_housing/mlp/tuning/reproduced.toml')
p.write_text(p.read_text().replace('n_trials = 100', 'n_trials = 5'))
"
python bin/tune.py output/california_housing/mlp/tuning/reproduced.toml

The result of your tuning will be located at output/california_housing/mlp/tuning/reproduced, you can compare it with ours: output/california_housing/mlp/tuning/0. The file best.toml contains the best configuration that we will evaluate in the next section.

Evaluation

Now we have to evaluate the tuned configuration with 15 different random seeds.

# create a directory for evaluation
mkdir -p output/california_housing/mlp/tuned_reproduced

# clone the best config from the tuning stage with 15 different random seeds
python -c "
for seed in range(15):
    open(f'output/california_housing/mlp/tuned_reproduced/{seed}.toml', 'w').write(
        open('output/california_housing/mlp/tuning/reproduced/best.toml').read().replace('seed = 0', f'seed = {seed}')
    )
"

# train MLP with all 15 configs
for seed in {0..14}
do
    python bin/mlp.py output/california_housing/mlp/tuned_reproduced/${seed}.toml
done

Our directory with evaluation results is located right next to yours, namely, at output/california_housing/mlp/tuned.

Ensembling

# just run this single command
python bin/ensemble.py mlp output/california_housing/mlp/tuned_reproduced

Your results will be located at output/california_housing/mlp/tuned_reproduced_ensemble, you can compare it with ours: output/california_housing/mlp/tuned_ensemble.

"Visualize" results

Use the approach described here to summarize the results of the conducted experiment (modify the path filter in .glob(...) accordingly: tuned -> tuned_reproduced).

What about other models and datasets?

Similar steps can be performed for all models and datasets. The tuning process is slightly different in the case of grid search: you have to run all desired configurations and manually choose the best one based on the validation performance. For example, see output/epsilon/ft_transformer.

How to work with the repository

How to run scripts

You should run Python scripts from the root of the repository. Most programs expect a configuration file as the only argument. The output will be a directory with the same name as the config, but without the extention. Configs are written in TOML. The lists of possible arguments for the programs are not provided and should be inferred from scripts (usually, the config is represented with the args variable in scripts). If you want to use CUDA, you must explicitly set the CUDA_VISIBLE_DEVICES environment variable. For example:

# The result will be at "path/to/my_experiment"
CUDA_VISIBLE_DEVICES=0 python bin/mlp.py path/to/my_experiment.toml

# The following example will run WITHOUT CUDA
python bin/mlp.py path/to/my_experiment.toml

If you are going to use CUDA all the time, you can save the environment variable in the Conda environment:

conda env config vars set CUDA_VISIBLE_DEVICES="0"

The -f (--force) option will remove the existing results and run the script from scratch:

python bin/whatever.py path/to/config.toml -f  # rewrites path/to/config

bin/tune.py supports continuation:

python bin/tune.py path/to/config.toml --continue

stats.json and other results

For all scripts, stats.json is the most important part of output. The content varies from program to program. It can contain:

• metrics
• config that was passed to the program
• hardware info
• execution time
• and other information

Predictions for train, validation and test sets are usually also saved.

Conclusion

Now, you know everything you need to reproduce all the results and extend this repository for your needs. The tutorial also should be more clear now. Feel free to open issues and ask questions.

How to cite

@inproceedings{gorishniy2021revisiting,
    title={Revisiting Deep Learning Models for Tabular Data},
    author={Yury Gorishniy and Ivan Rubachev and Valentin Khrulkov and Artem Babenko},
    booktitle={{NeurIPS}},
    year={2021},
}