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demo.py
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demo.py
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import os
import torch
import numpy as np
import random
import time
from sklearn.model_selection import KFold
from torch_geometric.utils import get_laplacian, to_dense_adj
from torch_geometric.data import Data, InMemoryDataset, DataLoader
from torch.distributions import normal
import argparse
from scipy import io
import seaborn as sns
from torch.distributions import normal, kl
from plot import plot, plot_matrix
import matplotlib.pyplot as plt
from model_rbgm import GNN_1,frobenious_distance
import timeit
from data_utils import timer
from torch_geometric.utils import dense_to_sparse
from torch_geometric.data import Data
import networkx as nx
import copy
#from lion_pytorch import Lion
# random seed
manualSeed = 1
np.random.seed(manualSeed)
random.seed(manualSeed)
torch.manual_seed(manualSeed)
if torch.cuda.is_available():
device = torch.device('cuda:0')
print('running on GPU')
# if you are using GPU
torch.cuda.manual_seed(manualSeed)
torch.cuda.manual_seed_all(manualSeed)
torch.backends.cudnn.enabled = False
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
else:
device = torch.device("cpu")
print('running on CPU')
def get_args():
parser = argparse.ArgumentParser(description='Args for graph predition')
parser.add_argument('-mode', type=str, default="weighted_weight_exchange", help='training technique')
parser.add_argument('-num_folds', type=int, default=5, help='cv number')
parser.add_argument('-num_hospitals', type=int, default=4, help='hospitals')
parser.add_argument('--num_regions', type=int, default=35,
help='Number of regions')
parser.add_argument('--num_timepoints', type=int, default=3,
help='Number of timepoints')
parser.add_argument('-num_epochs', type=int, default=60, help='number of epochs')
parser.add_argument('--lr_g', type=float, default=0.01, help='Generator learning rate')
parser.add_argument('--lr_d', type=float, default=0.0002, help='Discriminator learning rate')
parser.add_argument('--decay', type=float, default=0.0, help='Weight Decay')
parser.add_argument('-C', type=int, default=14, help='number of round before averaging')
parser.add_argument('-D', type=int, default=7, help='number of rounds before daisy chain')
parser.add_argument('-batch_num', type=int, default=1, help='batch number')
parser.add_argument('--tp_coeff', type=float, default=0.0, help='Coefficient of topology loss')
parser.add_argument('--g_coeff', type=float, default=2.0, help='Coefficient of adversarial loss')
parser.add_argument('--i_coeff', type=float, default=2.0, help='Coefficient of identity loss')
parser.add_argument('--kl_coeff', type=float, default=0.001, help='Coefficient of KL loss')
parser.add_argument('--exp', type=int, default=1, help='Which experiment are you running')
parser.add_argument('--lr', type=float, default=0.001, help="Learninng rate")
parser.add_argument('--tp_coef', type=float, default=10, help="KL Loss Coefficient")
parser.add_argument('-save_path',type=str,default = '/Users/pavelbozmarov/Desktop/Python_Projects/Imperial/Dissertation/Code/4D-FedGNN-Plus_mine/results/',help='Path to the saved results')
args, _ = parser.parse_known_args()
return args
def create_edge_index_attribute(adj_matrix):
"""
Given an adjacency matrix, this function creates the edge index and edge attribute matrix
suitable to graph representation in PyTorch Geometric.
"""
rows, cols = adj_matrix.shape[0], adj_matrix.shape[1]
edge_index = torch.zeros((2, rows * cols), dtype=torch.long)
edge_attr = torch.zeros((rows * cols, 1), dtype=torch.float)
counter = 0
for src, attrs in enumerate(adj_matrix):
for dest, attr in enumerate(attrs):
edge_index[0][counter], edge_index[1][counter] = src, dest
edge_attr[counter] = attr
counter += 1
return edge_index, edge_attr, rows, cols
def get_order_original(table):
"""
Computes the order of the hospitals
A hospital score is calculated as: number of 1s + the last timepoint availability point
"""
sums = np.sum(table, axis=1)
sums += table[:, -1]
order = np.argsort(sums)
order = np.flip(order)
return order
def get_order_weighted(table):
"""
Computes the order of the hospitals
A hospital score is calculated as: number of 1s + the last timepoint availability point
"""
sums = np.sum(table, axis=1).astype(int)
# Get sorted indices in descending order
sorted_indices = np.argsort(sums)[::-1]
# Create list with sorted indices and items
result = [[index, sums[index]] for index in sorted_indices]
return result
def node_features_from_adj_matrix(adj_matrix,device):
if device.type=='cpu':
# Create a NetworkX graph from the adjacency matrix
G = nx.from_numpy_array(adj_matrix.detach().numpy())
elif device.type=='cuda':
# Create a NetworkX graph from the adjacency matrix
G = nx.from_numpy_array(adj_matrix.detach().cpu().numpy())
# Compute the weighted degree (strength) for each node
strength = dict(G.degree(weight='weight'))
# Compute the degree for each node
degree = dict(G.degree())
# Compute the clustering coefficient for each node
clustering = nx.clustering(G, weight='weight')
# Compute the closeness centrality for each node
closeness_centrality = nx.closeness_centrality(G)
# Compute the PageRank for each node
pagerank = nx.pagerank(G, weight='weight')
# Let's convert these features into numpy arrays so we can stack them together
strength_array = np.array(list(strength.values()))
degree_array = np.array(list(degree.values()))
clustering_array = np.array(list(clustering.values()))
closeness_centrality_array = np.array(list(closeness_centrality.values()))
pagerank_array = np.array(list(pagerank.values()))
# Now we can stack these features together to get a node feature matrix
x = torch.Tensor(np.vstack([strength_array, degree_array, clustering_array, closeness_centrality_array, pagerank_array]).T)
return x
def adj_matrix_to_pytorch_geometric_data(adj_matrix,device):
# calculate edge_index and edge_weights
edge_indices, edge_weights = dense_to_sparse(adj_matrix)
#edge attributes
edge_attr = torch.cat([edge_indices.T,edge_weights.view(len(edge_weights),1)],1)
# calculate the node features
x = node_features_from_adj_matrix(adj_matrix,device)
data = Data(x=x.to(device),edge_index=edge_indices.to(device),edge_weights=edge_weights.to(device),adj_matrix=adj_matrix.to(device),edge_attr=edge_attr.to(device))
return data
def create_edge_index_attribute_new(adj_matrix):
"""
Given an adjacency matrix, this function creates the edge index and edge attribute matrix
suitable to graph representation in PyTorch Geometric.
"""
rows, cols = adj_matrix.shape[0], adj_matrix.shape[1]
edge_index = [[],[]]
edge_attr = []
for i in range(rows):
for j in range(cols):
if adj_matrix[i,j] > 0:
edge_index[0].append(i)
edge_index[1].append(j)
edge_attr.append(adj_matrix[i,j])
return torch.tensor(edge_index), torch.Tensor(edge_attr), rows, cols
def get_adjacency_matrix(num_nodes, edge_indices, edge_weights):
# Initialize an empty adjacency matrix
adjacency_matrix = np.zeros((num_nodes, num_nodes))
# Fill in the adjacency matrix
for ((node1, node2), weight) in zip(edge_indices, edge_weights):
adjacency_matrix[node1, node2] = weight
return adjacency_matrix
def train_gnns_final(args, dataset,seed=10,ratio=4/8,verbose=False,train_validate_verbose=True,train_validate_verbosity_epochs=1):
"""
Arguments:
args: arguments
dataset: the whole dataset (train and test set)
table: [num_hospitals, num_timepoints], holds timepoint-wise availability of hospitals
This function performs training and testing reporting Mean Absolute Error (MAE) of the testing brain graphs.
"""
# Create the results folders
print('Train NEW')
os.makedirs(args.save_path, exist_ok=True)
os.makedirs(args.save_path+f'{args.save_name}', exist_ok=True)
os.makedirs(args.save_path+f'{args.save_name}/real_and_predicted_graphs', exist_ok=True)
os.makedirs(args.save_path+f'{args.save_name}/train_losses', exist_ok=True)
os.makedirs(args.save_path+f'{args.save_name}/train_losses/mae_losses', exist_ok=True)
os.makedirs(args.save_path+f'{args.save_name}/train_losses/tp_losses', exist_ok=True)
os.makedirs(args.save_path+f'{args.save_name}/train_losses/total_losses', exist_ok=True)
os.makedirs(args.save_path+f'{args.save_name}/test_mae_losses', exist_ok=True)
os.makedirs(args.save_path+f'{args.save_name}/trained_models', exist_ok=True)
# Create the results folders
manualSeed = seed
np.random.seed(manualSeed)
random.seed(manualSeed)
torch.manual_seed(manualSeed)
# show the fixed seed
print(f'Fixed seed:{seed}')
print()
# create the table that shows the data availability by timepoint
table = np.zeros((args.num_folds - 1, args.num_timepoints))
table = random_table(args, ratio)
print(f'Table:')
print(table)
print(f'Ratio:{ratio}')
print()
if torch.cuda.is_available():
device = torch.device('cuda:0')
print('running on GPU')
# if you are using GPU
torch.cuda.manual_seed(manualSeed)
torch.cuda.manual_seed_all(manualSeed)
torch.backends.cudnn.enabled = False
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = False
os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8' # !!! necessary for the below line
torch.use_deterministic_algorithms(False)
print('TRAIN deterministic algorithms')
else:
device = torch.device("cpu")
print('running on CPU')
# change the save path
args.save_path = args.save_path+f'{args.save_name}/'
# Choosing the right get_order function
if args.mode == 'weighted_weight_exchange':
get_order = get_order_weighted
else:
get_order = get_order_gnns
# Getting our fold dict
fold_dict,X = mf.create_fold_dict_new(dataset,num_hospitals=4,num_folds=5)
# Perform the 5-fold Cross-Validation
num_hospitals = args.num_folds - 1
for f in range(args.num_folds):
# fix the seeds
np.random.seed(manualSeed)
random.seed(manualSeed)
torch.manual_seed(manualSeed)
tic0 = timeit.default_timer()
print(
f'------------------------------------Fold [{f + 1}/{args.num_folds}]-----------------------------------------')
# Create hospitals
hospitals = []
for i in range(num_hospitals):
hospitals.append(Hospital_meta(args,device))
print('GAT')
# Train data for each hospital
train_data_list = []
# Current test data
test_data = X[-1][f].to(device)
for i in range(num_hospitals):
# Append the fold related to Hospital i - fold f. Note, here we append the real data, not the indices
train_data_list.append(X[i][fold_dict[f'Hospital_{i}'][f'fold_{f}'][0]].to(device))
# Start measuring the epochs time
epochs_start = time.time()
# Initiate Training
for epoch in range(args.num_epochs):
epoch +=1
# order the hospitals based on the data availability
ordered_hospitals = get_order_gnns(table)
for h_i,hospital in enumerate(hospitals):
# get the train data for the hospital
train_data = train_data_list[h_i]
# get the table for th current hospital
table_hospital = table[h_i]
#if h_i ==3:
# return args,hospital,train_data,table_hospital
# Train the current hospital at the current timepoint for 1 epoch
mae_loss,hospital = train_one_epoch_gnns(args,hospital,train_data,table_hospital)
# Updating the hospital
hospitals[h_i] = hospital
if verbose:
print(f'Epoch:{epoch} Hospital:{h_i}, Train MAE Loss:{mae_loss}')
# Perform validation during training
if train_validate_verbose and (epoch%train_validate_verbosity_epochs==0 or epoch==1):
val_loss,val_mean_loss = validate_during_training_gnns(args, hospitals, test_data)
print(f"Epoch:{epoch},val_loss:")
print(f'Total MAE Loss')
print(val_loss)
print()
print(f'Average MAE Loss:')
print(val_mean_loss)
print()
for h_i,l in enumerate(val_mean_loss):
hospitals[h_i].scheduler.step(l)
if epoch != args.num_epochs - 1 or epoch != 0:
if epoch % args.C == 0 and args.mode != "4D-GNN":
print('Central Aggregation')
hospitals = update_main_by_average_gnns(hospitals)
elif epoch % args.D == 0 and args.mode == "4D-FED-GNN+":
hospitals = exchange_models(hospitals, t)
