{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/3"}],"enrichment":{"capability":"Provides a unified Python interface to compute standard information retrieval evaluation metrics (AP, nDCG, P@k, RR, etc.) from qrels and ranked runs, with both programmatic and command-line APIs.","skillfed_tags":["information-retrieval","evaluation-metrics","trec-benchmark"],"use_cases":["Evaluate a search or ranking system against standard TREC benchmarks using nDCG, AP, and precision metrics.","Batch-compute metrics from TREC-formatted qrels and run files via command line without writing Python.","Integrate metric computation into PyTerrier experiments to compare retrieval algorithms.","Load qrels from ir_datasets and compute per-query and aggregate metrics programmatically.","Prototype custom ranking evaluation with flexible input formats (dict, DataFrame, or iterables)."],"what_it_does":"ir_measures is a Python library that standardizes how you compute information retrieval evaluation metrics across different underlying tools. Instead of learning multiple APIs or command-line syntaxes, you import measure objects (AP, nDCG, P@10, etc.) and call calc_aggregate() or iter_calc() with your qrels (relevance judgments) and run (ranked results). It accepts input in multiple formats\u2014dicts, pandas DataFrames, namedtuples, or TREC-formatted files\u2014and outputs per-query or aggregated metric values.\n\nThe package wraps pytrec-eval-terrier as its computation engine and is maintained by the Terrier Team at Glasgow. It also exposes a command-line interface for batch evaluation and integrates with PyTerrier for experiment workflows. Active maintenance, no known vulnerabilities, and permissive licensing make it a stable choice for IR research and evaluation pipelines.","worth_installing":"Yes. ir_measures is actively maintained, has no security vulnerabilities, installs with low friction, and provides a clean, unified interface to a core task in IR research and evaluation. Use it if you need to evaluate ranked results against relevance judgments."},"id":"ir-measures","links":{"html":"https://skillfed.io/packages/ir-measures","md":"https://skillfed.io/packages/ir-measures.md","pypi":"https://pypi.org/project/ir-measures/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-11-25","license_spdx":null,"license_treatment":"permissive","name":"ir-measures","python_support":"supports_current","summary":"Provides a common interface to many IR measure tools"},"popularity":{"monthly_downloads":162501,"position":10598,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.4.3"}