Skip to content

Home

PyPI version Documentation Status License: MIT Open In Colab

๐Ÿ”ฅ News

  • ๐Ÿ“Œ [October 10, 2022] I released a new sharing platform for pre-computed runs called ranxhub, click here to learn more!

  • [November 2, 2022] ranx 0.3.3 is out!
    This release adds support for changing Qrels relevance level, i.e, the minimum relevance judgement score to consider a document to be relevant.
    You can now define metric-wise relevance levels by appending -l<num> to metric names (e.g., evaluate(qrels, run, ["map@100-l2", "ndcg-l3])), or setting the Qrels relevance level qrels-wise as qrels.set_relevance_level(2).

  • [October 10, 2022] ranx 0.3 is out!
    This release adds integration with ranxhub, a new sharing platform for pre-computed runs.
    Click here for a quick example.
    Click here to learn how to share your own runs with the community and lead by example!

โšก๏ธ Introduction

ranx is a library of fast ranking evaluation metrics implemented in Python, leveraging Numba for high-speed vector operations and automatic parallelization. It offers a user-friendly interface to evaluate and compare Information Retrieval and Recommender Systems. ranx allows you to perform statistical tests and export LaTeX tables for your scientific publications. Moreover, ranx provides several fusion algorithms and normalization strategies, and an automatic fusion optimization functionality. ranx was featured in ECIR 2022 and CIKM 2022.

If you use ranx to evaluate results or conducting experiments involving fusion for your scientific publication, please consider it: evaluation bibtex, fusion bibtex.

For a quick overview, follow the Usage section.

For a in-depth overview, follow the Examples section.

โœจ Features

Metrics

The metrics have been tested against TREC Eval for correctness.

Statistical Tests

Please, refer to Smucker et al., Carterette, and Fuhr for additional information on statistical tests for Information Retrieval.

Off-the-shelf Qrels

You can load qrels from ir-datasets as simply as:

qrels = Qrels.from_ir_datasets("msmarco-document/dev")
A full list of the available qrels is provided here.

Off-the-shelf Runs

You can load runs from ranxhub as simply as:

run = Run.from_ranxhub("run-id")
A full list of the available runs is provided here.

Fusion Algorithms

Name Name Name Name Name
CombMIN CombMNZ RRF MAPFuse BordaFuse
CombMED CombGMNZ RBC PosFuse Weighted BordaFuse
CombANZ ISR WMNZ ProbFuse Condorcet
CombMAX Log_ISR Mixed SegFuse Weighted Condorcet
CombSUM LogN_ISR BayesFuse SlideFuse Weighted Sum

Please, refer to the documentation for further details.

Normalization Strategies

Please, refer to the documentation for further details.

๐Ÿ”Œ Installation

pip install ranx

๐Ÿ’ก Usage

Create Qrels and Run

from ranx import Qrels, Run

qrels_dict = { "q_1": { "d_12": 5, "d_25": 3 },
               "q_2": { "d_11": 6, "d_22": 1 } }

run_dict = { "q_1": { "d_12": 0.9, "d_23": 0.8, "d_25": 0.7,
                      "d_36": 0.6, "d_32": 0.5, "d_35": 0.4  },
             "q_2": { "d_12": 0.9, "d_11": 0.8, "d_25": 0.7,
                      "d_36": 0.6, "d_22": 0.5, "d_35": 0.4  } }

qrels = Qrels(qrels_dict)
run = Run(run_dict)

Evaluate

from ranx import 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}

Compare

from ranx import compare

# Compare different runs and perform Two-sided Paired Student's t-Test
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
)
Output:
print(report)
#    Model    MAP@100    MRR@100    NDCG@10
---  -------  --------   --------   ---------
a    model_1  0.320แต‡     0.320แต‡     0.368แต‡แถœ
b    model_2  0.233      0.234      0.239
c    model_3  0.308แต‡     0.309แต‡     0.330แต‡
d    model_4  0.366แตƒแต‡แถœ   0.367แตƒแต‡แถœ   0.408แตƒแต‡แถœ
e    model_5  0.405แตƒแต‡แถœแตˆ  0.406แตƒแต‡แถœแตˆ  0.451แตƒแต‡แถœแตˆ

Fusion

from ranx import fuse, optimize_fusion

best_params = optimize_fusion(
    qrels=train_qrels,
    runs=[train_run_1, train_run_2, train_run_3],
    norm="min-max",     # The norm. to apply before fusion
    method="wsum",      # The fusion algorithm to use (Weighted Sum)
    metric="ndcg@100",  # The metric to maximize
)

combined_test_run = fuse(
    runs=[test_run_1, test_run_2, test_run_3],  
    norm="min-max",       
    method="wsum",        
    params=best_params,
)

๐Ÿ“– Examples

Name Link
Overview Open In Colab
Qrels and Run Open In Colab
Evaluation Open In Colab
Comparison and Report Open In Colab
Fusion Open In Colab
Share your runs with ranxhub Open In Colab

๐Ÿ“š Documentation

Browse the documentation for more details and examples.

๐ŸŽ“ Citation

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

@inproceedings{bassani2022ranx,
  author    = {Elias Bassani},
  title     = {ranx: {A} Blazing-Fast Python Library for Ranking Evaluation and Comparison},
  booktitle = {{ECIR} {(2)}},
  series    = {Lecture Notes in Computer Science},
  volume    = {13186},
  pages     = {259--264},
  publisher = {Springer},
  year      = {2022}
}

๐ŸŽ Feature Requests

Would you like to see other features implemented? Please, open a feature request.

๐Ÿค˜ Want to contribute?

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

๐Ÿ“„ License

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