'''
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
PM4Py is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
PM4Py is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with PM4Py. If not, see <https://www.gnu.org/licenses/>.
'''
from typing import List, Any, Dict
from enum import Enum
from pm4py.util import exec_utils
[docs]class Parameters(Enum):
WEIGHT_THRESHOLD = "weight_threshold"
[docs]def sna_result_to_nx_graph(sna_results: List[List[Any]], parameters=None):
"""
Transforms the results of SNA to a NetworkX Graph / DiGraph object
(depending on the type of analysis).
Parameters
------------------
sna_results
Result of a SNA operation
parameters
Parameters of the algorithm, including:
- Parameters.WEIGHT_THRESHOLD => the weight threshold (used to filter out edges)
Returns
-----------------
nx_graph
NetworkX Graph / DiGraph
"""
if parameters is None:
parameters = {}
import networkx as nx
import numpy as np
weight_threshold = exec_utils.get_param_value(Parameters.WEIGHT_THRESHOLD, parameters, 0.0)
directed = sna_results[2]
rows, cols = np.where(sna_results[0] > weight_threshold)
edges = zip(rows.tolist(), cols.tolist())
if directed:
graph = nx.DiGraph()
else:
graph = nx.Graph()
labels = {}
nodes = []
for index, item in enumerate(sna_results[1]):
labels[index] = item
nodes.append(item)
edges = [(labels[e[0]], labels[e[1]]) for e in edges]
graph.add_nodes_from(nodes)
graph.add_edges_from(edges)
return graph
[docs]def cluster_affinity_propagation(sna_results: List[List[Any]], parameters=None) -> Dict[str, List[str]]:
"""
Performs a clustering using the affinity propagation algorithm provided by Scikit Learn
Parameters
--------------
sna_results
Values for a SNA metric
parameters
Parameters of the algorithm
Returns
--------------
clustering
Dictionary that contains, for each cluster that has been identified,
the list of resources of the cluster
"""
from sklearn.cluster import AffinityPropagation
if parameters is None:
parameters = {}
matrix = sna_results[0]
originators = sna_results[1]
affinity_propagation = AffinityPropagation(**parameters)
affinity_propagation.fit(matrix)
clusters = affinity_propagation.predict(matrix)
ret = {}
for i in range(len(clusters)):
res = originators[i]
cluster = str(clusters[i])
if cluster not in ret:
ret[cluster] = []
ret[cluster].append(res)
return ret