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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 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 = 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 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 = 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 MGTABlarge(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):
# Read data into huge `Data` list.
edge_index0 = torch.load(self.root + "/large_edge_index0.pt")
edge_index1 = torch.load(self.root + "/large_edge_index1.pt")
edge_index2 = torch.load(self.root + "/large_edge_index2.pt")
edge_index3 = torch.load(self.root + "/large_edge_index3.pt")
edge_index4 = torch.load(self.root + "/large_edge_index4.pt")
edge_index5 = torch.load(self.root + "/large_edge_index5.pt")
edge_index6 = torch.load(self.root + "/large_edge_index6.pt")
edge_index = torch.cat([edge_index0, edge_index1, edge_index2, edge_index3, edge_index4, edge_index5, edge_index6], axis =1)
edge_type0 = 0*torch.ones(edge_index0.shape[1], dtype=torch.int64)
edge_type1 = 1*torch.ones(edge_index1.shape[1], dtype=torch.int64)
edge_type2 = 2*torch.ones(edge_index2.shape[1], dtype=torch.int64)
edge_type3 = 3*torch.ones(edge_index3.shape[1], dtype=torch.int64)
edge_type4 = 4*torch.ones(edge_index4.shape[1], dtype=torch.int64)
edge_type5 = 5*torch.ones(edge_index4.shape[1], dtype=torch.int64)
edge_type6 = 6*torch.ones(edge_index4.shape[1], dtype=torch.int64)
edge_type = torch.cat([edge_type0, edge_type1, edge_type2, edge_type3, edge_type4, edge_index5, edge_index6],axis =0)
edge_weight = torch.ones(edge_index.shape[1], dtype=torch.int64)
stance_label = torch.load(self.root + "/labels_stance.pt")
bot_label = torch.load(self.root + "/labels_bot.pt")
features = torch.load(self.root + "/large_features.pt")
x = features.to(torch.float32)
data = Data(x=x, edge_index=edge_index)
data.edge_type = edge_type
data.edge_weight = edge_weight
data.y1 = stance_label
data.y2 = bot_label
labeled_sample_number = len(data.y1)
all_sample_number = data.x.shape[0]
train_idx = range(int(0.7*labeled_sample_number))
val_idx = range(int(0.7*labeled_sample_number), int(0.9*labeled_sample_number))
test_idx = range(int(0.9*labeled_sample_number), int(labeled_sample_number))
data.train_mask = sample_mask(train_idx, all_sample_number)
data.val_mask = sample_mask(val_idx, all_sample_number)
data.test_mask = sample_mask(test_idx, all_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])