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 2023-12-15 16:55 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, ProcessEdgeAttribute, TypeCheckPreprocessor, TypeDifferentiatedProcessor, UseDefaultIfNone, UseValIfNone
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',
        }
        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)
        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, 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	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	ownership_processor = ProcessEdgeAttribute(
36		'ownership',
37		TypeDifferentiatedProcessor({
38		str: lambda x: frozenset(x.split()),
39		dict: lambda x: frozenset(x.keys()),
40		list: lambda x: frozenset(x),
41		type(None): None
42		})
43		)
44	1	self._filter_functions = {
45		"ownership": EdgeFilter(
46		operator.contains,
47		UseValIfNone(ownership_processor)
48		),
49		"not_ownership": EdgeFilter(
50		lambda a, b: not (a & b),
51		UseDefaultIfNone(ownership_processor, frozenset()),
52		TypeDifferentiatedProcessor({
53		str: lambda val: frozenset(val.split(',')),
54		list: lambda val: frozenset(val)
55		})
56		),
57		"bandwidth": EdgeFilter(
58		operator.ge,
59		'bandwidth'
60		),
61		"reliability": EdgeFilter(
62		operator.ge,
63		'reliability'
64		),
65		"priority": EdgeFilter(
66		operator.le,
67		'priority'
68		),
69		"utilization": EdgeFilter(
70		operator.le,
71		'utilization'
72		),
73		"delay": EdgeFilter(
74		operator.le,
75		'delay'
76		),
77		}
78	1	self.spf_edge_data_cbs = {
79		"hop": nx_edge_data_weight,
80		"delay": nx_edge_data_delay,
81		"priority": nx_edge_data_priority,
82		}
83
84	1	def clear(self):
85		"""Remove all nodes and links registered."""
86	1	self.graph.clear()
87
88	1	def update_topology(self, topology):
89		"""Update all nodes and links inside the graph."""
90	1	self.graph.clear()
91	1	self.update_nodes(topology.switches)
92	1	self.update_links(topology.links)
93
94	1	def update_nodes(self, nodes):
95		"""Update all nodes inside the graph."""
96	1	for node in nodes.values():
97	1	try:
98	1	if node.status != EntityStatus.UP:
99	1	continue
100	1	self.graph.add_node(node.id)
101
102	1	for interface in node.interfaces.values():
103	1	if interface.status == EntityStatus.UP:
104	1	self.graph.add_node(interface.id)
105	1	self.graph.add_edge(node.id, interface.id)
106
107	1	except AttributeError as err:
108	1	raise TypeError(
109		f"Error when updating nodes inside the graph: {str(err)}"
110		)
111
112	1	def update_links(self, links):
113		"""Update all links inside the graph."""
114	1	for link in links.values():
115	1	if link.status == EntityStatus.UP:
116	1	self.graph.add_edge(link.endpoint_a.id, link.endpoint_b.id)
117	1	self.update_link_metadata(link)
118
119	1	def update_link_metadata(self, link):
120		"""Update link metadata."""
121	1	for key, value in link.metadata.items():
122	1	if key not in self._accepted_metadata:
123	1	continue
124	1	endpoint_a = link.endpoint_a.id
125	1	endpoint_b = link.endpoint_b.id
126	1	self.graph[endpoint_a][endpoint_b][key] = value
127
128	1	def get_link_metadata(self, endpoint_a, endpoint_b):
129		"""Return the metadata of a link."""
130	1	return self.graph.get_edge_data(endpoint_a, endpoint_b)
131
132	1	@staticmethod
133	1	def _remove_switch_hops(circuit):
134		"""Remove switch hops from a circuit hops list."""
135	1	for hop in circuit["hops"]:
136	1	if len(hop.split(":")) == 8:
137	1	circuit["hops"].remove(hop)
138
139	1	def _path_cost(self, path, weight="hop", default_cost=1):
140		"""Compute the path cost given an attribute."""
