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<img alt="BOOMER - Gradient Boosted Multi-Label Classification Rules" src="https://github.com/mrapp-ke/MLRL-Boomer/raw/main/.assets/logo_boomer_light.svg">
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[](https://opensource.org/licenses/MIT) [](https://badge.fury.io/py/mlrl-boomer) [](https://mlrl-boomer.readthedocs.io/en/latest/?badge=latest)
**🔗 Important links:** [Documentation](https://mlrl-boomer.readthedocs.io/en/latest/) | [Issue Tracker](https://github.com/mrapp-ke/MLRL-Boomer/issues) | [Changelog](https://mlrl-boomer.readthedocs.io/en/latest/misc/CHANGELOG.html) | [License](https://mlrl-boomer.readthedocs.io/en/latest/misc/LICENSE.html)
<!-- documentation-start -->
This software package provides the official implementation of **BOOMER - an algorithm for learning gradient boosted multi-output rules** that uses [gradient boosting](https://en.wikipedia.org/wiki/Gradient_boosting) for learning an ensemble of rules that is built with respect to a specific multivariate loss function. It integrates with the popular [scikit-learn](https://scikit-learn.org) machine learning framework.
The problem domains addressed by this algorithm include the following:
- **Multi-label classification**: The goal of [multi-label classification](https://en.wikipedia.org/wiki/Multi-label_classification) is the automatic assignment of sets of labels to individual data points, for example, the annotation of text documents with topics.
- **Multi-output regression**: Multivariate [regression](https://en.wikipedia.org/wiki/Regression_analysis) problems require to predict for more than a single numerical output variable.
# The BOOMER Algorithm
To provide a versatile tool for different use cases, great emphasis is put on the *efficiency* of the implementation. Moreover, to ensure its *flexibility*, it is designed in a modular fashion and can therefore easily be adjusted to different requirements. This modular approach enables implementing different kind of rule learning algorithms (see packages [mlrl-common](https://pypi.org/project/mlrl-common/) and [mlrl-seco](https://pypi.org/project/mlrl-seco/)).
## 📖 References
The algorithm was first published in the following [paper](https://doi.org/10.1007/978-3-030-67664-3_8). A preprint version is publicly available [here](https://arxiv.org/pdf/2006.13346.pdf).
*Michael Rapp, Eneldo Loza MencÃa, Johannes Fürnkranz Vu-Linh Nguyen and Eyke Hüllermeier. Learning Gradient Boosted Multi-label Classification Rules. In: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), 2020, Springer.*
If you use the algorithm in a scientific publication, we would appreciate citations to the mentioned paper.
## 🔧 Functionalities
The algorithm that is provided by this project currently supports the following core functionalities for learning ensembles of boosted classification or regression rules.
### Deliberate Loss Optimization
- **Decomposable or non-decomposable loss functions** can be optimized in expectation.
- **L1 and L2 regularization** can be used.
- **Shrinkage (a.k.a. the learning rate) can be adjusted** for controlling the impact of individual rules on the overall ensemble.
### Different Prediction Strategies
- **Various strategies for predicting scores, binary labels or probabilities** are available, depending on whether a classification or regression model is used.
- **Isotonic regression models can be used to calibrate marginal and joint probabilities** predicted by a classification model.
### Flexible Handling of Input Data
- **Native support for numerical, ordinal, and nominal features** eliminates the need for pre-processing techniques such as one-hot encoding.
- **Handling of missing feature values**, i.e., occurrences of *NaN* in the feature matrix, is implemented by the algorithm.
### Fine-grained Control over Model Characteristics
- **Rules can be constructed via a greedy search or a beam search.** The latter may help to improve the quality of individual rules.
- **Single-output, partial, or complete heads** can be used by rules, i.e., they can predict for a single output, a subset of the available outputs, or all of them. Predicting for multiple outputs simultaneously enables to model local dependencies between them.
- **Fine-grained control over the specificity/generality of rules** is provided via hyperparameters.
