diff --git a/.commitlintrc.json b/.commitlintrc.json new file mode 100644 index 00000000..a0af52ad --- /dev/null +++ b/.commitlintrc.json @@ -0,0 +1,28 @@ +{ + "extends": ["@commitlint/config-conventional"], + "rules": { + "type-enum": [ + 2, + "always", + [ + "build", + "chore", + "ci", + "docs", + "feat", + "fix", + "perf", + "refactor", + "revert", + "style", + "test" + ] + ], + "subject-case": [2, "never", ["upper-case", "pascal-case"]], + "subject-empty": [2, "never"], + "subject-full-stop": [2, "never", "."], + "header-max-length": [2, "always", 72], + "body-leading-blank": [2, "always"], + "footer-leading-blank": [2, "always"] + } +} diff --git a/.github/workflows/build-publish-pypi.yml b/.github/workflows/build-publish-pypi.yml deleted file mode 100644 index 0ae304a0..00000000 --- a/.github/workflows/build-publish-pypi.yml +++ /dev/null @@ -1,34 +0,0 @@ -name: Publish Package to PyPi - -on: - push: - branches: - - release - -jobs: - build-publish: - runs-on: ubuntu-latest - - steps: - - name: Checkout code - uses: actions/checkout@v2 - - - name: Set up Python - uses: actions/setup-python@v2 - with: - python-version: "3.10" - - - name: Install Poetry - uses: abatilo/actions-poetry@v2 - - - name: Install dependencies - run: poetry install - - - name: Build package - run: poetry build - - - name: Publish to PyPI - env: - TWINE_USERNAME: __token__ - TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }} - run: poetry publish --username $TWINE_USERNAME --password $TWINE_PASSWORD diff --git a/.github/workflows/semantic-release.yml b/.github/workflows/semantic-release.yml new file mode 100644 index 00000000..7882d6e5 --- /dev/null +++ b/.github/workflows/semantic-release.yml @@ -0,0 +1,62 @@ +name: Semantic Release + +on: + push: + branches: + - master + - main + +permissions: + contents: write + issues: write + pull-requests: write + +jobs: + release: + runs-on: ubuntu-latest + concurrency: release + + steps: + - name: Checkout code + uses: actions/checkout@v4 + with: + fetch-depth: 0 + token: ${{ secrets.GITHUB_TOKEN }} + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.10" + + - name: Install Poetry + uses: snok/install-poetry@v1 + with: + version: latest + virtualenvs-create: true + virtualenvs-in-project: true + + - name: Cache Poetry dependencies + uses: actions/cache@v4 + with: + path: .venv + key: venv-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }} + + - name: Install dependencies + run: | + poetry install + + - name: Python Semantic Release + id: release + uses: python-semantic-release/python-semantic-release@v9.12.0 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + env: + PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }} + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + + - name: Publish package distributions to GitHub Releases + uses: python-semantic-release/upload-to-gh-release@main + if: steps.release.outputs.released == 'true' + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + tag: ${{ steps.release.outputs.tag }} diff --git a/.gitignore b/.gitignore index 31456c2b..f6a5ec46 100644 --- a/.gitignore +++ b/.gitignore @@ -172,5 +172,5 @@ examples/lightning_logs docs/_build/doctrees/* docs/_build/html/* - +dev dev/* \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8d3d7285..2173ee0a 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,3 +29,9 @@ repos: - yaml - markdown - json + + - repo: https://github.com/commitizen-tools/commitizen + rev: v3.29.1 + hooks: + - id: commitizen + stages: [commit-msg] diff --git a/README.md b/README.md index 91c51465..e11997d9 100644 --- a/README.md +++ b/README.md @@ -1,18 +1,17 @@
- [![PyPI](https://img.shields.io/pypi/v/deeptab)](https://pypi.org/project/deeptab) ![PyPI - Downloads](https://img.shields.io/pypi/dm/deeptab) [![docs build](https://readthedocs.org/projects/deeptab/badge/?version=latest)](https://deeptab.readthedocs.io/en/latest/?badge=latest) [![docs](https://img.shields.io/badge/docs-latest-blue)](https://deeptab.readthedocs.io/en/latest/) [![open issues](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/OpenTabular/deeptab/issues) - [πŸ“˜Documentation](https://deeptab.readthedocs.io/en/latest/index.html) | [πŸ› οΈInstallation](https://deeptab.readthedocs.io/en/latest/installation.html) | [Models](https://deeptab.readthedocs.io/en/latest/api/models/index.html) | [πŸ€”Report Issues](https://github.com/OpenTabular/deeptab/issues) +
@@ -31,9 +30,6 @@ deeptab is a Python library for tabular deep learning. It includes models that l
  • Define custom training metrics
    • - - -

    Table of Contents

