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run_exp_gp_pfs_exact_metric.py
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#
# DKAFT
#
# Copyright (c) Siemens AG, 2021
# Authors:
# Zhiliang Wu <[email protected]>
# License-Identifier: MIT
import shutil
import uuid
from pathlib import Path
import mlflow
import numpy as np
import pandas as pd
from sklearn.metrics import r2_score, mean_squared_error
import gpytorch
import torch
from torch.utils.data import TensorDataset
from torch.optim.lr_scheduler import StepLR
from tqdm import tqdm
from gp_layer import ExactGPModel
from plot_utils import residual_plot
from logging_conf import logger
from pytorchtools import EarlyStopping
from pml_pfs import run_metric_learning
def run(lr=3e-4, alpha=1e-3, n_hidden_sta=4, n_hidden_temp=32,
model_name='lstm', n_embedding_temp=32, epoch=50, fold_idx=0,
device=torch.device('cpu'), exp_name='dataset_xxx',
run_name='model_xxx', seed=42):
np.random.seed(seed)
torch.manual_seed(seed)
backbone, _ = run_metric_learning(batch_size=128,
lr=lr, alpha=alpha,
n_hidden_sta=n_hidden_sta,
n_hidden_temp=n_hidden_temp,
model_name=model_name,
n_embedding_temp=n_embedding_temp,
epoch=200, fold_idx=fold_idx,
device=device,
exp_name=exp_name,
run_name=f'{model_name}_metrics',
seed=seed
)
dp = Path(f'{str(Path.home())}/pfs/')
fn = 'pfs_data.pt'
data_fp = dp / fn
split_df = pd.read_csv(dp / f'{fn[:-3]}_split.csv', index_col=0)
data = torch.load(data_fp)
X_padded, lengths, static_data, target = data
X_padded = X_padded.to(device)
lengths = lengths.to(device)
static_data = static_data.to(device)
target = target.to(device)
x_dataset = TensorDataset(static_data, X_padded, lengths)
y_dataset = TensorDataset(target)
train_idx = split_df[split_df[f'fold_{fold_idx}'] == 1].index.to_list()
valid_idx = split_df[split_df[f'fold_{fold_idx}'] == 2].index.to_list()
test_idx = split_df[split_df[f'fold_{fold_idx}'] == 3].index.to_list()
train_x = x_dataset[train_idx]
train_y = y_dataset[train_idx][0]
valid_x = x_dataset[valid_idx]
valid_y = y_dataset[valid_idx][0]
test_x = x_dataset[test_idx]
test_y = y_dataset[test_idx][0]
likelihood = gpytorch.likelihoods.GaussianLikelihood()
model = ExactGPModel(train_x, train_y, backbone, likelihood)
likelihood = likelihood.to(device)
model = model.to(device)
optimizer = torch.optim.Adam([{'params': model.feature_extractor.parameters(),
'weight_decay': alpha,
'lr': lr
},
{'params': model.covar_module.parameters()},
{'params': model.mean_module.parameters()},
{'params': likelihood.parameters()}
], lr=0.01)
# step_scheduler = StepLR(optimizer, step_size=int(epoch/2), gamma=0.1)
# scheduler = LRScheduler(step_scheduler)
mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)
temp_name = f'temp_{uuid.uuid4()}'
Path(temp_name).mkdir(parents=True, exist_ok=True)
early_stopping = EarlyStopping(patience=25, verbose=True,
path=f'./{temp_name}/checkpoint.pt')
mlflow.set_experiment(exp_name)
with mlflow.start_run(run_name=run_name):
mlflow.log_params({
'seed': seed,
'num_epoch': epoch,
'model': model_name,
'weight_decay': alpha,
'embedding_temp': n_embedding_temp,
'hidden_dim_sta': n_hidden_sta,
'hidden_dim_temp': n_hidden_temp,
'fold_index': fold_idx,
'file_data': fn,
'pytorch version': torch.__version__,
'cuda version': torch.version.cuda,
'device name': torch.cuda.get_device_name(0)
})
iterator = tqdm(range(epoch))
for i in iterator:
model.train()
likelihood.train()
# Zero backprop gradients
optimizer.zero_grad()
# Get output from model
output = model(*train_x)
# Calc loss and backprop derivatives
loss = -mll(output, train_y)
loss.backward()
iterator.set_postfix(loss=loss.item())
optimizer.step()
model.eval()
likelihood.eval()
with torch.no_grad():
valid_output = model(*valid_x)
valid_y_pred = valid_output.mean.cpu().numpy()
mse_valid = mean_squared_error(valid_y.cpu().numpy(), valid_y_pred)
r2s_valid = r2_score(valid_y.cpu().numpy(), valid_y_pred)
models = {'model': model,
'likelihood': likelihood}
early_stopping(mse_valid, models)
training_metrics = {'training mll': -loss.item(),
'validation mse': mse_valid,
'validation r2score': r2s_valid
}
mlflow.log_metrics(training_metrics, step=i)
if early_stopping.early_stop:
print("Early stopping")
break
# https://pytorch.org/tutorials/beginner/saving_loading_models.html#saving-loading-a-general-checkpoint-for-inference-and-or-resuming-training
# model.load_state_dict(torch.load(f'./{temp_name}/checkpoint.pt'))
checkpoint = torch.load(f'./{temp_name}/checkpoint.pt')
for k, m in models.items():
m.load_state_dict(checkpoint[k])
print('final test on the test set')
model.eval()
likelihood.eval()
with torch.no_grad():
train_output = model(*train_x)
train_y_pred = train_output.mean.cpu().numpy()
valid_output = model(*valid_x)
valid_y_pred = valid_output.mean.cpu().numpy()
test_output = model(*test_x)
test_y_pred = test_output.mean.cpu().numpy()
mse_train = mean_squared_error(train_y.cpu().numpy(), train_y_pred)
r2s_train = r2_score(train_y.cpu().numpy(), train_y_pred)
mse_valid = mean_squared_error(valid_y.cpu().numpy(), valid_y_pred)
r2s_valid = r2_score(valid_y.cpu().numpy(), valid_y_pred)
mse_test = mean_squared_error(test_y.cpu().numpy(), test_y_pred)
r2s_test = r2_score(test_y.cpu().numpy(), test_y_pred)
test_metrics = {'training mse': mse_train,
'training r2score': r2s_train,
'validation mse': mse_valid,
'validation r2score': r2s_valid,
'test mse': mse_test,
'test r2score': r2s_test}
mlflow.log_metrics(test_metrics, step=i)
residual_plot(train_y.cpu().numpy(), train_y_pred,
valid_y.cpu().numpy(), valid_y_pred, dp=temp_name,
n_epoch=i, label='y_valid')
residual_plot(train_y.cpu().numpy(), train_y_pred,
test_y.cpu().numpy(), test_y_pred, dp=temp_name,
n_epoch=i, label='y_test')
mlflow.log_artifacts(f'./{temp_name}/')
try:
shutil.rmtree(temp_name)
except FileNotFoundError:
logger.warning('Temp drectory not found!')
raise
if __name__ == '__main__':
sd = 42
dc = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# dataset parameter
exp = 'Progression_free_survival'
n_epoch = 400
m_name = 'lstm' # or 'gru'
lrt = 1e-4 # this is from lr finder
for f in range(5):
run(lr=lrt, alpha=1e-7,
n_hidden_sta=4,
n_hidden_temp=128,
model_name=m_name,
n_embedding_temp=32,
epoch=n_epoch,
fold_idx=f, device=dc, exp_name=exp,
run_name=f'{m_name}_exact_gp_metric',
seed=sd)