Generate a diagnostic (validity) report for synthetic data

Checks whether synthetic data is structurally valid against the real data and metadata — independent of how closely it matches the real distributions (that is the job of quality_report()). Mirrors the SDMetrics DiagnosticReport two-property hierarchy:

Usage

diagnostic_report(real, synthetic, metadata)

Arguments

real: A data frame of real data.
synthetic: A data frame of synthetic data.
metadata: An rsdv_metadata object.

Value

An rsdv_diagnostic_report object.

Details

Data Validity — per-column checks:
- numerical: boundary adherence (fraction of values within the real min/max range),
- categorical: category adherence (fraction of values whose category was seen in the real data),
- boolean: always valid,
- primary key: key uniqueness (all values unique and non-missing).
Data Structure — fraction of expected columns present in the synthetic data.

Missing (NA) values are excluded from adherence denominators, since missingness is modeled separately.

Examples

# \donttest{
meta  <- metadata(adult_income)
syn   <- gaussian_copula_synthesizer(meta) |> fit(adult_income)
synth <- sample(syn, n = 500)
diagnostic_report(adult_income, synth, meta)
#> == rsdv Diagnostic Report ==
#> 
#> Data Validity (per column):
#>   id                   boundary adherence   1.000
#>   age                  boundary adherence   1.000
#>   fnlwgt               boundary adherence   1.000
#>   education_num        boundary adherence   1.000
#>   capital_gain         boundary adherence   1.000
#>   capital_loss         boundary adherence   1.000
#>   hours_per_week       boundary adherence   1.000
#>   workclass            category adherence   1.000
#>   education            category adherence   1.000
#>   marital_status       category adherence   1.000
#>   occupation           category adherence   1.000
#>   relationship         category adherence   1.000
#>   race                 category adherence   1.000
#>   sex                  category adherence   1.000
#>   native_country       category adherence   1.000
#>   income               category adherence   1.000
#> 
#> Data Validity score:   1.000
#> Data Structure score:  1.000
#> 
#> Overall Score:         1.000
# }