Metadata-Version: 2.1
Name: pyplt
Version: 0.1.3
Summary: A toolbox for preference learning implemented in Python.
Home-page: https://github.com/institutedigitalgames/PLT
Author: Institute of Digital Games, University of Malta
Author-email: plt.digitalgames@um.edu.mt
License: UNKNOWN
Description: # PLT - Preference Learning Toolbox
        
        > A toolbox for preference learning implemented in Python.
        
        ![Image](http://plt.institutedigitalgames.com/img/plt_header_logo_final_transparent_2_small.png "PLT logo")
        
        Preference learning (PL) is a core area of machine learning that handles datasets with ordinal relations. As the
        number of generated data of ordinal nature such as ranks and subjective ratings is increasing, the importance and
        role of the PL field becomes central within machine learning research and practice.
        
        The Preference Learning Toolbox (PLT) is an open source software application and package which supports the key
        data modelling phases incorporating various popular data pre-processing, feature selection and preference
        learning methods.
        
        ![Image](http://plt.institutedigitalgames.com/img/index_plt.png "PLT Screenshot")
        
        PLT may be used either via its GUI or its API. This ``README`` is based on the API which is made available via the Python package **pyplt**. For more information on the GUI or to download the GUI application, please visit the [PLT website](http://plt.institutedigitalgames.com/index.php).
        
        The API documentation may be found at: [https://plt.readthedocs.io/](https://plt.readthedocs.io/en/latest/).
        
        ## Features:
        * Dataset Pre-processing
        * Automatic Feature Selection (SFS)
        * Preference Learning Algorithms (RankSVM, ANN-Backpropagation)
        * Experiment Reporting and Model Storage
        
        ## Installation:
        
        The Python package for PLT, **pyplt**, may be installed via pip:
        
        ```bash
        pip install pyplt
        ```
        
        ## Usage Example:
        
        The following example loads a dataset in the single file format (refer to [Detailed Guidelines](http://plt.institutedigitalgames.com/howto.php) for more information about file formats) and carries out preference learning using the RankSVM algorithm and K-Fold Cross Validation. At the end, the results are saved to file.
        
        ```python
        from pyplt.experiment import Experiment
        from pyplt.plalgorithms.ranksvm import RankSVM
        from pyplt.util.enums import KernelType
        from pyplt.evaluation.cross_validation import KFoldCrossValidation
        import time
        
        exp = Experiment()
        
        # load ratings data
        exp.load_single_data("sample data sets\\single_synth.csv", has_ids=True, has_fnames=True)
        
        # set up RankSVM algorithm
        pl_algorithm = RankSVM(kernel=KernelType.RBF, gamma=1)
        exp.set_pl_algorithm(pl_algorithm)
        
        # set up K-Fold Cross Validation
        pl_evaluator = KFoldCrossValidation(k=3)
        exp.set_pl_evaluator(pl_evaluator)
        
        # run the experiment
        exp.run()
        
        # save the results
        t = time.time()
        exp.save_exp_log(t, path="my_results.csv")
        
        ```
        
        For more a more detailed usage guide, please check out the [tutorial](http://plt.institutedigitalgames.com/docs/tutorial_experiment.html).
        
        ## Development Setup
        
        PLT has the following package dependencies:
        * ttkthemes
        * numpy
        * matplotlib
        * pandas
        * tensorflow
        * scikit_learn
        * scipy
        
        These depenencies may be easily istalled via pip:
        
        ```bash
        pip install -r requirements.txt
        ```
        
        ## Citing
        
        The tool is free for scientific use. If you use PLT in your scientific work, please cite as:
        
        Farrugia, Vincent E., HÃ©ctor P. MartÃ­nez, and Georgios N. Yannakakis. 
        "The Preference Learning Toolbox." arXiv preprint arXiv:1506.01709 (2015)
        
        ## License
        
        PLT is licensed under the GNU General Public License v3.0. See ``LICENSE`` for more information.
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Operating System :: Microsoft :: Windows :: Windows 10
Classifier: Operating System :: POSIX :: Linux
Description-Content-Type: text/markdown
