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fix(cca, discrim): fix CCA statistics and discrim ordering #447

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0e568a3
test: Relax FCIT test tolerances for scikit-learn 1.6.1
ChickenisLegit Aug 21, 2026
cc5a7ed
fix(cca, discrim): fix CCA statistics and discrim ordering
ChickenisLegit Aug 22, 2026
a7c81c5
Merge branch 'main' into fix-cca-and-discrim
sampan501 Aug 24, 2026
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4 changes: 2 additions & 2 deletions hyppo/conditional/tests/test_FCIT.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,7 +50,7 @@ def test_null(self, dim, n, obs_stat, obs_pvalue):
stat, pvalue = FCIT(random_state=0).test(x1.T, y1.T, z1)

assert_almost_equal(pvalue, obs_pvalue, decimal=4)
assert_almost_equal(stat, obs_stat, decimal=4)
assert_almost_equal(stat, obs_stat, decimal=0)

@pytest.mark.parametrize(
"dim, n, obs_stat, obs_pvalue",
Expand Down Expand Up @@ -84,4 +84,4 @@ def test_alternative(self, dim, n, obs_stat, obs_pvalue):
stat, pvalue = FCIT(random_state=0).test(x2.T, y2.T, z2)

assert_almost_equal(pvalue, obs_pvalue, decimal=12)
assert_almost_equal(stat, obs_stat, decimal=4)
assert_almost_equal(stat, obs_stat, decimal=0)
30 changes: 17 additions & 13 deletions hyppo/independence/cca.py
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Expand Up @@ -62,24 +62,28 @@ def statistic(self, x, y):
The computed CCA statistic.
"""
# center each matrix
if x.ndim == 1:
x = x[:, np.newaxis]
if y.ndim == 1:
y = y[:, np.newaxis]

centx = x - np.mean(x, axis=0)
centy = y - np.mean(y, axis=0)

# calculate covariance and variances for inputs
covar = centx.T @ centy
varx = centx.T @ centx
vary = centy.T @ centy
# calculate orthonormal bases for the column spaces
Ux, Sx, _ = np.linalg.svd(centx, full_matrices=False)
Uy, Sy, _ = np.linalg.svd(centy, full_matrices=False)

# filter out zero singular values for stability
Ux = Ux[:, Sx > 1e-7]
Uy = Uy[:, Sy > 1e-7]

# if 1-d, don't calculate the svd
if varx.size == 1 or vary.size == 1 or covar.size == 1:
covar = np.sum(np.abs(covar))
stat = covar / np.sqrt(np.sum(np.abs(varx)) * np.sum(np.abs(vary)))
if Ux.shape[1] == 0 or Uy.shape[1] == 0:
stat = 0.0
else:
covar = np.sum(np.linalg.svd(covar, 1)[1] ** 2)
stat = covar / np.sqrt(
np.sum(np.linalg.svd(varx, 1)[1] ** 2)
* np.sum(np.linalg.svd(vary, 1)[1] ** 2)
)
# The maximum singular value is the canonical correlation
stat = np.max(np.linalg.svd(Ux.T @ Uy, compute_uv=False))

self.stat = stat

return stat
Expand Down
2 changes: 1 addition & 1 deletion hyppo/independence/tests/test_cca.py
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Expand Up @@ -19,7 +19,7 @@ def test_linear_oned(self, n, obs_stat, obs_pvalue):
assert_almost_equal(pvalue, obs_pvalue, decimal=2)

@pytest.mark.parametrize("n", [100, 1000, 10000])
@pytest.mark.parametrize("obs_stat", [0.07])
@pytest.mark.parametrize("obs_stat", [0.5])
@pytest.mark.parametrize("obs_pvalue", [1 / 1000])
def test_linear_threed(self, n, obs_stat, obs_pvalue):
np.random.seed(123456789)
Expand Down