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# os.environ["CUDA_VISIBLE_DEVICES"] = str(3)
import pytorch_lightning as pl
from torch.nn import functional as F
import PATH, utils
import torch
from torch import nn, optim
from models import reader
from models.popen import Auto_popen
import pandas as pd
import numpy as np
from collections import OrderedDict
import torchmetrics
from torch.utils.data import DataLoader
from pytorch_lightning import callbacks
from sklearn.model_selection import train_test_split
import argparse
################
if __name__ == '__main__':
global_seed = int(sys.argv[6])
torch.manual_seed(global_seed)
torch.cuda.manual_seed_all(global_seed)
###### data ######
def get_kmer_input_shape(csv_name, kernel_size):
MPA_U_test = pd.read_csv(os.path.join(utils.data_dir, f"{csv_name}_test.csv"))
# dataset
test_DS = reader.kmer_scan_dataset(MPA_U_test, seq_col='utr',
kmer_size=kernel_size, aux_columns='rl')
return np.multiply(*test_DS[0][0].shape)
def get_kmer_dls(csv_name, kernel_size, seed, seq_col, label_col):
print("seed = %s"%seed)
train_df, val_df, test_df = reader.split_DF(csv_name, None, [0.8,0.1,0.1], kfold_cv=True, kfold_index=seed,seed=43)
# dataset
train_DS = reader.kmer_scan_dataset(train_df, seq_col=seq_col, kmer_size=kernel_size, aux_columns=label_col)
val_DS = reader.kmer_scan_dataset(val_df, seq_col=seq_col, kmer_size=kernel_size, aux_columns=label_col)
test_DS = reader.kmer_scan_dataset(test_df, seq_col=seq_col, kmer_size=kernel_size, aux_columns=label_col)
# dataloader
train_dl = DataLoader(train_DS, batch_size = 64, shuffle=True)
val_dl = DataLoader(val_DS, batch_size = 64, shuffle=False)
test_dl = DataLoader(test_DS, batch_size = 64, shuffle=False)
return train_dl, val_dl, test_dl
##################
# PyTorch Light model #
class mlp_models(pl.LightningModule):
def __init__(self, dims):
super().__init__()
self.dims = dims
n_layer = len(dims) - 1
self.train_r2 = torchmetrics.R2Score()
self.val_r2 = torchmetrics.R2Score()
# add layers
nns = []
i = 1
for in_dim , out_dim in zip(dims[:-1], dims[1:]):
nns.append( (f"Linear_{i}", nn.Linear(in_dim, out_dim)) )
if i < n_layer:
nns += [(f"BN_{i}", nn.BatchNorm1d(out_dim)), (f"act_{i}", nn.Mish()) ]
i += 1
self.network = nn.Sequential(OrderedDict(nns))
def forward(self, x):
return self.network(x)
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
return optimizer
def training_step(self, train_batch, batch_idx):
x, y = train_batch
b = x.shape[0]
x = x.view(b, -1).float()
y = y.view(b,).float()
y_hat = self.network(x).view(b,)
loss = F.mse_loss(y_hat, y.float())
r2 = self.train_r2(y_hat, y)
self.log('train_loss', loss)
self.log('train_acc', self.train_r2)
return loss
def validation_step(self, val_batch, batch_idx):
x, y = val_batch
b = x.shape[0]
x = x.view(b, -1).float()
y = y.view(b,).float()
y_hat = self.network(x).view(b,)
loss = F.mse_loss(y_hat, y.float())
r2 = self.val_r2(y_hat, y)
self.log('val_loss', loss)
self.log('val_acc', self.val_r2)
# PyTorch Light model #
class rnn_models(pl.LightningModule):
def __init__(self, k, hidden=128):
super().__init__()
self.save_hyperparameters()
self.train_F1 = torchmetrics.F1Score()
self.train_AUROC = torchmetrics.AUROC()
self.train_ACC = torchmetrics.Accuracy()
self.val_F1 = torchmetrics.F1Score()
self.val_AUROC = torchmetrics.AUROC()
self.val_ACC = torchmetrics.Accuracy()
self.test_F1 = torchmetrics.F1Score()
self.test_AUROC = torchmetrics.AUROC()
