Merge pull request #393 from pybop-team/fixes-ahead-v24.6

General additions ahead of v24.6

CHANGELOG.md

@@ -3,12 +3,14 @@
 ## Features
 
 - [#319](https://github.com/pybop-team/PyBOP/pull/319/) - Adds `CuckooSearch` optimiser with corresponding tests.
+- [#359](https://github.com/pybop-team/PyBOP/pull/359/) - Aligning Inputs between problem, observer and model.
 - [#379](https://github.com/pybop-team/PyBOP/pull/379) - Adds model.simulateS1 to weekly benchmarks.
 - [#174](https://github.com/pybop-team/PyBOP/issues/174) - Adds new logo and updates Readme for accessibility.
 - [#316](https://github.com/pybop-team/PyBOP/pull/316) - Adds Adam with weight decay (AdamW) optimiser, adds depreciation warning for pints.Adam implementation.
 - [#271](https://github.com/pybop-team/PyBOP/issues/271) - Aligns the output of the optimisers via a generalisation of Result class.
 - [#315](https://github.com/pybop-team/PyBOP/pull/315) - Updates __init__ structure to remove circular import issues and minimises dependancy imports across codebase for faster PyBOP module import. Adds type-hints to BaseModel and refactors rebuild parameter variables.
 - [#236](https://github.com/pybop-team/PyBOP/issues/236) - Restructures the optimiser classes, adds a new optimisation API through direct construction and keyword arguments, and fixes the setting of `max_iterations`, and `_minimising`. Introduces `pybop.BaseOptimiser`, `pybop.BasePintsOptimiser`, and `pybop.BaseSciPyOptimiser` classes.
+- [#322](https://github.com/pybop-team/PyBOP/pull/322) - Add `Parameters` class to store and access multiple parameters in one object.
 - [#321](https://github.com/pybop-team/PyBOP/pull/321) - Updates Prior classes with BaseClass, adds a `problem.sample_initial_conditions` method to improve stability of SciPy.Minimize optimiser.
 - [#249](https://github.com/pybop-team/PyBOP/pull/249) - Add WeppnerHuggins model and GITT example.
 - [#304](https://github.com/pybop-team/PyBOP/pull/304) - Decreases the testing suite completion time.
@@ -26,6 +28,7 @@
 
 ## Bug Fixes
 
+- [#393](https://github.com/pybop-team/PyBOP/pull/393) - General integration test fixes. Adds UserWarning when using Plot2d with prior generated bounds.
 - [#338](https://github.com/pybop-team/PyBOP/pull/338) - Fixes GaussianLogLikelihood class, adds integration tests, updates non-bounded parameter implementation by applying bounds from priors and `boundary_multiplier` argument. Bugfixes to CMAES construction.
 - [#339](https://github.com/pybop-team/PyBOP/issues/339) - Updates the calculation of the cyclable lithium capacity in the spme_max_energy example.
 - [#387](https://github.com/pybop-team/PyBOP/issues/387) - Adds keys to ParameterSet and updates ECM OCV check.
@@ -44,6 +47,13 @@
 - [#270](https://github.com/pybop-team/PyBOP/pull/270) - Updates PR template.
 - [#91](https://github.com/pybop-team/PyBOP/issues/91) - Adds a check on the number of parameters for CMAES and makes XNES the default optimiser.
 
+## Breaking Changes
+
+- [#322](https://github.com/pybop-team/PyBOP/pull/322) - Add `Parameters` class to store and access multiple parameters in one object (API change).
+- [#285](https://github.com/pybop-team/PyBOP/pull/285) - Drop support for Python 3.8.
+- [#251](https://github.com/pybop-team/PyBOP/pull/251) - Drop support for PyBaMM v23.5
+- [#236](https://github.com/pybop-team/PyBOP/issues/236) - Restructures the optimiser classes (API change).
+
 # [v24.3.1](https://github.com/pybop-team/PyBOP/tree/v24.3.1) - 2024-06-17
 
