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.
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 (>= 0.18)
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.
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