From 4539f9e9f818e40818fbade0afe6646048ebc437 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Mon, 22 Nov 2021 10:28:04 +0100 Subject: [PATCH 001/225] rename sharpness to dispersion --- R/eval_forecasts.R | 6 +++--- R/eval_forecasts_quantile.R | 8 ++++---- R/interval_score.R | 10 +++++----- R/plot.R | 18 +++++++++--------- man/eval_forecasts.Rd | 6 +++--- man/interval_score.Rd | 2 +- man/range_plot.Rd | 14 +++++++------- man/wis_components.Rd | 2 +- vignettes/scoringutils.Rmd | 4 ++-- 9 files changed, 35 insertions(+), 35 deletions(-) diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index d17029090..a3185ba11 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -15,7 +15,7 @@ #' \item {aem} Absolute error of the median prediction #' \item {Bias} 0 is good, 1 and -1 are bad. #' See [bias()] for more information. -#' \item {Sharpness} Smaller is better. See [sharpness()] for more +#' \item {Sharpness/dispersion} Smaller is better. See [sharpness()] for more #' information. #' \item {Calibration} represented through the p-value of the #' Anderson-Darling test for the uniformity of the Probability Integral @@ -130,8 +130,8 @@ #' #' @return A data.table with appropriate scores. For binary predictions, #' the Brier Score will be returned, for quantile predictions the interval -#' score, as well as adapted metrics for calibration, sharpness and bias. -#' For integer forecasts, Sharpness, Bias, DSS, CRPS, LogS, and +#' score, as well as adapted metrics for calibration, sharpness/dispersion and +#' bias. For integer forecasts, Sharpness, Bias, DSS, CRPS, LogS, and #' pit_p_val (as an indicator of calibration) are returned. For integer #' forecasts, pit_sd is returned (to account for the randomised PIT), #' but no Log Score is returned (the internal estimation relies on a diff --git a/R/eval_forecasts_quantile.R b/R/eval_forecasts_quantile.R index 036b97ea1..9e92e9ada 100644 --- a/R/eval_forecasts_quantile.R +++ b/R/eval_forecasts_quantile.R @@ -60,7 +60,7 @@ eval_forecasts_quantile <- function(data, # compute separate results if desired if (interval_score_arguments$separate_results) { res <- res[, c("interval_score", - "sharpness", + "dispersion", "underprediction", "overprediction") := do.call(scoringutils::interval_score, c(list(true_value, @@ -164,7 +164,7 @@ eval_forecasts_quantile <- function(data, res <- add_quantiles(res, c("interval_score", "coverage", "overprediction", "underprediction", - "coverage_deviation", "bias", "sharpness", "aem", + "coverage_deviation", "bias", "dispersion", "aem", "ae_point"), quantiles, by = c(summarise_by)) @@ -174,7 +174,7 @@ eval_forecasts_quantile <- function(data, res <- add_sd(res, varnames = c("interval_score", "bias", "coverage", "overprediction", "underprediction", - "coverage_deviation", "sharpness", "aem", + "coverage_deviation", "dispersion", "aem", "ae_point"), by = c(summarise_by)) } @@ -183,7 +183,7 @@ eval_forecasts_quantile <- function(data, res <- res[, lapply(.SD, mean, na.rm = TRUE), by = c(summarise_by), .SDcols = colnames(res) %like% - "coverage|bias|sharpness|coverage_deviation|interval_score|overprediction|underprediction|aem|ae_point|relative_skill|scaled_rel_skill"] + "coverage|bias|dispersion|coverage_deviation|interval_score|overprediction|underprediction|aem|ae_point|relative_skill|scaled_rel_skill"] } # if neither quantile nor range are in summarise_by, remove coverage and quantile_coverage diff --git a/R/interval_score.R b/R/interval_score.R index 077e3aae4..36f6ae9e5 100644 --- a/R/interval_score.R +++ b/R/interval_score.R @@ -36,7 +36,7 @@ #' into an interval score that, in the limit, corresponds to CRPS. Default: #' `FALSE`. #' @param separate_results if `TRUE` (default is `FALSE`), then the separate -#' parts of the interval score (sharpness, penalties for over- and +#' parts of the interval score (dispersion penalty, penalties for over- and #' under-prediction get returned as separate elements of a list). If you want a #' `data.frame` instead, simply call [as.data.frame()] on the output. #' @return vector with the scoring values, or a list with separate entries if @@ -91,21 +91,21 @@ interval_score <- function(true_values, alpha <- (100 - interval_range) / 100 # calculate three components of WIS - sharpness <- (upper - lower) + dispersion <- (upper - lower) overprediction <- 2/alpha * (lower - true_values) * (true_values < lower) underprediction <- 2/alpha * (true_values - upper) * (true_values > upper) if (weigh) { - sharpness <- sharpness * alpha / 2 + dispersion <- dispersion * alpha / 2 underprediction <- underprediction * alpha / 2 overprediction <- overprediction * alpha / 2 } - score <- sharpness + underprediction + overprediction + score <- dispersion + underprediction + overprediction if (separate_results) { return(list(interval_score = score, - sharpness = sharpness, + dispersion = dispersion, underprediction = underprediction, overprediction = overprediction)) } else { diff --git a/R/plot.R b/R/plot.R index 7f418449f..327f574cc 100644 --- a/R/plot.R +++ b/R/plot.R @@ -265,7 +265,7 @@ correlation_plot <- function(scores, #' #' @description #' Visualise the components of the weighted interval score: penalties for -#' over-prediction, under-prediction and for a lack of sharpness +#' over-prediction, under-prediction and for high dispersion (lack of sharpness) #' #' @param scores A data.frame of scores based on quantile forecasts as #' produced by [eval_forecasts()] @@ -323,7 +323,7 @@ wis_components <- function(scores, scores <- data.table::melt(scores, measure.vars = c("overprediction", "underprediction", - "sharpness"), + "dispersion"), variable.name = "wis_component_name", value.name = "component_value") @@ -363,14 +363,14 @@ wis_components <- function(scores, #' #' @description #' Visualise the metrics by range, e.g. if you are interested how different -#' interval ranges contribute to the overall interval score, or how sharpness -#' changes by range. +#' interval ranges contribute to the overall interval score, or how +#' sharpness / dispersion changes by range. #' #' @param scores A data.frame of scores based on quantile forecasts as #' produced by [eval_forecasts()]. Note that "range" must be included #' in the `summarise_by` argument when running `eval_forecasts` #' @param y The variable from the scores you want to show on the y-Axis. -#' This could be something like "interval_score" (the default) or "sharpness" +#' This could be something like "interval_score" (the default) or "dispersion" #' @param x The variable from the scores you want to show on the x-Axis. #' Usually this will be "model" #' @param colour Character vector of length one used to determine a variable @@ -401,15 +401,15 @@ wis_components <- function(scores, #' summarise_by = c("model", "value_desc", "range")) #' scoringutils::range_plot(scores, x = "model", facet_formula = ~ value_desc) #' -#' # visualise sharpness instead of interval score -#' scoringutils::range_plot(scores, y = "sharpness", x = "model", +#' # visualise dispersion instead of interval score +#' scoringutils::range_plot(scores, y = "dispersion", x = "model", #' facet_formula = ~value_desc) #' -#' # we saw above that sharpness values crossed. Let's look at the unweighted WIS +#' # we saw above that dispersion values crossed. Let's look at the unweighted WIS #' scores <- scoringutils::eval_forecasts(scoringutils::range_example_data_long, #' interval_score_arguments = list(weigh = FALSE), #' summarise_by = c("model", "value_desc", "range")) -#' scoringutils::range_plot(scores, y = "sharpness", x = "model", +#' scoringutils::range_plot(scores, y = "dispersion", x = "model", #' facet_formula = ~value_desc) diff --git a/man/eval_forecasts.Rd b/man/eval_forecasts.Rd index de4ed1cb4..cbdcf267b 100644 --- a/man/eval_forecasts.Rd +++ b/man/eval_forecasts.Rd @@ -143,8 +143,8 @@ respect to a baseline model.} \value{ A data.table with appropriate scores. For binary predictions, the Brier Score will be returned, for quantile predictions the interval -score, as well as adapted metrics for calibration, sharpness and bias. -For integer forecasts, Sharpness, Bias, DSS, CRPS, LogS, and +score, as well as adapted metrics for calibration, sharpness/dispersion and +bias. For integer forecasts, Sharpness, Bias, DSS, CRPS, LogS, and pit_p_val (as an indicator of calibration) are returned. For integer forecasts, pit_sd is returned (to account for the randomised PIT), but no Log Score is returned (the internal estimation relies on a @@ -171,7 +171,7 @@ Smaller is better. See \code{\link[=brier_score]{brier_score()}} for more inform \item {aem} Absolute error of the median prediction \item {Bias} 0 is good, 1 and -1 are bad. See \code{\link[=bias]{bias()}} for more information. -\item {Sharpness} Smaller is better. See \code{\link[=sharpness]{sharpness()}} for more +\item {Sharpness/dispersion} Smaller is better. See \code{\link[=sharpness]{sharpness()}} for more information. \item {Calibration} represented through the p-value of the Anderson-Darling test for the uniformity of the Probability Integral diff --git a/man/interval_score.Rd b/man/interval_score.Rd index d37f79ad3..0db4c463d 100644 --- a/man/interval_score.Rd +++ b/man/interval_score.Rd @@ -32,7 +32,7 @@ into an interval score that, in the limit, corresponds to CRPS. Default: \code{FALSE}.} \item{separate_results}{if \code{TRUE} (default is \code{FALSE}), then the separate -parts of the interval score (sharpness, penalties for over- and +parts of the interval score (dispersion penalty, penalties for over- and under-prediction get returned as separate elements of a list). If you want a \code{data.frame} instead, simply call \code{\link[=as.data.frame]{as.data.frame()}} on the output.} } diff --git a/man/range_plot.Rd b/man/range_plot.Rd index 3c1d64a9c..82a8fc5dc 100644 --- a/man/range_plot.Rd +++ b/man/range_plot.Rd @@ -23,7 +23,7 @@ produced by \code{\link[=eval_forecasts]{eval_forecasts()}}. Note that "range" m in the \code{summarise_by} argument when running \code{eval_forecasts}} \item{y}{The variable from the scores you want to show on the y-Axis. -This could be something like "interval_score" (the default) or "sharpness"} +This could be something like "interval_score" (the default) or "dispersion"} \item{x}{The variable from the scores you want to show on the x-Axis. Usually this will be "model"} @@ -55,8 +55,8 @@ the weighted interval score } \description{ Visualise the metrics by range, e.g. if you are interested how different -interval ranges contribute to the overall interval score, or how sharpness -changes by range. +interval ranges contribute to the overall interval score, or how +sharpness / dispersion changes by range. } \examples{ scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, @@ -66,14 +66,14 @@ scores <- scoringutils::eval_forecasts(scoringutils::range_example_data_long, summarise_by = c("model", "value_desc", "range")) scoringutils::range_plot(scores, x = "model", facet_formula = ~ value_desc) -# visualise sharpness instead of interval score -scoringutils::range_plot(scores, y = "sharpness", x = "model", +# visualise dispersion instead of interval score +scoringutils::range_plot(scores, y = "dispersion", x = "model", facet_formula = ~value_desc) -# we saw above that sharpness values crossed. Let's look at the unweighted WIS +# we saw above that dispersion values crossed. Let's look at the unweighted WIS scores <- scoringutils::eval_forecasts(scoringutils::range_example_data_long, interval_score_arguments = list(weigh = FALSE), summarise_by = c("model", "value_desc", "range")) -scoringutils::range_plot(scores, y = "sharpness", x = "model", +scoringutils::range_plot(scores, y = "dispersion", x = "model", facet_formula = ~value_desc) } diff --git a/man/wis_components.Rd b/man/wis_components.Rd index 0ed11f7c6..622ad9a1f 100644 --- a/man/wis_components.Rd +++ b/man/wis_components.Rd @@ -57,7 +57,7 @@ the weighted interval score } \description{ Visualise the components of the weighted interval score: penalties for -over-prediction, under-prediction and for a lack of sharpness +over-prediction, under-prediction and for high dispersion (lack of sharpness) } \examples{ scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, diff --git a/vignettes/scoringutils.Rmd b/vignettes/scoringutils.Rmd index 80cafec5b..daeed602a 100644 --- a/vignettes/scoringutils.Rmd +++ b/vignettes/scoringutils.Rmd @@ -98,7 +98,7 @@ Let us look at the individual components of the weighted interval score: ```{r} scores <- scoringutils::eval_forecasts(data, - summarise_by = c("model", "value_desc")) + summarise_by = c("model", "value_desc")) scoringutils::wis_components(scores, facet_formula = ~ value_desc) ``` @@ -203,7 +203,7 @@ Sharpness is the ability of the model to generate predictions within a narrow range. It is a data-independent measure, and is purely a feature of the forecasts themselves. -Shaprness of predictive samples corresponding to one single true value is +Shaprness / dispersion of predictive samples corresponding to one single true value is measured as the normalised median of the absolute deviation from the median of the predictive samples. For details, see `?stats::mad` From 22c7c0afa8d2c683427048defd4a75fcf77daecd Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Wed, 24 Nov 2021 09:43:00 +0100 Subject: [PATCH 002/225] eliminate 'verbose' argument in scoringutils --- R/absolute_error.R | 8 +- R/eval_forecasts.R | 49 ++------- R/eval_forecasts_binary.R | 6 +- R/eval_forecasts_continuous_integer.R | 7 +- R/eval_forecasts_quantile.R | 7 +- R/pairwise-comparisons.R | 7 +- R/pit.R | 18 +-- R/plot.R | 8 +- man/add_rel_skill_to_eval_forecasts.Rd | 5 +- man/ae_median_quantile.Rd | 4 +- man/eval_forecasts.Rd | 6 +- man/eval_forecasts_binary.Rd | 8 +- man/eval_forecasts_sample.Rd | 5 +- man/pit.Rd | 6 +- man/pit_df.Rd | 6 +- man/plot_predictions.Rd | 5 +- tests/testthat/test-eval_forecasts.R | 128 +--------------------- tests/testthat/test-pairwise_comparison.R | 8 +- 18 files changed, 43 insertions(+), 248 deletions(-) diff --git a/R/absolute_error.R b/R/absolute_error.R index 1025c1601..f83558f0e 100644 --- a/R/absolute_error.R +++ b/R/absolute_error.R @@ -54,7 +54,6 @@ ae_median_sample <- function(true_values, predictions) { #' in `predictions`. Only those predictions where `quantiles == 0.5` will #' be kept. If `quantiles` is `NULL`, then all `predictions` and #' `true_values` will be used (this is then the same as [abs_error()]) -#' @param verbose logical, return a warning is something unexpected happens #' @return vector with the scoring values #' @importFrom stats median #' @examples @@ -62,14 +61,11 @@ ae_median_sample <- function(true_values, predictions) { #' predicted_values <- rnorm(30, mean = 1:30) #' ae_median_quantile(true_values, predicted_values, quantiles = 0.5) #' @export -ae_median_quantile <- function(true_values, predictions, quantiles = NULL, - verbose = TRUE) { +ae_median_quantile <- function(true_values, predictions, quantiles = NULL) { if (!is.null(quantiles)) { if (!any(quantiles == 0.5) && !any(is.na(quantiles))) { return(NA_real_) - if (verbose) { - warning("in order to compute the absolute error of the median, `0.5` must be among the quantiles given. Maybe you want to use `abs_error()`?") - } + warning("in order to compute the absolute error of the median, `0.5` must be among the quantiles given. Maybe you want to use `abs_error()`?") } true_values <- true_values[quantiles == 0.5] predictions <- predictions[quantiles == 0.5] diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index 064f8de11..5c03bf500 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -105,7 +105,6 @@ #' be better understood as an average of quantile scores.) #' @param summarised Summarise arguments (i.e. take the mean per group #' specified in group_by. Default is `TRUE.` -#' @param verbose print out additional helpful messages (default is `TRUE`) #' @param forecasts data.frame with forecasts, that should follow the same #' general guidelines as the `data` input. Argument can be used to supply #' forecasts and truth data independently. Default is `NULL`. @@ -149,8 +148,7 @@ #' binary_example <- data.table::setDT(scoringutils::binary_example_data) #' eval <- scoringutils::eval_forecasts(binary_example, #' summarise_by = c("model"), -#' quantiles = c(0.5), sd = TRUE, -#' verbose = FALSE) +#' quantiles = c(0.5), sd = TRUE) #' #' ## Quantile Forecasts #' # wide format example (this examples shows usage of both wide formats) @@ -201,7 +199,6 @@ eval_forecasts <- function(data = NULL, separate_results = TRUE), pit_plots = FALSE, summarised = TRUE, - verbose = TRUE, forecasts = NULL, truth_data = NULL, merge_by = NULL, @@ -218,9 +215,7 @@ eval_forecasts <- function(data = NULL, if (is.null(data)) { data <- merge_pred_and_obs(forecasts, truth_data, by = merge_by) if (nrow(data) == 0) { - if (verbose) { - warning("After attempting to merge, only an empty data.table was left") - } + warning("After attempting to merge, only an empty data.table was left") return(data) } } @@ -229,31 +224,22 @@ eval_forecasts <- function(data = NULL, data <- data.table::as.data.table(data) # error handling for relative skill computation - # should probably wrap this in a function warn_if_verbose(warning, verbose) if (compute_relative_skill) { if (!("model" %in% colnames(data))) { - if (verbose) { - warning("to compute relative skills, there must column present called 'model'. Relative skill will not be computed") - } + warning("to compute relative skills, there must column present called 'model'. Relative skill will not be computed") compute_relative_skill <- FALSE } models <- unique(data$model) if (length(models) < 2 + (!is.null(baseline))) { - if (verbose) { - warning("you need more than one model non-baseline model to make model comparisons. Relative skill will not be computed") - } + warning("you need more than one model non-baseline model to make model comparisons. Relative skill will not be computed") compute_relative_skill <- FALSE } if (!is.null(baseline) && !(baseline %in% models)) { - if (verbose){ - warning("The baseline you provided for the relative skill is not one of the models in the data. Relative skill will not be computed") - } + warning("The baseline you provided for the relative skill is not one of the models in the data. Relative skill will not be computed") compute_relative_skill <- FALSE } if (rel_skill_metric != "auto" && !(rel_skill_metric %in% list_of_avail_metrics())) { - if (verbose) { - warning("argument 'rel_skill_metric' must either be 'auto' or one of the metrics that can be computed. Relative skill will not be computed") - } + warning("argument 'rel_skill_metric' must either be 'auto' or one of the metrics that can be computed. Relative skill will not be computed") compute_relative_skill <- FALSE } } @@ -262,16 +248,12 @@ eval_forecasts <- function(data = NULL, unique_data <- unique(data) if (nrow(unique_data) != nrow(data)) { data <- unique_data - if(verbose) { - warning("There are duplicate rows in data. These were removed") - } + warning("There are duplicate rows in data. These were removed") } # check and remove any rows where the true value is missing if (any(is.na(data$true_value))) { - if(verbose) { - warning("There are NA values in the true values provided. These will be removed") - } + warning("There are NA values in the true values provided. These will be removed") } data <- data[!is.na(true_value)] @@ -303,11 +285,9 @@ eval_forecasts <- function(data = NULL, metrics <- available_metrics } else { if (!all(metrics %in% available_metrics)) { - if (verbose) { - msg <- paste("The following metrics are not currently implemented and", + msg <- paste("The following metrics are not currently implemented and", "will not be computed:", paste(setdiff(metrics, available_metrics), collapse = ", ")) - } warning(msg) } } @@ -336,9 +316,7 @@ eval_forecasts <- function(data = NULL, # remove any rows where the prediction is missing ---------------------------- data <- data[!is.na(prediction)] if (nrow(data) == 0) { - if (verbose) { - message("After removing all NA true values and predictions, there were no observations left") - } + message("After removing all NA true values and predictions, there were no observations left") return(data) } @@ -351,8 +329,7 @@ eval_forecasts <- function(data = NULL, metrics = metrics, quantiles = quantiles, sd = sd, - summarised = summarised, - verbose = verbose) + summarised = summarised) return(res) } @@ -367,7 +344,6 @@ eval_forecasts <- function(data = NULL, pit_plots = pit_plots, interval_score_arguments = interval_score_arguments, summarised = summarised, - verbose = verbose, compute_relative_skill = compute_relative_skill, rel_skill_metric = rel_skill_metric, baseline = baseline) @@ -387,8 +363,7 @@ eval_forecasts <- function(data = NULL, quantiles = quantiles, sd = sd, pit_plots = pit_plots, - summarised = summarised, - verbose = verbose) + summarised = summarised) return(res) } } diff --git a/R/eval_forecasts_binary.R b/R/eval_forecasts_binary.R index 28584906d..2a037b8c5 100644 --- a/R/eval_forecasts_binary.R +++ b/R/eval_forecasts_binary.R @@ -11,8 +11,7 @@ #' binary_example <- data.table::setDT(scoringutils::binary_example_data) #' eval <- scoringutils::eval_forecasts(data = binary_example, #' summarise_by = c("model"), -#' quantiles = c(0.5), sd = TRUE, -#' verbose = FALSE) +#' quantiles = c(0.5), sd = TRUE) #' #' @author Nikos Bosse \email{nikosbosse@@gmail.com} @@ -22,8 +21,7 @@ eval_forecasts_binary <- function(data, metrics, quantiles, sd, - summarised, - verbose){ + summarised){ res <- data[, "brier_score" := scoringutils::brier_score(true_value, prediction), by = by] diff --git a/R/eval_forecasts_continuous_integer.R b/R/eval_forecasts_continuous_integer.R index a90abf2aa..98f1b1ae3 100644 --- a/R/eval_forecasts_continuous_integer.R +++ b/R/eval_forecasts_continuous_integer.R @@ -42,8 +42,7 @@ eval_forecasts_sample <- function(data, quantiles, sd, pit_plots, - summarised, - verbose) { + summarised) { if (missing(prediction_type)) { if (all.equal(data$prediction, as.integer(data$prediction)) == TRUE) { @@ -92,9 +91,7 @@ eval_forecasts_sample <- function(data, # check if by == summarise_by - in that case no pit values can be computed if (identical(by, summarise_by)) { data[, c("pit_p_val", "pit_sd") := NA] - if (verbose) { - message("In order to compute PIT values, 'summarise_by' must be different from 'by'") - } + message("In order to compute PIT values, 'summarise_by' must be different from 'by'") } # if they are not identical, pit p-values can be computed diff --git a/R/eval_forecasts_quantile.R b/R/eval_forecasts_quantile.R index 5e7d25410..fe752a212 100644 --- a/R/eval_forecasts_quantile.R +++ b/R/eval_forecasts_quantile.R @@ -7,7 +7,6 @@ eval_forecasts_quantile <- function(data, pit_plots, interval_score_arguments, summarised, - verbose, compute_relative_skill, rel_skill_metric, baseline) { @@ -98,8 +97,7 @@ eval_forecasts_quantile <- function(data, if ("aem" %in% metrics) { quantile_data[, aem := ae_median_quantile(true_value, prediction, - quantile, - verbose = verbose), + quantile), by = by] } @@ -143,8 +141,7 @@ eval_forecasts_quantile <- function(data, rel_skill_metric = rel_skill_metric, baseline = baseline, by = by, - summarise_by = summarise_by, - verbose = verbose) + summarise_by = summarise_by) res <- merge(res, relative_res, by = by) } diff --git a/R/pairwise-comparisons.R b/R/pairwise-comparisons.R index 148b96e30..b5436142a 100644 --- a/R/pairwise-comparisons.R +++ b/R/pairwise-comparisons.R @@ -117,8 +117,7 @@ add_rel_skill_to_eval_forecasts <- function(unsummarised_scores, rel_skill_metric, baseline, by, - summarise_by, - verbose) { + summarise_by) { # infer the correct relative skill if only "auto" is given if (rel_skill_metric == "auto") { @@ -145,9 +144,7 @@ add_rel_skill_to_eval_forecasts <- function(unsummarised_scores, # if summarise_by is equal to by, then pairwise comparisons don't make sense if (identical(sort(summarise_by), sort(by))) { summarise_by <- "model" - if (verbose) { - message("relative skill can only be computed if `summarise_by` is different from `by`. `summarise_by` was set to 'model'") - } + message("relative skill can only be computed if `summarise_by` is different from `by`. `summarise_by` was set to 'model'") } # do pairwise comparison diff --git a/R/pit.R b/R/pit.R index 5e3faf56f..06717ef60 100644 --- a/R/pit.R +++ b/R/pit.R @@ -64,8 +64,6 @@ #' each time re-randomising the PIT #' @param full_output return all individual p_values and computed u_t values #' for the randomised PIT. Usually not needed. -#' @param verbose if `TRUE` (default is `FALSE`) more error messages are printed. -#' Usually, this should not be needed, but may help with debugging. #' @return a list with the following components: #' \itemize{ #' \item `p_value`: p-value of the Anderson-Darling test on the @@ -107,8 +105,7 @@ pit <- function(true_values, plot = TRUE, full_output = FALSE, n_replicates = 50, - num_bins = NULL, - verbose = FALSE) { + num_bins = NULL) { @@ -122,9 +119,7 @@ pit <- function(true_values, # check if there is more than one observation n <- length(true_values) if (n == 1) { - if (verbose) { - message("you need more than one observation to assess uniformity of the PIT") - } + message("you need more than one observation to assess uniformity of the PIT") out <- list(p_value = NA, sd = NA) if (full_output) { @@ -259,8 +254,7 @@ pit_df <- function(data, plot = TRUE, full_output = FALSE, n_replicates = 100, - num_bins = NULL, - verbose = FALSE) { + num_bins = NULL) { data <- data.table::as.data.table(data) @@ -281,8 +275,7 @@ pit_df <- function(data, plot = plot, full_output = full_output, n_replicates = n_replicates, - num_bins = num_bins, - verbose = verbose) + num_bins = num_bins) # call pit with samples and true values res <- do.call(pit, pit_arguments) @@ -347,8 +340,7 @@ pit_df_fast <- function(data, pit_arguments = list(plot = FALSE, full_output = FALSE, n_replicates = n_replicates, - num_bins = 1, - verbose = FALSE) + num_bins = 1) # reformat data.table to wide format for PIT data_wide <- data.table::dcast(data, ... ~ paste("sampl_", sample, sep = ""), diff --git a/R/plot.R b/R/plot.R index baa60dcb4..e31635d20 100644 --- a/R/plot.R +++ b/R/plot.R @@ -595,7 +595,6 @@ score_heatmap <- function(scores, #' (the default), these get filtered out. #' @param xlab Label for the x-axis. Default is the variable name on the x-axis #' @param ylab Label for the y-axis. Default is "True and predicted values" -#' @param verbose print out additional helpful messages (default is TRUE) #' @return ggplot object with a plot of true vs predicted values #' @importFrom ggplot2 ggplot scale_colour_manual scale_fill_manual #' facet_wrap facet_grid @@ -637,8 +636,7 @@ plot_predictions <- function(data = NULL, allow_truth_without_pred = FALSE, remove_from_truth = c("model", "forecaster", "quantile", "prediction", "sample", "interval"), xlab = x, - ylab = "True and predicted values", - verbose = TRUE) { + ylab = "True and predicted values") { # preparations --------------------------------------------------------------- # check data argument is provided @@ -649,9 +647,7 @@ plot_predictions <- function(data = NULL, if (is.null(data)) { data <- merge_pred_and_obs(forecasts, truth_data, by = merge_by, join = "full") if (nrow(data) == 0) { - if (verbose) { - warning("After attempting to merge, only an empty data.table was left") - } + warning("After attempting to merge, only an empty data.table was left") return(data) } } diff --git a/man/add_rel_skill_to_eval_forecasts.Rd b/man/add_rel_skill_to_eval_forecasts.Rd index 713d38ac8..c6976841a 100644 --- a/man/add_rel_skill_to_eval_forecasts.Rd +++ b/man/add_rel_skill_to_eval_forecasts.Rd @@ -9,8 +9,7 @@ add_rel_skill_to_eval_forecasts( rel_skill_metric, baseline, by, - summarise_by, - verbose + summarise_by ) } \arguments{ @@ -40,8 +39,6 @@ But sometimes you may want to to summarise over categories different from the scoring. \code{summarise_by} is also the grouping level used to compute (and possibly plot) the probability integral transform(pit).} - -\item{verbose}{print out additional helpful messages (default is \code{TRUE})} } \description{ This function will only be called within \code{\link[=eval_forecasts]{eval_forecasts()}} and serves to diff --git a/man/ae_median_quantile.Rd b/man/ae_median_quantile.Rd index ed2b60fc0..a2eab21cb 100644 --- a/man/ae_median_quantile.Rd +++ b/man/ae_median_quantile.Rd @@ -4,7 +4,7 @@ \alias{ae_median_quantile} \title{Absolute Error of the Median (Quantile-based Version)} \usage{ -ae_median_quantile(true_values, predictions, quantiles = NULL, verbose = TRUE) +ae_median_quantile(true_values, predictions, quantiles = NULL) } \arguments{ \item{true_values}{A vector with the true observed values of size n} @@ -16,8 +16,6 @@ quantiles in a second vector, \code{quantiles}.} in \code{predictions}. Only those predictions where \code{quantiles == 0.5} will be kept. If \code{quantiles} is \code{NULL}, then all \code{predictions} and \code{true_values} will be used (this is then the same as \code{\link[=abs_error]{abs_error()}})} - -\item{verbose}{logical, return a warning is something unexpected happens} } \value{ vector with the scoring values diff --git a/man/eval_forecasts.Rd b/man/eval_forecasts.Rd index 2734d6827..c91124250 100644 --- a/man/eval_forecasts.Rd +++ b/man/eval_forecasts.Rd @@ -15,7 +15,6 @@ eval_forecasts( separate_results = TRUE), pit_plots = FALSE, summarised = TRUE, - verbose = TRUE, forecasts = NULL, truth_data = NULL, merge_by = NULL, @@ -111,8 +110,6 @@ details see \code{\link[=pit]{pit()}}.} \item{summarised}{Summarise arguments (i.e. take the mean per group specified in group_by. Default is \code{TRUE.}} -\item{verbose}{print out additional helpful messages (default is \code{TRUE})} - \item{forecasts}{data.frame with forecasts, that should follow the same general guidelines as the \code{data} input. Argument can be used to supply forecasts and truth data independently. Default is \code{NULL}.} @@ -191,8 +188,7 @@ See \code{\link[=logs]{logs()}} for more information. binary_example <- data.table::setDT(scoringutils::binary_example_data) eval <- scoringutils::eval_forecasts(binary_example, summarise_by = c("model"), - quantiles = c(0.5), sd = TRUE, - verbose = FALSE) + quantiles = c(0.5), sd = TRUE) ## Quantile Forecasts # wide format example (this examples shows usage of both wide formats) diff --git a/man/eval_forecasts_binary.Rd b/man/eval_forecasts_binary.Rd index 0c99dbf94..8f2b26db7 100644 --- a/man/eval_forecasts_binary.Rd +++ b/man/eval_forecasts_binary.Rd @@ -11,8 +11,7 @@ eval_forecasts_binary( metrics, quantiles, sd, - summarised, - verbose + summarised ) } \arguments{ @@ -87,8 +86,6 @@ metrics will be returned when summarising.} \item{summarised}{Summarise arguments (i.e. take the mean per group specified in group_by. Default is \code{TRUE.}} - -\item{verbose}{print out additional helpful messages (default is \code{TRUE})} } \value{ A data.table with appropriate scores. For more information see @@ -102,8 +99,7 @@ Evaluate forecasts in a Binary Format binary_example <- data.table::setDT(scoringutils::binary_example_data) eval <- scoringutils::eval_forecasts(data = binary_example, summarise_by = c("model"), - quantiles = c(0.5), sd = TRUE, - verbose = FALSE) + quantiles = c(0.5), sd = TRUE) } \author{ diff --git a/man/eval_forecasts_sample.Rd b/man/eval_forecasts_sample.Rd index 0a742bb7a..aa7f7100d 100644 --- a/man/eval_forecasts_sample.Rd +++ b/man/eval_forecasts_sample.Rd @@ -13,8 +13,7 @@ eval_forecasts_sample( quantiles, sd, pit_plots, - summarised, - verbose + summarised ) } \arguments{ @@ -94,8 +93,6 @@ details see \code{\link[=pit]{pit()}}.} \item{summarised}{Summarise arguments (i.e. take the mean per group specified in group_by. Default is \code{TRUE.}} - -\item{verbose}{print out additional helpful messages (default is \code{TRUE})} } \value{ A data.table with appropriate scores. For more information see diff --git a/man/pit.Rd b/man/pit.Rd index c7668e670..d34d9a92e 100644 --- a/man/pit.Rd +++ b/man/pit.Rd @@ -10,8 +10,7 @@ pit( plot = TRUE, full_output = FALSE, n_replicates = 50, - num_bins = NULL, - verbose = FALSE + num_bins = NULL ) } \arguments{ @@ -32,9 +31,6 @@ each time re-randomising the PIT} \item{num_bins}{the number of bins in the PIT histogram (if \code{plot == TRUE}) If not given, the square root of n will be used} - -\item{verbose}{if \code{TRUE} (default is \code{FALSE}) more error messages are printed. -Usually, this should not be needed, but may help with debugging.} } \value{ a list with the following components: diff --git a/man/pit_df.Rd b/man/pit_df.Rd index f719f9580..44fb185c3 100644 --- a/man/pit_df.Rd +++ b/man/pit_df.Rd @@ -9,8 +9,7 @@ pit_df( plot = TRUE, full_output = FALSE, n_replicates = 100, - num_bins = NULL, - verbose = FALSE + num_bins = NULL ) } \arguments{ @@ -28,9 +27,6 @@ each time re-randomising the PIT} \item{num_bins}{the number of bins in the PIT histogram (if \code{plot == TRUE}) If not given, the square root of n will be used} - -\item{verbose}{if \code{TRUE} (default is \code{FALSE}) more error messages are printed. -Usually, this should not be needed, but may help with debugging.} } \value{ a list with the following components: diff --git a/man/plot_predictions.Rd b/man/plot_predictions.Rd index e12231140..acad52699 100644 --- a/man/plot_predictions.Rd +++ b/man/plot_predictions.Rd @@ -22,8 +22,7 @@ plot_predictions( remove_from_truth = c("model", "forecaster", "quantile", "prediction", "sample", "interval"), xlab = x, - ylab = "True and predicted values", - verbose = TRUE + ylab = "True and predicted values" ) } \arguments{ @@ -92,8 +91,6 @@ period covered by a certain model).} \item{xlab}{Label for the x-axis. Default is the variable name on the x-axis} \item{ylab}{Label for the y-axis. Default is "True and predicted values"} - -\item{verbose}{print out additional helpful messages (default is TRUE)} } \value{ ggplot object with a plot of true vs predicted values diff --git a/tests/testthat/test-eval_forecasts.R b/tests/testthat/test-eval_forecasts.R index cbca78028..540ca319b 100644 --- a/tests/testthat/test-eval_forecasts.R +++ b/tests/testthat/test-eval_forecasts.R @@ -10,8 +10,7 @@ test_that("function produces output for a binary case", { binary_example <- data.table::setDT(scoringutils::binary_example_data) eval <- eval_forecasts(binary_example[!is.na(prediction)], summarise_by = c("model", "value_desc"), - quantiles = c(0.5), sd = TRUE, - verbose = FALSE) + quantiles = c(0.5), sd = TRUE) expect_equal(nrow(eval) > 1, TRUE) }) @@ -129,128 +128,3 @@ test_that("function produces output for a continuous format case", { TRUE) }) - - - - - - - - - - - -# -# # tests that function returns the same results for scoringutils2 and scoringutils1 -# test_that("scoringutils and scoringutils2 are the same for a binary case", { -# binary_example <- data.table::setDT(scoringutils::binary_example_data) -# eval2 <- scoringutils::eval_forecasts(binary_example, -# summarise_by = c("model", "value_desc"), -# quantiles = c(0.5), sd = TRUE, -# verbose = FALSE) -# -# eval <- scoringutils::eval_forecasts(binary_example[!is.na(prediction)], -# summarise_by = c("model", "value_desc"), -# quantiles = c(0.5), sd = TRUE, -# verbose = FALSE) -# -# all(eval == eval2) -# -# expect_equal(eval, eval2) -# }) -# -# -# test_that("scoringutils and scoringutils2 are the same for a continuous case", { -# example <- data.table::setDT(scoringutils::continuous_example_data) -# eval2 <- scoringutils::eval_forecasts(example, -# summarise_by = c("model", "value_desc"), -# quantiles = c(0.5), sd = TRUE, -# verbose = FALSE) -# -# eval <- scoringutils::eval_forecasts(example[!is.na(prediction)], -# summarise_by = c("model", "value_desc"), -# quantiles = c(0.5), sd = TRUE, -# verbose = FALSE) -# -# eval2 <- eval2[, .SD, .SDcols = names(eval2)[names(eval2) %in% names(eval)]] -# data.table::setcolorder(eval2, names(eval)) -# -# expect_equal(eval, eval2) -# }) -# -# -# test_that("scoringutils and scoringutils2 are the same for an integer case", { -# set.seed(1) -# example <- data.table::setDT(scoringutils::integer_example_data) -# eval2 <- scoringutils::eval_forecasts(example, -# summarise_by = c("model", "value_desc"), -# quantiles = c(0.5), sd = TRUE, -# verbose = FALSE) -# set.seed(1) -# eval <- scoringutils::eval_forecasts(example[!is.na(prediction)], -# summarise_by = c("model", "value_desc"), -# quantiles = c(0.5), sd = TRUE, -# verbose = FALSE) -# -# eval <- eval[order(model)] -# eval[, c("pit_p_val", "pit_sd", "pit_p_val_0.5") := NULL] -# eval2 <- eval2[order(model)] -# eval2 <- eval2[, .SD, .SDcols = names(eval2)[names(eval2) %in% names(eval)]] -# data.table::setcolorder(eval2, names(eval)) -# -# expect_equal(eval, eval2) -# }) -# -# -# -# -# test_that("scoringutils and scoringutils2 are the same for a quantile case", { -# example <- data.table::setDT(scoringutils::quantile_example_data) -# eval2 <- scoringutils::eval_forecasts(example, -# summarise_by = c("model", "value_desc"), -# interval_score_arguments = list(count_median_twice = FALSE), -# quantiles = c(0.5), sd = TRUE, -# verbose = FALSE) -# -# eval <- scoringutils::eval_forecasts(example[!is.na(prediction)], -# summarise_by = c("model", "value_desc"), -# quantiles = c(0.5), sd = TRUE, -# verbose = FALSE) -# -# eval <- eval[order(model)] -# eval2 <- eval2[order(model)] -# eval2 <- eval2[, .SD, .SDcols = names(eval2)[names(eval2) %in% names(eval)]] -# eval <- eval[, .SD, .SDcols = names(eval)[names(eval) %in% names(eval2)]] -# data.table::setcolorder(eval2, names(eval)) -# -# expect_equal(eval, eval2) -# }) -# -# -# test_that("scoringutils and scoringutils2 are the same for a range format case", { -# example <- data.table::setDT(scoringutils::range_example_data_long) -# eval2 <- scoringutils::eval_forecasts(example, -# summarise_by = c("model", "value_desc"), -# interval_score_arguments = list(count_median_twice = FALSE), -# quantiles = c(0.5), sd = TRUE, -# verbose = FALSE) -# -# eval <- scoringutils::eval_forecasts(example[!is.na(prediction)], -# summarise_by = c("model", "value_desc"), -# quantiles = c(0.5), sd = TRUE, -# verbose = FALSE) -# -# eval <- eval[order(model)] -# eval2 <- eval2[order(model)] -# eval2 <- eval2[, .SD, .SDcols = names(eval2)[names(eval2) %in% names(eval)]] -# eval <- eval[, .SD, .SDcols = names(eval)[names(eval) %in% names(eval2)]] -# data.table::setcolorder(eval2, names(eval)) -# -# expect_equal(eval, eval2) -# }) - - - -## test for separate truth and forecast data - - diff --git a/tests/testthat/test-pairwise_comparison.R b/tests/testthat/test-pairwise_comparison.R index c560ebd7e..3f2432e49 100644 --- a/tests/testthat/test-pairwise_comparison.R +++ b/tests/testthat/test-pairwise_comparison.R @@ -24,10 +24,10 @@ test_that("pairwise comparisons works", { truth_formatted) # evaluate the toy forecasts, once with and once without a baseline model specified - eval_without_baseline <- scoringutils::eval_forecasts(data_formatted, - by = c("location", "target_end_date", "model"), - compute_relative_skill = TRUE, - interval_score_arguments = list(count_median_twice = FALSE)) + eval_without_baseline <- eval_forecasts(data_formatted, + by = c("location", "target_end_date", "model"), + compute_relative_skill = TRUE, + interval_score_arguments = list(count_median_twice = FALSE)) eval_with_baseline <- scoringutils::eval_forecasts(data_formatted, by = c("location", "target_end_date", "model"), baseline = "m1", From 295a023cfb932712f99ff549698f4e87068765c6 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Wed, 24 Nov 2021 09:55:04 +0100 Subject: [PATCH 003/225] simplify data check --- R/eval_forecasts.R | 25 ++----------------------- 1 file changed, 2 insertions(+), 23 deletions(-) diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index 5c03bf500..28f111370 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -220,8 +220,8 @@ eval_forecasts <- function(data = NULL, } } - # do a copy to avoid that the input may be altered in any way. - data <- data.table::as.data.table(data) + # check relevant columns and remove NA values in true_values and prediction + data <- check_clean_data(data) # error handling for relative skill computation if (compute_relative_skill) { @@ -244,19 +244,6 @@ eval_forecasts <- function(data = NULL, } } - # check that everything is unique - unique_data <- unique(data) - if (nrow(unique_data) != nrow(data)) { - data <- unique_data - warning("There are duplicate rows in data. These were removed") - } - - # check and remove any rows where the true value is missing - if (any(is.na(data$true_value))) { - warning("There are NA values in the true values provided. These will be removed") - } - data <- data[!is.na(true_value)] - # obtain a value for by if nothing was provided by the user if (is.null(by)) { protected_columns <- c("prediction", "true_value", "sample", "quantile", @@ -313,14 +300,6 @@ eval_forecasts <- function(data = NULL, target_type = "continuous" } - # remove any rows where the prediction is missing ---------------------------- - data <- data[!is.na(prediction)] - if (nrow(data) == 0) { - message("After removing all NA true values and predictions, there were no observations left") - return(data) - } - - # Score binary predictions --------------------------------------------------- if (target_type == "binary") { res <- eval_forecasts_binary(data = data, From 83eb6c6077521d007aa1f6c55ecd3570bdfbaaa6 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Wed, 24 Nov 2021 10:12:51 +0100 Subject: [PATCH 004/225] get rid of possibility to add separate truth data and forecasts to eval_forecasts --- R/check_forecasts.R | 11 ++++++++--- R/eval_forecasts.R | 25 +----------------------- man/check_clean_data.Rd | 4 +++- man/eval_forecasts.Rd | 14 ------------- tests/testthat/test-absolute_error.R | 21 ++++++++++++-------- tests/testthat/test-merge_pred_and_obs.R | 8 +++++--- 6 files changed, 30 insertions(+), 53 deletions(-) diff --git a/R/check_forecasts.R b/R/check_forecasts.R index 22db7b56b..683c0478b 100644 --- a/R/check_forecasts.R +++ b/R/check_forecasts.R @@ -230,6 +230,7 @@ print.scoringutils_check <- function(x, ...) { #' or similar and remove rows with no value for `prediction` or `true_value` #' #' @param data A data.frame or similar as it gets passed to [eval_forecasts()]. +#' @param verbose Boolean, whether or not to print warnings #' #' @return A data.table with NA values in `true_value` or `prediction` removed. #' @@ -237,7 +238,7 @@ print.scoringutils_check <- function(x, ...) { #' #'@keywords internal -check_clean_data <- function(data) { +check_clean_data <- function(data, verbose = TRUE) { if(!is.data.frame(data)) { stop("Input should be a data.frame or similar") @@ -251,12 +252,16 @@ check_clean_data <- function(data) { # remove rows where prediction or true value are NA if (anyNA(data$true_value)) { - warning("Some values for `true_value` are NA in the data provided") + if (verbose) { + warning("Some values for `true_value` are NA in the data provided") + } } data <- data[!is.na(true_value)] if (anyNA(data$prediction)) { - warning("Some values for `prediction` are NA in the data provided") + if (verbose) { + warning("Some values for `prediction` are NA in the data provided") + } } data <- data[!is.na(prediction)] if (nrow(data) == 0) { diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index 28f111370..33ab31aa1 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -105,14 +105,6 @@ #' be better understood as an average of quantile scores.) #' @param summarised Summarise arguments (i.e. take the mean per group #' specified in group_by. Default is `TRUE.` -#' @param forecasts data.frame with forecasts, that should follow the same -#' general guidelines as the `data` input. Argument can be used to supply -#' forecasts and truth data independently. Default is `NULL`. -#' @param truth_data data.frame with a column called `true_value` to be merged -#' with `forecasts` -#' @param merge_by character vector with column names that `forecasts` and -#' `truth_data` should be merged on. Default is `NULL` and merge will be -#' attempted automatically. #' @param compute_relative_skill logical, whether or not to compute relative #' performance between models. If `TRUE` (default is `FALSE`), then a column called #' 'model' must be present in the input data. For more information on @@ -199,29 +191,14 @@ eval_forecasts <- function(data = NULL, separate_results = TRUE), pit_plots = FALSE, summarised = TRUE, - forecasts = NULL, - truth_data = NULL, - merge_by = NULL, compute_relative_skill = FALSE, rel_skill_metric = "auto", baseline = NULL) { # preparations --------------------------------------------------------------- - # check data argument is provided - if (is.null(data) && (is.null(truth_data) | is.null(forecasts))) { - stop("need arguments 'data' in function 'eval_forecasts()', or alternatively 'forecasts' and 'truth_data'") - } - if (is.null(data)) { - data <- merge_pred_and_obs(forecasts, truth_data, by = merge_by) - if (nrow(data) == 0) { - warning("After attempting to merge, only an empty data.table was left") - return(data) - } - } - # check relevant columns and remove NA values in true_values and prediction - data <- check_clean_data(data) + data <- check_clean_data(data, verbose = FALSE) # error handling for relative skill computation if (compute_relative_skill) { diff --git a/man/check_clean_data.Rd b/man/check_clean_data.Rd index 565592c12..0e6d4c375 100644 --- a/man/check_clean_data.Rd +++ b/man/check_clean_data.Rd @@ -4,10 +4,12 @@ \alias{check_clean_data} \title{Clean forecast data} \usage{ -check_clean_data(data) +check_clean_data(data, verbose = TRUE) } \arguments{ \item{data}{A data.frame or similar as it gets passed to \code{\link[=eval_forecasts]{eval_forecasts()}}.} + +\item{verbose}{Boolean, whether or not to print warnings} } \value{ A data.table with NA values in \code{true_value} or \code{prediction} removed. diff --git a/man/eval_forecasts.Rd b/man/eval_forecasts.Rd index c91124250..c0aa33e9a 100644 --- a/man/eval_forecasts.Rd +++ b/man/eval_forecasts.Rd @@ -15,9 +15,6 @@ eval_forecasts( separate_results = TRUE), pit_plots = FALSE, summarised = TRUE, - forecasts = NULL, - truth_data = NULL, - merge_by = NULL, compute_relative_skill = FALSE, rel_skill_metric = "auto", baseline = NULL @@ -110,17 +107,6 @@ details see \code{\link[=pit]{pit()}}.} \item{summarised}{Summarise arguments (i.e. take the mean per group specified in group_by. Default is \code{TRUE.}} -\item{forecasts}{data.frame with forecasts, that should follow the same -general guidelines as the \code{data} input. Argument can be used to supply -forecasts and truth data independently. Default is \code{NULL}.} - -\item{truth_data}{data.frame with a column called \code{true_value} to be merged -with \code{forecasts}} - -\item{merge_by}{character vector with column names that \code{forecasts} and -\code{truth_data} should be merged on. Default is \code{NULL} and merge will be -attempted automatically.} - \item{compute_relative_skill}{logical, whether or not to compute relative performance between models. If \code{TRUE} (default is \code{FALSE}), then a column called 'model' must be present in the input data. For more information on diff --git a/tests/testthat/test-absolute_error.R b/tests/testthat/test-absolute_error.R index 646392494..c6f1344fd 100644 --- a/tests/testthat/test-absolute_error.R +++ b/tests/testthat/test-absolute_error.R @@ -61,8 +61,10 @@ test_that("abs error is correct within eval_forecasts, point forecast only", { data.table::setnames(fc_scoringutils, old = "value", new = "prediction") truth_scoringutils[, model := NULL] - eval <- scoringutils::eval_forecasts(forecasts = fc_scoringutils, - truth_data = truth_scoringutils) + data_scoringutils <- merge_pred_and_obs(forecasts = fc_scoringutils, + observations = truth_scoringutils) + + eval <- scoringutils::eval_forecasts(data_scoringutils) # actual <- score_forecasts(forecasts = test_forecasts, truth = test_truth) @@ -131,10 +133,10 @@ test_that("abs error is correct, point and median forecasts different", { data.table::setnames(fc_scoringutils, old = "value", new = "prediction") truth_scoringutils[, model := NULL] - eval <- scoringutils::eval_forecasts(forecasts = fc_scoringutils, - truth_data = truth_scoringutils) + data_scoringutils <- merge_pred_and_obs(forecasts = fc_scoringutils, + observations = truth_scoringutils) - # actual <- score_forecasts(forecasts = test_forecasts, truth = test_truth) + eval <- scoringutils::eval_forecasts(data_scoringutils) expected <- abs(y - point_forecast) # expect_equal(actual$abs_error, expected) @@ -200,9 +202,12 @@ test_that("abs error is correct, point and median forecasts same", { data.table::setnames(fc_scoringutils, old = "value", new = "prediction") truth_scoringutils[, model := NULL] - eval <- scoringutils::eval_forecasts(forecasts = fc_scoringutils, - truth_data = truth_scoringutils, - summarise_by = c("location", "target_end_date", "target_variable", "horizon")) + data_scoringutils <- merge_pred_and_obs(forecasts = fc_scoringutils, + observations = truth_scoringutils) + + eval <- scoringutils::eval_forecasts(data = data_scoringutils, + summarise_by = c("location", "target_end_date", + "target_variable", "horizon")) # actual <- score_forecasts(forecasts = test_forecasts, truth = test_truth) diff --git a/tests/testthat/test-merge_pred_and_obs.R b/tests/testthat/test-merge_pred_and_obs.R index 1be322de2..b4bbabe6b 100644 --- a/tests/testthat/test-merge_pred_and_obs.R +++ b/tests/testthat/test-merge_pred_and_obs.R @@ -1,4 +1,4 @@ -test_that("merge pred and obs works within eval_forecasts", { +test_that("merge pred and obs works", { data <- scoringutils::quantile_example_data forecasts <- scoringutils::example_quantile_forecasts_only @@ -6,8 +6,10 @@ test_that("merge pred and obs works within eval_forecasts", { eval1 <- scoringutils::eval_forecasts(data = data) - eval2 <- scoringutils::eval_forecasts(forecasts = forecasts, - truth_data = truth_data) + data2 <- merge_pred_and_obs(forecasts = forecasts, + observations = truth_data) + + eval2 <- scoringutils::eval_forecasts(data = data2) data.table::setcolorder(eval1, colnames(eval2)) From 5baab8db9e016ae18a47274048dd8ba7cb0d8a52 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Wed, 24 Nov 2021 10:44:11 +0100 Subject: [PATCH 005/225] replace 'interval_score_arguments list with ... in eval_forecasts --- R/eval_forecasts.R | 31 +- R/eval_forecasts_quantile.R | 32 +- R/plot.R | 4 +- man/eval_forecasts.Rd | 30 +- man/range_plot.Rd | 4 +- tests/testthat/test-interval_score.R | 462 ++++++++++------------ tests/testthat/test-pairwise_comparison.R | 6 +- 7 files changed, 267 insertions(+), 302 deletions(-) diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index 33ab31aa1..df0c0bccb 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -93,16 +93,6 @@ #' metrics will be returned when summarising. #' @param pit_plots if `TRUE` (not the default), pit plots will be returned. For #' details see [pit()]. -#' @param interval_score_arguments list with arguments for the calculation of -#' the interval score. These arguments get passed down to -#' `interval_score`, except for the argument `count_median_twice` that -#' controls how the interval scores for different intervals are summed up. This -#' should be a logical (default is `FALSE`) that indicates whether or not -#' to count the median twice when summarising. This would conceptually treat the -#' median as a 0% prediction interval, where the median is the lower as well as -#' the upper bound. The alternative is to treat the median as a single quantile -#' forecast instead of an interval. The interval score would then -#' be better understood as an average of quantile scores.) #' @param summarised Summarise arguments (i.e. take the mean per group #' specified in group_by. Default is `TRUE.` #' @param compute_relative_skill logical, whether or not to compute relative @@ -118,6 +108,17 @@ #' given, then a scaled relative skill with respect to the baseline will be #' returned. By default (`NULL`), relative skill will not be scaled with #' respect to a baseline model. +#' @param ... additional parameters passed down to lower-level functions. +#' For example, the following arguments can change how weighted interval +#' scores are computed: +#' - `count_median_twice` that controls how the interval scores for different +#' intervals are summed up. This should be a logical (default is `FALSE`) that +#' indicates whether or not to count the median twice when summarising. +#' This would conceptually treat the +#' median as a 0% prediction interval, where the median is the lower as well as +#' the upper bound. The alternative is to treat the median as a single quantile +#' forecast instead of an interval. The interval score would then +#' be better understood as an average of quantile scores.) #' #' @return A data.table with appropriate scores. For binary predictions, #' the Brier Score will be returned, for quantile predictions the interval @@ -186,14 +187,12 @@ eval_forecasts <- function(data = NULL, metrics = NULL, quantiles = c(), sd = FALSE, - interval_score_arguments = list(weigh = TRUE, - count_median_twice = FALSE, - separate_results = TRUE), pit_plots = FALSE, summarised = TRUE, compute_relative_skill = FALSE, rel_skill_metric = "auto", - baseline = NULL) { + baseline = NULL, + ...) { # preparations --------------------------------------------------------------- @@ -298,11 +297,11 @@ eval_forecasts <- function(data = NULL, quantiles = quantiles, sd = sd, pit_plots = pit_plots, - interval_score_arguments = interval_score_arguments, summarised = summarised, compute_relative_skill = compute_relative_skill, rel_skill_metric = rel_skill_metric, - baseline = baseline) + baseline = baseline, + ...) return(res) } diff --git a/R/eval_forecasts_quantile.R b/R/eval_forecasts_quantile.R index fe752a212..28a762036 100644 --- a/R/eval_forecasts_quantile.R +++ b/R/eval_forecasts_quantile.R @@ -5,7 +5,9 @@ eval_forecasts_quantile <- function(data, quantiles, sd, pit_plots, - interval_score_arguments, + weigh = TRUE, + count_median_twice = FALSE, + separate_results = TRUE, summarised, compute_relative_skill, rel_skill_metric, @@ -32,39 +34,25 @@ eval_forecasts_quantile <- function(data, range_data[, c("upper", "lower") := NA] } - # update interval_score arguments based on what was provided by user - interval_score_arguments <- update_list(defaults = list(weigh = TRUE, - count_median_twice = FALSE, - separate_results = TRUE), - optional = interval_score_arguments) - - # store separately, as this doesn't get passed down to interval_score() - count_median_twice <- interval_score_arguments$count_median_twice - interval_score_arguments$count_median_twice <- NULL - # set up results data.table that will then be modified throughout ------------ res <- data.table::copy(range_data) # calculate scores on range format ------------------------------------------- if ("interval_score" %in% metrics) { # compute separate results if desired - if (interval_score_arguments$separate_results) { + if (separate_results) { res <- res[, c("interval_score", "sharpness", "underprediction", "overprediction") := do.call(scoringutils::interval_score, - c(list(true_value, - lower, - upper, - range), - interval_score_arguments))] + list(true_value, lower, + upper,range, + weigh, separate_results))] } else { res <- res[, c("interval_score") := do.call(scoringutils::interval_score, - c(list(true_value, - lower, - upper, - range), - interval_score_arguments))] + list(true_value, lower, + upper,range, + weigh, separate_results))] } # res[, .(unlist(interval_score)), by = setdiff(colnames(res), "interval_score")] } diff --git a/R/plot.R b/R/plot.R index e31635d20..4b1e3873e 100644 --- a/R/plot.R +++ b/R/plot.R @@ -405,8 +405,8 @@ wis_components <- function(scores, #' #' # we saw above that sharpness values crossed. Let's look at the unweighted WIS #' scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, -#' interval_score_arguments = list(weigh = FALSE), -#' summarise_by = c("model", "value_desc", "range")) +#' weigh = FALSE, +#' summarise_by = c("model", "value_desc", "range")) #' scoringutils::range_plot(scores, y = "sharpness", x = "model", #' facet_formula = ~value_desc) diff --git a/man/eval_forecasts.Rd b/man/eval_forecasts.Rd index c0aa33e9a..ab4659b34 100644 --- a/man/eval_forecasts.Rd +++ b/man/eval_forecasts.Rd @@ -11,13 +11,12 @@ eval_forecasts( metrics = NULL, quantiles = c(), sd = FALSE, - interval_score_arguments = list(weigh = TRUE, count_median_twice = FALSE, - separate_results = TRUE), pit_plots = FALSE, summarised = TRUE, compute_relative_skill = FALSE, rel_skill_metric = "auto", - baseline = NULL + baseline = NULL, + ... ) } \arguments{ @@ -90,17 +89,6 @@ returned.} \item{sd}{if \code{TRUE} (the default is \code{FALSE}) the standard deviation of all metrics will be returned when summarising.} -\item{interval_score_arguments}{list with arguments for the calculation of -the interval score. These arguments get passed down to -\code{interval_score}, except for the argument \code{count_median_twice} that -controls how the interval scores for different intervals are summed up. This -should be a logical (default is \code{FALSE}) that indicates whether or not -to count the median twice when summarising. This would conceptually treat the -median as a 0\% prediction interval, where the median is the lower as well as -the upper bound. The alternative is to treat the median as a single quantile -forecast instead of an interval. The interval score would then -be better understood as an average of quantile scores.)} - \item{pit_plots}{if \code{TRUE} (not the default), pit plots will be returned. For details see \code{\link[=pit]{pit()}}.} @@ -122,6 +110,20 @@ one of interval score, crps or brier score will be used where appropriate} given, then a scaled relative skill with respect to the baseline will be returned. By default (\code{NULL}), relative skill will not be scaled with respect to a baseline model.} + +\item{...}{additional parameters passed down to lower-level functions. +For example, the following arguments can change how weighted interval +scores are computed: +\itemize{ +\item \code{count_median_twice} that controls how the interval scores for different +intervals are summed up. This should be a logical (default is \code{FALSE}) that +indicates whether or not to count the median twice when summarising. +This would conceptually treat the +median as a 0\% prediction interval, where the median is the lower as well as +the upper bound. The alternative is to treat the median as a single quantile +forecast instead of an interval. The interval score would then +be better understood as an average of quantile scores.) +}} } \value{ A data.table with appropriate scores. For binary predictions, diff --git a/man/range_plot.Rd b/man/range_plot.Rd index ccd4e1b81..7cc5ad89c 100644 --- a/man/range_plot.Rd +++ b/man/range_plot.Rd @@ -70,8 +70,8 @@ scoringutils::range_plot(scores, y = "sharpness", x = "model", # we saw above that sharpness values crossed. Let's look at the unweighted WIS scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, - interval_score_arguments = list(weigh = FALSE), - summarise_by = c("model", "value_desc", "range")) + weigh = FALSE, + summarise_by = c("model", "value_desc", "range")) scoringutils::range_plot(scores, y = "sharpness", x = "model", facet_formula = ~value_desc) } diff --git a/tests/testthat/test-interval_score.R b/tests/testthat/test-interval_score.R index 53f989750..fe0b9d8f6 100644 --- a/tests/testthat/test-interval_score.R +++ b/tests/testthat/test-interval_score.R @@ -3,9 +3,9 @@ test_that("wis works, median only", { lower <- upper <- c(1, 2, 3) quantile_probs <- 0.5 - actual <- scoringutils::interval_score(y, lower = lower, upper = upper, - weigh = TRUE, - interval_range = 0) + actual <- interval_score(y, lower = lower, upper = upper, + weigh = TRUE, + interval_range = 0) expected <- abs(y - lower) expect_identical(actual, expected) @@ -18,7 +18,7 @@ test_that("WIS works within eval_forecasts for median forecast", { model = "model1", date = 1:3) eval <- scoringutils::eval_forecasts(test_data, - interval_score_arguments = list(count_median_twice = TRUE)) + count_median_twice = TRUE) expect_equal(eval$aem, eval$interval_score) }) @@ -47,7 +47,7 @@ test_that("WIS works within eval_forecasts for one interval", { date = rep(1:3, times = 2)) eval <- scoringutils::eval_forecasts(test_data, - interval_score_arguments = list(count_median_twice = TRUE)) + count_median_twice = TRUE) lower = c(0, 1, 0) upper = c(2, 2, 3) @@ -71,7 +71,7 @@ test_that("wis works, 1 interval and median", { date = rep(1:3, times = 3)) eval <- scoringutils::eval_forecasts(test_data, - interval_score_arguments = list(count_median_twice = TRUE)) + count_median_twice = TRUE) y <- c(1, -15, 22) @@ -99,7 +99,7 @@ test_that("wis works, 2 intervals and median", { date = rep(1:3, times = 5)) eval <- scoringutils::eval_forecasts(test_data, - interval_score_arguments = list(count_median_twice = TRUE)) + count_median_twice = TRUE) y <- c(1, -15, 22) quantiles <- rbind(c(-1, 0, 1, 2, 3), c(-2, 1, 2, 2, 4), c(-2, 0, 3, 3, 4)) @@ -125,240 +125,216 @@ test_that("wis works, 2 intervals and median", { -# # additional tests from the covidhubutils repo -# -# test_that("wis is correct, median only - covidHubUtils check", { -# library(covidHubUtils) -# y <- c(1, -15, 22) -# forecast_quantiles_matrix <- rbind( -# c(-1, 0, 1, 2, 3), -# c(-2, 1, 2, 2, 4), -# c(-2, 0, 3, 3, 4)) -# forecast_quantile_probs <- c(0.1, 0.25, 0.5, 0.75, 0.9) -# forecast_quantiles_matrix <- forecast_quantiles_matrix[, 3, drop = FALSE] -# forecast_quantile_probs <- forecast_quantile_probs[3] -# -# target_end_dates <- as.Date("2020-01-01") + c(7, 14, 7) -# horizons <- c("1", "2", "1") -# locations <- c("01", "01", "02") -# target_variables <- rep("inc death", length(y)) -# -# forecast_target_end_dates <- -# rep(target_end_dates, times = ncol(forecast_quantiles_matrix)) -# forecast_horizons <- rep(horizons, times = ncol(forecast_quantiles_matrix)) -# forecast_locations <- rep(locations, times = ncol(forecast_quantiles_matrix)) -# forecast_target_variables <- -# rep(target_variables, times = ncol(forecast_quantiles_matrix)) -# forecast_quantile_probs <- rep(forecast_quantile_probs, each = length(y)) -# forecast_quantiles <- forecast_quantiles_matrix -# dim(forecast_quantiles) <- prod(dim(forecast_quantiles)) -# -# test_truth <- data.frame( -# model = rep("truth_source", length(y)), -# target_variable = target_variables, -# target_end_date = target_end_dates, -# location = locations, -# value = y, -# stringsAsFactors = FALSE -# ) -# -# n_forecasts <- length(forecast_quantiles) -# test_forecasts <- data.frame( -# model = rep("m1", n_forecasts), -# forecast_date = rep(as.Date("2020-01-01"), n_forecasts), -# location = forecast_locations, -# horizon = forecast_horizons, -# temporal_resolution = rep("wk", n_forecasts), -# target_variable = forecast_target_variables, -# target_end_date = forecast_target_end_dates, -# type = rep("quantile", n_forecasts), -# quantile = forecast_quantile_probs, -# value = forecast_quantiles, -# stringsAsFactors = FALSE -# ) -# -# # make a version that conforms to scoringutils format -# truth_formatted <- data.table::as.data.table(test_truth) -# truth_formatted[, `:=`(model = NULL)] -# data.table::setnames(truth_formatted, old = "value", new = "true_value") -# -# forecasts_formated <- data.table::as.data.table(test_forecasts) -# data.table::setnames(forecasts_formated, old = "value", new = "prediction") -# -# data_formatted <- merge(forecasts_formated, truth_formatted) -# -# eval <- scoringutils::eval_forecasts(data_formatted, -# interval_score_arguments = list(count_median_twice = FALSE)) -# -# actual <- covidHubUtils::score_forecasts(forecasts = test_forecasts, truth = test_truth, -# use_median_as_point = TRUE) -# -# expected <- abs(y - forecast_quantiles_matrix[, 1]) -# -# if(!all(eval$interval_score == actual$wis)) { -# warning("eval_forecasts() and covidHubUtils don't match") -# } -# -# expect_equal(eval$interval_score, expected) -# }) -# -# -# -# -# test_that("wis is correct, 1 interval only - covidHubUtils check", { -# library(covidHubUtils) -# y <- c(1, -15, 22) -# forecast_quantiles_matrix <- rbind( -# c(-1, 0, 1, 2, 3), -# c(-2, 1, 2, 2, 4), -# c(-2, 0, 3, 3, 4)) -# forecast_quantile_probs <- c(0.1, 0.25, 0.5, 0.75, 0.9) -# forecast_quantiles_matrix <- forecast_quantiles_matrix[, c(1, 5), drop = FALSE] -# forecast_quantile_probs <- forecast_quantile_probs[c(1, 5)] -# -# target_end_dates <- as.Date("2020-01-01") + c(7, 14, 7) -# horizons <- c("1", "2", "1") -# locations <- c("01", "01", "02") -# target_variables <- rep("inc death", length(y)) -# -# forecast_target_end_dates <- -# rep(target_end_dates, times = ncol(forecast_quantiles_matrix)) -# forecast_horizons <- rep(horizons, times = ncol(forecast_quantiles_matrix)) -# forecast_locations <- rep(locations, times = ncol(forecast_quantiles_matrix)) -# forecast_target_variables <- -# rep(target_variables, times = ncol(forecast_quantiles_matrix)) -# forecast_quantile_probs <- rep(forecast_quantile_probs, each = length(y)) -# forecast_quantiles <- forecast_quantiles_matrix -# dim(forecast_quantiles) <- prod(dim(forecast_quantiles)) -# -# test_truth <- data.frame( -# model = rep("truth_source", length(y)), -# target_variable = target_variables, -# target_end_date = target_end_dates, -# location = locations, -# value = y, -# stringsAsFactors = FALSE -# ) -# -# n_forecasts <- length(forecast_quantiles) -# test_forecasts <- data.frame( -# model = rep("m1", n_forecasts), -# forecast_date = rep(as.Date("2020-01-01"), n_forecasts), -# location = forecast_locations, -# horizon = forecast_horizons, -# temporal_resolution = rep("wk", n_forecasts), -# target_variable = forecast_target_variables, -# target_end_date = forecast_target_end_dates, -# type = rep("quantile", n_forecasts), -# quantile = forecast_quantile_probs, -# value = forecast_quantiles, -# stringsAsFactors = FALSE -# ) -# -# # make a version that conforms to scoringutils format -# truth_formatted <- data.table::as.data.table(test_truth) -# truth_formatted[, `:=`(model = NULL)] -# data.table::setnames(truth_formatted, old = "value", new = "true_value") -# -# forecasts_formated <- data.table::as.data.table(test_forecasts) -# data.table::setnames(forecasts_formated, old = "value", new = "prediction") -# -# data_formatted <- merge(forecasts_formated, truth_formatted) -# -# eval <- scoringutils::eval_forecasts(data_formatted, -# interval_score_arguments = list(count_median_twice = FALSE)) -# -# actual <- score_forecasts(forecasts = test_forecasts, truth = test_truth, -# use_median_as_point = TRUE) -# -# alpha1 <- 0.2 -# expected <- (forecast_quantiles_matrix[, 2] - forecast_quantiles_matrix[, 1]) * (alpha1 / 2) + -# c(0, (-2) - (-15), 22 - 4) -# -# if(!all(eval$interval_score == actual$wis)) { -# warning("eval_forecasts() and covidHubUtils don't match") -# } -# -# expect_equal(eval$interval_score, expected) -# }) -# -# -# test_that("wis is correct, 2 intervals and median - covidHubUtils check", { -# library(covidHubUtils) -# y <- c(1, -15, 22) -# forecast_quantiles_matrix <- rbind( -# c(-1, 0, 1, 2, 3), -# c(-2, 1, 2, 2, 4), -# c(-2, 0, 3, 3, 4)) -# forecast_quantile_probs <- c(0.1, 0.25, 0.5, 0.75, 0.9) -# -# target_end_dates <- as.Date("2020-01-01") + c(7, 14, 7) -# horizons <- c("1", "2", "1") -# locations <- c("01", "01", "02") -# target_variables <- rep("inc death", length(y)) -# -# forecast_target_end_dates <- -# rep(target_end_dates, times = ncol(forecast_quantiles_matrix)) -# forecast_horizons <- rep(horizons, times = ncol(forecast_quantiles_matrix)) -# forecast_locations <- rep(locations, times = ncol(forecast_quantiles_matrix)) -# forecast_target_variables <- -# rep(target_variables, times = ncol(forecast_quantiles_matrix)) -# forecast_quantile_probs <- rep(forecast_quantile_probs, each = length(y)) -# forecast_quantiles <- forecast_quantiles_matrix -# dim(forecast_quantiles) <- prod(dim(forecast_quantiles)) -# -# test_truth <- data.frame( -# model = rep("truth_source", length(y)), -# target_variable = target_variables, -# target_end_date = target_end_dates, -# location = locations, -# value = y, -# stringsAsFactors = FALSE -# ) -# -# n_forecasts <- length(forecast_quantiles) -# test_forecasts <- data.frame( -# model = rep("m1", n_forecasts), -# forecast_date = rep(as.Date("2020-01-01"), n_forecasts), -# location = forecast_locations, -# horizon = forecast_horizons, -# temporal_resolution = rep("wk", n_forecasts), -# target_variable = forecast_target_variables, -# target_end_date = forecast_target_end_dates, -# type = rep("quantile", n_forecasts), -# quantile = forecast_quantile_probs, -# value = forecast_quantiles, -# stringsAsFactors = FALSE -# ) -# -# # make a version that conforms to scoringutils format -# truth_formatted <- data.table::as.data.table(test_truth) -# truth_formatted[, `:=`(model = NULL)] -# data.table::setnames(truth_formatted, old = "value", new = "true_value") -# -# forecasts_formated <- data.table::as.data.table(test_forecasts) -# data.table::setnames(forecasts_formated, old = "value", new = "prediction") -# -# data_formatted <- merge(forecasts_formated, truth_formatted) -# -# eval <- scoringutils::eval_forecasts(data_formatted, -# interval_score_arguments = list(count_median_twice = FALSE)) -# -# actual <- score_forecasts(forecasts = test_forecasts, truth = test_truth, -# use_median_as_point = TRUE) -# -# alpha1 <- 0.2 -# alpha2 <- 0.5 -# expected <- (1 / 2.5) * ( -# 0.5 * abs(y - forecast_quantiles_matrix[, 3]) + -# (forecast_quantiles_matrix[, 5] - forecast_quantiles_matrix[, 1])*(alpha1/2) + c(0, (-2)-(-15), 22-4) + -# (forecast_quantiles_matrix[, 4] - forecast_quantiles_matrix[, 2])*(alpha2/2) + c(0, 1-(-15), 22-3) -# ) -# -# if(!all(eval$interval_score == actual$wis)) { -# warning("eval_forecasts() and covidHubUtils don't match") -# } -# -# expect_equal(eval$interval_score, expected) -# }) +# additional tests from the covidhubutils repo + +test_that("wis is correct, median only - test corresponds to covidHubUtils", { + y <- c(1, -15, 22) + forecast_quantiles_matrix <- rbind( + c(-1, 0, 1, 2, 3), + c(-2, 1, 2, 2, 4), + c(-2, 0, 3, 3, 4)) + forecast_quantile_probs <- c(0.1, 0.25, 0.5, 0.75, 0.9) + forecast_quantiles_matrix <- forecast_quantiles_matrix[, 3, drop = FALSE] + forecast_quantile_probs <- forecast_quantile_probs[3] + + target_end_dates <- as.Date("2020-01-01") + c(7, 14, 7) + horizons <- c("1", "2", "1") + locations <- c("01", "01", "02") + target_variables <- rep("inc death", length(y)) + + forecast_target_end_dates <- + rep(target_end_dates, times = ncol(forecast_quantiles_matrix)) + forecast_horizons <- rep(horizons, times = ncol(forecast_quantiles_matrix)) + forecast_locations <- rep(locations, times = ncol(forecast_quantiles_matrix)) + forecast_target_variables <- + rep(target_variables, times = ncol(forecast_quantiles_matrix)) + forecast_quantile_probs <- rep(forecast_quantile_probs, each = length(y)) + forecast_quantiles <- forecast_quantiles_matrix + dim(forecast_quantiles) <- prod(dim(forecast_quantiles)) + + test_truth <- data.frame( + model = rep("truth_source", length(y)), + target_variable = target_variables, + target_end_date = target_end_dates, + location = locations, + value = y, + stringsAsFactors = FALSE + ) + + n_forecasts <- length(forecast_quantiles) + test_forecasts <- data.frame( + model = rep("m1", n_forecasts), + forecast_date = rep(as.Date("2020-01-01"), n_forecasts), + location = forecast_locations, + horizon = forecast_horizons, + temporal_resolution = rep("wk", n_forecasts), + target_variable = forecast_target_variables, + target_end_date = forecast_target_end_dates, + type = rep("quantile", n_forecasts), + quantile = forecast_quantile_probs, + value = forecast_quantiles, + stringsAsFactors = FALSE + ) + + # make a version that conforms to scoringutils format + truth_formatted <- data.table::as.data.table(test_truth) + truth_formatted[, `:=`(model = NULL)] + data.table::setnames(truth_formatted, old = "value", new = "true_value") + + forecasts_formated <- data.table::as.data.table(test_forecasts) + data.table::setnames(forecasts_formated, old = "value", new = "prediction") + + data_formatted <- merge(forecasts_formated, truth_formatted) + + eval <- scoringutils::eval_forecasts(data_formatted, + count_median_twice = FALSE) + + expected <- abs(y - forecast_quantiles_matrix[, 1]) + + expect_equal(eval$interval_score, expected) +}) + + + + +test_that("wis is correct, 1 interval only - test corresponds to covidHubUtils", { + y <- c(1, -15, 22) + forecast_quantiles_matrix <- rbind( + c(-1, 0, 1, 2, 3), + c(-2, 1, 2, 2, 4), + c(-2, 0, 3, 3, 4)) + forecast_quantile_probs <- c(0.1, 0.25, 0.5, 0.75, 0.9) + forecast_quantiles_matrix <- forecast_quantiles_matrix[, c(1, 5), drop = FALSE] + forecast_quantile_probs <- forecast_quantile_probs[c(1, 5)] + + target_end_dates <- as.Date("2020-01-01") + c(7, 14, 7) + horizons <- c("1", "2", "1") + locations <- c("01", "01", "02") + target_variables <- rep("inc death", length(y)) + + forecast_target_end_dates <- + rep(target_end_dates, times = ncol(forecast_quantiles_matrix)) + forecast_horizons <- rep(horizons, times = ncol(forecast_quantiles_matrix)) + forecast_locations <- rep(locations, times = ncol(forecast_quantiles_matrix)) + forecast_target_variables <- + rep(target_variables, times = ncol(forecast_quantiles_matrix)) + forecast_quantile_probs <- rep(forecast_quantile_probs, each = length(y)) + forecast_quantiles <- forecast_quantiles_matrix + dim(forecast_quantiles) <- prod(dim(forecast_quantiles)) + + test_truth <- data.frame( + model = rep("truth_source", length(y)), + target_variable = target_variables, + target_end_date = target_end_dates, + location = locations, + value = y, + stringsAsFactors = FALSE + ) + + n_forecasts <- length(forecast_quantiles) + test_forecasts <- data.frame( + model = rep("m1", n_forecasts), + forecast_date = rep(as.Date("2020-01-01"), n_forecasts), + location = forecast_locations, + horizon = forecast_horizons, + temporal_resolution = rep("wk", n_forecasts), + target_variable = forecast_target_variables, + target_end_date = forecast_target_end_dates, + type = rep("quantile", n_forecasts), + quantile = forecast_quantile_probs, + value = forecast_quantiles, + stringsAsFactors = FALSE + ) + + # make a version that conforms to scoringutils format + truth_formatted <- data.table::as.data.table(test_truth) + truth_formatted[, `:=`(model = NULL)] + data.table::setnames(truth_formatted, old = "value", new = "true_value") + + forecasts_formated <- data.table::as.data.table(test_forecasts) + data.table::setnames(forecasts_formated, old = "value", new = "prediction") + + data_formatted <- merge(forecasts_formated, truth_formatted) + + eval <- scoringutils::eval_forecasts(data_formatted, + count_median_twice = FALSE) + + alpha1 <- 0.2 + expected <- (forecast_quantiles_matrix[, 2] - forecast_quantiles_matrix[, 1]) * (alpha1 / 2) + + c(0, (-2) - (-15), 22 - 4) + + expect_equal(eval$interval_score, expected) +}) + + +test_that("wis is correct, 2 intervals and median - test corresponds to covidHubUtils", { + y <- c(1, -15, 22) + forecast_quantiles_matrix <- rbind( + c(-1, 0, 1, 2, 3), + c(-2, 1, 2, 2, 4), + c(-2, 0, 3, 3, 4)) + forecast_quantile_probs <- c(0.1, 0.25, 0.5, 0.75, 0.9) + + target_end_dates <- as.Date("2020-01-01") + c(7, 14, 7) + horizons <- c("1", "2", "1") + locations <- c("01", "01", "02") + target_variables <- rep("inc death", length(y)) + + forecast_target_end_dates <- + rep(target_end_dates, times = ncol(forecast_quantiles_matrix)) + forecast_horizons <- rep(horizons, times = ncol(forecast_quantiles_matrix)) + forecast_locations <- rep(locations, times = ncol(forecast_quantiles_matrix)) + forecast_target_variables <- + rep(target_variables, times = ncol(forecast_quantiles_matrix)) + forecast_quantile_probs <- rep(forecast_quantile_probs, each = length(y)) + forecast_quantiles <- forecast_quantiles_matrix + dim(forecast_quantiles) <- prod(dim(forecast_quantiles)) + + test_truth <- data.frame( + model = rep("truth_source", length(y)), + target_variable = target_variables, + target_end_date = target_end_dates, + location = locations, + value = y, + stringsAsFactors = FALSE + ) + + n_forecasts <- length(forecast_quantiles) + test_forecasts <- data.frame( + model = rep("m1", n_forecasts), + forecast_date = rep(as.Date("2020-01-01"), n_forecasts), + location = forecast_locations, + horizon = forecast_horizons, + temporal_resolution = rep("wk", n_forecasts), + target_variable = forecast_target_variables, + target_end_date = forecast_target_end_dates, + type = rep("quantile", n_forecasts), + quantile = forecast_quantile_probs, + value = forecast_quantiles, + stringsAsFactors = FALSE + ) + + # make a version that conforms to scoringutils format + truth_formatted <- data.table::as.data.table(test_truth) + truth_formatted[, `:=`(model = NULL)] + data.table::setnames(truth_formatted, old = "value", new = "true_value") + + forecasts_formated <- data.table::as.data.table(test_forecasts) + data.table::setnames(forecasts_formated, old = "value", new = "prediction") + + data_formatted <- merge(forecasts_formated, truth_formatted) + + eval <- scoringutils::eval_forecasts(data_formatted, + count_median_twice = FALSE) + + alpha1 <- 0.2 + alpha2 <- 0.5 + expected <- (1 / 2.5) * ( + 0.5 * abs(y - forecast_quantiles_matrix[, 3]) + + (forecast_quantiles_matrix[, 5] - forecast_quantiles_matrix[, 1])*(alpha1/2) + c(0, (-2)-(-15), 22-4) + + (forecast_quantiles_matrix[, 4] - forecast_quantiles_matrix[, 2])*(alpha2/2) + c(0, 1-(-15), 22-3) + ) + + expect_equal(eval$interval_score, expected) +}) diff --git a/tests/testthat/test-pairwise_comparison.R b/tests/testthat/test-pairwise_comparison.R index 3f2432e49..8d89d3eb8 100644 --- a/tests/testthat/test-pairwise_comparison.R +++ b/tests/testthat/test-pairwise_comparison.R @@ -27,12 +27,12 @@ test_that("pairwise comparisons works", { eval_without_baseline <- eval_forecasts(data_formatted, by = c("location", "target_end_date", "model"), compute_relative_skill = TRUE, - interval_score_arguments = list(count_median_twice = FALSE)) + count_median_twice = FALSE) eval_with_baseline <- scoringutils::eval_forecasts(data_formatted, by = c("location", "target_end_date", "model"), baseline = "m1", compute_relative_skill = TRUE, - interval_score_arguments = list(count_median_twice = FALSE)) + count_median_twice = FALSE) # extract the relative_skill values relative_skills_without <- eval_without_baseline[, .