import torch
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from torch import nn, optim
from torch.utils.data import Dataset, DataLoader
from torch.nn import functional as F
from torch.utils.tensorboard import SummaryWriter
from earlystoping import Earlystopping
from sklearn import metrics
def ExprOmiVAE(input_path, expr_df, random_seed=42, no_cuda=False, model_parallelism=True,
separate_testing=True, batch_size=32, latent_dim=128, learning_rate=1e-3, p1_epoch_num=50,
p2_epoch_num=100, output_loss_record=True, classifier=True, early_stopping=True):
torch.manual_seed(random_seed)
torch.cuda.manual_seed_all(random_seed)
device = torch.device('cuda:0' if not no_cuda and torch.cuda.is_available() else 'cpu')
parallel = torch.cuda.device_count() > 1 and model_parallelism
# Sample ID and order that has both gene expression and DNA methylation data
sample_id = np.loadtxt(input_path + 'both_samples.tsv', delimiter='\t', dtype='str')
# Loading label
label = pd.read_csv(input_path + 'both_samples_tumour_type_digit.tsv', sep='\t', header=0, index_col=0)
class_num = len(label.tumour_type.unique())
label_array = label['tumour_type'].values
if separate_testing:
# Get testing set index and training set index
# Separate according to different tumour types
testset_ratio = 0.2
valset_ratio = 0.5
train_index, test_index, train_label, test_label = train_test_split(sample_id, label_array,
test_size=testset_ratio,
random_state=random_seed,
stratify=label_array)
val_index, test_index, val_label, test_label = train_test_split(test_index, test_label, test_size=valset_ratio,
random_state=random_seed, stratify=test_label)
expr_df_test = expr_df[test_index]
expr_df_val = expr_df[val_index]
expr_df_train = expr_df[train_index]
# Get multi-omics dataset information
sample_num = len(sample_id)
expr_feature_num = expr_df.shape[0]
print('\nNumber of samples: {}'.format(sample_num))
print('Number of gene expression features: {}'.format(expr_feature_num))
if classifier:
print('Number of classes: {}'.format(class_num))
class ExprOmiDataset(Dataset):
def __init__(self, expr_df, labels):
self.expr_df = expr_df
self.labels = labels
def __len__(self):
return self.expr_df.shape[1]
def __getitem__(self, index):
expr_line = self.expr_df.iloc[:, index].values
expr_line_tensor = torch.Tensor(expr_line)
label = self.labels[index]
return [expr_line_tensor, label]
# DataSets and DataLoaders
if separate_testing:
train_dataset = ExprOmiDataset(expr_df=expr_df_train, labels=train_label)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=6)
val_dataset = ExprOmiDataset(expr_df=expr_df_val, labels=val_label)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True, num_workers=6)
test_dataset = ExprOmiDataset(expr_df=expr_df_test, labels=test_label)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True, num_workers=6)
else:
train_dataset = ExprOmiDataset(expr_df=expr_df, labels=label_array)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=6)
full_dataset = ExprOmiDataset(expr_df=expr_df, labels=label_array)
full_loader = DataLoader(full_dataset, batch_size=batch_size, num_workers=6)
# Setting dimensions
latent_space_dim = latent_dim
input_dim_expr = expr_feature_num
level_2_dim_expr = 4096
level_3_dim_expr = 1024
level_4_dim = 512
classifier_1_dim = 128
classifier_2_dim = 64
classifier_out_dim = class_num
class VAE(nn.Module):
def __init__(self):
super(VAE, self).__init__()
# ENCODER fc layers
# level 1
# Expr input
self.e_fc1_expr = self.fc_layer(input_dim_expr, level_2_dim_expr)
# Level 2
self.e_fc2_expr = self.fc_layer(level_2_dim_expr, level_3_dim_expr)
# self.e_fc2_expr = self.fc_layer(level_2_dim_expr, level_3_dim_expr, dropout=True)
# Level 3
self.e_fc3 = self.fc_layer(level_3_dim_expr, level_4_dim)
# self.e_fc3 = self.fc_layer(level_3_dim_expr, level_4_dim, dropout=True)
# Level 4
self.e_fc4_mean = self.fc_layer(level_4_dim, latent_space_dim, activation=0)
self.e_fc4_log_var = self.fc_layer(level_4_dim, latent_space_dim, activation=0)
# model parallelism
if parallel:
self.e_fc1_expr.to('cuda:0')
