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preprocess.py
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preprocess.py
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import numpy as np
from scipy.io import loadmat
from torch_geometric.data import Data
import torch
from config import N_TARGET_NODES_F, N_SOURCE_NODES_F,N_TARGET_NODES,N_SOURCE_NODES
def convert_vector_to_graph_RH(data):
"""
convert subject vector to adjacency matrix then use it to create a graph
edge_index:
edge_attr:
x:
"""
data.reshape(1, N_SOURCE_NODES_F)
# create adjacency matrix
tri = np.zeros((N_SOURCE_NODES, N_SOURCE_NODES))
tri[np.triu_indices(N_SOURCE_NODES, 1)] = data
tri = tri + tri.T
tri[np.diag_indices(N_SOURCE_NODES)] = 1
edge_attr = torch.Tensor(tri).view(N_SOURCE_NODES**2, 1)
edge_attr = torch.tensor(edge_attr, dtype=torch.float)
counter = 0
pos_counter = 0
neg_counter = 0
N_ROI = N_SOURCE_NODES
pos_edge_index = torch.zeros(2, N_ROI * N_ROI)
neg_edge_indexe = []
# pos_edge_indexe = []
for i in range(N_ROI):
for j in range(N_ROI):
pos_edge_index[:, counter] = torch.tensor([i, j])
counter += 1
# xx = torch.ones(160, 160, dtype=torch.float)
x = torch.tensor(tri, dtype=torch.float)
pos_edge_index = torch.tensor(pos_edge_index, dtype=torch.long)
return Data(x=x, pos_edge_index=pos_edge_index, edge_attr=edge_attr)
def convert_vector_to_graph_HHR(data):
"""
convert subject vector to adjacency matrix then use it to create a graph
edge_index:
edge_attr:
x:
"""
data.reshape(1, 35778)
# create adjacency matrix
tri = np.zeros((268, 268))
tri[np.triu_indices(268, 1)] = data
tri = tri + tri.T
tri[np.diag_indices(268)] = 1
edge_attr = torch.Tensor(tri).view(71824, 1)
edge_attr = torch.tensor(edge_attr, dtype=torch.float)
counter = 0
pos_counter = 0
neg_counter = 0
N_ROI = 268
pos_edge_index = torch.zeros(2, N_ROI * N_ROI)
neg_edge_indexe = []
# pos_edge_indexe = []
for i in range(N_ROI):
for j in range(N_ROI):
pos_edge_index[:, counter] = torch.tensor([i, j])
counter += 1
# xx = torch.ones(268, 268, dtype=torch.float)
x = torch.tensor(tri, dtype=torch.float)
pos_edge_index = torch.tensor(pos_edge_index, dtype=torch.long)
return Data(x=x, pos_edge_index=pos_edge_index, edge_attr=edge_attr)
def convert_vector_to_graph_FC(data):
"""
convert subject vector to adjacency matrix then use it to create a graph
edge_index:
edge_attr:
x:
"""
data.reshape(1, N_TARGET_NODES_F)
# create adjacency matrix
tri = np.zeros((N_TARGET_NODES, N_TARGET_NODES))
tri[np.triu_indices(N_TARGET_NODES, 1)] = data
tri = tri + tri.T
tri[np.diag_indices(N_TARGET_NODES)] = 1
edge_attr = torch.Tensor(tri).view(N_TARGET_NODES**2, 1)
edge_attr = torch.tensor(edge_attr, dtype=torch.float)
counter = 0
pos_counter = 0
neg_counter = 0
N_ROI = N_TARGET_NODES
pos_edge_index = torch.zeros(2, N_ROI * N_ROI)
neg_edge_indexe = []
# pos_edge_indexe = []
for i in range(N_ROI):
for j in range(N_ROI):
pos_edge_index[:, counter] = torch.tensor([i, j])
counter += 1
# xx = torch.ones(160, 160, dtype=torch.float)
x = torch.tensor(tri, dtype=torch.float)
pos_edge_index = torch.tensor(pos_edge_index, dtype=torch.long)
return Data(x=x, pos_edge_index=pos_edge_index, edge_attr=edge_attr)
def cast_data_vector_RH(dataset):
"""
convert subject vectors to graph and append it in a list
"""
dataset_g = []
for subj in range(dataset.shape[0]):
dataset_g.append(convert_vector_to_graph_RH(dataset[subj]))
return dataset_g
def cast_data_vector_HHR(dataset):
"""
convert subject vectors to graph and append it in a list
"""
dataset_g = []
for subj in range(dataset.shape[0]):
dataset_g.append(convert_vector_to_graph_HHR(dataset[subj]))
return dataset_g
def cast_data_vector_FC(dataset):
"""
convert subject vectors to graph and append it in a list
"""
dataset_g = []
for subj in range(dataset.shape[0]):
dataset_g.append(convert_vector_to_graph_FC(dataset[subj]))
return dataset_g
def convert_generated_to_graph_268(data1):
"""
convert generated output from G to a graph
"""
dataset = []
for data in data1:
counter = 0
N_ROI = 268
pos_edge_index = torch.zeros(2, N_ROI * N_ROI, dtype=torch.long)
for i in range(N_ROI):
for j in range(N_ROI):
pos_edge_index[:, counter] = torch.tensor([i, j])
counter += 1
x = data
pos_edge_index = torch.tensor(pos_edge_index, dtype=torch.long)
data = Data(x=x, pos_edge_index= pos_edge_index, edge_attr=data.view(71824, 1))
dataset.append(data)
return dataset
def convert_generated_to_graph(data):
"""
convert generated output from G to a graph
"""
dataset = []
# for data in data1:
counter = 0
N_ROI = N_TARGET_NODES
pos_edge_index = torch.zeros(2, N_ROI * N_ROI, dtype=torch.long)
for i in range(N_ROI):
for j in range(N_ROI):
pos_edge_index[:, counter] = torch.tensor([i, j])
counter += 1
x = data
pos_edge_index = torch.tensor(pos_edge_index, dtype=torch.long)
data = Data(x=x, pos_edge_index= pos_edge_index, edge_attr=data.view(N_TARGET_NODES**2, 1))
dataset.append(data)
return dataset
def convert_generated_to_graph_Al(data1):
"""
convert generated output from G to a graph
"""
dataset = []
# for data in data1:
counter = 0
N_ROI = N_SOURCE_NODES
pos_edge_index = torch.zeros(2, N_ROI * N_ROI, dtype=torch.long)
for i in range(N_ROI):
for j in range(N_ROI):
pos_edge_index[:, counter] = torch.tensor([i, j])
counter += 1
# x = data
pos_edge_index = torch.tensor(pos_edge_index, dtype=torch.long)
data = Data(x=data1, pos_edge_index=pos_edge_index, edge_attr=data1.view(N_SOURCE_NODES*N_SOURCE_NODES, 1))
dataset.append(data)
return dataset