forecastML (version 0.5.0)

plot.forecast_model_hyper: Plot hyperparameters

Description

Plot hyperparameter stability and relationship with error metrics across validation datasets and horizons.

Usage

# S3 method for forecast_model_hyper
plot(x, data_results, data_error,
  type = c("stability", "error"), horizons = NULL, windows = NULL,
  ...)

Arguments

x

An object of class 'forecast_model_hyper' from return_hyper().

data_results

An object of class 'training_results' from predict.forecast_model().

data_error

An object of class 'validation_error' from return_error().

type

Select plot type; 'stability' is the default.

horizons

Optional. A numeric vector to filter results by horizon.

windows

Optional. A numeric vector to filter results by validation window number.

...

Not used.

Value

Hyperparameter plots of class 'ggplot'.

Examples

# NOT RUN {
# Sampled Seatbelts data from the R package datasets.
data("data_seatbelts", package = "forecastML")

# Example - Training data for 2 horizon-specific models w/ common lags per predictor.
horizons <- c(1, 12)
lookback <- 1:15

data_train <- create_lagged_df(data_seatbelts, type = "train", outcome_col = 1,
                               lookback = lookback, horizon = horizons)

windows <- create_windows(data_train, window_length = 12)

# User-define model - LASSO
# A user-defined wrapper function for model training that takes the following
# arguments: (1) a horizon-specific data.frame made with create_lagged_df(..., type = "train")
# (e.g., my_lagged_df$horizon_h) and, optionally, (2) any number of additional named arguments
# which are passed as '...' in train_model().
library(glmnet)
model_function <- function(data, my_outcome_col) {

  x <- data[, -(my_outcome_col), drop = FALSE]
  y <- data[, my_outcome_col, drop = FALSE]
  x <- as.matrix(x, ncol = ncol(x))
  y <- as.matrix(y, ncol = ncol(y))

  model <- glmnet::cv.glmnet(x, y, nfolds = 3)
  return(model)
}

# my_outcome_col = 1 is passed in ... but could have been defined in model_function().
model_results <- train_model(data_train, windows, model_name = "LASSO", model_function,
                             my_outcome_col = 1)

# User-defined prediction function - LASSO
# The predict() wrapper takes two positional arguments. First,
# the returned model from the user-defined modeling function (model_function() above).
# Second, a data.frame of predictors--identical to the datasets returned from
# create_lagged_df(..., type = "train"). The function can return a 1- or 3-column data.frame
# with either (a) point forecasts or (b) point forecasts plus lower and upper forecast
# bounds (column order and column names do not matter).
prediction_function <- function(model, data_features) {

  x <- as.matrix(data_features, ncol = ncol(data_features))

  data_pred <- data.frame("y_pred" = predict(model, x, s = "lambda.min"))
  return(data_pred)
}

# Predict on the validation datasets.
data_valid <- predict(model_results, prediction_function = list(prediction_function),
                      data = data_train)

# User-defined hyperparameter function - LASSO
# The hyperparameter function should take one positional argument--the returned model
# from the user-defined modeling function (model_function() above). It should
# return a 1-row data.frame of the optimal hyperparameters.
hyper_function <- function(model) {

  lambda_min <- model$lambda.min
  lambda_1se <- model$lambda.1se

  data_hyper <- data.frame("lambda_min" = lambda_min, "lambda_1se" = lambda_1se)
  return(data_hyper)
}

data_error <- return_error(data_valid)

data_hyper <- return_hyper(model_results, hyper_function)

plot(data_hyper, data_valid, data_error, type = "stability",
     horizons = c(1, 12))
# }