malss 1.0.4

MALSS: 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 and the other scikit-learn-compatible algorithms.

Requirements

These are external packages which you will need to install before installing malss.

• python (>= 2.7 or >= 3.4)

• numpy (>= 1.10.2)

• scipy (>= 0.16.1)

• scikit-learn (>= 0.17)

• matplotlib (>= 1.5.1)

• pandas (>= 0.14.1)

• jinja2 (>= 2.8)

I highly recommend Anaconda. Anaconda conveniently installs packages listed above.

Installation

If you already have a working installation of numpy and scipy:

pip install malss

If you have not installed numpy or scipy yet, you can also install these using pip.

Example

Classification:

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:

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:

from malss import MALSS
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier as RF
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.