Update/optimize evaluation modules with a benchmark script for testing multiple data-recommender-model pairs #61
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Codecov ReportBase: 80.24% // Head: 80.30% // Increases project coverage by
Additional details and impacted files@@ Coverage Diff @@
## master #61 +/- ##
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+ Coverage 80.24% 80.30% +0.05%
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Files 26 26
Lines 815 853 +38
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+ Hits 654 685 +31
- Misses 161 168 +7
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If a truth element is not found in prediction vector, its 'pseudo' rank must be set to the lowest position i.e., `length(pred)` rather than 0.
* Each recommender chooses if it uses ranking or accuracy metrics, or both. * Evaluate mutiple metrics at once with `evaluate(..., metrics::AbstractArray[<:Metrics], ...)`
with a note on multi-threading
depending on whether the outputs can be evaluated by accuracy metrics.
takuti
changed the title
Fix `kwargs` for `AggregatedMetric` to `topk` to unify the function interfaces.
allowing a recommender to recommend previously-observed items.
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#26
IntraListMetricas part of benchmark script (and by theevaluate()interface accordingly)recommend()with bulk prediction #64