Correlation similarity between real and synthetic numerical column pairs

For each pair of numerical columns, computes 1 - |corr_real - corr_syn| / 2 (the SDMetrics CorrelationSimilarity score), where corr is the Pearson correlation. Returns one row per pair plus the mean.

Usage

correlation_similarity(real, synthetic, meta)

Arguments

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

Value

A list with pairs (a tibble of column_1, column_2, score) and score (the mean over pairs). score is NA_real_ when there are fewer than two numerical columns — there is no dependence to measure, so propagating NA (rather than 1) avoids overstating fidelity in the aggregated quality report.

Examples

# \donttest{
syn       <- gaussian_copula_synthesizer(metadata(adult_income)) |> fit(adult_income)
synth_data <- sample(syn, n = 500)
correlation_similarity(adult_income, synth_data, metadata(adult_income))
#> $pairs
#> # A tibble: 21 × 3
#>    column_1 column_2       score
#>    <chr>    <chr>          <dbl>
#>  1 id       age            0.977
#>  2 id       fnlwgt         0.998
#>  3 id       education_num  0.990
#>  4 id       capital_gain   0.997
#>  5 id       capital_loss   0.956
#>  6 id       hours_per_week 0.969
#>  7 age      fnlwgt         0.993
#>  8 age      education_num  0.979
#>  9 age      capital_gain   0.979
#> 10 age      capital_loss   0.999
#> # ℹ 11 more rows
#> 
#> $score
#> [1] 0.9744202
#> 
# }