self.e_fc2_expr.to('cuda:0')
self.e_fc3.to('cuda:0')
self.e_fc4_mean.to('cuda:0')
self.e_fc4_log_var.to('cuda:0')
# DECODER fc layers
# Level 4
self.d_fc4 = self.fc_layer(latent_space_dim, level_4_dim)
# Level 3
self.d_fc3 = self.fc_layer(level_4_dim, level_3_dim_expr)
# self.d_fc3 = self.fc_layer(level_4_dim, level_3_dim_expr, dropout=True)
# Level 2
self.d_fc2_expr = self.fc_layer(level_3_dim_expr, level_2_dim_expr)
# self.d_fc2_expr = self.fc_layer(level_3_dim_expr, level_2_dim_expr, dropout=True)
# level 1
# Expr output
self.d_fc1_expr = self.fc_layer(level_2_dim_expr, input_dim_expr, activation=2)
# model parallelism
if parallel:
self.d_fc4.to('cuda:1')
self.d_fc3.to('cuda:1')
self.d_fc2_expr.to('cuda:1')
self.d_fc1_expr.to('cuda:1')
# CLASSIFIER fc layers
self.c_fc1 = self.fc_layer(latent_space_dim, classifier_1_dim)
self.c_fc2 = self.fc_layer(classifier_1_dim, classifier_2_dim)
# self.c_fc2 = self.fc_layer(classifier_1_dim, classifier_2_dim, dropout=True)
self.c_fc3 = self.fc_layer(classifier_2_dim, classifier_out_dim, activation=0)
# model parallelism
if parallel:
self.c_fc1.to('cuda:1')
self.c_fc2.to('cuda:1')
self.c_fc3.to('cuda:1')
# Activation - 0: no activation, 1: ReLU, 2: Sigmoid
def fc_layer(self, in_dim, out_dim, activation=1, dropout=False, dropout_p=0.5):
if activation == 0:
layer = nn.Sequential(
nn.Linear(in_dim, out_dim),
nn.BatchNorm1d(out_dim))
elif activation == 2:
layer = nn.Sequential(
nn.Linear(in_dim, out_dim),
nn.BatchNorm1d(out_dim),
nn.Sigmoid())
else:
if dropout:
layer = nn.Sequential(
nn.Linear(in_dim, out_dim),
nn.BatchNorm1d(out_dim),
nn.ReLU(),
nn.Dropout(p=dropout_p))
else:
layer = nn.Sequential(
nn.Linear(in_dim, out_dim),
nn.BatchNorm1d(out_dim),
nn.ReLU())
return layer
def encode(self, x):
expr_level2_layer = self.e_fc1_expr(x)
level_3_layer = self.e_fc2_expr(expr_level2_layer)
level_4_layer = self.e_fc3(level_3_layer)
latent_mean = self.e_fc4_mean(level_4_layer)
latent_log_var = self.e_fc4_log_var(level_4_layer)
return latent_mean, latent_log_var
def reparameterize(self, mean, log_var):
sigma = torch.exp(0.5 * log_var)
eps = torch.randn_like(sigma)
return mean + eps * sigma
def decode(self, z):
level_4_layer = self.d_fc4(z)
level_3_layer = self.d_fc3(level_4_layer)
expr_level2_layer = self.d_fc2_expr(level_3_layer)
recon_x = self.d_fc1_expr(expr_level2_layer)
return recon_x
def classifier(self, mean):
level_1_layer = self.c_fc1(mean)
level_2_layer = self.c_fc2(level_1_layer)
output_layer = self.c_fc3(level_2_layer)
return output_layer
def forward(self, x):
mean, log_var = self.encode(x)
z = self.reparameterize(mean, log_var)
classifier_x = mean
if parallel:
z = z.to('cuda:1')
classifier_x = classifier_x.to('cuda:1')
recon_x = self.decode(z)
pred_y = self.classifier(classifier_x)
return z, recon_x, mean, log_var, pred_y
# Instantiate VAE
if parallel:
vae_model = VAE()
else:
vae_model = VAE().to(device)
# Early Stopping
if early_stopping:
early_stop_ob = Earlystopping()
# Tensorboard writer
train_writer = SummaryWriter(log_dir='logs/train')
val_writer = SummaryWriter(log_dir='logs/val')
# print the model information
# print('\nModel information:')
# print(vae_model)
total_params = sum(params.numel() for params in vae_model.parameters())
print('Number of parameters: {}'.format(total_params))
optimizer = optim.Adam(vae_model.parameters(), lr=learning_rate)
def expr_recon_loss(recon_x, x):
loss = F.binary_cross_entropy(recon_x, x, reduction='sum')
return loss
def kl_loss(mean, log_var):
loss = -0.5 * torch.sum(1 + log_var - mean.pow(2) - log_var.exp())
return loss
def classifier_loss(pred_y, y):
loss = F.cross_entropy(pred_y, y, reduction='sum')
return loss
# k_expr_recon = 1
# k_kl = 1
# k_class = 1
# loss record
loss_array = np.zeros(shape=(9, p1_epoch_num+p2_epoch_num+1))
# performance metrics
metrics_array = np.zeros(4)
def train(e_index, e_num, k_expr_recon, k_kl, k_c):
vae_model.train()
train_expr_recon = 0
train_kl = 0
train_classifier = 0
train_correct_num = 0
train_total_loss = 0
for batch_index, sample in enumerate(train_loader):
