Simple ensemble

DESCRIPTION

@@ -1,16 +1,32 @@
 Package: hubEnsembles
-Title: What the Package Does (One Line, Title Case)
+Title: Ensemble methods for combining hub model outputs.
 Version: 0.0.0.9000
-Authors@R: 
+Authors@R: c(
     person("Anna", "Krystalli", , "[email protected]", role = c("aut", "cre"),
-           comment = c(ORCID = "0000-0002-2378-4915"))
-Description: What the package does (one paragraph).
+           comment = c(ORCID = "0000-0002-2378-4915")),
+    person(given = "Evan L",
+           family = "Ray",
+           role = c("aut")),
+    person(given = "Li",
+           family = "Shandross",
+           role = c("aut")))
+Description: Functions for combining model outputs (e.g. predictions or
+    estimates) from multiple models into an aggregated ensemble model output.
 License: MIT + file LICENSE
 Suggests: 
     testthat (>= 3.0.0)
 Config/testthat/edition: 3
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.2
+RoxygenNote: 7.2.3
 URL: https://github.com/Infectious-Disease-Modeling-Hubs/hubEnsembles
 BugReports: https://github.com/Infectious-Disease-Modeling-Hubs/hubEnsembles/issues
+Imports: 
+    cli,
+    dplyr,
+    hubUtils,
+    magrittr,
+    matrixStats,
+    rlang
+Remotes:
+    Infectious-Disease-Modeling-Hubs/hubUtils

NAMESPACE

@@ -1,2 +1,4 @@
 # Generated by roxygen2: do not edit by hand
 
+export("%>%")
+importFrom(magrittr,"%>%")

