| Paper | Documentation | RealMLP-TD-S standalone implementation | Grinsztajn et al. benchmark code | Data archive |
|---|
PyTabKit provides scikit-learn interfaces for modern tabular classification and regression methods benchmarked in our paper, see below. It also contains the code we used for benchmarking these methods on our benchmarks.
pip install pytabkit
- If you want to use TabR, you have to manually install faiss, which is only available on conda.
- Please install torch separately if you want to control the version (CPU/GPU etc.)
- Use
pytabkit[autogluon,extra,hpo,bench,dev]to install additional dependencies for AutoGluon models, extra preprocessing, hyperparameter optimization methods beyond random search (hyperopt/SMAC), the benchmarking part, and testing/documentation. For the hpo part, you might need to install swig (e.g. via pip) if the build of pyrfr fails. See also the documentation. To run the data download, you need one of rar, unrar, or 7-zip to be installed on the system.
Most of our machine learning models are directly available via scikit-learn interfaces. For example, you can use RealMLP-TD for classification as follows:
from pytabkit import RealMLP_TD_Classifier
model = RealMLP_TD_Classifier() # or TabR_S_D_Classifier, CatBoost_TD_Classifier, etc.
model.fit(X_train, y_train)
model.predict(X_test)The code above will automatically select a GPU if available,
try to detect categorical columns in dataframes,
preprocess numerical variables and regression targets (no standardization required),
and use a training-validation split for early stopping.
All of this (and much more) can be configured through the constructor
and the parameters of the fit() method.
For example, it is possible to do bagging
(ensembling of models on 5-fold cross-validation)
simply by passing n_cv=5 to the constructor.
Here is an example for some of the parameters that can be set explicitly:
from pytabkit import RealMLP_TD_Classifier
model = RealMLP_TD_Classifier(device='cpu', random_state=0, n_cv=1, n_refit=0,
n_epochs=256, batch_size=256, hidden_sizes=[256] * 3,
val_metric_name='cross_entropy',
use_ls=False, # for metrics like AUC / log-loss
lr=0.04, verbosity=2)
model.fit(X_train, y_train, X_val, y_val, cat_col_names=['Education'])
model.predict_proba(X_test)See this notebook for more examples. Missing numerical values are currently not allowed and need to be imputed beforehand.
Our ML models are available in up to three variants, all with best-epoch selection:
- library defaults (D)
- our tuned defaults (TD)
- random search hyperparameter optimization (HPO), sometimes also tree parzen estimator (HPO-TPE)
We provide the following ML models:
- RealMLP (TD, HPO): Our new neural net models with tuned defaults (TD) or random search hyperparameter optimization (HPO)
- XGB, LGBM, CatBoost (D, TD, HPO, HPO-TPE): Interfaces for gradient-boosted tree libraries XGBoost, LightGBM, CatBoost
- MLP, ResNet, FTT (D, HPO): Models from Revisiting Deep Learning Models for Tabular Data
- MLP-PLR (D, HPO): MLP with numerical embeddings from On Embeddings for Numerical Features in Tabular Deep Learning
- TabR (D, HPO): TabR model from TabR: Tabular Deep Learning Meets Nearest Neighbors
- TabM (D): TabM model from TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling
- RealTabR (D): Our new TabR variant with default parameters
- Ensemble-TD: Weighted ensemble of all TD models (RealMLP, XGB, LGBM, CatBoost)
Our benchmarking code has functionality for
- dataset download
- running methods highly parallel on single-node/multi-node/multi-GPU hardware, with automatic scheduling and trying to respect RAM constraints
- analyzing/plotting results
For more details, we refer to the documentation.
While many preprocessing methods are implemented in this repository, a standalone version of our robust scaling + smooth clipping can be found here.
If you use this repository for research purposes, please cite our paper:
@inproceedings{holzmuller2024better,
title={Better by default: {S}trong pre-tuned {MLPs} and boosted trees on tabular data},
author={Holzm{\"u}ller, David and Grinsztajn, Leo and Steinwart, Ingo},
booktitle = {Neural {Information} {Processing} {Systems}},
year={2024}
}
- David Holzmüller (main developer)
- Léo Grinsztajn (deep learning baselines, plotting)
- Ingo Steinwart (UCI dataset download)
- Katharina Strecker (PyTorch-Lightning interface)
- Lennart Purucker (some features/fixes)
- Jérôme Dockès (deployment, continuous integration)
Code from other repositories is acknowledged as well as possible in code comments. Especially, we used code from https://github.com/yandex-research/rtdl and sub-packages (Apache 2.0 license), code from https://github.com/catboost/benchmarks/ (Apache 2.0 license), and https://docs.ray.io/en/latest/cluster/vms/user-guides/community/slurm.html (Apache 2.0 license).
- v1.1.0:
- Included TabM
- Replaced
__by_in parameter names for MLP, MLP-PLR, ResNet, and FTT, to comply with scikit-learn interface requirements. - Fixed non-determinism in NN baselines by initializing the random state of quantile (and KDI) preprocessing transforms.
- n_threads parameter is not ignored by NNs anymore.
- Changes by Lennart Purucker:
Add time limit for RealMLP,
add support for
lightning(but also still allowingpytorch-lightning), making skorch a lazy import, removed msgpack_numpy dependency.
- v1.0.0: Release for the NeurIPS version and arXiv v2.
- More baselines (MLP-PLR, FT-Transformer, TabR-HPO, RF-HPO), also some un-polished internal interfaces for other methods, esp. the ones in AutoGluon.
- Updated benchmarking code (configurations, plots) including the new version of the Grinsztajn et al. benchmark
- Updated fit() parameters in scikit-learn interfaces, etc.
- v0.0.1: First release for arXiv v1. Code and data are archived at DaRUS.