Metadata-Version: 2.3
Name: tabpackage
Version: 0.0.1
Summary: TabPack: efficient parallel training and ensembling of MLPs for tabular deep learning.
Keywords: artificial intelligence,classification,deep learning,library,machine learning,python,pytorch,regression,research,torch,tabular,tabular data
Author: Yury Gorishniy
License:                                  Apache License
                                    Version 2.0, January 2004
                                 http://www.apache.org/licenses/
         
            TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
         
            1. Definitions.
         
               "License" shall mean the terms and conditions for use, reproduction,
               and distribution as defined by Sections 1 through 9 of this document.
         
               "Licensor" shall mean the copyright owner or entity authorized by
               the copyright owner that is granting the License.
         
               "Legal Entity" shall mean the union of the acting entity and all
               other entities that control, are controlled by, or are under common
               control with that entity. For the purposes of this definition,
               "control" means (i) the power, direct or indirect, to cause the
               direction or management of such entity, whether by contract or
               otherwise, or (ii) ownership of fifty percent (50%) or more of the
               outstanding shares, or (iii) beneficial ownership of such entity.
         
               "You" (or "Your") shall mean an individual or Legal Entity
               exercising permissions granted by this License.
         
               "Source" form shall mean the preferred form for making modifications,
               including but not limited to software source code, documentation
               source, and configuration files.
         
               "Object" form shall mean any form resulting from mechanical
               transformation or translation of a Source form, including but
               not limited to compiled object code, generated documentation,
               and conversions to other media types.
         
               "Work" shall mean the work of authorship, whether in Source or
               Object form, made available under the License, as indicated by a
               copyright notice that is included in or attached to the work
               (an example is provided in the Appendix below).
         
               "Derivative Works" shall mean any work, whether in Source or Object
               form, that is based on (or derived from) the Work and for which the
               editorial revisions, annotations, elaborations, or other modifications
               represent, as a whole, an original work of authorship. For the purposes
               of this License, Derivative Works shall not include works that remain
               separable from, or merely link (or bind by name) to the interfaces of,
               the Work and Derivative Works thereof.
         
               "Contribution" shall mean any work of authorship, including
               the original version of the Work and any modifications or additions
               to that Work or Derivative Works thereof, that is intentionally
               submitted to Licensor for inclusion in the Work by the copyright owner
               or by an individual or Legal Entity authorized to submit on behalf of
               the copyright owner. For the purposes of this definition, "submitted"
               means any form of electronic, verbal, or written communication sent
               to the Licensor or its representatives, including but not limited to
               communication on electronic mailing lists, source code control systems,
               and issue tracking systems that are managed by, or on behalf of, the
               Licensor for the purpose of discussing and improving the Work, but
               excluding communication that is conspicuously marked or otherwise
               designated in writing by the copyright owner as "Not a Contribution."
         
               "Contributor" shall mean Licensor and any individual or Legal Entity
               on behalf of whom a Contribution has been received by Licensor and
               subsequently incorporated within the Work.
         
            2. Grant of Copyright License. Subject to the terms and conditions of
               this License, each Contributor hereby grants to You a perpetual,
               worldwide, non-exclusive, no-charge, royalty-free, irrevocable
               copyright license to reproduce, prepare Derivative Works of,
               publicly display, publicly perform, sublicense, and distribute the
               Work and such Derivative Works in Source or Object form.
         
            3. Grant of Patent License. Subject to the terms and conditions of
               this License, each Contributor hereby grants to You a perpetual,
               worldwide, non-exclusive, no-charge, royalty-free, irrevocable
               (except as stated in this section) patent license to make, have made,
               use, offer to sell, sell, import, and otherwise transfer the Work,
               where such license applies only to those patent claims licensable
               by such Contributor that are necessarily infringed by their
               Contribution(s) alone or by combination of their Contribution(s)
               with the Work to which such Contribution(s) was submitted. If You
               institute patent litigation against any entity (including a
               cross-claim or counterclaim in a lawsuit) alleging that the Work
               or a Contribution incorporated within the Work constitutes direct
               or contributory patent infringement, then any patent licenses
               granted to You under this License for that Work shall terminate
               as of the date such litigation is filed.
         
