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_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)
(latvar1, latvar2, latvar1, latvar2, latvar1)).T
# Split each dataset into a training set and test set (10% of dataset is training data)
train1 = data1[:int(nSamples / 10)]
train2 = data2[:int(nSamples / 10)]
test1 = data1[int(nSamples / 10):]
test2 = data2[int(nSamples / 10):]
n_components = 4
# Initialize a linear kCCA class
kcca_l = KCCA(ktype="linear", reg=0.001, n_components=n_components)
# Use the methods to find a kCCA mapping and transform the views of data
kcca_ft = kcca_l.fit_transform([train1, train2])
kcca_f = kcca_l.fit([train1, train2])
kcca_t = kcca_l.transform([train1, train2])
# Test that cancorrs_ is equal to n_components
def test_numCC_cancorrs_():
assert len(kcca_ft.cancorrs_) == n_components
# Test that number of views is equal to number of ws_
def test_numCC_ws_():
assert len(kcca_ft.weights_) == 2
# Test that number of views is equal to number of comps_
def test_numCC_comps_():
# ICD is run on two views of data that each have two dimensions that are
# sinuisoidally related. The data has 100 samples and thus the fully decomposed
# kernel matrix would have dimensions (100, 100). Instead we implement ICD with
# a kernel matrix of rank 50 (mrank = 50).
np.random.seed(1)
Xsg = make_data('gaussian', 100)
crossviews_plot(Xsg, ax_ticks=False, ax_labels=True, equal_axes=True)
###############################################################################
# Full Decomposition
# ^^^^^^^^^^^^^^^^^^
kcca_g = KCCA(ktype="gaussian", n_components=2, reg=0.01)
kcca_g.fit(Xsg)
gausskcca = kcca_g.transform(Xsg)
crossviews_plot(gausskcca, ax_ticks=False, ax_labels=True, equal_axes=True)
(gr1, _) = stats.pearsonr(gausskcca[0][:, 0], gausskcca[1][:, 0])
(gr2, _) = stats.pearsonr(gausskcca[0][:, 1], gausskcca[1][:, 1])
print("Below are the canonical correlation of the two components:")
print(gr1, gr2)
###############################################################################
# ICD Decomposition
# ^^^^^^^^^^^^^^^^^
kcca_g_icd = KCCA(ktype="gaussian",
