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data_utils.py
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data_utils.py
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"""
Data utils for processing bAbI datasets
"""
import os
import string
import dgl
import torch
from dgl.data.utils import (
_get_dgl_url,
download,
extract_archive,
get_download_dir,
)
from torch.utils.data import DataLoader
def get_babi_dataloaders(batch_size, train_size=50, task_id=4, q_type=0):
_download_babi_data()
node_dict = dict(
zip(list(string.ascii_uppercase), range(len(string.ascii_uppercase)))
)
if task_id == 4:
edge_dict = {"n": 0, "s": 1, "w": 2, "e": 3}
reverse_edge = {}
return _ns_dataloader(
train_size,
q_type,
batch_size,
node_dict,
edge_dict,
reverse_edge,
"04",
)
elif task_id == 15:
edge_dict = {"is": 0, "has_fear": 1}
reverse_edge = {}
return _ns_dataloader(
train_size,
q_type,
batch_size,
node_dict,
edge_dict,
reverse_edge,
"15",
)
elif task_id == 16:
edge_dict = {"is": 0, "has_color": 1}
reverse_edge = {0: 0}
return _ns_dataloader(
train_size,
q_type,
batch_size,
node_dict,
edge_dict,
reverse_edge,
"16",
)
elif task_id == 18:
edge_dict = {">": 0, "<": 1}
label_dict = {"false": 0, "true": 1}
reverse_edge = {0: 1, 1: 0}
return _gc_dataloader(
train_size,
q_type,
batch_size,
node_dict,
edge_dict,
label_dict,
reverse_edge,
"18",
)
elif task_id == 19:
edge_dict = {"n": 0, "s": 1, "w": 2, "e": 3, "<end>": 4}
reverse_edge = {0: 1, 1: 0, 2: 3, 3: 2}
max_seq_length = 2
return _path_finding_dataloader(
train_size,
batch_size,
node_dict,
edge_dict,
reverse_edge,
"19",
max_seq_length,
)
def _ns_dataloader(
train_size, q_type, batch_size, node_dict, edge_dict, reverse_edge, path
):
def _collate_fn(batch):
graphs = []
labels = []
for d in batch:
edges = d["edges"]
node_ids = []
for s, e, t in edges:
if s not in node_ids:
node_ids.append(s)
if t not in node_ids:
node_ids.append(t)
g = dgl.graph([])
g.add_nodes(len(node_ids))
g.ndata["node_id"] = torch.tensor(node_ids, dtype=torch.long)
nid2idx = dict(zip(node_ids, list(range(len(node_ids)))))
# convert label to node index
label = d["eval"][2]
label_idx = nid2idx[label]
labels.append(label_idx)
edge_types = []
for s, e, t in edges:
g.add_edges(nid2idx[s], nid2idx[t])
edge_types.append(e)
if e in reverse_edge:
g.add_edges(nid2idx[t], nid2idx[s])
edge_types.append(reverse_edge[e])
g.edata["type"] = torch.tensor(edge_types, dtype=torch.long)
annotation = torch.zeros(len(node_ids), dtype=torch.long)
annotation[nid2idx[d["eval"][0]]] = 1
g.ndata["annotation"] = annotation.unsqueeze(-1)
graphs.append(g)
batch_graph = dgl.batch(graphs)
labels = torch.tensor(labels, dtype=torch.long)
return batch_graph, labels
def _get_dataloader(data, shuffle):
return DataLoader(
dataset=data,
batch_size=batch_size,
shuffle=shuffle,
collate_fn=_collate_fn,
)
train_set, dev_set, test_sets = _convert_ns_dataset(
train_size, node_dict, edge_dict, path, q_type
)
train_dataloader = _get_dataloader(train_set, True)
dev_dataloader = _get_dataloader(dev_set, False)
test_dataloaders = []
for d in test_sets:
dl = _get_dataloader(d, False)
test_dataloaders.append(dl)
return train_dataloader, dev_dataloader, test_dataloaders
def _convert_ns_dataset(train_size, node_dict, edge_dict, path, q_type):
total_num = 11000
def convert(file):