    - [πŸƒ Quickstart](#-quickstart) @@ -46,8 +42,8 @@ deeptab is a Python library for tabular deep learning. It includes models that l - [🏷️ Citation](#️-citation) - [License](#license) - # πŸƒ Quickstart + Similar to any sklearn model, deeptab models can be fit as easy as this: ```python @@ -60,38 +56,34 @@ model.fit(X, y, max_epochs=150, lr=1e-04) ``` # πŸ“– Introduction -deeptab is a Python package that brings the power of advanced deep learning architectures to tabular data, offering a suite of models for regression, classification, and distributional regression tasks. Designed with ease of use in mind, deeptab models adhere to scikit-learn's `BaseEstimator` interface, making them highly compatible with the familiar scikit-learn ecosystem. This means you can fit, predict, and evaluate using deeptab models just as you would with any traditional scikit-learn model, but with the added performance and flexibility of deep learning. +deeptab is a Python package that brings the power of advanced deep learning architectures to tabular data, offering a suite of models for regression, classification, and distributional regression tasks. Designed with ease of use in mind, deeptab models adhere to scikit-learn's `BaseEstimator` interface, making them highly compatible with the familiar scikit-learn ecosystem. This means you can fit, predict, and evaluate using deeptab models just as you would with any traditional scikit-learn model, but with the added performance and flexibility of deep learning. # πŸ€– Models -| Model | Description | -| ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- | -| `Mambular` | A sequential model using Mamba blocks specifically designed for various tabular data tasks introduced [here](https://arxiv.org/abs/2408.06291). | -| `TabM` | Batch Ensembling for a MLP as introduced by [Gorishniy et al.](https://arxiv.org/abs/2410.24210) | -| `NODE` | Neural Oblivious Decision Ensembles as introduced by [Popov et al.](https://arxiv.org/abs/1909.06312) | -| `FTTransformer` | A model leveraging transformer encoders, as introduced by [Gorishniy et al.](https://arxiv.org/abs/2106.11959), for tabular data. | -| `MLP` | A classical Multi-Layer Perceptron (MLP) model for handling tabular data tasks. | -| `ResNet` | An adaptation of the ResNet architecture for tabular data applications. | -| `TabTransformer` | A transformer-based model for tabular data introduced by [Huang et al.](https://arxiv.org/abs/2012.06678), enhancing feature learning capabilities. | -| `MambaTab` | A tabular model using a Mamba-Block on a joint input representation described [here](https://arxiv.org/abs/2401.08867) . Not a sequential model. | -| `TabulaRNN` | A Recurrent Neural Network for Tabular data, introduced [here](https://arxiv.org/pdf/2411.17207). | -| `MambAttention` | A combination between Mamba and Transformers, also introduced [here](https://arxiv.org/pdf/2411.17207). | +| Model | Description | +| ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `Mambular` | A sequential model using Mamba blocks specifically designed for various tabular data tasks introduced [here](https://arxiv.org/abs/2408.06291). | +| `TabM` | Batch Ensembling for a MLP as introduced by [Gorishniy et al.](https://arxiv.org/abs/2410.24210) | +| `NODE` | Neural Oblivious Decision Ensembles as introduced by [Popov et al.](https://arxiv.org/abs/1909.06312) | +| `FTTransformer` | A model leveraging transformer encoders, as introduced by [Gorishniy et al.](https://arxiv.org/abs/2106.11959), for tabular data. | +| `MLP` | A classical Multi-Layer Perceptron (MLP) model for handling tabular data tasks. | +| `ResNet` | An adaptation of the ResNet architecture for tabular data applications. | +| `TabTransformer` | A transformer-based model for tabular data introduced by [Huang et al.](https://arxiv.org/abs/2012.06678), enhancing feature learning capabilities. | +| `MambaTab` | A tabular model using a Mamba-Block on a joint input representation described [here](https://arxiv.org/abs/2401.08867) . Not a sequential model. | +| `TabulaRNN` | A Recurrent Neural Network for Tabular data, introduced [here](https://arxiv.org/pdf/2411.17207). | +| `MambAttention` | A combination between Mamba and Transformers, also introduced [here](https://arxiv.org/pdf/2411.17207). | | `NDTF` | A neural decision forest using soft decision trees. See [Kontschieder et al.](https://openaccess.thecvf.com/content_iccv_2015/html/Kontschieder_Deep_Neural_Decision_ICCV_2015_paper.html) for inspiration. | -| `SAINT` | Improve neural networs via Row Attention and Contrastive Pre-Training, introduced [here](https://arxiv.org/pdf/2106.01342). | -| `AutoInt` | Automatic Feature Interaction Learning via Self-Attentive Neural Networks introduced [here](https://arxiv.org/abs/1810.11921). | -| `Trompt` | Trompt: Towards a Better Deep Neural Network for Tabular Data introduced [here](https://arxiv.org/abs/2305.18446). | -| `Tangos` | Tangos: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization