def load_fb15k_data(dataset, sim_relations, keep_fraction=100):
train_network = FB15kReader(dataset)
dev_network = FB15kReader(dataset)
test_network = FB15kReader(dataset)
train_network.read_network(data_dir="data/FB15k-237/",
keep_fraction=keep_fraction,
split="train")
train_network.print_summary()
node_list = train_network.graph.iter_nodes()
node_degrees = [node.get_degree() for node in node_list]
degree_counter = Counter(node_degrees)
avg_degree = sum([k * v for k, v in degree_counter.items()]) / sum(
[v for k, v in degree_counter.items()])
print("Average Degree: ", avg_degree)
dev_network.read_network(data_dir="data/FB15k-237/",
split="valid",
train_network=train_network)
test_network.read_network(data_dir="data/FB15k-237/",
split="test",
train_network=train_network)
word_vocab = train_network.graph.node2id
train_data, _ = reader_utils.prepare_batch_dgl(word_vocab, train_network,
train_network)
test_data, test_labels = reader_utils.prepare_batch_dgl(
word_vocab, test_network, train_network)
valid_data, valid_labels = reader_utils.prepare_batch_dgl(
word_vocab, dev_network, train_network)
return len(
train_network.graph.nodes), train_data, valid_data, test_data, len(
train_network.graph.relations
), valid_labels, test_labels, train_network
def load_cn_data(dataset, sim_relations, eval_accuracy=False):
train_network = ConceptNetTSVReader(dataset)
dev1_network = ConceptNetTSVReader(dataset)
dev2_network = ConceptNetTSVReader(dataset)
test_network = ConceptNetTSVReader(dataset)
positive_only = not eval_accuracy
train_network.read_network(data_dir="data/", split="train")
train_network.print_summary()
#node_list = train_network.graph.iter_nodes()
#node_degrees = [node.get_degree() for node in node_list]
#degree_counter = Counter(node_degrees)
#avg_degree = sum([k*v for k,v in degree_counter.items()]) / sum([v for k,v in degree_counter.items()])
#print("Average Degree: ", avg_degree)
dev1_network.read_network(data_dir="data/",
split="valid1",
train_network=train_network,
positive_only=positive_only)
dev2_network.read_network(data_dir="data/",
split="valid2",
train_network=train_network,
positive_only=positive_only)
test_network.read_network(data_dir="data/",
split="valid2",
train_network=train_network,
positive_only=positive_only)
# Add sim nodes
if sim_relations:
print("Adding sim edges..")
train_network.add_sim_edges_bert()
#word_vocab, word_freqs = reader_utils.create_vocab(train_network)
word_vocab = train_network.graph.node2id
train_data, _ = reader_utils.prepare_batch_dgl(word_vocab, train_network,
train_network)
test_data, test_labels = reader_utils.prepare_batch_dgl(
word_vocab, test_network, train_network)
valid1_data, valid1_labels = reader_utils.prepare_batch_dgl(
word_vocab, dev1_network, train_network)
valid2_data, valid2_labels = reader_utils.prepare_batch_dgl(
word_vocab, dev2_network, train_network)
return len(
train_network.graph.nodes), train_data, valid1_data, test_data, len(
train_network.graph.relations
), valid1_labels, test_labels, train_network
def load_cn_full_data(dataset, sim_relations):
train_network = ConceptNetFullReader(dataset)
dev_network = ConceptNetFullReader(dataset)
test_network = ConceptNetFullReader(dataset)
train_network.read_network(
data_dir="/net/efs.mosaic/chaitanyam/ConvE/data/", split="train")
train_network.print_summary()
node_list = train_network.graph.iter_nodes()
node_degrees = [node.get_degree() for node in node_list]
degree_counter = Counter(node_degrees)
avg_degree = sum([k * v for k, v in degree_counter.items()]) / sum(
[v for k, v in degree_counter.items()])
print("Average Degree: ", avg_degree)
dev_network.read_network(data_dir="/net/efs.mosaic/chaitanyam/ConvE/data/",
split="valid",
train_network=train_network)
test_network.read_network(
data_dir="/net/efs.mosaic/chaitanyam/ConvE/data/",
split="test",
train_network=train_network)
#node_names = []
#for node in train_network.graph.iter_nodes():
# node_names.append(node.name)
#with open("cn-full_node_names.txt", 'w') as f:
# f.writelines([n.split("/")[-2].replace("_", " ")+"\n" for n in node_names if n not in string.punctuation and not n.isdigit()])
#import sys; sys.exit(0)
if sim_relations:
print("Adding sim edges..")
