Metadata-Version: 1.1
Name: malss
Version: 0.5.1
Summary: MALSS: MAchine Learning Support System
Home-page: https://github.com/canard0328/malss/
Author: Ryota KAMOSHIDA
Author-email: https://github.com/canard0328/malss/
License: MIT License: http://www.opensource.org/licenses/mit-license.php
Description: MAchine Learning Support System
        ###############################
        
        ``malss`` is a python module to facilitate machine learning tasks.
        This module is written to be compatible with the `scikit-learn algorithms <http://scikit-learn.org/stable/supervised_learning.html>`_ and the other scikit-learn-compatible algorithms.
        
        .. image:: https://travis-ci.org/canard0328/malss.svg?branch=master
            :target: https://travis-ci.org/canard0328/malss
        
        Requirements
        ************
        
        These are external packages which you will need to install before installing malss.
        
        * python (>= 2.7, 3.x's are not supported)
        * numpy (>= 1.6.1)
        * scipy (>= 0.9)
        * scikit-learn (>= 0.15)
        * matplotlib (>= 1.1)
        * pandas (>= 0.13)
        * jinja2 (>= 2.6)
        
        **Windows**
        
        If there are no binary packages matching your Python version you might to try to install these dependencies from `Christoph Gohlke Unofficial Windows installers <http://www.lfd.uci.edu/~gohlke/pythonlibs/>`_.
        
        Installation
        ************
        ::
        
          pip install malss
        
        Example
        *******
        
        Classification:
        
        .. code-block:: python
        
          from malss import MALSS
          from sklearn.datasets import load_iris
          iris = load_iris()
          clf = MALSS('classification')
          clf.fit(iris.data, iris.target, 'classification_result')
          clf.generate_module_sample('classification_module_sample.py')
        
        Regression:
        
        .. code-block:: python
        
          from malss import MALSS
          from sklearn.datasets import load_boston
          boston = load_boston()
          clf = MALSS('regression')
          clf.fit(boston.data, boston.target, 'regression_result')
          clf.generate_module_sample('regression_module_sample.py')
        
        Change algorithm:
        
        .. code-block:: python
        
          from malss import MALSS
          from sklearn.datasets import load_iris
          iris = load_iris()
          clf = MALSS('classification')
          clf.fit(iris.data, iris.target, algorithm_selection_only=True)
          algorithms = clf.get_algorithms()
          # check algorithms here
          clf.remove_algorithm(0)
          clf.add_algorithm(RF(n_jobs=3),
                            [{'n_estimators': [10, 30, 50],
                              'max_depth': [3, 5, None],
                              'max_features': [0.3, 0.6, 'auto']}],
                            'Random Forest')
          clf.fit(iris.data, iris.target, 'classification_result')
          clf.generate_module_sample('classification_module_sample.py')
        
        API
        ***
        View the `documentation here <https://pythonhosted.org/malss/>`_.
        
Keywords: machine learning support system
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
