Move old forecasters

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.0.1
+Version: 0.0.2
 Authors@R: c(
     person("Daniel", "McDonald", , "[email protected]", role = c("aut","cre")),
     person("Jacob", "Bien", role = "aut"),
@@ -19,6 +19,7 @@ Description: A forecasting "framework" for creating epidemiological forecasts
 License: MIT + file LICENSE
 URL: https://github.com/cmu-delphi/epipredict/,
     https://cmu-delphi.github.io/epipredict
+BugReports: https://github.com/cmu-delphi/epipredict/issues/
 Depends: 
     R (>= 3.5.0)
 Imports: 

NAMESPACE

@@ -64,9 +64,7 @@ export(add_frosting)
 export(add_layer)
 export(apply_frosting)
 export(arx_args_list)
-export(arx_epi_forecaster)
 export(arx_forecaster)
-export(create_lags_and_leads)
 export(create_layer)
 export(default_epi_recipe_blueprint)
 export(detect_layer)
@@ -82,18 +80,13 @@ export(extract_layers)
 export(extrapolate_quantiles)
 export(flatline)
 export(flatline_args_list)
-export(flatline_epi_forecaster)
+export(flatline_forecaster)
 export(frosting)
-export(get_precision)
 export(get_test_data)
 export(grab_names)
 export(is_epi_recipe)
 export(is_epi_workflow)
 export(is_layer)
-export(knn_iteraive_ar_args_list)
-export(knn_iteraive_ar_forecaster)
-export(knnarx_args_list)
-export(knnarx_forecaster)
 export(layer)
 export(layer_add_forecast_date)
 export(layer_add_target_date)
@@ -108,8 +101,6 @@ export(new_default_epi_recipe_blueprint)
 export(new_epi_recipe_blueprint)
 export(remove_frosting)
 export(slather)
-export(smooth_arx_args_list)
-export(smooth_arx_forecaster)
 export(step_epi_ahead)
 export(step_epi_lag)
 export(step_epi_naomit)
@@ -139,5 +130,4 @@ importFrom(stats,predict)
 importFrom(stats,qnorm)
 importFrom(stats,quantile)
 importFrom(stats,residuals)
-importFrom(stats,setNames)
 importFrom(tibble,tibble)

R/arx_forecaster.R

@@ -1,68 +1,87 @@
-#' AR forecaster with optional covariates
+#' Direct autoregressive forecaster with covariates
 #'
-#' @param x Covariates. Allowed to be missing (resulting in AR on `y`).
-#' @param y Response.
-#' @param key_vars Factor(s). A prediction will be made for each unique
-#'   combination.
-#' @param time_value the time value associated with each row of measurements.
-#' @param args Additional arguments specifying the forecasting task. Created
-#'   by calling `arx_args_list()`.
+#' This is an autoregressive forecasting model for
+#' [epiprocess::epi_df] data. It does "direct" forecasting, meaning
+#' that it estimates a model for a particular target horizon.
 #'
-#' @return A data frame of point (and optionally interval) forecasts at a single
-#'   ahead (unique horizon) for each unique combination of `key_vars`.
+#'
+#' @param epi_data An `epi_df` object
+#' @param outcome A character (scalar) specifying the outcome (in the
+#'   `epi_df`).
+#' @param predictors A character vector giving column(s) of predictor
+#'   variables.
+#' @param trainer A `{parsnip}` model describing the type of estimation.
+#'   For now, we enforce `mode = "regression"`.
+#' @param args_list A list of customization arguments to determine
+#'   the type of forecasting model. See [arx_args_list()].
+#'
+#' @return A list with (1) `predictions` an `epi_df` of predicted values
+#'   and (2) `epi_workflow`, a list that encapsulates the entire estimation
+#'   workflow
 #' @export
-arx_forecaster <- function(x, y, key_vars, time_value,
-                           args = arx_args_list()) {
+#'
+#' @examples
+#' jhu <- case_death_rate_subset %>%
+#'   dplyr::filter(time_value >= as.Date("2021-12-01"))
+#'
+#' out <- arx_forecaster(jhu, "death_rate",
+#'   c("case_rate", "death_rate"))
+arx_forecaster <- function(epi_data,
+                               outcome,
+                               predictors,
+                               trainer = parsnip::linear_reg(),
+                               args_list = arx_args_list()) {
 
