malss 2.3.0

Dependencies

malss requires:

• python (>= 3.6)

• numpy (>= 1.10.2)

• scipy (>= 0.16.1)

• scikit-learn (>= 0.20)

• matplotlib (>= 1.5.1)

• pandas (>= 0.14.1)

• jinja2 (>= 2.8)

• PyQt5 (>= 5.12) (only for interactive mode)

All modules except PyQt5 are automatically installed when installing malss.

Installation

pip install malss

For interactive mode, you need to install PyQt5 using pip.

pip install PyQt5

Example

Classification:

from malss import MALSS
from sklearn.datasets import load_iris
iris = load_iris()
clf = MALSS(task='classification', lang='en')
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(task='regression', lang='en')
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(task='classification', lang='en')
clf.fit(iris.data, iris.target, algorithm_selection_only=True)
algorithms = clf.get_algorithms()
# check algorithms here
clf.remove_algorithm(0)  # remove the first algorithm
# add random forest classifier
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')

Interactive mode:

In the interactive mode, you can interactively analyze data through a GUI.

from malss import MALSS

MALSS(lang='en', interactive=True)