Use objects for solver outcomes, not errors and tuples

src/causalprog/solvers/sgd.py

@@ -8,6 +8,7 @@
 import numpy.typing as npt
 import optax
 
+from causalprog.solvers.solver_result import SolverResult
 from causalprog.utils.norms import PyTree, l2_normsq
 
 
@@ -22,7 +23,7 @@ def stochastic_gradient_descent(
     maxiter: int = 1000,
     optimiser: optax.GradientTransformationExtraArgs | None = None,
     tolerance: float = 1.0e-8,
-) -> tuple[PyTree, npt.ArrayLike, npt.ArrayLike, int]:
+) -> SolverResult:
     """
     Minimise a function of one argument using Stochastic Gradient Descent (SGD).
 
@@ -87,20 +88,33 @@ def objective(x: npt.ArrayLike) -> npt.ArrayLike:
     def is_converged(x: npt.ArrayLike, dx: npt.ArrayLike) -> bool:
         return convergence_criteria(x, dx) < tolerance
 
-    gradient = jax.grad(objective)
-    opt_state = optimiser.init(initial_guess)
+    converged = False
 
+    opt_state = optimiser.init(initial_guess)
     current_params = deepcopy(initial_guess)
-    gradient_value = gradient(current_params)
-    for i in range(maxiter):
-        updates, opt_state = optimiser.update(gradient_value, opt_state)
-        current_params = optax.apply_updates(current_params, updates)
+    gradient = jax.grad(objective)
 
+    for _ in range(maxiter + 1):
         objective_value = objective(current_params)
         gradient_value = gradient(current_params)
 
-        if is_converged(objective_value, gradient_value):
-            return current_params, objective_value, gradient_value, i + 1
+        if converged := is_converged(objective_value, gradient_value):
+            break
+
+        updates, opt_state = optimiser.update(gradient_value, opt_state)
+        current_params = optax.apply_updates(current_params, updates)
 
-    msg = f"Did not converge after {i + 1} iterations."
-    raise RuntimeError(msg)
+    iters_used = _
+    reason_msg = (
+        f"Did not converge after {iters_used} iterations" if not converged else ""
+    )
+
+    return SolverResult(
+        fn_args=current_params,
+        grad_val=gradient_value,
+        iters=iters_used,
+        maxiter=maxiter,
+        obj_val=objective_value,
+        reason=reason_msg,
+        successful=converged,
+    )

src/causalprog/solvers/solver_result.py

@@ -0,0 +1,38 @@
+"""Container class for outputs from solver methods."""
+
+from dataclasses import dataclass
+
+import numpy.typing as npt
+
+from causalprog.utils.norms import PyTree
+
+
+@dataclass(frozen=True)
+class SolverResult:
+    """
+    Container class for outputs from solver methods.
+
+    Instances of this class provide a container for useful information that
+    comes out of running one of the solver methods on a causal problem.
+
+    Attributes:
+        fn_args: Argument to the objective function at final iteration (the solution,
+            if `successful is `True`).
+        grad_val: Value of the gradient of the objective function at the `fn_args`.
+        iters: Number of iterations performed.
+        maxiter: Maximum number of iterations the solver was permitted to perform.
+        obj_val: Value of the objective function at `fn_args`.
+        reason: Human-readable string explaining success or reasons for solver failure.
+        successful: `True` if solver converged, in which case `fn_args` is the
+            argument to the objective function at the solution of the problem being
+            solved. `False` otherwise.
+
+    """
+
+    fn_args: PyTree
+    grad_val: PyTree
+    iters: int
+    maxiter: int
+    obj_val: npt.ArrayLike
+    reason: str
+    successful: bool

tests/test_integration/test_two_normal_example.py

@@ -107,7 +107,7 @@ def objective(x, key):
     }
     l_mult = jnp.atleast_1d(lagrange_mult_sol)
 
-    opt_params, _, _, _ = stochastic_gradient_descent(
+    result = stochastic_gradient_descent(
         objective,
         (params, l_mult),
         convergence_criteria=lambda x, _: jnp.abs(x),
@@ -116,8 +116,10 @@ def objective(x, key):
         maxiter=maxiter,
         tolerance=minimisation_tolerance,
     )
+    assert result.successful, "SGD did not converge."
+
     # Unpack concatenated arguments
-    params, l_mult = opt_params
+    params, l_mult = result.fn_args
 
     # The lagrangian is independent of nu_x, thus it should not have changed value.
     assert jnp.isclose(params["cov2"], nu_x_starting_value), (

tests/test_solvers/test_sgd.py

@@ -28,7 +28,7 @@
         pytest.param(
             lambda x: (x**2).sum(),
             jnp.atleast_1d(10.0),
-            RuntimeError("Did not converge after 1 iterations"),
+            "Did not converge after 1 iterations",
             {"maxiter": 1},
             id="Reaches iteration limit",
         ),
@@ -67,8 +67,7 @@ def test_sgd(
     obj_fn: Callable[[PyTree], npt.ArrayLike],
     initial_guess: PyTree,
     kwargs_to_sgd: dict[str, Any],
-    expected: PyTree | Exception,
-    raises_context,
+    expected: PyTree | str,
 ) -> None:
     """Test the SGD method on a (deterministic) problem.
 
@@ -79,12 +78,12 @@ def test_sgd(
     if not kwargs_to_sgd:
         kwargs_to_sgd = {}
 
-    if isinstance(expected, Exception):
-        with raises_context(expected):
-            stochastic_gradient_descent(obj_fn, initial_guess, **kwargs_to_sgd)
-    else:
-        result = stochastic_gradient_descent(obj_fn, initial_guess, **kwargs_to_sgd)[0]
+    result = stochastic_gradient_descent(obj_fn, initial_guess, **kwargs_to_sgd)
 
+    if isinstance(expected, str):
+        assert not result.successful
+        assert result.reason == expected
+    else:
         assert jax.tree_util.tree_all(
-            jax.tree_util.tree_map(jax.numpy.allclose, result, expected)
+            jax.tree_util.tree_map(jax.numpy.allclose, result.fn_args, expected)
         )