def run_main(args):
# Define parameters
epochs = args.epochs
dim_au_out = args.bottleneck #8, 16, 32, 64, 128, 256,512
dim_dnn_in = dim_au_out
dim_dnn_out = 1
select_drug = args.drug
na = args.missing_value
data_path = args.data_path
label_path = args.label_path
test_size = args.test_size
valid_size = args.valid_size
g_disperson = args.var_genes_disp
model_path = args.model_store_path
pretrain_path = args.pretrain_path
log_path = args.logging_file
batch_size = args.batch_size
encoder_hdims = args.ft_h_dims.split(",")
preditor_hdims = args.p_h_dims.split(",")
reduce_model = args.dimreduce
prediction = args.predition
encoder_hdims = list(map(int, encoder_hdims))
preditor_hdims = list(map(int, preditor_hdims))
# Read data
data_r = pd.read_csv(data_path, index_col=0)
label_r = pd.read_csv(label_path, index_col=0)
label_r = label_r.fillna(na)
now = time.strftime("%Y-%m-%d-%H-%M-%S")
log_path = log_path + now + ".txt"
log = open(log_path, "w")
sys.stdout = log
print(args)
# data = data_r
# Filter out na values
selected_idx = label_r.loc[:, select_drug] != na
if (g_disperson != None):
hvg, adata = ut.highly_variable_genes(data_r, min_disp=g_disperson)
# Rename columns if duplication exist
data_r.columns = adata.var_names
# Extract hvgs
data = data_r.loc[selected_idx, hvg]
else:
data = data_r.loc[selected_idx, :]
# Extract labels
label = label_r.loc[selected_idx, select_drug]
# Scaling data
mmscaler = preprocessing.MinMaxScaler()
lbscaler = preprocessing.MinMaxScaler()
data = mmscaler.fit_transform(data)
label = label.values.reshape(-1, 1)
if prediction == "regression":
label = lbscaler.fit_transform(label)
dim_model_out = 1
else:
le = LabelEncoder()
label = le.fit_transform(label)
dim_model_out = 2
#label = label.values.reshape(-1,1)
print(np.std(data))
print(np.mean(data))
# Split traning valid test set
X_train_all, X_test, Y_train_all, Y_test = train_test_split(
data, label, test_size=test_size, random_state=42)
X_train, X_valid, Y_train, Y_valid = train_test_split(X_train_all,
Y_train_all,
test_size=valid_size,
random_state=42)
# up-sampling
X_train, Y_train = SMOTE().fit_sample(X_train, Y_train)
print(data.shape)
print(label.shape)
print(X_train.shape, Y_train.shape)
print(X_test.shape, Y_test.shape)
print(X_train.max())
print(X_train.min())
# Select the Training device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Assuming that we are on a CUDA machine, this should print a CUDA device:
print(device)
torch.cuda.set_device(device)
# Construct datasets and data loaders
X_trainTensor = torch.FloatTensor(X_train).to(device)
X_validTensor = torch.FloatTensor(X_valid).to(device)
X_testTensor = torch.FloatTensor(X_test).to(device)
X_allTensor = torch.FloatTensor(data).to(device)
if prediction == "regression":
Y_trainTensor = torch.FloatTensor(Y_train).to(device)
Y_validTensor = torch.FloatTensor(Y_valid).to(device)
else:
Y_trainTensor = torch.LongTensor(Y_train).to(device)
Y_validTensor = torch.LongTensor(Y_valid).to(device)
train_dataset = TensorDataset(X_trainTensor, X_trainTensor)
valid_dataset = TensorDataset(X_validTensor, X_validTensor)
test_dataset = TensorDataset(X_testTensor, X_testTensor)
all_dataset = TensorDataset(X_allTensor, X_allTensor)
X_trainDataLoader = DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
X_validDataLoader = DataLoader(dataset=valid_dataset,
batch_size=batch_size,