(model = unique(model), @@ -149,7 +149,7 @@ test_that("pairwise comparisons works", { summarise_by = c("model", "location"), baseline = "m1", compute_relative_skill = TRUE, - interval_score_arguments = list(count_median_twice = FALSE)) + count_median_twice = FALSE) relative_skills_with <- eval_with_baseline[location == "location_3", .(model = unique(model), relative_skill = unique(scaled_rel_skill))] From b75c62aff340f53b1a9ff1e97b47217fbac8f4c5 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Wed, 24 Nov 2021 11:19:07 +0100 Subject: [PATCH 006/225] small simplification for the computation of the interval score in eval_forecasts --- R/eval_forecasts_quantile.R | 19 +++++++------------ 1 file changed, 7 insertions(+), 12 deletions(-) diff --git a/R/eval_forecasts_quantile.R b/R/eval_forecasts_quantile.R index 28a762036..a94613e05 100644 --- a/R/eval_forecasts_quantile.R +++ b/R/eval_forecasts_quantile.R @@ -41,20 +41,15 @@ eval_forecasts_quantile <- function(data, if ("interval_score" %in% metrics) { # compute separate results if desired if (separate_results) { - res <- res[, c("interval_score", - "sharpness", - "underprediction", - "overprediction") := do.call(scoringutils::interval_score, - list(true_value, lower, - upper,range, - weigh, separate_results))] + outcols <- c("interval_score", "sharpness", + "underprediction", "overprediction") } else { - res <- res[, c("interval_score") := do.call(scoringutils::interval_score, - list(true_value, lower, - upper,range, - weigh, separate_results))] + outcols <- "interval_score" } - # res[, .(unlist(interval_score)), by = setdiff(colnames(res), "interval_score")] + res <- res[, eval(outcols) := do.call(scoringutils::interval_score, + list(true_value, lower, + upper,range, + weigh, separate_results = TRUE))] } # compute coverage for every single observation From e420b9f0064489d5b132ee151bb80151c6216912 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Wed, 24 Nov 2021 11:22:01 +0100 Subject: [PATCH 007/225] small update to documentation --- R/eval_forecasts.R | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index df0c0bccb..f491a8203 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -82,7 +82,9 @@ #' But sometimes you may want to to summarise #' over categories different from the scoring. #' `summarise_by` is also the grouping level used to compute -#' (and possibly plot) the probability integral transform(pit). +#' (and possibly plot) the probability integral transform(pit). Sometimes you +#' may want to include 'range', 'quantile' or 'sample', to summarise by +#' range, quantile or sample. #' @param metrics the metrics you want to have in the output. If `NULL` (the #' default), all available metrics will be computed. #' @param quantiles numeric vector of quantiles to be returned when summarising. From 959c5fef4e1a8443e4f7c2b117df7998c464efa6 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Wed, 24 Nov 2021 11:36:43 +0100 Subject: [PATCH 008/225] get rid of the summarised argument in eval_forecasts --- R/eval_forecasts.R | 10 +---- R/eval_forecasts_binary.R | 29 +++++++------- R/eval_forecasts_continuous_integer.R | 34 ++++++++--------- R/eval_forecasts_quantile.R | 52 +++++++++++++------------- man/add_rel_skill_to_eval_forecasts.Rd | 4 +- man/eval_forecasts.Rd | 8 ++-- man/eval_forecasts_binary.Rd | 17 ++------- man/eval_forecasts_sample.Rd | 10 ++--- tests/testthat/test-eval_forecasts.R | 18 ++++----- 9 files changed, 77 insertions(+), 105 deletions(-) diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index f491a8203..746c2ee7b 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -95,8 +95,6 @@ #' metrics will be returned when summarising. #' @param pit_plots if `TRUE` (not the default), pit plots will be returned. For #' details see [pit()]. -#' @param summarised Summarise arguments (i.e. take the mean per group -#' specified in group_by. Default is `TRUE.` #' @param compute_relative_skill logical, whether or not to compute relative #' performance between models. If `TRUE` (default is `FALSE`), then a column called #' 'model' must be present in the input data. For more information on @@ -190,7 +188,6 @@ eval_forecasts <- function(data = NULL, quantiles = c(), sd = FALSE, pit_plots = FALSE, - summarised = TRUE, compute_relative_skill = FALSE, rel_skill_metric = "auto", baseline = NULL, @@ -285,8 +282,7 @@ eval_forecasts <- function(data = NULL, summarise_by = summarise_by, metrics = metrics, quantiles = quantiles, - sd = sd, - summarised = summarised) + sd = sd) return(res) } @@ -299,7 +295,6 @@ eval_forecasts <- function(data = NULL, quantiles = quantiles, sd = sd, pit_plots = pit_plots, - summarised = summarised, compute_relative_skill = compute_relative_skill, rel_skill_metric = rel_skill_metric, baseline = baseline, @@ -319,8 +314,7 @@ eval_forecasts <- function(data = NULL, prediction_type = prediction_type, quantiles = quantiles, sd = sd, - pit_plots = pit_plots, - summarised = summarised) + pit_plots = pit_plots) return(res) } } diff --git a/R/eval_forecasts_binary.R b/R/eval_forecasts_binary.R index 2a037b8c5..82e785963 100644 --- a/R/eval_forecasts_binary.R +++ b/R/eval_forecasts_binary.R @@ -20,29 +20,26 @@ eval_forecasts_binary <- function(data, summarise_by, metrics, quantiles, - sd, - summarised){ + sd){ res <- data[, "brier_score" := scoringutils::brier_score(true_value, prediction), by = by] - if (summarised) { - # add quantiles - if (!is.null(quantiles)) { - res <- add_quantiles(res, "brier_score", quantiles, by = summarise_by) - } + # add quantiles + if (!is.null(quantiles)) { + res <- add_quantiles(res, "brier_score", quantiles, by = summarise_by) + } - # add standard deviation - if (sd) { - res <- add_sd(res, "brier_score", by = c(summarise_by)) - } + # add standard deviation + if (sd) { + res <- add_sd(res, "brier_score", by = c(summarise_by)) + } - # summarise by taking the mean over all relevant columns - res <- res[, lapply(.SD, mean, na.rm = TRUE), - .SDcols = colnames(res) %like% "brier", - by = summarise_by] + # summarise by taking the mean over all relevant columns + res <- res[, lapply(.SD, mean, na.rm = TRUE), + .SDcols = colnames(res) %like% "brier", + by = summarise_by] - } return(res[]) } diff --git a/R/eval_forecasts_continuous_integer.R b/R/eval_forecasts_continuous_integer.R index 98f1b1ae3..60d1df28d 100644 --- a/R/eval_forecasts_continuous_integer.R +++ b/R/eval_forecasts_continuous_integer.R @@ -1,6 +1,5 @@ #' @title Evaluate forecasts in a Sample-Based Format (Integer or Continuous) #' -#' #' @inheritParams eval_forecasts #' @param prediction_type character, should be either "continuous" or "integer" #' @@ -41,8 +40,7 @@ eval_forecasts_sample <- function(data, prediction_type, quantiles, sd, - pit_plots, - summarised) { + pit_plots) { if (missing(prediction_type)) { if (all.equal(data$prediction, as.integer(data$prediction)) == TRUE) { @@ -137,25 +135,23 @@ eval_forecasts_sample <- function(data, by = c(by)] # summarise output if desired ------------------------------------------------ - if (summarised) { - # add quantiles - if (!is.null(quantiles)) { - quantile_vars <- c("crps", "dss", "log_score", "pit_p_val", "bias", "sharpness") - res <- add_quantiles(res, quantile_vars, quantiles, by = c(summarise_by)) - } - - if (sd) { - # add standard deviations - sd_vars <- c("crps", "dss", "log_score", "bias", "sharpness") - res <- add_sd(res, sd_vars, by = c(summarise_by)) - } + # add quantiles + if (!is.null(quantiles)) { + quantile_vars <- c("crps", "dss", "log_score", "pit_p_val", "bias", "sharpness") + res <- add_quantiles(res, quantile_vars, quantiles, by = c(summarise_by)) + } - # take mean - res <- res[, lapply(.SD, mean, na.rm = TRUE), - .SDcols = colnames(res) %like% "pit_|bias|sharpness|dss|crps|log_score", - by = summarise_by] + if (sd) { + # add standard deviations + sd_vars <- c("crps", "dss", "log_score", "bias", "sharpness") + res <- add_sd(res, sd_vars, by = c(summarise_by)) } + # take mean + res <- res[, lapply(.SD, mean, na.rm = TRUE), + .SDcols = colnames(res) %like% "pit_|bias|sharpness|dss|crps|log_score", + by = summarise_by] + # if pit_plots is TRUE, add the plots as an output --------------------------- if (pit_plots) { diff --git a/R/eval_forecasts_quantile.R b/R/eval_forecasts_quantile.R index a94613e05..25982988d 100644 --- a/R/eval_forecasts_quantile.R +++ b/R/eval_forecasts_quantile.R @@ -128,34 +128,32 @@ eval_forecasts_quantile <- function(data, res <- merge(res, relative_res, by = by) } - # summarise scores if desired ------------------------------------------------ - if (summarised) { - # add quantiles for the scores - if (!is.null(quantiles)) { - res <- add_quantiles(res, - c("interval_score", "coverage", - "overprediction", "underprediction", - "coverage_deviation", "bias", "sharpness", "aem", - "ae_point"), - quantiles, - by = c(summarise_by)) - } - # add standard deviation - if (sd) { - res <- add_sd(res, - varnames = c("interval_score", "bias", "coverage", - "overprediction", "underprediction", - "coverage_deviation", "sharpness", "aem", - "ae_point"), - by = c(summarise_by)) - } - - # summarise by taking the mean and omitting unnecessary columns - res <- res[, lapply(.SD, mean, na.rm = TRUE), - by = c(summarise_by), - .SDcols = colnames(res) %like% - "coverage|bias|sharpness|coverage_deviation|interval_score|overprediction|underprediction|aem|ae_point|relative_skill|scaled_rel_skill"] + # summarise scores ----------------------------------------------------------- + # add quantiles for the scores + if (!is.null(quantiles)) { + res <- add_quantiles(res, + c("interval_score", "coverage", + "overprediction", "underprediction", + "coverage_deviation", "bias", "sharpness", "aem", + "ae_point"), + quantiles, + by = c(summarise_by)) } + # add standard deviation + if (sd) { + res <- add_sd(res, + varnames = c("interval_score", "bias", "coverage", + "overprediction", "underprediction", + "coverage_deviation", "sharpness", "aem", + "ae_point"), + by = c(summarise_by)) + } + + # summarise by taking the mean and omitting unnecessary columns + res <- res[, lapply(.SD, mean, na.rm = TRUE), + by = c(summarise_by), + .SDcols = colnames(res) %like% + "coverage|bias|sharpness|coverage_deviation|interval_score|overprediction|underprediction|aem|ae_point|relative_skill|scaled_rel_skill"] # if neither quantile nor range are in summarise_by, remove coverage and quantile_coverage if (!("range" %in% summarise_by) & ("coverage" %in% colnames(res))) { diff --git a/man/add_rel_skill_to_eval_forecasts.Rd b/man/add_rel_skill_to_eval_forecasts.Rd index c6976841a..7f92f4c66 100644 --- a/man/add_rel_skill_to_eval_forecasts.Rd +++ b/man/add_rel_skill_to_eval_forecasts.Rd @@ -38,7 +38,9 @@ default, this is equal to \code{by} and no summary takes place. But sometimes you may want to to summarise over categories different from the scoring. \code{summarise_by} is also the grouping level used to compute -(and possibly plot) the probability integral transform(pit).} +(and possibly plot) the probability integral transform(pit). Sometimes you +may want to include 'range', 'quantile' or 'sample', to summarise by +range, quantile or sample.} } \description{ This function will only be called within \code{\link[=eval_forecasts]{eval_forecasts()}} and serves to diff --git a/man/eval_forecasts.Rd b/man/eval_forecasts.Rd index ab4659b34..fdc534ac7 100644 --- a/man/eval_forecasts.Rd +++ b/man/eval_forecasts.Rd @@ -12,7 +12,6 @@ eval_forecasts( quantiles = c(), sd = FALSE, pit_plots = FALSE, - summarised = TRUE, compute_relative_skill = FALSE, rel_skill_metric = "auto", baseline = NULL, @@ -76,7 +75,9 @@ default, this is equal to \code{by} and no summary takes place. But sometimes you may want to to summarise over categories different from the scoring. \code{summarise_by} is also the grouping level used to compute -(and possibly plot) the probability integral transform(pit).} +(and possibly plot) the probability integral transform(pit). Sometimes you +may want to include 'range', 'quantile' or 'sample', to summarise by +range, quantile or sample.} \item{metrics}{the metrics you want to have in the output. If \code{NULL} (the default), all available metrics will be computed.} @@ -92,9 +93,6 @@ metrics will be returned when summarising.} \item{pit_plots}{if \code{TRUE} (not the default), pit plots will be returned. For details see \code{\link[=pit]{pit()}}.} -\item{summarised}{Summarise arguments (i.e. take the mean per group -specified in group_by. Default is \code{TRUE.}} - \item{compute_relative_skill}{logical, whether or not to compute relative performance between models. If \code{TRUE} (default is \code{FALSE}), then a column called 'model' must be present in the input data. For more information on diff --git a/man/eval_forecasts_binary.Rd b/man/eval_forecasts_binary.Rd index 8f2b26db7..f6d1eb3ec 100644 --- a/man/eval_forecasts_binary.Rd +++ b/man/eval_forecasts_binary.Rd @@ -4,15 +4,7 @@ \alias{eval_forecasts_binary} \title{Evaluate forecasts in a Binary Format} \usage{ -eval_forecasts_binary( - data, - by, - summarise_by, - metrics, - quantiles, - sd, - summarised -) +eval_forecasts_binary(data, by, summarise_by, metrics, quantiles, sd) } \arguments{ \item{data}{A data.frame or data.table with the predictions and observations. @@ -71,7 +63,9 @@ default, this is equal to \code{by} and no summary takes place. But sometimes you may want to to summarise over categories different from the scoring. \code{summarise_by} is also the grouping level used to compute -(and possibly plot) the probability integral transform(pit).} +(and possibly plot) the probability integral transform(pit). Sometimes you +may want to include 'range', 'quantile' or 'sample', to summarise by +range, quantile or sample.} \item{metrics}{the metrics you want to have in the output. If \code{NULL} (the default), all available metrics will be computed.} @@ -83,9 +77,6 @@ returned.} \item{sd}{if \code{TRUE} (the default is \code{FALSE}) the standard deviation of all metrics will be returned when summarising.} - -\item{summarised}{Summarise arguments (i.e. take the mean per group -specified in group_by. Default is \code{TRUE.}} } \value{ A data.table with appropriate scores. For more information see diff --git a/man/eval_forecasts_sample.Rd b/man/eval_forecasts_sample.Rd index aa7f7100d..2555f4d08 100644 --- a/man/eval_forecasts_sample.Rd +++ b/man/eval_forecasts_sample.Rd @@ -12,8 +12,7 @@ eval_forecasts_sample( prediction_type, quantiles, sd, - pit_plots, - summarised + pit_plots ) } \arguments{ @@ -73,7 +72,9 @@ default, this is equal to \code{by} and no summary takes place. But sometimes you may want to to summarise over categories different from the scoring. \code{summarise_by} is also the grouping level used to compute -(and possibly plot) the probability integral transform(pit).} +(and possibly plot) the probability integral transform(pit). Sometimes you +may want to include 'range', 'quantile' or 'sample', to summarise by +range, quantile or sample.} \item{metrics}{the metrics you want to have in the output. If \code{NULL} (the default), all available metrics will be computed.} @@ -90,9 +91,6 @@ metrics will be returned when summarising.} \item{pit_plots}{if \code{TRUE} (not the default), pit plots will be returned. For details see \code{\link[=pit]{pit()}}.} - -\item{summarised}{Summarise arguments (i.e. take the mean per group -specified in group_by. Default is \code{TRUE.}} } \value{ A data.table with appropriate scores. For more information see diff --git a/tests/testthat/test-eval_forecasts.R b/tests/testthat/test-eval_forecasts.R index 540ca319b..118c90785 100644 --- a/tests/testthat/test-eval_forecasts.R +++ b/tests/testthat/test-eval_forecasts.R @@ -110,19 +110,17 @@ test_that("WIS is the same with other metrics omitted or included", { test_that("function produces output for a continuous format case", { example <- data.table::setDT(scoringutils::continuous_example_data) eval <- eval_forecasts(example[!is.na(prediction)], - summarised = TRUE, summarise_by = c("model"), quantiles = c(0.5), sd = TRUE) - # eval2 <- scoringutils::eval_forecasts(example, - # summarised = TRUE, - # summarise_by = c("model"), - # quantiles = c(0.5), sd = TRUE) - # - # setcolorder(eval2, colnames(eval)) - # eval <- eval[order(model)] - # eval2 <- eval2[order(model)] - # all(eval == eval2, na.rm = TRUE) + eval2 <- scoringutils::eval_forecasts(example, + summarise_by = c("model"), + quantiles = c(0.5), sd = TRUE) + + data.table::setcolorder(eval2, colnames(eval)) + eval <- eval[order(model)] + eval2 <- eval2[order(model)] + all(eval == eval2, na.rm = TRUE) expect_equal(nrow(eval) > 1, TRUE) From ca760b67d22e5a0c09e7444c872ff5213e2fe62f Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Wed, 24 Nov 2021 14:50:37 +0100 Subject: [PATCH 009/225] remove pit plots argument from eval_forecasts_quantile --- R/eval_forecasts.R | 1 - R/eval_forecasts_quantile.R | 1 - 2 files changed, 2 deletions(-) diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index 746c2ee7b..1fac53fec 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -294,7 +294,6 @@ eval_forecasts <- function(data = NULL, metrics = metrics, quantiles = quantiles, sd = sd, - pit_plots = pit_plots, compute_relative_skill = compute_relative_skill, rel_skill_metric = rel_skill_metric, baseline = baseline, diff --git a/R/eval_forecasts_quantile.R b/R/eval_forecasts_quantile.R index 25982988d..a536e17dd 100644 --- a/R/eval_forecasts_quantile.R +++ b/R/eval_forecasts_quantile.R @@ -4,7 +4,6 @@ eval_forecasts_quantile <- function(data, metrics, quantiles, sd, - pit_plots, weigh = TRUE, count_median_twice = FALSE, separate_results = TRUE, From 9160039c1a13f9a765b3a4320a4e6f642fd46d64 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Wed, 24 Nov 2021 16:33:48 +0100 Subject: [PATCH 010/225] change line to avoid merge conflict --- R/eval_forecasts.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index 1fac53fec..adee954a9 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -213,7 +213,7 @@ eval_forecasts <- function(data = NULL, warning("The baseline you provided for the relative skill is not one of the models in the data. Relative skill will not be computed") compute_relative_skill <- FALSE } - if (rel_skill_metric != "auto" && !(rel_skill_metric %in% list_of_avail_metrics())) { + if (rel_skill_metric != "auto" && !(rel_skill_metric %in% available_metrics())) { warning("argument 'rel_skill_metric' must either be 'auto' or one of the metrics that can be computed. Relative skill will not be computed") compute_relative_skill <- FALSE } From 7c17b7a3a1ba5c18133c789d3b93eac512b6a4ba Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Wed, 24 Nov 2021 22:55:00 +0100 Subject: [PATCH 011/225] restructure PIT functions --- NAMESPACE | 3 +- R/eval_forecasts_continuous_integer.R | 52 ---- R/pit.R | 375 +++++++------------------- R/utils.R | 1 + man/hist_PIT.Rd | 13 +- man/hist_PIT_quantile.Rd | 23 -- man/pit.Rd | 48 +--- man/pit_df.Rd | 44 +-- man/pit_df_fast.Rd | 40 --- tests/testthat/test-pit.R | 35 +-- 10 files changed, 146 insertions(+), 488 deletions(-) delete mode 100644 man/hist_PIT_quantile.Rd delete mode 100644 man/pit_df_fast.Rd diff --git a/NAMESPACE b/NAMESPACE index 3f5a5dd2e..c93500e51 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -11,6 +11,7 @@ export(correlation_plot) export(crps) export(dss) export(eval_forecasts) +export(hist_PIT) export(interval_coverage) export(interval_score) export(logs) @@ -19,7 +20,6 @@ export(mse) export(pairwise_comparison) export(pit) export(pit_df) -export(pit_df_fast) export(plot_pairwise_comparison) export(plot_predictions) export(quantile_bias) @@ -84,7 +84,6 @@ importFrom(ggplot2,theme_light) importFrom(ggplot2,unit) importFrom(ggplot2,xlab) importFrom(ggplot2,ylab) -importFrom(goftest,ad.test) importFrom(methods,hasArg) importFrom(scoringRules,crps_sample) importFrom(scoringRules,dss_sample) diff --git a/R/eval_forecasts_continuous_integer.R b/R/eval_forecasts_continuous_integer.R index 60d1df28d..f38c2bab4 100644 --- a/R/eval_forecasts_continuous_integer.R +++ b/R/eval_forecasts_continuous_integer.R @@ -82,51 +82,6 @@ eval_forecasts_sample <- function(data, if ("coverage" %in% metrics) { } - - # Compute PIT if specified --------------------------------------------------- - if (any(grepl("pit", metrics)) || pit_plots) { - - # check if by == summarise_by - in that case no pit values can be computed - if (identical(by, summarise_by)) { - data[, c("pit_p_val", "pit_sd") := NA] - message("In order to compute PIT values, 'summarise_by' must be different from 'by'") - } - - # if they are not identical, pit p-values can be computed - if (!identical(by, summarise_by)) { - # if plots are not desired, a quick way to do computation can be chosen - if (!pit_plots) { - data <- pit_df_fast(data, by = summarise_by) - } else { - # split data into chunks as determined by summarise_by, since we need to - # get one PIT per element of summarise_by - split_data <- split(data, by = summarise_by) - - # calculate pit for every element of the split data.frame - pits <- lapply(split_data, - FUN = pit_df, plot = pit_plots) - - # extract data frames with added p-values. Bind data together again - data_with_pit_values <- extract_from_list(pits, "data") - data <- data.table::rbindlist(data_with_pit_values) - - if (pit_plots) { - # extract pit histograms if plots are desired - pit_histograms <- extract_from_list(pits, "hist_PIT") - - # add another histogram for the entire data set - pit_histograms[["overall_pit"]] <- scoringutils::pit_df(data)$hist_PIT - } - - } - } - - # remove sd if not asked for - if (!sd) { - data[, "pit_sd" := NULL] - } - } - res <- data.table::copy(data) # make scores unique to avoid redundancy. @@ -152,12 +107,5 @@ eval_forecasts_sample <- function(data, .SDcols = colnames(res) %like% "pit_|bias|sharpness|dss|crps|log_score", by = summarise_by] - - # if pit_plots is TRUE, add the plots as an output --------------------------- - if (pit_plots) { - res <- list(scores = res, - pit_plots = pit_histograms) - } - return(res[]) } diff --git a/R/pit.R b/R/pit.R index 06717ef60..3b8c6e134 100644 --- a/R/pit.R +++ b/R/pit.R @@ -56,41 +56,26 @@ #' @param predictions nxN matrix of predictive samples, n (number of rows) being #' the number of data points and N (number of columns) the #' number of Monte Carlo samples -#' @param plot logical. If `TRUE`, a histogram of the PIT values will be -#' returned as well -#' @param num_bins the number of bins in the PIT histogram (if `plot == TRUE`) -#' If not given, the square root of n will be used -#' @param n_replicates the number of tests to perform, -#' each time re-randomising the PIT -#' @param full_output return all individual p_values and computed u_t values -#' for the randomised PIT. Usually not needed. -#' @return a list with the following components: -#' \itemize{ -#' \item `p_value`: p-value of the Anderson-Darling test on the -#' PIT values. In case of integer forecasts, this will be the mean p_value -#' from the `n_replicates` replicates -#' \item `sd`: standard deviation of the p_value returned. In case of -#' continuous forecasts, this will be NA as there is only one p_value returned. -#' \item `hist_PIT` a plot object with the PIT histogram. Only returned -#' if `plot = TRUE`. Call `plot(PIT(...)$hist_PIT)` to display the histogram. -#' \item `p_values`: all p_values generated from the Anderson-Darling tests -#' on the (randomised) PIT. Only returned if `full_output = TRUE` -#' \item `u`: the u_t values internally computed. Only returned if -#' `full_output = TRUE` -#' } -#' @importFrom goftest ad.test -#' @importFrom stats runif sd +#' @param n_replicates the number of draws for the ranomised PIT for +#' integer predictions. +#' @return A vector with PIT-values. For continuous forecasts, the vector will +#' correspond to the length of `true_values`. For integer forecasts, a +#' randomised PIT will be returned of length +#' `length(true_values) * n_replicates` +#' @importFrom stats runif #' @examples -#' +#' library(scoringutils) #' ## continuous predictions #' true_values <- rnorm(30, mean = 1:30) #' predictions <- replicate(200, rnorm(n = 30, mean = 1:30)) -#' pit(true_values, predictions) +#' pit <- pit(true_values, predictions) +#' hist_PIT(pit) #' #' ## integer predictions #' true_values <- rpois(100, lambda = 1:100) #' predictions <- replicate(5000, rpois(n = 100, lambda = 1:100)) -#' pit(true_values, predictions, n_replicates = 5) +#' pit <- pit(true_values, predictions, n_replicates = 50) +#' hist_PIT(pit) #' #' @export #' @references @@ -99,15 +84,9 @@ #' real-time epidemic forecasts: A case study of Ebola in the Western Area #' region of Sierra Leone, 2014-15, - pit <- function(true_values, predictions, - plot = TRUE, - full_output = FALSE, - n_replicates = 50, - num_bins = NULL) { - - + n_replicates = 100) { # error handling-------------------------------------------------------------- # check al arguments are provided @@ -120,13 +99,7 @@ pit <- function(true_values, n <- length(true_values) if (n == 1) { message("you need more than one observation to assess uniformity of the PIT") - out <- list(p_value = NA, - sd = NA) - if (full_output) { - out <- list(p_values = NA, - calibration = NA, - u = NA) - } + return(NA) } # check and handle format of predictions @@ -145,7 +118,6 @@ pit <- function(true_values, stop(msg) } - # check data type ------------------------------------------------------------ # check whether continuous or integer if (all.equal(as.vector(predictions), as.integer(predictions)) != TRUE) { @@ -154,8 +126,7 @@ pit <- function(true_values, continuous_predictions <- FALSE } - - # calculate PIT -------------------------------------------------------------- + # calculate PIT-values ------------------------------------------------------- n_pred <- ncol(predictions) # calculate emipirical cumulative distribution function as @@ -164,161 +135,92 @@ pit <- function(true_values, # calculate PIT for continuous predictions case if (continuous_predictions) { - p_value <- goftest::ad.test(P_x)$p.value - out <- list(p_value = p_value, - sd = NA) - if (plot) { - hist_PIT <- hist_PIT(P_x, num_bins = num_bins, caption = p_value) - out$hist_PIT = hist_PIT - } - if(full_output) { - out$u <- P_x - out$p_values <- p_value - } - } - - # calculate PIT for integer predictions case - if (!continuous_predictions) { - # empirical cdf for (y-1) for integer-valued predictions + pit_values <- P_x + } else { P_xm1 <- rowSums(predictions <= (true_values - 1)) / n_pred - # do n_replicates times for randomised PIT - u <- replicate(n_replicates, P_xm1 + stats::runif(n) * (P_x - P_xm1)) - # apply Anderson Darling test on u values - p_values <- apply( - u, - MARGIN = 2, - FUN = function (x) { - goftest::ad.test(x)$p.value - } - ) - out <- list(p_value = mean(p_values), - sd = stats::sd(p_values)) - # add additional output if desired - if (full_output) { - out$u <- u - out$p_values <- p_values - } - # make plot if desired - if (plot) { - hist_PIT <- hist_PIT(rowMeans(u), num_bins = num_bins, - caption = mean(p_values)) - out$hist_PIT = hist_PIT - } + pit_values <- as.vector(replicate(n_replicates, P_xm1 + runif(1) * (P_x - P_xm1))) } - - return(out) + return(pit_values) } - - -#' @title Probability Integral Transformation (data.frame Format) -#' -#' @description Wrapper around `pit()` for use in data.frames +#' @title PIT Histogram #' -#' @details -#' see [pit()] +#' @description +#' Make a simple histogram of the probability integral transformed values to +#' visually check whether a uniform distribution seems likely. #' -#' @param data a data.frame with the following columns: `true_value`, -#' `prediction`, `sample` -#' @inheritParams pit -#' @return a list with the following components: -#' \itemize{ -#' \item `data`: the input data.frame (not including rows where prediction is `NA`), -#' with added columns `pit_p_val` and `pit_sd` -#' \item `hist_PIT` a plot object with the PIT histogram. Only returned -#' if `plot == TRUE`. Call -#' `plot(PIT(...)$hist_PIT)` to display the histogram. -#' \item `p_values`: all p_values generated from the Anderson-Darling tests on the -#' (randomised) PIT. Only returned if `full_output = TRUE` -#' \item `u`: the u_t values internally computed. Only returned if -#' `full_output = TRUE` -#' } -#' @importFrom goftest ad.test -#' @importFrom stats runif sd +#' @param PIT_samples A vector with the PIT values of size n +#' @param num_bins the number of bins in the PIT histogram. +#' @return vector with the scoring values #' @examples -#' example <- scoringutils::continuous_example_data -#' result <- pit_df(example, full_output = TRUE) +#' library(scoringutils) +#' true_values <- rnorm(30, mean = 1:30) +#' predictions <- replicate(200, rnorm(n = 30, mean = 1:30)) +#' pit <- pit(true_values, predictions) +#' hist_PIT(pit) #' +#' @importFrom ggplot2 ggplot aes xlab ylab geom_histogram stat theme_light #' @export -#' @references -#' Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, -#' Rosalind M. Eggo, W. John Edmunds (2019) Assessing the performance of -#' real-time epidemic forecasts: A case study of Ebola in the Western Area -#' region of Sierra Leone, 2014-15, - -pit_df <- function(data, - plot = TRUE, - full_output = FALSE, - n_replicates = 100, - num_bins = NULL) { - data <- data.table::as.data.table(data) - # filter out instances where prediction is NA - data <- data[!is.na(prediction)] - - # reformat data.table to wide format for PIT - data_wide <- data.table::dcast(data, ... ~ paste("sampl_", sample, sep = ""), - value.var = "prediction") - - samples <- as.matrix(data_wide[, grepl("sampl_", colnames(data_wide)), - with = FALSE]) - # extract true values - true_values <- data_wide$true_value - - pit_arguments = list(true_values = true_values, - predictions = samples, - plot = plot, - full_output = full_output, - n_replicates = n_replicates, - num_bins = num_bins) - - # call pit with samples and true values - res <- do.call(pit, pit_arguments) +hist_PIT <- function(PIT_samples, + num_bins = NULL) { - # add results back to the data.frame - data[, `:=` (pit_p_val = res$p_value, - pit_sd = res$sd)] + single_PIT_hist <- function(PIT_samples, + num_bins) { + if (is.null(num_bins)) { + n <- length(PIT_samples) + num_bins = round(sqrt(n)) + } - out <- list(data = data, - hist_PIT = res$hist_PIT) + hist_PIT <- ggplot2::ggplot(data = data.frame(x = PIT_samples), + ggplot2::aes(x = x)) + + ggplot2::geom_histogram(ggplot2::aes(y = stat(count) / sum(count)), + breaks = seq(0, 1, length.out = num_bins + 1), + colour = "grey") + + ggplot2::xlab("PIT") + + ggplot2::ylab("Frequency") + + ggplot2::theme_light() - if (full_output) { - out$p_values <- res$p_values - out$u <- res$u + return(hist_PIT) } + if (is.list(PIT_samples) && !is.data.frame(PIT_samples)) { + out <- lapply(PIT_samples, single_PIT_hist, num_bins = num_bins) + } else { + out <- single_PIT_hist(PIT_samples, num_bins) + } return(out) } - - -#' @title Probability Integral Transformation (data.frame Format, fast version) +#' @title Probability Integral Transformation (data.frame Format) #' -#' @description Wrapper around `pit()` for fast use in data.frames. This version -#' of the pit does not do allow any plotting, but can iterate over categories -#' in a data.frame as specified in the `by` argument. +#' @description Wrapper around `pit()` for use in data.frames #' #' @details #' see [pit()] #' #' @param data a data.frame with the following columns: `true_value`, -#' `prediction`, `sample` +#' `prediction`, `sample`. +#' @param summarise_by Character vector with the columns according to which the +#' PIT values shall be grouped. If you e.g. have the columns 'model' and +#' 'location' in the data and want to have a PIT histogram for +#' every model and location, specify `summarise_by = c("model", "location")`. #' @inheritParams pit -#' @param by character vector with categories to iterate over -#' @return the input data.frame (not including rows where prediction is `NA`), -#' with added columns `pit_p_val` and `pit_sd` -#' @importFrom goftest ad.test -#' @importFrom stats runif sd +#' @param include_full Boolean (default is `FALSE`). Whether or not to also +#' return PIT values for the full data set without any grouping. +#' @return a named list with PIT values according to the grouping specified in +#' `summarised_by` #' @examples #' example <- scoringutils::continuous_example_data -#' result <- pit_df(example, full_output = TRUE) +#' result <- pit_df(example, summarise_by = "model") +#' hist_PIT(result) #' #' @export #' @references @@ -327,120 +229,49 @@ pit_df <- function(data, #' real-time epidemic forecasts: A case study of Ebola in the Western Area #' region of Sierra Leone, 2014-15, -pit_df_fast <- function(data, - n_replicates = 100, - by = by) { - - data <- data.table::as.data.table(data) - - # filter out instances where prediction is NA - data <- data[!is.na(prediction)] +pit_df <- function(data, + summarise_by, + n_replicates = 100, + include_full = FALSE) { - # define arguments for call to PIT function - pit_arguments = list(plot = FALSE, - full_output = FALSE, - n_replicates = n_replicates, - num_bins = 1) + data <- check_clean_data(data, verbose = FALSE) # reformat data.table to wide format for PIT - data_wide <- data.table::dcast(data, ... ~ paste("sampl_", sample, sep = ""), + data_wide <- data.table::dcast(data, + ... ~ paste("InternalSampl_", sample, sep = ""), value.var = "prediction") - # calculate PIT values - data_wide[, c("pit_p_val", "pit_sd") := do.call(pit, c(list(true_value, - as.matrix(.SD)), - pit_arguments)), - .SDcols = names(data_wide)[grepl("sampl_", names(data_wide))], by = by] - - # melt data back - sample_names <- names(data_wide)[grepl("sampl_", names(data_wide))] - data <- data.table::melt(data_wide, - measure.vars = sample_names, - variable.name = "sample", - value.name = "prediction") - - - return(data) -} - - - - - - - - - -#' @title PIT Histogram -#' -#' @description -#' Make a simple histogram of the probability integral transformed values to -#' visually check whether a uniform distribution seems likely. -#' -#' @param PIT_samples A vector with the PIT values of size n -#' @param num_bins the number of bins in the PIT histogram. -#' @param caption provide a caption that gets passed to the plot -#' If not given, the square root of n will be used -#' @return vector with the scoring values -#' @importFrom ggplot2 ggplot aes xlab ylab geom_histogram stat - - -hist_PIT <- function(PIT_samples, - num_bins = NULL, - caption = NULL) { - - if (is.null(num_bins)) { - n <- length(PIT_samples) - num_bins = round(sqrt(n)) - } - - hist_PIT <- ggplot2::ggplot(data = data.frame(x = PIT_samples), - ggplot2::aes(x = x)) + - ggplot2::geom_histogram(ggplot2::aes(y = stat(count) / sum(count)), - breaks = seq(0, 1, length.out = num_bins + 1), - colour = "grey") + - ggplot2::xlab("PIT") + - ggplot2::ylab("Frequency") + - ggplot2::labs(caption = paste0("p-value of Andersen-Darling test for uniformity: ", - round(caption, 3))) - - return(hist_PIT) -} - - - -#' @title PIT Histogram Quantile -#' -#' @description -#' Make a simple histogram of the probability integral transformed values to -#' visually check whether a uniform distribution seems likely. -#' -#' @param PIT_samples A vector with the PIT values of size n -#' @param num_bins the number of bins in the PIT histogram. -#' @param caption provide a caption that gets passed to the plot -#' If not given, the square root of n will be used -#' @return vector with the scoring values -#' @importFrom ggplot2 ggplot aes xlab ylab geom_histogram stat - - -hist_PIT_quantile <- function(PIT_samples, - num_bins = NULL, - caption = NULL) { - - if (is.null(num_bins)) { - n <- length(PIT_samples) - num_bins = round(sqrt(n)) + # implementation idea for a data.table version. Probably only makes sense + # in a non-randomised version of the integer PIT + # could have a return = c("Data.table", "list") argument to distinguish + # data_wide[, "pit_values" := list(pit(true_value, as.matrix(.SD), + # n_replicates = n_replicates)), + # .SDcols = names(data_wide)[grepl("InternalSampl_", names(data_wide))], + # by = summarise_by] + # sample_names <- names(data_wide)[grepl("InternalSampl_", names(data_wide))] + # data <- data.table::melt(data_wide, + # measure.vars = sample_names, + # variable.name = "sample", + # value.name = "prediction") + # data[, sample := as.integer(gsub(pattern = "InternalSampl_", replacement = "", + # x = sample))] + + # include the full data set once in addition to the subsets. + if (include_full) { + split_data <- list(data_wide) + } else { + split_data <- list() } - hist_PIT <- ggplot2::ggplot(data = data.frame(x = PIT_samples), - ggplot2::aes(x = x)) + - ggplot2::geom_histogram(ggplot2::aes(y = stat(count) / sum(count)), - breaks = seq(0, 1, length.out = num_bins + 1), - colour = "grey") + - ggplot2::xlab("PIT") + - ggplot2::ylab("Frequency") + - ggplot2::labs() + split_data <- c(split_data, split(data_wide, by = summarise_by)) - return(hist_PIT) + pit_values <- lapply(split_data, + FUN = function(data) { + true_values <- data$true_value + samplecols <- names(data)[grepl("InternalSampl_", names(data))] + predictions <- as.matrix(data[, ..samplecols]) + return(pit(true_values, predictions, n_replicates)) + }) + return(pit_values) } diff --git a/R/utils.R b/R/utils.R index f5292f88a..98821a335 100644 --- a/R/utils.R +++ b/R/utils.R @@ -87,6 +87,7 @@ globalVariables(c("..index", "component_value", "..colnames_x", "..colnames_y", + "..samplecols", "compare_against", "count", "coverage_deviation", diff --git a/man/hist_PIT.Rd b/man/hist_PIT.Rd index d1ffaf938..36083f892 100644 --- a/man/hist_PIT.Rd +++ b/man/hist_PIT.Rd @@ -4,15 +4,12 @@ \alias{hist_PIT} \title{PIT Histogram} \usage{ -hist_PIT(PIT_samples, num_bins = NULL, caption = NULL) +hist_PIT(PIT_samples, num_bins = NULL) } \arguments{ \item{PIT_samples}{A vector with the PIT values of size n} \item{num_bins}{the number of bins in the PIT histogram.} - -\item{caption}{provide a caption that gets passed to the plot -If not given, the square root of n will be used} } \value{ vector with the scoring values @@ -21,3 +18,11 @@ vector with the scoring values Make a simple histogram of the probability integral transformed values to visually check whether a uniform distribution seems likely. } +\examples{ +library(scoringutils) +true_values <- rnorm(30, mean = 1:30) +predictions <- replicate(200, rnorm(n = 30, mean = 1:30)) +pit <- pit(true_values, predictions) +hist_PIT(pit) + +} diff --git a/man/hist_PIT_quantile.Rd b/man/hist_PIT_quantile.Rd deleted file mode 100644 index ccb5ec2d0..000000000 --- a/man/hist_PIT_quantile.Rd +++ /dev/null @@ -1,23 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/pit.R -\name{hist_PIT_quantile} -\alias{hist_PIT_quantile} -\title{PIT Histogram Quantile} -\usage{ -hist_PIT_quantile(PIT_samples, num_bins = NULL, caption = NULL) -} -\arguments{ -\item{PIT_samples}{A vector with the PIT values of size n} - -\item{num_bins}{the number of bins in the PIT histogram.} - -\item{caption}{provide a caption that gets passed to the plot -If not given, the square root of n will be used} -} -\value{ -vector with the scoring values -} -\description{ -Make a simple histogram of the probability integral transformed values to -visually check whether a uniform distribution seems likely. -} diff --git a/man/pit.Rd b/man/pit.Rd index d34d9a92e..be9e5c785 100644 --- a/man/pit.Rd +++ b/man/pit.Rd @@ -4,14 +4,7 @@ \alias{pit} \title{Probability Integral Transformation} \usage{ -pit( - true_values, - predictions, - plot = TRUE, - full_output = FALSE, - n_replicates = 50, - num_bins = NULL -) +pit(true_values, predictions, n_replicates = 100) } \arguments{ \item{true_values}{A vector with the true observed values of size n} @@ -20,33 +13,14 @@ pit( the number of data points and N (number of columns) the number of Monte Carlo samples} -\item{plot}{logical. If \code{TRUE}, a histogram of the PIT values will be -returned as well} - -\item{full_output}{return all individual p_values and computed u_t values -for the randomised PIT. Usually not needed.} - -\item{n_replicates}{the number of tests to perform, -each time re-randomising the PIT} - -\item{num_bins}{the number of bins in the PIT histogram (if \code{plot == TRUE}) -If not given, the square root of n will be used} +\item{n_replicates}{the number of draws for the ranomised PIT for +integer predictions.} } \value{ -a list with the following components: -\itemize{ -\item \code{p_value}: p-value of the Anderson-Darling test on the -PIT values. In case of integer forecasts, this will be the mean p_value -from the \code{n_replicates} replicates -\item \code{sd}: standard deviation of the p_value returned. In case of -continuous forecasts, this will be NA as there is only one p_value returned. -\item \code{hist_PIT} a plot object with the PIT histogram. Only returned -if \code{plot = TRUE}. Call \code{plot(PIT(...)$hist_PIT)} to display the histogram. -\item \code{p_values}: all p_values generated from the Anderson-Darling tests -on the (randomised) PIT. Only returned if \code{full_output = TRUE} -\item \code{u}: the u_t values internally computed. Only returned if -\code{full_output = TRUE} -} +A vector with PIT-values. For continuous forecasts, the vector will +correspond to the length of \code{true_values}. For integer forecasts, a +randomised PIT will be returned of length +\code{length(true_values) * n_replicates} } \description{ Uses a Probability Integral Transformation (PIT) (or a @@ -102,16 +76,18 @@ In this context it should be noted, though, that uniformity of the PIT is a necessary but not sufficient condition of calibration. } \examples{ - +library(scoringutils) ## continuous predictions true_values <- rnorm(30, mean = 1:30) predictions <- replicate(200, rnorm(n = 30, mean = 1:30)) -pit(true_values, predictions) +pit <- pit(true_values, predictions) +hist_PIT(pit) ## integer predictions true_values <- rpois(100, lambda = 1:100) predictions <- replicate(5000, rpois(n = 100, lambda = 1:100)) -pit(true_values, predictions, n_replicates = 5) +pit <- pit(true_values, predictions, n_replicates = 50) +hist_PIT(pit) } \references{ diff --git a/man/pit_df.Rd b/man/pit_df.Rd index 44fb185c3..e512e25a6 100644 --- a/man/pit_df.Rd +++ b/man/pit_df.Rd @@ -4,43 +4,26 @@ \alias{pit_df} \title{Probability Integral Transformation (data.frame Format)} \usage{ -pit_df( - data, - plot = TRUE, - full_output = FALSE, - n_replicates = 100, - num_bins = NULL -) +pit_df(data, summarise_by, n_replicates = 100, include_full = FALSE) } \arguments{ \item{data}{a data.frame with the following columns: \code{true_value}, -\code{prediction}, \code{sample}} +\code{prediction}, \code{sample}.} -\item{plot}{logical. If \code{TRUE}, a histogram of the PIT values will be -returned as well} +\item{summarise_by}{Character vector with the columns according to which the +PIT values shall be grouped. If you e.g. have the columns 'model' and +'location' in the data and want to have a PIT histogram for +every model and location, specify \code{summarise_by = c("model", "location")}.} -\item{full_output}{return all individual p_values and computed u_t values -for the randomised PIT. Usually not needed.} +\item{n_replicates}{the number of draws for the ranomised PIT for +integer predictions.} -\item{n_replicates}{the number of tests to perform, -each time re-randomising the PIT} - -\item{num_bins}{the number of bins in the PIT histogram (if \code{plot == TRUE}) -If not given, the square root of n will be used} +\item{include_full}{Boolean (default is \code{FALSE}). Whether or not to also +return PIT values for the full data set without any grouping.} } \value{ -a list with the following components: -\itemize{ -\item \code{data}: the input data.frame (not including rows where prediction is \code{NA}), -with added columns \code{pit_p_val} and \code{pit_sd} -\item \code{hist_PIT} a plot object with the PIT histogram. Only returned -if \code{plot == TRUE}. Call -\code{plot(PIT(...)$hist_PIT)} to display the histogram. -\item \code{p_values}: all p_values generated from the Anderson-Darling tests on the -(randomised) PIT. Only returned if \code{full_output = TRUE} -\item \code{u}: the u_t values internally computed. Only returned if -\code{full_output = TRUE} -} +a named list with PIT values according to the grouping specified in +\code{summarised_by} } \description{ Wrapper around \code{pit()} for use in data.frames @@ -50,7 +33,8 @@ see \code{\link[=pit]{pit()}} } \examples{ example <- scoringutils::continuous_example_data -result <- pit_df(example, full_output = TRUE) +result <- pit_df(example, summarise_by = "model") +hist_PIT(result) } \references{ diff --git a/man/pit_df_fast.Rd b/man/pit_df_fast.Rd deleted file mode 100644 index 9a167c4ac..000000000 --- a/man/pit_df_fast.Rd +++ /dev/null @@ -1,40 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/pit.R -\name{pit_df_fast} -\alias{pit_df_fast} -\title{Probability Integral Transformation (data.frame Format, fast version)} -\usage{ -pit_df_fast(data, n_replicates = 100, by = by) -} -\arguments{ -\item{data}{a data.frame with the following columns: \code{true_value}, -\code{prediction}, \code{sample}} - -\item{n_replicates}{the number of tests to perform, -each time re-randomising the PIT} - -\item{by}{character vector with categories to iterate over} -} -\value{ -the input data.frame (not including rows where prediction is \code{NA}), -with added columns \code{pit_p_val} and \code{pit_sd} -} -\description{ -Wrapper around \code{pit()} for fast use in data.frames. This version -of the pit does not do allow any plotting, but can iterate over categories -in a data.frame as specified in the \code{by} argument. -} -\details{ -see \code{\link[=pit]{pit()}} -} -\examples{ -example <- scoringutils::continuous_example_data -result <- pit_df(example, full_output = TRUE) - -} -\references{ -Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, -Rosalind M. Eggo, W. John Edmunds (2019) Assessing the performance of -real-time epidemic forecasts: A case study of Ebola in the Western Area -region of Sierra Leone, 2014-15, \url{doi:10.1371/journal.pcbi.1006785} -} diff --git a/tests/testthat/test-pit.R b/tests/testthat/test-pit.R index f79d9b902..fa194374d 100644 --- a/tests/testthat/test-pit.R +++ b/tests/testthat/test-pit.R @@ -22,13 +22,10 @@ test_that("function works for integer true_values and predictions", true_values <- rpois(10, lambda = 1:10) predictions <- replicate(10, rpois(10, lambda = 1:10)) output <- pit(true_values = true_values, - predictions = predictions) + predictions = predictions, + n_replicates = 56) expect_equal(length(output), - 3) - expect_equal(class(output), - "list") - expect_equal(class(output[[1]]), - "numeric") + 560) }) test_that("function works for continuous true_values and predictions", @@ -36,34 +33,14 @@ test_that("function works for continuous true_values and predictions", true_values <- rnorm(10) predictions <- replicate(10, rnorm(10)) output <- pit(true_values = true_values, - predictions = predictions) + predictions = predictions, + n_replicates = 56) expect_equal(length(output), - 3) - expect_equal(class(output), - "list") - expect_equal(class(output[[1]]), - "numeric") + 10) }) -# compare results scoringutils2 with scoringutils - -test_that("function works for continuous true_values and predictions", - { - ## continuous predictions - true_values <- rnorm(30, mean = 1:30) - predictions <- replicate(200, rnorm(n = 30, mean = 1:30)) - scoringutils2 <- scoringutils::pit(true_values, predictions) - scoringutils <- scoringutils::pit(true_values, predictions) - - expect_equal(scoringutils2$p_value, scoringutils$p_value) - }) - - - - - From f22fd6565c74b89bf825e50e8d191cc563aa778a Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Thu, 25 Nov 2021 15:21:35 +0100 Subject: [PATCH 012/225] increment package version --- DESCRIPTION | 2 +- NEWS.md | 7 +++++++ 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 1ac107c00..f9f78f771 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: scoringutils Title: Utilities for Scoring and Assessing Predictions -Version: 0.1.8 +Version: 0.2.0 Language: en-GB Authors@R: c( person(given = "Nikos", diff --git a/NEWS.md b/NEWS.md index e3d090c50..7e6c38207 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,10 @@ +# scoringutils 0.2.0 + +## Breaking changes +- The 'sharpness' component of the weighted interval score was renamed to +dispersion. This was done to make it more clear what the component represents +and to maintain consistency with what is used in other places. + # scoringutils 0.1.8 ## Feature updates From beeb652dd3f76ad6a03daae071b743cfb5344bdd Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Thu, 25 Nov 2021 15:39:43 +0100 Subject: [PATCH 013/225] restyle check_forecasts.R file --- R/check_forecasts.R | 148 ++++++++++++++++++++++++++------------------ 1 file changed, 89 insertions(+), 59 deletions(-) diff --git a/R/check_forecasts.R b/R/check_forecasts.R index 683c0478b..d64f30731 100644 --- a/R/check_forecasts.R +++ b/R/check_forecasts.R @@ -11,7 +11,7 @@ #' #' @seealso Functions to move between different formats: #' [range_long_to_quantile()], [range_wide_to_long()] -#' @param data A data.frame or similiar as would be used as input to [eval_forecasts()] +#' @param data A data.frame or similar as would be used for [eval_forecasts()] #' #' @return A list with elements that give information about what `scoringutils` #' thinks you are trying to do and potential issues. @@ -52,7 +52,6 @@ #' @export check_forecasts <- function(data) { - check <- list() msg <- list() warnings <- list() @@ -60,32 +59,34 @@ check_forecasts <- function(data) { # check data looks ok and remove columns with no prediction or no true value data <- tryCatch(check_clean_data(data), - warning = function(w) { - warnings <<- c(warnings, w$message) - # not ideal to repeat, but not sure whether this could be solved differently - return(suppressWarnings(check_clean_data(data))) - }, - error = function(e) { - errors <<- c(errors, e$message) - return(data) - } + warning = function(w) { + warnings <<- c(warnings, w$message) + # not ideal to repeat, but not sure whether this could be solved differently + return(suppressWarnings(check_clean_data(data))) + }, + error = function(e) { + errors <<- c(errors, e$message) + return(data) + } ) if (length(errors) > 0 | !is.data.table(data)) { - stop(paste0("Can't check input. The following error has been produced:\n", - paste(errors, collapse = "\n"))) + stop(paste0( + "Can't check input. The following error has been produced:\n", + paste(errors, collapse = "\n") + )) } # obtain truth type if (isTRUE(all.equal(data$true_value, as.integer(data$true_value)))) { - if (all(data$true_value %in% c(0,1)) && - all(data$prediction >= 0) && all(data$prediction <= 1)) { - check[["target_type"]] = "binary" + if (all(data$true_value %in% c(0, 1)) && + all(data$prediction >= 0) && all(data$prediction <= 1)) { + check[["target_type"]] <- "binary" } else { - check[["target_type"]] = "integer" + check[["target_type"]] <- "integer" } } else { - check[["target_type"]] = "continuous" + check[["target_type"]] <- "continuous" } # obtain prediction type @@ -100,29 +101,42 @@ check_forecasts <- function(data) { check[["prediction_type"]] <- "continuous" } - msg <- c(msg, - paste0("Forecasts are for a `", check[["target_type"]], "` target ", - "using a `", check[["prediction_type"]], "` prediction format.")) + msg <- c( + msg, + paste0( + "Forecasts are for a `", check[["target_type"]], "` target ", + "using a `", check[["prediction_type"]], "` prediction format." + ) + ) # obtain unit of a single forecast - protected_columns <- c("prediction", "true_value", "sample", "quantile", - "range", "boundary") + protected_columns <- c( + "prediction", "true_value", "sample", "quantile", + "range", "boundary" + ) obs_unit <- setdiff(colnames(data), protected_columns) check[["forecast_unit"]] <- obs_unit - msg <- c(msg, - paste0("The unit of a single forecast is defined by `", - paste(check[["forecast_unit"]], collapse = "`, `"), "`. ", - "If this is not as intended, please DELETE UNNECESSARY columns or add new ones.")) + msg <- c( + msg, + paste0( + "The unit of a single forecast is defined by `", + paste(check[["forecast_unit"]], collapse = "`, `"), "`. ", + "If this is not as intended, please DELETE UNNECESSARY columns or add new ones." + ) + ) # check what format is has right now and tell user to convert it. if (!any(c("quantile", "sample") %in% colnames(data))) { if ("range" %in% colnames(data) | any(grepl("lower_", colnames(data)))) { - errors <- c(errors, - "It seems like you have a format based on forecast intervals (see `example_data_long`, `example_data_semi_wide`, `example_data_wide`). You need to convert this to a quantile-based format first using `range_wide_to_long()` and `range_long_to_quantile()`") - + errors <- c( + errors, + "It seems like you have a format based on forecast intervals (see `example_data_long`, `example_data_semi_wide`, `example_data_wide`). You need to convert this to a quantile-based format first using `range_wide_to_long()` and `range_long_to_quantile()`" + ) } else if (!check[["target_type"]] == "binary") { - errors <- c(errors, - "This forecast does not seem to be for a binary prediction target, so we need a column called quantile or sample") + errors <- c( + errors, + "This forecast does not seem to be for a binary prediction target, so we need a column called quantile or sample" + ) } } @@ -133,31 +147,42 @@ check_forecasts <- function(data) { data[, InternalDuplicateCheck := .N, by = c(obs_unit, type)] if (any(data$duplicatecheck) > 1) { - errors <- c(errors, - paste("There are instances with more than one forecast for the same target.", - "This can't be right and needs to be resolved. Maybe you need to check", - "the unit of a single forecast and add missing columns?")) + errors <- c( + errors, + paste( + "There are instances with more than one forecast for the same target.", + "This can't be right and needs to be resolved. Maybe you need to check", + "the unit of a single forecast and add missing columns?" + ) + ) check[["duplicate_forecasts"]] <- data[InternalDuplicateCheck > 1] } data[, InternalDuplicateCheck := NULL] # check whether there is a model column present. And if not, state what that means if (!("model" %in% colnames(data))) { - msg <- c(msg, - paste("There is no column called `model` in the data.", - "scoringutils therefore thinks that all forecasts come from the same model")) + msg <- c( + msg, + paste( + "There is no column called `model` in the data.", + "scoringutils therefore thinks that all forecasts come from the same model" + ) + ) data[, model := "Unspecified model"] - } # some checks whether there are the same number of quantiles, samples data[, InternalNumCheck := length(prediction), by = obs_unit] n <- unique(data$InternalNumCheck) if (length(n) > 1) { - warnings <- c(warnings, - paste0("Some forecasts have different numbers of rows (e.g. quantiles or samples). ", - "scoringutils found: ", paste(n, collapse = ", "), - ". This is not necessarily a problem, but make sure this is intended.")) + warnings <- c( + warnings, + paste0( + "Some forecasts have different numbers of rows (e.g. quantiles or samples). ", + "scoringutils found: ", paste(n, collapse = ", "), + ". This is not necessarily a problem, but make sure this is intended." + ) + ) } check[["rows_per_forecast"]] <- data[, .(rows_per_forecast = unique(InternalNumCheck)), by = model] @@ -192,26 +217,33 @@ check_forecasts <- function(data) { #' @export print.scoringutils_check <- function(x, ...) { - print_elements <- names(x)[!(names(x) %in% c("messages"))] print.default(x[print_elements]) - cat(paste0("\nBased on your input, scoringutils thinks:\n", - paste(x$messages, collapse = "\n"))) + cat(paste0( + "\nBased on your input, scoringutils thinks:\n", + paste(x$messages, collapse = "\n") + )) cat("\n$rows_per_forecast shows how many rows (usually quantiles or samples are available per forecast.") - cat("\n$unique_values shows how many unique values there are per column per model", - "(across the entire data).") + cat( + "\n$unique_values shows how many unique values there are per column per model", + "(across the entire data)." + ) if (length(x$warnings) > 0) { - cat(paste0("\n\n", - "You should be aware of the following warnings:\n", - paste(x$warnings, collapse = "\n"))) + cat(paste0( + "\n\n", + "You should be aware of the following warnings:\n", + paste(x$warnings, collapse = "\n") + )) } if (length(x$errors) > 0) { - cat(paste0("\n\n", - "The following things will likely result in an error:", - paste(x$errors, collapse = "\n"))) + cat(paste0( + "\n\n", + "The following things will likely result in an error:", + paste(x$errors, collapse = "\n") + )) } return(NULL) } @@ -236,11 +268,10 @@ print.scoringutils_check <- function(x, ...) { #' #' @importFrom data.table as.data.table #' -#'@keywords internal +#' @keywords internal check_clean_data <- function(data, verbose = TRUE) { - - if(!is.data.frame(data)) { + if (!is.data.frame(data)) { stop("Input should be a data.frame or similar") } data <- as.data.table(data) @@ -269,4 +300,3 @@ check_clean_data <- function(data, verbose = TRUE) { } return(data) } - From 8092f1b49db882ce69547422c915907152ba3205 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Thu, 25 Nov 2021 16:26:32 +0100 Subject: [PATCH 014/225] resolve merge conflicts --- R/check_forecasts.R | 102 ++++++++++++++++---------- R/eval_forecasts_quantile.R | 14 ++-- R/plot.R | 40 +++++----- man/check_clean_data.Rd | 2 +- man/check_forecasts.Rd | 2 +- man/get_prediction_type.Rd | 21 ++++++ man/get_target_type.Rd | 21 ++++++ man/range_plot.Rd | 20 ++--- man/wis_components.Rd | 2 +- tests/testthat/test-check_forecasts.R | 17 ++++- 10 files changed, 161 insertions(+), 80 deletions(-) create mode 100644 man/get_prediction_type.Rd create mode 100644 man/get_target_type.Rd diff --git a/R/check_forecasts.R b/R/check_forecasts.R index d64f30731..f0f5d2c57 100644 --- a/R/check_forecasts.R +++ b/R/check_forecasts.R @@ -58,48 +58,31 @@ check_forecasts <- function(data) { errors <- list() # check data looks ok and remove columns with no prediction or no true value - data <- tryCatch(check_clean_data(data), + + data <- withCallingHandlers( + tryCatch( + check_clean_data(data), + error = function(e) { + errors <<- c(errors, e$message) + } + ), warning = function(w) { warnings <<- c(warnings, w$message) - # not ideal to repeat, but not sure whether this could be solved differently - return(suppressWarnings(check_clean_data(data))) - }, - error = function(e) { - errors <<- c(errors, e$message) - return(data) + tryInvokeRestart("muffleWarning") } ) if (length(errors) > 0 | !is.data.table(data)) { - stop(paste0( + stop( "Can't check input. The following error has been produced:\n", paste(errors, collapse = "\n") - )) + ) } # obtain truth type - if (isTRUE(all.equal(data$true_value, as.integer(data$true_value)))) { - if (all(data$true_value %in% c(0, 1)) && - all(data$prediction >= 0) && all(data$prediction <= 1)) { - check[["target_type"]] <- "binary" - } else { - check[["target_type"]] <- "integer" - } - } else { - check[["target_type"]] <- "continuous" - } - # obtain prediction type - # if (any(grepl("lower_", names(data))) | "boundary" %in% names(data) | - # "quantile" %in% names(data) | "range" %in% names(data)) { - - if ("quantile" %in% names(data)) { - check[["prediction_type"]] <- "quantile" - } else if (all.equal(data$prediction, as.integer(data$prediction)) == TRUE) { - check[["prediction_type"]] <- "integer" - } else { - check[["prediction_type"]] <- "continuous" - } + check[["target_type"]] <- get_target_type(data) + check[["prediction_type"]] <- get_prediction_type(data) msg <- c( msg, @@ -146,7 +129,7 @@ check_forecasts <- function(data) { type <- c("sample", "quantile")[c("sample", "quantile") %in% colnames(data)] data[, InternalDuplicateCheck := .N, by = c(obs_unit, type)] - if (any(data$duplicatecheck) > 1) { + if (any(data$InternalDuplicateCheck > 1)) { errors <- c( errors, paste( @@ -189,15 +172,15 @@ check_forecasts <- function(data) { data[, InternalNumCheck := NULL] # get available unique values per model for the different columns - cols <- obs_unit[!