Phi_K Correlation Analyzer Library

• Version: 0.12.0. Released: Jul 2021
• Documentation: https://phik.readthedocs.io
• Repository: https://github.com/kaveio/phik
• Publication: [offical] [arxiv pre-print]

Phi_K is a practical correlation constant that works consistently between categorical, ordinal and interval variables. It is based on several refinements to Pearson's hypothesis test of independence of two variables.

The combined features of Phi_K form an advantage over existing coefficients. First, it works consistently between categorical, ordinal and interval variables. Second, it captures non-linear dependency. Third, it reverts to the Pearson correlation coefficient in case of a bi-variate normal input distribution. These are useful features when studying the correlation matrix of variables with mixed types.

The presented algorithms are easy to use and available through this public Python library: the correlation analyzer package. Emphasis is paid to the proper evaluation of statistical significance of correlations and to the interpretation of variable relationships in a contingency table, in particular in case of low statistics samples.

For example, the Phi_K correlation analyzer package has been used to study surveys, insurance claims, correlograms, etc. For details on the methodology behind the calculations, please see our publication.

Documentation

The entire Phi_K documentation including tutorials can be found at read-the-docs. See the tutorials for detailed examples on how to run the code with pandas. We also have one example on how calculate the Phi_K correlation matrix for a spark dataframe.

Check it out

The Phi_K library requires Python 3 and is pip friendly. To get started, simply do:

$ pip install phik

or check out the code from out GitHub repository:

$ git clone https://github.com/KaveIO/PhiK.git
$ pip install -e PhiK/

where in this example the code is installed in edit mode (option -e).

You can now use the package in Python with:

import phik

Congratulations, you are now ready to use the PhiK correlation analyzer library!

C++ Extension

Phi_K contains an optional C++ extension to compute the significance matrix using the hypergeometric method.

Note that the PyPi distributed wheels contain a pre-build extension for Linux, MacOS and Windows.

A manual (pip) setup will attempt to build and install the extension, if it fails it will install without the extension. If so, using the hypergeometric method without the extension will trigger a NotImplementedError.

Compiler requirements through Pybind11:

• Clang/LLVM 3.3 or newer (for Apple Xcode's clang, this is 5.0.0 or newer)
• GCC 4.8 or newer
• Microsoft Visual Studio 2015 Update 3 or newer
• Intel classic C++ compiler 18 or newer (ICC 20.2 tested in CI)
• Cygwin/GCC (previously tested on 2.5.1)
• NVCC (CUDA 11.0 tested in CI)
• NVIDIA PGI (20.9 tested in CI)

Quick run

As a quick example, you can do:

import pandas as pd
import phik
from phik import resources, report

# open fake car insurance data
df = pd.read_csv( resources.fixture('fake_insurance_data.csv.gz') )
df.head()

# Pearson's correlation matrix between numeric variables (pandas functionality)
df.corr()

# get the phi_k correlation matrix between all variables
df.phik_matrix()

# get global correlations based on phi_k correlation matrix
df.global_phik()

# get the significance matrix (expressed as one-sided Z)
# of the hypothesis test of each variable-pair dependency
df.significance_matrix()

# contingency table of two columns
cols = ['mileage','car_size']
df[cols].hist2d()

# normalized residuals of contingency test applied to cols
df[cols].outlier_significance_matrix()

# show the normalized residuals of each variable-pair
df.outlier_significance_matrices()

# generate a phik correlation report and save as test.pdf
report.correlation_report(df, pdf_file_name='test.pdf')

For all available examples, please see the tutorials at read-the-docs.

Contact and support

• Issues and Ideas: https://github.com/kaveio/phik/issues

Please note that support is (only) provided on a best-effort basis.