This repository is adapted from hfawaz/cd-diagram (tnx, @hfawaz!).
It contains the necessary code to generate a critical difference diagram based on the Wilcoxon-Holm method to detect pairwise significance.
Run python main.py --help
using Python 3 to see the options.
Quick start: python main.py --file ./data/example.csv --output-dir ./figs
will generate a critical difference diagram with Wilcoxon-Holm post-hoc analysis for the data present in data/example.csv (!!!!!!!!!!!!!!place url!!!!!!!!!!!).
First the Friedman test is performed to reject the null hypothesis, we then proceed with a post-hoc analysis based on the Wilcoxon-Holm method.
We can clearly see how on average clf3
and clf5
were the best algorithms over the 15 datasets.
A thick horizontal line groups a set of classifiers that are not significantly different.
To run this code you will need the following python packages:
This repo is just my touch to make the script more flexible and nice to use, the real authors is @hfawaz, who wrote hfawaz/cd-diagram.
As demanded in his repo, if you re-use this work, please cite:
@article{IsmailFawaz2018deep,
Title = {Deep learning for time series classification: a review},
Author = {Ismail Fawaz, Hassan and Forestier, Germain and Weber, Jonathan and Idoumghar, Lhassane and Muller, Pierre-Alain},
journal = {Data Mining and Knowledge Discovery},
Year = {2019},
volume = {33},
number = {4},
pages = {917--963},
}