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MALSS: MAchine Learning Support System

Project description

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


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


pip install malss

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

pip install PyQt5

Example


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)

Project details


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