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MALSS: MAchine Learning Support System

Project description

malss is a python module to facilitate machine learning tasks. This module is written to be compatible with the scikit-learn algorithms and the other scikit-learn-compatible algorithms.

https://travis-ci.org/canard0328/malss.svg?branch=master

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.

Installation

pip install malss

Example

Classification:

from malss import MALSS
from sklearn.datasets import load_iris
iris = load_iris()
cls = MALSS(iris.data, iris.target, task='classification')
cls.execute()
cls.make_report('classification_result')
cls.make_sample_code('classification_sample_code.py')

Regression:

from malss import MALSS
from sklearn.datasets import load_boston
boston = load_boston()
cls = MALSS(boston.data, boston.target, task='regression')
cls.execute()
cls.make_report('regression_result')
cls.make_sample_code('regression_sample_code.py')

Change algorithm:

from malss import MALSS
from sklearn.datasets import load_iris
iris = load_iris()
cls = MALSS(iris.data, iris.target, task='classification')
algorithms = cls.get_algorithms()
# check algorithms here
cls.remove_algorithm(0)
cls.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')
cls.execute()
cls.make_report('classification_result')
cls.make_sample_code('classification_sample_code.py')

API

View the documentation here.

Project details


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