Comments (2)
Hi Stefano,
Almost all the metrics ignore qrels with zero scores, including NDCG.
So the difference you get is not because of that.
However, I think your results are entirely possible if you have many relevance judgments for each query.
Note that the Average Precision denominator is equal to the number of relevant documents, regardless of the cut-off.
Conversely, DCG and, therefore, NDCG depends on the cut-off and not on the number of relevant documents.
(Please, cross check this for correctness.)
Here is a toy example:
from ranx import Qrels, Run, evaluate
# 100 relevant docs
qrels = Qrels({
"q1": {f"d{i}": 1 for i in range(100)}
})
# Only one relevant doc is returned
run = Run({
"q1": {**{"d1":1000}, **{f"dd{i}": i for i in range(99)}}
})
>>>
{
"map": 0.01,
"map@10": 0.01,
"ndcg@10": 0.22009176629808017,
"ndcg@100": 0.047758523260819974,
}
From my experience, MAP is commonly used with larger cut-offs than 10 (usually 100) or with no cut-off at all.
Best,
Elias
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Closing for inactivity.
Feel free to re-open if needed.
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Related Issues (20)
- [BUG] dcg and dcg_burges do not work in the compare function HOT 2
- [Feature Request] Use black to indent the code HOT 1
- [BUG] RBP with multiple relevance levels HOT 3
- [Feature Request] Support gzipped files? HOT 3
- [Feature Request] memory issue / make Run more efficient HOT 2
- Incorrect result for f1 score HOT 13
- Zero-scored documents HOT 10
- [BUG] Misleading exception message on dataframe types HOT 2
- [BUG] Issues when storing/loading Qrels from a dataframe and a parquet file. HOT 6
- [Feature Request] Run.from_df and Run.from_parquet does not allow specifying run name HOT 1
- Question on rank aggregation usage HOT 4
- Getting "Segmentation fault (core dumped)" error HOT 2
- [Feature Request] stddev statistic HOT 3
- Couldn't find any documentation about Qrel and run score range HOT 2
- [Feature Request] Propensity-scored Metrics HOT 1
- How do we compare different runs with multiple folds per run? HOT 1
- [Question] About the correction among multiple hypotheses HOT 1
- [Question] How to compute precision for a retriever operating at passage-level HOT 1
- JIT compilation on serverless (i.e. Modal Labs) HOT 1
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