diff --git a/.all-contributorsrc b/.all-contributorsrc index b2da537f7..c3fb03f35 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -62,7 +62,8 @@ "profile": "https://www.brosaplanella.xyz", "contributions": [ "review", - "code" + "code", + "example" ] }, { @@ -97,7 +98,7 @@ { "login": "IntelLiGent", "name": "Horizon Europe IntelLiGent Consortium", - "avatar_url": "assets/logo-farger.pdf", + "avatar_url": "assets/logo-farger.svg", "profile": "https://heuintelligent.eu/", "contributions": [ "financial" @@ -111,6 +112,33 @@ "contributions": [ "code" ] + }, + { + "login": "MarkBlyth", + "name": "MarkBlyth", + "avatar_url": "https://avatars.githubusercontent.com/u/20501619?v=4", + "profile": "https://github.com/MarkBlyth", + "contributions": [ + "code" + ] + }, + { + "login": "f-g-r-i-m-m", + "name": "f-g-r-i-m-m", + "avatar_url": "https://avatars.githubusercontent.com/u/137511310?v=4", + "profile": "https://github.com/f-g-r-i-m-m", + "contributions": [ + "example" + ] + }, + { + "login": "Dibyendu-IITKGP", + "name": "Dibyendu-IITKGP", + "avatar_url": "https://avatars.githubusercontent.com/u/32595915?v=4", + "profile": "https://github.com/Dibyendu-IITKGP", + "contributions": [ + "example" + ] } ], "contributorsPerLine": 7, diff --git a/.github/release_workflow.md b/.github/release_workflow.md index a655f9fee..97fc65d67 100644 --- a/.github/release_workflow.md +++ b/.github/release_workflow.md @@ -11,6 +11,7 @@ To create a new release, follow these steps: - Increment the following; - The version number in the `pyproject.toml` and `CITATION.cff` files following CalVer versioning. - The`CHANGELOG.md` version with the changes for the new version. + - Add a new entry for the documentation site version switcher located at `docs/_static/switcher.json` - Open a PR to the `main` branch. Once the PR is merged, proceed to the next step. 2. **Tag the Release:** diff --git a/.github/workflows/lychee_links.yaml b/.github/workflows/lychee_links.yaml new file mode 100644 index 000000000..8d1127b49 --- /dev/null +++ b/.github/workflows/lychee_links.yaml @@ -0,0 +1,61 @@ +# Lychee Link Checking + +name: Links +on: + workflow_dispatch: + pull_request: + push: + branches: + - main + schedule: + - cron: '0 6 * * 0' # Run weekly on Sundays at 06:00 UTC + +jobs: + Lychee: + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Restore lychee cache + uses: actions/cache@v4 + with: + path: .lycheecache + key: cache-lychee-${{ github.sha }} + restore-keys: cache-lychee- + + - name: Set up Lychee + uses: lycheeverse/lychee-action@v1.10.0 + with: + args: >- + --cache + --no-progress + --max-cache-age 2d + --timeout 10 + --max-retries 5 + --skip-missing + --exclude-loopback + --accept 200,403,429,999 + --exclude "https://tiles.stadiamaps.com/*|https://b.tile.openstreetmap.org/*" + --exclude "https://cartodb-basemaps-c.global.ssl.fastly.net/*" + --exclude "https://events.mapbox.com/*|https://events.mapbox.cn/*|https://api.mapbox.cn/*" + --exclude "https://github.com/mikolalysenko/glsl-read-float/*" + --exclude "https://fonts.openmaptiles.org/*" + --exclude "https://a.tile.openstreetmap.org/*" + --exclude "https://openstreetmap.org/*|https://www.openstreetmap.org/*" + --exclude "https://cdn.plot.ly/*" + --exclude "http://www.w3.org/*|https://www.w3.org/*" + --exclude "https://doi.org/*" + --exclude "https://raw.githubusercontent.com/paramm-team/pybamm-param/develop/pbparam/input/data/" + --exclude-path ./CHANGELOG.md + --exclude-path asv.conf.json + --exclude-path docs/conf.py + './**/*.rst' + './**/*.md' + './**/*.py' + './**/*.ipynb' + './**/*.json' + './**/*.toml' + fail: true + jobSummary: true + format: markdown diff --git a/.github/workflows/release_action.yaml b/.github/workflows/release_action.yaml index 0e4ee04d4..499b40022 100644 --- a/.github/workflows/release_action.yaml +++ b/.github/workflows/release_action.yaml @@ -72,7 +72,7 @@ jobs: ./dist/*.tar.gz ./dist/*.whl - name: Publish artifacts and signatures to GitHub Releases - uses: softprops/action-gh-release@9d7c94cfd0a1f3ed45544c887983e9fa900f0564 # v2.0.4 + uses: softprops/action-gh-release@v2 with: # `dist/` contains the built packages, and the # sigstore-produced signatures and certificates. diff --git a/.github/workflows/scheduled_tests.yaml b/.github/workflows/scheduled_tests.yaml index 159152f05..f8d54a927 100644 --- a/.github/workflows/scheduled_tests.yaml +++ b/.github/workflows/scheduled_tests.yaml @@ -6,13 +6,14 @@ on: branches: - main - # runs every day at 09:00 UTC + # runs every day at 03:00 UTC schedule: - - cron: '0 9 * * *' + - cron: '0 3 * * *' # Check noxfile.py for associated environment variables env: PYBOP_SCHEDULED: 1 + FORCE_COLOR: 3 jobs: # Dynamically create a matrix of OS, Python, and PyBaMM versions diff --git a/.github/workflows/test_on_push.yaml b/.github/workflows/test_on_pull_request.yaml similarity index 83% rename from .github/workflows/test_on_push.yaml rename to .github/workflows/test_on_pull_request.yaml index e2b6d19d9..cdad41770 100644 --- a/.github/workflows/test_on_push.yaml +++ b/.github/workflows/test_on_pull_request.yaml @@ -4,6 +4,10 @@ on: workflow_dispatch: pull_request: +# Add colors to nox/pytest +env: + FORCE_COLOR: 3 + concurrency: # github.workflow: name of the workflow, so that we don't cancel other workflows # github.event.pull_request.number || github.ref: pull request number or branch name if not a pull request @@ -105,6 +109,37 @@ jobs: run: | nox -s examples + # Quick benchmarks on macos-14 + benchmarks: + needs: style + runs-on: macos-14 + strategy: + fail-fast: false + name: Benchmarks + + steps: + - name: Check out PyBOP repository + uses: actions/checkout@v4 + with: + fetch-depth: 2 + + - name: Set up Python 3.12 + id: setup-python + uses: actions/setup-python@v4 + with: + python-version: 3.12 + + - name: Install dependencies + shell: bash + run: | + python -m pip install --upgrade pip asv[virtualenv] + + - name: Run quick benchmarks + shell: bash + run: | + asv machine --machine "GitHubRunner" + asv run --machine "GitHubRunner" --quick --show-stderr + # Runs only on macos-14 with Python 3.12 check_coverage: needs: style diff --git a/.gitignore b/.gitignore index 3c3bb708d..2bafcca63 100644 --- a/.gitignore +++ b/.gitignore @@ -314,3 +314,6 @@ $RECYCLE.BIN/ # Airspeed Velocity *.asv/ results/ + +# Pycharm +*.idea/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index e5c6f51f5..db0a8319c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,7 +4,7 @@ ci: repos: - repo: https://github.com/astral-sh/ruff-pre-commit - rev: "v0.4.10" + rev: "v0.7.4" hooks: - id: ruff args: [--fix, --show-fixes] @@ -13,7 +13,7 @@ repos: types_or: [python, pyi, jupyter] - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.6.0 + rev: v5.0.0 hooks: - id: check-added-large-files args: ['--maxkb=2000'] @@ -32,3 +32,9 @@ repos: - id: rst-backticks - id: rst-directive-colons - id: rst-inline-touching-normal + + - repo: https://github.com/kynan/nbstripout + rev: 0.8.1 + hooks: + - id: nbstripout + args: ['--keep-output', '--drop-empty-cells'] diff --git a/CHANGELOG.md b/CHANGELOG.md index 93752e1d8..bcfe6c905 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,12 +2,102 @@ ## Features +- [#282](https://github.com/pybop-team/PyBOP/issues/282) - Restructures the examples directory. +- [#396](https://github.com/pybop-team/PyBOP/issues/396) - Adds `ecm_with_tau.py` example script. +- [#452](https://github.com/pybop-team/PyBOP/issues/452) - Extends `cell_mass` and `approximate_capacity` for half-cell models. +- [#544](https://github.com/pybop-team/PyBOP/issues/544) - Allows iterative plotting using `StandardPlot`. +- [#541](https://github.com/pybop-team/PyBOP/pull/541) - Adds `ScaledLogLikelihood` and `BaseMetaLikelihood` classes. +- [#409](https://github.com/pybop-team/PyBOP/pull/409) - Adds plotting and convergence methods for Monte Carlo sampling. Includes open-access Tesla 4680 dataset for Bayesian inference example. Fixes transformations for sampling. +- [#531](https://github.com/pybop-team/PyBOP/pull/531) - Adds Voronoi optimiser surface plot (`pybop.plot.surface`) for fast optimiser aligned cost visualisation. +- [#532](https://github.com/pybop-team/PyBOP/issues/532) - Adds `linked_parameters` example script which shows how to update linked parameters during design optimisation. +- [#529](https://github.com/pybop-team/PyBOP/issues/529) - Adds `GravimetricPowerDensity` and `VolumetricPowerDensity` costs, along with the mathjax extension for Sphinx. + +## Optimisations + +- [#512](https://github.com/pybop-team/PyBOP/pull/513) - Refactors `LogPosterior` with attributes pointing to composed likelihood object. +- [#551](https://github.com/pybop-team/PyBOP/pull/551) - Refactors Optimiser arguments, `population_size` and `max_iterations` as default args, improves optimiser docstrings + +## Bug Fixes + +- [#505](https://github.com/pybop-team/PyBOP/pull/505) - Bug fixes for `LogPosterior` with transformed `GaussianLogLikelihood` likelihood. + +## Breaking Changes + +- [#531](https://github.com/pybop-team/PyBOP/pull/531) - Plot methods moved to `pybop.plot` with mostly minimal renaming. For example, `pybop.plot_parameters` is now `pybop.plot.parameters`. Other breaking changes include: `pybop.plot2d` to `pybop.plot.contour`. +- [#526](https://github.com/pybop-team/PyBOP/pull/526) - Refactor `OptimisationResults` classes, with `optim.run()` now return the full object. Adds finite cost value check for optimised parameters. + +# [v24.9.1](https://github.com/pybop-team/PyBOP/tree/v24.9.0) - 2024-09-16 + +## Features + +## Bug Fixes + +- [#495](https://github.com/pybop-team/PyBOP/pull/495) - Bugfixes for Transformation class, adds `apply_transform` optional arg to `BaseCost` for transformation functionality. + +## Breaking Changes + +# [v24.9.0](https://github.com/pybop-team/PyBOP/tree/v24.9.0) - 2024-09-10 + +## Features + +- [#462](https://github.com/pybop-team/PyBOP/pull/462) - Enables multidimensional learning rate for `pybop.AdamW` with updated (more robust) integration testing. Fixes bug in `Minkowski` and `SumofPower` cost functions for gradient-based optimisers. +- [#411](https://github.com/pybop-team/PyBOP/pull/411) - Updates notebooks with README in `examples/` directory, removes kaleido dependency and moves to nbviewer rendering, displays notebook figures with `notebook_connected` plotly renderer +- [#6](https://github.com/pybop-team/PyBOP/issues/6) - Adds Monte Carlo functionality, with methods based on Pints' algorithms. A base class is added `BaseSampler`, in addition to `PintsBaseSampler`. +- [#353](https://github.com/pybop-team/PyBOP/issues/353) - Allow user-defined check_params functions to enforce nonlinear constraints, and enable SciPy constrained optimisation methods +- [#222](https://github.com/pybop-team/PyBOP/issues/222) - Adds an example for performing and electrode balancing. +- [#441](https://github.com/pybop-team/PyBOP/issues/441) - Adds an example for estimating constants within a `pybamm.FunctionalParameter`. +- [#405](https://github.com/pybop-team/PyBOP/pull/405) - Adds frequency-domain based EIS prediction methods via `model.simulateEIS` and updates to `problem.evaluate` with examples and tests. +- [#460](https://github.com/pybop-team/PyBOP/pull/460) - Notebook example files added for ECM and folder structure updated. +- [#450](https://github.com/pybop-team/PyBOP/pull/450) - Adds support for IDAKLU with output variables, and corresponding examples, tests. +- [#364](https://github.com/pybop-team/PyBOP/pull/364) - Adds the MultiFittingProblem class and the multi_fitting example script. +- [#444](https://github.com/pybop-team/PyBOP/issues/444) - Merge `BaseModel` `build()` and `rebuild()` functionality. +- [#435](https://github.com/pybop-team/PyBOP/pull/435) - Adds SLF001 linting for private members. +- [#418](https://github.com/pybop-team/PyBOP/issues/418) - Wraps the `get_parameter_info` method from PyBaMM to get a dictionary of parameter names and types. +- [#413](https://github.com/pybop-team/PyBOP/pull/413) - Adds `DesignCost` functionality to `WeightedCost` class with additional tests. +- [#357](https://github.com/pybop-team/PyBOP/pull/357) - Adds `Transformation()` class with `LogTransformation()`, `IdentityTransformation()`, and `ScaledTransformation()`, `ComposedTransformation()` implementations with corresponding examples and tests. +- [#427](https://github.com/pybop-team/PyBOP/issues/427) - Adds the nbstripout pre-commit hook to remove unnecessary metadata from notebooks. +- [#327](https://github.com/pybop-team/PyBOP/issues/327) - Adds the `WeightedCost` subclass, defines when to evaluate a problem and adds the `spm_weighted_cost` example script. +- [#393](https://github.com/pybop-team/PyBOP/pull/383) - Adds Minkowski and SumofPower cost classes, with an example and corresponding tests. +- [#403](https://github.com/pybop-team/PyBOP/pull/403/) - Adds lychee link checking action. + +## Bug Fixes + +- [#473](https://github.com/pybop-team/PyBOP/pull/473) - Bugfixes for transformation class, adds optional `apply_transform` arg to `BaseCost.__call__()`, adds `log_update()` method to `BaseOptimiser` +- [#464](https://github.com/pybop-team/PyBOP/issues/464) - Fix order of design `parameter_set` updates and refactor `update_capacity`. +- [#468](https://github.com/pybop-team/PyBOP/issue/468) - Renames `quick_plot.py` to `standard_plots.py`. +- [#454](https://github.com/pybop-team/PyBOP/issue/454) - Fixes benchmarking suite. +- [#421](https://github.com/pybop-team/PyBOP/issues/421) - Adds a default value for the initial SOC for design problems. + +## Breaking Changes + +- [#499](https://github.com/pybop-team/PyBOP/pull/499) - BPX is added as an optional dependency. +- [#483](https://github.com/pybop-team/PyBOP/pull/483) - Replaces `pybop.MAP` with `pybop.LogPosterior` with an updated call args and bugfixes. +- [#436](https://github.com/pybop-team/PyBOP/pull/436) - **API Change:** The functionality from `BaseCost.evaluate/S1` & `BaseCost._evaluate/S1` is represented in `BaseCost.__call__` & `BaseCost.compute`. `BaseCost.compute` directly acts on the predictions, while `BaseCost.__call__` calls `BaseProblem.evaluate/S1` before `BaseCost.compute`. `compute` has optional args for gradient cost calculations. +- [#424](https://github.com/pybop-team/PyBOP/issues/424) - Replaces the `init_soc` input to `FittingProblem` with the option to pass an initial OCV value, updates `BaseModel` and fixes `multi_model_identification.ipynb` and `spm_electrode_design.ipynb`. + +# [v24.6.1](https://github.com/pybop-team/PyBOP/tree/v24.6.1) - 2024-07-31 + +## Features + +- [#313](https://github.com/pybop-team/PyBOP/pull/313/) - Fixes for PyBaMM v24.5, drops support for PyBaMM v23.9, v24.1 + +## Bug Fixes + +## Breaking Changes + +# [v24.6](https://github.com/pybop-team/PyBOP/tree/v24.6) - 2024-07-08 + +## Features + +- [#319](https://github.com/pybop-team/PyBOP/pull/319/) - Adds `CuckooSearch` optimiser with corresponding tests. +- [#359](https://github.com/pybop-team/PyBOP/pull/359/) - Aligning Inputs between problem, observer and model. - [#379](https://github.com/pybop-team/PyBOP/pull/379) - Adds model.simulateS1 to weekly benchmarks. - [#174](https://github.com/pybop-team/PyBOP/issues/174) - Adds new logo and updates Readme for accessibility. - [#316](https://github.com/pybop-team/PyBOP/pull/316) - Adds Adam with weight decay (AdamW) optimiser, adds depreciation warning for pints.Adam implementation. - [#271](https://github.com/pybop-team/PyBOP/issues/271) - Aligns the output of the optimisers via a generalisation of Result class. - [#315](https://github.com/pybop-team/PyBOP/pull/315) - Updates __init__ structure to remove circular import issues and minimises dependancy imports across codebase for faster PyBOP module import. Adds type-hints to BaseModel and refactors rebuild parameter variables. - [#236](https://github.com/pybop-team/PyBOP/issues/236) - Restructures the optimiser classes, adds a new optimisation API through direct construction and keyword arguments, and fixes the setting of `max_iterations`, and `_minimising`. Introduces `pybop.BaseOptimiser`, `pybop.BasePintsOptimiser`, and `pybop.BaseSciPyOptimiser` classes. +- [#322](https://github.com/pybop-team/PyBOP/pull/322) - Add `Parameters` class to store and access multiple parameters in one object. - [#321](https://github.com/pybop-team/PyBOP/pull/321) - Updates Prior classes with BaseClass, adds a `problem.sample_initial_conditions` method to improve stability of SciPy.Minimize optimiser. - [#249](https://github.com/pybop-team/PyBOP/pull/249) - Add WeppnerHuggins model and GITT example. - [#304](https://github.com/pybop-team/PyBOP/pull/304) - Decreases the testing suite completion time. @@ -25,6 +115,11 @@ ## Bug Fixes +- [#393](https://github.com/pybop-team/PyBOP/pull/393) - General integration test fixes. Adds UserWarning when using Plot2d with prior generated bounds. +- [#338](https://github.com/pybop-team/PyBOP/pull/338) - Fixes GaussianLogLikelihood class, adds integration tests, updates non-bounded parameter implementation by applying bounds from priors and `boundary_multiplier` argument. Bugfixes to CMAES construction. +- [#339](https://github.com/pybop-team/PyBOP/issues/339) - Updates the calculation of the cyclable lithium capacity in the spme_max_energy example. +- [#387](https://github.com/pybop-team/PyBOP/issues/387) - Adds keys to ParameterSet and updates ECM OCV check. +- [#380](https://github.com/pybop-team/PyBOP/pull/380) - Restore self._boundaries construction for `pybop.PSO` - [#372](https://github.com/pybop-team/PyBOP/pull/372) - Converts `np.array` to `np.asarray` for Numpy v2.0 support. - [#165](https://github.com/pybop-team/PyBOP/issues/165) - Stores the attempted and best parameter values and the best cost for each iteration in the log attribute of the optimiser and updates the associated plots. - [#354](https://github.com/pybop-team/PyBOP/issues/354) - Fixes the calculation of the gradient in the `RootMeanSquaredError` cost. @@ -39,6 +134,13 @@ - [#270](https://github.com/pybop-team/PyBOP/pull/270) - Updates PR template. - [#91](https://github.com/pybop-team/PyBOP/issues/91) - Adds a check on the number of parameters for CMAES and makes XNES the default optimiser. +## Breaking Changes + +- [#322](https://github.com/pybop-team/PyBOP/pull/322) - Add `Parameters` class to store and access multiple parameters in one object (API change). +- [#285](https://github.com/pybop-team/PyBOP/pull/285) - Drop support for Python 3.8. +- [#251](https://github.com/pybop-team/PyBOP/pull/251) - Drop support for PyBaMM v23.5 +- [#236](https://github.com/pybop-team/PyBOP/issues/236) - Restructures the optimiser classes (API change). + # [v24.3.1](https://github.com/pybop-team/PyBOP/tree/v24.3.1) - 2024-06-17 ## Features diff --git a/CITATION.cff b/CITATION.cff index a14af062e..bcf8d5872 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -11,5 +11,5 @@ authors: family-names: Courtier - given-names: David family-names: Howey -version: "24.3.1" # Update this when you release a new version +version: "24.9.1" # Update this when you release a new version repository-code: 'https://www.github.com/pybop-team/pybop' diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 0f3a7f07d..f237f5221 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -21,7 +21,7 @@ pip install -e .[all,dev] Before you commit any code, please perform the following checks using [Nox](https://nox.thea.codes/en/stable/index.html): -- [All tests pass](#testing): `$ nox -s unit` +- [All tests pass](#testing): `$ nox -s quick` ### Installing and using pre-commit @@ -62,15 +62,15 @@ You now have everything you need to start making changes! ### B. Writing your code -6. PyBOP is developed in [Python](https://en.wikipedia.org/wiki/Python_(programming_language)), and makes heavy use of [NumPy](https://en.wikipedia.org/wiki/NumPy) (see also [NumPy for MatLab users](https://numpy.org/doc/stable/user/numpy-for-matlab-users.html) and [Python for R users](http://blog.hackerearth.com/how-can-r-users-learn-python-for-data-science)). +6. PyBOP is developed in [Python](https://en.wikipedia.org/wiki/Python_(programming_language)), and makes heavy use of [NumPy](https://en.wikipedia.org/wiki/NumPy) (see also [NumPy for MatLab users](https://numpy.org/doc/stable/user/numpy-for-matlab-users.html) and [Python for R users](https://rebeccabarter.com/blog/2023-09-11-from_r_to_python)). 7. Make sure to follow our [coding style guidelines](#coding-style-guidelines). -8. Commit your changes to your branch with [useful, descriptive commit messages](https://chris.beams.io/posts/git-commit/): Remember these are publicly visible and should still make sense a few months ahead in time. While developing, you can keep using the GitHub issue you're working on as a place for discussion. [Refer to your commits](https://stackoverflow.com/questions/8910271/how-can-i-reference-a-commit-in-an-issue-comment-on-github) when discussing specific lines of code. +8. Commit your changes to your branch with [useful, descriptive commit messages](https://chris.beams.io/posts/git-commit/): Remember these are publicly visible and should still make sense a few months ahead in time. While developing, you can keep using the GitHub issue you're working on as a place for discussion. Refer to your commits when discussing specific lines of code. This is achieved by referencing the SHA-hash in the comment. An example of this looks like: `the commit 3e5c1e6 solved the issue...` 9. If you want to add a dependency on another library, or re-use code you found somewhere else, have a look at [these guidelines](#dependencies-and-reusing-code). ### C. Merging your changes with PyBOP 10. [Test your code!](#testing) -12. If you added a major new feature, perhaps it should be showcased in an [example notebook](#example-notebooks). +12. If you added a major new feature, perhaps it should be showcased in an [example notebook](https://github.com/pybop-team/PyBOP/tree/develop/examples/notebooks). 13. If you've added new functionality, please add additional tests to ensure ample code coverage in PyBOP. 13. When you feel your code is finished, or at least warrants serious discussion, create a [pull request](https://help.github.com/articles/about-pull-requests/) (PR) on [PyBOP's GitHub page](https://github.com/pybop-team/PyBOP). 14. Once a PR has been created, it will be reviewed by any member of the community. Changes might be suggested which you can make by simply adding new commits to the branch. When everything's finished, someone with the right GitHub permissions will merge your changes into PyBOP main repository. @@ -83,16 +83,20 @@ PyBOP follows the [PEP8 recommendations](https://www.python.org/dev/peps/pep-000 ### Ruff -We use [ruff](https://github.com/charliermarsh/ruff) to check our PEP8 adherence. To try this on your system, navigate to the PyBOP directory in a console and type +We use [ruff](https://github.com/charliermarsh/ruff) to lint and ensure adherence to Python PEP standards. To manually trigger `ruff`, navigate to the PyBOP directory in a console and type ```bash python -m pip install pre-commit pre-commit run ruff ``` -ruff is configured inside the file `pre-commit-config.yaml`, allowing us to ignore some errors. If you think this should be added or removed, please submit an [issue](https://guides.github.com/features/issues/). +ruff is configured inside the file `pyproject.toml`, allowing us to ignore some errors. If you think a rule should be added or removed, please submit an [issue](https://guides.github.com/features/issues/). -When you commit your changes they will be checked against ruff automatically (see [Pre-commit checks](#pre-commit-checks)). +When you commit your changes they will be checked against ruff automatically (see [Pre-commit checks](#pre-commit-checks)). If you are having issues getting your commit to pass the linting, it +is possible to skip linting for single lines (this should only be done as a **last resort**) by adding a line comment of `#noqa: $ruff_rule` where the `$ruff_rule` is replaced with the rule in question. +It is also possible to skip linting altogether by committing your changes by using the +`--no-verify` command-line flag. +These rules can be found in the ruff configuration in `pyproject.toml` or in the failed pre-commit output. Please note the lint skipping in the pull request for reviewers. ### Naming @@ -105,7 +109,7 @@ Class names are CamelCase, and start with an upper case letter, for example `MyO While it's a bad idea for developers to "reinvent the wheel", it's important for users to get a _reasonably sized download and an easy install_. In addition, external libraries can sometimes cease to be supported, and when they contain bugs it might take a while before fixes become available as automatic downloads to PyBOP users. For these reasons, all dependencies in PyBOP should be thought about carefully and discussed on GitHub. -Direct inclusion of code from other packages is possible, as long as their license permits it and is compatible with ours, but again should be considered carefully and discussed in the group. Snippets from blogs and [stackoverflow](https://stackoverflow.com/) can often be included but must include attribution to the original by commenting with a link in the source code. +Direct inclusion of code from other packages is possible, as long as their license permits it and is compatible with ours, but again should be considered carefully and discussed in the group. Snippets from blogs and stackoverflow can often be included but must include attribution to the original by commenting with a link in the source code. ### Separating dependencies @@ -158,16 +162,24 @@ If you have nox installed, to run unit tests, type nox -s unit ``` -Alternatively, to run tests standalone with pytest, run, +For individual tests, use: + +```bash +nox -s tests -- tests/unit/test_costs.py::TestCosts::test_costs +``` + +which will run the specified test, alternatively you can run all tests within a file by removing the trailing `::test_costs` in the above command. + +Alternatively, to run tests standalone with pytest, use: ```bash pytest --unit -v ``` -To run individual test files, you can use +To run individual test files with nox, you can use ```bash -pytest tests/unit/path/to/test --unit -v +pytest tests/unit/path/to/test.py --unit -v ``` And for individual tests, @@ -314,8 +326,6 @@ Configuration files: pyproject.toml ``` -Note that this file must be kept in sync with the version number in [pybop/**init**.py](https://github.com/pybop-team/PyBOP/blob/develop/pybop/__init__.py). - ### Continuous Integration using GitHub actions Each change pushed to the PyBOP GitHub repository will trigger the test and benchmark suites to be run, using [GitHub actions](https://github.com/features/actions). diff --git a/README.md b/README.md index 99f7a031e..418405b7d 100644 --- a/README.md +++ b/README.md @@ -9,18 +9,18 @@ [![Contributors](https://img.shields.io/github/contributors/pybop-team/PyBOP)](https://github.com/pybop-team/PyBOP/graphs/contributors) [![Last Commit](https://img.shields.io/github/last-commit/pybop-team/PyBOP/develop?color=purple)](https://github.com/pybop-team/PyBOP/commits/develop) [![Python Versions from PEP 621 TOML](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Fpybop-team%2FPyBOP%2Fdevelop%2Fpyproject.toml&label=Python)](https://pypi.org/project/pybop/) - [![Forks](https://img.shields.io/github/forks/pybop-team/PyBOP?style=flat)](https://github.com/pybop-team/PyBOPe/network/members) + [![Forks](https://img.shields.io/github/forks/pybop-team/PyBOP?style=flat)](https://github.com/pybop-team/PyBOP/network/members) [![Stars](https://img.shields.io/github/stars/pybop-team/PyBOP?style=flat&color=gold)](https://github.com/pybop-team/PyBOP/stargazers) [![Codecov](https://codecov.io/gh/pybop-team/PyBOP/branch/develop/graph/badge.svg)](https://codecov.io/gh/pybop-team/PyBOP) [![Open Issues](https://img.shields.io/github/issues/pybop-team/PyBOP)](https://github.com/pybop-team/PyBOP/issues/) [![License](https://img.shields.io/github/license/pybop-team/PyBOP?color=blue)](https://github.com/pybop-team/PyBOP/blob/develop/LICENSE) [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pybop-team/PyBOP/blob/develop/) + [![nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/pybop-team/PyBOP/tree/develop/examples/notebooks/) [![Static Badge](https://img.shields.io/badge/https%3A%2F%2Fpybop-team.github.io%2Fpybop-bench%2F?label=Benchmarks)](https://pybop-team.github.io/pybop-bench/) [![Releases](https://img.shields.io/github/v/release/pybop-team/PyBOP?color=gold)](https://github.com/pybop-team/PyBOP/releases) -## PyBOP PyBOP provides a complete set of tools for parameterisation and optimisation of battery models, using both Bayesian and frequentist approaches, with [example workflows](https://github.com/pybop-team/PyBOP/tree/develop/examples/notebooks) to assist the user. PyBOP can be used to parameterise various battery models, including electrochemical and equivalent circuit models available in [PyBaMM](https://pybamm.org/). PyBOP prioritises clear and informative diagnostics for the user, while also allowing for advanced probabilistic methods. The diagram below shows the conceptual framework of PyBOP. This package is currently under development, so users can expect the API to evolve with future releases. @@ -54,44 +54,50 @@ To check that PyBOP is installed correctly, run one of the examples in the follo ## Using PyBOP PyBOP has two intended uses: -1. Parameter estimation from battery test data. +1. Parameter inference from battery test data. 2. Design optimisation under battery manufacturing/use constraints. These include a wide variety of optimisation problems that require careful consideration due to the choice of battery model, data availability and/or the choice of design parameters. -### Notebooks -PyBOP comes with a number of [example notebooks](https://github.com/pybop-team/PyBOP/blob/develop/examples), which can be found in the examples folder. A few noteworthy ones are listed below. +### Jupyter Notebooks -- [Gravimetric design optimisation of a single particle model](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/spm_electrode_design.ipynb) -- [Experimental GITT fitting of an ECM for an LG M50](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/LG_M50_ECM/1-single-pulse-circuit-model.ipynb) -- [Compare PyBOP optimisers for parameter identification](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/multi_optimiser_identification.ipynb) -- [Parameter identification for a spatial pouch cell model](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/pouch_cell_identification.ipynb) +Explore our [example notebooks](https://github.com/pybop-team/PyBOP/blob/develop/examples) for hands-on demonstrations: -### Scripts -Additional script-based examples can be found in the [examples directory](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/). Some notable scripts are listed below. +- [Gravimetric design optimisation (SPM)](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/energy_based_electrode_design.ipynb) +- [Non-linear constrained ECM parameter identification](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/ecm_trust-constr.ipynb) +- [Optimiser comparison for parameter identification](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/multi_optimiser_identification.ipynb) +- [Parameter identification for spatial pouch cell model](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/pouch_cell_identification.ipynb) +- [Estimating ECM parameters from HPPC pulse](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/equivalent_circuit_identification_hppc.ipynb) + +### Python Scripts + +Find additional script-based examples in the [examples directory](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/): + +- [UKF parameter identification (SPM)](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/spm_UKF.py) +- [BPX format parameter import/export](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/BPX_spm.py) +- [Electrochemical Impendence Spectroscopy (EIS) parameter identification](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/eis_fitting.py) +- [Maximum a Posteriori parameter identification (SPM)](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/BPX_spm.py) +- [Gradient-based parameter identification (SPM)](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/spm_AdamW.py) -- [Unscented Kalman filter parameter identification of a SPM](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/spm_UKF.py) -- [Import and export parameters using Faraday's BPX format](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/BPX_spm.py) -- [Maximum a posteriori parameter identification of a SPM](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/BPX_spm.py) -- [Gradient based parameter identification of a SPM](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/spm_adam.py) ### Supported Methods The table below lists the currently supported [models](https://github.com/pybop-team/PyBOP/tree/develop/pybop/models), [optimisers](https://github.com/pybop-team/PyBOP/tree/develop/pybop/optimisers), and [cost functions](https://github.com/pybop-team/PyBOP/tree/develop/pybop/costs) in PyBOP.

-| Battery Models | Optimization Algorithms | Cost Functions | -|-----------------------------------------------|-------------------------------------------------------------|------------------------------------------| -| Single Particle Model (SPM) | Covariance Matrix Adaptation Evolution Strategy (CMA-ES) | Sum of Squared Errors (SSE) | -| Single Particle Model with Electrolyte (SPMe) | Particle Swarm Optimization (PSO) | Root Mean Squared Error (RMSE) | -| Doyle-Fuller-Newman (DFN) | Exponential Natural Evolution Strategy (xNES) | Gaussian Log Likelihood | -| Many Particle Model (MPM) | Separable Natural Evolution Strategy (sNES) | Gaussian Log Likelihood w/ known variance | -| Multi-Species Multi-Reactants (MSMR) | Adaptive Moment Estimation with Weight Decay (AdamW) | Maximum a Posteriori (MAP) | -| Equivalent Circuit Models (ECM) | Improved Resilient Backpropagation (iRProp-) | Unscented Kalman Filter (UKF) | -| | SciPy Minimize & Differential Evolution | Gravimetric Energy Density | -| | Gradient Descent| Volumetric Energy Density | -| | Nelder-Mead | | +| Battery Models | Optimization Algorithms | Cost Functions | +|-----------------------------------------------|----------------------------------------------------------|------------------------------------------| +| Single Particle Model (SPM) | Covariance Matrix Adaptation Evolution Strategy (CMA-ES) | Sum of Squared Errors (SSE) | +| Single Particle Model with Electrolyte (SPMe) | Particle Swarm Optimization (PSO) | Root Mean Squared Error (RMSE) | +| Doyle-Fuller-Newman (DFN) | Exponential Natural Evolution Strategy (xNES) | Minkowski | +| Many Particle Model (MPM) | Separable Natural Evolution Strategy (sNES) | Sum of Power | +| Multi-Species Multi-Reactants (MSMR) | Adaptive Moment Estimation with Weight Decay (AdamW) | Gaussian Log Likelihood | +| Weppner-Huggins | Improved Resilient Backpropagation (iRProp-) | Log Posterior | +| Equivalent Circuit Models (ECM) | SciPy Minimize & Differential Evolution | Unscented Kalman Filter (UKF) | +| | Cuckoo Search | Gravimetric Energy Density | +| | Gradient Descent | Volumetric Energy Density | +| | Nelder-Mead | |

@@ -106,6 +112,10 @@ PyBOP aims to foster a broad consortium of developers and users, building on and - User-friendliness (putting user requirements first via user-assistance & workflows) +## License + +PyBOP is released under the [BSD 3-Clause License](https://github.com/pybop-team/PyBOP/blob/develop/LICENSE). + ## Contributors ✨ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)): @@ -120,14 +130,17 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d NicolaCourtier
NicolaCourtier
💻 👀 💡 ⚠️ David Howey
David Howey
🤔 🧑‍🏫 Martin Robinson
Martin Robinson
🤔 🧑‍🏫 👀 💻 ⚠️ - Ferran Brosa Planella
Ferran Brosa Planella
👀 💻 + Ferran Brosa Planella
Ferran Brosa Planella
👀 💻 💡 Agriya Khetarpal
Agriya Khetarpal
💻 🚇 👀 + Faraday Institution
Faraday Institution
💵 - Faraday Institution
Faraday Institution
💵 UK Research and Innovation
UK Research and Innovation
💵 - EU IntelLiGent Project
IntelLiGent Consortium
💵 + Horizon Europe IntelLiGent Consortium
Horizon Europe IntelLiGent Consortium
💵 Muhammed Nedim Sogut
Muhammed Nedim Sogut
💻 + MarkBlyth
MarkBlyth
💻 + f-g-r-i-m-m
f-g-r-i-m-m
💡 + Dibyendu-IITKGP
Dibyendu-IITKGP
💡 diff --git a/asv.conf.json b/asv.conf.json index fdd830cea..e6b508e06 100644 --- a/asv.conf.json +++ b/asv.conf.json @@ -8,7 +8,7 @@ "python -m build --wheel -o {build_cache_dir} {build_dir}" ], "default_benchmark_timeout": 180, - "branches": ["develop"], + "branches": ["HEAD"], "environment_type": "virtualenv", "matrix": { "req":{ diff --git a/benchmarks/benchmark_model.py b/benchmarks/benchmark_model.py index 843b03bcc..2941cba88 100644 --- a/benchmarks/benchmark_model.py +++ b/benchmarks/benchmark_model.py @@ -1,8 +1,7 @@ import numpy as np import pybop - -from .benchmark_utils import set_random_seed +from benchmarks.benchmark_utils import set_random_seed class BenchmarkModel: @@ -27,19 +26,20 @@ def setup(self, model, parameter_set): self.model = model(parameter_set=pybop.ParameterSet.pybamm(parameter_set)) # Define fitting parameters - parameters = [ + parameters = pybop.Parameters( pybop.Parameter( "Current function [A]", prior=pybop.Gaussian(0.4, 0.02), bounds=[0.2, 0.7], initial_value=0.4, ) - ] + ) # Generate synthetic data sigma = 0.001 self.t_eval = np.arange(0, 900, 2) - values = self.model.predict(t_eval=self.t_eval) + self.init_state = {"Initial SoC": 0.5} + values = self.model.predict(t_eval=self.t_eval, initial_state=self.init_state) corrupt_values = values["Voltage [V]"].data + np.random.normal( 0, sigma, len(self.t_eval) ) @@ -59,7 +59,7 @@ def setup(self, model, parameter_set): # Create fitting problem self.problem = pybop.FittingProblem( - model=self.model, dataset=dataset, parameters=parameters, init_soc=0.5 + model=self.model, dataset=dataset, parameters=parameters ) def time_model_predict(self, model, parameter_set): @@ -70,7 +70,9 @@ def time_model_predict(self, model, parameter_set): model (pybop.Model): The model class being benchmarked. parameter_set (str): The name of the parameter set being used. """ - self.model.predict(inputs=self.inputs, t_eval=self.t_eval) + self.model.predict( + inputs=self.inputs, t_eval=self.t_eval, initial_state=self.init_state + ) def time_model_simulate(self, model, parameter_set): """ @@ -80,7 +82,7 @@ def time_model_simulate(self, model, parameter_set): model (pybop.Model): The model class being benchmarked. parameter_set (str): The name of the parameter set being used. """ - self.problem._model.simulate(inputs=self.inputs, t_eval=self.t_eval) + self.problem.model.simulate(inputs=self.inputs, t_eval=self.t_eval) def time_model_simulateS1(self, model, parameter_set): """ @@ -90,4 +92,4 @@ def time_model_simulateS1(self, model, parameter_set): model (pybop.Model): The model class being benchmarked. parameter_set (str): The name of the parameter set being used. """ - self.problem._model.simulateS1(inputs=self.inputs, t_eval=self.t_eval) + self.problem.model.simulateS1(inputs=self.inputs, t_eval=self.t_eval) diff --git a/benchmarks/benchmark_optim_construction.py b/benchmarks/benchmark_optim_construction.py index fee5f0789..ccf60a045 100644 --- a/benchmarks/benchmark_optim_construction.py +++ b/benchmarks/benchmark_optim_construction.py @@ -1,8 +1,7 @@ import numpy as np import pybop - -from .benchmark_utils import set_random_seed +from benchmarks.benchmark_utils import set_random_seed class BenchmarkOptimisationConstruction: @@ -20,7 +19,7 @@ def setup(self, model, parameter_set, optimiser): Args: model (pybop.Model): The model class to be benchmarked. parameter_set (str): The name of the parameter set to be used. - optimiser (pybop.Optimiser): The optimizer class to be used. + optimiser (pybop.Optimiser): The optimiser class to be used. """ # Set random seed set_random_seed() @@ -29,7 +28,7 @@ def setup(self, model, parameter_set, optimiser): model_instance = model(parameter_set=pybop.ParameterSet.pybamm(parameter_set)) # Define fitting parameters - parameters = [ + parameters = pybop.Parameters( pybop.Parameter( "Negative electrode active material volume fraction", prior=pybop.Gaussian(0.6, 0.02), @@ -42,7 +41,7 @@ def setup(self, model, parameter_set, optimiser): bounds=[0.375, 0.625], initial_value=0.51, ), - ] + ) # Generate synthetic data sigma = 0.001 @@ -76,7 +75,7 @@ def time_optimisation_construction(self, model, parameter_set, optimiser): Args: model (pybop.Model): The model class being benchmarked. parameter_set (str): The name of the parameter set being used. - optimiser (pybop.Optimiser): The optimizer class being used. + optimiser (pybop.Optimiser): The optimiser class being used. """ self.optim = pybop.Optimisation(self.cost, optimiser=optimiser) @@ -87,6 +86,6 @@ def time_cost_evaluate(self, model, parameter_set, optimiser): Args: model (pybop.Model): The model class being benchmarked. parameter_set (str): The name of the parameter set being used. - optimiser (pybop.Optimiser): The optimizer class being used. + optimiser (pybop.Optimiser): The optimiser class being used. """ self.cost([0.63, 0.51]) diff --git a/benchmarks/benchmark_parameterisation.py b/benchmarks/benchmark_parameterisation.py index a64116a48..4e723da8f 100644 --- a/benchmarks/benchmark_parameterisation.py +++ b/benchmarks/benchmark_parameterisation.py @@ -1,8 +1,7 @@ import numpy as np import pybop - -from .benchmark_utils import set_random_seed +from benchmarks.benchmark_utils import set_random_seed class BenchmarkParameterisation: @@ -30,7 +29,7 @@ def setup(self, model, parameter_set, optimiser): Args: model (pybop.Model): The model class to be benchmarked. parameter_set (str): The name of the parameter set to be used. - optimiser (pybop.Optimiser): The optimizer class to be used. + optimiser (pybop.Optimiser): The optimiser class to be used. """ # Set random seed set_random_seed() @@ -46,7 +45,7 @@ def setup(self, model, parameter_set, optimiser): model_instance = model(parameter_set=params) # Define fitting parameters - parameters = [ + parameters = pybop.Parameters( pybop.Parameter( "Negative electrode active material volume fraction", prior=pybop.Gaussian(0.55, 0.03), @@ -57,7 +56,7 @@ def setup(self, model, parameter_set, optimiser): prior=pybop.Gaussian(0.55, 0.03), bounds=[0.375, 0.7], ), - ] + ) # Generate synthetic data sigma = 0.003 @@ -111,7 +110,7 @@ def time_parameterisation(self, model, parameter_set, optimiser): Args: model (pybop.Model): The model class being benchmarked (unused). parameter_set (str): The name of the parameter set being used (unused). - optimiser (pybop.Optimiser): The optimizer class being used (unused). + optimiser (pybop.Optimiser): The optimiser class being used (unused). """ self.optim.run() @@ -125,4 +124,4 @@ def time_optimiser_ask(self, model, parameter_set, optimiser): optimiser (pybop.Optimiser): The optimizer class being used. """ if optimiser not in [pybop.SciPyMinimize, pybop.SciPyDifferentialEvolution]: - self.optim.pints_optimiser.ask() + self.optim.optimiser.ask() diff --git a/benchmarks/benchmark_track_parameterisation.py b/benchmarks/benchmark_track_parameterisation.py index 9180ffecb..ccd8c3f88 100644 --- a/benchmarks/benchmark_track_parameterisation.py +++ b/benchmarks/benchmark_track_parameterisation.py @@ -1,8 +1,7 @@ import numpy as np import pybop - -from .benchmark_utils import set_random_seed +from benchmarks.benchmark_utils import set_random_seed class BenchmarkTrackParameterisation: @@ -30,7 +29,7 @@ def setup(self, model, parameter_set, optimiser): Args: model (pybop.Model): The model class to be benchmarked. parameter_set (str): The name of the parameter set to be used. - optimiser (pybop.Optimiser): The optimizer class to be used. + optimiser (pybop.Optimiser): The optimiser class to be used. """ # Set random seed set_random_seed() @@ -46,7 +45,7 @@ def setup(self, model, parameter_set, optimiser): model_instance = model(parameter_set=params) # Define fitting parameters - parameters = [ + parameters = pybop.Parameters( pybop.Parameter( "Negative electrode active material volume fraction", prior=pybop.Gaussian(0.55, 0.03), @@ -57,7 +56,7 @@ def setup(self, model, parameter_set, optimiser): prior=pybop.Gaussian(0.55, 0.03), bounds=[0.375, 0.7], ), - ] + ) # Generate synthetic data sigma = 0.003 @@ -122,7 +121,7 @@ def results_tracking(self, model, parameter_set, optimiser): Args: model (pybop.Model): The model class being benchmarked (unused). parameter_set (str): The name of the parameter set being used (unused). - optimiser (pybop.Optimiser): The optimizer class being used (unused). + optimiser (pybop.Optimiser): The optimiser class being used (unused). """ - x, _ = self.optim.run() - return x + results = self.optim.run() + return results.x diff --git a/conftest.py b/conftest.py index e2d9a55bf..808503fe8 100644 --- a/conftest.py +++ b/conftest.py @@ -20,7 +20,7 @@ def pytest_addoption(parser): "--examples", action="store_true", default=False, help="run examples tests" ) parser.addoption( - "--plots", action="store_true", default=False, help="run plotting tests" + "--plots", action="store_true", default=False, help="run plot tests" ) parser.addoption( "--notebooks", action="store_true", default=False, help="run notebook tests" diff --git a/docs/_extension/gallery_directive.py b/docs/_extension/gallery_directive.py index 3579ffcd8..4ab88d996 100644 --- a/docs/_extension/gallery_directive.py +++ b/docs/_extension/gallery_directive.py @@ -12,7 +12,7 @@ """ from pathlib import Path -from typing import Any, Dict, List +from typing import Any from docutils import nodes from docutils.parsers.rst import directives @@ -68,7 +68,7 @@ class GalleryGridDirective(SphinxDirective): "class-card": directives.unchanged, } - def run(self) -> List[nodes.Node]: + def run(self) -> list[nodes.Node]: """Create the gallery grid.""" if self.arguments: # If an argument is given, assume it's a path to a YAML file @@ -129,7 +129,7 @@ def run(self) -> List[nodes.Node]: return [container.children[0]] -def setup(app: Sphinx) -> Dict[str, Any]: +def setup(app: Sphinx) -> dict[str, Any]: """Add custom configuration to sphinx app. Args: diff --git a/docs/_static/switcher.json b/docs/_static/switcher.json index 5db09bc13..b3f464b27 100644 --- a/docs/_static/switcher.json +++ b/docs/_static/switcher.json @@ -4,9 +4,24 @@ "url": "https://pybop-docs.readthedocs.io/en/latest/" }, { - "name": "v23.12 (stable)", - "version": "v23.12", - "url": "https://pybop-docs.readthedocs.io/en/v23.12/", + "name": "v24.9 (stable)", + "version": "v24.9", + "url": "https://pybop-docs.readthedocs.io/en/v24.9.1/", "preferred": true + }, + { + "name": "v24.6", + "version": "v24.6", + "url": "https://pybop-docs.readthedocs.io/en/v24.6.1/" + }, + { + "name": "v24.3", + "version": "v24.3", + "url": "https://pybop-docs.readthedocs.io/en/v24.3/" + }, + { + "name": "v23.12", + "version": "v23.12", + "url": "https://pybop-docs.readthedocs.io/en/v23.12/" } ] diff --git a/docs/_templates/autoapi/index.rst b/docs/_templates/autoapi/index.rst index d60759952..7cc11171a 100644 --- a/docs/_templates/autoapi/index.rst +++ b/docs/_templates/autoapi/index.rst @@ -15,4 +15,6 @@ This page contains auto-generated API reference documentation [#f1]_. {% endif %} {% endfor %} + pybop/index + .. [#f1] Created with `sphinx-autoapi `_ diff --git a/docs/conf.py b/docs/conf.py index cfc37a90f..e602d17f3 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -7,11 +7,11 @@ from pathlib import Path sys.path.append(str(Path(".").resolve())) -from pybop._version import __version__ # noqa: E402 +from pybop._version import __version__ # -- Project information ----------------------------------------------------- project = "PyBOP" -copyright = "2023, The PyBOP Team" +copyright = "2023, The PyBOP Team" # noqa A001 author = "The PyBOP Team" release = f"v{__version__}" @@ -20,12 +20,13 @@ "sphinx.ext.napoleon", "sphinx.ext.autodoc", "sphinx.ext.autosummary", + "sphinx.ext.mathjax", "sphinx.ext.todo", "sphinx.ext.viewcode", "sphinx_design", "sphinx_copybutton", "autoapi.extension", - # custom extentions + # custom extensions "_extension.gallery_directive", # For extension examples and demos "myst_parser", @@ -39,7 +40,7 @@ # -- Options for autoapi ------------------------------------------------------- autoapi_type = "python" autoapi_dirs = ["../pybop"] -autoapi_keep_files = True +autoapi_keep_files = False autoapi_root = "api" autoapi_member_order = "groupwise" diff --git a/docs/index.md b/docs/index.md index d45182ae1..4d5621654 100644 --- a/docs/index.md +++ b/docs/index.md @@ -8,7 +8,7 @@ html_theme.sidebar_secondary.remove: true # PyBOP: Optimise and Parameterise Battery Models -Welcome to PyBOP, a Python package dedicated to the optimization and parameterization of battery models. PyBOP is designed to streamline your workflow, whether you are conducting academic research, working in industry, or simply interested in battery technology and modelling. +Welcome to PyBOP, a Python package dedicated to the optimisation and parameterisation of battery models. PyBOP is designed to streamline your workflow, whether you are conducting academic research, working in industry, or simply interested in battery technology and modelling. ```{gallery-grid} :grid-columns: 1 2 2 2 diff --git a/docs/installation.rst b/docs/installation.rst index 3c0080c1d..50138d81e 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -3,7 +3,7 @@ Installation ***************************** -PyBOP is a versatile Python package designed for optimization and parameterization of battery models. Follow the instructions below to install PyBOP and set up your environment to begin utilizing its capabilities. +PyBOP is a versatile Python package designed for optimisation and parameterisation of battery models. Follow the instructions below to install PyBOP and set up your environment to begin utilising its capabilities. Installing PyBOP with pip ------------------------- @@ -42,6 +42,28 @@ For those who prefer to install PyBOP from a local clone of the repository or wi In editable mode, changes you make to the source code will immediately affect the PyBOP installation without the need for reinstallation. +Optional Dependencies +----------------- +``plotly`` - For plotting, PyBOP uses plotly. It can be installed with: + +.. code-block:: console + + pip install pybop[plot] + +``scikit-fem`` - This is a dependency for the multi-dimensional pybamm models, and can be installed using: + +.. code-block:: console + + pip install pybop[scifem] + +``bpx`` - To use the Faraday Institution's Battery Parameter eXchange (BPX) package install the optional requirement: + +.. code-block:: console + + pip install pybop[bpx] + +To install all the optional dependencies, the command ``pip install pybop[all]`` is available. For more information on the optional packages, users are directed towards the `pyproject.toml `_. + Verifying Installation ---------------------- @@ -55,7 +77,7 @@ To verify that PyBOP has been installed successfully, try running one of the pro For Developers -------------- -If you are installing PyBOP for development purposes, such as contributing to the project, please ensure that you follow the guidelines outlined in the `Contributing Guide <../Contributing.html>`_. It includes additional steps that might be necessary for setting up a development environment, including the installation of dependencies and setup of pre-commit hooks. +If you are installing PyBOP for development purposes, such as contributing to the project, please ensure that you follow the guidelines outlined in the `Contributing Guide `_. It includes additional steps that might be necessary for setting up a development environment, including the installation of dependencies and setup of pre-commit hooks. Further Assistance ------------------ @@ -68,4 +90,4 @@ Next Steps After installing PyBOP, you might want to: * Explore the `Quick Start Guide `_ to begin using PyBOP. -* Check out the `API Reference <../api/index.html>`_ for detailed information on PyBOP's programming interface. +* Check out the `API Reference `_ for detailed information on PyBOP's programming interface. diff --git a/docs/quick_start.rst b/docs/quick_start.rst index 683c82b40..1a1617bd2 100644 --- a/docs/quick_start.rst +++ b/docs/quick_start.rst @@ -6,7 +6,7 @@ Welcome to the Quick Start Guide for PyBOP. This guide will help you get up and Getting Started with PyBOP -------------------------- -PyBOP is equipped with a series of robust tools that can help you optimize various parameters within your battery models to better match empirical data or to explore the effects of different parameters on battery behavior. +PyBOP is equipped with a series of robust tools that can help you optimise various parameters within your battery models to better match empirical data or to explore the effects of different parameters on battery behavior. To begin using PyBOP: @@ -24,14 +24,14 @@ To begin using PyBOP: import pybop - Now you're ready to utilize PyBOP's functionality in your projects! + Now you're ready to utilise PyBOP's functionality in your projects! Exploring Examples ------------------ To help you get acquainted with PyBOP's capabilities, we provide a collection of examples that demonstrate common use cases and features of the package: -- **Jupyter Notebooks**: Interactive notebooks that include detailed explanations alongside the live code, visualizations, and results. These are an excellent resource for learning and can be easily modified and executed to suit your needs. +- **Jupyter Notebooks**: Interactive notebooks that include detailed explanations alongside the live code, visualisations, and results. These are an excellent resource for learning and can be easily modified and executed to suit your needs. - **Python Scripts**: For those who prefer working in a text editor, IDE, or for integrating into larger projects, we provide equivalent examples in plain Python script format. @@ -55,4 +55,4 @@ If you encounter any issues or have questions as you start using PyBOP, don't he - **GitHub Issues**: Report bugs or request new features by opening an `Issue `_ - **GitHub Discussions**: Post your questions or feedback on our `GitHub Discussions `_ -- **Contributions**: Interested in contributing to PyBOP? Check out our `Contributing Guide <../Contributing.html>`_ for guidelines. +- **Contributions**: Interested in contributing to PyBOP? Check out our `Contributing Guide `_ for guidelines. diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 000000000..913f1e4cf --- /dev/null +++ b/examples/README.md @@ -0,0 +1,43 @@ +# Examples + +This directory contains example notebooks and scripts demonstrating how to use PyBOP. + +## Directory Structure + +- `notebooks/`: Jupyter notebooks of example functionality with explanations +- `scripts/`: Python scripts for quick reference and command-line usage +- `standalone/`: Example scripts for using standalone classes + +## Notebooks + +The `notebooks/` directory contains Jupyter notebooks that provide detailed, interactive examples of various features and use cases. These notebooks include explanations, code snippets, and visualisations. + +To view the notebooks with interactive figures without downloading the repository, please use nbviewer: + +
+ +[Notebooks on nbviewer](https://nbviewer.org/github/pybop-team/PyBOP/tree/develop/examples/notebooks/) + +
+ +## Scripts + +The `scripts/` directory contains standalone Python scripts that demonstrate specific tasks or workflows. These scripts are designed for quick reference and can be run directly from the command line. + +## Getting Started + +1. Clone the repository: `git clone https://github.com/pybop-team/pybop.git` +2. Navigate to the examples directory: `cd pybop/examples` +3. Explore the notebooks and scripts in their respective directories. +4. To run the Jupyter notebooks locally: + - Install Jupyter: `pip install jupyter` + - Start Jupyter Notebook: `jupyter notebook` + - Navigate to the `notebooks/` directory and open the desired notebook + +5. To run the Python scripts: + - Install PyBOP: `pip install pybop` + - Run a script using Python: `python scripts/script_name.py` + +## Contributing + +If you have additional examples or improvements to existing ones, please feel free to submit a pull request. We appreciate your contributions! diff --git a/examples/scripts/Chen_example.csv b/examples/data/Chen_example.csv similarity index 100% rename from examples/scripts/Chen_example.csv rename to examples/data/Chen_example.csv diff --git a/examples/data/LG_M50_ECM/data/LGM50_5Ah_OCV.mat b/examples/data/LG_M50_ECM/data/LGM50_5Ah_OCV.mat new file mode 100644 index 000000000..bdcb44452 Binary files /dev/null and b/examples/data/LG_M50_ECM/data/LGM50_5Ah_OCV.mat differ diff --git a/examples/data/LG_M50_ECM/data/LGM50_5Ah_Pulse.mat b/examples/data/LG_M50_ECM/data/LGM50_5Ah_Pulse.mat new file mode 100644 index 000000000..bcb1468cd Binary files /dev/null and b/examples/data/LG_M50_ECM/data/LGM50_5Ah_Pulse.mat differ diff --git a/examples/data/LG_M50_ECM/data/LGM50_5Ah_RateTest.mat b/examples/data/LG_M50_ECM/data/LGM50_5Ah_RateTest.mat new file mode 100644 index 000000000..63354591b Binary files /dev/null and b/examples/data/LG_M50_ECM/data/LGM50_5Ah_RateTest.mat differ diff --git 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+242649.9260471743 2.8188078 0.0 +242650.9260472786 2.8188472 0.0 +242651.8660473768 2.8188865 0.0 +242651.9240473825 2.8189261 0.0 diff --git a/examples/data/Tesla_4680/README.md b/examples/data/Tesla_4680/README.md new file mode 100644 index 000000000..7236e6624 --- /dev/null +++ b/examples/data/Tesla_4680/README.md @@ -0,0 +1,17 @@ +# Tesla 4680 Dataset + +This repository contains a subset of the data from the supplementary material of the research paper: +**M. Ank et al., "Lithium-Ion Cells in Automotive Applications: Tesla 4680 Cylindrical Cell Teardown and Characterization,"** *Journal of the Electrochemical Society*, vol. 170, no. 12, p. 120536, December 2023. [DOI: 10.1149/1945-7111/ad14d0](https://doi.org/10.1149/1945-7111/ad14d0) + +## Accessing the Supplemental Material + +The supplemental material can be accessed through the following link: +[Supplemental Material on MediaTUM](https://mediatum.ub.tum.de/1725661) + + +## Citation + +If you use this dataset in your work, please cite the original publication as follows: + +```plaintext +M. Ank et al., "Lithium-Ion Cells in Automotive Applications: Tesla 4680 Cylindrical Cell Teardown and Characterization," Journal of the Electrochemical Society, vol. 170, no. 12, p. 120536, Dec. 2023, doi: 10.1149/1945-7111/ad14d0. diff --git a/examples/data/Tesla_4680/T-cell_pOCV_data.txt b/examples/data/Tesla_4680/T-cell_pOCV_data.txt new file mode 100644 index 000000000..b4abc9baf --- /dev/null +++ b/examples/data/Tesla_4680/T-cell_pOCV_data.txt @@ -0,0 +1,9741 @@ +Ewe/V time/s Ece/V I/mA Efficiency/% Ewe-Ece/V Capacity/mA.h time/h arealCapacity Ewe/VReal Ece/VReal +3,3091569 183903,1284 0,20197234 0,091570303 0 3,1071846 0 51,08420233 0 3,6191569 0,51197234 +3,3205402 183935,1284 0,14970249 0,092138432 0 3,1708376 0,000819002 51,09309122 0,000869721 3,6305402 0,45970249 +3,3223355 183967,1284 0,14387779 0,092132702 0 3,1784577 0,001637999 51,10198011 0,001739435 3,6323355 0,45387779 +3,3233094 183999,1284 0,13977186 0,092139073 0 3,1835375 0,002456997 51,110869 0,002609151 3,6333094 0,44977186 +3,3238058 184031,1284 0,13612427 0,092132702 0 3,1876814 0,003275995 51,11975789 0,003478867 3,6338058 0,44612427 +3,3243787 184063,1284 0,13318327 0,092139073 0 3,1911955 0,00409499 51,12864678 0,004348581 3,6343787 0,44318327 +3,3247991 184095,1284 0,13024229 0,092142262 0 3,1945567 0,004913984 51,13753567 0,005218293 3,6347991 0,44024229 +3,3251429 184127,1284 0,12743497 0,09213461 0 3,1977079 0,00573298 51,14642456 0,006088007 3,6351429 0,43743497 +3,3254673 184159,1284 0,12497142 0,092142262 0 3,200496 0,006551975 51,15531345 0,00695772 3,6354673 0,43497142 +3,3257539 184191,1284 0,12266064 0,092133977 0 3,2030933 0,007370969 51,16420234 0,007827431 3,6357539 0,43266064 +3,3260593 184223,1284 0,12042625 0,092133336 0 3,2056332 0,008189965 51,17309123 0,008697146 3,6360593 0,43042625 +3,3262312 184255,1284 0,11842103 0,092135251 0 3,2078102 0,00900896 51,18198012 0,009566859 3,6362312 0,42842103 +3,326575 184287,1284 0,11654948 0,092135884 0 3,2100255 0,009827957 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495429,812 0,41853592 -0,092108302 99,443871 2,9001136 3,974845117 137,6193922 4,220995601 3,6286495 0,72853592 diff --git a/examples/notebooks/battery_parameterisation/ecm_trust-constr.ipynb b/examples/notebooks/battery_parameterisation/ecm_trust-constr.ipynb new file mode 100644 index 000000000..f5506b3a8 --- /dev/null +++ b/examples/notebooks/battery_parameterisation/ecm_trust-constr.ipynb @@ -0,0 +1,465 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Empirical models with scipy trust-constr:\n", + "## Identifying equivalent circuit parameters using bounds, nonlinear constraints, and gradient-based optimisers\n", + "\n", + "Here, we provide a short example of how to identify equivalent-circuit parameters using the [scipy trust-constr](https://docs.scipy.org/doc/scipy-1.14.0/reference/generated/scipy.optimize.minimize.html) method -- a trust-region based optimiser that can handle parameter bounds, and both linear and nonlinear constraints. As shown here, these turn out to be useful tools for fitting empirical battery models.\n", + "\n", + "### Importing libraries\n", + "\n", + "If you don't already have PyBOP installed, check out the [installation guide](https://pybop-docs.readthedocs.io/en/latest/installation.html) first.\n", + "\n", + "We begin by importing the necessary libraries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy.optimize\n", + "\n", + "import pybop\n", + "\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "### Initialising the model and parameters\n", + "\n", + "PyBOP needs to know some model parameters in order to run. Where these are unknown, and to be fitted, any sensible initial guess can be provided.\n", + "\n", + "Model parameters can either be defined using a PyBOP ```ParameterSet``` object, or by importing from a JSON file. For clarity, we will define the initial parameters from a ```ParameterSet```.\n", + "\n", + "Here, we'll fit a Thevenin model with two RC pairs. The initial parameter set must contain all the parameters that the PyBaMM model will use, and therefore it will need definitions for each circuit component, and each RC pair overpotential." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "parameter_set = {\n", + " \"chemistry\": \"ecm\",\n", + " \"Initial SoC\": 0.5,\n", + " \"Initial temperature [K]\": 25 + 273.15,\n", + " \"Cell capacity [A.h]\": 5,\n", + " \"Nominal cell capacity [A.h]\": 5,\n", + " \"Ambient temperature [K]\": 25 + 273.15,\n", + " \"Current function [A]\": 5,\n", + " \"Upper voltage cut-off [V]\": 4.2,\n", + " \"Lower voltage cut-off [V]\": 3.0,\n", + " \"Cell thermal mass [J/K]\": 1000,\n", + " \"Cell-jig heat transfer coefficient [W/K]\": 10,\n", + " \"Jig thermal mass [J/K]\": 500,\n", + " \"Jig-air heat transfer coefficient [W/K]\": 10,\n", + " \"Open-circuit voltage [V]\": pybop.empirical.Thevenin().default_parameter_values[\n", + " \"Open-circuit voltage [V]\"\n", + " ],\n", + " \"R0 [Ohm]\": 0.01,\n", + " \"Element-1 initial overpotential [V]\": 0,\n", + " \"Element-2 initial overpotential [V]\": 0,\n", + " \"R1 [Ohm]\": 0.005,\n", + " \"R2 [Ohm]\": 0.0003,\n", + " \"C1 [F]\": 10000,\n", + " \"C2 [F]\": 5000,\n", + " \"Entropic change [V/K]\": 0.0004,\n", + "}\n", + "\n", + "model = pybop.empirical.Thevenin(\n", + " parameter_set=parameter_set, options={\"number of rc elements\": 2}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "### Generating synthetic data\n", + "\n", + "Ordinarily, we would want to fit models to experimental data. For simplicity we'll use our model to generate some synthetic data, by simulating a discharge and corrupting the results with random noise. We will then use this synthetic data to form a PyBOP ```Dataset```, from which we can demonstrate the model fitting procedure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "sigma = 0.001\n", + "t_eval = np.linspace(0, 500, 500)\n", + "values = model.predict(t_eval=t_eval)\n", + "corrupt_values = values[\"Voltage [V]\"].data + np.random.normal(0, sigma, len(t_eval))\n", + "\n", + "# Form dataset\n", + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": t_eval,\n", + " \"Current function [A]\": values[\"Current [A]\"].data,\n", + " \"Voltage [V]\": corrupt_values,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "### Identifying parameters\n", + "\n", + "Now for the fun part! We begin by defining the parameters that we want to fit. In this case, it's the series resistance $R_0$, and the RC parameters $R_1$ and $C_1$. Since we don't want to make things too easy, we've set up our parameters to have quite different values to those used in simulating the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"R0 [Ohm]\",\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(1e-3, 1e-4),\n", + " bounds=[1e-5, 1e-2],\n", + " ),\n", + " pybop.Parameter(\n", + " \"C1 [F]\",\n", + " prior=pybop.Gaussian(5000, 300),\n", + " bounds=[2500, 5e4],\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "We can now set up a problem and cost for identifying these parameters from our synthetic dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "problem = pybop.FittingProblem(model, parameters, dataset)\n", + "cost = pybop.SumSquaredError(problem)" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "The voltage over any given RC branch will evolve over a timescale $\\tau = R \\times C$. The data we work with will place some limits on which parameters can realistically be identified. We can't expect to fit fast timescales with low sample-rate data; similarly, we can't get good parameters for a long timescale if we only have short amounts of data. \n", + "\n", + "To illustrate, imagine trying to fit a timescale of $\\tau=0.1$ s from data with a sample rate of 1 Hz. Virtually all the interesting dynamics will have happened over the course of a single sample, so there's not enough information to work from for parameter identification. Similarly, it would be unreasonable to fit a timescale of $\\tau=1000$ s from only one minute of data; across the range of data that we have, the dynamics will have barely changed, giving us very little to work from for fitting $R$ and $C$.\n", + "\n", + "In general therefore, we need to be careful to make sure our parameter values are sensible. To make sure that the optimiser doesn't propose excessively long or short timescales, we can use nonlinear constraints. The ```scipy.optimize.NonlinearConstraint``` function handles this for us." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "tau_constraint = scipy.optimize.NonlinearConstraint(lambda x: x[1] * x[2], 5, 750)" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "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 $5\\leq R_1 \\times C_1 \\leq 750$. 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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], + "source": [ + "optim = pybop.SciPyMinimize(\n", + " cost,\n", + " method=\"trust-constr\",\n", + " constraints=tau_constraint,\n", + " max_iterations=100,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "14", + "metadata": {}, + "source": [ + "Note that ```COBYLA```, ```COBYQA```, and ```SLSQP``` can also be used as the method; these are all different Scipy optimisers with constraint capabilities.\n", + "\n", + "Finally, we run the parameteriser, and plot some results. Don't worry if the solver sometimes terminates early -- this happens when the model receives a bad set of parameters, but PyBOP will catch and handle these cases automatically." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OptimisationResult:\n", + " Initial parameters: [2.01470532e-03 1.05171055e-03 4.57653422e+03]\n", + " Optimised parameters: [8.41410880e-03 6.45916310e-03 4.57841684e+03]\n", + " Final cost: 0.0013884834785099927\n", + " Optimisation time: 8.70180082321167 seconds\n", + " Number of iterations: 100\n", + " SciPy result available: Yes\n" + ] + }, + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "results = optim.run()\n", + "\n", + "# Plot the time series\n", + "pybop.plot.dataset(dataset)\n", + "\n", + "# Plot the timeseries output\n", + "pybop.plot.quick(problem, problem_inputs=results.x, title=\"Optimised Comparison\")\n", + "\n", + "# Plot convergence\n", + "pybop.plot.convergence(optim)\n", + "# Plot the parameter traces\n", + "pybop.plot.parameters(optim);" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": {}, + "source": [ + "### Conclusion\n", + "\n", + "Here, we have considered parameter estimation for an equivalent circuit model. We have introduced some issues of practical identifiability, whereby particularly fast or slow timescales can't be parameterised very well. To work around this challenge, nonlinear constraints have been implemented.\n", + "\n", + "A difficulty here is that fitting RC parameters is a [sloppy problem](https://sethna.lassp.cornell.edu/Sloppy/FittingExponentials.html) -- there's a range of timescales that will all produce reasonable looking results, and an optimiser will struggle to choose any one timescale over another. Gradient-free methods such as ```pybop.CMAES``` may do a better job of fitting timescales. Nevertheless, the resistance values can be fitted very accurately with gradient-based methods, whereas gradient-free methods don't always come up with such a good solution.\n", + "\n", + "The ideal approach would be to fit resistances with gradient-based or ordinary least-squares methods, and timescales using gradient-free optimisers. In the absence of this approach, general recommendations are as follows...\n", + "\n", + "+ If good initial guesses are available for the RC timescales, use a gradient-based method in the way we've done here, as it'll typically give the most accurate fit;\n", + "+ Otherwise, use a gradient-free method to estimate timescales, then maybe consider moving back to a gradient-based method!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/battery_parameterisation/electrode_balancing.ipynb b/examples/notebooks/battery_parameterisation/electrode_balancing.ipynb new file mode 100644 index 000000000..e929b2b9e --- /dev/null +++ b/examples/notebooks/battery_parameterisation/electrode_balancing.ipynb @@ -0,0 +1,432 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Electrode balancing\n", + "\n", + "In this notebook we provide an example on how to perform electrode balancing for a half cell. The goal is to find the conversion from capacity to stoichiometry for a given measured electrode, by using a reference dataset for which voltage is known as a function of the stoichiometry.\n", + "\n", + "## Set the environment\n", + "We start by installing and importing the necessary libraries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q\n", + "\n", + "# Import the necessary libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pybamm\n", + "\n", + "import pybop\n", + "\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load the data\n", + "We start by loading the data, which is available in the [pybamm-param repository](https://www.github.com/paramm-team/pybamm-param). We load half cell data (which is the versus stoichiometry and thus the reference one) and the three-electrode data (which is the one we want to balance). The measurements are for an LGM50 cell, with a graphite and SiOx negative electrode and an NMC811 positive electrode." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# .csv files are uploaded for Anode\n", + "base_url = \"https://raw.githubusercontent.com/paramm-team/pybamm-param/develop/pbparam/input/data/\"\n", + "reference_data = pd.read_csv(\n", + " base_url + \"anode_OCP_2_lit.csv\"\n", + ") # half cell lithiation data\n", + "measured_data = pd.read_csv(\n", + " base_url + \"anode_OCP_3_lit.csv\"\n", + ") # three-electrode full cell lithiation data\n", + "\n", + "# Drop negative capacity values\n", + "measured_data = measured_data.drop(\n", + " measured_data[measured_data[\"Capacity [A.h]\"] < 0].index\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup model, parameters and data\n", + "\n", + "To perform the electrode balancing, we will use an ECM model consisting only of the OCV component. To achieve that, we will set the resistance to zero. We will also change the upper and lower voltage limits to ensure we do not hit them during the optimisation. For the OCV, we will use the reference data we just loaded." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def ocv(sto):\n", + " return pybamm.Interpolant(\n", + " reference_data[\"Stoichiometry\"].to_numpy(),\n", + " reference_data[\"Voltage [V]\"].to_numpy(),\n", + " sto,\n", + " \"reference OCV\",\n", + " )\n", + "\n", + "\n", + "parameter_set = pybop.empirical.Thevenin().default_parameter_values\n", + "parameter_set.update(\n", + " {\n", + " \"Initial SoC\": 0,\n", + " \"Entropic change [V/K]\": 0,\n", + " \"R0 [Ohm]\": 0,\n", + " \"Lower voltage cut-off [V]\": 0,\n", + " \"Upper voltage cut-off [V]\": 5,\n", + " \"Open-circuit voltage [V]\": ocv,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can just assemble the model. We use the `Thevenin` model with no RC elements:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = pybop.empirical.Thevenin(\n", + " parameter_set=parameter_set, options={\"number of rc elements\": 0}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We define the parameters we want to optimise. In this case, we need to optimise the initial SoC and the cell capacity, which will be needed to convert the capacity to stoichiometry." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"Initial SoC\",\n", + " prior=pybop.Uniform(0, 0.5),\n", + " bounds=[0, 0.5],\n", + " ),\n", + " pybop.Parameter(\n", + " \"Cell capacity [A.h]\",\n", + " prior=pybop.Uniform(0.01, 50),\n", + " bounds=[0.01, 50],\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we need to assemble the dataset. This is a bit tricky, as we are doing an electrode balancing but in theory we are solving a discharge problem. However, we can use that if we impose a 1 A discharge, the time (in hours) will be the same as the capacity in (in Ah). Therefore, we can treat time as capacity, we just need to scale it to the correct units (as PyBaMM models take time in seconds). Note that in this case current is negative as we are lithiating the electrode." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Form dataset\n", + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": measured_data[\"Capacity [A.h]\"].to_numpy() * 3600,\n", + " \"Current function [A]\": -np.ones(len(measured_data[\"Capacity [A.h]\"])),\n", + " \"Voltage [V]\": measured_data[\"Voltage [V]\"].to_numpy(),\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Identifying the parameters\n", + "\n", + "Once we have defined the model, parameters and dataset, we can just proceed to the optimisation. We define the `FittingProblem` and the cost, for which we choose the sum squared error." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "problem = pybop.FittingProblem(model, parameters, dataset)\n", + "cost = pybop.SumSquaredError(problem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We choose the `SciPyMinimize` optimiser and we solve the optimisation problem. We can then print and plot the results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/lib/python3.12/site-packages/pybamm/solvers/base_solver.py:762: SolverWarning:\n", + "\n", + "Explicit interpolation times not implemented for CasADi solver with 'safe' mode\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OptimisationResult:\n", + " Initial parameters: [1.90442636e-02 3.24021877e+01]\n", + " Optimised parameters: [0.00923553 4.97209063]\n", + " Final cost: 0.0038212683454862052\n", + " Optimisation time: 2.6001641750335693 seconds\n", + " Number of iterations: 57\n", + " SciPy result available: Yes\n" + ] + } + ], + "source": [ + "optim = pybop.SciPyMinimize(cost, max_iterations=300)\n", + "results = optim.run()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.quick(problem, problem_inputs=results.x, title=\"Optimised Comparison\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Converting capacity to stoichiometry\n", + "\n", + "However, the whole goal of the electrode balancing was to convert capacity to stoichiometry, so how can we do that? To convert capacity $Q$ to stoichiometry $x$, we can simply use the following equation:\n", + "\n", + "$$\n", + "x = \\pm \\frac{Q}{Q_{\\text{cell}}} + x_0.\n", + "$$\n", + "Here, the choice of plus and minus depends on whether we are lithiating or delithiating the electrode (it is related to whether the current in the data is positive or negative). $Q_{\\text{cell}}$ is the cell capacity and $x_0$ is the initial stoichiometry, which are the two parameters we fitted. We can now convert the measured data and plot it against the reference data to check that the electrode balancing was correct." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from plotly import graph_objects as go\n", + "\n", + "fig = go.Figure(\n", + " layout=go.Layout(title=\"OCP Balance\", width=800, height=600),\n", + ")\n", + "\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=reference_data[\"Stoichiometry\"],\n", + " y=reference_data[\"Voltage [V]\"],\n", + " mode=\"lines\",\n", + " name=\"reference\",\n", + " ),\n", + ")\n", + "\n", + "Q = results.x[1]\n", + "sto_0 = results.x[0]\n", + "\n", + "sto = measured_data[\"Capacity [A.h]\"].to_numpy() / Q + sto_0\n", + "\n", + "fig.add_trace(\n", + " go.Scatter(x=sto, y=measured_data[\"Voltage [V]\"], mode=\"lines\", name=\"fitted\"),\n", + ")\n", + "\n", + "# Update axes labels\n", + "fig.update_xaxes(title_text=\"Stoichiometry\")\n", + "fig.update_yaxes(title_text=\"Voltage [V]\")\n", + "\n", + "# Show figure\n", + "fig.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/notebooks/battery_parameterisation/equivalent_circuit_identification.ipynb b/examples/notebooks/battery_parameterisation/equivalent_circuit_identification.ipynb new file mode 100644 index 000000000..1d5f6262b --- /dev/null +++ b/examples/notebooks/battery_parameterisation/equivalent_circuit_identification.ipynb @@ -0,0 +1,552 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Equivalent Circuit Parameter Identification\n", + "\n", + "This notebook provides example usage for identifying stationary parameters for a two RC branch Thevenin model. The Thevenin model represents an electrochemical battery through an empirical circuit model capable of capturing the electrical response of the battery. This model can be extended with a thermal submodel, as well as additional parallel resistor-capacitor branches.\n", + "\n", + "### Setting up the Environment\n", + "\n", + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP from its development branch and upgrade some dependencies:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "### Importing Libraries\n", + "\n", + "With the environment set up, we can now import PyBOP alongside other libraries we will need:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pybamm\n", + "\n", + "import pybop\n", + "\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(8)" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "## Importing Parameters\n", + "\n", + "This can be completed by importing a JSON representation, such as the one in the PyBOP [examples](https://github.com/pybop-team/PyBOP/blob/develop/examples/parameters/initial_ecm_parameters.json). To import via JSON, either download the example file, or create your own and update the path below to reference the corresponding file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "# parameter_set = pybop.ParameterSet(\n", + "# json_path=\"examples/parameters/initial_ecm_parameters.json\"\n", + "# )\n", + "# parameter_set.import_parameters()" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "Alternatively, define the initial parameter set with a dictionary. Ensure you have definitions for all R's, C's, and initial overpotentials for any additional RC elements.\n", + "\n", + "In this example, we use the default parameter value for the \"Open-circuit voltage [V] as provided by the original PyBaMM class. To update this, provide a function definition that matches this [function](https://github.com/pybamm-team/PyBaMM/blob/1943aa5ab2895b5378220595923dbae3d66b13c9/pybamm/input/parameters/ecm/example_set.py#L17)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "parameter_set = pybamm.ParameterValues(\"ECM_Example\")\n", + "parameter_set.update(\n", + " {\n", + " \"Cell capacity [A.h]\": 5,\n", + " \"Nominal cell capacity [A.h]\": 5,\n", + " \"Current function [A]\": 5,\n", + " \"Initial SoC\": 0.5,\n", + " \"Element-1 initial overpotential [V]\": 0,\n", + " \"Upper voltage cut-off [V]\": 4.2,\n", + " \"Lower voltage cut-off [V]\": 3.0,\n", + " \"R0 [Ohm]\": 1e-3,\n", + " \"R1 [Ohm]\": 2e-4,\n", + " \"C1 [F]\": 1e4,\n", + " \"Open-circuit voltage [V]\": pybop.empirical.Thevenin().default_parameter_values[\n", + " \"Open-circuit voltage [V]\"\n", + " ],\n", + " }\n", + ")\n", + "# Optional arguments - only needed for two RC pairs\n", + "parameter_set.update(\n", + " {\n", + " \"R2 [Ohm]\": 0.0003,\n", + " \"C2 [F]\": 40000,\n", + " \"Element-2 initial overpotential [V]\": 0,\n", + " },\n", + " check_already_exists=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "## Identifying the Parameters\n", + "\n", + "Now that the initial parameter set is constructed, we can start the PyBOP fitting process. First, we define the model class with two RC elements." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "model = pybop.empirical.Thevenin(\n", + " parameter_set=parameter_set, options={\"number of rc elements\": 2}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "In this example, we are going to try to fit all five parameters at once. This isn't recommend for real-life application as identifiablity is challenging to guarantee with this large a parameter space. To do this, we define the `pybop.Parameters` as," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], + "source": [ + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"R0 [Ohm]\",\n", + " prior=pybop.Gaussian(2 - 4, 1e-4),\n", + " bounds=[1e-4, 1e-2],\n", + " ),\n", + " pybop.Parameter(\n", + " \"R1 [Ohm]\",\n", + " prior=pybop.Gaussian(1e-4, 1e-4),\n", + " bounds=[1e-5, 1e-2],\n", + " ),\n", + " pybop.Parameter(\n", + " \"R2 [Ohm]\",\n", + " prior=pybop.Gaussian(1e-4, 1e-4),\n", + " bounds=[1e-5, 1e-2],\n", + " ),\n", + " pybop.Parameter(\n", + " \"C1 [F]\",\n", + " prior=pybop.Gaussian(1e4, 5e2),\n", + " bounds=[2.5e3, 5e4],\n", + " ),\n", + " pybop.Parameter(\n", + " \"C2 [F]\",\n", + " prior=pybop.Gaussian(1e4, 5e2),\n", + " bounds=[2.5e3, 5e4],\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "14", + "metadata": {}, + "source": [ + "Let's create some synthetic data to identify the parameters. This data is then corrupted with a small amount of Gaussian noise to represent some additional uncertainty in the measured values. We can then form the `pybop.Dataset` from this data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [], + "source": [ + "sigma = 0.001\n", + "t_eval = np.arange(0, 900, 3)\n", + "values = model.predict(t_eval=t_eval)\n", + "corrupt_values = values[\"Voltage [V]\"].data + np.random.normal(0, sigma, len(t_eval))\n", + "\n", + "# Form dataset\n", + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": t_eval,\n", + " \"Current function [A]\": values[\"Current [A]\"].data,\n", + " \"Voltage [V]\": corrupt_values,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": {}, + "source": [ + "The `FittingProblem` class provides us with a single class that holds all of the objects we need to evaluate our selected `SumSquaredError` cost function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17", + "metadata": {}, + "outputs": [], + "source": [ + "problem = pybop.FittingProblem(model, parameters, dataset)\n", + "cost = pybop.SumSquaredError(problem)" + ] + }, + { + "cell_type": "markdown", + "id": "18", + "metadata": {}, + "source": [ + "The cost function can be interrogated manually via the `cost([params])` API. In this example, that would look like the following," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.017324210933474568" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cost([0.001, 0.001, 0.001, 5000, 5000])" + ] + }, + { + "cell_type": "markdown", + "id": "20", + "metadata": {}, + "source": [ + "Next, we construct the optimisation class with our algorithm of choice and run it. In this case, we select the CMA-ES method as it provides global optimisation capability. For the sake of reducing the runtime of this example, we limit the maximum iterations to 100; however, feel free to update this value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: No significant change for 50 iterations.\n", + "OptimisationResult:\n", + " Initial parameters: [1.00990000e-04 2.09128273e-04 1.09990000e-05 9.30682523e+03\n", + " 8.85175421e+03]\n", + " Optimised parameters: [7.15236427e-04 4.90629050e-04 2.80089449e-04 9.29462538e+03\n", + " 8.75074954e+03]\n", + " Final cost: 0.0002759966220781992\n", + " Optimisation time: 6.4131858348846436 seconds\n", + " Number of iterations: 59\n", + " SciPy result available: No\n" + ] + } + ], + "source": [ + "optim = pybop.XNES(\n", + " cost,\n", + " sigma0=[1e-4, 1e-4, 1e-4, 10, 10],\n", + " max_unchanged_iterations=50,\n", + " max_iterations=250,\n", + ")\n", + "results = optim.run()" + ] + }, + { + "cell_type": "markdown", + "id": "22", + "metadata": {}, + "source": [ + "## Plotting and Visualisation\n", + "\n", + "PyBOP provides various plotting utilities to visualize the results of the optimisation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.quick(problem, problem_inputs=results.x, title=\"Optimised Comparison\");" + ] + }, + { + "cell_type": "markdown", + "id": "24", + "metadata": {}, + "source": [ + "### Convergence and Parameter Trajectories\n", + "\n", + "To assess the optimisation process, we can plot the convergence of the cost function and the trajectories of the parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.convergence(optim)\n", + "pybop.plot.parameters(optim);" + ] + }, + { + "cell_type": "markdown", + "id": "26", + "metadata": {}, + "source": [ + "### Conclusion\n", + "\n", + "This notebook illustrates how to perform parameter estimation using CMA-ES in PyBOP, providing insights into the optimisation process through various visualisations." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/battery_parameterisation/equivalent_circuit_identification_hppc.ipynb b/examples/notebooks/battery_parameterisation/equivalent_circuit_identification_hppc.ipynb new file mode 100644 index 000000000..cb13dc55d --- /dev/null +++ b/examples/notebooks/battery_parameterisation/equivalent_circuit_identification_hppc.ipynb @@ -0,0 +1,570 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Estimating ECM Parameters & Running a Thermal Submodel\n", + "\n", + "This notebook provides example usage for estimating stationary parameters for a two RC branch Thevenin model. With the estimated parameters, a thermal model is created and predictions are made.\n", + "\n", + "### Setting up the Environment\n", + "\n", + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP from its development branch and upgrade some dependencies:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets openpyxl pandas -q\n", + "%pip install pybop -q\n", + "\n", + "import pandas as pd\n", + "import pybamm\n", + "\n", + "import pybop\n", + "\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "In this example, we use the default parameter value for the `\"Open-circuit voltage [V]\"` as provided by the original PyBaMM class. To update this, provide a function definition that matches this [function](https://github.com/pybamm-team/PyBaMM/blob/1943aa5ab2895b5378220595923dbae3d66b13c9/pybamm/input/parameters/ecm/example_set.py#L17). First, we load and update the ECM input parameters," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "parameter_set = pybop.ParameterSet.pybamm(\"ECM_Example\")\n", + "parameter_set.update(\n", + " {\n", + " \"Cell capacity [A.h]\": 3,\n", + " \"Nominal cell capacity [A.h]\": 3,\n", + " \"Element-1 initial overpotential [V]\": 0,\n", + " \"Upper voltage cut-off [V]\": 4.2,\n", + " \"Lower voltage cut-off [V]\": 2.5,\n", + " \"R0 [Ohm]\": 1e-3,\n", + " \"R1 [Ohm]\": 3e-3,\n", + " \"C1 [F]\": 5e2,\n", + " \"Open-circuit voltage [V]\": pybop.empirical.Thevenin().default_parameter_values[\n", + " \"Open-circuit voltage [V]\"\n", + " ],\n", + " }\n", + ")\n", + "# Optional arguments - only needed for two RC pairs\n", + "parameter_set.update(\n", + " {\n", + " \"R2 [Ohm]\": 0.002,\n", + " \"C2 [F]\": 3e4,\n", + " \"Element-2 initial overpotential [V]\": 0,\n", + " },\n", + " check_already_exists=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "Now that the initial parameter set is constructed, we can start the PyBOP fitting process. First, we define the model class with two RC elements." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "model = pybop.empirical.Thevenin(\n", + " parameter_set=parameter_set,\n", + " options={\"number of rc elements\": 2},\n", + " solver=pybamm.CasadiSolver(mode=\"safe\", dt_max=10),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "Next we need to select the data for parameter identification. In this example we use a single HPPC pulse from the \n", + "`Kollmeyer, Phillip; Skells, Michael (2020), “Samsung INR21700 30T 3Ah Li-ion Battery Data”, Mendeley Data, V1, doi: 10.17632/9xyvy2njj3.1` dataset. This is imported and used to construct the `pybop.Dataset` class," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "file_loc = r\"../../data/Samsung_INR21700/sample_hppc_pulse.xlsx\"\n", + "df = pd.read_excel(file_loc, index_col=None, na_values=[\"NA\"])\n", + "df = df.drop_duplicates(subset=[\"Time\"], keep=\"first\")\n", + "\n", + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": df[\"Time\"].to_numpy(),\n", + " \"Current function [A]\": df[\"Current\"].to_numpy(),\n", + " \"Voltage [V]\": df[\"Voltage\"].to_numpy(),\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "Next, we need to define the parameter for identification. In this example, we've construct a two-branch Thevenin model, so we will select those parameters for identification. The initial guess for the resistance parameter is generated from a random sample of the prior distributions. These are influenced by the `r_guess` parameter below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "r_guess = 0.005\n", + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"R0 [Ohm]\",\n", + " prior=pybop.Gaussian(r_guess, r_guess / 10),\n", + " bounds=[0, 0.2],\n", + " ),\n", + " pybop.Parameter(\n", + " \"R1 [Ohm]\",\n", + " prior=pybop.Gaussian(r_guess, r_guess / 10),\n", + " bounds=[0, 0.2],\n", + " ),\n", + " pybop.Parameter(\n", + " \"R2 [Ohm]\",\n", + " prior=pybop.Gaussian(r_guess, r_guess / 10),\n", + " bounds=[0, 0.2],\n", + " ),\n", + " pybop.Parameter(\n", + " \"C1 [F]\",\n", + " prior=pybop.Gaussian(500, 100),\n", + " bounds=[100, 10000],\n", + " ),\n", + " pybop.Parameter(\n", + " \"C2 [F]\",\n", + " prior=pybop.Gaussian(2000, 500),\n", + " bounds=[100, 10000],\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "The `FittingProblem` class provides us with a single class that holds the objects we need to evaluate our selected `SumSquaredError` cost function. As we haven't built the model, we first do that with an initial OCV state selected from the first data point in the HPPC pulse." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "model.build(\n", + " initial_state={\"Initial open-circuit voltage [V]\": df[\"Voltage\"].to_numpy()[0]}\n", + ")\n", + "problem = pybop.FittingProblem(\n", + " model,\n", + " parameters,\n", + " dataset,\n", + ")\n", + "\n", + "cost = pybop.SumSquaredError(problem)" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "Next, we construct the optimisation class with our algorithm of choice and run it. In this case, we select the XNES method as it provides global optimisation capability. For the sake of reducing the runtime of this example, we limit the maximum iterations to 100; however, feel free to update this value. Due to the scale differences in the parameters, we update the optimiser step-size (`sigma0`) to be parameter specific, which helps ensure the optimiser explores the complete parameter space." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/lib/python3.12/site-packages/pybamm/solvers/base_solver.py:762: SolverWarning:\n", + "\n", + "Explicit interpolation times not implemented for CasADi solver with 'safe' mode\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: Maximum number of iterations (100) reached.\n", + "OptimisationResult:\n", + " Initial parameters: [5.68306791e-03 4.62654456e-03 4.63133641e-03 5.39464654e+02\n", + " 1.88179670e+03]\n", + " Optimised parameters: [9.35370665e-03 4.13523900e-03 2.14724423e-02 2.65501768e+02\n", + " 1.57309807e+03]\n", + " Final cost: 0.00017890709960192562\n", + " Optimisation time: 13.175962924957275 seconds\n", + " Number of iterations: 100\n", + " SciPy result available: No\n" + ] + } + ], + "source": [ + "optim = pybop.XNES(\n", + " cost,\n", + " sigma0=[1e-3, 1e-3, 1e-3, 20, 20],\n", + " max_unchanged_iterations=30,\n", + " max_iterations=100,\n", + ")\n", + "results = optim.run()" + ] + }, + { + "cell_type": "markdown", + "id": "14", + "metadata": {}, + "source": [ + "## Plotting and Visualisation\n", + "\n", + "Next, we use PyBOP's plotting utilities to visualise the results of the optimisation. This provides us with a visual confirmation of the optimisers' converged parameter values in the time-domain output." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.quick(problem, problem_inputs=results.x, title=\"Optimised Comparison\");" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": {}, + "source": [ + "## Convergence and Parameter Trajectories\n", + "\n", + "To assess the optimisation process, we can plot the convergence of the cost function and the trajectories of the parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.convergence(optim)\n", + "pybop.plot.parameters(optim);" + ] + }, + { + "cell_type": "markdown", + "id": "18", + "metadata": {}, + "source": [ + "## Using the estimated parameter for thermal predictions\n", + "With the estimated RC parameters, the temperature distribution for a given drive cycle can be calculated using the identified Thevenin model. We now we use a `.xlsx` file containing time-series current data as a pybamm experiment. A sample file is used here, but user's may choose to upload customized drive cycle." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19", + "metadata": {}, + "outputs": [], + "source": [ + "file_loc = r\"../../data/Samsung_INR21700/sample_drive_cycle.xlsx\"\n", + "df = pd.read_excel(file_loc, sheet_name=\"Sheet3\", index_col=None, na_values=[\"NA\"])\n", + "\n", + "# Remove duplicate rows, keeping the first occurrence\n", + "df = df.drop_duplicates(subset=[\"Time\"], keep=\"first\")\n", + "\n", + "# Create the pybamm experiment\n", + "experiment = pybamm.Experiment([pybamm.step.current(df.to_numpy())])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Step([[ 0 3]\n", + " [ 1 3]\n", + " [ 2 3]\n", + " ...\n", + " [3598 3]\n", + " [3599 3]\n", + " [3600 3]], duration=3600, period=1, direction=Discharge)]" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "experiment.steps" + ] + }, + { + "cell_type": "markdown", + "id": "21", + "metadata": {}, + "source": [ + "Update the estimated RC values. These values will be used to calculate heat generation and corresponding temperature distribution in the thermal submodel. Given `model.predict` is a light wrapper on the `PyBaMM.Simulation` class, we interact with it the same way. Visualisation of voltage response and cell temperature is plotted below using the PyBaMM solution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-21 17:28:38.587 - [WARNING] callbacks.on_experiment_infeasible_time(240): \n", + "\n", + "\tExperiment is infeasible: default duration (3600 seconds) was reached during 'Step([[ 0 3]\n", + " [ 1 3]\n", + " [ 2 3]\n", + " ...\n", + " [3598 3]\n", + " [3599 3]\n", + " [3600 3]], duration=3600, period=1, direction=Discharge)'. The returned solution only contains up to step 1 of cycle 1. Please specify a duration in the step instructions.\n" + ] + } + ], + "source": [ + "sol = model.predict(\n", + " inputs=results.x,\n", + " experiment=experiment,\n", + " parameter_set=parameter_set,\n", + " initial_state={\"Initial SoC\": 0.95},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "23", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This notebook illustrates how to extract EC parameters from an HPPC pulse using XNES in PyBOP, providing insights into the optimisation process through various visualisations. The estimated parameters are then used to run a thermal submodel." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/battery_parameterisation/equivalent_circuit_identification_multipulse.ipynb b/examples/notebooks/battery_parameterisation/equivalent_circuit_identification_multipulse.ipynb new file mode 100644 index 000000000..e4e4a7401 --- /dev/null +++ b/examples/notebooks/battery_parameterisation/equivalent_circuit_identification_multipulse.ipynb @@ -0,0 +1,605 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Estimating ECM Parameters from Multi-Pulse HPPC Data\n", + "\n", + "This notebook provides example usage for estimating stationary parameters for a two RC branch Thevenin model using multi-pulse HPPC data.\n", + "\n", + "### Setting up the Environment\n", + "\n", + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP from its development branch and upgrade some dependencies:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets openpyxl pandas -q\n", + "%pip install pybop -q\n", + "\n", + "import pandas as pd\n", + "import plotly.graph_objects as go\n", + "import pybamm\n", + "\n", + "import pybop\n", + "\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "In this example, we use the default parameter value for the \"Open-circuit voltage [V] as provided by the original PyBaMM class. The other relevant parameters for the ECM model implementation are updated as per the cell specification." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the parameters\n", + "parameter_set = pybop.ParameterSet.pybamm(\"ECM_Example\")\n", + "parameter_set.update(\n", + " {\n", + " \"Cell capacity [A.h]\": 3,\n", + " \"Nominal cell capacity [A.h]\": 3,\n", + " \"Element-1 initial overpotential [V]\": 0,\n", + " \"Upper voltage cut-off [V]\": 4.2,\n", + " \"Lower voltage cut-off [V]\": 2.5,\n", + " \"R0 [Ohm]\": 1e-3,\n", + " \"R1 [Ohm]\": 3e-3,\n", + " \"C1 [F]\": 5e2,\n", + " \"Open-circuit voltage [V]\": pybop.empirical.Thevenin().default_parameter_values[\n", + " \"Open-circuit voltage [V]\"\n", + " ],\n", + " }\n", + ")\n", + "# Optional arguments - only needed for two RC pairs\n", + "parameter_set.update(\n", + " {\n", + " \"R2 [Ohm]\": 2e-3,\n", + " \"C2 [F]\": 3e4,\n", + " \"Element-2 initial overpotential [V]\": 0,\n", + " },\n", + " check_already_exists=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "## Identifying the Parameters\n", + "\n", + "Now that the initial parameter set is constructed, we can start the PyBOP fitting process. First, we define the model class with two RC elements. One important thing here to note is \"maximum solver timestep\" (dt_max) needs to be set correctly to have a good fit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "model = pybop.empirical.Thevenin(\n", + " parameter_set=parameter_set,\n", + " options={\"number of rc elements\": 2},\n", + " solver=pybamm.CasadiSolver(mode=\"safe\", dt_max=40),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "We use multiple HPPC pulses from the dataset: Kollmeyer, Phillip; Skells, Michael (2020), “Samsung INR21700 30T 3Ah Li-ion Battery Data”, Mendeley Data, V1, doi: 10.17632/9xyvy2njj3.1 " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "file_loc = r\"../../data/Samsung_INR21700/multipulse_hppc.xlsx\"\n", + "df = pd.read_excel(file_loc, index_col=None, na_values=[\"NA\"])\n", + "df = df.drop_duplicates(subset=[\"Time\"], keep=\"first\")\n", + "\n", + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": df[\"Time\"].to_numpy(),\n", + " \"Current function [A]\": df[\"Current\"].to_numpy(),\n", + " \"Voltage [V]\": df[\"Voltage\"].to_numpy(),\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8", + "metadata": {}, + "outputs": [], + "source": [ + "r0_guess = 0.005\n", + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"R0 [Ohm]\",\n", + " prior=pybop.Gaussian(r0_guess, r0_guess / 10),\n", + " bounds=[0, 0.1],\n", + " ),\n", + " pybop.Parameter(\n", + " \"R1 [Ohm]\",\n", + " prior=pybop.Gaussian(r0_guess, r0_guess / 10),\n", + " bounds=[0, 0.1],\n", + " ),\n", + " pybop.Parameter(\n", + " \"R2 [Ohm]\",\n", + " prior=pybop.Gaussian(r0_guess, r0_guess / 10),\n", + " bounds=[0, 0.1],\n", + " ),\n", + " pybop.Parameter(\n", + " \"C1 [F]\",\n", + " prior=pybop.Gaussian(500, 100),\n", + " bounds=[100, 1000],\n", + " ),\n", + " pybop.Parameter(\n", + " \"C2 [F]\",\n", + " prior=pybop.Gaussian(2000, 500),\n", + " bounds=[1000, 10000],\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# To see current vs time profile.\n", + "fig1 = go.Figure()\n", + "# Add a line trace for current vs. time\n", + "fig1.add_trace(\n", + " go.Scatter(\n", + " x=df[\"Time\"].to_numpy(),\n", + " y=df[\"Current\"].to_numpy(),\n", + " mode=\"lines\", # 'lines', 'markers', or 'lines+markers'\n", + " name=\"Current vs Time\",\n", + " )\n", + ")\n", + "\n", + "# Customize layout\n", + "fig1.update_layout(\n", + " title=\"Current vs Time\",\n", + " xaxis_title=\"Time (s)\",\n", + " yaxis_title=\"Current (A)\",\n", + " template=\"plotly\", # Use a Plotly template (optional)\n", + ")\n", + "\n", + "# Show the plot\n", + "fig1.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# To see voltage vs time profile.\n", + "fig2 = go.Figure()\n", + "# Add a line trace for current vs. time\n", + "fig2.add_trace(\n", + " go.Scatter(\n", + " x=df[\"Time\"].to_numpy(),\n", + " y=df[\"Voltage\"].to_numpy(),\n", + " mode=\"lines\", # 'lines', 'markers', or 'lines+markers'\n", + " name=\"Voltage vs Time\",\n", + " )\n", + ")\n", + "\n", + "# Customize layout\n", + "fig2.update_layout(\n", + " title=\"Voltage vs Time\",\n", + " xaxis_title=\"Time (s)\",\n", + " yaxis_title=\"Voltage (V)\",\n", + " template=\"plotly\", # Use a Plotly template (optional)\n", + ")\n", + "\n", + "# Show the plot\n", + "fig2.show()" + ] + }, + { + "cell_type": "markdown", + "id": "11", + "metadata": {}, + "source": [ + "The `FittingProblem` class provides us with a single class that holds all of the objects we need to evaluate our selected `SumSquaredError` cost function.\n", + "\n", + "Initial state can be either \"Initial SoC\" or \"Initial open-circuit voltage [V]\". In this example, we get the initial OCV by accessing the voltage data. However, user can simply use a value instead, e.g., {\"Initial open-circuit voltage [V]\": 4.1}. Similarly, if SOC input is required, {\"Initial SoC\": 0.95}." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12", + "metadata": {}, + "outputs": [], + "source": [ + "model.build(\n", + " initial_state={\"Initial open-circuit voltage [V]\": df[\"Voltage\"].to_numpy()[0]}\n", + ")\n", + "problem = pybop.FittingProblem(\n", + " model,\n", + " parameters,\n", + " dataset,\n", + ")\n", + "\n", + "cost = pybop.SumSquaredError(problem)" + ] + }, + { + "cell_type": "markdown", + "id": "13", + "metadata": {}, + "source": [ + "Next, we construct the optimisation class with our algorithm of choice and run it. In this case, we select the CMA-ES method as it provides global optimisation capability. For the sake of reducing the runtime of this example, we limit the maximum iterations to 100; however, feel free to update this value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/lib/python3.12/site-packages/pybamm/solvers/base_solver.py:762: SolverWarning:\n", + "\n", + "Explicit interpolation times not implemented for CasADi solver with 'safe' mode\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: Maximum number of iterations (100) reached.\n", + "OptimisationResult:\n", + " Initial parameters: [3.97387634e-03 4.25500910e-03 3.85039109e-03 4.55295623e+02\n", + " 1.93038193e+03]\n", + " Optimised parameters: [9.55811673e-03 3.31919040e-03 1.76755904e-02 2.41500501e+02\n", + " 1.46563700e+03]\n", + " Final cost: 0.00186863751963031\n", + " Optimisation time: 35.69897198677063 seconds\n", + " Number of iterations: 100\n", + " SciPy result available: No\n" + ] + } + ], + "source": [ + "optim = pybop.XNES(\n", + " cost,\n", + " sigma0=[1e-3, 1e-3, 1e-3, 10, 10],\n", + " max_unchanged_iterations=20,\n", + " max_iterations=100,\n", + ")\n", + "results = optim.run()" + ] + }, + { + "cell_type": "markdown", + "id": "15", + "metadata": {}, + "source": [ + "## Plotting and Visualisation\n", + "\n", + "PyBOP provides various plotting utilities to visualize the results of the optimisation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.quick(problem, problem_inputs=results.x, title=\"Optimised Comparison\");" + ] + }, + { + "cell_type": "markdown", + "id": "17", + "metadata": {}, + "source": [ + "### Convergence and Parameter Trajectories\n", + "\n", + "To assess the optimisation process, we can plot the convergence of the cost function and the trajectories of the parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.convergence(optim)\n", + "pybop.plot.parameters(optim);" + ] + }, + { + "cell_type": "markdown", + "id": "19", + "metadata": {}, + "source": [ + "### Conclusion\n", + "\n", + "This notebook illustrates how to perform parameter estimation for multi-pulse HPPC data, providing insights into the optimisation process through various visualisations." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/battery_parameterisation/monte_carlo_ecm_identification.ipynb b/examples/notebooks/battery_parameterisation/monte_carlo_ecm_identification.ipynb new file mode 100644 index 000000000..730a51311 --- /dev/null +++ b/examples/notebooks/battery_parameterisation/monte_carlo_ecm_identification.ipynb @@ -0,0 +1,998 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "## Equivalent Circuit Bayesian Inference\n", + "\n", + "In this notebook, we introduce the Monte Carlo sampling methods for an example Thevenin model. This notebook includes importing experimental data for a Tesla 4680 NCA/Gr cell, and identifying the resistance elements of a two-RC Thevenin model. First, we import PyBOP and the required packages," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n", + "Note: you may need to restart the kernel to use updated packages.\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q\n", + "%pip install pandas -q\n", + "\n", + "import sys\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pybamm\n", + "\n", + "import pybop\n", + "\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "parallel = True if sys.platform != \"win32\" else False" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "Next, we will import and process the experimental data. The data for this notebook was obtained from the supplemental data provided in: \n", + "\n", + "[1] M. Ank et al., ‘Lithium-Ion Cells in Automotive Applications: Tesla 4680 Cylindrical Cell Teardown and Characterization’, doi: 10.1149/1945-7111/ad14d0.\n", + "\n", + "A portion of this data is retained in the PyBOP repository for use in parameterisation and optimisation examples. However, users' are pointed to the official location https://mediatum.ub.tum.de/1725661 for more information. First, we import the three-electrode pOCV dataset,\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "ocv_df = pd.read_csv(\n", + " \"../../data/Tesla_4680/T-cell_pOCV_data.txt\",\n", + " sep=\"\\t\",\n", + " decimal=\",\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "Next, let's plot the terminal voltage to visualise the protocol. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ocv_df.plot(y=\"Ewe-Ece/V\", x=\"time/h\", kind=\"line\")" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "As we are aiming to identify the resistance elements in an equivalent circuit model, we will construct an OCV function from the discharge portion of the experimental data. This is done by filtering the dataframe and appending an additional point to ensure the OCV function has data across the operating region (i.e. up to 4.2V)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "sto_measured = (\n", + " 1\n", + " - ocv_df.loc[(ocv_df[\"I/mA\"] <= 0.0), \"Capacity/mA.h\"]\n", + " / ocv_df.loc[(ocv_df[\"I/mA\"] <= 0.0), \"Capacity/mA.h\"].iloc[-1]\n", + ")\n", + "V_measured = ocv_df.loc[(ocv_df[\"I/mA\"] <= 0.0), \"Ewe-Ece/V\"]\n", + "V_measured.iloc[0] = 4.2 # Extend for improved interpolation (a bit of a fudge)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8", + "metadata": {}, + "outputs": [], + "source": [ + "def ocv(sto):\n", + " name = \"OCV\"\n", + " x = np.flip(sto_measured.to_numpy())\n", + " y = np.flip(V_measured.to_numpy())\n", + " return pybamm.Interpolant(x, y, sto, name)" + ] + }, + { + "cell_type": "markdown", + "id": "9", + "metadata": {}, + "source": [ + "Next, let's construct the parameter set and update it with the known information and placeholder values for this cell." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [], + "source": [ + "parameter_set = pybop.ParameterSet.pybamm(\"ECM_Example\")\n", + "parameter_set.update(\n", + " {\n", + " \"Cell capacity [A.h]\": 22.651, # 083/828 - C/20\n", + " \"Nominal cell capacity [A.h]\": 22.651,\n", + " \"Current function [A]\": 22.651,\n", + " \"Initial SoC\": 1.0,\n", + " \"Upper voltage cut-off [V]\": 4.25, # Extended to avoid hitting event\n", + " \"Lower voltage cut-off [V]\": 2.5,\n", + " \"Open-circuit voltage [V]\": ocv,\n", + " \"R2 [Ohm]\": 1e-4, # placeholder\n", + " \"C2 [F]\": 4e5, # placeholder\n", + " \"Element-2 initial overpotential [V]\": 0,\n", + " },\n", + " check_already_exists=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "11", + "metadata": {}, + "source": [ + "## Import Cycling Data\n", + "Now we import the corresponding constant current tests for this cell and construct a dataset from a subset of this time-series. This is done to reduce the computational time for inference, but also to improve the robustness of the inference task." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cycling_df = pd.read_csv(\n", + " \"../../data/Tesla_4680/601-828_Capacity_03_MB_CB1_subset.txt\",\n", + " sep=\"\\t\",\n", + ")\n", + "filter_cycling = cycling_df.loc[54811:61000].copy() # Full cycle is [54811:127689]\n", + "filter_cycling[\"time/s\"] = filter_cycling[\"time/s\"] - filter_cycling[\"time/s\"].iloc[0]\n", + "\n", + "# Take every 100th point\n", + "filtered_cycling_df = filter_cycling.iloc[\n", + " [i for i in range(len(filter_cycling)) if not i % 100 != 0]\n", + "]\n", + "filtered_cycling_df.plot(x=\"time/s\", y=\"Ecell/V\", kind=\"line\")" + ] + }, + { + "cell_type": "markdown", + "id": "13", + "metadata": {}, + "source": [ + "### Defining the parameters for inference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14", + "metadata": {}, + "outputs": [], + "source": [ + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"R0 [Ohm]\",\n", + " prior=pybop.Gaussian(3e-3, 1e-3),\n", + " bounds=[1e-5, 1e-2],\n", + " ),\n", + " pybop.Parameter(\n", + " \"R1 [Ohm]\",\n", + " prior=pybop.Gaussian(5e-3, 1e-3),\n", + " bounds=[1e-5, 1e-2],\n", + " ),\n", + " pybop.Parameter(\n", + " \"R2 [Ohm]\",\n", + " prior=pybop.Gaussian(1e-4, 5e-5),\n", + " bounds=[1e-5, 1e-2],\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "15", + "metadata": {}, + "source": [ + "### Form the dataset using the filtered data\n", + "In this parameter inference task, we use the filtered time-series data to reduce the computation time. This choice can be validated by comparing the inference results to the full time-series inference, which isn't covered in this example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16", + "metadata": {}, + "outputs": [], + "source": [ + "dataset_hundred = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": filtered_cycling_df[\"time/s\"].to_numpy(),\n", + " \"Current function [A]\": -filtered_cycling_df[\"I/mA\"].to_numpy()\n", + " / 1000, # Convert mA to A\n", + " \"Voltage [V]\": filtered_cycling_df[\"Ecell/V\"].to_numpy(),\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "17", + "metadata": {}, + "source": [ + "### Construct the model, problem, and non-scaled posterior\n", + "\n", + "We now construct a two-pair RC model and build the model with an initial SOC based on the first voltage point in the experimental data. \n", + "The `FittingProblem` and likelihood classes are constructed with posterior built from the GaussianLogLikelihood." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18", + "metadata": {}, + "outputs": [], + "source": [ + "model = pybop.empirical.Thevenin(\n", + " parameter_set=parameter_set, options={\"number of rc elements\": 2}\n", + ")\n", + "model.build(\n", + " initial_state={\n", + " \"Initial open-circuit voltage [V]\": dataset_hundred[\"Voltage [V]\"][0]\n", + " }\n", + ")\n", + "\n", + "# Generate problem, likelihood, and sampler\n", + "problem = pybop.FittingProblem(model, parameters, dataset_hundred)\n", + "likelihood = pybop.GaussianLogLikelihood(problem)\n", + "posterior = pybop.LogPosterior(likelihood)" + ] + }, + { + "cell_type": "markdown", + "id": "19", + "metadata": {}, + "source": [ + "### Let's find the Maximum a Posteriori values\n", + "Below we identify the parameters using the Maximum a Posterior estimate and the Covariance Matrix Adaptation Evolution Strategy (CMAES). We will use this estimate later in the Bayesian inference task as a starting position for the chains." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/engs2510/.pyenv/versions/3.12.4/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning:\n", + "\n", + "os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OptimisationResult:\n", + " Optimised parameters: [4.15690229e-03 1.64236988e-03 8.78939716e-05 2.78474178e-04]\n", + " Final cost: 431.8611377082031\n", + " Number of iterations: 78\n", + " SciPy result available: No\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/engs2510/.pyenv/versions/pybop/lib/python3.12/site-packages/pybamm/solvers/base_solver.py:762: SolverWarning:\n", + "\n", + "Explicit interpolation times not implemented for CasADi solver with 'safe' mode\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "optim = pybop.CMAES(\n", + " posterior,\n", + " sigma0=[1e-4, 1e-4, 1e-4, 1e-4],\n", + " max_iterations=200,\n", + " max_unchanged_iterations=40,\n", + " parallel=parallel,\n", + ")\n", + "results_optim = optim.run()\n", + "print(results_optim)\n", + "pybop.plot.quick(problem, results_optim.x)\n", + "pybop.plot.convergence(optim)\n", + "pybop.plot.parameters(optim);" + ] + }, + { + "cell_type": "markdown", + "id": "21", + "metadata": {}, + "source": [ + "### Monte Carlo Sampling\n", + "Below we construct the Monte Carlo sampler, specifically the Hamiltionian sampler, which samples from the posterior using Hamiltonion dynamics with gradient information. To minimise the time to execute this notebook, we greatly limit the number of samples; however, this should be increased for actual inference tasks. We initialise the chains from the Maximum a Posteriori point-estimate obtained from the CMAES optimisation above, as this should be close to the mode of the posterior." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using Haario-Bardenet adaptive covariance MCMC\n", + "Generating 3 chains.\n", + "Running in parallel with 12 worker processes.\n", + "/Users/engs2510/.pyenv/versions/3.12.4/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning:\n", + "\n", + "os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + "\n", + "| Iteration: 1 | Iter/s: 0.00 |\n", + "| Iteration: 2 | Iter/s: 28.66 |\n", + "| Iteration: 3 | Iter/s: 25.26 |\n", + "| Iteration: 4 | Iter/s: 74.38 |\n", + "| Iteration: 5 | Iter/s: 28.77 |\n", + "| Iteration: 6 | Iter/s: 25.46 |\n", + "| Iteration: 7 | Iter/s: 52.50 |\n", + "| Iteration: 8 | Iter/s: 82.83 |\n", + "| Iteration: 9 | Iter/s: 63.31 |\n", + "| Iteration: 10 | Iter/s: 1.61 |\n", + "| Iteration: 50 | Iter/s: 15.56 |\n", + "| Iteration: 100 | Iter/s: 16.85 |\n", + "| Iteration: 150 | Iter/s: 44.17 |\n", + "| Iteration: 200 | Iter/s: 125.50 |\n", + "| Iteration: 250 | Iter/s: 145.31 |\n", + "Initial phase completed.\n", + "| Iteration: 300 | Iter/s: 18.41 |\n", + "| Iteration: 350 | Iter/s: 124.50 |\n", + "| Iteration: 400 | Iter/s: 49.71 |\n", + "| Iteration: 450 | Iter/s: 15.42 |\n", + "| Iteration: 500 | Iter/s: 12.31 |\n", + "| Iteration: 550 | Iter/s: 12.83 |\n", + "| Iteration: 600 | Iter/s: 16.69 |\n", + "| Iteration: 650 | Iter/s: 18.65 |\n", + "| Iteration: 700 | Iter/s: 23.45 |\n", + "| Iteration: 750 | Iter/s: 9.57 |\n", + "| Iteration: 800 | Iter/s: 24.36 |\n", + "| Iteration: 850 | Iter/s: 6.11 |\n", + "| Iteration: 900 | Iter/s: 7.26 |\n", + "| Iteration: 950 | Iter/s: 12.78 |\n", + "| Iteration: 1000 | Iter/s: 9.71 |\n", + "| Iteration: 1050 | Iter/s: 10.11 |\n", + "| Iteration: 1100 | Iter/s: 10.57 |\n", + "| Iteration: 1150 | Iter/s: 9.25 |\n", + "| Iteration: 1200 | Iter/s: 14.29 |\n", + "| Iteration: 1250 | Iter/s: 12.10 |\n", + "| Iteration: 1300 | Iter/s: 37.08 |\n", + "| Iteration: 1350 | Iter/s: 11.71 |\n", + "| Iteration: 1400 | Iter/s: 30.06 |\n", + "| Iteration: 1450 | Iter/s: 48.85 |\n", + "| Iteration: 1500 | Iter/s: 9.15 |\n", + "Halting: Maximum number of iterations (1500) reached.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.00493998 1.00643067 1.00368448 1.01600809]\n" + ] + } + ], + "source": [ + "sampler = pybop.HaarioBardenetACMC(\n", + " posterior,\n", + " chains=3,\n", + " x0=results_optim.x, # Initialise at the MAP estimate\n", + " max_iterations=1500, # Increase for accurate posteriors\n", + " warm_up=350,\n", + " verbose=True,\n", + " parallel=parallel, # Only supported for macOS/WSL/Linux\n", + ")\n", + "\n", + "chains = sampler.run()\n", + "\n", + "# Create summary statistics\n", + "posterior_summary = pybop.PosteriorSummary(chains)\n", + "\n", + "# Create rhat of chains\n", + "print(posterior_summary.rhat())" + ] + }, + { + "cell_type": "markdown", + "id": "23", + "metadata": {}, + "source": [ + "### Plotting\n", + "Next, we plot the parameter traces as well as the combined posterior (across all chains) and each chain individually. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "posterior_summary.plot_trace()\n", + "posterior_summary.plot_posterior()\n", + "posterior_summary.plot_chains()" + ] + }, + { + "cell_type": "markdown", + "id": "25", + "metadata": {}, + "source": [ + "As expected, these chains haven't fully converged yet, so the values obtained from the posterior will be biased based on the initial conditions for sampling. Increasing the number of iterations for the sampler while also calibrating the `warm_up` period will provide better results. Next, for completeness, we will plot the identified time-series model against the experimental data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/engs2510/.pyenv/versions/pybop/lib/python3.12/site-packages/pybamm/solvers/base_solver.py:762: SolverWarning:\n", + "\n", + "Explicit interpolation times not implemented for CasADi solver with 'safe' mode\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.quick(problem, posterior_summary.mean);" + ] + }, + { + "cell_type": "markdown", + "id": "27", + "metadata": {}, + "source": [ + "Now, we can compare the identified parameters for each method. As the Bayesian sampler provides us with samples from the posterior, we use the statistical moments when comparing to the point-based result from the optimiser." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The XNES result is: [4.15690229e-03 1.64236988e-03 8.78939716e-05 2.78474178e-04]\n", + "The posterior means are:[4.14717807e-03 1.64491239e-03 9.20455193e-05 2.86617819e-04]\n", + "The difference between the two methods is: [9.72421663e-06 2.54251113e-06 4.15154776e-06 8.14364117e-06]\n" + ] + } + ], + "source": [ + "print(f\"The XNES result is: {results_optim.x}\")\n", + "print(f\"The posterior means are:{posterior_summary.mean}\")\n", + "print(\n", + " f\"The difference between the two methods is: {np.abs(results_optim.x - posterior_summary.mean)}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "29", + "metadata": {}, + "source": [ + "The sampled posterior also gives us information of the uncertainty in the inference process. This is present in the standard deviations as well as the lower and upper confidence intervals, shown below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "posterior_summary.summary_table()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/battery_parameterisation/multi_model_identification.ipynb b/examples/notebooks/battery_parameterisation/multi_model_identification.ipynb new file mode 100644 index 000000000..efdf61522 --- /dev/null +++ b/examples/notebooks/battery_parameterisation/multi_model_identification.ipynb @@ -0,0 +1,674 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "expmkveO04pw" + }, + "source": [ + "## Parameter identification for various models\n", + "\n", + "To investigate the performance of parameter identification for different electrochemical models we will start with synthetic data from the highest order model in PyBOP (Many-particle DFN) and try to identify the correct parameter values on the reduced order models.\n", + "\n", + "### Setting up the Environment\n", + "\n", + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X87NUGPW04py", + "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets pybamm -q\n", + "%pip install pybop -q" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jAvD5fk104p0" + }, + "source": [ + "Next, we import the added packages plus any additional dependencies," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SQdt4brD04p1" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pybamm\n", + "\n", + "import pybop\n", + "\n", + "go = pybop.plot.PlotlyManager().go\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(8)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X8-tubYY04p_" + }, + "source": [ + "## Optimising the Parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PQqhvSZN04p_" + }, + "source": [ + "First, we define the model to be used for the parameter optimisation," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "zuvGHWID04p_" + }, + "outputs": [], + "source": [ + "parameter_set = pybop.ParameterSet.pybamm(\"Chen2020\")\n", + "parameter_set = pybamm.get_size_distribution_parameters(parameter_set)\n", + "synth_model = pybop.lithium_ion.DFN(\n", + " parameter_set=parameter_set, options={\"particle size\": \"distribution\"}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulating Forward Model\n", + "\n", + "We can then simulate the model using the `predict` method, with a default constant current to generate voltage data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_points = 450\n", + "t_eval = np.linspace(0, 1600 + 1000, n_points)\n", + "current = np.concatenate(\n", + " [np.ones(200) * parameter_set[\"Nominal cell capacity [A.h]\"], np.zeros(250)]\n", + ")\n", + "initial_state = {\"Initial SoC\": 0.5}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": t_eval,\n", + " \"Current function [A]\": current,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "synth_model.build(dataset=dataset, initial_state=initial_state)\n", + "synth_model.signal = [\"Voltage [V]\"]\n", + "values = synth_model.simulate(t_eval=t_eval, inputs={})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding Noise to Voltage Data\n", + "\n", + "To make the parameter estimation more realistic, we add Gaussian noise to the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sigma = 0.001\n", + "corrupt_values = values[\"Voltage [V]\"].data + np.random.normal(\n", + " 0, sigma, len(values[\"Voltage [V]\"].data)\n", + ")\n", + "go.Figure(\n", + " data=go.Scatter(x=t_eval, y=corrupt_values, mode=\"lines\"),\n", + " layout=go.Layout(title=\"Corrupted Voltage\", width=800, height=600),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Identifying the Parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now set up the parameter estimation process by defining the datasets for optimisation and selecting the model parameters we wish to estimate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating a Dataset\n", + "\n", + "The dataset for optimisation is composed of time, current, and the noisy voltage data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": t_eval,\n", + " \"Current function [A]\": current,\n", + " \"Voltage [V]\": corrupt_values,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ffS3CF_704qA" + }, + "source": [ + "Next, we define the model parameters for optimisation. Furthermore, PyBOP provides functionality to define a prior for the parameters. The initial parameter values used in the optimisation will be randomly drawn from the prior distribution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WPCybXIJ04qA" + }, + "outputs": [], + "source": [ + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"Positive electrode thickness [m]\",\n", + " prior=pybop.Gaussian(7.56e-05, 0.05e-05),\n", + " bounds=[65e-06, 85e-06],\n", + " true_value=parameter_set[\"Positive electrode thickness [m]\"],\n", + " ),\n", + " pybop.Parameter(\n", + " \"Negative electrode thickness [m]\",\n", + " prior=pybop.Gaussian(8.52e-05, 0.05e-05),\n", + " bounds=[75e-06, 95e-06],\n", + " true_value=parameter_set[\"Negative electrode thickness [m]\"],\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n4OHa-aF04qA" + }, + "source": [ + "We can now define the output signal, the problem (which combines the model with the dataset) and construct a cost function which in this example is the `GravimetricEnergyDensity()` used to maximise the gravimetric energy density of the cell." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "models = [\n", + " pybop.lithium_ion.SPM(parameter_set=parameter_set),\n", + " pybop.lithium_ion.SPMe(parameter_set=parameter_set),\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eQiGurUV04qB" + }, + "source": [ + "Let's construct PyBOP's optimisation class for each model. This class provides the methods needed to fit the forward model. For this example, we use an evolution strategy (XNES) as the optimiser." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "etMzRtx404qA" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running Single Particle Model\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: No significant change for 15 iterations.\n", + "OptimisationResult:\n", + " Initial parameters: [7.61508933e-05 8.59364316e-05]\n", + " Optimised parameters: [6.50355739e-05 8.10453665e-05]\n", + " Final cost: 0.33026298961068246\n", + " Optimisation time: 22.5766921043396 seconds\n", + " Number of iterations: 40\n", + " SciPy result available: No\n", + "Running Single Particle Model with Electrolyte\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: No significant change for 15 iterations.\n", + "OptimisationResult:\n", + " Initial parameters: [7.61508933e-05 8.59364316e-05]\n", + " Optimised parameters: [7.08088980e-05 8.53871336e-05]\n", + " Final cost: 0.005807228768217647\n", + " Optimisation time: 22.70540690422058 seconds\n", + " Number of iterations: 23\n", + " SciPy result available: No\n" + ] + } + ], + "source": [ + "optims = []\n", + "xs = []\n", + "for model in models:\n", + " print(f\"Running {model.name}\")\n", + " model.set_initial_state(initial_state)\n", + " problem = pybop.FittingProblem(model, parameters, dataset)\n", + " cost = pybop.SumSquaredError(problem)\n", + " optim = pybop.XNES(\n", + " cost, verbose=True, max_iterations=60, max_unchanged_iterations=15\n", + " )\n", + " results = optim.run()\n", + " optims.append(optim)\n", + " xs.append(results.x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "N3FtAhrT04qB" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| Model: Single Particle Model | Results: [6.50355739e-05 8.10453665e-05] |\n", + "| Model: Single Particle Model with Electrolyte | Results: [7.08088980e-05 8.53871336e-05] |\n" + ] + } + ], + "source": [ + "for optim, x in zip(optims, xs):\n", + " print(f\"| Model: {optim.cost.problem.model.name} | Results: {x} |\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KxKURtH704qC" + }, + "source": [ + "## Plotting and Visualisation\n", + "\n", + "PyBOP provides various plotting utilities to visualise the results of the optimisation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-cWCOiqR04qC" + }, + "source": [ + "### Comparing System Response\n", + "\n", + "We can quickly plot the system's response using the estimated parameters compared to the initial parameters:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZVfozY0A04qC" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for optim, x in zip(optims, xs):\n", + " pybop.plot.quick(\n", + " optim.cost.problem, problem_inputs=x, title=optim.cost.problem.model.name\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ntIvAJmA04qD" + }, + "source": [ + "### Cost Landscape\n", + "\n", + "Finally, we can visualise the cost landscape and the path taken by the optimiser:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 467 + }, + "id": "tJUJ80Ve04qD", + "outputId": "855fbaa2-1e09-4935-eb1a-8caf7f99eb75" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for optim in optims:\n", + " pybop.plot.surface(optim, title=optim.cost.problem.model.name)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/notebooks/battery_parameterisation/pouch_cell_identification.ipynb b/examples/notebooks/battery_parameterisation/pouch_cell_identification.ipynb new file mode 100644 index 000000000..61a65d629 --- /dev/null +++ b/examples/notebooks/battery_parameterisation/pouch_cell_identification.ipynb @@ -0,0 +1,783 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "expmkveO04pw" + }, + "source": [ + "## Pouch Cell Model Parameter Identification\n", + "\n", + "In this notebook, we present the single particle model with a two dimensional current collector. This is achieved via the potential-pair models introduced in Marquis et al. [[1]](https://doi.org/10.1149/1945-7111/abbce4) as implemented in PyBaMM. At a high-level this is accomplished as a potential-pair model which is resolved across the discretised spatial locations.\n", + "\n", + "### Setting up the Environment\n", + "\n", + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X87NUGPW04py", + "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jAvD5fk104p0" + }, + "source": [ + "### Importing Libraries\n", + "\n", + "With the environment set up, we can now import PyBOP alongside other libraries we will need:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SQdt4brD04p1" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import pybop\n", + "\n", + "go = pybop.plot.PlotlyManager().go\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5XU-dMtU04p2" + }, + "source": [ + "## Generating Synthetic Data\n", + "\n", + "To demonstrate parameter estimation, we first need some data. We will generate synthetic data using a forward model, which requires defining a parameter set and the model itself.\n", + "\n", + "### Defining Parameters and Model\n", + "\n", + "We start by creating an example parameter set and then instantiate the single-particle model (SPM):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parameter_set = pybop.ParameterSet.pybamm(\"Marquis2019\")\n", + "parameter_set.update(\n", + " {\n", + " \"Negative electrode active material volume fraction\": 0.495,\n", + " \"Positive electrode active material volume fraction\": 0.612,\n", + " }\n", + ")\n", + "model = pybop.lithium_ion.SPM(\n", + " parameter_set=parameter_set,\n", + " options={\"current collector\": \"potential pair\", \"dimensionality\": 2},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Updating the Spatial Grid\n", + "\n", + "Next, we update the number of spatial locations to solve the potential-pair model," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.var_pts[\"y\"] = 5\n", + "model.var_pts[\"z\"] = 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulating the Forward Model\n", + "\n", + "We can then simulate the model using the `predict` method, with a default constant current to generate voltage data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sBasxv8U04p3" + }, + "outputs": [], + "source": [ + "t_eval = np.arange(0, 900, 3)\n", + "values = model.predict(t_eval=t_eval)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding Noise to Voltage Data\n", + "\n", + "To make the parameter estimation more realistic, we add Gaussian noise to the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sigma = 0.001\n", + "corrupt_values = values[\"Voltage [V]\"].data + np.random.normal(0, sigma, len(t_eval))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X8-tubYY04p_" + }, + "source": [ + "## Identifying the Parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PQqhvSZN04p_" + }, + "source": [ + "We will now set up the parameter estimation process by defining the datasets for optimisation and selecting the model parameters we wish to estimate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating a Dataset\n", + "\n", + "The dataset for optimisation is composed of time, current, and the noisy voltage data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "zuvGHWID04p_" + }, + "outputs": [], + "source": [ + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": t_eval,\n", + " \"Current function [A]\": values[\"Current [A]\"].data,\n", + " \"Voltage [V]\": corrupt_values,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ffS3CF_704qA" + }, + "source": [ + "### Defining Parameters to Estimate\n", + "\n", + "We select the parameters for estimation and set up their prior distributions and bounds:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WPCybXIJ04qA" + }, + "outputs": [], + "source": [ + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"Negative electrode active material volume fraction\",\n", + " prior=pybop.Gaussian(0.7, 0.05),\n", + " bounds=[0.45, 0.9],\n", + " ),\n", + " pybop.Parameter(\n", + " \"Positive electrode active material volume fraction\",\n", + " prior=pybop.Gaussian(0.58, 0.05),\n", + " bounds=[0.5, 0.8],\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For plotting purposes, we want additional variables to be stored in the problem class. These are defined as," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "additional_variables = [\n", + " \"Negative current collector potential [V]\",\n", + " \"Positive current collector potential [V]\",\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n4OHa-aF04qA" + }, + "source": [ + "### Setting up the Optimisation Problem\n", + "\n", + "With the datasets and parameters defined, we can set up the optimisation problem, its cost function, and the optimiser." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "etMzRtx404qA" + }, + "outputs": [], + "source": [ + "problem = pybop.FittingProblem(\n", + " model, parameters, dataset, additional_variables=additional_variables\n", + ")\n", + "cost = pybop.SumSquaredError(problem)\n", + "optim = pybop.CMAES(cost, max_iterations=30)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "caprp-bV04qB" + }, + "source": [ + "### Running the Optimisation\n", + "\n", + "We proceed to run the CMA-ES optimisation algorithm to estimate the parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-9OVt0EQ04qB" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: Maximum number of iterations (30) reached.\n", + "OptimisationResult:\n", + " Initial parameters: [0.65919975 0.65030567]\n", + " Optimised parameters: [0.49949096 0.60907043]\n", + " Final cost: 0.0002845276080247215\n", + " Optimisation time: 36.19517993927002 seconds\n", + " Number of iterations: 30\n", + " SciPy result available: No\n" + ] + } + ], + "source": [ + "results = optim.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-4pZsDmS04qC" + }, + "source": [ + "### Viewing the Estimated Parameters\n", + "\n", + "After the optimisation, we can examine the estimated parameter values:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Hgz8SV4i04qC", + "outputId": "e1e42ae7-5075-4c47-dd68-1b22ecc170f6" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.49949096, 0.60907043])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.x" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KxKURtH704qC" + }, + "source": [ + "## Plotting and Visualisation\n", + "\n", + "PyBOP provides various plotting utilities to visualise the results of the optimisation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-cWCOiqR04qC" + }, + "source": [ + "### Comparing System Response\n", + "\n", + "We can quickly plot the system's response using the estimated parameters compared to the target:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 467 + }, + "id": "tJUJ80Ve04qD", + "outputId": "855fbaa2-1e09-4935-eb1a-8caf7f99eb75" + }, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.quick(problem, problem_inputs=results.x, title=\"Optimised Comparison\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Spatial Plotting\n", + "\n", + "We can now plot the spatial variables from the solution object. First, the final negative current collector potential can be displayed. In this example, this is just a reference variable, but could be used for fitting or optimisation in the correct workflows." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sol = problem.evaluate(parameters.as_dict(results.x))\n", + "\n", + "go.Figure(\n", + " [\n", + " go.Contour(\n", + " x=np.arange(0, model.var_pts[\"y\"] - 1, 1),\n", + " y=np.arange(0, model.var_pts[\"z\"] - 1, 1),\n", + " z=sol[\"Negative current collector potential [V]\"][:, :, -1],\n", + " colorscale=\"Viridis\",\n", + " )\n", + " ],\n", + " layout=dict(\n", + " title=\"Negative current collector potential [V]\",\n", + " xaxis_title=\"x node\",\n", + " yaxis_title=\"y node\",\n", + " width=600,\n", + " height=600,\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We plot can then plot the positive current collector potential," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "go.Figure(\n", + " [\n", + " go.Contour(\n", + " x=np.arange(0, model.var_pts[\"y\"] - 1, 1),\n", + " y=np.arange(0, model.var_pts[\"z\"] - 1, 1),\n", + " z=sol[\"Positive current collector potential [V]\"][:, :, -1],\n", + " colorscale=\"Viridis\",\n", + " )\n", + " ],\n", + " layout=dict(\n", + " title=\"Positive current collector potential [V]\",\n", + " xaxis_title=\"x node\",\n", + " yaxis_title=\"y node\",\n", + " width=600,\n", + " height=600,\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Convergence and Parameter Trajectories\n", + "\n", + "To assess the optimisation process, we can plot the convergence of the cost function and the trajectories of the parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "N5XYkevi04qD" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.convergence(optim)\n", + "pybop.plot.parameters(optim);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cost Landscape\n", + "\n", + "Finally, we can visualise the cost landscape and the path taken by the optimiser:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.surface(optim);" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/notebooks/battery_parameterisation/single_pulse_circuit_model.ipynb b/examples/notebooks/battery_parameterisation/single_pulse_circuit_model.ipynb new file mode 100644 index 000000000..dec6e812f --- /dev/null +++ b/examples/notebooks/battery_parameterisation/single_pulse_circuit_model.ipynb @@ -0,0 +1,807 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "## LG M50 Single Pulse Parameter Identification\n", + "\n", + "This example presents an experimental parameter identification method for a two-RC circuit model. The data for this notebook is located within the same directory and was obtained from WDWidanage/Simscape-Battery-Library [[1]](https://github.com/WDWidanage/Simscape-Battery-Library/tree/a3842b91b3ccda006bc9be5d59c8bcbd167ceef7/Examples/parameterEstimation_TECMD/Data).\n", + "\n", + "\n", + "### Setting up the Environment\n", + "\n", + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP for the fitting and Pandas for the data ingestion and manipulation:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n", + "zsh:1: no matches found: pybop[plot]\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop[plot] -q\n", + "%pip install pandas -q" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "### Importing Libraries\n", + "\n", + "With the environment set up, we can now import PyBOP alongside other libraries we will need:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import pybamm\n", + "from scipy.io import loadmat\n", + "\n", + "import pybop\n", + "\n", + "go = pybop.plot.PlotlyManager().go\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(8)" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "## Importing Data\n", + "\n", + "The data is imported as a dictionary with the following key level:\n", + "- [\"LGM50_5Ah_Pulse\"]\n", + " - Temperature-> [\"T0\"]\n", + " - State of Charge ->[\"SoC3\"]\n", + " - Cell number -> [\"Cell1\"]\n", + " - Corresponding data -> [\"data\"]\n", + "\n", + "Using SciPy's `loadmat`, we import the data from the MAT containers:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "ocp = loadmat(\n", + " \"../../data/LG_M50_ECM/data/LGM50_5Ah_OCV.mat\", simplify_cells=True, mat_dtype=False\n", + ")\n", + "pulse_data = loadmat(\n", + " \"../../data/LG_M50_ECM/data/LGM50_5Ah_Pulse.mat\",\n", + " simplify_cells=True,\n", + " mat_dtype=False,\n", + ")\n", + "rate_data = loadmat(\n", + " \"../../data/LG_M50_ECM/data/LGM50_5Ah_RateTest.mat\",\n", + " simplify_cells=True,\n", + " mat_dtype=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "### Convert to Dataframes\n", + "\n", + "Next, we construct a dataframe from a selected pulse. In this case, we select the data for zero degrees (`T0`) with a state-of-charge of 90% (`SoC9`) and the 19th cell (`Cell19`). This can be extended to multiple pulses, or a different selection.\n", + "\n", + "Additionally, we apply two filters to the dataframe to ensure the data contains only monotonically increasing time samples without duplicates." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(pulse_data[\"LGM50_5Ah_Pulse\"][\"T0\"][\"SoC9\"][\"Cell19\"][\"data\"])\n", + "df[\"ProgTime\"] = df[\"ProgTime\"] - df[\"ProgTime\"].min()\n", + "df.drop_duplicates(subset=[\"ProgTime\"], inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "A plot of time vs voltage confirms the data looks correct for fitting. In this situation, we would prefer to have additional samples from the relaxation, but as we will show below, PyBOP is still able to identify parameter values that fit this system." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "go.Figure(\n", + " data=go.Scatter(\n", + " x=df[\"ProgTime\"],\n", + " y=df[\"Voltage\"],\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "Next, we construct the OCV function from the imported `OCV` data. This is completed with a wrapper method on the `pybamm.Interpolant` function," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], + "source": [ + "def ocv_LGM50(sto):\n", + " name = \"OCV\"\n", + " x = (ocp[\"LGM50_5Ah_OCV\"][\"T25\"][\"refSoC\"].reshape(-1) / 100,)\n", + " y = ocp[\"LGM50_5Ah_OCV\"][\"T25\"][\"meanOCV\"].reshape(-1)\n", + " return pybamm.Interpolant(x, y, sto, name)" + ] + }, + { + "cell_type": "markdown", + "id": "14", + "metadata": {}, + "source": [ + "We can construct the two RC parameter set with initial values as listed. Note, the initial SOC is shifted slightly to better match the zero degree data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [], + "source": [ + "params = pybop.ParameterSet(\n", + " params_dict={\n", + " \"chemistry\": \"ecm\",\n", + " \"Initial SoC\": 0.9 - 0.01,\n", + " \"Initial temperature [K]\": 25 + 273.15,\n", + " \"Cell capacity [A.h]\": 5,\n", + " \"Nominal cell capacity [A.h]\": 5,\n", + " \"Ambient temperature [K]\": 25 + 273.15,\n", + " \"Current function [A]\": 4.85,\n", + " \"Upper voltage cut-off [V]\": 4.2,\n", + " \"Lower voltage cut-off [V]\": 3.0,\n", + " \"Cell thermal mass [J/K]\": 1000,\n", + " \"Cell-jig heat transfer coefficient [W/K]\": 10,\n", + " \"Jig thermal mass [J/K]\": 500,\n", + " \"Jig-air heat transfer coefficient [W/K]\": 10,\n", + " \"Open-circuit voltage [V]\": ocv_LGM50,\n", + " \"R0 [Ohm]\": 0.005,\n", + " \"Element-1 initial overpotential [V]\": 0,\n", + " \"Element-2 initial overpotential [V]\": 0,\n", + " \"R1 [Ohm]\": 0.0001,\n", + " \"R2 [Ohm]\": 0.0001,\n", + " \"C1 [F]\": 3000,\n", + " \"C2 [F]\": 6924,\n", + " \"Entropic change [V/K]\": 0.0004,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": {}, + "source": [ + "## Identifying the Parameters\n", + "\n", + "Now that the initial parameter set is defined, we can start the PyBOP fitting process. First, we construct the model class with two RC elements and the corresponding parameter set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17", + "metadata": {}, + "outputs": [], + "source": [ + "model = pybop.empirical.Thevenin(\n", + " parameter_set=params, options={\"number of rc elements\": 2}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "18", + "metadata": {}, + "source": [ + "In this example, we are going to try to fit all five parameters at once. To do this, we define a `pybop.Parameter` for each fitting parameter and compile them in pybop.Parameters," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19", + "metadata": {}, + "outputs": [], + "source": [ + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"R0 [Ohm]\",\n", + " prior=pybop.Gaussian(0.005, 0.0001),\n", + " bounds=[1e-6, 2e-1],\n", + " ),\n", + " pybop.Parameter(\n", + " \"R1 [Ohm]\",\n", + " prior=pybop.Gaussian(0.0001, 0.0001),\n", + " bounds=[1e-6, 1],\n", + " ),\n", + " pybop.Parameter(\n", + " \"R2 [Ohm]\",\n", + " prior=pybop.Gaussian(0.0001, 0.0001),\n", + " bounds=[1e-6, 1],\n", + " ),\n", + " pybop.Parameter(\n", + " \"C1 [F]\",\n", + " prior=pybop.Gaussian(3000, 2500),\n", + " bounds=[0.5, 1e4],\n", + " ),\n", + " pybop.Parameter(\n", + " \"C2 [F]\",\n", + " prior=pybop.Gaussian(3000, 2500),\n", + " bounds=[0.5, 1e4],\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "20", + "metadata": {}, + "source": [ + "We can now form the `pybop.Dataset` from the experimental data. In this situation, the applied current convention is different between the experimental data and PyBOP's convention. This is solved by negating the vector on construction." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21", + "metadata": {}, + "outputs": [], + "source": [ + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": df[\"ProgTime\"].values,\n", + " \"Current function [A]\": -df[\"Current\"].values,\n", + " \"Voltage [V]\": df[\"Voltage\"].values,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "22", + "metadata": {}, + "source": [ + "The `FittingProblem` class provides us with a class to hold all of the objects we need to evaluate our selected `SumSquaredError` cost function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23", + "metadata": {}, + "outputs": [], + "source": [ + "problem = pybop.FittingProblem(model, parameters, dataset)\n", + "cost = pybop.SumSquaredError(problem)" + ] + }, + { + "cell_type": "markdown", + "id": "24", + "metadata": {}, + "source": [ + "The cost function can be interrogated manually via the `cost([params])` API. In this example, that would look like the following," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/lib/python3.12/site-packages/pybamm/solvers/base_solver.py:762: SolverWarning:\n", + "\n", + "Explicit interpolation times not implemented for CasADi solver with 'safe' mode\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "1.176754404015497" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cost([0.01, 0.01, 0.01, 20000, 20000])" + ] + }, + { + "cell_type": "markdown", + "id": "26", + "metadata": {}, + "source": [ + "Next, we construct the optimisation class with our algorithm of choice and run it. In this case, we select the PSO method as it provides global optimisation capability. After optimisation, we can compare the initial parameters to the identified parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: No significant change for 55 iterations.\n", + "OptimisationResult:\n", + " Initial parameters: [5.00912047e-03 2.09128273e-04 1.00999900e-04 1.49995000e+00\n", + " 1.49995000e+00]\n", + " Optimised parameters: [4.63261982e-02 1.68622272e-01 1.29049708e-02 7.68290523e+03\n", + " 5.36867333e+02]\n", + " Final cost: 0.026161662141767638\n", + " Optimisation time: 2.983907699584961 seconds\n", + " Number of iterations: 126\n", + " SciPy result available: No\n" + ] + } + ], + "source": [ + "optim = pybop.PSO(cost, max_unchanged_iterations=55, threshold=1e-6)\n", + "results = optim.run()" + ] + }, + { + "cell_type": "markdown", + "id": "28", + "metadata": {}, + "source": [ + "## Plotting and Visualisation\n", + "\n", + "PyBOP provides various plotting utilities to visualise the results of the optimisation. The `pybop.plot.quick` method provides a fast scatter of two variables. For this example, we plot the identified parameter set to the target dataset. The default plot is voltage vs time as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.quick(problem, problem_inputs=results.x, title=\"Optimised Comparison\");" + ] + }, + { + "cell_type": "markdown", + "id": "30", + "metadata": {}, + "source": [ + "### Convergence and Parameter Trajectories\n", + "\n", + "To assess the optimisation process, we can plot the convergence of the cost function and the trajectories of the parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.convergence(optim)\n", + "pybop.plot.parameters(optim);" + ] + }, + { + "cell_type": "markdown", + "id": "32", + "metadata": {}, + "source": [ + "## Validating the Fit\n", + "\n", + "We've identified parameters from a single discharge pulse. Now, let's ask ourselves how well these parameters extrapolate to other operational conditions. To accomplish this, let's load a new pulse dataset at 80% SOC and compare it to our identified forward model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33", + "metadata": {}, + "outputs": [], + "source": [ + "df_pulse_two = pd.DataFrame(\n", + " pulse_data[\"LGM50_5Ah_Pulse\"][\"T0\"][\"SoC8\"][\"Cell19\"][\"data\"]\n", + ")\n", + "df_pulse_two[\"ProgTime\"] = df_pulse_two[\"ProgTime\"] - df_pulse_two[\"ProgTime\"].min()\n", + "df_pulse_two.drop_duplicates(subset=[\"ProgTime\"], inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "34", + "metadata": {}, + "source": [ + "Next, we construct a new `pybop.Dataset` from the second pulse data," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35", + "metadata": {}, + "outputs": [], + "source": [ + "dataset_two_pulse = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": df_pulse_two[\"ProgTime\"].values,\n", + " \"Current function [A]\": -df_pulse_two[\"Current\"].values,\n", + " \"Voltage [V]\": df_pulse_two[\"Voltage\"].values,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "36", + "metadata": {}, + "source": [ + "Now that we have a new dataset, we update the target within the problem class as well as the `Initial SoC` value. Once that has been completed, we rebuild the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37", + "metadata": {}, + "outputs": [], + "source": [ + "problem.set_target(dataset_two_pulse)\n", + "model.build(dataset=dataset_two_pulse, initial_state={\"Initial SoC\": 0.8 - 0.0075})" + ] + }, + { + "cell_type": "markdown", + "id": "38", + "metadata": {}, + "source": [ + "Let's plot the parameterised forward model against the new pulse data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.quick(problem, problem_inputs=results.x, title=\"Parameter Extrapolation\");" + ] + }, + { + "cell_type": "markdown", + "id": "40", + "metadata": {}, + "source": [ + "As expected, when identifying parameters from a single pulse, extrapolation to different operating conditions is challenging. To solve this issue, parameter identification with various pulse datasets is recommended." + ] + }, + { + "cell_type": "markdown", + "id": "41", + "metadata": {}, + "source": [ + "### Conclusion\n", + "\n", + "This notebook illustrates how to perform circuit model parameter identification using PSO in PyBOP, providing insights into the optimisation process through various visualisations." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/comparison_examples/comparing_cost_functions.ipynb b/examples/notebooks/comparison_examples/comparing_cost_functions.ipynb new file mode 100644 index 000000000..3e79ebb8f --- /dev/null +++ b/examples/notebooks/comparison_examples/comparing_cost_functions.ipynb @@ -0,0 +1,768 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Investigating different cost functions\n", + "\n", + "In this notebook, we take a look at the different fitting cost functions offered in PyBOP. Cost functions for fitting problems conventionally describe the distance between two points (the target and the prediction) which is to be minimised via PyBOP's optimisation algorithms. \n", + "\n", + "First, we install and import the required packages below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q\n", + "\n", + "import numpy as np\n", + "\n", + "import pybop\n", + "\n", + "go = pybop.plot.PlotlyManager().go\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(8)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this notebook, we need to construct parameters, a model and a problem class before we can compare differing cost functions. We start with two parameters, but this is an arbitrary selection and can be expanded given the model and data in question." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"Positive electrode thickness [m]\",\n", + " prior=pybop.Gaussian(7.56e-05, 0.5e-05),\n", + " bounds=[65e-06, 10e-05],\n", + " ),\n", + " pybop.Parameter(\n", + " \"Positive particle radius [m]\",\n", + " prior=pybop.Gaussian(5.22e-06, 0.5e-06),\n", + " bounds=[2e-06, 9e-06],\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will construct the Single Particle Model (SPM) with the Chen2020 parameter set, but like the above, this is an arbitrary selection and can be replaced with any PyBOP model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parameter_set = pybop.ParameterSet.pybamm(\"Chen2020\")\n", + "model = pybop.lithium_ion.SPM(parameter_set=parameter_set)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, as we will need reference data to compare our model predictions to (via the cost function), we will create synthetic data from the model constructed above. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t_eval = np.arange(0, 900, 10)\n", + "values = model.predict(t_eval=t_eval)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then construct the PyBOP dataset class with the synthetic data as," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": t_eval,\n", + " \"Current function [A]\": values[\"Current [A]\"].data,\n", + " \"Voltage [V]\": values[\"Voltage [V]\"].data,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can put this all together and construct the problem class. In this situation, we are going to compare differing fitting cost functions, so we construct the `FittingProblem`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "problem = pybop.FittingProblem(model, parameters, dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sum of Squared Errors and Root Mean Squared Error\n", + "\n", + "First, let's start with two commonly-used cost functions: the sum of squared errors (SSE) and the root mean squared error (RMSE). Constructing these classes is very concise in PyBOP, and only requires the problem class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cost_SSE = pybop.SumSquaredError(problem)\n", + "cost_RMSE = pybop.RootMeanSquaredError(problem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can investigate how these functions differ when fitting the parameters. To acquire the cost value for each of these, we can simply use the call method of the constructed class, such as:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.0269652551213878e-10" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cost_SSE([7.56e-05, 5.22e-06])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can use the `Parameters` class for this," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[7.56e-05 5.22e-06]\n" + ] + }, + { + "data": { + "text/plain": [ + "2.0269652551213878e-10" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(parameters.current_value())\n", + "cost_SSE(parameters.current_value())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to generate a random sample of candidate solutions from the parameter class prior, we can also do that as:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[8.19241247e-05 5.09035035e-06]\n" + ] + }, + { + "data": { + "text/plain": [ + "0.030472774516224807" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample = parameters.rvs()\n", + "print(sample)\n", + "cost_SSE(sample)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Comparing RMSE and SSE\n", + "\n", + "Now, let's vary one of the parameters, and keep a fixed value for the other, to create a scatter plot comparing the cost values for the RMSE and SSE functions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_range = np.linspace(4.72e-06, 5.72e-06, 75)\n", + "y_SSE = []\n", + "y_RMSE = []\n", + "for i in x_range:\n", + " y_SSE.append(cost_SSE([7.56e-05, i]))\n", + " y_RMSE.append(cost_RMSE([7.56e-05, i]))\n", + "\n", + "fig = go.Figure()\n", + "fig.add_trace(go.Scatter(x=x_range, y=y_SSE, mode=\"lines\", name=\"SSE\"))\n", + "fig.add_trace(go.Scatter(x=x_range, y=y_RMSE, mode=\"lines\", name=\"RMSE\"))\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this situation, it's clear that the curvature of the SSE cost is greater than that of the RMSE. This can improve the rate of convergence for certain optimisation algorithms. However, with incorrect hyperparameter values, larger gradients can also result in the algorithm not converging due to sampling locations outside of the \"cost valley\", e.g. infeasible parameter values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Minkowski distance\n", + "\n", + "Next, let's investigate the Minkowski distance. The Minkowski cost takes a general form, which allows for hyperparameter calibration on the cost function itself, given by\n", + "\n", + "$\\mathcal{L_p} = \\displaystyle \\Big(\\sum_i |\\hat{y_i}-y_i|^p\\Big)^{1/p}$\n", + "\n", + "where $p ≥ 0$ is the order of the Minkowski distance.\n", + "\n", + "For $p = 1$, it is the Manhattan distance. \n", + "For $p = 2$, it is the Euclidean distance. \n", + "For $p ≥ 1$, the Minkowski distance is a metric, but for $0
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_minkowski = []\n", + "for i in x_range:\n", + " y_minkowski.append(cost_minkowski([7.56e-05, i]))\n", + "\n", + "fig = go.Figure()\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=x_range,\n", + " y=np.asarray(y_RMSE) * np.sqrt(len(t_eval)),\n", + " mode=\"lines\",\n", + " name=\"RMSE*N\",\n", + " )\n", + ")\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=x_range,\n", + " y=np.sqrt(y_SSE),\n", + " mode=\"lines\",\n", + " line=dict(dash=\"dash\"),\n", + " name=\"sqrt(SSE)\",\n", + " )\n", + ")\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=x_range, y=y_minkowski, mode=\"lines\", line=dict(dash=\"dot\"), name=\"Minkowski\"\n", + " )\n", + ")\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, these lines lie on top of one another. Now, let's take a look at how the Minkowski cost changes for different orders, `p`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "p_orders = np.append(0.75, np.linspace(1, 3, 5))\n", + "y_minkowski = tuple(\n", + " [pybop.Minkowski(problem, p=j)([7.56e-05, i]) for i in x_range] for j in p_orders\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure()\n", + "for k, _ in enumerate(p_orders):\n", + " fig.add_trace(\n", + " go.Scatter(x=x_range, y=y_minkowski[k], mode=\"lines\", name=f\"Minkowski {_}\")\n", + " )\n", + "fig.update_yaxes(range=[0, np.max(y_minkowski[2])])\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As seen above, the Minkowski cost allows for a range of different cost functions to be created. This provides users with another hyperparameter to calibrate for optimisation algorithm convergence. This addition does expand the global search space, and should be carefully considered before deciding upon.\n", + "\n", + "### Sum of Power\n", + "Next, we introduce a similar cost function, the `SumofPower` implementation. This cost function is the $p$-th power of the Minkowski distance of order $p$. It provides a generalised formulation for the Sum of Squared Errors (SSE) cost function, and is given by,\n", + "\n", + "$\\mathcal{L_p} = \\displaystyle \\sum_i |\\hat{y_i}-y_i|^p$\n", + "\n", + "where $p ≥ 0$ is the power order. A few special cases include,\n", + "\n", + "$p = 1$: Sum of Absolute Differences\n", + "$p = 2$: Sum of Squared Differences\n", + "\n", + "Next we repeat the above examples with the addition of the `SumofPower` class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cost_sumofpower = pybop.SumofPower(problem, p=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_sumofpower = []\n", + "for i in x_range:\n", + " y_sumofpower.append(cost_sumofpower([7.56e-05, i]))\n", + "\n", + "fig = go.Figure()\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=x_range,\n", + " y=np.asarray(y_RMSE) * np.sqrt(len(t_eval)),\n", + " mode=\"lines\",\n", + " name=\"RMSE*N\",\n", + " )\n", + ")\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=x_range,\n", + " y=y_SSE,\n", + " mode=\"lines\",\n", + " line=dict(dash=\"dash\"),\n", + " name=\"SSE\",\n", + " )\n", + ")\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=x_range,\n", + " y=y_sumofpower,\n", + " mode=\"lines\",\n", + " line=dict(dash=\"dot\"),\n", + " name=\"Sum of Power\",\n", + " )\n", + ")\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the `SumofPower` with order `p=2` equates to the `SSE` implementation. Next, we compare the `Minkowski` to the `SumofPower`," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "p_orders = np.append(0.75, np.linspace(1, 2, 2))\n", + "\n", + "y_minkowski = tuple(\n", + " [pybop.Minkowski(problem, p=j)([7.56e-05, i]) for i in x_range] for j in p_orders\n", + ")\n", + "\n", + "y_sumofpower = tuple(\n", + " [pybop.SumofPower(problem, p=j)([7.56e-05, i]) for i in x_range] for j in p_orders\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure()\n", + "for k, _ in enumerate(p_orders):\n", + " fig.add_trace(\n", + " go.Scatter(x=x_range, y=y_minkowski[k], mode=\"lines\", name=f\"Minkowski {_}\")\n", + " )\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=x_range,\n", + " y=y_sumofpower[k],\n", + " mode=\"lines\",\n", + " line=dict(dash=\"dash\"),\n", + " name=f\"Sum of Power {_}\",\n", + " )\n", + " )\n", + "fig.update_yaxes(range=[0, 2.5])\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The figure demonstrates the distinct behaviour of the `Minkowski` distance and the `SumofPower` function. One notable difference is the effect of the `1/p` exponent in the `Minkowski` distance, which has a linearising impact on the response. This linearisation can enhance the robustness of certain optimisation algorithms, potentially making them less sensitive to outliers or extreme values. However, this increased robustness may come at the cost of a slower convergence rate, as the linearised response might require more iterations to reach the optimal solution. In contrast, the `SumofPower` function does not exhibit this linearising effect, which can lead to faster convergence in some cases but may be more susceptible to the influence of outliers or extreme values.\n", + "\n", + "In this notebook, we've shown the different fitting cost functions offered in PyBOP. Selection between these functions can affect the optimisation result in the case that the optimiser hyperparameter values are not properly calibrated. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pybop-3.12", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/notebooks/comparison_examples/multi_optimiser_identification.ipynb b/examples/notebooks/comparison_examples/multi_optimiser_identification.ipynb new file mode 100644 index 000000000..357877eab --- /dev/null +++ b/examples/notebooks/comparison_examples/multi_optimiser_identification.ipynb @@ -0,0 +1,2183 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "expmkveO04pw" + }, + "source": [ + "## Different Optimisers for SPMe Parameter Estimation\n", + "\n", + "In this notebook, we demonstrate parameter estimation for a single-particle model for various PyBOP optimisers. PyBOP offers a variety of gradient and non-gradient based optimisers, with a table of the currently supported methods shown in the Readme. In this example, we will set up the model, problem, and cost function and investigate how the different optimisers perform under this task.\n", + "\n", + "### Setting up the Environment\n", + "\n", + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X87NUGPW04py", + "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jAvD5fk104p0" + }, + "source": [ + "### Importing Libraries\n", + "\n", + "With the environment set up, we can now import PyBOP alongside other libraries we will need:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SQdt4brD04p1" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import pybop\n", + "\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(8)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5XU-dMtU04p2" + }, + "source": [ + "## Generating Synthetic Data\n", + "\n", + "To demonstrate the parameter estimation, we first need some data. We will generate synthetic data using a PyBOP DFN forward model, which requires defining a parameter set and the model itself.\n", + "\n", + "### Defining Parameters and Model\n", + "\n", + "We start by creating an example parameter set, constructing the DFN for synthetic generation, and the model we will be fitting (SPMe)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parameter_set = pybop.ParameterSet.pybamm(\"Chen2020\")\n", + "synth_model = pybop.lithium_ion.DFN(parameter_set=parameter_set)\n", + "model = pybop.lithium_ion.SPMe(parameter_set=parameter_set)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulating the Forward Model\n", + "\n", + "We can then simulate the model using the `predict` method, with a default constant current discharge to generate the voltage data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sBasxv8U04p3" + }, + "outputs": [], + "source": [ + "t_eval = np.arange(0, 2000, 10)\n", + "initial_state = {\"Initial SoC\": 1.0}\n", + "values = synth_model.predict(t_eval=t_eval, initial_state=initial_state)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding Noise to Voltage Data\n", + "\n", + "To make the parameter estimation more realistic, we add Gaussian noise to the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sigma = 0.002\n", + "corrupt_values = values[\"Voltage [V]\"].data + np.random.normal(0, sigma, len(t_eval))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X8-tubYY04p_" + }, + "source": [ + "## Identifying the Parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PQqhvSZN04p_" + }, + "source": [ + "We will now set up the parameter estimation process by defining the datasets for optimisation and selecting the model parameters we wish to estimate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating a Dataset\n", + "\n", + "The dataset for optimisation is composed of time, current, and the noisy voltage data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "zuvGHWID04p_" + }, + "outputs": [], + "source": [ + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": t_eval,\n", + " \"Current function [A]\": values[\"Current [A]\"].data,\n", + " \"Voltage [V]\": corrupt_values,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ffS3CF_704qA" + }, + "source": [ + "### Defining Parameters to Estimate\n", + "\n", + "We select the parameters for estimation and set up their prior distributions and bounds. In this example, non-geometric parameters for each electrode's active material volume fraction are selected." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WPCybXIJ04qA" + }, + "outputs": [], + "source": [ + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"Negative electrode active material volume fraction\",\n", + " prior=pybop.Gaussian(0.6, 0.02),\n", + " bounds=[0.5, 0.8],\n", + " ),\n", + " pybop.Parameter(\n", + " \"Positive electrode active material volume fraction\",\n", + " prior=pybop.Gaussian(0.48, 0.02),\n", + " bounds=[0.4, 0.7],\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Selecting the Optimisers\n", + "\n", + "Now, we can select the optimisers to investigate. The first object is a list of non-gradient-based PINTS's optimisers. The next object comprises the gradient-based PINTS's optimisers (AdamW, GradientDescent, IRPropMin). The final object forms the SciPy optimisers which can have gradient and non-gradient-based algorithms." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gradient_optimisers = [\n", + " pybop.AdamW,\n", + " pybop.GradientDescent,\n", + " pybop.IRPropMin,\n", + "]\n", + "\n", + "non_gradient_optimisers = [\n", + " pybop.CMAES,\n", + " pybop.SNES,\n", + " pybop.PSO,\n", + " pybop.XNES,\n", + " pybop.NelderMead,\n", + " pybop.CuckooSearch,\n", + "]\n", + "\n", + "scipy_optimisers = [\n", + " pybop.SciPyMinimize,\n", + " pybop.SciPyDifferentialEvolution,\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n4OHa-aF04qA" + }, + "source": [ + "### Setting up the Optimisation Problem\n", + "\n", + "With the datasets, parameters, and optimisers defined, we can set up the optimisation problem and cost function. In this example we loop through all of the above optimisers and store the results for later visualisation and analysis." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "etMzRtx404qA" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running AdamW\n", + "NOTE: Boundaries ignored by AdamW\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: Maximum number of iterations (60) reached.\n", + "OptimisationResult:\n", + " Initial parameters: [0.61271354 0.47586173]\n", + " Optimised parameters: [0.72077396 0.674774 ]\n", + " Final cost: 0.003046118194519812\n", + " Optimisation time: 3.55043888092041 seconds\n", + " Number of iterations: 60\n", + " SciPy result available: No\n", + "Running GradientDescent\n", + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: Maximum number of iterations (60) reached.\n", + "OptimisationResult:\n", + " Initial parameters: [0.61271354 0.47586173]\n", + " Optimised parameters: [0.67939892 0.6815674 ]\n", + " Final cost: 0.0038166067064497105\n", + " Optimisation time: 3.2792601585388184 seconds\n", + " Number of iterations: 60\n", + " SciPy result available: No\n", + "Running IRPropMin\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: Maximum number of iterations (60) reached.\n", + "OptimisationResult:\n", + " Initial parameters: [0.61271354 0.47586173]\n", + " Optimised parameters: [0.72167189 0.67300586]\n", + " Final cost: 0.0029223029353733646\n", + " Optimisation time: 3.3285961151123047 seconds\n", + " Number of iterations: 60\n", + " SciPy result available: No\n" + ] + } + ], + "source": [ + "optims = []\n", + "xs = []\n", + "model.set_initial_state(initial_state)\n", + "problem = pybop.FittingProblem(model, parameters, dataset)\n", + "cost = pybop.SumSquaredError(problem)\n", + "for optimiser in gradient_optimisers:\n", + " print(f\"Running {optimiser.__name__}\")\n", + " sigma0 = 0.01 if optimiser is pybop.GradientDescent else None\n", + " optim = optimiser(\n", + " cost, sigma0=sigma0, max_unchanged_iterations=20, max_iterations=60\n", + " )\n", + " results = optim.run()\n", + " optims.append(optim)\n", + " xs.append(results.x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running CMAES\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: No significant change for 20 iterations.\n", + "OptimisationResult:\n", + " Initial parameters: [0.61271354 0.47586173]\n", + " Optimised parameters: [0.72099642 0.673128 ]\n", + " Final cost: 0.0029219755355584616\n", + " Optimisation time: 2.9584238529205322 seconds\n", + " Number of iterations: 42\n", + " SciPy result available: No\n", + "Running SNES\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: No significant change for 20 iterations.\n", + "OptimisationResult:\n", + " Initial parameters: [0.61271354 0.47586173]\n", + " Optimised parameters: [0.72125026 0.67307644]\n", + " Final cost: 0.0029220074793387843\n", + " Optimisation time: 2.7991578578948975 seconds\n", + " Number of iterations: 52\n", + " SciPy result available: No\n", + "Running PSO\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: No significant change for 20 iterations.\n", + "OptimisationResult:\n", + " Initial parameters: [0.61271354 0.47586173]\n", + " Optimised parameters: [0.7335151 0.67158104]\n", + " Final cost: 0.0030254785798547734\n", + " Optimisation time: 2.0981178283691406 seconds\n", + " Number of iterations: 43\n", + " SciPy result available: No\n", + "Running XNES\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: Maximum number of iterations (60) reached.\n", + "OptimisationResult:\n", + " Initial parameters: [0.61271354 0.47586173]\n", + " Optimised parameters: [0.67677389 0.68423077]\n", + " Final cost: 0.004170543052960678\n", + " Optimisation time: 3.208811044692993 seconds\n", + " Number of iterations: 60\n", + " SciPy result available: No\n", + "Running NelderMead\n", + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: Maximum number of iterations (60) reached.\n", + "OptimisationResult:\n", + " Initial parameters: [0.61271354 0.47586173]\n", + " Optimised parameters: [0.72127038 0.67308243]\n", + " Final cost: 0.002922015000486046\n", + " Optimisation time: 0.572443962097168 seconds\n", + " Number of iterations: 60\n", + " SciPy result available: No\n", + "Running CuckooSearch\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: No significant change for 20 iterations.\n", + "OptimisationResult:\n", + " Initial parameters: [0.61271354 0.47586173]\n", + " Optimised parameters: [0.71228325 0.67482605]\n", + " Final cost: 0.0029570571571777425\n", + " Optimisation time: 2.216571807861328 seconds\n", + " Number of iterations: 40\n", + " SciPy result available: No\n" + ] + } + ], + "source": [ + "for optimiser in non_gradient_optimisers:\n", + " print(f\"Running {optimiser.__name__}\")\n", + " optim = optimiser(cost, max_unchanged_iterations=20, max_iterations=60)\n", + " results = optim.run()\n", + " optims.append(optim)\n", + " xs.append(results.x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running SciPyMinimize\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OptimisationResult:\n", + " Initial parameters: [0.61271354 0.47586173]\n", + " Optimised parameters: [0.62747952 0.7 ]\n", + " Final cost: 0.010270031808476705\n", + " Optimisation time: 0.4111368656158447 seconds\n", + " Number of iterations: 23\n", + " SciPy result available: Yes\n", + "Running SciPyDifferentialEvolution\n", + "Ignoring x0. Initial conditions are not used for differential_evolution.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OptimisationResult:\n", + " Initial parameters: [0.61271354 0.47586173]\n", + " Optimised parameters: [0.72099808 0.67312761]\n", + " Final cost: 0.0029219755546384587\n", + " Optimisation time: 5.829385042190552 seconds\n", + " Number of iterations: 20\n", + " SciPy result available: Yes\n" + ] + } + ], + "source": [ + "for optimiser in scipy_optimisers:\n", + " print(f\"Running {optimiser.__name__}\")\n", + " optim = optimiser(cost, max_iterations=60)\n", + " results = optim.run()\n", + " optims.append(optim)\n", + " xs.append(results.x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we can compare the identified parameters across the optimisers. This gives us insight into how well each optimiser traversed the cost landscape. The ground-truth parameter values for the `Chen2020` parameter set are: \n", + "\n", + "- Negative active material volume fraction: `0.75`\n", + "- Positive active material volume fraction: `0.665`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| Optimiser: AdamW | Results: [0.72077396 0.674774 ] |\n", + "| Optimiser: Gradient descent | Results: [0.67939892 0.6815674 ] |\n", + "| Optimiser: iRprop- | Results: [0.72167189 0.67300586] |\n", + "| Optimiser: Covariance Matrix Adaptation Evolution Strategy (CMA-ES) | Results: [0.72099642 0.673128 ] |\n", + "| Optimiser: Seperable Natural Evolution Strategy (SNES) | Results: [0.72125026 0.67307644] |\n", + "| Optimiser: Particle Swarm Optimisation (PSO) | Results: [0.7335151 0.67158104] |\n", + "| Optimiser: Exponential Natural Evolution Strategy (xNES) | Results: [0.67677389 0.68423077] |\n", + "| Optimiser: Nelder-Mead | Results: [0.72127038 0.67308243] |\n", + "| Optimiser: Cuckoo Search | Results: [0.71228325 0.67482605] |\n", + "| Optimiser: SciPyMinimize | Results: [0.62747952 0.7 ] |\n", + "| Optimiser: SciPyDifferentialEvolution | Results: [0.72099808 0.67312761] |\n" + ] + } + ], + "source": [ + "for optim in optims:\n", + " print(f\"| Optimiser: {optim.name()} | Results: {optim.result.x} |\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Many of the above optimisers found the correct value for the positive active material volume fraction. However, none of them found the correct value for the negative electrode. Next, we can investigate if this was an optimiser or parameter observability failure." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KxKURtH704qC" + }, + "source": [ + "## Plotting and Visualisation\n", + "\n", + "PyBOP provides various plotting utilities to visualise the results of the optimisation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-cWCOiqR04qC" + }, + "source": [ + "### Comparing Solutions\n", + "\n", + "We can quickly plot the system's response using the estimated parameters for each optimiser and the target dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 467 + }, + "id": "tJUJ80Ve04qD", + "outputId": "855fbaa2-1e09-4935-eb1a-8caf7f99eb75" + }, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the cost landscape with optimisation path and updated bounds\n", + "bounds = np.asarray([[0.5, 0.8], [0.55, 0.8]])\n", + "for optim in optims:\n", + " pybop.plot.surface(optim, bounds=bounds, title=optim.name())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given the synthetic data and corresponding system excitation, the observability of the negative electrode active material fraction is quite low. As such, we would need to excite the system in a different way or observe a different signal to acquire a unique value." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusion\n", + "\n", + "This notebook illustrates how to perform parameter estimation using PyBOP, across both gradient and non-gradient-based optimisers. " + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/notebooks/comparison_examples/optimiser_calibration.ipynb b/examples/notebooks/comparison_examples/optimiser_calibration.ipynb new file mode 100644 index 000000000..53a34a063 --- /dev/null +++ b/examples/notebooks/comparison_examples/optimiser_calibration.ipynb @@ -0,0 +1,1559 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "expmkveO04pw" + }, + "source": [ + "## Learning Rate Calibration of Gradient Descent in PyBOP\n", + "\n", + "In this notebook, we calibrate the learning rate for the gradient descent optimiser on a parameter identification problem. The gradient descent learning rate is taken as the `sigma0` value passed to the `pybop.Optimisation` class, or via `problem.sigma0` or `cost.sigma0` if it is passed earlier in the workflow.\n", + "\n", + "### Setting up the Environment\n", + "\n", + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X87NUGPW04py", + "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jAvD5fk104p0" + }, + "source": [ + "### Importing Libraries\n", + "\n", + "With the environment set up, we can now import PyBOP alongside other libraries we will need:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SQdt4brD04p1" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import pybop\n", + "\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(8)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5XU-dMtU04p2" + }, + "source": [ + "## Generating Synthetic Data\n", + "\n", + "To demonstrate parameter estimation, we first need some data. We will generate synthetic data using a forward model, which requires defining a parameter set and the model itself.\n", + "\n", + "### Defining Parameters and Model\n", + "\n", + "We start by creating an example parameter set, constructing the single-particle model (SPM) and generating the synthetic data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parameter_set = pybop.ParameterSet.pybamm(\"Chen2020\")\n", + "parameter_set.update(\n", + " {\n", + " \"Negative electrode active material volume fraction\": 0.65,\n", + " \"Positive electrode active material volume fraction\": 0.51,\n", + " }\n", + ")\n", + "model = pybop.lithium_ion.SPM(parameter_set=parameter_set)\n", + "initial_state = {\"Initial SoC\": 0.4}\n", + "experiment = pybop.Experiment(\n", + " [\n", + " (\n", + " \"Discharge at 0.5C for 6 minutes (4 second period)\",\n", + " \"Charge at 0.5C for 6 minutes (4 second period)\",\n", + " ),\n", + " ]\n", + " * 2\n", + ")\n", + "values = model.predict(initial_state=initial_state, experiment=experiment)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding Noise to Voltage Data\n", + "\n", + "To make the parameter estimation more realistic, we add Gaussian noise to the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sigma = 0.002\n", + "corrupt_values = values[\"Voltage [V]\"].data + np.random.normal(\n", + " 0, sigma, len(values[\"Voltage [V]\"].data)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X8-tubYY04p_" + }, + "source": [ + "## Identifying the Parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PQqhvSZN04p_" + }, + "source": [ + "We will now set up the parameter estimation process by defining the datasets for optimisation and selecting the model parameters we wish to estimate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating a Dataset\n", + "\n", + "The dataset for optimisation is composed of time, current, and the noisy voltage data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "zuvGHWID04p_" + }, + "outputs": [], + "source": [ + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": values[\"Time [s]\"].data,\n", + " \"Current function [A]\": values[\"Current [A]\"].data,\n", + " \"Voltage [V]\": corrupt_values,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ffS3CF_704qA" + }, + "source": [ + "### Defining Parameters to Estimate\n", + "\n", + "We select the parameters for estimation and set up their prior distributions and bounds:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WPCybXIJ04qA" + }, + "outputs": [], + "source": [ + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"Negative electrode active material volume fraction\",\n", + " prior=pybop.Uniform(0.45, 0.7),\n", + " bounds=[0.4, 0.8],\n", + " true_value=0.65,\n", + " ),\n", + " pybop.Parameter(\n", + " \"Positive electrode active material volume fraction\",\n", + " prior=pybop.Uniform(0.45, 0.7),\n", + " bounds=[0.4, 0.8],\n", + " true_value=0.51,\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n4OHa-aF04qA" + }, + "source": [ + "### Setting up the Optimisation Problem with incorrect sigma value\n", + "\n", + "With the datasets and parameters defined, we can set up the optimisation problem, its cost function, and the optimiser. For gradient descent, the `sigma0` value corresponds to the learning rate. Let's set this hyperparmeter incorrectly to view how we calibrate it. In this example, let's start with `sigma0=0.2`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "etMzRtx404qA" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NOTE: Boundaries ignored by \n" + ] + } + ], + "source": [ + "problem = pybop.FittingProblem(model, parameters, dataset)\n", + "cost = pybop.SumSquaredError(problem)\n", + "optim = pybop.GradientDescent(cost, sigma0=0.2, max_iterations=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "caprp-bV04qB" + }, + "source": [ + "### Running the Optimisation\n", + "\n", + "We proceed to run the optimisation algorithm to estimate the parameters with the updated learning rate (`sigma0`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-9OVt0EQ04qB" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: No significant change for 15 iterations.\n", + "OptimisationResult:\n", + " Initial parameters: [0.46478836 0.56896942]\n", + " Optimised parameters: [0.64605501 0.51469905]\n", + " Final cost: 0.0013514039920203883\n", + " Optimisation time: 2.287174940109253 seconds\n", + " Number of iterations: 52\n", + " SciPy result available: No\n" + ] + } + ], + "source": [ + "results = optim.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-4pZsDmS04qC" + }, + "source": [ + "### Viewing the Estimated Parameters\n", + "\n", + "After the optimisation, we can examine the estimated parameter values. In this case, the optimiser misses the optimal solution by a large amount." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Hgz8SV4i04qC", + "outputId": "e1e42ae7-5075-4c47-dd68-1b22ecc170f6" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.64605501, 0.51469905])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.x # This will output the estimated parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot the time-series prediction for the given solution. As we suspected, the optimiser found a very poor solution. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.quick(problem, problem_inputs=results.x, title=\"Optimised Comparison\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calibrating the Learning Rate \n", + "\n", + "Now that we've seen how poor an incorrect `sigma0` value is for this optimisation problem, let's calibrate this value to find the optimal solution in the lowest number of iterations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.001\n", + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: Maximum number of iterations (100) reached.\n", + "OptimisationResult:\n", + " Initial parameters: [0.46478836 0.56896942]\n", + " Optimised parameters: [0.53090948 0.59339862]\n", + " Final cost: 0.014783332157851634\n", + " Optimisation time: 4.26934027671814 seconds\n", + " Number of iterations: 100\n", + " SciPy result available: No\n", + "0.012285714285714285\n", + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: Maximum number of iterations (100) reached.\n", + "OptimisationResult:\n", + " Initial parameters: [0.46478836 0.56896942]\n", + " Optimised parameters: [0.60345692 0.56598814]\n", + " Final cost: 0.002790217328868725\n", + " Optimisation time: 4.251996040344238 seconds\n", + " Number of iterations: 100\n", + " SciPy result available: No\n", + "0.023571428571428573\n", + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: Maximum number of iterations (100) reached.\n", + "OptimisationResult:\n", + " Initial parameters: [0.46478836 0.56896942]\n", + " Optimised parameters: [0.62409599 0.5385165 ]\n", + " Final cost: 0.0017177540172860569\n", + " Optimisation time: 4.208575963973999 seconds\n", + " Number of iterations: 100\n", + " SciPy result available: No\n", + "0.03485714285714286\n", + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: Maximum number of iterations (100) reached.\n", + "OptimisationResult:\n", + " Initial parameters: [0.46478836 0.56896942]\n", + " Optimised parameters: [0.63571019 0.52533286]\n", + " Final cost: 0.0014363025957628353\n", + " Optimisation time: 4.245105028152466 seconds\n", + " Number of iterations: 100\n", + " SciPy result available: No\n", + "0.046142857142857145\n", + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: Maximum number of iterations (100) reached.\n", + "OptimisationResult:\n", + " Initial parameters: [0.46478836 0.56896942]\n", + " Optimised parameters: [0.641494 0.51930683]\n", + " Final cost: 0.0013701538891978493\n", + " Optimisation time: 4.226551055908203 seconds\n", + " Number of iterations: 100\n", + " SciPy result available: No\n", + "0.05742857142857143\n", + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: Maximum number of iterations (100) reached.\n", + "OptimisationResult:\n", + " Initial parameters: [0.46478836 0.56896942]\n", + " Optimised parameters: [0.64414678 0.5165881 ]\n", + " Final cost: 0.0013574904717929912\n", + " Optimisation time: 4.241044044494629 seconds\n", + " Number of iterations: 100\n", + " SciPy result available: No\n", + "0.06871428571428571\n", + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: No significant change for 15 iterations.\n", + "OptimisationResult:\n", + " Initial parameters: [0.46478836 0.56896942]\n", + " Optimised parameters: [0.64460623 0.51615892]\n", + " Final cost: 0.0013563752974500437\n", + " Optimisation time: 3.6841771602630615 seconds\n", + " Number of iterations: 87\n", + " SciPy result available: No\n", + "0.08\n", + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: No significant change for 15 iterations.\n", + "OptimisationResult:\n", + " Initial parameters: [0.46478836 0.56896942]\n", + " Optimised parameters: [0.64463942 0.51613016]\n", + " Final cost: 0.001356018556480287\n", + " Optimisation time: 3.1757869720458984 seconds\n", + " Number of iterations: 75\n", + " SciPy result available: No\n" + ] + } + ], + "source": [ + "sigmas = np.linspace(0.001, 0.08, 8) # Change this to a smaller range for a quicker run\n", + "xs = []\n", + "optims = []\n", + "for sigma in sigmas:\n", + " print(sigma)\n", + " problem = pybop.FittingProblem(model, parameters, dataset)\n", + " cost = pybop.SumSquaredError(problem)\n", + " optim = pybop.GradientDescent(cost, sigma0=sigma, max_iterations=100)\n", + " results = optim.run()\n", + " optims.append(optim)\n", + " xs.append(results.x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| Sigma: 0.001 | Num Iterations: 100 | Best Cost: 0.014783332157851634 | Results: [0.53090948 0.59339862] |\n", + "| Sigma: 0.012285714285714285 | Num Iterations: 100 | Best Cost: 0.002790217328868725 | Results: [0.60345692 0.56598814] |\n", + "| Sigma: 0.023571428571428573 | Num Iterations: 100 | Best Cost: 0.0017177540172860569 | Results: [0.62409599 0.5385165 ] |\n", + "| Sigma: 0.03485714285714286 | Num Iterations: 100 | Best Cost: 0.0014363025957628353 | Results: [0.63571019 0.52533286] |\n", + "| Sigma: 0.046142857142857145 | Num Iterations: 100 | Best Cost: 0.0013701538891978493 | Results: [0.641494 0.51930683] |\n", + "| Sigma: 0.05742857142857143 | Num Iterations: 100 | Best Cost: 0.0013574904717929912 | Results: [0.64414678 0.5165881 ] |\n", + "| Sigma: 0.06871428571428571 | Num Iterations: 87 | Best Cost: 0.0013563752974500437 | Results: [0.64460623 0.51615892] |\n", + "| Sigma: 0.08 | Num Iterations: 75 | Best Cost: 0.001356018556480287 | Results: [0.64463942 0.51613016] |\n" + ] + } + ], + "source": [ + "for optim, sigma in zip(optims, sigmas):\n", + " print(\n", + " f\"| Sigma: {sigma} | Num Iterations: {optim.result.n_iterations} | Best Cost: {optim.optimiser.f_best()} | Results: {optim.optimiser.x_best()} |\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Perhaps a better way to view this information is to plot the optimiser convergences," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the cost landscape with optimisation path and updated bounds\n", + "bounds = np.array([[0.4, 0.8], [0.4, 0.8]])\n", + "for optim, sigma in zip(optims, sigmas):\n", + " pybop.plot.surface(optim, bounds=bounds, title=f\"Sigma: {sigma}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Updating the Learning Rate\n", + "\n", + "Let's take `sigma0 = 0.08` as the best learning rate for this problem and look at the time-series trajectories." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NOTE: Boundaries ignored by \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: No significant change for 15 iterations.\n", + "OptimisationResult:\n", + " Initial parameters: [0.46478836 0.56896942]\n", + " Optimised parameters: [0.64463942 0.51613016]\n", + " Final cost: 0.001356018556480287\n", + " Optimisation time: 3.1555721759796143 seconds\n", + " Number of iterations: 75\n", + " SciPy result available: No\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "optim = pybop.Optimisation(cost, optimiser=pybop.GradientDescent, sigma0=0.08)\n", + "results = optim.run()\n", + "pybop.plot.quick(problem, problem_inputs=results.x, title=\"Optimised Comparison\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusion\n", + "\n", + "This notebook covers how to calibrate the learning rate for the gradient descent optimiser. This provides an introduction into hyper-parameter tuning that will be discussed in further notebooks." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/notebooks/comparison_examples/solver_selection.ipynb b/examples/notebooks/comparison_examples/solver_selection.ipynb new file mode 100644 index 000000000..3a9e99db3 --- /dev/null +++ b/examples/notebooks/comparison_examples/solver_selection.ipynb @@ -0,0 +1,319 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "expmkveO04pw" + }, + "source": [ + "## Investigate Different PyBaMM Solvers\n", + "\n", + "In this notebook, we discuss the process of changing PyBaMM solvers and the corresponding performance trade-offs with each. For further reading on different solvers, see the PyBaMM solver documentation:\n", + "\n", + "[[1]: PyBaMM Solvers](https://docs.pybamm.org/en/stable/source/api/solvers/index.html#)\n", + "\n", + "### Setting up the Environment\n", + "\n", + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X87NUGPW04py", + "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q\n", + "\n", + "import time\n", + "\n", + "import numpy as np\n", + "import pybamm\n", + "\n", + "import pybop\n", + "\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(8)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5XU-dMtU04p2" + }, + "source": [ + "### Setting up the model, and problem\n", + "\n", + "We start by constructing a pybop model, and a synthetic dataset needed for the pybop problem we will be using for the solver benchmarking " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Model\n", + "parameter_set = pybop.ParameterSet.pybamm(\"Chen2020\")\n", + "model = pybop.lithium_ion.SPM(parameter_set=parameter_set)\n", + "\n", + "# Synthetic data\n", + "t_eval = np.arange(0, 900, 2)\n", + "values = model.predict(t_eval=t_eval)\n", + "\n", + "# Dataset\n", + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": t_eval,\n", + " \"Current function [A]\": values[\"Current [A]\"].data,\n", + " \"Voltage [V]\": values[\"Voltage [V]\"].data,\n", + " }\n", + ")\n", + "\n", + "# Parameters\n", + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"Negative electrode active material volume fraction\",\n", + " prior=pybop.Gaussian(0.6, 0.02),\n", + " bounds=[0.5, 0.8],\n", + " ),\n", + " pybop.Parameter(\n", + " \"Positive electrode active material volume fraction\",\n", + " prior=pybop.Gaussian(0.48, 0.02),\n", + " bounds=[0.4, 0.7],\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n4OHa-aF04qA" + }, + "source": [ + "### Defining the solvers for benchmarking\n", + "\n", + "Now that we have set up the majority of the pybop objects, we construct the solvers we want to benchmark on the given model, and applied current." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "solvers = [\n", + " pybamm.IDAKLUSolver(atol=1e-6, rtol=1e-6),\n", + " pybamm.CasadiSolver(atol=1e-6, rtol=1e-6, mode=\"safe\"),\n", + " pybamm.CasadiSolver(atol=1e-6, rtol=1e-6, mode=\"fast\"),\n", + " pybamm.CasadiSolver(atol=1e-6, rtol=1e-6, mode=\"fast with events\"),\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we construct a range of inputs for the parameters defined above, and select the number of instances in that range to benchmark on. For more statistically repeatable results, increase the variable `n` below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n = 50 # Number of solves\n", + "inputs = list(zip(np.linspace(0.45, 0.6, n), np.linspace(0.45, 0.6, n)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's benchmark the solvers without sensitivities. This provides a reference for the non-gradient based pybop optimisers and samplers. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time Evaluate IDA KLU solver: 0.320\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time Evaluate CasADi solver with 'safe' mode: 1.176\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time Evaluate CasADi solver with 'fast' mode: 0.912\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time Evaluate CasADi solver with 'fast with events' mode: 0.943\n" + ] + } + ], + "source": [ + "for solver in solvers:\n", + " model.solver = solver\n", + " problem = pybop.FittingProblem(model, parameters, dataset)\n", + "\n", + " start_time = time.time()\n", + " for input_values in inputs:\n", + " problem.evaluate(inputs=input_values)\n", + " print(f\"Time Evaluate {solver.name}: {time.time() - start_time:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Excellent, given the above results, we know which solver we should select for optimisation on your machine, i.e. the one with the smallest time. \n", + "\n", + "Next, let's repeat the same toy problem, but for the gradient-based cost evaluation," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time EvaluateS1 IDA KLU solver: 0.786\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time EvaluateS1 CasADi solver with 'safe' mode: 3.751\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time EvaluateS1 CasADi solver with 'fast' mode: 3.326\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time EvaluateS1 CasADi solver with 'fast with events' mode: 3.349\n" + ] + } + ], + "source": [ + "for solver in solvers:\n", + " model.solver = solver\n", + " problem = pybop.FittingProblem(model, parameters, dataset)\n", + "\n", + " start_time = time.time()\n", + " for input_values in inputs:\n", + " problem.evaluateS1(inputs=input_values)\n", + " print(f\"Time EvaluateS1 {solver.name}: {time.time() - start_time:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have the relevant information for the gradient-based optimisers. Likewise to the above results, we should select the solver with the smallest time." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/notebooks/design_optimisation/energy_based_electrode_design.ipynb b/examples/notebooks/design_optimisation/energy_based_electrode_design.ipynb new file mode 100644 index 000000000..96871b9f8 --- /dev/null +++ b/examples/notebooks/design_optimisation/energy_based_electrode_design.ipynb @@ -0,0 +1,475 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "expmkveO04pw" + }, + "source": [ + "## An Electrode Design Optimisation Example\n", + "\n", + "A design optimisation example loosely based on work by L.D. Couto available at [[1]](https://doi.org/10.1016/j.energy.2022.125966).\n", + "\n", + "The target is to maximise the gravimetric energy density over a range of possible design parameter values, including for example:\n", + "\n", + "cross-sectional area = height x width (only need change one), electrode widths, particle radii, volume fractions and separator width.\n", + "\n", + "### Setting up the Environment\n", + "\n", + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X87NUGPW04py", + "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n", + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jAvD5fk104p0" + }, + "source": [ + "Next, we import the added packages plus any additional dependencies," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SQdt4brD04p1" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from pybamm import Parameter\n", + "\n", + "import pybop\n", + "\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(8)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X8-tubYY04p_" + }, + "source": [ + "## Optimising the Parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PQqhvSZN04p_" + }, + "source": [ + "First, we define the model to be used for the parameter optimisation," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "zuvGHWID04p_" + }, + "outputs": [], + "source": [ + "parameter_set = pybop.ParameterSet.pybamm(\"Chen2020\")\n", + "parameter_set.update(\n", + " {\n", + " \"Electrolyte density [kg.m-3]\": Parameter(\"Separator density [kg.m-3]\"),\n", + " \"Negative electrode active material density [kg.m-3]\": Parameter(\n", + " \"Negative electrode density [kg.m-3]\"\n", + " ),\n", + " \"Negative electrode carbon-binder density [kg.m-3]\": Parameter(\n", + " \"Negative electrode density [kg.m-3]\"\n", + " ),\n", + " \"Positive electrode active material density [kg.m-3]\": Parameter(\n", + " \"Positive electrode density [kg.m-3]\"\n", + " ),\n", + " \"Positive electrode carbon-binder density [kg.m-3]\": Parameter(\n", + " \"Positive electrode density [kg.m-3]\"\n", + " ),\n", + " },\n", + " check_already_exists=False,\n", + ")\n", + "model = pybop.lithium_ion.SPMe(parameter_set=parameter_set)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ffS3CF_704qA" + }, + "source": [ + "Next, we define the model parameters for optimisation. Furthermore, PyBOP provides functionality to define a prior for the parameters. The initial parameters values used in the optimisation will be randomly drawn from the prior distribution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WPCybXIJ04qA" + }, + "outputs": [], + "source": [ + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"Positive electrode thickness [m]\",\n", + " prior=pybop.Gaussian(7.56e-05, 0.05e-05),\n", + " bounds=[65e-06, 10e-05],\n", + " ),\n", + " pybop.Parameter(\n", + " \"Positive particle radius [m]\",\n", + " prior=pybop.Gaussian(5.22e-06, 0.05e-06),\n", + " bounds=[2e-06, 9e-06],\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we construct the experiment for design optimisation and the initial state-of-charge," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "experiment = pybop.Experiment(\n", + " [\"Discharge at 1C until 2.5 V (5 seconds period)\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n4OHa-aF04qA" + }, + "source": [ + "We can now define the output signal, the problem (which combines the model with the dataset) and construct a cost function which in this example is the `GravimetricEnergyDensity()` used to maximise the gravimetric energy density of the cell." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "etMzRtx404qA" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/2s/pn1xwy191_7bv761mlf_cgzh0000gq/T/ipykernel_45535/2045688338.py:1: UserWarning:\n", + "\n", + "The nominal capacity is fixed at the initial model value.\n", + "\n" + ] + } + ], + "source": [ + "problem = pybop.DesignProblem(\n", + " model, parameters, experiment, initial_state={\"Initial SoC\": 0.7}\n", + ")\n", + "cost = pybop.GravimetricEnergyDensity(problem)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eQiGurUV04qB" + }, + "source": [ + "Let's construct PyBOP's optimisation class. This class provides the methods needed to fit the forward model. For this example, we use particle swarm optimisation (PSO). Due to the computational requirements of the design optimisation methods, we limit the number of iterations to 15 for this example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "N3FtAhrT04qB" + }, + "outputs": [], + "source": [ + "optim = pybop.PSO(cost, verbose=True, max_iterations=15)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "caprp-bV04qB" + }, + "source": [ + "Finally, we run the optimisation and return the values obtained," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-9OVt0EQ04qB" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: Maximum number of iterations (15) reached.\n", + "OptimisationResult:\n", + " Initial parameters: [7.56456024e-05 5.27456414e-06]\n", + " Optimised parameters: [8.27568156e-05 2.07975193e-06]\n", + " Final cost: 279.3737537368842\n", + " Optimisation time: 26.728662967681885 seconds\n", + " Number of iterations: 15\n", + " SciPy result available: No\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial gravimetric energy density: 256.31 Wh.kg-1\n", + "Optimised gravimetric energy density: 279.37 Wh.kg-1\n" + ] + } + ], + "source": [ + "results = optim.run()\n", + "print(f\"Initial gravimetric energy density: {cost(results.x0):.2f} Wh.kg-1\")\n", + "print(f\"Optimised gravimetric energy density: {results.final_cost:.2f} Wh.kg-1\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KxKURtH704qC" + }, + "source": [ + "## Plotting and Visualisation\n", + "\n", + "PyBOP provides various plotting utilities to visualise the results of the optimisation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-cWCOiqR04qC" + }, + "source": [ + "### Comparing System Response\n", + "\n", + "We can quickly plot the system's response using the estimated parameters compared to the initial parameters:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZVfozY0A04qC" + }, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.quick(problem, problem_inputs=results.x, title=\"Optimised Comparison\");" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ntIvAJmA04qD" + }, + "source": [ + "### Cost Landscape\n", + "\n", + "Finally, we can visualise the cost landscape and the path taken by the optimiser:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 467 + }, + "id": "tJUJ80Ve04qD", + "outputId": "855fbaa2-1e09-4935-eb1a-8caf7f99eb75" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.surface(optim);" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/notebooks/getting_started/adamw_identification.ipynb b/examples/notebooks/getting_started/adamw_identification.ipynb new file mode 100644 index 000000000..ed9af4637 --- /dev/null +++ b/examples/notebooks/getting_started/adamw_identification.ipynb @@ -0,0 +1,631 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "expmkveO04pw" + }, + "source": [ + "## Parameter Estimation with AdamW in PyBOP\n", + "\n", + "In this notebook, we demonstrate an example of parameter estimation for a single-particle model using the AdamW optimiser [1][2]. The AdamW optimiser is an algorithm for gradient-based optimisation, combining the advantages of the Adaptive Gradient Algorithm (AdaGrad) and Root Mean Square Propagation (RMSProp).