From 6672a1517338164dbc1db452607adc186ae3f78f Mon Sep 17 00:00:00 2001
From: Github Actions CI <lshan1@msn.com>
Date: Fri, 17 Feb 2023 12:30:06 -0500
Subject: [PATCH 01/24] Create simple_ensemble.R

based on Issue #1 draft code
---
 R/simple_ensemble.R | 78 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 78 insertions(+)
 create mode 100644 R/simple_ensemble.R

diff --git a/R/simple_ensemble.R b/R/simple_ensemble.R
new file mode 100644
index 0000000..b8c02e4
--- /dev/null
+++ b/R/simple_ensemble.R
@@ -0,0 +1,78 @@
+#' Compute ensemble predictions by summarizing component predictions for each
+#' combination of model task, output type, and type id. Supported output types
+#' include `mean`, `median`, `quantile`, `cdf`, and `category`.
+#'
+#' @param predictions a `data.frame` with component model predictions. It should
+#'   have columns `team_abbr`, `model_abbr`, one column for each task id
+#'   variable, `output_type`, `output_type_id`, and `value`.
+#' @param task_id_vars an optional `character` vector naming the columns of
+#'   `predictions` that correspond to task id variables. The default is `NULL`,
+#'   in which case the task id variables are looked up from the `hub_con` if one
+#'   is provided. If neither `task_id_vars` nor `hub_con` are provided, all
+#'   columns in `predictions` _other than_ `team_abbr`, `model_abbr`,
+#'   `output_type`, `output_id`, and `value` will be used as task id
+#'   variables.
+#' @param hub_con an optional hub connection object; see `hubUtils::connect_hub`
+#' @param weights an optional `data.frame` with component model weights. If
+#'   provided, it should have columns `team_name`, `model_abbr`, `weight`,
+#'   and optionally, additional columns corresponding to task id variables,
+#'   `output_type`, or `output_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 predictions into the ensemble prediction. The default
+#'   is `mean`, in which case the ensemble prediction is the simple average of
+#'   the component model predictions. The provided function 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.
+#' @param agg_args a named list of any additional arguments that will be passed
+#'   to `agg_fun`.
+#' @param team_abbr, model_abbr `character` strings with the name of the team
+#'   and model to use for the ensemble predictions.
+#'
+#' @return a data.frame with columns `team_abbr`, `model_abbr`, one column for
+#'   each task id variable, `output_type`, `output_id`, and `value`.
+
+simple_ensemble <- function(predictions, task_id_vars = NULL, hub_con = NULL,
+                     weights = NULL, agg_fun = mean, agg_args = list(),
+                     team_abbr = "Hub", model_abbr = "ensemble") {
+
+  # require(matrixStats)
+    
+  if (is.null(task_id_vars) && is.null(hub_con)) {
+    temp <- colnames(predictions)
+    task_id_vars <- temp[!temp %in% c("team_abbr", "model_abbr", "output_type", "output_id", "value")]
+  } else if (is.null(task_id_vars)) {
+    # task_id variables looked up from `hub_con`
+  }
+  
+  col_names <- c("team_abbr", "model_abbr", task_id_vars, "output_type", "output_id", "value")
+  if ((length(predictions) == 0) || !all(names(predictions) %in% col_names)) {
+    stop("predictions did not have required columns", call. = FALSE)
+  } else if (!all(names(predictions) == col_names) && names(predictions) %in% col_names) {
+    predictions <- relocate(predictions, all_of(col_names))
+  }
+  
+  if (!all(predictions$output_type %in% c("mean", "median", "quantile", "cdf", "category"))) # throw warning or error
+
+  if (!all(names(weights) %in% c("team_abbr", "model_abbr", "weight"))) {
+    stop("weights did not have required columns", call. = FALSE)
+  }
+  
+  if (is.null(weights)) {
+    weights <- predictions %>%
+      distinct(team_abbr, model_abbr) %>%
+      mutate(weight = 1/n())
+  }
+  
+  if (agg_fun == "mean") agg_fun = "weightedMean"
+  if (agg_fun == "median") agg_fun = "weightedMedian"
+    
+  
+  predictions %>%
+      dplyr::left_join(weights) %>%
+      dplyr::group_by(across(all_of(c(task_id_vars, "output_type", "output_id")))) %>%
+      dplyr::summarize(value = do.call(agg_fun, args = c(agg_args, list(x=value, w=weight)))) %>%
+      dplyr::mutate(team_abbr = team_abbr, model_abbr = model_abbr, .before = 1)
+      # do we want to have the horizon column before target?
+}

From 2a37506fcd613851428b2cad35933dfe4098e00e Mon Sep 17 00:00:00 2001
From: Github Actions CI <lshan1@msn.com>
Date: Fri, 17 Feb 2023 12:30:50 -0500
Subject: [PATCH 02/24] add example-data for function testing

---
 .../2022-10-01-simple_hub-baseline.csv        |  25 ++
 .../2022-10-08-simple_hub-baseline.csv        | 277 ++++++++++++++++++
 .../2022-10-08-team1-goodmodel.csv            |  24 ++
 3 files changed, 326 insertions(+)
 create mode 100644 inst/example-data/2022-10-01-simple_hub-baseline.csv
 create mode 100644 inst/example-data/2022-10-08-simple_hub-baseline.csv
 create mode 100644 inst/example-data/2022-10-08-team1-goodmodel.csv

diff --git a/inst/example-data/2022-10-01-simple_hub-baseline.csv b/inst/example-data/2022-10-01-simple_hub-baseline.csv
new file mode 100644
index 0000000..82c1d62
--- /dev/null
+++ b/inst/example-data/2022-10-01-simple_hub-baseline.csv
@@ -0,0 +1,25 @@
+"origin_date","target","horizon","location","type","type_id","value"
+2022-10-01,"wk inc flu hosp",1,"US","mean",NA,150
+2022-10-01,"wk inc flu hosp",1,"US","quantile","0.01",135
+2022-10-01,"wk inc flu hosp",1,"US","quantile","0.025",137
+2022-10-01,"wk inc flu hosp",1,"US","quantile","0.05",139
+2022-10-01,"wk inc flu hosp",1,"US","quantile","0.1",140
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+2022-10-01,"wk inc flu hosp",1,"US","quantile","0.975",176
+2022-10-01,"wk inc flu hosp",1,"US","quantile","0.99",205
diff --git a/inst/example-data/2022-10-08-simple_hub-baseline.csv b/inst/example-data/2022-10-08-simple_hub-baseline.csv
new file mode 100644
index 0000000..98ad58c
--- /dev/null
+++ b/inst/example-data/2022-10-08-simple_hub-baseline.csv
@@ -0,0 +1,277 @@
+"origin_date","target","horizon","location","type","type_id","value"
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+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.025",137
+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.05",139
+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.1",140
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+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.2",141
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+2022-10-08,"wk inc flu hosp",4,"01","quantile","0.7",155
+2022-10-08,"wk inc flu hosp",4,"01","quantile","0.75",161
+2022-10-08,"wk inc flu hosp",4,"01","quantile","0.8",165
+2022-10-08,"wk inc flu hosp",4,"01","quantile","0.85",170
+2022-10-08,"wk inc flu hosp",4,"01","quantile","0.9",175
+2022-10-08,"wk inc flu hosp",4,"01","quantile","0.95",176
+2022-10-08,"wk inc flu hosp",4,"01","quantile","0.975",176
+2022-10-08,"wk inc flu hosp",4,"01","quantile","0.99",205
diff --git a/inst/example-data/2022-10-08-team1-goodmodel.csv b/inst/example-data/2022-10-08-team1-goodmodel.csv
new file mode 100644
index 0000000..3e6f875
--- /dev/null
+++ b/inst/example-data/2022-10-08-team1-goodmodel.csv
@@ -0,0 +1,24 @@
+"origin_date","target","horizon","location","type","type_id","value"
+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.01",135
+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.025",137
+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.05",139
+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.1",140
+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.15",141
+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.2",141
+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.25",142
+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.3",143
+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.35",144
+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.4",145
+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.45",147
+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.5",148
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+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.8",165
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+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.9",175
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+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.99",205

From f67619090099ca1c9ca4c69ad497185866a3c9a9 Mon Sep 17 00:00:00 2001
From: Github Actions CI <lshan1@msn.com>
Date: Mon, 24 Apr 2023 15:28:38 -0400
Subject: [PATCH 03/24] fix weighted vs unweighted ensemble code

---
 R/simple_ensemble.R | 36 +++++++++++++++++++++---------------
 1 file changed, 21 insertions(+), 15 deletions(-)

diff --git a/R/simple_ensemble.R b/R/simple_ensemble.R
index b8c02e4..c96c660 100644
--- a/R/simple_ensemble.R
+++ b/R/simple_ensemble.R
@@ -53,26 +53,32 @@ simple_ensemble <- function(predictions, task_id_vars = NULL, hub_con = NULL,
     predictions <- relocate(predictions, all_of(col_names))
   }
   
-  if (!all(predictions$output_type %in% c("mean", "median", "quantile", "cdf", "category"))) # throw warning or error
+  if (!all(predictions$output_type %in% c("mean", "median", "quantile", "cdf", "category"))) stop("Predictions contains unsupported output type") # throw warning or error
 
-  if (!all(names(weights) %in% c("team_abbr", "model_abbr", "weight"))) {
-    stop("weights did not have required columns", call. = FALSE)
-  }
-  
   if (is.null(weights)) {
-    weights <- predictions %>%
-      distinct(team_abbr, model_abbr) %>%
-      mutate(weight = 1/n())
-  }
-  
-  if (agg_fun == "mean") agg_fun = "weightedMean"
-  if (agg_fun == "median") agg_fun = "weightedMedian"
+    if (agg_fun == "mean") agg_fun = "mean"
+    if (agg_fun == "median") agg_fun = "median"
     
-  
-  predictions %>%
+    ensemble_predictions <- predictions %>%
+      dplyr::group_by(across(all_of(c(task_id_vars, "output_type", "output_id")))) %>%
+      dplyr::summarize(value = do.call(agg_fun, args = c(agg_args, list(x=value)))) %>%
+      dplyr::mutate(team_abbr = team_abbr, model_abbr = model_abbr, .before = 1)
+      # do we want to have the horizon column before target?
+  } else {
+    if (!all(names(weights) %in% c("team_abbr", "model_abbr", "weight"))) {
+      stop("weights did not have required columns", call. = FALSE)
+    }
+    
+    if (agg_fun == "mean") agg_fun = "weightedMean"
+    if (agg_fun == "median") agg_fun = "weightedMedian"
+    
+    ensemble_predictions <- predictions %>%
       dplyr::left_join(weights) %>%
       dplyr::group_by(across(all_of(c(task_id_vars, "output_type", "output_id")))) %>%
-      dplyr::summarize(value = do.call(agg_fun, args = c(agg_args, list(x=value, w=weight)))) %>%
+      dplyr::summarize(value = do.call(agg_fun, args = c(agg_args, list(x=value, w=weights)))) %>%
       dplyr::mutate(team_abbr = team_abbr, model_abbr = model_abbr, .before = 1)
       # do we want to have the horizon column before target?
+  }  
+    
+  return (ensemble_predictions)
 }

From cf1a4593e1851115828534b0c34ecfb20d1b22db Mon Sep 17 00:00:00 2001
From: Github Actions CI <lshan1@msn.com>
Date: Tue, 25 Apr 2023 18:48:29 -0400
Subject: [PATCH 04/24] minor fixes to simple_ensemble() function

---
 R/simple_ensemble.R | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/R/simple_ensemble.R b/R/simple_ensemble.R
index c96c660..dad2fe4 100644
--- a/R/simple_ensemble.R
+++ b/R/simple_ensemble.R
@@ -34,7 +34,7 @@
 #'   each task id variable, `output_type`, `output_id`, and `value`.
 
 simple_ensemble <- function(predictions, task_id_vars = NULL, hub_con = NULL,
-                     weights = NULL, agg_fun = mean, agg_args = list(),
+                     weights = NULL, agg_fun = "mean", agg_args = list(),
                      team_abbr = "Hub", model_abbr = "ensemble") {
 
   # require(matrixStats)
@@ -62,7 +62,8 @@ simple_ensemble <- function(predictions, task_id_vars = NULL, hub_con = NULL,
     ensemble_predictions <- predictions %>%
       dplyr::group_by(across(all_of(c(task_id_vars, "output_type", "output_id")))) %>%
       dplyr::summarize(value = do.call(agg_fun, args = c(agg_args, list(x=value)))) %>%
-      dplyr::mutate(team_abbr = team_abbr, model_abbr = model_abbr, .before = 1)
+      dplyr::mutate(team_abbr = team_abbr, model_abbr = model_abbr, .before = 1) %>%
+      dplyr::ungroup()
       # do we want to have the horizon column before target?
   } else {
     if (!all(names(weights) %in% c("team_abbr", "model_abbr", "weight"))) {
@@ -76,7 +77,8 @@ simple_ensemble <- function(predictions, task_id_vars = NULL, hub_con = NULL,
       dplyr::left_join(weights) %>%
       dplyr::group_by(across(all_of(c(task_id_vars, "output_type", "output_id")))) %>%
       dplyr::summarize(value = do.call(agg_fun, args = c(agg_args, list(x=value, w=weights)))) %>%
-      dplyr::mutate(team_abbr = team_abbr, model_abbr = model_abbr, .before = 1)
+      dplyr::mutate(team_abbr = team_abbr, model_abbr = model_abbr, .before = 1) %>%
+      dplyr::ungroup()
       # do we want to have the horizon column before target?
   }  
     

From 10f1db55c70999d4b4b42d5112100a110cbc2643 Mon Sep 17 00:00:00 2001
From: Github Actions CI <lshan1@msn.com>
Date: Tue, 25 Apr 2023 18:48:57 -0400
Subject: [PATCH 05/24] add unweighted ensemble testing protocol and test data