self.test_ACC = torchmetrics.Accuracy()
tower = { f"GRU_layer" : nn.GRU(input_size=4**k, hidden_size=hidden,
num_layers=2,batch_first=True),
f"fc_out" : nn.Linear(hidden, 1),
}
self.sigmod = nn.Sigmoid()
self.tower = nn.ModuleDict(tower)
def forward(self, x):
x = x.transpose(1,2) # B C L -> B L C
h_prim,(c1,c2) = self.tower['GRU_layer'](x)
out = self.tower['fc_out'](c2)
return self.sigmod(out)
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
return optimizer
def training_step(self, train_batch, batch_idx):
x, y = train_batch
b = x.shape[0]
x = x.float()
y = y.view(b,)
y_hat = self.forward(x).view(b,)
loss = F.binary_cross_entropy(y_hat, y.float())
F1 = self.train_F1(y_hat, y.long())
auroc = self.train_AUROC(y_hat, y.long())
acc = self.train_ACC(y_hat, y.long())
self.log('train_loss', loss)
self.log('train_f1', self.train_F1)
self.log('train_AUROC', self.train_AUROC)
self.log('train_ACC', self.train_ACC)
return loss
def validation_step(self, val_batch, batch_idx):
x, y = val_batch
b = x.shape[0]
x = x.float()
y = y.view(b,)
y_hat = self.forward(x).view(b,)
loss = F.binary_cross_entropy(y_hat, y.float())
self.val_F1(y_hat, y.long())
self.val_AUROC(y_hat, y.long())
self.val_ACC(y_hat, y.long())
self.log('val_loss', loss)
self.log('val_F1', self.val_F1)
self.log('val_AUROC', self.val_AUROC)
self.log('val_ACC', self.val_ACC)
def test_step(self, test_batch, batch_idx):
x, y = test_batch
b = x.shape[0]
x = x.float()
y = y.view(b,)
y_hat = self.forward(x).view(b,)
loss = F.binary_cross_entropy(y_hat, y.float())
self.test_F1(y_hat, y.long())
self.test_AUROC(y_hat, y.long())
self.test_ACC(y_hat, y.long())
self.log('test_loss', loss)
self.log('test_F1', self.test_F1)
self.log('test_AUROC', self.test_AUROC)
self.log('test_ACC', self.test_ACC)
################
if __name__ == '__main__':
csv_name = sys.argv[1]
kmer_size = int(sys.argv[2])
hidden = int(sys.argv[3])
seq_col = sys.argv[4]
label_col = sys.argv[5]
train_dl, val_dl, test_dl = get_kmer_dls(csv_name, kmer_size, global_seed, seq_col, label_col)
#### hyper-params ####
Input_length = np.multiply(*train_dl.dataset[0][0].shape)
# dims = [Input_length] + hidden + [1]
model = rnn_models(kmer_size, hidden)
default_root_dir="/data/users/wergillius/UTR_VAE/pth/Kmer_Alan"
# default_root_dir="/ssd/users/wergillius/Project/MTtrans/evaluation/Kmer_results"
data_name = os.path.basename(csv_name).split("_")[0]
log_dir = os.path.join(default_root_dir, f"{data_name}_K{kmer_size}H{hidden}_sd{global_seed}")
################
# training
trainer = pl.Trainer(accelerator='gpu',devices=1, auto_select_gpus=False, #
default_root_dir=log_dir,
limit_train_batches=0.5, max_epochs=600,
#plugins=pl.plugins.DDPPlugin(find_unused_parameters=False),
callbacks=[
callbacks.ModelCheckpoint(monitor="val_loss",save_top_k=1),
callbacks.EarlyStopping(monitor="val_F1", mode="min", patience=15)
])
trainer.fit(model, train_dl, val_dl)
ckpt_dir = os.path.join(trainer.log_dir,'checkpoints')
ckpt_path = os.path.join(ckpt_dir, [file for file in os.listdir(ckpt_dir) if file.endswith('.ckpt')][0])
print('model saved to : %s\n'%ckpt_path)
# trainer.validate(model, test_dl)
saved_model = rnn_models.load_from_checkpoint(ckpt_path)
trainer.validate(saved_model, val_dl)
trainer.test(saved_model, test_dl)
# debugging args:
# "/data/users/wergillius/UTR_VAE/Alan_dataset/AlanAll_binary_10pctg.csv",
# "3",
# "64",
# "seq",
# "Binary_10pc",
# "1" |