\n", + "\n", + "[[1]: Adam: A Method for Stochastic Optimization](https://arxiv.org/abs/1412.6980) \n", + "\n", + "[[2]: Decoupled Weight Decay Regularization](https://doi.org/10.48550/arXiv.1711.05101)\n", + "\n", + "### Setting up the Environment\n", + "\n", + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X87NUGPW04py", + "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jAvD5fk104p0" + }, + "source": [ + "### Importing Libraries\n", + "\n", + "With the environment set up, we can now import PyBOP alongside other libraries we will need:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SQdt4brD04p1" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import pybop\n", + "\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(8)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5XU-dMtU04p2" + }, + "source": [ + "### Generate Synthetic Data\n", + "\n", + "To demonstrate parameter estimation, we first need some data. We will generate synthetic data using the PyBOP forward model, which requires defining a parameter set and the model itself.\n", + "\n", + "#### Defining Parameters and Model\n", + "\n", + "We start by creating an example parameter set and then instantiate the single-particle model (SPM):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parameter_set = pybop.ParameterSet.pybamm(\"Chen2020\")\n", + "model = pybop.lithium_ion.SPM(parameter_set=parameter_set)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulating Forward Model\n", + "\n", + "We can then simulate the model using the `predict` method, with a default constant current to generate voltage data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sBasxv8U04p3" + }, + "outputs": [], + "source": [ + "t_eval = np.arange(0, 900, 2)\n", + "values = model.predict(t_eval=t_eval)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding Noise to Voltage Data\n", + "\n", + "To make the parameter estimation more realistic, we add Gaussian noise to the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sigma = 0.001\n", + "corrupt_values = values[\"Voltage [V]\"].data + np.random.normal(0, sigma, len(t_eval))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X8-tubYY04p_" + }, + "source": [ + "## Identify the Parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PQqhvSZN04p_" + }, + "source": [ + "We will now set up the parameter estimation process by defining the datasets for optimisation and selecting the model parameters we wish to estimate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating Optimisation Dataset\n", + "\n", + "The dataset for optimisation is composed of time, current, and the noisy voltage data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "zuvGHWID04p_" + }, + "outputs": [], + "source": [ + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": t_eval,\n", + " \"Current function [A]\": values[\"Current [A]\"].data,\n", + " \"Voltage [V]\": corrupt_values,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ffS3CF_704qA" + }, + "source": [ + "### Defining Parameters to Estimate\n", + "\n", + "We select the parameters for estimation and set up their prior distributions and bounds:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WPCybXIJ04qA" + }, + "outputs": [], + "source": [ + "parameters = [\n", + " pybop.Parameter(\n", + " \"Negative electrode active material volume fraction\",\n", + " prior=pybop.Gaussian(0.6, 0.02),\n", + " bounds=[0.5, 0.8],\n", + " ),\n", + " pybop.Parameter(\n", + " \"Positive electrode active material volume fraction\",\n", + " prior=pybop.Gaussian(0.48, 0.02),\n", + " bounds=[0.4, 0.7],\n", + " ),\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n4OHa-aF04qA" + }, + "source": [ + "### Setting up the Optimisation Problem\n", + "\n", + "With the datasets and parameters defined, we can set up the optimisation problem, its cost function, and the optimiser." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "etMzRtx404qA" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NOTE: Boundaries ignored by AdamW\n" + ] + } + ], + "source": [ + "problem = pybop.FittingProblem(model, parameters, dataset)\n", + "cost = pybop.SumSquaredError(problem)\n", + "optim = pybop.Optimisation(cost, optimiser=pybop.AdamW)\n", + "optim.set_max_unchanged_iterations(40)\n", + "optim.set_max_iterations(150)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "caprp-bV04qB" + }, + "source": [ + "### Running the Optimisation\n", + "\n", + "We proceed to run the AdamW optimisation algorithm to estimate the parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-9OVt0EQ04qB" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: Maximum number of iterations (150) reached.\n", + "OptimisationResult:\n", + " Initial parameters: [0.60593931 0.46706684]\n", + " Optimised parameters: [0.76335438 0.66225687]\n", + " Final cost: 0.0004830773369301502\n", + " Optimisation time: 14.283170223236084 seconds\n", + " Number of iterations: 150\n", + " SciPy result available: No\n" + ] + } + ], + "source": [ + "results = optim.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-4pZsDmS04qC" + }, + "source": [ + "### Viewing the Estimated Parameters\n", + "\n", + "After the optimisation, we can examine the estimated parameter values:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Hgz8SV4i04qC", + "outputId": "e1e42ae7-5075-4c47-dd68-1b22ecc170f6" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.76335438, 0.66225687])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.x # This will output the estimated parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KxKURtH704qC" + }, + "source": [ + "## Plotting and Visualisation\n", + "\n", + "PyBOP provides various plotting utilities to visualise the results of the optimisation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-cWCOiqR04qC" + }, + "source": [ + "### Comparing System Response\n", + "\n", + "We can quickly plot the system's response using the estimated parameters compared to the target:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 467 + }, + "id": "tJUJ80Ve04qD", + "outputId": "855fbaa2-1e09-4935-eb1a-8caf7f99eb75" + }, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.quick(problem, problem_inputs=results.x, title=\"Optimised Comparison\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Convergence and Parameter Trajectories\n", + "\n", + "To assess the optimisation process, we can plot the convergence of the cost function and the trajectories of the parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "N5XYkevi04qD" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.convergence(optim)\n", + "pybop.plot.parameters(optim);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cost Landscape\n", + "\n", + "Finally, we can visualise the cost landscape and the path taken by the optimiser:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the cost landscape with updated bounds\n", + "bounds = np.asarray([[0.6, 0.9], [0.5, 0.8]])\n", + "pybop.plot.surface(optim, bounds=bounds);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusion\n", + "\n", + "This notebook illustrates how to perform parameter estimation using AdamW in PyBOP, providing insights into the optimisation process through various visualisations." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/notebooks/getting_started/cost_compute_methods.ipynb b/examples/notebooks/getting_started/cost_compute_methods.ipynb new file mode 100644 index 000000000..9af25b2e7 --- /dev/null +++ b/examples/notebooks/getting_started/cost_compute_methods.ipynb @@ -0,0 +1,393 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Using the Cost/Likelihood classes\n", + "This example will introduce the cost function methods used for both evaluating the output of and predicting the forward model. This example will use a cost class (`pybop.SumofPower`) as an example, but the methods discussed here are transferable to the other cost classes as well as the likelihood classes.\n", + "\n", + "### Setting up the Environment\n", + "\n", + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "### Importing Libraries\n", + "\n", + "With the environment set up, we can now import PyBOP alongside other libraries we will need:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import pybop\n", + "\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "First, to construct a `pybop.Cost` class, we need the following objects:\n", + "- Model\n", + "- Dataset\n", + "- Parameters to identify\n", + "- Problem\n", + "\n", + "Given the above, we will first construct the model, then the parameters and corresponding dataset. Once that is complete, the problem will be created. With the cost class created, we will showcase the different interactions users can have with the class. A small example with evaluation as well as computation is presented." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "parameter_set = pybop.ParameterSet.pybamm(\"Chen2020\")\n", + "model = pybop.lithium_ion.SPM(parameter_set=parameter_set)" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "Now that we have the model constructed, let's define the parameters for identification." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"Negative electrode active material volume fraction\",\n", + " initial_value=0.6,\n", + " ),\n", + " pybop.Parameter(\n", + " \"Positive electrode active material volume fraction\",\n", + " initial_value=0.6,\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "Next, we generate some synthetic data from the model using the `model.predict` method. This then gets corrupted with Gaussian noise and used to create the Dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "t_eval = np.linspace(0, 10, 100)\n", + "values = model.predict(t_eval=t_eval)\n", + "\n", + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": t_eval,\n", + " \"Current function [A]\": values[\"Current [A]\"].data,\n", + " \"Voltage [V]\": values[\"Voltage [V]\"].data,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "Now that we have the model, parameters, and dataset, we can combine them and construct the problem class. This class forms the basis for evaluating the forward model for the defined fitting process (parameters and operating conditions)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "problem = pybop.FittingProblem(model, parameters, dataset)" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "Perfect, let's now construct the cost class and move onto the main point of this example," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], + "source": [ + "cost = pybop.SumofPower(problem)" + ] + }, + { + "cell_type": "markdown", + "id": "14", + "metadata": {}, + "source": [ + "The conventional way to use the cost class is through the `cost.__call__` method, which is completed below," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.08963993888559865" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cost([0.5, 0.5])" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": {}, + "source": [ + "This does two things, it first evaluates the forward model at the given parameter values of `[0.5,0.5]`, then it computes the cost for the forward models prediction compared to the problem target values, which are provided from the dataset we constructed above. \n", + "\n", + "However, there is an alternative method to achieve this which provides the user with more flexibility in their assessment of the cost function, this is done through the `cost.compute` method, as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.08963993888559865" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out = problem.evaluate([0.5, 0.5])\n", + "cost.compute(out)" + ] + }, + { + "cell_type": "markdown", + "id": "18", + "metadata": {}, + "source": [ + "This splits the evaluation of the forward model and the computation of the cost function into two separate calls, allowing for the model evaluation to be decoupled from the cost computation. This decoupling can be helpful in the case where you want to assess the problem across multiple costs (see pybop.WeightedCost for a PyBOP implementation of this), or want to modify the problem output before assessing a cost.\n", + "\n", + "Next, let's present a few of these use-cases. In the first use-case, the problem is evaluated once, with random noise added and the cost computed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19", + "metadata": {}, + "outputs": [], + "source": [ + "def my_cost(inputs):\n", + " y = problem.evaluate(inputs)\n", + " y[\"Voltage [V]\"] += np.random.normal(0, 0.003, len(t_eval))\n", + " return cost.compute(y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.08910088339381227" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_cost([0.5, 0.5])" + ] + }, + { + "cell_type": "markdown", + "id": "21", + "metadata": {}, + "source": [ + "The above method showcases how the `cost.__call__` method can be constructed at the user level. Furthermore, the above example can be reimplemented with gradient calculations as well via the `calculate_gradient` argument within the `cost.compute` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22", + "metadata": {}, + "outputs": [], + "source": [ + "def my_cost_gradient(inputs):\n", + " y, dy = problem.evaluateS1(inputs)\n", + " y[\"Voltage [V]\"] += np.random.normal(0, 0.003, len(t_eval))\n", + " return cost.compute(y, dy=dy, calculate_grad=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.08917807157201464, array([-0.57688969, -0.48453944]))" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_cost_gradient([0.5, 0.5])" + ] + }, + { + "cell_type": "markdown", + "id": "24", + "metadata": {}, + "source": [ + "This provides the computed cost for the parameter values, alongside the gradient with respect to those parameters. This is the exact structure that is used within PyBOP's gradient-based optimisers. Finally, the above can be easily reproduced via the `cost.__call__` method with the corresponding `calculate_gradient=True` argument." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.08963668887423992, array([-0.58045629, -0.48653053]))" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cost([0.5, 0.5], calculate_grad=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/getting_started/creating_a_model.ipynb b/examples/notebooks/getting_started/creating_a_model.ipynb new file mode 100644 index 000000000..526346ab2 --- /dev/null +++ b/examples/notebooks/getting_started/creating_a_model.ipynb @@ -0,0 +1,162 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "expmkveO04pw" + }, + "source": [ + "## Creating a Model\n", + "\n", + "In this notebook, we create and solve a single particle model (SPM). This is achieved using a predefined parameter set introduced in Marquis et al. [[1]](https://doi.org/10.1149/1945-7111/abbce4) \n", + "\n", + "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X87NUGPW04py", + "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q\n", + "\n", + "import pybop\n", + "\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5XU-dMtU04p2" + }, + "source": [ + "## Creating a Model\n", + "\n", + "PyBOP offers the both forward emperical and physics-based forward models. These are provided by PyBaMM, with PyBOP adding wrappers on the underlying classes to reduce complexity and improve stability with parameter inference and design optimisation. Likewise, PyBOP provides a light wrapper on the PyBaMM parameter sets, with user-defined parameters available through the same `pybop.ParameterSet` class.\n", + "\n", + "Let's construct the parameter set and then the single-particle model (SPM):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parameter_set = pybop.ParameterSet.pybamm(\"Marquis2019\")\n", + "model = pybop.lithium_ion.SPM(parameter_set=parameter_set)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the model is constructed with the Maquis parameter set, we can use the `model.predict` method as a light wrapper on the `PyBaMM.Simulation` class. This is the recommended way to generate synthetic data, but not for parameter inference as the performance cost of constructing the `Simulation` class is high. For parameter inference, `model.simulate` and `model.simulateS1` offer a performant way to solve the forward model with and without sensitivities respectively." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Testing the Model\n", + "Having constructed the model, we can now have a look at its voltage discharge curve to verify that it is working. The discharge curve is evaluated on the time interval specified by `t_eval`. `model.predict` returns the `PyBaMM.solution` object with all of its functionality. As we are only working with the forward model, PyBaMM plotting methods will be used; however, when performing parameter inference or design optimisation, PyBOP plotting methods are recommended." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t_eval = [0, 3700]\n", + "solution = model.predict([], t_eval) # No inputs i.e []\n", + "\n", + "# Plot with PyBaMM\n", + "solution.plot_voltage_components()" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/notebooks/getting_started/maximum_a_posteriori.ipynb b/examples/notebooks/getting_started/maximum_a_posteriori.ipynb new file mode 100644 index 000000000..a30bf1a84 --- /dev/null +++ b/examples/notebooks/getting_started/maximum_a_posteriori.ipynb @@ -0,0 +1,908 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "expmkveO04pw" + }, + "source": [ + "## Maximum a Posteriori Parameter Inference\n", + "\n", + "In this notebook, we introduce the Maximum a Posteriori (MAP), which extends Maximum Likelihood Estimation (MLE) by inclusion of a prior $p(\\theta)$ into the cost function. To include this prior information, we construct a Bayesian Posterior with Bayesian's Theorem given as,\n", + "\n", + "$$\n", + "P(\\theta|D) = \\frac{P(D|\\theta)P(\\theta)}{P(D)}\n", + "$$\n", + "\n", + "where, \n", + "$~$ \n", + "$P(\\theta|D)$ represents the posterior and can be read as \"the probability of the parameters $(\\theta)$ given the data $(D)$\", \n", + "$P(D|\\theta)$ is the probability of the data given the parameters, commonly called the likelihood, \n", + "$P(\\theta)$ represents the probability of the parameters commonly called the prior, \n", + "$P(D)$ is the probability of the data and is commonly called the marginal probability. \n", + "\n", + "However, as the marginal probability is commonly difficult to compute and represents a normalisation constant, in the case of MAP this term is forgone and the proportional posterior is optmised instead. This is given as,\n", + "\n", + "$$\n", + "P(\\theta|D) \\propto P(D|\\theta)P(\\theta)\n", + "$$\n", + "\n", + "### Setting up the Environment\n", + "\n", + "Before we begin, we need to ensure that we have all the necessary tools. We will install and import PyBOP as well as upgrade dependencies. We also fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X87NUGPW04py", + "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q\n", + "\n", + "import time\n", + "\n", + "import numpy as np\n", + "\n", + "import pybop\n", + "\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"\n", + "np.random.seed(8)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5XU-dMtU04p2" + }, + "source": [ + "### Creating the model\n", + "\n", + "To demonstrate the MAP process, we will first need a forward model and data to run parameter inference on. As we are introducing this as a simple example, we will use the PyBOP forward model with white noise as the reference. This requires defining a parameter set and the model itself." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parameter_set = pybop.ParameterSet.pybamm(\"Chen2020\")\n", + "model = pybop.lithium_ion.SPM(parameter_set=parameter_set)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulating Forward Model\n", + "\n", + "We can now simulate the model using the `model.predict` method. This method is a light wrapper on the `Pybamm.Simulation` class and be used as such. For this example, we use the default current function for the `Chen2020` parameter set (5A) to generate the voltage data. As the goal is to investigate the MAP method, we will generate a range of observations from the forward model. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sBasxv8U04p3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of observations in each trajectory: [2, 4, 8, 16, 32, 64, 128, 256]\n" + ] + } + ], + "source": [ + "n = 8 # Number of time-series trajectories\n", + "observations = [\n", + " 2**j for j in range(1, n + 1)\n", + "] # Number of observations in each trajectory\n", + "values = []\n", + "for i in observations:\n", + " t_eval = np.linspace(0, 10, i)\n", + " values.append(model.predict(t_eval=t_eval)) # predict and store\n", + "\n", + "print(f\"Number of observations in each trajectory: {observations}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding noise to synthetic voltage data\n", + "\n", + "To make the parameter inference more realistic, we add gaussian noise with zero mean to the data. While this doesn't truly represent the challenge of parameter inference with experimental data, this does ensure the cost landscape curvature isn't perfect. For a more realistic representation of experimental data, a different noise function could be used. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sigma = 0.005\n", + "corrupt_values = values[1][\"Voltage [V]\"].data + np.random.normal(\n", + " 0, sigma, len(values[1][\"Voltage [V]\"].data)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating the PyBOP dataset\n", + "\n", + "The reference trajectory needs to be included in the optimisation task, which is handed within the `Dataset` class. In this situation, this class is composed of the time, current, and the noisy voltage data; however, if we were performing parameter inference from a different measured signal, such as 'Cell Temperature', that would need to be included." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "zuvGHWID04p_" + }, + "outputs": [], + "source": [ + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": values[1][\"Time [s]\"].data,\n", + " \"Current function [A]\": values[1][\"Current [A]\"].data,\n", + " \"Voltage [V]\": corrupt_values,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ffS3CF_704qA" + }, + "source": [ + "### Constructing Parameters Class\n", + "Next, we need to select the forward model parameters for inference. The PyBOP parameters class composes as many individual PyBOP parameters as the user wants (whether these parameters can be identified is left out of this example). This class requires the parameter name, which must resolve to a parameter within the `ParameterSet` defined above. Additionally, this class can accept an `initial_value` which will be used by the optimiser, as well as bounds. For this example, we will provide a `prior` to the parameter class, which will be used later by the MAP process." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WPCybXIJ04qA" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Default bounds applied based on prior distribution.\n", + "Default bounds applied based on prior distribution.\n" + ] + } + ], + "source": [ + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"Negative particle radius [m]\",\n", + " prior=pybop.Gaussian(4e-6, 1e-6),\n", + " true_value=parameter_set[\"Negative particle radius [m]\"],\n", + " ),\n", + " pybop.Parameter(\n", + " \"Positive particle radius [m]\",\n", + " prior=pybop.Gaussian(5e-6, 1e-6),\n", + " true_value=parameter_set[\"Positive particle radius [m]\"],\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n4OHa-aF04qA" + }, + "source": [ + "### Setting up the Fitting Problem, Likelihood, and Posterior\n", + "\n", + "With the datasets and parameters defined, we can now construct the `FittingProblem` which composes the model, parameters, and dataset providing a single class with the required information for simulating and assessing the forward model. \n", + "\n", + "As described in the introduction to this notebook, the MAP method uses the non-normalised posterior for optimisation. This is defined in PyBOP as the `LogPosterior` class, and has arguments for the likelihood and prior functions. If a prior is not provided, the parameter priors are used as default. In this example, we will use a `GaussianLogLikelihoodKnownSigma` likelihood function, and the default priors set above. For numerical reasons, we optimise the log of the posterior; however this doesn't affect the final results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "etMzRtx404qA" + }, + "outputs": [], + "source": [ + "problem = pybop.FittingProblem(model, parameters, dataset)\n", + "likelihood = pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=sigma)\n", + "posterior = pybop.LogPosterior(likelihood)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "caprp-bV04qB" + }, + "source": [ + "### Plotting the Posterior components\n", + "\n", + "Next, to investigate the individual components of the Posterior. The `LogPosterior` class provides attributes of the prior and likelihood. To investigate the contributions of each to the Posterior we plot the landscapes across a selected parameter range." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-9OVt0EQ04qB" + }, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "steps = 10 # Number of discretisation points\n", + "bounds = np.asarray([[1e-6, 9e-6], [1e-6, 9e-6]])\n", + "pybop.plot.contour(posterior.prior, bounds=bounds, steps=steps, title=\"Log Prior\")\n", + "pybop.plot.contour(\n", + " posterior.likelihood, bounds=bounds, steps=steps, title=\"Log Likelihood\"\n", + ")\n", + "pybop.plot.contour(posterior, bounds=bounds, steps=steps, title=\"Log Posterior\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the prior represents a two-dimensional gaussian distribution with a mode at $[4e-6,5e-6]$. The likelihood appears to have a banded shape with ridge of optimal points traversing the higher parameter values. Finally, the Posterior forms the combination of the two. This is the benefit of the MAP process, as it allows for previous information to be included in the parameter inference task. Previous knowledge is encapsulated within the prior function and influences the Posterior, depending on the magnitude of the likelihood function.\n", + "\n", + "To show how this is used within a PyBOP optimisation task, we select the Covariance Matrix Adaptation Evolution Strategy optimiser and run the optimisation. We can then plot the parameter trajectories to investigate how the optimiser performed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: No significant change for 15 iterations.\n", + "OptimisationResult:\n", + " Initial parameters: [4.65599972e-06 4.66268190e-06]\n", + " Optimised parameters: [5.35425483e-06 5.58415228e-06]\n", + " Final cost: 41.54150469038368\n", + " Optimisation time: 10.932653188705444 seconds\n", + " Number of iterations: 21\n", + " SciPy result available: No\n", + "Inferred Parameters: [5.35425483e-06 5.58415228e-06] in 10.933147192001343 seconds\n", + "True Parameters: [5.86e-06 5.22e-06]\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "optim = pybop.CMAES(\n", + " posterior,\n", + " min_iterations=20,\n", + " max_iterations=100,\n", + ")\n", + "start_time = time.time()\n", + "results = optim.run()\n", + "print(f\"Inferred Parameters: {results.x} in {time.time() - start_time} seconds\")\n", + "print(f\"True Parameters: {parameters.true_value()}\")\n", + "pybop.plot.parameters(optim);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the optimisation process returns values close to the true optimal. In this case, as the synthetic data is drawn from the forward model (with additive noise), the underlying structure is close to identical. This gives the optimisation process a very well posed landscape, and as such it finds the correct parameter values. \n", + "\n", + "This is not always the case, especially in inference tasks with low-quality data, sloppy parameters, or poor excitation. In these cases, the prior influence can help 'steer' the optimisation process towards the combination of the likelihood and the user's prior knowledge of the parameters." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-4pZsDmS04qC" + }, + "source": [ + "## Investigating how the number of observations effects the Posterior\n", + "\n", + "We've seen above that the proportional posterior can be represented from its components, the log-likelihood and log-prior. Next, to better understand when each of these terms can become dominating within the parameter inference problem we vary the number of measurement observations (i.e. the number of samples in the dataset) and inspect the construct posterior.\n", + "\n", + "This is completed below with an increasing series of $2^n$, where $n$ is set above in our original creation of the trajectories. This will give us a visual representation of how the posterior changes with increasing observations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Hgz8SV4i04qC", + "outputId": "e1e42ae7-5075-4c47-dd68-1b22ecc170f6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fixed Sigma for output 1: 0.005\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i, val in enumerate(values): # Loop through trajectories\n", + " dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": val[\"Time [s]\"].data,\n", + " \"Current function [A]\": val[\"Current [A]\"].data,\n", + " \"Voltage [V]\": val[\"Voltage [V]\"].data\n", + " + np.random.normal(0, sigma, len(val[\"Voltage [V]\"].data)),\n", + " }\n", + " )\n", + " problem = pybop.FittingProblem(model, parameters, dataset)\n", + " likelihood = pybop.GaussianLogLikelihood(problem, sigma0=sigma)\n", + " posterior = pybop.LogPosterior(likelihood)\n", + " pybop.plot.contour(\n", + " posterior,\n", + " bounds=bounds,\n", + " steps=steps,\n", + " title=f\"Posterior with {observations[i]} observations\",\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above contour plots showcase the influence decay of the prior on the posterior as observations are increased. This is expected as prior knowledge on the posterior should be reduced as additional high fidelity observations are obtained. In the case of lower quality, or noisy data, the prior influence can maintain influence.\n", + "\n", + "The influence of the prior and likelihood on the posterior should be investigated during a parameter inference process, as presented above. This provides insight into how much influence prior knowledge has on the optimisation task, whether the likelihood function is well posed with smooth curvature, and finally the overall scale of the posterior." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Concluding Thoughts\n", + "\n", + "This notebook illustrates the process of parameter inference with the Maximum a Posteriori method. This process enables encapsulation of prior knowledge into the optimisation process with influence decay as observations of the system increase. This influence decay has been presented above across observations obtained from the set $({2^n \\mid n \\in \\mathbb{N}})$." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/notebooks/getting_started/optimiser_interface.ipynb b/examples/notebooks/getting_started/optimiser_interface.ipynb new file mode 100644 index 000000000..7c3fb6ac5 --- /dev/null +++ b/examples/notebooks/getting_started/optimiser_interface.ipynb @@ -0,0 +1,319 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Interacting with PyBOP optimisers\n", + "\n", + "This notebook introduces two interfaces to interact with PyBOP's optimiser classes.\n", + "\n", + "### Set the Environment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q\n", + "\n", + "# Import the necessary libraries\n", + "import numpy as np\n", + "\n", + "import pybop\n", + "\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(8)" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "## Setup the model, problem, and cost\n", + "\n", + "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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting open-circuit voltage to default function\n" + ] + } + ], + "source": [ + "# Load the parameters\n", + "parameter_set = pybop.ParameterSet(\n", + " json_path=\"../../parameters/initial_ecm_parameters.json\"\n", + ")\n", + "parameter_set.import_parameters()\n", + "# Define the model\n", + "model = pybop.empirical.Thevenin(\n", + " parameter_set=parameter_set, options={\"number of rc elements\": 1}\n", + ")\n", + "\n", + "# Define the parameters\n", + "parameters = pybop.Parameter(\n", + " \"R0 [Ohm]\",\n", + " prior=pybop.Gaussian(0.0002, 0.0001),\n", + " bounds=[1e-4, 1e-2],\n", + ")\n", + "\n", + "# Generate synthetic data\n", + "t_eval = np.arange(0, 900, 2)\n", + "values = model.predict(t_eval=t_eval)\n", + "\n", + "# Form dataset\n", + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": t_eval,\n", + " \"Current function [A]\": values[\"Current [A]\"].data,\n", + " \"Voltage [V]\": values[\"Voltage [V]\"].data,\n", + " }\n", + ")\n", + "\n", + "# Construct problem and cost\n", + "problem = pybop.FittingProblem(model, parameters, dataset)\n", + "cost = pybop.SumSquaredError(problem)" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "## Interacting with the Optimisers\n", + "\n", + "Now that we have set up the required objects, we can introduce the two interfaces for interacting with PyBOP optimisers. These are:\n", + " \n", + "1. The direct optimiser (e.g. `pybop.XNES`)\n", + "2. The optimisation class (i.e. `pybop.Optimisation`)\n", + " \n", + "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. \n", + "\n", + "First, the direct interface is presented. With this interface the user can select from the [list of optimisers](https://github.com/pybop-team/PyBOP?tab=readme-ov-file#supported-methods) 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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: No significant change for 15 iterations.\n", + "OptimisationResult:\n", + " Initial parameters: [0.00032648]\n", + " Optimised parameters: [0.00099965]\n", + " Final cost: 1.363466506257511e-09\n", + " Optimisation time: 1.6153881549835205 seconds\n", + " Number of iterations: 23\n", + " SciPy result available: No\n" + ] + } + ], + "source": [ + "optim_one = pybop.XNES(\n", + " cost, max_iterations=50\n", + ") # Direct optimiser class with options as kwargs\n", + "optim_one.set_max_iterations(\n", + " 50\n", + ") # Alternative set() / get() methods for PINTS optimisers\n", + "results = optim_one.run()" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Halt: No significant change for 15 iterations.\n", + "OptimisationResult:\n", + " Initial parameters: [0.00032648]\n", + " Optimised parameters: [0.00099985]\n", + " Final cost: 2.431998416403876e-10\n", + " Optimisation time: 1.972628116607666 seconds\n", + " Number of iterations: 25\n", + " SciPy result available: No\n" + ] + } + ], + "source": [ + "optim_two = pybop.Optimisation(\n", + " cost, optimiser=pybop.XNES, max_iterations=50\n", + ") # Optimisation class with options as kwargs\n", + "optim_two.set_max_iterations(\n", + " 50\n", + ") # Alternative set() / get() methods for PINTS optimisers\n", + "results2 = optim_two.run()" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "We can show the equivalence of these two methods by comparing the optimiser objects:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "isinstance(optim_one, type(optim_two.optimiser))" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "For completeness, we can show the optimiser solutions:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated parameters x1: [0.00099965]\n", + "Estimated parameters x2: [0.00099985]\n" + ] + } + ], + "source": [ + "print(\"Estimated parameters x1:\", results.x)\n", + "print(\"Estimated parameters x2:\", results2.x)" + ] + }, + { + "cell_type": "markdown", + "id": "14", + "metadata": {}, + "source": [ + "## Closing Comments\n", + "\n", + "As both of these API's provide access to the same optimisers, please use either as you prefer. A couple things to note:\n", + "\n", + "- 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.\n", + "- The optimiser passed to `pybop.Optimisation` must not be a constructed object." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/getting_started/transformation_introduction.ipynb b/examples/notebooks/getting_started/transformation_introduction.ipynb new file mode 100644 index 000000000..c18e6f5c6 --- /dev/null +++ b/examples/notebooks/getting_started/transformation_introduction.ipynb @@ -0,0 +1,516 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Introduction to Transformations\n", + "This example introduces the `pybop.BaseTransformation` class and it's instances. This class adds functionality for the cost and likelihood functions to be transformed into a separate search space. This search space is used by the optimiser and sampler classes during inference. These transformations can be both linear (`pybop.ScaledTransformation`) and non-linear (`pybop.LogTransformation`). By default, if transformations are applied, the sampling and optimisers will search in the transformed space.\n", + "\n", + "Transformation can be helpful when the difference in parameter magnitudes is large, or to create a search space that is better posed for the optimisation algorithm. Before we begin, we need to ensure that we have all the necessary tools. We will install and import PyBOP alongside any other package dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/engs2510/Documents/Git/Second_PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade pip ipywidgets -q\n", + "%pip install pybop -q\n", + "\n", + "import numpy as np\n", + "\n", + "import pybop\n", + "\n", + "pybop.plot.PlotlyManager().pio.renderers.default = \"notebook_connected\"" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "First, to showcase the transformation functionality, we need to construct the `pybop.Cost` class. This class needs the following objects:\n", + "- Model\n", + "- Dataset\n", + "- Parameters to identify\n", + "- Problem\n", + "\n", + "We will first construct the model, then the parameters and corresponding dataset. Once that is complete, the problem will be created. With the cost class created, we will showcase the different interactions users can have with the class. A small example with evaluation as well as computation is presented." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "model = pybop.lithium_ion.SPM()" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "Now that we have the model constructed, let's define the parameters for identification. At this point, we define the corresponding transformations applied to each parameter. PyBOP allows for transformations to be applied at the individual parameter level, which is then combined for application on each cost call. Below we will apply a linear transformation using the pybop `ScaledTransformation` class. This class has arguments for a `coefficient` which defines the linear stretch or scaling of the search space, and `intercept` which defines the space shift. The equation for this transformation is:\n", + "\n", + "$$\n", + "y_{search} = m*x_{model}+b\n", + "$$\n", + "\n", + "where $m$ is the linear scale coefficient, $b$ is the intercept, $x_{model}$ is the model parameter space, and $y_{search}$ is the transformed space." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "parameters = pybop.Parameters(\n", + " pybop.Parameter(\n", + " \"Negative electrode active material volume fraction\",\n", + " initial_value=0.6,\n", + " bounds=[0.35, 0.7],\n", + " transformation=pybop.ScaledTransformation(coefficient=2.0, intercept=-0.6),\n", + " ),\n", + " pybop.Parameter(\n", + " \"Positive electrode active material volume fraction\",\n", + " initial_value=0.6,\n", + " bounds=[0.45, 0.625],\n", + " transformation=pybop.ScaledTransformation(coefficient=2.0, intercept=-0.6),\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "Next, to create the `pybop.Dataset` we generate some synthetic data from the model using the `model.predict` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "t_eval = np.linspace(0, 10, 100)\n", + "values = model.predict(t_eval=t_eval)\n", + "\n", + "dataset = pybop.Dataset(\n", + " {\n", + " \"Time [s]\": t_eval,\n", + " \"Current function [A]\": values[\"Current [A]\"].data,\n", + " \"Voltage [V]\": values[\"Voltage [V]\"].data,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "Now that we have the model, parameters, and dataset, we can combine them and construct the problem class. This is the last step needed before investigating how the transformation functionality works." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "problem = pybop.FittingProblem(model, parameters, dataset)\n", + "cost = pybop.SumofPower(problem)" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "The conventional way to use the cost class is through the `cost.__call__` method, which is completed below without transformations applied." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.006904000484442387" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cost([0.6, 0.6])" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "However, we can also interact with the cost function with transformations applied via the `apply_transform` optional arugment. This arg is by default set to `False`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.006904000484442387" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cost([0.0, 0.0], apply_transform=True)" + ] + }, + { + "cell_type": "markdown", + "id": "14", + "metadata": {}, + "source": [ + "Given the transformations applied in the parameter class above, we can confirm the alignment between the search space and the model space by passing values that coincide:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cost([0.0, 0.0], apply_transform=True) == cost([0.6, 0.6])" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": {}, + "source": [ + "We can more thoroughly test the transformation by testing that the search space is scaled by a value of two through the following:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cost([0.05, 0.05], apply_transform=True) == cost([0.625, 0.625])" + ] + }, + { + "cell_type": "markdown", + "id": "18", + "metadata": {}, + "source": [ + "Next, we can plot cost landscapes of these two spaces. In the first instance, we plot the model space through the conventional method:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.contour(cost);" + ] + }, + { + "cell_type": "markdown", + "id": "20", + "metadata": {}, + "source": [ + "Next, we can use the `apply_transform` argument when constructing the cost landscape to via the search space. First, we will transform the bounds used above to the search space using each parameters transformation instance and the `to_search` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.contour(cost, steps=15, apply_transform=True);" + ] + }, + { + "cell_type": "markdown", + "id": "22", + "metadata": {}, + "source": [ + "Note the difference in axis scale compared to the non-transformed landscape. Next, let's change the transformation on the 'Positive electrode active material volume fraction' to a non-linear log space." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23", + "metadata": {}, + "outputs": [], + "source": [ + "parameters[\n", + " \"Positive electrode active material volume fraction\"\n", + "].transformation = pybop.LogTransformation()\n", + "cost.transformation = parameters.construct_transformation()" + ] + }, + { + "cell_type": "markdown", + "id": "24", + "metadata": {}, + "source": [ + "Let's update the bounds and plot the cost landscape again:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pybop.plot.contour(cost, steps=15, apply_transform=True);" + ] + }, + { + "cell_type": "markdown", + "id": "26", + "metadata": {}, + "source": [ + "## Concluding Thoughts\n", + "\n", + "In the notebook we've introduced the transformation class and it's interaction with the `pybop.Parameters` and `pybop.BaseCost` classes. Transformation allow for the optimisation or sampling search space to scaled for improved convergence in situations where the optimisation hyperparameters are poorly tuned, or in optimisation tasks with high variance in the parameter magnitudes. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/scripts/parameters/example_BPX.json b/examples/parameters/example_BPX.json similarity index 94% rename from examples/scripts/parameters/example_BPX.json rename to examples/parameters/example_BPX.json index 43bbcf905..1e1efaadb 100644 --- a/examples/scripts/parameters/example_BPX.json +++ b/examples/parameters/example_BPX.json @@ -1,7 +1,7 @@ { "Header": { - "BPX": 0.1, - "Title": "Parameterisation example of an LFP|graphite 2 Ah cylindrical 18650 cell.", + "BPX": 0.4, + "Title": "Parameterisation example of an LFP|graphite 2 Ah cylindrical 18650 cell. File downloaded on 19/3/24 from https://github.com/FaradayInstitution/BPX/blob/main/examples/lfp_18650_cell_BPX.json", "Description": "LFP|graphite 2 Ah cylindrical 18650 cell. Parameterisation by About:Energy Limited (aboutenergy.io), December 2022, based on cell cycling data, and electrode data gathered after cell teardown. Electrolyte properties from Nyman et al. 2008 (doi:10.1016/j.electacta.2008.04.023). Negative electrode entropic coefficient data are from O'Regan et al. 2022 (doi:10.1016/j.electacta.2022.140700). Positive electrode entropic coefficient data are from Gerver and Meyers 2011 (doi:10.1149/1.3591799). Other thermal properties are estimated.", "Model": "DFN" }, @@ -70,9 +70,6 @@ "Thickness [m]": 2e-05, "Porosity": 0.47, "Transport efficiency": 0.3222 - }, - "User-defined": { - "Source:": "An example BPX json file downloaded on 19/3/24 from https://github.com/FaradayInstitution/BPX/blob/main/examples/lfp_18650_cell_BPX.json" } } } diff --git a/examples/scripts/parameters/initial_ecm_parameters.json b/examples/parameters/initial_ecm_parameters.json similarity index 100% rename from examples/scripts/parameters/initial_ecm_parameters.json rename to examples/parameters/initial_ecm_parameters.json diff --git a/examples/scripts/ecm_CMAES.py b/examples/scripts/battery_parameterisation/ecm_CMAES.py similarity index 86% rename from examples/scripts/ecm_CMAES.py rename to examples/scripts/battery_parameterisation/ecm_CMAES.py index 96a36ec44..01d8dca05 100644 --- a/examples/scripts/ecm_CMAES.py +++ b/examples/scripts/battery_parameterisation/ecm_CMAES.py @@ -77,8 +77,7 @@ cost = pybop.SumSquaredError(problem) optim = pybop.CMAES(cost, max_iterations=100) -x, final_cost = optim.run() -print("Estimated parameters:", x) +results = optim.run() # Export the parameters to JSON parameter_set.export_parameters( @@ -86,20 +85,16 @@ ) # Plot the time series -pybop.plot_dataset(dataset) +pybop.plot.dataset(dataset) # Plot the timeseries output -pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") # Plot convergence -pybop.plot_convergence(optim) +pybop.plot.convergence(optim) # Plot the parameter traces -pybop.plot_parameters(optim) +pybop.plot.parameters(optim) # Plot the cost landscape -pybop.plot2d(cost, steps=15) - -# Plot the cost landscape with optimisation path and updated bounds -bounds = np.asarray([[1e-4, 1e-2], [1e-5, 1e-2]]) -pybop.plot2d(optim, bounds=bounds, steps=15) +pybop.plot.surface(optim) diff --git a/examples/scripts/battery_parameterisation/ecm_tau_constraints.py b/examples/scripts/battery_parameterisation/ecm_tau_constraints.py new file mode 100644 index 000000000..6d2bf85e7 --- /dev/null +++ b/examples/scripts/battery_parameterisation/ecm_tau_constraints.py @@ -0,0 +1,181 @@ +from typing import Union + +import numpy as np + +import pybop + +""" +When fitting empirical models, the parameters we are able to identify +will be constrained from the data that's available. For example, it's +no good trying to fit an RC timescale of 0.1 s from data sampled at +1 Hz! Likewise, an RC timescale of 100 s cannot be meaningfully fitted +to just 10 s of data. To ensure the optimiser doesn't propose +excessively long or short timescales - beyond what can reasonably be +inferred from the data - it is common to apply nonlinear constraints +on the parameter space. This script fits an RC pair with the +constraint 0.5 <= R1 * C1 <= 1, to highlight a possible method for +applying constraints on the timescales. + +An alternative approach is given i the ecm_trust-constr notebook, +which can lead to better results and higher optimisation efficiency +when good timescale guesses are available. +""" + +# Define the initial parameter set +parameter_set = pybop.ParameterSet.pybamm("ECM_Example") +parameter_set.update( + { + "Initial SoC": 0.75, + "Cell capacity [A.h]": 5, + "Nominal cell capacity [A.h]": 5, + "Current function [A]": 5, + "Upper voltage cut-off [V]": 4.2, + "Lower voltage cut-off [V]": 3.0, + "Open-circuit voltage [V]": pybop.empirical.Thevenin().default_parameter_values[ + "Open-circuit voltage [V]" + ], + "R0 [Ohm]": 0.001, + "R1 [Ohm]": 0.0002, + "C1 [F]": 10000, + "Element-1 initial overpotential [V]": 0, + } +) +# Add definitions for R's, C's, and initial overpotentials for any additional RC elements +parameter_set.update( + { + "R2 [Ohm]": 0.0003, + "C2 [F]": 5000, + "Element-2 initial overpotential [V]": 0, + }, + check_already_exists=False, +) + + +def get_parameter_checker( + tau_mins: Union[float, list[float]], + tau_maxs: Union[float, list[float]], + fitted_rc_pair_indices: Union[int, list[int]], +): + """Returns a function to check parameters against given tau bounds. + The resulting check_params function will be sent off to PyBOP; the + rest of the code does some light checking of the constraints. + + Parameters + ---------- + tau_mins: float or list[float] + Lower bounds on timescale tau_i = Ri * Ci + tau_maxs: float or list[float] + Upper bounds on timescale tau_i = Ri * Ci + fitted_rc_pair_indices: int or list[float] + The index of each RC pair whose parameters are to be fitted. + Eg. [1, 2] means fitting R1, R2, C1, C2. The timescale of RC + pair fitted_rc_pair_indices[j] is constrained to be in the + range tau_mins[j] <= R * C <= tau_maxs[j] + + Returns + ------- + check_params + Function to check the proposed parameter values match the + requested constraints + + """ + # Ensure inputs are lists + tau_mins = [tau_mins] if not isinstance(tau_mins, list) else tau_mins + tau_maxs = [tau_maxs] if not isinstance(tau_maxs, list) else tau_maxs + fitted_rc_pair_indices = ( + [fitted_rc_pair_indices] + if not isinstance(fitted_rc_pair_indices, list) + else fitted_rc_pair_indices + ) + + # Validate input lengths + if len(tau_mins) != len(fitted_rc_pair_indices) or len(tau_maxs) != len( + fitted_rc_pair_indices + ): + raise ValueError( + "tau_mins and tau_maxs must have the same length as fitted_rc_pair_indices" + ) + + def check_params( + inputs: dict[str, float] = None, + parameter_set=None, + allow_infeasible_solutions: bool = False, + ) -> bool: + """Checks if the given inputs are within the tau bounds.""" + # Allow simulation to run if inputs are None + if inputs is None or inputs == {}: + return True + + # Check every respective R*C against tau bounds + for i, tau_min, tau_max in zip(fitted_rc_pair_indices, tau_mins, tau_maxs): + tau = inputs[f"R{i} [Ohm]"] * inputs[f"C{i} [F]"] + if not tau_min <= tau <= tau_max: + return False + return True + + return check_params + + +# Define the model +model = pybop.empirical.Thevenin( + parameter_set=parameter_set, + check_params=get_parameter_checker( + 0, 3.0, 1 + ), # Set the model up to automatically check parameters + options={"number of rc elements": 2}, +) + +# Fitting parameters +parameters = pybop.Parameters( + pybop.Parameter( + "R0 [Ohm]", + prior=pybop.Gaussian(0.0002, 0.0001), + bounds=[1e-4, 1e-2], + ), + pybop.Parameter( + "R1 [Ohm]", + prior=pybop.Gaussian(0.0001, 0.0001), + bounds=[1e-5, 1e-2], + ), + pybop.Parameter( + "C1 [F]", + prior=pybop.Gaussian(10000, 2500), + bounds=[2500, 5e4], + ), +) + +sigma = 0.001 +t_eval = np.arange(0, 600, 3) +values = model.predict(t_eval=t_eval) +corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval)) + +# Form dataset +dataset = pybop.Dataset( + { + "Time [s]": t_eval, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": corrupt_values, + } +) + +# Generate problem, cost function, and optimisation class +problem = pybop.FittingProblem(model, parameters, dataset) +cost = pybop.RootMeanSquaredError(problem) +optim = pybop.XNES( + cost, + sigma0=[1e-4, 1e-4, 100], # Set parameter specific step size + allow_infeasible_solutions=False, + max_unchanged_iterations=30, + max_iterations=125, +) + +results = optim.run() + +# Plot the timeseries output +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") + +# Plot convergence +pybop.plot.convergence(optim) + +# Plot the parameter traces +pybop.plot.parameters(optim) diff --git a/examples/scripts/battery_parameterisation/ecm_with_tau.py b/examples/scripts/battery_parameterisation/ecm_with_tau.py new file mode 100644 index 000000000..a1942efbe --- /dev/null +++ b/examples/scripts/battery_parameterisation/ecm_with_tau.py @@ -0,0 +1,124 @@ +import numpy as np +import pybamm + +import pybop + +""" +An alternative approach to the problem described in +examples/scripts/ecm_tau_constraints.py in which constraints are placed +on tau1 = R1 * C1. Here, tau1 is introduced as a parameter of the model +and C1 is replaced by 1/R1 so that the bounds can be applied directly. +""" + +# Define the initial parameter set +parameter_set = pybop.ParameterSet.pybamm("ECM_Example") +parameter_set.update( + { + "Initial SoC": 0.75, + "Cell capacity [A.h]": 5, + "Nominal cell capacity [A.h]": 5, + "Current function [A]": 5, + "Upper voltage cut-off [V]": 4.2, + "Lower voltage cut-off [V]": 3.0, + "Open-circuit voltage [V]": pybop.empirical.Thevenin().default_parameter_values[ + "Open-circuit voltage [V]" + ], + "R0 [Ohm]": 0.001, + "R1 [Ohm]": 0.0002, + "C1 [F]": 10000, + "Element-1 initial overpotential [V]": 0, + } +) +# Add definitions for R's, C's, and initial overpotentials for any additional RC elements +parameter_set.update( + { + "R2 [Ohm]": 0.0003, + "C2 [F]": 5000, + "Element-2 initial overpotential [V]": 0, + }, + check_already_exists=False, +) + +# Define the model +parameter_set.update( + { + "tau1 [s]": parameter_set["R1 [Ohm]"] * parameter_set["C1 [F]"], + "tau2 [s]": parameter_set["R2 [Ohm]"] * parameter_set["C2 [F]"], + }, + check_already_exists=False, +) +parameter_set.update( + { + "C1 [F]": pybamm.Parameter("tau1 [s]") / pybamm.Parameter("R1 [Ohm]"), + "C2 [F]": pybamm.Parameter("tau2 [s]") / pybamm.Parameter("R2 [Ohm]"), + } +) +model = pybop.empirical.Thevenin( + parameter_set=parameter_set, + options={"number of rc elements": 2}, +) + +# Fitting parameters +parameters = pybop.Parameters( + pybop.Parameter( + "R0 [Ohm]", + prior=pybop.Gaussian(0.0002, 0.0001), + bounds=[1e-4, 1e-2], + ), + pybop.Parameter( + "R1 [Ohm]", + prior=pybop.Gaussian(0.0001, 0.0001), + bounds=[1e-5, 1e-2], + ), + pybop.Parameter( + "tau1 [s]", + prior=pybop.Gaussian(1.0, 0.025), + bounds=[0, 3.0], + ), +) + +sigma = 0.001 +t_eval = np.arange(0, 600, 3) +values = model.predict(t_eval=t_eval) +corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval)) + +# Form dataset +dataset = pybop.Dataset( + { + "Time [s]": t_eval, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": corrupt_values, + } +) + +# Generate problem, cost function, and optimisation class +problem = pybop.FittingProblem(model, parameters, dataset) +cost = pybop.RootMeanSquaredError(problem) +optim = pybop.XNES( + cost, + sigma0=[1e-4, 1e-4, 0.02], # Set parameter specific step size + allow_infeasible_solutions=False, + max_unchanged_iterations=30, + max_iterations=125, +) + +results = optim.run() +print( + "True parameters:", + [ + parameter_set["R0 [Ohm]"], + parameter_set["R1 [Ohm]"], + parameter_set["tau1 [s]"], + parameter_set.evaluate(pybamm.Parameter("C1 [F]")), + ], +) +print("Estimated parameters:", results.x.tolist() + [results.x[2] / results.x[1]]) + +# Plot the timeseries output +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") + +# Plot convergence +pybop.plot.convergence(optim) + +# Plot the parameter traces +pybop.plot.parameters(optim) diff --git a/examples/scripts/battery_parameterisation/eis_fitting.py b/examples/scripts/battery_parameterisation/eis_fitting.py new file mode 100644 index 000000000..b93f1c47e --- /dev/null +++ b/examples/scripts/battery_parameterisation/eis_fitting.py @@ -0,0 +1,81 @@ +import numpy as np + +import pybop + +# Define model +parameter_set = pybop.ParameterSet.pybamm("Chen2020") +parameter_set["Contact resistance [Ohm]"] = 0.0 +initial_state = {"Initial SoC": 0.5} +n_frequency = 20 +sigma0 = 1e-4 +f_eval = np.logspace(-4, 5, n_frequency) +model = pybop.lithium_ion.SPM( + parameter_set=parameter_set, + eis=True, + options={"surface form": "differential", "contact resistance": "true"}, +) + +# Create synthetic data for parameter inference +sim = model.simulateEIS( + inputs={ + "Negative electrode active material volume fraction": 0.531, + "Positive electrode active material volume fraction": 0.732, + }, + f_eval=f_eval, + initial_state=initial_state, +) + +# Fitting parameters +parameters = 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], + ), +) + + +def noise(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 + + +# Form dataset +dataset = pybop.Dataset( + { + "Frequency [Hz]": f_eval, + "Current function [A]": np.ones(n_frequency) * 0.0, + "Impedance": sim["Impedance"] + noise(sigma0, len(sim["Impedance"])), + } +) + +signal = ["Impedance"] +# Generate problem, cost function, and optimisation class +problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) +cost = pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=sigma0) +optim = pybop.CMAES(cost, max_iterations=100, sigma0=0.25, max_unchanged_iterations=30) + +results = optim.run() + +# Plot the nyquist +pybop.plot.nyquist(problem, problem_inputs=results.x, title="Optimised Comparison") + +# Plot convergence +pybop.plot.convergence(optim) + +# Plot the parameter traces +pybop.plot.parameters(optim) + +# Plot 2d landscape +pybop.plot.surface(optim) diff --git a/examples/scripts/battery_parameterisation/gitt.py b/examples/scripts/battery_parameterisation/gitt.py new file mode 100644 index 000000000..059aac861 --- /dev/null +++ b/examples/scripts/battery_parameterisation/gitt.py @@ -0,0 +1,67 @@ +import numpy as np + +import pybop + +# Define model +parameter_set = pybop.ParameterSet.pybamm("Xu2019") +model = pybop.lithium_ion.SPM( + parameter_set=parameter_set, options={"working electrode": "positive"} +) + +# Generate data +sigma = 0.005 +t_eval = np.arange(0, 150, 2) +values = model.predict(t_eval=t_eval) +corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval)) + +# Form dataset +dataset = pybop.Dataset( + { + "Time [s]": t_eval, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": corrupt_values, + } +) + +# Define parameter set +parameter_set.update( + { + "Reference OCP [V]": 4.1821, + "Derivative of the OCP wrt stoichiometry [V]": -1.38636, + }, + check_already_exists=False, +) + +# Define the cost to optimise +model = pybop.lithium_ion.WeppnerHuggins(parameter_set=parameter_set) + +parameters = pybop.Parameter( + "Positive particle diffusivity [m2.s-1]", + prior=pybop.Gaussian(5e-14, 1e-13), + bounds=[1e-16, 1e-11], + true_value=parameter_set["Positive particle diffusivity [m2.s-1]"], +) + +problem = pybop.FittingProblem( + model, + parameters, + dataset, + signal=["Voltage [V]"], +) + +cost = pybop.RootMeanSquaredError(problem) + +# Build the optimisation problem +optim = pybop.PSO(cost=cost, verbose=True) + +# Run the optimisation problem +results = optim.run() + +# Plot the timeseries output +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") + +# Plot convergence +pybop.plot.convergence(optim) + +# Plot the parameter traces +pybop.plot.parameters(optim) diff --git a/examples/scripts/battery_parameterisation/ocp_balancing.py b/examples/scripts/battery_parameterisation/ocp_balancing.py new file mode 100644 index 000000000..b6a861760 --- /dev/null +++ b/examples/scripts/battery_parameterisation/ocp_balancing.py @@ -0,0 +1,100 @@ +import numpy as np + +import pybop + +# Parameter set and model definition +parameter_set = pybop.ParameterSet.pybamm("Chen2020") +model = pybop.lithium_ion.SPM(parameter_set=parameter_set) + +# Set objects for initial conditions +init_soc = {"Initial SoC": 1} +parameter_set.set_initial_stoichiometries(initial_value=init_soc["Initial SoC"]) +cs_n_max = parameter_set["Maximum concentration in negative electrode [mol.m-3]"] +cs_p_max = parameter_set["Maximum concentration in positive electrode [mol.m-3]"] +v_min = parameter_set["Open-circuit voltage at 0% SOC [V]"] +v_max = parameter_set["Open-circuit voltage at 100% SOC [V]"] + +# Define fitting parameters for OCP balancing +parameters = pybop.Parameters( + pybop.Parameter( + "Open-circuit voltage at 0% SOC [V]", + prior=pybop.Gaussian(v_min, 1e-1), + bounds=[2.35, 2.65], + true_value=v_min, + transformation=pybop.LogTransformation(), + ), + pybop.Parameter( + "Open-circuit voltage at 100% SOC [V]", + prior=pybop.Gaussian(v_max, 1e-1), + bounds=[4.1, 4.3], + true_value=v_max, + transformation=pybop.LogTransformation(), + ), + pybop.Parameter( + "Maximum concentration in negative electrode [mol.m-3]", + prior=pybop.Gaussian(cs_n_max, 6e3), + bounds=[cs_n_max * 0.75, cs_n_max * 1.25], + true_value=cs_n_max, + transformation=pybop.LogTransformation(), + ), + pybop.Parameter( + "Maximum concentration in positive electrode [mol.m-3]", + prior=pybop.Gaussian(cs_p_max, 6e3), + bounds=[cs_p_max * 0.75, cs_p_max * 1.25], + true_value=cs_p_max, + transformation=pybop.LogTransformation(), + ), +) + +# Generate synthetic data +sigma = 5e-4 # Volts +experiment = pybop.Experiment( + [ + ( + "Discharge at 0.1C until 2.5V (3 min period)", + "Charge at 0.1C until 4.2V (3 min period)", + ), + ] +) +values = model.predict(initial_state=init_soc, experiment=experiment) + + +def noise(sigma): + return np.random.normal(0, sigma, len(values["Voltage [V]"].data)) + + +# Form dataset +dataset = pybop.Dataset( + { + "Time [s]": values["Time [s]"].data, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": values["Voltage [V]"].data + noise(sigma), + } +) + +# Generate problem, cost function, and optimisation class +problem = pybop.FittingProblem(model, parameters, dataset, initial_state=init_soc) +cost = pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=sigma) +optim = pybop.CMAES( + cost, + verbose=True, + sigma0=1e-1, + max_iterations=100, + max_unchanged_iterations=20, +) + +# Run optimisation for Maximum Likelihood Estimate (MLE) +results = optim.run() +print("True parameters:", parameters.true_value()) + +# Plot the timeseries output +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") + +# Plot convergence +pybop.plot.convergence(optim) + +# Plot the parameter traces +pybop.plot.parameters(optim) + +# Plot the cost landscape with optimisation path +pybop.plot.contour(optim, steps=5) diff --git a/examples/scripts/spm_MAP.py b/examples/scripts/comparison_examples/cuckoo.py similarity index 54% rename from examples/scripts/spm_MAP.py rename to examples/scripts/comparison_examples/cuckoo.py index d8460915c..0ad7b22e9 100644 --- a/examples/scripts/spm_MAP.py +++ b/examples/scripts/comparison_examples/cuckoo.py @@ -4,6 +4,12 @@ # Define model parameter_set = pybop.ParameterSet.pybamm("Chen2020") +parameter_set.update( + { + "Negative electrode active material volume fraction": 0.7, + "Positive electrode active material volume fraction": 0.67, + } +) model = pybop.lithium_ion.SPM(parameter_set=parameter_set) # Fitting parameters @@ -11,32 +17,35 @@ pybop.Parameter( "Negative electrode active material volume fraction", prior=pybop.Gaussian(0.6, 0.05), - bounds=[0.5, 0.8], + bounds=[0.4, 0.75], + initial_value=0.41, ), pybop.Parameter( "Positive electrode active material volume fraction", prior=pybop.Gaussian(0.48, 0.05), - bounds=[0.4, 0.7], + bounds=[0.4, 0.75], + initial_value=0.41, ), ) +experiment = pybop.Experiment( + [ + ( + "Discharge at 0.5C for 3 minutes (4 second period)", + "Charge at 0.5C for 3 minutes (4 second period)", + ), + ] +) +values = model.predict(initial_state={"Initial SoC": 0.7}, experiment=experiment) -# Set initial parameter values -parameter_set.update( - { - "Negative electrode active material volume fraction": 0.63, - "Positive electrode active material volume fraction": 0.51, - } +sigma = 0.002 +corrupt_values = values["Voltage [V]"].data + np.random.normal( + 0, sigma, len(values["Voltage [V]"].data) ) -# Generate data -sigma = 0.005 -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)) # 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, } @@ -44,30 +53,23 @@ # Generate problem, cost function, and optimisation class problem = pybop.FittingProblem(model, parameters, dataset) -cost = pybop.MAP(problem, pybop.GaussianLogLikelihoodKnownSigma) -optim = pybop.CMAES( +cost = pybop.GaussianLogLikelihood(problem, sigma0=sigma * 4) +optim = pybop.Optimisation( cost, - max_unchanged_iterations=20, - min_iterations=20, + optimiser=pybop.CuckooSearch, max_iterations=100, ) -# Run the optimisation -x, final_cost = optim.run() -print("Estimated parameters:", x) +results = optim.run() # Plot the timeseries output -pybop.quick_plot(problem, parameter_values=x[0:2], title="Optimised Comparison") +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") # Plot convergence -pybop.plot_convergence(optim) +pybop.plot.convergence(optim) # Plot the parameter traces -pybop.plot_parameters(optim) - -# Plot the cost landscape -pybop.plot2d(cost, steps=15) +pybop.plot.parameters(optim) # Plot the cost landscape with optimisation path -bounds = np.asarray([[0.55, 0.77], [0.48, 0.68]]) -pybop.plot2d(optim, bounds=bounds, steps=15) +pybop.plot.contour(optim, steps=15) diff --git a/examples/scripts/comparison_examples/exp_UKF.py b/examples/scripts/comparison_examples/exp_UKF.py new file mode 100644 index 000000000..9a529cf65 --- /dev/null +++ b/examples/scripts/comparison_examples/exp_UKF.py @@ -0,0 +1,112 @@ +import numpy as np + +import pybop +from examples.standalone.model import ExponentialDecay + +# Parameter set and model definition +parameter_set = {"k": 0.1, "y0": 1.0} +model = ExponentialDecay(parameter_set=parameter_set, n_states=1) + +# Fitting parameters +parameters = pybop.Parameters( + pybop.Parameter( + "k", + prior=pybop.Gaussian(0.1, 0.05), + bounds=[0, 1], + true_value=parameter_set["k"], + ), + pybop.Parameter( + "y0", + prior=pybop.Gaussian(1, 0.05), + bounds=[0, 3], + true_value=parameter_set["y0"], + ), +) + +# Make a prediction with measurement noise +sigma = 1e-2 +t_eval = np.linspace(0, 20, 10) +true_inputs = parameters.as_dict("true") +values = model.predict(t_eval=t_eval) +values = values["2y"].data +corrupt_values = values + np.random.normal(0, sigma, len(t_eval)) + +# Verification step: compute the analytical solution for 2y +expected_values = ( + 2 * parameters["y0"].true_value * np.exp(-parameters["k"].true_value * t_eval) +) + +# Verification step: make another prediction using the Observer class +model.build(parameters=parameters) +simulator = pybop.Observer(parameters, model, signal=["2y"]) +simulator.domain_data = t_eval +measurements = simulator.evaluate(true_inputs) + +# Verification step: Compare by plot +go = pybop.plot.PlotlyManager().go +line1 = go.Scatter(x=t_eval, y=corrupt_values, name="Corrupt values", mode="markers") +line2 = go.Scatter( + x=t_eval, y=expected_values, name="Expected trajectory", mode="lines" +) +line3 = go.Scatter( + x=t_eval, y=measurements["2y"], name="Observed values", mode="markers" +) +fig = go.Figure(data=[line1, line2, line3]) + +# Form dataset +dataset = pybop.Dataset( + { + "Time [s]": t_eval, + "Current function [A]": 0 * t_eval, # placeholder + "2y": corrupt_values, + } +) + +# Build the model to get the number of states +model.build(dataset=dataset.data, parameters=parameters) + +# Define the UKF observer +signal = ["2y"] +n_states = model.n_states +n_signals = len(signal) +covariance = np.diag([sigma**2] * n_states) +process_noise = np.diag([1e-6] * n_states) +measurement_noise = np.diag([sigma**2] * n_signals) +observer = pybop.UnscentedKalmanFilterObserver( + parameters, + model, + covariance, + process_noise, + measurement_noise, + dataset, + signal=signal, +) + +# Verification step: Find the maximum likelihood estimate given the true parameters +estimation = observer.evaluate(true_inputs) + +# Verification step: Add the estimate to the plot +line4 = go.Scatter( + x=t_eval, y=estimation["2y"], name="Estimated trajectory", mode="lines" +) +fig.add_trace(line4) +fig.show() + +# Generate problem, cost function, and optimisation class +cost = pybop.ObserverCost(observer) +optim = pybop.CMAES(cost, verbose=True) + +# Run optimisation +results = optim.run() + +# Plot the timeseries output (requires model that returns Voltage) +pybop.plot.quick(observer, problem_inputs=results.x, title="Optimised Comparison") + +# Plot convergence +pybop.plot.convergence(optim) + +# Plot the parameter traces +pybop.plot.parameters(optim) + +# Plot the cost landscape with optimisation path +pybop.plot.surface(optim) diff --git a/examples/scripts/comparison_examples/maximum_a_posteriori.py b/examples/scripts/comparison_examples/maximum_a_posteriori.py new file mode 100644 index 000000000..b04fa8fbb --- /dev/null +++ b/examples/scripts/comparison_examples/maximum_a_posteriori.py @@ -0,0 +1,92 @@ +import numpy as np + +import pybop + +# Set variables +sigma = 0.002 + +# Construct and update initial parameter values +parameter_set = pybop.ParameterSet.pybamm("Chen2020") +parameter_set.update( + { + "Negative electrode active material volume fraction": 0.43, + "Positive electrode active material volume fraction": 0.56, + } +) + +# Define model +model = pybop.lithium_ion.SPM(parameter_set=parameter_set) + +# Fitting parameters +parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Uniform(0.3, 0.8), + bounds=[0.3, 0.8], + initial_value=0.653, + true_value=parameter_set["Negative electrode active material volume fraction"], + transformation=pybop.LogTransformation(), + ), + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Uniform(0.3, 0.8), + bounds=[0.4, 0.7], + initial_value=0.657, + true_value=parameter_set["Positive electrode active material volume fraction"], + transformation=pybop.LogTransformation(), + ), +) + +# Generate data and corrupt it with noise +experiment = pybop.Experiment( + [ + ( + "Discharge at 0.5C for 3 minutes (4 second period)", + "Charge at 0.5C for 3 minutes (4 second period)", + ), + ] +) +values = model.predict(initial_state={"Initial SoC": 0.5}, experiment=experiment) +corrupt_values = values["Voltage [V]"].data + np.random.normal( + 0, sigma, len(values["Voltage [V]"].data) +) + +# Form dataset +dataset = pybop.Dataset( + { + "Time [s]": values["Time [s]"].data, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": corrupt_values, + } +) + +# Generate problem, cost function, and optimisation class +problem = pybop.FittingProblem(model, parameters, dataset) +cost = pybop.LogPosterior(pybop.GaussianLogLikelihood(problem)) +optim = pybop.IRPropMin( + cost, + sigma0=0.05, + max_unchanged_iterations=20, + min_iterations=20, + max_iterations=100, +) + +# Run the optimisation +results = optim.run() +print("True parameters:", parameters.true_value()) + +# Plot the timeseries output +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") + +# Plot convergence +pybop.plot.convergence(optim) + +# Plot the parameter traces +pybop.plot.parameters(optim) + +# Plot the cost landscape +pybop.plot.contour(cost, steps=15) + +# Plot the cost landscape with optimisation path +bounds = np.asarray([[0.35, 0.7], [0.45, 0.625]]) +pybop.plot.contour(optim, bounds=bounds, steps=15) diff --git a/examples/scripts/spm_MLE.py b/examples/scripts/comparison_examples/maximum_likelihood.py similarity index 51% rename from examples/scripts/spm_MLE.py rename to examples/scripts/comparison_examples/maximum_likelihood.py index 6fc0238ca..bff7a0e72 100644 --- a/examples/scripts/spm_MLE.py +++ b/examples/scripts/comparison_examples/maximum_likelihood.py @@ -2,8 +2,14 @@ import pybop -# Define model +# Define model and set initial parameter values parameter_set = pybop.ParameterSet.pybamm("Chen2020") +parameter_set.update( + { + "Negative electrode active material volume fraction": 0.63, + "Positive electrode active material volume fraction": 0.51, + } +) model = pybop.lithium_ion.SPM(parameter_set=parameter_set) # Fitting parameters @@ -16,36 +22,43 @@ pybop.Parameter( "Positive electrode active material volume fraction", prior=pybop.Gaussian(0.48, 0.05), - bounds=[0.4, 0.7], ), ) -# Set initial parameter values -parameter_set.update( - { - "Negative electrode active material volume fraction": 0.63, - "Positive electrode active material volume fraction": 0.51, - } -) # Generate data -sigma = 0.005 -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)) +sigma = 0.002 +experiment = pybop.Experiment( + [ + ( + "Discharge at 0.5C for 3 minutes (3 second period)", + "Charge at 0.5C for 3 minutes (3 second period)", + ), + ] +) +values = model.predict(initial_state={"Initial SoC": 0.5}, experiment=experiment) + + +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), + "Bulk open-circuit voltage [V]": values["Bulk open-circuit voltage [V]"].data + + noise(sigma), } ) + +signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] # Generate problem, cost function, and optimisation class -problem = pybop.FittingProblem(model, parameters, dataset) -likelihood = pybop.GaussianLogLikelihoodKnownSigma(problem, sigma=[0.03, 0.03]) -optim = pybop.CMAES( +problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) +likelihood = pybop.GaussianLogLikelihood(problem, sigma0=sigma * 4) +optim = pybop.IRPropMin( likelihood, max_unchanged_iterations=20, min_iterations=20, @@ -53,21 +66,20 @@ ) # Run the optimisation -x, final_cost = optim.run() -print("Estimated parameters:", x) +results = optim.run() # Plot the timeseries output -pybop.quick_plot(problem, parameter_values=x[0:2], title="Optimised Comparison") +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") # Plot convergence -pybop.plot_convergence(optim) +pybop.plot.convergence(optim) # Plot the parameter traces -pybop.plot_parameters(optim) +pybop.plot.parameters(optim) # Plot the cost landscape -pybop.plot2d(likelihood, steps=15) +pybop.plot.contour(likelihood, steps=15) # Plot the cost landscape with optimisation path bounds = np.asarray([[0.55, 0.77], [0.48, 0.68]]) -pybop.plot2d(optim, bounds=bounds, steps=15) +pybop.plot.contour(optim, bounds=bounds, steps=15) diff --git a/examples/scripts/comparison_examples/selecting_a_solver.py b/examples/scripts/comparison_examples/selecting_a_solver.py new file mode 100644 index 000000000..248d943f5 --- /dev/null +++ b/examples/scripts/comparison_examples/selecting_a_solver.py @@ -0,0 +1,60 @@ +import time + +import numpy as np +import pybamm + +import pybop + +# Parameter set and model definition +parameter_set = pybop.ParameterSet.pybamm("Chen2020") +model = pybop.lithium_ion.SPM(parameter_set=parameter_set) + +solvers = [ + pybamm.IDAKLUSolver(atol=1e-6, rtol=1e-6), + pybamm.CasadiSolver(mode="safe", atol=1e-6, rtol=1e-6), + pybamm.CasadiSolver(mode="fast with events", atol=1e-6, rtol=1e-6), +] + +# Fitting parameters +parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", initial_value=0.55 + ), + pybop.Parameter( + "Positive electrode active material volume fraction", initial_value=0.55 + ), +) + +# Define test protocol and generate data +experiment = pybop.Experiment([("Discharge at 0.5C for 10 minutes (3 second period)")]) +values = model.predict( + initial_state={"Initial open-circuit voltage [V]": 4.2}, experiment=experiment +) + +# Form dataset +dataset = pybop.Dataset( + { + "Time [s]": values["Time [s]"].data, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": values["Voltage [V]"].data, + } +) + +# Create the list of input dicts +n = 150 # Number of solves +inputs = list(zip(np.linspace(0.45, 0.6, n), np.linspace(0.45, 0.6, n))) + +# Iterate over the solvers and print benchmarks +for solver in solvers: + model.solver = solver + problem = pybop.FittingProblem(model, parameters, dataset) + + start_time = time.time() + for input_values in inputs: + problem.evaluate(inputs=input_values) + print(f"Time Evaluate {solver.name}: {time.time() - start_time:.3f}") + + start_time = time.time() + for input_values in inputs: + problem.evaluateS1(inputs=input_values) + print(f"Time EvaluateS1 {solver.name}: {time.time() - start_time:.3f}") diff --git a/examples/scripts/spm_AdamW.py b/examples/scripts/comparison_examples/spm_AdamW.py similarity index 70% rename from examples/scripts/spm_AdamW.py rename to examples/scripts/comparison_examples/spm_AdamW.py index 44bbf8b11..5937ed726 100644 --- a/examples/scripts/spm_AdamW.py +++ b/examples/scripts/comparison_examples/spm_AdamW.py @@ -1,10 +1,12 @@ import numpy as np +import pybamm import pybop -# Parameter set and model definition +# Define model and use high-performant solver for sensitivities +solver = pybamm.IDAKLUSolver() parameter_set = pybop.ParameterSet.pybamm("Chen2020") -model = pybop.lithium_ion.SPM(parameter_set=parameter_set) +model = pybop.lithium_ion.SPM(parameter_set=parameter_set, solver=solver) # Fitting parameters parameters = pybop.Parameters( @@ -19,7 +21,6 @@ ) # Generate data -init_soc = 0.5 sigma = 0.003 experiment = pybop.Experiment( [ @@ -30,7 +31,7 @@ ] * 2 ) -values = model.predict(init_soc=init_soc, experiment=experiment) +values = model.predict(initial_state={"Initial SoC": 0.5}, experiment=experiment) def noise(sigma): @@ -50,32 +51,28 @@ def noise(sigma): signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] # Generate problem, cost function, and optimisation class -problem = pybop.FittingProblem( - model, parameters, dataset, signal=signal, init_soc=init_soc -) -cost = pybop.RootMeanSquaredError(problem) +problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) +cost = pybop.Minkowski(problem, p=2) optim = pybop.AdamW( cost, verbose=True, allow_infeasible_solutions=True, - sigma0=0.05, + sigma0=0.02, max_iterations=100, max_unchanged_iterations=20, ) # Run optimisation -x, final_cost = optim.run() -print("Estimated parameters:", x) +results = optim.run() # Plot the timeseries output -pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") # Plot convergence -pybop.plot_convergence(optim) +pybop.plot.convergence(optim) # Plot the parameter traces -pybop.plot_parameters(optim) +pybop.plot.parameters(optim) # Plot the cost landscape with optimisation path -bounds = np.asarray([[0.5, 0.8], [0.4, 0.7]]) -pybop.plot2d(optim, bounds=bounds, steps=15) +pybop.plot.surface(optim) diff --git a/examples/scripts/spm_CMAES.py b/examples/scripts/comparison_examples/spm_CMAES.py similarity index 77% rename from examples/scripts/spm_CMAES.py rename to examples/scripts/comparison_examples/spm_CMAES.py index 1fc051cc2..bd125710c 100644 --- a/examples/scripts/spm_CMAES.py +++ b/examples/scripts/comparison_examples/spm_CMAES.py @@ -13,18 +13,20 @@ prior=pybop.Gaussian(6e-06, 0.1e-6), bounds=[1e-6, 9e-6], true_value=parameter_set["Negative particle radius [m]"], + transformation=pybop.LogTransformation(), ), pybop.Parameter( "Positive particle radius [m]", prior=pybop.Gaussian(4.5e-06, 0.1e-6), bounds=[1e-6, 9e-6], true_value=parameter_set["Positive particle radius [m]"], + transformation=pybop.LogTransformation(), ), ) # Generate data sigma = 0.001 -t_eval = np.arange(0, 900, 3) +t_eval = np.arange(0, 900, 5) values = model.predict(t_eval=t_eval) corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval)) @@ -42,27 +44,23 @@ # Generate problem, cost function, and optimisation class problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) cost = pybop.SumSquaredError(problem) -optim = pybop.CMAES(cost, max_iterations=100) +optim = pybop.CMAES(cost, sigma0=0.25, max_unchanged_iterations=10, max_iterations=40) # Run the optimisation -x, final_cost = optim.run() +results = optim.run() print("True parameters:", parameters.true_value()) -print("Estimated parameters:", x) # Plot the time series -pybop.plot_dataset(dataset) +pybop.plot.dataset(dataset) # Plot the timeseries output -pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") # Plot convergence -pybop.plot_convergence(optim) +pybop.plot.convergence(optim) # Plot the parameter traces -pybop.plot_parameters(optim) +pybop.plot.parameters(optim) # Plot the cost landscape -pybop.plot2d(cost, steps=15) - -# Plot the cost landscape with optimisation path -pybop.plot2d(optim, steps=15) +pybop.plot.surface(optim) diff --git a/examples/scripts/spm_IRPropMin.py b/examples/scripts/comparison_examples/spm_IRPropMin.py similarity index 60% rename from examples/scripts/spm_IRPropMin.py rename to examples/scripts/comparison_examples/spm_IRPropMin.py index 727536ff8..bd3f9c50b 100644 --- a/examples/scripts/spm_IRPropMin.py +++ b/examples/scripts/comparison_examples/spm_IRPropMin.py @@ -1,20 +1,22 @@ import numpy as np +import pybamm import pybop -# Define model +# Define model and use high-performant solver for sensitivities +solver = pybamm.IDAKLUSolver() parameter_set = pybop.ParameterSet.pybamm("Chen2020") -model = pybop.lithium_ion.SPM(parameter_set=parameter_set) +model = pybop.lithium_ion.SPM(parameter_set=parameter_set, solver=solver) # Fitting parameters parameters = pybop.Parameters( pybop.Parameter( "Negative electrode active material volume fraction", - prior=pybop.Gaussian(0.6, 0.05), + prior=pybop.Gaussian(0.6, 0.02), ), pybop.Parameter( "Positive electrode active material volume fraction", - prior=pybop.Gaussian(0.48, 0.05), + prior=pybop.Gaussian(0.48, 0.02), ), ) @@ -35,21 +37,20 @@ # Generate problem, cost function, and optimisation class problem = pybop.FittingProblem(model, parameters, dataset) -cost = pybop.SumSquaredError(problem) -optim = pybop.IRPropMin(cost, max_iterations=100) +cost = pybop.Minkowski(problem, p=2) +optim = pybop.IRPropMin(cost, max_iterations=100, sigma0=0.1) -x, final_cost = optim.run() -print("Estimated parameters:", x) +results = optim.run() # Plot the timeseries output -pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") # Plot convergence -pybop.plot_convergence(optim) +pybop.plot.convergence(optim) # Plot the parameter traces -pybop.plot_parameters(optim) +pybop.plot.parameters(optim) -# Plot the cost landscape with optimisation path +# Contour plot with optimisation path bounds = np.asarray([[0.5, 0.8], [0.4, 0.7]]) -pybop.plot2d(optim, bounds=bounds, steps=15) +pybop.plot.surface(optim, bounds=bounds) diff --git a/examples/scripts/spm_NelderMead.py b/examples/scripts/comparison_examples/spm_NelderMead.py similarity index 77% rename from examples/scripts/spm_NelderMead.py rename to examples/scripts/comparison_examples/spm_NelderMead.py index 569dbadf2..08be57421 100644 --- a/examples/scripts/spm_NelderMead.py +++ b/examples/scripts/comparison_examples/spm_NelderMead.py @@ -19,7 +19,6 @@ ) # Generate data -init_soc = 0.5 sigma = 0.003 experiment = pybop.Experiment( [ @@ -30,7 +29,7 @@ ] * 2 ) -values = model.predict(init_soc=init_soc, experiment=experiment) +values = model.predict(initial_state={"Initial SoC": 0.5}, experiment=experiment) def noise(sigma): @@ -48,11 +47,9 @@ def noise(sigma): } ) -signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] # Generate problem, cost function, and optimisation class -problem = pybop.FittingProblem( - model, parameters, dataset, signal=signal, init_soc=init_soc -) +signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] +problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) cost = pybop.RootMeanSquaredError(problem) optim = pybop.NelderMead( cost, @@ -64,18 +61,16 @@ def noise(sigma): ) # Run optimisation -x, final_cost = optim.run() -print("Estimated parameters:", x) +results = optim.run() # Plot the timeseries output -pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") # Plot convergence -pybop.plot_convergence(optim) +pybop.plot.convergence(optim) # Plot the parameter traces -pybop.plot_parameters(optim) +pybop.plot.parameters(optim) # Plot the cost landscape with optimisation path -bounds = np.asarray([[0.5, 0.8], [0.4, 0.7]]) -pybop.plot2d(optim, bounds=bounds, steps=15) +pybop.plot.surface(optim) diff --git a/examples/scripts/spm_SNES.py b/examples/scripts/comparison_examples/spm_SNES.py similarity index 81% rename from examples/scripts/spm_SNES.py rename to examples/scripts/comparison_examples/spm_SNES.py index d2afcc85a..6e1747b32 100644 --- a/examples/scripts/spm_SNES.py +++ b/examples/scripts/comparison_examples/spm_SNES.py @@ -35,20 +35,18 @@ # Generate problem, cost function, and optimisation class problem = pybop.FittingProblem(model, parameters, dataset) -cost = pybop.SumSquaredError(problem) +cost = pybop.SumofPower(problem, p=2) optim = pybop.SNES(cost, max_iterations=100) -x, final_cost = optim.run() -print("Estimated parameters:", x) - +results = optim.run() # Plot the timeseries output -pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") # Plot convergence -pybop.plot_convergence(optim) +pybop.plot.convergence(optim) # Plot the parameter traces -pybop.plot_parameters(optim) +pybop.plot.parameters(optim) # Plot the cost landscape with optimisation path -pybop.plot2d(optim, steps=15) +pybop.plot.surface(optim) diff --git a/examples/scripts/spm_UKF.py b/examples/scripts/comparison_examples/spm_UKF.py similarity index 74% rename from examples/scripts/spm_UKF.py rename to examples/scripts/comparison_examples/spm_UKF.py index e9972bd0f..33b38de1e 100644 --- a/examples/scripts/spm_UKF.py +++ b/examples/scripts/comparison_examples/spm_UKF.py @@ -22,7 +22,7 @@ # Make a prediction with measurement noise sigma = 0.001 -t_eval = np.arange(0, 300, 2) +t_eval = np.arange(0, 250, 0.5) values = model.predict(t_eval=t_eval) corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval)) @@ -36,14 +36,11 @@ ) # Build the model to get the number of states -model.build(dataset=dataset.data, parameters=parameters) +model.build(dataset=dataset, parameters=parameters) # Define the UKF observer, setting the particle boundaries as uncertain states -signal = ["Voltage [V]"] -n_states = model.n_states -n_signals = len(signal) -covariance = np.diag([0] * 19 + [sigma**2] + [0] * 19 + [sigma**2]) -process_noise = np.diag([0] * 19 + [1e-6] + [0] * 19 + [1e-6]) +covariance = np.diag([0] * 20 + [sigma**2] + [0] * 20 + [sigma**2]) +process_noise = np.diag([0] * 20 + [1e-6] + [0] * 20 + [1e-6]) measurement_noise = np.diag([sigma**2]) observer = pybop.UnscentedKalmanFilterObserver( parameters, @@ -52,29 +49,27 @@ process_noise, measurement_noise, dataset, - signal=signal, ) # Generate problem, cost function, and optimisation class cost = pybop.ObserverCost(observer) -optim = pybop.PSO(cost, verbose=True) +optim = pybop.XNES(cost, verbose=True) # Parameter identification using the current observer implementation is very slow # so let's restrict the number of iterations and reduce the number of plots optim.set_max_iterations(5) # Run optimisation -x, final_cost = optim.run() -print("Estimated parameters:", x) +results = optim.run() # Plot the timeseries output (requires model that returns Voltage) -pybop.quick_plot(observer, parameter_values=x, title="Optimised Comparison") +pybop.plot.quick(observer, problem_inputs=results.x, title="Optimised Comparison") # # Plot convergence -# pybop.plot_convergence(optim) +# pybop.plot.convergence(optim) # # Plot the parameter traces -# pybop.plot_parameters(optim) +# pybop.plot.parameters(optim) # # Plot the cost landscape with optimisation path -# pybop.plot2d(optim, steps=15) +# pybop.plot.surface(optim) diff --git a/examples/scripts/spm_XNES.py b/examples/scripts/comparison_examples/spm_XNES.py similarity index 84% rename from examples/scripts/spm_XNES.py rename to examples/scripts/comparison_examples/spm_XNES.py index 59b6eca87..09d0ab395 100644 --- a/examples/scripts/spm_XNES.py +++ b/examples/scripts/comparison_examples/spm_XNES.py @@ -39,17 +39,16 @@ cost = pybop.SumSquaredError(problem) optim = pybop.XNES(cost, max_iterations=100) -x, final_cost = optim.run() -print("Estimated parameters:", x) +results = optim.run() # Plot the timeseries output -pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") # Plot convergence -pybop.plot_convergence(optim) +pybop.plot.convergence(optim) # Plot the parameter traces -pybop.plot_parameters(optim) +pybop.plot.parameters(optim) # Plot the cost landscape with optimisation path -pybop.plot2d(optim, steps=15) +pybop.plot.surface(optim) diff --git a/examples/scripts/spm_descent.py b/examples/scripts/comparison_examples/spm_descent.py similarity index 71% rename from examples/scripts/spm_descent.py rename to examples/scripts/comparison_examples/spm_descent.py index 7c7629b04..92332849f 100644 --- a/examples/scripts/spm_descent.py +++ b/examples/scripts/comparison_examples/spm_descent.py @@ -1,10 +1,12 @@ import numpy as np +import pybamm import pybop -# Parameter set and model definition +# Define model and use high-performant solver for sensitivities +solver = pybamm.IDAKLUSolver() parameter_set = pybop.ParameterSet.pybamm("Chen2020") -model = pybop.lithium_ion.SPM(parameter_set=parameter_set) +model = pybop.lithium_ion.SPM(parameter_set=parameter_set, solver=solver) # Fitting parameters parameters = pybop.Parameters( @@ -35,27 +37,25 @@ # Generate problem, cost function, and optimisation class problem = pybop.FittingProblem(model, parameters, dataset) -cost = pybop.SumSquaredError(problem) +cost = pybop.RootMeanSquaredError(problem) optim = pybop.GradientDescent( cost, - sigma0=0.011, + sigma0=0.05, verbose=True, max_iterations=125, ) # Run optimisation -x, final_cost = optim.run() -print("Estimated parameters:", x) +results = optim.run() # Plot the timeseries output -pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") # Plot convergence -pybop.plot_convergence(optim) +pybop.plot.convergence(optim) # Plot the parameter traces -pybop.plot_parameters(optim) +pybop.plot.parameters(optim) # Plot the cost landscape with optimisation path -bounds = np.asarray([[0.5, 0.8], [0.4, 0.7]]) -pybop.plot2d(optim, bounds=bounds, steps=15) +pybop.plot.surface(optim) diff --git a/examples/scripts/spm_pso.py b/examples/scripts/comparison_examples/spm_pso.py similarity index 71% rename from examples/scripts/spm_pso.py rename to examples/scripts/comparison_examples/spm_pso.py index acb3e1c6e..e5c305bd1 100644 --- a/examples/scripts/spm_pso.py +++ b/examples/scripts/comparison_examples/spm_pso.py @@ -20,7 +20,7 @@ ), ) -sigma = 0.001 +sigma = 0.002 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)) @@ -37,19 +37,28 @@ # Generate problem, cost function, and optimisation class problem = pybop.FittingProblem(model, parameters, dataset) cost = pybop.SumSquaredError(problem) -optim = pybop.Optimisation(cost, optimiser=pybop.PSO, max_iterations=100) +optim = pybop.Optimisation( + cost, + optimiser=pybop.PSO, + sigma0=0.05, + max_unchanged_iterations=20, + max_iterations=100, +) -x, final_cost = optim.run() -print("Estimated parameters:", x) +results = optim.run() # Plot the timeseries output -pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") # Plot convergence -pybop.plot_convergence(optim) +pybop.plot.convergence(optim) # Plot the parameter traces -pybop.plot_parameters(optim) +pybop.plot.parameters(optim) + +# Plot the cost surface +bounds = np.asarray([[0.5, 0.8], [0.4, 0.7]]) +pybop.plot.surface(optim, bounds) # Plot the cost landscape with optimisation path -pybop.plot2d(optim, steps=15) +pybop.plot.contour(optim, steps=15, bounds=bounds) diff --git a/examples/scripts/spm_scipymin.py b/examples/scripts/comparison_examples/spm_scipymin.py similarity index 77% rename from examples/scripts/spm_scipymin.py rename to examples/scripts/comparison_examples/spm_scipymin.py index 8c7b80c5a..36265b9e8 100644 --- a/examples/scripts/spm_scipymin.py +++ b/examples/scripts/comparison_examples/spm_scipymin.py @@ -3,7 +3,7 @@ import pybop # Form dataset -Measurements = pd.read_csv("examples/scripts/Chen_example.csv", comment="#").to_numpy() +Measurements = pd.read_csv("examples/data/Chen_example.csv", comment="#").to_numpy() dataset = pybop.Dataset( { "Time [s]": Measurements[:, 0], @@ -34,24 +34,23 @@ # Define the cost to optimise signal = ["Voltage [V]"] -problem = pybop.FittingProblem(model, parameters, dataset, signal=signal, init_soc=0.98) +problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) cost = pybop.RootMeanSquaredError(problem) # Build the optimisation problem optim = pybop.SciPyMinimize(cost) # Run the optimisation problem -x, final_cost = optim.run() -print("Estimated parameters:", x) +results = optim.run() # Plot the timeseries output -pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") # Plot convergence -pybop.plot_convergence(optim) +pybop.plot.convergence(optim) # Plot the parameter traces -pybop.plot_parameters(optim) +pybop.plot.parameters(optim) # Plot the cost landscape with optimisation path -pybop.plot2d(optim, steps=15) +pybop.plot.surface(optim) diff --git a/examples/scripts/design_optimisation/maximising_energy.py b/examples/scripts/design_optimisation/maximising_energy.py new file mode 100644 index 000000000..23364664c --- /dev/null +++ b/examples/scripts/design_optimisation/maximising_energy.py @@ -0,0 +1,90 @@ +from pybamm import Parameter + +import pybop + +# A design optimisation example loosely based on work by L.D. Couto +# available at https://doi.org/10.1016/j.energy.2022.125966. + +# The target is to maximise the energy density over a range of +# possible design parameter values, including for example: +# cross-sectional area = height x width (only need change one) +# electrode widths, particle radii, volume fractions and +# separator width. + +# Define parameter set and additional parameters needed for the cost function +parameter_set = pybop.ParameterSet.pybamm("Chen2020", formation_concentrations=True) +parameter_set.update( + { + "Electrolyte density [kg.m-3]": Parameter("Separator density [kg.m-3]"), + "Negative electrode active material density [kg.m-3]": Parameter( + "Negative electrode density [kg.m-3]" + ), + "Negative electrode carbon-binder density [kg.m-3]": Parameter( + "Negative electrode density [kg.m-3]" + ), + "Positive electrode active material density [kg.m-3]": Parameter( + "Positive electrode density [kg.m-3]" + ), + "Positive electrode carbon-binder density [kg.m-3]": Parameter( + "Positive electrode density [kg.m-3]" + ), + }, + check_already_exists=False, +) + +# Define model +model = pybop.lithium_ion.SPMe(parameter_set=parameter_set) + +# Fitting parameters +parameters = pybop.Parameters( + pybop.Parameter( + "Positive electrode thickness [m]", + prior=pybop.Gaussian(7.56e-05, 0.1e-05), + bounds=[65e-06, 10e-05], + ), + pybop.Parameter( + "Positive particle radius [m]", + prior=pybop.Gaussian(5.22e-06, 0.1e-06), + bounds=[2e-06, 9e-06], + ), +) + +# Define test protocol +experiment = pybop.Experiment( + [ + "Discharge at 1C until 2.5 V (5 seconds period)", + "Hold at 2.5 V for 30 minutes or until 10 mA (5 seconds period)", + ], +) +signal = ["Voltage [V]", "Current [A]"] + +# Generate problem +problem = pybop.DesignProblem( + model, + parameters, + experiment, + signal=signal, + initial_state={"Initial SoC": 1.0}, + update_capacity=True, +) + +# Generate multiple cost functions and combine them +cost1 = pybop.GravimetricEnergyDensity(problem) +cost2 = pybop.VolumetricEnergyDensity(problem) +cost = pybop.WeightedCost(cost1, cost2, weights=[1, 1e-3]) + +# Run optimisation +optim = pybop.PSO( + cost, verbose=True, allow_infeasible_solutions=False, max_iterations=10 +) +results = optim.run() +print(f"Initial gravimetric energy density: {cost1(optim.x0):.2f} Wh.kg-1") +print(f"Optimised gravimetric energy density: {cost1(results.