import math
import pathlib
import pickle
import random
import numpy as np
import pandas as pd
import torch
import torch.nn.functional as F
import torch.nn.utils.rnn as rnn_utils
from sklearn.model_selection import (
KFold,
StratifiedKFold,
StratifiedShuffleSplit,
train_test_split,
)
from sklearn.tree import DecisionTreeRegressor
from torch import nn
from torch.autograd import Variable
from torch.utils import data
from torch.utils.data import (
ConcatDataset,
DataLoader,
Dataset,
Subset,
SubsetRandomSampler,
TensorDataset,
random_split,
)
from app.core.evaluation import eval_metrics
from app.core.utils import init_random
from app.datasets import get_dataset, load_data
from app.datasets.dl import Dataset
from app.datasets.ml import flatten_dataset, numpy_dataset
from app.models import (
build_model_from_cfg,
get_multi_task_loss,
predict_all_visits_bce_loss,
predict_all_visits_mse_loss,
)
from app.utils import perflog
def train_epoch(model, device, dataloader, loss_fn, optimizer, info):
train_loss = []
model.train()
for step, data in enumerate(dataloader):
batch_x, batch_y, batch_x_lab_length = data
batch_x, batch_y, batch_x_lab_length = (
batch_x.float().to(device),
batch_y.float().to(device),
batch_x_lab_length.float().to(device),
)
batch_y = batch_y[:, :, 1] # 0: outcome, 1: los
batch_y = batch_y.unsqueeze(-1)
optimizer.zero_grad()
output = model(batch_x, device, info)
loss = loss_fn(output, batch_y, batch_x_lab_length)
train_loss.append(loss.item())
loss.backward()
optimizer.step()
return np.array(train_loss).mean()
def val_epoch(model, device, dataloader, loss_fn, los_statistics, info):
"""
val / test
"""
val_loss = []
y_pred = []
y_true = []
model.eval()
with torch.no_grad():
for step, data in enumerate(dataloader):
batch_x, batch_y, batch_x_lab_length = data
batch_x, batch_y, batch_x_lab_length = (
batch_x.float().to(device),
batch_y.float().to(device),
batch_x_lab_length.float().to(device),
)
batch_y = batch_y[:, :, 1] # 0: outcome, 1: los
batch_y = batch_y.unsqueeze(-1)
output = model(batch_x, device, info)
loss = loss_fn(output, batch_y, batch_x_lab_length)
val_loss.append(loss.item())
output = torch.squeeze(output)
batch_y = torch.squeeze(batch_y)
for i in range(len(batch_y)):
y_pred.extend(output[i][: batch_x_lab_length[i].long()].tolist())
y_true.extend(batch_y[i][: batch_x_lab_length[i].long()].tolist())
y_pred = np.array(y_pred)
y_true = np.array(y_true)
y_pred = reverse_zscore_los(y_pred, los_statistics)
y_true = reverse_zscore_los(y_true, los_statistics)
evaluation_scores = eval_metrics.print_metrics_regression(y_true, y_pred, verbose=0)
return np.array(val_loss).mean(), evaluation_scores
def calculate_los_statistics(dataset, train_idx):
"""calculate los's mean/std"""
# y = dataset.y[train_idx][:, :, 1]
y = []
for i in train_idx:
# print(dataset.y[i][:dataset.x_lab_length[i].long()])
for j in range(dataset.x_lab_length[i]):
y.append(dataset.y[i][j][1])
# y.extend(dataset.y[i][:dataset.x_lab_length[i].long()].tolist())
y = np.array(y)
mean, std = y.mean(), y.std()
los_statistics = {"los_mean": mean, "los_std": std}
return los_statistics
def zscore_los(dataset, los_statistics):
"""zscore scale y"""
dataset.y[:, :, 1] = (
dataset.y[:, :, 1] - los_statistics["los_mean"]
) / los_statistics["los_std"]
return dataset
def reverse_zscore_los(y, los_statistics):
"""reverse zscore y"""
y = y * los_statistics["los_std"] + los_statistics["los_mean"]
return y
def start_pipeline(cfg, device):
info = {"config": cfg, "epoch": 0}
val_info = {"config": cfg, "epoch": cfg.epochs}
dataset_type, method, num_folds, train_fold = (
cfg.dataset,
cfg.model,
cfg.num_folds,
cfg.train_fold,
)
# Load data
x, y, x_lab_length = load_data(dataset_type)
dataset = get_dataset(x, y, x_lab_length)
all_history = {}
test_performance = {
"test_loss": [],
"test_mad": [],
"test_mse": [],
"test_mape": [],
"test_rmse": [],
}
kfold_test = StratifiedKFold(
n_splits=num_folds, shuffle=True, random_state=cfg.dataset_split_seed
)
skf = kfold_test.split(np.arange(len(dataset)), dataset.y[:, 0, 0])
for fold_test in range(train_fold):
x, y, x_lab_length = load_data(dataset_type)
dataset = get_dataset(x, y, x_lab_length)
train_and_val_idx, test_idx = next(skf)
print("====== Test Fold {} ======".format(fold_test + 1))
sss = StratifiedShuffleSplit(
n_splits=1,
test_size=1 / (num_folds - 1),