---
 .../2022-10-01-simple_hub-baseline.csv        |  25 ++
 .../2022-10-08-simple_hub-baseline.csv        | 277 ++++++++++++++++++
 .../test-data/2022-10-08-team1-goodmodel.csv  |  24 ++
 tests/testthat/test-simple-ensemble.r         |  81 +++++
 tests/testthat/test-simple_ensemble.R         |  91 ++++++
 5 files changed, 498 insertions(+)
 create mode 100644 tests/testthat/test-data/2022-10-01-simple_hub-baseline.csv
 create mode 100644 tests/testthat/test-data/2022-10-08-simple_hub-baseline.csv
 create mode 100644 tests/testthat/test-data/2022-10-08-team1-goodmodel.csv
 create mode 100644 tests/testthat/test-simple-ensemble.r
 create mode 100644 tests/testthat/test-simple_ensemble.R

diff --git a/tests/testthat/test-data/2022-10-01-simple_hub-baseline.csv b/tests/testthat/test-data/2022-10-01-simple_hub-baseline.csv
new file mode 100644
index 0000000..82c1d62
--- /dev/null
+++ b/tests/testthat/test-data/2022-10-01-simple_hub-baseline.csv
@@ -0,0 +1,25 @@
+"origin_date","target","horizon","location","type","type_id","value"
+2022-10-01,"wk inc flu hosp",1,"US","mean",NA,150
+2022-10-01,"wk inc flu hosp",1,"US","quantile","0.01",135
+2022-10-01,"wk inc flu hosp",1,"US","quantile","0.025",137
+2022-10-01,"wk inc flu hosp",1,"US","quantile","0.05",139
+2022-10-01,"wk inc flu hosp",1,"US","quantile","0.1",140
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+2022-10-01,"wk inc flu hosp",1,"US","quantile","0.975",176
+2022-10-01,"wk inc flu hosp",1,"US","quantile","0.99",205
diff --git a/tests/testthat/test-data/2022-10-08-simple_hub-baseline.csv b/tests/testthat/test-data/2022-10-08-simple_hub-baseline.csv
new file mode 100644
index 0000000..98ad58c
--- /dev/null
+++ b/tests/testthat/test-data/2022-10-08-simple_hub-baseline.csv
@@ -0,0 +1,277 @@
+"origin_date","target","horizon","location","type","type_id","value"
+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.01",135
+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.025",137
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diff --git a/tests/testthat/test-data/2022-10-08-team1-goodmodel.csv b/tests/testthat/test-data/2022-10-08-team1-goodmodel.csv
new file mode 100644
index 0000000..3e6f875
--- /dev/null
+++ b/tests/testthat/test-data/2022-10-08-team1-goodmodel.csv
@@ -0,0 +1,24 @@
+"origin_date","target","horizon","location","type","type_id","value"
+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.01",135
+2022-10-08,"wk inc flu hosp",1,"US","quantile","0.025",137
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diff --git a/tests/testthat/test-simple-ensemble.r b/tests/testthat/test-simple-ensemble.r
new file mode 100644
index 0000000..ebdedbb
--- /dev/null
+++ b/tests/testthat/test-simple-ensemble.r
@@ -0,0 +1,81 @@
+context("simple_ensemble")
+library(hubEnsembles)
+library(matrixStats)
+
+tmp_dat <- readr::read_csv("test-data/minimal-forecast.csv")
+
+test_that("invalid method argument throws error", {
+  expect_error(
+    build_quantile_ensemble(tmp_dat,
+                            method="weighted mean",
+                            model_name = "example")
+    )
+})
+
+test_that("medians and means correctly calculated", {
+  fdat <- expand.grid(
+    stringsAsFactors = FALSE,
+    model = letters[1:4],
+    location = c("222", "888"),
+    horizon = 1,
+    temporal_resolution = "wk",
+    target_variable = "inc death",
+    target_end_date = as.Date("2021-12-25"),
+    type = "quantile",
+    quantile = c(.1, .5, .9))
+
+  fdat$value[fdat$location == "222" & fdat$quantile == .1] <- v2.1 <- c(10, 30, 15, 20)
+  fdat$value[fdat$location == "222" & fdat$quantile == .5] <- v2.5 <- c(40, 40, 45, 50)
+  fdat$value[fdat$location == "222" & fdat$quantile == .9] <- v2.9 <- c(60, 70, 75, 80)
+  fdat$value[fdat$location == "888" & fdat$quantile == .1] <- v8.1 <- c(100, 300, 400, 250)
+  fdat$value[fdat$location == "888" & fdat$quantile == .5] <- v8.5 <- c(150, 325, 500, 300)
+  fdat$value[fdat$location == "888" & fdat$quantile == .9] <- v8.9 <- c(250, 350, 500, 350)
+
+  median_vals <- sapply(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), median)
+  mean_vals <- sapply(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), mean)
+
+  fweight <- tibble(model = letters[1:4], weight = 0.1*(1:4))
+
+  weighted_median_vals <- map(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), weightedMedian, w = fweight$weight)
+  weighted_mean_vals <- map(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), weightedMean, w = fweight$weight)
+
+  median_actual <- build_quantile_ensemble(
+    forecast_data = fdat, weights_df = NULL, method = "median", model_name = "median_ens", forecast_date = "2021-12-20")
+  mean_actual <- build_quantile_ensemble(
+    forecast_data = fdat, weights_df = NULL, method = "mean", model_name = "mean_ens", forecast_date = "2021-12-20")
+    
+  weighted_median_actual <- build_quantile_ensemble(
+    forecast_data = fdat, weights_df = fweight, method = "median", model_name = "weighted_median_ens", forecast_date = "2021-12-20")
+  weighted_mean_actual <- build_quantile_ensemble(
+    forecast_data = fdat, weights_df = fweight, method = "mean", model_name = "weighted_mean_ens", forecast_date = "2021-12-20")
+
+
+  median_expected <- mean_expected <- weighted_median_expected <- weighted_mean_expected <- tibble::tibble(
+    location = rep(c("222", "888"), each = 3), 
+    horizon = 1, 
+    temporal_resolution = "wk", 
+    target_variable = "inc death",     
+    target_end_date = as.Date("2021-12-25"), 
+    type = "quantile", 
+    quantile = rep(c(.1, .5, .9), 2), 
+    forecast_count = 4,
+    value = 0,
+    model = NA,
+    forecast_date = as.Date("2021-12-20"))
+
+  median_expected$value <- sapply(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), median)
+  median_expected$model <- "median_ens"
+  mean_expected$value <- sapply(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), mean)
+  mean_expected$model <- "mean_ens"
+
+  weighted_mean_expected$value <- map_dbl(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), weightedMean, w = fweight$weight)
+  weighted_mean_expected$model <- "weighted_mean_ens"
+  weighted_median_expected$value <- map_dbl(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), weightedMedian, w = fweight$weight)
+  weighted_median_expected$model <- "weighted_median_ens"
+
+  expect_equal(median_actual, median_expected)
+  expect_equal(mean_actual, mean_expected)
+  
+  expect_equal(weighted_median_actual, weighted_median_expected)
+  expect_equal(weighted_mean_actual, weighted_mean_expected)
+})
diff --git a/tests/testthat/test-simple_ensemble.R b/tests/testthat/test-simple_ensemble.R
new file mode 100644
index 0000000..8456c69
--- /dev/null
+++ b/tests/testthat/test-simple_ensemble.R
@@ -0,0 +1,91 @@
+library(matrixStats)
+
+test_that("multiplication works", {
+  expect_equal(2 * 2, 4)
+})
+
+#tmp_dat <- readr::read_csv("test-data/minimal-forecast.csv")
+
+baseline_1001 <- readr::read_csv("inst/test-data/2022-10-01-simple_hub-baseline.csv") %>%
+  mutate(team_abbr = "simple_hub", model_abbr = "baseline", .before = origin_date)
+baseline_1008 <- readr::read_csv("inst/test-data/2022-10-08-simple_hub-baseline.csv") %>%
+  mutate(team_abbr = "simple_hub", model_abbr = "baseline", .before = origin_date)
+team1_1008 <- readr::read_csv("inst/test-data/2022-10-08-team1-goodmodel.csv") %>%
+  mutate(team_abbr = "team_1", model_abbr = "goodmodel1", .before = origin_date) %>%
+  mutate(value = value + 2)
+
+test_that("invalid method argument throws error", {
+  expect_error(
+    simple_ensemble(
+      baseline_1008,
+      agg_fun="linear pool",
+      model_abbr = "example"
+    )
+  )
+})
+
+test_that("medians and means correctly calculated", {
+  fdat <- expand.grid(
+    stringsAsFactors = FALSE,
+    model_abbr = letters[1:4],
+    location = c("222", "888"),
+    horizon = 1, #week
+    target = "inc death",
+    target_date = as.Date("2021-12-25"),
+    output_type = "quantile",
+    output_id = c(.1, .5, .9))
+
+  fdat <- cbind(team_abbr = letters[23:26], fdat)
+  fdat$value[fdat$location == "222" & fdat$output_id == .1] <- v2.1 <- c(10, 30, 15, 20)
+  fdat$value[fdat$location == "222" & fdat$output_id == .5] <- v2.5 <- c(40, 40, 45, 50)
+  fdat$value[fdat$location == "222" & fdat$output_id == .9] <- v2.9 <- c(60, 70, 75, 80)
+  fdat$value[fdat$location == "888" & fdat$output_id == .1] <- v8.1 <- c(100, 300, 400, 250)
+  fdat$value[fdat$location == "888" & fdat$output_id == .5] <- v8.5 <- c(150, 325, 500, 300)
+  fdat$value[fdat$location == "888" & fdat$output_id == .9] <- v8.9 <- c(250, 350, 500, 350)
+
+  median_vals <- sapply(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), median)
+  mean_vals <- sapply(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), mean)
+
+  fweight <- tibble(model_abbr = letters[1:4], weight = 0.1*(1:4))
+
+  weighted_median_vals <- map(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), weightedMedian, w = fweight$weight)
+  weighted_mean_vals <- map(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), weightedMean, w = fweight$weight)
+
+  median_actual <- simple_ensemble(
+    predictions = fdat, weights = NULL, agg_fun = "median", model_abbr = "median_ens")
+  mean_actual <- simple_ensemble(
+    predictions = fdat, weights = NULL, agg_fun = "mean", model_abbr = "mean_ens")
+    
+  weighted_median_actual <- simple_ensemble(
+    predictions = fdat, weights = fweight, agg_fun = "median", model_abbr = "weighted_median_ens")
+  weighted_mean_actual <- simple_ensemble(
+    predictions = fdat, weights = fweight, agg_fun = "mean", model_abbr = "weighted_mean_ens")
+
+
+  median_expected <- mean_expected <- weighted_median_expected <- weighted_mean_expected <- tibble::tibble(
+    team_abbr = "Hub",
+    model_abbr = NA,
+    location = rep(c("222", "888"), each = 3), 
+    horizon = 1, #week
+    target = "inc death",     
+    target_date = as.Date("2021-12-25"), 
+    output_type = "quantile", 
+    output_id = rep(c(.1, .5, .9), 2), 
+    value = 0)
+
+  median_expected$value <- sapply(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), median)
+  median_expected$model_abbr <- "median_ens"
+  mean_expected$value <- sapply(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), mean)
+  mean_expected$model_abbr <- "mean_ens"
+
+  weighted_mean_expected$value <- map_dbl(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), weightedMean, w = fweight$weight)
+  weighted_mean_expected$model_abbr <- "weighted_mean_ens"
+  weighted_median_expected$value <- map_dbl(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), weightedMedian, w = fweight$weight)
+  weighted_median_expected$model_abbr <- "weighted_median_ens"
+
+  expect_equal(median_actual, median_expected)
+  expect_equal(mean_actual, mean_expected)
+  
+  expect_equal(weighted_median_actual, weighted_median_expected)
+  expect_equal(weighted_mean_actual, weighted_mean_expected)
+})
\ No newline at end of file

From 4420a9f35567de2f25077977d998618743c8ae55 Mon Sep 17 00:00:00 2001
From: Github Actions CI <lshan1@msn.com>
Date: Fri, 28 Apr 2023 15:24:44 -0400
Subject: [PATCH 06/24] Remove hard coding of output_type, output_id, value
 columns

---
 R/simple_ensemble.R | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/R/simple_ensemble.R b/R/simple_ensemble.R
index dad2fe4..3fb8ab9 100644
--- a/R/simple_ensemble.R
+++ b/R/simple_ensemble.R
@@ -29,38 +29,41 @@
 #'   to `agg_fun`.
 #' @param team_abbr, model_abbr `character` strings with the name of the team
 #'   and model to use for the ensemble predictions.
+#' @param output_type_col, output_id_col, value_col `character` strings with the 
+#'   names of the columns in `predictions` for the output's type, additional 
+#'   identifying information, and value (of the prediction)
 #'
 #' @return a data.frame with columns `team_abbr`, `model_abbr`, one column for
 #'   each task id variable, `output_type`, `output_id`, and `value`.
 
 simple_ensemble <- function(predictions, task_id_vars = NULL, hub_con = NULL,
                      weights = NULL, agg_fun = "mean", agg_args = list(),
-                     team_abbr = "Hub", model_abbr = "ensemble") {
+                     team_abbr = "Hub", model_abbr = "ensemble", output_type_col = "output_type", output_id_col = "output_id", value_col = "value") {
 
   # require(matrixStats)
     
   if (is.null(task_id_vars) && is.null(hub_con)) {
     temp <- colnames(predictions)
-    task_id_vars <- temp[!temp %in% c("team_abbr", "model_abbr", "output_type", "output_id", "value")]
+    task_id_vars <- temp[!temp %in% c("team_abbr", "model_abbr", output_type_col, output_id_col, value_col)]
   } else if (is.null(task_id_vars)) {
     # task_id variables looked up from `hub_con`
   }
   
