build.graph.graph.KytosGraph.constrained_k_shortest_paths() - Code Metrics - kytos-ng/pathfinder - Measure and Improve Code Quality continuously with Scrutinizer

KytosGraph.constrained_k_shortest_paths() B
last analyzed 2025-06-23 13:30 UTC

↳ Parent: build.graph.graph

Complexity

Conditions

Size

Total Lines	50
Code Lines	41

Duplication

Lines	0
Ratio	0 %

Code Coverage

Tests	22
CRAP Score	7

Importance

Changes

Metric	Value
cc	7
eloc	41
nop	8
dl	0
loc	50
ccs	22
cts	22
cp	1
crap	7
rs	7.496
c	0
b	0
f	0

How to fix Many Parameters

"""Module Graph of kytos/pathfinder Kytos Network Application."""

# pylint: enable=too-many-arguments,too-many-locals
from itertools import combinations, islice
import operator

from kytos.core import log
from kytos.core.common import EntityStatus
 
from .filters import EdgeFilter, ProcessEdgeAttribute, TypeCheckPreprocessor, TypeDifferentiatedProcessor, UseDefaultIfNone, UseValIfNone
from .weights import (nx_edge_data_delay, nx_edge_data_priority, nx_edge_data_weight)


import networkx as nx
from networkx.exception import NetworkXNoPath, NodeNotFound


class KytosGraph:
    """Class responsible for the graph generation."""

    def __init__(self):
        self.graph = nx.Graph()
        self._accepted_metadata = {
            'ownership',
            'bandwidth',
            'reliability',
            'priority',
            'utilization',
            'delay',
        }
        ownership_processor = ProcessEdgeAttribute(
            'ownership',
            TypeDifferentiatedProcessor({
                str: lambda x: frozenset(x.split()),
                dict: lambda x: frozenset(x.keys()),
                list: lambda x: frozenset(x),
                type(None): None
            })
        )
        self._filter_functions = {
            "ownership": EdgeFilter(
                operator.contains,
                UseValIfNone(ownership_processor)
            ),
            "not_ownership": EdgeFilter(
                lambda a, b: not (a & b),
                UseDefaultIfNone(ownership_processor, frozenset()),
                TypeDifferentiatedProcessor({
                    str: lambda val: frozenset(val.split(',')),
                    list: lambda val: frozenset(val)
                })
            ),
            "bandwidth": EdgeFilter(
                operator.ge,
                'bandwidth'
            ),
            "reliability": EdgeFilter(
                operator.ge,
                'reliability'
            ),
            "priority": EdgeFilter(
                operator.le,
                'priority'
            ),
            "utilization": EdgeFilter(
                operator.le,
                'utilization'
            ),
            "delay": EdgeFilter(
                operator.le,
                'delay'
            ),
        }
        self.spf_edge_data_cbs = {
            "hop": nx_edge_data_weight,
            "delay": nx_edge_data_delay,
            "priority": nx_edge_data_priority,
        }

    def clear(self):
        """Remove all nodes and links registered."""
        self.graph.clear()

    def update_topology(self, topology):
        """Update all nodes and links inside the graph."""
        self.graph.clear()
        self.update_nodes(topology.switches.copy())
        self.update_links(topology.links.copy())

    def update_nodes(self, nodes):
        """Update all nodes inside the graph."""
        for node in nodes.values():
            try:
                if node.status != EntityStatus.UP:
                    continue
                self.graph.add_node(node.id)

                for interface in node.interfaces.copy().values():
                    if interface.status == EntityStatus.UP:
                        self.graph.add_node(interface.id)
                        self.graph.add_edge(node.id, interface.id)

            except AttributeError as err:
                raise TypeError(
                    f"Error when updating nodes inside the graph: {str(err)}"
                )

    def update_links(self, links):
        """Update all links inside the graph."""
        for link in links.values():
            if link.status == EntityStatus.UP:
                self.graph.add_edge(link.endpoint_a.id, link.endpoint_b.id)
                self.update_link_metadata(link)

    def update_link_metadata(self, link):
        """Update link metadata."""
        for key, value in link.metadata.copy().items():
            if key not in self._accepted_metadata:
                continue
            endpoint_a = link.endpoint_a.id
            endpoint_b = link.endpoint_b.id
            self.graph[endpoint_a][endpoint_b][key] = value

    def get_link_metadata(self, endpoint_a, endpoint_b):
        """Return the metadata of a link."""
        return self.graph.get_edge_data(endpoint_a, endpoint_b)