introduced [here](https://openreview.net/pdf?id=n6H86gW8u0d). | -| `ModernNCA` | Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later introduced [here](https://arxiv.org/abs/2407.03257). | -| `TabR` | TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023 [here](https://arxiv.org/abs/2307.14338) | - - - +| `SAINT` | Improve neural networs via Row Attention and Contrastive Pre-Training, introduced [here](https://arxiv.org/pdf/2106.01342). | +| `AutoInt` | Automatic Feature Interaction Learning via Self-Attentive Neural Networks introduced [here](https://arxiv.org/abs/1810.11921). | +| `Trompt` | Trompt: Towards a Better Deep Neural Network for Tabular Data introduced [here](https://arxiv.org/abs/2305.18446). | +| `Tangos` | Tangos: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization introduced [here](https://openreview.net/pdf?id=n6H86gW8u0d). | +| `ModernNCA` | Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later introduced [here](https://arxiv.org/abs/2407.03257). | +| `TabR` | TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023 [here](https://arxiv.org/abs/2307.14338) | All models are available for `regression`, `classification` and distributional regression, denoted by `LSS`. Hence, they are available as e.g. `MambularRegressor`, `MambularClassifier` or `MambularLSS` - # πŸ“š Documentation You can find the deeptab API documentation [here](https://deeptab.readthedocs.io/en/latest/). @@ -99,6 +91,7 @@ You can find the deeptab API documentation [here](https://deeptab.readthedocs.io # πŸ› οΈ Installation Install deeptab using pip: + ```sh pip install deeptab ``` @@ -128,21 +121,18 @@ For an overview over all available methods: [pretab](https://github.com/OpenTabu

    Data Type Detection and Transformation

    -- **Ordinal & One-Hot Encoding**: Automatically transforms categorical data into numerical formats using continuous ordinal encoding or one-hot encoding. Includes options for transforming outputs to `float` for compatibility with downstream models. -- **Binning**: Discretizes numerical features into bins, with support for both fixed binning strategies and optimal binning derived from decision tree models. -- **MinMax**: Scales numerical data to a specific range, such as [-1, 1], using Min-Max scaling or similar techniques. -- **Standardization**: Centers and scales numerical features to have a mean of zero and unit variance for better compatibility with certain models. -- **Quantile Transformations**: Normalizes numerical data to follow a uniform or normal distribution, handling distributional shifts effectively. -- **Spline Transformations**: Captures nonlinearity in numerical features using spline-based transformations, ideal for complex relationships. -- **Piecewise Linear Encodings (PLE)**: Captures complex numerical patterns by applying piecewise linear encoding, suitable for data with periodic or nonlinear structures. -- **Polynomial Features**: Automatically generates polynomial and interaction terms for numerical features, enhancing the ability to capture higher-order relationships. -- **Box-Cox & Yeo-Johnson Transformations**: Performs power transformations to stabilize variance and normalize distributions. -- **Custom Binning**: Enables user-defined bin edges for precise discretization of numerical data. +- **Ordinal & One-Hot Encoding**: Automatically transforms categorical data into numerical formats using continuous ordinal encoding or one-hot encoding. Includes options for transforming outputs to `float` for compatibility with downstream models. +- **Binning**: Discretizes numerical features into bins, with support for both fixed binning strategies and optimal binning derived from decision tree models. +- **MinMax**: Scales numerical data to a specific range, such as [-1, 1], using Min-Max scaling or similar techniques. +- **Standardization**: Centers and scales numerical features to have a mean of zero and unit variance for better compatibility with certain models. +- **Quantile Transformations**: Normalizes numerical data to follow a uniform or normal distribution, handling distributional shifts effectively. +- **Spline Transformations**: Captures nonlinearity in numerical features using spline-based transformations, ideal for complex relationships. +- **Piecewise Linear Encodings (PLE)**: Captures complex numerical patterns by applying piecewise linear encoding, suitable for data with periodic or nonlinear structures. +- **Polynomial Features**: Automatically generates polynomial and interaction terms for numerical features, enhancing the ability to capture higher-order relationships. +- **Box-Cox & Yeo-Johnson Transformations**: Performs power transformations to stabilize variance and normalize distributions. +- **Custom Binning**: Enables user-defined bin edges for precise discretization of numerical data. - **Pre-trained Encoding**: Use sentence transformers to encode categorical features. - - -