(obs_unit == "model")] + cols <- obs_unit[obs_unit != "model"] check[["unique_values"]] <- - data[, lapply(.SD, FUN = function(x) length(unique(x))), by = "model"] + data[, vapply(.SD, FUN = function(x) length(unique(x)), integer(1)), by = "model"] check[["messages"]] <- unlist(msg) check[["warnings"]] <- unlist(warnings) check[["errors"]] <- unlist(errors) - attr(check, "class") <- "scoringutils_check" + class(check) <- c("scoringutils_check", "list") return(check) } @@ -213,7 +196,6 @@ check_forecasts <- function(data) { #' @param ... additional arguments (not used here) #' #' @return NULL -#' @method print scoringutils_check #' @export print.scoringutils_check <- function(x, ...) { @@ -245,15 +227,60 @@ print.scoringutils_check <- function(x, ...) { paste(x$errors, collapse = "\n") )) } - return(NULL) + return(invisible(x)) } +#' @title Get prediction type of a forecast +#' +#' @description Internal helper function to get the prediction type of a +#' forecast. That is inferred based on the properties of the values in the +#' `prediction` column. +#' +#' @inheritParams check_forecasts +#' +#' @return Character vector of length one with either "quantile", "integer", or +#' "continuous". +#' +#' @keywords internal +get_prediction_type <- function(data) { + if ("quantile" %in% names(data)) { + return("quantile") + } else if (all.equal(data$prediction, as.integer(data$prediction)) == TRUE) { + return("integer") + } else { + return("continuous") + } +} +#' @title Get type of the target true values of a forecast +#' +#' @description Internal helper function to get the type of the target +#' true values of a forecast. That is inferred based on the which columns +#' are present in the data. +#' +#' @inheritParams check_forecasts +#' +#' @return Character vector of length one with either "binary", "integer", or +#' "continous" +#' +#' @keywords internal +get_target_type <- function(data) { + if (isTRUE(all.equal(data$true_value, as.integer(data$true_value)))) { + if (all(data$true_value %in% c(0, 1)) && + all(data$prediction >= 0) && all(data$prediction <= 1)) { + return("binary") + } else { + return("integer") + } + } else { + return("continuous") + } +} #' @title Clean forecast data @@ -262,7 +289,7 @@ print.scoringutils_check <- function(x, ...) { #' or similar and remove rows with no value for `prediction` or `true_value` #' #' @param data A data.frame or similar as it gets passed to [eval_forecasts()]. -#' @param verbose Boolean, whether or not to print warnings +#' @param verbose Boolean (default is `TRUE`), whether or not to print warnings #' #' @return A data.table with NA values in `true_value` or `prediction` removed. #' @@ -293,6 +320,7 @@ check_clean_data <- function(data, verbose = TRUE) { if (verbose) { warning("Some values for `prediction` are NA in the data provided") } + warning("Some values for `prediction` are NA in the data provided") } data <- data[!is.na(prediction)] if (nrow(data) == 0) { diff --git a/R/eval_forecasts_quantile.R b/R/eval_forecasts_quantile.R index a536e17dd..1aa4cf4d1 100644 --- a/R/eval_forecasts_quantile.R +++ b/R/eval_forecasts_quantile.R @@ -40,15 +40,15 @@ eval_forecasts_quantile <- function(data, if ("interval_score" %in% metrics) { # compute separate results if desired if (separate_results) { - outcols <- c("interval_score", "sharpness", - "underprediction", "overprediction") + outcols <- c("interval_score", "dispersion", + "underprediction", "overprediction") } else { outcols <- "interval_score" } res <- res[, eval(outcols) := do.call(scoringutils::interval_score, - list(true_value, lower, - upper,range, - weigh, separate_results = TRUE))] + list(true_value, lower, + upper,range, + weigh, separate_results = TRUE))] } # compute coverage for every single observation @@ -133,7 +133,7 @@ eval_forecasts_quantile <- function(data, res <- add_quantiles(res, c("interval_score", "coverage", "overprediction", "underprediction", - "coverage_deviation", "bias", "sharpness", "aem", + "coverage_deviation", "bias", "dispersion", "aem", "ae_point"), quantiles, by = c(summarise_by)) @@ -143,7 +143,7 @@ eval_forecasts_quantile <- function(data, res <- add_sd(res, varnames = c("interval_score", "bias", "coverage", "overprediction", "underprediction", - "coverage_deviation", "sharpness", "aem", + "coverage_deviation", "dispersion", "aem", "ae_point"), by = c(summarise_by)) } diff --git a/R/plot.R b/R/plot.R index 339d9bde0..efa39c41f 100644 --- a/R/plot.R +++ b/R/plot.R @@ -265,7 +265,7 @@ correlation_plot <- function(scores, #' #' @description #' Visualise the components of the weighted interval score: penalties for -#' over-prediction, under-prediction and for a lack of sharpness +#' over-prediction, under-prediction and for high dispersion (lack of sharpness) #' #' @param scores A data.frame of scores based on quantile forecasts as #' produced by [eval_forecasts()] @@ -323,7 +323,7 @@ wis_components <- function(scores, scores <- data.table::melt(scores, measure.vars = c("overprediction", "underprediction", - "sharpness"), + "dispersion"), variable.name = "wis_component_name", value.name = "component_value") @@ -363,14 +363,14 @@ wis_components <- function(scores, #' #' @description #' Visualise the metrics by range, e.g. if you are interested how different -#' interval ranges contribute to the overall interval score, or how sharpness -#' changes by range. +#' interval ranges contribute to the overall interval score, or how +#' sharpness / dispersion changes by range. #' #' @param scores A data.frame of scores based on quantile forecasts as #' produced by [eval_forecasts()]. Note that "range" must be included #' in the `summarise_by` argument when running `eval_forecasts` #' @param y The variable from the scores you want to show on the y-Axis. -#' This could be something like "interval_score" (the default) or "sharpness" +#' This could be something like "interval_score" (the default) or "dispersion" #' @param x The variable from the scores you want to show on the x-Axis. #' Usually this will be "model" #' @param colour Character vector of length one used to determine a variable @@ -399,16 +399,16 @@ wis_components <- function(scores, #' #' scoringutils::range_plot(scores, x = "model", facet_formula = ~ value_desc) #' -#' # visualise sharpness instead of interval score -#' scoringutils::range_plot(scores, y = "sharpness", x = "model", +#' # visualise dispersion instead of interval score +#' scoringutils::range_plot(scores, y = "dispersion", x = "model", #' facet_formula = ~value_desc) #' -#' # we saw above that sharpness values crossed. Let's look at the unweighted WIS +#' # we saw above that dispersion values crossed. Let's look at the unweighted WIS #' scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, -#' weigh = FALSE, -#' summarise_by = c("model", "value_desc", "range")) -#' scoringutils::range_plot(scores, y = "sharpness", x = "model", -#' facet_formula = ~value_desc) +#' interval_score_arguments = list(weigh = FALSE), +#' summarise_by = c("model", "value_desc", "range")) +#' scoringutils::range_plot(scores, y = "dispersion", x = "model", +#' facet_formula = ~value_desc) range_plot <- function(scores, @@ -423,9 +423,9 @@ range_plot <- function(scores, ylab = y) { plot <- ggplot2::ggplot(scores, - ggplot2::aes_string(x = x, - y = y, - colour = colour)) + + ggplot2::aes_string(x = x, + y = y, + colour = colour)) + ggplot2::geom_point(size = 2) + ggplot2::geom_line(ggplot2::aes(group = range), colour = "black", @@ -514,9 +514,9 @@ score_heatmap <- function(scores, scores[, eval(metric) := round(get(metric), 2)] plot <- ggplot2::ggplot(scores, - ggplot2::aes_string(y = y, - x = x, - fill = metric)) + + ggplot2::aes_string(y = y, + x = x, + fill = metric)) + ggplot2::geom_tile() + ggplot2::geom_text(ggplot2::aes_string(label = metric)) + ggplot2::scale_fill_gradient2(low = "skyblue", high = "red") + @@ -744,7 +744,7 @@ plot_predictions <- function(data = NULL, ggplot2::geom_line(data = median, mapping = ggplot2::aes(y = prediction, colour = "median"), lwd = 0.4) - } + } } # add true_values @@ -1059,5 +1059,3 @@ show_avail_forecasts <- function(data, return(plot) } - - diff --git a/man/check_clean_data.Rd b/man/check_clean_data.Rd index 0e6d4c375..5cfe79af5 100644 --- a/man/check_clean_data.Rd +++ b/man/check_clean_data.Rd @@ -9,7 +9,7 @@ check_clean_data(data, verbose = TRUE) \arguments{ \item{data}{A data.frame or similar as it gets passed to \code{\link[=eval_forecasts]{eval_forecasts()}}.} -\item{verbose}{Boolean, whether or not to print warnings} +\item{verbose}{Boolean (default is \code{TRUE}), whether or not to print warnings} } \value{ A data.table with NA values in \code{true_value} or \code{prediction} removed. diff --git a/man/check_forecasts.Rd b/man/check_forecasts.Rd index ba39d8c43..34edae005 100644 --- a/man/check_forecasts.Rd +++ b/man/check_forecasts.Rd @@ -7,7 +7,7 @@ check_forecasts(data) } \arguments{ -\item{data}{A data.frame or similiar as would be used as input to \code{\link[=eval_forecasts]{eval_forecasts()}}} +\item{data}{A data.frame or similar as would be used for \code{\link[=eval_forecasts]{eval_forecasts()}}} } \value{ A list with elements that give information about what \code{scoringutils} diff --git a/man/get_prediction_type.Rd b/man/get_prediction_type.Rd new file mode 100644 index 000000000..8b861830a --- /dev/null +++ b/man/get_prediction_type.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/check_forecasts.R +\name{get_prediction_type} +\alias{get_prediction_type} +\title{Get prediction type of a forecast} +\usage{ +get_prediction_type(data) +} +\arguments{ +\item{data}{A data.frame or similar as would be used for \code{\link[=eval_forecasts]{eval_forecasts()}}} +} +\value{ +Character vector of length one with either "quantile", "integer", or +"continuous". +} +\description{ +Internal helper function to get the prediction type of a +forecast. That is inferred based on the properties of the values in the +\code{prediction} column. +} +\keyword{internal} diff --git a/man/get_target_type.Rd b/man/get_target_type.Rd new file mode 100644 index 000000000..ebd2f7452 --- /dev/null +++ b/man/get_target_type.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/check_forecasts.R +\name{get_target_type} +\alias{get_target_type} +\title{Get type of the target true values of a forecast} +\usage{ +get_target_type(data) +} +\arguments{ +\item{data}{A data.frame or similar as would be used for \code{\link[=eval_forecasts]{eval_forecasts()}}} +} +\value{ +Character vector of length one with either "binary", "integer", or +"continous" +} +\description{ +Internal helper function to get the type of the target +true values of a forecast. That is inferred based on the which columns +are present in the data. +} +\keyword{internal} diff --git a/man/range_plot.Rd b/man/range_plot.Rd index 7cc5ad89c..76d892435 100644 --- a/man/range_plot.Rd +++ b/man/range_plot.Rd @@ -23,7 +23,7 @@ produced by \code{\link[=eval_forecasts]{eval_forecasts()}}. Note that "range" m in the \code{summarise_by} argument when running \code{eval_forecasts}} \item{y}{The variable from the scores you want to show on the y-Axis. -This could be something like "interval_score" (the default) or "sharpness"} +This could be something like "interval_score" (the default) or "dispersion"} \item{x}{The variable from the scores you want to show on the x-Axis. Usually this will be "model"} @@ -55,8 +55,8 @@ the weighted interval score } \description{ Visualise the metrics by range, e.g. if you are interested how different -interval ranges contribute to the overall interval score, or how sharpness -changes by range. +interval ranges contribute to the overall interval score, or how +sharpness / dispersion changes by range. } \examples{ scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, @@ -64,14 +64,14 @@ scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, scoringutils::range_plot(scores, x = "model", facet_formula = ~ value_desc) -# visualise sharpness instead of interval score -scoringutils::range_plot(scores, y = "sharpness", x = "model", +# visualise dispersion instead of interval score +scoringutils::range_plot(scores, y = "dispersion", x = "model", facet_formula = ~value_desc) -# we saw above that sharpness values crossed. Let's look at the unweighted WIS +# we saw above that dispersion values crossed. Let's look at the unweighted WIS scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, - weigh = FALSE, - summarise_by = c("model", "value_desc", "range")) -scoringutils::range_plot(scores, y = "sharpness", x = "model", - facet_formula = ~value_desc) + interval_score_arguments = list(weigh = FALSE), + summarise_by = c("model", "value_desc", "range")) +scoringutils::range_plot(scores, y = "dispersion", x = "model", + facet_formula = ~value_desc) } diff --git a/man/wis_components.Rd b/man/wis_components.Rd index 0ed11f7c6..622ad9a1f 100644 --- a/man/wis_components.Rd +++ b/man/wis_components.Rd @@ -57,7 +57,7 @@ the weighted interval score } \description{ Visualise the components of the weighted interval score: penalties for -over-prediction, under-prediction and for a lack of sharpness +over-prediction, under-prediction and for high dispersion (lack of sharpness) } \examples{ scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, diff --git a/tests/testthat/test-check_forecasts.R b/tests/testthat/test-check_forecasts.R index 19b5c314d..465cca420 100644 --- a/tests/testthat/test-check_forecasts.R +++ b/tests/testthat/test-check_forecasts.R @@ -1,9 +1,22 @@ test_that("check_forecasts() function works", { check <- check_forecasts(quantile_example_data) - expect_equal(class(check), "scoringutils_check") + expect_s3_class(check, "scoringutils_check") }) - test_that("check_forecasts() function has an error for empty data.frame", { expect_error(check_forecasts(data.frame())) }) + +test_that("check_forecasts() function returns a warning with NA in the data", { + check <- check_forecasts(quantile_example_data) + expect_equal(unlist(check$warnings), + "Some values for `prediction` are NA in the data provided") +}) + +test_that("check_forecasts() function returns warnings with NA in the data", { + example <- data.table::copy(quantile_example_data) + example[horizon == 7, true_value := NA] + check <- check_forecasts(example) + + expect_equal(length(check$warnings), 2) +}) From 0df22629ec352bd545ba5230d3a2893f4d6922f0 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Thu, 25 Nov 2021 16:46:33 +0100 Subject: [PATCH 015/225] correct a few minor typos --- R/check_forecasts.R | 1 - R/eval_forecasts_quantile.R | 2 +- R/plot.R | 1 - man/range_plot.Rd | 1 - 4 files changed, 1 insertion(+), 4 deletions(-) diff --git a/R/check_forecasts.R b/R/check_forecasts.R index f0f5d2c57..71c7e2b38 100644 --- a/R/check_forecasts.R +++ b/R/check_forecasts.R @@ -320,7 +320,6 @@ check_clean_data <- function(data, verbose = TRUE) { if (verbose) { warning("Some values for `prediction` are NA in the data provided") } - warning("Some values for `prediction` are NA in the data provided") } data <- data[!is.na(prediction)] if (nrow(data) == 0) { diff --git a/R/eval_forecasts_quantile.R b/R/eval_forecasts_quantile.R index 1aa4cf4d1..2cf39b699 100644 --- a/R/eval_forecasts_quantile.R +++ b/R/eval_forecasts_quantile.R @@ -152,7 +152,7 @@ eval_forecasts_quantile <- function(data, res <- res[, lapply(.SD, mean, na.rm = TRUE), by = c(summarise_by), .SDcols = colnames(res) %like% - "coverage|bias|sharpness|coverage_deviation|interval_score|overprediction|underprediction|aem|ae_point|relative_skill|scaled_rel_skill"] + "coverage|bias|dispersion|coverage_deviation|interval_score|overprediction|underprediction|aem|ae_point|relative_skill|scaled_rel_skill"] # if neither quantile nor range are in summarise_by, remove coverage and quantile_coverage if (!("range" %in% summarise_by) & ("coverage" %in% colnames(res))) { diff --git a/R/plot.R b/R/plot.R index 6fd348d95..59c1a6314 100644 --- a/R/plot.R +++ b/R/plot.R @@ -405,7 +405,6 @@ wis_components <- function(scores, #' #' # we saw above that dispersion values crossed. Let's look at the unweighted WIS #' scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, -#' interval_score_arguments = list(weigh = FALSE), #' summarise_by = c("model", "value_desc", "range")) #' scoringutils::range_plot(scores, y = "dispersion", x = "model", #' facet_formula = ~value_desc) diff --git a/man/range_plot.Rd b/man/range_plot.Rd index 76d892435..1442588e5 100644 --- a/man/range_plot.Rd +++ b/man/range_plot.Rd @@ -70,7 +70,6 @@ scoringutils::range_plot(scores, y = "dispersion", x = "model", # we saw above that dispersion values crossed. Let's look at the unweighted WIS scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, - interval_score_arguments = list(weigh = FALSE), summarise_by = c("model", "value_desc", "range")) scoringutils::range_plot(scores, y = "dispersion", x = "model", facet_formula = ~value_desc) From e2bff333305f0575fb805bfc7111dc4b70b6fa8a Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Thu, 25 Nov 2021 17:02:42 +0100 Subject: [PATCH 016/225] Update NEWS file --- NEWS.md | 20 +++++++++++++++++++- 1 file changed, 19 insertions(+), 1 deletion(-) diff --git a/NEWS.md b/NEWS.md index 7e6c38207..ba7b95cba 100644 --- a/NEWS.md +++ b/NEWS.md @@ -4,7 +4,25 @@ - The 'sharpness' component of the weighted interval score was renamed to dispersion. This was done to make it more clear what the component represents and to maintain consistency with what is used in other places. - +- Several changes have been introduced to [eval_forecasts()] and other functions: + - the verbose argument has to be removed from almost all functions + - the `by` argument has been removed from [eval_forecasts()]. Instead, all + functions now expect you to remove any additional columns beforehand to + avoid confusion. + - PIT plots have been removed from [eval_forecasts()]. Instead the function + [pit_df()] can now be used on a `data.frame` and its output can then be + passed to [hist_PIT()] + - the `interval_score_arguments` were now replaced by `...` in [eval_forecasts()] + - the argument `summarised = TRUE` was dropped. If you want no summarising + at all, please let `summarise_by` be equal to all avaialable column names + plus 'sample' or 'quantile' and 'range' +- the function [pit()] now returns PIT values rather than p-values from an +Anderson-Darling test for uniformity of the PIT values. The AD test is +sometimes not reliable in practice and we do not recommend its use in most cases. +The function also does not return plots anymore. Instead, plots can be generated +by running [hist_PIT()]. + + # scoringutils 0.1.8 ## Feature updates From aac1fa4289bea55f5c8d5d8b0aac1d6840437df8 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Thu, 25 Nov 2021 17:07:57 +0100 Subject: [PATCH 017/225] remove pit_plots argument from eval_forecasts --- R/eval_forecasts.R | 9 ++------- R/eval_forecasts_continuous_integer.R | 6 ++---- 2 files changed, 4 insertions(+), 11 deletions(-) diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index 2850cbbf4..5c8f01116 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -94,8 +94,6 @@ #' returned. #' @param sd if `TRUE` (the default is `FALSE`) the standard deviation of all #' metrics will be returned when summarising. -#' @param pit_plots if `TRUE` (not the default), pit plots will be returned. For -#' details see [pit()]. #' @param compute_relative_skill logical, whether or not to compute relative #' performance between models. If `TRUE` (default is `FALSE`), then a column called #' 'model' must be present in the input data. For more information on @@ -163,8 +161,7 @@ #' eval <- scoringutils::eval_forecasts(integer_example, #' summarise_by = c("model"), #' quantiles = c(0.1, 0.9), -#' sd = TRUE, -#' pit_plots = TRUE) +#' sd = TRUE) #' eval <- scoringutils::eval_forecasts(integer_example) #' #' ## Continuous Forecasts @@ -188,7 +185,6 @@ eval_forecasts <- function(data = NULL, metrics = NULL, quantiles = c(), sd = FALSE, - pit_plots = FALSE, compute_relative_skill = FALSE, rel_skill_metric = "auto", baseline = NULL, @@ -313,8 +309,7 @@ eval_forecasts <- function(data = NULL, metrics = metrics, prediction_type = prediction_type, quantiles = quantiles, - sd = sd, - pit_plots = pit_plots) + sd = sd) return(res) } } diff --git a/R/eval_forecasts_continuous_integer.R b/R/eval_forecasts_continuous_integer.R index a340dd688..b083494a7 100644 --- a/R/eval_forecasts_continuous_integer.R +++ b/R/eval_forecasts_continuous_integer.R @@ -16,8 +16,7 @@ #' eval <- scoringutils::eval_forecasts(integer_example, #' summarise_by = c("model"), #' quantiles = c(0.1, 0.9), -#' sd = TRUE, -#' pit_plots = TRUE) +#' sd = TRUE) #' eval <- scoringutils::eval_forecasts(integer_example) #' #' ## Continuous Forecasts @@ -39,8 +38,7 @@ eval_forecasts_sample <- function(data, metrics, prediction_type, quantiles, - sd, - pit_plots) { + sd) { if (missing(prediction_type)) { if (isTRUE(all.equal(data$prediction, as.integer(data$prediction)))) { From 68ff7b4bf00bdabf760bd029d526a3f33580b0bc Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Thu, 25 Nov 2021 17:15:00 +0100 Subject: [PATCH 018/225] change default from data = NULL to just data in eval_forecasts --- R/eval_forecasts.R | 2 +- R/eval_forecasts_quantile.R | 1 - man/eval_forecasts.Rd | 9 ++------- man/eval_forecasts_sample.Rd | 9 ++------- 4 files changed, 5 insertions(+), 16 deletions(-) diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index 5c8f01116..06562df9d 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -179,7 +179,7 @@ #' PLoS Comput Biol 15(2): e1006785. #' @export -eval_forecasts <- function(data = NULL, +eval_forecasts <- function(data, by = NULL, summarise_by = by, metrics = NULL, diff --git a/R/eval_forecasts_quantile.R b/R/eval_forecasts_quantile.R index 2cf39b699..936bbd689 100644 --- a/R/eval_forecasts_quantile.R +++ b/R/eval_forecasts_quantile.R @@ -73,7 +73,6 @@ eval_forecasts_quantile <- function(data, } } - # calculate scores on quantile format ---------------------------------------- # compute absolute error of the median if ("aem" %in% metrics) { diff --git a/man/eval_forecasts.Rd b/man/eval_forecasts.Rd index a32f95d2e..e49c9ddcc 100644 --- a/man/eval_forecasts.Rd +++ b/man/eval_forecasts.Rd @@ -5,13 +5,12 @@ \title{Evaluate forecasts} \usage{ eval_forecasts( - data = NULL, + data, by = NULL, summarise_by = by, metrics = NULL, quantiles = c(), sd = FALSE, - pit_plots = FALSE, compute_relative_skill = FALSE, rel_skill_metric = "auto", baseline = NULL, @@ -91,9 +90,6 @@ returned.} \item{sd}{if \code{TRUE} (the default is \code{FALSE}) the standard deviation of all metrics will be returned when summarising.} -\item{pit_plots}{if \code{TRUE} (not the default), pit plots will be returned. For -details see \code{\link[=pit]{pit()}}.} - \item{compute_relative_skill}{logical, whether or not to compute relative performance between models. If \code{TRUE} (default is \code{FALSE}), then a column called 'model' must be present in the input data. For more information on @@ -196,8 +192,7 @@ integer_example <- data.table::setDT(scoringutils::integer_example_data) eval <- scoringutils::eval_forecasts(integer_example, summarise_by = c("model"), quantiles = c(0.1, 0.9), - sd = TRUE, - pit_plots = TRUE) + sd = TRUE) eval <- scoringutils::eval_forecasts(integer_example) ## Continuous Forecasts diff --git a/man/eval_forecasts_sample.Rd b/man/eval_forecasts_sample.Rd index 834acd84b..f144b2ada 100644 --- a/man/eval_forecasts_sample.Rd +++ b/man/eval_forecasts_sample.Rd @@ -11,8 +11,7 @@ eval_forecasts_sample( metrics, prediction_type, quantiles, - sd, - pit_plots + sd ) } \arguments{ @@ -89,9 +88,6 @@ returned.} \item{sd}{if \code{TRUE} (the default is \code{FALSE}) the standard deviation of all metrics will be returned when summarising.} - -\item{pit_plots}{if \code{TRUE} (not the default), pit plots will be returned. For -details see \code{\link[=pit]{pit()}}.} } \value{ A data.table with appropriate scores. For more information see @@ -107,8 +103,7 @@ integer_example <- data.table::setDT(scoringutils::integer_example_data) eval <- scoringutils::eval_forecasts(integer_example, summarise_by = c("model"), quantiles = c(0.1, 0.9), - sd = TRUE, - pit_plots = TRUE) + sd = TRUE) eval <- scoringutils::eval_forecasts(integer_example) ## Continuous Forecasts From 3a8f6e4c0517ee37becf3dc105c3b871f5d0b307 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Thu, 25 Nov 2021 21:55:42 +0100 Subject: [PATCH 019/225] create helper function to get unit of a single forecast --- R/check_forecasts.R | 34 +++++++++++++++++++++++++--------- 1 file changed, 25 insertions(+), 9 deletions(-) diff --git a/R/check_forecasts.R b/R/check_forecasts.R index 71c7e2b38..3b9a85a10 100644 --- a/R/check_forecasts.R +++ b/R/check_forecasts.R @@ -58,7 +58,6 @@ check_forecasts <- function(data) { errors <- list() # check data looks ok and remove columns with no prediction or no true value - data <- withCallingHandlers( tryCatch( check_clean_data(data), @@ -71,7 +70,6 @@ check_forecasts <- function(data) { tryInvokeRestart("muffleWarning") } ) - if (length(errors) > 0 | !is.data.table(data)) { stop( "Can't check input. The following error has been produced:\n", @@ -79,8 +77,6 @@ check_forecasts <- function(data) { ) } - # obtain truth type - check[["target_type"]] <- get_target_type(data) check[["prediction_type"]] <- get_prediction_type(data) @@ -93,12 +89,9 @@ check_forecasts <- function(data) { ) # obtain unit of a single forecast - protected_columns <- c( - "prediction", "true_value", "sample", "quantile", - "range", "boundary" - ) - obs_unit <- setdiff(colnames(data), protected_columns) + obs_unit <- get_unit_of_forecast(data) check[["forecast_unit"]] <- obs_unit + msg <- c( msg, paste0( @@ -327,3 +320,26 @@ check_clean_data <- function(data, verbose = TRUE) { } return(data) } + + +#' @title Get unit of a single forecast +#' +#' @description Helper function to get the unit of a single forecast, i.e. +#' the column names that define where a single forecast was made for +#' +#' @inheritParams check_forecast +#' +#' @return A character vector with the column names that define the unit of +#' a single forecast +#' +#' @keywords internal + +get_unit_of_forecast <- function(data) { + protected_columns <- c( + "prediction", "true_value", "sample", "quantile", + "range", "boundary" + ) + obs_unit <- setdiff(colnames(data), protected_columns) + return(obs_unit) +} + From 4f2602211ba9fd3afc1b33af149999f141605bcb Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Thu, 25 Nov 2021 21:59:18 +0100 Subject: [PATCH 020/225] get rid of by argument --- R/eval_forecasts.R | 34 ++++++++--------------- R/eval_forecasts_binary.R | 8 ++++-- R/eval_forecasts_continuous_integer.R | 18 ++++++------ R/eval_forecasts_quantile.R | 12 ++++---- R/pairwise-comparisons.R | 7 ----- man/add_rel_skill_to_eval_forecasts.Rd | 8 ------ man/eval_forecasts.Rd | 11 +------- man/eval_forecasts_binary.Rd | 19 +++++++------ man/eval_forecasts_sample.Rd | 12 +++----- man/get_unit_of_forecast.Rd | 17 ++++++++++++ tests/testthat/test-pairwise_comparison.R | 2 -- 11 files changed, 66 insertions(+), 82 deletions(-) create mode 100644 man/get_unit_of_forecast.Rd diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index 06562df9d..55e562c63 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -70,13 +70,6 @@ #' - pairs of columns called something like 'upper_90' and 'lower_90', #' or 'upper_50' and 'lower_50', where the number denotes the interval range. #' For the median, you need to provide columns called 'upper_0' and 'lower_0' -#' @param by character vector of columns to group scoring by. This should be the -#' lowest level of grouping possible, i.e. the unit of the individual -#' observation. This is important as many functions work on individual -#' observations. If you want a different level of aggregation, you should use -#' `summarise_by` to aggregate the individual scores. -#' Also not that the pit will be computed using `summarise_by` -#' instead of `by` #' @param summarise_by character vector of columns to group the summary by. By #' default, this is equal to `by` and no summary takes place. #' But sometimes you may want to to summarise @@ -180,8 +173,7 @@ #' @export eval_forecasts <- function(data, - by = NULL, - summarise_by = by, + summarise_by = NULL, metrics = NULL, quantiles = c(), sd = FALSE, @@ -216,22 +208,18 @@ eval_forecasts <- function(data, } } - # obtain a value for by if nothing was provided by the user - if (is.null(by)) { - protected_columns <- c("prediction", "true_value", "sample", "quantile", - "range", "boundary") - by <- setdiff(colnames(data), protected_columns) + # obtain a value for the unit of a single observation + forecast_unit <- get_unit_of_forecast(data) - if (is.null(summarise_by)) { - summarise_by <- by - } + if (is.null(summarise_by)) { + summarise_by <- forecast_unit } # check that the arguments in by and summarise_by are actually present - if (!all(c(by, summarise_by) %in% c(colnames(data), "range", "quantile"))) { - not_present <- setdiff(unique(c(by, summarise_by)), + if (!all(c(forecast_unit, summarise_by) %in% c(colnames(data), "range", "quantile"))) { + not_present <- setdiff(unique(c(forecast_unit, summarise_by)), c(colnames(data), "range", "quantile")) - msg <- paste0("The following items in `by` or `summarise_by` are not", + msg <- paste0("The following items in `summarise_by` are not", "valid column names of the data: '", paste(not_present, collapse = ", "), "'. Check and run `eval_forecasts()` again") @@ -275,7 +263,7 @@ eval_forecasts <- function(data, # Score binary predictions --------------------------------------------------- if (target_type == "binary") { res <- eval_forecasts_binary(data = data, - by = by, + forecast_unit = forecast_unit, summarise_by = summarise_by, metrics = metrics, quantiles = quantiles, @@ -286,7 +274,7 @@ eval_forecasts <- function(data, # Score quantile predictions ------------------------------------------------- if (prediction_type == "quantile") { res <- eval_forecasts_quantile(data = data, - by = by, + forecast_unit = forecast_unit, summarise_by = summarise_by, metrics = metrics, quantiles = quantiles, @@ -304,7 +292,7 @@ eval_forecasts <- function(data, # compute scores ----------------------------------------------------------- res <- eval_forecasts_sample(data = data, - by = by, + forecast_unit = forecast_unit, summarise_by = summarise_by, metrics = metrics, prediction_type = prediction_type, diff --git a/R/eval_forecasts_binary.R b/R/eval_forecasts_binary.R index 82e785963..987321d51 100644 --- a/R/eval_forecasts_binary.R +++ b/R/eval_forecasts_binary.R @@ -1,6 +1,10 @@ #' @title Evaluate forecasts in a Binary Format #' #' @inheritParams eval_forecasts +#' @param forecast_unit A character vector with the column names that define +#' the unit of a single forecast, i.e. a forecast was made for a combination +#' of the values in `forecast_unit` +#' #' @return A data.table with appropriate scores. For more information see #' [eval_forecasts()] #' @@ -16,14 +20,14 @@ #' @author Nikos Bosse \email{nikosbosse@@gmail.com} eval_forecasts_binary <- function(data, - by, + forecast_unit, summarise_by, metrics, quantiles, sd){ res <- data[, "brier_score" := scoringutils::brier_score(true_value, prediction), - by = by] + by = forecast_unit] # add quantiles if (!is.null(quantiles)) { diff --git a/R/eval_forecasts_continuous_integer.R b/R/eval_forecasts_continuous_integer.R index b083494a7..b38d3884a 100644 --- a/R/eval_forecasts_continuous_integer.R +++ b/R/eval_forecasts_continuous_integer.R @@ -2,6 +2,9 @@ #' #' @inheritParams eval_forecasts #' @param prediction_type character, should be either "continuous" or "integer" +#' @param forecast_unit A character vector with the column names that define +#' the unit of a single forecast, i.e. a forecast was made for a combination +#' of the values in `forecast_unit` #' #' @return A data.table with appropriate scores. For more information see #' [eval_forecasts()] @@ -31,9 +34,8 @@ #' @author Nikos Bosse \email{nikosbosse@@gmail.com} #' @inherit eval_forecasts references - eval_forecasts_sample <- function(data, - by, + forecast_unit, summarise_by, metrics, prediction_type, @@ -51,29 +53,29 @@ eval_forecasts_sample <- function(data, # calculate scores ----------------------------------------------------------- # sharpness if ("sharpness" %in% metrics) { - data[, sharpness := scoringutils::sharpness(t(prediction)), by = c(by)] + data[, sharpness := scoringutils::sharpness(t(prediction)), by = forecast_unit] } # bias if ("bias" %in% metrics) { data[, bias := scoringutils::bias(unique(true_value), - t(prediction)), by = c(by)] + t(prediction)), by = forecast_unit] } # DSS if ("dss" %in% metrics) { data[, dss := scoringutils::dss(unique(true_value), - t(prediction)), by = c(by)] + t(prediction)), by = forecast_unit] } # CRPS if ("crps" %in% metrics) { data[, crps := scoringutils::crps(unique(true_value), - t(prediction)), by = c(by)] + t(prediction)), by = forecast_unit] } # Log Score if ("log_score" %in% metrics) { # only compute if prediction type is continuous if (prediction_type == "continuous") { data[, log_score := scoringutils::logs(unique(true_value), - t(prediction)), by = c(by)] + t(prediction)), by = forecast_unit] } } # coverage @@ -85,7 +87,7 @@ eval_forecasts_sample <- function(data, # make scores unique to avoid redundancy. res <- res[, lapply(.SD, unique), .SDcols = colnames(res) %like% "pit_|bias|sharpness|dss|crps|log_score|pit", - by = c(by)] + by = forecast_unit] # summarise output if desired ------------------------------------------------ # add quantiles diff --git a/R/eval_forecasts_quantile.R b/R/eval_forecasts_quantile.R index 936bbd689..b4e889cd3 100644 --- a/R/eval_forecasts_quantile.R +++ b/R/eval_forecasts_quantile.R @@ -1,5 +1,5 @@ eval_forecasts_quantile <- function(data, - by, + forecast_unit, summarise_by, metrics, quantiles, @@ -61,7 +61,7 @@ eval_forecasts_quantile <- function(data, if ("bias" %in% metrics) { res[, bias := quantile_bias(range = range, lower = lower, upper = upper, true_value = unique(true_value)), - by = by] + by = forecast_unit] } # score absolute error for point forecasts @@ -79,7 +79,7 @@ eval_forecasts_quantile <- function(data, quantile_data[, aem := ae_median_quantile(true_value, prediction, quantile), - by = by] + by = forecast_unit] } # compute quantile coverage based on quantile version @@ -94,7 +94,7 @@ eval_forecasts_quantile <- function(data, # if we computed either the interval score or the aem or quantile coverage if (any(c("aem", "interval_score", "quantile_coverage") %in% metrics)) { # delete unnecessary columns before merging back - keep_cols <- unique(c(by, "quantile", "aem", "quantile_coverage", + keep_cols <- unique(c(forecast_unit, "quantile", "aem", "quantile_coverage", "boundary", "range")) delete_cols <- names(quantile_data)[!(names(quantile_data) %in% keep_cols)] quantile_data[, eval(delete_cols) := NULL] @@ -121,9 +121,9 @@ eval_forecasts_quantile <- function(data, relative_res <- add_rel_skill_to_eval_forecasts(unsummarised_scores = res, rel_skill_metric = rel_skill_metric, baseline = baseline, - by = by, + by = forecast_unit, summarise_by = summarise_by) - res <- merge(res, relative_res, by = by) + res <- merge(res, relative_res, by = forecast_unit) } # summarise scores ----------------------------------------------------------- diff --git a/R/pairwise-comparisons.R b/R/pairwise-comparisons.R index bac6c55ee..fbfe53271 100644 --- a/R/pairwise-comparisons.R +++ b/R/pairwise-comparisons.R @@ -13,12 +13,6 @@ #' [eval_forecasts()] #' @param metric A character vector of length one with the metric to do #' the comparison on. -#' @param by character vector of columns to group scoring by. This should be the -#' lowest level of grouping possible, i.e. the unit of the individual -#' observation. This is important as many functions work on individual -#' observations. If you want a different level of aggregation, you should use -#' `summarise_by` to aggregate the individual scores. -#' Also not that the pit will be computed using `summarise_by` instead of `by` #' @param summarise_by character vector of columns to group the summary by. By #' default, this is equal to `by` and no summary takes place. But sometimes you #' may want to to summarise over categories different from the scoring. @@ -60,7 +54,6 @@ pairwise_comparison <- function(scores, test_type = c("non_parametric", "permuation"), n_permutations = 999), baseline = NULL, - by = NULL, summarise_by = c("model")) { scores <- data.table::as.data.table(scores) diff --git a/man/add_rel_skill_to_eval_forecasts.Rd b/man/add_rel_skill_to_eval_forecasts.Rd index 7f92f4c66..e63cf8e5e 100644 --- a/man/add_rel_skill_to_eval_forecasts.Rd +++ b/man/add_rel_skill_to_eval_forecasts.Rd @@ -25,14 +25,6 @@ given, then a scaled relative skill with respect to the baseline will be returned. By default (\code{NULL}), relative skill will not be scaled with respect to a baseline model.} -\item{by}{character vector of columns to group scoring by. This should be the -lowest level of grouping possible, i.e. the unit of the individual -observation. This is important as many functions work on individual -observations. If you want a different level of aggregation, you should use -\code{summarise_by} to aggregate the individual scores. -Also not that the pit will be computed using \code{summarise_by} -instead of \code{by}} - \item{summarise_by}{character vector of columns to group the summary by. By default, this is equal to \code{by} and no summary takes place. But sometimes you may want to to summarise diff --git a/man/eval_forecasts.Rd b/man/eval_forecasts.Rd index e49c9ddcc..e4c8b9760 100644 --- a/man/eval_forecasts.Rd +++ b/man/eval_forecasts.Rd @@ -6,8 +6,7 @@ \usage{ eval_forecasts( data, - by = NULL, - summarise_by = by, + summarise_by = NULL, metrics = NULL, quantiles = c(), sd = FALSE, @@ -61,14 +60,6 @@ or 'upper_50' and 'lower_50', where the number denotes the interval range. For the median, you need to provide columns called 'upper_0' and 'lower_0' }} -\item{by}{character vector of columns to group scoring by. This should be the -lowest level of grouping possible, i.e. the unit of the individual -observation. This is important as many functions work on individual -observations. If you want a different level of aggregation, you should use -\code{summarise_by} to aggregate the individual scores. -Also not that the pit will be computed using \code{summarise_by} -instead of \code{by}} - \item{summarise_by}{character vector of columns to group the summary by. By default, this is equal to \code{by} and no summary takes place. But sometimes you may want to to summarise diff --git a/man/eval_forecasts_binary.Rd b/man/eval_forecasts_binary.Rd index 7595ccc92..23a0e0559 100644 --- a/man/eval_forecasts_binary.Rd +++ b/man/eval_forecasts_binary.Rd @@ -4,7 +4,14 @@ \alias{eval_forecasts_binary} \title{Evaluate forecasts in a Binary Format} \usage{ -eval_forecasts_binary(data, by, summarise_by, metrics, quantiles, sd) +eval_forecasts_binary( + data, + forecast_unit, + summarise_by, + metrics, + quantiles, + sd +) } \arguments{ \item{data}{A data.frame or data.table with the predictions and observations. @@ -50,13 +57,9 @@ or 'upper_50' and 'lower_50', where the number denotes the interval range. For the median, you need to provide columns called 'upper_0' and 'lower_0' }} -\item{by}{character vector of columns to group scoring by. This should be the -lowest level of grouping possible, i.e. the unit of the individual -observation. This is important as many functions work on individual -observations. If you want a different level of aggregation, you should use -\code{summarise_by} to aggregate the individual scores. -Also not that the pit will be computed using \code{summarise_by} -instead of \code{by}} +\item{forecast_unit}{A character vector with the column names that define +the unit of a single forecast, i.e. a forecast was made for a combination +of the values in \code{forecast_unit}} \item{summarise_by}{character vector of columns to group the summary by. By default, this is equal to \code{by} and no summary takes place. diff --git a/man/eval_forecasts_sample.Rd b/man/eval_forecasts_sample.Rd index f144b2ada..8bd45bfd7 100644 --- a/man/eval_forecasts_sample.Rd +++ b/man/eval_forecasts_sample.Rd @@ -6,7 +6,7 @@ \usage{ eval_forecasts_sample( data, - by, + forecast_unit, summarise_by, metrics, prediction_type, @@ -58,13 +58,9 @@ or 'upper_50' and 'lower_50', where the number denotes the interval range. For the median, you need to provide columns called 'upper_0' and 'lower_0' }} -\item{by}{character vector of columns to group scoring by. This should be the -lowest level of grouping possible, i.e. the unit of the individual -observation. This is important as many functions work on individual -observations. If you want a different level of aggregation, you should use -\code{summarise_by} to aggregate the individual scores. -Also not that the pit will be computed using \code{summarise_by} -instead of \code{by}} +\item{forecast_unit}{A character vector with the column names that define +the unit of a single forecast, i.e. a forecast was made for a combination +of the values in \code{forecast_unit}} \item{summarise_by}{character vector of columns to group the summary by. By default, this is equal to \code{by} and no summary takes place. diff --git a/man/get_unit_of_forecast.Rd b/man/get_unit_of_forecast.Rd new file mode 100644 index 000000000..fa63f0011 --- /dev/null +++ b/man/get_unit_of_forecast.Rd @@ -0,0 +1,17 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/check_forecasts.R +\name{get_unit_of_forecast} +\alias{get_unit_of_forecast} +\title{Get unit of a single forecast} +\usage{ +get_unit_of_forecast(data) +} +\value{ +A character vector with the column names that define the unit of +a single forecast +} +\description{ +Helper function to get the unit of a single forecast, i.e. +the column names that define where a single forecast was made for +} +\keyword{internal} diff --git a/tests/testthat/test-pairwise_comparison.R b/tests/testthat/test-pairwise_comparison.R index 8d89d3eb8..0fee6db53 100644 --- a/tests/testthat/test-pairwise_comparison.R +++ b/tests/testthat/test-pairwise_comparison.R @@ -25,11 +25,9 @@ test_that("pairwise comparisons works", { # evaluate the toy forecasts, once with and once without a baseline model specified eval_without_baseline <- eval_forecasts(data_formatted, - by = c("location", "target_end_date", "model"), compute_relative_skill = TRUE, count_median_twice = FALSE) eval_with_baseline <- scoringutils::eval_forecasts(data_formatted, - by = c("location", "target_end_date", "model"), baseline = "m1", compute_relative_skill = TRUE, count_median_twice = FALSE) From 0a62ef6df65e9262682eaeb7a14ab091fc21a0e2 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Thu, 25 Nov 2021 22:12:39 +0100 Subject: [PATCH 021/225] temporarily reintroduce by argument to pairwise comparisons --- R/pairwise-comparisons.R | 5 +++-- man/compare_two_models.Rd | 7 +------ man/pairwise_comparison.Rd | 13 ++++--------- man/pairwise_comparison_one_group.Rd | 7 +------ 4 files changed, 9 insertions(+), 23 deletions(-) diff --git a/R/pairwise-comparisons.R b/R/pairwise-comparisons.R index fbfe53271..9e25fdcff 100644 --- a/R/pairwise-comparisons.R +++ b/R/pairwise-comparisons.R @@ -13,6 +13,7 @@ #' [eval_forecasts()] #' @param metric A character vector of length one with the metric to do #' the comparison on. +#' @param by character vector that denotes the unit of a single forecast. #' @param summarise_by character vector of columns to group the summary by. By #' default, this is equal to `by` and no summary takes place. But sometimes you #' may want to to summarise over categories different from the scoring. @@ -54,7 +55,8 @@ pairwise_comparison <- function(scores, test_type = c("non_parametric", "permuation"), n_permutations = 999), baseline = NULL, - summarise_by = c("model")) { + summarise_by = c("model"), + by = NULL) { scores <- data.table::as.data.table(scores) @@ -76,7 +78,6 @@ pairwise_comparison <- function(scores, split_scores <- split(scores, by = split_by) - results <- lapply(split_scores, FUN = function(scores) { out <- pairwise_comparison_one_group(scores = scores, diff --git a/man/compare_two_models.Rd b/man/compare_two_models.Rd index f6c5d382c..2e8755d75 100644 --- a/man/compare_two_models.Rd +++ b/man/compare_two_models.Rd @@ -23,12 +23,7 @@ the name of the argument you want to change. All elements not included in the list will be set to the default (so passing an empty list would result in the default options).} -\item{by}{character vector of columns to group scoring by. This should be the -lowest level of grouping possible, i.e. the unit of the individual -observation. This is important as many functions work on individual -observations. If you want a different level of aggregation, you should use -\code{summarise_by} to aggregate the individual scores. -Also not that the pit will be computed using \code{summarise_by} instead of \code{by}} +\item{by}{character vector that denotes the unit of a single forecast.} } \description{ This function compares two models based on the subset of forecasts for which diff --git a/man/pairwise_comparison.Rd b/man/pairwise_comparison.Rd index 9beab3189..9a192e87d 100644 --- a/man/pairwise_comparison.Rd +++ b/man/pairwise_comparison.Rd @@ -10,8 +10,8 @@ pairwise_comparison( test_options = list(oneSided = FALSE, test_type = c("non_parametric", "permuation"), n_permutations = 999), baseline = NULL, - by = NULL, - summarise_by = c("model") + summarise_by = c("model"), + by = NULL ) } \arguments{ @@ -30,18 +30,13 @@ default options).} \item{baseline}{character vector of length one that denotes the baseline model against which to compare other models.} -\item{by}{character vector of columns to group scoring by. This should be the -lowest level of grouping possible, i.e. the unit of the individual -observation. This is important as many functions work on individual -observations. If you want a different level of aggregation, you should use -\code{summarise_by} to aggregate the individual scores. -Also not that the pit will be computed using \code{summarise_by} instead of \code{by}} - \item{summarise_by}{character vector of columns to group the summary by. By default, this is equal to \code{by} and no summary takes place. But sometimes you may want to to summarise over categories different from the scoring. \code{summarise_by} is also the grouping level used to compute (and possibly plot) the probability integral transform(pit).} + +\item{by}{character vector that denotes the unit of a single forecast.} } \value{ A ggplot2 object with a coloured table of summarised scores diff --git a/man/pairwise_comparison_one_group.Rd b/man/pairwise_comparison_one_group.Rd index 54f7b0bf3..1a92d2eb3 100644 --- a/man/pairwise_comparison_one_group.Rd +++ b/man/pairwise_comparison_one_group.Rd @@ -29,12 +29,7 @@ default options).} \item{baseline}{character vector of length one that denotes the baseline model against which to compare other models.} -\item{by}{character vector of columns to group scoring by. This should be the -lowest level of grouping possible, i.e. the unit of the individual -observation. This is important as many functions work on individual -observations. If you want a different level of aggregation, you should use -\code{summarise_by} to aggregate the individual scores. -Also not that the pit will be computed using \code{summarise_by} instead of \code{by}} +\item{by}{character vector that denotes the unit of a single forecast.} \item{summarise_by}{character vector of columns to group the summary by. By default, this is equal to \code{by} and no summary takes place. But sometimes you From 651b72b5803ed60263e188647a91be005d0db698 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Thu, 25 Nov 2021 22:58:05 +0100 Subject: [PATCH 022/225] write helper function to simplify checks for eval_forecasts --- R/check_forecasts.R | 2 +- R/eval_forecasts.R | 134 +++++++++++++++-------------- man/check_eval_forecasts_params.Rd | 94 ++++++++++++++++++++ man/get_unit_of_forecast.Rd | 3 + 4 files changed, 168 insertions(+), 65 deletions(-) create mode 100644 man/check_eval_forecasts_params.Rd diff --git a/R/check_forecasts.R b/R/check_forecasts.R index 3b9a85a10..58395f13b 100644 --- a/R/check_forecasts.R +++ b/R/check_forecasts.R @@ -327,7 +327,7 @@ check_clean_data <- function(data, verbose = TRUE) { #' @description Helper function to get the unit of a single forecast, i.e. #' the column names that define where a single forecast was made for #' -#' @inheritParams check_forecast +#' @inheritParams check_forecasts #' #' @return A character vector with the column names that define the unit of #' a single forecast diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index 55e562c63..c4e748f76 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -187,78 +187,31 @@ eval_forecasts <- function(data, # check relevant columns and remove NA values in true_values and prediction data <- check_clean_data(data, verbose = FALSE) - # error handling for relative skill computation - if (compute_relative_skill) { - if (!("model" %in% colnames(data))) { - warning("to compute relative skills, there must column present called 'model'. Relative skill will not be computed") - compute_relative_skill <- FALSE - } - models <- unique(data$model) - if (length(models) < 2 + (!is.null(baseline))) { - warning("you need more than one model non-baseline model to make model comparisons. Relative skill will not be computed") - compute_relative_skill <- FALSE - } - if (!is.null(baseline) && !(baseline %in% models)) { - warning("The baseline you provided for the relative skill is not one of the models in the data. Relative skill will not be computed") - compute_relative_skill <- FALSE - } - if (rel_skill_metric != "auto" && !(rel_skill_metric %in% available_metrics())) { - warning("argument 'rel_skill_metric' must either be 'auto' or one of the metrics that can be computed. Relative skill will not be computed") - compute_relative_skill <- FALSE - } + # use all available metrics if none are given + if (is.null(metrics)) { + metrics <- available_metrics() } # obtain a value for the unit of a single observation forecast_unit <- get_unit_of_forecast(data) - if (is.null(summarise_by)) { summarise_by <- forecast_unit } - # check that the arguments in by and summarise_by are actually present - if (!all(c(forecast_unit, summarise_by) %in% c(colnames(data), "range", "quantile"))) { - not_present <- setdiff(unique(c(forecast_unit, summarise_by)), - c(colnames(data), "range", "quantile")) - msg <- paste0("The following items in `summarise_by` are not", - "valid column names of the data: '", - paste(not_present, collapse = ", "), - "'. Check and run `eval_forecasts()` again") - stop(msg) - } - - # check metrics to be computed - available_metrics <- available_metrics() - if (is.null(metrics)) { - metrics <- available_metrics - } else { - if (!all(metrics %in% available_metrics)) { - msg <- paste("The following metrics are not currently implemented and", - "will not be computed:", - paste(setdiff(metrics, available_metrics), collapse = ", ")) - warning(msg) - } - } - + # check input parameters and whether computation of relative skill is possible + compute_rel_skill <- check_eval_forecasts_params( + data, + forecast_unit, + metrics, + summarise_by, + compute_relative_skill, + baseline, + rel_skill_metric + ) # check prediction and target type ------------------------------------------- - if (any(grepl("lower", names(data))) | "boundary" %in% names(data) | - "quantile" %in% names(data) | "range" %in% names(data)) { - prediction_type <- "quantile" - } else if (isTRUE(all.equal(data$prediction, as.integer(data$prediction)))) { - prediction_type <- "integer" - } else { - prediction_type <- "continuous" - } - - if (isTRUE(all.equal(data$true_value, as.integer(data$true_value)))) { - if (all(data$true_value %in% c(0,1)) && all(data$prediction >= 0) && all(data$prediction <= 1)) { - target_type = "binary" - } else { - target_type = "integer" - } - } else { - target_type = "continuous" - } + prediction_type <- get_prediction_type(data) + target_type <- get_target_type(data) # Score binary predictions --------------------------------------------------- if (target_type == "binary") { @@ -286,11 +239,9 @@ eval_forecasts <- function(data, return(res) } - # Score integer or continuous predictions ------------------------------------ if (prediction_type %in% c("integer", "continuous")) { - # compute scores ----------------------------------------------------------- res <- eval_forecasts_sample(data = data, forecast_unit = forecast_unit, summarise_by = summarise_by, @@ -305,9 +256,64 @@ eval_forecasts <- function(data, +#' @title Check input parameters for [eval_forecasts()] +#' +#' @description A helper function to check the input parameters for +#' [eval_forecasts()]. +#' +#' @inheritParams eval_forecasts +#' +#' @keywords internal +check_eval_forecasts_params <- function(data, + forecast_unit, + metrics, + summarise_by, + compute_relative_skill, + baseline, + rel_skill_metric) { + # check that the arguments in by and summarise_by are actually present + if (!all(c(forecast_unit, summarise_by) %in% c(colnames(data), "range", "quantile"))) { + not_present <- setdiff(unique(c(forecast_unit, summarise_by)), + c(colnames(data), "range", "quantile")) + msg <- paste0("The following items in `summarise_by` are not", + "valid column names of the data: '", + paste(not_present, collapse = ", "), + "'. Check and run `eval_forecasts()` again") + stop(msg) + } + # check metrics to be computed + available_metrics <- available_metrics() + if (!all(metrics %in% available_metrics)) { + msg <- paste("The following metrics are not currently implemented and", + "will not be computed:", + paste(setdiff(metrics, available_metrics), collapse = ", ")) + warning(msg) + } + # error handling for relative skill computation + if (compute_relative_skill) { + if (!("model" %in% colnames(data))) { + warning("to compute relative skills, there must column present called 'model'. Relative skill will not be computed") + compute_relative_skill <- FALSE + } + models <- unique(data$model) + if (length(models) < 2 + (!is.null(baseline))) { + warning("you need more than one model non-baseline model to make model comparisons. Relative skill will not be computed") + compute_relative_skill <- FALSE + } + if (!is.null(baseline) && !(baseline %in% models)) { + warning("The baseline you provided for the relative skill is not one of the models in the data. Relative skill will not be computed") + compute_relative_skill <- FALSE + } + if (rel_skill_metric != "auto" && !(rel_skill_metric %in% available_metrics())) { + warning("argument 'rel_skill_metric' must either be 'auto' or one of the metrics that can be computed. Relative skill will not be computed") + compute_relative_skill <- FALSE + } + } + return(compute_relative_skill) +} diff --git a/man/check_eval_forecasts_params.Rd b/man/check_eval_forecasts_params.Rd new file mode 100644 index 000000000..bb9cf9f03 --- /dev/null +++ b/man/check_eval_forecasts_params.Rd @@ -0,0 +1,94 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/eval_forecasts.R +\name{check_eval_forecasts_params} +\alias{check_eval_forecasts_params} +\title{Check input parameters for \code{\link[=eval_forecasts]{eval_forecasts()}}} +\usage{ +check_eval_forecasts_params( + data, + forecast_unit, + metrics, + summarise_by, + compute_relative_skill, + baseline, + rel_skill_metric +) +} +\arguments{ +\item{data}{A data.frame or data.table with the predictions and observations. +Note: it is easiest to have a look at the example files provided in the +package and in the examples below. +The following columns need to be present: +\itemize{ +\item \code{true_value} - the true observed values +\item \code{prediction} - predictions or predictive samples for one +true value. (You only don't need to provide a prediction column if +you want to score quantile forecasts in a wide range format.)} +For integer and continuous forecasts a \code{sample} column is needed: +\itemize{ +\item \code{sample} - an index to identify the predictive samples in the +prediction column generated by one model for one true value. Only +necessary for continuous and integer forecasts, not for +binary predictions.} +For quantile forecasts the data can be provided in variety of formats. You +can either use a range-based format or a quantile-based format. (You can +convert between formats using \code{\link[=quantile_to_range_long]{quantile_to_range_long()}}, +\code{\link[=range_long_to_quantile]{range_long_to_quantile()}}, +\code{\link[=sample_to_range_long]{sample_to_range_long()}}, +\code{\link[=sample_to_quantile]{sample_to_quantile()}}) +For a quantile-format forecast you should provide: +\itemize{ +\item \code{prediction}: prediction to the corresponding quantile +\item \code{quantile}: quantile to which the prediction corresponds +For a range format (long) forecast you need +\item \code{prediction}: the quantile forecasts +\item \code{boundary}: values should be either "lower" or "upper", depending +on whether the prediction is for the lower or upper bound of a given range +\item \code{range} the range for which a forecast was made. For a 50\% interval +the range should be 50. The forecast for the 25\% quantile should have +the value in the \code{prediction} column, the value of \code{range} +should be 50 and the value of \code{boundary} should be "lower". +If you want to score the median (i.e. \code{range = 0}), you still +need to include a lower and an upper estimate, so the median has to +appear twice. +Alternatively you can also provide the format in a wide range format. +This format needs: +\item pairs of columns called something like 'upper_90' and 'lower_90', +or 'upper_50' and 'lower_50', where the number denotes the interval range. +For the median, you need to provide columns called 'upper_0' and 'lower_0' +}} + +\item{metrics}{the metrics you want to have in the output. If \code{NULL} (the +default), all available metrics will be computed. For a list of available +metrics see \code{\link[=available_metrics]{available_metrics()}}} + +\item{summarise_by}{character vector of columns to group the summary by. By +default, this is equal to \code{by} and no summary takes place. +But sometimes you may want to to summarise +over categories different from the scoring. +\code{summarise_by} is also the grouping level used to compute +(and possibly plot) the probability integral transform(pit). Sometimes you +may want to include 'range', 'quantile' or 'sample', to summarise by +range, quantile or sample.} + +\item{compute_relative_skill}{logical, whether or not to compute relative +performance between models. If \code{TRUE} (default is \code{FALSE}), then a column called +'model' must be present in the input data. For more information on +the computation of relative skill, see \code{\link[=pairwise_comparison]{pairwise_comparison()}}. +Relative skill will be calculated for the aggregation level specified in +\code{summarise_by}.} + +\item{baseline}{character string with the name of a model. If a baseline is +given, then a scaled relative skill with respect to the baseline will be +returned. By default (\code{NULL}), relative skill will not be scaled with +respect to a baseline model.} + +\item{rel_skill_metric}{character string with the name of the metric for which +a relative skill shall be computed. If equal to 'auto' (the default), then +one of interval score, crps or brier score will be used where appropriate} +} +\description{ +A helper function to check the input parameters for +\code{\link[=eval_forecasts]{eval_forecasts()}}. +} +\keyword{internal} diff --git a/man/get_unit_of_forecast.Rd b/man/get_unit_of_forecast.Rd index fa63f0011..100bfd86e 100644 --- a/man/get_unit_of_forecast.Rd +++ b/man/get_unit_of_forecast.Rd @@ -6,6 +6,9 @@ \usage{ get_unit_of_forecast(data) } +\arguments{ +\item{data}{A data.frame or similar as would be used for \code{\link[=eval_forecasts]{eval_forecasts()}}} +} \value{ A character vector with the column names that define the unit of a single forecast From 30ee8454af6550612981d45d143ea004ea85547d Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Fri, 26 Nov 2021 22:54:54 +0100 Subject: [PATCH 023/225] sketch for a version of available_metrics() that returns explanations --- R/utils.R | 92 ++++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 91 insertions(+), 1 deletion(-) diff --git a/R/utils.R b/R/utils.R index b0d426d11..62371678c 100644 --- a/R/utils.R +++ b/R/utils.R @@ -143,12 +143,102 @@ globalVariables(c("..index", #' @export available_metrics <- function() { - available_metrics <- c("ae_point", "aem", "log_score", "sharpness", "bias", "dss", "crps", + available_metrics <- c("ae_point", "aem", "log_score", "sharpness", + "dispersion", "bias", "dss", "crps", "brier_score", "coverage", "coverage_deviation", "quantile_coverage", "pit_p_val", "pit_sd","interval_score", "underprediction", "overprediction", "relative_skill", "scaled_rel_skill") + metrics <- list() + ae_point <- list( + id = "ae_point", + name = "Absolute error of the point forecast", + description = "" + ) + aem <- list( + id = "", + name = "", + description = "" + ) + log_score <- list( + id = "", + name = "", + description = "" + ) + + sharpness <- list( + id = "", + name = "", + description = "" + ) + bias <- list( + id = "", + name = "", + description = "" + ) + dss <- list( + id = "", + name = "", + description = "" + ) + crps <- list( + id = "", + name = "", + description = "" + ) + + brier_score <- list( + id = "", + name = "", + description = "" + ) + coverage <- list( + id = "", + name = "", + description = "" + ) + coverage_deviation <- list( + id = "", + name = "", + description = "" + ) + quantile_coverage <- list( + id = "", + name = "", + description = "" + ) + pit_values <- list( + id = "", + name = "", + description = "" + ) + interval_score <- list( + id = "", + name = "", + description = "" + ) + underprediction <- list( + id = "", + name = "", + description = "" + ) + overprediction <- list( + id = "", + name = "", + description = "" + ) + relative_skill <- list( + id = "", + name = "", + description = "" + ) + scaled_rel_skill <- list( + id = "", + name = "", + description = "" + ) + return(available_metrics) } From 13aa44c942f360044541ca6982bd7176f65e0279 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Fri, 26 Nov 2021 22:55:29 +0100 Subject: [PATCH 024/225] add warning to check_clean_data if columns correspond to names of metrics --- R/check_forecasts.R | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/R/check_forecasts.R b/R/check_forecasts.R index 58395f13b..321784d81 100644 --- a/R/check_forecasts.R +++ b/R/check_forecasts.R @@ -301,6 +301,12 @@ check_clean_data <- function(data, verbose = TRUE) { stop("Data needs to have columns called `true_value` and `prediction`") } + if (any(colnames(data) %in% available_metrics())) { + warning("At least one column in the data corresponds to the name of a ", + "metric that will be computed by scoringutils. This may be a ", + "problem. Please check `available_metrics()`") + } + # remove rows where prediction or true value are NA if (anyNA(data$true_value)) { if (verbose) { @@ -337,7 +343,7 @@ check_clean_data <- function(data, verbose = TRUE) { get_unit_of_forecast <- function(data) { protected_columns <- c( "prediction", "true_value", "sample", "quantile", - "range", "boundary" + "range", "boundary", available_metrics() ) obs_unit <- setdiff(colnames(data), protected_columns) return(obs_unit) From 8f50d0133587558706b5205713cacb0a14c99766 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Fri, 26 Nov 2021 22:56:37 +0100 Subject: [PATCH 025/225] simplify eval_forecasts by adding check functions and a summarise_scores() function --- NAMESPACE | 1 + R/eval_forecasts.R | 141 +++++++++++--------------- R/eval_forecasts_binary.R | 21 +--- R/eval_forecasts_continuous_integer.R | 23 +---- R/eval_forecasts_helper.R | 74 ++++++++++++-- R/eval_forecasts_quantile.R | 44 +------- man/add_quantiles.Rd | 6 +- man/add_sd.Rd | 2 +- man/check_eval_forecasts_params.Rd | 2 +- man/eval_forecasts_binary.Rd | 26 +---- man/eval_forecasts_sample.Rd | 27 +---- man/summarise_scores.Rd | 40 ++++++++ 12 files changed, 182 insertions(+), 225 deletions(-) create mode 100644 man/summarise_scores.Rd diff --git a/NAMESPACE b/NAMESPACE index 965cf9e1b..0f05458a3 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -41,6 +41,7 @@ export(score_heatmap) export(score_table) export(sharpness) export(show_avail_forecasts) +export(summarise_scores) export(wis_components) importFrom(data.table,"%like%") importFrom(data.table,':=') diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index c4e748f76..45a5eead1 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -182,7 +182,6 @@ eval_forecasts <- function(data, baseline = NULL, ...) { - # preparations --------------------------------------------------------------- # check relevant columns and remove NA values in true_values and prediction data <- check_clean_data(data, verbose = FALSE) @@ -215,108 +214,86 @@ eval_forecasts <- function(data, # Score binary predictions --------------------------------------------------- if (target_type == "binary") { - res <- eval_forecasts_binary(data = data, - forecast_unit = forecast_unit, - summarise_by = summarise_by, - metrics = metrics, - quantiles = quantiles, - sd = sd) - return(res) + scores <- eval_forecasts_binary(data = data, + forecast_unit = forecast_unit, + metrics = metrics) } # Score quantile predictions ------------------------------------------------- if (prediction_type == "quantile") { - res <- eval_forecasts_quantile(data = data, - forecast_unit = forecast_unit, - summarise_by = summarise_by, - metrics = metrics, - quantiles = quantiles, - sd = sd, - compute_relative_skill = compute_relative_skill, - rel_skill_metric = rel_skill_metric, - baseline = baseline, - ...) - return(res) + scores <- eval_forecasts_quantile(data = data, + forecast_unit = forecast_unit, + metrics = metrics, + summarise_by = summarise_by, + compute_relative_skill = compute_relative_skill, + rel_skill_metric = rel_skill_metric, + baseline = baseline, + ...) } # Score integer or continuous predictions ------------------------------------ - if (prediction_type %in% c("integer", "continuous")) { + if (prediction_type %in% c("integer", "continuous") && (target_type != "binary")) { - res <- eval_forecasts_sample(data = data, - forecast_unit = forecast_unit, - summarise_by = summarise_by, - metrics = metrics, - prediction_type = prediction_type, - quantiles = quantiles, - sd = sd) - return(res) + scores <- eval_forecasts_sample(data = data, + forecast_unit = forecast_unit, + metrics = metrics, + prediction_type = prediction_type) } -} + scores <- summarise_scores(scores, + summarise_by, + quantiles = quantiles, + sd = sd) +} -#' @title Check input parameters for [eval_forecasts()] + +#' @title Summarise scores as produced by [eval_forecasts()] #' -#' @description A helper function to check the input parameters for -#' [eval_forecasts()]. +#' @description Summarise scores as produced by [eval_forecasts()]- #' +#' @param scores a data.table of unsummarised scores as produced by +#' [eval_forecasts()], when running it with the option `summarise_by = NULL` #' @inheritParams eval_forecasts #' -#' @keywords internal -check_eval_forecasts_params <- function(data, - forecast_unit, - metrics, - summarise_by, - compute_relative_skill, - baseline, - rel_skill_metric) { +#' @examples +#' library(scoringutils) +#' data <- quantile_example_data +#' scores <- eval_forecasts(data) +#' summarise_scores(scores, +#' summarise_by = c("model")) +#' +#' @export - # check that the arguments in by and summarise_by are actually present - if (!all(c(forecast_unit, summarise_by) %in% c(colnames(data), "range", "quantile"))) { - not_present <- setdiff(unique(c(forecast_unit, summarise_by)), - c(colnames(data), "range", "quantile")) - msg <- paste0("The following items in `summarise_by` are not", - "valid column names of the data: '", - paste(not_present, collapse = ", "), - "'. Check and run `eval_forecasts()` again") - stop(msg) - } +summarise_scores <- function(scores, + summarise_by, + quantiles = c(), + sd = FALSE) { - # check metrics to be computed - available_metrics <- available_metrics() - if (!all(metrics %in% available_metrics)) { - msg <- paste("The following metrics are not currently implemented and", - "will not be computed:", - paste(setdiff(metrics, available_metrics), collapse = ", ")) - warning(msg) - } + forecast_unit <- get_unit_of_forecast(scores) - # error handling for relative skill computation - if (compute_relative_skill) { - if (!("model" %in% colnames(data))) { - warning("to compute relative skills, there must column present called 'model'. Relative skill will not be computed") - compute_relative_skill <- FALSE - } - models <- unique(data$model) - if (length(models) < 2 + (!is.null(baseline))) { - warning("you need more than one model non-baseline model to make model comparisons. Relative skill will not be computed") - compute_relative_skill <- FALSE - } - if (!is.null(baseline) && !(baseline %in% models)) { - warning("The baseline you provided for the relative skill is not one of the models in the data. Relative skill will not be computed") - compute_relative_skill <- FALSE - } - if (rel_skill_metric != "auto" && !(rel_skill_metric %in% available_metrics())) { - warning("argument 'rel_skill_metric' must either be 'auto' or one of the metrics that can be computed. Relative skill will not be computed") - compute_relative_skill <- FALSE - } + if (!is.null(quantiles) && length(quantiles) > 1) { + scores <- add_quantiles(scores, quantiles, summarise_by) + } + # add standard deviation + if (sd) { + scores <- add_sd(scores, summarise_by) } - return(compute_relative_skill) -} - - - + # summarise by taking the mean and omitting unnecessary columns + cols_to_summarise <- paste0(available_metrics(), collapse = "|") + scores <- scores[, lapply(.SD, mean, na.rm = TRUE), + by = c(summarise_by), + .SDcols = colnames(scores) %like% cols_to_summarise] + # if neither quantile nor range are in summarise_by, remove coverage and quantile_coverage + if (!("range" %in% summarise_by) & ("coverage" %in% colnames(scores))) { + scores[, c("coverage") := NULL] + } + if (!