R/simple_ensemble.R

@@ -0,0 +1,140 @@
+#' Compute ensemble model outputs by summarizing component model outputs for
+#' each combination of model task, output type, and output type id. Supported
+#' output types include `mean`, `median`, `quantile`, `cdf`, and `pmf`.
+#'
+#' @param model_outputs an object of class `model_output_df` with component
+#'   model outputs (e.g., predictions).
+#' @param weights an optional `data.frame` with component model weights. If
+#'   provided, it should have columns `model_id`, `weight`, and optionally,
+#'   additional columns corresponding to task id variables, `output_type`, or
+#'   `output_type_id`, if weights are specific to values of those variables. The
+#'   default is `NULL`, in which case an equally-weighted ensemble is calculated.
+#' @param agg_fun a function or character string name of a function to use for
+#'   aggregating component model outputs into the ensemble outputs. See the
+#'   details for more information.
+#' @param agg_args a named list of any additional arguments that will be passed
+#'   to `agg_fun`.
+#' @param model_id `character` string with the identifier to use for the
+#'   ensemble model.
+#' @param task_id_cols, output_type_col, output_type_id_col, value_col
+#'   `character` vectors with the names of the columns in `model_outputs` for
+#'   the output's type, additional identifying information, and value of the
+#'   model output.
+#'
+#' @details The default for `agg_fun` is `mean`, in which case the ensemble's
+#'   output is the average of the component model outputs within each group
+#'   defined by a combination of values in the task id columns, output type, and
+#'   output type id. The provided `agg_fun` should have an argument `x` for the
+#'   vector of numeric values to summarize, and for weighted methods, an
+#'   argument `w` with a numeric vector of weights. For weighted methods,
+#'  `agg_fun = "mean"` and `agg_fun = "median"` are translated to use
+#'  `matrixStats::weightedMean` and `matrixStats::weightedMedian` respectively.
+#'
+#' @return a data.frame with columns `team_abbr`, `model_abbr`, one column for
+#'   each task id variable, `output_type`, `output_id`, and `value`. Note that
+#'   any additional columns in the input `model_outputs` are dropped.
+simple_ensemble <- function(model_outputs, weights = NULL,
+                            agg_fun = "mean", agg_args = list(),
+                            model_id = "hub-ensemble",
+                            task_id_cols = NULL,
+                            output_type_col = "output_type",
+                            output_type_id_col = "output_type_id",
+                            hub_connection = NULL) {
+  model_out_cols <- colnames(model_outputs)
+  if (!is.data.frame(model_outputs)) {
+    cli::cli_abort(c("x" = "{.arg model_outputs} must be a `data.frame`."))
+  }
+
+  non_task_cols <- c("model_id", output_type_col, output_type_id_col, "value")
+  if (is.null(task_id_cols)) {
+    task_id_cols <- model_out_cols[!model_out_cols %in% non_task_cols]
+  }
+
+  req_col_names <- c(non_task_cols, task_id_cols)
+  if (!all(req_col_names %in% model_out_cols)) {
+    cli::cli_abort(c(
+      "x" = "{.arg model_outputs} did not have all required columns
+             {.val {req_col_names}}."
+    ))
+  }
+
+  ## Validations above this point to be relocated to hubUtils
+  # hubUtils::validate_model_output_df(model_outputs)
+
+  if (nrow(model_outputs) == 0) {
+    cli::cli_warn(c("!" = "{.arg model_outputs} has zero rows."))
+  }
+
+  valid_types <- c("mean", "median", "quantile", "cdf", "pmf")
+  unique_types <- unique(model_outputs[[output_type_col]])
+  invalid_types <- unique_types[!unique_types %in% valid_types]
+  if (length(invalid_types) > 0) {
+    cli::cli_abort(c(
+      "x" = "{.arg model_outputs} contains unsupported output type.",
+      "i" = "Included output type{?s}: {.val {invalid_types}}.",
+      "i" = "Supported output types: {.val {valid_types}}."
+    ))
+  }
+
+  if (is.null(weights)) {
+    agg_args <- c(agg_args, list(x = quote(.data[["value"]])))
+  } else {
+    req_weight_cols <- c("model_id", "weight")
+    if (!all(req_weight_cols %in% colnames(weights))) {
+      cli::cli_abort(c(
+        "x" = "{.arg weights} did not include required columns
+               {.val {req_weight_cols}}."
+      ))
+    }
+
+    weight_by_cols <- colnames(weights)[colnames(weights) != "weight"]
+
+    if ("value" %in% weight_by_cols) {
+      cli::cli_abort(c(
+        "x" = "{.arg weights} included a column named {.val {\"value\"}},
+               which is not allowed."
+      ))
+    }
+
+    invalid_cols <- weight_by_cols[!weight_by_cols %in% colnames(model_outputs)]
+    if (!all(weight_by_cols %in% colnames(model_outputs))) {
+      cli::cli_abort(c(
+        "x" = "{.arg weights} included {length(invalid_cols)} column{?s} that
+               {?was/were} not present in {.arg model_outputs}:
+               {.val {invalid_cols}}"
+      ))
+    }
+
+    if ("weight" %in% colnames(model_outputs)) {
+      weight_col_name <- paste0("weight_", rlang::hash(colnames(model_outputs)))
+      weights <- weights %>% dplyr::rename(!!weight_col_name := "weight")
+    } else {
+      weight_col_name <- "weight"
+    }
+
+    model_outputs <- model_outputs %>%
+      dplyr::left_join(weights, by = weight_by_cols)
+
+    if (is.character(agg_fun)) {
+      if (agg_fun == "mean") {
+        agg_fun <- matrixStats::weightedMean
+      } else if (agg_fun == "median") {
+        agg_fun <- matrixStats::weightedMedian
+      }
+    }
+
+    agg_args <- c(agg_args, list(x = quote(.data[["value"]]),
+                                 w = quote(.data[[weight_col_name]])))
+  }
+
+  group_by_cols <- c(task_id_cols, output_type_col, output_type_id_col)
+  ensemble_model_outputs <- model_outputs %>%
+    dplyr::group_by(dplyr::across(dplyr::all_of(group_by_cols))) %>%
+    dplyr::summarize(value = do.call(agg_fun, args = agg_args)) %>%
+    dplyr::mutate(model_id = model_id, .before = 1) %>%
+    dplyr::ungroup()
+
+  # hubUtils::as_model_output_df(ensemble_model_outputs)
+
+  return(ensemble_model_outputs)
+}

R/utils-pipe.R

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+#' Pipe operator
+#'
+#' See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details.
+#'
+#' @name %>%
+#' @rdname pipe
+#' @keywords internal
+#' @export
+#' @importFrom magrittr %>%
+#' @usage lhs \%>\% rhs
+#' @param lhs A value or the magrittr placeholder.
+#' @param rhs A function call using the magrittr semantics.
+#' @return The result of calling `rhs(lhs)`.
+NULL