Check a project for the faults that invalidate an analysis

Runs the checks that are cheap to automate and expensive to discover late.

Usage

check_project(p, mad_k = 3, max_missing = 0.2)

Arguments

p

An agri_project().

mad_k

Number of median absolute deviations beyond which a value is flagged. Three is conventional.

max_missing

Proportion of missing values in a feature above which it is reported.

Value

A data frame of issues with columns severity, check and detail, invisibly returned and printed. Zero rows means every check passed.

Details

The most important is the leakage guard: a covariate window that extends past the harvest it is supposed to predict produces a model that cannot be deployed and a skill estimate that means nothing. It is easy to introduce by fetching a fixed date range for every site, and hard to see afterwards.

Also checked: duplicated unit-season keys, coordinates outside plausible bounds, missingness by column, and univariate outliers by the median absolute deviation, which is resistant to the outliers it is looking for.

See also

build_features(), train_model()

Examples

p <- agri_project(demo_agri_data(n_units = 6, n_seasons = 2))
p <- add_climate(p, source = "demo")
check_project(p)
#> check_project(): no issues found.