Merge pull request #442 from Thomas-Christie/update-dependencies

Update dependencies

README.md

@@ -123,7 +123,7 @@ import jax.numpy as jnp
 import jax.random as jr
 import optax as ox
 
-key = jr.PRNGKey(123)
+key = jr.key(123)
 
 f = lambda x: 10 * jnp.sin(x)
 

benchmarks/kernels.py

@@ -12,7 +12,7 @@ class Kernels:
     params = [[10, 100, 500, 1000, 2000], [1, 2, 5]]
 
     def setup(self, n_datapoints: int, n_dims: int):
-        key = jr.PRNGKey(123)
+        key = jr.key(123)
         self.X = jr.uniform(
             key=key, minval=-3.0, maxval=3.0, shape=(n_datapoints, n_dims)
         )

benchmarks/objectives.py

@@ -16,15 +16,15 @@ class Gaussian:
     params = [[10, 100, 200, 500, 1000], [1, 2, 5]]
 
     def setup(self, n_datapoints: int, n_dims: int):
-        key = jr.PRNGKey(123)
+        key = jr.key(123)
         self.X = jr.normal(key=key, shape=(n_datapoints, n_dims))
         self.y = jnp.sin(self.X[:, :1])
         self.data = gpx.Dataset(X=self.X, y=self.y)
         kernel = gpx.kernels.RBF(active_dims=list(range(n_dims)))
         meanf = gpx.mean_functions.Constant()
         self.prior = gpx.gps.Prior(kernel=kernel, mean_function=meanf)
         self.likelihood = gpx.likelihoods.Gaussian(num_datapoints=self.data.n)
-        self.objective = gpx.ConjugateMLL()
+        self.objective = gpx.objectives.ConjugateMLL()
         self.posterior = self.prior * self.likelihood
 
     def time_eval(self, n_datapoints: int, n_dims: int):
@@ -42,15 +42,15 @@ class Bernoulli:
     params = [[10, 100, 200, 500, 1000], [1, 2, 5]]
 
     def setup(self, n_datapoints: int, n_dims: int):
-        key = jr.PRNGKey(123)
+        key = jr.key(123)
         self.X = jr.normal(key=key, shape=(n_datapoints, n_dims))
         self.y = jnp.where(jnp.sin(self.X[:, :1]) > 0, 1, 0)
         self.data = gpx.Dataset(X=self.X, y=self.y)
         kernel = gpx.kernels.RBF(active_dims=list(range(n_dims)))
         meanf = gpx.mean_functions.Constant()
         self.prior = gpx.gps.Prior(kernel=kernel, mean_function=meanf)
         self.likelihood = gpx.likelihoods.Bernoulli(num_datapoints=self.data.n)
-        self.objective = gpx.LogPosteriorDensity()
+        self.objective = gpx.objectives.LogPosteriorDensity()
         self.posterior = self.prior * self.likelihood
 
     def time_eval(self, n_datapoints: int, n_dims: int):
@@ -68,7 +68,7 @@ class Poisson:
     params = [[10, 100, 200, 500, 1000], [1, 2, 5]]
 
     def setup(self, n_datapoints: int, n_dims: int):
-        key = jr.PRNGKey(123)
+        key = jr.key(123)
         self.X = jr.normal(key=key, shape=(n_datapoints, n_dims))
         f = lambda x: 2.0 * jnp.sin(3 * x) + 0.5 * x  # latent function
         self.y = jr.poisson(key, jnp.exp(f(self.X)))
@@ -77,7 +77,7 @@ def setup(self, n_datapoints: int, n_dims: int):
         meanf = gpx.mean_functions.Constant()
         self.prior = gpx.gps.Prior(kernel=kernel, mean_function=meanf)
         self.likelihood = gpx.likelihoods.Poisson(num_datapoints=self.data.n)
-        self.objective = gpx.LogPosteriorDensity()
+        self.objective = gpx.objectives.LogPosteriorDensity()
         self.posterior = self.prior * self.likelihood
 
