cddiagram 0.0.4

Critical Difference diagram generator in pure Python

cddiagram

A pure Python library for generating Critical Difference (CD) diagrams as SVG.

CD diagrams visualize the statistical comparison of multiple classifiers (or models) over multiple datasets, as introduced by Demsar (2006). They show the average rank of each model and connect groups of models whose performance differences are not statistically significant.

J. Demsar, "Statistical Comparisons of Classifiers over Multiple Data Sets", Journal of Machine Learning Research, vol. 7, pp. 1-30, 2006. https://jmlr.org/papers/v7/demsar06a.html

How it works

1. A Friedman test checks whether at least one model differs significantly from the others (at alpha = 0.05).
2. If significant, the Nemenyi post-hoc test computes a critical distance (CD) threshold.
3. Models whose average rank difference is less than CD are grouped together — they are not statistically distinguishable.
4. The result is rendered as an SVG diagram showing ranked models and significance groups.

Install

pip install cddiagram

Requires Python 3.12+ and depends on numpy and scipy.

Usage

Write to file

import numpy as np
from cddiagram import draw_cd_diagram

rng = np.random.default_rng(1)

models = {
    "model1": rng.normal(loc=0.2, scale=0.1, size=30),
    "model2": rng.normal(loc=0.2, scale=0.1, size=30),
    "model3": rng.normal(loc=0.4, scale=0.1, size=30),
    "model4": rng.normal(loc=0.5, scale=0.1, size=30),
    "model5": rng.normal(loc=0.7, scale=0.1, size=30),
    "model6": rng.normal(loc=0.7, scale=0.1, size=30),
    "model7": rng.normal(loc=0.8, scale=0.1, size=30),
    "model8": rng.normal(loc=0.9, scale=0.1, size=30),
}

samples = np.column_stack(list(models.values()))
draw_cd_diagram(samples, labels=list(models.keys()), out_file="out.svg", title="Model comparison")

Non-significant results

If the Friedman test is not significant, the function issues a warning and returns None — no diagram is produced because the data does not support ranking the models.

API

draw_cd_diagram(
    samples,           # 2D array-like (rows=datasets, columns=models)
    labels,            # Sequence of model names (one per column)
    title=None,        # Optional diagram title
    out_file=None,     # Optional path to write SVG file
    fig_size=None,     # Optional (width, height) tuple in pixels
) -> Element | None

Input formats: NumPy arrays, pandas DataFrames, or any object with a .to_numpy() / .values attribute.

Release Notes

0.0.2

• Replaced hardcoded Nemenyi critical-value lookup table with SciPy's studentized_range computation.
• Updated CD diagram drawing to follow continuous rank-axis placement and first-anchor non-significant grouping.
• Improved readability for larger numbers of algorithms with adaptive multi-row label layout.

License

MIT