build.graph.graph.KytosGraph.constrained_k_shortest_paths() - Code Metrics - Inspection of "Feat/negative ownership" - kytos-ng/pathfinder - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#65)

unknown

created 2023-12-01 23:18 UTC

KytosGraph.constrained_k_shortest_paths() B

↳ 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, TypeCheckPreprocessor                                       
from .weights import (nx_edge_data_delay, nx_edge_data_priority, nx_edge_data_weight)


try:
    import networkx as nx
    from networkx.exception import NetworkXNoPath, NodeNotFound
except ImportError:
    PACKAGE = "networkx==2.5.1"
    log.error(f"Package {PACKAGE} not found. Please 'pip install {PACKAGE}'")


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

    def __init__(self):
        self.graph = nx.Graph()
        self._accepted_metadata = {
            'ownership',
            'bandwidth',
            'reliability',
            'priority',
            'utilization',
            'delay',
        }
        self._filter_functions = {
            "ownership": EdgeFilter(
                operator.and_,
                lambda edge, val: frozenset(edge[2].get('ownership', {}).keys()) or val,
                TypeCheckPreprocessor(
                    str,
                    lambda val: frozenset(val.split(','))
                )
            ),
            "not_ownership": EdgeFilter(
                lambda a, b: not (a & b),
                lambda edge, _: frozenset(edge[2].get('ownership', {}).keys()),
                TypeCheckPreprocessor(
                    str,
                    lambda val: frozenset(val.split(','))
                )
            ),
            "bandwidth": EdgeFilter(
                operator.ge,
                'bandwidth',
                TypeCheckPreprocessor((int, float))

            ),
            "reliability": EdgeFilter(
                operator.ge,
                'reliability',
                TypeCheckPreprocessor((int, float))
            ),
            "priority": EdgeFilter(
                operator.le,
                'priority',
                TypeCheckPreprocessor((int, float))
            ),
            "utilization": EdgeFilter(
                operator.le,
                'utilization',
                TypeCheckPreprocessor((int, float))
            ),
            "delay": EdgeFilter(
                operator.le,
                'delay',
                TypeCheckPreprocessor((int, float))
            ),
        }
        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)
        self.update_links(topology.links)

