Metadata-Version: 1.2
Name: malss
Version: 2.2.1
Summary: MALSS: MAchine Learning Support System
Home-page: https://github.com/canard0328/malss/
Author: Ryota KAMOSHIDA
Author-email: malss@malss.com
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
        
        Dependencies
        ************
        
        malss requires:
        
        * python (>= 3.6)
        * numpy (>= 1.10.2)
        * scipy (>= 0.16.1)
        * scikit-learn (>= 0.19)
        * matplotlib (>= 1.5.1)
        * pandas (>= 0.14.1)
        * jinja2 (>= 2.8)
        * PyQt5 (>= 5.12) (only for interactive mode)
        
        All modules except PyQt5 are automatically installed when installing malss.
        
        Installation
        ************
        
          pip install malss
        
        For interactive mode, you need to install PyQt5 using pip.
        
          pip install PyQt5
        
        Example
        *******
        
        Classification:
        
        .. code-block:: python
        
          from malss import MALSS
          from sklearn.datasets import load_iris
          iris = load_iris()
          clf = MALSS(task='classification', lang='en')
          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(task='regression', lang='en')
          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
          from sklearn.ensemble import RandomForestClassifier as RF
          iris = load_iris()
          clf = MALSS(task='classification', lang='en')
          clf.fit(iris.data, iris.target, algorithm_selection_only=True)
          algorithms = clf.get_algorithms()
          # check algorithms here
          clf.remove_algorithm(0)  # remove the first algorithm
          # add random forest classifier
          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')
        
        Interactive mode:
        
        In the interactive mode, you can interactively analyze data through a GUI.
        
        .. code-block:: python
        
          from malss import MALSS
        
          MALSS(lang='en', interactive=True)
Keywords: machine learning support system
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
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
Requires-Python: >=3.6
