{"categories":[{"label":"General","url":"https://skillfed.io/packages/category/text-processing-general"}],"enrichment":{"capability":"ranx computes ranking evaluation metrics (precision, recall, NDCG, MAP, MRR, and others) for information retrieval and recommender systems, with Numba-accelerated computation and built-in statistical testing.","skillfed_tags":["information-retrieval","ranking-evaluation","numba-accelerated"],"use_cases":["Compute NDCG, MAP, and other metrics to evaluate a search engine or recommender system against ground-truth relevance judgments.","Run statistical tests to determine whether one ranking algorithm significantly outperforms another.","Combine multiple ranking runs using fusion algorithms and automatically optimize fusion weights.","Load standard IR evaluation datasets and pre-computed runs from ranxhub to benchmark new models.","Generate publication-ready LaTeX tables comparing multiple ranking systems and their metric scores.","Normalize and compare ranking scores across different systems or datasets using built-in normalization strategies."],"what_it_does":"ranx is a Python library for evaluating and comparing rankings in information retrieval and recommender systems. It implements standard metrics like NDCG, MAP, MRR, precision, recall, and others, using Numba to accelerate vector operations and automatic parallelization. The library also provides statistical tests (paired t-test, Fisher's randomization test, Tukey's HSD) to determine whether differences between ranked results are significant, and can export results as LaTeX tables for scientific publications.\n\nBeyond basic metrics, ranx includes fusion algorithms (CombMNZ, RRF, BayesFuse, and others) to combine multiple ranking runs, normalization strategies to standardize scores across runs, and automatic fusion optimization. It integrates with ir-datasets to load standard IR benchmarks (MSMARCO, etc.) and ranxhub to download and share pre-computed runs. The package is designed specifically for ranking evaluation\u2014not classifier evaluation\u2014and has been presented at ECIR 2022, CIKM 2022, and SIGIR 2023.","worth_installing":"Yes, if you are evaluating information retrieval or recommender system rankings. ranx is a mature, well-cited library with no known vulnerabilities, permissive licensing, and low install friction. The 372-day gap since the last release suggests aging maintenance, but the repository is not archived and the package remains functional for its core use case. Install it if you need fast, standard-compliant ranking metrics and statistical testing; avoid it if you need active feature development or cutting-edge research implementations."},"id":"ranx","links":{"html":"https://skillfed.io/packages/ranx","md":"https://skillfed.io/packages/ranx.md","pypi":"https://pypi.org/project/ranx/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-08-07","license_spdx":null,"license_treatment":"permissive","name":"ranx","python_support":"supports_current","summary":"ranx: A Blazing-Fast Python Library for Ranking Evaluation, Comparison, and Fusion"},"popularity":{"monthly_downloads":147163,"position":11077,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.3.21"}