Implement and document custom constraints

docs/dev_docs/creating_example_notebook.md

@@ -34,7 +34,7 @@ Probdiffeq hosts numerous tutorials and benchmarks that demonstrate the library.
    Ensure the corresponding script is excluded under `mkdocs.yml -> exclude:`; if needed, add it there.
 
 4. **Makefile:**  
-   Add the new example or benchmark to the appropriate Makefile target (e.g., `examples-and-benchmarks`).
+   Check whether the new example or benchmark needs to be added to the appropriate Makefile target (e.g., `examples-and-benchmarks`). Generally, new files are detected automatically, but check nevertheless.
 
 5. **Pyproject.toml:**  
    If your example requires external dependencies, list them under the `doc` optional dependencies in `pyproject.toml`.

docs/examples_advanced/equinox_while_loop.py

@@ -26,8 +26,6 @@
 
 from probdiffeq import ivpsolve, probdiffeq, taylor
 
-# -
-
 
 def solution_routine(while_loop):
     """Construct a parameter-to-solution function and an initial value."""
@@ -64,17 +62,32 @@ def simulate(init_val):
     return simulate, init
 
 
-# This is the default behaviour
+# -
+
+
+# This is the default behaviour.
+
+
+# +
+
+
 solve, x = solution_routine(jax.lax.while_loop)
 
 try:
     solution, gradient = jax.jit(jax.value_and_grad(solve))(x)
 except ValueError as err:
     print(f"Caught error:\n\t {err}")
 
+
+# -
+
+
 # This while-loop makes the solver differentiable
 
 
+# +
+
+
 def while_loop_func(*a, **kw):
     """Evaluate a bounded while loop."""
     return equinox.internal.while_loop(*a, **kw, kind="bounded", max_steps=100)
@@ -87,3 +100,5 @@ def while_loop_func(*a, **kw):
 
 print(solution)
 print(gradient)
+
+# -

docs/examples_advanced/neural_ode.py

@@ -85,6 +85,11 @@ def main(num_data=100, epochs=500, print_every=50, hidden=(20,), lr=0.2):
     plt.show()
 
 
+# -
+
+# +
+
+
 def vf_neural_ode(*, hidden: tuple, t0: float, t1: float):
     """Build a neural ODE."""
     f_args, mlp = model_mlp(hidden=hidden, shape_in=(2,), shape_out=(1,))
@@ -99,6 +104,11 @@ def vf(y, *, t, p):
     return vf, (u0,), (t0, t1), f_args
 
 
+# -
+
+# +
+
+
 def model_mlp(
     *, hidden: tuple, shape_in: tuple = (), shape_out: tuple = (), activation=jnp.tanh
 ):
@@ -133,6 +143,11 @@ def fwd(w, x):
     return unravel(p_init), fwd
 
 
+# -
+
+# +
+
+
 def loss_log_marginal_likelihood(vf, *, t0):
     """Build a loss function from an ODE problem."""
 
@@ -177,6 +192,11 @@ def loss(
     return loss
 
 
+# -
+
+# +
+
+
 def train_step_optax(optimizer, loss):
     """Implement a training step using Optax."""
 
@@ -194,5 +214,10 @@ def update(params, opt_state, **loss_kwargs):
     return update
 
 
+# -
+
+# +
+
+
 if __name__ == "__main__":
     main()

docs/examples_advanced/parameter_estimation_blackjax.py

@@ -162,7 +162,11 @@ def vf(y, *, t):  # noqa: ARG001
 theta_guess = u0  # initial guess
 
 
+# -
+
 # +
+
+
 def plot_solution(t, u, *, ax, marker=".", **plotting_kwargs):
     """Plot the IVP solution."""
     for d in [0, 1]:
@@ -205,9 +209,15 @@ def solve_adaptive(theta, *, save_at):
 save_at = jnp.linspace(t0, t1, num=250, endpoint=True)
 solve_save_at = functools.partial(solve_adaptive, save_at=save_at)
 
-# +
+
+# -
+
 # Visualise the initial guess and the data
 
+
+# +
+
+
 fig, ax = plt.subplots(figsize=(5, 3))
 
 data_kwargs = {"alpha": 0.5, "color": "gray"}
@@ -220,6 +230,7 @@ def solve_adaptive(theta, *, save_at):
 sol = solve_save_at(theta_guess)
 ax = plot_solution(sol.t, sol.u.mean[0], ax=ax, **guess_kwargs)
 plt.show()
+
 # -
 
 # ## Log-posterior densities via ProbDiffEq
@@ -244,17 +255,30 @@ def logposterior_fn(theta, *, data, ts, obs_stdev=0.1):
     return logpdf_data + logpdf_prior
 
 
+# -
+
+
 # Fixed steps for reverse-mode differentiability:
 
 
+# +
+
+
 ts = jnp.linspace(t0, t1, endpoint=True, num=100)
 data = solve_fixed(theta_true, ts=ts).u.mean[0][-1]
 
 log_M = functools.partial(logposterior_fn, data=data, ts=ts)
+
+
 # -
 
+# +
+
+
 print(jnp.exp(log_M(theta_true)), ">=", jnp.exp(log_M(theta_guess)), "?")
 
+# -
+
 
 # ## Sampling with BlackJAX
 #
@@ -263,6 +287,9 @@ def logposterior_fn(theta, *, data, ts, obs_stdev=0.1):
 # Set up a sampler.
 
