Picasso Python Package
Project description
PICASSO: Penalized Generalized Linear Model Solver - Unleash the Power of Non-convex Penalty
Unleash the power of nonconvex penalty
L1 penalized regression (LASSO) is great for feature selection. However when you use LASSO in very noisy setting, especially when some columns in your data have strong colinearity, LASSO tends to give biased estimator due to the penalty term. As demonstrated in the example below, the lowest estimation error among all the lambdas computed is as high as 16.41%.
Installation
Install from source file (Github):
Clone picasso.git via git clone https://github.com/jasonge27/picasso.git
Make sure python-package/lib is deleted before installing.
Build the source file first via the cmake with CMakeLists.txt in the root directory. (You will see a lib file under (root)/lib/ )
Make sure you have setuptools
Install with cd python-package; python setup.py install command from this directory.
Install from PyPI:
pip install pycasso
Note: Owing to the setting on different OS, our binary distribution might not be working in your environment. Thus please build from source.
You can test if the package has been successfully installed by:
import pycasso
picasso.test()
Usage
import pycasso
picasso.test()
For Developer
Please follow the sphinx syntax style
To update the document: cd doc; make html
Copy Right
- Author:
Jason(Jian) Ge, Haoming Jiang
- Maintainer:
Haoming Jiang <jianghm@gatech.edu>
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