     def time_eval(self, n_datapoints: int, n_dims: int):

benchmarks/predictions.py

@@ -15,7 +15,7 @@ class Gaussian:
     params = [[100, 200, 500, 1000, 2000, 3000], [1, 2, 5]]
 
     def setup(self, n_test: int, n_dims: int):
-        key = jr.PRNGKey(123)
+        key = jr.key(123)
         self.X = jr.normal(key=key, shape=(100, n_dims))
         self.y = jnp.sin(self.X[:, :1])
         self.data = gpx.Dataset(X=self.X, y=self.y)
@@ -39,7 +39,7 @@ class Bernoulli:
     params = [[100, 200, 500, 1000, 2000, 3000], [1, 2, 5]]
 
     def setup(self, n_test: int, n_dims: int):
-        key = jr.PRNGKey(123)
+        key = jr.key(123)
         self.X = jr.normal(key=key, shape=(100, n_dims))
         self.y = jnp.sin(self.X[:, :1])
         self.y = jnp.array(jnp.where(self.y > 0, 1, 0), dtype=jnp.float64)
@@ -64,7 +64,7 @@ class Poisson:
     params = [[100, 200, 500, 1000, 2000, 3000], [1, 2, 5]]
 
     def setup(self, n_test: int, n_dims: int):
-        key = jr.PRNGKey(123)
+        key = jr.key(123)
         self.X = jr.normal(key=key, shape=(100, n_dims))
         f = lambda x: 2.0 * jnp.sin(3 * x) + 0.5 * x  # latent function
         self.y = jnp.array(jr.poisson(key, jnp.exp(f(self.X))), dtype=jnp.float64)

benchmarks/sparse.py

@@ -13,7 +13,7 @@ class Sparse:
     params = [[2000, 5000, 10000, 20000], [10, 20, 50, 100, 200]]
 
     def setup(self, n_datapoints: int, n_inducing: int):
-        key = jr.PRNGKey(123)
+        key = jr.key(123)
         self.X = jr.normal(key=key, shape=(n_datapoints, 1))
         self.y = jnp.sin(self.X[:, :1])
         self.data = gpx.Dataset(X=self.X, y=self.y)
@@ -24,10 +24,10 @@ def setup(self, n_datapoints: int, n_inducing: int):
         self.posterior = self.prior * self.likelihood
 
         Z = jnp.linspace(self.X.min(), self.X.max(), n_inducing).reshape(-1, 1)
-        self.q = gpx.CollapsedVariationalGaussian(
+        self.q = gpx.variational_families.CollapsedVariationalGaussian(
             posterior=self.posterior, inducing_inputs=Z
         )
-        self.objective = gpx.CollapsedELBO(negative=True)
+        self.objective = gpx.objectives.CollapsedELBO(negative=True)
 
     def time_eval(self, n_datapoints: int, n_dims: int):
         self.objective(self.q, self.data)

benchmarks/stochastic.py

@@ -14,7 +14,7 @@ class Sparse:
     params = [[10000, 20000, 50000], [10, 20, 50, 100, 200], [32, 64, 128, 256]]
 
     def setup(self, n_datapoints: int, n_inducing: int, batch_size: int):
-        key = jr.PRNGKey(123)
+        key = jr.key(123)
         self.X = jr.normal(key=key, shape=(n_datapoints, 1))
         self.y = jnp.sin(self.X[:, :1])
         self.data = gpx.Dataset(X=self.X, y=self.y)
@@ -25,15 +25,17 @@ def setup(self, n_datapoints: int, n_inducing: int, batch_size: int):
         self.posterior = self.prior * self.likelihood
 