("quantile" %in% summarise_by) & "quantile_coverage" %in% names(scores)) { + scores[, c("quantile_coverage") := NULL] + } + return(scores[]) +} diff --git a/R/eval_forecasts_binary.R b/R/eval_forecasts_binary.R index 987321d51..c234c6303 100644 --- a/R/eval_forecasts_binary.R +++ b/R/eval_forecasts_binary.R @@ -21,30 +21,11 @@ eval_forecasts_binary <- function(data, forecast_unit, - summarise_by, - metrics, - quantiles, - sd){ + metrics){ res <- data[, "brier_score" := scoringutils::brier_score(true_value, prediction), by = forecast_unit] - # add quantiles - if (!is.null(quantiles)) { - res <- add_quantiles(res, "brier_score", quantiles, by = summarise_by) - } - - # add standard deviation - if (sd) { - res <- add_sd(res, "brier_score", by = c(summarise_by)) - } - - # summarise by taking the mean over all relevant columns - res <- res[, lapply(.SD, mean, na.rm = TRUE), - .SDcols = colnames(res) %like% "brier", - by = summarise_by] - - return(res[]) } diff --git a/R/eval_forecasts_continuous_integer.R b/R/eval_forecasts_continuous_integer.R index b38d3884a..8149c8992 100644 --- a/R/eval_forecasts_continuous_integer.R +++ b/R/eval_forecasts_continuous_integer.R @@ -36,11 +36,8 @@ eval_forecasts_sample <- function(data, forecast_unit, - summarise_by, metrics, - prediction_type, - quantiles, - sd) { + prediction_type) { if (missing(prediction_type)) { if (isTRUE(all.equal(data$prediction, as.integer(data$prediction)))) { @@ -89,23 +86,5 @@ eval_forecasts_sample <- function(data, .SDcols = colnames(res) %like% "pit_|bias|sharpness|dss|crps|log_score|pit", by = forecast_unit] - # summarise output if desired ------------------------------------------------ - # add quantiles - if (!is.null(quantiles)) { - quantile_vars <- c("crps", "dss", "log_score", "pit_p_val", "bias", "sharpness") - res <- add_quantiles(res, quantile_vars, quantiles, by = c(summarise_by)) - } - - if (sd) { - # add standard deviations - sd_vars <- c("crps", "dss", "log_score", "bias", "sharpness") - res <- add_sd(res, sd_vars, by = c(summarise_by)) - } - - # take mean - res <- res[, lapply(.SD, mean, na.rm = TRUE), - .SDcols = colnames(res) %like% "pit_|bias|sharpness|dss|crps|log_score", - by = summarise_by] - return(res[]) } diff --git a/R/eval_forecasts_helper.R b/R/eval_forecasts_helper.R index e359ad89e..d59732c14 100644 --- a/R/eval_forecasts_helper.R +++ b/R/eval_forecasts_helper.R @@ -11,14 +11,14 @@ #' @return `data.table` with quantiles added #' #' @keywords internal -add_quantiles <- function(dt, varnames, quantiles, by) { +add_quantiles <- function(dt, quantiles, summarise_by) { # make sure that the desired varnames are actually present - varnames <- intersect(varnames, colnames(dt)) + varnames <- intersect(available_metrics(), colnames(dt)) for (varname in varnames) { dt[, paste0(varname, "_", quantiles) := as.list(quantile(get(varname), probs = quantiles, na.rm = TRUE)), - by = c(by)] + by = summarise_by] } return(dt[]) } @@ -35,11 +35,73 @@ add_quantiles <- function(dt, varnames, quantiles, by) { #' @return `data.table` with sd added #' #' @keywords internal -add_sd <- function(dt, varnames, by) { +add_sd <- function(dt, summarise_by) { # make sure that the desired varnames are actually present - varnames <- intersect(varnames, colnames(dt)) + varnames <- intersect(available_metrics(), colnames(dt)) for (varname in varnames) { - dt[, paste0(varname, "_sd") := sd(get(varname), na.rm = TRUE), by = by] + dt[, paste0(varname, "_sd") := sd(get(varname), na.rm = TRUE), + by = summarise_by] } return(dt[]) } + + + +#' @title Check input parameters for [eval_forecasts()] +#' +#' @description A helper function to check the input parameters for +#' [eval_forecasts()]. +#' +#' @inheritParams eval_forecasts +#' +#' @keywords internal +check_eval_forecasts_params <- function(data, + forecast_unit, + metrics, + summarise_by, + compute_relative_skill, + baseline, + rel_skill_metric) { + + # check that the arguments in by and summarise_by are actually present + if (!all(c(forecast_unit, summarise_by) %in% c(colnames(data), "range", "quantile"))) { + not_present <- setdiff(unique(c(forecast_unit, summarise_by)), + c(colnames(data), "range", "quantile")) + msg <- paste0("The following items in `summarise_by` are not", + "valid column names of the data: '", + paste(not_present, collapse = ", "), + "'. Check and run `eval_forecasts()` again") + stop(msg) + } + + # check metrics to be computed + available_metrics <- available_metrics() + if (!all(metrics %in% available_metrics)) { + msg <- paste("The following metrics are not currently implemented and", + "will not be computed:", + paste(setdiff(metrics, available_metrics), collapse = ", ")) + warning(msg) + } + + # error handling for relative skill computation + if (compute_relative_skill) { + if (!("model" %in% colnames(data))) { + warning("to compute relative skills, there must column present called 'model'. Relative skill will not be computed") + compute_relative_skill <- FALSE + } + models <- unique(data$model) + if (length(models) < 2 + (!is.null(baseline))) { + warning("you need more than one model non-baseline model to make model comparisons. Relative skill will not be computed") + compute_relative_skill <- FALSE + } + if (!is.null(baseline) && !(baseline %in% models)) { + warning("The baseline you provided for the relative skill is not one of the models in the data. Relative skill will not be computed") + compute_relative_skill <- FALSE + } + if (rel_skill_metric != "auto" && !(rel_skill_metric %in% available_metrics())) { + warning("argument 'rel_skill_metric' must either be 'auto' or one of the metrics that can be computed. Relative skill will not be computed") + compute_relative_skill <- FALSE + } + } + return(compute_relative_skill) +} diff --git a/R/eval_forecasts_quantile.R b/R/eval_forecasts_quantile.R index b4e889cd3..4ccd668d9 100644 --- a/R/eval_forecasts_quantile.R +++ b/R/eval_forecasts_quantile.R @@ -1,13 +1,10 @@ eval_forecasts_quantile <- function(data, forecast_unit, - summarise_by, metrics, - quantiles, - sd, weigh = TRUE, + summarise_by, count_median_twice = FALSE, separate_results = TRUE, - summarised, compute_relative_skill, rel_skill_metric, baseline) { @@ -118,6 +115,10 @@ eval_forecasts_quantile <- function(data, if (compute_relative_skill) { + if (is.null(summarise_by)) { + summarise_by <- forecast_unit + } + relative_res <- add_rel_skill_to_eval_forecasts(unsummarised_scores = res, rel_skill_metric = rel_skill_metric, baseline = baseline, @@ -126,40 +127,5 @@ eval_forecasts_quantile <- function(data, res <- merge(res, relative_res, by = forecast_unit) } - # summarise scores ----------------------------------------------------------- - # add quantiles for the scores - if (!is.null(quantiles)) { - res <- add_quantiles(res, - c("interval_score", "coverage", - "overprediction", "underprediction", - "coverage_deviation", "bias", "dispersion", "aem", - "ae_point"), - quantiles, - by = c(summarise_by)) - } - # add standard deviation - if (sd) { - res <- add_sd(res, - varnames = c("interval_score", "bias", "coverage", - "overprediction", "underprediction", - "coverage_deviation", "dispersion", "aem", - "ae_point"), - by = c(summarise_by)) - } - - # summarise by taking the mean and omitting unnecessary columns - res <- res[, lapply(.SD, mean, na.rm = TRUE), - by = c(summarise_by), - .SDcols = colnames(res) %like% - "coverage|bias|dispersion|coverage_deviation|interval_score|overprediction|underprediction|aem|ae_point|relative_skill|scaled_rel_skill"] - - # if neither quantile nor range are in summarise_by, remove coverage and quantile_coverage - if (!("range" %in% summarise_by) & ("coverage" %in% colnames(res))) { - res[, c("coverage") := NULL] - } - if (!("quantile" %in% summarise_by) & "quantile_coverage" %in% names(res)) { - res[, c("quantile_coverage") := NULL] - } - return(res[]) } diff --git a/man/add_quantiles.Rd b/man/add_quantiles.Rd index 5fe384de3..70e3c5143 100644 --- a/man/add_quantiles.Rd +++ b/man/add_quantiles.Rd @@ -4,15 +4,15 @@ \alias{add_quantiles} \title{Add Quantiles to Predictions When Summarising} \usage{ -add_quantiles(dt, varnames, quantiles, by) +add_quantiles(dt, quantiles, summarise_by) } \arguments{ \item{dt}{the data.table operated on} -\item{varnames}{names of the variables for which to calculate quantiles} - \item{quantiles}{the desired quantiles} +\item{varnames}{names of the variables for which to calculate quantiles} + \item{by}{grouping variable in \code{\link[=eval_forecasts]{eval_forecasts()}}} } \value{ diff --git a/man/add_sd.Rd b/man/add_sd.Rd index 192ff83bc..fdaedd304 100644 --- a/man/add_sd.Rd +++ b/man/add_sd.Rd @@ -4,7 +4,7 @@ \alias{add_sd} \title{Add Standard Deviation to Predictions When Summarising} \usage{ -add_sd(dt, varnames, by) +add_sd(dt, summarise_by) } \arguments{ \item{dt}{the data.table operated on} diff --git a/man/check_eval_forecasts_params.Rd b/man/check_eval_forecasts_params.Rd index bb9cf9f03..420cc748a 100644 --- a/man/check_eval_forecasts_params.Rd +++ b/man/check_eval_forecasts_params.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/eval_forecasts.R +% Please edit documentation in R/eval_forecasts_helper.R \name{check_eval_forecasts_params} \alias{check_eval_forecasts_params} \title{Check input parameters for \code{\link[=eval_forecasts]{eval_forecasts()}}} diff --git a/man/eval_forecasts_binary.Rd b/man/eval_forecasts_binary.Rd index 23a0e0559..91113f5ea 100644 --- a/man/eval_forecasts_binary.Rd +++ b/man/eval_forecasts_binary.Rd @@ -4,14 +4,7 @@ \alias{eval_forecasts_binary} \title{Evaluate forecasts in a Binary Format} \usage{ -eval_forecasts_binary( - data, - forecast_unit, - summarise_by, - metrics, - quantiles, - sd -) +eval_forecasts_binary(data, forecast_unit, metrics) } \arguments{ \item{data}{A data.frame or data.table with the predictions and observations. @@ -61,26 +54,9 @@ For the median, you need to provide columns called 'upper_0' and 'lower_0' the unit of a single forecast, i.e. a forecast was made for a combination of the values in \code{forecast_unit}} -\item{summarise_by}{character vector of columns to group the summary by. By -default, this is equal to \code{by} and no summary takes place. -But sometimes you may want to to summarise -over categories different from the scoring. -\code{summarise_by} is also the grouping level used to compute -(and possibly plot) the probability integral transform(pit). Sometimes you -may want to include 'range', 'quantile' or 'sample', to summarise by -range, quantile or sample.} - \item{metrics}{the metrics you want to have in the output. If \code{NULL} (the default), all available metrics will be computed. For a list of available metrics see \code{\link[=available_metrics]{available_metrics()}}} - -\item{quantiles}{numeric vector of quantiles to be returned when summarising. -Instead of just returning a mean, quantiles will be returned for the -groups specified through \code{summarise_by}. By default, no quantiles are -returned.} - -\item{sd}{if \code{TRUE} (the default is \code{FALSE}) the standard deviation of all -metrics will be returned when summarising.} } \value{ A data.table with appropriate scores. For more information see diff --git a/man/eval_forecasts_sample.Rd b/man/eval_forecasts_sample.Rd index 8bd45bfd7..c53ec4aec 100644 --- a/man/eval_forecasts_sample.Rd +++ b/man/eval_forecasts_sample.Rd @@ -4,15 +4,7 @@ \alias{eval_forecasts_sample} \title{Evaluate forecasts in a Sample-Based Format (Integer or Continuous)} \usage{ -eval_forecasts_sample( - data, - forecast_unit, - summarise_by, - metrics, - prediction_type, - quantiles, - sd -) +eval_forecasts_sample(data, forecast_unit, metrics, prediction_type) } \arguments{ \item{data}{A data.frame or data.table with the predictions and observations. @@ -62,28 +54,11 @@ For the median, you need to provide columns called 'upper_0' and 'lower_0' the unit of a single forecast, i.e. a forecast was made for a combination of the values in \code{forecast_unit}} -\item{summarise_by}{character vector of columns to group the summary by. By -default, this is equal to \code{by} and no summary takes place. -But sometimes you may want to to summarise -over categories different from the scoring. -\code{summarise_by} is also the grouping level used to compute -(and possibly plot) the probability integral transform(pit). Sometimes you -may want to include 'range', 'quantile' or 'sample', to summarise by -range, quantile or sample.} - \item{metrics}{the metrics you want to have in the output. If \code{NULL} (the default), all available metrics will be computed. For a list of available metrics see \code{\link[=available_metrics]{available_metrics()}}} \item{prediction_type}{character, should be either "continuous" or "integer"} - -\item{quantiles}{numeric vector of quantiles to be returned when summarising. -Instead of just returning a mean, quantiles will be returned for the -groups specified through \code{summarise_by}. By default, no quantiles are -returned.} - -\item{sd}{if \code{TRUE} (the default is \code{FALSE}) the standard deviation of all -metrics will be returned when summarising.} } \value{ A data.table with appropriate scores. For more information see diff --git a/man/summarise_scores.Rd b/man/summarise_scores.Rd new file mode 100644 index 000000000..a6dc89591 --- /dev/null +++ b/man/summarise_scores.Rd @@ -0,0 +1,40 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/eval_forecasts.R +\name{summarise_scores} +\alias{summarise_scores} +\title{Summarise scores as produced by \code{\link[=eval_forecasts]{eval_forecasts()}}} +\usage{ +summarise_scores(scores, summarise_by, quantiles = c(), sd = FALSE) +} +\arguments{ +\item{scores}{a data.table of unsummarised scores as produced by +\code{\link[=eval_forecasts]{eval_forecasts()}}, when running it with the option \code{summarise_by = NULL}} + +\item{summarise_by}{character vector of columns to group the summary by. By +default, this is equal to \code{by} and no summary takes place. +But sometimes you may want to to summarise +over categories different from the scoring. +\code{summarise_by} is also the grouping level used to compute +(and possibly plot) the probability integral transform(pit). Sometimes you +may want to include 'range', 'quantile' or 'sample', to summarise by +range, quantile or sample.} + +\item{quantiles}{numeric vector of quantiles to be returned when summarising. +Instead of just returning a mean, quantiles will be returned for the +groups specified through \code{summarise_by}. By default, no quantiles are +returned.} + +\item{sd}{if \code{TRUE} (the default is \code{FALSE}) the standard deviation of all +metrics will be returned when summarising.} +} +\description{ +Summarise scores as produced by \code{\link[=eval_forecasts]{eval_forecasts()}}- +} +\examples{ +library(scoringutils) +data <- quantile_example_data +scores <- eval_forecasts(data) +summarise_scores(scores, + summarise_by = c("model")) + +} From 0f401e6d1b9be695e0c903d6d99f613026e6d4f2 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Sat, 27 Nov 2021 09:51:30 +0100 Subject: [PATCH 026/225] add return statement to eval_forecasts and add two tests --- R/check_forecasts.R | 2 +- R/eval_forecasts.R | 2 +- R/eval_forecasts_quantile.R | 2 +- tests/testthat/test-eval_forecasts.R | 37 ++++++++++++++++++++++++++++ 4 files changed, 40 insertions(+), 3 deletions(-) diff --git a/R/check_forecasts.R b/R/check_forecasts.R index 321784d81..c44b899d5 100644 --- a/R/check_forecasts.R +++ b/R/check_forecasts.R @@ -216,7 +216,7 @@ print.scoringutils_check <- function(x, ...) { if (length(x$errors) > 0) { cat(paste0( "\n\n", - "The following things will likely result in an error:", + "The following things will likely result in an error: ", paste(x$errors, collapse = "\n") )) } diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index 45a5eead1..c8a58aab6 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -245,7 +245,7 @@ eval_forecasts <- function(data, quantiles = quantiles, sd = sd) - + return(scores[]) } diff --git a/R/eval_forecasts_quantile.R b/R/eval_forecasts_quantile.R index 4ccd668d9..58a7c4afe 100644 --- a/R/eval_forecasts_quantile.R +++ b/R/eval_forecasts_quantile.R @@ -96,7 +96,7 @@ eval_forecasts_quantile <- function(data, delete_cols <- names(quantile_data)[!(names(quantile_data) %in% keep_cols)] quantile_data[, eval(delete_cols) := NULL] - # duplicate median column before merging if median is too be counted twice + # duplicate median column before merging if median is to be counted twice # if this is false, then the res will have one entry for every quantile, # which translates to two rows for every interval, but only one for the median if (count_median_twice) { diff --git a/tests/testthat/test-eval_forecasts.R b/tests/testthat/test-eval_forecasts.R index 118c90785..87a746c73 100644 --- a/tests/testthat/test-eval_forecasts.R +++ b/tests/testthat/test-eval_forecasts.R @@ -3,6 +3,17 @@ test_that("function throws an error if data is missing", { expect_error(eval_forecasts(data = NULL)) }) +test_that("eval_forecasts() warns if column name equals a metric name", { + data <- data.frame(true_value = rep(1:10, each = 2), + prediction = rep(c(-0.3, 0.3), 10) + rep(1:10, each = 2), + model = "Model 1", + date = as.Date("2020-01-01") + rep(1:10, each = 2), + quantile = rep(c(0.1, 0.9), times = 10), + bias = 3) + + expect_warning(eval_forecasts(data = data)) +}) + # test binary case ------------------------------------------------------------- @@ -16,6 +27,17 @@ test_that("function produces output for a binary case", { }) +test_that("function produces score for a binary case", { + binary_example <- data.table::setDT(scoringutils::binary_example_data) + eval <- eval_forecasts(binary_example[!is.na(prediction)], + summarise_by = c("model", "value_desc"), + quantiles = c(0.5), sd = TRUE) + expect_true("brier_score" %in% names(eval)) +}) + + + + # test quantile case ----------------------------------------------------------- test_that("function produces output for a quantile format case", { quantile_example <- data.table::setDT(scoringutils::quantile_example_data) @@ -27,6 +49,21 @@ test_that("function produces output for a quantile format case", { TRUE) }) +test_that("eval_forecasts() quantile produces desired metrics", { + data <- data.frame(true_value = rep(1:10, each = 2), + prediction = rep(c(-0.3, 0.3), 10) + rep(1:10, each = 2), + model = "Model 1", + date = as.Date("2020-01-01") + rep(1:10, each = 2), + quantile = rep(c(0.1, 0.9), times = 10)) + + out <- eval_forecasts(data = data) + metric_names <- c("dispersion", "underprediction", "overprediction", + "bias", "aem", "coverage_deviation") + + expect_true(all(metric_names %in% colnames(out))) +}) + + test_that("calculation of aem is correct for a quantile format case", { quantile_example <- data.table::setDT(scoringutils::quantile_example_data) eval <- eval_forecasts(quantile_example[!is.na(prediction)], From 23a3c047d3c52f3ebefcd13cf15a54733830a265 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Sat, 27 Nov 2021 10:19:27 +0100 Subject: [PATCH 027/225] rename function in test to increase clarity --- tests/testthat/test-pairwise_comparison.R | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tests/testthat/test-pairwise_comparison.R b/tests/testthat/test-pairwise_comparison.R index 0fee6db53..1cf7dd7f5 100644 --- a/tests/testthat/test-pairwise_comparison.R +++ b/tests/testthat/test-pairwise_comparison.R @@ -47,7 +47,7 @@ test_that("pairwise comparisons works", { # -----------------------------------------------------------------------------# ## rerun code from Johannes Bracher to see whether results agree - pairwise_comparison <- function(scores, mx, my, subset = rep(TRUE, nrow(scores)), + pairwise_comparison_jb <- function(scores, mx, my, subset = rep(TRUE, nrow(scores)), permutation_test = FALSE){ # apply subset: scores <- scores[subset, ] @@ -90,8 +90,8 @@ test_that("pairwise comparisons works", { set.seed(123) # set seed for permutation tests for(mx in seq_along(models)){ for(my in 1:mx){ - pwc <- pairwise_comparison(scores = scores_johannes, mx = models[mx], my = models[my], - permutation_test = TRUE) + pwc <- pairwise_comparison_jb(scores = scores_johannes, mx = models[mx], my = models[my], + permutation_test = TRUE) results_ratio[mx, my] <- pwc$ratio results_ratio[my, mx] <- 1/pwc$ratio results_pval[mx, my] <- @@ -127,8 +127,8 @@ test_that("pairwise comparisons works", { set.seed(123) # set seed for permutation tests for(mx in seq_along(models)){ for(my in 1:mx){ - pwc <- pairwise_comparison(scores = scores_johannes_subset, mx = models[mx], my = models[my], - permutation_test = TRUE) + pwc <- pairwise_comparison_jb(scores = scores_johannes_subset, mx = models[mx], my = models[my], + permutation_test = TRUE) results_ratio[mx, my] <- pwc$ratio results_ratio[my, mx] <- 1/pwc$ratio results_pval[mx, my] <- From 74da2458d4e859e3db6161626ec1fb9b0947e051 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Sat, 27 Nov 2021 11:26:47 +0100 Subject: [PATCH 028/225] move computation of pairwise comparisons outside eval_forecasts_quantile into eval_forecasts --- R/eval_forecasts.R | 13 +++++++++++++ R/eval_forecasts_quantile.R | 15 --------------- 2 files changed, 13 insertions(+), 15 deletions(-) diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index c8a58aab6..c397b97ac 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -240,6 +240,19 @@ eval_forecasts <- function(data, prediction_type = prediction_type) } + if (compute_relative_skill) { + if (is.null(summarise_by)) { + summarise_by <- forecast_unit + } + + relative_res <- add_rel_skill_to_eval_forecasts(unsummarised_scores = scores, + rel_skill_metric = rel_skill_metric, + baseline = baseline, + by = forecast_unit, + summarise_by = summarise_by) + scores <- merge(scores, relative_res, by = forecast_unit) + } + scores <- summarise_scores(scores, summarise_by, quantiles = quantiles, diff --git a/R/eval_forecasts_quantile.R b/R/eval_forecasts_quantile.R index 58a7c4afe..b3f6e2742 100644 --- a/R/eval_forecasts_quantile.R +++ b/R/eval_forecasts_quantile.R @@ -112,20 +112,5 @@ eval_forecasts_quantile <- function(data, res <- merge(res, quantile_data, by = merge_cols, all.x = TRUE) } - - if (compute_relative_skill) { - - if (is.null(summarise_by)) { - summarise_by <- forecast_unit - } - - relative_res <- add_rel_skill_to_eval_forecasts(unsummarised_scores = res, - rel_skill_metric = rel_skill_metric, - baseline = baseline, - by = forecast_unit, - summarise_by = summarise_by) - res <- merge(res, relative_res, by = forecast_unit) - } - return(res[]) } From 35e7dafcd1a0dc64e3994295fabe4d2021813cc7 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Sat, 27 Nov 2021 11:35:42 +0100 Subject: [PATCH 029/225] add helper function to infer metric for pairwise comparisons --- R/pairwise-comparisons.R | 36 ++++++++++++++++++++++++++--------- man/infer_rel_skill_metric.Rd | 17 +++++++++++++++++ 2 files changed, 44 insertions(+), 9 deletions(-) create mode 100644 man/infer_rel_skill_metric.Rd diff --git a/R/pairwise-comparisons.R b/R/pairwise-comparisons.R index 9e25fdcff..56f84d5c7 100644 --- a/R/pairwise-comparisons.R +++ b/R/pairwise-comparisons.R @@ -115,15 +115,7 @@ add_rel_skill_to_eval_forecasts <- function(unsummarised_scores, # infer the correct relative skill if only "auto" is given if (rel_skill_metric == "auto") { - if ("interval_score" %in% colnames(unsummarised_scores)) { - rel_skill_metric <- "interval_score" - } else if ("crps" %in% colnames(unsummarised_scores)) { - rel_skill_metric <- "crps" - } else if ("brier_score" %in% colnames(unsummarised_scores)) { - rel_skill_metric <- "brier_score" - } else { - stop("automatically assign a metric to add relative skill failed. Please provide a metric.") - } + rel_skill_metric <- infer_rel_skill_metric(unsummarised_scores) } # summarise scores over all quantiles, ranges or samples in order to not @@ -643,6 +635,32 @@ plot_pairwise_comparison <- function(comparison_result, +#' @title Infer metric for pairwise comparisons +#' +#' @description +#' Helper function to infer the metric for which pairwise comparisons shall +#' be made. The function simply checks the names of the available columns and +#' chooses the most widely used metric. +#' @param scores A data.table of scores as produced by [eval_forecasts()] +#' @keywords internal + +infer_rel_skill_metric <- function(scores) { + + if ("interval_score" %in% colnames(scores)) { + rel_skill_metric <- "interval_score" + } else if ("crps" %in% colnames(scores)) { + rel_skill_metric <- "crps" + } else if ("brier_score" %in% colnames(scores)) { + rel_skill_metric <- "brier_score" + } else { + stop("automatically assigning a metric to compute relative skills on failed. ", + "Please provide a metric.") + } + + return(rel_skill_metric) +} + + diff --git a/man/infer_rel_skill_metric.Rd b/man/infer_rel_skill_metric.Rd new file mode 100644 index 000000000..b47808c6e --- /dev/null +++ b/man/infer_rel_skill_metric.Rd @@ -0,0 +1,17 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/pairwise-comparisons.R +\name{infer_rel_skill_metric} +\alias{infer_rel_skill_metric} +\title{Infer metric for pairwise comparisons} +\usage{ +infer_rel_skill_metric(scores) +} +\arguments{ +\item{scores}{A data.table of scores as produced by \code{\link[=eval_forecasts]{eval_forecasts()}}} +} +\description{ +Helper function to infer the metric for which pairwise comparisons shall +be made. The function simply checks the names of the available columns and +chooses the most widely used metric. +} +\keyword{internal} From de4f67cfdeb6e63591a782b75abf467d3650ee49 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Sat, 27 Nov 2021 11:41:26 +0100 Subject: [PATCH 030/225] add pairwise comparison tests for binary and integer data --- tests/testthat/test-pairwise_comparison.R | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/tests/testthat/test-pairwise_comparison.R b/tests/testthat/test-pairwise_comparison.R index 1cf7dd7f5..2c451c9ca 100644 --- a/tests/testthat/test-pairwise_comparison.R +++ b/tests/testthat/test-pairwise_comparison.R @@ -154,3 +154,24 @@ test_that("pairwise comparisons works", { expect_equal(relative_skills_with$relative_skill, ratios_scaled) }) + +test_that("Pairwise comparisons work in eval_forecasts() with integer data", { + eval <- eval_forecasts(data = integer_example_data, + summarise_by = "model", + compute_relative_skill = TRUE) + + expect_true("relative_skill" %in% colnames(eval)) +}) + + +test_that("Pairwise comparisons work in eval_forecasts() with binary data", { + eval <- suppressWarnings( + eval_forecasts(data = binary_example_data, + summarise_by = "model", + compute_relative_skill = TRUE) + ) + + expect_true("relative_skill" %in% colnames(eval)) +}) + + From 3d37201295d0c745bee7584c9aa9a69afeb294ec Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Sat, 27 Nov 2021 11:47:49 +0100 Subject: [PATCH 031/225] add return statement to pairwise_comparison() --- R/pairwise-comparisons.R | 2 ++ 1 file changed, 2 insertions(+) diff --git a/R/pairwise-comparisons.R b/R/pairwise-comparisons.R index 56f84d5c7..aec37487e 100644 --- a/R/pairwise-comparisons.R +++ b/R/pairwise-comparisons.R @@ -89,6 +89,8 @@ pairwise_comparison <- function(scores, }) out <- data.table::rbindlist(results) + + return(out) } From f95c7a3a63bc9aaedcc38b572b90dfa6b38727ba Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Sat, 27 Nov 2021 11:54:44 +0100 Subject: [PATCH 032/225] add more tests for pairwise comparisons --- tests/testthat/test-pairwise_comparison.R | 32 +++++++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/tests/testthat/test-pairwise_comparison.R b/tests/testthat/test-pairwise_comparison.R index 2c451c9ca..2b509db6d 100644 --- a/tests/testthat/test-pairwise_comparison.R +++ b/tests/testthat/test-pairwise_comparison.R @@ -175,3 +175,35 @@ test_that("Pairwise comparisons work in eval_forecasts() with binary data", { }) +# tests for pairwise comparison function --------------------------------------- + +test_that("pairwise_comparison() works", { + df <- data.frame(model = rep(c("model1", "model2", "model3"), each = 10), + date = as.Date("2020-01-01") + rep(1:5, each = 2), + location = c(1, 2), + interval_score = (abs(rnorm(30))), + aem = (abs(rnorm(30)))) + + res <- pairwise_comparison(df, + baseline = "model1") + + colnames <- c("model", "compare_against", "mean_scores_ratio", + "pval", "adj_pval", "relative_skill", "scaled_rel_skill") + + expect_true(all(colnames %in% colnames(res))) +}) + + +test_that("pairwise_comparison() works inside and outside of eval_forecasts()", { + eval <- eval_forecasts(data = continuous_example_data) + + pairwise <- pairwise_comparison(eval, summarise_by = "model", + metric = "crps") + + eval2 <- eval_forecasts(data = continuous_example_data, + summarise_by = "model", + compute_relative_skill = TRUE) + + expect_equal(sort(unique(pairwise$relative_skill)), + sort(eval2$relative_skill)) +}) From 64656ec6cd974c641b7a5b3c2ae71e3e231fac8b Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Fri, 10 Dec 2021 12:17:04 +0100 Subject: [PATCH 033/225] add a quantile version to pit_df and hist_PIT --- R/pit.R | 77 ++++++++++++++++++++++----------------- man/hist_PIT.Rd | 10 ++++- man/pit_df.Rd | 8 ++-- tests/testthat/test-pit.R | 7 ++++ 4 files changed, 63 insertions(+), 39 deletions(-) diff --git a/R/pit.R b/R/pit.R index 17e9c1ffe..3df5b0a83 100644 --- a/R/pit.R +++ b/R/pit.R @@ -152,8 +152,14 @@ pit <- function(true_values, #' Make a simple histogram of the probability integral transformed values to #' visually check whether a uniform distribution seems likely. #' -#' @param PIT_samples A vector with the PIT values of size n -#' @param num_bins the number of bins in the PIT histogram. +#' @param PIT_samples A named or unnamed vector with the PIT values of size n. +#' Alternatively a list of such vectors. +#' @param num_bins the number of bins in the PIT histogram. [hist_PIT()] will +#' always attempt to use the names of the vector (these should be interpretable +#' as numeric quantiles between 0 and 1) in order to construct the bins. +#' If this is not desired please provide unnamed vectors. If no names are +#' provided, and `num_bins` is `NULL`, then +#' the number of bins will be determined heuristically. #' @return vector with the scoring values #' @examples #' library(scoringutils) @@ -171,15 +177,19 @@ hist_PIT <- function(PIT_samples, single_PIT_hist <- function(PIT_samples, num_bins) { - if (is.null(num_bins)) { + + if (!is.null(names(PIT_samples))) { + breaks <- as.numeric(names(PIT_samples)) + } else if (is.null(num_bins)) { n <- length(PIT_samples) num_bins = round(sqrt(n)) + breaks <- seq(0, 1, length.out = num_bins + 1) } hist_PIT <- ggplot2::ggplot(data = data.frame(x = PIT_samples), ggplot2::aes(x = x)) + ggplot2::geom_histogram(ggplot2::aes(y = stat(count) / sum(count)), - breaks = seq(0, 1, length.out = num_bins + 1), + breaks = breaks, colour = "grey") + ggplot2::xlab("PIT") + ggplot2::ylab("Frequency") + @@ -212,8 +222,6 @@ hist_PIT <- function(PIT_samples, #' 'location' in the data and want to have a PIT histogram for #' every model and location, specify `summarise_by = c("model", "location")`. #' @inheritParams pit -#' @param include_full Boolean (default is `FALSE`). Whether or not to also -#' return PIT values for the full data set without any grouping. #' @return a named list with PIT values according to the grouping specified in #' `summarised_by` #' @examples @@ -221,6 +229,9 @@ hist_PIT <- function(PIT_samples, #' result <- pit_df(example, summarise_by = "model") #' hist_PIT(result) #' +#' # example with quantile data +#' result <- pit_df(quantile_example_data, summarise_by = "model") +#' hist_PIT(result) #' @export #' @references #' Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, @@ -230,39 +241,40 @@ hist_PIT <- function(PIT_samples, pit_df <- function(data, summarise_by, - n_replicates = 100, - include_full = FALSE) { + n_replicates = 100) { + # clean data by removing NA values data <- check_clean_data(data, verbose = FALSE) - # reformat data.table to wide format for PIT + # get prediction type + prediction_type <- get_prediction_type(data) + + # if prediction type is quantile, simply extract coverage values from + # eval_forecasts and returned a list with named vectors + if (prediction_type == "quantile") { + coverage <- + eval_forecasts(data, + summarise_by = unique(c("quantile", summarise_by)), + metrics = "quantile_coverage") + + split_data <- split(coverage, by = summarise_by) + + pit_values <- lapply(split_data, + FUN = function(data) { + data <- data[order(quantile)] + pit_values <- data$quantile_coverage + names(pit_values) <- data$quantile + return(pit_values) + }) + return(pit_values) + } + + # if prediction type is not quantile, calculate PIT values based on samples data_wide <- data.table::dcast(data, ... ~ paste("InternalSampl_", sample, sep = ""), value.var = "prediction") - # implementation idea for a data.table version. Probably only makes sense - # in a non-randomised version of the integer PIT - # could have a return = c("Data.table", "list") argument to distinguish - # data_wide[, "pit_values" := list(pit(true_value, as.matrix(.SD), - # n_replicates = n_replicates)), - # .SDcols = names(data_wide)[grepl("InternalSampl_", names(data_wide))], - # by = summarise_by] - # sample_names <- names(data_wide)[grepl("InternalSampl_", names(data_wide))] - # data <- data.table::melt(data_wide, - # measure.vars = sample_names, - # variable.name = "sample", - # value.name = "prediction") - # data[, sample := as.integer(gsub(pattern = "InternalSampl_", replacement = "", - # x = sample))] - - # include the full data set once in addition to the subsets. - if (include_full) { - split_data <- list(data_wide) - } else { - split_data <- list() - } - - split_data <- c(split_data, split(data_wide, by = summarise_by)) + split_data <- split(data_wide, by = summarise_by) pit_values <- lapply(split_data, FUN = function(data) { @@ -273,4 +285,3 @@ pit_df <- function(data, }) return(pit_values) } - diff --git a/man/hist_PIT.Rd b/man/hist_PIT.Rd index 36083f892..78f08599e 100644 --- a/man/hist_PIT.Rd +++ b/man/hist_PIT.Rd @@ -7,9 +7,15 @@ hist_PIT(PIT_samples, num_bins = NULL) } \arguments{ -\item{PIT_samples}{A vector with the PIT values of size n} +\item{PIT_samples}{A named or unnamed vector with the PIT values of size n. +Alternatively a list of such vectors.} -\item{num_bins}{the number of bins in the PIT histogram.} +\item{num_bins}{the number of bins in the PIT histogram. \code{\link[=hist_PIT]{hist_PIT()}} will +always attempt to use the names of the vector (these should be interpretable +as numeric quantiles between 0 and 1) in order to construct the bins. +If this is not desired please provide unnamed vectors. If no names are +provided, and \code{num_bins} is \code{NULL}, then +the number of bins will be determined heuristically.} } \value{ vector with the scoring values diff --git a/man/pit_df.Rd b/man/pit_df.Rd index e512e25a6..be9e4df40 100644 --- a/man/pit_df.Rd +++ b/man/pit_df.Rd @@ -4,7 +4,7 @@ \alias{pit_df} \title{Probability Integral Transformation (data.frame Format)} \usage{ -pit_df(data, summarise_by, n_replicates = 100, include_full = FALSE) +pit_df(data, summarise_by, n_replicates = 100) } \arguments{ \item{data}{a data.frame with the following columns: \code{true_value}, @@ -17,9 +17,6 @@ every model and location, specify \code{summarise_by = c("model", "location")}.} \item{n_replicates}{the number of draws for the ranomised PIT for integer predictions.} - -\item{include_full}{Boolean (default is \code{FALSE}). Whether or not to also -return PIT values for the full data set without any grouping.} } \value{ a named list with PIT values according to the grouping specified in @@ -36,6 +33,9 @@ example <- scoringutils::continuous_example_data result <- pit_df(example, summarise_by = "model") hist_PIT(result) +# example with quantile data +result <- pit_df(quantile_example_data, summarise_by = "model") +hist_PIT(result) } \references{ Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, diff --git a/tests/testthat/test-pit.R b/tests/testthat/test-pit.R index fa194374d..6077e7307 100644 --- a/tests/testthat/test-pit.R +++ b/tests/testthat/test-pit.R @@ -39,6 +39,13 @@ test_that("function works for continuous true_values and predictions", 10) }) +test_that("pit_df function works for continuous and quantile data", + { + pit_df(quantile_example_data, summarise_by = "model") + pit_df(continuous_example_data, summarise_by = "model") + }) + + From 19a742924acafa852d9a6d0e2ae5caa133ec086b Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Fri, 10 Dec 2021 12:38:06 +0100 Subject: [PATCH 034/225] remove by argument from pairwise_comparison --- R/pairwise-comparisons.R | 15 ++++----------- man/compare_two_models.Rd | 2 -- man/pairwise_comparison.Rd | 5 +---- man/pairwise_comparison_one_group.Rd | 2 -- 4 files changed, 5 insertions(+), 19 deletions(-) diff --git a/R/pairwise-comparisons.R b/R/pairwise-comparisons.R index aec37487e..ca7a022fd 100644 --- a/R/pairwise-comparisons.R +++ b/R/pairwise-comparisons.R @@ -13,7 +13,6 @@ #' [eval_forecasts()] #' @param metric A character vector of length one with the metric to do #' the comparison on. -#' @param by character vector that denotes the unit of a single forecast. #' @param summarise_by character vector of columns to group the summary by. By #' default, this is equal to `by` and no summary takes place. But sometimes you #' may want to to summarise over categories different from the scoring. @@ -55,8 +54,7 @@ pairwise_comparison <- function(scores, test_type = c("non_parametric", "permuation"), n_permutations = 999), baseline = NULL, - summarise_by = c("model"), - by = NULL) { + summarise_by = c("model")) { scores <- data.table::as.data.table(scores) @@ -66,13 +64,9 @@ pairwise_comparison <- function(scores, n_permutations = 999), optional = test_options) - # identify unit of single observation if it is not given. - # usually, by = NULL should be fine and only needs to be specified if there - # are additional columns that are not metrics and not related to the unit of observation - if (is.null(by)) { - all_metrics <- available_metrics() - by <- setdiff(names(scores), c(all_metrics, "model")) - } + # identify unit of single observation. + all_metrics <- available_metrics() + by <- setdiff(names(scores), c(all_metrics, "model")) split_by <- setdiff(summarise_by, "model") @@ -139,7 +133,6 @@ add_rel_skill_to_eval_forecasts <- function(unsummarised_scores, pairwise <- pairwise_comparison(scores = scores, metric = rel_skill_metric, baseline = baseline, - by = by, summarise_by = summarise_by) # delete unnecessary columns from the output diff --git a/man/compare_two_models.Rd b/man/compare_two_models.Rd index 2e8755d75..fe0ad309b 100644 --- a/man/compare_two_models.Rd +++ b/man/compare_two_models.Rd @@ -22,8 +22,6 @@ To change only one of the default options, just pass a list as input with the name of the argument you want to change. All elements not included in the list will be set to the default (so passing an empty list would result in the default options).} - -\item{by}{character vector that denotes the unit of a single forecast.} } \description{ This function compares two models based on the subset of forecasts for which diff --git a/man/pairwise_comparison.Rd b/man/pairwise_comparison.Rd index 9a192e87d..5cf5f0a94 100644 --- a/man/pairwise_comparison.Rd +++ b/man/pairwise_comparison.Rd @@ -10,8 +10,7 @@ pairwise_comparison( test_options = list(oneSided = FALSE, test_type = c("non_parametric", "permuation"), n_permutations = 999), baseline = NULL, - summarise_by = c("model"), - by = NULL + summarise_by = c("model") ) } \arguments{ @@ -35,8 +34,6 @@ default, this is equal to \code{by} and no summary takes place. But sometimes yo may want to to summarise over categories different from the scoring. \code{summarise_by} is also the grouping level used to compute (and possibly plot) the probability integral transform(pit).} - -\item{by}{character vector that denotes the unit of a single forecast.} } \value{ A ggplot2 object with a coloured table of summarised scores diff --git a/man/pairwise_comparison_one_group.Rd b/man/pairwise_comparison_one_group.Rd index 1a92d2eb3..def1369f4 100644 --- a/man/pairwise_comparison_one_group.Rd +++ b/man/pairwise_comparison_one_group.Rd @@ -29,8 +29,6 @@ default options).} \item{baseline}{character vector of length one that denotes the baseline model against which to compare other models.} -\item{by}{character vector that denotes the unit of a single forecast.} - \item{summarise_by}{character vector of columns to group the summary by. By default, this is equal to \code{by} and no summary takes place. But sometimes you may want to to summarise over categories different from the scoring. From 6022536fada77a16b886c2e7f0fe2daaba2b6026 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Fri, 10 Dec 2021 12:53:40 +0100 Subject: [PATCH 035/225] replace test_option argument with ... in pariwise_comparison --- R/pairwise-comparisons.R | 55 +++++++++++++--------------- man/compare_two_models.Rd | 17 +++++---- man/pairwise_comparison.Rd | 19 +++++----- man/pairwise_comparison_one_group.Rd | 23 +++++------- 4 files changed, 54 insertions(+), 60 deletions(-) diff --git a/R/pairwise-comparisons.R b/R/pairwise-comparisons.R index ca7a022fd..91819f98c 100644 --- a/R/pairwise-comparisons.R +++ b/R/pairwise-comparisons.R @@ -18,13 +18,15 @@ #' may want to to summarise over categories different from the scoring. #' `summarise_by` is also the grouping level used to compute (and possibly plot) #' the probability integral transform(pit). -#' @param test_options list with options to pass down to [compare_two_models()]. -#' To change only one of the default options, just pass a list as input with -#' the name of the argument you want to change. All elements not included in the -#' list will be set to the default (so passing an empty list would result in the -#' default options). #' @param baseline character vector of length one that denotes the baseline #' model against which to compare other models. +#' @param ... additional arguments, such as test options that can get passed +#' down to lower level functions. The following options are available: +#' `oneSided` (Boolean, default is `FALSE`, whether two conduct a one-sided +#' instead of a two-sided test), `test_type` (character, either "non_parametric" +#' or "permutation" determining which kind of test shall be conducted to +#' determine p-values. Default is "non-parametric), `n_permutations` (number of +#' permutations for a permutation test. Default is 999) #' @return A ggplot2 object with a coloured table of summarised scores #' @importFrom data.table as.data.table data.table setnames copy #' @importFrom stats sd rbinom wilcox.test p.adjust @@ -50,36 +52,27 @@ pairwise_comparison <- function(scores, metric = "interval_score", # maybe the default can happen automatically, - test_options = list(oneSided = FALSE, - test_type = c("non_parametric", "permuation"), - n_permutations = 999), baseline = NULL, - summarise_by = c("model")) { + summarise_by = c("model"), + ...) { scores <- data.table::as.data.table(scores) - # update test options - test_options <- update_list(defaults = list(oneSided = FALSE, - test_type = c("non_parametric", "permuation"), - n_permutations = 999), - optional = test_options) - # identify unit of single observation. - all_metrics <- available_metrics() - by <- setdiff(names(scores), c(all_metrics, "model")) + forecast_unit <- scoringutils:::get_unit_of_forecast(scores) + # split data set into groups determined by summarise_by split_by <- setdiff(summarise_by, "model") - split_scores <- split(scores, by = split_by) results <- lapply(split_scores, FUN = function(scores) { out <- pairwise_comparison_one_group(scores = scores, metric = metric, - test_options = test_options, baseline = baseline, - by = by, - summarise_by = summarise_by) + by = forecast_unit, + summarise_by = summarise_by, + ...) }) out <- data.table::rbindlist(results) @@ -170,10 +163,10 @@ add_rel_skill_to_eval_forecasts <- function(unsummarised_scores, pairwise_comparison_one_group <- function(scores, metric, - test_options, baseline, by, - summarise_by) { + summarise_by, + ...) { @@ -200,7 +193,7 @@ pairwise_comparison_one_group <- function(scores, # create a data.frame with results # we only need to do the calculation once, because for the ratio that - # should just be the inverse and for the permuation the result should + # should just be the inverse and for the permutation the result should # be the same # set up initial data.frame with all possible pairwise comparisons @@ -211,8 +204,8 @@ pairwise_comparison_one_group <- function(scores, name_model1 = model, name_model2 = compare_against, metric = metric, - test_options = test_options, - by = by), + by = by, + ...), by = seq_len(NROW(combinations))] combinations <- combinations[order(ratio)] @@ -313,7 +306,9 @@ compare_two_models <- function(scores, name_model1, name_model2, metric, - test_options, + oneSided = FALSE, + test_type = c("non_parametric", "permutation"), + n_permutations = 999, by) { scores <- data.table::as.data.table(scores) @@ -348,11 +343,11 @@ unique(overlap) # test whether the ratio is significantly different from one # equivalently, one can test whether the difference between the two values # is significantly different from zero. - if (test_options$test_type[1] == "permutation") { + if (test_type[1] == "permutation") { # adapted from the surveillance package pval <- permutation_test(values_x, values_y, - nPermutation = test_options$n_permutations, - oneSided = test_options$oneSided, + nPermutation = n_permutations, + oneSided = oneSided, comparison_mode = "difference") } else { # this probably needs some more thought diff --git a/man/compare_two_models.Rd b/man/compare_two_models.Rd index fe0ad309b..f9c7e6777 100644 --- a/man/compare_two_models.Rd +++ b/man/compare_two_models.Rd @@ -4,7 +4,16 @@ \alias{compare_two_models} \title{Compare Two Models Based on Subset of Common Forecasts} \usage{ -compare_two_models(scores, name_model1, name_model2, metric, test_options, by) +compare_two_models( + scores, + name_model1, + name_model2, + metric, + oneSided = FALSE, + test_type = c("non_parametric", "permutation"), + n_permutations = 999, + by +) } \arguments{ \item{scores}{A data.frame of unsummarised scores as produced by @@ -16,12 +25,6 @@ compare_two_models(scores, name_model1, name_model2, metric, test_options, by) \item{metric}{A character vector of length one with the metric to do the comparison on.} - -\item{test_options}{list with options to pass down to \code{\link[=compare_two_models]{compare_two_models()}}. -To change only one of the default options, just pass a list as input with -the name of the argument you want to change. All elements not included in the -list will be set to the default (so passing an empty list would result in the -default options).} } \description{ This function compares two models based on the subset of forecasts for which diff --git a/man/pairwise_comparison.Rd b/man/pairwise_comparison.Rd index 5cf5f0a94..91f38b696 100644 --- a/man/pairwise_comparison.Rd +++ b/man/pairwise_comparison.Rd @@ -7,10 +7,9 @@ pairwise_comparison( scores, metric = "interval_score", - test_options = list(oneSided = FALSE, test_type = c("non_parametric", "permuation"), - n_permutations = 999), baseline = NULL, - summarise_by = c("model") + summarise_by = c("model"), + ... ) } \arguments{ @@ -20,12 +19,6 @@ pairwise_comparison( \item{metric}{A character vector of length one with the metric to do the comparison on.} -\item{test_options}{list with options to pass down to \code{\link[=compare_two_models]{compare_two_models()}}. -To change only one of the default options, just pass a list as input with -the name of the argument you want to change. All elements not included in the -list will be set to the default (so passing an empty list would result in the -default options).} - \item{baseline}{character vector of length one that denotes the baseline model against which to compare other models.} @@ -34,6 +27,14 @@ default, this is equal to \code{by} and no summary takes place. But sometimes yo may want to to summarise over categories different from the scoring. \code{summarise_by} is also the grouping level used to compute (and possibly plot) the probability integral transform(pit).} + +\item{...}{additional arguments, such as test options that can get passed +down to lower level functions. The following options are available: +\code{oneSided} (Boolean, default is \code{FALSE}, whether two conduct a one-sided +instead of a two-sided test), \code{test_type} (character, either "non_parametric" +or "permutation" determining which kind of test shall be conducted to +determine p-values. Default is "non-parametric), \code{n_permutations} (number of +permutations for a permutation test. Default is 999)} } \value{ A ggplot2 object with a coloured table of summarised scores diff --git a/man/pairwise_comparison_one_group.Rd b/man/pairwise_comparison_one_group.Rd index def1369f4..d4581af4a 100644 --- a/man/pairwise_comparison_one_group.Rd +++ b/man/pairwise_comparison_one_group.Rd @@ -4,14 +4,7 @@ \alias{pairwise_comparison_one_group} \title{Do Pairwise Comparison for one Set of Forecasts} \usage{ -pairwise_comparison_one_group( - scores, - metric, - test_options, - baseline, - by, - summarise_by -) +pairwise_comparison_one_group(scores, metric, baseline, by, summarise_by, ...) } \arguments{ \item{scores}{A data.frame of unsummarised scores as produced by @@ -20,12 +13,6 @@ pairwise_comparison_one_group( \item{metric}{A character vector of length one with the metric to do the comparison on.} -\item{test_options}{list with options to pass down to \code{\link[=compare_two_models]{compare_two_models()}}. -To change only one of the default options, just pass a list as input with -the name of the argument you want to change. All elements not included in the -list will be set to the default (so passing an empty list would result in the -default options).} - \item{baseline}{character vector of length one that denotes the baseline model against which to compare other models.} @@ -34,6 +21,14 @@ default, this is equal to \code{by} and no summary takes place. But sometimes yo may want to to summarise over categories different from the scoring. \code{summarise_by} is also the grouping level used to compute (and possibly plot) the probability integral transform(pit).} + +\item{...}{additional arguments, such as test options that can get passed +down to lower level functions. The following options are available: +\code{oneSided} (Boolean, default is \code{FALSE}, whether two conduct a one-sided +instead of a two-sided test), \code{test_type} (character, either "non_parametric" +or "permutation" determining which kind of test shall be conducted to +determine p-values. Default is "non-parametric), \code{n_permutations} (number of +permutations for a permutation test. Default is 999)} } \description{ This function does the pairwise comparison for one set of forecasts, but From 5873f5c61fb5cfffb921c9617f388820f91b18a6 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Fri, 10 Dec 2021 13:25:36 +0100 Subject: [PATCH 036/225] get rid of by argument in all pairwise comparison functions --- R/pairwise-comparisons.R | 39 ++++++++++++++-------------- man/compare_two_models.Rd | 13 ++++++++-- man/pairwise_comparison.Rd | 3 ++- man/pairwise_comparison_one_group.Rd | 5 ++-- 4 files changed, 35 insertions(+), 25 deletions(-) diff --git a/R/pairwise-comparisons.R b/R/pairwise-comparisons.R index 91819f98c..14c58d007 100644 --- a/R/pairwise-comparisons.R +++ b/R/pairwise-comparisons.R @@ -26,7 +26,8 @@ #' instead of a two-sided test), `test_type` (character, either "non_parametric" #' or "permutation" determining which kind of test shall be conducted to #' determine p-values. Default is "non-parametric), `n_permutations` (number of -#' permutations for a permutation test. Default is 999) +#' permutations for a permutation test. Default is 999). See +#' [compare_two_models()] for more information. #' @return A ggplot2 object with a coloured table of summarised scores #' @importFrom data.table as.data.table data.table setnames copy #' @importFrom stats sd rbinom wilcox.test p.adjust @@ -59,7 +60,11 @@ pairwise_comparison <- function(scores, scores <- data.table::as.data.table(scores) # identify unit of single observation. - forecast_unit <- scoringutils:::get_unit_of_forecast(scores) + forecast_unit <- get_unit_of_forecast(scores) + + # get rid of all unnecessary columns and keep only metric and forecast unit + scores <- scores[, .SD, .SDcols = c(forecast_unit, metric)] + scores <- unique(scores) # split data set into groups determined by summarise_by split_by <- setdiff(summarise_by, "model") @@ -70,7 +75,6 @@ pairwise_comparison <- function(scores, out <- pairwise_comparison_one_group(scores = scores, metric = metric, baseline = baseline, - by = forecast_unit, summarise_by = summarise_by, ...) }) @@ -164,12 +168,9 @@ add_rel_skill_to_eval_forecasts <- function(unsummarised_scores, pairwise_comparison_one_group <- function(scores, metric, baseline, - by, summarise_by, ...) { - - if (!("model" %in% names(scores))) { stop("pairwise compairons require a column called 'model'") } @@ -182,15 +183,6 @@ pairwise_comparison_one_group <- function(scores, return(NULL) } - # the overlap is obtained by merging the available data for one model with - # the avaialble data from the other model. - # for efficiency when merging, remove everything that is not in c(by, var) - cols_to_remove <- setdiff(names(scores), c(by, "model", metric)) - if (length(cols_to_remove > 0)) { - scores[, eval(cols_to_remove) := NULL] - scores <- unique(scores) - } - # create a data.frame with results # we only need to do the calculation once, because for the ratio that # should just be the inverse and for the permutation the result should @@ -204,7 +196,6 @@ pairwise_comparison_one_group <- function(scores, name_model1 = model, name_model2 = compare_against, metric = metric, - by = by, ...), by = seq_len(NROW(combinations))] @@ -299,6 +290,13 @@ pairwise_comparison_one_group <- function(scores, #' @inheritParams pairwise_comparison #' @param name_model1 character, name of the first model #' @param name_model2 character, name of the model to compare against +#' @param oneSided Boolean, default is `FALSE`, whether two conduct a one-sided +#' instead of a two-sided test to determine significance in a pairwise comparison +#' @param test_type character, either "non_parametric" (the default) or +#' "permutation". This determines which kind of test shall be conducted to +#' determine p-values. +#' @param n_permutations numeric, the number of permutations for a +#' permutation test. Default is 999. #' @author Johannes Bracher, \email{johannes.bracher@@kit.edu} #' @author Nikos Bosse \email{nikosbosse@@gmail.com} @@ -308,11 +306,12 @@ compare_two_models <- function(scores, metric, oneSided = FALSE, test_type = c("non_parametric", "permutation"), - n_permutations = 999, - by) { + n_permutations = 999) { scores <- data.table::as.data.table(scores) + forecast_unit <- get_unit_of_forecast(scores) + if (!("model" %in% names(scores))) { stop("pairwise comparisons require a column called 'model'") } @@ -322,9 +321,9 @@ compare_two_models <- function(scores, b <- scores[model == name_model2, ] # remove "model" from 'by' before merging - by <- setdiff(by, "model") + merge_by <- setdiff(forecast_unit, "model") - overlap <- merge(a, b, by = by, allow.cartesian = TRUE) + overlap <- merge(a, b, by = merge_by, allow.cartesian = TRUE) unique(overlap) if (nrow(overlap) == 0) { diff --git a/man/compare_two_models.Rd b/man/compare_two_models.Rd index f9c7e6777..9fff6d5e6 100644 --- a/man/compare_two_models.Rd +++ b/man/compare_two_models.Rd @@ -11,8 +11,7 @@ compare_two_models( metric, oneSided = FALSE, test_type = c("non_parametric", "permutation"), - n_permutations = 999, - by + n_permutations = 999 ) } \arguments{ @@ -25,6 +24,16 @@ compare_two_models( \item{metric}{A character vector of length one with the metric to do the comparison on.} + +\item{oneSided}{Boolean, default is \code{FALSE}, whether two conduct a one-sided +instead of a two-sided test to determine significance in a pairwise comparison} + +\item{test_type}{character, either "non_parametric" (the default) or +"permutation". This determines which kind of test shall be conducted to +determine p-values.} + +\item{n_permutations}{numeric, the number of permutations for a +permutation test. Default is 999.} } \description{ This function compares two models based on the subset of forecasts for which diff --git a/man/pairwise_comparison.Rd b/man/pairwise_comparison.Rd index 91f38b696..4f6b54082 100644 --- a/man/pairwise_comparison.Rd +++ b/man/pairwise_comparison.Rd @@ -34,7 +34,8 @@ down to lower level functions. The following options are available: instead of a two-sided test), \code{test_type} (character, either "non_parametric" or "permutation" determining which kind of test shall be conducted to determine p-values. Default is "non-parametric), \code{n_permutations} (number of -permutations for a permutation test. Default is 999)} +permutations for a permutation test. Default is 999). See +\code{\link[=compare_two_models]{compare_two_models()}} for more information.} } \value{ A ggplot2 object with a coloured table of summarised scores diff --git a/man/pairwise_comparison_one_group.Rd b/man/pairwise_comparison_one_group.Rd index d4581af4a..b0953d731 100644 --- a/man/pairwise_comparison_one_group.Rd +++ b/man/pairwise_comparison_one_group.Rd @@ -4,7 +4,7 @@ \alias{pairwise_comparison_one_group} \title{Do Pairwise Comparison for one Set of Forecasts} \usage{ -pairwise_comparison_one_group(scores, metric, baseline, by, summarise_by, ...) +pairwise_comparison_one_group(scores, metric, baseline, summarise_by, ...) } \arguments{ \item{scores}{A data.frame of unsummarised scores as produced by @@ -28,7 +28,8 @@ down to lower level functions. The following options are available: instead of a two-sided test), \code{test_type} (character, either "non_parametric" or "permutation" determining which kind of test shall be conducted to determine p-values. Default is "non-parametric), \code{n_permutations} (number of -permutations for a permutation test. Default is 999)} +permutations for a permutation test. Default is 999). See +\code{\link[=compare_two_models]{compare_two_models()}} for more information.} } \description{ This function does the pairwise comparison for one set of forecasts, but From cbc169c2f406e2226598657fb6e3da9c42995456 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Fri, 10 Dec 2021 14:27:33 +0100 Subject: [PATCH 037/225] set metric argument in pairwise_comparison to "auto" --- R/pairwise-comparisons.R | 21 ++++++++++++++++++--- man/compare_two_models.Rd | 6 ++++-- man/pairwise_comparison.Rd | 8 +++++--- man/pairwise_comparison_one_group.Rd | 6 ++++-- 4 files changed, 31 insertions(+), 10 deletions(-) diff --git a/R/pairwise-comparisons.R b/R/pairwise-comparisons.R index 14c58d007..750b6088b 100644 --- a/R/pairwise-comparisons.R +++ b/R/pairwise-comparisons.R @@ -11,8 +11,10 @@ #' #' @param scores A data.frame of unsummarised scores as produced by #' [eval_forecasts()] -#' @param metric A character vector of length one with the metric to do -#' the comparison on. +#' @param metric A character vector of length one with the metric to do the +#' comparison on. The default is "auto", meaning that either "interval_score", +#' "crps", or "brier_score" will be selected where available. +#' See [available_metrics()] for available metrics. #' @param summarise_by character vector of columns to group the summary by. By #' default, this is equal to `by` and no summary takes place. But sometimes you #' may want to to summarise over categories different from the scoring. @@ -52,13 +54,26 @@ #' @author Johannes Bracher, \email{johannes.bracher@@kit.edu} pairwise_comparison <- function(scores, - metric = "interval_score", # maybe the default can happen automatically, + metric = "auto", baseline = NULL, summarise_by = c("model"), ...) { scores <- data.table::as.data.table(scores) + # determine metric automatically + if (metric == "auto") { + if ("interval_score" %in% names(scores)) { + metric <- "interval_score" + } else if ("crps" %in% names(scores)) { + metric <- "crps" + } else if ("brier_score" %in% names(scores)) { + metric <- "brier_score" + } else ( + stop("determining a metric automatically failed. Please set the argument `metric` to an appropriate metric.") + ) + } + # identify unit of single observation. forecast_unit <- get_unit_of_forecast(scores) diff --git a/man/compare_two_models.Rd b/man/compare_two_models.Rd index 9fff6d5e6..f0f7a7d28 100644 --- a/man/compare_two_models.Rd +++ b/man/compare_two_models.Rd @@ -22,8 +22,10 @@ compare_two_models( \item{name_model2}{character, name of the model to compare against} -\item{metric}{A character vector of length one with the metric to do -the comparison on.} +\item{metric}{A character vector of length one with the metric to do the +comparison on. The default is "auto", meaning that either "interval_score", +"crps", or "brier_score" will be selected where available. +See \code{\link[=available_metrics]{available_metrics()}} for available metrics.} \item{oneSided}{Boolean, default is \code{FALSE}, whether two conduct a one-sided instead of a two-sided test to determine significance in a pairwise comparison} diff --git a/man/pairwise_comparison.Rd b/man/pairwise_comparison.Rd index 4f6b54082..b9342589c 100644 --- a/man/pairwise_comparison.Rd +++ b/man/pairwise_comparison.Rd @@ -6,7 +6,7 @@ \usage{ pairwise_comparison( scores, - metric = "interval_score", + metric = "auto", baseline = NULL, summarise_by = c("model"), ... @@ -16,8 +16,10 @@ pairwise_comparison( \item{scores}{A data.frame of unsummarised scores as produced by \code{\link[=eval_forecasts]{eval_forecasts()}}} -\item{metric}{A character vector of length one with the metric to do -the comparison on.} +\item{metric}{A character vector of length one with the metric to do the +comparison on. The default is "auto", meaning that either "interval_score", +"crps", or "brier_score" will be selected where available. +See \code{\link[=available_metrics]{available_metrics()}} for available metrics.} \item{baseline}{character vector of length one that denotes the baseline model against which to compare other models.} diff --git a/man/pairwise_comparison_one_group.Rd b/man/pairwise_comparison_one_group.Rd index b0953d731..592f4e259 100644 --- a/man/pairwise_comparison_one_group.Rd +++ b/man/pairwise_comparison_one_group.Rd @@ -10,8 +10,10 @@ pairwise_comparison_one_group(scores, metric, baseline, summarise_by, ...) \item{scores}{A data.frame of unsummarised scores as produced by \code{\link[=eval_forecasts]{eval_forecasts()}}} -\item{metric}{A character vector of length one with the metric to do -the comparison on.} +\item{metric}{A character vector of length one with the metric to do the +comparison on. The default is "auto", meaning that either "interval_score", +"crps", or "brier_score" will be selected where available. +See \code{\link[=available_metrics]{available_metrics()}} for available metrics.} \item{baseline}{character vector of length one that denotes the baseline model against which to compare other models.} From 269a45951d5a7b7723fbe48b81529f4650bcb7f1 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Fri, 10 Dec 2021 14:32:39 +0100 Subject: [PATCH 038/225] clean code for inferring a relative skill metric in pairwise_comparison() --- R/pairwise-comparisons.R | 17 ++--------------- 1 file changed, 2 insertions(+), 15 deletions(-) diff --git a/R/pairwise-comparisons.R b/R/pairwise-comparisons.R index 750b6088b..740ae7573 100644 --- a/R/pairwise-comparisons.R +++ b/R/pairwise-comparisons.R @@ -62,16 +62,8 @@ pairwise_comparison <- function(scores, scores <- data.table::as.data.table(scores) # determine metric automatically - if (metric == "auto") { - if ("interval_score" %in% names(scores)) { - metric <- "interval_score" - } else if ("crps" %in% names(scores)) { - metric <- "crps" - } else if ("brier_score" %in% names(scores)) { - metric <- "brier_score" - } else ( - stop("determining a metric automatically failed. Please set the argument `metric` to an appropriate metric.") - ) + if (rel_skill_metric == "auto") { + metric <- infer_rel_skill_metric(unsummarised_scores) } # identify unit of single observation. @@ -121,11 +113,6 @@ add_rel_skill_to_eval_forecasts <- function(unsummarised_scores, by, summarise_by) { - # infer the correct relative skill if only "auto" is given - if (rel_skill_metric == "auto") { - rel_skill_metric <- infer_rel_skill_metric(unsummarised_scores) - } - # summarise scores over all quantiles, ranges or samples in order to not # include them in the calculation of relative scores scores <- unsummarised_scores[, lapply(.SD, mean, na.rm = TRUE), From 96669f285f616289ddd13d6792eddce553befb81 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Fri, 10 Dec 2021 19:25:35 +0100 Subject: [PATCH 039/225] correct typos --- R/pairwise-comparisons.R | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/R/pairwise-comparisons.R b/R/pairwise-comparisons.R index 740ae7573..53dcc36ee 100644 --- a/R/pairwise-comparisons.R +++ b/R/pairwise-comparisons.R @@ -62,8 +62,8 @@ pairwise_comparison <- function(scores, scores <- data.table::as.data.table(scores) # determine metric automatically - if (rel_skill_metric == "auto") { - metric <- infer_rel_skill_metric(unsummarised_scores) + if (metric == "auto") { + metric <- infer_rel_skill_metric(scores) } # identify unit of single observation. @@ -115,6 +115,14 @@ add_rel_skill_to_eval_forecasts <- function(unsummarised_scores, # summarise scores over all quantiles, ranges or samples in order to not # include them in the calculation of relative scores + + + # determine metric automatically + if (rel_skill_metric == "auto") { + rel_skill_metric <- infer_rel_skill_metric(unsummarised_scores) + } + + scores <- unsummarised_scores[, lapply(.SD, mean, na.rm = TRUE), by = c(by), .SDcols = colnames(unsummarised_scores) %in% c(rel_skill_metric)] From c4ba205c334c229fcad174933cfa54a1b89488e1 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Fri, 10 Dec 2021 21:01:21 +0100 Subject: [PATCH 040/225] clean code for add_rel_skill_to_eval_forecasts() --- R/pairwise-comparisons.R | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/R/pairwise-comparisons.R b/R/pairwise-comparisons.R index 53dcc36ee..28d6e0b6c 100644 --- a/R/pairwise-comparisons.R +++ b/R/pairwise-comparisons.R @@ -113,18 +113,17 @@ add_rel_skill_to_eval_forecasts <- function(unsummarised_scores, by, summarise_by) { - # summarise scores over all quantiles, ranges or samples in order to not - # include them in the calculation of relative scores - - # determine metric automatically if (rel_skill_metric == "auto") { rel_skill_metric <- infer_rel_skill_metric(unsummarised_scores) } + forecast_unit <- get_unit_of_forecast(unsummarised_scores) + # summarise scores over all quantiles, ranges or samples in order to not + # include them in the calculation of relative scores scores <- unsummarised_scores[, lapply(.SD, mean, na.rm = TRUE), - by = c(by), + by = by, .SDcols = colnames(unsummarised_scores) %in% c(rel_skill_metric)] # remove range and quantile from summarise_by if they are present From 53cc41ebb63cf29e3dc930300aa72593f7e919f9 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Fri, 10 Dec 2021 21:09:56 +0100 Subject: [PATCH 041/225] add "upper" and "lower" to protected columns --- R/check_forecasts.R | 2 +- R/pairwise-comparisons.R | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R/check_forecasts.R b/R/check_forecasts.R index c44b899d5..6a026be3a 100644 --- a/R/check_forecasts.R +++ b/R/check_forecasts.R @@ -342,7 +342,7 @@ check_clean_data <- function(data, verbose = TRUE) { get_unit_of_forecast <- function(data) { protected_columns <- c( - "prediction", "true_value", "sample", "quantile", + "prediction", "true_value", "sample", "quantile", "upper", "lower", "range", "boundary", available_metrics() ) obs_unit <- setdiff(colnames(data), protected_columns) diff --git a/R/pairwise-comparisons.R b/R/pairwise-comparisons.R index 28d6e0b6c..98a97676f 100644 --- a/R/pairwise-comparisons.R +++ b/R/pairwise-comparisons.R @@ -123,7 +123,7 @@ add_rel_skill_to_eval_forecasts <- function(unsummarised_scores, # summarise scores over all quantiles, ranges or samples in order to not # include them in the calculation of relative scores scores <- unsummarised_scores[, lapply(.SD, mean, na.rm = TRUE), - by = by, + by = forecast_unit, .SDcols = colnames(unsummarised_scores) %in% c(rel_skill_metric)] # remove range and quantile from summarise_by if they are present From 41cfa32113b210b50e5704fca6f4bdb9f5394080 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Fri, 10 Dec 2021 21:13:46 +0100 Subject: [PATCH 042/225] remove by argument from add_rel_skill_to_eval_forecasts() --- R/eval_forecasts.R | 1 - R/pairwise-comparisons.R | 7 +++---- man/add_rel_skill_to_eval_forecasts.Rd | 1 - 3 files changed, 3 insertions(+), 6 deletions(-) diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index c397b97ac..3ae3592a9 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -248,7 +248,6 @@ eval_forecasts <- function(data, relative_res <- add_rel_skill_to_eval_forecasts(unsummarised_scores = scores, rel_skill_metric = rel_skill_metric, baseline = baseline, - by = forecast_unit, summarise_by = summarise_by) scores <- merge(scores, relative_res, by = forecast_unit) } diff --git a/R/pairwise-comparisons.R b/R/pairwise-comparisons.R index 98a97676f..eb4398eaa 100644 --- a/R/pairwise-comparisons.R +++ b/R/pairwise-comparisons.R @@ -110,7 +110,6 @@ pairwise_comparison <- function(scores, add_rel_skill_to_eval_forecasts <- function(unsummarised_scores, rel_skill_metric, baseline, - by, summarise_by) { # determine metric automatically @@ -129,10 +128,10 @@ add_rel_skill_to_eval_forecasts <- function(unsummarised_scores, # remove range and quantile from summarise_by if they are present summarise_by <- setdiff(summarise_by, c("range", "quantile", "sample")) - # if summarise_by is equal to by, then pairwise comparisons don't make sense - if (identical(sort(summarise_by), sort(by))) { + # if summarise_by is equal to forecast_unit, then pairwise comparisons don't make sense + if (identical(sort(summarise_by), sort(forecast_unit))) { summarise_by <- "model" - message("relative skill can only be computed if `summarise_by` is different from `by`. `summarise_by` was set to 'model'") + message("relative skill can only be computed if `summarise_by` is different from the unit of a single forecast. `summarise_by` was set to 'model'") } # do pairwise comparison diff --git a/man/add_rel_skill_to_eval_forecasts.Rd b/man/add_rel_skill_to_eval_forecasts.Rd index e63cf8e5e..bbe355a88 100644 --- a/man/add_rel_skill_to_eval_forecasts.Rd +++ b/man/add_rel_skill_to_eval_forecasts.Rd @@ -8,7 +8,6 @@ add_rel_skill_to_eval_forecasts( unsummarised_scores, rel_skill_metric, baseline, - by, summarise_by ) } From 3c4f27a75ead38b3535ab66a3cdf78b331d8eeae Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Fri, 10 Dec 2021 21:21:03 +0100 Subject: [PATCH 043/225] move code to summarise scores from add_rel_skill_to_eval_forecasts to pairwise_comparison() --- R/pairwise-comparisons.R | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/R/pairwise-comparisons.R b/R/pairwise-comparisons.R index eb4398eaa..efb81afed 100644 --- a/R/pairwise-comparisons.R +++ b/R/pairwise-comparisons.R @@ -69,9 +69,18 @@ pairwise_comparison <- function(scores, # identify unit of single observation. forecast_unit <- get_unit_of_forecast(scores) - # get rid of all unnecessary columns and keep only metric and forecast unit - scores <- scores[, .SD, .SDcols = c(forecast_unit, metric)] - scores <- unique(scores) + # if summarise_by is equal to forecast_unit, then pairwise comparisons don't make sense + if (identical(sort(summarise_by), sort(forecast_unit))) { + summarise_by <- "model" + message("relative skill can only be computed if `summarise_by` is different from the unit of a single forecast. `summarise_by` was set to 'model'") + } + + # summarise scores over everything (e.g. quantiles, ranges or samples) in + # order to not to include those in the calculation of relative scores. Also + # gets rid of all unnecessary columns and keep only metric and forecast unit + scores <- scores[, lapply(.SD, mean, na.rm = TRUE), + by = forecast_unit, + .SDcols = metric] # split data set into groups determined by summarise_by split_by <- setdiff(summarise_by, "model") From 4ac65569c165584da142d5d9b5a04c33f3931714 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Fri, 10 Dec 2021 21:54:52 +0100 Subject: [PATCH 044/225] replace add_rel_skill_to_eval_forecasts() function with pairwise_comparison() --- R/eval_forecasts.R | 26 ++++++++++++++++++-------- 1 file changed, 18 insertions(+), 8 deletions(-) diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index 3ae3592a9..a22f8fe40 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -241,15 +241,25 @@ eval_forecasts <- function(data, } if (compute_relative_skill) { - if (is.null(summarise_by)) { - summarise_by <- forecast_unit - } - relative_res <- add_rel_skill_to_eval_forecasts(unsummarised_scores = scores, - rel_skill_metric = rel_skill_metric, - baseline = baseline, - summarise_by = summarise_by) - scores <- merge(scores, relative_res, by = forecast_unit) + # relative_res <- add_rel_skill_to_eval_forecasts(unsummarised_scores = scores, + # rel_skill_metric = rel_skill_metric, + # baseline = baseline, + # summarise_by = summarise_by) + # scores <- merge(scores, relative_res, by = forecast_unit) + + pairwise <- pairwise_comparison(scores = scores, + metric = rel_skill_metric, + baseline = baseline, + summarise_by = summarise_by) + + pairwise[, c("compare_against", "mean_scores_ratio", + "pval", "adj_pval") := NULL] + pairwise <- unique(pairwise) + + scores <- merge(scores, pairwise, all.x = TRUE, + by = get_unit_of_forecast(pairwise)) + } scores <- summarise_scores(scores, From 1cad5c26a121990e98f2a186f98f299e4a445f45 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Fri, 10 Dec 2021 21:59:00 +0100 Subject: [PATCH 045/225] clean code, remove add_rel_skill_to_eval_forecasts() function --- R/eval_forecasts.R | 9 +--- R/pairwise-comparisons.R | 67 -------------------------- man/add_rel_skill_to_eval_forecasts.Rd | 43 ----------------- 3 files changed, 2 insertions(+), 117 deletions(-) delete mode 100644 man/add_rel_skill_to_eval_forecasts.Rd diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index a22f8fe40..cd50330a2 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -241,22 +241,17 @@ eval_forecasts <- function(data, } if (compute_relative_skill) { - - # relative_res <- add_rel_skill_to_eval_forecasts(unsummarised_scores = scores, - # rel_skill_metric = rel_skill_metric, - # baseline = baseline, - # summarise_by = summarise_by) - # scores <- merge(scores, relative_res, by = forecast_unit) - pairwise <- pairwise_comparison(scores = scores, metric = rel_skill_metric, baseline = baseline, summarise_by = summarise_by) + # delete unnecessary columns pairwise[, c("compare_against", "mean_scores_ratio", "pval", "adj_pval") := NULL] pairwise <- unique(pairwise) + # merge back scores <- merge(scores, pairwise, all.x = TRUE, by = get_unit_of_forecast(pairwise)) diff --git a/R/pairwise-comparisons.R b/R/pairwise-comparisons.R index efb81afed..8f933a7f2 100644 --- a/R/pairwise-comparisons.R +++ b/R/pairwise-comparisons.R @@ -101,73 +101,6 @@ pairwise_comparison <- function(scores, } -#' @title Add relative skill to eval_forecasts() -#' -#' @description -#' -#' This function will only be called within [eval_forecasts()] and serves to -#' make pairwise comparisons from within that function. It uses the -#' `summarise_by` argument as well as the data from [eval_forecasts()]. -#' Essentially, it wraps [pairwise_comparison()] and deals with the specifics -#' necessary to work with [eval_forecasts()]. -#' @inheritParams eval_forecasts -#' @param unsummarised_scores unsummarised scores to be passed from -#' [eval_forecasts()] -#' -#' @keywords internal - -add_rel_skill_to_eval_forecasts <- function(unsummarised_scores, - rel_skill_metric, - baseline, - summarise_by) { - - # determine metric automatically - if (rel_skill_metric == "auto") { - rel_skill_metric <- infer_rel_skill_metric(unsummarised_scores) - } - - forecast_unit <- get_unit_of_forecast(unsummarised_scores) - - # summarise scores over all quantiles, ranges or samples in order to not - # include them in the calculation of relative scores - scores <- unsummarised_scores[, lapply(.SD, mean, na.rm = TRUE), - by = forecast_unit, - .SDcols = colnames(unsummarised_scores) %in% c(rel_skill_metric)] - - # remove range and quantile from summarise_by if they are present - summarise_by <- setdiff(summarise_by, c("range", "quantile", "sample")) - - # if summarise_by is equal to forecast_unit, then pairwise comparisons don't make sense - if (identical(sort(summarise_by), sort(forecast_unit))) { - summarise_by <- "model" - message("relative skill can only be computed if `summarise_by` is different from the unit of a single forecast. `summarise_by` was set to 'model'") - } - - # do pairwise comparison - pairwise <- pairwise_comparison(scores = scores, - metric = rel_skill_metric, - baseline = baseline, - summarise_by = summarise_by) - - # delete unnecessary columns from the output - cols_to_delete <- setdiff(colnames(pairwise), - unique(c(summarise_by, "model", "relative_skill", "scaled_rel_skill"))) - if (length(cols_to_delete > 1)) { - pairwise[, eval(cols_to_delete) := NULL] - } - pairwise <- unique(pairwise) - out <- merge(scores, pairwise, all.x = TRUE, - by = unique(c("model", summarise_by))) - - # also delete skill metric from output - out[, eval(rel_skill_metric) := NULL] - - return(out[]) -} - - - - #' @title Do Pairwise Comparison for one Set of Forecasts #' #' @description diff --git a/man/add_rel_skill_to_eval_forecasts.Rd b/man/add_rel_skill_to_eval_forecasts.Rd deleted file mode 100644 index bbe355a88..000000000 --- a/man/add_rel_skill_to_eval_forecasts.Rd +++ /dev/null @@ -1,43 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/pairwise-comparisons.R -\name{add_rel_skill_to_eval_forecasts} -\alias{add_rel_skill_to_eval_forecasts} -\title{Add relative skill to eval_forecasts()} -\usage{ -add_rel_skill_to_eval_forecasts( - unsummarised_scores, - rel_skill_metric, - baseline, - summarise_by -) -} -\arguments{ -\item{unsummarised_scores}{unsummarised scores to be passed from -\code{\link[=eval_forecasts]{eval_forecasts()}}} - -\item{rel_skill_metric}{character string with the name of the metric for which -a relative skill shall be computed. If equal to 'auto' (the default), then -one of interval score, crps or brier score will be used where appropriate} - -\item{baseline}{character string with the name of a model. If a baseline is -given, then a scaled relative skill with respect to the baseline will be -returned. By default (\code{NULL}), relative skill will not be scaled with -respect to a baseline model.