Merge branch 'feature/semantic-linking-refactoring' into develop

data-hub/semantic-linking-microservice/app/configs/swagger.yml

@@ -28,3 +28,39 @@ paths:
       responses:
         200:
           description: "Successful echo of request data"
+  /graphinfo:
+    get:
+        operationId: "rest.graphinfo.get"
+        tags:
+          - "GraphInfo"
+        summary: "Get info about clustered nodes"
+        description: "Returns multiple metrics for all nodes created by analyzing and clustering the blockchain traces"
+        parameters: []
+        responses:
+          200:
+            description: "Successful operation"
+            schema:
+              $ref: "#/definitions/NodeInfo"
+
+definitions:
+  NodeInfo:
+    type: "object"
+    properties:
+      label:
+        type: string
+      centrality:
+        type: number
+      adjacencies:
+        type: integer
+      degree:
+        type: number
+      betweenness:
+        type: object
+        properties:
+            to_node:
+                type: integer
+            value: 
+                type: number
+      betweenness_centrality:
+        type: number
+    
\ No newline at end of file

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

 import json
 
-nodeIds = []
-destIds = []
-clusterlabels = []
-destclusterlabel = []
-cluster = []
-labalvlues = []
+class HyperGraph:
 
-def classify():
-    df_nodes = load_values()
+    cluster_labels = []
+    dest_cluster_labels = []
+    label_values = []
 
-    for row in df_nodes:
+    def __init__(self):
+        pass
+
+    def classify(self):
+        df_nodes = self.load_values()
+
+        ret_val = self.init(df_nodes)
+        nodeIds = ret_val['nodeIds']
+        clusterlabels = ret_val['clusterlabels']
+        destIds = ret_val['destIds']
+        
+        clusterlabels = self.classify_input(nodeIds, clusterlabels)
+
+        labelvals = self.calc_cluster_num(clusterlabels)
+
+        cluster = self.cluster_with_labels(nodeIds, clusterlabels, labelvals)
+
+        cluster = self.remove_duplicates(cluster)
+
+        destclusterlabel = self.cluster_dest_ids(labelvals, cluster, destIds)
+
+        self.cluster_labels = clusterlabels
+        self.dest_cluster_labels = destclusterlabel
+        self.labelvals = labelvals
 
+    def load_values(self):
+        with open("mult_in_out_large.json", "r") as json_file:
+            df_nodes = json.load(json_file)
+
+        return df_nodes
+
+    def init(self, df_nodes):
+        nodeIds = []
+        clusterlabels = []
+        destIds = []
+
+        for row in df_nodes:
             for j in range(len(row['TransactionFrom'])):
                 print("   Input Ids:  ", row['TransactionFrom'][j])
             nodeIds.append(row['TransactionFrom'])
             print("This is nodes:  ", nodeIds)
 
-
         for row in df_nodes:
             destIds.append(row['TransactionTo'])
 
-
         for row in range(len(nodeIds)):
             print(nodeIds[row])
 
         print("Finish InputIDs")
         i = 0
         for row in range(len(nodeIds)):
-
             clusterlabels.append(row)
             i += 1
+
         print(i)
+        return {'nodeIds': nodeIds,
+                'clusterlabels': clusterlabels,
+                'destIds': destIds}
 
+    def classify_input(self, nodeIds, clusterlabels):
         """" classifying Inputs"""
         """" Labaling inputs"""
         for row in range(len(nodeIds)):
@@ -55,42 +88,43 @@ def classify():
                                         clusterlabels[row2] = clusterlabels[row1]
                                 clusterlabels[row] = clusterlabels[row1]
 
-
         print(clusterlabels)
         print("cluster labels:", len(clusterlabels))
         print("NodeIDs:   ", len(nodeIds))
+        return clusterlabels
 
-
-
+    def calc_cluster_num(self, clusterlabels):
         """" Calculating the number of clusters"""
-    clusternum = 1
-    labalvlues.append(clusterlabels[0])
+        labelvals = []
+
+        labelvals.append(clusterlabels[0])
         for row in range(len(clusterlabels)):
             flag = True
-        for row1 in range(len(labalvlues)):
-          if(clusterlabels[row]== labalvlues[row1]):
+            for row1 in range(len(labelvals)):
+                if(clusterlabels[row]== labelvals[row1]):
                     flag = False
 
             if (flag):
-            clusternum = + 1
-            labalvlues.append(clusterlabels[row])
-
-    print("label values (source Ids in the network):  ", labalvlues, " and the number of clusters is: ", len(labalvlues))
-
+                labelvals.append(clusterlabels[row])
 
