scoringutils (version 1.2.2)

score: Evaluate forecasts

Description

This function allows automatic scoring of forecasts using a range of metrics. For most users it will be the workhorse for scoring forecasts as it wraps the lower level functions package functions. However, these functions are also available if you wish to make use of them independently.

A range of forecasts formats are supported, including quantile-based, sample-based, binary forecasts. Prior to scoring, users may wish to make use of check_forecasts() to ensure that the input data is in a supported format though this will also be run internally by score(). Examples for each format are also provided (see the documentation for data below or in check_forecasts()).

Each format has a set of required columns (see below). Additional columns may be present to indicate a grouping of forecasts. For example, we could have forecasts made by different models in various locations at different time points, each for several weeks into the future. It is important, that there are only columns present which are relevant in order to group forecasts. A combination of different columns should uniquely define the unit of a single forecast, meaning that a single forecast is defined by the values in the other columns. Adding additional unrelated columns may alter results.

To obtain a quick overview of the currently supported evaluation metrics, have a look at the metrics data included in the package. The column metrics$Name gives an overview of all available metric names that can be computed. If interested in an unsupported metric please open a feature request or consider contributing a pull request.

For additional help and examples, check out the Getting Started Vignette as well as the paper Evaluating Forecasts with scoringutils in R.

Usage

score(data, metrics = NULL, ...)

Value

A data.table with unsummarised scores. There will be one score per quantile or sample, which is usually not desired, so you should almost always run summarise_scores() on the unsummarised scores.

Arguments

data

A data.frame or data.table with the predictions and observations. For scoring using score(), the following columns need to be present:

• true_value - the true observed values

• 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 scoring integer and continuous forecasts a sample column is needed:

• 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 scoring predictions in a quantile-format forecast you should provide a column called quantile:

• quantile: quantile to which the prediction corresponds

In addition a model column is suggested and if not present this will be flagged and added to the input data with all forecasts assigned as an "unspecified model").

You can check the format of your data using check_forecasts() and there are examples for each format (example_quantile, example_continuous, example_integer, and example_binary).

metrics

the metrics you want to have in the output. If NULL (the default), all available metrics will be computed. For a list of available metrics see available_metrics(), or check the metrics data set.

...

additional parameters passed down to score_quantile() (internal function used for scoring forecasts in a quantile-based format).

Author

Nikos Bosse

References

Funk S, Camacho A, Kucharski AJ, Lowe R, Eggo RM, Edmunds WJ (2019) Assessing the performance of real-time epidemic forecasts: A case study of Ebola in the Western Area region of Sierra Leone, 2014-15. PLoS Comput Biol 15(2): e1006785. tools:::Rd_expr_doi("10.1371/journal.pcbi.1006785")

Examples

library(magrittr) # pipe operator
# \dontshow{
  data.table::setDTthreads(2) # restricts number of cores used on CRAN
# }

check_forecasts(example_quantile)
score(example_quantile) %>%
  add_coverage(by = c("model", "target_type")) %>%
  summarise_scores(by = c("model", "target_type"))

# set forecast unit manually (to avoid issues with scoringutils trying to
# determine the forecast unit automatically), check forecasts before scoring
example_quantile %>%
  set_forecast_unit(
    c("location", "target_end_date", "target_type", "horizon", "model")
  ) %>%
  check_forecasts() %>%
  score()

# forecast formats with different metrics
if (FALSE) {
score(example_binary)
score(example_quantile)
score(example_integer)
score(example_continuous)
}

# score point forecasts (marked by 'NA' in the quantile column)
score(example_point) %>%
  summarise_scores(by = "model", na.rm = TRUE)