1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
High-level implementations of the apriori algorithm.
"""
import typing
from efficient_apriori.itemsets import itemsets_from_transactions
from efficient_apriori.rules import generate_rules_apriori
def apriori(
transactions: typing.Iterable[typing.Union[set, tuple, list]],
min_support: float = 0.5,
min_confidence: float = 0.5,
max_length: int = 8,
verbosity: int = 0,
output_transaction_ids: bool = False,
):
"""
The classic apriori algorithm as described in 1994 by Agrawal et al.
The Apriori algorithm works in two phases. Phase 1 iterates over the
transactions several times to build up itemsets of the desired support
level. Phase 2 builds association rules of the desired confidence given the
itemsets found in Phase 1. Both of these phases may be correctly
implemented by exhausting the search space, i.e. generating every possible
itemset and checking it's support. The Apriori prunes the search space
efficiently by deciding apriori if an itemset possibly has the desired
support, before iterating over the entire dataset and checking.
Parameters
----------
transactions : list of transactions (sets/tuples/lists). Each element in
the transactions must be hashable.
min_support : float
The minimum support of the rules returned. The support is frequency of
which the items in the rule appear together in the data set.
min_confidence : float
The minimum confidence of the rules returned. Given a rule X -> Y, the
confidence is the probability of Y, given X, i.e. P(Y|X) = conf(X -> Y)
max_length : int
The maximum length of the itemsets and the rules.
verbosity : int
The level of detail printing when the algorithm runs. Either 0, 1 or 2.
output_transaction_ids : bool
If set to true, the output contains the ids of transactions that
contain a frequent itemset. The ids are the enumeration of the
transactions in the sequence they appear.
Examples
--------
>>> transactions = [('a', 'b', 'c'), ('a', 'b', 'd'), ('f', 'b', 'g')]
>>> itemsets, rules = apriori(transactions, min_confidence=1)
>>> rules
[{a} -> {b}]
"""
itemsets, num_trans = itemsets_from_transactions(
transactions,
min_support,
max_length,
verbosity,
output_transaction_ids=True,
)
itemsets_raw = {
length: {item: counter.itemset_count for (item, counter) in itemsets.items()}
for (length, itemsets) in itemsets.items()
}
rules = generate_rules_apriori(itemsets_raw, min_confidence, num_trans, verbosity)
if output_transaction_ids:
return itemsets, list(rules)
return itemsets_raw, list(rules)
if __name__ == "__main__":
import pytest
pytest.main(args=[".", "--doctest-modules", "-v"])
|