} - -\item{summarise_by}{character vector of columns to group the summary by. By -default, this is equal to \code{by} and no summary takes place. -But sometimes you may want to to summarise -over categories different from the scoring. -\code{summarise_by} is also the grouping level used to compute -(and possibly plot) the probability integral transform(pit). Sometimes you -may want to include 'range', 'quantile' or 'sample', to summarise by -range, quantile or sample.} -} -\description{ -This function will only be called within \code{\link[=eval_forecasts]{eval_forecasts()}} and serves to -make pairwise comparisons from within that function. It uses the -\code{summarise_by} argument as well as the data from \code{\link[=eval_forecasts]{eval_forecasts()}}. -Essentially, it wraps \code{\link[=pairwise_comparison]{pairwise_comparison()}} and deals with the specifics -necessary to work with \code{\link[=eval_forecasts]{eval_forecasts()}}. -} -\keyword{internal} From 41331db9c94ec17a23801d87fa919a50e139c3e4 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Sat, 11 Dec 2021 00:24:15 +0100 Subject: [PATCH 046/225] generate example data based on European Forecast Hub forecasts --- data/example_binary.rda | Bin 0 -> 218776 bytes data/example_continuous.rda | Bin 0 -> 206362 bytes data/example_integer.rda | Bin 0 -> 14618 bytes data/example_quantile.rda | Bin 0 -> 28688 bytes data/example_quantile_forecasts_only.rda | Bin 8657 -> 29873 bytes data/example_range_long.rda | Bin 0 -> 30190 bytes data/example_range_semi_wide.rda | Bin 0 -> 29919 bytes data/example_range_wide.rda | Bin 0 -> 32497 bytes data/example_truth_only.rda | Bin 0 -> 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You can also download the folders manually instead. + +system("svn checkout https://github.com/epiforecasts/covid19-forecast-hub-europe/trunk/data-processed/EuroCOVIDhub-ensemble") +system("svn checkout https://github.com/epiforecasts/covid19-forecast-hub-europe/trunk/data-processed/EuroCOVIDhub-baseline") +system("svn checkout https://github.com/epiforecasts/covid19-forecast-hub-europe/trunk/data-processed/UMass-MechBayes") +system("svn checkout https://github.com/epiforecasts/covid19-forecast-hub-europe/trunk/data-processed/epiforecasts-EpiNow2") +system("svn checkout https://github.com/epiforecasts/covid19-forecast-hub-europe/trunk/data-processed/UVA-Ensemble") + +# load truth data using the covidHubutils package ------------------------------ +truth <- covidHubUtils::load_truth(hub = "ECDC") |> + filter(target_variable %in% c("inc case", "inc death")) |> + mutate(target_variable = ifelse(target_variable == "inc case", + "Cases", "Deaths")) |> + rename(target_type = target_variable, + true_value = value) |> + select(-model) + +# get the correct file paths to all forecasts ---------------------------------- +folders <- here(c("EuroCOVIDhub-ensemble", "EuroCOVIDhub-baseline", "UMASS-MechBayes", "UVA-Ensemble")) + +file_paths <- purrr::map(folders, + .f = function(folder) { + files <- list.files(folder) + out <- here::here(folder, files) + return(out)}) %>% + unlist() +file_paths <- file_paths[grepl(".csv", file_paths)] + +# load all past forecasts ------------------------------------------------------ +# ceate a helper function to get model name from a file path +get_model_name <- function(file_path) { + split <- str_split(file_path, pattern = "/")[[1]] + model <- split[length(split) - 1] + return(model) +} -# install package from github repository -# devtools::install_github("epiforecasts/covid19.forecasts.uk") - - - - - -# create quantile example ------------------------------------------------------ -# load forecasts and do some filtering -data <- covid19.forecasts.uk::uk_forecasts %>% - dplyr::mutate(horizon = as.numeric(value_date - creation_date), - quantile = round(quantile, 3)) %>% - dplyr::filter(model %in% c("EpiSoon", "SIRCOVID", "DetSEIRwithNB MCMC"), - creation_date > "2020-06-01", - geography %in% c("England", "Scotland", "Wales", "Northern Ireland"), - horizon %in% c(7, 14, 21)) %>% - dplyr::rename(prediction = value) - -# get available dates -dates <- data$value_date %>% - unique() - -# load observations and keep a couple of weeks before any forecasts were made -obs <- covid19.forecasts.uk::covid_uk_data %>% - dplyr::filter(value_date %in% c(as.Date(c("2020-06-08", "2020-06-01", "2020-05-25", - "2020-05-18", "2020-05-11", "2020-05-04")), - dates), - geography %in% c("England", "Scotland", "Wales", "Northern Ireland")) %>% - dplyr::rename(true_value = value) %>% - dplyr::select(-truncation) +# load forecasts +prediction_data <- map_dfr(file_paths, + .f = function(file_path) { + data <- fread(file_path) + data[, `:=`( + target_end_date = as.Date(target_end_date), + quantile = as.numeric(quantile), + forecast_date = as.Date(forecast_date), + model = get_model_name(file_path) + )] + return(data) + }) %>% + filter(grepl("case", target) | grepl("death", target)) %>% + mutate(target_type = ifelse(grepl("death", target), + "Deaths", "Cases"), + horizon = as.numeric(substr(target, 1, 1))) %>% + rename(prediction = value) %>% + filter(type == "quantile", + grepl("inc", target)) %>% + select(location, forecast_date, quantile, prediction, + model, target_end_date, target, target_type, horizon) + +# merge forecast data and truth data and save +hub_data <- merge_pred_and_obs(prediction_data, truth, + by = c("location", "target_end_date", + "target_type")) |> + filter(target_end_date >= "2021-01-01") |> + select(-location_name, -population, -target) + +# harmonise forecast dates to be the date a submission was made +hub_data <- mutate(hub_data, + forecast_date = calc_submission_due_date(forecast_date)) + +hub_data <- hub_data |> + filter(horizon <= 3, + forecast_date > "2021-05-01", + forecast_date < "2021-07-15", + location %in% c("DE", "GB", "FR", "IT")) + +truth <- truth |> + filter(target_end_date > "2021-01-01", + target_end_date < max(hub_data$target_end_date), + location %in% c("DE", "GB", "FR", "IT")) # save example data with forecasts only -example_quantile_forecasts_only <- data +example_quantile_forecasts_only <- hub_data usethis::use_data(example_quantile_forecasts_only, overwrite = TRUE) -example_truth_data_only <- obs -usethis::use_data(example_truth_data_only, overwrite = TRUE) +example_truth_only <- truth +usethis::use_data(example_truth_only, overwrite = TRUE) # join -quantile_example_data <- dplyr::left_join(obs, data) %>% +example_quantile <- dplyr::left_join(truth, hub_data) %>% dplyr::mutate(model = as.character(model)) -data.table::setDT(quantile_example_data) +data.table::setDT(example_quantile) # make model a character instead of a factor -usethis::use_data(quantile_example_data, overwrite = TRUE) +usethis::use_data(example_quantile, overwrite = TRUE) # create long range example ---------------------------------------------------- -range_example_data_long <- quantile_to_range_long(quantile_example_data, - keep_quantile_col = FALSE) -usethis::use_data(range_example_data_long, overwrite = TRUE) +example_range_long <- quantile_to_range_long(example_quantile, + keep_quantile_col = FALSE) +usethis::use_data(example_range_long, overwrite = TRUE) # create wide range example ---------------------------------------------------- -range_example_data_wide <- range_long_to_wide(range_example_data_long) -range_example_data_wide[, NA_NA := NULL] -usethis::use_data(range_example_data_wide, overwrite = TRUE) - - +example_range_wide <- range_long_to_wide(example_range_long) +example_range_wide[, NA_NA := NULL] +usethis::use_data(example_range_wide, overwrite = TRUE) #create semi-wide range example ------------------------------------------------ -range_example_data_semi_wide <- data.table::copy(range_example_data_long) -range_example_data_semi_wide <- data.table::dcast(range_example_data_semi_wide, +example_range_semi_wide <- data.table::copy(example_range_long) +example_range_semi_wide <- data.table::dcast(example_range_semi_wide, ... ~ boundary, value.var = "prediction") -range_example_data_semi_wide[, "NA" := NULL] -usethis::use_data(range_example_data_semi_wide, overwrite = TRUE) +example_range_semi_wide[, "NA" := NULL] +usethis::use_data(example_range_semi_wide, overwrite = TRUE) @@ -114,26 +168,25 @@ get_samples <- function(values, quantiles, n_samples = 1000) { } # calculate samples -setDT(quantile_example_data) -n_samples <- 50 -continuous_example_data <- quantile_example_data[, .(prediction = get_samples(prediction, - quantile, - n_samples = n_samples), - sample = 1:n_samples, - true_value = unique(true_value)), - by = c("value_date", "value_type", "geography", - "value_desc", "model", "creation_date", - "horizon")] +setDT(example_quantile) +n_samples <- 40 +example_continuous <- example_quantile[, .(prediction = get_samples(prediction, + quantile, + n_samples = n_samples), + sample = 1:n_samples, + true_value = unique(true_value)), + by = c("location", "target_end_date", "target_type", + "forecast_date", "model", "horizon")] # remove unnecessary rows where no predictions are available -continuous_example_data[is.na(prediction), sample := NA] -continuous_example_data <- unique(continuous_example_data) -usethis::use_data(continuous_example_data, overwrite = TRUE) +example_continuous[is.na(prediction), sample := NA] +example_continuous <- unique(continuous_example_data) +usethis::use_data(example_continuous, overwrite = TRUE) # get integer sample data ------------------------------------------------------ -integer_example_data <- data.table::copy(continuous_example_data) -integer_example_data <- integer_example_data[, prediction := round(prediction)] -usethis::use_data(integer_example_data, overwrite = TRUE) +example_integer <- data.table::copy(example_continuous) +example_integer <- integer_example_data[, prediction := round(prediction)] +usethis::use_data(example_integer, overwrite = TRUE) @@ -145,26 +198,26 @@ usethis::use_data(integer_example_data, overwrite = TRUE) # observed value was below or above that mean prediction. # Take this as a way to create example data, not as sound statistical practice -binary_example_data <- data.table::copy(continuous_example_data) +example_binary <- data.table::copy(example_continuous) # store grouping variable -by <- c("value_date", "value_type", "geography", "value_desc", - "model", "creation_date", "horizon") +by = c("location", "target_end_date", "target_type", + "forecast_date", "model", "horizon") # calculate mean value -binary_example_data[, mean_val := mean(prediction), - by = by] +example_binary[, mean_val := mean(prediction), + by = by] # calculate binary prediction as percentage above mean -binary_example_data[, prediction := mean(prediction > mean_val), - by = by] +example_binary[, prediction := mean(prediction > mean_val), + by = by] # calculate true value as whether or not observed was above mean -binary_example_data[, true_value := true_value > mean_val] +example_binary[, true_value := true_value > mean_val] # delete unnecessary columns and take unique values -binary_example_data[, `:=`(sample = NULL, mean_val = NULL, - true_value = as.numeric(true_value))] -binary_example_data <- unique(binary_example_data) -usethis::use_data(binary_example_data, overwrite = TRUE) +example_binary[, `:=`(sample = NULL, mean_val = NULL, + true_value = as.numeric(true_value))] +example_binary <- unique(binary_example_data) +usethis::use_data(example_binary, overwrite = TRUE) From 1f9f49d8160f79aa4b5adeff7794da26ac1c0941 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Sat, 11 Dec 2021 11:32:37 +0100 Subject: [PATCH 047/225] switch to Forecast Hub example data --- R/check_forecasts.R | 10 +- R/data.R | 185 +- R/eval_forecasts.R | 14 +- R/eval_forecasts_binary.R | 2 +- R/eval_forecasts_continuous_integer.R | 4 +- R/pairwise-comparisons.R | 8 +- R/pit.R | 4 +- R/plot.R | 80 +- R/utils_data_handling.R | 38 +- README.Rmd | 14 +- README.md | 14 +- data/binary_example_data.rda | Bin 1740 -> 0 bytes data/continuous_example_data.rda | Bin 109171 -> 0 bytes data/example_binary.rda | Bin 218776 -> 1996 bytes data/example_continuous.rda | Bin 206362 -> 285330 bytes data/example_integer.rda | Bin 14618 -> 78021 bytes data/example_quantile.rda | Bin 28688 -> 40189 bytes data/example_quantile_forecasts_only.rda | Bin 29873 -> 39100 bytes data/example_range_long.rda | Bin 30190 -> 42008 bytes data/example_range_semi_wide.rda | Bin 29919 -> 41870 bytes data/example_range_wide.rda | Bin 32497 -> 44818 bytes data/example_truth_data_only.rda | Bin 947 -> 0 bytes data/example_truth_only.rda | Bin 1590 -> 1537 bytes data/integer_example_data.rda | Bin 14657 -> 0 bytes data/quantile_example_data.rda | Bin 7404 -> 0 bytes data/range_example_data_long.rda | Bin 8526 -> 0 bytes data/range_example_data_semi_wide.rda | Bin 6065 -> 0 bytes data/range_example_data_wide.rda | Bin 6854 -> 0 bytes inst/create_example_data.R | 35 +- man/check_forecasts.Rd | 10 +- man/continuous_example_data.Rd | 29 - man/correlation_plot.Rd | 2 +- man/eval_forecasts.Rd | 10 +- man/eval_forecasts_binary.Rd | 2 +- man/eval_forecasts_sample.Rd | 4 +- ...nary_example_data.Rd => example_binary.Rd} | 26 +- man/example_continuous.Rd | 29 + man/example_integer.Rd | 29 + man/example_quantile.Rd | 31 + man/example_quantile_forecasts_only.Rd | 20 +- man/example_range_long.Rd | 29 + ...emi_wide.Rd => example_range_semi_wide.Rd} | 21 +- ...ple_data_wide.Rd => example_range_wide.Rd} | 25 +- man/example_truth_data_only.Rd | 25 - man/example_truth_only.Rd | 24 + man/integer_example_data.Rd | 29 - man/interval_coverage.Rd | 2 +- man/pairwise_comparison.Rd | 2 +- man/pit_df.Rd | 4 +- man/plot_pairwise_comparison.Rd | 6 +- man/plot_predictions.Rd | 30 +- man/quantile_coverage.Rd | 2 +- man/quantile_example_data.Rd | 30 - man/quantile_to_long.Rd | 2 +- man/quantile_to_range.Rd | 2 +- man/quantile_to_range_long.Rd | 8 +- man/quantile_to_wide.Rd | 2 +- man/range_example_data_long.Rd | 30 - man/range_long_to_quantile.Rd | 10 +- man/range_long_to_wide.Rd | 8 +- man/range_plot.Rd | 14 +- man/range_to_quantile.Rd | 2 +- man/range_wide_to_long.Rd | 8 +- man/sample_to_quantile.Rd | 2 +- man/sample_to_range_long.Rd | 2 +- man/score_heatmap.Rd | 6 +- man/score_table.Rd | 16 +- man/show_avail_forecasts.Rd | 4 +- man/summarise_scores.Rd | 2 +- man/wis_components.Rd | 8 +- .../many-quantiles-from-sample.svg | 1492 ++-- .../plot_predictions/many-quantiles.svg | 7620 ++++++++++++++--- .../plot_predictions/point-forecasts.svg | 1619 ++-- tests/testthat/test-check_forecasts.R | 10 +- tests/testthat/test-eval_forecasts.R | 29 +- tests/testthat/test-merge_pred_and_obs.R | 14 +- tests/testthat/test-pairwise_comparison.R | 8 +- tests/testthat/test-pit.R | 7 +- tests/testthat/test-plot_predictions.R | 44 +- vignettes/scoringutils.Rmd | 52 +- 80 files changed, 8502 insertions(+), 3348 deletions(-) delete mode 100644 data/binary_example_data.rda delete mode 100644 data/continuous_example_data.rda delete mode 100644 data/example_truth_data_only.rda delete mode 100644 data/integer_example_data.rda delete mode 100644 data/quantile_example_data.rda delete mode 100644 data/range_example_data_long.rda delete mode 100644 data/range_example_data_semi_wide.rda delete mode 100644 data/range_example_data_wide.rda delete mode 100644 man/continuous_example_data.Rd rename man/{binary_example_data.Rd => example_binary.Rd} (55%) create mode 100644 man/example_continuous.Rd create mode 100644 man/example_integer.Rd create mode 100644 man/example_quantile.Rd create mode 100644 man/example_range_long.Rd rename man/{range_example_data_semi_wide.Rd => example_range_semi_wide.Rd} (51%) rename man/{range_example_data_wide.Rd => example_range_wide.Rd} (68%) delete mode 100644 man/example_truth_data_only.Rd create mode 100644 man/example_truth_only.Rd delete mode 100644 man/integer_example_data.Rd delete mode 100644 man/quantile_example_data.Rd delete mode 100644 man/range_example_data_long.Rd diff --git a/R/check_forecasts.R b/R/check_forecasts.R index 6a026be3a..1b3a5ba01 100644 --- a/R/check_forecasts.R +++ b/R/check_forecasts.R @@ -4,10 +4,10 @@ #' [eval_forecasts()]. #' #' The data should come in one of three different formats: -#' - A format for binary predictions (see [binary_example_data]) +#' - A format for binary predictions (see [example_binary]) #' - A sample-based format for discrete or continuous predictions -#' (see [continuous_example_data] and [integer_example_data]) -#' - A quantile-based format (see [quantile_example_data]) +#' (see [example_continuous] and [example_integer]) +#' - A quantile-based format (see [example_quantile]) #' #' @seealso Functions to move between different formats: #' [range_long_to_quantile()], [range_wide_to_long()] @@ -45,9 +45,9 @@ #' #' @examples #' library(scoringutils) -#' check <- check_forecasts(quantile_example_data) +#' check <- check_forecasts(example_quantile) #' print(check) -#' check_forecasts(binary_example_data) +#' check_forecasts(example_binary) #' @author Nikos Bosse \email{nikosbosse@@gmail.com} #' @export diff --git a/R/data.R b/R/data.R index 1d11cfef1..0542188ab 100644 --- a/R/data.R +++ b/R/data.R @@ -1,63 +1,60 @@ #' Quantile Example Data #' -#' A data set with predictions for different quantities relevant in the -#' 2020 UK Covid-19 epidemic. +#' A data set with predictions for COVID-19 cases and deaths submitted to the European Forecast Hub. #' #' @format A data frame with #' \describe{ -#' \item{value_date}{the date for which a prediction was made} -#' \item{value_type}{the target to be predicted (short form)} -#' \item{geography}{the region for which a prediction was made} -#' \item{value_desc}{long form description of the prediction target} +#' \item{location}{the country for which a prediction was made} +#' \item{target_end_date}{the date for which a prediction was made} +#' \item{target_type}{the target to be predicted (cases or deaths)} #' \item{true_value}{true observed values} -#' \item{model}{name of the model that generated the forecasts} -#' \item{creation_date}{date on which the forecast was made} +#' \item{location_name}{name of the country for which a prediction was made} +#' \item{forecast_date}{the date on which a prediction was made} #' \item{quantile}{quantile of the corresponding prediction} -#' \item{prediction}{quantile predictions} -#' \item{horizon}{forecast horizon in days} -#' +#' \item{prediction}{predicted value} +#' \item{model}{name of the model that generated the forecasts} +#' \item{horizon}{forecast horizon in weeks} #' } -"quantile_example_data" +#' @source \url{https://github.com/epiforecasts/covid19-forecast-hub-europe/} +"example_quantile" #' Range Forecast Example Data (Long Format) #' -#' A data set with predictions with different interval ranges relevant in the -#' 2020 UK Covid-19 epidemic. +#' A data set with predictions for COVID-19 cases and deaths submitted to the European Forecast Hub. #' #' @format A data frame with: #' \describe{ -#' \item{value_date}{the date for which a prediction was made} -#' \item{value_type}{the target to be predicted (short form)} -#' \item{geography}{the region for which a prediction was made} -#' \item{value_desc}{long form description of the prediction target} +#' \item{location}{the country for which a prediction was made} +#' \item{target_end_date}{the date for which a prediction was made} +#' \item{target_type}{the target to be predicted (cases or deaths)} #' \item{true_value}{true observed values} +#' \item{location_name}{name of the country for which a prediction was made} +#' \item{forecast_date}{the date on which a prediction was made} +#' \item{prediction}{predicted value} #' \item{model}{name of the model that generated the forecasts} -#' \item{creation_date}{date on which the forecast was made} -#' \item{prediction}{value for the lower or upper bound of the given prediction interval} -#' \item{horizon}{forecast horizon in days} +#' \item{horizon}{forecast horizon in weeks} #' \item{boundary}{indicate lower or upper bound of prediction interval} #' \item{range}{range of the corresponding prediction interval} #' } -"range_example_data_long" +"example_range_long" #' Range Forecast Example Data (Wide Format) #' -#' A data set with predictions with different interval ranges relevant in the -#' 2020 UK Covid-19 epidemic. +#' A data set with predictions for COVID-19 cases and deaths submitted to the European Forecast Hub. #' #' @format A data frame with: #' \describe{ -#' \item{value_date}{the date for which a prediction was made} -#' \item{value_type}{the target to be predicted (short form)} -#' \item{geography}{the region for which a prediction was made} -#' \item{value_desc}{long form description of the prediction target} +#' \item{location}{the country for which a prediction was made} +#' \item{target_end_date}{the date for which a prediction was made} +#' \item{target_type}{the target to be predicted (cases or deaths)} #' \item{true_value}{true observed values} +#' \item{location_name}{name of the country for which a prediction was made} +#' \item{forecast_date}{the date on which a prediction was made} #' \item{model}{name of the model that generated the forecasts} -#' \item{creation_date}{date on which the forecast was made} -#' \item{horizon}{forecast horizon in days} +#' \item{horizon}{forecast horizon in weeks} #' \item{lower_0}{prediction for the lower bound of the 0% interval range (median)} #' \item{lower_10}{prediction for the lower bound of the 10% interval range} #' \item{lower_20}{prediction for the lower bound of the 20% interval range} @@ -68,6 +65,8 @@ #' \item{lower_70}{prediction for the lower bound of the 70% interval range} #' \item{lower_80}{prediction for the lower bound of the 80% interval range} #' \item{lower_90}{prediction for the lower bound of the 90% interval range} +#' \item{lower_95}{prediction for the lower bound of the 95% interval range} +#' \item{lower_98}{prediction for the lower bound of the 98% interval range} #' \item{upper_0}{prediction for the upper bound of the 0% interval range} #' \item{upper_10}{prediction for the upper bound of the 1% interval range} #' \item{upper_20}{prediction for the upper bound of the 20% interval range} @@ -78,78 +77,79 @@ #' \item{upper_70}{prediction for the upper bound of the 70% interval range} #' \item{upper_80}{prediction for the upper bound of the 80% interval range} #' \item{upper_90}{prediction for the upper bound of the 90% interval range} +#' \item{upper_95}{prediction for the upper bound of the 95% interval range} +#' \item{upper_98}{prediction for the upper bound of the 98% interval range} #' } -"range_example_data_wide" +"example_range_wide" #' Range Forecast Example Data (Semi-Wide Format) #' -#' A data set with predictions with different interval ranges relevant in the -#' 2020 UK Covid-19 epidemic. +#' A data set with predictions for COVID-19 cases and deaths submitted to the European Forecast Hub. #' #' @format A data frame with 5,419 rows and 12 columns: #' \describe{ -#' \item{value_date}{the date for which a prediction was made} -#' \item{value_type}{the target to be predicted (short form)} -#' \item{geography}{the region for which a prediction was made} -#' \item{value_desc}{long form description of the prediction target} +#' \item{location}{the country for which a prediction was made} +#' \item{target_end_date}{the date for which a prediction was made} +#' \item{target_type}{the target to be predicted (cases or deaths)} #' \item{true_value}{true observed values} +#' \item{location_name}{name of the country for which a prediction was made} +#' \item{forecast_date}{the date on which a prediction was made} #' \item{model}{name of the model that generated the forecasts} -#' \item{creation_date}{date on which the forecast was made} -#' \item{horizon}{forecast horizon in days} +#' \item{horizon}{forecast horizon in weeks} #' \item{range}{range of the corresponding prediction interval} #' \item{lower}{prediction for the lower bound of the corresponding interval} #' \item{upper}{prediction for the upper bound of the corresponding interval} #' } -"range_example_data_semi_wide" +"example_range_semi_wide" #' Continuous Forecast Example Data #' -#' A data set with continuous predictions in a sample-based format relevant in the -#' 2020 UK Covid-19 epidemic. +#' A data set with continuous predictions for COVID-19 cases and deaths constructed from data +#' submitted to the European Forecast Hub. #' #' @format A data frame with 13,429 rows and 10 columns: #' \describe{ -#' \item{value_date}{the date for which a prediction was made} -#' \item{value_type}{the target to be predicted (short form)} -#' \item{geography}{the region for which a prediction was made} -#' \item{value_desc}{long form description of the prediction target} +#' \item{location}{the country for which a prediction was made} +#' \item{target_end_date}{the date for which a prediction was made} +#' \item{target_type}{the target to be predicted (cases or deaths)} +#' \item{true_value}{true observed values} +#' \item{location_name}{name of the country for which a prediction was made} +#' \item{forecast_date}{the date on which a prediction was made} #' \item{model}{name of the model that generated the forecasts} -#' \item{creation_date}{date on which the forecast was made} -#' \item{horizon}{forecast horizon in days} -#' \item{prediction}{prediction value for the corresponding sample} +#' \item{horizon}{forecast horizon in weeks} +#' \item{prediction}{predicted value} #' \item{sample}{id for the corresponding sample} -#' \item{true_value}{true observed values} #' } -"continuous_example_data" +"example_continuous" #' Integer Forecast Example Data #' -#' A data set with integer predictions in a sample-based format relevant in the -#' 2020 UK Covid-19 epidemic. +#' A data set with integer predictions for COVID-19 cases and deaths constructed from data +#' submitted to the European Forecast Hub. #' #' @format A data frame with 13,429 rows and 10 columns: #' \describe{ -#' \item{value_date}{the date for which a prediction was made} -#' \item{value_type}{the target to be predicted (short form)} -#' \item{geography}{the region for which a prediction was made} -#' \item{value_desc}{long form description of the prediction target} +#' \item{location}{the country for which a prediction was made} +#' \item{target_end_date}{the date for which a prediction was made} +#' \item{target_type}{the target to be predicted (cases or deaths)} +#' \item{true_value}{true observed values} +#' \item{location_name}{name of the country for which a prediction was made} +#' \item{forecast_date}{the date on which a prediction was made} #' \item{model}{name of the model that generated the forecasts} -#' \item{creation_date}{date on which the forecast was made} -#' \item{horizon}{forecast horizon in days} -#' \item{prediction}{prediction value for the corresponding sample} +#' \item{horizon}{forecast horizon in weeks} +#' \item{prediction}{predicted value} #' \item{sample}{id for the corresponding sample} -#' \item{true_value}{true observed values} #' } -"integer_example_data" +"example_integer" #' Binary Forecast Example Data #' -#' A data set with (constructed) binary predictions relevant in the -#' 2020 UK Covid-19 epidemic. +#' A data set with binary predictions for COVID-19 cases and deaths constructed from data +#' submitted to the European Forecast Hub. #' #' Predictions in the data set were constructed based on the continuous example #' data by looking at the number of samples below the mean prediction. @@ -160,56 +160,49 @@ #' #' @format A data frame with 346 rows and 10 columns: #' \describe{ -#' \item{value_date}{the date for which a prediction was made} -#' \item{value_type}{the target to be predicted (short form)} -#' \item{geography}{the region for which a prediction was made} -#' \item{value_desc}{long form description of the prediction target} -#' \item{model}{name of the model that generated the forecasts} -#' \item{creation_date}{date on which the forecast was made} -#' \item{horizon}{forecast horizon in days} -#' \item{prediction}{probability prediction that true value would be 1} +#' \item{location}{the country for which a prediction was made} +#' \item{location_name}{name of the country for which a prediction was made} +#' \item{target_end_date}{the date for which a prediction was made} +#' \item{target_type}{the target to be predicted (cases or deaths)} #' \item{true_value}{true observed values} +#' \item{forecast_date}{the date on which a prediction was made} +#' \item{model}{name of the model that generated the forecasts} +#' \item{horizon}{forecast horizon in weeks} +#' \item{prediction}{predicted value} #' } -"binary_example_data" +"example_binary" #' Quantile Example Data - Forecasts only #' -#' A data set with predictions for different quantities relevant in the -#' 2020 UK Covid-19 epidemic, but no true_values +#' A data set with quantile predictions for COVID-19 cases and deaths +#' submitted to the European Forecast Hub. #' #' @format A data frame with 7,581 rows and 9 columns: #' \describe{ -#' \item{value_date}{the date for which a prediction was made} -#' \item{value_type}{the target to be predicted (short form)} -#' \item{geography}{the region for which a prediction was made} -#' \item{model}{name of the model that generated the forecasts} -#' \item{creation_date}{date on which the forecast was made} +#' \item{location}{the country for which a prediction was made} +#' \item{target_end_date}{the date for which a prediction was made} +#' \item{target_type}{the target to be predicted (cases or deaths)} +#' \item{forecast_date}{the date on which a prediction was made} #' \item{quantile}{quantile of the corresponding prediction} -#' \item{prediction}{quantile predictions} -#' \item{value_desc}{long form description of the prediction target} -#' \item{horizon}{forecast horizon in days} -#' +#' \item{prediction}{predicted value} +#' \item{model}{name of the model that generated the forecasts} +#' \item{horizon}{forecast horizon in weeks} #' } "example_quantile_forecasts_only" #' Truth data only #' -#' A data set with truth data for different quantities relevant in the -#' 2020 UK Covid-19 epidemic, but no predictions +#' A data set with truth values for COVID-19 cases and deaths +#' submitted to the European Forecast Hub. #' #' @format A data frame with 140 rows and 5 columns: #' \describe{ -#' \item{value_date}{the date for which a prediction was made} -#' \item{value_type}{the target to be predicted (short form)} -#' \item{geography}{the region for which a prediction was made} -#' \item{value_desc}{long form description of the prediction target} +#' \item{location}{the country for which a prediction was made} +#' \item{target_end_date}{the date for which a prediction was made} +#' \item{target_type}{the target to be predicted (cases or deaths)} #' \item{true_value}{true observed values} -#' +#' \item{location_name}{name of the country for which a prediction was made} #' } -"example_truth_data_only" - - - - +"example_truth_only" diff --git a/R/eval_forecasts.R b/R/eval_forecasts.R index cd50330a2..2e87baeee 100644 --- a/R/eval_forecasts.R +++ b/R/eval_forecasts.R @@ -130,16 +130,16 @@ #' #' @examples #' ## Probability Forecast for Binary Target -#' binary_example <- data.table::setDT(scoringutils::binary_example_data) +#' binary_example <- data.table::setDT(scoringutils::example_binary) #' eval <- scoringutils::eval_forecasts(binary_example, #' summarise_by = c("model"), #' quantiles = c(0.5), sd = TRUE) #' #' ## Quantile Forecasts #' # wide format example (this examples shows usage of both wide formats) -#' range_example_wide <- data.table::setDT(scoringutils::range_example_data_wide) +#' range_example_wide <- data.table::setDT(scoringutils::example_range_wide) #' range_example <- scoringutils::range_wide_to_long(range_example_wide) -#' wide2 <- data.table::setDT(scoringutils::range_example_data_semi_wide) +#' wide2 <- data.table::setDT(scoringutils::example_range_semi_wide) #' range_example <- scoringutils::range_wide_to_long(wide2) #' example <- scoringutils::range_long_to_quantile(range_example) #' eval <- scoringutils::eval_forecasts(example, @@ -150,7 +150,7 @@ #' #' #' ## Integer Forecasts -#' integer_example <- data.table::setDT(scoringutils::integer_example_data) +#' integer_example <- data.table::setDT(scoringutils::example_integer) #' eval <- scoringutils::eval_forecasts(integer_example, #' summarise_by = c("model"), #' quantiles = c(0.1, 0.9), @@ -158,7 +158,7 @@ #' eval <- scoringutils::eval_forecasts(integer_example) #' #' ## Continuous Forecasts -#' continuous_example <- data.table::setDT(scoringutils::continuous_example_data) +#' continuous_example <- data.table::setDT(scoringutils::example_continuous) #' eval <- scoringutils::eval_forecasts(continuous_example) #' eval <- scoringutils::eval_forecasts(continuous_example, #' quantiles = c(0.5, 0.9), @@ -276,7 +276,7 @@ eval_forecasts <- function(data, #' #' @examples #' library(scoringutils) -#' data <- quantile_example_data +#' data <- example_quantile #' scores <- eval_forecasts(data) #' summarise_scores(scores, #' summarise_by = c("model")) @@ -290,7 +290,7 @@ summarise_scores <- function(scores, forecast_unit <- get_unit_of_forecast(scores) - if (!is.null(quantiles) && length(quantiles) > 1) { + if (!is.null(quantiles) && length(quantiles) >= 1) { scores <- add_quantiles(scores, quantiles, summarise_by) } # add standard deviation diff --git a/R/eval_forecasts_binary.R b/R/eval_forecasts_binary.R index c234c6303..e16fdbd96 100644 --- a/R/eval_forecasts_binary.R +++ b/R/eval_forecasts_binary.R @@ -12,7 +12,7 @@ #' #' @examples #' # Probability Forecast for Binary Target -#' binary_example <- data.table::setDT(scoringutils::binary_example_data) +#' binary_example <- data.table::setDT(scoringutils::example_binary) #' eval <- scoringutils::eval_forecasts(data = binary_example, #' summarise_by = c("model"), #' quantiles = c(0.5), sd = TRUE) diff --git a/R/eval_forecasts_continuous_integer.R b/R/eval_forecasts_continuous_integer.R index 8149c8992..87479651c 100644 --- a/R/eval_forecasts_continuous_integer.R +++ b/R/eval_forecasts_continuous_integer.R @@ -15,7 +15,7 @@ #' @examples #' #' ## Integer Forecasts -#' integer_example <- data.table::setDT(scoringutils::integer_example_data) +#' integer_example <- data.table::setDT(scoringutils::example_integer) #' eval <- scoringutils::eval_forecasts(integer_example, #' summarise_by = c("model"), #' quantiles = c(0.1, 0.9), @@ -23,7 +23,7 @@ #' eval <- scoringutils::eval_forecasts(integer_example) #' #' ## Continuous Forecasts -#' continuous_example <- data.table::setDT(scoringutils::continuous_example_data) +#' continuous_example <- data.table::setDT(scoringutils::example_continuous) #' eval <- scoringutils::eval_forecasts(continuous_example)#' #' #' eval <- scoringutils::eval_forecasts(continuous_example, diff --git a/R/pairwise-comparisons.R b/R/pairwise-comparisons.R index 8f933a7f2..47f20a2d6 100644 --- a/R/pairwise-comparisons.R +++ b/R/pairwise-comparisons.R @@ -48,7 +48,7 @@ #' baseline = "model1") #' scoringutils::plot_pairwise_comparison(res) #' -#' eval <- scoringutils::eval_forecasts(scoringutils::quantile_example_data) +#' eval <- scoringutils::eval_forecasts(scoringutils::example_quantile) #' pairwise <- pairwise_comparison(eval, summarise_by = c("model")) #' @author Nikos Bosse \email{nikosbosse@@gmail.com} #' @author Johannes Bracher, \email{johannes.bracher@@kit.edu} @@ -363,12 +363,12 @@ unique(overlap) #' interval_score = abs(rnorm(30, mean = rep(c(1, 1.3, 2), each = 10))), #' aem = (abs(rnorm(30)))) #' -#' data <- scoringutils::quantile_example_data +#' data <- scoringutils::example_quantile #' scores <- scoringutils::eval_forecasts(data) #' pairwise <- pairwise_comparison(scores, -#' summarise_by = "value_desc") +#' summarise_by = "target_type") #' scoringutils::plot_pairwise_comparison(pairwise, -#' facet_formula = ~ value_desc, +#' facet_formula = ~ target_type, #' scales = "fixed") diff --git a/R/pit.R b/R/pit.R index 3df5b0a83..20b99894d 100644 --- a/R/pit.R +++ b/R/pit.R @@ -225,12 +225,12 @@ hist_PIT <- function(PIT_samples, #' @return a named list with PIT values according to the grouping specified in #' `summarised_by` #' @examples -#' example <- scoringutils::continuous_example_data +#' example <- scoringutils::example_continuous #' result <- pit_df(example, summarise_by = "model") #' hist_PIT(result) #' #' # example with quantile data -#' result <- pit_df(quantile_example_data, summarise_by = "model") +#' result <- pit_df(example_quantile, summarise_by = "model") #' hist_PIT(result) #' @export #' @references diff --git a/R/plot.R b/R/plot.R index 59c1a6314..c3af6f504 100644 --- a/R/plot.R +++ b/R/plot.R @@ -29,25 +29,25 @@ #' @export #' #' @examples -#' scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, -#' summarise_by = c("model", "value_desc")) -#' scoringutils::score_table(scores, y = "model", facet_formula = ~ value_desc, +#' scores <- scoringutils::eval_forecasts(scoringutils::example_quantile, +#' summarise_by = c("model", "target_type")) +#' scoringutils::score_table(scores, y = "model", facet_formula = ~ target_type, #' ncol = 1) #' #' # can also put target description on the y-axis -#' scoringutils::score_table(scores, y = c("model", "value_desc")) +#' scoringutils::score_table(scores, y = c("model", "target_type")) #' #' # yields the same result in this case #' scoringutils::score_table(scores) #' #' -#' scores <- scoringutils::eval_forecasts(scoringutils::integer_example_data, -#' summarise_by = c("model", "value_desc")) -#' scoringutils::score_table(scores, y = "model", facet_formula = ~ value_desc, +#' scores <- scoringutils::eval_forecasts(scoringutils::example_integer, +#' summarise_by = c("model", "target_type")) +#' scoringutils::score_table(scores, y = "model", facet_formula = ~ target_type, #' ncol = 1) #' #' # only show selected metrics -#' scoringutils::score_table(scores, y = "model", facet_formula = ~ value_desc, +#' scoringutils::score_table(scores, y = "model", facet_formula = ~ target_type, #' ncol = 1, select_metrics = c("crps", "bias")) score_table <- function(summarised_scores, @@ -188,7 +188,7 @@ score_table <- function(summarised_scores, #' @export #' #' @examples -#' scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data) +#' scores <- scoringutils::eval_forecasts(scoringutils::example_quantile) #' scoringutils::correlation_plot(scores) @@ -295,11 +295,11 @@ correlation_plot <- function(scores, #' @export #' #' @examples -#' scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, -#' summarise_by = c("model", "value_desc")) -#' scoringutils::wis_components(scores, x = "model", facet_formula = ~ value_desc, +#' scores <- scoringutils::eval_forecasts(scoringutils::example_quantile, +#' summarise_by = c("model", "target_type")) +#' scoringutils::wis_components(scores, x = "model", facet_formula = ~ target_type, #' relative_contributions = TRUE) -#' scoringutils::wis_components(scores, x = "model", facet_formula = ~ value_desc, +#' scoringutils::wis_components(scores, x = "model", facet_formula = ~ target_type, #' relative_contributions = FALSE) #' @references #' Bracher J, Ray E, Gneiting T, Reich, N (2020) Evaluating epidemic forecasts @@ -394,20 +394,20 @@ wis_components <- function(scores, #' @export #' #' @examples -#' scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, -#' summarise_by = c("model", "value_desc", "range")) +#' scores <- scoringutils::eval_forecasts(scoringutils::example_quantile, +#' summarise_by = c("model", "target_type", "range")) #' -#' scoringutils::range_plot(scores, x = "model", facet_formula = ~ value_desc) +#' scoringutils::range_plot(scores, x = "model", facet_formula = ~ target_type) #' #' # visualise dispersion instead of interval score #' scoringutils::range_plot(scores, y = "dispersion", x = "model", -#' facet_formula = ~value_desc) +#' facet_formula = ~target_type) #' #' # we saw above that dispersion values crossed. Let's look at the unweighted WIS -#' scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, -#' summarise_by = c("model", "value_desc", "range")) +#' scores <- scoringutils::eval_forecasts(scoringutils::example_quantile, +#' summarise_by = c("model", "target_type", "range")) #' scoringutils::range_plot(scores, y = "dispersion", x = "model", -#' facet_formula = ~value_desc) +#' facet_formula = ~target_type) range_plot <- function(scores, y = "interval_score", @@ -487,10 +487,10 @@ range_plot <- function(scores, #' @export #' #' @examples -#' scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, -#' summarise_by = c("model", "value_desc", "range")) +#' scores <- scoringutils::eval_forecasts(scoringutils::example_quantile, +#' summarise_by = c("model", "target_type", "range")) #' -#' scoringutils::score_heatmap(scores, x = "value_desc", metric = "bias") +#' scoringutils::score_heatmap(scores, x = "target_type", metric = "bias") #' @@ -600,23 +600,19 @@ score_heatmap <- function(scores, #' @export #' #' @examples -#' example1 <- scoringutils::continuous_example_data -#' example2 <- scoringutils::range_example_data_long +#' example1 <- scoringutils::example_continuous #' -#' scoringutils::plot_predictions(example1, x = "value_date", -#' filter_truth = list('value_date <= "2020-06-22"', -#' 'value_date > "2020-05-01"'), -#' filter_forecasts = list("model == 'SIRCOVID'", -#' 'creation_date == "2020-06-22"'), -#' facet_formula = geography ~ value_desc) +#' plot_predictions( +#' example1, +#' x = "target_end_date", +#' filter_truth = list('target_end_date <= "2021-07-22"', +#' 'target_end_date > "2021-05-01"'), +#' filter_forecasts = list("model == 'EuroCOVIDhub-ensemble'", +#' 'forecast_date == "2021-06-07"'), +#' facet_formula = location ~ target_type, +#' range = c(0, 50, 90, 95) +#' ) #' -#' scoringutils::plot_predictions(example2, x = "value_date", -#' filter_truth = list('value_date <= "2020-06-22"', -#' 'value_date > "2020-05-01"'), -#' filter_forecasts = list("model == 'SIRCOVID'", -#' 'creation_date == "2020-06-22"'), -#' allow_truth_without_pred = TRUE, -#' facet_formula = geography ~ value_desc) plot_predictions <- function(data = NULL, forecasts = NULL, @@ -804,7 +800,7 @@ plot_predictions <- function(data = NULL, #' @export #' #' @examples -#' example1 <- scoringutils::range_example_data_long +#' example1 <- scoringutils::example_range_long #' example1 <- scoringutils::range_long_to_quantile(example1) #' scores <- scoringutils::eval_forecasts(example1, #' summarise_by = c("model", "range")) @@ -877,7 +873,7 @@ interval_coverage <- function(summarised_scores, #' @export #' #' @examples -#' example1 <- scoringutils::quantile_example_data +#' example1 <- scoringutils::example_quantile #' scores <- scoringutils::eval_forecasts(example1, #' summarise_by = c("model", "quantile")) #' quantile_coverage(scores) @@ -982,8 +978,8 @@ quantile_coverage <- function(summarised_scores, #' @export #' #' @examples -#' example1 <- scoringutils::range_example_data_long -#' show_avail_forecasts(example1, x = "value_date", facet_formula = ~ value_desc) +#' example1 <- scoringutils::example_range_long +#' show_avail_forecasts(example1, x = "target_end_date", facet_formula = ~ target_type) show_avail_forecasts <- function(data, y = "model", diff --git a/R/utils_data_handling.R b/R/utils_data_handling.R index 09b6269f3..0bc5d82c9 100644 --- a/R/utils_data_handling.R +++ b/R/utils_data_handling.R @@ -2,19 +2,19 @@ #' #' @description #' Given a data.frame that follows the structure shown in -#' [range_example_data_long()], the function outputs the same +#' [example_range_long()], the function outputs the same #' data in a long format as (as shown in -#' [range_example_data_wide()]). This can be useful e.g. for +#' [example_range_wide()]). This can be useful e.g. for #' plotting. #' #' @param data a data.frame following the specifications from #' [eval_forecasts()]) for quantile forecasts. For an example, see -#' [range_example_data_long()]) +#' [example_range_long()]) #' @return a data.frame in wide format #' @importFrom data.table dcast #' @export #' @examples -#' long <- scoringutils::range_example_data_long +#' long <- scoringutils::example_range_long #' wide <- scoringutils::range_long_to_wide(long) #' @@ -55,19 +55,19 @@ quantile_to_wide <- function(data) { #' #' @description #' Given a data.frame that follows the structure shown in -#' [range_example_data_wide()], the function outputs the same +#' [example_range_wide()], the function outputs the same #' data in a long format as (as shown in -#' [range_example_data_long()]). This can be useful e.g. for +#' [example_range_long()]). This can be useful e.g. for #' plotting. #' #' @param data a data.frame following the specifications from #' [eval_forecasts()]) for quantile forecasts. For an example, see -#' [range_example_data_wide()]) +#' [example_range_wide()]) #' @return a data.frame in long format #' @importFrom data.table melt #' @export #' @examples -#' wide <- scoringutils::range_example_data_wide +#' wide <- scoringutils::example_range_wide #' long <- scoringutils::range_wide_to_long(wide) #' @@ -133,23 +133,23 @@ quantile_to_long <- function(data) { #' to a format that uses quantiles only. #' #' Given a data.frame that follows the structure shown in -#' [range_example_data_long()], the function outputs the same +#' [example_range_long()], the function outputs the same #' data in a long format as (as shown in -#' [range_example_data_long()]). This can be useful e.g. for +#' [example_range_long()]). This can be useful e.g. for #' plotting. If you're data.frame is in a different format, consider running #' [range_long_to_wide()] first. #' #' @param data a data.frame following the specifications from #' [eval_forecasts()]) for quantile forecasts. For an example, see -#' [range_example_data_long()]) +#' [example_range_long()]) #' @param keep_range_col keep the range and boundary columns after #' transformation (default is FALSE) #' @return a data.frame in a plain quantile format #' @importFrom data.table copy #' @export #' @examples -#' wide <- range_example_data_wide -#' semiwide <- range_example_data_semi_wide +#' wide <- example_range_wide +#' semiwide <- example_range_semi_wide #' #' long <- range_wide_to_long(wide) #' long2 <- range_wide_to_long(semiwide) @@ -206,12 +206,12 @@ range_to_quantile <- function(data, #' interval ranges to denote quantiles. #' #' Given a data.frame that follows the structure shown in -#' [quantile_example_data()], the function outputs the same +#' [example_quantile()], the function outputs the same #' data in a long format as (as shown in -#' [range_example_data_long()]). +#' [example_range_long()]). #' #' @param data a data.frame following the specifications shown in the example -#' [range_example_data_long()]) +#' [example_range_long()]) #' @param keep_quantile_col keep the quantile column in the final #' output after transformation (default is FALSE) #' @return a data.frame in a long interval range format @@ -219,7 +219,7 @@ range_to_quantile <- function(data, #' @export #' #' @examples -#' quantile <- scoringutils::quantile_example_data +#' quantile <- scoringutils::example_quantile #' #' long <- scoringutils::quantile_to_range_long(quantile) #' @@ -288,7 +288,7 @@ quantile_to_range <- function(data, #' @export #' #' @examples -#' example_data <- scoringutils::integer_example_data +#' example_data <- scoringutils::example_integer #' #' quantile_data <- scoringutils::sample_to_quantile(example_data) #' @@ -334,7 +334,7 @@ sample_to_quantile <- function(data, #' @export #' #' @examples -#' example_data <- scoringutils::integer_example_data +#' example_data <- scoringutils::example_integer #' #' quantile_data <- scoringutils::sample_to_range_long(example_data) #' diff --git a/README.Rmd b/README.Rmd index 711bde75f..0a2c224bf 100644 --- a/README.Rmd +++ b/README.Rmd @@ -53,7 +53,7 @@ library(data.table) ``` ```{r} -data <- scoringutils::quantile_example_data +data <- scoringutils::example_quantile print(data, 3, 3) scores <- scoringutils::eval_forecasts(data, @@ -138,22 +138,22 @@ The `eval_forecasts` function is designed to work with various different input f quantile forecasts in either a plain quantile format or in a format that specifies interval ranges and the boundary of a given interval range. ``` {r} -print(scoringutils::quantile_example_data, 3, 3) -print(scoringutils::range_example_data_long, 3, 3) -print(scoringutils::range_example_data_wide, 3, 3) +print(scoringutils::example_quantile, 3, 3) +print(scoringutils::example_range_long, 3, 3) +print(scoringutils::example_range_wide, 3, 3) ``` sample based format with either continuous or integer values ``` {r} -print(scoringutils::integer_example_data, 3, 3) -print(scoringutils::continuous_example_data, 3, 3) +print(scoringutils::example_integer, 3, 3) +print(scoringutils::example_continuous, 3, 3) ``` forecasts in a binary format: ``` {r} -print(scoringutils::binary_example_data, 3, 3) +print(scoringutils::example_binary, 3, 3) ``` It also offers functionality to convert between these formats. For more information have a look at the documentation of the following functions: diff --git a/README.md b/README.md index 80d77794e..ba62dfb8e 100644 --- a/README.md +++ b/README.md @@ -85,7 +85,7 @@ library(data.table) ``` ``` r -data <- scoringutils::quantile_example_data +data <- scoringutils::example_quantile print(data, 3, 3) #> value_date value_type geography value_desc true_value #> 1: 2020-05-04 hospital_inc England Hospital admissions 1043 @@ -204,7 +204,7 @@ quantile forecasts in either a plain quantile format or in a format that specifies interval ranges and the boundary of a given interval range. ``` r -print(scoringutils::quantile_example_data, 3, 3) +print(scoringutils::example_quantile, 3, 3) #> value_date value_type geography value_desc true_value #> 1: 2020-05-04 hospital_inc England Hospital admissions 1043 #> 2: 2020-05-04 hospital_prev England Total beds occupied 10648 @@ -221,7 +221,7 @@ print(scoringutils::quantile_example_data, 3, 3) #> 5150: SIRCOVID 2020-07-13 0.85 4 21 #> 5151: DetSEIRwithNB MCMC 2020-07-13 0.90 2 21 #> 5152: SIRCOVID 2020-07-13 0.90 6 21 -print(scoringutils::range_example_data_long, 3, 3) +print(scoringutils::example_range_long, 3, 3) #> value_date value_type geography value_desc true_value #> 1: 2020-05-04 hospital_inc England Hospital admissions 1043 #> 2: 2020-05-04 hospital_prev England Total beds occupied 10648 @@ -238,7 +238,7 @@ print(scoringutils::range_example_data_long, 3, 3) #> 5417: SIRCOVID 2020-07-13 1 14 upper 0 #> 5418: DetSEIRwithNB MCMC 2020-07-13 0 21 upper 0 #> 5419: SIRCOVID 2020-07-13 1 21 upper 0 -print(scoringutils::range_example_data_wide, 3, 3) +print(scoringutils::example_range_wide, 3, 3) #> value_date value_type geography value_desc true_value #> 1: 2020-05-04 death_inc_line England Deaths 448 #> 2: 2020-05-04 death_inc_line Northern Ireland Deaths 9 @@ -284,7 +284,7 @@ print(scoringutils::range_example_data_wide, 3, 3) sample based format with either continuous or integer values ``` r -print(scoringutils::integer_example_data, 3, 3) +print(scoringutils::example_integer, 3, 3) #> value_date value_type geography value_desc model #> 1: 2020-05-04 hospital_inc England Hospital admissions #> 2: 2020-05-04 hospital_prev England Total beds occupied @@ -301,7 +301,7 @@ print(scoringutils::integer_example_data, 3, 3) #> 13427: 2020-07-13 21 0 48 1 #> 13428: 2020-07-13 21 0 49 1 #> 13429: 2020-07-13 21 0 50 1 -print(scoringutils::continuous_example_data, 3, 3) +print(scoringutils::example_continuous, 3, 3) #> value_date value_type geography value_desc model #> 1: 2020-05-04 hospital_inc England Hospital admissions #> 2: 2020-05-04 hospital_prev England Total beds occupied @@ -323,7 +323,7 @@ print(scoringutils::continuous_example_data, 3, 3) forecasts in a binary format: ``` r -print(scoringutils::binary_example_data, 3, 3) +print(scoringutils::example_binary, 3, 3) #> value_date value_type geography value_desc model #> 1: 2020-05-04 hospital_inc England Hospital admissions 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AYybcN diff --git a/inst/create_example_data.R b/inst/create_example_data.R index 28d921f77..334dc9aa8 100644 --- a/inst/create_example_data.R +++ b/inst/create_example_data.R @@ -6,6 +6,7 @@ library(covidHubUtils) # devtools::install_github("reichlab/covidHubUtils") library(purrr) library(data.table) library(stringr) +library(scoringutils) # download data from the European Forecast Hub Github Repository using @@ -15,7 +16,6 @@ system("svn checkout https://github.com/epiforecasts/covid19-forecast-hub-europe system("svn checkout https://github.com/epiforecasts/covid19-forecast-hub-europe/trunk/data-processed/EuroCOVIDhub-baseline") system("svn checkout https://github.com/epiforecasts/covid19-forecast-hub-europe/trunk/data-processed/UMass-MechBayes") system("svn checkout https://github.com/epiforecasts/covid19-forecast-hub-europe/trunk/data-processed/epiforecasts-EpiNow2") -system("svn checkout https://github.com/epiforecasts/covid19-forecast-hub-europe/trunk/data-processed/UVA-Ensemble") # load truth data using the covidHubutils package ------------------------------ truth <- covidHubUtils::load_truth(hub = "ECDC") |> @@ -27,7 +27,7 @@ truth <- covidHubUtils::load_truth(hub = "ECDC") |> select(-model) # get the correct file paths to all forecasts ---------------------------------- -folders <- here(c("EuroCOVIDhub-ensemble", "EuroCOVIDhub-baseline", "UMASS-MechBayes", "UVA-Ensemble")) +folders <- here(c("EuroCOVIDhub-ensemble", "EuroCOVIDhub-baseline", "UMass-MechBayes", "epiforecasts-EpiNow2")) file_paths <- purrr::map(folders, .f = function(folder) { @@ -67,27 +67,23 @@ prediction_data <- map_dfr(file_paths, select(location, forecast_date, quantile, prediction, model, target_end_date, target, target_type, horizon) -# merge forecast data and truth data and save -hub_data <- merge_pred_and_obs(prediction_data, truth, - by = c("location", "target_end_date", - "target_type")) |> - filter(target_end_date >= "2021-01-01") |> - select(-location_name, -population, -target) - # harmonise forecast dates to be the date a submission was made -hub_data <- mutate(hub_data, +hub_data <- mutate(prediction_data, forecast_date = calc_submission_due_date(forecast_date)) hub_data <- hub_data |> filter(horizon <= 3, forecast_date > "2021-05-01", forecast_date < "2021-07-15", - location %in% c("DE", "GB", "FR", "IT")) + # quantile %in% c(seq(0.05, 0.45, 0.1), 0.5, seq(0.55, 0.95, 0.1)), + location %in% c("DE", "GB", "FR", "IT")) |> + select(-target) truth <- truth |> filter(target_end_date > "2021-01-01", target_end_date < max(hub_data$target_end_date), - location %in% c("DE", "GB", "FR", "IT")) + location %in% c("DE", "GB", "FR", "IT")) |> + select(-population) # save example data with forecasts only example_quantile_forecasts_only <- hub_data @@ -96,10 +92,8 @@ usethis::use_data(example_quantile_forecasts_only, overwrite = TRUE) example_truth_only <- truth usethis::use_data(example_truth_only, overwrite = TRUE) - -# join -example_quantile <- dplyr::left_join(truth, hub_data) %>% - dplyr::mutate(model = as.character(model)) +# merge forecast data and truth data and save +example_quantile <- merge_pred_and_obs(hub_data, truth) data.table::setDT(example_quantile) # make model a character instead of a factor usethis::use_data(example_quantile, overwrite = TRUE) @@ -175,17 +169,18 @@ example_continuous <- example_quantile[, .