    Fit a Model

    Fitting a model in deeptab is as simple as it gets. All models in deeptab are sklearn BaseEstimators. Thus the `.fit` method is implemented for all of them. Additionally, this allows for using all other sklearn inherent methods such as their built in hyperparameter optimization tools. @@ -162,6 +152,7 @@ model.fit(X, y, max_epochs=150, lr=1e-04) ``` Predictions are also easily obtained: + ```python # simple predictions preds = model.predict(X) @@ -171,12 +162,14 @@ preds = model.predict_proba(X) ``` Get latent representations for each feature: + ```python # simple encoding model.encode(X) ``` Use unstructured data: + ```python # load pretrained models image_model = ... @@ -190,8 +183,6 @@ txt_embs = nlp_model.encode(texts) model.fit(X_train, y_train, embeddings=[img_embs, txt_embs]) ``` - -

    Hyperparameter Optimization

    Since all of the models are sklearn base estimators, you can use the built-in hyperparameter optimizatino from sklearn. @@ -199,8 +190,8 @@ Since all of the models are sklearn base estimators, you can use the built-in hy from sklearn.model_selection import RandomizedSearchCV param_dist = { - 'd_model': randint(32, 128), - 'n_layers': randint(2, 10), + 'd_model': randint(32, 128), + 'n_layers': randint(2, 10), 'lr': uniform(1e-5, 1e-3) } @@ -222,29 +213,28 @@ random_search.fit(X, y, **fit_params) print("Best Parameters:", random_search.best_params_) print("Best Score:", random_search.best_score_) ``` + Note, that using this, you can also optimize the preprocessing. Just specify the necessary parameters when specifying the preprocessor arguments you want to optimize: + ```python param_dist = { - 'd_model': randint(32, 128), - 'n_layers': randint(2, 10), + 'd_model': randint(32, 128), + 'n_layers': randint(2, 10), 'lr': uniform(1e-5, 1e-3), "numerical_preprocessing": ["ple", "standardization", "box-cox"] } ``` - Since we have early stopping integrated and return the best model with respect to the validation loss, setting max_epochs to a large number is sensible. - Or use the built-in bayesian hpo simply by running: ```python best_params = model.optimize_hparams(X, y) ``` -This automatically sets the search space based on the default config from ``deeptab.configs``. See the documentation for all params with regard to ``optimize_hparams()``. However, the preprocessor arguments are fixed and cannot be optimized here. - +This automatically sets the search space based on the default config from `deeptab.configs`. See the documentation for all params with regard to `optimize_hparams()`. However, the preprocessor arguments are fixed and cannot be optimized here.

    βš–οΈ Distributional Regression with MambularLSS

    @@ -271,10 +261,8 @@ MambularLSS allows you to model the full distribution of a response variable, no - **categorical**: For data with more than two categories. - **Quantile**: For quantile regression using the pinball loss. - These distribution classes make MambularLSS versatile in modeling various data types and distributions. -

    Getting Started with MambularLSS:

    To integrate distributional regression into your workflow with `MambularLSS`, start by initializing the model with your desired configuration, similar to other deeptab models: @@ -296,13 +284,12 @@ model.fit( y, max_epochs=150, lr=1e-04, - patience=10, + patience=10, family="normal" # define your distribution ) ``` - # πŸ’» Implement Your Own Model deeptab allows users to easily integrate their custom models into the existing logic. This process is designed to be straightforward, making it simple to create a PyTorch model and define its forward pass. Instead of inheriting from `nn.Module`, you inherit from deeptab's `BaseModel`. Each deeptab model takes three main arguments: the number of classes (e.g., 1 for regression or 2 for binary classification), `cat_feature_info`, and `num_feature_info` for categorical and numerical feature information, respectively. Additionally, you can provide a config argument, which can either be a custom configuration or one of the provided default configs. @@ -393,11 +380,29 @@ Here's how you can implement a custom model with deeptab: regressor.evaluate(X_test, y_test) ``` +# 🀝 Contributing + +We welcome contributions! This project uses [Conventional Commits](https://www.conventionalcommits.org/) and automated semantic versioning. + +**Quick Start for Contributors:** +```bash +# Install dependencies with pre-commit hooks +just install + +# Make your changes and commit using the interactive tool +just commit + +# Or commit manually following conventional commits format +git commit -m "feat(models): add