Source code for pm4py.algo.organizational_mining.sna.util

'''
    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