Metadata-Version: 1.2
Name: korr
Version: 0.9.1
Summary: collection of utility functions for correlation analysis
Home-page: http://github.com/kmedian/korr
Author: Ulf Hamster
Author-email: 554c46@gmail.com
License: Apache License 2.0
Description: |PyPI version| |korr| |Total alerts| |Language grade: Python| |deepcode|
        
        korr
        ====
        
        collection of utility functions for correlation analysis
        
        Usage
        -----
        
        Check the
        `examples <https://github.com/kmedian/korr/tree/master/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
        
        -  `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
        
        Parameter Stability
        
        -  `bootcorr <https://github.com/kmedian/korr/blob/master/examples/bootcorr.ipynb>`__
           – Estimate multiple correlation matrices based on bootstrapped
           samples. From there you can assess how stable correlation estimates
           are (how sensitive against in-sample variation). For example, stable
           estimates are good candidates for modeling, and unstable correlation
           pairs are good candidates for P-hacking and non-reproducibility.
        
        Variable Selection, Search Functions
        
        -  `mincorr <https://github.com/kmedian/korr/blob/master/examples/mincorr.ipynb>`__
           – From all estimated correlation pairs, pick a given ``n=3,5,..`` of
           variables with low and insignificant correlations among each other.
           (See `binsel <https://github.com/kmedian/binsel>`__ package for an
           application.)
        -  ``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)
        
        Appendix
        --------
        
        Installation
        ~~~~~~~~~~~~
        
        The ``korr`` `git repo <http://github.com/kmedian/korr>`__ is available
        as `PyPi package <https://pypi.org/project/korr>`__
        
        ::
        
           pip install korr
        
        Install a virtual environment
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        ::
        
           python3.6 -m venv .venv
           source .venv/bin/activate
           pip install --upgrade pip
           pip install -r requirements.txt --no-cache-dir
           pip install -r requirements-dev.txt --no-cache-dir
           pip install -r requirements-demo.txt --no-cache-dir
        
        (If your git repo is stored in a folder with whitespaces, then don’t use
        the subfolder ``.venv``. Use an absolute path without whitespaces.)
        
        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``
        
        Publish
        
        .. code:: sh
        
           pandoc README.md --from markdown --to rst -s -o README.rst
           python setup.py sdist 
           twine upload -r pypi dist/*
        
        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/>`__.
        
        .. |PyPI version| image:: https://badge.fury.io/py/korr.svg
           :target: https://badge.fury.io/py/korr
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           :target: https://snyk.io/advisor/python/korr
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           :target: https://lgtm.com/projects/g/kmedian/korr/alerts/
        .. |Language grade: Python| image:: https://img.shields.io/lgtm/grade/python/g/kmedian/korr.svg?logo=lgtm&logoWidth=18
           :target: https://lgtm.com/projects/g/kmedian/korr/context:python
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Platform: UNKNOWN
Requires-Python: >=3.6
