Call hypergraph impl

data-hub/semantic-linking-microservice/app/initialdemo/SemanticLinking.py

 import networkx as nx
 import matplotlib.pyplot as plt
 from collections import Counter
-import HyperGraph as hg
+from HyperGraph import HyperGraph
 import warnings
 
 # pip install networkx
@@ -13,9 +13,18 @@ import warnings
 
 class SemanticLinking:
 
-    def __init__(self):
-        hg.classify()
+    hg: HyperGraph = None
+    df_nodes = []
+    destf_nodes = []
 
+    def __init__(self):
+        warnings.filterwarnings('ignore')
+        self.hg = HyperGraph()
+        self.hg.classify()
+        
+        self.df_nodes = self.hg.cluster_labels
+        self.destf_nodes = self.hg.dest_cluster_labels
+        
     def _color_network(self, G):
         """Colors the network so that neighboring nodes all have distinct colors.
 
@@ -30,7 +39,6 @@ class SemanticLinking:
                 coloring[color] = set([node])
         return coloring
 
-
     def _labeling_complete(self, labeling, G):
         """Determines whether or not LPA is done.
 
@@ -42,7 +50,6 @@ class SemanticLinking:
         return all(labeling[v] in self._most_frequent_labels(v, labeling, G)
                    for v in G if len(G[v]) > 0)
 
-
     def _most_frequent_labels(self, node, labeling, G):
         """Returns a set of all labels with maximum frequency in `labeling`.
 
@@ -58,7 +65,6 @@ class SemanticLinking:
         max_freq = max(freqs.values())
         return {label for label, freq in freqs.items() if freq == max_freq}
 
-
     def _update_label(self, node, labeling, G):
         """Updates the label of a node using the Prec-Max tie breaking algorithm
 
@@ -75,16 +81,8 @@ class SemanticLinking:
              labeling[node] = max(high_labels)
 
 
-    warnings.filterwarnings('ignore')
-
-
-
-
-    #G = nx.DiGraph(directed=True)
     G = nx.MultiDiGraph(day="Stackoverflow")
 
-    df_nodes = hg.clusterlabels
-    destf_nodes = hg.destclusterlabel
     color_map = {1: '#f09494', 2: '#eebcbc', 3: '#72bbd0', 4: '#91f0a1', 5: '#629fff', 6: '#bcc2f2',
                  7: '#eebcbc', 8: '#f1f0c0', 9: '#d2ffe7', 10: '#caf3a6', 11: '#ffdf55', 12: '#ef77aa',
                  13: '#d6dcff', 14: '#d2f5f0'}
@@ -102,24 +100,23 @@ class SemanticLinking:
     labeling = {}
 
     def drawedges(self):
-
         """drawing edges in graph"""
+        labelvalues = self.hg.label_values
 
         for drow in range(len(self.df_nodes)):
             for row in range(len(self.destf_nodes[drow])):
                self.G.add_edge(self.df_nodes[drow], self.destf_nodes[drow][row])
 
-        for row in range(len(hg.labalvlues)):
-            for row1 in range(len(hg.labalvlues)):
-               self.weight1.append(self.G.number_of_edges(hg.labalvlues[row], hg.labalvlues[row1]))
-               print("The number of coccurance from node ", hg.labalvlues[row],"to node ", hg.labalvlues[row1], ": ", self.weight1[row1])
+        for row in range(len(labelvalues)):
+            for row1 in range(len(labelvalues)):
+               self.weight1.append(self.G.number_of_edges(labelvalues[row], labelvalues[row1]))
+               print("The number of coccurance from node ", labelvalues[row],"to node ", labelvalues[row1], ": ", self.weight1[row1])
 
         self.G.__setattr__('weight', self.weight1)
 
     def dolabeling(self):
         """label_propagation_communities(G) """
 
-
         coloring = self._color_network(self.G)
             # Create a unique label for each node in the graph
         labeling = {v: k for k, v in enumerate(self.G)}