elif epoch % args.D == 0 and args.mode == "4D-FED-GNN++":
print('4D-FED-GNN++')
hospitals = exchange_models_based_on_order_gnns(hospitals, ordered_hospitals)
elif epoch % args.D == 0 and args.mode == "weighted_weight_exchange":
print('weighted_weight_exchange')
hospitals = exchange_models_weights_pairs_extreme(hospitals, t, ordered_hospitals)
epochs_end = time.time()-epochs_start
print()
print(f'epochs finished with time:{epochs_end}')
print()
validate_gnns(args, hospitals, test_data, f)
tic1 = timeit.default_timer()
timer(tic0,tic1)
def exchange_models_based_on_order_gnns(hospitals, order):
"""
This function exchanges GNN-layer weights of paired hospitals at timepoint t with each other
"""
pre_model = None
for i, h_i in enumerate(order):
next_model = copy.deepcopy(hospitals[h_i].model.state_dict())
if not pre_model is None:
hospitals[h_i].model.load_state_dict(pre_model)
pre_model = copy.deepcopy(next_model)
if i == 0:
hospitals[h_i].model.load_state_dict(copy.deepcopy(hospitals[order[-1]].model.state_dict()))
return hospitals
def update_main_by_average_gnns(hospitals):
"""
This function takes the GNN-layer weights of the GNN at timepoint t and computes the global model by averaging,
then broadcats the weights to the hospitals (updates each GNN with the global model)
"""
target_state_dict = copy.deepcopy(hospitals[0].model.state_dict())
mux = 1 / len(hospitals)
model_state_dict_list = [copy.deepcopy(hospitals[i].model.state_dict()) for i in range(1, len(hospitals))]
for key in target_state_dict:
if target_state_dict[key].data.dtype == torch.float32:
target_state_dict[key].data = target_state_dict[key].data.clone() * mux
for model_state_dict in model_state_dict_list:
target_state_dict[key].data += mux * model_state_dict[key].data.clone()
for i in range(len(hospitals)):
hospitals[i].model.load_state_dict(target_state_dict)
return hospitals
def validate_during_training_gnns(args, hospitals, test_data):
"""
This function calculates the average MAE of predicted brain graphs during validation.
"""
mael = torch.nn.L1Loss()
hloss=np.array([np.zeros(args.num_timepoints-1) for i in range(args.num_hospitals)])
for h_i, hospital in enumerate(hospitals):
hospital.model.eval()
with torch.no_grad():
for data in test_data:
input = data[0]
mae_loss_hospital = np.zeros(args.num_timepoints-1)
for t in range(args.num_timepoints-1):
pred = hospital.model(input)
hloss[h_i,t] += mael(pred, data[t+1])
input = pred
# Calculate and save the average MAE Loss for each hospital
avg_hloss = hloss/len(test_data)
avg_hloss_mean = np.mean(avg_hloss,axis=1)
return avg_hloss,avg_hloss_mean
def validate_gnns(args, hospitals, test_data,f,verbose=False):
"""
This function calculates the average MAE of predicted brain graphs during validation.
"""
mael = torch.nn.L1Loss()
hloss=np.array([np.zeros(args.num_timepoints-1) for i in range(args.num_hospitals)])
for h_i, hospital in enumerate(hospitals):
hospital.model.eval()
with torch.no_grad():
for subject_index,data in enumerate(test_data):
input = data[0]
mae_loss_hospital = np.zeros(args.num_timepoints-1)
for t in range(args.num_timepoints-1):
pred = hospital.model(input)
hloss[h_i,t] += mael(pred, data[t+1])
input = pred
#plot and save the brain graph of patient(sample) i through all the timepoints
plot_matrix(data[t+1].cpu().detach().numpy(),f'Real Graph, Hospital:{h_i}, Subject:{subject_index}, Timepoint:{t+1}',
args.save_path+'real_and_predicted_graphs/'+f'hospital_{h_i}_subject_{subject_index}_timepoint_{t+1}_fold_{f}_real_graph',verbose)
plot_matrix(pred.cpu().detach().numpy(),f'Predicted Graph, Hospital:{h_i}, Subject:{subject_index}, Timepoint:{t+1}',
args.save_path+'real_and_predicted_graphs/'+f'hospital_{h_i}_subject_{subject_index}_timepoint_{t+1}_fold_{f}_predicted_graph',verbose)
# Calculate and save the average MAE Loss for each hospital
avg_hloss = hloss/len(test_data)
for h_i,loss in enumerate(avg_hloss):
# Save the MAE Loss
np.save(args.save_path+f"test_mae_losses/mae_test_loss_hospital_{h_i}_fold_{f}", loss)
# Save the loss
print(avg_hloss)
return avg_hloss
class Hospital_gnns():
def __init__(self, args,device):
"""
Hospital object contains a GNN and an optimizer for each timepoint
Hospital object can update GNN-layer wise weights of its GNNs
