def test_get_stats_wrong():
kcca_bad = KCCA()
with pytest.raises(NameError):
kcca_bad.get_stats()
with pytest.raises(NameError):
kcca_bad.fit([train1, train2])
stats = kcca_bad.get_stats()
def test_get_stats_nonlinear_kernel():
kcca_poly = KCCA(ktype='poly')
kcca_poly.fit([train1, train2]).transform([train1, train2])
stats = kcca_poly.get_stats()
assert np.all(stats['r']>0)
assert stats['r'].shape == (2,)
kcca_gaussian = KCCA(ktype='gaussian')
kcca_gaussian.fit([train1, train2]).transform([train1, train2])
stats = kcca_gaussian.get_stats()
assert np.all(stats['r']>0)
assert stats['r'].shape == (2,)
def test_get_stats_icd_check_corrs():
X = np.vstack((np.eye(3,3), 2*np.eye(3,3)))
Y1 = np.fliplr(np.eye(3,3))
Y = np.vstack((Y1, 0.1*np.eye(3,3)))
kcca = KCCA(n_components=3, decomp='icd')
out = kcca.fit([X, Y]).transform([X, Y])
stats = kcca.get_stats()
assert np.allclose(stats['r'], np.array([0.51457091, 0.3656268]))
def test_get_stats_1_feature_vs_matlab():
X = np.arange(1, 11).reshape(-1, 1)
Y = np.arange(2, 21, 2).reshape(-1, 1)
matlab_stats = {'r': np.array([1]),
'Wilks': np.array([0]),
'df1': np.array([1]),
'df2': np.array([8]),
'F': np.array([np.inf]),
'pF': np.array([0]),
'chisq': np.array([np.inf]),
'pChisq': np.array([0])
}
kcca = KCCA(n_components=1)
out = kcca.fit([X, Y]).transform([X, Y])
stats = kcca.get_stats()
for key in stats:
assert np.allclose(stats[key], matlab_stats[key], rtol=1e-3, atol=1e-4)
def test_get_stats_2_components():
np.random.seed(12)
X = X = np.random.rand(100,3)
Y = np.random.rand(100,4)
past_stats = {'r': np.array([0.22441608, 0.19056307]),
'Wilks': np.array([0.91515202, 0.96368572]),
'df1': np.array([12, 6]),
'df2': np.array([246.34637455, 188]),
'F': np.array([0.69962605, 0.58490315]),
'pF': np.array([0.75134965, 0.74212361]),
'chisq': np.array([8.42318331, 4.2115406 ]),
'pChisq': np.array([0.75124771, 0.64807349])
}
kcca2 = KCCA(n_components=2)
kcca2.fit_transform([X,Y])
stats = kcca2.get_stats()
nondegen = np.argwhere(stats['r'] < 1 - 2 * np.finfo(float).eps).squeeze()
assert np.array_equal(nondegen, np.array([0, 1]))
for key in stats:
assert np.allclose(stats[key], past_stats[key], rtol=1e-3, atol=1e-4)
def test_get_stats_1_component():
np.random.seed(12)
X = X = np.random.rand(100,3)
Y = np.random.rand(100,4)
past_stats = {'r': np.array([0.22441608326082138]),
'Wilks': np.array([0.94963742]),
'df1': np.array([12]),
'df2': np.array([246.34637455]),
'F': np.array([0.40489714]),
'pF': np.array([0.96096493]),
'chisq': np.array([4.90912773]),
'pChisq': np.array([0.9609454])
}
kcca1 = KCCA(n_components=1)
kcca1.fit_transform([X,Y])
stats = kcca1.get_stats()
assert not stats['r'] == 1
assert not stats['r'] + 2 * np.finfo(float).eps >= 1
for key in stats:
assert np.allclose(stats[key], past_stats[key], rtol=1e-3, atol=1e-4)
def test_get_stats_vs_matlab():
X = np.vstack((np.eye(3,3), 2*np.eye(3,3)))
Y1 = np.fliplr(np.eye(3,3))
Y = np.vstack((Y1, 0.1*np.eye(3,3)))
matlab_stats = {'r': np.array([1.000000000000000, 0.533992991387982, 0.355995327591988]),
'Wilks': np.array([0, 0.624256445446525, 0.873267326732673]),
'df1': np.array([9, 4, 1]),
'df2': np.array([0.150605850666856, 2, 2]),
'F': np.array([np.inf, 0.132832080200501, 0.290249433106576]),
'pF': np.array([0, 0.955941574355455, 0.644004672408012]),
'chisq': np.array([np.inf, 0.706791037156489, 0.542995281660087]),
'pChisq': np.array([0, 0.950488814632803, 0.461194028737338])
}
kcca = KCCA(n_components=3)
out = kcca.fit([X, Y]).transform([X, Y])
stats = kcca.get_stats()
assert np.allclose(stats['r'][0], 1)
nondegen = np.argwhere(stats['r'] < 1 - 2 * np.finfo(float).eps).squeeze()
assert np.array_equal(nondegen, np.array([1, 2]))
for key in stats:
assert np.allclose(stats[key], matlab_stats[key], rtol=1e-3, atol=1e-4)
