Remove HSGP as it became a part of PyMC (#136)

* remove HSGP as it became a part of PyMC

* isort

pymc_experimental/gp/__init__.py

@@ -13,8 +13,4 @@
 # limitations under the License.
 
 
-from pymc_experimental.gp.latent_approx import (
- HSGP,
- KarhunenLoeveExpansion,
- ProjectedProcess,
-)
+from pymc_experimental.gp.latent_approx import KarhunenLoeveExpansion, ProjectedProcess

pymc_experimental/gp/latent_approx.py

@@ -75,108 +75,6 @@ def conditional(self, name, Xnew, jitter=1e-6, **kwargs):
  return pm.MvNormal(name, mu=mu, chol=chol)
 
 
-class HSGP(pm.gp.Latent):
- ## inputs: M, c
-
- def __init__(
- self, n_basis, c=3 / 2, *, mean_func=pm.gp.mean.Zero(), cov_func=pm.gp.cov.Constant(0.0)
- ):
- ## TODO: specify either c or L
- self.M = n_basis
- self.c = c
- super().__init__(mean_func=mean_func, cov_func=cov_func)
-
- def _validate_cov_func(self, cov_func):
- ## TODO: actually validate it. Right now this fails unless cov func is exactly
- # in the form eta**2 * pm.gp.cov.Matern12(...) and will error otherwise.
- cov, scaling_factor = cov_func.factor_list
- return scaling_factor, cov.ls, cov.spectral_density
-
- def prior(self, name, X, **kwargs):
- f, Phi, L, spd, beta, Xmu, Xsd = self._build_prior(name, X, **kwargs)
- self.X, self.f = X, f
- self.Phi, self.L, self.spd, self.beta = Phi, L, spd, beta
- self.Xmu, self.Xsd = Xmu, Xsd
- return f
-
- def _generate_basis(self, X, L):
- indices = pt.arange(1, self.M + 1)
- m1 = (np.pi / (2.0 * L)) * pt.tile(L + X, self.M)
- m2 = pt.diag(indices)
- Phi = pt.sin(m1 @ m2) / pt.sqrt(L)
- omega = (np.pi * indices) / (2.0 * L)
- return Phi, omega
-
- def _build_prior(self, name, X, **kwargs):
- n_obs = np.shape(X)[0]
-
- # standardize input scale
- X = pt.as_tensor_variable(X)
- Xmu = pt.mean(X, axis=0)
- Xsd = pt.std(X, axis=0)
- Xz = (X - Xmu) / Xsd
-
- # define L using Xz and c
- La = pt.abs(pt.min(Xz)) # .eval()?
- Lb = pt.max(Xz)
- L = self.c * pt.max([La, Lb])
-
- # make basis and omega, spectral density
- Phi, omega = self._generate_basis(Xz, L)
- scale, ls, spectral_density = self._validate_cov_func(self.cov_func)
- spd = scale * spectral_density(omega, ls / Xsd).flatten()
-
- beta = pm.Normal(f"{name}_coeffs_", size=self.M)
- f = pm.Deterministic(name, self.mean_func(X) + pt.dot(Phi * pt.sqrt(spd), beta))
- return f, Phi, L, spd, beta, Xmu, Xsd
-
- def _build_conditional(self, Xnew, Xmu, Xsd, L, beta):
- Xnewz = (Xnew - Xmu) / Xsd
- Phi, omega = self._generate_basis(Xnewz, L)
- scale, ls, spectral_density = self._validate_cov_func(self.cov_func)
- spd = scale * spectral_density(omega, ls / Xsd).flatten()
- return self.mean_func(Xnew) + pt.dot(Phi * pt.sqrt(spd), beta)
-
- def conditional(self, name, Xnew):
- # warn about extrapolation
- fnew = self._build_conditional(Xnew, self.Xmu, self.Xsd, self.L, self.beta)
- return pm.Deterministic(name, fnew)
-
-
-class ExpQuad(pm.gp.cov.ExpQuad):
- @staticmethod
- def spectral_density(omega, ls):
- # univariate spectral denisty, implement multi
- return pt.sqrt(2 * np.pi) * ls * pt.exp(-0.5 * ls**2 * omega**2)
-
-
-class Matern52(pm.gp.cov.Matern52):
- @staticmethod
- def spectral_density(omega, ls):
- # univariate spectral denisty, implement multi
- # https://arxiv.org/pdf/1611.06740.pdf
- lam = pt.sqrt(5) * (1.0 / ls)
- return (16.0 / 3.0) * lam**5 * (1.0 / (lam**2 + omega**2) ** 3)
-
-
-class Matern32(pm.gp.cov.Matern32):
- @staticmethod
- def spectral_density(omega, ls):
- # univariate spectral denisty, implement multi
- # https://arxiv.org/pdf/1611.06740.pdf
- lam = np.sqrt(3.0) * (1.0 / ls)
- return 4.0 * lam**3 * (1.0 / pt.square(lam**2 + omega**2))
-
-
-class Matern12(pm.gp.cov.Matern12):
- @staticmethod
- def spectral_density(omega, ls):
- # univariate spectral denisty, implement multi
- # https://arxiv.org/pdf/1611.06740.pdf
- lam = 1.0 / ls
- return 2.0 * lam * (1.0 / (lam**2 + omega**2))
-
-
 class KarhunenLoeveExpansion(pm.gp.Latent):
  def __init__(
  self,