"""
self.model = RGCN(in_channels=3,hidden_size=32,num_nodes=35,window=1,dropout=0.4,device=device).to(device)
self.optimizer = torch.optim.Adam(self.model.parameters(),lr=args.lr)
def train_one_epoch_gnns(args,hospital,train_data,table_hospital):
# Set the model in training mode
hospital.model.train()
num_timepoints = train_data.shape[1]
#loss types definition
MAE_loss = torch.nn.L1Loss()
# this is our loss for all the data
mae_loss_overall = []
# loop through the data batches
for data_id,data in enumerate(train_data):
# zero the gradients
hospital.optimizer.zero_grad()
num_preds=0 # the number of times that we are able to predict
mae_loss = 0
t=0
# loop through the time dependent adj matrices in the batches
while t < num_timepoints-1:
#print(data_id,t)
# check if the next timepoint is available
if table_hospital[t+1]==1:
pred = hospital.model(data[t])
real = data[t+1]
mae_loss += MAE_loss(pred,real)
#print(f'MAE LOSS FROM CURRENT PREDICTION:{MAE_loss(pred,real) }')
num_preds+=1
t+=1
# if the next timepoint is not available
elif table_hospital[t+1]==0:
pred = hospital.model(data[t])
#return pred
# find the next closes available 1 to use it for a label. We use the pred as input until then
reached = False
for t in range(t+1,num_timepoints-1):
#print(f'Search t:{t}')
# using the previous prediction as input
pred = hospital.model(pred)
# if the next timepoint has data available we break
if table_hospital[t+1]==1:
real = data[t+1]
mae_loss += MAE_loss(pred,real)
#print(f'MAE LOSS FROM CURRENT PREDICTION:{MAE_loss(pred,real)}')
num_preds+=1
t+=1
reached = True
break
# if there are no more 1s in our table, we break from the loop and use whatever losses we accumulated in this loop
if not reached:
break
#print()
#print(f'the new t is:{t}')
#print()
#print(mae_loss)
# Calculate the total MAE Loss for the current batch
if num_preds == 0:
return 100,hospital
mae_loss=mae_loss/num_preds
# Append to the total MAE Los
mae_loss_overall.append(mae_loss.item())
#print(f'Num Predictions:{num_preds}')
# Update the weights of the neural network
mae_loss.backward()
hospital.optimizer.step()
mae_loss_overall = np.mean(np.array(mae_loss_overall))
return mae_loss_overall,hospital
def get_order_gnns(table):
hospital_distances=[]
hospital_number_of_points=[]
for row in table:
hospital_number_of_points.append(int(row.sum()))
ones_indexes = np.where(row==1)[0]
distances = np.diff(ones_indexes)-1
distance = distances.sum()
hospital_distances.append(distance)
hospital_number_of_points = np.array(hospital_number_of_points)
hospital_distances = np.array(hospital_distances)
order = np.lexsort((-hospital_distances,hospital_number_of_points))
order = np.flip(order)
return order
######################################################## 4D-FED-GNN++ ######################################################################
class Hospital():
def __init__(self, args):
"""
Hospital object contains a GNN and an optimizer for each timepoint
Hospital object can update GNN-layer wise weights of its GNNs
"""
self.models = []
self.optimizers = []
for i in range(args.num_timepoints - 1):
self.models.append(GNN_1().to(device))
self.optimizers.append(torch.optim.Adam(self.models[i].parameters(), lr=args.lr))
#self.optimizers.append(Lion(self.models[i].parameters(), lr=args.lr))
def update_hospital(self, main_model):
for i in range(len(self.models)):
self.models[i].load_state_dict(main_model.models[i].state_dict())
def get_folds(length, num_folds):
"""
Arguments:
length: number of subjects
num_folds: number of folds
This function returns a list of subjects for each fold (list of lists)
"""
indexes = list(range(length))
random.shuffle(indexes)
n = length // num_folds
folds = []
for fold in range(num_folds):
if fold == num_folds - 1:
folds.append(indexes[fold * n: -1])
else:
folds.append(indexes[fold * n: (fold * n) + n])
return folds
def train(args, dataset, table,verbose,train_validate_verbose,train_validate_verbosity_epochs):
"""
Arguments:
args: arguments
dataset: the whole dataset (train and test set)
table: [num_hospitals, num_timepoints], holds timepoint-wise availability of hospitals
This function performs training and testing reporting Mean Absolute Error (MAE) of the testing brain graphs.