new model architecture" +``` + +See our [Contributing Guide](docs/contributing.md) for detailed guidelines and [Conventional Commits Reference](CONVENTIONAL_COMMITS.md) for commit message formatting. # 🏷️ Citation If you find this project useful in your research, please consider cite: + ```BibTeX @article{thielmann2024mambular, title={Mambular: A Sequential Model for Tabular Deep Learning}, @@ -408,6 +413,7 @@ If you find this project useful in your research, please consider cite: ``` If you use TabulaRNN please consider to cite: + ```BibTeX @article{thielmann2024efficiency, title={On the Efficiency of NLP-Inspired Methods for Tabular Deep Learning}, diff --git a/docs/contributing.md b/docs/contributing.md index a6b3eb4a..d2b449cb 100644 --- a/docs/contributing.md +++ b/docs/contributing.md @@ -1,4 +1,3 @@ - ## Contribution Guidelines Thank you for considering contributing to our Python package! We appreciate your time and effort in helping us improve our project. Please take a moment to review the following guidelines to ensure a smooth and efficient contribution process. @@ -16,9 +15,9 @@ To set up the development environment for this Python package, follow these step 1. Clone the repository to your local machine using the command: ``` -git clone https://github.com/basf/mamba-tabular +git clone https://github.com/OpenTabular/DeepTab -cd mamba-tabular +cd DeepTab ``` 2. Install tools required for setting up development environment: @@ -45,7 +44,7 @@ just install In case you are not able to install `just`, you can follow the below steps to set up the development environment: ``` -cd mamaba-tabular +cd DeepTab poetry install @@ -60,26 +59,142 @@ pip install -r docs/requirements_docs.txt **Note:** You can also set up a virtual environment to isolate your development environment. - ### How to Contribute 1. Create a new branch from the `develop` branch for your contributions. Please use descriptive and concise branch names. 2. Make your desired changes or additions to the codebase. 3. Ensure that your code adheres to [PEP8](https://peps.python.org/pep-0008/) coding style guidelines. 4. Write appropriate tests for your changes, ensuring that they pass. - - `make test` + - `make test` 5. Update the documentation and examples, if necessary. 6. Build the html documentation and verify if it works as expected. We have used Sphinx for documentation, you could build the documents as follows: - - `cd src/docs` - - `make clean` - - `make html` + - `cd src/docs` + - `make clean` + - `make html` 7. Verify the html documents created under `docs/_build/html` directory. `index.html` file is the main file which contains link to all other files and doctree. -8. Commit your changes with a clear and concise commit message. +8. Commit your changes following the Conventional Commits specification (see below). 9. Submit a pull request from your branch to the development branch of the original repository. 10. Wait for the maintainers to review your pull request. Address any feedback or comments if required. 11. Once approved, your changes will be merged into the main codebase. +### Release Workflow + +This project uses automated semantic versioning and releases. Here's how releases work: + +#### Automated Release Process + +``` +1. Make Changes β†’ 2. Conventional Commit β†’ 3. Merge to Master β†’ 4. Automated Release +``` + +**Step-by-Step:** + +1. **Development Phase** + + - Create feature branch from `develop` + - Make your changes + - Commit using conventional commits (e.g., `feat:`, `fix:`) + +2. **Merge to Develop** + + - Create PR to `develop` branch + - After review, merge to `develop` + - ReadTheDocs dev documentation updates automatically + +3. **Merge to Master** (Triggers Release) + + - Merge `develop` to `master` + - GitHub Actions semantic-release workflow runs automatically + +4. **Automated Release (on Master)** + - βœ… Analyzes conventional commits since last release + - βœ… Determines version bump (major/minor/patch) + - βœ… Updates version in `pyproject.toml` and `__version__.py` + - βœ… Generates/updates `CHANGELOG.md` + - βœ… Creates git tag (e.g., `v1.7.0`) + - βœ… Builds package (`poetry build`) + - βœ… Publishes to PyPI + - βœ… Creates GitHub Release with notes + +#### What Triggers a Release? + +| Commit Type | Version Bump | PyPI Release | +| -------------------------------------------------------- | ------------- | ------------ | +| `feat:` | Minor (1.x.0) | βœ… Yes | +| `fix:` | Patch (1.6.x) | βœ… Yes | +| `perf:` | Patch (1.6.x) | βœ… Yes | +| `feat!:` or `BREAKING CHANGE:` | Major (x.0.0) | βœ… Yes | +| `docs:`, `style:`, `refactor:`, `test:`, `chore:`, `ci:` | None | ❌ No | + +#### Example Scenarios + +**Scenario 1: Documentation Update (No Release)** + +```bash +git commit -m "docs: update API