    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.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.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, TypeCheckPreprocessor
11	1	from .weights import (nx_edge_data_delay, nx_edge_data_priority, nx_edge_data_weight)
12
13
14	1	try:
15	1	import networkx as nx
16	1	from networkx.exception import NetworkXNoPath, NodeNotFound
17		except ImportError:
18		PACKAGE = "networkx==2.5.1"
19		log.error(f"Package {PACKAGE} not found. Please 'pip install {PACKAGE}'")
20
21
22	1	class KytosGraph:
23		"""Class responsible for the graph generation."""
24
25	1	def __init__(self):
26	1	self.graph = nx.Graph()
27	1	self._accepted_metadata = {
28		'ownership',
29		'bandwidth',
30		'reliability',
31		'priority',
32		'utilization',
33		'delay',
34		}
35	1	self._filter_functions = {
36		"ownership": EdgeFilter(
37		operator.and_,
38		lambda edge, val: frozenset(edge[2].get('ownership', {}).keys()) or val,
39		TypeCheckPreprocessor(
40		str,
41		lambda val: frozenset(val.split(','))
42		)
43		),
44		"not_ownership": EdgeFilter(
45		lambda a, b: not (a & b),
46		lambda edge, _: frozenset(edge[2].get('ownership', {}).keys()),
47		TypeCheckPreprocessor(
48		str,
49		lambda val: frozenset(val.split(','))
50		)
51		),
52		"bandwidth": EdgeFilter(
53		operator.ge,
54		'bandwidth',
55		TypeCheckPreprocessor((int, float))
56
57		),
58		"reliability": EdgeFilter(
59		operator.ge,
60		'reliability',
61		TypeCheckPreprocessor((int, float))
62		),
63		"priority": EdgeFilter(
64		operator.le,
65		'priority',
66		TypeCheckPreprocessor((int, float))
67		),
68		"utilization": EdgeFilter(
69		operator.le,
70		'utilization',
71		TypeCheckPreprocessor((int, float))
72		),
73		"delay": EdgeFilter(
74		operator.le,
75		'delay',
76		TypeCheckPreprocessor((int, float))
77		),
78		}
79	1	self.spf_edge_data_cbs = {
80		"hop": nx_edge_data_weight,
81		"delay": nx_edge_data_delay,
82		"priority": nx_edge_data_priority,
83		}
84
85	1	def clear(self):
86		"""Remove all nodes and links registered."""
87	1	self.graph.clear()
88
89	1	def update_topology(self, topology):
90		"""Update all nodes and links inside the graph."""
91	1	self.graph.clear()
92	1	self.update_nodes(topology.switches)
93	1	self.update_links(topology.links)
94
95	1	def update_nodes(self, nodes):
96		"""Update all nodes inside the graph."""
97	1	for node in nodes.values():
98	1	try:
99	1	if node.status != EntityStatus.UP:
100	1	continue
101	1	self.graph.add_node(node.id)
102
103	1	for interface in node.interfaces.values():
104	1	if interface.status == EntityStatus.UP:
105	1	self.graph.add_node(interface.id)
106	1	self.graph.add_edge(node.id, interface.id)
107
108	1	except AttributeError as err:
109	1	raise TypeError(
110		f"Error when updating nodes inside the graph: {str(err)}"
111		)
112
113	1	def update_links(self, links):
114		"""Update all links inside the graph."""
115	1	for link in links.values():
116	1	if link.status == EntityStatus.UP:
117	1	self.graph.add_edge(link.endpoint_a.id, link.endpoint_b.id)
118	1	self.update_link_metadata(link)
119
120	1	def update_link_metadata(self, link):
121		"""Update link metadata."""
122	1	for key, value in link.metadata.items():
123	1	if key not in self._accepted_metadata:
124	1	continue
125	1	endpoint_a = link.endpoint_a.id
126	1	endpoint_b = link.endpoint_b.id
127	1	self.graph[endpoint_a][endpoint_b][key] = value
128
129	1	def get_link_metadata(self, endpoint_a, endpoint_b):
130		"""Return the metadata of a link."""
131	1	return self.graph.get_edge_data(endpoint_a, endpoint_b)
132
133	1	@staticmethod
134	1	def _remove_switch_hops(circuit):
135		"""Remove switch hops from a circuit hops list."""
136	1	for hop in circuit["hops"]:
137	1	if len(hop.split(":")) == 8:
138	1	circuit["hops"].remove(hop)
139
140	1	def _path_cost(self, path, weight="hop", default_cost=1):
141		"""Compute the path cost given an attribute."""
142	1	cost = 0
143	1	for node, nbr in nx.utils.pairwise(path):
144	1	cost += self.graph[node][nbr].get(weight, default_cost)
145	1	return cost
146
147	1	def path_cost_builder(self, paths, weight="hop", default_weight=1):
148		"""Build the cost of a path given a list of paths."""
149	1	paths_acc = []
150	1	for path in paths:
151	1	if isinstance(path, list):
152	1	paths_acc.append(
153		{
154		"hops": path,
155		"cost": self._path_cost(
156		path, weight=weight, default_cost=default_weight
157		),
158		}
159		)
160	1	elif isinstance(path, dict):
161	1	path["cost"] = self._path_cost(
162		path["hops"], weight=weight, default_cost=default_weight
163		)
164	1	paths_acc.append(path)
165		else:
166		raise TypeError(
167		f"type: '{type(path)}' must be be either list or dict. "
168		f"path: {path}"
169		)
170	1	return paths_acc
171
172	1	def k_shortest_paths(
173		self, source, destination, weight=None, k=1, graph=None
174		):
175		"""
176		Compute up to k shortest paths and return them.
177
178		This procedure is based on algorithm by Jin Y. Yen [1].
179		Since Yen's algorithm calls Dijkstra's up to k times, the time
180		complexity will be proportional to K * Dijkstra's, average
181		O(K(\|V\| + \|E\|)logV), assuming it's using a heap, where V is the
182		number of vertices and E number of egdes.
183
184		References
185		----------
186		.. [1] Jin Y. Yen, "Finding the K Shortest Loopless Paths in a
187		Network", Management Science, Vol. 17, No. 11, Theory Series
188		(Jul., 1971), pp. 712-716.
189		"""
190	1	try:
191	1	return list(
192		islice(
193		nx.shortest_simple_paths(
194		graph or self.graph,
195		source,
196		destination,
197		weight=weight,
198		),
199		k,
200		)
201		)
202	1	except (NodeNotFound, NetworkXNoPath):
203	1	return []
204
205	1	def constrained_k_shortest_paths(
206		self,
207		source,
208		destination,
209		weight=None,
210		k=1,
211		graph=None,
212		minimum_hits=None,
213		**metrics,
214		):
215		"""Calculate the constrained shortest paths with flexibility."""
216	1	graph = graph or self.graph
217	1	mandatory_metrics = metrics.get("mandatory_metrics", {})
218	1	flexible_metrics = metrics.get("flexible_metrics", {})
219	1	first_pass_links = list(
220		self._filter_links(
221		graph.edges(data=True), **mandatory_metrics
222		)
223		)
224	1	length = len(flexible_metrics)
225	1	if minimum_hits is None:
226	1	minimum_hits = 0
227	1	minimum_hits = min(length, max(0, minimum_hits))
228
229	1	paths = []
230	1	for i in range(length, minimum_hits - 1, -1):
231	1	for combo in combinations(flexible_metrics.items(), i):
232	1	additional = dict(combo)
233	1	filtered_links = self._filter_links(
234		first_pass_links, **additional
235		)
236	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...
237	1	for path in self.k_shortest_paths(
238		source,
239		destination,
240		weight=weight,
241		k=k,
242		graph=graph.edge_subgraph(filtered_links),
243		):
244	1	paths.append(
245		{
246		"hops": path,
247		"metrics": {mandatory_metrics, additional},
248		}
249		)
250	1	if len(paths) == k:
251	1	return paths
252	1	if paths:
253	1	return paths
254	1	return paths
255
256	1	def _filter_links(self, links, **metrics):
257	1	for metric, value in metrics.items():
258	1	filter_func = self._filter_functions.get(metric, None)
259	1	if filter_func is not None:
260	1	try:
261	1	links = filter_func(value, links)
262	1	except TypeError as err:
263	1	raise TypeError(
264		f"Error in {metric} value: {value} err: {err}"
265		)
266		return links
267

kytos-ng / pathfinder

Pull Request — master (#65)

KytosGraph.constrained_k_shortest_paths() B

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How to fix Many Parameters

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