dataset = []
d = dict()
with open(file, "r") as f:
for i, line in enumerate(f.readlines()):
line = line.strip().split()
if line[0] == "1" and len(d) > 0:
d = dict()
if line[1] == "eval":
# (src, edge, label)
d["eval"] = (
node_dict[line[2]],
edge_dict[line[3]],
node_dict[line[4]],
)
if d["eval"][1] == q_type:
dataset.append(d)
if len(dataset) >= total_num:
break
else:
if "edges" not in d:
d["edges"] = []
d["edges"].append(
(
node_dict[line[1]],
edge_dict[line[2]],
node_dict[line[3]],
)
)
return dataset
download_dir = get_download_dir()
filename = os.path.join(download_dir, "babi_data", path, "data.txt")
data = convert(filename)
assert len(data) == total_num
train_set = data[:train_size]
dev_set = data[950:1000]
test_sets = []
for i in range(10):
test = data[1000 * (i + 1) : 1000 * (i + 2)]
test_sets.append(test)
return train_set, dev_set, test_sets
def _gc_dataloader(
train_size,
q_type,
batch_size,
node_dict,
edge_dict,
label_dict,
reverse_edge,
path,
):
def _collate_fn(batch):
graphs = []
labels = []
for d in batch:
edges = d["edges"]
node_ids = []
for s, e, t in edges:
if s not in node_ids:
node_ids.append(s)
if t not in node_ids:
node_ids.append(t)
g = dgl.graph([])
g.add_nodes(len(node_ids))
g.ndata["node_id"] = torch.tensor(node_ids, dtype=torch.long)
nid2idx = dict(zip(node_ids, list(range(len(node_ids)))))
labels.append(d["eval"][-1])
edge_types = []
for s, e, t in edges:
g.add_edges(nid2idx[s], nid2idx[t])
edge_types.append(e)
if e in reverse_edge:
g.add_edges(nid2idx[t], nid2idx[s])
edge_types.append(reverse_edge[e])
g.edata["type"] = torch.tensor(edge_types, dtype=torch.long)
annotation = torch.zeros([len(node_ids), 2], dtype=torch.long)
annotation[nid2idx[d["eval"][0]]][0] = 1
annotation[nid2idx[d["eval"][2]]][1] = 1
g.ndata["annotation"] = annotation
graphs.append(g)
batch_graph = dgl.batch(graphs)
labels = torch.tensor(labels, dtype=torch.long)
return batch_graph, labels
def _get_dataloader(data, shuffle):
return DataLoader(
dataset=data,
batch_size=batch_size,
shuffle=shuffle,
collate_fn=_collate_fn,
)
train_set, dev_set, test_sets = _convert_gc_dataset(
train_size, node_dict, edge_dict, label_dict, path, q_type
)
train_dataloader = _get_dataloader(train_set, True)
dev_dataloader = _get_dataloader(dev_set, False)
test_dataloaders = []
for d in test_sets:
dl = _get_dataloader(d, False)
test_dataloaders.append(dl)
return train_dataloader, dev_dataloader, test_dataloaders
def _convert_gc_dataset(
train_size, node_dict, edge_dict, label_dict, path, q_type
):
total_num = 11000
def convert(file):
dataset = []
d = dict()
with open(file, "r") as f:
for i, line in enumerate(f.readlines()):
line = line.strip().split()
if line[0] == "1" and len(d) > 0:
d = dict()
if line[1] == "eval":
# (src, edge, label)
if "eval" not in d:
d["eval"] = (
node_dict[line[2]],
edge_dict[line[3]],
node_dict[line[4]],
label_dict[line[5]],
)
if d["eval"][1] == q_type:
dataset.append(d)
if len(dataset) >= total_num:
break
else:
if "edges" not in d:
d["edges"] = []
d["edges"].append(
(
node_dict[line[1]],
edge_dict[line[2]],
node_dict[line[3]],
)
)
return dataset
download_dir = get_download_dir()
filename = os.path.join(download_dir, "babi_data", path, "data.txt")
data = convert(filename)
assert len(data) == total_num
train_set = data[:train_size]
dev_set = data[950:1000]
