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.
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)
All modules except PyQt5 are automatically installed when installing malss.
pip install malss
For interactive mode, you need to install PyQt5 using pip.
pip install PyQt5
Classification:
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:
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:
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:
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.)Clustering:
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)