Commit d94169eb authored by Alexander Lercher's avatar Alexander Lercher

Semantic Linking: working semantic linking logic without integration

parent b365f613
Index: data-hub/semantic-linking-microservice/app/initialdemo/HyperGraph.py
IDEA additional info:
Subsystem: com.intellij.openapi.diff.impl.patch.CharsetEP
<+>UTF-8
===================================================================
--- data-hub/semantic-linking-microservice/app/initialdemo/HyperGraph.py (date 1568037363000)
+++ data-hub/semantic-linking-microservice/app/initialdemo/HyperGraph.py (date 1568038969230)
@@ -1,140 +1,137 @@
-import networkx as nx
-import matplotlib.pyplot as plt
-import pandas as pd
import json
-
-
-with open("mult_in_out.json", "r") as json_file:
- df_nodes = json.load(json_file)
-
-
nodeIds = []
-destIds= []
-clusterlabels= []
+destIds = []
+clusterlabels = []
destclusterlabel = []
-cluster= []
+cluster = []
labalvlues = []
-i = 0
+
+def classify():
+
+ with open("mult_in_out.json", "r") as json_file:
+ df_nodes = json.load(json_file)
+
-for row in df_nodes:
+ 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 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 df_nodes:
+ destIds.append(row['TransactionTo'])
-for row in range(len(nodeIds)):
- print(nodeIds[row])
+ for row in range(len(nodeIds)):
+ print(nodeIds[row])
-print("Finish InputIDs")
-for row in range(len(nodeIds)):
+ print("Finish InputIDs")
+ i = 0
+ for row in range(len(nodeIds)):
- clusterlabels.append(row)
- i += 1
-print(i)
+ clusterlabels.append(row)
+ i += 1
+ print(i)
-"""" classifying Inputs"""
-"""" Labaling inputs"""
-for row in range(len(nodeIds)):
+ """" classifying Inputs"""
+ """" Labaling inputs"""
+ for row in range(len(nodeIds)):
- for rown in range(len(nodeIds[row])):
+ for rown in range(len(nodeIds[row])):
- for row1 in range(len(nodeIds)):
- for rown1 in range(len(nodeIds[row1])):
- if(nodeIds[row][rown]==nodeIds[row1][rown1]):
- # print("row: ",row,"row1: ",row1)
- if(row < row1):
- for row2 in clusterlabels:
- if( clusterlabels[row1]== clusterlabels[row2]):
- clusterlabels[row2]=clusterlabels[row]
- clusterlabels[row1] = clusterlabels[row]
+ for row1 in range(len(nodeIds)):
+ for rown1 in range(len(nodeIds[row1])):
+ if(nodeIds[row][rown]==nodeIds[row1][rown1]):
+ # print("row: ",row,"row1: ",row1)
+ if(row < row1):
+ for row2 in clusterlabels:
+ if( clusterlabels[row1]== clusterlabels[row2]):
+ clusterlabels[row2]=clusterlabels[row]
+ clusterlabels[row1] = clusterlabels[row]
- else:
- for row2 in clusterlabels:
- if (clusterlabels[row] == clusterlabels[row2]):
- clusterlabels[row2] = clusterlabels[row1]
- clusterlabels[row] = clusterlabels[row1]
+ else:
+ for row2 in clusterlabels:
+ if (clusterlabels[row] == clusterlabels[row2]):
+ clusterlabels[row2] = clusterlabels[row1]
+ clusterlabels[row] = clusterlabels[row1]
-print(clusterlabels)
-print("cluster labels:", len(clusterlabels))
-print("NodeIDs: ", len(nodeIds))
+ print(clusterlabels)
+ print("cluster labels:", len(clusterlabels))
+ print("NodeIDs: ", len(nodeIds))
-"""" Calculating the number of clusters"""
-clusternum = 1
-labalvlues.append(clusterlabels[0])
-for row in range(len(clusterlabels)):
- flag = True
- for row1 in range(len(labalvlues)):
- if(clusterlabels[row]== labalvlues[row1]):
- flag = False
+ """" Calculating the number of clusters"""
+ clusternum = 1
+ labalvlues.append(clusterlabels[0])
+ for row in range(len(clusterlabels)):
+ flag = True
+ for row1 in range(len(labalvlues)):
+ if(clusterlabels[row]== labalvlues[row1]):
+ flag = False
- if (flag):
- clusternum = + 1
- labalvlues.append(clusterlabels[row])
+ 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))
+ print("label values (source Ids in the network): ", labalvlues, " and the number of clusters is: ", len(labalvlues))
-"""" clustering Ids according to their labels"""
+ """" clustering Ids according to their labels"""
-for row in range(len(labalvlues)):
- cluster.append([])
- for row3 in range(len(nodeIds)):
- if (labalvlues[row] == clusterlabels[row3]):
- cluster[row].extend(nodeIds[row3])
-print("clusters: ", cluster)
+ for row in range(len(labalvlues)):
