From 30b6101d30891a64a8f9ece3b58c7985595b8222 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Tue, 27 Aug 2024 08:44:04 +0100 Subject: [PATCH] fix: updates scipy bounds with keep_feasible, clipping groundtruth for integration tests, small fixes on examples --- examples/notebooks/ecm_trust-constr.ipynb | 3811 +---------------- examples/scripts/ecm_tau_constraints.py | 5 +- pybop/optimisers/scipy_optimisers.py | 23 +- .../test_thevenin_parameterisation.py | 6 +- tests/integration/test_transformation.py | 12 +- tests/integration/test_weighted_cost.py | 6 +- 6 files changed, 60 insertions(+), 3803 deletions(-) diff --git a/examples/notebooks/ecm_trust-constr.ipynb b/examples/notebooks/ecm_trust-constr.ipynb index 26b7992d..4c7d4551 100644 --- a/examples/notebooks/ecm_trust-constr.ipynb +++ b/examples/notebooks/ecm_trust-constr.ipynb @@ -132,23 +132,23 @@ "metadata": {}, "outputs": [], "source": [ - "parameters = [\n", + "parameters = pybop.Parameters(\n", " pybop.Parameter(\n", " \"R0 [Ohm]\",\n", - " prior=pybop.Gaussian(0.002, 0.0001),\n", + " prior=pybop.Gaussian(2e-3, 1e-4),\n", " bounds=[1e-4, 1e-1],\n", " ),\n", " pybop.Parameter(\n", " \"R1 [Ohm]\",\n", - " prior=pybop.Gaussian(0.001, 0.0001),\n", + " prior=pybop.Gaussian(1e-3, 1e-4),\n", " bounds=[1e-5, 1e-2],\n", " ),\n", " pybop.Parameter(\n", " \"C1 [F]\",\n", - " prior=pybop.Gaussian(5000, 2500),\n", + " prior=pybop.Gaussian(5000, 300),\n", " bounds=[2500, 5e4],\n", " ),\n", - "]" + ")" ] }, { @@ -189,7 +189,7 @@ "metadata": {}, "outputs": [], "source": [ - "tau_constraint = scipy.optimize.NonlinearConstraint(lambda x: x[1] * x[2], 25, 750)" + "tau_constraint = scipy.optimize.NonlinearConstraint(lambda x: x[1] * x[2], 5, 750)" ] }, { @@ -200,7 +200,7 @@ "Let's dig into what's going on here.\n", "\n", "The nonlinear constraint will receive a parameter vector from PyBOP. We know from our parameters list that parameter 0 will be $R_0$, \n", - "parameter 1 will be $R_1$, and parameter 2 will be $C_1$. We can therefore compute the RC timescale $R_1 \\times C_1$ using ```lambda x: x[1] * x[2]```. Next, we tell scipy that we want this to be constrained to the range $25\\leq R_1 \\times C_1 \\leq 750$. There's a lot of flexibility in our choice of lower and upper bounds, however these are reasonable numbers given our sampling rate (1 Hz), and data time-span (500 s).\n", + "parameter 1 will be $R_1$, and parameter 2 will be $C_1$. We can therefore compute the RC timescale $R_1 \\times C_1$ using ```lambda x: x[1] * x[2]```. Next, we tell scipy that we want this to be constrained to the range $5\\leq R_1 \\times C_1 \\leq 750$. There's a lot of flexibility in our choice of lower and upper bounds, however these are reasonable numbers given our sampling rate (1 Hz), and data time-span (500 s).\n", "\n", "Now all we need to do is set up an optimiser to use this." ] @@ -216,6 +216,7 @@ " cost,\n", " method=\"trust-constr\",\n", " constraints=tau_constraint,\n", + " max_iterations=100,\n", ")" ] }, @@ -236,3782 +237,25 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimated parameters: [8.97270417e-03 5.94267303e-03 6.01565024e+03]\n" + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m x, final_cost \u001b[38;5;241m=\u001b[39m \u001b[43moptim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEstimated parameters:\u001b[39m\u001b[38;5;124m\"\u001b[39m, x)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Plot the time series\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/Git/PyBOP/pybop/optimisers/base_optimiser.py:177\u001b[0m, in \u001b[0;36mBaseOptimiser.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 167\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 168\u001b[0m \u001b[38;5;124;03m Run the optimisation and return the optimised parameters and final cost.