 ## Features

examples/scripts/cuckoo.py

@@ -0,0 +1,75 @@
+import numpy as np
+
+import pybop
+
+# Define model
+parameter_set = pybop.ParameterSet.pybamm("Chen2020")
+model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
+
+# Fitting parameters
+parameters = pybop.Parameters(
+    pybop.Parameter(
+        "Negative electrode active material volume fraction",
+        prior=pybop.Gaussian(0.6, 0.05),
+        bounds=[0.4, 0.75],
+        initial_value=0.41,
+        true_value=0.7,
+    ),
+    pybop.Parameter(
+        "Positive electrode active material volume fraction",
+        prior=pybop.Gaussian(0.48, 0.05),
+        bounds=[0.4, 0.75],
+        initial_value=0.41,
+        true_value=0.67,
+    ),
+)
+init_soc = 0.7
+experiment = pybop.Experiment(
+    [
+        (
+            "Discharge at 0.5C for 3 minutes (4 second period)",
+            "Charge at 0.5C for 3 minutes (4 second period)",
+        ),
+    ]
+)
+values = model.predict(
+    init_soc=init_soc, experiment=experiment, inputs=parameters.as_dict("true")
+)
+
+sigma = 0.002
+corrupt_values = values["Voltage [V]"].data + np.random.normal(
+    0, sigma, len(values["Voltage [V]"].data)
+)
+
+# Form dataset
+dataset = pybop.Dataset(
+    {
+        "Time [s]": values["Time [s]"].data,
+        "Current function [A]": values["Current [A]"].data,
+        "Voltage [V]": corrupt_values,
+    }
+)
+
+# Generate problem, cost function, and optimisation class
+problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc)
+cost = pybop.GaussianLogLikelihood(problem, sigma0=sigma * 4)
+optim = pybop.Optimisation(
+    cost,
+    optimiser=pybop.CuckooSearch,
+    max_iterations=100,
+)
+
+x, final_cost = optim.run()
+print("Estimated parameters:", x)
+
+# Plot the timeseries output
+pybop.quick_plot(problem, problem_inputs=x, title="Optimised Comparison")
+
+# Plot convergence
+pybop.plot_convergence(optim)
+
+# Plot the parameter traces
+pybop.plot_parameters(optim)
+
+# Plot the cost landscape with optimisation path
+pybop.plot2d(optim, steps=15)

pybop/optimisers/_cuckoo.py

@@ -47,7 +47,7 @@ class CuckooSearchImpl(PopulationBasedOptimiser):
       https://doi.org/10.1016/j.chaos.2011.06.004.
     """
 
-    def __init__(self, x0, sigma0=0.01, boundaries=None, pa=0.25):
+    def __init__(self, x0, sigma0=0.05, boundaries=None, pa=0.25):
         super().__init__(x0, sigma0, boundaries=boundaries)
 
         # Problem dimensionality

pybop/parameters/parameter.py

@@ -1,3 +1,4 @@
+import warnings
 from collections import OrderedDict
 from typing import Dict, List, Union
 
@@ -46,6 +47,7 @@ def __init__(
         self.true_value = true_value
         self.initial_value = initial_value
         self.value = initial_value
+        self.applied_prior_bounds = False
         self.set_bounds(bounds)
         self.margin = 1e-4
 
@@ -153,6 +155,7 @@ def set_bounds(self, bounds=None, boundary_multiplier=6):
                 self.lower_bound = bounds[0]
                 self.upper_bound = bounds[1]
         elif self.prior is not None:
+            self.applied_prior_bounds = True
             self.lower_bound = self.prior.mean - boundary_multiplier * self.prior.sigma
             self.upper_bound = self.prior.mean + boundary_multiplier * self.prior.sigma
             bounds = [self.lower_bound, self.upper_bound]
@@ -417,7 +420,14 @@ def get_bounds_for_plotly(self):
         bounds = np.empty((len(self), 2))
 
         for i, param in enumerate(self.param.values()):
-            if param.bounds is not None:
+            if param.applied_prior_bounds:
+                warnings.warn(
+                    "Bounds were created from prior distributions. "
+                    "Please provide bounds for better plotting results.",
+                    UserWarning,
+                    stacklevel=2,
+                )
+            elif param.bounds is not None:
                 bounds[i] = param.bounds
             else:
                 raise ValueError("All parameters require bounds for plotting.")

