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.
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.
pip install malss
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.make_sample_code('classification_sample_code.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.make_sample_code('regression_sample_code.py')Change algorithm:
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.make_sample_code('classification_sample_code.py')View the documentation here.