shuffle=True)
X_allDataLoader = DataLoader(dataset=all_dataset,
batch_size=batch_size,
shuffle=True)
# construct TensorDataset
trainreducedDataset = TensorDataset(X_trainTensor, Y_trainTensor)
validreducedDataset = TensorDataset(X_validTensor, Y_validTensor)
trainDataLoader_p = DataLoader(dataset=trainreducedDataset,
batch_size=batch_size,
shuffle=True)
validDataLoader_p = DataLoader(dataset=validreducedDataset,
batch_size=batch_size,
shuffle=True)
dataloaders_train = {'train': trainDataLoader_p, 'val': validDataLoader_p}
if (bool(args.pretrain) == True):
dataloaders_pretrain = {
'train': X_trainDataLoader,
'val': X_validDataLoader
}
if reduce_model == "AE":
encoder = AEBase(input_dim=data.shape[1],
latent_dim=dim_au_out,
h_dims=encoder_hdims)
elif reduce_model == "VAE":
encoder = VAEBase(input_dim=data.shape[1],
latent_dim=dim_au_out,
h_dims=encoder_hdims)
#model = VAE(dim_au_in=data_r.shape[1],dim_au_out=128)
if torch.cuda.is_available():
encoder.cuda()
print(encoder)
encoder.to(device)
optimizer_e = optim.Adam(encoder.parameters(), lr=1e-2)
loss_function_e = nn.MSELoss()
exp_lr_scheduler_e = lr_scheduler.ReduceLROnPlateau(optimizer_e)
if reduce_model == "AE":
encoder, loss_report_en = ut.train_extractor_model(
net=encoder,
data_loaders=dataloaders_pretrain,
optimizer=optimizer_e,
loss_function=loss_function_e,
n_epochs=epochs,
scheduler=exp_lr_scheduler_e,
save_path=pretrain_path)
elif reduce_model == "VAE":
encoder, loss_report_en = ut.train_VAE_model(
net=encoder,
data_loaders=dataloaders_pretrain,
optimizer=optimizer_e,
n_epochs=epochs,
scheduler=exp_lr_scheduler_e,
save_path=pretrain_path)
print("Pretrained finished")
# Train model of predictor
if reduce_model == "AE":
model = PretrainedPredictor(input_dim=X_train.shape[1],
latent_dim=dim_au_out,
h_dims=encoder_hdims,
hidden_dims_predictor=preditor_hdims,
output_dim=dim_model_out,
pretrained_weights=pretrain_path,
freezed=bool(args.freeze_pretrain))
elif reduce_model == "VAE":
model = PretrainedVAEPredictor(input_dim=X_train.shape[1],
latent_dim=dim_au_out,
h_dims=encoder_hdims,
hidden_dims_predictor=preditor_hdims,
output_dim=dim_model_out,
pretrained_weights=pretrain_path,
freezed=bool(args.freeze_pretrain))
print(model)
if torch.cuda.is_available():
model.cuda()
model.to(device)
# Define optimizer
optimizer = optim.Adam(model.parameters(), lr=1e-2)
if prediction == "regression":
loss_function = nn.MSELoss()
else:
loss_function = nn.CrossEntropyLoss()
exp_lr_scheduler = lr_scheduler.ReduceLROnPlateau(optimizer)
preditor_path = model_path + reduce_model + select_drug + '.pkl'
load_model = os.path.exists(preditor_path)
model, report = ut.train_predictor_model(model,
dataloaders_train,
optimizer,
loss_function,
epochs,
exp_lr_scheduler,
load=load_model,
save_path=preditor_path)
dl_result = model(X_testTensor).detach().cpu().numpy()
#torch.save(model.feature_extractor.state_dict(), preditor_path+"encoder.pkl")
print('Performances: R/Pearson/Mse/')
if prediction == "regression":
print(r2_score(dl_result, Y_test))
print(pearsonr(dl_result.flatten(), Y_test.flatten()))
print(mean_squared_error(dl_result, Y_test))
else:
lb_results = np.argmax(dl_result, axis=1)
pb_results = np.max(dl_result, axis=1)
print(classification_report(Y_test, lb_results))
print(average_precision_score(Y_test, pb_results))
print(roc_auc_score(Y_test, pb_results))
