diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..a532df8 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,25 @@ +default_language_version: + python: python3.10 + +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v5.0.0 + hooks: + - id: check-added-large-files + - id: check-toml + - id: check-yaml + - id: end-of-file-fixer + - id: trailing-whitespace + + - repo: https://github.com/pycqa/isort + rev: 6.0.1 + hooks: + - id: isort + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.11.12 + hooks: + - id: ruff + args: + - --fix + - id: ruff-format diff --git a/README.md b/README.md index c1fe156..2204294 100644 --- a/README.md +++ b/README.md @@ -27,7 +27,7 @@ Version 0.1.2 of **LinearBoost Classifier** is released. Here are the changes: - Both SEFR and LinearBoostClassifier classes are refactored to fully adhere to Scikit-learn's conventions and API. Now, they are standard Scikit-learn estimators that can be used in Scikit-learn pipelines, grid search, etc. - Added unit tests (using pytest) to ensure the estimators adhere to Scikit-learn conventions. - Added fit_intercept parameter to SEFR similar to other linear estimators in Scikit-learn (e.g., LogisticRegression, LinearRegression, etc.). -- Removed random_state parameter from LinearBoostClassifier as it doesn't affect the result, since SEFR doesn't expose a random_state argument. According to Scikit-learn documentation for this parameter in AdaBoostClassifier: +- Removed random_state parameter from LinearBoostClassifier as it doesn't affect the result, since SEFR doesn't expose a random_state argument. According to Scikit-learn documentation for this parameter in AdaBoostClassifier: > it is only used when estimator exposes a random_state. - Added docstring to both SEFR and LinearBoostClassifier classes. - Used uv for project and package management. @@ -45,20 +45,20 @@ The documentation is available at https://linearboost.readthedocs.io/. The following parameters yielded optimal results during testing. All results are based on 10-fold Cross-Validation: -- **`n_estimators`**: +- **`n_estimators`**: A range of 10 to 200 is suggested, with higher values potentially improving performance at the cost of longer training times. -- **`learning_rate`**: +- **`learning_rate`**: Values between 0.01 and 1 typically perform well. Adjust based on the dataset's complexity and noise. -- **`algorithm`**: +- **`algorithm`**: Use either `SAMME` or `SAMME.R`. The choice depends on the specific problem: - `SAMME`: May be better for datasets with clearer separations between classes. - `SAMME.R`: Can handle more nuanced class probabilities. **Note:** As of scikit-learn v1.6, the `algorithm` parameter is deprecated and will be removed in v1.8. LinearBoostClassifier will only implement the 'SAMME' algorithm in newer versions. -- **`scaler`**: +- **`scaler`**: The following scaling methods are recommended based on dataset characteristics: - `minmax`: Best for datasets where features are on different scales but bounded. - `robust`: Effective for datasets with outliers. @@ -200,10 +200,10 @@ params = { LinearBoost's combination of **runtime efficiency** and **high accuracy** makes it a powerful choice for real-world machine learning tasks, particularly in resource-constrained or real-time applications. ### 📰 Featured in: -- [LightGBM Alternatives: A Comprehensive Comparison](https://nightwatcherai.com/blog/lightgbm-alternatives) - _by Jordan Cole, March 11, 2025_ +- [LightGBM Alternatives: A Comprehensive Comparison](https://nightwatcherai.com/blog/lightgbm-alternatives) + _by Jordan Cole, March 11, 2025_ *Discusses how LinearBoost outperforms traditional boosting frameworks in terms of speed while maintaining accuracy.* - + Future Developments ----------------------------- @@ -224,7 +224,7 @@ This project is licensed under the terms of the MIT license. See [LICENSE](https Some portions of this code are adapted from the scikit-learn project (https://scikit-learn.org), which is licensed under the BSD 3-Clause License. -See the `licenses/` folder for details. The modifications and additions made to the original code are licensed under the MIT License © 2025 Hamidreza Keshavarz, Reza Rawassizadeh. +See the `licenses/` folder for details. The modifications and additions made to the original code are licensed under the MIT License © 2025 Hamidreza Keshavarz, Reza Rawassizadeh. The original code from scikit-learn is available at [scikit-learn GitHub repository](https://github.com/scikit-learn/scikit-learn) Special Thanks to: diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 0000000..3feb504 --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,19 @@ +# Security Policy + +## Reporting a Vulnerability + +If you think you found a vulnerability, and even if you are not sure about it, please report it right away by sending an email to: `hamid9 at outlook dot com`. Please try to be as explicit as possible, describing all the steps and example code to reproduce the security issue. + +## Vulnerability Disclosures + +Critical vulnerabilities will be disclosed via GitHub's [security advisory](https://github.com/LinearBoost/linearboost-classifier/security) system. + +## Public Discussions + +Please restrain from publicly discussing a potential security vulnerability. + +It's better to discuss privately and try to find a solution first, to limit the potential impact as much as possible. + +--- + +Thanks for your help! diff --git a/pyproject.toml b/pyproject.toml index a451e21..8adb6a0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,13 +38,16 @@ dependencies = [ [dependency-groups] dev = [ "isort", + "pre-commit>=3.5.0", "pytest>=7.0.0", "ruff>=0.9.2", ] [project.urls] Homepage = "https://github.com/LinearBoost/linearboost-classifier" -Source = "https://github.com/LinearBoost/linearboost-classifier" +Documentation = "https://linearboost.readthedocs.io" +Repository = "https://github.com/LinearBoost/linearboost-classifier" +Issues = "https://github.com/LinearBoost/linearboost-classifier/issues" [tool.hatch.version] path = "src/linearboost/__init__.py" @@ -66,4 +69,4 @@ line-length = 120 atomic = true profile = "black" skip_gitignore = true -known_first_party = ["black", "blib2to3", "blackd", "_black_version"] +known_first_party = ["linearboost"] diff --git a/requirements.txt b/requirements.txt index b52fd23..c1e6cc8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,2 +1,2 @@ scikit-learn>=1.2.2 -typing-extensions>=4.1.0; python_version < "3.11" \ No newline at end of file +typing-extensions>=4.1.0; python_version < "3.11" diff --git a/src/linearboost/linear_boost.py b/src/linearboost/linear_boost.py index 2dd9bed..e6fac67 100644 --- a/src/linearboost/linear_boost.py +++ b/src/linearboost/linear_boost.py @@ -67,7 +67,7 @@ class LinearBoostClassifier(AdaBoostClassifier): """A LinearBoost classifier. A LinearBoost classifier is a meta-estimator based on AdaBoost and SEFR. - It is a fast and accurate classification algorithm built to enhance the + It is a fast and accurate classification algorithm built to enhance the performance of the linear classifier SEFR. Parameters @@ -107,7 +107,7 @@ class LinearBoostClassifier(AdaBoostClassifier): class_weight : {"balanced", "balanced_subsample"}, dict or list of dicts, \ default=None Weights associated with classes in the form ``{class_label: weight}``. - If not given, all classes are supposed to have weight one. + If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data @@ -122,9 +122,9 @@ class LinearBoostClassifier(AdaBoostClassifier): loss_function : callable, default=None Custom loss function for optimization. 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