+        print("label values (source Ids in the network):  ", labelvals, " and the number of clusters is: ", len(labelvals))
 
+        return labelvals
 
+    def cluster_with_labels(self, nodeIds, clusterlabels, labelvals):
         """" clustering Ids according to their labels"""
+        cluster = []
 
-    for row in range(len(labalvlues)):
+        for row in range(len(labelvals)):
             cluster.append([])
             for row3 in range(len(nodeIds)):
-            if (labalvlues[row] == clusterlabels[row3]):
+                if (labelvals[row] == clusterlabels[row3]):
                     cluster[row].extend(nodeIds[row3])
         print("clusters:   ", cluster)
 
+        return cluster
 
-
+    def remove_duplicates(self, cluster):
         """ Removing duplicating items in cluster"""
 
         flag = True
@@ -113,27 +147,25 @@ def classify():
 
         print("cluster:", cluster)
         
+        return cluster
 
-
-
+    def cluster_dest_ids(self, labelvals, cluster, destIds):
         """" Clustering  Destination Ids """
+        destclusterlabel = []
+
         for row in range(len(destIds)):
             destclusterlabel.append([])
             for row2 in range(len(destIds[row])):
                 flag = True
-            for rownum in range(len(labalvlues)):
+                for rownum in range(len(labelvals)):
                     for row1 in range(len(cluster[rownum])):
 
                         if(destIds[row][row2]== cluster[rownum][row1]):
-                        destclusterlabel[row].append(labalvlues[rownum])
+                            destclusterlabel[row].append(labelvals[rownum])
                             flag = False
                 if(flag):
                     destclusterlabel.append(destIds[row][row2])
 
         print("destination labels (destination Ids):  ", destclusterlabel)
 
-def load_values():
-    with open("mult_in_out_large.json", "r") as json_file:
-        df_nodes = json.load(json_file)
-
-    return df_nodes
\ No newline at end of file
+        return destclusterlabel
\ No newline at end of file

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

+class NodeInfo:
+    '''Contains information about the individual nodes in the generated graph'''
+
+    label = None
+    centrality = None
+    adjacencies = None
+    degree = None
+    betweenness = None
+    betweenness_centrality = None
+
+    def __init__(self):
+        self.label = 'Node123'
+        self.centrality = 0
+        self.adjacencies = 0
+        self.degree = 0
+        self.betweenness = None
+        self.betweenness_centrality = 0

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
@@ -13,8 +13,28 @@ import warnings
 
 class SemanticLinking:
 
+    hg: HyperGraph = None
+    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):
-        hg.classify()
+        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.
@@ -30,7 +50,6 @@ class SemanticLinking:
                 coloring[color] = set([node])
         return coloring
 
-
     def _labeling_complete(self, labeling, G):
         """Determines whether or not LPA is done.
 
@@ -42,7 +61,6 @@ 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 +76,6 @@ 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
 
@@ -71,55 +88,28 @@ class SemanticLinking:
         elif len(high_labels) > 1:
             # Prec-Max
             if labeling[node] not in high_labels:
-
                 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'}
-    i=0
-
-    graphedge = []
-    weigth = []
-    sourcedestination = []
-    source = []
-    dest = []
-    edge_width = []
-    weight1 = []
-
-    node_adjacencies = []
-    labeling = {}
-
     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])):
                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)):
+               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.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
         labeling = {v: k for k, v in enumerate(self.G)}
@@ -132,24 +122,26 @@ 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 print_metrics(self, weight1, labeling, node_adjacencies):
+        weigth = []
+        edge_width = []
 
-    def result(self):
         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]
@@ -157,18 +149,19 @@ 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])
-        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]
+        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])
+       
+        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))
 
+    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
@@ -177,15 +170,21 @@ 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"
 
         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')
 
@@ -193,18 +192,23 @@ 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.print_metrics(weight1, labeling, node_adjacencies)
+
+        self.draw_edges(weight1, labeling, node_adjacencies)
 
-linking = SemanticLinking()
-linking.main()
\ No newline at end of file
+if __name__ == '__main__':
+    linking = SemanticLinking()
+    linking.main()
\ No newline at end of file

data-hub/semantic-linking-microservice/app/rest/graphinfo.py

+from flask import request, Response
+from initialdemo.NodeInfo import NodeInfo
+import pickle as json
+
+def get():
+    # TODO return real graph infos
+    ni = NodeInfo()
+    return [ni.__dict__]

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()
\ No newline at end of file