            4. Redistribution. You may reproduce and distribute copies of the
               Work or Derivative Works thereof in any medium, with or without
               modifications, and in Source or Object form, provided that You
               meet the following conditions:
         
               (a) You must give any other recipients of the Work or
                   Derivative Works a copy of this License; and
         
               (b) You must cause any modified files to carry prominent notices
                   stating that You changed the files; and
         
               (c) You must retain, in the Source form of any Derivative Works
                   that You distribute, all copyright, patent, trademark, and
                   attribution notices from the Source form of the Work,
                   excluding those notices that do not pertain to any part of
                   the Derivative Works; and
         
               (d) If the Work includes a "NOTICE" text file as part of its
                   distribution, then any Derivative Works that You distribute must
                   include a readable copy of the attribution notices contained
                   within such NOTICE file, excluding those notices that do not
                   pertain to any part of the Derivative Works, in at least one
                   of the following places: within a NOTICE text file distributed
                   as part of the Derivative Works; within the Source form or
                   documentation, if provided along with the Derivative Works; or,
                   within a display generated by the Derivative Works, if and
                   wherever such third-party notices normally appear. The contents
                   of the NOTICE file are for informational purposes only and
                   do not modify the License. You may add Your own attribution
                   notices within Derivative Works that You distribute, alongside
                   or as an addendum to the NOTICE text from the Work, provided
                   that such additional attribution notices cannot be construed
                   as modifying the License.
         
               You may add Your own copyright statement to Your modifications and
               may provide additional or different license terms and conditions
               for use, reproduction, or distribution of Your modifications, or
               for any such Derivative Works as a whole, provided Your use,
               reproduction, and distribution of the Work otherwise complies with
               the conditions stated in this License.
         
            5. Submission of Contributions. Unless You explicitly state otherwise,
               any Contribution intentionally submitted for inclusion in the Work
               by You to the Licensor shall be under the terms and conditions of
               this License, without any additional terms or conditions.
               Notwithstanding the above, nothing herein shall supersede or modify
               the terms of any separate license agreement you may have executed
               with Licensor regarding such Contributions.
         
            6. Trademarks. This License does not grant permission to use the trade
               names, trademarks, service marks, or product names of the Licensor,
               except as required for reasonable and customary use in describing the
               origin of the Work and reproducing the content of the NOTICE file.
         
            7. Disclaimer of Warranty. Unless required by applicable law or
               agreed to in writing, Licensor provides the Work (and each
               Contributor provides its Contributions) on an "AS IS" BASIS,
               WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
               implied, including, without limitation, any warranties or conditions
               of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
               PARTICULAR PURPOSE. You are solely responsible for determining the
               appropriateness of using or redistributing the Work and assume any
               risks associated with Your exercise of permissions under this License.
         
            8. Limitation of Liability. In no event and under no legal theory,
               whether in tort (including negligence), contract, or otherwise,
               unless required by applicable law (such as deliberate and grossly
               negligent acts) or agreed to in writing, shall any Contributor be
               liable to You for damages, including any direct, indirect, special,
               incidental, or consequential damages of any character arising as a
               result of this License or out of the use or inability to use the
               Work (including but not limited to damages for loss of goodwill,
               work stoppage, computer failure or malfunction, or any and all
               other commercial damages or losses), even if such Contributor
               has been advised of the possibility of such damages.
         
            9. Accepting Warranty or Additional Liability. While redistributing
               the Work or Derivative Works thereof, You may choose to offer,
               and charge a fee for, acceptance of support, warranty, indemnity,
               or other liability obligations and/or rights consistent with this
               License. However, in accepting such obligations, You may act only
               on Your own behalf and on Your sole responsibility, not on behalf
               of any other Contributor, and only if You agree to indemnify,
               defend, and hold each Contributor harmless for any liability
               incurred by, or claims asserted against, such Contributor by reason
               of your accepting any such warranty or additional liability.
         
