Note: This documentation
refers to the development version of scoringutils. You can also view
the documentation of the stable
version.
The scoringutils package facilitates the process of evaluating
forecasts in R, using a convenient and flexible data.table-based
framework. It provides broad functionality to check the input data and
diagnose issues, to visualise forecasts and missing data, to transform
data before scoring, to handle missing forecasts, to aggregate scores,
and to visualise the results of the evaluation. The package is easily
extendable, meaning that users can supply their own scoring rules or
extend existing classes to handle new types of forecasts.
The package underwent a major re-write. The most comprehensive
documentation for the updated package is the revised
version
of our original
scoringutils paper.
Another good starting point are the vignettes on Getting started, Details on the metrics implemented and Scoring forecasts directly.
For further details on the specific issue of transforming forecasts for scoring see:
Nikos I. Bosse, Sam Abbott, Anne Cori, Edwin van Leeuwen, Johannes Bracher* and Sebastian Funk* (*: equal contribution) (2023).
Scoring epidemiological forecasts on transformed scales, PLoS Comput Biol 19(8): e1011393 https://doi.org/10.1371/journal.pcbi.1011393
Install the CRAN version of this package using
install.packages("scoringutils")Install the unstable development version from GitHub using
remotes::install_github("epiforecasts/scoringutils", dependencies = TRUE)scoringutils currently supports scoring the following forecast
types: - binary: a probability for a binary (yes/no) outcome
variable. - point: a forecast for a continuous or discrete outcome
variable that is represented by a single number. - quantile: a
probabilistic forecast for a continuous or discrete outcome variable,
with the forecast distribution represented by a set of predictive
quantiles. - sample: a probabilistic forecast for a continuous or
discrete outcome variable, with the forecast represented by a finite set
of samples drawn from the predictive distribution.
The expected input format is generally a data.frame (or similar) with
required columns observed, predicted, and model that holds the
forecasts and observed values. Exact requirements depend on the forecast
type. For more information, have a look at the
paper,
call ?as_forecast(), or have a look at the example data provided in
the package (example_binary, example_point, example_quantile,
example_sample_continuous, example_sample_discrete).
Before scoring, input data needs to be validated and transformed into a
forecast object using the function as_forecast().
forecast_quantile <- example_quantile |>
as_forecast(
forecast_unit = c(
"location", "forecast_date", "target_end_date", "target_type", "model", "horizon"
),
forecast_type = "quantile"
)
#> ℹ Some rows containing NA values may be removed. This is fine if not
#> unexpected.
print(forecast_quantile, 2)
#> Forecast type:
#> quantile
#> Forecast unit:
#> location, forecast_date, target_end_date, target_type, model, and horizon
#>
#> Key: <location, target_end_date, target_type>
#> observed quantile_level predicted location forecast_date target_end_date
#> <num> <num> <int> <char> <Date> <Date>
#> 1: 127300 NA NA DE <NA> 2021-01-02
#> 2: 4534 NA NA DE <NA> 2021-01-02
#> ---
#> 20544: 78 0.975 611 IT 2021-07-12 2021-07-24
#> 20545: 78 0.990 719 IT 2021-07-12 2021-07-24
#> target_type model horizon
#> <char> <char> <num>
#> 1: Cases <NA> NA
#> 2: Deaths <NA> NA
#> ---
#> 20544: Deaths epiforecasts-EpiNow2 2
#> 20545: Deaths epiforecasts-EpiNow2 2For quantile-based and sample-based forecasts, a single prediction is
represented by a set of several quantiles (or samples) from the
predictive distribution, i.e. several rows in the input data.
scoringutils therefore needs to group rows together that form a single
forecast. scoringutils uses all other existing columns in the input
data to achieve this - the values in all other columns should uniquely
identify a single forecast. Additional columns unrelated to the forecast
unit can mess this up. The forecast_unit argument in as_forecast()
makes sure that only those columns are retained which are relevant for
defining the unit of a single forecast.