-  # TODO: function to verify standard forecaster signature inputs
+  validate_forecaster_inputs(epi_data, outcome, predictors)
+  if (!is.list(trainer) || trainer$mode != "regression")
+    cli_stop("{trainer} must be a `parsnip` method of mode 'regression'.")
+  lags <- arx_lags_validator(predictors, args_list$lags)
 
-  assign_arg_list(args)
-  if (is.null(key_vars)) { # this is annoying/repetitive, seemingly necessary?
-    keys <- NULL
-    distinct_keys <- tibble(.dump = NA)
-  } else {
-    keys <- tibble::tibble(key_vars)
-    distinct_keys <- dplyr::distinct(keys)
+  r <- epi_recipe(epi_data)
+  for (l in seq_along(lags)) {
+    p <- predictors[l]
+    r <- step_epi_lag(r, !!p, lag = lags[[l]])
   }
+  r <- r %>%
+    step_epi_ahead(dplyr::all_of(!!outcome), ahead = args_list$ahead) %>%
+    step_epi_naomit()
+  # should limit the training window here (in an open PR)
+  # What to do if insufficient training data? Add issue.
 
-  # Return NA if insufficient training data
-  if (length(y) < min_train_window + max_lags + ahead) {
-    qnames <- probs_to_string(levels)
-    out <- dplyr::bind_cols(distinct_keys, point = NA) %>%
-      dplyr::select(!dplyr::any_of(".dump"))
-    return(enframer(out, qnames))
-  }
+  forecast_date <- args_list$forecast_date %||% max(epi_data$time_value)
+  target_date <- args_list$target_date %||% forecast_date + args_list$ahead
+  f <- frosting() %>%
+    layer_predict() %>%
+    # layer_naomit(.pred) %>%
+    layer_residual_quantiles(
+      probs = args_list$levels,
+      symmetrize = args_list$symmetrize) %>%
+    layer_add_forecast_date(forecast_date = forecast_date) %>%
+    layer_add_target_date(target_date = target_date)
+  if (args_list$nonneg) f <- layer_threshold(f, dplyr::starts_with(".pred"))
 
-  dat <- create_lags_and_leads(x, y, lags, ahead, time_value, keys)
-  dat$x0 <- 1
+  latest <- get_test_data(r, epi_data)
 
-  obj <- stats::lm(
-    y1 ~ . + 0,
-    data = dat %>% dplyr::select(starts_with(c("x", "y")))
+  wf <- epi_workflow(r, trainer, f) %>% generics::fit(epi_data)
+  list(
+    predictions = predict(wf, new_data = latest),
+    epi_workflow = wf
   )
+}
 
-  point <- make_predictions(obj, dat, time_value, keys)
-
-  # Residuals, simplest case, requires
-  # 1. same quantiles for all keys
-  # 2. `residuals(obj)` works
-  r <- residuals(obj)
-  q <- residual_quantiles(r, point, levels, symmetrize)
 
-  # Harder case requires handling failures of 1 and or 2, neither implemented
-  # 1. different quantiles by key, need to bind the keys, then group_modify
-  # 2 fails. need to bind the keys, grab, y and yhat, subtract
-  if (nonneg) {
-    q <- dplyr::mutate(q, dplyr::across(dplyr::everything(), ~ pmax(.x, 0)))
+arx_lags_validator <- function(predictors, lags) {
+  p <- length(predictors)
+  if (!is.list(lags)) lags <- list(lags)
+  if (length(lags) == 1) lags <- rep(lags, p)
+  else if (length(lags) < p) {
+    cli_stop(
+      "You have requested {p} predictors but lags cannot be recycled to match."
+    )
   }
-
-  return(
-    dplyr::bind_cols(distinct_keys, q) %>%
-      dplyr::select(!dplyr::any_of(".dump"))
-  )
+  lags
 }
 
-
 #' ARX forecaster argument constructor
 #'
 #' Constructs a list of arguments for [arx_forecaster()].