"""
# Create the results folders
print('hi')
os.makedirs(args.save_path+'results', exist_ok=True)
os.makedirs(args.save_path+f'results/{args.save_name}', exist_ok=True)
os.makedirs(args.save_path+f'results/{args.save_name}/real_and_predicted_graphs', exist_ok=True)
os.makedirs(args.save_path+f'results/{args.save_name}/train_losses', exist_ok=True)
os.makedirs(args.save_path+f'results/{args.save_name}/train_losses/mae_losses', exist_ok=True)
os.makedirs(args.save_path+f'results/{args.save_name}/train_losses/tp_losses', exist_ok=True)
os.makedirs(args.save_path+f'results/{args.save_name}/train_losses/total_losses', exist_ok=True)
os.makedirs(args.save_path+f'results/{args.save_name}/test_mae_losses', exist_ok=True)
os.makedirs(args.save_path+f'results/{args.save_name}/trained_models', exist_ok=True)
# Create the results folders
manualSeed = 1
np.random.seed(manualSeed)
random.seed(manualSeed)
torch.manual_seed(manualSeed)
if torch.cuda.is_available():
device = torch.device('cuda:0')
print('running on GPU')
# if you are using GPU
torch.cuda.manual_seed(manualSeed)
torch.cuda.manual_seed_all(manualSeed)
torch.backends.cudnn.enabled = False
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8' # !!! necessary for the below line
torch.use_deterministic_algorithms(True)
print('TRAIN deterministic algorithms')
else:
device = torch.device("cpu")
print('running on CPU')
# change the save path
args.save_path = args.save_path+f'results/{args.save_name}/'
# Choosing the right get_order function
if args.mode == 'weighted_weight_exchange':
get_order = get_order_weighted
else:
get_order = get_order_original
folds = get_folds(dataset.shape[0], args.num_folds)
indexes = range(args.num_folds)
kfold = KFold(n_splits=args.num_folds)
f = 0
for train, test in kfold.split(indexes):
tic0 = timeit.default_timer()
print(
f'------------------------------------Fold [{f + 1}/{args.num_folds}]-----------------------------------------')
# initialize hospitals
hospitals = []
train_data_list = []
for h in range(args.num_folds - 1):
hospitals.append(Hospital(args))
train_data_list.append(dataset[folds[train[h]]])
# start training
for t in range(1, args.num_timepoints):
print('time point:',t)
mae_list, tp_list, tot_list = list(), list(), list()
if verbose:
print("-----------------------------------------------------------------------------")
# determine the order
ordered_hospitals = get_order(table[:, t - 1:t + 1])
if verbose:
print("Ordering of the hospitals: ", ordered_hospitals)
epochs_start = time.time()
for epoch in range(args.num_epochs):
if verbose:
print(f'Epoch [{epoch + 1}/{args.num_epochs}]')
tot_mae, tot, tp = 0.0, 0.0, 0.0
for item in ordered_hospitals:
if args.mode == 'weighted_weight_exchange':
h_i,w_i = item
else:
h_i = item
h = hospitals[h_i]
train_data = train_data_list[h_i]
if verbose:
print(f'Hospital [{h_i + 1}/{len(hospitals)}]')
hospitals[h_i], tot_l, tp_l, mae_l = train_one_epoch(args, h, train_data, f, table, [h_i, t])
tot_mae += mae_l
tot += tot_l
tp += tp_l
if verbose:
print(f'[Train] Loss T' + str(t) + f': {mae_l:.5f}',
