Rare RLDA test failure #125
Description
Describe the bug
This test failed during a random test. Using removenan branch, possibly doesn't exist on main
_________________________________________________________________________________ test_rlda _________________________________________________________________________________
def test_rlda():
ns = 6
n_components = 3
for nv in [1, 2, 4]:
for nb in [1, 4, 8, 12]:
> rlda_inner_test(ns, min(nb, n_components), nb, nv)
tests/test_rlda.py:85:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
ns = 6, n_components = 1, nb = 1, nv = 2
def rlda_inner_test(ns, n_components, nb, nv):
nc = 2**nb
n_model = 2000
n_test = 500
noise = 2000
assert nv <= ns, "Test case error, use higher ns"
# Generate profiling data
m = np.ones((nv, nc, ns), dtype=np.int16)
for v in range(nv):
m[v, :, v] = (get_bits(np.arange(nc), nbits=nb).astype(np.int64) + 1).sum(
axis=1
) * (v + 1)
traces = np.random.randint(-noise, noise, (n_model, ns), dtype=np.int16)
labels = np.random.randint(0, nc, (nv, n_model), dtype=np.uint64)
for v in range(nv):
traces += m[v, labels[v]].astype(np.int16)
# Generate test data
test_traces = np.random.randint(-noise, noise, (n_test, ns), dtype=np.int16)
test_labels = np.random.randint(0, nc, (nv, n_test), dtype=np.uint16)
for v in range(nv):
test_traces += m[v, test_labels[v]]
## Run SCALib RLDA and compare projection matrices
rlda = RLDAClassifier(nb, ns, nv, n_components)
rlda.fit_u(traces, labels.T, 1)
rlda.solve()
for v in range(nv):
## Start computing RLDA in python
label_bits = get_bits(labels[v], nbits=nb).astype(np.int64)
B = label_bits.T @ label_bits
C = label_bits.T @ traces
S_L = traces.astype(np.int64).T @ traces.astype(np.int64)
A = np.linalg.solve(B, C)
mu = C[0] / n_model
SB = A.T @ B @ A - n_model * np.outer(mu, mu)
SW = S_L + A.T @ B @ A - C.T @ A - A.T @ C
ev, W = eigh(SB, SW, subset_by_index=(ns - n_components, ns - 1))
W = W.T
cov = W @ SW @ W.T * 1 / n_model
evals, evecs = np.linalg.eigh(cov)
Wnorm = evecs * (evals[:, np.newaxis] ** -0.5)
Weff = W.T @ Wnorm
Aeff = A @ Weff
prs_ref = (test_traces @ Weff)[:, :, np.newaxis] - (
get_bits(np.arange(2**nb, dtype=np.uint64), nbits=nb) @ Aeff
).T[np.newaxis]
prs_ref = np.exp(-0.5 * (prs_ref**2).sum(axis=1))
prs_ref = prs_ref / prs_ref.sum(axis=1, keepdims=True)
prs = rlda.predict_proba(test_traces, v)
> assert np.allclose(prs, prs_ref)
E assert False
E + where False = <function allclose at 0x7fcb457fb8b0>(array([[0.49879052, 0.50120948],\n [0.50548702, 0.49451298],\n [0.53497155, 0.46502845],\n [0.51253122,...28571, 0.49671429],\n [0.46444726, 0.53555274],\n [0.50274778, 0.49725222],\n [0.54640572, 0.45359428]]), array([[0.49878063, 0.50121937],\n [0.50548422, 0.49451578],\n [0.53497949, 0.46502051],\n [0.51252112,...28582, 0.49671418],\n [0.46445038, 0.53554962],\n [0.50274603, 0.49725397],\n [0.54640589, 0.45359411]]))
E + where <function allclose at 0x7fcb457fb8b0> = np.allclose
tests/test_rlda.py:77: AssertionError
--------------------------------------------------------------------------- Captured stdout setup ---------------------------------------------------------------------------
Random seeds: random.seed(488548030); np.random.seed(546442009)