Add coverage deviation as a metric #417
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…he name is terrible
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## rework-add_coverage() #417 +/- ##
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…intervals from quantiles
… and other functions with the same arguments
… other data documentation (seems it was ignored anyway)
…ion of coverage_deviation per quantile as a metric
…or now that cannot be run anymore.
…differing quantiles
…ame of the coverage columns
…eviously failing due to `add_coverage()`
…cause we can't pass the `forecast_unit` arg to apply_metrics
…wasn't working and everything else feels more complicated.
| @@ -32,6 +32,7 @@ export(crps_sample) | |||
| export(dispersion) | |||
| export(dss_sample) | |||
| export(get_duplicate_forecasts) | |||
| export(interval_coverage_deviation_quantile) | |||
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so the reason we have to do all this stuff is we are only supporting one metric per function call vs allowing the output to be a data.frame and unnesting?
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I mean, we could have all functions return a data.frame and then merging things together in score().
Or a wrapper in score() that checks whether the output is a data.frame, list or a vector? I'd be open to that, I just haven't thought about it long enough to have a good vision for how it would look like.
I created an issue, $455. Could you please elaborate a bit on how you'd imagine this?
| #' ) | ||
| #' quantile <- c(0.1, 0.25, 0.5, 0.75, 0.9) | ||
| #' interval_coverage_deviation_quantile(observed, predicted, quantile) | ||
| interval_coverage_deviation_quantile <- function(observed, predicted, quantile) { |
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this feels like it has a lot of duplication from add_coverage
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Opened an issue #454
Update functions to compute the absolute median
…uantile()2 Rework score.scoringutils quantile()2
Move tests around
Expand tests2
Rework add coverage to work with raw forecasts
Simplify `score()`
Fix set forecast unit
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Yeah that's interval_coverage_deviation. I created an issue #453 as a reminder to create an equivalent function for quantile coverage |
This PR adds coverage deviation as a new metric. The function is obviously named terribly.
Maybe
interval_coveragedeviation_quantile()is better?At the moment, just
coverage_deviationor something like that would also work. But in theory you could also doquantile_coverage_deviationthat does the same thing for quantiles instead of intervals...I guess a question is whether we think it's a useful metric overall? Not great, not terrible?