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(results.x):.2f} Wh.m-3") + +# Plot the timeseries output +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") + +# Plot the cost landscape with optimisation path +pybop.plot.surface(optim) diff --git a/examples/scripts/design_optimisation/maximising_power.py b/examples/scripts/design_optimisation/maximising_power.py new file mode 100644 index 000000000..d1a8f8578 --- /dev/null +++ b/examples/scripts/design_optimisation/maximising_power.py @@ -0,0 +1,85 @@ +from pybamm import Parameter + +import pybop + +# Define parameter set and additional parameters needed for the cost function +parameter_set = pybop.ParameterSet.pybamm("Chen2020", formation_concentrations=True) +parameter_set.update( + { + "Electrolyte density [kg.m-3]": Parameter("Separator density [kg.m-3]"), + "Negative electrode active material density [kg.m-3]": Parameter( + "Negative electrode density [kg.m-3]" + ), + "Negative electrode carbon-binder density [kg.m-3]": Parameter( + "Negative electrode density [kg.m-3]" + ), + "Positive electrode active material density [kg.m-3]": Parameter( + "Positive electrode density [kg.m-3]" + ), + "Positive electrode carbon-binder density [kg.m-3]": Parameter( + "Positive electrode density [kg.m-3]" + ), + }, + check_already_exists=False, +) + +# Define model +model = pybop.lithium_ion.SPMe(parameter_set=parameter_set) + +# Define useful quantities +nominal_capacity = parameter_set["Nominal cell capacity [A.h]"] +target_c_rate = 2 +discharge_rate = target_c_rate * nominal_capacity + +# Fitting parameters +parameters = pybop.Parameters( + pybop.Parameter( + "Positive electrode thickness [m]", + prior=pybop.Gaussian(7.56e-05, 0.5e-05), + bounds=[65e-06, 10e-05], + ), + pybop.Parameter( + "Nominal cell capacity [A.h]", # controls the C-rate in the experiment + prior=pybop.Gaussian(discharge_rate, 0.2), + bounds=[0.8 * discharge_rate, 1.2 * discharge_rate], + ), +) + +# Define test protocol +experiment = pybop.Experiment( + ["Discharge at 1C for 30 minutes or until 2.5 V (5 seconds period)"], +) +signal = ["Voltage [V]", "Current [A]"] + +# Generate problem +problem = pybop.DesignProblem( + model, + parameters, + experiment, + signal=signal, + initial_state={"Initial SoC": 1.0}, +) + +# Generate multiple cost functions and combine them +cost1 = pybop.GravimetricPowerDensity(problem, target_time=3600 / target_c_rate) +cost2 = pybop.VolumetricPowerDensity(problem, target_time=3600 / target_c_rate) +cost = pybop.WeightedCost(cost1, cost2, weights=[1, 1e-3]) + +# Run optimisation +optim = pybop.XNES( + cost, verbose=True, allow_infeasible_solutions=False, max_iterations=10 +) +results = optim.run() +print(f"Initial gravimetric power density: {cost1(optim.x0):.2f} W.kg-1") +print(f"Optimised gravimetric power density: {cost1(results.x):.2f} W.kg-1") +print(f"Initial volumetric power density: {cost2(optim.x0):.2f} W.m-3") +print(f"Optimised volumetric power density: {cost2(results.x):.2f} W.m-3") +print( + f"Optimised discharge rate: {results.x[-1]:.2f} A = {results.x[-1]/nominal_capacity:.2f} C" +) + +# Plot the timeseries output +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") + +# Plot the cost landscape with optimisation path +pybop.plot.surface(optim) diff --git a/examples/scripts/BPX_spm.py b/examples/scripts/getting_started/BPX_spm.py similarity index 78% rename from examples/scripts/BPX_spm.py rename to examples/scripts/getting_started/BPX_spm.py index 6fdb76490..49087da87 100644 --- a/examples/scripts/BPX_spm.py +++ b/examples/scripts/getting_started/BPX_spm.py @@ -27,7 +27,7 @@ # Generate data sigma = 0.001 -t_eval = np.arange(0, 900, 2) +t_eval = np.arange(0, 900, 5) values = model.predict(t_eval=t_eval) corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval)) @@ -43,21 +43,20 @@ # Generate problem, cost function, and optimisation class problem = pybop.FittingProblem(model, parameters, dataset) cost = pybop.SumSquaredError(problem) -optim = pybop.CMAES(cost, max_iterations=100) +optim = pybop.CMAES(cost, max_iterations=40) # Run the optimisation -x, final_cost = optim.run() +results = optim.run() print("True parameters:", parameters.true_value()) -print("Estimated parameters:", x) # Plot the timeseries output -pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") # Plot convergence -pybop.plot_convergence(optim) +pybop.plot.convergence(optim) # Plot the parameter traces -pybop.plot_parameters(optim) +pybop.plot.parameters(optim) -# Plot the cost landscape with optimisation path and updated bounds -pybop.plot2d(optim, steps=15) +# Plot the cost landscape with optimisation path +pybop.plot.surface(optim) diff --git a/examples/scripts/getting_started/functional_parameters.py b/examples/scripts/getting_started/functional_parameters.py new file mode 100644 index 000000000..a8ff75b6e --- /dev/null +++ b/examples/scripts/getting_started/functional_parameters.py @@ -0,0 +1,96 @@ +import numpy as np +import pybamm + +import pybop + +# This example demonstrates how to use a pybamm.FunctionalParameter to +# optimise functional parameters using PyBOP. + +# Method: Define a new scalar parameter for use in a functional parameter +# that already exists in the model, for example an exchange current density. + + +# Load parameter set +parameter_set = pybop.ParameterSet.pybamm("Chen2020") + + +# Define a new function using pybamm parameters +def positive_electrode_exchange_current_density(c_e, c_s_surf, c_s_max, T): + # New parameters + j0_ref = pybamm.Parameter( + "Positive electrode reference exchange-current density [A.m-2]" + ) + alpha = pybamm.Parameter("Positive electrode charge transfer coefficient") + + # Existing parameters + c_e_init = pybamm.Parameter("Initial concentration in electrolyte [mol.m-3]") + + return ( + j0_ref + * ((c_e / c_e_init) * (c_s_surf / c_s_max) * (1 - c_s_surf / c_s_max)) ** alpha + ) + + +# Give default values to the new scalar parameters and pass the new function +parameter_set.update( + { + "Positive electrode reference exchange-current density [A.m-2]": 1, + "Positive electrode charge transfer coefficient": 0.5, + }, + check_already_exists=False, +) +parameter_set["Positive electrode exchange-current density [A.m-2]"] = ( + positive_electrode_exchange_current_density +) + +# Model definition +model = pybop.lithium_ion.SPM( + parameter_set=parameter_set, options={"contact resistance": "true"} +) + +# Fitting parameters +parameters = pybop.Parameters( + pybop.Parameter( + "Positive electrode reference exchange-current density [A.m-2]", + prior=pybop.Gaussian(1, 0.1), + ), + pybop.Parameter( + "Positive electrode charge transfer coefficient", + prior=pybop.Gaussian(0.5, 0.1), + ), +) + +# 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)) + +# Form dataset +dataset = pybop.Dataset( + { + "Time [s]": t_eval, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": corrupt_values, + } +) + +# Generate problem, cost function, and optimisation class +problem = pybop.FittingProblem(model, parameters, dataset) +cost = pybop.RootMeanSquaredError(problem) +optim = pybop.SciPyMinimize(cost, sigma0=0.1, max_iterations=125) + +# Run optimisation +results = optim.run() + +# Plot the timeseries output +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") + +# Plot convergence +pybop.plot.convergence(optim) + +# Plot the parameter traces +pybop.plot.parameters(optim) + +# Plot the cost landscape with optimisation path +pybop.plot.surface(optim) diff --git a/examples/scripts/getting_started/linked_parameters.py b/examples/scripts/getting_started/linked_parameters.py new file mode 100644 index 000000000..9d4797bd0 --- /dev/null +++ b/examples/scripts/getting_started/linked_parameters.py @@ -0,0 +1,88 @@ +from pybamm import Parameter + +import pybop + +# The aim of this script is to show how to systematically update +# design parameters which depend on the optimisation parameters. + +# Define parameter set and additional parameters needed for the cost function +parameter_set = pybop.ParameterSet.pybamm("Chen2020", formation_concentrations=True) +parameter_set.update( + { + "Electrolyte density [kg.m-3]": Parameter("Separator density [kg.m-3]"), + "Negative electrode active material density [kg.m-3]": Parameter( + "Negative electrode density [kg.m-3]" + ), + "Negative electrode carbon-binder density [kg.m-3]": Parameter( + "Negative electrode density [kg.m-3]" + ), + "Positive electrode active material density [kg.m-3]": Parameter( + "Positive electrode density [kg.m-3]" + ), + "Positive electrode carbon-binder density [kg.m-3]": Parameter( + "Positive electrode density [kg.m-3]" + ), + }, + check_already_exists=False, +) + +# Link parameters +parameter_set.update( + { + "Positive electrode porosity": 1 + - Parameter("Positive electrode active material volume fraction") + } +) + +# Define model +model = pybop.lithium_ion.SPMe(parameter_set=parameter_set) + +# Fitting parameters +parameters = pybop.Parameters( + pybop.Parameter( + "Positive electrode thickness [m]", + prior=pybop.Gaussian(7.56e-05, 0.1e-05), + bounds=[65e-06, 10e-05], + ), + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Gaussian(0.6, 0.15), + bounds=[0.1, 0.9], + ), +) + +# Define test protocol +experiment = pybop.Experiment( + [ + "Discharge at 1C until 2.5 V (5 seconds period)", + "Hold at 2.5 V for 30 minutes or until 10 mA (5 seconds period)", + ], +) +signal = ["Voltage [V]", "Current [A]"] + +# Generate problem +problem = pybop.DesignProblem( + model, + parameters, + experiment, + signal=signal, + initial_state={"Initial SoC": 1.0}, + update_capacity=True, +) + +# Define the cost +cost = pybop.GravimetricEnergyDensity(problem) + +# Run optimisation +optim = pybop.XNES( + cost, verbose=True, allow_infeasible_solutions=False, max_iterations=10 +) +results = optim.run() +print(f"Initial gravimetric energy density: {cost(optim.x0):.2f} Wh.kg-1") +print(f"Optimised gravimetric energy density: {cost(results.x):.2f} Wh.kg-1") + +# Plot the timeseries output +pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") + +# Plot the cost landscape with optimisation path +pybop.plot.surface(optim) diff --git a/examples/scripts/getting_started/mcmc_example.py b/examples/scripts/getting_started/mcmc_example.py new file mode 100644 index 000000000..1b25a2d0d --- /dev/null +++ b/examples/scripts/getting_started/mcmc_example.py @@ -0,0 +1,86 @@ +import numpy as np +import pybamm + +import pybop + +# Parameter set and model definition +solver = pybamm.IDAKLUSolver() +parameter_set = pybop.ParameterSet.pybamm("Chen2020") +parameter_set.update( + { + "Negative electrode active material volume fraction": 0.63, + "Positive electrode active material volume fraction": 0.71, + } +) +synth_model = pybop.lithium_ion.DFN(parameter_set=parameter_set, solver=solver) + +# Fitting parameters +parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.68, 0.02), + transformation=pybop.LogTransformation(), + ), + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Gaussian(0.65, 0.02), + transformation=pybop.LogTransformation(), + ), +) + +# Generate data +init_soc = 0.5 +sigma = 0.005 +experiment = pybop.Experiment( + [ + ("Discharge at 0.5C for 6 minutes (5 second period)",), + ] +) +values = synth_model.predict( + initial_state={"Initial SoC": init_soc}, experiment=experiment +) + + +def noise(sigma): + return np.random.normal(0, sigma, len(values["Voltage [V]"].data)) + + +# Form dataset +dataset = pybop.Dataset( + { + "Time [s]": values["Time [s]"].data, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": values["Voltage [V]"].data + noise(sigma), + "Bulk open-circuit voltage [V]": values["Bulk open-circuit voltage [V]"].data + + noise(sigma), + } +) + +model = pybop.lithium_ion.SPM(parameter_set=parameter_set, solver=pybamm.IDAKLUSolver()) +model.build(initial_state={"Initial SoC": init_soc}) +signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] + +# Generate problem, likelihood, and sampler +problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) +likelihood = pybop.GaussianLogLikelihood(problem) +posterior = pybop.LogPosterior(likelihood) + +sampler = pybop.DifferentialEvolutionMCMC( + posterior, + chains=3, + max_iterations=300, + warm_up=100, + verbose=True, + # parallel=True, # uncomment to enable parallelisation (macOS/WSL/Linux only) +) +chains = sampler.run() + +# Summary statistics +posterior_summary = pybop.PosteriorSummary(chains) +print(posterior_summary.get_summary_statistics()) +posterior_summary.plot_trace() +posterior_summary.summary_table() +posterior_summary.plot_posterior() +posterior_summary.plot_chains() +posterior_summary.effective_sample_size() +print(f"rhat: {posterior_summary.rhat()}") diff --git a/examples/scripts/getting_started/multi_fitting.py b/examples/scripts/getting_started/multi_fitting.py new file mode 100644 index 000000000..9380974ab --- /dev/null +++ b/examples/scripts/getting_started/multi_fitting.py @@ -0,0 +1,84 @@ +import numpy as np + +import pybop + +# Parameter set and model definition +parameter_set = pybop.ParameterSet.pybamm("Chen2020") +model = pybop.lithium_ion.SPM(parameter_set=parameter_set) + +# Create initial SOC, experiment objects +init_soc = [{"Initial SoC": 0.8}, {"Initial SoC": 0.6}] +experiment = [ + pybop.Experiment([("Discharge at 0.5C for 2 minutes (4 second period)")]), + pybop.Experiment([("Discharge at 1C for 1 minutes (4 second period)")]), +] + +# Fitting parameters +parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.68, 0.05), + true_value=parameter_set["Negative electrode active material volume fraction"], + ), + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Gaussian(0.58, 0.05), + true_value=parameter_set["Positive electrode active material volume fraction"], + ), +) + +# Generate a dataset and a fitting problem +sigma = 0.002 +values = model.predict(initial_state=init_soc[0], experiment=experiment[0]) +dataset_1 = pybop.Dataset( + { + "Time [s]": values["Time [s]"].data, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": values["Voltage [V]"].data + + np.random.normal(0, sigma, len(values["Voltage [V]"].data)), + } +) +problem_1 = pybop.FittingProblem(model, parameters, dataset_1) + +# Generate a second dataset and problem +model = model.new_copy() +values = model.predict(initial_state=init_soc[1], experiment=experiment[1]) +dataset_2 = pybop.Dataset( + { + "Time [s]": values["Time [s]"].data, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": values["Voltage [V]"].data + + np.random.normal(0, sigma, len(values["Voltage [V]"].data)), + } +) +problem_2 = pybop.FittingProblem(model, parameters, dataset_2) + +# Combine the problems into one +problem = pybop.MultiFittingProblem(problem_1, problem_2) + +# Generate the cost function and optimisation class +cost = pybop.SumSquaredError(problem) +optim = pybop.CuckooSearch( + cost, + verbose=True, + sigma0=0.05, + max_unchanged_iterations=20, + max_iterations=100, +) + +# Run optimisation +results = optim.run() +print("True parameters:", parameters.true_value()) + +# Plot the timeseries output +pybop.plot.quick(problem_1, problem_inputs=results.x, title="Optimised Comparison") +pybop.plot.quick(problem_2, problem_inputs=results.x, title="Optimised Comparison") + +# Plot convergence +pybop.plot.convergence(optim) + +# Plot the parameter traces +pybop.plot.parameters(optim) + +# Plot the cost landscape with optimisation path +pybop.plot.surface(optim) diff --git a/examples/scripts/getting_started/spm_weighted_cost.py b/examples/scripts/getting_started/spm_weighted_cost.py new file mode 100644 index 000000000..6aa71d20d --- /dev/null +++ b/examples/scripts/getting_started/spm_weighted_cost.py @@ -0,0 +1,72 @@ +import numpy as np + +import pybop + +# Parameter set and model definition +parameter_set = pybop.ParameterSet.pybamm("Chen2020") +model = pybop.lithium_ion.SPM(parameter_set=parameter_set) + +# Fitting parameters +parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.68, 0.05), + bounds=[0.5, 0.8], + true_value=parameter_set["Negative electrode active material volume fraction"], + ), + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Gaussian(0.58, 0.05), + bounds=[0.4, 0.7], + true_value=parameter_set["Positive electrode active material volume fraction"], + ), +) + +# Generate data +sigma = 0.001 +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, initial_state={"Initial SoC": 0.5}) + + +def noise(sigma): + return np.random.normal(0, sigma, len(values["Voltage [V]"].data)) + + +# Form dataset +dataset = pybop.Dataset( + { + "Time [s]": values["Time [s]"].data, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": values["Voltage [V]"].data + noise(sigma), + } +) + +# Generate problem, cost function, and optimisation class +problem = pybop.FittingProblem(model, parameters, dataset) +cost1 = pybop.SumSquaredError(problem) +cost2 = pybop.RootMeanSquaredError(problem) +weighted_cost = pybop.WeightedCost(cost1, cost2, weights=[0.1, 1]) + +for cost in [weighted_cost, cost1, cost2]: + optim = pybop.IRPropMin(cost, max_iterations=60) + + # Run the optimisation + results = optim.run() + print("True parameters:", parameters.true_value()) + + # Plot the timeseries output + pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison") + + # Plot convergence + pybop.plot.convergence(optim) + + # Plot the cost landscape with optimisation path + pybop.plot.surface(optim) diff --git a/examples/standalone/cost.py b/examples/standalone/cost.py index 806bc0eab..1a0558cef 100644 --- a/examples/standalone/cost.py +++ b/examples/standalone/cost.py @@ -1,3 +1,5 @@ +import numpy as np + import pybop @@ -21,7 +23,7 @@ class StandaloneCost(pybop.BaseCost): Methods ------- - __call__(x, grad=None) + __call__(x) Calculate the cost for a given parameter value. """ @@ -34,35 +36,18 @@ def __init__(self, problem=None): """ super().__init__(problem) - self.parameters = pybop.Parameters( - pybop.Parameter( - "x", - initial_value=4.2, - bounds=[-1, 10], - ), - ) - self.x0 = self.parameters.initial_value() - - def _evaluate(self, x, grad=None): + def compute( + self, y: dict = None, dy: np.ndarray = None, calculate_grad: bool = False + ): """ - Calculate the cost for a given parameter value. + Compute the cost for a given parameter value. The cost function is defined as cost(x) = x^2 + 42, where x is the parameter value. - Parameters - ---------- - x : array-like - A one-element array containing the parameter value for which to - evaluate the cost. - grad : array-like, optional - Unused parameter, present for compatibility with gradient-based - optimizers. - Returns ------- float The calculated cost value for the given parameter. """ - - return x[0] ** 2 + 42 + return np.asarray([sum(y[signal]) for signal in self.signal]) ** 2 + 43 diff --git a/examples/standalone/model.py b/examples/standalone/model.py index e67477466..82a9f2d87 100644 --- a/examples/standalone/model.py +++ b/examples/standalone/model.py @@ -18,7 +18,8 @@ def __init__( parameter_set: pybamm.ParameterValues = None, n_states: int = 1, ): - super().__init__() + super().__init__(name=name, parameter_set=parameter_set) + self.n_states = n_states if n_states < 1: raise ValueError("The number of states (n_states) must be greater than 0") @@ -38,19 +39,20 @@ def __init__( ) self._unprocessed_model = self.pybamm_model - self.name = name self.default_parameter_values = ( - default_parameter_values if parameter_set is None else parameter_set + default_parameter_values + if self._parameter_set is None + else self._parameter_set ) self._parameter_set = self.default_parameter_values self._unprocessed_parameter_set = self._parameter_set - self.geometry = self.pybamm_model.default_geometry - self.submesh_types = self.pybamm_model.default_submesh_types - self.var_pts = self.pybamm_model.default_var_pts - self.spatial_methods = self.pybamm_model.default_spatial_methods - self.solver = pybamm.CasadiSolver(mode="fast") + self._geometry = self.pybamm_model.default_geometry + self._submesh_types = self.pybamm_model.default_submesh_types + self._var_pts = self.pybamm_model.default_var_pts + self._spatial_methods = self.pybamm_model.default_spatial_methods + self._solver = pybamm.CasadiSolver(mode="fast") self._model_with_set_params = None self._built_model = None self._built_initial_soc = None diff --git a/examples/standalone/optimiser.py b/examples/standalone/optimiser.py index 326edd443..2e577958c 100644 --- a/examples/standalone/optimiser.py +++ b/examples/standalone/optimiser.py @@ -1,7 +1,6 @@ -import numpy as np from scipy.optimize import minimize -from pybop import BaseOptimiser, Result +from pybop import BaseOptimiser, OptimisationResult class StandaloneOptimiser(BaseOptimiser): @@ -9,15 +8,9 @@ class StandaloneOptimiser(BaseOptimiser): Defines an example standalone optimiser without a Cost. """ - def __init__(self, cost=None, **optimiser_kwargs): - # Define cost function - def cost(x): - x1, x2 = x - return (x1 - 2) ** 2 + (x2 - 4) ** 4 - + def __init__(self, cost, **optimiser_kwargs): # Set initial values and other options optimiser_options = dict( - x0=np.array([0, 0]), bounds=None, method="Nelder-Mead", jac=False, @@ -54,7 +47,7 @@ def _run(self): Returns ------- x : numpy.ndarray - The best parameter set found by the optimization. + The best parameter set found by the optimisation. final_cost : float The final cost associated with the best parameters. """ @@ -73,11 +66,13 @@ def callback(x): **self._options, ) - return Result( + return OptimisationResult( x=result.x, + cost=self.cost, final_cost=self.cost(result.x), n_iterations=result.nit, scipy_result=result, + optim=self, ) def name(self): diff --git a/examples/standalone/problem.py b/examples/standalone/problem.py index d6d1f4b01..ecdfd0644 100644 --- a/examples/standalone/problem.py +++ b/examples/standalone/problem.py @@ -16,57 +16,58 @@ def __init__( check_model=True, signal=None, additional_variables=None, - init_soc=None, + initial_state=None, ): - super().__init__( - parameters, model, check_model, signal, additional_variables, init_soc - ) + super().__init__(parameters, model, check_model, signal, additional_variables) self._dataset = dataset.data # Check that the dataset contains time and current - for name in ["Time [s]"] + self.signal: + for name in ["Time [s]", *self.signal]: if name not in self._dataset: raise ValueError(f"expected {name} in list of dataset") - self._time_data = self._dataset["Time [s]"] - self.n_time_data = len(self._time_data) - if np.any(self._time_data < 0): + self._domain_data = self._dataset[self.domain] + self.n_data = len(self._domain_data) + if np.any(self._domain_data < 0): raise ValueError("Times can not be negative.") - if np.any(self._time_data[:-1] >= self._time_data[1:]): + if np.any(self._domain_data[:-1] >= self._domain_data[1:]): raise ValueError("Times must be increasing.") for signal in self.signal: - if len(self._dataset[signal]) != self.n_time_data: + if len(self._dataset[signal]) != self.n_data: raise ValueError( f"Time data and {signal} data must be the same length." ) self._target = {signal: self._dataset[signal] for signal in self.signal} - def evaluate(self, x): + def evaluate(self, inputs): """ Evaluate the model with the given parameters and return the signal. Parameters ---------- - x : np.ndarray - Parameter values to evaluate the model at. + inputs : Dict + Parameters for evaluation of the model. Returns ------- y : np.ndarray - The model output y(t) simulated with inputs x. + The model output y(t) simulated with given inputs. """ - return {signal: x[0] * self._time_data + x[1] for signal in self.signal} + return { + signal: inputs["Gradient"] * self._domain_data + inputs["Intercept"] + for signal in self.signal + } - def evaluateS1(self, x): + def evaluateS1(self, inputs): """ Evaluate the model with the given parameters and return the signal and its derivatives. Parameters ---------- - x : np.ndarray - Parameter values to evaluate the model at. + inputs : Dict + Parameters for evaluation of the model. Returns ------- @@ -75,9 +76,9 @@ def evaluateS1(self, x): with given inputs x. """ - y = {signal: x[0] * self._time_data + x[1] for signal in self.signal} + y = self.evaluate(inputs) - dy = np.zeros((self.n_time_data, self.n_outputs, self.n_parameters)) - dy[:, 0, 0] = self._time_data + dy = np.zeros((self.n_data, self.n_outputs, self.n_parameters)) + dy[:, 0, 0] = self._domain_data return (y, dy) diff --git a/noxfile.py b/noxfile.py index 1cbbd08f9..4b4b34779 100644 --- a/noxfile.py +++ b/noxfile.py @@ -14,7 +14,6 @@ @nox.session def unit(session): - session.install("setuptools", "wheel") session.install("-e", ".[all,dev]", silent=False) if PYBOP_SCHEDULED: session.run("pip", "install", f"pybamm=={PYBAMM_VERSION}", silent=False) @@ -23,7 +22,6 @@ def unit(session): @nox.session def coverage(session): - session.install("setuptools", "wheel") session.install("-e", ".[all,dev]", silent=False) session.install("pip") if PYBOP_SCHEDULED: @@ -44,7 +42,6 @@ def coverage(session): @nox.session def plots(session): """Run the tests that generate plots.""" - session.install("setuptools", "wheel") session.install("-e", ".[plot,dev]", silent=False) session.install("pip") session.run("pytest", "--plots", "-n", "0") @@ -53,7 +50,6 @@ def plots(session): @nox.session def integration(session): """Run the integration tests.""" - session.install("setuptools", "wheel") session.install("-e", ".[all,dev]", silent=False) session.run("pytest", "--integration") @@ -61,7 +57,6 @@ def integration(session): @nox.session def examples(session): """Run the examples and notebooks""" - session.install("setuptools", "wheel") session.install("-e", ".[all,dev]", silent=False) session.run("pytest", "--examples") notebooks(session) @@ -70,7 +65,7 @@ def examples(session): @nox.session def notebooks(session): """Run the Jupyter notebooks.""" - session.install("setuptools", "wheel") + session.install("openpyxl", "ipywidgets") session.install("-e", ".[all,dev]", silent=False) if PYBOP_SCHEDULED: session.run("pip", "install", f"pybamm=={PYBAMM_VERSION}", silent=False) @@ -83,14 +78,39 @@ def notebooks(session): ) +@nox.session(name="notebooks-overwrite") +def notebooks_overwrite(session): + """Run the Jupyter notebooks.""" + session.install("openpyxl", "ipywidgets") + session.install("-e", ".[all,dev]", silent=False) + if PYBOP_SCHEDULED: + session.run("pip", "install", f"pybamm=={PYBAMM_VERSION}", silent=False) + session.run( + "pytest", + "--notebooks", + "--nbmake", + "--overwrite", + "--nbmake-timeout=1000", + "examples/", + ) + + @nox.session(name="tests") def run_tests(session): - """Run all the tests.""" + """Run all or a user-defined set of tests.""" session.install("-e", ".[all,dev]", silent=False) if PYBOP_SCHEDULED: session.run("pip", "install", f"pybamm=={PYBAMM_VERSION}", silent=False) + specific_tests = session.posargs if session.posargs else [] session.run( - "pytest", "--unit", "--integration", "--nbmake", "--examples", "-n", "auto" + "pytest", + "--unit", + "--integration", + "--nbmake", + "--examples", + "-n", + "auto", + *specific_tests, ) @@ -100,7 +120,6 @@ def run_doc_tests(session): Checks if the documentation can be built, runs any doctests (currently not used). """ - session.install("setuptools", "wheel") session.install("-e", ".[plot,docs,dev]", silent=False) session.run("pytest", "--docs", "-n", "0") @@ -112,7 +131,6 @@ def lint(session): Credit: PyBaMM Team """ - session.install("setuptools", "wheel") session.install("pre-commit", silent=False) session.run("pre-commit", "run", "--all-files") @@ -131,7 +149,6 @@ def run_quick(session): @nox.session def benchmarks(session): """Run the benchmarks.""" - session.install("setuptools", "wheel") session.install("-e", ".[all,dev]", silent=False) session.install("asv[virtualenv]") session.run("asv", "run", "--show-stderr", "--python=same") @@ -143,7 +160,6 @@ def docs(session): Build the documentation and load it in a browser tab, rebuilding on changes. Credit: PyBaMM Team """ - session.install("setuptools", "wheel") envbindir = session.bin session.install("-e", ".[all,docs]", silent=False) session.chdir("docs") diff --git a/pybop/__init__.py b/pybop/__init__.py index 61971e95c..9eab3d5d6 100644 --- a/pybop/__init__.py +++ b/pybop/__init__.py @@ -43,7 +43,7 @@ # # Utilities # -from ._utils import is_numeric +from ._utils import is_numeric, SymbolReplacer # # Experiment class @@ -55,12 +55,23 @@ # from ._dataset import Dataset +# +# Transformation classes +# +from .transformation.base_transformation import Transformation +from .transformation.transformations import ( + IdentityTransformation, + ScaledTransformation, + LogTransformation, + ComposedTransformation, +) + # # Parameter classes # from .parameters.parameter import Parameter, Parameters from .parameters.parameter_set import ParameterSet -from .parameters.priors import BasePrior, Gaussian, Uniform, Exponential +from .parameters.priors import BasePrior, Gaussian, Uniform, Exponential, JointLogPrior # # Model classes @@ -72,39 +83,47 @@ from .models.base_model import Inputs # -# Problem class +# Problem classes # from .problems.base_problem import BaseProblem from .problems.fitting_problem import FittingProblem +from .problems.multi_fitting_problem import MultiFittingProblem from .problems.design_problem import DesignProblem # -# Cost function class +# Cost classes # from .costs.base_cost import BaseCost from .costs.fitting_costs import ( RootMeanSquaredError, SumSquaredError, + Minkowski, + SumofPower, ObserverCost, - MAP, ) from .costs.design_costs import ( DesignCost, GravimetricEnergyDensity, VolumetricEnergyDensity, + GravimetricPowerDensity, + VolumetricPowerDensity, ) from .costs._likelihoods import ( BaseLikelihood, GaussianLogLikelihood, GaussianLogLikelihoodKnownSigma, + ScaledLogLikelihood, + LogPosterior, ) +from .costs._weighted_cost import WeightedCost # -# Optimiser class +# Optimiser classes # +from .optimisers._cuckoo import CuckooSearchImpl from .optimisers._adamw import AdamWImpl -from .optimisers.base_optimiser import BaseOptimiser, Result +from .optimisers.base_optimiser import BaseOptimiser, OptimisationResult from .optimisers.base_pints_optimiser import BasePintsOptimiser from .optimisers.scipy_optimisers import ( BaseSciPyOptimiser, @@ -120,10 +139,29 @@ PSO, SNES, XNES, + CuckooSearch, AdamW, ) from .optimisers.optimisation import Optimisation +# +# Monte Carlo classes +# +from .samplers.base_sampler import BaseSampler +from .samplers.base_pints_sampler import BasePintsSampler +from .samplers.pints_samplers import ( + NUTS, DREAM, AdaptiveCovarianceMCMC, + DifferentialEvolutionMCMC, DramACMC, + EmceeHammerMCMC, + HaarioACMC, HaarioBardenetACMC, + HamiltonianMCMC, MALAMCMC, + MetropolisRandomWalkMCMC, MonomialGammaHamiltonianMCMC, + PopulationMCMC, RaoBlackwellACMC, + RelativisticMCMC, SliceDoublingMCMC, + SliceRankShrinkingMCMC, SliceStepoutMCMC, +) +from .samplers.mcmc_sampler import MCMCSampler + # # Observer classes # @@ -131,15 +169,10 @@ from .observers.observer import Observer # -# Plotting class +# Plotting classes # -from .plotting.plotly_manager import PlotlyManager -from .plotting.quick_plot import StandardPlot, StandardSubplot, plot_trajectories -from .plotting.plot2d import plot2d -from .plotting.plot_dataset import plot_dataset -from .plotting.plot_convergence import plot_convergence -from .plotting.plot_parameters import plot_parameters -from .plotting.plot_problem import quick_plot +from . import plot as plot +from .samplers.mcmc_summary import PosteriorSummary # # Remove any imported modules, so we don't expose them as part of pybop diff --git a/pybop/_dataset.py b/pybop/_dataset.py index 0da8be4be..120fcb61d 100644 --- a/pybop/_dataset.py +++ b/pybop/_dataset.py @@ -1,6 +1,7 @@ +from typing import Union + import numpy as np -from pybamm import Interpolant, solvers -from pybamm import t as pybamm_t +from pybamm import solvers class Dataset: @@ -77,58 +78,68 @@ def __getitem__(self, key): return self.data[key] - def Interpolant(self): - """ - Create an interpolation function of the dataset based on the independent variable. - - Currently, only time-based interpolation is supported. This method modifies - the instance's Interpolant attribute to be an interpolation function that - can be evaluated at different points in time. - - Raises - ------ - NotImplementedError - If the independent variable for interpolation is not supported. - """ - - if self.variable == "time": - self.Interpolant = Interpolant(self.x, self.y, pybamm_t) - else: - NotImplementedError("Only time interpolation is supported") - - def check(self, signal=["Voltage [V]"]): + def check(self, domain: str = None, signal: Union[str, list[str]] = None) -> bool: """ Check the consistency of a PyBOP Dataset against the expected format. + Parameters + ---------- + domain : str, optional + The domain of the dataset. Defaults to "Time [s]". + signal : str or List[str], optional + The signal(s) to check. Defaults to ["Voltage [V]"]. + Returns ------- bool - If True, the dataset has the expected attributes. + True if the dataset has the expected attributes. Raises ------ ValueError If the time series and the data series are not consistent. """ - if isinstance(signal, str): - signal = [signal] + self.domain = domain or "Time [s]" + signals = [signal] if isinstance(signal, str) else (signal or ["Voltage [V]"]) + + # Check that the dataset contains domain and chosen signals + missing_attributes = set([self.domain, *signals]) - set(self.names) + if missing_attributes: + raise ValueError( + f"Expected {', '.join(missing_attributes)} in list of dataset" + ) + + domain_data = self.data[self.domain] + + # Check domain-specific constraints + if self.domain == "Time [s]": + self._check_time_constraints(domain_data) + elif self.domain == "Frequency [Hz]": + self._check_frequency_constraints(domain_data) - # Check that the dataset contains time and chosen signal - for name in ["Time [s]"] + signal: - if name not in self.names: - raise ValueError(f"expected {name} in list of dataset") + # Check for consistent data length + self._check_data_consistency(domain_data, signals) - # Check for increasing times - time_data = self.data["Time [s]"] + return True + + @staticmethod + def _check_time_constraints(time_data: np.ndarray) -> None: if np.any(time_data < 0): - raise ValueError("Times can not be negative.") + raise ValueError("Times cannot be negative.") if np.any(time_data[:-1] >= time_data[1:]): raise ValueError("Times must be increasing.") - # Check for consistent data - n_time_data = len(time_data) - for s in signal: - if len(self.data[s]) != n_time_data: - raise ValueError(f"Time data and {s} data must be the same length.") - - return True + @staticmethod + def _check_frequency_constraints(freq_data: np.ndarray) -> None: + if np.any(freq_data < 0): + raise ValueError("Frequencies cannot be negative.") + + def _check_data_consistency( + self, domain_data: np.ndarray, signals: list[str] + ) -> None: + n_domain_data = len(domain_data) + for s in signals: + if len(self.data[s]) != n_domain_data: + raise ValueError( + f"{self.domain} data and {s} data must be the same length." + ) diff --git a/pybop/_experiment.py b/pybop/_experiment.py index 1c4953849..b674c14dc 100644 --- a/pybop/_experiment.py +++ b/pybop/_experiment.py @@ -3,7 +3,7 @@ class Experiment(Experiment): """ - Wraps the Experiment class for generating experiment conditions for PyBaMM models. + Light wrapper of the PyBaMM Experiment class for generating experiment conditions for PyBaMM models. Credit: PyBaMM Base class for experimental conditions under which to run the model. In general, a @@ -38,17 +38,13 @@ class Experiment(Experiment): def __init__( self, operating_conditions, - period="1 minute", + period=None, temperature=None, termination=None, - drive_cycles=None, - cccv_handling=None, ): super().__init__( operating_conditions, period, temperature, termination, - drive_cycles, - cccv_handling, ) diff --git a/pybop/_utils.py b/pybop/_utils.py index 6fbfeaab5..426b0d277 100644 --- a/pybop/_utils.py +++ b/pybop/_utils.py @@ -1,4 +1,7 @@ +from typing import Optional + import numpy as np +import pybamm def is_numeric(x): @@ -6,3 +9,168 @@ def is_numeric(x): Check if a variable is numeric. """ return isinstance(x, (int, float, np.number)) + + +class SymbolReplacer: + """ + Helper class to replace all instances of one or more symbols in an expression tree + with another symbol, as defined by the dictionary `symbol_replacement_map` + Originally developed by pybamm: https://github.com/pybamm-team/pybamm + + Parameters + ---------- + symbol_replacement_map : dict {:class:`pybamm.Symbol` -> :class:`pybamm.Symbol`} + Map of which symbols should be replaced by which. + processed_symbols: dict {:class:`pybamm.Symbol` -> :class:`pybamm.Symbol`}, optional + cached replaced symbols + process_initial_conditions: bool, optional + Whether to process initial conditions, default is True + """ + + def __init__( + self, + symbol_replacement_map: dict[pybamm.Symbol, pybamm.Symbol], + processed_symbols: Optional[dict[pybamm.Symbol, pybamm.Symbol]] = None, + process_initial_conditions: bool = True, + ): + self._symbol_replacement_map = symbol_replacement_map + self._processed_symbols = processed_symbols or {} + self._process_initial_conditions = process_initial_conditions + + def process_model(self, unprocessed_model, inplace=True): + """ + Replace all instances of a symbol in a PyBaMM model class. + + Parameters + ---------- + unprocessed_model : :class:`pybamm.BaseModel` + Model class to assign parameter values to + inplace: bool, optional + If True, replace the parameters in the model in place. Otherwise, return a + new model with parameter values set. Default is True. + """ + + model = unprocessed_model if inplace else unprocessed_model.new_copy() + + for variable, equation in unprocessed_model.rhs.items(): + pybamm.logger.verbose(f"Replacing symbols in {variable!r} (rhs)") + model.rhs[self.process_symbol(variable)] = self.process_symbol(equation) + + for variable, equation in unprocessed_model.algebraic.items(): + pybamm.logger.verbose(f"Replacing symbols in {variable!r} (algebraic)") + model.algebraic[self.process_symbol(variable)] = self.process_symbol( + equation + ) + + for variable, equation in unprocessed_model.initial_conditions.items(): + pybamm.logger.verbose( + f"Replacing symbols in {variable!r} (initial conditions)" + ) + if self._process_initial_conditions: + model.initial_conditions[self.process_symbol(variable)] = ( + self.process_symbol(equation) + ) + else: + model.initial_conditions[self.process_symbol(variable)] = equation + + model.boundary_conditions = self.process_boundary_conditions(unprocessed_model) + + for variable, equation in unprocessed_model.variables.items(): + pybamm.logger.verbose(f"Replacing symbols in {variable!r} (variables)") + model.variables[variable] = self.process_symbol(equation) + + model.events = self._process_events(unprocessed_model.events) + pybamm.logger.info(f"Finish replacing symbols in {model.name}") + + return model + + def _process_events(self, events: list) -> list: + new_events = [] + for event in events: + pybamm.logger.verbose(f"Replacing symbols in event '{event.name}'") + new_events.append( + pybamm.Event( + event.name, self.process_symbol(event.expression), event.event_type + ) + ) + return new_events + + def process_boundary_conditions(self, model): + """ + Process boundary conditions for a PybaMM model class + Boundary conditions are dictionaries {"left": left bc, "right": right bc} + in general, but may be imposed on the tabs (or *not* on the tab) for a + small number of variables, e.g. {"negative tab": neg. tab bc, + "positive tab": pos. tab bc "no tab": no tab bc}. + """ + boundary_conditions = {} + sides = ["left", "right", "negative tab", "positive tab", "no tab"] + for variable, bcs in model.boundary_conditions.items(): + processed_variable = self.process_symbol(variable) + boundary_conditions[processed_variable] = {} + + for side in sides: + try: + bc, typ = bcs[side] + pybamm.logger.verbose( + f"Replacing symbols in {variable!r} ({side} bc)" + ) + processed_bc = (self.process_symbol(bc), typ) + boundary_conditions[processed_variable][side] = processed_bc + except KeyError as err: + # Don't raise if side is not in the boundary conditions + if err.args[0] in side: + pass + # Raise otherwise + else: # pragma: no cover + raise KeyError(err) from err + + return boundary_conditions + + def process_symbol(self, symbol): + """ + This function recurses down the tree, replacing any symbols in + self._symbol_replacement_map.keys() with their corresponding value + + Parameters + ---------- + symbol : :class:`pybamm.Symbol` + The symbol to replace + + Returns + ------- + :class:`pybamm.Symbol` + Symbol with all replacements performed + """ + if symbol in self._processed_symbols: + return self._processed_symbols[symbol] + + processed_symbol = self._process_symbol(symbol) + self._processed_symbols[symbol] = processed_symbol + return processed_symbol + + def _process_symbol(self, symbol: pybamm.Symbol) -> pybamm.Symbol: + if symbol in self._symbol_replacement_map: + return self._symbol_replacement_map[symbol] + + if isinstance(symbol, pybamm.BinaryOperator): + # process children + new_left = self.process_symbol(symbol.left) + new_right = self.process_symbol(symbol.right) + return symbol._binary_new_copy(new_left, new_right) # noqa: SLF001 + + if isinstance(symbol, pybamm.UnaryOperator): + new_child = self.process_symbol(symbol.child) + return symbol._unary_new_copy(new_child) # noqa: SLF001 + + if isinstance(symbol, pybamm.Function): + new_children = [self.process_symbol(child) for child in symbol.children] + # Return a new copy with the replaced symbols + return symbol._function_new_copy(new_children) # noqa: SLF001 + + if isinstance(symbol, pybamm.Concatenation): + new_children = [self.process_symbol(child) for child in symbol.children] + return symbol._concatenation_new_copy(new_children) # noqa: SLF001 + + # Return leaf + return symbol diff --git a/pybop/costs/_likelihoods.py b/pybop/costs/_likelihoods.py index cce09f9bb..2472f351e 100644 --- a/pybop/costs/_likelihoods.py +++ b/pybop/costs/_likelihoods.py @@ -1,6 +1,13 @@ +from typing import Optional, Union + import numpy as np +import scipy.stats as stats +import pybop from pybop.costs.base_cost import BaseCost +from pybop.parameters.parameter import Parameter, Parameters +from pybop.parameters.priors import BasePrior, JointLogPrior, Uniform +from pybop.problems.base_problem import BaseProblem class BaseLikelihood(BaseCost): @@ -8,9 +15,42 @@ class BaseLikelihood(BaseCost): Base class for likelihoods """ - def __init__(self, problem): - super(BaseLikelihood, self).__init__(problem) - self.n_time_data = problem.n_time_data + def __init__(self, problem: BaseProblem): + super().__init__(problem) + self.n_data = problem.n_data + + +class BaseMetaLikelihood(BaseLikelihood): + """ + Base class for likelihood classes which have a meta-likelihood + such as `LogPosterior` or `ScaledLoglikelihood`. This class + points the required attributes towards the composed likelihood + class. + """ + + def __init__(self, log_likelihood: BaseLikelihood): + self._log_likelihood = log_likelihood + super().__init__(log_likelihood.problem) + + @property + def transformation(self): + return self._log_likelihood.transformation + + @property + def has_separable_problem(self): + return self._log_likelihood.has_separable_problem + + @property + def parameters(self): + return self._log_likelihood.parameters + + @property + def n_parameters(self): + return self._log_likelihood.n_parameters + + @property + def likelihood(self) -> BaseLikelihood: + return self._log_likelihood class GaussianLogLikelihoodKnownSigma(BaseLikelihood): @@ -21,91 +61,60 @@ class GaussianLogLikelihoodKnownSigma(BaseLikelihood): Parameters ---------- - sigma : scalar or array + sigma0 : scalar or array Initial standard deviation around ``x0``. Either a scalar value (one standard deviation for all coordinates) or an array with one entry - per dimension. Not all methods will use this information. + per dimension. """ - def __init__(self, problem, sigma): - super(GaussianLogLikelihoodKnownSigma, self).__init__(problem) - self.sigma = None - self.set_sigma(sigma) - self._offset = -0.5 * self.n_time_data * np.log(2 * np.pi / self.sigma) - self._multip = -1 / (2.0 * self.sigma**2) - self.sigma2 = self.sigma**-2 - self._dl = np.ones(self.n_parameters) - - def set_sigma(self, sigma): - """ - Setter for sigma parameter + def __init__(self, problem: BaseProblem, sigma0: Union[list[float], float]): + super().__init__(problem) + sigma0 = self.check_sigma0(sigma0) + self.sigma2 = sigma0**2.0 + self._offset = -0.5 * self.n_data * np.log(2 * np.pi * self.sigma2) + self._multip = -1 / (2.0 * self.sigma2) + + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> Union[float, tuple[float, np.ndarray]]: """ - if sigma is None: - raise ValueError( - "The GaussianLogLikelihoodKnownSigma cost requires sigma to be " - + "either a scalar value or an array with one entry per dimension." - ) - - if not isinstance(sigma, np.ndarray): - sigma = np.asarray(sigma) - - if not np.issubdtype(sigma.dtype, np.number): - raise ValueError("Sigma must contain only numeric values") + Compute the Gaussian log-likelihood for the given parameters with known sigma. - if np.any(sigma <= 0): - raise ValueError("Sigma must be positive") - else: - self.sigma = sigma - - def get_sigma(self): - """ - Getter for sigma parameter + This method only computes the likelihood, without calling the problem.evaluateS1. """ - return self.sigma + # Verify we have dy if calculate_grad is True + self.verify_args(dy, calculate_grad) - def _evaluate(self, x, grad=None): - """ - Calls the problem.evaluate method and calculates - the log-likelihood - """ - y = self.problem.evaluate(x) + # Early return if the prediction is not verified + if not self.verify_prediction(y): + return (-np.inf, -self.grad_fail) if calculate_grad else -np.inf - for key in self.signal: - if len(y.get(key, [])) != len(self._target.get(key, [])): - return -np.float64(np.inf) # prediction doesn't match target + # Calculate residuals and error + r = np.asarray([self._target[signal] - y[signal] for signal in self.signal]) + e = np.sum(self._offset + self._multip * np.sum(np.real(r * np.conj(r)))) - e = np.asarray( - [ - np.sum( - self._offset - + self._multip * np.sum((self._target[signal] - y[signal]) ** 2) - ) - for signal in self.signal - ] - ) + if calculate_grad: + dl = np.sum((np.sum((r * dy.T), axis=2) / self.sigma2), axis=1) + return e, dl - if self.n_outputs == 1: - return e.item() - else: - return np.sum(e) + return e - def _evaluateS1(self, x, grad=None): + def check_sigma0(self, sigma0: Union[np.ndarray, float]): """ - Calls the problem.evaluateS1 method and calculates - the log-likelihood + Check the validity of sigma0. """ - y, dy = self.problem.evaluateS1(x) - - for key in self.signal: - if len(y.get(key, [])) != len(self._target.get(key, [])): - likelihood = np.float64(np.inf) - dl = self._dl * np.ones(self.n_parameters) - return -likelihood, -dl - - r = np.asarray([self._target[signal] - y[signal] for signal in self.signal]) - likelihood = self._evaluate(x) - dl = np.sum((self.sigma2 * np.sum((r * dy.T), axis=2)), axis=1) - return likelihood, dl + sigma0 = np.asarray(sigma0, dtype=float) + if not np.all(sigma0 > 0): + raise ValueError("Sigma0 must be positive") + if np.shape(sigma0) not in [(), (1,), (self.n_outputs,)]: + raise ValueError( + "sigma0 must be either a scalar value (one standard deviation for " + "all coordinates) or an array with one entry per dimension." + ) + return sigma0 class GaussianLogLikelihood(BaseLikelihood): @@ -114,76 +123,256 @@ class GaussianLogLikelihood(BaseLikelihood): data follows a Gaussian distribution and computes the log-likelihood of observed data under this assumption. + This class estimates the standard deviation of the Gaussian distribution + alongside the parameters of the model. + Attributes ---------- _logpi : float Precomputed offset value for the log-likelihood function. + _dsigma_scale : float + Scale factor for derivative of standard deviation. """ - def __init__(self, problem): - super(GaussianLogLikelihood, self).__init__(problem) - self._logpi = -0.5 * self.n_time_data * np.log(2 * np.pi) - self._dl = np.ones(self.n_parameters + self.n_outputs) + def __init__( + self, + problem: BaseProblem, + sigma0: Union[float, list[float], list[Parameter]] = 1e-2, + dsigma_scale: float = 1.0, + ): + super().__init__(problem) + self._dsigma_scale = dsigma_scale + self._logpi = -0.5 * self.n_data * np.log(2 * np.pi) + + # Add sigma parameter, join with self.parameters, reapply transformations + self.sigma = Parameters() + self._add_sigma_parameters(sigma0) + self.join_parameters(self.sigma) + self.transformation = self._parameters.construct_transformation() + + def _add_sigma_parameters(self, sigma0): + sigma0 = [sigma0] if not isinstance(sigma0, list) else sigma0 + sigma0 = self._pad_sigma0(sigma0) + + for i, value in enumerate(sigma0): + self._add_single_sigma(i, value) + + def _pad_sigma0(self, sigma0): + if len(sigma0) < self.n_outputs: + return np.pad( + sigma0, + (0, self.n_outputs - len(sigma0)), + constant_values=sigma0[-1], + ) + return sigma0 + + def _add_single_sigma(self, index, value): + if isinstance(value, Parameter): + self.sigma.add(value) + elif isinstance(value, (int, float)): + self.sigma.add( + Parameter( + f"Sigma for output {index+1}", + initial_value=value, + prior=Uniform(1e-8 * value, 3 * value), + bounds=[1e-8, 3 * value], + ) + ) + else: + raise TypeError( + f"Expected sigma0 to contain Parameter objects or numeric values. " + f"Received {type(value)}" + ) - def _evaluate(self, x, grad=None): + @property + def dsigma_scale(self): + """ + Scaling factor for the dsigma term in the gradient calculation. + """ + return self._dsigma_scale + + @dsigma_scale.setter + def dsigma_scale(self, new_value): + if new_value < 0: + raise ValueError("dsigma_scale must be non-negative") + self._dsigma_scale = new_value + + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> Union[float, tuple[float, np.ndarray]]: """ - Evaluates the Gaussian log-likelihood for the given parameters. + Compute the Gaussian log-likelihood for the given parameters. - Args: - x (array_like): The parameters for which to evaluate the log-likelihood. - The last `self.n_outputs` elements are assumed to be the - standard deviations of the Gaussian distributions. + This method only computes the likelihood, without calling problem.evaluate(). - Returns: - float: The log-likelihood value, or -inf if the standard deviations are received as non-positive. + Returns + ------- + float + The log-likelihood value, or -inf if the standard deviations are non-positive. """ - sigma = np.asarray(x[-self.n_outputs :]) + # Verify we have dy if calculate_grad is True + self.verify_args(dy, calculate_grad) + sigma = self.sigma.current_value() - if np.any(sigma <= 0): - return -np.inf + if not self.verify_prediction(y): + return (-np.inf, -self.grad_fail) if calculate_grad else -np.inf - y = self.problem.evaluate(x[: -self.n_outputs]) + # Calculate residuals and error + r = np.asarray([self._target[signal] - y[signal] for signal in self.signal]) + e = np.sum( + self._logpi + - self.n_data * np.log(sigma) + - np.sum(np.real(r * np.conj(r)), axis=1) / (2.0 * sigma**2.0) + ) - for key in self.signal: - if len(y.get(key, [])) != len(self._target.get(key, [])): - return -np.float64(np.inf) # prediction doesn't match target + if calculate_grad: + dl = np.sum((np.sum((r * dy.T), axis=2) / (sigma**2.0)), axis=1) + dsigma = ( + -self.n_data / sigma + np.sum(r**2.0, axis=1) / (sigma**3.0) + ) / self._dsigma_scale + dl = np.concatenate((dl.flatten(), dsigma)) + return e, dl - e = np.asarray( - [ - np.sum( - self._logpi - - self.n_time_data * np.log(sigma) - - np.sum((self._target[signal] - y[signal]) ** 2) / (2.0 * sigma**2) - ) - for signal in self.signal - ] - ) + return e - if self.n_outputs == 1: - return e.item() - else: - return np.sum(e) - def _evaluateS1(self, x, grad=None): +class ScaledLogLikelihood(BaseMetaLikelihood): + r""" + This class scaled a `BaseLogLikelihood` class by the number of observations. + The scaling factor is given below: + + .. math:: + \mathcal{\hat{L(\theta)}} = \frac{1}{N} \mathcal{L(\theta)} + + This class aims to provide numerical values with lower magnitude than the + canonical likelihoods, which can improve optimiser convergence in certain + cases. + """ + + def __init__(self, log_likelihood: BaseLikelihood): + super().__init__(log_likelihood) + + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> Union[float, tuple[float, np.ndarray]]: + likelihood = self._log_likelihood.compute(y, dy, calculate_grad) + normalised_val = 1 / self.n_data + + if isinstance(likelihood, tuple): + return tuple(val * normalised_val for val in likelihood) + + return likelihood * normalised_val + + +class LogPosterior(BaseMetaLikelihood): + """ + The Log Posterior for a given problem. + + Computes the log posterior which is proportional to the sum of the log + likelihood and the log prior. + + Parameters + ---------- + log_likelihood : BaseLikelihood + The likelihood class of type ``BaseLikelihood``. + log_prior : Optional, Union[pybop.BasePrior, stats.rv_continuous] + The prior class of type ``BasePrior`` or ``stats.rv_continuous``. + If not provided, the prior class will be taken from the parameter priors + constructed in the `pybop.Parameters` class. + gradient_step : float, default: 1e-3 + The step size for the finite-difference gradient calculation + if the ``log_prior`` is not of type ``BasePrior``. + """ + + def __init__( + self, + log_likelihood: BaseLikelihood, + log_prior: Optional[Union[pybop.BasePrior, stats.rv_continuous]] = None, + gradient_step: float = 1e-3, + ): + self.gradient_step = gradient_step + super().__init__(log_likelihood) + + if log_prior is None: + self._prior = JointLogPrior(*self.parameters.priors()) + else: + self._prior = log_prior + + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> Union[float, tuple[float, np.ndarray]]: """ - Calls the problem.evaluateS1 method and calculates - the log-likelihood + Calculate the posterior cost for a given forward model prediction. + + Parameters + ---------- + y : dict + The data for which to evaluate the cost. + dy : np.ndarray, optional + The correspond sensitivities in the data. + calculate_grad : bool, optional + Whether to calculate the gradient of the cost function. + + Returns + ------- + Union[float, Tuple[float, np.ndarray]] + The posterior cost, and optionally the gradient. """ - sigma = np.asarray(x[-self.n_outputs :]) + # Verify we have dy if calculate_grad is True + self.verify_args(dy, calculate_grad) + + if calculate_grad: + if isinstance(self._prior, BasePrior): + log_prior, dp = self._prior.logpdfS1(self.parameters.current_value()) + else: + # Compute log prior first + log_prior = self._prior.logpdf(self.parameters.current_value()) + + # Compute a finite difference approximation of the gradient of the log prior + delta = self.parameters.initial_value() * self.gradient_step + dp = [] + + for parameter, step_size in zip(self.parameters, delta): + param_value = parameter.value + upper_value = param_value * (1 + step_size) + lower_value = param_value * (1 - step_size) + + log_prior_upper = parameter.prior.logpdf(upper_value) + log_prior_lower = parameter.prior.logpdf(lower_value) + + gradient = (log_prior_upper - log_prior_lower) / ( + 2 * step_size * param_value + np.finfo(float).eps + ) + dp.append(gradient) + else: + log_prior = self._prior.logpdf(self.parameters.current_value()) + + if not np.isfinite(log_prior).any(): + return (-np.inf, -self.grad_fail) if calculate_grad else -np.inf - if np.any(sigma <= 0): - return -np.float64(np.inf), -self._dl * np.ones(self.n_parameters) + if calculate_grad: + log_likelihood, dl = self._log_likelihood.compute( + y, dy, calculate_grad=True + ) - y, dy = self.problem.evaluateS1(x[: -self.n_outputs]) - for key in self.signal: - if len(y.get(key, [])) != len(self._target.get(key, [])): - likelihood = np.float64(np.inf) - dl = self._dl * np.ones(self.n_parameters) - return -likelihood, -dl + posterior = log_likelihood + log_prior + total_gradient = dl + dp - r = np.asarray([self._target[signal] - y[signal] for signal in self.signal]) - likelihood = self._evaluate(x) - dl = sigma ** (-2.0) * np.sum((r * dy.T), axis=2) - dsigma = -self.n_time_data / sigma + sigma**-(3.0) * np.sum(r**2, axis=1) - dl = np.concatenate((dl.flatten(), dsigma)) - return likelihood, dl + return posterior, total_gradient + + log_likelihood = self._log_likelihood.compute(y) + posterior = log_likelihood + log_prior + return posterior + + @property + def prior(self) -> BasePrior: + return self._prior diff --git a/pybop/costs/_weighted_cost.py b/pybop/costs/_weighted_cost.py new file mode 100644 index 000000000..39c42ef67 --- /dev/null +++ b/pybop/costs/_weighted_cost.py @@ -0,0 +1,125 @@ +from typing import Optional, Union + +import numpy as np + +from pybop import BaseCost, BaseLikelihood, DesignCost + + +class WeightedCost(BaseCost): + """ + A subclass for constructing a linear combination of cost functions as + a single weighted cost function. + + Parameters + ---------- + costs : pybop.BaseCost + The individual PyBOP cost objects. + weights : list[float] + A list of values with which to weight the cost values. + has_identical_problems : bool + If True, the shared problem will be evaluated once and saved before the + self.compute() method of each cost is called (default: False). + has_separable_problem: bool + This attribute must be set to False for WeightedCost objects. If the + corresponding attribute of an individual cost is True, the problem is + separable from the cost function and will be evaluated before the + individual cost evaluation is called. + """ + + def __init__(self, *costs, weights: Optional[list[float]] = None): + if not all(isinstance(cost, BaseCost) for cost in costs): + raise TypeError("All costs must be instances of BaseCost.") + self.costs = [cost for cost in costs] + if len(set(type(cost.problem) for cost in self.costs)) > 1: + raise TypeError("All problems must be of the same class type.") + self.minimising = not any( + isinstance(cost, (BaseLikelihood, DesignCost)) for cost in self.costs + ) + + # Check if weights are provided + if weights is not None: + try: + self.weights = np.asarray(weights, dtype=float) + except ValueError: + raise ValueError("Weights must be numeric values.") from None + + if self.weights.size != len(self.costs): + raise ValueError("Number of weights must match number of costs.") + else: + self.weights = np.ones(len(self.costs)) + + # Check if all costs depend on the same problem + self._has_identical_problems = all( + cost.has_separable_problem and cost.problem is self.costs[0].problem + for cost in self.costs + ) + + if self._has_identical_problems: + super().__init__(self.costs[0].problem) + else: + super().__init__() + + for cost in self.costs: + self.join_parameters(cost.parameters) + + # Weighted costs do not use this functionality + self._has_separable_problem = False + + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> Union[float, tuple[float, np.ndarray]]: + """ + Computes the cost function for the given predictions. + + Parameters + ---------- + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. + + Returns + ------- + float + The weighted cost value. + """ + if self._has_identical_problems: + inputs = self.problem.parameters.as_dict() + if calculate_grad: + y, dy = self.problem.evaluateS1(inputs) + else: + y = self.problem.evaluate(inputs) + + e = np.empty_like(self.costs) + de = np.empty((len(self.parameters), len(self.costs))) + + for i, cost in enumerate(self.costs): + if self._has_identical_problems: + y, dy = (y, dy) + elif cost.has_separable_problem: + inputs = cost.parameters.as_dict() + if calculate_grad: + y, dy = cost.problem.evaluateS1(inputs) + else: + y = cost.problem.evaluate(inputs) + + if calculate_grad: + e[i], de[:, i] = cost.compute(y, dy=dy, calculate_grad=True) + else: + e[i] = cost.compute(y) + + e = np.dot(e, self.weights) + if calculate_grad: + de = np.dot(de, self.weights) + return e, de + + return e + + @property + def has_identical_problems(self): + return self._has_identical_problems diff --git a/pybop/costs/base_cost.py b/pybop/costs/base_cost.py index 04d0a3934..b53e80dff 100644 --- a/pybop/costs/base_cost.py +++ b/pybop/costs/base_cost.py @@ -1,4 +1,10 @@ +from typing import Optional, Union + +import numpy as np +from numpy import ndarray + from pybop import BaseProblem +from pybop.parameters.parameter import Inputs, Parameters class BaseCost: @@ -15,46 +21,53 @@ class BaseCost: problem : object A problem instance containing the data and functions necessary for evaluating the cost function. - _target : array-like + target : array-like An array containing the target data to fit. - x0 : array-like - The initial guess for the model parameters. n_outputs : int The number of outputs in the model. + has_separable_problem : bool + If True, the problem is separable from the cost function and will be + evaluated in advance of the call to self.compute() (default: False). + _de : float + The gradient of the cost function to use if an error occurs during + evaluation. Defaults to 1.0. """ - def __init__(self, problem=None): - self.parameters = None + def __init__(self, problem: Optional[BaseProblem] = None): + self._parameters = Parameters() + self._transformation = None self.problem = problem - self.x0 = None + self.verbose = False + self._has_separable_problem = False + self.y = None + self.dy = None + self._de = 1.0 if isinstance(self.problem, BaseProblem): - self._target = self.problem._target - self.parameters = self.problem.parameters - self.x0 = self.problem.x0 + self._target = self.problem.target + self._parameters.join(self.problem.parameters) self.n_outputs = self.problem.n_outputs self.signal = self.problem.signal - - @property - def n_parameters(self): - return len(self.parameters) - - def __call__(self, x, grad=None): + self._transformation = self._parameters.construct_transformation() + self._has_separable_problem = True + self.grad_fail = None + self.set_fail_gradient() + + def __call__( + self, + inputs: Union[Inputs, list], + calculate_grad: bool = False, + apply_transform: bool = False, + ): """ - Call the evaluate function for a given set of parameters. - """ - return self.evaluate(x, grad) - - def evaluate(self, x, grad=None): - """ - Call the evaluate function for a given set of parameters. + This method calls the forward model via problem.evaluate(inputs), + and computes the cost for the given output by calling self.compute(). Parameters ---------- - x : array-like - The parameters for which to evaluate the cost. - grad : array-like, optional - An array to store the gradient of the cost function with respect - to the parameters. + inputs : Inputs or array-like + The parameters for which to compute the cost and gradient. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. Returns ------- @@ -66,28 +79,44 @@ def evaluate(self, x, grad=None): ValueError If an error occurs during the calculation of the cost. """ - try: - return self._evaluate(x, grad) - - except NotImplementedError as e: - raise e - - except Exception as e: - raise ValueError(f"Error in cost calculation: {e}") - - def _evaluate(self, x, grad=None): + # Apply transformation if needed + # Note, we use the transformation and parameter properties here to enable + # differing attributes within the `LogPosterior` class + self.has_transform = self.transformation is not None and apply_transform + if self.has_transform: + inputs = self.transformation.to_model(inputs) + inputs = self.parameters.verify(inputs) + self.parameters.update(values=list(inputs.values())) + + y, dy = None, None + if self._has_separable_problem: + if calculate_grad: + y, dy = self.problem.evaluateS1(self.problem.parameters.as_dict()) + cost, grad = self.compute(y, dy=dy, calculate_grad=calculate_grad) + if self.has_transform and np.isfinite(cost): + jac = self.transformation.jacobian(inputs) + grad = np.matmul(grad, jac) + return cost, grad + + y = self.problem.evaluate(self.problem.parameters.as_dict()) + return self.compute(y, dy=dy, calculate_grad=calculate_grad) + + def compute(self, y: dict, dy: ndarray, calculate_grad: bool = False): """ - Calculate the cost function value for a given set of parameters. + Compute the cost and if `calculate_grad` is True, its gradient with + respect to the predictions. + This method only computes the cost, without calling the `problem.evaluate()`. This method must be implemented by subclasses. Parameters ---------- - x : array-like - The parameters for which to evaluate the cost. - grad : array-like, optional - An array to store the gradient of the cost function with respect - to the parameters. + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. Returns ------- @@ -101,53 +130,83 @@ def _evaluate(self, x, grad=None): """ raise NotImplementedError - def evaluateS1(self, x): + def set_fail_gradient(self, de: float = 1.0): """ - Call _evaluateS1 for a given set of parameters. + Set the fail gradient to a specified value. + + The fail gradient is used if an error occurs during the calculation + of the gradient. This method allows updating the default gradient value. Parameters ---------- - x : array-like - The parameters for which to compute the cost and gradient. - - Returns - ------- - tuple - A tuple containing the cost and the gradient. The cost is a float, - and the gradient is an array-like of the same length as `x`. - - Raises - ------ - ValueError - If an error occurs during the calculation of the cost or gradient. + de : float + The new fail gradient value to be used. """ - try: - return self._evaluateS1(x) - - except NotImplementedError as e: - raise e - - except Exception as e: - raise ValueError(f"Error in cost calculation: {e}") + if not isinstance(de, float): + de = float(de) + self._de = de + self.grad_fail = self._de * np.ones(self.n_parameters) - def _evaluateS1(self, x): + def verify_prediction(self, y): """ - Compute the cost and its gradient with respect to the parameters. + Verify that the prediction matches the target data. Parameters ---------- - x : array-like - The parameters for which to compute the cost and gradient. + y : dict + The model predictions. Returns ------- - tuple - A tuple containing the cost and the gradient. The cost is a float, - and the gradient is an array-like of the same length as `x`. + bool + True if the prediction matches the target data, otherwise False. + """ + if any( + len(y.get(key, [])) != len(self._target.get(key, [])) for key in self.signal + ): + return False - Raises - ------ - NotImplementedError - If the method has not been implemented by the subclass. + return True + + def verify_args(self, dy: ndarray, calculate_grad: bool): + if calculate_grad and dy is None: + raise ValueError( + "Forward model sensitivities need to be provided alongside `calculate_grad=True` for `cost.compute`." + ) + + def join_parameters(self, parameters): """ - raise NotImplementedError + Setter for joining parameters. This method sets the fail gradient if the join adds parameters. + """ + original_n_params = self.n_parameters + self._parameters.join(parameters) + if original_n_params != self.n_parameters: + self.set_fail_gradient() + + @property + def n_parameters(self): + return len(self._parameters) + + @property + def has_separable_problem(self): + return self._has_separable_problem + + @property + def target(self): + return self._target + + @property + def parameters(self): + return self._parameters + + @parameters.setter + def parameters(self, parameters): + self._parameters = parameters + + @property + def transformation(self): + return self._transformation + + @transformation.setter + def transformation(self, transformation): + self._transformation = transformation diff --git a/pybop/costs/design_costs.py b/pybop/costs/design_costs.py index 60064c65c..49714cff7 100644 --- a/pybop/costs/design_costs.py +++ b/pybop/costs/design_costs.py @@ -1,8 +1,7 @@ -import warnings +from typing import Union import numpy as np -from pybop import is_numeric from pybop.costs.base_cost import BaseCost @@ -16,13 +15,9 @@ class DesignCost(BaseCost): --------------------- problem : object The associated problem containing model and evaluation methods. - parameter_set : object) - The set of parameters from the problem's model. - dt : float - The time step size used in the simulation. """ - def __init__(self, problem, update_capacity=False): + def __init__(self, problem): """ Initialises the gravimetric energy density calculator with a problem. @@ -31,173 +26,248 @@ def __init__(self, problem, update_capacity=False): problem : object The problem instance containing the model and data. """ - super(DesignCost, self).__init__(problem) + super().__init__(problem) self.problem = problem - if update_capacity is True: - nominal_capacity_warning = ( - "The nominal capacity is approximated for each iteration." - ) - else: - nominal_capacity_warning = ( - "The nominal capacity is fixed at the initial model value." - ) - warnings.warn(nominal_capacity_warning, UserWarning) - self.update_capacity = update_capacity - self.parameter_set = problem.model.parameter_set - self.update_simulation_data(self.x0) - - def update_simulation_data(self, x0): + + +class GravimetricEnergyDensity(DesignCost): + """ + Calculates the gravimetric energy density (specific energy) of a battery cell, + when applied to a normalised discharge from upper to lower voltage limits. The + goal of maximising the energy density is achieved by setting minimising = False + in the optimiser settings. + + The gravimetric energy density [Wh.kg-1] is calculated as + + .. math:: + \\frac{1}{3600 m} \\int_{t=0}^{t=T} I(t) V(t) \\mathrm{d}t + + where m is the cell mass, t is the time, T is the total time, I is the current + and V is the voltage. The factor of 1/3600 is included to convert from seconds + to hours. + + Inherits all parameters and attributes from ``DesignCost``. + """ + + def __init__(self, problem): + super().__init__(problem) + + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> float: """ - Updates the simulation data based on the initial parameter values. + Computes the cost function for the given predictions. Parameters ---------- - x0 : array - The initial parameter values for the simulation. + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + Note: not used in design optimisation classes. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. + + Returns + ------- + float + The gravimetric energy density or -infinity in case of infeasible parameters. """ - if self.update_capacity: - self.problem.model.approximate_capacity(x0) - solution = self.problem.evaluate(x0) + if not any(np.isfinite(y[signal][0]) for signal in self.signal): + return -np.inf - if "Time [s]" not in solution: - raise ValueError("The solution does not contain time data.") - self.problem._time_data = solution["Time [s]"] - self.problem._target = {key: solution[key] for key in self.problem.signal} - self.dt = solution["Time [s]"][1] - solution["Time [s]"][0] + voltage, current = y["Voltage [V]"], y["Current [A]"] + dt = y["Time [s]"][1] - y["Time [s]"][0] + energy_density = np.trapz(voltage * current, dx=dt) / ( + 3600 * self.problem.model.cell_mass() + ) - def _evaluate(self, x, grad=None): - """ - Computes the value of the cost function. + return energy_density + + +class VolumetricEnergyDensity(DesignCost): + """ + Calculates the (volumetric) energy density of a battery cell, when applied to a + normalised discharge from upper to lower voltage limits. The goal of maximising + the energy density is achieved by setting minimising = False in the optimiser + settings. - This method must be implemented by subclasses. + The volumetric energy density [Wh.m-3] is calculated as + + .. math:: + \\frac{1}{3600 v} \\int_{t=0}^{t=T} I(t) V(t) \\mathrm{d}t + + where v is the cell volume, t is the time, T is the total time, I is the current + and V is the voltage. The factor of 1/3600 is included to convert from seconds + to hours. + + Inherits all parameters and attributes from ``DesignCost``. + """ + + def __init__(self, problem): + super().__init__(problem) + + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> float: + """ + Computes the cost function for the given predictions. Parameters ---------- - x : array - The parameter set for which to compute the cost. - grad : array, optional - Gradient information, not used in this method. - - Raises - ------ - NotImplementedError - If the method has not been implemented by the subclass. + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + Note: not used in design optimisation classes. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. + + Returns + ------- + float + The volumetric energy density or -infinity in case of infeasible parameters. """ - raise NotImplementedError + if not any(np.isfinite(y[signal][0]) for signal in self.signal): + return -np.inf + voltage, current = y["Voltage [V]"], y["Current [A]"] + dt = y["Time [s]"][1] - y["Time [s]"][0] + energy_density = np.trapz(voltage * current, dx=dt) / ( + 3600 * self.problem.model.cell_volume() + ) -class GravimetricEnergyDensity(DesignCost): + return energy_density + + +class GravimetricPowerDensity(DesignCost): """ - Represents the gravimetric energy density of a battery cell, calculated based - on a normalised discharge from upper to lower voltage limits. The goal is to - maximise the energy density, which is achieved by setting minimising = False - in the optimiser settings. + Calculates the gravimetric power density (specific power) of a battery cell, + when applied to a discharge from upper to lower voltage limits. The goal of + maximising the power density is achieved by setting minimising = False in the + optimiser settings. + + The time-averaged gravimetric power density [W.kg-1] is calculated as + + .. math:: + \\frac{1}{3600 m T} \\int_{t=0}^{t=T} I(t) V(t) \\mathrm{d}t + + where m is the cell mass, t is the time, T is the total time, I is the current + and V is the voltage. The factor of 1/3600 is included to convert from seconds + to hours. Inherits all parameters and attributes from ``DesignCost``. + + Additional parameters + --------------------- + target_time : int + The length of time (seconds) over which the power should be sustained. """ - def __init__(self, problem, update_capacity=False): - super(GravimetricEnergyDensity, self).__init__(problem, update_capacity) + def __init__(self, problem, target_time: Union[int, float] = 3600): + super().__init__(problem) + self.target_time = target_time - def _evaluate(self, x, grad=None): + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> float: """ - Computes the cost function for the energy density. + Computes the cost function for the given predictions. Parameters ---------- - x : array - The parameter set for which to compute the cost. - grad : array, optional - Gradient information, not used in this method. + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + Note: not used in design optimisation classes. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. Returns ------- float - The gravimetric energy density or -infinity in case of infeasible parameters. + The gravimetric power density or -infinity in case of infeasible parameters. """ - if not all(is_numeric(i) for i in x): - raise ValueError("Input must be a numeric array.") + if not any(np.isfinite(y[signal][0]) for signal in self.signal): + return -np.inf - try: - with warnings.catch_warnings(): - # Convert UserWarning to an exception - warnings.filterwarnings("error", category=UserWarning) + voltage, current = y["Voltage [V]"], y["Current [A]"] + dt = y["Time [s]"][1] - y["Time [s]"][0] + time_averaged_power_density = np.trapz(voltage * current, dx=dt) / ( + self.target_time * 3600 * self.problem.model.cell_mass() + ) - if self.update_capacity: - self.problem.model.approximate_capacity(x) - solution = self.problem.evaluate(x) + return time_averaged_power_density - voltage, current = solution["Voltage [V]"], solution["Current [A]"] - energy_density = np.trapz(voltage * current, dx=self.dt) / ( - 3600 * self.problem.model.cell_mass(self.parameter_set) - ) - return energy_density +class VolumetricPowerDensity(DesignCost): + """ + Calculates the (volumetric) power density of a battery cell, when applied to a + discharge from upper to lower voltage limits. The goal of maximising the power + density is achieved by setting minimising = False in the optimiser settings. - # Catch infeasible solutions and return infinity - except UserWarning as e: - print(f"Ignoring this sample due to: {e}") - return -np.inf + The time-averaged volumetric power density [W.m-3] is calculated as - # Catch any other exception and return infinity - except Exception as e: - print(f"An error occurred during the evaluation: {e}") - return -np.inf + .. math:: + \\frac{1}{3600 v T} \\int_{t=0}^{t=T} I(t) V(t) \\mathrm{d}t - -class VolumetricEnergyDensity(DesignCost): - """ - Represents the volumetric energy density of a battery cell, calculated based - on a normalised discharge from upper to lower voltage limits. The goal is to - maximise the energy density, which is achieved by setting minimising = False - in the optimiser settings. + where v is the cell volume, t is the time, T is the total time, I is the current + and V is the voltage. The factor of 1/3600 is included to convert from seconds + to hours. Inherits all parameters and attributes from ``DesignCost``. + + Additional parameters + --------------------- + target_time : int + The length of time (seconds) over which the power should be sustained. """ - def __init__(self, problem, update_capacity=False): - super(VolumetricEnergyDensity, self).__init__(problem, update_capacity) + def __init__(self, problem, target_time: Union[int, float] = 3600): + super().__init__(problem) + self.target_time = target_time - def _evaluate(self, x, grad=None): + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> float: """ - Computes the cost function for the energy density. + Computes the cost function for the given predictions. Parameters ---------- - x : array - The parameter set for which to compute the cost. - grad : array, optional - Gradient information, not used in this method. + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + Note: not used in design optimisation classes. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. Returns ------- float - The volumetric energy density or -infinity in case of infeasible parameters. + The volumetric power density or -infinity in case of infeasible parameters. """ - if not all(is_numeric(i) for i in x): - raise ValueError("Input must be a numeric array.") - try: - with warnings.catch_warnings(): - # Convert UserWarning to an exception - warnings.filterwarnings("error", category=UserWarning) - - if self.update_capacity: - self.problem.model.approximate_capacity(x) - solution = self.problem.evaluate(x) - - voltage, current = solution["Voltage [V]"], solution["Current [A]"] - energy_density = np.trapz(voltage * current, dx=self.dt) / ( - 3600 * self.problem.model.cell_volume(self.parameter_set) - ) - - return energy_density - - # Catch infeasible solutions and return infinity - except UserWarning as e: - print(f"Ignoring this sample due to: {e}") + if not any(np.isfinite(y[signal][0]) for signal in self.signal): return -np.inf - # Catch any other exception and return infinity - except Exception as e: - print(f"An error occurred during the evaluation: {e}") - return -np.inf + voltage, current = y["Voltage [V]"], y["Current [A]"] + dt = y["Time [s]"][1] - y["Time [s]"][0] + time_averaged_power_density = np.trapz(voltage * current, dx=dt) / ( + self.target_time * 3600 * self.problem.model.cell_volume() + ) + + return time_averaged_power_density diff --git a/pybop/costs/fitting_costs.py b/pybop/costs/fitting_costs.py index eff56059c..19b8f458a 100644 --- a/pybop/costs/fitting_costs.py +++ b/pybop/costs/fitting_costs.py @@ -1,6 +1,7 @@ +from typing import Union + import numpy as np -from pybop.costs._likelihoods import BaseLikelihood from pybop.costs.base_cost import BaseCost from pybop.observers.observer import Observer @@ -18,98 +19,51 @@ class RootMeanSquaredError(BaseCost): """ def __init__(self, problem): - super(RootMeanSquaredError, self).__init__(problem) - - # Default fail gradient - self._de = 1.0 - - def _evaluate(self, x, grad=None): + super().__init__(problem) + + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> Union[float, tuple[float, np.ndarray]]: """ - Calculate the root mean square error for a given set of parameters. + Computes the cost function for the given predictions. Parameters ---------- - x : array-like - The parameters for which to evaluate the cost. - grad : array-like, optional - An array to store the gradient of the cost function with respect - to the parameters. + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. Returns ------- float The root mean square error. - - """ - prediction = self.problem.evaluate(x) - - for key in self.signal: - if len(prediction.get(key, [])) != len(self._target.get(key, [])): - return np.float64(np.inf) # prediction doesn't match target - - e = np.asarray( - [ - np.sqrt(np.mean((prediction[signal] - self._target[signal]) ** 2)) - for signal in self.signal - ] - ) - - if self.n_outputs == 1: - return e.item() - else: - return np.sum(e) - - def _evaluateS1(self, x): - """ - Compute the cost and its gradient with respect to the parameters. - - Parameters - ---------- - x : array-like - The parameters for which to compute the cost and gradient. - - Returns - ------- - tuple - A tuple containing the cost and the gradient. The cost is a float, - and the gradient is an array-like of the same length as `x`. - - Raises - ------ - ValueError - If an error occurs during the calculation of the cost or gradient. """ - y, dy = self.problem.evaluateS1(x) + # Verify we have dy if calculate_grad is True + self.verify_args(dy, calculate_grad) - for key in self.signal: - if len(y.get(key, [])) != len(self._target.get(key, [])): - e = np.float64(np.inf) - de = self._de * np.ones(self.n_parameters) - return e, de + # Early return if the prediction is not verified + if not self.verify_prediction(y): + return (np.inf, self.grad_fail) if calculate_grad else np.inf + # Calculate residuals and error r = np.asarray([y[signal] - self._target[signal] for signal in self.signal]) - e = np.sqrt(np.mean(r**2, axis=1)) - de = np.mean((r * dy.T), axis=2) / (e + np.finfo(float).eps) - - if self.n_outputs == 1: - return e.item(), de.flatten() - else: - return np.sum(e), np.sum(de, axis=1) - - def set_fail_gradient(self, de): - """ - Set the fail gradient to a specified value. - - The fail gradient is used if an error occurs during the calculation - of the gradient. This method allows updating the default gradient value. + e = np.sqrt(np.mean(np.abs(r) ** 2, axis=1)) + + if calculate_grad: + de = np.mean((r * dy.T), axis=2) / (e + np.finfo(float).eps) + return ( + (e.item(), de.flatten()) + if self.n_outputs == 1 + else (e.sum(), de.sum(1)) + ) - Parameters - ---------- - de : float - The new fail gradient value to be used. - """ - de = float(de) - self._de = de + return e.item() if self.n_outputs == 1 else np.sum(e) class SumSquaredError(BaseCost): @@ -122,246 +76,261 @@ class SumSquaredError(BaseCost): Inherits all parameters and attributes from ``BaseCost``. - Additional Attributes - --------------------- - _de : float - The gradient of the cost function to use if an error occurs during - evaluation. Defaults to 1.0. - """ def __init__(self, problem): - super(SumSquaredError, self).__init__(problem) - - # Default fail gradient - self._de = 1.0 - - def _evaluate(self, x, grad=None): + super().__init__(problem) + + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> Union[float, tuple[float, np.ndarray]]: """ - Calculate the sum of squared errors for a given set of parameters. + Computes the cost function for the given predictions. Parameters ---------- - x : array-like - The parameters for which to evaluate the cost. - grad : array-like, optional - An array to store the gradient of the cost function with respect - to the parameters. + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. Returns ------- float - The sum of squared errors. - """ - prediction = self.problem.evaluate(x) - - for key in self.signal: - if len(prediction.get(key, [])) != len(self._target.get(key, [])): - return np.float64(np.inf) # prediction doesn't match target - - e = np.asarray( - [ - np.sum(((prediction[signal] - self._target[signal]) ** 2)) - for signal in self.signal - ] - ) - if self.n_outputs == 1: - return e.item() - else: - return np.sum(e) - - def _evaluateS1(self, x): + The Sum of Squared Error. """ - Compute the cost and its gradient with respect to the parameters. + # Verify we have dy if calculate_grad is True + self.verify_args(dy, calculate_grad) - Parameters - ---------- - x : array-like - The parameters for which to compute the cost and gradient. - - Returns - ------- - tuple - A tuple containing the cost and the gradient. The cost is a float, - and the gradient is an array-like of the same length as `x`. - - Raises - ------ - ValueError - If an error occurs during the calculation of the cost or gradient. - """ - y, dy = self.problem.evaluateS1(x) - for key in self.signal: - if len(y.get(key, [])) != len(self._target.get(key, [])): - e = np.float64(np.inf) - de = self._de * np.ones(self.n_parameters) - return e, de + # Early return if the prediction is not verified + if not self.verify_prediction(y): + return (np.inf, self.grad_fail) if calculate_grad else np.inf + # Calculate residuals and error r = np.asarray([y[signal] - self._target[signal] for signal in self.signal]) - e = np.sum(np.sum(r**2, axis=0), axis=0) - de = 2 * np.sum(np.sum((r * dy.T), axis=2), axis=1) + e = np.sum(np.sum(np.abs(r) ** 2, axis=0), axis=0) - return e, de + if calculate_grad: + de = 2 * np.sum((r * dy.T), axis=(1, 2)) + return e, de - def set_fail_gradient(self, de): - """ - Set the fail gradient to a specified value. + return e - The fail gradient is used if an error occurs during the calculation - of the gradient. This method allows updating the default gradient value. - Parameters - ---------- - de : float - The new fail gradient value to be used. - """ - de = float(de) - self._de = de +class Minkowski(BaseCost): + """ + The Minkowski distance is a generalisation of several distance metrics, + including the Euclidean and Manhattan distances. It is defined as: + .. math:: + L_p(x, y) = ( \\sum_i |x_i - y_i|^p )^(1/p) -class ObserverCost(BaseCost): - """ - Observer cost function. + where p > 0 is the order of the Minkowski distance. For p ≥ 1, the + Minkowski distance is a metric. For 0 < p < 1, it is not a metric, as it + does not satisfy the triangle inequality, although a metric can be + obtained by removing the (1/p) exponent. - Computes the cost function for an observer model, which is log likelihood - of the data points given the model parameters. + Special cases: - Inherits all parameters and attributes from ``BaseCost``. + * p = 1: Manhattan distance + * p = 2: Euclidean distance + * p → ∞: Chebyshev distance (not implemented as yet) - """ + This class implements the Minkowski distance as a cost function for + optimisation problems, allowing for flexible distance-based optimisation + across various problem domains. - def __init__(self, observer: Observer): - super().__init__(problem=observer) - self._observer = observer + Additional Attributes + --------------------- + p : float, optional + The order of the Minkowski distance. + """ - def _evaluate(self, x, grad=None): + def __init__(self, problem, p: float = 2.0): + super().__init__(problem) + if p < 0: + raise ValueError( + "The order of the Minkowski distance must be greater than 0." + ) + elif not np.isfinite(p): + raise ValueError( + "For p = infinity, an implementation of the Chebyshev distance is required." + ) + self.p = float(p) + + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> Union[float, tuple[float, np.ndarray]]: """ - Calculate the observer cost for a given set of parameters. + Computes the cost function for the given predictions. Parameters ---------- - x : array-like - The parameters for which to evaluate the cost. - grad : array-like, optional - An array to store the gradient of the cost function with respect - to the parameters. + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. Returns ------- float - The observer cost (negative of the log likelihood). + The Minkowski cost. """ - inputs = self._observer.parameters.as_dict(x) - log_likelihood = self._observer.log_likelihood( - self._target, self._observer.time_data(), inputs - ) - return -log_likelihood + # Verify we have dy if calculate_grad is True + self.verify_args(dy, calculate_grad) - def evaluateS1(self, x): - """ - Compute the cost and its gradient with respect to the parameters. + # Early return if the prediction is not verified + if not self.verify_prediction(y): + return (np.inf, self.grad_fail) if calculate_grad else np.inf - Parameters - ---------- - x : array-like - The parameters for which to compute the cost and gradient. + # Calculate residuals and error + r = np.asarray([y[signal] - self._target[signal] for signal in self.signal]) + e = np.sum(np.abs(r) ** self.p) ** (1 / self.p) - Returns - ------- - tuple - A tuple containing the cost and the gradient. The cost is a float, - and the gradient is an array-like of the same length as `x`. - - Raises - ------ - ValueError - If an error occurs during the calculation of the cost or gradient. - """ - raise NotImplementedError + if calculate_grad: + de = np.sum( + np.sum(np.sign(r) * np.abs(r) ** (self.p - 1) * dy.T, axis=2) + / (e ** (self.p - 1) + np.finfo(float).eps), + axis=1, + ) + return e, de + + return e -class MAP(BaseLikelihood): +class SumofPower(BaseCost): """ - Maximum a posteriori cost function. + The Sum of Power [1] is a generalised cost function based on the p-th power + of absolute differences between two vectors. It is defined as: - Computes the maximum a posteriori cost function, which is the sum of the - log likelihood and the log prior. The goal of maximising is achieved by - setting minimising = False in the optimiser settings. + .. math:: + C_p(x, y) = \\sum_i |x_i - y_i|^p - Inherits all parameters and attributes from ``BaseLikelihood``. + where p ≥ 0 is the power order. - """ + This class implements the Sum of Power as a cost function for + optimisation problems, allowing for flexible power-based optimisation + across various problem domains. - def __init__(self, problem, likelihood, sigma=None): - super(MAP, self).__init__(problem) - self.sigma0 = sigma - if self.sigma0 is None: - self.sigma0 = [] - for param in self.problem.parameters: - self.sigma0.append(param.prior.sigma) - - try: - self.likelihood = likelihood(problem=self.problem, sigma=self.sigma0) - except Exception as e: - raise ValueError( - f"An error occurred when constructing the Likelihood class: {e}" - ) + Special cases: + + * p = 1: Sum of Absolute Differences + * p = 2: Sum of Squared Differences + * p → ∞: Maximum Absolute Difference + + Note that this is not normalised, unlike distance metrics. To get a + distance metric, you would need to take the p-th root of the result. - if hasattr(self, "likelihood") and not isinstance( - self.likelihood, BaseLikelihood - ): - raise ValueError(f"{self.likelihood} must be a subclass of BaseLikelihood") + [1]: https://mathworld.wolfram.com/PowerSum.html - def _evaluate(self, x, grad=None): + Additional Attributes + --------------------- + p : float, optional + The power order for Sum of Power. + """ + + def __init__(self, problem, p: float = 2.0): + super().__init__(problem) + if p < 0: + raise ValueError("The order of 'p' must be greater than 0.") + elif not np.isfinite(p): + raise ValueError("p = np.inf is not yet supported.") + self.p = float(p) + + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> Union[float, tuple[float, np.ndarray]]: """ - Calculate the maximum a posteriori cost for a given set of parameters. + Computes the cost function for the given predictions. Parameters ---------- - x : array-like - The parameters for which to evaluate the cost. - grad : array-like, optional - An array to store the gradient of the cost function with respect - to the parameters. + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. Returns ------- float - The maximum a posteriori cost. + The Sum of Power cost. """ - log_likelihood = self.likelihood.evaluate(x) - log_prior = sum( - param.prior.logpdf(x_i) for x_i, param in zip(x, self.problem.parameters) - ) + # Verify we have dy if calculate_grad is True + self.verify_args(dy, calculate_grad) + + # Early return if the prediction is not verified + if not self.verify_prediction(y): + return (np.inf, self.grad_fail) if calculate_grad else np.inf + + # Calculate residuals and error + r = np.asarray([y[signal] - self._target[signal] for signal in self.signal]) + e = np.sum(np.abs(r) ** self.p) + + if calculate_grad: + de = self.p * np.sum( + np.sign(r) * np.abs(r) ** (self.p - 1) * dy.T, axis=(1, 2) + ) + return e, de - posterior = log_likelihood + log_prior - return posterior + return e - def _evaluateS1(self, x): + +class ObserverCost(BaseCost): + """ + Observer cost function. + + Computes the cost function for an observer model, which is log likelihood + of the data points given the model parameters. + + Inherits all parameters and attributes from ``BaseCost``. + + """ + + def __init__(self, observer: Observer): + super().__init__(problem=observer) + self._observer = observer + self._has_separable_problem = False + + def compute( + self, + y: dict, + dy: np.ndarray = None, + calculate_grad: bool = False, + ) -> float: """ - Compute the maximum a posteriori with respect to the parameters. - The method passes the likelihood gradient to the optimiser without modification. + Computes the cost function for the given predictions. Parameters ---------- - x : array-like - The parameters for which to compute the cost and gradient. + y : dict + The dictionary of predictions with keys designating the signals for fitting. + dy : np.ndarray, optional + The corresponding gradient with respect to the parameters for each signal. + calculate_grad : bool, optional + A bool condition designating whether to calculate the gradient. Returns ------- - tuple - A tuple containing the cost and the gradient. The cost is a float, - and the gradient is an array-like of the same length as `x`. - - Raises - ------ - ValueError - If an error occurs during the calculation of the cost or gradient. + float + The observer cost (negative of the log likelihood). """ - log_likelihood, dl = self.likelihood.evaluateS1(x) - log_prior = sum( - param.prior.logpdf(x_i) for x_i, param in zip(x, self.problem.parameters) + inputs = self._parameters.as_dict() + log_likelihood = self._observer.log_likelihood( + self._target, self._observer.domain_data, inputs ) - - posterior = log_likelihood + log_prior - return posterior, dl + return -log_likelihood diff --git a/pybop/models/base_model.py b/pybop/models/base_model.py index a05062672..fd1e0b87f 100644 --- a/pybop/models/base_model.py +++ b/pybop/models/base_model.py @@ -1,18 +1,20 @@ import copy from dataclasses import dataclass -from typing import Any, Dict, Optional, Union +from typing import Callable, Optional, Union import casadi import numpy as np import pybamm +from pybamm import IDAKLUSolver as IDAKLUSolver +from scipy.sparse import csc_matrix +from scipy.sparse.linalg import spsolve -from pybop import Dataset, Experiment, Parameters, ParameterSet - -Inputs = Dict[str, float] +from pybop import Dataset, Experiment, Parameters, ParameterSet, SymbolReplacer +from pybop.parameters.parameter import Inputs @dataclass -class TimeSeriesState(object): +class TimeSeriesState: """ The current state of a time series model that is a pybamm model. """ @@ -39,54 +41,98 @@ class BaseModel: """ A base class for constructing and simulating models using PyBaMM. - This class serves as a foundation for building specific models in PyBaMM. - It provides methods to set up the model, define parameters, and perform - simulations. The class is designed to be subclassed for creating models - with custom behaviour. - + This class serves as a foundation for constructing models based on PyBaMM models. It + provides methods to set up the model, define parameters, and perform simulations. The + class is designed to be subclassed for creating models with custom behaviour. + + This class is based on PyBaMM's Simulation class. A PyBOP model is set up via a + similar 3-step process. The `pybamm_model` attributes echoes the `model` attribute of + a simulation, which tracks the model through the build process. Firstly, note that a + PyBaMM `model` must first be built via `build_model` before a simulation or PyBOP + model can be built. The 3-step process is then as follows. + + The `pybamm_model` attribute is first defined as an instance of the imported PyBaMM + model, using any given model options. This initial version of the model is saved as + the `_unprocessed_model` for future reference. Next, the type of each parameter in + the parameter set as well as the geometry of the model is set. Parameters may be set + as an input, interpolant, functional or just a standard PyBaMM parameter. This + version of the model is referred to as the `model_with_set_params`. After its + creation, the `pybamm_model` attribute is updated to point at this version of the + model. Finally, the model required for simulations is built by defining the mesh and + processing the discretisation. The complete model is referred to as the `built_model` + and this version is used to run simulations. + + In order to rebuild a model with a different initial state or geometry, the + `built_model` and the `model_with_set_params` must be cleared and the `pybamm_model` + reset to the `unprocessed_model` in order to start the build process again. """ - def __init__(self, name="Base Model", parameter_set=None): + def __init__( + self, + name: str = "Base Model", + parameter_set: Optional[ParameterSet] = None, + check_params: Callable = None, + eis=False, + ): """ - Initialize the BaseModel with an optional name. + Initialise the BaseModel with an optional name and a parameter set. Parameters ---------- name : str, optional The name given to the model instance. + parameter_set : Union[pybop.ParameterSet, pybamm.ParameterValues], optional + A dict-like object containing the parameter values. + check_params : Callable, optional + A compatibility check for the model parameters. Function, with + signature + check_params( + inputs: dict, + allow_infeasible_solutions: bool, optional + ) + Returns true if parameters are valid, False otherwise. Can be + used to impose constraints on valid parameters. + + Additional Attributes + --------------------- + pybamm_model : pybamm.BaseModel + An instance of a PyBaMM model. + parameters : pybop.Parameters + The input parameters. + param_check_counter : int + A counter for the number of parameter checks (default: 0). + allow_infeasible_solutions : bool, optional + If True, parameter values will be simulated whether or not they are feasible + (default: True). """ self.name = name + self.eis = eis if parameter_set is None: self._parameter_set = None elif isinstance(parameter_set, dict): - self._parameter_set = pybamm.ParameterValues(parameter_set) + self._parameter_set = pybamm.ParameterValues(parameter_set).copy() elif isinstance(parameter_set, pybamm.ParameterValues): - self._parameter_set = parameter_set + self._parameter_set = parameter_set.copy() else: # a pybop parameter set - self._parameter_set = pybamm.ParameterValues(parameter_set.params) - - self.parameters = None - self.dataset = None - self.signal = None - self.additional_variables = [] - self.rebuild_parameters = {} - self.standard_parameters = {} + self._parameter_set = pybamm.ParameterValues(parameter_set.params).copy() + self.param_checker = check_params + + self.pybamm_model = None + self.parameters = Parameters() self.param_check_counter = 0 self.allow_infeasible_solutions = True - @property - def n_parameters(self): - return len(self.parameters) - def build( self, - dataset: Dataset = None, - parameters: Union[Parameters, Dict] = None, + parameters: Union[Parameters, dict] = None, + inputs: Optional[Inputs] = None, + initial_state: Optional[dict] = None, + dataset: Optional[Dataset] = None, check_model: bool = True, - init_soc: float = None, ) -> None: """ - Construct the PyBaMM model if not already built, and set parameters. + Construct the PyBaMM model, if not already built or if there are changes to any + `rebuild_parameters` or the initial state. This method initializes the model components, applies the given parameters, sets up the mesh and discretisation if needed, and prepares the model @@ -94,167 +140,251 @@ def build( Parameters ---------- - dataset : pybamm.Dataset, optional - The dataset to be used in the model construction. parameters : pybop.Parameters or Dict, optional A pybop Parameters class or dictionary containing parameter values to apply to the model. + inputs : Inputs + The input parameters to be used when building the model. + initial_state : dict, optional + A valid initial state, e.g. the initial state of charge or open-circuit voltage. + Defaults to None, indicating that the existing initial state of charge (for an ECM) + or initial concentrations (for an EChem model) will be used. + Accepted keys either `"Initial open-circuit voltage [V]"` or ``"Initial SoC"` + dataset : pybop.Dataset or dict, optional + The dataset to be used in the model construction. check_model : bool, optional If True, the model will be checked for correctness after construction. - init_soc : float, optional - The initial state of charge to be used in simulations. """ - self.dataset = dataset - self.parameters = parameters - if self.parameters is not None: - self.classify_and_update_parameters(self.parameters) + if parameters is not None or inputs is not None: + # Classify parameters and clear the model if rebuild required + inputs = self.classify_parameters(parameters, inputs=inputs) + + if initial_state is not None: + # Clear the model if rebuild required (currently if any initial state) + self.set_initial_state(initial_state, inputs=inputs) + + if not self.pybamm_model._built: # noqa: SLF001 + self.pybamm_model.build_model() - if init_soc is not None: - self.set_init_soc(init_soc) + if self.eis: + self.set_up_for_eis(self.pybamm_model) + self._parameter_set["Current function [A]"] = 0 + + V_scale = getattr(self.pybamm_model.variables["Voltage [V]"], "scale", 1) + I_scale = getattr(self.pybamm_model.variables["Current [A]"], "scale", 1) + self.z_scale = self._parameter_set.evaluate(V_scale / I_scale) + + if dataset is not None and not self.eis: + self.set_current_function(dataset) if self._built_model: return - elif self.pybamm_model.is_discretised: self._model_with_set_params = self.pybamm_model self._built_model = self.pybamm_model - else: - if not self.pybamm_model._built: - self.pybamm_model.build_model() - self.set_params() - + self.set_parameters() self._mesh = pybamm.Mesh(self.geometry, self.submesh_types, self.var_pts) - self._disc = pybamm.Discretisation(self.mesh, self.spatial_methods) + self._disc = pybamm.Discretisation( + mesh=self.mesh, + spatial_methods=self.spatial_methods, + check_model=check_model, + ) self._built_model = self._disc.process_model( - self._model_with_set_params, inplace=False, check_model=check_model + self._model_with_set_params, inplace=False ) # Clear solver and setup model - self._solver._model_set_up = {} + self._solver._model_set_up = {} # noqa: SLF001 self.n_states = self._built_model.len_rhs_and_alg # len_rhs + len_alg - def set_init_soc(self, init_soc: float): + def convert_to_pybamm_initial_state(self, initial_state: dict): """ - Set the initial state of charge for the battery model. + Convert an initial state of charge into a float and an initial open-circuit + voltage into a string ending in "V". Parameters ---------- - init_soc : float - The initial state of charge to be used in the model. - """ - if self._built_initial_soc != init_soc: - # reset - self._model_with_set_params = None - self._built_model = None - self.op_conds_to_built_models = None - self.op_conds_to_built_solvers = None - - param = self.pybamm_model.param - self._parameter_set = ( - self._unprocessed_parameter_set.set_initial_stoichiometries( - init_soc, param=param, inplace=False - ) - ) - # Save solved initial SOC in case we need to rebuild the model - self._built_initial_soc = init_soc + initial_state : dict + A valid initial state, e.g. the initial state of charge or open-circuit voltage. + + Returns + ------- + float or str + If float, this value is used as the initial state of charge (as a decimal between 0 + and 1). If str ending in "V", this value is used as the initial open-circuit voltage. - def set_params(self, rebuild=False): + Raises + ------ + ValueError + If the input is not a dictionary with a single, valid key. + """ + if len(initial_state) > 1: + raise ValueError("Expecting only one initial state.") + elif "Initial SoC" in initial_state.keys(): + return initial_state["Initial SoC"] + elif "Initial open-circuit voltage [V]" in initial_state.keys(): + return str(initial_state["Initial open-circuit voltage [V]"]) + "V" + else: + raise ValueError(f'Unrecognised initial state: "{list(initial_state)[0]}"') + + def set_initial_state(self, initial_state: dict, inputs: Optional[Inputs] = None): """ - Assign the parameters to the model. + Set the initial state of charge or concentrations for the battery model. - This method processes the model with the given parameters, sets up - the geometry, and updates the model instance. + Parameters + ---------- + initial_state : dict + A valid initial state, e.g. the initial state of charge or open-circuit voltage. + inputs : Inputs + The input parameters to be used when building the model. """ - if self.model_with_set_params and not rebuild: - return + self.clear() - # Mark any simulation inputs in the parameter set - for key in self.standard_parameters.keys(): - self._parameter_set[key] = "[input]" + initial_state = self.convert_to_pybamm_initial_state(initial_state) + + if isinstance(self.pybamm_model, pybamm.equivalent_circuit.Thevenin): + initial_state = self.get_initial_state(initial_state, inputs=inputs) + self._unprocessed_parameter_set.update({"Initial SoC": initial_state}) + + else: + if not self.pybamm_model._built: # noqa: SLF001 + self.pybamm_model.build_model() + + # Temporary construction of attributes for PyBaMM + self._model = self.pybamm_model + self._unprocessed_parameter_values = self._unprocessed_parameter_set + + # Set initial state via PyBaMM's Simulation class + pybamm.Simulation.set_initial_soc(self, initial_state, inputs=inputs) + + # Update the default parameter set for consistency + self._unprocessed_parameter_set = self._parameter_values + + # Clear the pybamm objects + del self._model + del self._unprocessed_parameter_values + del self._parameter_values + + # Use a copy of the updated default parameter set + self._parameter_set = self._unprocessed_parameter_set.copy() - if self.dataset is not None and (not self.rebuild_parameters or not rebuild): - if ( - self.parameters is None - or "Current function [A]" not in self.parameters.keys() - ): - self._parameter_set["Current function [A]"] = pybamm.Interpolant( - self.dataset["Time [s]"], - self.dataset["Current function [A]"], + def set_current_function(self, dataset: Union[Dataset, dict]): + """ + Update the input current function according to the data. + + Parameters + ---------- + dataset : pybop.Dataset or dict, optional + The dataset to be used in the model construction. + """ + if "Current function [A]" in self._parameter_set.keys(): + if "Current function [A]" not in self.parameters.keys(): + current = pybamm.Interpolant( + dataset["Time [s]"], + dataset["Current function [A]"], pybamm.t, ) - # Set t_eval - self.time_data = self._parameter_set["Current function [A]"].x[0] + # Update both the active and unprocessed parameter sets for consistency + self._parameter_set["Current function [A]"] = current + self._unprocessed_parameter_set["Current function [A]"] = current + + def set_parameters(self): + """ + Assign the parameters to the model. + + This method processes the model with the given parameters, sets up + the geometry, and updates the model instance. + """ + if self._model_with_set_params: + return self._model_with_set_params = self._parameter_set.process_model( self._unprocessed_model, inplace=False ) - if self.geometry is not None: - self._parameter_set.process_geometry(self.geometry) + self._parameter_set.process_geometry(self._geometry) self.pybamm_model = self._model_with_set_params - def rebuild( - self, - dataset: Dataset = None, - parameters: Union[Parameters, Dict] = None, - parameter_set: ParameterSet = None, - check_model: bool = True, - init_soc: float = None, - ) -> None: + def set_up_for_eis(self, model): """ - Rebuild the PyBaMM model for a given parameter set. + Set up the model for electrochemical impedance spectroscopy (EIS) simulations. - This method requires the self.build() method to be called first, and - then rebuilds the model for a given parameter set. Specifically, - this method applies the given parameters, sets up the mesh and - discretisation if needed, and prepares the model for simulations. + This method sets up the model for EIS simulations by adding the necessary + algebraic equations and variables to the model. + Originally developed by pybamm-eis: https://github.com/pybamm-team/pybamm-eis Parameters ---------- - dataset : pybamm.Dataset, optional - The dataset to be used in the model construction. - parameters : pybop.Parameters or Dict, optional - A pybop Parameters class or dictionary containing parameter values to apply to the model. - parameter_set : pybop.parameter_set, optional - A PyBOP parameter set object or a dictionary containing the parameter values - check_model : bool, optional - If True, the model will be checked for correctness after construction. - init_soc : float, optional - The initial state of charge to be used in simulations. - """ - self.dataset = dataset - if parameters is not None: - self.parameters = parameters - self.classify_and_update_parameters(parameters) - - if init_soc is not None: - self.set_init_soc(init_soc) - - if self._built_model is None: - raise ValueError("Model must be built before calling rebuild") + model : pybamm.Model + The PyBaMM model to be used for EIS simulations. + """ + V_cell = pybamm.Variable("Voltage variable [V]") + model.variables["Voltage variable [V]"] = V_cell + V = model.variables["Voltage [V]"] + + # Add algebraic equation for the voltage + model.algebraic[V_cell] = V_cell - V + model.initial_conditions[V_cell] = model.param.ocv_init + + # Create the FunctionControl submodel and extract variables + external_circuit_variables = pybamm.external_circuit.FunctionControl( + model.param, None, model.options, control="algebraic" + ).get_fundamental_variables() + + # Perform the replacement + symbol_replacement_map = { + model.variables[name]: variable + for name, variable in external_circuit_variables.items() + } - self.set_params(rebuild=True) - self._mesh = pybamm.Mesh(self.geometry, self.submesh_types, self.var_pts) - self._disc = pybamm.Discretisation(self.mesh, self.spatial_methods) - self._built_model = self._disc.process_model( - self._model_with_set_params, inplace=False, check_model=check_model + # Don't replace initial conditions, as these should not contain + # Variable objects + replacer = SymbolReplacer( + symbol_replacement_map, process_initial_conditions=False + ) + replacer.process_model(model, inplace=True) + + # Add an algebraic equation for the current density variable + # External circuit submodels are always equations on the current + I_cell = model.variables["Current variable [A]"] + I = model.variables["Current [A]"] + I_applied = pybamm.FunctionParameter( + "Current function [A]", {"Time [s]": pybamm.t} ) + model.algebraic[I_cell] = I - I_applied + model.initial_conditions[I_cell] = 0 - # Clear solver and setup model - self._solver._model_set_up = {} + def clear(self): + """ + Clear any built PyBaMM model. + """ + self._model_with_set_params = None + self._built_model = None + self._built_initial_soc = None + self._mesh = None + self._disc = None - def classify_and_update_parameters(self, parameters: Union[Parameters, Dict]): + def classify_parameters( + self, parameters: Optional[Parameters] = None, inputs: Optional[Inputs] = None + ): """ - Update the parameter values according to their classification as either - 'rebuild_parameters' which require a model rebuild and - 'standard_parameters' which do not. + Check for any 'rebuild_parameters' which require a model rebuild and + update the unprocessed_parameter_set if a rebuild is required. Parameters ---------- - parameters : pybop.ParameterSet - + parameters : Parameters, optional + The optimisation parameters. Defaults to None, resulting in the internal + `pybop.Parameters` object to be used. + inputs : Inputs, optional + The input parameters for the simulation (default: None). """ - parameter_dictionary = parameters.as_dict() + self.parameters = parameters or self.parameters + + # Compile all parameters and inputs + parameter_dictionary = self.parameters.as_dict() + parameter_dictionary.update(inputs or {}) + rebuild_parameters = { param: parameter_dictionary[param] for param in parameter_dictionary @@ -266,14 +396,25 @@ def classify_and_update_parameters(self, parameters: Union[Parameters, Dict]): if param not in self.geometric_parameters } - self.rebuild_parameters.update(rebuild_parameters) - self.standard_parameters.update(standard_parameters) + # Mark any standard parameters in the active parameter set and pass as inputs + for key in standard_parameters.keys(): + self._parameter_set[key] = "[input]" - # Update the parameter set and geometry for rebuild parameters - if self.rebuild_parameters: - self._parameter_set.update(self.rebuild_parameters) - self._unprocessed_parameter_set = self._parameter_set - self.geometry = self.pybamm_model.default_geometry + # Clear any built model, update the parameter set and geometry if rebuild required + if rebuild_parameters: + requires_rebuild = False + # A rebuild is required if any of the rebuild parameter values have changed + for key, value in rebuild_parameters.items(): + if value != self._unprocessed_parameter_set[key]: + requires_rebuild = True + if requires_rebuild: + self.clear() + self._geometry = self.pybamm_model.default_geometry + # Update both the active and unprocessed parameter sets for consistency + self._parameter_set.update(rebuild_parameters) + self._unprocessed_parameter_set.update(rebuild_parameters) + + return standard_parameters def reinit( self, inputs: Inputs, t: float = 0.0, x: Optional[np.ndarray] = None @@ -284,17 +425,14 @@ def reinit( if self._built_model is None: raise ValueError("Model must be built before calling reinit") - if not isinstance(inputs, dict): - inputs = self.parameters.as_dict(inputs) + inputs = self.parameters.verify(inputs) self._solver.set_up(self._built_model, inputs=inputs) if x is None: x = self._built_model.y0 - sol = pybamm.Solution([np.asarray([t])], [x], self._built_model, inputs) - - return TimeSeriesState(sol=sol, inputs=inputs, t=t) + return self.get_state(inputs, t, x) def get_state(self, inputs: Inputs, t: float, x: np.ndarray) -> TimeSeriesState: """ @@ -320,23 +458,72 @@ def step(self, state: TimeSeriesState, time: np.ndarray) -> TimeSeriesState: """ dt = time - state.t new_sol = self._solver.step( - state.sol, self.built_model, dt, npts=2, inputs=state.inputs, save=False + state.sol, self._built_model, dt, npts=2, inputs=state.inputs, save=False ) return TimeSeriesState(sol=new_sol, inputs=state.inputs, t=time) def simulate( - self, inputs: Inputs, t_eval: np.array - ) -> Dict[str, np.ndarray[np.float64]]: + self, inputs: Inputs, t_eval: np.array, initial_state: Optional[dict] = None + ) -> Union[pybamm.Solution, list[np.float64]]: """ Execute the forward model simulation and return the result. + Parameters + ---------- + inputs : Inputs + The input parameters for the simulation. + t_eval : array-like + An array of time points at which to evaluate the solution. + initial_state : dict, optional + A valid initial state, e.g. the initial state of charge or open-circuit voltage. + Defaults to None, indicating that the existing initial state of charge (for an ECM) + or initial concentrations (for an EChem model) will be used. + + Returns + ------- + pybamm.Solution + The solution object returned by a PyBaMM simulation, or a pybamm error in the case + where the parameter values are infeasible and infeasible solutions are not allowed. + + Raises + ------ + ValueError + If the model has not been built before simulation. + """ + inputs = self.parameters.verify(inputs) + + # Build or rebuild if required + self.build(inputs=inputs, initial_state=initial_state) + + if not self.check_params( + inputs=inputs, + allow_infeasible_solutions=self.allow_infeasible_solutions, + ): + raise ValueError("These parameter values are infeasible.") + + return self.solver.solve( + self._built_model, + inputs=inputs, + t_eval=[t_eval[0], t_eval[-1]] + if isinstance(self._solver, IDAKLUSolver) + else t_eval, + t_interp=t_eval, + ) + + def simulateEIS( + self, inputs: Inputs, f_eval: list, initial_state: Optional[dict] = None + ) -> dict[str, np.ndarray]: + """ + Compute the forward model simulation with electrochemical impedance spectroscopy + and return the result. + Parameters ---------- inputs : dict or array-like The input parameters for the simulation. If array-like, it will be converted to a dictionary using the model's fit keys. - t_eval : array-like - An array of time points at which to evaluate the solution. + f_eval : array-like + An array of frequency points at which to evaluate the solution. Returns ------- @@ -348,149 +535,181 @@ def simulate( ValueError If the model has not been built before simulation. """ - if self._built_model is None: - raise ValueError("Model must be built before calling simulate") - else: - if self.rebuild_parameters and not self.standard_parameters: - sol = self.solver.solve(self.built_model, t_eval=t_eval) - - else: - if not isinstance(inputs, dict): - inputs = self.parameters.as_dict(inputs) - - if self.check_params( - inputs=inputs, - allow_infeasible_solutions=self.allow_infeasible_solutions, - ): - try: - sol = self.solver.solve( - self.built_model, inputs=inputs, t_eval=t_eval - ) - except Exception as e: - print(f"Error: {e}") - return {signal: [np.inf] for signal in self.signal} - else: - return {signal: [np.inf] for signal in self.signal} - - y = { - signal: sol[signal].data - for signal in (self.signal + self.additional_variables) - } + inputs = self.parameters.verify(inputs) + + # Build or rebuild if required + self.build(inputs=inputs, initial_state=initial_state) + + if not self.check_params( + inputs=inputs, + allow_infeasible_solutions=self.allow_infeasible_solutions, + ): + raise ValueError("These parameter values are infeasible.") + + self.initialise_eis_simulation(inputs) + zs = [self.calculate_impedance(frequency) for frequency in f_eval] + + return {"Impedance": np.asarray(zs) * self.z_scale} + + def initialise_eis_simulation(self, inputs: Optional[Inputs] = None): + """ + Initialise the Electrochemical Impedance Spectroscopy (EIS) simulation. + + This method sets up the mass matrix and solver, converts inputs to the appropriate format, + extracts necessary attributes from the model, and prepares matrices for the simulation. + + Parameters + ---------- + inputs : dict (optional) + The input parameters for the simulation. + """ + # Setup mass matrix, solver + self.M = self._built_model.mass_matrix.entries + self._solver.set_up(self._built_model, inputs=inputs) + + # Convert inputs to casadi format if needed + casadi_inputs = ( + casadi.vertcat(*inputs.values()) + if inputs is not None and self._built_model.convert_to_format == "casadi" + else inputs or [] + ) + + # Extract necessary attributes from the model + self.y0 = self._built_model.concatenated_initial_conditions.evaluate( + 0, inputs=inputs + ) + self.J = self._built_model.jac_rhs_algebraic_eval( + 0, self.y0, casadi_inputs + ).sparse() + + # Convert to Compressed Sparse Column format + self.M = csc_matrix(self.M) + self.J = csc_matrix(self.J) + + # Add forcing to the RHS on the current density + self.b = np.zeros(self.y0.shape) + self.b[-1] = -1 + + def calculate_impedance(self, frequency): + """ + Calculate the impedance for a given frequency. + + This method computes the system matrix, solves the linear system, and calculates + the impedance based on the solution. + + Parameters + ---------- + frequency (np.ndarray | list like): The frequency at which to calculate the impedance. - return y + Returns + ------- + The calculated impedance (complex np.ndarray). + """ + # Compute the system matrix + A = 1.0j * 2 * np.pi * frequency * self.M - self.J + + # Solve the system + x = spsolve(A, self.b) - def simulateS1(self, inputs: Inputs, t_eval: np.array): + # Calculate the impedance + return -x[-2] / x[-1] + + def simulateS1( + self, inputs: Inputs, t_eval: np.array, initial_state: Optional[dict] = None + ): """ Perform the forward model simulation with sensitivities. Parameters ---------- - inputs : dict or array-like - The input parameters for the simulation. If array-like, it will be - converted to a dictionary using the model's fit keys. + inputs : Inputs + The input parameters for the simulation. t_eval : array-like An array of time points at which to evaluate the solution and its sensitivities. + initial_state : dict, optional + A valid initial state, e.g. the initial state of charge or open-circuit voltage. + Defaults to None, indicating that the existing initial state of charge (for an ECM) + or initial concentrations (for an EChem model) will be used. Returns ------- - tuple - A tuple containing the simulation result and the sensitivities. + pybamm.Solution + The solution object returned by a PyBaMM simulation, or a pybamm error in the case + where the parameter values are infeasible and infeasible solutions are not allowed. Raises ------ ValueError If the model has not been built before simulation. """ + inputs = self.parameters.verify(inputs) - if self._built_model is None: - raise ValueError("Model must be built before calling simulate") - else: - if self.rebuild_parameters: - raise ValueError( - "Cannot use sensitivies for parameters which require a model rebuild" - ) + if initial_state is not None or any( + key in self.geometric_parameters for key in inputs.keys() + ): + raise ValueError( + "Cannot use sensitivities for parameters which require a model rebuild" + ) + + # Build if required + self.build(inputs=inputs, initial_state=initial_state) - if not isinstance(inputs, dict): - inputs = self.parameters.as_dict(inputs) - - if self.check_params( - inputs=inputs, - allow_infeasible_solutions=self.allow_infeasible_solutions, - ): - try: - sol = self._solver.solve( - self.built_model, - inputs=inputs, - t_eval=t_eval, - calculate_sensitivities=True, - ) - y = {signal: sol[signal].data for signal in self.signal} - - # Extract the sensitivities and stack them along a new axis for each signal - dy = np.empty( - ( - sol[self.signal[0]].data.shape[0], - self.n_outputs, - self.n_parameters, - ) - ) - - for i, signal in enumerate(self.signal): - dy[:, i, :] = np.stack( - [ - sol[signal].sensitivities[key].toarray()[:, 0] - for key in self.parameters.keys() - ], - axis=-1, - ) - - return y, dy - except Exception as e: - print(f"Error: {e}") - return {signal: [np.inf] for signal in self.signal}, [np.inf] - - else: - return {signal: [np.inf] for signal in self.signal}, [np.inf] + if not self.check_params( + inputs=inputs, + allow_infeasible_solutions=self.allow_infeasible_solutions, + ): + raise ValueError("These parameter values are infeasible.") + + return self._solver.solve( + self._built_model, + inputs=inputs, + t_eval=[t_eval[0], t_eval[-1]] + if isinstance(self._solver, IDAKLUSolver) + else t_eval, + calculate_sensitivities=True, + t_interp=t_eval, + ) def predict( self, - inputs: Inputs = None, - t_eval: np.array = None, - parameter_set: ParameterSet = None, - experiment: Experiment = None, - init_soc: float = None, - ) -> Dict[str, np.ndarray[np.float64]]: + inputs: Optional[Inputs] = None, + t_eval: Optional[np.array] = None, + parameter_set: Optional[ParameterSet] = None, + experiment: Optional[Experiment] = None, + initial_state: Optional[dict] = None, + ) -> dict[str, np.ndarray[np.float64]]: """ Solve the model using PyBaMM's simulation framework and return the solution. This method sets up a PyBaMM simulation by configuring the model, parameters, experiment - (if any), and initial state of charge (if provided). It then solves the simulation and - returns the resulting solution object. + or time vector, and initial state of charge (if provided). Either 't_eval' or 'experiment' + must be provided. It then solves the simulation and returns the resulting solution object. Parameters ---------- - inputs : dict or array-like, optional - Input parameters for the simulation. If the input is array-like, it is converted - to a dictionary using the model's fitting keys. Defaults to None, indicating - that the default parameters should be used. + inputs : Inputs, optional + Input parameters for the simulation. Defaults to None, indicating that the + default parameters should be used. t_eval : array-like, optional An array of time points at which to evaluate the solution. Defaults to None, which means the time points need to be specified within experiment or elsewhere. - parameter_set : pybamm.ParameterValues, optional - A PyBaMM ParameterValues object or a dictionary containing the parameter values - to use for the simulation. Defaults to the model's current ParameterValues if None. + parameter_set : Union[pybop.ParameterSet, pybamm.ParameterValues], optional + A dict-like object containing the parameter values to use for the simulation. + Defaults to the model's current ParameterValues if None. experiment : pybamm.Experiment, optional A PyBaMM Experiment object specifying the experimental conditions under which the simulation should be run. Defaults to None, indicating no experiment. - init_soc : float, optional - The initial state of charge for the simulation, as a fraction (between 0 and 1). - Defaults to None. + initial_state : dict, optional + A valid initial state, e.g. the initial state of charge or open-circuit voltage. + Defaults to None, indicating that the existing initial state of charge (for an ECM) + or initial concentrations (for an EChem model) will be used. Returns ------- pybamm.Solution - The solution object returned after solving the simulation. + The solution object returned by a PyBaMM simulation, or a pybamm error in the case + where the parameter values are infeasible and infeasible solutions are not allowed. Raises ------ @@ -499,44 +718,56 @@ def predict( if PyBaMM models are not supported by the current simulation method. """ - if not self.pybamm_model._built: - self.pybamm_model.build_model() + if self.pybamm_model is None: + raise ValueError( + "The predict method currently only supports PyBaMM models." + ) + elif not self._unprocessed_model._built: # noqa: SLF001 + self._unprocessed_model.build_model() - parameter_set = parameter_set or self._unprocessed_parameter_set + no_parameter_set = parameter_set is None + parameter_set = parameter_set or self._unprocessed_parameter_set.copy() if inputs is not None: - if not isinstance(inputs, dict): - inputs = self.parameters.as_dict(inputs) + inputs = self.parameters.verify(inputs) parameter_set.update(inputs) - if self.check_params( - inputs=inputs, + if initial_state is not None: + if no_parameter_set: + # Update the default initial state for consistency + self.set_initial_state(initial_state) + + initial_state = self.convert_to_pybamm_initial_state(initial_state) + if isinstance(self.pybamm_model, pybamm.equivalent_circuit.Thevenin): + parameter_set["Initial SoC"] = self._parameter_set["Initial SoC"] + initial_state = None + + if not self.check_params( parameter_set=parameter_set, allow_infeasible_solutions=self.allow_infeasible_solutions, ): - if self._unprocessed_model is not None: - if experiment is None: - return pybamm.Simulation( - self._unprocessed_model, - parameter_values=parameter_set, - ).solve(t_eval=t_eval, initial_soc=init_soc) - else: - return pybamm.Simulation( - self._unprocessed_model, - experiment=experiment, - parameter_values=parameter_set, - ).solve(initial_soc=init_soc) - else: - raise ValueError( - "This sim method currently only supports PyBaMM models" - ) - + raise ValueError("These parameter values are infeasible.") + + if experiment is not None: + return pybamm.Simulation( + model=self._unprocessed_model, + experiment=experiment, + parameter_values=parameter_set, + ).solve(initial_soc=initial_state) + elif t_eval is not None: + return pybamm.Simulation( + model=self._unprocessed_model, + parameter_values=parameter_set, + ).solve(t_eval=t_eval, initial_soc=initial_state) else: - return [np.inf] + raise ValueError( + "The predict method requires either an experiment or t_eval " + "to be specified." + ) def check_params( self, - inputs: Inputs = None, - parameter_set: ParameterSet = None, + inputs: Optional[Inputs] = None, + parameter_set: Optional[ParameterSet] = None, allow_infeasible_solutions: bool = True, ): """ @@ -544,8 +775,10 @@ def check_params( Parameters ---------- - inputs : dict + inputs : Inputs The input parameters for the simulation. + parameter_set : Union[pybop.ParameterSet, pybamm.ParameterValues], optional + A dict-like object containing the parameter values. allow_infeasible_solutions : bool, optional If True, infeasible parameter values will be allowed in the optimisation (default: True). @@ -555,24 +788,20 @@ def check_params( A boolean which signifies whether the parameters are compatible. """ - if inputs is not None: - if not isinstance(inputs, dict): - if isinstance(inputs, list): - for entry in inputs: - if not isinstance(entry, (int, float)): - raise ValueError( - "Expecting inputs in the form of a dictionary, numeric list" - + f" or None, but received a list with type: {type(inputs)}" - ) - else: - inputs = self.parameters.as_dict(inputs) + inputs = self.parameters.verify(inputs) or {} + parameter_set = parameter_set or self._parameter_set return self._check_params( - inputs=inputs, allow_infeasible_solutions=allow_infeasible_solutions + inputs=inputs, + parameter_set=parameter_set, + allow_infeasible_solutions=allow_infeasible_solutions, ) def _check_params( - self, inputs: Inputs = None, allow_infeasible_solutions: bool = True + self, + inputs: Inputs, + parameter_set: ParameterSet, + allow_infeasible_solutions: bool = True, ): """ A compatibility check for the model parameters which can be implemented by subclasses @@ -580,8 +809,10 @@ def _check_params( Parameters ---------- - inputs : dict + inputs : Inputs The input parameters for the simulation. + parameter_set : Union[pybop.ParameterSet, pybamm.ParameterValues], optional + A dict-like object containing the parameter values. allow_infeasible_solutions : bool, optional If True, infeasible parameter values will be allowed in the optimisation (default: True). @@ -590,6 +821,8 @@ def _check_params( bool A boolean which signifies whether the parameters are compatible. """ + if self.param_checker: + return self.param_checker(inputs, allow_infeasible_solutions) return True def copy(self): @@ -603,6 +836,53 @@ def copy(self): """ return copy.copy(self) + def new_copy(self): + """ + Return a new copy of the model, explicitly copying all the mutable attributes + to avoid issues with shared objects. + + Returns + ------- + BaseModel + A new copy of the model. + """ + model_class = type(self) + if self.pybamm_model is None: + model_args = {"parameter_set": self._parameter_set.copy()} + else: + model_args = { + "options": self._unprocessed_model.options, + "parameter_set": self._unprocessed_parameter_set.copy(), + "geometry": self.pybamm_model.default_geometry.copy(), + "submesh_types": self.pybamm_model.default_submesh_types.copy(), + "var_pts": self.pybamm_model.default_var_pts.copy(), + "spatial_methods": self.pybamm_model.default_spatial_methods.copy(), + "solver": self.pybamm_model.default_solver.copy(), + "eis": copy.copy(self.eis), + } + + return model_class(**model_args) + + def get_parameter_info(self, print_info: bool = False): + """ + Extracts the parameter names and types and returns them as a dictionary. + """ + if not self.pybamm_model._built: # noqa: SLF001 + self.pybamm_model.build_model() + + info = self.pybamm_model.get_parameter_info() + + reduced_info = dict() + for param, param_type in info.values(): + param_name = getattr(param, "name", str(param)) + reduced_info[param_name] = param_type + + if print_info: + for param, param_type in info.values(): + print(param, " : ", param_type) + + return reduced_info + def cell_mass(self, parameter_set: ParameterSet = None): """ Calculate the cell mass in kilograms. @@ -611,9 +891,8 @@ def cell_mass(self, parameter_set: ParameterSet = None): Parameters ---------- - parameter_set : dict, optional - A dictionary containing the parameter values necessary for the mass - calculations. + parameter_set : Union[pybop.ParameterSet, pybamm.ParameterValues], optional + A dict-like object containing the parameter values. Raises ------ @@ -630,9 +909,8 @@ def cell_volume(self, parameter_set: ParameterSet = None): Parameters ---------- - parameter_set : dict, optional - A dictionary containing the parameter values necessary for the volume - calculation. + parameter_set : Union[pybop.ParameterSet, pybamm.ParameterValues], optional + A dict-like object containing the parameter values. Raises ------ @@ -641,17 +919,17 @@ def cell_volume(self, parameter_set: ParameterSet = None): """ raise NotImplementedError - def approximate_capacity(self, x): + def approximate_capacity(self, parameter_set: ParameterSet = None): """ - Calculate a new estimate for the nominal capacity based on the theoretical energy density - and an average voltage. + Calculate a new estimate for the nominal capacity based on the theoretical energy + density and an average voltage. This method must be implemented by subclasses. Parameters ---------- - x : array-like - An array of values representing the model inputs. + parameter_set : Union[pybop.ParameterSet, pybamm.ParameterValues], optional + A dict-like object containing the parameter values. Raises ------ @@ -664,14 +942,14 @@ def approximate_capacity(self, x): def built_model(self): return self._built_model + @property + def built_initial_soc(self): + return self._built_initial_soc + @property def parameter_set(self): return self._parameter_set - @parameter_set.setter - def parameter_set(self, parameter_set): - self._parameter_set = parameter_set.copy() - @property def model_with_set_params(self): return self._model_with_set_params @@ -680,42 +958,26 @@ def model_with_set_params(self): def geometry(self): return self._geometry - @geometry.setter - def geometry(self, geometry: Optional[pybamm.Geometry]): - self._geometry = geometry.copy() if geometry is not None else None - @property def submesh_types(self): return self._submesh_types - @submesh_types.setter - def submesh_types(self, submesh_types: Optional[Dict[str, Any]]): - self._submesh_types = ( - submesh_types.copy() if submesh_types is not None else None - ) - @property def mesh(self): return self._mesh + @property + def disc(self): + return self._disc + @property def var_pts(self): return self._var_pts - @var_pts.setter - def var_pts(self, var_pts: Optional[Dict[str, int]]): - self._var_pts = var_pts.copy() if var_pts is not None else None - @property def spatial_methods(self): return self._spatial_methods - @spatial_methods.setter - def spatial_methods(self, spatial_methods: Optional[Dict[str, Any]]): - self._spatial_methods = ( - spatial_methods.copy() if spatial_methods is not None else None - ) - @property def solver(self): return self._solver diff --git a/pybop/models/empirical/base_ecm.py b/pybop/models/empirical/base_ecm.py index 8d15442d1..9b0d9209e 100644 --- a/pybop/models/empirical/base_ecm.py +++ b/pybop/models/empirical/base_ecm.py @@ -1,4 +1,8 @@ -from pybop.models.base_model import BaseModel +import numpy as np +import pybamm + +from pybop.models.base_model import BaseModel, Inputs +from pybop.parameters.parameter_set import ParameterSet class ECircuitModel(BaseModel): @@ -41,27 +45,32 @@ def __init__( var_pts=None, spatial_methods=None, solver=None, + check_params=None, + eis=False, **model_kwargs, ): model_options = dict(build=False) for key, value in model_kwargs.items(): model_options[key] = value - self.pybamm_model = pybamm_model(**model_options) - self._unprocessed_model = self.pybamm_model + pybamm_model = pybamm_model(**model_options) # Correct OCP if set to default if ( parameter_set is not None - and "Open-circuit voltage [V]" in parameter_set.params + and "Open-circuit voltage [V]" in parameter_set.keys() ): - default_ocp = self.pybamm_model.default_parameter_values[ + default_ocp = pybamm_model.default_parameter_values[ "Open-circuit voltage [V]" ] - if parameter_set.params["Open-circuit voltage [V]"] == "default": + if parameter_set["Open-circuit voltage [V]"] == "default": print("Setting open-circuit voltage to default function") - parameter_set.params["Open-circuit voltage [V]"] = default_ocp + parameter_set["Open-circuit voltage [V]"] = default_ocp - super().__init__(name=name, parameter_set=parameter_set) + super().__init__( + name=name, parameter_set=parameter_set, check_params=check_params, eis=eis + ) + self.pybamm_model = pybamm_model + self._unprocessed_model = self.pybamm_model # Set parameters, using either the provided ones or the default self.default_parameter_values = self.pybamm_model.default_parameter_values @@ -69,13 +78,13 @@ def __init__( self._unprocessed_parameter_set = self._parameter_set # Define model geometry and discretization - self.geometry = geometry or self.pybamm_model.default_geometry - self.submesh_types = submesh_types or self.pybamm_model.default_submesh_types - self.var_pts = var_pts or self.pybamm_model.default_var_pts - self.spatial_methods = ( + self._geometry = geometry or self.pybamm_model.default_geometry + self._submesh_types = submesh_types or self.pybamm_model.default_submesh_types + self._var_pts = var_pts or self.pybamm_model.default_var_pts + self._spatial_methods = ( spatial_methods or self.pybamm_model.default_spatial_methods ) - self.solver = solver or self.pybamm_model.default_solver + self._solver = solver or self.pybamm_model.default_solver # Internal attributes for the built model are initialized but not set self._model_with_set_params = None @@ -85,14 +94,21 @@ def __init__( self._disc = None self.geometric_parameters = {} - def _check_params(self, inputs=None, allow_infeasible_solutions=True): + def _check_params( + self, + inputs: Inputs, + parameter_set: ParameterSet, + allow_infeasible_solutions: bool = True, + ): """ Check the compatibility of the model parameters. Parameters ---------- - inputs : dict + inputs : Inputs The input parameters for the simulation. + parameter_set : pybop.ParameterSet + A PyBOP parameter set object or a dictionary containing the parameter values. allow_infeasible_solutions : bool, optional If True, infeasible parameter values will be allowed in the optimisation (default: True). @@ -100,6 +116,90 @@ def _check_params(self, inputs=None, allow_infeasible_solutions=True): ------- bool A boolean which signifies whether the parameters are compatible. - """ + if self.param_checker: + return self.param_checker(inputs, allow_infeasible_solutions) return True + + def get_initial_state( + self, + initial_value, + parameter_values=None, + param=None, + options=None, + tol=1e-6, + inputs=None, + ): + """ + Calculate the initial state of charge given an open-circuit voltage, voltage limits + and the open-circuit voltage function defined by the parameter set. + + Parameters + ---------- + initial_value : float + Target initial value. + If float, interpreted as SOC, must be between 0 and 1. + If string e.g. "4 V", interpreted as voltage, must be between V_min and V_max. + parameter_values : :class:`pybamm.ParameterValues` + The parameter values class that will be used for the simulation. Required for + calculating appropriate initial stoichiometries. + param : :class:`pybamm.LithiumIonParameters`, optional + The symbolic parameter set to use for the simulation. + If not provided, the default parameter set will be used. + options : dict-like, optional + A dictionary of options to be passed to the model, see + :class:`pybamm.BatteryModelOptions`. + tol : float, optional + The tolerance for the solver used to compute the initial stoichiometries. + A lower value results in higher precision but may increase computation time. + Default is 1e-6. + + Returns + ------- + initial_soc + The initial state of charge + """ + parameter_values = parameter_values or self._unprocessed_parameter_set + param = param or self.pybamm_model.param + + if isinstance(initial_value, str) and initial_value.endswith("V"): + V_init = float(initial_value[:-1]) + V_min = parameter_values.evaluate(param.voltage_low_cut, inputs=inputs) + V_max = parameter_values.evaluate(param.voltage_high_cut, inputs=inputs) + + if not V_min <= V_init <= V_max: + raise ValueError( + f"Initial voltage {V_init}V is outside the voltage limits " + f"({V_min}, {V_max})" + ) + + # Solve simple model for initial soc based on target voltage + soc_model = pybamm.BaseModel() + soc = pybamm.Variable("soc") + ocv = param.ocv + soc_model.algebraic[soc] = ocv(soc) - V_init + + # initial guess for soc linearly interpolates between 0 and 1 + # based on V linearly interpolating between V_max and V_min + soc_model.initial_conditions[soc] = (V_init - V_min) / (V_max - V_min) + soc_model.variables["soc"] = soc + parameter_values.process_model(soc_model) + initial_soc = ( + pybamm.AlgebraicSolver(tol=tol).solve(soc_model, [0])["soc"].data[0] + ) + + # Ensure that the result lies between 0 and 1 + initial_soc = np.minimum(np.maximum(initial_soc, 0.0), 1.0) + + elif isinstance(initial_value, (int, float)): + if not 0 <= initial_value <= 1: + raise ValueError("Initial SOC should be between 0 and 1") + initial_soc = initial_value + + else: + raise ValueError( + "Initial value must be a float between 0 and 1, " + "or a string ending in 'V'" + ) + + return initial_soc diff --git a/pybop/models/empirical/ecm.py b/pybop/models/empirical/ecm.py index 031da3fde..95a1a170e 100644 --- a/pybop/models/empirical/ecm.py +++ b/pybop/models/empirical/ecm.py @@ -38,27 +38,12 @@ class Thevenin(ECircuitModel): def __init__( self, name="Equivalent Circuit Thevenin Model", + eis=False, **model_kwargs, ): super().__init__( - pybamm_model=pybamm_equivalent_circuit.Thevenin, name=name, **model_kwargs + pybamm_model=pybamm_equivalent_circuit.Thevenin, + name=name, + eis=eis, + **model_kwargs, ) - - def _check_params(self, inputs=None, allow_infeasible_solutions=True): - """ - Check the compatibility of the model parameters. - - Parameters - ---------- - inputs : dict - The input parameters for the simulation. - allow_infeasible_solutions : bool, optional - If True, infeasible parameter values will be allowed in the optimisation (default: True). - - Returns - ------- - bool - A boolean which signifies whether the parameters are compatible. - - """ - return True diff --git a/pybop/models/lithium_ion/base_echem.py b/pybop/models/lithium_ion/base_echem.py index 6947774bf..ade7e8fa2 100644 --- a/pybop/models/lithium_ion/base_echem.py +++ b/pybop/models/lithium_ion/base_echem.py @@ -1,8 +1,12 @@ +import sys import warnings +from typing import Optional +from pybamm import LithiumIonParameters from pybamm import lithium_ion as pybamm_lithium_ion -from pybop.models.base_model import BaseModel +from pybop.models.base_model import BaseModel, Inputs +from pybop.parameters.parameter_set import ParameterSet class EChemBaseModel(BaseModel): @@ -45,12 +49,10 @@ def __init__( var_pts=None, spatial_methods=None, solver=None, + eis=False, **model_kwargs, ): - super().__init__( - name=name, - parameter_set=parameter_set, - ) + super().__init__(name=name, parameter_set=parameter_set, eis=eis) model_options = dict(build=False) for key, value in model_kwargs.items(): @@ -64,18 +66,18 @@ def __init__( self._unprocessed_parameter_set = self._parameter_set # Define model geometry and discretization - self.geometry = geometry or self.pybamm_model.default_geometry - self.submesh_types = submesh_types or self.pybamm_model.default_submesh_types - self.var_pts = var_pts or self.pybamm_model.default_var_pts - self.spatial_methods = ( + self._geometry = geometry or self.pybamm_model.default_geometry + self._submesh_types = submesh_types or self.pybamm_model.default_submesh_types + self._var_pts = var_pts or self.pybamm_model.default_var_pts + self._spatial_methods = ( spatial_methods or self.pybamm_model.default_spatial_methods ) if solver is None: - self.solver = self.pybamm_model.default_solver - self.solver.mode = "fast with events" - self.solver.max_step_decrease_count = 1 + self._solver = self.pybamm_model.default_solver + self._solver.mode = "fast with events" + self._solver.max_step_decrease_count = 1 else: - self.solver = solver + self._solver = solver # Internal attributes for the built model are initialized but not set self._model_with_set_params = None @@ -85,18 +87,24 @@ def __init__( self._disc = None self._electrode_soh = pybamm_lithium_ion.electrode_soh + self._electrode_soh_half_cell = pybamm_lithium_ion.electrode_soh_half_cell self.geometric_parameters = self.set_geometric_parameters() def _check_params( - self, inputs=None, parameter_set=None, allow_infeasible_solutions=True + self, + inputs: Inputs, + parameter_set: ParameterSet, + allow_infeasible_solutions: bool = True, ): """ Check compatibility of the model parameters. Parameters ---------- - inputs : dict + inputs : Inputs The input parameters for the simulation. + parameter_set : pybop.ParameterSet + A PyBOP parameter set object or a dictionary containing the parameter values. allow_infeasible_solutions : bool, optional If True, infeasible parameter values will be allowed in the optimisation (default: True). @@ -105,8 +113,6 @@ def _check_params( bool A boolean which signifies whether the parameters are compatible. """ - parameter_set = parameter_set or self._parameter_set - if self.pybamm_model.options["working electrode"] == "positive": electrode_params = [ ( @@ -133,19 +139,22 @@ def _check_params( } for material_vol_fraction, porosity in electrode_params: - if ( + total_vol_fraction = ( related_parameters[material_vol_fraction] + related_parameters[porosity] - > 1 + ) + if ( + ParameterSet.evaluate_symbol(total_vol_fraction, parameter_set) + > 1 + sys.float_info.epsilon ): if self.param_check_counter <= len(electrode_params): infeasibility_warning = "Non-physical point encountered - [{material_vol_fraction} + {porosity}] > 1.0!" - warnings.warn(infeasibility_warning, UserWarning) + warnings.warn(infeasibility_warning, UserWarning, stacklevel=2) self.param_check_counter += 1 return allow_infeasible_solutions return True - def cell_volume(self, parameter_set=None): + def cell_volume(self, parameter_set: Optional[ParameterSet] = None): """ Calculate the total cell volume in m3. @@ -156,8 +165,7 @@ def cell_volume(self, parameter_set=None): Parameters ---------- parameter_set : dict, optional - A dictionary containing the parameter values necessary for the volume - calculation. + A dictionary containing the parameter values necessary for the calculation. Returns ------- @@ -180,10 +188,12 @@ def cell_volume(self, parameter_set=None): parameter_set["Electrode height [m]"] * parameter_set["Electrode width [m]"] ) - # Calculate and return total cell volume - return cross_sectional_area * cell_thickness + # Calculate total cell volume + cell_volume = cross_sectional_area * cell_thickness + + return ParameterSet.evaluate_symbol(cell_volume, parameter_set) - def cell_mass(self, parameter_set=None): + def cell_mass(self, parameter_set: Optional[ParameterSet] = None): """ Calculate the total cell mass in kilograms. @@ -195,8 +205,7 @@ def cell_mass(self, parameter_set=None): Parameters ---------- parameter_set : dict, optional - A dictionary containing the parameter values necessary for the mass - calculations. + A dictionary containing the parameter values necessary for the calculation. Returns ------- @@ -206,30 +215,36 @@ def cell_mass(self, parameter_set=None): parameter_set = parameter_set or self._parameter_set def mass_density( - active_material_vol_frac, density, porosity, electrolyte_density + active_material_vol_frac, + density, + porosity, + electrolyte_density, + carbon_binder_domain_density, ): - return (active_material_vol_frac * density) + ( - porosity * electrolyte_density + return ( + (active_material_vol_frac * density) + + (porosity * electrolyte_density) + + (1.0 - active_material_vol_frac - porosity) + * carbon_binder_domain_density ) def area_density(thickness, mass_density): return thickness * mass_density - # Approximations due to SPM(e) parameter set limitations - electrolyte_density = parameter_set["Separator density [kg.m-3]"] - # Calculate mass densities positive_mass_density = mass_density( parameter_set["Positive electrode active material volume fraction"], - parameter_set["Positive electrode density [kg.m-3]"], + parameter_set["Positive electrode active material density [kg.m-3]"], parameter_set["Positive electrode porosity"], - electrolyte_density, + parameter_set["Electrolyte density [kg.m-3]"], + parameter_set["Positive electrode carbon-binder density [kg.m-3]"], ) negative_mass_density = mass_density( parameter_set["Negative electrode active material volume fraction"], - parameter_set["Negative electrode density [kg.m-3]"], + parameter_set["Negative electrode active material density [kg.m-3]"], parameter_set["Negative electrode porosity"], - electrolyte_density, + parameter_set["Electrolyte density [kg.m-3]"], + parameter_set["Negative electrode carbon-binder density [kg.m-3]"], ) # Calculate area densities @@ -241,7 +256,7 @@ def area_density(thickness, mass_density): ) separator_area_density = area_density( parameter_set["Separator thickness [m]"], - parameter_set["Separator porosity"] * electrolyte_density, + parameter_set["Separator density [kg.m-3]"], ) positive_cc_area_density = area_density( parameter_set["Positive current collector thickness [m]"], @@ -257,7 +272,7 @@ def area_density(thickness, mass_density): parameter_set["Electrode height [m]"] * parameter_set["Electrode width [m]"] ) - # Calculate and return total cell mass + # Calculate total cell mass total_area_density = ( positive_area_density + negative_area_density @@ -265,61 +280,49 @@ def area_density(thickness, mass_density): + positive_cc_area_density + negative_cc_area_density ) - return cross_sectional_area * total_area_density + cell_mass = cross_sectional_area * total_area_density - def approximate_capacity(self, x): - """ - Calculate and update an estimate for the nominal cell capacity based on the theoretical - energy density and an average voltage. + return ParameterSet.evaluate_symbol(cell_mass, parameter_set) - The nominal capacity is computed by dividing the theoretical energy (in watt-hours) by - the average open circuit potential (voltage) of the cell. + def approximate_capacity(self, parameter_set: Optional[ParameterSet] = None): + """ + Calculate an estimate for the nominal cell capacity. The estimate is computed + by estimating the capacity of the positive electrode that lies between the + stoichiometric limits corresponding to the upper and lower voltage limits. Parameters ---------- - x : array-like - An array of values representing the model inputs. + parameter_set : dict, optional + A dictionary containing the parameter values necessary for the calculation. Returns ------- - None - The nominal cell capacity is updated directly in the model's parameter set. + float + The estimate of the nominal cell capacity [A.h]. """ - # Extract stoichiometries and compute mean values - ( - min_sto_neg, - max_sto_neg, - min_sto_pos, - max_sto_pos, - ) = self._electrode_soh.get_min_max_stoichiometries(self._parameter_set) - mean_sto_neg = (min_sto_neg + max_sto_neg) / 2 - mean_sto_pos = (min_sto_pos + max_sto_pos) / 2 - - inputs = { - key: x[i] for i, key in enumerate([param.name for param in self.parameters]) - } - self._parameter_set.update(inputs) - - # Calculate theoretical energy density - theoretical_energy = self._electrode_soh.calculate_theoretical_energy( - self._parameter_set - ) + parameter_set = parameter_set or self._parameter_set - # Calculate average voltage - positive_electrode_ocp = self._parameter_set["Positive electrode OCP [V]"] - negative_electrode_ocp = self._parameter_set["Negative electrode OCP [V]"] - try: - average_voltage = positive_electrode_ocp( - mean_sto_pos - ) - negative_electrode_ocp(mean_sto_neg) - except Exception as e: - raise ValueError(f"Error in average voltage calculation: {e}") - - # Calculate and update nominal capacity - theoretical_capacity = theoretical_energy / average_voltage - self._parameter_set.update( - {"Nominal cell capacity [A.h]": theoretical_capacity} - ) + # Calculate the theoretical capacity in the limit of low current + if self.pybamm_model.options["working electrode"] == "positive": + ( + max_sto_p, + min_sto_p, + ) = self._electrode_soh_half_cell.get_min_max_stoichiometries(parameter_set) + else: + ( + min_sto_n, + max_sto_n, + min_sto_p, + max_sto_p, + ) = self._electrode_soh.get_min_max_stoichiometries(parameter_set) + # Note that the stoichiometric limits correspond to 0 and 100% SOC. + # Stoichiometric balancing is performed within get_min_max_stoichiometries + # such that the capacity accessible between the limits should be the same + # for both electrodes, so we consider just the positive electrode below. + + Q_p = LithiumIonParameters().p.prim.Q_init + theoretical_capacity = Q_p * (max_sto_p - min_sto_p) + return ParameterSet.evaluate_symbol(theoretical_capacity, parameter_set) def set_geometric_parameters(self): """ @@ -341,6 +344,10 @@ def set_geometric_parameters(self): "Positive electrode thickness [m]", "Separator porosity", "Separator thickness [m]", + "Open-circuit voltage at 100% SOC [V]", + "Open-circuit voltage at 0% SOC [V]" + "Maximum concentration in positive electrode [mol.m-3]", + "Maximum concentration in negative electrode [mol.m-3]", ] ) diff --git a/pybop/models/lithium_ion/echem.py b/pybop/models/lithium_ion/echem.py index 8bd8ab636..aac05b738 100644 --- a/pybop/models/lithium_ion/echem.py +++ b/pybop/models/lithium_ion/echem.py @@ -1,8 +1,7 @@ from pybamm import lithium_ion as pybamm_lithium_ion from pybop.models.lithium_ion.base_echem import EChemBaseModel - -from .weppner_huggins import BaseWeppnerHuggins +from pybop.models.lithium_ion.weppner_huggins import BaseWeppnerHuggins class SPM(EChemBaseModel): @@ -39,11 +38,13 @@ class SPM(EChemBaseModel): def __init__( self, name="Single Particle Model", + eis=False, **model_kwargs, ): super().__init__( pybamm_model=pybamm_lithium_ion.SPM, name=name, + eis=eis, **model_kwargs, ) @@ -84,10 +85,11 @@ class SPMe(EChemBaseModel): def __init__( self, name="Single Particle Model with Electrolyte", + eis=False, **model_kwargs, ): super().__init__( - pybamm_model=pybamm_lithium_ion.SPMe, name=name, **model_kwargs + pybamm_model=pybamm_lithium_ion.SPMe, name=name, eis=eis, **model_kwargs ) @@ -127,9 +129,12 @@ class DFN(EChemBaseModel): def __init__( self, name="Doyle-Fuller-Newman", + eis=False, **model_kwargs, ): - super().__init__(pybamm_model=pybamm_lithium_ion.DFN, name=name, **model_kwargs) + super().__init__( + pybamm_model=pybamm_lithium_ion.DFN, name=name, eis=eis, **model_kwargs + ) class MPM(EChemBaseModel): @@ -166,10 +171,12 @@ class MPM(EChemBaseModel): def __init__( self, name="Many Particle Model", + eis=False, **model_kwargs, ): super().__init__( pybamm_model=pybamm_lithium_ion.MPM, + eis=eis, name=name, **model_kwargs, ) @@ -209,11 +216,13 @@ class MSMR(EChemBaseModel): def __init__( self, name="Multi Species Multi Reactions Model", + eis=False, **model_kwargs, ): super().__init__( pybamm_model=pybamm_lithium_ion.MSMR, name=name, + eis=eis, **model_kwargs, ) @@ -242,5 +251,7 @@ class WeppnerHuggins(EChemBaseModel): The solver to use for simulating the model. If None, the default solver from PyBaMM is used. """ - def __init__(self, name="Weppner & Huggins model", **model_kwargs): - super().__init__(pybamm_model=BaseWeppnerHuggins, name=name, **model_kwargs) + def __init__(self, name="Weppner & Huggins model", eis=False, **model_kwargs): + super().__init__( + pybamm_model=BaseWeppnerHuggins, name=name, eis=eis, **model_kwargs + ) diff --git a/pybop/models/lithium_ion/weppner_huggins.py b/pybop/models/lithium_ion/weppner_huggins.py index 5d8d626a4..9703269d3 100644 --- a/pybop/models/lithium_ion/weppner_huggins.py +++ b/pybop/models/lithium_ion/weppner_huggins.py @@ -36,7 +36,7 @@ def __init__(self, name="Weppner & Huggins model", **model_kwargs): # Model kwargs (build, options) are not implemented, keeping here for consistent interface if model_kwargs is not dict(build=True): unused_kwargs_warning = "The input model_kwargs are not currently used by the Weppner & Huggins model." - warnings.warn(unused_kwargs_warning, UserWarning) + warnings.warn(unused_kwargs_warning, UserWarning, stacklevel=2) super().__init__({}, name) @@ -65,7 +65,7 @@ def __init__(self, name="Weppner & Huggins model", **model_kwargs): # Parameters ###################### - d_s = Parameter("Positive electrode diffusivity [m2.s-1]") + d_s = Parameter("Positive particle diffusivity [m2.s-1]") c_s_max = Parameter("Maximum concentration in positive electrode [mol.m-3]") @@ -99,6 +99,7 @@ def __init__(self, name="Weppner & Huggins model", **model_kwargs): self.variables = { "Voltage [V]": V, "Time [s]": t, + "Current [A]": self.param.current_with_time, } # Set the built property on creation to prevent unnecessary model rebuilds diff --git a/pybop/observers/observer.py b/pybop/observers/observer.py index 1b81c5ac7..239f352a1 100644 --- a/pybop/observers/observer.py +++ b/pybop/observers/observer.py @@ -22,12 +22,12 @@ class Observer(BaseProblem): The model to observe. check_model : bool, optional Flag to indicate if the model should be checked (default: True). - signal: List[str] + signal: list[str] The signal to observe. - additional_variables : List[str], optional + additional_variables : list[str], optional Additional variables to observe and store in the solution (default: []). - init_soc : float, optional - Initial state of charge (default: None). + initial_state : dict, optional + A valid initial state, e.g. the initial open-circuit voltage (default: None). """ # define a subtype for covariance matrices for use by derived classes @@ -37,27 +37,19 @@ def __init__( self, parameters: Parameters, model: BaseModel, - check_model=True, - signal=["Voltage [V]"], - additional_variables=[], - init_soc=None, + check_model: bool = True, + signal: Optional[list[str]] = None, + additional_variables: Optional[list[str]] = None, + initial_state: Optional[dict] = None, ) -> None: super().__init__( - parameters, model, check_model, signal, additional_variables, init_soc + parameters, model, check_model, signal, additional_variables, initial_state ) - if model._built_model is None: + if model.built_model is None: raise ValueError("Only built models can be used in Observers") - if model.signal is None: - model.signal = self.signal - inputs = dict() - for param in self.parameters: - inputs[param.name] = param.value - - self._state = model.reinit(inputs) - self._model = model - self._signal = self.signal - self._n_outputs = len(self._signal) + inputs = self.parameters.as_dict("initial") + self._state = self.model.reinit(inputs) def reset(self, inputs: Inputs) -> None: self._state = self._model.reinit(inputs) @@ -96,8 +88,10 @@ def log_likelihood(self, values: dict, times: np.ndarray, inputs: Inputs) -> flo inputs : Inputs The inputs to the model. """ - if self._n_outputs == 1: - signal = self._signal[0] + inputs = self.parameters.verify(inputs) + + if self.n_outputs == 1: + signal = self.signal[0] if len(values[signal]) != len(times): raise ValueError("values and times must have the same length.") log_likelihood = 0.0 @@ -133,7 +127,7 @@ def get_current_covariance(self) -> Covariance: return np.zeros((n, n)) def get_measure(self, x: TimeSeriesState) -> np.ndarray: - measures = [x.sol[s].data[-1] for s in self._signal] + measures = [x.sol[s].data[-1] for s in self.signal] return np.asarray([[m] for m in measures]) def get_current_time(self) -> float: @@ -142,41 +136,36 @@ def get_current_time(self) -> float: """ return self._state.t - def evaluate(self, x): + def evaluate(self, inputs: Inputs): """ Evaluate the model with the given parameters and return the signal. Parameters ---------- - x : np.ndarray - Parameter values to evaluate the model at. + inputs : Inputs + Parameters for evaluation of the model. Returns ------- y : np.ndarray - The model output y(t) simulated with inputs x. + The model output y(t) simulated with given inputs. """ - inputs = dict() - if isinstance(x, Parameters): - for param in x: - inputs[param.name] = param.value - else: # x is an array of parameter values - for i, param in enumerate(self.parameters): - inputs[param.name] = x[i] + inputs = self.parameters.verify(inputs) + self.reset(inputs) output = {} ys = [] - if hasattr(self, "_dataset"): - for signal in self._signal: + if self._dataset is not None: + for signal in self.signal: ym = self._target[signal] - for i, t in enumerate(self._time_data): + for i, t in enumerate(self._domain_data): self.observe(t, ym[i]) ys.append(self.get_current_measure()) output[signal] = np.vstack(ys) else: - for signal in self._signal: - for t in self._time_data: + for signal in self.signal: + for t in self._domain_data: self.observe(t) ys.append(self.get_current_measure()) output[signal] = np.vstack(ys) diff --git a/pybop/observers/unscented_kalman.py b/pybop/observers/unscented_kalman.py index 0b6425db9..69e019ee6 100644 --- a/pybop/observers/unscented_kalman.py +++ b/pybop/observers/unscented_kalman.py @@ -1,9 +1,10 @@ from dataclasses import dataclass -from typing import List, Tuple, Union +from typing import Optional, Union import numpy as np import scipy.linalg as linalg +from pybop._dataset import Dataset from pybop.models.base_model import BaseModel, Inputs from pybop.observers.observer import Observer from pybop.parameters.parameter import Parameter @@ -15,8 +16,8 @@ class UnscentedKalmanFilterObserver(Observer): Parameters ---------- - parameters: List[Parameters] - The inputs to the model. + parameters: Parameters + The parameters for the model. model : BaseModel The model to observe. sigma0 : np.ndarray | float @@ -33,45 +34,49 @@ class UnscentedKalmanFilterObserver(Observer): Flag to indicate if the model should be checked (default: True). signal: str The signal to observe. - init_soc : float, optional - Initial state of charge (default: None). + initial_state : dict, optional + A valid initial state, e.g. the initial open-circuit voltage (default: None). """ Covariance = np.ndarray def __init__( self, - parameters: List[Parameter], + parameters: list[Parameter], model: BaseModel, sigma0: Union[Covariance, float], process: Union[Covariance, float], measure: Union[Covariance, float], - dataset=None, - check_model=True, - signal=["Voltage [V]"], - additional_variables=[], - init_soc=None, + dataset: Optional[Dataset] = None, + check_model: bool = True, + signal: Optional[list[str]] = None, + additional_variables: Optional[list[str]] = None, + initial_state: Optional[float] = None, ) -> None: + if model is not None: + # Clear any existing built model and its properties + if model.built_model is not None: + model.clear() + + # Build the model from scratch + model.build( + dataset=dataset, + parameters=parameters, + check_model=check_model, + ) + super().__init__( - parameters, model, check_model, signal, additional_variables, init_soc + parameters, model, check_model, signal, additional_variables, initial_state ) if dataset is not None: - self._dataset = dataset.data + # Check that the dataset contains necessary variables + dataset.check(signal=[*self.signal, "Current function [A]"]) - # Check that the dataset contains time and current - dataset.check(self.signal + ["Current function [A]"]) - - self._time_data = self._dataset["Time [s]"] - self.n_time_data = len(self._time_data) + self._dataset = dataset.data + self._domain_data = self._dataset["Time [s]"] + self.n_data = len(self._domain_data) self._target = {signal: self._dataset[signal] for signal in self.signal} - # Add useful parameters to model - if model is not None: - self._model.signal = self.signal - self._model.n_outputs = self.n_outputs - if dataset is not None: - self._model.n_time_data = self.n_time_data - # Observer initiation self._process = process @@ -152,7 +157,7 @@ def get_current_covariance(self) -> Covariance: @dataclass -class SigmaPoint(object): +class SigmaPoint: """ A sigma point is a point in the state space that is used to estimate the mean and covariance of a random variable. """ @@ -162,7 +167,7 @@ class SigmaPoint(object): w_c: float -class SquareRootUKF(object): +class SquareRootUKF: """ van der Menve, R., & Wan, E. A. (2001). THE SQUARE-ROOT UNSCENTED KALMAN FILTER FOR STATE AND PARAMETER-ESTIMATION. https://doi.org/10.1109/ICASSP.2001.940586 @@ -235,7 +240,7 @@ def reset(self, x: np.ndarray, S: np.ndarray) -> None: @staticmethod def gen_sigma_points( x: np.ndarray, S: np.ndarray, alpha: float, beta: float, states: np.ndarray - ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: """ Generates 2L+1 sigma points for the unscented transform, where L is the number of states. @@ -254,11 +259,11 @@ def gen_sigma_points( Returns ------- - List[np.ndarray] + list[np.ndarray] The sigma points - List[float] + list[float] The weights of the sigma points - List[float] + list[float] The weights of the covariance of the sigma points """ # Set the scaling parameters: sigma and eta @@ -290,14 +295,14 @@ def unscented_transform( w_m: np.ndarray, w_c: np.ndarray, sqrtR: np.ndarray, - states: Union[np.ndarray, None] = None, - ) -> Tuple[np.ndarray, np.ndarray]: + states: Optional[np.ndarray] = None, + ) -> tuple[np.ndarray, np.ndarray]: """ Performs the unscented transform Parameters ---------- - sigma_points : List[SigmaPoint] + sigma_points : list[SigmaPoint] The sigma points sqrtR : np.ndarray The square root of the covariance matrix diff --git a/pybop/optimisers/_adamw.py b/pybop/optimisers/_adamw.py index 24e5ec982..34c990206 100644 --- a/pybop/optimisers/_adamw.py +++ b/pybop/optimisers/_adamw.py @@ -26,7 +26,7 @@ class AdamWImpl(PintsOptimiser): m_j' = m_j[i] / (1 - beta1**(1 + i)) v_j' = v_j[i] / (1 - beta2**(1 + i)) - p_j[i] = p_j[i - 1] - alpha * (m_j' / (sqrt(v_j') + eps) + lambda * p_j[i - 1]) + p_j[i] = p_j[i - 1] - alpha * (m_j' / (sqrt(v_j') + eps) + lam * p_j[i - 1]) The initial values of the moments are ``m_j[0] = v_j[0] = 0``, after which they decay with rates ``beta1`` and ``beta2``. The default values for these are, @@ -38,7 +38,7 @@ class AdamWImpl(PintsOptimiser): The parameter ``alpha`` is a step size, which is set as ``min(sigma0)`` in this implementation. - The parameter ``lambda`` is the weight decay rate, which is set to ``0.01`` + The parameter ``lam`` is the weight decay rate, which is set to ``0.01`` by default in this implementation. Finally, ``eps`` is a small constant used to avoid division by zero, set to @@ -86,10 +86,9 @@ def __init__(self, x0, sigma0=0.015, boundaries=None): self._b2 = 0.999 # Step size - self._alpha = np.min(self._sigma0) - + self._alpha = self._sigma0 # Weight decay rate - self.set_lambda() + self._lam = 0.01 # Small number added to avoid divide-by-zero self._eps = np.finfo(float).eps @@ -157,7 +156,7 @@ def tell(self, reply): # Check ask-tell pattern if not self._ready_for_tell: - raise Exception("ask() not called before tell()") + raise RuntimeError("ask() not called before tell()") self._ready_for_tell = False # Unpack reply @@ -186,7 +185,7 @@ def tell(self, reply): # Take step with weight decay self._proposed = self._current - self._alpha * ( - m / (np.sqrt(v) + self._eps) + self._lambda * self._current + m / (np.sqrt(v) + self._eps) + self._lam * self._current ) # Update x_best and f_best @@ -206,17 +205,27 @@ def x_guessed(self): """ return self._current - def set_lambda(self, lambda_: float = 0.01) -> None: + @property + def lam(self): + return self._lam + + @lam.setter + def lam(self, lam: float = 0.01) -> None: """ - Sets the lambda_ decay constant. This is the weight decay rate + Sets the lam decay constant. This is the weight decay rate that helps in finding the optimal solution. """ - if not isinstance(lambda_, (int, float)) or not 0 < lambda_ <= 1: - raise ValueError("lambda_ must be a numeric value between 0 and 1.") + if not isinstance(lam, (int, float)) or not 0 < lam <= 1: + raise ValueError("lam must be a numeric value between 0 and 1.") - self._lambda = float(lambda_) + self._lam = float(lam) - def set_b1(self, b1: float) -> None: + @property + def b1(self): + return self._b1 + + @b1.setter + def b1(self, b1: float) -> None: """ Sets the b1 momentum decay constant. """ @@ -225,7 +234,12 @@ def set_b1(self, b1: float) -> None: self._b1 = float(b1) - def set_b2(self, b2: float) -> None: + @property + def b2(self): + return self._b2 + + @b2.setter + def b2(self, b2: float) -> None: """ Sets the b2 momentum decay constant. """ diff --git a/pybop/optimisers/_cuckoo.py b/pybop/optimisers/_cuckoo.py new file mode 100644 index 000000000..346052728 --- /dev/null +++ b/pybop/optimisers/_cuckoo.py @@ -0,0 +1,197 @@ +import numpy as np +from pints import PopulationBasedOptimiser +from scipy.special import gamma + + +class CuckooSearchImpl(PopulationBasedOptimiser): + """ + Cuckoo Search (CS) optimisation algorithm, inspired by the brood parasitism + of some cuckoo species. This algorithm was introduced by Yang and Deb in 2009. + + The algorithm uses a population of host nests (solutions), where each cuckoo + (new solution) tries to replace a worse nest in the population. The quality + or fitness of the nests is determined by the cost function. A fraction + of the worst nests is abandoned at each generation, and new ones are built + randomly. + + The pseudo-code for the Cuckoo Search is as follows: + + 1. Initialise population of n host nests + 2. While (t < max_generations): + a. Get a cuckoo randomly by Lévy flights + b. Evaluate its quality/fitness F + c. Choose a nest among n (say, j) randomly + d. If (F > fitness of j): + i. Replace j with the new solution + e. Abandon a fraction (pa) of the worst nests and build new ones + f. Keep the best solutions/nests + g. Rank the solutions and find the current best + 3. End While + + This implementation also uses a decreasing step size for the Lévy flights, calculated + as sigma = sigma0 / sqrt(iterations), where sigma0 is the initial step size and + iterations is the current iteration number. + + Parameters: + - pa: Probability of discovering alien eggs/solutions (abandoning rate) + + References: + - X. -S. Yang and Suash Deb, "Cuckoo Search via Lévy flights," + 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), + Coimbatore, India, 2009, pp. 210-214, https://doi.org/10.1109/NABIC.2009.5393690. + + - S. Walton, O. Hassan, K. Morgan, M.R. Brown, + Modified cuckoo search: A new gradient free optimisation algorithm, + Chaos, Solitons & Fractals, Volume 44, Issue 9, 2011, + Pages 710-718, ISSN 0960-0779, + https://doi.org/10.1016/j.chaos.2011.06.004. + """ + + def __init__(self, x0, sigma0=0.05, boundaries=None, pa=0.25): + super().__init__(x0, sigma0, boundaries=boundaries) + + # Problem dimensionality + self._dim = len(x0) + + # Population size and abandon rate + self._n = self._population_size + self._pa = pa + self.step_size = self._sigma0 + self.beta = 1.5 + + # Set states + self._running = False + self._ready_for_tell = False + + # Initialise nests + if self._boundaries: + self._nests = np.random.uniform( + low=self._boundaries.lower(), + high=self._boundaries.upper(), + size=(self._n, self._dim), + ) + else: + self._nests = np.random.normal( + self._x0, self._sigma0, size=(self._n, self._dim) + ) + + self._fitness = np.full(self._n, np.inf) + + # Initialise best solutions + self._x_best = np.copy(x0) + self._f_best = np.inf + + # Set iteration count + self._iterations = 0 + + def ask(self): + """ + Returns a list of next points in the parameter-space + to evaluate from the optimiser. + """ + # Set flag to indicate that the optimiser is ready to receive replies + self._ready_for_tell = True + self._running = True + + # Generate new solutions (cuckoos) by Lévy flights + self.step_size = self._sigma0 / max(1, np.sqrt(self._iterations)) + step = self.levy_flight(self.beta, self._dim) * self.step_size + self.cuckoos = self._nests + step + return self.clip_nests(self.cuckoos) + + def tell(self, replies): + """ + Receives a list of function values from the cost function from points + previously specified by `self.ask()`, and updates the optimiser state + accordingly. + """ + # Update iteration count + self._iterations += 1 + + # Compare cuckoos with current nests + for i in range(self._n): + f_new = replies[i] + if f_new < self._fitness[i]: + self._nests[i] = self.cuckoos[i] + self._fitness[i] = f_new + if f_new < self._f_best: + self._f_best = f_new + self._x_best = self.cuckoos[i] + + # Abandon some worse nests + n_abandon = int(self._pa * self._n) + worst_nests = np.argsort(self._fitness)[-n_abandon:] + for idx in worst_nests: + self.abandon_nests(idx) + self._fitness[idx] = np.inf # reset fitness + + def levy_flight(self, alpha, size): + """ + Generate step sizes via the Mantegna's algorithm for Levy flights + """ + from numpy import pi, power, random, sin + + sigma_u = power( + (gamma(1 + alpha) * sin(pi * alpha / 2)) + / (gamma((1 + alpha) / 2) * alpha * power(2, (alpha - 1) / 2)), + 1 / alpha, + ) + sigma_v = 1 + + u = random.normal(0, sigma_u, size=size) + v = random.normal(0, sigma_v, size=size) + step = u / power(abs(v), 1 / alpha) + + return step + + def abandon_nests(self, idx): + """ + Updates the nests to abandon the worst performers and reinitialise. + """ + if self._boundaries: + self._nests[idx] = np.random.uniform( + low=self._boundaries.lower(), + high=self._boundaries.upper(), + ) + else: + self._nests[idx] = np.random.normal(self._x0, self._sigma0) + + def clip_nests(self, x): + """ + Clip the input array to the boundaries if available. + """ + if self._boundaries: + x = np.clip(x, self._boundaries.lower(), self._boundaries.upper()) + return x + + def _suggested_population_size(self): + """ + Inherited from Pints:PopulationBasedOptimiser. + Returns a suggested population size, based on the + dimension of the parameter space. + """ + return 4 + int(3 * np.log(self._n_parameters)) + + def running(self): + """ + Returns ``True`` if the optimisation is in progress. + """ + return self._running + + def x_best(self): + """ + Returns the best parameter values found so far. + """ + return self._x_best + + def f_best(self): + """ + Returns the best score found so far. + """ + return self._f_best + + def name(self): + """ + Returns the name of the optimiser. + """ + return "Cuckoo Search" diff --git a/pybop/optimisers/base_optimiser.py b/pybop/optimisers/base_optimiser.py index b283b9d8e..d008f5eeb 100644 --- a/pybop/optimisers/base_optimiser.py +++ b/pybop/optimisers/base_optimiser.py @@ -1,8 +1,18 @@ import warnings +from typing import Optional, Union import numpy as np +from scipy.optimize import OptimizeResult -from pybop import BaseCost, BaseLikelihood, DesignCost +from pybop import ( + BaseCost, + BaseLikelihood, + DesignCost, + Inputs, + Parameter, + Parameters, + WeightedCost, +) class BaseOptimiser: @@ -37,7 +47,7 @@ class BaseOptimiser: If True, the target is to minimise the cost, else target is to maximise by minimising the negative cost (default: True). physical_viability : bool, optional - If True, the feasibility of the optimised parameters is checked (default: True). + If True, the feasibility of the optimised parameters is checked (default: False). allow_infeasible_solutions : bool, optional If True, infeasible parameter values will be allowed in the optimisation (default: True). log : dict @@ -50,12 +60,15 @@ def __init__( **optimiser_kwargs, ): # First set attributes to default values + self.parameters = Parameters() self.x0 = None self.bounds = None - self.sigma0 = 0.1 - self.verbose = False - self.log = dict(x=[], x_best=[], cost=[]) + self.sigma0 = 0.02 + self.verbose = True + self.log = dict(x=[], x_best=[], cost=[], cost_best=[]) self.minimising = True + self._transformation = None + self._needs_sensitivities = False self.physical_viability = False self.allow_infeasible_solutions = False self.default_max_iterations = 1000 @@ -63,41 +76,49 @@ def __init__( if isinstance(cost, BaseCost): self.cost = cost - self.x0 = cost.x0 + self.parameters = self.cost.parameters + self._transformation = self.cost.transformation self.set_allow_infeasible_solutions() + if isinstance(cost, WeightedCost): + self.minimising = cost.minimising if isinstance(cost, (BaseLikelihood, DesignCost)): self.minimising = False - # Set default bounds (for all or no parameters) - self.bounds = cost.parameters.get_bounds() - - # Set default initial standard deviation (for all or no parameters) - self.sigma0 = cost.parameters.get_sigma0() or self.sigma0 - else: try: - cost_test = cost(optimiser_kwargs.get("x0", [])) + self.x0 = optimiser_kwargs.get("x0", []) + cost_test = cost(self.x0) warnings.warn( "The cost is not an instance of pybop.BaseCost, but let's continue " - + "assuming that it is a callable function to be minimised.", + "assuming that it is a callable function to be minimised.", UserWarning, + stacklevel=2, ) self.cost = cost + for i, value in enumerate(self.x0): + self.parameters.add( + Parameter(name=f"Parameter {i}", initial_value=value) + ) self.minimising = True - except Exception: - raise Exception("The cost is not a recognised cost object or function.") + except Exception as e: + raise Exception( + "The cost is not a recognised cost object or function." + ) from e if not np.isscalar(cost_test) or not np.isreal(cost_test): raise TypeError( f"Cost returned {type(cost_test)}, not a scalar numeric value." ) + if len(self.parameters) == 0: + raise ValueError("There are no parameters to optimise.") + self.unset_options = optimiser_kwargs self.set_base_options() self._set_up_optimiser() - # Throw an warning if any options remain + # Throw a warning if any options remain if self.unset_options: warnings.warn( f"Unrecognised keyword arguments: {self.unset_options} will not be used.", @@ -109,9 +130,21 @@ def set_base_options(self): """ Update the base optimiser options and remove them from the options dictionary. """ - self.x0 = self.unset_options.pop("x0", self.x0) - self.bounds = self.unset_options.pop("bounds", self.bounds) - self.sigma0 = self.unset_options.pop("sigma0", self.sigma0) + # Set initial values, if x0 is None, initial values are unmodified. + self.parameters.update(initial_values=self.unset_options.pop("x0", None)) + self.x0 = self.parameters.reset_initial_value(apply_transform=True) + + # Set default bounds (for all or no parameters) + self.bounds = self.unset_options.pop( + "bounds", self.parameters.get_bounds(apply_transform=True) + ) + + # Set default initial standard deviation (for all or no parameters) + self.sigma0 = self.unset_options.pop( + "sigma0", self.parameters.get_sigma0(apply_transform=True) or self.sigma0 + ) + + # Set other options self.verbose = self.unset_options.pop("verbose", self.verbose) self.minimising = self.unset_options.pop("minimising", self.minimising) if "allow_infeasible_solutions" in self.unset_options.keys(): @@ -138,23 +171,18 @@ def run(self): Returns ------- - x : numpy.ndarray - The best parameter set found by the optimisation. - final_cost : float - The final cost associated with the best parameters. + results: OptimisationResult + The pybop optimisation result class. """ self.result = self._run() - # Store the optimised parameters - x = self.result.x - if hasattr(self.cost, "parameters"): - self.store_optimised_parameters(x) + if self.verbose: + print(self.result) - # Check if parameters are viable - if self.physical_viability: - self.check_optimal_parameters(x) + # Store the optimised parameters + self.parameters.update(values=self.result.x) - return x, self.result.final_cost + return self.result def _run(self): """ @@ -169,36 +197,54 @@ def _run(self): """ raise NotImplementedError - def store_optimised_parameters(self, x): + def log_update(self, x=None, x_best=None, cost=None, cost_best=None): """ - Update the problem parameters with optimised values. - - The optimised parameter values are stored within the associated PyBOP parameter class. + Update the log with new values. Parameters ---------- - x : array-like - Optimised parameter values. - """ - for i, param in enumerate(self.cost.parameters): - param.update(value=x[i]) - - def check_optimal_parameters(self, x): - """ - Check if the optimised parameters are physically viable. + x : list or array-like, optional + Parameter values (default: None). + x_best : list or array-like, optional + Parameter values corresponding to the best cost yet (default: None). + cost : list, optional + Cost values corresponding to x (default: None). + cost_best + Cost values corresponding to x_best (default: None). """ - if self.cost.problem._model.check_params( - inputs=x, allow_infeasible_solutions=False - ): - return - else: - warnings.warn( - "Optimised parameters are not physically viable! \nConsider retrying the optimisation" - + " with a non-gradient-based optimiser and the option allow_infeasible_solutions=False", - UserWarning, - stacklevel=2, - ) + def convert_to_list(array_like): + """Helper function to convert input to a list, if necessary.""" + if isinstance(array_like, (list, tuple, np.ndarray)): + return list(array_like) + elif isinstance(array_like, (int, float)): + return [array_like] + else: + raise TypeError("Input must be a list, tuple, or numpy array") + + def apply_transformation(values): + """Apply transformation if it exists.""" + if self._transformation: + return [self._transformation.to_model(value) for value in values] + return values + + if x is not None: + x = convert_to_list(x) + x = apply_transformation(x) + self.log["x"].extend(x) + + if x_best is not None: + x_best = convert_to_list(x_best) + x_best = apply_transformation(x_best) + self.log["x_best"].extend([x_best]) + + if cost is not None: + cost = convert_to_list(cost) + self.log["cost"].extend(cost) + + if cost_best is not None: + cost_best = convert_to_list(cost_best) + self.log["cost_best"].extend(cost_best) def name(self): """ @@ -207,11 +253,11 @@ def name(self): Returns ------- str - The name of the optimiser, which is "Optimisation" for this base class. + The name of the optimiser """ - return "Optimisation" + raise NotImplementedError # pragma: no cover - def set_allow_infeasible_solutions(self, allow=True): + def set_allow_infeasible_solutions(self, allow: bool = True): """ Set whether to allow infeasible solutions or not. @@ -224,8 +270,12 @@ def set_allow_infeasible_solutions(self, allow=True): self.physical_viability = allow self.allow_infeasible_solutions = allow - if hasattr(self.cost, "problem") and hasattr(self.cost.problem, "_model"): - self.cost.problem._model.allow_infeasible_solutions = ( + if ( + hasattr(self.cost, "problem") + and hasattr(self.cost.problem, "model") + and self.cost.problem.model is not None + ): + self.cost.problem.model.allow_infeasible_solutions = ( self.allow_infeasible_solutions ) else: @@ -233,8 +283,12 @@ def set_allow_infeasible_solutions(self, allow=True): self.physical_viability = False self.allow_infeasible_solutions = False + @property + def needs_sensitivities(self): + return self._needs_sensitivities + -class Result: +class OptimisationResult: """ Stores the result of the optimisation. @@ -252,12 +306,102 @@ class Result: def __init__( self, - x: np.ndarray = None, - final_cost: float = None, - n_iterations: int = None, + x: Union[Inputs, np.ndarray] = None, + cost: Union[BaseCost, None] = None, + final_cost: Optional[float] = None, + n_iterations: Optional[int] = None, + optim: Optional[BaseOptimiser] = None, + time: Optional[float] = None, scipy_result=None, ): self.x = x - self.final_cost = final_cost + self.cost = cost + self.final_cost = ( + final_cost if final_cost is not None else self._calculate_final_cost() + ) self.n_iterations = n_iterations self.scipy_result = scipy_result + self.optim = optim + self.time = time + if isinstance(self.optim, BaseOptimiser): + self.x0 = self.optim.parameters.initial_value() + else: + self.x0 = None + + # Check that the parameters produce finite cost, and are physically viable + self._validate_parameters() + self.check_physical_viability(self.x) + + def _calculate_final_cost(self) -> float: + """ + Calculate the final cost using the cost function and optimised parameters. + + Returns: + float: The calculated final cost. + """ + return self.cost(self.x) + + def get_scipy_result(self) -> Optional[OptimizeResult]: + """ + Get the SciPy optimisation result object. + + Returns: + OptimizeResult or None: The SciPy optimisation result object if available, None otherwise. + """ + return self.scipy_result + + def _validate_parameters(self) -> None: + """ + Validate the optimised parameters and ensure they produce a finite cost value. + + Raises: + ValueError: If the optimized parameters do not produce a finite cost value. + """ + if not np.isfinite(self.final_cost): + raise ValueError("Optimised parameters do not produce a finite cost value") + + def check_physical_viability(self, x): + """ + Check if the optimised parameters are physically viable. + + Parameters + ---------- + x : array-like + Optimised parameter values. + """ + if self.cost.problem.model is None: + warnings.warn( + "No model within problem class, can't check physical viability.", + UserWarning, + stacklevel=2, + ) + return + + if self.cost.problem.model.check_params( + inputs=x, allow_infeasible_solutions=False + ): + return + else: + warnings.warn( + "Optimised parameters are not physically viable! \nConsider retrying the optimisation" + " with a non-gradient-based optimiser and the option allow_infeasible_solutions=False", + UserWarning, + stacklevel=2, + ) + + def __str__(self) -> str: + """ + A string representation of the OptimisationResult object. + + Returns: + str: A formatted string containing optimisation result information. + """ + return ( + f"OptimisationResult:\n" + f" Initial parameters: {self.x0}\n" + f" Optimised parameters: {self.x}\n" + f" Final cost: {self.final_cost}\n" + f" Optimisation time: {self.time} seconds\n" + f" Number of iterations: {self.n_iterations}\n" + f" SciPy result available: {'Yes' if self.scipy_result else 'No'}" + ) diff --git a/pybop/optimisers/base_pints_optimiser.py b/pybop/optimisers/base_pints_optimiser.py index a5140df0b..f03fc7a19 100644 --- a/pybop/optimisers/base_pints_optimiser.py +++ b/pybop/optimisers/base_pints_optimiser.py @@ -1,3 +1,5 @@ +from time import time + import numpy as np from pints import PSO as PintsPSO from pints import Adam as PintsAdam @@ -10,7 +12,7 @@ from pints import SequentialEvaluator as PintsSequentialEvaluator from pints import strfloat as PintsStrFloat -from pybop import BaseOptimiser, Result +from pybop import BaseOptimiser, OptimisationResult class BasePintsOptimiser(BaseOptimiser): @@ -19,6 +21,18 @@ class BasePintsOptimiser(BaseOptimiser): Parameters ---------- + cost : callable + The cost function to be minimized. + pints_optimiser : class + The PINTS optimiser class to be used. + max_iterations : int, optional + Maximum number of iterations for the optimisation. + min_iterations : int, optional (default=2) + Minimum number of iterations before termination. + max_unchanged_iterations : int, optional (default=15) + Maximum number of iterations without improvement before termination. + parallel : bool, optional (default=False) + Whether to run the optimisation in parallel. **optimiser_kwargs : optional Valid PINTS option keys and their values, for example: x0 : array_like @@ -28,30 +42,51 @@ class BasePintsOptimiser(BaseOptimiser): bounds : dict A dictionary with 'lower' and 'upper' keys containing arrays for lower and upper bounds on the parameters. + use_f_guessed : bool + Whether to track guessed function values. + absolute_tolerance : float + Absolute tolerance for convergence checking. + relative_tolerance : float + Relative tolerance for convergence checking. + max_evaluations : int + Maximum number of function evaluations. + threshold : float + Threshold value for early termination. """ - def __init__(self, cost, pints_optimiser, **optimiser_kwargs): + def __init__( + self, + cost, + pints_optimiser, + max_iterations: int = None, + min_iterations: int = 2, + max_unchanged_iterations: int = 15, + parallel: bool = False, + **optimiser_kwargs, + ): # First set attributes to default values self._boundaries = None self._needs_sensitivities = None self._use_f_guessed = None - self._parallel = False self._n_workers = 1 self._callback = None - self._max_iterations = None - self._min_iterations = 2 - self._unchanged_max_iterations = 15 + self.set_parallel(parallel) + self.set_max_iterations(max_iterations) + self.set_min_iterations(min_iterations) + self._unchanged_max_iterations = max_unchanged_iterations self._absolute_tolerance = 1e-5 self._relative_tolerance = 1e-2 self._max_evaluations = None self._threshold = None self._evaluations = None self._iterations = None + self.option_methods = { + "use_f_guessed": self.set_f_guessed_tracking, + "max_evaluations": self.set_max_evaluations, + "threshold": self.set_threshold, + } - # PyBOP doesn't currently support the PINTS transformation class - self._transformation = None - - self.pints_optimiser = pints_optimiser + self.optimiser = pints_optimiser super().__init__(cost, **optimiser_kwargs) def _set_up_optimiser(self): @@ -62,47 +97,26 @@ def _set_up_optimiser(self): self._sanitise_inputs() # Create an instance of the PINTS optimiser class - if issubclass(self.pints_optimiser, PintsOptimiser): - self.pints_optimiser = self.pints_optimiser( + if issubclass(self.optimiser, PintsOptimiser): + self.optimiser = self.optimiser( self.x0, sigma0=self.sigma0, boundaries=self._boundaries ) else: - raise ValueError( - "The pints_optimiser is not a recognised PINTS optimiser class." - ) + raise ValueError("The optimiser is not a recognised PINTS optimiser class.") # Check if sensitivities are required - self._needs_sensitivities = self.pints_optimiser.needs_sensitivities() - - # Apply default maxiter - self.set_max_iterations() + self._needs_sensitivities = self.optimiser.needs_sensitivities() # Apply additional options and remove them from options - key_list = list(self.unset_options.keys()) - for key in key_list: - if key == "use_f_guessed": - self.set_f_guessed_tracking(self.unset_options.pop(key)) - elif key == "parallel": - self.set_parallel(self.unset_options.pop(key)) - elif key == "max_iterations": - self.set_max_iterations(self.unset_options.pop(key)) - elif key == "min_iterations": - self.set_min_iterations(self.unset_options.pop(key)) - elif key == "max_unchanged_iterations": - max_unchanged_kwargs = {"iterations": self.unset_options.pop(key)} - if "absolute_tolerance" in self.unset_options.keys(): - max_unchanged_kwargs["absolute_tolerance"] = self.unset_options.pop( - "absolute_tolerance" - ) - if "relative_tolerance" in self.unset_options.keys(): - max_unchanged_kwargs["relative_tolerance"] = self.unset_options.pop( - "relative_tolerance" - ) - self.set_max_unchanged_iterations(**max_unchanged_kwargs) - elif key == "max_evaluations": - self.set_max_evaluations(self.unset_options.pop(key)) - elif key == "threshold": - self.set_threshold(self.unset_options.pop(key)) + max_unchanged_kwargs = {"iterations": self._unchanged_max_iterations} + for key, method in self.option_methods.items(): + if key in self.unset_options: + method(self.unset_options.pop(key)) + + # Capture tolerance options + for tol_key in ["absolute_tolerance", "relative_tolerance"]: + if tol_key in self.unset_options: + max_unchanged_kwargs[tol_key] = self.unset_options.pop(tol_key) def _sanitise_inputs(self): """ @@ -120,50 +134,29 @@ def _sanitise_inputs(self): ) self.unset_options.pop("options") - # Check for duplicate keywords - expected_keys = ["max_iterations", "popsize"] - alternative_keys = ["maxiter", "population_size"] - for exp_key, alt_key in zip(expected_keys, alternative_keys): - if alt_key in self.unset_options.keys(): - if exp_key in self.unset_options.keys(): - raise Exception( - "The alternative {alt_key} option was passed in addition to the expected {exp_key} option." - ) - else: # rename - self.unset_options[exp_key] = self.unset_options.pop(alt_key) - # Convert bounds to PINTS boundaries if self.bounds is not None: - if issubclass( - self.pints_optimiser, - (PintsGradientDescent, PintsAdam, PintsNelderMead), - ): - print(f"NOTE: Boundaries ignored by {self.pints_optimiser}") + ignored_optimisers = (PintsGradientDescent, PintsAdam, PintsNelderMead) + if issubclass(self.optimiser, ignored_optimisers): + print(f"NOTE: Boundaries ignored by {self.optimiser}") self.bounds = None - elif issubclass(self.pints_optimiser, PintsPSO): - if not all( - np.isfinite(value) - for sublist in self.bounds.values() - for value in sublist - ): - raise ValueError( - "Either all bounds or no bounds must be set for Pints PSO." - ) else: + if issubclass(self.optimiser, PintsPSO): + if not all( + np.isfinite(value) + for sublist in self.bounds.values() + for value in sublist + ): + raise ValueError( + f"Either all bounds or no bounds must be set for {self.optimiser.__name__}." + ) self._boundaries = PintsRectangularBoundaries( self.bounds["lower"], self.bounds["upper"] ) def name(self): - """ - Provides the name of the optimisation strategy. - - Returns - ------- - str - The name given by PINTS. - """ - return self.pints_optimiser.name() + """Returns the name of the PINTS optimisation strategy.""" + return self.optimiser.name() def _run(self): """ @@ -178,6 +171,9 @@ def _run(self): -------- This method is heavily based on the run method in the PINTS.OptimisationController class. """ + # Timing + start_time = time() + # Check stopping criteria has_stopping_criterion = False has_stopping_criterion |= self._max_iterations is not None @@ -195,15 +191,14 @@ def _run(self): unchanged_iterations = 0 # Choose method to evaluate - if self._needs_sensitivities: - - def f(x): - L, dl = self.cost.evaluateS1(x) - return (L, dl) if self.minimising else (-L, -dl) - else: - - def f(x, grad=None): - return self.cost(x, grad) if self.minimising else -self.cost(x, grad) + def fun(x): + if self._needs_sensitivities: + L, dl = self.cost(x, calculate_grad=True, apply_transform=True) + else: + L = self.cost(x, apply_transform=True) + dl = None + sign = -1 if not self.minimising else 1 + return (sign * L, sign * dl) if dl is not None else sign * L # Create evaluator object if self._parallel: @@ -212,11 +207,11 @@ def f(x, grad=None): # For population based optimisers, don't use more workers than # particles! - if isinstance(self.pints_optimiser, PintsPopulationBasedOptimiser): - n_workers = min(n_workers, self.pints_optimiser.population_size()) - evaluator = PintsParallelEvaluator(f, n_workers=n_workers) + if isinstance(self.optimiser, PintsPopulationBasedOptimiser): + n_workers = min(n_workers, self.optimiser.population_size()) + evaluator = PintsParallelEvaluator(fun, n_workers=n_workers) else: - evaluator = PintsSequentialEvaluator(f) + evaluator = PintsSequentialEvaluator(fun) # Keep track of current best and best-guess scores. fb = fg = np.inf @@ -232,17 +227,17 @@ def f(x, grad=None): try: while running: # Ask optimiser for new points - xs = self.pints_optimiser.ask() + xs = self.optimiser.ask() # Evaluate points fs = evaluator.evaluate(xs) # Tell optimiser about function values - self.pints_optimiser.tell(fs) + self.optimiser.tell(fs) # Update the scores - fb = self.pints_optimiser.f_best() - fg = self.pints_optimiser.f_guessed() + fb = self.optimiser.f_best() + fg = self.optimiser.f_guessed() fg_user = (fb, fg) if self.minimising else (-fb, -fg) # Check for significant changes against the absolute and relative tolerance @@ -258,9 +253,13 @@ def f(x, grad=None): # Update counts evaluations += len(fs) iteration += 1 - self.log["x"].append(xs) - self.log["x_best"].append(self.pints_optimiser.x_best()) - self.log["cost"].append(fb if self.minimising else -fb) + _fs = [x[0] for x in fs] if self._needs_sensitivities else fs + self.log_update( + x=xs, + x_best=self.optimiser.x_best(), + cost=_fs if self.minimising else [-x for x in _fs], + cost_best=fb if self.minimising else -fb, + ) # Check stopping criteria: # Maximum number of iterations @@ -310,7 +309,7 @@ def f(x, grad=None): ) # Error in optimiser - error = self.pints_optimiser.stop() + error = self.optimiser.stop() if error: running = False halt_message = str(error) @@ -326,14 +325,15 @@ def f(x, grad=None): print("Current position:") # Show current parameters - x_user = self.pints_optimiser.x_guessed() - if self._transformation is not None: + x_user = self.optimiser.x_guessed() + if self._transformation: x_user = self._transformation.to_model(x_user) for p in x_user: print(PintsStrFloat(p)) print("-" * 40) raise + total_time = time() - start_time if self.verbose: print("Halt: " + halt_message) @@ -343,18 +343,23 @@ def f(x, grad=None): # Get best parameters if self._use_f_guessed: - x = self.pints_optimiser.x_guessed() - f = self.pints_optimiser.f_guessed() + x = self.optimiser.x_guessed() + f = self.optimiser.f_guessed() else: - x = self.pints_optimiser.x_best() - f = self.pints_optimiser.f_best() + x = self.optimiser.x_best() + f = self.optimiser.f_best() # Inverse transform search parameters - if self._transformation is not None: + if self._transformation: x = self._transformation.to_model(x) - return Result( - x=x, final_cost=f if self.minimising else -f, n_iterations=self._iterations + return OptimisationResult( + x=x, + cost=self.cost, + final_cost=f if self.minimising else -f, + n_iterations=self._iterations, + optim=self, + time=total_time, ) def f_guessed_tracking(self): diff --git a/pybop/optimisers/optimisation.py b/pybop/optimisers/optimisation.py index aaa0ab3bf..59d5c8e1a 100644 --- a/pybop/optimisers/optimisation.py +++ b/pybop/optimisers/optimisation.py @@ -15,11 +15,11 @@ class Optimisation: cost : pybop.BaseCost or pints.ErrorMeasure An objective function to be optimized, which can be either a pybop.Cost optimiser : pybop.Optimiser or subclass of pybop.BaseOptimiser, optional - An optimiser from either the PINTS or PyBOP framework to perform the optimization (default: None). + An optimiser from either the PINTS or PyBOP framework to perform the optimisation (default: None). sigma0 : float or sequence, optional Initial step size or standard deviation for the optimiser (default: None). verbose : bool, optional - If True, the optimization progress is printed (default: False). + If True, the optimisation progress is printed (default: False). physical_viability : bool, optional If True, the feasibility of the optimised parameters is checked (default: True). allow_infeasible_solutions : bool, optional @@ -32,24 +32,24 @@ class Optimisation: """ def __init__(self, cost, optimiser=None, **optimiser_kwargs): - self.__dict__["optimiser"] = ( + self.__dict__["optim"] = ( None # Pre-define optimiser to avoid recursion during initialisation ) if optimiser is None: - self.optimiser = XNES(cost, **optimiser_kwargs) + self.optim = XNES(cost, **optimiser_kwargs) elif issubclass(optimiser, BasePintsOptimiser): - self.optimiser = optimiser(cost, **optimiser_kwargs) + self.optim = optimiser(cost, **optimiser_kwargs) elif issubclass(optimiser, BaseSciPyOptimiser): - self.optimiser = optimiser(cost, **optimiser_kwargs) + self.optim = optimiser(cost, **optimiser_kwargs) else: raise ValueError("Unknown optimiser type") def run(self): - return self.optimiser.run() + return self.optim.run() def __getattr__(self, attr): - if "optimiser" in self.__dict__ and hasattr(self.optimiser, attr): - return getattr(self.optimiser, attr) + if "optim" in self.__dict__ and hasattr(self.optim, attr): + return getattr(self.optim, attr) raise AttributeError( f"'{self.__class__.__name__}' object has no attribute '{attr}'" ) @@ -57,9 +57,9 @@ def __getattr__(self, attr): def __setattr__(self, name: str, value) -> None: if ( name in self.__dict__ - or "optimiser" not in self.__dict__ - or not hasattr(self.optimiser, name) + or "optim" not in self.__dict__ + or not hasattr(self.optim, name) ): object.__setattr__(self, name, value) else: - setattr(self.optimiser, name, value) + setattr(self.optim, name, value) diff --git a/pybop/optimisers/pints_optimisers.py b/pybop/optimisers/pints_optimisers.py index 4872973a8..d4d89a6a4 100644 --- a/pybop/optimisers/pints_optimisers.py +++ b/pybop/optimisers/pints_optimisers.py @@ -9,53 +9,115 @@ from pints import IRPropMin as PintsIRPropMin from pints import NelderMead as PintsNelderMead -from pybop import AdamWImpl, BasePintsOptimiser +from pybop import AdamWImpl, BasePintsOptimiser, CuckooSearchImpl class GradientDescent(BasePintsOptimiser): """ - Implements a simple gradient descent optimization algorithm. + Implements a simple gradient descent optimisation algorithm. This class extends the gradient descent optimiser from the PINTS library, designed - to minimize a scalar function of one or more variables. Note that this optimiser - does not support boundary constraints. + to minimize a scalar function of one or more variables. + + Note that this optimiser does not support boundary constraints. Parameters ---------- + cost : callable + The cost function to be minimized. + max_iterations : int, optional + Maximum number of iterations for the optimisation. + min_iterations : int, optional (default=2) + Minimum number of iterations before termination. + max_unchanged_iterations : int, optional (default=15) + Maximum number of iterations without improvement before termination. + parallel : bool, optional (default=False) + Whether to run the optimisation in parallel. **optimiser_kwargs : optional Valid PINTS option keys and their values, for example: x0 : array_like Initial position from which optimisation will start. sigma0 : float - The learning rate / Initial step size (default: 0.02). + Initial step size or standard deviation depending on the optimiser. + bounds : dict + A dictionary with 'lower' and 'upper' keys containing arrays for lower and + upper bounds on the parameters. + use_f_guessed : bool + Whether to return the guessed function values. + absolute_tolerance : float + Absolute tolerance for convergence checking. + relative_tolerance : float + Relative tolerance for convergence checking. + max_evaluations : int + Maximum number of function evaluations. + threshold : float + Threshold value for early termination. See Also -------- pints.GradientDescent : The PINTS implementation this class is based on. """ - def __init__(self, cost, **optimiser_kwargs): - if "sigma0" not in optimiser_kwargs.keys(): - optimiser_kwargs["sigma0"] = 0.02 # set default - super().__init__(cost, PintsGradientDescent, **optimiser_kwargs) + def __init__( + self, + cost, + max_iterations: int = None, + min_iterations: int = 2, + max_unchanged_iterations: int = 15, + parallel: bool = False, + **optimiser_kwargs, + ): + super().__init__( + cost, + PintsGradientDescent, + max_iterations, + min_iterations, + max_unchanged_iterations, + parallel, + **optimiser_kwargs, + ) class Adam(BasePintsOptimiser): """ - Implements the Adam optimization algorithm. + Implements the Adam optimisation algorithm. This class extends the Adam optimiser from the PINTS library, which combines - ideas from RMSProp and Stochastic Gradient Descent with momentum. Note that - this optimiser does not support boundary constraints. + ideas from RMSProp and Stochastic Gradient Descent with momentum. + + Note that this optimiser does not support boundary constraints. Parameters ---------- + cost : callable + The cost function to be minimized. + max_iterations : int, optional + Maximum number of iterations for the optimisation. + min_iterations : int, optional (default=2) + Minimum number of iterations before termination. + max_unchanged_iterations : int, optional (default=15) + Maximum number of iterations without improvement before termination. + parallel : bool, optional (default=False) + Whether to run the optimisation in parallel. **optimiser_kwargs : optional Valid PINTS option keys and their values, for example: x0 : array_like Initial position from which optimisation will start. sigma0 : float - Initial step size. + Initial step size or standard deviation depending on the optimiser. + bounds : dict + A dictionary with 'lower' and 'upper' keys containing arrays for lower and + upper bounds on the parameters. + use_f_guessed : bool + Whether to return the guessed function values. + absolute_tolerance : float + Absolute tolerance for convergence checking. + relative_tolerance : float + Relative tolerance for convergence checking. + max_evaluations : int + Maximum number of function evaluations. + threshold : float + Threshold value for early termination. See Also -------- @@ -68,8 +130,24 @@ class Adam(BasePintsOptimiser): stacklevel=2, ) - def __init__(self, cost, **optimiser_kwargs): - super().__init__(cost, PintsAdam, **optimiser_kwargs) + def __init__( + self, + cost, + max_iterations: int = None, + min_iterations: int = 2, + max_unchanged_iterations: int = 15, + parallel: bool = False, + **optimiser_kwargs, + ): + super().__init__( + cost, + PintsAdam, + max_iterations, + min_iterations, + max_unchanged_iterations, + parallel, + **optimiser_kwargs, + ) class AdamW(BasePintsOptimiser): @@ -81,27 +159,68 @@ class AdamW(BasePintsOptimiser): robust and stable for training deep neural networks, particularly when using larger learning rates. + Note that this optimiser does not support boundary constraints. + Parameters ---------- + cost : callable + The cost function to be minimized. + max_iterations : int, optional + Maximum number of iterations for the optimisation. + min_iterations : int, optional (default=2) + Minimum number of iterations before termination. + max_unchanged_iterations : int, optional (default=15) + Maximum number of iterations without improvement before termination. + parallel : bool, optional (default=False) + Whether to run the optimisation in parallel. **optimiser_kwargs : optional - Valid PyBOP option keys and their values, for example: + Valid PINTS option keys and their values, for example: x0 : array_like Initial position from which optimisation will start. sigma0 : float - Initial step size. + Initial step size or standard deviation depending on the optimiser. + bounds : dict + A dictionary with 'lower' and 'upper' keys containing arrays for lower and + upper bounds on the parameters. + use_f_guessed : bool + Whether to return the guessed function values. + absolute_tolerance : float + Absolute tolerance for convergence checking. + relative_tolerance : float + Relative tolerance for convergence checking. + max_evaluations : int + Maximum number of function evaluations. + threshold : float + Threshold value for early termination. See Also -------- pybop.AdamWImpl : The PyBOP implementation this class is based on. """ - def __init__(self, cost, **optimiser_kwargs): - super().__init__(cost, AdamWImpl, **optimiser_kwargs) + def __init__( + self, + cost, + max_iterations: int = None, + min_iterations: int = 2, + max_unchanged_iterations: int = 15, + parallel: bool = False, + **optimiser_kwargs, + ): + super().__init__( + cost, + AdamWImpl, + max_iterations, + min_iterations, + max_unchanged_iterations, + parallel, + **optimiser_kwargs, + ) class IRPropMin(BasePintsOptimiser): """ - Implements the iRpropMin optimization algorithm. + Implements the iRpropMin optimisation algorithm. This class inherits from the PINTS IRPropMin class, which is an optimiser that uses resilient backpropagation with weight-backtracking. It is designed to handle @@ -109,57 +228,129 @@ class IRPropMin(BasePintsOptimiser): Parameters ---------- + cost : callable + The cost function to be minimized. + max_iterations : int, optional + Maximum number of iterations for the optimisation. + min_iterations : int, optional (default=2) + Minimum number of iterations before termination. + max_unchanged_iterations : int, optional (default=15) + Maximum number of iterations without improvement before termination. + parallel : bool, optional (default=False) + Whether to run the optimisation in parallel. **optimiser_kwargs : optional Valid PINTS option keys and their values, for example: x0 : array_like Initial position from which optimisation will start. sigma0 : float - Initial step size. + Initial step size or standard deviation depending on the optimiser. bounds : dict A dictionary with 'lower' and 'upper' keys containing arrays for lower and upper bounds on the parameters. + use_f_guessed : bool + Whether to return the guessed function values. + absolute_tolerance : float + Absolute tolerance for convergence checking. + relative_tolerance : float + Relative tolerance for convergence checking. + max_evaluations : int + Maximum number of function evaluations. + threshold : float + Threshold value for early termination. See Also -------- pints.IRPropMin : The PINTS implementation this class is based on. """ - def __init__(self, cost, **optimiser_kwargs): - super().__init__(cost, PintsIRPropMin, **optimiser_kwargs) + def __init__( + self, + cost, + max_iterations: int = None, + min_iterations: int = 2, + max_unchanged_iterations: int = 15, + parallel: bool = False, + **optimiser_kwargs, + ): + super().__init__( + cost, + PintsIRPropMin, + max_iterations, + min_iterations, + max_unchanged_iterations, + parallel, + **optimiser_kwargs, + ) class PSO(BasePintsOptimiser): """ - Implements a particle swarm optimization (PSO) algorithm. + Implements a particle swarm optimisation (PSO) algorithm. This class extends the PSO optimiser from the PINTS library. PSO is a - metaheuristic optimization method inspired by the social behavior of birds - flocking or fish schooling, suitable for global optimization problems. + metaheuristic optimisation method inspired by the social behavior of birds + flocking or fish schooling, suitable for global optimisation problems. Parameters ---------- + cost : callable + The cost function to be minimized. + max_iterations : int, optional + Maximum number of iterations for the optimisation. + min_iterations : int, optional (default=2) + Minimum number of iterations before termination. + max_unchanged_iterations : int, optional (default=15) + Maximum number of iterations without improvement before termination. + parallel : bool, optional (default=False) + Whether to run the optimisation in parallel. **optimiser_kwargs : optional Valid PINTS option keys and their values, for example: x0 : array_like - Initial positions of particles, which the optimisation will use. + Initial position from which optimisation will start. sigma0 : float - Spread of the initial particle positions. + Initial step size or standard deviation depending on the optimiser. bounds : dict A dictionary with 'lower' and 'upper' keys containing arrays for lower and upper bounds on the parameters. + use_f_guessed : bool + Whether to return the guessed function values. + absolute_tolerance : float + Absolute tolerance for convergence checking. + relative_tolerance : float + Relative tolerance for convergence checking. + max_evaluations : int + Maximum number of function evaluations. + threshold : float + Threshold value for early termination. See Also -------- pints.PSO : The PINTS implementation this class is based on. """ - def __init__(self, cost, **optimiser_kwargs): - super().__init__(cost, PintsPSO, **optimiser_kwargs) + def __init__( + self, + cost, + max_iterations: int = None, + min_iterations: int = 2, + max_unchanged_iterations: int = 15, + parallel: bool = False, + **optimiser_kwargs, + ): + super().__init__( + cost, + PintsPSO, + max_iterations, + min_iterations, + max_unchanged_iterations, + parallel, + **optimiser_kwargs, + ) class SNES(BasePintsOptimiser): """ - Implements the stochastic natural evolution strategy (SNES) optimization algorithm. + Implements the stochastic natural evolution strategy (SNES) optimisation algorithm. Inheriting from the PINTS SNES class, this optimiser is an evolutionary algorithm that evolves a probability distribution on the parameter space, guiding the search @@ -167,23 +358,59 @@ class SNES(BasePintsOptimiser): Parameters ---------- + cost : callable + The cost function to be minimized. + max_iterations : int, optional + Maximum number of iterations for the optimisation. + min_iterations : int, optional (default=2) + Minimum number of iterations before termination. + max_unchanged_iterations : int, optional (default=15) + Maximum number of iterations without improvement before termination. + parallel : bool, optional (default=False) + Whether to run the optimisation in parallel. **optimiser_kwargs : optional Valid PINTS option keys and their values, for example: x0 : array_like Initial position from which optimisation will start. sigma0 : float - Initial standard deviation of the sampling distribution. + Initial step size or standard deviation depending on the optimiser. bounds : dict A dictionary with 'lower' and 'upper' keys containing arrays for lower and upper bounds on the parameters. + use_f_guessed : bool + Whether to return the guessed function values. + absolute_tolerance : float + Absolute tolerance for convergence checking. + relative_tolerance : float + Relative tolerance for convergence checking. + max_evaluations : int + Maximum number of function evaluations. + threshold : float + Threshold value for early termination. See Also -------- pints.SNES : The PINTS implementation this class is based on. """ - def __init__(self, cost, **optimiser_kwargs): - super().__init__(cost, PintsSNES, **optimiser_kwargs) + def __init__( + self, + cost, + max_iterations: int = None, + min_iterations: int = 2, + max_unchanged_iterations: int = 15, + parallel: bool = False, + **optimiser_kwargs, + ): + super().