test_sets = []
for i in range(10):
test = data[1000 * (i + 1) : 1000 * (i + 2)]
test_sets.append(test)
return train_set, dev_set, test_sets
def _path_finding_dataloader(
train_size,
batch_size,
node_dict,
edge_dict,
reverse_edge,
path,
max_seq_length,
):
def _collate_fn(batch):
graphs = []
ground_truths = []
seq_lengths = []
for d in batch:
edges = d["edges"]
node_ids = []
for s, e, t in edges:
if s not in node_ids:
node_ids.append(s)
if t not in node_ids:
node_ids.append(t)
g = dgl.graph([])
g.add_nodes(len(node_ids))
g.ndata["node_id"] = torch.tensor(node_ids, dtype=torch.long)
nid2idx = dict(zip(node_ids, list(range(len(node_ids)))))
truth = d["seq_out"] + [edge_dict["<end>"]] * (
max_seq_length - len(d["seq_out"])
)
seq_len = len(d["seq_out"])
ground_truths.append(truth)
seq_lengths.append(seq_len)
edge_types = []
for s, e, t in edges:
g.add_edges(nid2idx[s], nid2idx[t])
edge_types.append(e)
if e in reverse_edge:
g.add_edges(nid2idx[t], nid2idx[s])
edge_types.append(reverse_edge[e])
g.edata["type"] = torch.tensor(edge_types, dtype=torch.long)
annotation = torch.zeros([len(node_ids), 2], dtype=torch.long)
annotation[nid2idx[d["eval"][0]]][0] = 1
annotation[nid2idx[d["eval"][1]]][1] = 1
g.ndata["annotation"] = annotation
graphs.append(g)
batch_graph = dgl.batch(graphs)
ground_truths = torch.tensor(ground_truths, dtype=torch.long)
seq_lengths = torch.tensor(seq_lengths, dtype=torch.long)
return batch_graph, ground_truths, seq_lengths
def _get_dataloader(data, shuffle):
return DataLoader(
dataset=data,
batch_size=batch_size,
shuffle=shuffle,
collate_fn=_collate_fn,
)
train_set, dev_set, test_sets = _convert_path_finding(
train_size, node_dict, edge_dict, path
)
train_dataloader = _get_dataloader(train_set, True)
dev_dataloader = _get_dataloader(dev_set, False)
test_dataloaders = []
for d in test_sets:
dl = _get_dataloader(d, False)
test_dataloaders.append(dl)
return train_dataloader, dev_dataloader, test_dataloaders
def _convert_path_finding(train_size, node_dict, edge_dict, path):
total_num = 11000
def convert(file):
dataset = []
d = dict()
with open(file, "r") as f:
for line in f.readlines():
line = line.strip().split()
if line[0] == "1" and len(d) > 0:
d = dict()
if line[1] == "eval":
# (src, edge, label)
d["eval"] = (node_dict[line[3]], node_dict[line[4]])
d["seq_out"] = []
seq_out = line[5].split(",")
for e in seq_out:
d["seq_out"].append(edge_dict[e])
dataset.append(d)
if len(dataset) >= total_num:
break
else:
if "edges" not in d:
d["edges"] = []
d["edges"].append(
(
node_dict[line[1]],
edge_dict[line[2]],
node_dict[line[3]],
)
)
return dataset
download_dir = get_download_dir()
filename = os.path.join(download_dir, "babi_data", path, "data.txt")
data = convert(filename)
assert len(data) == total_num
train_set = data[:train_size]
dev_set = data[950:1000]
test_sets = []
for i in range(10):
test = data[1000 * (i + 1) : 1000 * (i + 2)]
test_sets.append(test)
return train_set, dev_set, test_sets
def _download_babi_data():
download_dir = get_download_dir()
zip_file_path = os.path.join(download_dir, "babi_data.zip")
data_url = _get_dgl_url("models/ggnn_babi_data.zip")
download(data_url, path=zip_file_path)
extract_dir = os.path.join(download_dir, "babi_data")
if not os.path.exists(extract_dir):
extract_archive(zip_file_path, extract_dir)