korr 0.6.1

collection of utility functions for correlation analysis

korr

collection of utility functions for correlation analysis

Table of Contents

• Installation
• Usage
• Commands
• Support
• Contributing

Installation

The korr git repo is available as PyPi package

pip install korr

Usage

Check the examples folder for notebooks.

Compute correlation matrix and its p-values

• pearson -- Pearson/Sample correlation (interval- and ratio-scale data)
• kendall -- Kendall's tau rank correlation (ordinal data)
• spearman -- Spearman rho rank correlation (ordinal data)
• mcc -- Matthews correlation coefficient between binary variables

EDA, Dig deeper into results

• flatten -- 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 -- 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 -- Histogram to find p-value cutoffs (alpha) for a) highly correlated pairs, b) unrelated pairs, c) the mixed results.
• bracket_pval -- Histogram with more fine-grained p-value brackets.
• corrgram -- 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 -- Return variable indicies of unrelated pairs (in terms of insignificant p-value)
• confusion -- 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 folder

Support

Please open an issue for support.

Contributing

Please contribute using Github Flow. Create a branch, add commits, and open a pull request.