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Harmonize HSGP.prior dimension names and order #7562

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merged 2 commits into from
Nov 6, 2024
Merged

Harmonize HSGP.prior dimension names and order #7562

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c1e1733
suggest dimension improvement
juanitorduz Nov 5, 2024
af142c4
make mypy happy
juanitorduz Nov 5, 2024
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17 changes: 11 additions & 6 deletions pymc/gp/hsgp_approx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -428,8 +428,8 @@ def prior(
self,
name: str,
X: TensorLike,
dims: str | None = None,
hsgp_coeffs_dims: str | None = None,
gp_dims: str | None = None,
*args,
**kwargs,
):
Expand All @@ -444,10 +444,11 @@ def prior(
Name of the random variable
X: array-like
Function input values.
dims: str, default None
Dimension name for the GP random variable.
hsgp_coeffs_dims: str, default None
Dimension name for the HSGP basis vectors.
gp_dims: str, default None
Dimension name for the GP random variable.

"""
phi, sqrt_psd = self.prior_linearized(X)
self._sqrt_psd = sqrt_psd
Expand All @@ -469,7 +470,7 @@ def prior(
)
f = self.mean_func(X) + phi @ self._beta

self.f = pm.Deterministic(name, f, dims=gp_dims)
self.f = pm.Deterministic(name, f, dims=dims)
return self.f

def _build_conditional(self, Xnew):
Expand Down Expand Up @@ -695,7 +696,9 @@ def prior_linearized(self, X: TensorLike):
psd = self.scale * self.cov_func.power_spectral_density_approx(J)
return (phi_cos, phi_sin), psd

def prior(self, name: str, X: TensorLike, dims: str | None = None): # type: ignore[override]
def prior( # type: ignore[override]
self, name: str, X: TensorLike, dims: str | None = None, hsgp_coeffs_dims: str | None = None
):
R"""
Return the (approximate) GP prior distribution evaluated over the input locations `X`.

Expand All @@ -709,11 +712,13 @@ def prior(self, name: str, X: TensorLike, dims: str | None = None): # type: ign
Function input values.
dims: None
Dimension name for the GP random variable.
hsgp_coeffs_dims: str | None = None
Dimension name for the HSGPPeriodic basis vectors.
"""
(phi_cos, phi_sin), psd = self.prior_linearized(X)

m = self._m
self._beta = pm.Normal(f"{name}_hsgp_coeffs_", size=(m * 2 - 1))
self._beta = pm.Normal(f"{name}_hsgp_coeffs_", size=(m * 2 - 1), dims=hsgp_coeffs_dims)
# The first eigenfunction for the sine component is zero
# and so does not contribute to the approximation.
f = (
Expand Down
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