-  col_names <- c("team_abbr", "model_abbr", task_id_vars, "output_type", "output_id", "value")
+  col_names <- c("team_abbr", "model_abbr", task_id_vars, output_type_col, output_id_col, value_col)
   if ((length(predictions) == 0) || !all(names(predictions) %in% col_names)) {
     stop("predictions did not have required columns", call. = FALSE)
-  } else if (!all(names(predictions) == col_names) && names(predictions) %in% col_names) {
+  } else if (!all(names(predictions) == col_names) && all(names(predictions) %in% col_names)) {
     predictions <- relocate(predictions, all_of(col_names))
   }
   
-  if (!all(predictions$output_type %in% c("mean", "median", "quantile", "cdf", "category"))) stop("Predictions contains unsupported output type") # throw warning or error
+  if (!all(pull(predictions[temp == output_type_col], 1) %in% c("mean", "median", "quantile", "cdf", "category"))) stop("Predictions contains unsupported output type") # throw warning or error
 
   if (is.null(weights)) {
     if (agg_fun == "mean") agg_fun = "mean"
     if (agg_fun == "median") agg_fun = "median"
     
     ensemble_predictions <- predictions %>%
-      dplyr::group_by(across(all_of(c(task_id_vars, "output_type", "output_id")))) %>%
+      dplyr::group_by(across(all_of(c(task_id_vars, output_type_col, output_id_col)))) %>%
       dplyr::summarize(value = do.call(agg_fun, args = c(agg_args, list(x=value)))) %>%
       dplyr::mutate(team_abbr = team_abbr, model_abbr = model_abbr, .before = 1) %>%
       dplyr::ungroup()
@@ -75,8 +78,8 @@ simple_ensemble <- function(predictions, task_id_vars = NULL, hub_con = NULL,
     
     ensemble_predictions <- predictions %>%
       dplyr::left_join(weights) %>%
-      dplyr::group_by(across(all_of(c(task_id_vars, "output_type", "output_id")))) %>%
-      dplyr::summarize(value = do.call(agg_fun, args = c(agg_args, list(x=value, w=weights)))) %>%
+      dplyr::group_by(across(all_of(c(task_id_vars, output_type_col, output_id_col)))) %>%
+      dplyr::summarize(value = do.call(agg_fun, args = c(agg_args, list(x=value, w=weight)))) %>%
       dplyr::mutate(team_abbr = team_abbr, model_abbr = model_abbr, .before = 1) %>%
       dplyr::ungroup()
       # do we want to have the horizon column before target?

From d6cad091c0bafa984200330d707bc38cf22496ae Mon Sep 17 00:00:00 2001
From: Github Actions CI <lshan1@msn.com>
Date: Fri, 28 Apr 2023 16:02:09 -0400
Subject: [PATCH 07/24] Add additional tests for simple_ensemble()

---
 tests/testthat/test-simple_ensemble.R | 35 +++++++++++++++++++++++----
 1 file changed, 30 insertions(+), 5 deletions(-)

diff --git a/tests/testthat/test-simple_ensemble.R b/tests/testthat/test-simple_ensemble.R
index 8456c69..c7e6099 100644
--- a/tests/testthat/test-simple_ensemble.R
+++ b/tests/testthat/test-simple_ensemble.R
@@ -14,13 +14,38 @@ team1_1008 <- readr::read_csv("inst/test-data/2022-10-08-team1-goodmodel.csv") %
   mutate(team_abbr = "team_1", model_abbr = "goodmodel1", .before = origin_date) %>%
   mutate(value = value + 2)
 
+test_that("non-default column names are preserved in output data frame", {
+  output_names <- baseline_1008 %>%
+    simple_ensemble(
+      agg_fun="mean",
+      model_abbr = "example",
+      output_type_col = "type", 
+      output_id_col = "type_id" 
+    ) %>%
+    names()
+  expect_equal(names(baseline_1008), output_names)
+})
+
+test_that("invalid output type throws error", {
+  expect_error(
+    baseline_1008 %>%
+      rename(output_type = type, output_id=type_id) %>%
+      mutate(output_type="sample") %>%
+      simple_ensemble(
+        agg_fun="mean",
+        model_abbr = "example"
+      )
+  )
+})
+
 test_that("invalid method argument throws error", {
   expect_error(
-    simple_ensemble(
-      baseline_1008,
-      agg_fun="linear pool",
-      model_abbr = "example"
-    )
+    baseline_1008 %>%
+      rename(output_type = type, output_id=type_id) %>%
+      simple_ensemble(
+        agg_fun="linear pool",
+        model_abbr = "example"
+      )
   )
 })
 

From 3900a91bbcb853a9c7ace0ff1365c29a2f0a7a02 Mon Sep 17 00:00:00 2001
From: Github Actions CI <lshan1@msn.com>
Date: Tue, 2 May 2023 11:02:38 -0400
Subject: [PATCH 08/24] clean up simple_ensemble() testing file

---
 tests/testthat/test-simple_ensemble.R | 21 +++++----------------
 1 file changed, 5 insertions(+), 16 deletions(-)

diff --git a/tests/testthat/test-simple_ensemble.R b/tests/testthat/test-simple_ensemble.R
index c7e6099..e8fe157 100644
--- a/tests/testthat/test-simple_ensemble.R
+++ b/tests/testthat/test-simple_ensemble.R
@@ -1,21 +1,10 @@
 library(matrixStats)
 
-test_that("multiplication works", {
-  expect_equal(2 * 2, 4)
-})
-
-#tmp_dat <- readr::read_csv("test-data/minimal-forecast.csv")
-
-baseline_1001 <- readr::read_csv("inst/test-data/2022-10-01-simple_hub-baseline.csv") %>%
-  mutate(team_abbr = "simple_hub", model_abbr = "baseline", .before = origin_date)
-baseline_1008 <- readr::read_csv("inst/test-data/2022-10-08-simple_hub-baseline.csv") %>%
+pred <- readr::read_csv("inst/test-data/2022-10-08-simple_hub-baseline.csv") %>%
   mutate(team_abbr = "simple_hub", model_abbr = "baseline", .before = origin_date)
-team1_1008 <- readr::read_csv("inst/test-data/2022-10-08-team1-goodmodel.csv") %>%
-  mutate(team_abbr = "team_1", model_abbr = "goodmodel1", .before = origin_date) %>%
-  mutate(value = value + 2)
 
 test_that("non-default column names are preserved in output data frame", {
-  output_names <- baseline_1008 %>%
+  output_names <- pred %>%
     simple_ensemble(
       agg_fun="mean",
       model_abbr = "example",
@@ -23,12 +12,12 @@ test_that("non-default column names are preserved in output data frame", {
       output_id_col = "type_id" 
     ) %>%
     names()
-  expect_equal(names(baseline_1008), output_names)
+  expect_equal(names(pred), output_names)
 })
 
 test_that("invalid output type throws error", {
   expect_error(
-    baseline_1008 %>%
+    pred %>%
       rename(output_type = type, output_id=type_id) %>%
       mutate(output_type="sample") %>%
       simple_ensemble(
@@ -40,7 +29,7 @@ test_that("invalid output type throws error", {
 
 test_that("invalid method argument throws error", {
   expect_error(
-    baseline_1008 %>%
+    pred %>%
       rename(output_type = type, output_id=type_id) %>%
       simple_ensemble(
         agg_fun="linear pool",

From 935c4201ce226a2b40686938038d501170446ae0 Mon Sep 17 00:00:00 2001
From: Github Actions CI <lshan1@msn.com>
Date: Tue, 2 May 2023 11:07:02 -0400
Subject: [PATCH 09/24] remove extraneous test data

---
 .../2022-10-01-simple_hub-baseline.csv        | 25 -------------------
 .../test-data/2022-10-08-team1-goodmodel.csv  | 24 ------------------
 2 files changed, 49 deletions(-)
 delete mode 100644 tests/testthat/test-data/2022-10-01-simple_hub-baseline.csv
 delete mode 100644 tests/testthat/test-data/2022-10-08-team1-goodmodel.csv

diff --git a/tests/testthat/test-data/2022-10-01-simple_hub-baseline.csv b/tests/testthat/test-data/2022-10-01-simple_hub-baseline.csv
deleted file mode 100644
index 82c1d62..0000000
--- a/tests/testthat/test-data/2022-10-01-simple_hub-baseline.csv
+++ /dev/null
@@ -1,25 +0,0 @@
-"origin_date","target","horizon","location","type","type_id","value"
-2022-10-01,"wk inc flu hosp",1,"US","mean",NA,150
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.01",135
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.025",137
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.05",139
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.1",140
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.15",141
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.2",141
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.25",142
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.3",143
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.35",144
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.4",145
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.45",147
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.5",148
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.55",149
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.6",150
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.65",152
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.7",155
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.75",161
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.8",165
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.85",170
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.9",175
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.95",176
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.975",176
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.99",205
diff --git a/tests/testthat/test-data/2022-10-08-team1-goodmodel.csv b/tests/testthat/test-data/2022-10-08-team1-goodmodel.csv
deleted file mode 100644
index 3e6f875..0000000
--- a/tests/testthat/test-data/2022-10-08-team1-goodmodel.csv
+++ /dev/null
@@ -1,24 +0,0 @@
-"origin_date","target","horizon","location","type","type_id","value"
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.01",135
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.025",137
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.05",139
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.1",140
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.15",141
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.2",141
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.25",142
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.3",143
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.35",144
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.4",145
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.45",147
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.5",148
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.55",149
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.6",150
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.65",152
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.7",155
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.75",161
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.8",165
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.85",170
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.9",175
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.95",176
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.975",176
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.99",205

From 15a9299435faf735a8eec92af6168eb8b11a377c Mon Sep 17 00:00:00 2001
From: Github Actions CI <lshan1@msn.com>
Date: Tue, 2 May 2023 11:07:33 -0400
Subject: [PATCH 10/24] remove old testing file

---
 tests/testthat/test-simple-ensemble.r | 81 ---------------------------
 1 file changed, 81 deletions(-)
 delete mode 100644 tests/testthat/test-simple-ensemble.r

diff --git a/tests/testthat/test-simple-ensemble.r b/tests/testthat/test-simple-ensemble.r
deleted file mode 100644
index ebdedbb..0000000
--- a/tests/testthat/test-simple-ensemble.r
+++ /dev/null
@@ -1,81 +0,0 @@
-context("simple_ensemble")
-library(hubEnsembles)
-library(matrixStats)
-
-tmp_dat <- readr::read_csv("test-data/minimal-forecast.csv")
-
-test_that("invalid method argument throws error", {
-  expect_error(
-    build_quantile_ensemble(tmp_dat,
-                            method="weighted mean",
-                            model_name = "example")
-    )
-})
-
-test_that("medians and means correctly calculated", {
-  fdat <- expand.grid(
-    stringsAsFactors = FALSE,
-    model = letters[1:4],
-    location = c("222", "888"),
-    horizon = 1,
-    temporal_resolution = "wk",
-    target_variable = "inc death",
-    target_end_date = as.Date("2021-12-25"),
-    type = "quantile",
-    quantile = c(.1, .5, .9))
-
-  fdat$value[fdat$location == "222" & fdat$quantile == .1] <- v2.1 <- c(10, 30, 15, 20)
-  fdat$value[fdat$location == "222" & fdat$quantile == .5] <- v2.5 <- c(40, 40, 45, 50)
-  fdat$value[fdat$location == "222" & fdat$quantile == .9] <- v2.9 <- c(60, 70, 75, 80)
-  fdat$value[fdat$location == "888" & fdat$quantile == .1] <- v8.1 <- c(100, 300, 400, 250)
-  fdat$value[fdat$location == "888" & fdat$quantile == .5] <- v8.5 <- c(150, 325, 500, 300)
-  fdat$value[fdat$location == "888" & fdat$quantile == .9] <- v8.9 <- c(250, 350, 500, 350)
-
-  median_vals <- sapply(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), median)
-  mean_vals <- sapply(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), mean)
-
-  fweight <- tibble(model = letters[1:4], weight = 0.1*(1:4))
-
-  weighted_median_vals <- map(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), weightedMedian, w = fweight$weight)
-  weighted_mean_vals <- map(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), weightedMean, w = fweight$weight)
-
-  median_actual <- build_quantile_ensemble(
-    forecast_data = fdat, weights_df = NULL, method = "median", model_name = "median_ens", forecast_date = "2021-12-20")
-  mean_actual <- build_quantile_ensemble(
-    forecast_data = fdat, weights_df = NULL, method = "mean", model_name = "mean_ens", forecast_date = "2021-12-20")
-    
-  weighted_median_actual <- build_quantile_ensemble(
-    forecast_data = fdat, weights_df = fweight, method = "median", model_name = "weighted_median_ens", forecast_date = "2021-12-20")
-  weighted_mean_actual <- build_quantile_ensemble(
-    forecast_data = fdat, weights_df = fweight, method = "mean", model_name = "weighted_mean_ens", forecast_date = "2021-12-20")
-
-
-  median_expected <- mean_expected <- weighted_median_expected <- weighted_mean_expected <- tibble::tibble(
-    location = rep(c("222", "888"), each = 3), 
-    horizon = 1, 
-    temporal_resolution = "wk", 
-    target_variable = "inc death",     
-    target_end_date = as.Date("2021-12-25"), 
-    type = "quantile", 
-    quantile = rep(c(.1, .5, .9), 2), 
-    forecast_count = 4,
-    value = 0,
-    model = NA,
-    forecast_date = as.Date("2021-12-20"))
-
-  median_expected$value <- sapply(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), median)
-  median_expected$model <- "median_ens"
-  mean_expected$value <- sapply(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), mean)
-  mean_expected$model <- "mean_ens"
-
-  weighted_mean_expected$value <- map_dbl(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), weightedMean, w = fweight$weight)
-  weighted_mean_expected$model <- "weighted_mean_ens"
-  weighted_median_expected$value <- map_dbl(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), weightedMedian, w = fweight$weight)
-  weighted_median_expected$model <- "weighted_median_ens"
-
-  expect_equal(median_actual, median_expected)
-  expect_equal(mean_actual, mean_expected)
-  
-  expect_equal(weighted_median_actual, weighted_median_expected)
-  expect_equal(weighted_mean_actual, weighted_mean_expected)
-})

