Description:
ireval 0.1.1
ireval
This Python package provides an implementation of the most common information retrieval (IR) metrics.
Our goal is to return the same scores as trec_eval.
We achieve this by extensively comparing our implementations across many different datasets with their results.
ireval can be installed via
pip install ireval
Implemented metrics
The following metrics are currently implemented:
Name
Function
Description
Precision@k
precision_at_k
Precision is the fraction of retrieved documents that are relevant to the query. Precision@k considers only the documents with the highest k scores.
Precision@k%
precision_at_k_percent
Precision is the fraction of retrieved documents that are relevant to the query. Precision@k% considers only the documents with the highest k% scores.
Recall@k
recall_at_k
Recall is the fraction of the relevant documents that are successfully retrieved. Recall@k considers only the documents with the highest k scores.
Recall@k%
recall_at_k_percent
Recall is the fraction of the relevant documents that are successfully retrieved. Recall@k% considers only the documents with the highest k% scores.
Average precision
average_precision
Average precision is the area under the precision-recall curve.
R-precision
r_precision
R-Precision is the precision after R documents have been retrieved, where R is the number of relevant documents for the topic.
Usage
import ireval
relevancies = [1, 0, 1, 1, 0]
scores = [0.1, 0.4, 0.35, 0.8, .25]
p5 = ireval.precision_at_k(relevancies, scores, 5)
p5pct = ireval.precision_at_k_percent(relevancies, scores, 5)
r5 = ireval.recall_at_k(relevancies, scores, 5)
r5pct = ireval.recall_at_k_percent(relevancies, scores, 5)
ap = ireval.average_precision(relevancies, scores)
rprec = ireval.r_precision(relevancies, scores)
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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