    @staticmethod
    def _remove_switch_hops(circuit):
        """Remove switch hops from a circuit hops list."""
        for hop in circuit["hops"]:
            if len(hop.split(":")) == 8:
                circuit["hops"].remove(hop)

    def _path_cost(self, path, weight="hop", default_cost=1):
        """Compute the path cost given an attribute."""
        cost = 0
        for node, nbr in nx.utils.pairwise(path):
            cost += self.graph[node][nbr].get(weight, default_cost)
        return cost

    def path_cost_builder(self, paths, weight="hop", default_weight=1):
        """Build the cost of a path given a list of paths."""
        paths_acc = []
        for path in paths:
            if isinstance(path, list):
                paths_acc.append(
                    {
                        "hops": path,
                        "cost": self._path_cost(
                            path, weight=weight, default_cost=default_weight
                        ),
                    }
                )
            elif isinstance(path, dict):
                path["cost"] = self._path_cost(
                    path["hops"], weight=weight, default_cost=default_weight
                )
                paths_acc.append(path)
            else:
                raise TypeError(
                    f"type: '{type(path)}' must be be either list or dict. "
                    f"path: {path}"
                )
        return paths_acc

    def k_shortest_paths(
        self, source, destination, weight=None, k=1, graph=None
    ):
        """
        Compute up to k shortest paths and return them.

        This procedure is based on algorithm by Jin Y. Yen [1].
        Since Yen's algorithm calls Dijkstra's up to k times, the time
        complexity will be proportional to K * Dijkstra's, average
        O(K(|V| + |E|)logV), assuming it's using a heap, where V is the
        number of vertices and E number of egdes.

        References
        ----------
        .. [1] Jin Y. Yen, "Finding the K Shortest Loopless Paths in a
           Network", Management Science, Vol. 17, No. 11, Theory Series
           (Jul., 1971), pp. 712-716.
        """
        try:
            return list(
                islice(
                    nx.shortest_simple_paths(
                        graph or self.graph,
                        source,
                        destination,
                        weight=weight,
                    ),
                    k,
                )
            )
        except (NodeNotFound, NetworkXNoPath):
            return []

    def constrained_k_shortest_paths(
        self,
        source,
        destination,
        weight=None,
        k=1,
        graph=None,
        minimum_hits=None,
        **metrics,
    ):
        """Calculate the constrained shortest paths with flexibility."""
        graph = graph or self.graph
        mandatory_metrics = metrics.get("mandatory_metrics", {})
        flexible_metrics = metrics.get("flexible_metrics", {})
        first_pass_links = list(
            self._filter_links(
                graph.edges(data=True), **mandatory_metrics
            )
        )
        length = len(flexible_metrics)
        if minimum_hits is None:
            minimum_hits = 0
        minimum_hits = min(length, max(0, minimum_hits))

        paths = []
        for i in range(length, minimum_hits - 1, -1):
            for combo in combinations(flexible_metrics.items(), i):
                additional = dict(combo)
                filtered_links = self._filter_links(
                    first_pass_links, **additional
                )
                filtered_links = ((u, v) for u, v, d in filtered_links)

                for path in self.k_shortest_paths(
                    source,
                    destination,
                    weight=weight,
                    k=k,
                    graph=graph.edge_subgraph(filtered_links),
                ):
                    paths.append(
                        {
                            "hops": path,
                            "metrics": {**mandatory_metrics, **additional},
                        }
                    )
                if len(paths) == k:
                    return paths
            if paths:
                return paths
        return paths

    def _filter_links(self, links, **metrics):
        for metric, value in metrics.items():
            filter_func = self._filter_functions.get(metric, None)
            if filter_func is not None:
                try:
                    links = filter_func(value, links)
                except TypeError as err:
                    raise TypeError(
                        f"Error in {metric} value: {value} err: {err}"
                    )
        return links


