Picasso Python Package
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%.
- Linux or MacOS
It may take lots of effort to build on Windows. One way to do it is using CMAKE and MSVC. Be careful of issues like the system bits.
In the following process, you may need to be root (sudo).
Install from source file (Github):
Clone picasso.git via git clone https://github.com/jasonge27/picasso.git
Make sure you have setuptools
Run sudo make Pyinstall command.
Build the source file first via the cmake with CMakeLists.txt in the root directory. (You will see a .so or .lib file under (root)/lib/ )
Run cd python-package; sudo python setup.py install command.
Install from PyPI:
- pip install pycasso
- Note: Owing to the setting on different OS, our distribution might not be working in your environment (especially in Windows). Thus please build from source.
You can test if the package has been successfully installed by:
import pycasso pycasso.test()
import pycasso x = [[1,2,3,4,5,0],[3,4,1,7,0,1],[5,6,2,1,4,0]] y = [3.1,6.9,11.3] s = pycasso.Solver(x,y) s.train() s.predict()
Please follow the sphinx syntax style
To update the document: cd doc; make html
|Author:||Jason Ge, Haoming Jiang|
|Maintainer:||Haoming Jiang <email@example.com>|