https://github.com/Luwen-Zhang/tabular_ensemble
A framework to evaluate various models for tabular regression and classification tasks.
https://github.com/Luwen-Zhang/tabular_ensemble
machine-learning tabular-model
Last synced: 10 months ago
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A framework to evaluate various models for tabular regression and classification tasks.
- Host: GitHub
- URL: https://github.com/Luwen-Zhang/tabular_ensemble
- Owner: LuoXueling
- License: mit
- Created: 2023-07-21T05:07:35.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-09-25T16:33:36.000Z (almost 2 years ago)
- Last Synced: 2024-09-26T01:48:50.488Z (almost 2 years ago)
- Topics: machine-learning, tabular-model
- Language: Python
- Homepage: https://tabular-ensemble.readthedocs.io/en/latest/index.html
- Size: 23.8 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# tabular_ensemble
[](https://github.com/psf/black)
[](https://codecov.io/gh/Luwen-Zhang/tabular_ensemble)
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[](https://tabular-ensemble.readthedocs.io/en/latest/?badge=latest)
A framework to evaluate various models for tabular regression and classification tasks. The package integrates 25 machine learning (including deep learning) models for tabular prediction
tasks from the following well-established model bases:
* [`autogluon`](https://github.com/autogluon/autogluon)
* `"LightGBM"`, `"CatBoost"`, `"XGBoost"`, `"Random Forest"`, `"Extremely Randomized Trees"`, `"K-Nearest Neighbors"`, `"Linear Regression"`, `"Neural Network with MXNet"`, `"Neural Network with PyTorch"`, `"Neural Network with FastAI"`.
* [`pytorch_widedeep`](https://github.com/jrzaurin/pytorch-widedeep)
* `"TabMlp"`, `"TabResnet"`, `"TabTransformer"`, `"TabNet"`, `"SAINT"`, `"ContextAttentionMLP"`, `"SelfAttentionMLP"`, `"FTTransformer"`, `"TabPerceiver"`, `"TabFastFormer"`.
* [`pytorch_tabular`](https://github.com/manujosephv/pytorch_tabular)
* `"Category Embedding"`, `"NODE"`, `"TabNet"`, `"TabTransformer"`, `"AutoInt"`, `"FTTransformer"`.
You are able to implement your own models, data processing pipelines, and datasets under the flexible and
well-tested framework for consistent comparisons with baseline models, which is even easier when your own model is
based on `pytorch`.
Supported features for all model bases:
* Data processing
* Data splitting (training/validation/testing sets)
* Data imputation
* Data filtering
* Data scaling
* Data augmentation
* Feature augmentation
* Feature selection
* etc.
* Multi-modal data
* Loading [UCI datasets](https://archive.ics.uci.edu/datasets)
* Data/result analysis
* Leaderboard
* Box plot
* Pair plot
* Pearson correlation
* Partial dependency plot (with bootstrapping)
* Feature importance (Permutation and SHAP)
* etc.
* Building models upon other trained models
* `pytorch_lightning`-based training for `pytorch` models
* Gaussian-process-based Bayesian hyperparameter optimization
* Cross-validation (including continuing from a cross-validation checkpoint)
* Saving, loading, and migrating models
The package stands on the shoulder of the giants:
* [scikit-learn](https://scikit-learn.org/)
* [PyTorch](https://pytorch.org/)
* [PyTorch Lightning](https://lightning.ai/)
* etc. (See `requirements.txt`)
## Installation/Usage
A full documentation is available [here](https://tabular-ensemble.readthedocs.io/en/latest/index.html). For a quick start:
1. `tabular_ensemble` can be installed using pypi by running the following command:
```shell
pip install tabensemb[torch]
```
Please use `pip install tabensemb` instead if you already have `torch>=1.12.0` installed. Use `pip install tabensemb[test]` if you want to run unit tests.
To install from source,
```shell
pip install -e .[torch]
```
2. (Optional) Run unit tests after installed `tabensemb[test]`:
```shell
cd test
pytest .
```
3. Place your `.csv` or `.xlsx` file in a `data` subfolder (e.g., `data/sample.csv`), and generate a configuration file in a `configs` subfolder (e.g., `configs/sample.py`), containing the following content
```python
cfg = {
"database": "sample",
"continuous_feature_names": ["cont_0", "cont_1", "cont_2", "cont_3", "cont_4"],
"categorical_feature_names": ["cat_0", "cat_1", "cat_2"],
"label_name": ["target"],
}
```
4. Run the experiment using the configuration and the data using
```python
python main.py --base sample --epoch 10
```
where `--base` refers to the configuration file, and additional arguments (such as `--epoch` here) refer to those in `config/default.py`.
See the [documentation pages](https://tabular-ensemble.readthedocs.io/en/latest/index.html) for details.
## Citation
If you use this repository, please cite us as:
```text
(Will be updated after released on arXiv or published)
```