rank-validation 1.1.8

Lightweight, vectorised ranking-metric toolkit

rank‑validation

📊 One‑liner ranking evaluation for search, recommendation & IR.

rank‑validation turns a dataframe of truth ✨ vs prediction 🔮 into two ready‑to‑export reports—per‑query and overall—complete with industry‑standard metrics at any cut‑off k.

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✨ Key features

• Simple API – get_metrics_report(...) returns pandas DataFrames you already know how to use.
• Out‑of‑the‑box metrics – nDCG, Recall, Kendall’s τ‑b, Kendall’s τ‑ap, RBO (extendable).
• Arbitrary cut‑offs – evaluate at @1, @5, @20… whatever matters.
• Automatic score alignment – helper utilities map prediction lists onto truth scores for graded relevance.
• Vectorised NumPy & Pandas core – scales to millions of queries on a laptop.
• Pure Python ≥ 3.8 – zero native extensions.

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🚀 Installation

pip install rank-validation

The wheel is lightweight (< 30 KB) and pulls in only numpy, pandas, scipy & rbo.

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⚡ Quick start

import pandas as pd
from rank_validation.validation_generator import get_metrics_report

df = pd.DataFrame({
    "query": ["q1", "q2"],
    "truth_items":  [["A","B","C","D"], ["X","Y","Z"]],
    "truth_scores": [[3,2,1,0],          [2,1,0]],
    "pred_items":   [["B","A","E","C"], ["Y","X","Z"]],
})

metrics  = ["ndcg", "recall", "kendall_tau", "tau_ap", "rbo"]
cutoffs  = [3, 5]

query_report, overall_report = get_metrics_report(
    df,
    truth_item_col="truth_items",
    truth_score_col="truth_scores",
    pred_item_col="pred_items",
    metric_list=metrics,
    cutoff_list=cutoffs,
)

print(query_report.head())  # per‑query breakdown
print(overall_report)       # summary stats (mean, std, …)

Typical query_report:

  query  ndcg@3  recall@3  kendall_tau@3  tau_ap@3  rbo@3  ndcg@5  recall@5  kendall_tau@5  tau_ap@5  rbo@5
0    q1    0.91      0.67           0.33      0.40   0.79    0.90      1.00           0.33      0.46   0.79
1    q2    1.00      0.67           0.67      0.80   1.00    1.00      1.00           0.67      0.80   1.00

Typical overall_report:

       ndcg@3  recall@3  kendall_tau@3  tau_ap@3  rbo@3  ndcg@5  recall@5  kendall_tau@5  tau_ap@5  rbo@5
mean     0.96     0.67           0.50      0.60   0.90    0.95     1.00           0.50      0.63   0.90
std      0.06     0.00           0.24      0.28   0.15    0.05     0.00           0.24      0.24   0.15

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🧮 Supported metrics & formulas

Metric	What it measures	Reference
nDCG@k	Graded relevance with log‑discounted gain, normalised by ideal ranking	Järvelin & Kekäläinen (2002)
Recall@k	Proportion of ground‑truth items retrieved in top k	–
Kendall’s τ‑b@k	Rank correlation, tie‑adjusted	Kendall (1938)
Kendall’s τ‑ap@k	Top‑weighted rank correlation	Yilmaz et al. (2008)
RBO@k	Top‑weighted similarity between two indefinite rankings	Webber et al. (2010)

Heads‑up: RBO requires the two lists to have unique items and equalised lengths. If you hit RankingSimilarity errors, drop duplicates beforehand or omit RBO for that experiment.

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🛠️ API reference

def get_metrics_report(
    df: pd.DataFrame,
    truth_item_col: str,
    truth_score_col: str,
    pred_item_col: str,
    metric_list: list[str],
    cutoff_list: list[int],
) -> tuple[pd.DataFrame, pd.DataFrame]

Parameter	Description
df	DataFrame with at least the three list‑columns below.
truth_item_col	Column holding ground‑truth item IDs.
truth_score_col	Column with relevance grades (same order & length).
pred_item_col	Column holding system‑predicted ranked lists.
metric_list	Any subset of METRIC_REGISTRY keys, e.g. ndcg, tau_ap.
cutoff_list	Integers e.g. [1, 3, 10]. Each yields metric@k columns.

Returns (query_report, overall_report) where:

• query_report – original df plus metric columns.
• overall_report – query_report.describe().

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⚙️ Performance tips

• Core logic is vectorised; multi‑process pandas handles millions of rows out‑of‑the‑box.
• Chunk evaluation if truth lists are extremely long (> 1 K items) to limit memory.

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🤝 Contributing

Found a bug? Need MAP or MRR? PRs are welcome! Please open an issue first so we can discuss the approach.

1. Fork ➡️ branch ➡️ commit (with tests!)
2. pre‑commit run -a
3. Open a pull request describing the change.

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🛣️ Roadmap

• Mean Average Precision (MAP)
• Mean Reciprocal Rank (MRR)
• Optional GPU acceleration via cuDF / RAPIDS

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📝 License

MIT © 2025 Akash Dubey

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🔗 Links & citation

• Docs / examples: https://github.com/akashkdubey/ranking_validation
• PyPI: https://pypi.org/project/rank-validation/

@software{Dubey_2025_rank_validation,
  author = {Dubey, Akash},
  title  = {rank‑validation: A lightweight toolkit for ranking evaluation},
  year   = {2025},
  url    = {https://github.com/akashkdubey/ranking_validation}
}

_{Built with ❤️, Pandas & SciPy.}