tabensemb 0.2

A framework to ensemble model bases and evaluate various models for tabular predictions.

tabular_ensemble

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
  • "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
  • "TabMlp", "TabResnet", "TabTransformer", "TabNet", "SAINT", "ContextAttentionMLP", "SelfAttentionMLP", "FTTransformer", "TabPerceiver", "TabFastFormer".
• 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
• 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
• PyTorch
• PyTorch Lightning
• etc. (See requirements.txt)

Installation/Usage

A full documentation is available here. For a quick start:

1. tabular_ensemble can be installed using pypi by running the following command:

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.

2. (Optional) Run unit tests after installed tabensemb[test]:

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

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 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 for details.

Citation

If you use this repository, please cite us as:

(Will be updated after released on arXiv or published)