         Z = jnp.linspace(self.X.min(), self.X.max(), n_inducing).reshape(-1, 1)
-        self.q = gpx.VariationalGaussian(posterior=self.posterior, inducing_inputs=Z)
-        self.objective = gpx.ELBO(negative=True)
+        self.q = gpx.variational_families.VariationalGaussian(
+            posterior=self.posterior, inducing_inputs=Z
+        )
+        self.objective = gpx.objectives.ELBO(negative=True)
 
     def time_eval(self, n_datapoints: int, n_dims: int, batch_size: int):
-        key = jr.PRNGKey(123)
+        key = jr.key(123)
         batch = get_batch(train_data=self.data, batch_size=batch_size, key=key)
         self.objective(self.q, batch)
 
     def time_grad(self, n_datapoints: int, n_dims: int, batch_size: int):
-        key = jr.PRNGKey(123)
+        key = jr.key(123)
         batch = get_batch(train_data=self.data, batch_size=batch_size, key=key)
         jax.grad(self.objective)(self.q, batch)

docs/_static/jaxkern/main.py

@@ -6,7 +6,7 @@
 
 # import gpjax.kernels as jk
 
-# key = jr.PRNGKey(123)
+# key = jr.key(123)
 
 
 # def set_font(font_path):

docs/examples/barycentres.py

@@ -33,7 +33,7 @@
     import gpjax as gpx
 
 
-key = jr.PRNGKey(123)
+key = jr.key(123)
 plt.style.use(
     "https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle"
 )

docs/examples/bayesian_optimisation.py

@@ -28,7 +28,7 @@
 from gpjax.typing import Array, FunctionalSample, ScalarFloat
 from jaxopt import ScipyBoundedMinimize
 
-key = jr.PRNGKey(42)
+key = jr.key(42)
 plt.style.use(
     "https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle"
 )

docs/examples/classification.py

@@ -51,7 +51,7 @@
 
 tfd = tfp.distributions
 identity_matrix = jnp.eye
-key = jr.PRNGKey(123)
+key = jr.key(123)
 plt.style.use(
     "https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle"
 )

docs/examples/collapsed_vi.py

@@ -43,7 +43,7 @@
 with install_import_hook("gpjax", "beartype.beartype"):
     import gpjax as gpx
 
-key = jr.PRNGKey(123)
+key = jr.key(123)
 plt.style.use(
     "https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle"
 )

docs/examples/constructing_new_kernels.py

@@ -47,7 +47,7 @@
     import gpjax as gpx
     from gpjax.base.param import param_field
 
-key = jr.PRNGKey(123)
+key = jr.key(123)
 tfb = tfp.bijectors
 plt.style.use(
     "https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle"

docs/examples/decision_making.py

@@ -49,7 +49,7 @@
     Float,
 )
 
-key = jr.PRNGKey(42)
+key = jr.key(42)
 plt.style.use(
     "https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle"
 )

docs/examples/deep_kernels.py

@@ -44,7 +44,7 @@
     from gpjax.kernels.base import AbstractKernel
     from gpjax.kernels.computations import AbstractKernelComputation
 
-key = jr.PRNGKey(123)
+key = jr.key(123)
 plt.style.use(
     "https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle"
 )
@@ -103,7 +103,7 @@ class DeepKernelFunction(AbstractKernel):
     base_kernel: AbstractKernel = None
     network: nn.Module = static_field(None)
     dummy_x: jax.Array = static_field(None)
-    key: jr.PRNGKeyArray = static_field(jr.PRNGKey(123))
+    key: jax.Array = static_field(jr.key(123))
     nn_params: Any = field(init=False, repr=False)
 
     def __post_init__(self):

docs/examples/graph_kernels.py

@@ -27,7 +27,7 @@
 with install_import_hook("gpjax", "beartype.beartype"):
     import gpjax as gpx
 
-key = jr.PRNGKey(123)
+key = jr.key(123)
 plt.style.use(
     "https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle"
 )

docs/examples/intro_to_gps.py

@@ -158,7 +158,7 @@
 # determines the correlation of the multivariate Gaussian.
 