random_state=cfg.dataset_split_seed,
)
sub_dataset = Dataset(
dataset.x[train_and_val_idx],
dataset.y[train_and_val_idx],
dataset.x_lab_length[train_and_val_idx],
)
all_history["test_fold_{}".format(fold_test + 1)] = {}
history = {
"train_loss": [],
"val_loss": [],
"val_mad": [],
"val_mse": [],
"val_mape": [],
"val_rmse": [],
}
train_idx, val_idx = next(
sss.split(np.arange(len(train_and_val_idx)), sub_dataset.y[:, 0, 0])
)
# apply z-score transform los
los_statistics = calculate_los_statistics(sub_dataset, train_idx)
print(los_statistics)
sub_dataset = zscore_los(sub_dataset, los_statistics)
dataset = zscore_los(dataset, los_statistics)
test_sampler = SubsetRandomSampler(test_idx)
test_loader = DataLoader(
dataset,
batch_size=cfg.batch_size,
sampler=test_sampler,
num_workers=0,
)
train_sampler = SubsetRandomSampler(train_idx)
val_sampler = SubsetRandomSampler(val_idx)
train_loader = DataLoader(
sub_dataset,
batch_size=cfg.batch_size,
sampler=train_sampler,
num_workers=0,
)
val_loader = DataLoader(
sub_dataset,
batch_size=cfg.batch_size,
sampler=val_sampler,
num_workers=0,
)
for seed in cfg.model_init_seed:
init_random(seed)
model = build_model_from_cfg(cfg, device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
criterion = predict_all_visits_mse_loss
best_val_performance = 1e8
if cfg.train == True:
for epoch in range(cfg.epochs):
info["epoch"] = epoch + 1
train_loss = train_epoch(
model,
device,
train_loader,
criterion,
optimizer,
info=info,
)
val_loss, val_evaluation_scores = val_epoch(
model,
device,
val_loader,
criterion,
los_statistics,
info=val_info,
)
# save performance history on validation set
print(
"Epoch:{}/{} AVG Training Loss:{:.3f} AVG Val Loss:{:.3f}".format(
epoch + 1, cfg.epochs, train_loss, val_loss
)
)
history["train_loss"].append(train_loss)
history["val_loss"].append(val_loss)
history["val_mad"].append(val_evaluation_scores["mad"])
history["val_mse"].append(val_evaluation_scores["mse"])
history["val_mape"].append(val_evaluation_scores["mape"])
history["val_rmse"].append(val_evaluation_scores["rmse"])
# if mad is lower, than set the best mad, save the model, and test it on the test set
if val_evaluation_scores["mad"] < best_val_performance:
best_val_performance = val_evaluation_scores["mad"]
torch.save(
model.state_dict(),
f"checkpoints/{cfg.name}_{fold_test + 1}_seed{seed}.pth",
)
print("[best!!]", epoch)
es = 0
else:
es += 1
if es >= 20:
print(f"Early stopping break at epoch {epoch}")
break
print(
f"Best performance on val set {fold_test+1}: \
MAE = {best_val_performance}"
)
model = build_model_from_cfg(cfg, device)
model.load_state_dict(
torch.load(
f"checkpoints/{cfg.name}_{fold_test + 1}_seed{seed}.pth",
map_location=torch.device("cpu"),
)
)
test_loss, test_evaluation_scores = val_epoch(
model,
device,
test_loader,
criterion,
los_statistics,
info=val_info,
)
test_performance["test_loss"].append(test_loss)
test_performance["test_mad"].append(test_evaluation_scores["mad"])
test_performance["test_mse"].append(test_evaluation_scores["mse"])
test_performance["test_mape"].append(test_evaluation_scores["mape"])
test_performance["test_rmse"].append(test_evaluation_scores["rmse"])
print(
f"Performance on test set {fold_test+1}: \
MAE = {test_evaluation_scores['mad']}, \
MSE = {test_evaluation_scores['mse']}, \
MAPE = {test_evaluation_scores['mape']}, \
RMSE = {test_evaluation_scores['rmse']}"
)
all_history["test_fold_{}".format(fold_test + 1)] = history
# Calculate average performance on 10-fold test set
test_mad_list = np.array(test_performance["test_mad"])
test_mse_list = np.array(test_performance["test_mse"])
test_mape_list = np.array(test_performance["test_mape"])
test_rmse_list = np.array(test_performance["test_rmse"])
print("====================== TEST RESULT ======================")
print("MAE: {:.3f} ({:.3f})".format(test_mad_list.mean(), test_mad_list.std()))
print("MSE: {:.3f} ({:.3f})".format(test_mse_list.mean(), test_mse_list.std()))
print("MAPE: {:.3f} ({:.3f})".format(test_mape_list.mean(), test_mape_list.std()))
print("RMSE: {:.3f} ({:.3f})".format(test_rmse_list.mean(), test_rmse_list.std()))
print("=========================================================")
perflog.process_and_upload_performance(
cfg,
mae=test_mad_list,
mse=test_mse_list,
rmse=test_rmse_list,
mape=test_mape_list,
verbose=1,
upload=cfg.db,
)
