Make function ts_model_spec_tuner

[spsanderson]

ts_model_spec_tuner <- function(.parsnip_engine = NULL){
  
  # * Tidyeval ----
  pe <- base::as.character(.parsnip_engine)
  
  # * Checks ----
  if(!pe %in% c("auto_arima","auto_arima_xgboost",
    "ets","croston","theta",
    "stlm_ets","tbats","stlm_arima",
    "nnetar",
    "prophet","prophet_xgboost",
    "lm","glmnet","stan","spark","keras",
    "earth","xgboost")){
    stop(call. = FALSE, base::paste0("The parameter (.parsnip_engine) value of: ", pe, ", is not supported."))
  }
  
  if(pe %in% c("auto_arima","stlm_ets","tbats","stlm_arima")){
    stop(call. = FALSE, base::paste0("The parameter (.parsnip_engine) value of: ", pe, ", is an auto tuned model spec already."))
  }
  
  if(pe %in% c("lm")){
    stop(call. = FALSE, base::paste0("The parameter (.parsnip_engine) value of: ", pe, ", has no tuning parameters."))
  }
  
  # * Model Spec Tuner ----
  if (pe == "auto_arima_xgboost"){
    mst <- modeltime::arima_boost(
      trees            = tune::tune()
      , min_n          = tune::tune()
      , tree_depth     = tune::tune()
      , learn_rate     = tune::tune()
      , loss_reduction = tune::tune()
      , sample_size    = tune::tune()
      , stop_iter      = tune::tune()
    ) %>%
      parsnip::set_engine(pe)
  } else if (pe == "ets"){
    mst <- modeltime::exp_smoothing(
      seasonal_period = "auto"
      , error         = "auto"
      , trend         = "auto"
      , season        = "auto"
      , damping       = "auto"
      , smooth_level    = tune::tune()
      , smooth_trend    = tune::tune()
      , smooth_seasonal = tune::tune()
    ) %>%
      parsnip::set_engine(pe)
  } else if (pe == "croston"){
    mst <- modeltime::exp_smoothing(
      seasonal_period = "auto"
      , error         = "auto"
      , trend         = "auto"
      , season        = "auto"
      , damping       = "auto"
      , smooth_level    = tune::tune()
      , smooth_trend    = tune::tune()
      , smooth_seasonal = tune::tune()
    ) %>%
      parsnip::set_engine(pe)
  } else if (pe == "theta"){
    mst <- modeltime::exp_smoothing(
      seasonal_period = "auto"
      , error         = "auto"
      , trend         = "auto"
      , season        = "auto"
      , damping       = "auto"
      , smooth_level    = tune::tune()
      , smooth_trend    = tune::tune()
      , smooth_seasonal = tune::tune()
    ) %>%
      parsnip::set_engine(pe)
  } else if (pe == "nnetar"){
    mst <- modeltime::nnetar_reg(
      seasonal_period   = "auto"
      , non_seasonal_ar = tune::tune()
      , seasonal_ar     = tune::tune()
      , hidden_units    = tune::tune()
      , num_networks    = tune::tune()
      , penalty         = tune::tune()
      , epochs          = tune::tune()
    ) %>%
      parsnip::set_engine(pe)
  } else if (pe == "prophet"){
    mst <- modeltime::prophet_reg(
      changepoint_num      = tune::tune()
      , changepoint_range  = tune::tune()
      , seasonality_yearly = "auto"
      , seasonality_weekly = "auto"
      , seasonality_daily  = "auto"
      , prior_scale_changepoints = tune::tune()
      , prior_scale_seasonality  = tune::tune()
      , prior_scale_holidays     = tune::tune()
    ) %>%
      parsnip::set_engine(pe)
  } else if (pe == "prophet_xgboost"){
    mst <- modeltime::prophet_boost(
      changepoint_num      = tune::tune()
      , changepoint_range  = tune::tune()
      , seasonality_yearly = FALSE
      , seasonality_weekly = FALSE
      , seasonality_daily  = FALSE
      , prior_scale_changepoints = tune::tune()
      , prior_scale_seasonality  = tune::tune()
      , prior_scale_holidays     = tune::tune()
      , trees                    = tune::tune()
      , min_n                    = tune::tune()
      , tree_depth               = tune::tune()
      , learn_rate               = tune::tune()
      , loss_reduction           = tune::tune()
      , sample_size              = tune::tune()
      , stop_iter                = tune::tune()
    ) %>%
      parsnip::set_engine(pe)
  } else if (pe == "glmnet") {
    mst <- parsnip::linear_reg(
      penalty   = tune::tune()
      , mixture = tune::tune()
    ) %>%
      parsnip::set_engine(pe)
  } else if (pe == "stan"){
    mst <- parsnip::linear_reg() %>%
      parsnip::set_engine(
        engine   = pe
        , chains = tune::tune()
        , iter   = tune::tune()
        , seed   = 123
      )
  } else if (pe == "spark"){
    mst <- parsnip::linear_reg(
      penalty   = tune::tune()
      , mixture = tune::tune()
    ) %>%
      parsnip::set_engine(pe)
  } else if (pe == "keras"){
    mst <- parsnip::linear_reg(
      penalty   = tune::tune()
      , mixture = tune::tune()
    ) %>%
      parsnip::set_engine(pe)
  } else if (pe == "earth"){
    mst <- parsnip::mars(
      num_terms = tune::tune()
      , prod_degree = tune::tune()
    ) %>%
      parsnip::set_engine(pe)
  } else if (pe == "xgboost"){
    mst <- parsnip::boost_tree(
      mtry             = tune::tune()
      , trees          = tune::tune()
      , min_n          = tune::tune()
      , tree_depth     = tune::tune()
      , learn_rate     = tune::tune()
      , loss_reduction = tune::tune()
      , sample_size    = tune::tune()
    ) %>%
      parsnip::set_engine(pe)
  }
  
  # * Return ----
  return(mst)
  
}