 # %%
-key = jr.PRNGKey(123)
+key = jr.key(123)
 
 d1 = tfd.MultivariateNormalDiag(loc=jnp.zeros(2), scale_diag=jnp.ones(2))
 d2 = tfd.MultivariateNormalTriL(

docs/examples/intro_to_kernels.py

@@ -24,7 +24,7 @@
 from gpjax.typing import Array
 from sklearn.preprocessing import StandardScaler
 
-key = jr.PRNGKey(42)
+key = jr.key(42)
 plt.style.use(
     "https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle"
 )
@@ -249,17 +249,24 @@ def forrester(x: Float[Array, "N"]) -> Float[Array, "N"]:
 # with the optimised hyperparameters, and compare them to the predictions made using the
 # posterior with the default hyperparameters:
 
+
 # %%
 def plot_ribbon(ax, x, dist, color):
     mean = dist.mean()
     std = dist.stddev()
     ax.plot(x, mean, label="Predictive mean", color=color)
-    ax.fill_between(x.squeeze(), mean - 2 * std, mean + 2 * std, alpha=0.2, label="Two sigma", color=color)
+    ax.fill_between(
+        x.squeeze(),
+        mean - 2 * std,
+        mean + 2 * std,
+        alpha=0.2,
+        label="Two sigma",
+        color=color,
+    )
     ax.plot(x, mean - 2 * std, linestyle="--", linewidth=1, color=color)
     ax.plot(x, mean + 2 * std, linestyle="--", linewidth=1, color=color)
 
 
-
 # %%
 opt_latent_dist = opt_posterior.predict(test_x, train_data=D)
 opt_predictive_dist = opt_posterior.likelihood(opt_latent_dist)

docs/examples/likelihoods_guide.py

@@ -66,7 +66,7 @@
     "https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle"
 )
 cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
-key = jr.PRNGKey(123)
+key = jr.key(123)
 
 
 n = 50

docs/examples/oceanmodelling.py

@@ -30,7 +30,7 @@
     import gpjax as gpx
 
 # Enable Float64 for more stable matrix inversions.
-key = jr.PRNGKey(123)
+key = jr.key(123)
 plt.style.use(
     "https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle"
 )

docs/examples/poisson.py

@@ -40,7 +40,7 @@
 # Enable Float64 for more stable matrix inversions.
 config.update("jax_enable_x64", True)
 tfd = tfp.distributions
-key = jr.PRNGKey(123)
+key = jr.key(123)
 plt.style.use(
     "https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle"
 )

docs/examples/pytrees.md

@@ -397,9 +397,9 @@ class RBF(Module):
         init=False, bijector=tfb.Softplus(), trainable=True
     )
     variance: float = param_field(init=False, bijector=tfb.Softplus(), trainable=True)
-    key: jr.KeyArray = field(default_factory = lambda: jr.PRNGKey(42))
+    key: jax.Array = field(default_factory = lambda: jr.key(42))
     # Note, for Python <3.11 you may use the following:
-    # key: jr.KeyArray = jr.PRNGKey(42)
+    # key: jax.Array = jr.key(42)
 
     def __post_init__(self):
         # Split key into two keys
@@ -444,7 +444,7 @@ class RBF(Module):
         init=False, bijector=tfb.Softplus(), trainable=True
     )
     variance: float = param_field(init=False, bijector=tfb.Softplus(), trainable=True)
-    key: jr.KeyArray = static_field(default_factory=lambda: jr.PRNGKey(42))
+    key: jax.Array = static_field(default_factory=lambda: jr.key(42))
 
     def __post_init__(self):
         # Split key into two keys

docs/examples/regression.py

@@ -37,7 +37,7 @@
 with install_import_hook("gpjax", "beartype.beartype"):
     import gpjax as gpx
 
-key = jr.PRNGKey(123)
+key = jr.key(123)
 plt.style.use(
     "https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle"
 )