zadu

A-to-Z Python library for evalUating Dimensionality reduction

ZADU evaluates how faithfully a dimensionality-reduction embedding preserves its original data. It provides 22 local, cluster-level, global, and gap-based distortion measures through one consistent Python interface, with exact shared execution, bounded-memory strategies, repeated-embedding evaluation, and optional MLX and PyTorch backends.

Documentation · Measure reference · Performance report · ZADU paper

Installation

python -m pip install zadu

Visualization is optional:

python -m pip install "zadu[vis]"

See the installation guide for optional MLX and PyTorch backends.

Quick start

import numpy as np
from zadu import ZADU

rng = np.random.default_rng(0)
original = rng.normal(size=(200, 16))
embedding = original[:, :2] + 0.05 * rng.normal(size=(200, 2))

specs = [
    {"id": "tnc", "params": {"k": 20}},
    {"id": "mrre", "params": {"k": 20}},
]

scores = ZADU(specs, original).measure(embedding)
print(scores)

ZADU's execution DAG shares compatible exact distances, neighbors, ranks, densities, and pair reductions across measures. Scientific scores remain separate from backend, timing, and memory diagnostics in last_run_info.

Read the quickstart, choose measures, or browse the complete measure reference.

Contributing

To propose a distortion measure, provide its name, paper, and an optional reference implementation through the metric proposal form. You do not need to learn ZADU's internals or prepare repository files. See CONTRIBUTING.md for development and correctness requirements.

Citation

@INPROCEEDINGS{jeon23vis,
  author={Jeon, Hyeon and Cho, Aeri and Jang, Jinhwa and Lee, Soohyun and Hyun, Jake and Ko, Hyung-Kwon and Jo, Jaemin and Seo, Jinwook},
  booktitle={2023 IEEE Visualization and Visual Analytics (VIS)},
  title={ZADU: A Python Library for Evaluating the Reliability of Dimensionality Reduction Embeddings},
  year={2023},
  pages={196--200},
  doi={10.1109/VIS54172.2023.00048}
}

Each metric's original literature and later additions are credited in the measure reference.

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