### Support for Post-Optimization and Pruning
- **Incremental reduced error pruning** can be used for removing overly specific conditions from rules and preventing overfitting.
- **Post- and pre-pruning (a.k.a. early stopping)** allows to determine the optimal number of rules to be included in an ensemble.
- **Sequential post-optimization** may help improving the predictive performance of a model by reconstructing each rule in the context of the other rules.
## ⌚ Runtime and Memory Optimizations
In addition to the features mentioned above, several techniques that may speed up training or reduce the memory footprint are currently implemented.
### Approximation Techniques
- **Unsupervised feature binning** can be used to speed up the evaluation of a rule's potential conditions when dealing with numerical features.
- **Sampling techniques and stratification methods** can be used for learning new rules on a subset of the available training examples, features, or output variables.
- **[Gradient-based label binning (GBLB)](https://arxiv.org/pdf/2106.11690.pdf)** can be used for assigning the labels included in a multi-label classification dataset to a limited number of bins. This may speed up training significantly when minimizing a non-decomposable loss function using rules with partial or complete heads.
### Sparse Data Structures
- **Sparse feature matrices** can be used for training and prediction. This may speed up training significantly on some datasets.
- **Sparse ground truth matrices** can be used for training. This may reduce the memory footprint in case of large datasets.
- **Sparse prediction matrices** can be used for storing predicted labels. This may reduce the memory footprint in case of large datasets.
- **Sparse matrices for storing gradients and Hessians** can be used if supported by the loss function. This may speed up training significantly on datasets with many output variables.
### Parallelization
- **Multi-threading** can be used for parallelizing the evaluation of a rule's potential refinements across several features, updating the gradients and Hessians of individual examples in parallel, or obtaining predictions for several examples in parallel.
<!-- documentation-end -->
## 📚 Documentation
Our documentation provides an extensive [user guide](https://mlrl-boomer.readthedocs.io/en/latest/user_guide/boosting/index.html), as well as [Python](https://mlrl-boomer.readthedocs.io/en/latest/developer_guide/api/python/boosting/mlrl.boosting.html) and [C++](https://mlrl-boomer.readthedocs.io/en/latest/developer_guide/api/cpp/boosting/filelist.html) API references for developers. If you are new to the project, you probably want to read about the following topics:
- Instructions for [installing the software package](https://mlrl-boomer.readthedocs.io/en/latest/quickstart/installation.html) or [building the project from source](https://mlrl-boomer.readthedocs.io/en/latest/developer_guide/compilation.html).
- Examples of how to [use the algorithm](https://mlrl-boomer.readthedocs.io/en/latest/quickstart/usage.html) in your own Python code or how to use the [command line API](https://mlrl-boomer.readthedocs.io/en/latest/quickstart/testbed.html).
- An overview of available [parameters](https://mlrl-boomer.readthedocs.io/en/latest/user_guide/boosting/parameters.html).
A collection of benchmark datasets that are compatible with the algorithm are provided in a separate [repository](https://github.com/mrapp-ke/Boomer-Datasets).
For an overview of changes and new features that have been included in past releases, please refer to the [changelog](https://mlrl-boomer.readthedocs.io/en/latest/misc/CHANGELOG.html).
## 📜 License
This project is open source software licensed under the terms of the [MIT license](https://mlrl-boomer.readthedocs.io/en/latest/misc/LICENSE.html). We welcome contributions to the project to enhance its functionality and make it more accessible to a broader audience. A frequently updated list of contributors is available [here](https://mlrl-boomer.readthedocs.io/en/latest/misc/CONTRIBUTORS.html).
All contributions to the project and discussions on the [issue tracker](https://github.com/mrapp-ke/MLRL-Boomer/issues) are expected to follow the [code of conduct](https://mlrl-boomer.readthedocs.io/en/latest/misc/CODE_OF_CONDUCT.html).
