simplyfy function names and add author reference

docs/source/contrib.rst

@@ -251,7 +251,8 @@ approximate Bayesian Gaussian processes for probabilistic programming. Stat Comp
 5. `Gelman, Vehtari, Simpson, et al., Bayesian workflow book - Birthdays <https://avehtari.github.io/casestudies/Birthdays/birthdays.html>`_.
 
 .. note::
-    The code of this module is based on the code of the example `Example: Hilbert space approximation for Gaussian processes <https://num.pyro.ai/en/stable/examples/hsgp.html>`_.
+    The code of this module is based on the code of the example
+    `Example: Hilbert space approximation for Gaussian processes <https://num.pyro.ai/en/stable/examples/hsgp.html>`_ by `Omar Sosa Rodríguez <https://github.com/omarfsosa>`_.
 
 sqrt_eigenvalues
 ----------------
@@ -285,14 +286,14 @@ diag_spectral_density_periodic
 ------------------------------
 .. autofunction:: numpyro.contrib.hsgp.spectral_densities.diag_spectral_density_periodic
 
-hsgp_approximation_squared_exponential
---------------------------------------
-.. autofunction:: numpyro.contrib.hsgp.approximation.hsgp_approximation_squared_exponential
+hsgp_squared_exponential
+------------------------
+.. autofunction:: numpyro.contrib.hsgp.approximation.hsgp_squared_exponential
 
-hsgp_approximation_matern
--------------------------
-.. autofunction:: numpyro.contrib.hsgp.approximation.hsgp_approximation_matern
+hsgp_matern
+-----------
+.. autofunction:: numpyro.contrib.hsgp.approximation.hsgp_matern
 
-hsgp_approximation_periodic_non_centered
------------------------------------------
-.. autofunction:: numpyro.contrib.hsgp.approximation.hsgp_approximation_periodic_non_centered
+hsgp_periodic_non_centered
+--------------------------
+.. autofunction:: numpyro.contrib.hsgp.approximation.hsgp_periodic_non_centered

notebooks/source/hsgp_example.ipynb

@@ -44,7 +44,7 @@
     "import jax.numpy as jnp\n",
     "\n",
     "import numpyro\n",
-    "from numpyro.contrib.hsgp.approximation import hsgp_approximation_squared_exponential\n",
+    "from numpyro.contrib.hsgp.approximation import hsgp_squared_exponential\n",
     "from numpyro.contrib.hsgp.laplacian import eigenfunctions\n",
     "from numpyro.contrib.hsgp.spectral_densities import (\n",
     "    diag_spectral_density_squared_exponential,\n",
@@ -187,7 +187,7 @@
     "    length = numpyro.sample(\"length\", dist.InverseGamma(concentration=6, rate=1))\n",
     "    noise = numpyro.sample(\"noise\", dist.InverseGamma(concentration=12, rate=10))\n",
     "    # --- Parametrization ---\n",
-    "    f = hsgp_approximation_squared_exponential(\n",
+    "    f = hsgp_squared_exponential(\n",
     "        x=x, alpha=alpha, length=length, ell=ell, m=m, non_centered=non_centered\n",
     "    )\n",
     "    # --- Likelihood ---\n",

numpyro/contrib/hsgp/approximation.py

@@ -58,7 +58,7 @@ def linear_approximation(
     return _centered_approximation(phi, spd, m)
 
 
-def hsgp_approximation_squared_exponential(
+def hsgp_squared_exponential(
     x: ArrayImpl,
     alpha: float,
     length: float,
@@ -99,7 +99,7 @@ def hsgp_approximation_squared_exponential(
     return linear_approximation(phi=phi, spd=spd, m=m, non_centered=non_centered)
 
 
-def hsgp_approximation_matern(
+def hsgp_matern(
     x: ArrayImpl,
     nu: float,
     alpha: float,
@@ -140,7 +140,7 @@ def hsgp_approximation_matern(
     return linear_approximation(phi=phi, spd=spd, m=m, non_centered=non_centered)
 
 
-def hsgp_approximation_periodic_non_centered(
+def hsgp_periodic_non_centered(
     x: ArrayImpl, alpha: float, length: float, w0: float, m: int
 ) -> ArrayImpl:
     """

test/contrib/hsgp/test_approximation.py

@@ -8,9 +8,9 @@
 
 import numpyro
 from numpyro.contrib.hsgp.approximation import (
-    hsgp_approximation_matern,
-    hsgp_approximation_periodic_non_centered,
-    hsgp_approximation_squared_exponential,
+    hsgp_matern,
+    hsgp_periodic_non_centered,
+    hsgp_squared_exponential,
 )
 import numpyro.distributions as dist
 from numpyro.handlers import scope, seed, trace
@@ -48,9 +48,7 @@ def test_approximation_squared_exponential(x, alpha, length, ell, m, non_centere
     def model(x, alpha, length, ell, m, non_centered):
         numpyro.deterministic(
             "f",
-            hsgp_approximation_squared_exponential(
-                x, alpha, length, ell, m, non_centered
-            ),
+            hsgp_squared_exponential(x, alpha, length, ell, m, non_centered),
         )
 
     rng_key = random.PRNGKey(0)
@@ -74,7 +72,7 @@ def model(x, alpha, length, ell, m, non_centered):
 def test_approximation_matern(x, nu, alpha, length, ell, m, non_centered):
     def model(x, nu, alpha, length, ell, m, non_centered):
         numpyro.deterministic(
-            "f", hsgp_approximation_matern(x, nu, alpha, length, ell, m, non_centered)
+            "f", hsgp_matern(x, nu, alpha, length, ell, m, non_centered)
         )
 
     rng_key = random.PRNGKey(0)
@@ -102,7 +100,7 @@ def test_squared_exponential_gp_one_dim_model(
     def latent_gp(x, alpha, length, ell, m, non_centered):
         return numpyro.deterministic(
             "f",
-            hsgp_approximation_squared_exponential(
+            hsgp_squared_exponential(
                 x=x, alpha=alpha, length=length, ell=ell, m=m, non_centered=non_centered
             ),
         )
@@ -141,7 +139,7 @@ def test_matern_gp_one_dim_model(synthetic_one_dim_data, nu, ell, m, non_centere
     def latent_gp(x, nu, alpha, length, ell, m, non_centered):
         return numpyro.deterministic(
             "f",
-            hsgp_approximation_matern(
+            hsgp_matern(
                 x=x,
                 nu=nu,
                 alpha=alpha,
@@ -191,7 +189,7 @@ def test_periodic_gp_one_dim_model(synthetic_one_dim_data, w0, m):
     def latent_gp(x, alpha, length, w0, m):
         return numpyro.deterministic(
             "f",
-            hsgp_approximation_periodic_non_centered(
+            hsgp_periodic_non_centered(
                 x=x,
                 alpha=alpha,
                 length=length,