Removed global variables

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

@@ -17,13 +17,24 @@ class SemanticLinking:
     df_nodes = []
     destf_nodes = []
 
+    G: nx.MultiDiGraph = None
+
+    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'}
+
     def __init__(self):
         warnings.filterwarnings('ignore')
+
+        # init HyperGraph
         self.hg = HyperGraph()
         self.hg.classify()
         
         self.df_nodes = self.hg.cluster_labels
         self.destf_nodes = self.hg.dest_cluster_labels
+
+        # init visual graph
+        self.G = nx.MultiDiGraph(day="Stackoverflow")
         
     def _color_network(self, G):
         """Colors the network so that neighboring nodes all have distinct colors.
@@ -77,31 +88,12 @@ class SemanticLinking:
         elif len(high_labels) > 1:
             # Prec-Max
             if labeling[node] not in high_labels:
-
-             labeling[node] = max(high_labels)
-
-
-    G = nx.MultiDiGraph(day="Stackoverflow")
-
-    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'}
-    i=0
-
-    graphedge = []
-    weigth = []
-    sourcedestination = []
-    source = []
-    dest = []
-    edge_width = []
-    weight1 = []
-
-    node_adjacencies = []
-    labeling = {}
-
+                labeling[node] = max(high_labels)
+                
     def drawedges(self):
         """drawing edges in graph"""
         labelvalues = self.hg.label_values
+        weight1 = []
 
         for drow in range(len(self.df_nodes)):
             for row in range(len(self.destf_nodes[drow])):
@@ -109,16 +101,18 @@ class SemanticLinking:
 
         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])
+               weight1.append(self.G.number_of_edges(labelvalues[row], labelvalues[row1]))
+               print("The number of coccurance from node ", labelvalues[row],"to node ", labelvalues[row1], ": ", weight1[row1])
 
-        self.G.__setattr__('weight', self.weight1)
+        #self.G.__setattr__('weight', weight1)
+        self.G.weight = weight1
+        return weight1
 
     def dolabeling(self):
         """label_propagation_communities(G) """
 
         coloring = self._color_network(self.G)
-            # Create a unique label for each node in the graph
+        # Create a unique label for each node in the graph
         labeling = {v: k for k, v in enumerate(self.G)}
         print("lable value: ", labeling.values())
         while not self._labeling_complete(labeling, self.G):
@@ -129,19 +123,25 @@ class SemanticLinking:
                 self._update_label(n, labeling, self.G)
             for label in set(labeling.values()):
               print("lable value: ", labeling.values())
-        self.labeling = labeling
 
+        return labeling
 
     def findigneighbors(self):
         """ findig nodes' adjecencies"""
         node_text = []
+        node_adjacencies = []
+
         for node, adjacencies in enumerate(self.G.adjacency()):
-            self.node_adjacencies.append(len(adjacencies[1]))
+            node_adjacencies.append(len(adjacencies[1]))
             node_text.append('# of connections: '+str(len(adjacencies[1])))
 
-        self.G.color = self.node_adjacencies
+        self.G.color = node_adjacencies
+        return node_adjacencies
+
+    def result(self, weight1, labeling, node_adjacencies):
+        weigth = []
+        edge_width = []
 
-    def result(self):
         plt.figure(figsize=(25, 25))
         options = {
             'with_labels': True,
@@ -154,18 +154,19 @@ class SemanticLinking:
         d = nx.degree_centrality(self.G)
         d_list = list(d.values())
         print("node centrality: ", d_list)
-        print("node adjacencies: ", self.node_adjacencies)
-        for row in range(len(self.weigth)):
-            self.edge_width.append([])
-            for drow in range(len(self.weigth[row])):
-                self.edge_width[row].append(self.weigth[row][drow])
+        print("node adjacencies: ", node_adjacencies)
+        for row in range(len(weigth)):
+            edge_width.append([])
+            for drow in range(len(weigth[row])):
+                edge_width[row].append(weigth[row][drow])
         node_size = [v * 80 for v in d.values()]  # setting node size based on node centrality
-        edge_width = [row * 0.5 for row in self.weight1]
+        edge_width = [row * 0.5 for row in weight1]
 
         print("Nodes' Degree:  ", nx.degree(self.G))
         print("Nodes' Betweeness  ", nx.edge_betweenness_centrality(self.G))
         print("Nodes' Betweeness-centrality:  ", nx.betweenness_centrality(self.G))
 
+        # TODO extract method
         """
         Using the spring layout : 
         - k controls the distance between the nodes and varies between 0 and 1
@@ -180,9 +181,8 @@ class SemanticLinking:
 
         pos_nodes = nx.spring_layout(self.G, k=0.25, iterations=50)
 
-
-        nx.draw(self.G, pos_nodes, node_color=self.node_adjacencies, node_size=node_size, width=2, arrowstyle='->',
-                arrowsize=10, weight=self.weight1, edge_color='gray', **options)
+        nx.draw(self.G, pos_nodes, node_color=node_adjacencies, node_size=node_size, width=2, arrowstyle='->',
+                arrowsize=10, weight=weight1, edge_color='gray', **options)
 
         edge_labels = nx.get_edge_attributes(self.G, 'weight')
 
@@ -190,18 +190,23 @@ class SemanticLinking:
         for node, coords in pos_nodes.items():
             pos_attrs[node] = (coords[0], coords[1] + 0.02)
         nx.draw_networkx_edge_labels(self.G, pos_nodes, edge_labels=edge_labels, font_size=10, font_color='red')
-        nx.draw_networkx_labels(self.G, pos_attrs, labels=self.labeling, font_size=10, font_color='red')
+        nx.draw_networkx_labels(self.G, pos_attrs, labels=labeling, font_size=10, font_color='red')
 
         ax = plt.gca()
         ax.collections[0].set_edgecolor("#555555")
         plt.show()
 
-
     def main(self):
-        self.drawedges()
-        self.dolabeling()
-        self.findigneighbors()
-        self.result()
+        weight1 = self.drawedges()
+
+        labeling = self.dolabeling()
+       
+        node_adjacencies = self.findigneighbors()
+
+        self.result(weight1, labeling, node_adjacencies)
+
+
 
-linking = SemanticLinking()
-linking.main()
+if __name__ == '__main__':
+    linking = SemanticLinking()
\ No newline at end of file
+    linking.main()
\ No newline at end of file