Merge pull request #117 from pybop-team/116-add-pints-optimisers

Adds Pints' optimisers

CHANGELOG.md

@@ -1,7 +1,9 @@
 # [Unreleased](https://github.com/pybop-team/PyBOP)
 
+- [#116](https://github.com/pybop-team/PyBOP/issues/116) - Adds PSO, SNES, XNES, ADAM, and IPropMin optimisers to PintsOptimisers() class
+
 # [v23.11](https://github.com/pybop-team/PyBOP/releases/tag/v23.11)
-    - Initial release
-    - Adds Pints, NLOpt, and SciPy optimisers
-    - Adds SumofSquareError and RootMeanSquareError cost functions
-    - Adds Parameter and dataset classes
+- Initial release
+- Adds Pints, NLOpt, and SciPy optimisers
+- Adds SumofSquareError and RootMeanSquareError cost functions
+- Adds Parameter and dataset classes

conftest.py

@@ -8,17 +8,29 @@ def pytest_addoption(parser):
     parser.addoption(
         "--unit", action="store_true", default=False, help="run unit tests"
     )
+    parser.addoption(
+        "--examples", action="store_true", default=False, help="run examples tests"
+    )
 
 
 def pytest_configure(config):
     config.addinivalue_line("markers", "unit: mark test as a unit test")
+    config.addinivalue_line("markers", "examples: mark test as an example")
 
 
 def pytest_collection_modifyitems(config, items):
+    def skip_marker(marker_name, reason):
+        skip = pytest.mark.skip(reason=reason)
+        for item in items:
+            if marker_name in item.keywords:
+                item.add_marker(skip)
+
     if config.getoption("--unit"):
-        # --unit given in cli: do not skip unit tests
+        skip_marker("examples", "need --examples option to run")
         return
-    skip_unit = pytest.mark.skip(reason="need --unit option to run")
-    for item in items:
-        if "unit" in item.keywords:
-            item.add_marker(skip_unit)
+
+    if config.getoption("--examples"):
+        skip_marker("unit", "need --unit option to run")
+        return
+
+    skip_marker("unit", "need --unit option to run")

examples/scripts/CMAES.py → examples/scripts/spm_CMAES.py

@@ -3,7 +3,7 @@
 import matplotlib.pyplot as plt
 
 parameter_set = pybop.ParameterSet("pybamm", "Chen2020")
-model = pybop.lithium_ion.SPMe(parameter_set=parameter_set)
+model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
 
 # Fitting parameters
 parameters = [

examples/scripts/spm_IRPropMin.py

@@ -0,0 +1,55 @@
+import pybop
+import numpy as np
+import matplotlib.pyplot as plt
+
+parameter_set = pybop.ParameterSet("pybamm", "Chen2020")
+model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
+
+# Fitting parameters
+parameters = [
+    pybop.Parameter(
+        "Negative electrode active material volume fraction",
+        prior=pybop.Gaussian(0.7, 0.05),
+        bounds=[0.6, 0.9],
+    ),
+    pybop.Parameter(
+        "Positive electrode active material volume fraction",
+        prior=pybop.Gaussian(0.58, 0.05),
+        bounds=[0.5, 0.8],
+    ),
+]
+
+sigma = 0.001
+t_eval = np.arange(0, 900, 2)
+values = model.predict(t_eval=t_eval)
+CorruptValues = values["Terminal voltage [V]"].data + np.random.normal(
+    0, sigma, len(t_eval)
+)
+
+dataset = [
+    pybop.Dataset("Time [s]", t_eval),
+    pybop.Dataset("Current function [A]", values["Current [A]"].data),
+    pybop.Dataset("Terminal voltage [V]", CorruptValues),
+]
+
+# Generate problem, cost function, and optimisation class
+problem = pybop.Problem(model, parameters, dataset)
+cost = pybop.SumSquaredError(problem)
+optim = pybop.Optimisation(cost, optimiser=pybop.IRPropMin)
+optim.set_max_iterations(100)
+
+x, final_cost = optim.run()
+print("Estimated parameters:", x)
+
+# Show the generated data
+simulated_values = problem.evaluate(x)
+
+plt.figure(dpi=100)
+plt.xlabel("Time", fontsize=12)
+plt.ylabel("Values", fontsize=12)
+plt.plot(t_eval, CorruptValues, label="Measured")
+plt.fill_between(t_eval, simulated_values - sigma, simulated_values + sigma, alpha=0.2)
+plt.plot(t_eval, simulated_values, label="Simulated")
+plt.legend(bbox_to_anchor=(0.6, 1), loc="upper left", fontsize=12)
+plt.tick_params(axis="both", labelsize=12)
+plt.show()

examples/scripts/spm_SNES.py

@@ -0,0 +1,55 @@
+import pybop
+import numpy as np
+import matplotlib.pyplot as plt
+
+parameter_set = pybop.ParameterSet("pybamm", "Chen2020")
+model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
+
+# Fitting parameters
+parameters = [
+    pybop.Parameter(
+        "Negative electrode active material volume fraction",
+        prior=pybop.Gaussian(0.7, 0.05),
+        bounds=[0.6, 0.9],
+    ),
+    pybop.Parameter(
+        "Positive electrode active material volume fraction",
+        prior=pybop.Gaussian(0.58, 0.05),
+        bounds=[0.5, 0.8],
+    ),
+]
+
+sigma = 0.001
+t_eval = np.arange(0, 900, 2)
+values = model.predict(t_eval=t_eval)
+CorruptValues = values["Terminal voltage [V]"].data + np.random.normal(
+    0, sigma, len(t_eval)
+)
+
+dataset = [
+    pybop.Dataset("Time [s]", t_eval),
+    pybop.Dataset("Current function [A]", values["Current [A]"].data),
+    pybop.Dataset("Terminal voltage [V]", CorruptValues),
+]
+
+# Generate problem, cost function, and optimisation class
+problem = pybop.Problem(model, parameters, dataset)
+cost = pybop.SumSquaredError(problem)
+optim = pybop.Optimisation(cost, optimiser=pybop.SNES)
+optim.set_max_iterations(100)
+
+x, final_cost = optim.run()
+print("Estimated parameters:", x)
+
+# Show the generated data
+simulated_values = problem.evaluate(x)
+
+plt.figure(dpi=100)
+plt.xlabel("Time", fontsize=12)
+plt.ylabel("Values", fontsize=12)
+plt.plot(t_eval, CorruptValues, label="Measured")
+plt.fill_between(t_eval, simulated_values - sigma, simulated_values + sigma, alpha=0.2)
+plt.plot(t_eval, simulated_values, label="Simulated")
+plt.legend(bbox_to_anchor=(0.6, 1), loc="upper left", fontsize=12)
+plt.tick_params(axis="both", labelsize=12)
+plt.show()

examples/scripts/spm_XNES.py

@@ -0,0 +1,55 @@
+import pybop
+import numpy as np
+import matplotlib.pyplot as plt
+
+parameter_set = pybop.ParameterSet("pybamm", "Chen2020")
+model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
+
+# Fitting parameters
+parameters = [
+    pybop.Parameter(
+        "Negative electrode active material volume fraction",
+        prior=pybop.Gaussian(0.7, 0.05),
+        bounds=[0.6, 0.9],
+    ),
+    pybop.Parameter(
+        "Positive electrode active material volume fraction",
+        prior=pybop.Gaussian(0.58, 0.05),
+        bounds=[0.5, 0.8],
+    ),
+]
+
+sigma = 0.001
+t_eval = np.arange(0, 900, 2)
+values = model.predict(t_eval=t_eval)
+CorruptValues = values["Terminal voltage [V]"].data + np.random.normal(
+    0, sigma, len(t_eval)
+)
+
+dataset = [
+    pybop.Dataset("Time [s]", t_eval),
+    pybop.Dataset("Current function [A]", values["Current [A]"].data),
+    pybop.Dataset("Terminal voltage [V]", CorruptValues),
+]
+
+# Generate problem, cost function, and optimisation class
+problem = pybop.Problem(model, parameters, dataset)
+cost = pybop.SumSquaredError(problem)
+optim = pybop.Optimisation(cost, optimiser=pybop.XNES)
+optim.set_max_iterations(100)
+
+x, final_cost = optim.run()
+print("Estimated parameters:", x)
+
+# Show the generated data
+simulated_values = problem.evaluate(x)
+
+plt.figure(dpi=100)
+plt.xlabel("Time", fontsize=12)
+plt.ylabel("Values", fontsize=12)
+plt.plot(t_eval, CorruptValues, label="Measured")
+plt.fill_between(t_eval, simulated_values - sigma, simulated_values + sigma, alpha=0.2)
+plt.plot(t_eval, simulated_values, label="Simulated")
+plt.legend(bbox_to_anchor=(0.6, 1), loc="upper left", fontsize=12)
+plt.tick_params(axis="both", labelsize=12)
+plt.show()