            END OF TERMS AND CONDITIONS
         
            APPENDIX: How to apply the Apache License to your work.
         
               To apply the Apache License to your work, attach the following
               boilerplate notice, with the fields enclosed by brackets "[]"
               replaced with your own identifying information. (Don't include
               the brackets!)  The text should be enclosed in the appropriate
               comment syntax for the file format. We also recommend that a
               file or class name and description of purpose be included on the
               same "printed page" as the copyright notice for easier
               identification within third-party archives.
         
            Copyright 2026 Yandex LLC
         
            Licensed under the Apache License, Version 2.0 (the "License");
            you may not use this file except in compliance with the License.
            You may obtain a copy of the License at
         
                http://www.apache.org/licenses/LICENSE-2.0
         
            Unless required by applicable law or agreed to in writing, software
            distributed under the License is distributed on an "AS IS" BASIS,
            WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
            See the License for the specific language governing permissions and
            limitations under the License.
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Dist: torch>=2.7.1,<3
Requires-Dist: numpy>=1.24.2,<3 ; extra == 'sklearn'
Requires-Dist: scikit-learn>=1.2.2,<2 ; extra == 'sklearn'
Requires-Python: >=3.11, <4
Project-URL: Code, https://github.com/yandex-research/tabpack
Project-URL: Documentation, https://github.com/yandex-research/tabpack/tree/main/README.md
Provides-Extra: sklearn
Description-Content-Type: text/markdown

# TabPack & TabPackage

This is the official repository and Python package for the paper "TabPack: Efficient Hyperparameter Ensembles for Tabular Deep Learning" ([arXiv](https://arxiv.org/abs/2607.05380)).
TabPack provides:

**1. An effective and practical baseline** for supervised learning on medium-to-large tabular datasets.
It achieves competitive out-of-the-box performance on benchmarks and is efficient enough to get started on a modern MacBook.

**2. An efficient way to train and ensemble many MLPs with different hyperparameters** for fast experiments.
Train hundreds or thousands of MLPs and build many ensembles in one run.

# TL;DR

<img src="assets/tabpack-overview.png" alt="Overview of TabPack">

As a predictive tabular model, **TabPack is an efficient ensemble of MLPs with different model and optimizer hyperparameters**.
In a single run, TabPack samples and trains many MLPs with different hyperparameters efficiently in parallel and builds ensembles on the fly during training.
See the paper for details.

**TabPack is designed to be a good starting point for solving tabular ML tasks** on medium-to-large datasets:

- **Competitive out-of-the-box performance.**
  The results reported in the paper are achieved with zero hyperparameter tuning.
- **Better performance with less compute.**
  On a modern MacBook, training TabPack once and achieving good performance can be faster than tuning other baselines on GPUs.
- **Practical for deployment.**
  A trained TabPack is just an ensemble of MLPs that will meet efficiency requirements in many real-world applications.

Overall, **TabPack can reduce the time, compute, and tuning effort** needed to obtain a good tabular-learning baseline.

# TabPackage

> [!WARNING]
> The package should be considered experimental: expect rough edges and breaking changes.

TabPackage is the PyTorch-based Python package behind TabPack.
It supports efficient parallel training and ensembling of many MLP-like models with different hyperparameters.
The main features include:

- **High-level training API for TabPack** including PyTorch and scikit-learn interfaces.
- **PyTorch building blocks**, including:
  - **Module packs** for applying layers from many models with different hyperparameters in parallel.
  - **Optimizer packs** for training module packs with different optimization hyperparameters for each member.
  - **Ensembling utilities** for constructing ensembles of models on the fly while they are training.
  - **Metric functions** for computing metrics for each pack member.

## Installation

```
pip install tabpackage

# Or, with the scikit-learn API:
pip install "tabpackage[sklearn]"
```

> [!NOTE]
> When using the MPS device on Apple Silicon with PyTorch older than 2.12, enable CPU fallback **before importing PyTorch**:
>
> ```
> import os
> os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
>
> import torch
> import tabpackage
> ```
>
> This will make the package work with a harmless warning about the CPU fallback.