data = sample[0]
y = sample[1]
data = data.to(device)
y = y.to(device)
optimizer.zero_grad()
_, recon_data, mean, log_var, pred_y = vae_model(data)
if parallel:
recon_data = recon_data.to('cuda:0')
pred_y = pred_y.to('cuda:0')
expr_recon = expr_recon_loss(recon_data, data)
kl = kl_loss(mean, log_var)
class_loss = classifier_loss(pred_y, y)
loss = k_expr_recon * expr_recon + k_kl * kl + k_c * class_loss
loss.backward()
with torch.no_grad():
pred_y_softmax = F.softmax(pred_y, dim=1)
_, predicted = torch.max(pred_y_softmax, 1)
correct = (predicted == y).sum().item()
train_expr_recon += expr_recon.item()
train_kl += kl.item()
train_classifier += class_loss.item()
train_correct_num += correct
train_total_loss += loss.item()
optimizer.step()
# if batch_index % 15 == 0:
# print('Epoch {:3d}/{:3d} --- [{:5d}/{:5d}] ({:2d}%)\n'
# 'Expr Recon Loss: {:.2f} KL Loss: {:.2f} '
# 'Classification Loss: {:.2f}\nACC: {:.2f}%'.format(
# e_index + 1, e_num, batch_index * len(data), len(train_dataset),
# round(100. * batch_index / len(train_loader)),
# expr_recon.item() / len(data), kl.item() / len(data), class_loss.item() / len(data),
# correct / len(data) * 100))
train_expr_recon_ave = train_expr_recon / len(train_dataset)
train_kl_ave = train_kl / len(train_dataset)
train_classifier_ave = train_classifier / len(train_dataset)
train_accuracy = train_correct_num / len(train_dataset) * 100
train_total_loss_ave = train_total_loss / len(train_dataset)
print('Epoch {:3d}/{:3d}\n'
'Training\n'
'Expr Recon Loss: {:.2f} KL Loss: {:.2f} '
'Classification Loss: {:.2f}\nACC: {:.2f}%'.
format(e_index + 1, e_num, train_expr_recon_ave, train_kl_ave, train_classifier_ave, train_accuracy))
loss_array[0, e_index] = train_expr_recon_ave
loss_array[1, e_index] = train_kl_ave
loss_array[2, e_index] = train_classifier_ave
loss_array[3, e_index] = train_accuracy
# TB
train_writer.add_scalar('Total loss', train_total_loss_ave, e_index)
train_writer.add_scalar('Expr recon loss', train_expr_recon_ave, e_index)
train_writer.add_scalar('KL loss', train_kl_ave, e_index)
train_writer.add_scalar('Classification loss', train_classifier_ave, e_index)
train_writer.add_scalar('Accuracy', train_accuracy, e_index)
if separate_testing:
def val(e_index, get_metrics=False):
vae_model.eval()
val_expr_recon = 0
val_kl = 0
val_classifier = 0
val_correct_num = 0
val_total_loss = 0
y_store = torch.tensor([0])
predicted_store = torch.tensor([0])
with torch.no_grad():
for batch_index, sample in enumerate(val_loader):
data = sample[0]
y = sample[1]
data = data.to(device)
y = y.to(device)
_, recon_data, mean, log_var, pred_y = vae_model(data)
if parallel:
recon_data = recon_data.to('cuda:0')
pred_y = pred_y.to('cuda:0')
expr_recon = expr_recon_loss(recon_data, data)
kl = kl_loss(mean, log_var)
class_loss = classifier_loss(pred_y, y)
loss = expr_recon + kl + class_loss
pred_y_softmax = F.softmax(pred_y, dim=1)
_, predicted = torch.max(pred_y_softmax, 1)
correct = (predicted == y).sum().item()
y_store = torch.cat((y_store, y.cpu()))
predicted_store = torch.cat((predicted_store, predicted.cpu()))
val_expr_recon += expr_recon.item()
val_kl += kl.item()
val_classifier += class_loss.item()
val_correct_num += correct
val_total_loss += loss.item()
output_y = y_store[1:].numpy()
output_pred_y = predicted_store[1:].numpy()
if get_metrics:
metrics_array[0] = metrics.accuracy_score(output_y, output_pred_y)
metrics_array[1] = metrics.precision_score(output_y, output_pred_y, average='weighted')
metrics_array[2] = metrics.recall_score(output_y, output_pred_y, average='weighted')
metrics_array[3] = metrics.f1_score(output_y, output_pred_y, average='weighted')
val_expr_recon_ave = val_expr_recon / len(val_dataset)
val_kl_ave = val_kl / len(val_dataset)
val_classifier_ave = val_classifier / len(val_dataset)
val_accuracy = val_correct_num / len(val_dataset) * 100
val_total_loss_ave = val_total_loss / len(val_dataset)
print('Validation\n'
'Expr Recon Loss: {:.2f} KL Loss: {:.2f} Classification Loss: {:.2f}'
'\nACC: {:.2f}%\n'.
