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Dataset.py
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Dataset.py
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import torch
from torch_geometric.data import InMemoryDataset
from torch_geometric.data import Data
from utils import sample_mask
class cresci15(InMemoryDataset):
def __init__(self, root, transform=None, pre_transform=None):
super().__init__(root, transform, pre_transform)
self.data, self.slices = torch.load(self.processed_paths[0])
self.root = root
@property
def raw_file_names(self):
return ['some_file_1', 'some_file_2', ...]
@property
def processed_file_names(self):
return ['data.pt']
def sample_mask(self, idx, l):
"""Create mask."""
mask = torch.zeros(l)
mask[idx] = 1
return torch.as_tensor(mask, dtype=torch.bool)
def process(self):
# Read data into huge `Data` list.
edge_index = torch.load(self.root + "/edge_index.pt")
edge_type = torch.load(self.root + "/edge_type.pt")
label = torch.load(self.root + "/label.pt")
cat_prop = torch.load(self.root + "/cat_properties_tensor.pt")
num_prop = torch.load(self.root + "/num_properties_tensor.pt")
des_tensor = torch.load(self.root + "/des_tensor.pt")
tweets_tensor = torch.load(self.root + "/tweets_tensor.pt")
features = torch.cat([cat_prop, num_prop, des_tensor, tweets_tensor], axis=1)
data = Data(x=features, y =label, edge_index=edge_index)
data.edge_type = edge_type
sample_number = len(data.y)
train_idx = torch.load(self.root + "/train_idx.pt")
val_idx = torch.load(self.root + "/test_idx.pt")
test_idx = torch.load(self.root + "/val_idx.pt")
data.train_mask = self.sample_mask(train_idx, sample_number)
data.val_mask = self.sample_mask(val_idx, sample_number)
data.test_mask = self.sample_mask(test_idx, sample_number)
data_list = [data]
if self.pre_filter is not None:
data_list = [data for data in data_list if self.pre_filter(data)]
if self.pre_transform is not None:
data_list = [self.pre_transform(data) for data in data_list]
data, slices = self.collate(data_list)
torch.save((data, slices), self.processed_paths[0])
class Twibot20(InMemoryDataset):
def __init__(self, root, transform=None, pre_transform=None):
super().__init__(root, transform, pre_transform)
self.data, self.slices = torch.load(self.processed_paths[0])
self.root = root
@property
def raw_file_names(self):
return ['some_file_1', 'some_file_2', ...]
@property
def processed_file_names(self):
return ['data.pt']
def process(self):
labels = torch.load(self.root + "/label.pt")
des_tensor = torch.load(self.root + "/des_tensor.pt")
tweets_tensor1 = torch.load(self.root + "/tweets_tensor_p1.pt")
tweets_tensor2 = torch.load(self.root + "/tweets_tensor_p2.pt")
tweets_tensor = torch.cat([tweets_tensor1, tweets_tensor2], 0)
num_prop = torch.load(self.root + "/num_prop.pt")
category_prop = torch.load(self.root + "/category_prop.pt")
edge_index = torch.load(self.root + "/edge_index.pt")
edge_type = torch.load(self.root + "/edge_type.pt")
x = torch.cat([des_tensor, tweets_tensor, num_prop, category_prop], 1)
m0 = edge_index[0, :] > 11826
m1 = edge_index[1, :] > 11826
m = m0 + m1
x = x[:11826, :]
data = Data(x=x, y=labels, edge_index=edge_index)
data.edge_index = edge_index[:, ~m]
data.edge_type = edge_type[~m]
sample_number = len(data.x)
train_idx = range(8278)
val_idx = range(8278, 8278 + 2365)
test_idx = range(8278 + 2365, 8278 + 2365 + 1183)
data.train_mask = sample_mask(train_idx, sample_number)
data.val_mask = sample_mask(val_idx, sample_number)
data.test_mask = sample_mask(test_idx, sample_number)
data_list = [data]
if self.pre_filter is not None:
data_list = [data for data in data_list if self.pre_filter(data)]
if self.pre_transform is not None:
data_list = [self.pre_transform(data) for data in data_list]
data, slices = self.collate(data_list)
torch.save((data, slices), self.processed_paths[0])
class MGTAB(InMemoryDataset):
def __init__(self, root, transform=None, pre_transform=None):
super().__init__(root, transform, pre_transform)
self.data, self.slices = torch.load(self.processed_paths[0])
self.root = root
@property
def raw_file_names(self):
return ['some_file_1', 'some_file_2', ...]
@property
def processed_file_names(self):
return ['data.pt']
def sample_mask(self, idx, l):
"""Create mask."""
mask = torch.zeros(l)
mask[idx] = 1
return torch.as_tensor(mask, dtype=torch.bool)
def process(self):
# Read data into huge `Data` list.
edge_index = torch.load(self.root + "/edge_index.pt")
edge_index = torch.tensor(edge_index, dtype = torch.int64)
edge_type = torch.load(self.root + "/edge_type.pt")
edge_weight = torch.load(self.root + "/edge_weight.pt")
stance_label = torch.load(self.root + "/labels_stance.pt")
bot_label = torch.load(self.root + "/labels_bot.pt")
features = torch.load(self.root + "/features.pt")
features = features.to(torch.float32)
data = Data(x=features, edge_index=edge_index)
data.edge_type = edge_type
data.edge_weight = edge_weight
data.y1 = stance_label
data.y2 = bot_label
sample_number = len(data.y1)
train_idx = range(int(0.7*sample_number))
val_idx = range(int(0.7*sample_number), int(0.9*sample_number))
test_idx = range(int(0.9*sample_number), int(sample_number))
data.train_mask = self.sample_mask(train_idx, sample_number)
data.val_mask = self.sample_mask(val_idx, sample_number)
data.test_mask = self.sample_mask(test_idx, sample_number)
data_list = [data]
if self.pre_filter is not None:
data_list = [data for data in data_list if self.pre_filter(data)]
if self.pre_transform is not None:
data_list = [self.pre_transform(data) for data in data_list]
data, slices = self.collate(data_list)
torch.save((data, slices), self.processed_paths[0])