f'[Train] TP Loss T' + str(t) + f': {tp_l:.5f} ',
f'[Train] Total Loss T' + str(t) + f': {tot_l:.5f} ')
if train_validate_verbose and (epoch+1)%train_validate_verbosity_epochs==0 :
test_data = dataset[folds[test[0]]]
val_loss = validate_during_training(args, hospitals, test_data,t)
print(f"Epoch:{epoch+1},val_loss:{val_loss}")
if epoch != args.num_epochs - 1 or epoch != 0:
if epoch % args.C == 0 and args.mode != "4D-GNN":
hospitals = update_main_by_average(hospitals, t)
elif epoch % args.D == 0 and args.mode == "4D-FED-GNN+":
hospitals = exchange_models(hospitals, t)
elif epoch % args.D == 0 and args.mode == "4D-FED-GNN++":
hospitals = exchange_models_based_on_order(hospitals, t, ordered_hospitals)
elif epoch % args.D == 0 and args.mode == "weighted_weight_exchange":
hospitals = exchange_models_weights_pairs_extreme(hospitals, t, ordered_hospitals)
mae_list.append(tot_mae)
tot_list.append(tot)
tp_list.append(tp)
plot(tot_list,f"Total Train Loss Model {t} Fold {f}",'Total Loss',args.save_path+'train_losses/total_losses/'+ f"total_train_loss_model_{t}_fold_{f}", verbose)
plot(mae_list,f"MAE Loss Model {t} Fold {f}",'MAE Loss', args.save_path+'train_losses/mae_losses/'+ f"mae_train_loss_model_{t}_fold_{f}", verbose)
plot(tp_list,f"TP Loss Model {t} Fold {f}", 'TP Loss', args.save_path+'train_losses/mae_losses/'+ f"tp_train_loss_model_{t}_fold_{f}", verbose)
if verbose:
print(" ")
epochs_end = time.time()-epochs_start
print(f'epochs finished with time:{epochs_end}')
test_data = dataset[folds[test[0]]]
validate(args, hospitals, test_data, f,verbose)
tic1 = timeit.default_timer()
timer(tic0,tic1)
f+=1
# save the weights for the hospitals
for i,hospital in enumerate(hospitals):
for j,model in enumerate(hospital.models):
# save the model of the current hospital
torch.save(model.state_dict(),
args.save_path +f'trained_models/hospital_{i}_model_{j+1}')
def exchange_models(hospitals, t):
"""
This function exchanges GNNs of hospitals at timepoint t with each other
"""
pre_model = None
for i, hospital in enumerate(hospitals):
next_model = copy.deepcopy(hospitals[i].models[t - 1].state_dict())
if not pre_model is None:
hospitals[i].models[t - 1].load_state_dict(pre_model)
pre_model = copy.deepcopy(next_model)
if i == 0:
hospitals[i].models[t - 1].load_state_dict(copy.deepcopy(hospitals[-1].models[t - 1].state_dict()))
return hospitals
def exchange_models_based_on_order(hospitals, t, order):
"""
This function exchanges GNN-layer weights of paired hospitals at timepoint t with each other
"""
pre_model = None
for i, h_i in enumerate(order):
next_model = copy.deepcopy(hospitals[h_i].models[t - 1].state_dict())
if not pre_model is None:
hospitals[h_i].models[t - 1].load_state_dict(pre_model)
pre_model = copy.deepcopy(next_model)
if i == 0:
hospitals[h_i].models[t - 1].load_state_dict(copy.deepcopy(hospitals[order[-1]].models[t - 1].state_dict()))
return hospitals
def exchange_models_weights_pairs_extreme(hospitals,t,order):
"""
This function exchanges GNN-layer weights of paired hospitals at timepoint t with each other.
We pair strong strongest hospitals with weakest ones.