+ cluster.append([])
+ for row3 in range(len(nodeIds)):
+ if (labalvlues[row] == clusterlabels[row3]):
+ cluster[row].extend(nodeIds[row3])
+ print("clusters: ", cluster)
-""" Removing duplicating items in cluster"""
+ """ Removing duplicating items in cluster"""
-flag = True
-while(flag):
- for row in range(len(cluster)):
+ flag = True
+ while(flag):
+ for row in range(len(cluster)):
- flag= False
- for row1 in range(len(cluster[row])):
- flag= False
- for row2 in range (len(cluster[row])):
- if(row1 != row2):
- if(cluster[row][row1] == cluster[row][row2]):
- del cluster[row][row2]
- flag=True
- break
- if(flag):
- break
- if(flag):
- break
+ flag= False
+ for row1 in range(len(cluster[row])):
+ flag= False
+ for row2 in range (len(cluster[row])):
+ if(row1 != row2):
+ if(cluster[row][row1] == cluster[row][row2]):
+ del cluster[row][row2]
+ flag=True
+ break
+ if(flag):
+ break
+ if(flag):
+ break
-print("cluster:", cluster)
+ print("cluster:", cluster)
-"""" Clustering Destination Ids """
-for row in range(len(destIds)):
- destclusterlabel.append([])
- for row2 in range(len(destIds[row])):
- flag = True
- for rownum in range(len(labalvlues)):
- for row1 in range(len(cluster[rownum])):
+ """" Clustering Destination Ids """
+ for row in range(len(destIds)):
+ destclusterlabel.append([])
+ for row2 in range(len(destIds[row])):
+ flag = True
+ for rownum in range(len(labalvlues)):
+ for row1 in range(len(cluster[rownum])):
- if(destIds[row][row2]== cluster[rownum][row1]):
- destclusterlabel[row].append(labalvlues[rownum])
- flag = False
- if(flag):
- destclusterlabel.append(destIds[row][row2])
+ if(destIds[row][row2]== cluster[rownum][row1]):
+ destclusterlabel[row].append(labalvlues[rownum])
+ flag = False
+ if(flag):
+ destclusterlabel.append(destIds[row][row2])
-print("destination labels (destination Ids): ", destclusterlabel)
+ print("destination labels (destination Ids): ", destclusterlabel)
Index: data-hub/semantic-linking-microservice/app/initialdemo/SemanticLinking.py
IDEA additional info:
Subsystem: com.intellij.openapi.diff.impl.patch.CharsetEP
<+>UTF-8
===================================================================
--- data-hub/semantic-linking-microservice/app/initialdemo/SemanticLinking.py (date 1568037363000)
+++ data-hub/semantic-linking-microservice/app/initialdemo/SemanticLinking.py (date 1568040344378)
@@ -1,7 +1,7 @@
import networkx as nx
import matplotlib.pyplot as plt
from collections import Counter
-import HyperGraph as hg
+import initialdemo.HyperGraph as hg
import pandas as pd
import json
import warnings
@@ -12,194 +12,198 @@
import values as values
from matplotlib import colors
-def _color_network(G):
- """Colors the network so that neighboring nodes all have distinct colors.
+class SemanticLinking:
+
+ def __init__(self):
+ hg.classify()
+
+ def _color_network(self, G):
+ """Colors the network so that neighboring nodes all have distinct colors.
- Returns a dict keyed by color to a set of nodes with that color.
- """
- coloring = dict() # color => set(node)
- colors = nx.coloring.greedy_color(G)
- for node, color in colors.items():
- if color in coloring:
- coloring[color].add(node)
- else:
- coloring[color] = set([node])
- return coloring
+ Returns a dict keyed by color to a set of nodes with that color.
+ """
+ coloring = dict() # color => set(node)
+ colors = nx.coloring.greedy_color(G)
+ for node, color in colors.items():
+ if color in coloring:
+ coloring[color].add(node)
+ else:
+ coloring[color] = set([node])
+ return coloring
-def _labeling_complete(labeling, G):
- """Determines whether or not LPA is done.
+ def _labeling_complete(self, labeling, G):
+ """Determines whether or not LPA is done.
- Label propagation is complete when all nodes have a label that is
- in the set of highest frequency labels amongst its neighbors.
+ Label propagation is complete when all nodes have a label that is
+ in the set of highest frequency labels amongst its neighbors.
- Nodes with no neighbors are considered complete.
- """
- return all(labeling[v] in _most_frequent_labels(v, labeling, G)
- for v in G if len(G[v]) > 0)
+ Nodes with no neighbors are considered complete.
