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Documentation fix for Recall and Hit #369

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merged 3 commits into from
Mar 12, 2024
Merged

Documentation fix for Recall and Hit #369

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2ca1caa
Documentation fix for Recall and Hit
lukas-wegmeth Mar 11, 2024
ed9bd90
Fixed Hit function
lukas-wegmeth Mar 11, 2024
f4e1e27
Fixed the fix
lukas-wegmeth Mar 11, 2024
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10 changes: 3 additions & 7 deletions lenskit/metrics/topn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -74,7 +74,7 @@ def recall(recs, truth, k=None):
Compute recommendation recall. This is computed as:

.. math::
\\frac{|L \\cap I_u^{\\mathrm{test}}|}{\\operatorname{max}\\{|I_u^{\\mathrm{test}}|, k\\}}
\\frac{|L \\cap I_u^{\\mathrm{test}}|}{\\operatorname{min}\\{|I_u^{\\mathrm{test}}|, k\\}}

This metric has a bulk implementation.
"""
Expand Down Expand Up @@ -119,17 +119,13 @@ def hit(recs, truth, k=None):
is scored as 1, and lists with no relevant items as 0. When averaged over the recommendation
lists, this computes the *hit rate* :cite:p:`Deshpande2004-ht`.

.. math::
\\frac{|L \\cap I_u^{\\mathrm{test}}|}{\\operatorname{max}\\{|I_u^{\\mathrm{test}}|, k\\}}

This metric has a bulk implementation.
"""
nrel = len(truth)
if nrel == 0:

if len(truth) == 0:
return None

if k is not None:
nrel = min(nrel, k)
recs = recs.iloc[:k]

good = recs["item"].isin(truth.index)
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