Additions for #327 - Enable DesignCosts in WeightedCost

examples/scripts/spm_weighted_cost.py

@@ -24,24 +24,37 @@
 
 # Generate data
 sigma = 0.001
-t_eval = np.arange(0, 900, 3)
-values = model.predict(t_eval=t_eval)
-corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval))
+init_soc = 0.5
+experiment = pybop.Experiment(
+    [
+        (
+            "Discharge at 0.5C for 3 minutes (3 second period)",
+            "Charge at 0.5C for 3 minutes (3 second period)",
+        ),
+    ]
+    * 2
+)
+values = model.predict(experiment=experiment, init_soc=init_soc)
+
+
+def noise(sigma):
+    return np.random.normal(0, sigma, len(values["Voltage [V]"].data))
+
 
 # Form dataset
 dataset = pybop.Dataset(
     {
-        "Time [s]": t_eval,
+        "Time [s]": values["Time [s]"].data,
         "Current function [A]": values["Current [A]"].data,
-        "Voltage [V]": corrupt_values,
+        "Voltage [V]": values["Voltage [V]"].data + noise(sigma),
     }
 )
 
 # Generate problem, cost function, and optimisation class
-problem = pybop.FittingProblem(model, parameters, dataset)
+problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc)
 cost1 = pybop.SumSquaredError(problem)
 cost2 = pybop.RootMeanSquaredError(problem)
-weighted_cost = pybop.WeightedCost(cost1, cost2, weights=[1, 100])
+weighted_cost = pybop.WeightedCost(cost1, cost2, weights=[0.1, 1])
 
 for cost in [weighted_cost, cost1, cost2]:
     optim = pybop.IRPropMin(cost, max_iterations=60)

examples/scripts/spme_max_energy.py

@@ -9,12 +9,6 @@
 # electrode widths, particle radii, volume fractions and
 # separator width.
 
-# NOTE: This script can be easily adjusted to consider the volumetric
-# (instead of gravimetric) energy density by changing the line which
-# defines the cost and changing the output to:
-# print(f"Initial volumetric energy density: {cost(optim.x0):.2f} Wh.m-3")
-# print(f"Optimised volumetric energy density: {final_cost:.2f} Wh.m-3")
-
 # Define parameter set and model
 parameter_set = pybop.ParameterSet.pybamm("Chen2020", formation_concentrations=True)
 model = pybop.lithium_ion.SPMe(parameter_set=parameter_set)
@@ -45,17 +39,21 @@
     model, parameters, experiment, signal=signal, init_soc=init_soc
 )
 
-# Generate cost function and optimisation class:
-cost = pybop.GravimetricEnergyDensity(problem)
+# Generate multiple cost functions and combine them.
+cost1 = pybop.GravimetricEnergyDensity(problem, update_capacity=True)
+cost2 = pybop.VolumetricEnergyDensity(problem, update_capacity=True)
+cost = pybop.WeightedCost(cost1, cost2, weights=[1, 1])
+
+# Run optimisation
 optim = pybop.PSO(
     cost, verbose=True, allow_infeasible_solutions=False, max_iterations=15
 )
-
-# Run optimisation
 x, final_cost = optim.run()
 print("Estimated parameters:", x)
-print(f"Initial gravimetric energy density: {cost(optim.x0):.2f} Wh.kg-1")
-print(f"Optimised gravimetric energy density: {final_cost:.2f} Wh.kg-1")
+print(f"Initial gravimetric energy density: {cost1(optim.x0):.2f} Wh.kg-1")
+print(f"Optimised gravimetric energy density: {cost1(x):.2f} Wh.kg-1")
+print(f"Initial volumetric energy density: {cost2(optim.x0):.2f} Wh.m-3")
+print(f"Optimised volumetric energy density: {cost2(x):.2f} Wh.m-3")
 
 # Plot the timeseries output
 if cost.update_capacity:

examples/standalone/problem.py

@@ -42,7 +42,7 @@ def __init__(
                 )
         self._target = {signal: self._dataset[signal] for signal in self.signal}
 
-    def evaluate(self, inputs):
+    def evaluate(self, inputs, **kwargs):
         """
         Evaluate the model with the given parameters and return the signal.
 

pybop/costs/_likelihoods.py

@@ -43,17 +43,15 @@ def _evaluate(self, inputs: Inputs, grad: Union[None, np.ndarray] = None) -> flo
         """
         Evaluates the Gaussian log-likelihood for the given parameters with known sigma.
         """
-        if not self.verify_prediction(self._current_prediction):
+        if not self.verify_prediction(self.y):
             return -np.inf
 
         e = np.asarray(
             [
                 np.sum(
                     self._offset
                     + self._multip
-                    * np.sum(
-                        (self._target[signal] - self._current_prediction[signal]) ** 2.0
-                    )
+                    * np.sum((self._target[signal] - self.y[signal]) ** 2.0)
                 )
                 for signal in self.signal
             ]
@@ -65,20 +63,15 @@ def _evaluateS1(self, inputs: Inputs) -> tuple[float, np.ndarray]:
         """
         Calculates the log-likelihood and gradient.
         """
-        if not self.verify_prediction(self._current_prediction):
+        if not self.verify_prediction(self.y):
             return -np.inf, -self._de * np.ones(self.n_parameters)
 
