---
title: "Customizing Wrapper Functions"
author: "Nickalus Redell"
date: "`r lubridate::today()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Customizing Wrapper Functions}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(fig.width = 7.15, fig.height = 4)
```

## Purpose

The purpose of this vignette is to provide a closer look at how the user-supplied model training and 
predict wrapper functions can be modified to give greater control over the model-building process. 
The goal is to present examples of how the wrapper functions could be flexibly written to keep a linear workflow in 
`forecastML` while modeling across multiple forecast horizons and validation datasets. The alternative would be to 
train models across a single forecast horizon and/or validation window and customize the wrapper functions 
for this specific setup.

## Example 1 - Multiple forecast horizons and 1 model training function

* Load packages and data.

```{r, message = FALSE, warning = FALSE}
library(DT)
library(dplyr)
library(ggplot2)
library(forecastML)
library(randomForest)

data("data_seatbelts", package = "forecastML")
data <- data_seatbelts

data <- data[, c("DriversKilled", "kms", "PetrolPrice", "law")]

dates <- seq(as.Date("1969-01-01"), as.Date("1984-12-01"), by = "1 month")
```


* Create a lagged data frame for model training. We'll train 2 models: one across a 1:3-month 
horizon and the other across a 1:12-month horizon.

```{r}
data_train <- forecastML::create_lagged_df(data,
                                           type = "train",
                                           outcome_col = 1, 
                                           lookback = 1:12,
                                           horizons = c(3, 12),
                                           dates = dates,
                                           frequency = "1 month")

# View the horizon 3 lagged dataset.
DT::datatable(head((data_train$horizon_3)), options = list("scrollX" = TRUE))
```

<br>

* We'll train our models to predict on 1 validation dataset. Setting `window_length = 0` means that a 
single validation dataset will span from `window_start` to `window_stop`.

```{r}
windows <- forecastML::create_windows(data_train, window_length = 0, 
                                      window_start = as.Date("1984-01-01"),
                                      window_stop = as.Date("1984-12-01"))

plot(windows, data_train)
```

### User-defined model-training function

* The key to customizing training across forecast horizons--here we have 2--is to modify 
the model training wrapper function based on the horizon-specific dataset in our `lagged_df` object 
`data_train`.

* Each dataset's forecast horizon is stored as an attribute.

```{r}
attributes(data_train$horizon_3)$horizon
attributes(data_train$horizon_12)$horizon
```

* We'll train a Random Forest model with different settings for the 3-month and 12-month datasets.

* The first argument to the user-defined model training function is always the horizon-specific 
dataset from `create_lagged_df(type = "train")` and is passed into the wrapper function internally 
in `train_model()`. Any number of additional parameters can be defined in this 
wrapper function by either (a) setting arguments here--like below--or (b) setting the arguments in 
`train_model(...)`.

```{r}
model_function <- function(data, my_outcome_col = 1, n_tree = c(200, 100)) {

  outcome_names <- names(data)[my_outcome_col]
  model_formula <- formula(paste0(outcome_names,  "~ ."))
  
  if (attributes(data)$horizon == 3) {  # Model 1
    
          model <- randomForest::randomForest(formula = model_formula, 
                                              data = data, 
                                              ntree = n_tree[1])
          
          return(list("my_trained_model" = model, "n_tree" = n_tree[1], 
                      "meta_data" = attributes(data)$horizon))
      
  } else if (attributes(data)$horizon == 12) {  # Model 2
    
          model <- randomForest::randomForest(formula = model_formula, 
                                              data = data, 
                                              ntree = n_tree[2])
          
          return(list("my_trained_model" = model, "n_tree" = n_tree[2],
                      "meta_data" = attributes(data)$horizon))
  }
}
```


* Train the models.

```{r}
model_results <- forecastML::train_model(data_train, windows, model_name = "RF", model_function)
```


* View the `return()` values from the user-defined `model_function()`. The returned values are stored in 
`my_training_results$horizon_h$window_w$model`.

```{r}
model_results$horizon_3$window_1$model
model_results$horizon_12$window_1$model
```

### User-defined prediction function

* The user-defined prediction function only takes two positional parameters. The first parameter 
is the returned value of the user-defined modeling function; here, a list with 3 elements. The second parameter is the horizon-specific dataset with model features from `create_lagged_df()` (`type = "train"` or `type = "forecast"`).

```{r}
prediction_function <- function(model, data_features) {
  
    if (model$meta_data == 3) {  # Perform a transformation specific to model 1.
      
        data_pred <- data.frame("y_pred" = predict(model$my_trained_model, data_features))
    }
  
    if (model$meta_data == 12) {  # Perform a transformation specific to model 2.
      
        data_pred <- data.frame("y_pred" = predict(model$my_trained_model, data_features))
    }

  return(data_pred)
}
```


* Predict

```{r}
data_results <- predict(model_results,
                        prediction_function = list(prediction_function),
                        data = data_train)
```


* Plot the performance on the validation dataset.

```{r}
plot(data_results)
```

***