__init__( + cost, + PintsSNES, + max_iterations, + min_iterations, + max_unchanged_iterations, + parallel, + **optimiser_kwargs, + ) class XNES(BasePintsOptimiser): @@ -196,23 +423,59 @@ class XNES(BasePintsOptimiser): Parameters ---------- + cost : callable + The cost function to be minimized. + max_iterations : int, optional + Maximum number of iterations for the optimisation. + min_iterations : int, optional (default=2) + Minimum number of iterations before termination. + max_unchanged_iterations : int, optional (default=15) + Maximum number of iterations without improvement before termination. + parallel : bool, optional (default=False) + Whether to run the optimisation in parallel. **optimiser_kwargs : optional Valid PINTS option keys and their values, for example: x0 : array_like - The initial parameter vector to optimise. + Initial position from which optimisation will start. sigma0 : float - Initial standard deviation of the sampling distribution. + Initial step size or standard deviation depending on the optimiser. bounds : dict A dictionary with 'lower' and 'upper' keys containing arrays for lower and - upperbounds on the parameters. If ``None``, no bounds are enforced. + upper bounds on the parameters. + use_f_guessed : bool + Whether to return the guessed function values. + absolute_tolerance : float + Absolute tolerance for convergence checking. + relative_tolerance : float + Relative tolerance for convergence checking. + max_evaluations : int + Maximum number of function evaluations. + threshold : float + Threshold value for early termination. See Also -------- pints.XNES : PINTS implementation of XNES algorithm. """ - def __init__(self, cost, **optimiser_kwargs): - super().__init__(cost, PintsXNES, **optimiser_kwargs) + def __init__( + self, + cost, + max_iterations: int = None, + min_iterations: int = 2, + max_unchanged_iterations: int = 15, + parallel: bool = False, + **optimiser_kwargs, + ): + super().__init__( + cost, + PintsXNES, + max_iterations, + min_iterations, + max_unchanged_iterations, + parallel, + **optimiser_kwargs, + ) class NelderMead(BasePintsOptimiser): @@ -223,55 +486,195 @@ class NelderMead(BasePintsOptimiser): either one evaluation, or two sequential evaluations, so that it will not typically benefit from parallelisation. + Note that this optimiser does not support boundary constraints. + Parameters ---------- + cost : callable + The cost function to be minimized. + max_iterations : int, optional + Maximum number of iterations for the optimisation. + min_iterations : int, optional (default=2) + Minimum number of iterations before termination. + max_unchanged_iterations : int, optional (default=15) + Maximum number of iterations without improvement before termination. + parallel : bool, optional (default=False) + Whether to run the optimisation in parallel. **optimiser_kwargs : optional Valid PINTS option keys and their values, for example: x0 : array_like - The initial parameter vector to optimise. + Initial position from which optimisation will start. sigma0 : float - Initial standard deviation of the sampling distribution. - Does not appear to be used. + Initial step size or standard deviation depending on the optimiser. + bounds : dict + A dictionary with 'lower' and 'upper' keys containing arrays for lower and + upper bounds on the parameters. + use_f_guessed : bool + Whether to return the guessed function values. + absolute_tolerance : float + Absolute tolerance for convergence checking. + relative_tolerance : float + Relative tolerance for convergence checking. + max_evaluations : int + Maximum number of function evaluations. + threshold : float + Threshold value for early termination. See Also -------- pints.NelderMead : PINTS implementation of Nelder-Mead algorithm. """ - def __init__(self, cost, **optimiser_kwargs): - super().__init__(cost, PintsNelderMead, **optimiser_kwargs) + def __init__( + self, + cost, + max_iterations: int = None, + min_iterations: int = 2, + max_unchanged_iterations: int = 15, + parallel: bool = False, + **optimiser_kwargs, + ): + super().__init__( + cost, + PintsNelderMead, + max_iterations, + min_iterations, + max_unchanged_iterations, + parallel, + **optimiser_kwargs, + ) class CMAES(BasePintsOptimiser): """ Adapter for the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimiser in PINTS. - CMA-ES is an evolutionary algorithm for difficult non-linear non-convex optimization problems. + CMA-ES is an evolutionary algorithm for difficult non-linear non-convex optimisation problems. It adapts the covariance matrix of a multivariate normal distribution to capture the shape of the cost landscape. Parameters ---------- + cost : callable + The cost function to be minimized. + max_iterations : int, optional + Maximum number of iterations for the optimisation. + min_iterations : int, optional (default=2) + Minimum number of iterations before termination. + max_unchanged_iterations : int, optional (default=15) + Maximum number of iterations without improvement before termination. + parallel : bool, optional (default=False) + Whether to run the optimisation in parallel. **optimiser_kwargs : optional Valid PINTS option keys and their values, for example: x0 : array_like - The initial parameter vector to optimise. + Initial position from which optimisation will start. sigma0 : float - Initial standard deviation of the sampling distribution. + Initial step size or standard deviation depending on the optimiser. bounds : dict A dictionary with 'lower' and 'upper' keys containing arrays for lower and - upper bounds on the parameters. If ``None``, no bounds are enforced. + upper bounds on the parameters. + use_f_guessed : bool + Whether to return the guessed function values. + absolute_tolerance : float + Absolute tolerance for convergence checking. + relative_tolerance : float + Relative tolerance for convergence checking. + max_evaluations : int + Maximum number of function evaluations. + threshold : float + Threshold value for early termination. See Also -------- pints.CMAES : PINTS implementation of CMA-ES algorithm. """ - def __init__(self, cost, **optimiser_kwargs): - x0 = optimiser_kwargs.pop("x0", cost.x0) - if x0 is not None and len(x0) == 1: + def __init__( + self, + cost, + max_iterations: int = None, + min_iterations: int = 2, + max_unchanged_iterations: int = 15, + parallel: bool = False, + **optimiser_kwargs, + ): + x0 = optimiser_kwargs.get("x0", cost.parameters.initial_value()) + if len(x0) == 1 or len(cost.parameters) == 1: raise ValueError( "CMAES requires optimisation of >= 2 parameters at once. " - + "Please choose another optimiser." + "Please choose another optimiser." ) - super().__init__(cost, PintsCMAES, **optimiser_kwargs) + super().__init__( + cost, + PintsCMAES, + max_iterations, + min_iterations, + max_unchanged_iterations, + parallel, + **optimiser_kwargs, + ) + + +class CuckooSearch(BasePintsOptimiser): + """ + Adapter for the Cuckoo Search optimiser in PyBOP. + + Cuckoo Search is a population-based optimisation algorithm inspired by the brood parasitism of some cuckoo species. + It is designed to be simple, efficient, and robust, and is suitable for global optimisation problems. + + Parameters + ---------- + cost : callable + The cost function to be minimized. + max_iterations : int, optional + Maximum number of iterations for the optimisation. + min_iterations : int, optional (default=2) + Minimum number of iterations before termination. + max_unchanged_iterations : int, optional (default=15) + Maximum number of iterations without improvement before termination. + parallel : bool, optional (default=False) + Whether to run the optimisation in parallel. + **optimiser_kwargs : optional + Valid PINTS option keys and their values, for example: + x0 : array_like + Initial position from which optimisation will start. + sigma0 : float + Initial step size or standard deviation depending on the optimiser. + bounds : dict + A dictionary with 'lower' and 'upper' keys containing arrays for lower and + upper bounds on the parameters. + use_f_guessed : bool + Whether to return the guessed function values. + absolute_tolerance : float + Absolute tolerance for convergence checking. + relative_tolerance : float + Relative tolerance for convergence checking. + max_evaluations : int + Maximum number of function evaluations. + threshold : float + Threshold value for early termination. + + See Also + -------- + pybop.CuckooSearchImpl : PyBOP implementation of Cuckoo Search algorithm. + """ + + def __init__( + self, + cost, + max_iterations: int = None, + min_iterations: int = 2, + max_unchanged_iterations: int = 15, + parallel: bool = False, + **optimiser_kwargs, + ): + super().__init__( + cost, + CuckooSearchImpl, + max_iterations, + min_iterations, + max_unchanged_iterations, + parallel, + **optimiser_kwargs, + ) diff --git a/pybop/optimisers/scipy_optimisers.py b/pybop/optimisers/scipy_optimisers.py index 843423042..064cf3f02 100644 --- a/pybop/optimisers/scipy_optimisers.py +++ b/pybop/optimisers/scipy_optimisers.py @@ -1,7 +1,11 @@ +import warnings +from time import time +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 +from pybop import BaseOptimiser, OptimisationResult class BaseSciPyOptimiser(BaseOptimiser): @@ -19,8 +23,9 @@ class BaseSciPyOptimiser(BaseOptimiser): """ def __init__(self, cost, **optimiser_kwargs): - super().__init__(cost, **optimiser_kwargs) self.num_resamples = 40 + self.key_mapping = {"max_iterations": "maxiter", "population_size": "popsize"} + super().__init__(cost, **optimiser_kwargs) def _sanitise_inputs(self): """ @@ -38,75 +43,124 @@ def _sanitise_inputs(self): ) self.unset_options.pop("options") - # Check for duplicate keywords - expected_keys = ["maxiter", "popsize"] - alternative_keys = ["max_iterations", "population_size"] - for exp_key, alt_key in zip(expected_keys, alternative_keys): - if alt_key in self.unset_options.keys(): - if exp_key in self.unset_options.keys(): + # Convert PyBOP keys to SciPy + for pybop_key, scipy_key in self.key_mapping.items(): + if pybop_key in self.unset_options: + if scipy_key in self.unset_options: raise Exception( - "The alternative {alt_key} option was passed in addition to the expected {exp_key} option." + f"The alternative {pybop_key} option was passed in addition to the expected {scipy_key} option." ) - else: # rename - self.unset_options[exp_key] = self.unset_options.pop(alt_key) + # Rename the key + self.unset_options[scipy_key] = self.unset_options.pop(pybop_key) # 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): """ - Internal method to run the optimization using a PyBOP optimiser. + Internal method to run the optimisation using a PyBOP optimiser. Returns ------- result : pybop.Result The result of the optimisation including the optimised parameter values and cost. """ + # Run with timing + start_time = time() result = self._run_optimiser() - - return Result( - x=result.x, - final_cost=self.cost(result.x), - n_iterations=result.nit, + total_time = time() - start_time + + try: + nit = result.nit + except AttributeError: + nit = -1 + + return OptimisationResult( + x=self._transformation.to_model(result.x) + if self._transformation + else result.x, + cost=self.cost, + final_cost=self.cost(result.x, apply_transform=True), + n_iterations=nit, scipy_result=result, + optim=self, + time=total_time, ) class SciPyMinimize(BaseSciPyOptimiser): """ - Adapts SciPy's minimize function for use as an optimization strategy. + Adapts SciPy's minimize function for use as an optimisation strategy. - This class provides an interface to various scalar minimization algorithms implemented in SciPy, - allowing fine-tuning of the optimization process through method selection and option configuration. + This class provides an interface to various scalar minimisation algorithms implemented in SciPy, + allowing fine-tuning of the optimisation process through method selection and option configuration. Parameters ---------- **optimiser_kwargs : optional - Valid SciPy Minimize option keys and their values, For example: + Valid SciPy Minimize option keys and their values: x0 : array_like Initial position from which optimisation will start. - bounds : dict, sequence or scipy.optimize.Bounds - Bounds for variables as supported by the selected method. method : str The optimisation method, options include: 'Nelder-Mead', 'Powell', 'CG', 'BFGS', 'Newton-CG', 'L-BFGS-B', 'TNC', 'COBYLA', 'SLSQP', 'trust-constr', 'dogleg', 'trust-ncg', 'trust-exact', 'trust-krylov'. + jac : {callable, '2-point', '3-point', 'cs', bool}, optional + Method for computing the gradient vector. + hess : {callable, '2-point', '3-point', 'cs', HessianUpdateStrategy}, optional + Method for computing the Hessian matrix. + hessp : callable, optional + Hessian of objective function times an arbitrary vector p. + bounds : sequence or scipy.optimize.Bounds, optional + Bounds on variables for L-BFGS-B, TNC, SLSQP, trust-constr methods. + constraints : {Constraint, dict} or List of {Constraint, dict}, optional + Constraints definition for constrained optimisation. + tol : float, optional + Tolerance for termination. + options : dict, optional + Method-specific options. Common options include: + maxiter : int + Maximum number of iterations. + disp : bool + Set to True to print convergence messages. + ftol : float + Function tolerance for termination. + gtol : float + Gradient tolerance for termination. + eps : float + Step size for finite difference approximation. + maxfev : int + Maximum number of function evaluations. + maxcor : int + Maximum number of variable metric corrections (L-BFGS-B). See Also -------- scipy.optimize.minimize : The SciPy method this class is based on. + + Notes + ----- + Different optimisation methods may support different options. Consult SciPy's + documentation for method-specific options and constraints. """ def __init__(self, cost, **optimiser_kwargs): optimiser_options = dict(method="Nelder-Mead", jac=False) optimiser_options.update(**optimiser_kwargs) super().__init__(cost, **optimiser_options) + self._cost0 = 1.0 def _set_up_optimiser(self): """ @@ -141,6 +195,24 @@ def _set_up_optimiser(self): # Nest this option within an options dictionary for SciPy minimize self._options["options"]["maxiter"] = self.unset_options.pop(key) + def cost_wrapper(self, x): + """ + Scale the cost function, preserving the sign convention, and eliminate nan values + """ + if not self._options["jac"]: + cost = self.cost(x, apply_transform=True) + self.log_update(x=[x], cost=cost if self.minimising else -cost) + scaled_cost = cost / self._cost0 + if np.isinf(scaled_cost): + self.inf_count += 1 + scaled_cost = 1 + 0.9**self.inf_count # for fake finite gradient + return scaled_cost if self.minimising else -scaled_cost + + L, dl = self.cost(x, calculate_grad=True, apply_transform=True) + self.log_update(x=[x], cost=L if self.minimising else -L) + scaled_L = L / self._cost0 + return (scaled_L, dl) if self.minimising else (-scaled_L, -dl) + def _run_optimiser(self): """ Executes the optimisation process using SciPy's minimize function. @@ -150,20 +222,49 @@ def _run_optimiser(self): result : scipy.optimize.OptimizeResult The result of the optimisation including the optimised parameter values and cost. """ + self.inf_count = 0 # Add callback storing history of parameter values - def callback(intermediate_result: OptimizeResult): - self.log["x_best"].append(intermediate_result.x) - self.log["cost"].append( - intermediate_result.fun if self.minimising else -intermediate_result.fun + def base_callback(intermediate_result: Union[OptimizeResult, np.ndarray]): + """ + Log intermediate optimisation solutions. Depending on the + optimisation algorithm, intermediate_result may be either + a OptimizeResult or an array of parameter values, with a + try/except ensuring both cases are handled correctly. + """ + if isinstance(intermediate_result, OptimizeResult): + x_best = intermediate_result.x + cost_best = intermediate_result.fun + else: + x_best = intermediate_result + cost_best = self.cost(x_best, apply_transform=True) + + cost_log = (-1 if not self.minimising else 1) * cost_best * self._cost0 + self.log_update( + x_best=x_best, + cost_best=cost_log, ) + callback = ( + base_callback + if self._options["method"] != "trust-constr" + else lambda x, intermediate_result: base_callback(intermediate_result) + ) + # Compute the absolute initial cost and resample if required - self._cost0 = np.abs(self.cost(self.x0)) + self._cost0 = np.abs(self.cost(self.x0, apply_transform=True)) if np.isinf(self._cost0): - for i in range(1, self.num_resamples): - x0 = self.cost.parameters.rvs(1) - self._cost0 = np.abs(self.cost(x0)) + for _i in range(1, self.num_resamples): + try: + self.x0 = self.parameters.rvs(apply_transform=True) + except AttributeError: + warnings.warn( + "Parameter does not have a prior distribution. Stopping resampling.", + UserWarning, + stacklevel=2, + ) + break + self._cost0 = np.abs(self.cost(self.x0, apply_transform=True)) if not np.isinf(self._cost0): break if np.isinf(self._cost0): @@ -171,27 +272,8 @@ def callback(intermediate_result: OptimizeResult): "The initial parameter values return an infinite cost." ) - # Scale the cost function, preserving the sign convention, and eliminate nan values - self.inf_count = 0 - - if not self._options["jac"]: - - def cost_wrapper(x): - self.log["x"].append([x]) - cost = self.cost(x) / self._cost0 - if np.isinf(cost): - self.inf_count += 1 - cost = 1 + 0.9**self.inf_count # for fake finite gradient - return cost if self.minimising else -cost - elif self._options["jac"] is True: - - def cost_wrapper(x): - self.log["x"].append([x]) - L, dl = self.cost.evaluateS1(x) - return L, dl if self.minimising else -L, -dl - return minimize( - cost_wrapper, + self.cost_wrapper, self.x0, bounds=self._scipy_bounds, callback=callback, @@ -199,40 +281,85 @@ def cost_wrapper(x): ) def name(self): - """ - Provides the name of the optimization strategy. - - Returns - ------- - str - The name 'SciPyMinimize'. - """ + """Provides the name of the optimisation strategy.""" return "SciPyMinimize" class SciPyDifferentialEvolution(BaseSciPyOptimiser): """ - Adapts SciPy's differential_evolution function for global optimization. + Adapts SciPy's differential_evolution function for global optimisation. - This class provides a global optimization strategy based on differential evolution, useful for + This class provides a global optimisation strategy based on differential evolution, useful for problems involving continuous parameters and potentially multiple local minima. Parameters ---------- bounds : dict, sequence or scipy.optimize.Bounds Bounds for variables. Must be provided as it is essential for differential evolution. + Each element is a tuple (min, max) for the corresponding variable. **optimiser_kwargs : optional - Valid SciPy option keys and their values, for example: - strategy : str - The differential evolution strategy to use. - maxiter : int - Maximum number of iterations to perform. - popsize : int - The number of individuals in the population. + Valid SciPy differential_evolution options: + strategy : str, optional + The differential evolution strategy to use. Should be one of: + - 'best1bin' + - 'best1exp' + - 'rand1exp' + - 'randtobest1exp' + - 'currenttobest1exp' + - 'best2exp' + - 'rand2exp' + - 'randtobest1bin' + - 'currenttobest1bin' + - 'best2bin' + - 'rand2bin' + - 'rand1bin' + Default is 'best1bin'. + maxiter : int, optional + Maximum number of generations. Default is 1000. + popsize : int, optional + Multiplier for setting the total population size. The population has + popsize * len(x) individuals. Default is 15. + tol : float, optional + Relative tolerance for convergence. Default is 0.01. + mutation : float or tuple(float, float), optional + The mutation constant. If specified as a float, should be in [0, 2]. + If specified as a tuple (min, max), dithering is used. Default is (0.5, 1.0). + recombination : float, optional + The recombination constant, should be in [0, 1]. Default is 0.7. + seed : int, optional + Random seed for reproducibility. + disp : bool, optional + Display status messages. Default is False. + callback : callable, optional + Called after each iteration with the current result as argument. + polish : bool, optional + If True, performs a local optimisation on the solution. Default is True. + init : str or array-like, optional + Specify initial population. Can be 'latinhypercube', 'random', + or an array of shape (M, len(x)). + atol : float, optional + Absolute tolerance for convergence. Default is 0. + updating : {'immediate', 'deferred'}, optional + If 'immediate', best solution vector is continuously updated within + a single generation. Default is 'immediate'. + workers : int or map-like callable, optional + If workers is an int the population is subdivided into workers + sections and evaluated in parallel. Default is 1. + constraints : {NonlinearConstraint, LinearConstraint, Bounds}, optional + Constraints on the solver. See Also -------- scipy.optimize.differential_evolution : The SciPy method this class is based on. + + Notes + ----- + Differential Evolution is a stochastic population based method that is useful for + global optimisation problems. At each pass through the population the algorithm mutates + each candidate solution by mixing with other candidate solutions to create a trial + candidate. The fitness of all candidates is then evaluated and for each candidate if + the trial candidate is an improvement, it takes its place in the population for the next + iteration. """ def __init__(self, cost, **optimiser_kwargs): @@ -251,9 +378,8 @@ 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 @@ -302,14 +428,18 @@ def _run_optimiser(self): # Add callback storing history of parameter values def callback(intermediate_result: OptimizeResult): - self.log["x_best"].append(intermediate_result.x) - self.log["cost"].append( + cost = ( intermediate_result.fun if self.minimising else -intermediate_result.fun ) + self.log_update(x_best=intermediate_result.x, cost_best=cost) def cost_wrapper(x): - self.log["x"].append([x]) - return self.cost(x) if self.minimising else -self.cost(x) + if self.minimising: + cost = self.cost(x, apply_transform=True) + else: + cost = -self.cost(x, apply_transform=True) + self.log_update(x=[x], cost=cost) + return cost return differential_evolution( cost_wrapper, @@ -319,12 +449,5 @@ def cost_wrapper(x): ) def name(self): - """ - Provides the name of the optimization strategy. - - Returns - ------- - str - The name 'SciPyDifferentialEvolution'. - """ + """Provides the name of the optimisation strategy.""" return "SciPyDifferentialEvolution" diff --git a/pybop/parameters/parameter.py b/pybop/parameters/parameter.py index 76754847d..b683c34d4 100644 --- a/pybop/parameters/parameter.py +++ b/pybop/parameters/parameter.py @@ -1,8 +1,14 @@ +import warnings from collections import OrderedDict -from typing import Dict, List +from typing import Optional import numpy as np +from pybop import ComposedTransformation, IdentityTransformation +from pybop._utils import is_numeric + +Inputs = dict[str, float] + class Parameter: """ @@ -32,7 +38,13 @@ class Parameter: """ def __init__( - self, name, initial_value=None, true_value=None, prior=None, bounds=None + self, + name, + initial_value=None, + true_value=None, + prior=None, + bounds=None, + transformation=None, ): """ Construct the parameter class with a name, initial value, prior, and bounds. @@ -42,10 +54,15 @@ def __init__( self.true_value = true_value self.initial_value = initial_value self.value = initial_value + self.transformation = transformation + self.applied_prior_bounds = False + self.bounds = None + self.lower_bounds = None + self.upper_bounds = None self.set_bounds(bounds) self.margin = 1e-4 - def rvs(self, n_samples, random_state=None): + def rvs(self, n_samples: int = 1, random_state=None, apply_transform: bool = False): """ Draw random samples from the parameter's prior distribution. @@ -55,7 +72,11 @@ def rvs(self, n_samples, random_state=None): Parameters ---------- n_samples : int - The number of samples to draw. + The number of samples to draw (default: 1). + random_state : int, optional + The random state seed for reproducibility (default: None). + apply_transform : bool + If True, the transformation is applied to the output (default: False). Returns ------- @@ -71,9 +92,15 @@ def rvs(self, n_samples, random_state=None): samples, self.lower_bound + offset, self.upper_bound - offset ) + if apply_transform and self.transformation is not None: + samples = list(samples) + for i, x in enumerate(samples): + samples[i] = float(self.transformation.to_search(x)) + return np.asarray(samples) + return samples - def update(self, value=None, initial_value=None): + def update(self, initial_value=None, value=None): """ Update the parameter's current value. @@ -82,12 +109,12 @@ def update(self, value=None, initial_value=None): value : float The new value to be assigned to the parameter. """ - if value is not None: - self.value = value - elif initial_value is not None: + if initial_value is not None: self.initial_value = initial_value self.value = initial_value - else: + if value is not None: + self.value = value + if initial_value is None and value is None: raise ValueError("No value provided to update parameter") def __repr__(self): @@ -123,15 +150,21 @@ def set_margin(self, margin): self.margin = margin - def set_bounds(self, bounds=None): + def set_bounds(self, bounds=None, boundary_multiplier=15): """ - Set the upper and lower bounds. + Set the upper and lower bounds and applies default values + from the prior if no bounds are provided. The default values + are calculated using the boundary_multiplier and the parameters + prior sigma value. Parameters ---------- bounds : tuple, optional A tuple defining the lower and upper bounds for the parameter. Defaults to None. + boundary_multiplier : float, optional + Used to define the bounds when no bounds are passed but the parameter has + a prior distribution (default: 15). Raises ------ @@ -145,8 +178,41 @@ def set_bounds(self, bounds=None): else: self.lower_bound = bounds[0] self.upper_bound = bounds[1] + elif self.prior is not None: + self.applied_prior_bounds = True + self.lower_bound = self.prior.mean - boundary_multiplier * self.prior.sigma + self.upper_bound = self.prior.mean + boundary_multiplier * self.prior.sigma + print("Default bounds applied based on prior distribution.") + else: + self.bounds = None + return - self.bounds = bounds + self.bounds = [self.lower_bound, self.upper_bound] + + def get_initial_value(self, apply_transform: bool = False) -> float: + """ + Return the initial value of each parameter. + + Parameters + ---------- + apply_transform : bool + If True, the transformation is applied to the output (default: False). + """ + if self.initial_value is None: + if self.prior is not None: + sample = self.rvs(1)[0] + self.update(initial_value=sample) + else: + warnings.warn( + "Initial value and prior are None, proceeding without an initial value.", + UserWarning, + stacklevel=2, + ) + + if apply_transform and self.transformation is not None: + return float(self.transformation.to_search(self.initial_value)) + + return self.initial_value class Parameters: @@ -166,6 +232,7 @@ def __init__(self, *args): self.param = OrderedDict() for param in args: self.add(param) + self.initial_value() def __getitem__(self, key: str) -> Parameter: """ @@ -180,13 +247,21 @@ def __getitem__(self, key: str) -> Parameter: ------- pybop.Parameter The Parameter object. + + Raises + ------ + ValueError + The key must be the name of one of the parameters. """ + if key not in self.param.keys(): + raise ValueError(f"The key {key} is not the name of a parameter.") + return self.param[key] def __len__(self) -> int: return len(self.param) - def keys(self) -> List: + def keys(self) -> list: """ A list of parameter names """ @@ -212,7 +287,7 @@ def add(self, parameter): if parameter.name in self.param.keys(): raise ValueError( f"There is already a parameter with the name {parameter.name} " - + "in the Parameters object. Please remove the duplicate entry." + "in the Parameters object. Please remove the duplicate entry." ) self.param[parameter.name] = parameter elif isinstance(parameter, dict): @@ -222,7 +297,7 @@ def add(self, parameter): if name in self.param.keys(): raise ValueError( f"There is already a parameter with the name {name} " - + "in the Parameters object. Please remove the duplicate entry." + "in the Parameters object. Please remove the duplicate entry." ) self.param[name] = Parameter(**parameter) else: @@ -240,34 +315,66 @@ def remove(self, parameter_name): # Remove the parameter self.param.pop(parameter_name) - def get_bounds(self) -> Dict: + def join(self, parameters=None): + """ + Join two Parameters objects into the first by copying across each Parameter. + + Parameters + ---------- + parameters : pybop.Parameters + """ + for param in parameters: + if param not in self.param.values(): + self.add(param) + else: + print(f"Discarding duplicate {param.name}.") + + def get_bounds(self, apply_transform: bool = False) -> dict: """ Get bounds, for either all or no parameters. + + Parameters + ---------- + apply_transform : bool + If True, the transformation is applied to the output (default: False). """ - all_unbounded = True # assumption bounds = {"lower": [], "upper": []} - for param in self.param.values(): - if param.bounds is not None: - bounds["lower"].append(param.bounds[0]) - bounds["upper"].append(param.bounds[1]) - all_unbounded = False + if param.bounds is None: + lower, upper = -np.inf, np.inf else: - bounds["lower"].append(-np.inf) - bounds["upper"].append(np.inf) - if all_unbounded: - bounds = None + lower, upper = param.bounds + if apply_transform and param.transformation is not None: + lower = float(param.transformation.to_search(param.bounds[0])) + upper = float(param.transformation.to_search(param.bounds[1])) + if np.isnan(lower) or np.isnan(upper): + raise ValueError( + "Transformed bounds resulted in NaN values.\n" + "If you've not applied bounds, this is due to the defaults applied from the prior distribution,\n" + "consider bounding the parameters to avoid this error." + ) + + bounds["lower"].append(lower) + bounds["upper"].append(upper) return bounds - def update(self, values): + def update(self, initial_values=None, values=None, bounds=None): """ Set value of each parameter. """ for i, param in enumerate(self.param.values()): - param.update(value=values[i]) - - def rvs(self, n_samples: int) -> List: + if initial_values is not None: + param.update(initial_value=initial_values[i]) + if values is not None: + param.update(value=values[i]) + if bounds is not None: + if isinstance(bounds, dict): + param.set_bounds(bounds=[bounds["lower"][i], bounds["upper"][i]]) + else: + param.set_bounds(bounds=bounds[i]) + + def rvs(self, n_samples: int = 1, apply_transform: bool = False) -> np.ndarray: """ Draw random samples from each parameter's prior distribution. @@ -277,7 +384,9 @@ def rvs(self, n_samples: int) -> List: Parameters ---------- n_samples : int - The number of samples to draw. + The number of samples to draw (default: 1). + apply_transform : bool + If True, the transformation is applied to the output (default: False). Returns ------- @@ -287,51 +396,86 @@ def rvs(self, n_samples: int) -> List: all_samples = [] for param in self.param.values(): - samples = param.rvs(n_samples) - - # Constrain samples to be within bounds - if param.bounds is not None: - offset = param.margin * (param.upper_bound - param.lower_bound) - samples = np.clip( - samples, param.lower_bound + offset, param.upper_bound - offset - ) - + samples = param.rvs(n_samples, apply_transform=apply_transform) all_samples.append(samples) - return all_samples + return np.concatenate(all_samples) - def get_sigma0(self) -> List: + def get_sigma0(self, apply_transform: bool = False) -> list: """ Get the standard deviation, for either all or no parameters. + + Parameters + ---------- + apply_transform : bool + If True, the transformation is applied to the output (default: False). """ all_have_sigma = True # assumption sigma0 = [] for param in self.param.values(): if hasattr(param.prior, "sigma"): - sigma0.append(param.prior.sigma) + if apply_transform and param.transformation is not None: + sigma0.append( + np.ndarray.item( + param.transformation.convert_standard_deviation( + param.prior.sigma, + param.get_initial_value(apply_transform=True), + ) + ) + ) + else: + sigma0.append(param.prior.sigma) else: all_have_sigma = False if not all_have_sigma: sigma0 = None - return sigma0 - def initial_value(self) -> List: + def priors(self) -> list: + """ + Return the prior distribution of each parameter. + """ + return [param.prior for param in self.param.values()] + + def initial_value(self, apply_transform: bool = False) -> np.ndarray: """ Return the initial value of each parameter. + + Parameters + ---------- + apply_transform : bool + If True, the transformation is applied to the output (default: False). """ initial_values = [] for param in self.param.values(): - if param.initial_value is None: - initial_value = param.rvs(1) - param.update(initial_value=initial_value[0]) - initial_values.append(param.initial_value) + initial_value = param.get_initial_value(apply_transform=apply_transform) + initial_values.append(initial_value) - return initial_values + return np.asarray(initial_values) - def current_value(self) -> List: + def reset_initial_value(self, apply_transform: bool = False) -> np.ndarray: + """ + Reset and return the initial value of each parameter. + + Parameters + ---------- + apply_transform : bool + If True, the transformation is applied to the output (default: False). + """ + initial_values = [] + + for param in self.param.values(): + initial_value = param.get_initial_value(apply_transform=apply_transform) + if initial_value is not None: + # Reset the current value as well + param.update(value=param.get_initial_value()) + initial_values.append(initial_value) + + return np.asarray(initial_values) + + def current_value(self) -> np.ndarray: """ Return the current value of each parameter. """ @@ -340,9 +484,9 @@ def current_value(self) -> List: for param in self.param.values(): current_values.append(param.value) - return current_values + return np.asarray(current_values) - def true_value(self) -> List: + def true_value(self) -> np.ndarray: """ Return the true value of each parameter. """ @@ -351,9 +495,33 @@ def true_value(self) -> List: for param in self.param.values(): true_values.append(param.true_value) - return true_values + return np.asarray(true_values) - def get_bounds_for_plotly(self): + def get_transformations(self): + """ + Get the transformations for each parameter. + """ + transformations = [] + + for param in self.param.values(): + transformations.append(param.transformation) + + return transformations + + def construct_transformation(self): + """ + Create a ComposedTransformation object from the individual parameter transformations. + """ + transformations = self.get_transformations() + if not transformations or all(t is None for t in transformations): + return None + + valid_transformations = [ + t if t is not None else IdentityTransformation() for t in transformations + ] + return ComposedTransformation(valid_transformations) + + def get_bounds_for_plotly(self, apply_transform: bool = False) -> np.ndarray: """ Retrieve parameter bounds in the format expected by Plotly. @@ -362,17 +530,78 @@ def get_bounds_for_plotly(self): bounds : numpy.ndarray An array of shape (n_parameters, 2) containing the bounds for each parameter. """ - bounds = np.empty((len(self), 2)) + for param in self.param.values(): + if param.applied_prior_bounds: + warnings.warn( + "Bounds were created from prior distributions. " + "Please provide bounds for better plot results.", + UserWarning, + stacklevel=2, + ) - for i, param in enumerate(self.param.values()): - if param.bounds is not None: - bounds[i] = param.bounds - else: - raise ValueError("All parameters require bounds for plotting.") + bounds = self.get_bounds(apply_transform=apply_transform) - return bounds + # Validate that all parameters have bounds + if bounds is None or not np.isfinite(list(bounds.values())).all(): + raise ValueError("All parameters require bounds for plot.") + + return np.asarray(list(bounds.values())).T + + def as_dict(self, values=None) -> dict: + """ + Parameters + ---------- + values : list or str, optional + A list of parameter values or one of the strings "initial" or "true" which can be used + to obtain a dictionary of parameters. - def as_dict(self, values=None) -> Dict: + Returns + ------- + Inputs + A parameters dictionary. + """ if values is None: values = self.current_value() + elif isinstance(values, str): + if values == "initial": + values = self.initial_value() + elif values == "true": + values = self.true_value() return {key: values[i] for i, key in enumerate(self.param.keys())} + + def verify(self, inputs: Optional[Inputs] = None): + """ + Verify that the inputs are an Inputs dictionary or numeric values + which can be used to construct an Inputs dictionary + + Parameters + ---------- + inputs : Inputs or numeric + """ + if inputs is None or isinstance(inputs, dict): + return inputs + if isinstance(inputs, np.ndarray) and inputs.ndim == 0: + inputs = inputs[np.newaxis] + if (isinstance(inputs, list) and all(is_numeric(x) for x in inputs)) or all( + is_numeric(x) for x in list(inputs) + ): + return self.as_dict(inputs) + else: + raise TypeError( + f"Inputs must be a dictionary or numeric. Received {type(inputs)}" + ) + + def __repr__(self): + """ + Return a string representation of the Parameters instance. + + Returns + ------- + str + A string including the number of parameters and a summary of each parameter. + """ + param_summary = "\n".join( + f" {name}: prior= {param.prior}, value={param.value}, bounds={param.bounds}" + for name, param in self.param.items() + ) + return f"Parameters({len(self)}):\n{param_summary}" diff --git a/pybop/parameters/parameter_set.py b/pybop/parameters/parameter_set.py index a1ab63cd3..4c4e53471 100644 --- a/pybop/parameters/parameter_set.py +++ b/pybop/parameters/parameter_set.py @@ -1,7 +1,18 @@ import json import types +from numbers import Number +from typing import Union -from pybamm import ParameterValues, parameter_sets +import numpy as np +from pybamm import ( + FunctionParameter, + LithiumIonParameters, + Parameter, + ParameterValues, + Scalar, + Symbol, + parameter_sets, +) class ParameterSet: @@ -34,6 +45,39 @@ def __setitem__(self, key, value): def __getitem__(self, key): return self.params[key] + @staticmethod + def evaluate_symbol(symbol: Union[Symbol, Number], params: dict): + """ + Evaluate a parameter in the parameter set. + + Parameters + ---------- + symbol : pybamm.Symbol or Number + The parameter to evaluate. + + Returns + ------- + float + The value of the parameter. + """ + if isinstance(symbol, (Number, np.float64)): + return symbol + if isinstance(symbol, Scalar): + return symbol.value + if isinstance(symbol, (Parameter, FunctionParameter)): + return ParameterSet.evaluate_symbol(params[symbol.name], params) + new_children = [ + Scalar(ParameterSet.evaluate_symbol(child, params)) + for child in symbol.children + ] + return symbol.create_copy(new_children).evaluate() + + def keys(self) -> list: + """ + A list of parameter names + """ + return list(self.params.keys()) + def import_parameters(self, json_path=None): """ Imports parameters from a JSON file specified by the `json_path` attribute. @@ -60,7 +104,7 @@ def import_parameters(self, json_path=None): # Read JSON file if not self.params and self.json_path: - with open(self.json_path, "r") as file: + with open(self.json_path) as file: self.params = json.load(file) else: raise ValueError( @@ -132,7 +176,7 @@ def export_parameters(self, output_json_path, fit_params=None): # Update parameter set if fit_params is not None: - for i, param in enumerate(fit_params): + for _i, param in enumerate(fit_params): exportable_params.update({param.name: param.value}) # Replace non-serializable values @@ -167,7 +211,7 @@ def is_json_serializable(self, value): return False @classmethod - def pybamm(cls, name): + def pybamm(cls, name, formation_concentrations=False): """ Retrieves a PyBaMM parameter set by name. @@ -175,6 +219,8 @@ def pybamm(cls, name): ---------- name : str The name of the PyBaMM parameter set to retrieve. + set_formation_concentrations : bool, optional + If True, re-calculates the initial concentrations of lithium in the active material (default: False). Returns ------- @@ -187,4 +233,59 @@ def pybamm(cls, name): if name not in list(parameter_sets): raise ValueError(msg) - return ParameterValues(name).copy() + parameter_set = ParameterValues(name).copy() + + if formation_concentrations: + set_formation_concentrations(parameter_set) + + return parameter_set + + +def set_formation_concentrations(parameter_set): + """ + Compute the concentration of lithium in the positive electrode assuming that + all lithium in the active material originated from the positive electrode. + + Only perform the calculation if an initial concentration exists for both + electrodes, i.e. it is not a half cell. + + Parameters + ---------- + parameter_set : pybamm.ParameterValues + A PyBaMM parameter set containing standard lithium ion parameters. + """ + if ( + all( + key in parameter_set.keys() + for key in [ + "Initial concentration in negative electrode [mol.m-3]", + "Initial concentration in positive electrode [mol.m-3]", + ] + ) + and parameter_set["Initial concentration in negative electrode [mol.m-3]"] > 0 + ): + # Obtain the total amount of lithium in the active material + Q_Li_particles_init = parameter_set.evaluate( + LithiumIonParameters().Q_Li_particles_init + ) + + # Convert this total amount to a concentration in the positive electrode + c_init = ( + Q_Li_particles_init + * 3600 + / ( + parameter_set["Positive electrode active material volume fraction"] + * parameter_set["Positive electrode thickness [m]"] + * parameter_set["Electrode height [m]"] + * parameter_set["Electrode width [m]"] + * parameter_set["Faraday constant [C.mol-1]"] + ) + ) + + # Update the initial lithium concentrations + parameter_set.update( + {"Initial concentration in negative electrode [mol.m-3]": 0} + ) + parameter_set.update( + {"Initial concentration in positive electrode [mol.m-3]": c_init} + ) diff --git a/pybop/parameters/priors.py b/pybop/parameters/priors.py index 10472e17f..5c40c42c1 100644 --- a/pybop/parameters/priors.py +++ b/pybop/parameters/priors.py @@ -1,3 +1,5 @@ +from typing import Union + import numpy as np import scipy.stats as stats @@ -56,6 +58,38 @@ def logpdf(self, x): """ return self.prior.logpdf(x, loc=self.loc, scale=self.scale) + def icdf(self, q): + """ + Calculates the inverse cumulative distribution function (CDF) of the distribution at q. + + Parameters + ---------- + q : float + The point(s) at which to evaluate the inverse CDF. + + Returns + ------- + float + The inverse cumulative distribution function value at q. + """ + return self.prior.ppf(q, scale=self.scale, loc=self.loc) + + def cdf(self, x): + """ + Calculates the cumulative distribution function (CDF) of the distribution at x. + + Parameters + ---------- + x : float + The point(s) at which to evaluate the CDF. + + Returns + ------- + float + The cumulative distribution function value at x. + """ + return self.prior.cdf(x, scale=self.scale, loc=self.loc) + def rvs(self, size=1, random_state=None): """ Generates random variates from the distribution. @@ -90,6 +124,66 @@ def rvs(self, size=1, random_state=None): loc=self.loc, scale=self.scale, size=size, random_state=random_state ) + def __call__(self, x): + """ + Evaluates the distribution at x. + + Parameters + ---------- + x : float + The point(s) at which to evaluate the distribution. + + Returns + ------- + float + The value(s) of the distribution at x. + """ + inputs = self.verify(x) + return self.logpdf(inputs) + + def logpdfS1(self, x): + """ + Evaluates the first derivative of the distribution at x. + + Parameters + ---------- + x : float + The point(s) at which to evaluate the first derivative. + + Returns + ------- + float + The value(s) of the first derivative at x. + """ + inputs = self.verify(x) + return self._logpdfS1(inputs) + + def _logpdfS1(self, x): + """ + Evaluates the first derivative of the distribution at x. + + Parameters + ---------- + x : float + The point(s) at which to evaluate the first derivative. + + Returns + ------- + float + The value(s) of the first derivative at x. + """ + raise NotImplementedError + + def verify(self, x): + """ + Verifies that the input is a numpy array and converts it if necessary. + """ + if isinstance(x, dict): + x = np.asarray(list(x.values())) + elif not isinstance(x, np.ndarray): + x = np.asarray(x) + return x + def __repr__(self): """ Returns a string representation of the object. @@ -137,10 +231,31 @@ class Gaussian(BasePrior): """ def __init__(self, mean, sigma, random_state=None): + super().__init__() self.name = "Gaussian" self.loc = mean self.scale = sigma self.prior = stats.norm + self._offset = -0.5 * np.log(2 * np.pi * self.scale**2) + self.sigma2 = self.scale**2 + self._multip = -1 / (2.0 * self.sigma2) + self._n_parameters = 1 + + def _logpdfS1(self, x): + """ + Evaluates the first derivative of the gaussian (log) distribution at x. + + Parameters + ---------- + x : float + The point(s) at which to evaluate the first derivative. + + Returns + ------- + float + The value(s) of the first derivative at x. + """ + return self.__call__(x), -(x - self.loc) * self._multip class Uniform(BasePrior): @@ -159,12 +274,32 @@ class Uniform(BasePrior): """ def __init__(self, lower, upper, random_state=None): + super().__init__() self.name = "Uniform" self.lower = lower self.upper = upper self.loc = lower self.scale = upper - lower self.prior = stats.uniform + self._n_parameters = 1 + + def _logpdfS1(self, x): + """ + Evaluates the first derivative of the log uniform distribution at x. + + Parameters + ---------- + x : float + The point(s) at which to evaluate the first derivative. + + Returns + ------- + float + The value(s) of the first derivative at x. + """ + log_pdf = self.__call__(x) + dlog_pdf = np.zeros_like(x) + return log_pdf, dlog_pdf @property def mean(self): @@ -195,7 +330,106 @@ class Exponential(BasePrior): """ def __init__(self, scale, loc=0, random_state=None): + super().__init__() self.name = "Exponential" self.loc = loc self.scale = scale self.prior = stats.expon + self._n_parameters = 1 + + def _logpdfS1(self, x): + """ + Evaluates the first derivative of the log exponential distribution at x. + + Parameters + ---------- + x : float + The point(s) at which to evaluate the first derivative. + + Returns + ------- + float + The value(s) of the first derivative at x. + """ + log_pdf = self.__call__(x) + dlog_pdf = -1 / self.scale * np.ones_like(x) + return log_pdf, dlog_pdf + + +class JointLogPrior(BasePrior): + """ + Represents a joint prior distribution composed of multiple prior distributions. + + Parameters + ---------- + priors : BasePrior + One or more prior distributions to combine into a joint distribution. + """ + + def __init__(self, *priors: BasePrior): + super().__init__() + + if not all(isinstance(prior, BasePrior) for prior in priors): + raise ValueError("All priors must be instances of BasePrior") + + self._n_parameters = len(priors) + self._priors: list[BasePrior] = list(priors) + + def logpdf(self, x: Union[float, np.ndarray]) -> float: + """ + Evaluates the joint log-prior distribution at a given point. + + Parameters + ---------- + x : Union[float, np.ndarray] + The point(s) at which to evaluate the distribution. The length of `x` + should match the total number of parameters in the joint distribution. + + Returns + ------- + float + The joint log-probability density of the distribution at `x`. + """ + if len(x) != self._n_parameters: + raise ValueError( + f"Input x must have length {self._n_parameters}, got {len(x)}" + ) + + return sum(prior(x) for prior, x in zip(self._priors, x)) + + def _logpdfS1(self, x: Union[float, np.ndarray]) -> tuple[float, np.ndarray]: + """ + Evaluates the first derivative of the joint log-prior distribution at a given point. + + Parameters + ---------- + x : Union[float, np.ndarray] + The point(s) at which to evaluate the first derivative. The length of `x` + should match the total number of parameters in the joint distribution. + + Returns + ------- + Tuple[float, np.ndarray] + A tuple containing the log-probability density and its first derivative at `x`. + """ + if len(x) != self._n_parameters: + raise ValueError( + f"Input x must have length {self._n_parameters}, got {len(x)}" + ) + + log_probs = [] + derivatives = [] + + for prior, xi in zip(self._priors, x): + p, dp = prior.logpdfS1(xi) + log_probs.append(p) + derivatives.append(dp) + + output = sum(log_probs) + doutput = np.array(derivatives) + + return output, doutput + + def __repr__(self) -> str: + priors_repr = ", ".join([repr(prior) for prior in self._priors]) + return f"{self.__class__.__name__}(priors: [{priors_repr}])" diff --git a/pybop/plot/__init__.py b/pybop/plot/__init__.py new file mode 100644 index 000000000..b6c26a294 --- /dev/null +++ b/pybop/plot/__init__.py @@ -0,0 +1,12 @@ +# +# Import plots +# +from .plotly_manager import PlotlyManager +from .standard_plots import StandardPlot, StandardSubplot, trajectories +from .contour import contour +from .dataset import dataset +from .convergence import convergence +from .parameters import parameters +from .problem import quick +from .nyquist import nyquist +from .voronoi import surface diff --git a/pybop/plotting/plot2d.py b/pybop/plot/contour.py similarity index 69% rename from pybop/plotting/plot2d.py rename to pybop/plot/contour.py index 961bc7c49..922627028 100644 --- a/pybop/plotting/plot2d.py +++ b/pybop/plot/contour.py @@ -1,15 +1,19 @@ -import sys +import warnings +from functools import partial +from typing import Union import numpy as np from scipy.interpolate import griddata -from pybop import BaseOptimiser, Optimisation, PlotlyManager +from pybop import BaseCost, BaseOptimiser, Optimisation +from pybop.plot.plotly_manager import PlotlyManager -def plot2d( - cost_or_optim, +def contour( + call_object: Union[BaseCost, BaseOptimiser], gradient: bool = False, - bounds: np.ndarray = None, + bounds: Union[np.ndarray, None] = None, + apply_transform: bool = False, steps: int = 10, show: bool = True, use_optim_log: bool = False, @@ -23,7 +27,7 @@ def plot2d( Parameters ---------- - cost_or_optim : a callable cost function, pybop Cost or Optimisation object + call_object : Union([pybop.BaseCost,pybop.BaseOptimiser, pybop.BasePrior]) Either: - the cost function to be evaluated. Must accept a list of parameter values and return a cost value. - an Optimisation object which provides a specific optimisation trace overlaid on the cost landscape. @@ -53,19 +57,41 @@ def plot2d( ValueError If the cost function does not return a valid cost when called with a parameter list. """ + plot_optim = False + cost = cost_call = call_object # Assign input as a cost or optimisation object - if isinstance(cost_or_optim, (BaseOptimiser, Optimisation)): - optim = cost_or_optim + if isinstance(call_object, (BaseOptimiser, Optimisation)): plot_optim = True + optim = call_object cost = optim.cost - else: - cost = cost_or_optim - plot_optim = False + cost_call = partial(optim.cost, apply_transform=apply_transform) + elif isinstance(call_object, BaseCost): + cost = call_object + cost_call = partial(cost, apply_transform=apply_transform) + + if isinstance(cost, BaseCost) and len(cost.parameters) < 2: + raise ValueError("This cost function takes fewer than 2 parameters.") + + additional_values = [] + if isinstance(cost, BaseCost) and len(cost.parameters) > 2: + warnings.warn( + "This cost function requires more than 2 parameters. " + "Plotting in 2d with fixed values for the additional parameters.", + UserWarning, + stacklevel=2, + ) + for ( + i, + param, + ) in enumerate(cost.parameters): + if i > 1: + additional_values.append(param.value) + print(f"Fixed {param.name}:", param.value) # Set up parameter bounds if bounds is None: - bounds = cost.parameters.get_bounds_for_plotly() + bounds = cost.parameters.get_bounds_for_plotly(apply_transform=apply_transform) # Generate grid x = np.linspace(bounds[0, 0], bounds[0, 1], steps) @@ -74,36 +100,45 @@ def plot2d( # Initialize cost matrix costs = np.zeros((len(y), len(x))) - # Populate cost matrix - for i, xi in enumerate(x): - for j, yj in enumerate(y): - costs[j, i] = cost(np.asarray([xi, yj])) - if gradient: grad_parameter_costs = [] - # Determine the number of gradient outputs from cost.evaluateS1 - num_gradients = len(cost.evaluateS1(np.asarray([x[0], y[0]]))[1]) + # Determine the number of gradient outputs from cost.compute + num_gradients = cost_call( + np.asarray([x[0], y[0]] + additional_values), + calculate_grad=True, + )[1].shape[0] - # Create an array to hold each gradient output & populate + # Create an array to hold each gradient output grads = [np.zeros((len(y), len(x))) for _ in range(num_gradients)] - for i, xi in enumerate(x): - for j, yj in enumerate(y): - (*current_grads,) = cost.evaluateS1(np.asarray([xi, yj]))[1] + + # Populate cost matrix + for i, xi in enumerate(x): + for j, yj in enumerate(y): + if gradient: + costs[j, i], (*current_grads,) = cost_call( + np.asarray([xi, yj] + additional_values), + calculate_grad=True, + ) for k, grad_output in enumerate(current_grads): grads[k][j, i] = grad_output + else: + costs[j, i] = cost_call( + np.asarray([xi, yj] + additional_values), + ) - # Append the arrays to the grad_parameter_costs list + # Append the arrays to the grad_parameter_costs list + if gradient: grad_parameter_costs.extend(grads) - elif plot_optim and use_optim_log: + if plot_optim and use_optim_log: # Flatten the cost matrix and parameter values flat_x = np.tile(x, len(y)) flat_y = np.repeat(y, len(x)) flat_costs = costs.flatten() # Append the optimisation trace to the data - parameter_log = np.asarray(optim.log["x_best"]) + parameter_log = np.asarray(optim.log["x"]) flat_x = np.concatenate((flat_x, parameter_log[:, 0])) flat_y = np.concatenate((flat_y, parameter_log[:, 1])) flat_costs = np.concatenate((flat_costs, optim.log["cost"])) @@ -124,8 +159,8 @@ def plot2d( title_y=0.9, width=600, height=600, - xaxis=dict(range=bounds[0]), - yaxis=dict(range=bounds[1]), + xaxis=dict(range=bounds[0], showexponent="last", exponentformat="e"), + yaxis=dict(range=bounds[1], showexponent="last", exponentformat="e"), ) if hasattr(cost, "parameters"): name = cost.parameters.keys() @@ -140,9 +175,7 @@ def plot2d( if plot_optim: # Plot the optimisation trace - optim_trace = np.asarray( - [item for sublist in optim.log["x"] for item in sublist] - ) + optim_trace = np.asarray([item[:2] for item in optim.log["x"]]) optim_trace = optim_trace.reshape(-1, 2) fig.add_trace( go.Scatter( @@ -179,9 +212,7 @@ def plot2d( # Update the layout and display the figure fig.update_layout(**layout_kwargs) - if "ipykernel" in sys.modules and show: - fig.show("svg") - elif show: + if show: fig.show() if gradient: @@ -200,9 +231,7 @@ def plot2d( ) grad_fig.update_layout(**layout_kwargs) - if "ipykernel" in sys.modules and show: - grad_fig.show("svg") - elif show: + if show: grad_fig.show() # append grad_fig to list diff --git a/pybop/plotting/plot_convergence.py b/pybop/plot/convergence.py similarity index 76% rename from pybop/plotting/plot_convergence.py rename to pybop/plot/convergence.py index f8ec6948c..6ff0e828e 100644 --- a/pybop/plotting/plot_convergence.py +++ b/pybop/plot/convergence.py @@ -1,9 +1,7 @@ -import sys +from pybop.plot.standard_plots import StandardPlot -from pybop import StandardPlot - -def plot_convergence(optim, show=True, **layout_kwargs): +def convergence(optim, show=True, **layout_kwargs): """ Plot the convergence of the optimisation algorithm. @@ -25,17 +23,19 @@ def plot_convergence(optim, show=True, **layout_kwargs): """ # Extract log from the optimisation object - cost_log = optim.log["cost"] + cost_log = optim.log["cost_best"] # Generate a list of iteration numbers iteration_numbers = list(range(1, len(cost_log) + 1)) - # Create a plotting dictionary + # Create a plot dictionary plot_dict = StandardPlot( x=iteration_numbers, y=cost_log, layout_options=dict( - xaxis_title="Iteration", yaxis_title="Cost", title="Convergence" + xaxis_title="Iteration", + yaxis_title="Cost", + title="Convergence", ), trace_names=optim.name(), ) @@ -43,9 +43,7 @@ def plot_convergence(optim, show=True, **layout_kwargs): # Generate and display the figure fig = plot_dict(show=False) fig.update_layout(**layout_kwargs) - if "ipykernel" in sys.modules and show: - fig.show("svg") - elif show: + if show: fig.show() return fig diff --git a/pybop/plotting/plot_dataset.py b/pybop/plot/dataset.py similarity index 81% rename from pybop/plotting/plot_dataset.py rename to pybop/plot/dataset.py index 70573e476..3146d2bcf 100644 --- a/pybop/plotting/plot_dataset.py +++ b/pybop/plot/dataset.py @@ -1,11 +1,7 @@ -import sys +from pybop.plot.standard_plots import StandardPlot, trajectories -from pybop import StandardPlot, plot_trajectories - -def plot_dataset( - dataset, signal=["Voltage [V]"], trace_names=None, show=True, **layout_kwargs -): +def dataset(dataset, signal=None, trace_names=None, show=True, **layout_kwargs): """ Quickly plot a PyBOP Dataset using Plotly. @@ -31,7 +27,9 @@ def plot_dataset( """ # Get data dictionary - dataset.check(signal) + if signal is None: + signal = ["Voltage [V]"] + dataset.check(signal=signal) # Compile ydata and labels or legend y = [dataset[s] for s in signal] @@ -45,7 +43,7 @@ def plot_dataset( trace_names = StandardPlot.remove_brackets(signal) # Create the figure - fig = plot_trajectories( + fig = trajectories( x=dataset["Time [s]"], y=y, trace_names=trace_names, @@ -54,9 +52,7 @@ def plot_dataset( yaxis_title=yaxis_title, ) fig.update_layout(**layout_kwargs) - if "ipykernel" in sys.modules and show: - fig.show("svg") - elif show: + if show: fig.show() return fig diff --git a/pybop/plot/nyquist.py b/pybop/plot/nyquist.py new file mode 100644 index 000000000..08ab71295 --- /dev/null +++ b/pybop/plot/nyquist.py @@ -0,0 +1,139 @@ +from pybop.parameters.parameter import Inputs +from pybop.plot.standard_plots import StandardPlot + + +def nyquist(problem, problem_inputs: Inputs = None, show=True, **layout_kwargs): + """ + Generates Nyquist plots for the given problem by evaluating the model's output and target values. + + Parameters + ---------- + problem : pybop.BaseProblem + An instance of a problem class (e.g., `pybop.EISProblem`) that contains the parameters and methods + for evaluation and target retrieval. + problem_inputs : Inputs, optional + Input parameters for the problem. If not provided, the default parameters from the problem + instance will be used. These parameters are verified before use (default is None). + show : bool, optional + If True, the plots will be displayed. + **layout_kwargs : dict, optional + Additional keyword arguments for customising the plot layout. These arguments are passed to + `fig.update_layout()`. + + Returns + ------- + list + A list of plotly `Figure` objects, each representing a Nyquist plot for the model's output and target values. + + Notes + ----- + - The function extracts the real part of the impedance from the model's output and the real and imaginary parts + of the impedance from the target output. + - For each signal in the problem, a Nyquist plot is created with the model's impedance plotted as a scatter plot. + - An additional trace for the reference (target output) is added to the plot. + - The plot layout can be customised using `layout_kwargs`. + + Example + ------- + >>> problem = pybop.EISProblem() + >>> nyquist_figures = nyquist(problem, show=True, title="Nyquist Plot", xaxis_title="Real(Z)", yaxis_title="Imag(Z)") + >>> # The plots will be displayed and nyquist_figures will contain the list of figure objects. + """ + if problem_inputs is None: + problem_inputs = problem.parameters.as_dict() + else: + problem_inputs = problem.parameters.verify(problem_inputs) + + model_output = problem.evaluate(problem_inputs) + domain_data = model_output["Impedance"].real + target_output = problem.get_target() + + figure_list = [] + for i in problem.signal: + default_layout_options = dict( + title="Nyquist Plot", + font=dict(family="Arial", size=14), + plot_bgcolor="white", + paper_bgcolor="white", + xaxis=dict( + title=dict(text="Zre / Ω", font=dict(size=16), standoff=15), + showline=True, + linewidth=2, + linecolor="black", + mirror=True, + ticks="outside", + tickwidth=2, + tickcolor="black", + ticklen=5, + ), + yaxis=dict( + title=dict(text="-Zim / Ω", font=dict(size=16), standoff=15), + showline=True, + linewidth=2, + linecolor="black", + mirror=True, + ticks="outside", + tickwidth=2, + tickcolor="black", + ticklen=5, + scaleanchor="x", + scaleratio=1, + ), + legend=dict( + x=0.02, + y=0.98, + bgcolor="rgba(255, 255, 255, 0.5)", + bordercolor="black", + borderwidth=1, + ), + width=600, + height=600, + ) + + plot_dict = StandardPlot( + x=domain_data, + y=-model_output[i].imag, + layout_options=default_layout_options, + trace_names="Model", + ) + + plot_dict.traces[0].update( + mode="lines+markers", + line=dict(color="blue", width=2), + marker=dict(size=8, color="blue", symbol="circle"), + ) + + target_trace = plot_dict.create_trace( + x=target_output[i].real, + y=-target_output[i].imag, + name="Reference", + mode="markers", + marker=dict(size=8, color="red", symbol="circle-open"), + showlegend=True, + ) + plot_dict.traces.append(target_trace) + + fig = plot_dict(show=False) + + # Add minor gridlines + fig.update_xaxes( + showgrid=True, + gridwidth=1, + gridcolor="lightgray", + minor=dict(showgrid=True, gridwidth=0.5, gridcolor="lightgray"), + ) + fig.update_yaxes( + showgrid=True, + gridwidth=1, + gridcolor="lightgray", + minor=dict(showgrid=True, gridwidth=0.5, gridcolor="lightgray"), + ) + + # Overwrite with user-kwargs + fig.update_layout(**layout_kwargs) + if show: + fig.show() + + figure_list.append(fig) + + return figure_list diff --git a/pybop/plotting/plot_parameters.py b/pybop/plot/parameters.py similarity index 63% rename from pybop/plotting/plot_parameters.py rename to pybop/plot/parameters.py index bc1f9a7ac..8fbc65d95 100644 --- a/pybop/plotting/plot_parameters.py +++ b/pybop/plot/parameters.py @@ -1,9 +1,8 @@ -import sys +from pybop import GaussianLogLikelihood +from pybop.plot.standard_plots import StandardSubplot -from pybop import StandardSubplot - -def plot_parameters(optim, show=True, **layout_kwargs): +def parameters(optim, show=True, **layout_kwargs): """ Plot the evolution of parameters during the optimization process using Plotly. @@ -26,24 +25,8 @@ def plot_parameters(optim, show=True, **layout_kwargs): # Extract parameters and log from the optimisation object parameters = optim.cost.parameters - log = optim.log["x"] - - # Create a list of sequential integers for the x-axis - x = list(range(1, len(log[0]) * len(log) + 1)) - - # Determine the number of elements in the smallest arrays - num_elements = len(log[0][0]) - - # Initialize a list of lists to store our columns - y = [[] for _ in range(num_elements)] - - # Loop through each numpy array in y - for array in log: - # Loop through each item (which is a n-element array) in the numpy array - for item in array: - # Loop through each element in the item and append to the corresponding column - for i in range(num_elements): - y[i].append(item[i]) + x = list(range(len(optim.log["x"]))) + y = [list(item) for item in zip(*optim.log["x"])] # Create lists of axis titles and trace names axis_titles = [] @@ -51,6 +34,10 @@ def plot_parameters(optim, show=True, **layout_kwargs): for name in trace_names: axis_titles.append(("Function Call", name)) + if isinstance(optim.cost, GaussianLogLikelihood): + axis_titles.append(("Function Call", "Sigma")) + trace_names.append("Sigma") + # Set subplot layout options layout_options = dict( title="Parameter Convergence", @@ -59,7 +46,7 @@ def plot_parameters(optim, show=True, **layout_kwargs): legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1), ) - # Create a plotting dictionary + # Create a plot dictionary plot_dict = StandardSubplot( x=x, y=y, @@ -72,9 +59,7 @@ def plot_parameters(optim, show=True, **layout_kwargs): # Generate the figure and update the layout fig = plot_dict(show=False) fig.update_layout(**layout_kwargs) - if "ipykernel" in sys.modules and show: - fig.show("svg") - elif show: + if show: fig.show() return fig diff --git a/pybop/plotting/plotly_manager.py b/pybop/plot/plotly_manager.py similarity index 95% rename from pybop/plotting/plotly_manager.py rename to pybop/plot/plotly_manager.py index 7b4b079a4..d8ad14c88 100644 --- a/pybop/plotting/plotly_manager.py +++ b/pybop/plot/plotly_manager.py @@ -71,14 +71,13 @@ def prompt_for_plotly_installation(self): print("Installation cancelled by user.") sys.exit(1) # Exit if user cancels installation - def install_plotly(self): + @staticmethod + def install_plotly(): """ Install the Plotly package using pip. Exit if installation fails. """ try: - subprocess.check_call( - [sys.executable, "-m", "pip", "install", "plotly", "kaleido"] - ) + subprocess.check_call([sys.executable, "-m", "pip", "install", "plotly"]) except subprocess.CalledProcessError as e: print(f"Error installing plotly: {e}") sys.exit(1) # Exit if installation fails @@ -119,7 +118,7 @@ def check_browser_availability(self): if self.pio and self.pio.renderers.default == "browser": try: webbrowser.get() - except webbrowser.Error: + except webbrowser.Error as e: raise Exception( "\n **Browser Not Found** \nFor Windows users, in order to view figures in the browser using Plotly, " "you need to set the environment variable BROWSER equal to the " @@ -129,4 +128,4 @@ def check_browser_availability(self): "\n\nThen reactivate your virtual environment. Alternatively, you can use a " "different Plotly renderer. For more information see: " "https://plotly.com/python/renderers/#setting-the-default-renderer" - ) + ) from e diff --git a/pybop/plotting/plot_problem.py b/pybop/plot/problem.py similarity index 78% rename from pybop/plotting/plot_problem.py rename to pybop/plot/problem.py index 968da94d6..3cc4ba020 100644 --- a/pybop/plotting/plot_problem.py +++ b/pybop/plot/problem.py @@ -1,11 +1,11 @@ -import sys - import numpy as np -from pybop import DesignProblem, FittingProblem, StandardPlot +from pybop import DesignProblem, FittingProblem +from pybop.parameters.parameter import Inputs +from pybop.plot.standard_plots import StandardPlot -def quick_plot(problem, parameter_values=None, show=True, **layout_kwargs): +def quick(problem, problem_inputs: Inputs = None, show=True, **layout_kwargs): """ Quickly plot the target dataset against optimised model output. @@ -16,7 +16,7 @@ def quick_plot(problem, parameter_values=None, show=True, **layout_kwargs): ---------- problem : object Problem object with dataset and signal attributes. - parameter_values : array-like + problem_inputs : Inputs Optimised (or example) parameter values. show : bool, optional If True, the figure is shown upon creation (default: True). @@ -30,12 +30,14 @@ def quick_plot(problem, parameter_values=None, show=True, **layout_kwargs): plotly.graph_objs.Figure The Plotly figure object for the scatter plot. """ - if parameter_values is None: - parameter_values = problem.x0 + if problem_inputs is None: + problem_inputs = problem.parameters.as_dict() + else: + problem_inputs = problem.parameters.verify(problem_inputs) # Extract the time data and evaluate the model's output and target values - xaxis_data = problem.time_data() - model_output = problem.evaluate(parameter_values) + xaxis_data = problem.domain_data + model_output = problem.evaluate(problem_inputs) target_output = problem.get_target() # Create a plot for each output @@ -47,16 +49,16 @@ def quick_plot(problem, parameter_values=None, show=True, **layout_kwargs): yaxis_title=StandardPlot.remove_brackets(i), ) - # Create a plotting dictionary + # Create a plot dictionary if isinstance(problem, DesignProblem): trace_name = "Optimised" - opt_time_data = model_output["Time [s]"] + opt_domain_data = model_output["Time [s]"] else: trace_name = "Model" - opt_time_data = xaxis_data + opt_domain_data = xaxis_data plot_dict = StandardPlot( - x=opt_time_data, + x=opt_domain_data, y=model_output[i], layout_options=default_layout_options, trace_names=trace_name, @@ -71,7 +73,9 @@ def quick_plot(problem, parameter_values=None, show=True, **layout_kwargs): ) plot_dict.traces.append(target_trace) - if isinstance(problem, FittingProblem): + if isinstance(problem, FittingProblem) and len(model_output[i]) == len( + target_output[i] + ): # Compute the standard deviation as proxy for uncertainty plot_dict.sigma = np.std(model_output[i] - target_output[i]) @@ -97,9 +101,7 @@ def quick_plot(problem, parameter_values=None, show=True, **layout_kwargs): # Generate the figure and update the layout fig = plot_dict(show=False) fig.update_layout(**layout_kwargs) - if "ipykernel" in sys.modules and show: - fig.show("svg") - elif show: + if show: fig.show() figure_list.append(fig) diff --git a/pybop/plotting/quick_plot.py b/pybop/plot/standard_plots.py similarity index 65% rename from pybop/plotting/quick_plot.py rename to pybop/plot/standard_plots.py index 5be353a62..f5e5e3088 100644 --- a/pybop/plotting/quick_plot.py +++ b/pybop/plot/standard_plots.py @@ -1,22 +1,34 @@ import math -import sys import textwrap import numpy as np -from pybop import PlotlyManager +from pybop.plot.plotly_manager import PlotlyManager DEFAULT_LAYOUT_OPTIONS = dict( title=None, title_x=0.5, - xaxis=dict(title=None, titlefont_size=12, tickfont_size=12), - yaxis=dict(title=None, titlefont_size=12, tickfont_size=12), + xaxis=dict( + title=None, + showexponent="last", + exponentformat="e", + titlefont_size=12, + tickfont_size=12, + ), + yaxis=dict( + title=None, + showexponent="last", + exponentformat="e", + titlefont_size=12, + tickfont_size=12, + ), legend=dict(x=1, y=1, xanchor="right", yanchor="top", font_size=12), showlegend=True, autosize=False, - width=1024, - height=576, + width=600, + height=600, margin=dict(l=10, r=10, b=10, t=75, pad=4), + plot_bgcolor="white", ) DEFAULT_SUBPLOT_OPTIONS = dict( start_cell="bottom-left", @@ -31,9 +43,9 @@ class StandardPlot: Parameters ---------- - x : list or np.ndarray + x : list or np.ndarray, optional X-axis data points. - y : list or np.ndarray + y : list or np.ndarray, optional Primary Y-axis data points for simulated model output. layout : Plotly layout, optional A layout for the figure, overrides the layout options (default: None). @@ -54,87 +66,39 @@ class StandardPlot: def __init__( self, - x, - y, + x=None, + y=None, layout=None, - layout_options=DEFAULT_LAYOUT_OPTIONS.copy(), - trace_options=DEFAULT_TRACE_OPTIONS.copy(), + layout_options=None, + trace_options=None, trace_names=None, trace_name_width=40, ): - self.x = x - self.y = y + self.traces = [] self.layout = layout - self.layout_options = layout_options - self.trace_options = DEFAULT_TRACE_OPTIONS.copy() - if trace_options is not None: - for arg, value in trace_options.items(): - self.trace_options[arg] = value - if isinstance(trace_names, str): - self.trace_names = [trace_names] - else: - self.trace_names = trace_names self.trace_name_width = trace_name_width - # Check type and dimensions of data - # What we want is a list of 'things plotly can take', e.g. numpy arrays or lists of numbers - if isinstance(self.x, list): - # If it's a list of numpy arrays, it's fine - # If it's a list of lists, it's fine - # If it's neither, it's a list of numbers that we need to wrap - if not isinstance(self.x[0], np.ndarray) and not isinstance( - self.x[0], list - ): - self.x = [self.x] - elif isinstance(self.x, np.ndarray): - self.x = np.squeeze(self.x) - if self.x.ndim == 1: - self.x = [self.x] - else: - self.x = self.x.tolist() - if isinstance(self.y, list): - if not isinstance(self.y[0], np.ndarray) and not isinstance( - self.y[0], list - ): - self.y = [self.y] - if isinstance(self.y, np.ndarray): - self.y = np.squeeze(self.y) - if self.y.ndim == 1: - self.y = [self.y] - else: - self.y = self.y.tolist() - if len(self.x) > 1 and len(self.x) != len(self.y): - raise ValueError( - "Input x should have either one data series or the same number as y." - ) + # Set default layout options and update if provided + if self.layout is None: + self.layout_options = DEFAULT_LAYOUT_OPTIONS.copy() + if layout_options: + self.layout_options.update(layout_options) + + # Set default trace options and update if provided + self.trace_options = DEFAULT_TRACE_OPTIONS.copy() + if trace_options: + self.trace_options.update(trace_options) # Attempt to import plotly when an instance is created self.go = PlotlyManager().go # Create layout if self.layout is None: - self.layout = self.go.Layout(self.layout_options) - if self.layout_options is not None: - for arg, value in self.layout_options.items(): - self.layout[arg] = value - - # Wrap trace names - if self.trace_names is not None: - for i, name in enumerate(self.trace_names): - self.trace_names[i] = self.wrap_text(name, width=self.trace_name_width) + self.layout = self.go.Layout(**self.layout_options) - # Create a trace for each trajectory - self.traces = [] - x = self.x[0] - for i in range(0, len(self.y)): - if len(self.x) > 1: - x = self.x[i] - if self.trace_names is not None: - self.trace_options["name"] = self.trace_names[i] - else: - self.trace_options["showlegend"] = False - trace = self.create_trace(x, self.y[i], **self.trace_options) - self.traces.append(trace) + # Add traces + if x is not None and y is not None: + self.add_traces(x, y, trace_names) def __call__(self, show=True): """ @@ -146,13 +110,89 @@ def __call__(self, show=True): If True, the figure is shown upon creation (default: True). """ fig = self.go.Figure(data=self.traces, layout=self.layout) - if "ipykernel" in sys.modules and show: - fig.show("svg") - elif show: + if show: fig.show() return fig + def add_traces(self, x, y, trace_names=None, **trace_options): + """ + Add a set of traces to the plot dictionary. + + Parameters + ---------- + x : list or np.ndarray + X-axis data points. + y : list or np.ndarray + Primary Y-axis data points for simulated model output. + trace_names : str or list[str], optional + Name(s) for the primary trace(s) (default: None). + """ + options = self.trace_options.copy() + options.update(trace_options) + + # Check and wrap trace names + if trace_names is not None: + if isinstance(trace_names, str): + trace_names = [trace_names] + for i, name in enumerate(trace_names): + trace_names[i] = self.wrap_text(name, width=self.trace_name_width) + + # Parse the data + x, y = self.parse_data(x, y) + + # Create a trace for each trajectory + xi = x[0] + for i in range(0, len(y)): + trace_options = options.copy() + if len(x) > 1: + xi = x[i] + if trace_names is not None: + trace_options["name"] = trace_names[i] + else: + trace_options["showlegend"] = False + trace = self.create_trace(xi, y[i], **trace_options) + self.traces.append(trace) + + def parse_data(self, x, y): + """ + Check the type and dimensions of the data and convert if necessary to a list + of 'things plotly can take', e.g. numpy arrays or lists of numbers. + + Parameters + ---------- + x : list or np.ndarray, optional + X-axis data points. + y : list or np.ndarray, optional + Primary Y-axis data points for simulated model output. + """ + if isinstance(x, list): + # If it's a list of numpy arrays, it's fine + # If it's a list of lists, it's fine + # If it's neither, it's a list of numbers that we need to wrap + if not isinstance(x[0], np.ndarray) and not isinstance(x[0], list): + x = [x] + elif isinstance(x, np.ndarray): + x = np.squeeze(x) + if x.ndim == 1: + x = [x] + else: + x = x.tolist() + if isinstance(y, list): + if not isinstance(y[0], np.ndarray) and not isinstance(y[0], list): + y = [y] + if isinstance(y, np.ndarray): + y = np.squeeze(y) + if y.ndim == 1: + y = [y] + else: + y = y.tolist() + if len(x) > 1 and len(x) != len(y): + raise ValueError( + "Input x should have either one data series or the same number as y." + ) + return x, y + def create_trace(self, x, y, **trace_options): """ Create a trace for the Plotly figure. @@ -246,9 +286,9 @@ def __init__( num_cols=None, axis_titles=None, layout=None, - layout_options=DEFAULT_LAYOUT_OPTIONS.copy(), - subplot_options=DEFAULT_SUBPLOT_OPTIONS.copy(), - trace_options=DEFAULT_SUBPLOT_TRACE_OPTIONS.copy(), + layout_options=DEFAULT_LAYOUT_OPTIONS, + subplot_options=DEFAULT_SUBPLOT_OPTIONS, + trace_options=DEFAULT_SUBPLOT_TRACE_OPTIONS, trace_names=None, trace_name_width=40, ): @@ -267,7 +307,7 @@ def __init__( elif self.num_cols is None: self.num_cols = int(math.ceil(self.num_traces / self.num_rows)) self.axis_titles = axis_titles - self.subplot_options = DEFAULT_SUBPLOT_OPTIONS.copy() + self.subplot_options = subplot_options.copy() if subplot_options is not None: for arg, value in subplot_options.items(): self.subplot_options[arg] = value @@ -285,7 +325,11 @@ def __call__(self, show): If True, the figure is shown upon creation (default: True). """ fig = self.make_subplots( - rows=self.num_rows, cols=self.num_cols, **self.subplot_options + rows=self.num_rows, + cols=self.num_cols, + horizontal_spacing=0.1, + vertical_spacing=0.15, + **self.subplot_options, ) fig.update_layout(self.layout_options) @@ -297,17 +341,21 @@ def __call__(self, show): if self.axis_titles and idx < len(self.axis_titles): x_title, y_title = self.axis_titles[idx] fig.update_xaxes(title_text=x_title, row=row, col=col) - fig.update_yaxes(title_text=y_title, row=row, col=col) - - if "ipykernel" in sys.modules and show: - fig.show("svg") - elif show: + fig.update_yaxes( + title_text=y_title, + row=row, + col=col, + showexponent="last", + exponentformat="e", + ) + + if show: fig.show() return fig -def plot_trajectories(x, y, trace_names=None, show=True, **layout_kwargs): +def trajectories(x, y, trace_names=None, show=True, **layout_kwargs): """ Quickly plot one or more trajectories using Plotly. @@ -329,7 +377,7 @@ def plot_trajectories(x, y, trace_names=None, show=True, **layout_kwargs): plotly.graph_objs.Figure The Plotly figure object for the scatter plot. """ - # Create a plotting dictionary + # Create a plot dictionary plot_dict = StandardPlot( x=x, y=y, @@ -339,9 +387,7 @@ def plot_trajectories(x, y, trace_names=None, show=True, **layout_kwargs): # Generate the figure and update the layout fig = plot_dict(show=False) fig.update_layout(**layout_kwargs) - if "ipykernel" in sys.modules and show: - fig.show("svg") - elif show: + if show: fig.show() return fig diff --git a/pybop/plot/voronoi.py b/pybop/plot/voronoi.py new file mode 100644 index 000000000..4c3e2483a --- /dev/null +++ b/pybop/plot/voronoi.py @@ -0,0 +1,339 @@ +from typing import Union + +import numpy as np +from scipy.spatial import Voronoi, cKDTree + +from pybop import BaseOptimiser, Optimisation +from pybop.plot.plotly_manager import PlotlyManager + + +def _voronoi_regions(x, y, f, xlim, ylim): + """ + Takes a set of ``(x, y, f)`` points and returns the edgepoints of the + voronoi region around each point within the boundaries specified by + ``xlim`` and ``ylim``. Originally adapted from PINTS. + + Parameters + ---------- + x : array-like + A list of x-coordinates. + y : array-like + A list of y-coordinates. + f : array-like + The score function at the given x and y coordinates. + xlim : tuple + Lower and upper bound for the x coordinates. + ylim : tuple + Lower and upper bound for the y coordinates. + + Returns + ------- + tuple + A tuple ``(x, y, f, regions)`` where ``x``, ``y`` and ``f`` are the + coordinates of the accepted points and each ``regions[i]`` is a list of the + vertices making up the voronoi region for point ``(x[i], y[i])`` with score + ``f[i]``. + """ + # Convert inputs to numpy arrays + x, y, f = map(np.array, (x, y, f)) + + # Check limits + xmin, xmax = sorted(map(float, xlim)) + ymin, ymax = sorted(map(float, ylim)) + + # Create voronoi diagram + vor = Voronoi(np.column_stack((x, y))) + + # Calculate center and radius + center = vor.points.mean(axis=0) + radius2 = 2 * np.sqrt((xmax - xmin) ** 2 + (ymax - ymin) ** 2) + + # Create regions + regions = [set() for _ in range(len(x))] + for (p1, p2), (v1, v2) in zip(vor.ridge_points, vor.ridge_vertices): + v1, v2 = sorted([v1, v2]) # Sort the vertices + x2 = vor.vertices[v2] + y1, y2 = vor.points[p1], vor.points[p2] + x1 = ( + vor.vertices[v1] + if v1 >= 0 + else _compute_far_point(vor, y1, y2, x2, center, radius2) + ) + regions[p1].update((tuple(x1), tuple(x2))) + regions[p2].update((tuple(x1), tuple(x2))) + + # Process regions + selection = [] + for k, region in enumerate(regions): + # Check for empty regions + if not region: + continue # pragma: no cover + + # Check for regions completely outside of limits, skip if so. + region = np.asarray(list(region)) + xmn, xmx, ymn, ymx, all_outside = _is_region_outside_bounds( + region, xmin, xmax, ymin, ymax + ) + + if all_outside: + continue # pragma: no cover + + # Sort the vertices + regions[k] = _sort_region_vertices(region, vor.points[k]) + + # Region fully contained? Then keep in selection and continue + if not (np.any(xmn) or np.any(xmx) or np.any(ymn) or np.any(ymx)): + selection.append(k) + continue + + # Truncate points for x-axis and y-axis + region = truncate_region_by_axis(region, 0, xmin, xmax) + region = truncate_region_by_axis(region, 1, ymin, ymax) + + # Filter region if len > 2 + if len(region) > 2: + selection.append(k) + + # Filter out bad regions + regions = [regions[i] for i in selection] + x, y, f = x[selection], y[selection], f[selection] + + return x, y, f, regions + + +def _compute_far_point(vor, y1, y2, x2, center, radius2): + """ + Computes the far-point of the voronoi region. + Originally adapted from PINTS. + """ + t = y2 - y1 + t /= np.linalg.norm(t) + q = np.array([-t[1], t[0]]) + midpoint = np.mean([y1, y2], axis=0) + return x2 + np.sign(np.dot(midpoint - center, q)) * q * radius2 + + +def _is_region_outside_bounds(region, xmin, xmax, ymin, ymax): + """ + Check for points outside of bounds. Originally adapted from PINTS. + """ + xmn = region[:, 0] < xmin + xmx = region[:, 0] > xmax + ymn = region[:, 1] < ymin + ymx = region[:, 1] > ymax + combined = np.all(xmn) or np.all(xmx) or np.all(ymn) or np.all(ymx) + return xmn, xmx, ymn, ymx, combined + + +def _sort_region_vertices(region, point): + """ + Sorts the region vertices according to the given point. + Originally adapted from PINTS. + """ + angles = np.arctan2(region[:, 1] - point[1], region[:, 0] - point[0]) + return region[np.argsort(angles)] + + +def truncate_region_by_axis(region, axis, min_val, max_val): + """ + Truncates a region along a specific axis. Originally adapted from PINTS. + + Args: + region: A list of points (numpy arrays) representing the region. + axis: The axis to truncate along (0 for x, 1 for y). + min_val: The minimum value along the axis. + max_val: The maximum value along the axis. + + Returns: + A list of points representing the truncated region. + """ + + new_region = [] + for j, p in enumerate(region): + q = region[j - 1] if j > 0 else region[-1] + r = region[j + 1] if j < len(region) - 1 else region[0] + + if p[axis] < min_val: + if q[axis] < min_val and r[axis] < min_val: + continue + if q[axis] >= min_val: + new_region.append(interpolate_point(p, q, axis, min_val)) + if r[axis] >= min_val: + new_region.append(interpolate_point(p, r, axis, min_val)) + elif p[axis] > max_val: + if q[axis] > max_val and r[axis] > max_val: + continue # pragma: no cover + if q[axis] <= max_val: + new_region.append(interpolate_point(p, q, axis, max_val)) + if r[axis] <= max_val: + new_region.append(interpolate_point(p, r, axis, max_val)) + else: + new_region.append(p) + + return new_region + + +def interpolate_point(p, q, axis, boundary_val): + """ + Interpolates a new point on the line segment between p and q at the given boundary value. + + Args: + p: The first point. + q: The second point. + axis: The axis to interpolate along (0 for x, 1 for y). + boundary_val: The boundary value. + + Returns: + A numpy array representing the interpolated point. + """ + + other_axis = 1 - axis + s = p[other_axis] + (boundary_val - p[axis]) * (q[other_axis] - p[other_axis]) / ( + q[axis] - p[axis] + ) + return np.array([boundary_val, s]) if axis == 0 else np.array([s, boundary_val]) + + +def surface( + optim: Union[BaseOptimiser, Optimisation], + bounds=None, + resolution=250, + show=True, + **layout_kwargs, +): + """ + Plot a 2D representation of the Voronoi diagram with color-coded regions. + + Parameters: + ----------- + optim : pybop.BaseOptimiser | pybop.Optimisation + Solved optimisation object + bounds : numpy.ndarray, optional + A 2x2 array specifying the [min, max] bounds for each parameter. If None, uses + `cost.parameters.get_bounds_for_plotly`. + resolution : int, optional + Resolution of the plot. Default is 500. + show : bool, optional + If True, the figure is shown upon creation (default: True). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time [s]"` or + `xaxis={"title": "Time [s]", "titlefont_size": 18}`. + """ + + # Append the optimisation trace to the data + points = optim.log["x"] + + if points[0].shape[0] != 2: + raise ValueError("This plot method requires two parameters.") + + x_optim, y_optim = map(list, zip(*points)) + f = optim.log["cost"] + + # Translate bounds, taking only the first two elements + xlim, ylim = ( + bounds if bounds is not None else [param.bounds for param in optim.parameters] + )[:2] + + # Compute regions + x, y, f, regions = _voronoi_regions(x_optim, y_optim, f, xlim, ylim) + + # Create a grid for plot + xi = np.linspace(xlim[0], xlim[1], resolution) + yi = np.linspace(ylim[0], ylim[1], resolution) + xi, yi = np.meshgrid(xi, yi) + + # Create a KD-tree for efficient nearest neighbor search + tree = cKDTree(np.column_stack((x, y))) + + # Find the nearest point for each grid point + _, indices = tree.query(np.column_stack((xi.ravel(), yi.ravel()))) + zi = f[indices].reshape(xi.shape) + + # Calculate the size of each Voronoi region + region_sizes = np.array([len(region) for region in regions]) + normalized_sizes = (region_sizes - region_sizes.min()) / ( + region_sizes.max() - region_sizes.min() + ) + + # Construct figure + go = PlotlyManager().go + fig = go.Figure() + + # Heatmap + fig.add_trace( + go.Heatmap( + x=xi[0], + y=yi[:, 0], + z=zi, + colorscale="Viridis", + zsmooth="best", + ) + ) + + # Add Voronoi edges + for region, size in zip(regions, normalized_sizes): + x_region = region[:, 0].tolist() + [region[0, 0]] + y_region = region[:, 1].tolist() + [region[0, 1]] + + fig.add_trace( + go.Scatter( + x=x_region, + y=y_region, + mode="lines", + line=dict(color="white", width=0.5 + size * 0.1), + showlegend=False, + ) + ) + + # Add original points + fig.add_trace( + go.Scatter( + x=x_optim, + y=y_optim, + mode="markers", + marker=dict( + size=8, + symbol="x-thin", + line=dict(color="white", width=1), + showscale=False, + ), + text=[f"f={val:.2f}" for val in f], + hoverinfo="text", + showlegend=False, + ) + ) + + # Plot the initial guess + if optim.x0 is not None: + fig.add_trace( + go.Scatter( + x=[optim.x0[0]], + y=[optim.x0[1]], + mode="markers", + marker_symbol="circle", + marker=dict( + color="mediumspringgreen", + line_color="mediumspringgreen", + line_width=1, + size=14, + showscale=False, + ), + showlegend=False, + ) + ) + names = optim.cost.parameters.keys() + fig.update_layout( + title="Voronoi Cost Landscape", + title_x=0.5, + title_y=0.9, + xaxis_title=names[0], + yaxis_title=names[1], + width=600, + height=600, + xaxis=dict(range=xlim, showexponent="last", exponentformat="e"), + yaxis=dict(range=ylim, showexponent="last", exponentformat="e"), + ) + fig.update_layout(**layout_kwargs) + if show: + fig.show() diff --git a/pybop/problems/base_problem.py b/pybop/problems/base_problem.py index 48f53dab1..94c278976 100644 --- a/pybop/problems/base_problem.py +++ b/pybop/problems/base_problem.py @@ -1,4 +1,10 @@ +from typing import Optional + +import numpy as np +from pybamm import IDAKLUSolver + from pybop import BaseModel, Dataset, Parameter, Parameters +from pybop.parameters.parameter import Inputs class BaseProblem: @@ -13,23 +19,29 @@ class BaseProblem: The model to be used for the problem (default: None). check_model : bool, optional Flag to indicate if the model should be checked (default: True). - signal: List[str] + signal: list[str] The signal to observe. - additional_variables : List[str], optional + additional_variables : list[str], optional Additional variables to observe and store in the solution (default: []). - init_soc : float, optional - Initial state of charge (default: None). + initial_state : dict, optional + A valid initial state (default: None). """ def __init__( self, - parameters, - model=None, - check_model=True, - signal=["Voltage [V]"], - additional_variables=[], - init_soc=None, + parameters: Parameters, + model: Optional[BaseModel] = None, + check_model: bool = True, + signal: Optional[list[str]] = None, + additional_variables: Optional[list[str]] = None, + initial_state: Optional[dict] = None, ): + signal = signal or ["Voltage [V]"] + if isinstance(signal, str): + signal = [signal] + elif not all(isinstance(item, str) for item in signal): + raise ValueError("Signal should be either a string or list of strings.") + # Check if parameters is a list of pybop.Parameter objects if isinstance(parameters, list): if all(isinstance(param, Parameter) for param in parameters): @@ -48,38 +60,69 @@ def __init__( ) self.parameters = parameters + self.parameters.reset_initial_value() + self._model = model + self.eis = False + self.domain = "Time [s]" self.check_model = check_model - if isinstance(signal, str): - signal = [signal] - elif not all(isinstance(item, str) for item in signal): - raise ValueError("Signal should be either a string or list of strings.") - self.signal = signal - self.init_soc = init_soc - self.n_outputs = len(self.signal) - self._time_data = None + self.signal = signal or ["Voltage [V]"] + self.set_initial_state(initial_state) + self._dataset = None + self._domain_data = None self._target = None + self.verbose = False + self.failure_output = np.asarray([np.inf]) + if isinstance(self._model, BaseModel): + self.eis = self.model.eis + self.domain = "Frequency [Hz]" if self.eis else "Time [s]" + + # Add domain and remove duplicates + self.additional_variables = additional_variables or [] + self.additional_variables.extend([self.domain, "Current [A]"]) + self.additional_variables = list(set(self.additional_variables)) + + # If model.solver is IDAKLU, set output vars for improved performance + self.output_vars = tuple(self.signal + self.additional_variables) + if self._model is not None and isinstance(self._model.solver, IDAKLUSolver): + self._solver_copy = self._model.solver.copy() + self._model.solver = IDAKLUSolver( + atol=self._solver_copy.atol, + rtol=self._solver_copy.rtol, + root_method=self._solver_copy.root_method, + root_tol=self._solver_copy.root_tol, + extrap_tol=self._solver_copy.extrap_tol, + options=self._solver_copy._options, # noqa: SLF001 + output_variables=self.output_vars, + ) - if isinstance(model, BaseModel): - self.additional_variables = additional_variables - else: - self.additional_variables = [] + def set_initial_state(self, initial_state: Optional[dict] = None): + """ + Set the initial state to be applied to evaluations of the problem. - # Set initial values - self.x0 = self.parameters.initial_value() + Parameters + ---------- + initial_state : dict, optional + A valid initial state (default: None). + """ + self.initial_state = initial_state @property def n_parameters(self): return len(self.parameters) - def evaluate(self, x): + @property + def n_outputs(self): + return len(self.signal) + + def evaluate(self, inputs: Inputs, eis=False): """ Evaluate the model with the given parameters and return the signal. Parameters ---------- - x : np.ndarray - Parameter values to evaluate the model at. + inputs : Inputs + Parameters for evaluation of the model. Raises ------ @@ -88,15 +131,15 @@ def evaluate(self, x): """ raise NotImplementedError - def evaluateS1(self, x): + def evaluateS1(self, inputs: Inputs): """ Evaluate the model with the given parameters and return the signal and its derivatives. Parameters ---------- - x : np.ndarray - Parameter values to evaluate the model at. + inputs : Inputs + Parameters for evaluation of the model. Raises ------ @@ -105,17 +148,6 @@ def evaluateS1(self, x): """ raise NotImplementedError - def time_data(self): - """ - Returns the time data. - - Returns - ------- - np.ndarray - The time array. - """ - return self._time_data - def get_target(self): """ Return the target dataset. @@ -127,13 +159,13 @@ def get_target(self): """ return self._target - def set_target(self, dataset): + def set_target(self, dataset: Dataset): """ Set the target dataset. Parameters ---------- - target : np.ndarray + target : Dataset The target dataset array. """ if self.signal is None: @@ -141,8 +173,25 @@ def set_target(self, dataset): if not isinstance(dataset, Dataset): raise ValueError("Dataset must be a pybop Dataset object.") + self._domain_data = dataset[self.domain] self._target = {signal: dataset[signal] for signal in self.signal} @property def model(self): return self._model + + @property + def target(self): + return self._target + + @property + def domain_data(self): + return self._domain_data + + @domain_data.setter + def domain_data(self, domain_data): + self._domain_data = domain_data + + @property + def dataset(self): + return self._dataset diff --git a/pybop/problems/design_problem.py b/pybop/problems/design_problem.py index 3217ca95d..2eac66ed1 100644 --- a/pybop/problems/design_problem.py +++ b/pybop/problems/design_problem.py @@ -1,6 +1,13 @@ +import warnings +from typing import Optional + import numpy as np -from pybop import BaseProblem +from pybop import BaseModel, BaseProblem, Experiment, Parameters +from pybop.models.empirical.base_ecm import ECircuitModel +from pybop.models.lithium_ion.base_echem import EChemBaseModel +from pybop.parameters.parameter import Inputs +from pybop.parameters.parameter_set import set_formation_concentrations class DesignProblem(BaseProblem): @@ -21,81 +28,127 @@ class DesignProblem(BaseProblem): Flag to indicate if the model parameters should be checked for feasibility each iteration (default: True). signal : str, optional The signal to fit (default: "Voltage [V]"). - additional_variables : List[str], optional + additional_variables : list[str], optional Additional variables to observe and store in the solution (default additions are: ["Time [s]", "Current [A]"]). - init_soc : float, optional - Initial state of charge (default: None). + initial_state : dict, optional + A valid initial state (default: {"Initial SoC": 1.0}). + update_capacity : bool, optional + If True, the nominal capacity is updated with an approximate value for each design. """ def __init__( self, - model, - parameters, - experiment, - check_model=True, - signal=["Voltage [V]"], - additional_variables=[], - init_soc=None, + model: BaseModel, + parameters: Parameters, + experiment: Optional[Experiment], + check_model: bool = True, + signal: Optional[list[str]] = None, + additional_variables: Optional[list[str]] = None, + initial_state: Optional[dict] = None, + update_capacity: bool = False, ): - # Add time and current and remove duplicates - additional_variables.extend(["Time [s]", "Current [A]"]) - additional_variables = list(set(additional_variables)) - super().__init__( - parameters, - model, - check_model, - signal, - additional_variables, - init_soc, + parameters, model, check_model, signal, additional_variables, initial_state ) self.experiment = experiment - - # Build the model if required - if experiment is not None: - # Leave the build until later to apply the experiment - self._model.parameters = self.parameters - - elif self._model._built_model is None: - self._model.build( - experiment=self.experiment, - parameters=self.parameters, - check_model=self.check_model, - init_soc=self.init_soc, + self.warning_patterns = [ + "Ah is greater than", + "Non-physical point encountered", + ] + + # Set whether to update the nominal capacity along with the design parameters + if update_capacity is True: + nominal_capacity_warning = ( + "The nominal capacity is approximated for each evaluation." + ) + else: + nominal_capacity_warning = ( + "The nominal capacity is fixed at the initial model value." ) + warnings.warn(nominal_capacity_warning, UserWarning, stacklevel=2) + self.update_capacity = update_capacity - # Add an example dataset for plotting comparison - sol = self.evaluate(self.x0) - self._time_data = sol["Time [s]"] + # Add an example dataset for plot comparison + sol = self.evaluate(self.parameters.as_dict("initial")) + self._domain_data = sol["Time [s]"] self._target = {key: sol[key] for key in self.signal} self._dataset = None - def evaluate(self, x): + def set_initial_state(self, initial_state: dict): + """ + Set the initial state to be applied to evaluations of the problem. + + Parameters + ---------- + initial_state : dict, optional + A valid initial state (default: None). + """ + if initial_state is None: + if isinstance(self.model, ECircuitModel): + initial_state = {"Initial SoC": self.model.parameter_set["Initial SoC"]} + else: + initial_state = {"Initial SoC": 1.0} # default value + elif "Initial open-circuit voltage [V]" in initial_state.keys(): + warnings.warn( + "It is usually better to define an initial state of charge as the " + "initial_state for a DesignProblem because this state will scale with " + "design properties such as the capacity of the battery, as opposed to the " + "initial open-circuit voltage which may correspond to a different state " + "of charge for each design.", + UserWarning, + stacklevel=1, + ) + + self.initial_state = initial_state + + def evaluate(self, inputs: Inputs): """ Evaluate the model with the given parameters and return the signal. Parameters ---------- - x : np.ndarray - Parameter values to evaluate the model at. + inputs : Inputs + Parameters for evaluation of the model. Returns ------- y : np.ndarray - The model output y(t) simulated with inputs x. + The model output y(t) simulated with inputs. """ - sol = self._model.predict( - inputs=x, - experiment=self.experiment, - init_soc=self.init_soc, - ) - - if sol == [np.inf]: - return sol - - else: - predictions = {} - for signal in self.signal + self.additional_variables: - predictions[signal] = sol[signal].data - - return predictions + inputs = self.parameters.verify(inputs) + + # Update the active parameter set + parameter_set = self.model.parameter_set + if isinstance(self._model, EChemBaseModel): + set_formation_concentrations(parameter_set) + parameter_set.update(inputs) + if self.update_capacity: + approximate_capacity = self.model.approximate_capacity(parameter_set) + parameter_set.update({"Nominal cell capacity [A.h]": approximate_capacity}) + + try: + with warnings.catch_warnings(): + for pattern in self.warning_patterns: + warnings.filterwarnings( + "error", category=UserWarning, message=pattern + ) + + sol = self._model.predict( + parameter_set=parameter_set, + experiment=self.experiment, + initial_state=self.initial_state, + ) + + # Catch infeasible solutions and return infinity + except (UserWarning, Exception) as e: + if self.verbose: + print(f"Ignoring this sample due to: {e}") + return { + signal: np.asarray(np.ones(2) * -np.inf) + for signal in [*self.signal, *self.additional_variables] + } + + return { + signal: sol[signal].data + for signal in self.signal + self.additional_variables + } diff --git a/pybop/problems/fitting_problem.py b/pybop/problems/fitting_problem.py index 15d1ed7e2..16a1f4784 100644 --- a/pybop/problems/fitting_problem.py +++ b/pybop/problems/fitting_problem.py @@ -1,6 +1,10 @@ +import warnings +from typing import Optional + import numpy as np -from pybop import BaseProblem +from pybop import BaseModel, BaseProblem, Dataset +from pybop.parameters.parameter import Inputs, Parameters class FittingProblem(BaseProblem): @@ -17,109 +21,195 @@ class FittingProblem(BaseProblem): An object or list of the parameters for the problem. dataset : Dataset Dataset object containing the data to fit the model to. + check_model : bool, optional + Flag to indicate if the model should be checked (default: True). signal : str, optional The variable used for fitting (default: "Voltage [V]"). - additional_variables : List[str], optional + additional_variables : list[str], optional Additional variables to observe and store in the solution (default additions are: ["Time [s]"]). - init_soc : float, optional - Initial state of charge (default: None). + initial_state : dict, optional + A valid initial state, e.g. the initial open-circuit voltage (default: None). + + Additional Attributes + --------------------- + dataset : dictionary + The dictionary from a Dataset object containing the signal keys and values to fit the model to. + domain_data : np.ndarray + The domain points in the dataset. + n_domain_data : int + The number of domain points. + target : np.ndarray + The target values of the signals. """ def __init__( self, - model, - parameters, - dataset, - check_model=True, - signal=["Voltage [V]"], - additional_variables=[], - init_soc=None, + model: BaseModel, + parameters: Parameters, + dataset: Dataset, + check_model: bool = True, + signal: Optional[list[str]] = None, + additional_variables: Optional[list[str]] = None, + initial_state: Optional[dict] = None, ): - # Add time and remove duplicates - additional_variables.extend(["Time [s]"]) - additional_variables = list(set(additional_variables)) - super().