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
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Tests for algorithms related to association rules.
"""
import pytest
import itertools
import random
from efficient_apriori.itemsets import itemsets_from_transactions
from efficient_apriori.rules import (
Rule,
generate_rules_simple,
generate_rules_apriori,
)
from efficient_apriori.tests.test_itemsets import generate_transactions
def generate_rules_naively(itemsets, min_confidence, num_transactions):
"""
Generate association rules naively, for testing purposes.
"""
def proper_subsets(itemset: set):
"""
Yield every proper subset of a set.
"""
size = range(1, len(itemset))
arg = [itertools.combinations(itemset, i) for i in size]
yield from itertools.chain(*arg)
def count(itemset):
"""
Helper function to find the count of an itemset in the transactions.
"""
return itemsets[len(itemset)][itemset]
# For every itemset size greater than 1, yield every itemset of that size
itemsets_gen = (iset for size in filter(lambda x: x > 1, itemsets.keys()) for iset in itemsets[size].keys())
for itemset in itemsets_gen:
count_full = count(itemset)
# For every subset, get the difference, create a rule and check
for lhs in proper_subsets(itemset):
rhs = set(itemset).difference(set(lhs))
rhs = tuple(sorted(list(rhs)))
rule = Rule(lhs, rhs, count_full, count(lhs), count(rhs), num_transactions)
# If the confidence of the rule is high enough, yield it
if rule.confidence >= min_confidence:
yield rule
def test_generate_rules_apriori_large():
"""
Test with lots of data.
This test will fail if the second argument to `_ap_genrules` is not
validated as non-empty before the recursive function call. We must have
if H_m_copy:
yield from _ap_genrules
for this test to pass.
"""
transactions = generate_transactions(num_transactions=100, unique_items=30, items_row=(1, 20), seed=123)
itemsets, num_transactions = itemsets_from_transactions(transactions, 0.1)
min_conf = 0.3
rules_apri = generate_rules_apriori(itemsets, min_conf, num_transactions)
rules_naive = generate_rules_naively(itemsets, min_conf, num_transactions)
rules_apri = list(rules_apri)
rules_naive = list(rules_naive)
# Test equal length, since no duplicates should be returned by apriori
assert len(rules_apri) == len(rules_naive)
# Test equal results
assert set(rules_apri) == set(rules_naive)
input_data = [
list(
generate_transactions(
num_transactions=random.randint(15, 25),
unique_items=random.randint(1, 8),
items_row=(1, random.randint(2, 6)),
)
)
for i in range(10)
]
@pytest.mark.parametrize("transactions", input_data)
def test_generate_rules_simple_vs_naive(transactions):
"""
Test the naive rule finder vs. the simple one from the paper.
"""
itemsets, num_transactions = itemsets_from_transactions(transactions, 0.25)
min_conf = 0.1
rules_naive = generate_rules_naively(itemsets, min_conf, num_transactions)
rules_simple = generate_rules_simple(itemsets, min_conf, num_transactions)
assert set(rules_naive) == set(rules_simple)
@pytest.mark.parametrize("transactions", input_data)
def test_generate_rules_simple_vs_apriori(transactions):
"""
Test the naive rule finder vs. the simple one from the paper.
"""
itemsets, num_transactions = itemsets_from_transactions(transactions, 0.1)
min_conf = 0.1
rules_apri = generate_rules_apriori(itemsets, min_conf, num_transactions)
rules_simple = generate_rules_simple(itemsets, min_conf, num_transactions)
assert set(rules_apri) == set(rules_simple)
@pytest.mark.parametrize("transactions", input_data)
def test_generate_rules_naive_vs_apriori(transactions):
"""
Test the naive rule finder vs. the simple one from the paper.
"""
itemsets, num_transactions = itemsets_from_transactions(transactions, 0.15)
min_conf = 0.3
rules_apri = generate_rules_apriori(itemsets, min_conf, num_transactions)
rules_naive = generate_rules_naively(itemsets, min_conf, num_transactions)
rules_apri = list(rules_apri)
rules_naive = list(rules_naive)
# Test equal length, since no duplicates should be returned by apriori
assert len(rules_apri) == len(rules_naive)
# Test equal results
assert set(rules_apri) == set(rules_naive)
def speeds():
"""
Test the naive rule finder vs. the simple one from the paper.
"""
import random
random.seed(123456)
transactions = generate_transactions(
num_transactions=random.randint(250, 500),
unique_items=random.randint(8, 9),
items_row=(10, 50),
)
itemsets, num_transactions = itemsets_from_transactions(transactions, 0.1)
import time
min_conf = 0.5
print(itemsets)
st = time.perf_counter()
rules_apri = generate_rules_apriori(itemsets, min_conf, num_transactions)
rules_apri = list(rules_apri)
time_formatted = round(time.perf_counter() - st, 40)
print("Fast apriori ran in {} s".format(time_formatted))
st = time.perf_counter()
rules_simple = generate_rules_simple(itemsets, min_conf, num_transactions)
rules_simple = list(rules_simple)
time_formatted = round(time.perf_counter() - st, 40)
print("Simple apriori ran in {} s".format(time_formatted))
st = time.perf_counter()
rules_naive = generate_rules_naively(itemsets, min_conf, num_transactions)
rules_naive = list(rules_naive)
time_formatted = round(time.perf_counter() - st, 40)
print("Naive apriori ran in {} s".format(time_formatted))
if __name__ == "__main__":
pytest.main(args=[".", "--doctest-modules", "-v"])
|
