diff --git a/docs/examples/collapsed_vi.py b/docs/examples/collapsed_vi.py index 8dd442e3..80e0436a 100644 --- a/docs/examples/collapsed_vi.py +++ b/docs/examples/collapsed_vi.py @@ -244,11 +244,11 @@ full_rank_model = gpx.Prior(mean_function=gpx.Zero(), kernel=gpx.RBF()) * gpx.Gaussian( num_datapoints=D.n ) -negative_mll = jit(gpx.ConjugateMLL(negative=True)) +negative_mll = jit(gpx.ConjugateMLL(negative=True).step) # %timeit negative_mll(full_rank_model, D).block_until_ready() # %% -negative_elbo = jit(gpx.CollapsedELBO(negative=True)) +negative_elbo = jit(gpx.CollapsedELBO(negative=True).step) # %timeit negative_elbo(q, D).block_until_ready() # %% [markdown] diff --git a/docs/examples/graph_kernels.py b/docs/examples/graph_kernels.py index 82154b3a..2d77d79e 100644 --- a/docs/examples/graph_kernels.py +++ b/docs/examples/graph_kernels.py @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- # %% [markdown] # # Graph Kernels # @@ -119,7 +120,8 @@ cmap=plt.cm.inferno, norm=plt.Normalize(vmin=vmin, vmax=vmax) ) sm.set_array([]) -cbar = plt.colorbar(sm) +ax = plt.gca() +cbar = plt.colorbar(sm, ax=ax) # %% [markdown] # @@ -201,8 +203,8 @@ sm = plt.cm.ScalarMappable( cmap=plt.cm.inferno, norm=plt.Normalize(vmin=vmin, vmax=vmax) ) -sm.set_array([]) -cbar = plt.colorbar(sm) +ax = plt.gca() +cbar = plt.colorbar(sm, ax=ax) # %% [markdown] # diff --git a/gpjax/citation.py b/gpjax/citation.py index 57c12fc9..6fe15858 100644 --- a/gpjax/citation.py +++ b/gpjax/citation.py @@ -31,15 +31,15 @@ NonConjugateMLL, ) -CitationType = Union[str, Dict[str, str]] +CitationType = Union[None, str, Dict[str, str]] @dataclass(repr=False) class AbstractCitation: - citation_key: str = None - authors: str = None - title: str = None - year: str = None + citation_key: Union[str, None] = None + authors: Union[str, None] = None + title: Union[str, None] = None + year: Union[str, None] = None def as_str(self) -> str: citation_str = f"@{self.citation_type}{{{self.citation_key}," @@ -64,29 +64,24 @@ def __str__(self) -> str: ) -class JittedFnCitation(AbstractCitation): - def __str__(self) -> str: - return "Citation not available for jitted objects." - - @dataclass class PhDThesisCitation(AbstractCitation): - school: str = None - institution: str = None - citation_type: str = "phdthesis" + school: Union[str, None] = None + institution: Union[str, None] = None + citation_type: CitationType = "phdthesis" @dataclass class PaperCitation(AbstractCitation): - booktitle: str = None - citation_type: str = "inproceedings" + booktitle: Union[str, None] = None + citation_type: CitationType = "inproceedings" @dataclass class BookCitation(AbstractCitation): - publisher: str = None - volume: str = None - citation_type: str = "book" + publisher: Union[str, None] = None + volume: Union[str, None] = None + citation_type: CitationType = "book" #################### @@ -101,8 +96,8 @@ def cite(tree) -> AbstractCitation: # Default citation #################### @cite.register(PjitFunction) -def _(tree): - return JittedFnCitation() +def _(tree) -> None: + raise RuntimeError("Citation not available for jitted objects.") #################### diff --git a/gpjax/distributions.py b/gpjax/distributions.py index 6ec3dbbc..f638cef0 100644 --- a/gpjax/distributions.py +++ b/gpjax/distributions.py @@ -26,6 +26,7 @@ from cola.ops import ( Dense, Identity, + LinearOperator, ) from jax import vmap import jax.numpy as jnp @@ -45,6 +46,8 @@ tfd = tfp.distributions +from cola.linalg.decompositions.decompositions import Cholesky + def _check_loc_scale(loc: Optional[Any], scale: Optional[Any]) -> None: r"""Checks that the inputs are correct.""" @@ -60,9 +63,9 @@ def _check_loc_scale(loc: Optional[Any], scale: Optional[Any]) -> None: f"`scale.shape = {scale.shape}`." ) - if scale is not None and not isinstance(scale, cola.LinearOperator): + if scale is not None and not isinstance(scale, LinearOperator): raise ValueError( - f"The `scale` must be a cola.LinearOperator but got {type(scale)}" + f"The `scale` must be a CoLA LinearOperator but got {type(scale)}" ) if scale is not None and (scale.shape[-1] != scale.shape[-2]): @@ -84,7 +87,7 @@ class GaussianDistribution(tfd.Distribution): Args: loc (Optional[Float[Array, " N"]]): The mean of the distribution. Defaults to None. - scale (Optional[cola.LinearOperator]): The scale matrix of the distribution. Defaults to None. + scale (Optional[LinearOperator]): The scale matrix of the distribution. Defaults to None. Returns ------- @@ -99,7 +102,7 @@ class GaussianDistribution(tfd.Distribution): def __init__( self, loc: Optional[Float[Array, " N"]] = None, - scale: Optional[cola.LinearOperator] = None, + scale: Optional[LinearOperator] = None, ) -> None: r"""Initialises the distribution.""" _check_loc_scale(loc, scale) @@ -155,9 +158,7 @@ def entropy(self) -> ScalarFloat: r"""Calculates the entropy of the distribution.""" return 0.5 * ( self.event_shape[0] * (1.0 + jnp.log(2.0 * jnp.pi)) - + cola.logdet( - self.scale, method="dense" - ) # <--- Seems to be an issue with CoLA! + + cola.logdet(self.scale, Cholesky(), Cholesky()) ) def log_prob( @@ -191,8 +192,8 @@ def log_prob( # compute the pdf, -1/2[ n log(2π) + log|Σ| + (y - µ)ᵀΣ⁻¹(y - µ) ] return -0.5 * ( n * jnp.log(2.0 * jnp.pi) - + cola.logdet(sigma, method="dense") # <--- Seems to be an issue with CoLA! - + diff.T @ cola.solve(sigma, diff) + + cola.logdet(sigma, Cholesky(), Cholesky()) + + diff.T @ cola.solve(sigma, diff, Cholesky()) ) def _sample_n(self, key: KeyArray, n: int) -> Float[Array, "n N"]: @@ -347,17 +348,19 @@ def _kl_divergence(q: GaussianDistribution, p: GaussianDistribution) -> ScalarFl # trace term, tr[Σp⁻¹ Σq] = tr[(LpLpᵀ)⁻¹(LqLqᵀ)] = tr[(Lp⁻¹Lq)(Lp⁻¹Lq)ᵀ] = (fr[LqLp⁻¹])² trace = _frobenius_norm_squared( - cola.solve(sqrt_p, sqrt_q.to_dense()) + cola.solve(sqrt_p, sqrt_q.to_dense(), Cholesky()) ) # TODO: Not most efficient, given the `to_dense()` call (e.g., consider diagonal p and q). Need to abstract solving linear operator against another linear operator. # Mahalanobis term, (μp - μq)ᵀ Σp⁻¹ (μp - μq) = tr [(μp - μq)ᵀ [LpLpᵀ]⁻¹ (μp - μq)] = (fr[Lp⁻¹(μp - μq)])² - mahalanobis = jnp.sum( - jnp.square(cola.solve(sqrt_p, diff)) - ) # TODO: Need to improve this. Perhaps add a Mahalanobis method to ``LinearOperator``s. + mahalanobis = jnp.sum(jnp.square(cola.solve(sqrt_p, diff, Cholesky()))) # KL[q(x)||p(x)] = [ [(μp - μq)ᵀ Σp⁻¹ (μp - μq)] - n - log|Σq| + log|Σp| + tr[Σp⁻¹ Σq] ] / 2 return ( - mahalanobis - n_dim - cola.logdet(sigma_q) + cola.logdet(sigma_p) + trace + mahalanobis + - n_dim + - cola.logdet(sigma_q, Cholesky(), Cholesky()) + + cola.logdet(sigma_p, Cholesky(), Cholesky()) + + trace ) / 2.0 diff --git a/gpjax/gps.py b/gpjax/gps.py index d3469d96..638f408c 100644 --- a/gpjax/gps.py +++ b/gpjax/gps.py @@ -13,7 +13,6 @@ # limitations under the License. # ============================================================================== -# from __future__ import annotations from abc import abstractmethod from dataclasses import ( dataclass, @@ -25,8 +24,10 @@ Any, Callable, Optional, + Union, ) import cola +from cola.linalg.decompositions.decompositions import Cholesky from cola.ops import Dense import jax.numpy as jnp from jax.random import ( @@ -152,17 +153,17 @@ class Prior(AbstractPrior): ``` """ - @overload - def __mul__(self, other: Gaussian) -> "ConjugatePosterior": - ... + # @overload + # def __mul__(self, other: Gaussian) -> "ConjugatePosterior": + # ... - @overload - def __mul__(self, other: NonGaussianLikelihood) -> "NonConjugatePosterior": - ... + # @overload + # def __mul__(self, other: NonGaussianLikelihood) -> "NonConjugatePosterior": + # ... - @overload - def __mul__(self, other: AbstractLikelihood) -> "AbstractPosterior": - ... + # @overload + # def __mul__(self, other: AbstractLikelihood) -> "AbstractPosterior": + # ... def __mul__(self, other): r"""Combine the prior with a likelihood to form a posterior distribution. @@ -198,17 +199,17 @@ def __mul__(self, other): """ return construct_posterior(prior=self, likelihood=other) - @overload - def __rmul__(self, other: Gaussian) -> "ConjugatePosterior": - ... + # @overload + # def __rmul__(self, other: Gaussian) -> "ConjugatePosterior": + # ... - @overload - def __rmul__(self, other: NonGaussianLikelihood) -> "NonConjugatePosterior": - ... + # @overload + # def __rmul__(self, other: NonGaussianLikelihood) -> "NonConjugatePosterior": + # ... - @overload - def __rmul__(self, other: AbstractLikelihood) -> "AbstractPosterior": - ... + # @overload + # def __rmul__(self, other: AbstractLikelihood) -> "AbstractPosterior": + # ... def __rmul__(self, other): r"""Combine the prior with a likelihood to form a posterior distribution. @@ -540,7 +541,7 @@ def predict( # Σ⁻¹ Kxt if mask is not None: Kxt = jnp.where(mask * jnp.ones((1, n_train), dtype=bool), 0.0, Kxt) - Sigma_inv_Kxt = cola.solve(Sigma, Kxt) + Sigma_inv_Kxt = cola.solve(Sigma, Kxt, Cholesky()) # μt + Ktx (Kxx + Io²)⁻¹ (y - μx) mean = mean_t.flatten() + Sigma_inv_Kxt.T @ (y - mx).flatten() @@ -618,7 +619,9 @@ def sample_approx( y = train_data.y - self.prior.mean_function(train_data.X) # account for mean Phi = fourier_feature_fn(train_data.X) canonical_weights = cola.solve( - Sigma, y + eps - jnp.inner(Phi, fourier_weights) + Sigma, + y + eps - jnp.inner(Phi, fourier_weights), + Cholesky(), ) # [N, B] def sample_fn(test_inputs: Float[Array, "n D"]) -> Float[Array, "n B"]: @@ -656,7 +659,7 @@ class NonConjugatePosterior(AbstractPosterior): from, or optimise an approximation to, the posterior distribution. """ - latent: Float[Array, "N 1"] = param_field(None) + latent: Union[Float[Array, "N 1"], None] = param_field(None) key: KeyArray = static_field(PRNGKey(42)) def __post_init__(self): @@ -707,7 +710,7 @@ def predict( mean_t = mean_function(t) # Lx⁻¹ Kxt - Lx_inv_Kxt = cola.solve(Lx, Ktx.T) + Lx_inv_Kxt = cola.solve(Lx, Ktx.T, Cholesky()) # Whitened function values, wx, corresponding to the inputs, x wx = self.latent diff --git a/gpjax/integrators.py b/gpjax/integrators.py index e956b8fd..25271559 100644 --- a/gpjax/integrators.py +++ b/gpjax/integrators.py @@ -1,16 +1,23 @@ from abc import abstractmethod from dataclasses import dataclass -from typing import TYPE_CHECKING +from typing import ( + TypeVar, + Union, +) from beartype.typing import Callable import jax.numpy as jnp from jaxtyping import Float import numpy as np +import gpjax from gpjax.typing import Array -if TYPE_CHECKING: - import gpjax.likelihoods +Likelihood = TypeVar( + "Likelihood", + bound=Union["gpjax.likelihoods.AbstractLikelihood", None], # noqa: F821 +) +Gaussian = TypeVar("Gaussian", bound="gpjax.likelihoods.Gaussian") # noqa: F821 @dataclass @@ -24,7 +31,7 @@ def integrate( y: Float[Array, "N D"], mean: Float[Array, "N D"], variance: Float[Array, "N D"], - likelihood: "gpjax.likelihoods.AbstractLikelihood" = None, + likelihood: Likelihood, ) -> Float[Array, " N"]: r"""Integrate a function with respect to a Gaussian distribution. @@ -47,7 +54,7 @@ def __call__( y: Float[Array, "N D"], mean: Float[Array, "N D"], variance: Float[Array, "N D"], - likelihood: "gpjax.likelihoods.AbstractLikelihood" = None, + likelihood: Likelihood, ) -> Float[Array, " N"]: r"""Integrate a function with respect to a Gaussian distribution. @@ -86,7 +93,7 @@ def integrate( y: Float[Array, "N D"], mean: Float[Array, "N D"], variance: Float[Array, "N D"], - likelihood: "gpjax.likelihoods.AbstractLikelihood" = None, + likelihood: Likelihood, ) -> Float[Array, " N"]: r"""Compute a quadrature integral. @@ -127,7 +134,7 @@ def integrate( y: Float[Array, "N D"], mean: Float[Array, "N D"], variance: Float[Array, "N D"], - likelihood: "gpjax.likelihoods.Gaussian" = None, + likelihood: Gaussian, ) -> Float[Array, " N"]: r"""Compute a Gaussian integral. diff --git a/gpjax/kernels/approximations/rff.py b/gpjax/kernels/approximations/rff.py index 7a664fc8..0806df0f 100644 --- a/gpjax/kernels/approximations/rff.py +++ b/gpjax/kernels/approximations/rff.py @@ -1,6 +1,7 @@ """Compute Random Fourier Feature (RFF) kernel approximations. """ from dataclasses import dataclass +from beartype.typing import Union from jax.random import PRNGKey from jaxtyping import Float import tensorflow_probability.substrates.jax.bijectors as tfb @@ -34,9 +35,11 @@ class RFF(AbstractKernel): - 'On the Error of Random Fourier Features' by Sutherland and Schneider (2015). """ - base_kernel: AbstractKernel = None + base_kernel: Union[AbstractKernel, None] = None num_basis_fns: int = static_field(50) - frequencies: Float[Array, "M 1"] = param_field(None, bijector=tfb.Identity()) + frequencies: Union[Float[Array, "M D"], None] = param_field( + None, bijector=tfb.Identity() + ) compute_engine: BasisFunctionComputation = static_field( BasisFunctionComputation(), repr=False ) @@ -57,8 +60,9 @@ def __post_init__(self) -> None: ) self.name = f"{self.base_kernel.name} (RFF)" - def __call__(self, x: Array, y: Array) -> Array: + def __call__(self, x: Float[Array, "D 1"], y: Float[Array, "D 1"]) -> None: """Superfluous for RFFs.""" + raise RuntimeError("RFFs do not have a kernel function.") def _check_valid_base_kernel(self, kernel: AbstractKernel): r"""Verify that the base kernel is valid for RFF approximation. diff --git a/gpjax/kernels/non_euclidean/categorical.py b/gpjax/kernels/non_euclidean/categorical.py index 1d376956..1b0c9ead 100644 --- a/gpjax/kernels/non_euclidean/categorical.py +++ b/gpjax/kernels/non_euclidean/categorical.py @@ -67,7 +67,7 @@ class CatKernel(AbstractKernel): cholesky_lower: Float[Array, "N N"] = param_field( jnp.eye(2), bijector=tfb.CorrelationCholesky() ) - inspace_vals: list = static_field(None) + inspace_vals: Union[list, None] = static_field(None) name: str = "Categorical Kernel" input_1hot: bool = static_field(False) diff --git a/gpjax/kernels/non_euclidean/graph.py b/gpjax/kernels/non_euclidean/graph.py index 4f409491..9cf0a003 100644 --- a/gpjax/kernels/non_euclidean/graph.py +++ b/gpjax/kernels/non_euclidean/graph.py @@ -16,6 +16,7 @@ from dataclasses import dataclass +from beartype.typing import Union import jax.numpy as jnp from jaxtyping import ( Float, @@ -58,13 +59,13 @@ class GraphKernel(AbstractKernel): of a graph. """ - laplacian: Num[Array, "N N"] = static_field(None) + laplacian: Union[Num[Array, "N N"], None] = static_field(None) lengthscale: ScalarFloat = param_field(jnp.array(1.0), bijector=tfb.Softplus()) variance: ScalarFloat = param_field(jnp.array(1.0), bijector=tfb.Softplus()) smoothness: ScalarFloat = param_field(jnp.array(1.0), bijector=tfb.Softplus()) - eigenvalues: Float[Array, " N"] = static_field(None) - eigenvectors: Float[Array, "N N"] = static_field(None) - num_vertex: ScalarInt = static_field(None) + eigenvalues: Union[Float[Array, "N 1"], None] = static_field(None) + eigenvectors: Union[Float[Array, "N N"], None] = static_field(None) + num_vertex: Union[ScalarInt, None] = static_field(None) compute_engine: AbstractKernelComputation = static_field( EigenKernelComputation(), repr=False ) diff --git a/gpjax/objectives.py b/gpjax/objectives.py index 852918c1..88777214 100644 --- a/gpjax/objectives.py +++ b/gpjax/objectives.py @@ -22,15 +22,30 @@ tfd = tfp.distributions +from typing import TypeVar + import cola +ConjugatePosterior = TypeVar( + "ConjugatePosterior", bound="gpjax.gps.ConjugatePosterior" # noqa: F821 +) +NonConjugatePosterior = TypeVar( + "NonConjugatePosterior", bound="gpjax.gps.NonConjugatePosterior" # noqa: F821 +) +VariationalFamily = TypeVar( + "VariationalFamily", + bound="gpjax.variational_families.AbstractVariationalFamily", # noqa: F821 +) + +from cola.linalg.decompositions.decompositions import Cholesky + @dataclass class AbstractObjective(Module): r"""Abstract base class for objectives.""" negative: bool = static_field(False) - constant: float = static_field(init=False, repr=False) + constant: ScalarFloat = static_field(init=False, repr=False) def __post_init__(self) -> None: self.constant = jnp.array(-1.0) if self.negative else jnp.array(1.0) @@ -49,8 +64,8 @@ def step(self, *args, **kwargs) -> ScalarFloat: class ConjugateMLL(AbstractObjective): def step( self, - posterior: "gpjax.gps.ConjugatePosterior", # noqa: F821 - train_data: Dataset, # noqa: F821 + posterior: ConjugatePosterior, + train_data: Dataset, ) -> ScalarFloat: r"""Evaluate the marginal log-likelihood of the Gaussian process. @@ -148,9 +163,7 @@ class LogPosteriorDensity(AbstractObjective): sometimes referred to as the marginal log-likelihood. """ - def step( - self, posterior: "gpjax.gps.NonConjugatePosterior", data: Dataset # noqa: F821 - ) -> ScalarFloat: + def step(self, posterior: NonConjugatePosterior, data: Dataset) -> ScalarFloat: r"""Evaluate the log-posterior density of a Gaussian process. Compute the marginal log-likelihood, or log-posterior density of the Gaussian @@ -210,7 +223,7 @@ def step( class ELBO(AbstractObjective): def step( self, - variational_family: "gpjax.variational_families.AbstractVariationalFamily", # noqa: F821 + variational_family: VariationalFamily, train_data: Dataset, ) -> ScalarFloat: r"""Compute the evidence lower bound of a variational approximation. @@ -249,7 +262,7 @@ def step( def variational_expectation( - variational_family: "gpjax.variational_families.AbstractVariationalFamily", # noqa: F821 + variational_family: VariationalFamily, train_data: Dataset, ) -> Float[Array, " N"]: r"""Compute the variational expectation. @@ -304,7 +317,7 @@ class CollapsedELBO(AbstractObjective): def step( self, - variational_family: "gpjax.variational_families.AbstractVariationalFamily", # noqa: F821 + variational_family: VariationalFamily, train_data: Dataset, ) -> ScalarFloat: r"""Compute a single step of the collapsed evidence lower bound. @@ -373,7 +386,7 @@ def step( # # with A and B defined as above. - A = cola.solve(Lz, Kzx) / jnp.sqrt(noise) + A = cola.solve(Lz, Kzx, Cholesky()) / jnp.sqrt(noise) # AAᵀ AAT = jnp.matmul(A, A.T) diff --git a/gpjax/variational_families.py b/gpjax/variational_families.py index 2bfd157d..d66fbf19 100644 --- a/gpjax/variational_families.py +++ b/gpjax/variational_families.py @@ -16,8 +16,12 @@ import abc from dataclasses import dataclass -from beartype.typing import Any +from beartype.typing import ( + Any, + Union, +) import cola +from cola.linalg.decompositions.decompositions import Cholesky import jax.numpy as jnp import jax.scipy as jsp from jaxtyping import Float @@ -106,7 +110,7 @@ class VariationalGaussian(AbstractVariationalGaussian): $`\mu`$ and $`sqrt`$ with $`S = sqrt sqrt^{\top}`$. """ - variational_mean: Float[Array, "N 1"] = param_field(None) + variational_mean: Union[Float[Array, "N 1"], None] = param_field(None) variational_root_covariance: Float[Array, "N N"] = param_field( None, bijector=tfb.FillTriangular() ) @@ -199,10 +203,10 @@ def predict(self, test_inputs: Float[Array, "N D"]) -> GaussianDistribution: mut = mean_function(t) # Lz⁻¹ Kzt - Lz_inv_Kzt = cola.solve(Lz, Kzt) + Lz_inv_Kzt = cola.solve(Lz, Kzt, Cholesky()) # Kzz⁻¹ Kzt - Kzz_inv_Kzt = cola.solve(Lz.T, Lz_inv_Kzt) + Kzz_inv_Kzt = cola.solve(Lz.T, Lz_inv_Kzt, Cholesky()) # Ktz Kzz⁻¹ sqrt Ktz_Kzz_inv_sqrt = jnp.matmul(Kzz_inv_Kzt.T, sqrt) @@ -302,7 +306,7 @@ def predict(self, test_inputs: Float[Array, "N D"]) -> GaussianDistribution: mut = mean_function(t) # Lz⁻¹ Kzt - Lz_inv_Kzt = cola.solve(Lz, Kzt) + Lz_inv_Kzt = cola.solve(Lz, Kzt, Cholesky()) # Ktz Lz⁻ᵀ sqrt Ktz_Lz_invT_sqrt = jnp.matmul(Lz_inv_Kzt.T, sqrt) @@ -456,10 +460,10 @@ def predict(self, test_inputs: Float[Array, "N D"]) -> GaussianDistribution: mut = mean_function(test_inputs) # Lz⁻¹ Kzt - Lz_inv_Kzt = cola.solve(Lz, Kzt) + Lz_inv_Kzt = cola.solve(Lz, Kzt, Cholesky()) # Kzz⁻¹ Kzt - Kzz_inv_Kzt = cola.solve(Lz.T, Lz_inv_Kzt) + Kzz_inv_Kzt = cola.solve(Lz.T, Lz_inv_Kzt, Cholesky()) # Ktz Kzz⁻¹ L Ktz_Kzz_inv_L = jnp.matmul(Kzz_inv_Kzt.T, sqrt) @@ -605,10 +609,10 @@ def predict(self, test_inputs: Float[Array, "N D"]) -> GaussianDistribution: mut = mean_function(t) # Lz⁻¹ Kzt - Lz_inv_Kzt = cola.solve(Lz, Kzt) + Lz_inv_Kzt = cola.solve(Lz, Kzt, Cholesky()) # Kzz⁻¹ Kzt - Kzz_inv_Kzt = cola.solve(Lz.T, Lz_inv_Kzt) + Kzz_inv_Kzt = cola.solve(Lz.T, Lz_inv_Kzt, Cholesky()) # Ktz Kzz⁻¹ sqrt Ktz_Kzz_inv_sqrt = Kzz_inv_Kzt.T @ sqrt @@ -680,7 +684,7 @@ def predict( Lz = lower_cholesky(Kzz) # Lz⁻¹ Kzx - Lz_inv_Kzx = cola.solve(Lz, Kzx) + Lz_inv_Kzx = cola.solve(Lz, Kzx, Cholesky()) # A = Lz⁻¹ Kzt / o A = Lz_inv_Kzx / self.posterior.likelihood.obs_stddev @@ -698,14 +702,14 @@ def predict( Lz_inv_Kzx_diff = jsp.linalg.cho_solve((L, True), jnp.matmul(Lz_inv_Kzx, diff)) # Kzz⁻¹ Kzx (y - μx) - Kzz_inv_Kzx_diff = cola.solve(Lz.T, Lz_inv_Kzx_diff) + Kzz_inv_Kzx_diff = cola.solve(Lz.T, Lz_inv_Kzx_diff, Cholesky()) Ktt = kernel.gram(t) Kzt = kernel.cross_covariance(z, t) mut = mean_function(t) # Lz⁻¹ Kzt - Lz_inv_Kzt = cola.solve(Lz, Kzt) + Lz_inv_Kzt = cola.solve(Lz, Kzt, Cholesky()) # L⁻¹ Lz⁻¹ Kzt L_inv_Lz_inv_Kzt = jsp.linalg.solve_triangular(L, Lz_inv_Kzt, lower=True) diff --git a/poetry.lock b/poetry.lock index d01a47e6..ed9adfcb 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,22 +1,20 @@ -# This file is automatically @generated by Poetry 1.4.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. 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b/pyproject.toml index c9266f44..abe42fd3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,11 +22,11 @@ jaxtyping = "^0.2.15" tqdm = "^4.65.0" simple-pytree = "^0.1.7" tensorflow-probability = "^0.19.0" -beartype = "^0.13.1" +beartype = "^0.16.2" jax = ">=0.4.10" jaxlib = ">=0.4.10" orbax-checkpoint = ">=0.2.3" -cola-ml = "^0.0.1" +cola-ml = "^0.0.5" [tool.poetry.group.test.dependencies] pytest = "^7.2.2" diff --git a/tests/test_citations.py b/tests/test_citations.py index a5490da0..02eaf13b 100644 --- a/tests/test_citations.py +++ b/tests/test_citations.py @@ -10,7 +10,6 @@ from gpjax.citation import ( AbstractCitation, BookCitation, - JittedFnCitation, NullCitation, PaperCitation, PhDThesisCitation, @@ -209,6 +208,5 @@ def test_logarithmic_goldstein_price(): [gpx.ELBO(), gpx.CollapsedELBO(), gpx.LogPosteriorDensity(), gpx.ConjugateMLL()], ) def test_jitted_fallback(objective): - citation = cite(jit(objective)) - assert isinstance(citation, JittedFnCitation) - assert citation.__str__() == "Citation not available for jitted objects." + with pytest.raises(RuntimeError): + _ = cite(jit(objective)) diff --git a/tests/test_integrators.py b/tests/test_integrators.py index be34b82b..7ee6ffd2 100644 --- a/tests/test_integrators.py +++ b/tests/test_integrators.py @@ -47,6 +47,7 @@ def fun(x, y): mean=mean, variance=variance, y=jnp.ones_like(mean), + likelihood=None, ) return fn_val.squeeze().round(1)