rank-eval 0.1.3

rank_eval: A Blazing Fast Python Library for Ranking Evaluation and Comparison

🔥 News

rank_eval will be featured in ECIR 2022, the 44th European Conference on Information Retrieval!

🤖 Dev Bulletin

We are aware rank_eval is currently not working on Google Colab.
That's because Google Colab runs Python 3.6 while rank_eval currently requires a newer Python version.
I will try to downgrade the required Python version soon.

If you experienced the numba.typed issue, it should now be solved. Please, re-install rank_eval.

⚡️ Introduction

rank_eval is a library of fast ranking evaluation metrics implemented in Python, leveraging Numba for high-speed vector operations and automatic parallelization.

It allows you to compare different runs, perform statistical tests, and export a LaTeX table for your scientific publications.

We strongly incourage you to check the example folder to learn how to use rank_eval in just a few minutes.

✨ Available Metrics

• Hits
• Precision
• Recall
• rPrecision
• Mean Reciprocal Rank (MRR)
• Mean Average Precision (MAP)
• Normalized Discounted Cumulative Gain (NDCG)

The metrics have been tested against TREC Eval for correctness.

🔌 Installation

pip install rank_eval

💡 Usage

Create Qrels and Run

from rank_eval import Qrels, Run, evaluate

qrels = Qrels()
qrels.add_multi(
    q_ids=["q_1", "q_2"],
    doc_ids=[
        ["doc_12", "doc_25"],  # q_1 relevant documents
        ["doc_11", "doc_2"],  # q_2 relevant documents
    ],
    scores=[
        [5, 3],  # q_1 relevance judgements
        [6, 1],  # q_2 relevance judgements
    ],
)

run = Run()
run.add_multi(
    q_ids=["q_1", "q_2"],
    doc_ids=[
        ["doc_12", "doc_23", "doc_25", "doc_36", "doc_32", "doc_35"],
        ["doc_12", "doc_11", "doc_25", "doc_36", "doc_2",  "doc_35"],
    ],
    scores=[
        [0.9, 0.8, 0.7, 0.6, 0.5, 0.4],
        [0.9, 0.8, 0.7, 0.6, 0.5, 0.4],
    ],
)

Evaluate

# Compute score for a single metric
evaluate(qrels, run, "ndcg@5")
>>> 0.7861

# Compute scores for multiple metrics at once
evaluate(qrels, run, ["map@5", "mrr"])
>>> {"map@5": 0.6416, "mrr": 0.75}

# Computed metric scores are saved in the Run object
run.mean_scores
>>> {"ndcg@5": 0.7861, "map@5": 0.6416, "mrr": 0.75}

# Access scores for each query
dict(run.scores)
>>> {"ndcg@5": {"q_1": 0.9430, "q_2": 0.6292},
      "map@5": {"q_1": 0.8333, "q_2": 0.4500},
        "mrr": {"q_1": 1.0000, "q_2": 0.5000}}

Compare

# Compare different runs and perform statistical tests
report = compare(
    qrels=qrels,
    runs=[run_1, run_2, run_3, run_4, run_5],
    metrics=["map@100", "mrr@100", "ndcg@10"],
    max_p=0.01  # P-value threshold
)

print(report)

Output:

#    Model    MAP@100     MRR@100     NDCG@10
---  -------  ----------  ----------  ----------
a    model_1  0.3202ᵇ     0.3207ᵇ     0.3684ᵇᶜ
b    model_2  0.2332      0.2339      0.239
c    model_3  0.3082ᵇ     0.3089ᵇ     0.3295ᵇ
d    model_4  0.3664ᵃᵇᶜ   0.3668ᵃᵇᶜ   0.4078ᵃᵇᶜ
e    model_5  0.4053ᵃᵇᶜᵈ  0.4061ᵃᵇᶜᵈ  0.4512ᵃᵇᶜᵈ

📖 Examples

• Overview: This notebook shows the main features of rank_eval.
• Create Qrels and Run: This notebook shows different ways of creating Qrels and Run.

📚 Documentation

To be updated! Please, refer to the examples in the meantime.
Search the documentation for more details and examples.

🎓 Citation

If you use rank_eval to evaluate results for your scientific publication, please consider citing it:

@misc{rankEval2021,
  title = {Rank\_eval: Blazing Fast Ranking Evaluation Metrics in Python},
  author = {Bassani, Elias},
  year = {2021},
  publisher = {GitHub},
  howpublished = {\url{https://github.com/AmenRa/rank_eval}},
}

🎁 Feature Requests

Would you like to see a new metric implemented? Please, open a new issue.

🤘 Want to contribute?

Would you like to contribute? Please, drop me an e-mail.

📄 License

rank_eval is an open-sourced software licensed under the MIT license.