Evaluate forecasts
Source:R/class-forecast-binary.R, R/class-forecast-nominal.R, R/class-forecast-ordinal.R, and 4 more
score.Rdscore() applies a selection of scoring metrics to a forecast
object.
score() is a generic that dispatches to different methods depending on the
class of the input data.
See as_forecast_binary(), as_forecast_quantile() etc. for information on
how to create a forecast object.
See get_forecast_unit() for more information on the concept of a forecast
unit.
For additional help and examples, check out the paper Evaluating Forecasts with scoringutils in R.
Usage
# S3 method for class 'forecast_binary'
score(forecast, metrics = get_metrics(forecast), ...)
# S3 method for class 'forecast_nominal'
score(forecast, metrics = get_metrics(forecast), ...)
# S3 method for class 'forecast_ordinal'
score(forecast, metrics = get_metrics(forecast), ...)
# S3 method for class 'forecast_point'
score(forecast, metrics = get_metrics(forecast), ...)
# S3 method for class 'forecast_quantile'
score(forecast, metrics = get_metrics(forecast), ...)
# S3 method for class 'forecast_sample'
score(forecast, metrics = get_metrics(forecast), ...)
score(forecast, metrics, ...)Arguments
- forecast
A forecast object (a validated data.table with predicted and observed values).
- metrics
A named list of scoring functions. Names will be used as column names in the output. See
get_metrics()for more information on the default metrics used. See the Customising metrics section below for information on how to pass custom arguments to scoring functions.- ...
Currently unused. You cannot pass additional arguments to scoring functions via
.... See the Customising metrics section below for details on how to usepurrr::partial()to pass arguments to individual metrics.
Value
An object of class scores. This object is a data.table with
unsummarised scores (one score per forecast) and has an additional attribute
metrics with the names of the metrics used for scoring. See
summarise_scores()) for information on how to summarise
scores.
Details
Customising metrics
If you want to pass arguments to a scoring function, you need change the
scoring function itself via e.g. purrr::partial() and pass an updated list
of functions with your custom metric to the metrics argument in score().
For example, to use interval_coverage() with interval_range = 90, you
would define a new function, e.g.
interval_coverage_90 <- purrr::partial(interval_coverage, interval_range = 90)
and pass this new function to metrics in score().
Note that if you want to pass a variable as an argument, you can
unquote it with !! to make sure the value is evaluated only once when the
function is created. Consider the following example:
References
Bosse NI, Gruson H, Cori A, van Leeuwen E, Funk S, Abbott S (2022) Evaluating Forecasts with scoringutils in R. doi:10.48550/arXiv.2205.07090
Author
Nikos Bosse nikosbosse@gmail.com
Examples
library(magrittr) # pipe operator
validated <- as_forecast_quantile(example_quantile)
#> ℹ Some rows containing NA values may be removed. This is fine if not
#> unexpected.
score(validated) %>%
summarise_scores(by = c("model", "target_type"))
#> model target_type wis overprediction underprediction
#> <char> <char> <num> <num> <num>
#> 1: EuroCOVIDhub-ensemble Cases 17943.82383 10043.121943 4237.177310
#> 2: EuroCOVIDhub-baseline Cases 28483.57465 14096.100883 10284.972826
#> 3: epiforecasts-EpiNow2 Cases 20831.55662 11906.823030 3260.355639
#> 4: EuroCOVIDhub-ensemble Deaths 41.42249 7.138247 4.103261
#> 5: EuroCOVIDhub-baseline Deaths 159.40387 65.899117 2.098505
#> 6: UMass-MechBayes Deaths 52.65195 8.978601 16.800951
#> 7: epiforecasts-EpiNow2 Deaths 66.64282 18.892583 15.893314
#> dispersion bias interval_coverage_50 interval_coverage_90 ae_median
#> <num> <num> <num> <num> <num>
#> 1: 3663.52458 -0.05640625 0.3906250 0.8046875 24101.07031
#> 2: 4102.50094 0.09796875 0.3281250 0.8203125 38473.60156
#> 3: 5664.37795 -0.07890625 0.4687500 0.7890625 27923.81250
#> 4: 30.18099 0.07265625 0.8750000 1.0000000 53.13281
#> 5: 91.40625 0.33906250 0.6640625 1.0000000 233.25781
#> 6: 26.87239 -0.02234375 0.4609375 0.8750000 78.47656
#> 7: 31.85692 -0.00512605 0.4201681 0.9075630 104.74790
# set forecast unit manually (to avoid issues with scoringutils trying to
# determine the forecast unit automatically)
example_quantile %>%
as_forecast_quantile(
forecast_unit = c(
"location", "target_end_date", "target_type", "horizon", "model"
)
) %>%
score()
#> ℹ Some rows containing NA values may be removed. This is fine if not
#> unexpected.
#> location target_end_date target_type horizon model
#> <char> <Date> <char> <num> <char>
#> 1: DE 2021-05-08 Cases 1 EuroCOVIDhub-ensemble
#> 2: DE 2021-05-08 Cases 1 EuroCOVIDhub-baseline
#> 3: DE 2021-05-08 Cases 1 epiforecasts-EpiNow2
#> 4: DE 2021-05-08 Deaths 1 EuroCOVIDhub-ensemble
#> 5: DE 2021-05-08 Deaths 1 EuroCOVIDhub-baseline
#> ---
#> 883: IT 2021-07-24 Deaths 2 EuroCOVIDhub-baseline
#> 884: IT 2021-07-24 Deaths 3 UMass-MechBayes
#> 885: IT 2021-07-24 Deaths 2 UMass-MechBayes
#> 886: IT 2021-07-24 Deaths 3 epiforecasts-EpiNow2
#> 887: IT 2021-07-24 Deaths 2 epiforecasts-EpiNow2
#> wis overprediction underprediction dispersion bias
#> <num> <num> <num> <num> <num>
#> 1: 7990.854783 2.549870e+03 0.0000000 5440.985217 0.50
#> 2: 16925.046957 1.527583e+04 0.0000000 1649.220870 0.95
#> 3: 25395.960870 1.722226e+04 0.0000000 8173.700000 0.90
#> 4: 53.880000 0.000000e+00 0.6086957 53.271304 -0.10
#> 5: 46.793043 2.130435e+00 0.0000000 44.662609 0.30
#> ---
#> 883: 80.336957 3.608696e+00 0.0000000 76.728261 0.20
#> 884: 4.881739 4.347826e-02 0.0000000 4.838261 0.10
#> 885: 25.581739 1.782609e+01 0.0000000 7.755652 0.80
#> 886: 19.762609 5.478261e+00 0.0000000 14.284348 0.50
#> 887: 66.161739 4.060870e+01 0.0000000 25.553043 0.90
#> interval_coverage_50 interval_coverage_90 ae_median
#> <lgcl> <lgcl> <num>
#> 1: TRUE TRUE 12271
#> 2: FALSE FALSE 25620
#> 3: FALSE TRUE 44192
#> 4: TRUE TRUE 14
#> 5: TRUE TRUE 15
#> ---
#> 883: TRUE TRUE 53
#> 884: TRUE TRUE 1
#> 885: FALSE TRUE 46
#> 886: TRUE TRUE 26
#> 887: FALSE TRUE 108
# forecast formats with different metrics
if (FALSE) { # \dontrun{
score(as_forecast_binary(example_binary))
score(as_forecast_quantile(example_quantile))
score(as_forecast_point(example_point))
score(as_forecast_sample(example_sample_discrete))
score(as_forecast_sample(example_sample_continuous))
} # }