rgcn

link-prediction/citation/citation.py

@@ -85,7 +85,7 @@ sampling_rate = 2
 train_neg = construct_negative_graph(train, sampling_rate, ('judgement', 'citation', 'judgement'))
 test_neg = construct_negative_graph(test,sampling_rate,('judgement', 'citation', 'judgement'))
 print(train.etypes)
-model = Model(13, 10, 2, train.etypes)
+model = Model(27, 10, 2, train.etypes)
 judgement_feats = train.nodes['judgement'].data['feature']
 test_judgement_feats = test.nodes['judgement'].data['feature']
 node_features = {'judgement': judgement_feats}#, 'court': court_feats}

link-prediction/citation/model.py

@@ -9,13 +9,12 @@ import json
 import csv
 from hetDotProduct import HeteroDotProductPredictor
 from rgcnClass import RGCN
-#from sageClass import GraphSAGE
 
 class Model(nn.Module):
     def __init__(self, in_features, hidden_features, out_features, rel_names):
         super().__init__()
-        self.sage = RGCN(in_features, hidden_features, out_features, rel_names)
+        self.rgcn = RGCN(in_features, hidden_features, out_features, rel_names)
         self.pred = HeteroDotProductPredictor()
     def forward(self, g, neg_g, x, etype):
-        h = self.sage(g, x)
+        h = self.rgcn(g, x)
         return h
\ No newline at end of file

link-prediction/citation/output/not_predicted_results.csv

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link-prediction/citation/test_gsData.py

@@ -23,7 +23,9 @@ def return_edges():
 def return_features_labels():
     data = pd.read_csv("input/OE_LABELS.csv") #labels.csv
     data = data.drop(columns=['DATETIME'])
-    data = data[['tid','LAW','INFRINGEMENT','PERSON','NATION/RELIGIOUS/POL_GROUP','GEOGRAPHICAL_LOC','LANGUAGE','LOCATION_STRUCTURE','LOCATION','CONTACTNAME','ORGANIZATION','ORGANIZATION2','GEOGRAPHY2','PERCENT']]
+
+    #uncomment below for 13 features
+    #data = data[['tid','LAW','INFRINGEMENT','PERSON','NATION/RELIGIOUS/POL_GROUP','GEOGRAPHICAL_LOC','LANGUAGE','LOCATION_STRUCTURE','LOCATION','CONTACTNAME','ORGANIZATION','ORGANIZATION2','GEOGRAPHY2','PERCENT']]
     with open('input/MAPPING.json') as json_file: 
         j = json.load(json_file)
     for i in range(len(data)):

link-prediction/citation/train_gsData.py

@@ -23,7 +23,9 @@ def return_edges():
 def return_features_labels():
     data = pd.read_csv("input/OE_LABELS.csv") #labels.csv
     data = data.drop(columns=['DATETIME'])
-    data = data[['tid','LAW','INFRINGEMENT','PERSON','NATION/RELIGIOUS/POL_GROUP','GEOGRAPHICAL_LOC','LANGUAGE','LOCATION_STRUCTURE','LOCATION','CONTACTNAME','ORGANIZATION','ORGANIZATION2','GEOGRAPHY2','PERCENT']]
+
+    #uncomment below for 13 features
+    #data = data[['tid','LAW','INFRINGEMENT','PERSON','NATION/RELIGIOUS/POL_GROUP','GEOGRAPHICAL_LOC','LANGUAGE','LOCATION_STRUCTURE','LOCATION','CONTACTNAME','ORGANIZATION','ORGANIZATION2','GEOGRAPHY2','PERCENT']]
     
     with open('input/MAPPING.json') as json_file: 
         j = json.load(json_file)

link-prediction/similarity/model.py

@@ -9,13 +9,12 @@ import json
 import csv
 from hetDotProduct import HeteroDotProductPredictor
 from rgcnClass import RGCN
-#from sageClass import GraphSAGE
 
 class Model(nn.Module):
     def __init__(self, in_features, hidden_features, out_features, rel_names):
         super().__init__()
-        self.sage = RGCN(in_features, hidden_features, out_features, rel_names)
+        self.rgcn = RGCN(in_features, hidden_features, out_features, rel_names)
         self.pred = HeteroDotProductPredictor()
     def forward(self, g, neg_g, x, etype):
-        h = self.sage(g, x)
+        h = self.rgcn(g, x)
         return h
\ No newline at end of file

link-prediction/similarity/test_gsData.py

@@ -23,6 +23,8 @@ def return_edges():
 def return_features_labels():
     data = pd.read_csv("input/OE_LABELS.csv") #labels.csv
     data = data.drop(columns=['DATETIME'])
+
+    #uncomment below for 13 features
     #data = data[['tid','LAW','INFRINGEMENT','PERSON','NATION/RELIGIOUS/POL_GROUP','GEOGRAPHICAL_LOC','LANGUAGE','LOCATION_STRUCTURE','LOCATION','CONTACTNAME','ORGANIZATION','ORGANIZATION2','GEOGRAPHY2','PERCENT']]
     
     with open('input/MAPPING.json') as json_file: 

link-prediction/similarity/train_gsData.py

@@ -23,6 +23,8 @@ def return_edges():
 def return_features_labels():
     data = pd.read_csv("input/OE_LABELS.csv") #labels.csv
     data = data.drop(columns=['DATETIME'])
+
+    #uncomment below for 13 features
     #data = data[['tid','LAW','INFRINGEMENT','PERSON','NATION/RELIGIOUS/POL_GROUP','GEOGRAPHICAL_LOC','LANGUAGE','LOCATION_STRUCTURE','LOCATION','CONTACTNAME','ORGANIZATION','ORGANIZATION2','GEOGRAPHY2','PERCENT']]
     
     with open('input/MAPPING.json') as json_file: