--- a
+++ b/app/apis/dl_los_pipeline.py
@@ -0,0 +1,313 @@
+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,
+    )