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[ENH] Use np.argpartition for efficient top-k selection instead of np.argsort #2805

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Merged
merged 5 commits into from
Jun 26, 2025
Merged

[ENH] Use np.argpartition for efficient top-k selection instead of np.argsort #2805

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8a73957
use argpartition for efficient selection instead of argsort
Kaustbh May 14, 2025
fc81165
Merge branch 'main' into replace-argsort
Kaustbh May 23, 2025
9163cc2
made the required changes
Kaustbh May 23, 2025
88ca04c
Merge branch 'main' into replace-argsort
Kaustbh May 29, 2025
1a54753
Merge branch 'main' into replace-argsort
Kaustbh Jun 17, 2025
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9 changes: 5 additions & 4 deletions aeon/similarity_search/series/_commons.py
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Original file line number Diff line number Diff line change
Expand Up @@ -137,8 +137,10 @@ def _extract_top_k_from_dist_profile(
top_k_distances = np.full(k, np.inf, dtype=np.float64)
ub = np.full(k, np.inf)
lb = np.full(k, -1.0)
# Could be optimized by using argpartition
sorted_indexes = np.argsort(dist_profile)

k = min(k, len(dist_profile))
partitioned = np.argpartition(dist_profile, k)[:k]
sorted_indexes = partitioned[np.argsort(dist_profile[partitioned])]
_current_k = 0
if not allow_trivial_matches:
_current_j = 0
Expand All @@ -165,8 +167,7 @@ def _extract_top_k_from_dist_profile(
break
_current_j += 1
else:
_current_k += min(k, len(dist_profile))
dist_profile = dist_profile[sorted_indexes[:_current_k]]
dist_profile = dist_profile[sorted_indexes]
dist_profile = dist_profile[dist_profile <= threshold]
_current_k = len(dist_profile)

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