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%**.

Requirements
------------

- Linux or MacOS

**Windows User:** It may take lots of effort to build on Windows. One way to do it is using mingw/mingw64.
Be careful of issues like the system bits and environment variables.
Once the correct make tools and g++ are setted up, you can install the package from suorce with the following instruction.


Installation
------------

In the following process, you may need to be root (``sudo``).

Install from source file (Github) with Makefile:

- Clone ``picasso.git`` via ``git clone --recurse-submodules https://github.com/jasonge27/picasso.git``
- Make sure you have `setuptools <https://pypi.python.org/pypi/setuptools>`__
- Run ``sudo make Pyinstall`` command.


Install from source file (Github) with CMAKE:

- Clone ``picasso.git`` via ``git clone --recurse-submodules https://github.com/jasonge27/picasso.git``
- Make sure you have `setuptools <https://pypi.python.org/pypi/setuptools>`__
- 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:

.. code-block:: python

        import pycasso
        pycasso.test()

..

Usage
-----

.. code-block:: python

        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()

..

For Developer
-------------
Please follow the `sphinx syntax style
<https://thomas-cokelaer.info/tutorials/sphinx/docstring_python.html>`__

To update the document: ``cd doc; make html``

Copy Right
----------

:Author: Jason Ge, Haoming Jiang
:Maintainer: Haoming Jiang <jianghm@gatech.edu>