Metadata-Version: 2.1
Name: rulelist
Version: 0.0.1
Summary: Learn rule lists from data for classification, regression or subgroup discovery
Home-page: https://github.com/HMProenca/RuleList
Author: Hugo Proenca
Author-email: hugo.manuel.proenca@gmail.com
License: MIT License
Description: 
        
        # MDL Rule Lists for prediction and data mining
        
        This repository contains the code for using rule lists for univariate or multivariate classification or regression and its equivalents in Data Mining and Subgroup Discovery. 
        These models use the Minimum Description Length (MDL) principle as optimality criteria.
        
        
        ## Dependencies
        
        This project was written for Python 3.7. All required packages from PyPI are specified in the `requirements.txt`.
        
        *NOTE:* This list of packages includes the `gmpy2` package.
        
        
        ## Example of usage:
        
        ```python
        import pandas as pd
        from rulelist import RuleList
        from sklearn import datasets
        from sklearn.model_selection import train_test_split
        
        task = 'prediction'
        target_model = 'categorical'
        
        data = datasets.load_breast_cancer()
        Y = pd.Series(data.target)
        X = pd.DataFrame(data.data)
        
        X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.3)
        
        model = RuleList(task = task, target_model = target_model)
        
        model.fit(X_train, y_train)
        
        y_pred = model.predict(X_test)
        from sklearn.metrics import accuracy_score
        accuracy_score(y_test.values,y_pred)
        
        print(model)
        ```
        
        
        
        
        ## Contact
        
        If there are any questions or issues, please contact me by mail at `hugo.manuel.proenca@gmail.com` or open an issue here on Github.
        
        
        ## Citation
        
        In a machine learning (prediction) context for problems of classification, regression, multi-label classification, multi-category classification, or multivariate regression cite the corresponding bibtex of the first classification application of MDL rule lists:
        
        ```
        @article{proencca2020interpretable,
          title={Interpretable multiclass classification by MDL-based rule lists},
          author={Proen{\c{c}}a, Hugo M and van Leeuwen, Matthijs},
          journal={Information Sciences},
          volume={512},
          pages={1372--1393},
          year={2020},
          publisher={Elsevier}
        }
        ```
        
        in the context of data mining and subgroup discovery please refer to subgroup lists:
        
        ```
        @article{proencca2020discovering,
          title={Discovering outstanding subgroup lists for numeric targets using MDL},
          author={Proen{\c{c}}a, Hugo M and Gr{\"u}nwald, Peter and B{\"a}ck, Thomas and van Leeuwen, Matthijs},
          journal={arXiv preprint arXiv:2006.09186},
          year={2020}
        } 
        ```
        
        # References #
         * [Interpretable multiclass classification by MDL-based rule lists. Hugo M. ProenÃ§a, Matthijs van Leeuwen. Information Sciences 512 (2020): 1372-1393.](https://www.sciencedirect.com/science/article/pii/S0020025519310138) or publicly available in [ArXiv](https://arxiv.org/abs/1905.00328) -- experiments code (old version) available [here](https://github.com/HMProenca/MDLRuleLists)
         * [Discovering outstanding subgroup lists for numeric targets using MDL. Hugo M. ProenÃ§a,Thomas BÃ¤ck, Matthijs van Leeuwen. ECML-PKDD(2020): ](https://arxiv.org/abs/2006.09186) -- experiments code available [here](https://github.com/HMProenca/SSDpp-numeric)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.7
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Requires-Python: >=3.7
Description-Content-Type: text/markdown
