# Customizing Wrapper Functions

#### Nickalus Redell

#### 2020-02-28

## 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.

    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.

    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))

- 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`.

    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.

    attributes(data_train$horizon_3)$horizon

    ## [1] 3

    attributes(data_train$horizon_12)$horizon

    ## [1] 12

- 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(...)`.

    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.

    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`.

    model_results$horizon_3$window_1$model

    ## $my_trained_model
    ##
    ## Call:
    ##  randomForest(formula = model_formula, data = data, ntree = n_tree[1])
    ##                Type of random forest: regression
    ##                      Number of trees: 200
    ## No. of variables tried at each split: 13
    ##
    ##           Mean of squared residuals: 257.5402
    ##                     % Var explained: 59.83
    ##
    ## $n_tree
    ## [1] 200
    ##
    ## $meta_data
    ## [1] 3

    model_results$horizon_12$window_1$model

    ## $my_trained_model
    ##
    ## Call:
    ##  randomForest(formula = model_formula, data = data, ntree = n_tree[2])
    ##                Type of random forest: regression
    ##                      Number of trees: 100
    ## No. of variables tried at each split: 1
    ##
    ##           Mean of squared residuals: 389.5226
    ##                     % Var explained: 39.24
    ##
    ## $n_tree
    ## [1] 100
    ##
    ## $meta_data
    ## [1] 12

### 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"`).

    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

    data_results <- predict(model_results,
                            prediction_function = list(prediction_function),
                            data = data_train)

- Plot the performance on the validation dataset.

    plot(data_results)