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114 lines
3.8 KiB
Python
114 lines
3.8 KiB
Python
"""
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Apriori Algorithm is a Association rule mining technique, also known as market basket
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analysis, aims to discover interesting relationships or associations among a set of
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items in a transactional or relational database.
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For example, Apriori Algorithm states: "If a customer buys item A and item B, then they
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are likely to buy item C." This rule suggests a relationship between items A, B, and C,
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indicating that customers who purchased A and B are more likely to also purchase item C.
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WIKI: https://en.wikipedia.org/wiki/Apriori_algorithm
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Examples: https://www.kaggle.com/code/earthian/apriori-association-rules-mining
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"""
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from itertools import combinations
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def load_data() -> list[list[str]]:
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"""
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Returns a sample transaction dataset.
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>>> load_data()
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[['milk'], ['milk', 'butter'], ['milk', 'bread'], ['milk', 'bread', 'chips']]
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"""
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return [["milk"], ["milk", "butter"], ["milk", "bread"], ["milk", "bread", "chips"]]
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def prune(itemset: list, candidates: list, length: int) -> list:
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"""
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Prune candidate itemsets that are not frequent.
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The goal of pruning is to filter out candidate itemsets that are not frequent. This
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is done by checking if all the (k-1) subsets of a candidate itemset are present in
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the frequent itemsets of the previous iteration (valid subsequences of the frequent
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itemsets from the previous iteration).
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Prunes candidate itemsets that are not frequent.
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>>> itemset = ['X', 'Y', 'Z']
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>>> candidates = [['X', 'Y'], ['X', 'Z'], ['Y', 'Z']]
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>>> prune(itemset, candidates, 2)
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[['X', 'Y'], ['X', 'Z'], ['Y', 'Z']]
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>>> itemset = ['1', '2', '3', '4']
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>>> candidates = ['1', '2', '4']
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>>> prune(itemset, candidates, 3)
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[]
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"""
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pruned = []
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for candidate in candidates:
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is_subsequence = True
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for item in candidate:
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if item not in itemset or itemset.count(item) < length - 1:
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is_subsequence = False
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break
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if is_subsequence:
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pruned.append(candidate)
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return pruned
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def apriori(data: list[list[str]], min_support: int) -> list[tuple[list[str], int]]:
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"""
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Returns a list of frequent itemsets and their support counts.
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>>> data = [['A', 'B', 'C'], ['A', 'B'], ['A', 'C'], ['A', 'D'], ['B', 'C']]
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>>> apriori(data, 2)
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[(['A', 'B'], 1), (['A', 'C'], 2), (['B', 'C'], 2)]
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>>> data = [['1', '2', '3'], ['1', '2'], ['1', '3'], ['1', '4'], ['2', '3']]
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>>> apriori(data, 3)
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[]
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"""
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itemset = [list(transaction) for transaction in data]
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frequent_itemsets = []
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length = 1
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while itemset:
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# Count itemset support
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counts = [0] * len(itemset)
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for transaction in data:
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for j, candidate in enumerate(itemset):
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if all(item in transaction for item in candidate):
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counts[j] += 1
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# Prune infrequent itemsets
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itemset = [item for i, item in enumerate(itemset) if counts[i] >= min_support]
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# Append frequent itemsets (as a list to maintain order)
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for i, item in enumerate(itemset):
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frequent_itemsets.append((sorted(item), counts[i]))
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length += 1
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itemset = prune(itemset, list(combinations(itemset, length)), length)
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return frequent_itemsets
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if __name__ == "__main__":
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"""
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Apriori algorithm for finding frequent itemsets.
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Args:
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data: A list of transactions, where each transaction is a list of items.
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min_support: The minimum support threshold for frequent itemsets.
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Returns:
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A list of frequent itemsets along with their support counts.
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"""
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import doctest
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doctest.testmod()
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# user-defined threshold or minimum support level
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frequent_itemsets = apriori(data=load_data(), min_support=2)
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print("\n".join(f"{itemset}: {support}" for itemset, support in frequent_itemsets))
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