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feat: optimizing the prune function at the apriori_algorithm.py archive (#12992)

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* feat: optimizing the prune function at the apriori_algorithm.py archive

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* fix: fixing the unsorted importing statment

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* fix: fixing the key structure to a tuple that can be an hashable structure

* Update apriori_algorithm.py

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Update apriori_algorithm.py

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Maxim Smolskiy <mithridatus@mail.ru>
This commit is contained in:
João Neto
2025-10-19 19:21:00 -03:00
committed by GitHub
parent 3b08413ab3
commit 154cd3e400
1 changed files with 7 additions and 1 deletions
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8
machine_learning/apriori_algorithm.py
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@@ -11,6 +11,7 @@ WIKI: https://en.wikipedia.org/wiki/Apriori_algorithm
Examples: https://www.kaggle.com/code/earthian/apriori-association-rules-mining Examples: https://www.kaggle.com/code/earthian/apriori-association-rules-mining
""" """
from collections import Counter
from itertools import combinations from itertools import combinations
@@ -44,11 +45,16 @@ def prune(itemset: list, candidates: list, length: int) -> list:
>>> prune(itemset, candidates, 3) >>> prune(itemset, candidates, 3)
[] []
""" """
itemset_counter = Counter(tuple(item) for item in itemset)
pruned = [] pruned = []
for candidate in candidates: for candidate in candidates:
is_subsequence = True is_subsequence = True
for item in candidate: for item in candidate:
if item not in itemset or itemset.count(item) < length - 1: item_tuple = tuple(item)
if (
item_tuple not in itemset_counter
or itemset_counter[item_tuple] < length - 1
):
is_subsequence = False is_subsequence = False
break break
if is_subsequence: if is_subsequence: