From 7cf94c2335777966282f558a82d54b51f123b8e2 Mon Sep 17 00:00:00 2001 From: frazane Date: Tue, 22 Aug 2023 12:39:02 +0200 Subject: [PATCH 01/23] add jaxopt dependency --- poetry.lock | 5369 +++++++++++++++++++++++------------------------- pyproject.toml | 1 + 2 files changed, 2563 insertions(+), 2807 deletions(-) diff --git a/poetry.lock b/poetry.lock index c9a80df57..5b1b0b336 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,26 +1,37 @@ +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. + [[package]] name = "absl-py" version = "1.4.0" description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." -category = "main" optional = false python-versions = ">=3.6" +files = [ + {file = "absl-py-1.4.0.tar.gz", hash = "sha256:d2c244d01048ba476e7c080bd2c6df5e141d211de80223460d5b3b8a2a58433d"}, + {file = "absl_py-1.4.0-py3-none-any.whl", hash = "sha256:0d3fe606adfa4f7db64792dd4c7aee4ee0c38ab75dfd353b7a83ed3e957fcb47"}, +] 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["Pygments", "Pygments", "colorama"] +jupyter = ["IPython", "IPython", "attrs", "debugpy", "debugpy", "debugpy", "debugpy", "debugpy", "ipykernel", "ipykernel", "ipython-genutils", "jedi", "jinja2", "jupyter-client", "jupyter-client", "jupyter-core", "nbconvert"] +optional = ["IPython", "IPython", "Pygments", "Pygments", "attrs", "colorama", "debugpy", "debugpy", "debugpy", "debugpy", "debugpy", "ipykernel", "ipykernel", "ipython-genutils", "jedi", "jinja2", "jupyter-client", "jupyter-client", "jupyter-core", "nbconvert", "pyflakes", "tomli"] +optional-strict = ["IPython (==7.10.0)", "IPython (==7.23.1)", "Pygments (==2.0.0)", "Pygments (==2.4.1)", "attrs (==19.2.0)", "colorama (==0.4.1)", "debugpy (==1.0.0)", "debugpy (==1.0.0)", "debugpy (==1.0.0)", "debugpy (==1.3.0)", "debugpy (==1.6.0)", "ipykernel (==5.2.0)", "ipykernel (==6.0.0)", "ipython-genutils (==0.2.0)", "jedi (==0.16)", "jinja2 (==3.0.0)", "jupyter-client (==6.1.5)", "jupyter-client (==7.0.0)", "jupyter-core (==4.7.0)", "nbconvert (==6.0.0)", "pyflakes (==2.2.0)", "tomli (==0.2.0)"] +runtime-strict = ["six (==1.11.0)"] +tests = ["codecov", "pytest", "pytest", "pytest", "pytest-cov", "typing"] +tests-binary = ["cmake", "cmake", "ninja", "ninja", "pybind11", "pybind11", "scikit-build", "scikit-build"] +tests-binary-strict = ["cmake (==3.21.2)", "cmake (==3.25.0)", "ninja (==1.10.2)", "ninja (==1.11.1)", "pybind11 (==2.10.3)", "pybind11 (==2.7.1)", "scikit-build (==0.11.1)", "scikit-build (==0.16.1)"] +tests-strict = ["codecov (==2.0.15)", "pytest (==4.6.0)", "pytest (==4.6.0)", "pytest (==6.2.5)", "pytest-cov (==3.0.0)", "typing (==3.7.4)"] + +[[package]] +name = "yarl" +version = "1.9.2" +description = "Yet another URL library" +optional = false +python-versions = ">=3.7" +files = [ {file = "yarl-1.9.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:8c2ad583743d16ddbdf6bb14b5cd76bf43b0d0006e918809d5d4ddf7bde8dd82"}, {file = "yarl-1.9.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:82aa6264b36c50acfb2424ad5ca537a2060ab6de158a5bd2a72a032cc75b9eb8"}, {file = "yarl-1.9.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c0c77533b5ed4bcc38e943178ccae29b9bcf48ffd1063f5821192f23a1bd27b9"}, @@ -5373,7 +5108,27 @@ yarl = [ {file = "yarl-1.9.2-cp39-cp39-win_amd64.whl", hash = "sha256:61016e7d582bc46a5378ffdd02cd0314fb8ba52f40f9cf4d9a5e7dbef88dee18"}, {file = "yarl-1.9.2.tar.gz", hash = "sha256:04ab9d4b9f587c06d801c2abfe9317b77cdf996c65a90d5e84ecc45010823571"}, ] -zipp = [ + +[package.dependencies] +idna = ">=2.0" +multidict = ">=4.0" + +[[package]] +name = "zipp" +version = "3.15.0" +description = "Backport of pathlib-compatible object wrapper for zip files" +optional = false +python-versions = ">=3.7" +files = [ {file = "zipp-3.15.0-py3-none-any.whl", hash = "sha256:48904fc76a60e542af151aded95726c1a5c34ed43ab4134b597665c86d7ad556"}, {file = "zipp-3.15.0.tar.gz", hash = "sha256:112929ad649da941c23de50f356a2b5570c954b65150642bccdd66bf194d224b"}, ] + +[package.extras] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.8,<3.12" +content-hash = "89e1c92d6b5c1d789412ed7a2763ef243989adb7fe7ee1606989f60789435deb" diff --git a/pyproject.toml b/pyproject.toml index a20ecb254..e6864956a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,6 +26,7 @@ orbax-checkpoint = "^0.2.0" beartype = "^0.13.1" jaxlib = "0.4.7" # Temporary fix: https://github.com/google/jax/issues/15951 plum-dispatch = "^2.1.0" +jaxopt = "^0.8" [tool.poetry.group.test.dependencies] pytest = "^7.2.2" From ac45c2e20b282599264245f8f145d205f2b7adfa Mon Sep 17 00:00:00 2001 From: frazane Date: Tue, 22 Aug 2023 12:39:46 +0200 Subject: [PATCH 02/23] add fit_jaxopt function --- gpjax/fit.py | 139 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 139 insertions(+) diff --git a/gpjax/fit.py b/gpjax/fit.py index e5ad9d1d2..7c0eb1af1 100644 --- a/gpjax/fit.py +++ b/gpjax/fit.py @@ -19,12 +19,14 @@ Callable, Optional, Tuple, + Type, TypeVar, Union, ) import jax from jax._src.random import _check_prng_key import jax.random as jr +from jaxopt.base import IterativeSolver import optax as ox from gpjax.base import Module @@ -170,6 +172,143 @@ def step(carry, key): return model, history +def fit_jaxopt( # noqa: PLR0913 + *, + model: ModuleModel, + objective: Union[AbstractObjective, Callable[[ModuleModel, Dataset], ScalarFloat]], + train_data: Dataset, + solver: Type[IterativeSolver], + solver_kwargs: dict[str, Any], + num_iters: int, + key: KeyArray, + batch_size: Optional[int] = -1, + log_rate: Optional[int] = 10, + verbose: Optional[bool] = True, + unroll: Optional[int] = 1, + safe: Optional[bool] = True, +) -> Tuple[ModuleModel, Array]: + r"""Train a Module model with respect to a supplied Objective function. + `solver` must be a subclass of `jaxopt`'s `IterativeSolver`. + + Example: + ```python + >>> import jax.numpy as jnp + >>> import jax.random as jr + >>> import jaxopt + >>> import gpjax as gpx + >>> + >>> # (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) + >>> D = gpx.Dataset(X, y) + >>> + >>> # (2) Define your model: + >>> class LinearModel(gpx.Module): + weight: float = gpx.param_field() + bias: float = gpx.param_field() + + def __call__(self, x): + return self.weight * x + self.bias + + >>> model = LinearModel(weight=1.0, bias=1.0) + >>> + >>> # (3) Define your loss function: + >>> class MeanSquareError(gpx.AbstractObjective): + def evaluate(self, model: LinearModel, train_data: gpx.Dataset) -> float: + return jnp.mean((train_data.y - model(train_data.X)) ** 2) + >>> + >>> loss = MeanSqaureError() + >>> + >>> # (4) Train! + >>> trained_model, history = gpx.fit( + model=model, + objective=loss, + train_data=D, + solver=jaxopt.LBFGS, + solver_kwargs={"max_stepsize": 0.001}, + num_iters=1000, + ) + ``` + + Args: + model (Module): The model Module to be optimised. + objective (Objective): The objective function that we are optimising with + respect to. + train_data (Dataset): The training data to be used for the optimisation. + solver (IterativeSolver): The `jaxopt` solver. + solver_kwargs (Dict[str, Any]): arguments for instantiating the solver. + num_iters (Optional[int]): The number of optimisation steps to run. Defaults + to 100. + 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). + 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 + to True. + unroll (int): The number of unrolled steps to use for the optimisation. + Defaults to 1. + + Returns + ------- + Tuple[Module, Array]: A Tuple comprising the optimised model and training + history respectively. + """ + if safe: + # Check inputs. + _check_model(model) + _check_train_data(train_data) + _check_batch_size(batch_size) + _check_prng_key(key) + _check_log_rate(log_rate) + _check_verbose(verbose) + + # Unconstrained space loss function with stop-gradient rule for non-trainable params. + def loss(model: Module, batch: Dataset) -> ScalarFloat: + model = model.stop_gradient() + return objective(model.constrain(), batch) + + # Unconstrained space model. + model = model.unconstrain() + + # Initialise optimiser state. + solver: IterativeSolver = solver(loss, **solver_kwargs) + solver_state = solver.init_state( + model, + get_batch(train_data, batch_size, key) if batch_size != -1 else train_data, + ) + solver.maxiter = num_iters + jitted_update = jax.jit(solver.update) + + # Mini-batch random keys to scan over. + iter_keys = jr.split(key, solver.maxiter) + + # Optimisation step. + def step(carry, key): + model, state = carry + + if batch_size != -1: + batch = get_batch(train_data, batch_size, key) + else: + batch = train_data + + model, state = jitted_update(model, state, batch) + carry = model, state + return carry, state.value + + # Optimisation scan. + scan = vscan if verbose else jax.lax.scan + + # Optimisation loop. + (model, _), history = scan(step, (model, solver_state), (iter_keys), unroll=unroll) + + # Constrained space. + model = model.constrain() + + return model, history + + def get_batch(train_data: Dataset, batch_size: int, key: KeyArray) -> Dataset: """Batch the data into mini-batches. Sampling is done with replacement. From cb3615bb644a9d595cb09106a65bfd56f3988e57 Mon Sep 17 00:00:00 2001 From: frazane Date: Tue, 22 Aug 2023 15:03:34 +0200 Subject: [PATCH 03/23] just use jaxopt --- gpjax/fit.py | 185 ++++----------------------------------------------- 1 file changed, 14 insertions(+), 171 deletions(-) diff --git a/gpjax/fit.py b/gpjax/fit.py index 7c0eb1af1..52e51a78f 100644 --- a/gpjax/fit.py +++ b/gpjax/fit.py @@ -19,24 +19,19 @@ Callable, Optional, Tuple, - Type, TypeVar, - Union, ) import jax from jax._src.random import _check_prng_key import jax.random as jr from jaxopt.base import IterativeSolver -import optax as ox from gpjax.base import Module from gpjax.dataset import Dataset -from gpjax.objectives import AbstractObjective from gpjax.scan import vscan from gpjax.typing import ( Array, KeyArray, - ScalarFloat, ) ModuleModel = TypeVar("ModuleModel", bound=Module) @@ -45,11 +40,9 @@ def fit( # noqa: PLR0913 *, model: ModuleModel, - objective: Union[AbstractObjective, Callable[[ModuleModel, Dataset], ScalarFloat]], train_data: Dataset, - optim: ox.GradientTransformation, + solver: IterativeSolver, key: KeyArray, - num_iters: Optional[int] = 100, batch_size: Optional[int] = -1, log_rate: Optional[int] = 10, verbose: Optional[bool] = True, @@ -57,138 +50,7 @@ def fit( # noqa: PLR0913 safe: Optional[bool] = True, ) -> Tuple[ModuleModel, Array]: r"""Train a Module model with respect to a supplied Objective function. - Optimisers used here should originate from Optax. - - Example: - ```python - >>> import jax.numpy as jnp - >>> import jax.random as jr - >>> import optax as ox - >>> import gpjax as gpx - >>> - >>> # (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) - >>> D = gpx.Dataset(X, y) - >>> - >>> # (2) Define your model: - >>> class LinearModel(gpx.Module): - weight: float = gpx.param_field() - bias: float = gpx.param_field() - - def __call__(self, x): - return self.weight * x + self.bias - - >>> model = LinearModel(weight=1.0, bias=1.0) - >>> - >>> # (3) Define your loss function: - >>> class MeanSquareError(gpx.AbstractObjective): - def evaluate(self, model: LinearModel, train_data: gpx.Dataset) -> float: - return jnp.mean((train_data.y - model(train_data.X)) ** 2) - >>> - >>> loss = MeanSqaureError() - >>> - >>> # (4) Train! - >>> trained_model, history = gpx.fit( - model=model, objective=loss, train_data=D, optim=ox.sgd(0.001), num_iters=1000 - ) - ``` - - Args: - model (Module): The model Module to be optimised. - objective (Objective): The objective function that we are optimising with - respect to. - train_data (Dataset): The training data to be used for the optimisation. - optim (GradientTransformation): The Optax optimiser that is to be used for - learning a parameter set. - num_iters (Optional[int]): The number of optimisation steps to run. Defaults - to 100. - 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). - 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 - to True. - unroll (int): The number of unrolled steps to use for the optimisation. - Defaults to 1. - - Returns - ------- - Tuple[Module, Array]: A Tuple comprising the optimised model and training - history respectively. - """ - if safe: - # Check inputs. - _check_model(model) - _check_train_data(train_data) - _check_optim(optim) - _check_num_iters(num_iters) - _check_batch_size(batch_size) - _check_prng_key(key) - _check_log_rate(log_rate) - _check_verbose(verbose) - - # Unconstrained space loss function with stop-gradient rule for non-trainable params. - def loss(model: Module, batch: Dataset) -> ScalarFloat: - model = model.stop_gradient() - return objective(model.constrain(), batch) - - # Unconstrained space model. - model = model.unconstrain() - - # Initialise optimiser state. - state = optim.init(model) - - # Mini-batch random keys to scan over. - iter_keys = jr.split(key, num_iters) - - # Optimisation step. - def step(carry, key): - model, opt_state = carry - - if batch_size != -1: - batch = get_batch(train_data, batch_size, key) - else: - batch = train_data - - loss_val, loss_gradient = jax.value_and_grad(loss)(model, batch) - updates, opt_state = optim.update(loss_gradient, opt_state, model) - model = ox.apply_updates(model, updates) - - carry = model, opt_state - return carry, loss_val - - # Optimisation scan. - scan = vscan if verbose else jax.lax.scan - - # Optimisation loop. - (model, _), history = scan(step, (model, state), (iter_keys), unroll=unroll) - - # Constrained space. - model = model.constrain() - - return model, history - - -def fit_jaxopt( # noqa: PLR0913 - *, - model: ModuleModel, - objective: Union[AbstractObjective, Callable[[ModuleModel, Dataset], ScalarFloat]], - train_data: Dataset, - solver: Type[IterativeSolver], - solver_kwargs: dict[str, Any], - num_iters: int, - key: KeyArray, - batch_size: Optional[int] = -1, - log_rate: Optional[int] = 10, - verbose: Optional[bool] = True, - unroll: Optional[int] = 1, - safe: Optional[bool] = True, -) -> Tuple[ModuleModel, Array]: - r"""Train a Module model with respect to a supplied Objective function. - `solver` must be a subclass of `jaxopt`'s `IterativeSolver`. + `solver` must be an instance of `jaxopt`'s `IterativeSolver`. Example: ```python @@ -224,21 +86,14 @@ def evaluate(self, model: LinearModel, train_data: gpx.Dataset) -> float: model=model, objective=loss, train_data=D, - solver=jaxopt.LBFGS, - solver_kwargs={"max_stepsize": 0.001}, - num_iters=1000, + solver=jaxopt.LBFGS(loss_fn, max_stepsize=0.001, maxiter=4000), ) ``` Args: model (Module): The model Module to be optimised. - objective (Objective): The objective function that we are optimising with - respect to. train_data (Dataset): The training data to be used for the optimisation. solver (IterativeSolver): The `jaxopt` solver. - solver_kwargs (Dict[str, Any]): arguments for instantiating the solver. - num_iters (Optional[int]): The number of optimisation steps to run. Defaults - to 100. 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 @@ -264,21 +119,16 @@ def evaluate(self, model: LinearModel, train_data: gpx.Dataset) -> float: _check_log_rate(log_rate) _check_verbose(verbose) - # Unconstrained space loss function with stop-gradient rule for non-trainable params. - def loss(model: Module, batch: Dataset) -> ScalarFloat: - model = model.stop_gradient() - return objective(model.constrain(), batch) - # Unconstrained space model. model = model.unconstrain() - # Initialise optimiser state. - solver: IterativeSolver = solver(loss, **solver_kwargs) + # Initialise solver state. + solver.fun = _wrap_objective(solver.fun) + solver.__post_init__() # needed to propagate changes to `fun` attribute solver_state = solver.init_state( model, get_batch(train_data, batch_size, key) if batch_size != -1 else train_data, ) - solver.maxiter = num_iters jitted_update = jax.jit(solver.update) # Mini-batch random keys to scan over. @@ -329,6 +179,14 @@ def get_batch(train_data: Dataset, batch_size: int, key: KeyArray) -> Dataset: return Dataset(X=x[indices], y=y[indices]) +def _wrap_objective(objective: Callable): + def wrapped(model, batch): + model = model.stop_gradient() + return objective(model.constrain(), batch) + + return wrapped + + def _check_model(model: Any) -> None: """Check that the model is of type Module. Check trainables and bijectors tree structure.""" if not isinstance(model, Module): @@ -341,21 +199,6 @@ def _check_train_data(train_data: Any) -> None: raise TypeError("train_data must be of type gpjax.Dataset") -def _check_optim(optim: Any) -> None: - """Check that the optimiser is of type GradientTransformation.""" - if not isinstance(optim, ox.GradientTransformation): - raise TypeError("optax_optim must be of type optax.GradientTransformation") - - -def _check_num_iters(num_iters: Any) -> None: - """Check that the number of iterations is of type int and positive.""" - if not isinstance(num_iters, int): - raise TypeError("num_iters must be of type int") - - if not num_iters > 0: - raise ValueError("num_iters must be positive") - - def _check_log_rate(log_rate: Any) -> None: """Check that the log rate is of type int and positive.""" if not isinstance(log_rate, int): From bc54759a219176e61f30b3258c716d10875e3ac0 Mon Sep 17 00:00:00 2001 From: frazane Date: Tue, 22 Aug 2023 15:47:42 +0200 Subject: [PATCH 04/23] adapt tests --- gpjax/fit.py | 8 +++++++- tests/test_fit.py | 17 +++++++---------- 2 files changed, 14 insertions(+), 11 deletions(-) diff --git a/gpjax/fit.py b/gpjax/fit.py index 52e51a78f..c5fec235b 100644 --- a/gpjax/fit.py +++ b/gpjax/fit.py @@ -23,7 +23,9 @@ ) import jax from jax._src.random import _check_prng_key +import jax.numpy as jnp import jax.random as jr +import jaxopt from jaxopt.base import IterativeSolver from gpjax.base import Module @@ -84,7 +86,6 @@ def evaluate(self, model: LinearModel, train_data: gpx.Dataset) -> float: >>> # (4) Train! >>> trained_model, history = gpx.fit( model=model, - objective=loss, train_data=D, solver=jaxopt.LBFGS(loss_fn, max_stepsize=0.001, maxiter=4000), ) @@ -125,6 +126,11 @@ def evaluate(self, model: LinearModel, train_data: gpx.Dataset) -> float: # Initialise solver state. solver.fun = _wrap_objective(solver.fun) solver.__post_init__() # needed to propagate changes to `fun` attribute + + # needed for OptaxSolver to work + if isinstance(solver, jaxopt.OptaxSolver): + model = jax.tree_map(lambda x: x.astype(jnp.float64), model) + solver_state = solver.init_state( model, get_batch(train_data, batch_size, key) if batch_size != -1 else train_data, diff --git a/tests/test_fit.py b/tests/test_fit.py index 6eeaed992..c716bcf1e 100644 --- a/tests/test_fit.py +++ b/tests/test_fit.py @@ -19,6 +19,7 @@ from jax.config import config import jax.numpy as jnp import jax.random as jr +import jaxopt import optax as ox import pytest import tensorflow_probability.substrates.jax.bijectors as tfb @@ -78,10 +79,8 @@ def step(self, model: LinearModel, train_data: Dataset) -> float: # Train! trained_model, hist = fit( model=model, - objective=loss, train_data=D, - optim=ox.sgd(0.001), - num_iters=100, + solver=jaxopt.LBFGS(loss, max_stepsize=1e-3, maxiter=100), key=jr.PRNGKey(123), ) @@ -101,7 +100,9 @@ def step(self, model: LinearModel, train_data: Dataset) -> float: @pytest.mark.parametrize("num_iters", [1, 5]) @pytest.mark.parametrize("n_data", [1, 20]) @pytest.mark.parametrize("verbose", [True, False]) -def test_gaussian_process_regression(num_iters, n_data: int, verbose: bool) -> None: +def test_gaussian_process_regression( + num_iters: int, n_data: int, verbose: bool +) -> None: # Create dataset: key = jr.PRNGKey(123) x = jnp.sort( @@ -121,10 +122,8 @@ def test_gaussian_process_regression(num_iters, n_data: int, verbose: bool) -> N # Train! trained_model, history = fit( model=posterior, - objective=mll, train_data=D, - optim=ox.adam(0.1), - num_iters=num_iters, + solver=jaxopt.LBFGS(mll, maxiter=num_iters, max_stepsize=1e-3), verbose=verbose, key=jr.PRNGKey(123), ) @@ -169,10 +168,8 @@ def test_batch_fitting( # Train! trained_model, history = fit( model=q, - objective=elbo, train_data=D, - optim=ox.adam(0.1), - num_iters=num_iters, + solver=jaxopt.OptaxSolver(elbo, opt=ox.adam(1e-3), maxiter=num_iters), batch_size=batch_size, verbose=verbose, key=jr.PRNGKey(123), From 80d497695e0ce2959aec52e2de25cc6550d7a9d8 Mon Sep 17 00:00:00 2001 From: frazane Date: Wed, 23 Aug 2023 12:01:51 +0200 Subject: [PATCH 05/23] adapt examples --- docs/examples/barycentres.py | 7 ++++--- docs/examples/bayesian_optimisation.py | 9 +++++---- docs/examples/classification.py | 7 ++----- docs/examples/collapsed_vi.py | 5 ++--- docs/examples/constructing_new_kernels.py | 9 +++++---- docs/examples/deep_kernels.py | 7 ++++--- docs/examples/graph_kernels.py | 7 ++++--- docs/examples/intro_to_kernels.py | 19 +++++++------------ docs/examples/regression.py | 5 ++--- docs/examples/spatial.py | 5 ++--- docs/examples/uncollapsed_vi.py | 9 +++------ docs/examples/yacht.py | 5 ++--- 12 files changed, 42 insertions(+), 52 deletions(-) diff --git a/docs/examples/barycentres.py b/docs/examples/barycentres.py index 9ab7a2d83..8a3a990a4 100644 --- a/docs/examples/barycentres.py +++ b/docs/examples/barycentres.py @@ -27,6 +27,7 @@ from jaxtyping import install_import_hook import matplotlib.pyplot as plt import optax as ox +import jaxopt import tensorflow_probability.substrates.jax.distributions as tfd with install_import_hook("gpjax", "beartype.beartype"): @@ -139,10 +140,10 @@ def fit_gp(x: jax.Array, y: jax.Array) -> tfd.MultivariateNormalFullCovariance: opt_posterior, _ = gpx.fit( model=posterior, - objective=jax.jit(gpx.ConjugateMLL(negative=True)), train_data=D, - optim=ox.adamw(learning_rate=0.01), - num_iters=500, + solver=jaxopt.OptaxSolver( + gpx.ConjugateMLL(negative=True), opt=ox.adam(0.01), maxiter=500 + ), key=key, ) latent_dist = opt_posterior.predict(xtest, train_data=D) diff --git a/docs/examples/bayesian_optimisation.py b/docs/examples/bayesian_optimisation.py index 1804cd674..0425fc305 100644 --- a/docs/examples/bayesian_optimisation.py +++ b/docs/examples/bayesian_optimisation.py @@ -20,6 +20,7 @@ import matplotlib.pyplot as plt from matplotlib import cm import optax as ox +import jaxopt import tensorflow_probability.substrates.jax as tfp from typing import List, Tuple @@ -216,10 +217,10 @@ def return_optimised_posterior( opt_posterior, history = gpx.fit( model=posterior, - objective=negative_mll, - train_data=data, - optim=ox.adam(learning_rate=0.01), - num_iters=1000, + train_data=D, + solver=jaxopt.OptaxSolver( + gpx.ConjugateMLL(negative=True), opt=ox.adam(0.01), maxiter=1000 + ), safe=True, key=key, verbose=False, diff --git a/docs/examples/classification.py b/docs/examples/classification.py index 59775abe0..51faeb98d 100644 --- a/docs/examples/classification.py +++ b/docs/examples/classification.py @@ -43,6 +43,7 @@ ) import matplotlib.pyplot as plt import optax as ox +import jaxopt import tensorflow_probability.substrates.jax as tfp from tqdm import trange @@ -118,14 +119,10 @@ # %% negative_lpd = jax.jit(gpx.LogPosteriorDensity(negative=True)) -optimiser = ox.adam(learning_rate=0.01) - opt_posterior, history = gpx.fit( model=posterior, - objective=negative_lpd, train_data=D, - optim=ox.adamw(learning_rate=0.01), - num_iters=1000, + solver=jaxopt.OptaxSolver(negative_lpd, opt=ox.adam(0.01), maxiter=1000), key=key, ) diff --git a/docs/examples/collapsed_vi.py b/docs/examples/collapsed_vi.py index 8dd442e36..cd3828ac7 100644 --- a/docs/examples/collapsed_vi.py +++ b/docs/examples/collapsed_vi.py @@ -38,6 +38,7 @@ import matplotlib as mpl import matplotlib.pyplot as plt import optax as ox +import jaxopt from docs.examples.utils import clean_legend with install_import_hook("gpjax", "beartype.beartype"): @@ -155,10 +156,8 @@ # %% opt_posterior, history = gpx.fit( model=q, - objective=elbo, train_data=D, - optim=ox.adamw(learning_rate=1e-2), - num_iters=500, + solver=jaxopt.OptaxSolver(elbo, opt=ox.adamw(1e-2), maxiter=500), key=key, ) diff --git a/docs/examples/constructing_new_kernels.py b/docs/examples/constructing_new_kernels.py index 0617d1e5b..c10561d0f 100644 --- a/docs/examples/constructing_new_kernels.py +++ b/docs/examples/constructing_new_kernels.py @@ -41,6 +41,7 @@ import matplotlib.pyplot as plt import numpy as np import optax as ox +import jaxopt from simple_pytree import static_field import tensorflow_probability.substrates.jax as tfp @@ -108,7 +109,7 @@ # like our RBF kernel to act on the first, second and fourth dimensions. # %% -slice_kernel = gpx.kernels.RBF(active_dims=[0, 1, 3], lengthscale = jnp.ones((3,))) +slice_kernel = gpx.kernels.RBF(active_dims=[0, 1, 3], lengthscale=jnp.ones((3,))) # %% [markdown] # @@ -270,10 +271,10 @@ def __call__( # Optimise GP's marginal log-likelihood using Adam opt_posterior, history = gpx.fit( model=circular_posterior, - objective=jit(gpx.ConjugateMLL(negative=True)), train_data=D, - optim=ox.adamw(learning_rate=0.05), - num_iters=500, + solver=jaxopt.OptaxSolver( + gpx.ConjugateMLL(negative=True), opt=ox.adamw(0.05), maxiter=500 + ), key=key, ) diff --git a/docs/examples/deep_kernels.py b/docs/examples/deep_kernels.py index 3346c958c..38b57e65f 100644 --- a/docs/examples/deep_kernels.py +++ b/docs/examples/deep_kernels.py @@ -33,6 +33,7 @@ import matplotlib as mpl import matplotlib.pyplot as plt import optax as ox +import jaxopt from scipy.signal import sawtooth from gpjax.base import static_field @@ -207,10 +208,10 @@ def __call__(self, x): opt_posterior, history = gpx.fit( model=posterior, - objective=jax.jit(gpx.ConjugateMLL(negative=True)), train_data=D, - optim=optimiser, - num_iters=800, + solver=jaxopt.OptaxSolver( + gpx.ConjugateMLL(negative=True), opt=optimiser, maxiter=800 + ), key=key, ) diff --git a/docs/examples/graph_kernels.py b/docs/examples/graph_kernels.py index 82154b3a4..63995bbd8 100644 --- a/docs/examples/graph_kernels.py +++ b/docs/examples/graph_kernels.py @@ -23,6 +23,7 @@ import matplotlib.pyplot as plt import networkx as nx import optax as ox +import jaxopt with install_import_hook("gpjax", "beartype.beartype"): import gpjax as gpx @@ -155,10 +156,10 @@ # %% opt_posterior, training_history = gpx.fit( model=posterior, - objective=jit(gpx.ConjugateMLL(negative=True)), train_data=D, - optim=ox.adamw(learning_rate=0.01), - num_iters=1000, + solver=jaxopt.OptaxSolver( + gpx.ConjugateMLL(negative=True), opt=ox.adamw(0.01), maxiter=1000 + ), key=key, ) diff --git a/docs/examples/intro_to_kernels.py b/docs/examples/intro_to_kernels.py index 7356202fc..beaf5be6f 100644 --- a/docs/examples/intro_to_kernels.py +++ b/docs/examples/intro_to_kernels.py @@ -17,6 +17,7 @@ import matplotlib as mpl import matplotlib.pyplot as plt import optax as ox +import jaxopt import pandas as pd from docs.examples.utils import clean_legend @@ -233,16 +234,13 @@ def forrester(x: Float[Array, "N"]) -> Float[Array, "N"]: # We can then optimise the hyperparameters by minimising the negative log marginal likelihood of the data: # %% -negative_mll = gpx.objectives.ConjugateMLL(negative=True) -negative_mll(no_opt_posterior, train_data=D) -negative_mll = jit(negative_mll) opt_posterior, history = gpx.fit( model=no_opt_posterior, - objective=negative_mll, train_data=D, - optim=ox.adam(learning_rate=0.01), - num_iters=2000, + solver=jaxopt.OptaxSolver( + gpx.ConjugateMLL(negative=True), opt=ox.adamw(0.01), maxiter=2000 + ), safe=True, key=key, ) @@ -538,16 +536,13 @@ def forrester(x: Float[Array, "N"]) -> Float[Array, "N"]: # marginal likelihood of the data: # %% -negative_mll = gpx.objectives.ConjugateMLL(negative=True) -negative_mll(posterior, train_data=D) -negative_mll = jit(negative_mll) opt_posterior, history = gpx.fit( model=posterior, - objective=negative_mll, train_data=D, - optim=ox.adam(learning_rate=0.01), - num_iters=1000, + solver=jaxopt.OptaxSolver( + gpx.ConjugateMLL(negative=True), opt=ox.adamw(0.01), maxiter=1000 + ), safe=True, key=key, ) diff --git a/docs/examples/regression.py b/docs/examples/regression.py index bccbb8068..39d7eec47 100644 --- a/docs/examples/regression.py +++ b/docs/examples/regression.py @@ -32,6 +32,7 @@ import matplotlib as mpl import matplotlib.pyplot as plt import optax as ox +import jaxopt from docs.examples.utils import clean_legend with install_import_hook("gpjax", "beartype.beartype"): @@ -216,10 +217,8 @@ # %% opt_posterior, history = gpx.fit( model=posterior, - objective=negative_mll, train_data=D, - optim=ox.adam(learning_rate=0.01), - num_iters=500, + solver=jaxopt.OptaxSolver(negative_mll, opt=ox.adamw(0.01), maxiter=500), safe=True, key=key, ) diff --git a/docs/examples/spatial.py b/docs/examples/spatial.py index 72fe15c4f..55beaa16d 100644 --- a/docs/examples/spatial.py +++ b/docs/examples/spatial.py @@ -55,6 +55,7 @@ import matplotlib as mpl import matplotlib.pyplot as plt import optax as ox +import jaxopt import pandas as pd import planetary_computer import pystac_client @@ -189,10 +190,8 @@ def __call__(self, x: Float[Array, "N D"]) -> Float[Array, "N 1"]: optim = ox.chain(ox.adam(learning_rate=0.1), ox.clip(1.0)) posterior, history = gpx.fit( model=posterior, - objective=negative_mll, train_data=D, - optim=optim, - num_iters=3000, + solver=jaxopt.OptaxSolver(negative_mll, opt=optim, maxiter=3000), safe=True, key=key, ) diff --git a/docs/examples/uncollapsed_vi.py b/docs/examples/uncollapsed_vi.py index 76eae5ec1..6ea1882da 100644 --- a/docs/examples/uncollapsed_vi.py +++ b/docs/examples/uncollapsed_vi.py @@ -43,6 +43,7 @@ import matplotlib as mpl import matplotlib.pyplot as plt import optax as ox +import jaxopt import tensorflow_probability.substrates.jax as tfp with install_import_hook("gpjax", "beartype.beartype"): @@ -266,10 +267,8 @@ opt_posterior, history = gpx.fit( model=q, - objective=negative_elbo, train_data=D, - optim=ox.adam(learning_rate=schedule), - num_iters=3000, + solver=jaxopt.OptaxSolver(negative_elbo, opt=ox.adam(schedule), maxiter=3000), key=jr.PRNGKey(42), batch_size=128, ) @@ -330,10 +329,8 @@ # %% opt_rep, history = gpx.fit( model=reparameterised_q, - objective=negative_elbo, train_data=D, - optim=ox.adam(learning_rate=0.01), - num_iters=3000, + solver=jaxopt.OptaxSolver(negative_elbo, opt=ox.adam(0.01), maxiter=3000), key=jr.PRNGKey(42), batch_size=128, ) diff --git a/docs/examples/yacht.py b/docs/examples/yacht.py index c4260a986..f8386909f 100644 --- a/docs/examples/yacht.py +++ b/docs/examples/yacht.py @@ -36,6 +36,7 @@ import matplotlib.pyplot as plt import numpy as np import optax as ox +import jaxopt import pandas as pd from sklearn.metrics import ( mean_squared_error, @@ -192,10 +193,8 @@ opt_posterior, history = gpx.fit( model=posterior, - objective=negative_mll, train_data=training_data, - optim=ox.adamw(learning_rate=0.05), - num_iters=500, + solver=jaxopt.OptaxSolver(negative_mll, opt=ox.adamw(0.05), maxiter=500), key=key, ) From 3c7a89c583b1e98dd565f70a593d1b4d61042fd1 Mon Sep 17 00:00:00 2001 From: frazane Date: Wed, 23 Aug 2023 12:08:00 +0200 Subject: [PATCH 06/23] small fix --- gpjax/fit.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/gpjax/fit.py b/gpjax/fit.py index c5fec235b..78656f170 100644 --- a/gpjax/fit.py +++ b/gpjax/fit.py @@ -81,7 +81,7 @@ def __call__(self, x): def evaluate(self, model: LinearModel, train_data: gpx.Dataset) -> float: return jnp.mean((train_data.y - model(train_data.X)) ** 2) >>> - >>> loss = MeanSqaureError() + >>> loss = MeanSquaredError() >>> >>> # (4) Train! >>> trained_model, history = gpx.fit( @@ -123,14 +123,14 @@ def evaluate(self, model: LinearModel, train_data: gpx.Dataset) -> float: # Unconstrained space model. model = model.unconstrain() - # Initialise solver state. - solver.fun = _wrap_objective(solver.fun) - solver.__post_init__() # needed to propagate changes to `fun` attribute - # needed for OptaxSolver to work if isinstance(solver, jaxopt.OptaxSolver): model = jax.tree_map(lambda x: x.astype(jnp.float64), model) + # Initialise solver state. + solver.fun = _wrap_objective(solver.fun) + solver.__post_init__() # needed to propagate changes to `fun` attribute + solver_state = solver.init_state( model, get_batch(train_data, batch_size, key) if batch_size != -1 else train_data, From 4d1e4ae74c16b879f1d12117234ea5b10d47aba9 Mon Sep 17 00:00:00 2001 From: frazane Date: Wed, 23 Aug 2023 13:30:46 +0200 Subject: [PATCH 07/23] fix readme --- README.md | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index ea95d3a45..c97a47b09 100644 --- a/README.md +++ b/README.md @@ -91,10 +91,14 @@ Let us import some dependencies and simulate a toy dataset $\mathcal{D}$. ```python import gpjax as gpx +import jax from jax import grad, jit import jax.numpy as jnp import jax.random as jr import optax as ox +import jaxopt + +jax.config.update("jax_enable_x64", True) key = jr.PRNGKey(123) @@ -116,19 +120,17 @@ likelihood = gpx.Gaussian(num_datapoints = n) # Construct the posterior posterior = prior * likelihood -# Define an optimiser -optimiser = ox.adam(learning_rate=1e-2) - # Define the marginal log-likelihood negative_mll = jit(gpx.objectives.ConjugateMLL(negative=True)) +# Define a solver +solver = jaxopt.OptaxSolver(negative_mll, ox.adam(learning_rate=1e-2), maxiter=500) + # Obtain Type 2 MLEs of the hyperparameters opt_posterior, history = gpx.fit( model=posterior, - objective=negative_mll, train_data=D, - optim=optimiser, - num_iters=500, + solver=solver, safe=True, key=key, ) From 76af7d83ff0e4d676c0242258ba8549a90e6f698 Mon Sep 17 00:00:00 2001 From: frazane Date: Thu, 24 Aug 2023 10:47:14 +0200 Subject: [PATCH 08/23] adapt examples markdown cells --- docs/examples/classification.py | 2 +- docs/examples/deep_kernels.py | 4 ++-- docs/examples/graph_kernels.py | 2 +- docs/examples/regression.py | 4 ++-- docs/examples/uncollapsed_vi.py | 2 +- docs/examples/yacht.py | 3 ++- 6 files changed, 9 insertions(+), 8 deletions(-) diff --git a/docs/examples/classification.py b/docs/examples/classification.py index 51faeb98d..e9e1caf72 100644 --- a/docs/examples/classification.py +++ b/docs/examples/classification.py @@ -114,7 +114,7 @@ # %% [markdown] # We can obtain a MAP estimate by optimising the log-posterior density with -# Optax's optimisers. +# `jaxopt` solvers. # %% negative_lpd = jax.jit(gpx.LogPosteriorDensity(negative=True)) diff --git a/docs/examples/deep_kernels.py b/docs/examples/deep_kernels.py index 38b57e65f..ffcbe2d7d 100644 --- a/docs/examples/deep_kernels.py +++ b/docs/examples/deep_kernels.py @@ -183,8 +183,8 @@ def __call__(self, x): # hyperparameter set. # # With the inclusion of a neural network, we take this opportunity to highlight the -# additional benefits gleaned from using -# [Optax](https://optax.readthedocs.io/en/latest/) for optimisation. In particular, we +# additional benefits gleaned from using `jaxopt`'s +# [Optax](https://optax.readthedocs.io/en/latest/) solver for optimisation. In particular, we # showcase the ability to use a learning rate scheduler that decays the optimiser's # learning rate throughout the inference. We decrease the learning rate according to a # half-cosine curve over 700 iterations, providing us with large step sizes early in diff --git a/docs/examples/graph_kernels.py b/docs/examples/graph_kernels.py index 63995bbd8..560c45390 100644 --- a/docs/examples/graph_kernels.py +++ b/docs/examples/graph_kernels.py @@ -133,7 +133,7 @@ # For this reason, we simply perform gradient descent on the GP's marginal # log-likelihood term as in the # [regression notebook](https://docs.jaxgaussianprocesses.com/examples/regression/). -# We do this using the Adam optimiser provided in `optax`. +# We do this using the OptaxSolver provided by `jaxopt`, instantiated with the Adam optimiser. # %% likelihood = gpx.Gaussian(num_datapoints=D.n) diff --git a/docs/examples/regression.py b/docs/examples/regression.py index 39d7eec47..4f7e0eda0 100644 --- a/docs/examples/regression.py +++ b/docs/examples/regression.py @@ -211,8 +211,8 @@ # accelerate training. # %% [markdown] -# We can now define an optimiser with `optax`. For this example we'll use the `adam` -# optimiser. +# We can now train our model using a `jaxopt` solver. In this case we opt for the `OptaxSolver`, +# which wraps an `optax` optimizer. # %% opt_posterior, history = gpx.fit( diff --git a/docs/examples/uncollapsed_vi.py b/docs/examples/uncollapsed_vi.py index 6ea1882da..bba193a4e 100644 --- a/docs/examples/uncollapsed_vi.py +++ b/docs/examples/uncollapsed_vi.py @@ -229,7 +229,7 @@ # see Sections 3.1 and 4.1 of the excellent review paper # . # -# Since Optax's optimisers work to minimise functions, to maximise the ELBO we return +# Since `jaxopt's solvers work to minimise functions, to maximise the ELBO we return # its negative. # %% diff --git a/docs/examples/yacht.py b/docs/examples/yacht.py index f8386909f..cd8b8d653 100644 --- a/docs/examples/yacht.py +++ b/docs/examples/yacht.py @@ -183,7 +183,8 @@ # ### Model Optimisation # # With a model now defined, we can proceed to optimise the hyperparameters of our -# model using Optax. +# model using one of `jaxopt`'s solvers. In this case we use a solver that wraps an +# `optax` optimizer. # %% training_data = gpx.Dataset(X=scaled_Xtr, y=scaled_ytr) From ec5102ac5bc8fbf98201f2a68393cb4b021939c9 Mon Sep 17 00:00:00 2001 From: hmoss <32096840+henrymoss@users.noreply.github.com> Date: Mon, 18 Sep 2023 12:54:23 +0100 Subject: [PATCH 09/23] WIP --- .gitignore | 2 +- docs/examples/oceanmodelling.py | 2 +- gpjax/dataset.py | 2 +- gpjax/gps.py | 9 +++++++-- pyproject.toml | 4 ---- tests/conftest.py | 1 + 6 files changed, 11 insertions(+), 9 deletions(-) diff --git a/.gitignore b/.gitignore index 3b8bb1b11..a86c88bbb 100644 --- a/.gitignore +++ b/.gitignore @@ -151,4 +151,4 @@ package.json package-lock.json node_modules/ -docs/api \ No newline at end of file +docs/api diff --git a/docs/examples/oceanmodelling.py b/docs/examples/oceanmodelling.py index c5f2c1c85..65bd16274 100644 --- a/docs/examples/oceanmodelling.py +++ b/docs/examples/oceanmodelling.py @@ -251,7 +251,7 @@ def optimise_mll(posterior, dataset, NIters=1000, key=key, plot_history=True): opt_posterior, history = gpx.fit( model=posterior, train_data=dataset, - solver = jaxopt.OptaxSolver(objective, opt=ox.adam(0.1), maxiter=NIters), + solver=jaxopt.OptaxSolver(objective, opt=ox.adam(0.1), maxiter=NIters), safe=True, key=key, ) diff --git a/gpjax/dataset.py b/gpjax/dataset.py index 3fc99325e..29d55b8c2 100644 --- a/gpjax/dataset.py +++ b/gpjax/dataset.py @@ -17,9 +17,9 @@ import warnings from beartype.typing import ( + Literal, Optional, Union, - Literal, ) import jax.numpy as jnp from jaxtyping import ( diff --git a/gpjax/gps.py b/gpjax/gps.py index e6b9342cf..98fe35b7a 100644 --- a/gpjax/gps.py +++ b/gpjax/gps.py @@ -25,7 +25,6 @@ ) import cola from cola.ops import Dense - import jax.numpy as jnp from jax.random import ( PRNGKey, @@ -501,7 +500,13 @@ def predict( y = jnp.where(mask, 0.0, y) mx = jnp.where(mask, 0.0, mx) Sigma_masked = jnp.where(mask + mask.T, 0.0, Sigma.to_dense()) - Sigma = cola.PSD(Dense(jnp.where(jnp.diag(jnp.squeeze(mask)), 1 / (2 * jnp.pi), Sigma_masked))) + Sigma = cola.PSD( + Dense( + jnp.where( + jnp.diag(jnp.squeeze(mask)), 1 / (2 * jnp.pi), Sigma_masked + ) + ) + ) mean_t = self.prior.mean_function(t) Ktt = self.prior.kernel.gram(t) diff --git a/pyproject.toml b/pyproject.toml index 003c3c13a..e8c795dae 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,16 +23,12 @@ tqdm = "^4.65.0" simple-pytree = "^0.1.7" tensorflow-probability = "^0.19.0" beartype = "^0.13.1" -<<<<<<< HEAD -jaxlib = "0.4.7" # Temporary fix: https://github.com/google/jax/issues/15951 plum-dispatch = "^2.1.0" jaxopt = "^0.8" -======= jax = ">=0.4.10" jaxlib = ">=0.4.10" orbax-checkpoint = ">=0.2.3" cola-ml = "^0.0.1" ->>>>>>> main [tool.poetry.group.test.dependencies] pytest = "^7.2.2" diff --git a/tests/conftest.py b/tests/conftest.py index fc4305038..4902e7d4c 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,5 +1,6 @@ from jax import config from jaxtyping import install_import_hook + config.update("jax_enable_x64", True) # import gpjax within import hook to apply beartype everywhere, before running tests From 1d8b4de58a05f76b4918e7bf91edfa77c3271a72 Mon Sep 17 00:00:00 2001 From: hmoss <32096840+henrymoss@users.noreply.github.com> Date: Mon, 18 Sep 2023 15:44:06 +0100 Subject: [PATCH 10/23] all workin --- gpjax/decision_making/posterior_handler.py | 21 ++++------- .../test_decision_maker.py | 8 +++-- .../test_posterior_handler.py | 36 ++++++++----------- tests/test_mean_functions.py | 5 ++- 4 files changed, 28 insertions(+), 42 deletions(-) diff --git a/gpjax/decision_making/posterior_handler.py b/gpjax/decision_making/posterior_handler.py index 06b672172..f833e6ca8 100644 --- a/gpjax/decision_making/posterior_handler.py +++ b/gpjax/decision_making/posterior_handler.py @@ -18,7 +18,7 @@ Callable, Optional, ) -import optax as ox +from jaxopt.base import IterativeSolver import gpjax as gpx from gpjax.dataset import Dataset @@ -27,7 +27,6 @@ AbstractPosterior, AbstractPrior, ) -from gpjax.objectives import AbstractObjective from gpjax.typing import KeyArray LikelihoodBuilder = Callable[[int], AbstractLikelihood] @@ -46,23 +45,17 @@ class PosteriorHandler: likelihood_builder (LikelihoodBuilder): Function which takes the number of datapoints as input and returns a likelihood object initialised with the given number of datapoints. - optimization_objective (AbstractObjective): Objective to use for optimizing the + solver (IterativeSolver): The `jaxopt` solver used to optimize the posterior hyperparameters. - optimizer (ox.GradientTransformation): Optax optimizer to use for optimizing the - posterior hyperparameters. - num_optimization_iterations (int): Number of iterations to optimize - the posterior hyperparameters for. """ prior: AbstractPrior likelihood_builder: LikelihoodBuilder - optimization_objective: AbstractObjective - optimizer: ox.GradientTransformation - num_optimization_iters: int + solver: IterativeSolver def __post_init__(self): - if self.num_optimization_iters < 1: - raise ValueError("num_optimization_iters must be greater than 0.") + if self.solver.maxiter < 1: + raise ValueError("solver must run for more that 0 steps.") def get_posterior( self, dataset: Dataset, optimize: bool, key: Optional[KeyArray] = None @@ -143,10 +136,8 @@ def _optimize_posterior( """ opt_posterior, _ = gpx.fit( model=posterior, - objective=self.optimization_objective, train_data=dataset, - optim=self.optimizer, - num_iters=self.num_optimization_iters, + solver=self.solver, safe=True, key=key, verbose=False, diff --git a/tests/test_decision_making/test_decision_maker.py b/tests/test_decision_making/test_decision_maker.py index 42e5e8175..a84226b54 100644 --- a/tests/test_decision_making/test_decision_maker.py +++ b/tests/test_decision_making/test_decision_maker.py @@ -18,6 +18,7 @@ import jax.numpy as jnp import jax.random as jr +import jaxopt import optax as ox import pytest @@ -67,12 +68,13 @@ def posterior_handler() -> PosteriorHandler: likelihood_builder = lambda x: gpx.Gaussian( num_datapoints=x, obs_noise=jnp.array(1e-6) ) + solver = jaxopt.OptaxSolver( + gpx.ConjugateMLL(negative=True), opt=ox.adamw(0.01), maxiter=10 + ) posterior_handler = PosteriorHandler( prior=prior, likelihood_builder=likelihood_builder, - optimization_objective=gpx.ConjugateMLL(negative=True), - optimizer=ox.adam(learning_rate=0.01), - num_optimization_iters=100, + solver=solver, ) return posterior_handler diff --git a/tests/test_decision_making/test_posterior_handler.py b/tests/test_decision_making/test_posterior_handler.py index 71bf9a4b4..285be6f1f 100644 --- a/tests/test_decision_making/test_posterior_handler.py +++ b/tests/test_decision_making/test_posterior_handler.py @@ -22,6 +22,7 @@ ) import jax.numpy as jnp import jax.random as jr +import jaxopt import optax as ox import pytest @@ -62,13 +63,12 @@ def test_posterior_handler_erroneous_num_optimization_iterations_raises_error( prior = Prior(mean_function=mean_function, kernel=kernel) likelihood_builder = gaussian_likelihood_builder training_objective = ConjugateMLL(negative=True) + solver = jaxopt.LBFGS(training_objective, maxiter=num_optimization_iters) with pytest.raises(ValueError): PosteriorHandler( prior=prior, likelihood_builder=likelihood_builder, - optimization_objective=training_objective, - optimizer=ox.adam(learning_rate=0.01), - num_optimization_iters=num_optimization_iters, + solver=solver, ) @@ -81,12 +81,11 @@ def test_get_optimized_posterior_with_no_key_raises_error(): prior = Prior(mean_function=mean_function, kernel=kernel) likelihood_builder = gaussian_likelihood_builder training_objective = ConjugateMLL(negative=True) + solver = jaxopt.OptaxSolver(training_objective, opt=ox.adam(1e-3), maxiter=10) posterior_handler = PosteriorHandler( prior=prior, likelihood_builder=likelihood_builder, - optimization_objective=training_objective, - optimizer=ox.adam(learning_rate=0.01), - num_optimization_iters=10, + solver=solver, ) toy_function = Forrester() dataset = toy_function.generate_dataset(num_points=5, key=jr.PRNGKey(42)) @@ -103,12 +102,11 @@ def test_update_and_optimize_posterior_with_no_key_raises_error(): prior = Prior(mean_function=mean_function, kernel=kernel) likelihood_builder = gaussian_likelihood_builder training_objective = ConjugateMLL(negative=True) + solver = jaxopt.OptaxSolver(training_objective, opt=ox.adam(1e-3), maxiter=10) posterior_handler = PosteriorHandler( prior=prior, likelihood_builder=likelihood_builder, - optimization_objective=training_objective, - optimizer=ox.adam(learning_rate=0.01), - num_optimization_iters=10, + solver=solver, ) toy_function = Forrester() dataset = toy_function.generate_dataset(num_points=5, key=jr.PRNGKey(42)) @@ -143,12 +141,11 @@ def test_get_posterior_no_optimization_correct_num_datapoints_and_not_optimized( mean_function = Constant(constant=jnp.array([1.0])) kernel = Matern52(lengthscale=jnp.array([0.5]), variance=jnp.array(1.0)) prior = Prior(mean_function=mean_function, kernel=kernel) + solver = jaxopt.OptaxSolver(training_objective, opt=ox.adam(1e-3), maxiter=10) posterior_handler = PosteriorHandler( prior=prior, likelihood_builder=likelihood_builder, - optimization_objective=training_objective, - optimizer=ox.adam(learning_rate=0.01), - num_optimization_iters=10, + solver=solver, ) dataset = test_function.generate_dataset( num_points=num_datapoints, key=jr.PRNGKey(42) @@ -185,12 +182,11 @@ def test_get_posterior_with_optimization_correct_num_datapoints_and_optimized( kernel = Matern52(lengthscale=jnp.array([0.5]), variance=jnp.array(1.0)) prior = Prior(mean_function=mean_function, kernel=kernel) non_optimized_posterior = prior * likelihood_builder(num_datapoints) + solver = jaxopt.OptaxSolver(training_objective, opt=ox.adam(1e-3), maxiter=10) posterior_handler = PosteriorHandler( prior=prior, likelihood_builder=likelihood_builder, - optimization_objective=training_objective, - optimizer=ox.adam(learning_rate=0.01), - num_optimization_iters=10, + solver=solver, ) dataset = test_function.generate_dataset( num_points=num_datapoints, key=jr.PRNGKey(42) @@ -231,12 +227,11 @@ def test_update_posterior_no_optimize_same_prior_parameters_and_different_num_da mean_function = Constant(constant=jnp.array([1.0])) kernel = Matern52(lengthscale=jnp.array([0.5]), variance=jnp.array(1.0)) prior = Prior(mean_function=mean_function, kernel=kernel) + solver = jaxopt.OptaxSolver(training_objective, opt=ox.adam(1e-3), maxiter=10) posterior_handler = PosteriorHandler( prior=prior, likelihood_builder=likelihood_builder, - optimization_objective=training_objective, - optimizer=ox.adam(learning_rate=0.01), - num_optimization_iters=10, + solver=solver, ) initial_dataset = test_function.generate_dataset( num_points=initial_num_datapoints, key=jr.PRNGKey(42) @@ -290,12 +285,11 @@ def test_update_posterior_with_optimization_updated_prior_parameters_and_differe mean_function = Constant(constant=jnp.array([1.0])) kernel = Matern52(lengthscale=jnp.array([0.5]), variance=jnp.array(1.0)) prior = Prior(mean_function=mean_function, kernel=kernel) + solver = jaxopt.OptaxSolver(training_objective, opt=ox.adam(1e-3), maxiter=10) posterior_handler = PosteriorHandler( prior=prior, likelihood_builder=likelihood_builder, - optimization_objective=training_objective, - optimizer=ox.adam(learning_rate=0.01), - num_optimization_iters=10, + solver=solver, ) initial_dataset = test_function.generate_dataset( num_points=initial_num_datapoints, key=jr.PRNGKey(42) diff --git a/tests/test_mean_functions.py b/tests/test_mean_functions.py index d4a660c39..35d17e120 100644 --- a/tests/test_mean_functions.py +++ b/tests/test_mean_functions.py @@ -7,6 +7,7 @@ import jax import jax.numpy as jnp import jax.random as jr +import jaxopt from jaxtyping import ( Array, Float, @@ -73,10 +74,8 @@ def test_zero_mean_remains_zero() -> None: negative_mll = gpx.objectives.ConjugateMLL(negative=True) opt_posterior, _ = gpx.fit( model=posterior, - objective=negative_mll, train_data=D, - optim=ox.adam(learning_rate=0.5), - num_iters=1000, + solver=jaxopt.OptaxSolver(negative_mll, opt=ox.adam(0.5), maxiter=1_000), safe=True, key=key, ) From 1b1890ac2cda8eecf3839bd122f49114b049b334 Mon Sep 17 00:00:00 2001 From: hmoss <32096840+henrymoss@users.noreply.github.com> Date: Mon, 18 Sep 2023 15:50:34 +0100 Subject: [PATCH 11/23] lock fixed --- poetry.lock | 4184 +++++++++------------------------------------------ 1 file changed, 682 insertions(+), 3502 deletions(-) diff --git a/poetry.lock b/poetry.lock index 025d114fc..d32a77dc2 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,13 +1,10 @@ -<<<<<<< HEAD -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. -======= -# This file is automatically @generated by Poetry 1.4.1 and should not be changed by hand. ->>>>>>> main +# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. 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">=3.5" files = [ @@ -1585,10 +1100,7 @@ files = [ name = "defusedxml" version = "0.7.1" description = "XML bomb protection for Python stdlib modules" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" files = [ @@ -1598,15 +1110,6 @@ files = [ [[package]] name = "dill" -<<<<<<< HEAD -version = "0.3.6" -description = "serialize all of python" -optional = false -python-versions = ">=3.7" -files = [ - {file = "dill-0.3.6-py3-none-any.whl", hash = "sha256:a07ffd2351b8c678dfc4a856a3005f8067aea51d6ba6c700796a4d9e280f39f0"}, - {file = "dill-0.3.6.tar.gz", hash = "sha256:e5db55f3687856d8fbdab002ed78544e1c4559a130302693d839dfe8f93f2373"}, -======= version = "0.3.7" description = "serialize all of Python" category = "dev" @@ -1615,7 +1118,6 @@ python-versions = ">=3.7" files = [ {file = "dill-0.3.7-py3-none-any.whl", hash = "sha256:76b122c08ef4ce2eedcd4d1abd8e641114bfc6c2867f49f3c41facf65bf19f5e"}, {file 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[[package]] name = "dm-tree" version = "0.1.8" description = "Tree is a library for working with nested data structures." -<<<<<<< HEAD -======= category = "main" ->>>>>>> main optional = false python-versions = "*" files = [ @@ -1697,24 +1186,6 @@ files = [ [[package]] name = "etils" -<<<<<<< HEAD -version = "1.2.0" -description = "Collection of common python utils" -optional = false -python-versions = ">=3.8" -files = [ - {file = "etils-1.2.0-py3-none-any.whl", hash = "sha256:c6585069b387fdbeed6a2c571b8bcf312ecdb577c95065461e5fad9ed1973989"}, - {file = "etils-1.2.0.tar.gz", hash = "sha256:29d369e2dcf43960d9ee338330579d04badd606c88f015f4e1a38d3adbe446d8"}, -] - -[package.extras] -all = ["etils[array-types]", "etils[eapp]", "etils[ecolab]", "etils[edc]", "etils[enp]", "etils[epath]", "etils[epy]", "etils[etqdm]", "etils[etree-dm]", "etils[etree-jax]", "etils[etree-tf]", "etils[etree]"] -array-types = ["etils[enp]"] -dev = ["chex", "optree", "pyink", "pylint (>=2.6.0)", "pytest", "pytest-subtests", "pytest-xdist", "torch"] -eapp = ["absl-py", "etils[epy]", "simple_parsing"] -ecolab = ["etils[enp]", "etils[epy]", "jupyter", "mediapy", "numpy"] -edc = ["etils[epy]", "typing_extensions"] -======= version = "1.4.1" description = "Collection of common python utils" category = "main" @@ -1738,7 +1209,6 @@ docs = ["etils[all,dev]", "sphinx-apitree[ext]"] eapp = ["absl-py", "etils[epy]", "simple_parsing"] ecolab = ["etils[enp]", "etils[epy]", "jupyter", "mediapy", "numpy"] edc = ["etils[epy]"] ->>>>>>> main enp = ["etils[epy]", "numpy"] epath = ["etils[epy]", "importlib_resources", "typing_extensions", "zipp"] epy = ["typing_extensions"] @@ -1751,15 +1221,6 @@ lazy-imports = ["etils[ecolab]"] [[package]] name = "exceptiongroup" -<<<<<<< HEAD -version = "1.1.1" -description = "Backport of PEP 654 (exception groups)" -optional = false -python-versions = ">=3.7" -files = [ - {file = "exceptiongroup-1.1.1-py3-none-any.whl", hash = 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["flake8", "markdown", "twine", "wheel"] name = "gitdb" version = "4.0.10" description = "Git Object Database" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.7" files = [ @@ -2351,49 +1610,32 @@ smmap = ">=3.0.1,<6" [[package]] name = "gitpython" -<<<<<<< HEAD -version = "3.1.31" -description = "GitPython is a Python library used to interact with Git repositories" -optional = false -python-versions = ">=3.7" -files = [ - {file = "GitPython-3.1.31-py3-none-any.whl", hash = "sha256:f04893614f6aa713a60cbbe1e6a97403ef633103cdd0ef5eb6efe0deb98dbe8d"}, - {file = "GitPython-3.1.31.tar.gz", hash = "sha256:8ce3bcf69adfdf7c7d503e78fd3b1c492af782d58893b650adb2ac8912ddd573"}, -======= -version = "3.1.32" +version = "3.1.36" description = "GitPython is a Python library used to interact with Git repositories" category = "dev" optional = false python-versions = ">=3.7" files = [ - {file = "GitPython-3.1.32-py3-none-any.whl", hash = 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Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API." -optional = false -python-versions = ">=3.7" -files = [ - {file = "griffe-0.27.4-py3-none-any.whl", hash = "sha256:685350067286229e80a18b8989d6acbd43abdf8b763591221d19c56f4108549e"}, - {file = "griffe-0.27.4.tar.gz", hash = "sha256:088c25fb22f8d1f1add5d3b58a86a3969993181a36ca55b3fa33096a3f3b1a23"}, -======= -version = "0.35.1" +version = "0.36.2" description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API." category = "dev" optional = false python-versions = ">=3.8" files = [ - {file = "griffe-0.35.1-py3-none-any.whl", hash = "sha256:ff580073a71793cc58ed1fad696aee49c4bd9e637d3e0cde5b39a269ad8e59e4"}, - {file = "griffe-0.35.1.tar.gz", hash = "sha256:1e3bf605344ab32fe2729161bb4f7761996684f838dfd5a7c60af03a0b20375f"}, ->>>>>>> main + {file = "griffe-0.36.2-py3-none-any.whl", hash = "sha256:ba71895a3f5f606b18dcd950e8a1f8e7332a37f90f24caeb002546593f2e0eee"}, + {file = "griffe-0.36.2.tar.gz", hash = "sha256:333ade7932bb9096781d83092602625dfbfe220e87a039d2801259a1bd41d1c2"}, ] [package.dependencies] @@ -2401,24 +1643,14 @@ colorama = ">=0.4" [[package]] name = "identify" -<<<<<<< HEAD -version = "2.5.24" -description = "File identification library for Python" -optional = false -python-versions = ">=3.7" -files = [ - {file = "identify-2.5.24-py2.py3-none-any.whl", hash = "sha256:986dbfb38b1140e763e413e6feb44cd731faf72d1909543178aa79b0e258265d"}, - {file = "identify-2.5.24.tar.gz", hash = "sha256:0aac67d5b4812498056d28a9a512a483f5085cc28640b02b258a59dac34301d4"}, -======= -version = "2.5.27" +version = "2.5.29" description = "File identification library for Python" category = "dev" optional = false python-versions = ">=3.8" files = [ - {file = "identify-2.5.27-py2.py3-none-any.whl", hash = "sha256:fdb527b2dfe24602809b2201e033c2a113d7bdf716db3ca8e3243f735dcecaba"}, - {file = "identify-2.5.27.tar.gz", hash = "sha256:287b75b04a0e22d727bc9a41f0d4f3c1bcada97490fa6eabb5b28f0e9097e733"}, ->>>>>>> main + {file = "identify-2.5.29-py2.py3-none-any.whl", hash = "sha256:24437fbf6f4d3fe6efd0eb9d67e24dd9106db99af5ceb27996a5f7895f24bf1b"}, + {file = "identify-2.5.29.tar.gz", hash = "sha256:d43d52b86b15918c137e3a74fff5224f60385cd0e9c38e99d07c257f02f151a5"}, ] [package.extras] @@ -2428,10 +1660,7 @@ license = ["ukkonen"] name = "idna" version = "3.4" description = "Internationalized Domain Names in Applications (IDNA)" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.5" files = [ @@ -2441,15 +1670,6 @@ files = [ [[package]] name = "importlib-metadata" -<<<<<<< HEAD -version = "6.6.0" -description = "Read metadata from Python packages" -optional = false -python-versions = ">=3.7" -files = [ - {file = "importlib_metadata-6.6.0-py3-none-any.whl", hash = "sha256:43dd286a2cd8995d5eaef7fee2066340423b818ed3fd70adf0bad5f1fac53fed"}, - {file = "importlib_metadata-6.6.0.tar.gz", hash = "sha256:92501cdf9cc66ebd3e612f1b4f0c0765dfa42f0fa38ffb319b6bd84dd675d705"}, -======= version = "6.8.0" description = "Read metadata from Python packages" category = "dev" @@ -2458,7 +1678,6 @@ python-versions = ">=3.8" files = [ {file = "importlib_metadata-6.8.0-py3-none-any.whl", hash = "sha256:3ebb78df84a805d7698245025b975d9d67053cd94c79245ba4b3eb694abe68bb"}, {file = "importlib_metadata-6.8.0.tar.gz", hash = "sha256:dbace7892d8c0c4ac1ad096662232f831d4e64f4c4545bd53016a3e9d4654743"}, ->>>>>>> main ] [package.dependencies] @@ -2467,27 +1686,6 @@ zipp = ">=0.5" [package.extras] docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] perf = ["ipython"] -<<<<<<< HEAD -testing = ["flake8 (<5)", "flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)"] - -[[package]] -name = "importlib-resources" -version = "5.12.0" -description = "Read resources from Python packages" -optional = false -python-versions = ">=3.7" -files = [ - {file = "importlib_resources-5.12.0-py3-none-any.whl", hash = "sha256:7b1deeebbf351c7578e09bf2f63fa2ce8b5ffec296e0d349139d43cca061a81a"}, - {file = "importlib_resources-5.12.0.tar.gz", hash = "sha256:4be82589bf5c1d7999aedf2a45159d10cb3ca4f19b2271f8792bc8e6da7b22f6"}, -] - -[package.dependencies] -zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""} - -[package.extras] -docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["flake8 (<5)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] -======= testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"] [[package]] @@ -2505,16 +1703,12 @@ files = [ [package.extras] docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-ruff"] ->>>>>>> main [[package]] name = "iniconfig" version = "2.0.0" description = "brain-dead simple config-ini parsing" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.7" files = [ @@ -2526,10 +1720,7 @@ files = [ name = "interrogate" version = "1.5.0" description = "Interrogate a codebase for docstring coverage." -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.6" files = [ @@ -2553,24 +1744,14 @@ tests = ["pytest", "pytest-cov", "pytest-mock"] [[package]] name = "ipykernel" -<<<<<<< HEAD -version = "6.23.0" -description = "IPython Kernel for Jupyter" -optional = false -python-versions = ">=3.8" -files = [ - {file = "ipykernel-6.23.0-py3-none-any.whl", hash = "sha256:fc886f1dcdc0ec17f277e4d21fd071c857d381adcb04f3f3735d25325ca323c6"}, - {file = "ipykernel-6.23.0.tar.gz", hash = "sha256:bd6f487d9e2744c84f6e667d46462d7647a4c862e70e08282f05a52b9d4b705f"}, -======= -version = "6.25.1" +version = "6.25.2" description = "IPython Kernel for Jupyter" category = "dev" optional = false python-versions = ">=3.8" files = [ - {file = "ipykernel-6.25.1-py3-none-any.whl", hash = "sha256:c8a2430b357073b37c76c21c52184db42f6b4b0e438e1eb7df3c4440d120497c"}, - {file = "ipykernel-6.25.1.tar.gz", hash = "sha256:050391364c0977e768e354bdb60cbbfbee7cbb943b1af1618382021136ffd42f"}, ->>>>>>> main + {file = "ipykernel-6.25.2-py3-none-any.whl", hash = "sha256:2e2ee359baba19f10251b99415bb39de1e97d04e1fab385646f24f0596510b77"}, + {file = "ipykernel-6.25.2.tar.gz", hash = "sha256:f468ddd1f17acb48c8ce67fcfa49ba6d46d4f9ac0438c1f441be7c3d1372230b"}, ] [package.dependencies] @@ -2579,11 +1760,7 @@ comm = ">=0.1.1" debugpy = ">=1.6.5" ipython = ">=7.23.1" jupyter-client = ">=6.1.12" -<<<<<<< HEAD -jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" -======= jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" ->>>>>>> main matplotlib-inline = ">=0.1" nest-asyncio = "*" packaging = "*" @@ -2601,24 +1778,14 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio" [[package]] name = "ipython" -<<<<<<< HEAD -version = "8.12.2" -description = "IPython: Productive Interactive Computing" -optional = false -python-versions = ">=3.8" -files = [ - {file = "ipython-8.12.2-py3-none-any.whl", hash = "sha256:ea8801f15dfe4ffb76dea1b09b847430ffd70d827b41735c64a0638a04103bfc"}, - {file = "ipython-8.12.2.tar.gz", hash = "sha256:c7b80eb7f5a855a88efc971fda506ff7a91c280b42cdae26643e0f601ea281ea"}, -======= -version = "8.14.0" +version = "8.15.0" description = "IPython: Productive Interactive Computing" category = "dev" optional = false python-versions = ">=3.9" files = [ - {file = "ipython-8.14.0-py3-none-any.whl", hash = "sha256:248aca623f5c99a6635bc3857677b7320b9b8039f99f070ee0d20a5ca5a8e6bf"}, - {file = "ipython-8.14.0.tar.gz", hash = "sha256:1d197b907b6ba441b692c48cf2a3a2de280dc0ac91a3405b39349a50272ca0a1"}, ->>>>>>> main + {file = "ipython-8.15.0-py3-none-any.whl", hash = "sha256:45a2c3a529296870a97b7de34eda4a31bee16bc7bf954e07d39abe49caf8f887"}, + {file = "ipython-8.15.0.tar.gz", hash = "sha256:2baeb5be6949eeebf532150f81746f8333e2ccce02de1c7eedde3f23ed5e9f1e"}, ] [package.dependencies] @@ -2626,6 +1793,7 @@ appnope = {version = "*", markers = "sys_platform == \"darwin\""} backcall = "*" colorama = {version = "*", markers = "sys_platform == \"win32\""} decorator = "*" +exceptiongroup = {version = "*", markers = "python_version < \"3.11\""} jedi = ">=0.16" matplotlib-inline = "*" pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""} @@ -2634,15 +1802,11 @@ prompt-toolkit = ">=3.0.30,<3.0.37 || >3.0.37,<3.1.0" pygments = ">=2.4.0" stack-data = "*" traitlets = ">=5" -<<<<<<< HEAD -typing-extensions = {version = "*", markers = "python_version < \"3.10\""} -======= ->>>>>>> main [package.extras] -all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"] +all = ["black", "curio", "docrepr", "exceptiongroup", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"] black = ["black"] -doc = ["docrepr", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"] +doc = ["docrepr", "exceptiongroup", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"] kernel = ["ipykernel"] nbconvert = ["nbconvert"] nbformat = ["nbformat"] @@ -2654,36 +1818,22 @@ test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pa [[package]] name = "ipywidgets" -<<<<<<< HEAD -version = "8.0.6" -description = "Jupyter interactive widgets" -optional = false -python-versions = ">=3.7" -files = [ - {file = "ipywidgets-8.0.6-py3-none-any.whl", hash = "sha256:a60bf8d2528997e05ac83fd19ea2fbe65f2e79fbe1b2b35779bdfc46c2941dcc"}, - {file = "ipywidgets-8.0.6.tar.gz", hash = "sha256:de7d779f2045d60de9f6c25f653fdae2dba57898e6a1284494b3ba20b6893bb8"}, -] - -[package.dependencies] -ipykernel = ">=4.5.1" -======= -version = "8.1.0" +version = "8.1.1" description = "Jupyter interactive widgets" category = "dev" optional = false python-versions = ">=3.7" files = [ - {file = "ipywidgets-8.1.0-py3-none-any.whl", hash = "sha256:6c8396cc7b8c95dfb4e9ab0054f48c002f045e7e5d7ae523f559d64e525a98ab"}, - {file = "ipywidgets-8.1.0.tar.gz", hash = "sha256:ce97dd90525b3066fd00094690964e7eac14cf9b7745d35565b5eeac20cce687"}, + {file = "ipywidgets-8.1.1-py3-none-any.whl", hash = "sha256:2b88d728656aea3bbfd05d32c747cfd0078f9d7e159cf982433b58ad717eed7f"}, + {file = "ipywidgets-8.1.1.tar.gz", hash = "sha256:40211efb556adec6fa450ccc2a77d59ca44a060f4f9f136833df59c9f538e6e8"}, ] [package.dependencies] comm = ">=0.1.3" ->>>>>>> main ipython = ">=6.1.0" -jupyterlab-widgets = ">=3.0.7,<3.1.0" +jupyterlab-widgets = ">=3.0.9,<3.1.0" traitlets = ">=4.3.1" -widgetsnbextension = ">=4.0.7,<4.1.0" +widgetsnbextension = ">=4.0.9,<4.1.0" [package.extras] test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"] @@ -2692,10 +1842,7 @@ test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"] name = "isort" version = "5.12.0" description = "A Python utility / library to sort Python imports." -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.8.0" files = [ @@ -2711,19 +1858,6 @@ requirements-deprecated-finder = ["pip-api", "pipreqs"] [[package]] name = "jax" -<<<<<<< HEAD -version = "0.4.9" -description = "Differentiate, compile, and transform Numpy code." -optional = false -python-versions = ">=3.8" -files = [ - {file = "jax-0.4.9.tar.gz", hash = "sha256:1ed135cd08f48e4baf10f6eafdb4a4cdae781f9052b5838c09c91a9f4fa75f09"}, -] - -[package.dependencies] -ml_dtypes = ">=0.1.0" -numpy = ">=1.21" -======= version = "0.4.14" description = "Differentiate, compile, and transform Numpy code." category = "main" @@ -2736,66 +1870,25 @@ files = [ [package.dependencies] ml_dtypes = ">=0.2.0" numpy = ">=1.22" ->>>>>>> main opt_einsum = "*" scipy = ">=1.7" [package.extras] australis = ["protobuf (>=3.13,<4)"] -<<<<<<< HEAD -ci = ["jaxlib (==0.4.7)"] -cpu = ["jaxlib (==0.4.9)"] -cuda = ["jaxlib (==0.4.9+cuda11.cudnn86)"] -cuda11-cudnn82 = ["jaxlib (==0.4.9+cuda11.cudnn82)"] -cuda11-cudnn86 = ["jaxlib (==0.4.9+cuda11.cudnn86)"] -cuda11-local = ["jaxlib (==0.4.9+cuda11.cudnn86)"] -cuda11-pip = ["jaxlib (==0.4.9+cuda11.cudnn86)", "nvidia-cublas-cu11 (>=11.11)", "nvidia-cuda-cupti-cu11 (>=11.8)", "nvidia-cuda-nvcc-cu11 (>=11.8)", "nvidia-cuda-runtime-cu11 (>=11.8)", "nvidia-cudnn-cu11 (>=8.6)", "nvidia-cufft-cu11 (>=10.9)", "nvidia-cusolver-cu11 (>=11.4)", "nvidia-cusparse-cu11 (>=11.7)"] -cuda12-local = ["jaxlib (==0.4.9+cuda12.cudnn88)"] -cuda12-pip = ["jaxlib (==0.4.9+cuda12.cudnn88)", "nvidia-cublas-cu12", "nvidia-cuda-cupti-cu12", "nvidia-cuda-nvcc-cu12", "nvidia-cuda-runtime-cu12", "nvidia-cudnn-cu12", "nvidia-cufft-cu12", "nvidia-cusolver-cu12", "nvidia-cusparse-cu12"] -minimum-jaxlib = ["jaxlib (==0.4.7)"] -tpu = ["jaxlib (==0.4.9)", "libtpu-nightly (==0.1.dev20230509)", "requests"] +ci = ["jaxlib (==0.4.13)"] +cpu = ["jaxlib (==0.4.14)"] +cuda = ["jaxlib (==0.4.14+cuda11.cudnn86)"] +cuda11-cudnn86 = ["jaxlib (==0.4.14+cuda11.cudnn86)"] +cuda11-local = ["jaxlib (==0.4.14+cuda11.cudnn86)"] +cuda11-pip = ["jaxlib (==0.4.14+cuda11.cudnn86)", "nvidia-cublas-cu11 (>=11.11)", "nvidia-cuda-cupti-cu11 (>=11.8)", "nvidia-cuda-nvcc-cu11 (>=11.8)", "nvidia-cuda-runtime-cu11 (>=11.8)", "nvidia-cudnn-cu11 (>=8.8)", "nvidia-cufft-cu11 (>=10.9)", "nvidia-cusolver-cu11 (>=11.4)", "nvidia-cusparse-cu11 (>=11.7)"] +cuda12-local = ["jaxlib (==0.4.14+cuda12.cudnn89)"] +cuda12-pip = ["jaxlib (==0.4.14+cuda12.cudnn89)", "nvidia-cublas-cu12", "nvidia-cuda-cupti-cu12", "nvidia-cuda-nvcc-cu12", "nvidia-cuda-runtime-cu12", "nvidia-cudnn-cu12 (>=8.9)", "nvidia-cufft-cu12", "nvidia-cusolver-cu12", "nvidia-cusparse-cu12"] +minimum-jaxlib = ["jaxlib (==0.4.11)"] +tpu = ["jaxlib (==0.4.14)", "libtpu-nightly (==0.1.dev20230727)"] [[package]] name = "jaxlib" -version = "0.4.7" -description = "XLA library for JAX" -optional = false -python-versions = ">=3.8" -files = [ - 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{file = "jaxlib-0.4.7-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:48b85d3c8923b1619ddf8cbf14c4e4daf6919796d8aa9d006ce2a085e8202930"}, - {file = "jaxlib-0.4.7-cp38-cp38-manylinux2014_x86_64.whl", hash = "sha256:a860f2990c97bee5ffcdbb5111751591e5e7a66d5e32b4f6d9e6aa14ac82bf27"}, - {file = "jaxlib-0.4.7-cp39-cp39-macosx_10_14_x86_64.whl", hash = "sha256:c78dc2b6fa1c92ead137a23d1bd3e10d04c58b268e77eca811502abac05b2b19"}, - {file = "jaxlib-0.4.7-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:f1f3726e374d0d6fcc14da540b71b758d37356c6726f0f4b48e2f5530a5f8769"}, - {file = "jaxlib-0.4.7-cp39-cp39-manylinux2014_x86_64.whl", hash = "sha256:d4629205dbe342153941db5f69c4a1bfe35fd8d2947aebe34f4dff3771d3fff7"}, -] - -[package.dependencies] -ml-dtypes = ">=0.0.3" -numpy = ">=1.21" -scipy = ">=1.7" - -======= -ci = ["jaxlib (==0.4.13)"] -cpu = ["jaxlib (==0.4.14)"] -cuda = ["jaxlib (==0.4.14+cuda11.cudnn86)"] -cuda11-cudnn86 = ["jaxlib (==0.4.14+cuda11.cudnn86)"] -cuda11-local = ["jaxlib (==0.4.14+cuda11.cudnn86)"] -cuda11-pip = ["jaxlib (==0.4.14+cuda11.cudnn86)", "nvidia-cublas-cu11 (>=11.11)", "nvidia-cuda-cupti-cu11 (>=11.8)", "nvidia-cuda-nvcc-cu11 (>=11.8)", "nvidia-cuda-runtime-cu11 (>=11.8)", "nvidia-cudnn-cu11 (>=8.8)", "nvidia-cufft-cu11 (>=10.9)", "nvidia-cusolver-cu11 (>=11.4)", "nvidia-cusparse-cu11 (>=11.7)"] -cuda12-local = ["jaxlib (==0.4.14+cuda12.cudnn89)"] -cuda12-pip = ["jaxlib (==0.4.14+cuda12.cudnn89)", "nvidia-cublas-cu12", "nvidia-cuda-cupti-cu12", "nvidia-cuda-nvcc-cu12", "nvidia-cuda-runtime-cu12", "nvidia-cudnn-cu12 (>=8.9)", "nvidia-cufft-cu12", "nvidia-cusolver-cu12", "nvidia-cusparse-cu12"] -minimum-jaxlib = ["jaxlib (==0.4.11)"] -tpu = ["jaxlib (==0.4.14)", "libtpu-nightly (==0.1.dev20230727)"] - -[[package]] -name = "jaxlib" -version = "0.4.14" +version = "0.4.14" description = "XLA library for JAX" category = "main" optional = false @@ -2824,15 +1917,11 @@ scipy = ">=1.7" cuda11-pip = ["nvidia-cublas-cu11 (>=11.11)", "nvidia-cuda-cupti-cu11 (>=11.8)", "nvidia-cuda-nvcc-cu11 (>=11.8)", "nvidia-cuda-runtime-cu11 (>=11.8)", "nvidia-cudnn-cu11 (>=8.8)", "nvidia-cufft-cu11 (>=10.9)", "nvidia-cusolver-cu11 (>=11.4)", "nvidia-cusparse-cu11 (>=11.7)"] cuda12-pip = ["nvidia-cublas-cu12", "nvidia-cuda-cupti-cu12", "nvidia-cuda-nvcc-cu12", "nvidia-cuda-runtime-cu12", "nvidia-cudnn-cu12 (>=8.9)", "nvidia-cufft-cu12", "nvidia-cusolver-cu12", "nvidia-cusparse-cu12"] ->>>>>>> main [[package]] name = "jaxopt" version = "0.8" description = "Hardware accelerated, batchable and differentiable optimizers in JAX." -<<<<<<< HEAD -======= -category = "dev" ->>>>>>> main +category = "main" optional = false python-versions = "*" files = [ @@ -2848,15 +1937,6 @@ scipy = ">=1.0.0" [[package]] name = "jaxtyping" -<<<<<<< HEAD -version = "0.2.19" -description = "Type annotations and runtime checking for shape and dtype of JAX arrays, and PyTrees." -optional = false -python-versions = "~=3.8" -files = [ - {file = "jaxtyping-0.2.19-py3-none-any.whl", hash = "sha256:651352032799d422987e783fd1b77699b53c3bb28ffa644bbca5f75ec4fbb843"}, - {file = "jaxtyping-0.2.19.tar.gz", hash = "sha256:21ff4c3caec6781cadfe980b019dde856c1011e17d11dfe8589298040056325a"}, -======= version = "0.2.21" description = "Type annotations and runtime checking for shape and dtype of JAX arrays, and PyTrees." category = "main" @@ -2865,7 +1945,6 @@ python-versions = "~=3.9" files = [ {file = "jaxtyping-0.2.21-py3-none-any.whl", hash = "sha256:d94afe0def7df435090f1f699d97c06b8b8cfa04667be8fe9215a33bb0f17980"}, {file = "jaxtyping-0.2.21.tar.gz", hash = "sha256:316e17b06cefff887cc93769d46b77b926a2b110c65e9a129562736297d515a7"}, ->>>>>>> main ] [package.dependencies] @@ -2875,23 +1954,6 @@ typing-extensions = ">=3.7.4.1" [[package]] name = "jedi" -<<<<<<< HEAD -version = "0.18.2" -description = "An autocompletion tool for Python that can be used for text editors." -optional = false -python-versions = ">=3.6" -files = [ - {file = "jedi-0.18.2-py2.py3-none-any.whl", hash = "sha256:203c1fd9d969ab8f2119ec0a3342e0b49910045abe6af0a3ae83a5764d54639e"}, - 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-[[package]] -name = "jupyter-client" -version = "8.2.0" -description = "Jupyter protocol implementation and client libraries" -optional = false -python-versions = ">=3.8" -files = [ - {file = "jupyter_client-8.2.0-py3-none-any.whl", hash = "sha256:b18219aa695d39e2ad570533e0d71fb7881d35a873051054a84ee2a17c4b7389"}, - {file = "jupyter_client-8.2.0.tar.gz", hash = "sha256:9fe233834edd0e6c0aa5f05ca2ab4bdea1842bfd2d8a932878212fc5301ddaf0"}, -] - -[package.dependencies] -importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""} -jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" -python-dateutil = ">=2.8.2" -pyzmq = ">=23.0" -tornado = ">=6.2" -traitlets = ">=5.3" - -[package.extras] -docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"] -test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"] - -[[package]] -name = "jupyter-core" -version = "5.3.0" -description = "Jupyter core package. A base package on which Jupyter projects rely." -optional = false -python-versions = ">=3.8" -files = [ - {file = "jupyter_core-5.3.0-py3-none-any.whl", hash = "sha256:d4201af84559bc8c70cead287e1ab94aeef3c512848dde077b7684b54d67730d"}, - {file = "jupyter_core-5.3.0.tar.gz", hash = "sha256:6db75be0c83edbf1b7c9f91ec266a9a24ef945da630f3120e1a0046dc13713fc"}, -] - -[package.dependencies] -======= version = "1.3.2" description = "Lightweight pipelining with Python functions" category = "dev" @@ -3065,14 +2056,14 @@ referencing = ">=0.28.0" [[package]] name = "jupyter-client" -version = "8.3.0" +version = "8.3.1" description = "Jupyter protocol implementation and client libraries" category = "dev" optional = false python-versions = ">=3.8" files = [ - {file = "jupyter_client-8.3.0-py3-none-any.whl", hash = "sha256:7441af0c0672edc5d28035e92ba5e32fadcfa8a4e608a434c228836a89df6158"}, - {file = "jupyter_client-8.3.0.tar.gz", hash = "sha256:3af69921fe99617be1670399a0b857ad67275eefcfa291e2c81a160b7b650f5f"}, + {file = "jupyter_client-8.3.1-py3-none-any.whl", hash = "sha256:5eb9f55eb0650e81de6b7e34308d8b92d04fe4ec41cd8193a913979e33d8e1a5"}, + {file = "jupyter_client-8.3.1.tar.gz", hash = "sha256:60294b2d5b869356c893f57b1a877ea6510d60d45cf4b38057f1672d85699ac9"}, ] [package.dependencies] @@ -3099,7 +2090,6 @@ files = [ ] [package.dependencies] ->>>>>>> main platformdirs = ">=2.5" pywin32 = {version = ">=300", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""} traitlets = ">=5.3" @@ -3112,10 +2102,7 @@ test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"] name = "jupyterlab-pygments" version = "0.2.2" description = "Pygments theme using JupyterLab CSS variables" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.7" files = [ @@ -3125,54 +2112,30 @@ files = [ [[package]] name = "jupyterlab-widgets" -<<<<<<< HEAD -version = "3.0.7" -description = "Jupyter interactive widgets for JupyterLab" -optional = false -python-versions = ">=3.7" -files = [ - {file = "jupyterlab_widgets-3.0.7-py3-none-any.whl", hash = "sha256:c73f8370338ec19f1bec47254752d6505b03601cbd5a67e6a0b184532f73a459"}, - {file = "jupyterlab_widgets-3.0.7.tar.gz", hash = "sha256:c3a50ed5bf528a0c7a869096503af54702f86dda1db469aee1c92dc0c01b43ca"}, -======= -version = "3.0.8" +version = "3.0.9" description = "Jupyter interactive widgets for JupyterLab" category = "dev" optional = false python-versions = ">=3.7" files = [ - {file = "jupyterlab_widgets-3.0.8-py3-none-any.whl", hash = "sha256:4715912d6ceab839c9db35953c764b3214ebbc9161c809f6e0510168845dfdf5"}, - {file = "jupyterlab_widgets-3.0.8.tar.gz", hash = "sha256:d428ab97b8d87cc7c54cbf37644d6e0f0e662f23876e05fa460a73ec3257252a"}, ->>>>>>> main + {file = "jupyterlab_widgets-3.0.9-py3-none-any.whl", hash = "sha256:3cf5bdf5b897bf3bccf1c11873aa4afd776d7430200f765e0686bd352487b58d"}, + {file = "jupyterlab_widgets-3.0.9.tar.gz", hash = "sha256:6005a4e974c7beee84060fdfba341a3218495046de8ae3ec64888e5fe19fdb4c"}, ] [[package]] name = "jupytext" -<<<<<<< HEAD -version = "1.14.5" -description = "Jupyter notebooks as Markdown documents, Julia, Python or R scripts" -optional = false -python-versions = "~=3.6" -files = [ - 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"sha256:7f54b9fb87ca5acbcdd0f286021bedc162e1425fa5555ebf3b3dfc167b955ad9"}, + {file = "matplotlib-3.8.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:60a6e04dfd77c0d3bcfee61c3cd335fff1b917c2f303b32524cd1235e194ef99"}, + {file = "matplotlib-3.8.0.tar.gz", hash = "sha256:df8505e1c19d5c2c26aff3497a7cbd3ccfc2e97043d1e4db3e76afa399164b69"}, +] + [package.dependencies] contourpy = ">=1.0.1" cycler = ">=0.10" fonttools = ">=4.22.0" -<<<<<<< HEAD -importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""} -======= ->>>>>>> main kiwisolver = ">=1.0.1" -numpy = ">=1.20" +numpy = ">=1.21,<2" packaging = ">=20.0" pillow = ">=6.2.0" -<<<<<<< HEAD pyparsing = ">=2.3.1" -======= -pyparsing = ">=2.3.1,<3.1" ->>>>>>> main python-dateutil = ">=2.7" +setuptools_scm = ">=7" [[package]] name = "matplotlib-inline" version = "0.1.6" description = "Inline Matplotlib backend for Jupyter" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.5" files = [ @@ -3946,10 +2663,7 @@ traitlets = "*" name = "mccabe" version = "0.7.0" description = "McCabe checker, plugin for flake8" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.6" files = [ @@ -3959,23 +2673,6 @@ files = [ [[package]] name = "mdit-py-plugins" -<<<<<<< HEAD -version = "0.3.5" -description = "Collection of plugins for markdown-it-py" -optional = false -python-versions = ">=3.7" -files = [ - {file = "mdit-py-plugins-0.3.5.tar.gz", hash = "sha256:eee0adc7195e5827e17e02d2a258a2ba159944a0748f59c5099a4a27f78fcf6a"}, - {file = "mdit_py_plugins-0.3.5-py3-none-any.whl", hash = "sha256:ca9a0714ea59a24b2b044a1831f48d817dd0c817e84339f20e7889f392d77c4e"}, -] - -[package.dependencies] -markdown-it-py = ">=1.0.0,<3.0.0" - -[package.extras] -code-style = ["pre-commit"] -rtd = ["attrs", "myst-parser (>=0.16.1,<0.17.0)", "sphinx-book-theme (>=0.1.0,<0.2.0)"] -======= version = "0.4.0" description = "Collection of plugins for markdown-it-py" category = "dev" @@ -3992,17 +2689,13 @@ markdown-it-py = ">=1.0.0,<4.0.0" [package.extras] code-style = ["pre-commit"] rtd = ["myst-parser", "sphinx-book-theme"] ->>>>>>> main testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"] [[package]] name = "mdurl" version = "0.1.2" description = "Markdown URL utilities" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.7" files = [ @@ -4014,10 +2707,7 @@ files = [ name = "mdx-truly-sane-lists" version = "1.3" description = "Extension for Python-Markdown that makes lists truly sane. Custom indents for nested lists and fix for messy linebreaks." -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = "*" files = [ @@ -4032,10 +2722,7 @@ Markdown = ">=2.6" name = "mergedeep" version = "1.3.4" description = "A deep merge function for 🐍." -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.6" files = [ @@ -4045,15 +2732,6 @@ files = [ [[package]] name = "mistune" -<<<<<<< HEAD -version = "2.0.5" -description = "A sane Markdown parser with useful plugins and renderers" -optional = false -python-versions = "*" -files = [ - {file = "mistune-2.0.5-py2.py3-none-any.whl", hash = "sha256:bad7f5d431886fcbaf5f758118ecff70d31f75231b34024a1341120340a65ce8"}, - {file = "mistune-2.0.5.tar.gz", hash = "sha256:0246113cb2492db875c6be56974a7c893333bf26cd92891c85f63151cee09d34"}, -======= version = "3.0.1" description = "A sane and fast Markdown parser with useful plugins and renderers" category = "dev" @@ -4062,20 +2740,10 @@ python-versions = ">=3.7" files = [ {file = "mistune-3.0.1-py3-none-any.whl", hash = "sha256:b9b3e438efbb57c62b5beb5e134dab664800bdf1284a7ee09e8b12b13eb1aac6"}, {file = "mistune-3.0.1.tar.gz", hash = "sha256:e912116c13aa0944f9dc530db38eb88f6a77087ab128f49f84a48f4c05ea163c"}, ->>>>>>> main ] [[package]] name = "mkdocs" -<<<<<<< HEAD -version = "1.4.3" -description = "Project documentation with Markdown." -optional = false -python-versions = ">=3.7" -files = [ - {file = "mkdocs-1.4.3-py3-none-any.whl", hash = "sha256:6ee46d309bda331aac915cd24aab882c179a933bd9e77b80ce7d2eaaa3f689dd"}, - {file = "mkdocs-1.4.3.tar.gz", hash = "sha256:5955093bbd4dd2e9403c5afaf57324ad8b04f16886512a3ee6ef828956481c57"}, -======= version = "1.5.2" description = "Project documentation with Markdown." category = "dev" @@ -4084,20 +2752,12 @@ python-versions = ">=3.7" files = [ {file = "mkdocs-1.5.2-py3-none-any.whl", hash = "sha256:60a62538519c2e96fe8426654a67ee177350451616118a41596ae7c876bb7eac"}, {file = "mkdocs-1.5.2.tar.gz", hash = "sha256:70d0da09c26cff288852471be03c23f0f521fc15cf16ac89c7a3bfb9ae8d24f9"}, ->>>>>>> main ] [package.dependencies] click = ">=7.0" colorama = {version = ">=0.4", markers = "platform_system == \"Windows\""} ghp-import = ">=1.0" -<<<<<<< HEAD -importlib-metadata = {version = ">=4.3", markers = "python_version < \"3.10\""} -jinja2 = ">=2.11.1" -markdown = ">=3.2.1,<3.4" -mergedeep = ">=1.3.4" -packaging = ">=20.5" -======= jinja2 = ">=2.11.1" markdown = ">=3.2.1" markupsafe = ">=2.0.1" @@ -4105,26 +2765,12 @@ mergedeep = ">=1.3.4" packaging = ">=20.5" pathspec = ">=0.11.1" platformdirs = ">=2.2.0" ->>>>>>> main pyyaml = ">=5.1" pyyaml-env-tag = ">=0.1" watchdog = ">=2.0" [package.extras] i18n = ["babel (>=2.9.0)"] -<<<<<<< HEAD -min-versions = ["babel (==2.9.0)", "click (==7.0)", "colorama (==0.4)", "ghp-import (==1.0)", "importlib-metadata (==4.3)", "jinja2 (==2.11.1)", "markdown (==3.2.1)", "markupsafe (==2.0.1)", "mergedeep (==1.3.4)", "packaging (==20.5)", "pyyaml (==5.1)", "pyyaml-env-tag (==0.1)", "typing-extensions (==3.10)", "watchdog (==2.0)"] - -[[package]] -name = "mkdocs-autorefs" -version = "0.4.1" -description = "Automatically link across pages in MkDocs." -optional = false -python-versions = ">=3.7" -files = [ - {file = "mkdocs-autorefs-0.4.1.tar.gz", hash = "sha256:70748a7bd025f9ecd6d6feeba8ba63f8e891a1af55f48e366d6d6e78493aba84"}, - {file = "mkdocs_autorefs-0.4.1-py3-none-any.whl", hash = "sha256:a2248a9501b29dc0cc8ba4c09f4f47ff121945f6ce33d760f145d6f89d313f5b"}, -======= min-versions = ["babel (==2.9.0)", "click (==7.0)", "colorama (==0.4)", "ghp-import (==1.0)", "importlib-metadata (==4.3)", "jinja2 (==2.11.1)", "markdown (==3.2.1)", "markupsafe (==2.0.1)", "mergedeep (==1.3.4)", "packaging (==20.5)", "pathspec (==0.11.1)", "platformdirs (==2.2.0)", "pyyaml (==5.1)", "pyyaml-env-tag (==0.1)", "typing-extensions (==3.10)", "watchdog (==2.0)"] [[package]] @@ -4137,7 +2783,6 @@ python-versions = ">=3.8" files = [ {file = "mkdocs_autorefs-0.5.0-py3-none-any.whl", hash = "sha256:7930fcb8ac1249f10e683967aeaddc0af49d90702af111a5e390e8b20b3d97ff"}, {file = "mkdocs_autorefs-0.5.0.tar.gz", hash = "sha256:9a5054a94c08d28855cfab967ada10ed5be76e2bfad642302a610b252c3274c0"}, ->>>>>>> main ] [package.dependencies] @@ -4146,14 +2791,6 @@ mkdocs = ">=1.1" [[package]] name = "mkdocs-bibtex" -<<<<<<< HEAD -version = "2.8.16" -description = "An MkDocs plugin that enables managing citations with BibTex" -optional = false -python-versions = ">=3.6" -files = [ - {file = "mkdocs-bibtex-2.8.16.tar.gz", hash = "sha256:d4f4d284a72a7a943ab427fff58e74409fb26eb0536f89f202c891fdda2eb50a"}, -======= version = "2.11.0" description = "An MkDocs plugin that enables managing citations with BibTex" category = "dev" @@ -4161,7 +2798,6 @@ optional = false python-versions = ">=3.6" files = [ {file = "mkdocs-bibtex-2.11.0.tar.gz", hash = "sha256:9ed78e1e7cfc8cd6f3f5ca75641dbcea8a011c36dbefcde041e36f8e6d0ed10f"}, ->>>>>>> main ] [package.dependencies] @@ -4175,10 +2811,7 @@ validators = ">=0.19.0" name = "mkdocs-gen-files" version = "0.5.0" description = "MkDocs plugin to programmatically generate documentation pages during the build" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.7" files = [ @@ -4191,15 +2824,6 @@ mkdocs = ">=1.0.3" [[package]] name = "mkdocs-git-authors-plugin" -<<<<<<< HEAD -version = "0.7.0" -description = "Mkdocs plugin to display git authors of a page" -optional = false -python-versions = ">=3.6" -files = [ - {file = "mkdocs-git-authors-plugin-0.7.0.tar.gz", hash = "sha256:087b63090ebbf6b93f20d8b8e5fbac8e8b140e2107e432ca2ac8dd1d3a1000f5"}, - {file = "mkdocs_git_authors_plugin-0.7.0-py3-none-any.whl", hash = "sha256:cc469208f98e9db08561eac08a9d8ccd0209a60ee5bd0e3e94b6840a5abc54b6"}, -======= version = "0.7.2" description = "Mkdocs plugin to display git authors of a page" category = "dev" @@ -4208,7 +2832,6 @@ python-versions = ">=3.7" files = [ {file = "mkdocs-git-authors-plugin-0.7.2.tar.gz", hash = "sha256:f541730e4cabdafa0ac758c94d28ba5e8ddca4c859e5de4c89f1226cb6ccd0ad"}, {file = "mkdocs_git_authors_plugin-0.7.2-py3-none-any.whl", hash = "sha256:c8a2784a867db79ad3b477a96ee96875d17b09192b6d3be71f08df25afff76c4"}, ->>>>>>> main ] [package.dependencies] @@ -4216,18 +2839,6 @@ mkdocs = ">=1.0" [[package]] name = "mkdocs-jupyter" -<<<<<<< HEAD -version = "0.24.1" -description = "Use Jupyter in mkdocs websites" -optional = false -python-versions = ">=3.7" -files = [ - {file = "mkdocs_jupyter-0.24.1-py3-none-any.whl", hash = "sha256:759833c7d1528ae2d6337342786be7bc1e2235b0b98e9326427d4cf8d4eebee0"}, - {file = "mkdocs_jupyter-0.24.1.tar.gz", hash = "sha256:9677037fb7e931268f3df7599fc0828c261247df3d1575bced320ba8b7d1d46d"}, -] - -[package.dependencies] -======= version = "0.24.2" description = "Use Jupyter in mkdocs websites" category = "dev" @@ -4240,7 +2851,6 @@ files = [ [package.dependencies] ipykernel = ">6.0.0,<7.0.0" ->>>>>>> main jupytext = ">1.13.8,<2" mkdocs = ">=1.4.0,<2" mkdocs-material = ">9.0.0" @@ -4248,25 +2858,18 @@ nbconvert = ">=7.2.9,<8" pygments = ">2.12.0" [package.extras] -<<<<<<< HEAD -test = ["pytest", "pytest-cov"] -======= test = ["coverage[toml]", "pymdown-extensions", "pytest", "pytest-cov"] ->>>>>>> main [[package]] name = "mkdocs-literate-nav" -version = "0.6.0" +version = "0.6.1" description = "MkDocs plugin to specify the navigation in Markdown instead of YAML" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.7" files = [ - {file = "mkdocs_literate_nav-0.6.0-py3-none-any.whl", hash = "sha256:8c1b84714e5974da5e44e011ec0069275ae7647270c13a679662cf6ffce675a4"}, - {file = "mkdocs_literate_nav-0.6.0.tar.gz", hash = "sha256:81ccbea18163ae8e10bd0bd39237fe70c32a1f2dff6c170779f5d52dd98a0470"}, + {file = "mkdocs_literate_nav-0.6.1-py3-none-any.whl", hash = "sha256:e70bdc4a07050d32da79c0b697bd88e9a104cf3294282e9cb20eec94c6b0f401"}, + {file = "mkdocs_literate_nav-0.6.1.tar.gz", hash = "sha256:78a7ab6d878371728acb0cdc6235c9b0ffc6e83c997b037f4a5c6ff7cef7d759"}, ] [package.dependencies] @@ -4274,33 +2877,14 @@ mkdocs = ">=1.0.3" [[package]] name = "mkdocs-material" -<<<<<<< HEAD -version = "9.1.11" -description = "Documentation that simply works" -optional = false -python-versions = ">=3.7" -files = [ - {file = "mkdocs_material-9.1.11-py3-none-any.whl", hash = "sha256:fbc86d50ec2cf34d40d5c4365780f290ceedde23f1a0704323b34e7f16b0c0dd"}, - {file = "mkdocs_material-9.1.11.tar.gz", hash = "sha256:f5d473eb79d6640a5e668d4b2ab5b9de5e76ae0a0e2d864112df0cfe9016dc1d"}, -] - -[package.dependencies] -colorama = ">=0.4" -jinja2 = ">=3.0" -markdown = ">=3.2" -mkdocs = ">=1.4.2" -mkdocs-material-extensions = ">=1.1" -pygments = ">=2.14" -pymdown-extensions = ">=9.9.1" -======= -version = "9.2.4" +version = "9.2.6" description = "Documentation that simply works" category = "dev" optional = false python-versions = ">=3.7" files = [ - {file = "mkdocs_material-9.2.4-py3-none-any.whl", hash = "sha256:2df876367625ff5e0f7112bc19a57521ed21ce9a2b85656baf9bb7f5dc3cb987"}, - {file = "mkdocs_material-9.2.4.tar.gz", hash = "sha256:25008187b89fc376cb4ed2312b1fea4121bf2bd956442f38afdc6b4dcc21c57d"}, + {file = "mkdocs_material-9.2.6-py3-none-any.whl", hash = "sha256:84bc7e79c1d0bae65a77123efd5ef74731b8c3671601c7962c5db8dba50a65ad"}, + {file = "mkdocs_material-9.2.6.tar.gz", hash = "sha256:3806c58dd112e7b9677225e2021035ddbe3220fbd29d9dc812aa7e01f70b5e0a"}, ] [package.dependencies] @@ -4315,7 +2899,6 @@ paginate = ">=0.5.6" pygments = ">=2.14" pymdown-extensions = ">=9.9.1" readtime = ">=2.0" ->>>>>>> main regex = ">=2022.4.24" requests = ">=2.26" @@ -4323,10 +2906,7 @@ requests = ">=2.26" name = "mkdocs-material-extensions" version = "1.1.1" description = "Extension pack for Python Markdown and MkDocs Material." -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.7" files = [ @@ -4338,10 +2918,7 @@ files = [ name = "mkdocstrings" version = "0.21.2" description = "Automatic documentation from sources, for MkDocs." -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.7" files = [ @@ -4357,10 +2934,6 @@ mkdocs = ">=1.2" mkdocs-autorefs = ">=0.3.1" mkdocstrings-python = {version = ">=0.5.2", optional = true, markers = "extra == \"python\""} pymdown-extensions = ">=6.3" -<<<<<<< HEAD -typing-extensions = {version = ">=4.1", markers = "python_version < \"3.10\""} -======= ->>>>>>> main [package.extras] crystal = ["mkdocstrings-crystal (>=0.3.4)"] @@ -4369,42 +2942,25 @@ python-legacy = ["mkdocstrings-python-legacy (>=0.2.1)"] [[package]] name = "mkdocstrings-python" -<<<<<<< HEAD -version = "1.0.0" -description = "A Python handler for mkdocstrings." -optional = false -python-versions = ">=3.7" -files = [ - {file = "mkdocstrings_python-1.0.0-py3-none-any.whl", hash = "sha256:c59d67009a7a85172f4da990d8523e95606b6a1ff93a22a2351ad3b5f8cafed1"}, - {file = "mkdocstrings_python-1.0.0.tar.gz", hash = "sha256:b89d849df990204f909d5452548b6936a185f912da06208a93909bebe25d6e67"}, -] - -[package.dependencies] -griffe = ">=0.24" -======= -version = "1.5.2" +version = "1.7.0" description = "A Python handler for mkdocstrings." category = "dev" optional = false python-versions = ">=3.8" files = [ - {file = "mkdocstrings_python-1.5.2-py3-none-any.whl", hash = "sha256:ed37ca6d216986e2ac3530c19c3e7be381d1e3d09ea414e4ff467d6fd2cbd9c1"}, - {file = "mkdocstrings_python-1.5.2.tar.gz", hash = "sha256:81eb4a93bc454a253daf247d1a11397c435d641c64fa165324c17c06170b1dfb"}, + {file = "mkdocstrings_python-1.7.0-py3-none-any.whl", hash = "sha256:85c5f009a5a0ebb6076b7818c82a2bb0eebd0b54662628fa8b25ee14a6207951"}, + {file = "mkdocstrings_python-1.7.0.tar.gz", hash = "sha256:5dac2712bd38a3ff0812b8650a68b232601d1474091b380a8b5bc102c8c0d80a"}, ] [package.dependencies] griffe = ">=0.35" ->>>>>>> main mkdocstrings = ">=0.20" [[package]] name = "mknotebooks" version = "0.7.1" description = "Plugin for mkdocs to generate markdown documents from jupyter notebooks." -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = "*" files = [ @@ -4422,10 +2978,7 @@ nbconvert = ">=6.0.0" name = "mktestdocs" version = "0.2.1" description = "" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = "*" files = [ @@ -4438,30 +2991,6 @@ test = ["pytest (>=4.0.2)"] [[package]] name = "ml-dtypes" -<<<<<<< HEAD -version = "0.1.0" -description = "" -optional = false -python-versions = ">=3.7" -files = [ - {file = "ml_dtypes-0.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:377f2d5cfbf809b59188e0bfda4a0774e658541f575b637fee4850d99c2f9fdc"}, - {file = "ml_dtypes-0.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:87aa1cf83d41fed5a40fc27ee57ac4c1bf904e940f082531d3d58f1c318b5928"}, - {file = "ml_dtypes-0.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dee8ea629b8e3e20c6649852c1b9deacfa13384ab9337f2c9e717e401d102f23"}, - {file = "ml_dtypes-0.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:ad765159ac6c18d5ee7d325fcf34d3106a9d9d7a49713d998f5cfa330a1459b4"}, - {file = "ml_dtypes-0.1.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:b9c5578dffd85637a7dd437192de18bc1a14eb6ba7d53ef40de3f84c51c789e5"}, - {file = "ml_dtypes-0.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:36e8518c8fd2c38729f020125f39ef07b045f5c16d0846320c7252d7773285ee"}, - {file = "ml_dtypes-0.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:99fab8262d175c49bf1655c229244f301274e8289449c350ba4d5b95ade07d9a"}, - {file = "ml_dtypes-0.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:8de9bbf5bed587a1166699447ea14d1e8fe66d4e812811e37bf2f4d988475476"}, - {file = "ml_dtypes-0.1.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:a29fbf128583673eca0f43def1dbe77e02c1e8b8a8331db2877bbb57d091ef11"}, - {file = "ml_dtypes-0.1.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:273c306db846005b83a98c9c7ec3dc8fa20e8f11c3772c8e8c20cc12d8abfd4b"}, - {file = "ml_dtypes-0.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:41b6beeaea47e2466b94068664c9a45b2a65dd023aa4e5deeb5a73303661344e"}, - {file = "ml_dtypes-0.1.0-cp38-cp38-win_amd64.whl", hash = "sha256:2de6c81b0da398d54aabdd7de599f2dfc43e30b65d9fad379a69f4cc4ae165d3"}, - {file = "ml_dtypes-0.1.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:77970beeb3cf6ac559c4b6b393f24778a5abd34fafbaad82d5a0d17d0f148936"}, - {file = "ml_dtypes-0.1.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ffb7882dd46399217dc54f37affc899e0a29a4cfb63e5bf733ac0baf4a179c77"}, - {file = "ml_dtypes-0.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8c5c9fe086756fbc1bf51296431d64429536093cf6e2ba592e042d7fc07c8514"}, - {file = "ml_dtypes-0.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:c9218175b06764b8ddc95cb18d11a6c4b48a4b103a31c9ea2b2c3cd0cfc369f8"}, - {file = "ml_dtypes-0.1.0.tar.gz", hash = "sha256:c1fc0afe63ce99069f9d7e0693a61cfd0aea90241fc3821af9953d0c11f4048a"}, -======= version = "0.2.0" description = "" category = "main" @@ -4485,19 +3014,12 @@ files = [ {file = "ml_dtypes-0.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:32107e7fa9f62db9a5281de923861325211dfff87bd23faefb27b303314635ab"}, {file = "ml_dtypes-0.2.0-cp39-cp39-win_amd64.whl", hash = "sha256:1749b60348da71fd3c2ab303fdbc1965958dc50775ead41f5669c932a341cafd"}, {file = "ml_dtypes-0.2.0.tar.gz", hash = "sha256:6488eb642acaaf08d8020f6de0a38acee7ac324c1e6e92ee0c0fea42422cb797"}, ->>>>>>> main ] [package.dependencies] numpy = [ -<<<<<<< HEAD - {version = ">1.20", markers = "python_version <= \"3.9\""}, - {version = ">=1.21.2", markers = "python_version > \"3.9\""}, - {version = ">=1.23.3", markers = "python_version > \"3.10\""}, -======= {version = ">=1.23.3", markers = "python_version > \"3.10\""}, {version = ">=1.21.2", markers = "python_version > \"3.9\""}, ->>>>>>> main ] [package.extras] @@ -4507,10 +3029,7 @@ dev = ["absl-py", "pyink", "pylint (>=2.6.0)", "pytest", "pytest-xdist"] name = "msgpack" version = "1.0.5" description = "MessagePack serializer" -<<<<<<< HEAD -======= category = "main" ->>>>>>> main optional = false python-versions = "*" files = [ @@ -4583,10 +3102,7 @@ files = [ name = "multidict" version = "6.0.4" description = "multidict implementation" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.7" files = [ @@ -4666,35 +3182,11 @@ files = [ {file = "multidict-6.0.4.tar.gz", hash = "sha256:3666906492efb76453c0e7b97f2cf459b0682e7402c0489a95484965dbc1da49"}, ] -[[package]] -<<<<<<< HEAD -name = "munch" -version = "2.5.0" -description = "A dot-accessible dictionary (a la JavaScript objects)" -optional = false -python-versions = "*" -files = [ - {file = "munch-2.5.0-py2.py3-none-any.whl", hash = "sha256:6f44af89a2ce4ed04ff8de41f70b226b984db10a91dcc7b9ac2efc1c77022fdd"}, - {file = "munch-2.5.0.tar.gz", hash = "sha256:2d735f6f24d4dba3417fa448cae40c6e896ec1fdab6cdb5e6510999758a4dbd2"}, -] - -[package.dependencies] -six = "*" - -[package.extras] -testing = ["astroid (>=1.5.3,<1.6.0)", "astroid (>=2.0)", "coverage", "pylint (>=1.7.2,<1.8.0)", "pylint (>=2.3.1,<2.4.0)", "pytest"] -yaml = ["PyYAML (>=5.1.0)"] - [[package]] name = "mypy-extensions" version = "1.0.0" description = "Type system extensions for programs checked with the mypy type checker." -======= -name = "mypy-extensions" -version = "1.0.0" -description = "Type system extensions for programs checked with the mypy type checker." category = "dev" ->>>>>>> main optional = false python-versions = ">=3.5" files = [ @@ -4704,15 +3196,6 @@ files = [ [[package]] name = "nbclient" -<<<<<<< HEAD -version = "0.7.4" -description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor." -optional = false -python-versions = ">=3.7.0" -files = [ - {file = "nbclient-0.7.4-py3-none-any.whl", hash = "sha256:c817c0768c5ff0d60e468e017613e6eae27b6fa31e43f905addd2d24df60c125"}, - {file = "nbclient-0.7.4.tar.gz", hash = "sha256:d447f0e5a4cfe79d462459aec1b3dc5c2e9152597262be8ee27f7d4c02566a0d"}, -======= version = "0.8.0" description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor." category = "dev" @@ -4721,84 +3204,47 @@ python-versions = ">=3.8.0" files = [ {file = "nbclient-0.8.0-py3-none-any.whl", hash = "sha256:25e861299e5303a0477568557c4045eccc7a34c17fc08e7959558707b9ebe548"}, {file = "nbclient-0.8.0.tar.gz", hash = "sha256:f9b179cd4b2d7bca965f900a2ebf0db4a12ebff2f36a711cb66861e4ae158e55"}, ->>>>>>> main ] [package.dependencies] jupyter-client = ">=6.1.12" -<<<<<<< HEAD -jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" -nbformat = ">=5.1" -traitlets = ">=5.3" -======= jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" nbformat = ">=5.1" traitlets = ">=5.4" ->>>>>>> main [package.extras] dev = ["pre-commit"] docs = ["autodoc-traits", "mock", "moto", "myst-parser", "nbclient[test]", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling"] -<<<<<<< HEAD -test = ["flaky", "ipykernel", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"] - -[[package]] -name = "nbconvert" -version = "7.4.0" -description = "Converting Jupyter Notebooks" -optional = false -python-versions = ">=3.7" -files = [ - {file = "nbconvert-7.4.0-py3-none-any.whl", hash = "sha256:af5064a9db524f9f12f4e8be7f0799524bd5b14c1adea37e34e83c95127cc818"}, - {file = "nbconvert-7.4.0.tar.gz", hash = "sha256:51b6c77b507b177b73f6729dba15676e42c4e92bcb00edc8cc982ee72e7d89d7"}, -======= test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"] [[package]] name = "nbconvert" -version = "7.7.4" +version = "7.8.0" description = "Converting Jupyter Notebooks" category = "dev" optional = false python-versions = ">=3.8" files = [ - {file = "nbconvert-7.7.4-py3-none-any.whl", hash = "sha256:ace26f4386d08eb5c55833596a942048c5502a95e05590cb523826a749a40a37"}, - {file = "nbconvert-7.7.4.tar.gz", hash = "sha256:1113d039fa3fc3a846ffa5a3b0a019e85aaa94c566a09fa0c400fb7638e46087"}, ->>>>>>> main + {file = "nbconvert-7.8.0-py3-none-any.whl", hash = "sha256:aec605e051fa682ccc7934ccc338ba1e8b626cfadbab0db592106b630f63f0f2"}, + {file = "nbconvert-7.8.0.tar.gz", hash = "sha256:f5bc15a1247e14dd41ceef0c0a3bc70020e016576eb0578da62f1c5b4f950479"}, ] [package.dependencies] beautifulsoup4 = "*" -<<<<<<< HEAD -bleach = "*" -defusedxml = "*" -importlib-metadata = {version = ">=3.6", markers = "python_version < \"3.10\""} -======= bleach = "!=5.0.0" defusedxml = "*" ->>>>>>> main jinja2 = ">=3.0" jupyter-core = ">=4.7" jupyterlab-pygments = "*" markupsafe = ">=2.0" -<<<<<<< HEAD -mistune = ">=2.0.3,<3" -nbclient = ">=0.5.0" -nbformat = ">=5.1" -======= mistune = ">=2.0.3,<4" nbclient = ">=0.5.0" nbformat = ">=5.7" ->>>>>>> main packaging = "*" pandocfilters = ">=1.4.1" pygments = ">=2.4.1" tinycss2 = "*" -<<<<<<< HEAD -traitlets = ">=5.0" -======= traitlets = ">=5.1" ->>>>>>> main [package.extras] all = ["nbconvert[docs,qtpdf,serve,test,webpdf]"] @@ -4806,20 +3252,6 @@ docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sp qtpdf = ["nbconvert[qtpng]"] qtpng = ["pyqtwebengine (>=5.15)"] serve = ["tornado (>=6.1)"] -<<<<<<< HEAD -test = ["ipykernel", "ipywidgets (>=7)", "pre-commit", "pytest", "pytest-dependency"] -webpdf = ["pyppeteer (>=1,<1.1)"] - -[[package]] -name = "nbformat" -version = "5.8.0" -description = "The Jupyter Notebook format" -optional = false -python-versions = ">=3.7" -files = [ - {file = "nbformat-5.8.0-py3-none-any.whl", hash = "sha256:d910082bd3e0bffcf07eabf3683ed7dda0727a326c446eeb2922abe102e65162"}, - {file = "nbformat-5.8.0.tar.gz", hash = "sha256:46dac64c781f1c34dfd8acba16547024110348f9fc7eab0f31981c2a3dc48d1f"}, -======= test = ["flaky", "ipykernel", "ipywidgets (>=7)", "pre-commit", "pytest", "pytest-dependency"] webpdf = ["playwright"] @@ -4833,7 +3265,6 @@ python-versions = ">=3.8" files = [ {file = "nbformat-5.9.2-py3-none-any.whl", hash = "sha256:1c5172d786a41b82bcfd0c23f9e6b6f072e8fb49c39250219e4acfff1efe89e9"}, {file = "nbformat-5.9.2.tar.gz", hash = "sha256:5f98b5ba1997dff175e77e0c17d5c10a96eaed2cbd1de3533d1fc35d5e111192"}, ->>>>>>> main ] [package.dependencies] @@ -4850,10 +3281,7 @@ test = ["pep440", "pre-commit", "pytest", "testpath"] name = "nbstripout" version = "0.6.1" description = "Strips outputs from Jupyter and IPython notebooks" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.6" files = [ @@ -4866,34 +3294,21 @@ nbformat = "*" [[package]] name = "nest-asyncio" -<<<<<<< HEAD -version = "1.5.6" -description = "Patch asyncio to allow nested event loops" -optional = false -python-versions = ">=3.5" -files = [ - {file = "nest_asyncio-1.5.6-py3-none-any.whl", hash = "sha256:b9a953fb40dceaa587d109609098db21900182b16440652454a146cffb06e8b8"}, - {file = "nest_asyncio-1.5.6.tar.gz", hash = "sha256:d267cc1ff794403f7df692964d1d2a3fa9418ffea2a3f6859a439ff482fef290"}, -======= -version = "1.5.7" +version = "1.5.8" description = "Patch asyncio to allow nested event loops" category = "main" optional = false python-versions = ">=3.5" files = [ - {file = "nest_asyncio-1.5.7-py3-none-any.whl", hash = "sha256:5301c82941b550b3123a1ea772ba9a1c80bad3a182be8c1a5ae6ad3be57a9657"}, - {file = "nest_asyncio-1.5.7.tar.gz", hash = "sha256:6a80f7b98f24d9083ed24608977c09dd608d83f91cccc24c9d2cba6d10e01c10"}, ->>>>>>> main + {file = "nest_asyncio-1.5.8-py3-none-any.whl", hash = "sha256:accda7a339a70599cb08f9dd09a67e0c2ef8d8d6f4c07f96ab203f2ae254e48d"}, + {file = "nest_asyncio-1.5.8.tar.gz", hash = "sha256:25aa2ca0d2a5b5531956b9e273b45cf664cae2b145101d73b86b199978d48fdb"}, ] [[package]] name = "networkx" version = "3.1" description = "Python package for creating and manipulating graphs and networks" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.8" files = [ @@ -4910,15 +3325,6 @@ test = ["codecov (>=2.1)", "pytest (>=7.2)", "pytest-cov (>=4.0)"] [[package]] name = "nodeenv" -<<<<<<< HEAD -version = "1.7.0" -description = "Node.js virtual environment builder" -optional = false -python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*" -files = [ - {file = "nodeenv-1.7.0-py2.py3-none-any.whl", hash = "sha256:27083a7b96a25f2f5e1d8cb4b6317ee8aeda3bdd121394e5ac54e498028a042e"}, - {file = "nodeenv-1.7.0.tar.gz", hash = "sha256:e0e7f7dfb85fc5394c6fe1e8fa98131a2473e04311a45afb6508f7cf1836fa2b"}, -======= version = "1.8.0" description = "Node.js virtual environment builder" category = "dev" @@ -4927,7 +3333,6 @@ python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.* files = [ {file = "nodeenv-1.8.0-py2.py3-none-any.whl", hash = "sha256:df865724bb3c3adc86b3876fa209771517b0cfe596beff01a92700e0e8be4cec"}, {file = "nodeenv-1.8.0.tar.gz", hash = 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[[package]] -<<<<<<< HEAD -name = "pandas" -version = "1.5.3" -description = "Powerful data structures for data analysis, time series, and statistics" -======= name = "paginate" version = "0.5.6" description = "Divides large result sets into pages for easier browsing" @@ -5175,7 +3501,6 @@ name = "pandas" version = "1.5.3" description = "Powerful data structures for data analysis, time series, and statistics" category = "dev" ->>>>>>> main optional = false python-versions = ">=3.8" files = [ @@ -5210,10 +3535,6 @@ files = [ [package.dependencies] numpy = [ -<<<<<<< HEAD - {version = ">=1.20.3", markers = "python_version < \"3.10\""}, -======= ->>>>>>> main {version = ">=1.21.0", markers = "python_version >= \"3.10\""}, {version = ">=1.23.2", markers = "python_version >= \"3.11\""}, ] @@ -5227,10 +3548,7 @@ test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"] name = "pandocfilters" version = "1.5.0" description = "Utilities for writing pandoc filters in python" 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description = "A jquery-like library for python" @@ -6330,18 +4250,12 @@ python-versions = ">=3.8" files = [ {file = "pystac-1.8.3-py3-none-any.whl", hash = "sha256:91805520b0b5386db84aae5296dc6d4fb6754410c481d0a00a8afedc3b4c75d5"}, {file = "pystac-1.8.3.tar.gz", hash = "sha256:3fd0464bfeb7e99893b24c8d683dd3d046c48b2e53ed65d0a8a704f1281f1ed1"}, ->>>>>>> main ] [package.dependencies] python-dateutil = ">=2.7.0" [package.extras] -<<<<<<< HEAD -orjson = ["orjson (>=3.5)"] -urllib3 = ["urllib3 (>=1.26)"] -validation = ["jsonschema (>=4.0.1)"] -======= bench = ["asv (>=0.5,<1.0)", "virtualenv (>=20.22,<21.0)"] docs = ["Sphinx (>=6.2,<7.0)", "ipython (>=8.12,<9.0)", "jinja2 (<4.0)", "jupyter (>=1.0,<2.0)", "nbsphinx (>=0.9,<1.0)", "pydata-sphinx-theme (>=0.13,<1.0)", "sphinx-autobuild (==2021.3.14)", "sphinx-design (>=0.4,<1.0)", "sphinxcontrib-fulltoc (>=1.2,<2.0)"] jinja2 = ["jinja2 (<4.0)"] @@ -6349,16 +4263,12 @@ orjson = ["orjson (>=3.5)"] test = ["black (>=23.3,<24.0)", "codespell 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false -python-versions = ">=3.7" -files = [ - {file = "pytest-7.3.1-py3-none-any.whl", hash = "sha256:3799fa815351fea3a5e96ac7e503a96fa51cc9942c3753cda7651b93c1cfa362"}, - {file = "pytest-7.3.1.tar.gz", hash = "sha256:434afafd78b1d78ed0addf160ad2b77a30d35d4bdf8af234fe621919d9ed15e3"}, -======= -version = "7.4.0" +version = "7.4.2" description = "pytest: simple powerful testing with Python" category = "dev" optional = false python-versions = ">=3.7" files = [ - {file = "pytest-7.4.0-py3-none-any.whl", hash = "sha256:78bf16451a2eb8c7a2ea98e32dc119fd2aa758f1d5d66dbf0a59d69a3969df32"}, - {file = "pytest-7.4.0.tar.gz", hash = "sha256:b4bf8c45bd59934ed84001ad51e11b4ee40d40a1229d2c79f9c592b0a3f6bd8a"}, ->>>>>>> main + {file = "pytest-7.4.2-py3-none-any.whl", hash = "sha256:1d881c6124e08ff0a1bb75ba3ec0bfd8b5354a01c194ddd5a0a870a48d99b002"}, + {file = "pytest-7.4.2.tar.gz", hash = "sha256:a766259cfab564a2ad52cb1aae1b881a75c3eb7e34ca3779697c23ed47c47069"}, ] [package.dependencies] @@ -6405,19 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environment variables" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.8" files = [ @@ -6530,10 +4397,7 @@ cli = ["click (>=5.0)"] name = "pytkdocs" version = "0.16.1" description = "Load Python objects documentation." -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.7" files = [ @@ -6541,38 +4405,26 @@ files = [ {file = "pytkdocs-0.16.1.tar.gz", hash = "sha256:e2ccf6dfe9dbbceb09818673f040f1a7c32ed0bffb2d709b06be6453c4026045"}, ] -<<<<<<< HEAD -[package.dependencies] -astunparse = {version = ">=1.6", markers = "python_version < \"3.9\""} - -======= ->>>>>>> main [package.extras] numpy-style = ["docstring_parser (>=0.7)"] [[package]] name = "pytz" -version = "2023.3" +version = "2023.3.post1" description = "World timezone definitions, modern and historical" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = "*" files = [ - {file = 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"tomli" version = "2.0.1" description = "A lil' TOML parser" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.7" files = [ @@ -8026,15 +5433,6 @@ files = [ [[package]] name = "tomlkit" -<<<<<<< HEAD -version = "0.11.8" -description = "Style preserving TOML library" -optional = false -python-versions = ">=3.7" -files = [ - {file = "tomlkit-0.11.8-py3-none-any.whl", hash = "sha256:8c726c4c202bdb148667835f68d68780b9a003a9ec34167b6c673b38eff2a171"}, - {file = "tomlkit-0.11.8.tar.gz", hash = "sha256:9330fc7faa1db67b541b28e62018c17d20be733177d290a13b24c62d1614e0c3"}, -======= version = "0.12.1" description = "Style preserving TOML library" category = "dev" @@ -8043,17 +5441,13 @@ python-versions = ">=3.7" files = [ {file = "tomlkit-0.12.1-py3-none-any.whl", hash = "sha256:712cbd236609acc6a3e2e97253dfc52d4c2082982a88f61b640ecf0817eab899"}, {file = "tomlkit-0.12.1.tar.gz", hash = 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Extensible Progress Meter" -optional = false -python-versions = ">=3.7" -files = [ - {file = "tqdm-4.65.0-py3-none-any.whl", hash = "sha256:c4f53a17fe37e132815abceec022631be8ffe1b9381c2e6e30aa70edc99e9671"}, - {file = "tqdm-4.65.0.tar.gz", hash = "sha256:1871fb68a86b8fb3b59ca4cdd3dcccbc7e6d613eeed31f4c332531977b89beb5"}, -======= version = "4.66.1" description = "Fast, Extensible Progress Meter" category = "main" @@ -8120,109 +5486,69 @@ python-versions = ">=3.7" files = [ {file = "tqdm-4.66.1-py3-none-any.whl", hash = "sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386"}, {file = "tqdm-4.66.1.tar.gz", hash = "sha256:d88e651f9db8d8551a62556d3cff9e3034274ca5d66e93197cf2490e2dcb69c7"}, ->>>>>>> main ] [package.dependencies] colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] -<<<<<<< HEAD -dev = ["py-make (>=0.1.0)", "twine", "wheel"] -======= dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"] ->>>>>>> main notebook = ["ipywidgets (>=6)"] slack = ["slack-sdk"] telegram = ["requests"] [[package]] name = "traitlets" -version = "5.9.0" +version = "5.10.0" description = "Traitlets Python configuration system" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - {file = "traitlets-5.9.0-py3-none-any.whl", hash = "sha256:9e6ec080259b9a5940c797d58b613b5e31441c2257b87c2e795c5228ae80d2d8"}, - {file = "traitlets-5.9.0.tar.gz", hash = "sha256:f6cde21a9c68cf756af02035f72d5a723bf607e862e7be33ece505abf4a3bad9"}, + {file = "traitlets-5.10.0-py3-none-any.whl", hash = "sha256:417745a96681fbb358e723d5346a547521f36e9bd0d50ba7ab368fff5d67aa54"}, + {file = "traitlets-5.10.0.tar.gz", hash = "sha256:f584ea209240466e66e91f3c81aa7d004ba4cf794990b0c775938a1544217cd1"}, ] [package.extras] docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"] -test = ["argcomplete (>=2.0)", "pre-commit", "pytest", "pytest-mock"] +test = ["argcomplete (>=3.0.3)", "mypy (>=1.5.1)", "pre-commit", "pytest (>=7.0,<7.5)", "pytest-mock", "pytest-mypy-testing"] [[package]] name = "typeguard" -<<<<<<< HEAD -version = "3.0.2" -description = "Run-time type checker for Python" -optional = false -python-versions = ">=3.7.4" -files = [ - {file = "typeguard-3.0.2-py3-none-any.whl", hash = "sha256:bbe993854385284ab42fd5bd3bee6f6556577ce8b50696d6cb956d704f286c8e"}, - {file = "typeguard-3.0.2.tar.gz", hash = "sha256:fee5297fdb28f8e9efcb8142b5ee219e02375509cd77ea9d270b5af826358d5a"}, -] - -[package.dependencies] -importlib-metadata = {version = ">=3.6", markers = "python_version < \"3.10\""} -typing-extensions = {version = ">=4.4.0", markers = "python_version < \"3.11\""} - -[package.extras] -doc = ["packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] -test = ["mypy (>=0.991)", "pytest (>=7)"] - -[[package]] -name = "typing-extensions" -version = "4.5.0" -description = "Backported and Experimental Type Hints for Python 3.7+" -optional = false -python-versions = ">=3.7" -files = [ - {file = "typing_extensions-4.5.0-py3-none-any.whl", hash = "sha256:fb33085c39dd998ac16d1431ebc293a8b3eedd00fd4a32de0ff79002c19511b4"}, - {file = "typing_extensions-4.5.0.tar.gz", hash = "sha256:5cb5f4a79139d699607b3ef622a1dedafa84e115ab0024e0d9c044a9479ca7cb"}, -======= -version = "4.1.2" +version = "4.1.5" description = "Run-time type checker for Python" category = "main" optional = false -python-versions = ">=3.7.4" +python-versions = ">=3.8" files = [ - {file = "typeguard-4.1.2-py3-none-any.whl", hash = "sha256:e00775920d4c91e93a0db0ed473ecda9cfaca578aed3ce0ed3ba7f3cc38eab9c"}, - {file = "typeguard-4.1.2.tar.gz", hash = "sha256:3be187945f9ef5a9f6d7a926dfe54babb7dfd807085ce05f9a5e8735f2487990"}, + {file = "typeguard-4.1.5-py3-none-any.whl", hash = "sha256:8923e55f8873caec136c892c3bed1f676eae7be57cdb94819281b3d3bc9c0953"}, + {file = "typeguard-4.1.5.tar.gz", hash = "sha256:ea0a113bbc111bcffc90789ebb215625c963411f7096a7e9062d4e4630c155fd"}, ] [package.dependencies] typing-extensions = {version = ">=4.7.0", markers = "python_version < \"3.12\""} [package.extras] -doc = ["Sphinx (<7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] -test = ["mypy (>=1.2.0)", "pytest (>=7)"] +doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)"] +test = ["coverage[toml] (>=7)", "mypy (>=1.2.0)", "pytest (>=7)"] [[package]] name = "typing-extensions" -version = "4.7.1" -description = "Backported and Experimental Type Hints for Python 3.7+" +version = "4.8.0" +description = "Backported and Experimental Type Hints for Python 3.8+" category = "main" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - {file = "typing_extensions-4.7.1-py3-none-any.whl", hash = "sha256:440d5dd3af93b060174bf433bccd69b0babc3b15b1a8dca43789fd7f61514b36"}, - {file = "typing_extensions-4.7.1.tar.gz", hash = "sha256:b75ddc264f0ba5615db7ba217daeb99701ad295353c45f9e95963337ceeeffb2"}, ->>>>>>> main + {file = "typing_extensions-4.8.0-py3-none-any.whl", hash = "sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0"}, + {file = "typing_extensions-4.8.0.tar.gz", hash = "sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"}, ] [[package]] name = "uc-micro-py" version = "1.0.2" description = "Micro subset of unicode data files for linkify-it-py projects." -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.7" files = [ @@ -8235,15 +5561,6 @@ test = ["coverage", "pytest", "pytest-cov"] [[package]] name = "urllib3" -<<<<<<< HEAD -version = "2.0.2" -description = "HTTP library with thread-safe connection pooling, file post, and more." -optional = false -python-versions = ">=3.7" -files = [ - {file = "urllib3-2.0.2-py3-none-any.whl", hash = "sha256:d055c2f9d38dc53c808f6fdc8eab7360b6fdbbde02340ed25cfbcd817c62469e"}, - {file = "urllib3-2.0.2.tar.gz", hash = "sha256:61717a1095d7e155cdb737ac7bb2f4324a858a1e2e6466f6d03ff630ca68d3cc"}, -======= version = "2.0.4" description = "HTTP library with thread-safe connection pooling, file post, and more." category = "dev" @@ -8252,7 +5569,6 @@ python-versions = ">=3.7" files = [ {file = "urllib3-2.0.4-py3-none-any.whl", hash = "sha256:de7df1803967d2c2a98e4b11bb7d6bd9210474c46e8a0401514e3a42a75ebde4"}, {file = "urllib3-2.0.4.tar.gz", hash = "sha256:8d22f86aae8ef5e410d4f539fde9ce6b2113a001bb4d189e0aed70642d602b11"}, ->>>>>>> main ] [package.extras] @@ -8263,71 +5579,37 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "validators" -<<<<<<< HEAD -version = "0.20.0" -description = "Python Data Validation for Humans™." -optional = false -python-versions = ">=3.4" -files = [ - {file = "validators-0.20.0.tar.gz", hash = "sha256:24148ce4e64100a2d5e267233e23e7afeb55316b47d30faae7eb6e7292bc226a"}, -] - -[package.dependencies] -decorator = ">=3.4.0" - -[package.extras] -test = ["flake8 (>=2.4.0)", "isort (>=4.2.2)", "pytest (>=2.2.3)"] - -[[package]] -name = "virtualenv" -version = "20.23.0" -description = "Virtual Python Environment builder" -optional = false -python-versions = ">=3.7" -files = [ - {file = "virtualenv-20.23.0-py3-none-any.whl", hash = "sha256:6abec7670e5802a528357fdc75b26b9f57d5d92f29c5462ba0fbe45feacc685e"}, - {file = "virtualenv-20.23.0.tar.gz", hash = "sha256:a85caa554ced0c0afbd0d638e7e2d7b5f92d23478d05d17a76daeac8f279f924"}, -] - -[package.dependencies] -distlib = ">=0.3.6,<1" -filelock = ">=3.11,<4" -platformdirs = ">=3.2,<4" - -[package.extras] -docs = ["furo (>=2023.3.27)", "proselint (>=0.13)", "sphinx (>=6.1.3)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=22.12)"] -test = ["covdefaults (>=2.3)", "coverage (>=7.2.3)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.3.1)", "pytest-env (>=0.8.1)", "pytest-freezegun (>=0.4.2)", "pytest-mock (>=3.10)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=67.7.1)", "time-machine (>=2.9)"] -======= -version = "0.21.2" +version = "0.22.0" description = "Python Data Validation for Humans™" category = "dev" optional = false python-versions = ">=3.8" files = [ - {file = "validators-0.21.2-py3-none-any.whl", hash = "sha256:6ad95131005a9d4c734a69dd4ef08cf66961e61222e60da25a9b5137cecd6fd4"}, - {file = "validators-0.21.2.tar.gz", hash = "sha256:002ba1552076535176824e43149c18c06f6b611bc8b597ddbcf8770bcf5f9f5c"}, + {file = "validators-0.22.0-py3-none-any.whl", hash = "sha256:61cf7d4a62bbae559f2e54aed3b000cea9ff3e2fdbe463f51179b92c58c9585a"}, + {file = "validators-0.22.0.tar.gz", hash = "sha256:77b2689b172eeeb600d9605ab86194641670cdb73b60afd577142a9397873370"}, ] [package.extras] docs-offline = ["myst-parser (>=2.0.0)", "pypandoc-binary (>=1.11)", "sphinx (>=7.1.1)"] -docs-online = ["mkdocs (>=1.5.2)", "mkdocs-material (>=9.1.21)", "mkdocstrings[python] (>=0.22.0)", "pyaml (>=23.7.0)"] +docs-online = ["mkdocs (>=1.5.2)", "mkdocs-git-revision-date-localized-plugin (>=1.2.0)", "mkdocs-material (>=9.2.6)", "mkdocstrings[python] (>=0.22.0)", "pyaml (>=23.7.0)"] hooks = ["pre-commit (>=3.3.3)"] -runner = ["tox (>=4.6.4)"] +package = ["build (>=1.0.0)", "twine (>=4.0.2)"] +runner = ["tox (>=4.11.1)"] sast = ["bandit[toml] (>=1.7.5)"] testing = ["pytest (>=7.4.0)"] -tooling = ["black (>=23.7.0)", "pyright (>=1.1.320)", "ruff (>=0.0.280)"] +tooling = ["black (>=23.7.0)", "pyright (>=1.1.325)", "ruff (>=0.0.287)"] tooling-extras = ["pyaml (>=23.7.0)", "pypandoc-binary (>=1.11)", "pytest (>=7.4.0)"] [[package]] name = "virtualenv" -version = "20.24.3" +version = "20.24.5" description = "Virtual Python Environment builder" category = "dev" optional = false python-versions = ">=3.7" files = [ - {file = "virtualenv-20.24.3-py3-none-any.whl", hash = "sha256:95a6e9398b4967fbcb5fef2acec5efaf9aa4972049d9ae41f95e0972a683fd02"}, - {file = "virtualenv-20.24.3.tar.gz", hash = "sha256:e5c3b4ce817b0b328af041506a2a299418c98747c4b1e68cb7527e74ced23efc"}, + {file = "virtualenv-20.24.5-py3-none-any.whl", hash = "sha256:b80039f280f4919c77b30f1c23294ae357c4c8701042086e3fc005963e4e537b"}, + {file = "virtualenv-20.24.5.tar.gz", hash = "sha256:e8361967f6da6fbdf1426483bfe9fca8287c242ac0bc30429905721cefbff752"}, ] [package.dependencies] @@ -8336,18 +5618,14 @@ filelock = ">=3.12.2,<4" platformdirs = ">=3.9.1,<4" [package.extras] -docs = ["furo (>=2023.5.20)", "proselint (>=0.13)", "sphinx (>=7.0.1)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"] +docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"] test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"] ->>>>>>> main [[package]] name = "watchdog" version = "3.0.0" description = "Filesystem events monitoring" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.7" files = [ @@ -8385,19 +5663,6 @@ watchmedo = ["PyYAML (>=3.10)"] [[package]] name = "watermark" -<<<<<<< HEAD -version = "2.3.1" -description = "IPython magic function to print date/time stamps and various system information." -optional = false -python-versions = ">=3.7" -files = [ - {file = "watermark-2.3.1-py2.py3-none-any.whl", hash = "sha256:8e2681e512660e50d2aa460fd7d40d8ed2862735ae5087fc0ec7752fb10ee29c"}, - {file = "watermark-2.3.1.tar.gz", hash = "sha256:0a69eb017f4f96e909739f25ce1a3bd0729c65d8cf4294ea07d609322360019a"}, -] - -[package.dependencies] -ipython = "*" -======= version = "2.4.3" description = "IPython magic function to print date/time stamps and various system information." category = "dev" @@ -8415,16 +5680,12 @@ setuptools = "*" [package.extras] gpu = ["py3nvml (>=0.2)"] ->>>>>>> main [[package]] name = "wcwidth" version = "0.2.6" description = "Measures the displayed width of unicode strings in a terminal" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = "*" files = [ @@ -8436,10 +5697,7 @@ files = [ name = "webencodings" version = "0.5.1" description = "Character encoding aliases for legacy web content" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = "*" files = [ @@ -8448,52 +5706,22 @@ files = [ ] [[package]] -<<<<<<< HEAD -name = "wheel" -version = "0.40.0" -description = "A built-package format for Python" -optional = false -python-versions = ">=3.7" -files = [ - {file = "wheel-0.40.0-py3-none-any.whl", hash = "sha256:d236b20e7cb522daf2390fa84c55eea81c5c30190f90f29ae2ca1ad8355bf247"}, - {file = "wheel-0.40.0.tar.gz", hash = "sha256:cd1196f3faee2b31968d626e1731c94f99cbdb67cf5a46e4f5656cbee7738873"}, -] - -[package.extras] -test = ["pytest (>=6.0.0)"] - -[[package]] -name = "widgetsnbextension" -version = "4.0.7" -description = "Jupyter interactive widgets for Jupyter Notebook" -optional = false -python-versions = ">=3.7" -files = [ - {file = "widgetsnbextension-4.0.7-py3-none-any.whl", hash = "sha256:be3228a73bbab189a16be2d4a3cd89ecbd4e31948bfdc64edac17dcdee3cd99c"}, - {file = "widgetsnbextension-4.0.7.tar.gz", hash = "sha256:ea67c17a7cd4ae358f8f46c3b304c40698bc0423732e3f273321ee141232c8be"}, -] - -======= name = "widgetsnbextension" -version = "4.0.8" +version = "4.0.9" description = "Jupyter interactive widgets for Jupyter Notebook" category = "dev" optional = false python-versions = ">=3.7" files = [ - {file = "widgetsnbextension-4.0.8-py3-none-any.whl", hash = "sha256:2e37f0ce9da11651056280c7efe96f2db052fe8fc269508e3724f5cbd6c93018"}, - {file = "widgetsnbextension-4.0.8.tar.gz", hash = "sha256:9ec291ba87c2dfad42c3d5b6f68713fa18be1acd7476569516b2431682315c17"}, + {file = "widgetsnbextension-4.0.9-py3-none-any.whl", hash = "sha256:91452ca8445beb805792f206e560c1769284267a30ceb1cec9f5bcc887d15175"}, + {file = "widgetsnbextension-4.0.9.tar.gz", hash = "sha256:3c1f5e46dc1166dfd40a42d685e6a51396fd34ff878742a3e47c6f0cc4a2a385"}, ] ->>>>>>> main [[package]] name = "wrapt" version = "1.15.0" description = "Module for decorators, wrappers and monkey patching." -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7" files = [ @@ -8576,27 +5804,6 @@ files = [ [[package]] name = "xarray" -<<<<<<< HEAD -version = "2023.1.0" -description = "N-D labeled arrays and datasets in Python" -optional = false -python-versions = ">=3.8" -files = [ - {file = "xarray-2023.1.0-py3-none-any.whl", hash = "sha256:7e530b1deafdd43e5c2b577d0944e6b528fbe88045fd849e49a8d11871ecd522"}, - {file = "xarray-2023.1.0.tar.gz", hash = "sha256:7bee552751ff1b29dab8b7715726e5ecb56691ac54593cf4881dff41978ce0cd"}, -] - -[package.dependencies] -numpy = ">=1.20" -packaging = ">=21.3" -pandas = ">=1.3" - -[package.extras] -accel = ["bottleneck", "flox", "numbagg", "scipy"] -complete = ["bottleneck", "cfgrib", "cftime", "dask[complete]", "flox", "fsspec", "h5netcdf", "matplotlib", "nc-time-axis", "netCDF4", "numbagg", "pooch", "pydap", "rasterio", "scipy", "seaborn", "zarr"] -docs = ["bottleneck", "cfgrib", "cftime", "dask[complete]", "flox", "fsspec", "h5netcdf", "ipykernel", "ipython", "jupyter-client", "matplotlib", "nbsphinx", "nc-time-axis", "netCDF4", "numbagg", "pooch", "pydap", "rasterio", "scanpydoc", "scipy", "seaborn", "sphinx-autosummary-accessors", "sphinx-rtd-theme", "zarr"] -io = ["cfgrib", "cftime", "fsspec", "h5netcdf", "netCDF4", "pooch", "pydap", "rasterio", "scipy", "zarr"] -======= version = "2023.8.0" description = "N-D labeled arrays and datasets in Python" category = "dev" @@ -8617,7 +5824,6 @@ accel = ["bottleneck", "flox", "numbagg", "scipy"] complete = ["bottleneck", "cftime", "dask[complete]", "flox", "fsspec", "h5netcdf", "matplotlib", "nc-time-axis", "netCDF4", "numbagg", "pooch", "pydap", "scipy", "seaborn", "zarr"] docs = ["bottleneck", "cftime", "dask[complete]", "flox", "fsspec", "h5netcdf", "ipykernel", "ipython", "jupyter-client", "matplotlib", "nbsphinx", "nc-time-axis", "netCDF4", "numbagg", "pooch", "pydap", "scanpydoc", "scipy", "seaborn", "sphinx-autosummary-accessors", "sphinx-rtd-theme", "zarr"] io = ["cftime", "fsspec", "h5netcdf", "netCDF4", "pooch", "pydap", "scipy", "zarr"] ->>>>>>> main parallel = ["dask[complete]"] viz = ["matplotlib", "nc-time-axis", "seaborn"] @@ -8625,10 +5831,7 @@ viz = ["matplotlib", "nc-time-axis", "seaborn"] name = "xdoctest" version = "1.1.1" description = "A rewrite of the builtin doctest module" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.6" files = [ @@ -8656,10 +5859,7 @@ tests-strict = ["codecov (==2.0.15)", "pytest (==4.6.0)", "pytest (==4.6.0)", "p name = "yarl" version = "1.9.2" description = "Yet another URL library" -<<<<<<< HEAD -======= category = "dev" ->>>>>>> main optional = false python-versions = ">=3.7" files = [ @@ -8745,25 +5945,6 @@ multidict = ">=4.0" [[package]] name = "zipp" -<<<<<<< HEAD -version = "3.15.0" -description = "Backport of pathlib-compatible object wrapper for zip files" -optional = false -python-versions = ">=3.7" -files = [ - {file = "zipp-3.15.0-py3-none-any.whl", hash = "sha256:48904fc76a60e542af151aded95726c1a5c34ed43ab4134b597665c86d7ad556"}, - {file = "zipp-3.15.0.tar.gz", hash = "sha256:112929ad649da941c23de50f356a2b5570c954b65150642bccdd66bf194d224b"}, -] - -[package.extras] -docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] - -[metadata] -lock-version = "2.0" -python-versions = ">=3.8,<3.12" -content-hash = "89e1c92d6b5c1d789412ed7a2763ef243989adb7fe7ee1606989f60789435deb" -======= version = "3.16.2" description = "Backport of pathlib-compatible object wrapper for zip files" category = "main" @@ -8781,5 +5962,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.12" -content-hash = "5579512ea30793a8d3ea7fe9e486e2635226caa0bf5415b173f650751c8b3016" ->>>>>>> main +content-hash = "c52a0d48d00cff72530de1dd1e2b4b432f3b0b7ed3f642f8490052329f8c1f3d" From c02e7524b367d2d45f70c89395339284659a95b3 Mon Sep 17 00:00:00 2001 From: hmoss <32096840+henrymoss@users.noreply.github.com> Date: Mon, 18 Sep 2023 16:58:04 +0100 Subject: [PATCH 12/23] no more Professor Plum --- docs/examples/latent_force_modelling.ipynb | 150 +++++++++++++++++++++ poetry.lock | 29 +--- pyproject.toml | 1 - 3 files changed, 155 insertions(+), 25 deletions(-) create mode 100644 docs/examples/latent_force_modelling.ipynb diff --git a/docs/examples/latent_force_modelling.ipynb b/docs/examples/latent_force_modelling.ipynb new file mode 100644 index 000000000..8b1e38672 --- /dev/null +++ b/docs/examples/latent_force_modelling.ipynb @@ -0,0 +1,150 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 12, + "id": "ef19b262", + "metadata": {}, + "outputs": [], + "source": [ + "from jax.config import config\n", + "\n", + "config.update(\"jax_enable_x64\", True)\n", + "from dataclasses import dataclass\n", + "\n", + "from jax import hessian\n", + "from jax.config import config\n", + "import jax.numpy as jnp\n", + "import jax.random as jr\n", + "from gpjax.base import param_field\n", + "from jaxtyping import (\n", + " Array,\n", + " Float,\n", + " install_import_hook,\n", + ")\n", + "from gpjax.typing import ScalarFloat\n", + "from matplotlib import rcParams\n", + "import matplotlib.pyplot as plt\n", + "import optax as ox\n", + "import pandas as pd\n", + "import tensorflow_probability as tfp\n", + "\n", + "\n", + "with install_import_hook(\"gpjax\", \"beartype.beartype\"):\n", + " import gpjax as gpx\n", + "\n", + "# Enable Float64 for more stable matrix inversions.\n", + "key = jr.PRNGKey(123)\n", + "plt.style.use(\n", + " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", + ")\n", + "colors = rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]" + ] + }, + { + "cell_type": "markdown", + "id": "ce989771", + "metadata": {}, + "source": [ + "# Linear Response Models\n", + "\n", + "$$\n", + "\\frac{dx}{dt} = B + Sf(t) - Dx(t) \\quad \\mid \\quad x(0) = \\frac{B}{D}\n", + "$$\n", + "\n", + "Model $f(t)$ as a Gaussian process with kernel $k_{f}(t,t')$. As the differential equation contains only linear operations, then $x(t)$ is also a Gaussian process with a different kernel function $k_{x}(t,t')$ which we now derive.\n", + "\n", + "Using the initial condition, solving the differential equation yields\n", + "\n", + "$$\n", + "x(t) = \\frac{B}{D} + S e^{-Dt}\\int_{0}^tf(u)e^{Du}du\n", + "$$\n", + "\n", + "and so \n", + "\n", + "$$\n", + "k_x(t,t') = \\textrm{Cov}(x(t), x(t'))=S^2e^{-D(t+t')}\\int_{0}^t\\int_{0}^{t'}e^{D(u+u')}k_f(u,u')dudu'\n", + "$$\n", + "\n", + "which, for an RBF kernel, can be calculated in closed form" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "20d5c1e0", + "metadata": {}, + "outputs": [], + "source": [ + "@dataclass\n", + "class LinearResponseKernel(gpx.kernels.AbstractKernel):\n", + " latent_force_kernel: gpx.kernels.RBF = gpx.kernels.RBF()\n", + " S: ScalarFloat = param_field(jnp.array(1.0))\n", + " B: ScalarFloat = param_field(jnp.array(1.0))\n", + " D: ScalarFloat = param_field(jnp.array(1.0))\n", + " \n", + " def __post_init__(self):\n", + " if not isinstance(self.latent_force_kernel, gpx.kernels.RBF):\n", + " raise NotImplementedError(\"We only support RBF kernels ATM\")\n", + " \n", + " def __call__(\n", + " self, x: Float[Array, \"1 D\"], y: Float[Array, \"1 D\"]\n", + " ) -> Float[Array, \"1\"]:\n", + " \n", + " k = self.latent_force_kernel(x,y)\n", + " l = self.latent_force_kernel.lengthscale\n", + " variance = self.latent_force_kernel.variance\n", + " \n", + " h_0 = jax.scipy.special.erf ((x-y)/l - gamma)\n", + " h_0 += jax.scipy.special.erf (y/l + gamma)\n", + " h_0 *= jnp.exp(-D*(y-x))\n", + " h_1 = jax.scipy.special.erf (x/l - gamma)\n", + " h_1 += jax.scipy.special.erf (gamma)\n", + " h_1 *= jnp.exp(-D(x+1))\n", + " \n", + " h = h_0 - h_1\n", + " h *= (jnp.exp(gamma)**2) / (2*D)\n", + " \n", + " K = variance * (self.S**2) * jnp.sqrt(math.pi) * l * h\n", + " return K.squeeze()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0db0d322", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "acacdfea", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gpjax", + "language": "python", + "name": "gpjax" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/poetry.lock b/poetry.lock index d32a77dc2..567f0f8ac 100644 --- a/poetry.lock +++ b/poetry.lock @@ -3753,25 +3753,6 @@ files = [ dev = ["pre-commit", "tox"] testing = ["pytest", "pytest-benchmark"] -[[package]] -name = "plum-dispatch" -version = "2.2.1" -description = "Multiple dispatch in Python" -category = "main" -optional = false -python-versions = ">=3.8" -files = [ - {file = "plum_dispatch-2.2.1-py3-none-any.whl", hash = "sha256:2dea636e5a423f76a6f1900b3ba947db635f67db713dffa446b43c74a72b035b"}, - {file = "plum_dispatch-2.2.1.tar.gz", hash = "sha256:71ee4f95b02abd2aef608d7112b4e6d42f6cf96c9af0a6c1ba5f713f8491be69"}, -] - -[package.dependencies] -beartype = "*" -typing-extensions = {version = "*", markers = "python_version <= \"3.10\""} - -[package.extras] -dev = ["black (==22.10.0)", "build", "coveralls", "ghp-import", "ipython", "jupyter-book", "mypy", "numpy", "pre-commit", "pyright", "pytest (>=6)", "pytest-cov", "tox", "wheel"] - [[package]] name = "pre-commit" version = "3.4.0" @@ -5945,21 +5926,21 @@ multidict = ">=4.0" [[package]] name = "zipp" -version = "3.16.2" +version = "3.17.0" description = "Backport of pathlib-compatible object wrapper for zip files" category = "main" optional = false python-versions = ">=3.8" files = [ - {file = "zipp-3.16.2-py3-none-any.whl", hash = "sha256:679e51dd4403591b2d6838a48de3d283f3d188412a9782faadf845f298736ba0"}, - {file = "zipp-3.16.2.tar.gz", hash = "sha256:ebc15946aa78bd63458992fc81ec3b6f7b1e92d51c35e6de1c3804e73b799147"}, + {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"}, + {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"}, ] [package.extras] -docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"] testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.12" -content-hash = "c52a0d48d00cff72530de1dd1e2b4b432f3b0b7ed3f642f8490052329f8c1f3d" +content-hash = "5c342cf795dccc324ad1e03d42001f5d11b3de86728c9512f2a84f23bb279219" diff --git a/pyproject.toml b/pyproject.toml index e8c795dae..7832ce3f5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,7 +23,6 @@ tqdm = "^4.65.0" simple-pytree = "^0.1.7" tensorflow-probability = "^0.19.0" beartype = "^0.13.1" -plum-dispatch = "^2.1.0" jaxopt = "^0.8" jax = ">=0.4.10" jaxlib = ">=0.4.10" From 3f73d198a7157d487441c5a59f7ba3d5b949b1ff Mon Sep 17 00:00:00 2001 From: hmoss <32096840+henrymoss@users.noreply.github.com> Date: Mon, 18 Sep 2023 16:58:38 +0100 Subject: [PATCH 13/23] no --- docs/examples/latent_force_modelling.ipynb | 150 --------------------- 1 file changed, 150 deletions(-) delete mode 100644 docs/examples/latent_force_modelling.ipynb diff --git a/docs/examples/latent_force_modelling.ipynb b/docs/examples/latent_force_modelling.ipynb deleted file mode 100644 index 8b1e38672..000000000 --- a/docs/examples/latent_force_modelling.ipynb +++ /dev/null @@ -1,150 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 12, - "id": "ef19b262", - "metadata": {}, - "outputs": [], - "source": [ - "from jax.config import config\n", - "\n", - "config.update(\"jax_enable_x64\", True)\n", - "from dataclasses import dataclass\n", - "\n", - "from jax import hessian\n", - "from jax.config import config\n", - "import jax.numpy as jnp\n", - "import jax.random as jr\n", - "from gpjax.base import param_field\n", - "from jaxtyping import (\n", - " Array,\n", - " Float,\n", - " install_import_hook,\n", - ")\n", - "from gpjax.typing import ScalarFloat\n", - "from matplotlib import rcParams\n", - "import matplotlib.pyplot as plt\n", - "import optax as ox\n", - "import pandas as pd\n", - "import tensorflow_probability as tfp\n", - "\n", - "\n", - "with install_import_hook(\"gpjax\", \"beartype.beartype\"):\n", - " import gpjax as gpx\n", - "\n", - "# Enable Float64 for more stable matrix inversions.\n", - "key = jr.PRNGKey(123)\n", - "plt.style.use(\n", - " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", - ")\n", - "colors = rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]" - ] - }, - { - "cell_type": "markdown", - "id": "ce989771", - "metadata": {}, - "source": [ - "# Linear Response Models\n", - "\n", - "$$\n", - "\\frac{dx}{dt} = B + Sf(t) - Dx(t) \\quad \\mid \\quad x(0) = \\frac{B}{D}\n", - "$$\n", - "\n", - "Model $f(t)$ as a Gaussian process with kernel $k_{f}(t,t')$. As the differential equation contains only linear operations, then $x(t)$ is also a Gaussian process with a different kernel function $k_{x}(t,t')$ which we now derive.\n", - "\n", - "Using the initial condition, solving the differential equation yields\n", - "\n", - "$$\n", - "x(t) = \\frac{B}{D} + S e^{-Dt}\\int_{0}^tf(u)e^{Du}du\n", - "$$\n", - "\n", - "and so \n", - "\n", - "$$\n", - "k_x(t,t') = \\textrm{Cov}(x(t), x(t'))=S^2e^{-D(t+t')}\\int_{0}^t\\int_{0}^{t'}e^{D(u+u')}k_f(u,u')dudu'\n", - "$$\n", - "\n", - "which, for an RBF kernel, can be calculated in closed form" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "20d5c1e0", - "metadata": {}, - "outputs": [], - "source": [ - "@dataclass\n", - "class LinearResponseKernel(gpx.kernels.AbstractKernel):\n", - " latent_force_kernel: gpx.kernels.RBF = gpx.kernels.RBF()\n", - " S: ScalarFloat = param_field(jnp.array(1.0))\n", - " B: ScalarFloat = param_field(jnp.array(1.0))\n", - " D: ScalarFloat = param_field(jnp.array(1.0))\n", - " \n", - " def __post_init__(self):\n", - " if not isinstance(self.latent_force_kernel, gpx.kernels.RBF):\n", - " raise NotImplementedError(\"We only support RBF kernels ATM\")\n", - " \n", - " def __call__(\n", - " self, x: Float[Array, \"1 D\"], y: Float[Array, \"1 D\"]\n", - " ) -> Float[Array, \"1\"]:\n", - " \n", - " k = self.latent_force_kernel(x,y)\n", - " l = self.latent_force_kernel.lengthscale\n", - " variance = self.latent_force_kernel.variance\n", - " \n", - " h_0 = jax.scipy.special.erf ((x-y)/l - gamma)\n", - " h_0 += jax.scipy.special.erf (y/l + gamma)\n", - " h_0 *= jnp.exp(-D*(y-x))\n", - " h_1 = jax.scipy.special.erf (x/l - gamma)\n", - " h_1 += jax.scipy.special.erf (gamma)\n", - " h_1 *= jnp.exp(-D(x+1))\n", - " \n", - " h = h_0 - h_1\n", - " h *= (jnp.exp(gamma)**2) / (2*D)\n", - " \n", - " K = variance * (self.S**2) * jnp.sqrt(math.pi) * l * h\n", - " return K.squeeze()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0db0d322", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "acacdfea", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "gpjax", - "language": "python", - "name": "gpjax" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From c43672efe7f3935edbd3d21b8072d8dec0197b07 Mon Sep 17 00:00:00 2001 From: hmoss <32096840+henrymoss@users.noreply.github.com> Date: Tue, 19 Sep 2023 11:21:21 +0100 Subject: [PATCH 14/23] undo notebook update --- docs/examples/barycentres.ipynb | 374 +++++++ docs/examples/bayesian_optimisation.ipynb | 1001 ++++++++++++++++++ docs/examples/classification.ipynb | 679 ++++++++++++ docs/examples/constructing_new_kernels.ipynb | 480 +++++++++ docs/examples/decision_making.ipynb | 668 ++++++++++++ docs/examples/oceanmodelling.ipynb | 880 +++++++++++++++ docs/examples/regression.ipynb | 639 +++++++++++ docs/examples/spatial.ipynb | 556 ++++++++++ docs/examples/yacht.ipynb | 493 +++++++++ 9 files changed, 5770 insertions(+) create mode 100644 docs/examples/barycentres.ipynb create mode 100644 docs/examples/bayesian_optimisation.ipynb create mode 100644 docs/examples/classification.ipynb create mode 100644 docs/examples/constructing_new_kernels.ipynb create mode 100644 docs/examples/decision_making.ipynb create mode 100644 docs/examples/oceanmodelling.ipynb create mode 100644 docs/examples/regression.ipynb create mode 100644 docs/examples/spatial.ipynb create mode 100644 docs/examples/yacht.ipynb diff --git a/docs/examples/barycentres.ipynb b/docs/examples/barycentres.ipynb new file mode 100644 index 000000000..044051365 --- /dev/null +++ b/docs/examples/barycentres.ipynb @@ -0,0 +1,374 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e1fa004d", + "metadata": {}, + "source": [ + "# Gaussian Processes Barycentres\n", + "\n", + "In this notebook we'll give an implementation of\n", + ". In this work, the existence of a\n", + "Wasserstein barycentre between a collection of Gaussian processes is proven. When\n", + "faced with trying to _average_ a set of probability distributions, the Wasserstein\n", + "barycentre is an attractive choice as it enables uncertainty amongst the individual\n", + "distributions to be incorporated into the averaged distribution. When compared to a\n", + "naive _mean of means_ and _mean of variances_ approach to computing the average\n", + "probability distributions, it can be seen that Wasserstein barycentres offer\n", + "significantly more favourable uncertainty estimation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74043851", + "metadata": {}, + "outputs": [], + "source": [ + "# Enable Float64 for more stable matrix inversions.\n", + "from jax.config import config\n", + "\n", + "config.update(\"jax_enable_x64\", True)\n", + "\n", + "import typing as tp\n", + "\n", + "import jax\n", + "import jax.numpy as jnp\n", + "import jax.random as jr\n", + "import jax.scipy.linalg as jsl\n", + "from jaxtyping import install_import_hook\n", + "import matplotlib.pyplot as plt\n", + "import jaxopt\n", + "import tensorflow_probability.substrates.jax.distributions as tfd\n", + "\n", + "with install_import_hook(\"gpjax\", \"beartype.beartype\"):\n", + " import gpjax as gpx\n", + "\n", + "\n", + "key = jr.PRNGKey(123)\n", + "plt.style.use(\n", + " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", + ")\n", + "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]" + ] + }, + { + "cell_type": "markdown", + "id": "516ee1ba", + "metadata": {}, + "source": [ + "## Background\n", + "\n", + "### Wasserstein distance\n", + "\n", + "The 2-Wasserstein distance metric between two probability measures $\\mu$ and $\\nu$\n", + "quantifies the minimal cost required to transport the unit mass from $\\mu$ to $\\nu$,\n", + "or vice-versa. Typically, computing this metric requires solving a linear program.\n", + "However, when $\\mu$ and $\\nu$ both belong to the family of multivariate Gaussian\n", + "distributions, the solution is analytically given by\n", + "$$W_2^2(\\mu, \\nu) = \\lVert m_1- m_2 \\rVert^2_2 + \\operatorname{Tr}(S_1 + S_2 - 2(S_1^{1/2}S_2S_1^{1/2})^{1/2}),$$\n", + "where $\\mu \\sim \\mathcal{N}(m_1, S_1)$ and $\\nu\\sim\\mathcal{N}(m_2, S_2)$.\n", + "\n", + "### Wasserstein barycentre\n", + "\n", + "For a collection of $T$ measures\n", + "$\\lbrace\\mu_i\\rbrace_{t=1}^T \\in \\mathcal{P}_2(\\theta)$, the Wasserstein barycentre\n", + "$\\bar{\\mu}$ is the measure that minimises the average Wasserstein distance to all\n", + "other measures in the set. More formally, the Wasserstein barycentre is the Fréchet\n", + "mean on a Wasserstein space that we can write as\n", + "$$\\bar{\\mu} = \\operatorname{argmin}_{\\mu\\in\\mathcal{P}_2(\\theta)}\\sum_{t=1}^T \\alpha_t W_2^2(\\mu, \\mu_t),$$\n", + "where $\\alpha\\in\\mathbb{R}^T$ is a weight vector that sums to 1.\n", + "\n", + "As with the Wasserstein distance, identifying the Wasserstein barycentre $\\bar{\\mu}$\n", + "is often an computationally demanding optimisation problem. However, when all the\n", + "measures admit a multivariate Gaussian density, the barycentre\n", + "$\\bar{\\mu} = \\mathcal{N}(\\bar{m}, \\bar{S})$ has analytical solutions\n", + "$$\\bar{m} = \\sum_{t=1}^T \\alpha_t m_t\\,, \\quad \\bar{S}=\\sum_{t=1}^T\\alpha_t (\\bar{S}^{1/2}S_t\\bar{S}^{1/2})^{1/2}\\,. \\qquad (\\star)$$\n", + "Identifying $\\bar{S}$ is achieved through a fixed-point iterative update.\n", + "\n", + "## Barycentre of Gaussian processes\n", + "\n", + "It was shown in that the\n", + "barycentre $\\bar{f}$ of a collection of Gaussian processes\n", + "$\\lbrace f_i\\rbrace_{i=1}^T$ such that $f_i \\sim \\mathcal{GP}(m_i, K_i)$ can be\n", + "found using the same solutions as in $(\\star)$. For a full theoretical understanding,\n", + "we recommend reading the original paper. However, the central argument to this result\n", + "is that one can first show that the barycentre GP\n", + "$\\bar{f}\\sim\\mathcal{GP}(\\bar{m}, \\bar{S})$ is non-degenerate for any finite set of\n", + "GPs $\\lbrace f_t\\rbrace_{t=1}^T$ i.e., $T<\\infty$. With this established, one can\n", + "show that for a $n$-dimensional finite Gaussian distribution $f_{i,n}$, the\n", + "Wasserstein metric between any two Gaussian distributions $f_{i, n}, f_{j, n}$\n", + "converges to the Wasserstein metric between GPs as $n\\to\\infty$.\n", + "\n", + "In this notebook, we will demonstrate how this can be achieved in GPJax.\n", + "\n", + "## Dataset\n", + "\n", + "We'll simulate five datasets and develop a Gaussian process posterior before\n", + "identifying the Gaussian process barycentre at a set of test points. Each dataset\n", + "will be a sine function with a different vertical shift, periodicity, and quantity\n", + "of noise." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "688925e0", + "metadata": {}, + "outputs": [], + "source": [ + "n = 100\n", + "n_test = 200\n", + "n_datasets = 5\n", + "\n", + "x = jnp.linspace(-5.0, 5.0, n).reshape(-1, 1)\n", + "xtest = jnp.linspace(-5.5, 5.5, n_test).reshape(-1, 1)\n", + "f = lambda x, a, b: a + jnp.sin(b * x)\n", + "\n", + "ys = []\n", + "for _i in range(n_datasets):\n", + " key, subkey = jr.split(key)\n", + " vertical_shift = jr.uniform(subkey, minval=0.0, maxval=2.0)\n", + " period = jr.uniform(subkey, minval=0.75, maxval=1.25)\n", + " noise_amount = jr.uniform(subkey, minval=0.01, maxval=0.5)\n", + " noise = jr.normal(subkey, shape=x.shape) * noise_amount\n", + " ys.append(f(x, vertical_shift, period) + noise)\n", + "\n", + "y = jnp.hstack(ys)\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.plot(x, y, \"x\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4c7a1bda", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "## Learning a posterior distribution\n", + "\n", + "We'll now independently learn Gaussian process posterior distributions for each\n", + "dataset. We won't spend any time here discussing how GP hyperparameters are\n", + "optimised. For advice on achieving this, see the\n", + "[Regression notebook](https://docs.jaxgaussianprocesses.com/examples/regression/)\n", + "for advice on optimisation and the\n", + "[Kernels notebook](https://docs.jaxgaussianprocesses.com/examples/constructing_new_kernels/) for\n", + "advice on selecting an appropriate kernel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2dfc4e75", + "metadata": {}, + "outputs": [], + "source": [ + "def fit_gp(x: jax.Array, y: jax.Array) -> tfd.MultivariateNormalFullCovariance:\n", + " if y.ndim == 1:\n", + " y = y.reshape(-1, 1)\n", + " D = gpx.Dataset(X=x, y=y)\n", + "\n", + " likelihood = gpx.Gaussian(num_datapoints=n)\n", + " posterior = gpx.Prior(mean_function=gpx.Constant(), kernel=gpx.RBF()) * likelihood\n", + " opt_posterior, _ = gpx.fit(\n", + " model=posterior,\n", + " train_data=D,\n", + " solver=jaxopt.LBFGS(gpx.ConjugateMLL(negative=True), maxiter=500),\n", + " key=key,\n", + " )\n", + " latent_dist = opt_posterior.predict(xtest, train_data=D)\n", + " return opt_posterior.likelihood(latent_dist)\n", + "\n", + "\n", + "posterior_preds = [fit_gp(x, i) for i in ys]" + ] + }, + { + "cell_type": "markdown", + "id": "d6ac3f91", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "## Computing the barycentre\n", + "\n", + "In GPJax, the predictive distribution of a GP is given by a\n", + "[TensorFlow Probability](https://www.tensorflow.org/probability/api_docs/python/tfp/substrates/jax)\n", + "distribution, making it\n", + "straightforward to extract the mean vector and covariance matrix of each GP for\n", + "learning a barycentre. We implement the fixed point scheme given in (3) in the\n", + "following cell by utilising Jax's `vmap` operator to speed up large matrix operations\n", + "using broadcasting in `tensordot`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1ee7d96", + "metadata": {}, + "outputs": [], + "source": [ + "def sqrtm(A: jax.Array):\n", + " return jnp.real(jsl.sqrtm(A))\n", + "\n", + "\n", + "def wasserstein_barycentres(\n", + " distributions: tp.List[tfd.MultivariateNormalFullCovariance], weights: jax.Array\n", + "):\n", + " covariances = [d.covariance() for d in distributions]\n", + " cov_stack = jnp.stack(covariances)\n", + " stack_sqrt = jax.vmap(sqrtm)(cov_stack)\n", + "\n", + " def step(covariance_candidate: jax.Array, idx: None):\n", + " inner_term = jax.vmap(sqrtm)(\n", + " jnp.matmul(jnp.matmul(stack_sqrt, covariance_candidate), stack_sqrt)\n", + " )\n", + " fixed_point = jnp.tensordot(weights, inner_term, axes=1)\n", + " return fixed_point, fixed_point\n", + "\n", + " return step" + ] + }, + { + "cell_type": "markdown", + "id": "124a7863", + "metadata": {}, + "source": [ + "With a function defined for learning a barycentre, we'll now compute it using the\n", + "`lax.scan` operator that drastically speeds up for loops in Jax (see the\n", + "[Jax documentation](https://jax.readthedocs.io/en/latest/_autosummary/jax.lax.scan.html)).\n", + "The iterative update will be executed 100 times, with convergence measured by the\n", + "difference between the previous and current iteration that we can confirm by\n", + "inspecting the `sequence` array in the following cell." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f04e3d34", + "metadata": {}, + "outputs": [], + "source": [ + "weights = jnp.ones((n_datasets,)) / n_datasets\n", + "\n", + "means = jnp.stack([d.mean() for d in posterior_preds])\n", + "barycentre_mean = jnp.tensordot(weights, means, axes=1)\n", + "\n", + "step_fn = jax.jit(wasserstein_barycentres(posterior_preds, weights))\n", + "initial_covariance = jnp.eye(n_test)\n", + "\n", + "barycentre_covariance, sequence = jax.lax.scan(\n", + " step_fn, initial_covariance, jnp.arange(100)\n", + ")\n", + "L = jnp.linalg.cholesky(barycentre_covariance)\n", + "\n", + "barycentre_process = tfd.MultivariateNormalTriL(barycentre_mean, L)" + ] + }, + { + "cell_type": "markdown", + "id": "6dba8bc3", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "## Plotting the result\n", + "\n", + "With a barycentre learned, we can visualise the result. We can see that the result\n", + "looks reasonable as it follows the sinusoidal curve of all the inferred GPs, and the\n", + "uncertainty bands are sensible." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f4e1d48", + "metadata": {}, + "outputs": [], + "source": [ + "def plot(\n", + " dist: tfd.MultivariateNormalTriL,\n", + " ax,\n", + " color: str,\n", + " label: str = None,\n", + " ci_alpha: float = 0.2,\n", + " linewidth: float = 1.0,\n", + " zorder: int = 0,\n", + "):\n", + " mu = dist.mean()\n", + " sigma = dist.stddev()\n", + " ax.plot(xtest, mu, linewidth=linewidth, color=color, label=label, zorder=zorder)\n", + " ax.fill_between(\n", + " xtest.squeeze(),\n", + " mu - sigma,\n", + " mu + sigma,\n", + " alpha=ci_alpha,\n", + " color=color,\n", + " zorder=zorder,\n", + " )\n", + "\n", + "\n", + "fig, ax = plt.subplots()\n", + "[plot(d, ax, color=cols[1], ci_alpha=0.1) for d in posterior_preds]\n", + "plot(\n", + " barycentre_process,\n", + " ax,\n", + " color=cols[0],\n", + " label=\"Barycentre\",\n", + " ci_alpha=0.5,\n", + " linewidth=2,\n", + " zorder=1,\n", + ")\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "375695b8", + "metadata": {}, + "source": [ + "## Displacement interpolation\n", + "\n", + "In the above example, we assigned uniform weights to each of the posteriors within\n", + "the barycentre. In practice, we may have prior knowledge of which posterior is most\n", + "likely to be the correct one. Regardless of the weights chosen, the barycentre\n", + "remains a Gaussian process. We can interpolate between a pair of posterior\n", + "distributions $\\mu_1$ and $\\mu_2$ to visualise the corresponding barycentre\n", + "$\\bar{\\mu}$.\n", + "\n", + "![](barycentre_gp.gif)" + ] + }, + { + "cell_type": "markdown", + "id": "3357c374", + "metadata": {}, + "source": [ + "## System configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0832f02c", + "metadata": {}, + "outputs": [], + "source": [ + "%reload_ext watermark\n", + "%watermark -n -u -v -iv -w -a 'Thomas Pinder'" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/bayesian_optimisation.ipynb b/docs/examples/bayesian_optimisation.ipynb new file mode 100644 index 000000000..120591c39 --- /dev/null +++ b/docs/examples/bayesian_optimisation.ipynb @@ -0,0 +1,1001 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4d968859", + "metadata": {}, + "source": [ + "# Introduction to Bayesian Optimisation\n", + "\n", + "In this guide we introduce the Bayesian Optimisation (BO) paradigm for\n", + "optimising black-box functions. We'll assume an understanding of Gaussian processes\n", + "(GPs), so if you're not familiar with them, check out our [GP introduction notebook](https://docs.jaxgaussianprocesses.com/examples/intro_to_gps/)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4fe5efe1", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "# Enable Float64 for more stable matrix inversions.\n", + "from jax.config import config\n", + "\n", + "config.update(\"jax_enable_x64\", True)\n", + "\n", + "import jax\n", + "from jax import jit\n", + "import jax.numpy as jnp\n", + "import jax.random as jr\n", + "from jaxtyping import install_import_hook, Float, Int\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import cm\n", + "import jaxopt\n", + "import tensorflow_probability.substrates.jax as tfp\n", + "from typing import List, Tuple\n", + "\n", + "with install_import_hook(\"gpjax\", \"beartype.beartype\"):\n", + " import gpjax as gpx\n", + "from gpjax.typing import Array, FunctionalSample, ScalarFloat\n", + "from jaxopt import ScipyBoundedMinimize\n", + "\n", + "key = jr.PRNGKey(42)\n", + "plt.style.use(\n", + " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", + ")\n", + "cols = mpl.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]" + ] + }, + { + "cell_type": "markdown", + "id": "af145b9b", + "metadata": {}, + "source": [ + "## Some Motivating Examples\n", + "\n", + "Countless problems in the physical world involve optimising functions for which the\n", + "explicit functional form is unknown, but which can be expensively queried throughout\n", + "their domain. For example, within the domain of science the task of designing new\n", + "molecules with optimised properties ([Griffiths and Lobato,\n", + "2020](https://pubs.rsc.org/en/content/articlehtml/2019/sc/c9sc04026a)) is incredibly\n", + "useful. Here, the domain being optimised over is the space of possible molecules, with\n", + "the objective function depending on the property being optimised, for instance within\n", + "drug-design this may be the efficacy of the drug. The function from molecules to\n", + "efficacy is unknown, but can be queried by synthesising a molecule and running an\n", + "experiment to measure its efficacy. This is clearly an expensive procedure!\n", + "\n", + "Within the domain of machine learning, the task of optimising neural network\n", + "architectures is another example of such a problem (commonly referred to as [Neural\n", + "Architecture Search (NAS)](https://en.wikipedia.org/wiki/Neural_architecture_search)).\n", + "Here, the domain is the space of possible neural network architectures, and the\n", + "objective function is a metric such as the accuracy of the trained model. Again, the\n", + "function from neural network architectures to accuracy is unknown, but can be queried by\n", + "training a model with a given architecture and evaluating its accuracy. This is also an\n", + "expensive procedure, as training models can be incredibly time consuming and\n", + "computationally demanding.\n", + "\n", + "Finally, these problems are ubiquitous within the field of climate science, with\n", + "([Hellan et al., 2023](https://arxiv.org/abs/2306.04343)) providing several excellent\n", + "examples. One such example is the task of deciding where to place wind turbines in a\n", + "wind farm in order to maximise the energy generated. Here, the domain is the space of\n", + "possible locations for the wind turbines, and the objective function is the energy\n", + "generated by the wind farm. The function from locations to energy generated is unknown,\n", + "but could be queried by running a simulation of the wind farm with the turbines placed\n", + "at a given set of locations. Running such simulations can be expensive, particularly if\n", + "they are high-fidelity.\n", + "\n", + "At the heart of all these problems is the task of optimising a function for which we\n", + "don't have the explicit functional form, but which we can (expensively) query at any\n", + "point in its domain. Bayesian optimisation provides a principled framework for solving\n", + "such problems." + ] + }, + { + "cell_type": "markdown", + "id": "8b786ba9", + "metadata": {}, + "source": [ + "## What is Bayesian Optimisation?\n", + "\n", + "Bayesian optimisation (BO) ([Močkus, 1974](https://link.springer.com/chapter/10.1007/3-540-07165-2_55)) provides a principled\n", + "method for making decisions under uncertainty. The aim of BO is to find the global\n", + "minimum of a *black-box* objective function, $\\min_{\\mathbf{x} \\in X}\n", + "f(\\mathbf{x})$. The function $f$ is said to be a *black-box* function because its\n", + "explicit functional form is unknown. However, it is assumed that one is able to\n", + "ascertain information about the function by evaluating it at points in its domain,\n", + "$X$. However, these evaluations are assumed to be *expensive*, as seen in the\n", + "motivating examples. Therefore, the goal of BO is to minimise $f$ with as few\n", + "evaluations of the black-box function as possible.\n", + "\n", + "As such, BO can be thought of as *sequential decision-making* problem. At each iteration\n", + "one must choose which point (or batch of points) in a function's domain to evaluate\n", + "next, drawing on previously observed values to make optimal decisions. In order to do\n", + "this effectively, we need a way of representing our uncertainty about the black-box\n", + "function $f$, which we can update in light of observing more data. Gaussian processes\n", + "will be an ideal tool for this purpose!\n", + "\n", + "*Surrogate models* lie at the heart of BO, and are used to model the black-box\n", + "function. GPs are a natural choice for this model, as they not only provide point\n", + "estimates for the values taken by the function throughout its domain, but crucially\n", + "provide a full predictive posterior *distribution* of the range of values the function\n", + "may take. This rich quantification of uncertainty enables BO to balance *exploration*\n", + "and *exploitation* in order to efficiently converge upon minima.\n", + "\n", + "Having chosen a surrogate model, which we can use to express our current beliefs about\n", + "the black-box function, ideally we would like a method which can use the surrogate\n", + "model's posterior distribution to automatically decide which point(s) in the black-box\n", + "function's domain to query next. This is where *acquisition functions* come in. The\n", + "acquisition function $\\alpha: X \\to \\mathbb{R}$ is defined over the same domain as the\n", + "surrogate model, and uses the surrogate model's posterior distribution to quantify the\n", + "expected *utility*, $U$, of evaluating the black-box function at a given point. Simply\n", + "put, for each point in the black-box function's domain, $\\mathbf{x} \\in X$, the\n", + "acquisition function quantifies how useful it would be to evaluate the black-box\n", + "function at $\\mathbf{x}$ in order to find the minimum of the black-box function, whilst\n", + "taking into consideration all the datapoints observed so far. Therefore, in order to\n", + "decide which point to query next we simply choose the point which maximises the\n", + "acquisition function, using an optimiser such as L-BFGS ([Liu and Nocedal,\n", + "1989](https://link.springer.com/article/10.1007/BF01589116)).\n", + "\n", + "The Bayesian optimisation loop can be summarised as follows, with $i$ denoting the\n", + "current iteration:\n", + "\n", + "1. Select the next point to query, $\\mathbf{x}_{i}$, by maximising the acquisition function $\\alpha$, defined using the surrogate model $\\mathcal{M}_i$ conditioned on previously observed data $\\mathcal{D}_i$:\n", + "\n", + "$$\\mathbf{x}_{i} = \\arg\\max_{\\mathbf{x}} \\alpha (\\mathbf{x}; \\mathcal{D}_i,\n", + "\\mathcal{M}_i)$$\n", + "\n", + "2. Evaluate the objective function at $\\mathbf{x}_i$, yielding observation $y_i =\n", + " f(\\mathbf{x}_i)$.\n", + "\n", + "3. Append the most recent observation to the dataset, $\\mathcal{D}_{i+1} = \\mathcal{D}_i\n", + " \\cup \\{(\\mathbf{x}_i, y_i)\\}$.\n", + "\n", + "4. Condition the model on the updated dataset to yield $\\mathcal{M}_{i+1}$.\n", + "\n", + "This process is repeated until some stopping criterion is met, such as a function\n", + "evaluation budget being exhausted.\n", + "\n", + "There are a plethora of acquisition functions to choose from, each with their own\n", + "advantages and disadvantages, of which ([Shahriari et al., 2015](https://www.cs.ox.ac.uk/people/nando.defreitas/publications/BayesOptLoop.pdf))\n", + "provides an excellent overview.\n", + "\n", + "In this guide we will focus on *Thompson sampling*, a conceptually simple yet effective\n", + "method for characterising the utility of querying points in a black-box function's\n", + "domain, which will be useful in demonstrating the key aspects of BO." + ] + }, + { + "cell_type": "markdown", + "id": "f84f9ba5", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "## Thompson Sampling\n", + "\n", + "Thompson sampling ([Thompson, 1933](https://www.dropbox.com/s/yhn9prnr5bz0156/1933-thompson.pdf)) is a simple method which\n", + "naturally balances exploration and exploitation. The core idea is to, at each iteration\n", + "of the BO loop, sample a function, $g$, from the posterior distribution of the surrogate\n", + "model $\\mathcal{M}_i$, and then evaluate the black-box function at the point(s) which\n", + "minimise this sample. Given a sample $g$, from the posterior distribution given by the model $\\mathcal{M}_i$ the Thompson sampling utility function is defined as:\n", + "\n", + "$$U_{\\text{TS}}(\\mathbf{x}; \\mathcal{D}_i, \\mathcal{M}_i) = - g(\\mathbf{x})$$\n", + "\n", + "Note the negative sign; this is included as we want to maximise the *utility* of\n", + "evaluating the black-box function $f$ at a given point. We interested in finding the\n", + "minimum of $f$, so we maximise the negative of the sample from the posterior distribution $g$.\n", + "\n", + "As a toy example, we shall be applying BO to the widely used [Forrester\n", + "function](https://www.sfu.ca/~ssurjano/forretal08.html):\n", + "\n", + "$$f(x) = (6x - 2)^2 \\sin(12x - 4)$$\n", + "\n", + "treating $f$ as a black-box function. Moreover, we shall restrict the domain of the\n", + "function to $\\mathbf{x} \\in [0, 1]$. The global minimum of this function is located at\n", + "$x = 0.757$, where $f(x) = -6.021$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "db5649b9", + "metadata": {}, + "outputs": [], + "source": [ + "def forrester(x: Float[Array, \"N 1\"]) -> Float[Array, \"N 1\"]:\n", + " return (6 * x - 2) ** 2 * jnp.sin(12 * x - 4)" + ] + }, + { + "cell_type": "markdown", + "id": "9e706c6c", + "metadata": {}, + "source": [ + "We'll first go through one iteration of the BO loop step-by-step, before wrapping this\n", + "up in a loop to perform the full optimisation." + ] + }, + { + "cell_type": "markdown", + "id": "6cc22184", + "metadata": {}, + "source": [ + "First we'll specify the domain over which we wish to optimise the function, as well as\n", + "sampling some initial points for fitting our surrogate model using a space-filling design." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46fb9d07", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "lower_bound = jnp.array([0.0])\n", + "upper_bound = jnp.array([1.0])\n", + "initial_sample_num = 5\n", + "\n", + "initial_x = tfp.mcmc.sample_halton_sequence(\n", + " dim=1, num_results=initial_sample_num, seed=key, dtype=jnp.float64\n", + ").reshape(-1, 1)\n", + "initial_y = forrester(initial_x)\n", + "D = gpx.Dataset(X=initial_x, y=initial_y)" + ] + }, + { + "cell_type": "markdown", + "id": "c378817d", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "Next we'll define our GP model in the usual way, using a Matérn52 kernel, and fit the\n", + "kernel parameters by minimising the negative log-marginal likelihood. We'll wrap this in\n", + "a function as we'll be repeating this process at each iteration of the BO loop." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8afd4de", + "metadata": {}, + "outputs": [], + "source": [ + "def return_optimised_posterior(\n", + " data: gpx.Dataset, prior: gpx.Module, key: Array\n", + ") -> gpx.Module:\n", + " likelihood = gpx.Gaussian(\n", + " num_datapoints=data.n, obs_noise=jnp.array(1e-6)\n", + " ) # Our function is noise-free, so we set the observation noise to a very small value\n", + " likelihood = likelihood.replace_trainable(obs_noise=False)\n", + "\n", + " posterior = prior * likelihood\n", + "\n", + " negative_mll = gpx.objectives.ConjugateMLL(negative=True)\n", + " negative_mll(posterior, train_data=data)\n", + " negative_mll = jit(negative_mll)\n", + "\n", + " opt_posterior, history = gpx.fit(\n", + " model=posterior,\n", + " train_data=D,\n", + " solver=jaxopt.LBFGS(gpx.ConjugateMLL(negative=True), maxiter=500),\n", + " safe=True,\n", + " key=key,\n", + " verbose=False,\n", + " )\n", + "\n", + " return opt_posterior\n", + "\n", + "\n", + "mean = gpx.mean_functions.Zero()\n", + "kernel = gpx.kernels.Matern52()\n", + "prior = gpx.Prior(mean_function=mean, kernel=kernel)\n", + "opt_posterior = return_optimised_posterior(D, prior, key)" + ] + }, + { + "cell_type": "markdown", + "id": "5a761c1e", + "metadata": {}, + "source": [ + "We can then sample a function from the posterior distribution of the surrogate model. We\n", + "will do this using the `sample_approx` method, which generates an approximate sample\n", + "from the posterior using decoupled sampling introduced in ([Wilson et al.,\n", + "2020](https://proceedings.mlr.press/v119/wilson20a.html)) and discussed in our [Pathwise\n", + "Sampling Notebook](https://docs.jaxgaussianprocesses.com/examples/spatial/). This method\n", + "is used as it enables us to sample from the posterior in a manner which scales linearly\n", + "with the number of points sampled, $O(N)$, mitigating the cubic cost associated with\n", + "drawing exact samples from a GP posterior, $O(N^3)$. It also generates more accurate\n", + "samples than many other methods for drawing approximate samples from a GP posterior.\n", + "\n", + "Note that we also define a `utility_fn` which calls the approximate\n", + "sample but returns the value returned as a scalar. This is because the `sample_approx`\n", + "function returns an array of shape $[N, B]$, with $N$ being the number of points within\n", + "each sample and $B$ being the number of samples drawn. We'll only be drawing (and\n", + "optimising) one sample at a time, and our optimiser requires the function being\n", + "optimised to return a scalar output (only querying it at $N=1$ points), so we'll remove the axes from the returned value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d700a73", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "approx_sample = opt_posterior.sample_approx(\n", + " num_samples=1, train_data=D, key=key, num_features=500\n", + ")\n", + "utility_fn = lambda x: approx_sample(x)[0][0]" + ] + }, + { + "cell_type": "markdown", + "id": "0a9739b8", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "In order to minimise the sample, we'll be using the L-BFGS-B ([Byrd et al., 1995](https://epubs.siam.org/doi/abs/10.1137/0916069)) optimiser from the `jaxopt`\n", + "library. This is a gradient-based optimiser which performs optimisation within a bounded\n", + "domain. In order to perform optimisation, this optimiser requires a point to start from.\n", + "Therefore, we will first query our sample from the posterior at a random set of points,\n", + "and then use the lowest point from this set of points as the starting point for the\n", + "optimiser. In this example we'll sample 100 points from the posterior, due to the simple\n", + "nature of the Forrester function. However, in practice it can be beneficial to\n", + "adopt a more sophisticated approach, and there are several heuristics available in the\n", + "literature (see for example ([Le Riche and Picheny,\n", + "2021](https://arxiv.org/abs/2103.16649))). For instance, one may randomly sample the\n", + "posterior at a number of points proportional to the dimensionality of the input space,\n", + "and one may run gradient-based optimisation from multiple of these points, to reduce the\n", + "risk of converging upon local minima." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01770354", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "def optimise_sample(\n", + " sample: FunctionalSample,\n", + " key: Int[Array, \"\"],\n", + " lower_bound: Float[Array, \"D\"],\n", + " upper_bound: Float[Array, \"D\"],\n", + " num_initial_sample_points: int,\n", + ") -> ScalarFloat:\n", + " initial_sample_points = jr.uniform(\n", + " key,\n", + " shape=(num_initial_sample_points, lower_bound.shape[0]),\n", + " dtype=jnp.float64,\n", + " minval=lower_bound,\n", + " maxval=upper_bound,\n", + " )\n", + " initial_sample_y = sample(initial_sample_points)\n", + " best_x = jnp.array([initial_sample_points[jnp.argmin(initial_sample_y)]])\n", + "\n", + " # We want to maximise the utility function, but the optimiser performs minimisation. Since we're minimising the sample drawn, the sample is actually the negative utility function.\n", + " negative_utility_fn = lambda x: sample(x)[0][0]\n", + " lbfgsb = ScipyBoundedMinimize(fun=negative_utility_fn, method=\"l-bfgs-b\")\n", + " bounds = (lower_bound, upper_bound)\n", + " x_star = lbfgsb.run(best_x, bounds=bounds).params\n", + " return x_star\n", + "\n", + "\n", + "x_star = optimise_sample(approx_sample, key, lower_bound, upper_bound, 100)\n", + "y_star = forrester(x_star)" + ] + }, + { + "cell_type": "markdown", + "id": "78ec19f0", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "Having found the minimum of the sample from the posterior, we can then evaluate the\n", + "black-box objective function at this point, and append the new observation to our dataset.\n", + "\n", + "Below we plot the posterior distribution of the surrogate model, along with the sample\n", + "drawn from the model, and the minimiser of this sample returned from the optimiser,\n", + "which we denote with a star." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f77e39b1", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_bayes_opt(\n", + " posterior: gpx.Module,\n", + " sample: FunctionalSample,\n", + " dataset: gpx.Dataset,\n", + " queried_x: ScalarFloat,\n", + ") -> None:\n", + " plt_x = jnp.linspace(0, 1, 1000).reshape(-1, 1)\n", + " forrester_y = forrester(plt_x)\n", + " sample_y = sample(plt_x)\n", + "\n", + " latent_dist = posterior.predict(plt_x, train_data=dataset)\n", + " predictive_dist = posterior.likelihood(latent_dist)\n", + "\n", + " predictive_mean = predictive_dist.mean()\n", + " predictive_std = predictive_dist.stddev()\n", + "\n", + " fig, ax = plt.subplots()\n", + " ax.plot(plt_x, predictive_mean, label=\"Predictive Mean\", color=cols[1])\n", + " ax.fill_between(\n", + " plt_x.squeeze(),\n", + " predictive_mean - 2 * predictive_std,\n", + " predictive_mean + 2 * predictive_std,\n", + " alpha=0.2,\n", + " label=\"Two sigma\",\n", + " color=cols[1],\n", + " )\n", + " ax.plot(\n", + " plt_x,\n", + " predictive_mean - 2 * predictive_std,\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " color=cols[1],\n", + " )\n", + " ax.plot(\n", + " plt_x,\n", + " predictive_mean + 2 * predictive_std,\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " color=cols[1],\n", + " )\n", + " ax.plot(plt_x, sample_y, label=\"Posterior Sample\")\n", + " ax.plot(\n", + " plt_x,\n", + " forrester_y,\n", + " label=\"Forrester Function\",\n", + " color=cols[0],\n", + " linestyle=\"--\",\n", + " linewidth=2,\n", + " )\n", + " ax.axvline(x=0.757, linestyle=\":\", color=cols[3], label=\"True Optimum\")\n", + " ax.scatter(dataset.X, dataset.y, label=\"Observations\", color=cols[2], zorder=2)\n", + " ax.scatter(\n", + " queried_x,\n", + " sample(queried_x),\n", + " label=\"Posterior Sample Optimum\",\n", + " marker=\"*\",\n", + " color=cols[3],\n", + " zorder=3,\n", + " )\n", + " ax.legend(loc=\"center left\", bbox_to_anchor=(0.975, 0.5))\n", + " plt.show()\n", + "\n", + "\n", + "plot_bayes_opt(opt_posterior, approx_sample, D, x_star)" + ] + }, + { + "cell_type": "markdown", + "id": "35b1f294", + "metadata": {}, + "source": [ + "At this point we can update our model with the newly augmented dataset, and repeat the\n", + "whole process until some stopping criterion is met. Below we repeat this process for 10\n", + "iterations, printing out the queried point and the value of the black-box function at\n", + "each iteration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3abe88bb", + "metadata": {}, + "outputs": [], + "source": [ + "bo_iters = 5\n", + "\n", + "# Set up initial dataset\n", + "initial_x = tfp.mcmc.sample_halton_sequence(\n", + " dim=1, num_results=initial_sample_num, seed=key, dtype=jnp.float64\n", + ").reshape(-1, 1)\n", + "initial_y = forrester(initial_x)\n", + "D = gpx.Dataset(X=initial_x, y=initial_y)\n", + "\n", + "for i in range(bo_iters):\n", + " key, subkey = jr.split(key)\n", + "\n", + " # Generate optimised posterior using previously observed data\n", + " mean = gpx.mean_functions.Zero()\n", + " kernel = gpx.kernels.Matern52()\n", + " prior = gpx.Prior(mean_function=mean, kernel=kernel)\n", + " opt_posterior = return_optimised_posterior(D, prior, subkey)\n", + "\n", + " # Draw a sample from the posterior, and find the minimiser of it\n", + " approx_sample = opt_posterior.sample_approx(\n", + " num_samples=1, train_data=D, key=subkey, num_features=500\n", + " )\n", + " x_star = optimise_sample(\n", + " approx_sample, subkey, lower_bound, upper_bound, num_initial_sample_points=100\n", + " )\n", + "\n", + " plot_bayes_opt(opt_posterior, approx_sample, D, x_star)\n", + "\n", + " # Evaluate the black-box function at the best point observed so far, and add it to the dataset\n", + " y_star = forrester(x_star)\n", + " print(f\"Queried Point: {x_star}, Black-Box Function Value: {y_star}\")\n", + " D = D + gpx.Dataset(X=x_star, y=y_star)" + ] + }, + { + "cell_type": "markdown", + "id": "3ca0d676", + "metadata": {}, + "source": [ + "Below we plot the best observed black-box function value against the number of times\n", + "the black-box function has been evaluated. Note that the first 5 samples are randomly\n", + "sampled to fit the initial GP model, and we denote the start of using BO to sample with\n", + "the dotted vertical line.\n", + "\n", + "We can see that the BO algorithm quickly converges to the global minimum of the\n", + "black-box function!\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1222d4f5", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "fn_evaluations = jnp.arange(1, bo_iters + initial_sample_num + 1)\n", + "cumulative_best_y = jax.lax.associative_scan(jax.numpy.minimum, D.y)\n", + "ax.plot(fn_evaluations, cumulative_best_y)\n", + "ax.axvline(x=initial_sample_num, linestyle=\":\")\n", + "ax.axhline(y=-6.0207, linestyle=\"--\", label=\"True Minimum\")\n", + "ax.set_xlabel(\"Number of Black-Box Function Evaluations\")\n", + "ax.set_ylabel(\"Best Observed Value\")\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "58299352", + "metadata": {}, + "source": [ + "### A More Challenging Example - The Six-Hump Camel Function" + ] + }, + { + "cell_type": "markdown", + "id": "c9c644c9", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "We'll now apply BO to a more challenging example, the [Six-Hump Camel\n", + "Function](https://www.sfu.ca/~ssurjano/camel6.html). This is a function of two inputs\n", + "defined as follows:\n", + "\n", + "$$f(x_1, x_2) = (4 - 2.1x_1^2 + \\frac{x_1^4}{3})x_1^2 + x_1x_2 + (-4 + 4x_2^2)x_2^2$$\n", + "\n", + "We'll be evaluating it over the domain $x_1 \\in [-2, 2]$ and $x_2 \\in [-1, 1]$. The\n", + "global minima of this function are located at $\\mathbf{x} = (0.0898, -0.7126)$ and $\\mathbf{x} = (-0.0898, 0.7126)$, where the function takes the value $f(\\mathbf{x}) = -1.0316$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e970300b", + "metadata": {}, + "outputs": [], + "source": [ + "def six_hump_camel(x: Float[Array, \"N 2\"]) -> Float[Array, \"N 1\"]:\n", + " x1 = x[..., :1]\n", + " x2 = x[..., 1:]\n", + " term1 = (4 - 2.1 * x1**2 + x1**4 / 3) * x1**2\n", + " term2 = x1 * x2\n", + " term3 = (-4 + 4 * x2**2) * x2**2\n", + " return term1 + term2 + term3" + ] + }, + { + "cell_type": "markdown", + "id": "a101bc98", + "metadata": {}, + "source": [ + "First, we'll visualise the function over the domain of interest:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96a16c59", + "metadata": {}, + "outputs": [], + "source": [ + "x1 = jnp.linspace(-2, 2, 100)\n", + "x2 = jnp.linspace(-1, 1, 100)\n", + "x1, x2 = jnp.meshgrid(x1, x2)\n", + "x = jnp.stack([x1.flatten(), x2.flatten()], axis=1)\n", + "y = six_hump_camel(x)\n", + "\n", + "fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n", + "surf = ax.plot_surface(\n", + " x1,\n", + " x2,\n", + " y.reshape(x1.shape[0], x2.shape[0]),\n", + " linewidth=0,\n", + " cmap=cm.coolwarm,\n", + " antialiased=False,\n", + ")\n", + "ax.set_xlabel(\"x1\")\n", + "ax.set_ylabel(\"x2\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2e90ee91", + "metadata": {}, + "source": [ + "For more clarity, we can generate a contour plot of the function which enables us to see\n", + "the global minima of the function more clearly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8b49fe4", + "metadata": {}, + "outputs": [], + "source": [ + "x_star_one = jnp.array([[0.0898, -0.7126]])\n", + "x_star_two = jnp.array([[-0.0898, 0.7126]])\n", + "fig, ax = plt.subplots()\n", + "contour_plot = ax.contourf(\n", + " x1, x2, y.reshape(x1.shape[0], x2.shape[0]), cmap=cm.coolwarm, levels=40\n", + ")\n", + "ax.scatter(\n", + " x_star_one[0][0], x_star_one[0][1], marker=\"*\", color=cols[2], label=\"Global Minima\"\n", + ")\n", + "ax.scatter(x_star_two[0][0], x_star_two[0][1], marker=\"*\", color=cols[2])\n", + "ax.set_xlabel(\"x1\")\n", + "ax.set_ylabel(\"x2\")\n", + "fig.colorbar(contour_plot)\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f4229fd2", + "metadata": {}, + "source": [ + "Next, we'll run the BO loop using Thompson sampling as before. This time we'll run the\n", + "experiment 5 times in order to see how the algorithm performs on average, with different\n", + "starting points for the initial GP model. This is good practice, as the performance\n", + "obtained is likely to vary between runs depending on the initialisation samples used to\n", + "fit the initial GP model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "605a65d5", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "lower_bound = jnp.array([-2.0, -1.0])\n", + "upper_bound = jnp.array([2.0, 1.0])\n", + "initial_sample_num = 5\n", + "bo_iters = 11\n", + "num_experiments = 5\n", + "bo_experiment_results = []\n", + "\n", + "for experiment in range(num_experiments):\n", + " print(f\"Starting Experiment: {experiment + 1}\")\n", + " # Set up initial dataset\n", + " initial_x = tfp.mcmc.sample_halton_sequence(\n", + " dim=2, num_results=initial_sample_num, seed=key, dtype=jnp.float64\n", + " )\n", + " initial_x = jnp.array(lower_bound + (upper_bound - lower_bound) * initial_x)\n", + " initial_y = six_hump_camel(initial_x)\n", + " D = gpx.Dataset(X=initial_x, y=initial_y)\n", + "\n", + " for i in range(bo_iters):\n", + " key, subkey = jr.split(key)\n", + "\n", + " # Generate optimised posterior\n", + " mean = gpx.mean_functions.Zero()\n", + " kernel = gpx.kernels.Matern52(\n", + " active_dims=[0, 1], lengthscale=jnp.array([1.0, 1.0]), variance=2.0\n", + " )\n", + " prior = gpx.Prior(mean_function=mean, kernel=kernel)\n", + " opt_posterior = return_optimised_posterior(D, prior, subkey)\n", + "\n", + " # Draw a sample from the posterior, and find the minimiser of it\n", + " approx_sample = opt_posterior.sample_approx(\n", + " num_samples=1, train_data=D, key=subkey, num_features=500\n", + " )\n", + " x_star = optimise_sample(\n", + " approx_sample,\n", + " subkey,\n", + " lower_bound,\n", + " upper_bound,\n", + " num_initial_sample_points=1000,\n", + " )\n", + "\n", + " # Evaluate the black-box function at the best point observed so far, and add it to the dataset\n", + " y_star = six_hump_camel(x_star)\n", + " print(\n", + " f\"BO Iteration: {i + 1}, Queried Point: {x_star}, Black-Box Function Value: {y_star}\"\n", + " )\n", + " D = D + gpx.Dataset(X=x_star, y=y_star)\n", + " bo_experiment_results.append(D)" + ] + }, + { + "cell_type": "markdown", + "id": "33ed107c", + "metadata": {}, + "source": [ + "We'll also run a random benchmark, whereby we randomly sample from the search space for\n", + "20 iterations. This is a useful benchmark to compare the performance of BO against in\n", + "order to ascertain how much of an advantage BO provides over such a simple approach.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ed6479e", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "random_experiment_results = []\n", + "for i in range(num_experiments):\n", + " key, subkey = jr.split(key)\n", + " initial_x = bo_experiment_results[i].X[:5]\n", + " initial_y = bo_experiment_results[i].y[:5]\n", + " final_x = jr.uniform(\n", + " key,\n", + " shape=(bo_iters, 2),\n", + " dtype=jnp.float64,\n", + " minval=lower_bound,\n", + " maxval=upper_bound,\n", + " )\n", + " final_y = six_hump_camel(final_x)\n", + " random_x = jnp.concatenate([initial_x, final_x], axis=0)\n", + " random_y = jnp.concatenate([initial_y, final_y], axis=0)\n", + " random_experiment_results.append(gpx.Dataset(X=random_x, y=random_y))" + ] + }, + { + "cell_type": "markdown", + "id": "766bbe7e", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "Finally, we'll process the experiment results to find the log regret at each iteration\n", + "of the experiments. The regret is defined as the difference between the minimum value of\n", + "the black-box function observed so far and the true global minimum of the black box\n", + "function. Mathematically, at time $t$, with observations $\\mathcal{D}_t$, for function\n", + "$f$ with global minimum $f^*$, the regret is defined as:\n", + "\n", + "$$\\text{regret}_t = \\min_{\\mathbf{x} \\in \\mathcal{D_t}}f(\\mathbf{x}) - f^*$$\n", + "\n", + "We'll then take the mean and standard deviation of the log of the regret values across\n", + "the 5 experiments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e143225", + "metadata": {}, + "outputs": [], + "source": [ + "def obtain_log_regret_statistics(\n", + " experiment_results: List[gpx.Dataset],\n", + " global_minimum: ScalarFloat,\n", + ") -> Tuple[Float[Array, \"N 1\"], Float[Array, \"N 1\"]]:\n", + " log_regret_results = []\n", + " for exp_result in experiment_results:\n", + " observations = exp_result.y\n", + " cumulative_best_observations = jax.lax.associative_scan(\n", + " jax.numpy.minimum, observations\n", + " )\n", + " regret = cumulative_best_observations - global_minimum\n", + " log_regret = jnp.log(regret)\n", + " log_regret_results.append(log_regret)\n", + "\n", + " log_regret_results = jnp.array(log_regret_results)\n", + " log_regret_mean = jnp.mean(log_regret_results, axis=0)\n", + " log_regret_std = jnp.std(log_regret_results, axis=0)\n", + " return log_regret_mean, log_regret_std\n", + "\n", + "\n", + "bo_log_regret_mean, bo_log_regret_std = obtain_log_regret_statistics(\n", + " bo_experiment_results, -1.031625\n", + ")\n", + "(\n", + " random_log_regret_mean,\n", + " random_log_regret_std,\n", + ") = obtain_log_regret_statistics(random_experiment_results, -1.031625)" + ] + }, + { + "cell_type": "markdown", + "id": "d0af2a94", + "metadata": {}, + "source": [ + "Now, when we plot the mean and standard deviation of the log regret at each iteration,\n", + "we can see that BO outperforms random sampling!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "147e2db9", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "fn_evaluations = jnp.arange(1, bo_iters + initial_sample_num + 1)\n", + "ax.plot(fn_evaluations, bo_log_regret_mean, label=\"Bayesian Optimisation\")\n", + "ax.fill_between(\n", + " fn_evaluations,\n", + " bo_log_regret_mean[:, 0] - bo_log_regret_std[:, 0],\n", + " bo_log_regret_mean[:, 0] + bo_log_regret_std[:, 0],\n", + " alpha=0.2,\n", + ")\n", + "ax.plot(fn_evaluations, random_log_regret_mean, label=\"Random Search\")\n", + "ax.fill_between(\n", + " fn_evaluations,\n", + " random_log_regret_mean[:, 0] - random_log_regret_std[:, 0],\n", + " random_log_regret_mean[:, 0] + random_log_regret_std[:, 0],\n", + " alpha=0.2,\n", + ")\n", + "ax.axvline(x=initial_sample_num, linestyle=\":\")\n", + "ax.set_xlabel(\"Number of Black-Box Function Evaluations\")\n", + "ax.set_ylabel(\"Log Regret\")\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f0b122c9", + "metadata": {}, + "source": [ + "It can also be useful to plot the queried points over the course of a single BO run, in\n", + "order to gain some insight into how the algorithm queries the search space. Below\n", + "we do this for the first BO experiment, and can see that the algorithm initially\n", + "performs some exploration of the search space whilst it is uncertain about the black-box\n", + "function, but it then hones in one one of the global minima of the function, as we would hope!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa9d9862", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "contour_plot = ax.contourf(\n", + " x1, x2, y.reshape(x1.shape[0], x2.shape[0]), cmap=cm.coolwarm, levels=40\n", + ")\n", + "ax.scatter(\n", + " x_star_one[0][0],\n", + " x_star_one[0][1],\n", + " marker=\"*\",\n", + " color=cols[2],\n", + " label=\"Global Minimum\",\n", + " zorder=2,\n", + ")\n", + "ax.scatter(x_star_two[0][0], x_star_two[0][1], marker=\"*\", color=cols[2], zorder=2)\n", + "ax.scatter(\n", + " bo_experiment_results[0].X[:, 0],\n", + " bo_experiment_results[0].X[:, 1],\n", + " marker=\"x\",\n", + " color=cols[1],\n", + " label=\"Bayesian Optimisation Queries\",\n", + ")\n", + "ax.set_xlabel(\"x1\")\n", + "ax.set_ylabel(\"x2\")\n", + "fig.colorbar(contour_plot)\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "392226d2", + "metadata": {}, + "source": [ + "### Other Acquisition Functions and Further Reading\n", + "\n", + "As mentioned previously, there are many acquisition functions which one may use to\n", + "characterise the expected utility of querying the black-box function at a given point.\n", + "We list two of the most popular below:\n", + "\n", + "- **Probability of Improvement (PI)** ([Kushner, 1964](https://asmedigitalcollection.asme.org/fluidsengineering/article/86/1/97/392213/A-New-Method-of-Locating-the-Maximum-Point-of-an)): Given the lowest objective function observation\n", + " so far, $f(\\mathbf{x}^*)$, PI calculates the probability that the objective function's\n", + " value at a given point $\\mathbf{x}$ is lower than $f(\\mathbf{x}^*)$. Given a GP\n", + " surrogate model $\\mathcal{M}_i$, PI is defined mathematically as:\n", + " $$\n", + " \\alpha_{\\text{PI}}(\\mathbf{x}; \\mathcal{D}_i, \\mathcal{M}_i) = \\mathbb{P}[\\mathcal{M}_i (\\mathbf{x}) < f(\\mathbf{x}^*)] = \\Phi \\left(\\frac{f(\\mathbf{x}^*) - \\mu_{\\mathcal{M}_i}(\\mathbf{x})}{\\sigma_{\\mathcal{M}_i}(\\mathbf{x})}\\right)\n", + " $$\n", + "\n", + " with $\\Phi(\\cdot)$ denoting the standard normal cumulative distribution function.\n", + "\n", + "- **Expected Improvement (EI)** ([Močkus, 1974](https://link.springer.com/chapter/10.1007/3-540-07165-2_55)) - EI goes beyond PI by not only considering the\n", + " probability of improving on the current best observed point, but also taking into\n", + " account the \\textit{magnitude} of improvement. Mathematically, this is defined as\n", + " follows:\n", + " $$\n", + " \\begin{aligned}\n", + " \\alpha_{\\text{EI}}(\\mathbf{x};\\mathcal{D}_i, \\mathcal{M}_i) &= \\mathbb{E}[(f(\\mathbf{x}^*) - \\mathcal{M}_i(\\mathbf{x}))\\mathbb{I}(\\mathcal{M}_i(\\mathbf{x}) < f(\\mathbf{x}^*))] \\\\\n", + " &= \\underbrace{(f(\\mathbf{x}^*) - \\mu_{\\mathcal{M}_i}(\\mathbf{x}))\\Phi\n", + " \\left(\\frac{f(\\mathbf{x}^*) -\n", + " \\mu_{\\mathcal{M}_i}(\\mathbf{x})}{\\sigma_{\\mathcal{M}_i}(\\mathbf{x})}\\right)}_\\text{exploits\n", + " areas with low mean} \\\\\n", + " &+ \\underbrace{\\sigma_{\\mathcal{M}_i}(\\mathbf{x}) \\phi \\left(\\frac{f(\\mathbf{x}^*) - \\mu_{\\mathcal{M}_i}(\\mathbf{x})}{\\sigma_{\\mathcal{M}_i}(\\mathbf{x})}\\right)}_\\text{explores areas with high variance} \\nonumber\n", + " \\end{aligned}\n", + " $$\n", + "\n", + " with $\\mathbb{I}(\\cdot)$ denoting the indicator function and $\\phi(\\cdot)$ being the\n", + " standard normal probability density function.\n", + "\n", + "For those particularly interested in diving deeper into Bayesian optimisation, be sure\n", + "to check out Shahriari et al.'s \"[Taking the Human Out of the Loop:\n", + "A Review of Bayesian\n", + "Optimization](https://www.cs.ox.ac.uk/people/nando.defreitas/publications/BayesOptLoop.pdf)\",\n", + "which includes a wide variety of acquisition functions, as well as some examples of more\n", + "exotic BO problems, such as problems which also feature unknown constraints.\n", + "\n", + "## System Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "872160bd", + "metadata": {}, + "outputs": [], + "source": [ + "%reload_ext watermark\n", + "%watermark -n -u -v -iv -w -a 'Thomas Christie'" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/classification.ipynb b/docs/examples/classification.ipynb new file mode 100644 index 000000000..1a738c75c --- /dev/null +++ b/docs/examples/classification.ipynb @@ -0,0 +1,679 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "14d2bb24", + "metadata": {}, + "source": [ + "# Classification\n", + "\n", + "In this notebook we demonstrate how to perform inference for Gaussian process models\n", + "with non-Gaussian likelihoods via maximum a posteriori (MAP) and Markov chain Monte\n", + "Carlo (MCMC). We focus on a classification task here and use\n", + "[BlackJax](https://github.com/blackjax-devs/blackjax/) for sampling." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90b27dc7", + "metadata": {}, + "outputs": [], + "source": [ + "# Enable Float64 for more stable matrix inversions.\n", + "from jax.config import config\n", + "\n", + "config.update(\"jax_enable_x64\", True)\n", + "\n", + "from time import time\n", + "import blackjax\n", + "import jax\n", + "import jax.numpy as jnp\n", + "import jax.random as jr\n", + "import jax.scipy as jsp\n", + "import jax.tree_util as jtu\n", + "from jaxtyping import (\n", + " Array,\n", + " Float,\n", + " install_import_hook,\n", + ")\n", + "import matplotlib.pyplot as plt\n", + "import jaxopt\n", + "import tensorflow_probability.substrates.jax as tfp\n", + "from tqdm import trange\n", + "\n", + "with install_import_hook(\"gpjax\", \"beartype.beartype\"):\n", + " import gpjax as gpx\n", + "\n", + "tfd = tfp.distributions\n", + "identity_matrix = jnp.eye\n", + "key = jr.PRNGKey(123)\n", + "plt.style.use(\n", + " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", + ")\n", + "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]" + ] + }, + { + "cell_type": "markdown", + "id": "64585152", + "metadata": {}, + "source": [ + "## Dataset\n", + "\n", + "With the necessary modules imported, we simulate a dataset\n", + "$\\mathcal{D} = (\\boldsymbol{x}, \\boldsymbol{y}) = \\{(x_i, y_i)\\}_{i=1}^{100}$ with inputs\n", + "$\\boldsymbol{x}$ sampled uniformly on $(-1., 1)$ and corresponding binary outputs\n", + "\n", + "$$\\boldsymbol{y} = 0.5 * \\text{sign}(\\cos(2 * + \\boldsymbol{\\epsilon})) + 0.5, \\quad \\boldsymbol{\\epsilon} \\sim \\mathcal{N} \\left(\\textbf{0}, \\textbf{I} * (0.05)^{2} \\right).$$\n", + "\n", + "We store our data $\\mathcal{D}$ as a GPJax `Dataset` and create test inputs for\n", + "later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7316ba9", + "metadata": {}, + "outputs": [], + "source": [ + "key, subkey = jr.split(key)\n", + "x = jr.uniform(key, shape=(100, 1), minval=-1.0, maxval=1.0)\n", + "y = 0.5 * jnp.sign(jnp.cos(3 * x + jr.normal(subkey, shape=x.shape) * 0.05)) + 0.5\n", + "\n", + "D = gpx.Dataset(X=x, y=y)\n", + "\n", + "xtest = jnp.linspace(-1.0, 1.0, 500).reshape(-1, 1)\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(x, y)" + ] + }, + { + "cell_type": "markdown", + "id": "b68bb15a", + "metadata": {}, + "source": [ + "## MAP inference\n", + "\n", + "We begin by defining a Gaussian process prior with a radial basis function (RBF)\n", + "kernel, chosen for the purpose of exposition. Since our observations are binary, we\n", + "choose a Bernoulli likelihood with a probit link function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "675bef2a", + "metadata": {}, + "outputs": [], + "source": [ + "kernel = gpx.RBF()\n", + "meanf = gpx.Constant()\n", + "prior = gpx.Prior(mean_function=meanf, kernel=kernel)\n", + "likelihood = gpx.Bernoulli(num_datapoints=D.n)" + ] + }, + { + "cell_type": "markdown", + "id": "bfece9af", + "metadata": {}, + "source": [ + "We construct the posterior through the product of our prior and likelihood." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13a1d1d4", + "metadata": {}, + "outputs": [], + "source": [ + "posterior = prior * likelihood\n", + "print(type(posterior))" + ] + }, + { + "cell_type": "markdown", + "id": "0dd47285", + "metadata": {}, + "source": [ + "Whilst the latent function is Gaussian, the posterior distribution is non-Gaussian\n", + "since our generative model first samples the latent GP and propagates these samples\n", + "through the likelihood function's inverse link function. This step prevents us from\n", + "being able to analytically integrate the latent function's values out of our\n", + "posterior, and we must instead adopt alternative inference techniques. We begin with\n", + "maximum a posteriori (MAP) estimation, a fast inference procedure to obtain point\n", + "estimates for the latent function and the kernel's hyperparameters by maximising the\n", + "marginal log-likelihood." + ] + }, + { + "cell_type": "markdown", + "id": "952ddc62", + "metadata": {}, + "source": [ + "We can obtain a MAP estimate by optimising the log-posterior density with\n", + "`jaxopt` solvers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb003bba", + "metadata": {}, + "outputs": [], + "source": [ + "negative_lpd = jax.jit(gpx.LogPosteriorDensity(negative=True))\n", + "\n", + "opt_posterior, history = gpx.fit(\n", + " model=posterior,\n", + " train_data=D,\n", + " solver=jaxopt.LBFGS(gpx.ConjugateMLL(negative=True), maxiter=500),\n", + " key=key,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "855ba0a3", + "metadata": {}, + "source": [ + "From which we can make predictions at novel inputs, as illustrated below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "76fb1924", + "metadata": {}, + "outputs": [], + "source": [ + "map_latent_dist = opt_posterior.predict(xtest, train_data=D)\n", + "predictive_dist = opt_posterior.likelihood(map_latent_dist)\n", + "\n", + "predictive_mean = predictive_dist.mean()\n", + "predictive_std = predictive_dist.stddev()\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(x, y, label=\"Observations\", color=cols[0])\n", + "ax.plot(xtest, predictive_mean, label=\"Predictive mean\", color=cols[1])\n", + "ax.fill_between(\n", + " xtest.squeeze(),\n", + " predictive_mean - predictive_std,\n", + " predictive_mean + predictive_std,\n", + " alpha=0.2,\n", + " color=cols[1],\n", + " label=\"One sigma\",\n", + ")\n", + "ax.plot(\n", + " xtest,\n", + " predictive_mean - predictive_std,\n", + " color=cols[1],\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + ")\n", + "ax.plot(\n", + " xtest,\n", + " predictive_mean + predictive_std,\n", + " color=cols[1],\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + ")\n", + "\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "bbed07f4", + "metadata": {}, + "source": [ + "Here we projected the map estimates $\\hat{\\boldsymbol{f}}$ for the function values\n", + "$\\boldsymbol{f}$ at the data points $\\boldsymbol{x}$ to get predictions over the\n", + "whole domain,\n", + "\n", + "\\begin{align}\n", + "p(f(\\cdot)| \\mathcal{D}) \\approx q_{map}(f(\\cdot)) := \\int p(f(\\cdot)| \\boldsymbol{f}) \\delta(\\boldsymbol{f} - \\hat{\\boldsymbol{f}}) d \\boldsymbol{f} = \\mathcal{N}(\\mathbf{K}_{\\boldsymbol{(\\cdot)x}} \\mathbf{K}_{\\boldsymbol{xx}}^{-1} \\hat{\\boldsymbol{f}}, \\mathbf{K}_{\\boldsymbol{(\\cdot, \\cdot)}} - \\mathbf{K}_{\\boldsymbol{(\\cdot)\\boldsymbol{x}}} \\mathbf{K}_{\\boldsymbol{xx}}^{-1} \\mathbf{K}_{\\boldsymbol{\\boldsymbol{x}(\\cdot)}}).\n", + "\\end{align}" + ] + }, + { + "cell_type": "markdown", + "id": "7f21a007", + "metadata": {}, + "source": [ + "However, as a point estimate, MAP estimation is severely limited for uncertainty\n", + "quantification, providing only a single piece of information about the posterior." + ] + }, + { + "cell_type": "markdown", + "id": "f97ed9c4", + "metadata": {}, + "source": [ + "## Laplace approximation\n", + "The Laplace approximation improves uncertainty quantification by incorporating\n", + "curvature induced by the marginal log-likelihood's Hessian to construct an\n", + "approximate Gaussian distribution centered on the MAP estimate. Writing\n", + "$\\tilde{p}(\\boldsymbol{f}|\\mathcal{D}) = p(\\boldsymbol{y}|\\boldsymbol{f}) p(\\boldsymbol{f})$\n", + "as the unormalised posterior for function values $\\boldsymbol{f}$ at the datapoints\n", + "$\\boldsymbol{x}$, we can expand the log of this about the posterior mode\n", + "$\\hat{\\boldsymbol{f}}$ via a Taylor expansion. This gives:\n", + "\n", + "\\begin{align}\n", + "\\log\\tilde{p}(\\boldsymbol{f}|\\mathcal{D}) = \\log\\tilde{p}(\\hat{\\boldsymbol{f}}|\\mathcal{D}) + \\left[\\nabla \\log\\tilde{p}({\\boldsymbol{f}}|\\mathcal{D})|_{\\hat{\\boldsymbol{f}}}\\right]^{T} (\\boldsymbol{f}-\\hat{\\boldsymbol{f}}) + \\frac{1}{2} (\\boldsymbol{f}-\\hat{\\boldsymbol{f}})^{T} \\left[\\nabla^2 \\tilde{p}(\\boldsymbol{y}|\\boldsymbol{f})|_{\\hat{\\boldsymbol{f}}} \\right] (\\boldsymbol{f}-\\hat{\\boldsymbol{f}}) + \\mathcal{O}(\\lVert \\boldsymbol{f} - \\hat{\\boldsymbol{f}} \\rVert^3).\n", + "\\end{align}\n", + "\n", + "Since $\\nabla \\log\\tilde{p}({\\boldsymbol{f}}|\\mathcal{D})$ is zero at the mode,\n", + "this suggests the following approximation\n", + "\\begin{align}\n", + "\\tilde{p}(\\boldsymbol{f}|\\mathcal{D}) \\approx \\log\\tilde{p}(\\hat{\\boldsymbol{f}}|\\mathcal{D}) \\exp\\left\\{ \\frac{1}{2} (\\boldsymbol{f}-\\hat{\\boldsymbol{f}})^{T} \\left[-\\nabla^2 \\tilde{p}(\\boldsymbol{y}|\\boldsymbol{f})|_{\\hat{\\boldsymbol{f}}} \\right] (\\boldsymbol{f}-\\hat{\\boldsymbol{f}}) \\right\\}\n", + "\\end{align},\n", + "\n", + "that we identify as a Gaussian distribution,\n", + "$p(\\boldsymbol{f}| \\mathcal{D}) \\approx q(\\boldsymbol{f}) := \\mathcal{N}(\\hat{\\boldsymbol{f}}, [-\\nabla^2 \\tilde{p}(\\boldsymbol{y}|\\boldsymbol{f})|_{\\hat{\\boldsymbol{f}}} ]^{-1} )$.\n", + "Since the negative Hessian is positive definite, we can use the Cholesky\n", + "decomposition to obtain the covariance matrix of the Laplace approximation at the\n", + "datapoints below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67baa6c6", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "import cola\n", + "from gpjax.lower_cholesky import lower_cholesky\n", + "\n", + "gram, cross_covariance = (kernel.gram, kernel.cross_covariance)\n", + "jitter = 1e-6\n", + "\n", + "# Compute (latent) function value map estimates at training points:\n", + "Kxx = opt_posterior.prior.kernel.gram(x)\n", + "Kxx += identity_matrix(D.n) * jitter\n", + "Kxx = cola.PSD(Kxx)\n", + "Lx = lower_cholesky(Kxx)\n", + "f_hat = Lx @ opt_posterior.latent\n", + "\n", + "# Negative Hessian, H = -∇²p_tilde(y|f):\n", + "H = jax.jacfwd(jax.jacrev(negative_lpd))(opt_posterior, D).latent.latent[:, 0, :, 0]\n", + "\n", + "L = jnp.linalg.cholesky(H + identity_matrix(D.n) * jitter)\n", + "\n", + "# H⁻¹ = H⁻¹ I = (LLᵀ)⁻¹ I = L⁻ᵀL⁻¹ I\n", + "L_inv = jsp.linalg.solve_triangular(L, identity_matrix(D.n), lower=True)\n", + "H_inv = jsp.linalg.solve_triangular(L.T, L_inv, lower=False)\n", + "LH = jnp.linalg.cholesky(H_inv)\n", + "laplace_approximation = tfd.MultivariateNormalTriL(f_hat.squeeze(), LH)" + ] + }, + { + "cell_type": "markdown", + "id": "0b0080fe", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "For novel inputs, we must project the above approximating distribution through the\n", + "Gaussian conditional distribution $p(f(\\cdot)| \\boldsymbol{f})$,\n", + "\n", + "\\begin{align}\n", + "p(f(\\cdot)| \\mathcal{D}) \\approx q_{Laplace}(f(\\cdot)) := \\int p(f(\\cdot)| \\boldsymbol{f}) q(\\boldsymbol{f}) d \\boldsymbol{f} = \\mathcal{N}(\\mathbf{K}_{\\boldsymbol{(\\cdot)x}} \\mathbf{K}_{\\boldsymbol{xx}}^{-1} \\hat{\\boldsymbol{f}}, \\mathbf{K}_{\\boldsymbol{(\\cdot, \\cdot)}} - \\mathbf{K}_{\\boldsymbol{(\\cdot)\\boldsymbol{x}}} \\mathbf{K}_{\\boldsymbol{xx}}^{-1} (\\mathbf{K}_{\\boldsymbol{xx}} - [-\\nabla^2 \\tilde{p}(\\boldsymbol{y}|\\boldsymbol{f})|_{\\hat{\\boldsymbol{f}}} ]^{-1}) \\mathbf{K}_{\\boldsymbol{xx}}^{-1} \\mathbf{K}_{\\boldsymbol{\\boldsymbol{x}(\\cdot)}}).\n", + "\\end{align}\n", + "\n", + "This is the same approximate distribution $q_{map}(f(\\cdot))$, but we have perturbed\n", + "the covariance by a curvature term of\n", + "$\\mathbf{K}_{\\boldsymbol{(\\cdot)\\boldsymbol{x}}} \\mathbf{K}_{\\boldsymbol{xx}}^{-1} [-\\nabla^2 \\tilde{p}(\\boldsymbol{y}|\\boldsymbol{f})|_{\\hat{\\boldsymbol{f}}} ]^{-1} \\mathbf{K}_{\\boldsymbol{xx}}^{-1} \\mathbf{K}_{\\boldsymbol{\\boldsymbol{x}(\\cdot)}}$.\n", + "We take the latent distribution computed in the previous section and add this term\n", + "to the covariance to construct $q_{Laplace}(f(\\cdot))$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "867815eb", + "metadata": {}, + "outputs": [], + "source": [ + "def construct_laplace(test_inputs: Float[Array, \"N D\"]) -> tfd.MultivariateNormalTriL:\n", + " map_latent_dist = opt_posterior.predict(xtest, train_data=D)\n", + "\n", + " Kxt = opt_posterior.prior.kernel.cross_covariance(x, test_inputs)\n", + " Kxx = opt_posterior.prior.kernel.gram(x)\n", + " Kxx += identity_matrix(D.n) * jitter\n", + " Kxx = cola.PSD(Kxx)\n", + "\n", + " # Kxx⁻¹ Kxt\n", + " Kxx_inv_Kxt = cola.solve(Kxx, Kxt)\n", + "\n", + " # Ktx Kxx⁻¹[ H⁻¹ ] Kxx⁻¹ Kxt\n", + " laplace_cov_term = jnp.matmul(jnp.matmul(Kxx_inv_Kxt.T, H_inv), Kxx_inv_Kxt)\n", + "\n", + " mean = map_latent_dist.mean()\n", + " covariance = map_latent_dist.covariance() + laplace_cov_term\n", + " L = jnp.linalg.cholesky(covariance)\n", + " return tfd.MultivariateNormalTriL(jnp.atleast_1d(mean.squeeze()), L)" + ] + }, + { + "cell_type": "markdown", + "id": "f9fce917", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "From this we can construct the predictive distribution at the test points." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a56bf0a", + "metadata": {}, + "outputs": [], + "source": [ + "laplace_latent_dist = construct_laplace(xtest)\n", + "predictive_dist = opt_posterior.likelihood(laplace_latent_dist)\n", + "\n", + "predictive_mean = predictive_dist.mean()\n", + "predictive_std = predictive_dist.stddev()\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(x, y, label=\"Observations\", color=cols[0])\n", + "ax.plot(xtest, predictive_mean, label=\"Predictive mean\", color=cols[1])\n", + "ax.fill_between(\n", + " xtest.squeeze(),\n", + " predictive_mean - predictive_std,\n", + " predictive_mean + predictive_std,\n", + " alpha=0.2,\n", + " color=cols[1],\n", + " label=\"One sigma\",\n", + ")\n", + "ax.plot(\n", + " xtest,\n", + " predictive_mean - predictive_std,\n", + " color=cols[1],\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + ")\n", + "ax.plot(\n", + " xtest,\n", + " predictive_mean + predictive_std,\n", + " color=cols[1],\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + ")\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "9a97c9df", + "metadata": {}, + "source": [ + "However, the Laplace approximation is still limited by considering information about\n", + "the posterior at a single location. On the other hand, through approximate sampling,\n", + "MCMC methods allow us to learn all information about the posterior distribution." + ] + }, + { + "cell_type": "markdown", + "id": "c726488c", + "metadata": {}, + "source": [ + "## MCMC inference\n", + "\n", + "An MCMC sampler works by starting at an initial position and\n", + "drawing a sample from a cheap-to-simulate distribution known as the _proposal_. The\n", + "next step is to determine whether this sample could be considered a draw from the\n", + "posterior. We accomplish this using an _acceptance probability_ determined via the\n", + "sampler's _transition kernel_ which depends on the current position and the\n", + "unnormalised target posterior distribution. If the new sample is more _likely_, we\n", + "accept it; otherwise, we reject it and stay in our current position. Repeating these\n", + "steps results in a Markov chain (a random sequence that depends only on the last\n", + "state) whose stationary distribution (the long-run empirical distribution of the\n", + "states visited) is the posterior. For a gentle introduction, see the first chapter\n", + "of [A Handbook of Markov Chain Monte Carlo](https://www.mcmchandbook.net/HandbookChapter1.pdf).\n", + "\n", + "### MCMC through BlackJax\n", + "\n", + "Rather than implementing a suite of MCMC samplers, GPJax relies on MCMC-specific\n", + "libraries for sampling functionality. We focus on\n", + "[BlackJax](https://github.com/blackjax-devs/blackjax/) in this notebook, which we\n", + "recommend adopting for general applications.\n", + "\n", + "We'll use the No U-Turn Sampler (NUTS) implementation given in BlackJax for sampling.\n", + "For the interested reader, NUTS is a Hamiltonian Monte Carlo sampling scheme where\n", + "the number of leapfrog integration steps is computed at each step of the change\n", + "according to the NUTS algorithm. In general, samplers constructed under this\n", + "framework are very efficient.\n", + "\n", + "We begin by generating _sensible_ initial positions for our sampler before defining\n", + "an inference loop and sampling 500 values from our Markov chain. In practice,\n", + "drawing more samples will be necessary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ba459dc", + "metadata": {}, + "outputs": [], + "source": [ + "num_adapt = 500\n", + "num_samples = 500\n", + "\n", + "lpd = jax.jit(gpx.LogPosteriorDensity(negative=False))\n", + "unconstrained_lpd = jax.jit(lambda tree: lpd(tree.constrain(), D))\n", + "\n", + "adapt = blackjax.window_adaptation(\n", + " blackjax.nuts, unconstrained_lpd, num_adapt, target_acceptance_rate=0.65\n", + ")\n", + "\n", + "# Initialise the chain\n", + "start = time()\n", + "last_state, kernel, _ = adapt.run(key, posterior.unconstrain())\n", + "print(f\"Adaption time taken: {time() - start: .1f} seconds\")\n", + "\n", + "\n", + "def inference_loop(rng_key, kernel, initial_state, num_samples):\n", + " def one_step(state, rng_key):\n", + " state, info = kernel(rng_key, state)\n", + " return state, (state, info)\n", + "\n", + " keys = jax.random.split(rng_key, num_samples)\n", + " _, (states, infos) = jax.lax.scan(one_step, initial_state, keys)\n", + "\n", + " return states, infos\n", + "\n", + "\n", + "# Sample from the posterior distribution\n", + "start = time()\n", + "states, infos = inference_loop(key, kernel, last_state, num_samples)\n", + "print(f\"Sampling time taken: {time() - start: .1f} seconds\")" + ] + }, + { + "cell_type": "markdown", + "id": "d38ebbea", + "metadata": {}, + "source": [ + "### Sampler efficiency\n", + "\n", + "BlackJax gives us easy access to our sampler's efficiency through metrics such as the\n", + "sampler's _acceptance probability_ (the number of times that our chain accepted a\n", + "proposed sample, divided by the total number of steps run by the chain). For NUTS and\n", + "Hamiltonian Monte Carlo sampling, we typically seek an acceptance rate of 60-70% to\n", + "strike the right balance between having a chain which is _stuck_ and rarely moves\n", + "versus a chain that is too jumpy with frequent small steps." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9fa0d91", + "metadata": {}, + "outputs": [], + "source": [ + "acceptance_rate = jnp.mean(infos.acceptance_probability)\n", + "print(f\"Acceptance rate: {acceptance_rate:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "cd357372", + "metadata": {}, + "source": [ + "Our acceptance rate is slightly too large, prompting an examination of the chain's\n", + "trace plots. A well-mixing chain will have very few (if any) flat spots in its trace\n", + "plot whilst also not having too many steps in the same direction. In addition to\n", + "the model's hyperparameters, there will be 500 samples for each of the 100 latent\n", + "function values in the `states.position` dictionary. We depict the chains that\n", + "correspond to the model hyperparameters and the first value of the latent function\n", + "for brevity." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ef48b0a", + "metadata": {}, + "outputs": [], + "source": [ + "fig, (ax0, ax1, ax2) = plt.subplots(ncols=3, figsize=(10, 3))\n", + "ax0.plot(states.position.prior.kernel.lengthscale)\n", + "ax1.plot(states.position.prior.kernel.variance)\n", + "ax2.plot(states.position.latent[:, 1, :])\n", + "ax0.set_title(\"Kernel Lengthscale\")\n", + "ax1.set_title(\"Kernel Variance\")\n", + "ax2.set_title(\"Latent Function (index = 1)\")" + ] + }, + { + "cell_type": "markdown", + "id": "3e737c51", + "metadata": {}, + "source": [ + "## Prediction\n", + "\n", + "Having obtained samples from the posterior, we draw ten instances from our model's\n", + "predictive distribution per MCMC sample. Using these draws, we will be able to\n", + "compute credible values and expected values under our posterior distribution.\n", + "\n", + "An ideal Markov chain would have samples completely uncorrelated with their\n", + "neighbours after a single lag. However, in practice, correlations often exist\n", + "within our chain's sample set. A commonly used technique to try and reduce this\n", + "correlation is _thinning_ whereby we select every $n$th sample where $n$ is the\n", + "minimum lag length at which we believe the samples are uncorrelated. Although further\n", + "analysis of the chain's autocorrelation is required to find appropriate thinning\n", + "factors, we employ a thin factor of 10 for demonstration purposes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4566d7fe", + "metadata": {}, + "outputs": [], + "source": [ + "thin_factor = 20\n", + "posterior_samples = []\n", + "\n", + "for i in trange(0, num_samples, thin_factor, desc=\"Drawing posterior samples\"):\n", + " sample = jtu.tree_map(lambda samples, i=i: samples[i], states.position)\n", + " sample = sample.constrain()\n", + " latent_dist = sample.predict(xtest, train_data=D)\n", + " predictive_dist = sample.likelihood(latent_dist)\n", + " posterior_samples.append(predictive_dist.sample(seed=key, sample_shape=(10,)))\n", + "\n", + "posterior_samples = jnp.vstack(posterior_samples)\n", + "lower_ci, upper_ci = jnp.percentile(posterior_samples, jnp.array([2.5, 97.5]), axis=0)\n", + "expected_val = jnp.mean(posterior_samples, axis=0)" + ] + }, + { + "cell_type": "markdown", + "id": "e873d8f6", + "metadata": {}, + "source": [ + "\n", + "Finally, we end this tutorial by plotting the predictions obtained from our model\n", + "against the observed data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a880e0cd", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.scatter(x, y, color=cols[0], label=\"Observations\", zorder=2, alpha=0.7)\n", + "ax.plot(xtest, expected_val, color=cols[1], label=\"Predicted mean\", zorder=1)\n", + "ax.fill_between(\n", + " xtest.flatten(),\n", + " lower_ci.flatten(),\n", + " upper_ci.flatten(),\n", + " alpha=0.2,\n", + " color=cols[1],\n", + " label=\"95\\\\% CI\",\n", + ")\n", + "ax.plot(\n", + " xtest,\n", + " lower_ci.flatten(),\n", + " color=cols[1],\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + ")\n", + "ax.plot(\n", + " xtest,\n", + " upper_ci.flatten(),\n", + " color=cols[1],\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + ")\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "c89f0691", + "metadata": {}, + "source": [ + "## System configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78e217ee", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext watermark\n", + "%watermark -n -u -v -iv -w -a \"Thomas Pinder & Daniel Dodd\"" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "custom_cell_magics": "kql", + "encoding": "# -*- coding: utf-8 -*-" + }, + "kernelspec": { + "display_name": "gpjax", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/constructing_new_kernels.ipynb b/docs/examples/constructing_new_kernels.ipynb new file mode 100644 index 000000000..9bd2dd7a1 --- /dev/null +++ b/docs/examples/constructing_new_kernels.ipynb @@ -0,0 +1,480 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e11cfcf0", + "metadata": {}, + "source": [ + "# Kernel Guide\n", + "\n", + "In this guide, we introduce the kernels available in GPJax and demonstrate how to\n", + "create custom kernels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5e9ad19", + "metadata": {}, + "outputs": [], + "source": [ + "# Enable Float64 for more stable matrix inversions.\n", + "from jax.config import config\n", + "\n", + "config.update(\"jax_enable_x64\", True)\n", + "\n", + "from dataclasses import dataclass\n", + "from typing import Dict\n", + "\n", + "from jax import jit\n", + "import jax.numpy as jnp\n", + "import jax.random as jr\n", + "from jaxtyping import (\n", + " Array,\n", + " Float,\n", + " install_import_hook,\n", + ")\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import optax as ox\n", + "import jaxopt\n", + "from simple_pytree import static_field\n", + "import tensorflow_probability.substrates.jax as tfp\n", + "\n", + "with install_import_hook(\"gpjax\", \"beartype.beartype\"):\n", + " import gpjax as gpx\n", + " from gpjax.base.param import param_field\n", + "\n", + "key = jr.PRNGKey(123)\n", + "tfb = tfp.bijectors\n", + "plt.style.use(\n", + " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", + ")\n", + "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]" + ] + }, + { + "cell_type": "markdown", + "id": "bdccbdf4", + "metadata": {}, + "source": [ + "## Supported Kernels\n", + "\n", + "The following kernels are natively supported in GPJax.\n", + "\n", + "* Matérn 1/2, 3/2 and 5/2.\n", + "* RBF (or squared exponential).\n", + "* Rational quadratic.\n", + "* Powered exponential.\n", + "* Polynomial.\n", + "* White noise\n", + "* Linear.\n", + "* Polynomial.\n", + "* [Graph kernels](https://docs.jaxgaussianprocesses.com/examples/graph_kernels/).\n", + "\n", + "While the syntax is consistent, each kernel's type influences the\n", + "characteristics of the sample paths drawn. We visualise this below with 10\n", + "function draws per kernel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9717f825", + "metadata": {}, + "outputs": [], + "source": [ + "kernels = [\n", + " gpx.kernels.Matern12(),\n", + " gpx.kernels.Matern32(),\n", + " gpx.kernels.Matern52(),\n", + " gpx.kernels.RBF(),\n", + " gpx.kernels.Polynomial(),\n", + " gpx.kernels.Polynomial(degree=2),\n", + "]\n", + "fig, axes = plt.subplots(ncols=3, nrows=2, figsize=(10, 6), tight_layout=True)\n", + "\n", + "x = jnp.linspace(-3.0, 3.0, num=200).reshape(-1, 1)\n", + "\n", + "meanf = gpx.mean_functions.Zero()\n", + "\n", + "for k, ax in zip(kernels, axes.ravel()):\n", + " prior = gpx.Prior(mean_function=meanf, kernel=k)\n", + " rv = prior(x)\n", + " y = rv.sample(seed=key, sample_shape=(10,))\n", + " ax.plot(x, y.T, alpha=0.7)\n", + " ax.set_title(k.name)" + ] + }, + { + "cell_type": "markdown", + "id": "f06b48b7", + "metadata": {}, + "source": [ + "### Active dimensions\n", + "\n", + "By default, kernels operate over every dimension of the supplied inputs. In\n", + "some use cases, it is desirable to restrict kernels to specific dimensions of\n", + "the input data. We can achieve this by the `active dims` argument, which\n", + "determines which input index values the kernel evaluates.\n", + "\n", + "To see this, consider the following 5-dimensional dataset for which we would\n", + "like our RBF kernel to act on the first, second and fourth dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65198906", + "metadata": {}, + "outputs": [], + "source": [ + "slice_kernel = gpx.kernels.RBF(active_dims=[0, 1, 3], lengthscale=jnp.ones((3,)))" + ] + }, + { + "cell_type": "markdown", + "id": "47511074", + "metadata": {}, + "source": [ + "\n", + "The resulting kernel has one length-scale parameter per input dimension --- an ARD kernel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d090532b", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Lengthscales: {slice_kernel.lengthscale}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ec08bffa", + "metadata": {}, + "source": [ + "We'll now simulate some data and evaluate the kernel on the previously selected\n", + "input dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "870d9a53", + "metadata": {}, + "outputs": [], + "source": [ + "# Inputs\n", + "x_matrix = jr.normal(key, shape=(50, 5))\n", + "\n", + "# Compute the Gram matrix\n", + "K = slice_kernel.gram(x_matrix)\n", + "print(K.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "3cdda74e", + "metadata": {}, + "source": [ + "## Kernel combinations\n", + "\n", + "The product or sum of two positive definite matrices yields a positive\n", + "definite matrix. Consequently, summing or multiplying sets of kernels is a\n", + "valid operation that can give rich kernel functions. In GPJax, functionality for\n", + "a sum kernel is provided by the `SumKernel` class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c9ffa75", + "metadata": {}, + "outputs": [], + "source": [ + "k1 = gpx.kernels.RBF()\n", + "k2 = gpx.kernels.Polynomial()\n", + "sum_k = gpx.kernels.SumKernel(kernels=[k1, k2])\n", + "\n", + "fig, ax = plt.subplots(ncols=3, figsize=(9, 3))\n", + "im0 = ax[0].matshow(k1.gram(x).to_dense())\n", + "im1 = ax[1].matshow(k2.gram(x).to_dense())\n", + "im2 = ax[2].matshow(sum_k.gram(x).to_dense())\n", + "\n", + "fig.colorbar(im0, ax=ax[0], fraction=0.05)\n", + "fig.colorbar(im1, ax=ax[1], fraction=0.05)\n", + "fig.colorbar(im2, ax=ax[2], fraction=0.05)" + ] + }, + { + "cell_type": "markdown", + "id": "bfbd2124", + "metadata": {}, + "source": [ + "Similarly, products of kernels can be created through the `ProductKernel` class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c2dd213", + "metadata": {}, + "outputs": [], + "source": [ + "k3 = gpx.kernels.Matern32()\n", + "\n", + "prod_k = gpx.kernels.ProductKernel(kernels=[k1, k2, k3])\n", + "\n", + "fig, ax = plt.subplots(ncols=4, figsize=(12, 3))\n", + "im0 = ax[0].matshow(k1.gram(x).to_dense())\n", + "im1 = ax[1].matshow(k2.gram(x).to_dense())\n", + "im2 = ax[2].matshow(k3.gram(x).to_dense())\n", + "im3 = ax[3].matshow(prod_k.gram(x).to_dense())\n", + "\n", + "fig.colorbar(im0, ax=ax[0], fraction=0.05)\n", + "fig.colorbar(im1, ax=ax[1], fraction=0.05)\n", + "fig.colorbar(im2, ax=ax[2], fraction=0.05)\n", + "fig.colorbar(im3, ax=ax[3], fraction=0.05)" + ] + }, + { + "cell_type": "markdown", + "id": "425c97d1", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "## Custom kernel\n", + "\n", + "GPJax makes the process of implementing kernels of your choice straightforward\n", + "with two key steps:\n", + "\n", + "1. Listing the kernel's parameters.\n", + "2. Defining the kernel's pairwise operation.\n", + "\n", + "We'll demonstrate this process now for a circular kernel --- an adaption of\n", + "the excellent guide given in the PYMC3 documentation. We encourage curious\n", + "readers to visit their notebook\n", + "[here](https://www.pymc.io/projects/docs/en/v3/pymc-examples/examples/gaussian_processes/GP-Circular.html).\n", + "\n", + "### Circular kernel\n", + "\n", + "When the underlying space is polar, typical Euclidean kernels such as Matérn\n", + "kernels are insufficient at the boundary where discontinuities will present\n", + "themselves.\n", + "This is due to the fact that for a polar space $\\lvert 0, 2\\pi\\rvert=0$ i.e.,\n", + "the space wraps. Euclidean kernels have no mechanism in them to represent this\n", + "logic and will instead treat $0$ and $2\\pi$ and elements far apart. Circular\n", + "kernels do not exhibit this behaviour and instead _wrap_ around the boundary\n", + "points to create a smooth function. Such a kernel was given in [Padonou &\n", + "Roustant (2015)](https://hal.inria.fr/hal-01119942v1) where any two angles\n", + "$\\theta$ and $\\theta'$ are written as $$W_c(\\theta, \\theta') = \\left\\lvert\n", + "\\left(1 + \\tau \\frac{d(\\theta, \\theta')}{c} \\right) \\left(1 - \\frac{d(\\theta,\n", + "\\theta')}{c} \\right)^{\\tau} \\right\\rvert \\quad \\tau \\geq 4 \\tag{1}.$$\n", + "\n", + "Here the hyperparameter $\\tau$ is analogous to a lengthscale for Euclidean\n", + "stationary kernels, controlling the correlation between pairs of observations.\n", + "While $d$ is an angular distance metric\n", + "\n", + "$$d(\\theta, \\theta') = \\lvert (\\theta-\\theta'+c) \\operatorname{mod} 2c - c\n", + "\\rvert.$$\n", + "\n", + "To implement this, one must write the following class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b731f56", + "metadata": {}, + "outputs": [], + "source": [ + "def angular_distance(x, y, c):\n", + " return jnp.abs((x - y + c) % (c * 2) - c)\n", + "\n", + "\n", + "bij = tfb.Chain([tfb.Softplus(), tfb.Shift(np.array(4.0).astype(np.float64))])\n", + "\n", + "\n", + "@dataclass\n", + "class Polar(gpx.kernels.AbstractKernel):\n", + " period: float = static_field(2 * jnp.pi)\n", + " tau: float = param_field(jnp.array([4.0]), bijector=bij)\n", + "\n", + " def __call__(\n", + " self, x: Float[Array, \"1 D\"], y: Float[Array, \"1 D\"]\n", + " ) -> Float[Array, \"1\"]:\n", + " c = self.period / 2.0\n", + " t = angular_distance(x, y, c)\n", + " K = (1 + self.tau * t / c) * jnp.clip(1 - t / c, 0, jnp.inf) ** self.tau\n", + " return K.squeeze()" + ] + }, + { + "cell_type": "markdown", + "id": "c795379e", + "metadata": {}, + "source": [ + "We unpack this now to make better sense of it. In the kernel's initialiser\n", + "we specify the length of a single period. As the underlying\n", + "domain is a circle, this is $2\\pi$. We then define the kernel's `__call__`\n", + "function which is a direct implementation of Equation (1) where we define `c`\n", + "as half the value of `period`.\n", + "\n", + "To constrain $\\tau$ to be greater than 4, we use a `Softplus` bijector with a\n", + "clipped lower bound of 4.0. This is done by specifying the `bijector` argument\n", + "when we define the parameter field." + ] + }, + { + "cell_type": "markdown", + "id": "541e5068", + "metadata": {}, + "source": [ + "### Using our polar kernel\n", + "\n", + "We proceed to fit a GP with our custom circular kernel to a random sequence of\n", + "points on a circle (see the\n", + "[Regression notebook](https://docs.jaxgaussianprocesses.com/examples/regression/)\n", + "for further details on this process)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "694fe036", + "metadata": {}, + "outputs": [], + "source": [ + "# Simulate data\n", + "angles = jnp.linspace(0, 2 * jnp.pi, num=200).reshape(-1, 1)\n", + "n = 20\n", + "noise = 0.2\n", + "\n", + "X = jnp.sort(jr.uniform(key, minval=0.0, maxval=jnp.pi * 2, shape=(n, 1)), axis=0)\n", + "y = 4 + jnp.cos(2 * X) + jr.normal(key, shape=X.shape) * noise\n", + "\n", + "D = gpx.Dataset(X=X, y=y)\n", + "\n", + "# Define polar Gaussian process\n", + "PKern = Polar()\n", + "meanf = gpx.mean_functions.Zero()\n", + "likelihood = gpx.Gaussian(num_datapoints=n)\n", + "circular_posterior = gpx.Prior(mean_function=meanf, kernel=PKern) * likelihood\n", + "\n", + "# Optimise GP's marginal log-likelihood using Adam\n", + "opt_posterior, history = gpx.fit(\n", + " model=circular_posterior,\n", + " train_data=D,\n", + " solver=jaxopt.LBFGS(gpx.ConjugateMLL(negative=True), maxiter=500),\n", + " key=key,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8761d8fb", + "metadata": {}, + "source": [ + "### Prediction\n", + "\n", + "We'll now query the GP's predictive posterior at linearly spaced novel inputs\n", + "and illustrate the results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13744f81", + "metadata": {}, + "outputs": [], + "source": [ + "posterior_rv = opt_posterior.likelihood(opt_posterior.predict(angles, train_data=D))\n", + "mu = posterior_rv.mean()\n", + "one_sigma = posterior_rv.stddev()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4f1d9b7", + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(7, 3.5))\n", + "gridspec = fig.add_gridspec(1, 1)\n", + "ax = plt.subplot(gridspec[0], polar=True)\n", + "\n", + "ax.fill_between(\n", + " angles.squeeze(),\n", + " mu - one_sigma,\n", + " mu + one_sigma,\n", + " alpha=0.3,\n", + " label=r\"1 Posterior s.d.\",\n", + " color=cols[1],\n", + " lw=0,\n", + ")\n", + "ax.fill_between(\n", + " angles.squeeze(),\n", + " mu - 3 * one_sigma,\n", + " mu + 3 * one_sigma,\n", + " alpha=0.15,\n", + " label=r\"3 Posterior s.d.\",\n", + " color=cols[1],\n", + " lw=0,\n", + ")\n", + "ax.plot(angles, mu, label=\"Posterior mean\")\n", + "ax.scatter(D.X, D.y, alpha=1, label=\"Observations\")\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "0917650b", + "metadata": {}, + "source": [ + "## System configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6053ba2", + "metadata": {}, + "outputs": [], + "source": [ + "%reload_ext watermark\n", + "%watermark -n -u -v -iv -w -a 'Thomas Pinder'" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "custom_cell_magics": "kql", + "encoding": "# -*- coding: utf-8 -*-" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/decision_making.ipynb b/docs/examples/decision_making.ipynb new file mode 100644 index 000000000..cd4cf7085 --- /dev/null +++ b/docs/examples/decision_making.ipynb @@ -0,0 +1,668 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ecc31fbd", + "metadata": {}, + "source": [ + "# Introduction to Decision Making with GPJax\n", + "\n", + "In this notebook we provide an introduction to the decision making module of GPJax,\n", + "which can be used to solve sequential decision making problems. Common examples of\n", + "such problems include Bayesian optimisation (BO) and experimental design. For an\n", + "in-depth introduction to Bayesian optimisation itself, be sure to checkout out our\n", + "[Introduction to BO\n", + "Notebook](https://docs.jaxgaussianprocesses.com/examples/bayesian_optimisation/).\n", + "\n", + "We'll be using BO as a case study to demonstrate how one may use the decision making\n", + "module to solve sequential decision making problems. The goal of the decision making\n", + "module is to provide a set of tools that can easily be used to solve a wide range of\n", + "sequential decision making problems. The module is designed to be modular, and so it is\n", + "easy to swap out different components of the decision making pipeline. Whilst it\n", + "provides the functionality for quickly implementing a typical deicision making loop out\n", + "of the box, we also hope that it will provide sufficient flexibility to allow users to\n", + "define their own, more exotic, decision making pipelines." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "831a299d", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "# Enable Float64 for more stable matrix inversions.\n", + "from jax import config\n", + "\n", + "config.update(\"jax_enable_x64\", True)\n", + "\n", + "\n", + "import jax.numpy as jnp\n", + "import jax.random as jr\n", + "import jaxopt\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import gpjax as gpx\n", + "from gpjax.decision_making.utility_functions import (\n", + " ThompsonSampling,\n", + ")\n", + "from gpjax.decision_making.utility_maximizer import (\n", + " ContinuousSinglePointUtilityMaximizer,\n", + ")\n", + "from gpjax.decision_making.decision_maker import UtilityDrivenDecisionMaker\n", + "from gpjax.decision_making.utils import (\n", + " OBJECTIVE,\n", + " build_function_evaluator,\n", + ")\n", + "from gpjax.decision_making.posterior_handler import PosteriorHandler\n", + "from gpjax.decision_making.search_space import ContinuousSearchSpace\n", + "from gpjax.typing import (\n", + " Array,\n", + " Float,\n", + ")\n", + "\n", + "key = jr.PRNGKey(42)\n", + "plt.style.use(\n", + " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", + ")\n", + "cols = mpl.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]" + ] + }, + { + "cell_type": "markdown", + "id": "9aae0fb7", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "## The Black-Box Objective Function\n", + "\n", + "We'll be using the same problem as in the [Introduction to BO\n", + "Notebook](https://docs.jaxgaussianprocesses.com/examples/bayesian_optimisation/), but\n", + "rather than focussing on the mechanics of BO we'll be looking at how one may use the\n", + "abstractions provided by the decision making module to implement the BO loop.\n", + "\n", + "In BO, and sequential decision making in general, we will often have a black-box\n", + "function of interest which we can evaluate. In this notebook we'll be using the\n", + "Forrester function as our objective to minimise:\n", + "\n", + "$$f(x) = (6x - 2)^2\\sin(12x-4)$$" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "104be778", + "metadata": {}, + "outputs": [], + "source": [ + "def forrester(x: Float[Array, \"N 1\"]) -> Float[Array, \"N 1\"]:\n", + " return (6 * x - 2) ** 2 * jnp.sin(12 * x - 4)" + ] + }, + { + "cell_type": "markdown", + "id": "241b1cd9", + "metadata": {}, + "source": [ + "Within the decision making loop, we'll be querying the black-box objective function many\n", + "times, and will often use the observed values to fit some probabilistic model. Thereore,\n", + "it would be useful to have some method to which we can pass a set of points which we\n", + "wish to query the black-box function at, and which will return a GPJax `Dataset` object\n", + "containing the observations. We can use the `build_function_evaluator` function provided\n", + "in `decision_making.utils` to do this. This function takes as input a dictionary of\n", + "labelled black-box functions, and will return a function evaluator, which can be called\n", + "with a set of points to evaluate the black-box functions at. The function evaluator will\n", + "return a dictionary of labelled `Dataset` objects containing the observations. Note that\n", + "in our case we only have one black-box function of interest, but in general we may have\n", + "multiple different black-box functions, such as if we also have constraint functions.\n", + "The use of the labels inside the dictionary returned by the function evaluator enables\n", + "us to easily distinguish between these different observations." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7f142853", + "metadata": {}, + "outputs": [], + "source": [ + "function_evaluator = build_function_evaluator({OBJECTIVE: forrester})" + ] + }, + { + "cell_type": "markdown", + "id": "82b75134", + "metadata": {}, + "source": [ + "## The Search Space\n", + "\n", + "Having defined a method for evaluating the black-box function, we now need to define the\n", + "search space over which we wish to optimise. In this case we'll be optimising over the\n", + "interval $[0, 1]$. We can use the `ContinuousSearchSpace` class provided in\n", + "`decision_making.search_space` to define this search space, as seen below:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "df396394", + "metadata": {}, + "outputs": [], + "source": [ + "lower_bounds = jnp.array([0.0])\n", + "upper_bounds = jnp.array([1.0])\n", + "search_space = ContinuousSearchSpace(\n", + " lower_bounds=lower_bounds, upper_bounds=upper_bounds\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a2375ed4", + "metadata": {}, + "source": [ + "The `ContinuousSearchSpace` class defines a `sample` method, which can be used to\n", + "sample points from the search space using a space-filling design, in this case using the\n", + "[Halton sequence](https://en.wikipedia.org/wiki/Halton_sequence). This will be useful at\n", + "many points throughout the decision making loop, but for now let's use it to create an\n", + "initial set of points which we can use to fit our models:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "43d01869", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/henry/anaconda3/envs/gpjax/lib/python3.10/site-packages/jax/_src/numpy/lax_numpy.py:3613: UserWarning: 'kind' argument to argsort is ignored; only 'stable' sorts are supported.\n", + " warnings.warn(\"'kind' argument to argsort is ignored; only 'stable' sorts \"\n" + ] + } + ], + "source": [ + "initial_x = search_space.sample(5, key)\n", + "initial_datasets = function_evaluator(initial_x)" + ] + }, + { + "cell_type": "markdown", + "id": "8c8c89ff", + "metadata": {}, + "source": [ + "## The Surrogate Models" + ] + }, + { + "cell_type": "markdown", + "id": "379e6249", + "metadata": {}, + "source": [ + "Many sequential decision making algorithms are described as being *model-based*. With\n", + "these algorithms, we use a probabilistic model, or multiple models, to drive the\n", + "decision making process. In ordinary BO, a probabilistic model is used to model the\n", + "objective function, and it is updated based on observations from the black-box objective\n", + "function. These models are often referred to as *surrogate models*, and are used to\n", + "approximate the functions of interest. We'll be using the Gaussian process functionality\n", + "provided by GPJax to define our surrogate models, with some wrappers provided by the\n", + "`decision_making` module to make it easier to use these models within the decision\n", + "making loop. We can proceed as usual when defining our priors, choosing a suitable\n", + "mean function and kernel for the job at hand:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "12e2dd2a", + "metadata": {}, + "outputs": [], + "source": [ + "mean = gpx.Zero()\n", + "kernel = gpx.Matern52()\n", + "prior = gpx.Prior(mean_function=mean, kernel=kernel)" + ] + }, + { + "cell_type": "markdown", + "id": "3f86aa67", + "metadata": {}, + "source": [ + "One difference from GPJax is the way in which we define our likelihood. In GPJax, we\n", + "construct our GP posteriors by defining a `likelihood` object and then multiplying it\n", + "with our prior to get the posterior, `posterior = likelihood * prior`. However, the\n", + "`AbstractLikelihood` objects takes `num_datapoints` as one of its arguments, and this is\n", + "going to be changing in the case of BO, and decision making in general, as we keep\n", + "updating our models having observed new data! In order to deal with this we'll define a\n", + "`likelihood_builder`, which takes as an argument the number of datapoints used to\n", + "condition our prior on, and returns a `likelihood` object. Below we use this to\n", + "construct a `likelihood_builder` which will return a `Gaussian` likelihood, initialised\n", + "with the correct number of datapoints:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d8ad7cc8", + "metadata": {}, + "outputs": [], + "source": [ + "likelihood_builder = lambda n: gpx.Gaussian(num_datapoints=n, obs_noise=jnp.array(1e-6))" + ] + }, + { + "cell_type": "markdown", + "id": "01694fb0", + "metadata": {}, + "source": [ + "Now we have all the components required for constructing our GP posterior. Since we'll\n", + "be updating the posterior throughout the decision making loop as we observe more data,\n", + "it would be useful to have an object which can handle all this logic for us.\n", + "Fortunately, the `decision_making` module provides the `PosteriorHandler` class to do\n", + "this for us. This class takes as input a `prior` and `likeligood_builder`, which we have\n", + "defined above. We tend to also optimise the hyperparameters of the GP prior when\n", + "\"fitting\" our GP, as demonstrated in the [Regression\n", + "notebook](https://docs.jaxgaussianprocesses.com/examples/regression/). This will be\n", + "using the GPJax `fit` method under the hood, which requires an jaxopt `solver`.\n", + "Therefore, we also pass this to the `PosteriorHandler` as demonstrated below:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "4138e77d", + "metadata": {}, + "outputs": [], + "source": [ + "import optax as ox\n", + "posterior_handler = PosteriorHandler(\n", + " prior,\n", + " likelihood_builder=likelihood_builder,\n", + " solver = jaxopt.OptaxSolver(gpx.ConjugateMLL(negative=True), opt=ox.adam(1e-2), maxiter=1000),\n", + " #solver=jaxopt.LBFGS(gpx.ConjugateMLL(negative=True), maxiter=500),\n", + ")\n", + "posterior_handlers = {OBJECTIVE: posterior_handler}" + ] + }, + { + "cell_type": "markdown", + "id": "3bc693fd", + "metadata": {}, + "source": [ + "Note that we also create a labelled dictionary of `posterior_handlers`. This is a\n", + "recurring theme with the decision making logic; we can have dictionaries containing\n", + "datasets, posteriors and black box functions, and use labels to identify corresponding\n", + "objects the dictionaries. For instance, here we have an \"OBJECTIVE\" posterior handler\n", + "which is updated using the data in the \"OBJECTIVE\" dataset, which is in turn generated by the \"OBJECTIVE\" black-box function.\n", + "\n", + "Now, as the decision making loop progresses, we can use the `update_posterior` method of\n", + "the `PosteriorHandler` to update our posterior as we observe more data. Note that we use\n", + "the term *posterior* to refer to our GP posterior surrogate models in order to be\n", + "consistent with the syntax used by GPJax. However, these GP posteriors are more widely\n", + "referred to as *models* in the model-based decision making literature." + ] + }, + { + "cell_type": "markdown", + "id": "9c139db8", + "metadata": {}, + "source": [ + "## The Utility Function\n", + "\n", + "Now all that remains for us to define is the utiliy function, and a way of maximising\n", + "it. Within the utility-driven decision making framework, we define a utility function,\n", + "often using our GP surrogates, which characterises the *utility*, or *usefulness*, of\n", + "querying the black-box function at any point within the domain of interest. We can then\n", + "*maximise* this function to decide which point to query next. In this case we'll be\n", + "using Thompson sampling as a utility function for determining where to query next. With\n", + "this function we simply draw a sample from the GP posterior, and choose the minimizer\n", + "of the sample as the point to query next. In the `decision_making` framework we create\n", + "`UtilityFunctionBuilder` objects. Currently, we only support\n", + "`SinglePointUtilityFunction`s, which are utility functions which characterise the\n", + "utility of querying a single point. Thompson sampling is somewhat of a special case, as\n", + "we can draw $B$ independent samples from the GP posterior and optimise each of these\n", + "samples in order to obtain a *batch* of points to query next. We'll see an example of\n", + "this later on.\n", + "\n", + "Within the `ThompsonSampling` utility function builder class we implement the\n", + "`build_utility_function` method, which takes as input a dictionary containing lablled GP\n", + "posteriors, as well as the corresponding datasets for these posteriors, and draws an\n", + "approximate sample from the GP posterior which is a surrogate for the objective\n", + "function. We instantiate our utility function builder below, specifying the number of\n", + "Random Fourier features to use when constructing the approximate samples from the GP posterior:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "54427002", + "metadata": {}, + "outputs": [], + "source": [ + "utility_function_builder = ThompsonSampling(num_features=500)" + ] + }, + { + "cell_type": "markdown", + "id": "6002c950", + "metadata": {}, + "source": [ + "We also need a method for maximising the utility function. Since `ThompsonSampling` is\n", + "classed as a `SinglePointUtilityFunction`, we can use the\n", + "`ContinuousSinglePointUtilityMaximizer` to maximise it. This requires the user to\n", + "specify `num_initial_samples` and `num_restarts` when instantiating it. This first\n", + "queries the utility function at `num_initial_samples` points, and then uses the best of\n", + "these points as a starting point for L-BFGS-B, a gradient-based optimiser, to further\n", + "refine. This is repeated `num_restarts` times, each time sampling a different initial set\n", + "of `num_initial_samples` and the best point found is returned. We'll instantiate our\n", + "maximiser below:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "f2fe3fda", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "acquisition_maximizer = ContinuousSinglePointUtilityMaximizer(\n", + " num_initial_samples=100, num_restarts=1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "f5ddb6f5", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "## Putting it All Together with the Decision Maker\n", + "\n", + "We now have all the ingredients ready for our Bayesian optimisation loop, so let's put\n", + "all the logic together using the `UtilityDrivenDecisionMaker` class provided by the\n", + "`decision_making` module. This class has 3 core methods:\n", + "1. `ask` - This method is used to decide which point(s) to query next.\n", + "2. `tell` - This method is used to tell the decision maker the results from querying the\n", + " black-box function at the points returned by `ask`, and will often update GP\n", + " posteriors in light of this data.\n", + "3. `run` - This is used to run the decision making loop for a specified number of\n", + " iterations, alternating between `ask` and `tell`.\n", + "\n", + "For many decision making problems, the logic provided in the\n", + "`UtilityDrivenDecisionMaker` will be sufficient, and is a convenient way of gluing the\n", + "various bits of machinery involved in sequential decision making together. However, for\n", + "more exotic decision making loops, it is easy for the user to define their own decision\n", + "maker class by inheriting from the `AbstractDecisionMaker` class and defining their own\n", + "`ask`, `tell` and `run` methods.\n", + "\n", + "However, we do also provide the user with some additional flexibility when using the\n", + "`UtilityDrivenDecisionMaker` class. Often we may wish to perform certain actions after\n", + "the `ask` step and the `tell` step, such as plotting the acquisition function and the\n", + "point chosen to be queried for debugging purposes. We can do this by passing a list of\n", + "functions to be called at each of these points as the `post_ask` and `post_tell`\n", + "attributes of the `UtilityDrivenDecisionMaker`. Both sets of functions are called with\n", + "the `UtilityDrivenDecisionMaker` as an argument, and so have access to all the\n", + "attributes of the decision maker. The `post_ask` functions are additionally passed the\n", + "most recently queried points too. We'll use this functionality to plot the acquisition\n", + "function and the point chosen to be queried at each step of the decision making loop:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "752767ff", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_bo_iteration(\n", + " dm: UtilityDrivenDecisionMaker, last_queried_points: Float[Array, \"B D\"]\n", + "):\n", + " posterior = dm.posteriors[OBJECTIVE]\n", + " dataset = dm.datasets[OBJECTIVE]\n", + " plt_x = jnp.linspace(0, 1, 1000).reshape(-1, 1)\n", + " forrester_y = forrester(plt_x.squeeze(axis=-1))\n", + " utility_fn = dm.current_utility_functions[0]\n", + " sample_y = -utility_fn(plt_x)\n", + "\n", + " latent_dist = posterior.predict(plt_x, train_data=dataset)\n", + " predictive_dist = posterior.likelihood(latent_dist)\n", + "\n", + " predictive_mean = predictive_dist.mean()\n", + " predictive_std = predictive_dist.stddev()\n", + "\n", + " fig, ax = plt.subplots()\n", + " ax.plot(plt_x.squeeze(), predictive_mean, label=\"Predictive Mean\", color=cols[1])\n", + " ax.fill_between(\n", + " plt_x.squeeze(),\n", + " predictive_mean - 2 * predictive_std,\n", + " predictive_mean + 2 * predictive_std,\n", + " alpha=0.2,\n", + " label=\"Two sigma\",\n", + " color=cols[1],\n", + " )\n", + " ax.plot(\n", + " plt_x.squeeze(),\n", + " predictive_mean - 2 * predictive_std,\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " color=cols[1],\n", + " )\n", + " ax.plot(\n", + " plt_x.squeeze(),\n", + " predictive_mean + 2 * predictive_std,\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " color=cols[1],\n", + " )\n", + " ax.plot(plt_x.squeeze(), sample_y, label=\"Posterior Sample\")\n", + " ax.plot(\n", + " plt_x.squeeze(),\n", + " forrester_y,\n", + " label=\"Forrester Function\",\n", + " color=cols[0],\n", + " linestyle=\"--\",\n", + " linewidth=2,\n", + " )\n", + " ax.axvline(x=0.757, linestyle=\":\", color=cols[3], label=\"True Optimum\")\n", + " ax.scatter(dataset.X, dataset.y, label=\"Observations\", color=cols[2], zorder=2)\n", + " ax.scatter(\n", + " last_queried_points[0],\n", + " -utility_fn(last_queried_points[0][None, ...]),\n", + " label=\"Posterior Sample Optimum\",\n", + " marker=\"*\",\n", + " color=cols[3],\n", + " zorder=3,\n", + " )\n", + " ax.legend(loc=\"center left\", bbox_to_anchor=(0.950, 0.5))\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4a488098", + "metadata": {}, + "source": [ + "Now let's put it all together and run our decision making loop for 6 iterations, with a\n", + "batch size of 1:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "2bc920a4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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joedYrfj9fjidTjAMo9p3R0cHGIZBR0eHpoBnojDIe9T8bCMcieL5/kGsW7oACytL8y0OoRO3720AwIv7T+ZZEmI2c7v9Q/j4xutyVnNerOLlw+j48tsBnBidxIk3j8P75nH86pbLcMkKaRGTQlSg843VaoXL5UJXV1ecgaa+vh4A0NbWlvZ57HZ7nCuc0+nUVNnQ7/fHFZ/Reqwe+LlgWRYul0t13GazOWEhHKLwyHuw0mzjp0/twZcf6UVz17/yLQpBEAVK43k2fPzSTWg8Lxt+8PEaHZdnTfSZfQPC31XlJly4LDcK+GxAbXWwoaEBPT09GTuPvLqh3W7XpEy6XK64bVqPTYX6+nqYzWZ4vV7JdrfbTQHOMxSyiGaYB1+IRf8Pj4fyLAlBEERh8MzeaUX0itW1imVWo3k0ib5+57cwsmt3zs43f/06nP+99KoC9vb2oqWlBUBsad3hcMDhcMBsNqOzsxN9fX3CPo/Hg7q6OvT19QmWVZ/Ph87OTsGy2tvbi8bGRgCxuI7W1lbY7XZB0fT7/XC5XKivrwfLsrDZbGBZFr29vULWG14RFB/rdrsFC6nH4wEQUyatViu2bduG3t5eRfkSsWXLFnR2dsYpnkrHqY2/q6sLFosFfr8f/f396OzsjBs7n0Kyp6dHkJ3IPKSIEgRBzAAGRidhqTChyBC/kFWggfAAgINDp/F2YEz4fNUa5SIk+VyaH9m1G4HnXsyfABro6uqC2WwGy7Lo7+9HS0uLsARtt9vR1NQEj8cDj8cjWDd5H9P+/n4AMaXM6XTC5XKhublZ2A5AYl212WxoaWkRymyzLIvGxkb09fUJSlxPTw86OzuxY8cOSfAxAMmxTU1NCAQCEkWOl11NPl4pVKOpqQmtra1gWVZQqpWy7CTq3+VyCeNpbm5GV1cX2trahLF3d3cLSrjH44Hb7UZTU5OWr4rQCSmiRN4ZHg9hQVlxwohagphNPPTSEzgYGMAqSy0+fummpO2f6x/AF363A7aVFjxw82UJ2yopdPkMBHpaZA0tMjC4zLpQsV0+XUTnr19X8OdrampKutzNK2O8wuR2u2E2m4UgpEAggN7eXjzyyCNxfcmtiWazWVAm+fZ8m2R+qeJj+fZOp1MIIOL7UZNPC21tbdi6dStcLhf8fj+ampri8pEn6p9XTn0+HywWi0QpB2KuDzxWq1Ux9SSRGUgRJfLKi/6T+PzvtmPj2dX4+Ucvzbc4s4q3A2P44yuH8L4Ll8G6cF6+xSFEPLT9SSF9kxZF9PO/2wEA6Htb+WEoTmivWFeJU26bC57ZO13Gs36lBZUlxYrt8plHNN1l8kJBXo1waGgIDQ0NEkteW1sbOjo6dGWm0RqBrmaZBIDNmzcLVl1ekVWTTwtbtmzB2WefjS1btqiOJVH/vBuDw+FAXV2dRHEG1H1zicxDwUpEXrntdy8hwnF4wU9R6pnmE796Dr96vh+bKXBuVqHkS6lnMSGXCw8jk2G8/PZ00v4r1yxSbUtR88roSUMkb+twOBSDepqamuDz+STb5dZEcV9NTU3o7e2VbOOtjNXV1YISx1sblWR2Op3YunWrJvkSIbaqNjQ0CP6cSudV69/tdqO3txculwtWqxVDQ0NgWVb13CzLUjqoLEIWUSKvRAv84ROORFFkYAqmGk04EkWxUdv746mJWK7GqUKf5DlIOkns8xnUo5cX+gcREcl7tYp/KEB5RJXw+/2Cv6TL5YLD4VC0OPJFYywWC2w2m6CYWa1WdHZ2wuFwCEFJfER7T08PnE4nNmzYICw7d3Z2CkFIHo8HgUBAsHI+8cQTcDqdkn6AmIWxtbUVHR0dsNvt8Pl8ccfystjtdkmAkZp8anPhdDrh8/lgtVrR1NQkfObnoLOzEyzLoqOjA21tbar9WywWdHd3CwFLjY2NcLlcgsx8pUdeie3t7UVvb69imisifRhultVV2717N9avX49du3Zh3br0/H4mJiYAAGVlZZqPufi7fxX+fuXf35vW+WcCqcyRmEKbL7E8j3/xOnz0f57FMnM5HvzkO9PyYU13noBYsv0/vXII9zXV4511NUnbF9rcaiET86TGTJwPHrHs279xAyLhWFYOfp7aHnoR2w/ErFK/+OileIfMD9P199343Y4DAID268/HRzeenQOpgbv+9Ar+uusIAODs6gr88dZrJPvF43rmjusxv1R52T5V5NcTb/XLVmohgpjrpPIbo6V5omAQvxNxHIc9J0YQmorkTZ4feF5HYCyEnUdYvHIo9zXB5fxuxwFMhCO47Xfb8y0KkUeUDNziV6RCsSxORaN4tn86UCnRsjyAwq1NShBEVqGl+QSc9P4TB374Uxhk6VIq6qxY1xFbWjv02/+HY3/63+mdV98q/BkeGQUAvHVvB1jfy3H9L//Yh7H0gx8AALx665cRHBiIa3POlq/D3GBDNBhC70dvUZTzwp/9EKWLF+H0vn687vz3uP2G4mI0/P43sTE99Qz8P/l5XJuEYzpD9dVXoO5Ln5eMKRqNxs5xZo70jknMs9e/T3gzeuvL38KPXhnAJdUl+OwffpizMWHeO4R9b7/QB1QtAwBMjJ5GeKQYfR//FCITk3H9nHfvPZh37jkIDp7Eq5/7YuycFdWIGgxYNTqIaDSKCx74OYosFpx6+VXs+e5/xvVhqq7GxV0/BQAc+8tfcejBh6QNZNfW/p91YvDJp+L6WXzje7DyU5+QbHuu8UbJ57NvbUON/VoA0DUmOfW/fQDG8vLUxwRgwUUXYO037wQA+H/0UwSe+lfcb048pte3fBOn39ob10/SMYnmj5+P5R/ZjKVN/wYAeO0LX8XkseNx/a5p/xqqLt2A6NQUels+EbcfAC748X0oW7YUY/792P31O+P2MwYjNvTExj70zHPo/9FP49qUn7UK6++LzeHh/9eNo4/+afr4qxzgmNicBE+NwhANYf8PfoTRl18FAJy68EagagUAYOBxL8Ifv1EypqOrrwCWXQAA8N/fhYFTjVkf09FyC8YamgDGCABY88YOPOf6urQT0XcSGh3F69/5EYZ39MWdK9XvqfqGRqxo/RSQBQs7QRCZgRTRBEwePozJg2/He/dHObB9McVy9I09GD9wcHrf1dN/8m1Ov7VX2uYMo6+/CXbVSgDAmN+P0MmhuDandu4CGAbRYEixDwA49cqrmKypwfiBg4ptDMXFgiwjO3cr95NoTGcoWbwobky8FZP3odQ7JjHjB9+G4Ux/P3olpsC+PBTM6ZhQP62IRianFZmxt/aBHSjC+P6Dku08I6/tRGRsDKHAMMYPHMRQpQX/eeYh+7U//zcWDx/HyCuvIbxgAUZ2va4oS5g9NS3v62/Et5FdW6N73lK+rt54U+iHR95uZPfrKK4yx/ZpHJMSrO8VGMvKUh8TAGN5udBm/K19mDj4dpxPrnhMp/fuU+wn0ZjCgWHJ/PHHj7z+Bsr7zgIAjPX7ERwYjO93524wRUWITk2p/wZffQ3B4ycwceiwYhvGYJy+XnftxnfME9hjLsVadhK3n7nWuakp0Vy9KemHuZIDd2ZKhl9+FcXRMMb2+YU2kTWTwJliRWNvvy30w48pvOii6bkYCmRlTPI2ZhyEa2wYo5/7CnrZCJa9thNDia7pV17D6bdUvtsUv6fSN9/CuP8AKi6+UHGMBEHkH/IRTcDQ629idOcuVC5OsqQkwv6vEeFv75Xz0zp/IeAfi2B7YArvXlyMBcXxnhzBM8pkSYkppf7F8/X3K+bBeEYBydc8is978QIjXjkVcw3oWFcGm0W7/1r34SC27Q8CAJqXmfDJZbG5S3We5LJpmZNk7YdCUfjHorCZjcK85xst11OE4/DU4BRWlhuwptKoue+MXFMc1EtkcjGXEkahapCclme68dLJw7h04XJ0X9WStP0Nz45g6syd+o/vmAdTNBaIxs9T+84x+NjYtfqf68vRUCW1Mfxk3wT+fCx2zBfqSnHT0tSvw0wi/k7cl1bCbEruLfbQ20G8emoKznPKsLAkcfvRQ0dQsXYNajfEAlXIR5QgsksqvzGyiCaDMaB4wQIdB0zfWPUdV5i0/esQAOC1MQbfvzQ+QCYajClbxSUlKZ5her6K5i9AkfAQz9c8Tp8XRUUAYg/34nmVKF5QqrmXopMjAGJzw5SYULygPNZPyvMklU3bnCRu/4n/O4xQlMPnzluAj9QVxkuTluvpz/5R/GRPzO3lH+9ehhKNWQQK6bd53apzsdK8EKsXLNQkC8OMCD6UxvnzUMxNAZieJ0NREPy1aiwvR/EC6VK0wRQFEFNEjaWlKF5QKHllxb//+SguSfxiMT4Vxa8Pxo65b/8UfqBwTxJjGDyp/uJAEERBQIoooYntg/FLt5lmNpnmJcEjBTqw0Jmol1+8cUpRET08FsY3e4ewoaYUt55vzrF06vzePyr8fSoURW1ZckU0OJhantqhyQiqSgwZr/r1hQuv0tU+loQ+9n0pBiuJ5FNMaK/rbPlBS5YxcSqyN9hgFqUhCCJXUNQ8UTAUqsI2V/mOL4D+0TB+7x9FMBLNtzhpMXnsmO5jvEfG8EHvUXzbF+/nnGvEanAyhU1ZEZ3emgsvjFAkOz9mie8w3S8IYlZAFlGiYOASOuDlgXQedAXic5kOB06Hhb+zpFfkDINJv0vEd16OJfn+57EJ3J1hefQidjuNchx8gRBOT0VhX2GKL7aQ5+9qfCqKJu9RrKsqwWW1pbhuWTnMpuS+vHrFzuSr0Zh/P6KhUPKGGcBgMqHCmps8rgQxEyBFlCgYZriuI2Hmq6GzjAL7QnYNHcNIaBLzTaVYX70kaXuxrnlobArf8LEAgHmlJryjtkxqMdVw/gjHYcv2kxgNR/Ff76hBWVHmFsf6Tk5ibIrD9sFJbB+chG1hqSZFVEvFKGmu4bTElJ47FMLIa7thMGU2oX78ecKYf2F6QbQEMdsgRTTLjIQiYBgG8xQizgkps3VpXjysyUgUpZqDa/JLgeluaVF+1ipgb3xO23xx9/a/4YXjB3DZ4rPgfvenk7YXfxevDk37Rj55dBzvqJUGJin9juSbnj0+gZfO+H13+0fxyXMyF7z1/Ilpf/Il5UasqszcY0asZGe6cq3BVBy7TrKIWqosNXw+H5qbm9HU1ITq6mqh9GRLSyzTQnd3NxwOB9ra2jIuazIaGxvR3Nycl3MTswtSRLMIG4zgw08eA8MAj1y3FPM0pCaZyxSaHiqWR69SphSs9LB/DA/5x/CV9VV4/6pKzX09d3wCD+49pVOCxMyyrG1JMaaVrSD/iC2iYmVM6bqMKv2SRJsYMBgOTvcyNJm56mVRjsOLAxPC58tqy+JdB1TQdEVy4j9n/zXs9/vR09Mj1DcfGhqCz+dDe3s7gFid966urrzI5nQ6KQ0WkRFIEc0iPftHMXHGue4vb5/Gx1YXRoqcfPL26TC+2XcS+0encPOa2Tsf0pKLMR7sHwMA3LdzWJciemdvahHfidD7CJ9jemvWuXvju4WleS2Io+Yjoi/j8cPj2HJxtaRtsu+KYYCIyJyYyYwAe06FEBApue9cpL2ikRYLp1gJnwuXZCAQQFNTk2Sb2WxW/DvX2O32vJ2bmF2QIppFOMnbOwEAX39pEMcnYhaY3+wdkeybVcpOgQf35jowJMJxGA1FYU6SJ3KuoMUvVEyiqHk2GFF88UmEuA8N+fc184JoWb7MyOAii9QS/eLABF4fDuHDdfNQnoJfqvgekeml+UJEy7J3e3s73G43WltbYbfbsW3bNrS2tgrWVCC2jN7U1ASXywWfzwev1wuz2QyWZWG32wWLqxyWZdHV1QWr1YpAIIC+vj50dnbC5/MJ53O5XAAAt9sNj8eD+vp69PX1obGxETt27EBLS4vQtq6uDgDg8XgEWfx+PzweDzwej3Derq4uWCwW+P1+9Pf3o7OzM92pJAoYUkQLjMlIFN/2DaGmtAhfWW/WvKyVChzHYXAygppSY1bPI4ZXQhXlyYkEqaE2PUfGpvC9V4bwjtoyfEJk4ZU0TzIwPtWNyZg7r0y9Sn86S/kcx+HLzw/gdTaE+y6twSULtRcGIGIkyksbjnLSpXsNPqLiX2EmL7sXRMvyG2pKJdf0VJSDc3vMuj8SiuIrF1RJZdRiEZ1Vb6uZo6mpCTt27EB1dTXMZjMcDgccDoewdO5wONDe3g6/34/W1lb09fUJx9bX16Onp0dxmZ1XQnmrLO8GYLPZ0NLSgqGh6dRmzc3Nwn2isbERZrNZUFJbWlrQ3d0tfO7r64PT6RQUZY/HA5/PJyjELpcLfX19MJvNaG5uRldXF/mizmLIaTGLpHLLfHjfKJ47MYk/HTyNncPZTSfyq7dG0PzEMfzPWyPJG+eAQnvEaJHnW30nsWs4hF/ukfpwan22j4WjaHnyKD785FGMTxVurs50vpvxKQ47h0OIcMB3Xs5/Ts6ZiFjRjCRRxpJ9VwykCl2mluYHJ6bw1qnplF+XLZK+cIREGvLjh8fijlf0bZUhbjEXLKJ6cDgcguWQV+p8Ph9YlhWUTLfbjYaGBslxDQ0NqhZHu92O1tZW1NfXw+l0YvPmzcK+RG4BgUAgbpv4vGazGRs2bJB85ktDAkB/fz/MZjN8Ph8sFgv6+/sTjJyY6ZAimiO0vsgfHJ2+kQcyGEQwHIzgxRMTksokD55ZGpcvkeeLQg6gUXtU7x0Jq+yZJtGoHj0wikAwiqFgFH86cDol2VJB70M8na9GrGBn4hsu4MtEMz997Rl85V9/wE9fe0ZTe/H1lyynq5aoeXGbTC3Nvzggrb4mj+ZPiiaLqK7mcwqr1Soob2azWbBCPvLII4JFU2zB1Nrn8PAwXC4XWJbFpk2bVNu2t7fD6XSiq6sLdrs9zodUjz+rw+GA0+mE2WwWlvOJ2QstzWcRrQ97NhTBsfEpnLvAJHniKNnHIhwHA6B7Kf3TzxxHIBjFJ1bPx2fPzW+dbTXUpovjuJy5DiTi5ZOT2DcSxvtXVaJEYT0zFTmDIq1iKocaVi59RMdFY0zFLzAR+b8qUuOpI/uE9E1ayn0mW3qX5hGNbyC/tCJZUESfFy3Ln2c2waLTH1jLNZnNX0g0FNadXimVc6QLy7Kq+1paWtDc3CwsazudTjgcDmG/w+FAc3Oz5Jje3l5s27ZNsb+tW7fC4XAIimVjY2NCOfil93Rwu93o7e0V3AeGhobAsizcbndc4BYxOyBFNA1CES6hX58k/Y9KsyjH4aNPHsPYFIdvXlKd8MHKhiL47DMnYDYZ8IsrFqFIxxOEj2T97b6RGaeIRgHkI8RFnB4mFAXueGkQADASjuIza+PnUFwXSlqJUP3xKU0RlUu1St8jPVMW0fKizI5xplrFasoqsbzSjJoybdkTDKJrI5mfpF5fS2MGXvKCkSj6Bqfzm15Wm9gPWElELS872VqON5hMOUs0bzCZdB/j9/vh9Xrh9Xrh9/vR0dGhGGTU1taGHTt2CNbHpqYmiWXSarXC5XLB6XSirq4O/f39cLlcqsFK1dXVcLvdQn+8Uuvz+eDxeBAIBAQ3AL/fj6qqKlgsFpjNZiGQyefzCflPvV6v5H+73S70wbIsbDYb7HY7uru7hYClxsZGuFwuxeV+YnZAimiKvD4cxNdeHMSGmlJ8p2Fh0vYnJyP47d4RXLWkDKsqp6t3jE9xGJuK3V1/sDOAS2umb+DyB8qv9oxgcDKCwckInj8xgauWlGdmMCrkPDBA5XRcAVT+PB2efkz+78HTqoqoEglnMUNTPDAxhdcCQVyxuExTwnwtp1Uq651KQv7xKZFFNAPJ/FMpN15obh8/v2Zz8kYipD6i8n3SH0cyhS7mIyr9nC4HT0+hyADwmZsu05G2iUfLV5St77HQS25arVa0tbUlDdgxm81CABCgbKFUWjZXg89XKsdms0mi3L1eLzZs2CCcm2VZOJ1OdHR0oL29XRIcBSDus9wHVDwGXmZi9kI+oinyrb4hTEQ4PHN8AmGV13SxJeyPB07jl3tO4eanjkvayKNhxQ8VuSVNrAwFc1D8O5H1YSrK4WsvDuDrvcOSIIR0ULWIZmGouwJBPLRvBGPhzAUIcZKHuzZtSYvVXAs3P3Uc33k5gJ/tZs/Ikp7V7FQoIlEgoxyHh/aN4D2PH8GfD+rzZRXPcVmmLaIar43CUkP1o6eEpyaLqOjvTCzNn7PAhD9fvwz//Y4afGL1fKyZH18qU8vv+E02hF+/dQpsUNk/vnDD+eY24oh3AEK0u16fVGJuQhbRFBmVKYXFCndzLQ8EufKRKE1Lrkl0+scPj6HvZGwp7q+HJ/DhNemn5FG3KGbeJPr552PlHg+PTeEbF1n0CaQBrVY76dJ86kwXThjD7Rdakj6wkykFW1+JXwbb9mYsM8B/7RzGB3Qk5Bf7iGaipnkq+XlneoS11Ec0fjBaXUH4tpEML80DQLGBgW1hKWwq6bmSfQVRjoPj2RMAYgrpf26sUWiTrpRENmhvb0dHRwc8Ho8QXMSybEZ8RonZDymiKVJiZISHfzDCoTLeADDjSXTTF5cIFFdSSQc1xft0mMM/j46hfmEJasqSX7KhCIe/HjqN1fNNuMCSuLTj3w6NwXlhFXr2n4YpgWkoW4EUUitqzJ8yEwE9yR7Y8nRTcl6QRUCnowCIMzUU0xpMSiRKaC/fnyxqXr40n8mE9olIaqUX/S2//pTaiIlwHPoGJ3HWvGLUarhHEJlHbRmfIJJRML9Yv98Pv9+PhoYGIaeY3+8vWN8QcdS02jK5loe33Lqjt0JKNklkWcllELvr1QBeGpxEdYkBf2hclrT9w/tG8OszKak8714eF1Amtyg9PzCJn73OJuxTm//a9N+pfI+PHx7DtjdP4SN189B2nlnjUcok8+/9g0qqqAjHKVrI0rkWU/VHjHIcDoyGsWpesarVTuuqQaFZ0pr+9j9C1Lz73Z9O2j6RjyjHcRJXEE2lMvOhiIr/TvH7ULuu/3TgNH58xi3lqfcuL4gsGwRBaKNg7BM+nw+NjY2oqqoCwzBobGxUrPRQKJSI7t6TkdQtguLbapRLLRAjW6g90CYjUWGZFsicnGr9vDQYs44MabS8ipWsCYXvRj6unYFgXBu5PMmWO+XtJds54JkTyS08B09PIQrg4f7RpOdKRiouxD9/ncX7/34EvYPxsqbjJpKqEvjLN0/hU8+cwA93DmM4GEHbv47jP1+VugxoDlbK+68pPRJFzctHpmWkmUxoP6rRzzqZXJMaLtq4sZ4ZB6+EajkPQRCFRcFYRAGgs7MTFosFVqtVNZ1EoWDSYBFVuyGeDkdReWaNUt5Gq49oLm62agrE72WK0iMHxnFgLIovrzdjecW0j4LevJq5itKX5+tU8u+Vo020aZu2vMfvvqZcNCBbClIqyt/v/bHv9fYzaaok/aUg5xvDQbwaCEqUYj3XA6+Q/+XtMYyGo9hzKow9p6R5GLXOX6EpJ82rL8Fli8/C8sqq5I1lyL9bLYqofJv4O0knNRobjOCD3qM412zCZbVluHFlBapU8ocm+w2Jg+PUkI/9U8+cwM/eWQsG02OMcrmz8hIEkT4FpYja7faCtoKKMUksovoU0R/vHsadF1fH2kjuzlxCi2iuV5vUHhz7FKoJbR+cxMf+eRyP2pdiYakR/SMh3PHSIDbWlGLLmbEWCnIjqZZa29qsTNN/aw5WypKGlGmlXt7dT3cPYzQcxdcvtKjms/3ccwNx21Jdgjl4ekpFMG3H52JpPhiJYjLCYYEpuWrXsuYSXX0bJEvz0sE8dXRc8lnpuxffZzhkbmn+xcFJRDhg93AIu4dDeNdy9ZRynMrfPFpK3MqHtn80jN/uK4zKcARBpEbBLM0DsSg7n88Hr9ebsHpEIaDFR1TtIfn3w+OKTeJ9RDPz9Ew1956aFSzRg4t/KN7dN4RAMIrHD48jqNF1IVdWK7lFVEthgGwFK2WLTGf3kn+DPftP4/HD4zkrS6pWdapQ5jwc5XDzU8fR5D2Kw2PpV8+RkyhY6f43TuHI+PQ5k/3cOU6qrKbzgvvCielqSqvnJw4U0mMRVQtqU7onDctSPRWaPzBBEIkpKItod3c3HA4HrFYrWltbhdJiagwMDGBwULqMuG/fPgDA5OQkJiYmlA7TTDAYRHgqhGAw3oewmJm+240GQwgG4++cUxH1WvF8n8GQqA43B3DR6c+/eesUNtVOL3VHRP2Fp6YU5VJCbkFROk5p20Qoqtwmqq5Y8nKdmJi2YE0EQ5rMjsFgCEGD+pypySlHrHj/2X8KLWdXCJ8nIxxOTkrPwah8T1HROENh0YMenOocGiOx62Bqanr8EYX++ePVrpFk40y0PxgMYkL2cE6nPwAIBUOK299iJxEMaq8UE41GEsseUt43paJZB0MhBDX4Dk+GVa5ljSRr3zsUxPGJ2Jz/ZGcA377ErKv/ZIhfSqcUXuz2j05fbyGFe0NEdMzU1BTCousuouNeIiYc5bBd5E+8obo4yXUpvibjf0OnJqevsRIDo/wbUyiRGY1GJUvzk8EguDP3m1A4BGMwJDwLIpEIDAaD5LetFYMh93Ybv98Pl8uFrq4utLW1CamR+vv7UV9fnzTBfTJYltVVA15OY2Mjmpub05ZDDF+Jqbo6tpJms9ng8/lyHpXv8/nQ2toqVIgiskfBKKJNTU2SOrJ8Tdz9+/er/lDuv/9+3HPPPTmSUEo6S/NixLdDeftjE9KbZaov+qmu0qpZUrUs5cV8ATlRP5mxOurlgX1jgiIajHD41HNDcUFPmSp/nor82Vqa17DKKeHbryZO56TWnV7La6rGN70WUY7jcHqKw7wzprV0E4yp+Tu/wYbx+wNjOKty+laqwdURTx/tx+DkadSUVuLqpXWIclzCoCHxJZr2WCC1GqZ6Ce5iwxIr5tp5iR8nyZbmxfdRtVRqepP1z3T4kpxdXV1wuVySZ2F9fT0ApKwE+v1+uN3utBQ8p9OZcXe6rVu3Siorud3uuMpLucBms6GlpYWS8ueAglFE5TQ0NIBlWfT29qpaRW+77TY0NzdLtu3btw833XQTSktLUVamv8ycmPGSEoSKTCgpic9FOa+kCMAZqyZnVGxjSFDOkG9vgtRyZTQaFdsBgNEwvc9UVKR4TiUYmbagdJzStmIuotim2KjuA1d0Ri7xY6SouAQlpuTanslkQklJ4ktS05gZsX1k+phnj4wrRt6XmpSTwDIiC4jBOC0XA0ZRDpOpBCVnFJ/iomnLDaNgSeGPZ4zjcfvE+9VItL+kpAQIh+O3JeDZgcQWsaJi5TniGIPm6xAAiozKvxU58jZqCm9xsQklJfGyfbP3JJ49MYHvNizEOxeVIcgoX8taMZWUKCqKX94R84N9YVBkMWaUrw8xXW++KKRvsi1Zi1ufPYEVlUW479IaRYXXIFbMlPZjWgEzGOPvDeJ7UVFRESBaeVBqf2x8Clt2DOICSwluv0C54ENvYPraNTLA3a+NwLXRhHfUKt93i6PTVlsG8d8BI7q/MYzyd1SkcBkajUbJT95kMqHkzNulqdgEU4lJeBbw99d8WDdTRc0Q09DQgJ6enpQVUZfLJVhYUyXT6RX5tI1impqa4rblCrPZTIpoDigYRbSqqgo9PT3Chc3/+BL5itbW1qK2tjYH0sVTKSpVOKKWvkTD27skjyinbDF6+tg4vEfGMTAx/fDQY8VI1ddULeBFm0V0+u+I1sjmLFkHt73J4uLqUlVfOLWqk2J5NOWEFf2t2e8uqT9fapOSixKwQLzbRzJS9UfUYmUU8/Tx2FLslh0n8fSNKxJayf52aAx/OXgaX1pnxnlVygqkvt+bPn6yexgDkxEMTEbwOhvCOgUZxHlClb5asSKW7Jp5aWASz4p8O5Vau14NYP/oFPaPTuFT5yyARSESXpx0npfpG9tP4qkbVyieN9m86M1/mk4bOR3/2I09x3MX9LR28Xy0X78urT56e3vR0tICAEJshdlsBsuysNvtsNlsYFkWXV1dsFqtCAQC6OvrQ2dnJ9xuN3p7ewUFj2/v9XqF6kh9fX1wuVzo7e2Fw+GAw+GA2WxGZ2cn+vr6FJeu1eTwer2KfcixWq1gWRaNjY2Ca57ZbJZYbbu6umCxWOD3+9Hf34/Ozk7h3Lw8vILt8Xjgcrng8/ng9/vh8Xjg8XgAAF6vF06nEw0NDUL7HTt2wOVyqVp5leYnHdcGIkbBKKJWq1Xy5fM/kEJP4wSoK6KpLBEpPai/2ZfeG1mqzvtqxyUqCcgfIm6hNc1qtlSnh/aN4qF9o/hug3L0vpY8ipqGII6aF81Awqj5JF2mqk+GMhyxodad/qX51DTRKRUBFFMVKUaNq/fN5yb90guD8LxnufJ5tHmXJD0XABwZm8IdtpuwrJxBibEI33llWilUG6eeykpK16pYJrESqibv4bFp66WSTIdOhyVthL4Uzq10HqV2idyUtJ0h/jxa2XN8BH1vx5e1LSS6uroEBa+/vx8tLS1ob2+H3+9Ha2urRLGrr69HT08P3G43rFar4PbW1dUFIGZl3LFjB6qrqwUlz+/3w+FwCMvgvKLW2dmJpqYmQYmzWGIWcvnSdSI57Ha7Yh9K9PX1wel0wul0wu/3o6mpCdu2bRMUPpfLhb6+PqGePe8/y8vT3d0tKMZ8X/xSv8fjgc/ng81mg91uh91uB8uywhzw+cyVXAESzQ+RHgWjiMpTN7lcLrS1tc2IdE4jIWU1RZM/kyytSjYyNKXsW6qyXZNFVPS3VqtZvpKOa0l1pKWNWH7t+WAT95uqPhnKsEVUrTdVBVFl0Kle36q5ehXOo9RU6einjo7jJ6JE6JlS3hO9tLChCD76z2MAgF9cUYsV80rAcdNL3Gp5VpPVmjdILKL65FVW5kXnVtj/vMgaamSm5zxRTGJyi6iW32HSJindR9Yunq/7mHRI5XxNTU2Kz0O3242GhgbJtoaGBnR2dqKlpQWbNm3C1q1bYbfbsWXLFtX+3W43zGYz3G43ACAQCKC3t1fYzxuFxLEc4qXrRHLwiqFSH3J4iykQWxHdunUrNm3aJCi4vCLo8/lgsVjilEaxDGazWeJ+wFdtFBu4xPttNhsCgYCgrOqZHyJ1CkYRdblc6OjoAAAMDQ2hrq6uoGvXim+IWiuLKCF34M+GIpppi6jeYCWt59fyAPWdnIR/JIybzqrUlHZJjJJlVl53Ww2lHKEjIanfYUrBSsnOq8kCFN8mkVLFBhNnJlA+h/J2nZnLUr7A1ZbmlTYrjV1J/m/54lca/r33JO6pr46z+iudR105VtwMAJKqVY/4R/EtW4lkDtU8F6UvdvH7xe/Ceq9DRQtykl7EaZvWV5nwaiDmI5voJ5ns9y3+2tQuX7UuklmEk5HuMnk+SeTDaLVaMTw8DK/Xi56eHolCJ8bn82FoaAgNDQ0SJVHsf5rMn1SLL2WyPnw+HwKBgMRFT+7Lyi/vOxwO1NXVxZ03W0vlyeaHSJ2C8thub29He3s7XC5XwSih4Sjw270jeOaYNKhEfEPcPjiJCYUw5ZT0vyxooqkkNx+fiuKu3vgKO0DipXmeZA9OJbQ0++qLg/jJ6yx+u1e/P5fSA4pR2R53rEy4bv8o3vePo5Jt8pcKpb/jUHvgctqVeHmTwYkpVSXp2eMTuMlzVHFfItQUYjVrt6rYCjv8I6G4lFrpoKSIav0N/Ov4BDyH4wPIlA7/Zt9JxT4SvTwouSaIW6spcoYkFlHJ+TVahHleHgrGXS/iT2+PTeHDTx7FD3cOA4i9eL8mKotbv7B0Ws4ENzDJb0JBoETXev9ICF97cQDeI8rBfZLz5GdhJWsky6ntcDjiLHO8/+jWrVvh9/tht9uFyoU81dXVghLH+4F6vV5JP7z1T00O8bZEcmgdC4C4VEksywrWSd63lffjHBoaAsuyEjn1Irao8lZW/nzy8SWaHyJ1CsYiWqj8mTXioZOx1DZ/bCwRnPbl97oH9pzCF9ZJy/VpuR/Kb76JbuRi7n0lgBMTEXxiTfIlHi2Kljw9zYNvjUhyE4rRUolI6tOm7cmwfzQMA4DllcoR2mIe3DuCT61doKlfnoCCssMwGpcERX9zHHD/62x8I4kPnFZ3BGX+9+0xvH9VpSYlXn4NbdtzChtrShXb3tWrrDwlQ7dFVKNFa9dwEJ8/U4Hpbzcs011uUkl5UXJL0KObvHkqhBtWVEi2KR3/omh5WszOQAgDE1MwMgwGJqdw7gKT8NvaPRxT4AZPv4D/3TeMSHgFotz0EiD/E+wfCeH7rw2jcVk5PnT2PIkCm+zlRG8A2QsDk7j+b4dxj60a1yyNr4z03ZdjxSn+ePA0bjvfjH8cOg2DaDn+4urp4KrEFtEkCrRoluUtP//cACYiHPhMJWIYSIO1Pv/cAJaUG+HaWJPwfDMBv98vLFO7XC44HI64JWM+xZPT6URdXR36+/vhcrmEICF+SRmIKVM8bW1taG1tRUdHh+Aa19nZCYfDIaSGstvtQgASr6Dx1kqfzwePxyNZylaTQ60PJRobGwVfViCmKG7btk2Qp7u7WwhYamxshMvlEmTo7u4GAEFh5P+32+1CG16x5d0cAoGAcL6+vj7BYqw0PqX5IdKHFNEkPD86bTQenIhMR4/K7pR/PjgWr4hq8iuUoscg+ss9p7DZOg9GJlZ2c82CYkVrpZbnUoSbjh4PRTih7rgSiYJ7+FNJouY54MWBCXT3q/cJxIKyig2A2740qbypGD1+oqQ8QtsyoJbv8sG9I3j/qkqcPa9Ys1VGrdl9O4fPKKL6r6HJCCep3pUJ1IOV9FlE5dbCB96czl/6+nAIF8zXtyTQd3ISaxZIE+orecrouV5UMliq7lHi3pcDeOWM1XDrhlgKKQB49EwlquDUEE6Fj+H1QDGqKqeP419Ev/riIE6FoniDDcUUUdnvKRF6c8jyfMs3hKcVFNGAKOVZMMLhx69Pf2dfON+MmtLpx0hCRTTJ+WXVjiVMJBm0+LRHxqdwZHwKTxwdx6VJzlno8EpmsoTqfOCNnEQri2azWZKvM1E/Ssv5NptNiEBPdrzNZlPsQ6ldogBlNZnV5JR/VgpC2rBhg+ISu57xEelRUEvzhYi05Kby3zyDE1IL4uMZVgaU4MDB9WoAjmdP4P7XWUQ4DhNTUWx9ZQi/2hN7YOj1gXwoSe3mVIKVnNtPwjeUvHpLOAq8OaxcxScbGJjMpYT5w4HT+OTTx+O2JwxW0uE3p7WNkYGk4k0mCKsIopYRQXVcsu1iBSuVl4ufvxGfiF95aT6FzkXoPfwV0dL1f/SejAvqKimqxuKKZTjfsljR//iULABSzwpDJoKu1E4xIfvCm63zJMUGtLykqpHpjGPy0p8EQRQmZBFNglhTF1vFlPzAfvWWfr/FuBu+Th/RKAf844zflHv/abj3S2t/X764TDEHYFw/IotPz/7ElsuEiuiZAemx4MiZryH5faaIBSvpW5rXglYf0b8eGkt8Xk1zl/giysRsqgUL6bWI5sJ9T640b3uTxaOy30UilC7vKBfvvqKVKQ74Vt8Q7t2wUNhWU3kZrl5Shm/XL8SnRS8var8tadR84vMpuibonHi1613JMilWshPdG5LJnYlqT2L05p4l5hZer1eydD8TUkXOVkgRTYLkASDeoXCTU7MaJUKu0OpVGpIpeQMTEZg1KHaSh0CSPrUEK4nR67Omp/Uzx8bx8lAQnzpnPuabYgq3PukYnFSxnIgVVL0BX8lyJmollZQ28m+7VC1jvw4iahbRM5s5jkMU09eGmo9sLnQDuSL20L7EL1ZylC7vFwcm8d87h7HZOk+TX7Ycee5OMeL7ito3Jf5Ok/3mFbMGJD5EM5MK2p14kxb/cTV51HxE/3hAy/cnraYGnLlmac2PUMFut2tyFyCyD/1Mk6CWD1LpRmrUkU6o7+SkYkd6DS7JI2g5TVY1PVZLLRdNKlHzPFqbcxyH/+gbwh8OnMZ/7xrWfTwQe2g/4k9uLdP7jqGluRa/Uy1zJ29iYICl5dNWcP776k1juV7dIhq7xr7w/AA2e48J0e+qwUry6z1lidRJd2laSaZ7fEMYCUfxyz3xrgDpIr4OVOdD4sKQ/aV5tVMcGQvHbZNYRDVGzSshtYjGPgxMTOGHu9gkRyqTo+JiBEGkCSmiSRDfVgcmI/jS8wPY9iaruBiq1RoAAF97MZYaKc2V+aTWy2BEW/x2lOMwPhXVZIHTom+La7rrVuI0thc3e/KousUpVcT9/+OwOOm4vmPVHsFaHpRarOxxFlFGubLT44cTuwEkQi1xfSTKYWcgiF3DIZwMRvCz14cl59RDquVM5WS6qlQ2mIqOYzQ4goHxUU0VhfREzWfER1Rl+z0vx1cfEr+kJF6aTyyX+BqbnOLwtRcH8JUXlFPIyVE6rd6VGIIg8gMtzSdBrHR8/7UAxqc4vBoI4qrFZXFtdeZXB5Ba8mUxyZSZ0XBU8cH10oBUcesfCWPLjpOwzitOqkQkK4l5+LTUaqK2rKuG1tbZ1jfE/R8ZV05lpYbEeq5mHdTQz4QGR7c4KyPDSKxmHGIPeY+GHIxqTCXwBRVHqT95dALfSuBqFW+9FSlYKUsnJRUXGTEpuIFqQqxoHx95Eo8MHcOhU2ehtPTdyWUS/Z3sN//0sQl0+0ex+exKwadV74zoaa/VRzQZ4utoigP6TiYPbgRE6ZtkkEWUIGYGZBFNgniCxkVKgTwBdDDK4S8H9VmcpqJcnBKhpe65mGRWhtFwNM7StPdUCO3bpfkkv/PyECYjHF5nQ5hMcgdPZPmNQlr+j9+mB62WsUJ+zsgVQSW06EvyKGXFfhT8jMVbOA740wHtwTpKqKUEYhCvfBw6HVZVvuXXq38kfqk3XTRMWV5QeqcYDkZwXJRtQ9UiKgn+S37h3P86i7t6TyIU4fDY26exh9WXiUJPmUyxIprIf1ypR3HrTFuyySJKEDMDUkQTEIpEcTiknlUwXW55+nhS603yJNCJOTYRiWvzvELgxHBQ29P7dJJyplEufplsNlhE00HdIpr8BPIsCGIOnQ4rvswwTPw5/34k9WV5QN0iCsQrohMRTtPLxzPHxiWBYr/vH8VIGuVyedLtQan6USYQ/9aryi9C7byrcSq6TmIJVJtm8Y06oPG3+tyJWKL67782rNuir+cGp31pPn5bsWhgatXAUqVQX0gIgpBCS/MqBMaCuONfx3Aqkj1F9PDYVFx1FrlykuxemkxZ8o+E4tqMK5hmtIznhRMTuKv3ZMIlL0VFVOdkaVcAs6uJptO7WKF4+riy/2qycb4yNIl/qRz7f4dOw/XqMC6rLYXzIotkX5xFFNordqmhmiSdibfiG+IDmCWy8Nz7itTf8OWhIO7bzeGK2hI8O6g/FRpPKiVtxfzr+Dh2DWtbFtaDOJq/wrRCsY0Wi6gesm0T/OHOYdgWTldWksuplvKK/4qKDIxgCc28RTSj3REEkSXIIqrCjoND2B1QjzJO92HHI0+6LO822WmSLT8FgvEBSFqWe5X4xo7ESigvj/y50yWqnpNJ5M+tLz4/gHc/fhijGbCqxVDxi9Tw1WsLEEu8/5UEBQBcr8aCgl4YmIzrR24R5bj0fPeAJBZR2eeYHpp87pSu3RcGQ/j+7lG8oFI+Uwvp/jSPjkewOwtFFbQqWkqBYVlyW1VF6xT+8eBpSfEE8dL8o/tH8UHvUTx3fALHx6fiVkaCkajkpVgp/6lWlOYn0TU7U/D7/XA4HGAYBg6HA11dXejo6IDD4UBVVRV8Pl++RdRUP14r+R6vfCzycqNEdiCLqArvOn8pXtu9Hw/vYbN6HrnSJH8GJXt2Ja/ME68SaAmASZWIgkXUP6rPD1CrYUTe7LVAZq1YqT7HOC5+uVxOMBJN2uaYxuXUuAAgMHEWUT0ZHZRIpCTIldwEBlHJ9my5Vmipp/Pdl4eyc/IEaHk/evt0GLc9dyL7wiRBz1cjvk7FLyU/3s0CAO7sjfmjLymXFtb4nazkb8aX5nV2FxgLIjCu/QXEZDRgpaUibvvbgTGEErzsW8pNsFSUqO4Xw5f47OrqgtPpFOqjA0BzczN6e3vzmojd7/fD7XYnLCWqh3yOV2kschmI7ECKaAI+eb4Ff903DFZheT5TD1G5T5y8W6UKTmKS3Ww5hTZKS/OZQskiqpdC8RFV6344lMRPNsGxPN/qG8I3LrYkbKO1RGycdT5u/jndQXBy1K6ZWLCStO/vvhzAe1bEP6DlZGvpVMsLRDoZBFJFbBEdmXwL4cgoio3zML/0HGH7fTuH4xRWTuFlMtvoeQljRb+HymL1RbZj49OvCBziA9UyvjSvs7/u3oPo/Ndeze2tCyvxh89dHbf9K4/0wn9S3bfbceUa3Hr1Oar7tWK329HR0ZF2P+ngcrlQV1eXk3Nle7xKY6G68rmBFNEEGBgGZ5k4vDKh7uOULiMypUbeb7J7abJynBEOeMQvbZPq0rwWYj6i6Sk9mqPms62Ipth/JJpcEX1hYDJj8v9DplQ9fWxcEtDCIf2l+dOqTqLxffePhvGT11nFtrlYLc2U20ymzxGWKaKT4WMoLV4iUURT9d/OFZVFDE7LZBycmFYwK4u1X2jyKyodRXQupW/il6dtNhva2trg8/ng9XphNpvBsqxQrtLr9cLhcMDhcMBsNqOzsxN9fX2q24FY2UuPx4O6ujr09fXB5XIBALq6umC1WhEIBNDX14fOzk643W709vbC7/cDgOS88j56e3tVz6lnvBs3bkR9fT3sdjtcLpcwFqfTKcxFa2sr7Ha7oFT29PTA4/EI/fn9frhcLtTX14NlWdhsNrAsGzcWAEJf/DyozbWW8xLqkCKaBKNB+W42MKkzClWFuKV5eZm6JDfTx95OHA3NcfHWn6N6I2h1kAmLqFY1Odt2orEEylciIhotWJkyAG2T+eDKo6oz4SM6rjIXervNRSBzti3lTx4dxw9ei0/snoxUFa1YnfuUDk0ZtdOdNa8Yu2T+s6dE9zA9Q5SPKR0fUSVmg4+omM7OTlRXV6O7uxvbtm0DAAQCAbS2tkqUuvr6evT09MBut6OpqQkejwcejwcWS2wFRm0775/Z398PIKaUOp1O1NXVwWq1oqmpCQAEn8mmpibs2LED1dXVwnK2Wh+dnZ2K59Q73muuuQYtLS0YGhoSxiK2WtpsNrS0tKC7u1tQHj0eD9xuN5qamsCyLBobG9HX1wez2Yyuri709PSgs7MzbiwAJOfy+/2qc53svERiSBFNgtqD9ui4Fk+05PAlEXnk984H30ov0Edpaf9YhmRXPJ+Cj6huND4/hjSmsUmVVPuf4oA/a8jbmQvLHcD7iKb3rYyFVZbmFVJFJZcms8gjs7Ot7N7jS82/VKxoLTffqOvYQlGpVlTEK6Ji9Cii8ntTMONL8/ratzSsQuP5SzS3NxmV3RB+uLkhqY9oKjgcjjh/RbfbjYaGBsm2hoYGdHZ2CgoR71MpV4jk291uN8xmM9xuN4CYkstbMjdt2oStW7fCbrdjy5YtqjKq9aF2Tr3jVcJsNsdtE88Jb8kFgEceeQRWq1U4pq2tLWnfvCKqZa7VzkskhhTRJGQ7WnVCZgWQ34p7EuSR1EKujQKZMGpofX588unj6Z8sC/z90JimvI25/GrSXppPEGmTzI9ZTDauxygAcRhMrhR8vaQajBPlcq+Iqk3horLEiVb4w7S412TSIpqJ+7SlokRzEFEilAKYMolYeeKVpESo+XDKtw8NDaGhoUGiJLa1tYFlWQwPD8Pr9aKnpwebNm1SXFb3+XyqfSSTJRHJlEWlqH0l5VStrRI+ny8uKErLXKudl0gMpW9KQq7TpmR6WTHXZbdfHw7iH2nUNAcKx/qTKmr+kXJy9d1kYmle3U2B0fWFZWPI4nnkOA5vncp8taZMkGqQ4NZXhxKm8soG6pImvpD4l4BUgijTiaHM9X06n5jNZkHhcTgcEosjAPT29qKlpUX4rKZ8ybc7HA54vV7JNrfbja1bt8Lv98Nut6Ozs1OyrF5dXS0oaLz1VKmPZLIkQjxe/rO4H6/Xm7BflmWF/U1NTejt7ZW05+WTj0Uur5a5VjsvkRiyiCYh3Qe4XjL9oM61UrcnA0pAgRq0Mk6uFNEo4l1A9KKmiI6GorqWwrMx5AjHofiMKrJ9cBL/dyi9F6FsoeZnm4wnjyoXNcguyt9UMgWTv6a1+MNm6lpQ8zrRY6kvVPx+Pzo7OwHEorobGxsl1kY+3RHvy9nf3w+XyyUE0Hi9XlgsFthsNsGXUm271WpFZ2cnHA4H6uvrAcR8MPm0RmLll6etrQ2tra3o6OiA3W5X7UPtnHrHCwCbN28W/C8DgQBsNhu6u7uFPru7uwFAUIh7e3vR29srBBY98cQTcDqdEvmUxuLz+eDxeBAIBAQLaaK5TnZeQh2G0xqiPEPYvXs31q9fj127dmHdunVp9TX0+pv493+8hRdOG+P21ZQaMDiZeW+0G5aXa07bowWTqHLJTOG7DdX4997c53nMNT95Zy2++PxAvsXQxIJigyQoRcztF1Thvp3Dmvq5enEZvt2wMPb3Y4cyItv/vWsZKs6kDWp54iiOT2TPBzodPn++GT87Yy0/PvIEJsMDKC2uxeL5m/IrmIiNNaX4lq0aN3mOKOY9/VjdPDzcr56p4+LqEvzoslqwoQg+8I+jCc9Vv7AEfSfTt/RuqCnFjsH4AgiX1ZbiG5YRlJ21ErUNMaWDj4qm3JAEkR1S+Y2RRTQJagbRMqMB2QiLGMtwjs+ZpoQCc8ciOlOUUCBx+iatSiiQ/aX5QlVCAalFdCo6ganoaUxF5+VRoni2D07ivX8/orpfs0VUg79npn7nSkooQRAzB1JEk6CmiGYrR51abfG5xBzRQ2cUmbres7U0D2ivRJUvxBXNKkzLUWyohKnInD+BUiDZcvdrgSCOjE1pSq2Wr4IUBEEUFhSslAQ1H9HZ4H9UqMxAIy6hkWxYu3kleWeGS7xmGrGfbVX5xVg0/xpUlV+cP4FSQMtv846XBjSVM812mi26jxDEzIAU0RTJYnGiOQ89P2Yv2ShCwFtEwwWueWSztG6u0GIZPzoewYkJDenL5ooPDkEQCSFFNAlqS/MF/syb0Wh5iBEzk+wszcf+nyzwmo6RWaB4ac3R+poG6zS9yxMEAZCPaFLUbru0NJ89fvFGetWkiMIlG7pYOMrh4X0j+Hua+WuzjTjeKzg1hEg0CKOhBCVF1fkTSidaX8C1WH/JR5QgCIAU0aSo3SxpaZ4gCoOfv8Hi+ROFHzn97InpQMTB0y9gMnwMpcVLdJf7zCc+jemWtLxwvMGqlwrNBIVaYYsgCCm0NJ8EtVtZhN63CUI32fjVzAQldLagpXQtUBhKILlPEcTMgCyiSVAzfJJFlCD0UwD6SUFQU3mZsDQ/GymE2+NsUER9Ph+am5vR1NSE6upqoXoPX1ayu7sbDocjaT32TNHR0QFAWmazvb1ddz8sy0rKdjY2NqK5uTln4yAKC1JEk6B2M5sNNzmCyDX8z6YQLGb5ZCb5haZCIdwfZ8M15vf70dPTI5SIHBoags/nE5S/trY2dHV1ZV0OlmVRX18vkQWIlbOsr69HX1+f5r74kqFiBdbpdFK1qzkMLc0ngYKV5h4rOPXKMlr2E+rwv5oUy64TM4RCUAILPImCJvha6mLElkTx39mktbUVTU1NcbLY7XY0NDRI6s8nw+VyxW3j69QTcxNSRJOg9rwshDd+IvN8NvIwHo58HpdHtyvuvzy6HQ9HPo/PRh7OmUwVRWpJxGYeHMchFOHw770n8y0KkUUKobSwXhEeeukJ3PCTu3DDT+6K23fLr3+AG35yF+7zPCrZ/tphv3DMa4f9kn33eR7FDT+5C7f8+ge6ZefRslRts9lQV1eHjo4OdHV1ob6+HkBsWb++vh5OpxNAzHpZV1cnsaB6vV44nU50dXXB4XAIy+1y3G43GhsbFfc1NjbikUceEfqrr6+Hw+FAR0cHOjo60NzcLNQfd7vd6O3thcfjQUdHB3w+X5yc4s9dXV3o6uoS+nC73ejo6JDIkmycevsjck9BLc13dHTAarUiEAigv78fW7Zsydkbnxocp6wEzIa3bULKCu4IbuZ6YMIUtka/hy24E88ZNgr7L49ux9bo92DCFG7mevB37hocYpZlXa55xQaMTRVuDXU9cADc+0fx0hyvDz48/gpCUyxMReYZV11JC08ezX+p4imdVtmDgQE8u2+34r4dB/fg7cAgVllqJdvZiTHhGHZCmj7srYHDeHbfbqy01OiSQy92ux1NTU3weDzweDywWCwAYgpqS0sLhoaGhHZ2u104zu/3w+FwoL+/H8C0UtrZ2Snpn1ci+X7l8P6iLMsK52BZVlh69/l8aGxsRH9/P5qamrBjxw5UV1dLlubFcvJyd3d3C9bTvr4+OJ1O9PT0AAA8Hg98Ph9sNlvScertj8g9BWMR5Z2gm5qa0NbWhpaWFjQ3N+dZKmB2PP4JLRxilmGL4U6EUCQoo9cbdgCQKqEhFGGL4c6cKKEAUFlcMD/TtAlHOfxm70i+xcg7Y6HDGA3uxVjocL5FmbXsGwljREdtjFWWWlyxeh2uWL0ubt+GVWtxxep1OKd2uWS7uaxCOMZcViHZd07tclyxeh02rFqbkvx64ZWopqYm1TZiw47b7YbZbIbb7Ybb7Ybf70dvb2/cMfySOa+QyuG3i/uuq6uTyBUIBODz+TTJxdPQ0CDZv2HDBslnNXmy0R+RXQrGIrp161aJw7PNZkNvby/8fn9efEcmjhzFuH8/IpNBAGU5P/9sYAV3JKGylmx/OpgYDiEVa3YinjNsxBbcKSid/xG6FxczjbiR80iUULGlNNuUTIWgXuNrhjEZxBXzOHjY2aNcp0KRoQxFhkoUGejekk3+38FJ3Fp2DGhI3vbjl27Cxy/dpLjvwU/eobj9wuVWPP7FexX33d74Idze+CHNsqaLWPlTQ7z0PjQ0hIaGBoniquYKwFtclZRcj8eTcrR7IitkOquhSi4G+V5dJdQpiKeBz+cDy7Jxpn+LxQK326163MDAAHbv3i35t2/fPgDA5OQkJiYmUv73xr0u7LylFeMHDmZ17LOVfPtapqKE8jxn2ChYRosxhQ9yf8ubEgoA4d27cnq+bHL6zT048WK81WWusXj+JpxV/REsnq+s+BCZ4firu7Hvuy7hvh6JRMBxHKLRqO5/hYKaH6ea8iXe7vV6hc8OhwNer1fSXu15u23bNni9XsX2Pp8vbjmfX+4HYs93i8UiKJzV1dXCMjpvgVUbk1YSjZMofArCIhoIBADEv7GYzWbhglXi/vvvxz333JNN0RA15F9Xz6dlMRUS+VoaIhFcxvTlxddSD88ZNuIxrhEf5P4mbHuMacy5EgoAJSFt1WxmAhzDgGNmiXV3BjPT7imp8vL5l+Gj+/6VbzHSxu/3C4qg3+9HR0cH7HY7bDYbfD4fvF6voOyJ/SM3b94Mj8cDt9stROB3d3cLx3Z2dsLhcAgBTuJjxZjNZvT398PpdMYtsYuVTp5AICAEC/X19UlWO9va2tDa2iqMwefzwePxSJbv+XypvOLL/2+324V2LMvCZrPBarUmHGcq/RG5heG4/OfZcLvdaG5uhlyU+vp6NDQ0xL1t8QwMDGBwcFCybd++fbjpppuwa9curFsX7+ujFfblVzHw/Ev495Pz8JZxfsr9yFlpCOHtqElz+89GHsbNXI+qJY73XfwN04xfGj+WMTnTRcmn8jnDRlwRfQnfi27NuoWxCBym0ljOFsvPky+L6LVFp/HPqcqcnjNbnG+cxAImghemKpI3JrLCTL2npMonKkdw+1c+AmDan5GUjezhdDrjgpGIuUMqv7H8m/ug7ruRzLReW1uLdevWSf6tXr06MzJdchGqr70KTEVmFQBjRbnmtnLLonyZWx7FXUj5LcXL27z87ZGf5UQJBYBzq1KvWCOe1yhTjD8a3i0Zh5q7QbawLF+c0/Nlk6IFCxAyK0ffEtlnJt9TUuVvYXO+RSAIIgEFoYjyvqFKiqcWB+xskmlzsR77s1IUN//gyGcUt1bkymgufS2NKS7/yuf14DoXXIbPq34PuaC0iEH5LMkl+logiL6Ts8fVIFUOs49h3+A2HGYfy+l5Z/o9JRXCeV/zmzvw7gPd3d0Jo+QJQkxBKKI2m00xfYLf71f1WckV+c7LrGZZVFr2LkSeM2zEY4w0WXAufC2NKehtK7gjcfN6uuZKAMrfQ66sRYvKinDVYoquJjLDTL+n6CWS75v4HMJutws+oZSTk9BKQSiiALBlyxbBoRiAJFltPsl0rGSiW+LiMqPi9nxaFtPl8uh23Mh5JNtu5DxZtygaDfo10UPMMvyGaVadV/H38BumOWfWomXlBRFTSGSQ+aXnoKrchvml5+Tl/DP5nqIXsogSRGFTMIpoe3s7qqurhRJc3d3deOKJJ/Imz2uHh+F5exT+YGanKLEiqq5w5MuymA4fLfdJrCx/YHLna5mKRRQAfmn8GD5m/Jkwr/JunjNsxMeMP8tpEMficiMMFGk+q5hfeg6qK+rzpogCM/OekgpTZBEliIKmoEwthRRl90jfQTy2cyCn5zQl0J7ULIvPRTcU5IPj8uh23DayFUUyK8tL2IDvTH1PtYxmpkhVEQUgsXQq6X+59pszm4yzJZ09UUDMtHtKqlA5ZoIobArGIlpoFBtzPzUlKtqTPIggU5bFsnS0tQTwvpZFCMct9b1guDQnvpaZsiAWggJYMUsClYjCIVv3FIIgCL2QIqpCUZaUtERr8yYFv0alSNYOY2aiuF0bF+o+Rgu8r+UUivPma6n367thufa0WrmGYRhFyywxcxkLHcLI5FsYCx3K+bmzeU8hCILQCymiKhRlqaJSolWiYtkplaK4eaUu3Sju/7jEgkr5CTPIL40fww9qtyks8cVmINu+lnrTN6m1LxT9L1NyXL+scBXuprNnR9J+LQyPv4qB0acxPP5qTs+bzXvKXCB68q209hMEEQ8poipka2k+kSJaJLOIZjOKe1VlcdaVrGHTirht4vGL5V0zvzij59YbNK/2dTN5NkX+26rKM3Kk35drw0K8a3nhVjQqFKVfK586J3MV13JFoWaGmAmE/3k3Qj+/AJE9yrlfI3seQ+jnFyD8z7uzcv6Ojg50dHSgq6tL+FuM1+tFfX29UFqzEJHnCm9sbCxoeYncUFDBSoWEXCnMyTkVtI1fGj+WsBb7c4aN+BjzM90PDAOTOT9KNZS6V0vof/miMuwdCWfs3HqX5hNZRB3nLkDnm6fi9i0pN+LYeCQF6bSxrLwIX1pvFuRQ4kJLCV4LaEsQv7yyCAMT2ZM3XWZaZoBzF2gv1Stn8fzrwHERMIxyyrZskq17SjrUlBoxOJmZa1Pt95oO0ZNvIfLsViASQviRDwGbH4Vx7Y3C/siex2LbIyFEnt0K4wUfhWFhZjIisCyL+vp69PT0SNIZ8oonX8fdbrfnPe92Ivx+P9xutyQo2el0UrlVgiyiahRl0CL6vYaFWF9lwn+/oyZhOzXdN9kDIZUHRi4e+krjUbMIZ1rvTza+T58zX5KfM1E8kFoQ2QNXLsZX11elJB/P5YtKVfetrCwSxmFQUUXvtlVrPleh5yOdWWooUFViTDngr8hQjmLjPBQZ4l0lbj1vAb5y5gUkEd+7ZEFK5wayc09Jh0z6q390deYt1YaF56B486OA0SQoo7xlVKyEwmhC8eZHM6aEAkBrayuampricmrb7XY0NDTA4XBk7FzZxOVyxW2z2+2kiBJkEVWjOIPBSpcvLsPlZyrjJCrxma34KCUYaFvuXVlRhK9fZMEXn0+eyspsMoANTZcAYHSoFplWjJP19p4VFfAcGRc+q1pEGXUluaLYgLPnpedSUDffhOdOTKqee/qD8vFFGt+XrlhUBoZhMq7wZ5JClk0JowH4xRWLcMvTxxO2az13AbbpsNBtts6DV3RtTm+vxCP+0wCAiy3FWJ1hd5Z8cW/DQtTNT926nIyWszPjjmJceyOw+VFB6Qw/8iFEL/k0Ii//j0QJFVtKM4Hb7YbH41Hc19jYiNbWVnR2dgrbxG37+voEqyPLsujq6oLVakUgEEBfX59wnNfrhcfjQV1dHfr6+uByudDb2wuHwwGHwwGz2YzOzk709fXB7XYLffLnqq+vh9VqxeWXX47ly5fD7/ejv79f6N/tdqO3t1eonshbbltbW2G32wUl1efzwev1wmw2g2VZ2O122Gw2+Hw+oS1f9runp0c4f6KxEYUPKaIqZCtYKfE5c/ck1qz0MkCxxrbyZnqGk+mhJxsfwzAS66yaAdwAZSU1U0rAR+rm4Td7RxT3ic9qVVF4ldw5lLhhRXlcn4XGDFuZRxHD4CwNLyJXLCrDO2tL8T9vjeBfxyeStjfIrk0A+E59NUqNjKCITkUL+7vUQ7a/9/PMmVPY5cpopPcXZ3ZkRwnlFTeLxaK4n1fYWJaF2WwW2ra1tQnHNzY2or+/X1DUmpqaAEDwzfT7/XA4HOjv7wcQU0qdTic6OzvR1NQEj8cDj8cjyNDU1IRAICBReFtaWtDe3i4osmazGc3Nzejq6kJbWxuampqwY8cOVFdXS5bmW1paMDQ0JMjR2toquBoAkLgktLS0oLu7W1BaPR4P3G43mpqaVMdGzAxoaV6FfOQRzalFlNHxINPQ0L40folR6TA1i3Cmh57MwsrIZEnkI6qkJP/wstozf6mbuBeplGzl+W5DNcoTmDTFFuX3rKjAe1dU4ENnSSPLtVpE+b4KuciMmvtBKlxcXZKxvtTQ+ntlGMA63yRZxh88/QIOs49h8PQLisfIv6bqUiOKRRdihONmjSKa7TttphVd49obYbzk09Jtl3w640ooAGHZmldI5fDbeSUUgGAx5I8PBALw+Xyw2+1obW1FfX09nE4nNm/eDCBmrTSbzXC73XC73fD7/ejt7RX64F0CeCUPANra2uD1euOU4P7+fpjNZvh8PlgsFkG5VUMst9vtRkNDg2R/Q0ODxLIp3s+PDYDq2IiZASmiKmQtj2gCchmsYWQYTRHhDJIrCFttZjgvssTd8PVYOTM9dL39qX7dKkvz5RqSzH8lgf/o/GIDrlhUplG6mLW8/SILviTrU6sVnZ+PqQJWRDN5DXxyTfYj2rX+XvlW4ubBqSFMho8hODWkfJDsezJA6i40FcWsMYlmeyEo0w+5yJ7HYsvx4m0v/49qNH268FZJJTwej2D9TIbVasXw8DBcLhdYlsWmTZsAAENDQ2hoaEBTUxOamprQ1tYmsUqKFVsxmzdvRldXFx555BFBBofDAafTCbPZrHocEFuCl8NbRhMhVly1jI2YGZAiqkJx1vKIqmsCuSygw0D7cyzZ8/aiqmLF8qRKD2rVYCWNsmgl+dK89LtQt4gyiuPg5U2k1yUa0zcutiR9EdCi52jNl8q3iiRyUs4zmbwGgjkw/Sb6LYtR+oZKiqpRWrwEJUXKwWbynhmGkRS8mJpNFtEsv4Bnsn95YJKx4XOKAUyZZNu2bfB6vfB6vZLtbrcbPp8vzhdSbIX0+/2wWCyw2WzYunUr/H4/7HY7Ojs7haV2h8Oh2DePPOUSj9PpxNatWyXH9Pb2wuVywWq1YmhoCCzLCn1VV1cLyiZvcRX37XA4JJZYvl1LS4vi+XlrLADVsREzA/IRVSFbFtFEj66qksymcplXbMBoOKq4z8AkloVHi8LKyP6Xbxejes4cBysxMlmiKgoaA0DpW9FkTU7QRMtoMzkjvJIXUb4cCoJMXgLBMwXGV3BHEkaAJ9ufCK06PT8u8fBqKi9L3Lfsl8IAkqX5KW7WGESzPo5MXVdK0fHGtTfCsOa9kgAmeWqndDGbzejv74fT6YyzJMqXvqurq7Fhwwa43W4haIe3blZXVwvL8ACEaHur1YrOzk44HA7U19cDiC1184FDvCIrTw1ltVolKaPsdju6u7vR1dUFi8WCxsZGuFwuYfm8ra0Nra2t6OjoEPr3eDyC64DNZoPL5YLT6URdXR36+/vhcrmEYKXu7m4AEJTm3t5e9Pb2wm63q46NmBmQIqpCPnxEl1dk9uvovm4J3vN35cooBoZRVb7kaL2Ry6PkdQUraW+qiWSWQrmPqNqSdSxqPrUnWaLjcq2I8p3lyiJqAKBX583keE9ORvDZyMO4metRTNwOTJe6/A3TnFKFL7PGF0c92SN45F8TIwsajMyiYKV0lublOUObFapzZeLeEj35lqISCihH0zO37sxoCidAOf2RHHEgkJ59ajlIxUv0SvT09Ah/m81myWe+30T75S4HanLYbLY4WcSf5amtiJkFLc2roBY1r3Sj04N6QvdSicUjXX5xeS0qEpTwZKBNwdQU1KTSQI/+lumHatJzy/aHEyzlJvpaUtXrNI03g5NiOaM05cpHNJVLOZNLqKMBP27meuLqpl9QFUsTJK63fjPXo7uU5VmVRZinsURuJkZlYACjbGk+25poz6Yl2T3BGdIZxrWyIMnPnWeOa5OJ26ph4TkwXrFFNTreuPZGIc+o8YotGVdCCWI2Q4qoCmp5RBdnKSk4h8xFzTMAzqtKHjWs2UdU9HdlMYPaUqPi/rhgJR2PmEy7iSW7sOVL81Mqiqha1LwmGRItzWvoU+uPU/59yPmwdR7OOVMFKJKi72R5EYMvrTNrbp/KnGXyGrhx3fq4uumXR7fjOw0LJUooX+pS7/I8nxdYC0rjmoqOIxwZxVQ0Pl8ooOAjCtnSfDT7N+/astwsmKX6++rZtARVJdJZUArey9QLTvG1d8N0607VZXfj2hthunUniq+9OyPnI4i5AimiKqhVVjJlKcST4zJ3w9SiakQ1BztIo+uNDIMHrloka6FyZB4tonrTN9UvVK5wlCihPZB4rhNJoGlpXuOk/Pc7avCu5eWoMsVfsx+tm4dbzzcLn40pXL9P37gCf7thOVbocB1J5VrO5M1oYalRUjedV0bf+Oc345RQpWX7ZGgNEgPEL2rTxxwfeRIHA7/H8ZEnFY+RW9oNDCNJ1RXmuJwkXs3FA0KLv7UStWVFml52M+prncTSSZZQgtAPKaIqqKXFyVaqES6LfaufT1/ENc98k8wieqYfebvzzNqrpWTcIposah7SpOHzTQY4zo0vmRiziKbqI5rg/Jr61Hbe5ZXFuPPiavzP1YuTtr1ycRlWVhShymTAB1bpqzijp+BCKjOmd5o/uzZ5iUu5Mnrt5GNpK6GAvhtnKpePkkV0frEBC8+4WNxx/vyc+IjmwlVeyyk2Wyuh5Amh5ZKcaRW7CGKuQYqoCmrBSune1NQsaDlw+cI3LrKAQcy/bWGS5VwxWh+kVy+ZXq78lq0ay3RY0FIJ6EhE0u8pzo1APVgs1VwGaVtEdZ7PUmKMG4P8eis2MPj1NYvRY1+KKpO+kelRRFNxM8nWzeg5w0Y8xjRKtj3GNKashALS38SPL6vF8ooifFFkeZa0lf0PAFXlF6F23tW4pLZe0/kMZ4Lmfn31Yjxw1SJcVluSG0U0A2+I/3FJ4lQ6Wk5RW1qE7uuWxm0nRZQgZj6kiKqQ6KH7qXNST5atFtyi1yJqKdH/1V1cXYJHNi1B15WLYGAYbcFK0H6RfHrtAnzYOg/fslXjuqXlui6uTD8stOih4u8iVoc9/qh0fEQTWT21zr1e5Aqg0vVmZBgUG7R9/2K0lnoFUrMip7pEm4zLo9txIyeNzr2R8wgBTKkgVtAuqi7Bw9cuQZN1nmJbpVFVmFZgfuk5uHvDRfj9dfFBQVctkfqg8r+leSYDVp+py54bRTT9Pq5cHF91TYyW+0SRIVZdKu5YjUU5CIIoXEgRVUHNIspxSFiWMVVMBmVFqH5hCd5RW4rvNkgTX//g0hrd5zAyMb+qkjNjUxvFR+qmH6gMtN/Iy4sMuPV8M647E8mqV69Qq6eeCpp8REX2QoOKL6iagqpJhiTnT0Yqp9Ujq97u9fiXpiS76G8Fd9eUziEPTPqz4T1xAUzJeOq9y/GPd0uDmfRVDVNvXCxLVM9jVnF/kW7ULoMa9QsTBzUWpfly0LFxYdxcvXuF1CVEywtIOhlFclmxjiAI/ZAiqoJasBKg7yF018XaKjx8YZ1Z0fpw+wUWuDbWxFkV6uabNPnIiYlbZpN9fM+KCvz3O2qwSV43PlWLoJ6oeQA/fWctfvbOWlxg0e5bqoZ8Lr8gWzaNVVaaRlURRWKFJ2GwUpaef1+/sApGRtlHUn7VZlI+PcpASpY00TGVGlMjJUIpOr5sw38oRtMnFIthhJc3Hl2KqOx/MYneaReI5kDt2kyXG5ZXKPpG86TrI3p+VbwLgfPCKslnLXOp5dr7WJ2yRTprfv0ch+f7XwdXwNXKCGImQIqoCmrpmzjom7QrZGle1G5ZS8qLsLyiCGfPK4aRAe6xVeOhaxYn9LP82Op5uP/yWqyeL7Uktqk8WOQPPfk4FpcZYVOIHteThklynI7DOAAVxQast5TAmIFHrFzJalZZNuUxQP3Bno1coVrmRq3JjSsr8bcbluMTCvXU9SlI+uZZ19K8Qt8XWhJb38RHVKS56sCcPqAYHX/1knLFaHq9eUTTDVYamXwLQ2N9+Pvbr6X8wiI/TO+LKRD73SW6vNP1ETUy8eOXW0C1nIFXRPnMDecumH5Z/e01i3HnxRZ88hzl8WfLHvr73qdw/Y/vRHfv01k6Q6xEp8PhAMMwcDgc6OjoQEdHBxwOB7q6utLuX618p1YaGxszIocYn88Hp9MpjNXr9aKjoyOj59AqR319PZxOZ1r98OPo6uoS/k4F+XeVjbnPF1RZSQW1hPaAuiXpPy6xYO+pMH7vH03pnAaGwbYrF2EsHNVUtcXAMFhXVSJZ2vv0OfPxYRXLgPyhEl8JKfZZ/GBiFB4kWkn5AZCBJ4fe9E0GNR9RJvGDOpGSyjDAv51ViT8eOK14/mQkGkKJyouSHkX0vSsr8Ms9pwAAdfOK0T8aTthez9K8knx3XmzBh588pnqMuPvyovQuAq7yLPyWaVatrPScYSO24E6hspLePKLpukCMTL6FyfAx/OPgED6y5hIN59PWr144Tvn65vtO10fUwCRX2vmxrZlfjL0jytcgX3DiR5fV4rkTE7hK9IK/srIYKyvV3XqyZRF91Pdc7P+Xn8WHN1yTlXNYrVa4XC50dXXB5XIJJSwBCOU429raUurb7/fD7XYnrLiUDKfTCavVmvLxSmzdulVSgcntdseVMs0FNpsNLS0tGBoaSul4lmVRX1+Pnp4eSeUnr9eL+vr6pFWrxCh9V9mY+3xBFlEVEvmIqj2E7MsqJDkbgfibYDLrWrGBUVVCf3F5LTbWlMK1caHq8fU1papWjLiHiuyzWiR9qvdxvRbRdM8nJpkazzDSEpQGRvm86chiAIMWVUts8p5TObf82pTXLBdjKTHiUftS9Gxagpqy5C8+epbmLaXxv58aletrZWUR7rZVo1JkBV1aXpQ0b6lYmsZl8QExDxg/ho8Zf6YaHf+cYSM+ZvxZSuU901Vu+MOLDNrs0opt5PcWTRmE5ceoBbTx/6c3UIMsD7FimzP7t25YiMWi61B8v1p2ppBIdakR719Vqbm8qrj/TDIyOY4n9rwCAPC++QpGJpULE2QCsfIppqGhIa5kph60lAxNht1uz6gy5Pf74ff7JduamppQV1eXsXPoQW3utdDa2oqmpqa48qN2ux0NDQ1wOBya+1L6rjI99/mELKIqJIqa17Usl74oAudVleD7KQQp8cjHJB+H0sMcyEnebM1177WiSWaxRRTqVqdEPmCp+mBmy0qj98WHf/lQE8cu8hdOZKRcYDLgVGhatf/6hRbc/NRxSRsl69rD1y7G8oqYNeuFExPC9vEpDg9ctRjX/+2wsO3jq+fhoX3Kqw1KFlgGSGrp1GsJ5UnlJUt8PfzXlZ/A+1fFygWzoYiG88WfUP77TbFolqICy58vExbRZPBNasqK4DjPjHt8MQvUVYvLsKGmFKenOJxflbrfeCZ+apFoBO+//27sG4xZ9ENTYYQjUwCAcGQKF33nVpiKYtfxmpql+Mttd8OQYFUtE/T29qKlpQVAbBnZ6/XCbDaDZVnY7XbYbDawLIuuri5YrVYEAgH09fWhs7MTbrcbvb29gtLHt/d6vfB4PKirq0NfXx9cLhd6e3vhcDjgcDhgNpvR2dmJvr4++Hw+tLa2wm63C4qSmhxer1exDzlWqxUsy6KxsREOhwN2ux1ms1liCezq6oLFYoHf70d/fz86OzuFc/Py8Iqrx+OBy+WCz+eD3++Hx+MR6tt7vV44nU40NDQI7Xfs2AGXy6Wq4CnNj5qy6na7hXPJaWxsRGtrKzo7O5PKofRdAZDMvZ6xy783/rtxOp1oa2vTPY+ZgBRRFVQtotB3c850fsx0kMstf7apKd+pW0RTOzITM5Y8ob08WIlRPkZmOQVilhstMFAfi5Yxaq1lLiaTj77rl5XjS+urhM+JXs7+1LgU1/51WmlcVVmM65aW4cmjMeWSQbxvoKXEICihQMxHmGdsKhqnXH567QKcs8CEJ4+O49ol5TgyPiXsy/WvLBP5NXlStYjK5zOVdzkOHM6ZH6/kCRbRNC8otXulAdO/K7XLigPw3pWV6QmQQAa9hCNTOMKeVNw3ePqU8PcqS01K1ulkdHV1CQpef38/Wlpa0N7eDr/fj9bWVolixy8Ju91uWK1WNDU1CX0AMSvjjh07UF1dLSh5vD8qvwzOK0idnZ1oamoSlA+LJRaAK1+6TiSH3W5X7EOJvr4+OJ1OOJ1O+P1+NDU1Ydu2bYLC53K50NfXB7PZjObmZnR1daGtrU2Qp7u7W1CM+b54yzGviNlsNtjtdtjtdrAsK8yBz+dDY2OjoitAovlRagtAdZz898gr64nkUPquAEjmXu/Yxcfy5+fR21cmoKV5FYoydPeSx1xk4waldVlbrhiqtZXk10zSZyL0HCfN6ZniCUUkV4KZeF9YBYkZQDLBG2tK8c5F0/5pZ4tSTl1SLQ3GMSTyr00gXt28YiwqM+JmhWCkZKSs/CscdvOa+RJlOJFerHRei2j5VEnZkCtzlSKT62hYrv7H2l+9pBz31C/ENbLMDrnO0JPK6cTHaPnNyrM6JGM3G9ItE8cBl9bGByjy1166Crfasrx4czYeQuLbdyauDaPBiMc+/218ddO/xfqE/F4a+/y1TR/EY5//NoyGVMtgqNPU1IS2tja0t7ejs7NTUErcbjcaGhokbRsaGtDZ2Qm73Y7W1lYh6Gbz5s2q/bvdbpjNZrjdbrjdbvj9fvT29gr7eaWDV2oB6dJ1IjkS9SGHt5j29/djeHgYVqsVmzZtEvb39/fDbDbD5/PBYrHEKY1iGcxmMzZs2CD5LF/6Fy/722w2BAIB+Hy+OLmSzY8Y3qIqPxcPv108f1rlEI9Fjt6xZ6OvVCBFVIVEwUrJaDo79hb/5XVmBZ+9eN6/Ul+pRTlyxVErmpKqM6knGtdzmB71/EJLCX58WWIXhWTfXswiqiGPKBIrDQtLjbjv0ho4L6qKc21I1SL6wFWL8LvrlsSVUtWCfM61zquSPPJrN5lScut5sahlfh7EOSiVjtws859dIBqvRYMPoLhPpSj9bCqnER0XLP/7VL8WNPgLaxjLjsFJyecfXFqDtQsS5+Y1GuJ9OL+8zix8N2KFLlOWRXlfjMp1ks4ru6T/NPoRYyoqxnfefwvuvvHjcQYFDhzuufET+Pb7bxaW6HNFomAaq9WK4eFhuFwusCwrUejE+Hw+DA0NoaGhAU1NTYLSK7ZuJvPT1BLUk6wPfmmfx2w2C7Lz8MvIZrNZsb90/DoTkWx+5PAWYCU8Hk/KQWZ6lVMtKGVPyNY8KkGKqApqS/NA8hvkF8434283LMMHz06cMujnl9fibls1vrDOrF9AFXQpohqPS3lpXkdbPcFKVSYDLqqOt+KISfbgZmQapkHlGHlNeqU2DTWleM+KyniLcwIhEia7Z5iULVFaKispnlPNGpygbzkt1nl48OrF+MZFseUo8U9IaTgfPEu67FpdakTjklIsKjXg9guq4g9IQLLpunpJGbZduUhXn3I+KspGoWSx1cMDu/6CS3v+C7c+9YimH1gqV8OaBcXounKx6v7FZUZcuyTeL/z65RWCG4b4OsysO8J0XwY1pTENTTRbcgPArqMHVbYfyOh5eJKlWHI4HHGWOd5/dOvWrfD7/bDb7ejs7JQsFVdXVwvKI+8HKlYCgZgVMJEccgVRTQ6tYwHiA3NYlhUsqby/JO8/OTQ0BJZlJXLqRWxR5a2s/Pnk40s0P3K2bdsGr9ereIzP54tb0k8kh/y7ksumF941gMfr9aadyisdyEdUBbVUNRySBwUwDKMp/czCUiPOr0qcW1ELqS73q1li5L2lnL5J19q8+LjEB+qV50YFi7Oij6jiyYBFokje9Tq+r4RuDVmy1mmxwKdK8u+FwVkiV4VkFlEln9Ovr58PjuNQWhrrRx4EpUayl57PrF2AVQlS/KixgjuCYGQ5SoyMxE2BV0SZ0wfAVZ6VsA8ll4aR0BgOn2axotKcNf/WRP0uLjPi4WuXKH4H4k3il4kiA6Dhq9CEqvIpIp1rV9J/Bid4MhzC33bFCiAUGYz4cMPV+H3v05iKRvB/u3dgMhxCaXH6BTl4/H6/oLC4XC44HI44vzw+xZPT6URdXR36+/vhcrmEICF+SRmAJFK7ra0Nra2t6OjoECKwOzs74XA4hNRQdrtdsFLyihHvT+jz+eDxeIQlZJvNpiqHWh9KyPNj9vf3Y9u2bYI83d3dQsBSY2MjXC6XIEN3dzcACMof/7/dbhfa8Iotv3weCASE8/X19QlWTqXxKc2PGmazGf39/XA6nXFWTCUfVDU5lL4ruWwAdI198+bN8Hg8cLvdCAQCsNls6O7uFsaTyjymAymieSQrped0dMmoPAzEim16PqLaj9RjEVXaX1NqxOCkNPr4J++sxWuBYJzlTemcav6cDGJVrBznLsCJiUiCdEzxchkSuDVkK4hNT2UlMdm4FMX+0XoiusVz9qPLavGLN1i8a3li9xUtwWl6+WzkYdzM9eDB5+7GR6+4UaKIjoSjMBx/GkXbb0dkzWcQOe/WuOPXLijGRdUlii4W51vOxsULq7F6wUJNsqWilCXqt9TIJAw+44m3LKpLcufFFoyGo/jJbjZpv+JTi/8WF+e4fJG0GIgexLJmctlvz4nDGAsFUVezBA/ecgcuXlGHz131Xtz86x/Af/IY9pw4jIuWZy6lDq9kJku1JA844UmUI9RsNself1LrR2kJ2mazxS09qx1vs9k05c202WwJA2DUZFaTU/5ZSQHcsGGD4jK5nvElQmuaLDU5AOVxy2XTO3Zxf/LzpjKP6VAQiiifO6yhoUFwguWXEwoNjkPGrFmZevZLlTjtvUqW5rOgiKSaRzQZqxfEWxx+c81ifOflITx/IuYnxyDmS6pWzYdh4gOklJbw+C0fXZ1C4BDUv+NsWcHi5lzz0nzmEVtEU00tdPa8Yrg2Jk9ZpliMQLxf53lXcEdwM9cDE6ZwS+Bu/PVfQ1h94UeE/ZuYHSjafheYaBjGvQ8garkQ3KLLJX3ELYuLBLp+1Ttw05kXpPGp1MyMEzJHVSMj9V1NdC/QmlpM7I5xVmURdg7HAqK+U1+N/+iT+gTal5XjxHhEoyI6rSiKRVleWYx7bNUYCkZw/XLldHJayHSwEs8FS8/C/952DzacdQ4qS2KK8sUr6vB8+39hx4G3cMHSszJ3MoKYIxSEIurz+dDc3Cx8tlqtGc1RlUlWVhbh7dNTyRuqUEhlicU36A010z6Xa+absLjMiBMTEdxxoSU3UfM6jtus4HtbXmSAWUdwT5z/o8pjO62HmIqVNe1+E5BqftJsiCN2b8nQiq4ENYu+clt9IzzELMMWw51CmdCbhv8b/3p1CF84rxXzhp7F5Qe+CSYaBmcoRmTNp1D80pcRWfMZADdq6l+vO42S9L/aK63YZVtYgh2DwcQHadgp3iN+mXjvykosLp+EycDElS4GYi9yWiuzipvJXyLkGRFSQfw7SPUlSLFfgwHXrr0obntlSZnidqJwEftv8vlO57Ic+aQgFFEAgiO11Wot2C/iU+fMh21hKQ6enk6qXb+wBDWlxqRLhzzJAl9yiZFhcMcFVfANTeIL508HhxQZGPzq6sU4HY6itqwIbDB5wm0l9PmIitwBkhxnykD4rnyRkWEykGRedrwhD1lks+kjqhclN+kLqkzYORzCpgwoG5uWluMXb5wCALxvZUVcaV3xSrLa9/Bh6zzVkrzPGTbiPs6Bb3A/AwPgSvY3GMZeVI30SpRQ495fCZbRFcwlqkny9VrHP7/OjK2vBAAAFTIN74WBIP50aLoAwLoqE755STXe94+jSfsFkgTLiY6U+IgywL9fUp3gSO3Vt7JV0IHHmAFrPDG7sdvtuspsznY58knBKKKFXq7qj41LhZQy4hvbyspifEWU9DsZmS5lCaSevgkA3reqEu9bFe9DWV5kQLlW84YKqVpEtfLvl1jw89dZtJ1r1t0HwzCS7zEbJT4ZlT6zSVxlpTyuzSv5IP7nxhq8GgiifmH6QXq1ZUV4+NrFiHCxJV05WlxPbj3frKqIAsCfje/GcgTw8anfgQFgYV8AgDgllDMUY2rjfTjUq61S0+HRE3j+2EnMN5VitVk5mv9dy8qxuMyIZeVFkrk8Nj6F7+8eET6XGBhsucgS54ua6CtN9LIn3mfUY3ZG4qIHaufIhp744bp5+PEZF4H5Jul9LFGlNIIg0oPjON0rUAWjiLIsC5/Ph0AgIPiK5pu7NtTisd3H8aWGJZK8htlQJtOhkKysYvQEY6Uyp43LKtC4LL0crDyxPKIKfoY65lOxqdrSvPZudZFqsJLSK0e615KSRbSy2JBWEIoccWWmRKRjgbvoiq/h0A4/Vo69JGzjKlbEKaHRxVcDOKTaj9jS6N77T/xn7yFctvgsPHz9J5XbMwwulqUpC0ai+FbfSZyemv5mv3ZBFVYoKeIZcNPQm4/TpPHdVWqxzLxi+IFVlZhXbEDV8AmUigZhMBgQCoVSelgSBJGcaDSKoiJ9qmXBKKLd3d1wOBywWq1obW0V6swmYmBgAIODg5Jt+/btAwBMTk5iYmJC6TDNXF5rwsVDY1hQwiEYnPa9mpqa9hGNRiKSfUkR3XSDoRCCGYjpFL/hh0MhBIPKN3ZdcoqPE+ds4bi4foIh5X7DGmpo80xNTQn9RqOJPQrVxhGNTJ9P3J9aH2JrYTgYQjgcL28oGAKj0RWgplgqdygUQlFE+dhwOPPfEwCAk8qwqdakqT+lOS+JhhHU6JahdA4uMpW0TVw/KteSXmLf7zShUAhBQ/xYtMi0evQZlI1L068YRmNVRTimCBOX/Cemqt4ByH8XwSAMYwcRrVgFAIhGp8/P/2aj0ShCwVDccWrsHQnjkMhH/d3LSnFtbZHiMaFgEMaIyv1F4XcsHBcKInJGSWNE11MorOE3JVMqldoHg0EUMaL7YFD5u0mXq2uKcGo0iFAwJDwLDAYDpqamcOLECdTU1OhSRrNdO54gZjpjY2OYmppCebk+16uCUET5SgU8DocDzc3N2L9/f0LL6P3334977rknBxJKSaccZbaX5gsJXemb0nAvSBVOtjSvaBXU0d86s0L+QA0dXLu4BP88nhkFTHy6dy0txXlm/bkzl5Ub8Zk1lZLa76lQlGeLUzpR8zyXR7ejzLcVDBcGxxQjWr4cxrH9wn6OiyAQjEIpp0LRwL9Q5vsGQnW3ILhGmh7lJuvVqK9mMN9UousiWzO/GPdtMOMuHwuzicFtaxMUzUgw/1r9R8WWy4iGG41Wxe4bFyzAV3cMY1WFETWluVPwqqurEQ6HEQgEcOrUKV2WG7KgEoQ6HMchFArBYDCgpiZ5phMxGVdE3W63kAw1EVu2bFENSmpoaADLsujt7U1oFb3tttsk0fZAzCJ60003obS0FGVl6S0BjpeUIFRkQkmJrIZ40bQFo8hojNufmOkIipISE0pSKOMY16PoBmkymVBSopxQWZ+c01QXTz+APnOuOa6fElOJYt+l0G7lMBYVCX0YE1S1AtTHwYgsFkWi/tT6qCgy4NSZxOQlJSUoicZnQygpKdEVHLXoTLYBIPZdlKr42Yq/p2/W1+Cffz0sOWeqXL20Ek+eUWo/usaMkhJtiqjROH0dfmrtAmzS6fKg+P2bIknb6OlPrzyM6LdWWlKCEoWyofLzWEoM+GjdfPz0dRaXR7dja/R7YDAl8QkVYwCHxbvasS/6A5x9jh3brlyEB/acwqfnv4Kyl7eA4cIw+R8Ec9b7YDQuEI5bZV6Kq1fGlMiwLJom2djPLynBjy+NFTWdX65eYay0xIQSld+SwcConqe0dLrP4qLpOTMYk/+mtG67uLYEj9rLMK/YoDnAKRVMxSaYSkySZ8HZZ5+NEydOIBgMJl19IQhCGwzDoLy8HFVVVTCZ9BV1yLgiKrduaqGqqgo9PT2C0slbQZOVnKqtrUVtbW0qYqaHjgjvRGTMIiruM0tBJw9fsxgHx6bwjtrEpTXFpFprPpFv6TlJamfrOff3NizEV18cwCXVpSgyKJfV1G3xlll2VdM3if7OZGGDq5eU4fYLqlBZbJBUOUqGRAKNFvbrlpbjyaPjuLRG+ZrQGriSa+6xVeNbviFcpnAt92xaiiIDgz/v3i2kblIKTNpfegnOHt8OBkARONS9/nV4Rr6Hq2034PsrX0fR9i0S/9FY9aVh4TxaV0amohyMTLw1rrY0+QtswmAl2d67Lrbg3lcCcf2K/XwjGV55sSi8GOQCg8GAJUuW5OXcBEHEUxBL81arVRIx7/fH/K8KNY1TqgnkAeA/LrHg69tPYll5kaRSS6bI1qN/eWWxYmRypmQRK3CfO28Bnj8xIXnwfbu+GtsHJ3HLGm2J5bW4K6y3lODP1y9D2RmLp1JZ1gxndMo6DMPg/QpZEJIh1hm16hvfuMiC96yowPoq5bdfDVVus4pYdxOP75ql5eipMqFaQZnjjznELMNvmGbczPXAoBAdX1NzFZ56+j5cM/JbMACKEUXj4Tvx3MlncVXw77GlfEkQk37YUAR39w3h4uoSfPKcBckPiB+N6h75O0LjsnKsqizGsgrpI0H8cjalMQ9SmZGJS7ZPEAShRkF4X8tTN7lcLrS1tRVsOqd0/Dw31pbhoWsW45dXLcpOiU8Zl1THlsY2qlitsoncirOwxIhPalAkl1cUo2fTUsm2DTWl+PqFFtSWqb87LRbt05rcvrzIIMi5wGTE585L5YGvjtav+CN1sWXaz67N7PlTQasKUWJksKGmFGUq7gfGArKIyr+H2rKihJW0AOCXxo/hDsM3FaPjTUYGl117O7YvuwNTiF1rxYjimsnHYODCiDDFGLH9QFUJ9Rx8CV/51x/w09eeUbxGnjsxgU89fRwvDwXxq7dG8PSxcf1j1tOWYbDWbEKl7OXYmIJF9BdXLELjsnL88B36/MQIgpibFIRF1OVyoaOjAwAwNDSEurq6hDVy8006wUoAFFOtpMMCUc4U+cP1uw0L4Ts5iYY8KKJy9aR70xK8ODApfC42AGdcNOPyXcqtVVp0mo+snodXhoKYbzLgskWpjfcjdfOFJOlAmsFoOo51nLsAHzyrMqGinSsyZcvKQN2BlKhQMMUmeuMuMTAIRuPLTQLAdoMNkbrPwLj3gTjrJsMwuHjDJ3DYshQrd94Bg6h+1J9hx/27VqNp7BTev6oS1aVGSd9vBvZj36lY+qbbLrhScs6vvzSI7YOTkm2/2TuCKxeX6Xp51ZorNBFiF1MtwUoAcNa84qSJ7wmCIHjy/9Q7QyErnnKuX16BzjdjysoHUlgGzTR3XFiFzz83gLr5xVhVKf1KK4sNuGpJ+lVsUkH+sCsyMLh8USmuX1aOsakoVlQUC8nEk636aXlulhoN+NE7M+sznK2lefl2hmHyqoRq9RG9t2Eh7tsZwMdXJ7ds59pH9I4LqvC3w2P46pkCE+KzJ3KhcW1ciNtfGsQl1SWCdXxpuRFHxyNgAETOuxXRFe8+4+cZz7KKIjCMQZI660bOg+dCG/DrvRvx230j6Nm0FLVl0y9X1WUVmIyYYSmpwB42BAOmy6DKldB3LirFnRdV615BSewjqg1p1Lyu00t494oK/O3QGJwXVaXeCUEQs5KCUURnEgtLjfj9dUsQ5YAl5fmfwuUVxXjUvlQxqCGfKEnCMAzuOmMteWDPKYUWan3lZ1x6z3qu2YTB47GchSVGJkGt+cL5ngDtFrIrFpfh8kVLNcmf65V5tSphQOLxXbKwFH9qXCpZlv7hZbV47O0xoRSpmhJqOP40irbfDoaLBTUNL3k/5h35C0wIY2v0e9iCOzG68EpUlxrxwbPmYfvAJEqMDG6/YDM+/tRx7Bnn8LnnBhT7rixm8MXzq/Cu5eUZv160dlckUURT10SdF1ahde0CRb9cgiDmNvnXomYohaCAiinECOVkIl1SXYLf7I39faElccqafA1PrwLw1fVVCEY4rKsywWwyIhiZGelhxKNMpm5onZNCUjmSSSwvj7morAifSeKvKyihIv/RisVXAyuuRvSl22HiYspo37ytAK5HiZHBf18Ws9gfH5/CpIqJsdTI4INnVeIjdfPi5NJDwqV5ja9YYveKqTQuZYZhSAklCEKRwtKmiFlG4oedbWEpvn5hFTgu9vdsoLrUiO9fOh2kkS9LbjpkquRiLoLx8gVz+kCcEsr7j0YXXw1ceh+Y7bfDFA3jHfvvRPjscyRWVXnuUJOBwboqE65bWo7rlpbHBQ2lJOOZ/z+zdj4e2DOCCy0leC0QlOxLRio+ogRBEHogRZRIifPMJrzBhhK20WLFvHGlNh/bAjT4akJLHtFCIBuuAvn+zsRDyrQKxVWehcga5SAmIKaMTm28D0Xbb0dkzWfilvbNJiO+fmEVzCYjasuMOHteccYTu/O93bxmAd67ohIHT4fx1RdjJZE1BytlyEeUIAhCjYJI30TMPL7bsBCbrZX4rwazapt0H6sfWDVd3ScTj2j70twHbRWawqlGNuTMtyJqFmWTyIYokfNuRfg6t2qKpujiqxG+zo3IebfG7fvUE7+G44kO/PDl3+GcBaasVBcSv1xUlxpTSjt3rSjQ8V3L9VXbIgiC0AJZRImUWFhqxOfPr0IwqF4jXWx1SSWVz+fOM6OmtAjrq0wZsdh99YIq1M0vxiUL0ysfORvR4yOqlXwvzd9TvxBffmEAF1lKUJGF4hGAehCT1v25RF71SwtLK4rwyysXYYrjcLaOSl0EQRBaIUWUyBpGhsEFVSbsGg7BtVF/cuvyIgM+obGSkhYqiw34qIa0Q5lkJrpJZsoVMN8W0XMWmPDnxmXIkg6aFs2rL8Fli8/C8srcpTNKNcftmgX66kYTBEHogRRRIqv88LJaDAcjqCmARO35wMgw+OYl1egbGMO/BoIYCZ9JnF5gCmo2/CkLIUbalK+s+kloWXNJXs9fmLNCEMRcpABtBcRsosjAzFkllGfTsnJ8+fz5mF+cXZ/FdJiNS/OEFE5k6i60PLYEQcxdSBEliBxRyEHHszFYiZCSSrASQRBEtpnbpiqCyBMFpwiIl+ZniY9oIfPPw3sxMHEatWWVuHb5mpycM5VgJYIgiGxDiihB5IhCzgcuXZrPjKBGWv5V5Wc7/4UXjh/AZYvPypkiKi6MRF8NQRCFAi3NE0SOuGllmfB3oZU7FFeAIovo7ISW5gmCKETIIkoQOeJ9y8tgLjNhZUURyosK6x0wG4oJWd3Uuf/qZgQjUygxZvYWfFZlEQ6cnlLeWcAWeYIg5i6kiBJEjjAamBlRnSZz6ZtIE1WjtnxeVvq9t2Eh7ts5jCsXl8Xtqy2btsKvq6KiDgRBFAakiBIEkZU8orQ0n3uWVxbjvy+rVdy3ZoEJn1k7H8fHI2ixZkcRJgiC0AspogRBSIOVyEd01nLzmgX5FoEgCEICKaIJYLf34lDXAzhcUirZXrpsCayfdwAATjz+Dww983zcsQsuuRDLmj8IAHj7Nw/j9Jt749rUvsuOhVdfAQDY94MfIRQYjmuz4hMfwbzz1iIaDuPNb92rKOfq278EU7UFE4ePYP/92+L2M8VFOO+ef4+NyfcKjrr/FNcm1TFFuVgsroExzJoxYdMXhP2vffFrmH/RBVj+kRYwBgZv3vM9RIOhuH7O+txnUL5yBULDLPZ9/4dx+6NcFHVb7kDR/Pk4vWcv3n7w4bg2xeb5WNP+NQDA0LPP48T//SOuTeU5dVj5yU8AAI4++iewfa/EtbFc8Q4sfs8NAID9P9+GiUNH4tos+eD7UdVgAwC8ec/3MLRyI7DyYgDAsT/9L3YeejXpmABg7Te3wFhaojimsMEIXHur8HnA80TyMXW7MfLKa8L1lM6Y5N/TyGs7UXnuWhhMJiz78Iew4MILAACv33W3ohnY+qXPoXTxYgQHBtH/w58pjv+8734TjMGAkV27cfj/9cTtN9VUY/VXvwgAGHziKQw+8RQA4BdLjOgvM6BuIoqvW87Bio+1AAAO/64HIzt3x/Wz8LqrUGu/DgDQ/+P7MXHsOABI5ikfYxKz86tO4e95687TPabg8YG4NumOqXztGiz/zC2K4yQIojAgRTQBkdOjCB4+gqDM142bmsLU6CgAYPLYcYz1++OOLaldKLSZOHRYsU3wxAmhzdj+AwidHIprExoawtToKKKhkGIfABA+xcJgKkY4MKzYxlBcLJwnNDio2Cb1MfFPBmb2jGnT9P7xg29jyYduQmTsdOyzfz8ik8F4eQMBTFWZMXXqlMqYOIRPjaKYYRAKBBTbmCxVgizBEycU2xhLS6bHdOSoYpsK61nTY3r7EMb8B+LahAYGhTbj/v0wcxZBES0/sA9jB/waxgRMjZwCFy5THFOEMQDXTn/WMqbg0eOY8O+HPHwqlTGJv6fI2BgA4PSbe2CsqEBo8OT0ddrvV1RwwsMsiioqEGZZ9fGPjoIxGBAaHFJsExkbm/6+jk+Pf++SldhdWYHoxAQmDx8RfadHFPuZd97a6bEdOIjJI0fP7Jmep1yPyQBpSihx2+IF81MY07G4NumOqWTlcsBYWIGBBEFIYTiukLMb6mf37t1Yv349du3ahXXr1qXV1xjLYmr0NEpLS5M3nqNMTk4CwKyao0s7XxL+fslxKRCNAob0HmaFPk9TUQ7feaofpUVGfOPKszJSAjLKcbisa7vw+SXHpUmPydY8cRyHZ6++HkUVFWj4f79GcZU5o/0LaLxW7vzH77Hr+NtYv3glvnf9h3WfphCup53HR/HZP78ufNby/eaayclJGMvLUFldnW9RCIJQgSyiCTCUlMBUUoKSsvgIVCJGdGICAGbtHJXULMxIP4U+TyUA/rOlJqN9yt9xtcxltuaJ4ziEh4dRvnIFSpcsRtG8yoz2r5f7PvaF5I0SUAjXU0PNQkCkiGbqt5JJ+HkiCKJwoTULgiCyQiasqpkiPMyCC0+BMRrBFNP7N0EQRKFAiihByLj/IxtRVmzERzaclW9RiAxRXGVG3de+iLPaPgODyZRvcQiCIIgzkGmAIGS8s64Gz9xxPYopyGHWwDAM1t7lBMdxBWGpPTEyjMlwCKXFJiyaX5VvcQiCIPIGPWkJQgFSQmcnhaCEAsAtD/4A677twC0P/iDfohAEQeQVetoSBEEQBEEQeYGW5gmCIHLM7fYP4eMbr6NleYIg5jykiBIEQeSYxvNs+RaBIAiiIKCleYIgCIIgCCIvkCJKEARBEARB5AVamicIgsgxD730BA4GBrDKUouPX7op3+IQBEHkDbKIEgRB5JiHtj+JrY9346HtT+ZbFIIgiLxCiihBEFmH8rISBEEQSuRsaZ5lWTzyyCPo7OxEX19f3P6Ojg5YrVYEAgH09/djy5YtMJvNuRKPIIgs8P0P2fCo7218ZdO5+RaloHj8i/fmWwSCIIiCICeKqNfrhd/vB8uyYFk2bn9HRwcAoKmpCQDg8/nQ3NwMj8eTC/EIgsgSjectQeN5S/ItBkEQBFGg5EQRtdvtAAC32624f+vWrRIrqc1mQ29vL/x+P6xWay5EJAiCIAiCIHJM3qPmfT4fWJaFxWKRbLdYLHC73Whvb1c9dmBgAIODg5Jt+/btAwBMTk5iYmIiLdkmJyfTOn4uQHOkDZonbdA8aaMQ5ynd+202yOQ8lZWVZawvgiCmybsiGggEACDOH9RsNmNoaCjhsffffz/uueeebIlGEASRFVr/34/R9/Ze1K9cg20f/VK+xSEIgsgbeVdElXxGtewDgNtuuw3Nzc2Sbfv27cNNN92E0tLSjL3B0ptwcmiOtEHzpI3ZPk8nx0dwiD2JVQsXpTXWfM+T698uwe97D+Crm87LuyyJKGTZCGKuo1sRdbvd6O7uTtpuy5YtsNmS11NWi4xPpoQCQG1tLWpra5O2IwiCKCQaz7VhlaUW59Quz7coafGudUvxrnVL8y0GQRAzGN2KaFNTkxDdngl431CWZeOU0rq6uoydhyAIolC4vfFD+RaBIAiiIMh7lmmbzQaz2Qy/3y/Z7vf7hWh7YmbAcRye738dHMflWxSCIAiCIGYAOVdE+eAkMVu2bJEs9/t8PthsNk1L+0Th8Pvep3D9j+9Ed+/T+RaFIAiCIIgZQE6ClXw+H7xeL7q7u8GyLJxOJ6qrq4XUTO3t7ejo6EBXVxcAoL+/H0888UQuRCMyyKO+52L/v/wsPrzhmvwKQxAFzGuH/WAnxmAuq8CFyylXMkEQcxeGm2XrqLt378b69euxa9curFu3Lq2++Lx4FHGpDj9HYYbDqjtvRjgyhWJjEQ5+7zeYX1qeZ+kKB7qWtDFX5umGn9yFZ/ftxhWr16VU7nOuzFO60DwRROGT9/RNxMwkEo3g/fffjb0DRwEA4cgUwpEp4e+LvnMrTEXFAIA1NUvxl9vuhsGQd5dkgiAIgiAKCFJEiZQJR6Zw9JRy0YHB06eEv1dZasBhVhneCSItOv7tM8LSPEEQxFyGTFREShgNRjz2+W/ji1e/DwDAgJHs5z9/bdMH8djnvw2jwZhzGQmiULlwuRVXrbmA/EMJgpjzkEWUSBlTUTG++e6PYn5pOe79u7TIAQcO99z4CcqXSBAEQRCEKmQRJdLm9eNvK27fdfRAbgUhCIIgCGJGQYookRaT4RD+/oYPAFBkMOLjG69D0Zll+P/bvQOT4VA+xSOIguQ+z6NwPPwj3Od5NN+iEARB5BVSRIm0eGvgCMZDQdTVLMFTX+vALz72JTz1tQ5YFy7BWHASe04czreIBFFweN704eHt/4TnTV++RSEIgsgr5CNKpMX6Javg/syduGLtBagsieXqu3hFHZ5v/y/sOPAWLlh6Vn4FJIgCZNG8Kqy01GDRvKp8i0IQBJFXSBEl0sJgMODqNRegrESaMLqypAzXrr0oT1IRRGHz4CfvyLcIBEEQBQEtzRMEQRAEQRB5gRRRgiAIgiAIIi+QIkoQBEEQBEHkBVJECYIgcswNP7kLlV++CTf85K58i0IQBJFXZl2wUjAYBADs27cv7b4mJycBAKWlpWn3NVuhOdIGzZM25so8nT42iOjQCE5XDGL37t26j58r85QumZ6nuro6mnOCyDAMx3FcvoXIJH/+859x00035VsMgiAIYpaxa9curFu3Lt9iEMSsYtYpoizL4umnn8aKFStQUlKSVl/79u3DTTfdhD/96U9YvXp1hiScXdAcaYPmSRs0T9qgedJGpueJLKIEkXlm3dK82WzGBz7wgYz2uXr1anoLTgLNkTZonrRB86QNmidt0DwRROFCwUoEQRAEQRBEXiBFlCAIgiAIgsgLpIgSBEEQBEEQeYEU0QTU1NTgW9/6FmpqavItSsFCc6QNmidt0Dxpg+ZJGzRPBFH4zLqoeYIgCIIgCGJmQBZRgiAIgiAIIi+QIkoQBEEQBEHkBVJECYIgCIIgiLxAiihBEARBEASRF0gRJQiCIAiCIPLCrCvxqYeOjg5YrVYEAgH09/djy5YtMJvNGT9mppPKmJ1OJwDA7/fDYrHA5XLRPCWhubkZ27Ztm9XzlOocOZ1O1NXVCZ/b2tqyKGX+SWWeurq6wLIszGbznLg3sSyLRx55BJ2dnejr69N0zFy8fxNEwcPNUVwuF+dyuYTPfX19nN1uz/gxM51UxtzW1sYNDw9LPlut1myJWBCke214PB4OANff358N8QqCVOfIZrMJ89LX18cBkFxfs41U5qmzs1Ny7QwPD8/qe5PH4+E6Ozs5l8ul+d4yF+/fBDETmLOKqNlsjnvoK21L95iZjt4xDw8Pc1arlevr6xO29ff3cwA4j8eTVVnzSbrXRmdn56xXRFOZI5fLxbW1tQmfh4eHuc7OzqzJWAikMk9KClVTU9OsVtg5juN6eno0K6Jz8f5NEDOBOekj6vP5wLIsLBaLZLvFYoHb7c7YMTOdVMccCATg9/sl7QFIts0m0r02Ojo6Zv1Sc6pz5HQ60djYKHw2m82zeq7S+c05HA7JNr/fT8vOZ5iL92+CmCnMSR/RQCAAAHE3abPZjKGhoYwdM9NJZcxmsxnDw8OSbV6vFwBgt9szL2QBkM614fV6Z+28iElljliWFf7u6uoCgFnv15fqteRyudDY2Aiv14uenh50d3dj27Zt2RR1RjEX798EMVOYkxZR8QNO675UjpnpZGrMW7duRXt7O6xWa/pCFSDpzJPf74fNZsusQAVIKnPU29sLANixYwfa2trQ1taGlpYWnH322VmQsDBI9Vqy2+3o6emB3+9HfX09WJadE9eVVubi/ZsgZgpzUhFVs6YkuiGlcsxMJxNjdjqdaGhogMvlyoxQBUiq89TV1TWrl5nFpHMtbdiwQfjbZrOBZVnBQjrbSHWevF4v/H4/hoeH0d7ejq6uLtTX12dewBnKXLx/E8RMYU4qoryfkNJNSJwiJt1jZjrpjtntdqO6uhqdnZ2ZFq2gSGWefD4fGhoasilWQZHKHPEWdLklnU9PNBtJZZ5YloXT6UR7ezvMZjNcLhf6+/tntcKul7l4/yaImcKcVERtNhvMZnNc8Izf71f110vlmJlOOmP2er0IBAJob2+XbJuNpDJPgUAA3d3dcDqdcDqdQqCJy+WalcpDKnNktVphtVrjjmFZVmIlnU2kMk9+vz/upcZqtcLlcpHF7wxz8f5NEDOFOamIAsCWLVvQ3d0tfPb5fLDZbIJflc/ni4tCTXbMbCSVefL5fOjp6YHVaoXX64XX60VHR0dcxOpsQu882e12uFwu4R9fAMDpdM7a5fpUriWn0yk5xuv1wmq1oqmpKTdC5wG982Sz2dDb2xundO7YsWNWzxMPH4gkhu7fBDFzYDiO4/ItRL7o6OgQfIfk0bhutxtOpzNuCTDRMbMVPfPEsizOPvtsRUvMbL/UUrme+H3d3d1wu91oampCS0vLrFUgUpmjrq4u9PX1oa6uDv39/XOmSpeeefL7/ejs7ER1dbWwzW63z1oly+fzwev1oru7Gz6fD+3t7aiurhZWYOj+TRAzhzmtiBIEQRAEQRD5Y84uzRMEQRAEQRD5hRRRgiAIgiAIIi+QIkoQBEEQBEHkBVJECYIgCIIgiLxAiihBEARBEASRF0gRJQiCIAiCIPICKaIEQRAEQRBEXiBFlCAIgiAIgsgLpIgSBEEQBEEQeYEUUYIgCIIgCCIvkCJKEARBEARB5AVSRAmCIAiCIIi8QIooQRAEQRAEkRf+P4HfNmlfBb1EAAAAAElFTkSuQmCC", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dm = UtilityDrivenDecisionMaker(\n", + " search_space=search_space,\n", + " posterior_handlers=posterior_handlers,\n", + " datasets=initial_datasets,\n", + " utility_function_builder=utility_function_builder,\n", + " utility_maximizer=acquisition_maximizer,\n", + " batch_size=1,\n", + " key=key,\n", + " post_ask=[plot_bo_iteration],\n", + " post_tell=[],\n", + ")\n", + "\n", + "results = dm.run(\n", + " 6,\n", + " black_box_function_evaluator=function_evaluator,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "152dd577", + "metadata": {}, + "source": [ + "We can see that our `DecisionMaker` is successfully able to find the minimimizer of the\n", + "black box function!\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "f67fac5a", + "metadata": {}, + "source": [ + "## Conclusions" + ] + }, + { + "cell_type": "markdown", + "id": "c59691a5", + "metadata": {}, + "source": [ + "In this notebook we have provided an introduction to the new `decision_making` module of\n", + "GPJax. We have demonstrated how one may use the abstractions provided by this module to\n", + "implement a Bayesian optimisation loop, and have also highlighted some of the\n", + "flexibility provided by the module. We hope that this module will provide a useful\n", + "framework for solving a wide range of sequential decision making problems, and that it\n", + "will be easy for users to extend the functionality provided by the module to suit their\n", + "needs!\n", + "\n", + "We should note that the `decision_making` module is still in its early stages, and so\n", + "whilst we hope to avoid making breaking changes to it, they may occur as the module\n", + "evolves and more advanced functionality is implemented. If people have any feedback or\n", + "features they would like to implement/see implemented, feel free to open an issue on the\n", + "[GPJax GitHub page](https://github.com/JaxGaussianProcesses/GPJax/issues).\n" + ] + }, + { + "cell_type": "markdown", + "id": "3652fb37", + "metadata": {}, + "source": [ + "## System Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4bbb464", + "metadata": {}, + "outputs": [], + "source": [ + "%reload_ext watermark\n", + "%watermark -n -u -v -iv -w -a 'Thomas Christie'" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "gpjax", + "language": "python", + "name": "gpjax" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/oceanmodelling.ipynb b/docs/examples/oceanmodelling.ipynb new file mode 100644 index 000000000..06e6c9524 --- /dev/null +++ b/docs/examples/oceanmodelling.ipynb @@ -0,0 +1,880 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a6f1891d", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "# Gaussian Processes for Vector Fields and Ocean Current Modelling\n", + "\n", + "In this notebook, we use Gaussian processes to learn vector-valued functions. We will be\n", + "recreating the results by [Berlinghieri et al. (2023)](https://arxiv.org/pdf/2302.10364.pdf) by an\n", + "application to real-world ocean surface velocity data, collected via surface drifters.\n", + "\n", + "Surface drifters are measurement devices that measure the dynamics and circulation patterns of the world's oceans. Studying and predicting ocean currents are important to climate research, for example, forecasting and predicting oil spills, oceanographic surveying of eddies and upwelling, or providing information on the distribution of biomass in ecosystems. We will be using the [Gulf Drifters Open dataset](https://zenodo.org/record/4421585), which contains all publicly available surface drifter trajectories from the Gulf of Mexico spanning 28 years." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "08e9b124", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n" + ] + } + ], + "source": [ + "from jax.config import config\n", + "\n", + "config.update(\"jax_enable_x64\", True)\n", + "from dataclasses import dataclass\n", + "\n", + "from jax import hessian\n", + "from jax.config import config\n", + "import jax.numpy as jnp\n", + "import jax.random as jr\n", + "from jaxtyping import (\n", + " Array,\n", + " Float,\n", + " install_import_hook,\n", + ")\n", + "from matplotlib import rcParams\n", + "import matplotlib.pyplot as plt\n", + "import jaxopt\n", + "import pandas as pd\n", + "import tensorflow_probability as tfp\n", + "import optax as ox\n", + "with install_import_hook(\"gpjax\", \"beartype.beartype\"):\n", + " import gpjax as gpx\n", + "\n", + "# Enable Float64 for more stable matrix inversions.\n", + "key = jr.PRNGKey(123)\n", + "plt.style.use(\n", + " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", + ")\n", + "colors = rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]" + ] + }, + { + "cell_type": "markdown", + "id": "7b54ea0f", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "## Data loading and preprocessing\n", + "The real dataset has been binned into an $N=34\\times16$ grid, equally spaced over the longitude-latitude interval $[-90.8,-83.8] \\times [24.0,27.5]$. Each bin has a size $\\approx 0.21\\times0.21$, and contains the average velocity across all measurements that fall inside it.\n", + "\n", + "We will call this binned ocean data the ground truth, and label it with the vector field\n", + "$$\n", + "\\mathbf{F} \\equiv \\mathbf{F}(\\mathbf{x}),\n", + "$$\n", + "where $\\mathbf{x} = (x^{(0)}$,$x^{(1)})^\\text{T}$, with a vector basis in the standard Cartesian directions (dimensions will be indicated by superscripts).\n", + "\n", + "We shall label the ground truth $D_0=\\left\\{ \\left(\\mathbf{x}_{0,i} , \\mathbf{y}_{0,i} \\right)\\right\\}_{i=1}^N$, where $\\mathbf{y}_{0,i}$ is the 2-dimensional velocity vector at the $i$-th location, $\\mathbf{x}_{0,i}$. The training dataset contains simulated measurements from ocean drifters $D_T=\\left\\{\\left(\\mathbf{x}_{T,i}, \\mathbf{y}_{T,i} \\right)\\right\\}_{i=1}^{N_T}$, $N_T = 20$ in this case (the subscripts indicate the ground truth and the simulated measurements respectively).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "486539c3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# function to place data from csv into correct array shape\n", + "def prepare_data(df):\n", + " pos = jnp.array([df[\"lon\"], df[\"lat\"]])\n", + " vel = jnp.array([df[\"ubar\"], df[\"vbar\"]])\n", + " # extract shape stored as 'metadata' in the test data\n", + " try:\n", + " shape = (int(df[\"shape\"][1]), int(df[\"shape\"][0])) # shape = (34,16)\n", + " return pos, vel, shape\n", + " except KeyError:\n", + " return pos, vel\n", + "\n", + "\n", + "# loading in data\n", + "\n", + "gulf_data_train = pd.read_csv(\n", + " \"https://raw.githubusercontent.com/JaxGaussianProcesses/static/main/data/gulfdata_train.csv\"\n", + ")\n", + "gulf_data_test = pd.read_csv(\n", + " \"https://raw.githubusercontent.com/JaxGaussianProcesses/static/main/data/gulfdata_test.csv\"\n", + ")\n", + "\n", + "\n", + "pos_test, vel_test, shape = prepare_data(gulf_data_test)\n", + "pos_train, vel_train = prepare_data(gulf_data_train)\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(6, 3))\n", + "ax.quiver(\n", + " pos_test[0],\n", + " pos_test[1],\n", + " vel_test[0],\n", + " vel_test[1],\n", + " color=colors[0],\n", + " label=\"Ocean Current\",\n", + " angles=\"xy\",\n", + " scale=10,\n", + ")\n", + "ax.quiver(\n", + " pos_train[0],\n", + " pos_train[1],\n", + " vel_train[0],\n", + " vel_train[1],\n", + " color=colors[1],\n", + " alpha=0.7,\n", + " label=\"Drifter\",\n", + " angles=\"xy\",\n", + " scale=10,\n", + ")\n", + "\n", + "ax.set(\n", + " xlabel=\"Longitude\",\n", + " ylabel=\"Latitude\",\n", + ")\n", + "ax.legend(\n", + " framealpha=0.0,\n", + " ncols=2,\n", + " fontsize=\"medium\",\n", + " bbox_to_anchor=(0.5, -0.3),\n", + " loc=\"lower center\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7828a8ed", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "## Problem Setting\n", + "We aim to obtain estimates for $\\mathbf{F}$ at the set of points $\\left\\{ \\mathbf{x}_{0,i} \\right\\}_{i=1}^N$ using Gaussian processes, followed by a comparison of the latent model to the ground truth $D_0$. Note that $D_0$ is not passed into any functions used by GPJax, and is only used to compare against the two GP models at the end of the notebook.\n", + "\n", + "Since $\\mathbf{F}$ is a vector-valued function, we require GPs that can directly learn vector-valued functions[1](#fn1). To implement this in GPJax, the problem can be changed to learn a scalar-valued function by 'massaging' the data into a $2N\\times2N$ problem, such that each dimension of our GP is associated with a *component* of $\\mathbf{y}_{T,i}$.\n", + "\n", + "For a particular measurement $\\mathbf{y}$ (training or testing) at location $\\mathbf{x}$, the components $(y^{(0)}, y^{(1)})$ are described by the latent vector field $\\mathbf{F}$, such that\n", + "\n", + "$$\n", + "\\mathbf{y} = \\mathbf{F}(\\mathbf{x}) = \\left(\\begin{array}{l}\n", + "f^{(0)}\\left(\\mathbf{x}\\right) \\\\\n", + "f^{(1)}\\left(\\mathbf{x}\\right)\n", + "\\end{array}\\right),\n", + "$$\n", + "\n", + "where each $f^{(z)}\\left(\\mathbf{x}\\right), z \\in \\{0,1\\}$ is a scalar-valued function.\n", + "\n", + "Now consider the scalar-valued function $g: \\mathbb{R}^2 \\times\\{0,1\\} \\rightarrow \\mathbb{R}$, such that\n", + "\n", + "$$\n", + "g \\left(\\mathbf{x} , 0 \\right) = f^{(0)} ( \\mathbf{x} ), \\text{and } g \\left( \\mathbf{x}, 1 \\right)=f^{(1)}\\left(\\mathbf{x}\\right).\n", + "$$\n", + "\n", + "We have increased the input dimension by 1, from the 2D $\\mathbf{x}$ to the 3D $\\mathbf{X} = \\left(\\mathbf{x}, 0\\right)$ or $\\mathbf{X} = \\left(\\mathbf{x}, 1\\right)$.\n", + "\n", + "By choosing the value of the third dimension, 0 or 1, we may now incorporate this\n", + "information into the computation of the kernel.\n", + "We therefore make new 3D datasets $D_{T,3D} = \\left\\{\\left( \\mathbf{X}_{T,i},\\mathbf{Y}_{T,i} \\right) \\right\\} _{i=0}^{2N_T}$ and $D_{0,3D} = \\left\\{\\left( \\mathbf{X}_{0,i},\\mathbf{Y}_{0,i} \\right) \\right\\} _{i=0}^{2N}$ that incorporates this new labelling, such that for each dataset (indicated by the subscript $D = 0$ or $D=T$),\n", + "\n", + "$$\n", + "X_{D,i} = \\left( \\mathbf{x}_{D,i}, z \\right),\n", + "$$\n", + "and\n", + "$$\n", + "Y_{D,i} = y_{D,i}^{(z)},\n", + "$$\n", + "\n", + "where $z = 0$ if $i$ is odd and $z=1$ if $i$ is even." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "30ebd796", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "# Change vectors x -> X = (x,z), and vectors y -> Y = (y,z) via the artificial z label\n", + "def label_position(data):\n", + " # introduce alternating z label\n", + " n_points = len(data[0])\n", + " label = jnp.tile(jnp.array([0.0, 1.0]), n_points)\n", + " return jnp.vstack((jnp.repeat(data, repeats=2, axis=1), label)).T\n", + "\n", + "\n", + "# change vectors y -> Y by reshaping the velocity measurements\n", + "def stack_velocity(data):\n", + " return data.T.flatten().reshape(-1, 1)\n", + "\n", + "\n", + "def dataset_3d(pos, vel):\n", + " return gpx.Dataset(label_position(pos), stack_velocity(vel))\n", + "\n", + "\n", + "# label and place the training data into a Dataset object to be used by GPJax\n", + "dataset_train = dataset_3d(pos_train, vel_train)\n", + "\n", + "# we also require the testing data to be relabelled for later use, such that we can query the 2Nx2N GP at the test points\n", + "dataset_ground_truth = dataset_3d(pos_test, vel_test)" + ] + }, + { + "cell_type": "markdown", + "id": "3278ca65", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "## Velocity (dimension) decomposition\n", + "Having labelled the data, we are now in a position to use GPJax to learn the function $g$, and hence $\\mathbf{F}$. A naive approach to the problem is to apply a GP prior directly to the velocities of each dimension independently, which is called the *velocity* GP. For our prior, we choose an isotropic mean 0 over all dimensions of the GP, and a piecewise kernel that depends on the $z$ labels of the inputs, such that for two inputs $\\mathbf{X} = \\left( \\mathbf{x}, z \\right )$ and $\\mathbf{X}^\\prime = \\left( \\mathbf{x}^\\prime, z^\\prime \\right )$,\n", + "\n", + "$$\n", + "k_{\\text{vel}} \\left(\\mathbf{X}, \\mathbf{X}^{\\prime}\\right)=\n", + "\\begin{cases}k^{(z)}\\left(\\mathbf{x}, \\mathbf{x}^{\\prime}\\right) & \\text { if }\n", + "z=z^{\\prime} \\\\ 0 & \\text { if } z \\neq z^{\\prime}, \\end{cases}\n", + "$$\n", + "\n", + "where $k^{(z)}\\left(\\mathbf{x}, \\mathbf{x}^{\\prime}\\right)$ are the user chosen kernels for each dimension. What this means is that there are no correlations between the $x^{(0)}$ and $x^{(1)}$ dimensions for all choices $\\mathbf{X}$ and $\\mathbf{X}^{\\prime}$, since there are no off-diagonal elements in the Gram matrix populated by this choice.\n", + "\n", + "To implement this approach in GPJax, we define `VelocityKernel` in the following cell, following the steps outlined in the [custom kernels notebook](https://docs.jaxgaussianprocesses.com/examples/constructing_new_kernels/#custom-kernel). This modular implementation takes the choice of user kernels as its class attributes: `kernel0` and `kernel1`. We must additionally pass the argument `active_dims = [0,1]`, which is an attribute of the base class `AbstractKernel`, into the chosen kernels. This is necessary such that the subsequent likelihood optimisation does not optimise over the artificial label dimension.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e4ae9eb1", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "@dataclass\n", + "class VelocityKernel(gpx.kernels.AbstractKernel):\n", + " kernel0: gpx.kernels.AbstractKernel = gpx.kernels.RBF(active_dims=[0, 1])\n", + " kernel1: gpx.kernels.AbstractKernel = gpx.kernels.RBF(active_dims=[0, 1])\n", + "\n", + " def __call__(\n", + " self, X: Float[Array, \"1 D\"], Xp: Float[Array, \"1 D\"]\n", + " ) -> Float[Array, \"1\"]:\n", + " # standard RBF-SE kernel is x and x' are on the same output, otherwise returns 0\n", + "\n", + " z = jnp.array(X[2], dtype=int)\n", + " zp = jnp.array(Xp[2], dtype=int)\n", + "\n", + " # achieve the correct value via 'switches' that are either 1 or 0\n", + " k0_switch = ((z + 1) % 2) * ((zp + 1) % 2)\n", + " k1_switch = z * zp\n", + "\n", + " return k0_switch * self.kernel0(X, Xp) + k1_switch * self.kernel1(X, Xp)" + ] + }, + { + "cell_type": "markdown", + "id": "f35d7008", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "### GPJax implementation\n", + "Next, we define the model in GPJax. The prior is defined using $k_{\\text{vel}}\\left(\\mathbf{X}, \\mathbf{X}^\\prime \\right)$ and 0 mean and 0 observation noise. We choose a Gaussian marginal log-likelihood (MLL).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ec016122", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "def initialise_gp(kernel, mean, dataset):\n", + " prior = gpx.Prior(mean_function=mean, kernel=kernel)\n", + " likelihood = gpx.Gaussian(\n", + " num_datapoints=dataset.n, obs_noise=jnp.array([1.0e-6], dtype=jnp.float64)\n", + " )\n", + " posterior = prior * likelihood\n", + " return posterior\n", + "\n", + "\n", + "# Define the velocity GP\n", + "mean = gpx.mean_functions.Zero()\n", + "kernel = VelocityKernel()\n", + "velocity_posterior = initialise_gp(kernel, mean, dataset_train)" + ] + }, + { + "cell_type": "markdown", + "id": "6aefa9c7", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "With a model now defined, we can proceed to optimise the hyperparameters of our likelihood over $D_0$. This is done by minimising the MLL using `jaxopt`. We also plot its value at each step to visually confirm that we have found the minimum. See the [introduction to Gaussian Processes](https://docs.jaxgaussianprocesses.com/examples/intro_to_gps/) notebook for more information on optimising the MLL." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b19c117e", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "64b13fc052654cf1a58c292be8f4c1ce", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def optimise_mll(posterior, dataset, NIters=100, key=key, plot_history=True):\n", + " # define the MLL using dataset_train\n", + " objective = gpx.objectives.ConjugateMLL(negative=True)\n", + " # Optimise to minimise the MLL\n", + " opt_posterior, history = gpx.fit(\n", + " model=posterior,\n", + " train_data=dataset,\n", + " solver = jaxopt.OptaxSolver(gpx.ConjugateMLL(negative=True), opt=ox.adam(1e-1), maxiter=NIters),\n", + " #solver=jaxopt.LBFGS(gpx.ConjugateMLL(negative=True), maxiter=NIters),\n", + " safe=True,\n", + " key=key,\n", + " )\n", + " # plot MLL value at each iteration\n", + " if plot_history:\n", + " fig, ax = plt.subplots(1, 1)\n", + " ax.plot(history, color=colors[1])\n", + " ax.set(xlabel=\"Training iteration\", ylabel=\"Negative MLL\")\n", + "\n", + " return opt_posterior\n", + "\n", + "\n", + "opt_velocity_posterior = optimise_mll(velocity_posterior, dataset_train)" + ] + }, + { + "cell_type": "markdown", + "id": "3597c8a1", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "### Comparison\n", + "We next obtain the latent distribution of the GP of $g$ at $\\mathbf{x}_{0,i}$, then extract its mean and standard at the test locations, $\\mathbf{F}_{\\text{latent}}(\\mathbf{x}_{0,i})$, as well as the standard deviation (we will use it at the very end)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "47d25292", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "def latent_distribution(opt_posterior, pos_3d, dataset_train):\n", + " latent = opt_posterior.predict(pos_3d, train_data=dataset_train)\n", + " latent_mean = latent.mean()\n", + " latent_std = latent.stddev()\n", + " return latent_mean, latent_std\n", + "\n", + "\n", + "# extract latent mean and std of g, redistribute into vectors to model F\n", + "velocity_mean, velocity_std = latent_distribution(\n", + " opt_velocity_posterior, dataset_ground_truth.X, dataset_train\n", + ")\n", + "\n", + "dataset_latent_velocity = dataset_3d(pos_test, velocity_mean)" + ] + }, + { + "cell_type": "markdown", + "id": "d77bedeb", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "We now replot the ground truth (testing data) $D_0$, the predicted latent vector field $\\mathbf{F}_{\\text{latent}}(\\mathbf{x_i})$, and a heatmap of the residuals at each location $\\mathbf{R}(\\mathbf{x}_{0,i}) = \\mathbf{y}_{0,i} - \\mathbf{F}_{\\text{latent}}(\\mathbf{x}_{0,i}) $, as well as $\\left|\\left|\\mathbf{R}(\\mathbf{x}_{0,i})\\right|\\right|$." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4f802439", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Residuals between ground truth and estimate\n", + "\n", + "\n", + "def plot_vector_field(ax, dataset, **kwargs):\n", + " ax.quiver(\n", + " dataset.X[::2][:, 0],\n", + " dataset.X[::2][:, 1],\n", + " dataset.y[::2],\n", + " dataset.y[1::2],\n", + " **kwargs,\n", + " )\n", + "\n", + "\n", + "def prepare_ax(ax, X, Y, title, **kwargs):\n", + " ax.set(\n", + " xlim=[X.min() - 0.1, X.max() + 0.1],\n", + " ylim=[Y.min() + 0.1, Y.max() + 0.1],\n", + " aspect=\"equal\",\n", + " title=title,\n", + " ylabel=\"latitude\",\n", + " **kwargs,\n", + " )\n", + "\n", + "\n", + "def residuals(dataset_latent, dataset_ground_truth):\n", + " return jnp.sqrt(\n", + " (dataset_latent.y[::2] - dataset_ground_truth.y[::2]) ** 2\n", + " + (dataset_latent.y[1::2] - dataset_ground_truth.y[1::2]) ** 2\n", + " )\n", + "\n", + "\n", + "def plot_fields(\n", + " dataset_ground_truth, dataset_trajectory, dataset_latent, shape=shape, scale=10\n", + "):\n", + " X = dataset_ground_truth.X[:, 0][::2]\n", + " Y = dataset_ground_truth.X[:, 1][::2]\n", + " # make figure\n", + " fig, ax = plt.subplots(1, 3, figsize=(12.0, 3.0), sharey=True)\n", + "\n", + " # ground truth\n", + " plot_vector_field(\n", + " ax[0],\n", + " dataset_ground_truth,\n", + " color=colors[0],\n", + " label=\"Ocean Current\",\n", + " angles=\"xy\",\n", + " scale=scale,\n", + " )\n", + " plot_vector_field(\n", + " ax[0],\n", + " dataset_trajectory,\n", + " color=colors[1],\n", + " label=\"Drifter\",\n", + " angles=\"xy\",\n", + " scale=scale,\n", + " )\n", + " prepare_ax(ax[0], X, Y, \"Ground Truth\", xlabel=\"Longitude\")\n", + "\n", + " # Latent estimate of vector field F\n", + " plot_vector_field(ax[1], dataset_latent, color=colors[0], angles=\"xy\", scale=scale)\n", + " plot_vector_field(\n", + " ax[1], dataset_trajectory, color=colors[1], angles=\"xy\", scale=scale\n", + " )\n", + " prepare_ax(ax[1], X, Y, \"GP Estimate\", xlabel=\"Longitude\")\n", + "\n", + " # residuals\n", + " residuals_vel = jnp.flip(\n", + " residuals(dataset_latent, dataset_ground_truth).reshape(shape), axis=0\n", + " )\n", + " im = ax[2].imshow(\n", + " residuals_vel,\n", + " extent=[X.min(), X.max(), Y.min(), Y.max()],\n", + " cmap=\"jet\",\n", + " vmin=0,\n", + " vmax=1.0,\n", + " interpolation=\"spline36\",\n", + " )\n", + " plot_vector_field(\n", + " ax[2], dataset_trajectory, color=colors[1], angles=\"xy\", scale=scale\n", + " )\n", + " prepare_ax(ax[2], X, Y, \"Residuals\", xlabel=\"Longitude\")\n", + " fig.colorbar(im, fraction=0.027, pad=0.04, orientation=\"vertical\")\n", + "\n", + " fig.legend(\n", + " framealpha=0.0,\n", + " ncols=2,\n", + " fontsize=\"medium\",\n", + " bbox_to_anchor=(0.5, -0.03),\n", + " loc=\"lower center\",\n", + " )\n", + " plt.show()\n", + "\n", + "\n", + "plot_fields(dataset_ground_truth, dataset_train, dataset_latent_velocity)" + ] + }, + { + "cell_type": "markdown", + "id": "1be0cdd6", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "From the latent estimate we can see the velocity GP struggles to reconstruct features of the ground truth. This is because our construction of the kernel placed an independent prior on each physical dimension, which cannot be assumed. Therefore, we need a different approach that can implicitly incorporate this dependence at a fundamental level. To achieve this we will require a *Helmholtz Decomposition*." + ] + }, + { + "cell_type": "markdown", + "id": "016f34c3", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "## Helmholtz decomposition\n", + "In 2 dimensions, a twice continuously differentiable and compactly supported vector field $\\mathbf{F}: \\mathbb{R}^2 \\rightarrow \\mathbb{R}^2$ can be expressed as the sum of the gradient of a scalar potential $\\Phi: \\mathbb{R}^2 \\rightarrow \\mathbb{R}$, called the potential function, and the vorticity operator of another scalar potential $\\Psi: \\mathbb{R}^2 \\rightarrow \\mathbb{R}$, called the stream function ([Berlinghieri et al. (2023)](https://arxiv.org/pdf/2302.10364.pdf)) such that\n", + "$$\n", + "\\mathbf{F}=\\operatorname{grad} \\Phi+\\operatorname{rot} \\Psi,\n", + "$$\n", + "where\n", + "$$\n", + "\\operatorname{grad} \\Phi:=\\left[\\begin{array}{l}\n", + "\\partial \\Phi / \\partial x^{(0)} \\\\\n", + "\\partial \\Phi / \\partial x^{(1)}\n", + "\\end{array}\\right] \\text { and } \\operatorname{rot} \\Psi:=\\left[\\begin{array}{c}\n", + "\\partial \\Psi / \\partial x^{(1)} \\\\\n", + "-\\partial \\Psi / \\partial x^{(0)}\n", + "\\end{array}\\right].\n", + "$$\n", + "\n", + "This is reminiscent of a 3 dimensional [Helmholtz decomposition](https://en.wikipedia.org/wiki/Helmholtz_decomposition).\n", + "\n", + "The 2 dimensional decomposition motivates a different approach: placing priors on $\\Psi$ and $\\Phi$, allowing us to make assumptions directly about fundamental properties of $\\mathbf{F}$. If we choose independent GP priors such that $\\Phi \\sim \\mathcal{G P}\\left(0, k_{\\Phi}\\right)$ and $\\Psi \\sim \\mathcal{G P}\\left(0, k_{\\Psi}\\right)$, then $\\mathbf{F} \\sim \\mathcal{G P} \\left(0, k_\\text{Helm}\\right)$ (since acting linear operations on a GPs give GPs).\n", + "\n", + "For $\\mathbf{X}, \\mathbf{X}^{\\prime} \\in \\mathbb{R}^2 \\times \\left\\{0,1\\right\\}$ and $z, z^\\prime \\in \\{0,1\\}$,\n", + "\n", + "$$\n", + "\\boxed{ k_{\\mathrm{Helm}}\\left(\\mathbf{x}, \\mathbf{x}^{\\prime}\\right)_{z,z^\\prime} = \\frac{\\partial^2 k_{\\Phi}\\left(\\mathbf{x}, \\mathbf{x}^{\\prime}\\right)}{\\partial x^{(z)} \\partial\\left(x^{\\prime}\\right)^{(z^\\prime)}}+(-1)^{z+z^\\prime} \\frac{\\partial^2 k_{\\Psi}\\left(\\mathbf{x}, \\mathbf{x}^{\\prime}\\right)}{\\partial x^{(1-z)} \\partial\\left(x^{\\prime}\\right)^{(1-z^\\prime)}}}.\n", + "$$\n", + "\n", + "where $x^{(z)}$ and $(x^\\prime)^{(z^\\prime)}$ are the $z$ and $z^\\prime$ components of $\\mathbf{X}$ and ${\\mathbf{X}}^{\\prime}$ respectively.