From 7588b8a53eeed8431aad2174bc8f36948106e3b8 Mon Sep 17 00:00:00 2001
From: Github Actions CI <lshan1@msn.com>
Date: Wed, 24 May 2023 10:09:39 -0400
Subject: [PATCH 11/24] fix file path to test data

---
 tests/testthat/test-simple_ensemble.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/testthat/test-simple_ensemble.R b/tests/testthat/test-simple_ensemble.R
index e8fe157..e116a83 100644
--- a/tests/testthat/test-simple_ensemble.R
+++ b/tests/testthat/test-simple_ensemble.R
@@ -1,6 +1,6 @@
 library(matrixStats)
 
-pred <- readr::read_csv("inst/test-data/2022-10-08-simple_hub-baseline.csv") %>%
+pred <- readr::read_csv("inst/example-data/2022-10-08-simple_hub-baseline.csv") %>%
   mutate(team_abbr = "simple_hub", model_abbr = "baseline", .before = origin_date)
 
 test_that("non-default column names are preserved in output data frame", {

From a88c6939438b18ffa4113fea0a012326fc92b5a4 Mon Sep 17 00:00:00 2001
From: Evan Ray <elray@umass.edu>
Date: Mon, 5 Jun 2023 17:11:01 -0400
Subject: [PATCH 12/24] progress on refactoring simple_ensemble to use cli for
 messaging and streamline logic

---
 R/simple_ensemble.R | 127 +++++++++++++++++++++++++-------------------
 R/utils-pipe.R      |  14 +++++
 2 files changed, 85 insertions(+), 56 deletions(-)
 create mode 100644 R/utils-pipe.R

diff --git a/R/simple_ensemble.R b/R/simple_ensemble.R
index 3fb8ab9..18201c4 100644
--- a/R/simple_ensemble.R
+++ b/R/simple_ensemble.R
@@ -1,18 +1,15 @@
 #' Compute ensemble predictions by summarizing component predictions for each
-#' combination of model task, output type, and type id. Supported output types
-#' include `mean`, `median`, `quantile`, `cdf`, and `category`.
+#' combination of model task, output type, and output type id. Supported output
+#' types include `mean`, `median`, `quantile`, `cdf`, and `pmf`.
 #'
 #' @param predictions a `data.frame` with component model predictions. It should
 #'   have columns `team_abbr`, `model_abbr`, one column for each task id
 #'   variable, `output_type`, `output_type_id`, and `value`.
-#' @param task_id_vars an optional `character` vector naming the columns of
+#' @param task_id_cols an optional `character` vector naming the columns of
 #'   `predictions` that correspond to task id variables. The default is `NULL`,
-#'   in which case the task id variables are looked up from the `hub_con` if one
-#'   is provided. If neither `task_id_vars` nor `hub_con` are provided, all
-#'   columns in `predictions` _other than_ `team_abbr`, `model_abbr`,
-#'   `output_type`, `output_id`, and `value` will be used as task id
-#'   variables.
-#' @param hub_con an optional hub connection object; see `hubUtils::connect_hub`
+#'   in which case all columns in `predictions` _other than_ `team_abbr`,
+#'   `model_abbr`, and the specified `output_type_col`, `output_id_col`, and
+#'   `value_col` are used as task id variables.
 #' @param weights an optional `data.frame` with component model weights. If
 #'   provided, it should have columns `team_name`, `model_abbr`, `weight`,
 #'   and optionally, additional columns corresponding to task id variables,
@@ -23,67 +20,85 @@
 #'   aggregating component predictions into the ensemble prediction. The default
 #'   is `mean`, in which case the ensemble prediction is the simple average of
 #'   the component model predictions. The provided function 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.
+#'   argument `x` for the vector of numeric values to summarize, and for
+#'   weighted methods, an argument `w` with a numeric vector of weights.
 #' @param agg_args a named list of any additional arguments that will be passed
 #'   to `agg_fun`.
 #' @param team_abbr, model_abbr `character` strings with the name of the team
 #'   and model to use for the ensemble predictions.
-#' @param output_type_col, output_id_col, value_col `character` strings with the 
-#'   names of the columns in `predictions` for the output's type, additional 
+#' @param output_type_col, output_id_col, value_col `character` strings with the
+#'   names of the columns in `predictions` for the output's type, additional
 #'   identifying information, and value (of the prediction)
 #'
 #' @return a data.frame with columns `team_abbr`, `model_abbr`, one column for
 #'   each task id variable, `output_type`, `output_id`, and `value`.
+simple_ensemble <- function(predictions, task_id_cols = NULL,
+                            weights = NULL, agg_fun = "mean", agg_args = list(),
+                            team_abbr = "Hub", model_abbr = "ensemble",
+                            output_type_col = "output_type",
+                            output_id_col = "output_id",
+                            value_col = "value") {
+  if (!is.data.frame(predictions)) {
+    cli::cli_abort(c("x" = "{.arg predictions} must be a `data.frame`."))
+  }
 
-simple_ensemble <- function(predictions, task_id_vars = NULL, hub_con = NULL,
-                     weights = NULL, agg_fun = "mean", agg_args = list(),
-                     team_abbr = "Hub", model_abbr = "ensemble", output_type_col = "output_type", output_id_col = "output_id", value_col = "value") {
+  if (is.null(task_id_cols)) {
+    cols <- colnames(predictions)
+    non_task_cols <- c("team_abbr", "model_abbr", output_type_col,
+                       output_id_col, value_col)
+    task_id_cols <- cols[!cols %in% non_task_cols]
+  }
 
-  # require(matrixStats)
-    
-  if (is.null(task_id_vars) && is.null(hub_con)) {
-    temp <- colnames(predictions)
-    task_id_vars <- temp[!temp %in% c("team_abbr", "model_abbr", output_type_col, output_id_col, value_col)]
-  } else if (is.null(task_id_vars)) {
-    # task_id variables looked up from `hub_con`
+  col_names <- c("team_abbr", "model_abbr", task_id_cols, output_type_col,
+                 output_id_col, value_col)
+  if (!all(names(predictions) %in% col_names)) {
+    cli::cli_abort(c(
+      "x" = "{.arg predictions} did not have all required columns
+             {.val {col_names}}."
+    ))
   }
-  
-  col_names <- c("team_abbr", "model_abbr", task_id_vars, output_type_col, output_id_col, value_col)
-  if ((length(predictions) == 0) || !all(names(predictions) %in% col_names)) {
-    stop("predictions did not have required columns", call. = FALSE)
-  } else if (!all(names(predictions) == col_names) && all(names(predictions) %in% col_names)) {
-    predictions <- relocate(predictions, all_of(col_names))
+
+  valid_types <- c("mean", "median", "quantile", "cdf", "pmf")
+  unique_types <- unique(predictions[[output_type_col]])
+  invalid_types <- unique_types[!unique_types %in% valid_types]
+  if (length(invalid_types) > 0) {
+    cli::cli_abort(c(
+      "x" = "{.arg predictions} contains unsupported output type.",
+      "i" = "Included type{?s}: {.val {invalid_types}}.",
+      "i" = "Supported output types: {.val {valid_types}}."
+    ))
   }
-  
-  if (!all(pull(predictions[temp == output_type_col], 1) %in% c("mean", "median", "quantile", "cdf", "category"))) stop("Predictions contains unsupported output type") # throw warning or error
 
   if (is.null(weights)) {
-    if (agg_fun == "mean") agg_fun = "mean"
-    if (agg_fun == "median") agg_fun = "median"
-    
-    ensemble_predictions <- predictions %>%
-      dplyr::group_by(across(all_of(c(task_id_vars, output_type_col, output_id_col)))) %>%
-      dplyr::summarize(value = do.call(agg_fun, args = c(agg_args, list(x=value)))) %>%
-      dplyr::mutate(team_abbr = team_abbr, model_abbr = model_abbr, .before = 1) %>%
-      dplyr::ungroup()
-      # do we want to have the horizon column before target?
+    if (agg_fun == "mean") agg_fun <- mean
+    if (agg_fun == "median") agg_fun <- median
+
+    agg_args <- c(agg_args, list(x = "value"))
   } else {
-    if (!all(names(weights) %in% c("team_abbr", "model_abbr", "weight"))) {
-      stop("weights did not have required columns", call. = FALSE)
+    req_weight_cols <- c("model_abbr", "team_abbr", "weight")
+    if (!isTRUE(all.equal(sort(names(weights)), req_weight_cols))) {
+      cli::cli_abort(c(
+        "x" = "{.arg weights} did not have required columns
+               {.val {req_weight_cols}}."
+      ))
     }
-    
-    if (agg_fun == "mean") agg_fun = "weightedMean"
-    if (agg_fun == "median") agg_fun = "weightedMedian"
-    
-    ensemble_predictions <- predictions %>%
-      dplyr::left_join(weights) %>%
-      dplyr::group_by(across(all_of(c(task_id_vars, output_type_col, output_id_col)))) %>%
-      dplyr::summarize(value = do.call(agg_fun, args = c(agg_args, list(x=value, w=weight)))) %>%
-      dplyr::mutate(team_abbr = team_abbr, model_abbr = model_abbr, .before = 1) %>%
-      dplyr::ungroup()
-      # do we want to have the horizon column before target?
-  }  
-    
-  return (ensemble_predictions)
+
+    predictions <- predictions %>%
+      dplyr::left_join(weights, by = c("team_abbr", "model_abbr"))
+
+    if (agg_fun == "mean") agg_fun <- matrixStats::weightedMean
+    if (agg_fun == "median") agg_fun <- matrixStats::weightedMedian
+
+    agg_args <- c(agg_args, list(x = "value", w = "weight"))
+  }
+
+  ensemble_predictions <- predictions %>%
+    dplyr::group_by(dplyr::across(dplyr::all_of(c(task_id_cols, output_type_col,
+                                                  output_id_col)))) %>%
+    dplyr::summarize(value = do.call(agg_fun, args = agg_args)) %>%
+    dplyr::mutate(team_abbr = team_abbr, model_abbr = model_abbr,
+                  .before = 1) %>%
+    dplyr::ungroup()
+
+  return(ensemble_predictions)
 }
diff --git a/R/utils-pipe.R b/R/utils-pipe.R
new file mode 100644
index 0000000..fd0b1d1
--- /dev/null
+++ b/R/utils-pipe.R
@@ -0,0 +1,14 @@
+#' 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

From 39f601ed8ec044adb55b8a81716ab2d1e7d5e3f4 Mon Sep 17 00:00:00 2001
From: Evan Ray <elray@umass.edu>
Date: Mon, 5 Jun 2023 22:58:00 -0400
Subject: [PATCH 13/24] warn if 0 rows

---
 R/simple_ensemble.R | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/R/simple_ensemble.R b/R/simple_ensemble.R
index 18201c4..9dcd3b4 100644
--- a/R/simple_ensemble.R
+++ b/R/simple_ensemble.R
@@ -42,6 +42,10 @@ simple_ensemble <- function(predictions, task_id_cols = NULL,
     cli::cli_abort(c("x" = "{.arg predictions} must be a `data.frame`."))
   }
 
+  if (nrow(predictions) == 0) {
+    cli::cli_warn(c("!" = "{.arg predictions} has zero rows."))
+  }
+
   if (is.null(task_id_cols)) {
     cols <- colnames(predictions)
     non_task_cols <- c("team_abbr", "model_abbr", output_type_col,

From 0794377e29ae50462c3b3fde27d754492f24d0c7 Mon Sep 17 00:00:00 2001
From: Evan Ray <elray@umass.edu>
Date: Tue, 6 Jun 2023 12:07:05 -0400
Subject: [PATCH 14/24] add package imports, validations for weights

---
 DESCRIPTION         |  5 ++++
 R/simple_ensemble.R | 62 ++++++++++++++++++++++++++++++++-------------
 2 files changed, 50 insertions(+), 17 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 90bc71c..4dd6a11 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -14,3 +14,8 @@ Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.2.2
 URL: https://github.com/Infectious-Disease-Modeling-Hubs/hubEnsembles
 BugReports: https://github.com/Infectious-Disease-Modeling-Hubs/hubEnsembles/issues
+Imports: 
+    cli,
+    dplyr,
+    matrixStats,
+    rlang
diff --git a/R/simple_ensemble.R b/R/simple_ensemble.R
index 9dcd3b4..45afcf8 100644
--- a/R/simple_ensemble.R
+++ b/R/simple_ensemble.R
@@ -32,35 +32,38 @@
 #'
 #' @return a data.frame with columns `team_abbr`, `model_abbr`, one column for
 #'   each task id variable, `output_type`, `output_id`, and `value`.
-simple_ensemble <- function(predictions, task_id_cols = NULL,
-                            weights = NULL, agg_fun = "mean", agg_args = list(),
-                            team_abbr = "Hub", model_abbr = "ensemble",
+simple_ensemble <- function(predictions, weights = NULL,
+                            agg_fun = "mean", agg_args = list(),
+                            team_abbr = "hub", model_abbr = "ensemble",
+                            task_id_cols = NULL,
                             output_type_col = "output_type",
-                            output_id_col = "output_id",
-                            value_col = "value") {
+                            output_id_col = "output_type_id",
+                            hub_connection = NULL) {
   if (!is.data.frame(predictions)) {
     cli::cli_abort(c("x" = "{.arg predictions} must be a `data.frame`."))
   }
 
-  if (nrow(predictions) == 0) {
-    cli::cli_warn(c("!" = "{.arg predictions} has zero rows."))
-  }
-
   if (is.null(task_id_cols)) {
     cols <- colnames(predictions)
     non_task_cols <- c("team_abbr", "model_abbr", output_type_col,
-                       output_id_col, value_col)
+                       output_id_col, "value")
     task_id_cols <- cols[!cols %in% non_task_cols]
   }
 
   col_names <- c("team_abbr", "model_abbr", task_id_cols, output_type_col,
-                 output_id_col, value_col)
-  if (!all(names(predictions) %in% col_names)) {
+                 output_id_col, "value")
+  if (!all(colnames(predictions) %in% col_names)) {
     cli::cli_abort(c(
       "x" = "{.arg predictions} did not have all required columns
              {.val {col_names}}."
     ))
   }
+  
+  ## Validations above this point to be relocated to hubUtils
+
+  if (nrow(predictions) == 0) {
+    cli::cli_warn(c("!" = "{.arg predictions} has zero rows."))
+  }
 
   valid_types <- c("mean", "median", "quantile", "cdf", "pmf")
   unique_types <- unique(predictions[[output_type_col]])
@@ -68,7 +71,7 @@ simple_ensemble <- function(predictions, task_id_cols = NULL,
   if (length(invalid_types) > 0) {
     cli::cli_abort(c(
       "x" = "{.arg predictions} contains unsupported output type.",
-      "i" = "Included type{?s}: {.val {invalid_types}}.",
+      "i" = "Included output type{?s}: {.val {invalid_types}}.",
       "i" = "Supported output types: {.val {valid_types}}."
     ))
   }
@@ -80,20 +83,45 @@ simple_ensemble <- function(predictions, task_id_cols = NULL,
     agg_args <- c(agg_args, list(x = "value"))
   } else {
     req_weight_cols <- c("model_abbr", "team_abbr", "weight")
-    if (!isTRUE(all.equal(sort(names(weights)), req_weight_cols))) {
+    if (!all(req_weight_cols %in% colnames(weights))) {
       cli::cli_abort(c(
-        "x" = "{.arg weights} did not have required columns
+        "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(predictions)]
+    if (!all(weight_by_cols %in% colnames(predictions))) {
+      cli::cli_abort(c(
+        "x" = "{.arg weights} included {length(invalid_cols)} column{?s} that
+               {?was/were} not present in {.arg predictions}:
+               {.val {invalid_cols}}"
+      ))
+    }
+
+    if ("weight" %in% colnames(predictions)) {
+      weight_col_name <- paste0("weight_", rlang::hash(colnames(predictions)))
+      weights <- weights %>% dplyr::rename(!!weight_col_name := "weight")
+    } else {
+      weight_col_name <- "weight"
+    }
+
     predictions <- predictions %>%
-      dplyr::left_join(weights, by = c("team_abbr", "model_abbr"))
+      dplyr::left_join(weights, by = weight_by_cols)
 
     if (agg_fun == "mean") agg_fun <- matrixStats::weightedMean
     if (agg_fun == "median") agg_fun <- matrixStats::weightedMedian
 
-    agg_args <- c(agg_args, list(x = "value", w = "weight"))
+    agg_args <- c(agg_args, list(x = "value", w = weight_col_name))
   }
 
   ensemble_predictions <- predictions %>%

From 783e936e6d36b0d119d94a15e780b4ad19363841 Mon Sep 17 00:00:00 2001
From: Evan Ray <elray@umass.edu>
Date: Wed, 7 Jun 2023 14:48:01 -0400
Subject: [PATCH 15/24] misc updates

---
 DESCRIPTION                                   |   2 +-
 NAMESPACE                                     |   2 +
 R/simple_ensemble.R                           | 136 ++++-----
 .../2022-10-01-simple_hub-baseline.csv        |   2 +-
 .../2022-10-08-simple_hub-baseline.csv        |   2 +-
 .../2022-10-08-team1-goodmodel.csv            |   2 +-
 man/pipe.Rd                                   |  20 ++
 man/simple_ensemble.Rd                        |  65 ++++
 .../2022-10-08-simple_hub-baseline.csv        | 277 ------------------
 tests/testthat/test-simple_ensemble.R         | 153 +++++-----
 10 files changed, 245 insertions(+), 416 deletions(-)
 create mode 100644 man/pipe.Rd
 create mode 100644 man/simple_ensemble.Rd
 delete mode 100644 tests/testthat/test-data/2022-10-08-simple_hub-baseline.csv

diff --git a/DESCRIPTION b/DESCRIPTION
index 4dd6a11..1abb6bd 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -11,7 +11,7 @@ Suggests:
 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: 
diff --git a/NAMESPACE b/NAMESPACE
index 6ae9268..ea39623 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,2 +1,4 @@
 # Generated by roxygen2: do not edit by hand
 
+export("%>%")
+importFrom(magrittr,"%>%")
diff --git a/R/simple_ensemble.R b/R/simple_ensemble.R
index 45afcf8..a2763c0 100644
--- a/R/simple_ensemble.R
+++ b/R/simple_ensemble.R
@@ -1,88 +1,85 @@
-#' Compute ensemble predictions by summarizing component predictions for each
-#' combination of model task, output type, and output type id. Supported output
-#' types include `mean`, `median`, `quantile`, `cdf`, and `pmf`.
+#' 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 predictions a `data.frame` with component model predictions. It should
-#'   have columns `team_abbr`, `model_abbr`, one column for each task id
-#'   variable, `output_type`, `output_type_id`, and `value`.
-#' @param task_id_cols an optional `character` vector naming the columns of
-#'   `predictions` that correspond to task id variables. The default is `NULL`,
-#'   in which case all columns in `predictions` _other than_ `team_abbr`,
-#'   `model_abbr`, and the specified `output_type_col`, `output_id_col`, and
-#'   `value_col` are used as task id variables.
+#' @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 `team_name`, `model_abbr`, `weight`,
-#'   and optionally, additional columns corresponding to task id variables,
-#'   `output_type`, or `output_id`, if weights are specific to values of
-#'   those variables. The default is `NULL`, in which case an equally-weighted
-#'   ensemble is calculated.
+#'   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 predictions into the ensemble prediction. The default
-#'   is `mean`, in which case the ensemble prediction is the simple average of
-#'   the component model predictions. The provided function 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.
+#'   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 team_abbr, model_abbr `character` strings with the name of the team
-#'   and model to use for the ensemble predictions.
-#' @param output_type_col, output_id_col, value_col `character` strings with the
-#'   names of the columns in `predictions` for the output's type, additional
-#'   identifying information, and value (of the prediction)
+#' @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`.
-simple_ensemble <- function(predictions, weights = NULL,
+#'   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(),
-                            team_abbr = "hub", model_abbr = "ensemble",
+                            model_id = "hub-ensemble",
                             task_id_cols = NULL,
                             output_type_col = "output_type",
-                            output_id_col = "output_type_id",
+                            output_type_id_col = "output_type_id",
                             hub_connection = NULL) {
-  if (!is.data.frame(predictions)) {
-    cli::cli_abort(c("x" = "{.arg predictions} must be a `data.frame`."))
+  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)) {
-    cols <- colnames(predictions)
-    non_task_cols <- c("team_abbr", "model_abbr", output_type_col,
-                       output_id_col, "value")
-    task_id_cols <- cols[!cols %in% non_task_cols]
+    task_id_cols <- model_out_cols[!model_out_cols %in% non_task_cols]
   }
 
-  col_names <- c("team_abbr", "model_abbr", task_id_cols, output_type_col,
-                 output_id_col, "value")
-  if (!all(colnames(predictions) %in% col_names)) {
+  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 predictions} did not have all required columns
-             {.val {col_names}}."
+      "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(predictions) == 0) {
-    cli::cli_warn(c("!" = "{.arg predictions} has zero rows."))
+  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(predictions[[output_type_col]])
+  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 predictions} contains unsupported output type.",
+      "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)) {
-    if (agg_fun == "mean") agg_fun <- mean
-    if (agg_fun == "median") agg_fun <- median
-
-    agg_args <- c(agg_args, list(x = "value"))
+    agg_args <- c(agg_args, list(x = quote(.data[["value"]])))
   } else {
-    req_weight_cols <- c("model_abbr", "team_abbr", "weight")
+    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
@@ -99,38 +96,45 @@ simple_ensemble <- function(predictions, weights = NULL,
       ))
     }
 
-    invalid_cols <- weight_by_cols[!weight_by_cols %in% colnames(predictions)]
-    if (!all(weight_by_cols %in% colnames(predictions))) {
+    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 predictions}:
+               {?was/were} not present in {.arg model_outputs}:
                {.val {invalid_cols}}"
       ))
     }
 
-    if ("weight" %in% colnames(predictions)) {
-      weight_col_name <- paste0("weight_", rlang::hash(colnames(predictions)))
+    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"
     }
 
-    predictions <- predictions %>%
+    model_outputs <- model_outputs %>%
       dplyr::left_join(weights, by = weight_by_cols)
 
-    if (agg_fun == "mean") agg_fun <- matrixStats::weightedMean
-    if (agg_fun == "median") agg_fun <- matrixStats::weightedMedian
+    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 = "value", w = weight_col_name))
+    agg_args <- c(agg_args, list(x = quote(.data[["value"]]),
+                                 w = quote(.data[[weight_col_name]])))
   }
 
-  ensemble_predictions <- predictions %>%
-    dplyr::group_by(dplyr::across(dplyr::all_of(c(task_id_cols, output_type_col,
-                                                  output_id_col)))) %>%
+  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(team_abbr = team_abbr, model_abbr = model_abbr,
-                  .before = 1) %>%
+    dplyr::mutate(model_id = model_id, .before = 1) %>%
     dplyr::ungroup()
 