reference" +# Merge to master β†’ No version bump, no PyPI release +``` + +**Scenario 2: Bug Fix (Patch Release)** + +```bash +git commit -m "fix: resolve memory leak in dataloader" +# Merge to master β†’ Version 1.6.1 β†’ 1.6.2 β†’ PyPI release +``` + +**Scenario 3: New Feature (Minor Release)** + +```bash +git commit -m "feat(models): add TabNet architecture" +# Merge to master β†’ Version 1.6.1 β†’ 1.7.0 β†’ PyPI release +``` + +**Scenario 4: Breaking Change (Major Release)** + +```bash +git commit -m "feat!: remove Python 3.9 support + +BREAKING CHANGE: Python 3.10 is now the minimum required version" +# Merge to master β†’ Version 1.6.1 β†’ 2.0.0 β†’ PyPI release +``` + +#### Important Notes + +- **Only master branch** triggers releases +- **Semantic-release is fully automated** - no manual version bumping needed +- **Never manually edit version numbers** in `pyproject.toml` or `deeptab/__version__.py` - they are automatically updated by semantic-release +- **PyPI token** is configured in GitHub repository secrets +- **Review commits carefully** before merging to master (they determine the version!) + +#### Working with Updated Versions + +**Q: What happens if I create a branch from `develop` after a release?** + +After a release is merged to `master`, the version files are updated automatically. To get the latest version: + +1. **Sync develop with master** (maintainers do this): + + ```bash + git checkout develop + git merge master + git push origin develop + ``` + +2. **Update your local develop branch** (before creating new branches): + + ```bash + git checkout develop + git pull origin develop + ``` + +3. **Create your feature branch from updated develop**: + ```bash + git checkout -b feature/my-new-feature + ``` + +This ensures your branch has the correct version number as a starting point. Don't worry if the version seems "old" in your branch - semantic-release will calculate the correct new version based on commits when merging to master. + ### Submitting Contributions When submitting your contributions, please ensure the following: @@ -93,7 +208,7 @@ When submitting your contributions, please ensure the following: ### Issue Tracker -If you encounter any bugs, have feature requests, or need assistance, please visit our [Issue Tracker](https://github.com/basf/mamba-tabular/issues). Make sure to search for existing issues before creating a new one. +If you encounter any bugs, have feature requests, or need assistance, please visit our [Issue Tracker](https://github.com/OpenTabular/DeepTab/issues). Make sure to search for existing issues before creating a new one. ### License diff --git a/justfile b/justfile index 391d1ac9..cd4e8fa3 100644 --- a/justfile +++ b/justfile @@ -32,3 +32,7 @@ lint: format: poetry run docformatter --in-place --recursive --wrap-summaries 120 --wrap-descriptions 120 . poetry run ruff format . + +# create a conventional commit using commitizen +commit: + poetry run cz commit diff --git a/poetry.lock b/poetry.lock index fe8d3e33..9bc92864 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand. [[package]] name = "accelerate" @@ -169,6 +169,33 @@ files = [ frozenlist = ">=1.1.0" typing-extensions = {version = ">=4.2", markers = "python_version < \"3.13\""} +[[package]] +name = "annotated-types" +version = "0.7.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +groups = ["dev"] +files = [ + {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, + {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, +] + +[[package]] +name = "argcomplete" +version = "3.5.3" +description = "Bash tab completion for argparse" +optional = false +python-versions = ">=3.8" +groups = ["dev"] +files = [ + {file = "argcomplete-3.5.3-py3-none-any.whl", hash = "sha256:2ab2c4a215c59fd6caaff41a869480a23e8f6a5f910b266c1808037f4e375b61"}, + {file = "argcomplete-3.5.3.tar.gz", hash = "sha256:c12bf50eded8aebb298c7b7da7a5ff3ee24dffd9f5281867dfe1424b58c55392"}, +] + +[package.extras] +test = ["coverage", "mypy", "pexpect", "ruff", "wheel"] + [[package]] name = "async-timeout" version = "5.0.1" @@ -208,7 +235,7 @@ version = "2025.1.31" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.6" -groups = ["main"] +groups = ["main", "dev"] files = [ {file = "certifi-2025.1.31-py3-none-any.whl", hash = "sha256:ca78db4565a652026a4db2bcdf68f2fb589ea80d0be70e03929ed730746b84fe"}, {file = "certifi-2025.1.31.tar.gz", hash = "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651"}, @@ -328,6 +355,42 @@ files = [ {file = "charset_normalizer-3.4.1.tar.gz", hash = "sha256:44251f18cd68a75b56585dd00dae26183e102cd5e0f9f1466e6df5da2ed64ea3"}, ] +[[package]] +name = "click" +version = "8.3.1" +description = "Composable command line interface toolkit" +optional = false +python-versions = ">=3.10" +groups = ["dev"] +files = [ + {file = "click-8.3.1-py3-none-any.whl", hash = "sha256:981153a64e25f12d547d3426c367a4857371575ee7ad18df2a6183ab0545b2a6"}, + {file = "click-8.3.1.tar.gz", hash = "sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a"}, +] + +[package.dependencies] +colorama = {version = "*", markers = "platform_system == \"Windows\""} + +[[package]] +name = "click-option-group" +version = "0.5.9" +description = "Option groups missing in Click" +optional = false +python-versions = ">=3.7" +groups = ["dev"] +files = [ + {file = "click_option_group-0.5.9-py3-none-any.whl", hash = "sha256:ad2599248bd373e2e19bec5407967c3eec1d0d4fc4a5e77b08a0481e75991080"}, + {file = "click_option_group-0.5.9.tar.gz", hash = "sha256:f94ed2bc4cf69052e0f29592bd1e771a1789bd7bfc482dd0bc482134aff95823"}, +] + +[package.dependencies] +click = ">=7.0" + +[package.extras] +dev = ["pre-commit", "pytest"] +docs = ["m2r2", "pallets-sphinx-themes", "sphinx"] +test = ["pytest"] +test-cov = ["pytest", "pytest-cov"] + [[package]] name = "colorama" version = "0.4.6" @@ -339,7 +402,31 @@ files = [ {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"}, {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, ] -markers = {main = "platform_system == \"Windows\"", dev = "sys_platform == \"win32\""} +markers = {main = "platform_system == \"Windows\""} + +[[package]] +name = "commitizen" +version = "3.31.0" +description = "Python commitizen client tool" +optional = false +python-versions = ">=3.8" +groups = ["dev"] +files = [ + {file = "commitizen-3.31.0-py3-none-any.whl", hash = "sha256:a28df7ab5b8665d48796c422a97dcfae0d0fce7e2d28404c0e386cf1ebd42c8f"}, + {file = "commitizen-3.31.0.tar.gz", hash = "sha256:6ab973e91d07c1e745c6c0efe6dd0708b1f6d8fd7e4ab5e7c773b5ceb3df4ff0"}, +] + +[package.dependencies] +argcomplete = ">=1.12.1,<3.6" +charset-normalizer = ">=2.1.0,<4" +colorama = ">=0.4.1,<0.5.0" +decli = ">=0.6.0,<0.7.0" +jinja2 = ">=2.10.3" +packaging = ">=19" +pyyaml = ">=3.08" +questionary = ">=2.0,<3.0" +termcolor = ">=1.1,<3" +tomlkit = ">=0.5.3,<1.0.0" [[package]] name = "coverage" @@ -420,6 +507,36 @@ tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.1 [package.extras] toml = ["tomli ; python_full_version <= \"3.11.0a6\""] +[[package]] +name = "decli" +version = "0.6.3" +description = "Minimal, easy-to-use, declarative cli tool" +optional = false +python-versions = ">=3.9" +groups = ["dev"] +files = [ + {file = "decli-0.6.3-py3-none-any.whl", hash = "sha256:5152347c7bb8e3114ad65db719e5709b28d7f7f45bdb709f70167925e55640f3"}, + {file = "decli-0.6.3.tar.gz", hash = "sha256:87f9d39361adf7f16b9ca6e3b614badf7519da13092f2db3c80ca223c53c7656"}, +] + +[[package]] +name = "deprecated" +version = "1.3.1" +description = "Python @deprecated decorator to deprecate old python classes, functions or methods." +optional = false +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7" +groups = ["dev"] +files = [ + {file = "deprecated-1.3.1-py2.py3-none-any.whl", hash = "sha256:597bfef186b6f60181535a29fbe44865ce137a5079f295b479886c82729d5f3f"}, + {file = "deprecated-1.3.1.tar.gz", hash = "sha256:b1b50e0ff0c1fddaa5708a2c6b0a6588bb09b892825ab2b214ac9ea9d92a5223"}, +] + +[package.dependencies] +wrapt = ">=1.10,<3" + +[package.extras] +dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "setuptools ; python_version >= \"3.12\"", "tox"] + [[package]] name = "distlib" version = "0.3.9" @@ -451,6 +568,18 @@ untokenize = ">=0.1.1,<0.2.0" [package.extras] tomli = ["tomli (>=2.0.0,<3.0.0) ; python_version < \"3.11\""] +[[package]] +name = "dotty-dict" +version = "1.3.1" +description = "Dictionary wrapper for quick access to deeply nested keys." +optional = false +python-versions = ">=3.5,<4.0" +groups = ["dev"] +files = [ + {file = "dotty_dict-1.3.1-py3-none-any.whl", hash = "sha256:5022d234d9922f13aa711b4950372a06a6d64cb6d6db9ba43d0ba133ebfce31f"}, + {file = "dotty_dict-1.3.1.tar.gz", hash = "sha256:4b016e03b8ae265539757a53eba24b9bfda506fb94fbce0bee843c6f05541a15"}, +] + [[package]] name = "einops" version = "0.8.1" @@ -641,6 +770,40 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"] tqdm = ["tqdm"] +[[package]] +name = "gitdb" +version = "4.0.12" +description = "Git Object Database" +optional = false +python-versions = ">=3.7" +groups = ["dev"] +files = [ + {file = "gitdb-4.0.12-py3-none-any.whl", hash = "sha256:67073e15955400952c6565cc3e707c554a4eea2e428946f7a4c162fab9bd9bcf"}, + {file = "gitdb-4.0.12.tar.gz", hash = "sha256:5ef71f855d191a3326fcfbc0d5da835f26b13fbcba60c32c21091c349ffdb571"}, +] + +[package.dependencies] +smmap = ">=3.0.1,<6" + +[[package]] +name = "gitpython" +version = "3.1.45" +description = "GitPython is a Python library used to interact with Git repositories" +optional = false +python-versions = ">=3.7" +groups = ["dev"] +files = [ + {file = "gitpython-3.1.45-py3-none-any.whl", hash = "sha256:8908cb2e02fb3b93b7eb0f2827125cb699869470432cc885f019b8fd0fccff77"}, + {file = "gitpython-3.1.45.tar.gz", hash = "sha256:85b0ee964ceddf211c41b9f27a49086010a190fd8132a24e21f362a4b36a791c"}, +] + +[package.dependencies] +gitdb = ">=4.0.1,<5" + +[package.extras] +doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"] +test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock ; 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"db6bdae055eb3aac67ee10e3cb523e517b56b5e42c2aa2e0cd3392c8600aaa14" +content-hash = "c6aed75ce372f38a297e13463961256c0fcebe60c437fdba5315783c322e42b6" diff --git a/pyproject.toml b/pyproject.toml index 57be3dc5..bad470af 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,6 +35,8 @@ pytest-cov = "^4.1" ruff = ">=0.3" pre-commit = "^3.6" docformatter = "^1.4" +commitizen = "^3.29.1" +python-semantic-release = "^9.12.0" [tool.poetry.urls] @@ -100,3 +102,75 @@ in-place = true wrap-summaries = 120 wrap-descriptions = 120 exclude = ["build", "dist", ".venv", ".git"] + +# Commitizen configuration +[tool.commitizen] +name = "cz_conventional_commits" +version = "1.6.1" +version_files = ["pyproject.toml:version", "deeptab/__version__.py"] +tag_format = "v$version" +update_changelog_on_bump = true +major_version_zero = false + +# Python Semantic Release configuration +[tool.semantic_release] +version_toml = ["pyproject.toml:tool.poetry.version"] +version_variables = ["deeptab/__version__.py:__version__"] +build_command = "poetry build" +branch = "master" +upload_to_pypi = true +upload_to_release = true +commit_parser = "angular" + +[tool.semantic_release.branches.master] +match = "(master|main)" +prerelease = false + +[tool.semantic_release.changelog] +template_dir = "templates" +changelog_file = "CHANGELOG.md" +exclude_commit_patterns = [] + +[tool.semantic_release.changelog.environment] +block_start_string = "{%" +block_end_string = "%}" +variable_start_string = "{{" +variable_end_string = "}}" +comment_start_string = "{#" +comment_end_string = "#}" +trim_blocks = false +lstrip_blocks = false +newline_sequence = "\n" +keep_trailing_newline = false +extensions = [] +autoescape = true + +[tool.semantic_release.commit_parser_options] +allowed_tags = [ + "build", + "chore", + "ci", + "docs", + "feat", + "fix", + "perf", + "style", + "refactor", + "test", +] +minor_tags = ["feat"] +patch_tags = ["fix", "perf"] + +[tool.semantic_release.remote] +name = "origin" +type = "github" +ignore_token_for_push = false + +[tool.semantic_release.publish] +dist_glob_patterns = ["dist/*"] +upload_to_vcs_release = true + +[[tool.semantic_release.publish.repositories]] +name = "pypi" +url = "https://upload.pypi.org/legacy/" +token_var = "PYPI_TOKEN" diff --git a/templates/CHANGELOG.md.j2 b/templates/CHANGELOG.md.j2 new file mode 100644 index 00000000..9d700e97 --- /dev/null +++ b/templates/CHANGELOG.md.j2 @@ -0,0 +1,51 @@ +# CHANGELOG + +{% for version, release in context.history.items() %} +## {{ version }} ({{ release.tagged_date.strftime("%Y-%m-%d") }}) + +{% if release.elements.breaking %} +### πŸ’₯ Breaking Changes +{% for commit in release.elements.breaking %} +- {{ commit.descriptions[0] }} ([`{{ commit.short_hash }}`]({{ commit.hexsha_link }})) +{% if commit.descriptions | length > 1 %} + {{ commit.descriptions[1] }} +{% endif %} +{% endfor %} +{% endif %} + +{% if release.elements.feature %} +### πŸš€ Features +{% for commit in release.elements.feature %} +- {% if commit.scope %}**{{ commit.scope }}**: {% endif %}{{ commit.descriptions[0] }} ([`{{ commit.short_hash }}`]({{ commit.hexsha_link }})) +{% endfor %} +{% endif %} + +{% if release.elements.fix %} +### πŸ› Bug Fixes +{% for commit in release.elements.fix %} +- {% if commit.scope %}**{{ commit.scope }}**: {% endif %}{{ commit.descriptions[0] }} ([`{{ commit.short_hash }}`]({{ commit.hexsha_link }})) +{% endfor %} +{% endif %} + +{% if release.elements.documentation %} +### πŸ“š Documentation +{% for commit in release.elements.documentation %} +- {% if commit.scope %}**{{ commit.scope }}**: {% endif %}{{ commit.descriptions[0] }} ([`{{ commit.short_hash }}`]({{ commit.hexsha_link }})) +{% endfor %} +{% endif %} + +{% if release.elements.performance %} +### ⚑ Performance Improvements +{% for commit in release.elements.performance %} +- {% if commit.scope %}**{{ commit.scope }}**: {% endif %}{{ commit.descriptions[0] }} ([`{{ commit.short_hash }}`]({{ commit.hexsha_link }})) +{% endfor %} +{% endif %} + +{% if release.elements.refactoring %} +### πŸ”¨ Refactoring +{% for commit in release.elements.refactoring %} +- {% if commit.scope %}**{{ commit.scope }}**: {% endif %}{{ commit.descriptions[0] }} ([`{{ commit.short_hash }}`]({{ commit.hexsha_link }})) +{% endfor %} +{% endif %} + +{% endfor %}