From a4176e454d2f73663b8f2f16ce4732a6e021b6bd Mon Sep 17 00:00:00 2001 From: Thomas-Christie Date: Mon, 11 Mar 2024 21:15:56 +0000 Subject: [PATCH] Update dependencies --- README.md | 2 +- benchmarks/kernels.py | 2 +- benchmarks/linops.py | 32 - benchmarks/objectives.py | 12 +- benchmarks/predictions.py | 6 +- benchmarks/sparse.py | 6 +- benchmarks/stochastic.py | 12 +- docs/_static/jaxkern/main.py | 2 +- docs/examples/barycentres.py | 2 +- docs/examples/bayesian_optimisation.py | 2 +- docs/examples/classification.py | 2 +- docs/examples/collapsed_vi.py | 2 +- docs/examples/constructing_new_kernels.py | 2 +- docs/examples/decision_making.py | 2 +- docs/examples/deep_kernels.py | 4 +- docs/examples/graph_kernels.py | 2 +- docs/examples/intro_to_gps.py | 2 +- docs/examples/intro_to_kernels.py | 13 +- docs/examples/likelihoods_guide.py | 2 +- docs/examples/oceanmodelling.py | 2 +- docs/examples/poisson.py | 2 +- docs/examples/pytrees.md | 6 +- docs/examples/regression.py | 2 +- docs/examples/uncollapsed_vi.py | 6 +- docs/examples/yacht.py | 2 +- docs/sharp_bits.md | 2 +- .../test_functions/non_conjugate_functions.py | 2 +- gpjax/fit.py | 6 +- gpjax/gps.py | 3 +- poetry.lock | 3817 +++++++++-------- pyproject.toml | 10 +- .../test_decision_maker.py | 54 +- .../test_posterior_handler.py | 24 +- .../test_decision_making/test_search_space.py | 16 +- .../test_continuous_functions.py | 18 +- .../test_non_conjugate_functions.py | 16 +- .../test_thompson_sampling.py | 14 +- .../test_utility_maximizer.py | 8 +- tests/test_fit.py | 14 +- tests/test_gaussian_distribution.py | 2 +- tests/test_gps.py | 16 +- tests/test_kernels/test_approximations.py | 12 +- tests/test_kernels/test_nonstationary.py | 4 +- tests/test_likelihoods.py | 2 +- tests/test_mean_functions.py | 2 +- tests/test_objectives.py | 10 +- tests/test_variational_families.py | 2 +- 47 files changed, 2117 insertions(+), 2066 deletions(-) delete mode 100644 benchmarks/linops.py diff --git a/README.md b/README.md index 3917f14d7..b5f2d9082 100644 --- a/README.md +++ b/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) diff --git a/benchmarks/kernels.py b/benchmarks/kernels.py index d9d202931..34e6582fb 100644 --- a/benchmarks/kernels.py +++ b/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) ) diff --git a/benchmarks/linops.py b/benchmarks/linops.py deleted file mode 100644 index a9fd6bd01..000000000 --- a/benchmarks/linops.py +++ /dev/null @@ -1,32 +0,0 @@ -from jax import config - -config.update("jax_enable_x64", True) - -import jax.numpy as jnp -import jax.random as jr -from sklearn.datasets import make_spd_matrix - -from gpjax.linops import DenseLinearOperator - - -class LinOps: - param_names = ["n_data"] - params = [[10, 100, 200, 500, 1000]] - - def setup(self, n_datapoints: int): - key = jr.PRNGKey(123) - self.X = jnp.asarray(make_spd_matrix(n_dim=n_datapoints, random_state=123)) - self.y = jr.normal(key=key, shape=(n_datapoints, 1)) - self.linop = DenseLinearOperator(matrix=self.X) - - def time_root(self, n_datapoints: int): - self.linop.to_root() - - def time_inverse(self, n_datapoints: int): - self.linop.inverse() - - def time_logdet(self, n_datapoints: int): - self.linop.log_det() - - def time_solve(self, n_datapoints: int): - self.linop.solve(self.y) diff --git a/benchmarks/objectives.py b/benchmarks/objectives.py index 65b8569c2..832b580d8 100644 --- a/benchmarks/objectives.py +++ b/benchmarks/objectives.py @@ -16,7 +16,7 @@ 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) @@ -24,7 +24,7 @@ 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.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): @@ -42,7 +42,7 @@ 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) @@ -50,7 +50,7 @@ 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.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): diff --git a/benchmarks/predictions.py b/benchmarks/predictions.py index a3dd4fa8e..6cc3136a8 100644 --- a/benchmarks/predictions.py +++ b/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) diff --git a/benchmarks/sparse.py b/benchmarks/sparse.py index de7d47054..5b3b66828 100644 --- a/benchmarks/sparse.py +++ b/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) diff --git a/benchmarks/stochastic.py b/benchmarks/stochastic.py index 1e530c731..14872ba12 100644 --- a/benchmarks/stochastic.py +++ b/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) diff --git a/docs/_static/jaxkern/main.py b/docs/_static/jaxkern/main.py index 076a871e0..a0b1cc617 100644 --- a/docs/_static/jaxkern/main.py +++ b/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): diff --git a/docs/examples/barycentres.py b/docs/examples/barycentres.py index ef5f0062a..55639e851 100644 --- a/docs/examples/barycentres.py +++ b/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" ) diff --git a/docs/examples/bayesian_optimisation.py b/docs/examples/bayesian_optimisation.py index 39feba64f..8182beae1 100644 --- a/docs/examples/bayesian_optimisation.py +++ b/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" ) diff --git a/docs/examples/classification.py b/docs/examples/classification.py index 4b9552368..e8e5f1790 100644 --- a/docs/examples/classification.py +++ b/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" ) diff --git a/docs/examples/collapsed_vi.py b/docs/examples/collapsed_vi.py index 6ee7d81d7..ea1caed69 100644 --- a/docs/examples/collapsed_vi.py +++ b/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" ) diff --git a/docs/examples/constructing_new_kernels.py b/docs/examples/constructing_new_kernels.py index 073015ca5..9df2b8fae 100644 --- a/docs/examples/constructing_new_kernels.py +++ b/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" diff --git a/docs/examples/decision_making.py b/docs/examples/decision_making.py index 66dc77a93..1460fc25a 100644 --- a/docs/examples/decision_making.py +++ b/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" ) diff --git a/docs/examples/deep_kernels.py b/docs/examples/deep_kernels.py index 97123fbd5..28e03dcb8 100644 --- a/docs/examples/deep_kernels.py +++ b/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): diff --git a/docs/examples/graph_kernels.py b/docs/examples/graph_kernels.py index d475d119b..8ca54842a 100644 --- a/docs/examples/graph_kernels.py +++ b/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" ) diff --git a/docs/examples/intro_to_gps.py b/docs/examples/intro_to_gps.py index ccf9242ab..363e3d43c 100644 --- a/docs/examples/intro_to_gps.py +++ b/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( diff --git a/docs/examples/intro_to_kernels.py b/docs/examples/intro_to_kernels.py index 1519ee014..fea3d781a 100644 --- a/docs/examples/intro_to_kernels.py +++ b/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) diff --git a/docs/examples/likelihoods_guide.py b/docs/examples/likelihoods_guide.py index 9199d9e3b..e46b73e2c 100644 --- a/docs/examples/likelihoods_guide.py +++ b/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 diff --git a/docs/examples/oceanmodelling.py b/docs/examples/oceanmodelling.py index 77f125b12..a5920e231 100644 --- a/docs/examples/oceanmodelling.py +++ b/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" ) diff --git a/docs/examples/poisson.py b/docs/examples/poisson.py index 61ff0f615..8fd78a08b 100644 --- a/docs/examples/poisson.py +++ b/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" ) diff --git a/docs/examples/pytrees.md b/docs/examples/pytrees.md index 3f63004a7..966e01054 100644 --- a/docs/examples/pytrees.md +++ b/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 diff --git a/docs/examples/regression.py b/docs/examples/regression.py index f1fc77a32..7a2596f76 100644 --- a/docs/examples/regression.py +++ b/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" ) diff --git a/docs/examples/uncollapsed_vi.py b/docs/examples/uncollapsed_vi.py index 0c099464e..349a3a3bc 100644 --- a/docs/examples/uncollapsed_vi.py +++ b/docs/examples/uncollapsed_vi.py @@ -49,7 +49,7 @@ import gpjax as gpx import gpjax.kernels as jk -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" @@ -270,7 +270,7 @@ train_data=D, optim=ox.adam(learning_rate=schedule), num_iters=3000, - key=jr.PRNGKey(42), + key=jr.key(42), batch_size=128, ) # %% [markdown] @@ -334,7 +334,7 @@ train_data=D, optim=ox.adam(learning_rate=0.01), num_iters=3000, - key=jr.PRNGKey(42), + key=jr.key(42), batch_size=128, ) diff --git a/docs/examples/yacht.py b/docs/examples/yacht.py index b5d7fd0dc..7a7ae3193 100644 --- a/docs/examples/yacht.py +++ b/docs/examples/yacht.py @@ -47,7 +47,7 @@ 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" ) diff --git a/docs/sharp_bits.md b/docs/sharp_bits.md index 91d544814..f0beccd70 100644 --- a/docs/sharp_bits.md +++ b/docs/sharp_bits.md @@ -12,7 +12,7 @@ To examine what it means for a PRNG to be stateful, consider the following examp ```python import numpy as np import jax.random as jr -key = jr.PRNGKey(123) +key = jr.key(123) # NumPy print('NumPy:') diff --git a/gpjax/decision_making/test_functions/non_conjugate_functions.py b/gpjax/decision_making/test_functions/non_conjugate_functions.py index 94fa4ecb1..eec353be2 100644 --- a/gpjax/decision_making/test_functions/non_conjugate_functions.py +++ b/gpjax/decision_making/test_functions/non_conjugate_functions.py @@ -85,6 +85,6 @@ def evaluate(self, x: Float[Array, "N 1"]) -> Integer[Array, "N 1"]: Returns: Integer[Array, 'N 1']: Values of the test function at the points. """ - key = jr.PRNGKey(42) + key = jr.key(42) f = lambda x: 2.0 * jnp.sin(3 * x) + 0.5 * x return jr.poisson(key, jnp.exp(f(x))) diff --git a/gpjax/fit.py b/gpjax/fit.py index 45627e08a..df27ba5f4 100644 --- a/gpjax/fit.py +++ b/gpjax/fit.py @@ -73,7 +73,7 @@ def fit( # noqa: PLR0913 >>> >>> # (1) Create a dataset: >>> X = jnp.linspace(0.0, 10.0, 100)[:, None] - >>> y = 2.0 * X + 1.0 + 10 * jr.normal(jr.PRNGKey(0), X.shape) + >>> y = 2.0 * X + 1.0 + 10 * jr.normal(jr.key(0), X.shape) >>> D = gpx.Dataset(X, y) >>> >>> # (2) Define your model: @@ -111,7 +111,7 @@ def evaluate(self, model: LinearModel, train_data: gpx.Dataset) -> float: batch_size (Optional[int]): The size of the mini-batch to use. Defaults to -1 (i.e. full batch). key (Optional[KeyArray]): The random key to use for the optimisation batch - selection. Defaults to jr.PRNGKey(42). + selection. Defaults to jr.key(42). log_rate (Optional[int]): How frequently the objective function's value should be printed. Defaults to 10. verbose (Optional[bool]): Whether to print the training loading bar. Defaults @@ -131,7 +131,7 @@ def evaluate(self, model: LinearModel, train_data: gpx.Dataset) -> float: _check_optim(optim) _check_num_iters(num_iters) _check_batch_size(batch_size) - _check_prng_key(key) + _check_prng_key("fit", key) _check_log_rate(log_rate) _check_verbose(verbose) diff --git a/gpjax/gps.py b/gpjax/gps.py index 32a06212d..f5fb80475 100644 --- a/gpjax/gps.py +++ b/gpjax/gps.py @@ -147,6 +147,7 @@ class Prior(AbstractPrior[MeanFunction, Kernel]): >>> prior = gpx.gps.Prior(mean_function=meanf, kernel = kernel) ``` """ + if TYPE_CHECKING: @overload @@ -315,7 +316,7 @@ def sample_approx( >>> import gpjax as gpx >>> import jax.numpy as jnp >>> import jax.random as jr - >>> key = jr.PRNGKey(123) + >>> key = jr.key(123) >>> >>> meanf = gpx.mean_functions.Zero() >>> kernel = gpx.kernels.RBF() diff --git a/poetry.lock b/poetry.lock index eb9f9cc49..9a03aea78 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,14 +1,14 @@ -# This file is automatically @generated by Poetry 1.7.0 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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simple-pytree = "^0.1.7" -tensorflow-probability = "^0.19.0" +tensorflow-probability = "^0.22.0" beartype = "^0.16.2" -jax = ">=0.4.10" -jaxlib = ">=0.4.10" +jax = ">=0.4.16" +jaxlib = ">=0.4.16" orbax-checkpoint = ">=0.2.3" cola-ml = "^0.0.5" @@ -46,7 +46,7 @@ isort = "^5.12.0" pylint = "^2.17.1" nox = "^2022.11.21" ruff = "^0.0.259" -flax = "^0.6.8" +flax = ">=0.7.5" pre-commit = "^3.2.2" nbstripout = "^0.6.1" pydocstyle = "^6.3.0" @@ -70,7 +70,7 @@ ipywidgets = "^8.0.5" pandas = "^1.5.3" geopandas = "^0.12.2" scikit-learn = "^1.2.2" -flax = "^0.6.8" +flax = ">=0.7.5" xarray = "^2023.1" pystac-client = "^0.6.1" planetary-computer = "^1.0.0" diff --git a/tests/test_decision_making/test_decision_maker.py b/tests/test_decision_making/test_decision_maker.py index 23a87a2f1..98d4658f2 100644 --- a/tests/test_decision_making/test_decision_maker.py +++ b/tests/test_decision_making/test_decision_maker.py @@ -116,9 +116,9 @@ def test_invalid_batch_size_raises_error( utility_maximizer: AbstractSinglePointUtilityMaximizer, batch_size: int, ): - key = jr.PRNGKey(42) + key = jr.key(42) posterior_handlers = {OBJECTIVE: posterior_handler} - objective_dataset = get_dataset(num_points=5, key=jr.PRNGKey(42)) + objective_dataset = get_dataset(num_points=5, key=jr.key(42)) datasets = {"OBJECTIVE": objective_dataset} with pytest.raises(ValueError): UtilityDrivenDecisionMaker( @@ -143,9 +143,9 @@ def test_non_thompson_sampling_non_one_batch_size_raises_error( utility_function_builder: AbstractSinglePointUtilityFunctionBuilder, utility_maximizer: AbstractSinglePointUtilityMaximizer, ): - key = jr.PRNGKey(42) + key = jr.key(42) posterior_handlers = {OBJECTIVE: posterior_handler} - objective_dataset = get_dataset(num_points=5, key=jr.PRNGKey(42)) + objective_dataset = get_dataset(num_points=5, key=jr.key(42)) datasets = {"OBJECTIVE": objective_dataset} with pytest.raises(NotImplementedError): UtilityDrivenDecisionMaker( @@ -170,9 +170,9 @@ def test_invalid_tags_raises_error( utility_function_builder: AbstractSinglePointUtilityFunctionBuilder, utility_maximizer: AbstractSinglePointUtilityMaximizer, ): - key = jr.PRNGKey(42) + key = jr.key(42) posterior_handlers = {OBJECTIVE: posterior_handler} - dataset = get_dataset(num_points=5, key=jr.PRNGKey(42)) + dataset = get_dataset(num_points=5, key=jr.key(42)) datasets = {"CONSTRAINT": dataset} # Dataset tag doesn't match posterior tag with pytest.raises(ValueError): UtilityDrivenDecisionMaker( @@ -197,10 +197,10 @@ def test_initialisation_optimizes_posterior_hyperparameters( utility_function_builder: AbstractSinglePointUtilityFunctionBuilder, utility_maximizer: AbstractSinglePointUtilityMaximizer, ): - key = jr.PRNGKey(42) + key = jr.key(42) posterior_handlers = {OBJECTIVE: posterior_handler, CONSTRAINT: posterior_handler} - objective_dataset = get_dataset(num_points=5, key=jr.PRNGKey(42)) - constraint_dataset = get_dataset(num_points=5, key=jr.PRNGKey(10)) + objective_dataset = get_dataset(num_points=5, key=jr.key(42)) + constraint_dataset = get_dataset(num_points=5, key=jr.key(10)) datasets = {"OBJECTIVE": objective_dataset, CONSTRAINT: constraint_dataset} decision_maker = UtilityDrivenDecisionMaker( search_space=search_space, @@ -241,9 +241,9 @@ def test_decision_maker_ask( utility_function_builder: AbstractSinglePointUtilityFunctionBuilder, utility_maximizer: AbstractSinglePointUtilityMaximizer, ): - key = jr.PRNGKey(42) + key = jr.key(42) posterior_handlers = {OBJECTIVE: posterior_handler} - objective_dataset = get_dataset(num_points=5, key=jr.PRNGKey(42)) + objective_dataset = get_dataset(num_points=5, key=jr.key(42)) datasets = {"OBJECTIVE": objective_dataset} decision_maker = UtilityDrivenDecisionMaker( search_space=search_space, @@ -277,9 +277,9 @@ def test_decision_maker_ask_multi_batch_ts( utility_maximizer: AbstractSinglePointUtilityMaximizer, batch_size: int, ): - key = jr.PRNGKey(42) + key = jr.key(42) posterior_handlers = {OBJECTIVE: posterior_handler} - objective_dataset = get_dataset(num_points=5, key=jr.PRNGKey(42)) + objective_dataset = get_dataset(num_points=5, key=jr.key(42)) datasets = {"OBJECTIVE": objective_dataset} decision_maker = UtilityDrivenDecisionMaker( search_space=search_space, @@ -313,10 +313,10 @@ def test_decision_maker_tell_with_inconsistent_observations_raises_error( utility_function_builder: AbstractSinglePointUtilityFunctionBuilder, utility_maximizer: AbstractSinglePointUtilityMaximizer, ): - key = jr.PRNGKey(42) + key = jr.key(42) posterior_handlers = {OBJECTIVE: posterior_handler, CONSTRAINT: posterior_handler} - initial_objective_dataset = get_dataset(num_points=5, key=jr.PRNGKey(42)) - initial_constraint_dataset = get_dataset(num_points=5, key=jr.PRNGKey(10)) + initial_objective_dataset = get_dataset(num_points=5, key=jr.key(42)) + initial_constraint_dataset = get_dataset(num_points=5, key=jr.key(10)) datasets = { OBJECTIVE: initial_objective_dataset, CONSTRAINT: initial_constraint_dataset, @@ -332,8 +332,8 @@ def test_decision_maker_tell_with_inconsistent_observations_raises_error( post_tell=[], batch_size=1, ) - mock_objective_observation = get_dataset(num_points=1, key=jr.PRNGKey(1)) - mock_constraint_observation = get_dataset(num_points=1, key=jr.PRNGKey(2)) + mock_objective_observation = get_dataset(num_points=1, key=jr.key(1)) + mock_constraint_observation = get_dataset(num_points=1, key=jr.key(2)) observations = { OBJECTIVE: mock_objective_observation, "CONSTRAINT_ONE": mock_constraint_observation, # Deliberately incorrect tag @@ -351,10 +351,10 @@ def test_decision_maker_tell_updates_datasets_and_models( utility_function_builder: AbstractSinglePointUtilityFunctionBuilder, utility_maximizer: AbstractSinglePointUtilityMaximizer, ): - key = jr.PRNGKey(42) + key = jr.key(42) posterior_handlers = {OBJECTIVE: posterior_handler, CONSTRAINT: posterior_handler} - initial_objective_dataset = get_dataset(num_points=5, key=jr.PRNGKey(42)) - initial_constraint_dataset = get_dataset(num_points=5, key=jr.PRNGKey(10)) + initial_objective_dataset = get_dataset(num_points=5, key=jr.key(42)) + initial_constraint_dataset = get_dataset(num_points=5, key=jr.key(10)) datasets = { "OBJECTIVE": initial_objective_dataset, CONSTRAINT: initial_constraint_dataset, @@ -373,8 +373,8 @@ def test_decision_maker_tell_updates_datasets_and_models( initial_decision_maker_key = decision_maker.key initial_objective_posterior = decision_maker.posteriors[OBJECTIVE] initial_constraint_posterior = decision_maker.posteriors[CONSTRAINT] - mock_objective_observation = get_dataset(num_points=1, key=jr.PRNGKey(1)) - mock_constraint_observation = get_dataset(num_points=1, key=jr.PRNGKey(2)) + mock_objective_observation = get_dataset(num_points=1, key=jr.key(1)) + mock_constraint_observation = get_dataset(num_points=1, key=jr.key(2)) observations = { OBJECTIVE: mock_objective_observation, CONSTRAINT: mock_constraint_observation, @@ -416,9 +416,9 @@ def test_decision_maker_run( utility_maximizer: AbstractSinglePointUtilityMaximizer, n_steps: int, ): - key = jr.PRNGKey(42) + key = jr.key(42) posterior_handlers = {OBJECTIVE: posterior_handler} - initial_objective_dataset = get_dataset(num_points=5, key=jr.PRNGKey(42)) + initial_objective_dataset = get_dataset(num_points=5, key=jr.key(42)) initial_datasets = { "OBJECTIVE": initial_objective_dataset, } @@ -464,9 +464,9 @@ def test_decision_maker_run_ts( n_steps: int, batch_size: int, ): - key = jr.PRNGKey(42) + key = jr.key(42) posterior_handlers = {OBJECTIVE: posterior_handler} - initial_objective_dataset = get_dataset(num_points=5, key=jr.PRNGKey(42)) + initial_objective_dataset = get_dataset(num_points=5, key=jr.key(42)) initial_datasets = { "OBJECTIVE": initial_objective_dataset, } diff --git a/tests/test_decision_making/test_posterior_handler.py b/tests/test_decision_making/test_posterior_handler.py index 71bf9a4b4..ade638fca 100644 --- a/tests/test_decision_making/test_posterior_handler.py +++ b/tests/test_decision_making/test_posterior_handler.py @@ -89,7 +89,7 @@ def test_get_optimized_posterior_with_no_key_raises_error(): num_optimization_iters=10, ) toy_function = Forrester() - dataset = toy_function.generate_dataset(num_points=5, key=jr.PRNGKey(42)) + dataset = toy_function.generate_dataset(num_points=5, key=jr.key(42)) with pytest.raises(ValueError): posterior_handler.get_posterior(dataset=dataset, optimize=True) @@ -111,7 +111,7 @@ def test_update_and_optimize_posterior_with_no_key_raises_error(): num_optimization_iters=10, ) toy_function = Forrester() - dataset = toy_function.generate_dataset(num_points=5, key=jr.PRNGKey(42)) + dataset = toy_function.generate_dataset(num_points=5, key=jr.key(42)) initial_posterior = posterior_handler.get_posterior(dataset=dataset, optimize=False) with pytest.raises(ValueError): posterior_handler.update_posterior( @@ -150,9 +150,7 @@ def test_get_posterior_no_optimization_correct_num_datapoints_and_not_optimized( optimizer=ox.adam(learning_rate=0.01), num_optimization_iters=10, ) - dataset = test_function.generate_dataset( - num_points=num_datapoints, key=jr.PRNGKey(42) - ) + dataset = test_function.generate_dataset(num_points=num_datapoints, key=jr.key(42)) posterior = posterior_handler.get_posterior(dataset=dataset, optimize=False) assert posterior.likelihood.num_datapoints == num_datapoints assert posterior.prior.mean_function.constant == jnp.array([1.0]) @@ -192,11 +190,9 @@ def test_get_posterior_with_optimization_correct_num_datapoints_and_optimized( optimizer=ox.adam(learning_rate=0.01), num_optimization_iters=10, ) - dataset = test_function.generate_dataset( - num_points=num_datapoints, key=jr.PRNGKey(42) - ) + dataset = test_function.generate_dataset(num_points=num_datapoints, key=jr.key(42)) optimized_posterior = posterior_handler.get_posterior( - dataset=dataset, optimize=True, key=jr.PRNGKey(42) + dataset=dataset, optimize=True, key=jr.key(42) ) assert optimized_posterior.likelihood.num_datapoints == num_datapoints assert optimized_posterior.prior.mean_function.constant != jnp.array([1.0]) @@ -239,13 +235,13 @@ def test_update_posterior_no_optimize_same_prior_parameters_and_different_num_da num_optimization_iters=10, ) initial_dataset = test_function.generate_dataset( - num_points=initial_num_datapoints, key=jr.PRNGKey(42) + num_points=initial_num_datapoints, key=jr.key(42) ) initial_posterior = posterior_handler.get_posterior( dataset=initial_dataset, optimize=False ) updated_dataset = initial_dataset + test_function.generate_dataset( - num_points=1, key=jr.PRNGKey(42) + num_points=1, key=jr.key(42) ) assert updated_dataset.n == initial_dataset.n + 1 updated_posterior = posterior_handler.update_posterior( @@ -298,13 +294,13 @@ def test_update_posterior_with_optimization_updated_prior_parameters_and_differe num_optimization_iters=10, ) initial_dataset = test_function.generate_dataset( - num_points=initial_num_datapoints, key=jr.PRNGKey(42) + num_points=initial_num_datapoints, key=jr.key(42) ) initial_posterior = posterior_handler.get_posterior( dataset=initial_dataset, optimize=False ) updated_dataset = initial_dataset + test_function.generate_dataset( - num_points=1, key=jr.PRNGKey(42) + num_points=1, key=jr.key(42) ) assert updated_dataset.n == initial_dataset.n + 1 non_optimized_updated_posterior = posterior_handler.update_posterior( @@ -314,7 +310,7 @@ def test_update_posterior_with_optimization_updated_prior_parameters_and_differe dataset=updated_dataset, previous_posterior=initial_posterior, optimize=True, - key=jr.PRNGKey(42), + key=jr.key(42), ) assert ( optimized_updated_posterior.prior.kernel.lengthscale diff --git a/tests/test_decision_making/test_search_space.py b/tests/test_decision_making/test_search_space.py index 95c47f8bb..5fc3e1e3d 100644 --- a/tests/test_decision_making/test_search_space.py +++ b/tests/test_decision_making/test_search_space.py @@ -115,7 +115,7 @@ def test_continous_search_space_invalid_sample_num_points( continuous_search_space: ContinuousSearchSpace, num_points: int ): with pytest.raises(ValueError): - continuous_search_space.sample(num_points=num_points, key=jr.PRNGKey(42)) + continuous_search_space.sample(num_points=num_points, key=jr.key(42)) @pytest.mark.parametrize( @@ -138,7 +138,7 @@ def test_continous_search_space_invalid_sample_num_points( def test_continuous_search_space_sample_shape( continuous_search_space: ContinuousSearchSpace, dimensionality: int, num_points: int ): - samples = continuous_search_space.sample(num_points=num_points, key=jr.PRNGKey(42)) + samples = continuous_search_space.sample(num_points=num_points, key=jr.key(42)) assert samples.shape[0] == num_points assert samples.shape[1] == dimensionality @@ -151,12 +151,12 @@ def test_continuous_search_space_sample_shape( ContinuousSearchSpace(jnp.array([0.0, 0.0, 0.0]), jnp.array([1.0, 1.0, 