Format and increment

Author: thomaspinder

docs/conf.py

@@ -28,7 +28,7 @@
 author = "Thomas Pinder"
 
 # The full version, including alpha/beta/rc tags
-release = "0.2.0"
+release = "0.3"
 
 
 # -- General configuration ---------------------------------------------------

gpjax/__init__.py

@@ -5,4 +5,4 @@
 from .predict import mean, variance
 from .sampling import random_variable, sample
 
-__version__ = "0.3.0"
+__version__ = "0.3.1"

gpjax/kernels.py

@@ -36,7 +36,7 @@ def __call__(self, x: Array, y: Array) -> Array:
 
 @dispatch(RBF)
 def initialise(kernel: RBF):
-    return {"lengthscale": jnp.array([1.0]*kernel.ndims), "variance": jnp.array([1.0])}
+    return {"lengthscale": jnp.array([1.0] * kernel.ndims), "variance": jnp.array([1.0])}
 
 
 def squared_distance(x: Array, y: Array):

gpjax/mean_functions.py

@@ -23,7 +23,7 @@ class Zero(MeanFunction):
 
     def __call__(self, x: Array) -> Array:
         out_shape = (x.shape[0], self.output_dim)
-        return jnp.zeros(shape = out_shape)
+        return jnp.zeros(shape=out_shape)
 
 
 @dispatch(Zero)

gpjax/objectives/mlls.py

@@ -39,6 +39,7 @@ def mll(params: dict, x: Array, y: Array, priors: dict = None):
         log_prior_density = evaluate_prior(params, priors)
         constant = jnp.array(-1.0) if negative else jnp.array(1.0)
         return constant * (random_variable.log_prob(y.squeeze()).mean() + log_prior_density)
+
     return mll
 
 
@@ -49,7 +50,9 @@ def marginal_ll(
     negative: bool = False,
     jitter: float = 1e-6,
 ) -> Callable:
-    def mll(params: dict, x: Array, y: Array, priors: dict = {'latent': tfd.Normal(loc=0., scale=1.)}):
+    def mll(
+        params: dict, x: Array, y: Array, priors: dict = {"latent": tfd.Normal(loc=0.0, scale=1.0)}
+    ):
         params = untransform(params, transformation)
         n = x.shape[0]
         link = link_function(gp.likelihood)

gpjax/parameters/priors.py

@@ -1,11 +1,12 @@
+import warnings
+
 import jax.numpy as jnp
 from jax.interpreters.ad import JVPTracer
 from jax.interpreters.partial_eval import DynamicJaxprTracer
 from multipledispatch import dispatch
 from tensorflow_probability.substrates.jax import distributions as tfd
-from ..gps import NonConjugatePosterior
-import warnings
 
+from ..gps import NonConjugatePosterior
 from ..types import Array, NoneType
 
 
@@ -16,24 +17,26 @@ def log_density(param: jnp.DeviceArray, density: tfd.Distribution) -> Array:
 
 @dispatch(dict, NoneType)
 def evaluate_prior(params: dict, priors: dict) -> Array:
-    return jnp.array(0.)
+    return jnp.array(0.0)
 
 
 @dispatch(dict, dict)
 def evaluate_prior(params: dict, priors: dict) -> Array:
     lpd = jnp.array(0)
     for param, val in priors.items():
-        lpd+=jnp.sum(log_density(params[param], priors[param]))
+        lpd += jnp.sum(log_density(params[param], priors[param]))
     return lpd
 
 
 @dispatch(NonConjugatePosterior, dict)
 def prior_checks(gp: NonConjugatePosterior, priors: dict) -> dict:
-    if 'latent' in priors.keys():
-        latent_prior = priors['latent']
-        if latent_prior.name != 'Normal':
-            warnings.warn(f'A {latent_prior.name} distribution prior has been placed on the latent function. It is strongly afvised that a unit-Gaussian prior is used.')
+    if "latent" in priors.keys():
+        latent_prior = priors["latent"]
+        if latent_prior.name != "Normal":
+            warnings.warn(
+                f"A {latent_prior.name} distribution prior has been placed on the latent function. It is strongly afvised that a unit-Gaussian prior is used."
+            )
         return priors
     else:
-        priors['latent'] = tfd.Normal(loc=0., scale=1.)
-        return priors
+        priors["latent"] = tfd.Normal(loc=0.0, scale=1.0)
+        return priors

gpjax/types.py

@@ -6,4 +6,4 @@
 
 # Array = Union[jnp.ndarray, ShardedDeviceArray, jnp.DeviceArray]  # Cannot currently dispatch on a Union type
 # Data = Tuple[Array, Array]
-NoneType = type(None)
+NoneType = type(None)

gpjax/utils.py

@@ -1,5 +1,6 @@
-import jax.numpy as jnp
 from typing import Tuple
+
+import jax.numpy as jnp
 from multipledispatch import dispatch
 
 from .types import Array
@@ -50,11 +51,13 @@ def standardise(x: jnp.DeviceArray) -> Tuple[jnp.DeviceArray, jnp.DeviceArray, j
     """
     xmean = jnp.mean(x, axis=0)
     xstd = jnp.std(x, axis=0)
-    return (x-xmean)/xstd, xmean, xstd
+    return (x - xmean) / xstd, xmean, xstd
 