\u001b[39;00m\n\u001b[1;32m 169\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;124;03m The final cost associated with the best parameters.\u001b[39;00m\n\u001b[1;32m 176\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 177\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresult \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 179\u001b[0m \u001b[38;5;66;03m# Store the optimised parameters\u001b[39;00m\n\u001b[1;32m 180\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresult\u001b[38;5;241m.\u001b[39mx\n", + "File \u001b[0;32m~/Documents/Git/PyBOP/pybop/optimisers/scipy_optimisers.py:71\u001b[0m, in \u001b[0;36m_run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_run\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 65\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;124;03m Internal method to run the optimization using a PyBOP optimiser.\u001b[39;00m\n\u001b[1;32m 67\u001b[0m \n\u001b[1;32m 68\u001b[0m \u001b[38;5;124;03m Returns\u001b[39;00m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;124;03m -------\u001b[39;00m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;124;03m result : pybop.Result\u001b[39;00m\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;124;03m The result of the optimisation including the optimised parameter values and cost.\u001b[39;00m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 73\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run_optimiser()\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[0;32m~/Documents/Git/PyBOP/pybop/optimisers/scipy_optimisers.py:229\u001b[0m, in \u001b[0;36m_run_optimiser\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cost0):\n\u001b[1;32m 223\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 224\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe initial parameter values return an infinite cost.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 225\u001b[0m )\n\u001b[1;32m 227\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m minimize(\n\u001b[1;32m 228\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcost_wrapper,\n\u001b[0;32m--> 229\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mx0,\n\u001b[1;32m 230\u001b[0m bounds\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_scipy_bounds,\n\u001b[1;32m 231\u001b[0m callback\u001b[38;5;241m=\u001b[39mcallback,\n\u001b[1;32m 232\u001b[0m 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", - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -4026,9 +270,8 @@ "\n", "# Plot convergence\n", "pybop.plot_convergence(optim)\n", - "\n", "# Plot the parameter traces\n", - "pybop.plot_parameters(optim)" + "pybop.plot_parameters(optim);" ] }, { diff --git a/examples/scripts/ecm_tau_constraints.py b/examples/scripts/ecm_tau_constraints.py index 92306d5b..e800dff7 100644 --- a/examples/scripts/ecm_tau_constraints.py +++ b/examples/scripts/ecm_tau_constraints.py @@ -134,7 +134,7 @@ def check_params( ) # Fitting parameters -parameters = [ +parameters = pybop.Parameters( pybop.Parameter( "R0 [Ohm]", prior=pybop.Gaussian(0.0002, 0.0001), @@ -150,7 +150,7 @@ def check_params( prior=pybop.Gaussian(10000, 2500), bounds=[2500, 5e4], ), -] +) sigma = 0.001 t_eval = np.arange(0, 900, 3) @@ -172,6 +172,7 @@ def check_params( optim = pybop.XNES( cost, allow_infeasible_solutions=False, + max_iterations=100, ) x, final_cost = optim.run() diff --git a/pybop/optimisers/scipy_optimisers.py b/pybop/optimisers/scipy_optimisers.py index a6fa3f98..03fa2437 100644 --- a/pybop/optimisers/scipy_optimisers.py +++ b/pybop/optimisers/scipy_optimisers.py @@ -2,7 +2,7 @@ from typing import Union