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Fix imports from gp.util #243

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Fix imports from gp.util #243

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b2cdffd
Do not predefine custom Cholesky and SolveTriangular Ops
jessegrabowski Sep 16, 2023
2a76fec
Import `solve_triangular` from `pytensor.linalg.slinalg` for better c…
jessegrabowski Sep 16, 2023
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13 changes: 7 additions & 6 deletions pymc_experimental/gp/latent_approx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,8 @@
import numpy as np
import pymc as pm
import pytensor.tensor as pt
from pymc.gp.util import JITTER_DEFAULT, cholesky, solve_lower, solve_upper, stabilize
from pymc.gp.util import JITTER_DEFAULT, stabilize
from pytensor.tensor.slinalg import solve_triangular


class LatentApprox(pm.gp.Latent):
Expand All @@ -35,14 +36,14 @@ def __init__(
def _build_prior(self, name, X, Xu, jitter=JITTER_DEFAULT, **kwargs):
mu = self.mean_func(X)
Kuu = self.cov_func(Xu)
L = cholesky(stabilize(Kuu, jitter))
L = pt.linalg.cholesky(stabilize(Kuu, jitter))

n_inducing_points = np.shape(Xu)[0]
v = pm.Normal(name + "_u_rotated_", mu=0.0, sigma=1.0, size=n_inducing_points, **kwargs)
u = pm.Deterministic(name + "_u", L @ v)

Kfu = self.cov_func(X, Xu)
Kuuiu = solve_upper(pt.transpose(L), solve_lower(L, u))
Kuuiu = solve_triangular(pt.transpose(L), solve_triangular(L, u), lower=False)

return pm.Deterministic(name, mu + Kfu @ Kuuiu), Kuuiu, L

Expand All @@ -62,10 +63,10 @@ def prior(self, name, X, Xu=None, jitter=JITTER_DEFAULT, **kwargs):
def _build_conditional(self, name, Xnew, Xu, L, Kuuiu, jitter, **kwargs):
Ksu = self.cov_func(Xnew, Xu)
mu = self.mean_func(Xnew) + Ksu @ Kuuiu
tmp = solve_lower(L, pt.transpose(Ksu))
tmp = solve_triangular(L, pt.transpose(Ksu))
Qss = pt.transpose(tmp) @ tmp # Qss = tt.dot(tt.dot(Ksu, tt.nlinalg.pinv(Kuu)), Ksu.T)
Kss = self.cov_func(Xnew)
Lss = cholesky(stabilize(Kss - Qss, jitter))
Lss = pt.linalg.cholesky(stabilize(Kss - Qss, jitter))
return mu, Lss

def conditional(self, name, Xnew, jitter=1e-6, **kwargs):
Expand Down Expand Up @@ -123,7 +124,7 @@ def _build_conditional(self, Xnew, X, f, U, s, jitter):
Kxxpinv = U @ pt.diag(1.0 / s) @ U.T
mus = Kxs.T @ Kxxpinv @ f
K = Kss - Kxs.T @ Kxxpinv @ Kxs
L = pm.gp.util.cholesky(pm.gp.util.stabilize(K, jitter))
L = pt.linalg.cholesky(stabilize(K, jitter))
return mus, L

def conditional(self, name, Xnew, jitter=1e-6, **kwargs):
Expand Down
7 changes: 3 additions & 4 deletions pymc_experimental/statespace/filters/kalman_filter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@
from pytensor.raise_op import Assert
from pytensor.tensor import TensorVariable
from pytensor.tensor.nlinalg import matrix_dot
from pytensor.tensor.slinalg import SolveTriangular
from pytensor.tensor.slinalg import solve_triangular

from pymc_experimental.statespace.filters.utilities import (
quad_form_sym,
Expand All @@ -22,7 +22,6 @@
MVN_CONST = pt.log(2 * pt.constant(np.pi, dtype="float64"))
PARAM_NAMES = ["c", "d", "T", "Z", "R", "H", "Q"]

solve_lower_triangular = SolveTriangular(lower=True)
assert_data_is_1d = Assert("UnivariateTimeSeries filter requires data be at most 1-dimensional")
assert_time_varying_dim_correct = Assert(
"The first dimension of a time varying matrix (the time dimension) must be "
Expand Down Expand Up @@ -684,13 +683,13 @@ def update(self, a, P, y, c, d, Z, H, all_nan_flag):
F_chol = pt.linalg.cholesky(F)

# If everything is missing, K = 0, IKZ = I
K = solve_lower_triangular(F_chol.T, solve_lower_triangular(F_chol, PZT.T)).T
K = solve_triangular(F_chol.T, solve_triangular(F_chol, PZT.T)).T
I_KZ = self.eye_states - K.dot(Z)

a_filtered = a + K.dot(v)
P_filtered = quad_form_sym(I_KZ, P) + quad_form_sym(K, H)

inner_term = solve_lower_triangular(F_chol.T, solve_lower_triangular(F_chol, v))
inner_term = solve_triangular(F_chol.T, solve_triangular(F_chol, v))
n = y.shape[0]

ll = pt.switch(
Expand Down