format(val_expr_recon_ave, val_kl_ave, val_classifier_ave, val_accuracy))
loss_array[4, e_index] = val_expr_recon_ave
loss_array[5, e_index] = val_kl_ave
loss_array[6, e_index] = val_classifier_ave
loss_array[7, e_index] = val_accuracy
# TB
val_writer.add_scalar('Total loss', val_total_loss_ave, e_index)
val_writer.add_scalar('Expr recon loss', val_expr_recon_ave, e_index)
val_writer.add_scalar('KL loss', val_kl_ave, e_index)
val_writer.add_scalar('Classification loss', val_classifier_ave, e_index)
val_writer.add_scalar('Accuracy', val_accuracy, e_index)
return val_accuracy, output_pred_y
print('\nUNSUPERVISED PHASE\n')
# unsupervised phase
for epoch_index in range(p1_epoch_num):
train(e_index=epoch_index, e_num=p1_epoch_num+p2_epoch_num, k_expr_recon=1, k_kl=1, k_c=0)
if separate_testing:
_, out_pred_y = val(epoch_index)
print('\nSUPERVISED PHASE\n')
# supervised phase
epoch_number = p1_epoch_num
for epoch_index in range(p1_epoch_num, p1_epoch_num+p2_epoch_num):
epoch_number += 1
train(e_index=epoch_index, e_num=p1_epoch_num+p2_epoch_num, k_expr_recon=0, k_kl=0, k_c=1)
if separate_testing:
if epoch_index == p1_epoch_num+p2_epoch_num-1:
val_classification_acc, out_pred_y = val(epoch_index, get_metrics=True)
else:
val_classification_acc, out_pred_y = val(epoch_index)
if early_stopping:
early_stop_ob(vae_model, val_classification_acc)
if early_stop_ob.stop_now:
print('Early stopping\n')
break
if early_stopping:
best_epoch = p1_epoch_num + early_stop_ob.best_epoch_num
loss_array[8, 0] = best_epoch
print('Load model of Epoch {:d}'.format(best_epoch))
vae_model.load_state_dict(torch.load('../ssd/checkpoint.pt'))
_, out_pred_y = val(epoch_number, get_metrics=True)
# Encode all of the data into the latent space
print('Encoding all the data into latent space...')
vae_model.eval()
with torch.no_grad():
d_z_store = torch.zeros(1, latent_dim).to(device)
for i, sample in enumerate(full_loader):
d = sample[0]
d = d.to(device)
_, _, d_z, _, _ = vae_model(d)
d_z_store = torch.cat((d_z_store, d_z), 0)
all_data_z = d_z_store[1:]
all_data_z_np = all_data_z.cpu().numpy()
# Output file
print('Preparing the output files... ')
input_path_name = input_path.split('/')[-1]
latent_space_path = 'results/' + input_path_name + str(latent_dim) + 'D_latent_space.tsv'
all_data_z_df = pd.DataFrame(all_data_z_np, index=sample_id)
all_data_z_df.to_csv(latent_space_path, sep='\t')
if separate_testing:
pred_y_path = 'results/' + input_path_name + str(latent_dim) + 'D_pred_y.tsv'
np.savetxt(pred_y_path, out_pred_y, delimiter='\t')
metrics_record_path = 'results/' + input_path_name + str(latent_dim) + 'D_metrics.tsv'
np.savetxt(metrics_record_path, metrics_array, delimiter='\t')
if output_loss_record:
loss_record_path = 'results/' + input_path_name + str(latent_dim) + 'D_loss_record.tsv'
np.savetxt(loss_record_path, loss_array, delimiter='\t')
return all_data_z_df
Datasets
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