Multi-Comparison Matrix (MCM)

This repository contains the software for our paper titled "An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set". This work has been done by Ali Ismail-Fawaz, Angus Dempster, Chang Wei Tan, Matthieu Herrmann, Lynn Miller, Daniel Schmidt, Stefano Berretti, Jonathan Weber, Maxime Devanne, Germain Forestier and Geoff I. Webb.

Can now be used through PyPl

Simply install using pip install multi-comp-matrix and use it as explained in the below example section

Papers Using the MCM:

1. Middlehurst et al. 2024 "Bake off redux: a review and experimental evaluation of recent time series classification algorithms" Data Mining and Knowledge Discovery
2. Ismail-Fawaz et al. 2024 "Finding foundation models for time series classification with a pretext task" The Pacific-Asia Conference on Knowledge Discovery and Data Mining - International Workshop on Temporal Analytics
3. Foumani et al. 2023 "Series2Vec: Similarity-based Self-supervised Representation Learning for Time Series Classification" Data Mining and Knowledge Discovery
4. Holder et al. 2023 "[A review and evaluation of elastic distance functions for time series clustering](A review and evaluation of elastic distance functions for time series clustering)" Knowledge and Information Systems
5. Ismail-Fawaz et al. 2023 "LITE: Light Inception with boosTing tEchniques for Time Series Classification" IEEE 10th International Conference on Data Science and Advanced Analytics
6. Koh et al. 2023 "PSICHIC: physicochemical graph neural network for learning protein-ligand interaction fingerprints from sequence data" bioRxiv
7. Ayllón-Gavilán et al. 2023 "Convolutional and Deep Learning based techniques for Time Series Ordinal Classification"
8. Ismail-Fawaz et al. 2023 "ShapeDBA: Generating Effective Time Series Prototypes Using ShapeDTW Barycenter Averaging" The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshop on Advanced Analytics and Learning on Temporal Data
9. Dempster et al. 2023 "QUANT: A Minimalist Interval Method for Time Series Classification" Data Mining and Knowledge Discovery
10. Holder et al. 2023 "Clustering Time Series with k-Medoids Based Algorithms" The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshop on Advanced Analytics and Learning on Temporal Data
11. Guijo-Rubio et al. 2023 "Unsupervised feature based algorithms for time series extrinsic regression" Data Mining and Knowledge Discovery
12. Fischer et al. 2024 "Towards more sustainable and trustworthy reporting in machine learning." Data Mining and Knowledge Discovery
13. Middlehurst et al. 2024 "aeon: a Python toolkit for learning from time series."
14. da Silva et al. 2024 "Artist Similarity based on Heterogeneous Graph Neural Networks." IEEE/ACM Transactions on Audio, Speech, and Language Processing
15. Renault, Aurélien, et al. "Early Classification of Time Series: Taxonomy and Benchmark." arXiv preprint arXiv:2406.18332 (2024).
16. Spinnato, Francesco, et al. "Fast, Interpretable and Deterministic Time Series Classification with a Bag-Of-Receptive-Fields." Data Mining and Knowledge Discovery (2024).
17. Lo, Mouhamadou Mansour, et al. "Time series classification with random convolution kernels based transforms: pooling operators and input representations matter." arXiv preprint arXiv:2409.01115 (2024).

Summary

This repo is a long term used benchmark method that generates a Multi-Comparison Matrix where the user ca choose whether to include a full pairwise multi-comparate comparison or to choose which ones to be included or excluded in the rows and columns of the matrix.

Input Format

The input format is in a .csv file containing the statistics of each classifiers as the format of this example.

Usage of Code - Plot the MCM

In order for the user to plot the MCM, first thing is to load the .csv file into a pandas dataframe and feed it to the compare function. The user should specify the pdf_savename, png_savename, csv_savename or tex_savename parameter in order to save the output figure in pdf, png, csv or tex formats respecively.

Examples

Generating the MCM on the following example produces the following. To generate the following figure, the user follows this simple code:

import pandas as pd
from multi_comp_matrix import MCM

df_results = pd.read_csv('path/to/csv')

output_dir = '/output/directory/desired'

MCM.compare(
        output_dir=output_dir,
        df_results=df_results,
        pdf_savename="heatmap",
        png_savename="heatmap",
    )

Generating the MCM on the following example by excluding clf1 and clf3 from the columns.

import pandas as pd
from multi_comp_matrix import MCM

df_results = pd.read_csv('path/to/csv')

output_dir = '/output/directory/desired'

MCM.compare(
        output_dir=output_dir,
        df_results=df_results,
        excluded_col_comparates=['clf1','clf3'],
        png_savename='heatline_vertical',
        tex_savename='heatline_vertical',
        include_ProbaWinTieLoss=True
    )

and by excluding them in the rows.

import pandas as pd
from multi_comp_matrix import MCM

df_results = pd.read_csv('path/to/csv')

output_dir = '/output/directory/desired'

MCM.compare(
        output_dir=output_dir,
        df_results=df_results,
        excluded_row_comparates=['clf1','clf3'],
        png_savename='heatline_horizontal',
        csv_savename='heatline_horizontal',
    )

Colormap

The colormap can be customized to suit different needs. Users can modify it to enhance readability, including options for colorblind-friendly palettes (e.g., viridis, cividis).

Requirements

The following python packages are required for the usage of the module:

1. numpy==1.24.4
2. pandas==2.0.3
3. matplotlib==3.7.4
4. scipy==1.10.0
5. baycomp==1.0
6. tqdm==4.66.1

Citation

If you use this work please make sure you cite this paper:

@article{ismail2023approach,
  title={An Approach To Multiple Comparison Benchmark Evaluations That Is Stable Under Manipulation Of The Comparate Set},
  author={Ismail-Fawaz, Ali and Dempster, Angus and Tan, Chang Wei and Herrmann, Matthieu and Miller, Lynn and Schmidt, Daniel F and Berretti, Stefano and Weber, Jonathan and Devanne, Maxime and Forestier, Germain and Webb, Geoff I},
  journal={arXiv preprint arXiv:2305.11921},
  year={2023}
}

Acknowledgments

The work reported in this paper has been supported by the Australian Research Council under grant DP210100072; the ANR TIMES project (grant ANR-17- CE23-0015); and ANR DELEGATION project (grant ANR-21-CE23-0014) of the French Agence Nationale de la Recherche. The authors would like to thank Professor Eamonn Keogh and all the people who have contributed to the UCR time series classification archive.