-  return(ensemble_predictions)
+  # hubUtils::as_model_output_df(ensemble_model_outputs)
+
+  return(ensemble_model_outputs)
 }
diff --git a/inst/example-data/2022-10-01-simple_hub-baseline.csv b/inst/example-data/2022-10-01-simple_hub-baseline.csv
index 82c1d62..703831b 100644
--- a/inst/example-data/2022-10-01-simple_hub-baseline.csv
+++ b/inst/example-data/2022-10-01-simple_hub-baseline.csv
@@ -1,4 +1,4 @@
-"origin_date","target","horizon","location","type","type_id","value"
+"origin_date","target","horizon","location","output_type","output_type_id","value"
 2022-10-01,"wk inc flu hosp",1,"US","mean",NA,150
 2022-10-01,"wk inc flu hosp",1,"US","quantile","0.01",135
 2022-10-01,"wk inc flu hosp",1,"US","quantile","0.025",137
diff --git a/inst/example-data/2022-10-08-simple_hub-baseline.csv b/inst/example-data/2022-10-08-simple_hub-baseline.csv
index 98ad58c..7387c87 100644
--- a/inst/example-data/2022-10-08-simple_hub-baseline.csv
+++ b/inst/example-data/2022-10-08-simple_hub-baseline.csv
@@ -1,4 +1,4 @@
-"origin_date","target","horizon","location","type","type_id","value"
+"origin_date","target","horizon","location","output_type","output_type_id","value"
 2022-10-08,"wk inc flu hosp",1,"US","quantile","0.01",135
 2022-10-08,"wk inc flu hosp",1,"US","quantile","0.025",137
 2022-10-08,"wk inc flu hosp",1,"US","quantile","0.05",139
diff --git a/inst/example-data/2022-10-08-team1-goodmodel.csv b/inst/example-data/2022-10-08-team1-goodmodel.csv
index 3e6f875..71108b3 100644
--- a/inst/example-data/2022-10-08-team1-goodmodel.csv
+++ b/inst/example-data/2022-10-08-team1-goodmodel.csv
@@ -1,4 +1,4 @@
-"origin_date","target","horizon","location","type","type_id","value"
+"origin_date","target","horizon","location","output_type","output_type_id","value"
 2022-10-08,"wk inc flu hosp",1,"US","quantile","0.01",135
 2022-10-08,"wk inc flu hosp",1,"US","quantile","0.025",137
 2022-10-08,"wk inc flu hosp",1,"US","quantile","0.05",139
diff --git a/man/pipe.Rd b/man/pipe.Rd
new file mode 100644
index 0000000..a648c29
--- /dev/null
+++ b/man/pipe.Rd
@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-pipe.R
+\name{\%>\%}
+\alias{\%>\%}
+\title{Pipe operator}
+\usage{
+lhs \%>\% rhs
+}
+\arguments{
+\item{lhs}{A value or the magrittr placeholder.}
+
+\item{rhs}{A function call using the magrittr semantics.}
+}
+\value{
+The result of calling \code{rhs(lhs)}.
+}
+\description{
+See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details.
+}
+\keyword{internal}
diff --git a/man/simple_ensemble.Rd b/man/simple_ensemble.Rd
new file mode 100644
index 0000000..b231d55
--- /dev/null
+++ b/man/simple_ensemble.Rd
@@ -0,0 +1,65 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/simple_ensemble.R
+\name{simple_ensemble}
+\alias{simple_ensemble}
+\title{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 \code{mean}, \code{median}, \code{quantile}, \code{cdf}, and \code{pmf}.}
+\usage{
+simple_ensemble(
+  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
+)
+}
+\arguments{
+\item{model_outputs}{an object of class \code{model_output_df} with component
+model outputs (e.g., predictions).}
+
+\item{weights}{an optional \code{data.frame} with component model weights. If
+provided, it should have columns \code{model_id}, \code{weight}, and optionally,
+additional columns corresponding to task id variables, \code{output_type}, or
+\code{output_type_id}, if weights are specific to values of those variables. The
+default is \code{NULL}, in which case an equally-weighted ensemble is calculated.}
+
+\item{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.}
+
+\item{agg_args}{a named list of any additional arguments that will be passed
+to \code{agg_fun}.}
+
+\item{model_id}{\code{character} string with the identifier to use for the
+ensemble model.}
+
+\item{task_id_cols, }{output_type_col, output_type_id_col, value_col
+\code{character} vectors with the names of the columns in \code{model_outputs} for
+the output's type, additional identifying information, and value of the
+model output.}
+}
+\value{
+a data.frame with columns \code{team_abbr}, \code{model_abbr}, one column for
+each task id variable, \code{output_type}, \code{output_id}, and \code{value}. Note that
+any additional columns in the input \code{model_outputs} are dropped.
+}
+\description{
+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 \code{mean}, \code{median}, \code{quantile}, \code{cdf}, and \code{pmf}.
+}
+\details{
+The default for \code{agg_fun} is \code{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 \code{agg_fun} should have an argument \code{x} for the
+vector of numeric values to summarize, and for weighted methods, an
+argument \code{w} with a numeric vector of weights. For weighted methods,
+\code{agg_fun = "mean"} and \code{agg_fun = "median"} are translated to use
+\code{matrixStats::weightedMean} and \code{matrixStats::weightedMedian} respectively.
+}
diff --git a/tests/testthat/test-data/2022-10-08-simple_hub-baseline.csv b/tests/testthat/test-data/2022-10-08-simple_hub-baseline.csv
deleted file mode 100644
index 98ad58c..0000000
--- a/tests/testthat/test-data/2022-10-08-simple_hub-baseline.csv
+++ /dev/null
@@ -1,277 +0,0 @@
-"origin_date","target","horizon","location","type","type_id","value"
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.01",135
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.025",137
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.05",139
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.1",140
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.15",141
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.2",141
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.25",142
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.3",143
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.35",144
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.4",145
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.45",147
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.5",148
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.55",149
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.6",150
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.65",152
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.7",155
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.75",161
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.8",165
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.85",170
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.9",175
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.95",176
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.975",176
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.99",205
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.01",135
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.025",137
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.05",139
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.1",140
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.15",141
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.2",141
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.25",142
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.3",143
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.35",144
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.4",145
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.45",147
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.5",148
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.55",149
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.6",150
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.65",152
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.7",155
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.75",161
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.8",165
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.85",170
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.9",175
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.95",176
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.975",176
-2022-10-08,"wk inc flu hosp",2,"US","quantile","0.99",205
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.01",135
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.025",137
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.05",139
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.1",140
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.15",141
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.2",141
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.25",142
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.3",143
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.35",144
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.4",145
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.45",147
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.5",148
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.55",149
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.6",150
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.65",152
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.7",155
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.75",161
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.8",165
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.85",170
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.9",175
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.95",176
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.975",176
-2022-10-08,"wk inc flu hosp",3,"US","quantile","0.99",205
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.01",135
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.025",137
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.05",139
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.1",140
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.15",141
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.2",141
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.25",142
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.3",143
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.35",144
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.4",145
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.45",147
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.5",148
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.55",149
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.6",150
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.65",152
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.7",155
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.75",161
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.8",165
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.85",170
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.9",175
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.95",176
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.975",176
-2022-10-08,"wk inc flu hosp",4,"US","quantile","0.99",205
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.01",135
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.025",137
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.05",139
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.1",140
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.15",141
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.2",141
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.25",142
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.3",143
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.35",144
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.4",145
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.45",147
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.5",148
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.55",149
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.6",150
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.65",152
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.7",155
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.75",161
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.8",165
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.85",170
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.9",175
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.95",176
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.975",176
-2022-10-08,"wk inc flu hosp",1,"04","quantile","0.99",205
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.01",135
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.025",137
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.05",139
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.1",140
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.15",141
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.2",141
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.25",142
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.3",143
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.35",144
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.4",145
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.45",147
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.5",148
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.55",149
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.6",150
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.65",152
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.7",155
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.75",161
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.8",165
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.85",170
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.9",175
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.95",176
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.975",176
-2022-10-08,"wk inc flu hosp",2,"04","quantile","0.99",205
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.01",135
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.025",137
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.05",139
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.1",140
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.15",141
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.2",141
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.25",142
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.3",143
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.35",144
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.4",145
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.45",147
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.5",148
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.55",149
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.6",150
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.65",152
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.7",155
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.75",161
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.8",165
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.85",170
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.9",175
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.95",176
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.975",176
-2022-10-08,"wk inc flu hosp",3,"04","quantile","0.99",205
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.01",135
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.025",137
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.05",139
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.1",140
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.15",141
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.2",141
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.25",142
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.3",143
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.35",144
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.4",145
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.45",147
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.5",148
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.55",149
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.6",150
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.65",152
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.7",155
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.75",161
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.8",165
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.85",170
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.9",175
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.95",176
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.975",176
-2022-10-08,"wk inc flu hosp",4,"04","quantile","0.99",205
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.01",135
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.025",137
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.05",139
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.1",140
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.15",141
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.2",141
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.25",142
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.3",143
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.35",144
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.4",145
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.45",147
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.5",148
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.55",149
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.6",150
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.65",152
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.7",155
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.75",161
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.8",165
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.85",170
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.9",175
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.95",176
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.975",176
-2022-10-08,"wk inc flu hosp",1,"01","quantile","0.99",205
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.01",135
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.025",137
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.05",139
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.1",140
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.15",141
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.2",141
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.25",142
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.3",143
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.35",144
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.4",145
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.45",147
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.5",148
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.55",149
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.6",150
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.65",152
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.7",155
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.75",161
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.8",165
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.85",170
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.9",175
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.95",176
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.975",176
-2022-10-08,"wk inc flu hosp",2,"01","quantile","0.99",205
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.01",135
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.025",137
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.05",139
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.1",140
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.15",141
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.2",141
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.25",142
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.3",143
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.35",144
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.4",145
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.45",147
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.5",148
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.55",149
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.6",150
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.65",152
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.7",155
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.75",161
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.8",165
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.85",170
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.9",175
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.95",176
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.975",176
-2022-10-08,"wk inc flu hosp",3,"01","quantile","0.99",205
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.01",135
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.025",137
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.05",139
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.1",140
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.15",141
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.2",141
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.25",142
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.3",143
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.35",144
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.4",145
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.45",147
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.5",148
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.55",149
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.6",150
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.65",152
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.7",155
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.75",161
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.8",165
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.85",170
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.9",175
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.95",176
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.975",176
-2022-10-08,"wk inc flu hosp",4,"01","quantile","0.99",205
diff --git a/tests/testthat/test-simple_ensemble.R b/tests/testthat/test-simple_ensemble.R
index e116a83..a9f6907 100644
--- a/tests/testthat/test-simple_ensemble.R
+++ b/tests/testthat/test-simple_ensemble.R
@@ -1,105 +1,120 @@
 library(matrixStats)
+library(dplyr)
 
-pred <- readr::read_csv("inst/example-data/2022-10-08-simple_hub-baseline.csv") %>%
-  mutate(team_abbr = "simple_hub", model_abbr = "baseline", .before = origin_date)
+pred <- read.csv(system.file("example-data/2022-10-08-simple_hub-baseline.csv",
+                             package = "hubEnsembles")) %>%
+  dplyr::mutate(model_id = "simple_hub-baseline", .before = origin_date)
 
-test_that("non-default column names are preserved in output data frame", {
+test_that("non-default columns are dropped from output", {
   output_names <- pred %>%
+    dplyr::mutate(extra_col_1 = "a", extra_col_2 = "a") %>%
     simple_ensemble(
-      agg_fun="mean",
-      model_abbr = "example",
-      output_type_col = "type", 
-      output_id_col = "type_id" 
+      task_id_cols = c("origin_date", "target", "horizon", "location")
     ) %>%
     names()
-  expect_equal(names(pred), output_names)
+
+  expect_equal(sort(names(pred)), sort(output_names))
 })
 
+
 test_that("invalid output type throws error", {
   expect_error(
     pred %>%
-      rename(output_type = type, output_id=type_id) %>%
-      mutate(output_type="sample") %>%
-      simple_ensemble(
-        agg_fun="mean",
-        model_abbr = "example"
-      )
+      dplyr::mutate(output_type = "sample") %>%
+      simple_ensemble()
   )
 })
 
+
 test_that("invalid method argument throws error", {
   expect_error(
-    pred %>%
-      rename(output_type = type, output_id=type_id) %>%
-      simple_ensemble(
-        agg_fun="linear pool",
-        model_abbr = "example"
-      )
+    simple_ensemble(pred, agg_fun="linear pool")
   )
 })
 
-test_that("medians and means correctly calculated", {
+
+test_that("(weighted) medians and means correctly calculated", {
   fdat <- expand.grid(
     stringsAsFactors = FALSE,
-    model_abbr = letters[1:4],
+    model_id = letters[1:4],
     location = c("222", "888"),
     horizon = 1, #week
     target = "inc death",
     target_date = as.Date("2021-12-25"),
     output_type = "quantile",
-    output_id = c(.1, .5, .9))
+    output_type_id = c(.1, .5, .9),
+    value = NA_real_)
 
-  fdat <- cbind(team_abbr = letters[23:26], fdat)
-  fdat$value[fdat$location == "222" & fdat$output_id == .1] <- v2.1 <- c(10, 30, 15, 20)
-  fdat$value[fdat$location == "222" & fdat$output_id == .5] <- v2.5 <- c(40, 40, 45, 50)
-  fdat$value[fdat$location == "222" & fdat$output_id == .9] <- v2.9 <- c(60, 70, 75, 80)
-  fdat$value[fdat$location == "888" & fdat$output_id == .1] <- v8.1 <- c(100, 300, 400, 250)
-  fdat$value[fdat$location == "888" & fdat$output_id == .5] <- v8.5 <- c(150, 325, 500, 300)
-  fdat$value[fdat$location == "888" & fdat$output_id == .9] <- v8.9 <- c(250, 350, 500, 350)
+  fdat$value[fdat$location == "222" & fdat$output_type_id == .1] <- v2.1 <-
+    c(10, 30, 15, 20)
+  fdat$value[fdat$location == "222" & fdat$output_type_id == .5] <- v2.5 <-
+    c(40, 40, 45, 50)
+  fdat$value[fdat$location == "222" & fdat$output_type_id == .9] <- v2.9 <-
+    c(60, 70, 75, 80)
+  fdat$value[fdat$location == "888" & fdat$output_type_id == .1] <- v8.1 <-
+    c(100, 300, 400, 250)
+  fdat$value[fdat$location == "888" & fdat$output_type_id == .5] <- v8.5 <-
+    c(150, 325, 500, 300)
+  fdat$value[fdat$location == "888" & fdat$output_type_id == .9] <- v8.9 <-
+    c(250, 350, 500, 350)
+
+  fweight2 <- data.frame(model_id = letters[1:4],
+                         location = "222",
+                         weight = 0.1 * (1:4))
+  fweight8 <- data.frame(model_id = letters[1:4],
+                         location = "888",
+                         weight = 0.1 * (4:1))
+  fweight <- bind_rows(fweight2, fweight8)
+
+  median_expected <- mean_expected <-
+    weighted_median_expected <- weighted_mean_expected <- data.frame(
+      model_id = "hub-ensemble",
+      location = rep(c("222", "888"), each = 3),
+      horizon = 1,
+      target = "inc death",
+      target_date = as.Date("2021-12-25"), 
+      output_type = "quantile", 
+      output_type_id = rep(c(.1, .5, .9), 2), 
+      value = NA_real_)
 
   median_vals <- sapply(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), median)
   mean_vals <- sapply(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), mean)
 
-  fweight <- tibble(model_abbr = letters[1:4], weight = 0.1*(1:4))
+  weighted_median_vals <- c(
+    sapply(list(v2.1, v2.5, v2.9),
+           matrixStats::weightedMedian,
+           w = fweight2$weight),
+    sapply(list(v8.1, v8.5, v8.9),
+           matrixStats::weightedMedian,
+           w = fweight8$weight))
+  weighted_mean_vals <- c(
+    sapply(list(v2.1, v2.5, v2.9),
+           matrixStats::weightedMean,
+           w = fweight2$weight),
+    sapply(list(v8.1, v8.5, v8.9),
+           matrixStats::weightedMean,
+           w = fweight8$weight))
 
-  weighted_median_vals <- map(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), weightedMedian, w = fweight$weight)
-  weighted_mean_vals <- map(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), weightedMean, w = fweight$weight)
+  median_expected$value <- median_vals
+  mean_expected$value <- mean_vals
+  weighted_mean_expected$value <- weighted_mean_vals
+  weighted_median_expected$value <- weighted_median_vals
 
-  median_actual <- simple_ensemble(
-    predictions = fdat, weights = NULL, agg_fun = "median", model_abbr = "median_ens")
-  mean_actual <- simple_ensemble(
-    predictions = fdat, weights = NULL, agg_fun = "mean", model_abbr = "mean_ens")
-    
-  weighted_median_actual <- simple_ensemble(
-    predictions = fdat, weights = fweight, agg_fun = "median", model_abbr = "weighted_median_ens")
-  weighted_mean_actual <- simple_ensemble(
-    predictions = fdat, weights = fweight, agg_fun = "mean", model_abbr = "weighted_mean_ens")
+  median_actual <- simple_ensemble(model_outputs = fdat, weights = NULL,
+                                   agg_fun = "median")
+  mean_actual <- simple_ensemble(model_outputs = fdat, weights = NULL,
+                                 agg_fun = "mean")
 
+  weighted_median_actual <- simple_ensemble(model_outputs = fdat,
+                                            weights = fweight,
+                                            agg_fun = "median")
+  weighted_mean_actual <- simple_ensemble(model_outputs = fdat,
+                                          weights = fweight,
+                                          agg_fun = "mean")
 