## Basics

For most users, the [examples](#examples) are the recommended entry point to the package.
The following subsections provide high-level sketches of what to expect from the package.

### PyTorch training API

Use `tabpackage.train` to train TabPack on a supervised tabular-learning task with PyTorch:

```py
# Prepare datasets.
raw_datasets: dict[str, dict[str, torch.Tensor]] = {
    'train': {'x_num': ..., 'x_cat': ..., 'y': ...},
    # At least one additional split is required.
    # See the examples for details.
    'val': ...,
    ...
}
datasets = preprocess(raw_datasets)

# Train TabPack.
tabpack = tabpackage.train(datasets=datasets, ...)

# Optionally, retrain TabPack on the combined training and validation data
# for better performance.
if retrain:
    raw_datasets['train'] = {
        'x_num': torch.cat([
            raw_datasets['train']['x_num'],
            raw_datasets['val']['x_num'],
        ]),
        ...
    }
    datasets = preprocess(raw_datasets)  # Redo the preprocessing.
    tabpack = tabpackage.retrain(tabpack, ...)

# Create a predictor, which is a vanilla PyTorch module.
predictor = tabpack.make_predictor(...)

# Make predictions.
y_pred, y_pred_ensembles = predictor(x_num, x_cat)

# Export the model.
...
torch.save(predictor, ...)
torch.onnx.export(predictor, ...)
torch.export.save(torch.export.export(predictor, ...), ...)
```

### scikit-learn API

Use `tabpackage.sklearn` to train TabPack with a scikit-learn interface:

```py
from tabpackage.sklearn import TabPackClassifier, TabPackRegressor

# Prepare datasets.
X_train, y_train = ...
X_val, y_val = ...
X_test = ...

# Train TabPack.
tabpack = TabPackClassifier(...)  # Or TabPackRegressor(...).
# The extra dataset split is required. See the examples for details.
tabpack.fit(X_train, y_train, extra_set=(X_val, y_val))

# Optionally, retrain TabPack on the combined training and validation data
# for better performance.
if retrain:
    X_full, y_full = ...  # Merge train + val and redo data preprocessing.
    tabpack.refit(X_full, y_full)

# Make predictions.
y_pred = tabpack.predict(X_test)
y_proba = tabpack.predict_proba(X_test)  # Classification only.
```

## API Overview

The package is designed to be easy to learn from well-documented [examples](#examples) and code.
A high-level summary:

- **`tabpackage.train` is the central function of the package.**
  Everything else either uses `tabpackage.train` or is used by `tabpackage.train`.
- `tabpackage.sklearn` provides scikit-learn estimators that are thin wrappers around `tabpackage.train`.
- The remaining components provide building blocks for implementing TabPack-like systems.

## Examples

- [`examples/tabpack.py`](./examples/tabpack.py) is a well-documented end-to-end example covering TabPack training and export.
- [`examples/tabpack_sklearn.ipynb`](./examples/tabpack_sklearn.ipynb) ([Colab](https://colab.research.google.com/github/yandex-research/tabpack/blob/main/examples/tabpack_sklearn.ipynb)) showcases scikit-learn API for TabPack.
- `tabpackage._training` is the reference TabPack training implementation that can be copied and customized as needed.
- The following classes form a good set of examples showing how to implement module packs:
  - `tabpackage.nn.DropoutPack`: a pack of dropout layers with different dropout rates.
  - `tabpackage.nn.LinearPack`: a pack of linear layers with different input and output sizes.
  - `tabpackage.nn.MLPBackbonePack`: a pack of MLP backbones with different depth, width and dropout rates.
  - `tabpackage.nn.TabPack`: a pack of full-fledged tabular MLPs with different hyperparameters.
    Pay extra attention to the use of `tabpackage.nn.PackView` in this module.

# How to cite

```
@inproceedings{gorishniy2026tabpack,
    title={{TabPack: Efficient Hyperparameter Ensembles for Tabular Deep Learning}},
    author={Yury Gorishniy and Akim Kotelnikov and Ivan Rubachev and Artem Babenko},
    booktitle={ICML},
    year={2026},
}
```
