--- a
+++ b/app/apis/dl_outcome_pipeline.py
@@ -0,0 +1,314 @@
+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 covid_metrics, 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[:, :, 0]  # 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, info):
+    """
+    val / test
+    """
+    val_loss = []
+    y_pred = []
+    y_true = []
+    y_true_all = []
+    len_list = []
+    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),
+            )
+            all_y = batch_y
+            batch_y = batch_y[:, :, 0]  # 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())
+            len_list.extend(batch_x_lab_length.long().tolist())
+            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_true_all.extend(all_y[i][: batch_x_lab_length[i].long()].tolist())
+    y_true = np.array(y_true)
+    y_pred = np.array(y_pred)
+    y_true_all = np.array(y_true_all)
+    len_list = np.array(len_list)
+    # print("len:", len(y_true), len_list.sum(), len_list)
+    early_prediction_score = covid_metrics.early_prediction_outcome_metric(
+        y_true_all,
+        y_pred,
+        len_list,
+        info["config"].thresholds,
+        verbose=0,
+    )
+    y_pred = np.stack([1 - y_pred, y_pred], axis=1)
+    evaluation_scores = eval_metrics.print_metrics_binary(y_true, y_pred, verbose=0)
+    evaluation_scores["early_prediction_score"] = early_prediction_score
+    return np.array(val_loss).mean(), evaluation_scores
+
+
+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_accuracy": [],
+        "test_auroc": [],
+        "test_auprc": [],
+        "test_early_prediction_score": [],
+    }
+    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):
+        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,
+        )
+
+        test_sampler = SubsetRandomSampler(test_idx)
+        test_loader = DataLoader(
+            dataset,
+            batch_size=cfg.batch_size,
+            sampler=test_sampler,
+            num_workers=0,
+        )
+        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_accuracy": [],
+            "val_auroc": [],
+            "val_auprc": [],
+            "val_early_prediction_score": [],
+        }
+        train_idx, val_idx = next(
+            sss.split(np.arange(len(train_and_val_idx)), sub_dataset.y[:, 0, 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_bce_loss
+            best_val_performance = 0.0
+
+            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,
+                        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_accuracy"].append(val_evaluation_scores["acc"])
+                    history["val_auroc"].append(val_evaluation_scores["auroc"])
+                    history["val_auprc"].append(val_evaluation_scores["auprc"])
+                    history["val_early_prediction_score"].append(
+                        val_evaluation_scores["early_prediction_score"]
+                    )
+                    # if auroc is better, than set the best auroc, save the model, and test it on the test set
+                    if val_evaluation_scores["auprc"] > best_val_performance:
+                        best_val_performance = val_evaluation_scores["auprc"]
+                        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}: \
+                    AUPRC = {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,
+                info=val_info,
+            )
+            test_performance["test_loss"].append(test_loss)
+            test_performance["test_accuracy"].append(test_evaluation_scores["acc"])
+            test_performance["test_auroc"].append(test_evaluation_scores["auroc"])
+            test_performance["test_auprc"].append(test_evaluation_scores["auprc"])
+            test_performance["test_early_prediction_score"].append(
+                test_evaluation_scores["early_prediction_score"]
+            )
+            print(
+                f"Performance on test set {fold_test+1}: \
+                ACC = {test_evaluation_scores['acc']}, \
+                AUROC = {test_evaluation_scores['auroc']}, \
+                AUPRC = {test_evaluation_scores['auprc']}, \
+                EarlyPredictionScore = {test_evaluation_scores['early_prediction_score']}"
+            )
+        all_history["test_fold_{}".format(fold_test + 1)] = history
+    # Calculate average performance on 10-fold test set
+    test_accuracy_list = np.array(test_performance["test_accuracy"])
+    test_auroc_list = np.array(test_performance["test_auroc"])
+    test_auprc_list = np.array(test_performance["test_auprc"])
+    test_early_prediction_list = np.array(
+        test_performance["test_early_prediction_score"]
+    )
+
+    print("====================== TEST RESULT ======================")
+    print(
+        "ACC: {:.3f} ({:.3f})".format(
+            test_accuracy_list.mean(), test_accuracy_list.std()
+        )
+    )
+    print(
+        "AUROC: {:.3f} ({:.3f})".format(test_auroc_list.mean(), test_auroc_list.std())
+    )
+    print(
+        "AUPRC: {:.3f} ({:.3f})".format(test_auprc_list.mean(), test_auprc_list.std())
+    )
+    print(
+        "EarlyPredictionScore:",
+        (
+            test_early_prediction_list.mean(axis=0),
+            test_early_prediction_list.std(axis=0),
+        ),
+    )
+    for i in range(len(cfg.thresholds)):
+        print(
+            cfg.thresholds[i],
+            test_early_prediction_list.mean(axis=0)[i],
+            test_early_prediction_list.std(axis=0)[i],
+        )
+    print("=========================================================")
+    perflog.process_and_upload_performance(
+        cfg,
+        acc=test_accuracy_list,
+        auroc=test_auroc_list,
+        auprc=test_auprc_list,
+        early_prediction_score=test_early_prediction_list,
+        verbose=1,
+        upload=cfg.db,
+    )