assert_forecast: Assert that input is a forecast object and passes validations

In epiforecasts/scoringutils: Utilities for Scoring and Assessing Predictions

Assert that input is a forecast object and passes validations

Description

Assert that an object is a forecast object (i.e. a data.table with a class forecast and an additional class ⁠forecast_*⁠ corresponding to the forecast type).

Usage

assert_forecast(forecast, forecast_type = NULL, verbose = TRUE, ...)

## Default S3 method:
assert_forecast(forecast, forecast_type = NULL, verbose = TRUE, ...)

## S3 method for class 'forecast_binary'
assert_forecast(forecast, forecast_type = NULL, verbose = TRUE, ...)

## S3 method for class 'forecast_point'
assert_forecast(forecast, forecast_type = NULL, verbose = TRUE, ...)

## S3 method for class 'forecast_quantile'
assert_forecast(forecast, forecast_type = NULL, verbose = TRUE, ...)

## S3 method for class 'forecast_sample'
assert_forecast(forecast, forecast_type = NULL, verbose = TRUE, ...)

Arguments

forecast	A forecast object (a validated data.table with predicted and observed values, see as_forecast())
forecast_type	(optional) The forecast type you expect the forecasts to have. If the forecast type as determined by scoringutils based on the input does not match this, an error will be thrown. If NULL (the default), the forecast type will be inferred from the data.
verbose	Logical. If FALSE (default is TRUE), no messages and warnings will be created.
...	Currently unused. You cannot pass additional arguments to scoring functions via .... See the Customising metrics section below for details on how to use purrr::partial() to pass arguments to individual metrics.

Value

Returns NULL invisibly.

Forecast types and input formats

Various different forecast types / forecast formats are supported. At the moment, those are:

• point forecasts

• binary forecasts ("soft binary classification")

• nominal forecasts ("soft classification with multiple unordered classes")

• Probabilistic forecasts in a quantile-based format (a forecast is represented as a set of predictive quantiles)

• Probabilistic forecasts in a sample-based format (a forecast is represented as a set of predictive samples)

Forecast types are determined based on the columns present in the input data. Here is an overview of the required format for each forecast type:

All forecast types require a data.frame or similar with columns observed predicted, and model.

Point forecasts require a column observed of type numeric and a column predicted of type numeric.

Binary forecasts require a column observed of type factor with exactly two levels and a column predicted of type numeric with probabilities, corresponding to the probability that observed is equal to the second factor level. See details here for more information.

Nominal forecasts require a column observed of type factor with N levels, (where N is the number of possible outcomes), a column predicted of type numeric with probabilities (which sum to one across all possible outcomes), and a column predicted_label of type factor with N levels, denoting the outcome for which a probability is given. Forecasts must be complete, i.e. there must be a probability assigned to every possible outcome.

Quantile-based forecasts require a column observed of type numeric, a column predicted of type numeric, and a column quantile_level of type numeric with quantile-levels (between 0 and 1).

Sample-based forecasts require a column observed of type numeric, a column predicted of type numeric, and a column sample_id of type numeric with sample indices.

For more information see the vignettes and the example data (example_quantile, example_sample_continuous, example_sample_discrete, example_point(), example_binary, and example_nominal).

Examples

forecast <- as_forecast_binary(example_binary)
assert_forecast(forecast)

epiforecasts/scoringutils documentation built on Aug. 20, 2024, 9:58 p.m.