def encode_types(graph, node_types, edge_types):
node_iterator = multidigraph_node_data_iterator(graph)
encode_categorically(node_iterator, node_types, 'type', 'categorical_type')
edge_iterator = multidigraph_edge_data_iterator(graph)
encode_categorically(edge_iterator, edge_types, 'type', 'categorical_type')
return graph
def __call__(self, graph):
if self.obfuscate:
obfuscate_labels(graph, self.obfuscate)
# Encode attribute values as number
graph = encode_values(graph, self.categorical, self.continuous)
graph = nx.convert_node_labels_to_integers(
graph, label_attribute=self.label_attribute
)
if self.duplicate:
graph = duplicate_edges_in_reverse(graph)
# Node or Edge Type as int
graph = encode_types(graph, multidigraph_node_data_iterator, self.node_types)
graph = encode_types(graph, multidigraph_edge_data_iterator, self.edge_types)
for data in multidigraph_node_data_iterator(graph):
features = create_feature_vector(data)
target = data[self.target_name]
data.clear()
data["x"] = features
data["y"] = target
for data in multidigraph_edge_data_iterator(graph):
features = create_feature_vector(data)
target = data[self.target_name]
data.clear()
data["edge_attr"] = features
data["y_edge"] = target
return graph
def encode_values(graph, categorical_attributes, continuous_attributes):
for node_data in multidigraph_node_data_iterator(graph):
typ = node_data['type']
if categorical_attributes is not None and typ in categorical_attributes.keys():
# Add the integer value of the category for each categorical attribute instance
category_values = categorical_attributes[typ]
node_data['encoded_value'] = category_values.index(node_data['value'])
elif continuous_attributes is not None and typ in continuous_attributes.keys():
min_val, max_val = continuous_attributes[typ]
node_data['encoded_value'] = (node_data['value'] - min_val) / (max_val - min_val)
else:
node_data['encoded_value'] = 0
for edge_data in multidigraph_edge_data_iterator(graph):
edge_data['encoded_value'] = 0
return graph
def pipeline(graphs,
tr_ge_split,
node_types,
edge_types,
num_processing_steps_tr=10,
num_processing_steps_ge=10,
num_training_iterations=10000,
continuous_attributes=None,
categorical_attributes=None,
type_embedding_dim=5,
attr_embedding_dim=6,
edge_output_size=3,
node_output_size=3,
output_dir=None):
############################################################
# Manipulate the graph data
############################################################
# Encode attribute values
for graph in graphs:
for node_data in multidigraph_node_data_iterator(graph):
typ = node_data['type']
if categorical_attributes is not None and typ in categorical_attributes.keys(
):
# Add the integer value of the category for each categorical attribute instance
category_values = categorical_attributes[typ]
node_data['encoded_value'] = category_values.index(
node_data['value'])
elif continuous_attributes is not None and typ in continuous_attributes.keys(
):
min_val, max_val = continuous_attributes[typ]
node_data['encoded_value'] = (node_data['value'] -
min_val) / (max_val - min_val)
else:
node_data['encoded_value'] = 0
for edge_data in multidigraph_edge_data_iterator(graph):
edge_data['encoded_value'] = 0
indexed_graphs = [
nx.convert_node_labels_to_integers(graph, label_attribute='concept')
for graph in graphs
]
graphs = [duplicate_edges_in_reverse(graph) for graph in indexed_graphs]
graphs = [encode_types(graph, node_types, edge_types) for graph in graphs]
input_graphs = [create_input_graph(graph) for graph in graphs]
target_graphs = [create_target_graph(graph) for graph in graphs]
tr_input_graphs = input_graphs[:tr_ge_split]
tr_target_graphs = target_graphs[:tr_ge_split]
ge_input_graphs = input_graphs[tr_ge_split:]
ge_target_graphs = target_graphs[tr_ge_split:]
############################################################
# Build and run the KGCN
############################################################
attr_embedders = configure_embedders(node_types, attr_embedding_dim,
categorical_attributes,
continuous_attributes)
kgcn = KGCN(len(node_types),
len(edge_types),
type_embedding_dim,
attr_embedding_dim,
attr_embedders,
edge_output_size=edge_output_size,
node_output_size=node_output_size)
learner = KGCNLearner(kgcn,
num_processing_steps_tr=num_processing_steps_tr,
num_processing_steps_ge=num_processing_steps_ge)
train_values, test_values, tr_info = learner(
tr_input_graphs,
tr_target_graphs,
ge_input_graphs,
ge_target_graphs,
num_training_iterations=num_training_iterations,
log_dir=output_dir)
plot_across_training(*tr_info, output_file=f'{output_dir}learning.png')
plot_predictions(ge_input_graphs,
test_values,
num_processing_steps_ge,
output_file=f'{output_dir}graph.png')
logit_graphs = graphs_tuple_to_networkxs(test_values["outputs"][-1])
indexed_ge_graphs = indexed_graphs[tr_ge_split:]
ge_graphs = [
apply_logits_to_graphs(graph, logit_graph)
for graph, logit_graph in zip(indexed_ge_graphs, logit_graphs)
]
for ge_graph in ge_graphs:
for data in multidigraph_data_iterator(ge_graph):
data['probabilities'] = softmax(data['logits'])
data['prediction'] = int(np.argmax(data['probabilities']))
_, _, _, _, _, solveds_tr, solveds_ge = tr_info
return ge_graphs, solveds_tr, solveds_ge
def pipeline(graphs,
tr_ge_split,
node_types,
edge_types,
num_processing_steps_tr=10,
num_processing_steps_ge=10,
num_training_iterations=10000,
categorical_attributes=None,
type_embedding_dim=5,
attr_embedding_dim=6,
edge_output_size=3,
node_output_size=3):
############################################################
# Manipulate the graph data
############################################################
# Encode attribute values
for graph in graphs:
for data in multidigraph_data_iterator(graph):
data['encoded_value'] = 0
for node_data in multidigraph_node_data_iterator(graph):
typ = node_data['type']
# Add the integer value of the category for each categorical attribute instance
for attr_typ, category_values in categorical_attributes.items():
if typ == attr_typ:
node_data['encoded_value'] = category_values.index(
node_data['value'])
indexed_graphs = [
nx.convert_node_labels_to_integers(graph, label_attribute='concept')
for graph in graphs
]
graphs = [duplicate_edges_in_reverse(graph) for graph in indexed_graphs]
graphs = [encode_types(graph, node_types, edge_types) for graph in graphs]
input_graphs = [create_input_graph(graph) for graph in graphs]
target_graphs = [create_target_graph(graph) for graph in graphs]
tr_input_graphs = input_graphs[:tr_ge_split]
tr_target_graphs = target_graphs[:tr_ge_split]
ge_input_graphs = input_graphs[tr_ge_split:]
ge_target_graphs = target_graphs[tr_ge_split:]
############################################################
# Build and run the KGCN
############################################################
type_categories_list = [i for i, _ in enumerate(node_types)]
non_attribute_nodes = type_categories_list.copy()
attr_embedders = dict()
# Construct categorical attribute embedders
for attr_typ, category_values in categorical_attributes.items():
num_categories = len(category_values)
def make_embedder():
return CategoricalAttribute(num_categories,
attr_embedding_dim,
name=attr_typ + '_cat_embedder')
attr_typ_index = node_types.index(attr_typ)
# Record the embedder, and the index of the type that it should encode
attr_embedders[make_embedder] = [attr_typ_index]
non_attribute_nodes.pop(attr_typ_index)
# All entities and relations (non-attributes) also need an embedder with matching output dimension, which does
# nothing. This is provided as a list of their indices
def make_blank_embedder():
return BlankAttribute(attr_embedding_dim)
attr_embedders[make_blank_embedder] = non_attribute_nodes
kgcn = KGCN(len(node_types),
len(edge_types),
type_embedding_dim,
attr_embedding_dim,
attr_embedders,
edge_output_size=edge_output_size,
node_output_size=node_output_size)
learner = KGCNLearner(kgcn,
num_processing_steps_tr=num_processing_steps_tr,
num_processing_steps_ge=num_processing_steps_ge)
train_values, test_values, tr_info = learner(
tr_input_graphs,
tr_target_graphs,
ge_input_graphs,
ge_target_graphs,
num_training_iterations=num_training_iterations)
plot_across_training(*tr_info)
plot_predictions(ge_input_graphs, test_values, num_processing_steps_ge)
logit_graphs = graphs_tuple_to_networkxs(test_values["outputs"][-1])
indexed_ge_graphs = indexed_graphs[tr_ge_split:]
ge_graphs = [
apply_logits_to_graphs(graph, logit_graph)
for graph, logit_graph in zip(indexed_ge_graphs, logit_graphs)
]
for ge_graph in ge_graphs:
for data in multidigraph_data_iterator(ge_graph):
data['probabilities'] = softmax(data['logits'])
data['prediction'] = int(np.argmax(data['probabilities']))
_, _, _, _, _, solveds_tr, solveds_ge = tr_info
return ge_graphs, solveds_tr, solveds_ge