"""
if len(order)%2==1:
h_last,w_last = order[-1]
order = order[:-1]
for i in range(int(len(order)/2)):
h_strong,w_strong = order[i]
h_weak,w_weak = order[len(order)-(i+1)]
# Calculate the total weight for normalization
total_weight = w_strong + w_weak
# Initialize an empty state dictionary to hold the weighted average of the models
avg_state_dict = {name: torch.zeros_like(param) for name, param in hospitals[h_strong].models[t - 1].state_dict().items()}
# Calculate the weighted average of the strong and weak models
for name, param_strong in hospitals[h_strong].models[t - 1].state_dict().items():
param_weak = hospitals[h_weak].models[t - 1].state_dict()[name]
avg_state_dict[name] = (w_strong * param_strong + w_weak * param_weak) / total_weight
# Update both models in the pair with the weighted average
hospitals[h_strong].models[t - 1].load_state_dict(avg_state_dict)
hospitals[h_weak].models[t - 1].load_state_dict(avg_state_dict)
return hospitals
def validate(args, hospitals, test_data, f,verbose):
"""
Output:
plotting of each predicted testing brain graph, also saved as a numpy file
average MAE of predicted brain graphs
"""
mael = torch.nn.L1Loss().to(device)
for j, hospital in enumerate(hospitals):
hloss = []
for k in range(len(hospital.models)):
hospital.models[k].eval()
hloss.append(torch.tensor(0))
with torch.no_grad():
for i, data in enumerate(test_data):
data = data.to(device)
out_1 = data[0] #
for k, model in enumerate(hospital.models):
temp = model.rnn[0].hidden_state
out_1 = model(out_1)
model.rnn[0].hidden_state = temp
# Updating the MAE loss for hospital j, model k
hloss[k] = hloss[k] + mael(out_1, data[k + 1])
#print(f'MAE LOSS Hospital_{j}_Subject_{i}_Timepoint_{k+1}_Fold_{f}: {mael(out_1, data[k + 1])}')
# plot and save the brain graph of patient(sample) i through all the timepoints
plot_matrix(data[k+1].cpu().detach().numpy(),f'Real Graph, Hospital:{j}, Subject:{i}, Timepoint:{k+1}',
args.save_path+'real_and_predicted_graphs/'+f'hospital_{j}_subject_{i}_timepoint_{k+1}_fold_{f}_real_graph',verbose)
plot_matrix(out_1.cpu().detach().numpy(),f'Predicted Graph, Hospital:{j}, Subject:{i}, Timepoint:{k+1}',
args.save_path+'real_and_predicted_graphs/'+f'hospital_{j}_subject_{i}_timepoint_{k+1}_fold_{f}_predicted_graph',verbose)
print(F'OVERALL PER PIXEL MAE LOSS FOR HOSPITAL:{j} FOR ALL MODELS')
print(np.array([item.cpu()/len(test_data) for item in hloss]))
# Save the MAE Loss
np.save(args.save_path+f"test_mae_losses/mae_test_loss_hospital_{j}_fold_{f}", np.array([item.cpu()/len(test_data) for item in hloss]))
if verbose:
print(f'Hospital:{j}')
for k in range(args.num_timepoints-1):
print(
'[Val]: MAE Loss Model' + str(k) + f': {hloss[k] / len(test_data):.5f}', sep=' ', end='')
print(" ")
def validate_during_training(args, hospitals, test_data,t):
"""
This function calculates the average MAE of predicted brain graphs during validation.
We only use the models that are related to data prediction for timepoint t. These are the
models with indices t-1
"""
mael = torch.nn.L1Loss().to(device)
val_hos = len(test_data)
hloss=[]
for i, hospital in enumerate(hospitals):
hospital.models[t-1].eval()
hloss.append(0)
with torch.no_grad():
for data in test_data:
data = data.to(device)
# here our input data is the data at timepoint t-1
input = data[t-1]
model = hospital.models[t-1]
temp = model.rnn[0].hidden_state
output= model(input)
model.rnn[0].hidden_state = temp
hloss[i] += mael(output, data[t])
# Calculate and save the average MAE Loss for each hospital
avg_hloss = np.array([loss.cpu()/val_hos for loss in hloss])
return avg_hloss
def update_main_by_average(hospitals, t):
"""
This function takes the GNN-layer weights of the GNN at timepoint t and computes the global model by averaging,
then broadcats the weights to the hospitals (updates each GNN with the global model)
"""
target_state_dict = copy.deepcopy(hospitals[0].models[t - 1].state_dict())
mux = 1 / len(hospitals)
model_state_dict_list = [copy.deepcopy(hospitals[i].models[t - 1].state_dict()) for i in range(1, len(hospitals))]
for key in target_state_dict:
if target_state_dict[key].data.dtype == torch.float32:
target_state_dict[key].data = target_state_dict[key].data.clone() * mux
for model_state_dict in model_state_dict_list:
target_state_dict[key].data += mux * model_state_dict[key].data.clone()