 
+# +
+
+
 @functools.partial(jax.jit, static_argnames=["kernel", "num_samples"])
 def inference_loop(rng_key, kernel, initial_state, num_samples):
     """Run BlackJAX' inference loop."""
@@ -277,13 +304,24 @@ def one_step(state, rng_key):
     return states
 
 
+# -
+
+
 # Initialise the sampler, warm it up, and run the inference loop.
 
+
+# +
+
+
 initial_position = theta_guess
 rng_key = jax.random.PRNGKey(0)
 
+# -
+
+# Warm up.
+
 # +
-# WARMUP
+
 warmup = blackjax.window_adaptation(blackjax.nuts, log_M, progress_bar=True)
 
 warmup_results, _ = warmup.run(rng_key, initial_position, num_steps=200)
@@ -296,21 +334,40 @@ def one_step(state, rng_key):
 )
 # -
 
-# INFERENCE LOOP
+# Inference loop
+
+
+# +
+
+
 rng_key, _ = jax.random.split(rng_key, 2)
 states = inference_loop(
     rng_key, kernel=nuts_kernel, initial_state=initial_state, num_samples=150
 )
 
+# -
+
+
 # ## Visualisation
 #
 # Now that we have samples of $\theta$, let's plot the corresponding solutions:
 
+
+# +
+
+
 solution_samples = jax.vmap(solve_save_at)(states.position)
 
+# -
+
 # +
+
 # Visualise the initial guess and the data
 
+
+# +
+
+
 fig, ax = plt.subplots()
 
 sample_kwargs = {"color": "C0"}
@@ -330,20 +387,29 @@ def one_step(state, rng_key):
     sol.t, sol.u.mean[0], ax=ax, linestyle="dashed", alpha=0.75, **guess_kwargs
 )
 plt.show()
+
 # -
 
 # The samples cover a perhaps surpringly large range of
 # potential initial conditions, but lead to the "correct" data.
 #
 # In parameter space, this is what it looks like:
 
+
+# +
+
+
 plt.title("Posterior samples (parameter space)")
 plt.plot(states.position[:, 0], states.position[:, 1], "o", alpha=0.5, markersize=4)
 plt.plot(theta_true[0], theta_true[1], "P", label="Truth", markersize=8)
 plt.plot(theta_guess[0], theta_guess[1], "P", label="Initial guess", markersize=8)
 plt.legend()
 plt.show()
 
+
+# -
+
+
 # Let's add the value of $M$ to the plot to see whether
 # the sampler covers the entire region of interest.
 
@@ -360,7 +426,11 @@ def one_step(state, rng_key):
 log_M_vmapped = jax.vmap(log_M_vmapped_x, in_axes=-1, out_axes=-1)
 Zs = log_M_vmapped(Thetas)
 
+
+# -
+
 # +
+
 fig, ax = plt.subplots(ncols=2, sharex=True, sharey=True, figsize=(8, 3))
 
 ax_samples, ax_heatmap = ax
@@ -377,6 +447,7 @@ def one_step(state, rng_key):
 im = ax_heatmap.contourf(Xs, Ys, jnp.exp(Zs), cmap="cividis", alpha=0.8)
 plt.colorbar(im)
 plt.show()
+
 # -
 
 # Looks great!

docs/examples_advanced/parameter_estimation_optax.py

@@ -33,7 +33,6 @@
 
 from probdiffeq import ivpsolve, probdiffeq
 
-# +
 if not backend.has_been_selected:
     backend.select("jax")  # ivp examples in jax
 
@@ -87,16 +86,21 @@ def solve(p):
 data = solution_true.u.mean[0]
 plt.plot(ts, data, "P-")
 plt.show()
+
 # -
 
 # We make an initial guess, but it does not lead to a good data fit:
 
+# +
+
 solution_guess = solve(parameter_guess)
 plt.plot(ts, data, color="k", linestyle="solid", linewidth=6, alpha=0.125)
 plt.plot(ts, solution_guess.u.mean[0])
 plt.show()
 
 
+# -
+
 # Use the probdiffeq functionality to compute a parameter-to-data fit function.
 #
 # This incorporates the likelihood of the data under the distribution induced
@@ -123,9 +127,10 @@ def parameter_to_data_fit(parameters_, /, standard_deviation=1e-1):
 # We can differentiate the function forward- and reverse-mode
 # (the latter is possible because we use fixed steps)
 
+# +
 parameter_to_data_fit(parameter_guess)
 sensitivities(parameter_guess)
-
+# -
 
 # Now, enter optax: build an optimizer,
 # and optimise the parameter-to-model-fit function.
@@ -151,7 +156,11 @@ def update(params, opt_state):
 optim = optax.adam(learning_rate=1e-2)
 update_fn = build_update_fn(optimizer=optim, loss_fn=parameter_to_data_fit)
 
+# -
+
 # +
+
+
 p = parameter_guess
 state = optim.init(p)
 
@@ -165,7 +174,11 @@ def update(params, opt_state):
 
 # The solution looks much better:
 
+# +
+
 solution_better = solve(p)
 plt.plot(ts, data, color="k", linestyle="solid", linewidth=6, alpha=0.125)
 plt.plot(ts, solution_better.u.mean[0])
 plt.show()
+
+# -