Metadata-Version: 1.1
Name: green_magic
Version: 0.5.6
Summary: The Green Magic library of the Green-Machine
Home-page: https://github.com/boromir674/green-magic
Author: Konstantinos
Author-email: k.lampridis@hotmail.com
License: GNU GPLv3
Description-Content-Type: UNKNOWN
Description: Green Magic - Python Library
        ================================
        
        Green Magic is a library containing class models allowing users to train machine learning models as well as visualize cannabis strain data. It has functionality for encoding raw cannabis strain data into features usefull for visualization and cluster analysis. It contains implementations for model evaluation and methods for data exploration.
        
        Key features of the Library:
        
        * Data cleaning
        * Seemless dataset creation
        * Extendable feature extraction system
        * Usage of the Somoclu library [1] as the backend, which allows for 'fast execution of Self-Organizing Maps by parallelization: OpenMP and CUDA are supported'.
        * Visualization of maps
        * Kmeans and Affinity-propagation based clustering
        * Formatted print of statistics and distributions
        
        
        Usage
        -----
        A simple example is below.
        
        ::
        
            from green_magic import WeedMaster
            from green_magic.clustering import ClusteringFactory, DistroReporter, get_model_quality_reporter
            all_vars = ['type', 'effects', 'medical', 'negatives', 'flavors']
            active_vars = ['type', 'effects', 'medical', 'negatives', 'flavors']
            wd = 'pd'
            wm = WeedMaster()
            dt = wm.create_weedataset(dt_path, wd)
            dt.use_variables(active_vars)
            dt.clean()
            vectors = wm.get_feature_vectors(dt)
            print(dt)
            wm.save_dataset(wd)
            som = wm.map_manager.get_som('toroid.rectangular.30.30.pca')
            wm.map_manager.show_mmap(som)
            clf = ClusteringFactory(wm)
            cls = clf.create_clusters(som, 'kmeans', nb_clusters=10, vars=all_vars, ngrams=1)
            print(cls)
            cls.print_map()
            r = DistroReporter()
            r.print_distros(cls0, 'type', prec=3)
            qr = get_model_quality_reporter(wm, wd)
            print(qr.measure(cls, metric='silhouette'))
            print(qr.measure(cls, metric='cali-hara'))
        
        
        Installation
        ------------
        The code is available on PyPI, hence it can be installed by
        
        ::
        
            $ pip install green_magic
        
        Citation
        --------
        
        1. Peter Wittek, Shi Chao Gao, Ik Soo Lim, Li Zhao (2017). Somoclu: An Efficient Parallel Library for Self-Organizing Maps.  Journal of Statistical Software, 78(9), pp.1--21. DOI:`10.18637/jss.v078.i09 <https://doi.org/10.18637/jss.v078.i09>`_. arXiv:`1305.1422 <https://arxiv.org/abs/1305.1422>`_.
        
Keywords: cannabis strain self-organizing maps visualization
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Intended Audience :: Science/Research
