From b33bb3a687fd44998b053b83cc41a8355e4a5591 Mon Sep 17 00:00:00 2001 From: marouenbg Date: Sun, 10 May 2026 13:09:41 -0400 Subject: [PATCH] Add DRAGON cross-language parity test Add a parity test asserting that the netZooPy DRAGON pipeline (lambdas, shrunken covariance, precision matrix, partial correlation matrix) matches a fixed snapshot. The same shared inputs and gold values are mirrored in netZooR's tests/testthat/dragon_parity/ -- if both tests pass, the two implementations agree to 1e-5 (atol). Inputs are generated deterministically by tests/dragon_parity/regenerate.py from simulate_dragon_data with seed=20250510, then byte-identical CSVs are committed to both repos. Kappa / p-values are not covered, since netZooR's estimate_kappa_dragon and estimate_p_values_dragon are unimplemented stubs. Co-Authored-By: Claude Opus 4.7 (1M context) --- tests/dragon_parity/X1.csv | 50 ++++++++++++++++++++++++++++ tests/dragon_parity/X2.csv | 50 ++++++++++++++++++++++++++++ tests/dragon_parity/cov.csv | 35 ++++++++++++++++++++ tests/dragon_parity/ggm.csv | 35 ++++++++++++++++++++ tests/dragon_parity/lambdas.txt | 2 ++ tests/dragon_parity/prec.csv | 35 ++++++++++++++++++++ tests/dragon_parity/regenerate.py | 41 +++++++++++++++++++++++ tests/test_dragon_parity.py | 55 +++++++++++++++++++++++++++++++ 8 files changed, 303 insertions(+) create mode 100644 tests/dragon_parity/X1.csv create mode 100644 tests/dragon_parity/X2.csv create mode 100644 tests/dragon_parity/cov.csv create mode 100644 tests/dragon_parity/ggm.csv create mode 100644 tests/dragon_parity/lambdas.txt create mode 100644 tests/dragon_parity/prec.csv create mode 100644 tests/dragon_parity/regenerate.py create mode 100644 tests/test_dragon_parity.py diff --git a/tests/dragon_parity/X1.csv b/tests/dragon_parity/X1.csv new file 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+Run this from the netZooPy repo root after intentional changes to DRAGON's +covariance / precision / partial-correlation pipeline. The same files must +then be copied into netZooR's tests/testthat/dragon_parity/ to keep the +two repos in sync. + + python tests/dragon_parity/regenerate.py +""" +import os +import numpy as np +from netZooPy import dragon +from netZooPy.dragon.dragon import Scale + +OUT = os.path.dirname(os.path.abspath(__file__)) + +n, p1, p2 = 50, 20, 15 +SEED = 20250510 + +X1_raw, X2_raw, _, _ = dragon.simulate_dragon_data( + eta11=0.05, eta12=0.05, eta22=0.05, + p1=p1, p2=p2, epsilon=[0.1, 0.1], n=n, seed=SEED, +) +X1 = Scale(X1_raw) +X2 = Scale(X2_raw) + +lambdas, _ = dragon.estimate_penalty_parameters_dragon(X1, X2) +cov = dragon.get_shrunken_covariance_dragon(X1, X2, lambdas) +prec, _ = dragon.get_precision_matrix_dragon(X1, X2, lambdas) +ggm = dragon.get_partial_correlation_dragon(X1, X2, lambdas) + +np.savetxt(os.path.join(OUT, "X1.csv"), X1, delimiter=",", fmt="%.17g") +np.savetxt(os.path.join(OUT, "X2.csv"), X2, delimiter=",", fmt="%.17g") +np.savetxt(os.path.join(OUT, "cov.csv"), cov, delimiter=",", fmt="%.17g") +np.savetxt(os.path.join(OUT, "prec.csv"), prec, delimiter=",", fmt="%.17g") +np.savetxt(os.path.join(OUT, "ggm.csv"), ggm, delimiter=",", fmt="%.17g") +with open(os.path.join(OUT, "lambdas.txt"), "w") as f: + f.write("%.17g\n%.17g\n" % (lambdas[0], lambdas[1])) + +print("seed:", SEED, "n,p1,p2:", n, p1, p2, "lambdas:", lambdas) +print("wrote:", OUT) diff --git a/tests/test_dragon_parity.py b/tests/test_dragon_parity.py new file mode 100644 index 00000000..357fc27d --- /dev/null +++ b/tests/test_dragon_parity.py @@ -0,0 +1,55 @@ +"""DRAGON cross-language parity test. + +Runs the netZooPy DRAGON pipeline on shared inputs and asserts the outputs +match the snapshot in tests/dragon_parity/. The same inputs and gold values +live byte-identical in netZooR, where a mirror test asserts the same numbers. +If both tests pass, the two implementations agree to the documented tolerances. + +Coverage: lambdas, shrunken covariance, precision matrix, partial correlation +matrix. Kappa / p-values are not covered because netZooR's +estimate_kappa_dragon and estimate_p_values_dragon are unimplemented stubs. +""" +import os +import numpy as np +import pytest +from netZooPy import dragon + +FIXTURE_DIR = os.path.join(os.path.dirname(__file__), "dragon_parity") + + +def _load(name): + return np.loadtxt(os.path.join(FIXTURE_DIR, name), delimiter=",") + + +def test_dragon_parity_lambdas(): + X1 = _load("X1.csv") + X2 = _load("X2.csv") + expected = np.loadtxt(os.path.join(FIXTURE_DIR, "lambdas.txt")) + lambdas, _ = dragon.estimate_penalty_parameters_dragon(X1, X2) + assert np.allclose(lambdas, expected, atol=1e-5, rtol=0), ( + "lambdas drift from gold: got=%s expected=%s" % (lambdas, expected) + ) + + +def test_dragon_parity_matrices(): + X1 = _load("X1.csv") + X2 = _load("X2.csv") + lambdas = tuple(np.loadtxt(os.path.join(FIXTURE_DIR, "lambdas.txt"))) + + cov_gold = _load("cov.csv") + prec_gold = _load("prec.csv") + ggm_gold = _load("ggm.csv") + + cov = dragon.get_shrunken_covariance_dragon(X1, X2, lambdas) + prec, _ = dragon.get_precision_matrix_dragon(X1, X2, lambdas) + ggm = dragon.get_partial_correlation_dragon(X1, X2, lambdas) + + assert np.allclose(cov, cov_gold, atol=1e-5, rtol=0), ( + "cov max|diff|=%g" % np.abs(cov - cov_gold).max() + ) + assert np.allclose(prec, prec_gold, atol=1e-5, rtol=0), ( + "prec max|diff|=%g" % np.abs(prec - prec_gold).max() + ) + assert np.allclose(ggm, ggm_gold, atol=1e-5, rtol=0), ( + "ggm max|diff|=%g" % np.abs(ggm - ggm_gold).max() + )