-  median_expected <- mean_expected <- weighted_median_expected <- weighted_mean_expected <- tibble::tibble(
-    team_abbr = "Hub",
-    model_abbr = NA,
-    location = rep(c("222", "888"), each = 3), 
-    horizon = 1, #week
-    target = "inc death",     
-    target_date = as.Date("2021-12-25"), 
-    output_type = "quantile", 
-    output_id = rep(c(.1, .5, .9), 2), 
-    value = 0)
-
-  median_expected$value <- sapply(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), median)
-  median_expected$model_abbr <- "median_ens"
-  mean_expected$value <- sapply(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), mean)
-  mean_expected$model_abbr <- "mean_ens"
-
-  weighted_mean_expected$value <- map_dbl(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), weightedMean, w = fweight$weight)
-  weighted_mean_expected$model_abbr <- "weighted_mean_ens"
-  weighted_median_expected$value <- map_dbl(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), weightedMedian, w = fweight$weight)
-  weighted_median_expected$model_abbr <- "weighted_median_ens"
-
-  expect_equal(median_actual, median_expected)
-  expect_equal(mean_actual, mean_expected)
-  
-  expect_equal(weighted_median_actual, weighted_median_expected)
-  expect_equal(weighted_mean_actual, weighted_mean_expected)
-})
\ No newline at end of file
+  expect_equal(as.data.frame(median_actual), median_expected)
+  expect_equal(as.data.frame(mean_actual), mean_expected)
+
+  expect_equal(as.data.frame(weighted_median_actual), weighted_median_expected)
+  expect_equal(as.data.frame(weighted_mean_actual), weighted_mean_expected)
+})

From 6671da285e7e112cee35843983c33c7fb5b2d383 Mon Sep 17 00:00:00 2001
From: Evan Ray <elray@umass.edu>
Date: Wed, 7 Jun 2023 15:14:17 -0400
Subject: [PATCH 16/24] remove stale example data, update data example in unit
 tests

---
 .../2022-10-01-simple_hub-baseline.csv        |  25 --
 .../2022-10-08-simple_hub-baseline.csv        | 277 ------------------
 .../2022-10-08-team1-goodmodel.csv            |  24 --
 tests/testthat/test-simple_ensemble.R         |  92 +++---
 4 files changed, 48 insertions(+), 370 deletions(-)
 delete mode 100644 inst/example-data/2022-10-01-simple_hub-baseline.csv
 delete mode 100644 inst/example-data/2022-10-08-simple_hub-baseline.csv
 delete mode 100644 inst/example-data/2022-10-08-team1-goodmodel.csv

diff --git a/inst/example-data/2022-10-01-simple_hub-baseline.csv b/inst/example-data/2022-10-01-simple_hub-baseline.csv
deleted file mode 100644
index 703831b..0000000
--- a/inst/example-data/2022-10-01-simple_hub-baseline.csv
+++ /dev/null
@@ -1,25 +0,0 @@
-"origin_date","target","horizon","location","output_type","output_type_id","value"
-2022-10-01,"wk inc flu hosp",1,"US","mean",NA,150
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.01",135
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.025",137
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.05",139
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.1",140
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.15",141
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.2",141
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.25",142
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.3",143
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.35",144
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.4",145
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.45",147
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.5",148
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.55",149
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.6",150
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.65",152
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.7",155
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.75",161
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.8",165
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.85",170
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.9",175
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.95",176
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.975",176
-2022-10-01,"wk inc flu hosp",1,"US","quantile","0.99",205
diff --git a/inst/example-data/2022-10-08-simple_hub-baseline.csv b/inst/example-data/2022-10-08-simple_hub-baseline.csv
deleted file mode 100644
index 7387c87..0000000
--- a/inst/example-data/2022-10-08-simple_hub-baseline.csv
+++ /dev/null
@@ -1,277 +0,0 @@
-"origin_date","target","horizon","location","output_type","output_type_id","value"
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.01",135
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diff --git a/inst/example-data/2022-10-08-team1-goodmodel.csv b/inst/example-data/2022-10-08-team1-goodmodel.csv
deleted file mode 100644
index 71108b3..0000000
--- a/inst/example-data/2022-10-08-team1-goodmodel.csv
+++ /dev/null
@@ -1,24 +0,0 @@
-"origin_date","target","horizon","location","output_type","output_type_id","value"
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-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.85",170
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.9",175
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.95",176
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.975",176
-2022-10-08,"wk inc flu hosp",1,"US","quantile","0.99",205
diff --git a/tests/testthat/test-simple_ensemble.R b/tests/testthat/test-simple_ensemble.R
index a9f6907..d23fae4 100644
--- a/tests/testthat/test-simple_ensemble.R
+++ b/tests/testthat/test-simple_ensemble.R
@@ -1,25 +1,61 @@
 library(matrixStats)
 library(dplyr)
 
-pred <- read.csv(system.file("example-data/2022-10-08-simple_hub-baseline.csv",
-                             package = "hubEnsembles")) %>%
-  dplyr::mutate(model_id = "simple_hub-baseline", .before = origin_date)
+# set up simple data for test cases
+model_outputs <- expand.grid(
+  stringsAsFactors = FALSE,
+  model_id = letters[1:4],
+  location = c("222", "888"),
+  horizon = 1, #week
+  target = "inc death",
+  target_date = as.Date("2021-12-25"),
+  output_type = "quantile",
+  output_type_id = c(.1, .5, .9),
+  value = NA_real_)
+
+v2.1 <- model_outputs$value[model_outputs$location == "222" &
+                            model_outputs$output_type_id == .1] <-
+  c(10, 30, 15, 20)
+v2.5 <- model_outputs$value[model_outputs$location == "222" &
+                            model_outputs$output_type_id == .5] <-
+  c(40, 40, 45, 50)
+v2.9 <- model_outputs$value[model_outputs$location == "222" &
+                            model_outputs$output_type_id == .9] <-
+  c(60, 70, 75, 80)
+v8.1 <- model_outputs$value[model_outputs$location == "888" &
+                            model_outputs$output_type_id == .1] <-
+  c(100, 300, 400, 250)
+v8.5 <- model_outputs$value[model_outputs$location == "888" &
+                            model_outputs$output_type_id == .5] <-
+  c(150, 325, 500, 300)
+v8.9 <- model_outputs$value[model_outputs$location == "888" &
+                            model_outputs$output_type_id == .9] <-
+  c(250, 350, 500, 350)
+
+fweight2 <- data.frame(model_id = letters[1:4],
+                       location = "222",
+                       weight = 0.1 * (1:4))
+fweight8 <- data.frame(model_id = letters[1:4],
+                       location = "888",
+                       weight = 0.1 * (4:1))
+fweight <- bind_rows(fweight2, fweight8)
+
 
 test_that("non-default columns are dropped from output", {
-  output_names <- pred %>%
+  output_names <- model_outputs %>%
     dplyr::mutate(extra_col_1 = "a", extra_col_2 = "a") %>%
     simple_ensemble(
-      task_id_cols = c("origin_date", "target", "horizon", "location")
+      task_id_cols = c("target_date", "target", "horizon", "location")
     ) %>%
     names()
 
-  expect_equal(sort(names(pred)), sort(output_names))
+  expect_equal(sort(names(model_outputs)), sort(output_names))
 })
 
 
 test_that("invalid output type throws error", {
   expect_error(
-    pred %>%
+    model_outputs %>%
       dplyr::mutate(output_type = "sample") %>%
       simple_ensemble()
   )
@@ -28,44 +64,12 @@ test_that("invalid output type throws error", {
 
 test_that("invalid method argument throws error", {
   expect_error(
-    simple_ensemble(pred, agg_fun="linear pool")
+    simple_ensemble(model_outputs, agg_fun="linear pool")
   )
 })
 
 
 test_that("(weighted) medians and means correctly calculated", {
-  fdat <- expand.grid(
-    stringsAsFactors = FALSE,
-    model_id = letters[1:4],
-    location = c("222", "888"),
-    horizon = 1, #week
-    target = "inc death",
-    target_date = as.Date("2021-12-25"),
-    output_type = "quantile",
-    output_type_id = c(.1, .5, .9),
-    value = NA_real_)
-
-  fdat$value[fdat$location == "222" & fdat$output_type_id == .1] <- v2.1 <-
-    c(10, 30, 15, 20)
-  fdat$value[fdat$location == "222" & fdat$output_type_id == .5] <- v2.5 <-
-    c(40, 40, 45, 50)
-  fdat$value[fdat$location == "222" & fdat$output_type_id == .9] <- v2.9 <-
-    c(60, 70, 75, 80)
-  fdat$value[fdat$location == "888" & fdat$output_type_id == .1] <- v8.1 <-
-    c(100, 300, 400, 250)
-  fdat$value[fdat$location == "888" & fdat$output_type_id == .5] <- v8.5 <-
-    c(150, 325, 500, 300)
-  fdat$value[fdat$location == "888" & fdat$output_type_id == .9] <- v8.9 <-
-    c(250, 350, 500, 350)
-
-  fweight2 <- data.frame(model_id = letters[1:4],
-                         location = "222",
-                         weight = 0.1 * (1:4))
-  fweight8 <- data.frame(model_id = letters[1:4],
-                         location = "888",
-                         weight = 0.1 * (4:1))
-  fweight <- bind_rows(fweight2, fweight8)
-
   median_expected <- mean_expected <-
     weighted_median_expected <- weighted_mean_expected <- data.frame(
       model_id = "hub-ensemble",
@@ -100,15 +104,15 @@ test_that("(weighted) medians and means correctly calculated", {
   weighted_mean_expected$value <- weighted_mean_vals
   weighted_median_expected$value <- weighted_median_vals
 
-  median_actual <- simple_ensemble(model_outputs = fdat, weights = NULL,
+  median_actual <- simple_ensemble(model_outputs = model_outputs, weights = NULL,
                                    agg_fun = "median")
-  mean_actual <- simple_ensemble(model_outputs = fdat, weights = NULL,
+  mean_actual <- simple_ensemble(model_outputs = model_outputs, weights = NULL,
                                  agg_fun = "mean")
 
-  weighted_median_actual <- simple_ensemble(model_outputs = fdat,
+  weighted_median_actual <- simple_ensemble(model_outputs = model_outputs,
                                             weights = fweight,
                                             agg_fun = "median")
-  weighted_mean_actual <- simple_ensemble(model_outputs = fdat,
+  weighted_mean_actual <- simple_ensemble(model_outputs = model_outputs,
                                           weights = fweight,
                                           agg_fun = "mean")
 

From dbe05e1a91c6e0d4a994c185589aeb71fa7b6727 Mon Sep 17 00:00:00 2001
From: Evan Ray <elray@umass.edu>
Date: Wed, 7 Jun 2023 22:02:41 -0400
Subject: [PATCH 17/24] updates to description

---
 DESCRIPTION | 16 +++++++++++++---
 1 file changed, 13 insertions(+), 3 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 1abb6bd..397bd39 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,10 +1,17 @@
 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: 
     person("Anna", "Krystalli", , "annakrystalli@googlemail.com", 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)
@@ -17,5 +24,8 @@ BugReports: https://github.com/Infectious-Disease-Modeling-Hubs/hubEnsembles/iss
 Imports: 
     cli,
     dplyr,
+    hubUtils,
     matrixStats,
     rlang
+Remotes:
+    Infectious-Disease-Modeling-Hubs/hubUtils

From 79515c08936a56b020330a632cffd51a9ec482ff Mon Sep 17 00:00:00 2001
From: Evan Ray <elray@umass.edu>
Date: Wed, 7 Jun 2023 22:40:22 -0400
Subject: [PATCH 18/24] fix authors field in DESCRIPTION

---
 DESCRIPTION | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 397bd39..4b76f73 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: hubEnsembles
 Title: Ensemble methods for combining hub model outputs.
 Version: 0.0.0.9000
-Authors@R: 
+Authors@R: c(
     person("Anna", "Krystalli", , "annakrystalli@googlemail.com", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-2378-4915")),
     person(given = "Evan L",
@@ -9,7 +9,7 @@ Authors@R:
            role = c("aut")),
     person(given = "Li",
            family = "Shandross",
-           role = c("aut"))
+           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

From ac7e964d11c9aaef27468312d718b633e638f82d Mon Sep 17 00:00:00 2001
From: Evan Ray <elray@umass.edu>
Date: Wed, 7 Jun 2023 22:44:28 -0400
Subject: [PATCH 19/24] add magrittr to package imports

---
 DESCRIPTION | 1 +
 1 file changed, 1 insertion(+)

diff --git a/DESCRIPTION b/DESCRIPTION
index 4b76f73..04875dc 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -25,6 +25,7 @@ Imports:
     cli,
     dplyr,
     hubUtils,
+    magrittr,
     matrixStats,
     rlang
 Remotes:

From a8e6ecee20a63ae29bd18004bf45cdeaa2330593 Mon Sep 17 00:00:00 2001
From: Evan Ray <elray1@users.noreply.github.com>
Date: Thu, 8 Jun 2023 10:50:03 -0400
Subject: [PATCH 20/24] Update R/simple_ensemble.R

Co-authored-by: Anna Krystalli <annakrystalli@googlemail.com>
---
 R/simple_ensemble.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/simple_ensemble.R b/R/simple_ensemble.R
index a2763c0..338ce83 100644
--- a/R/simple_ensemble.R
+++ b/R/simple_ensemble.R
@@ -71,7 +71,7 @@ simple_ensemble <- function(model_outputs, weights = NULL,
   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}}.",
+      "!" = "Included invalid output type{?s}: {.val {invalid_types}}.",
       "i" = "Supported output types: {.val {valid_types}}."
     ))
   }

From 4bc4d1dab857e32f799777945a00a76f92407ddd Mon Sep 17 00:00:00 2001
From: Evan Ray <elray@umass.edu>
Date: Thu, 8 Jun 2023 12:09:19 -0400
Subject: [PATCH 21/24] updates in response to anna's pr comments,
 weights_col_name as argument

