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)
