my vasvas will kill me

GraphTransformer.py

@@ -75,23 +75,38 @@ def feature_matrix(g):
         piazche[i][v-1] = 1
     return piazche
 
-def remove_random_node(graph, max_size=40, min_size=10):
-    '''
-    removes a random node from the gragh
-    returns the remaining graph matrix and the removed node links
-    '''
-    if len(graph.nodes()) >= max_size or len(graph.nodes()) < min_size:
-        return None
-    relabeled_graph = nx.relabel.convert_node_labels_to_integers(graph)
-    choice = np.random.choice(list(relabeled_graph.nodes()))
-    remaining_graph = nx.to_numpy_matrix(relabeled_graph.subgraph(filter(lambda x: x != choice, list(relabeled_graph.nodes()))))
-    removed_node = nx.to_numpy_matrix(relabeled_graph)[choice]
-    graph_length = len(remaining_graph)
-    # source_graph = np.pad(remaining_graph, [(0, max_size - graph_length), (0, max_size - graph_length)])
-    # target_graph = np.copy(source_graph)
-    removed_node_row = np.asarray(removed_node)[0]
-    # target_graph[graph_length] = np.pad(removed_node_row, [(0, max_size - len(removed_node_row))])
-    return remaining_graph, removed_node_row
+# def remove_random_node(graph, max_size=40, min_size=10):
+#     '''
+#     removes a random node from the gragh
+#     returns the remaining graph matrix and the removed node links
+#     '''
+#     if len(graph.nodes()) >= max_size or len(graph.nodes()) < min_size:
+#         return None
+#     relabeled_graph = nx.relabel.convert_node_labels_to_integers(graph)
+#     choice = np.random.choice(list(relabeled_graph.nodes()))
+#     remaining_graph = nx.to_numpy_matrix(relabeled_graph.subgraph(filter(lambda x: x != choice, list(relabeled_graph.nodes()))))
+#     removed_node = nx.to_numpy_matrix(relabeled_graph)[choice]
+#     graph_length = len(remaining_graph)
+#     # source_graph = np.pad(remaining_graph, [(0, max_size - graph_length), (0, max_size - graph_length)])
+#     # target_graph = np.copy(source_graph)
+#     removed_node_row = np.asarray(removed_node)[0]
+#     # target_graph[graph_length] = np.pad(removed_node_row, [(0, max_size - len(removed_node_row))])
+#     return remaining_graph, removed_node_row
+
+def prepare_graph_data(graph, max_size=40, min_size=10):
+  '''
+  gets a graph as an input
+  returns a graph with a randomly removed node adj matrix [0], its feature matrix [0], the removed node true links [2]
+  '''
+  if len(graph.nodes()) >= max_size or len(graph.nodes()) < min_size:
+    return None
+  relabeled_graph = nx.relabel.convert_node_labels_to_integers(graph)
+  choice = np.random.choice(list(relabeled_graph.nodes()))
+  remaining_graph = relabeled_graph.subgraph(filter(lambda x: x != choice, list(relabeled_graph.nodes())))
+  remaining_graph_adj = nx.to_numpy_matrix(remaining_graph)
+  removed_node = nx.to_numpy_matrix(relabeled_graph)[choice]
+  removed_node_row = np.asarray(removed_node)[0]
+  return remaining_graph_adj, feature_matrix(remaining_graph),  removed_node_row
 
 """"
 Layers:
@@ -146,5 +161,6 @@ class GraphAttn(nn.Module):
     scores = attention(query, key, value, self.key_dim)
     concat = scores.transpose(1,2).contiguous().view(bs, -1, self.model_dim)
     output = self.out(concat)
+    output = output.view(bs, self.model_dim)
 
     return output