(prediction = get_samples(prediction, n_samples = n_samples), sample = 1:n_samples, true_value = unique(true_value)), - by = c("location", "target_end_date", "target_type", + by = c("location", "location_name", + "target_end_date", "target_type", "forecast_date", "model", "horizon")] # remove unnecessary rows where no predictions are available example_continuous[is.na(prediction), sample := NA] -example_continuous <- unique(continuous_example_data) +example_continuous <- unique(example_continuous) usethis::use_data(example_continuous, overwrite = TRUE) # get integer sample data ------------------------------------------------------ example_integer <- data.table::copy(example_continuous) -example_integer <- integer_example_data[, prediction := round(prediction)] +example_integer <- example_integer[, prediction := round(prediction)] usethis::use_data(example_integer, overwrite = TRUE) @@ -218,6 +213,6 @@ example_binary[, true_value := true_value > mean_val] # delete unnecessary columns and take unique values example_binary[, `:=`(sample = NULL, mean_val = NULL, true_value = as.numeric(true_value))] -example_binary <- unique(binary_example_data) +example_binary <- unique(example_binary) usethis::use_data(example_binary, overwrite = TRUE) diff --git a/man/check_forecasts.Rd b/man/check_forecasts.Rd index 34edae005..4a76c1125 100644 --- a/man/check_forecasts.Rd +++ b/man/check_forecasts.Rd @@ -45,17 +45,17 @@ Function to check the input data before running The data should come in one of three different formats: \itemize{ -\item A format for binary predictions (see \link{binary_example_data}) +\item A format for binary predictions (see \link{example_binary}) \item A sample-based format for discrete or continuous predictions -(see \link{continuous_example_data} and \link{integer_example_data}) -\item A quantile-based format (see \link{quantile_example_data}) +(see \link{example_continuous} and \link{example_integer}) +\item A quantile-based format (see \link{example_quantile}) } } \examples{ library(scoringutils) -check <- check_forecasts(quantile_example_data) +check <- check_forecasts(example_quantile) print(check) -check_forecasts(binary_example_data) +check_forecasts(example_binary) } \seealso{ Functions to move between different formats: diff --git a/man/continuous_example_data.Rd b/man/continuous_example_data.Rd deleted file mode 100644 index 94282bf3a..000000000 --- a/man/continuous_example_data.Rd +++ /dev/null @@ -1,29 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/data.R -\docType{data} -\name{continuous_example_data} -\alias{continuous_example_data} -\title{Continuous Forecast Example Data} -\format{ -A data frame with 13,429 rows and 10 columns: -\describe{ -\item{value_date}{the date for which a prediction was made} -\item{value_type}{the target to be predicted (short form)} -\item{geography}{the region for which a prediction was made} -\item{value_desc}{long form description of the prediction target} -\item{model}{name of the model that generated the forecasts} -\item{creation_date}{date on which the forecast was made} -\item{horizon}{forecast horizon in days} -\item{prediction}{prediction value for the corresponding sample} -\item{sample}{id for the corresponding sample} -\item{true_value}{true observed values} -} -} -\usage{ -continuous_example_data -} -\description{ -A data set with continuous predictions in a sample-based format relevant in the -2020 UK Covid-19 epidemic. -} -\keyword{datasets} diff --git a/man/correlation_plot.Rd b/man/correlation_plot.Rd index d3c3f829c..f896060e7 100644 --- a/man/correlation_plot.Rd +++ b/man/correlation_plot.Rd @@ -23,6 +23,6 @@ Plots a coloured table of scores obtained using \code{\link[=eval_forecasts]{eval_forecasts()}} } \examples{ -scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data) +scores <- scoringutils::eval_forecasts(scoringutils::example_quantile) scoringutils::correlation_plot(scores) } diff --git a/man/eval_forecasts.Rd b/man/eval_forecasts.Rd index e4c8b9760..d400a2a42 100644 --- a/man/eval_forecasts.Rd +++ b/man/eval_forecasts.Rd @@ -159,16 +159,16 @@ See \code{\link[=logs]{logs()}} for more information. } \examples{ ## Probability Forecast for Binary Target -binary_example <- data.table::setDT(scoringutils::binary_example_data) +binary_example <- data.table::setDT(scoringutils::example_binary) eval <- scoringutils::eval_forecasts(binary_example, summarise_by = c("model"), quantiles = c(0.5), sd = TRUE) ## Quantile Forecasts # wide format example (this examples shows usage of both wide formats) -range_example_wide <- data.table::setDT(scoringutils::range_example_data_wide) +range_example_wide <- data.table::setDT(scoringutils::example_range_wide) range_example <- scoringutils::range_wide_to_long(range_example_wide) -wide2 <- data.table::setDT(scoringutils::range_example_data_semi_wide) +wide2 <- data.table::setDT(scoringutils::example_range_semi_wide) range_example <- scoringutils::range_wide_to_long(wide2) example <- scoringutils::range_long_to_quantile(range_example) eval <- scoringutils::eval_forecasts(example, @@ -179,7 +179,7 @@ eval <- scoringutils::eval_forecasts(example) ## Integer Forecasts -integer_example <- data.table::setDT(scoringutils::integer_example_data) +integer_example <- data.table::setDT(scoringutils::example_integer) eval <- scoringutils::eval_forecasts(integer_example, summarise_by = c("model"), quantiles = c(0.1, 0.9), @@ -187,7 +187,7 @@ eval <- scoringutils::eval_forecasts(integer_example, eval <- scoringutils::eval_forecasts(integer_example) ## Continuous Forecasts -continuous_example <- data.table::setDT(scoringutils::continuous_example_data) +continuous_example <- data.table::setDT(scoringutils::example_continuous) eval <- scoringutils::eval_forecasts(continuous_example) eval <- scoringutils::eval_forecasts(continuous_example, quantiles = c(0.5, 0.9), diff --git a/man/eval_forecasts_binary.Rd b/man/eval_forecasts_binary.Rd index 91113f5ea..3ccad1556 100644 --- a/man/eval_forecasts_binary.Rd +++ b/man/eval_forecasts_binary.Rd @@ -67,7 +67,7 @@ Evaluate forecasts in a Binary Format } \examples{ # Probability Forecast for Binary Target -binary_example <- data.table::setDT(scoringutils::binary_example_data) +binary_example <- data.table::setDT(scoringutils::example_binary) eval <- scoringutils::eval_forecasts(data = binary_example, summarise_by = c("model"), quantiles = c(0.5), sd = TRUE) diff --git a/man/eval_forecasts_sample.Rd b/man/eval_forecasts_sample.Rd index c53ec4aec..1f4b62cb1 100644 --- a/man/eval_forecasts_sample.Rd +++ b/man/eval_forecasts_sample.Rd @@ -70,7 +70,7 @@ Evaluate forecasts in a Sample-Based Format (Integer or Continuous) \examples{ ## Integer Forecasts -integer_example <- data.table::setDT(scoringutils::integer_example_data) +integer_example <- data.table::setDT(scoringutils::example_integer) eval <- scoringutils::eval_forecasts(integer_example, summarise_by = c("model"), quantiles = c(0.1, 0.9), @@ -78,7 +78,7 @@ eval <- scoringutils::eval_forecasts(integer_example, eval <- scoringutils::eval_forecasts(integer_example) ## Continuous Forecasts -continuous_example <- data.table::setDT(scoringutils::continuous_example_data) +continuous_example <- data.table::setDT(scoringutils::example_continuous) eval <- scoringutils::eval_forecasts(continuous_example)#' eval <- scoringutils::eval_forecasts(continuous_example, diff --git a/man/binary_example_data.Rd b/man/example_binary.Rd similarity index 55% rename from man/binary_example_data.Rd rename to man/example_binary.Rd index c0da1cc25..7bd34703a 100644 --- a/man/binary_example_data.Rd +++ b/man/example_binary.Rd @@ -1,29 +1,29 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} -\name{binary_example_data} -\alias{binary_example_data} +\name{example_binary} +\alias{example_binary} \title{Binary Forecast Example Data} \format{ A data frame with 346 rows and 10 columns: \describe{ -\item{value_date}{the date for which a prediction was made} -\item{value_type}{the target to be predicted (short form)} -\item{geography}{the region for which a prediction was made} -\item{value_desc}{long form description of the prediction target} -\item{model}{name of the model that generated the forecasts} -\item{creation_date}{date on which the forecast was made} -\item{horizon}{forecast horizon in days} -\item{prediction}{probability prediction that true value would be 1} +\item{location}{the country for which a prediction was made} +\item{location_name}{name of the country for which a prediction was made} +\item{target_end_date}{the date for which a prediction was made} +\item{target_type}{the target to be predicted (cases or deaths)} \item{true_value}{true observed values} +\item{forecast_date}{the date on which a prediction was made} +\item{model}{name of the model that generated the forecasts} +\item{horizon}{forecast horizon in weeks} +\item{prediction}{predicted value} } } \usage{ -binary_example_data +example_binary } \description{ -A data set with (constructed) binary predictions relevant in the -2020 UK Covid-19 epidemic. +A data set with binary predictions for COVID-19 cases and deaths constructed from data +submitted to the European Forecast Hub. } \details{ Predictions in the data set were constructed based on the continuous example diff --git a/man/example_continuous.Rd b/man/example_continuous.Rd new file mode 100644 index 000000000..d1cd3aae4 --- /dev/null +++ b/man/example_continuous.Rd @@ -0,0 +1,29 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\docType{data} +\name{example_continuous} +\alias{example_continuous} +\title{Continuous Forecast Example Data} +\format{ +A data frame with 13,429 rows and 10 columns: +\describe{ +\item{location}{the country for which a prediction was made} +\item{target_end_date}{the date for which a prediction was made} +\item{target_type}{the target to be predicted (cases or deaths)} +\item{true_value}{true observed values} +\item{location_name}{name of the country for which a prediction was made} +\item{forecast_date}{the date on which a prediction was made} +\item{model}{name of the model that generated the forecasts} +\item{horizon}{forecast horizon in weeks} +\item{prediction}{predicted value} +\item{sample}{id for the corresponding sample} +} +} +\usage{ +example_continuous +} +\description{ +A data set with continuous predictions for COVID-19 cases and deaths constructed from data +submitted to the European Forecast Hub. +} +\keyword{datasets} diff --git a/man/example_integer.Rd b/man/example_integer.Rd new file mode 100644 index 000000000..97b4fd7c2 --- /dev/null +++ b/man/example_integer.Rd @@ -0,0 +1,29 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\docType{data} +\name{example_integer} +\alias{example_integer} +\title{Integer Forecast Example Data} +\format{ +A data frame with 13,429 rows and 10 columns: +\describe{ +\item{location}{the country for which a prediction was made} +\item{target_end_date}{the date for which a prediction was made} +\item{target_type}{the target to be predicted (cases or deaths)} +\item{true_value}{true observed values} +\item{location_name}{name of the country for which a prediction was made} +\item{forecast_date}{the date on which a prediction was made} +\item{model}{name of the model that generated the forecasts} +\item{horizon}{forecast horizon in weeks} +\item{prediction}{predicted value} +\item{sample}{id for the corresponding sample} +} +} +\usage{ +example_integer +} +\description{ +A data set with integer predictions for COVID-19 cases and deaths constructed from data +submitted to the European Forecast Hub. +} +\keyword{datasets} diff --git a/man/example_quantile.Rd b/man/example_quantile.Rd new file mode 100644 index 000000000..f02700cab --- /dev/null +++ b/man/example_quantile.Rd @@ -0,0 +1,31 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\docType{data} +\name{example_quantile} +\alias{example_quantile} +\title{Quantile Example Data} +\format{ +A data frame with +\describe{ +\item{location}{the country for which a prediction was made} +\item{target_end_date}{the date for which a prediction was made} +\item{target_type}{the target to be predicted (cases or deaths)} +\item{true_value}{true observed values} +\item{location_name}{name of the country for which a prediction was made} +\item{forecast_date}{the date on which a prediction was made} +\item{quantile}{quantile of the corresponding prediction} +\item{prediction}{predicted value} +\item{model}{name of the model that generated the forecasts} +\item{horizon}{forecast horizon in weeks} +} +} +\source{ +\url{https://github.com/epiforecasts/covid19-forecast-hub-europe/} +} +\usage{ +example_quantile +} +\description{ +A data set with predictions for COVID-19 cases and deaths submitted to the European Forecast Hub. +} +\keyword{datasets} diff --git a/man/example_quantile_forecasts_only.Rd b/man/example_quantile_forecasts_only.Rd index 68c4c93b1..b7193705d 100644 --- a/man/example_quantile_forecasts_only.Rd +++ b/man/example_quantile_forecasts_only.Rd @@ -7,23 +7,21 @@ \format{ A data frame with 7,581 rows and 9 columns: \describe{ -\item{value_date}{the date for which a prediction was made} -\item{value_type}{the target to be predicted (short form)} -\item{geography}{the region for which a prediction was made} -\item{model}{name of the model that generated the forecasts} -\item{creation_date}{date on which the forecast was made} +\item{location}{the country for which a prediction was made} +\item{target_end_date}{the date for which a prediction was made} +\item{target_type}{the target to be predicted (cases or deaths)} +\item{forecast_date}{the date on which a prediction was made} \item{quantile}{quantile of the corresponding prediction} -\item{prediction}{quantile predictions} -\item{value_desc}{long form description of the prediction target} -\item{horizon}{forecast horizon in days} - +\item{prediction}{predicted value} +\item{model}{name of the model that generated the forecasts} +\item{horizon}{forecast horizon in weeks} } } \usage{ example_quantile_forecasts_only } \description{ -A data set with predictions for different quantities relevant in the -2020 UK Covid-19 epidemic, but no true_values +A data set with quantile predictions for COVID-19 cases and deaths +submitted to the European Forecast Hub. } \keyword{datasets} diff --git a/man/example_range_long.Rd b/man/example_range_long.Rd new file mode 100644 index 000000000..0d986bcbc --- /dev/null +++ b/man/example_range_long.Rd @@ -0,0 +1,29 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\docType{data} +\name{example_range_long} +\alias{example_range_long} +\title{Range Forecast Example Data (Long Format)} +\format{ +A data frame with: +\describe{ +\item{location}{the country for which a prediction was made} +\item{target_end_date}{the date for which a prediction was made} +\item{target_type}{the target to be predicted (cases or deaths)} +\item{true_value}{true observed values} +\item{location_name}{name of the country for which a prediction was made} +\item{forecast_date}{the date on which a prediction was made} +\item{prediction}{predicted value} +\item{model}{name of the model that generated the forecasts} +\item{horizon}{forecast horizon in weeks} +\item{boundary}{indicate lower or upper bound of prediction interval} +\item{range}{range of the corresponding prediction interval} +} +} +\usage{ +example_range_long +} +\description{ +A data set with predictions for COVID-19 cases and deaths submitted to the European Forecast Hub. +} +\keyword{datasets} diff --git a/man/range_example_data_semi_wide.Rd b/man/example_range_semi_wide.Rd similarity index 51% rename from man/range_example_data_semi_wide.Rd rename to man/example_range_semi_wide.Rd index dea418c8b..11fdb63c3 100644 --- a/man/range_example_data_semi_wide.Rd +++ b/man/example_range_semi_wide.Rd @@ -1,30 +1,29 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} -\name{range_example_data_semi_wide} -\alias{range_example_data_semi_wide} +\name{example_range_semi_wide} +\alias{example_range_semi_wide} \title{Range Forecast Example Data (Semi-Wide Format)} \format{ A data frame with 5,419 rows and 12 columns: \describe{ -\item{value_date}{the date for which a prediction was made} -\item{value_type}{the target to be predicted (short form)} -\item{geography}{the region for which a prediction was made} -\item{value_desc}{long form description of the prediction target} +\item{location}{the country for which a prediction was made} +\item{target_end_date}{the date for which a prediction was made} +\item{target_type}{the target to be predicted (cases or deaths)} \item{true_value}{true observed values} +\item{location_name}{name of the country for which a prediction was made} +\item{forecast_date}{the date on which a prediction was made} \item{model}{name of the model that generated the forecasts} -\item{creation_date}{date on which the forecast was made} -\item{horizon}{forecast horizon in days} +\item{horizon}{forecast horizon in weeks} \item{range}{range of the corresponding prediction interval} \item{lower}{prediction for the lower bound of the corresponding interval} \item{upper}{prediction for the upper bound of the corresponding interval} } } \usage{ -range_example_data_semi_wide +example_range_semi_wide } \description{ -A data set with predictions with different interval ranges relevant in the -2020 UK Covid-19 epidemic. +A data set with predictions for COVID-19 cases and deaths submitted to the European Forecast Hub. } \keyword{datasets} diff --git a/man/range_example_data_wide.Rd b/man/example_range_wide.Rd similarity index 68% rename from man/range_example_data_wide.Rd rename to man/example_range_wide.Rd index fce617c92..707ddf969 100644 --- a/man/range_example_data_wide.Rd +++ b/man/example_range_wide.Rd @@ -1,20 +1,20 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} -\name{range_example_data_wide} -\alias{range_example_data_wide} +\name{example_range_wide} +\alias{example_range_wide} \title{Range Forecast Example Data (Wide Format)} \format{ A data frame with: \describe{ -\item{value_date}{the date for which a prediction was made} -\item{value_type}{the target to be predicted (short form)} -\item{geography}{the region for which a prediction was made} -\item{value_desc}{long form description of the prediction target} +\item{location}{the country for which a prediction was made} +\item{target_end_date}{the date for which a prediction was made} +\item{target_type}{the target to be predicted (cases or deaths)} \item{true_value}{true observed values} +\item{location_name}{name of the country for which a prediction was made} +\item{forecast_date}{the date on which a prediction was made} \item{model}{name of the model that generated the forecasts} -\item{creation_date}{date on which the forecast was made} -\item{horizon}{forecast horizon in days} +\item{horizon}{forecast horizon in weeks} \item{lower_0}{prediction for the lower bound of the 0\% interval range (median)} \item{lower_10}{prediction for the lower bound of the 10\% interval range} \item{lower_20}{prediction for the lower bound of the 20\% interval range} @@ -25,6 +25,8 @@ A data frame with: \item{lower_70}{prediction for the lower bound of the 70\% interval range} \item{lower_80}{prediction for the lower bound of the 80\% interval range} \item{lower_90}{prediction for the lower bound of the 90\% interval range} +\item{lower_95}{prediction for the lower bound of the 95\% interval range} +\item{lower_98}{prediction for the lower bound of the 98\% interval range} \item{upper_0}{prediction for the upper bound of the 0\% interval range} \item{upper_10}{prediction for the upper bound of the 1\% interval range} \item{upper_20}{prediction for the upper bound of the 20\% interval range} @@ -35,13 +37,14 @@ A data frame with: \item{upper_70}{prediction for the upper bound of the 70\% interval range} \item{upper_80}{prediction for the upper bound of the 80\% interval range} \item{upper_90}{prediction for the upper bound of the 90\% interval range} +\item{upper_95}{prediction for the upper bound of the 95\% interval range} +\item{upper_98}{prediction for the upper bound of the 98\% interval range} } } \usage{ -range_example_data_wide +example_range_wide } \description{ -A data set with predictions with different interval ranges relevant in the -2020 UK Covid-19 epidemic. +A data set with predictions for COVID-19 cases and deaths submitted to the European Forecast Hub. } \keyword{datasets} diff --git a/man/example_truth_data_only.Rd b/man/example_truth_data_only.Rd deleted file mode 100644 index 2add5b491..000000000 --- a/man/example_truth_data_only.Rd +++ /dev/null @@ -1,25 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/data.R -\docType{data} -\name{example_truth_data_only} -\alias{example_truth_data_only} -\title{Truth data only} -\format{ -A data frame with 140 rows and 5 columns: -\describe{ -\item{value_date}{the date for which a prediction was made} -\item{value_type}{the target to be predicted (short form)} -\item{geography}{the region for which a prediction was made} -\item{value_desc}{long form description of the prediction target} -\item{true_value}{true observed values} - -} -} -\usage{ -example_truth_data_only -} -\description{ -A data set with truth data for different quantities relevant in the -2020 UK Covid-19 epidemic, but no predictions -} -\keyword{datasets} diff --git a/man/example_truth_only.Rd b/man/example_truth_only.Rd new file mode 100644 index 000000000..73783ba92 --- /dev/null +++ b/man/example_truth_only.Rd @@ -0,0 +1,24 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\docType{data} +\name{example_truth_only} +\alias{example_truth_only} +\title{Truth data only} +\format{ +A data frame with 140 rows and 5 columns: +\describe{ +\item{location}{the country for which a prediction was made} +\item{target_end_date}{the date for which a prediction was made} +\item{target_type}{the target to be predicted (cases or deaths)} +\item{true_value}{true observed values} +\item{location_name}{name of the country for which a prediction was made} +} +} +\usage{ +example_truth_only +} +\description{ +A data set with truth values for COVID-19 cases and deaths +submitted to the European Forecast Hub. +} +\keyword{datasets} diff --git a/man/integer_example_data.Rd b/man/integer_example_data.Rd deleted file mode 100644 index 19e7fe9f3..000000000 --- a/man/integer_example_data.Rd +++ /dev/null @@ -1,29 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/data.R -\docType{data} -\name{integer_example_data} -\alias{integer_example_data} -\title{Integer Forecast Example Data} -\format{ -A data frame with 13,429 rows and 10 columns: -\describe{ -\item{value_date}{the date for which a prediction was made} -\item{value_type}{the target to be predicted (short form)} -\item{geography}{the region for which a prediction was made} -\item{value_desc}{long form description of the prediction target} -\item{model}{name of the model that generated the forecasts} -\item{creation_date}{date on which the forecast was made} -\item{horizon}{forecast horizon in days} -\item{prediction}{prediction value for the corresponding sample} -\item{sample}{id for the corresponding sample} -\item{true_value}{true observed values} -} -} -\usage{ -integer_example_data -} -\description{ -A data set with integer predictions in a sample-based format relevant in the -2020 UK Covid-19 epidemic. -} -\keyword{datasets} diff --git a/man/interval_coverage.Rd b/man/interval_coverage.Rd index 694fa9582..b9fe64fd4 100644 --- a/man/interval_coverage.Rd +++ b/man/interval_coverage.Rd @@ -38,7 +38,7 @@ ggplot object with a plot of interval coverage Plot interval coverage } \examples{ -example1 <- scoringutils::range_example_data_long +example1 <- scoringutils::example_range_long example1 <- scoringutils::range_long_to_quantile(example1) scores <- scoringutils::eval_forecasts(example1, summarise_by = c("model", "range")) diff --git a/man/pairwise_comparison.Rd b/man/pairwise_comparison.Rd index b9342589c..2960bb04c 100644 --- a/man/pairwise_comparison.Rd +++ b/man/pairwise_comparison.Rd @@ -61,7 +61,7 @@ res <- scoringutils::pairwise_comparison(df, baseline = "model1") scoringutils::plot_pairwise_comparison(res) -eval <- scoringutils::eval_forecasts(scoringutils::quantile_example_data) +eval <- scoringutils::eval_forecasts(scoringutils::example_quantile) pairwise <- pairwise_comparison(eval, summarise_by = c("model")) } \author{ diff --git a/man/pit_df.Rd b/man/pit_df.Rd index be9e4df40..a00b24d7e 100644 --- a/man/pit_df.Rd +++ b/man/pit_df.Rd @@ -29,12 +29,12 @@ Wrapper around \code{pit()} for use in data.frames see \code{\link[=pit]{pit()}} } \examples{ -example <- scoringutils::continuous_example_data +example <- scoringutils::example_continuous result <- pit_df(example, summarise_by = "model") hist_PIT(result) # example with quantile data -result <- pit_df(quantile_example_data, summarise_by = "model") +result <- pit_df(example_quantile, summarise_by = "model") hist_PIT(result) } \references{ diff --git a/man/plot_pairwise_comparison.Rd b/man/plot_pairwise_comparison.Rd index 09da847d4..3b4f244d6 100644 --- a/man/plot_pairwise_comparison.Rd +++ b/man/plot_pairwise_comparison.Rd @@ -50,11 +50,11 @@ df <- data.frame(model = rep(c("model1", "model2", "model3"), each = 10), interval_score = abs(rnorm(30, mean = rep(c(1, 1.3, 2), each = 10))), aem = (abs(rnorm(30)))) -data <- scoringutils::quantile_example_data +data <- scoringutils::example_quantile scores <- scoringutils::eval_forecasts(data) pairwise <- pairwise_comparison(scores, - summarise_by = "value_desc") + summarise_by = "target_type") scoringutils::plot_pairwise_comparison(pairwise, - facet_formula = ~ value_desc, + facet_formula = ~ target_type, scales = "fixed") } diff --git a/man/plot_predictions.Rd b/man/plot_predictions.Rd index acad52699..58ee2108e 100644 --- a/man/plot_predictions.Rd +++ b/man/plot_predictions.Rd @@ -99,21 +99,17 @@ ggplot object with a plot of true vs predicted values Make a plot of observed and predicted values } \examples{ -example1 <- scoringutils::continuous_example_data -example2 <- scoringutils::range_example_data_long - -scoringutils::plot_predictions(example1, x = "value_date", - filter_truth = list('value_date <= "2020-06-22"', - 'value_date > "2020-05-01"'), - filter_forecasts = list("model == 'SIRCOVID'", - 'creation_date == "2020-06-22"'), - facet_formula = geography ~ value_desc) - -scoringutils::plot_predictions(example2, x = "value_date", - filter_truth = list('value_date <= "2020-06-22"', - 'value_date > "2020-05-01"'), - filter_forecasts = list("model == 'SIRCOVID'", - 'creation_date == "2020-06-22"'), - allow_truth_without_pred = TRUE, - facet_formula = geography ~ value_desc) +example1 <- scoringutils::example_continuous + +plot_predictions( + example1, + x = "target_end_date", + filter_truth = list('target_end_date <= "2021-07-22"', + 'target_end_date > "2021-05-01"'), + filter_forecasts = list("model == 'EuroCOVIDhub-ensemble'", + 'forecast_date == "2021-06-07"'), + facet_formula = location ~ target_type, + range = c(0, 50, 90, 95) +) + } diff --git a/man/quantile_coverage.Rd b/man/quantile_coverage.Rd index 5589709cb..b3ad51ab7 100644 --- a/man/quantile_coverage.Rd +++ b/man/quantile_coverage.Rd @@ -38,7 +38,7 @@ ggplot object with a plot of interval coverage Plot quantile coverage } \examples{ -example1 <- scoringutils::quantile_example_data +example1 <- scoringutils::example_quantile scores <- scoringutils::eval_forecasts(example1, summarise_by = c("model", "quantile")) quantile_coverage(scores) diff --git a/man/quantile_example_data.Rd b/man/quantile_example_data.Rd deleted file mode 100644 index 98f5b5bea..000000000 --- a/man/quantile_example_data.Rd +++ /dev/null @@ -1,30 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/data.R -\docType{data} -\name{quantile_example_data} -\alias{quantile_example_data} -\title{Quantile Example Data} -\format{ -A data frame with -\describe{ -\item{value_date}{the date for which a prediction was made} -\item{value_type}{the target to be predicted (short form)} -\item{geography}{the region for which a prediction was made} -\item{value_desc}{long form description of the prediction target} -\item{true_value}{true observed values} -\item{model}{name of the model that generated the forecasts} -\item{creation_date}{date on which the forecast was made} -\item{quantile}{quantile of the corresponding prediction} -\item{prediction}{quantile predictions} -\item{horizon}{forecast horizon in days} - -} -} -\usage{ -quantile_example_data -} -\description{ -A data set with predictions for different quantities relevant in the -2020 UK Covid-19 epidemic. -} -\keyword{datasets} diff --git a/man/quantile_to_long.Rd b/man/quantile_to_long.Rd index 33fcd4905..c9ca1067e 100644 --- a/man/quantile_to_long.Rd +++ b/man/quantile_to_long.Rd @@ -9,7 +9,7 @@ quantile_to_long(data) \arguments{ \item{data}{a data.frame following the specifications from \code{\link[=eval_forecasts]{eval_forecasts()}}) for quantile forecasts. For an example, see -\code{\link[=range_example_data_long]{range_example_data_long()}})} +\code{\link[=example_range_long]{example_range_long()}})} } \value{ a data.frame in long format diff --git a/man/quantile_to_range.Rd b/man/quantile_to_range.Rd index a63f5e828..0b8fda17f 100644 --- a/man/quantile_to_range.Rd +++ b/man/quantile_to_range.Rd @@ -8,7 +8,7 @@ quantile_to_range(data, keep_quantile_col = FALSE) } \arguments{ \item{data}{a data.frame following the specifications shown in the example -\code{\link[=range_example_data_long]{range_example_data_long()}})} +\code{\link[=example_range_long]{example_range_long()}})} \item{keep_quantile_col}{keep the quantile column in the final output after transformation (default is FALSE)} diff --git a/man/quantile_to_range_long.Rd b/man/quantile_to_range_long.Rd index 1e91d6803..6f0e693cd 100644 --- a/man/quantile_to_range_long.Rd +++ b/man/quantile_to_range_long.Rd @@ -8,7 +8,7 @@ quantile_to_range_long(data, keep_quantile_col = TRUE) } \arguments{ \item{data}{a data.frame following the specifications shown in the example -\code{\link[=range_example_data_long]{range_example_data_long()}})} +\code{\link[=example_range_long]{example_range_long()}})} \item{keep_quantile_col}{keep the quantile column in the final output after transformation (default is FALSE)} @@ -21,12 +21,12 @@ Transform data from a format that uses quantiles only to one that uses interval ranges to denote quantiles. Given a data.frame that follows the structure shown in -\code{\link[=quantile_example_data]{quantile_example_data()}}, the function outputs the same +\code{\link[=example_quantile]{example_quantile()}}, the function outputs the same data in a long format as (as shown in -\code{\link[=range_example_data_long]{range_example_data_long()}}). +\code{\link[=example_range_long]{example_range_long()}}). } \examples{ -quantile <- scoringutils::quantile_example_data +quantile <- scoringutils::example_quantile long <- scoringutils::quantile_to_range_long(quantile) diff --git a/man/quantile_to_wide.Rd b/man/quantile_to_wide.Rd index 13bf435c2..33e54ebd6 100644 --- a/man/quantile_to_wide.Rd +++ b/man/quantile_to_wide.Rd @@ -9,7 +9,7 @@ quantile_to_wide(data) \arguments{ \item{data}{a data.frame following the specifications from \code{\link[=eval_forecasts]{eval_forecasts()}}) for quantile forecasts. For an example, see -\code{\link[=range_example_data_long]{range_example_data_long()}})} +\code{\link[=example_range_long]{example_range_long()}})} } \value{ a data.frame in wide format diff --git a/man/range_example_data_long.Rd b/man/range_example_data_long.Rd deleted file mode 100644 index bd92db768..000000000 --- a/man/range_example_data_long.Rd +++ /dev/null @@ -1,30 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/data.R -\docType{data} -\name{range_example_data_long} -\alias{range_example_data_long} -\title{Range Forecast Example Data (Long Format)} -\format{ -A data frame with: -\describe{ -\item{value_date}{the date for which a prediction was made} -\item{value_type}{the target to be predicted (short form)} -\item{geography}{the region for which a prediction was made} -\item{value_desc}{long form description of the prediction target} -\item{true_value}{true observed values} -\item{model}{name of the model that generated the forecasts} -\item{creation_date}{date on which the forecast was made} -\item{prediction}{value for the lower or upper bound of the given prediction interval} -\item{horizon}{forecast horizon in days} -\item{boundary}{indicate lower or upper bound of prediction interval} -\item{range}{range of the corresponding prediction interval} -} -} -\usage{ -range_example_data_long -} -\description{ -A data set with predictions with different interval ranges relevant in the -2020 UK Covid-19 epidemic. -} -\keyword{datasets} diff --git a/man/range_long_to_quantile.Rd b/man/range_long_to_quantile.Rd index 5342d40f8..51ed73abb 100644 --- a/man/range_long_to_quantile.Rd +++ b/man/range_long_to_quantile.Rd @@ -9,7 +9,7 @@ range_long_to_quantile(data, keep_range_col = FALSE) \arguments{ \item{data}{a data.frame following the specifications from \code{\link[=eval_forecasts]{eval_forecasts()}}) for quantile forecasts. For an example, see -\code{\link[=range_example_data_long]{range_example_data_long()}})} +\code{\link[=example_range_long]{example_range_long()}})} \item{keep_range_col}{keep the range and boundary columns after transformation (default is FALSE)} @@ -22,15 +22,15 @@ Transform data from a format that uses interval ranges to denote quantiles to a format that uses quantiles only. Given a data.frame that follows the structure shown in -\code{\link[=range_example_data_long]{range_example_data_long()}}, the function outputs the same +\code{\link[=example_range_long]{example_range_long()}}, the function outputs the same data in a long format as (as shown in -\code{\link[=range_example_data_long]{range_example_data_long()}}). This can be useful e.g. for +\code{\link[=example_range_long]{example_range_long()}}). This can be useful e.g. for plotting. If you're data.frame is in a different format, consider running \code{\link[=range_long_to_wide]{range_long_to_wide()}} first. } \examples{ -wide <- range_example_data_wide -semiwide <- range_example_data_semi_wide +wide <- example_range_wide +semiwide <- example_range_semi_wide long <- range_wide_to_long(wide) long2 <- range_wide_to_long(semiwide) diff --git a/man/range_long_to_wide.Rd b/man/range_long_to_wide.Rd index 0107af8f4..e9eeaf2ad 100644 --- a/man/range_long_to_wide.Rd +++ b/man/range_long_to_wide.Rd @@ -9,20 +9,20 @@ range_long_to_wide(data) \arguments{ \item{data}{a data.frame following the specifications from \code{\link[=eval_forecasts]{eval_forecasts()}}) for quantile forecasts. For an example, see -\code{\link[=range_example_data_long]{range_example_data_long()}})} +\code{\link[=example_range_long]{example_range_long()}})} } \value{ a data.frame in wide format } \description{ Given a data.frame that follows the structure shown in -\code{\link[=range_example_data_long]{range_example_data_long()}}, the function outputs the same +\code{\link[=example_range_long]{example_range_long()}}, the function outputs the same data in a long format as (as shown in -\code{\link[=range_example_data_wide]{range_example_data_wide()}}). This can be useful e.g. for +\code{\link[=example_range_wide]{example_range_wide()}}). This can be useful e.g. for plotting. } \examples{ -long <- scoringutils::range_example_data_long +long <- scoringutils::example_range_long wide <- scoringutils::range_long_to_wide(long) } diff --git a/man/range_plot.Rd b/man/range_plot.Rd index 1442588e5..95d90b2ec 100644 --- a/man/range_plot.Rd +++ b/man/range_plot.Rd @@ -59,18 +59,18 @@ interval ranges contribute to the overall interval score, or how sharpness / dispersion changes by range. } \examples{ -scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, - summarise_by = c("model", "value_desc", "range")) +scores <- scoringutils::eval_forecasts(scoringutils::example_quantile, + summarise_by = c("model", "target_type", "range")) -scoringutils::range_plot(scores, x = "model", facet_formula = ~ value_desc) +scoringutils::range_plot(scores, x = "model", facet_formula = ~ target_type) # visualise dispersion instead of interval score scoringutils::range_plot(scores, y = "dispersion", x = "model", - facet_formula = ~value_desc) + facet_formula = ~target_type) # we saw above that dispersion values crossed. Let's look at the unweighted WIS -scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, - summarise_by = c("model", "value_desc", "range")) +scores <- scoringutils::eval_forecasts(scoringutils::example_quantile, + summarise_by = c("model", "target_type", "range")) scoringutils::range_plot(scores, y = "dispersion", x = "model", - facet_formula = ~value_desc) + facet_formula = ~target_type) } diff --git a/man/range_to_quantile.Rd b/man/range_to_quantile.Rd index d605c94b4..0fdb48fcb 100644 --- a/man/range_to_quantile.Rd +++ b/man/range_to_quantile.Rd @@ -9,7 +9,7 @@ range_to_quantile(data, keep_range_col = FALSE) \arguments{ \item{data}{a data.frame following the specifications from \code{\link[=eval_forecasts]{eval_forecasts()}}) for quantile forecasts. For an example, see -\code{\link[=range_example_data_long]{range_example_data_long()}})} +\code{\link[=example_range_long]{example_range_long()}})} \item{keep_range_col}{keep the range and boundary columns after transformation (default is FALSE)} diff --git a/man/range_wide_to_long.Rd b/man/range_wide_to_long.Rd index 4165c560e..556a5d387 100644 --- a/man/range_wide_to_long.Rd +++ b/man/range_wide_to_long.Rd @@ -9,20 +9,20 @@ range_wide_to_long(data) \arguments{ \item{data}{a data.frame following the specifications from \code{\link[=eval_forecasts]{eval_forecasts()}}) for quantile forecasts. For an example, see -\code{\link[=range_example_data_wide]{range_example_data_wide()}})} +\code{\link[=example_range_wide]{example_range_wide()}})} } \value{ a data.frame in long format } \description{ Given a data.frame that follows the structure shown in -\code{\link[=range_example_data_wide]{range_example_data_wide()}}, the function outputs the same +\code{\link[=example_range_wide]{example_range_wide()}}, the function outputs the same data in a long format as (as shown in -\code{\link[=range_example_data_long]{range_example_data_long()}}). This can be useful e.g. for +\code{\link[=example_range_long]{example_range_long()}}). This can be useful e.g. for plotting. } \examples{ -wide <- scoringutils::range_example_data_wide +wide <- scoringutils::example_range_wide long <- scoringutils::range_wide_to_long(wide) } diff --git a/man/sample_to_quantile.Rd b/man/sample_to_quantile.Rd index e4caedb95..867fe4f22 100644 --- a/man/sample_to_quantile.Rd +++ b/man/sample_to_quantile.Rd @@ -22,7 +22,7 @@ Transform data from a format that is based on predictive samples to a format based on plain quantiles. } \examples{ -example_data <- scoringutils::integer_example_data +example_data <- scoringutils::example_integer quantile_data <- scoringutils::sample_to_quantile(example_data) diff --git a/man/sample_to_range_long.Rd b/man/sample_to_range_long.Rd index 19524a66d..09b647d88 100644 --- a/man/sample_to_range_long.Rd +++ b/man/sample_to_range_long.Rd @@ -30,7 +30,7 @@ Transform data from a format that is based on predictive samples to a format based on interval ranges } \examples{ -example_data <- scoringutils::integer_example_data +example_data <- scoringutils::example_integer quantile_data <- scoringutils::sample_to_range_long(example_data) diff --git a/man/score_heatmap.Rd b/man/score_heatmap.Rd index b1bf9af1a..a03904e0d 100644 --- a/man/score_heatmap.Rd +++ b/man/score_heatmap.Rd @@ -57,9 +57,9 @@ groups, e.g. the interval score obtained by several forecasting models in different locations. } \examples{ -scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, - summarise_by = c("model", "value_desc", "range")) +scores <- scoringutils::eval_forecasts(scoringutils::example_quantile, + summarise_by = c("model", "target_type", "range")) -scoringutils::score_heatmap(scores, x = "value_desc", metric = "bias") +scoringutils::score_heatmap(scores, x = "target_type", metric = "bias") } diff --git a/man/score_table.Rd b/man/score_table.Rd index 8f8ab2c80..1d910285c 100644 --- a/man/score_table.Rd +++ b/man/score_table.Rd @@ -46,24 +46,24 @@ Plots a coloured table of summarised scores obtained using \code{\link[=eval_forecasts]{eval_forecasts()}} } \examples{ -scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, - summarise_by = c("model", "value_desc")) -scoringutils::score_table(scores, y = "model", facet_formula = ~ value_desc, +scores <- scoringutils::eval_forecasts(scoringutils::example_quantile, + summarise_by = c("model", "target_type")) +scoringutils::score_table(scores, y = "model", facet_formula = ~ target_type, ncol = 1) # can also put target description on the y-axis -scoringutils::score_table(scores, y = c("model", "value_desc")) +scoringutils::score_table(scores, y = c("model", "target_type")) # yields the same result in this case scoringutils::score_table(scores) -scores <- scoringutils::eval_forecasts(scoringutils::integer_example_data, - summarise_by = c("model", "value_desc")) -scoringutils::score_table(scores, y = "model", facet_formula = ~ value_desc, +scores <- scoringutils::eval_forecasts(scoringutils::example_integer, + summarise_by = c("model", "target_type")) +scoringutils::score_table(scores, y = "model", facet_formula = ~ target_type, ncol = 1) # only show selected metrics -scoringutils::score_table(scores, y = "model", facet_formula = ~ value_desc, +scoringutils::score_table(scores, y = "model", facet_formula = ~ target_type, ncol = 1, select_metrics = c("crps", "bias")) } diff --git a/man/show_avail_forecasts.Rd b/man/show_avail_forecasts.Rd index bdcd76546..a03bb0f6c 100644 --- a/man/show_avail_forecasts.Rd +++ b/man/show_avail_forecasts.Rd @@ -75,6 +75,6 @@ ggplot object with a plot of interval coverage Visualise Where Forecasts Are Available } \examples{ -example1 <- scoringutils::range_example_data_long -show_avail_forecasts(example1, x = "value_date", facet_formula = ~ value_desc) +example1 <- scoringutils::example_range_long +show_avail_forecasts(example1, x = "target_end_date", facet_formula = ~ target_type) } diff --git a/man/summarise_scores.Rd b/man/summarise_scores.Rd index a6dc89591..3c16dac1a 100644 --- a/man/summarise_scores.Rd +++ b/man/summarise_scores.Rd @@ -32,7 +32,7 @@ Summarise scores as produced by \code{\link[=eval_forecasts]{eval_forecasts()}}- } \examples{ library(scoringutils) -data <- quantile_example_data +data <- example_quantile scores <- eval_forecasts(data) summarise_scores(scores, summarise_by = c("model")) diff --git a/man/wis_components.Rd b/man/wis_components.Rd index 622ad9a1f..6eb23bfbc 100644 --- a/man/wis_components.Rd +++ b/man/wis_components.Rd @@ -60,11 +60,11 @@ Visualise the components of the weighted interval score: penalties for over-prediction, under-prediction and for high dispersion (lack of sharpness) } \examples{ -scores <- scoringutils::eval_forecasts(scoringutils::quantile_example_data, - summarise_by = c("model", "value_desc")) -scoringutils::wis_components(scores, x = "model", facet_formula = ~ value_desc, +scores <- scoringutils::eval_forecasts(scoringutils::example_quantile, + summarise_by = c("model", "target_type")) +scoringutils::wis_components(scores, x = "model", facet_formula = ~ target_type, relative_contributions = TRUE) -scoringutils::wis_components(scores, x = "model", facet_formula = ~ value_desc, +scoringutils::wis_components(scores, x = "model", facet_formula = ~ target_type, relative_contributions = FALSE) } \references{ diff --git a/tests/testthat/_snaps/plot_predictions/many-quantiles-from-sample.svg b/tests/testthat/_snaps/plot_predictions/many-quantiles-from-sample.svg index 22fd773ff..637a6c372 100644 --- a/tests/testthat/_snaps/plot_predictions/many-quantiles-from-sample.svg +++ b/tests/testthat/_snaps/plot_predictions/many-quantiles-from-sample.svg @@ -21,919 +21,925 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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- - - - -May 01 -May 15 -Jun 01 -Jun 15 -Jul 01 - - - - - -May 01 -May 15 -Jun 01 -Jun 15 -Jul 01 -0 -5 -10 -15 -20 - - - - - -10 -20 -30 -40 -50 - - - - - -100 -200 -300 -400 - - - - -0 -25 -50 -75 - - - - -400 -600 -800 -1000 - - - - -500 -700 -900 -1100 -1300 - - - - - -5 -10 -15 -20 - - - - -4000 -6000 -8000 -10000 - - - - -0.0 -2.5 -5.0 -7.5 - - - - -0 -10 -20 -30 -40 - - - - - -value_date + + + + + +May 15 +Jun 01 +Jun 15 +Jul 01 +Jul 15 + + + + + +May 15 +Jun 01 +Jun 15 +Jul 01 +Jul 15 + + + + + +May 15 +Jun 01 +Jun 15 +Jul 01 +Jul 15 +-2e+05 +-1e+05 +0e+00 +1e+05 + + + + +100 +200 + + +400 +800 +1200 +1600 + + + + +0e+00 +1e+05 +2e+05 +3e+05 + + + + +500 +1000 +1500 + + + +0 +30000 +60000 +90000 + + + + +400 +800 +1200 + + + +20000 +40000 +60000 + + + +target_end_date True and predicted values diff --git a/tests/testthat/test-check_forecasts.R b/tests/testthat/test-check_forecasts.R index b91fb5b9b..e63d6d749 100644 --- a/tests/testthat/test-check_forecasts.R +++ b/tests/testthat/test-check_forecasts.R @@ -1,5 +1,5 @@ test_that("check_forecasts() function works", { - check <- check_forecasts(quantile_example_data) + check <- check_forecasts(example_quantile) expect_s3_class(check, "scoringutils_check") }) @@ -8,15 +8,15 @@ test_that("check_forecasts() function has an error for empty data.frame", { }) test_that("check_forecasts() function returns a warning with NA in the data", { - check <- check_forecasts(quantile_example_data) + check <- check_forecasts(example_quantile) expect_equal(unlist(check$warnings), "Some values for `prediction` are NA in the data provided") }) test_that("check_forecasts() function returns warnings with NA in the data", { - example <- data.table::copy(quantile_example_data) - example[horizon == 7, true_value := NA] + example <- data.table::copy(example_quantile) + example[horizon == 2, true_value := NA] check <- check_forecasts(example) expect_equal(length(check$warnings), 2) -}) \ No newline at end of file +}) diff --git a/tests/testthat/test-eval_forecasts.R b/tests/testthat/test-eval_forecasts.R index 87a746c73..2dfe14133 100644 --- a/tests/testthat/test-eval_forecasts.R +++ b/tests/testthat/test-eval_forecasts.R @@ -18,19 +18,22 @@ test_that("eval_forecasts() warns if column name equals a metric name", { # test binary case ------------------------------------------------------------- test_that("function produces output for a binary case", { - binary_example <- data.table::setDT(scoringutils::binary_example_data) + binary_example <- data.table::setDT(scoringutils::example_binary) eval <- eval_forecasts(binary_example[!is.na(prediction)], - summarise_by = c("model", "value_desc"), + summarise_by = c("model", "target_type"), quantiles = c(0.5), sd = TRUE) expect_equal(nrow(eval) > 1, TRUE) + expect_equal(colnames(eval), + c("model", "target_type", + "brier_score", "brier_score_0.5", "brier_score_sd")) }) test_that("function produces score for a binary case", { - binary_example <- data.table::setDT(scoringutils::binary_example_data) + binary_example <- data.table::setDT(scoringutils::example_binary) eval <- eval_forecasts(binary_example[!is.na(prediction)], - summarise_by = c("model", "value_desc"), + summarise_by = c("model", "target_type"), quantiles = c(0.5), sd = TRUE) expect_true("brier_score" %in% names(eval)) }) @@ -40,7 +43,7 @@ test_that("function produces score for a binary case", { # test quantile case ----------------------------------------------------------- test_that("function produces output for a quantile format case", { - quantile_example <- data.table::setDT(scoringutils::quantile_example_data) + quantile_example <- data.table::setDT(scoringutils::example_quantile) eval <- eval_forecasts(quantile_example[!is.na(prediction)], summarise_by = c("model"), quantiles = c(0.5), sd = TRUE) @@ -65,7 +68,7 @@ test_that("eval_forecasts() quantile produces desired metrics", { test_that("calculation of aem is correct for a quantile format case", { - quantile_example <- data.table::setDT(scoringutils::quantile_example_data) + quantile_example <- data.table::setDT(scoringutils::example_quantile) eval <- eval_forecasts(quantile_example[!is.na(prediction)], summarise_by = c("model"), quantiles = c(0.5), sd = TRUE) @@ -80,16 +83,16 @@ test_that("calculation of aem is correct for a quantile format case", { test_that("all quantile and range formats yield the same result", { - quantile_example1 <- data.table::setDT(scoringutils::quantile_example_data) + quantile_example1 <- data.table::setDT(scoringutils::example_quantile) - quantile_example2 <- data.table::setDT(scoringutils::range_example_data_long) + quantile_example2 <- data.table::setDT(scoringutils::example_range_long) quantile_example2 <- range_long_to_quantile(quantile_example2) - quantile_example3 <- data.table::setDT(scoringutils::range_example_data_semi_wide) + quantile_example3 <- data.table::setDT(scoringutils::example_range_semi_wide) quantile_example3 <- range_wide_to_long(quantile_example3) quantile_example3 <- range_long_to_quantile(quantile_example3) - wide <- data.table::setDT(scoringutils::range_example_data_wide) + wide <- data.table::setDT(scoringutils::example_range_wide) quantile_example4 <- scoringutils::range_wide_to_long(wide) @@ -110,7 +113,7 @@ test_that("all quantile and range formats yield the same result", { }) test_that("function produces output even if only some metrics are chosen", { - range_example_wide <- data.table::setDT(scoringutils::range_example_data_wide) + range_example_wide <- data.table::setDT(scoringutils::example_range_wide) range_example <- scoringutils::range_wide_to_long(range_example_wide) example <- range_long_to_quantile(range_example) @@ -124,7 +127,7 @@ test_that("function produces output even if only some metrics are chosen", { }) test_that("WIS is the same with other metrics omitted or included", { - range_example_wide <- data.table::setDT(scoringutils::range_example_data_wide) + range_example_wide <- data.table::setDT(scoringutils::example_range_wide) range_example <- scoringutils::range_wide_to_long(range_example_wide) example <- scoringutils::range_long_to_quantile(range_example) @@ -145,7 +148,7 @@ test_that("WIS is the same with other metrics omitted or included", { # test integer and continuous case --------------------------------------------- test_that("function produces output for a continuous format case", { - example <- data.table::setDT(scoringutils::continuous_example_data) + example <- data.table::setDT(scoringutils::example_continuous) eval <- eval_forecasts(example[!is.na(prediction)], summarise_by = c("model"), quantiles = c(0.5), sd = TRUE) diff --git a/tests/testthat/test-merge_pred_and_obs.R b/tests/testthat/test-merge_pred_and_obs.R index b4bbabe6b..d75c80024 100644 --- a/tests/testthat/test-merge_pred_and_obs.R +++ b/tests/testthat/test-merge_pred_and_obs.R @@ -1,26 +1,26 @@ test_that("merge pred and obs works", { - data <- scoringutils::quantile_example_data - forecasts <- scoringutils::example_quantile_forecasts_only - truth_data <- scoringutils::example_truth_data_only + data <- example_quantile + forecasts <- example_quantile_forecasts_only + truth_data <- example_truth_only - eval1 <- scoringutils::eval_forecasts(data = data) + eval1 <- eval_forecasts(data = data) data2 <- merge_pred_and_obs(forecasts = forecasts, observations = truth_data) - eval2 <- scoringutils::eval_forecasts(data = data2) + eval2 <- eval_forecasts(data = data2) data.table::setcolorder(eval1, colnames(eval2)) - eval1 <- eval1[order(geography, value_type, model, value_date)] + eval1 <- eval1[order(location, target_type, model, forecast_date, horizon)] # for some reason merge sometimes turns characters into factors. # Reverse this here. # not sure this needs a general solution eval2[, model := as.character(model)] - eval2 <- eval2[order(geography, value_type, model, value_date)] + eval2 <- eval2[order(location, target_type, model, forecast_date, horizon)] expect_equal(as.data.frame(eval1), as.data.frame(eval2), ignore_attr = TRUE) }) diff --git a/tests/testthat/test-pairwise_comparison.R b/tests/testthat/test-pairwise_comparison.R index 2b509db6d..7b9f06cc1 100644 --- a/tests/testthat/test-pairwise_comparison.R +++ b/tests/testthat/test-pairwise_comparison.R @@ -156,7 +156,7 @@ test_that("pairwise comparisons works", { }) test_that("Pairwise comparisons work in eval_forecasts() with integer data", { - eval <- eval_forecasts(data = integer_example_data, + eval <- eval_forecasts(data = example_integer, summarise_by = "model", compute_relative_skill = TRUE) @@ -166,7 +166,7 @@ test_that("Pairwise comparisons work in eval_forecasts() with integer data", { test_that("Pairwise comparisons work in eval_forecasts() with binary data", { eval <- suppressWarnings( - eval_forecasts(data = binary_example_data, + eval_forecasts(data = example_binary, summarise_by = "model", compute_relative_skill = TRUE) ) @@ -195,12 +195,12 @@ test_that("pairwise_comparison() works", { test_that("pairwise_comparison() works inside and outside of eval_forecasts()", { - eval <- eval_forecasts(data = continuous_example_data) + eval <- eval_forecasts(data = example_continuous) pairwise <- pairwise_comparison(eval, summarise_by = "model", metric = "crps") - eval2 <- eval_forecasts(data = continuous_example_data, + eval2 <- eval_forecasts(data = example_continuous, summarise_by = "model", compute_relative_skill = TRUE) diff --git a/tests/testthat/test-pit.R b/tests/testthat/test-pit.R index 6077e7307..f921be411 100644 --- a/tests/testthat/test-pit.R +++ b/tests/testthat/test-pit.R @@ -41,8 +41,11 @@ test_that("function works for continuous true_values and predictions", test_that("pit_df function works for continuous and quantile data", { - pit_df(quantile_example_data, summarise_by = "model") - pit_df(continuous_example_data, summarise_by = "model") + pit1 <- pit_df(example_quantile, summarise_by = "model") + pit2 <- pit_df(example_continuous, summarise_by = "model") + + expect_equal(length(pit1), 4) + expect_equal(length(pit2), 4) }) diff --git a/tests/testthat/test-plot_predictions.R b/tests/testthat/test-plot_predictions.R index 2f1cdacaf..4e4756405 100644 --- a/tests/testthat/test-plot_predictions.R +++ b/tests/testthat/test-plot_predictions.R @@ -1,16 +1,16 @@ test_that("plot_predictions() works with point forecasts", { - d <- range_example_data_long + d <- example_range_long d <- d[d$range == 0 | is.na(d$range), ] p <- scoringutils::plot_predictions( d, - x = "value_date", - filter_truth = list('value_date <= "2020-06-22"', - 'value_date > "2020-05-01"'), - filter_forecasts = list("model == 'SIRCOVID'", - 'creation_date == "2020-06-22"'), + x = "target_end_date", + filter_truth = list('target_end_date <= "2021-07-22"', + 'target_end_date > "2021-05-01"'), + filter_forecasts = list("model == 'EuroCOVIDhub-ensemble'", + 'forecast_date == "2021-06-07"'), allow_truth_without_pred = TRUE, - facet_formula = geography ~ value_desc + facet_formula = location ~ target_type ) expect_s3_class(p, "ggplot") @@ -22,16 +22,17 @@ test_that("plot_predictions() works with point forecasts", { test_that("plot_predictions() can handle an arbitrary number of quantiles", { - example2 <- scoringutils::range_example_data_long + example2 <- scoringutils::example_range_long p <- scoringutils::plot_predictions( - example2, x = "value_date", - filter_truth = list('value_date <= "2020-06-22"', - 'value_date > "2020-05-01"'), - filter_forecasts = list("model == 'SIRCOVID'", - 'creation_date == "2020-06-22"'), + example2, + x = "target_end_date", + filter_truth = list('target_end_date <= "2021-07-22"', + 'target_end_date > "2021-05-01"'), + filter_forecasts = list("model == 'EuroCOVIDhub-ensemble'", + 'forecast_date == "2021-06-07"'), allow_truth_without_pred = TRUE, - facet_formula = geography ~ value_desc, + facet_formula = location ~ target_type, range = c(0, 10, 20, 30, 40, 50, 60) ) expect_s3_class(p, "ggplot") @@ -39,15 +40,16 @@ test_that("plot_predictions() can handle an arbitrary number of quantiles", { skip_on_cran() vdiffr::expect_doppelganger('many_quantiles', p) - example1 <- scoringutils::continuous_example_data + example1 <- scoringutils::example_continuous p2 <- scoringutils::plot_predictions( - example1, x = "value_date", - filter_truth = list('value_date <= "2020-06-22"', - 'value_date > "2020-05-01"'), - filter_forecasts = list("model == 'SIRCOVID'", - 'creation_date == "2020-06-22"'), - facet_formula = geography ~ value_desc, + example1, + x = "target_end_date", + filter_truth = list('target_end_date <= "2021-07-22"', + 'target_end_date > "2021-05-01"'), + filter_forecasts = list("model == 'EuroCOVIDhub-ensemble'", + 'forecast_date == "2021-06-07"'), + facet_formula = location ~ target_type, range = c(0, 50, 90, 95) ) expect_s3_class(p2, "ggplot") diff --git a/vignettes/scoringutils.Rmd b/vignettes/scoringutils.Rmd index daeed602a..96858a51b 100644 --- a/vignettes/scoringutils.Rmd +++ b/vignettes/scoringutils.Rmd @@ -47,7 +47,7 @@ library(scoringutils) ``` ```{r} -data <- scoringutils::quantile_example_data +data <- scoringutils::example_quantile print(data, 3, 3) scores <- scoringutils::eval_forecasts(data, @@ -58,26 +58,26 @@ print(scores, 3, 3) Using an appropriate level of summary, we can easily use the output for visualisation. The `scoringutils` package offers some built-in functions to help get a sense of the data ```{r} -# -# filtered_data <- data[geography == "England" & -# creation_date <= "2020-06-29" & -# value_desc == "Deaths"] - -scoringutils::plot_predictions(data = data, - filter_both = list("geography == 'England'"), - filter_forecasts = list("creation_date == '2020-07-06'"), - filter_truth = list("as.Date(value_date) <= '2020-07-06'"), - x = "value_date", - range = c(0, 50, 90), - scale = "free", - facet_formula = value_desc ~ model) +scoringutils::plot_predictions( + data = data,, + x = "target_end_date", + filter_truth = list('target_end_date <= "2021-07-22"', + 'target_end_date > "2021-05-01"'), + filter_forecasts = list("model == 'EuroCOVIDhub-ensemble'", + 'forecast_date == "2021-06-28"'), + facet_formula = location ~ target_type, + range = c(0, 50, 90, 95), + scale = "free" +) ``` ```{r} scores <- scoringutils::eval_forecasts(data, - summarise_by = c("model")) -scoringutils::score_table(scores) + summarise_by = c("model", "target_type")) +scoringutils::score_table(scores, + y = "model", + facet_formula = ~ target_type) ``` Given this level of aggregation, not all metrics may make sense. In this case, for example, averaging over different quantiles to compute quantile coverage does not make much sense. If you like, you can select specific metrics for the visualisation. @@ -98,17 +98,17 @@ Let us look at the individual components of the weighted interval score: ```{r} scores <- scoringutils::eval_forecasts(data, - summarise_by = c("model", "value_desc")) -scoringutils::wis_components(scores, facet_formula = ~ value_desc) + summarise_by = c("model", "target_type")) +scoringutils::wis_components(scores, facet_formula = ~ target_type) ``` We can also look at contributions to different metrics by range: ```{r} scores <- scoringutils::eval_forecasts(data, - summarise_by = c("model", "range", "value_desc")) + summarise_by = c("model", "range", "target_type")) scoringutils::range_plot(scores, y = "interval_score", - facet_formula = ~ value_desc) + facet_formula = ~ target_type) ``` We can also visualise metrics using a heatmap: @@ -129,22 +129,22 @@ The `eval_forecasts()` function is designed to work with various different input quantile forecasts in either a plain quantile format or in a format that specifies interval ranges and the boundary of a given interval range. ``` {r} -print(scoringutils::quantile_example_data, 3, 3) -print(scoringutils::range_example_data_long, 3, 3) -print(scoringutils::range_example_data_wide, 3, 3) +print(scoringutils::example_quantile, 3, 3) +print(scoringutils::example_range_long, 3, 3) +print(scoringutils::example_range_wide, 3, 3) ``` sample based format with either continuous or integer values ``` {r} -print(scoringutils::integer_example_data, 3, 3) -print(scoringutils::continuous_example_data, 3, 3) +print(scoringutils::example_integer, 3, 3) +print(scoringutils::example_continuous, 3, 3) ``` forecasts in a binary format: ``` {r} -print(scoringutils::binary_example_data, 3, 3) +print(scoringutils::example_binary, 3, 3) ``` It also offers functionality to convert between these formats. For more information have a look at the documentation of the following functions: From 75f808d9ed3928c1cc5dc3f2724114b4ed4e023a Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Sun, 12 Dec 2021 11:20:01 +0100 Subject: [PATCH 048/225] update the way unique values for columns are shown in check_forecasts() --- R/check_forecasts.R | 19 ++++++++----------- 1 file changed, 8 insertions(+), 11 deletions(-) diff --git a/R/check_forecasts.R b/R/check_forecasts.R index 1b3a5ba01..a3401712d 100644 --- a/R/check_forecasts.R +++ b/R/check_forecasts.R @@ -89,8 +89,8 @@ check_forecasts <- function(data) { ) # obtain unit of a single forecast - obs_unit <- get_unit_of_forecast(data) - check[["forecast_unit"]] <- obs_unit + forecast_unit <- get_unit_of_forecast(data) + check[["forecast_unit"]] <- forecast_unit msg <- c( msg, @@ -120,7 +120,7 @@ check_forecasts <- function(data) { # the length of prediction is greater 1 for a sample / quantile for # a single forecast type <- c("sample", "quantile")[c("sample", "quantile") %in% colnames(data)] - data[, InternalDuplicateCheck := .N, by = c(obs_unit, type)] + data[, InternalDuplicateCheck := .N, by = c(forecast_unit, type)] if (any(data$InternalDuplicateCheck > 1)) { errors <- c( @@ -148,7 +148,7 @@ check_forecasts <- function(data) { } # some checks whether there are the same number of quantiles, samples - data[, InternalNumCheck := length(prediction), by = obs_unit] + data[, InternalNumCheck := length(prediction), by = forecast_unit] n <- unique(data$InternalNumCheck) if (length(n) > 1) { warnings <- c( @@ -160,14 +160,12 @@ check_forecasts <- function(data) { ) ) } - check[["rows_per_forecast"]] <- - data[, .(rows_per_forecast = unique(InternalNumCheck)), by = model] data[, InternalNumCheck := NULL] # get available unique values per model for the different columns - cols <- obs_unit[obs_unit != "model"] + cols <- forecast_unit[forecast_unit != "model"] check[["unique_values"]] <- - data[, vapply(.SD, FUN = function(x) length(unique(x)), integer(1)), by = "model"] + data[, lapply(.SD, FUN = function(x) length(unique(x))), by = "model"] check[["messages"]] <- unlist(msg) check[["warnings"]] <- unlist(warnings) @@ -199,7 +197,6 @@ print.scoringutils_check <- function(x, ...) { "\nBased on your input, scoringutils thinks:\n", paste(x$messages, collapse = "\n") )) - cat("\n$rows_per_forecast shows how many rows (usually quantiles or samples are available per forecast.") cat( "\n$unique_values shows how many unique values there are per column per model", "(across the entire data)." @@ -345,7 +342,7 @@ get_unit_of_forecast <- function(data) { "prediction", "true_value", "sample", "quantile", "upper", "lower", "range", "boundary", available_metrics() ) - obs_unit <- setdiff(colnames(data), protected_columns) - return(obs_unit) + forecast_unit <- setdiff(colnames(data), protected_columns) + return(forecast_unit) } From f1cc50265180415c47327383318903bb6e3624a8 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Mon, 13 Dec 2021 14:44:07 +0100 Subject: [PATCH 049/225] add vignette with details on scoring metrics --- DESCRIPTION | 3 +- scoringutils.Rproj | 2 +- vignettes/details-metrics.Rmd | 342 ++++++++++++++++++++++++++++++++++ 3 files changed, 345 insertions(+), 2 deletions(-) create mode 100644 vignettes/details-metrics.Rmd diff --git a/DESCRIPTION b/DESCRIPTION index f9f78f771..b226d82ec 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -63,12 +63,13 @@ Imports: data.table, forcats, ggplot2, - goftest, scoringRules, stats, methods Suggests: testthat, + kableExtra, + magrittr, knitr, rmarkdown, vdiffr diff --git a/scoringutils.Rproj b/scoringutils.Rproj index 0ee12cdee..052e507fb 100644 --- a/scoringutils.Rproj +++ b/scoringutils.Rproj @@ -19,4 +19,4 @@ BuildType: Package PackageUseDevtools: Yes PackageInstallArgs: --no-multiarch --with-keep.source PackageCheckArgs: --as-cran -PackageRoxygenize: rd,collate,namespace +PackageRoxygenize: rd,collate,namespace,vignette diff --git a/vignettes/details-metrics.Rmd b/vignettes/details-metrics.Rmd new file mode 100644 index 000000000..e9f7f18c2 --- /dev/null +++ b/vignettes/details-metrics.Rmd @@ -0,0 +1,342 @@ +--- +title: "Details on the metrics implemented in `scoringutils`" +author: "Nikos Bosse" +date: "`r Sys.Date()`" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Details on the metrics implemented in `scoringutils`} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +library(scoringutils) +library(kableExtra) +library(magrittr) +library(knitr) +library(data.table) +``` + +## Applicability of different metrics for different types of forecasts and formats + +This table gives an overview for when which metric can be applied and gives a very brief description. Note that this table on shows the metrics as implemented in `scoringutils`. For example, only scoring of sample-based discrete and continuous distributions is implemented in `scoringutils`, but closed-form solutions often exist (e.g. in the `scoringRules` package). + +```{r, echo = FALSE, results = "asis"} +crps <- list( + `Metric` = "(Continuous) ranked probability score (CRPS)", + `Discrete` = r"($\checkmark$)", + `Continuous` = r"($\checkmark$)", + `Binary` = r"($-$)", + `Quantiles` = r"($-$)", + `Properties` = "proper scoring rule, global, stable handling of outliers", + `References` = "" +) + +log_score <- list( + `Metric` = "Log score (logS)", + `Discrete` = r"($-$)", + `Continuous` = r"($\checkmark$)", + `Binary` = r"($-$)", + `Quantiles` = r"($-$)", + `Properties` = "proper scoring rule, log of predictive density evaluated at observed value, local, unstable for outliers", + `References` = "" +) + +wis <- list( + Metric = "(Weighted) interval score (WIS)", + `Discrete` = r"($\checkmark$)", + `Continuous` = r"($\checkmark$)", + `Binary` = r"($-$)", + `Quantiles` = r"($\checkmark$)", + `Properties` = "proper scoring rule, global, stable handling of outliers, converges to crps for an increasing numbre of equally spaced intervals", + `References` = "" +) + +dss <- list( + `Metric` = "Dawid-Sebastiani score (DSS)", + `Discrete` = r"($\checkmark$)", + `Continuous` = r"($\checkmark$)", + `Binary` = r"($-$)", + `Quantiles` = r"($-$)", + `Properties` = "proper scoring rule, somewhat global, somewhat stable handling of outliers", + `References` = "" +) + +brier_score <- list( + `Metric` = "Brier score (BS)", + `Discrete` = r"($-$)", + `Continuous` = r"($-$)", + `Binary` = r"($\checkmark$)", + `Quantiles` = r"($-$)", + `Properties` = "proper scoring rule", + `References` = "" +) + +interval_coverage <- list( + `Metric` = "Interval coverage", + `Discrete` = r"($\checkmark$)", + `Continuous` = r"($\checkmark$)", + `Binary` = r"($-$)", + `Quantiles` = r"($-$)", + `Properties` = "measure for calibration", + `References` = "" +) + +quantile_coverage <- list( + `Metric` = "Quantile coverage", + `Discrete` = r"($\checkmark$)", + `Continuous` = r"($\checkmark$)", + `Binary` = r"($-$)", + `Quantiles` = r"($-$)", + `Properties` = "measure for calibration", + `References` = "" +) + +sharpness <- list( + `Metric` = "Sharpness", + `Discrete` = r"($\checkmark$)", + `Continuous` = r"($\checkmark$)", + `Binary` = r"($-$)", + `Quantiles` = r"($\checkmark$)", + `Properties` = "measures forecast dispersions", + `References` = "" +) + +bias <- list( + `Metric` = "Bias", + `Discrete` = r"($\checkmark$)", + `Continuous` = r"($\checkmark$)", + `Binary` = r"($-$)", + `Quantiles` = r"($\checkmark$)", + `Properties` = "captures tendency to over-or underpredict (aspect of calibration)", + `References` = "" +) + +pit <- list( + `Metric` = "Probability integral transform (PIT)", + `Discrete` = r"($\checkmark$)", + `Continuous` = r"($\checkmark$)", + `Binary` = r"($-$)", + `Quantiles` = r"($\checkmark$)", + `Properties` = "assesses calibration", + `References` = "" +) + +mean_score_ratio <- list( + `Metric` = "Mean score ratio", + `Discrete` = r"($\sim$)", + `Continuous` = r"($\sim$)", + `Binary` = r"($\sim$)", + `Quantiles` = r"($\sim$)", + `Properties` = "compares performance of two models. Properties depend on the metric chosen for the comparison.", + `References` = "" +) + +relative_skill <- list( + `Metric` = "Relative skill", + `Discrete` = r"($\sim$)", + `Continuous` = r"($\sim$)", + `Binary` = r"($\sim$)", + `Quantiles` = r"($\sim$)", + `Properties` = "Ranks models based on pairwise comparisons. Properties depend on the metric chosen for the comparison.", + `References` = "" +) + +data <- rbind(as.data.table(crps), + as.data.table(log_score), + as.data.table(wis), + as.data.table(dss), + as.data.table(brier_score), + as.data.table(interval_coverage), + as.data.table(quantile_coverage), + as.data.table(pit), + as.data.table(sharpness), + as.data.table(bias), + as.data.table(mean_score_ratio), + as.data.table(relative_skill)) + +data[, 1:6] %>% + kbl(format = "html", + escape = FALSE, + align = c("lccccl"), + linesep = c('\\addlinespace')) %>% + column_spec(1, width = "3.2cm") %>% + column_spec(2, width = "1.5cm") %>% + column_spec(3, width = "1.5cm") %>% + column_spec(4, width = "1.3cm") %>% + column_spec(5, width = "1.5cm") %>% + column_spec(6, width = "6.0cm") %>% + add_header_above(c(" " = 1, "Sample-based" = 2, " " = 3)) %>% + row_spec(seq(1, nrow(data), 2), background = "Gainsboro") %>% + kable_styling() + + +``` + + +## Detailed explanation of the metrics implemented in `scoringutils` + +```{r, echo = FALSE, results = "asis"} +crps <- list( + `Metric` = "CRPS (Continuous) ranked probability score", + `Explanation` = r"(The crps is a proper scoring rule that generalises the absolute error to probabilistic forecasts. It measures the 'distance' of the predictive distribution to the observed data-generating distribution. The CRPS is given as + $$\text{CRPS}(F, y) = \int_{-\infty}^\infty \left( F(x) - 1(x \geq y) \right)^2 dx,$$ + where y is the true observed value and F the CDF of predictive distribution. Often An alternative representation is used: + $$ \text{CRPS}(F, y) = \frac{1}{2} \mathbb{E}_{F} |X - X'| - \mathbb{E}_P |X - y|,$$ where $X$ and $X'$ are independent realisations from the predictive distributions $F$ with finite first moment and $y$ is the true value. In this represenation we can simply replace $X$ and $X'$ by samples sum over all possible combinations to obtain the CRPS. + For integer-valued forecasts, the RPS is given as + $$ \text{RPS}(F, y) = \sum_{x = 0}^\infty (F(x) - 1(x \geq y))^2. $$ + +**Usage and caveats** Smaller values are better. The crps is a good choice for most practical purposes that involve decision making, as it takes the entire predictive distribution into account. If two forecasters assign the same probability to the true event $y$, then the forecaster who assigned high probability to events far away from $y$ will still get a worse score. The crps (in contrast to the log score) can at times be quite lenient towards extreme mispredictions. Also, due to it's similarity to the absolute error, the level of scores depend a lot on the absolute value of what is predicted, which makes it hard to compare scores of forecasts for quantities that are orders of magnitude apart.)" +) + + +log_score <- list( + `Metric` = "Log score", + `Explanation` = r"(The Log score is a proper scoring rule that is simply compuated as the log of the predictive density evaluated at the true observed value. It is given as + $$ \text{log score} = \log f(y), $$ + where $f$ is the predictive density function and y is the true value. For integer-valued forecasts, the log score can be computed as + $$ \text{log score} = \log p_y, $$ + where $p_y$ is the probability assigned to outcome p by the forecast F. + +**Usage and caveats**: Larger values are better, but sometimes the sign is reversed. The log score is ensitive to outliers, as individual negative log score contributions quickly can become very large if the event falls in the tails of the predictive distribution, where $f(y)$ (or $p_y$) is close to zero. Whether or not that is desirable depends ont the application. In scoringutils, the log score cannot be used for integer-valued forecasts, as the implementation requires a predictive density. In contrast to the crps, the log score is a local scoring rule: it's value only depends only on the probability that was assigned to the actual outcome. This property is desirable for inferential purposes, for example in a Bayesian context (@winklerScoringRulesEvaluation1996). In settings where forecasts inform decision making, it may be more appropriate to score forecasts based on the entire predictive distribution.)" +) + +wis <- list( + Metric = "WIS (Weighted) interval score", + `Explanation` = r"(The (weighted) interval score is a proper scoring rule for quantile forecasts that converges to the crps for an increasing number of intervals. The score can be decomposed into a sharpness (uncertainty) component and penalties for over- and underprediction. For a single interval, the score is computed as + $$IS_\alpha(F,y) = (u-l) + \frac{2}{\alpha} \cdot (l-y) \cdot 1(y \leq l) + \frac{2}{\alpha} \cdot (y-u) \cdot 1(y \geq u), $$ + where $1()$ is the indicator function, $y$ is the true value, and $l$ and $u$ are the $\frac{\alpha}{2}$ and $1 - \frac{\alpha}{2}$ quantiles of the predictive distribution $F$, i.e. the lower and upper bound of a single prediction interval. For a set of $K$ prediction intervals and the median $m$, the score is computed as a weighted sum, + $$WIS = \frac{1}{K + 0.5} \cdot (w_0 \cdot |y - m| + \sum_{k = 1}^{K} w_k \cdot IS_{\alpha}(F, y)),$$ + where $w_k$ is a weight for every interval. Usually, $w_k = \frac{\alpha_k}{2}$ and $w_0 = 0.5$. + +**Usage and caveats**: + Smaller scores are better. Applicable to all quantile forecasts, takes the entire predictive distribution into account. Just as the crps, the wis is based on measures of absolute error. When averaging across multiple targets, it will therefore be dominated by targets with higher absolute values. The decomposition into sharpness, over- and underprediction make it easy to interpret scores and use them for model improvement. )" +) + +quantile_score <- "yet to come" + + +dss <- list( + `Metric` = "DSS Dawid-Sebastiani score", + `Explanation` = r"(The Dawid-Sebastiani-Score is a proper scoring rule proposed by Gneiting and Raftery in [@gneitingStrictlyProperScoring2007] that only relies on the first moments of the predictive distribution and is therefore easy to compute. It is given as + +$$\text{dss}(F, y) = \left( \frac{y - \mu}{\sigma} \right)^2 + 2 \cdot \log \sigma,$$ +where $F$ is the predictive distribution with mean $\mu$ and standard deviation $\sigma$ and $y$ is the true observed value. + +**Usage and caveats** The dss is applicable to continuous and integer forecasts and easy to compute. Apart from the ease of computation we see little advantage in using it over other scores.)" +) + +brier_score <- list( + `Metric` = "Brier score", + `Explanation` = r"(Proper scoring rule for binary forecasts. The Brier score is computed as +$$\text{Brier Score} = \frac{1}{N} \sum_{n = 1}^{N} (f_n - y_n),$$ +where $f_n$, with $n = 1, \dots, N$ are the predicted probablities that the corresponding events, $y_n \in (0, 1)$ will be equal to one.) + +**Usage**: Applicable to all binary forecasts.)" +) + +interval_coverage <- list( + `Metric` = "Interval coverage", + `Explanation` = r"(Interval coverage measures the proportion of observed values that fall in a given prediction interval range. Interval coverage for a single prediction interval range can be calculated as $$IC_{\alpha} = \text{nominal coverage} - \text{empirical coverage},$$ +where nominal coverage is $1 - \alpha$ and empirical coverage is the percentage of true values actually covered by all $1 - \alpha$ prediction intervals. + +To summarise interval coverage over different over multiple interval ranges, we can compute coverage deviation defined as the mean interval coverage over all $K$ interval ranges $\alpha_k$ with $k = 1, \dots, K$: +$$\text{Coverage deviation} = \frac{1}{K} \sum_{k = 1}^{K} \text{IC}_{\alpha_k}$$ + +**Usage**: Interval coverage for a set of chosen intervals, (e.g. 50\% and 90\%) gives a good indication of marginal calibration and is easy to interpret. Reporting coverage deviation has the advantage of summarising calibration in a single number, but loses some of the nuance.)" +) + +quantile_coverage <- list( + `Metric` = "Quantile coverage", + `Explanation` = r"(Quantile coverage for a given quantile level is the percentage of true values smaller than the predictions corresponding to that quantile level. + +**Usage**: Quantile coverage is similar to interval coverage, but conveys more information. For example, it allows us to look at the 5\% and 95\% quantile separately, instead of jointly at the 90\% prediction interval). This helps to diagnose whether it is the upper or lower end of a prediction interval that is causing problems. Plots of quantile coverage are conceptually very similar to PIT histograms.)" +) + +sharpness <- list( + `Metric` = "Sharpness", + `Explanation` = r"(Sharpness is the ability to produce narrow forecasts and is a feature of the forecasts only and does not depend on the observations. Sharpness is therefore only of interest conditional on calibration: a very precise forecast is not useful if it is clearly wrong. + +As suggested by @funkAssessingPerformanceRealtime2019, we measure sharpness for continuous and integer forecasts represented by predictive samples as the normalised median absolute deviation about the median (MADN) ), i.e. +$$ S(F) = \frac{1}{0.675} \cdot \text{median}(|x - \text{median(x)}|), $$ +where $x$ is the vector of all predictive samples and $\frac{1}{0.675}$ is a normalising constant. If the predictive distribution $F$ is the CDF of a normal distribution, then sharpness will equal the standard deviation of $F$. + +For quantile forecasts we can directly use the sharpness component of the weighted interval score. Sharpness is then simply the weighted mean of the widths of the central prediction intervals.)" +) + +bias <- list( + `Metric` = "Bias", + `Explanation` = r"(Bias is a measure of the tendency of a forecaster to over- or underpredict. For continuous forecasts, bias is given as +$$B(F, y) = 1 - 2 \cdot (F (y)), $$ +where $F$ is the CDF of the predictive distribution and $y$ is the observed value. + +For integer-valued forecasts, bias can be calculated as +$$B(P, y) = 1 - (P(y) + P(y + 1)), $$ +where $P(y)$ is the cumulative probability assigned to all outcomes smaller or equal to $y$. + +For quantile forecasts, Bias can be calculated as the maximum percentile rank for which the prediction is smaller than $y$, if the true value is smaller than the median of the predictive distribution. If the true value is above the median of the predictive distribution, then bias is the minimum percentile rank for which the corresponding quantile is still larger than the true value. If the true value is exactly the median, bias is zero. For a large enough number of quantiles, the percentile rank will equal the proportion of predictive samples below the observed true value, and this metric coincides with the one for continuous forecasts. + +**Usage**: +In contrast to the over- and underprediction penalties of the interval score it is bound between 0 and 1 and represents the tendency of forecasts to be biased rather than the absolute amount of over- and underprediction. It is therefore a more robust measurement, but harder to interpet. It largely depends on the application whether one is more interested in the tendency to be biased or in the absolute value of over- and underpredictions.)" +) + +pit <- list( + `Metric` = "Probability integral transform (PIT)", + `Explanation` = r"(The probability integral transform (PIT) @dawidPresentPositionPotential1984 represents a succinct way to visualise deviations between the predictive distribution $F$ and the true data-generating distribution $G$. The idea is to transform the observed values such that agreement between forecasts and data can then be examined by observing whether or not the transformed values follow a uniform distribution. The PIT is given by +$$u = F (y),$$ +where $u$ is the transformed variable and $F(y)$ is the predictive distribution $F$ evaluated at the true observed value $y$. If $F = G$, then $u$ follows a uniform distribution (for a proof see e.g. @angusProbabilityIntegralTransform1994). + +For integer outcomes, the PIT is no longer uniform even when forecasts are ideal. Instead, a randomised PIT (@funkAssessingPerformanceRealtime2019) can be used: +$$u = P(y) + v \cdot (P(y) - P(y - 1) ),$$ +where $y$ is again the observed value $P()$ is the cumulative probability assigned to all values smaller or equal to $y$ (where $P(-1) = 0$ by definition, and $v$ is a standard uniform variable independent of $y$. If $P$ is equal to the true data-generating distribution function, then $u$ is standard uniform. also propose a non-randomised version of the PIT for count data that could be used alternatively. + +**Usage**: +One can plot a histogram of $u$ values to look for deviations from uniformity. U-shaped histograms often result from predictions that are too narrow, while hump-shaped histograms indicate that predictions may be too wide. Biased predictions will usually result in a triangle-shaped histogram. One can also test for deviations from normality, using for example an Anderson-Darling test. This, however, proves to be overly strict in practice and even slight deviations from perfect calibration are punished in a way that makes it very hard to compare models at all. In addition, errors from forecasts may be correlated (i.e. forecasts made on a given date), potentially violating the assumptions of the Anderson-Darling test. We therefore do not recommend it for most use cases.)" +) + +mean_score_ratio <- list( + `Metric` = "Mean score ratio", + `Explanation` = r"(The mean score ratio is used to compare two models on the overlapping set of forecast targets for which both models have made a prediction. The mean score ratio is calculated as the mean score achieved by the first model over the mean score achieved by the second model. More precisely, for two models $i, j$, we determine the set of overlapping forecasts, denoted by $\mathcal{A}_{ij}$ and compute the mean score ratio $\theta_{ij}$ as +$$\theta_{ij} =\frac{\text{mean score model } i \text{ on } \mathcal{A}_{ij}}{\text{mean score model } j \text{ on } \mathcal{A}_{ij}}.