import numpy as np -from scipy.optimize import OptimizeResult, differential_evolution, minimize +from scipy.optimize import Bounds, OptimizeResult, differential_evolution, minimize from pybop import BaseOptimiser, Result @@ -55,12 +55,18 @@ def _sanitise_inputs(self): # Convert bounds to SciPy format if isinstance(self.bounds, dict): - self._scipy_bounds = [ - (lower, upper) - for lower, upper in zip(self.bounds["lower"], self.bounds["upper"]) - ] - else: + self._scipy_bounds = Bounds( + self.bounds["lower"], self.bounds["upper"], True + ) + elif isinstance(self.bounds, list): + lb, ub = zip(*self.bounds) + self._scipy_bounds = Bounds(lb, ub, True) + elif isinstance(self.bounds, Bounds) or self.bounds is None: self._scipy_bounds = self.bounds + else: + raise TypeError( + "Bounds provided must be either type dict, list or SciPy.optimize.bounds object." + ) def _run(self): """ @@ -286,9 +292,8 @@ def _set_up_optimiser(self): if self._scipy_bounds is None: raise ValueError("Bounds must be specified for differential_evolution.") else: - if not all( - np.isfinite(value) for pair in self._scipy_bounds for value in pair - ): + bnds = self._scipy_bounds + if not (np.isfinite(bnds.lb).all() and np.isfinite(bnds.ub).all()): raise ValueError("Bounds must be specified for differential_evolution.") # Apply default maxiter and tolerance diff --git a/tests/integration/test_thevenin_parameterisation.py b/tests/integration/test_thevenin_parameterisation.py index 08ebace4..75530c89 100644 --- a/tests/integration/test_thevenin_parameterisation.py +++ b/tests/integration/test_thevenin_parameterisation.py @@ -11,8 +11,10 @@ class TestTheveninParameterisation: @pytest.fixture(autouse=True) def setup(self): - self.ground_truth = np.asarray([0.05, 0.05]) + np.random.normal( - loc=0.0, scale=0.01, size=2 + self.ground_truth = np.clip( + np.asarray([0.05, 0.05]) + np.random.normal(loc=0.0, scale=0.01, size=2), + a_min=0.0, + a_max=0.1, ) @pytest.fixture diff --git a/tests/integration/test_transformation.py b/tests/integration/test_transformation.py index 9b5ff31d..65806501 100644 --- a/tests/integration/test_transformation.py +++ b/tests/integration/test_transformation.py @@ -11,8 +11,10 @@ class TestTransformation: @pytest.fixture(autouse=True) def setup(self): - self.ground_truth = np.array([0.5, 0.2]) + np.random.normal( - loc=0.0, scale=0.05, size=2 + self.ground_truth = np.clip( + np.array([0.5, 0.1]) + np.random.normal(loc=0.0, scale=0.05, size=2), + a_min=[0.375, 0.02], + a_max=[0.7, 0.5], ) @pytest.fixture @@ -39,7 +41,7 @@ def parameters(self): pybop.Parameter( "Positive electrode conductivity [S.m-1]", prior=pybop.Uniform(0.05, 0.45), - bounds=[0.04, 0.5], + bounds=[0.02, 0.5], transformation=pybop.LogTransformation(), ), ) @@ -78,10 +80,12 @@ def cost(self, model, parameters, init_soc): @pytest.mark.integration def test_spm_optimisers(self, optimiser, cost): x0 = cost.parameters.initial_value() + sigma0 = None if optimiser is pybop.AdamW else 0.2 optim = optimiser( cost=cost, + sigma0=sigma0, max_unchanged_iterations=35, - max_iterations=125, + max_iterations=150, ) initial_cost = optim.cost(x0) diff --git a/tests/integration/test_weighted_cost.py b/tests/integration/test_weighted_cost.py index c7f1f85f..2bc78ae7 100644 --- a/tests/integration/test_weighted_cost.py +++ b/tests/integration/test_weighted_cost.py @@ -12,8 +12,10 @@ class TestWeightedCost: @pytest.fixture(autouse=True) def setup(self): self.sigma0 = 0.002 - self.ground_truth = np.asarray([0.55, 0.55]) + np.random.normal( - loc=0.0, scale=0.05, size=2 + self.ground_truth = np.clip( + np.asarray([0.55, 0.55]) + np.random.normal(loc=0.0, scale=0.05, size=2), + a_min=0.4, + a_max=0.75, ) @pytest.fixture