malss 2.4.2

MALSS: MAchine Learning Support System

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 <http://scikit-learn.org/stable/supervised_learning.html>_ and the other scikit-learn-compatible algorithms.

.. image:: https://travis-ci.org/canard0328/malss.svg?branch=master :target: https://travis-ci.org/canard0328/malss

Dependencies

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malss requires:

• python (>= 3.9)
• numpy (>= 1.21.2)
• scipy (>= 1.7.1)
• scikit-learn (>= 1.1.1)
• matplotlib (>= 3.4.3)
• pandas (>= 1.3.3)
• jinja2 (>= 3.1.2)

.. * PyQt5 (== 5.10) (only for interactive mode)

All modules except PyQt5 are automatically installed when installing malss.

Installation

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pip install malss

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

pip install PyQt5

Example

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Supervised learning

Classification:

.. code-block:: python

from malss import MALSS from sklearn.datasets import load_iris iris = load_iris() model = MALSS(task='classification', lang='en') model.fit(iris.data, iris.target, 'classification_result') model.generate_module_sample('classification_module_sample.py')

Regression:

.. code-block:: python

from malss import MALSS from sklearn.datasets import load_boston boston = load_boston() model = MALSS(task='regression', lang='en') model.fit(boston.data, boston.target, 'regression_result') model.generate_module_sample('regression_module_sample.py')

Change algorithm:

.. code-block:: python

from malss import MALSS from sklearn.datasets import load_iris from sklearn.ensemble import RandomForestClassifier as RF iris = load_iris() model = MALSS(task='classification', lang='en') model.fit(iris.data, iris.target, algorithm_selection_only=True) algorithms = model.get_algorithms()

check algorithms here

model.remove_algorithm(0) # remove the first algorithm

add random forest classifier

model.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') model.fit(iris.data, iris.target, 'classification_result') model.generate_module_sample('classification_module_sample.py')

Feature selection:

.. code-block:: python

from malss import MALSS from sklearn.datasets import make_friedman1 X, y = make_friedman1(n_samples=1000, n_features=20, noise=0.0, random_state=0) model = MALSS(task='regression', lang='en') model.fit(X, y, dname='default')

check the analysis report

model.select_features() model.fit(X, y, dname='feature_selection')

You can set the original data after feature selection

(You do not need to select features by yourself.)

.. Interactive mode:

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

.. code-block:: python

from malss import MALSS

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

Unsupervised learning

Clustering:

.. code-block:: python

from malss import MALSS from sklearn.datasets import load_iris

iris = load_iris() model = MALSS(task='clustering', lang='en') model.fit(iris.data, None, 'clustering_result') pred_dict = model.predict(iris.data)