Removes depreciated Adam optimiser

CHANGELOG.md

@@ -31,6 +31,7 @@
 
 ## Breaking Changes
 
+- [#598](https://github.com/pybop-team/PyBOP/pull/598) - Depreciated `Adam` optimiser has been removed, see `AdamW` for replacement.
 - [#531](https://github.com/pybop-team/PyBOP/pull/531) - Plot methods moved to `pybop.plot` with mostly minimal renaming. For example, `pybop.plot_parameters` is now `pybop.plot.parameters`. Other breaking changes include: `pybop.plot2d` to `pybop.plot.contour`.
 - [#526](https://github.com/pybop-team/PyBOP/pull/526) - Refactor `OptimisationResults` classes, with `optim.run()` now return the full object. Adds finite cost value check for optimised parameters.
 

pybop/__init__.py

@@ -134,7 +134,6 @@
 )
 from .optimisers.pints_optimisers import (
     GradientDescent,
-    Adam,
     CMAES,
     IRPropMin,
     NelderMead,

pybop/optimisers/base_pints_optimiser.py

@@ -2,7 +2,6 @@
 
 import numpy as np
 from pints import PSO as PintsPSO
-from pints import Adam as PintsAdam
 from pints import NelderMead as PintsNelderMead
 from pints import Optimiser as PintsOptimiser
 from pints import ParallelEvaluator as PintsParallelEvaluator
@@ -12,7 +11,7 @@
 from pints import SequentialEvaluator as PintsSequentialEvaluator
 from pints import strfloat as PintsStrFloat
 
-from pybop import BaseOptimiser, GradientDescentImpl, OptimisationResult
+from pybop import AdamWImpl, BaseOptimiser, GradientDescentImpl, OptimisationResult
 
 
 class BasePintsOptimiser(BaseOptimiser):
@@ -143,7 +142,7 @@ def _sanitise_inputs(self):
 
         # Convert bounds to PINTS boundaries
         if self.bounds is not None:
-            ignored_optimisers = (GradientDescentImpl, PintsAdam, PintsNelderMead)
+            ignored_optimisers = (GradientDescentImpl, AdamWImpl, PintsNelderMead)
             if issubclass(self._pints_optimiser, ignored_optimisers):
                 print(f"NOTE: Boundaries ignored by {self._pints_optimiser}")
                 self.bounds = None

pybop/optimisers/pints_optimisers.py

@@ -1,10 +1,7 @@
-from warnings import warn
-
 from pints import CMAES as PintsCMAES
 from pints import PSO as PintsPSO
 from pints import SNES as PintsSNES
 from pints import XNES as PintsXNES
-from pints import Adam as PintsAdam
 from pints import IRPropMin as PintsIRPropMin
 from pints import NelderMead as PintsNelderMead
 
@@ -87,83 +84,6 @@ def __init__(
         )
 
 
-class Adam(BasePintsOptimiser):
-    """
-    Implements the Adam optimisation algorithm.
-
-    This class extends the Adam optimiser from the PINTS library, which combines
-    ideas from RMSProp and Stochastic Gradient Descent with momentum.
-
-    Note that this optimiser does not support boundary constraints.
-
-    Parameters
-    ----------
-    cost : callable
-        The cost function to be minimized.
-    max_iterations : int, optional
-        Maximum number of iterations for the optimisation.
-    min_iterations : int, optional (default=2)
-        Minimum number of iterations before termination.
-    max_unchanged_iterations : int, optional (default=15)
-        Maximum number of iterations without improvement before termination.
-    multistart : int, optional (default=1)
-        Number of optimiser restarts from randomly sample position. These positions
-        are sampled from the priors.
-    parallel : bool, optional (default=False)
-        Whether to run the optimisation in parallel.
-    **optimiser_kwargs : optional
-        Valid PINTS option keys and their values, for example:
-        x0 : array_like
-            Initial position from which optimisation will start.
-        sigma0 : float
-            Initial step size or standard deviation depending on the optimiser.
-        bounds : dict
-            A dictionary with 'lower' and 'upper' keys containing arrays for lower and
-            upper bounds on the parameters.
-        use_f_guessed : bool
-            Whether to return the guessed function values.
-        absolute_tolerance : float
-            Absolute tolerance for convergence checking.
-        relative_tolerance : float
-            Relative tolerance for convergence checking.
-        max_evaluations : int
-            Maximum number of function evaluations.
-        threshold : float
-            Threshold value for early termination.
-
-    See Also
-    --------
-    pints.Adam : The PINTS implementation this class is based on.
-    """
-
-    warn(
-        "Adam is deprecated and will be removed in a future release. Please use AdamW instead.",
-        DeprecationWarning,
-        stacklevel=2,
-    )
-
-    def __init__(
-        self,
-        cost,
-        max_iterations: int = None,
-        min_iterations: int = 2,
-        max_unchanged_iterations: int = 15,
-        multistart: int = 1,
-        parallel: bool = False,
-        **optimiser_kwargs,
-    ):
-        super().__init__(
-            cost,
-            PintsAdam,
-            max_iterations,
-            min_iterations,
-            max_unchanged_iterations,
-            multistart,
-            parallel,
-            **optimiser_kwargs,
-        )
-
-
 class AdamW(BasePintsOptimiser):
     """
     Implements the AdamW optimisation algorithm in PyBOP.

tests/unit/test_optimisation.py

@@ -76,7 +76,6 @@ def two_param_cost(self, model, two_parameters, dataset):
             (pybop.SciPyMinimize, "SciPyMinimize", False),
             (pybop.SciPyDifferentialEvolution, "SciPyDifferentialEvolution", False),
             (pybop.GradientDescent, "Gradient descent", True),
-            (pybop.Adam, "Adam", True),
             (pybop.AdamW, "AdamW", True),
             (
                 pybop.CMAES,
@@ -131,7 +130,6 @@ def test_no_optimisation_parameters(self, model, dataset):
             pybop.SciPyMinimize,
             pybop.SciPyDifferentialEvolution,
             pybop.GradientDescent,
-            pybop.Adam,
             pybop.AdamW,
             pybop.SNES,
             pybop.XNES,
@@ -184,7 +182,7 @@ def check_multistart(optim, n_iters, multistarts):
         multistart_optim = optimiser(cost, multistart=2, max_iterations=6)
         check_multistart(multistart_optim, 6, 2)
 
-        if optimiser in [pybop.GradientDescent, pybop.Adam, pybop.NelderMead]:
+        if optimiser in [pybop.GradientDescent, pybop.AdamW, pybop.NelderMead]:
             optim = optimiser(cost=cost, bounds=cost_bounds)
             assert optim.bounds is None
         elif optimiser in [pybop.PSO]: