Extracted drawing method; example unit tests

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

@@ -104,7 +104,6 @@ class SemanticLinking:
                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', weight1)
         self.G.weight = weight1
         return weight1
 
@@ -138,15 +137,11 @@ class SemanticLinking:
         self.G.color = node_adjacencies
         return node_adjacencies
 
-    def result(self, weight1, labeling, node_adjacencies):
+    def print_metrics(self, weight1, labeling, node_adjacencies):
         weigth = []
         edge_width = []
 
         plt.figure(figsize=(25, 25))
-        options = {
-            'with_labels': True,
-            'font_weight': 'regular',
-        }
       
         # colors = [color_map[G.node[node][1]] for node in G]
         # sizes = [G.node[node]['Timestamp'] * 10 for node in G]
@@ -159,14 +154,14 @@ class SemanticLinking:
             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 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
+    def draw_edges(self, weight1, labeling, node_adjacencies):
         """
         Using the spring layout : 
         - k controls the distance between the nodes and varies between 0 and 1
@@ -175,6 +170,13 @@ class SemanticLinking:
         """
 
         labels2 = {}  
+        options = {
+            'with_labels': True,
+            'font_weight': 'regular',
+        }
+
+        d = nx.degree_centrality(self.G)
+        node_size = [v * 80 for v in d.values()]  # setting node size based on node centrality
 
         for idx, edge in enumerate(self.G.edges):
             labels2[edge] = "s"
@@ -203,9 +205,9 @@ class SemanticLinking:
        
         node_adjacencies = self.findigneighbors()
 
-        self.result(weight1, labeling, node_adjacencies)
-
+        self.print_metrics(weight1, labeling, node_adjacencies)
 
+        self.draw_edges(weight1, labeling, node_adjacencies)
 
 if __name__ == '__main__':
     linking = SemanticLinking()

data-hub/semantic-linking-microservice/tests/manage_sys_paths.py

+# add modules folder to interpreter path
+import sys
+import os
+modules_paths = ['../app/', '../../../modules/']
+for path in modules_paths:
+    if os.path.exists(path):
+        sys.path.insert(1, path)
+        print(f"added {path}")

data-hub/semantic-linking-microservice/tests/test_HyperGraph.py

+import unittest
+import manage_sys_paths
+import json
+
+from initialdemo.HyperGraph import HyperGraph
+
+class Test_HyperGraph(unittest.TestCase):
+    hypergraph: HyperGraph = None
+
+    def setUp(self):
+        self.hypergraph = HyperGraph()
+
+    def test_removeDuplicates_noDupOrdered_sameContent(self):
+        list_ = [[1,2,3]]
+        set_ = self.hypergraph.remove_duplicates(list_)
+        self.assertEqual(list_, set_)
+
+    def test_removeDuplicates_oneDupOrdered_removed(self):
+        list_ = [[1,2,3,3]]
+        set_ = self.hypergraph.remove_duplicates(list_)
+        self.assertEqual([[1,2,3]], set_)
+
+    def test_removeDuplicates_multDupOrdered_allRemoved(self):
+        list_ = [[1,1,2,3,3,4]]
+        set_ = self.hypergraph.remove_duplicates(list_)
+        self.assertEqual([[1,2,3,4]], set_)
+
+    def test_removeDuplicates_noDupUnordered_sameContent(self):
+        list_ = [[1,2,3,5,9,4,30,15]]
+        set_ = self.hypergraph.remove_duplicates(list_)
+        self.assertEqual(list_, set_)
+
+    def test_removeDuplicates_oneDupUnordered_removed(self):
+        list_ = [[1,2,3,5,9,4,30,5,15]]
+        set_ = self.hypergraph.remove_duplicates(list_)
+        self.assertEqual([[1,2,3,5,9,4,30,15]], set_)
+
+    def test_removeDuplicates_multDupUnordered_allRemoved(self):
+        list_ = [[1,2,5,3,1,70,25,-1,7,-1]]
+        set_ = self.hypergraph.remove_duplicates(list_)
+        self.assertEqual([[1,2,5,3,70,25,-1,7]], set_)
+
+    def test_removeDuplicates_oneDupOrderedMultDim_removed(self):
+        list_ = [[1,1,2],[2,2,3]]
+        set_ = self.hypergraph.remove_duplicates(list_)
+        self.assertEqual([[1,2],[2,3]], set_)
+
+    def test_removeDuplicates_multDupOrderedMultDim_allRemoved(self):
+        list_ = [[1,1,2,3,3],[2,2,3,4,4,5]]
+        set_ = self.hypergraph.remove_duplicates(list_)
+        self.assertEqual([[1,2,3],[2,3,4,5]], set_)
+
+    def test_removeDuplicates_multDupUnorderedMultDim_allRemoved(self):
+        list_ = [[1,2,5,2,7,3],[-10,5,3,20,-10,-7]]
+        set_ = self.hypergraph.remove_duplicates(list_)
+        self.assertEqual([[1,2,5,7,3],[-10,5,3,20,-7]], set_)
+
+    def test_removeDuplicates_multDupUnorderedMultDim2_allRemoved(self):
+        list_ = [[1,2,5,2,7,3],[-10,5,3,20,-10,-7],[1,2]]
+        set_ = self.hypergraph.remove_duplicates(list_)
+        self.assertEqual([[1,2,5,7,3],[-10,5,3,20,-7],[1,2]], set_)
+    
+    def test_removeDuplicates_multDupUnorderedTripleDim_noDupRemoved(self):
+        list_ = [[[1,2,5,2,7,3],[-10,5,3,20,-10,-7],[1,2]]]
+        set_ = self.hypergraph.remove_duplicates(list_)
+        self.assertEqual(list_, set_)
+
+if __name__ == '__main__':
+    unittest.main()
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