use std::{fs, path::PathBuf};
use ndarray::Array2;
use rand::{SeedableRng, rngs::StdRng};
use serde::Deserialize;
use rscopulas::{
CopulaModel, EvalOptions, PseudoObs, SampleOptions, VineCopula, VineStructureKind,
};
#[derive(Debug, Deserialize)]
struct Metadata {
source_package: String,
source_version: String,
}
#[derive(Debug, Deserialize)]
struct VineLogPdfFixture {
metadata: Metadata,
structure: String,
order: Vec<usize>,
correlation: Vec<Vec<f64>>,
pair_parameters: Vec<f64>,
inputs: Vec<Vec<f64>>,
expected_log_pdf: Vec<f64>,
}
#[derive(Debug, Deserialize)]
struct VineSampleSummaryFixture {
metadata: Metadata,
structure: String,
order: Vec<usize>,
correlation: Vec<Vec<f64>>,
pair_parameters: Vec<f64>,
seed: u64,
sample_size: usize,
expected_mean: Vec<f64>,
expected_kendall_tau: Vec<Vec<f64>>,
}
#[test]
fn gaussian_c_vine_matches_vinecopula_fixture() {
let fixture: VineLogPdfFixture = load_fixture("gaussian_c_vine_log_pdf_d4_case01.json");
assert_eq!(fixture.metadata.source_package, "VineCopula");
assert_eq!(fixture.structure, "C");
let model = VineCopula::gaussian_c_vine(fixture.order.clone(), array2(&fixture.correlation))
.expect("fixture parameters should be valid");
assert_eq!(model.structure(), VineStructureKind::C);
assert_eq!(model.order(), fixture.order.as_slice());
for (actual, expected) in model
.pair_parameters()
.iter()
.zip(fixture.pair_parameters.iter())
{
assert!((actual - expected).abs() < 1e-10);
}
let input = PseudoObs::new(array2(&fixture.inputs)).expect("fixture inputs should be valid");
let actual = model
.log_pdf(&input, &EvalOptions::default())
.expect("log pdf should evaluate");
for (idx, (left, right)) in actual
.iter()
.zip(fixture.expected_log_pdf.iter())
.enumerate()
{
assert!(
(left - right).abs() < 2e-10,
"fixture {} mismatch at row {idx}: left={left}, right={right}",
fixture.metadata.source_version
);
}
}
#[test]
fn gaussian_d_vine_matches_vinecopula_fixture() {
let fixture: VineLogPdfFixture = load_fixture("gaussian_d_vine_log_pdf_d4_case01.json");
assert_eq!(fixture.metadata.source_package, "VineCopula");
assert_eq!(fixture.structure, "D");
let model = VineCopula::gaussian_d_vine(fixture.order.clone(), array2(&fixture.correlation))
.expect("fixture parameters should be valid");
assert_eq!(model.structure(), VineStructureKind::D);
assert_eq!(model.order(), fixture.order.as_slice());
for (actual, expected) in model
.pair_parameters()
.iter()
.zip(fixture.pair_parameters.iter())
{
assert!((actual - expected).abs() < 1e-10);
}
let input = PseudoObs::new(array2(&fixture.inputs)).expect("fixture inputs should be valid");
let actual = model
.log_pdf(&input, &EvalOptions::default())
.expect("log pdf should evaluate");
for (idx, (left, right)) in actual
.iter()
.zip(fixture.expected_log_pdf.iter())
.enumerate()
{
assert!(
(left - right).abs() < 1e-10,
"fixture {} mismatch at row {idx}: left={left}, right={right}",
fixture.metadata.source_version
);
}
}
#[test]
fn gaussian_c_vine_sample_statistics_match_fixture() {
let fixture: VineSampleSummaryFixture =
load_fixture("gaussian_c_vine_sample_summary_d4_case01.json");
assert_eq!(fixture.metadata.source_package, "VineCopula");
assert_eq!(fixture.structure, "C");
let model = VineCopula::gaussian_c_vine(fixture.order.clone(), array2(&fixture.correlation))
.expect("fixture parameters should be valid");
let mut rng = StdRng::seed_from_u64(fixture.seed);
let samples = model
.sample(fixture.sample_size, &mut rng, &SampleOptions::default())
.expect("sampling should succeed");
let sample_obs = PseudoObs::new(samples).expect("sample should be valid");
let means = column_means(&sample_obs);
let tau = rscopulas::stats::kendall_tau_matrix(&sample_obs);
for (actual, expected) in model
.pair_parameters()
.iter()
.zip(fixture.pair_parameters.iter())
{
assert!((actual - expected).abs() < 1e-10);
}
for (idx, (actual, expected)) in means.iter().zip(fixture.expected_mean.iter()).enumerate() {
assert!(
(actual - expected).abs() < 1.2e-2,
"mean mismatch at column {idx}: left={actual}, right={expected}"
);
}
for row in 0..tau.nrows() {
for col in 0..tau.ncols() {
let expected = fixture.expected_kendall_tau[row][col];
assert!((tau[(row, col)] - expected).abs() < 2.5e-2);
}
}
}
#[test]
fn gaussian_d_vine_sample_statistics_match_fixture() {
let fixture: VineSampleSummaryFixture =
load_fixture("gaussian_d_vine_sample_summary_d4_case01.json");
assert_eq!(fixture.metadata.source_package, "VineCopula");
assert_eq!(fixture.structure, "D");
let model = VineCopula::gaussian_d_vine(fixture.order.clone(), array2(&fixture.correlation))
.expect("fixture parameters should be valid");
let mut rng = StdRng::seed_from_u64(fixture.seed);
let samples = model
.sample(fixture.sample_size, &mut rng, &SampleOptions::default())
.expect("sampling should succeed");
let sample_obs = PseudoObs::new(samples).expect("sample should be valid");
let means = column_means(&sample_obs);
let tau = rscopulas::stats::kendall_tau_matrix(&sample_obs);
for (actual, expected) in model
.pair_parameters()
.iter()
.zip(fixture.pair_parameters.iter())
{
assert!((actual - expected).abs() < 1e-10);
}
for (idx, (actual, expected)) in means.iter().zip(fixture.expected_mean.iter()).enumerate() {
assert!(
(actual - expected).abs() < 1e-2,
"mean mismatch at column {idx}: left={actual}, right={expected}"
);
}
for row in 0..tau.nrows() {
for col in 0..tau.ncols() {
let expected = fixture.expected_kendall_tau[row][col];
assert!((tau[(row, col)] - expected).abs() < 2e-2);
}
}
}
fn fixture_dir() -> PathBuf {
PathBuf::from(env!("CARGO_MANIFEST_DIR")).join("../../fixtures/reference/vinecopula/v2")
}
fn load_fixture<T: for<'de> Deserialize<'de>>(name: &str) -> T {
let path = fixture_dir().join(name);
let bytes = fs::read(path).expect("fixture should exist");
serde_json::from_slice(&bytes).expect("fixture should deserialize")
}
fn array2(rows: &[Vec<f64>]) -> Array2<f64> {
let nrows = rows.len();
let ncols = rows.first().map_or(0, Vec::len);
let data = rows
.iter()
.flat_map(|row| row.iter().copied())
.collect::<Vec<_>>();
Array2::from_shape_vec((nrows, ncols), data).expect("rows should form a matrix")
}
fn column_means(data: &PseudoObs) -> Vec<f64> {
(0..data.dim())
.map(|col| data.as_view().column(col).iter().sum::<f64>() / data.n_obs() as f64)
.collect()
}