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.
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)1 maintainer
Release files for malss 2.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| malss-2.3.0.tar.gz | 68.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| malss-2.3.0-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details |
Total release size: 1.3 MB
Release files / malss-2.3.0.tar.gz
| Download URL | malss-2.3.0.tar.gz |
|---|---|
| Size | 68.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
52b9900e3a1d7dc7771541bdeeee5f1488c65eabbdb913ad959b228e5a57ee50
|
|
BLAKE2b-256 checksum How to use checksums |
ef2da757a7f8d75c5b85b6e9281fb4eca110940c085c7e0b77ffb013fd7dbf6f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/28.8.0 requests-toolbelt/0.9.1 tqdm/4.42.1 CPython/3.6.3
|
Release files / malss-2.3.0-py2.py3-none-any.whl
| Download URL | malss-2.3.0-py2.py3-none-any.whl |
|---|---|
| Size | 1.2 MB |
| Tags | Python 2 Python 3 |
|
SHA-256 checksum How to use checksums |
b61a0de150e6151c516f3d90fcabf1df9dbe6347d667f701a7b6c77f1ac25e3d
|
|
BLAKE2b-256 checksum How to use checksums |
9fa4fdeeb0ab39dd7fb9df5835cb257e6567a12f1318f894f4283abab6770c40
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/28.8.0 requests-toolbelt/0.9.1 tqdm/4.42.1 CPython/3.6.3
|
