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, 3.x’s are not supported)
numpy (>= 1.6.1)
scipy (>= 0.9)
scikit-learn (>= 0.14)
matplotlib (>= 1.3)
pandas (>= 0.13)
jinja2 (>= 2.7)
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.
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