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Users/mitokic/pca feature #80

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Nov 12, 2021
Merged

Users/mitokic/pca feature #80

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b81dd53
update to daily runs and pca feature to simplify lag features
mitokic Nov 3, 2021
6984c0a
add default handling of PCA based on date type and combo value
mitokic Nov 11, 2021
8d1d050
Merge branch 'main' into users/mitokic/pca-feature
mitokic Nov 12, 2021
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6 changes: 2 additions & 4 deletions R/configure_forecast_run.R
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@ get_fourier_periods <- function(fourier_periods,
#'
#' @param lag_periods lag_periods override
#' @param date_type year, quarter, month, week, day
#' @param forecast_horizon horion input from user
#' @param forecast_horizon horizon input from user
#'
#' @return Returns lag_periods
#' @noRd
Expand All @@ -49,9 +49,7 @@ get_lag_periods <- function(lag_periods,
"quarter" = c(1,2,3,4),
"month" = c(1, 2, 3, 6, 9, 12),
"week" = c(1, 2, 3, 4, 8, 12, 24, 48, 52),
"day" = c(1, 2, 3, 4, 5, 6, 7, 14,
21, 28, 28*2, 28*3, 28*6,
28*9, 28*12, 365)
"day" = c(7, 14, 21, 28, 60, 90, 180, 365)
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)

oplist <- c(oplist,forecast_horizon)
Expand Down
74 changes: 45 additions & 29 deletions R/forecast_models.R
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Original file line number Diff line number Diff line change
Expand Up @@ -164,7 +164,8 @@ invoke_forecast_function <- function(fn_to_invoke,
date_rm_regex,
back_test_spacing,
fiscal_year_start,
model_type){
model_type,
pca){

exp_arg_list <- formalArgs(fn_to_invoke)

Expand All @@ -177,7 +178,8 @@ invoke_forecast_function <- function(fn_to_invoke,
'date_rm_regex' = date_rm_regex,
'back_test_spacing' = back_test_spacing,
'fiscal_year_start' = fiscal_year_start,
'model_type' = model_type)
'model_type' = model_type,
"pca" = pca)

avail_names <- names(avail_arg_list)

Expand Down Expand Up @@ -248,9 +250,10 @@ construct_forecast_models <- function(full_data_tbl,
back_test_scenarios,
date_regex,
fiscal_year_start,
seasonal_periods
){

seasonal_periods,
pca
){

forecast_models <- function(combo_value) {

cli::cli_h2("Running Combo: {combo_value}")
Expand Down Expand Up @@ -335,6 +338,8 @@ construct_forecast_models <- function(full_data_tbl,

cli::cli_h3("Individual Model Training")


# models to run
model_list <- get_model_functions(models_to_run,
models_not_to_run,
run_deep_learning)
Expand All @@ -347,7 +352,13 @@ construct_forecast_models <- function(full_data_tbl,

models_to_go_over <- names(model_list)


# PCA
if(sum(pca == TRUE) == 1 | (combo_value == "All-Data" & is.null(pca)) | (is.null(pca) & date_type %in% c("day", "week"))) {
run_pca <- TRUE
} else {
run_pca <- FALSE
}

for(model_name in models_to_go_over){

model_fn <- as.character(model_list[model_name])
Expand All @@ -372,7 +383,9 @@ construct_forecast_models <- function(full_data_tbl,
fiscal_year_start = fiscal_year_start,
tscv_inital = hist_periods_80,
date_rm_regex = date_regex,
model_type = "single"))
model_type = "single",
pca = run_pca))


try(combined_models_recipe_1 <- modeltime::add_modeltime_model(combined_models_recipe_1,
mdl_called,
Expand All @@ -392,25 +405,26 @@ construct_forecast_models <- function(full_data_tbl,
freq_val <- gluon_ts_frequency
add_name <- paste0(model_name,model_name_suffix)
}


try(mdl_called <- invoke_forecast_function(fn_to_invoke = model_fn,
train_data = train_data_recipe_1,
frequency = freq_val,
parallel = run_model_parallel,
horizon = forecast_horizon,
seasonal_period =seasonal_periods,
back_test_spacing = back_test_spacing,
fiscal_year_start = fiscal_year_start,
tscv_inital = hist_periods_80,
date_rm_regex = date_regex,
model_type = "single"))