R/blueprint-epi_recipe-default.R

@@ -9,6 +9,7 @@
 #' @details The `bake_dependent_roles` are automatically set to `epi_df` defaults.
 #' @return A recipe blueprint.
 #'
+#' @keywords internal
 #' @export
 new_epi_recipe_blueprint <-
   function(intercept = FALSE, allow_novel_levels = FALSE, fresh = TRUE,

R/df_mat_mul.R

@@ -13,6 +13,7 @@
 #' @return A data.frame with the new columns at the right. Original
 #'   columns are removed.
 #' @export
+#' @keywords internal
 #'
 #' @examples
 #' df <- data.frame(matrix(1:200, ncol = 10))

R/epi_keys.R

@@ -3,8 +3,8 @@
 #' @param x a data.frame, tibble, or epi_df
 #'
 #' @return If an `epi_df`, this returns all "keys". Otherwise `NULL`
+#' @keywords internal
 #' @export
-#'
 epi_keys <- function(x) {
   UseMethod("epi_keys")
 }

R/epi_recipe.R

@@ -215,6 +215,7 @@ epi_form2args <- function(formula, data, ...) {
 #' @param x An object.
 #' @return `TRUE` if the object inherits from `epi_recipe`.
 #'
+#' @keywords internal
 #' @export
 is_epi_recipe <- function(x) {
   inherits(x, "epi_recipe")

R/epi_shift.R

@@ -10,6 +10,8 @@
 #' @param keys Data frame, vector, or `NULL`. Additional grouping vars.
 #' @param out_name Chr. The output list will use this as a prefix.
 #'
+#' @keywords internal
+#'
 #' @return a list of tibbles
 epi_shift <- function(x, shifts, time_value, keys = NULL, out_name = "x") {
   if (!is.data.frame(x)) x <- data.frame(x)

R/epi_workflow.R

@@ -57,6 +57,7 @@ epi_workflow <- function(preprocessor = NULL, spec = NULL, postprocessor = NULL)
 #' @param x An object.
 #' @return `TRUE` if the object inherits from `epi_workflow`.
 #'
+#' @keywords internal
 #' @export
 is_epi_workflow <- function(x) {
   inherits(x, "epi_workflow")

R/extract.R

@@ -8,6 +8,8 @@
 #' @return An object originally passed as an argument to a layer or step
 #' @export
 #'
+#' @keywords internal
+#'
 #' @examples
 #' f <- frosting() %>%
 #'   layer_predict() %>%

R/flatline.R

@@ -10,7 +10,7 @@
 #'   horizon. The right hand side must contain any keys (locations) for the
 #'   panel data separated by plus. The observed time series must come last.
 #'   For example
-#'   ```
+#'   ```r
 #'   form <- as.formula(lead7_y ~ state + age + y)
 #'   ```
 #'   Note that this function doesn't DO the shifting, that has to be done
@@ -26,6 +26,7 @@
 #'   predictions for future data (the last observed of the outcome for each
 #'   combination of keys.
 #' @export
+#' @keywords internal
 #'
 #' @examples
 #' tib <- data.frame(y = runif(100),

R/flatline_epi_forecaster.R → R/flatline_forecaster.R

@@ -26,10 +26,10 @@
 #' jhu <- case_death_rate_subset %>%
 #'   dplyr::filter(time_value >= as.Date("2021-12-01"))
 #'
-#' out <- flatline_epi_forecaster(jhu, "death_rate")
-flatline_epi_forecaster <- function(epi_data,
-                                    outcome,
-                                    args_list = flatline_args_list()) {
+#' out <- flatline_forecaster(jhu, "death_rate")
+flatline_forecaster <- function(epi_data,
+                                outcome,
+                                args_list = flatline_args_list()) {
 
   validate_forecaster_inputs(epi_data, outcome, "time_value")
   keys <- epi_keys(epi_data)
@@ -71,7 +71,7 @@ flatline_epi_forecaster <- function(epi_data,
 
 #' Flatline forecaster argument constructor
 #'
-#' Constructs a list of arguments for [flatline_epi_forecaster()].
+#' Constructs a list of arguments for [flatline_forecaster()].
 #'
 #' @inheritParams arx_args_list
 #'

R/grab_names.R

@@ -12,6 +12,7 @@
 #'   be used to select a range of variables.
 #'
 #' @export
+#' @keywords internal
 #' @return a character vector
 #' @examples
 #' df <- data.frame(a = 1, b = 2, cc = rep(NA, 3))