snha4py

Snha4py is a Python implementation of the St. Nicholas House algorithm (Groth et al. 2019, Hermanussen et al. 2021, Hake et al. 2023). The algorithm infers a graph based on the correlation coefficient between variables of a data set. Here, the nodes and the edges would be the variables of the data set and the correlation coefficient, respectively. The correlation coefficient is evaluated in terms of association chains. Therfore, the absolute coefficient is ranked and compared pairwise. An association chain is characterized by a sequence of variables for which reversing the start and end point does not change the order of elements. These chains then build the graph.

Next, to the algorithm the package offers a variety of graph implementations, as well as a vizualization routine for the graphs and the correlation matrix.

Also, a module to create correlated data along a directed graph based on a Monte Carlo approach is included (Novine et al. 2022).

You can use the St. Nicolas House algorithm and other features of the package as a command-line interface (CLI) or you can import the package in your own code.

There is also an implementation of the SNHA in R available.

Documentation

Install

pip install snha4py

pip3 install git+https://github.com/thake93/snha4py.git --user

after the installation you should be able to run:

pip show snha4py

Create your 1^st "St. Nicolas House"

Command-line interface

We start with creating a directory to store the test files.

mkdir test_snha
cd test_snha

In the first place we create test data in two steps:

We create a directed graph.

python3 -m snha4py --create-graph -t werner -o werner.csv

We create correlated data along the edges of the graph.

python3 -m snha4py --create-data --graph werner.csv -o data.csv --steps 25 --iterations 200 --plot

Now, we can run the algortihm to predict the graph based on the correlation of the data.

python3 -m snha4py --snha --data data.csv -o graph_pred.csv -p

Also, we are able to apply bootstrapping to increase the prediction (Hake et al. (2023)).

python3 -m snha4py --snha --data data.csv -o graph_pred_bt.csv --bootstrap -i 10 -p

Note: The result might differ from the picture below.

As import in your own code

python3

Python 3.x.xx ()

>>> from snha4py.Snha import Snha
>>> import matplotlib.pyplot as plt

>>> s = Snha()
>>> s.new_graph()
>>> s.create_corr_data(200,25)
>>> s.comp_corr(method='spearman')
>>> s.st_nich_alg()

>>> fig,ax = plt.subplots(1,3, figsize=(30,10))

>>> s.plot_graph(pred=False, ax=ax[0])
>>> s.plot_corr(ax=ax[1])
>>> s.plot_graph(ax=ax[2])

>>> ax[0].set_title('True Graph')
>>> ax[1].set_title('Correlation Coefficient')
>>> ax[2].set_title('Predicted Graph')

>>> plt.show()

References

Groth, D., Scheffler, C., and Hermanussen, M. (2019). Body height in stunted indonesian children depends directly on parental education and not via a nutrition mediated pathway - evidence from tracing association chains by st. nicolas house analysis. Anthropologischer Anzeiger, 76(5):445–451. DOI

Hake, T., Bodenberger, B., Groth, D. (2023). In Python available: St. Nicolas House Algorithm (SNHA) with bootstrap support for improved performance in dense networks. Human Biology and Public Health 4. (Accepted, doi follows)

Hermanussen, M., Aßmann, C., and Groth, D. (2021). Chain reversion for detecting associations in interacting variables—st. nicolas house analysis. International Journal of Environmental Research and Public Health, 18(4). DOI

Novine, M., Mattsson, C. C., & Groth, D. (2022). Network reconstruction based on synthetic data generated by a Monte Carlo approach. Human Biology and Public Health, 3. DOI

License

MIT License

thake93/snha4py