Forecasts can be scored by calling score() on a validated forecast
object.
scores <- forecast_quantile |>
score()score() takes an additional argument, metrics, with a list of
scoring rules. Every forecast type has a default list of metrics. You
can easily add your own scoring functions, as long as they conform with
the format for that forecast type. See the
paper
for more information.
You can summarise scores using the function summarise_scores(). The
by argument is used to specify the desired level of summary. fun
let’s you specify any summary function, although it is recommended to
stick to the mean as a primary summary function, as other functions can
lead to improper scores.
scores |>
summarise_scores(by = c("model", "target_type")) |>
summarise_scores(by = c("model", "target_type"), fun = signif, digits = 3)
#> model target_type wis overprediction underprediction
#> <char> <char> <num> <num> <num>
#> 1: EuroCOVIDhub-ensemble Cases 17900.0 10000.00 4240.0
#> 2: EuroCOVIDhub-baseline Cases 28500.0 14100.00 10300.0
#> 3: epiforecasts-EpiNow2 Cases 20800.0 11900.00 3260.0
#> 4: EuroCOVIDhub-ensemble Deaths 41.4 7.14 4.1
#> 5: EuroCOVIDhub-baseline Deaths 159.0 65.90 2.1
#> 6: UMass-MechBayes Deaths 52.7 8.98 16.8
#> 7: epiforecasts-EpiNow2 Deaths 66.6 18.90 15.9
#> dispersion bias interval_coverage_50 interval_coverage_90
#> <num> <num> <num> <num>
#> 1: 3660.0 -0.05640 0.391 0.805
#> 2: 4100.0 0.09800 0.328 0.820
#> 3: 5660.0 -0.07890 0.469 0.789
#> 4: 30.2 0.07270 0.875 1.000
#> 5: 91.4 0.33900 0.664 1.000
#> 6: 26.9 -0.02230 0.461 0.875
#> 7: 31.9 -0.00513 0.420 0.908
#> interval_coverage_deviation ae_median
#> <num> <num>
#> 1: -0.1020 24100.0
#> 2: -0.1170 38500.0
#> 3: -0.0696 27900.0
#> 4: 0.2040 53.1
#> 5: 0.1210 233.0
#> 6: -0.0249 78.5
#> 7: -0.0452 105.0The following depicts the suggested workflow for evaluating forecasts
with scoringutils (sections refer to the paper). Please find more
information in the
paper,
the function documentation and the vignettes.
If you are using scoringutils in your work please consider citing it
using the output of citation("scoringutils") (or
print(citation("scoringutils"), bibtex = TRUE)):
#> To cite scoringutils in publications use the following. If you use the
#> CRPS, DSS, or Log Score, please also cite scoringRules.
#>
#> Nikos I. Bosse, Hugo Gruson, Sebastian Funk, Anne Cori, Edwin van
#> Leeuwen, and Sam Abbott (2022). Evaluating Forecasts with
#> scoringutils in R, arXiv. DOI: 10.48550/ARXIV.2205.07090
#>
#> To cite scoringRules in publications use:
#>
#> Alexander Jordan, Fabian Krueger, Sebastian Lerch (2019). Evaluating
#> Probabilistic Forecasts with scoringRules. Journal of Statistical
#> Software, 90(12), 1-37. DOI 10.18637/jss.v090.i12
#>
#> To see these entries in BibTeX format, use 'print(<citation>,
#> bibtex=TRUE)', 'toBibtex(.)', or set
#> 'options(citation.bibtex.max=999)'.
Please briefly describe your problem and what output you expect in an issue. If you have a question, please don’t open an issue. Instead, ask on our Q and A page.
We welcome contributions and new contributors! We particularly appreciate help on priority problems in the issues. Please check and add to the issues, and/or add a pull request.
Please note that the scoringutils project is released with a
Contributor Code of
Conduct. By
contributing to this project, you agree to abide by its terms.