Metadata-Version: 2.1
Name: korr
Version: 0.8.1
Summary: collection of utility functions for correlation analysis
Home-page: http://github.com/kmedian/korr
Author: Ulf Hamster
Author-email: 554c46@gmail.com
License: MIT
Description: [![Build Status](https://travis-ci.org/kmedian/korr.svg?branch=master)](https://travis-ci.org/kmedian/korr)
        [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/kmedian/korr/master?urlpath=lab)
        
        # korr
        collection of utility functions for correlation analysis
        
        
        ## Table of Contents
        * [Installation](#installation)
        * [Usage](#usage)
        * [Commands](#commands)
        * [Support](#support)
        * [Contributing](#contributing)
        
        
        ## Installation
        The `korr` [git repo](http://github.com/kmedian/korr) is available as [PyPi package](https://pypi.org/project/korr)
        
        ```
        pip install korr
        ```
        
        
        ## Usage
        Check the [examples](examples) folder for notebooks.
        
        Compute correlation matrix and its p-values
        
        * [pearson](https://github.com/kmedian/korr/blob/master/examples/pearson.ipynb) -- Pearson/Sample correlation (interval- and ratio-scale data)
        * [kendall](https://github.com/kmedian/korr/blob/master/examples/kendall.ipynb) -- Kendall's tau rank correlation (ordinal data)
        * [spearman](https://github.com/kmedian/korr/blob/master/examples/spearman.ipynb) -- Spearman rho rank correlation (ordinal data)
        * [mcc](https://github.com/kmedian/korr/blob/master/examples/mcc%20(Matthews%20correlation).ipynb) -- Matthews correlation coefficient between binary variables 
        
        EDA, Dig deeper into results
        
        * [flatten](https://github.com/kmedian/korr/blob/master/examples/flatten.ipynb) -- A table (pandas) with one row for each correlation pairs with the variable indicies, corr., p-value. For example, try to find "good" cutoffs with `corr_vs_pval` and then look up the variable indicies with `flatten` afterwards.
        * [slice_yx](https://github.com/kmedian/korr/blob/master/examples/slice_yx.ipynb) -- slice a correlation and p-value matrix of a (y,X) dataset into a (y,x_i) vector and (x_j, x_k) matrices
        * [corr_vs_pval](https://github.com/kmedian/korr/blob/master/examples/corr_vs_pval.ipynb)  -- Histogram to find p-value cutoffs (alpha) for a) highly correlated pairs, b) unrelated pairs, c) the mixed results. 
        * [bracket_pval](hhttps://github.com/kmedian/korr/blob/master/examples/bracket_pval.ipynb) -- Histogram with more fine-grained p-value brackets. 
        * [corrgram](https://github.com/kmedian/korr/blob/master/examples/corrgram.ipynb) -- Correlogram, heatmap of correlations with p-values in brackets
        
        Utility functions
        
        * `find_best` -- Find the N "best", i.e. high and most significant, correlations
        * `find_worst` -- Find the N "worst", i.e. insignificant/random and low, correlations
        * [find_unrelated](https://github.com/kmedian/korr/blob/master/examples/find_unrelated.ipynb) -- Return variable indicies of unrelated pairs (in terms of insignificant p-value)
        * [confusion](https://github.com/kmedian/korr/blob/master/examples/confusion.ipynb) -- Confusion matrix. Required for Matthews correlation (mcc) and is a bitter faster than sklearn's 
        
        ## Commands
        * Check syntax: `flake8 --ignore=F401`
        * Run Unit Tests: `python -W ignore -m unittest discover`
        * Remove `.pyc` files: `find . -type f -name "*.pyc" | xargs rm`
        * Remove `__pycache__` folders: `find . -type d -name "__pycache__" | xargs rm -rf`
        * Upload to PyPi with twine: `python setup.py sdist && twine upload -r pypi dist/*`
        
        
        ## Debugging
        * Notebooks to profile python code are in the [profile](profile) folder
        
        
        ## Support
        Please [open an issue](https://github.com/kmedian/korr/issues/new) for support.
        
        
        ## Contributing
        Please contribute using [Github Flow](https://guides.github.com/introduction/flow/). Create a branch, add commits, and [open a pull request](https://github.com/kmedian/korr/compare/).
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
