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Merge pull request #192 from JesseLivezey/1.0.0
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.flake8

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[flake8]
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ignore = E401,W503,W504
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max-line-length = 80
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exclude = bin/run_uoinmf.py

.travis.yml

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language: python
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os: linux
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python:
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- 3.5
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- 3.6
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- 3.7
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matrix:

README.md

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<img src="https://travis-ci.org/BouchardLab/PyUoI.svg?branch=master"> [![codecov](https://codecov.io/gh/BouchardLab/PyUoI/branch/master/graph/badge.svg)](https://codecov.io/gh/BouchardLab/PyUoI)
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[![Documentation Status](https://readthedocs.org/projects/pyuoi/badge/?version=latest)](https://pyuoi.readthedocs.io/en/latest/?badge=latest)
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![PyPI](https://img.shields.io/pypi/v/pyuoi)
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![PyPI](https://img.shields.io/pypi/v/pyuoi)[![Anaconda-Server Badge](https://anaconda.org/conda-forge/pyuoi/badges/installer/conda.svg)](https://conda.anaconda.org/conda-forge)
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PyUoI contains implementations of Union of Intersections framework for a variety
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of penalized generalized linear models as well as dimensionality reductions
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# Installation
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PyUoI is available for Python>=3.5 on PyPI:
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PyUoI is available for Python 3 on PyPI:
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```
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pip install pyuoi
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```
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and will soon be through conda-forge:
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and through conda-forge:
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```
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conda install pyuoi -c conda-forge

bin/generate_build.sh

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eval "$(conda shell.bash hook)"
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mkdir dist
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for py in 3.5 3.6 3.7; do
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for py in 3.6 3.7; do
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git clone https://github.com/BouchardLab/pyuoi.git
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cd pyuoi
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conda create -y -n temp_build_env python=$py

docs/source/installation.rst

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Installation
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============
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PyUoI is available for Python>=3.5 on PyPI:
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PyUoI is available for Python 3 on PyPI:
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.. code-block:: bash
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$ pip install pyuoi
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and will soon be through conda-forge:
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and through conda-forge:
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.. code-block:: bash
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paper/paper.md

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\end{equation}
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where $\epsilon \sim \mathcal{N}(0, \sigma^2)$ for each sample. When the true
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$\boldsymbol{\beta}$ is thought to be sparse (i.e., some subset of the $\beta$
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$\boldsymbol{\beta}$ is thought to be sparse (i.e., some subset of the $\boldsymbol{\beta}$
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are exactly zero), an estimate of $\boldsymbol{\beta}$ can be found by solving a
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constrained optimization problem of the form
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selection profiles that are more robust and parameter estimates that have less bias. This can be
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contrasted with a typical Lasso fit wherein parameter selection and estimation are performed
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simultaneously. The Lasso procedure can lead to selection profiles that are not robust
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to data resampling and estimates that are biased by the penalty on $\beta$. For
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to data resampling and estimates that are biased by the penalty on $\boldsymbol{\beta}$. For
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UoI~Lasso~, the procedure is as follows (see Algorithm 1 for a more detailed pseudocode):
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* **Model Selection:** For each $\lambda_j$ in the Lasso path, generate estimates on $N_S$
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prediction accuracy for the response variable $y$.
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\begin{algorithm}[t]
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\caption{\textsc{UoI-Lasso}}
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\caption{\textsc{UoI$_\textsc{Lasso}$}}
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\label{alg:uoi}
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\hspace*{\algorithmicindent} \textbf{Input}:
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$X \in \mathbb{R}^{N\times p}$ design matrix \\

setup.py

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# Versions should comply with PEP440. For a discussion on single-sourcing
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# the version across setup.py and the project code, see
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# https://packaging.python.org/en/latest/single_source_version.html
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version='0.99.0',
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version='1.0.0',
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description='The Union of Intersections framework in Python.',
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long_description=long_description,
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# Specify the Python versions you support here. In particular, ensure
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# that you indicate whether you support Python 2, Python 3 or both.
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'Programming Language :: Python :: 3',
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'Programming Language :: Python :: 3.5',
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'Programming Language :: Python :: 3.6',
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'Programming Language :: Python :: 3.7',
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'Programming Language :: Python :: 3.8',
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],
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# What does your project relate to?

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