---
 R/simple_ensemble.R                   | 57 ++++++++++++++++-----------
 tests/testthat/test-simple_ensemble.R | 49 ++++++++++++++++++++---
 2 files changed, 77 insertions(+), 29 deletions(-)

diff --git a/R/simple_ensemble.R b/R/simple_ensemble.R
index 338ce83..6c4fbbe 100644
--- a/R/simple_ensemble.R
+++ b/R/simple_ensemble.R
@@ -5,10 +5,13 @@
 #' @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.
+#'   provided, it should have a column named `model_id` and a column containing
+#'   model weights. Optionally, it may contain 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 weights_col_name `character` string naming the column in `weights`
+#'   with model weights. Defaults to `"weights"`
 #' @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.
@@ -16,35 +19,43 @@
 #'   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.
+#' @param task_id_cols `character` vector with names of columns in
+#'   `model_outputs` that specify modeling tasks.
+#' @param output_type_col `character` string with the name of the column in
+#'   `model_outputs` that contains the output type.
+#' @param output_type_id_col `character` string with the name of the column in
+#'   `model_outputs` that contains the output type id.
+#' @param value_col `character` string with the name of the column in
+#'   `model_outputs` that contains model output values.
 #'
-#' @details The default for `agg_fun` is `mean`, in which case the ensemble's
+#' @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.
+#'   argument `w` with a numeric vector of weights. If it desired to use an
+#'   aggregation function that does not accept these arguments, a wrapper
+#'   would need to be written. 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
+#' @return a data.frame with columns `model_id`, 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,
+                            weights_col_name = "weight",
                             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`."))
   }
 
+  model_out_cols <- colnames(model_outputs)
+
   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]
@@ -79,7 +90,7 @@ simple_ensemble <- function(model_outputs, weights = NULL,
   if (is.null(weights)) {
     agg_args <- c(agg_args, list(x = quote(.data[["value"]])))
   } else {
-    req_weight_cols <- c("model_id", "weight")
+    req_weight_cols <- c("model_id", weights_col_name)
     if (!all(req_weight_cols %in% colnames(weights))) {
       cli::cli_abort(c(
         "x" = "{.arg weights} did not include required columns
@@ -87,7 +98,7 @@ simple_ensemble <- function(model_outputs, weights = NULL,
       ))
     }
 
-    weight_by_cols <- colnames(weights)[colnames(weights) != "weight"]
+    weight_by_cols <- colnames(weights)[colnames(weights) != weights_col_name]
 
     if ("value" %in% weight_by_cols) {
       cli::cli_abort(c(
@@ -97,7 +108,7 @@ simple_ensemble <- function(model_outputs, weights = NULL,
     }
 
     invalid_cols <- weight_by_cols[!weight_by_cols %in% colnames(model_outputs)]
-    if (!all(weight_by_cols %in% colnames(model_outputs))) {
+    if (length(invalid_cols) > 0) {
       cli::cli_abort(c(
         "x" = "{.arg weights} included {length(invalid_cols)} column{?s} that
                {?was/were} not present in {.arg model_outputs}:
@@ -105,11 +116,11 @@ simple_ensemble <- function(model_outputs, weights = NULL,
       ))
     }
 
-    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"
+    if (weights_col_name %in% colnames(model_outputs)) {
+      cli::cli_abort(c(
+        "x" = "The specified {.arg weights_col_name}, {.val {weights_col_name}},
+               is already a column in {.arg model_outputs}."
+      ))
     }
 
     model_outputs <- model_outputs %>%
@@ -124,7 +135,7 @@ simple_ensemble <- function(model_outputs, weights = NULL,
     }
 
     agg_args <- c(agg_args, list(x = quote(.data[["value"]]),
-                                 w = quote(.data[[weight_col_name]])))
+                                 w = quote(.data[[weights_col_name]])))
   }
 
   group_by_cols <- c(task_id_cols, output_type_col, output_type_id_col)
diff --git a/tests/testthat/test-simple_ensemble.R b/tests/testthat/test-simple_ensemble.R
index d23fae4..854e324 100644
--- a/tests/testthat/test-simple_ensemble.R
+++ b/tests/testthat/test-simple_ensemble.R
@@ -64,7 +64,16 @@ test_that("invalid output type throws error", {
 
 test_that("invalid method argument throws error", {
   expect_error(
-    simple_ensemble(model_outputs, agg_fun="linear pool")
+    simple_ensemble(model_outputs, agg_fun = "linear pool")
+  )
+})
+
+
+test_that("weights column already in model_outputs generates error", {
+  expect_error(
+    model_outputs %>%
+      dplyr::mutate(weight = "a") %>%
+      simple_ensemble(weights = fweight)
   )
 })
 
@@ -76,9 +85,9 @@ test_that("(weighted) medians and means correctly calculated", {
       location = rep(c("222", "888"), each = 3),
       horizon = 1,
       target = "inc death",
-      target_date = as.Date("2021-12-25"), 
-      output_type = "quantile", 
-      output_type_id = rep(c(.1, .5, .9), 2), 
+      target_date = as.Date("2021-12-25"),
+      output_type = "quantile",
+      output_type_id = rep(c(.1, .5, .9), 2),
       value = NA_real_)
 
   median_vals <- sapply(list(v2.1, v2.5, v2.9, v8.1, v8.5, v8.9), median)
@@ -104,9 +113,11 @@ test_that("(weighted) medians and means correctly calculated", {
   weighted_mean_expected$value <- weighted_mean_vals
   weighted_median_expected$value <- weighted_median_vals
 
-  median_actual <- simple_ensemble(model_outputs = model_outputs, weights = NULL,
+  median_actual <- simple_ensemble(model_outputs = model_outputs,
+                                   weights = NULL,
                                    agg_fun = "median")
-  mean_actual <- simple_ensemble(model_outputs = model_outputs, weights = NULL,
+  mean_actual <- simple_ensemble(model_outputs = model_outputs,
+                                 weights = NULL,
                                  agg_fun = "mean")
 
   weighted_median_actual <- simple_ensemble(model_outputs = model_outputs,
@@ -122,3 +133,29 @@ test_that("(weighted) medians and means correctly calculated", {
   expect_equal(as.data.frame(weighted_median_actual), weighted_median_expected)
   expect_equal(as.data.frame(weighted_mean_actual), weighted_mean_expected)
 })
+
+
+test_that("(weighted) medians and means work with alternate name for weights columns", {
+  weighted_median_actual <- simple_ensemble(
+    model_outputs = model_outputs,
+    weights = fweight %>%
+      dplyr::rename(w = weight),
+    weights_col_name = "w",
+    agg_fun = "median")
+  weighted_mean_actual <- simple_ensemble(
+    model_outputs = model_outputs,
+    weights = fweight %>%
+      dplyr::rename(w = weight),
+    weights_col_name = "w",
+    agg_fun = "mean")
+
+  weighted_median_expected <- simple_ensemble(model_outputs = model_outputs,
+                                              weights = fweight,
+                                              agg_fun = "median")
+  weighted_mean_expected <- simple_ensemble(model_outputs = model_outputs,
+                                            weights = fweight,
+                                            agg_fun = "mean")
+
+  expect_equal(weighted_mean_actual, weighted_mean_expected)
+  expect_equal(weighted_median_actual, weighted_median_expected)
+})

From 38d1a29de2749820080f0fae5efbdec76d326d69 Mon Sep 17 00:00:00 2001
From: Evan Ray <elray@umass.edu>
Date: Thu, 8 Jun 2023 12:14:18 -0400
Subject: [PATCH 22/24] update docs for simple_ensemble

---
 man/simple_ensemble.Rd | 40 +++++++++++++++++++++++++++-------------
 1 file changed, 27 insertions(+), 13 deletions(-)

diff --git a/man/simple_ensemble.Rd b/man/simple_ensemble.Rd
index b231d55..d922a62 100644
--- a/man/simple_ensemble.Rd
+++ b/man/simple_ensemble.Rd
@@ -9,6 +9,7 @@ output types include \code{mean}, \code{median}, \code{quantile}, \code{cdf}, an
 simple_ensemble(
   model_outputs,
   weights = NULL,
+  weights_col_name = "weight",
   agg_fun = "mean",
   agg_args = list(),
   model_id = "hub-ensemble",
@@ -23,10 +24,14 @@ simple_ensemble(
 model outputs (e.g., predictions).}
 
 \item{weights}{an optional \code{data.frame} with component model weights. If
-provided, it should have columns \code{model_id}, \code{weight}, and optionally,
-additional columns corresponding to task id variables, \code{output_type}, or
-\code{output_type_id}, if weights are specific to values of those variables. The
-default is \code{NULL}, in which case an equally-weighted ensemble is calculated.}
+provided, it should have a column named \code{model_id} and a column containing
+model weights. Optionally, it may contain additional columns corresponding
+to task id variables, \code{output_type}, or \code{output_type_id}, if weights are
+specific to values of those variables. The default is \code{NULL}, in which case
+an equally-weighted ensemble is calculated.}
+
+\item{weights_col_name}{\code{character} string naming the column in \code{weights}
+with model weights. Defaults to \code{"weights"}}
 
 \item{agg_fun}{a function or character string name of a function to use for
 aggregating component model outputs into the ensemble outputs. See the
@@ -38,13 +43,20 @@ to \code{agg_fun}.}
 \item{model_id}{\code{character} string with the identifier to use for the
 ensemble model.}
 
-\item{task_id_cols, }{output_type_col, output_type_id_col, value_col
-\code{character} vectors with the names of the columns in \code{model_outputs} for
-the output's type, additional identifying information, and value of the
-model output.}
+\item{task_id_cols}{\code{character} vector with names of columns in
+\code{model_outputs} that specify modeling tasks.}
+
+\item{output_type_col}{\code{character} string with the name of the column in
+\code{model_outputs} that contains the output type.}
+
+\item{output_type_id_col}{\code{character} string with the name of the column in
+\code{model_outputs} that contains the output type id.}
+
+\item{value_col}{\code{character} string with the name of the column in
+\code{model_outputs} that contains model output values.}
 }
 \value{
-a data.frame with columns \code{team_abbr}, \code{model_abbr}, one column for
+a data.frame with columns \code{model_id}, one column for
 each task id variable, \code{output_type}, \code{output_id}, and \code{value}. Note that
 any additional columns in the input \code{model_outputs} are dropped.
 }
@@ -54,12 +66,14 @@ each combination of model task, output type, and output type id. Supported
 output types include \code{mean}, \code{median}, \code{quantile}, \code{cdf}, and \code{pmf}.
 }
 \details{
-The default for \code{agg_fun} is \code{mean}, in which case the ensemble's
+The default for \code{agg_fun} is \code{"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 \code{agg_fun} should have an argument \code{x} for the
 vector of numeric values to summarize, and for weighted methods, an
-argument \code{w} with a numeric vector of weights. For weighted methods,
-\code{agg_fun = "mean"} and \code{agg_fun = "median"} are translated to use
-\code{matrixStats::weightedMean} and \code{matrixStats::weightedMedian} respectively.
+argument \code{w} with a numeric vector of weights. If it desired to use an
+aggregation function that does not accept these arguments, a wrapper
+would need to be written. For weighted methods, \code{agg_fun = "mean"} and
+\code{agg_fun = "median"} are translated to use \code{matrixStats::weightedMean} and
+\code{matrixStats::weightedMedian} respectively.
 }

From 2a92d7af5a79002942ecf44a7c05fba9f0f086c1 Mon Sep 17 00:00:00 2001
From: Evan Ray <elray@umass.edu>
Date: Thu, 8 Jun 2023 12:18:17 -0400
Subject: [PATCH 23/24] remove hub_connection as argument to simple_ensemble

---
 R/simple_ensemble.R    | 3 +--
 man/simple_ensemble.Rd | 3 +--
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/R/simple_ensemble.R b/R/simple_ensemble.R
index 6c4fbbe..c1ee0ff 100644
--- a/R/simple_ensemble.R
+++ b/R/simple_ensemble.R
@@ -48,8 +48,7 @@ simple_ensemble <- function(model_outputs, weights = NULL,
                             model_id = "hub-ensemble",
                             task_id_cols = NULL,
                             output_type_col = "output_type",
-                            output_type_id_col = "output_type_id",
-                            hub_connection = NULL) {
+                            output_type_id_col = "output_type_id") {
   if (!is.data.frame(model_outputs)) {
     cli::cli_abort(c("x" = "{.arg model_outputs} must be a `data.frame`."))
   }
diff --git a/man/simple_ensemble.Rd b/man/simple_ensemble.Rd
index d922a62..3fd6ea4 100644
--- a/man/simple_ensemble.Rd
+++ b/man/simple_ensemble.Rd
@@ -15,8 +15,7 @@ simple_ensemble(
   model_id = "hub-ensemble",
   task_id_cols = NULL,
   output_type_col = "output_type",
-  output_type_id_col = "output_type_id",
-  hub_connection = NULL
+  output_type_id_col = "output_type_id"
 )
 }
 \arguments{

From 483b9605c31a309ff1528dc9e434f4c60b0f9e7e Mon Sep 17 00:00:00 2001
From: Evan Ray <elray@umass.edu>
Date: Thu, 8 Jun 2023 12:22:18 -0400
Subject: [PATCH 24/24] remove doc for removed parameter value_col from
 simple_ensemble

---
 R/simple_ensemble.R    | 2 --
 man/simple_ensemble.Rd | 3 ---
 2 files changed, 5 deletions(-)

diff --git a/R/simple_ensemble.R b/R/simple_ensemble.R
index c1ee0ff..6bf2c9e 100644
--- a/R/simple_ensemble.R
+++ b/R/simple_ensemble.R
@@ -25,8 +25,6 @@
 #'   `model_outputs` that contains the output type.
 #' @param output_type_id_col `character` string with the name of the column in
 #'   `model_outputs` that contains the output type id.
-#' @param value_col `character` string with the name of the column in
-#'   `model_outputs` that contains model output values.
 #'
 #' @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
diff --git a/man/simple_ensemble.Rd b/man/simple_ensemble.Rd
index 3fd6ea4..d2b4caf 100644
--- a/man/simple_ensemble.Rd
+++ b/man/simple_ensemble.Rd
@@ -50,9 +50,6 @@ ensemble model.}
 
 \item{output_type_id_col}{\code{character} string with the name of the column in
 \code{model_outputs} that contains the output type id.}
-
-\item{value_col}{\code{character} string with the name of the column in
-\code{model_outputs} that contains model output values.}
 }
 \value{
 a data.frame with columns \code{model_id}, one column for