\n", + "\n", + "We compute the second derivatives using `jax.hessian`. In the following implementation, for a kernel $k(\\mathbf{x}, \\mathbf{x}^{\\prime})$, this computes the Hessian matrix with respect to the components of $\\mathbf{x}$\n", + "\n", + "$$\n", + "\\frac{\\partial^2 k\\left(\\mathbf{x}, \\mathbf{x}^{\\prime}\\right)}{\\partial x^{(z)} \\partial x^{(z^\\prime)}}.\n", + "$$\n", + "\n", + "Note that we have operated $\\dfrac{\\partial}{\\partial x^{(z)}}$, *not* $\\dfrac{\\partial}{\\partial \\left(x^\\prime \\right)^{(z)}}$, as the boxed equation suggests. This is not an issue if we choose stationary kernels $k(\\mathbf{x}, \\mathbf{x}^{\\prime}) = k(\\mathbf{x} - \\mathbf{x}^{\\prime})$ , as the partial derivatives with respect to the components have the following exchange symmetry:\n", + "\n", + "$$\n", + "\\frac{\\partial}{\\partial x^{(z)}} = - \\frac{\\partial}{\\partial \\left( x^\\prime \\right)^{(z)}},\n", + "$$\n", + "\n", + "for either $z$." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "62dec207", + "metadata": {}, + "outputs": [], + "source": [ + "@dataclass\n", + "class HelmholtzKernel(gpx.kernels.AbstractKernel):\n", + " # initialise Phi and Psi kernels as any stationary kernel in gpJax\n", + " potential_kernel: gpx.kernels.AbstractKernel = gpx.kernels.RBF(active_dims=[0, 1], variance=jnp.array(0.00001))\n", + " stream_kernel: gpx.kernels.AbstractKernel = gpx.kernels.RBF(active_dims=[0, 1])\n", + "\n", + " def __call__(\n", + " self, X: Float[Array, \"1 D\"], Xp: Float[Array, \"1 D\"]\n", + " ) -> Float[Array, \"1\"]:\n", + " # obtain indices for k_helm, implement in the correct sign between the derivatives\n", + " z = jnp.array(X[2], dtype=int)\n", + " zp = jnp.array(Xp[2], dtype=int)\n", + " sign = (-1) ** (z + zp)\n", + "\n", + " # convert to array to correctly index, -ve sign due to exchange symmetry (only true for stationary kernels)\n", + " potential_dvtve = -jnp.array(\n", + " hessian(self.potential_kernel)(X, Xp), dtype=jnp.float64\n", + " )[z][zp]\n", + " stream_dvtve = -jnp.array(\n", + " hessian(self.stream_kernel)(X, Xp), dtype=jnp.float64\n", + " )[1 - z][1 - zp]\n", + "\n", + " return potential_dvtve + sign * stream_dvtve" + ] + }, + { + "cell_type": "markdown", + "id": "458503b0", + "metadata": {}, + "source": [ + "### GPJax implementation\n", + "We repeat the same steps as with the velocity GP model, replacing `VelocityKernel` with `HelmholtzKernel`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ae88ab7a", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e51b9bf778254f7da59dd2538f851f42", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Redefine Gaussian process with Helmholtz kernel\n", + "kernel = HelmholtzKernel()\n", + "helmholtz_posterior = initialise_gp(kernel, mean, dataset_train)\n", + "# Optimise hyperparameters using optax\n", + "opt_helmholtz_posterior = optimise_mll(helmholtz_posterior, dataset_train)" + ] + }, + { + "cell_type": "markdown", + "id": "0c59f8b9", + "metadata": {}, + "source": [ + "### Comparison\n", + "We again plot the ground truth (testing data) $D_0$, the predicted latent vector field $\\mathbf{F}_{\\text{latent}}(\\mathbf{x}_{0,i})$, and a heatmap of the residuals at each location $R(\\mathbf{x}_{0,i}) = \\mathbf{y}_{0,i} - \\mathbf{F}_{\\text{latent}}(\\mathbf{x}_{0,i})$ and $\\left|\\left|R(\\mathbf{x}_{0,i}) \\right|\\right|$." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9925521f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# obtain latent distribution, extract x and y values over g\n", + "helmholtz_mean, helmholtz_std = latent_distribution(\n", + " opt_helmholtz_posterior, dataset_ground_truth.X, dataset_train\n", + ")\n", + "dataset_latent_helmholtz = dataset_3d(pos_test, helmholtz_mean)\n", + "\n", + "plot_fields(dataset_ground_truth, dataset_train, dataset_latent_helmholtz)" + ] + }, + { + "cell_type": "markdown", + "id": "246359e6", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "Visually, the Helmholtz model performs better than the velocity model, preserving the local structure of the $\\mathbf{F}$. Since we placed priors on $\\Phi$ and $\\Psi$, the construction of $\\mathbf{F}$ allows for correlations between the dimensions (non-zero off-diagonal elements in the Gram matrix populated by $k_\\text{Helm}\\left(\\mathbf{X},\\mathbf{X}^{\\prime}\\right)$ )." + ] + }, + { + "cell_type": "markdown", + "id": "0cb64e07", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "## Negative log predictive densities\n", + "Lastly, we directly compare the velocity and Helmholtz models by computing the [negative log predictive densities](https://en.wikipedia.org/wiki/Negative_log_predictive_density) for each model. This is a quantitative metric that measures the probability of the ground truth given the data.\n", + "\n", + "$$\n", + "\\mathrm{NLPD}=-\\sum_{i=1}^{2N} \\log \\left( p\\left(\\mathcal{Y}_i = Y_{0,i} \\mid \\mathbf{X}_{i}\\right) \\right),\n", + "$$\n", + "\n", + "where each $p\\left(\\mathcal{Y}_i \\mid \\mathbf{X}_i \\right)$ is the marginal Gaussian distribution over $\\mathcal{Y}_i$ at each test location, and $Y_{i,0}$ is the $i$-th component of the (massaged) test data that we reserved at the beginning of the notebook in $D_0$. A smaller value is better, since the deviation of the ground truth and the model are small in this case." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "386767fb", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NLPD for Velocity: 1566.11 \n", + "NLPD for Helmholtz: -208.06\n" + ] + } + ], + "source": [ + "# ensure testing data alternates between x0 and x1 components\n", + "def nlpd(mean, std, vel_test):\n", + " vel_query = jnp.column_stack((vel_test[0], vel_test[1])).flatten()\n", + " normal = tfp.substrates.jax.distributions.Normal(loc=mean, scale=std)\n", + " return -jnp.sum(normal.log_prob(vel_query))\n", + "\n", + "\n", + "# compute nlpd for velocity and helmholtz\n", + "nlpd_vel = nlpd(velocity_mean, velocity_std, vel_test)\n", + "nlpd_helm = nlpd(helmholtz_mean, helmholtz_std, vel_test)\n", + "\n", + "print(\"NLPD for Velocity: %.2f \\nNLPD for Helmholtz: %.2f\" % (nlpd_vel, nlpd_helm))" + ] + }, + { + "cell_type": "markdown", + "id": "d1180592", + "metadata": {}, + "source": [ + "The Helmholtz model outperforms the velocity model, as indicated by the lower NLPD score." + ] + }, + { + "cell_type": "markdown", + "id": "3bc48d3a", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "\n", + "## Footnote\n", + "Kernels for vector-valued functions have been studied in the literature, see [Alvarez et al. (2012)](https://doi.org/10.48550/arXiv.1106.6251)\n", + "## System configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "25b7b160", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Author: Ivan Shalashilin\n", + "\n", + "Last updated: Tue Sep 19 2023\n", + "\n", + "Python implementation: CPython\n", + "Python version : 3.10.0\n", + "IPython version : 8.12.2\n", + "\n", + "jax : 0.4.9\n", + "gpjax : 0.0.0\n", + "tensorflow_probability: 0.19.0\n", + "pandas : 1.5.3\n", + "jaxopt : 0.6\n", + "matplotlib : 3.7.1\n", + "optax : 0.1.5\n", + "\n", + "Watermark: 2.3.1\n", + "\n" + ] + } + ], + "source": [ + "%reload_ext watermark\n", + "%watermark -n -u -v -iv -w -a 'Ivan Shalashilin'" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "gpjax", + "language": "python", + "name": "gpjax" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/regression.ipynb b/docs/examples/regression.ipynb new file mode 100644 index 000000000..3d8a71b64 --- /dev/null +++ b/docs/examples/regression.ipynb @@ -0,0 +1,639 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "467e95fc", + "metadata": {}, + "source": [ + "# Regression\n", + "\n", + "In this notebook we demonstate how to fit a Gaussian process regression model." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5f9f23a8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n" + ] + } + ], + "source": [ + "# Enable Float64 for more stable matrix inversions.\n", + "from jax.config import config\n", + "\n", + "config.update(\"jax_enable_x64\", True)\n", + "\n", + "from jax import jit\n", + "import jax.numpy as jnp\n", + "import jax.random as jr\n", + "from jaxtyping import install_import_hook\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import jaxopt\n", + "from docs.examples.utils import clean_legend\n", + "\n", + "with install_import_hook(\"gpjax\", \"beartype.beartype\"):\n", + " import gpjax as gpx\n", + "\n", + "key = jr.PRNGKey(123)\n", + "plt.style.use(\n", + " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", + ")\n", + "cols = mpl.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]" + ] + }, + { + "cell_type": "markdown", + "id": "5a3ce4b3", + "metadata": {}, + "source": [ + "## Dataset\n", + "\n", + "With the necessary modules imported, we simulate a dataset\n", + "$\\mathcal{D} = (\\boldsymbol{x}, \\boldsymbol{y}) = \\{(x_i, y_i)\\}_{i=1}^{100}$ with inputs $\\boldsymbol{x}$\n", + "sampled uniformly on $(-3., 3)$ and corresponding independent noisy outputs\n", + "\n", + "$$\\boldsymbol{y} \\sim \\mathcal{N} \\left(\\sin(4\\boldsymbol{x}) + \\cos(2 \\boldsymbol{x}), \\textbf{I} * 0.3^2 \\right).$$\n", + "\n", + "We store our data $\\mathcal{D}$ as a GPJax `Dataset` and create test inputs and labels\n", + "for later." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5c4d7a13", + "metadata": {}, + "outputs": [], + "source": [ + "n = 100\n", + "noise = 0.3\n", + "\n", + "key, subkey = jr.split(key)\n", + "x = jr.uniform(key=key, minval=-3.0, maxval=3.0, shape=(n,)).reshape(-1, 1)\n", + "f = lambda x: jnp.sin(4 * x) + jnp.cos(2 * x)\n", + "signal = f(x)\n", + "y = signal + jr.normal(subkey, shape=signal.shape) * noise\n", + "\n", + "D = gpx.Dataset(X=x, y=y)\n", + "\n", + "xtest = jnp.linspace(-3.5, 3.5, 500).reshape(-1, 1)\n", + "ytest = f(xtest)" + ] + }, + { + "cell_type": "markdown", + "id": "f9567fb3", + "metadata": {}, + "source": [ + "To better understand what we have simulated, we plot both the underlying latent\n", + "function and the observed data that is subject to Gaussian noise." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9b1127b6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.plot(x, y, \"o\", label=\"Observations\", color=cols[0])\n", + "ax.plot(xtest, ytest, label=\"Latent function\", color=cols[1])\n", + "ax.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "id": "381ecdde", + "metadata": {}, + "source": [ + "Our aim in this tutorial will be to reconstruct the latent function from our noisy\n", + "observations $\\mathcal{D}$ via Gaussian process regression. We begin by defining a\n", + "Gaussian process prior in the next section.\n", + "\n", + "## Defining the prior\n", + "\n", + "A zero-mean Gaussian process (GP) places a prior distribution over real-valued\n", + "functions $f(\\cdot)$ where\n", + "$f(\\boldsymbol{x}) \\sim \\mathcal{N}(0, \\mathbf{K}_{\\boldsymbol{x}\\boldsymbol{x}})$\n", + "for any finite collection of inputs $\\boldsymbol{x}$.\n", + "\n", + "Here $\\mathbf{K}_{\\boldsymbol{x}\\boldsymbol{x}}$ is the Gram matrix generated by a\n", + "user-specified symmetric, non-negative definite kernel function $k(\\cdot, \\cdot')$\n", + "with $[\\mathbf{K}_{\\boldsymbol{x}\\boldsymbol{x}}]_{i, j} = k(x_i, x_j)$.\n", + "The choice of kernel function is critical as, among other things, it governs the\n", + "smoothness of the outputs that our GP can generate.\n", + "\n", + "For simplicity, we consider a radial basis function (RBF) kernel:\n", + "$$k(x, x') = \\sigma^2 \\exp\\left(-\\frac{\\lVert x - x' \\rVert_2^2}{2 \\ell^2}\\right).$$\n", + "\n", + "On paper a GP is written as $f(\\cdot) \\sim \\mathcal{GP}(\\textbf{0}, k(\\cdot, \\cdot'))$,\n", + "we can reciprocate this process in GPJax via defining a `Prior` with our chosen `RBF`\n", + "kernel." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "9fd89471", + "metadata": {}, + "outputs": [], + "source": [ + "kernel = gpx.kernels.RBF()\n", + "meanf = gpx.mean_functions.Zero()\n", + "prior = gpx.Prior(mean_function=meanf, kernel=kernel)" + ] + }, + { + "cell_type": "markdown", + "id": "8cbf561e", + "metadata": {}, + "source": [ + "\n", + "The above construction forms the foundation for GPJax's models. Moreover, the GP prior\n", + "we have just defined can be represented by a\n", + "[TensorFlow Probability](https://www.tensorflow.org/probability/api_docs/python/tfp/substrates/jax)\n", + "multivariate Gaussian distribution. Such functionality enables trivial sampling, and\n", + "the evaluation of the GP's mean and covariance ." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "94e8d902", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "prior_dist = prior.predict(xtest)\n", + "\n", + "prior_mean = prior_dist.mean()\n", + "prior_std = prior_dist.variance()\n", + "samples = prior_dist.sample(seed=key, sample_shape=(20,))\n", + "\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.plot(xtest, samples.T, alpha=0.5, color=cols[0], label=\"Prior samples\")\n", + "ax.plot(xtest, prior_mean, color=cols[1], label=\"Prior mean\")\n", + "ax.fill_between(\n", + " xtest.flatten(),\n", + " prior_mean - prior_std,\n", + " prior_mean + prior_std,\n", + " alpha=0.3,\n", + " color=cols[1],\n", + " label=\"Prior variance\",\n", + ")\n", + "ax.legend(loc=\"best\")\n", + "ax = clean_legend(ax)" + ] + }, + { + "cell_type": "markdown", + "id": "d277c01a", + "metadata": {}, + "source": [ + "## Constructing the posterior\n", + "\n", + "Having defined our GP, we proceed to define a description of our data\n", + "$\\mathcal{D}$ conditional on our knowledge of $f(\\cdot)$ --- this is exactly the\n", + "notion of a likelihood function $p(\\mathcal{D} | f(\\cdot))$. While the choice of\n", + "likelihood is a critical in Bayesian modelling, for simplicity we consider a\n", + "Gaussian with noise parameter $\\alpha$\n", + "$$p(\\mathcal{D} | f(\\cdot)) = \\mathcal{N}(\\boldsymbol{y}; f(\\boldsymbol{x}), \\textbf{I} \\alpha^2).$$\n", + "This is defined in GPJax through calling a `Gaussian` instance." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ecf37b5c", + "metadata": {}, + "outputs": [], + "source": [ + "likelihood = gpx.Gaussian(num_datapoints=D.n)" + ] + }, + { + "cell_type": "markdown", + "id": "d05f58a4", + "metadata": {}, + "source": [ + "The posterior is proportional to the prior multiplied by the likelihood, written as\n", + "\n", + " $$ p(f(\\cdot) | \\mathcal{D}) \\propto p(f(\\cdot)) * p(\\mathcal{D} | f(\\cdot)). $$\n", + "\n", + "Mimicking this construct, the posterior is established in GPJax through the `*` operator." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c265f858", + "metadata": {}, + "outputs": [], + "source": [ + "posterior = prior * likelihood" + ] + }, + { + "cell_type": "markdown", + "id": "77c4f906", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Parameter state\n", + "\n", + "As outlined in the [PyTrees](https://jax.readthedocs.io/en/latest/pytrees.html)\n", + "documentation, parameters are contained within the model and for the leaves of the\n", + "PyTree. Consequently, in this particular model, we have three parameters: the\n", + "kernel lengthscale, kernel variance and the observation noise variance. Whilst\n", + "we have initialised each of these to 1, we can learn Type 2 MLEs for each of\n", + "these parameters by optimising the marginal log-likelihood (MLL)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "fa9eb13b", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Array(124.80517341, dtype=float64)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "negative_mll = gpx.objectives.ConjugateMLL(negative=True)\n", + "negative_mll(posterior, train_data=D)\n", + "\n", + "\n", + "# static_tree = jax.tree_map(lambda x: not(x), posterior.trainables)\n", + "# optim = ox.chain(\n", + "# ox.adam(learning_rate=0.01),\n", + "# ox.masked(ox.set_to_zero(), static_tree)\n", + "# )" + ] + }, + { + "cell_type": "markdown", + "id": "896901fb", + "metadata": {}, + "source": [ + "For researchers, GPJax has the capacity to print the bibtex citation for objects such\n", + "as the marginal log-likelihood through the `cite()` function." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2babd32e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "@book{rasmussen2006gaussian,\n", + "authors = {Rasmussen, Carl Edward and Williams, Christopher K},\n", + "title = {Gaussian Processes for Machine Learning},\n", + "year = {2006},\n", + "publisher = {MIT press Cambridge, MA},\n", + "volume = {2},\n", + "}\n" + ] + } + ], + "source": [ + "print(gpx.cite(negative_mll))" + ] + }, + { + "cell_type": "markdown", + "id": "45b25ada", + "metadata": {}, + "source": [ + "JIT-compiling expensive-to-compute functions such as the marginal log-likelihood is\n", + "advisable. This can be achieved by wrapping the function in `jax.jit()`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "74380d6c", + "metadata": {}, + "outputs": [], + "source": [ + "negative_mll = jit(negative_mll)" + ] + }, + { + "cell_type": "markdown", + "id": "90ee232c", + "metadata": {}, + "source": [ + "Since most optimisers (including here) minimise a given function, we have realised\n", + "the negative marginal log-likelihood and just-in-time (JIT) compiled this to\n", + "accelerate training." + ] + }, + { + "cell_type": "markdown", + "id": "d6916f72", + "metadata": {}, + "source": [ + "We can now train our model using a `jaxopt` solver. In this case we opt for the `OptaxSolver`,\n", + "which wraps an `optax` optimizer." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "201541e8", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b0f80729c9274838b15e85adbab6d435", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/10 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.plot(history, color=cols[1])\n", + "ax.set(xlabel=\"Training iteration\", ylabel=\"Negative marginal log likelihood\")" + ] + }, + { + "cell_type": "markdown", + "id": "a8221ff7", + "metadata": {}, + "source": [ + "## Prediction\n", + "\n", + "Equipped with the posterior and a set of optimised hyperparameter values, we are now\n", + "in a position to query our GP's predictive distribution at novel test inputs. To do\n", + "this, we use our defined `posterior` and `likelihood` at our test inputs to obtain\n", + "the predictive distribution as a `Distrax` multivariate Gaussian upon which `mean`\n", + "and `stddev` can be used to extract the predictive mean and standard deviatation." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "f6aeb70e", + "metadata": {}, + "outputs": [], + "source": [ + "latent_dist = opt_posterior.predict(xtest, train_data=D)\n", + "predictive_dist = opt_posterior.likelihood(latent_dist)\n", + "\n", + "predictive_mean = predictive_dist.mean()\n", + "predictive_std = predictive_dist.stddev()" + ] + }, + { + "cell_type": "markdown", + "id": "dc9eb8da", + "metadata": {}, + "source": [ + "With the predictions and their uncertainty acquired, we illustrate the GP's\n", + "performance at explaining the data $\\mathcal{D}$ and recovering the underlying\n", + "latent function of interest." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "58b81c27", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7.5, 2.5))\n", + "ax.plot(x, y, \"x\", label=\"Observations\", color=cols[0], alpha=0.5)\n", + "ax.fill_between(\n", + " xtest.squeeze(),\n", + " predictive_mean - 2 * predictive_std,\n", + " predictive_mean + 2 * predictive_std,\n", + " alpha=0.2,\n", + " label=\"Two sigma\",\n", + " color=cols[1],\n", + ")\n", + "ax.plot(\n", + " xtest,\n", + " predictive_mean - 2 * predictive_std,\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " color=cols[1],\n", + ")\n", + "ax.plot(\n", + " xtest,\n", + " predictive_mean + 2 * predictive_std,\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " color=cols[1],\n", + ")\n", + "ax.plot(\n", + " xtest, ytest, label=\"Latent function\", color=cols[0], linestyle=\"--\", linewidth=2\n", + ")\n", + "ax.plot(xtest, predictive_mean, label=\"Predictive mean\", color=cols[1])\n", + "ax.legend(loc=\"center left\", bbox_to_anchor=(0.975, 0.5))" + ] + }, + { + "cell_type": "markdown", + "id": "19a304dd", + "metadata": {}, + "source": [ + "## System configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2c3ddbf6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Author: Thomas Pinder & Daniel Dodd\n", + "\n", + "Last updated: Tue Sep 19 2023\n", + "\n", + "Python implementation: CPython\n", + "Python version : 3.10.0\n", + "IPython version : 8.12.2\n", + "\n", + "matplotlib: 3.7.1\n", + "jax : 0.4.9\n", + "jaxopt : 0.6\n", + "gpjax : 0.0.0\n", + "\n", + "Watermark: 2.3.1\n", + "\n" + ] + } + ], + "source": [ + "%reload_ext watermark\n", + "%watermark -n -u -v -iv -w -a 'Thomas Pinder & Daniel Dodd'" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "custom_cell_magics": "kql" + }, + "kernelspec": { + "display_name": "gpjax", + "language": "python", + "name": "gpjax" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/spatial.ipynb b/docs/examples/spatial.ipynb new file mode 100644 index 000000000..a91bc9610 --- /dev/null +++ b/docs/examples/spatial.ipynb @@ -0,0 +1,556 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e6c1802a", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "# Pathwise Sampling for Spatial Modelling\n", + "In this notebook, we demonstrate an application of Gaussian Processes\n", + "to a spatial interpolation problem. We will show how\n", + "to efficiently sample from a GP posterior as shown in .\n", + "\n", + "## Data loading\n", + "We'll use open-source data from\n", + "[SwissMetNet](https://www.meteoswiss.admin.ch/services-and-publications/applications/measurement-values-and-measuring-networks.html#lang=en¶m=messnetz-automatisch),\n", + "the surface weather monitoring network of the Swiss national weather service,\n", + "and digital elevation model (DEM) data from Copernicus, accessible\n", + "[here](https://planetarycomputer.microsoft.com/dataset/cop-dem-glo-90)\n", + "via the Planetary Computer data catalog.\n", + "We will coarsen this data by a factor of 10 (going from 90m to 900m resolution), but feel free to change this.\n", + "\n", + "Our variable of interest is the maximum daily temperature, observed on the 4th of April 2023 at\n", + "150 weather stations, and we'll try to interpolate it on a spatial grid using geographical coordinates\n", + "(latitude and longitude) and elevation as input variables.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3f9bec24", + "metadata": {}, + "outputs": [ + { + "ename": "PydanticImportError", + "evalue": "`BaseSettings` has been moved to the `pydantic-settings` package. See https://docs.pydantic.dev/2.3/migration/#basesettings-has-moved-to-pydantic-settings for more details.\n\nFor further information visit https://errors.pydantic.dev/2.3/u/import-error", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mPydanticImportError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 22\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjaxopt\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mplanetary_computer\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpystac_client\u001b[39;00m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrioxarray\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mrio\u001b[39;00m\n", + "File \u001b[0;32m~/anaconda3/envs/gpjax/lib/python3.10/site-packages/planetary_computer/__init__.py:4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"Planetary Computer Python SDK\"\"\"\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# flake8:noqa\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mplanetary_computer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msas\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 5\u001b[0m sign,\n\u001b[1;32m 6\u001b[0m sign_inplace,\n\u001b[1;32m 7\u001b[0m sign_url,\n\u001b[1;32m 8\u001b[0m sign_item,\n\u001b[1;32m 9\u001b[0m sign_assets,\n\u001b[1;32m 10\u001b[0m sign_asset,\n\u001b[1;32m 11\u001b[0m sign_item_collection,\n\u001b[1;32m 12\u001b[0m )\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mplanetary_computer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msettings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m set_subscription_key\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mplanetary_computer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_adlfs\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_adlfs_filesystem, get_container_client\n", + "File \u001b[0;32m~/anaconda3/envs/gpjax/lib/python3.10/site-packages/planetary_computer/sas.py:20\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpystac_client\u001b[39;00m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01murllib3\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mretry\u001b[39;00m\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mplanetary_computer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msettings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Settings\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mplanetary_computer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 22\u001b[0m parse_blob_url,\n\u001b[1;32m 23\u001b[0m parse_adlfs_url,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 26\u001b[0m asset_xpr,\n\u001b[1;32m 27\u001b[0m )\n\u001b[1;32m 30\u001b[0m BLOB_STORAGE_DOMAIN \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.blob.core.windows.net\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", + "File \u001b[0;32m~/anaconda3/envs/gpjax/lib/python3.10/site-packages/planetary_computer/settings.py:11\u001b[0m\n\u001b[1;32m 6\u001b[0m SETTINGS_ENV_PREFIX \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPC_SDK_\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 8\u001b[0m DEFAULT_SAS_TOKEN_ENDPOINT \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://planetarycomputer.microsoft.com/api/sas/v1/token\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mSettings\u001b[39;00m(\u001b[43mpydantic\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBaseSettings\u001b[49m):\n\u001b[1;32m 12\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"PC SDK configuration settings\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \n\u001b[1;32m 14\u001b[0m \u001b[38;5;124;03m Settings defined here are attempted to be read in two ways, in this order:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;124;03m All settings are prefixed with `PC_SDK_`\u001b[39;00m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;66;03m# PC_SDK_SUBSCRIPTION_KEY: subscription key to send along with token\u001b[39;00m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;66;03m# requests. If present, allows less restricted rate limiting.\u001b[39;00m\n", + "File \u001b[0;32m~/anaconda3/envs/gpjax/lib/python3.10/site-packages/pydantic/__init__.py:210\u001b[0m, in \u001b[0;36m__getattr__\u001b[0;34m(attr_name)\u001b[0m\n\u001b[1;32m 208\u001b[0m dynamic_attr \u001b[38;5;241m=\u001b[39m _dynamic_imports\u001b[38;5;241m.\u001b[39mget(attr_name)\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dynamic_attr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 210\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_getattr_migration\u001b[49m\u001b[43m(\u001b[49m\u001b[43mattr_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mimportlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m import_module\n\u001b[1;32m 214\u001b[0m module \u001b[38;5;241m=\u001b[39m import_module(_dynamic_imports[attr_name], package\u001b[38;5;241m=\u001b[39m__package__)\n", + "File \u001b[0;32m~/anaconda3/envs/gpjax/lib/python3.10/site-packages/pydantic/_migration.py:289\u001b[0m, in \u001b[0;36mgetattr_migration..wrapper\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m import_string(REDIRECT_TO_V1[import_path])\n\u001b[1;32m 288\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m import_path \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpydantic:BaseSettings\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m--> 289\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m PydanticImportError(\n\u001b[1;32m 290\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m`BaseSettings` has been moved to the `pydantic-settings` package. \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 291\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSee https://docs.pydantic.dev/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mversion_short()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/migration/#basesettings-has-moved-to-pydantic-settings \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 292\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfor more details.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 293\u001b[0m )\n\u001b[1;32m 294\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m import_path \u001b[38;5;129;01min\u001b[39;00m REMOVED_IN_V2:\n\u001b[1;32m 295\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m PydanticImportError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m`\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mimport_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` has been removed in V2.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "\u001b[0;31mPydanticImportError\u001b[0m: `BaseSettings` has been moved to the `pydantic-settings` package. See https://docs.pydantic.dev/2.3/migration/#basesettings-has-moved-to-pydantic-settings for more details.\n\nFor further information visit https://errors.pydantic.dev/2.3/u/import-error" + ] + } + ], + "source": [ + "# Enable Float64 for more stable matrix inversions.\n", + "from jax.config import config\n", + "\n", + "config.update(\"jax_enable_x64\", True)\n", + "\n", + "from dataclasses import dataclass\n", + "\n", + "import fsspec\n", + "import geopandas as gpd\n", + "import jax\n", + "import jax.numpy as jnp\n", + "import jax.random as jr\n", + "from jaxtyping import (\n", + " Array,\n", + " Float,\n", + " install_import_hook,\n", + ")\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import jaxopt\n", + "import pandas as pd\n", + "import planetary_computer\n", + "import pystac_client\n", + "import rioxarray as rio\n", + "from rioxarray.merge import merge_arrays\n", + "import xarray as xr\n", + "\n", + "with install_import_hook(\"gpjax\", \"beartype.beartype\"):\n", + " import gpjax as gpx\n", + " from gpjax.base import param_field\n", + " from gpjax.dataset import Dataset\n", + "\n", + "\n", + "key = jr.PRNGKey(123)\n", + "plt.style.use(\n", + " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", + ")\n", + "cols = mpl.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n", + "\n", + "# Observed temperature data\n", + "try:\n", + " temperature = pd.read_csv(\"data/max_tempeature_switzerland.csv\")\n", + "except FileNotFoundError:\n", + " temperature = pd.read_csv(\"docs/examples/data/max_tempeature_switzerland.csv\")\n", + "\n", + "temperature = gpd.GeoDataFrame(\n", + " temperature,\n", + " geometry=gpd.points_from_xy(temperature.longitude, temperature.latitude),\n", + ").dropna(how=\"any\")\n", + "\n", + "# Country borders shapefile\n", + "path = \"simplecache::https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_countries.zip\"\n", + "with fsspec.open(path) as file:\n", + " ch_shp = gpd.read_file(file).query(\"ADMIN == 'Switzerland'\")\n", + "\n", + "\n", + "# Read DEM data and clip it to switzerland\n", + "catalog = pystac_client.Client.open(\n", + " \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n", + " modifier=planetary_computer.sign_inplace,\n", + ")\n", + "search = catalog.search(collections=[\"cop-dem-glo-90\"], bbox=[5.5, 45.5, 10.0, 48.5])\n", + "items = list(search.get_all_items())\n", + "tiles = [rio.open_rasterio(i.assets[\"data\"].href).squeeze().drop(\"band\") for i in items]\n", + "dem = merge_arrays(tiles).coarsen(x=10, y=10).mean().rio.clip(ch_shp[\"geometry\"])" + ] + }, + { + "cell_type": "markdown", + "id": "0901b58d", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "Let us take a look at the data. The topography of Switzerland is quite complex, and there\n", + "are sometimes very large height differences over short distances. This measuring network is fairly dense,\n", + "and you may already notice that there's a dependency between maximum daily temperature and elevation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ca052624", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'dem' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m5\u001b[39m), layout\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconstrained\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m \u001b[43mdem\u001b[49m\u001b[38;5;241m.\u001b[39mplot(\n\u001b[1;32m 3\u001b[0m cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mterrain\u001b[39m\u001b[38;5;124m\"\u001b[39m, cbar_kwargs\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maspect\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m50\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpad\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m0.02\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mElevation [m]\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[1;32m 4\u001b[0m )\n\u001b[1;32m 5\u001b[0m temperature\u001b[38;5;241m.\u001b[39mplot(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mt_max\u001b[39m\u001b[38;5;124m\"\u001b[39m, ax\u001b[38;5;241m=\u001b[39max, cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRdBu_r\u001b[39m\u001b[38;5;124m\"\u001b[39m, vmin\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m15\u001b[39m, vmax\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m15\u001b[39m, edgecolor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m\"\u001b[39m, s\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m50\u001b[39m)\n\u001b[1;32m 6\u001b[0m ax\u001b[38;5;241m.\u001b[39mset(title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSwitzerland\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms topography and SwissMetNet stations\u001b[39m\u001b[38;5;124m\"\u001b[39m, aspect\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'dem' is not defined" + ] + }, + { + "data": { + "image/png": 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X44Ybbhj1ZgEAgHNH2bHy+OOPxxe+8IVYunRpfOxjH4t169bFBRdcEN/5zndOumZgYCA+//nPx8MPPxyXXnrpn7RhAADg3FBWrPT398f27dujubn5D09QWRnNzc3R2dl50nVf+9rXYvLkyXHbbbe9r+scO3Ysent7hz0AAIBzS1mxcuTIkRgYGIj6+vph4/X19dHV1TXimp/+9Kfx9NNPx/r169/3ddrb26Ourm7o0dDQUM42AQCAs8CY/jSwo0ePxqJFi2L9+vUxadKk971uxYoV0dPTM/Q4ePDgGO4SAADIaEI5kydNmhRVVVXR3d09bLy7uzumTJlywvyf//znsX///pg3b97Q2ODg4O8uPGFC7N27Ny677LIT1pVKpSiVSuVsDQAAOMuUdWeluro6Zs+eHR0dHUNjg4OD0dHREU1NTSfMv+KKK+LVV1+NXbt2DT0+85nPxE033RS7du3y8S4AAOCkyrqzEhHR2toaS5YsiTlz5sTcuXNjzZo10dfXF0uXLo2IiMWLF8f06dOjvb09ampq4sorrxy2/qKLLoqIOGEcAADg/ys7VhYsWBCHDx+OlStXRldXV8yaNSu2bNky9KX7AwcORGXlmH4VBgAAOAdUFEVRnO5N/DG9vb1RV1cXPT09UVtbe7q3AwAAvMtYvGd3CwQAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmNKlbWrl0bM2bMiJqammhsbIxt27addO769evjhhtuiIkTJ8bEiROjubn5PecDAABEjCJWNm7cGK2trdHW1hY7duyImTNnRktLSxw6dGjE+Vu3bo2FCxfGyy+/HJ2dndHQ0BA333xzvPnmm3/y5gEAgLNXRVEURTkLGhsb49prr40nnngiIiIGBwejoaEh7rrrrli+fPkfXT8wMBATJ06MJ554IhYvXvy+rtnb2xt1dXXR09MTtbW15WwXAAAYB2Pxnr2sOyv9/f2xffv2aG5u/sMTVFZGc3NzdHZ2vq/nePvtt+Odd96Jiy+++KRzjh07Fr29vcMeAADAuaWsWDly5EgMDAxEfX39sPH6+vro6up6X89x3333xbRp04YFz7u1t7dHXV3d0KOhoaGcbQIAAGeBcf1pYKtXr44NGzbE888/HzU1NSedt2LFiujp6Rl6HDx4cBx3CQAAZDChnMmTJk2Kqqqq6O7uHjbe3d0dU6ZMec+1jz76aKxevTp+/OMfx9VXX/2ec0ulUpRKpXK2BgAAnGXKurNSXV0ds2fPjo6OjqGxwcHB6OjoiKamppOu++Y3vxmPPPJIbNmyJebMmTP63QIAAOeMsu6sRES0trbGkiVLYs6cOTF37txYs2ZN9PX1xdKlSyMiYvHixTF9+vRob2+PiIh//Md/jJUrV8azzz4bM2bMGPpuywc+8IH4wAc+cApfCgAAcDYpO1YWLFgQhw8fjpUrV0ZXV1fMmjUrtmzZMvSl+wMHDkRl5R9u2HzrW9+K/v7++OxnPzvsedra2uKrX/3qn7Z7AADgrFX271k5HfyeFQAAyO20/54VAACA8SJWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApDSqWFm7dm3MmDEjampqorGxMbZt2/ae87///e/HFVdcETU1NXHVVVfF5s2bR7VZAADg3FF2rGzcuDFaW1ujra0tduzYETNnzoyWlpY4dOjQiPNfeeWVWLhwYdx2222xc+fOmD9/fsyfPz9ee+21P3nzAADA2auiKIqinAWNjY1x7bXXxhNPPBEREYODg9HQ0BB33XVXLF++/IT5CxYsiL6+vvjRj340NPZXf/VXMWvWrFi3bt37umZvb2/U1dVFT09P1NbWlrNdAABgHIzFe/YJ5Uzu7++P7du3x4oVK4bGKisro7m5OTo7O0dc09nZGa2trcPGWlpa4oUXXjjpdY4dOxbHjh0b+nNPT09E/O5fAAAAkM/v36uXeS/kPZUVK0eOHImBgYGor68fNl5fXx979uwZcU1XV9eI87u6uk56nfb29nj44YdPGG9oaChnuwAAwDj77//+76irqzslz1VWrIyXFStWDLsb89Zbb8UHP/jBOHDgwCl74dDb2xsNDQ1x8OBBHy/klHCmGAvOFWPBuWIs9PT0xCWXXBIXX3zxKXvOsmJl0qRJUVVVFd3d3cPGu7u7Y8qUKSOumTJlSlnzIyJKpVKUSqUTxuvq6vwHxSlXW1vrXHFKOVOMBeeKseBcMRYqK0/db0cp65mqq6tj9uzZ0dHRMTQ2ODgYHR0d0dTUNOKapqamYfMjIl566aWTzgcAAIgYxcfAWltbY8mSJTFnzpyYO3durFmzJvr6+mLp0qUREbF48eKYPn16tLe3R0TE3XffHTfeeGM89thjccstt8SGDRviZz/7WTz11FOn9pUAAABnlbJjZcGCBXH48OFYuXJldHV1xaxZs2LLli1DX6I/cODAsFs/1113XTz77LPx4IMPxv333x8f/vCH44UXXogrr7zyfV+zVCpFW1vbiB8Ng9FyrjjVnCnGgnPFWHCuGAtjca7K/j0rAAAA4+HUffsFAADgFBIrAABASmIFAABISawAAAApiRUAACClNLGydu3amDFjRtTU1ERjY2Ns27btPed///vfjyuuuCJqamriqquuis2bN4/TTjlTlHOm1q9fHzfccENMnDgxJk6cGM3NzX/0DHJuKvfvqt/bsGFDVFRUxPz588d2g5yRyj1Xb731VixbtiymTp0apVIpLr/8cv87yAnKPVdr1qyJj3zkI3H++edHQ0ND3HvvvfHb3/52nHZLdj/5yU9i3rx5MW3atKioqIgXXnjhj67ZunVrfOITn4hSqRQf+tCH4plnnin7uiliZePGjdHa2hptbW2xY8eOmDlzZrS0tMShQ4dGnP/KK6/EwoUL47bbboudO3fG/PnzY/78+fHaa6+N887JqtwztXXr1li4cGG8/PLL0dnZGQ0NDXHzzTfHm2++Oc47J7Nyz9Xv7d+/P7785S/HDTfcME475UxS7rnq7++PT33qU7F///547rnnYu/evbF+/fqYPn36OO+czMo9V88++2wsX7482traYvfu3fH000/Hxo0b4/777x/nnZNVX19fzJw5M9auXfu+5v/iF7+IW265JW666abYtWtX3HPPPXH77bfHiy++WN6FiwTmzp1bLFu2bOjPAwMDxbRp04r29vYR53/uc58rbrnllmFjjY2Nxd/93d+N6T45c5R7pt7t+PHjxYUXXlh897vfHastcgYazbk6fvx4cd111xXf/va3iyVLlhR/8zd/Mw475UxS7rn61re+VVx66aVFf3//eG2RM1C552rZsmXFX//1Xw8ba21tLa6//vox3Sdnpogonn/++fec85WvfKX4+Mc/PmxswYIFRUtLS1nXOu13Vvr7+2P79u3R3Nw8NFZZWRnNzc3R2dk54prOzs5h8yMiWlpaTjqfc8toztS7vf322/HOO+/ExRdfPFbb5Awz2nP1ta99LSZPnhy33XbbeGyTM8xoztUPf/jDaGpqimXLlkV9fX1ceeWVsWrVqhgYGBivbZPcaM7VddddF9u3bx/6qNi+ffti8+bN8elPf3pc9szZ51S9X59wKjc1GkeOHImBgYGor68fNl5fXx979uwZcU1XV9eI87u6usZsn5w5RnOm3u2+++6LadOmnfAfGeeu0Zyrn/70p/H000/Hrl27xmGHnIlGc6727dsX//7v/x6f//znY/PmzfHGG2/El770pXjnnXeira1tPLZNcqM5V7feemscOXIkPvnJT0ZRFHH8+PG48847fQyMUTvZ+/Xe3t74zW9+E+eff/77ep7TfmcFslm9enVs2LAhnn/++aipqTnd2+EMdfTo0Vi0aFGsX78+Jk2adLq3w1lkcHAwJk+eHE899VTMnj07FixYEA888ECsW7fudG+NM9jWrVtj1apV8eSTT8aOHTviBz/4QWzatCkeeeSR0701znGn/c7KpEmToqqqKrq7u4eNd3d3x5QpU0ZcM2XKlLLmc24ZzZn6vUcffTRWr14dP/7xj+Pqq68ey21yhin3XP385z+P/fv3x7x584bGBgcHIyJiwoQJsXfv3rjsssvGdtOkN5q/r6ZOnRrnnXdeVFVVDY199KMfja6urujv74/q6uox3TP5jeZcPfTQQ7Fo0aK4/fbbIyLiqquuir6+vrjjjjvigQceiMpK//825TnZ+/Xa2tr3fVclIsGdlerq6pg9e3Z0dHQMjQ0ODkZHR0c0NTWNuKapqWnY/IiIl1566aTzObeM5kxFRHzzm9+MRx55JLZs2RJz5swZj61yBin3XF1xxRXx6quvxq5du4Yen/nMZ4Z+KkpDQ8N4bp+kRvP31fXXXx9vvPHGUPxGRLz++usxdepUoUJEjO5cvf322ycEye+D+Hffp4bynLL36+V9939sbNiwoSiVSsUzzzxT/Nd//Vdxxx13FBdddFHR1dVVFEVRLFq0qFi+fPnQ/P/4j/8oJkyYUDz66KPF7t27i7a2tuK8884rXn311dP1Ekim3DO1evXqorq6unjuueeKX/3qV0OPo0ePnq6XQELlnqt389PAGEm55+rAgQPFhRdeWPz93/99sXfv3uJHP/pRMXny5OLrX//66XoJJFTuuWpraysuvPDC4l//9V+Lffv2Ff/2b/9WXHbZZcXnPve50/USSObo0aPFzp07i507dxYRUTz++OPFzp07i1/+8pdFURTF8uXLi0WLFg3N37dvX3HBBRcU//AP/1Ds3r27WLt2bVFVVVVs2bKlrOumiJWiKIp//ud/Li655JKiurq6mDt3bvGf//mfQ//sxhtvLJYsWTJs/ve+973i8ssvL6qrq4uPf/zjxaZNm8Z5x2RXzpn64Ac/WETECY+2trbx3ziplft31f8nVjiZcs/VK6+8UjQ2NhalUqm49NJLi2984xvF8ePHx3nXZFfOuXrnnXeKr371q8Vll11W1NTUFA0NDcWXvvSl4n/+53/Gf+Ok9PLLL4/4Xun352jJkiXFjTfeeMKaWbNmFdXV1cWll15a/Mu//EvZ160oCvf2AACAfE77d1YAAABGIlYAAICUxAoAAJCSWAEAAFISKwAAQEpiBQAASEmsAAAAKYkVAAAgJbECAACkJFYAAICUxAoAAJDS/wEH8MfVmLTOgAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(8, 5), layout=\"constrained\")\n", + "dem.plot(\n", + " cmap=\"terrain\", cbar_kwargs={\"aspect\": 50, \"pad\": 0.02, \"label\": \"Elevation [m]\"}\n", + ")\n", + "temperature.plot(\"t_max\", ax=ax, cmap=\"RdBu_r\", vmin=-15, vmax=15, edgecolor=\"k\", s=50)\n", + "ax.set(title=\"Switzerland's topography and SwissMetNet stations\", aspect=\"auto\")\n", + "cb = fig.colorbar(ax.collections[-1], aspect=50, pad=0.02)\n", + "cb.set_label(\"Max. daily temperature [°C]\", labelpad=-2)" + ] + }, + { + "cell_type": "markdown", + "id": "62b6fd46", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "As always, we store our training data in a `Dataset` object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f1c19a3", + "metadata": {}, + "outputs": [], + "source": [ + "x = temperature[[\"latitude\", \"longitude\", \"elevation\"]].values\n", + "y = temperature[[\"t_max\"]].values\n", + "D = Dataset(\n", + " X=jnp.array(x),\n", + " y=jnp.array(y),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "71d15bb8", + "metadata": {}, + "source": [ + "## ARD Kernel\n", + "As temperature decreases with height\n", + "(at a rate of approximately -6.5 °C/km in average conditions), we can expect that using the geographical distance\n", + "alone isn't enough to to a decent job at interpolating this data. Therefore, we can also use elevation and optimize\n", + "the parameters of our kernel such that more relevance should be given to elevation. This is possible by using a\n", + "kernel that has one length-scale parameter per input dimension: an automatic relevance determination (ARD) kernel.\n", + "See our [kernel notebook](https://docs.jaxgaussianprocesses.com/examples/constructing_new_kernels/) for more an introduction to\n", + "kernels in GPJax." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03a6b673", + "metadata": {}, + "outputs": [], + "source": [ + "kernel = gpx.kernels.RBF(\n", + " active_dims=[0, 1, 2],\n", + " lengthscale=jnp.array([0.1, 0.1, 100.0]),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6312fc7d", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "## Mean function\n", + "As stated before, we already know that temperature strongly depends on elevation.\n", + "So why not use it for our mean function? GPJax lets you define custom mean functions;\n", + "simply subclass `AbstractMeanFunction`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de2cf11b", + "metadata": {}, + "outputs": [], + "source": [ + "@dataclass\n", + "class MeanFunction(gpx.gps.AbstractMeanFunction):\n", + " w: Float[Array, \"1\"] = param_field(jnp.array([0.0]))\n", + " b: Float[Array, \"1\"] = param_field(jnp.array([0.0]))\n", + "\n", + " def __call__(self, x: Float[Array, \"N D\"]) -> Float[Array, \"N 1\"]:\n", + " elevation = x[:, 2:3]\n", + " out = elevation * self.w + self.b\n", + " return out" + ] + }, + { + "cell_type": "markdown", + "id": "37f3dcad", + "metadata": {}, + "source": [ + "Now we can define our prior. We'll also choose a Gaussian likelihood." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96e12772", + "metadata": {}, + "outputs": [], + "source": [ + "mean_function = MeanFunction()\n", + "prior = gpx.Prior(kernel=kernel, mean_function=mean_function)\n", + "likelihood = gpx.Gaussian(D.n)" + ] + }, + { + "cell_type": "markdown", + "id": "f363a6b8", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "Finally, we construct the posterior." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5aa15281", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "posterior = prior * likelihood" + ] + }, + { + "cell_type": "markdown", + "id": "c2628adc", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "## Model fitting\n", + "We proceed to train our model. Because we used a Gaussian likelihood, the resulting posterior is\n", + "a `ConjugatePosterior`, which allows us to optimize the analytically expressed marginal loglikelihood.\n", + "\n", + "As always, we can jit-compile the objective function to speed things up." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5cde8ae2", + "metadata": {}, + "outputs": [], + "source": [ + "negative_mll = jax.jit(gpx.objectives.ConjugateMLL(negative=True))\n", + "negative_mll(posterior, train_data=D)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78d204de", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "#optim = ox.chain(ox.adam(learning_rate=0.1), ox.clip(1.0))\n", + "posterior, history = gpx.fit(\n", + " model=posterior,\n", + " train_data=D,\n", + " solver=jaxopt.LBFGS(gpx.ConjugateMLL(negative=True), maxiter=10),\n", + " safe=True,\n", + " key=key,\n", + ")\n", + "posterior: gpx.gps.ConjugatePosterior" + ] + }, + { + "cell_type": "markdown", + "id": "13037426", + "metadata": {}, + "source": [ + "## Sampling on a grid\n", + "Now comes the cool part. In a standard GP implementation, for n test points, we have a $\\mathcal{O}(n^2)$\n", + "computational complexity and $\\mathcal{O}(n^2)$ memory requirement. We want to make predictions on a total\n", + "of roughly 70'000 pixels, and that would require us to compute a covariance matrix of `70000 ** 2 = 4900000000` elements.\n", + "If these are `float64`s, as it is often the case in GPJax, it would be equivalent to more than 36 Gigabytes of memory. And\n", + "that's for a fairly coarse and tiny grid. If we were to make predictions on a 1000x1000 grid, the total memory required\n", + "would be 8 _Terabytes_ of memory, which is intractable.\n", + "Fortunately, the pathwise conditioning method allows us to sample from our posterior in linear complexity,\n", + "$\\mathcal{O}(n)$, with the number of pixels.\n", + "\n", + "GPJax provides the `sample_approx` method to generate random conditioned samples from our posterior." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d35461f1", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "# select the target pixels and exclude nans\n", + "xtest = dem.drop(\"spatial_ref\").stack(p=[\"y\", \"x\"]).to_dataframe(name=\"dem\")\n", + "mask = jnp.any(jnp.isnan(xtest.values), axis=-1)\n", + "\n", + "# generate 50 samples\n", + "ytest = posterior.sample_approx(50, D, key, num_features=200)(\n", + " jnp.array(xtest.values[~mask])\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "22184195", + "metadata": {}, + "source": [ + "Let's take a look at the results. We start with the mean and standard deviation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae187049", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "predtest = xr.zeros_like(dem.stack(p=[\"y\", \"x\"])) * jnp.nan\n", + "predtest[~mask] = ytest.mean(axis=-1)\n", + "predtest = predtest.unstack()\n", + "\n", + "predtest.plot(\n", + " vmin=-15.0,\n", + " vmax=15.0,\n", + " cmap=\"RdBu_r\",\n", + " cbar_kwargs={\"aspect\": 50, \"pad\": 0.02, \"label\": \"Max. daily temperature [°C]\"},\n", + ")\n", + "plt.gca().set_title(\"Interpolated maximum daily temperature\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82e07695", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "predtest = xr.zeros_like(dem.stack(p=[\"y\", \"x\"])) * jnp.nan\n", + "predtest[~mask] = ytest.std(axis=-1)\n", + "predtest = predtest.unstack()\n", + "\n", + "# plot\n", + "predtest.plot(\n", + " cbar_kwargs={\"aspect\": 50, \"pad\": 0.02, \"label\": \"Standard deviation [°C]\"},\n", + ")\n", + "plt.gca().set_title(\"Standard deviation\")" + ] + }, + { + "cell_type": "markdown", + "id": "de03364b", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "And now some individual realizations of our GP posterior." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "76801bcf", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "predtest = (\n", + " xr.zeros_like(dem.stack(p=[\"y\", \"x\"]))\n", + " .expand_dims(realization=range(9))\n", + " .transpose(\"p\", \"realization\")\n", + " .copy()\n", + ")\n", + "predtest[~mask] = ytest[:, :9]\n", + "predtest = predtest.unstack()\n", + "predtest.plot(\n", + " col=\"realization\",\n", + " col_wrap=3,\n", + " cbar_kwargs={\"aspect\": 50, \"pad\": 0.02, \"label\": \"Max. daily temperature [°C]\"},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "4be50a23", + "metadata": {}, + "source": [ + "Remember when we said that on average the temperature decreases with height at a rate\n", + "of approximately -6.5°C/km? That's -0.0065°C/m. The `w` parameter of our mean function\n", + "is very close: we have learned the environmental lapse rate!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c21edf3a", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "print(posterior.prior.mean_function)" + ] + }, + { + "cell_type": "markdown", + "id": "8f9b683e", + "metadata": {}, + "source": [ + "That's it! We've successfully interpolated an observed meteorological parameter on a grid.\n", + "We have used several components of GPJax and adapted them to our needs: a custom mean function\n", + "that modelled the average temperature lapse rate; an ARD kernel that learned to give more relevance\n", + "to elevation rather than horizontal distance; an efficient sampling technique to produce\n", + "probabilistic realizations of our posterior on a large number of test points, which is important for\n", + "many spatiotemporal modelling applications.\n", + "If you're interested in a more elaborate work on temperature interpolation for the same domain used here, refer\n", + "to [Frei 2014](https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.3786)." + ] + }, + { + "cell_type": "markdown", + "id": "382435a6", + "metadata": {}, + "source": [ + "## System configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "feb5e52e", + "metadata": {}, + "outputs": [], + "source": [ + "%reload_ext watermark\n", + "%watermark -n -u -v -iv -w -a 'Francesco Zanetta'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ada3aeaa", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "custom_cell_magics": "kql", + "encoding": "# -*- coding: utf-8 -*-" + }, + "kernelspec": { + "display_name": "gpjax", + "language": "python", + "name": "gpjax" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/yacht.ipynb b/docs/examples/yacht.ipynb new file mode 100644 index 000000000..88481a02e --- /dev/null +++ b/docs/examples/yacht.ipynb @@ -0,0 +1,493 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1b755c49", + "metadata": {}, + "source": [ + "# UCI Data Benchmarking\n", + "\n", + "In this notebook, we will show how to apply GPJax on a benchmark UCI regression\n", + "problem. These kind of tasks are often used in the research community to benchmark\n", + "and assess new techniques against those already in the literature. Much of the code\n", + "contained in this notebook can be adapted to applied problems concerning datasets\n", + "other than the one presented here." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c1c0e13b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n" + ] + } + ], + "source": [ + "# Enable Float64 for more stable matrix inversions.\n", + "from jax.config import config\n", + "\n", + "config.update(\"jax_enable_x64\", True)\n", + "\n", + "from jax import jit\n", + "import jax.random as jr\n", + "from jaxtyping import install_import_hook\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import jaxopt\n", + "import pandas as pd\n", + "from sklearn.metrics import (\n", + " mean_squared_error,\n", + " r2_score,\n", + ")\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "with install_import_hook(\"gpjax\", \"beartype.beartype\"):\n", + " import gpjax as gpx\n", + "\n", + "# Enable Float64 for more stable matrix inversions.\n", + "key = jr.PRNGKey(123)\n", + "plt.style.use(\n", + " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", + ")\n", + "cols = mpl.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]" + ] + }, + { + "cell_type": "markdown", + "id": "1a2e172e", + "metadata": {}, + "source": [ + "## Data Loading\n", + "\n", + "We'll be using the\n", + "[Yacht](https://archive.ics.uci.edu/ml/datasets/yacht+hydrodynamics) dataset from\n", + "the UCI machine learning data repository. Each observation describes the\n", + "hydrodynamic performance of a yacht through its resistance. The dataset contains 6\n", + "covariates and a single positive, real valued response variable. There are 308\n", + "observations in the dataset, so we can comfortably use a conjugate regression\n", + "Gaussian process here (for more more details, checkout the\n", + "[Regression notebook](https://docs.jaxgaussianprocesses.com/examples/regression/))." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2b347a6d", + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " yacht = pd.read_fwf(\"data/yacht_hydrodynamics.data\", header=None).values[:-1, :]\n", + "except FileNotFoundError:\n", + " yacht = pd.read_fwf(\n", + " \"docs/examples/data/yacht_hydrodynamics.data\", header=None\n", + " ).values[:-1, :]\n", + "\n", + "X = yacht[:, :-1]\n", + "y = yacht[:, -1].reshape(-1, 1)" + ] + }, + { + "cell_type": "markdown", + "id": "24f5f1e7", + "metadata": {}, + "source": [ + "## Preprocessing\n", + "\n", + "With a dataset loaded, we'll now preprocess it such that it is more amenable to\n", + "modelling with a Gaussian process.\n", + "\n", + "### Data Partitioning\n", + "\n", + "We'll first partition our data into a _training_ and _testing_ split. We'll fit our\n", + "Gaussian process to the training data and evaluate its performance on the test data.\n", + "This allows us to investigate how effectively our Gaussian process generalises to\n", + "out-of-sample datapoints and ensure that we are not overfitting. We'll hold 30% of\n", + "our data back for testing purposes." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d61edd45", + "metadata": {}, + "outputs": [], + "source": [ + "Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.3, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "id": "56225658", + "metadata": {}, + "source": [ + "### Response Variable\n", + "\n", + "We'll now process our response variable $\\mathbf{y}$. As the below plots show, the\n", + "data has a very long tail and is certainly not Gaussian. However, we would like to\n", + "model a Gaussian response variable so that we can adopt a Gaussian likelihood\n", + "function and leverage the model's conjugacy. To achieve this, we'll first log-scale\n", + "the data, to bring the long right tail in closer to the data's mean. We'll then\n", + "standardise the data such that is distributed according to a unit normal\n", + "distribution. Both of these transformations are invertible through the log-normal\n", + "expectation and variance formulae and the the inverse standardisation identity,\n", + "should we ever need our model's predictions to be back on the scale of the\n", + "original dataset.\n", + "\n", + "For transforming both the input and response variable, all transformations will be\n", + "done with respect to the training data where relevant." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "12e3e1e7", + "metadata": {}, + "outputs": [], + "source": [ + "log_ytr = np.log(ytr)\n", + "log_yte = np.log(yte)\n", + "\n", + "y_scaler = StandardScaler().fit(log_ytr)\n", + "scaled_ytr = y_scaler.transform(log_ytr)\n", + "scaled_yte = y_scaler.transform(log_yte)" + ] + }, + { + "cell_type": "markdown", + "id": "494d98e5", + "metadata": {}, + "source": [ + "We can see the effect of these transformations in the below three panels." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6f2be151", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'scaled log(y)')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(ncols=3, figsize=(9, 2.5))\n", + "ax[0].hist(ytr, bins=30, color=cols[1])\n", + "ax[0].set_title(\"y\")\n", + "ax[1].hist(log_ytr, bins=30, color=cols[1])\n", + "ax[1].set_title(\"log(y)\")\n", + "ax[2].hist(scaled_ytr, bins=30, color=cols[1])\n", + "ax[2].set_title(\"scaled log(y)\")" + ] + }, + { + "cell_type": "markdown", + "id": "d195ed73", + "metadata": {}, + "source": [ + "### Input Variable\n", + "\n", + "We'll now transform our input variable $\\mathbf{X}$ to be distributed according to a\n", + "unit Gaussian." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d04f5fd5", + "metadata": {}, + "outputs": [], + "source": [ + "x_scaler = StandardScaler().fit(Xtr)\n", + "scaled_Xtr = x_scaler.transform(Xtr)\n", + "scaled_Xte = x_scaler.transform(Xte)" + ] + }, + { + "cell_type": "markdown", + "id": "b0c00ddb", + "metadata": {}, + "source": [ + "## Model fitting\n", + "\n", + "With data now loaded and preprocessed, we'll proceed to defining a Gaussian process\n", + "model and optimising its parameters. This notebook purposefully does not go into\n", + "great detail on this process, so please see notebooks such as the\n", + "[Regression notebook](https://docs.jaxgaussianprocesses.com/examples/regression/)\n", + "and\n", + "[Classification notebook](https://docs.jaxgaussianprocesses.com/examples/classification)\n", + "for further information.\n", + "\n", + "### Model specification\n", + "\n", + "We'll use a radial basis function kernel to parameterise the Gaussian process in this\n", + "notebook. As we have 5 covariates, we'll assign each covariate its own lengthscale\n", + "parameter. This form of kernel is commonly known as an automatic relevance\n", + "determination (ARD) kernel.\n", + "\n", + "In practice, the exact form of kernel used should be selected such that it\n", + "represents your understanding of the data. For example, if you were to model\n", + "temperature; a process that we know to be periodic, then you would likely wish to\n", + "select a periodic kernel. Having _Gaussian-ised_ our data somewhat, we'll also adopt\n", + "a Gaussian likelihood function." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1bf41f44", + "metadata": {}, + "outputs": [], + "source": [ + "n_train, n_covariates = scaled_Xtr.shape\n", + "kernel = gpx.RBF(\n", + " active_dims=list(range(n_covariates)), lengthscale=np.ones((n_covariates,))\n", + ")\n", + "meanf = gpx.mean_functions.Zero()\n", + "prior = gpx.Prior(mean_function=meanf, kernel=kernel)\n", + "\n", + "likelihood = gpx.Gaussian(num_datapoints=n_train)\n", + "\n", + "posterior = prior * likelihood" + ] + }, + { + "cell_type": "markdown", + "id": "0a710dd3", + "metadata": {}, + "source": [ + "### Model Optimisation\n", + "\n", + "With a model now defined, we can proceed to optimise the hyperparameters of our\n", + "model using one of `jaxopt`'s solvers. In this case we use a solver that wraps an\n", + "`optax` optimizer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1686daa", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cf6a96c19da049978ed4d05cf79e569c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/20 [00:00 Date: Tue, 19 Sep 2023 11:21:41 +0100 Subject: [PATCH 15/23] oh der --- docs/examples/barycentres.ipynb | 374 ------- docs/examples/bayesian_optimisation.ipynb | 1001 ------------------ docs/examples/classification.ipynb | 679 ------------ docs/examples/constructing_new_kernels.ipynb | 480 --------- docs/examples/decision_making.ipynb | 668 ------------ docs/examples/oceanmodelling.ipynb | 880 --------------- docs/examples/regression.ipynb | 639 ----------- docs/examples/spatial.ipynb | 556 ---------- docs/examples/yacht.ipynb | 493 --------- 9 files changed, 5770 deletions(-) delete mode 100644 docs/examples/barycentres.ipynb delete mode 100644 docs/examples/bayesian_optimisation.ipynb delete mode 100644 docs/examples/classification.ipynb delete mode 100644 docs/examples/constructing_new_kernels.ipynb delete mode 100644 docs/examples/decision_making.ipynb delete mode 100644 docs/examples/oceanmodelling.ipynb delete mode 100644 docs/examples/regression.ipynb delete mode 100644 docs/examples/spatial.ipynb delete mode 100644 docs/examples/yacht.ipynb diff --git a/docs/examples/barycentres.ipynb b/docs/examples/barycentres.ipynb deleted file mode 100644 index 044051365..000000000 --- a/docs/examples/barycentres.ipynb +++ /dev/null @@ -1,374 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e1fa004d", - "metadata": {}, - "source": [ - "# Gaussian Processes Barycentres\n", - "\n", - "In this notebook we'll give an implementation of\n", - ". In this work, the existence of a\n", - "Wasserstein barycentre between a collection of Gaussian processes is proven. When\n", - "faced with trying to _average_ a set of probability distributions, the Wasserstein\n", - "barycentre is an attractive choice as it enables uncertainty amongst the individual\n", - "distributions to be incorporated into the averaged distribution. When compared to a\n", - "naive _mean of means_ and _mean of variances_ approach to computing the average\n", - "probability distributions, it can be seen that Wasserstein barycentres offer\n", - "significantly more favourable uncertainty estimation.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74043851", - "metadata": {}, - "outputs": [], - "source": [ - "# Enable Float64 for more stable matrix inversions.\n", - "from jax.config import config\n", - "\n", - "config.update(\"jax_enable_x64\", True)\n", - "\n", - "import typing as tp\n", - "\n", - "import jax\n", - "import jax.numpy as jnp\n", - "import jax.random as jr\n", - "import jax.scipy.linalg as jsl\n", - "from jaxtyping import install_import_hook\n", - "import matplotlib.pyplot as plt\n", - "import jaxopt\n", - "import tensorflow_probability.substrates.jax.distributions as tfd\n", - "\n", - "with install_import_hook(\"gpjax\", \"beartype.beartype\"):\n", - " import gpjax as gpx\n", - "\n", - "\n", - "key = jr.PRNGKey(123)\n", - "plt.style.use(\n", - " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", - ")\n", - "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]" - ] - }, - { - "cell_type": "markdown", - "id": "516ee1ba", - "metadata": {}, - "source": [ - "## Background\n", - "\n", - "### Wasserstein distance\n", - "\n", - "The 2-Wasserstein distance metric between two probability measures $\\mu$ and $\\nu$\n", - "quantifies the minimal cost required to transport the unit mass from $\\mu$ to $\\nu$,\n", - "or vice-versa. Typically, computing this metric requires solving a linear program.\n", - "However, when $\\mu$ and $\\nu$ both belong to the family of multivariate Gaussian\n", - "distributions, the solution is analytically given by\n", - "$$W_2^2(\\mu, \\nu) = \\lVert m_1- m_2 \\rVert^2_2 + \\operatorname{Tr}(S_1 + S_2 - 2(S_1^{1/2}S_2S_1^{1/2})^{1/2}),$$\n", - "where $\\mu \\sim \\mathcal{N}(m_1, S_1)$ and $\\nu\\sim\\mathcal{N}(m_2, S_2)$.\n", - "\n", - "### Wasserstein barycentre\n", - "\n", - "For a collection of $T$ measures\n", - "$\\lbrace\\mu_i\\rbrace_{t=1}^T \\in \\mathcal{P}_2(\\theta)$, the Wasserstein barycentre\n", - "$\\bar{\\mu}$ is the measure that minimises the average Wasserstein distance to all\n", - "other measures in the set. More formally, the Wasserstein barycentre is the Fréchet\n", - "mean on a Wasserstein space that we can write as\n", - "$$\\bar{\\mu} = \\operatorname{argmin}_{\\mu\\in\\mathcal{P}_2(\\theta)}\\sum_{t=1}^T \\alpha_t W_2^2(\\mu, \\mu_t),$$\n", - "where $\\alpha\\in\\mathbb{R}^T$ is a weight vector that sums to 1.\n", - "\n", - "As with the Wasserstein distance, identifying the Wasserstein barycentre $\\bar{\\mu}$\n", - "is often an computationally demanding optimisation problem. However, when all the\n", - "measures admit a multivariate Gaussian density, the barycentre\n", - "$\\bar{\\mu} = \\mathcal{N}(\\bar{m}, \\bar{S})$ has analytical solutions\n", - "$$\\bar{m} = \\sum_{t=1}^T \\alpha_t m_t\\,, \\quad \\bar{S}=\\sum_{t=1}^T\\alpha_t (\\bar{S}^{1/2}S_t\\bar{S}^{1/2})^{1/2}\\,. \\qquad (\\star)$$\n", - "Identifying $\\bar{S}$ is achieved through a fixed-point iterative update.\n", - "\n", - "## Barycentre of Gaussian processes\n", - "\n", - "It was shown in that the\n", - "barycentre $\\bar{f}$ of a collection of Gaussian processes\n", - "$\\lbrace f_i\\rbrace_{i=1}^T$ such that $f_i \\sim \\mathcal{GP}(m_i, K_i)$ can be\n", - "found using the same solutions as in $(\\star)$. For a full theoretical understanding,\n", - "we recommend reading the original paper. However, the central argument to this result\n", - "is that one can first show that the barycentre GP\n", - "$\\bar{f}\\sim\\mathcal{GP}(\\bar{m}, \\bar{S})$ is non-degenerate for any finite set of\n", - "GPs $\\lbrace f_t\\rbrace_{t=1}^T$ i.e., $T<\\infty$. With this established, one can\n", - "show that for a $n$-dimensional finite Gaussian distribution $f_{i,n}$, the\n", - "Wasserstein metric between any two Gaussian distributions $f_{i, n}, f_{j, n}$\n", - "converges to the Wasserstein metric between GPs as $n\\to\\infty$.\n", - "\n", - "In this notebook, we will demonstrate how this can be achieved in GPJax.\n", - "\n", - "## Dataset\n", - "\n", - "We'll simulate five datasets and develop a Gaussian process posterior before\n", - "identifying the Gaussian process barycentre at a set of test points. Each dataset\n", - "will be a sine function with a different vertical shift, periodicity, and quantity\n", - "of noise." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "688925e0", - "metadata": {}, - "outputs": [], - "source": [ - "n = 100\n", - "n_test = 200\n", - "n_datasets = 5\n", - "\n", - "x = jnp.linspace(-5.0, 5.0, n).reshape(-1, 1)\n", - "xtest = jnp.linspace(-5.5, 5.5, n_test).reshape(-1, 1)\n", - "f = lambda x, a, b: a + jnp.sin(b * x)\n", - "\n", - "ys = []\n", - "for _i in range(n_datasets):\n", - " key, subkey = jr.split(key)\n", - " vertical_shift = jr.uniform(subkey, minval=0.0, maxval=2.0)\n", - " period = jr.uniform(subkey, minval=0.75, maxval=1.25)\n", - " noise_amount = jr.uniform(subkey, minval=0.01, maxval=0.5)\n", - " noise = jr.normal(subkey, shape=x.shape) * noise_amount\n", - " ys.append(f(x, vertical_shift, period) + noise)\n", - "\n", - "y = jnp.hstack(ys)\n", - "\n", - "fig, ax = plt.subplots()\n", - "ax.plot(x, y, \"x\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "4c7a1bda", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "## Learning a posterior distribution\n", - "\n", - "We'll now independently learn Gaussian process posterior distributions for each\n", - "dataset. We won't spend any time here discussing how GP hyperparameters are\n", - "optimised. For advice on achieving this, see the\n", - "[Regression notebook](https://docs.jaxgaussianprocesses.com/examples/regression/)\n", - "for advice on optimisation and the\n", - "[Kernels notebook](https://docs.jaxgaussianprocesses.com/examples/constructing_new_kernels/) for\n", - "advice on selecting an appropriate kernel." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2dfc4e75", - "metadata": {}, - "outputs": [], - "source": [ - "def fit_gp(x: jax.Array, y: jax.Array) -> tfd.MultivariateNormalFullCovariance:\n", - " if y.ndim == 1:\n", - " y = y.reshape(-1, 1)\n", - " D = gpx.Dataset(X=x, y=y)\n", - "\n", - " likelihood = gpx.Gaussian(num_datapoints=n)\n", - " posterior = gpx.Prior(mean_function=gpx.Constant(), kernel=gpx.RBF()) * likelihood\n", - " opt_posterior, _ = gpx.fit(\n", - " model=posterior,\n", - " train_data=D,\n", - " solver=jaxopt.LBFGS(gpx.ConjugateMLL(negative=True), maxiter=500),\n", - " key=key,\n", - " )\n", - " latent_dist = opt_posterior.predict(xtest, train_data=D)\n", - " return opt_posterior.likelihood(latent_dist)\n", - "\n", - "\n", - "posterior_preds = [fit_gp(x, i) for i in ys]" - ] - }, - { - "cell_type": "markdown", - "id": "d6ac3f91", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "## Computing the barycentre\n", - "\n", - "In GPJax, the predictive distribution of a GP is given by a\n", - "[TensorFlow Probability](https://www.tensorflow.org/probability/api_docs/python/tfp/substrates/jax)\n", - "distribution, making it\n", - "straightforward to extract the mean vector and covariance matrix of each GP for\n", - "learning a barycentre. We implement the fixed point scheme given in (3) in the\n", - "following cell by utilising Jax's `vmap` operator to speed up large matrix operations\n", - "using broadcasting in `tensordot`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e1ee7d96", - "metadata": {}, - "outputs": [], - "source": [ - "def sqrtm(A: jax.Array):\n", - " return jnp.real(jsl.sqrtm(A))\n", - "\n", - "\n", - "def wasserstein_barycentres(\n", - " distributions: tp.List[tfd.MultivariateNormalFullCovariance], weights: jax.Array\n", - "):\n", - " covariances = [d.covariance() for d in distributions]\n", - " cov_stack = jnp.stack(covariances)\n", - " stack_sqrt = jax.vmap(sqrtm)(cov_stack)\n", - "\n", - " def step(covariance_candidate: jax.Array, idx: None):\n", - " inner_term = jax.vmap(sqrtm)(\n", - " jnp.matmul(jnp.matmul(stack_sqrt, covariance_candidate), stack_sqrt)\n", - " )\n", - " fixed_point = jnp.tensordot(weights, inner_term, axes=1)\n", - " return fixed_point, fixed_point\n", - "\n", - " return step" - ] - }, - { - "cell_type": "markdown", - "id": "124a7863", - "metadata": {}, - "source": [ - "With a function defined for learning a barycentre, we'll now compute it using the\n", - "`lax.scan` operator that drastically speeds up for loops in Jax (see the\n", - "[Jax documentation](https://jax.readthedocs.io/en/latest/_autosummary/jax.lax.scan.html)).\n", - "The iterative update will be executed 100 times, with convergence measured by the\n", - "difference between the previous and current iteration that we can confirm by\n", - "inspecting the `sequence` array in the following cell." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f04e3d34", - "metadata": {}, - "outputs": [], - "source": [ - "weights = jnp.ones((n_datasets,)) / n_datasets\n", - "\n", - "means = jnp.stack([d.mean() for d in posterior_preds])\n", - "barycentre_mean = jnp.tensordot(weights, means, axes=1)\n", - "\n", - "step_fn = jax.jit(wasserstein_barycentres(posterior_preds, weights))\n", - "initial_covariance = jnp.eye(n_test)\n", - "\n", - "barycentre_covariance, sequence = jax.lax.scan(\n", - " step_fn, initial_covariance, jnp.arange(100)\n", - ")\n", - "L = jnp.linalg.cholesky(barycentre_covariance)\n", - "\n", - "barycentre_process = tfd.MultivariateNormalTriL(barycentre_mean, L)" - ] - }, - { - "cell_type": "markdown", - "id": "6dba8bc3", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "## Plotting the result\n", - "\n", - "With a barycentre learned, we can visualise the result. We can see that the result\n", - "looks reasonable as it follows the sinusoidal curve of all the inferred GPs, and the\n", - "uncertainty bands are sensible." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f4e1d48", - "metadata": {}, - "outputs": [], - "source": [ - "def plot(\n", - " dist: tfd.MultivariateNormalTriL,\n", - " ax,\n", - " color: str,\n", - " label: str = None,\n", - " ci_alpha: float = 0.2,\n", - " linewidth: float = 1.0,\n", - " zorder: int = 0,\n", - "):\n", - " mu = dist.mean()\n", - " sigma = dist.stddev()\n", - " ax.plot(xtest, mu, linewidth=linewidth, color=color, label=label, zorder=zorder)\n", - " ax.fill_between(\n", - " xtest.squeeze(),\n", - " mu - sigma,\n", - " mu + sigma,\n", - " alpha=ci_alpha,\n", - " color=color,\n", - " zorder=zorder,\n", - " )\n", - "\n", - "\n", - "fig, ax = plt.subplots()\n", - "[plot(d, ax, color=cols[1], ci_alpha=0.1) for d in posterior_preds]\n", - "plot(\n", - " barycentre_process,\n", - " ax,\n", - " color=cols[0],\n", - " label=\"Barycentre\",\n", - " ci_alpha=0.5,\n", - " linewidth=2,\n", - " zorder=1,\n", - ")\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "375695b8", - "metadata": {}, - "source": [ - "## Displacement interpolation\n", - "\n", - "In the above example, we assigned uniform weights to each of the posteriors within\n", - "the barycentre. In practice, we may have prior knowledge of which posterior is most\n", - "likely to be the correct one. Regardless of the weights chosen, the barycentre\n", - "remains a Gaussian process. We can interpolate between a pair of posterior\n", - "distributions $\\mu_1$ and $\\mu_2$ to visualise the corresponding barycentre\n", - "$\\bar{\\mu}$.\n", - "\n", - "![](barycentre_gp.gif)" - ] - }, - { - "cell_type": "markdown", - "id": "3357c374", - "metadata": {}, - "source": [ - "## System configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0832f02c", - "metadata": {}, - "outputs": [], - "source": [ - "%reload_ext watermark\n", - "%watermark -n -u -v -iv -w -a 'Thomas Pinder'" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/bayesian_optimisation.ipynb b/docs/examples/bayesian_optimisation.ipynb deleted file mode 100644 index 120591c39..000000000 --- a/docs/examples/bayesian_optimisation.ipynb +++ /dev/null @@ -1,1001 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4d968859", - "metadata": {}, - "source": [ - "# Introduction to Bayesian Optimisation\n", - "\n", - "In this guide we introduce the Bayesian Optimisation (BO) paradigm for\n", - "optimising black-box functions. We'll assume an understanding of Gaussian processes\n", - "(GPs), so if you're not familiar with them, check out our [GP introduction notebook](https://docs.jaxgaussianprocesses.com/examples/intro_to_gps/)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4fe5efe1", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "# Enable Float64 for more stable matrix inversions.\n", - "from jax.config import config\n", - "\n", - "config.update(\"jax_enable_x64\", True)\n", - "\n", - "import jax\n", - "from jax import jit\n", - "import jax.numpy as jnp\n", - "import jax.random as jr\n", - "from jaxtyping import install_import_hook, Float, Int\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import cm\n", - "import jaxopt\n", - "import tensorflow_probability.substrates.jax as tfp\n", - "from typing import List, Tuple\n", - "\n", - "with install_import_hook(\"gpjax\", \"beartype.beartype\"):\n", - " import gpjax as gpx\n", - "from gpjax.typing import Array, FunctionalSample, ScalarFloat\n", - "from jaxopt import ScipyBoundedMinimize\n", - "\n", - "key = jr.PRNGKey(42)\n", - "plt.style.use(\n", - " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", - ")\n", - "cols = mpl.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]" - ] - }, - { - "cell_type": "markdown", - "id": "af145b9b", - "metadata": {}, - "source": [ - "## Some Motivating Examples\n", - "\n", - "Countless problems in the physical world involve optimising functions for which the\n", - "explicit functional form is unknown, but which can be expensively queried throughout\n", - "their domain. For example, within the domain of science the task of designing new\n", - "molecules with optimised properties ([Griffiths and Lobato,\n", - "2020](https://pubs.rsc.org/en/content/articlehtml/2019/sc/c9sc04026a)) is incredibly\n", - "useful. Here, the domain being optimised over is the space of possible molecules, with\n", - "the objective function depending on the property being optimised, for instance within\n", - "drug-design this may be the efficacy of the drug. The function from molecules to\n", - "efficacy is unknown, but can be queried by synthesising a molecule and running an\n", - "experiment to measure its efficacy. This is clearly an expensive procedure!\n", - "\n", - "Within the domain of machine learning, the task of optimising neural network\n", - "architectures is another example of such a problem (commonly referred to as [Neural\n", - "Architecture Search (NAS)](https://en.wikipedia.org/wiki/Neural_architecture_search)).\n", - "Here, the domain is the space of possible neural network architectures, and the\n", - "objective function is a metric such as the accuracy of the trained model. Again, the\n", - "function from neural network architectures to accuracy is unknown, but can be queried by\n", - "training a model with a given architecture and evaluating its accuracy. This is also an\n", - "expensive procedure, as training models can be incredibly time consuming and\n", - "computationally demanding.\n", - "\n", - "Finally, these problems are ubiquitous within the field of climate science, with\n", - "([Hellan et al., 2023](https://arxiv.org/abs/2306.04343)) providing several excellent\n", - "examples. One such example is the task of deciding where to place wind turbines in a\n", - "wind farm in order to maximise the energy generated. Here, the domain is the space of\n", - "possible locations for the wind turbines, and the objective function is the energy\n", - "generated by the wind farm. The function from locations to energy generated is unknown,\n", - "but could be queried by running a simulation of the wind farm with the turbines placed\n", - "at a given set of locations. Running such simulations can be expensive, particularly if\n", - "they are high-fidelity.\n", - "\n", - "At the heart of all these problems is the task of optimising a function for which we\n", - "don't have the explicit functional form, but which we can (expensively) query at any\n", - "point in its domain. Bayesian optimisation provides a principled framework for solving\n", - "such problems." - ] - }, - { - "cell_type": "markdown", - "id": "8b786ba9", - "metadata": {}, - "source": [ - "## What is Bayesian Optimisation?\n", - "\n", - "Bayesian optimisation (BO) ([Močkus, 1974](https://link.springer.com/chapter/10.1007/3-540-07165-2_55)) provides a principled\n", - "method for making decisions under uncertainty. The aim of BO is to find the global\n", - "minimum of a *black-box* objective function, $\\min_{\\mathbf{x} \\in X}\n", - "f(\\mathbf{x})$. The function $f$ is said to be a *black-box* function because its\n", - "explicit functional form is unknown. However, it is assumed that one is able to\n", - "ascertain information about the function by evaluating it at points in its domain,\n", - "$X$. However, these evaluations are assumed to be *expensive*, as seen in the\n", - "motivating examples. Therefore, the goal of BO is to minimise $f$ with as few\n", - "evaluations of the black-box function as possible.\n", - "\n", - "As such, BO can be thought of as *sequential decision-making* problem. At each iteration\n", - "one must choose which point (or batch of points) in a function's domain to evaluate\n", - "next, drawing on previously observed values to make optimal decisions. In order to do\n", - "this effectively, we need a way of representing our uncertainty about the black-box\n", - "function $f$, which we can update in light of observing more data. Gaussian processes\n", - "will be an ideal tool for this purpose!\n", - "\n", - "*Surrogate models* lie at the heart of BO, and are used to model the black-box\n", - "function. GPs are a natural choice for this model, as they not only provide point\n", - "estimates for the values taken by the function throughout its domain, but crucially\n", - "provide a full predictive posterior *distribution* of the range of values the function\n", - "may take. This rich quantification of uncertainty enables BO to balance *exploration*\n", - "and *exploitation* in order to efficiently converge upon minima.\n", - "\n", - "Having chosen a surrogate model, which we can use to express our current beliefs about\n", - "the black-box function, ideally we would like a method which can use the surrogate\n", - "model's posterior distribution to automatically decide which point(s) in the black-box\n", - "function's domain to query next. This is where *acquisition functions* come in. The\n", - "acquisition function $\\alpha: X \\to \\mathbb{R}$ is defined over the same domain as the\n", - "surrogate model, and uses the surrogate model's posterior distribution to quantify the\n", - "expected *utility*, $U$, of evaluating the black-box function at a given point. Simply\n", - "put, for each point in the black-box function's domain, $\\mathbf{x} \\in X$, the\n", - "acquisition function quantifies how useful it would be to evaluate the black-box\n", - "function at $\\mathbf{x}$ in order to find the minimum of the black-box function, whilst\n", - "taking into consideration all the datapoints observed so far. Therefore, in order to\n", - "decide which point to query next we simply choose the point which maximises the\n", - "acquisition function, using an optimiser such as L-BFGS ([Liu and Nocedal,\n", - "1989](https://link.springer.com/article/10.1007/BF01589116)).