25485096441

Committed 07 May 2026 08:33AM UTC coverage: 76.981% (+0.1%) from 76.853%

Build # 25485096441

Build Type

push

github

Committed by

Quang00

Commit Message

refactor

Coverage Stats

0 of 1 new or added line in 1 file covered. (0.0%)

2 existing lines in 1 file now uncovered.

923 of 1199 relevant lines covered (76.98%)

2.31 hits per line

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0.0

/utils/predictor.py

from itertools import combinations

import networkx as nx


def topology_stats(gml_path: str) -> dict[str, float]:
    """Compute statistics for a network topology represented by a GML file.
    Args:
        gml_path (str): GML file path representing the network topology.

    Returns:
        dict[str, float]: avg_disjoint_paths, avg_hops_all, avg_hops_shortest.
    """
    G = nx.read_gml(gml_path)
    disjoint_path_counts = []
    hops_count_all_paths = []

    for u, v in combinations(G.nodes(), 2):
        paths = list(nx.edge_disjoint_paths(G, u, v))
        disjoint_path_counts.append(len(paths))
        hops_count_all_paths.extend(len(path) - 1 for path in paths)

    avg_disjoint_paths = (
        sum(disjoint_path_counts) / len(disjoint_path_counts)
        if disjoint_path_counts
        else 0
    )
    avg_hops_all = (
        sum(hops_count_all_paths) / len(hops_count_all_paths)
        if hops_count_all_paths
        else 0
    )
    avg_hops_shortest = nx.average_shortest_path_length(G)

    stats = {
        "avg_disjoint_paths": avg_disjoint_paths,
        "avg_hops_all": avg_hops_all,
        "avg_hops_shortest": avg_hops_shortest,
    }
    return stats


def predictor(gml_path: str) -> dict[str, float | str]:
    """Predict whether a network topology is more suitable for static or
    dynamic routing based on its statistics.

    Args:
        gml_path (str): GML file path representing the network topology.

    Returns:
        dict[str, float | str]: path_redundancy, path_stretch, score,
        prediction.
    """
    stats = topology_stats(gml_path)
    path_redundancy = stats["avg_disjoint_paths"] - 1
    path_stretch = stats["avg_hops_all"] / stats["avg_hops_shortest"] - 1
    if path_stretch == 0:
        score = 0 if path_redundancy == 0 else float("inf")
    else:
        score = path_redundancy / path_stretch

    if score == 0:
        prediction = "equal"
    elif path_redundancy < 0.3:
        prediction = "static"
    elif score >= 3.3:
        prediction = "dynamic"
    else:
        prediction = "static"

    topology_prediction = {
        "path_redundancy": path_redundancy,
        "path_stretch": path_stretch,
        "score": score,
        "prediction": prediction,
    }
    return topology_prediction

1	from itertools import combinations	×
2
3	import networkx as nx	×
4
5
6	def topology_stats(gml_path: str) -> dict[str, float]:	×
7	"""Compute statistics for a network topology represented by a GML file.
8	Args:
9	gml_path (str): GML file path representing the network topology.
10
11	Returns:
12	dict[str, float]: avg_disjoint_paths, avg_hops_all, avg_hops_shortest.
13	"""
14	G = nx.read_gml(gml_path)	×
15	disjoint_path_counts = []	×
16	hops_count_all_paths = []	×
17
UNCOV 18	for u, v in combinations(G.nodes(), 2):	×
19	paths = list(nx.edge_disjoint_paths(G, u, v))	×
20	disjoint_path_counts.append(len(paths))	×
21	hops_count_all_paths.extend(len(path) - 1 for path in paths)	×
22
UNCOV 23	avg_disjoint_paths = (	×
24	sum(disjoint_path_counts) / len(disjoint_path_counts)
25	if disjoint_path_counts
26	else 0
27	)
28	avg_hops_all = (	×
29	sum(hops_count_all_paths) / len(hops_count_all_paths)
30	if hops_count_all_paths
31	else 0
32	)
NEW 33	avg_hops_shortest = nx.average_shortest_path_length(G)	×
34
35	stats = {	×
36	"avg_disjoint_paths": avg_disjoint_paths,
37	"avg_hops_all": avg_hops_all,
38	"avg_hops_shortest": avg_hops_shortest,
39	}
40	return stats	×
41
42
43	def predictor(gml_path: str) -> dict[str, float \| str]:	×
44	"""Predict whether a network topology is more suitable for static or
45	dynamic routing based on its statistics.
46
47	Args:
48	gml_path (str): GML file path representing the network topology.
49
50	Returns:
51	dict[str, float \| str]: path_redundancy, path_stretch, score,
52	prediction.
53	"""
54	stats = topology_stats(gml_path)	×
55	path_redundancy = stats["avg_disjoint_paths"] - 1	×
56	path_stretch = stats["avg_hops_all"] / stats["avg_hops_shortest"] - 1	×
57	if path_stretch == 0:	×
58	score = 0 if path_redundancy == 0 else float("inf")	×
59	else:
60	score = path_redundancy / path_stretch	×
61
62	if score == 0:	×
63	prediction = "equal"	×
64	elif path_redundancy < 0.3:	×
65	prediction = "static"	×
66	elif score >= 3.3:	×
67	prediction = "dynamic"	×
68	else:
69	prediction = "static"	×
70
71	topology_prediction = {	×
72	"path_redundancy": path_redundancy,
73	"path_stretch": path_stretch,
74	"score": score,
75	"prediction": prediction,
76	}
77	return topology_prediction	×

Quang00 / qnscheduling / 25485096441

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