1.0])), ], ) -@pytest.mark.parametrize("key", [jr.PRNGKey(42), jr.PRNGKey(5)]) +@pytest.mark.parametrize("key", [jr.key(42), jr.key(5)]) @pytest.mark.filterwarnings( "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort def test_continous_search_space_sample_same_key_same_samples( - continuous_search_space: ContinuousSearchSpace, key: jr.PRNGKey + continuous_search_space: ContinuousSearchSpace, key: jr.key ): sample_one = continuous_search_space.sample(num_points=100, key=key) sample_two = continuous_search_space.sample(num_points=100, key=key) @@ -173,15 +173,15 @@ def test_continous_search_space_sample_same_key_same_samples( ) @pytest.mark.parametrize( "key_one, key_two", - [(jr.PRNGKey(42), jr.PRNGKey(5)), (jr.PRNGKey(1), jr.PRNGKey(2))], + [(jr.key(42), jr.key(5)), (jr.key(1), jr.key(2))], ) @pytest.mark.filterwarnings( "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort def test_continuous_search_space_different_keys_different_samples( continuous_search_space: ContinuousSearchSpace, - key_one: jr.PRNGKey, - key_two: jr.PRNGKey, + key_one: jr.key, + key_two: jr.key, ): sample_one = continuous_search_space.sample(num_points=100, key=key_one) sample_two = continuous_search_space.sample(num_points=100, key=key_two) @@ -212,7 +212,7 @@ def test_continuous_search_space_different_keys_different_samples( def test_continuous_search_space_valid_sample_ranges( continuous_search_space: ContinuousSearchSpace, ): - samples = continuous_search_space.sample(num_points=100, key=jr.PRNGKey(42)) + samples = continuous_search_space.sample(num_points=100, key=jr.key(42)) for i in range(continuous_search_space.dimensionality): assert jnp.all(samples[:, i] >= continuous_search_space.lower_bounds[i]) assert jnp.all(samples[:, i] <= continuous_search_space.upper_bounds[i]) diff --git a/tests/test_decision_making/test_test_functions/test_continuous_functions.py b/tests/test_decision_making/test_test_functions/test_continuous_functions.py index 6272a6694..1b57d43af 100644 --- a/tests/test_decision_making/test_test_functions/test_continuous_functions.py +++ b/tests/test_decision_making/test_test_functions/test_continuous_functions.py @@ -50,7 +50,7 @@ def test_minimizer_evaluates_to_minimum(test_function: AbstractContinuousTestFun @pytest.mark.parametrize( "test_function", [Quadratic(), Forrester(), LogarithmicGoldsteinPrice()] ) -@pytest.mark.parametrize("key", [jr.PRNGKey(42), jr.PRNGKey(10)]) +@pytest.mark.parametrize("key", [jr.key(42), jr.key(10)]) @pytest.mark.filterwarnings( "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort @@ -71,8 +71,8 @@ def test_correct_minimum(test_function: AbstractContinuousTestFunction, key: Key def test_correct_dtypes(test_function: AbstractContinuousTestFunction): minimizer = test_function.minimizer minimum = test_function.minimum - dataset = test_function.generate_dataset(10, jr.PRNGKey(42)) - test_x = test_function.generate_test_points(10, jr.PRNGKey(42)) + dataset = test_function.generate_dataset(10, jr.key(42)) + test_x = test_function.generate_test_points(10, jr.key(42)) assert minimizer.dtype == jnp.float64 assert minimum.dtype == jnp.float64 assert dataset.X.dtype == jnp.float64 @@ -91,7 +91,7 @@ def test_correct_dtypes(test_function: AbstractContinuousTestFunction): def test_test_points_shape( test_function: AbstractContinuousTestFunction, dimensionality: int, num_samples: int ): - test_X = test_function.generate_test_points(num_samples, jr.PRNGKey(42)) + test_X = test_function.generate_test_points(num_samples, jr.key(42)) assert test_X.shape == (num_samples, dimensionality) @@ -106,7 +106,7 @@ def test_test_points_shape( def test_dataset_shapes( test_function: AbstractContinuousTestFunction, dimensionality: int, num_samples: int ): - dataset = test_function.generate_dataset(num_samples, jr.PRNGKey(42)) + dataset = test_function.generate_dataset(num_samples, jr.key(42)) assert dataset.X.shape == (num_samples, dimensionality) assert dataset.y.shape == (num_samples, 1) @@ -115,7 +115,7 @@ def test_dataset_shapes( "test_function", [Quadratic(), Forrester(), LogarithmicGoldsteinPrice()] ) @pytest.mark.parametrize("num_samples", [1, 10, 100]) -@pytest.mark.parametrize("key", [jr.PRNGKey(42), jr.PRNGKey(10)]) +@pytest.mark.parametrize("key", [jr.key(42), jr.key(10)]) @pytest.mark.filterwarnings( "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort @@ -132,7 +132,7 @@ def test_same_key_same_dataset( "test_function", [Quadratic(), Forrester(), LogarithmicGoldsteinPrice()] ) @pytest.mark.parametrize("num_samples", [1, 10, 100]) -@pytest.mark.parametrize("key", [jr.PRNGKey(42), jr.PRNGKey(10)]) +@pytest.mark.parametrize("key", [jr.key(42), jr.key(10)]) @pytest.mark.filterwarnings( "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort @@ -150,7 +150,7 @@ def test_different_key_different_dataset( "test_function", [Quadratic(), Forrester(), LogarithmicGoldsteinPrice()] ) @pytest.mark.parametrize("num_samples", [1, 10, 100]) -@pytest.mark.parametrize("key", [jr.PRNGKey(42), jr.PRNGKey(10)]) +@pytest.mark.parametrize("key", [jr.key(42), jr.key(10)]) @pytest.mark.filterwarnings( "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort @@ -166,7 +166,7 @@ def test_same_key_same_test_points( "test_function", [Quadratic(), Forrester(), LogarithmicGoldsteinPrice()] ) @pytest.mark.parametrize("num_samples", [1, 10, 100]) -@pytest.mark.parametrize("key", [jr.PRNGKey(42), jr.PRNGKey(10)]) +@pytest.mark.parametrize("key", [jr.key(42), jr.key(10)]) @pytest.mark.filterwarnings( "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort diff --git a/tests/test_decision_making/test_test_functions/test_non_conjugate_functions.py b/tests/test_decision_making/test_test_functions/test_non_conjugate_functions.py index f6d999b5c..5e973c4ee 100644 --- a/tests/test_decision_making/test_test_functions/test_non_conjugate_functions.py +++ b/tests/test_decision_making/test_test_functions/test_non_conjugate_functions.py @@ -29,8 +29,8 @@ "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort def test_correct_dtypes(test_function: PoissonTestFunction): - dataset = test_function.generate_dataset(10, jr.PRNGKey(42)) - test_x = test_function.generate_test_points(10, jr.PRNGKey(42)) + dataset = test_function.generate_dataset(10, jr.key(42)) + test_x = test_function.generate_test_points(10, jr.key(42)) assert dataset.X.dtype == jnp.float64 assert dataset.y.dtype == jnp.integer assert test_x.dtype == jnp.float64 @@ -47,7 +47,7 @@ def test_correct_dtypes(test_function: PoissonTestFunction): def test_test_points_shape( test_function: PoissonTestFunction, dimensionality: int, num_samples: int ): - test_X = test_function.generate_test_points(num_samples, jr.PRNGKey(42)) + test_X = test_function.generate_test_points(num_samples, jr.key(42)) assert test_X.shape == (num_samples, dimensionality) @@ -62,14 +62,14 @@ def test_test_points_shape( def test_dataset_shapes( test_function: PoissonTestFunction, dimensionality: int, num_samples: int ): - dataset = test_function.generate_dataset(num_samples, jr.PRNGKey(42)) + dataset = test_function.generate_dataset(num_samples, jr.key(42)) assert dataset.X.shape == (num_samples, dimensionality) assert dataset.y.shape == (num_samples, 1) @pytest.mark.parametrize("test_function", [PoissonTestFunction()]) @pytest.mark.parametrize("num_samples", [1, 10, 100]) -@pytest.mark.parametrize("key", [jr.PRNGKey(42), jr.PRNGKey(10)]) +@pytest.mark.parametrize("key", [jr.key(42), jr.key(10)]) @pytest.mark.filterwarnings( "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort @@ -84,7 +84,7 @@ def test_same_key_same_dataset( @pytest.mark.parametrize("test_function", [PoissonTestFunction()]) @pytest.mark.parametrize("num_samples", [10, 100]) -@pytest.mark.parametrize("key", [jr.PRNGKey(42), jr.PRNGKey(10)]) +@pytest.mark.parametrize("key", [jr.key(42), jr.key(10)]) @pytest.mark.filterwarnings( "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort @@ -100,7 +100,7 @@ def test_different_key_different_dataset( @pytest.mark.parametrize("test_function", [PoissonTestFunction()]) @pytest.mark.parametrize("num_samples", [1, 10, 100]) -@pytest.mark.parametrize("key", [jr.PRNGKey(42), jr.PRNGKey(10)]) +@pytest.mark.parametrize("key", [jr.key(42), jr.key(10)]) @pytest.mark.filterwarnings( "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort @@ -114,7 +114,7 @@ def test_same_key_same_test_points( @pytest.mark.parametrize("test_function", [PoissonTestFunction()]) @pytest.mark.parametrize("num_samples", [1, 10, 100]) -@pytest.mark.parametrize("key", [jr.PRNGKey(42), jr.PRNGKey(10)]) +@pytest.mark.parametrize("key", [jr.key(42), jr.key(10)]) @pytest.mark.filterwarnings( "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort diff --git a/tests/test_decision_making/test_utility_functions/test_thompson_sampling.py b/tests/test_decision_making/test_utility_functions/test_thompson_sampling.py index 2ffd41ffe..f2a726274 100644 --- a/tests/test_decision_making/test_utility_functions/test_thompson_sampling.py +++ b/tests/test_decision_making/test_utility_functions/test_thompson_sampling.py @@ -65,7 +65,7 @@ def generate_dummy_non_conjugate_posterior(dataset: Dataset) -> NonConjugatePost "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort def test_thompson_sampling_no_objective_posterior_raises_error(): - key = jr.PRNGKey(42) + key = jr.key(42) forrester = Forrester() dataset = forrester.generate_dataset(num_points=10, key=key) posterior = generate_dummy_conjugate_posterior(dataset) @@ -82,7 +82,7 @@ def test_thompson_sampling_no_objective_posterior_raises_error(): "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort def test_thompson_sampling_no_objective_dataset_raises_error(): - key = jr.PRNGKey(42) + key = jr.key(42) forrester = Forrester() dataset = forrester.generate_dataset(num_points=10, key=key) posterior = generate_dummy_conjugate_posterior(dataset) @@ -99,7 +99,7 @@ def test_thompson_sampling_no_objective_dataset_raises_error(): "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort def test_thompson_sampling_non_conjugate_posterior_raises_error(): - key = jr.PRNGKey(42) + key = jr.key(42) forrester = Forrester() dataset = forrester.generate_dataset(num_points=10, key=key) posterior = generate_dummy_non_conjugate_posterior(dataset) @@ -117,7 +117,7 @@ def test_thompson_sampling_non_conjugate_posterior_raises_error(): "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort def test_thompson_sampling_invalid_rff_num_raises_error(num_rff_features: int): - key = jr.PRNGKey(42) + key = jr.key(42) forrester = Forrester() dataset = forrester.generate_dataset(num_points=10, key=key) posterior = generate_dummy_conjugate_posterior(dataset) @@ -135,7 +135,7 @@ def test_thompson_sampling_invalid_rff_num_raises_error(num_rff_features: int): [(Forrester()), (LogarithmicGoldsteinPrice())], ) @pytest.mark.parametrize("num_test_points", [50, 100]) -@pytest.mark.parametrize("key", [jr.PRNGKey(42), jr.PRNGKey(10)]) +@pytest.mark.parametrize("key", [jr.key(42), jr.key(10)]) @pytest.mark.filterwarnings( "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort @@ -163,7 +163,7 @@ def test_thompson_sampling_utility_function_correct_shapes( [(Forrester()), (LogarithmicGoldsteinPrice())], ) @pytest.mark.parametrize("num_test_points", [50, 100]) -@pytest.mark.parametrize("key", [jr.PRNGKey(42), jr.PRNGKey(10)]) +@pytest.mark.parametrize("key", [jr.key(42), jr.key(10)]) @pytest.mark.filterwarnings( "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort @@ -198,7 +198,7 @@ def test_thompson_sampling_utility_function_same_key_same_function( [(Forrester()), (LogarithmicGoldsteinPrice())], ) @pytest.mark.parametrize("num_test_points", [50, 100]) -@pytest.mark.parametrize("key", [jr.PRNGKey(42), jr.PRNGKey(10)]) +@pytest.mark.parametrize("key", [jr.key(42), jr.key(10)]) @pytest.mark.filterwarnings( "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort diff --git a/tests/test_decision_making/test_utility_maximizer.py b/tests/test_decision_making/test_utility_maximizer.py index 13b7287c8..1040b60ca 100644 --- a/tests/test_decision_making/test_utility_maximizer.py +++ b/tests/test_decision_making/test_utility_maximizer.py @@ -43,7 +43,7 @@ def test_abstract_single_batch_utility_maximizer(): "test_function, dimensionality", [(Forrester(), 