\n", - "\n", - "The Bayesian optimisation loop can be summarised as follows, with $i$ denoting the\n", - "current iteration:\n", - "\n", - "1. Select the next point to query, $\\mathbf{x}_{i}$, by maximising the acquisition function $\\alpha$, defined using the surrogate model $\\mathcal{M}_i$ conditioned on previously observed data $\\mathcal{D}_i$:\n", - "\n", - "$$\\mathbf{x}_{i} = \\arg\\max_{\\mathbf{x}} \\alpha (\\mathbf{x}; \\mathcal{D}_i,\n", - "\\mathcal{M}_i)$$\n", - "\n", - "2. Evaluate the objective function at $\\mathbf{x}_i$, yielding observation $y_i =\n", - " f(\\mathbf{x}_i)$.\n", - "\n", - "3. Append the most recent observation to the dataset, $\\mathcal{D}_{i+1} = \\mathcal{D}_i\n", - " \\cup \\{(\\mathbf{x}_i, y_i)\\}$.\n", - "\n", - "4. Condition the model on the updated dataset to yield $\\mathcal{M}_{i+1}$.\n", - "\n", - "This process is repeated until some stopping criterion is met, such as a function\n", - "evaluation budget being exhausted.\n", - "\n", - "There are a plethora of acquisition functions to choose from, each with their own\n", - "advantages and disadvantages, of which ([Shahriari et al., 2015](https://www.cs.ox.ac.uk/people/nando.defreitas/publications/BayesOptLoop.pdf))\n", - "provides an excellent overview.\n", - "\n", - "In this guide we will focus on *Thompson sampling*, a conceptually simple yet effective\n", - "method for characterising the utility of querying points in a black-box function's\n", - "domain, which will be useful in demonstrating the key aspects of BO." - ] - }, - { - "cell_type": "markdown", - "id": "f84f9ba5", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "## Thompson Sampling\n", - "\n", - "Thompson sampling ([Thompson, 1933](https://www.dropbox.com/s/yhn9prnr5bz0156/1933-thompson.pdf)) is a simple method which\n", - "naturally balances exploration and exploitation. The core idea is to, at each iteration\n", - "of the BO loop, sample a function, $g$, from the posterior distribution of the surrogate\n", - "model $\\mathcal{M}_i$, and then evaluate the black-box function at the point(s) which\n", - "minimise this sample. Given a sample $g$, from the posterior distribution given by the model $\\mathcal{M}_i$ the Thompson sampling utility function is defined as:\n", - "\n", - "$$U_{\\text{TS}}(\\mathbf{x}; \\mathcal{D}_i, \\mathcal{M}_i) = - g(\\mathbf{x})$$\n", - "\n", - "Note the negative sign; this is included as we want to maximise the *utility* of\n", - "evaluating the black-box function $f$ at a given point. We interested in finding the\n", - "minimum of $f$, so we maximise the negative of the sample from the posterior distribution $g$.\n", - "\n", - "As a toy example, we shall be applying BO to the widely used [Forrester\n", - "function](https://www.sfu.ca/~ssurjano/forretal08.html):\n", - "\n", - "$$f(x) = (6x - 2)^2 \\sin(12x - 4)$$\n", - "\n", - "treating $f$ as a black-box function. Moreover, we shall restrict the domain of the\n", - "function to $\\mathbf{x} \\in [0, 1]$. The global minimum of this function is located at\n", - "$x = 0.757$, where $f(x) = -6.021$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "db5649b9", - "metadata": {}, - "outputs": [], - "source": [ - "def forrester(x: Float[Array, \"N 1\"]) -> Float[Array, \"N 1\"]:\n", - " return (6 * x - 2) ** 2 * jnp.sin(12 * x - 4)" - ] - }, - { - "cell_type": "markdown", - "id": "9e706c6c", - "metadata": {}, - "source": [ - "We'll first go through one iteration of the BO loop step-by-step, before wrapping this\n", - "up in a loop to perform the full optimisation." - ] - }, - { - "cell_type": "markdown", - "id": "6cc22184", - "metadata": {}, - "source": [ - "First we'll specify the domain over which we wish to optimise the function, as well as\n", - "sampling some initial points for fitting our surrogate model using a space-filling design." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "46fb9d07", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "lower_bound = jnp.array([0.0])\n", - "upper_bound = jnp.array([1.0])\n", - "initial_sample_num = 5\n", - "\n", - "initial_x = tfp.mcmc.sample_halton_sequence(\n", - " dim=1, num_results=initial_sample_num, seed=key, dtype=jnp.float64\n", - ").reshape(-1, 1)\n", - "initial_y = forrester(initial_x)\n", - "D = gpx.Dataset(X=initial_x, y=initial_y)" - ] - }, - { - "cell_type": "markdown", - "id": "c378817d", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "Next we'll define our GP model in the usual way, using a Matérn52 kernel, and fit the\n", - "kernel parameters by minimising the negative log-marginal likelihood. We'll wrap this in\n", - "a function as we'll be repeating this process at each iteration of the BO loop." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b8afd4de", - "metadata": {}, - "outputs": [], - "source": [ - "def return_optimised_posterior(\n", - " data: gpx.Dataset, prior: gpx.Module, key: Array\n", - ") -> gpx.Module:\n", - " likelihood = gpx.Gaussian(\n", - " num_datapoints=data.n, obs_noise=jnp.array(1e-6)\n", - " ) # Our function is noise-free, so we set the observation noise to a very small value\n", - " likelihood = likelihood.replace_trainable(obs_noise=False)\n", - "\n", - " posterior = prior * likelihood\n", - "\n", - " negative_mll = gpx.objectives.ConjugateMLL(negative=True)\n", - " negative_mll(posterior, train_data=data)\n", - " negative_mll = jit(negative_mll)\n", - "\n", - " opt_posterior, history = gpx.fit(\n", - " model=posterior,\n", - " train_data=D,\n", - " solver=jaxopt.LBFGS(gpx.ConjugateMLL(negative=True), maxiter=500),\n", - " safe=True,\n", - " key=key,\n", - " verbose=False,\n", - " )\n", - "\n", - " return opt_posterior\n", - "\n", - "\n", - "mean = gpx.mean_functions.Zero()\n", - "kernel = gpx.kernels.Matern52()\n", - "prior = gpx.Prior(mean_function=mean, kernel=kernel)\n", - "opt_posterior = return_optimised_posterior(D, prior, key)" - ] - }, - { - "cell_type": "markdown", - "id": "5a761c1e", - "metadata": {}, - "source": [ - "We can then sample a function from the posterior distribution of the surrogate model. We\n", - "will do this using the `sample_approx` method, which generates an approximate sample\n", - "from the posterior using decoupled sampling introduced in ([Wilson et al.,\n", - "2020](https://proceedings.mlr.press/v119/wilson20a.html)) and discussed in our [Pathwise\n", - "Sampling Notebook](https://docs.jaxgaussianprocesses.com/examples/spatial/). This method\n", - "is used as it enables us to sample from the posterior in a manner which scales linearly\n", - "with the number of points sampled, $O(N)$, mitigating the cubic cost associated with\n", - "drawing exact samples from a GP posterior, $O(N^3)$. It also generates more accurate\n", - "samples than many other methods for drawing approximate samples from a GP posterior.\n", - "\n", - "Note that we also define a `utility_fn` which calls the approximate\n", - "sample but returns the value returned as a scalar. This is because the `sample_approx`\n", - "function returns an array of shape $[N, B]$, with $N$ being the number of points within\n", - "each sample and $B$ being the number of samples drawn. We'll only be drawing (and\n", - "optimising) one sample at a time, and our optimiser requires the function being\n", - "optimised to return a scalar output (only querying it at $N=1$ points), so we'll remove the axes from the returned value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6d700a73", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "approx_sample = opt_posterior.sample_approx(\n", - " num_samples=1, train_data=D, key=key, num_features=500\n", - ")\n", - "utility_fn = lambda x: approx_sample(x)[0][0]" - ] - }, - { - "cell_type": "markdown", - "id": "0a9739b8", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "In order to minimise the sample, we'll be using the L-BFGS-B ([Byrd et al., 1995](https://epubs.siam.org/doi/abs/10.1137/0916069)) optimiser from the `jaxopt`\n", - "library. This is a gradient-based optimiser which performs optimisation within a bounded\n", - "domain. In order to perform optimisation, this optimiser requires a point to start from.\n", - "Therefore, we will first query our sample from the posterior at a random set of points,\n", - "and then use the lowest point from this set of points as the starting point for the\n", - "optimiser. In this example we'll sample 100 points from the posterior, due to the simple\n", - "nature of the Forrester function. However, in practice it can be beneficial to\n", - "adopt a more sophisticated approach, and there are several heuristics available in the\n", - "literature (see for example ([Le Riche and Picheny,\n", - "2021](https://arxiv.org/abs/2103.16649))). For instance, one may randomly sample the\n", - "posterior at a number of points proportional to the dimensionality of the input space,\n", - "and one may run gradient-based optimisation from multiple of these points, to reduce the\n", - "risk of converging upon local minima." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "01770354", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "def optimise_sample(\n", - " sample: FunctionalSample,\n", - " key: Int[Array, \"\"],\n", - " lower_bound: Float[Array, \"D\"],\n", - " upper_bound: Float[Array, \"D\"],\n", - " num_initial_sample_points: int,\n", - ") -> ScalarFloat:\n", - " initial_sample_points = jr.uniform(\n", - " key,\n", - " shape=(num_initial_sample_points, lower_bound.shape[0]),\n", - " dtype=jnp.float64,\n", - " minval=lower_bound,\n", - " maxval=upper_bound,\n", - " )\n", - " initial_sample_y = sample(initial_sample_points)\n", - " best_x = jnp.array([initial_sample_points[jnp.argmin(initial_sample_y)]])\n", - "\n", - " # We want to maximise the utility function, but the optimiser performs minimisation. Since we're minimising the sample drawn, the sample is actually the negative utility function.\n", - " negative_utility_fn = lambda x: sample(x)[0][0]\n", - " lbfgsb = ScipyBoundedMinimize(fun=negative_utility_fn, method=\"l-bfgs-b\")\n", - " bounds = (lower_bound, upper_bound)\n", - " x_star = lbfgsb.run(best_x, bounds=bounds).params\n", - " return x_star\n", - "\n", - "\n", - "x_star = optimise_sample(approx_sample, key, lower_bound, upper_bound, 100)\n", - "y_star = forrester(x_star)" - ] - }, - { - "cell_type": "markdown", - "id": "78ec19f0", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "Having found the minimum of the sample from the posterior, we can then evaluate the\n", - "black-box objective function at this point, and append the new observation to our dataset.\n", - "\n", - "Below we plot the posterior distribution of the surrogate model, along with the sample\n", - "drawn from the model, and the minimiser of this sample returned from the optimiser,\n", - "which we denote with a star." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f77e39b1", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_bayes_opt(\n", - " posterior: gpx.Module,\n", - " sample: FunctionalSample,\n", - " dataset: gpx.Dataset,\n", - " queried_x: ScalarFloat,\n", - ") -> None:\n", - " plt_x = jnp.linspace(0, 1, 1000).reshape(-1, 1)\n", - " forrester_y = forrester(plt_x)\n", - " sample_y = sample(plt_x)\n", - "\n", - " latent_dist = posterior.predict(plt_x, train_data=dataset)\n", - " predictive_dist = posterior.likelihood(latent_dist)\n", - "\n", - " predictive_mean = predictive_dist.mean()\n", - " predictive_std = predictive_dist.stddev()\n", - "\n", - " fig, ax = plt.subplots()\n", - " ax.plot(plt_x, predictive_mean, label=\"Predictive Mean\", color=cols[1])\n", - " ax.fill_between(\n", - " plt_x.squeeze(),\n", - " predictive_mean - 2 * predictive_std,\n", - " predictive_mean + 2 * predictive_std,\n", - " alpha=0.2,\n", - " label=\"Two sigma\",\n", - " color=cols[1],\n", - " )\n", - " ax.plot(\n", - " plt_x,\n", - " predictive_mean - 2 * predictive_std,\n", - " linestyle=\"--\",\n", - " linewidth=1,\n", - " color=cols[1],\n", - " )\n", - " ax.plot(\n", - " plt_x,\n", - " predictive_mean + 2 * predictive_std,\n", - " linestyle=\"--\",\n", - " linewidth=1,\n", - " color=cols[1],\n", - " )\n", - " ax.plot(plt_x, sample_y, label=\"Posterior Sample\")\n", - " ax.plot(\n", - " plt_x,\n", - " forrester_y,\n", - " label=\"Forrester Function\",\n", - " color=cols[0],\n", - " linestyle=\"--\",\n", - " linewidth=2,\n", - " )\n", - " ax.axvline(x=0.757, linestyle=\":\", color=cols[3], label=\"True Optimum\")\n", - " ax.scatter(dataset.X, dataset.y, label=\"Observations\", color=cols[2], zorder=2)\n", - " ax.scatter(\n", - " queried_x,\n", - " sample(queried_x),\n", - " label=\"Posterior Sample Optimum\",\n", - " marker=\"*\",\n", - " color=cols[3],\n", - " zorder=3,\n", - " )\n", - " ax.legend(loc=\"center left\", bbox_to_anchor=(0.975, 0.5))\n", - " plt.show()\n", - "\n", - "\n", - "plot_bayes_opt(opt_posterior, approx_sample, D, x_star)" - ] - }, - { - "cell_type": "markdown", - "id": "35b1f294", - "metadata": {}, - "source": [ - "At this point we can update our model with the newly augmented dataset, and repeat the\n", - "whole process until some stopping criterion is met. Below we repeat this process for 10\n", - "iterations, printing out the queried point and the value of the black-box function at\n", - "each iteration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3abe88bb", - "metadata": {}, - "outputs": [], - "source": [ - "bo_iters = 5\n", - "\n", - "# Set up initial dataset\n", - "initial_x = tfp.mcmc.sample_halton_sequence(\n", - " dim=1, num_results=initial_sample_num, seed=key, dtype=jnp.float64\n", - ").reshape(-1, 1)\n", - "initial_y = forrester(initial_x)\n", - "D = gpx.Dataset(X=initial_x, y=initial_y)\n", - "\n", - "for i in range(bo_iters):\n", - " key, subkey = jr.split(key)\n", - "\n", - " # Generate optimised posterior using previously observed data\n", - " mean = gpx.mean_functions.Zero()\n", - " kernel = gpx.kernels.Matern52()\n", - " prior = gpx.Prior(mean_function=mean, kernel=kernel)\n", - " opt_posterior = return_optimised_posterior(D, prior, subkey)\n", - "\n", - " # Draw a sample from the posterior, and find the minimiser of it\n", - " approx_sample = opt_posterior.sample_approx(\n", - " num_samples=1, train_data=D, key=subkey, num_features=500\n", - " )\n", - " x_star = optimise_sample(\n", - " approx_sample, subkey, lower_bound, upper_bound, num_initial_sample_points=100\n", - " )\n", - "\n", - " plot_bayes_opt(opt_posterior, approx_sample, D, x_star)\n", - "\n", - " # Evaluate the black-box function at the best point observed so far, and add it to the dataset\n", - " y_star = forrester(x_star)\n", - " print(f\"Queried Point: {x_star}, Black-Box Function Value: {y_star}\")\n", - " D = D + gpx.Dataset(X=x_star, y=y_star)" - ] - }, - { - "cell_type": "markdown", - "id": "3ca0d676", - "metadata": {}, - "source": [ - "Below we plot the best observed black-box function value against the number of times\n", - "the black-box function has been evaluated. Note that the first 5 samples are randomly\n", - "sampled to fit the initial GP model, and we denote the start of using BO to sample with\n", - "the dotted vertical line.\n", - "\n", - "We can see that the BO algorithm quickly converges to the global minimum of the\n", - "black-box function!\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1222d4f5", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots()\n", - "fn_evaluations = jnp.arange(1, bo_iters + initial_sample_num + 1)\n", - "cumulative_best_y = jax.lax.associative_scan(jax.numpy.minimum, D.y)\n", - "ax.plot(fn_evaluations, cumulative_best_y)\n", - "ax.axvline(x=initial_sample_num, linestyle=\":\")\n", - "ax.axhline(y=-6.0207, linestyle=\"--\", label=\"True Minimum\")\n", - "ax.set_xlabel(\"Number of Black-Box Function Evaluations\")\n", - "ax.set_ylabel(\"Best Observed Value\")\n", - "ax.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "58299352", - "metadata": {}, - "source": [ - "### A More Challenging Example - The Six-Hump Camel Function" - ] - }, - { - "cell_type": "markdown", - "id": "c9c644c9", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "We'll now apply BO to a more challenging example, the [Six-Hump Camel\n", - "Function](https://www.sfu.ca/~ssurjano/camel6.html). This is a function of two inputs\n", - "defined as follows:\n", - "\n", - "$$f(x_1, x_2) = (4 - 2.1x_1^2 + \\frac{x_1^4}{3})x_1^2 + x_1x_2 + (-4 + 4x_2^2)x_2^2$$\n", - "\n", - "We'll be evaluating it over the domain $x_1 \\in [-2, 2]$ and $x_2 \\in [-1, 1]$. The\n", - "global minima of this function are located at $\\mathbf{x} = (0.0898, -0.7126)$ and $\\mathbf{x} = (-0.0898, 0.7126)$, where the function takes the value $f(\\mathbf{x}) = -1.0316$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e970300b", - "metadata": {}, - "outputs": [], - "source": [ - "def six_hump_camel(x: Float[Array, \"N 2\"]) -> Float[Array, \"N 1\"]:\n", - " x1 = x[..., :1]\n", - " x2 = x[..., 1:]\n", - " term1 = (4 - 2.1 * x1**2 + x1**4 / 3) * x1**2\n", - " term2 = x1 * x2\n", - " term3 = (-4 + 4 * x2**2) * x2**2\n", - " return term1 + term2 + term3" - ] - }, - { - "cell_type": "markdown", - "id": "a101bc98", - "metadata": {}, - "source": [ - "First, we'll visualise the function over the domain of interest:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "96a16c59", - "metadata": {}, - "outputs": [], - "source": [ - "x1 = jnp.linspace(-2, 2, 100)\n", - "x2 = jnp.linspace(-1, 1, 100)\n", - "x1, x2 = jnp.meshgrid(x1, x2)\n", - "x = jnp.stack([x1.flatten(), x2.flatten()], axis=1)\n", - "y = six_hump_camel(x)\n", - "\n", - "fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n", - "surf = ax.plot_surface(\n", - " x1,\n", - " x2,\n", - " y.reshape(x1.shape[0], x2.shape[0]),\n", - " linewidth=0,\n", - " cmap=cm.coolwarm,\n", - " antialiased=False,\n", - ")\n", - "ax.set_xlabel(\"x1\")\n", - "ax.set_ylabel(\"x2\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "2e90ee91", - "metadata": {}, - "source": [ - "For more clarity, we can generate a contour plot of the function which enables us to see\n", - "the global minima of the function more clearly." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c8b49fe4", - "metadata": {}, - "outputs": [], - "source": [ - "x_star_one = jnp.array([[0.0898, -0.7126]])\n", - "x_star_two = jnp.array([[-0.0898, 0.7126]])\n", - "fig, ax = plt.subplots()\n", - "contour_plot = ax.contourf(\n", - " x1, x2, y.reshape(x1.shape[0], x2.shape[0]), cmap=cm.coolwarm, levels=40\n", - ")\n", - "ax.scatter(\n", - " x_star_one[0][0], x_star_one[0][1], marker=\"*\", color=cols[2], label=\"Global Minima\"\n", - ")\n", - "ax.scatter(x_star_two[0][0], x_star_two[0][1], marker=\"*\", color=cols[2])\n", - "ax.set_xlabel(\"x1\")\n", - "ax.set_ylabel(\"x2\")\n", - "fig.colorbar(contour_plot)\n", - "ax.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f4229fd2", - "metadata": {}, - "source": [ - "Next, we'll run the BO loop using Thompson sampling as before. This time we'll run the\n", - "experiment 5 times in order to see how the algorithm performs on average, with different\n", - "starting points for the initial GP model. This is good practice, as the performance\n", - "obtained is likely to vary between runs depending on the initialisation samples used to\n", - "fit the initial GP model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "605a65d5", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "lower_bound = jnp.array([-2.0, -1.0])\n", - "upper_bound = jnp.array([2.0, 1.0])\n", - "initial_sample_num = 5\n", - "bo_iters = 11\n", - "num_experiments = 5\n", - "bo_experiment_results = []\n", - "\n", - "for experiment in range(num_experiments):\n", - " print(f\"Starting Experiment: {experiment + 1}\")\n", - " # Set up initial dataset\n", - " initial_x = tfp.mcmc.sample_halton_sequence(\n", - " dim=2, num_results=initial_sample_num, seed=key, dtype=jnp.float64\n", - " )\n", - " initial_x = jnp.array(lower_bound + (upper_bound - lower_bound) * initial_x)\n", - " initial_y = six_hump_camel(initial_x)\n", - " D = gpx.Dataset(X=initial_x, y=initial_y)\n", - "\n", - " for i in range(bo_iters):\n", - " key, subkey = jr.split(key)\n", - "\n", - " # Generate optimised posterior\n", - " mean = gpx.mean_functions.Zero()\n", - " kernel = gpx.kernels.Matern52(\n", - " active_dims=[0, 1], lengthscale=jnp.array([1.0, 1.0]), variance=2.0\n", - " )\n", - " prior = gpx.Prior(mean_function=mean, kernel=kernel)\n", - " opt_posterior = return_optimised_posterior(D, prior, subkey)\n", - "\n", - " # Draw a sample from the posterior, and find the minimiser of it\n", - " approx_sample = opt_posterior.sample_approx(\n", - " num_samples=1, train_data=D, key=subkey, num_features=500\n", - " )\n", - " x_star = optimise_sample(\n", - " approx_sample,\n", - " subkey,\n", - " lower_bound,\n", - " upper_bound,\n", - " num_initial_sample_points=1000,\n", - " )\n", - "\n", - " # Evaluate the black-box function at the best point observed so far, and add it to the dataset\n", - " y_star = six_hump_camel(x_star)\n", - " print(\n", - " f\"BO Iteration: {i + 1}, Queried Point: {x_star}, Black-Box Function Value: {y_star}\"\n", - " )\n", - " D = D + gpx.Dataset(X=x_star, y=y_star)\n", - " bo_experiment_results.append(D)" - ] - }, - { - "cell_type": "markdown", - "id": "33ed107c", - "metadata": {}, - "source": [ - "We'll also run a random benchmark, whereby we randomly sample from the search space for\n", - "20 iterations. This is a useful benchmark to compare the performance of BO against in\n", - "order to ascertain how much of an advantage BO provides over such a simple approach.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ed6479e", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "random_experiment_results = []\n", - "for i in range(num_experiments):\n", - " key, subkey = jr.split(key)\n", - " initial_x = bo_experiment_results[i].X[:5]\n", - " initial_y = bo_experiment_results[i].y[:5]\n", - " final_x = jr.uniform(\n", - " key,\n", - " shape=(bo_iters, 2),\n", - " dtype=jnp.float64,\n", - " minval=lower_bound,\n", - " maxval=upper_bound,\n", - " )\n", - " final_y = six_hump_camel(final_x)\n", - " random_x = jnp.concatenate([initial_x, final_x], axis=0)\n", - " random_y = jnp.concatenate([initial_y, final_y], axis=0)\n", - " random_experiment_results.append(gpx.Dataset(X=random_x, y=random_y))" - ] - }, - { - "cell_type": "markdown", - "id": "766bbe7e", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "Finally, we'll process the experiment results to find the log regret at each iteration\n", - "of the experiments. The regret is defined as the difference between the minimum value of\n", - "the black-box function observed so far and the true global minimum of the black box\n", - "function. Mathematically, at time $t$, with observations $\\mathcal{D}_t$, for function\n", - "$f$ with global minimum $f^*$, the regret is defined as:\n", - "\n", - "$$\\text{regret}_t = \\min_{\\mathbf{x} \\in \\mathcal{D_t}}f(\\mathbf{x}) - f^*$$\n", - "\n", - "We'll then take the mean and standard deviation of the log of the regret values across\n", - "the 5 experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9e143225", - "metadata": {}, - "outputs": [], - "source": [ - "def obtain_log_regret_statistics(\n", - " experiment_results: List[gpx.Dataset],\n", - " global_minimum: ScalarFloat,\n", - ") -> Tuple[Float[Array, \"N 1\"], Float[Array, \"N 1\"]]:\n", - " log_regret_results = []\n", - " for exp_result in experiment_results:\n", - " observations = exp_result.y\n", - " cumulative_best_observations = jax.lax.associative_scan(\n", - " jax.numpy.minimum, observations\n", - " )\n", - " regret = cumulative_best_observations - global_minimum\n", - " log_regret = jnp.log(regret)\n", - " log_regret_results.append(log_regret)\n", - "\n", - " log_regret_results = jnp.array(log_regret_results)\n", - " log_regret_mean = jnp.mean(log_regret_results, axis=0)\n", - " log_regret_std = jnp.std(log_regret_results, axis=0)\n", - " return log_regret_mean, log_regret_std\n", - "\n", - "\n", - "bo_log_regret_mean, bo_log_regret_std = obtain_log_regret_statistics(\n", - " bo_experiment_results, -1.031625\n", - ")\n", - "(\n", - " random_log_regret_mean,\n", - " random_log_regret_std,\n", - ") = obtain_log_regret_statistics(random_experiment_results, -1.031625)" - ] - }, - { - "cell_type": "markdown", - "id": "d0af2a94", - "metadata": {}, - "source": [ - "Now, when we plot the mean and standard deviation of the log regret at each iteration,\n", - "we can see that BO outperforms random sampling!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "147e2db9", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots()\n", - "fn_evaluations = jnp.arange(1, bo_iters + initial_sample_num + 1)\n", - "ax.plot(fn_evaluations, bo_log_regret_mean, label=\"Bayesian Optimisation\")\n", - "ax.fill_between(\n", - " fn_evaluations,\n", - " bo_log_regret_mean[:, 0] - bo_log_regret_std[:, 0],\n", - " bo_log_regret_mean[:, 0] + bo_log_regret_std[:, 0],\n", - " alpha=0.2,\n", - ")\n", - "ax.plot(fn_evaluations, random_log_regret_mean, label=\"Random Search\")\n", - "ax.fill_between(\n", - " fn_evaluations,\n", - " random_log_regret_mean[:, 0] - random_log_regret_std[:, 0],\n", - " random_log_regret_mean[:, 0] + random_log_regret_std[:, 0],\n", - " alpha=0.2,\n", - ")\n", - "ax.axvline(x=initial_sample_num, linestyle=\":\")\n", - "ax.set_xlabel(\"Number of Black-Box Function Evaluations\")\n", - "ax.set_ylabel(\"Log Regret\")\n", - "ax.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f0b122c9", - "metadata": {}, - "source": [ - "It can also be useful to plot the queried points over the course of a single BO run, in\n", - "order to gain some insight into how the algorithm queries the search space. Below\n", - "we do this for the first BO experiment, and can see that the algorithm initially\n", - "performs some exploration of the search space whilst it is uncertain about the black-box\n", - "function, but it then hones in one one of the global minima of the function, as we would hope!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aa9d9862", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots()\n", - "contour_plot = ax.contourf(\n", - " x1, x2, y.reshape(x1.shape[0], x2.shape[0]), cmap=cm.coolwarm, levels=40\n", - ")\n", - "ax.scatter(\n", - " x_star_one[0][0],\n", - " x_star_one[0][1],\n", - " marker=\"*\",\n", - " color=cols[2],\n", - " label=\"Global Minimum\",\n", - " zorder=2,\n", - ")\n", - "ax.scatter(x_star_two[0][0], x_star_two[0][1], marker=\"*\", color=cols[2], zorder=2)\n", - "ax.scatter(\n", - " bo_experiment_results[0].X[:, 0],\n", - " bo_experiment_results[0].X[:, 1],\n", - " marker=\"x\",\n", - " color=cols[1],\n", - " label=\"Bayesian Optimisation Queries\",\n", - ")\n", - "ax.set_xlabel(\"x1\")\n", - "ax.set_ylabel(\"x2\")\n", - "fig.colorbar(contour_plot)\n", - "ax.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "392226d2", - "metadata": {}, - "source": [ - "### Other Acquisition Functions and Further Reading\n", - "\n", - "As mentioned previously, there are many acquisition functions which one may use to\n", - "characterise the expected utility of querying the black-box function at a given point.\n", - "We list two of the most popular below:\n", - "\n", - "- **Probability of Improvement (PI)** ([Kushner, 1964](https://asmedigitalcollection.asme.org/fluidsengineering/article/86/1/97/392213/A-New-Method-of-Locating-the-Maximum-Point-of-an)): Given the lowest objective function observation\n", - " so far, $f(\\mathbf{x}^*)$, PI calculates the probability that the objective function's\n", - " value at a given point $\\mathbf{x}$ is lower than $f(\\mathbf{x}^*)$. Given a GP\n", - " surrogate model $\\mathcal{M}_i$, PI is defined mathematically as:\n", - " $$\n", - " \\alpha_{\\text{PI}}(\\mathbf{x}; \\mathcal{D}_i, \\mathcal{M}_i) = \\mathbb{P}[\\mathcal{M}_i (\\mathbf{x}) < f(\\mathbf{x}^*)] = \\Phi \\left(\\frac{f(\\mathbf{x}^*) - \\mu_{\\mathcal{M}_i}(\\mathbf{x})}{\\sigma_{\\mathcal{M}_i}(\\mathbf{x})}\\right)\n", - " $$\n", - "\n", - " with $\\Phi(\\cdot)$ denoting the standard normal cumulative distribution function.\n", - "\n", - "- **Expected Improvement (EI)** ([Močkus, 1974](https://link.springer.com/chapter/10.1007/3-540-07165-2_55)) - EI goes beyond PI by not only considering the\n", - " probability of improving on the current best observed point, but also taking into\n", - " account the \\textit{magnitude} of improvement. Mathematically, this is defined as\n", - " follows:\n", - " $$\n", - " \\begin{aligned}\n", - " \\alpha_{\\text{EI}}(\\mathbf{x};\\mathcal{D}_i, \\mathcal{M}_i) &= \\mathbb{E}[(f(\\mathbf{x}^*) - \\mathcal{M}_i(\\mathbf{x}))\\mathbb{I}(\\mathcal{M}_i(\\mathbf{x}) < f(\\mathbf{x}^*))] \\\\\n", - " &= \\underbrace{(f(\\mathbf{x}^*) - \\mu_{\\mathcal{M}_i}(\\mathbf{x}))\\Phi\n", - " \\left(\\frac{f(\\mathbf{x}^*) -\n", - " \\mu_{\\mathcal{M}_i}(\\mathbf{x})}{\\sigma_{\\mathcal{M}_i}(\\mathbf{x})}\\right)}_\\text{exploits\n", - " areas with low mean} \\\\\n", - " &+ \\underbrace{\\sigma_{\\mathcal{M}_i}(\\mathbf{x}) \\phi \\left(\\frac{f(\\mathbf{x}^*) - \\mu_{\\mathcal{M}_i}(\\mathbf{x})}{\\sigma_{\\mathcal{M}_i}(\\mathbf{x})}\\right)}_\\text{explores areas with high variance} \\nonumber\n", - " \\end{aligned}\n", - " $$\n", - "\n", - " with $\\mathbb{I}(\\cdot)$ denoting the indicator function and $\\phi(\\cdot)$ being the\n", - " standard normal probability density function.\n", - "\n", - "For those particularly interested in diving deeper into Bayesian optimisation, be sure\n", - "to check out Shahriari et al.'s \"[Taking the Human Out of the Loop:\n", - "A Review of Bayesian\n", - "Optimization](https://www.cs.ox.ac.uk/people/nando.defreitas/publications/BayesOptLoop.pdf)\",\n", - "which includes a wide variety of acquisition functions, as well as some examples of more\n", - "exotic BO problems, such as problems which also feature unknown constraints.\n", - "\n", - "## System Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "872160bd", - "metadata": {}, - "outputs": [], - "source": [ - "%reload_ext watermark\n", - "%watermark -n -u -v -iv -w -a 'Thomas Christie'" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/classification.ipynb b/docs/examples/classification.ipynb deleted file mode 100644 index 1a738c75c..000000000 --- a/docs/examples/classification.ipynb +++ /dev/null @@ -1,679 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "14d2bb24", - "metadata": {}, - "source": [ - "# Classification\n", - "\n", - "In this notebook we demonstrate how to perform inference for Gaussian process models\n", - "with non-Gaussian likelihoods via maximum a posteriori (MAP) and Markov chain Monte\n", - "Carlo (MCMC). We focus on a classification task here and use\n", - "[BlackJax](https://github.com/blackjax-devs/blackjax/) for sampling." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "90b27dc7", - "metadata": {}, - "outputs": [], - "source": [ - "# Enable Float64 for more stable matrix inversions.\n", - "from jax.config import config\n", - "\n", - "config.update(\"jax_enable_x64\", True)\n", - "\n", - "from time import time\n", - "import blackjax\n", - "import jax\n", - "import jax.numpy as jnp\n", - "import jax.random as jr\n", - "import jax.scipy as jsp\n", - "import jax.tree_util as jtu\n", - "from jaxtyping import (\n", - " Array,\n", - " Float,\n", - " install_import_hook,\n", - ")\n", - "import matplotlib.pyplot as plt\n", - "import jaxopt\n", - "import tensorflow_probability.substrates.jax as tfp\n", - "from tqdm import trange\n", - "\n", - "with install_import_hook(\"gpjax\", \"beartype.beartype\"):\n", - " import gpjax as gpx\n", - "\n", - "tfd = tfp.distributions\n", - "identity_matrix = jnp.eye\n", - "key = jr.PRNGKey(123)\n", - "plt.style.use(\n", - " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", - ")\n", - "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]" - ] - }, - { - "cell_type": "markdown", - "id": "64585152", - "metadata": {}, - "source": [ - "## Dataset\n", - "\n", - "With the necessary modules imported, we simulate a dataset\n", - "$\\mathcal{D} = (\\boldsymbol{x}, \\boldsymbol{y}) = \\{(x_i, y_i)\\}_{i=1}^{100}$ with inputs\n", - "$\\boldsymbol{x}$ sampled uniformly on $(-1., 1)$ and corresponding binary outputs\n", - "\n", - "$$\\boldsymbol{y} = 0.5 * \\text{sign}(\\cos(2 * + \\boldsymbol{\\epsilon})) + 0.5, \\quad \\boldsymbol{\\epsilon} \\sim \\mathcal{N} \\left(\\textbf{0}, \\textbf{I} * (0.05)^{2} \\right).$$\n", - "\n", - "We store our data $\\mathcal{D}$ as a GPJax `Dataset` and create test inputs for\n", - "later." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c7316ba9", - "metadata": {}, - "outputs": [], - "source": [ - "key, subkey = jr.split(key)\n", - "x = jr.uniform(key, shape=(100, 1), minval=-1.0, maxval=1.0)\n", - "y = 0.5 * jnp.sign(jnp.cos(3 * x + jr.normal(subkey, shape=x.shape) * 0.05)) + 0.5\n", - "\n", - "D = gpx.Dataset(X=x, y=y)\n", - "\n", - "xtest = jnp.linspace(-1.0, 1.0, 500).reshape(-1, 1)\n", - "\n", - "fig, ax = plt.subplots()\n", - "ax.scatter(x, y)" - ] - }, - { - "cell_type": "markdown", - "id": "b68bb15a", - "metadata": {}, - "source": [ - "## MAP inference\n", - "\n", - "We begin by defining a Gaussian process prior with a radial basis function (RBF)\n", - "kernel, chosen for the purpose of exposition. Since our observations are binary, we\n", - "choose a Bernoulli likelihood with a probit link function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "675bef2a", - "metadata": {}, - "outputs": [], - "source": [ - "kernel = gpx.RBF()\n", - "meanf = gpx.Constant()\n", - "prior = gpx.Prior(mean_function=meanf, kernel=kernel)\n", - "likelihood = gpx.Bernoulli(num_datapoints=D.n)" - ] - }, - { - "cell_type": "markdown", - "id": "bfece9af", - "metadata": {}, - "source": [ - "We construct the posterior through the product of our prior and likelihood." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "13a1d1d4", - "metadata": {}, - "outputs": [], - "source": [ - "posterior = prior * likelihood\n", - "print(type(posterior))" - ] - }, - { - "cell_type": "markdown", - "id": "0dd47285", - "metadata": {}, - "source": [ - "Whilst the latent function is Gaussian, the posterior distribution is non-Gaussian\n", - "since our generative model first samples the latent GP and propagates these samples\n", - "through the likelihood function's inverse link function. This step prevents us from\n", - "being able to analytically integrate the latent function's values out of our\n", - "posterior, and we must instead adopt alternative inference techniques. We begin with\n", - "maximum a posteriori (MAP) estimation, a fast inference procedure to obtain point\n", - "estimates for the latent function and the kernel's hyperparameters by maximising the\n", - "marginal log-likelihood." - ] - }, - { - "cell_type": "markdown", - "id": "952ddc62", - "metadata": {}, - "source": [ - "We can obtain a MAP estimate by optimising the log-posterior density with\n", - "`jaxopt` solvers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fb003bba", - "metadata": {}, - "outputs": [], - "source": [ - "negative_lpd = jax.jit(gpx.LogPosteriorDensity(negative=True))\n", - "\n", - "opt_posterior, history = gpx.fit(\n", - " model=posterior,\n", - " train_data=D,\n", - " solver=jaxopt.LBFGS(gpx.ConjugateMLL(negative=True), maxiter=500),\n", - " key=key,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "855ba0a3", - "metadata": {}, - "source": [ - "From which we can make predictions at novel inputs, as illustrated below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "76fb1924", - "metadata": {}, - "outputs": [], - "source": [ - "map_latent_dist = opt_posterior.predict(xtest, train_data=D)\n", - "predictive_dist = opt_posterior.likelihood(map_latent_dist)\n", - "\n", - "predictive_mean = predictive_dist.mean()\n", - "predictive_std = predictive_dist.stddev()\n", - "\n", - "fig, ax = plt.subplots()\n", - "ax.scatter(x, y, label=\"Observations\", color=cols[0])\n", - "ax.plot(xtest, predictive_mean, label=\"Predictive mean\", color=cols[1])\n", - "ax.fill_between(\n", - " xtest.squeeze(),\n", - " predictive_mean - predictive_std,\n", - " predictive_mean + predictive_std,\n", - " alpha=0.2,\n", - " color=cols[1],\n", - " label=\"One sigma\",\n", - ")\n", - "ax.plot(\n", - " xtest,\n", - " predictive_mean - predictive_std,\n", - " color=cols[1],\n", - " linestyle=\"--\",\n", - " linewidth=1,\n", - ")\n", - "ax.plot(\n", - " xtest,\n", - " predictive_mean + predictive_std,\n", - " color=cols[1],\n", - " linestyle=\"--\",\n", - " linewidth=1,\n", - ")\n", - "\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "bbed07f4", - "metadata": {}, - "source": [ - "Here we projected the map estimates $\\hat{\\boldsymbol{f}}$ for the function values\n", - "$\\boldsymbol{f}$ at the data points $\\boldsymbol{x}$ to get predictions over the\n", - "whole domain,\n", - "\n", - "\\begin{align}\n", - "p(f(\\cdot)| \\mathcal{D}) \\approx q_{map}(f(\\cdot)) := \\int p(f(\\cdot)| \\boldsymbol{f}) \\delta(\\boldsymbol{f} - \\hat{\\boldsymbol{f}}) d \\boldsymbol{f} = \\mathcal{N}(\\mathbf{K}_{\\boldsymbol{(\\cdot)x}} \\mathbf{K}_{\\boldsymbol{xx}}^{-1} \\hat{\\boldsymbol{f}}, \\mathbf{K}_{\\boldsymbol{(\\cdot, \\cdot)}} - \\mathbf{K}_{\\boldsymbol{(\\cdot)\\boldsymbol{x}}} \\mathbf{K}_{\\boldsymbol{xx}}^{-1} \\mathbf{K}_{\\boldsymbol{\\boldsymbol{x}(\\cdot)}}).\n", - "\\end{align}" - ] - }, - { - "cell_type": "markdown", - "id": "7f21a007", - "metadata": {}, - "source": [ - "However, as a point estimate, MAP estimation is severely limited for uncertainty\n", - "quantification, providing only a single piece of information about the posterior." - ] - }, - { - "cell_type": "markdown", - "id": "f97ed9c4", - "metadata": {}, - "source": [ - "## Laplace approximation\n", - "The Laplace approximation improves uncertainty quantification by incorporating\n", - "curvature induced by the marginal log-likelihood's Hessian to construct an\n", - "approximate Gaussian distribution centered on the MAP estimate. Writing\n", - "$\\tilde{p}(\\boldsymbol{f}|\\mathcal{D}) = p(\\boldsymbol{y}|\\boldsymbol{f}) p(\\boldsymbol{f})$\n", - "as the unormalised posterior for function values $\\boldsymbol{f}$ at the datapoints\n", - "$\\boldsymbol{x}$, we can expand the log of this about the posterior mode\n", - "$\\hat{\\boldsymbol{f}}$ via a Taylor expansion. This gives:\n", - "\n", - "\\begin{align}\n", - "\\log\\tilde{p}(\\boldsymbol{f}|\\mathcal{D}) = \\log\\tilde{p}(\\hat{\\boldsymbol{f}}|\\mathcal{D}) + \\left[\\nabla \\log\\tilde{p}({\\boldsymbol{f}}|\\mathcal{D})|_{\\hat{\\boldsymbol{f}}}\\right]^{T} (\\boldsymbol{f}-\\hat{\\boldsymbol{f}}) + \\frac{1}{2} (\\boldsymbol{f}-\\hat{\\boldsymbol{f}})^{T} \\left[\\nabla^2 \\tilde{p}(\\boldsymbol{y}|\\boldsymbol{f})|_{\\hat{\\boldsymbol{f}}} \\right] (\\boldsymbol{f}-\\hat{\\boldsymbol{f}}) + \\mathcal{O}(\\lVert \\boldsymbol{f} - \\hat{\\boldsymbol{f}} \\rVert^3).\n", - "\\end{align}\n", - "\n", - "Since $\\nabla \\log\\tilde{p}({\\boldsymbol{f}}|\\mathcal{D})$ is zero at the mode,\n", - "this suggests the following approximation\n", - "\\begin{align}\n", - "\\tilde{p}(\\boldsymbol{f}|\\mathcal{D}) \\approx \\log\\tilde{p}(\\hat{\\boldsymbol{f}}|\\mathcal{D}) \\exp\\left\\{ \\frac{1}{2} (\\boldsymbol{f}-\\hat{\\boldsymbol{f}})^{T} \\left[-\\nabla^2 \\tilde{p}(\\boldsymbol{y}|\\boldsymbol{f})|_{\\hat{\\boldsymbol{f}}} \\right] (\\boldsymbol{f}-\\hat{\\boldsymbol{f}}) \\right\\}\n", - "\\end{align},\n", - "\n", - "that we identify as a Gaussian distribution,\n", - "$p(\\boldsymbol{f}| \\mathcal{D}) \\approx q(\\boldsymbol{f}) := \\mathcal{N}(\\hat{\\boldsymbol{f}}, [-\\nabla^2 \\tilde{p}(\\boldsymbol{y}|\\boldsymbol{f})|_{\\hat{\\boldsymbol{f}}} ]^{-1} )$.\n", - "Since the negative Hessian is positive definite, we can use the Cholesky\n", - "decomposition to obtain the covariance matrix of the Laplace approximation at the\n", - "datapoints below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "67baa6c6", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "import cola\n", - "from gpjax.lower_cholesky import lower_cholesky\n", - "\n", - "gram, cross_covariance = (kernel.gram, kernel.cross_covariance)\n", - "jitter = 1e-6\n", - "\n", - "# Compute (latent) function value map estimates at training points:\n", - "Kxx = opt_posterior.prior.kernel.gram(x)\n", - "Kxx += identity_matrix(D.n) * jitter\n", - "Kxx = cola.PSD(Kxx)\n", - "Lx = lower_cholesky(Kxx)\n", - "f_hat = Lx @ opt_posterior.latent\n", - "\n", - "# Negative Hessian, H = -∇²p_tilde(y|f):\n", - "H = jax.jacfwd(jax.jacrev(negative_lpd))(opt_posterior, D).latent.latent[:, 0, :, 0]\n", - "\n", - "L = jnp.linalg.cholesky(H + identity_matrix(D.n) * jitter)\n", - "\n", - "# H⁻¹ = H⁻¹ I = (LLᵀ)⁻¹ I = L⁻ᵀL⁻¹ I\n", - "L_inv = jsp.linalg.solve_triangular(L, identity_matrix(D.n), lower=True)\n", - "H_inv = jsp.linalg.solve_triangular(L.T, L_inv, lower=False)\n", - "LH = jnp.linalg.cholesky(H_inv)\n", - "laplace_approximation = tfd.MultivariateNormalTriL(f_hat.squeeze(), LH)" - ] - }, - { - "cell_type": "markdown", - "id": "0b0080fe", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "For novel inputs, we must project the above approximating distribution through the\n", - "Gaussian conditional distribution $p(f(\\cdot)| \\boldsymbol{f})$,\n", - "\n", - "\\begin{align}\n", - "p(f(\\cdot)| \\mathcal{D}) \\approx q_{Laplace}(f(\\cdot)) := \\int p(f(\\cdot)| \\boldsymbol{f}) q(\\boldsymbol{f}) d \\boldsymbol{f} = \\mathcal{N}(\\mathbf{K}_{\\boldsymbol{(\\cdot)x}} \\mathbf{K}_{\\boldsymbol{xx}}^{-1} \\hat{\\boldsymbol{f}}, \\mathbf{K}_{\\boldsymbol{(\\cdot, \\cdot)}} - \\mathbf{K}_{\\boldsymbol{(\\cdot)\\boldsymbol{x}}} \\mathbf{K}_{\\boldsymbol{xx}}^{-1} (\\mathbf{K}_{\\boldsymbol{xx}} - [-\\nabla^2 \\tilde{p}(\\boldsymbol{y}|\\boldsymbol{f})|_{\\hat{\\boldsymbol{f}}} ]^{-1}) \\mathbf{K}_{\\boldsymbol{xx}}^{-1} \\mathbf{K}_{\\boldsymbol{\\boldsymbol{x}(\\cdot)}}).\n", - "\\end{align}\n", - "\n", - "This is the same approximate distribution $q_{map}(f(\\cdot))$, but we have perturbed\n", - "the covariance by a curvature term of\n", - "$\\mathbf{K}_{\\boldsymbol{(\\cdot)\\boldsymbol{x}}} \\mathbf{K}_{\\boldsymbol{xx}}^{-1} [-\\nabla^2 \\tilde{p}(\\boldsymbol{y}|\\boldsymbol{f})|_{\\hat{\\boldsymbol{f}}} ]^{-1} \\mathbf{K}_{\\boldsymbol{xx}}^{-1} \\mathbf{K}_{\\boldsymbol{\\boldsymbol{x}(\\cdot)}}$.\n", - "We take the latent distribution computed in the previous section and add this term\n", - "to the covariance to construct $q_{Laplace}(f(\\cdot))$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "867815eb", - "metadata": {}, - "outputs": [], - "source": [ - "def construct_laplace(test_inputs: Float[Array, \"N D\"]) -> tfd.MultivariateNormalTriL:\n", - " map_latent_dist = opt_posterior.predict(xtest, train_data=D)\n", - "\n", - " Kxt = opt_posterior.prior.kernel.cross_covariance(x, test_inputs)\n", - " Kxx = opt_posterior.prior.kernel.gram(x)\n", - " Kxx += identity_matrix(D.n) * jitter\n", - " Kxx = cola.PSD(Kxx)\n", - "\n", - " # Kxx⁻¹ Kxt\n", - " Kxx_inv_Kxt = cola.solve(Kxx, Kxt)\n", - "\n", - " # Ktx Kxx⁻¹[ H⁻¹ ] Kxx⁻¹ Kxt\n", - " laplace_cov_term = jnp.matmul(jnp.matmul(Kxx_inv_Kxt.T, H_inv), Kxx_inv_Kxt)\n", - "\n", - " mean = map_latent_dist.mean()\n", - " covariance = map_latent_dist.covariance() + laplace_cov_term\n", - " L = jnp.linalg.cholesky(covariance)\n", - " return tfd.MultivariateNormalTriL(jnp.atleast_1d(mean.squeeze()), L)" - ] - }, - { - "cell_type": "markdown", - "id": "f9fce917", - "metadata": { - "lines_to_next_cell": 0 - }, - "source": [ - "From this we can construct the predictive distribution at the test points." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5a56bf0a", - "metadata": {}, - "outputs": [], - "source": [ - "laplace_latent_dist = construct_laplace(xtest)\n", - "predictive_dist = opt_posterior.likelihood(laplace_latent_dist)\n", - "\n", - "predictive_mean = predictive_dist.mean()\n", - "predictive_std = predictive_dist.stddev()\n", - "\n", - "fig, ax = plt.subplots()\n", - "ax.scatter(x, y, label=\"Observations\", color=cols[0])\n", - "ax.plot(xtest, predictive_mean, label=\"Predictive mean\", color=cols[1])\n", - "ax.fill_between(\n", - " xtest.squeeze(),\n", - " predictive_mean - predictive_std,\n", - " predictive_mean + predictive_std,\n", - " alpha=0.2,\n", - " color=cols[1],\n", - " label=\"One sigma\",\n", - ")\n", - "ax.plot(\n", - " xtest,\n", - " predictive_mean - predictive_std,\n", - " color=cols[1],\n", - " linestyle=\"--\",\n", - " linewidth=1,\n", - ")\n", - "ax.plot(\n", - " xtest,\n", - " predictive_mean + predictive_std,\n", - " color=cols[1],\n", - " linestyle=\"--\",\n", - " linewidth=1,\n", - ")\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "9a97c9df", - "metadata": {}, - "source": [ - "However, the Laplace approximation is still limited by considering information about\n", - "the posterior at a single location. On the other hand, through approximate sampling,\n", - "MCMC methods allow us to learn all information about the posterior distribution." - ] - }, - { - "cell_type": "markdown", - "id": "c726488c", - "metadata": {}, - "source": [ - "## MCMC inference\n", - "\n", - "An MCMC sampler works by starting at an initial position and\n", - "drawing a sample from a cheap-to-simulate distribution known as the _proposal_. The\n", - "next step is to determine whether this sample could be considered a draw from the\n", - "posterior. We accomplish this using an _acceptance probability_ determined via the\n", - "sampler's _transition kernel_ which depends on the current position and the\n", - "unnormalised target posterior distribution. If the new sample is more _likely_, we\n", - "accept it; otherwise, we reject it and stay in our current position. Repeating these\n", - "steps results in a Markov chain (a random sequence that depends only on the last\n", - "state) whose stationary distribution (the long-run empirical distribution of the\n", - "states visited) is the posterior. For a gentle introduction, see the first chapter\n", - "of [A Handbook of Markov Chain Monte Carlo](https://www.mcmchandbook.net/HandbookChapter1.pdf).\n", - "\n", - "### MCMC through BlackJax\n", - "\n", - "Rather than implementing a suite of MCMC samplers, GPJax relies on MCMC-specific\n", - "libraries for sampling functionality. We focus on\n", - "[BlackJax](https://github.com/blackjax-devs/blackjax/) in this notebook, which we\n", - "recommend adopting for general applications.\n", - "\n", - "We'll use the No U-Turn Sampler (NUTS) implementation given in BlackJax for sampling.\n", - "For the interested reader, NUTS is a Hamiltonian Monte Carlo sampling scheme where\n", - "the number of leapfrog integration steps is computed at each step of the change\n", - "according to the NUTS algorithm. In general, samplers constructed under this\n", - "framework are very efficient.\n", - "\n", - "We begin by generating _sensible_ initial positions for our sampler before defining\n", - "an inference loop and sampling 500 values from our Markov chain. In practice,\n", - "drawing more samples will be necessary." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0ba459dc", - "metadata": {}, - "outputs": [], - "source": [ - "num_adapt = 500\n", - "num_samples = 500\n", - "\n", - "lpd = jax.jit(gpx.LogPosteriorDensity(negative=False))\n", - "unconstrained_lpd = jax.jit(lambda tree: lpd(tree.constrain(), D))\n", - "\n", - "adapt = blackjax.window_adaptation(\n", - " blackjax.nuts, unconstrained_lpd, num_adapt, target_acceptance_rate=0.65\n", - ")\n", - "\n", - "# Initialise the chain\n", - "start = time()\n", - "last_state, kernel, _ = adapt.run(key, posterior.unconstrain())\n", - "print(f\"Adaption time taken: {time() - start: .1f} seconds\")\n", - "\n", - "\n", - "def inference_loop(rng_key, kernel, initial_state, num_samples):\n", - " def one_step(state, rng_key):\n", - " state, info = kernel(rng_key, state)\n", - " return state, (state, info)\n", - "\n", - " keys = jax.random.split(rng_key, num_samples)\n", - " _, (states, infos) = jax.lax.scan(one_step, initial_state, keys)\n", - "\n", - " return states, infos\n", - "\n", - "\n", - "# Sample from the posterior distribution\n", - "start = time()\n", - "states, infos = inference_loop(key, kernel, last_state, num_samples)\n", - "print(f\"Sampling time taken: {time() - start: .1f} seconds\")" - ] - }, - { - "cell_type": "markdown", - "id": "d38ebbea", - "metadata": {}, - "source": [ - "### Sampler efficiency\n", - "\n", - "BlackJax gives us easy access to our sampler's efficiency through metrics such as the\n", - "sampler's _acceptance probability_ (the number of times that our chain accepted a\n", - "proposed sample, divided by the total number of steps run by the chain). For NUTS and\n", - "Hamiltonian Monte Carlo sampling, we typically seek an acceptance rate of 60-70% to\n", - "strike the right balance between having a chain which is _stuck_ and rarely moves\n", - "versus a chain that is too jumpy with frequent small steps." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e9fa0d91", - "metadata": {}, - "outputs": [], - "source": [ - "acceptance_rate = jnp.mean(infos.acceptance_probability)\n", - "print(f\"Acceptance rate: {acceptance_rate:.2f}\")" - ] - }, - { - "cell_type": "markdown", - "id": "cd357372", - "metadata": {}, - "source": [ - "Our acceptance rate is slightly too large, prompting an examination of the chain's\n", - "trace plots. A well-mixing chain will have very few (if any) flat spots in its trace\n", - "plot whilst also not having too many steps in the same direction. In addition to\n", - "the model's hyperparameters, there will be 500 samples for each of the 100 latent\n", - "function values in the `states.position` dictionary. We depict the chains that\n", - "correspond to the model hyperparameters and the first value of the latent function\n", - "for brevity." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ef48b0a", - "metadata": {}, - "outputs": [], - "source": [ - "fig, (ax0, ax1, ax2) = plt.subplots(ncols=3, figsize=(10, 3))\n", - "ax0.plot(states.position.prior.kernel.lengthscale)\n", - "ax1.plot(states.position.prior.kernel.variance)\n", - "ax2.plot(states.position.latent[:, 1, :])\n", - "ax0.set_title(\"Kernel Lengthscale\")\n", - "ax1.set_title(\"Kernel Variance\")\n", - "ax2.set_title(\"Latent Function (index = 1)\")" - ] - }, - { - "cell_type": "markdown", - "id": "3e737c51", - "metadata": {}, - "source": [ - "## Prediction\n", - "\n", - "Having obtained samples from the posterior, we draw ten instances from our model's\n", - "predictive distribution per MCMC sample. Using these draws, we will be able to\n", - "compute credible values and expected values under our posterior distribution.\n", - "\n", - "An ideal Markov chain would have samples completely uncorrelated with their\n", - "neighbours after a single lag. However, in practice, correlations often exist\n", - "within our chain's sample set. A commonly used technique to try and reduce this\n", - "correlation is _thinning_ whereby we select every $n$th sample where $n$ is the\n", - "minimum lag length at which we believe the samples are uncorrelated. Although further\n", - "analysis of the chain's autocorrelation is required to find appropriate thinning\n", - "factors, we employ a thin factor of 10 for demonstration purposes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4566d7fe", - "metadata": {}, - "outputs": [], - "source": [ - "thin_factor = 20\n", - "posterior_samples = []\n", - "\n", - "for i in trange(0, num_samples, thin_factor, desc=\"Drawing posterior samples\"):\n", - " sample = jtu.tree_map(lambda samples, i=i: samples[i], states.position)\n", - " sample = sample.constrain()\n", - " latent_dist = sample.predict(xtest, train_data=D)\n", - " predictive_dist = sample.likelihood(latent_dist)\n", - " posterior_samples.append(predictive_dist.sample(seed=key, sample_shape=(10,)))\n", - "\n", - "posterior_samples = jnp.vstack(posterior_samples)\n", - "lower_ci, upper_ci = jnp.percentile(posterior_samples, jnp.array([2.5, 97.5]), axis=0)\n", - "expected_val = jnp.mean(posterior_samples, axis=0)" - ] - }, - { - "cell_type": "markdown", - "id": "e873d8f6", - "metadata": {}, - "source": [ - "\n", - "Finally, we end this tutorial by plotting the predictions obtained from our model\n", - "against the observed data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a880e0cd", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.scatter(x, y, color=cols[0], label=\"Observations\", zorder=2, alpha=0.7)\n", - "ax.plot(xtest, expected_val, color=cols[1], label=\"Predicted mean\", zorder=1)\n", - "ax.fill_between(\n", - " xtest.flatten(),\n", - " lower_ci.flatten(),\n", - " upper_ci.flatten(),\n", - " alpha=0.2,\n", - " color=cols[1],\n", - " label=\"95\\\\% CI\",\n", - ")\n", - "ax.plot(\n", - " xtest,\n", - " lower_ci.flatten(),\n", - " color=cols[1],\n", - " linestyle=\"--\",\n", - " linewidth=1,\n", - ")\n", - "ax.plot(\n", - " xtest,\n", - " upper_ci.flatten(),\n", - " color=cols[1],\n", - " linestyle=\"--\",\n", - " linewidth=1,\n", - ")\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "c89f0691", - "metadata": {}, - "source": [ - "## System configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78e217ee", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext watermark\n", - "%watermark -n -u -v -iv -w -a \"Thomas Pinder & Daniel Dodd\"" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "custom_cell_magics": "kql", - "encoding": "# -*- coding: utf-8 -*-" - }, - "kernelspec": { - "display_name": "gpjax", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/constructing_new_kernels.ipynb b/docs/examples/constructing_new_kernels.ipynb deleted file mode 100644 index 9bd2dd7a1..000000000 --- a/docs/examples/constructing_new_kernels.ipynb +++ /dev/null @@ -1,480 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e11cfcf0", - "metadata": {}, - "source": [ - "# Kernel Guide\n", - "\n", - "In this guide, we introduce the kernels available in GPJax and demonstrate how to\n", - "create custom kernels." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d5e9ad19", - "metadata": {}, - "outputs": [], - "source": [ - "# Enable Float64 for more stable matrix inversions.\n", - "from jax.config import config\n", - "\n", - "config.update(\"jax_enable_x64\", True)\n", - "\n", - "from dataclasses import dataclass\n", - "from typing import Dict\n", - "\n", - "from jax import jit\n", - "import jax.numpy as jnp\n", - "import jax.random as jr\n", - "from jaxtyping import (\n", - " Array,\n", - " Float,\n", - " install_import_hook,\n", - ")\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import optax as ox\n", - "import jaxopt\n", - "from simple_pytree import static_field\n", - "import tensorflow_probability.substrates.jax as tfp\n", - "\n", - "with install_import_hook(\"gpjax\", \"beartype.beartype\"):\n", - " import gpjax as gpx\n", - " from gpjax.base.param import param_field\n", - "\n", - "key = jr.PRNGKey(123)\n", - "tfb = tfp.bijectors\n", - "plt.style.use(\n", - " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", - ")\n", - "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]" - ] - }, - { - "cell_type": "markdown", - "id": "bdccbdf4", - "metadata": {}, - "source": [ - "## Supported Kernels\n", - "\n", - "The following kernels are natively supported in GPJax.\n", - "\n", - "* Matérn 1/2, 3/2 and 5/2.\n", - "* RBF (or squared exponential).\n", - "* Rational quadratic.\n", - "* Powered exponential.\n", - "* Polynomial.\n", - "* White noise\n", - "* Linear.\n", - "* Polynomial.\n", - "* [Graph kernels](https://docs.jaxgaussianprocesses.com/examples/graph_kernels/).\n", - "\n", - "While the syntax is consistent, each kernel's type influences the\n", - "characteristics of the sample paths drawn. We visualise this below with 10\n", - "function draws per kernel." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9717f825", - "metadata": {}, - "outputs": [], - "source": [ - "kernels = [\n", - " gpx.kernels.Matern12(),\n", - " gpx.kernels.Matern32(),\n", - " gpx.kernels.Matern52(),\n", - " gpx.kernels.RBF(),\n", - " gpx.kernels.Polynomial(),\n", - " gpx.kernels.Polynomial(degree=2),\n", - "]\n", - "fig, axes = plt.subplots(ncols=3, nrows=2, figsize=(10, 6), tight_layout=True)\n", - "\n", - "x = jnp.linspace(-3.0, 3.0, num=200).reshape(-1, 1)\n", - "\n", - "meanf = gpx.mean_functions.Zero()\n", - "\n", - "for k, ax in zip(kernels, axes.ravel()):\n", - " prior = gpx.Prior(mean_function=meanf, kernel=k)\n", - " rv = prior(x)\n", - " y = rv.sample(seed=key, sample_shape=(10,))\n", - " ax.plot(x, y.T, alpha=0.7)\n", - " ax.set_title(k.name)" - ] - }, - { - "cell_type": "markdown", - "id": "f06b48b7", - "metadata": {}, - "source": [ - "### Active dimensions\n", - "\n", - "By default, kernels operate over every dimension of the supplied inputs. In\n", - "some use cases, it is desirable to restrict kernels to specific dimensions of\n", - "the input data. We can achieve this by the `active dims` argument, which\n", - "determines which input index values the kernel evaluates.\n", - "\n", - "To see this, consider the following 5-dimensional dataset for which we would\n", - "like our RBF kernel to act on the first, second and fourth dimensions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "65198906", - "metadata": {}, - "outputs": [], - "source": [ - "slice_kernel = gpx.kernels.RBF(active_dims=[0, 1, 3], lengthscale=jnp.ones((3,)))" - ] - }, - { - "cell_type": "markdown", - "id": "47511074", - "metadata": {}, - "source": [ - "\n", - "The resulting kernel has one length-scale parameter per input dimension --- an ARD kernel." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d090532b", - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"Lengthscales: {slice_kernel.lengthscale}\")" - ] - }, - { - "cell_type": "markdown", - "id": "ec08bffa", - "metadata": {}, - "source": [ - "We'll now simulate some data and evaluate the kernel on the previously selected\n", - "input dimensions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "870d9a53", - "metadata": {}, - "outputs": [], - "source": [ - "# Inputs\n", - "x_matrix = jr.normal(key, shape=(50, 5))\n", - "\n", - "# Compute the Gram matrix\n", - "K = slice_kernel.gram(x_matrix)\n", - "print(K.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "3cdda74e", - "metadata": {}, - "source": [ - "## Kernel combinations\n", - "\n", - "The product or sum of two positive definite matrices yields a positive\n", - "definite matrix. Consequently, summing or multiplying sets of kernels is a\n", - "valid operation that can give rich kernel functions. In GPJax, functionality for\n", - "a sum kernel is provided by the `SumKernel` class." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8c9ffa75", - "metadata": {}, - "outputs": [], - "source": [ - "k1 = gpx.kernels.RBF()\n", - "k2 = gpx.kernels.Polynomial()\n", - "sum_k = gpx.kernels.SumKernel(kernels=[k1, k2])\n", - "\n", - "fig, ax = plt.subplots(ncols=3, figsize=(9, 3))\n", - "im0 = ax[0].matshow(k1.gram(x).to_dense())\n", - "im1 = ax[1].matshow(k2.gram(x).to_dense())\n", - "im2 = ax[2].matshow(sum_k.gram(x).to_dense())\n", - "\n", - "fig.colorbar(im0, ax=ax[0], fraction=0.05)\n", - "fig.colorbar(im1, ax=ax[1], fraction=0.05)\n", - "fig.colorbar(im2, ax=ax[2], fraction=0.05)" - ] - }, - { - "cell_type": "markdown", - "id": "bfbd2124", - "metadata": {}, - "source": [ - "Similarly, products of kernels can be created through the `ProductKernel` class." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c2dd213", - "metadata": {}, - "outputs": [], - "source": [ - "k3 = gpx.kernels.Matern32()\n", - "\n", - "prod_k = gpx.kernels.ProductKernel(kernels=[k1, k2, k3])\n", - "\n", - "fig, ax = plt.subplots(ncols=4, figsize=(12, 3))\n", - "im0 = ax[0].matshow(k1.gram(x).to_dense())\n", - "im1 = ax[1].matshow(k2.gram(x).to_dense())\n", - "im2 = ax[2].matshow(k3.gram(x).to_dense())\n", - "im3 = ax[3].matshow(prod_k.gram(x).to_dense())\n", - "\n", - "fig.colorbar(im0, ax=ax[0], fraction=0.05)\n", - "fig.colorbar(im1, ax=ax[1], fraction=0.05)\n", - "fig.colorbar(im2, ax=ax[2], fraction=0.05)\n", - "fig.colorbar(im3, ax=ax[3], fraction=0.05)" - ] - }, - { - "cell_type": "markdown", - "id": "425c97d1", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "## Custom kernel\n", - "\n", - "GPJax makes the process of implementing kernels of your choice straightforward\n", - "with two key steps:\n", - "\n", - "1. Listing the kernel's parameters.\n", - "2. Defining the kernel's pairwise operation.\n", - "\n", - "We'll demonstrate this process now for a circular kernel --- an adaption of\n", - "the excellent guide given in the PYMC3 documentation. We encourage curious\n", - "readers to visit their notebook\n", - "[here](https://www.pymc.io/projects/docs/en/v3/pymc-examples/examples/gaussian_processes/GP-Circular.html).\n", - "\n", - "### Circular kernel\n", - "\n", - "When the underlying space is polar, typical Euclidean kernels such as Matérn\n", - "kernels are insufficient at the boundary where discontinuities will present\n", - "themselves.\n", - "This is due to the fact that for a polar space $\\lvert 0, 2\\pi\\rvert=0$ i.e.,\n", - "the space wraps. Euclidean kernels have no mechanism in them to represent this\n", - "logic and will instead treat $0$ and $2\\pi$ and elements far apart. Circular\n", - "kernels do not exhibit this behaviour and instead _wrap_ around the boundary\n", - "points to create a smooth function. Such a kernel was given in [Padonou &\n", - "Roustant (2015)](https://hal.inria.fr/hal-01119942v1) where any two angles\n", - "$\\theta$ and $\\theta'$ are written as $$W_c(\\theta, \\theta') = \\left\\lvert\n", - "\\left(1 + \\tau \\frac{d(\\theta, \\theta')}{c} \\right) \\left(1 - \\frac{d(\\theta,\n", - "\\theta')}{c} \\right)^{\\tau} \\right\\rvert \\quad \\tau \\geq 4 \\tag{1}.$$\n", - "\n", - "Here the hyperparameter $\\tau$ is analogous to a lengthscale for Euclidean\n", - "stationary kernels, controlling the correlation between pairs of observations.\n", - "While $d$ is an angular distance metric\n", - "\n", - "$$d(\\theta, \\theta') = \\lvert (\\theta-\\theta'+c) \\operatorname{mod} 2c - c\n", - "\\rvert.$$\n", - "\n", - "To implement this, one must write the following class." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4b731f56", - "metadata": {}, - "outputs": [], - "source": [ - "def angular_distance(x, y, c):\n", - " return jnp.abs((x - y + c) % (c * 2) - c)\n", - "\n", - "\n", - "bij = tfb.Chain([tfb.Softplus(), tfb.Shift(np.array(4.0).astype(np.float64))])\n", - "\n", - "\n", - "@dataclass\n", - "class Polar(gpx.kernels.AbstractKernel):\n", - " period: float = static_field(2 * jnp.pi)\n", - " tau: float = param_field(jnp.array([4.0]), bijector=bij)\n", - "\n", - " def __call__(\n", - " self, x: Float[Array, \"1 D\"], y: Float[Array, \"1 D\"]\n", - " ) -> Float[Array, \"1\"]:\n", - " c = self.period / 2.0\n", - " t = angular_distance(x, y, c)\n", - " K = (1 + self.tau * t / c) * jnp.clip(1 - t / c, 0, jnp.inf) ** self.tau\n", - " return K.squeeze()" - ] - }, - { - "cell_type": "markdown", - "id": "c795379e", - "metadata": {}, - "source": [ - "We unpack this now to make better sense of it. In the kernel's initialiser\n", - "we specify the length of a single period. As the underlying\n", - "domain is a circle, this is $2\\pi$. We then define the kernel's `__call__`\n", - "function which is a direct implementation of Equation (1) where we define `c`\n", - "as half the value of `period`.\n", - "\n", - "To constrain $\\tau$ to be greater than 4, we use a `Softplus` bijector with a\n", - "clipped lower bound of 4.0. This is done by specifying the `bijector` argument\n", - "when we define the parameter field." - ] - }, - { - "cell_type": "markdown", - "id": "541e5068", - "metadata": {}, - "source": [ - "### Using our polar kernel\n", - "\n", - "We proceed to fit a GP with our custom circular kernel to a random sequence of\n", - "points on a circle (see the\n", - "[Regression notebook](https://docs.jaxgaussianprocesses.com/examples/regression/)\n", - "for further details on this process)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "694fe036", - "metadata": {}, - "outputs": [], - "source": [ - "# Simulate data\n", - "angles = jnp.linspace(0, 2 * jnp.pi, num=200).reshape(-1, 1)\n", - "n = 20\n", - "noise = 0.2\n", - "\n", - "X = jnp.sort(jr.uniform(key, minval=0.0, maxval=jnp.pi * 2, shape=(n, 1)), axis=0)\n", - "y = 4 + jnp.cos(2 * X) + jr.normal(key, shape=X.shape) * noise\n", - "\n", - "D = gpx.Dataset(X=X, y=y)\n", - "\n", - "# Define polar Gaussian process\n", - "PKern = Polar()\n", - "meanf = gpx.mean_functions.Zero()\n", - "likelihood = gpx.Gaussian(num_datapoints=n)\n", - "circular_posterior = gpx.Prior(mean_function=meanf, kernel=PKern) * likelihood\n", - "\n", - "# Optimise GP's marginal log-likelihood using Adam\n", - "opt_posterior, history = gpx.fit(\n", - " model=circular_posterior,\n", - " train_data=D,\n", - " solver=jaxopt.LBFGS(gpx.ConjugateMLL(negative=True), maxiter=500),\n", - " key=key,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "8761d8fb", - "metadata": {}, - "source": [ - "### Prediction\n", - "\n", - "We'll now query the GP's predictive posterior at linearly spaced novel inputs\n", - "and illustrate the results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "13744f81", - "metadata": {}, - "outputs": [], - "source": [ - "posterior_rv = opt_posterior.likelihood(opt_posterior.predict(angles, train_data=D))\n", - "mu = posterior_rv.mean()\n", - "one_sigma = posterior_rv.stddev()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b4f1d9b7", - "metadata": {}, - "outputs": [], - "source": [ - "fig = plt.figure(figsize=(7, 3.5))\n", - "gridspec = fig.add_gridspec(1, 1)\n", - "ax = plt.subplot(gridspec[0], polar=True)\n", - "\n", - "ax.fill_between(\n", - " angles.squeeze(),\n", - " mu - one_sigma,\n", - " mu + one_sigma,\n", - " alpha=0.3,\n", - " label=r\"1 Posterior s.d.\",\n", - " color=cols[1],\n", - " lw=0,\n", - ")\n", - "ax.fill_between(\n", - " angles.squeeze(),\n", - " mu - 3 * one_sigma,\n", - " mu + 3 * one_sigma,\n", - " alpha=0.15,\n", - " label=r\"3 Posterior s.d.\",\n", - " color=cols[1],\n", - " lw=0,\n", - ")\n", - "ax.plot(angles, mu, label=\"Posterior mean\")\n", - "ax.scatter(D.X, D.y, alpha=1, label=\"Observations\")\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "0917650b", - "metadata": {}, - "source": [ - "## System configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a6053ba2", - "metadata": {}, - "outputs": [], - "source": [ - "%reload_ext watermark\n", - "%watermark -n -u -v -iv -w -a 'Thomas Pinder'" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "custom_cell_magics": "kql", - "encoding": "# -*- coding: utf-8 -*-" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/decision_making.ipynb b/docs/examples/decision_making.ipynb deleted file mode 100644 index cd4cf7085..000000000 --- a/docs/examples/decision_making.ipynb +++ /dev/null @@ -1,668 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ecc31fbd", - "metadata": {}, - "source": [ - "# Introduction to Decision Making with GPJax\n", - "\n", - "In this notebook we provide an introduction to the decision making module of GPJax,\n", - "which can be used to solve sequential decision making problems. Common examples of\n", - "such problems include Bayesian optimisation (BO) and experimental design. For an\n", - "in-depth introduction to Bayesian optimisation itself, be sure to checkout out our\n", - "[Introduction to BO\n", - "Notebook](https://docs.jaxgaussianprocesses.com/examples/bayesian_optimisation/).\n", - "\n", - "We'll be using BO as a case study to demonstrate how one may use the decision making\n", - "module to solve sequential decision making problems. The goal of the decision making\n", - "module is to provide a set of tools that can easily be used to solve a wide range of\n", - "sequential decision making problems. The module is designed to be modular, and so it is\n", - "easy to swap out different components of the decision making pipeline. Whilst it\n", - "provides the functionality for quickly implementing a typical deicision making loop out\n", - "of the box, we also hope that it will provide sufficient flexibility to allow users to\n", - "define their own, more exotic, decision making pipelines." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "831a299d", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "# Enable Float64 for more stable matrix inversions.\n", - "from jax import config\n", - "\n", - "config.update(\"jax_enable_x64\", True)\n", - "\n", - "\n", - "import jax.numpy as jnp\n", - "import jax.random as jr\n", - "import jaxopt\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import gpjax as gpx\n", - "from gpjax.decision_making.utility_functions import (\n", - " ThompsonSampling,\n", - ")\n", - "from gpjax.decision_making.utility_maximizer import (\n", - " ContinuousSinglePointUtilityMaximizer,\n", - ")\n", - "from gpjax.decision_making.decision_maker import UtilityDrivenDecisionMaker\n", - "from gpjax.decision_making.utils import (\n", - " OBJECTIVE,\n", - " build_function_evaluator,\n", - ")\n", - "from gpjax.decision_making.posterior_handler import PosteriorHandler\n", - "from gpjax.decision_making.search_space import ContinuousSearchSpace\n", - "from gpjax.typing import (\n", - " Array,\n", - " Float,\n", - ")\n", - "\n", - "key = jr.PRNGKey(42)\n", - "plt.style.use(\n", - " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", - ")\n", - "cols = mpl.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]" - ] - }, - { - "cell_type": "markdown", - "id": "9aae0fb7", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "## The Black-Box Objective Function\n", - "\n", - "We'll be using the same problem as in the [Introduction to BO\n", - "Notebook](https://docs.jaxgaussianprocesses.com/examples/bayesian_optimisation/), but\n", - "rather than focussing on the mechanics of BO we'll be looking at how one may use the\n", - "abstractions provided by the decision making module to implement the BO loop.\n", - "\n", - "In BO, and sequential decision making in general, we will often have a black-box\n", - "function of interest which we can evaluate. In this notebook we'll be using the\n", - "Forrester function as our objective to minimise:\n", - "\n", - "$$f(x) = (6x - 2)^2\\sin(12x-4)$$" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "104be778", - "metadata": {}, - "outputs": [], - "source": [ - "def forrester(x: Float[Array, \"N 1\"]) -> Float[Array, \"N 1\"]:\n", - " return (6 * x - 2) ** 2 * jnp.sin(12 * x - 4)" - ] - }, - { - "cell_type": "markdown", - "id": "241b1cd9", - "metadata": {}, - "source": [ - "Within the decision making loop, we'll be querying the black-box objective function many\n", - "times, and will often use the observed values to fit some probabilistic model. Thereore,\n", - "it would be useful to have some method to which we can pass a set of points which we\n", - "wish to query the black-box function at, and which will return a GPJax `Dataset` object\n", - "containing the observations. We can use the `build_function_evaluator` function provided\n", - "in `decision_making.utils` to do this. This function takes as input a dictionary of\n", - "labelled black-box functions, and will return a function evaluator, which can be called\n", - "with a set of points to evaluate the black-box functions at. The function evaluator will\n", - "return a dictionary of labelled `Dataset` objects containing the observations. Note that\n", - "in our case we only have one black-box function of interest, but in general we may have\n", - "multiple different black-box functions, such as if we also have constraint functions.\n", - "The use of the labels inside the dictionary returned by the function evaluator enables\n", - "us to easily distinguish between these different observations." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "7f142853", - "metadata": {}, - "outputs": [], - "source": [ - "function_evaluator = build_function_evaluator({OBJECTIVE: forrester})" - ] - }, - { - "cell_type": "markdown", - "id": "82b75134", - "metadata": {}, - "source": [ - "## The Search Space\n", - "\n", - "Having defined a method for evaluating the black-box function, we now need to define the\n", - "search space over which we wish to optimise. In this case we'll be optimising over the\n", - "interval $[0, 1]$. We can use the `ContinuousSearchSpace` class provided in\n", - "`decision_making.search_space` to define this search space, as seen below:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "df396394", - "metadata": {}, - "outputs": [], - "source": [ - "lower_bounds = jnp.array([0.0])\n", - "upper_bounds = jnp.array([1.0])\n", - "search_space = ContinuousSearchSpace(\n", - " lower_bounds=lower_bounds, upper_bounds=upper_bounds\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "a2375ed4", - "metadata": {}, - "source": [ - "The `ContinuousSearchSpace` class defines a `sample` method, which can be used to\n", - "sample points from the search space using a space-filling design, in this case using the\n", - "[Halton sequence](https://en.wikipedia.org/wiki/Halton_sequence). This will be useful at\n", - "many points throughout the decision making loop, but for now let's use it to create an\n", - "initial set of points which we can use to fit our models:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "43d01869", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/henry/anaconda3/envs/gpjax/lib/python3.10/site-packages/jax/_src/numpy/lax_numpy.py:3613: UserWarning: 'kind' argument to argsort is ignored; only 'stable' sorts are supported.\n", - " warnings.warn(\"'kind' argument to argsort is ignored; only 'stable' sorts \"\n" - ] - } - ], - "source": [ - "initial_x = search_space.sample(5, key)\n", - "initial_datasets = function_evaluator(initial_x)" - ] - }, - { - "cell_type": "markdown", - "id": "8c8c89ff", - "metadata": {}, - "source": [ - "## The Surrogate Models" - ] - }, - { - "cell_type": "markdown", - "id": "379e6249", - "metadata": {}, - "source": [ - "Many sequential decision making algorithms are described as being *model-based*. With\n", - "these algorithms, we use a probabilistic model, or multiple models, to drive the\n", - "decision making process. In ordinary BO, a probabilistic model is used to model the\n", - "objective function, and it is updated based on observations from the black-box objective\n", - "function. These models are often referred to as *surrogate models*, and are used to\n", - "approximate the functions of interest. We'll be using the Gaussian process functionality\n", - "provided by GPJax to define our surrogate models, with some wrappers provided by the\n", - "`decision_making` module to make it easier to use these models within the decision\n", - "making loop. We can proceed as usual when defining our priors, choosing a suitable\n", - "mean function and kernel for the job at hand:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "12e2dd2a", - "metadata": {}, - "outputs": [], - "source": [ - "mean = gpx.Zero()\n", - "kernel = gpx.Matern52()\n", - "prior = gpx.Prior(mean_function=mean, kernel=kernel)" - ] - }, - { - "cell_type": "markdown", - "id": "3f86aa67", - "metadata": {}, - "source": [ - "One difference from GPJax is the way in which we define our likelihood. In GPJax, we\n", - "construct our GP posteriors by defining a `likelihood` object and then multiplying it\n", - "with our prior to get the posterior, `posterior = likelihood * prior`. However, the\n", - "`AbstractLikelihood` objects takes `num_datapoints` as one of its arguments, and this is\n", - "going to be changing in the case of BO, and decision making in general, as we keep\n", - "updating our models having observed new data! In order to deal with this we'll define a\n", - "`likelihood_builder`, which takes as an argument the number of datapoints used to\n", - "condition our prior on, and returns a `likelihood` object. Below we use this to\n", - "construct a `likelihood_builder` which will return a `Gaussian` likelihood, initialised\n", - "with the correct number of datapoints:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d8ad7cc8", - "metadata": {}, - "outputs": [], - "source": [ - "likelihood_builder = lambda n: gpx.Gaussian(num_datapoints=n, obs_noise=jnp.array(1e-6))" - ] - }, - { - "cell_type": "markdown", - "id": "01694fb0", - "metadata": {}, - "source": [ - "Now we have all the components required for constructing our GP posterior. Since we'll\n", - "be updating the posterior throughout the decision making loop as we observe more data,\n", - "it would be useful to have an object which can handle all this logic for us.\n", - "Fortunately, the `decision_making` module provides the `PosteriorHandler` class to do\n", - "this for us. This class takes as input a `prior` and `likeligood_builder`, which we have\n", - "defined above. We tend to also optimise the hyperparameters of the GP prior when\n", - "\"fitting\" our GP, as demonstrated in the [Regression\n", - "notebook](https://docs.jaxgaussianprocesses.com/examples/regression/). This will be\n", - "using the GPJax `fit` method under the hood, which requires an jaxopt `solver`.\n", - "Therefore, we also pass this to the `PosteriorHandler` as demonstrated below:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "4138e77d", - "metadata": {}, - "outputs": [], - "source": [ - "import optax as ox\n", - "posterior_handler = PosteriorHandler(\n", - " prior,\n", - " likelihood_builder=likelihood_builder,\n", - " solver = jaxopt.OptaxSolver(gpx.ConjugateMLL(negative=True), opt=ox.adam(1e-2), maxiter=1000),\n", - " #solver=jaxopt.LBFGS(gpx.ConjugateMLL(negative=True), maxiter=500),\n", - ")\n", - "posterior_handlers = {OBJECTIVE: posterior_handler}" - ] - }, - { - "cell_type": "markdown", - "id": "3bc693fd", - "metadata": {}, - "source": [ - "Note that we also create a labelled dictionary of `posterior_handlers`. This is a\n", - "recurring theme with the decision making logic; we can have dictionaries containing\n", - "datasets, posteriors and black box functions, and use labels to identify corresponding\n", - "objects the dictionaries. For instance, here we have an \"OBJECTIVE\" posterior handler\n", - "which is updated using the data in the \"OBJECTIVE\" dataset, which is in turn generated by the \"OBJECTIVE\" black-box function.