+ """
+ 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(node, labeling, G):
- """Returns a set of all labels with maximum frequency in `labeling`.
+ def _most_frequent_labels(self, node, labeling, G):
+ """Returns a set of all labels with maximum frequency in `labeling`.
- Input `labeling` should be a dict keyed by node to labels.
- """
- if not G[node]:
- # Nodes with no neighbors are themselves a community and are labeled
- # accordingly, hence the immediate if statement.
- return {labeling[node]}
+ Input `labeling` should be a dict keyed by node to labels.
+ """
+ if not G[node]:
+ # Nodes with no neighbors are themselves a community and are labeled
+ # accordingly, hence the immediate if statement.
+ return {labeling[node]}
- # Compute the frequencies of all neighbours of node
- freqs = Counter(labeling[q] for q in G[node])
- max_freq = max(freqs.values())
- return {label for label, freq in freqs.items() if freq == max_freq}
+ # Compute the frequencies of all neighbours of node
+ freqs = Counter(labeling[q] for q in G[node])
+ max_freq = max(freqs.values())
+ return {label for label, freq in freqs.items() if freq == max_freq}
-def _update_label(node, labeling, G):
- """Updates the label of a node using the Prec-Max tie breaking algorithm
+ def _update_label(self, node, labeling, G):
+ """Updates the label of a node using the Prec-Max tie breaking algorithm
- The algorithm is explained in: 'Community Detection via Semi-Synchronous
- Label Propagation Algorithms' Cordasco and Gargano, 2011
- """
- high_labels = _most_frequent_labels(node, labeling, G)
- if len(high_labels) == 1:
- labeling[node] = high_labels.pop()
- elif len(high_labels) > 1:
- # Prec-Max
- if labeling[node] not in high_labels:
+ The algorithm is explained in: 'Community Detection via Semi-Synchronous
+ Label Propagation Algorithms' Cordasco and Gargano, 2011
+ """
+ high_labels = self._most_frequent_labels(node, labeling, G)
+ if len(high_labels) == 1:
+ labeling[node] = high_labels.pop()
+ elif len(high_labels) > 1:
+ # Prec-Max
+ if labeling[node] not in high_labels:
- labeling[node] = max(high_labels)
+ labeling[node] = max(high_labels)
-warnings.filterwarnings('ignore')
+ warnings.filterwarnings('ignore')
-#G = nx.DiGraph(directed=True)
-G = nx.MultiDiGraph(day="Stackoverflow")
+ #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
+ 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 = []
+ graphedge = []
+ weigth = []
+ sourcedestination = []
+ source = []
+ dest = []
+ edge_width = []
+ weight1 = []
+ node_adjacencies = []
-""""drawing edges in graph"""
+ def drawedges(self):
-for drow in range(len(df_nodes)):
- for row in range(len(destf_nodes[drow])):
- G.add_edge(df_nodes[drow], destf_nodes[drow][row])
+ """drawing edges in graph"""
+
+ 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)):
- weight1.append(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], ": ", weight1[row1])
-
-G.__setattr__('weight', weight1)
+ 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])
- # print(float(row['Timestamp']))
- #G.add_weighted_edges_from([(row['TransactionFrom'], row['TransactionTo'], i*j)])
+ self.G.__setattr__('weight', self.weight1)
-#print dict_pos
-
-"""label_propagation_communities(G) """
+ def labeling(self):
+ """label_propagation_communities(G) """
-coloring = _color_network(G)
- # Create a unique label for each node in the graph
-labeling = {v: k for k, v in enumerate(G)}
-print("lable value: ", labeling.values())
-while not _labeling_complete(labeling, G):
-# Update the labels of every node with the same color.
- print("lable value: ", labeling.values())
- for color, nodes in coloring.items():
- for n in nodes:
- _update_label(n, labeling, G)
- for label in set(labeling.values()):
- print("lable value: ", labeling.values())
+ 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)}
+ print("lable value: ", labeling.values())
+ while not self._labeling_complete(labeling, self.G):
+ # Update the labels of every node with the same color.