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"description": "<p align=\"center\">\n <picture>\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"https://github.com/mrapp-ke/MLRL-Boomer/raw/main/doc/_static/logo_boomer_dark.svg\">\n <source media=\"(prefers-color-scheme: light)\" srcset=\"https://github.com/mrapp-ke/MLRL-Boomer/raw/main/doc/_static/logo_boomer_light.svg\">\n <img alt=\"BOOMER - Gradient Boosted Multi-Label Classification Rules\" src=\"https://github.com/mrapp-ke/MLRL-Boomer/raw/main/.assets/logo_boomer_light.svg\">\n </picture>\n</p>\n\n[](https://opensource.org/licenses/MIT) [](https://badge.fury.io/py/mlrl-boomer) [](https://mlrl-boomer.readthedocs.io/en/latest/?badge=latest)\n\n**\ud83d\udd17 Important links:** [Documentation](https://mlrl-boomer.readthedocs.io/en/latest/) | [Issue Tracker](https://github.com/mrapp-ke/MLRL-Boomer/issues) | [Changelog](https://mlrl-boomer.readthedocs.io/en/latest/misc/CHANGELOG.html) | [License](https://mlrl-boomer.readthedocs.io/en/latest/misc/LICENSE.html)\n\n<!-- documentation-start -->\n\nThis software package provides the official implementation of **BOOMER - an algorithm for learning gradient boosted multi-output rules** that uses [gradient boosting](https://en.wikipedia.org/wiki/Gradient_boosting) for learning an ensemble of rules that is built with respect to a specific multivariate loss function. It integrates with the popular [scikit-learn](https://scikit-learn.org) machine learning framework.\n\nThe problem domains addressed by this algorithm include the following:\n\n- **Multi-label classification**: The goal of [multi-label classification](https://en.wikipedia.org/wiki/Multi-label_classification) is the automatic assignment of sets of labels to individual data points, for example, the annotation of text documents with topics.\n- **Multi-output regression**: Multivariate [regression](https://en.wikipedia.org/wiki/Regression_analysis) problems require to predict for more than a single numerical output variable.\n\n# The BOOMER Algorithm\n\nTo provide a versatile tool for different use cases, great emphasis is put on the *efficiency* of the implementation. Moreover, to ensure its *flexibility*, it is designed in a modular fashion and can therefore easily be adjusted to different requirements. This modular approach enables implementing different kind of rule learning algorithms (see packages [mlrl-common](https://pypi.org/project/mlrl-common/) and [mlrl-seco](https://pypi.org/project/mlrl-seco/)).\n\n## \ud83d\udcd6 References\n\nThe algorithm was first published in the following [paper](https://doi.org/10.1007/978-3-030-67664-3_8). A preprint version is publicly available [here](https://arxiv.org/pdf/2006.13346.pdf).\n\n*Michael Rapp, Eneldo Loza Menc\u00eda, Johannes F\u00fcrnkranz Vu-Linh Nguyen and Eyke H\u00fcllermeier. Learning Gradient Boosted Multi-label Classification Rules. In: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), 2020, Springer.*\n\nIf you use the algorithm in a scientific publication, we would appreciate citations to the mentioned paper.\n\n## \ud83d\udd27 Functionalities\n\nThe algorithm that is provided by this project currently supports the following core functionalities for learning ensembles of boosted classification or regression rules.\n\n### Deliberate Loss Optimization\n\n- **Decomposable or non-decomposable loss functions** can be optimized in expectation.\n- **L1 and L2 regularization** can be used.\n- **Shrinkage (a.k.a. the learning rate) can be adjusted** for controlling the impact of individual rules on the overall ensemble.\n\n### Different Prediction Strategies\n\n- **Various strategies for predicting scores, binary labels or probabilities** are available, depending on whether a classification or regression model is used.\n- **Isotonic regression models can be used to calibrate marginal and joint probabilities** predicted by a classification model.\n\n### Flexible Handling of Input Data\n\n- **Native support for numerical, ordinal, and nominal features** eliminates the need for pre-processing techniques such as one-hot encoding.\n- **Handling of missing feature values**, i.e., occurrences of *NaN* in the feature matrix, is implemented by the algorithm.\n\n### Fine-grained Control over Model Characteristics\n\n- **Rules can be constructed via a greedy search or a beam search.