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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
###### 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):
train_val = pd.read_csv(os.path.join(utils.data_dir, f"{csv_name}_train_val.csv"))
MPA_U_test = pd.read_csv(os.path.join(utils.data_dir, f"{csv_name}_test.csv"))
MPA_U_val = train_val.sample(frac=0.1, random_state=seed)
MPA_U_train = pd.concat([train_val, MPA_U_val]).drop_duplicates(keep=False)
# dataset
train_DS = reader.kmer_scan_dataset(MPA_U_train, seq_col='utr', kmer_size=kernel_size, aux_columns='rl')
val_DS = reader.kmer_scan_dataset(MPA_U_val, seq_col='utr', kmer_size=kernel_size, aux_columns='rl')
test_DS = reader.kmer_scan_dataset(MPA_U_test, seq_col='utr', kmer_size=kernel_size, aux_columns='rl')
# dataloader
train_dl = DataLoader(train_DS, batch_size = 64, shuffle=True)
val_dl = DataLoader(test_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):
super().__init__()
self.train_r2 = torchmetrics.R2Score()
self.val_r2 = torchmetrics.R2Score()
tower = { f"GRU_layer" : nn.GRU(input_size=4**k, hidden_size=128,
num_layers=2,batch_first=True),
f"fc_out" : nn.Linear(128, 1),
}
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 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,).float()
y_hat = self.forward(x).view(b,)
loss = F.mse_loss(y_hat, y)
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.float()
y = y.view(b,).float()
y_hat = self.forward(x).view(b,)
loss = F.mse_loss(y_hat, y)
self.val_r2(y_hat, y)
self.log('val_loss', loss)
self.log('val_acc', self.val_r2)
################
if __name__ == '__main__':
csv_name = sys.argv[1]
kmer_size = sys.argv[2]
hidden = sys.argv[3]
#### hyper-params ####
Input_length = np.multiply(*train_DS[0][0].shape)
hidden = [512]
dims = [Input_length] + hidden + [1]
################
# training
trainer = pl.Trainer(gpus=1, num_processes=8,
default_root_dir="/ssd/users/wergillius/Project/MTtrans/evaluation/Kmer_results",
limit_train_batches=0.5, max_epochs=6,
callbacks=[callbacks.EarlyStopping(monitor="val_loss", mode="min", patience=5)])
trainer.fit(model, train_dl, val_dl)
trainer.test(test_dl) |