         likelihood = self._evaluate(inputs)
 
         r = np.asarray(
-            [
-                self._target[signal] - self._current_prediction[signal]
-                for signal in self.signal
-            ]
-        )
-        dl = np.sum(
-            (np.sum((r * self._current_sensitivities.T), axis=2) / self.sigma2), axis=1
+            [self._target[signal] - self.y[signal] for signal in self.signal]
         )
+        dl = np.sum((np.sum((r * self.dy.T), axis=2) / self.sigma2), axis=1)
 
         return likelihood, dl
 
@@ -123,7 +116,7 @@ def __init__(
         super().__init__(problem)
         self._dsigma_scale = dsigma_scale
         self._logpi = -0.5 * self.n_time_data * np.log(2 * np.pi)
-        self._fixed_problem = False  # keep problem evaluation within _evaluate
+        self._has_separable_problem = False
 
         self.sigma = Parameters()
         self._add_sigma_parameters(sigma0)
@@ -196,20 +189,16 @@ def _evaluate(self, inputs: Inputs, grad: Union[None, np.ndarray] = None) -> flo
         if np.any(sigma <= 0):
             return -np.inf
 
-        self._current_prediction = self.problem.evaluate(
-            self.problem.parameters.as_dict()
-        )
-        if not self.verify_prediction(self._current_prediction):
+        self.y = self.problem.evaluate(self.problem.parameters.as_dict())
+        if not self.verify_prediction(self.y):
             return -np.inf
 
         e = np.asarray(
             [
                 np.sum(
                     self._logpi
                     - self.n_time_data * np.log(sigma)
-                    - np.sum(
-                        (self._target[signal] - self._current_prediction[signal]) ** 2.0
-                    )
+                    - np.sum((self._target[signal] - self.y[signal]) ** 2.0)
                     / (2.0 * sigma**2.0)
                 )
                 for signal in self.signal
@@ -238,23 +227,16 @@ def _evaluateS1(self, inputs: Inputs) -> tuple[float, np.ndarray]:
         if np.any(sigma <= 0):
             return -np.inf, -self._de * np.ones(self.n_parameters)
 
-        self._current_prediction, self._current_sensitivities = self.problem.evaluateS1(
-            self.problem.parameters.as_dict()
-        )
-        if not self.verify_prediction(self._current_prediction):
+        self.y, self.dy = self.problem.evaluateS1(self.problem.parameters.as_dict())
+        if not self.verify_prediction(self.y):
             return -np.inf, -self._de * np.ones(self.n_parameters)
 
         likelihood = self._evaluate(inputs)
 
         r = np.asarray(
-            [
-                self._target[signal] - self._current_prediction[signal]
-                for signal in self.signal
-            ]
-        )
-        dl = np.sum(
-            (np.sum((r * self._current_sensitivities.T), axis=2) / (sigma**2.0)), axis=1
+            [self._target[signal] - self.y[signal] for signal in self.signal]
         )
+        dl = np.sum((np.sum((r * self.dy.T), axis=2) / (sigma**2.0)), axis=1)
         dsigma = (
             -self.n_time_data / sigma + np.sum(r**2.0, axis=1) / (sigma**3.0)
         ) / self._dsigma_scale
@@ -296,6 +278,9 @@ def __init__(self, problem, likelihood, sigma0=None, gradient_step=1e-3):
         ):
             raise ValueError(f"{self.likelihood} must be a subclass of BaseLikelihood")
 
+        self.parameters = self.likelihood.parameters
+        self._has_separable_problem = self.likelihood._has_separable_problem
+
     def _evaluate(self, inputs: Inputs, grad=None) -> float:
         """
         Calculate the maximum a posteriori cost for a given set of parameters.
@@ -320,9 +305,9 @@ def _evaluate(self, inputs: Inputs, grad=None) -> float:
         if not np.isfinite(log_prior).any():
             return -np.inf
 
-        if self._fixed_problem:
-            self.likelihood._current_prediction = self._current_prediction
-        log_likelihood = self.likelihood._evaluate(inputs)
+        if self._has_separable_problem:
+            self.likelihood.y = self.y
+        log_likelihood = self.likelihood.evaluate(inputs)
 
         posterior = log_likelihood + log_prior
         return posterior
@@ -354,12 +339,9 @@ def _evaluateS1(self, inputs: Inputs) -> tuple[float, np.ndarray]:
         if not np.isfinite(log_prior).any():
             return -np.inf, -self._de * np.ones(self.n_parameters)
 
-        if self._fixed_problem:
-            (
-                self.likelihood._current_prediction,
-                self.likelihood._current_sensitivities,
-            ) = self._current_prediction, self._current_sensitivities
-        log_likelihood, dl = self.likelihood._evaluateS1(inputs)
+        if self._has_separable_problem:
+            self.likelihood.y, self.likelihood.dy = (self.y, self.dy)
+        log_likelihood, dl = self.likelihood.evaluateS1(inputs)
 
         # Compute a finite difference approximation of the gradient of the log prior
         delta = self.parameters.initial_value() * self.gradient_step