** The latter may help to improve the quality of individual rules.\n- **Single-output, partial, or complete heads** can be used by rules, i.e., they can predict for a single output, a subset of the available outputs, or all of them. Predicting for multiple outputs simultaneously enables to model local dependencies between them.\n- **Fine-grained control over the specificity/generality of rules** is provided via hyperparameters.\n\n### Support for Post-Optimization and Pruning\n\n- **Incremental reduced error pruning** can be used for removing overly specific conditions from rules and preventing overfitting.\n- **Post- and pre-pruning (a.k.a. early stopping)** allows to determine the optimal number of rules to be included in an ensemble.\n- **Sequential post-optimization** may help improving the predictive performance of a model by reconstructing each rule in the context of the other rules.\n\n## \u231a Runtime and Memory Optimizations\n\nIn addition to the features mentioned above, several techniques that may speed up training or reduce the memory footprint are currently implemented.\n\n### Approximation Techniques\n\n- **Unsupervised feature binning** can be used to speed up the evaluation of a rule's potential conditions when dealing with numerical features.\n- **Sampling techniques and stratification methods** can be used for learning new rules on a subset of the available training examples, features, or output variables.\n- **[Gradient-based label binning (GBLB)](https://arxiv.org/pdf/2106.11690.pdf)** can be used for assigning the labels included in a multi-label classification dataset to a limited number of bins. This may speed up training significantly when minimizing a non-decomposable loss function using rules with partial or complete heads.\n\n### Sparse Data Structures\n\n- **Sparse feature matrices** can be used for training and prediction. This may speed up training significantly on some datasets.\n- **Sparse ground truth matrices** can be used for training. This may reduce the memory footprint in case of large datasets.\n- **Sparse prediction matrices** can be used for storing predicted labels. This may reduce the memory footprint in case of large datasets.\n- **Sparse matrices for storing gradients and Hessians** can be used if supported by the loss function. This may speed up training significantly on datasets with many output variables.\n\n### Parallelization\n\n- **Multi-threading** can be used for parallelizing the evaluation of a rule's potential refinements across several features, updating the gradients and Hessians of individual examples in parallel, or obtaining predictions for several examples in parallel.\n\n<!-- documentation-end -->\n\n## \ud83d\udcda Documentation\n\nOur documentation provides an extensive [user guide](https://mlrl-boomer.readthedocs.io/en/latest/user_guide/boosting/index.html), as well as [Python](https://mlrl-boomer.readthedocs.io/en/latest/developer_guide/api/python/boosting/mlrl.boosting.html) and [C++](https://mlrl-boomer.readthedocs.io/en/latest/developer_guide/api/cpp/boosting/filelist.html) API references for developers. If you are new to the project, you probably want to read about the following topics:\n\n- Instructions for [installing the software package](https://mlrl-boomer.readthedocs.io/en/latest/quickstart/installation.html) or [building the project from source](https://mlrl-boomer.readthedocs.io/en/latest/developer_guide/compilation.html).\n- Examples of how to [use the algorithm](https://mlrl-boomer.readthedocs.io/en/latest/quickstart/usage.html) in your own Python code or how to use the [command line API](https://mlrl-boomer.readthedocs.io/en/latest/quickstart/testbed.html).\n- An overview of available [parameters](https://mlrl-boomer.readthedocs.io/en/latest/user_guide/boosting/parameters.html).\n\nA collection of benchmark datasets that are compatible with the algorithm are provided in a separate [repository](https://github.com/mrapp-ke/Boomer-Datasets).\n\nFor an overview of changes and new features that have been included in past releases, please refer to the [changelog](https://mlrl-boomer.readthedocs.io/en/latest/misc/CHANGELOG.html).\n\n## \ud83d\udcdc License\n\nThis project is open source software licensed under the terms of the [MIT license](https://mlrl-boomer.readthedocs.io/en/latest/misc/LICENSE.html). 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