1		"""Module Graph of kytos/pathfinder Kytos Network Application."""
2
3		# pylint: enable=too-many-arguments,too-many-locals
4	1	from itertools import combinations, islice
5	1	import operator
6
7	1	from kytos.core import log
8	1	from kytos.core.common import EntityStatus
9
10	1	from .filters import EdgeFilter, ProcessEdgeAttribute, TypeCheckPreprocessor, TypeDifferentiatedProcessor, UseDefaultIfNone, UseValIfNone
11	1	from .weights import (nx_edge_data_delay, nx_edge_data_priority, nx_edge_data_weight)
12
13
14	1	import networkx as nx
15	1	from networkx.exception import NetworkXNoPath, NodeNotFound
16
17
18	1	class KytosGraph:
19		"""Class responsible for the graph generation."""
20
21	1	def __init__(self):
22	1	self.graph = nx.Graph()
23	1	self._accepted_metadata = {
24		'ownership',
25		'bandwidth',
26		'reliability',
27		'priority',
28		'utilization',
29		'delay',
30		}
31	1	ownership_processor = ProcessEdgeAttribute(
32		'ownership',
33		TypeDifferentiatedProcessor({
34		str: lambda x: frozenset(x.split()),
35		dict: lambda x: frozenset(x.keys()),
36		list: lambda x: frozenset(x),
37		type(None): None
38		})
39		)
40	1	self._filter_functions = {
41		"ownership": EdgeFilter(
42		operator.contains,
43		UseValIfNone(ownership_processor)
44		),
45		"not_ownership": EdgeFilter(
46		lambda a, b: not (a & b),
47		UseDefaultIfNone(ownership_processor, frozenset()),
48		TypeDifferentiatedProcessor({
49		str: lambda val: frozenset(val.split(',')),
50		list: lambda val: frozenset(val)
51		})
52		),
53		"bandwidth": EdgeFilter(
54		operator.ge,
55		'bandwidth'
56		),
57		"reliability": EdgeFilter(
58		operator.ge,
59		'reliability'
60		),
61		"priority": EdgeFilter(
62		operator.le,
63		'priority'
64		),
65		"utilization": EdgeFilter(
66		operator.le,
67		'utilization'
68		),
69		"delay": EdgeFilter(
70		operator.le,
71		'delay'
72		),
73		}
74	1	self.spf_edge_data_cbs = {
75		"hop": nx_edge_data_weight,
76		"delay": nx_edge_data_delay,
77		"priority": nx_edge_data_priority,
78		}
79
80	1	def clear(self):
81		"""Remove all nodes and links registered."""
82	1	self.graph.clear()
83
84	1	def update_topology(self, topology):
85		"""Update all nodes and links inside the graph."""
86	1	self.graph.clear()
87	1	self.update_nodes(topology.switches.copy())
88	1	self.update_links(topology.links.copy())
89
90	1	def update_nodes(self, nodes):
91		"""Update all nodes inside the graph."""
92	1	for node in nodes.values():
93	1	try:
94	1	if node.status != EntityStatus.UP:
95	1	continue
96	1	self.graph.add_node(node.id)
97
98	1	for interface in node.interfaces.copy().values():
99	1	if interface.status == EntityStatus.UP:
100	1	self.graph.add_node(interface.id)
101	1	self.graph.add_edge(node.id, interface.id)
102
103	1	except AttributeError as err:
104	1	raise TypeError(
105		f"Error when updating nodes inside the graph: {str(err)}"
106		)
107
108	1	def update_links(self, links):
109		"""Update all links inside the graph."""
110	1	for link in links.values():
111	1	if link.status == EntityStatus.UP:
112	1	self.graph.add_edge(link.endpoint_a.id, link.endpoint_b.id)
113	1	self.update_link_metadata(link)
114
115	1	def update_link_metadata(self, link):
116		"""Update link metadata."""
117	1	for key, value in link.metadata.copy().items():
118	1	if key not in self._accepted_metadata:
119	1	continue
120	1	endpoint_a = link.endpoint_a.id
121	1	endpoint_b = link.endpoint_b.id
122	1	self.graph[endpoint_a][endpoint_b][key] = value
123
124	1	def get_link_metadata(self, endpoint_a, endpoint_b):
125		"""Return the metadata of a link."""
126	1	return self.graph.get_edge_data(endpoint_a, endpoint_b)
127
128	1	@staticmethod
129	1	def _remove_switch_hops(circuit):
130		"""Remove switch hops from a circuit hops list."""
131	1	for hop in circuit["hops"]:
132	1	if len(hop.split(":")) == 8:
133	1	circuit["hops"].remove(hop)
134
135	1	def _path_cost(self, path, weight="hop", default_cost=1):
136		"""Compute the path cost given an attribute."""
137	1	cost = 0
138	1	for node, nbr in nx.utils.pairwise(path):
