---
title: "0. One-Click Run All Models"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{0. One-Click Run All Models}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 10,
  fig.height = 8
)
```

```{r setup}
library(E2E)
```

```{r, include=FALSE}
# Set up parallel processing
cl <- parallel::makeCluster(2)
doParallel::registerDoParallel(cl)
```

## Overview

E2E provides three powerful one-click functions that automatically run comprehensive modeling pipelines:

* **`int_dia()`**: Diagnostic modeling pipeline (52 model variants)
* **`int_imbalance()`**: Imbalanced data diagnostic pipeline (64 model variants)
* **`int_pro()`**: Prognostic modeling pipeline (24 model variants)

---

## 1. Diagnostic Pipeline

```{r, eval=FALSE}
# Run all diagnostic models
results_dia <- int_dia(
  train_dia,
  test_dia,
  test_dia, #can be any other data
  tune = TRUE,
  n_estimators = 5,
  seed = 123
)

# Visualize results
#plot_integrated_results(results_dia, metric_name = "AUROC")
```

---

## 2. Imbalanced Data Pipeline

```{r, eval=FALSE}
# Run all models including imbalance handling methods
results_imb <- int_imbalance(
  train_dia,
  test_dia,
  test_dia, #can be any other data
  tune = TRUE,
  n_estimators = 5,
  seed = 123
)

# Visualize results
#plot_integrated_results(results_imb, metric_name = "AUROC")
```

---

## 3. Prognostic Pipeline

```{r, eval=FALSE}
# Run all prognostic models
results_pro <- int_pro(
  train_pro,
  test_pro,
  test_pro, #can be any other data
  tune = TRUE,
  n_estimators = 5,
  time_unit = "day",
  years_to_evaluate = c(1, 3, 5),
  seed = 123
)

# Visualize results (C-index)
#plot_integrated_results(results_pro, metric_name = "C-index")
```

---

## Key Features

✅ **Fully Automated**: Run dozens of models with one line of code
✅ **Multi-Dataset Evaluation**: Evaluate on training and multiple test sets simultaneously
✅ **Diverse Methods**: Covers single models, Bagging, Stacking, and Voting
✅ **Clear Visualization**: Heatmaps display all results intuitively

```{r, include=FALSE}
# Stop parallel cluster
parallel::stopCluster(cl)
```