$$ +The mean score ratio can in principle be computed for any arbitrary score. + +**Usage**: +Mean scores ratios are usually calculated in the context of pairwise comparisons, where a set of models is compared by looking at mean score ratios of all possible parings. Whether smaller or larger values are better depends on the orientation of the original score used)" +) + +relative_skill <- list( + `Metric` = "Relative skill", + `Explanation` = r"(Relative skill scores can be used to obtain a ranking of models based on pairwise comparisons between all models. To compute the relative skill $\theta_i$ of model $i$, we take the geometric mean of all mean score ratios that involve model $i$, i.e. +$$ \theta_{i} = \left(\prod_{m = 1}^M \theta_{im}\right)^{1/M}, $$ +where M is the number of models. + +**Usage and caveats**: +Relative skill is a helpful way to obtain a model ranking. Whether smaller or larger values are better depends on the orientation of the original score used. +It is in principle relatively robust against biases that arise when models only forecast some of the available targets and is a reasonable way to handle missing forecasts. One possible precautionary measure to reduces issues with missing forecasts is to only compare models that have forecasted at least half of all possible targets (this ensures that there is always an overlap between models). If there is no overlap between models, the relative skill implicitly estimates how a model would have forecasted on those missing targets. )" +) + +data <- rbind(as.data.frame(crps), + as.data.frame(log_score), + as.data.frame(wis), + as.data.frame(dss), + as.data.frame(brier_score), + as.data.frame(interval_coverage), + as.data.frame(quantile_coverage), + as.data.frame(pit), + as.data.frame(sharpness), + as.data.frame(bias), + as.data.frame(mean_score_ratio), + as.data.frame(relative_skill)) + +data[, 1:2] %>% + kbl(format = "html", + escape = TRUE) %>% + column_spec(1, width = "3.5cm") %>% + row_spec(seq(1, nrow(data), 2), background = "Gainsboro") %>% + column_spec(2, width = "15.5cm") %>% + kable_styling() +``` From e715416c699ea80e898efbf9817a4561e538e1b1 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Mon, 13 Dec 2021 15:37:56 +0100 Subject: [PATCH 050/225] move tables with metric details in inst/ --- inst/metrics-overview/metrics-detailed.Rda | Bin 0 -> 5089 bytes 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`Binary` = r"($-$)", + `Quantiles` = r"($-$)", + `Properties` = "proper scoring rule, global, stable handling of outliers", + `References` = "" +) + +log_score <- list( + `Metric` = "Log score (logS)", + `Discrete` = r"($-$)", + `Continuous` = r"($\checkmark$)", + `Binary` = r"($-$)", + `Quantiles` = r"($-$)", + `Properties` = "proper scoring rule, log of predictive density evaluated at observed value, local, unstable for outliers", + `References` = "" +) + +wis <- list( + Metric = "(Weighted) interval score (WIS)", + `Discrete` = r"($\checkmark$)", + `Continuous` = r"($\checkmark$)", + `Binary` = r"($-$)", + `Quantiles` = r"($\checkmark$)", + `Properties` = "proper scoring rule, global, stable handling of outliers, converges to crps for an increasing numbre of equally spaced intervals", + `References` = "" +) + +dss <- list( + `Metric` = "Dawid-Sebastiani score (DSS)", + `Discrete` = r"($\checkmark$)", + `Continuous` = r"($\checkmark$)", + `Binary` = r"($-$)", + `Quantiles` = r"($-$)", + `Properties` = "proper scoring rule, somewhat global, somewhat stable handling of outliers", + `References` = "" +) + +brier_score <- list( + `Metric` = "Brier score (BS)", + `Discrete` = r"($-$)", + `Continuous` = r"($-$)", + `Binary` = r"($\checkmark$)", + `Quantiles` = r"($-$)", + `Properties` = "proper scoring rule", + `References` = "" +) + +interval_coverage <- list( + `Metric` = "Interval coverage", + `Discrete` = r"($\checkmark$)", + `Continuous` = r"($\checkmark$)", + `Binary` = r"($-$)", + `Quantiles` = r"($-$)", + `Properties` = "measure for calibration", + `References` = "" +) + +quantile_coverage <- list( + `Metric` = "Quantile coverage", + `Discrete` = r"($\checkmark$)", + `Continuous` = r"($\checkmark$)", + `Binary` = r"($-$)", + `Quantiles` = r"($-$)", + `Properties` = "measure for calibration", + `References` = "" +) + +sharpness <- list( + `Metric` = "Sharpness", + `Discrete` = r"($\checkmark$)", + `Continuous` = r"($\checkmark$)", + `Binary` = r"($-$)", + `Quantiles` = r"($\checkmark$)", + `Properties` = "measures forecast dispersions", + `References` = "" +) + +bias <- list( + `Metric` = "Bias", + `Discrete` = r"($\checkmark$)", + `Continuous` = r"($\checkmark$)", + `Binary` = r"($-$)", + `Quantiles` = r"($\checkmark$)", + `Properties` = "captures tendency to over-or underpredict (aspect of calibration)", + `References` = "" +) + +pit <- list( + `Metric` = "Probability integral transform (PIT)", + `Discrete` = r"($\checkmark$)", + `Continuous` = r"($\checkmark$)", + `Binary` = r"($-$)", + `Quantiles` = r"($\checkmark$)", + `Properties` = "assesses calibration", + `References` = "" +) + +mean_score_ratio <- list( + `Metric` = "Mean score ratio", + `Discrete` = r"($\sim$)", + `Continuous` = r"($\sim$)", + `Binary` = r"($\sim$)", + `Quantiles` = r"($\sim$)", + `Properties` = "compares performance of two models. Properties depend on the metric chosen for the comparison.", + `References` = "" +) + +relative_skill <- list( + `Metric` = "Relative skill", + `Discrete` = r"($\sim$)", + `Continuous` = r"($\sim$)", + `Binary` = r"($\sim$)", + `Quantiles` = r"($\sim$)", + `Properties` = "Ranks models based on pairwise comparisons. Properties depend on the metric chosen for the comparison.", + `References` = "" +) + +data <- rbind(as.data.table(crps), + as.data.table(log_score), + as.data.table(wis), + as.data.table(dss), + as.data.table(brier_score), + as.data.table(interval_coverage), + as.data.table(quantile_coverage), + as.data.table(pit), + as.data.table(sharpness), + as.data.table(bias), + as.data.table(mean_score_ratio), + as.data.table(relative_skill)) + +saveRDS(data, file = "inst/metrics-overview/metrics-summary.Rda") + + + +#------------------------------------------------------------------------------# +#------------------ Detailed explanation of all the metrics -------------------# +#------------------------------------------------------------------------------# + +crps <- list( + `Metric` = "CRPS (Continuous) ranked probability score", + `Explanation` = r"(The crps is a proper scoring rule that generalises the absolute error to probabilistic forecasts. It measures the 'distance' of the predictive distribution to the observed data-generating distribution. The CRPS is given as + $$\text{CRPS}(F, y) = \int_{-\infty}^\infty \left( F(x) - 1(x \geq y) \right)^2 dx,$$ + where y is the true observed value and F the CDF of predictive distribution. Often An alternative representation is used: + $$ \text{CRPS}(F, y) = \frac{1}{2} \mathbb{E}_{F} |X - X'| - \mathbb{E}_P |X - y|,$$ where $X$ and $X'$ are independent realisations from the predictive distributions $F$ with finite first moment and $y$ is the true value. In this represenation we can simply replace $X$ and $X'$ by samples sum over all possible combinations to obtain the CRPS. + For integer-valued forecasts, the RPS is given as + $$ \text{RPS}(F, y) = \sum_{x = 0}^\infty (F(x) - 1(x \geq y))^2. $$ + + **Usage and caveats** Smaller values are better. The crps is a good choice for most practical purposes that involve decision making, as it takes the entire predictive distribution into account. If two forecasters assign the same probability to the true event $y$, then the forecaster who assigned high probability to events far away from $y$ will still get a worse score. The crps (in contrast to the log score) can at times be quite lenient towards extreme mispredictions. Also, due to it's similarity to the absolute error, the level of scores depend a lot on the absolute value of what is predicted, which makes it hard to compare scores of forecasts for quantities that are orders of magnitude apart.)" +) + + +log_score <- list( + `Metric` = "Log score", + `Explanation` = r"(The Log score is a proper scoring rule that is simply compuated as the log of the predictive density evaluated at the true observed value. It is given as + $$ \text{log score} = \log f(y), $$ + where $f$ is the predictive density function and y is the true value. For integer-valued forecasts, the log score can be computed as + $$ \text{log score} = \log p_y, $$ + where $p_y$ is the probability assigned to outcome p by the forecast F. + + **Usage and caveats**: Larger values are better, but sometimes the sign is reversed. The log score is ensitive to outliers, as individual negative log score contributions quickly can become very large if the event falls in the tails of the predictive distribution, where $f(y)$ (or $p_y$) is close to zero. Whether or not that is desirable depends ont the application. In scoringutils, the log score cannot be used for integer-valued forecasts, as the implementation requires a predictive density. In contrast to the crps, the log score is a local scoring rule: it's value only depends only on the probability that was assigned to the actual outcome. This property is desirable for inferential purposes, for example in a Bayesian context (@winklerScoringRulesEvaluation1996). In settings where forecasts inform decision making, it may be more appropriate to score forecasts based on the entire predictive distribution.)" +) + +wis <- list( + Metric = "WIS (Weighted) interval score", + `Explanation` = r"(The (weighted) interval score is a proper scoring rule for quantile forecasts that converges to the crps for an increasing number of intervals. The score can be decomposed into a sharpness (uncertainty) component and penalties for over- and underprediction. For a single interval, the score is computed as + $$IS_\alpha(F,y) = (u-l) + \frac{2}{\alpha} \cdot (l-y) \cdot 1(y \leq l) + \frac{2}{\alpha} \cdot (y-u) \cdot 1(y \geq u), $$ + where $1()$ is the indicator function, $y$ is the true value, and $l$ and $u$ are the $\frac{\alpha}{2}$ and $1 - \frac{\alpha}{2}$ quantiles of the predictive distribution $F$, i.e. the lower and upper bound of a single prediction interval. For a set of $K$ prediction intervals and the median $m$, the score is computed as a weighted sum, + $$WIS = \frac{1}{K + 0.5} \cdot (w_0 \cdot |y - m| + \sum_{k = 1}^{K} w_k \cdot IS_{\alpha}(F, y)),$$ + where $w_k$ is a weight for every interval. Usually, $w_k = \frac{\alpha_k}{2}$ and $w_0 = 0.5$. + + **Usage and caveats**: + Smaller scores are better. Applicable to all quantile forecasts, takes the entire predictive distribution into account. Just as the crps, the wis is based on measures of absolute error. When averaging across multiple targets, it will therefore be dominated by targets with higher absolute values. The decomposition into sharpness, over- and underprediction make it easy to interpret scores and use them for model improvement. )" +) + +quantile_score <- "yet to come" + + +dss <- list( + `Metric` = "DSS Dawid-Sebastiani score", + `Explanation` = r"(The Dawid-Sebastiani-Score is a proper scoring rule proposed by Gneiting and Raftery in [@gneitingStrictlyProperScoring2007] that only relies on the first moments of the predictive distribution and is therefore easy to compute. It is given as + + $$\text{dss}(F, y) = \left( \frac{y - \mu}{\sigma} \right)^2 + 2 \cdot \log \sigma,$$ + where $F$ is the predictive distribution with mean $\mu$ and standard deviation $\sigma$ and $y$ is the true observed value. + + **Usage and caveats** The dss is applicable to continuous and integer forecasts and easy to compute. Apart from the ease of computation we see little advantage in using it over other scores.)" +) + +brier_score <- list( + `Metric` = "Brier score", + `Explanation` = r"(Proper scoring rule for binary forecasts. The Brier score is computed as + $$\text{Brier Score} = \frac{1}{N} \sum_{n = 1}^{N} (f_n - y_n),$$ + where $f_n$, with $n = 1, \dots, N$ are the predicted probablities that the corresponding events, $y_n \in (0, 1)$ will be equal to one.) + + **Usage**: Applicable to all binary forecasts.)" +) + +interval_coverage <- list( + `Metric` = "Interval coverage", + `Explanation` = r"(Interval coverage measures the proportion of observed values that fall in a given prediction interval range. Interval coverage for a single prediction interval range can be calculated as $$IC_{\alpha} = \text{nominal coverage} - \text{empirical coverage},$$ + where nominal coverage is $1 - \alpha$ and empirical coverage is the percentage of true values actually covered by all $1 - \alpha$ prediction intervals. + + To summarise interval coverage over different over multiple interval ranges, we can compute coverage deviation defined as the mean interval coverage over all $K$ interval ranges $\alpha_k$ with $k = 1, \dots, K$: + $$\text{Coverage deviation} = \frac{1}{K} \sum_{k = 1}^{K} \text{IC}_{\alpha_k}$$ + + **Usage**: Interval coverage for a set of chosen intervals, (e.g. 50\% and 90\%) gives a good indication of marginal calibration and is easy to interpret. Reporting coverage deviation has the advantage of summarising calibration in a single number, but loses some of the nuance.)" +) + +quantile_coverage <- list( + `Metric` = "Quantile coverage", + `Explanation` = r"(Quantile coverage for a given quantile level is the percentage of true values smaller than the predictions corresponding to that quantile level. + + **Usage**: Quantile coverage is similar to interval coverage, but conveys more information. For example, it allows us to look at the 5\% and 95\% quantile separately, instead of jointly at the 90\% prediction interval). This helps to diagnose whether it is the upper or lower end of a prediction interval that is causing problems. Plots of quantile coverage are conceptually very similar to PIT histograms.)" +) + +sharpness <- list( + `Metric` = "Sharpness", + `Explanation` = r"(Sharpness is the ability to produce narrow forecasts and is a feature of the forecasts only and does not depend on the observations. Sharpness is therefore only of interest conditional on calibration: a very precise forecast is not useful if it is clearly wrong. + + As suggested by @funkAssessingPerformanceRealtime2019, we measure sharpness for continuous and integer forecasts represented by predictive samples as the normalised median absolute deviation about the median (MADN) ), i.e. + $$ S(F) = \frac{1}{0.675} \cdot \text{median}(|x - \text{median(x)}|), $$ + where $x$ is the vector of all predictive samples and $\frac{1}{0.675}$ is a normalising constant. If the predictive distribution $F$ is the CDF of a normal distribution, then sharpness will equal the standard deviation of $F$. + + For quantile forecasts we can directly use the sharpness component of the weighted interval score. Sharpness is then simply the weighted mean of the widths of the central prediction intervals.)" +) + +bias <- list( + `Metric` = "Bias", + `Explanation` = r"(Bias is a measure of the tendency of a forecaster to over- or underpredict. For continuous forecasts, bias is given as + $$B(F, y) = 1 - 2 \cdot (F (y)), $$ + where $F$ is the CDF of the predictive distribution and $y$ is the observed value. + + For integer-valued forecasts, bias can be calculated as + $$B(P, y) = 1 - (P(y) + P(y + 1)), $$ + where $P(y)$ is the cumulative probability assigned to all outcomes smaller or equal to $y$. + + For quantile forecasts, Bias can be calculated as the maximum percentile rank for which the prediction is smaller than $y$, if the true value is smaller than the median of the predictive distribution. If the true value is above the median of the predictive distribution, then bias is the minimum percentile rank for which the corresponding quantile is still larger than the true value. If the true value is exactly the median, bias is zero. For a large enough number of quantiles, the percentile rank will equal the proportion of predictive samples below the observed true value, and this metric coincides with the one for continuous forecasts. + + **Usage**: + In contrast to the over- and underprediction penalties of the interval score it is bound between 0 and 1 and represents the tendency of forecasts to be biased rather than the absolute amount of over- and underprediction. It is therefore a more robust measurement, but harder to interpet. It largely depends on the application whether one is more interested in the tendency to be biased or in the absolute value of over- and underpredictions.)" +) + +pit <- list( + `Metric` = "Probability integral transform (PIT)", + `Explanation` = r"(The probability integral transform (PIT) @dawidPresentPositionPotential1984 represents a succinct way to visualise deviations between the predictive distribution $F$ and the true data-generating distribution $G$. The idea is to transform the observed values such that agreement between forecasts and data can then be examined by observing whether or not the transformed values follow a uniform distribution. The PIT is given by + $$u = F (y),$$ + where $u$ is the transformed variable and $F(y)$ is the predictive distribution $F$ evaluated at the true observed value $y$. If $F = G$, then $u$ follows a uniform distribution (for a proof see e.g. @angusProbabilityIntegralTransform1994). + + For integer outcomes, the PIT is no longer uniform even when forecasts are ideal. Instead, a randomised PIT (@funkAssessingPerformanceRealtime2019) can be used: + $$u = P(y) + v \cdot (P(y) - P(y - 1) ),$$ + where $y$ is again the observed value $P()$ is the cumulative probability assigned to all values smaller or equal to $y$ (where $P(-1) = 0$ by definition, and $v$ is a standard uniform variable independent of $y$. If $P$ is equal to the true data-generating distribution function, then $u$ is standard uniform. also propose a non-randomised version of the PIT for count data that could be used alternatively. + + **Usage**: + One can plot a histogram of $u$ values to look for deviations from uniformity. U-shaped histograms often result from predictions that are too narrow, while hump-shaped histograms indicate that predictions may be too wide. Biased predictions will usually result in a triangle-shaped histogram. One can also test for deviations from normality, using for example an Anderson-Darling test. This, however, proves to be overly strict in practice and even slight deviations from perfect calibration are punished in a way that makes it very hard to compare models at all. In addition, errors from forecasts may be correlated (i.e. forecasts made on a given date), potentially violating the assumptions of the Anderson-Darling test. We therefore do not recommend it for most use cases.)" +) + +mean_score_ratio <- list( + `Metric` = "Mean score ratio", + `Explanation` = r"(The mean score ratio is used to compare two models on the overlapping set of forecast targets for which both models have made a prediction. The mean score ratio is calculated as the mean score achieved by the first model over the mean score achieved by the second model. More precisely, for two models $i, j$, we determine the set of overlapping forecasts, denoted by $\mathcal{A}_{ij}$ and compute the mean score ratio $\theta_{ij}$ as + $$\theta_{ij} =\frac{\text{mean score model } i \text{ on } \mathcal{A}_{ij}}{\text{mean score model } j \text{ on } \mathcal{A}_{ij}}.$$ + The mean score ratio can in principle be computed for any arbitrary score. + + **Usage**: + Mean scores ratios are usually calculated in the context of pairwise comparisons, where a set of models is compared by looking at mean score ratios of all possible parings. Whether smaller or larger values are better depends on the orientation of the original score used)" +) + +relative_skill <- list( + `Metric` = "Relative skill", + `Explanation` = r"(Relative skill scores can be used to obtain a ranking of models based on pairwise comparisons between all models. To compute the relative skill $\theta_i$ of model $i$, we take the geometric mean of all mean score ratios that involve model $i$, i.e. + $$ \theta_{i} = \left(\prod_{m = 1}^M \theta_{im}\right)^{1/M}, $$ + where M is the number of models. + + **Usage and caveats**: + Relative skill is a helpful way to obtain a model ranking. Whether smaller or larger values are better depends on the orientation of the original score used. + It is in principle relatively robust against biases that arise when models only forecast some of the available targets and is a reasonable way to handle missing forecasts. One possible precautionary measure to reduces issues with missing forecasts is to only compare models that have forecasted at least half of all possible targets (this ensures that there is always an overlap between models). If there is no overlap between models, the relative skill implicitly estimates how a model would have forecasted on those missing targets. )" +) + +data <- rbind(as.data.frame(crps), + as.data.frame(log_score), + as.data.frame(wis), + as.data.frame(dss), + as.data.frame(brier_score), + as.data.frame(interval_coverage), + as.data.frame(quantile_coverage), + as.data.frame(pit), + as.data.frame(sharpness), + as.data.frame(bias), + as.data.frame(mean_score_ratio), + as.data.frame(relative_skill)) + +saveRDS(data, "inst/metrics-overview/metrics-detailed.Rda") diff --git a/vignettes/details-metrics.Rmd b/vignettes/details-metrics.Rmd index e9f7f18c2..f40227095 100644 --- a/vignettes/details-metrics.Rmd +++ b/vignettes/details-metrics.Rmd @@ -26,138 +26,8 @@ library(data.table) This table gives an overview for when which metric can be applied and gives a very brief description. Note that this table on shows the metrics as implemented in `scoringutils`. For example, only scoring of sample-based discrete and continuous distributions is implemented in `scoringutils`, but closed-form solutions often exist (e.g. in the `scoringRules` package). ```{r, echo = FALSE, results = "asis"} -crps <- list( - `Metric` = "(Continuous) ranked probability score (CRPS)", - `Discrete` = r"($\checkmark$)", - `Continuous` = r"($\checkmark$)", - `Binary` = r"($-$)", - `Quantiles` = r"($-$)", - `Properties` = "proper scoring rule, global, stable handling of outliers", - `References` = "" -) - -log_score <- list( - `Metric` = "Log score (logS)", - `Discrete` = r"($-$)", - `Continuous` = r"($\checkmark$)", - `Binary` = r"($-$)", - `Quantiles` = r"($-$)", - `Properties` = "proper scoring rule, log of predictive density evaluated at observed value, local, unstable for outliers", - `References` = "" -) - -wis <- list( - Metric = "(Weighted) interval score (WIS)", - `Discrete` = r"($\checkmark$)", - `Continuous` = r"($\checkmark$)", - `Binary` = r"($-$)", - `Quantiles` = r"($\checkmark$)", - `Properties` = "proper scoring rule, global, stable handling of outliers, converges to crps for an increasing numbre of equally spaced intervals", - `References` = "" -) - -dss <- list( - `Metric` = "Dawid-Sebastiani score (DSS)", - `Discrete` = r"($\checkmark$)", - `Continuous` = r"($\checkmark$)", - `Binary` = r"($-$)", - `Quantiles` = r"($-$)", - `Properties` = "proper scoring rule, somewhat global, somewhat stable handling of outliers", - `References` = "" -) - -brier_score <- list( - `Metric` = "Brier score (BS)", - `Discrete` = r"($-$)", - `Continuous` = r"($-$)", - `Binary` = r"($\checkmark$)", - `Quantiles` = r"($-$)", - `Properties` = "proper scoring rule", - `References` = "" -) -interval_coverage <- list( - `Metric` = "Interval coverage", - `Discrete` = r"($\checkmark$)", - `Continuous` = r"($\checkmark$)", - `Binary` = r"($-$)", - `Quantiles` = r"($-$)", - `Properties` = "measure for calibration", - `References` = "" -) - -quantile_coverage <- list( - `Metric` = "Quantile coverage", - `Discrete` = r"($\checkmark$)", - `Continuous` = r"($\checkmark$)", - `Binary` = r"($-$)", - `Quantiles` = r"($-$)", - `Properties` = "measure for calibration", - `References` = "" -) - -sharpness <- list( - `Metric` = "Sharpness", - `Discrete` = r"($\checkmark$)", - `Continuous` = r"($\checkmark$)", - `Binary` = r"($-$)", - `Quantiles` = r"($\checkmark$)", - `Properties` = "measures forecast dispersions", - `References` = "" -) - -bias <- list( - `Metric` = "Bias", - `Discrete` = r"($\checkmark$)", - `Continuous` = r"($\checkmark$)", - `Binary` = r"($-$)", - `Quantiles` = r"($\checkmark$)", - `Properties` = "captures tendency to over-or underpredict (aspect of calibration)", - `References` = "" -) - -pit <- list( - `Metric` = "Probability integral transform (PIT)", - `Discrete` = r"($\checkmark$)", - `Continuous` = r"($\checkmark$)", - `Binary` = r"($-$)", - `Quantiles` = r"($\checkmark$)", - `Properties` = "assesses calibration", - `References` = "" -) - -mean_score_ratio <- list( - `Metric` = "Mean score ratio", - `Discrete` = r"($\sim$)", - `Continuous` = r"($\sim$)", - `Binary` = r"($\sim$)", - `Quantiles` = r"($\sim$)", - `Properties` = "compares performance of two models. Properties depend on the metric chosen for the comparison.", - `References` = "" -) - -relative_skill <- list( - `Metric` = "Relative skill", - `Discrete` = r"($\sim$)", - `Continuous` = r"($\sim$)", - `Binary` = r"($\sim$)", - `Quantiles` = r"($\sim$)", - `Properties` = "Ranks models based on pairwise comparisons. Properties depend on the metric chosen for the comparison.", - `References` = "" -) - -data <- rbind(as.data.table(crps), - as.data.table(log_score), - as.data.table(wis), - as.data.table(dss), - as.data.table(brier_score), - as.data.table(interval_coverage), - as.data.table(quantile_coverage), - as.data.table(pit), - as.data.table(sharpness), - as.data.table(bias), - as.data.table(mean_score_ratio), - as.data.table(relative_skill)) +data <- readRDS(system.file("metrics-overview/metrics-summary.Rda", package = "scoringutils")) data[, 1:6] %>% kbl(format = "html", @@ -173,164 +43,26 @@ data[, 1:6] %>% add_header_above(c(" " = 1, "Sample-based" = 2, " " = 3)) %>% row_spec(seq(1, nrow(data), 2), background = "Gainsboro") %>% kable_styling() - - -``` -## Detailed explanation of the metrics implemented in `scoringutils` +setDT(data) -```{r, echo = FALSE, results = "asis"} -crps <- list( - `Metric` = "CRPS (Continuous) ranked probability score", - `Explanation` = r"(The crps is a proper scoring rule that generalises the absolute error to probabilistic forecasts. It measures the 'distance' of the predictive distribution to the observed data-generating distribution. The CRPS is given as - $$\text{CRPS}(F, y) = \int_{-\infty}^\infty \left( F(x) - 1(x \geq y) \right)^2 dx,$$ - where y is the true observed value and F the CDF of predictive distribution. Often An alternative representation is used: - $$ \text{CRPS}(F, y) = \frac{1}{2} \mathbb{E}_{F} |X - X'| - \mathbb{E}_P |X - y|,$$ where $X$ and $X'$ are independent realisations from the predictive distributions $F$ with finite first moment and $y$ is the true value. In this represenation we can simply replace $X$ and $X'$ by samples sum over all possible combinations to obtain the CRPS. - For integer-valued forecasts, the RPS is given as - $$ \text{RPS}(F, y) = \sum_{x = 0}^\infty (F(x) - 1(x \geq y))^2. $$ - -**Usage and caveats** Smaller values are better. The crps is a good choice for most practical purposes that involve decision making, as it takes the entire predictive distribution into account. If two forecasters assign the same probability to the true event $y$, then the forecaster who assigned high probability to events far away from $y$ will still get a worse score. The crps (in contrast to the log score) can at times be quite lenient towards extreme mispredictions. Also, due to it's similarity to the absolute error, the level of scores depend a lot on the absolute value of what is predicted, which makes it hard to compare scores of forecasts for quantities that are orders of magnitude apart.)" -) - - -log_score <- list( - `Metric` = "Log score", - `Explanation` = r"(The Log score is a proper scoring rule that is simply compuated as the log of the predictive density evaluated at the true observed value. It is given as - $$ \text{log score} = \log f(y), $$ - where $f$ is the predictive density function and y is the true value. For integer-valued forecasts, the log score can be computed as - $$ \text{log score} = \log p_y, $$ - where $p_y$ is the probability assigned to outcome p by the forecast F. +data[, lapply(.SD, FUN = function(x) { + x <- gsub("$\\checkmark$", '+', x, fixed = TRUE) + x <- gsub("$-$", '-', x, fixed = TRUE) + x <- gsub("$\\sim$", '~', x, fixed = TRUE) + return(x) + })] -**Usage and caveats**: Larger values are better, but sometimes the sign is reversed. The log score is ensitive to outliers, as individual negative log score contributions quickly can become very large if the event falls in the tails of the predictive distribution, where $f(y)$ (or $p_y$) is close to zero. Whether or not that is desirable depends ont the application. In scoringutils, the log score cannot be used for integer-valued forecasts, as the implementation requires a predictive density. In contrast to the crps, the log score is a local scoring rule: it's value only depends only on the probability that was assigned to the actual outcome. This property is desirable for inferential purposes, for example in a Bayesian context (@winklerScoringRulesEvaluation1996). In settings where forecasts inform decision making, it may be more appropriate to score forecasts based on the entire predictive distribution.)" -) - -wis <- list( - Metric = "WIS (Weighted) interval score", - `Explanation` = r"(The (weighted) interval score is a proper scoring rule for quantile forecasts that converges to the crps for an increasing number of intervals. The score can be decomposed into a sharpness (uncertainty) component and penalties for over- and underprediction. For a single interval, the score is computed as - $$IS_\alpha(F,y) = (u-l) + \frac{2}{\alpha} \cdot (l-y) \cdot 1(y \leq l) + \frac{2}{\alpha} \cdot (y-u) \cdot 1(y \geq u), $$ - where $1()$ is the indicator function, $y$ is the true value, and $l$ and $u$ are the $\frac{\alpha}{2}$ and $1 - \frac{\alpha}{2}$ quantiles of the predictive distribution $F$, i.e. the lower and upper bound of a single prediction interval. For a set of $K$ prediction intervals and the median $m$, the score is computed as a weighted sum, - $$WIS = \frac{1}{K + 0.5} \cdot (w_0 \cdot |y - m| + \sum_{k = 1}^{K} w_k \cdot IS_{\alpha}(F, y)),$$ - where $w_k$ is a weight for every interval. Usually, $w_k = \frac{\alpha_k}{2}$ and $w_0 = 0.5$. - -**Usage and caveats**: - Smaller scores are better. Applicable to all quantile forecasts, takes the entire predictive distribution into account. Just as the crps, the wis is based on measures of absolute error. When averaging across multiple targets, it will therefore be dominated by targets with higher absolute values. The decomposition into sharpness, over- and underprediction make it easy to interpret scores and use them for model improvement. )" -) - -quantile_score <- "yet to come" - - -dss <- list( - `Metric` = "DSS Dawid-Sebastiani score", - `Explanation` = r"(The Dawid-Sebastiani-Score is a proper scoring rule proposed by Gneiting and Raftery in [@gneitingStrictlyProperScoring2007] that only relies on the first moments of the predictive distribution and is therefore easy to compute. It is given as - -$$\text{dss}(F, y) = \left( \frac{y - \mu}{\sigma} \right)^2 + 2 \cdot \log \sigma,$$ -where $F$ is the predictive distribution with mean $\mu$ and standard deviation $\sigma$ and $y$ is the true observed value. - -**Usage and caveats** The dss is applicable to continuous and integer forecasts and easy to compute. Apart from the ease of computation we see little advantage in using it over other scores.)" -) - -brier_score <- list( - `Metric` = "Brier score", - `Explanation` = r"(Proper scoring rule for binary forecasts. The Brier score is computed as -$$\text{Brier Score} = \frac{1}{N} \sum_{n = 1}^{N} (f_n - y_n),$$ -where $f_n$, with $n = 1, \dots, N$ are the predicted probablities that the corresponding events, $y_n \in (0, 1)$ will be equal to one.) - -**Usage**: Applicable to all binary forecasts.)" -) - -interval_coverage <- list( - `Metric` = "Interval coverage", - `Explanation` = r"(Interval coverage measures the proportion of observed values that fall in a given prediction interval range. Interval coverage for a single prediction interval range can be calculated as $$IC_{\alpha} = \text{nominal coverage} - \text{empirical coverage},$$ -where nominal coverage is $1 - \alpha$ and empirical coverage is the percentage of true values actually covered by all $1 - \alpha$ prediction intervals. - -To summarise interval coverage over different over multiple interval ranges, we can compute coverage deviation defined as the mean interval coverage over all $K$ interval ranges $\alpha_k$ with $k = 1, \dots, K$: -$$\text{Coverage deviation} = \frac{1}{K} \sum_{k = 1}^{K} \text{IC}_{\alpha_k}$$ - -**Usage**: Interval coverage for a set of chosen intervals, (e.g. 50\% and 90\%) gives a good indication of marginal calibration and is easy to interpret. Reporting coverage deviation has the advantage of summarising calibration in a single number, but loses some of the nuance.)" -) - -quantile_coverage <- list( - `Metric` = "Quantile coverage", - `Explanation` = r"(Quantile coverage for a given quantile level is the percentage of true values smaller than the predictions corresponding to that quantile level. - -**Usage**: Quantile coverage is similar to interval coverage, but conveys more information. For example, it allows us to look at the 5\% and 95\% quantile separately, instead of jointly at the 90\% prediction interval). This helps to diagnose whether it is the upper or lower end of a prediction interval that is causing problems. Plots of quantile coverage are conceptually very similar to PIT histograms.)" -) - -sharpness <- list( - `Metric` = "Sharpness", - `Explanation` = r"(Sharpness is the ability to produce narrow forecasts and is a feature of the forecasts only and does not depend on the observations. Sharpness is therefore only of interest conditional on calibration: a very precise forecast is not useful if it is clearly wrong. -As suggested by @funkAssessingPerformanceRealtime2019, we measure sharpness for continuous and integer forecasts represented by predictive samples as the normalised median absolute deviation about the median (MADN) ), i.e. -$$ S(F) = \frac{1}{0.675} \cdot \text{median}(|x - \text{median(x)}|), $$ -where $x$ is the vector of all predictive samples and $\frac{1}{0.675}$ is a normalising constant. If the predictive distribution $F$ is the CDF of a normal distribution, then sharpness will equal the standard deviation of $F$. - -For quantile forecasts we can directly use the sharpness component of the weighted interval score. Sharpness is then simply the weighted mean of the widths of the central prediction intervals.)" -) - -bias <- list( - `Metric` = "Bias", - `Explanation` = r"(Bias is a measure of the tendency of a forecaster to over- or underpredict. For continuous forecasts, bias is given as -$$B(F, y) = 1 - 2 \cdot (F (y)), $$ -where $F$ is the CDF of the predictive distribution and $y$ is the observed value. - -For integer-valued forecasts, bias can be calculated as -$$B(P, y) = 1 - (P(y) + P(y + 1)), $$ -where $P(y)$ is the cumulative probability assigned to all outcomes smaller or equal to $y$. - -For quantile forecasts, Bias can be calculated as the maximum percentile rank for which the prediction is smaller than $y$, if the true value is smaller than the median of the predictive distribution. If the true value is above the median of the predictive distribution, then bias is the minimum percentile rank for which the corresponding quantile is still larger than the true value. If the true value is exactly the median, bias is zero. For a large enough number of quantiles, the percentile rank will equal the proportion of predictive samples below the observed true value, and this metric coincides with the one for continuous forecasts. - -**Usage**: -In contrast to the over- and underprediction penalties of the interval score it is bound between 0 and 1 and represents the tendency of forecasts to be biased rather than the absolute amount of over- and underprediction. It is therefore a more robust measurement, but harder to interpet. It largely depends on the application whether one is more interested in the tendency to be biased or in the absolute value of over- and underpredictions.)" -) - -pit <- list( - `Metric` = "Probability integral transform (PIT)", - `Explanation` = r"(The probability integral transform (PIT) @dawidPresentPositionPotential1984 represents a succinct way to visualise deviations between the predictive distribution $F$ and the true data-generating distribution $G$. The idea is to transform the observed values such that agreement between forecasts and data can then be examined by observing whether or not the transformed values follow a uniform distribution. The PIT is given by -$$u = F (y),$$ -where $u$ is the transformed variable and $F(y)$ is the predictive distribution $F$ evaluated at the true observed value $y$. If $F = G$, then $u$ follows a uniform distribution (for a proof see e.g. @angusProbabilityIntegralTransform1994). - -For integer outcomes, the PIT is no longer uniform even when forecasts are ideal. Instead, a randomised PIT (@funkAssessingPerformanceRealtime2019) can be used: -$$u = P(y) + v \cdot (P(y) - P(y - 1) ),$$ -where $y$ is again the observed value $P()$ is the cumulative probability assigned to all values smaller or equal to $y$ (where $P(-1) = 0$ by definition, and $v$ is a standard uniform variable independent of $y$. If $P$ is equal to the true data-generating distribution function, then $u$ is standard uniform. also propose a non-randomised version of the PIT for count data that could be used alternatively. - -**Usage**: -One can plot a histogram of $u$ values to look for deviations from uniformity. U-shaped histograms often result from predictions that are too narrow, while hump-shaped histograms indicate that predictions may be too wide. Biased predictions will usually result in a triangle-shaped histogram. One can also test for deviations from normality, using for example an Anderson-Darling test. This, however, proves to be overly strict in practice and even slight deviations from perfect calibration are punished in a way that makes it very hard to compare models at all. In addition, errors from forecasts may be correlated (i.e. forecasts made on a given date), potentially violating the assumptions of the Anderson-Darling test. We therefore do not recommend it for most use cases.)" -) - -mean_score_ratio <- list( - `Metric` = "Mean score ratio", - `Explanation` = r"(The mean score ratio is used to compare two models on the overlapping set of forecast targets for which both models have made a prediction. The mean score ratio is calculated as the mean score achieved by the first model over the mean score achieved by the second model. More precisely, for two models $i, j$, we determine the set of overlapping forecasts, denoted by $\mathcal{A}_{ij}$ and compute the mean score ratio $\theta_{ij}$ as -$$\theta_{ij} =\frac{\text{mean score model } i \text{ on } \mathcal{A}_{ij}}{\text{mean score model } j \text{ on } \mathcal{A}_{ij}}.$$ -The mean score ratio can in principle be computed for any arbitrary score. +``` -**Usage**: -Mean scores ratios are usually calculated in the context of pairwise comparisons, where a set of models is compared by looking at mean score ratios of all possible parings. Whether smaller or larger values are better depends on the orientation of the original score used)" -) -relative_skill <- list( - `Metric` = "Relative skill", - `Explanation` = r"(Relative skill scores can be used to obtain a ranking of models based on pairwise comparisons between all models. To compute the relative skill $\theta_i$ of model $i$, we take the geometric mean of all mean score ratios that involve model $i$, i.e. -$$ \theta_{i} = \left(\prod_{m = 1}^M \theta_{im}\right)^{1/M}, $$ -where M is the number of models. +## Detailed explanation of the metrics implemented in `scoringutils` -**Usage and caveats**: -Relative skill is a helpful way to obtain a model ranking. Whether smaller or larger values are better depends on the orientation of the original score used. -It is in principle relatively robust against biases that arise when models only forecast some of the available targets and is a reasonable way to handle missing forecasts. One possible precautionary measure to reduces issues with missing forecasts is to only compare models that have forecasted at least half of all possible targets (this ensures that there is always an overlap between models). If there is no overlap between models, the relative skill implicitly estimates how a model would have forecasted on those missing targets. )" -) +```{r, echo = FALSE, results = "asis"} -data <- rbind(as.data.frame(crps), - as.data.frame(log_score), - as.data.frame(wis), - as.data.frame(dss), - as.data.frame(brier_score), - as.data.frame(interval_coverage), - as.data.frame(quantile_coverage), - as.data.frame(pit), - as.data.frame(sharpness), - as.data.frame(bias), - as.data.frame(mean_score_ratio), - as.data.frame(relative_skill)) +data <- readRDS(system.file("metrics-overview/metrics-detailed.Rda", package = "scoringutils")) data[, 1:2] %>% kbl(format = "html", From dfe5b2248fc172a7884fc0f0224ceac96d85be55 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Tue, 14 Dec 2021 10:46:55 +0100 Subject: [PATCH 051/225] add table with different forecast types --- inst/metrics-overview/forecast-types.Rda | Bin 0 -> 334 bytes inst/tables-metric-overview.R | 26 +++++++++++++++++++++++ 2 files changed, 26 insertions(+) create mode 100644 inst/metrics-overview/forecast-types.Rda diff --git a/inst/metrics-overview/forecast-types.Rda b/inst/metrics-overview/forecast-types.Rda new file mode 100644 index 0000000000000000000000000000000000000000..3ff41d5408e4703ae9f13f76cbf4061052e831db GIT binary patch literal 334 zcmV-U0kQrciwFP!000001HDo`Ps1<}b<+l^MMWS!M&uneGyH+h6)FwvAmNRuEQZXzk7G`^Gpaa7L!pX#yKBF{I z$=Z2T5GbKHEAL7uwb6mJ`YStBzE#eWwykS}oXJWD<<&rMBR^ly&`URw7SQ>QMO3 zS(HKBjX`PKrbHhtNP0oJ^;l^|SV`BCa!oJvkO=+8FQM5QjMe083tT*1lyQL{f(6rY zTVSGUlLW2~PPjnooHPa@CK+?1l#^R%A~-NRU#96u>bAX|(>3@F4xq&&vd4gyfV8vH z)ju1GzIis}b=gMxsPfdi_oA;p=g2;I+3)wC#}<`=&|AzBIIDo5ASewEklM52k-$a4 gzXd(ds0e6kk9GVHDRwFTqkpmc0xX8N$LIn80Kr(C6aWAK literal 0 HcmV?d00001 diff --git a/inst/tables-metric-overview.R b/inst/tables-metric-overview.R index 2b53586f8..020391d16 100644 --- a/inst/tables-metric-overview.R +++ b/inst/tables-metric-overview.R @@ -1,3 +1,29 @@ +#------------------------------------------------------------------------------# +#------------------ Overview of the different forecast types ------------------# +#------------------------------------------------------------------------------# + +point_forecast <- list( + `Forecast type` = c("Point forecast"), + `Target type` = c("continuous \n discrete \n binary"), + `Representation of the predictive distribution` = c("one single number for the predicted outcome") +) + + +probabilistic_forecast <- list( + `Forecast type` = c("Probabilistic forecast", "Probabilistic forecast"), + `Target type` = c("continuous \n discrete", + "binary"), + `Representation of the predictive distribution` = c( + "predictive samples \n quantiles \n closed analytical form", + "binary probabilities" + ) +) + +data <- rbind(as.data.table(point_forecast), + as.data.table(probabilistic_forecast)) + +saveRDS(data, "inst/metrics-overview/forecast-types.Rda") + #------------------------------------------------------------------------------# #----------------- Overview with applicability of the metrics -----------------# #------------------------------------------------------------------------------# From d2695be6b6edbbf13f8e1850206437edd29648a6 Mon Sep 17 00:00:00 2001 From: nikosbosse Date: Tue, 14 Dec 2021 10:47:26 +0100 Subject: [PATCH 052/225] integrate scoringutils paper manuscript into package --- .Rinstignore | 5 + .gitignore | 5 + inst/manuscript/R/toy-example-calibration.R | 99 + ...ring forecasts using scoringutils in R.Rmd | 533 ++++++ inst/manuscript/jss.bst | 1653 +++++++++++++++++ inst/manuscript/jss.cls | 494 +++++ inst/manuscript/jsslogo.jpg | Bin 0 -> 22731 bytes .../plots/calibration-diagnostic-examples.png | Bin 0 -> 588307 bytes inst/manuscript/plots/forecast-paradigm.png | Bin 0 -> 156417 bytes inst/manuscript/references.bib | 10 + inst/manuscript/scoringutils-paper.bib | 133 ++ 11 files changed, 2932 insertions(+) create mode 100644 .Rinstignore create mode 100644 inst/manuscript/R/toy-example-calibration.R create mode 100644 inst/manuscript/Scoring forecasts using scoringutils in R.Rmd create mode 100644 inst/manuscript/jss.bst create mode 100644 inst/manuscript/jss.cls create mode 100644 inst/manuscript/jsslogo.jpg create mode 100644 inst/manuscript/plots/calibration-diagnostic-examples.png create mode 100644 inst/manuscript/plots/forecast-paradigm.png create mode 100644 inst/manuscript/references.bib create mode 100644 inst/manuscript/scoringutils-paper.bib diff --git a/.Rinstignore b/.Rinstignore new file mode 100644 index 000000000..9ebf01875 --- /dev/null +++ b/.Rinstignore @@ -0,0 +1,5 @@ +inst/manuscript/Scoring-forecasts-using-scoringutils-in-R_cache/ +inst/manuscript/Scoring-forecasts-using-scoringutils-in-R.log +inst/manuscript/Scoring-forecasts-using-scoringutils-in-R.pdf +inst/manuscript/Scoring-forecasts-using-scoringutils-in-R.tex +inst/manuscript/Scoring-forecasts-using-scoringutils-in-R_files/ diff --git a/.gitignore b/.gitignore index 250f09584..54d0e7eff 100644 --- a/.gitignore +++ b/.gitignore @@ -6,3 +6,8 @@ inst/doc R/sandbox.R doc Meta +inst/manuscript/Scoring-forecasts-using-scoringutils-in-R_cache/ +inst/manuscript/Scoring-forecasts-using-scoringutils-in-R.log +inst/manuscript/Scoring-forecasts-using-scoringutils-in-R.pdf +inst/manuscript/Scoring-forecasts-using-scoringutils-in-R.tex +inst/manuscript/Scoring-forecasts-using-scoringutils-in-R_files/ diff --git a/inst/manuscript/R/toy-example-calibration.R b/inst/manuscript/R/toy-example-calibration.R new file mode 100644 index 000000000..4bc59d47f --- /dev/null +++ b/inst/manuscript/R/toy-example-calibration.R @@ -0,0 +1,99 @@ +library(scoringutils) +library(patchwork) +library(ggplot2) +library(data.table) + + +# Calibration Diagnostic plot -------------------------------------------------- +n_truth = 1000 +n_samples = 100 +true_values <- rnorm(n_truth, 0, 1) +predictions1 <- rnorm(n_truth * n_samples) +predictions2 <- rnorm(n_truth * n_samples, mean = 0.5) +predictions3 <- rnorm(n_truth * n_samples, sd = 1.4) +predictions4 <- rnorm(n_truth * n_samples, sd = 0.7) +# predictions5 <- rnorm(n_truth * n_samples, mean = 0.2, sd = 0.7) + +df <- data.table::data.table(true_value = rep(true_values, each = n_samples), + id = rep(1:n_truth, each = n_samples), + prediction = c(predictions1, predictions2, + predictions3, predictions4), + sample = 1:n_samples, + model = rep(c("Normal(0, 1)", "Normal(0.5, 1)", + "Normal(0, 1.4)", "Normal(0, 0.7)"), + each = n_truth * n_samples)) + +# get scores +res <- eval_forecasts(df, + summarise_by = c("model")) + +# create pit plots +pit <- scoringutils::pit_df(df, summarise_by = "model") +pit_plots <- scoringutils::hist_PIT(pit) + +# create coverage plots by transforming to quantile format first +quantiles <- c(0.01, 0.025, seq(0.05, 0.95, 0.05), 0.975, 0.99) +df_quantile <- scoringutils::sample_to_quantile(df, + quantiles = quantiles) + +res_quantile <- eval_forecasts(df_quantile, + summarise_by = c("model", "range", "quantile")) + +res_quantile[, model := factor(model, levels = c("Normal(0, 1)", "Normal(0.5, 1)", "Normal(0, 1.4)", "Normal(0, 0.7)"))] + +interval_coverage <- scoringutils::interval_coverage(res_quantile) + + facet_wrap(~ model, nrow = 1) + +quantile_coverage <- scoringutils::quantile_coverage(res_quantile) + + facet_wrap(~ model, nrow = 1) + + +# plot with observations +true_value_plot <- ggplot2::ggplot(data = data.frame(x = true_values), + ggplot2::aes(x = x)) + + ggplot2::geom_histogram(ggplot2::aes(y = ..density..), + fill = "grey", + colour = "dark grey") + + theme_minimal() + + ggplot2::labs(x = "True values", + y = "Density") + + ggplot2::theme(legend.position = "bottom") + +# plot with standard normal distribution +standard_normal <- true_value_plot + + ggplot2::geom_function(fun = dnorm, colour = "black") + + ggplot2::ggtitle("Normal(0, 1)") + +# plot with shifted mean +shifted_mean <- true_value_plot + + ggplot2::geom_function(fun = dnorm, colour = "black", args = list(mean = 0.5)) + + ggplot2::ggtitle("Normal(0.5, 1)") + +# plot with overdispersion +overdispersion <- true_value_plot + + ggplot2::geom_function(fun = dnorm, colour = "black", args = list(sd = 1.4)) + + ggplot2::ggtitle("Normal(0, 1.4)") + +# plot with underdispersion +underdispersion <- true_value_plot + + ggplot2::geom_function(fun = dnorm, colour = "black", args = list(sd = 0.7)) + + ggplot2::ggtitle("Normal(0, 0.7)") + +scores_table <- dcast(melt(res, id.vars = "model", + variable.name = "score"), + score ~ model) +scores_table <- scores_table[, lapply(.SD, round, 2), by = score] + +setcolorder( + scores_table, + c("score", "Normal(0, 1)", "Normal(0.5, 1)", "Normal(0, 1.4)", "Normal(0, 0.7)") +) + +(standard_normal | shifted_mean | overdispersion | underdispersion) / + (pit_plots$`Normal(0, 1)` | pit_plots$`Normal(0.5, 1)` | pit_plots$`Normal(0, 1.4)` | pit_plots$`Normal(0, 0.7)`) / + interval_coverage / + quantile_coverage / + gridExtra::tableGrob(scores_table) + + +ggsave("inst/manuscript/plots/calibration-diagnostic-examples.png", width = 12.5, height = 10) diff --git a/inst/manuscript/Scoring forecasts using scoringutils in R.Rmd b/inst/manuscript/Scoring forecasts using scoringutils in R.Rmd new file mode 100644 index 000000000..d3ee4cd8f --- /dev/null +++ b/inst/manuscript/Scoring forecasts using scoringutils in R.Rmd @@ -0,0 +1,533 @@ +--- +documentclass: jss +author: + - name: Nikos I. Bosse + affiliation: London School of Hygiene & Tropical Medicine + # use this syntax to add text on several lines + address: | + | Centre for Mathematical Modelling of Infectious Diseases + | London School of Hygiene & Tropical Medicine + | Keppel Street + | London WC1E 7HT + email: \email{nikosbosse@lshtm.ac.uk} + url: https://lshtm.ac.uk + - name: Second Author + affiliation: 'Affiliation \AND' + # To add another line, use \AND at the end of the previous one as above + - name: Third Author + address: | + | Department of Statistics and Mathematics, + | Faculty of Biosciences, + | Universitat Autònoma de Barcelona + affiliation: | + | Universitat Autònoma + | de Barcelona + # use a different affiliation in adress field (differently formated here) + affiliation2: Universitat Autònoma de Barcelona +title: + formatted: "Evaluating Covid-19 Short-Term Forecasts using \\pkg{scoringutils} in \\proglang{R}" + # If you use tex in the formatted title, also supply version without + plain: "Evaluating Covid-19 Short-Term Forecasts using scoringutils in R" + # For running headers, if needed + short: "Evaluating Forecasts with \\pkg{scoringutils} in \\proglang{R}" +abstract: > + Forecasts play an important role in a variety of fields. Their role in informing public policy has attracted increased attention from the general public with the emergence of the Covid-19 pandemic. Much theoretical work has been done on the development of proper scoring rules and other scoring metrics that can help evaluate these forecasts. Making these theoretical insights available to end users, however, has been less of a focus. In this paper we introduce \pkg{scoringutils}, an \proglang{R} package that facilitates automated forecast evaluation. It gives the user access to a wide range of scoring metrics for various types of forecasts as well as a variety of ways to visualise the evaluation. We give an overview of the evaluation process and the metrics implemented in \pkg{scoringutils} and show an example evaluation of forecasts for COVID-19 cases and deaths submitted to the European Forecast Hub between May und September 2021. +keywords: + # at least one keyword must be supplied + formatted: [keywords, not capitalized, "\\proglang{R}"] + plain: [keywords, not capitalized, R] +preamble: > + \usepackage{amsmath} + \usepackage{amssymb} + \usepackage{caption} + \captionsetup[table]{skip=10pt} + \newcommand{\class}[1]{`\code{#1}'} + \newcommand{\fct}[1]{\code{#1()}} + +bibliography: + - references.bib + - scoringutils-paper.bib +output: + rticles::jss_article: + citation_package: natbib + toc: true +--- + +```{r, setup, include=FALSE} +options(prompt = 'R> ', continue = '+ ') + +library(knitr) +library(dplyr) +library(magrittr) +library(kableExtra) + +opts_chunk$set( + cache = TRUE, + warning = FALSE, + message = FALSE, + out.width = "100%" +) +``` + +# Introduction + +Good forecasts are of great interest to decision makers in various fields like finance \citep{timmermannForecastingMethodsFinance2018} [CITATION], weather predictions [CITATION] or infectious disease modeling \citep{funkShorttermForecastsInform2020} [CITATIONS]. An integral part of assessing and improving their usefulness is forecast evaluation. For decades, researchers have developed and refined an arsenal of techniques not only to forecast, but also to evaluate these forecasts (see e.g. \cite{bracherEvaluatingEpidemicForecasts2021}, \cite{funkAssessingPerformanceRealtime2019}, \cite{gneitingProbabilisticForecastsCalibration2007}, and \cite{gneitingStrictlyProperScoring2007}). Yet even with this rich body of research available, implementing a complete forecast evaluation in \proglang{R} is not trivial. + +Some packages exist that bundle different scoring metrics together, but none offer the user a standalone solution to forecast evaluation. The \pkg{scoringRules} package \citep{scoringRules} offers a very extensive collection of proper scoring rules. Its exclusive focus on proper scoring rules and the fact that all functions are implemented using vectors and matrices instead of data.frames, however, make it more suitable for experienced users or as a building block in larger application. It also lacks features like pairwise comparisons between forecast models and plotting functionality that are invaluable in the evaluation process. Other packages like [\pkg{Metrics}](https://cran.r-project.org/web/packages/Metrics/Metrics.pdf) and [\pkg{MLmetrics}](https://cran.r-project.org/web/packages/MLmetrics/MLmetrics.pdf) are geared towards machine learning problems and don't implement the set of metrics and scoring rules desired for forecast evaluation. The \pkg{scoringutils} package aims to bring forth a standardised and tested toolkit. It offers convenient automated forecast evaluation in a data.table format, but also provides experienced users with a set of reliable lower-level scoring metrics they can build upon in other applications. In addition it implements a wide range of flexible plots that are able to cover most day-to-day use cases. + +This paper provides an overview of the fundamental ideas behind forecast evaluation, gives a detailed explanation of the evaluation metrics in \pkg{scoringutils} and discusses what to think about when applying them in practice. It then presents a case study based on the evaluation of Covid-19 related short-term forecasts in the UK \citep{funkShorttermForecastsInform2020}. +\subsection{Forecast types and forecast formats} + +In its most general sense, a forecast is the forecaster’s stated belief about the future \citep{gneitingStrictlyProperScoring2007} that can come in many different forms. Quantitative forecasts are either point forecasts or probabilistic in nature and can make statements about continuous, discrete or binary outcome variables. Point forecasts only give one single number for the most likely outcome, but do not quantify the forecaster's uncertainty. This limits their usefulness, as a very certain forecast may, for example, warrant a very different course of actions than does a very uncertain one. Probabilistic forecasts, in contrast, by definition provide a full predictive distribution. This makes them much more useful in any applied setting, as we learn about the forecaster's uncertainty and their belief about all aspects of the underlying data-generating distribution (including e.g. skewness or the width of its tails). Probabilistic forecasts are therefore the focus of this paper as well as the \pkg{scoringutils} package. + +The predictive distribution of a probabilistic forecast can be represented in different ways with implications for the appropriate evaluation approach. For most forecasting problems, predictive distributions are not readily available in a closed form (and the \pkg{scoringutils} package therefore does not support scoring them directly). Instead, predictive distributions are often represented by a set of quantiles or predictive samples. Predictive samples require a lot of storage space and also come with a loss of precision that is especially pronounced in the tails of the predictive distribution, where quite a lot of samples are needed to accurately characterise the distribution. For that reason, often quantiles or central prediction intervals are reported instead [citation FORECAST HUBS]. For binary or multinomial prediction targets, common in many classification problems, a probabilistic forecasts is represented by the probability that an outcome will come true. Table \ref{tab:forecast-types} summarises the different forecast types and formats. While specific metrics may differ depending on the forecast type or format, the general forecasting paradigm \cite{gneitingProbabilisticForecastsCalibration2007} that guides the evaluation process is the same. + + +```{r forecast-types, echo = FALSE} + +data <- readRDS(system.file("metrics-overview/forecast-types.Rda", + package = "scoringutils")) + +data %>% + dplyr::mutate_all(linebreak) %>% + kableExtra::kbl(format = "latex", + valign = "middle", + caption = "Forecasts can be distinguished by whether they are probabilistic in nature, or a point forecast only. Depending on the type of the target (discrete, continuous or binary) different representation of the predictive distribution are possible.", + booktabs = TRUE, + escape = FALSE, + linesep = c('\\addlinespace')) %>% + # kableExtra::column_spec(1, width = "3.5cm") %>% + # kableExtra::column_spec(2, width = "2.5cm") %>% + # kableExtra::column_spec(3, width = "4.5cm") %>% + kableExtra::row_spec(0, bold = TRUE) %>% + # kableExtra::row_spec(1, hline = TRUE) %>% + kableExtra::kable_styling() %>% + kableExtra::collapse_rows() +``` + +## The Forecasting paradigm + +Any forecaster should aim to minimise the difference between the (cumulative) predictive distribution $F$ and the unknown true data-generating distribution $G$ \citep{gneitingProbabilisticForecastsCalibration2007}. For an ideal forecast, we therefore have + +$$ F = G, $$ + +where $F$ and $G$ are both cumulative distribution functions. As we don't know the true data-generating distribution, we cannot assess the difference between the two distributions directly. \cite{gneitingProbabilisticForecastsCalibration2007} instead suggest to focus on two central aspects of the predictive distribution, calibration and sharpness (illustrated in Figure \ref{fig:forecast-paradigm}. Calibration refers to the statistical consistency (i.e. absence of systematic deviations) between the predictive distribution and the observations. Sharpness is a feature of the forecast only and describes how concentrated the predictive distribution is, i.e. how precise the forecasts are. The general forecasting paradigm states that we should maximise sharpness of the predictive distribution subject to calibration. A model that made very precise forecasts would at best be useless if the forecasts were wrong most of the time. On the other hand, a model may be well calibrated, but not sharp enough to be useful. Take a weather forecast that would assign 30 percent rain probability for every single day. It may be (marginally) calibrated when looking at the average rainfall over the course of a year, but it doesn't give much guidance on a day to day basis. \cite{gneitingProbabilisticForecastsCalibration2007} discuss different forms of calibration in more detail. + + +```{r forecast-paradigm, echo = FALSE, fig.cap= "Schematic illustration of calibration and sharpness. The true value is represented in red, the predictive distribution is shown in black"} +include_graphics("plots/forecast-paradigm.png") +``` + + +# Scoring metrics implemented in \pkg{scoringutils} {short-title="Scoring metrics implemented in scoringutils"} + +Some of the metrics in \pkg{scoringutils} focus only on sharpness or on calibration. Others, called proper scoring rules, combine both aspects into a single number. The former can be helpful to learn about specific model aspects and improve them, the latter are especially useful to assess and rank predictive performance of a forecaster. The following gives an introduction to how these metrics can be used to evaluate forecasts. Table \ref{tab:table-summary-scores} shows an overview of the metrics implemented in \pkg{scoringutils}. Table \ref{tab:score-table} in the Appendix gives a mathematical definition and thorough explanation of all the metrics. Figure \ref{fig:calibration-plots} gives an applied example of different scoring metrics and visualisations for simple toy data. + +\newpage + +```{r metrics-summary, echo = FALSE} + +data <- readRDS(system.file("metrics-overview/metrics-summary.Rda", package = "scoringutils")) + +data[, 1:6] %>% + kableExtra::kbl(format = "latex", booktabs = TRUE, + escape = FALSE, + longtable = TRUE, + caption = "Summary table of scores available in \\pkg{scoringutils} and when they are applicable. ", + align = c("lccccl"), + linesep = c('\\addlinespace')) %>% + kableExtra::column_spec(1, width = "3.3cm") %>% + # kableExtra::column_spec(2, width = "1.2cm") %>% + # kableExtra::column_spec(3, width = "1.2cm") %>% + # kableExtra::column_spec(4, width = "1.0cm") %>% + # kableExtra::column_spec(5, width = "1.0cm") %>% + kableExtra::column_spec(6, width = "4.8cm") %>% + # kableExtra::column_spec(7, width = "1.1cm") %>% + # kableExtra::column_spec(8, width = "3.4cm") %>% + kableExtra::kable_styling(latex_options = c("striped", + "repeat_header, scale_down"), + # full_width = TRUE, + font_size = 8) +``` + +\newpage + +```{r calibration-plots, echo = FALSE, fig.cap= "Calibration plots for different forecast distributions."