try(combined_models_recipe_1 <- modeltime::add_modeltime_model(combined_models_recipe_1,
mdl_called,
location = "top") %>%
update_model_description(1, add_name),
silent = TRUE)

try(mdl_called <- invoke_forecast_function(fn_to_invoke = model_fn,
train_data = train_data_recipe_1,
frequency = freq_val,
parallel = run_model_parallel,
horizon = forecast_horizon,
seasonal_period =seasonal_periods,
back_test_spacing = back_test_spacing,
fiscal_year_start = fiscal_year_start,
tscv_inital = hist_periods_80,
date_rm_regex = date_regex,
model_type = "single",
pca = run_pca))

try(combined_models_recipe_1 <- modeltime::add_modeltime_model(combined_models_recipe_1,
mdl_called,
location = "top") %>%
update_model_description(1, add_name),
silent = TRUE)

}

if(model_name %in% r2_models & ("R2" %in% recipes_to_run | sum(recipes_to_run == "all") == 1 | (is.null(recipes_to_run) & date_type %in% c("month", "quarter", "year")))){
Expand All @@ -426,8 +440,9 @@ construct_forecast_models <- function(full_data_tbl,
fiscal_year_start = fiscal_year_start,
tscv_inital = hist_periods_80,
date_rm_regex = date_regex,
model_type = "single"))

model_type = "single",
pca = run_pca))

try(combined_models_recipe_2 <- modeltime::add_modeltime_model(combined_models_recipe_2,
mdl_called,
location = "top") %>%
Expand Down Expand Up @@ -630,7 +645,8 @@ construct_forecast_models <- function(full_data_tbl,
fiscal_year_start = fiscal_year_start,
tscv_inital = "1 year",
date_rm_regex = date_regex,
model_type = "ensemble"))
model_type = "ensemble",
pca = FALSE))

try(combined_ensemble_models <- modeltime::add_modeltime_model(combined_ensemble_models,
mdl_ensemble,
Expand Down
8 changes: 6 additions & 2 deletions R/forecast_time_series.R
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Original file line number Diff line number Diff line change
Expand Up @@ -51,10 +51,12 @@
#' @param lag_periods List of values to use in creating lag features. Default of NULL automatically chooses these values
#' based on date_type.
#' @param rolling_window_periods List of values to use in creating rolling window features. Default of NULL automatically
#' chooses these values based on date_type.
#' chooses these values based on date type.
#' @param recipes_to_run List of recipes to run on multivariate models that can run different recipes. A value of NULL runs
#' all recipes, but only runs the R1 recipe for weekly and daily date types. A value of "all" runs all recipes, regardless
#' of date type. A list like c("R1") or c("R2") would only run models with the R1 or R2 recipe.
#' @param pca Run principle component analysis on any lagged features to speed up model run time. Default of NULL runs
#' PCA on day and week date types across all local multivariate models, and also for global models across all date types.
#' @param reticulate_environment File path to python environment to use when training gluonts deep learning models.
#' Only important when parallel_processing is not set to 'azure_batch'. Azure Batch should use its own docker image
#' that has python environment already installed.
Expand Down Expand Up @@ -116,6 +118,7 @@ forecast_time_series <- function(input_data,
lag_periods = NULL,
rolling_window_periods = NULL,
recipes_to_run = NULL,
pca = NULL,
reticulate_environment = NULL,
models_to_run = NULL,
models_not_to_run = NULL,
Expand Down Expand Up @@ -278,7 +281,8 @@ forecast_time_series <- function(input_data,
back_test_scenarios,
date_regex,
fiscal_year_start,
seasonal_periods)
seasonal_periods,
pca)

# * Run Forecast ----
if(forecast_approach == "bottoms_up" & length(unique(full_data_tbl$Combo)) > 1 & (sum(run_global_models == TRUE) == 1 | (is.null(run_global_models) & date_type %in% c("month", "quarter", "year"))) & run_local_models) {
Expand Down
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