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
|
# ==================================================================================================================== #
# _____ _ _ ____ _ #
# _ __ _ |_ _|__ ___ | (_)_ __ __ _ / ___|_ __ __ _ _ __ | |__ #
# | '_ \| | | || |/ _ \ / _ \| | | '_ \ / _` || | _| '__/ _` | '_ \| '_ \ #
# | |_) | |_| || | (_) | (_) | | | | | | (_| || |_| | | | (_| | |_) | | | | #
# | .__/ \__, ||_|\___/ \___/|_|_|_| |_|\__, (_)____|_| \__,_| .__/|_| |_| #
# |_| |___/ |___/ |_| #
# ==================================================================================================================== #
# Authors: #
# Patrick Lehmann #
# #
# License: #
# ==================================================================================================================== #
# Copyright 2017-2025 Patrick Lehmann - Bötzingen, Germany #
# #
# Licensed under the Apache License, Version 2.0 (the "License"); #
# you may not use this file except in compliance with the License. #
# You may obtain a copy of the License at #
# #
# http://www.apache.org/licenses/LICENSE-2.0 #
# #
# Unless required by applicable law or agreed to in writing, software #
# distributed under the License is distributed on an "AS IS" BASIS, #
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #
# See the License for the specific language governing permissions and #
# limitations under the License. #
# #
# SPDX-License-Identifier: Apache-2.0 #
# ==================================================================================================================== #
#
"""Performance tests for pyTooling.Graph."""
import timeit
from dataclasses import dataclass
from pathlib import Path
from statistics import median
from time import perf_counter_ns
from typing import Callable, Iterable
from unittest import TestCase
from pyTooling.Graph import Graph as pt_Graph
if __name__ == "__main__": # pragma: no cover
print("ERROR: you called a testcase declaration file as an executable module.")
print("Use: 'python -m unittest <testcase module>'")
exit(1)
@dataclass
class BiggestNetwork:
startNodeID: int
size: int
@dataclass
class EdgeFile:
vertexCount: int
edgeCount: int
biggestNetwork: BiggestNetwork
file: Path
class PerformanceTest(TestCase):
counts: Iterable[int] = (10, 100, 1000, 10000)
edgeFiles: Iterable[EdgeFile] = (
EdgeFile( 100, 150, BiggestNetwork( 92, 72), Path("graph_n100_m150_dir_w0_100.edgelist")),
EdgeFile( 1000, 1500, BiggestNetwork( 489, 626), Path("graph_n1000_m1500_dir_w0_100.edgelist")),
EdgeFile( 10000, 15000, BiggestNetwork(3056, 5741), Path("graph_n10000_m15000_dir_w0_100.edgelist")),
# EdgeFile(100000, 150000, BiggestNetwork(9671, 58243), Path("graph_n100000_m150000_dir_w0_100.edgelist")),
)
@staticmethod
def minMaxSumMean(array):
minimum = 1.0e9
maximum = 0.0
sum = 0.0
for value in array:
minimum = value if value < minimum else minimum
maximum = value if value > maximum else maximum
sum += value
return minimum, maximum, sum, sum/len(array)
def runSizedTests(self, func: Callable[[int], Callable[[], None]], counts: Iterable[int]):
print()
print(f" min mean median max")
for count in counts:
results = timeit.repeat(func(count), repeat=20, number=50)
norm = count / 10
minimum, maximum, _, mean = self.minMaxSumMean(results)
print(f"{count:>6}x: {minimum/norm:.6f} s {mean/norm:.6f} s {median(results)/norm:.6f} s {maximum/norm:.6f} s")
def runFileBasedTests(self, setup: Callable[[Path, int], pt_Graph], func: Callable[[pt_Graph, int, int], Callable[[], None]], edgeFiles: Iterable[EdgeFile]):
print()
print(f" min mean median max construct")
for edgeFile in edgeFiles:
file = Path("tests/data/Graph/EdgeLists") / edgeFile.file
start = perf_counter_ns()
graph = setup(file, edgeFile.vertexCount)
construct = (perf_counter_ns() - start) / 1e9
results = timeit.repeat(func(graph, edgeFile.biggestNetwork.startNodeID, edgeFile.biggestNetwork.size), repeat=20, number=50)
norm = edgeFile.biggestNetwork.size
minimum, maximum, _, mean = self.minMaxSumMean(results)
print(f"{edgeFile.vertexCount:>6}x: {minimum/norm:.6f} s {mean/norm:.6f} s {median(results)/norm:.6f} s {maximum/norm:.6f} s {construct/norm:.6f} s")
|