139	1	cost += self.graph[node][nbr].get(weight, default_cost)
140	1	return cost
141
142	1	def path_cost_builder(self, paths, weight="hop", default_weight=1):
143		"""Build the cost of a path given a list of paths."""
144	1	paths_acc = []
145	1	for path in paths:
146	1	if isinstance(path, list):
147	1	paths_acc.append(
148		{
149		"hops": path,
150		"cost": self._path_cost(
151		path, weight=weight, default_cost=default_weight
152		),
153		}
154		)
155	1	elif isinstance(path, dict):
156	1	path["cost"] = self._path_cost(
157		path["hops"], weight=weight, default_cost=default_weight
158		)
159	1	paths_acc.append(path)
160		else:
161		raise TypeError(
162		f"type: '{type(path)}' must be be either list or dict. "
163		f"path: {path}"
164		)
165	1	return paths_acc
166
167	1	def k_shortest_paths(
168		self, source, destination, weight=None, k=1, graph=None
169		):
170		"""
171		Compute up to k shortest paths and return them.
172
173		This procedure is based on algorithm by Jin Y. Yen [1].
174		Since Yen's algorithm calls Dijkstra's up to k times, the time
175		complexity will be proportional to K * Dijkstra's, average
176		O(K(\|V\| + \|E\|)logV), assuming it's using a heap, where V is the
177		number of vertices and E number of egdes.
178
179		References
180		----------
181		.. [1] Jin Y. Yen, "Finding the K Shortest Loopless Paths in a
182		Network", Management Science, Vol. 17, No. 11, Theory Series
183		(Jul., 1971), pp. 712-716.
184		"""
185	1	try:
186	1	return list(
187		islice(
188		nx.shortest_simple_paths(
189		graph or self.graph,
190		source,
191		destination,
192		weight=weight,
193		),
194		k,
195		)
196		)
197	1	except (NodeNotFound, NetworkXNoPath):
198	1	return []
199
200	1	def constrained_k_shortest_paths(
201		self,
202		source,
203		destination,
204		weight=None,
205		k=1,
206		graph=None,
207		minimum_hits=None,
208		**metrics,
209		):
210		"""Calculate the constrained shortest paths with flexibility."""
211	1	graph = graph or self.graph
212	1	mandatory_metrics = metrics.get("mandatory_metrics", {})
213	1	flexible_metrics = metrics.get("flexible_metrics", {})
214	1	first_pass_links = list(
215		self._filter_links(
216		graph.edges(data=True), **mandatory_metrics
217		)
218		)
219	1	length = len(flexible_metrics)
220	1	if minimum_hits is None:
221	1	minimum_hits = 0
222	1	minimum_hits = min(length, max(0, minimum_hits))
223
224	1	paths = []
225	1	for i in range(length, minimum_hits - 1, -1):
226	1	for combo in combinations(flexible_metrics.items(), i):
227	1	additional = dict(combo)
228	1	filtered_links = self._filter_links(
229		first_pass_links, **additional
230		)
231	1	filtered_links = ((u, v) for u, v, d in filtered_links)
		0 ignored issues – show introduced 2021-11-30 20:18 UTC by Report Bug Copy Issue Report The variable `u` does not seem to be defined for all execution paths. Loading history... introduced 2021-11-30 20:18 UTC by Report Bug Copy Issue Report The variable `v` does not seem to be defined for all execution paths. Loading history...
232	1	for path in self.k_shortest_paths(
233		source,
234		destination,
235		weight=weight,
236		k=k,
237		graph=graph.edge_subgraph(filtered_links),
238		):
239	1	paths.append(
240		{
241		"hops": path,
242		"metrics": {mandatory_metrics, additional},
243		}
244		)
245	1	if len(paths) == k:
246	1	return paths
247	1	if paths:
248	1	return paths
249	1	return paths
250
251	1	def _filter_links(self, links, **metrics):
252	1	for metric, value in metrics.items():
253	1	filter_func = self._filter_functions.get(metric, None)
254	1	if filter_func is not None:
255	1	try:
256	1	links = filter_func(value, links)
257		except TypeError as err:
258		raise TypeError(
259		f"Error in {metric} value: {value} err: {err}"
260		)
261		return links
262

kytos-ng / pathfinder

KytosGraph.constrained_k_shortest_paths() B last analyzed 2025-06-23 13:30 UTC

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KytosGraph.constrained_k_shortest_paths() B
last analyzed 2025-06-23 13:30 UTC