tests/integration/test_spm_parameterisations.py

@@ -26,12 +26,12 @@ def parameters(self):
         return pybop.Parameters(
             pybop.Parameter(
                 "Negative electrode active material volume fraction",
-                prior=pybop.Uniform(0.4, 0.7),
-                bounds=[0.375, 0.725],
+                prior=pybop.Uniform(0.4, 0.75),
+                bounds=[0.375, 0.75],
             ),
             pybop.Parameter(
                 "Positive electrode active material volume fraction",
-                prior=pybop.Uniform(0.4, 0.7),
+                prior=pybop.Uniform(0.4, 0.75),
                 # no bounds
             ),
         )
@@ -100,21 +100,26 @@ def test_spm_optimisers(self, optimiser, spm_costs):
         common_args = {
             "cost": spm_costs,
             "max_iterations": 250,
+            "absolute_tolerance": 1e-6,
         }
 
         # Add sigma0 to ground truth for GaussianLogLikelihood
         if isinstance(spm_costs, pybop.GaussianLogLikelihood):
             self.ground_truth = np.concatenate(
                 (self.ground_truth, np.asarray([self.sigma0]))
             )
-
+        if isinstance(spm_costs, pybop.MAP):
+            for i in spm_costs.parameters.keys():
+                spm_costs.parameters[i].prior = pybop.Uniform(
+                    0.4, 2.0
+                )  # Increase range to avoid prior == np.inf
         # Set sigma0 and create optimiser
-        sigma0 = 0.006 if isinstance(spm_costs, pybop.MAP) else None
+        sigma0 = 0.05 if isinstance(spm_costs, pybop.MAP) else None
         optim = optimiser(sigma0=sigma0, **common_args)
 
         # Set max unchanged iterations for BasePintsOptimisers
         if issubclass(optimiser, pybop.BasePintsOptimiser):
-            optim.set_max_unchanged_iterations(iterations=45, absolute_tolerance=1e-5)
+            optim.set_max_unchanged_iterations(iterations=55)
 
         # AdamW will use lowest sigma0 for learning rate, so allow more iterations
         if issubclass(optimiser, (pybop.AdamW, pybop.IRPropMin)) and isinstance(

tests/unit/test_plots.py

@@ -1,3 +1,5 @@
+import warnings
+
 import numpy as np
 import pytest
 from packaging import version
@@ -193,3 +195,25 @@ def test_plot2d_incorrect_number_of_parameters(self, model, dataset):
         fitting_problem = pybop.FittingProblem(model, parameters, dataset)
         cost = pybop.SumSquaredError(fitting_problem)
         pybop.plot2d(cost)
+
+    @pytest.mark.unit
+    def test_plot2d_prior_bounds(self, model, dataset):
+        # Test with prior bounds
+        parameters = pybop.Parameters(
+            pybop.Parameter(
+                "Negative electrode active material volume fraction",
+                prior=pybop.Gaussian(0.68, 0.01),
+            ),
+            pybop.Parameter(
+                "Positive electrode active material volume fraction",
+                prior=pybop.Gaussian(0.58, 0.01),
+            ),
+        )
+        fitting_problem = pybop.FittingProblem(model, parameters, dataset)
+        cost = pybop.SumSquaredError(fitting_problem)
+        with pytest.warns(
+            UserWarning,
+            match="Bounds were created from prior distributions.",
+        ):
+            warnings.simplefilter("always")
+            pybop.plot2d(cost)