Notebook

Interacting with PyBOP optimisers¶

This notebook introduces two interfaces to interact with PyBOP's optimiser classes.

Set the Environment¶

In [ ]:

%pip install --upgrade pip ipywidgets -q
%pip install pybop -q

# Import the necessary libraries
import numpy as np

import pybop

pybop.plot.PlotlyManager().pio.renderers.default = "notebook_connected"

/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip
Note: you may need to restart the kernel to use updated packages.
/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip
Note: you may need to restart the kernel to use updated packages.

Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice.

In [ ]:

np.random.seed(8)

Setup the model, problem, and cost¶

The code block below sets up the model, problem, and cost objects. For more information on this process, take a look at other notebooks in the examples directory.

In [ ]:

# Load the parameters
parameter_set = pybop.ParameterSet(
    json_path="../scripts/parameters/initial_ecm_parameters.json"
)
parameter_set.import_parameters()
# Define the model
model = pybop.empirical.Thevenin(
    parameter_set=parameter_set, options={"number of rc elements": 1}
)

# Define the parameters
parameters = pybop.Parameter(
    "R0 [Ohm]",
    prior=pybop.Gaussian(0.0002, 0.0001),
    bounds=[1e-4, 1e-2],
)

# Generate synthetic data
t_eval = np.arange(0, 900, 2)
values = model.predict(t_eval=t_eval)

# Form dataset
dataset = pybop.Dataset(
    {
        "Time [s]": t_eval,
        "Current function [A]": values["Current [A]"].data,
        "Voltage [V]": values["Voltage [V]"].data,
    }
)

# Construct problem and cost
problem = pybop.FittingProblem(model, parameters, dataset)
cost = pybop.SumSquaredError(problem)

Setting open-circuit voltage to default function

Interacting with the Optimisers¶

Now that we have set up the required objects, we can introduce the two interfaces for interacting with PyBOP optimisers. These are:

The direct optimiser (e.g. pybop.XNES)
The optimisation class (i.e. pybop.Optimisation)

These two methods provide two equivalent ways of interacting with PyBOP's optimisers. The first method provides a direct way to select the Optimiser, with the second method being a more general method with a default optimiser (pybop.XNES) set if you don't provide an optimiser.

First, the direct interface is presented. With this interface the user can select from the list of optimisers supported in PyBOP and construct them directly. Options can be passed as kwargs, or through get() / set() methods in the case of PINTS-based optimisers.

In [ ]:

optim_one = pybop.XNES(
    cost, max_iterations=50
)  # Direct optimiser class with options as kwargs
optim_one.set_max_iterations(
    50
)  # Alternative set() / get() methods for PINTS optimisers
results = optim_one.run()

Halt: No significant change for 15 iterations.
OptimisationResult:
  Initial parameters: [0.00032648]
  Optimised parameters: [0.00099965]
  Final cost: 1.363466506257511e-09
  Optimisation time: 1.6153881549835205 seconds
  Number of iterations: 23
  SciPy result available: No

Next, the Optimisation interface is less direct than the previous one, but provides a single class to work with across PyBOP workflows. The options are passed the same way as the above method, through kwargs or get() / set() methods.

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optim_two = pybop.Optimisation(
    cost, optimiser=pybop.XNES, max_iterations=50
)  # Optimisation class with options as kwargs
optim_two.set_max_iterations(
    50
)  # Alternative set() / get() methods for PINTS optimisers
results2 = optim_two.run()

Halt: No significant change for 15 iterations.
OptimisationResult:
  Initial parameters: [0.00032648]
  Optimised parameters: [0.00099985]
  Final cost: 2.431998416403876e-10
  Optimisation time: 1.972628116607666 seconds
  Number of iterations: 25
  SciPy result available: No

We can show the equivalence of these two methods by comparing the optimiser objects:

In [ ]:

isinstance(optim_one, type(optim_two.optimiser))

Out[ ]:

True

For completeness, we can show the optimiser solutions:

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print("Estimated parameters x1:", results.x)
print("Estimated parameters x2:", results2.x)

Estimated parameters x1: [0.00099965]
Estimated parameters x2: [0.00099985]

Closing Comments¶

As both of these API's provide access to the same optimisers, please use either as you prefer. A couple things to note:

If you are using a SciPy-based optimiser (pybop.SciPyMinimize, pybop.SciPyDifferentialEvolution), the set() / get() methods for the optimiser options are not currently supported. These optimisers require options to be passed as kwargs.
The optimiser passed to pybop.Optimisation must not be a constructed object.