train_network.add_sim_edges_bert()
#word_vocab, word_freqs = reader_utils.create_vocab(train_network)
word_vocab = train_network.graph.node2id
train_data, _ = reader_utils.prepare_batch_dgl(word_vocab, train_network,
train_network)
test_data, test_labels = reader_utils.prepare_batch_dgl(
word_vocab, test_network, train_network)
valid_data, valid_labels = reader_utils.prepare_batch_dgl(
word_vocab, dev_network, train_network)
return len(
train_network.graph.nodes), train_data, valid_data, test_data, len(
train_network.graph.relations
), valid_labels, test_labels, train_network
def load_atomic_data(dataset, sim_relations):
train_network = AtomicTSVReader(dataset)
dev_network = AtomicTSVReader(dataset)
test_network = AtomicTSVReader(dataset)
#train_network.read_network(data_dir="data/atomic-original/", split="train")
train_network.read_network(data_dir="data/atomic/", split="train")
train_network.print_summary()
node_list = train_network.graph.iter_nodes()
node_degrees = [node.get_degree() for node in node_list]
degree_counter = Counter(node_degrees)
avg_degree = sum([k * v for k, v in degree_counter.items()]) / sum(
[v for k, v in degree_counter.items()])
print("Average Degree: ", avg_degree)
#dev_network.read_network(data_dir="data/atomic-original/", split="valid", train_network=train_network)
#test_network.read_network(data_dir="data/atomic-original/", split="test", train_network=train_network)
dev_network.read_network(data_dir="data/atomic/",
split="valid",
train_network=train_network)
test_network.read_network(data_dir="data/atomic/",
split="test",
train_network=train_network)
word_vocab = train_network.graph.node2id
#node_names = []
#for node in train_network.graph.iter_nodes():??????
# node_names.append(node.name + "\n")
#with open("atomic_node_names.txt", 'w') as f:
# f.writelines([reader_utils.preprocess_atomic_sentence(n.replace("-", " ??????")) for n in node_names])
#import sys; sys.exit(0)
# Add sim nodes
if sim_relations:
print("Adding sim edges..")
train_network.add_sim_edges_bert()
train_data, _ = reader_utils.prepare_batch_dgl(word_vocab, train_network,
train_network)
test_data, test_labels = reader_utils.prepare_batch_dgl(
word_vocab, test_network, train_network)
valid_data, valid_labels = reader_utils.prepare_batch_dgl(
word_vocab, dev_network, train_network)
return len(
train_network.graph.nodes), train_data, valid_data, test_data, len(
train_network.graph.relations
), valid_labels, test_labels, train_network
def load_data(dataset, reader_cls, data_dir, sim_relations):
train_network = reader_cls(dataset)
dev_network = reader_cls(dataset)
test_network = reader_cls(dataset)
train_network.read_network(data_dir=data_dir, split="train")
train_network.print_summary()
node_list = train_network.graph.iter_nodes()
node_degrees = [node.get_degree() for node in node_list]
degree_counter = Counter(node_degrees)
avg_degree = sum([k * v for k, v in degree_counter.items()]) / sum(
[v for k, v in degree_counter.items()])
print("Average Degree: ", avg_degree)
dev_network.read_network(data_dir=data_dir,
split="valid",
train_network=train_network)
test_network.read_network(data_dir=data_dir,
split="test",
train_network=train_network)
word_vocab = train_network.graph.node2id
# Add sim nodes
if sim_relations:
print("Adding sim edges..")
train_network.add_sim_edges_bert()
train_data, _ = reader_utils.prepare_batch_dgl(word_vocab, train_network,
train_network)
test_data, test_labels = reader_utils.prepare_batch_dgl(
word_vocab, test_network, train_network)
valid_data, valid_labels = reader_utils.prepare_batch_dgl(
word_vocab, dev_network, train_network)
return train_data, valid_data, test_data, valid_labels, test_labels, train_network