examples/scripts/spm_adam.py

@@ -0,0 +1,59 @@
+import pybop
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Parameter set and model definition
+parameter_set = pybop.ParameterSet("pybamm", "Chen2020")
+model = pybop.lithium_ion.SPMe(parameter_set=parameter_set)
+
+# Fitting parameters
+parameters = [
+    pybop.Parameter(
+        "Negative electrode active material volume fraction",
+        prior=pybop.Gaussian(0.7, 0.05),
+        bounds=[0.6, 0.9],
+    ),
+    pybop.Parameter(
+        "Positive electrode active material volume fraction",
+        prior=pybop.Gaussian(0.58, 0.05),
+        bounds=[0.5, 0.8],
+    ),
+]
+
+# Generate data
+sigma = 0.001
+t_eval = np.arange(0, 900, 2)
+values = model.predict(t_eval=t_eval)
+corrupt_values = values["Terminal voltage [V]"].data + np.random.normal(
+    0, sigma, len(t_eval)
+)
+
+# Dataset definition
+dataset = [
+    pybop.Dataset("Time [s]", t_eval),
+    pybop.Dataset("Current function [A]", values["Current [A]"].data),
+    pybop.Dataset("Terminal voltage [V]", corrupt_values),
+]
+
+# Generate problem, cost function, and optimisation class
+problem = pybop.Problem(model, parameters, dataset)
+cost = pybop.SumSquaredError(problem)
+optim = pybop.Optimisation(cost, optimiser=pybop.Adam)
+optim.set_max_iterations(100)
+
+# Run optimisation
+x, final_cost = optim.run()
+print("Estimated parameters:", x)
+
+# Show the generated data
+simulated_values = problem.evaluate(x)
+
+plt.figure(dpi=100)
+plt.xlabel("Time", fontsize=12)
+plt.ylabel("Values", fontsize=12)
+plt.plot(t_eval, corrupt_values, label="Measured")
+plt.fill_between(t_eval, simulated_values - sigma, simulated_values + sigma, alpha=0.2)
+plt.plot(t_eval, simulated_values, label="Simulated")
+plt.legend(bbox_to_anchor=(0.6, 1), loc="upper left", fontsize=12)
+plt.tick_params(axis="both", labelsize=12)
+plt.show()

examples/scripts/spm_pso.py

@@ -0,0 +1,55 @@
+import pybop
+import numpy as np
+import matplotlib.pyplot as plt
+
+parameter_set = pybop.ParameterSet("pybamm", "Chen2020")
+model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
+
+# Fitting parameters
+parameters = [
+    pybop.Parameter(
+        "Negative electrode active material volume fraction",
+        prior=pybop.Gaussian(0.7, 0.05),
+        bounds=[0.6, 0.9],
+    ),
+    pybop.Parameter(
+        "Positive electrode active material volume fraction",
+        prior=pybop.Gaussian(0.58, 0.05),
+        bounds=[0.5, 0.8],
+    ),
+]
+
+sigma = 0.001
+t_eval = np.arange(0, 900, 2)
+values = model.predict(t_eval=t_eval)
+CorruptValues = values["Terminal voltage [V]"].data + np.random.normal(
+    0, sigma, len(t_eval)
+)
+
+dataset = [
+    pybop.Dataset("Time [s]", t_eval),
+    pybop.Dataset("Current function [A]", values["Current [A]"].data),
+    pybop.Dataset("Terminal voltage [V]", CorruptValues),
+]
+
+# Generate problem, cost function, and optimisation class
+problem = pybop.Problem(model, parameters, dataset)
+cost = pybop.SumSquaredError(problem)
+optim = pybop.Optimisation(cost, optimiser=pybop.PSO)
+optim.set_max_iterations(100)
+
+x, final_cost = optim.run()
+print("Estimated parameters:", x)
+
+# Show the generated data
+simulated_values = problem.evaluate(x)
+
+plt.figure(dpi=100)
+plt.xlabel("Time", fontsize=12)
+plt.ylabel("Values", fontsize=12)
+plt.plot(t_eval, CorruptValues, label="Measured")
+plt.fill_between(t_eval, simulated_values - sigma, simulated_values + sigma, alpha=0.2)
+plt.plot(t_eval, simulated_values, label="Simulated")
+plt.legend(bbox_to_anchor=(0.6, 1), loc="upper left", fontsize=12)
+plt.tick_params(axis="both", labelsize=12)
+plt.show()

noxfile.py

@@ -8,14 +8,22 @@
 def unit(session):
     session.run_always("pip", "install", "-e", ".")
     session.install("pytest")
-    session.run("pytest", "--unit", "-v")
+    session.run("pytest", "--unit", "-v", "--showlocals")
 
 
 @nox.session
 def coverage(session):
     session.run_always("pip", "install", "-e", ".")
     session.install("pytest-cov")
-    session.run("pytest", "--unit", "-v", "--cov", "--cov-report=xml")
+    session.run(
+        "pytest",
+        "--unit",
+        "--examples",
+        "-v",
+        "--cov",
+        "--cov-report=xml",
+        "--showlocals",
+    )
 
 
 @nox.session

pybop/__init__.py

@@ -50,7 +50,15 @@
 from .optimisers.base_optimiser import BaseOptimiser
 from .optimisers.nlopt_optimize import NLoptOptimize
 from .optimisers.scipy_minimize import SciPyMinimize
-from .optimisers.pints_optimisers import GradientDescent, CMAES
+from .optimisers.pints_optimisers import (
+    GradientDescent,
+    Adam,
+    CMAES,
+    IRPropMin,
+    PSO,
+    SNES,
+    XNES,
+)
 
 #
 # Parameter classes