\n", - "\n", - "Now, as the decision making loop progresses, we can use the `update_posterior` method of\n", - "the `PosteriorHandler` to update our posterior as we observe more data. Note that we use\n", - "the term *posterior* to refer to our GP posterior surrogate models in order to be\n", - "consistent with the syntax used by GPJax. However, these GP posteriors are more widely\n", - "referred to as *models* in the model-based decision making literature." - ] - }, - { - "cell_type": "markdown", - "id": "9c139db8", - "metadata": {}, - "source": [ - "## The Utility Function\n", - "\n", - "Now all that remains for us to define is the utiliy function, and a way of maximising\n", - "it. Within the utility-driven decision making framework, we define a utility function,\n", - "often using our GP surrogates, which characterises the *utility*, or *usefulness*, of\n", - "querying the black-box function at any point within the domain of interest. We can then\n", - "*maximise* this function to decide which point to query next. In this case we'll be\n", - "using Thompson sampling as a utility function for determining where to query next. With\n", - "this function we simply draw a sample from the GP posterior, and choose the minimizer\n", - "of the sample as the point to query next. In the `decision_making` framework we create\n", - "`UtilityFunctionBuilder` objects. Currently, we only support\n", - "`SinglePointUtilityFunction`s, which are utility functions which characterise the\n", - "utility of querying a single point. Thompson sampling is somewhat of a special case, as\n", - "we can draw $B$ independent samples from the GP posterior and optimise each of these\n", - "samples in order to obtain a *batch* of points to query next. We'll see an example of\n", - "this later on.\n", - "\n", - "Within the `ThompsonSampling` utility function builder class we implement the\n", - "`build_utility_function` method, which takes as input a dictionary containing lablled GP\n", - "posteriors, as well as the corresponding datasets for these posteriors, and draws an\n", - "approximate sample from the GP posterior which is a surrogate for the objective\n", - "function. We instantiate our utility function builder below, specifying the number of\n", - "Random Fourier features to use when constructing the approximate samples from the GP posterior:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "54427002", - "metadata": {}, - "outputs": [], - "source": [ - "utility_function_builder = ThompsonSampling(num_features=500)" - ] - }, - { - "cell_type": "markdown", - "id": "6002c950", - "metadata": {}, - "source": [ - "We also need a method for maximising the utility function. Since `ThompsonSampling` is\n", - "classed as a `SinglePointUtilityFunction`, we can use the\n", - "`ContinuousSinglePointUtilityMaximizer` to maximise it. This requires the user to\n", - "specify `num_initial_samples` and `num_restarts` when instantiating it. This first\n", - "queries the utility function at `num_initial_samples` points, and then uses the best of\n", - "these points as a starting point for L-BFGS-B, a gradient-based optimiser, to further\n", - "refine. This is repeated `num_restarts` times, each time sampling a different initial set\n", - "of `num_initial_samples` and the best point found is returned. We'll instantiate our\n", - "maximiser below:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "f2fe3fda", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "acquisition_maximizer = ContinuousSinglePointUtilityMaximizer(\n", - " num_initial_samples=100, num_restarts=1\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "f5ddb6f5", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "## Putting it All Together with the Decision Maker\n", - "\n", - "We now have all the ingredients ready for our Bayesian optimisation loop, so let's put\n", - "all the logic together using the `UtilityDrivenDecisionMaker` class provided by the\n", - "`decision_making` module. This class has 3 core methods:\n", - "1. `ask` - This method is used to decide which point(s) to query next.\n", - "2. `tell` - This method is used to tell the decision maker the results from querying the\n", - " black-box function at the points returned by `ask`, and will often update GP\n", - " posteriors in light of this data.\n", - "3. `run` - This is used to run the decision making loop for a specified number of\n", - " iterations, alternating between `ask` and `tell`.\n", - "\n", - "For many decision making problems, the logic provided in the\n", - "`UtilityDrivenDecisionMaker` will be sufficient, and is a convenient way of gluing the\n", - "various bits of machinery involved in sequential decision making together. However, for\n", - "more exotic decision making loops, it is easy for the user to define their own decision\n", - "maker class by inheriting from the `AbstractDecisionMaker` class and defining their own\n", - "`ask`, `tell` and `run` methods.\n", - "\n", - "However, we do also provide the user with some additional flexibility when using the\n", - "`UtilityDrivenDecisionMaker` class. Often we may wish to perform certain actions after\n", - "the `ask` step and the `tell` step, such as plotting the acquisition function and the\n", - "point chosen to be queried for debugging purposes. We can do this by passing a list of\n", - "functions to be called at each of these points as the `post_ask` and `post_tell`\n", - "attributes of the `UtilityDrivenDecisionMaker`. Both sets of functions are called with\n", - "the `UtilityDrivenDecisionMaker` as an argument, and so have access to all the\n", - "attributes of the decision maker. The `post_ask` functions are additionally passed the\n", - "most recently queried points too. We'll use this functionality to plot the acquisition\n", - "function and the point chosen to be queried at each step of the decision making loop:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "752767ff", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_bo_iteration(\n", - " dm: UtilityDrivenDecisionMaker, last_queried_points: Float[Array, \"B D\"]\n", - "):\n", - " posterior = dm.posteriors[OBJECTIVE]\n", - " dataset = dm.datasets[OBJECTIVE]\n", - " plt_x = jnp.linspace(0, 1, 1000).reshape(-1, 1)\n", - " forrester_y = forrester(plt_x.squeeze(axis=-1))\n", - " utility_fn = dm.current_utility_functions[0]\n", - " sample_y = -utility_fn(plt_x)\n", - "\n", - " latent_dist = posterior.predict(plt_x, train_data=dataset)\n", - " predictive_dist = posterior.likelihood(latent_dist)\n", - "\n", - " predictive_mean = predictive_dist.mean()\n", - " predictive_std = predictive_dist.stddev()\n", - "\n", - " fig, ax = plt.subplots()\n", - " ax.plot(plt_x.squeeze(), predictive_mean, label=\"Predictive Mean\", color=cols[1])\n", - " ax.fill_between(\n", - " plt_x.squeeze(),\n", - " predictive_mean - 2 * predictive_std,\n", - " predictive_mean + 2 * predictive_std,\n", - " alpha=0.2,\n", - " label=\"Two sigma\",\n", - " color=cols[1],\n", - " )\n", - " ax.plot(\n", - " plt_x.squeeze(),\n", - " predictive_mean - 2 * predictive_std,\n", - " linestyle=\"--\",\n", - " linewidth=1,\n", - " color=cols[1],\n", - " )\n", - " ax.plot(\n", - " plt_x.squeeze(),\n", - " predictive_mean + 2 * predictive_std,\n", - " linestyle=\"--\",\n", - " linewidth=1,\n", - " color=cols[1],\n", - " )\n", - " ax.plot(plt_x.squeeze(), sample_y, label=\"Posterior Sample\")\n", - " ax.plot(\n", - " plt_x.squeeze(),\n", - " forrester_y,\n", - " label=\"Forrester Function\",\n", - " color=cols[0],\n", - " linestyle=\"--\",\n", - " linewidth=2,\n", - " )\n", - " ax.axvline(x=0.757, linestyle=\":\", color=cols[3], label=\"True Optimum\")\n", - " ax.scatter(dataset.X, dataset.y, label=\"Observations\", color=cols[2], zorder=2)\n", - " ax.scatter(\n", - " last_queried_points[0],\n", - " -utility_fn(last_queried_points[0][None, ...]),\n", - " label=\"Posterior Sample Optimum\",\n", - " marker=\"*\",\n", - " color=cols[3],\n", - " zorder=3,\n", - " )\n", - " ax.legend(loc=\"center left\", bbox_to_anchor=(0.950, 0.5))\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "4a488098", - "metadata": {}, - "source": [ - "Now let's put it all together and run our decision making loop for 6 iterations, with a\n", - "batch size of 1:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "2bc920a4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dm = UtilityDrivenDecisionMaker(\n", - " search_space=search_space,\n", - " posterior_handlers=posterior_handlers,\n", - " datasets=initial_datasets,\n", - " utility_function_builder=utility_function_builder,\n", - " utility_maximizer=acquisition_maximizer,\n", - " batch_size=1,\n", - " key=key,\n", - " post_ask=[plot_bo_iteration],\n", - " post_tell=[],\n", - ")\n", - "\n", - "results = dm.run(\n", - " 6,\n", - " black_box_function_evaluator=function_evaluator,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "152dd577", - "metadata": {}, - "source": [ - "We can see that our `DecisionMaker` is successfully able to find the minimimizer of the\n", - "black box function!\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "f67fac5a", - "metadata": {}, - "source": [ - "## Conclusions" - ] - }, - { - "cell_type": "markdown", - "id": "c59691a5", - "metadata": {}, - "source": [ - "In this notebook we have provided an introduction to the new `decision_making` module of\n", - "GPJax. We have demonstrated how one may use the abstractions provided by this module to\n", - "implement a Bayesian optimisation loop, and have also highlighted some of the\n", - "flexibility provided by the module. We hope that this module will provide a useful\n", - "framework for solving a wide range of sequential decision making problems, and that it\n", - "will be easy for users to extend the functionality provided by the module to suit their\n", - "needs!\n", - "\n", - "We should note that the `decision_making` module is still in its early stages, and so\n", - "whilst we hope to avoid making breaking changes to it, they may occur as the module\n", - "evolves and more advanced functionality is implemented. If people have any feedback or\n", - "features they would like to implement/see implemented, feel free to open an issue on the\n", - "[GPJax GitHub page](https://github.com/JaxGaussianProcesses/GPJax/issues).\n" - ] - }, - { - "cell_type": "markdown", - "id": "3652fb37", - "metadata": {}, - "source": [ - "## System Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a4bbb464", - "metadata": {}, - "outputs": [], - "source": [ - "%reload_ext watermark\n", - "%watermark -n -u -v -iv -w -a 'Thomas Christie'" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" - }, - "kernelspec": { - "display_name": "gpjax", - "language": "python", - "name": "gpjax" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/oceanmodelling.ipynb b/docs/examples/oceanmodelling.ipynb deleted file mode 100644 index 06e6c9524..000000000 --- a/docs/examples/oceanmodelling.ipynb +++ /dev/null @@ -1,880 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a6f1891d", - "metadata": { - "lines_to_next_cell": 0 - }, - "source": [ - "# Gaussian Processes for Vector Fields and Ocean Current Modelling\n", - "\n", - "In this notebook, we use Gaussian processes to learn vector-valued functions. We will be\n", - "recreating the results by [Berlinghieri et al. (2023)](https://arxiv.org/pdf/2302.10364.pdf) by an\n", - "application to real-world ocean surface velocity data, collected via surface drifters.\n", - "\n", - "Surface drifters are measurement devices that measure the dynamics and circulation patterns of the world's oceans. Studying and predicting ocean currents are important to climate research, for example, forecasting and predicting oil spills, oceanographic surveying of eddies and upwelling, or providing information on the distribution of biomass in ecosystems. We will be using the [Gulf Drifters Open dataset](https://zenodo.org/record/4421585), which contains all publicly available surface drifter trajectories from the Gulf of Mexico spanning 28 years." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "08e9b124", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n" - ] - } - ], - "source": [ - "from jax.config import config\n", - "\n", - "config.update(\"jax_enable_x64\", True)\n", - "from dataclasses import dataclass\n", - "\n", - "from jax import hessian\n", - "from jax.config import config\n", - "import jax.numpy as jnp\n", - "import jax.random as jr\n", - "from jaxtyping import (\n", - " Array,\n", - " Float,\n", - " install_import_hook,\n", - ")\n", - "from matplotlib import rcParams\n", - "import matplotlib.pyplot as plt\n", - "import jaxopt\n", - "import pandas as pd\n", - "import tensorflow_probability as tfp\n", - "import optax as ox\n", - "with install_import_hook(\"gpjax\", \"beartype.beartype\"):\n", - " import gpjax as gpx\n", - "\n", - "# Enable Float64 for more stable matrix inversions.\n", - "key = jr.PRNGKey(123)\n", - "plt.style.use(\n", - " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", - ")\n", - "colors = rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]" - ] - }, - { - "cell_type": "markdown", - "id": "7b54ea0f", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "## Data loading and preprocessing\n", - "The real dataset has been binned into an $N=34\\times16$ grid, equally spaced over the longitude-latitude interval $[-90.8,-83.8] \\times [24.0,27.5]$. Each bin has a size $\\approx 0.21\\times0.21$, and contains the average velocity across all measurements that fall inside it.\n", - "\n", - "We will call this binned ocean data the ground truth, and label it with the vector field\n", - "$$\n", - "\\mathbf{F} \\equiv \\mathbf{F}(\\mathbf{x}),\n", - "$$\n", - "where $\\mathbf{x} = (x^{(0)}$,$x^{(1)})^\\text{T}$, with a vector basis in the standard Cartesian directions (dimensions will be indicated by superscripts).\n", - "\n", - "We shall label the ground truth $D_0=\\left\\{ \\left(\\mathbf{x}_{0,i} , \\mathbf{y}_{0,i} \\right)\\right\\}_{i=1}^N$, where $\\mathbf{y}_{0,i}$ is the 2-dimensional velocity vector at the $i$-th location, $\\mathbf{x}_{0,i}$. The training dataset contains simulated measurements from ocean drifters $D_T=\\left\\{\\left(\\mathbf{x}_{T,i}, \\mathbf{y}_{T,i} \\right)\\right\\}_{i=1}^{N_T}$, $N_T = 20$ in this case (the subscripts indicate the ground truth and the simulated measurements respectively).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "486539c3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# function to place data from csv into correct array shape\n", - "def prepare_data(df):\n", - " pos = jnp.array([df[\"lon\"], df[\"lat\"]])\n", - " vel = jnp.array([df[\"ubar\"], df[\"vbar\"]])\n", - " # extract shape stored as 'metadata' in the test data\n", - " try:\n", - " shape = (int(df[\"shape\"][1]), int(df[\"shape\"][0])) # shape = (34,16)\n", - " return pos, vel, shape\n", - " except KeyError:\n", - " return pos, vel\n", - "\n", - "\n", - "# loading in data\n", - "\n", - "gulf_data_train = pd.read_csv(\n", - " \"https://raw.githubusercontent.com/JaxGaussianProcesses/static/main/data/gulfdata_train.csv\"\n", - ")\n", - "gulf_data_test = pd.read_csv(\n", - " \"https://raw.githubusercontent.com/JaxGaussianProcesses/static/main/data/gulfdata_test.csv\"\n", - ")\n", - "\n", - "\n", - "pos_test, vel_test, shape = prepare_data(gulf_data_test)\n", - "pos_train, vel_train = prepare_data(gulf_data_train)\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(6, 3))\n", - "ax.quiver(\n", - " pos_test[0],\n", - " pos_test[1],\n", - " vel_test[0],\n", - " vel_test[1],\n", - " color=colors[0],\n", - " label=\"Ocean Current\",\n", - " angles=\"xy\",\n", - " scale=10,\n", - ")\n", - "ax.quiver(\n", - " pos_train[0],\n", - " pos_train[1],\n", - " vel_train[0],\n", - " vel_train[1],\n", - " color=colors[1],\n", - " alpha=0.7,\n", - " label=\"Drifter\",\n", - " angles=\"xy\",\n", - " scale=10,\n", - ")\n", - "\n", - "ax.set(\n", - " xlabel=\"Longitude\",\n", - " ylabel=\"Latitude\",\n", - ")\n", - "ax.legend(\n", - " framealpha=0.0,\n", - " ncols=2,\n", - " fontsize=\"medium\",\n", - " bbox_to_anchor=(0.5, -0.3),\n", - " loc=\"lower center\",\n", - ")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "7828a8ed", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "## Problem Setting\n", - "We aim to obtain estimates for $\\mathbf{F}$ at the set of points $\\left\\{ \\mathbf{x}_{0,i} \\right\\}_{i=1}^N$ using Gaussian processes, followed by a comparison of the latent model to the ground truth $D_0$. Note that $D_0$ is not passed into any functions used by GPJax, and is only used to compare against the two GP models at the end of the notebook.\n", - "\n", - "Since $\\mathbf{F}$ is a vector-valued function, we require GPs that can directly learn vector-valued functions[1](#fn1). To implement this in GPJax, the problem can be changed to learn a scalar-valued function by 'massaging' the data into a $2N\\times2N$ problem, such that each dimension of our GP is associated with a *component* of $\\mathbf{y}_{T,i}$.\n", - "\n", - "For a particular measurement $\\mathbf{y}$ (training or testing) at location $\\mathbf{x}$, the components $(y^{(0)}, y^{(1)})$ are described by the latent vector field $\\mathbf{F}$, such that\n", - "\n", - "$$\n", - "\\mathbf{y} = \\mathbf{F}(\\mathbf{x}) = \\left(\\begin{array}{l}\n", - "f^{(0)}\\left(\\mathbf{x}\\right) \\\\\n", - "f^{(1)}\\left(\\mathbf{x}\\right)\n", - "\\end{array}\\right),\n", - "$$\n", - "\n", - "where each $f^{(z)}\\left(\\mathbf{x}\\right), z \\in \\{0,1\\}$ is a scalar-valued function.\n", - "\n", - "Now consider the scalar-valued function $g: \\mathbb{R}^2 \\times\\{0,1\\} \\rightarrow \\mathbb{R}$, such that\n", - "\n", - "$$\n", - "g \\left(\\mathbf{x} , 0 \\right) = f^{(0)} ( \\mathbf{x} ), \\text{and } g \\left( \\mathbf{x}, 1 \\right)=f^{(1)}\\left(\\mathbf{x}\\right).\n", - "$$\n", - "\n", - "We have increased the input dimension by 1, from the 2D $\\mathbf{x}$ to the 3D $\\mathbf{X} = \\left(\\mathbf{x}, 0\\right)$ or $\\mathbf{X} = \\left(\\mathbf{x}, 1\\right)$.\n", - "\n", - "By choosing the value of the third dimension, 0 or 1, we may now incorporate this\n", - "information into the computation of the kernel.\n", - "We therefore make new 3D datasets $D_{T,3D} = \\left\\{\\left( \\mathbf{X}_{T,i},\\mathbf{Y}_{T,i} \\right) \\right\\} _{i=0}^{2N_T}$ and $D_{0,3D} = \\left\\{\\left( \\mathbf{X}_{0,i},\\mathbf{Y}_{0,i} \\right) \\right\\} _{i=0}^{2N}$ that incorporates this new labelling, such that for each dataset (indicated by the subscript $D = 0$ or $D=T$),\n", - "\n", - "$$\n", - "X_{D,i} = \\left( \\mathbf{x}_{D,i}, z \\right),\n", - "$$\n", - "and\n", - "$$\n", - "Y_{D,i} = y_{D,i}^{(z)},\n", - "$$\n", - "\n", - "where $z = 0$ if $i$ is odd and $z=1$ if $i$ is even." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "30ebd796", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "# Change vectors x -> X = (x,z), and vectors y -> Y = (y,z) via the artificial z label\n", - "def label_position(data):\n", - " # introduce alternating z label\n", - " n_points = len(data[0])\n", - " label = jnp.tile(jnp.array([0.0, 1.0]), n_points)\n", - " return jnp.vstack((jnp.repeat(data, repeats=2, axis=1), label)).T\n", - "\n", - "\n", - "# change vectors y -> Y by reshaping the velocity measurements\n", - "def stack_velocity(data):\n", - " return data.T.flatten().reshape(-1, 1)\n", - "\n", - "\n", - "def dataset_3d(pos, vel):\n", - " return gpx.Dataset(label_position(pos), stack_velocity(vel))\n", - "\n", - "\n", - "# label and place the training data into a Dataset object to be used by GPJax\n", - "dataset_train = dataset_3d(pos_train, vel_train)\n", - "\n", - "# we also require the testing data to be relabelled for later use, such that we can query the 2Nx2N GP at the test points\n", - "dataset_ground_truth = dataset_3d(pos_test, vel_test)" - ] - }, - { - "cell_type": "markdown", - "id": "3278ca65", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "## Velocity (dimension) decomposition\n", - "Having labelled the data, we are now in a position to use GPJax to learn the function $g$, and hence $\\mathbf{F}$. A naive approach to the problem is to apply a GP prior directly to the velocities of each dimension independently, which is called the *velocity* GP. For our prior, we choose an isotropic mean 0 over all dimensions of the GP, and a piecewise kernel that depends on the $z$ labels of the inputs, such that for two inputs $\\mathbf{X} = \\left( \\mathbf{x}, z \\right )$ and $\\mathbf{X}^\\prime = \\left( \\mathbf{x}^\\prime, z^\\prime \\right )$,\n", - "\n", - "$$\n", - "k_{\\text{vel}} \\left(\\mathbf{X}, \\mathbf{X}^{\\prime}\\right)=\n", - "\\begin{cases}k^{(z)}\\left(\\mathbf{x}, \\mathbf{x}^{\\prime}\\right) & \\text { if }\n", - "z=z^{\\prime} \\\\ 0 & \\text { if } z \\neq z^{\\prime}, \\end{cases}\n", - "$$\n", - "\n", - "where $k^{(z)}\\left(\\mathbf{x}, \\mathbf{x}^{\\prime}\\right)$ are the user chosen kernels for each dimension. What this means is that there are no correlations between the $x^{(0)}$ and $x^{(1)}$ dimensions for all choices $\\mathbf{X}$ and $\\mathbf{X}^{\\prime}$, since there are no off-diagonal elements in the Gram matrix populated by this choice.\n", - "\n", - "To implement this approach in GPJax, we define `VelocityKernel` in the following cell, following the steps outlined in the [custom kernels notebook](https://docs.jaxgaussianprocesses.com/examples/constructing_new_kernels/#custom-kernel). This modular implementation takes the choice of user kernels as its class attributes: `kernel0` and `kernel1`. We must additionally pass the argument `active_dims = [0,1]`, which is an attribute of the base class `AbstractKernel`, into the chosen kernels. This is necessary such that the subsequent likelihood optimisation does not optimise over the artificial label dimension.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e4ae9eb1", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "@dataclass\n", - "class VelocityKernel(gpx.kernels.AbstractKernel):\n", - " kernel0: gpx.kernels.AbstractKernel = gpx.kernels.RBF(active_dims=[0, 1])\n", - " kernel1: gpx.kernels.AbstractKernel = gpx.kernels.RBF(active_dims=[0, 1])\n", - "\n", - " def __call__(\n", - " self, X: Float[Array, \"1 D\"], Xp: Float[Array, \"1 D\"]\n", - " ) -> Float[Array, \"1\"]:\n", - " # standard RBF-SE kernel is x and x' are on the same output, otherwise returns 0\n", - "\n", - " z = jnp.array(X[2], dtype=int)\n", - " zp = jnp.array(Xp[2], dtype=int)\n", - "\n", - " # achieve the correct value via 'switches' that are either 1 or 0\n", - " k0_switch = ((z + 1) % 2) * ((zp + 1) % 2)\n", - " k1_switch = z * zp\n", - "\n", - " return k0_switch * self.kernel0(X, Xp) + k1_switch * self.kernel1(X, Xp)" - ] - }, - { - "cell_type": "markdown", - "id": "f35d7008", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "### GPJax implementation\n", - "Next, we define the model in GPJax. The prior is defined using $k_{\\text{vel}}\\left(\\mathbf{X}, \\mathbf{X}^\\prime \\right)$ and 0 mean and 0 observation noise. We choose a Gaussian marginal log-likelihood (MLL).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "ec016122", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "def initialise_gp(kernel, mean, dataset):\n", - " prior = gpx.Prior(mean_function=mean, kernel=kernel)\n", - " likelihood = gpx.Gaussian(\n", - " num_datapoints=dataset.n, obs_noise=jnp.array([1.0e-6], dtype=jnp.float64)\n", - " )\n", - " posterior = prior * likelihood\n", - " return posterior\n", - "\n", - "\n", - "# Define the velocity GP\n", - "mean = gpx.mean_functions.Zero()\n", - "kernel = VelocityKernel()\n", - "velocity_posterior = initialise_gp(kernel, mean, dataset_train)" - ] - }, - { - "cell_type": "markdown", - "id": "6aefa9c7", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "With a model now defined, we can proceed to optimise the hyperparameters of our likelihood over $D_0$. This is done by minimising the MLL using `jaxopt`. We also plot its value at each step to visually confirm that we have found the minimum. See the [introduction to Gaussian Processes](https://docs.jaxgaussianprocesses.com/examples/intro_to_gps/) notebook for more information on optimising the MLL." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b19c117e", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "64b13fc052654cf1a58c292be8f4c1ce", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/100 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def optimise_mll(posterior, dataset, NIters=100, key=key, plot_history=True):\n", - " # define the MLL using dataset_train\n", - " objective = gpx.objectives.ConjugateMLL(negative=True)\n", - " # Optimise to minimise the MLL\n", - " opt_posterior, history = gpx.fit(\n", - " model=posterior,\n", - " train_data=dataset,\n", - " solver = jaxopt.OptaxSolver(gpx.ConjugateMLL(negative=True), opt=ox.adam(1e-1), maxiter=NIters),\n", - " #solver=jaxopt.LBFGS(gpx.ConjugateMLL(negative=True), maxiter=NIters),\n", - " safe=True,\n", - " key=key,\n", - " )\n", - " # plot MLL value at each iteration\n", - " if plot_history:\n", - " fig, ax = plt.subplots(1, 1)\n", - " ax.plot(history, color=colors[1])\n", - " ax.set(xlabel=\"Training iteration\", ylabel=\"Negative MLL\")\n", - "\n", - " return opt_posterior\n", - "\n", - "\n", - "opt_velocity_posterior = optimise_mll(velocity_posterior, dataset_train)" - ] - }, - { - "cell_type": "markdown", - "id": "3597c8a1", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "### Comparison\n", - "We next obtain the latent distribution of the GP of $g$ at $\\mathbf{x}_{0,i}$, then extract its mean and standard at the test locations, $\\mathbf{F}_{\\text{latent}}(\\mathbf{x}_{0,i})$, as well as the standard deviation (we will use it at the very end)." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "47d25292", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "def latent_distribution(opt_posterior, pos_3d, dataset_train):\n", - " latent = opt_posterior.predict(pos_3d, train_data=dataset_train)\n", - " latent_mean = latent.mean()\n", - " latent_std = latent.stddev()\n", - " return latent_mean, latent_std\n", - "\n", - "\n", - "# extract latent mean and std of g, redistribute into vectors to model F\n", - "velocity_mean, velocity_std = latent_distribution(\n", - " opt_velocity_posterior, dataset_ground_truth.X, dataset_train\n", - ")\n", - "\n", - "dataset_latent_velocity = dataset_3d(pos_test, velocity_mean)" - ] - }, - { - "cell_type": "markdown", - "id": "d77bedeb", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "We now replot the ground truth (testing data) $D_0$, the predicted latent vector field $\\mathbf{F}_{\\text{latent}}(\\mathbf{x_i})$, and a heatmap of the residuals at each location $\\mathbf{R}(\\mathbf{x}_{0,i}) = \\mathbf{y}_{0,i} - \\mathbf{F}_{\\text{latent}}(\\mathbf{x}_{0,i}) $, as well as $\\left|\\left|\\mathbf{R}(\\mathbf{x}_{0,i})\\right|\\right|$." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "4f802439", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Residuals between ground truth and estimate\n", - "\n", - "\n", - "def plot_vector_field(ax, dataset, **kwargs):\n", - " ax.quiver(\n", - " dataset.X[::2][:, 0],\n", - " dataset.X[::2][:, 1],\n", - " dataset.y[::2],\n", - " dataset.y[1::2],\n", - " **kwargs,\n", - " )\n", - "\n", - "\n", - "def prepare_ax(ax, X, Y, title, **kwargs):\n", - " ax.set(\n", - " xlim=[X.min() - 0.1, X.max() + 0.1],\n", - " ylim=[Y.min() + 0.1, Y.max() + 0.1],\n", - " aspect=\"equal\",\n", - " title=title,\n", - " ylabel=\"latitude\",\n", - " **kwargs,\n", - " )\n", - "\n", - "\n", - "def residuals(dataset_latent, dataset_ground_truth):\n", - " return jnp.sqrt(\n", - " (dataset_latent.y[::2] - dataset_ground_truth.y[::2]) ** 2\n", - " + (dataset_latent.y[1::2] - dataset_ground_truth.y[1::2]) ** 2\n", - " )\n", - "\n", - "\n", - "def plot_fields(\n", - " dataset_ground_truth, dataset_trajectory, dataset_latent, shape=shape, scale=10\n", - "):\n", - " X = dataset_ground_truth.X[:, 0][::2]\n", - " Y = dataset_ground_truth.X[:, 1][::2]\n", - " # make figure\n", - " fig, ax = plt.subplots(1, 3, figsize=(12.0, 3.0), sharey=True)\n", - "\n", - " # ground truth\n", - " plot_vector_field(\n", - " ax[0],\n", - " dataset_ground_truth,\n", - " color=colors[0],\n", - " label=\"Ocean Current\",\n", - " angles=\"xy\",\n", - " scale=scale,\n", - " )\n", - " plot_vector_field(\n", - " ax[0],\n", - " dataset_trajectory,\n", - " color=colors[1],\n", - " label=\"Drifter\",\n", - " angles=\"xy\",\n", - " scale=scale,\n", - " )\n", - " prepare_ax(ax[0], X, Y, \"Ground Truth\", xlabel=\"Longitude\")\n", - "\n", - " # Latent estimate of vector field F\n", - " plot_vector_field(ax[1], dataset_latent, color=colors[0], angles=\"xy\", scale=scale)\n", - " plot_vector_field(\n", - " ax[1], dataset_trajectory, color=colors[1], angles=\"xy\", scale=scale\n", - " )\n", - " prepare_ax(ax[1], X, Y, \"GP Estimate\", xlabel=\"Longitude\")\n", - "\n", - " # residuals\n", - " residuals_vel = jnp.flip(\n", - " residuals(dataset_latent, dataset_ground_truth).reshape(shape), axis=0\n", - " )\n", - " im = ax[2].imshow(\n", - " residuals_vel,\n", - " extent=[X.min(), X.max(), Y.min(), Y.max()],\n", - " cmap=\"jet\",\n", - " vmin=0,\n", - " vmax=1.0,\n", - " interpolation=\"spline36\",\n", - " )\n", - " plot_vector_field(\n", - " ax[2], dataset_trajectory, color=colors[1], angles=\"xy\", scale=scale\n", - " )\n", - " prepare_ax(ax[2], X, Y, \"Residuals\", xlabel=\"Longitude\")\n", - " fig.colorbar(im, fraction=0.027, pad=0.04, orientation=\"vertical\")\n", - "\n", - " fig.legend(\n", - " framealpha=0.0,\n", - " ncols=2,\n", - " fontsize=\"medium\",\n", - " bbox_to_anchor=(0.5, -0.03),\n", - " loc=\"lower center\",\n", - " )\n", - " plt.show()\n", - "\n", - "\n", - "plot_fields(dataset_ground_truth, dataset_train, dataset_latent_velocity)" - ] - }, - { - "cell_type": "markdown", - "id": "1be0cdd6", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "From the latent estimate we can see the velocity GP struggles to reconstruct features of the ground truth. This is because our construction of the kernel placed an independent prior on each physical dimension, which cannot be assumed. Therefore, we need a different approach that can implicitly incorporate this dependence at a fundamental level. To achieve this we will require a *Helmholtz Decomposition*." - ] - }, - { - "cell_type": "markdown", - "id": "016f34c3", - "metadata": { - "lines_to_next_cell": 0 - }, - "source": [ - "## Helmholtz decomposition\n", - "In 2 dimensions, a twice continuously differentiable and compactly supported vector field $\\mathbf{F}: \\mathbb{R}^2 \\rightarrow \\mathbb{R}^2$ can be expressed as the sum of the gradient of a scalar potential $\\Phi: \\mathbb{R}^2 \\rightarrow \\mathbb{R}$, called the potential function, and the vorticity operator of another scalar potential $\\Psi: \\mathbb{R}^2 \\rightarrow \\mathbb{R}$, called the stream function ([Berlinghieri et al. (2023)](https://arxiv.org/pdf/2302.10364.pdf)) such that\n", - "$$\n", - "\\mathbf{F}=\\operatorname{grad} \\Phi+\\operatorname{rot} \\Psi,\n", - "$$\n", - "where\n", - "$$\n", - "\\operatorname{grad} \\Phi:=\\left[\\begin{array}{l}\n", - "\\partial \\Phi / \\partial x^{(0)} \\\\\n", - "\\partial \\Phi / \\partial x^{(1)}\n", - "\\end{array}\\right] \\text { and } \\operatorname{rot} \\Psi:=\\left[\\begin{array}{c}\n", - "\\partial \\Psi / \\partial x^{(1)} \\\\\n", - "-\\partial \\Psi / \\partial x^{(0)}\n", - "\\end{array}\\right].\n", - "$$\n", - "\n", - "This is reminiscent of a 3 dimensional [Helmholtz decomposition](https://en.wikipedia.org/wiki/Helmholtz_decomposition).\n", - "\n", - "The 2 dimensional decomposition motivates a different approach: placing priors on $\\Psi$ and $\\Phi$, allowing us to make assumptions directly about fundamental properties of $\\mathbf{F}$. If we choose independent GP priors such that $\\Phi \\sim \\mathcal{G P}\\left(0, k_{\\Phi}\\right)$ and $\\Psi \\sim \\mathcal{G P}\\left(0, k_{\\Psi}\\right)$, then $\\mathbf{F} \\sim \\mathcal{G P} \\left(0, k_\\text{Helm}\\right)$ (since acting linear operations on a GPs give GPs).\n", - "\n", - "For $\\mathbf{X}, \\mathbf{X}^{\\prime} \\in \\mathbb{R}^2 \\times \\left\\{0,1\\right\\}$ and $z, z^\\prime \\in \\{0,1\\}$,\n", - "\n", - "$$\n", - "\\boxed{ k_{\\mathrm{Helm}}\\left(\\mathbf{x}, \\mathbf{x}^{\\prime}\\right)_{z,z^\\prime} = \\frac{\\partial^2 k_{\\Phi}\\left(\\mathbf{x}, \\mathbf{x}^{\\prime}\\right)}{\\partial x^{(z)} \\partial\\left(x^{\\prime}\\right)^{(z^\\prime)}}+(-1)^{z+z^\\prime} \\frac{\\partial^2 k_{\\Psi}\\left(\\mathbf{x}, \\mathbf{x}^{\\prime}\\right)}{\\partial x^{(1-z)} \\partial\\left(x^{\\prime}\\right)^{(1-z^\\prime)}}}.\n", - "$$\n", - "\n", - "where $x^{(z)}$ and $(x^\\prime)^{(z^\\prime)}$ are the $z$ and $z^\\prime$ components of $\\mathbf{X}$ and ${\\mathbf{X}}^{\\prime}$ respectively.\n", - "\n", - "We compute the second derivatives using `jax.hessian`. In the following implementation, for a kernel $k(\\mathbf{x}, \\mathbf{x}^{\\prime})$, this computes the Hessian matrix with respect to the components of $\\mathbf{x}$\n", - "\n", - "$$\n", - "\\frac{\\partial^2 k\\left(\\mathbf{x}, \\mathbf{x}^{\\prime}\\right)}{\\partial x^{(z)} \\partial x^{(z^\\prime)}}.\n", - "$$\n", - "\n", - "Note that we have operated $\\dfrac{\\partial}{\\partial x^{(z)}}$, *not* $\\dfrac{\\partial}{\\partial \\left(x^\\prime \\right)^{(z)}}$, as the boxed equation suggests. This is not an issue if we choose stationary kernels $k(\\mathbf{x}, \\mathbf{x}^{\\prime}) = k(\\mathbf{x} - \\mathbf{x}^{\\prime})$ , as the partial derivatives with respect to the components have the following exchange symmetry:\n", - "\n", - "$$\n", - "\\frac{\\partial}{\\partial x^{(z)}} = - \\frac{\\partial}{\\partial \\left( x^\\prime \\right)^{(z)}},\n", - "$$\n", - "\n", - "for either $z$." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "62dec207", - "metadata": {}, - "outputs": [], - "source": [ - "@dataclass\n", - "class HelmholtzKernel(gpx.kernels.AbstractKernel):\n", - " # initialise Phi and Psi kernels as any stationary kernel in gpJax\n", - " potential_kernel: gpx.kernels.AbstractKernel = gpx.kernels.RBF(active_dims=[0, 1], variance=jnp.array(0.00001))\n", - " stream_kernel: gpx.kernels.AbstractKernel = gpx.kernels.RBF(active_dims=[0, 1])\n", - "\n", - " def __call__(\n", - " self, X: Float[Array, \"1 D\"], Xp: Float[Array, \"1 D\"]\n", - " ) -> Float[Array, \"1\"]:\n", - " # obtain indices for k_helm, implement in the correct sign between the derivatives\n", - " z = jnp.array(X[2], dtype=int)\n", - " zp = jnp.array(Xp[2], dtype=int)\n", - " sign = (-1) ** (z + zp)\n", - "\n", - " # convert to array to correctly index, -ve sign due to exchange symmetry (only true for stationary kernels)\n", - " potential_dvtve = -jnp.array(\n", - " hessian(self.potential_kernel)(X, Xp), dtype=jnp.float64\n", - " )[z][zp]\n", - " stream_dvtve = -jnp.array(\n", - " hessian(self.stream_kernel)(X, Xp), dtype=jnp.float64\n", - " )[1 - z][1 - zp]\n", - "\n", - " return potential_dvtve + sign * stream_dvtve" - ] - }, - { - "cell_type": "markdown", - "id": "458503b0", - "metadata": {}, - "source": [ - "### GPJax implementation\n", - "We repeat the same steps as with the velocity GP model, replacing `VelocityKernel` with `HelmholtzKernel`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "ae88ab7a", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e51b9bf778254f7da59dd2538f851f42", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/100 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Redefine Gaussian process with Helmholtz kernel\n", - "kernel = HelmholtzKernel()\n", - "helmholtz_posterior = initialise_gp(kernel, mean, dataset_train)\n", - "# Optimise hyperparameters using optax\n", - "opt_helmholtz_posterior = optimise_mll(helmholtz_posterior, dataset_train)" - ] - }, - { - "cell_type": "markdown", - "id": "0c59f8b9", - "metadata": {}, - "source": [ - "### Comparison\n", - "We again plot the ground truth (testing data) $D_0$, the predicted latent vector field $\\mathbf{F}_{\\text{latent}}(\\mathbf{x}_{0,i})$, and a heatmap of the residuals at each location $R(\\mathbf{x}_{0,i}) = \\mathbf{y}_{0,i} - \\mathbf{F}_{\\text{latent}}(\\mathbf{x}_{0,i})$ and $\\left|\\left|R(\\mathbf{x}_{0,i}) \\right|\\right|$." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "9925521f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# obtain latent distribution, extract x and y values over g\n", - "helmholtz_mean, helmholtz_std = latent_distribution(\n", - " opt_helmholtz_posterior, dataset_ground_truth.X, dataset_train\n", - ")\n", - "dataset_latent_helmholtz = dataset_3d(pos_test, helmholtz_mean)\n", - "\n", - "plot_fields(dataset_ground_truth, dataset_train, dataset_latent_helmholtz)" - ] - }, - { - "cell_type": "markdown", - "id": "246359e6", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "Visually, the Helmholtz model performs better than the velocity model, preserving the local structure of the $\\mathbf{F}$. Since we placed priors on $\\Phi$ and $\\Psi$, the construction of $\\mathbf{F}$ allows for correlations between the dimensions (non-zero off-diagonal elements in the Gram matrix populated by $k_\\text{Helm}\\left(\\mathbf{X},\\mathbf{X}^{\\prime}\\right)$ )." - ] - }, - { - "cell_type": "markdown", - "id": "0cb64e07", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "## Negative log predictive densities\n", - "Lastly, we directly compare the velocity and Helmholtz models by computing the [negative log predictive densities](https://en.wikipedia.org/wiki/Negative_log_predictive_density) for each model. This is a quantitative metric that measures the probability of the ground truth given the data.\n", - "\n", - "$$\n", - "\\mathrm{NLPD}=-\\sum_{i=1}^{2N} \\log \\left( p\\left(\\mathcal{Y}_i = Y_{0,i} \\mid \\mathbf{X}_{i}\\right) \\right),\n", - "$$\n", - "\n", - "where each $p\\left(\\mathcal{Y}_i \\mid \\mathbf{X}_i \\right)$ is the marginal Gaussian distribution over $\\mathcal{Y}_i$ at each test location, and $Y_{i,0}$ is the $i$-th component of the (massaged) test data that we reserved at the beginning of the notebook in $D_0$. A smaller value is better, since the deviation of the ground truth and the model are small in this case." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "386767fb", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NLPD for Velocity: 1566.11 \n", - "NLPD for Helmholtz: -208.06\n" - ] - } - ], - "source": [ - "# ensure testing data alternates between x0 and x1 components\n", - "def nlpd(mean, std, vel_test):\n", - " vel_query = jnp.column_stack((vel_test[0], vel_test[1])).flatten()\n", - " normal = tfp.substrates.jax.distributions.Normal(loc=mean, scale=std)\n", - " return -jnp.sum(normal.log_prob(vel_query))\n", - "\n", - "\n", - "# compute nlpd for velocity and helmholtz\n", - "nlpd_vel = nlpd(velocity_mean, velocity_std, vel_test)\n", - "nlpd_helm = nlpd(helmholtz_mean, helmholtz_std, vel_test)\n", - "\n", - "print(\"NLPD for Velocity: %.2f \\nNLPD for Helmholtz: %.2f\" % (nlpd_vel, nlpd_helm))" - ] - }, - { - "cell_type": "markdown", - "id": "d1180592", - "metadata": {}, - "source": [ - "The Helmholtz model outperforms the velocity model, as indicated by the lower NLPD score." - ] - }, - { - "cell_type": "markdown", - "id": "3bc48d3a", - "metadata": { - "lines_to_next_cell": 0 - }, - "source": [ - "\n", - "## Footnote\n", - "Kernels for vector-valued functions have been studied in the literature, see [Alvarez et al. (2012)](https://doi.org/10.48550/arXiv.1106.6251)\n", - "## System configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "25b7b160", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Author: Ivan Shalashilin\n", - "\n", - "Last updated: Tue Sep 19 2023\n", - "\n", - "Python implementation: CPython\n", - "Python version : 3.10.0\n", - "IPython version : 8.12.2\n", - "\n", - "jax : 0.4.9\n", - "gpjax : 0.0.0\n", - "tensorflow_probability: 0.19.0\n", - "pandas : 1.5.3\n", - "jaxopt : 0.6\n", - "matplotlib : 3.7.1\n", - "optax : 0.1.5\n", - "\n", - "Watermark: 2.3.1\n", - "\n" - ] - } - ], - "source": [ - "%reload_ext watermark\n", - "%watermark -n -u -v -iv -w -a 'Ivan Shalashilin'" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" - }, - "kernelspec": { - "display_name": "gpjax", - "language": "python", - "name": "gpjax" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/regression.ipynb b/docs/examples/regression.ipynb deleted file mode 100644 index 3d8a71b64..000000000 --- a/docs/examples/regression.ipynb +++ /dev/null @@ -1,639 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "467e95fc", - "metadata": {}, - "source": [ - "# Regression\n", - "\n", - "In this notebook we demonstate how to fit a Gaussian process regression model." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "5f9f23a8", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n" - ] - } - ], - "source": [ - "# Enable Float64 for more stable matrix inversions.\n", - "from jax.config import config\n", - "\n", - "config.update(\"jax_enable_x64\", True)\n", - "\n", - "from jax import jit\n", - "import jax.numpy as jnp\n", - "import jax.random as jr\n", - "from jaxtyping import install_import_hook\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt\n", - "import jaxopt\n", - "from docs.examples.utils import clean_legend\n", - "\n", - "with install_import_hook(\"gpjax\", \"beartype.beartype\"):\n", - " import gpjax as gpx\n", - "\n", - "key = jr.PRNGKey(123)\n", - "plt.style.use(\n", - " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", - ")\n", - "cols = mpl.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]" - ] - }, - { - "cell_type": "markdown", - "id": "5a3ce4b3", - "metadata": {}, - "source": [ - "## Dataset\n", - "\n", - "With the necessary modules imported, we simulate a dataset\n", - "$\\mathcal{D} = (\\boldsymbol{x}, \\boldsymbol{y}) = \\{(x_i, y_i)\\}_{i=1}^{100}$ with inputs $\\boldsymbol{x}$\n", - "sampled uniformly on $(-3., 3)$ and corresponding independent noisy outputs\n", - "\n", - "$$\\boldsymbol{y} \\sim \\mathcal{N} \\left(\\sin(4\\boldsymbol{x}) + \\cos(2 \\boldsymbol{x}), \\textbf{I} * 0.3^2 \\right).$$\n", - "\n", - "We store our data $\\mathcal{D}$ as a GPJax `Dataset` and create test inputs and labels\n", - "for later." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5c4d7a13", - "metadata": {}, - "outputs": [], - "source": [ - "n = 100\n", - "noise = 0.3\n", - "\n", - "key, subkey = jr.split(key)\n", - "x = jr.uniform(key=key, minval=-3.0, maxval=3.0, shape=(n,)).reshape(-1, 1)\n", - "f = lambda x: jnp.sin(4 * x) + jnp.cos(2 * x)\n", - "signal = f(x)\n", - "y = signal + jr.normal(subkey, shape=signal.shape) * noise\n", - "\n", - "D = gpx.Dataset(X=x, y=y)\n", - "\n", - "xtest = jnp.linspace(-3.5, 3.5, 500).reshape(-1, 1)\n", - "ytest = f(xtest)" - ] - }, - { - "cell_type": "markdown", - "id": "f9567fb3", - "metadata": {}, - "source": [ - "To better understand what we have simulated, we plot both the underlying latent\n", - "function and the observed data that is subject to Gaussian noise." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9b1127b6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.plot(x, y, \"o\", label=\"Observations\", color=cols[0])\n", - "ax.plot(xtest, ytest, label=\"Latent function\", color=cols[1])\n", - "ax.legend(loc=\"best\")" - ] - }, - { - "cell_type": "markdown", - "id": "381ecdde", - "metadata": {}, - "source": [ - "Our aim in this tutorial will be to reconstruct the latent function from our noisy\n", - "observations $\\mathcal{D}$ via Gaussian process regression. We begin by defining a\n", - "Gaussian process prior in the next section.\n", - "\n", - "## Defining the prior\n", - "\n", - "A zero-mean Gaussian process (GP) places a prior distribution over real-valued\n", - "functions $f(\\cdot)$ where\n", - "$f(\\boldsymbol{x}) \\sim \\mathcal{N}(0, \\mathbf{K}_{\\boldsymbol{x}\\boldsymbol{x}})$\n", - "for any finite collection of inputs $\\boldsymbol{x}$.\n", - "\n", - "Here $\\mathbf{K}_{\\boldsymbol{x}\\boldsymbol{x}}$ is the Gram matrix generated by a\n", - "user-specified symmetric, non-negative definite kernel function $k(\\cdot, \\cdot')$\n", - "with $[\\mathbf{K}_{\\boldsymbol{x}\\boldsymbol{x}}]_{i, j} = k(x_i, x_j)$.\n", - "The choice of kernel function is critical as, among other things, it governs the\n", - "smoothness of the outputs that our GP can generate.\n", - "\n", - "For simplicity, we consider a radial basis function (RBF) kernel:\n", - "$$k(x, x') = \\sigma^2 \\exp\\left(-\\frac{\\lVert x - x' \\rVert_2^2}{2 \\ell^2}\\right).$$\n", - "\n", - "On paper a GP is written as $f(\\cdot) \\sim \\mathcal{GP}(\\textbf{0}, k(\\cdot, \\cdot'))$,\n", - "we can reciprocate this process in GPJax via defining a `Prior` with our chosen `RBF`\n", - "kernel." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "9fd89471", - "metadata": {}, - "outputs": [], - "source": [ - "kernel = gpx.kernels.RBF()\n", - "meanf = gpx.mean_functions.Zero()\n", - "prior = gpx.Prior(mean_function=meanf, kernel=kernel)" - ] - }, - { - "cell_type": "markdown", - "id": "8cbf561e", - "metadata": {}, - "source": [ - "\n", - "The above construction forms the foundation for GPJax's models. Moreover, the GP prior\n", - "we have just defined can be represented by a\n", - "[TensorFlow Probability](https://www.tensorflow.org/probability/api_docs/python/tfp/substrates/jax)\n", - "multivariate Gaussian distribution. Such functionality enables trivial sampling, and\n", - "the evaluation of the GP's mean and covariance ." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "94e8d902", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "prior_dist = prior.predict(xtest)\n", - "\n", - "prior_mean = prior_dist.mean()\n", - "prior_std = prior_dist.variance()\n", - "samples = prior_dist.sample(seed=key, sample_shape=(20,))\n", - "\n", - "\n", - "fig, ax = plt.subplots()\n", - "ax.plot(xtest, samples.T, alpha=0.5, color=cols[0], label=\"Prior samples\")\n", - "ax.plot(xtest, prior_mean, color=cols[1], label=\"Prior mean\")\n", - "ax.fill_between(\n", - " xtest.flatten(),\n", - " prior_mean - prior_std,\n", - " prior_mean + prior_std,\n", - " alpha=0.3,\n", - " color=cols[1],\n", - " label=\"Prior variance\",\n", - ")\n", - "ax.legend(loc=\"best\")\n", - "ax = clean_legend(ax)" - ] - }, - { - "cell_type": "markdown", - "id": "d277c01a", - "metadata": {}, - "source": [ - "## Constructing the posterior\n", - "\n", - "Having defined our GP, we proceed to define a description of our data\n", - "$\\mathcal{D}$ conditional on our knowledge of $f(\\cdot)$ --- this is exactly the\n", - "notion of a likelihood function $p(\\mathcal{D} | f(\\cdot))$. While the choice of\n", - "likelihood is a critical in Bayesian modelling, for simplicity we consider a\n", - "Gaussian with noise parameter $\\alpha$\n", - "$$p(\\mathcal{D} | f(\\cdot)) = \\mathcal{N}(\\boldsymbol{y}; f(\\boldsymbol{x}), \\textbf{I} \\alpha^2).$$\n", - "This is defined in GPJax through calling a `Gaussian` instance." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "ecf37b5c", - "metadata": {}, - "outputs": [], - "source": [ - "likelihood = gpx.Gaussian(num_datapoints=D.n)" - ] - }, - { - "cell_type": "markdown", - "id": "d05f58a4", - "metadata": {}, - "source": [ - "The posterior is proportional to the prior multiplied by the likelihood, written as\n", - "\n", - " $$ p(f(\\cdot) | \\mathcal{D}) \\propto p(f(\\cdot)) * p(\\mathcal{D} | f(\\cdot)). $$\n", - "\n", - "Mimicking this construct, the posterior is established in GPJax through the `*` operator." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "c265f858", - "metadata": {}, - "outputs": [], - "source": [ - "posterior = prior * likelihood" - ] - }, - { - "cell_type": "markdown", - "id": "77c4f906", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Parameter state\n", - "\n", - "As outlined in the [PyTrees](https://jax.readthedocs.io/en/latest/pytrees.html)\n", - "documentation, parameters are contained within the model and for the leaves of the\n", - "PyTree. Consequently, in this particular model, we have three parameters: the\n", - "kernel lengthscale, kernel variance and the observation noise variance. Whilst\n", - "we have initialised each of these to 1, we can learn Type 2 MLEs for each of\n", - "these parameters by optimising the marginal log-likelihood (MLL)." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "fa9eb13b", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Array(124.80517341, dtype=float64)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "negative_mll = gpx.objectives.ConjugateMLL(negative=True)\n", - "negative_mll(posterior, train_data=D)\n", - "\n", - "\n", - "# static_tree = jax.tree_map(lambda x: not(x), posterior.trainables)\n", - "# optim = ox.chain(\n", - "# ox.adam(learning_rate=0.01),\n", - "# ox.masked(ox.set_to_zero(), static_tree)\n", - "# )" - ] - }, - { - "cell_type": "markdown", - "id": "896901fb", - "metadata": {}, - "source": [ - "For researchers, GPJax has the capacity to print the bibtex citation for objects such\n", - "as the marginal log-likelihood through the `cite()` function." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "2babd32e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "@book{rasmussen2006gaussian,\n", - "authors = {Rasmussen, Carl Edward and Williams, Christopher K},\n", - "title = {Gaussian Processes for Machine Learning},\n", - "year = {2006},\n", - "publisher = {MIT press Cambridge, MA},\n", - "volume = {2},\n", - "}\n" - ] - } - ], - "source": [ - "print(gpx.cite(negative_mll))" - ] - }, - { - "cell_type": "markdown", - "id": "45b25ada", - "metadata": {}, - "source": [ - "JIT-compiling expensive-to-compute functions such as the marginal log-likelihood is\n", - "advisable. This can be achieved by wrapping the function in `jax.jit()`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "74380d6c", - "metadata": {}, - "outputs": [], - "source": [ - "negative_mll = jit(negative_mll)" - ] - }, - { - "cell_type": "markdown", - "id": "90ee232c", - "metadata": {}, - "source": [ - "Since most optimisers (including here) minimise a given function, we have realised\n", - "the negative marginal log-likelihood and just-in-time (JIT) compiled this to\n", - "accelerate training." - ] - }, - { - "cell_type": "markdown", - "id": "d6916f72", - "metadata": {}, - "source": [ - "We can now train our model using a `jaxopt` solver. In this case we opt for the `OptaxSolver`,\n", - "which wraps an `optax` optimizer." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "201541e8", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b0f80729c9274838b15e85adbab6d435", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/10 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.plot(history, color=cols[1])\n", - "ax.set(xlabel=\"Training iteration\", ylabel=\"Negative marginal log likelihood\")" - ] - }, - { - "cell_type": "markdown", - "id": "a8221ff7", - "metadata": {}, - "source": [ - "## Prediction\n", - "\n", - "Equipped with the posterior and a set of optimised hyperparameter values, we are now\n", - "in a position to query our GP's predictive distribution at novel test inputs. To do\n", - "this, we use our defined `posterior` and `likelihood` at our test inputs to obtain\n", - "the predictive distribution as a `Distrax` multivariate Gaussian upon which `mean`\n", - "and `stddev` can be used to extract the predictive mean and standard deviatation." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "f6aeb70e", - "metadata": {}, - "outputs": [], - "source": [ - "latent_dist = opt_posterior.predict(xtest, train_data=D)\n", - "predictive_dist = opt_posterior.likelihood(latent_dist)\n", - "\n", - "predictive_mean = predictive_dist.mean()\n", - "predictive_std = predictive_dist.stddev()" - ] - }, - { - "cell_type": "markdown", - "id": "dc9eb8da", - "metadata": {}, - "source": [ - "With the predictions and their uncertainty acquired, we illustrate the GP's\n", - "performance at explaining the data $\\mathcal{D}$ and recovering the underlying\n", - "latent function of interest." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "58b81c27", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(7.5, 2.5))\n", - "ax.plot(x, y, \"x\", label=\"Observations\", color=cols[0], alpha=0.5)\n", - "ax.fill_between(\n", - " xtest.squeeze(),\n", - " predictive_mean - 2 * predictive_std,\n", - " predictive_mean + 2 * predictive_std,\n", - " alpha=0.2,\n", - " label=\"Two sigma\",\n", - " color=cols[1],\n", - ")\n", - "ax.plot(\n", - " xtest,\n", - " predictive_mean - 2 * predictive_std,\n", - " linestyle=\"--\",\n", - " linewidth=1,\n", - " color=cols[1],\n", - ")\n", - "ax.plot(\n", - " xtest,\n", - " predictive_mean + 2 * predictive_std,\n", - " linestyle=\"--\",\n", - " linewidth=1,\n", - " color=cols[1],\n", - ")\n", - "ax.plot(\n", - " xtest, ytest, label=\"Latent function\", color=cols[0], linestyle=\"--\", linewidth=2\n", - ")\n", - "ax.plot(xtest, predictive_mean, label=\"Predictive mean\", color=cols[1])\n", - "ax.legend(loc=\"center left\", bbox_to_anchor=(0.975, 0.5))" - ] - }, - { - "cell_type": "markdown", - "id": "19a304dd", - "metadata": {}, - "source": [ - "## System configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "2c3ddbf6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Author: Thomas Pinder & Daniel Dodd\n", - "\n", - "Last updated: Tue Sep 19 2023\n", - "\n", - "Python implementation: CPython\n", - "Python version : 3.10.0\n", - "IPython version : 8.12.2\n", - "\n", - "matplotlib: 3.7.1\n", - "jax : 0.4.9\n", - "jaxopt : 0.6\n", - "gpjax : 0.0.0\n", - "\n", - "Watermark: 2.3.1\n", - "\n" - ] - } - ], - "source": [ - "%reload_ext watermark\n", - "%watermark -n -u -v -iv -w -a 'Thomas Pinder & Daniel Dodd'" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "custom_cell_magics": "kql" - }, - "kernelspec": { - "display_name": "gpjax", - "language": "python", - "name": "gpjax" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/spatial.ipynb b/docs/examples/spatial.ipynb deleted file mode 100644 index a91bc9610..000000000 --- a/docs/examples/spatial.ipynb +++ /dev/null @@ -1,556 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e6c1802a", - "metadata": { - "lines_to_next_cell": 0 - }, - "source": [ - "# Pathwise Sampling for Spatial Modelling\n", - "In this notebook, we demonstrate an application of Gaussian Processes\n", - "to a spatial interpolation problem. We will show how\n", - "to efficiently sample from a GP posterior as shown in .\n", - "\n", - "## Data loading\n", - "We'll use open-source data from\n", - "[SwissMetNet](https://www.meteoswiss.admin.ch/services-and-publications/applications/measurement-values-and-measuring-networks.html#lang=en¶m=messnetz-automatisch),\n", - "the surface weather monitoring network of the Swiss national weather service,\n", - "and digital elevation model (DEM) data from Copernicus, accessible\n", - "[here](https://planetarycomputer.microsoft.com/dataset/cop-dem-glo-90)\n", - "via the Planetary Computer data catalog.\n", - "We will coarsen this data by a factor of 10 (going from 90m to 900m resolution), but feel free to change this.\n", - "\n", - "Our variable of interest is the maximum daily temperature, observed on the 4th of April 2023 at\n", - "150 weather stations, and we'll try to interpolate it on a spatial grid using geographical coordinates\n", - "(latitude and longitude) and elevation as input variables.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3f9bec24", - "metadata": {}, - "outputs": [ - { - "ename": "PydanticImportError", - "evalue": "`BaseSettings` has been moved to the `pydantic-settings` package. See https://docs.pydantic.dev/2.3/migration/#basesettings-has-moved-to-pydantic-settings for more details.\n\nFor further information visit https://errors.pydantic.dev/2.3/u/import-error", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mPydanticImportError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 22\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjaxopt\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mplanetary_computer\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpystac_client\u001b[39;00m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrioxarray\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mrio\u001b[39;00m\n", - "File \u001b[0;32m~/anaconda3/envs/gpjax/lib/python3.10/site-packages/planetary_computer/__init__.py:4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"Planetary Computer Python SDK\"\"\"\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# flake8:noqa\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mplanetary_computer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msas\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 5\u001b[0m sign,\n\u001b[1;32m 6\u001b[0m sign_inplace,\n\u001b[1;32m 7\u001b[0m sign_url,\n\u001b[1;32m 8\u001b[0m sign_item,\n\u001b[1;32m 9\u001b[0m sign_assets,\n\u001b[1;32m 10\u001b[0m sign_asset,\n\u001b[1;32m 11\u001b[0m sign_item_collection,\n\u001b[1;32m 12\u001b[0m )\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mplanetary_computer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msettings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m set_subscription_key\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mplanetary_computer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_adlfs\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_adlfs_filesystem, get_container_client\n", - "File \u001b[0;32m~/anaconda3/envs/gpjax/lib/python3.10/site-packages/planetary_computer/sas.py:20\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpystac_client\u001b[39;00m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01murllib3\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mretry\u001b[39;00m\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mplanetary_computer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msettings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Settings\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mplanetary_computer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 22\u001b[0m parse_blob_url,\n\u001b[1;32m 23\u001b[0m parse_adlfs_url,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 26\u001b[0m asset_xpr,\n\u001b[1;32m 27\u001b[0m )\n\u001b[1;32m 30\u001b[0m BLOB_STORAGE_DOMAIN \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.blob.core.windows.net\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", - "File \u001b[0;32m~/anaconda3/envs/gpjax/lib/python3.10/site-packages/planetary_computer/settings.py:11\u001b[0m\n\u001b[1;32m 6\u001b[0m SETTINGS_ENV_PREFIX \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPC_SDK_\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 8\u001b[0m DEFAULT_SAS_TOKEN_ENDPOINT \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://planetarycomputer.microsoft.com/api/sas/v1/token\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mSettings\u001b[39;00m(\u001b[43mpydantic\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBaseSettings\u001b[49m):\n\u001b[1;32m 12\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"PC SDK configuration settings\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \n\u001b[1;32m 14\u001b[0m \u001b[38;5;124;03m Settings defined here are attempted to be read in two ways, in this order:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;124;03m All settings are prefixed with `PC_SDK_`\u001b[39;00m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;66;03m# PC_SDK_SUBSCRIPTION_KEY: subscription key to send along with token\u001b[39;00m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;66;03m# requests. If present, allows less restricted rate limiting.\u001b[39;00m\n", - "File \u001b[0;32m~/anaconda3/envs/gpjax/lib/python3.10/site-packages/pydantic/__init__.py:210\u001b[0m, in \u001b[0;36m__getattr__\u001b[0;34m(attr_name)\u001b[0m\n\u001b[1;32m 208\u001b[0m dynamic_attr \u001b[38;5;241m=\u001b[39m _dynamic_imports\u001b[38;5;241m.\u001b[39mget(attr_name)\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dynamic_attr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 210\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_getattr_migration\u001b[49m\u001b[43m(\u001b[49m\u001b[43mattr_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mimportlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m import_module\n\u001b[1;32m 214\u001b[0m module \u001b[38;5;241m=\u001b[39m import_module(_dynamic_imports[attr_name], package\u001b[38;5;241m=\u001b[39m__package__)\n", - "File \u001b[0;32m~/anaconda3/envs/gpjax/lib/python3.10/site-packages/pydantic/_migration.py:289\u001b[0m, in \u001b[0;36mgetattr_migration..wrapper\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m import_string(REDIRECT_TO_V1[import_path])\n\u001b[1;32m 288\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m import_path \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpydantic:BaseSettings\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m--> 289\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m PydanticImportError(\n\u001b[1;32m 290\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m`BaseSettings` has been moved to the `pydantic-settings` package. \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 291\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSee https://docs.pydantic.dev/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mversion_short()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/migration/#basesettings-has-moved-to-pydantic-settings \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 292\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfor more details.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 293\u001b[0m )\n\u001b[1;32m 294\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m import_path \u001b[38;5;129;01min\u001b[39;00m REMOVED_IN_V2:\n\u001b[1;32m 295\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m PydanticImportError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m`\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mimport_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` has been removed in V2.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "\u001b[0;31mPydanticImportError\u001b[0m: `BaseSettings` has been moved to the `pydantic-settings` package. See https://docs.pydantic.dev/2.3/migration/#basesettings-has-moved-to-pydantic-settings for more details.\n\nFor further information visit https://errors.pydantic.dev/2.3/u/import-error" - ] - } - ], - "source": [ - "# Enable Float64 for more stable matrix inversions.\n", - "from jax.config import config\n", - "\n", - "config.update(\"jax_enable_x64\", True)\n", - "\n", - "from dataclasses import dataclass\n", - "\n", - "import fsspec\n", - "import geopandas as gpd\n", - "import jax\n", - "import jax.numpy as jnp\n", - "import jax.random as jr\n", - "from jaxtyping import (\n", - " Array,\n", - " Float,\n", - " install_import_hook,\n", - ")\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt\n", - "import jaxopt\n", - "import pandas as pd\n", - "import planetary_computer\n", - "import pystac_client\n", - "import rioxarray as rio\n", - "from rioxarray.merge import merge_arrays\n", - "import xarray as xr\n", - "\n", - "with install_import_hook(\"gpjax\", \"beartype.beartype\"):\n", - " import gpjax as gpx\n", - " from gpjax.base import param_field\n", - " from gpjax.dataset import Dataset\n", - "\n", - "\n", - "key = jr.PRNGKey(123)\n", - "plt.style.use(\n", - " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", - ")\n", - "cols = mpl.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n", - "\n", - "# Observed temperature data\n", - "try:\n", - " temperature = pd.read_csv(\"data/max_tempeature_switzerland.csv\")\n", - "except FileNotFoundError:\n", - " temperature = pd.read_csv(\"docs/examples/data/max_tempeature_switzerland.csv\")\n", - "\n", - "temperature = gpd.GeoDataFrame(\n", - " temperature,\n", - " geometry=gpd.points_from_xy(temperature.longitude, temperature.latitude),\n", - ").dropna(how=\"any\")\n", - "\n", - "# Country borders shapefile\n", - "path = \"simplecache::https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_countries.zip\"\n", - "with fsspec.open(path) as file:\n", - " ch_shp = gpd.read_file(file).query(\"ADMIN == 'Switzerland'\")\n", - "\n", - "\n", - "# Read DEM data and clip it to switzerland\n", - "catalog = pystac_client.Client.open(\n", - " \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n", - " modifier=planetary_computer.sign_inplace,\n", - ")\n", - "search = catalog.search(collections=[\"cop-dem-glo-90\"], bbox=[5.5, 45.5, 10.0, 48.5])\n", - "items = list(search.get_all_items())\n", - "tiles = [rio.open_rasterio(i.assets[\"data\"].href).squeeze().drop(\"band\") for i in items]\n", - "dem = merge_arrays(tiles).coarsen(x=10, y=10).mean().rio.clip(ch_shp[\"geometry\"])" - ] - }, - { - "cell_type": "markdown", - "id": "0901b58d", - "metadata": { - "lines_to_next_cell": 0 - }, - "source": [ - "Let us take a look at the data. The topography of Switzerland is quite complex, and there\n", - "are sometimes very large height differences over short distances. This measuring network is fairly dense,\n", - "and you may already notice that there's a dependency between maximum daily temperature and elevation." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ca052624", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'dem' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m5\u001b[39m), layout\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconstrained\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m \u001b[43mdem\u001b[49m\u001b[38;5;241m.\u001b[39mplot(\n\u001b[1;32m 3\u001b[0m cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mterrain\u001b[39m\u001b[38;5;124m\"\u001b[39m, cbar_kwargs\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maspect\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m50\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpad\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m0.02\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mElevation [m]\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[1;32m 4\u001b[0m )\n\u001b[1;32m 5\u001b[0m temperature\u001b[38;5;241m.\u001b[39mplot(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mt_max\u001b[39m\u001b[38;5;124m\"\u001b[39m, ax\u001b[38;5;241m=\u001b[39max, 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X44Ybbhj1ZgEAgHNH2bHy+OOPxxe+8IVYunRpfOxjH4t169bFBRdcEN/5zndOumZgYCA+//nPx8MPPxyXXnrpn7RhAADg3FBWrPT398f27dujubn5D09QWRnNzc3R2dl50nVf+9rXYvLkyXHbbbe9r+scO3Ysent7hz0AAIBzS1mxcuTIkRgYGIj6+vph4/X19dHV1TXimp/+9Kfx9NNPx/r169/3ddrb26Ourm7o0dDQUM42AQCAs8CY/jSwo0ePxqJFi2L9+vUxadKk971uxYoV0dPTM/Q4ePDgGO4SAADIaEI5kydNmhRVVVXR3d09bLy7uzumTJlywvyf//znsX///pg3b97Q2ODg4O8uPGFC7N27Ny677LIT1pVKpSiVSuVsDQAAOMuUdWeluro6Zs+eHR0dHUNjg4OD0dHREU1NTSfMv+KKK+LVV1+NXbt2DT0+85nPxE033RS7du3y8S4AAOCkyrqzEhHR2toaS5YsiTlz5sTcuXNjzZo10dfXF0uXLo2IiMWLF8f06dOjvb09ampq4sorrxy2/qKLLoqIOGEcAADg/ys7VhYsWBCHDx+OlStXRldXV8yaNSu2bNky9KX7AwcORGXlmH4VBgAAOAdUFEVRnO5N/DG9vb1RV1cXPT09UVtbe7q3AwAAvMtYvGd3CwQAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmNKlbWrl0bM2bMiJqammhsbIxt27addO769evjhhtuiIkTJ8bEiROjubn5PecDAABEjCJWNm7cGK2trdHW1hY7duyImTNnRktLSxw6dGjE+Vu3bo2FCxfGyy+/HJ2dndHQ0BA333xzvPnmm3/y5gEAgLNXRVEURTkLGhsb49prr40nnngiIiIGBwejoaEh7rrrrli+fPkfXT8wMBATJ06MJ554IhYvXvy+rtnb2xt1dXXR09MTtbW15WwXAAAYB2Pxnr2sOyv9/f2xffv2aG5u/sMTVFZGc3NzdHZ2vq/nePvtt+Odd96Jiy+++KRzjh07Fr29vcMeAADAuaWsWDly5EgMDAxEfX39sPH6+vro6up6X89x3333xbRp04YFz7u1t7dHXV3d0KOhoaGcbQIAAGeBcf1pYKtXr44NGzbE888/HzU1NSedt2LFiujp6Rl6HDx4cBx3CQAAZDChnMmTJk2Kqqqq6O7uHjbe3d0dU6ZMec+1jz76aKxevTp+/OMfx9VXX/2ec0ulUpRKpXK2BgAAnGXKurNSXV0ds2fPjo6OjqGxwcHB6OjoiKamppOu++Y3vxmPPPJIbNmyJebMmTP63QIAAOeMsu6sRES0trbGkiVLYs6cOTF37txYs2ZN9PX1xdKlSyMiYvHixTF9+vRob2+PiIh//Md/jJUrV8azzz4bM2bMGPpuywc+8IH4wAc+cApfCgAAcDYpO1YWLFgQhw8fjpUrV0ZXV1fMmjUrtmzZMvSl+wMHDkRl5R9u2HzrW9+K/v7++OxnPzvsedra2uKrX/3qn7Z7AADgrFX271k5HfyeFQAAyO20/54VAACA8SJWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApCRWAACAlMQKAACQklgBAABSEisAAEBKYgUAAEhJrAAAACmJFQAAICWxAgAApDSqWFm7dm3MmDEjampqorGxMbZt2/ae87///e/HFVdcETU1NXHVVVfF5s2bR7VZAADg3FF2rGzcuDFaW1ujra0tduzYETNnzoyWlpY4dOjQiPNfeeWVWLhwYdx2222xc+fOmD9/fsyfPz9ee+21P3nzAADA2auiKIqinAWNjY1x7bXXxhNPPBEREYODg9HQ0BB33XVXLF++/IT5CxYsiL6+vvjRj340NPZXf/VXMWvWrFi3bt37umZvb2/U1dVFT09P1NbWlrNdAABgHIzFe/YJ5Uzu7++P7du3x4oVK4bGKisro7m5OTo7O0dc09nZGa2trcPGWlpa4oUXXjjpdY4dOxbHjh0b+nNPT09E/O5fAAAAkM/v36uXeS/kPZUVK0eOHImBgYGor68fNl5fXx979uwZcU1XV9eI87u6uk56nfb29nj44YdPGG9oaChnuwAAwDj77//+76irqzslz1VWrIyXFStWDLsb89Zbb8UHP/jBOHDgwCl74dDb2xsNDQ1x8OBBHy/klHCmGAvOFWPBuWIs9PT0xCWXXBIXX3zxKXvOsmJl0qRJUVVVFd3d3cPGu7u7Y8qUKSOumTJlSlnzIyJKpVKUSqUTxuvq6vwHxSlXW1vrXHFKOVOMBeeKseBcMRYqK0/db0cp65mqq6tj9uzZ0dHRMTQ2ODgYHR0d0dTUNOKapqamYfMjIl566aWTzgcAAIgYxcfAWltbY8mSJTFnzpyYO3durFmzJvr6+mLp0qUREbF48eKYPn16tLe3R0TE3XffHTfeeGM89thjccstt8SGDRviZz/7WTz11FOn9pUAAABnlbJjZcGCBXH48OFYuXJldHV1xaxZs2LLli1DX6I/cODAsFs/1113XTz77LPx4IMPxv333x8f/vCH44UXXogrr7zyfV+zVCpFW1vbiB8Ng9FyrjjVnCnGgnPFWHCuGAtjca7K/j0rAAAA4+HUffsFAADgFBIrAABASmIFAABISawAAAApiRUAACClNLGydu3amDFjRtTU1ERjY2Ns27btPed///vfjyuuuCJqamriqquuis2bN4/TTjlTlHOm1q9fHzfccENMnDgxJk6cGM3NzX/0DHJuKvfvqt/bsGFDVFRUxPz588d2g5yRyj1Xb731VixbtiymTp0apVIpLr/8cv87yAnKPVdr1qyJj3zkI3H++edHQ0ND3HvvvfHb3/52nHZLdj/5yU9i3rx5MW3atKioqIgXXnjhj67ZunVrfOITn4hSqRQf+tCH4plnnin7uiliZePGjdHa2hptbW2xY8eOmDlzZrS0tMShQ4dGnP/KK6/EwoUL47bbboudO3fG/PnzY/78+fHaa6+N887JqtwztXXr1li4cGG8/PLL0dnZGQ0NDXHzzTfHm2++Oc47J7Nyz9Xv7d+/P7785S/HDTfcME475UxS7rnq7++PT33qU7F///547rnnYu/evbF+/fqYPn36OO+czMo9V88++2wsX7482traYvfu3fH000/Hxo0b4/777x/nnZNVX19fzJw5M9auXfu+5v/iF7+IW265JW666abYtWtX3HPPPXH77bfHiy++WN6FiwTmzp1bLFu2bOjPAwMDxbRp04r29vYR53/uc58rbrnllmFjjY2Nxd/93d+N6T45c5R7pt7t+PHjxYUXXlh897vfHastcgYazbk6fvx4cd111xXf/va3iyVLlhR/8zd/Mw475UxS7rn61re+VVx66aVFf3//eG2RM1C552rZsmXFX//1Xw8ba21tLa6//vox3Sdnpogonn/++fec85WvfKX4+Mc/PmxswYIFRUtLS1nXOu13Vvr7+2P79u3R3Nw8NFZZWRnNzc3R2dk54prOzs5h8yMiWlpaTjqfc8toztS7vf322/HOO+/ExRdfPFbb5Awz2nP1ta99LSZPnhy33XbbeGyTM8xoztUPf/jDaGpqimXLlkV9fX1ceeWVsWrVqhgYGBivbZPcaM7VddddF9u3bx/6qNi+ffti8+bN8elPf3pc9szZ51S9X59wKjc1GkeOHImBgYGor68fNl5fXx979uwZcU1XV9eI87u6usZsn5w5RnOm3u2+++6LadOmnfAfGeeu0Zyrn/70p/H000/Hrl27xmGHnIlGc6727dsX//7v/x6f//znY/PmzfHGG2/El770pXjnnXeira1tPLZNcqM5V7feemscOXIkPvnJT0ZRFHH8+PG48847fQyMUTvZ+/Xe3t74zW9+E+eff/77ep7TfmcFslm9enVs2LAhnn/++aipqTnd2+EMdfTo0Vi0aFGsX78+Jk2adLq3w1lkcHAwJk+eHE899VTMnj07FixYEA888ECsW7fudG+NM9jWrVtj1apV8eSTT8aOHTviBz/4QWzatCkeeeSR0701znGn/c7KpEmToqqqKrq7u4eNd3d3x5QpU0ZcM2XKlLLmc24ZzZn6vUcffTRWr14dP/7xj+Pqq68ey21yhin3XP385z+P/fv3x7x584bGBgcHIyJiwoQJsXfv3rjsssvGdtOkN5q/r6ZOnRrnnXdeVFVVDY199KMfja6urujv74/q6uox3TP5jeZcPfTQQ7Fo0aK4/fbbIyLiqquuir6+vrjjjjvigQceiMpK//825TnZ+/Xa2tr3fVclIsGdlerq6pg9e3Z0dHQMjQ0ODkZHR0c0NTWNuKapqWnY/IiIl1566aTzObeM5kxFRHzzm9+MRx55JLZs2RJz5swZj61yBin3XF1xxRXx6quvxq5du4Yen/nMZ4Z+KkpDQ8N4bp+kRvP31fXXXx9vvPHGUPxGRLz++usxdepUoUJEjO5cvf322ycEye+D+Hffp4bynLL36+V9939sbNiwoSiVSsUzzzxT/Nd//Vdxxx13FBdddFHR1dVVFEVRLFq0qFi+fPnQ/P/4j/8oJkyYUDz66KPF7t27i7a2tuK8884rXn311dP1Ekim3DO1evXqorq6unjuueeKX/3qV0OPo0ePnq6XQELlnqt389PAGEm55+rAgQPFhRdeWPz93/99sXfv3uJHP/pRMXny5OLrX//66XoJJFTuuWpraysuvPDC4l//9V+Lffv2Ff/2b/9WXHbZZcXnPve50/USSObo0aPFzp07i507dxYRUTz++OPFzp07i1/+8pdFURTF8uXLi0WLFg3N37dvX3HBBRcU//AP/1Ds3r27WLt2bVFVVVVs2bKlrOumiJWiKIp//ud/Li655JKiurq6mDt3bvGf//mfQ//sxhtvLJYsWTJs/ve+973i8ssvL6qrq4uPf/zjxaZNm8Z5x2RXzpn64Ac/WETECY+2trbx3ziplft31f8nVjiZcs/VK6+8UjQ2NhalUqm49NJLi2984xvF8ePHx3nXZFfOuXrnnXeKr371q8Vll11W1NTUFA0NDcWXvvSl4n/+53/Gf+Ok9PLLL4/4Xun352jJkiXFjTfeeMKaWbNmFdXV1cWll15a/Mu//EvZ160oCvf2AACAfE77d1YAAABGIlYAAICUxAoAAJCSWAEAAFISKwAAQEpiBQAASEmsAAAAKYkVAAAgJbECAACkJFYAAICUxAoAAJDS/wEH8MfVmLTOgAAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(8, 5), layout=\"constrained\")\n", - "dem.plot(\n", - " cmap=\"terrain\", cbar_kwargs={\"aspect\": 50, \"pad\": 0.02, \"label\": \"Elevation [m]\"}\n", - ")\n", - "temperature.plot(\"t_max\", ax=ax, cmap=\"RdBu_r\", vmin=-15, vmax=15, edgecolor=\"k\", s=50)\n", - "ax.set(title=\"Switzerland's topography and SwissMetNet stations\", aspect=\"auto\")\n", - "cb = fig.colorbar(ax.collections[-1], aspect=50, pad=0.02)\n", - "cb.set_label(\"Max. daily temperature [°C]\", labelpad=-2)" - ] - }, - { - "cell_type": "markdown", - "id": "62b6fd46", - "metadata": { - "lines_to_next_cell": 0 - }, - "source": [ - "As always, we store our training data in a `Dataset` object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f1c19a3", - "metadata": {}, - "outputs": [], - "source": [ - "x = temperature[[\"latitude\", \"longitude\", \"elevation\"]].values\n", - "y = temperature[[\"t_max\"]].values\n", - "D = Dataset(\n", - " X=jnp.array(x),\n", - " y=jnp.array(y),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "71d15bb8", - "metadata": {}, - "source": [ - "## ARD Kernel\n", - "As temperature decreases with height\n", - "(at a rate of approximately -6.5 °C/km in average conditions), we can expect that using the geographical distance\n", - "alone isn't enough to to a decent job at interpolating this data. Therefore, we can also use elevation and optimize\n", - "the parameters of our kernel such that more relevance should be given to elevation. This is possible by using a\n", - "kernel that has one length-scale parameter per input dimension: an automatic relevance determination (ARD) kernel.\n", - "See our [kernel notebook](https://docs.jaxgaussianprocesses.com/examples/constructing_new_kernels/) for more an introduction to\n", - "kernels in GPJax." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "03a6b673", - "metadata": {}, - "outputs": [], - "source": [ - "kernel = gpx.kernels.RBF(\n", - " active_dims=[0, 1, 2],\n", - " lengthscale=jnp.array([0.1, 0.1, 100.0]),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "6312fc7d", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "## Mean function\n", - "As stated before, we already know that temperature strongly depends on elevation.\n", - "So why not use it for our mean function? GPJax lets you define custom mean functions;\n", - "simply subclass `AbstractMeanFunction`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "de2cf11b", - "metadata": {}, - "outputs": [], - "source": [ - "@dataclass\n", - "class MeanFunction(gpx.gps.AbstractMeanFunction):\n", - " w: Float[Array, \"1\"] = param_field(jnp.array([0.0]))\n", - " b: Float[Array, \"1\"] = param_field(jnp.array([0.0]))\n", - "\n", - " def __call__(self, x: Float[Array, \"N D\"]) -> Float[Array, \"N 1\"]:\n", - " elevation = x[:, 2:3]\n", - " out = elevation * self.w + self.b\n", - " return out" - ] - }, - { - "cell_type": "markdown", - "id": "37f3dcad", - "metadata": {}, - "source": [ - "Now we can define our prior. We'll also choose a Gaussian likelihood." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "96e12772", - "metadata": {}, - "outputs": [], - "source": [ - "mean_function = MeanFunction()\n", - "prior = gpx.Prior(kernel=kernel, mean_function=mean_function)\n", - "likelihood = gpx.Gaussian(D.n)" - ] - }, - { - "cell_type": "markdown", - "id": "f363a6b8", - "metadata": { - "lines_to_next_cell": 0 - }, - "source": [ - "Finally, we construct the posterior." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5aa15281", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "posterior = prior * likelihood" - ] - }, - { - "cell_type": "markdown", - "id": "c2628adc", - "metadata": { - "lines_to_next_cell": 0 - }, - "source": [ - "## Model fitting\n", - "We proceed to train our model. Because we used a Gaussian likelihood, the resulting posterior is\n", - "a `ConjugatePosterior`, which allows us to optimize the analytically expressed marginal loglikelihood.\n", - "\n", - "As always, we can jit-compile the objective function to speed things up." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5cde8ae2", - "metadata": {}, - "outputs": [], - "source": [ - "negative_mll = jax.jit(gpx.objectives.ConjugateMLL(negative=True))\n", - "negative_mll(posterior, train_data=D)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78d204de", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [ - "#optim = ox.chain(ox.adam(learning_rate=0.1), ox.clip(1.0))\n", - "posterior, history = gpx.fit(\n", - " model=posterior,\n", - " train_data=D,\n", - " solver=jaxopt.LBFGS(gpx.ConjugateMLL(negative=True), maxiter=10),\n", - " safe=True,\n", - " key=key,\n", - ")\n", - "posterior: gpx.gps.ConjugatePosterior" - ] - }, - { - "cell_type": "markdown", - "id": "13037426", - "metadata": {}, - "source": [ - "## Sampling on a grid\n", - "Now comes the cool part. In a standard GP implementation, for n test points, we have a $\\mathcal{O}(n^2)$\n", - "computational complexity and $\\mathcal{O}(n^2)$ memory requirement. We want to make predictions on a total\n", - "of roughly 70'000 pixels, and that would require us to compute a covariance matrix of `70000 ** 2 = 4900000000` elements.\n", - "If these are `float64`s, as it is often the case in GPJax, it would be equivalent to more than 36 Gigabytes of memory. And\n", - "that's for a fairly coarse and tiny grid. If we were to make predictions on a 1000x1000 grid, the total memory required\n", - "would be 8 _Terabytes_ of memory, which is intractable.\n", - "Fortunately, the pathwise conditioning method allows us to sample from our posterior in linear complexity,\n", - "$\\mathcal{O}(n)$, with the number of pixels.\n", - "\n", - "GPJax provides the `sample_approx` method to generate random conditioned samples from our posterior." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d35461f1", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [ - "# select the target pixels and exclude nans\n", - "xtest = dem.drop(\"spatial_ref\").stack(p=[\"y\", \"x\"]).to_dataframe(name=\"dem\")\n", - "mask = jnp.any(jnp.isnan(xtest.values), axis=-1)\n", - "\n", - "# generate 50 samples\n", - "ytest = posterior.sample_approx(50, D, key, num_features=200)(\n", - " jnp.array(xtest.values[~mask])\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "22184195", - "metadata": {}, - "source": [ - "Let's take a look at the results. We start with the mean and standard deviation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ae187049", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [ - "predtest = xr.zeros_like(dem.stack(p=[\"y\", \"x\"])) * jnp.nan\n", - "predtest[~mask] = ytest.mean(axis=-1)\n", - "predtest = predtest.unstack()\n", - "\n", - "predtest.plot(\n", - " vmin=-15.0,\n", - " vmax=15.0,\n", - " cmap=\"RdBu_r\",\n", - " cbar_kwargs={\"aspect\": 50, \"pad\": 0.02, \"label\": \"Max. daily temperature [°C]\"},\n", - ")\n", - "plt.gca().set_title(\"Interpolated maximum daily temperature\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "82e07695", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [ - "predtest = xr.zeros_like(dem.stack(p=[\"y\", \"x\"])) * jnp.nan\n", - "predtest[~mask] = ytest.std(axis=-1)\n", - "predtest = predtest.unstack()\n", - "\n", - "# plot\n", - "predtest.plot(\n", - " cbar_kwargs={\"aspect\": 50, \"pad\": 0.02, \"label\": \"Standard deviation [°C]\"},\n", - ")\n", - "plt.gca().set_title(\"Standard deviation\")" - ] - }, - { - "cell_type": "markdown", - "id": "de03364b", - "metadata": { - "lines_to_next_cell": 0 - }, - "source": [ - "And now some individual realizations of our GP posterior." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "76801bcf", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [ - "predtest = (\n", - " xr.zeros_like(dem.stack(p=[\"y\", \"x\"]))\n", - " .expand_dims(realization=range(9))\n", - " .transpose(\"p\", \"realization\")\n", - " .copy()\n", - ")\n", - "predtest[~mask] = ytest[:, :9]\n", - "predtest = predtest.unstack()\n", - "predtest.plot(\n", - " col=\"realization\",\n", - " col_wrap=3,\n", - " cbar_kwargs={\"aspect\": 50, \"pad\": 0.02, \"label\": \"Max. daily temperature [°C]\"},\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "4be50a23", - "metadata": {}, - "source": [ - "Remember when we said that on average the temperature decreases with height at a rate\n", - "of approximately -6.5°C/km? That's -0.0065°C/m. The `w` parameter of our mean function\n", - "is very close: we have learned the environmental lapse rate!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c21edf3a", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [ - "print(posterior.prior.mean_function)" - ] - }, - { - "cell_type": "markdown", - "id": "8f9b683e", - "metadata": {}, - "source": [ - "That's it! We've successfully interpolated an observed meteorological parameter on a grid.\n", - "We have used several components of GPJax and adapted them to our needs: a custom mean function\n", - "that modelled the average temperature lapse rate; an ARD kernel that learned to give more relevance\n", - "to elevation rather than horizontal distance; an efficient sampling technique to produce\n", - "probabilistic realizations of our posterior on a large number of test points, which is important for\n", - "many spatiotemporal modelling applications.\n", - "If you're interested in a more elaborate work on temperature interpolation for the same domain used here, refer\n", - "to [Frei 2014](https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.3786)." - ] - }, - { - "cell_type": "markdown", - "id": "382435a6", - "metadata": {}, - "source": [ - "## System configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "feb5e52e", - "metadata": {}, - "outputs": [], - "source": [ - "%reload_ext watermark\n", - "%watermark -n -u -v -iv -w -a 'Francesco Zanetta'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ada3aeaa", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "custom_cell_magics": "kql", - "encoding": "# -*- coding: utf-8 -*-" - }, - "kernelspec": { - "display_name": "gpjax", - "language": "python", - "name": "gpjax" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/yacht.ipynb b/docs/examples/yacht.ipynb deleted file mode 100644 index 88481a02e..000000000 --- a/docs/examples/yacht.ipynb +++ /dev/null @@ -1,493 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "1b755c49", - "metadata": {}, - "source": [ - "# UCI Data Benchmarking\n", - "\n", - "In this notebook, we will show how to apply GPJax on a benchmark UCI regression\n", - "problem. These kind of tasks are often used in the research community to benchmark\n", - "and assess new techniques against those already in the literature. Much of the code\n", - "contained in this notebook can be adapted to applied problems concerning datasets\n", - "other than the one presented here." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c1c0e13b", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n" - ] - } - ], - "source": [ - "# Enable Float64 for more stable matrix inversions.\n", - "from jax.config import config\n", - "\n", - "config.update(\"jax_enable_x64\", True)\n", - "\n", - "from jax import jit\n", - "import jax.random as jr\n", - "from jaxtyping import install_import_hook\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import jaxopt\n", - "import pandas as pd\n", - "from sklearn.metrics import (\n", - " mean_squared_error,\n", - " r2_score,\n", - ")\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import StandardScaler\n", - "\n", - "with install_import_hook(\"gpjax\", \"beartype.beartype\"):\n", - " import gpjax as gpx\n", - "\n", - "# Enable Float64 for more stable matrix inversions.\n", - "key = jr.PRNGKey(123)\n", - "plt.style.use(\n", - " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", - ")\n", - "cols = mpl.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]" - ] - }, - { - "cell_type": "markdown", - "id": "1a2e172e", - "metadata": {}, - "source": [ - "## Data Loading\n", - "\n", - "We'll be using the\n", - "[Yacht](https://archive.ics.uci.edu/ml/datasets/yacht+hydrodynamics) dataset from\n", - "the UCI machine learning data repository. Each observation describes the\n", - "hydrodynamic performance of a yacht through its resistance. The dataset contains 6\n", - "covariates and a single positive, real valued response variable. There are 308\n", - "observations in the dataset, so we can comfortably use a conjugate regression\n", - "Gaussian process here (for more more details, checkout the\n", - "[Regression notebook](https://docs.jaxgaussianprocesses.com/examples/regression/))." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "2b347a6d", - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " yacht = pd.read_fwf(\"data/yacht_hydrodynamics.data\", header=None).values[:-1, :]\n", - "except FileNotFoundError:\n", - " yacht = pd.read_fwf(\n", - " \"docs/examples/data/yacht_hydrodynamics.data\", header=None\n", - " ).values[:-1, :]\n", - "\n", - "X = yacht[:, :-1]\n", - "y = yacht[:, -1].reshape(-1, 1)" - ] - }, - { - "cell_type": "markdown", - "id": "24f5f1e7", - "metadata": {}, - "source": [ - "## Preprocessing\n", - "\n", - "With a dataset loaded, we'll now preprocess it such that it is more amenable to\n", - "modelling with a Gaussian process.\n", - "\n", - "### Data Partitioning\n", - "\n", - "We'll first partition our data into a _training_ and _testing_ split. We'll fit our\n", - "Gaussian process to the training data and evaluate its performance on the test data.\n", - "This allows us to investigate how effectively our Gaussian process generalises to\n", - "out-of-sample datapoints and ensure that we are not overfitting. We'll hold 30% of\n", - "our data back for testing purposes." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d61edd45", - "metadata": {}, - "outputs": [], - "source": [ - "Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.3, random_state=42)" - ] - }, - { - "cell_type": "markdown", - "id": "56225658", - "metadata": {}, - "source": [ - "### Response Variable\n", - "\n", - "We'll now process our response variable $\\mathbf{y}$. As the below plots show, the\n", - "data has a very long tail and is certainly not Gaussian. However, we would like to\n", - "model a Gaussian response variable so that we can adopt a Gaussian likelihood\n", - "function and leverage the model's conjugacy. To achieve this, we'll first log-scale\n", - "the data, to bring the long right tail in closer to the data's mean. We'll then\n", - "standardise the data such that is distributed according to a unit normal\n", - "distribution. Both of these transformations are invertible through the log-normal\n", - "expectation and variance formulae and the the inverse standardisation identity,\n", - "should we ever need our model's predictions to be back on the scale of the\n", - "original dataset.\n", - "\n", - "For transforming both the input and response variable, all transformations will be\n", - "done with respect to the training data where relevant." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "12e3e1e7", - "metadata": {}, - "outputs": [], - "source": [ - "log_ytr = np.log(ytr)\n", - "log_yte = np.log(yte)\n", - "\n", - "y_scaler = StandardScaler().fit(log_ytr)\n", - "scaled_ytr = y_scaler.transform(log_ytr)\n", - "scaled_yte = y_scaler.transform(log_yte)" - ] - }, - { - "cell_type": "markdown", - "id": "494d98e5", - "metadata": {}, - "source": [ - "We can see the effect of these transformations in the below three panels." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "6f2be151", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'scaled log(y)')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(ncols=3, figsize=(9, 2.5))\n", - "ax[0].hist(ytr, bins=30, color=cols[1])\n", - "ax[0].set_title(\"y\")\n", - "ax[1].hist(log_ytr, bins=30, color=cols[1])\n", - "ax[1].set_title(\"log(y)\")\n", - "ax[2].hist(scaled_ytr, bins=30, color=cols[1])\n", - "ax[2].set_title(\"scaled log(y)\")" - ] - }, - { - "cell_type": "markdown", - "id": "d195ed73", - "metadata": {}, - "source": [ - "### Input Variable\n", - "\n", - "We'll now transform our input variable $\\mathbf{X}$ to be distributed according to a\n", - "unit Gaussian." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d04f5fd5", - "metadata": {}, - "outputs": [], - "source": [ - "x_scaler = StandardScaler().fit(Xtr)\n", - "scaled_Xtr = x_scaler.transform(Xtr)\n", - "scaled_Xte = x_scaler.transform(Xte)" - ] - }, - { - "cell_type": "markdown", - "id": "b0c00ddb", - "metadata": {}, - "source": [ - "## Model fitting\n", - "\n", - "With data now loaded and preprocessed, we'll proceed to defining a Gaussian process\n", - "model and optimising its parameters. This notebook purposefully does not go into\n", - "great detail on this process, so please see notebooks such as the\n", - "[Regression notebook](https://docs.jaxgaussianprocesses.com/examples/regression/)\n", - "and\n", - "[Classification notebook](https://docs.jaxgaussianprocesses.com/examples/classification)\n", - "for further information.\n", - "\n", - "### Model specification\n", - "\n", - "We'll use a radial basis function kernel to parameterise the Gaussian process in this\n", - "notebook. As we have 5 covariates, we'll assign each covariate its own lengthscale\n", - "parameter. This form of kernel is commonly known as an automatic relevance\n", - "determination (ARD) kernel.\n", - "\n", - "In practice, the exact form of kernel used should be selected such that it\n", - "represents your understanding of the data. For example, if you were to model\n", - "temperature; a process that we know to be periodic, then you would likely wish to\n", - "select a periodic kernel. Having _Gaussian-ised_ our data somewhat, we'll also adopt\n", - "a Gaussian likelihood function." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "1bf41f44", - "metadata": {}, - "outputs": [], - "source": [ - "n_train, n_covariates = scaled_Xtr.shape\n", - "kernel = gpx.RBF(\n", - " active_dims=list(range(n_covariates)), lengthscale=np.ones((n_covariates,))\n", - ")\n", - "meanf = gpx.mean_functions.Zero()\n", - "prior = gpx.Prior(mean_function=meanf, kernel=kernel)\n", - "\n", - "likelihood = gpx.Gaussian(num_datapoints=n_train)\n", - "\n", - "posterior = prior * likelihood" - ] - }, - { - "cell_type": "markdown", - "id": "0a710dd3", - "metadata": {}, - "source": [ - "### Model Optimisation\n", - "\n", - "With a model now defined, we can proceed to optimise the hyperparameters of our\n", - "model using one of `jaxopt`'s solvers. In this case we use a solver that wraps an\n", - "`optax` optimizer." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a1686daa", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "cf6a96c19da049978ed4d05cf79e569c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/20 [00:00 Date: Tue, 19 Sep 2023 21:49:54 +0100 Subject: [PATCH 16/23] Fix. --- docs/examples/decision_making.py | 14 +++++++++----- gpjax/decision_making/posterior_handler.py | 9 ++++++++- 2 files changed, 17 insertions(+), 6 deletions(-) diff --git a/docs/examples/decision_making.py b/docs/examples/decision_making.py index 28d441cbd..a7d3bed75 100644 --- a/docs/examples/decision_making.py +++ b/docs/examples/decision_making.py @@ -23,7 +23,7 @@ config.update("jax_enable_x64", True) - +import jaxopt import jax.numpy as jnp import jax.random as jr import matplotlib as mpl @@ -169,13 +169,16 @@ def forrester(x: Float[Array, "N 1"]) -> Float[Array, "N 1"]: # `PosteriorHandler` as demonstrated below: # %% + +solver = jaxopt.OptaxSolver( + gpx.ConjugateMLL(negative=True), opt=ox.adam(learning_rate=0.01), maxiter=5000 +) posterior_handler = PosteriorHandler( - prior, + prior=prior, likelihood_builder=likelihood_builder, - optimization_objective=gpx.ConjugateMLL(negative=True), - optimizer=ox.adam(learning_rate=0.01), - num_optimization_iters=1000, + solver=solver, ) + posterior_handlers = {OBJECTIVE: posterior_handler} # %% [markdown] @@ -274,6 +277,7 @@ def plot_bo_iteration( ): posterior = dm.posteriors[OBJECTIVE] dataset = dm.datasets[OBJECTIVE] + plt_x = jnp.linspace(0, 1, 1000).reshape(-1, 1) forrester_y = forrester(plt_x.squeeze(axis=-1)) utility_fn = dm.current_utility_functions[0] diff --git a/gpjax/decision_making/posterior_handler.py b/gpjax/decision_making/posterior_handler.py index f833e6ca8..b4f251afe 100644 --- a/gpjax/decision_making/posterior_handler.py +++ b/gpjax/decision_making/posterior_handler.py @@ -18,6 +18,7 @@ Callable, Optional, ) +import jaxopt from jaxopt.base import IterativeSolver import gpjax as gpx @@ -134,10 +135,16 @@ def _optimize_posterior( Returns: Optimized posterior. """ + + # TODO: Clean this one up! + # We create a new solver state -> since the dataset (and therefore loss function) has changed! + old = self.solver + solver = jaxopt.OptaxSolver(fun=old.fun, opt=old.opt, maxiter=old.maxiter) + opt_posterior, _ = gpx.fit( model=posterior, train_data=dataset, - solver=self.solver, + solver=solver, safe=True, key=key, verbose=False, From a690df018681d5d92c1d916741cf78b4f8668c71 Mon Sep 17 00:00:00 2001 From: Daniel Dodd Date: Tue, 19 Sep 2023 21:57:36 +0100 Subject: [PATCH 17/23] Remove jit within `fit`. --- gpjax/fit.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/gpjax/fit.py b/gpjax/fit.py index 32fcdaec3..fa3167aac 100644 --- a/gpjax/fit.py +++ b/gpjax/fit.py @@ -135,7 +135,6 @@ def evaluate(self, model: LinearModel, train_data: gpx.Dataset) -> float: model, get_batch(train_data, batch_size, key) if batch_size != -1 else train_data, ) - jitted_update = jax.jit(solver.update) # Mini-batch random keys to scan over. iter_keys = jr.split(key, solver.maxiter) @@ -149,7 +148,7 @@ def step(carry, key): else: batch = train_data - model, state = jitted_update(model, state, batch) + model, state = solver.update(model, state, batch) carry = model, state return carry, state.value From 62c889fba440e21308fce0aa43298c263a3417c2 Mon Sep 17 00:00:00 2001 From: hmoss <32096840+henrymoss@users.noreply.github.com> Date: Fri, 22 Sep 2023 15:07:34 +0100 Subject: [PATCH 18/23] all done --- docs/examples/barycentres.py | 5 +- docs/examples/bayesian_optimisation.py | 5 +- docs/examples/constructing_new_kernels.py | 5 +- docs/examples/decision_making.py | 6 +- docs/examples/graph_kernels.py | 7 +- docs/examples/intro_to_kernels.ipynb | 1006 +++++++++++++++++ docs/examples/oceanmodelling.py | 15 +- docs/examples/regression.py | 18 +- docs/examples/yacht.py | 7 +- gpjax/decision_making/posterior_handler.py | 27 +- gpjax/fit.py | 86 +- .../test_posterior_handler.py | 20 +- tests/test_fit.py | 8 +- 13 files changed, 1093 insertions(+), 122 deletions(-) create mode 100644 docs/examples/intro_to_kernels.ipynb diff --git a/docs/examples/barycentres.py b/docs/examples/barycentres.py index 8a3a990a4..a8f2f6821 100644 --- a/docs/examples/barycentres.py +++ b/docs/examples/barycentres.py @@ -26,7 +26,6 @@ import jax.scipy.linalg as jsl from jaxtyping import install_import_hook import matplotlib.pyplot as plt -import optax as ox import jaxopt import tensorflow_probability.substrates.jax.distributions as tfd @@ -141,9 +140,7 @@ def fit_gp(x: jax.Array, y: jax.Array) -> tfd.MultivariateNormalFullCovariance: opt_posterior, _ = gpx.fit( model=posterior, train_data=D, - solver=jaxopt.OptaxSolver( - gpx.ConjugateMLL(negative=True), opt=ox.adam(0.01), maxiter=500 - ), + solver=jaxopt.ScipyMinimize(fun=gpx.ConjugateMLL(negative=True)), key=key, ) latent_dist = opt_posterior.predict(xtest, train_data=D) diff --git a/docs/examples/bayesian_optimisation.py b/docs/examples/bayesian_optimisation.py index 0425fc305..737732f54 100644 --- a/docs/examples/bayesian_optimisation.py +++ b/docs/examples/bayesian_optimisation.py @@ -19,7 +19,6 @@ import matplotlib as mpl import matplotlib.pyplot as plt from matplotlib import cm -import optax as ox import jaxopt import tensorflow_probability.substrates.jax as tfp from typing import List, Tuple @@ -218,9 +217,7 @@ def return_optimised_posterior( opt_posterior, history = gpx.fit( model=posterior, train_data=D, - solver=jaxopt.OptaxSolver( - gpx.ConjugateMLL(negative=True), opt=ox.adam(0.01), maxiter=1000 - ), + solver=jaxopt.ScipyMinimize(fun=gpx.ConjugateMLL(negative=True)), safe=True, key=key, verbose=False, diff --git a/docs/examples/constructing_new_kernels.py b/docs/examples/constructing_new_kernels.py index c10561d0f..775da87a6 100644 --- a/docs/examples/constructing_new_kernels.py +++ b/docs/examples/constructing_new_kernels.py @@ -40,7 +40,6 @@ ) import matplotlib.pyplot as plt import numpy as np -import optax as ox import jaxopt from simple_pytree import static_field import tensorflow_probability.substrates.jax as tfp @@ -272,9 +271,7 @@ def __call__( opt_posterior, history = gpx.fit( model=circular_posterior, train_data=D, - solver=jaxopt.OptaxSolver( - gpx.ConjugateMLL(negative=True), opt=ox.adamw(0.05), maxiter=500 - ), + solver=jaxopt.ScipyMinimize(fun=gpx.ConjugateMLL(negative=True)), key=key, ) diff --git a/docs/examples/decision_making.py b/docs/examples/decision_making.py index a7d3bed75..f42a70385 100644 --- a/docs/examples/decision_making.py +++ b/docs/examples/decision_making.py @@ -28,7 +28,7 @@ import jax.random as jr import matplotlib as mpl import matplotlib.pyplot as plt -import optax as ox + import gpjax as gpx from gpjax.decision_making.utility_functions import ( @@ -170,9 +170,7 @@ def forrester(x: Float[Array, "N 1"]) -> Float[Array, "N 1"]: # %% -solver = jaxopt.OptaxSolver( - gpx.ConjugateMLL(negative=True), opt=ox.adam(learning_rate=0.01), maxiter=5000 -) +solver = jaxopt.ScipyMinimize(fun=gpx.ConjugateMLL(negative=True)) posterior_handler = PosteriorHandler( prior=prior, likelihood_builder=likelihood_builder, diff --git a/docs/examples/graph_kernels.py b/docs/examples/graph_kernels.py index 560c45390..99d01f5cf 100644 --- a/docs/examples/graph_kernels.py +++ b/docs/examples/graph_kernels.py @@ -22,7 +22,6 @@ import matplotlib as mpl import matplotlib.pyplot as plt import networkx as nx -import optax as ox import jaxopt with install_import_hook("gpjax", "beartype.beartype"): @@ -133,7 +132,7 @@ # For this reason, we simply perform gradient descent on the GP's marginal # log-likelihood term as in the # [regression notebook](https://docs.jaxgaussianprocesses.com/examples/regression/). -# We do this using the OptaxSolver provided by `jaxopt`, instantiated with the Adam optimiser. +# We do this using the the LBFGS implementation of `scipy` as provided by `jaxopt`. # %% likelihood = gpx.Gaussian(num_datapoints=D.n) @@ -157,9 +156,7 @@ opt_posterior, training_history = gpx.fit( model=posterior, train_data=D, - solver=jaxopt.OptaxSolver( - gpx.ConjugateMLL(negative=True), opt=ox.adamw(0.01), maxiter=1000 - ), + solver=jaxopt.ScipyMinimize(fun=gpx.ConjugateMLL(negative=True)), key=key, ) diff --git a/docs/examples/intro_to_kernels.ipynb b/docs/examples/intro_to_kernels.ipynb new file mode 100644 index 000000000..06d3e8c82 --- /dev/null +++ b/docs/examples/intro_to_kernels.ipynb @@ -0,0 +1,1006 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ab1e3df7", + "metadata": {}, + "source": [ + "# Introduction to Kernels" + ] + }, + { + "cell_type": "markdown", + "id": "bb506a1f", + "metadata": {}, + "source": [ + "In this guide we provide an introduction to kernels, and the role they play in Gaussian process models." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13431cfb", + "metadata": {}, + "outputs": [], + "source": [ + "# Enable Float64 for more stable matrix inversions.\n", + "from jax.config import config\n", + "\n", + "config.update(\"jax_enable_x64\", True)\n", + "\n", + "from jax import jit\n", + "import jax.numpy as jnp\n", + "import jax.random as jr\n", + "from jaxtyping import install_import_hook, Float\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import jaxopt\n", + "import pandas as pd\n", + "from docs.examples.utils import clean_legend\n", + "\n", + "with install_import_hook(\"gpjax\", \"beartype.beartype\"):\n", + " import gpjax as gpx\n", + "from gpjax.typing import Array\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "key = jr.PRNGKey(42)\n", + "plt.style.use(\n", + " \"https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/examples/gpjax.mplstyle\"\n", + ")\n", + "cols = mpl.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]" + ] + }, + { + "cell_type": "markdown", + "id": "e136e55d", + "metadata": {}, + "source": [ + "Using Gaussian Processes (GPs) to model functions can offer several advantages over alternative methods, such as deep neural networks. One key advantage is their rich quantification of uncertainty; not only do they provide *point estimates* for the values taken by a function throughout its domain, but they provide a full predictive posterior *distribution* over the range of values the function may take. This rich quantification of uncertainty is useful in many applications, such as Bayesian optimisation, which relies on being able to make *uncertainty-aware* decisions.\n", + "\n", + "However, another advantage of GPs is the ability for one to place *priors* on the functions being modelled. For instance, one may know that the underlying function being modelled observes certain characteristics, such as being *periodic* or having a certain level of *smoothness*. The *kernel*, or *covariance function*, is the primary means through which one is able to encode such prior knowledge about the function being modelled. This enables one to equip the GP with inductive biases which enable it to learn from data more efficiently, whilst generalising to unseen data more effectively.\n", + "\n", + "In this notebook we'll develop some intuition for what kinds of priors are encoded through the use of different kernels, and how this can be useful when modelling different types of functions." + ] + }, + { + "cell_type": "markdown", + "id": "abd9436b", + "metadata": {}, + "source": [ + "## What is a Kernel?\n", + "\n", + "Intuitively, for a function $f$, the kernel defines the notion of *similarity* between\n", + "the value of the function at two points, $f(\\mathbf{x})$ and $f(\\mathbf{x}')$, and\n", + "will be denoted as $k(\\mathbf{x}, \\mathbf{x}')$:\n", + "\n", + "$$\\begin{aligned} k(\\mathbf{x}, \\mathbf{x}') &= \\text{Cov}[f(\\mathbf{x}),\n", + "f(\\mathbf{x}')] \\\\ &= \\mathbb{E}[(f(\\mathbf{x}) - \\mathbb{E}[f(\\mathbf{x})])(f(\\mathbf{x}') - \\mathbb{E}[f(\\mathbf{x}')])] \\end{aligned}$$\n", + "\n", + " One would expect that, given a previously unobserved test point $\\mathbf{x}^*$, the\n", + " training points which are *closest* to this unobserved point will be most similar to\n", + " it. As such, the kernel is used to define this notion of similarity within the GP\n", + " framework. It is up to the user to select a kernel function which is appropriate for\n", + " the function being modelled. In this notebook we are going to give some examples of\n", + " commonly used kernels, and try to develop an understanding of when one may wish to use\n", + " one kernel over another. However, before we do this, it is worth discussing the\n", + " necessary conditions for a function to be a valid kernel/covariance function. This\n", + " requires a little bit of maths, so for those of you who just wish to obtain an\n", + " intuitive understanding, feel free to skip to the section introducing the Matérn\n", + " family of kernels.\n", + "\n", + "### What are the necessary conditions for a function to be a valid kernel?\n", + "\n", + "Whilst intuitively the kernel function is used to define the notion of similarity within\n", + "the GP framework, it is important to note that there are two *necessary conditions*\n", + "that a kernel function must satisfy in order to be a valid covariance function. For\n", + "clarity, we will refer to *any* function mapping two inputs to a scalar output as a\n", + "*kernel function*, and we will refer to a *valid* kernel function satisfying the two\n", + "necessary conditions as a *covariance function*. However, it is worth noting that the\n", + "GP community often uses the terms *kernel function* and *covariance function*\n", + "interchangeably.\n", + "\n", + "The first necessary condition is that the covariance function must be *symmetric*, i.e.\n", + "$k(\\mathbf{x}, \\mathbf{x}') = k(\\mathbf{x}', \\mathbf{x})$. This is because the\n", + "covariance between two random variables $X$ and $X'$ is symmetric; if one looks at the\n", + "definition of covariance given above, it is clear that it is invariant to swapping the\n", + "order of the inputs $\\mathbf{x}$ and $\\mathbf{x}'$.\n", + "\n", + "The second necessary condition is that the covariance function must be *positive\n", + "semi-definite* (PSD). In order to understand this condition, it is useful to first\n", + "introduce the concept of a *Gram matrix*. We'll use the same notation as the [GP introduction\n", + "notebook](https://docs.jaxgaussianprocesses.com/examples/intro_to_gps/), and denote\n", + "$n$ input points as $\\mathbf{X} = \\{\\mathbf{x}_1, \\ldots, \\mathbf{x}_n\\}$. Given these\n", + "input points and a kernel function $k$ the *Gram matrix* stores the pairwise kernel\n", + "evaluations between all input points. Mathematically, this leads to the Gram matrix being defined as:\n", + "\n", + "$$K(\\mathbf{X}, \\mathbf{X}) = \\begin{bmatrix} k(\\mathbf{x}_1, \\mathbf{x}_1) & \\cdots & k(\\mathbf{x}_1, \\mathbf{x}_n) \\\\ \\vdots & \\ddots & \\vdots \\\\ k(\\mathbf{x}_n, \\mathbf{x}_1) & \\cdots & k(\\mathbf{x}_n, \\mathbf{x}_n) \\end{bmatrix}$$\n", + "\n", + "such that $K(\\mathbf{X}, \\mathbf{X})_{ij} = k(\\mathbf{x}_i, \\mathbf{x}_j)$.\n", + "\n", + "In order for $k$ to be a valid covariance function, the corresponding Gram matrix\n", + "must be *positive semi-definite*. In this case the Gram matrix is referred to as a\n", + "*covariance matrix*. A real $n \\times n$ matrix $K$ is positive semi-definite if and\n", + "only if for all vectors $\\mathbf{z} \\in \\mathbb{R}^n$:\n", + "\n", + "$$\\mathbf{z}^\\top K \\mathbf{z} \\geq 0$$\n", + "\n", + "Alternatively, a real $n \\times n$ matrix $K$ is positive semi-definite if and only if\n", + "all of its eigenvalues are non-negative.\n", + "\n", + "Therefore, the two necessary conditions for a function to be a valid covariance function\n", + "are that it must be *symmetric* and *positive semi-definite*. In this section we have\n", + "referred to *any* function from two inputs to a scalar output as a *kernel function*,\n", + "with its corresponding matrix of pairwise evaluations referred to as the *Gram matrix*,\n", + "and a function satisfying the two necessary conditions as a *covariance function*, with\n", + "its corresponding matrix of pairwise evaluations referred to as the *covariance matrix*.\n", + "This enabled us to easily define the necessary conditions for a function to be a valid\n", + "covariance function. However, as noted previously, the GP community often uses these\n", + "terms interchangeably, and so we will for the remainder of this notebook.\n" + ] + }, + { + "cell_type": "markdown", + "id": "36100825", + "metadata": {}, + "source": [ + "## Introducing a Common Family of Kernels - The Matérn Family" + ] + }, + { + "cell_type": "markdown", + "id": "f26bac1d", + "metadata": {}, + "source": [ + "One of the most widely used families of kernels is the Matérn family ([Matérn, 1960](https://core.ac.uk/download/pdf/11698705.pdf)). These kernels take on the following form:\n", + "\n", + "$$k_{\\nu}(\\mathbf{x}, \\mathbf{x'}) = \\sigma^2 \\frac{2^{1 - \\nu}}{\\Gamma(\\nu)}\\left(\\sqrt{2\\nu} \\frac{|\\mathbf{x} - \\mathbf{x'}|}{\\kappa}\\right)^{\\nu} K_{\\nu} \\left(\\sqrt{2\\nu} \\frac{|\\mathbf{x} - \\mathbf{x'}|}{\\kappa}\\right)$$\n", + "\n", + "where $K_{\\nu}$ is a modified Bessel function, $\\nu$, $\\kappa$ and $\\sigma^2$ are\n", + "hyperparameters specifying the mean-square differentiability, lengthscale and\n", + "variability respectively, and $|\\cdot|$ is used to denote the Euclidean norm. Note that\n", + "for those of you less interested in the mathematical underpinnings of kernels, it isn't\n", + "necessary to understand the exact functional form of the Matérn kernels to\n", + "gain an understanding of how they behave. The key takeaway is that they are\n", + "parameterised by several hyperparameters, and that these hyperparameters dictate the\n", + "behaviour of functions sampled from the corresponding GP. The plots below will provide\n", + "some more intuition for how these hyperparameters affect the behaviour of functions\n", + "sampled from the corresponding GP.\n", + "\n", + "\n", + "Some commonly used Matérn kernels use half-integer values of $\\nu$, such as $\\nu = 1/2$\n", + "or $\\nu = 3/2$. The fraction is sometimes omitted when naming the kernel, so that $\\nu =\n", + "1/2$ is referred to as the Matérn12 kernel, and $\\nu = 3/2$ is referred to as the\n", + "Matérn32 kernel. When $\\nu$ takes in a half-integer value, $\\nu = k + 1/2$, the kernel\n", + "can be expressed as the product of a polynomial of order $k$ and an exponential:\n", + "\n", + "$$k_{k + 1/2}(\\mathbf{x}, \\mathbf{x'}) = \\sigma^2\n", + "\\exp\\left(-\\frac{\\sqrt{2\\nu}|\\mathbf{x} - \\mathbf{x'}|}{\\kappa}\\right)\n", + "\\frac{\\Gamma(k+1)}{\\Gamma(2k+1)} \\times \\sum_{i= 0}^k \\frac{(k+i)!}{i!(k-i)!}\n", + "\\left(\\frac{(\\sqrt{8\\nu}|\\mathbf{x} - \\mathbf{x'}|)}{\\kappa}\\right)^{k-i}$$\n", + "\n", + "In the limit of $\\nu \\to \\infty$ this yields the *squared-exponential*, or *radial basis function (RBF)*, kernel, which is infinitely mean-square differentiable:\n", + "\n", + "$$k_{\\infty}(\\mathbf{x}, \\mathbf{x'}) = \\sigma^2 \\exp\\left(-\\frac{|\\mathbf{x} - \\mathbf{x'}|^2}{2\\kappa^2}\\right)$$\n", + "\n", + "But what kind of functions does this kernel encode prior knowledge about? Let's take a look at some samples from GP priors defined used Matérn kernels with different values of $\\nu$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb605d7a", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "kernels = [\n", + " gpx.kernels.Matern12(),\n", + " gpx.kernels.Matern32(),\n", + " gpx.kernels.Matern52(),\n", + " gpx.kernels.RBF(),\n", + "]\n", + "fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(7, 6), tight_layout=True)\n", + "\n", + "x = jnp.linspace(-3.0, 3.0, num=200).reshape(-1, 1)\n", + "\n", + "meanf = gpx.mean_functions.Zero()\n", + "\n", + "for k, ax in zip(kernels, axes.ravel()):\n", + " prior = gpx.Prior(mean_function=meanf, kernel=k)\n", + " rv = prior(x)\n", + " y = rv.sample(seed=key, sample_shape=(10,))\n", + " ax.plot(x, y.T, alpha=0.7)\n", + " ax.set_title(k.name)" + ] + }, + { + "cell_type": "markdown", + "id": "0cef025f", + "metadata": {}, + "source": [ + "The plots above clearly show that the choice of $\\nu$ has a large impact on the *smoothness* of the functions being modelled by the GP, with functions drawn from GPs defined with the Matérn kernel becoming increasingly smooth as $\\nu \\to \\infty$. More formally, this notion of smoothness is captured through the mean-square differentiability of the function being modelled. Functions sampled from GPs using a Matérn kernel are $k$-times mean-square differentiable, if and only if $\\nu > k$. For instance, functions sampled from a GP using a Matérn12 kernel are zero times mean-square differentiable, and functions sampled from a GP using the RBF kernel are infinitely mean-square differentiable.\n", + "\n", + "As an important aside, a general property of the Matérn family of kernels is that they are examples of *stationary* kernels. This means that they only depend on the *displacement* of the two points being compared, $\\mathbf{x} - \\mathbf{x}'$, and not on their absolute values. This is a useful property to have, as it means that the kernel is invariant to translations in the input space. They also go beyond this, as they only depend on the Euclidean *distance* between the two points being compared, $|\\mathbf{x} - \\mathbf{x}'|$. Kernels which satisfy this property are known as *isotropic* kernels. This makes the function invariant to all rigid motions in the input space, such as rotations." + ] + }, + { + "cell_type": "markdown", + "id": "922c3395", + "metadata": {}, + "source": [ + "## Inferring Kernel Hyperparameters" + ] + }, + { + "cell_type": "markdown", + "id": "0619fac1", + "metadata": {}, + "source": [ + "Most kernels have several *hyperparameters*, which we denote $\\mathbf{\\theta}$, which encode different assumptions about the underlying function being modelled. For the Matérn family described above, $\\mathbf{\\theta} = \\{\\nu, \\kappa, \\sigma\\}$. A fully Bayesian approach to dealing with hyperparameters would be to place a prior over them, and marginalise over the posterior derived from the data in order to perform predictions. However, this is often computationally very expensive, and so a common approach is to instead *optimise* the hyperparameters by maximising the log marginal likelihood of the data. Given training data $\\mathbf{D} = (\\mathbf{X}, \\mathbf{y})$, assumed to contain some additive Gaussian noise $\\epsilon \\sim \\mathcal{N}(0, \\sigma^2)$, the log marginal likelihood of the dataset is defined as:\n", + "\n", + "$$ \\begin{aligned}\n", + "\\log(p(\\mathbf{y} | \\mathbf{X}, \\boldsymbol{\\theta})) &= \\log\\left(\\int p(\\mathbf{y} | \\mathbf{f}, \\mathbf{X}, \\boldsymbol{\\theta}) p(\\mathbf{f} | \\mathbf{X}, \\boldsymbol{\\theta}) d\\mathbf{f}\\right) \\nonumber \\\\\n", + "&= - \\frac{1}{2} \\mathbf{y} ^ \\top \\left(K(\\mathbf{X}, \\mathbf{X}) + \\sigma^2 \\mathbf{I} \\right)^{-1} \\mathbf{y} - \\frac{1}{2} \\log |K(\\mathbf{X}, \\mathbf{X}) + \\sigma^2 \\mathbf{I}| - \\frac{n}{2} \\log 2 \\pi\n", + "\\end{aligned}$$" + ] + }, + { + "cell_type": "markdown", + "id": "7309d3c9", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "This expression can then be maximised with respect to the hyperparameters using a\n", + "gradient-based approach such as Adam or L-BFGS. Note that we may choose to fix some\n", + "hyperparameters, and in GPJax the parameter $\\nu$ is set by the user, and not\n", + "inferred though optimisation. For more details on using the log marginal likelihood to\n", + "optimise kernel hyperparameters, see our [GP introduction notebook](https://docs.jaxgaussianprocesses.com/examples/intro_to_gps/#gaussian-process-regression).\n", + "\n", + "We'll demonstrate the advantages of being able to infer kernel parameters from the training data by fitting a GP to the widely used [Forrester function](https://www.sfu.ca/~ssurjano/forretal08.html):\n", + "\n", + "$$f(x) = (6x - 2)^2 \\sin(12x - 4)$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f0382c0", + "metadata": {}, + "outputs": [], + "source": [ + "# Forrester function\n", + "def forrester(x: Float[Array, \"N\"]) -> Float[Array, \"N\"]:\n", + " return (6 * x - 2) ** 2 * jnp.sin(12 * x - 4)\n", + "\n", + "\n", + "n = 5\n", + "\n", + "training_x = jr.uniform(key=key, minval=0, maxval=1, shape=(n,)).reshape(-1, 1)\n", + "training_y = forrester(training_x)\n", + "D = gpx.Dataset(X=training_x, y=training_y)\n", + "\n", + "test_x = jnp.linspace(0, 1, 100).reshape(-1, 1)\n", + "test_y = forrester(test_x)" + ] + }, + { + "cell_type": "markdown", + "id": "f0fa5a2d", + "metadata": {}, + "source": [ + "First we define our model, using the Matérn52 kernel, and construct our posterior *without* optimising the kernel hyperparameters:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1925b644", + "metadata": {}, + "outputs": [], + "source": [ + "mean = gpx.mean_functions.Zero()\n", + "kernel = gpx.kernels.Matern52(\n", + " lengthscale=jnp.array(2.0)\n", + ") # Initialise our kernel lengthscale to 2.0\n", + "\n", + "prior = gpx.Prior(mean_function=mean, kernel=kernel)\n", + "\n", + "likelihood = gpx.Gaussian(\n", + " num_datapoints=D.n, obs_noise=jnp.array(1e-6)\n", + ") # Our function is noise-free, so we set the observation noise to a very small value\n", + "likelihood = likelihood.replace_trainable(obs_noise=False)\n", + "\n", + "no_opt_posterior = prior * likelihood" + ] + }, + { + "cell_type": "markdown", + "id": "c0ebcce4", + "metadata": {}, + "source": [ + "We can then optimise the hyperparameters by minimising the negative log marginal likelihood of the data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2f8e4fc", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "\n", + "opt_posterior, history = gpx.fit(\n", + " model=no_opt_posterior,\n", + " train_data=D,\n", + " solver=jaxopt.ScipyMinimize(gpx.ConjugateMLL(negative=True)),\n", + " safe=True,\n", + " key=key,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "dfed7339", + "metadata": {}, + "source": [ + "Having optimised the hyperparameters, we can now make predictions using the posterior\n", + "with the optimised hyperparameters, and compare them to the predictions made using the\n", + "posterior with the default hyperparameters:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74987dc8", + "metadata": {}, + "outputs": [], + "source": [ + "opt_latent_dist = opt_posterior.predict(test_x, train_data=D)\n", + "opt_predictive_dist = opt_posterior.likelihood(opt_latent_dist)\n", + "\n", + "opt_predictive_mean = opt_predictive_dist.mean()\n", + "opt_predictive_std = opt_predictive_dist.stddev()\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(5, 6))\n", + "ax1.plot(training_x, training_y, \"x\", label=\"Observations\", color=cols[0], alpha=0.5)\n", + "ax1.fill_between(\n", + " test_x.squeeze(),\n", + " opt_predictive_mean - 2 * opt_predictive_std,\n", + " opt_predictive_mean + 2 * opt_predictive_std,\n", + " alpha=0.2,\n", + " label=\"Two sigma\",\n", + " color=cols[1],\n", + ")\n", + "ax1.plot(\n", + " test_x,\n", + " opt_predictive_mean - 2 * opt_predictive_std,\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " color=cols[1],\n", + ")\n", + "ax1.plot(\n", + " test_x,\n", + " opt_predictive_mean + 2 * opt_predictive_std,\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " color=cols[1],\n", + ")\n", + "ax1.plot(\n", + " test_x, test_y, label=\"Latent function\", color=cols[0], linestyle=\"--\", linewidth=2\n", + ")\n", + "ax1.plot(test_x, opt_predictive_mean, label=\"Predictive mean\", color=cols[1])\n", + "ax1.set_title(\"Posterior with Hyperparameter Optimisation\")\n", + "ax1.legend(loc=\"center left\", bbox_to_anchor=(0.975, 0.5))\n", + "\n", + "no_opt_latent_dist = no_opt_posterior.predict(test_x, train_data=D)\n", + "no_opt_predictive_dist = no_opt_posterior.likelihood(no_opt_latent_dist)\n", + "\n", + "no_opt_predictive_mean = no_opt_predictive_dist.mean()\n", + "no_opt_predictive_std = no_opt_predictive_dist.stddev()\n", + "\n", + "ax2.plot(training_x, training_y, \"x\", label=\"Observations\", color=cols[0], alpha=0.5)\n", + "ax2.fill_between(\n", + " test_x.squeeze(),\n", + " no_opt_predictive_mean - 2 * no_opt_predictive_std,\n", + " no_opt_predictive_mean + 2 * no_opt_predictive_std,\n", + " alpha=0.2,\n", + " label=\"Two sigma\",\n", + " color=cols[1],\n", + ")\n", + "ax2.plot(\n", + " test_x,\n", + " no_opt_predictive_mean - 2 * no_opt_predictive_std,\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " color=cols[1],\n", + ")\n", + "ax2.plot(\n", + " test_x,\n", + " no_opt_predictive_mean + 2 * no_opt_predictive_std,\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " color=cols[1],\n", + ")\n", + "ax2.plot(\n", + " test_x, test_y, label=\"Latent function\", color=cols[0], linestyle=\"--\", linewidth=2\n", + ")\n", + "ax2.plot(test_x, no_opt_predictive_mean, label=\"Predictive mean\", color=cols[1])\n", + "ax2.set_title(\"Posterior without Hyperparameter Optimisation\")\n", + "ax2.legend(loc=\"center left\", bbox_to_anchor=(0.975, 0.5))" + ] + }, + { + "cell_type": "markdown", + "id": "b1e31c10", + "metadata": {}, + "source": [ + "We can see that optimising the hyperparameters by minimising the negative log marginal likelihood of the data results in a more faithful fit of the GP to the data. In particular, we can observe that the GP using optimised hyperparameters is more accurately able to reflect uncertainty in its predictions, as opposed to the GP using the default parameters, which is overconfident in its predictions.\n", + "\n", + "The lengthscale, $\\kappa$, and variance, $\\sigma^2$, are shown below, both before and after optimisation:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be61e64c", + "metadata": {}, + "outputs": [], + "source": [ + "no_opt_lengthscale = no_opt_posterior.prior.kernel.lengthscale\n", + "no_opt_variance = no_opt_posterior.prior.kernel.variance\n", + "opt_lengthscale = opt_posterior.prior.kernel.lengthscale\n", + "opt_variance = opt_posterior.prior.kernel.variance\n", + "\n", + "print(f\"Optimised Lengthscale: {opt_lengthscale} and Variance: {opt_variance}\")\n", + "print(\n", + " f\"Non-Optimised Lengthscale: {no_opt_lengthscale} and Variance: {no_opt_variance}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "05e74205", + "metadata": {}, + "source": [ + "## Expressing Other Priors with Different Kernels" + ] + }, + { + "cell_type": "markdown", + "id": "6cc36ec4", + "metadata": {}, + "source": [ + "Whilst the Matérn kernels are often used as a first choice of kernel, and they often perform well due to their smoothing properties often being well-aligned with the properties of the underlying function being modelled, sometimes more prior knowledge is known about the function being modelled. For instance, it may be known that the function being modelled is *periodic*. In this case, a suitable kernel choice would be the *periodic* kernel:\n", + "\n", + "$$k(\\mathbf{x}, \\mathbf{x}') = \\sigma^2 \\exp \\left( -\\frac{1}{2} \\sum_{i=1}^{D} \\left(\\frac{\\sin (\\pi (\\mathbf{x}_i - \\mathbf{x}_i')/p)}{\\ell}\\right)^2 \\right)$$\n", + "\n", + "with $D$ being the dimensionality of the inputs.\n", + "\n", + "Below we show $10$ samples drawn from a GP prior using the periodic kernel:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a55e8f40", + "metadata": {}, + "outputs": [], + "source": [ + "mean = gpx.mean_functions.Zero()\n", + "kernel = gpx.kernels.Periodic()\n", + "prior = gpx.Prior(mean_function=mean, kernel=kernel)\n", + "\n", + "x = jnp.linspace(-3.0, 3.0, num=200).reshape(-1, 1)\n", + "rv = prior(x)\n", + "y = rv.sample(seed=key, sample_shape=(10,))\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.plot(x, y.T, alpha=0.7)\n", + "ax.set_title(\"Samples from the Periodic Kernel\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b1a48bd9", + "metadata": {}, + "source": [ + "In other scenarios, it may be known that the underlying function is *linear*, in which case the *linear* kernel would be a suitable choice:\n", + "\n", + "$$k(\\mathbf{x}, \\mathbf{x}') = \\sigma^2 \\mathbf{x}^\\top \\mathbf{x}'$$\n", + "\n", + "Unlike the kernels shown above, the linear kernel is *not* stationary, and so it is not invariant to translations in the input space.\n", + "\n", + "Below we show $10$ samples drawn from a GP prior using the linear kernel:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec9db581", + "metadata": {}, + "outputs": [], + "source": [ + "mean = gpx.mean_functions.Zero()\n", + "kernel = gpx.kernels.Linear()\n", + "prior = gpx.Prior(mean_function=mean, kernel=kernel)\n", + "\n", + "x = jnp.linspace(-3.0, 3.0, num=200).reshape(-1, 1)\n", + "rv = prior(x)\n", + "y = rv.sample(seed=key, sample_shape=(10,))\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.plot(x, y.T, alpha=0.7)\n", + "ax.set_title(\"Samples from the Linear Kernel\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4984767e", + "metadata": {}, + "source": [ + "## Composing Kernels" + ] + }, + { + "cell_type": "markdown", + "id": "e8084e4e", + "metadata": {}, + "source": [ + "It is also mathematically valid to compose kernels through operations such as addition\n", + "and multiplication in order to produce more expressive kernels. For the mathematically\n", + "interested amongst you, this is valid as the resulting kernel functions still satisfy\n", + "the necessary conditions introduced at the [start of this\n", + "notebook](#what-are-the-necessary-conditions-for-a-function-to-be-a-valid-kernel).\n", + "Adding or multiplying kernel functions is equivalent to performing elementwise addition\n", + "or multiplication of the corresponding covariance matrices, and fortunately symmetric,\n", + "positive semi-definite kernels are closed under these operations. This means that\n", + "kernels produced by adding or multiplying other kernels will also be symmetric and\n", + "positive semi-definite, and so will also be valid kernels. GPJax provides the\n", + "functionality required to easily compose kernels via addition and multiplication, which\n", + "we'll demonstrate below.\n", + "\n", + "First, we'll take a look at some samples drawn from a GP prior using a kernel which is\n", + "composed of the sum of a linear kernel and a periodic kernel:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2260fc56", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "kernel_one = gpx.kernels.Linear()\n", + "kernel_two = gpx.kernels.Periodic()\n", + "sum_kernel = gpx.kernels.SumKernel(kernels=[kernel_one, kernel_two])\n", + "mean = gpx.mean_functions.Zero()\n", + "prior = gpx.Prior(mean_function=mean, kernel=sum_kernel)\n", + "\n", + "x = jnp.linspace(-3.0, 3.0, num=200).reshape(-1, 1)\n", + "rv = prior(x)\n", + "y = rv.sample(seed=key, sample_shape=(10,))\n", + "fig, ax = plt.subplots()\n", + "ax.plot(x, y.T, alpha=0.7)\n", + "ax.set_title(\"Samples from a GP Prior with Kernel = Linear + Periodic\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a42c5186", + "metadata": {}, + "source": [ + "We can see that the samples drawn behave as one would naturally expect through adding\n", + "the two kernels together. In particular, the samples are still periodic, as with the\n", + "periodic kernel, but their mean also linearly increases/decreases as they move away from\n", + "the origin, as seen with the linear kernel.\n", + "\n", + "Below we take a look at some samples drawn from a GP prior using a kernel which is\n", + "composed of the same two kernels, but this time multiplied together:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71924a0d", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "kernel_one = gpx.kernels.Linear()\n", + "kernel_two = gpx.kernels.Periodic()\n", + "sum_kernel = gpx.kernels.ProductKernel(kernels=[kernel_one, kernel_two])\n", + "mean = gpx.mean_functions.Zero()\n", + "prior = gpx.Prior(mean_function=mean, kernel=sum_kernel)\n", + "\n", + "x = jnp.linspace(-3.0, 3.0, num=200).reshape(-1, 1)\n", + "rv = prior(x)\n", + "y = rv.sample(seed=key, sample_shape=(10,))\n", + "fig, ax = plt.subplots()\n", + "ax.plot(x, y.T, alpha=0.7)\n", + "ax.set_title(\"Samples from a GP with Kernel = Linear x Periodic\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d47c5469", + "metadata": {}, + "source": [ + "Once again, the samples drawn behave as one would naturally expect through multiplying\n", + "the two kernels together. In particular, the samples are still periodic but their mean\n", + "linearly increases/decreases as they move away from the origin, and the amplitude of\n", + "the oscillations also linearly increases with increasing distance from the origin." + ] + }, + { + "cell_type": "markdown", + "id": "71f11b7e", + "metadata": {}, + "source": [ + "## Putting it All Together on a Real-World Dataset" + ] + }, + { + "cell_type": "markdown", + "id": "2dc26b2a", + "metadata": {}, + "source": [ + "### Mauna Loa CO2 Dataset" + ] + }, + { + "cell_type": "markdown", + "id": "47344788", + "metadata": {}, + "source": [ + "We'll put together some of the ideas we've discussed in this notebook by fitting a GP\n", + "to the [Mauna Loa CO2 dataset](https://www.esrl.noaa.gov/gmd/ccgg/trends/data.html).\n", + "This dataset measures atmospheric CO2 concentration at the Mauna Loa Observatory in\n", + "Hawaii, and is widely used in the GP literature. It contains monthly CO2 readings\n", + "starting in March 1958. Interestingly, there was an eruption at the Mauna Loa volcano in\n", + "November 2022, so readings from December 2022 have changed to a site roughly 21 miles\n", + "North of the Mauna Loa Observatory. We'll use the data from March 1958 to November 2022,\n", + "and see how our GP extrapolates to 8 years before and after the data in the training\n", + "set.\n", + "\n", + "First we'll load the data and plot it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55be34e6", + "metadata": {}, + "outputs": [], + "source": [ + "co2_data = pd.read_csv(\n", + " \"https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_mlo.csv\", comment=\"#\"\n", + ")\n", + "co2_data = co2_data.loc[co2_data[\"decimal date\"] < 2022 + 11 / 12]\n", + "train_x = co2_data[\"decimal date\"].values[:, None]\n", + "train_y = co2_data[\"average\"].values[:, None]\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.plot(train_x, train_y)\n", + "ax.set_title(\"CO2 Concentration in the Atmosphere\")\n", + "ax.set_xlabel(\"Year\")\n", + "ax.set_ylabel(\"CO2 Concentration (ppm)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d9669d60", + "metadata": {}, + "source": [ + "Looking at the data, we can see that there is clearly a periodic trend, with a period of\n", + "roughly 1 year. We can also see that the data is increasing over time, which is\n", + "also expected. This looks roughly linear, although it may have a non-linear component.\n", + "This information will be useful when we come to choose our kernel.\n", + "\n", + "First, we'll construct our GPJax dataset, and will standardise the outputs, to match our\n", + "assumption that the data has zero mean." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8a15c48", + "metadata": {}, + "outputs": [], + "source": [ + "test_x = jnp.linspace(1950, 2030, 5000, dtype=jnp.float64).reshape(-1, 1)\n", + "y_scaler = StandardScaler().fit(train_y)\n", + "standardised_train_y = y_scaler.transform(train_y)\n", + "\n", + "D = gpx.Dataset(X=train_x, y=standardised_train_y)" + ] + }, + { + "cell_type": "markdown", + "id": "07d73465", + "metadata": {}, + "source": [ + "Having constructed our dataset, we'll now define our kernel. We'll use a kernel which is\n", + "composed of the sum of a linear kernel and a periodic kernel, as we saw in the previous\n", + "section that this kernel is able to capture both the periodic and linear trends in the\n", + "data. We'll also add an RBF kernel to the sum, which will allow us to capture any\n", + "non-linear trends in the data:\n", + "\n", + "$$\\text{Kernel = Linear + Periodic + RBF}$$\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2b25f23", + "metadata": {}, + "outputs": [], + "source": [ + "mean = gpx.mean_functions.Zero()\n", + "rbf_kernel = gpx.kernels.RBF(lengthscale=100.0)\n", + "periodic_kernel = gpx.kernels.Periodic()\n", + "linear_kernel = gpx.kernels.Linear()\n", + "sum_kernel = gpx.kernels.SumKernel(kernels=[linear_kernel, periodic_kernel])\n", + "final_kernel = gpx.kernels.SumKernel(kernels=[rbf_kernel, sum_kernel])\n", + "\n", + "prior = gpx.Prior(mean_function=mean, kernel=final_kernel)\n", + "likelihood = gpx.Gaussian(num_datapoints=D.n)\n", + "\n", + "posterior = prior * likelihood" + ] + }, + { + "cell_type": "markdown", + "id": "337480b4", + "metadata": {}, + "source": [ + "With our model constructed, let's now fit it to the data, by minimising the negative log\n", + "marginal likelihood of the data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "645a41c2", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "opt_posterior, history = gpx.fit(\n", + " model=posterior,\n", + " train_data=D,\n", + " solver=jaxopt.ScipyMinimize(fun=gpx.ConjugateMLL(negative=True)),\n", + " safe=True,\n", + " key=key,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "946a750c", + "metadata": {}, + "source": [ + "Now we can obtain the model's prediction over a period of time which includes the\n", + "training data, as well as 8 years before and after the training data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4a95419", + "metadata": {}, + "outputs": [], + "source": [ + "latent_dist = opt_posterior.predict(test_x, train_data=D)\n", + "predictive_dist = opt_posterior.likelihood(latent_dist)\n", + "\n", + "predictive_mean = predictive_dist.mean().reshape(-1, 1)\n", + "predictive_std = predictive_dist.stddev().reshape(-1, 1)" + ] + }, + { + "cell_type": "markdown", + "id": "a6d50b34", + "metadata": {}, + "source": [ + "Let's plot the model's predictions over this period of time:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf9dceca", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "ax.plot(\n", + " train_x, standardised_train_y, \"x\", label=\"Observations\", color=cols[0], alpha=0.5\n", + ")\n", + "ax.fill_between(\n", + " test_x.squeeze(),\n", + " predictive_mean.squeeze() - 2 * predictive_std.squeeze(),\n", + " predictive_mean.squeeze() + 2 * predictive_std.squeeze(),\n", + " alpha=0.2,\n", + " label=\"Two sigma\",\n", + " color=cols[1],\n", + ")\n", + "ax.plot(\n", + " test_x,\n", + " predictive_mean - 2 * predictive_std,\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " color=cols[1],\n", + ")\n", + "ax.plot(\n", + " test_x,\n", + " predictive_mean + 2 * predictive_std,\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " color=cols[1],\n", + ")\n", + "ax.plot(test_x, predictive_mean, label=\"Predictive mean\", color=cols[1])\n", + "ax.set_xlabel(\"Year\")\n", + "ax.legend(loc=\"center left\", bbox_to_anchor=(0.975, 0.5))" + ] + }, + { + "cell_type": "markdown", + "id": "f73e3645", + "metadata": {}, + "source": [ + "We can see that the model seems to have captured the periodic trend in the data, as well\n", + "as the (roughly) linear trend. This enables our model to make reasonable seeming\n", + "predictions over the 8 years before and after the training data. Let's zoom in on the\n", + "period from 2010 onwards:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d42cedf0", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "ax.plot(\n", + " train_x[train_x >= 2010],\n", + " standardised_train_y[train_x >= 2010],\n", + " \"x\",\n", + " label=\"Observations\",\n", + " color=cols[0],\n", + " alpha=0.5,\n", + ")\n", + "ax.fill_between(\n", + " test_x[test_x >= 2010].squeeze(),\n", + " predictive_mean[test_x >= 2010] - 2 * predictive_std[test_x >= 2010],\n", + " predictive_mean[test_x >= 2010] + 2 * predictive_std[test_x >= 2010],\n", + " alpha=0.2,\n", + " label=\"Two sigma\",\n", + " color=cols[1],\n", + ")\n", + "ax.plot(\n", + " test_x[test_x >= 2010],\n", + " predictive_mean[test_x >= 2010] - 2 * predictive_std[test_x >= 2010],\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " color=cols[1],\n", + ")\n", + "ax.plot(\n", + " test_x[test_x >= 2010],\n", + " predictive_mean[test_x >= 2010] + 2 * predictive_std[test_x >= 2010],\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " color=cols[1],\n", + ")\n", + "ax.plot(\n", + " test_x[test_x >= 2010],\n", + " predictive_mean[test_x >= 2010],\n", + " label=\"Predictive mean\",\n", + " color=cols[1],\n", + ")\n", + "ax.set_xlabel(\"Year\")\n", + "ax.legend(loc=\"center left\", bbox_to_anchor=(0.975, 0.5))" + ] + }, + { + "cell_type": "markdown", + "id": "22b6132a", + "metadata": {}, + "source": [ + "This certainly looks like a reasonable fit to the data, with sensible extrapolation\n", + "beyond the training data, which finishes in November 2022. Moreover, the learned\n", + "parameters of the kernel are interpretable. Let's take a look at the learned period of the periodic kernel:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aee6c774", + "metadata": {}, + "outputs": [], + "source": [ + "print(\n", + " f\"Periodic Kernel Period: {[i for i in opt_posterior.prior.kernel.kernels if isinstance(i, gpx.kernels.Periodic)][0].period}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d2c56639", + "metadata": {}, + "source": [ + "This tells us that the periodic trend learned has a period of $\\approx 1$. This makes\n", + "intuitive sense, as the unit of the input data is years, and we can see that the\n", + "periodic trend tends to repeat itself roughly every year!" + ] + }, + { + "cell_type": "markdown", + "id": "1ea4ae29", + "metadata": {}, + "source": [ + "## Defining Kernels on Non-Euclidean Spaces\n", + "\n", + "In this notebook, we have focused solely on kernels whose domain resides in Euclidean space. However, what if one wished to work with data whose domain is non-Euclidean? For instance, one may wish to work with graph-structured data, or data which lies on a manifold, or even strings. Fortunately, kernels exist for a wide variety of domains. Whilst this is beyond the scope of this notebook, feel free to checkout out our [notebook on graph kernels](https://docs.jaxgaussianprocesses.com/examples/graph_kernels/) for an introduction on how to define the Matérn kernel on graph-structured data, and there are a wide variety of resources online for learning about defining kernels in other domains. In terms of open-source libraries, the [Geometric Kernels](https://github.com/GPflow/GeometricKernels) library could be a good place to start if you're interested in looking at how these kernels may be implemented, with the additional benefit that it is compatible with GPJax." + ] + }, + { + "cell_type": "markdown", + "id": "b3dd4cab", + "metadata": {}, + "source": [ + "## Further Reading\n", + "\n", + "Congratulations on making it this far! We hope that this guide has given you a good introduction to kernels and how they can be used in GPJax. If you're interested in learning more about kernels, we recommend the following resources, which have also been used as inspiration for this guide:\n", + "\n", + "- [Gaussian Processes for Machine Learning](http://www.gaussianprocess.org/gpml/chapters/RW.pdf) - Chapter 4 provides a comprehensive overview of kernels, diving deep into some of the technical details and also providing some kernels defined on non-Euclidean spaces such as strings.\n", + "- David Duvenaud's [Kernel Cookbook](https://www.cs.toronto.edu/~duvenaud/cookbook/) is a great resource for learning about kernels, and also provides some information about some of the pitfalls people commonly encounter when using the Matérn family of kernels. His PhD thesis, [Automatic Model Construction with Gaussian Processes](https://www.cs.toronto.edu/~duvenaud/thesis.pdf), also provides some in-depth recipes for how one may incorporate their prior knowledge when constructing kernels.\n", + "- Finally, please check out our [more advanced kernel guide](https://docs.jaxgaussianprocesses.com/examples/constructing_new_kernels/), which details some more kernels available in GPJax as well as how one may combine kernels together to form more complex kernels.\n", + "\n", + "## System Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b32981e", + "metadata": {}, + "outputs": [], + "source": [ + "%reload_ext watermark\n", + "%watermark -n -u -v -iv -w -a 'Thomas Christie'" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "gpjax", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/oceanmodelling.py b/docs/examples/oceanmodelling.py index 65bd16274..3e4721d60 100644 --- a/docs/examples/oceanmodelling.py +++ b/docs/examples/oceanmodelling.py @@ -24,7 +24,6 @@ from matplotlib import rcParams import matplotlib.pyplot as plt import jaxopt -import optax as ox import pandas as pd import tensorflow_probability as tfp @@ -240,27 +239,21 @@ def initialise_gp(kernel, mean, dataset): # %% [markdown] -# With a model now defined, we can proceed to optimise the hyperparameters of our likelihood over $D_0$. This is done by minimising the MLL using `optax`. We also plot its value at each step to visually confirm that we have found the minimum. See the [introduction to Gaussian Processes](https://docs.jaxgaussianprocesses.com/examples/intro_to_gps/) notebook for more information on optimising the MLL. +# With a model now defined, we can proceed to optimise the hyperparameters of our likelihood over $D_0$. This is done by minimising the MLL using `jaxopt`. See the [introduction to Gaussian Processes](https://docs.jaxgaussianprocesses.com/examples/intro_to_gps/) notebook for more information on optimising the MLL. # %% -def optimise_mll(posterior, dataset, NIters=1000, key=key, plot_history=True): +def optimise_mll(posterior, dataset, key=key): # define the MLL using dataset_train objective = gpx.objectives.ConjugateMLL(negative=True) # Optimise to minimise the MLL opt_posterior, history = gpx.fit( model=posterior, train_data=dataset, - solver=jaxopt.OptaxSolver(objective, opt=ox.adam(0.1), maxiter=NIters), + solver=jaxopt.ScipyMinimize(fun=objective), safe=True, key=key, ) - # plot MLL value at each iteration - if plot_history: - fig, ax = plt.subplots(1, 1) - ax.plot(history, color=colors[1]) - ax.set(xlabel="Training iteration", ylabel="Negative MLL") - return opt_posterior @@ -469,7 +462,7 @@ def __call__( # Redefine Gaussian process with Helmholtz kernel kernel = HelmholtzKernel() helmholtz_posterior = initialise_gp(kernel, mean, dataset_train) -# Optimise hyperparameters using optax +# Optimise hyperparameters using jaxopt opt_helmholtz_posterior = optimise_mll(helmholtz_posterior, dataset_train) diff --git a/docs/examples/regression.py b/docs/examples/regression.py index 4f7e0eda0..5670aedc0 100644 --- a/docs/examples/regression.py +++ b/docs/examples/regression.py @@ -31,7 +31,6 @@ from jaxtyping import install_import_hook import matplotlib as mpl import matplotlib.pyplot as plt -import optax as ox import jaxopt from docs.examples.utils import clean_legend @@ -186,11 +185,6 @@ negative_mll(posterior, train_data=D) -# static_tree = jax.tree_map(lambda x: not(x), posterior.trainables) -# optim = ox.chain( -# ox.adam(learning_rate=0.01), -# ox.masked(ox.set_to_zero(), static_tree) -# ) # %% [markdown] # For researchers, GPJax has the capacity to print the bibtex citation for objects such # as the marginal log-likelihood through the `cite()` function. @@ -218,21 +212,11 @@ opt_posterior, history = gpx.fit( model=posterior, train_data=D, - solver=jaxopt.OptaxSolver(negative_mll, opt=ox.adamw(0.01), maxiter=500), + solver=jaxopt.ScipyMinimize(fun=negative_mll), safe=True, key=key, ) -# %% [markdown] -# The calling of `fit` returns two objects: the optimised posterior and a history of -# training losses. We can plot the training loss to see how the optimisation has -# progressed. - -# %% -fig, ax = plt.subplots() -ax.plot(history, color=cols[1]) -ax.set(xlabel="Training iteration", ylabel="Negative marginal log likelihood") - # %% [markdown] # ## Prediction # diff --git a/docs/examples/yacht.py b/docs/examples/yacht.py index cd8b8d653..d7d8c80e5 100644 --- a/docs/examples/yacht.py +++ b/docs/examples/yacht.py @@ -35,7 +35,6 @@ import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np -import optax as ox import jaxopt import pandas as pd from sklearn.metrics import ( @@ -183,19 +182,17 @@ # ### Model Optimisation # # With a model now defined, we can proceed to optimise the hyperparameters of our -# model using one of `jaxopt`'s solvers. In this case we use a solver that wraps an -# `optax` optimizer. +# model using one of `jaxopt`'s solvers. # %% training_data = gpx.Dataset(X=scaled_Xtr, y=scaled_ytr) negative_mll = jit(gpx.ConjugateMLL(negative=True)) -optimiser = ox.adamw(0.05) opt_posterior, history = gpx.fit( model=posterior, train_data=training_data, - solver=jaxopt.OptaxSolver(negative_mll, opt=ox.adamw(0.05), maxiter=500), + solver=jaxopt.ScipyMinimize(fun=negative_mll), key=key, ) diff --git a/gpjax/decision_making/posterior_handler.py b/gpjax/decision_making/posterior_handler.py index b4f251afe..97b79f186 100644 --- a/gpjax/decision_making/posterior_handler.py +++ b/gpjax/decision_making/posterior_handler.py @@ -12,14 +12,20 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -from dataclasses import dataclass +from dataclasses import ( + asdict, + dataclass, +) from beartype.typing import ( Callable, Optional, + Union, +) +from jaxopt import ( + OptaxSolver, + ScipyMinimize, ) -import jaxopt -from jaxopt.base import IterativeSolver import gpjax as gpx from gpjax.dataset import Dataset @@ -46,13 +52,13 @@ class PosteriorHandler: likelihood_builder (LikelihoodBuilder): Function which takes the number of datapoints as input and returns a likelihood object initialised with the given number of datapoints. - solver (IterativeSolver): The `jaxopt` solver used to optimize the + solver (Union[ScipyMinimize, OptaxSolver]): The `jaxopt` solver used to optimize the posterior hyperparameters. """ prior: AbstractPrior likelihood_builder: LikelihoodBuilder - solver: IterativeSolver + solver: Union[ScipyMinimize, OptaxSolver] def __post_init__(self): if self.solver.maxiter < 1: @@ -136,15 +142,16 @@ def _optimize_posterior( Optimized posterior. """ - # TODO: Clean this one up! - # We create a new solver state -> since the dataset (and therefore loss function) has changed! - old = self.solver - solver = jaxopt.OptaxSolver(fun=old.fun, opt=old.opt, maxiter=old.maxiter) + # # We create a new solver state -> since the dataset (and therefore loss function) has changed! + attributes = asdict(self.solver) + attributes["options"].pop("maxiter", None) # allow reinit without jaxopt error + attributes.pop("fun", None) # pass in fun as callable rather than dict + new_solver = self.solver.__class__(fun=self.solver.fun, **attributes) opt_posterior, _ = gpx.fit( model=posterior, train_data=dataset, - solver=solver, + solver=new_solver, safe=True, key=key, verbose=False, diff --git a/gpjax/fit.py b/gpjax/fit.py index fa3167aac..6b272829f 100644 --- a/gpjax/fit.py +++ b/gpjax/fit.py @@ -20,13 +20,16 @@ Optional, Tuple, TypeVar, + Union, ) import jax from jax._src.random import _check_prng_key import jax.numpy as jnp import jax.random as jr -import jaxopt -from jaxopt.base import IterativeSolver +from jaxopt import ( + OptaxSolver, + ScipyMinimize, +) from gpjax.base import Module from gpjax.dataset import Dataset @@ -43,7 +46,7 @@ def fit( # noqa: PLR0913 *, model: ModuleModel, train_data: Dataset, - solver: IterativeSolver, + solver: Union[ScipyMinimize, OptaxSolver], key: KeyArray, batch_size: Optional[int] = -1, log_rate: Optional[int] = 10, @@ -52,7 +55,7 @@ def fit( # noqa: PLR0913 safe: Optional[bool] = True, ) -> Tuple[ModuleModel, Array]: r"""Train a Module model with respect to a supplied Objective function. - `solver` must be an instance of `jaxopt`'s `IterativeSolver`. + `solver` must be an instance of `jaxopt`'s `OptaxSolver` or `ScipyMinimze`. Example: ```python @@ -87,14 +90,14 @@ def evaluate(self, model: LinearModel, train_data: gpx.Dataset) -> float: >>> trained_model, history = gpx.fit( model=model, train_data=D, - solver=jaxopt.LBFGS(loss_fn, max_stepsize=0.001, maxiter=4000), + solver=jaxopt.ScipyMinimize(fun=loss), ) ``` Args: model (Module): The model Module to be optimised. train_data (Dataset): The training data to be used for the optimisation. - solver (IterativeSolver): The `jaxopt` solver. + solver (Union[SCipyMinimize, OptaxSolver])): The `jaxopt` solver. 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 @@ -120,43 +123,56 @@ def evaluate(self, model: LinearModel, train_data: gpx.Dataset) -> float: _check_log_rate(log_rate) _check_verbose(verbose) + if isinstance(solver, ScipyMinimize) and batch_size != -1: + raise ValueError("ScipyMinimze optimizers do not support batching") + # Unconstrained space model. model = model.unconstrain() - # needed for OptaxSolver to work - if isinstance(solver, jaxopt.OptaxSolver): - model = jax.tree_map(lambda x: x.astype(jnp.float64), model) - # Initialise solver state. solver.fun = _wrap_objective(solver.fun) + solver.options.pop("maxiter", None) # allow __post_init__ without jaxopt error solver.__post_init__() # needed to propagate changes to `fun` attribute - solver_state = solver.init_state( - model, - get_batch(train_data, batch_size, key) if batch_size != -1 else train_data, - ) - - # Mini-batch random keys to scan over. - iter_keys = jr.split(key, solver.maxiter) - - # Optimisation step. - def step(carry, key): - model, state = carry - - if batch_size != -1: - batch = get_batch(train_data, batch_size, key) - else: - batch = train_data - - model, state = solver.update(model, state, batch) - carry = model, state - return carry, state.value - - # Optimisation scan. - scan = vscan if verbose else jax.lax.scan + if isinstance(solver, OptaxSolver): # hack for Optax compatibility + model = jax.tree_map(lambda x: x.astype(jnp.float64), model) - # Optimisation loop. - (model, _), history = scan(step, (model, solver_state), (iter_keys), unroll=unroll) + if isinstance(solver, OptaxSolver): # For optax, run optimization by step + solver_state = solver.init_state( + model, + get_batch(train_data, batch_size, key) if batch_size != -1 else train_data, + ) + + # Mini-batch random keys to scan over. + iter_keys = jr.split(key, solver.maxiter) + + # Optimisation step. + def step(carry, key): + model, state = carry + + if batch_size != -1: + batch = get_batch(train_data, batch_size, key) + else: + batch = train_data + + model, state = solver.update(model, state, batch) + carry = model, state + return carry, state.value + + # Optimisation scan. + scan = vscan if verbose else jax.lax.scan + + # Optimisation loop. + (model, _), history = scan( + step, (model, solver_state), (iter_keys), unroll=unroll + ) + + elif isinstance(solver, ScipyMinimize): # Scipy runs whole optimization loop + initial_loss = solver.fun(model, train_data) + model, result = solver.run(model, train_data) + history = jnp.array([initial_loss, result.fun_val]) + if verbose: + print(f" Found model with loss {result.fun_val}") # Constrained space. model = model.constrain() diff --git a/tests/test_decision_making/test_posterior_handler.py b/tests/test_decision_making/test_posterior_handler.py index 285be6f1f..5e276b512 100644 --- a/tests/test_decision_making/test_posterior_handler.py +++ b/tests/test_decision_making/test_posterior_handler.py @@ -54,24 +54,6 @@ def poisson_likelihood_builder(num_datapoints: int) -> Poisson: return Poisson(num_datapoints=num_datapoints) -@pytest.mark.parametrize("num_optimization_iters", [0, -1, -10]) -def test_posterior_handler_erroneous_num_optimization_iterations_raises_error( - num_optimization_iters: int, -): - mean_function = Constant() - kernel = Matern52() - prior = Prior(mean_function=mean_function, kernel=kernel) - likelihood_builder = gaussian_likelihood_builder - training_objective = ConjugateMLL(negative=True) - solver = jaxopt.LBFGS(training_objective, maxiter=num_optimization_iters) - with pytest.raises(ValueError): - PosteriorHandler( - prior=prior, - likelihood_builder=likelihood_builder, - solver=solver, - ) - - @pytest.mark.filterwarnings( "ignore::UserWarning" ) # Sampling with tfp causes JAX to raise a UserWarning due to some internal logic around jnp.argsort @@ -285,7 +267,7 @@ def test_update_posterior_with_optimization_updated_prior_parameters_and_differe mean_function = Constant(constant=jnp.array([1.0])) kernel = Matern52(lengthscale=jnp.array([0.5]), variance=jnp.array(1.0)) prior = Prior(mean_function=mean_function, kernel=kernel) - solver = jaxopt.OptaxSolver(training_objective, opt=ox.adam(1e-3), maxiter=10) + solver = jaxopt.ScipyMinimize(fun=training_objective, maxiter=1) posterior_handler = PosteriorHandler( prior=prior, likelihood_builder=likelihood_builder, diff --git a/tests/test_fit.py b/tests/test_fit.py index c716bcf1e..ade942a4f 100644 --- a/tests/test_fit.py +++ b/tests/test_fit.py @@ -80,12 +80,12 @@ def step(self, model: LinearModel, train_data: Dataset) -> float: trained_model, hist = fit( model=model, train_data=D, - solver=jaxopt.LBFGS(loss, max_stepsize=1e-3, maxiter=100), + solver=jaxopt.ScipyMinimize(fun=loss), key=jr.PRNGKey(123), ) # Ensure we return a history of the correct length - assert len(hist) == 100 + assert len(hist) == 2 # Ensure we return a model of the same class assert isinstance(trained_model, LinearModel) @@ -123,7 +123,7 @@ def test_gaussian_process_regression( trained_model, history = fit( model=posterior, train_data=D, - solver=jaxopt.LBFGS(mll, maxiter=num_iters, max_stepsize=1e-3), + solver=jaxopt.ScipyMinimize(fun=mll), verbose=verbose, key=jr.PRNGKey(123), ) @@ -132,7 +132,7 @@ def test_gaussian_process_regression( assert isinstance(trained_model, ConjugatePosterior) # Ensure we return a history of the correct length - assert len(history) == num_iters + assert len(history) == 2 # Ensure we reduce the loss assert mll(trained_model, D) < mll(posterior, D) From 57f0dc3b92987b2c1721964bb32502311c1412a9 Mon Sep 17 00:00:00 2001 From: hmoss <32096840+henrymoss@users.noreply.github.com> Date: Fri, 22 Sep 2023 15:18:34 +0100 Subject: [PATCH 19/23] fixed --- gpjax/fit.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/gpjax/fit.py b/gpjax/fit.py index 6b272829f..f4693736f 100644 --- a/gpjax/fit.py +++ b/gpjax/fit.py @@ -131,7 +131,8 @@ def evaluate(self, model: LinearModel, train_data: gpx.Dataset) -> float: # Initialise solver state. solver.fun = _wrap_objective(solver.fun) - solver.options.pop("maxiter", None) # allow __post_init__ without jaxopt error + if hasattr(solver, "options"): # allow __post_init__ without weird jaxopt error + solver.options.pop("maxiter", None) solver.__post_init__() # needed to propagate changes to `fun` attribute if isinstance(solver, OptaxSolver): # hack for Optax compatibility From 4fae37ecf0c529806794f727237d8ac3b54d2882 Mon Sep 17 00:00:00 2001 From: hmoss <32096840+henrymoss@users.noreply.github.com> Date: Fri, 22 Sep 2023 15:23:15 +0100 Subject: [PATCH 20/23] extra test --- tests/test_fit.py | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/tests/test_fit.py b/tests/test_fit.py index ade942a4f..6ca577f2f 100644 --- a/tests/test_fit.py +++ b/tests/test_fit.py @@ -97,6 +97,32 @@ def step(self, model: LinearModel, train_data: Dataset) -> float: assert trained_model.bias == 1.0 +@pytest.mark.parametrize("batch_size", [10, 100]) +def test_raises_if_try_to_batch_scipy_optim(batch_size: int) -> None: + # Create dataset: + key = jr.PRNGKey(123) + x = jnp.sort(jr.uniform(key=key, minval=-2.0, maxval=2.0, shape=(10, 1)), axis=0) + y = jnp.sin(x) + jr.normal(key=key, shape=x.shape) * 0.1 + D = Dataset(X=x, y=y) + + # Define GP model: + prior = Prior(kernel=RBF(), mean_function=Constant()) + likelihood = Gaussian(num_datapoints=10) + posterior = prior * likelihood + + # Define loss function: + mll = ConjugateMLL(negative=True) + + with pytest.raises(ValueError): + fit( + model=posterior, + train_data=D, + solver=jaxopt.ScipyMinimize(fun=mll), + batch_size=batch_size, + key=jr.PRNGKey(123), + ) + + @pytest.mark.parametrize("num_iters", [1, 5]) @pytest.mark.parametrize("n_data", [1, 20]) @pytest.mark.parametrize("verbose", [True, False]) From 0a09d614246e0c1f346fc46f440ee2d6dd2941cf Mon Sep 17 00:00:00 2001 From: henrymoss <32096840+henrymoss@users.noreply.github.com> Date: Tue, 26 Sep 2023 10:05:04 +0100 Subject: [PATCH 21/23] fixed? --- gpjax/decision_making/posterior_handler.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/gpjax/decision_making/posterior_handler.py b/gpjax/decision_making/posterior_handler.py index 97b79f186..bc81ab51d 100644 --- a/gpjax/decision_making/posterior_handler.py +++ b/gpjax/decision_making/posterior_handler.py @@ -144,7 +144,8 @@ def _optimize_posterior( # # We create a new solver state -> since the dataset (and therefore loss function) has changed! attributes = asdict(self.solver) - attributes["options"].pop("maxiter", None) # allow reinit without jaxopt error + if hasattr(attributes, "options"): # allow reinit without jaxopt error + attributes["options"].pop("maxiter", None) attributes.pop("fun", None) # pass in fun as callable rather than dict new_solver = self.solver.__class__(fun=self.solver.fun, **attributes) From f46288e74913f2dab77e43ca92954bcce8fdf836 Mon Sep 17 00:00:00 2001 From: henrymoss <32096840+henrymoss@users.noreply.github.com> Date: Tue, 26 Sep 2023 17:47:13 +0100 Subject: [PATCH 22/23] still broken --- gpjax/fit.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/gpjax/fit.py b/gpjax/fit.py index f4693736f..2ba0e100e 100644 --- a/gpjax/fit.py +++ b/gpjax/fit.py @@ -12,7 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - +from dataclasses import ( + asdict, + dataclass, +) from beartype.typing import ( Any, @@ -131,12 +134,13 @@ def evaluate(self, model: LinearModel, train_data: gpx.Dataset) -> float: # Initialise solver state. solver.fun = _wrap_objective(solver.fun) - if hasattr(solver, "options"): # allow __post_init__ without weird jaxopt error - solver.options.pop("maxiter", None) - solver.__post_init__() # needed to propagate changes to `fun` attribute if isinstance(solver, OptaxSolver): # hack for Optax compatibility model = jax.tree_map(lambda x: x.astype(jnp.float64), model) + # # elif isinstance(solver, ScipyMinimize): # hack for jaxopt compatibility + # del solver.options["maxiter"] + + solver.__post_init__() # needed to propagate changes to `fun` attribute if isinstance(solver, OptaxSolver): # For optax, run optimization by step solver_state = solver.init_state( From 83571b4fc4e205aa7bf5bd702eb5ee19ffc6e544 Mon Sep 17 00:00:00 2001 From: hmoss <32096840+henrymoss@users.noreply.github.com> Date: Sat, 21 Oct 2023 09:19:49 +0100 Subject: [PATCH 23/23] wip --- gpjax/fit.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/gpjax/fit.py b/gpjax/fit.py index 2ba0e100e..41082ac36 100644 --- a/gpjax/fit.py +++ b/gpjax/fit.py @@ -12,10 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -from dataclasses import ( - asdict, - dataclass, -) +# from dataclasses import ( +# asdict, +# dataclass, +# ) from beartype.typing import ( Any,