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tests: adds integration tests for eis fitting, update problem.evaluat…
…e eis args, correct remainging cost functions for complex numbers
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,199 @@ | ||
| import numpy as np | ||
| import pytest | ||
|
|
||
| import pybop | ||
|
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||
|
|
||
| class TestEISParameterisation: | ||
| """ | ||
| A class to test the eis parameterisation methods. | ||
| """ | ||
|
|
||
| @pytest.fixture(autouse=True) | ||
| def setup(self): | ||
| self.sigma0 = 5e-4 | ||
| self.ground_truth = np.asarray([0.55, 0.55]) + np.random.normal( | ||
| loc=0.0, scale=0.05, size=2 | ||
| ) | ||
|
|
||
| @pytest.fixture | ||
| def model(self): | ||
| parameter_set = pybop.ParameterSet.pybamm("Chen2020") | ||
| x = self.ground_truth | ||
| parameter_set.update( | ||
| { | ||
| "Negative electrode active material volume fraction": x[0], | ||
| "Positive electrode active material volume fraction": x[1], | ||
| } | ||
| ) | ||
| return pybop.lithium_ion.SPM( | ||
| parameter_set=parameter_set, | ||
| eis=True, | ||
| options={"surface form": "differential"}, | ||
| ) | ||
|
|
||
| @pytest.fixture | ||
| def parameters(self): | ||
| return pybop.Parameters( | ||
| pybop.Parameter( | ||
| "Negative electrode active material volume fraction", | ||
| 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, 0.75), | ||
| bounds=[0.375, 0.75], | ||
| ), | ||
| ) | ||
|
|
||
| @pytest.fixture(params=[0.5]) | ||
| def init_soc(self, request): | ||
| return request.param | ||
|
|
||
| @pytest.fixture( | ||
| params=[ | ||
| pybop.GaussianLogLikelihoodKnownSigma, | ||
| pybop.GaussianLogLikelihood, | ||
| pybop.RootMeanSquaredError, | ||
| pybop.SumSquaredError, | ||
| pybop.SumofPower, | ||
| pybop.Minkowski, | ||
| pybop.MAP, | ||
| ] | ||
| ) | ||
| def cost(self, request): | ||
| return request.param | ||
|
|
||
| def noise(self, sigma, values): | ||
| # Generate real part noise | ||
| real_noise = np.random.normal(0, sigma, values) | ||
|
|
||
| # Generate imaginary part noise | ||
| imag_noise = np.random.normal(0, sigma, values) | ||
|
|
||
| # Combine them into a complex noise | ||
| return real_noise + 1j * imag_noise | ||
|
|
||
| @pytest.fixture( | ||
| params=[ | ||
| pybop.SciPyDifferentialEvolution, | ||
| pybop.CMAES, | ||
| pybop.CuckooSearch, | ||
| pybop.NelderMead, | ||
| pybop.SNES, | ||
| pybop.XNES, | ||
| ] | ||
| ) | ||
| def optimiser(self, request): | ||
| return request.param | ||
|
|
||
| @pytest.fixture | ||
| def optim(self, optimiser, model, parameters, cost, init_soc): | ||
| n_frequency = 12 | ||
| # Set frequency set | ||
| f_eval = np.logspace(-4, 5, n_frequency) | ||
|
|
||
| # Form dataset | ||
| solution = self.get_data(model, init_soc, f_eval) | ||
| dataset = pybop.Dataset( | ||
| { | ||
| "Frequency [Hz]": f_eval, | ||
| "Current function [A]": np.ones(n_frequency) * 0.0, | ||
| "Impedance": solution["Impedance"] | ||
| + self.noise(self.sigma0, len(solution["Impedance"])), | ||
| } | ||
| ) | ||
|
|
||
| # Define the problem | ||
| signal = ["Impedance"] | ||
| problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) | ||
|
|
||
| # Construct the cost | ||
| if cost in [pybop.GaussianLogLikelihoodKnownSigma]: | ||
| cost = cost(problem, sigma0=self.sigma0) | ||
| elif cost in [pybop.GaussianLogLikelihood]: | ||
| cost = cost(problem, sigma0=self.sigma0 * 4) # Initial sigma0 guess | ||
| elif cost in [pybop.MAP]: | ||
| cost = cost( | ||
| problem, pybop.GaussianLogLikelihoodKnownSigma, sigma0=self.sigma0 | ||
| ) | ||
| elif cost in [pybop.SumofPower, pybop.Minkowski]: | ||
| cost = cost(problem, p=2) | ||
| else: | ||
| cost = cost(problem) | ||
|
|
||
| # Construct optimisation object | ||
| common_args = { | ||
| "cost": cost, | ||
| "max_iterations": 250, | ||
| "absolute_tolerance": 1e-6, | ||
| "max_unchanged_iterations": 25, | ||
| } | ||
|
|
||
| if isinstance(cost, pybop.MAP): | ||
| for i in cost.parameters.keys(): | ||
| cost.parameters[i].prior = pybop.Uniform( | ||
| 0.2, 2.0 | ||
| ) # Increase range to avoid prior == np.inf | ||
|
|
||
| # Set sigma0 and create optimiser | ||
| sigma0 = 0.05 if isinstance(cost, pybop.MAP) else None | ||
| optim = optimiser(sigma0=sigma0, **common_args) | ||
|
|
||
| return optim | ||
|
|
||
| @pytest.mark.integration | ||
| def test_eis_optimisers(self, optim): | ||
| x0 = optim.parameters.initial_value() | ||
|
|
||
| # Add sigma0 to ground truth for GaussianLogLikelihood | ||
| if isinstance(optim.cost, pybop.GaussianLogLikelihood): | ||
| self.ground_truth = np.concatenate( | ||
| (self.ground_truth, np.asarray([self.sigma0])) | ||
| ) | ||
|
|
||
| initial_cost = optim.cost(x0) | ||
| x, final_cost = optim.run() | ||
|
|
||
| # Assertions | ||
| if np.allclose(x0, self.ground_truth, atol=1e-5): | ||
| raise AssertionError("Initial guess is too close to ground truth") | ||
|
|
||
| if isinstance(optim.cost, pybop.GaussianLogLikelihood): | ||
| np.testing.assert_allclose(x, self.ground_truth, atol=1.5e-2) | ||
| np.testing.assert_allclose(x[-1], self.sigma0, atol=5e-4) | ||
| else: | ||
| assert ( | ||
| (initial_cost > final_cost) | ||
| if optim.minimising | ||
| else (initial_cost < final_cost) | ||
| ) | ||
| np.testing.assert_allclose(x, self.ground_truth, atol=1.5e-2) | ||
|
|
||
| def get_data(self, model, init_soc, f_eval): | ||
| initial_state = {"Initial SoC": init_soc} | ||
| model.build( | ||
| inputs={ | ||
| "Negative electrode active material volume fraction": self.ground_truth[ | ||
| 0 | ||
| ], | ||
| "Positive electrode active material volume fraction": self.ground_truth[ | ||
| 1 | ||
| ], | ||
| }, | ||
| initial_state=initial_state, | ||
| ) | ||
| sim = model.simulateEIS( | ||
| inputs={ | ||
| "Negative electrode active material volume fraction": self.ground_truth[ | ||
| 0 | ||
| ], | ||
| "Positive electrode active material volume fraction": self.ground_truth[ | ||
| 1 | ||
| ], | ||
| }, | ||
| f_eval=f_eval, | ||
| ) | ||
|
|
||
| return sim |