} +include_graphics("plots/calibration-diagnostic-examples.png") +``` + +## Evaluating calibration and sharpness independently + + +Evaluating calibration and sharpness independently is helpful for model diagnostics. To that end \pkg{scoringutils} makes numerous metrics available that aim to capture different aspects of sharpness and calibration. + +### Assessing calibration + +Calibration means consistency between forecasts and observed values, but there are various ways in which a forecast can systematically deviate from the observations (see \cite{gneitingProbabilisticForecastsCalibration2007} for a discussion of different forms of calibration MAYBE IT WOULD BE A GOOD IDEA TO INCLUDE THESE DIFFERENT FORMS OF CALIBRATION HERE). +\pkg{scoringutils} allows the user to examine three different sub-aspects of calibration: bias, empirical coverage, and the probability integral transform (PIT). + +% Bias +Bias, i.e. systematic over- or underprediction, is a very common form of miscalibration which therefore deserves separate attention. The bias metric (with slightly different versions for the various forecast types and formats) captures a general tendency to over- and underpredict that is bound to be between minus one (underpredicton) and one (overprediction), where zero is ideal. It is derived by looking at how much of the probability mass of the predictive distribution is below or above the true observed value. For quantile forecasts we have second alternative approach available to assess over- and underprediction - by simply looking at the corresponding components of the weighted interval score. What is different between the over- and underprediction components and bias as described above is its sensitivity to outliers. The former are derived from absolute differences, while the latter is bound and rather captures a general tendency to be biased. + +% interval coverage and quantile coverage +Another way to look at calibration (precisely: probabilistic calibration in \cite{gneitingProbabilisticForecastsCalibration2007}) is to compare the proportion of observed values covered by different parts of the predictive distribution with the nominal coverage implied by the CDF of the distribution. This is most easily understood in the context of quantile forecasts, but can easily be transferred to sample-based continuous and discrete forecasts as well. +% +To assess empirical coverage at a certain interval range, we simply measure the proportion of true observed values that fall into corresponding range of the predictive distribution. If the 0.05, 0.25, 0.75, and 0.95 quantiles are given, then 50\% of the true values should fall between the 0.25 and 0.75 quantiles and 90\% should fall between the 0.05 and 0.95 quantiles. We can calculate and plot these values to inspect how well different parts of the forecast distribution are calibrated. +% +To get an even more precise picture, we can also look at the percentage of true values below every single quantile of the predictive distribution. This allows to diagnose issues in the lower and upper tails of the prediction intervals separately. A similar way to visualise the same information is a PIT histogram. In order to conveniently assess deviations between the predictive distribution and the true data-generating distribution we can transform the observed values using the probability integral transformation (PIT) \citep{dawidPresentPositionPotential1984} (see more details in Table \ref{tab:score-table}). If both distributions are equal, the transformed values will follow a uniform distribution. A histogram of the transformed values can help to diagnose systematic differences between the predictions and the observed values. Figure \ref{fig:calibration-plots} exemplifies the characteristic shape of certain systematic deviations of the predicitve distribution from the true data-generating distribution. In the PIT histograms, bias leads to a triangular shape, overdispersion results in a hump shaped form and underdispersion in a U-shape. ADD INTERPRETATION FOR QUANTILE AND INTERVAL COVERAGE PLOTS HERE. + +### Assessing sharpness + +Sharpness is the ability to produce narrow forecasts. It does not depend on the actual observations and is a quality of the forecast only \ref{gneitingProbabilisticForecastsCalibration2007}. Sharpness is therefore only useful subject to calibration, as exemplified above in Figure \ref{fig:calibration-example}. We may be willing to trade off a little calibration for a lot more sharpness, but usually not much. For sample-based forecasts, \pkg{scoringutils} calculates sharpness as the normalised median absolute deviation about the median (MADN) [funkAssessingPerformanceRealtime2019] (for details see Table \ref{tab:score-overview}). For quantile forecasts, we take the sharpness component of the WIS which corresponds to a weighted average of the individual interval widths. + +WHAT WOULD BE REALLY COOL IS TO DETERMINE A WAY TO FIND THE OPTIMAL SHARPNESS OF A FORECAST. AS IS, I FEEL THIS PARAGRAPH IS SOMEWHAT USELESS. + + + + +## Proper scoring rules + +Proper scoring rules \citep{gneitingStrictlyProperScoring2007} jointly assess sharpness and calibration and assign a single numeric value to a forecast. A scoring rule is proper if a perfect forecaster (the predictive distribution equals the data-generating distribution) receives the lowest score on average. This makes sure that a forecaster evaluated by a proper scoring rule is always incentivised to state their best estimate. As summarised in Table \ref{tab:table-summary-scores}, not all proper scoring rules are suitable for all forecast formats. + +## Proper scoring rules for sample-based forecasts (CRPS, logS and DSS) +For sample-based forecasts, the \pkg{scoringutils} provides the following proper scoring rules: the (continuous) ranked probability score (CRPS) [CITATION], the log score (logS) [CITATION], and the Dawid-Sebastiani-score (DSS) [CITATION] (formal definitions are given in Table \ref{tab:score-table}). The corresponding functions are imported from the \pkg{scoringRules} package and exposed to the user through a slightly adapted interface. Other, non-sample-based variants of the CRPS, logS and DSS are available directly in the \pkg{scoringRules} package, but not in \pkg{scoringutils}. All three scores are in principle applicable to continuous as well as discrete forecasts. The \pkg{scoringRules} implementation of the log score, however, requires a kernel density estimation that may be inappropriate for discrete values (see also Table \ref{tab:table-summary-scores}). The logS is therefore not computed for discrete predictions in \pkg{scoringutils}. + +When scoring forecasts in a sample-based format, the choice is usually between the logS and the CRPS. The DSS is much less commonly used. It is easier to compute, but apart from that does not have immediate advantages over the former two. CRPS and logS differ in three important aspects: sensitivity to distance \cite{winklerScoringRulesEvaluation1996}, sensitivity to outlier predictions, and sensitivity to the order of magnitude of the forecasted quantity. + +The CRPS is a so-called global scoring rule, which means that the entire predictive distribution is taken into account when scoring a single forecast. The log score, on the other hand is local. The resulting score does not depend on the overall distance between the observed value and the distribution, but only on the probability density assigned to the actual outcome. Imagine two forecasters, A and B, who forecast the number of goals scored by a team in a football match. If both forecasters assigned the same probability to the true outcome (e.g. 2 points), but A assigned higher probability to extreme outcomes far away from the actually observed outcome (e.g. A stated 10 goals may be quite likely, while B's predictions are concentrated around 1, 2 or 3 points), then A will receive a worse score than B. The log score, in contrast, is a local scoring rule that only scores the probability assigned to the actual outcome and ignores the rest of the predictive distribution. Judged by the log score, A and B would receive exactly the same score. Sensitivity to distance (taking the entire predictive distribution into account) may be an advantage in most settings that involve decision making. Forecaster A's prediction that assigns high probability to results far away from the observed value is arguably less useful than B's forecast that assigns higher probability to values closer to it (the probability assigned to the actual outcome being equal for both forecasts). The log score is only implicitly sensitive to distance if we assume that values close to the observed value are actually more likely to occur. The logS may, however, be more appropriate for inferential purposes (see \cite{winklerScoringRulesEvaluation1996}) and is commonly used in Bayesian statistics [CITATION]. + +% stable vs. non-stable +A second important difference is how forecasts are treated that deviate strongly from the observed outcome. The CRPS can be thought of as a generalisation of the absolute error to a predictive distribution. It therefore scales linearly with the distance between forecast distribution and true value. The log score, however, is the log of the predictive density evaluated at the observed value. It can quickly go to negative infinity if the probability assigned to the observed outcome is close to zero. The CRPS is therefore considered more stable than the log score. The behaviour of the DSS is in between the two. Whether or not harsh punishment of bad predictions is desirable or not depends of course on the setting. \cite{bracherEvaluatingEpidemicForecasts2021} exemplify that in practice there may indeed be substantial differences between how the CRPS and log score judge the same forecast. + +As the CRPS is a generalisation of the absolute value, overall scores depend on the order of magnitude of the quantity we try to forecast. This makes it harder to compare forecasts for very different targets, or assess average performance if the quantity of interest varies substantially over time. Average scores are dominated by forecasts for targets with high absolute numbers. This may be desirable,if we care most about forecasts in situations where numbers are high, but usually it is not. LogS and DSS are more robust against this effect. Another way to address this issue by using pairwise comparisons will be introduced later. + +## Proper scoring rules for non-sample-based forecasts (WIS and BS) +For forecasts in an interval or quantile format, \pkg{scoringutils} offers the weighted interval score (WIS) \citep{bracherEvaluatingEpidemicForecasts2021}. +% properties of the WIS + decomposition +The WIS has very similar properties to the CRPS and can be thought of as a quantile-based approximation. For an increasing number of equally-spaced prediction intervals the WIS converges to the CRPS. One additional benefit of the WIS is that it can easily be decomposed into three additive components: an uncertainty penalty (called dispersion or sharpness) for the width of a prediction interval and penalties for over- and underprediction (if a value falls outside of a prediction interval). This can be very helpful in diagnosing model problems. It may even be useful to convert samples into quantiles and use the WIS instead of the CRPS to make use of this decomposition for the purpose of model diagnostics. + +% Brier Score +Binary forecasts can be scored using the Brier score (BS) [CITATION], which corresponds to the squared difference between the given probability and the outcome (either 0 or 1). + +## Pairwise comparisons + +If what we care about is to determine which model performs best, pairwise comparisons between models are a suitable approach [CITATION CRAMER et al.]. In turn, each pair of models is evaluated based on the targets that both models have predicted. The mean score by one model is divided by the mean score of the other model to obtain the mean score ratio (see Table \ref{tab:score-overview}, a measure of relative performance. To obtain an overall relative skill score for a model, we take the geomatric mean of all mean score ratios that involve that model (omitting comparisons where there is no overlapping set of forecasts). This gives us an indicator of performance relative to all other models. The orientation depends on the score used. For the proper scoring rules described above, smaller is better and a relative skill score smaller than 1 indicates that a model is performing better than the average model. We can obtain a scaled relative skill score by dividing a model's relative skill by the relative skill of a baseline model. A scaled relative skill smaller than one then means that the model in question performed better than the baseline. + +It is in principle possible to obtain p-values that help determine whether two models perform significantly differently. \pkg{scoringutils} allows to compute these using either the Wilcoxon rank sum test or a permutation test. In practice, this is slightly complicated by the fact that both tests assume independent observations. In reality, however, forecasts by a model may be correlated across time or another dimension (e.g. if a forecaster has a bad day, they will likely perform badly across different targets for a given forecast date). P-values may therefore be too quick to suggest significant differences where there aren't any. One way to mitigate this is to aggregate observations over a category where one suspects correlation. A test that is performed on aggregate scores will likely be more conservative. + +# Example evaluation of Short-term forecasts of COVID-19 + +The following section shows an example evaluation of short-term predictions for COVID-19 cases and deaths submitted to the European Forecast Hub [CITATION]. The full oficial hub evaluations, which also use \pkg{scoringutils}, can be seen at https://covid19forecasthub.eu/. The European Forecast Hub each week collates, aggregates and evaluates one to four week ahead predictions of different COVID-19 related targets submitted by different research groups. Forecasts are submitted in a quantile-based format with a set of 22 quantiles plus the median ($0.01, 0.025, 0.05, ..., 0.5, ... 0.95, 0.975, 0.99$). The following example evaluation is based on a subset of forecasts from the European Forecast Hub which includes one to three week ahead forecasts made between May and September 2021 for COVID-19 cases and deaths from four different models. + +This example data follows a quantile-based format, but the evaluation process would work analogously for forecasts in a different format. We will point out differences to scoring different forecast formats where appropriate. The example data set (as well as versions of it in different formats) is also included in the \pkg{scoringutils} package. + +The evaluation process looks as follows: First we load, prepare and visualise (some of) the data. Then we obtain forecast scores and a model ranking based on pairwise-comparisons, followed by a more detailed analysis of calibration and sharpness. + +## Checking the data + +```{r} +# load packages +library(scoringutils) +library(dplyr) +library(ggplot2) + +# load forecasts +data(example_quantile) +``` + +All higher level functions that allow for convenient evaluation of forecasts expect a \code{data.frame} or similar. Lower-level functions (discussed in a later section) accept inputs as vectors and matrices. + +We omit \code{NA} values as the data set also contains entries for which we have an observed value, but no forecast. + +```{r} +example_quantile |> + na.omit() |> + glimpse() +``` + +For forecasts in a quantile-based format it is necessary to have columns called \code{true_value}, \code{prediction} and \code{quantile}. It is recommended (although not necessary except in order to do pairwise comparisons) to have a column called "model" with an identifier for the forecaster. +Table \ref{tab:column-requirements} shows the columns expected for different input formats. + +```{r, column-requirements} +requirements <- + data.table( + "Format" = c("quantile-based", "sample-based", "binary", "pairwise-comparisons"), + `Required columns` = c("'true_value', 'prediction', 'quantile'", + "'true_value', 'prediction', 'sample'", + "'true_value', 'prediction'", + "additionally a column 'model'") + ) + +requirements |> + kbl(format = "latex", + caption = "Overview of the columns required for different input formats.") +``` + +We can check whether the data conforms to the requirements by running \fct{check\_forecasts}. + +```{r} +check_forecasts(example_quantile) +``` + +This returns a list with different entries giving information about what \pkg{scoringutils} infers from the data. \code{target_type} \code{prediction_type} refer to the forecast format. \code{forecast_unit} contains a vector of the columns which \pkg{scoringutils} thinks denote the unit of a single forecast. This means that in this instance a single forecast (with a set of 23 quantiles) can uniquely be identified by the values in the columns "location", "target\_end\_date", "target\_type", "location\_name", "forecast\_date", "model", "horizon". In this example, having "location" as well as "location\_name" included does not make a difference, as they contain duplicated information. In general, however, it is strongly advised to remove all unnecessary columns that do not help identify a single forecast. \code{unique_values} gives an overview of the number of unique values per column across the entire data set, providing a first hint as to whether the forecast set is complete. \code{warnings} shows potential warnings about the data. In this example, \pkg{scoringutils} warns that there are observed values present for which there is no corresponding forecast. These warnings can often be ignored, but may provide important information. If there are errors that cannot be ignored, a list entry \code{errors} will appear. + +It is helpful to start the evaluation process by visualising data availability, as missing forecasts can impact the evaluation if missingness is not random, but instead correlates with performance. The function \fct{show\_avail\_forecasts} returns a heatmap with the number of available forecasts. By default, the function treats a set of different quantiles or samples as one forecast. However, the user can specify manually which elements to treat as one forecast and which categories to sum over to count the number of available forecasts. + + +```{r avail-forecasts, echo=TRUE, fig.width = 10, eval = TRUE, fig.cap="Overview of the number of available forecasts."} +show_avail_forecasts(data = example_quantile, + x = "target_end_date", + show_numbers = FALSE, + legend_position = "bottom", + facet_formula = ~ target_type) +``` + +The forecasts and observed values themselves can be visualised using the \fct{plot\_predictions} function. Data visualisation is of course highly context-dependent, but the function tries to accommodate most practical use-cases. In addition to basic plotting functionality it offers the user an optional ad hoc way to filter both forecasts and observed values. This makes it possible to tweak the plot in a beginner-friendly way without having to manipulate the data separately. Forecasts and observed values can be passed in separately (and are merged internally) or as a single data.frame. Conditions to filter on need to be provided as a list of strings, where each of the strings represents an expression that can be evaluated to filter the data. To display, for example, short-term forecasts for COVID-19 cases and deaths made by the EuroCOVIDhub-ensemble model on June 28 2021 as well as 5 weeks of prior data, we can call: % + + +```{r forecast-visualisation, fig.width = 10, fig.cap = "Short-term forecasts for COVID-19 cases and deaths made by the EuroCOVIDhub-ensemble model on June 28 2021."} +plot_predictions(data = example_quantile, + x = "target_end_date", + filter_truth = list('target_end_date <= "2021-07-15"', + 'target_end_date > "2021-05-22"'), + filter_forecasts = list("model == 'EuroCOVIDhub-ensemble'", + 'forecast_date == "2021-06-28"'), + facet_formula = target_type ~ location, + ncol = 4) + + theme(legend.position = "bottom") +``` + +The output is shown in Figure \ref{fig:forecast-visualisation}. + +## Scoring forecasts with \fct{eval\_forecasts} {short-title="Scoring forecasts with eval_forecasts()"} + +The actual scoring of forecasts based on observed values can be performed using the function \fct{eval\_forecasts}. The function automatically detects the forecast type and format (as shown in the output of \fct{check\_forecasts}), applies the appropriate scoring metrics and can in addition also aggregate results as well as perform pairwise comparisons between models. Internally, operations are handled using \pkg{data.table} to allow for fast and efficient computation. + +```{r} +scores <- eval_forecasts(example_quantile) +glimpse(scores) +``` + +The above produces one score for every forecast. However, we usually like to summarise scores to learn about average performance. + +We can either run a separate function to summarise the scores, + + +```{r} +summarised_scores <- + summarise_scores(scores = scores, + summarise_by = c("model", "target_type")) +``` + +or we score and summarise in one function call: + +```{r} +summarised_scores <- + eval_forecasts(example_quantile, + summarise_by = c("model", "target_type")) +``` + +```{r} +summarised_scores +``` + +\fct{eval\_forecasts} and \fct{summarise\_scores} aggregate scores by taking the mean over according to the grouping specified in \code{summarise_by}. In the above example, if \code{summarise_by = c("model", "target_type")}, then scores would be averaged over all other categories to obtain one score per model and forecast target type. For a more detailed analysis we could for example specify \code{summarise_by = c("mode", "target_type", "location")} to additionally stratify by country. Summarised scores can be visualised using the function \fct{scores\_table} as is shown in Figure \ref{fig:score-table}. + +If we wanted to have one score per quantile or one per prediction interval range, we could use something like \code{summarise_by = c("model", "quantile", "range")}. This would allow us to examine interval or quantile coverage or makes it possible to analyse the accuracy of the tails of the forecasts. In addition to the mean, we can also obtain the standard deviation of the scores over which we average, as well as any desired quantile, by specifying \code{sd = TRUE} and for example \code{quantiles = c(0.5)} for the median. + +The user must, however, exercise some caution when aggregating scores. As explained above, many of the metrics are absolute and scale with the magnitude of the quantity to forecast. This makes it sometimes ill-advised to average over them. In the given example, looking at one score per model (i.e. specifying \code{summarise_by = c("model")}) is problematic, as overall aggregate scores would be dominated by case forecasts, while errors on death forecasts would have little influence. Similarly, aggregating over different forecast horizons is often not a good idea as the mean will be dominated by further ahead forecast horizons. + +```{r score-table, fig.width = 12, fig.cap="Coloured table to visualise the computed scores."} +score_table(summarised_scores, y = "model", facet_formula = ~ target_type) +``` + +In order to obtain a model ranking, we recommend to look at the relative skill in terms of an appropriate proper scoring rule instead of the raw score. Relative skill scores can be aggregated more easily across different forecast targets as they are less influenced by the order of magnitude of the quantity to forecast than e.g. the WIS or the CRPS. + +Again there are two possible ways to do pairwise comparisons. First, the function \fct{pairiwse\_comparison} can be called to compare models against each other. Here \code{summarise_by} denotes what relative skill scores shall be computed for. \code{summarise_by = "model"} means there will be one relative skill score per model, while \code{summarise_by = c("target_type", "target_type")} would mean there is one relative skill score per model, computed completely separately for different target types. + +When using \fct{pairwise\_comparison}, it is important that the scores used as input have not been summarised in any way. + +```{r} +pairwise <- pairwise_comparison(scores, + baseline = "EuroCOVIDhub-baseline") + +pairwise2 <- pairwise_comparison(scores, + summarise_by = c("model", "target_type"), + baseline = "EuroCOVIDhub-baseline") +``` + +Pairwise comparisons between models [CITATION] can be obtained in two different ways. First, relative skill scores based on pairwise comparisons are by default returned from \fct{eval\_forecasts}. These will be computed separately for the categories defined in the \code{summarise_by} argument (excluding the category 'model'). Alternatively, a set of scores can be post-processed using the separate function \fct{pairwise\_comparison}. This approach is to be used for visualisation and if p-values for the pairwise comparisons are needed, as those are not returned from \fct{eval\_forecasts}. Usually, one would compute scores without specifying a \code{summarise_by} argument, but sometimes it may be sensible to average over certain scores, for example for predictions generated at a certain date. This allows to reduce the correlation between observations that enter the computation of p-values, which in turn makes the test less liberal. +Using the function \fct{plot\_pairwise\_comparison} we can visualise the mean score ratios between all models as well as the + +The result is a \code{data.table} with different scores and metrics in a tidy format that can easily be used for further manipulation and plotting. + + +```{r pairwise-plot, echo=TRUE, fig.width = 10, fig.cap="Ratios of mean scores based on overlapping forecast sets. If a tile is blue, then the model on the y-axis performed better. If it is red, the model on the x-axis performed better in direct comparison."} + +``` + +# Visualisation and interpretation of evaluation results + +## Visualising aggregate scores and rankings +A good starting point for an evaluation is the following score table that visusalises the scores we produced above. We can facet the table to account for the different forecast targets: + + +The most informative metric in terms of model ranking is the relative\_skill. However, interpretation is not always straightforward and has to be done carefully. We can see that performance varied quite a bit across different metrics, where some models did well on one target, but poorly on another. Especially the Exponential growth/decline model stands out as it received the lowest relative skill score for hospital admissions, but the highest for the total number of beds occupied. Looking back at Figure \ref{fig:avail-forecasts}, we see that the model has only submitted very few forecasts over all. It may therefore be sensible to require all models to have submitted forecasts for at least 50\% of all forecast targets in order to enter the pairwise comparisons. For similar reasons, the interval score may be deceiving if looked at in isolation. As can be seen, the DetSEIRwithNB MLE model received a lower relative skill score, but a higher interval score than the DetSEIRwithNB MCMC model. This, again, can be explained by the fact that they forecasted different targets. The interval score, as an absolute metric, is highly influenced by the absolute value of the quantity that is forecasted. For the same reason, one should be careful when summarising interval scores from different locations or forecast targets, as the average score will be dominated by outliers as well as differences in the absolute level. Assuming a large enough set of available overlapping forecasts, the relative skill score is more robust. It therefore is reasonable to assume that the DetSEIRwithNB MLE forecasted quantities with a higher absolute value, but tended to perform worse than the DetSEIRwithNB MCMC model as far as we can tell based on the set of all pariwise comparisons. This can be confirmed for the direct comparison between the two by looking at the mean score ratios from the pairwise comparisons. These can be obtained by calling + +In terms of actually understanding \textit{why} one model performs well or badly, the other metrics shown in Figure \ref{fig:score-table} provide additional insight. We turn to them in the following. + +## Visual model diagnostics + +For forecasts in an interval format, looking at the components of the weighted interval score separately is a natural next step. We can see in Figure \ref{fig:wis-components} that the majority of penalties come from over-and underprediction, instead of the sharpness component. We also see that most models tended to either over- or underpredict actual numbers. + + + +```{r wis-components, fig.width = 10, fig.cap = "CAPTION"} +wis_components(summarised_scores, + facet_formula = ~ target_type, + scales = "free_x", + relative_contributions = TRUE, + x_text_angle = 0) + + ggplot2::coord_flip() + + ggplot2::theme(legend.position = "bottom") +``` + +We can have a closer look at calibration using the functions \fct{interval\_coverage} and \fct{quantile\_coverage}. The interval coverage plot shows the proportion of all true values that fall within all the different prediction intervals. This gives a visual impression of probabilistic calibration \ref{gneitingProbabilisticForecastsCalibration2007}. Ideally, $x$ percent of true values should be covered by the $x$\%-prediction intervals, resulting in a 45° line. Areas shaded in green indicate that the model is covering more true values than it actually should, while areas in white indicate that the model fails to cover the desired proportion of true values with its prediction intervals. The majority of the models were too confident in their predictions, while some showed showed good calibration. The quantile coverage plot shows the proportion of all true values below certain predictive quantiles. While this plot is slightly harder to interpret, it also includes information about bias as and allows to separate the lower and upper boundaries of the prediction intervals. We can see, for example, that the Exponential growth/decline model was consistently biased downwards. Figure \ref{fig:coverage} + + +```{r pit-plots, out.width="49%", fig.show='hold'} +pit <- pit_df(example_quantile, + summarise_by = "model", "target") + +hist_PIT(pit) +``` + + + +```{r coverage, fig.width = 10, fig.show='hold', fig.cap = "Interval coverage"} +cov_scores <- eval_forecasts(example_quantile, + summarise_by = c("model", "target_type", + "range", "quantile")) + +scoringutils::interval_coverage(cov_scores, facet_formula = ~ target_type) +``` + +```{r quantile-coverage, fig.width = 10, fig.show='hold', fig.cap = "Quantile coverage"} +scoringutils::quantile_coverage(cov_scores, facet_formula = ~ target_type) +``` + +DO I WANT TO INCLUDE PIT PLOTS AS WELL? I GUESS? NEED TO LOOK AT THE IMPLEMENTATION FOR QUANTILE FORECASTS + +```{r calibration-pit, fig.cap = "PIT histograms"} +# pit-plots, echo=TRUE, fig=TRUE, height=5.2, width=12>>= +# out <- eval_forecasts(example_quantile, +# summarise_by = c("model"), +# pit_plots = TRUE) + +``` + +Look at e.g. bias by location? Figure \ref{fig:bias-heatmap} + +```{r heatmap-location, fig.width = 10, fig.cap="Heatmap of interval scores by location"} +scores <- eval_forecasts(example_quantile, + summarise_by = c("model", + "target_type", + "location_name"), + compute_relative_skill = FALSE) +scoringutils::score_heatmap(scores, metric = "interval_score", + x = "location_name", facet_formula = ~ target_type, + scale = "free_x") +``` + + +## Summary and discussion + +COMING SOON. + + + + + + + + + + +## Acknowledgments + + + + + + + + + + + + + + + + + + + + +\newpage + +\appendix + +# (APPENDIX) Supplementary information {-} + +```{r score-table-detailed, echo=FALSE} + +data <- readRDS(system.file("metrics-overview/metrics-detailed.Rda", package = "scoringutils")) + +data[, 1:2] %>% + kableExtra::kbl(format = "latex", booktabs = TRUE, + escape = FALSE, + caption = "Detailed explanation of all the metrics,", + longtable = TRUE, + linesep = c('\\addlinespace')) %>% + kableExtra::column_spec(1, width = "1.1in") %>% + kableExtra::column_spec(2, width = "4.625in") %>% + kableExtra::kable_styling(latex_options = c("striped", "repeat_header")) + +``` + + +\newpage + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/inst/manuscript/jss.bst b/inst/manuscript/jss.bst new file mode 100644 index 000000000..ae7541547 --- /dev/null +++ b/inst/manuscript/jss.bst @@ -0,0 +1,1653 @@ +%% +%% This is file `jss.bst', +%% generated with the docstrip utility. +%% +%% The original source files were: +%% +%% merlin.mbs (with options: `ay,nat,nm-rvx,keyxyr,dt-beg,yr-par,note-yr,tit-qq,atit-u,trnum-it,vol-bf,volp-com,num-xser,pre-edn,isbn,issn,edpar,pp,ed,xedn,xand,etal-it,revdata,eprint,url,url-blk,doi,nfss') +%% +%% ** BibTeX style file for JSS publications (http://www.jstatsoft.org/) +%% +%% License: GPL-2 | GPL-3 + % =============================================================== + % IMPORTANT NOTICE: + % This bibliographic style (bst) file has been generated from one or + % more master bibliographic style (mbs) files, listed above, provided + % with kind permission of Patrick W Daly. + % + % This generated file can be redistributed and/or modified under the terms + % of the General Public License (Version 2 or 3). + % =============================================================== + % Name and version information of the main mbs file: + % \ProvidesFile{merlin.mbs}[2011/11/18 4.33 (PWD, AO, DPC)] + % For use with BibTeX version 0.99a or later + %------------------------------------------------------------------- + % This bibliography style file is intended for texts in ENGLISH + % This is an author-year citation style bibliography. 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'output.state := +} + +FUNCTION {date.block} +{ + new.block +} + +FUNCTION {not} +{ { #0 } + { #1 } + if$ +} +FUNCTION {and} +{ 'skip$ + { pop$ #0 } + if$ +} +FUNCTION {or} +{ { pop$ #1 } + 'skip$ + if$ +} +FUNCTION {non.stop} +{ duplicate$ + "}" * add.period$ + #-1 #1 substring$ "." = +} + +STRINGS {z} + +FUNCTION {remove.dots} +{ 'z := + "" + { z empty$ not } + { z #1 #2 substring$ + duplicate$ "\." = + { z #3 global.max$ substring$ 'z := * } + { pop$ + z #1 #1 substring$ + z #2 global.max$ substring$ 'z := + duplicate$ "." = 'pop$ + { * } + if$ + } + if$ + } + while$ +} +FUNCTION {new.block.checkb} +{ empty$ + swap$ empty$ + and + 'skip$ + 'new.block + if$ +} +FUNCTION {field.or.null} +{ duplicate$ empty$ + { pop$ "" } + 'skip$ + if$ +} +FUNCTION {emphasize} +{ duplicate$ empty$ + { pop$ "" } + { "\emph{" swap$ * "}" * } + if$ +} +FUNCTION {bolden} +{ duplicate$ empty$ + { pop$ "" } + { "\textbf{" swap$ * "}" * } + if$ +} +FUNCTION {tie.or.space.prefix} +{ duplicate$ text.length$ #3 < + { "~" } + { " " } + if$ + swap$ +} + +FUNCTION {capitalize} +{ "u" change.case$ "t" change.case$ } + +FUNCTION {space.word} +{ " " swap$ * " " * } + % Here are the language-specific definitions for explicit words. + % Each function has a name bbl.xxx where xxx is the English word. + % The language selected here is ENGLISH +FUNCTION {bbl.and} +{ "and"} + +FUNCTION {bbl.etal} +{ "et~al." } + +FUNCTION {bbl.editors} +{ "eds." } + +FUNCTION {bbl.editor} +{ "ed." } + +FUNCTION {bbl.edby} +{ "edited by" } + +FUNCTION {bbl.edition} +{ "edition" } + +FUNCTION {bbl.volume} +{ "volume" } + +FUNCTION {bbl.of} +{ "of" } + +FUNCTION {bbl.number} +{ "number" } + +FUNCTION {bbl.nr} +{ "no." } + +FUNCTION {bbl.in} +{ "in" } + +FUNCTION {bbl.pages} +{ "pp." } + +FUNCTION {bbl.page} +{ "p." } + +FUNCTION {bbl.eidpp} +{ "pages" } + +FUNCTION {bbl.chapter} +{ "chapter" } + +FUNCTION {bbl.techrep} +{ "Technical Report" } + +FUNCTION {bbl.mthesis} +{ "Master's thesis" } + +FUNCTION {bbl.phdthesis} +{ "Ph.D. thesis" } + +MACRO {jan} {"January"} + +MACRO {feb} {"February"} + +MACRO {mar} {"March"} + +MACRO {apr} {"April"} + +MACRO {may} {"May"} + +MACRO {jun} {"June"} + +MACRO {jul} {"July"} + +MACRO {aug} {"August"} + +MACRO {sep} {"September"} + +MACRO {oct} {"October"} + +MACRO {nov} {"November"} + +MACRO {dec} {"December"} + +MACRO {acmcs} {"ACM Computing Surveys"} + +MACRO {acta} {"Acta Informatica"} + +MACRO {cacm} {"Communications of the ACM"} + +MACRO {ibmjrd} {"IBM Journal of Research and Development"} + +MACRO {ibmsj} {"IBM Systems Journal"} + +MACRO {ieeese} {"IEEE Transactions on Software Engineering"} + +MACRO {ieeetc} {"IEEE Transactions on Computers"} + +MACRO {ieeetcad} + {"IEEE Transactions on Computer-Aided Design of Integrated Circuits"} + +MACRO {ipl} {"Information Processing Letters"} + +MACRO {jacm} {"Journal of the ACM"} + +MACRO {jcss} {"Journal of Computer and System Sciences"} + +MACRO {scp} {"Science of Computer Programming"} + +MACRO {sicomp} {"SIAM Journal on Computing"} + +MACRO {tocs} {"ACM Transactions on Computer Systems"} + +MACRO {tods} {"ACM Transactions on Database Systems"} + +MACRO {tog} {"ACM Transactions on Graphics"} + +MACRO {toms} {"ACM Transactions on Mathematical Software"} + +MACRO {toois} {"ACM Transactions on Office Information Systems"} + +MACRO {toplas} {"ACM Transactions on Programming Languages and Systems"} + +MACRO {tcs} {"Theoretical Computer Science"} +FUNCTION {bibinfo.check} +{ swap$ + duplicate$ missing$ + { + pop$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ pop$ + } + { swap$ + pop$ + } + if$ + } + if$ +} +FUNCTION {bibinfo.warn} +{ swap$ + duplicate$ missing$ + { + swap$ "missing " swap$ * " in " * cite$ * warning$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ "empty " swap$ * " in " * cite$ * warning$ + } + { swap$ + pop$ + } + if$ + } + if$ +} +FUNCTION {format.eprint} +{ eprint duplicate$ empty$ + 'skip$ + { "\eprint" + archive empty$ + 'skip$ + { "[" * archive * "]" * } + if$ + "{" * swap$ * "}" * + } + if$ +} +FUNCTION {format.url} +{ + url + duplicate$ empty$ + { pop$ "" } + { "\urlprefix\url{" swap$ * "}" * } + if$ +} + +INTEGERS { nameptr namesleft numnames } + + +STRINGS { bibinfo} + +FUNCTION {format.names} +{ 'bibinfo := + duplicate$ empty$ 'skip$ { + 's := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{vv~}{ll}{ jj}{ f{}}" + format.name$ + remove.dots + bibinfo bibinfo.check + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + "," * + t "others" = + { + " " * bbl.etal emphasize * + } + { " " * t * } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ + } if$ +} +FUNCTION {format.names.ed} +{ + 'bibinfo := + duplicate$ empty$ 'skip$ { + 's := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{f{}~}{vv~}{ll}{ jj}" + format.name$ + remove.dots + bibinfo bibinfo.check + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + "," * + t "others" = + { + + " " * bbl.etal emphasize * + } + { " " * t * } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ + } if$ +} +FUNCTION {format.key} +{ empty$ + { key field.or.null } + { "" } + if$ +} + +FUNCTION {format.authors} +{ author "author" format.names + duplicate$ empty$ 'skip$ + { collaboration "collaboration" bibinfo.check + duplicate$ empty$ 'skip$ + { " (" swap$ * ")" * } + if$ + * + } + if$ +} +FUNCTION {get.bbl.editor} +{ editor num.names$ #1 > 'bbl.editors 'bbl.editor if$ } + +FUNCTION {format.editors} +{ editor "editor" format.names duplicate$ empty$ 'skip$ + { + " " * + get.bbl.editor + "(" swap$ * ")" * + * + } + if$ +} +FUNCTION {format.isbn} +{ isbn "isbn" bibinfo.check + duplicate$ empty$ 'skip$ + { + new.block + "ISBN " swap$ * + } + if$ +} + +FUNCTION {format.issn} +{ issn "issn" bibinfo.check + duplicate$ empty$ 'skip$ + { + new.block + "ISSN " swap$ * + } + if$ +} + +FUNCTION {format.doi} +{ doi empty$ + { "" } + { + new.block + "\doi{" doi * "}" * + } + if$ +} +FUNCTION {format.note} +{ + note empty$ + { "" } + { note #1 #1 substring$ + duplicate$ "{" = + 'skip$ + { output.state mid.sentence = + { "l" } + { "u" } + if$ + change.case$ + } + if$ + note #2 global.max$ substring$ * "note" bibinfo.check + } + if$ +} + +FUNCTION {format.title} +{ title + "title" bibinfo.check + duplicate$ empty$ 'skip$ + { + "\enquote{" swap$ * + add.period$ "}" * + } + if$ +} +FUNCTION {format.full.names} +{'s := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{vv~}{ll}" format.name$ + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + " " * bbl.etal emphasize * + } + { + numnames #2 > + { "," * } + 'skip$ + if$ + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {author.editor.key.full} +{ author empty$ + { editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.full.names } + if$ + } + { author format.full.names } + if$ +} + +FUNCTION {author.key.full} +{ author empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { author format.full.names } + if$ +} + +FUNCTION {editor.key.full} +{ editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.full.names } + if$ +} + +FUNCTION {make.full.names} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.key.full + { type$ "proceedings" = + 'editor.key.full + 'author.key.full + if$ + } + if$ +} + +FUNCTION {output.bibitem} +{ newline$ + "\bibitem[{" write$ + label write$ + ")" make.full.names duplicate$ short.list = + { pop$ } + { * } + if$ + "}]{" * write$ + cite$ write$ + "}" write$ + newline$ + "" + before.all 'output.state := +} + +FUNCTION {n.dashify} +{ + 't := + "" + { t empty$ not } + { t #1 #1 substring$ "-" = + { t #1 #2 substring$ "--" = not + { "--" * + t #2 global.max$ substring$ 't := + } + { { t #1 #1 substring$ "-" = } + { "-" * + t #2 global.max$ substring$ 't := + } + while$ + } + if$ + } + { t #1 #1 substring$ * + t #2 global.max$ substring$ 't := + } + if$ + } + while$ +} + +FUNCTION {word.in} +{ bbl.in capitalize + " " * } + +FUNCTION {format.date} +{ year "year" bibinfo.check duplicate$ empty$ + { + "empty year in " cite$ * "; set to ????" * warning$ + pop$ "????" + } + 'skip$ + if$ + extra.label * + before.all 'output.state := + " (" swap$ * ")" * +} +FUNCTION {format.btitle} +{ title "title" bibinfo.check + duplicate$ empty$ 'skip$ + { + emphasize + } + if$ +} +FUNCTION {either.or.check} +{ empty$ + 'pop$ + { "can't use both " swap$ * " fields in " * cite$ * warning$ } + if$ +} +FUNCTION {format.bvolume} +{ volume empty$ + { "" } + { bbl.volume volume tie.or.space.prefix + "volume" bibinfo.check * * + series "series" bibinfo.check + duplicate$ empty$ 'pop$ + { swap$ bbl.of space.word * swap$ + emphasize * } + if$ + "volume and number" number either.or.check + } + if$ +} +FUNCTION {format.number.series} +{ volume empty$ + { number empty$ + { series field.or.null } + { series empty$ + { number "number" bibinfo.check } + { output.state mid.sentence = + { bbl.number } + { bbl.number capitalize } + if$ + number tie.or.space.prefix "number" bibinfo.check * * + bbl.in space.word * + series "series" bibinfo.check * + } + if$ + } + if$ + } + { "" } + if$ +} + +FUNCTION {format.edition} +{ edition duplicate$ empty$ 'skip$ + { + output.state mid.sentence = + { "l" } + { "t" } + if$ change.case$ + "edition" bibinfo.check + " " * bbl.edition * + } + if$ +} +INTEGERS { multiresult } +FUNCTION {multi.page.check} +{ 't := + #0 'multiresult := + { multiresult not + t empty$ not + and + } + { t #1 #1 substring$ + duplicate$ "-" = + swap$ duplicate$ "," = + swap$ "+" = + or or + { #1 'multiresult := } + { t #2 global.max$ substring$ 't := } + if$ + } + while$ + multiresult +} +FUNCTION {format.pages} +{ pages duplicate$ empty$ 'skip$ + { duplicate$ multi.page.check + { + bbl.pages swap$ + n.dashify + } + { + bbl.page swap$ + } + if$ + tie.or.space.prefix + "pages" bibinfo.check + * * + } + if$ +} +FUNCTION {format.journal.pages} +{ pages duplicate$ empty$ 'pop$ + { swap$ duplicate$ empty$ + { pop$ pop$ format.pages } + { + ", " * + swap$ + n.dashify + "pages" bibinfo.check + * + } + if$ + } + if$ +} +FUNCTION {format.journal.eid} +{ eid "eid" bibinfo.check + duplicate$ empty$ 'pop$ + { swap$ duplicate$ empty$ 'skip$ + { + ", " * + } + if$ + swap$ * + numpages empty$ 'skip$ + { bbl.eidpp numpages tie.or.space.prefix + "numpages" bibinfo.check * * + " (" swap$ * ")" * * + } + if$ + } + if$ +} +FUNCTION {format.vol.num.pages} +{ volume field.or.null + duplicate$ empty$ 'skip$ + { + "volume" bibinfo.check + } + if$ + bolden + number "number" bibinfo.check duplicate$ empty$ 'skip$ + { + swap$ duplicate$ empty$ + { "there's a number but no volume in " cite$ * warning$ } + 'skip$ + if$ + swap$ + "(" swap$ * ")" * + } + if$ * + eid empty$ + { format.journal.pages } + { format.journal.eid } + if$ +} + +FUNCTION {format.chapter.pages} +{ chapter empty$ + 'format.pages + { type empty$ + { bbl.chapter } + { type "l" change.case$ + "type" bibinfo.check + } + if$ + chapter tie.or.space.prefix + "chapter" bibinfo.check + * * + pages empty$ + 'skip$ + { ", " * format.pages * } + if$ + } + if$ +} + +FUNCTION {format.booktitle} +{ + booktitle "booktitle" bibinfo.check + emphasize +} +FUNCTION {format.in.ed.booktitle} +{ format.booktitle duplicate$ empty$ 'skip$ + { + editor "editor" format.names.ed duplicate$ empty$ 'pop$ + { + " " * + get.bbl.editor + "(" swap$ * "), " * + * swap$ + * } + if$ + word.in swap$ * + } + if$ +} +FUNCTION {format.thesis.type} +{ type duplicate$ empty$ + 'pop$ + { swap$ pop$ + "t" change.case$ "type" bibinfo.check + } + if$ +} +FUNCTION {format.tr.number} +{ number "number" bibinfo.check + type duplicate$ empty$ + { pop$ bbl.techrep } + 'skip$ + if$ + "type" bibinfo.check + swap$ duplicate$ empty$ + { pop$ "t" change.case$ } + { tie.or.space.prefix * * } + if$ +} +FUNCTION {format.article.crossref} +{ + word.in + " \cite{" * crossref * "}" * +} +FUNCTION {format.book.crossref} +{ volume duplicate$ empty$ + { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ + pop$ word.in + } + { bbl.volume + capitalize + swap$ tie.or.space.prefix "volume" bibinfo.check * * bbl.of space.word * + } + if$ + " \cite{" * crossref * "}" * +} +FUNCTION {format.incoll.inproc.crossref} +{ + word.in + " \cite{" * crossref * "}" * +} +FUNCTION {format.org.or.pub} +{ 't := + "" + address empty$ t empty$ and + 'skip$ + { + t empty$ + { address "address" bibinfo.check * + } + { t * + address empty$ + 'skip$ + { ", " * address "address" bibinfo.check * } + if$ + } + if$ + } + if$ +} +FUNCTION {format.publisher.address} +{ publisher "publisher" bibinfo.warn format.org.or.pub +} + +FUNCTION {format.organization.address} +{ organization "organization" bibinfo.check format.org.or.pub +} + +FUNCTION {article} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + format.title "title" output.check + new.block + crossref missing$ + { + journal + "journal" bibinfo.check + emphasize + "journal" output.check + format.vol.num.pages output + } + { format.article.crossref output.nonnull + format.pages output + } + if$ + format.issn output + format.doi output + new.block + format.note output + format.eprint output + format.url output + fin.entry +} +FUNCTION {book} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + editor format.key output + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.date "year" output.check + date.block + format.btitle "title" output.check + crossref missing$ + { format.bvolume output + new.block + format.number.series output + format.edition output + new.sentence + format.publisher.address output + } + { + new.block + format.book.crossref output.nonnull + } + if$ + format.isbn output + format.doi output + new.block + format.note output + format.eprint output + format.url output + fin.entry +} +FUNCTION {booklet} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + format.title "title" output.check + new.block + howpublished "howpublished" bibinfo.check output + address "address" bibinfo.check output + format.isbn output + format.doi output + new.block + format.note output + format.eprint output + format.url output + fin.entry +} + +FUNCTION {inbook} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + editor format.key output + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.date "year" output.check + date.block + format.btitle "title" output.check + crossref missing$ + { + format.bvolume output + format.chapter.pages "chapter and pages" output.check + new.block + format.number.series output + format.edition output + new.sentence + format.publisher.address output + } + { + format.chapter.pages "chapter and pages" output.check + new.block + format.book.crossref output.nonnull + } + if$ + crossref missing$ + { format.isbn output } + 'skip$ + if$ + format.doi output + new.block + format.note output + format.eprint output + format.url output + fin.entry +} + +FUNCTION {incollection} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + format.title "title" output.check + new.block + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + format.bvolume output + format.number.series output + format.edition output + format.chapter.pages output + new.sentence + format.publisher.address output + format.isbn output + } + { format.incoll.inproc.crossref output.nonnull + format.chapter.pages output + } + if$ + format.doi output + new.block + format.note output + format.eprint output + format.url output + fin.entry +} +FUNCTION {inproceedings} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + format.title "title" output.check + new.block + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + format.bvolume output + format.number.series output + format.pages output + new.sentence + publisher empty$ + { format.organization.address output } + { organization "organization" bibinfo.check output + format.publisher.address output + } + if$ + format.isbn output + format.issn output + } + { format.incoll.inproc.crossref output.nonnull + format.pages output + } + if$ + format.doi output + new.block + format.note output + format.eprint output + format.url output + fin.entry +} +FUNCTION {conference} { inproceedings } +FUNCTION {manual} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + format.btitle "title" output.check + organization address new.block.checkb + organization "organization" bibinfo.check output + address "address" bibinfo.check output + format.edition output + format.doi output + new.block + format.note output + format.eprint output + format.url output + fin.entry +} + +FUNCTION {mastersthesis} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + format.btitle + "title" output.check + new.block + bbl.mthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + format.doi output + new.block + format.note output + format.eprint output + format.url output + fin.entry +} + +FUNCTION {misc} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + format.title output + new.block + howpublished "howpublished" bibinfo.check output + format.doi output + new.block + format.note output + format.eprint output + format.url output + fin.entry +} +FUNCTION {phdthesis} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + format.btitle + "title" output.check + new.block + bbl.phdthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + format.doi output + new.block + format.note output + format.eprint output + format.url output + fin.entry +} + +FUNCTION {proceedings} +{ output.bibitem + format.editors output + editor format.key output + format.date "year" output.check + date.block + format.btitle "title" output.check + format.bvolume output + format.number.series output + new.sentence + publisher empty$ + { format.organization.address output } + { organization "organization" bibinfo.check output + format.publisher.address output + } + if$ + format.isbn output + format.issn output + format.doi output + new.block + format.note output + format.eprint output + format.url output + fin.entry +} + +FUNCTION {techreport} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + format.title + "title" output.check + new.block + format.tr.number emphasize output.nonnull + institution "institution" bibinfo.warn output + address "address" bibinfo.check output + format.doi output + new.block + format.note output + format.eprint output + format.url output + fin.entry +} + +FUNCTION {unpublished} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + format.title "title" output.check + format.doi output + new.block + format.note "note" output.check + format.eprint output + format.url output + fin.entry +} + +FUNCTION {default.type} { misc } +READ +FUNCTION {sortify} +{ purify$ + "l" change.case$ +} +INTEGERS { len } +FUNCTION {chop.word} +{ 's := + 'len := + s #1 len substring$ = + { s len #1 + global.max$ substring$ } + 's + if$ +} +FUNCTION {format.lab.names} +{ 's := + "" 't := + s #1 "{vv~}{ll}" format.name$ + s num.names$ duplicate$ + #2 > + { pop$ + " " * bbl.etal emphasize * + } + { #2 < + 'skip$ + { s #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = + { + " " * bbl.etal emphasize * + } + { bbl.and space.word * s #2 "{vv~}{ll}" format.name$ + * } + if$ + } + if$ + } + if$ +} + +FUNCTION {author.key.label} +{ author empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {author.editor.key.label} +{ author empty$ + { editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.lab.names } + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {editor.key.label} +{ editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.lab.names } + if$ +} + +FUNCTION {calc.short.authors} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.key.label + { type$ "proceedings" = + 'editor.key.label + 'author.key.label + if$ + } + if$ + 'short.list := +} + +FUNCTION {calc.label} +{ calc.short.authors + short.list + "(" + * + year duplicate$ empty$ + short.list key field.or.null = or + { pop$ "" } + 'skip$ + if$ + * + 'label := +} + +FUNCTION {sort.format.names} +{ 's := + #1 'nameptr := + "" + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{vv{ } }{ll{ }}{ f{ }}{ jj{ }}" + format.name$ 't := + nameptr #1 > + { + " " * + namesleft #1 = t "others" = and + { "zzzzz" 't := } + 'skip$ + if$ + t sortify * + } + { t sortify * } + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {sort.format.title} +{ 't := + "A " #2 + "An " #3 + "The " #4 t chop.word + chop.word + chop.word + sortify + #1 global.max$ substring$ +} +FUNCTION {author.sort} +{ author empty$ + { key empty$ + { "to sort, need author or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {author.editor.sort} +{ author empty$ + { editor empty$ + { key empty$ + { "to sort, need author, editor, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {editor.sort} +{ editor empty$ + { key empty$ + { "to sort, need editor or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ +} +FUNCTION {presort} +{ calc.label + label sortify + " " + * + type$ "book" = + type$ "inbook" = + or + 'author.editor.sort + { type$ "proceedings" = + 'editor.sort + 'author.sort + if$ + } + if$ + #1 entry.max$ substring$ + 'sort.label := + sort.label + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} + +ITERATE {presort} +SORT +STRINGS { last.label next.extra } +INTEGERS { last.extra.num last.extra.num.extended last.extra.num.blank number.label } +FUNCTION {initialize.extra.label.stuff} +{ #0 int.to.chr$ 'last.label := + "" 'next.extra := + #0 'last.extra.num := + "a" chr.to.int$ #1 - 'last.extra.num.blank := + last.extra.num.blank 'last.extra.num.extended := + #0 'number.label := +} +FUNCTION {forward.pass} +{ last.label label = + { last.extra.num #1 + 'last.extra.num := + last.extra.num "z" chr.to.int$ > + { "a" chr.to.int$ 'last.extra.num := + last.extra.num.extended #1 + 'last.extra.num.extended := + } + 'skip$ + if$ + last.extra.num.extended last.extra.num.blank > + { last.extra.num.extended int.to.chr$ + last.extra.num int.to.chr$ + * 'extra.label := } + { last.extra.num int.to.chr$ 'extra.label := } + if$ + } + { "a" chr.to.int$ 'last.extra.num := + "" 'extra.label := + label 'last.label := + } + if$ + number.label #1 + 'number.label := +} +FUNCTION {reverse.pass} +{ next.extra "b" = + { "a" 'extra.label := } + 'skip$ + if$ + extra.label 'next.extra := + extra.label + duplicate$ empty$ + 'skip$ + { "{\natexlab{" swap$ * "}}" * } + if$ + 'extra.label := + label extra.label * 'label := +} +EXECUTE {initialize.extra.label.stuff} +ITERATE {forward.pass} +REVERSE {reverse.pass} +FUNCTION {bib.sort.order} +{ sort.label + " " + * + year field.or.null sortify + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} +ITERATE {bib.sort.order} +SORT +FUNCTION {begin.bib} +{ preamble$ empty$ + 'skip$ + { preamble$ write$ newline$ } + if$ + "\begin{thebibliography}{" number.label int.to.str$ * "}" * + write$ newline$ + "\newcommand{\enquote}[1]{``#1''}" + write$ newline$ + "\providecommand{\natexlab}[1]{#1}" + write$ newline$ + "\providecommand{\url}[1]{\texttt{#1}}" + write$ newline$ + "\providecommand{\urlprefix}{URL }" + write$ newline$ + "\expandafter\ifx\csname urlstyle\endcsname\relax" + write$ newline$ + " \providecommand{\doi}[1]{doi:\discretionary{}{}{}#1}\else" + write$ newline$ + " \providecommand{\doi}{doi:\discretionary{}{}{}\begingroup \urlstyle{rm}\Url}\fi" + write$ newline$ + "\providecommand{\eprint}[2][]{\url{#2}}" + write$ newline$ +} +EXECUTE {begin.bib} +EXECUTE {init.state.consts} +ITERATE {call.type$} +FUNCTION {end.bib} +{ newline$ + "\end{thebibliography}" write$ newline$ +} +EXECUTE {end.bib} +%% End of customized bst file +%% +%% End of file `jss.bst'. diff --git a/inst/manuscript/jss.cls b/inst/manuscript/jss.cls new file mode 100644 index 000000000..c2e9a479c --- /dev/null +++ b/inst/manuscript/jss.cls @@ -0,0 +1,494 @@ +%% My own packages added here +\usepackage{colortbl} +\usepackage{xcolor} + +%% +%% This is file `jss.cls' +\def\fileversion{3.2} +\def\filename{jss} +\def\filedate{2020/12/09} +%% +%% Package `jss' to use with LaTeX2e for JSS publications (http://www.jstatsoft.org/) +%% License: GPL-2 | GPL-3 +%% Copyright: (C) Achim Zeileis +%% Please report errors to Achim.Zeileis@R-project.org +%% +\NeedsTeXFormat{LaTeX2e} +\ProvidesClass{jss}[\filedate\space\fileversion\space jss class by Achim Zeileis] +%% options +\newif\if@article +\newif\if@codesnippet +\newif\if@bookreview +\newif\if@softwarereview +\newif\if@review +\newif\if@shortnames +\newif\if@nojss +\newif\if@notitle +\newif\if@noheadings +\newif\if@nofooter + +\@articletrue +\@codesnippetfalse +\@bookreviewfalse +\@softwarereviewfalse +\@reviewfalse +\@shortnamesfalse +\@nojssfalse +\@notitlefalse +\@noheadingsfalse +\@nofooterfalse + +\DeclareOption{article}{\@articletrue% + \@codesnippetfalse \@bookreviewfalse \@softwarereviewfalse} +\DeclareOption{codesnippet}{\@articlefalse% + \@codesnippettrue \@bookreviewfalse \@softwarereviewfalse} +\DeclareOption{bookreview}{\@articlefalse% + \@codesnippetfalse \@bookreviewtrue \@softwarereviewfalse} +\DeclareOption{softwarereview}{\@articlefalse% + \@codesnippetfalse \@bookreviewfalse \@softwarereviewtrue} +\DeclareOption{shortnames}{\@shortnamestrue} +\DeclareOption{nojss}{\@nojsstrue} +\DeclareOption{notitle}{\@notitletrue} +\DeclareOption{noheadings}{\@noheadingstrue} +\DeclareOption{nofooter}{\@nofootertrue} + +\ProcessOptions +\LoadClass[11pt,a4paper,twoside]{article} +%% required packages +\RequirePackage{graphicx,color,ae,fancyvrb} +\RequirePackage[T1]{fontenc} +\IfFileExists{upquote.sty}{\RequirePackage{upquote}}{} +\IfFileExists{lmodern.sty}{\RequirePackage{lmodern}}{} +%% bibliography +\if@shortnames + \usepackage[authoryear,round]{natbib} +\else + \usepackage[authoryear,round,longnamesfirst]{natbib} +\fi +\bibpunct{(}{)}{;}{a}{}{,} +\bibliographystyle{jss} +%% page layout +\topmargin 0pt +\textheight 46\baselineskip +\advance\textheight by \topskip +\oddsidemargin 0.1in +\evensidemargin 0.15in +\marginparwidth 1in +\oddsidemargin 0.125in +\evensidemargin 0.125in +\marginparwidth 0.75in +\textwidth 6.125in +%% paragraphs +\setlength{\parskip}{0.7ex plus0.1ex minus0.1ex} +\setlength{\parindent}{0em} +%% for all publications +\newcommand{\Address}[1]{\def\@Address{#1}} +\newcommand{\Plaintitle}[1]{\def\@Plaintitle{#1}} +\newcommand{\Shorttitle}[1]{\def\@Shorttitle{#1}} +\newcommand{\Plainauthor}[1]{\def\@Plainauthor{#1}} +\newcommand{\Volume}[1]{\def\@Volume{#1}} +\newcommand{\Year}[1]{\def\@Year{#1}} +\newcommand{\Month}[1]{\def\@Month{#1}} +\newcommand{\Issue}[1]{\def\@Issue{#1}} +\newcommand{\Submitdate}[1]{\def\@Submitdate{#1}} +%% for articles and code snippets +\newcommand{\Acceptdate}[1]{\def\@Acceptdate{#1}} +\newcommand{\Abstract}[1]{\def\@Abstract{#1}} +\newcommand{\Keywords}[1]{\def\@Keywords{#1}} +\newcommand{\Plainkeywords}[1]{\def\@Plainkeywords{#1}} +%% for book and software reviews +\newcommand{\Reviewer}[1]{\def\@Reviewer{#1}} +\newcommand{\Booktitle}[1]{\def\@Booktitle{#1}} +\newcommand{\Bookauthor}[1]{\def\@Bookauthor{#1}} +\newcommand{\Publisher}[1]{\def\@Publisher{#1}} +\newcommand{\Pubaddress}[1]{\def\@Pubaddress{#1}} +\newcommand{\Pubyear}[1]{\def\@Pubyear{#1}} +\newcommand{\ISBN}[1]{\def\@ISBN{#1}} +\newcommand{\Pages}[1]{\def\@Pages{#1}} +\newcommand{\Price}[1]{\def\@Price{#1}} +\newcommand{\Plainreviewer}[1]{\def\@Plainreviewer{#1}} +\newcommand{\Softwaretitle}[1]{\def\@Softwaretitle{#1}} +\newcommand{\URL}[1]{\def\@URL{#1}} +\newcommand{\DOI}[1]{\def\@DOI{#1}} +%% for internal use +\newcommand{\Seriesname}[1]{\def\@Seriesname{#1}} +\newcommand{\Hypersubject}[1]{\def\@Hypersubject{#1}} +\newcommand{\Hyperauthor}[1]{\def\@Hyperauthor{#1}} +\newcommand{\Footername}[1]{\def\@Footername{#1}} +\newcommand{\Firstdate}[1]{\def\@Firstdate{#1}} +\newcommand{\Seconddate}[1]{\def\@Seconddate{#1}} +\newcommand{\Reviewauthor}[1]{\def\@Reviewauthor{#1}} +%% defaults +\author{Firstname Lastname\\Affiliation} +\title{Title} +\Abstract{---!!!---an abstract is required---!!!---} +\Plainauthor{\@author} +\Volume{VV} +\Year{YYYY} +\Month{MMMMMM} +\Issue{II} +\Submitdate{yyyy-mm-dd} +\Acceptdate{yyyy-mm-dd} +\Address{ + Firstname Lastname\\ + Affiliation\\ + Address, Country\\ + E-mail: \email{name@address}\\ + URL: \url{http://link/to/webpage/} +} + +\Reviewer{Firstname Lastname\\Affiliation} +\Plainreviewer{Firstname Lastname} +\Booktitle{Book Title} +\Bookauthor{Book Author} +\Publisher{Publisher} +\Pubaddress{Publisher's Address} +\Pubyear{YYY} +\ISBN{x-xxxxx-xxx-x} +\Pages{xv + 123} +\Price{USD 69.95 (P)} +\URL{http://link/to/webpage/} +\DOI{10.18637/jss.v000.i00} +\if@article + \Seriesname{Issue} + \Hypersubject{Journal of Statistical Software} + \Plaintitle{\@title} + \Shorttitle{\@title} + \Plainkeywords{\@Keywords} +\fi + +\if@codesnippet + \Seriesname{Code Snippet} + \Hypersubject{Journal of Statistical Software -- Code Snippets} + \Plaintitle{\@title} + \Shorttitle{\@title} + \Plainkeywords{\@Keywords} +\fi + +\if@bookreview + \Seriesname{Book Review} + \Hypersubject{Journal of Statistical Software -- Book Reviews} + \Plaintitle{\@Booktitle} + \Shorttitle{\@Booktitle} + \Reviewauthor{\@Bookauthor\\ + \@Publisher, \@Pubaddress, \@Pubyear.\\ + ISBN~\@ISBN. \@Pages~pp. \@Price.\\ + \url{\@URL}} + \Plainkeywords{} + \@reviewtrue +\fi + +\if@softwarereview + \Seriesname{Software Review} + \Hypersubject{Journal of Statistical Software -- Software Reviews} + \Plaintitle{\@Softwaretitle} + \Shorttitle{\@Softwaretitle} + \Booktitle{\@Softwaretitle} + \Reviewauthor{\@Publisher, \@Pubaddress. \@Price.\\ + \url{\@URL}} + \Plainkeywords{} + \@reviewtrue +\fi + +\if@review + \Hyperauthor{\@Plainreviewer} + \Keywords{} + \Footername{Reviewer} + \Firstdate{\textit{Published:} \@Submitdate} + \Seconddate{} +\else + \Hyperauthor{\@Plainauthor} + \Keywords{---!!!---at least one keyword is required---!!!---} + \Footername{Affiliation} + \Firstdate{\textit{Submitted:} \@Submitdate} + \Seconddate{\textit{Accepted:} \@Acceptdate} +\fi +%% Sweave(-like) +\DefineVerbatimEnvironment{Sinput}{Verbatim}{fontshape=sl} +\DefineVerbatimEnvironment{Soutput}{Verbatim}{} +\DefineVerbatimEnvironment{Scode}{Verbatim}{fontshape=sl} +\newenvironment{Schunk}{}{} +\DefineVerbatimEnvironment{Code}{Verbatim}{} +\DefineVerbatimEnvironment{CodeInput}{Verbatim}{fontshape=sl} +\DefineVerbatimEnvironment{CodeOutput}{Verbatim}{} +\newenvironment{CodeChunk}{}{} +\setkeys{Gin}{width=0.8\textwidth} +%% footer +\newlength{\footerskip} +\setlength{\footerskip}{2.5\baselineskip plus 2ex minus 0.5ex} + +\newcommand{\makefooter}{% + \vspace{\footerskip} + + \if@nojss + \begin{samepage} + \textbf{\large \@Footername: \nopagebreak}\\[.3\baselineskip] \nopagebreak + \@Address \nopagebreak + \end{samepage} + \else + \begin{samepage} + \textbf{\large \@Footername: \nopagebreak}\\[.3\baselineskip] \nopagebreak + \@Address \nopagebreak + \vfill + \hrule \nopagebreak + \vspace{.1\baselineskip} + {\fontfamily{pzc} \fontsize{13}{15} \selectfont Journal of Statistical Software} + \hfill + \url{http://www.jstatsoft.org/}\\ \nopagebreak + published by the Foundation for Open Access Statistics + \hfill + \url{http://www.foastat.org/}\\[.3\baselineskip] \nopagebreak + {\@Month{} \@Year, Volume~\@Volume, \@Seriesname~\@Issue} + \hfill + \@Firstdate\\ \nopagebreak + {\href{https://doi.org/\@DOI}{\tt doi:\@DOI}} + \hfill + \@Seconddate \nopagebreak + \vspace{.3\baselineskip} + \hrule + \end{samepage} + \fi +} +\if@nofooter + %% \AtEndDocument{\makefooter} +\else + \AtEndDocument{\makefooter} +\fi +%% required packages +\RequirePackage{hyperref} +%% new \maketitle +\def\@myoddhead{ + {\color{white} JSS}\\[-1.42cm] + \hspace{-2em} \includegraphics[height=23mm,keepaspectratio]{jsslogo} \hfill + \parbox[b][23mm]{118mm}{\hrule height 3pt + \center{ + {\fontfamily{pzc} \fontsize{28}{32} \selectfont Journal of Statistical Software} + \vfill + {\it \small \@Month{} \@Year, Volume~\@Volume, \@Seriesname~\@Issue.% + \hfill \href{https://doi.org/\@DOI}{doi:\,\@DOI}}}\\[0.1cm] + \hrule height 3pt}} +\if@review + \renewcommand{\maketitle}{ + \if@nojss + %% \@oddhead{\@myoddhead}\\[3\baselineskip] + \else + \@oddhead{\@myoddhead}\\[3\baselineskip] + \fi + {\large + \noindent + Reviewer: \@Reviewer + \vspace{\baselineskip} + \hrule + \vspace{\baselineskip} + \textbf{\@Booktitle} + \begin{quotation} \noindent + \@Reviewauthor + \end{quotation} + \vspace{0.7\baselineskip} + \hrule + \vspace{1.3\baselineskip} + } + + \thispagestyle{empty} + \if@nojss + \markboth{\centerline{\@Shorttitle}}{\centerline{\@Hyperauthor}} + \else + \markboth{\centerline{\@Shorttitle}}{\centerline{\@Hypersubject}} + \fi + \pagestyle{myheadings} + } +\else + \def\maketitle{ + \if@nojss + %% \@oddhead{\@myoddhead} \par + \else + \@oddhead{\@myoddhead} \par + \fi + \begingroup + \def\thefootnote{\fnsymbol{footnote}} + \def\@makefnmark{\hbox to 0pt{$^{\@thefnmark}$\hss}} + \long\def\@makefntext##1{\parindent 1em\noindent + \hbox to1.8em{\hss $\m@th ^{\@thefnmark}$}##1} + \@maketitle \@thanks + \endgroup + \setcounter{footnote}{0} + + \if@noheadings + %% \markboth{\centerline{\@Shorttitle}}{\centerline{\@Hypersubject}} + \else + \thispagestyle{empty} + \if@nojss + \markboth{\centerline{\@Shorttitle}}{\centerline{\@Hyperauthor}} + \else + \markboth{\centerline{\@Shorttitle}}{\centerline{\@Hypersubject}} + \fi + \pagestyle{myheadings} + \fi + + \let\maketitle\relax \let\@maketitle\relax + \gdef\@thanks{}\gdef\@author{}\gdef\@title{}\let\thanks\relax + } + + \def\@maketitle{\vbox{\hsize\textwidth \linewidth\hsize + \if@nojss + %% \vskip 1in + \else + \vskip 1in + \fi + {\centering + {\LARGE\bf \@title\par} + \vskip 0.2in plus 1fil minus 0.1in + { + \def\and{\unskip\enspace{\rm and}\enspace}% + \def\And{\end{tabular}\hss \egroup \hskip 1in plus 2fil + \hbox to 0pt\bgroup\hss \begin{tabular}[t]{c}\large\bf\rule{\z@}{24pt}\ignorespaces}% + \def\AND{\end{tabular}\hss\egroup \hfil\hfil\egroup + \vskip 0.1in plus 1fil minus 0.05in + \hbox to \linewidth\bgroup\rule{\z@}{10pt} \hfil\hfil + \hbox to 0pt\bgroup\hss \begin{tabular}[t]{c}\large\bf\rule{\z@}{24pt}\ignorespaces} + \hbox to \linewidth\bgroup\rule{\z@}{10pt} \hfil\hfil + \hbox to 0pt\bgroup\hss \begin{tabular}[t]{c}\large\bf\rule{\z@}{24pt}\@author + \end{tabular}\hss\egroup + \hfil\hfil\egroup} + \vskip 0.3in minus 0.1in + \hrule + \begin{abstract} + \@Abstract + \end{abstract}} + \textit{Keywords}:~\@Keywords. + \vskip 0.1in minus 0.05in + \hrule + \vskip 0.2in minus 0.1in + }} +\fi +%% sections, subsections, and subsubsections +\newlength{\preXLskip} +\newlength{\preLskip} +\newlength{\preMskip} +\newlength{\preSskip} +\newlength{\postMskip} +\newlength{\postSskip} +\setlength{\preXLskip}{1.8\baselineskip plus 0.5ex minus 0ex} +\setlength{\preLskip}{1.5\baselineskip plus 0.3ex minus 0ex} +\setlength{\preMskip}{1\baselineskip plus 0.2ex minus 0ex} +\setlength{\preSskip}{.8\baselineskip plus 0.2ex minus 0ex} +\setlength{\postMskip}{.5\baselineskip plus 0ex minus 0.1ex} +\setlength{\postSskip}{.3\baselineskip plus 0ex minus 0.1ex} + +\newcommand{\jsssec}[2][default]{\vskip \preXLskip% + \pdfbookmark[1]{#1}{Section.\thesection.#1}% + \refstepcounter{section}% + \centerline{\textbf{\Large \thesection. #2}} \nopagebreak + \vskip \postMskip \nopagebreak} +\newcommand{\jsssecnn}[1]{\vskip \preXLskip% + \centerline{\textbf{\Large #1}} \nopagebreak + \vskip \postMskip \nopagebreak} + +\newcommand{\jsssubsec}[2][default]{\vskip \preMskip% + \pdfbookmark[2]{#1}{Subsection.\thesubsection.#1}% + \refstepcounter{subsection}% + \textbf{\large \thesubsection. #2} \nopagebreak + \vskip \postSskip \nopagebreak} +\newcommand{\jsssubsecnn}[1]{\vskip \preMskip% + \textbf{\large #1} \nopagebreak + \vskip \postSskip \nopagebreak} + +\newcommand{\jsssubsubsec}[2][default]{\vskip \preSskip% + \pdfbookmark[3]{#1}{Subsubsection.\thesubsubsection.#1}% + \refstepcounter{subsubsection}% + {\large \textit{#2}} \nopagebreak + \vskip \postSskip \nopagebreak} +\newcommand{\jsssubsubsecnn}[1]{\vskip \preSskip% + {\textit{\large #1}} \nopagebreak + \vskip \postSskip \nopagebreak} + +\newcommand{\jsssimplesec}[2][default]{\vskip \preLskip% +%% \pdfbookmark[1]{#1}{Section.\thesection.#1}% + \refstepcounter{section}% + \textbf{\large #1} \nopagebreak + \vskip \postSskip \nopagebreak} +\newcommand{\jsssimplesecnn}[1]{\vskip \preLskip% + \textbf{\large #1} \nopagebreak + \vskip \postSskip \nopagebreak} + +\if@review + \renewcommand{\section}{\secdef \jsssimplesec \jsssimplesecnn} + \renewcommand{\subsection}{\secdef \jsssimplesec \jsssimplesecnn} + \renewcommand{\subsubsection}{\secdef \jsssimplesec \jsssimplesecnn} +\else + \renewcommand{\section}{\secdef \jsssec \jsssecnn} + \renewcommand{\subsection}{\secdef \jsssubsec \jsssubsecnn} + \renewcommand{\subsubsection}{\secdef \jsssubsubsec \jsssubsubsecnn} +\fi +%% colors +\definecolor{Red}{rgb}{0.5,0,0} +\definecolor{Blue}{rgb}{0,0,0.5} +\if@review + \hypersetup{% + hyperindex = {true}, + colorlinks = {true}, + linktocpage = {true}, + plainpages = {false}, + linkcolor = {Blue}, + citecolor = {Blue}, + urlcolor = {Red}, + pdfstartview = {Fit}, + pdfpagemode = {None}, + pdfview = {XYZ null null null} + } +\else + \hypersetup{% + hyperindex = {true}, + colorlinks = {true}, + linktocpage = {true}, + plainpages = {false}, + linkcolor = {Blue}, + citecolor = {Blue}, + urlcolor = {Red}, + pdfstartview = {Fit}, + pdfpagemode = {UseOutlines}, + pdfview = {XYZ null null null} + } +\fi +\if@nojss + \AtBeginDocument{ + \hypersetup{% + pdfauthor = {\@Hyperauthor}, + pdftitle = {\@Plaintitle}, + pdfkeywords = {\@Plainkeywords} + } + } +\else + \AtBeginDocument{ + \hypersetup{% + pdfauthor = {\@Hyperauthor}, + pdftitle = {\@Plaintitle}, + pdfsubject = {\@Hypersubject}, + pdfkeywords = {\@Plainkeywords} + } + } +\fi +\if@notitle + %% \AtBeginDocument{\maketitle} +\else + \@ifundefined{AddToHook}{\AtBeginDocument{\maketitle}}{\AddToHook{begindocument}[maketitle]{\maketitle}} +\fi +%% commands +\newcommand\code{\bgroup\@makeother\_\@makeother\~\@makeother\$\@codex} +\def\@codex#1{{\normalfont\ttfamily\hyphenchar\font=-1 #1}\egroup} +%%\let\code=\texttt +\let\proglang=\textsf +\newcommand{\pkg}[1]{{\fontseries{m}\fontseries{b}\selectfont #1}} +\newcommand{\email}[1]{\href{mailto:#1}{\normalfont\texttt{#1}}} +\ifx\csname urlstyle\endcsname\relax + \newcommand\@doi[1]{doi:\discretionary{}{}{}#1}\else + \newcommand\@doi{doi:\discretionary{}{}{}\begingroup +\urlstyle{tt}\Url}\fi +\newcommand{\doi}[1]{\href{https://doi.org/#1}{\normalfont\texttt{\@doi{#1}}}} +\newcommand{\E}{\mathsf{E}} +\newcommand{\VAR}{\mathsf{VAR}} 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