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https://github.com/slipguru/l1l2py
l1l2py is a Python package to perform variable selection by means of l1l2 regularization with double optimization.
https://github.com/slipguru/l1l2py
feature-selection fista l1l2-regularization machinelearning python variable-selection
Last synced: about 2 months ago
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l1l2py is a Python package to perform variable selection by means of l1l2 regularization with double optimization.
- Host: GitHub
- URL: https://github.com/slipguru/l1l2py
- Owner: slipguru
- License: gpl-3.0
- Created: 2016-07-20T14:42:14.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-12-05T16:34:19.000Z (about 7 years ago)
- Last Synced: 2024-03-28T09:13:42.441Z (9 months ago)
- Topics: feature-selection, fista, l1l2-regularization, machinelearning, python, variable-selection
- Language: Python
- Size: 3.58 MB
- Stars: 9
- Watchers: 7
- Forks: 3
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
**l1l2py** is a Python package to perform variable selection by means
of l1l2 regularization with double optimization.
**l1l2py** makes use of `numpy` to provide fast
N-dimensional array manipulation and it is the Python implementation of the
method proposed and applied in [DeMol09].
**l1l2py** is fully compatible with `scikit-learn` Python toolbox for machine learning .
**l1l2py** is a project of SLIPGURU - Statistical Learning and Image Processing
Genoa University Research Group - Via Dodecaneso, 35 - 16146 Genova, ITALY
.
**l1l2py** is free software. It is licensed under the GNU General Public
License (GPL) version 3 .
## Installation
**l1l2py** supports Python 2.7
### Pip installation
`$ pip install L1L2py`
### Installing from sources
```bash
$ git clone https://github.com/slipguru/l1l2py
$ cd l1l2py
$ python setup.py install
```
## Try L1L2py
**l1l2py** can be used to solve both regression and classification problems.
### Regression
```python
>>> from l1l2py.linear_model import L1L2
>>> from sklearn.datasets import load_boston
>>> X, y = load_boston(return_X_y=True)
>>> l1l2 = L1L2(tau=1, mu=0.5).fit(X, y)
>>> l1l2
L1L2(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5, max_iter=10000,
mu=0.5, normalize=False, positive=False, precompute=False,
random_state=None, selection='cyclic', tau=1, tol=0.0001, use_gpu=False,
warm_start=False)
>>> l1l2.coef_
array([-0.07871197, 0.05147779, -0.00579328, 0. , -0. ,
1.03468713, 0.01992332, -0.74193449, 0.30231875, -0.01640304,
-0.77808095, 0.00851397, -0.75325201])
```
### Classification
```python
>>> from l1l2py.classification import L1L2Classifier
>>> from sklearn.datasets import load_breast_cancer
>>> X, y = load_breast_cancer(return_X_y=True)
>>> l1l2 = L1L2Classifier(tau=1, mu=0.5).fit(X, y)
>>> l1l2
L1L2Classifier(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,
max_iter=10000, mu=0.5, normalize=False, positive=False,
precompute=False, random_state=None, selection='cyclic', tau=1,
threshold=1e-16, tol=0.0001, use_gpu=False, warm_start=False)
>>> l1l2.coef_
array([[-0. , -0. , 0.00013437, 0.00046001, -0. ,
-0. , -0. , -0. , -0. , -0. ,
-0. , -0. , -0. , 0. , -0. ,
-0. , -0. , -0. , -0. , -0. ,
-0. , -0.01317881, -0.03120279, 0.00032235, -0. ,
-0. , -0. , -0. , -0. , -0. ]])
```
## Help and support
HTML documentation [L1L2py documentation](http://www.slipguru.unige.it/Software/L1L2Py/).
## Reference
[DeMol09] C. De Mol, S. Mosci, M. Traskine, A. Verri,
"A Regularized Method for Selecting Nested Group of Genes from
Microarray Data"
Journal of Computational Biology, vol. 16, pp. 677-690, 2009.