1), (LogarithmicGoldsteinPrice(), 2)], ) -@pytest.mark.parametrize("key", [jr.PRNGKey(42), jr.PRNGKey(10)]) +@pytest.mark.parametrize("key", [jr.key(42), jr.key(10)]) @pytest.mark.filterwarnings( "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort @@ -86,7 +86,7 @@ def test_continuous_maximizer_raises_error_with_erroneous_num_restarts( "test_function, dimensionality", [(Forrester(), 1), (LogarithmicGoldsteinPrice(), 2)], ) -@pytest.mark.parametrize("key", [jr.PRNGKey(42), jr.PRNGKey(10)]) +@pytest.mark.parametrize("key", [jr.key(42), jr.key(10)]) @pytest.mark.parametrize("num_restarts", [1, 3]) @pytest.mark.filterwarnings( "ignore::UserWarning" @@ -128,7 +128,7 @@ def test_continous_maximizer_returns_same_point_with_same_key( (LogarithmicGoldsteinPrice(), 2), ], ) -@pytest.mark.parametrize("key", [jr.PRNGKey(42), jr.PRNGKey(10)]) +@pytest.mark.parametrize("key", [jr.key(42), jr.key(10)]) @pytest.mark.parametrize("num_restarts", [1, 3]) @pytest.mark.filterwarnings( "ignore::UserWarning" @@ -154,7 +154,7 @@ def test_continuous_maximizer_finds_correct_point( assert jnp.allclose(maximizer, true_utility_maximizer, atol=1e-6).all() -@pytest.mark.parametrize("key", [jr.PRNGKey(42), jr.PRNGKey(10), jr.PRNGKey(1)]) +@pytest.mark.parametrize("key", [jr.key(42), jr.key(10), jr.key(1)]) @pytest.mark.parametrize("num_restarts", [1, 3]) @pytest.mark.filterwarnings( "ignore::UserWarning" diff --git a/tests/test_fit.py b/tests/test_fit.py index 3b492851f..40497872f 100644 --- a/tests/test_fit.py +++ b/tests/test_fit.py @@ -63,7 +63,7 @@ def test_simple_linear_model() -> None: # Create dataset: X = jnp.linspace(0.0, 10.0, 100).reshape(-1, 1) - y = 2.0 * X + 1.0 + 10 * jr.normal(jr.PRNGKey(0), X.shape).reshape(-1, 1) + y = 2.0 * X + 1.0 + 10 * jr.normal(jr.key(0), X.shape).reshape(-1, 1) D = Dataset(X, y) # Define linear model: @@ -92,7 +92,7 @@ def step(self, model: LinearModel, train_data: Dataset) -> float: train_data=D, optim=ox.sgd(0.001), num_iters=100, - key=jr.PRNGKey(123), + key=jr.key(123), ) # Ensure we return a history of the correct length @@ -132,7 +132,7 @@ def step(self, model: LinearModel, train_data: Dataset) -> float: @pytest.mark.parametrize("verbose", [True, False]) def test_gaussian_process_regression(n_data: int, verbose: bool) -> None: # Create dataset: - key = jr.PRNGKey(123) + key = jr.key(123) x = jnp.sort( jr.uniform(key=key, minval=-2.0, maxval=2.0, shape=(n_data, 1)), axis=0 ) @@ -155,7 +155,7 @@ def test_gaussian_process_regression(n_data: int, verbose: bool) -> None: optim=ox.adam(0.1), num_iters=15, verbose=verbose, - key=jr.PRNGKey(123), + key=jr.key(123), ) # Ensure the trained model is a Gaussian process posterior @@ -237,7 +237,7 @@ def test_batch_fitting( num_iters: int, batch_size: int, n_data: int, verbose: bool ) -> None: # Create dataset: - key = jr.PRNGKey(123) + key = jr.key(123) x = jnp.sort( jr.uniform(key=key, minval=-2.0, maxval=2.0, shape=(n_data, 1)), axis=0 ) @@ -265,7 +265,7 @@ def test_batch_fitting( num_iters=num_iters, batch_size=batch_size, verbose=verbose, - key=jr.PRNGKey(123), + key=jr.key(123), ) # Ensure the trained model is a Gaussian process posterior @@ -282,7 +282,7 @@ def test_batch_fitting( @pytest.mark.parametrize("n_dim", [1, 2, 3]) @pytest.mark.parametrize("batch_size", [1, 2, 50]) def test_get_batch(n_data: int, n_dim: int, batch_size: int): - key = jr.PRNGKey(123) + key = jr.key(123) # Create dataset: x = jnp.sort( diff --git a/tests/test_gaussian_distribution.py b/tests/test_gaussian_distribution.py index db8586c51..dfe07c89e 100644 --- a/tests/test_gaussian_distribution.py +++ b/tests/test_gaussian_distribution.py @@ -31,7 +31,7 @@ from gpjax.distributions import GaussianDistribution -_key = jr.PRNGKey(seed=42) +_key = jr.key(seed=42) from tensorflow_probability.substrates.jax.distributions import ( MultivariateNormalDiag, diff --git a/tests/test_gps.py b/tests/test_gps.py index 59af2bb91..d520c681a 100644 --- a/tests/test_gps.py +++ b/tests/test_gps.py @@ -14,9 +14,9 @@ # ============================================================================== try: - import beartype + import jaxtyping - ValidationErrors = (ValueError, beartype.roar.BeartypeCallHintParamViolation) + ValidationErrors = (ValueError, jaxtyping.TypeCheckError) except ImportError: ValidationErrors = ValueError @@ -122,7 +122,7 @@ def test_conjugate_posterior( kernel: AbstractKernel, ) -> None: # Create a dataset. - key = jr.PRNGKey(123) + key = jr.key(123) x = jr.uniform(key=key, minval=-2.0, maxval=2.0, shape=(num_datapoints, 1)) y = jnp.sin(x) + jr.normal(key=key, shape=x.shape) * 0.1 D = Dataset(X=x, y=y) @@ -169,7 +169,7 @@ def test_nonconjugate_posterior( kernel: AbstractKernel, ) -> None: # Create a dataset. - key = jr.PRNGKey(123) + key = jr.key(123) x = jr.uniform(key=key, minval=-2.0, maxval=2.0, shape=(num_datapoints, 1)) y = jnp.sin(x) + jr.normal(key=key, shape=x.shape) * 0.1 D = Dataset(X=x, y=y) @@ -277,7 +277,7 @@ def test_posterior_construct( def test_prior_sample_approx(num_datapoints, kernel, mean_function): kern = kernel(lengthscale=jnp.array([5.0, 1.0]), variance=0.1) p = Prior(kernel=kern, mean_function=mean_function) - key = jr.PRNGKey(123) + key = jr.key(123) with pytest.raises(ValueError): p.sample_approx(-1, key) @@ -304,7 +304,7 @@ def test_prior_sample_approx(num_datapoints, kernel, mean_function): max_delta = jnp.max(jnp.abs(evals - evals_2)) assert max_delta == 0.0 # samples same for same seed - new_key = jr.PRNGKey(12345) + new_key = jr.key(12345) sampled_fn_3 = p.sample_approx(1, new_key, 100) evals_3 = sampled_fn_3(x) max_delta = jnp.max(jnp.abs(evals - evals_3)) @@ -332,7 +332,7 @@ def test_conjugate_posterior_sample_approx(num_datapoints, kernel, mean_function p = Prior(kernel=kern, mean_function=mean_function) * Gaussian( num_datapoints=num_datapoints ) - key = jr.PRNGKey(123) + key = jr.key(123) x = jr.uniform(key=key, minval=-2.0, maxval=2.0, shape=(num_datapoints, 2)) y = ( @@ -366,7 +366,7 @@ def test_conjugate_posterior_sample_approx(num_datapoints, kernel, mean_function max_delta = jnp.max(jnp.abs(evals - evals_2)) assert max_delta == 0.0 # samples same for same seed - new_key = jr.PRNGKey(12345) + new_key = jr.key(12345) sampled_fn_3 = p.sample_approx(1, D, new_key, 100) evals_3 = sampled_fn_3(x) max_delta = jnp.max(jnp.abs(evals - evals_3)) diff --git a/tests/test_kernels/test_approximations.py b/tests/test_kernels/test_approximations.py index ddc7b7252..ca71f5901 100644 --- a/tests/test_kernels/test_approximations.py +++ b/tests/test_kernels/test_approximations.py @@ -41,7 +41,7 @@ def test_frequency_sampler(kernel: AbstractKernel, num_basis_fns: int, n_dims: i @pytest.mark.parametrize("n_dims", [1, 2, 5]) @pytest.mark.parametrize("n_data", [50, 100]) def test_gram(kernel: AbstractKernel, num_basis_fns: int, n_dims: int, n_data: int): - key = jr.PRNGKey(123) + key = jr.key(123) x = jr.uniform(key, shape=(n_data, 1), minval=-3.0, maxval=3.0).reshape(-1, 1) if n_dims > 1: x = jnp.hstack([x] * n_dims) @@ -74,7 +74,7 @@ def test_cross_covariance( n_datas: Tuple[int, int], ): nd1, nd2 = n_datas - key = jr.PRNGKey(123) + key = jr.key(123) x1 = jr.uniform(key, shape=(nd1, 1), minval=-3.0, maxval=3.0) if n_dims > 1: x1 = jnp.hstack([x1] * n_dims) @@ -97,7 +97,7 @@ def test_cross_covariance( @pytest.mark.parametrize("n_dim", [1, 2, 5]) def test_improvement(kernel, n_dim): n_data = 100 - key = jr.PRNGKey(123) + key = jr.key(123) x = jr.uniform(key, minval=-3.0, maxval=3.0, shape=(n_data, n_dim)) base_kernel = kernel(active_dims=list(range(n_dim))) @@ -120,7 +120,7 @@ def test_improvement(kernel, n_dim): @pytest.mark.parametrize("kernel", [RBF(), Matern12(), Matern32(), Matern52()]) def test_exactness(kernel): n_data = 100 - key = jr.PRNGKey(123) + key = jr.key(123) x = jr.uniform(key, minval=-3.0, maxval=3.0, shape=(n_data, 1)) exact_linop = kernel.gram(x).to_dense() @@ -143,7 +143,7 @@ def test_value_error(kernel): @pytest.mark.parametrize("kernel", [RBF(), Matern12(), Matern32(), Matern52()]) def test_stochastic_init(kernel: AbstractKernel): - k1 = RFF(base_kernel=kernel, num_basis_fns=10, key=jr.PRNGKey(123)) - k2 = RFF(base_kernel=kernel, num_basis_fns=10, key=jr.PRNGKey(42)) + k1 = RFF(base_kernel=kernel, num_basis_fns=10, key=jr.key(123)) + k2 = RFF(base_kernel=kernel, num_basis_fns=10, key=jr.key(42)) assert (k1.frequencies != k2.frequencies).any() diff --git a/tests/test_kernels/test_nonstationary.py b/tests/test_kernels/test_nonstationary.py index 803e44d23..9ab84cdd1 100644 --- a/tests/test_kernels/test_nonstationary.py +++ b/tests/test_kernels/test_nonstationary.py @@ -38,7 +38,7 @@ # Enable Float64 for more stable matrix inversions. config.update("jax_enable_x64", True) -_initialise_key = jr.PRNGKey(123) +_initialise_key = jr.key(123) _jitter = 1e-6 @@ -198,7 +198,7 @@ def test_values_by_monte_carlo_in_special_case(self, order: int) -> None: kernel: AbstractKernel = self.kernel( weight_variance=jnp.array([1.0, 1.0]), bias_variance=1e-25, order=order ) - key = jr.PRNGKey(123) + key = jr.key(123) # Inputs close(ish) together a = jnp.array([[0.0, 0.0]]) diff --git a/tests/test_likelihoods.py b/tests/test_likelihoods.py index 62e829712..2059876ec 100644 --- a/tests/test_likelihoods.py +++ b/tests/test_likelihoods.py @@ -43,7 +43,7 @@ # Enable Float64 for more stable matrix inversions. config.update("jax_enable_x64", True) -_initialise_key = jr.PRNGKey(123) +_initialise_key = jr.key(123) class BaseTestLikelihood: diff --git a/tests/test_mean_functions.py b/tests/test_mean_functions.py index 2fb74283d..dee85b095 100644 --- a/tests/test_mean_functions.py +++ b/tests/test_mean_functions.py @@ -54,7 +54,7 @@ def test_constant(constant: Float[Array, " Q"]) -> None: def test_zero_mean_remains_zero() -> None: - key = jr.PRNGKey(123) + key = jr.key(123) x = jr.uniform(key=key, minval=0, maxval=1, shape=(20, 1)) y = jnp.full((20, 1), 50, dtype=jnp.float64) # Dataset with non-zero mean diff --git a/tests/test_objectives.py b/tests/test_objectives.py index fbb53f14a..0645a31bf 100644 --- a/tests/test_objectives.py +++ b/tests/test_objectives.py @@ -57,7 +57,7 @@ def build_data(num_datapoints: int, num_dims: int, key, binary: bool): def test_conjugate_mll( num_datapoints: int, num_dims: int, negative: bool, jit_compile: bool, key_val: int ): - key = jr.PRNGKey(key_val) + key = jr.key(key_val) D = build_data(num_datapoints, num_dims, key, binary=False) # Build model @@ -87,7 +87,7 @@ def test_conjugate_mll( def test_conjugate_loocv( num_datapoints: int, num_dims: int, negative: bool, jit_compile: bool, key_val: int ): - key = jr.PRNGKey(key_val) + key = jr.key(key_val) D = build_data(num_datapoints, num_dims, key, binary=False) # Build model @@ -117,7 +117,7 @@ def test_conjugate_loocv( def test_non_conjugate_mll( num_datapoints: int, num_dims: int, negative: bool, jit_compile: bool, key_val: int ): - key = jr.PRNGKey(key_val) + key = jr.key(key_val) D = build_data(num_datapoints, num_dims, key, binary=True) # Build model @@ -152,7 +152,7 @@ def test_non_conjugate_mll( def test_collapsed_elbo( num_datapoints: int, num_dims: int, negative: bool, jit_compile: bool, key_val: int ): - key = jr.PRNGKey(key_val) + key = jr.key(key_val) D = build_data(num_datapoints, num_dims, key, binary=False) z = jr.uniform( key=key, minval=-2.0, maxval=2.0, shape=(num_datapoints // 2, num_dims) @@ -202,7 +202,7 @@ def test_elbo( key_val: int, binary: bool, ): - key = jr.PRNGKey(key_val) + key = jr.key(key_val) D = build_data(num_datapoints, num_dims, key, binary=binary) z = jr.uniform( key=key, minval=-2.0, maxval=2.0, shape=(num_datapoints // 2, num_dims) diff --git a/tests/test_variational_families.py b/tests/test_variational_families.py index 66a3c650c..1a5ad2e15 100644 --- a/tests/test_variational_families.py +++ b/tests/test_variational_families.py @@ -221,7 +221,7 @@ def test_collapsed_variational_gaussian( n_test: int, n_inducing: int, n_datapoints: int, point_dim: int ) -> None: x = jnp.linspace(-5.0, 5.0, n_datapoints).reshape(-1, 1) - y = jnp.sin(x) + jr.normal(key=jr.PRNGKey(123), shape=x.shape) * 0.1 + y = jnp.sin(x) + jr.normal(key=jr.key(123), shape=x.shape) * 0.1 x = jnp.hstack([x] * point_dim) D = gpx.Dataset(X=x, y=y)