141	1	cost = 0
142	1	for node, nbr in nx.utils.pairwise(path):
143	1	cost += self.graph[node][nbr].get(weight, default_cost)
144	1	return cost
145
146	1	def path_cost_builder(self, paths, weight="hop", default_weight=1):
147		"""Build the cost of a path given a list of paths."""
148	1	paths_acc = []
149	1	for path in paths:
150	1	if isinstance(path, list):
151	1	paths_acc.append(
152		{
153		"hops": path,
154		"cost": self._path_cost(
155		path, weight=weight, default_cost=default_weight
156		),
157		}
158		)
159	1	elif isinstance(path, dict):
160	1	path["cost"] = self._path_cost(
161		path["hops"], weight=weight, default_cost=default_weight
162		)
163	1	paths_acc.append(path)
164		else:
165		raise TypeError(
166		f"type: '{type(path)}' must be be either list or dict. "
167		f"path: {path}"
168		)
169	1	return paths_acc
170
171	1	def k_shortest_paths(
172		self, source, destination, weight=None, k=1, graph=None
173		):
174		"""
175		Compute up to k shortest paths and return them.
176
177		This procedure is based on algorithm by Jin Y. Yen [1].
178		Since Yen's algorithm calls Dijkstra's up to k times, the time
179		complexity will be proportional to K * Dijkstra's, average
180		O(K(\|V\| + \|E\|)logV), assuming it's using a heap, where V is the
181		number of vertices and E number of egdes.
182
183		References
184		----------
185		.. [1] Jin Y. Yen, "Finding the K Shortest Loopless Paths in a
186		Network", Management Science, Vol. 17, No. 11, Theory Series
187		(Jul., 1971), pp. 712-716.
188		"""
189	1	try:
190	1	return list(
191		islice(
192		nx.shortest_simple_paths(
193		graph or self.graph,
194		source,
195		destination,
196		weight=weight,
197		),
198		k,
199		)
200		)
201	1	except (NodeNotFound, NetworkXNoPath):
202	1	return []
203
204	1	def constrained_k_shortest_paths(
205		self,
206		source,
207		destination,
208		weight=None,
209		k=1,
210		graph=None,
211		minimum_hits=None,
212		**metrics,
213		):
214		"""Calculate the constrained shortest paths with flexibility."""
215	1	graph = graph or self.graph
216	1	mandatory_metrics = metrics.get("mandatory_metrics", {})
217	1	flexible_metrics = metrics.get("flexible_metrics", {})
218	1	first_pass_links = list(
219		self._filter_links(
220		graph.edges(data=True), **mandatory_metrics
221		)
222		)
223	1	length = len(flexible_metrics)
224	1	if minimum_hits is None:
225	1	minimum_hits = 0
226	1	minimum_hits = min(length, max(0, minimum_hits))
227
228	1	paths = []
229	1	for i in range(length, minimum_hits - 1, -1):
230	1	for combo in combinations(flexible_metrics.items(), i):
231	1	additional = dict(combo)
232	1	filtered_links = self._filter_links(
233		first_pass_links, **additional
234		)
235	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 `v` 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 `u` does not seem to be defined for all execution paths. Loading history...
236	1	for path in self.k_shortest_paths(
237		source,
238		destination,
239		weight=weight,
240		k=k,
241		graph=graph.edge_subgraph(filtered_links),
242		):
243	1	paths.append(
244		{
245		"hops": path,
246		"metrics": {mandatory_metrics, additional},
247		}
248		)
249	1	if len(paths) == k:
250	1	return paths
251	1	if paths:
252	1	return paths
253	1	return paths
254
255	1	def _filter_links(self, links, **metrics):
256	1	for metric, value in metrics.items():
257	1	filter_func = self._filter_functions.get(metric, None)
258	1	if filter_func is not None:
259	1	try:
260	1	links = filter_func(value, links)
261		except TypeError as err:
262		raise TypeError(
263		f"Error in {metric} value: {value} err: {err}"
264		)
265		return links
266

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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