+ print("lable value: ", labeling.values())
+ for color, nodes in coloring.items():
+ for n in nodes:
+ self._update_label(n, labeling, self.G)
+ for label in set(labeling.values()):
+ print("lable value: ", labeling.values())
-
-""" findig nodes' adjecencies"""
-node_adjacencies = []
-node_text = []
-for node, adjacencies in enumerate(G.adjacency()):
- node_adjacencies.append(len(adjacencies[1]))
- node_text.append('# of connections: '+str(len(adjacencies[1])))
+ def findigneighbors(self):
+ """ findig nodes' adjecencies"""
+ node_text = []
+ for node, adjacencies in enumerate(self.G.adjacency()):
+ self.node_adjacencies.append(len(adjacencies[1]))
+ node_text.append('# of connections: '+str(len(adjacencies[1])))
-G.color = node_adjacencies
+ self.G.color = self.node_adjacencies
-
-plt.figure(figsize=(25, 25))
-options = {
- 'with_labels': True,
- 'font_weight': 'regular',
-}
+ 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]
+ # colors = [color_map[G.node[node][1]] for node in G]
+ # sizes = [G.node[node]['Timestamp'] * 10 for node in G]
-
-d = nx.degree_centrality(G)
-d_list= list(d.values())
-print ("node centrality: ",d_list)
-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 weight1]
-
-print("Nodes' Degree: ", nx.degree(G))
-print("Nodes' Betweeness ", nx.edge_betweenness_centrality(G))
-print("Nodes' Betweeness-centrality: ", nx.betweenness_centrality(G))
+ 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("Nodes' Degree: ", nx.degree(self.G))
+ print("Nodes' Betweeness ", nx.edge_betweenness_centrality(self.G))
+ print("Nodes' Betweeness-centrality: ", nx.betweenness_centrality(self.G))
-"""
-Using the spring layout :
-- k controls the distance between the nodes and varies between 0 and 1
-- iterations is the number of times simulated annealing is run
-default k=0.1 and iterations=50
-"""
+ """
+ Using the spring layout :
+ - k controls the distance between the nodes and varies between 0 and 1
+ - iterations is the number of times simulated annealing is run
+ default k=0.1 and iterations=50
+ """
-labels2 = {}
+ labels2 = {}
-for idx, edge in enumerate(G.edges):
- labels2[edge] = "s"
+ for idx, edge in enumerate(self.G.edges):
+ labels2[edge] = "s"
-pos_nodes=nx.spring_layout(G, k=0.25, iterations=50)
-ax = plt.gca()
+ pos_nodes = nx.spring_layout(self.G, k=0.25, iterations=50)
-nx.draw(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(G, 'weight')
+ 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)
+ edge_labels = nx.get_edge_attributes(self.G, 'weight')
-pos_attrs = {}
-for node, coords in pos_nodes.items():
- pos_attrs[node] = (coords[0], coords[1] + 0.02)
-nx.draw_networkx_edge_labels(G, pos_nodes, edge_labels=edge_labels, font_size=10, font_color='red')
-nx.draw_networkx_labels(G, pos_attrs, labels=labeling,font_size=10, font_color='red')
-
-
+ pos_attrs = {}
+ 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')
-ax = plt.gca()
-ax.collections[0].set_edgecolor("#555555")
-plt.show()
+ ax = plt.gca()
+ ax.collections[0].set_edgecolor("#555555")
+ plt.show()
+ def main(self):
+ self.drawedges()
+ self.labeling()
+ self.findigneighbors()
+ self.result()
-
+linking = SemanticLinking()
+linking.main()
\ No newline at end of file
import networkx as nx
import matplotlib.pyplot as plt
from collections import Counter
import initialdemo.HyperGraph as hg
import HyperGraph as hg
import pandas as pd
import json
import warnings
......@@ -12,6 +12,13 @@ import mplleaflet
import values as values
from matplotlib import colors
# pip install networkx
# pip install matplotlib
# pip install pandas
# pip install community
# pip install mplleaflet
# pip install values
class SemanticLinking:
def __init__(self):
......@@ -100,6 +107,7 @@ class SemanticLinking:
weight1 = []
node_adjacencies = []
labeling = {}
def drawedges(self):
......@@ -116,7 +124,7 @@ class SemanticLinking:
self.G.__setattr__('weight', self.weight1)
def labeling(self):
def dolabeling(self):
"""label_propagation_communities(G) """
......@@ -201,7 +209,7 @@ class SemanticLinking:
def main(self):
self.drawedges()
self.labeling()
self.dolabeling()
self.findigneighbors()
self.result()
......
......@@ -2,16 +2,13 @@ import json
import requests
from threading import Thread
import network_constants as netconst
from intelligence_zahra.Processor import Processor
import logging
LOGGER = logging.getLogger(__name__)
class MessageHandler:
_processor: Processor = None
def __init__(self):
self._processor = Processor()
pass
def handle_generic(self, body):
LOGGER.info(f"Received message: {body}")
......@@ -43,6 +40,7 @@ class MessageHandler:
if response.status_code == 200:
traces = response.json()
Thread(target=self._processor.process(traces)).start()
# TODO integrate zahras code
# Thread(target=self._processor.process(traces)).start()
else:
LOGGER.error(f"Could not retrieve JSON from {url} with GET request ({response.status_code})")
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