 
 @dispatch(jnp.DeviceArray, jnp.DeviceArray, jnp.DeviceArray)
-def standardise(x: jnp.DeviceArray, xmean: jnp.DeviceArray, xstd: jnp.DeviceArray) -> jnp.DeviceArray:
+def standardise(
+    x: jnp.DeviceArray, xmean: jnp.DeviceArray, xstd: jnp.DeviceArray
+) -> jnp.DeviceArray:
     """
     Standardise a given matrix with respect to a given mean and standard deviation. This is primarily designed for
     standardising a test set of data with respect to the training data.
@@ -64,11 +67,12 @@ def standardise(x: jnp.DeviceArray, xmean: jnp.DeviceArray, xstd: jnp.DeviceArra
     :param xstd: A precomputed standard deviation vector
     :return: A matrix of standardised values
     """
-    return (x-xmean)/xstd
-
+    return (x - xmean) / xstd
 
 
-def unstandardise(x: jnp.DeviceArray, xmean: jnp.DeviceArray, xstd: jnp.DeviceArray) -> jnp.DeviceArray:
+def unstandardise(
+    x: jnp.DeviceArray, xmean: jnp.DeviceArray, xstd: jnp.DeviceArray
+) -> jnp.DeviceArray:
     """
     Unstandardise a given matrix with respect to a previously computed mean and standard deviation. This is designed
     for remapping a matrix back onto its original scale.
@@ -78,4 +82,4 @@ def unstandardise(x: jnp.DeviceArray, xmean: jnp.DeviceArray, xstd: jnp.DeviceAr
     :param xstd: A standard deviation vector.
     :return: A matrix of unstandardised values.
     """
-    return (x*xstd) + xmean
+    return (x * xstd) + xmean

setup.py

@@ -29,7 +29,7 @@ def parse_requirements_file(filename):
 
 setup(
     name="GPJax",
-    version="0.3.0",
+    version="0.3.1",
     author="Thomas Pinder",
     author_email="[email protected]",
     packages=find_packages(".", exclude=["tests"]),

tests/objectives/test_mlls.py

@@ -1,36 +1,38 @@
-from gpjax.objectives import marginal_ll
-from gpjax import Prior
-from gpjax.likelihoods import Bernoulli, Gaussian
-from gpjax.kernels import RBF
-from gpjax.parameters import transform, SoftplusTransformation, initialise
+from typing import Callable
+
 import jax.numpy as jnp
 import jax.random as jr
 import pytest
-from typing import Callable
 from tensorflow_probability.substrates.jax import distributions as tfd
 
+from gpjax import Prior
+from gpjax.kernels import RBF
+from gpjax.likelihoods import Bernoulli, Gaussian
+from gpjax.objectives import marginal_ll
+from gpjax.parameters import SoftplusTransformation, initialise, transform
+
 
 def test_conjugate():
-    posterior = Prior(kernel = RBF()) * Gaussian()
+    posterior = Prior(kernel=RBF()) * Gaussian()
     mll = marginal_ll(posterior)
     assert isinstance(mll, Callable)
     neg_mll = marginal_ll(posterior, negative=True)
-    x = jnp.linspace(-1., 1., 20).reshape(-1, 1)
+    x = jnp.linspace(-1.0, 1.0, 20).reshape(-1, 1)
     y = jnp.sin(x)
     params = transform(params=initialise(posterior), transformation=SoftplusTransformation)
-    assert neg_mll(params, x, y) == jnp.array(-1.)*mll(params, x, y)
+    assert neg_mll(params, x, y) == jnp.array(-1.0) * mll(params, x, y)
 
 
 def test_non_conjugate():
-    posterior = Prior(kernel = RBF()) * Bernoulli()
+    posterior = Prior(kernel=RBF()) * Bernoulli()
     mll = marginal_ll(posterior)
     assert isinstance(mll, Callable)
     neg_mll = marginal_ll(posterior, negative=True)
     n = 20
-    x = jnp.linspace(-1., 1., n).reshape(-1, 1)
+    x = jnp.linspace(-1.0, 1.0, n).reshape(-1, 1)
     y = jnp.sin(x)
     params = transform(params=initialise(posterior, n), transformation=SoftplusTransformation)
-    assert neg_mll(params, x, y) == jnp.array(-1.)*mll(params, x, y)
+    assert neg_mll(params, x, y) == jnp.array(-1.0) * mll(params, x, y)
 
 
 def test_prior_mll():
@@ -54,4 +56,4 @@ def test_prior_mll():
     mll_eval_priors = mll(params, x, y, priors)
 
     assert pytest.approx(mll_eval) == jnp.array(-115.72332969)
-    assert pytest.approx(mll_eval_priors) == jnp.array(-118.97202259)
+    assert pytest.approx(mll_eval_priors) == jnp.array(-118.97202259)