__init__( - parameters, model, check_model, signal, additional_variables, init_soc + parameters, model, check_model, signal, additional_variables, initial_state ) self._dataset = dataset.data - self.x = self.x0 + self._n_parameters = len(self.parameters) - # Check that the dataset contains time and current - dataset.check(self.signal + ["Current function [A]"]) + # Check that the dataset contains necessary variables + dataset.check(domain=self.domain, signal=[*self.signal, "Current function [A]"]) - # Unpack time and target data - self._time_data = self._dataset["Time [s]"] - self.n_time_data = len(self._time_data) + # Unpack domain and target data + self._domain_data = self._dataset[self.domain] + self.n_data = len(self._domain_data) self.set_target(dataset) - # Add useful parameters to model - if model is not None: - self._model.signal = self.signal - self._model.additional_variables = self.additional_variables - self._model.n_outputs = self.n_outputs - self._model.n_time_data = self.n_time_data - + if self._model is not None: # Build the model from scratch - if self._model._built_model is not None: - self._model._model_with_set_params = None - self._model._built_model = None - self._model._built_initial_soc = None - self._model._mesh = None - self._model._disc = None + if self._model.built_model is not None: + self._model.clear() self._model.build( dataset=self._dataset, parameters=self.parameters, check_model=self.check_model, - init_soc=self.init_soc, + initial_state=self.initial_state, ) - def evaluate(self, x): + def set_initial_state(self, initial_state: Optional[dict] = None): + """ + Set the initial state to be applied to evaluations of the problem. + + Parameters + ---------- + initial_state : dict, optional + A valid initial state (default: None). + """ + if initial_state is not None and "Initial SoC" in initial_state.keys(): + warnings.warn( + "It is usually better to define an initial open-circuit voltage as the " + "initial_state for a FittingProblem because this value can typically be " + "obtained from the data, unlike the intrinsic initial state of charge. " + "In the case where the fitting parameters do not change the OCV-SOC " + "relationship, the initial state of charge may be passed to the model " + 'using, for example, `model.set_initial_state({"Initial SoC": 1.0})` ' + "before constructing the FittingProblem.", + UserWarning, + stacklevel=1, + ) + + self.initial_state = initial_state + + def evaluate(self, inputs: Inputs) -> dict[str, np.ndarray[np.float64]]: """ Evaluate the model with the given parameters and return the signal. Parameters ---------- - x : np.ndarray - Parameter values to evaluate the model at. + inputs : Inputs + Parameters for evaluation of the model. Returns ------- y : np.ndarray - The model output y(t) simulated with inputs x. + The simulated model output y(t) for self.eis == False, and y(ω) for self.eis == True for the given inputs. + """ + inputs = self.parameters.verify(inputs) + if self.eis: + return self._evaluateEIS(inputs) + else: + try: + sol = self._model.simulate( + inputs=inputs, + t_eval=self._domain_data, + initial_state=self.initial_state, + ) + except Exception as e: + if self.verbose: + print(f"Simulation error: {e}") + return {signal: self.failure_output for signal in self.signal} + + return { + signal: sol[signal].data + for signal in (self.signal + self.additional_variables) + } + + def _evaluateEIS(self, inputs: Inputs) -> dict[str, np.ndarray[np.float64]]: """ - if np.any(x != self.x) and self._model.rebuild_parameters: - self.parameters.update(values=x) - self._model.rebuild(parameters=self.parameters) - self.x = x + Evaluate the model with the given parameters and return the signal. - y = self._model.simulate(inputs=x, t_eval=self._time_data) + Parameters + ---------- + inputs : Inputs + Parameters for evaluation of the model. + + Returns + ------- + y : np.ndarray + The simulated model output y(ω) for the given inputs. + """ + try: + sol = self._model.simulateEIS( + inputs=inputs, + f_eval=self._domain_data, + initial_state=self.initial_state, + ) + except Exception as e: + if self.verbose: + print(f"Simulation error: {e}") + return {signal: self.failure_output for signal in self.signal} - return y + return sol - def evaluateS1(self, x): + def evaluateS1(self, inputs: Inputs): """ Evaluate the model with the given parameters and return the signal and its derivatives. Parameters ---------- - x : np.ndarray - Parameter values to evaluate the model at. + inputs : Inputs + Parameters for evaluation of the model. Returns ------- - tuple - A tuple containing the simulation result y(t) and the sensitivities dy/dx(t) evaluated - with given inputs x. + tuple[dict, np.ndarray] + A tuple containing the simulation result y(t) as a dictionary and the sensitivities + dy/dx(t) evaluated with given inputs. """ - if self._model.rebuild_parameters: - raise RuntimeError( - "Gradient not available when using geometric parameters." + inputs = self.parameters.verify(inputs) + self.parameters.update(values=list(inputs.values())) + + try: + sol = self._model.simulateS1( + inputs=inputs, + t_eval=self._domain_data, + initial_state=self.initial_state, + ) + except Exception as e: + print(f"Error: {e}") + return { + signal: self.failure_output for signal in self.signal + }, self.failure_output + + y = {signal: sol[signal].data for signal in self.signal} + + # Extract the sensitivities and stack them along a new axis for each signal + dy = np.empty( + ( + sol[self.signal[0]].data.shape[0], + self.n_outputs, + self._n_parameters, ) - - y, dy = self._model.simulateS1( - inputs=x, - t_eval=self._time_data, ) - return (y, np.asarray(dy)) + for i, signal in enumerate(self.signal): + dy[:, i, :] = np.stack( + [ + sol[signal].sensitivities[key].toarray()[:, 0] + for key in self.parameters.keys() + ], + axis=-1, + ) + + return y, np.asarray(dy) diff --git a/pybop/problems/multi_fitting_problem.py b/pybop/problems/multi_fitting_problem.py new file mode 100644 index 000000000..72dd70c39 --- /dev/null +++ b/pybop/problems/multi_fitting_problem.py @@ -0,0 +1,141 @@ +from typing import Optional + +import numpy as np + +from pybop import BaseProblem, Dataset +from pybop.parameters.parameter import Inputs, Parameters + + +class MultiFittingProblem(BaseProblem): + """ + Problem class for joining mulitple fitting problems into one combined fitting problem. + + Extends `BaseProblem` in a similar way to FittingProblem but for multiple parameter + estimation problems, which must first be defined individually. + + Additional Attributes + --------------------- + problems : pybop.FittingProblem + The individual PyBOP fitting problems. + """ + + def __init__(self, *args): + self.problems = [] + models_to_check = [] + for problem in args: + self.problems.append(problem) + if problem.model is not None: + models_to_check.append(problem.model) + + # Check that there are no copies of the same model + if len(set(models_to_check)) < len(models_to_check): + raise ValueError("Make a new_copy of the model for each problem.") + + # Compile the set of parameters, ignoring duplicates + combined_parameters = Parameters() + for problem in self.problems: + combined_parameters.join(problem.parameters) + + # Combine the target datasets + combined_domain_data = [] + combined_signal = [] + for problem in self.problems: + for signal in problem.signal: + combined_domain_data.extend(problem.domain_data) + combined_signal.extend(problem.target[signal]) + + super().__init__( + parameters=combined_parameters, + model=None, + signal=["Combined signal"], + ) + + combined_dataset = Dataset( + { + self.domain: np.asarray(combined_domain_data), + "Combined signal": np.asarray(combined_signal), + } + ) + self._dataset = combined_dataset.data + self.parameters.initial_value() + + # Unpack domain and target data + self._domain_data = self._dataset[self.domain] + self.n_domain_data = len(self._domain_data) + self.set_target(combined_dataset) + + def set_initial_state(self, initial_state: Optional[dict] = None): + """ + Set the initial state to be applied to evaluations of the problem. + + Parameters + ---------- + initial_state : dict, optional + A valid initial state (default: None). + """ + for problem in self.problems: + problem.set_initial_state(initial_state) + + def evaluate(self, inputs: Inputs): + """ + Evaluate the model with the given parameters and return the signal. + + Parameters + ---------- + inputs : Inputs + Parameters for evaluation of the model. + + Returns + ------- + y : np.ndarray + The model output y(t) simulated with given inputs. + """ + inputs = self.parameters.verify(inputs) + self.parameters.update(values=list(inputs.values())) + + combined_signal = [] + + for problem in self.problems: + problem_inputs = problem.parameters.as_dict() + signal_values = problem.evaluate(problem_inputs) + + # Collect signals + for signal in problem.signal: + combined_signal.extend(signal_values[signal]) + + return {"Combined signal": np.asarray(combined_signal)} + + def evaluateS1(self, inputs: Inputs): + """ + Evaluate the model with the given parameters and return the signal and its derivatives. + + Parameters + ---------- + inputs : Inputs + Parameters for evaluation of the model. + + Returns + ------- + tuple[dict, np.ndarray] + A tuple containing the simulation result y(t) as a dictionary and the sensitivities + dy/dx(t) evaluated with given inputs. + """ + inputs = self.parameters.verify(inputs) + self.parameters.update(values=list(inputs.values())) + + combined_signal = [] + all_derivatives = [] + + for problem in self.problems: + problem_inputs = problem.parameters.as_dict() + signal_values, dyi = problem.evaluateS1(problem_inputs) + + # Collect signals and derivatives + for signal in problem.signal: + combined_signal.extend(signal_values[signal]) + all_derivatives.append(dyi) + + y = {"Combined signal": np.asarray(combined_signal)} + dy = np.concatenate(all_derivatives) if all_derivatives else None + + return (y, dy) diff --git a/pybop/plotting/__init__.py b/pybop/samplers/__init__.py similarity index 100% rename from pybop/plotting/__init__.py rename to pybop/samplers/__init__.py diff --git a/pybop/samplers/base_pints_sampler.py b/pybop/samplers/base_pints_sampler.py new file mode 100644 index 000000000..b72352e0c --- /dev/null +++ b/pybop/samplers/base_pints_sampler.py @@ -0,0 +1,291 @@ +import logging +import time +from functools import partial +from typing import Optional + +import numpy as np +from pints import ( + MultiSequentialEvaluator, + ParallelEvaluator, + SequentialEvaluator, + SingleChainMCMC, +) + +from pybop import BaseCost, BaseSampler, LogPosterior + + +class BasePintsSampler(BaseSampler): + """ + Base class for PINTS samplers. + + This class extends the BaseSampler class to provide a common interface for + PINTS samplers. The class provides a sample() method that can be used to + sample from the posterior distribution using a PINTS sampler. + """ + + def __init__( + self, + log_pdf: LogPosterior, + sampler, + chains: int = 1, + warm_up=None, + x0=None, + cov0=0.1, + **kwargs, + ): + """ + Initialise the base PINTS sampler. + + Args: + log_pdf (pybop.LogPosterior or List[pybop.LogPosterior]): The distribution(s) to be sampled. + chains (int): Number of chains to be used. + sampler: The sampler class to be used. + x0 (list): Initial states for the chains. + cov0: Initial standard deviation for the chains. + kwargs: Additional keyword arguments. + """ + super().__init__(log_pdf, x0, chains, cov0) + + # Set kwargs + self._max_iterations = kwargs.get("max_iterations", 500) + self._log_to_screen = kwargs.get("log_to_screen", True) + self._log_filename = kwargs.get("log_filename", None) + self._initial_phase_iterations = kwargs.get("initial_phase_iterations", 250) + self._chains_in_memory = kwargs.get("chains_in_memory", True) + self._chain_files = kwargs.get("chain_files", None) + self._evaluation_files = kwargs.get("evaluation_files", None) + self._parallel = kwargs.get("parallel", False) + self._verbose = kwargs.get("verbose", False) + self.sampler = sampler + self.iter_time = float(0) + self._iteration = 0 + self._loop_iters = 0 + self._warm_up = warm_up + + # Check log_pdf + if isinstance(self._log_pdf, BaseCost): + self._multi_log_pdf = False + else: + if len(self._log_pdf) != chains: + raise ValueError("Number of log pdf's must match number of chains") + + first_pdf_parameters = self._log_pdf[0].n_parameters + for pdf in self._log_pdf: + if not isinstance(pdf, BaseCost): + raise ValueError("All log pdf's must be instances of BaseCost") + if pdf.n_parameters != first_pdf_parameters: + raise ValueError( + "All log pdf's must have the same number of parameters" + ) + + self._multi_log_pdf = True + + # Single chain vs multiple chain samplers + self._single_chain = issubclass(self.sampler, SingleChainMCMC) + + # Construct the samplers object + if self._single_chain: + self._n_samplers = self._n_chains + self._samplers = [self.sampler(x0, sigma0=self._cov0) for x0 in self._x0] + else: + self._n_samplers = 1 + self._samplers = [self.sampler(self._n_chains, self._x0, self._cov0)] + + # Check for sensitivities from sampler and set evaluation + self._needs_sensitivities = self._samplers[0].needs_sensitivities() + + # Check initial phase + self._initial_phase = self._samplers[0].needs_initial_phase() + if self._initial_phase: + self.set_initial_phase_iterations() + + # Parallelisation (Might be able to move into parent class) + self._n_workers = 1 + self.set_parallel(self._parallel) + + def run(self) -> Optional[np.ndarray]: + """ + Executes the Monte Carlo sampling process and generates samples + from the posterior distribution. + + This method orchestrates the entire sampling process, managing + iterations, evaluations, logging, and stopping criteria. It + initialises the necessary structures, handles both single and + multi-chain scenarios, and manages parallel or sequential + evaluation based on the configuration. + + Returns: + np.ndarray: A numpy array containing the samples from the + posterior distribution if chains are stored in memory, + otherwise returns None. + + Raises: + ValueError: If no stopping criterion is set (i.e., + _max_iterations is None). + + Details: + - Checks and ensures at least one stopping criterion is set. + - Initialises iterations, evaluations, and other required + structures. + - Sets up the evaluator (parallel or sequential) based on the + configuration. + - Handles the initial phase, if applicable, and manages + intermediate steps in the sampling process. + - Logs progress and relevant information based on the logging + configuration. + - Iterates through the sampling process, evaluating the log + PDF, updating chains, and managing the stopping criteria. + - Finalises and returns the collected samples, or None if + chains are not stored in memory. + """ + + self._initialise_logging() + self._check_stopping_criteria() + + # Initialise iterations and evaluations + self._iteration = 0 + + evaluator = self._create_evaluator() + self._check_initial_phase() + self._initialise_storage() + + running = True + while running: + if ( + self._initial_phase + and self._iteration == self._initial_phase_iterations + ): + self._end_initial_phase() + + xs = self._ask_for_samples() + self.fxs = evaluator.evaluate(xs) + self._evaluations += len(self.fxs) + + if self._single_chain: + self._process_single_chain() + self._intermediate_step = min(self._n_samples) <= self._iteration + else: + self._process_multi_chain() + + if self._intermediate_step: + continue + + self._iteration += 1 + if self._log_to_screen and self._verbose: + if self._iteration <= 10 or self._iteration % 50 == 0: + timing_iterations = self._iteration - self._loop_iters + elapsed_time = time.time() - self.iter_time + iterations_per_second = ( + timing_iterations / elapsed_time if elapsed_time > 0 else 0 + ) + logging.info( + f"| Iteration: {self._iteration} | Iter/s: {iterations_per_second: .2f} |" + ) + self.iter_time = time.time() + self._loop_iters = self._iteration + if self._max_iterations and self._iteration >= self._max_iterations: + running = False + + self._finalise_logging() + + if self._warm_up: + self._samples = self._samples[:, self._warm_up :, :] + + return self._samples if self._chains_in_memory else None + + def _process_single_chain(self): + self.fxs_iterator = iter(self.fxs) + for i in list(self._active): + reply = self._samplers[i].tell(next(self.fxs_iterator)) + if reply: + y, fy, accepted = reply + y_store = self._inverse_transform( + y, self._log_pdf[i] if self._multi_log_pdf else self._log_pdf + ) + if self._chains_in_memory: + self._samples[i][self._n_samples[i]] = y_store + else: + self._samples[i] = y_store + + if accepted: + self._sampled_logpdf[i] = ( + fy[0] if self._needs_sensitivities else fy + ) # Not storing sensitivities + if self._prior: + self._sampled_prior[i] = self._prior(y) + + e = self._sampled_logpdf[i] + if self._prior: + e = [ + e, + self._sampled_logpdf[i] - self._sampled_prior[i], + self._sampled_prior[i], + ] + + self._evaluations[i][self._n_samples[i]] = e + self._n_samples[i] += 1 + if self._n_samples[i] == self._max_iterations: + self._active.remove(i) + + def _process_multi_chain(self): + reply = self._samplers[0].tell(self.fxs) + self._intermediate_step = reply is None + if reply: + ys, fys, accepted = reply + ys_store = np.asarray( + [self._inverse_transform(y, self._log_pdf) for y in ys] + ) + if self._chains_in_memory: + self._samples[:, self._iteration] = ys_store + else: + self._samples = ys_store + + es = [] + for i, _y in enumerate(ys): + if accepted[i]: + self._sampled_logpdf[i] = ( + fys[0][i] if self._needs_sensitivities else fys[i] + ) + if self._prior: + self._sampled_prior[i] = self._prior(ys[i]) + e = self._sampled_logpdf[i] + if self._prior: + e = [ + e, + self._sampled_logpdf[i] - self._sampled_prior[i], + self._sampled_prior[i], + ] + es.append(e) + + for i, e in enumerate(es): + self._evaluations[i, self._iteration] = e + + def _check_stopping_criteria(self): + has_stopping_criterion = False + has_stopping_criterion |= self._max_iterations is not None + if not has_stopping_criterion: + raise ValueError("At least one stopping criterion must be set.") + + def _create_evaluator(self): + common_args = {"apply_transform": True} + + if self._needs_sensitivities: + common_args["calculate_grad"] = True + if not self._multi_log_pdf: + f = partial(self._log_pdf, **common_args) + else: + f = [partial(pdf, **common_args) for pdf in self._log_pdf] + + if self._parallel: + if not self._multi_log_pdf: + self._n_workers = min(self._n_workers, self._n_chains) + return ParallelEvaluator(f, n_workers=self._n_workers) + else: + return ( + SequentialEvaluator(f) + if not self._multi_log_pdf + else MultiSequentialEvaluator(f) + ) + + def _inverse_transform(self, y, log_pdf): + return log_pdf.transformation.to_model(y) if log_pdf.transformation else y diff --git a/pybop/samplers/base_sampler.py b/pybop/samplers/base_sampler.py new file mode 100644 index 000000000..b9a6cffd2 --- /dev/null +++ b/pybop/samplers/base_sampler.py @@ -0,0 +1,188 @@ +import logging +from typing import Union + +import numpy as np +from pints import ParallelEvaluator + +from pybop import LogPosterior + + +class BaseSampler: + """ + Base class for Monte Carlo samplers. + """ + + def __init__( + self, log_pdf: LogPosterior, x0, chains: int, cov0: Union[np.ndarray, float] + ): + """ + Initialise the base sampler. + + Parameters + ---------------- + log_pdf (pybop.LogPosterior or List[pybop.LogPosterior]): The posterior or PDF to be sampled. + chains (int): Number of chains to be used. + x0: List-like initial condition for Monte Carlo sampling. + cov0: The covariance matrix to be sampled. + """ + self._log_pdf = log_pdf + self._cov0 = cov0 + + # Number of chains + self._n_chains = chains + if self._n_chains < 1: + raise ValueError("Number of chains must be greater than 0") + + # Set up parameters based on log_pdf + if isinstance(log_pdf, LogPosterior): + self.parameters = log_pdf.parameters + self.n_parameters = log_pdf.n_parameters + elif isinstance(log_pdf, (list, np.ndarray)) and isinstance( + log_pdf[0], LogPosterior + ): + self.parameters = log_pdf[0].parameters + self.n_parameters = log_pdf[0].n_parameters + else: + raise ValueError( + "log_pdf must be a LogPosterior or List[LogPosterior]" + ) # TODO: Update for more general sampling + + # Check initial conditions + if x0 is not None and len(x0) != self.n_parameters: + raise ValueError("x0 must have the same number of parameters as log_pdf") + + # Set initial values, if x0 is None, initial values are unmodified. + self.parameters.update(initial_values=x0 if x0 is not None else None) + self._x0 = self.parameters.reset_initial_value(apply_transform=True).reshape( + 1, -1 + ) + + if len(self._x0) != self._n_chains or len(self._x0) == 1: + self._x0 = np.tile(self._x0, (self._n_chains, 1)) + + def run(self) -> np.ndarray: + """ + Sample from the posterior distribution. + + Returns: + np.ndarray: Samples from the posterior distribution. + """ + raise NotImplementedError + + def set_initial_phase_iterations(self, iterations=250): + """ + Set the number of iterations for the initial phase of the sampler. + + Args: + iterations (int): Number of iterations for the initial phase. + """ + self._initial_phase_iterations = iterations + + def set_max_iterations(self, iterations=500): + """ + Set the maximum number of iterations for the sampler. + + Args: + iterations (int): Maximum number of iterations. + """ + iterations = int(iterations) + if iterations < 1: + raise ValueError("Number of iterations must be greater than 0") + + self._max_iterations = iterations + + def set_parallel(self, parallel=False): + """ + Enable or disable parallel evaluation. + Credit: PINTS + + Parameters + ---------- + parallel : bool or int, optional + If True, use as many worker processes as there are CPU cores. If an integer, use that many workers. + If False or 0, disable parallelism (default: False). + """ + if parallel is True: + self._parallel = True + self._n_workers = ParallelEvaluator.cpu_count() + elif parallel >= 1: + self._parallel = True + self._n_workers = int(parallel) + else: + self._parallel = False + self._n_workers = 1 + + def _ask_for_samples(self): + if self._single_chain: + return [self._samplers[i].ask() for i in self._active] + else: + return self._samplers[0].ask() + + def _check_initial_phase(self): + # Set initial phase if needed + if self._initial_phase: + for sampler in self._samplers: + sampler.set_initial_phase(True) + + def _end_initial_phase(self): + for sampler in self._samplers: + sampler.set_initial_phase(False) + if self._log_to_screen: + logging.info("Initial phase completed.") + + def _initialise_storage(self): + self._prior = None + if isinstance(self._log_pdf, LogPosterior): + self._prior = self._log_pdf.prior + + # Storage of the received samples + self._sampled_logpdf = np.zeros(self._n_chains) + self._sampled_prior = np.zeros(self._n_chains) + + # Pre-allocate arrays for chain storage + self._samples = np.zeros( + (self._n_chains, self._max_iterations, self.n_parameters) + ) + + # Pre-allocate arrays for evaluation storage + if self._prior: + # Store posterior, likelihood, prior + self._evaluations = np.zeros((self._n_chains, self._max_iterations, 3)) + else: + # Store pdf + self._evaluations = np.zeros((self._n_chains, self._max_iterations)) + + # From PINTS: + # Some samplers need intermediate steps, where `None` is returned instead + # of a sample. But samplers can run asynchronously, so that one can return + # `None` while another returns a sample. To deal with this, we maintain a + # list of 'active' samplers that have not reached `max_iterations`, + # and store the number of samples so far in each chain. + if self._single_chain: + self._active = list(range(self._n_chains)) + self._n_samples = [0] * self._n_chains + + def _initialise_logging(self): + logging.basicConfig(format="%(message)s", level=logging.INFO) + + if self._log_to_screen: + logging.info("Using " + str(self._samplers[0].name())) + logging.info("Generating " + str(self._n_chains) + " chains.") + if self._parallel: + logging.info( + f"Running in parallel with {self._n_workers} worker processes." + ) + else: + logging.info("Running in sequential mode.") + if self._chain_files: + logging.info("Writing chains to " + self._chain_files[0] + " etc.") + if self._evaluation_files: + logging.info( + "Writing evaluations to " + self._evaluation_files[0] + " etc." + ) + + def _finalise_logging(self): + if self._log_to_screen: + logging.info( + f"Halting: Maximum number of iterations ({self._iteration}) reached." + ) diff --git a/pybop/samplers/mcmc_sampler.py b/pybop/samplers/mcmc_sampler.py new file mode 100644 index 000000000..933c36957 --- /dev/null +++ b/pybop/samplers/mcmc_sampler.py @@ -0,0 +1,105 @@ +from pybop import AdaptiveCovarianceMCMC + + +class MCMCSampler: + """ + A high-level class for MCMC sampling. + + This class provides an alternative API to the `PyBOP.Sampler()` API, + specifically allowing for single user-friendly interface for the + optimisation process. + """ + + def __init__( + self, + log_pdf, + chains, + sampler=AdaptiveCovarianceMCMC, + x0=None, + cov0=None, + **kwargs, + ): + """ + Initialize the MCMCSampler. + + Parameters + ---------- + log_pdf : pybop.BaseCost + The log-probability density function to be sampled. + chains : int + The number of MCMC chains to be run. + sampler : pybop.MCMCSampler, optional + The MCMC sampler class to be used. Defaults to `pybop.MCMC`. + x0 : np.ndarray, optional + Initial positions for the MCMC chains. Defaults to None. + cov0 : np.ndarray, optional + Initial step sizes for the MCMC chains. Defaults to None. + **kwargs : dict + Additional keyword arguments to pass to the sampler. + + Raises + ------ + ValueError + If the sampler could not be constructed due to an exception. + """ + + self.sampler = sampler(log_pdf, chains, x0=x0, sigma0=cov0, **kwargs) + + def run(self): + """ + Run the MCMC sampling process. + + Returns + ------- + list + The result of the sampling process. + """ + return self.sampler.run() + + def __getattr__(self, attr): + """ + Delegate attribute access to the underlying sampler if the attribute + is not found in the MCMCSampler instance. + + Parameters + ---------- + attr : str + The attribute name to be accessed. + + Returns + ------- + Any + The attribute value from the underlying sampler. + + Raises + ------ + AttributeError + If the attribute is not found in both the MCMCSampler instance + and the underlying sampler. + """ + if "sampler" in self.__dict__ and hasattr(self.sampler, attr): + return getattr(self.sampler, attr) + raise AttributeError( + f"'{self.__class__.__name__}' object has no attribute '{attr}'" + ) + + def __setattr__(self, name: str, value) -> None: + """ + Delegate attribute setting to the underlying sampler if the attribute + exists in the sampler and not in the MCMCSampler instance. + + Parameters + ---------- + name : str + The attribute name to be set. + value : Any + The value to be set to the attribute. + """ + if ( + name in self.__dict__ + or "sampler" not in self.__dict__ + or not hasattr(self.sampler, name) + ): + object.__setattr__(self, name, value) + else: + setattr(self.sampler, name, value) diff --git a/pybop/samplers/mcmc_summary.py b/pybop/samplers/mcmc_summary.py new file mode 100644 index 000000000..c67a3b0fd --- /dev/null +++ b/pybop/samplers/mcmc_summary.py @@ -0,0 +1,213 @@ +import numpy as np +import pints +import scipy + +from pybop.plot import PlotlyManager + + +class PosteriorSummary: + def __init__(self, chains: np.ndarray, significant_digits: int = 4): + """ + Initialize with chains of posterior samples. + + Parameters: + chains (np.ndarray): List where each element is a NumPy array representing + a chain of posterior samples for a parameter. + significant_digits (int): Number of significant digits to display for summary statistics. + """ + self.chains = chains + self.all_samples = np.concatenate(chains, axis=0) + self.num_parameters = self.chains.shape[2] + self.sig_digits = significant_digits + self.get_summary_statistics() + self.go = PlotlyManager().go + + def signif(self, x, p: int): + """ + Rounds array `x` to `p` significant digits. + """ + x = np.asarray(x) + x_positive = np.where(np.isfinite(x) & (x != 0), np.abs(x), 10 ** (p - 1)) + mags = 10 ** (p - 1 - np.floor(np.log10(x_positive))) + return np.round(x * mags) / mags + + def _calculate_statistics(self, fun, attr_name, *args, **kwargs): + """ + Calculate statistics from callable `fun`. + """ + stat = fun(self.all_samples, *args, **kwargs) + if fun is scipy.stats.mode: + setattr(self, attr_name, stat[0]) + else: + setattr(self, attr_name, stat) + return self.signif(stat, self.sig_digits) + + def get_summary_statistics(self): + """ + Calculate summary statistics for the posterior samples. + + Returns: + dict: Summary statistics including mean, median, standard deviation, and 95% credible interval. + """ + summary_funs = { + "mean": np.mean, + "median": np.median, + "mode": scipy.stats.mode, + "max": np.max, + "min": np.min, + "std": np.std, + "ci_lower": lambda x, axis: np.percentile(x, 2.5, axis=axis), + "ci_upper": lambda x, axis: np.percentile(x, 97.5, axis=axis), + } + + return { + key: self._calculate_statistics(func, key, axis=0) + for key, func in summary_funs.items() + } + + def plot_trace(self): + """ + Plot trace plots for the posterior samples. + """ + + for i in range(self.num_parameters): + fig = self.go.Figure() + + for j, chain in enumerate(self.chains): + fig.add_trace( + self.go.Scatter(y=chain[:, i], mode="lines", name=f"Chain {j}") + ) + + fig.update_layout( + title=f"Parameter {i} Trace Plot", + xaxis_title="Sample Index", + yaxis_title="Value", + ) + fig.show() + + def plot_chains(self): + """ + Plot posterior distributions for each chain. + """ + fig = self.go.Figure() + + for i, chain in enumerate(self.chains): + for j in range(chain.shape[1]): + fig.add_trace( + self.go.Histogram( + x=chain[:, j], + name=f"Chain {i} - Parameter {j}", + opacity=0.75, + ) + ) + + fig.add_shape( + type="line", + x0=self.mean[j], + y0=0, + x1=self.mean[j], + y1=self.max[j], + name=f"Mean - Parameter {j}", + line=dict(color="Black", width=1.5, dash="dash"), + ) + + fig.update_layout( + barmode="overlay", + title="Posterior Distribution", + xaxis_title="Value", + yaxis_title="Density", + ) + fig.show() + + def plot_posterior(self): + """ + Plot the summed posterior distribution across chains. + """ + fig = self.go.Figure() + + for j in range(self.all_samples.shape[1]): + histogram = self.go.Histogram( + x=self.all_samples[:, j], + name=f"Parameter {j}", + opacity=0.75, + ) + fig.add_trace(histogram) + fig.add_vline( + x=self.mean[j], line_width=3, line_dash="dash", line_color="black" + ) + + fig.update_layout( + barmode="overlay", + title="Posterior Distribution", + xaxis_title="Value", + yaxis_title="Density", + ) + fig.show() + + def summary_table(self): + """ + Display summary statistics in a table. + """ + summary_stats = self.get_summary_statistics() + + header = ["Statistic", "Value"] + values = [ + ["Mean", summary_stats["mean"]], + ["Median", summary_stats["median"]], + ["Standard Deviation", summary_stats["std"]], + ["95% CI Lower", summary_stats["ci_lower"]], + ["95% CI Upper", summary_stats["ci_upper"]], + ] + + fig = self.go.Figure( + data=[ + self.go.Table( + header=dict(values=header), + cells=dict( + values=[[row[0] for row in values], [row[1] for row in values]] + ), + ) + ] + ) + + fig.update_layout(title="Summary Statistics") + fig.show() + + def autocorrelation(self, x: np.ndarray) -> np.ndarray: + """ + Computes the autocorrelation (Pearson correlation coefficient) + of a numpy array representing samples. + """ + x = (x - x.mean()) / (x.std() * np.sqrt(len(x)) + np.finfo(float).eps) + cor = np.correlate(x, x, mode="full") + return cor[len(x) : -1] + + def _autocorrelate_negative(self, autocorrelation): + """ + Returns the index of the first negative entry in ``autocorrelation``, or + ``len(autocorrelation)`` if a negative entry is not found. + """ + negative_indices = np.where(autocorrelation < 0)[0] + return ( + negative_indices[0] if negative_indices.size > 0 else len(autocorrelation) + ) + + def effective_sample_size(self): + """ + Computes the effective sample size for each parameter in each chain. + """ + + if self.all_samples.shape[0] < 2: + raise ValueError("At least two samples must be given.") + + ess = [] + for _, chain in enumerate(self.chains): + for j in range(self.num_parameters): + rho = self.autocorrelation(chain[:, j]) + T = self._autocorrelate_negative(rho) + ess.append(len(chain[:, j]) / (1 + 2 * rho[:T].sum())) + + return ess + + def rhat(self): + return pints.rhat(self.chains) diff --git a/pybop/samplers/pints_samplers.py b/pybop/samplers/pints_samplers.py new file mode 100644 index 000000000..481a5eaeb --- /dev/null +++ b/pybop/samplers/pints_samplers.py @@ -0,0 +1,607 @@ +from pints import MALAMCMC as PintsMALAMCMC +from pints import AdaptiveCovarianceMCMC as PintsAdaptiveCovarianceMCMC +from pints import DifferentialEvolutionMCMC as PintsDifferentialEvolutionMCMC +from pints import DramACMC as PintsDramACMC +from pints import DreamMCMC as PintsDREAM +from pints import EmceeHammerMCMC as PintsEmceeHammerMCMC +from pints import HaarioACMC as PintsHaarioACMC +from pints import HaarioBardenetACMC as PintsHaarioBardenetACMC +from pints import HamiltonianMCMC as PintsHamiltonianMCMC +from pints import MetropolisRandomWalkMCMC as PintsMetropolisRandomWalkMCMC +from pints import MonomialGammaHamiltonianMCMC as PintsMonomialGammaHamiltonianMCMC +from pints import NoUTurnMCMC +from pints import PopulationMCMC as PintsPopulationMCMC +from pints import RaoBlackwellACMC as PintsRaoBlackwellACMC +from pints import RelativisticMCMC as PintsRelativisticMCMC +from pints import SliceDoublingMCMC as PintsSliceDoublingMCMC +from pints import SliceRankShrinkingMCMC as PintsSliceRankShrinkingMCMC +from pints import SliceStepoutMCMC as PintsSliceStepoutMCMC + +from pybop import BasePintsSampler + + +class NUTS(BasePintsSampler): + """ + Implements the No-U-Turn Sampler (NUTS) algorithm. + + This class extends the NUTS sampler from the PINTS library. + NUTS is a Markov chain Monte Carlo (MCMC) method for sampling + from a probability distribution. It is an extension of the + Hamiltonian Monte Carlo (HMC) method, which uses a dynamic + integration time to explore the parameter space more efficiently. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the NUTS sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, NoUTurnMCMC, chains=chains, x0=x0, cov0=cov0, **kwargs + ) + + +class DREAM(BasePintsSampler): + """ + Implements the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. + + This class extends the DREAM sampler from the PINTS library. + DREAM is a Markov chain Monte Carlo (MCMC) method for sampling + from a probability distribution. It combines the Differential + Evolution (DE) algorithm with the Adaptive Metropolis (AM) algorithm + to explore the parameter space more efficiently. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the DREAM sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__(log_pdf, PintsDREAM, chains=chains, x0=x0, cov0=cov0, **kwargs) + + +class AdaptiveCovarianceMCMC(BasePintsSampler): + """ + Implements the Adaptive Covariance Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Adaptive Covariance MCMC sampler from the PINTS library. + This MCMC method adapts the proposal distribution covariance matrix + during the sampling process to improve efficiency and convergence. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Adaptive Covariance MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsAdaptiveCovarianceMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class DifferentialEvolutionMCMC(BasePintsSampler): + """ + Implements the Differential Evolution Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Differential Evolution MCMC sampler from the PINTS library. + This MCMC method uses the Differential Evolution algorithm to explore the + parameter space more efficiently by evolving a population of chains. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Differential Evolution MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsDifferentialEvolutionMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class DramACMC(BasePintsSampler): + """ + Implements the Delayed Rejection Adaptive Metropolis (DRAM) Adaptive Covariance Markov Chain + Monte Carlo (MCMC) algorithm. + + This class extends the DRAM Adaptive Covariance MCMC sampler from the PINTS library. + This MCMC method combines Delayed Rejection with Adaptive Metropolis to enhance + the efficiency and robustness of the sampling process. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the DRAM Adaptive Covariance MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsDramACMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class EmceeHammerMCMC(BasePintsSampler): + """ + Implements the Emcee Hammer Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Emcee Hammer MCMC sampler from the PINTS library. + The Emcee Hammer is an affine-invariant ensemble sampler for MCMC, which is + particularly effective for high-dimensional parameter spaces. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Emcee Hammer MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsEmceeHammerMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class HaarioACMC(BasePintsSampler): + """ + Implements the Haario Adaptive Covariance Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Haario Adaptive Covariance MCMC sampler from the PINTS library. + This MCMC method adapts the proposal distribution's covariance matrix based on the + history of the chain, improving sampling efficiency and convergence. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Haario Adaptive Covariance MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsHaarioACMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class HaarioBardenetACMC(BasePintsSampler): + """ + Implements the Haario-Bardenet Adaptive Covariance Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Haario-Bardenet Adaptive Covariance MCMC sampler from the PINTS library. + This MCMC method combines the adaptive covariance approach with an additional + mechanism to improve performance in high-dimensional parameter spaces. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Haario-Bardenet Adaptive Covariance MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsHaarioBardenetACMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class HamiltonianMCMC(BasePintsSampler): + """ + Implements the Hamiltonian Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Hamiltonian MCMC sampler from the PINTS library. + This MCMC method uses Hamiltonian dynamics to propose new states, + allowing for efficient exploration of high-dimensional parameter spaces. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Hamiltonian MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsHamiltonianMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class MALAMCMC(BasePintsSampler): + """ + Implements the Metropolis Adjusted Langevin Algorithm (MALA) Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the MALA MCMC sampler from the PINTS library. + This MCMC method combines the Metropolis-Hastings algorithm with + Langevin dynamics to improve sampling efficiency and convergence. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the MALA MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsMALAMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class MetropolisRandomWalkMCMC(BasePintsSampler): + """ + Implements the Metropolis Random Walk Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Metropolis Random Walk MCMC sampler from the PINTS library. + This classic MCMC method uses a simple random walk proposal distribution + and the Metropolis-Hastings acceptance criterion. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Metropolis Random Walk MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsMetropolisRandomWalkMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class MonomialGammaHamiltonianMCMC(BasePintsSampler): + """ + Implements the Monomial Gamma Hamiltonian Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Monomial Gamma Hamiltonian MCMC sampler from the PINTS library. + This MCMC method uses Hamiltonian dynamics with a monomial gamma distribution + for efficient exploration of the parameter space. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Monomial Gamma Hamiltonian MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsMonomialGammaHamiltonianMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class PopulationMCMC(BasePintsSampler): + """ + Implements the Population Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Population MCMC sampler from the PINTS library. + This MCMC method uses a population of chains at different temperatures + to explore the parameter space more efficiently and avoid local minima. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Population MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsPopulationMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class RaoBlackwellACMC(BasePintsSampler): + """ + Implements the Rao-Blackwell Adaptive Covariance Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Rao-Blackwell Adaptive Covariance MCMC sampler from the PINTS library. + This MCMC method improves sampling efficiency by combining Rao-Blackwellisation + with adaptive covariance strategies. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Rao-Blackwell Adaptive Covariance MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsRaoBlackwellACMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class RelativisticMCMC(BasePintsSampler): + """ + Implements the Relativistic Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Relativistic MCMC sampler from the PINTS library. + This MCMC method uses concepts from relativistic mechanics to propose new states, + allowing for efficient exploration of the parameter space. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Relativistic MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsRelativisticMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class SliceDoublingMCMC(BasePintsSampler): + """ + Implements the Slice Doubling Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Slice Doubling MCMC sampler from the PINTS library. + This MCMC method uses slice sampling with a doubling procedure to propose new states, + allowing for efficient exploration of the parameter space. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Slice Doubling MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsSliceDoublingMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class SliceRankShrinkingMCMC(BasePintsSampler): + """ + Implements the Slice Rank Shrinking Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Slice Rank Shrinking MCMC sampler from the PINTS library. + This MCMC method uses slice sampling with a rank shrinking procedure to propose new states, + allowing for efficient exploration of the parameter space. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Slice Rank Shrinking MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsSliceRankShrinkingMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) + + +class SliceStepoutMCMC(BasePintsSampler): + """ + Implements the Slice Stepout Markov Chain Monte Carlo (MCMC) algorithm. + + This class extends the Slice Stepout MCMC sampler from the PINTS library. + This MCMC method uses slice sampling with a stepout procedure to propose new states, + allowing for efficient exploration of the parameter space. + + Parameters + ---------- + log_pdf : (pybop.LogPosterior or List[pybop.LogPosterior]) + A function that calculates the log-probability density. + chains : int + The number of chains to run. + x0 : ndarray, optional + Initial positions for the chains. + cov0 : ndarray, optional + Initial covariance matrix. + **kwargs + Additional arguments to pass to the Slice Stepout MCMC sampler. + """ + + def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs): + super().__init__( + log_pdf, + PintsSliceStepoutMCMC, + chains=chains, + x0=x0, + cov0=cov0, + **kwargs, + ) diff --git a/pybop/transformation/base_transformation.py b/pybop/transformation/base_transformation.py new file mode 100644 index 000000000..a36ba8081 --- /dev/null +++ b/pybop/transformation/base_transformation.py @@ -0,0 +1,128 @@ +from abc import ABC, abstractmethod +from collections.abc import Sequence +from typing import Union + +import numpy as np + + +class Transformation(ABC): + """ + Abstract base class for transformations between two parameter spaces: the model + parameter space and a search space. + + If `transform` is an instance of a `Transformation` class, you can apply the + transformation of a parameter vector from the model space `p` to the search + space `q` using `q = transform.to_search(p)` and the inverse using `p = transform.to_model(q)`. + + Based on pints.transformation method. + + References + ---------- + .. [1] Erik Jorgensen and Asger Roer Pedersen. "How to Obtain Those Nasty Standard Errors From Transformed Data." + http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.47.9023 + .. [2] Kaare Brandt Petersen and Michael Syskind Pedersen. "The Matrix Cookbook." 2012. + """ + + # ---- To be implemented with Monte Carlo PR ------ # + # def convert_log_prior(self, log_prior): + # """Returns a transformed log-prior class.""" + # return TransformedLogPrior(log_prior, self) + + def convert_covariance_matrix(self, cov: np.ndarray, q: np.ndarray) -> np.ndarray: + """ + Converts a covariance matrix `covariance` from the model space to the search space + around a parameter vector `q` in the search space. + """ + jac_inv = np.linalg.pinv(self.jacobian(q)) + return jac_inv @ cov @ jac_inv.T + + def convert_standard_deviation( + self, std: Union[float, np.ndarray], q: np.ndarray + ) -> np.ndarray: + """ + Converts standard deviation `std`, either a scalar or a vector, from the model space + to the search space around a parameter vector `q` in the search space. + """ + if isinstance(q, (int, float)): + q = np.asarray([q]) + jac_inv = np.linalg.pinv(self.jacobian(q)) + cov = jac_inv @ jac_inv.T + return std * np.sqrt(np.diagonal(cov)) + + @abstractmethod + def jacobian(self, q: np.ndarray) -> np.ndarray: + """Returns the Jacobian matrix of the transformation at the parameter vector `q`.""" + + def jacobian_S1(self, q: np.ndarray) -> tuple[np.ndarray, Sequence[np.ndarray]]: + """ + Computes the Jacobian matrix and its partial derivatives at the parameter vector `q`. + + Returns a tuple `(jacobian, hessian)`. + """ + raise NotImplementedError("jacobian_S1 method must be implemented if used.") + + def log_jacobian_det(self, q: np.ndarray) -> float: + """ + Returns the logarithm of the absolute value of the determinant of the Jacobian matrix + at the parameter vector `q`. + """ + raise NotImplementedError( + "log_jacobian_det method must be implemented if used." + ) + + def log_jacobian_det_S1(self, q: np.ndarray) -> tuple[float, np.ndarray]: + """ + Computes the logarithm of the absolute value of the determinant of the Jacobian, + and returns it along with its partial derivatives. + """ + raise NotImplementedError( + "log_jacobian_det_S1 method must be implemented if used." + ) + + @property + def n_parameters(self): + return self._n_parameters + + def to_model(self, q: np.ndarray) -> np.ndarray: + """Transforms a parameter vector `q` from the search space to the model space.""" + return self._transform(q, method="to_model") + + def to_search(self, p: np.ndarray) -> np.ndarray: + """Transforms a parameter vector `p` from the model space to the search space.""" + return self._transform(p, method="to_search") + + @abstractmethod + def _transform(self, x: np.ndarray, method: str) -> np.ndarray: + """ + Transforms a parameter vector `x` from the search space to the model space if `method` + is "to_model", or from the model space to the search space if `method` is "to_search". + """ + + def is_elementwise(self) -> bool: + """ + Returns `True` if the transformation is element-wise, meaning it can be applied + element-by-element independently. + """ + raise NotImplementedError("is_elementwise method must be implemented if used.") + + def verify_input( + self, inputs: Union[float, int, list[float], np.ndarray, dict[str, float]] + ) -> np.ndarray: + """Set and validate the transformation parameter.""" + if isinstance(inputs, (float, int)): + return np.full(self._n_parameters, float(inputs)) + + if isinstance(inputs, dict): + inputs = list(inputs.values()) + + try: + input_array = np.asarray(inputs, dtype=float) + except (ValueError, TypeError) as e: + raise TypeError( + "Transform must be a float, int, list, numpy array, or dictionary" + ) from e + + if input_array.size != self._n_parameters: + raise ValueError(f"Transform must have {self._n_parameters} elements") + + return input_array diff --git a/pybop/transformation/transformations.py b/pybop/transformation/transformations.py new file mode 100644 index 000000000..8951c41db --- /dev/null +++ b/pybop/transformation/transformations.py @@ -0,0 +1,371 @@ +from typing import Union + +import numpy as np + +from pybop import Transformation + + +class IdentityTransformation(Transformation): + """ + This class implements a trivial transformation where the model parameter space + and the search space are identical. It extends the base Transformation class. + + The transformation is defined as: + - to_search: y = x + - to_model: x = y + + Key properties: + 1. Jacobian: Identity matrix + 2. Log determinant of Jacobian: Always 0 + 3. Elementwise: True (each output dimension depends only on the corresponding input dimension) + + Use cases: + 1. When no transformation is needed between spaces + 2. As a placeholder in composite transformations + 3. For testing and benchmarking other transformations + + Note: While this transformation doesn't change the parameters, it still provides + all the methods required by the Transformation interface, making it useful in + scenarios where a transformation object is expected but no actual transformation + is needed. + + Initially based on pints.IdentityTransformation method. + """ + + def __init__(self, n_parameters: int = 1): + self._n_parameters = n_parameters + + def is_elementwise(self) -> bool: + """See :meth:`Transformation.is_elementwise()`.""" + return True + + def jacobian(self, q: np.ndarray) -> np.ndarray: + """See :meth:`Transformation.jacobian()`.""" + return np.eye(self._n_parameters) + + def jacobian_S1(self, q: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """See :meth:`Transformation.jacobian_S1()`.""" + n = self._n_parameters + return self.jacobian(q), np.zeros((n, n, n)) + + def log_jacobian_det(self, q: np.ndarray) -> float: + """See :meth:`Transformation.log_jacobian_det()`.""" + return 0.0 + + def log_jacobian_det_S1(self, q: np.ndarray) -> tuple[float, np.ndarray]: + """See :meth:`Transformation.log_jacobian_det_S1()`.""" + return self.log_jacobian_det(q), np.zeros(self._n_parameters) + + def _transform(self, x: np.ndarray, method: str) -> np.ndarray: + """See :meth:`Transformation._transform`.""" + return np.asarray(x) + + +class ScaledTransformation(Transformation): + """ + This class implements a linear transformation between the model parameter space + and a search space, using a coefficient (scale factor) and an intercept (offset). + It extends the base Transformation class. + + The transformation is defined as: + - to_search: y = coefficient * (x + intercept) + - to_model: x = y / coefficient - intercept + + Where: + - x is in the model parameter space + - y is in the search space + - coefficient is the scaling factor + - intercept is the offset + + This transformation is useful for scaling and shifting parameters to a more + suitable range for optimisation algorithms. + + Based on pints.ScaledTransformation class. + """ + + def __init__( + self, + coefficient: Union[list, float, np.ndarray], + intercept: Union[list, float, np.ndarray] = 0, + n_parameters: int = 1, + ): + self._n_parameters = n_parameters + self.intercept = self.verify_input(intercept) + self.coefficient = self.verify_input(coefficient) + self.inverse_coeff = np.reciprocal(self.coefficient) + + def is_elementwise(self) -> bool: + """See :meth:`Transformation.is_elementwise()`.""" + return True + + def jacobian(self, q: np.ndarray) -> np.ndarray: + """See :meth:`Transformation.jacobian()`.""" + return np.diag(self.inverse_coeff) + + def jacobian_S1(self, q: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """See :meth:`Transformation.jacobian_S1()`.""" + n = self._n_parameters + return self.jacobian(q), np.zeros((n, n, n)) + + def log_jacobian_det(self, q: np.ndarray) -> float: + """See :meth:`Transformation.log_jacobian_det()`.""" + return np.log(np.abs(self.coefficient)).sum() + + def log_jacobian_det_S1(self, q: np.ndarray) -> tuple[float, np.ndarray]: + """See :meth:`Transformation.log_jacobian_det_S1()`.""" + return self.log_jacobian_det(q), np.zeros(self._n_parameters) + + def _transform(self, x: np.ndarray, method: str) -> np.ndarray: + """See :meth:`Transformation._transform`.""" + x = self.verify_input(x) + if method == "to_model": + return x * self.inverse_coeff - self.intercept + elif method == "to_search": + return self.coefficient * (x + self.intercept) + else: + raise ValueError(f"Unknown method: {method}") + + +class LogTransformation(Transformation): + """ + This class implements a logarithmic transformation between the model parameter space + and a search space. It extends the base Transformation class. + + The transformation is defined as: + - to_search: y = log(x) + - to_model: x = exp(y) + + Where: + - x is in the model parameter space (strictly positive) + - y is in the search space (can be any real number) + + This transformation is particularly useful for: + 1. Parameters that are strictly positive and may span several orders of magnitude. + 2. Converting multiplicative processes to additive ones in the search space. + 3. Ensuring positivity constraints without explicit bounds in optimisation. + + Note: Care should be taken when using this transformation, as it can introduce + bias in the parameter estimates if not accounted for properly in the likelihood + or cost function. Simply, E[log(x)] <= log(E[x]) as per to Jensen's inequality. + For more information, see Jensen's inequality: + https://en.wikipedia.org/w/index.php?title=Jensen%27s_inequality&oldid=1212437916#Probabilistic_form + + Initially based on pints.LogTransformation class. + """ + + def __init__(self, n_parameters: int = 1): + self._n_parameters = n_parameters + + def is_elementwise(self) -> bool: + """See :meth:`Transformation.is_elementwise()`.""" + return True + + def jacobian(self, q: np.ndarray) -> np.ndarray: + """See :meth:`Transformation.jacobian()`.""" + return np.diag(np.exp(q)) + + def jacobian_S1(self, q: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """See :meth:`Transformation.jacobian_S1()`.""" + n = self._n_parameters + jac = self.jacobian(q) + jac_S1 = np.zeros((n, n, n)) + rn = np.arange(n) + jac_S1[rn, rn, rn] = np.diagonal(jac) + return jac, jac_S1 + + def log_jacobian_det(self, q: np.ndarray) -> float: + """See :meth:`Transformation.log_jacobian_det()`.""" + return np.sum(q) + + def log_jacobian_det_S1(self, q: np.ndarray) -> tuple[float, np.ndarray]: + """See :meth:`Transformation.log_jacobian_det_S1()`.""" + logjacdet = self.log_jacobian_det(q) + dlogjacdet = np.ones(self._n_parameters) + return logjacdet, dlogjacdet + + def _transform(self, x: np.ndarray, method: str) -> np.ndarray: + """See :meth:`Transformation._transform`.""" + x = self.verify_input(x) + if method == "to_model": + return np.exp(x) + elif method == "to_search": + return np.log(x) + else: + raise ValueError(f"Unknown method: {method}") + + +class ComposedTransformation(Transformation): + """ + N-dimensional Transformation composed of one or more other N_i-dimensional + sub-transformations, where the sum of N_i equals N. + + This class allows for the composition of multiple transformations, each potentially + operating on a different number of dimensions. The total dimensionality of the + composed transformation is the sum of the dimensionalities of its components. + + The dimensionality of the individual transformations does not have to be + the same, i.e., N_i != N_j is allowed. + + Extends pybop.Transformation. Initially based on pints.ComposedTransformation class. + """ + + def __init__(self, transformations: list[Transformation]): + if not transformations: + raise ValueError("Must have at least one sub-transformation.") + self._transformations = [] + self._n_parameters = 0 + self._is_elementwise = True + for transformation in transformations: + self._append_transformation(transformation) + + def _append_transformation(self, transformation: Transformation): + """ + Append a transformation to the internal list of transformations. + + Parameters + ---------- + transformation : Transformation + Transformation to append. + + Raises + ------ + ValueError + If the appended object is not a Transformation. + """ + if not isinstance(transformation, Transformation): + raise TypeError("The appended object must be a Transformation.") + self._transformations.append(transformation) + self._n_parameters += transformation.n_parameters + self._is_elementwise = self._is_elementwise and transformation.is_elementwise() + + def append(self, transformation: Transformation): + """ + Append a new transformation to the existing composition. + + Parameters + ---------- + transformation : Transformation + The transformation to append. + """ + self._append_transformation(transformation) + + def is_elementwise(self) -> bool: + """See :meth:`Transformation.is_elementwise()`.""" + return self._is_elementwise + + def jacobian(self, q: np.ndarray) -> np.ndarray: + """ + Compute the elementwise Jacobian of the composed transformation. + + Parameters + ---------- + q : np.ndarray + Input array. + + Returns + ------- + np.ndarray + Diagonal matrix representing the elementwise Jacobian. + """ + q = self.verify_input(q) + diag = np.zeros(self._n_parameters) + lo = 0 + + for transformation in self._transformations: + hi = lo + transformation.n_parameters + diag[lo:hi] = np.diagonal(transformation.jacobian(q[lo:hi])) + lo = hi + + return np.diag(diag) + + def jacobian_S1(self, q: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """See :meth:`Transformation.jacobian_S1()`.""" + q = self.verify_input(q) + output_S1 = np.zeros( + (self._n_parameters, self._n_parameters, self._n_parameters) + ) + lo = 0 + + for transformation in self._transformations: + hi = lo + transformation.n_parameters + _, jac_S1 = transformation.jacobian_S1(q[lo:hi]) + for i, jac_S1_i in enumerate(jac_S1): + output_S1[lo + i, lo:hi, lo:hi] = jac_S1_i + lo = hi + + return self.jacobian(q), output_S1 + + def log_jacobian_det(self, q: np.ndarray) -> float: + """ + Compute the elementwise logarithm of the determinant of the Jacobian. + + Parameters + ---------- + q : np.ndarray + Input array. + + Returns + ------- + float + Sum of log determinants of individual transformations. + """ + q = self.verify_input(q) + return sum( + transformation.log_jacobian_det(q[lo : lo + transformation.n_parameters]) + for lo, transformation in self._iter_transformations() + ) + + def log_jacobian_det_S1(self, q: np.ndarray) -> tuple[float, np.ndarray]: + """ + Compute the elementwise logarithm of the determinant of the Jacobian and its first-order sensitivities. + + Parameters + ---------- + q : np.ndarray + Input array. + + Returns + ------- + Tuple[float, np.ndarray] + Tuple of sum of log determinants and concatenated first-order sensitivities. + """ + q = self.verify_input(q) + output = 0.0 + output_S1 = np.zeros(self._n_parameters) + lo = 0 + + for transformation in self._transformations: + hi = lo + transformation.n_parameters + j, j_S1 = transformation.log_jacobian_det_S1(q[lo:hi]) + output += j + output_S1[lo:hi] = j_S1 + lo = hi + + return output, output_S1 + + def _transform(self, data: np.ndarray, method: str) -> np.ndarray: + """See :meth:`Transformation._transform`.""" + data = self.verify_input(data) + output = np.zeros_like(data) + lo = 0 + + for transformation in self._transformations: + hi = lo + transformation.n_parameters + output[lo:hi] = getattr(transformation, method)(data[lo:hi]) + lo = hi + + return output + + def _iter_transformations(self): + """ + Iterate over the transformations in the composition. + + Yields + ------ + Tuple[int, Transformation] + Tuple of starting index and transformation object for each sub-transformation. + """ + lo = 0 + for transformation in self._transformations: + yield lo, transformation + lo += transformation.n_parameters diff --git a/pyproject.toml b/pyproject.toml index 6d2e1b61c..5e1c74444 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "pybop" -version = "24.3.1" +version = "24.9.1" authors = [ {name = "The PyBOP Team"}, ] @@ -26,20 +26,14 @@ classifiers = [ ] requires-python = ">=3.9, <3.13" dependencies = [ - "pybamm>=23.9", + "pybamm>=24.9", "numpy>=1.16, <2.0", "scipy>=1.3", "pints>=0.5", - "bpx>=0.4", ] [project.optional-dependencies] -# Split kaleido into two dependencies to avoid Windows hang -# See: https://github.com/plotly/Kaleido/issues/110 -plot = ["plotly>=5.0", - "kaleido==0.1.0.post1; sys_platform == 'win32'", - "kaleido>=0.2; sys_platform != 'win32'", -] +plot = ["plotly>=5.0"] docs = [ "pydata-sphinx-theme", "sphinx>=6", @@ -63,7 +57,10 @@ dev = [ scifem = [ "scikit-fem>=8.1.0" # scikit-fem is a dependency for the multi-dimensional pybamm models ] -all = ["pybop[plot]", "pybop[scifem]"] +bpx = [ + "bpx>=0.4", +] +all = ["pybop[plot,scifem,bpx]"] [tool.setuptools.packages.find] include = ["pybop", "pybop.*"] @@ -73,7 +70,7 @@ Homepage = "https://github.com/pybop-team/PyBOP" Documentation = "https://pybop-docs.readthedocs.io" Repository = "https://github.com/pybop-team/PyBOP" Releases = "https://github.com/pybop-team/PyBOP/releases" -Changelog = "https://github.com/pybop-team/PyBOP/CHANGELOG.md" +Changelog = "https://github.com/pybop-team/PyBOP/blob/develop/CHANGELOG.md" [tool.pytest.ini_options] addopts = "--showlocals -v -n auto" @@ -81,10 +78,27 @@ addopts = "--showlocals -v -n auto" [tool.ruff] extend-include = ["*.ipynb"] extend-exclude = ["__init__.py"] +fix = true [tool.ruff.lint] -extend-select = ["I"] +select = [ + "A", # flake8-builtins: Check for Python builtins being used as variables or parameters + "B", # flake8-bugbear: Find likely bugs and design problems + "E", # pycodestyle errors + "W", # pycodestyle warnings + "F", # pyflakes: Detect various errors by parsing the source file + "I", # isort: Check and enforce import ordering + "ISC", # flake8-implicit-str-concat: Check for implicit string concatenation + "TID", # flake8-tidy-imports: Validate import hygiene + "UP", # pyupgrade: Automatically upgrade syntax for newer versions of Python + "SLF001", # flake8-string-format: Check for private object name access +] + ignore = ["E501","E741"] [tool.ruff.lint.per-file-ignores] +"tests/*" = ["SLF001"] "**.ipynb" = ["E402", "E703"] + +[tool.ruff.lint.flake8-tidy-imports] +ban-relative-imports = "all" diff --git a/scripts/ci/build_matrix.sh b/scripts/ci/build_matrix.sh index 9dfe32249..491bb59df 100755 --- a/scripts/ci/build_matrix.sh +++ b/scripts/ci/build_matrix.sh @@ -11,8 +11,11 @@ python_version=("3.9" "3.10" "3.11" "3.12") os=("ubuntu-latest" "windows-latest" "macos-13" "macos-14") -# This command fetches the last two PyBaMM versions excluding release candidates from PyPI -pybamm_version=($(curl -s https://pypi.org/pypi/pybamm/json | jq -r '.releases | keys[]' | grep -v rc | tail -n 2 | paste -sd " " -)) +# This command fetches the last PyBaMM version excluding release candidates from PyPI +pybamm_version=($(curl -s https://pypi.org/pypi/pybamm/json | jq -r '.releases | keys[]' | grep -v rc | tail -n 1 | paste -sd " " -)) + +# This command fetches the last PyBaMM versions including release candidates from PyPI +#pybamm_version=($(curl -s https://pypi.org/pypi/pybamm/json | jq -r '.releases | keys[]' | tail -n 1 | paste -sd " " -)) # open dict json='{ @@ -40,7 +43,7 @@ json+=' ] }' -# Filter out incompatible combinations -json=$(echo "$json" | jq -c 'del(.include[] | select(.pybamm_version == "23.9" and .python_version == "3.12"))') +# Example for filtering out incompatible combinations +#json=$(echo "$json" | jq -c 'del(.include[] | select(.pybamm_version == "23.9" and .python_version == "3.12"))') echo "$json" | jq -c . diff --git a/tests/examples/test_examples.py b/tests/examples/test_examples.py index 1c45be840..2bebc6fc6 100644 --- a/tests/examples/test_examples.py +++ b/tests/examples/test_examples.py @@ -12,14 +12,14 @@ class TestExamples: """ def list_of_examples(): - list = [] + examples_list = [] path_to_example_scripts = os.path.join( pybop.script_path, "..", "examples", "scripts" ) for example in os.listdir(path_to_example_scripts): if example.endswith(".py"): - list.append(os.path.join(path_to_example_scripts, example)) - return list + examples_list.append(os.path.join(path_to_example_scripts, example)) + return examples_list @pytest.mark.parametrize("example", list_of_examples()) @pytest.mark.examples diff --git a/tests/integration/test_eis_parameterisation.py b/tests/integration/test_eis_parameterisation.py new file mode 100644 index 000000000..22c2cbc39 --- /dev/null +++ b/tests/integration/test_eis_parameterisation.py @@ -0,0 +1,185 @@ +import numpy as np +import pytest + +import pybop + + +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.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 + 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, 0.775], + ), + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Uniform(0.4, 0.75), + bounds=[0.375, 0.775], + ), + ) + + @pytest.fixture(params=[0.5]) + def init_soc(self, request): + return request.param + + @pytest.fixture( + params=[ + pybop.GaussianLogLikelihood, + pybop.SumSquaredError, + pybop.Minkowski, + pybop.LogPosterior, + ] + ) + 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.XNES, + ] + ) + def optimiser(self, request): + return request.param + + @pytest.fixture + def optim(self, optimiser, model, parameters, cost, init_soc): + n_frequency = 15 + # 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 is pybop.GaussianLogLikelihoodKnownSigma: + cost = cost(problem, sigma0=self.sigma0) + elif cost is pybop.GaussianLogLikelihood: + cost = cost(problem, sigma0=self.sigma0 * 4) # Initial sigma0 guess + elif cost is pybop.LogPosterior: + cost = cost( + pybop.GaussianLogLikelihoodKnownSigma(problem, 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": 35, + "sigma0": [0.05, 0.05, 1e-3] + if isinstance(cost, pybop.GaussianLogLikelihood) + else 0.02, + } + + if isinstance(cost, pybop.LogPosterior): + for i in cost.parameters.keys(): + cost.parameters[i].prior = pybop.Uniform( + 0.2, 2.0 + ) # Increase range to avoid prior == np.inf + + # Create optimiser + optim = optimiser(**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) + results = optim.run() + + # Assertions + if np.allclose(x0, self.ground_truth, atol=1e-5): + raise AssertionError("Initial guess is too close to ground truth") + + # Assert on identified values, without sigma for GaussianLogLikelihood + # as the sigma values are small (5e-4), this is a difficult identification process + # and requires a high number of iterations, and parameter dependent step sizes. + if optim.minimising: + assert initial_cost > results.final_cost + else: + assert initial_cost < results.final_cost + np.testing.assert_allclose(results.x, self.ground_truth, atol=1.5e-2) + + def get_data(self, model, init_soc, f_eval): + initial_state = {"Initial SoC": init_soc} + 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, + initial_state=initial_state, + ) + + return sim diff --git a/tests/integration/test_half_cell_model.py b/tests/integration/test_half_cell_model.py new file mode 100644 index 000000000..f3c3a2708 --- /dev/null +++ b/tests/integration/test_half_cell_model.py @@ -0,0 +1,163 @@ +import numpy as np +import pytest +from pybamm import Parameter + +import pybop + + +class TestHalfCellModel: + """ + A class to test optimisation of a PyBaMM half-cell model. + """ + + @pytest.fixture(autouse=True) + def setup(self): + self.sigma0 = 0.002 + self.ground_truth = np.clip( + np.asarray([0.5]) + np.random.normal(loc=0.0, scale=0.05, size=1), + a_min=0.4, + a_max=0.75, + ) + + @pytest.fixture + def model(self): + options = {"working electrode": "positive"} + parameter_set = pybop.lithium_ion.SPM(options=options).default_parameter_values + parameter_set.update( + { + "Electrolyte density [kg.m-3]": Parameter( + "Positive electrode density [kg.m-3]" + ), + "Negative current collector density [kg.m-3]": 0.0, + "Negative current collector thickness [m]": 0.0, + "Negative electrode active material density [kg.m-3]": 0.0, + "Negative electrode active material volume fraction": 0.0, + "Negative electrode porosity": 0.0, + "Negative electrode carbon-binder density [kg.m-3]": 0.0, + "Positive current collector density [kg.m-3]": 0.0, + "Positive current collector thickness [m]": 0.0, + "Positive electrode active material density [kg.m-3]": Parameter( + "Positive electrode density [kg.m-3]" + ), + "Positive electrode carbon-binder density [kg.m-3]": Parameter( + "Positive electrode density [kg.m-3]" + ), + "Positive electrode density [kg.m-3]": 3262.0, + "Separator density [kg.m-3]": 0.0, + }, + check_already_exists=False, + ) + x = self.ground_truth + parameter_set.update( + { + "Positive electrode active material volume fraction": x[0], + } + ) + return pybop.lithium_ion.SPM(parameter_set=parameter_set, options=options) + + @pytest.fixture + def parameters(self): + return pybop.Parameters( + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Uniform(0.4, 0.75), + # no bounds + ), + ) + + @pytest.fixture(params=[0.4]) + def init_soc(self, request): + return request.param + + def noise(self, sigma, values): + return np.random.normal(0, sigma, values) + + @pytest.fixture + def fitting_cost(self, model, parameters, init_soc): + # Form dataset + solution = self.get_data(model, init_soc) + dataset = pybop.Dataset( + { + "Time [s]": solution["Time [s]"].data, + "Current function [A]": solution["Current [A]"].data, + "Voltage [V]": solution["Voltage [V]"].data + + self.noise(self.sigma0, len(solution["Time [s]"].data)), + } + ) + + # Define the cost to optimise + problem = pybop.FittingProblem(model, parameters, dataset) + return pybop.SumSquaredError(problem) + + @pytest.mark.integration + def test_fitting_costs(self, fitting_cost): + x0 = fitting_cost.parameters.initial_value() + optim = pybop.CuckooSearch( + cost=fitting_cost, + sigma0=0.03, + max_iterations=250, + max_unchanged_iterations=35, + ) + + initial_cost = optim.cost(optim.parameters.initial_value()) + results = optim.run() + + # Assertions + if not np.allclose(x0, self.ground_truth, atol=1e-5): + if optim.minimising: + assert initial_cost > results.final_cost + else: + assert initial_cost < results.final_cost + np.testing.assert_allclose(results.x, self.ground_truth, atol=1.5e-2) + + @pytest.fixture + def design_cost(self, model): + initial_state = {"Initial SoC": 1.0} + parameters = pybop.Parameters( + pybop.Parameter( + "Positive electrode thickness [m]", + prior=pybop.Gaussian(5e-05, 5e-06), + bounds=[2e-06, 10e-05], + ), + ) + experiment = pybop.Experiment( + ["Discharge at 1C until 3.5 V (5 seconds period)"], + ) + + problem = pybop.DesignProblem( + model, + parameters, + experiment=experiment, + initial_state=initial_state, + update_capacity=True, + ) + return pybop.GravimetricEnergyDensity(problem) + + @pytest.mark.integration + def test_design_costs(self, design_cost): + optim = pybop.CuckooSearch( + design_cost, + max_iterations=15, + allow_infeasible_solutions=False, + ) + initial_values = optim.parameters.initial_value() + initial_cost = optim.cost(initial_values) + results = optim.run() + + # Assertions + assert initial_cost < results.final_cost + for i, _ in enumerate(results.x): + assert results.x[i] < initial_values[i] + + def get_data(self, model, init_soc): + initial_state = {"Initial SoC": init_soc} + experiment = pybop.Experiment( + [ + ( + "Discharge at 0.5C for 3 minutes (4 second period)", + "Charge at 0.5C for 3 minutes (4 second period)", + ), + ] + ) + sim = model.predict(initial_state=initial_state, experiment=experiment) + return sim diff --git a/tests/integration/test_model_experiment_changes.py b/tests/integration/test_model_experiment_changes.py index 6902f873e..4f915f759 100644 --- a/tests/integration/test_model_experiment_changes.py +++ b/tests/integration/test_model_experiment_changes.py @@ -22,7 +22,7 @@ class TestModelAndExperimentChanges: ), pybop.Parameters( pybop.Parameter( # non-geometric parameter - "Positive electrode diffusivity [m2.s-1]", + "Positive particle diffusivity [m2.s-1]", prior=pybop.Gaussian(3.43e-15, 1e-15), bounds=[1e-15, 5e-15], true_value=4e-15, @@ -39,18 +39,21 @@ def test_changing_experiment(self, parameters): # Change the experiment and check that the results are different. parameter_set = pybop.ParameterSet.pybamm("Chen2020") - init_soc = 0.5 + parameter_set.update(parameters.as_dict("true")) + initial_state = {"Initial SoC": 0.5} model = pybop.lithium_ion.SPM(parameter_set=parameter_set) t_eval = np.arange(0, 3600, 2) # Default 1C discharge to cut-off voltage - solution_1 = model.predict(init_soc=init_soc, t_eval=t_eval) - cost_1 = self.final_cost(solution_1, model, parameters, init_soc) + solution_1 = model.predict(initial_state=initial_state, t_eval=t_eval) + cost_1 = self.final_cost(solution_1, model, parameters) experiment = pybop.Experiment(["Charge at 1C until 4.1 V (2 seconds period)"]) solution_2 = model.predict( - init_soc=init_soc, experiment=experiment, inputs=parameters.true_value() + initial_state=initial_state, + experiment=experiment, + inputs=parameters.as_dict("true"), ) - cost_2 = self.final_cost(solution_2, model, parameters, init_soc) + cost_2 = self.final_cost(solution_2, model, parameters) with np.testing.assert_raises(AssertionError): np.testing.assert_array_equal( @@ -67,16 +70,17 @@ def test_changing_model(self, parameters): # Change the model and check that the results are different. parameter_set = pybop.ParameterSet.pybamm("Chen2020") - init_soc = 0.5 + parameter_set.update(parameters.as_dict("true")) + initial_state = {"Initial SoC": 0.5} experiment = pybop.Experiment(["Charge at 1C until 4.1 V (2 seconds period)"]) model = pybop.lithium_ion.SPM(parameter_set=parameter_set) - solution_1 = model.predict(init_soc=init_soc, experiment=experiment) - cost_1 = self.final_cost(solution_1, model, parameters, init_soc) + solution_1 = model.predict(initial_state=initial_state, experiment=experiment) + cost_1 = self.final_cost(solution_1, model, parameters) model = pybop.lithium_ion.SPMe(parameter_set=parameter_set) - solution_2 = model.predict(init_soc=init_soc, experiment=experiment) - cost_2 = self.final_cost(solution_2, model, parameters, init_soc) + solution_2 = model.predict(initial_state=initial_state, experiment=experiment) + cost_2 = self.final_cost(solution_2, model, parameters) with np.testing.assert_raises(AssertionError): np.testing.assert_array_equal( @@ -88,7 +92,7 @@ def test_changing_model(self, parameters): np.testing.assert_allclose(cost_1, 0, atol=1e-5) np.testing.assert_allclose(cost_2, 0, atol=1e-5) - def final_cost(self, solution, model, parameters, init_soc): + def final_cost(self, solution, model, parameters): # Compute the cost corresponding to a particular solution dataset = pybop.Dataset( { @@ -98,10 +102,58 @@ def final_cost(self, solution, model, parameters, init_soc): } ) signal = ["Voltage [V]"] - problem = pybop.FittingProblem( - model, parameters, dataset, signal=signal, init_soc=init_soc - ) + problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) cost = pybop.RootMeanSquaredError(problem) optim = pybop.PSO(cost) - x, final_cost = optim.run() - return final_cost + results = optim.run() + return results.final_cost + + @pytest.mark.integration + def test_multi_fitting_problem(self): + parameter_set = pybop.ParameterSet.pybamm("Chen2020") + parameters = pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.68, 0.05), + true_value=parameter_set[ + "Negative electrode active material volume fraction" + ], + ) + + model_1 = pybop.lithium_ion.SPM(parameter_set=parameter_set) + experiment_1 = pybop.Experiment( + ["Discharge at 1C until 3 V (4 seconds period)"] + ) + solution_1 = model_1.predict(experiment=experiment_1) + dataset_1 = pybop.Dataset( + { + "Time [s]": solution_1["Time [s]"].data, + "Current function [A]": solution_1["Current [A]"].data, + "Voltage [V]": solution_1["Voltage [V]"].data, + } + ) + + model_2 = pybop.lithium_ion.SPMe(parameter_set=parameter_set.copy()) + experiment_2 = pybop.Experiment( + ["Discharge at 3C until 3 V (4 seconds period)"] + ) + solution_2 = model_2.predict(experiment=experiment_2) + dataset_2 = pybop.Dataset( + { + "Time [s]": solution_2["Time [s]"].data, + "Current function [A]": solution_2["Current [A]"].data, + "Voltage [V]": solution_2["Voltage [V]"].data, + } + ) + + # Define a problem for each dataset and combine them into one + problem_1 = pybop.FittingProblem(model_1, parameters, dataset_1) + problem_2 = pybop.FittingProblem(model_2, parameters, dataset_2) + problem = pybop.MultiFittingProblem(problem_1, problem_2) + cost = pybop.RootMeanSquaredError(problem) + + # Test with a gradient and non-gradient-based optimiser + for optimiser in [pybop.SNES, pybop.IRPropMin]: + optim = optimiser(cost) + results = optim.run() + np.testing.assert_allclose(results.x, parameters.true_value, atol=2e-5) + np.testing.assert_allclose(results.final_cost, 0, atol=2e-5) diff --git a/tests/integration/test_monte_carlo.py b/tests/integration/test_monte_carlo.py new file mode 100644 index 000000000..3c04155d6 --- /dev/null +++ b/tests/integration/test_monte_carlo.py @@ -0,0 +1,142 @@ +import numpy as np +import pybamm +import pytest + +import pybop +from pybop import ( + DREAM, + DifferentialEvolutionMCMC, + HaarioACMC, + HaarioBardenetACMC, + MetropolisRandomWalkMCMC, + PopulationMCMC, +) + + +class Test_Sampling_SPM: + """ + A class to test the MCMC samplers on a physics-based model. + """ + + @pytest.fixture(autouse=True) + def setup(self): + self.ground_truth = np.clip( + np.asarray([0.55, 0.55, 3e-3]) + + np.random.normal(0, [5e-2, 5e-2, 1e-4], size=3), + [0.4, 0.4, 1e-5], + [0.7, 0.7, 1e-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], + } + ) + solver = pybamm.IDAKLUSolver() + return pybop.lithium_ion.SPM(parameter_set=parameter_set, solver=solver) + + @pytest.fixture + def parameters(self): + return pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Uniform(0.4, 0.7), + bounds=[0.375, 0.725], + ), + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Uniform(0.4, 0.7), + # no bounds + ), + ) + + @pytest.fixture(params=[0.5]) + def init_soc(self, request): + return request.param + + def noise(self, sigma, values): + return np.random.normal(0, sigma, values) + + @pytest.fixture + def log_posterior(self, model, parameters, init_soc): + # Form dataset + solution = self.get_data(model, init_soc) + dataset = pybop.Dataset( + { + "Time [s]": solution["Time [s]"].data, + "Current function [A]": solution["Current [A]"].data, + "Voltage [V]": solution["Voltage [V]"].data + + self.noise(0.002, len(solution["Time [s]"].data)), + } + ) + + # Define the posterior to optimise + problem = pybop.FittingProblem(model, parameters, dataset) + likelihood = pybop.GaussianLogLikelihood(problem, sigma0=0.002 * 1.2) + return pybop.LogPosterior(likelihood) + + @pytest.fixture + def map_estimate(self, log_posterior): + common_args = { + "max_iterations": 100, + "max_unchanged_iterations": 35, + "absolute_tolerance": 1e-7, + } + optim = pybop.CMAES(log_posterior, **common_args) + results = optim.run() + + return results.x + + @pytest.mark.parametrize( + "quick_sampler", + [ + DREAM, + DifferentialEvolutionMCMC, + HaarioACMC, + HaarioBardenetACMC, + MetropolisRandomWalkMCMC, + PopulationMCMC, + ], + ) + @pytest.mark.integration + def test_sampling_spm(self, quick_sampler, log_posterior, map_estimate): + x0 = np.clip( + map_estimate + np.random.normal(0, [5e-3, 5e-3, 1e-4], size=3), + [0.4, 0.4, 1e-5], + [0.75, 0.75, 5e-2], + ) + # set common args + common_args = { + "log_pdf": log_posterior, + "chains": 3, + "x0": x0, + "warm_up": 150, + "max_iterations": 550, + } + + # construct and run + sampler = quick_sampler(**common_args) + chains = sampler.run() + + # Assert both final sample and posterior mean + x = np.mean(chains, axis=1) + for i in range(len(x)): + np.testing.assert_allclose(x[i], self.ground_truth, atol=1.5e-2) + + def get_data(self, model, init_soc): + initial_state = {"Initial SoC": init_soc} + experiment = pybop.Experiment( + [ + ( + "Discharge at 0.5C for 4 minutes (12 second period)", + "Charge at 0.5C for 4 minutes (12 second period)", + ), + ] + ) + sim = model.predict(initial_state=initial_state, experiment=experiment) + return sim diff --git a/tests/integration/test_monte_carlo_thevenin.py b/tests/integration/test_monte_carlo_thevenin.py new file mode 100644 index 000000000..a23df6094 --- /dev/null +++ b/tests/integration/test_monte_carlo_thevenin.py @@ -0,0 +1,168 @@ +import numpy as np +import pytest + +import pybop +from pybop import ( + MALAMCMC, + NUTS, + DramACMC, + HamiltonianMCMC, + MonomialGammaHamiltonianMCMC, + RaoBlackwellACMC, + RelativisticMCMC, + SliceDoublingMCMC, + SliceRankShrinkingMCMC, + SliceStepoutMCMC, +) + + +class TestSamplingThevenin: + """ + A class to test a subset of samplers on the simple Thevenin Model. + """ + + @pytest.fixture(autouse=True) + def setup(self): + self.sigma0 = 1e-3 + self.ground_truth = np.clip( + np.asarray([0.05, 0.05]) + np.random.normal(loc=0.0, scale=0.01, size=2), + a_min=1e-4, + a_max=0.1, + ) + self.fast_samplers = [ + MALAMCMC, + RaoBlackwellACMC, + SliceDoublingMCMC, + SliceStepoutMCMC, + DramACMC, + ] + + @pytest.fixture + def model(self): + parameter_set = pybop.ParameterSet( + json_path="examples/parameters/initial_ecm_parameters.json" + ) + parameter_set.import_parameters() + parameter_set.params.update( + { + "C1 [F]": 1000, + "R0 [Ohm]": self.ground_truth[0], + "R1 [Ohm]": self.ground_truth[1], + } + ) + + return pybop.empirical.Thevenin(parameter_set=parameter_set) + + @pytest.fixture + def parameters(self): + return pybop.Parameters( + pybop.Parameter( + "R0 [Ohm]", + prior=pybop.Uniform(1e-2, 8e-2), + bounds=[1e-4, 1e-1], + ), + pybop.Parameter( + "R1 [Ohm]", + prior=pybop.Uniform(1e-2, 8e-2), + bounds=[1e-4, 1e-1], + ), + ) + + @pytest.fixture(params=[0.5]) + def init_soc(self, request): + return request.param + + def noise(self, sigma, values): + return np.random.normal(0, sigma, values) + + @pytest.fixture + def posterior(self, model, parameters, init_soc): + # Form dataset + solution = self.get_data(model, init_soc) + dataset = pybop.Dataset( + { + "Time [s]": solution["Time [s]"].data, + "Current function [A]": solution["Current [A]"].data, + "Voltage [V]": solution["Voltage [V]"].data + + self.noise(self.sigma0, len(solution["Time [s]"].data)), + } + ) + + # Define the cost to optimise + problem = pybop.FittingProblem(model, parameters, dataset) + likelihood = pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=self.sigma0) + return pybop.LogPosterior(likelihood) + + @pytest.fixture + def map_estimate(self, posterior): + common_args = { + "max_iterations": 100, + "max_unchanged_iterations": 35, + "absolute_tolerance": 1e-7, + "sigma0": [3e-4, 3e-4], + "verbose": True, + } + optim = pybop.CMAES(posterior, **common_args) + results = optim.run() + + return results.x + + # Parameterize the samplers + @pytest.mark.parametrize( + "sampler", + [ + NUTS, + HamiltonianMCMC, + MonomialGammaHamiltonianMCMC, + RelativisticMCMC, + SliceRankShrinkingMCMC, + MALAMCMC, + RaoBlackwellACMC, + SliceDoublingMCMC, + SliceStepoutMCMC, + DramACMC, + ], + ) + @pytest.mark.integration + def test_sampling_thevenin(self, sampler, posterior, map_estimate): + x0 = np.clip(map_estimate + np.random.normal(0, 1e-3, size=2), 1e-4, 1e-1) + common_args = { + "log_pdf": posterior, + "chains": 2, + "warm_up": 100, + "cov0": [2e-3, 2e-3], + "max_iterations": 1050, + "x0": x0, + } + + # construct and run + sampler = sampler(**common_args) + if isinstance(sampler, SliceRankShrinkingMCMC): + for i, _j in enumerate(sampler._samplers): + sampler._samplers[i].set_hyper_parameters([1e-3]) + chains = sampler.run() + + # Test PosteriorSummary + summary = pybop.PosteriorSummary(chains) + ess = summary.effective_sample_size() + np.testing.assert_array_less(0, ess) + if not isinstance(sampler, RelativisticMCMC): + np.testing.assert_array_less( + summary.rhat(), 1.5 + ) # Large rhat, to enable faster tests + + # Assert both final sample and posterior mean + x = np.mean(chains, axis=1) + for i in range(len(x)): + np.testing.assert_allclose(x[i], self.ground_truth, atol=5e-3) + np.testing.assert_allclose(chains[i][-1], self.ground_truth, atol=1e-2) + + def get_data(self, model, init_soc): + initial_state = {"Initial SoC": init_soc} + experiment = pybop.Experiment( + [ + ("Discharge at 0.5C for 6 minutes (20 second period)",), + ] + ) + sim = model.predict(initial_state=initial_state, experiment=experiment) + return sim diff --git a/tests/integration/test_observer_parameterisation.py b/tests/integration/test_observer_parameterisation.py new file mode 100644 index 000000000..6df290e8d --- /dev/null +++ b/tests/integration/test_observer_parameterisation.py @@ -0,0 +1,102 @@ +import numpy as np +import pytest + +import pybop +from examples.standalone.model import ExponentialDecay + + +class TestObservers: + """ + A class to run integration tests on the Observers class. + """ + + @pytest.fixture(autouse=True) + def setup(self): + self.ground_truth = np.clip( + np.array([0.1, 1.0]) + np.random.normal(loc=0.0, scale=0.05, size=2), + a_min=[0.04, 0.2], + a_max=[0.85, 1.15], + ) + + @pytest.fixture + def parameter_set(self): + return {"k": self.ground_truth[0], "y0": self.ground_truth[1]} + + @pytest.fixture + def model(self, parameter_set): + return ExponentialDecay(parameter_set=parameter_set, n_states=1) + + @pytest.fixture + def parameters(self, parameter_set): + return pybop.Parameters( + pybop.Parameter( + "k", + prior=pybop.Gaussian(0.1, 0.05), + bounds=[0, 1], + true_value=parameter_set["k"], + ), + pybop.Parameter( + "y0", + prior=pybop.Gaussian(1, 0.05), + bounds=[0, 3], + true_value=parameter_set["y0"], + ), + ) + + def noise(self, sigma, values): + return np.random.normal(0, sigma, values) + + @pytest.mark.integration + def test_observer_exponential_decay(self, parameters, model): + # Make a prediction with measurement noise + sigma = 1e-2 + t_eval = np.linspace(0, 20, 10) + values = model.predict(t_eval=t_eval)["2y"].data + corrupt_values = values + self.noise(sigma, len(t_eval)) + + # Form dataset + dataset = pybop.Dataset( + { + "Time [s]": t_eval, + "Current function [A]": 0 * t_eval, # placeholder + "2y": corrupt_values, + } + ) + + # Define the UKF observer + signal = ["2y"] + n_states = model.n_states + n_signals = len(signal) + covariance = np.diag([sigma**2] * n_states) + process_noise = np.diag([1e-6] * n_states) + measurement_noise = np.diag([sigma**2] * n_signals) + observer = pybop.UnscentedKalmanFilterObserver( + parameters, + model, + covariance, + process_noise, + measurement_noise, + dataset, + signal=signal, + ) + + # Generate cost function, and optimisation class + cost = pybop.ObserverCost(observer) + optim = pybop.CMAES(cost, max_unchanged_iterations=25, verbose=True) + + # Initial Cost + x0 = cost.parameters.initial_value() + initial_cost = optim.cost(x0) + + # Run optimisation + results = optim.run() + + # Assertions + if not np.allclose(x0, self.ground_truth, atol=1e-5): + if optim.minimising: + assert initial_cost > results.final_cost + else: + assert initial_cost < results.final_cost + else: + raise ValueError("Initial value is the same as the ground truth value.") + np.testing.assert_allclose(results.x, parameters.true_value(), atol=1.5e-2) diff --git a/tests/integration/test_optimisation_options.py b/tests/integration/test_optimisation_options.py index 01702ba24..b3bab9137 100644 --- a/tests/integration/test_optimisation_options.py +++ b/tests/integration/test_optimisation_options.py @@ -13,13 +13,22 @@ class TestOptimisation: @pytest.fixture(autouse=True) def setup(self): - 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 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) @pytest.fixture @@ -53,8 +62,8 @@ def noise(self, sigma, values): @pytest.fixture def spm_costs(self, model, parameters, cost_class): # Form dataset - init_soc = 0.5 - solution = self.get_data(model, parameters, self.ground_truth, init_soc) + initial_state = {"Initial SoC": 0.5} + solution = self.get_data(model, initial_state) dataset = pybop.Dataset( { "Time [s]": solution["Time [s]"].data, @@ -65,9 +74,9 @@ def spm_costs(self, model, parameters, cost_class): ) # Define the cost to optimise - problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc) + problem = pybop.FittingProblem(model, parameters, dataset) if cost_class in [pybop.GaussianLogLikelihoodKnownSigma]: - return cost_class(problem, sigma=[0.03, 0.03]) + return cost_class(problem, sigma0=0.002) else: return cost_class(problem) @@ -80,7 +89,7 @@ def spm_costs(self, model, parameters, cost_class): ) @pytest.mark.integration def test_optimisation_f_guessed(self, f_guessed, spm_costs): - x0 = spm_costs.x0 + x0 = spm_costs.parameters.initial_value() # Test each optimiser optim = pybop.XNES( cost=spm_costs, @@ -93,21 +102,23 @@ def test_optimisation_f_guessed(self, f_guessed, spm_costs): # Set parallelisation if not on Windows if sys.platform != "win32": - optim.set_parallel(True) + optim.set_parallel(1) initial_cost = optim.cost(x0) - x, final_cost = optim.run() + results = optim.run() # Assertions if not np.allclose(x0, self.ground_truth, atol=1e-5): if optim.minimising: - assert initial_cost > final_cost + assert initial_cost > results.final_cost else: - assert initial_cost < final_cost - np.testing.assert_allclose(x, self.ground_truth, atol=1.5e-2) + assert initial_cost < results.final_cost + else: + raise ValueError("Initial value is the same as the ground truth value.") + np.testing.assert_allclose(results.x, self.ground_truth, atol=1.5e-2) - def get_data(self, model, parameters, x, init_soc): - model.parameters = parameters + def get_data(self, model, initial_state): + # Update the initial state and save the ground truth initial concentrations experiment = pybop.Experiment( [ ( @@ -117,5 +128,5 @@ def get_data(self, model, parameters, x, init_soc): ] * 2 ) - sim = model.predict(init_soc=init_soc, experiment=experiment, inputs=x) + sim = model.predict(initial_state=initial_state, experiment=experiment) return sim diff --git a/tests/integration/test_spm_parameterisations.py b/tests/integration/test_spm_parameterisations.py index 7eeb0b7c0..d8f27fffb 100644 --- a/tests/integration/test_spm_parameterisations.py +++ b/tests/integration/test_spm_parameterisations.py @@ -1,4 +1,5 @@ import numpy as np +import pybamm import pytest import pybop @@ -11,13 +12,23 @@ class Test_SPM_Parameterisation: @pytest.fixture(autouse=True) def setup(self): - self.ground_truth = np.asarray([0.55, 0.55]) + np.random.normal( - loc=0.0, scale=0.05, size=2 + self.sigma0 = 0.002 + self.ground_truth = np.clip( + np.asarray([0.55, 0.55]) + np.random.normal(loc=0.0, scale=0.05, size=2), + a_min=0.425, + a_max=0.75, ) @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) @pytest.fixture @@ -25,12 +36,12 @@ def parameters(self): return pybop.Parameters( pybop.Parameter( "Negative electrode active material volume fraction", - prior=pybop.Uniform(0.4, 0.7), - bounds=[0.375, 0.725], + prior=pybop.Uniform(0.4, 0.75), + bounds=[0.375, 0.775], ), pybop.Parameter( "Positive electrode active material volume fraction", - prior=pybop.Uniform(0.4, 0.7), + prior=pybop.Uniform(0.4, 0.75), # no bounds ), ) @@ -42,158 +53,201 @@ def init_soc(self, request): @pytest.fixture( params=[ pybop.GaussianLogLikelihoodKnownSigma, + pybop.GaussianLogLikelihood, pybop.RootMeanSquaredError, pybop.SumSquaredError, - pybop.MAP, + pybop.SumofPower, + pybop.Minkowski, + pybop.LogPosterior, ] ) - def cost_class(self, request): + def cost(self, request): return request.param def noise(self, sigma, values): return np.random.normal(0, sigma, values) + @pytest.fixture( + params=[ + pybop.SciPyDifferentialEvolution, + pybop.CuckooSearch, + pybop.NelderMead, + pybop.IRPropMin, + pybop.AdamW, + pybop.CMAES, + pybop.SNES, + pybop.XNES, + ] + ) + def optimiser(self, request): + return request.param + @pytest.fixture - def spm_costs(self, model, parameters, cost_class, init_soc): + def optim(self, optimiser, model, parameters, cost, init_soc): # Form dataset - solution = self.get_data(model, parameters, self.ground_truth, init_soc) + solution = self.get_data(model, init_soc) dataset = pybop.Dataset( { "Time [s]": solution["Time [s]"].data, "Current function [A]": solution["Current [A]"].data, "Voltage [V]": solution["Voltage [V]"].data - + self.noise(0.002, len(solution["Time [s]"].data)), + + self.noise(self.sigma0, len(solution["Time [s]"].data)), } ) - # Define the cost to optimise - problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc) - if cost_class in [pybop.GaussianLogLikelihoodKnownSigma]: - return cost_class(problem, sigma=[0.03, 0.03]) - elif cost_class in [pybop.MAP]: - return cost_class( - problem, pybop.GaussianLogLikelihoodKnownSigma, sigma=[0.03, 0.03] + # IDAKLU Solver for Gradient-based optimisers + if optimiser in [pybop.AdamW, pybop.IRPropMin]: + model.solver = pybamm.IDAKLUSolver() + + # Define the problem + problem = pybop.FittingProblem(model, parameters, dataset) + + # Construct the cost + if cost is pybop.GaussianLogLikelihoodKnownSigma: + cost = cost(problem, sigma0=self.sigma0) + elif cost is pybop.GaussianLogLikelihood: + cost = cost(problem, sigma0=self.sigma0 * 4) # Initial sigma0 guess + elif cost is pybop.LogPosterior: + cost = cost( + pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=self.sigma0) ) + elif cost in [pybop.SumofPower, pybop.Minkowski]: + cost = cost(problem, p=2.5) else: - return cost_class(problem) + cost = cost(problem) + + sigma0 = 0.05 if optimiser == pybop.CuckooSearch else 0.02 + # Construct optimisation object + common_args = { + "cost": cost, + "max_iterations": 250, + "absolute_tolerance": 1e-6, + "max_unchanged_iterations": 55, + "sigma0": [0.05, 0.05, 1e-3] + if isinstance(cost, pybop.GaussianLogLikelihood) + else sigma0, + } + + if isinstance(cost, pybop.LogPosterior): + 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 + optim = optimiser(**common_args) + return optim - @pytest.mark.parametrize( - "optimiser", - [ - pybop.SciPyDifferentialEvolution, - pybop.AdamW, - pybop.CMAES, - pybop.IRPropMin, - pybop.NelderMead, - pybop.SNES, - pybop.XNES, - ], - ) @pytest.mark.integration - def test_spm_optimisers(self, optimiser, spm_costs): - x0 = spm_costs.x0 - # Some optimisers require a complete set of bounds - if optimiser in [ - pybop.SciPyDifferentialEvolution, - ]: - spm_costs.problem.parameters[ - "Positive electrode active material volume fraction" - ].set_bounds([0.375, 0.725]) # Large range to ensure IC within bounds - bounds = spm_costs.problem.parameters.get_bounds() - spm_costs.problem.bounds = bounds - spm_costs.bounds = bounds + def test_spm_optimisers(self, optim): + x0 = optim.parameters.initial_value() - # Test each optimiser - if optimiser in [pybop.PSO]: - optim = pybop.Optimisation( - cost=spm_costs, optimiser=optimiser, sigma0=0.05, max_iterations=250 + # 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])) ) - else: - optim = optimiser(cost=spm_costs, sigma0=0.05, max_iterations=250) - if issubclass(optimiser, pybop.BasePintsOptimiser): - optim.set_max_unchanged_iterations(iterations=35, absolute_tolerance=1e-5) initial_cost = optim.cost(x0) - x, final_cost = optim.run() + results = optim.run() # Assertions - if not np.allclose(x0, self.ground_truth, atol=1e-5): - if optim.minimising: - assert initial_cost > final_cost - else: - assert initial_cost < final_cost - np.testing.assert_allclose(x, self.ground_truth, atol=1.5e-2) + 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(results.x, self.ground_truth, atol=1.5e-2) + np.testing.assert_allclose(results.x[-1], self.sigma0, atol=5e-4) + else: + assert ( + (initial_cost > results.final_cost) + if optim.minimising + else (initial_cost < results.final_cost) + ) + np.testing.assert_allclose(results.x, self.ground_truth, atol=1.5e-2) @pytest.fixture - def spm_two_signal_cost(self, parameters, model, cost_class): + def spm_two_signal_cost(self, parameters, model, cost): # Form dataset - init_soc = 0.5 - solution = self.get_data(model, parameters, self.ground_truth, init_soc) + solution = self.get_data(model, init_soc=0.5) dataset = pybop.Dataset( { "Time [s]": solution["Time [s]"].data, "Current function [A]": solution["Current [A]"].data, "Voltage [V]": solution["Voltage [V]"].data - + self.noise(0.002, len(solution["Time [s]"].data)), + + self.noise(self.sigma0, len(solution["Time [s]"].data)), "Bulk open-circuit voltage [V]": solution[ "Bulk open-circuit voltage [V]" ].data - + self.noise(0.002, len(solution["Time [s]"].data)), + + self.noise(self.sigma0, len(solution["Time [s]"].data)), } ) # Define the cost to optimise signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] - problem = pybop.FittingProblem( - model, parameters, dataset, signal=signal, init_soc=init_soc - ) + problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) - if cost_class in [pybop.GaussianLogLikelihoodKnownSigma]: - return cost_class(problem, sigma=[0.05, 0.05]) - elif cost_class in [pybop.MAP]: - return cost_class(problem, pybop.GaussianLogLikelihoodKnownSigma) + if cost is pybop.GaussianLogLikelihoodKnownSigma: + return cost(problem, sigma0=self.sigma0) + elif cost is pybop.GaussianLogLikelihood: + return cost(problem, sigma0=self.sigma0 * 4) # Initial sigma0 guess + elif cost is pybop.LogPosterior: + return cost( + pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=self.sigma0) + ) else: - return cost_class(problem) + return cost(problem) @pytest.mark.parametrize( "multi_optimiser", [ pybop.SciPyDifferentialEvolution, pybop.IRPropMin, - pybop.XNES, + pybop.CMAES, ], ) @pytest.mark.integration def test_multiple_signals(self, multi_optimiser, spm_two_signal_cost): - x0 = spm_two_signal_cost.x0 - # Some optimisers require a complete set of bounds - if multi_optimiser in [pybop.SciPyDifferentialEvolution]: - spm_two_signal_cost.problem.parameters[ - "Positive electrode active material volume fraction" - ].set_bounds([0.375, 0.725]) # Large range to ensure IC within bounds - bounds = spm_two_signal_cost.problem.parameters.get_bounds() - spm_two_signal_cost.problem.bounds = bounds - spm_two_signal_cost.bounds = bounds + x0 = spm_two_signal_cost.parameters.initial_value() + combined_sigma0 = np.asarray([self.sigma0, self.sigma0]) + + common_args = { + "cost": spm_two_signal_cost, + "max_iterations": 250, + "absolute_tolerance": 1e-6, + "max_unchanged_iterations": 55, + "sigma0": [0.035, 0.035, 6e-3, 6e-3] + if isinstance(spm_two_signal_cost, pybop.GaussianLogLikelihood) + else 0.02, + } # Test each optimiser - optim = multi_optimiser( - cost=spm_two_signal_cost, - sigma0=0.03, - max_iterations=250, - ) + optim = multi_optimiser(**common_args) + + # Add sigma0 to ground truth for GaussianLogLikelihood + if isinstance(spm_two_signal_cost, pybop.GaussianLogLikelihood): + self.ground_truth = np.concatenate((self.ground_truth, combined_sigma0)) + if issubclass(multi_optimiser, pybop.BasePintsOptimiser): - optim.set_max_unchanged_iterations(iterations=35, absolute_tolerance=1e-5) + optim.set_max_unchanged_iterations(iterations=35, absolute_tolerance=1e-6) - initial_cost = optim.cost(spm_two_signal_cost.x0) - x, final_cost = optim.run() + initial_cost = optim.cost(optim.parameters.initial_value()) + results = optim.run() # Assertions - if not np.allclose(x0, self.ground_truth, atol=1e-5): - if optim.minimising: - assert initial_cost > final_cost - else: - assert initial_cost < final_cost - np.testing.assert_allclose(x, self.ground_truth, atol=1.5e-2) + if np.allclose(x0, self.ground_truth, atol=1e-5): + raise AssertionError("Initial guess is too close to ground truth") + + if isinstance(spm_two_signal_cost, pybop.GaussianLogLikelihood): + np.testing.assert_allclose(results.x, self.ground_truth, atol=1.5e-2) + np.testing.assert_allclose(results.x[-2:], combined_sigma0, atol=5e-4) + else: + assert ( + (initial_cost > results.final_cost) + if optim.minimising + else (initial_cost < results.final_cost) + ) + np.testing.assert_allclose(results.x, self.ground_truth, atol=1.5e-2) @pytest.mark.parametrize("init_soc", [0.4, 0.6]) @pytest.mark.integration @@ -204,7 +258,7 @@ def test_model_misparameterisation(self, parameters, model, init_soc): second_model = pybop.lithium_ion.SPMe(parameter_set=second_parameter_set) # Form dataset - solution = self.get_data(second_model, parameters, self.ground_truth, init_soc) + solution = self.get_data(second_model, init_soc) dataset = pybop.Dataset( { "Time [s]": solution["Time [s]"].data, @@ -214,7 +268,7 @@ def test_model_misparameterisation(self, parameters, model, init_soc): ) # Define the cost to optimise - problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc) + problem = pybop.FittingProblem(model, parameters, dataset) cost = pybop.RootMeanSquaredError(problem) # Select optimiser @@ -222,28 +276,27 @@ def test_model_misparameterisation(self, parameters, model, init_soc): # Build the optimisation problem optim = optimiser(cost=cost) - initial_cost = optim.cost(cost.x0) + initial_cost = optim.cost(optim.x0) # Run the optimisation problem - x, final_cost = optim.run() + results = optim.run() # Assertion for final_cost - assert initial_cost > final_cost + assert initial_cost > results.final_cost # Assertion for x with np.testing.assert_raises(AssertionError): - np.testing.assert_allclose(x, self.ground_truth, atol=2e-2) + np.testing.assert_allclose(results.x, self.ground_truth, atol=2e-2) - def get_data(self, model, parameters, x, init_soc): - model.parameters = parameters + def get_data(self, model, init_soc): + initial_state = {"Initial SoC": init_soc} experiment = pybop.Experiment( [ ( - "Discharge at 0.5C for 6 minutes (4 second period)", - "Charge at 0.5C for 6 minutes (4 second period)", + "Discharge at 0.5C for 3 minutes (4 second period)", + "Charge at 0.5C for 3 minutes (4 second period)", ), ] - * 2 ) - sim = model.predict(init_soc=init_soc, experiment=experiment, inputs=x) + sim = model.predict(initial_state=initial_state, experiment=experiment) return sim diff --git a/tests/integration/test_thevenin_parameterisation.py b/tests/integration/test_thevenin_parameterisation.py index 1ef1bc3eb..2b4bdabd1 100644 --- a/tests/integration/test_thevenin_parameterisation.py +++ b/tests/integration/test_thevenin_parameterisation.py @@ -11,17 +11,25 @@ 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 def model(self): parameter_set = pybop.ParameterSet( - json_path="examples/scripts/parameters/initial_ecm_parameters.json" + json_path="examples/parameters/initial_ecm_parameters.json" ) parameter_set.import_parameters() - parameter_set.params.update({"C1 [F]": 1000}) + parameter_set.params.update( + { + "C1 [F]": 1000, + "R0 [Ohm]": self.ground_truth[0], + "R1 [Ohm]": self.ground_truth[1], + } + ) return pybop.empirical.Thevenin(parameter_set=parameter_set) @pytest.fixture @@ -39,15 +47,11 @@ def parameters(self): ), ) - @pytest.fixture(params=[pybop.RootMeanSquaredError, pybop.SumSquaredError]) - def cost_class(self, request): - return request.param - @pytest.fixture - def cost(self, model, parameters, cost_class): + def dataset(self, model): # Form dataset - solution = self.get_data(model, parameters, self.ground_truth) - dataset = pybop.Dataset( + solution = self.get_data(model) + return pybop.Dataset( { "Time [s]": solution["Time [s]"].data, "Current function [A]": solution["Current [A]"].data, @@ -55,52 +59,62 @@ def cost(self, model, parameters, cost_class): } ) - # Define the cost to optimise - problem = pybop.FittingProblem(model, parameters, dataset) - return cost_class(problem) - @pytest.mark.parametrize( - "optimiser", - [pybop.SciPyMinimize, pybop.GradientDescent, pybop.PSO], + "cost_class", [pybop.RootMeanSquaredError, pybop.SumSquaredError] + ) + @pytest.mark.parametrize( + "optimiser, method", + [ + (pybop.SciPyMinimize, "trust-constr"), + (pybop.SciPyMinimize, "SLSQP"), + (pybop.SciPyMinimize, "COBYLA"), + (pybop.GradientDescent, ""), + (pybop.PSO, ""), + ], ) @pytest.mark.integration - def test_optimisers_on_simple_model(self, optimiser, cost): - x0 = cost.x0 + def test_optimisers_on_simple_model( + self, model, parameters, dataset, cost_class, optimiser, method + ): + # Define the cost to optimise + problem = pybop.FittingProblem(model, parameters, dataset) + cost = cost_class(problem) + + x0 = cost.parameters.initial_value() if optimiser in [pybop.GradientDescent]: optim = optimiser( - cost=cost, - sigma0=2.5e-4, - max_iterations=250, + cost=cost, sigma0=2.5e-4, max_iterations=250, method=method ) else: - optim = optimiser( - cost=cost, - sigma0=0.03, - max_iterations=250, - ) + optim = optimiser(cost=cost, sigma0=0.03, max_iterations=250, method=method) if isinstance(optimiser, pybop.BasePintsOptimiser): optim.set_max_unchanged_iterations(iterations=35, absolute_tolerance=1e-5) - initial_cost = optim.cost(x0) - x, final_cost = optim.run() + initial_cost = optim.cost(optim.parameters.initial_value()) + results = optim.run() # Assertions if not np.allclose(x0, self.ground_truth, atol=1e-5): if optim.minimising: - assert initial_cost > final_cost + assert initial_cost > results.final_cost else: - assert initial_cost < final_cost - np.testing.assert_allclose(x, self.ground_truth, atol=1.5e-2) + assert initial_cost < results.final_cost + else: + raise ValueError("Initial value is the same as the ground truth value.") + np.testing.assert_allclose(results.x, self.ground_truth, atol=1.5e-2) + + if isinstance(optimiser, pybop.SciPyMinimize): + assert results.scipy_result.success is True - def get_data(self, model, parameters, x): - model.parameters = parameters + def get_data(self, model): experiment = pybop.Experiment( [ ( "Discharge at 0.5C for 2 minutes (4 seconds period)", + "Rest for 20 seconds (4 seconds period)", "Charge at 0.5C for 2 minutes (4 seconds period)", ), ] ) - sim = model.predict(experiment=experiment, inputs=x) + sim = model.predict(experiment=experiment) return sim diff --git a/tests/integration/test_transformation.py b/tests/integration/test_transformation.py new file mode 100644 index 000000000..65a477755 --- /dev/null +++ b/tests/integration/test_transformation.py @@ -0,0 +1,166 @@ +import numpy as np +import pytest + +import pybop + + +class TestTransformation: + """ + A class for integration tests of the transformation methods. + """ + + @pytest.fixture(autouse=True) + def setup(self): + self.sigma0 = 2e-3 + 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 + def model(self): + parameter_set = pybop.ParameterSet( + json_path="examples/parameters/initial_ecm_parameters.json" + ) + parameter_set.import_parameters() + parameter_set.params.update( + { + "C1 [F]": 1000, + "R0 [Ohm]": self.ground_truth[0], + "R1 [Ohm]": self.ground_truth[1], + } + ) + return pybop.empirical.Thevenin(parameter_set=parameter_set) + + @pytest.fixture + def parameters(self): + return pybop.Parameters( + pybop.Parameter( + "R0 [Ohm]", + prior=pybop.Uniform(0.001, 0.1), + bounds=[0, 0.1], + transformation=pybop.ScaledTransformation( + coefficient=1 / 0.35, intercept=-0.375 + ), + ), + pybop.Parameter( + "R1 [Ohm]", + prior=pybop.Uniform(0.001, 0.1), + bounds=[0, 0.1], + transformation=pybop.LogTransformation(), + ), + ) + + @pytest.fixture(params=[0.5]) + def init_soc(self, request): + return request.param + + def noise(self, sigma, values): + return np.random.normal(0, sigma, values) + + @pytest.fixture( + params=[ + pybop.GaussianLogLikelihoodKnownSigma, + pybop.GaussianLogLikelihood, + pybop.RootMeanSquaredError, + pybop.SumSquaredError, + pybop.SumofPower, + pybop.Minkowski, + pybop.LogPosterior, + pybop.LogPosterior, # Second for GaussianLogLikelihood + ] + ) + def cost_cls(self, request): + return request.param + + @pytest.fixture + def cost(self, model, parameters, init_soc, cost_cls): + # Form dataset + solution = self.get_data(model, init_soc) + dataset = pybop.Dataset( + { + "Time [s]": solution["Time [s]"].data, + "Current function [A]": solution["Current [A]"].data, + "Voltage [V]": solution["Voltage [V]"].data + + self.noise(self.sigma0, len(solution["Time [s]"].data)), + } + ) + + # Construct problem + problem = pybop.FittingProblem(model, parameters, dataset) + + # Construct the cost + first_map = True + if cost_cls is pybop.GaussianLogLikelihoodKnownSigma: + return cost_cls(problem, sigma0=self.sigma0) + elif cost_cls is pybop.GaussianLogLikelihood: + return cost_cls(problem) + elif cost_cls is pybop.LogPosterior and first_map: + first_map = False + return cost_cls(log_likelihood=pybop.GaussianLogLikelihood(problem)) + elif cost_cls is pybop.LogPosterior: + return cost_cls( + log_likelihood=pybop.GaussianLogLikelihoodKnownSigma( + problem, sigma0=self.sigma0 + ) + ) + else: + return cost_cls(problem) + + @pytest.mark.parametrize( + "optimiser", + [ + pybop.IRPropMin, + pybop.NelderMead, + ], + ) + @pytest.mark.integration + def test_thevenin_transformation(self, optimiser, cost): + x0 = cost.parameters.initial_value() + optim = optimiser( + cost=cost, + sigma0=[0.03, 0.03, 1e-3] + if isinstance(cost, (pybop.GaussianLogLikelihood, pybop.LogPosterior)) + else [0.03, 0.03], + max_unchanged_iterations=35, + absolute_tolerance=1e-6, + max_iterations=250, + ) + + initial_cost = optim.cost(x0) + results = optim.run() + + # Add sigma0 to ground truth for GaussianLogLikelihood + if isinstance(optim.cost, (pybop.GaussianLogLikelihood, pybop.LogPosterior)): + self.ground_truth = np.concatenate( + (self.ground_truth, np.asarray([self.sigma0])) + ) + + # Assertions + if np.allclose(x0, self.ground_truth, atol=1e-5): + raise AssertionError("Initial guess is too close to ground truth") + + if isinstance(cost, pybop.GaussianLogLikelihood): + np.testing.assert_allclose(results.x, self.ground_truth, atol=1.5e-2) + np.testing.assert_allclose(results.x[-1], self.sigma0, atol=5e-4) + else: + assert ( + (initial_cost > results.final_cost) + if optim.minimising + else (initial_cost < results.final_cost) + ) + np.testing.assert_allclose(results.x, self.ground_truth, atol=1.5e-2) + + def get_data(self, model, init_soc): + initial_state = {"Initial SoC": init_soc} + experiment = pybop.Experiment( + [ + ( + "Discharge at 0.5C for 2 minutes (4 second period)", + "Rest for 1 minute (4 second period)", + ), + ] + ) + sim = model.predict(initial_state=initial_state, experiment=experiment) + return sim diff --git a/tests/integration/test_weighted_cost.py b/tests/integration/test_weighted_cost.py new file mode 100644 index 000000000..13df11cda --- /dev/null +++ b/tests/integration/test_weighted_cost.py @@ -0,0 +1,205 @@ +import numpy as np +import pytest +from pybamm import Parameter + +import pybop + + +class TestWeightedCost: + """ + A class to test the weighted cost function. + """ + + @pytest.fixture(autouse=True) + def setup(self): + self.sigma0 = 0.002 + 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 + def model(self): + parameter_set = pybop.ParameterSet.pybamm("Chen2020") + parameter_set.update( + { + "Electrolyte density [kg.m-3]": Parameter("Separator density [kg.m-3]"), + "Negative electrode active material density [kg.m-3]": Parameter( + "Negative electrode density [kg.m-3]" + ), + "Negative electrode carbon-binder density [kg.m-3]": Parameter( + "Negative electrode density [kg.m-3]" + ), + "Positive electrode active material density [kg.m-3]": Parameter( + "Positive electrode density [kg.m-3]" + ), + "Positive electrode carbon-binder density [kg.m-3]": Parameter( + "Positive electrode density [kg.m-3]" + ), + }, + check_already_exists=False, + ) + 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) + + @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), + # no bounds + ), + ) + + @pytest.fixture(params=[0.4]) + def init_soc(self, request): + return request.param + + @pytest.fixture( + params=[ + ( + pybop.GaussianLogLikelihoodKnownSigma, + pybop.RootMeanSquaredError, + pybop.SumSquaredError, + pybop.LogPosterior, + ) + ] + ) + def cost_class(self, request): + return request.param + + def noise(self, sigma, values): + return np.random.normal(0, sigma, values) + + @pytest.fixture + def weighted_fitting_cost(self, model, parameters, cost_class, init_soc): + # Form dataset + solution = self.get_data(model, init_soc) + dataset = pybop.Dataset( + { + "Time [s]": solution["Time [s]"].data, + "Current function [A]": solution["Current [A]"].data, + "Voltage [V]": solution["Voltage [V]"].data + + self.noise(self.sigma0, len(solution["Time [s]"].data)), + } + ) + + # Define the cost to optimise + problem = pybop.FittingProblem(model, parameters, dataset) + costs = [] + for cost in cost_class: + if issubclass(cost, pybop.LogPosterior): + costs.append( + cost( + pybop.GaussianLogLikelihoodKnownSigma( + problem, sigma0=self.sigma0 + ), + ) + ) + elif issubclass(cost, pybop.BaseLikelihood): + costs.append(cost(problem, sigma0=self.sigma0)) + else: + costs.append(cost(problem)) + + return pybop.WeightedCost(*costs, weights=[0.1, 1, 0.5, 0.6]) + + @pytest.mark.integration + def test_fitting_costs(self, weighted_fitting_cost): + x0 = weighted_fitting_cost.parameters.initial_value() + optim = pybop.CuckooSearch( + cost=weighted_fitting_cost, + sigma0=0.03, + max_iterations=250, + max_unchanged_iterations=35, + ) + + initial_cost = optim.cost(optim.parameters.initial_value()) + results = optim.run() + + # Assertions + if not np.allclose(x0, self.ground_truth, atol=1e-5): + if optim.minimising: + assert initial_cost > results.final_cost + else: + assert initial_cost < results.final_cost + np.testing.assert_allclose(results.x, self.ground_truth, atol=1.5e-2) + + @pytest.fixture( + params=[ + ( + pybop.GravimetricEnergyDensity, + pybop.VolumetricEnergyDensity, + ) + ] + ) + def design_cost(self, request): + return request.param + + @pytest.fixture + def weighted_design_cost(self, model, design_cost): + initial_state = {"Initial SoC": 1.0} + parameters = pybop.Parameters( + pybop.Parameter( + "Positive electrode thickness [m]", + prior=pybop.Gaussian(5e-05, 5e-06), + bounds=[2e-06, 10e-05], + ), + pybop.Parameter( + "Negative electrode thickness [m]", + prior=pybop.Gaussian(5e-05, 5e-06), + bounds=[2e-06, 10e-05], + ), + ) + experiment = pybop.Experiment( + ["Discharge at 1C until 3.5 V (5 seconds period)"], + ) + + problem = pybop.DesignProblem( + model, parameters, experiment=experiment, initial_state=initial_state + ) + costs = [cost(problem) for cost in design_cost] + + return pybop.WeightedCost(*costs, weights=[1.0, 0.1]) + + @pytest.mark.integration + def test_design_costs(self, weighted_design_cost): + cost = weighted_design_cost + optim = pybop.CuckooSearch( + cost, + max_iterations=15, + allow_infeasible_solutions=False, + ) + initial_values = optim.parameters.initial_value() + initial_cost = optim.cost(initial_values) + results = optim.run() + + # Assertions + assert initial_cost < results.final_cost + for i, _ in enumerate(results.x): + assert results.x[i] > initial_values[i] + + def get_data(self, model, init_soc): + initial_state = {"Initial SoC": init_soc} + experiment = pybop.Experiment( + [ + ( + "Discharge at 0.5C for 3 minutes (4 second period)", + "Charge at 0.5C for 3 minutes (4 second period)", + ), + ] + ) + sim = model.predict(initial_state=initial_state, experiment=experiment) + return sim diff --git a/tests/plotting/test_plotly_manager.py b/tests/plotting/test_plotly_manager.py index 80bc3bb58..70cca7704 100644 --- a/tests/plotting/test_plotly_manager.py +++ b/tests/plotting/test_plotly_manager.py @@ -7,7 +7,7 @@ import pytest import pybop -from pybop import PlotlyManager +from pybop.plot import PlotlyManager # Find the Python executable python_executable = which("python") @@ -21,7 +21,7 @@ def plotly_installed(): # If Plotly is not installed initially, install it if not initially_installed: - subprocess.check_call([python_executable, "-m", "pip", "install", "plotly"]) + PlotlyManager.install_plotly() # Yield control back to the tests yield @@ -50,7 +50,7 @@ def uninstall_plotly_if_installed(): # If Plotly was uninstalled for the test, reinstall it afterwards if was_installed: - subprocess.check_call([python_executable, "-m", "pip", "install", "plotly"]) + PlotlyManager.install_plotly() # Reset the default renderer for tests plotly.io.renderers.default = None @@ -95,7 +95,7 @@ def test_cancel_installation(mocker, uninstall_plotly_if_installed): with pytest.raises(SystemExit) as pytest_wrapped_e: PlotlyManager().prompt_for_plotly_installation() - assert pytest_wrapped_e.type == SystemExit + assert pytest_wrapped_e.type is SystemExit assert pytest_wrapped_e.value.code == 1 assert not is_package_installed("plotly") @@ -121,7 +121,6 @@ def is_package_installed(package_name): def dataset(plotly_installed): # Construct and simulate model model = pybop.lithium_ion.SPM() - model.parameter_set = model.pybamm_model.default_parameter_values solution = model.predict(t_eval=np.linspace(0, 10, 100)) # Form dataset @@ -136,37 +135,37 @@ def dataset(plotly_installed): @pytest.mark.unit def test_standard_plot(dataset, plotly_installed): # Check the StandardPlot class - pybop.StandardPlot(dataset["Time [s]"], dataset["Terminal voltage [V]"]) + pybop.plot.StandardPlot(dataset["Time [s]"], dataset["Terminal voltage [V]"]) # Check the StandardSubplot class - pybop.StandardSubplot( + pybop.plot.StandardSubplot( dataset["Time [s]"], [dataset["Terminal voltage [V]"], dataset["Current [A]"]], num_rows=1, ) - pybop.StandardSubplot( + pybop.plot.StandardSubplot( dataset["Time [s]"], [dataset["Terminal voltage [V]"], dataset["Current [A]"]], num_cols=1, ) - # Check plotting numpy arrays, lists, and lists of numpy arrays - pybop.plot_trajectories(dataset["Time [s]"], dataset["Terminal voltage [V]"]) - pybop.plot_trajectories( + # Check plot numpy arrays, lists, and lists of numpy arrays + pybop.plot.trajectories(dataset["Time [s]"], dataset["Terminal voltage [V]"]) + pybop.plot.trajectories( dataset["Time [s]"].tolist(), dataset["Terminal voltage [V]"].tolist() ) - pybop.plot_trajectories( + pybop.plot.trajectories( [dataset["Time [s]"]], [dataset["Terminal voltage [V]"], dataset["Current [A]"]], ) - pybop.plot_trajectories( + pybop.plot.trajectories( [dataset["Time [s]"], dataset["Time [s]"]], [dataset["Terminal voltage [V]"], dataset["Current [A]"]], ) # Test incorrect dimensions with pytest.raises(ValueError): - pybop.plot_trajectories( + pybop.plot.trajectories( [dataset["Time [s]"], dataset["Current [A]"]], dataset["Terminal voltage [V]"], ) @@ -174,6 +173,6 @@ def test_standard_plot(dataset, plotly_installed): @pytest.mark.unit def test_plot_dataset(dataset, plotly_installed): - # Test plotting of a dataset - pybop.plot_dataset(dataset, signal=["Terminal voltage [V]"]) - pybop.plot_dataset(dataset, signal=["Terminal voltage [V]", "Current [A]"]) + # Test plot of a dataset + pybop.plot.dataset(dataset, signal=["Terminal voltage [V]"]) + pybop.plot.dataset(dataset, signal=["Terminal voltage [V]", "Current [A]"]) diff --git a/tests/unit/test_cost.py b/tests/unit/test_cost.py index 3c0d81514..e9ad01dcd 100644 --- a/tests/unit/test_cost.py +++ b/tests/unit/test_cost.py @@ -1,4 +1,7 @@ +from copy import copy + import numpy as np +import pybamm import pytest import pybop @@ -9,21 +12,30 @@ class TestCosts: Class for tests cost functions """ + # Define an invalid likelihood class for MAP tests + class InvalidLikelihood: + def __init__(self, problem, sigma0): + pass + @pytest.fixture - def model(self): - return pybop.lithium_ion.SPM() + def model(self, ground_truth): + solver = pybamm.IDAKLUSolver() + model = pybop.lithium_ion.SPM(solver=solver) + model.parameter_set["Negative electrode active material volume fraction"] = ( + ground_truth + ) + return model @pytest.fixture def ground_truth(self): return 0.52 @pytest.fixture - def parameters(self, ground_truth): + def parameters(self): return pybop.Parameter( "Negative electrode active material volume fraction", prior=pybop.Gaussian(0.5, 0.01), bounds=[0.375, 0.625], - initial_value=ground_truth, ) @pytest.fixture @@ -35,13 +47,7 @@ def experiment(self): ) @pytest.fixture - def dataset(self, model, experiment, ground_truth): - model.parameter_set = model.pybamm_model.default_parameter_values - model.parameter_set.update( - { - "Negative electrode active material volume fraction": ground_truth, - } - ) + def dataset(self, model, experiment): solution = model.predict(experiment=experiment) return pybop.Dataset( { @@ -59,29 +65,30 @@ def signal(self): def problem(self, model, parameters, dataset, signal, request): cut_off = request.param model.parameter_set.update({"Lower voltage cut-off [V]": cut_off}) - problem = pybop.FittingProblem( - model, parameters, dataset, signal=signal, init_soc=1.0 - ) - problem.dataset = dataset # add this to pass the pybop dataset to cost + problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) return problem @pytest.fixture( params=[ pybop.RootMeanSquaredError, pybop.SumSquaredError, + pybop.Minkowski, + pybop.SumofPower, pybop.ObserverCost, - pybop.MAP, + pybop.LogPosterior, ] ) def cost(self, problem, request): cls = request.param if cls in [pybop.SumSquaredError, pybop.RootMeanSquaredError]: return cls(problem) - elif cls in [pybop.MAP]: - return cls(problem, pybop.GaussianLogLikelihoodKnownSigma) - elif cls in [pybop.ObserverCost]: + elif cls is pybop.LogPosterior: + return cls(pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=0.002)) + elif cls in [pybop.Minkowski, pybop.SumofPower]: + return cls(problem, p=2) + elif cls is pybop.ObserverCost: inputs = problem.parameters.initial_value() - state = problem._model.reinit(inputs) + state = problem.model.reinit(inputs) n = len(state) sigma_diag = [0.0] * n sigma_diag[0] = 1e-4 @@ -91,11 +98,11 @@ def cost(self, problem, request): process_diag[1] = 1e-4 sigma0 = np.diag(sigma_diag) process = np.diag(process_diag) - dataset = problem.dataset + dataset = pybop.Dataset(data_dictionary=problem.dataset) return cls( pybop.UnscentedKalmanFilterObserver( problem.parameters, - problem._model, + problem.model, sigma0=sigma0, process=process, measure=1e-4, @@ -110,18 +117,6 @@ def test_base(self, problem): assert base_cost.problem == problem with pytest.raises(NotImplementedError): base_cost([0.5]) - with pytest.raises(NotImplementedError): - base_cost.evaluateS1([0.5]) - - @pytest.mark.unit - def test_MAP(self, problem): - # Incorrect likelihood - with pytest.raises(ValueError): - pybop.MAP(problem, pybop.SumSquaredError) - - # Incorrect construction of likelihood - with pytest.raises(ValueError): - pybop.MAP(problem, pybop.GaussianLogLikelihoodKnownSigma, sigma="string") @pytest.mark.unit def test_costs(self, cost): @@ -132,16 +127,12 @@ def test_costs(self, cost): higher_cost = cost([0.55]) lower_cost = cost([0.52]) assert higher_cost > lower_cost or ( - higher_cost == lower_cost and higher_cost == np.inf + higher_cost == lower_cost and not np.isfinite(higher_cost) ) # Test type of returned value assert np.isscalar(cost([0.5])) - if isinstance(cost, pybop.ObserverCost): - with pytest.raises(NotImplementedError): - cost.evaluateS1([0.5]) - # Test UserWarnings if isinstance(cost, (pybop.SumSquaredError, pybop.RootMeanSquaredError)): assert cost([0.5]) >= 0 @@ -149,37 +140,99 @@ def test_costs(self, cost): cost([1.1]) # Test option setting - cost.set_fail_gradient(1) + cost.set_fail_gradient(10) + assert cost._de == 10 - if isinstance(cost, pybop.SumSquaredError): - e, de = cost.evaluateS1([0.5]) + if not isinstance(cost, (pybop.ObserverCost, pybop.LogPosterior)): + e, de = cost([0.5], calculate_grad=True) assert np.isscalar(e) - assert type(de) == np.ndarray + assert isinstance(de, np.ndarray) # Test exception for non-numeric inputs - with pytest.raises(ValueError): - cost.evaluateS1(["StringInputShouldNotWork"]) + with pytest.raises( + TypeError, match="Inputs must be a dictionary or numeric." + ): + cost(["StringInputShouldNotWork"], calculate_grad=True) with pytest.warns(UserWarning) as record: - cost.evaluateS1([1.1]) + cost([1.1], calculate_grad=True) for i in range(len(record)): assert "Non-physical point encountered" in str(record[i].message) # Test infeasible locations - cost.problem._model.allow_infeasible_solutions = False + cost.problem.model.allow_infeasible_solutions = False assert cost([1.1]) == np.inf - assert cost.evaluateS1([1.1]) == (np.inf, cost._de) + assert cost([1.1], calculate_grad=True) == (np.inf, cost._de) assert cost([0.01]) == np.inf - assert cost.evaluateS1([0.01]) == (np.inf, cost._de) + assert cost([0.01], calculate_grad=True) == (np.inf, cost._de) # Test exception for non-numeric inputs - with pytest.raises(ValueError): + with pytest.raises(TypeError, match="Inputs must be a dictionary or numeric."): cost(["StringInputShouldNotWork"]) - # Test treatment of simulations that terminated early - # by variation of the cut-off voltage. + # Test ValueError for none dy w/ calculate_grad == True + if not isinstance(cost, pybop.ObserverCost): + with pytest.raises( + ValueError, + match="Forward model sensitivities need to be provided alongside `calculate_grad=True` for `cost.compute`.", + ): + cost.compute([1.1], dy=None, calculate_grad=True) + + @pytest.mark.unit + def test_minkowski(self, problem): + # Incorrect order + with pytest.raises(ValueError, match="The order of the Minkowski distance"): + pybop.Minkowski(problem, p=-1) + with pytest.raises( + ValueError, + match="For p = infinity, an implementation of the Chebyshev distance is required.", + ): + pybop.Minkowski(problem, p=np.inf) + + @pytest.mark.unit + def test_sumofpower(self, problem): + # Incorrect order + with pytest.raises( + ValueError, match="The order of 'p' must be greater than 0." + ): + pybop.SumofPower(problem, p=-1) + + with pytest.raises(ValueError, match="p = np.inf is not yet supported."): + pybop.SumofPower(problem, p=np.inf) + + @pytest.fixture + def design_problem(self, parameters, experiment, signal): + parameter_set = pybop.ParameterSet.pybamm("Chen2020") + parameter_set.update( + { + "Electrolyte density [kg.m-3]": pybamm.Parameter( + "Separator density [kg.m-3]" + ), + "Negative electrode active material density [kg.m-3]": pybamm.Parameter( + "Negative electrode density [kg.m-3]" + ), + "Negative electrode carbon-binder density [kg.m-3]": pybamm.Parameter( + "Negative electrode density [kg.m-3]" + ), + "Positive electrode active material density [kg.m-3]": pybamm.Parameter( + "Positive electrode density [kg.m-3]" + ), + "Positive electrode carbon-binder density [kg.m-3]": pybamm.Parameter( + "Positive electrode density [kg.m-3]" + ), + }, + check_already_exists=False, + ) + model = pybop.lithium_ion.SPM(parameter_set=parameter_set) + return pybop.DesignProblem( + model, + parameters, + experiment, + signal=signal, + initial_state={"Initial SoC": 0.5}, + ) @pytest.mark.parametrize( "cost_class", @@ -187,24 +240,14 @@ def test_costs(self, cost): pybop.DesignCost, pybop.GravimetricEnergyDensity, pybop.VolumetricEnergyDensity, + pybop.GravimetricPowerDensity, + pybop.VolumetricPowerDensity, ], ) @pytest.mark.unit - def test_design_costs( - self, - cost_class, - model, - parameters, - experiment, - signal, - ): - # Construct Problem - problem = pybop.DesignProblem( - model, parameters, experiment, signal=signal, init_soc=0.5 - ) - + def test_design_costs(self, cost_class, design_problem): # Construct Cost - cost = cost_class(problem) + cost = cost_class(design_problem) if cost_class in [pybop.DesignCost]: with pytest.raises(NotImplementedError): @@ -220,13 +263,166 @@ def test_design_costs( assert cost([-0.1]) == -np.inf # Should not be a viable design # Test infeasible locations - cost.problem._model.allow_infeasible_solutions = False + cost.problem.model.allow_infeasible_solutions = False assert cost([1.1]) == -np.inf # Test exception for non-numeric inputs - with pytest.raises(ValueError): + with pytest.raises( + TypeError, match="Inputs must be a dictionary or numeric." + ): cost(["StringInputShouldNotWork"]) # Compute after updating nominal capacity - cost = cost_class(problem, update_capacity=True) + design_problem.update_capacity = True + cost = cost_class(design_problem) cost([0.4]) + + @pytest.fixture + def noisy_problem(self, ground_truth, parameters, experiment): + model = pybop.lithium_ion.SPM() + model.parameter_set["Negative electrode active material volume fraction"] = ( + ground_truth + ) + sol = model.predict(experiment=experiment) + noisy_dataset = pybop.Dataset( + { + "Time [s]": sol["Time [s]"].data, + "Current function [A]": sol["Current [A]"].data, + "Voltage [V]": sol["Voltage [V]"].data + + np.random.normal(0, 0.02, len(sol["Time [s]"].data)), + } + ) + return pybop.FittingProblem(model, parameters, noisy_dataset) + + @pytest.mark.unit + def test_weighted_fitting_cost(self, noisy_problem): + problem = noisy_problem + cost1 = pybop.SumSquaredError(problem) + cost2 = pybop.RootMeanSquaredError(problem) + + # Test with and without weights + weighted_cost = pybop.WeightedCost(cost1, cost2) + np.testing.assert_array_equal(weighted_cost.weights, np.ones(2)) + weighted_cost = pybop.WeightedCost(cost1, cost2, weights=[1, 1]) + np.testing.assert_array_equal(weighted_cost.weights, np.ones(2)) + weighted_cost = pybop.WeightedCost(cost1, cost2, weights=np.array([1, 1])) + np.testing.assert_array_equal(weighted_cost.weights, np.ones(2)) + with pytest.raises( + TypeError, + match="All costs must be instances of BaseCost.", + ): + pybop.WeightedCost(cost1.problem) + with pytest.raises( + ValueError, + match="Weights must be numeric values.", + ): + pybop.WeightedCost(cost1, cost2, weights="Invalid string") + with pytest.raises( + ValueError, + match="Number of weights must match number of costs.", + ): + pybop.WeightedCost(cost1, cost2, weights=[1]) + + # Test with identical problems + weight = 100 + weighted_cost_2 = pybop.WeightedCost(cost1, cost2, weights=[1, weight]) + assert weighted_cost_2.has_identical_problems is True + assert weighted_cost_2.has_separable_problem is False + assert weighted_cost_2.problem is problem + assert weighted_cost_2([0.5]) >= 0 + np.testing.assert_allclose( + weighted_cost_2([0.6]), + cost1([0.6]) + weight * cost2([0.6]), + atol=1e-5, + ) + + # Test with different problems + cost3 = pybop.RootMeanSquaredError(copy(problem)) + weighted_cost_3 = pybop.WeightedCost(cost1, cost3, weights=[1, weight]) + assert weighted_cost_3.has_identical_problems is False + assert weighted_cost_3.has_separable_problem is False + assert weighted_cost_3.problem is None + assert weighted_cost_3([0.5]) >= 0 + np.testing.assert_allclose( + weighted_cost_3([0.6]), + cost1([0.6]) + weight * cost3([0.6]), + atol=1e-5, + ) + + errors_2, sensitivities_2 = weighted_cost_2([0.5], calculate_grad=True) + errors_3, sensitivities_3 = weighted_cost_3([0.5], calculate_grad=True) + np.testing.assert_allclose(errors_2, errors_3, atol=1e-5) + np.testing.assert_allclose(sensitivities_2, sensitivities_3, atol=1e-5) + + # Test LogPosterior explicitly + cost4 = pybop.LogPosterior(pybop.GaussianLogLikelihood(problem)) + weighted_cost_4 = pybop.WeightedCost(cost1, cost4, weights=[1, -1 / weight]) + assert weighted_cost_4.has_identical_problems is True + assert weighted_cost_4.has_separable_problem is False + sigma = 0.01 + assert np.isfinite(cost4.parameters["Sigma for output 1"].prior.logpdf(sigma)) + assert np.isfinite(weighted_cost_4([0.5, sigma])) + np.testing.assert_allclose( + weighted_cost_4([0.6, sigma]), + cost1([0.6, sigma]) - 1 / weight * cost4([0.6, sigma]), + atol=1e-5, + ) + + @pytest.mark.unit + def test_weighted_design_cost(self, design_problem): + cost1 = pybop.GravimetricEnergyDensity(design_problem) + cost2 = pybop.VolumetricEnergyDensity(design_problem) + + # Test DesignCosts with identical problems + weighted_cost = pybop.WeightedCost(cost1, cost2) + assert weighted_cost.has_identical_problems is True + assert weighted_cost.has_separable_problem is False + assert weighted_cost.problem is design_problem + assert weighted_cost([0.5]) >= 0 + np.testing.assert_allclose( + weighted_cost([0.6]), + cost1([0.6]) + cost2([0.6]), + atol=1e-5, + ) + + # Test DesignCosts with different problems + cost3 = pybop.VolumetricEnergyDensity(copy(design_problem)) + weighted_cost = pybop.WeightedCost(cost1, cost3) + assert weighted_cost.has_identical_problems is False + assert weighted_cost.has_separable_problem is False + for i, _ in enumerate(weighted_cost.costs): + assert isinstance(weighted_cost.costs[i].problem, pybop.DesignProblem) + + assert weighted_cost([0.5]) >= 0 + np.testing.assert_allclose( + weighted_cost([0.6]), + cost1([0.6]) + cost2([0.6]), + atol=1e-5, + ) + + @pytest.mark.unit + def test_weighted_design_cost_with_update_capacity(self, design_problem): + design_problem.update_capacity = True + cost1 = pybop.GravimetricEnergyDensity(design_problem) + cost2 = pybop.VolumetricEnergyDensity(design_problem) + weighted_cost = pybop.WeightedCost(cost1, cost2, weights=[1, 1]) + + assert weighted_cost.has_identical_problems is True + assert weighted_cost.has_separable_problem is False + assert weighted_cost.problem is design_problem + assert weighted_cost([0.5]) >= 0 + np.testing.assert_allclose( + weighted_cost([0.6]), + cost1([0.6]) + cost2([0.6]), + atol=1e-5, + ) + + @pytest.mark.unit + def test_mixed_problem_classes(self, problem, design_problem): + cost1 = pybop.SumSquaredError(problem) + cost2 = pybop.GravimetricEnergyDensity(design_problem) + with pytest.raises( + TypeError, + match="All problems must be of the same class type.", + ): + pybop.WeightedCost(cost1, cost2) diff --git a/tests/unit/test_cost_transformations.py b/tests/unit/test_cost_transformations.py new file mode 100644 index 000000000..3d069528c --- /dev/null +++ b/tests/unit/test_cost_transformations.py @@ -0,0 +1,138 @@ +import numpy as np +import pytest + +import pybop + + +class TestCostsTransformations: + """ + Class for unit testing cost functions with transformations applied. + """ + + @pytest.fixture(autouse=True) + def setup(self): + self.x1 = [0.5, 1] + self.x2 = [(self.x1[0] + 1) * -2.5, np.log(1)] + + @pytest.fixture + def model(self): + return pybop.lithium_ion.SPM() + + @pytest.fixture + def parameters(self): + parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.5, 0.01), + bounds=[0.375, 0.625], + transformation=pybop.ScaledTransformation( + coefficient=-2.5, intercept=1 + ), + ), + pybop.Parameter( + "Positive electrode Bruggeman coefficient (electrode)", + prior=pybop.Gaussian(1, 0.1), + transformation=pybop.LogTransformation(), + ), + ) + return parameters + + @pytest.fixture + def experiment(self): + return pybop.Experiment( + [ + ("Discharge at 1C for 1 minutes (6 second period)"), + ] + ) + + @pytest.fixture + def dataset(self, model, experiment): + solution = model.predict(experiment=experiment) + return pybop.Dataset( + { + "Time [s]": solution["Time [s]"].data, + "Current function [A]": solution["Current [A]"].data, + "Voltage [V]": solution["Terminal voltage [V]"].data, + } + ) + + @pytest.fixture + def problem(self, model, parameters, dataset, request): + problem = pybop.FittingProblem(model, parameters, dataset) + return problem + + @pytest.fixture( + params=[ + pybop.RootMeanSquaredError, + pybop.SumSquaredError, + pybop.Minkowski, + pybop.SumofPower, + pybop.ObserverCost, + pybop.LogPosterior, + pybop.GaussianLogLikelihood, + pybop.GaussianLogLikelihoodKnownSigma, + ] + ) + def cost(self, problem, request): + cls = request.param + if cls in [pybop.SumSquaredError, pybop.RootMeanSquaredError]: + return cls(problem) + elif cls is pybop.LogPosterior: + return cls(pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=0.002)) + elif cls is pybop.GaussianLogLikelihoodKnownSigma: + return pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=0.002) + elif cls is pybop.ObserverCost: + inputs = problem.parameters.initial_value() + state = problem.model.reinit(inputs) + n = len(state) + sigma_diag = [0.0] * n + sigma_diag[0] = 1e-4 + sigma_diag[1] = 1e-4 + process_diag = [0.0] * n + process_diag[0] = 1e-4 + process_diag[1] = 1e-4 + sigma0 = np.diag(sigma_diag) + process = np.diag(process_diag) + dataset = pybop.Dataset(data_dictionary=problem.dataset) + return cls( + pybop.UnscentedKalmanFilterObserver( + problem.parameters, + problem.model, + sigma0=sigma0, + process=process, + measure=1e-4, + dataset=dataset, + signal=problem.signal, + ), + ) + else: + return cls(problem) + + @pytest.mark.unit + def test_cost_transformations(self, cost): + if isinstance(cost, pybop.GaussianLogLikelihood): + self.x1.append(0.002) + self.x2.append(0.002) + + # Asserts + non_transformed_cost = cost(self.x1) + transformed_cost = cost(self.x2, apply_transform=True) + np.testing.assert_allclose(non_transformed_cost, transformed_cost) + + @pytest.mark.unit + def test_cost_gradient_transformed(self, cost): + # Gradient transformations are not implmented on ObserverCost + if isinstance(cost, pybop.ObserverCost): + return + + if isinstance(cost, pybop.GaussianLogLikelihood): + self.x1.append(0.002) + self.x2.append(0.002) + # Asserts + non_transformed_cost, grad = cost(self.x1, calculate_grad=True) + transformed_cost, transformed_gradient = cost( + self.x2, calculate_grad=True, apply_transform=True + ) + np.testing.assert_allclose(transformed_cost, non_transformed_cost) + with pytest.raises(AssertionError): + np.testing.assert_allclose(transformed_gradient, grad) diff --git a/tests/unit/test_dataset.py b/tests/unit/test_dataset.py index 9c4eac13d..bb881429c 100644 --- a/tests/unit/test_dataset.py +++ b/tests/unit/test_dataset.py @@ -13,14 +13,13 @@ class TestDataset: def test_dataset(self): # Construct and simulate model model = pybop.lithium_ion.SPM() - model.parameter_set = model.pybamm_model.default_parameter_values solution = model.predict(t_eval=np.linspace(0, 10, 100)) # Form dataset data_dictionary = { "Time [s]": solution["Time [s]"].data, "Current [A]": solution["Current [A]"].data, - "Terminal voltage [V]": solution["Terminal voltage [V]"].data, + "Voltage [V]": solution["Voltage [V]"].data, } dataset = pybop.Dataset(data_dictionary) @@ -55,4 +54,15 @@ def test_dataset(self): dataset["Time"] # Test conversion of single signal to list - assert dataset.check(signal="Terminal voltage [V]") + assert dataset.check() + + # Form frequency dataset + data_dictionary = { + "Frequency [Hz]": np.linspace(-10, 0, 10), + "Current [A]": np.zeros(10), + "Impedance": np.zeros(10), + } + frequency_dataset = pybop.Dataset(data_dictionary) + + with pytest.raises(ValueError, match="Frequencies cannot be negative."): + frequency_dataset.check(domain="Frequency [Hz]", signal="Impedance") diff --git a/tests/unit/test_experiment.py b/tests/unit/test_experiment.py index 6d18ef509..71355ff84 100644 --- a/tests/unit/test_experiment.py +++ b/tests/unit/test_experiment.py @@ -18,9 +18,9 @@ def test_experiment(self): pybop_experiment = pybop.Experiment(protocol) pybamm_experiment = pybamm.Experiment(protocol) - assert [ - step.to_dict() for step in pybop_experiment.operating_conditions_steps - ] == [step.to_dict() for step in pybamm_experiment.operating_conditions_steps] + assert [step.to_dict() for step in pybop_experiment.steps] == [ + step.to_dict() for step in pybamm_experiment.steps + ] assert pybop_experiment.cycle_lengths == pybamm_experiment.cycle_lengths diff --git a/tests/unit/test_likelihoods.py b/tests/unit/test_likelihoods.py index 41ee36673..8791c2c06 100644 --- a/tests/unit/test_likelihoods.py +++ b/tests/unit/test_likelihoods.py @@ -10,38 +10,37 @@ class TestLikelihoods: """ @pytest.fixture - def model(self): - return pybop.lithium_ion.SPM() + def model(self, ground_truth): + model = pybop.lithium_ion.SPM() + model.parameter_set.update( + { + "Negative electrode active material volume fraction": ground_truth, + } + ) + return model @pytest.fixture def ground_truth(self): return 0.52 @pytest.fixture - def parameters(self, ground_truth): + def parameters(self): return pybop.Parameter( "Negative electrode active material volume fraction", prior=pybop.Gaussian(0.5, 0.01), bounds=[0.375, 0.625], - initial_value=ground_truth, ) @pytest.fixture def experiment(self): return pybop.Experiment( [ - ("Discharge at 1C for 10 minutes (20 second period)"), + ("Discharge at 1C for 1 minutes (5 second period)"), ] ) @pytest.fixture - def dataset(self, model, experiment, ground_truth): - model.parameter_set = model.pybamm_model.default_parameter_values - model.parameter_set.update( - { - "Negative electrode active material volume fraction": ground_truth, - } - ) + def dataset(self, model, experiment): solution = model.predict(experiment=experiment) return pybop.Dataset( { @@ -54,16 +53,12 @@ def dataset(self, model, experiment, ground_truth): @pytest.fixture def one_signal_problem(self, model, parameters, dataset): signal = ["Voltage [V]"] - return pybop.FittingProblem( - model, parameters, dataset, signal=signal, init_soc=1.0 - ) + return pybop.FittingProblem(model, parameters, dataset, signal=signal) @pytest.fixture def two_signal_problem(self, model, parameters, dataset): signal = ["Time [s]", "Voltage [V]"] - return pybop.FittingProblem( - model, parameters, dataset, signal=signal, init_soc=1.0 - ) + return pybop.FittingProblem(model, parameters, dataset, signal=signal) @pytest.mark.parametrize( "problem_name, n_outputs", @@ -75,10 +70,9 @@ def test_base_likelihood_init(self, problem_name, n_outputs, request): likelihood = pybop.BaseLikelihood(problem) assert likelihood.problem == problem assert likelihood.n_outputs == n_outputs - assert likelihood.n_time_data == problem.n_time_data - assert likelihood.x0 == problem.x0 + assert likelihood.n_data == problem.n_data assert likelihood.n_parameters == 1 - assert np.array_equal(likelihood._target, problem._target) + assert np.array_equal(likelihood.target, problem.target) @pytest.mark.unit def test_base_likelihood_call_raises_not_implemented_error( @@ -86,24 +80,25 @@ def test_base_likelihood_call_raises_not_implemented_error( ): likelihood = pybop.BaseLikelihood(one_signal_problem) with pytest.raises(NotImplementedError): - likelihood(np.array([0.5, 0.5])) + likelihood(np.array([0.5])) @pytest.mark.unit - def test_set_get_sigma(self, one_signal_problem): - likelihood = pybop.GaussianLogLikelihoodKnownSigma(one_signal_problem, 0.1) - likelihood.set_sigma(np.array([0.3])) - assert np.array_equal(likelihood.get_sigma(), np.array([0.3])) - + def test_likelihood_check_sigma0(self, one_signal_problem): with pytest.raises( ValueError, - match="The GaussianLogLikelihoodKnownSigma cost requires sigma to be " - + "either a scalar value or an array with one entry per dimension.", + match="Sigma0 must be positive", ): - pybop.GaussianLogLikelihoodKnownSigma(one_signal_problem, sigma=None) + pybop.GaussianLogLikelihoodKnownSigma(one_signal_problem, sigma0=None) likelihood = pybop.GaussianLogLikelihoodKnownSigma(one_signal_problem, 0.1) - with pytest.raises(ValueError): - likelihood.set_sigma(np.array([-0.2])) + sigma = likelihood.check_sigma0(0.2) + assert sigma == np.array(0.2) + + with pytest.raises( + ValueError, + match=r"sigma0 must be either a scalar value", + ): + pybop.GaussianLogLikelihoodKnownSigma(one_signal_problem, sigma0=[0.2, 0.3]) @pytest.mark.unit def test_base_likelihood_n_parameters_property(self, one_signal_problem): @@ -117,33 +112,66 @@ def test_base_likelihood_n_parameters_property(self, one_signal_problem): def test_gaussian_log_likelihood_known_sigma(self, problem_name, request): problem = request.getfixturevalue(problem_name) likelihood = pybop.GaussianLogLikelihoodKnownSigma( - problem, sigma=np.array([1.0]) + problem, sigma0=np.array([1.0]) ) result = likelihood(np.array([0.5])) - grad_result, grad_likelihood = likelihood.evaluateS1(np.array([0.5])) + grad_result, grad_likelihood = likelihood(np.array([0.5]), calculate_grad=True) assert isinstance(result, float) np.testing.assert_allclose(result, grad_result, atol=1e-5) - assert np.all(grad_likelihood <= 0) + # Since 0.5 < ground_truth, the likelihood should be increasing + assert grad_likelihood >= 0 @pytest.mark.unit def test_gaussian_log_likelihood(self, one_signal_problem): likelihood = pybop.GaussianLogLikelihood(one_signal_problem) - result = likelihood(np.array([0.5, 0.5])) - grad_result, grad_likelihood = likelihood.evaluateS1(np.array([0.5, 0.5])) + result = likelihood(np.array([0.8, 0.2])) + grad_result, grad_likelihood = likelihood( + np.array([0.8, 0.2]), calculate_grad=True + ) assert isinstance(result, float) np.testing.assert_allclose(result, grad_result, atol=1e-5) - assert np.all(grad_likelihood <= 0) + # Since 0.8 > ground_truth, the likelihood should be decreasing + assert grad_likelihood[0] <= 0 + # Since sigma < 0.5, the likelihood should be decreasing + assert grad_likelihood[1] <= 0 + + # Test construction with sigma as a Parameter + sigma = pybop.Parameter("sigma", prior=pybop.Uniform(0.4, 0.6)) + likelihood = pybop.GaussianLogLikelihood(one_signal_problem, sigma0=sigma) + + # Test invalid sigma + with pytest.raises( + TypeError, + match=r"Expected sigma0 to contain Parameter objects or numeric values.", + ): + likelihood = pybop.GaussianLogLikelihood( + one_signal_problem, sigma0="Invalid string" + ) + + # Test transformation fail dimensions + cost_fail, grad_fail = likelihood( + [0.01, 0.01], calculate_grad=True, apply_transform=True + ) + assert not np.isfinite(cost_fail) + assert grad_fail.shape == (2,) + + @pytest.mark.unit + def test_gaussian_log_likelihood_dsigma_scale(self, one_signal_problem): + likelihood = pybop.GaussianLogLikelihood(one_signal_problem, dsigma_scale=0.05) + assert likelihood.dsigma_scale == 0.05 + likelihood.dsigma_scale = 1e3 + assert likelihood.dsigma_scale == 1e3 + + # Test incorrect sigma scale + with pytest.raises(ValueError): + likelihood.dsigma_scale = -1e3 @pytest.mark.unit def test_gaussian_log_likelihood_returns_negative_inf(self, one_signal_problem): likelihood = pybop.GaussianLogLikelihood(one_signal_problem) - assert likelihood(np.array([-0.5, -0.5])) == -np.inf # negative sigma value - assert ( - likelihood.evaluateS1(np.array([-0.5, -0.5]))[0] == -np.inf - ) # negative sigma value assert likelihood(np.array([0.01, 0.1])) == -np.inf # parameter value too small assert ( - likelihood.evaluateS1(np.array([0.01, 0.1]))[0] == -np.inf + likelihood(np.array([0.01, 0.1]), calculate_grad=True)[0] == -np.inf ) # parameter value too small @pytest.mark.unit @@ -151,9 +179,36 @@ def test_gaussian_log_likelihood_known_sigma_returns_negative_inf( self, one_signal_problem ): likelihood = pybop.GaussianLogLikelihoodKnownSigma( - one_signal_problem, sigma=np.array([0.2]) + one_signal_problem, sigma0=np.array([0.2]) ) assert likelihood(np.array([0.01])) == -np.inf # parameter value too small assert ( - likelihood.evaluateS1(np.array([0.01]))[0] == -np.inf + likelihood(np.array([0.01]), calculate_grad=True)[0] == -np.inf + ) # parameter value too small + + @pytest.mark.unit + def test_scaled_log_likelihood(self, one_signal_problem): + likelihood = pybop.GaussianLogLikelihoodKnownSigma( + one_signal_problem, sigma0=0.02 + ) + scaled_likelihood = pybop.ScaledLogLikelihood(likelihood) + + assert scaled_likelihood._log_likelihood == likelihood + assert ( + scaled_likelihood(np.array([0.01])) == -np.inf ) # parameter value too small + assert ( + likelihood(np.array([0.01]), calculate_grad=True)[0] == -np.inf + ) # parameter value too small + + # Test scaling + scaled_values = likelihood([0.6]) / likelihood.n_data + np.testing.assert_allclose(scaled_values, scaled_likelihood([0.6])) + + # Test w/ gradient + values, grad = tuple( + i / likelihood.n_data for i in likelihood([0.6], calculate_grad=True) + ) + np.testing.assert_allclose( + grad, scaled_likelihood([0.6], calculate_grad=True)[1] + ) diff --git a/tests/unit/test_models.py b/tests/unit/test_models.py index 9c11b4c6b..0f789dbc5 100644 --- a/tests/unit/test_models.py +++ b/tests/unit/test_models.py @@ -1,3 +1,6 @@ +import sys +from io import StringIO + import numpy as np import pybamm import pytest @@ -57,18 +60,6 @@ def model(self, request): model = request.param return model.copy() - @pytest.mark.unit - def test_simulate_without_build_model(self, model): - with pytest.raises( - ValueError, match="Model must be built before calling simulate" - ): - model.simulate(None, None) - - with pytest.raises( - ValueError, match="Model must be built before calling simulate" - ): - model.simulateS1(None, None) - @pytest.mark.unit def test_non_default_solver(self): solver = pybamm.CasadiSolver( @@ -83,11 +74,20 @@ def test_non_default_solver(self): @pytest.mark.unit def test_predict_without_pybamm(self, model): - model._unprocessed_model = None + model.pybamm_model = None - with pytest.raises(ValueError): + with pytest.raises( + ValueError, + match="The predict method currently only supports PyBaMM models.", + ): model.predict(None, None) + # Test new_copy() without pybamm_model + if not isinstance(model, pybop.lithium_ion.MSMR): + new_model = model.new_copy() + assert new_model.pybamm_model is not None + assert new_model.parameter_set is not None + @pytest.mark.unit def test_predict_with_inputs(self, model): # Define inputs @@ -113,6 +113,12 @@ def test_predict_with_inputs(self, model): res = model.predict(t_eval=t_eval, inputs=inputs) assert len(res["Voltage [V]"].data) == 100 + with pytest.raises( + ValueError, + match="The predict method requires either an experiment or t_eval to be specified.", + ): + model.predict(inputs=inputs) + @pytest.mark.unit def test_predict_without_allow_infeasible_solutions(self, model): if isinstance(model, (pybop.lithium_ion.SPM, pybop.lithium_ion.SPMe)): @@ -123,17 +129,27 @@ def test_predict_without_allow_infeasible_solutions(self, model): "Positive electrode active material volume fraction": 0.9, } - res = model.predict(t_eval=t_eval, inputs=inputs) - assert np.isinf(res).any() + with pytest.raises( + ValueError, match="These parameter values are infeasible." + ): + model.predict(t_eval=t_eval, inputs=inputs) @pytest.mark.unit def test_build(self, model): - model.build() - assert model.built_model is not None + if isinstance(model, pybop.lithium_ion.SPMe): + model.build(initial_state={"Initial SoC": 1.0}) - # Test that the model can be built again - model.build() - assert model.built_model is not None + # Test attributes with init_soc + assert model.built_model is not None + assert model.disc is not None + assert model.built_initial_soc is not None + else: + model.build() + assert model.built_model is not None + + # Test that the model can be built again + model.build() + assert model.built_model is not None @pytest.mark.unit def test_rebuild(self, model): @@ -141,8 +157,11 @@ def test_rebuild(self, model): initial_built_model = model._built_model assert model._built_model is not None + model.set_parameters() + assert model.model_with_set_params is not None + # Test that the model can be built again - model.rebuild() + model.build() rebuilt_model = model._built_model assert rebuilt_model is not None @@ -171,18 +190,18 @@ def test_parameter_set_definition(self): # Test initilisation with different types of parameter set param_dict = {"Nominal cell capacity [A.h]": 5} model = pybop.BaseModel(parameter_set=None) - assert model._parameter_set is None + assert model.parameter_set is None model = pybop.BaseModel(parameter_set=param_dict) parameter_set = pybamm.ParameterValues(param_dict) - assert model._parameter_set == parameter_set + assert model.parameter_set == parameter_set model = pybop.BaseModel(parameter_set=parameter_set) - assert model._parameter_set == parameter_set + assert model.parameter_set == parameter_set pybop_parameter_set = pybop.ParameterSet(params_dict=param_dict) model = pybop.BaseModel(parameter_set=pybop_parameter_set) - assert model._parameter_set == parameter_set + assert model.parameter_set == parameter_set @pytest.mark.unit def test_rebuild_geometric_parameters(self): @@ -211,17 +230,23 @@ def test_rebuild_geometric_parameters(self): t_eval = np.linspace(0, 100, 100) out_init = initial_built_model.predict(t_eval=t_eval) + with pytest.raises( + ValueError, + match="Cannot use sensitivities for parameters which require a model rebuild", + ): + model.simulateS1(t_eval=t_eval, inputs=parameters.as_dict()) + # Test that the model can be rebuilt with different geometric parameters parameters["Positive particle radius [m]"].update(5e-06) parameters["Negative electrode thickness [m]"].update(45e-06) - model.rebuild(parameters=parameters) + model.build(parameters=parameters) rebuilt_model = model assert rebuilt_model._built_model is not None # Test model geometry assert ( - rebuilt_model._mesh["negative electrode"].nodes[1] - != initial_built_model._mesh["negative electrode"].nodes[1] + rebuilt_model.mesh["negative electrode"].nodes[1] + != initial_built_model.mesh["negative electrode"].nodes[1] ) assert ( rebuilt_model.geometry["negative electrode"]["x_n"]["max"] @@ -234,8 +259,8 @@ def test_rebuild_geometric_parameters(self): ) assert ( - rebuilt_model._mesh["positive particle"].nodes[1] - != initial_built_model._mesh["positive particle"].nodes[1] + rebuilt_model.mesh["positive particle"].nodes[1] + != initial_built_model.mesh["positive particle"].nodes[1] ) # Compare model results @@ -252,11 +277,17 @@ def test_reinit(self): k = 0.1 y0 = 1 model = ExponentialDecay(pybamm.ParameterValues({"k": k, "y0": y0})) + + with pytest.raises( + ValueError, match="Model must be built before calling get_state" + ): + model.get_state({"k": k, "y0": y0}, 0, np.array([0])) + model.build() state = model.reinit(inputs={}) np.testing.assert_array_almost_equal(state.as_ndarray(), np.array([[y0]])) - model.parameters = pybop.Parameters(pybop.Parameter("y0")) + model.classify_parameters(pybop.Parameters(pybop.Parameter("y0"))) state = model.reinit(inputs=[1]) np.testing.assert_array_almost_equal(state.as_ndarray(), np.array([[y0]])) @@ -277,11 +308,33 @@ def test_simulate(self): t_eval = np.linspace(0, 10, 100) expected = y0 * np.exp(-k * t_eval) solved = model.simulate(inputs, t_eval) - np.testing.assert_array_almost_equal(solved["y_0"], expected, decimal=5) + np.testing.assert_array_almost_equal(solved["y_0"].data, expected, decimal=5) with pytest.raises(ValueError): ExponentialDecay(n_states=-1) + @pytest.mark.unit + def test_simulateEIS(self): + # Test EIS on SPM + model = pybop.lithium_ion.SPM(eis=True) + + # Construct frequencies and solve + f_eval = np.linspace(100, 1000, 5) + sol = model.simulateEIS(inputs={}, f_eval=f_eval) + assert np.isfinite(sol["Impedance"]).all() + + # Test infeasible parameter values + model.allow_infeasible_solutions = False + inputs = { + "Negative electrode active material volume fraction": 0.9, + "Positive electrode active material volume fraction": 0.9, + } + # Rebuild model + model.build(inputs=inputs) + + with pytest.raises(ValueError, match="These parameter values are infeasible."): + model.simulateEIS(f_eval=f_eval, inputs=inputs) + @pytest.mark.unit def test_basemodel(self): base = pybop.BaseModel() @@ -296,10 +349,13 @@ def test_basemodel(self): with pytest.raises(NotImplementedError): base.approximate_capacity(x) + base.classify_parameters(parameters=None) + assert isinstance(base.parameters, pybop.Parameters) + @pytest.mark.unit def test_thevenin_model(self): parameter_set = pybop.ParameterSet( - json_path="examples/scripts/parameters/initial_ecm_parameters.json" + json_path="examples/parameters/initial_ecm_parameters.json" ) parameter_set.import_parameters() assert parameter_set["Open-circuit voltage [V]"] == "default" @@ -311,15 +367,79 @@ def test_thevenin_model(self): == model.pybamm_model.default_parameter_values["Open-circuit voltage [V]"] ) + model.predict(initial_state={"Initial SoC": 0.5}, t_eval=np.arange(0, 10, 5)) + assert model.parameter_set["Initial SoC"] == 0.5 + + model.set_initial_state({"Initial SoC": parameter_set["Initial SoC"] / 2}) + assert model.parameter_set["Initial SoC"] == parameter_set["Initial SoC"] / 2 + model.set_initial_state( + { + "Initial open-circuit voltage [V]": parameter_set[ + "Lower voltage cut-off [V]" + ] + } + ) + np.testing.assert_allclose(model.parameter_set["Initial SoC"], 0.0, atol=1e-2) + model.set_initial_state( + { + "Initial open-circuit voltage [V]": parameter_set[ + "Upper voltage cut-off [V]" + ] + } + ) + np.testing.assert_allclose(model.parameter_set["Initial SoC"], 1.0, atol=1e-2) + + with pytest.raises(ValueError, match="outside the voltage limits"): + model.set_initial_state({"Initial open-circuit voltage [V]": -1.0}) + with pytest.raises(ValueError, match="Initial SOC should be between 0 and 1"): + model.set_initial_state({"Initial SoC": -1.0}) + with pytest.raises( + ValueError, + match="Initial value must be a float between 0 and 1, or a string ending in 'V'", + ): + model.set_initial_state({"Initial SoC": "invalid string"}) + @pytest.mark.unit def test_check_params(self): base = pybop.BaseModel() assert base.check_params() assert base.check_params(inputs={"a": 1}) assert base.check_params(inputs=[1]) - with pytest.raises(ValueError, match="Expecting inputs in the form of"): + with pytest.raises(TypeError, match="Inputs must be a dictionary or numeric."): base.check_params(inputs=["unexpected_string"]) + @pytest.mark.unit + def test_base_ecircuit_model(self): + def check_params(inputs: dict, allow_infeasible_solutions: bool): + return True if inputs is None else inputs["a"] < 2 + + base_ecircuit_model = pybop.empirical.ECircuitModel( + pybamm_model=pybamm.equivalent_circuit.Thevenin, + check_params=check_params, + ) + assert base_ecircuit_model.check_params({"a": 1}) + + base_ecircuit_model = pybop.empirical.ECircuitModel( + pybamm_model=pybamm.equivalent_circuit.Thevenin, + ) + assert base_ecircuit_model.check_params() + + @pytest.mark.unit + def test_userdefined_check_params(self): + def check_params(inputs: dict, allow_infeasible_solutions: bool): + return True if inputs is None else inputs["a"] < 2 + + for model in [ + pybop.BaseModel(check_params=check_params), + pybop.empirical.Thevenin(check_params=check_params), + ]: + assert model.check_params(inputs={"a": 1}) + assert not model.check_params(inputs={"a": 2}) + with pytest.raises( + TypeError, match="Inputs must be a dictionary or numeric." + ): + model.check_params(inputs=["unexpected_string"]) + @pytest.mark.unit def test_non_converged_solution(self): model = pybop.lithium_ion.DFN() @@ -340,11 +460,111 @@ def test_non_converged_solution(self): "Voltage [V]": np.zeros(100), } ) - problem = pybop.FittingProblem(model, parameters=parameters, dataset=dataset) - res = problem.evaluate([-0.2, -0.2]) - _, res_grad = problem.evaluateS1([-0.2, -0.2]) + + # Simulate the DFN with active material values of 0. + # The solution elements will not change as the solver will not converge. + output = problem.evaluate([0, 0]) + output_S1, _ = problem.evaluateS1([0, 0]) for key in problem.signal: - assert np.isinf(res.get(key, [])).any() - assert np.isinf(res_grad).any() + assert np.allclose(output.get(key, [])[0], output.get(key, [])) + assert np.allclose(output_S1.get(key, [])[0], output_S1.get(key, [])) + + @pytest.mark.unit + def test_set_initial_state(self): + t_eval = np.linspace(0, 10, 100) + + model = pybop.lithium_ion.SPM() + model.build(initial_state={"Initial SoC": 0.7}) + values_1 = model.predict(t_eval=t_eval) + + model = pybop.lithium_ion.SPM() + model.build(initial_state={"Initial SoC": 0.4}) + model.set_initial_state({"Initial SoC": 0.7}) + values_2 = model.predict(t_eval=t_eval) + + np.testing.assert_allclose( + values_1["Voltage [V]"].data, values_2["Voltage [V]"].data, atol=1e-8 + ) + + init_ocp_p = model.parameter_set["Positive electrode OCP [V]"](0.7) + init_ocp_n = model.parameter_set["Negative electrode OCP [V]"](0.7) + model.set_initial_state( + {"Initial open-circuit voltage [V]": init_ocp_p - init_ocp_n} + ) + values_3 = model.predict(t_eval=t_eval) + + np.testing.assert_allclose( + values_1["Voltage [V]"].data, values_3["Voltage [V]"].data, atol=0.05 + ) + + with pytest.raises(ValueError, match="Expecting only one initial state."): + model.set_initial_state( + {"Initial open-circuit voltage [V]": 3.7, "Initial SoC": 0.7} + ) + with pytest.raises(ValueError, match="Unrecognised initial state"): + model.set_initial_state({"Initial voltage [V]": 3.7}) + + @pytest.mark.unit + def test_get_parameter_info(self, model): + if isinstance(model, pybop.empirical.Thevenin): + # Test at least one model without a built pybamm model + model = pybop.empirical.Thevenin(build=False) + + parameter_info = model.get_parameter_info() + assert isinstance(parameter_info, dict) + + captured_output = StringIO() + sys.stdout = captured_output + + model.get_parameter_info(print_info=True) + sys.stdout = sys.__stdout__ + + printed_messaage = captured_output.getvalue().strip() + + for key, value in parameter_info.items(): + assert key in printed_messaage + assert value in printed_messaage + + @pytest.mark.unit + def test_set_current_function(self): + dataset_1 = pybop.Dataset( + { + "Time [s]": np.linspace(0, 10, 100), + "Current function [A]": 3.0 * np.ones(100), + } + ) + dataset_2 = pybop.Dataset( + { + "Time [s]": np.linspace(0, 5, 100), + "Current function [A]": 6.0 * np.ones(100), + } + ) + + model = pybop.lithium_ion.SPM() + model.set_current_function(dataset=dataset_1) + values_1 = model.predict(t_eval=dataset_1["Time [s]"]) + + np.testing.assert_allclose( + values_1["Current [A]"].data, + dataset_1["Current function [A]"].data, + atol=1e-8, + ) + + model.set_current_function(dataset=dataset_2) + values_2 = model.predict(t_eval=dataset_2["Time [s]"]) + + np.testing.assert_allclose( + values_2["Current [A]"].data, + dataset_2["Current function [A]"].data, + atol=1e-8, + ) + + values_3 = model.simulate(inputs={}, t_eval=dataset_2["Time [s]"]) + + np.testing.assert_allclose( + values_3["Current [A]"].data, + dataset_2["Current function [A]"].data, + atol=1e-8, + ) diff --git a/tests/unit/test_observer_unscented_kalman.py b/tests/unit/test_observer_unscented_kalman.py index 2a947e716..c83fae315 100644 --- a/tests/unit/test_observer_unscented_kalman.py +++ b/tests/unit/test_observer_unscented_kalman.py @@ -14,15 +14,6 @@ class TestUKF: measure_noise = 1e-4 - @pytest.fixture(params=[1, 2, 3]) - def model(self, request): - model = ExponentialDecay( - parameter_set=pybamm.ParameterValues({"k": "[input]", "y0": "[input]"}), - n_states=request.param, - ) - model.build() - return model - @pytest.fixture def parameters(self): return pybop.Parameters( @@ -40,6 +31,15 @@ def parameters(self): ), ) + @pytest.fixture(params=[1, 2, 3]) + def model(self, parameters, request): + model = ExponentialDecay( + parameter_set=pybamm.ParameterValues({"k": "[input]", "y0": "[input]"}), + n_states=request.param, + ) + model.build(parameters=parameters) + return model + @pytest.fixture def dataset(self, model: pybop.BaseModel, parameters): observer = pybop.Observer(parameters, model, signal=["2y"]) @@ -93,6 +93,15 @@ def test_cholupdate(self): SquareRootUKF.cholupdate(R1_, u.copy(), 1.0) np.testing.assert_array_almost_equal(R1, R1_) + # Test hypot + f = 10.0 + j = 20.0 + out_1 = f * np.sqrt(1 + 1.0 * f**2 / j**2) + np.testing.assert_allclose(SquareRootUKF.hypot(f, j, 1.0), out_1) + + j = 10.0 + np.testing.assert_allclose(SquareRootUKF.hypot(f, j, 1.0), float(0)) + @pytest.mark.unit def test_unscented_kalman_filter(self, dataset, observer): t_eval = dataset["Time [s]"] @@ -116,6 +125,11 @@ def test_unscented_kalman_filter(self, dataset, observer): decimal=4, ) + # Test get covariance + cov = observer.get_current_measure() + assert cov.shape == (1, 1) + assert float(0) <= cov[0] + @pytest.mark.unit def test_observe_no_measurement(self, observer): with pytest.raises(ValueError): @@ -156,3 +170,22 @@ def test_wrong_input_shapes(self, model, parameters): pybop.UnscentedKalmanFilterObserver( parameters, model, sigma0, process, measure, signal=signal ) + + @pytest.mark.unit + def test_without_signal(self): + model = pybop.lithium_ion.SPM() + parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.5, 0.05), + ) + ) + model.build(parameters=parameters) + n = model.n_states + sigma0 = np.diag([1e-4] * n) + process = np.diag([1e-4] * n) + measure = np.diag([1e-4]) + observer = pybop.UnscentedKalmanFilterObserver( + parameters, model, sigma0, process, measure + ) + assert observer.signal == ["Voltage [V]"] diff --git a/tests/unit/test_observers.py b/tests/unit/test_observers.py index 46987bae9..2eda85612 100644 --- a/tests/unit/test_observers.py +++ b/tests/unit/test_observers.py @@ -1,5 +1,4 @@ import numpy as np -import pybamm import pytest import pybop @@ -11,15 +10,6 @@ class TestObserver: A class to test the observer class. """ - @pytest.fixture(params=[1, 2]) - def model(self, request): - model = ExponentialDecay( - parameter_set=pybamm.ParameterValues({"k": "[input]", "y0": "[input]"}), - n_states=request.param, - ) - model.build() - return model - @pytest.fixture def parameters(self): return pybop.Parameters( @@ -27,21 +17,26 @@ def parameters(self): "k", prior=pybop.Gaussian(0.1, 0.05), bounds=[0, 1], - initial_value=0.1, ), pybop.Parameter( "y0", prior=pybop.Gaussian(1, 0.05), bounds=[0, 3], - initial_value=1.0, ), ) + @pytest.fixture(params=[1, 2]) + def model(self, parameters, request): + parameter_set = {"k": 0.1, "y0": 1.0} + model = ExponentialDecay(parameter_set=parameter_set, n_states=request.param) + model.build(parameters=parameters) + return model + @pytest.mark.unit def test_observer(self, model, parameters): n = model.n_states observer = pybop.Observer(parameters, model, signal=["2y"]) - t_eval = np.linspace(0, 1, 100) + t_eval = np.linspace(0, 20, 10) expected = parameters["y0"].initial_value * np.exp( -parameters["k"].initial_value * t_eval ) @@ -59,9 +54,11 @@ def test_observer(self, model, parameters): ) # Test with invalid inputs - with pytest.raises(ValueError): + with pytest.raises(ValueError, match="Time must be increasing."): observer.observe(-1) - with pytest.raises(ValueError): + with pytest.raises( + ValueError, match="values and times must have the same length." + ): observer.log_likelihood( {"2y": t_eval}, np.array([1]), inputs=observer._state.inputs ) @@ -71,9 +68,9 @@ def test_observer(self, model, parameters): assert np.shape(covariance) == (n, n) # Test evaluate with different inputs - observer._time_data = t_eval - observer.evaluate(parameters.initial_value()) - observer.evaluate(parameters) + observer.domain_data = t_eval + observer.evaluate(parameters.as_dict()) + observer.evaluate(parameters.current_value()) # Test evaluate with dataset observer._dataset = pybop.Dataset( @@ -83,10 +80,31 @@ def test_observer(self, model, parameters): } ) observer._target = {"2y": expected} - observer.evaluate(parameters.initial_value()) + observer.evaluate(parameters.as_dict()) @pytest.mark.unit def test_unbuilt_model(self, parameters): model = ExponentialDecay() - with pytest.raises(ValueError): + with pytest.raises( + ValueError, match="Only built models can be used in Observers" + ): pybop.Observer(parameters, model) + + @pytest.mark.unit + def test_observer_inputs(self): + initial_state = {"Initial open-circuit voltage [V]": 4.0} + t_eval = np.linspace(0, 1, 100) + model = ExponentialDecay(n_states=1) + model.build() + observer = pybop.Observer( + pybop.Parameters(), model, signal=["y_0", "2y"], initial_state=initial_state + ) + assert observer.initial_state == initial_state + + with pytest.raises( + ValueError, + match="Observer.log_likelihood is currently restricted to single output models.", + ): + observer.log_likelihood( + {"2y": t_eval}, t_eval, inputs=observer._state.inputs + ) diff --git a/tests/unit/test_optimisation.py b/tests/unit/test_optimisation.py index 97fe12fc5..36d1ed9ff 100644 --- a/tests/unit/test_optimisation.py +++ b/tests/unit/test_optimisation.py @@ -1,3 +1,5 @@ +import io +import sys import warnings import numpy as np @@ -16,7 +18,7 @@ def dataset(self): return pybop.Dataset( { "Time [s]": np.linspace(0, 360, 10), - "Current function [A]": np.zeros(10), + "Current function [A]": 1e-2 * np.ones(10), "Voltage [V]": np.ones(10), } ) @@ -24,8 +26,8 @@ def dataset(self): @pytest.fixture def one_parameter(self): return pybop.Parameter( - "Negative electrode active material volume fraction", - prior=pybop.Gaussian(0.6, 0.2), + "Positive electrode active material volume fraction", + prior=pybop.Gaussian(0.6, 0.02), bounds=[0.58, 0.62], ) @@ -67,23 +69,30 @@ def two_param_cost(self, model, two_parameters, dataset): return pybop.SumSquaredError(problem) @pytest.mark.parametrize( - "optimiser, expected_name", + "optimiser, expected_name, sensitivities", [ - (pybop.SciPyMinimize, "SciPyMinimize"), - (pybop.SciPyDifferentialEvolution, "SciPyDifferentialEvolution"), - (pybop.GradientDescent, "Gradient descent"), - (pybop.Adam, "Adam"), - (pybop.AdamW, "AdamW"), - (pybop.CMAES, "Covariance Matrix Adaptation Evolution Strategy (CMA-ES)"), - (pybop.SNES, "Seperable Natural Evolution Strategy (SNES)"), - (pybop.XNES, "Exponential Natural Evolution Strategy (xNES)"), - (pybop.PSO, "Particle Swarm Optimisation (PSO)"), - (pybop.IRPropMin, "iRprop-"), - (pybop.NelderMead, "Nelder-Mead"), + (pybop.SciPyMinimize, "SciPyMinimize", False), + (pybop.SciPyDifferentialEvolution, "SciPyDifferentialEvolution", False), + (pybop.GradientDescent, "Gradient descent", True), + (pybop.Adam, "Adam", True), + (pybop.AdamW, "AdamW", True), + ( + pybop.CMAES, + "Covariance Matrix Adaptation Evolution Strategy (CMA-ES)", + False, + ), + (pybop.CuckooSearch, "Cuckoo Search", False), + (pybop.SNES, "Seperable Natural Evolution Strategy (SNES)", False), + (pybop.XNES, "Exponential Natural Evolution Strategy (xNES)", False), + (pybop.PSO, "Particle Swarm Optimisation (PSO)", False), + (pybop.IRPropMin, "iRprop-", True), + (pybop.NelderMead, "Nelder-Mead", False), ], ) @pytest.mark.unit - def test_optimiser_classes(self, two_param_cost, optimiser, expected_name): + def test_optimiser_classes( + self, two_param_cost, optimiser, expected_name, sensitivities + ): # Test class construction cost = two_param_cost optim = optimiser(cost=cost) @@ -96,6 +105,7 @@ def test_optimiser_classes(self, two_param_cost, optimiser, expected_name): assert optim.cost is not None assert optim.name() == expected_name + assert optim.needs_sensitivities == sensitivities if optimiser not in [pybop.SciPyDifferentialEvolution]: # Test construction without bounds @@ -104,6 +114,15 @@ def test_optimiser_classes(self, two_param_cost, optimiser, expected_name): if issubclass(optimiser, pybop.BasePintsOptimiser): assert optim._boundaries is None + @pytest.mark.unit + def test_no_optimisation_parameters(self, model, dataset): + problem = pybop.FittingProblem( + model=model, parameters=pybop.Parameters(), dataset=dataset + ) + cost = pybop.RootMeanSquaredError(problem) + with pytest.raises(ValueError, match="There are no parameters to optimise."): + pybop.Optimisation(cost=cost) + @pytest.mark.parametrize( "optimiser", [ @@ -117,16 +136,29 @@ def test_optimiser_classes(self, two_param_cost, optimiser, expected_name): pybop.PSO, pybop.IRPropMin, pybop.NelderMead, + pybop.CuckooSearch, ], ) @pytest.mark.unit def test_optimiser_kwargs(self, cost, optimiser): - optim = optimiser(cost=cost, maxiter=1) + optim = optimiser(cost=cost, max_iterations=3, tol=1e-6) cost_bounds = cost.parameters.get_bounds() # Check maximum iterations - optim.run() - assert optim.result.n_iterations == 1 + results = optim.run() + assert results.n_iterations == 3 + + # Test BaseOptimiser.log_update + optim.log_update(x=[0.7], cost=[0.01]) + + assert optim.log["x"][-1] == 0.7 + assert optim.log["cost"][-1] == 0.01 + + # Test incorrect update + with pytest.raises( + TypeError, match="Input must be a list, tuple, or numpy array" + ): + optim.log_update(x="Incorrect") if optimiser in [pybop.GradientDescent, pybop.Adam, pybop.NelderMead]: # Ignored bounds @@ -166,6 +198,7 @@ def test_optimiser_kwargs(self, cost, optimiser): ): warnings.simplefilter("always") optim = optimiser(cost=cost, unrecognised=10) + assert not optim.optimiser.running() else: # Check bounds in list format and update tol bounds = [ @@ -180,23 +213,28 @@ def test_optimiser_kwargs(self, cost, optimiser): pybop.XNES, ]: # Pass nested options - optim = optimiser(cost=cost, options=dict(maxiter=10)) + optim = optimiser(cost=cost, options=dict(max_iterations=10)) with pytest.raises( Exception, - match="A duplicate maxiter option was found in the options dictionary.", + match="A duplicate max_evaluations option was found in the options dictionary.", ): - optimiser(cost=cost, maxiter=5, options=dict(maxiter=10)) + optimiser( + cost=cost, max_evaluations=5, options=dict(max_evaluations=10) + ) - # Pass similar keywords - with pytest.raises( - Exception, - match="option was passed in addition to the expected", - ): - optimiser(cost=cost, maxiter=5, max_iterations=10) + if optimiser in [pybop.SciPyDifferentialEvolution, pybop.SciPyMinimize]: + # Pass duplicate keywords + with pytest.raises( + Exception, + match="option was passed in addition to the expected", + ): + optimiser(cost=cost, maxiter=5, max_iterations=10) if optimiser in [pybop.SciPyDifferentialEvolution]: - # Update population size - optimiser(cost=cost, popsize=5) + # Update population size, test maxiter arg + pop_maxiter_optim = optimiser(cost=cost, maxiter=3, popsize=5) + assert pop_maxiter_optim._options["maxiter"] == 3 + assert pop_maxiter_optim._options["popsize"] == 5 # Test invalid bounds with pytest.raises( @@ -214,51 +252,59 @@ def test_optimiser_kwargs(self, cost, optimiser): # Test AdamW hyperparameters if optimiser in [pybop.AdamW]: - optim = optimiser(cost=cost, b1=0.9, b2=0.999, lambda_=0.1) - optim.pints_optimiser.set_b1(0.9) - optim.pints_optimiser.set_b2(0.9) - optim.pints_optimiser.set_lambda(0.1) + optim = optimiser(cost=cost, b1=0.9, b2=0.999, lam=0.1) + optim.optimiser.b1 = 0.9 + optim.optimiser.b2 = 0.9 + optim.optimiser.lam = 0.1 - assert optim.pints_optimiser._b1 == 0.9 - assert optim.pints_optimiser._b2 == 0.9 - assert optim.pints_optimiser._lambda == 0.1 + assert optim.optimiser.b1 == 0.9 + assert optim.optimiser.b2 == 0.9 + assert optim.optimiser.lam == 0.1 # Incorrect values - for i, match in (("Value", -1),): + for i, _match in (("Value", -1),): with pytest.raises( Exception, match="must be a numeric value between 0 and 1." ): - optim.pints_optimiser.set_b1(i) + optim.optimiser.b1 = i with pytest.raises( Exception, match="must be a numeric value between 0 and 1." ): - optim.pints_optimiser.set_b2(i) + optim.optimiser.b2 = i with pytest.raises( Exception, match="must be a numeric value between 0 and 1." ): - optim.pints_optimiser.set_lambda(i) + optim.optimiser.lam = i # Check defaults - assert optim.pints_optimiser.n_hyper_parameters() == 5 - assert not optim.pints_optimiser.running() - assert optim.pints_optimiser.x_guessed() == optim.pints_optimiser._x0 - with pytest.raises(Exception): - optim.pints_optimiser.tell([0.1]) + assert optim.optimiser.n_hyper_parameters() == 5 + assert optim.optimiser.x_guessed() == optim.optimiser._x0 + with pytest.raises(RuntimeError): + optim.optimiser.tell([0.1]) else: # Check and update initial values - assert optim.x0 == cost.x0 + x0 = cost.parameters.initial_value() + assert optim.x0 == x0 x0_new = np.array([0.6]) optim = optimiser(cost=cost, x0=x0_new) assert optim.x0 == x0_new - assert optim.x0 != cost.x0 + assert optim.x0 != x0 + + @pytest.mark.unit + def test_cuckoo_no_bounds(self, cost): + optim = pybop.CuckooSearch(cost=cost, bounds=None, max_iterations=1) + optim.run() + assert optim.optimiser._boundaries is None @pytest.mark.unit def test_scipy_minimize_with_jac(self, cost): # Check a method that uses gradient information - optim = pybop.SciPyMinimize(cost=cost, method="L-BFGS-B", jac=True, maxiter=10) - optim.run() - assert optim.result.scipy_result.success is True + optim = pybop.SciPyMinimize( + cost=cost, method="L-BFGS-B", jac=True, max_iterations=1 + ) + results = optim.run() + assert results.get_scipy_result() == optim.result.scipy_result with pytest.raises( ValueError, @@ -266,6 +312,19 @@ def test_scipy_minimize_with_jac(self, cost): ): optim = pybop.SciPyMinimize(cost=cost, jac="Invalid string") + @pytest.mark.unit + def test_scipy_minimize_invalid_x0(self, cost): + # Check a starting point that returns an infinite cost + invalid_x0 = np.array([1.1]) + optim = pybop.SciPyMinimize( + cost=cost, + x0=invalid_x0, + max_iterations=10, + allow_infeasible_solutions=False, + ) + optim.run() + assert abs(optim._cost0) != np.inf + @pytest.mark.unit def test_single_parameter(self, cost): # Test catch for optimisers that can only run with multiple parameters @@ -293,17 +352,19 @@ def invalid_cost(x): ): pybop.Optimisation(cost=invalid_cost) + # Test valid cost + def valid_cost(x): + return x[0] ** 2 + + pybop.Optimisation(cost=valid_cost, x0=np.asarray([0.1])) + @pytest.mark.unit def test_default_optimiser(self, cost): optim = pybop.Optimisation(cost=cost) assert optim.name() == "Exponential Natural Evolution Strategy (xNES)" - # Test incorrect setting attribute - with pytest.raises( - AttributeError, - match="'Optimisation' object has no attribute 'not_a_valid_attribute'", - ): - optim.not_a_valid_attribute + # Test getting incorrect attribute + assert not hasattr(optim, "not_a_valid_attribute") @pytest.mark.unit def test_incorrect_optimiser_class(self, cost): @@ -312,7 +373,7 @@ class RandomClass: with pytest.raises( ValueError, - match="The pints_optimiser is not a recognised PINTS optimiser class.", + match="The optimiser is not a recognised PINTS optimiser class.", ): pybop.BasePintsOptimiser(cost=cost, pints_optimiser=RandomClass) @@ -322,19 +383,13 @@ class RandomClass: with pytest.raises(ValueError): pybop.Optimisation(cost=cost, optimiser=RandomClass) - @pytest.mark.unit - def test_prior_sampling(self, cost): - # Tests prior sampling - for i in range(50): - optim = pybop.Optimisation(cost=cost) - assert optim.x0[0] < 0.62 and optim.x0[0] > 0.58 - @pytest.mark.unit @pytest.mark.parametrize( "mean, sigma, expect_exception", [ (0.85, 0.2, False), (0.85, 0.001, True), + (1.0, 0.5, False), ], ) def test_scipy_prior_resampling( @@ -368,20 +423,78 @@ def test_scipy_prior_resampling( else: opt.run() + # Test cost_wrapper inf return + cost = opt.cost_wrapper(np.array([0.9])) + assert cost in [1.729, 1.81, 1.9] + + @pytest.mark.unit + def test_scipy_noprior(self, model, dataset): + # Test that Scipy minimize handles no-priors correctly + # Set up the parameter with no prior + parameter = pybop.Parameter( + "Negative electrode active material volume fraction", + initial_value=1, # Intentionally infeasible! + bounds=[0.55, 0.95], + ) + + # Define the problem and cost + problem = pybop.FittingProblem(model, parameter, dataset) + cost = pybop.SumSquaredError(problem) + + # Create the optimisation class with infeasible solutions disabled + opt = pybop.SciPyMinimize( + cost=cost, + allow_infeasible_solutions=False, + max_iterations=1, + ) + with pytest.raises( + ValueError, + match="The initial parameter values return an infinite cost.", + ): + opt.run() + + @pytest.mark.unit + def test_scipy_bounds(self, cost): + # Create the optimisation class with incorrect bounds type + with pytest.raises( + TypeError, + match="Bounds provided must be either type dict, list or SciPy.optimize.bounds object.", + ): + pybop.SciPyMinimize( + cost=cost, + bounds="This is a bad bound", + max_iterations=1, + ) + @pytest.mark.unit def test_halting(self, cost): + # Add a parameter transformation + cost.parameters[ + "Positive electrode active material volume fraction" + ].transformation = pybop.IdentityTransformation() + # Test max evalutions optim = pybop.GradientDescent(cost=cost, max_evaluations=1, verbose=True) - x, __ = optim.run() + optim.run() assert optim.result.n_iterations == 1 # Test max unchanged iterations optim = pybop.GradientDescent( cost=cost, max_unchanged_iterations=1, min_iterations=1 ) - x, __ = optim.run() + results = optim.run() assert optim.result.n_iterations == 2 + assert ( + str(results) == f"OptimisationResult:\n" + f" Initial parameters: {results.x0}\n" + f" Optimised parameters: {results.x}\n" + f" Final cost: {results.final_cost}\n" + f" Optimisation time: {results.time} seconds\n" + f" Number of iterations: {results.n_iterations}\n" + f" SciPy result available: No" + ) + # Test guessed values optim.set_f_guessed_tracking(True) assert optim.f_guessed_tracking() is True @@ -400,11 +513,31 @@ def test_halting(self, cost): with pytest.raises(ValueError): optim.set_max_evaluations(-1) - optim = pybop.Optimisation(cost=cost) + # Reset optim + optim = pybop.Optimisation(cost=cost, sigma0=0.015, verbose=True) + + # Confirm setting threshold == None + optim.set_threshold(None) + assert optim._threshold is None - # Trigger threshold + # Confirm threshold halts + # Redirect stdout to capture print output + captured_output = io.StringIO() + sys.stdout = captured_output optim.set_threshold(np.inf) - optim.run() + results = optim.run() + assert ( + captured_output.getvalue().strip() + == f"Halt: Objective function crossed threshold: inf.\n" + f"OptimisationResult:\n" + f" Initial parameters: {results.x0}\n" + f" Optimised parameters: {results.x}\n" + f" Final cost: {results.final_cost}\n" + f" Optimisation time: {results.time} seconds\n" + f" Number of iterations: {results.n_iterations}\n" + f" SciPy result available: No" + ) + optim.set_max_unchanged_iterations() # Test callback and halting output @@ -420,7 +553,7 @@ def callback_error(iteration, s): def optimiser_error(): return "Optimiser error message" - optim.pints_optimiser.stop = optimiser_error + optim.optimiser.stop = optimiser_error optim.run() assert optim.result.n_iterations == 1 @@ -438,12 +571,31 @@ def optimiser_error(): def test_infeasible_solutions(self, cost): # Test infeasible solutions for optimiser in [pybop.SciPyMinimize, pybop.GradientDescent]: - optim = optimiser(cost=cost, allow_infeasible_solutions=False, maxiter=1) + optim = optimiser( + cost=cost, allow_infeasible_solutions=False, max_iterations=1 + ) optim.run() assert optim.result.n_iterations == 1 @pytest.mark.unit def test_unphysical_result(self, cost): # Trigger parameters not physically viable warning - optim = pybop.Optimisation(cost=cost) - optim.check_optimal_parameters(np.array([2])) + optim = pybop.Optimisation(cost=cost, max_iterations=3) + results = optim.run() + results.check_physical_viability(np.array([2])) + + @pytest.mark.unit + def test_optimisation_results(self, cost): + # Construct OptimisationResult + results = pybop.OptimisationResult(x=[1e-3], cost=cost, n_iterations=1) + + # Asserts + assert results.x[0] == 1e-3 + assert results.final_cost == cost([1e-3]) + assert results.n_iterations == 1 + + # Test non-finite results + with pytest.raises( + ValueError, match="Optimised parameters do not produce a finite cost value" + ): + pybop.OptimisationResult(x=[1e-5], cost=cost, n_iterations=1) diff --git a/tests/unit/test_parameter_sets.py b/tests/unit/test_parameter_sets.py index 12b2c18a6..35dbd9c1c 100644 --- a/tests/unit/test_parameter_sets.py +++ b/tests/unit/test_parameter_sets.py @@ -1,5 +1,6 @@ import numpy as np import pytest +from pybamm import FunctionParameter, Parameter, Scalar import pybop @@ -37,7 +38,7 @@ def test_ecm_parameter_sets(self): json_params.import_parameters() json_params = pybop.ParameterSet( - json_path="examples/scripts/parameters/initial_ecm_parameters.json" + json_path="examples/parameters/initial_ecm_parameters.json" ) json_params.import_parameters() @@ -93,14 +94,14 @@ def test_ecm_parameter_sets(self): ), ) params.export_parameters( - "examples/scripts/parameters/fit_ecm_parameters.json", fit_params=parameters + "examples/parameters/fit_ecm_parameters.json", fit_params=parameters ) # Test error when there no parameters to export empty_params = pybop.ParameterSet() with pytest.raises(ValueError): empty_params.export_parameters( - "examples/scripts/parameters/fit_ecm_parameters.json" + "examples/parameters/fit_ecm_parameters.json" ) @pytest.mark.unit @@ -114,7 +115,7 @@ def test_bpx_parameter_sets(self): bpx_parameters.import_from_bpx() bpx_parameters = pybop.ParameterSet( - json_path="examples/scripts/parameters/example_BPX.json" + json_path="examples/parameters/example_BPX.json" ) bpx_parameters.import_from_bpx() @@ -123,3 +124,32 @@ def test_bpx_parameter_sets(self): match="Parameter set already constructed, or path to bpx file not provided.", ): bpx_parameters.import_from_bpx() + + @pytest.mark.unit + def test_set_formation_concentrations(self): + parameter_set = pybop.ParameterSet.pybamm( + "Chen2020", formation_concentrations=True + ) + + assert ( + parameter_set["Initial concentration in negative electrode [mol.m-3]"] == 0 + ) + assert ( + parameter_set["Initial concentration in positive electrode [mol.m-3]"] > 0 + ) + + @pytest.mark.unit + def test_evaluate_symbol(self): + parameter_set = pybop.ParameterSet.pybamm("Chen2020") + porosity = parameter_set["Positive electrode porosity"] + assert isinstance(porosity, float) + + for param in [ + 1.0 + porosity, + 1.0 + Scalar(porosity), + 1.0 + Parameter("Positive electrode porosity"), + 1.0 + FunctionParameter("Positive electrode porosity", inputs={}), + ]: + value = pybop.ParameterSet.evaluate_symbol(param, parameter_set) + assert isinstance(value, float) + np.testing.assert_allclose(value, 1.0 + porosity) diff --git a/tests/unit/test_parameters.py b/tests/unit/test_parameters.py index 736684fef..cc8e4dbcd 100644 --- a/tests/unit/test_parameters.py +++ b/tests/unit/test_parameters.py @@ -1,3 +1,4 @@ +import numpy as np import pytest import pybop @@ -62,6 +63,11 @@ def test_no_bounds(self): ) assert parameter.bounds is None + # Test get_bounds with bounds == None + parameters = pybop.Parameters(parameter) + bounds = parameters.get_bounds() + assert not np.isfinite(list(bounds.values())).all() + @pytest.mark.unit def test_invalid_inputs(self, parameter): # Test error with invalid value @@ -78,6 +84,16 @@ def test_invalid_inputs(self, parameter): ): pybop.Parameter("Name", bounds=[0.7, 0.3]) + @pytest.mark.unit + def test_sample_initial_values(self): + parameter = pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.6, 0.02), + bounds=[0.375, 0.7], + ) + sample = parameter.get_initial_value() + assert (sample >= 0.375) and (sample <= 0.7) + class TestParameters: """ @@ -105,11 +121,23 @@ def test_parameters_construction(self, parameter): assert parameter.name in params.param.keys() assert parameter in params.param.values() + params.join( + pybop.Parameters( + parameter, + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Gaussian(0.6, 0.02), + bounds=[0.375, 0.7], + initial_value=0.6, + ), + ) + ) + with pytest.raises( ValueError, match="There is already a parameter with the name " - + "Negative electrode active material volume fraction" - + " in the Parameters object. Please remove the duplicate entry.", + "Negative electrode active material volume fraction" + " in the Parameters object. Please remove the duplicate entry.", ): params.add(parameter) @@ -128,11 +156,16 @@ def test_parameters_construction(self, parameter): initial_value=0.6, ) ) + with pytest.raises( + Exception, + match="Parameter requires a name.", + ): + params.add(dict(value=1)) with pytest.raises( ValueError, match="There is already a parameter with the name " - + "Negative electrode active material volume fraction" - + " in the Parameters object. Please remove the duplicate entry.", + "Negative electrode active material volume fraction" + " in the Parameters object. Please remove the duplicate entry.", ): params.add( dict( @@ -156,6 +189,56 @@ def test_parameters_construction(self, parameter): ): params.remove(parameter_name=parameter) + @pytest.mark.unit + def test_parameters_naming(self, parameter): + params = pybop.Parameters(parameter) + param = params["Negative electrode active material volume fraction"] + assert param == parameter + + with pytest.raises( + ValueError, + match="is not the name of a parameter.", + ): + params["Positive electrode active material volume fraction"] + + @pytest.mark.unit + def test_parameters_transformation(self): + # Test unbounded transformation causes ValueError due to NaN + params = pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.01, 0.2), + transformation=pybop.LogTransformation(), + ) + ) + + with pytest.raises( + ValueError, + match="Transformed bounds resulted in NaN values.\nIf you've not applied bounds", + ): + params.get_bounds(apply_transform=True) + + @pytest.mark.unit + def test_parameters_update(self, parameter): + params = pybop.Parameters(parameter) + params.update(values=[0.5]) + assert parameter.value == 0.5 + params.update(bounds=[[0.38, 0.68]]) + assert parameter.bounds == [0.38, 0.68] + params.update(bounds=dict(lower=[0.37], upper=[0.7])) + assert parameter.bounds == [0.37, 0.7] + + @pytest.mark.unit + def test_parameters_rvs(self, parameter): + parameter.transformation = pybop.ScaledTransformation( + coefficient=0.2, intercept=-1 + ) + params = pybop.Parameters(parameter) + params.construct_transformation() + samples = params.rvs(n_samples=500, apply_transform=True) + assert (samples >= -0.125).all() and (samples <= -0.06).all() + parameter.transformation = None + @pytest.mark.unit def test_get_sigma(self, parameter): params = pybop.Parameters(parameter) @@ -163,3 +246,27 @@ def test_get_sigma(self, parameter): parameter.prior = None assert params.get_sigma0() is None + + @pytest.mark.unit + def test_initial_values_without_attributes(self): + # Test without initial values + parameter = pybop.Parameters( + pybop.Parameter( + "Negative electrode conductivity [S.m-1]", + ) + ) + with pytest.warns( + UserWarning, + match="Initial value and prior are None, proceeding without an initial value.", + ): + sample = parameter.initial_value() + + np.testing.assert_equal(sample, np.array([None])) + + @pytest.mark.unit + def test_parameters_repr(self, parameter): + params = pybop.Parameters(parameter) + assert ( + repr(params) + == "Parameters(1):\n Negative electrode active material volume fraction: prior= Gaussian, loc: 0.6, scale: 0.02, value=0.6, bounds=[0.375, 0.7]" + ) diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index b810e3f0e..733b81d42 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -1,3 +1,5 @@ +import warnings + import numpy as np import pytest from packaging import version @@ -7,14 +9,16 @@ class TestPlots: """ - A class to test the plotting classes. + A class to test the plot classes. """ @pytest.mark.unit def test_standard_plot(self): # Test standard plot - trace_names = pybop.StandardPlot.remove_brackets(["Trace [1]", "Trace [2]"]) - plot_dict = pybop.StandardPlot( + trace_names = pybop.plot.StandardPlot.remove_brackets( + ["Trace [1]", "Trace [2]"] + ) + plot_dict = pybop.plot.StandardPlot( x=np.ones((2, 10)), y=np.ones((2, 10)), trace_names=trace_names, @@ -58,13 +62,13 @@ def dataset(self, model): @pytest.mark.unit def test_dataset_plots(self, dataset): - # Test plotting of Dataset objects - pybop.plot_trajectories( + # Test plot of Dataset objects + pybop.plot.trajectories( dataset["Time [s]"], dataset["Voltage [V]"], trace_names=["Time [s]", "Voltage [V]"], ) - pybop.plot_dataset(dataset, signal=["Voltage [V]"]) + pybop.plot.dataset(dataset) @pytest.fixture def fitting_problem(self, model, parameters, dataset): @@ -80,13 +84,17 @@ def experiment(self): @pytest.fixture def design_problem(self, model, parameters, experiment): + model = pybop.lithium_ion.SPM() return pybop.DesignProblem(model, parameters, experiment) @pytest.mark.unit def test_problem_plots(self, fitting_problem, design_problem): - # Test plotting of Problem objects - pybop.quick_plot(fitting_problem, title="Optimised Comparison") - pybop.quick_plot(design_problem) + # Test plot of Problem objects + pybop.plot.quick(fitting_problem, title="Optimised Comparison") + pybop.plot.quick(design_problem) + + # Test conversion of values into inputs + pybop.plot.quick(fitting_problem, problem_inputs=[0.6, 0.6]) @pytest.fixture def cost(self, fitting_problem): @@ -95,15 +103,17 @@ def cost(self, fitting_problem): @pytest.mark.unit def test_cost_plots(self, cost): - # Test plotting of Cost objects - pybop.plot2d(cost, gradient=True, steps=5) + # Test plot of Cost objects + pybop.plot.contour(cost, gradient=True, steps=5) # Test without bounds - for param in cost.problem.parameters: + for param in cost.parameters: param.bounds = None - with pytest.raises(ValueError): - pybop.plot2d(cost, steps=5) - pybop.plot2d(cost, bounds=np.array([[0.5, 0.8], [0.4, 0.7]]), steps=5) + with pytest.raises(ValueError, match="All parameters require bounds for plot."): + pybop.plot.contour(cost, steps=5) + + # Test with bounds + pybop.plot.contour(cost, bounds=np.array([[0.5, 0.8], [0.4, 0.7]]), steps=5) @pytest.fixture def optim(self, cost): @@ -114,30 +124,172 @@ def optim(self, cost): @pytest.mark.unit def test_optim_plots(self, optim): + bounds = np.asarray([[0.5, 0.8], [0.4, 0.7]]) + # Plot convergence - pybop.plot_convergence(optim) + pybop.plot.convergence(optim) optim._minimising = False - pybop.plot_convergence(optim) + pybop.plot.convergence(optim) # Plot the parameter traces - pybop.plot_parameters(optim) + pybop.plot.parameters(optim) # Plot the cost landscape with optimisation path - pybop.plot2d(optim, steps=5) + pybop.plot.contour(optim, steps=3) + + # Plot the cost landscape w/ optim & bounds + pybop.plot.contour(optim, steps=3, bounds=bounds) # Plot the cost landscape using optimisation path - pybop.plot2d(optim, steps=5, use_optim_log=True) + pybop.plot.contour(optim, steps=3, use_optim_log=True) # Plot gradient cost landscape - pybop.plot2d(optim, gradient=True, steps=5) + pybop.plot.contour(optim, gradient=True, steps=5) + + # Plot voronoi + pybop.plot.surface(optim) + + # Plot voronoi w/ bounds + pybop.plot.surface(optim, bounds=bounds) + + @pytest.fixture + def posterior_summary(self, fitting_problem): + posterior = pybop.LogPosterior( + pybop.GaussianLogLikelihoodKnownSigma(fitting_problem, sigma0=2e-3) + ) + sampler = pybop.SliceStepoutMCMC(posterior, chains=1, iterations=1) + results = sampler.run() + return pybop.PosteriorSummary(results) + + @pytest.mark.unit + def test_posterior_plots(self, posterior_summary): + # Plot trace + posterior_summary.plot_trace() + + # Plot posterior + posterior_summary.plot_posterior() + + # Plot chains + posterior_summary.plot_chains() + + # Plot summary table + posterior_summary.summary_table() @pytest.mark.unit def test_with_ipykernel(self, dataset, cost, optim): import ipykernel assert version.parse(ipykernel.__version__) >= version.parse("0.6") - pybop.plot_dataset(dataset, signal=["Voltage [V]"]) - pybop.plot2d(cost, gradient=True, steps=5) - pybop.plot_convergence(optim) - pybop.plot_parameters(optim) - pybop.plot2d(optim, steps=5) + pybop.plot.dataset(dataset, signal=["Voltage [V]"]) + pybop.plot.contour(cost, gradient=True, steps=5) + pybop.plot.convergence(optim) + pybop.plot.parameters(optim) + pybop.plot.contour(optim, steps=5) + + @pytest.mark.unit + def test_gaussianlogliklihood_plots(self, fitting_problem): + # Test plot of GaussianLogLikelihood + likelihood = pybop.GaussianLogLikelihood(fitting_problem) + optim = pybop.CMAES(likelihood, max_iterations=5) + optim.run() + + # Plot parameters + pybop.plot.parameters(optim) + + @pytest.mark.unit + def test_contour_incorrect_number_of_parameters(self, model, dataset): + # Test with less than two paramters + parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.68, 0.05), + bounds=[0.5, 0.8], + ), + ) + fitting_problem = pybop.FittingProblem(model, parameters, dataset) + cost = pybop.SumSquaredError(fitting_problem) + with pytest.raises( + ValueError, match="This cost function takes fewer than 2 parameters." + ): + pybop.plot.contour(cost) + + # Test with more than two paramters + parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.68, 0.05), + bounds=[0.5, 0.8], + ), + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Gaussian(0.58, 0.05), + bounds=[0.4, 0.7], + ), + pybop.Parameter( + "Positive particle radius [m]", + prior=pybop.Gaussian(4.8e-06, 0.05e-06), + bounds=[4e-06, 6e-06], + ), + ) + fitting_problem = pybop.FittingProblem(model, parameters, dataset) + cost = pybop.SumSquaredError(fitting_problem) + pybop.plot.contour(cost) + + @pytest.mark.unit + def test_contour_prior_bounds(self, model, dataset): + # Test with prior bounds + parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.68, 0.01), + ), + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Gaussian(0.58, 0.01), + ), + ) + fitting_problem = pybop.FittingProblem(model, parameters, dataset) + cost = pybop.SumSquaredError(fitting_problem) + with pytest.warns( + UserWarning, + match="Bounds were created from prior distributions.", + ): + warnings.simplefilter("always") + pybop.plot.contour(cost) + + @pytest.mark.unit + def test_nyquist(self): + # Define model + model = pybop.lithium_ion.SPM( + eis=True, options={"surface form": "differential"} + ) + + # Fitting parameters + parameters = pybop.Parameters( + pybop.Parameter( + "Positive electrode thickness [m]", + prior=pybop.Gaussian(60e-6, 1e-6), + bounds=[10e-6, 80e-6], + ) + ) + + # Form dataset + dataset = pybop.Dataset( + { + "Frequency [Hz]": np.logspace(-4, 5, 10), + "Current function [A]": np.ones(10) * 0.0, + "Impedance": np.ones(10) * 0.0, + } + ) + + signal = ["Impedance"] + # Generate problem, cost function, and optimisation class + problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) + + # Plot the nyquist + pybop.plot.nyquist( + problem, problem_inputs=[60e-6], title="Optimised Comparison" + ) + + # Without inputs + pybop.plot.nyquist(problem, title="Optimised Comparison") diff --git a/tests/unit/test_posterior.py b/tests/unit/test_posterior.py new file mode 100644 index 000000000..8d45ddc28 --- /dev/null +++ b/tests/unit/test_posterior.py @@ -0,0 +1,131 @@ +import numpy as np +import pytest +import scipy.stats as st + +import pybop + + +class TestLogPosterior: + """ + Class for log posterior unit tests + """ + + @pytest.fixture + def model(self): + return pybop.lithium_ion.SPM() + + @pytest.fixture + def ground_truth(self): + return 0.52 + + @pytest.fixture + def parameters(self, ground_truth): + return pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.5, 0.01), + bounds=[0.375, 0.625], + initial_value=ground_truth, + ) + + @pytest.fixture + def experiment(self): + return pybop.Experiment( + [ + ("Discharge at 1C for 1 minutes (5 second period)"), + ] + ) + + @pytest.fixture + def dataset(self, model, experiment, ground_truth): + model._parameter_set = model.pybamm_model.default_parameter_values + model._parameter_set.update( + { + "Negative electrode active material volume fraction": ground_truth, + } + ) + solution = model.predict(experiment=experiment) + return pybop.Dataset( + { + "Time [s]": solution["Time [s]"].data, + "Current function [A]": solution["Current [A]"].data, + "Voltage [V]": solution["Terminal voltage [V]"].data, + } + ) + + @pytest.fixture + def one_signal_problem(self, model, parameters, dataset): + return pybop.FittingProblem(model, parameters, dataset) + + @pytest.fixture + def likelihood(self, one_signal_problem): + return pybop.GaussianLogLikelihoodKnownSigma(one_signal_problem, sigma0=0.01) + + @pytest.fixture + def prior(self): + return pybop.Gaussian(0.5, 0.01) + + @pytest.mark.unit + def test_log_posterior_construction(self, likelihood, prior): + # Test log posterior construction + posterior = pybop.LogPosterior(likelihood, prior) + keys = likelihood.parameters.keys() + + assert posterior._log_likelihood == likelihood + assert posterior._prior == prior + assert posterior.parameters[keys[0]] == likelihood.parameters[keys[0]] + assert posterior.has_separable_problem == likelihood.has_separable_problem + assert posterior.transformation == likelihood.transformation + + @pytest.mark.unit + def test_log_posterior_construction_no_prior(self, likelihood): + # Test log posterior construction without prior + posterior = pybop.LogPosterior(likelihood, None) + + assert posterior._prior is not None + assert isinstance(posterior._prior, pybop.JointLogPrior) + + for i, p in enumerate(posterior._prior._priors): + assert p == posterior._log_likelihood.problem.parameters.priors()[i] + + @pytest.fixture + def posterior(self, likelihood, prior): + return pybop.LogPosterior(likelihood, prior) + + @pytest.mark.unit + def test_log_posterior(self, posterior): + # Test log posterior + x = np.array([0.50]) + assert np.allclose(posterior(x), 51.5236, atol=2e-2) + + # Test log posterior evaluateS1 + p, dp = posterior(x, calculate_grad=True) + assert np.allclose(p, 51.5236, atol=2e-2) + assert np.allclose(dp, 2.0, atol=2e-2) + + # Get log likelihood and log prior + likelihood = posterior.likelihood + prior = posterior.prior + + assert likelihood == posterior._log_likelihood + assert prior == posterior._prior + + @pytest.fixture + def posterior_uniform_prior(self, likelihood): + return pybop.LogPosterior(likelihood, pybop.Uniform(0.45, 0.55)) + + @pytest.mark.unit + def test_log_posterior_inf(self, posterior_uniform_prior): + # Test prior np.inf + assert not np.isfinite(posterior_uniform_prior([1])) + assert not np.isfinite(posterior_uniform_prior([1], calculate_grad=True)[0]) + + @pytest.mark.unit + def test_non_logpdfS1_prior(self, likelihood): + # Scipy distribution + prior = st.norm(0.8, 0.01) + posterior = pybop.LogPosterior(likelihood, log_prior=prior) + p, dp = posterior([0.6], calculate_grad=True) + + # Assert to PyBOP.Gaussian + p2, dp2 = pybop.Gaussian(0.8, 0.01).logpdfS1(0.6) + np.testing.assert_allclose(dp, dp2, atol=2e-3) diff --git a/tests/unit/test_priors.py b/tests/unit/test_priors.py index b773049f8..3e9184374 100644 --- a/tests/unit/test_priors.py +++ b/tests/unit/test_priors.py @@ -21,13 +21,23 @@ def Uniform(self): def Exponential(self): return pybop.Exponential(scale=1) + @pytest.fixture + def JointPrior1(self, Gaussian, Uniform): + return pybop.JointLogPrior(Gaussian, Uniform) + + @pytest.fixture + def JointPrior2(self, Gaussian, Exponential): + return pybop.JointLogPrior(Gaussian, Exponential) + @pytest.mark.unit def test_base_prior(self): base = pybop.BasePrior() assert isinstance(base, pybop.BasePrior) + with pytest.raises(NotImplementedError): + base._logpdfS1(0.0) @pytest.mark.unit - def test_priors(self, Gaussian, Uniform, Exponential): + def test_priors(self, Gaussian, Uniform, Exponential, JointPrior1, JointPrior2): # Test pdf np.testing.assert_allclose(Gaussian.pdf(0.5), 0.3989422804014327, atol=1e-4) np.testing.assert_allclose(Uniform.pdf(0.5), 1, atol=1e-4) @@ -38,6 +48,69 @@ def test_priors(self, Gaussian, Uniform, Exponential): np.testing.assert_allclose(Uniform.logpdf(0.5), 0, atol=1e-4) np.testing.assert_allclose(Exponential.logpdf(1), -1, atol=1e-4) + # Test icdf + np.testing.assert_allclose(Gaussian.icdf(0.5), 0.5, atol=1e-4) + np.testing.assert_allclose(Uniform.icdf(0.5), 0.5, atol=1e-4) + np.testing.assert_allclose(Exponential.icdf(0.5), 0.6931471805599453, atol=1e-4) + + # Test cdf + np.testing.assert_allclose(Gaussian.cdf(0.5), 0.5, atol=1e-4) + np.testing.assert_allclose(Uniform.cdf(0.5), 0.5, atol=1e-4) + np.testing.assert_allclose(Exponential.cdf(1), 0.6321205588285577, atol=1e-4) + + # Test __call__ + assert Gaussian(0.5) == Gaussian.logpdf(0.5) + assert Uniform(0.5) == Uniform.logpdf(0.5) + assert Exponential(1) == Exponential.logpdf(1) + assert JointPrior1([0.5, 0.5]) == Gaussian.logpdf(0.5) + Uniform.logpdf(0.5) + assert JointPrior2([0.5, 1]) == Gaussian.logpdf(0.5) + Exponential.logpdf(1) + + # Test Gaussian.logpdfS1 + p, dp = Gaussian.logpdfS1(0.5) + assert p == Gaussian.logpdf(0.5) + assert dp == 0.0 + + # Test Uniform.logpdfS1 + p, dp = Uniform.logpdfS1(0.5) + assert p == Uniform.logpdf(0.5) + assert dp == 0.0 + + # Test Exponential.logpdfS1 + p, dp = Exponential.logpdfS1(1) + assert p == Exponential.logpdf(1) + assert dp == Exponential.logpdf(1) + + # Test JointPrior1.logpdfS1 + p, dp = JointPrior1.logpdfS1([0.5, 0.5]) + assert p == Gaussian.logpdf(0.5) + Uniform.logpdf(0.5) + np.testing.assert_allclose(dp, np.array([0.0, 0.0]), atol=1e-4) + + # Test JointPrior.logpdfS1 + p, dp = JointPrior2.logpdfS1([0.5, 1]) + assert p == Gaussian.logpdf(0.5) + Exponential.logpdf(1) + np.testing.assert_allclose( + dp, np.array([0.0, Exponential.logpdf(1)]), atol=1e-4 + ) + + # Test JointPrior1 non-symmetric + with pytest.raises(AssertionError): + np.testing.assert_allclose( + JointPrior1([0.4, 0.5]), JointPrior1([0.5, 0.4]), atol=1e-4 + ) + + # Test JointPrior2 non-symmetric + with pytest.raises(AssertionError): + np.testing.assert_allclose( + JointPrior2([0.4, 1]), JointPrior2([1, 0.4]), atol=1e-4 + ) + + # Test JointPrior with incorrect dimensions + with pytest.raises(ValueError, match="Input x must have length 2, got 1"): + JointPrior1([0.4]) + + with pytest.raises(ValueError, match="Input x must have length 2, got 1"): + JointPrior1.logpdfS1([0.4]) + # Test properties assert Uniform.mean == (Uniform.upper - Uniform.lower) / 2 np.testing.assert_allclose( @@ -74,10 +147,14 @@ def test_exponential_rvs(self, Exponential): assert abs(mean - 1) < 0.2 @pytest.mark.unit - def test_repr(self, Gaussian, Uniform, Exponential): + def test_repr(self, Gaussian, Uniform, Exponential, JointPrior1): assert repr(Gaussian) == "Gaussian, loc: 0.5, scale: 1" assert repr(Uniform) == "Uniform, loc: 0, scale: 1" assert repr(Exponential) == "Exponential, loc: 0, scale: 1" + assert ( + repr(JointPrior1) + == "JointLogPrior(priors: [Gaussian, loc: 0.5, scale: 1, Uniform, loc: 0, scale: 1])" + ) @pytest.mark.unit def test_invalid_size(self, Gaussian, Uniform, Exponential): @@ -87,3 +164,14 @@ def test_invalid_size(self, Gaussian, Uniform, Exponential): Uniform.rvs(-1) with pytest.raises(ValueError): Exponential.rvs(-1) + + @pytest.mark.unit + def test_incorrect_composed_priors(self, Gaussian, Uniform): + with pytest.raises( + ValueError, match="All priors must be instances of BasePrior" + ): + pybop.JointLogPrior(Gaussian, Uniform, "string") + with pytest.raises( + ValueError, match="All priors must be instances of BasePrior" + ): + pybop.JointLogPrior(Gaussian, Uniform, 0.5) diff --git a/tests/unit/test_problem.py b/tests/unit/test_problem.py index c2c40a038..c02a0aa17 100644 --- a/tests/unit/test_problem.py +++ b/tests/unit/test_problem.py @@ -45,7 +45,6 @@ def experiment(self): @pytest.fixture def dataset(self, model, experiment): - model.parameter_set = model.pybamm_model.default_parameter_values x0 = np.array([2e-5, 0.5e-5]) model.parameter_set.update( { @@ -71,7 +70,7 @@ def test_base_problem(self, parameters, model, dataset): # Construct Problem problem = pybop.BaseProblem(parameters, model=model) - assert problem._model == model + assert problem.model == model with pytest.raises(NotImplementedError): problem.evaluate([1e-5, 1e-5]) @@ -109,11 +108,29 @@ def test_base_problem(self, parameters, model, dataset): @pytest.mark.unit def test_fitting_problem(self, parameters, dataset, model, signal): + with pytest.warns(UserWarning) as record: + problem = pybop.FittingProblem( + model, + parameters, + dataset, + signal=signal, + initial_state={"Initial SoC": 0.8}, + ) + assert "It is usually better to define an initial open-circuit voltage" in str( + record[0].message + ) + # Construct Problem - problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) + problem = pybop.FittingProblem( + model, + parameters, + dataset, + signal=signal, + initial_state={"Initial open-circuit voltage [V]": 4.0}, + ) - assert problem._model == model - assert problem._model._built_model is not None + assert problem.model == model + assert problem.model.built_model is not None # Test get target target = problem.get_target()["Voltage [V]"] @@ -130,6 +147,14 @@ def test_fitting_problem(self, parameters, dataset, model, signal): # Test model.simulate model.simulate(inputs=[1e-5, 1e-5], t_eval=np.linspace(0, 10, 100)) + # Test model.simulate with an initial state + problem.evaluate(inputs=[1e-5, 1e-5]) + + # Test try-except + problem.verbose = True + out = problem.evaluate(inputs=[0.0, 0.0]) + assert not np.isfinite(out["Voltage [V]"]) + # Test problem construction errors for bad_dataset in [ pybop.Dataset({"Time [s]": np.array([0])}), @@ -162,19 +187,120 @@ def test_fitting_problem(self, parameters, dataset, model, signal): with pytest.raises(ValueError): pybop.FittingProblem(model, parameters, bad_dataset, signal=two_signals) + @pytest.mark.unit + def test_fitting_problem_eis(self, parameters): + model = pybop.lithium_ion.SPM(eis=True) + + dataset = pybop.Dataset( + { + "Frequency [Hz]": np.logspace(-4, 5, 30), + "Current function [A]": np.ones(30) * 0.0, + "Impedance": np.ones(30) * 0.0, + } + ) + + # Construct Problem + problem = pybop.FittingProblem( + model, + parameters, + dataset, + signal=["Impedance"], + initial_state={"Initial open-circuit voltage [V]": 4.0}, + ) + assert problem.eis == model.eis + assert problem.domain == "Frequency [Hz]" + + # Test try-except + problem.verbose = True + out = problem.evaluate(inputs=[0.0, 0.0]) + assert not np.isfinite(out["Impedance"]) + + @pytest.mark.unit + def test_multi_fitting_problem(self, model, parameters, dataset, signal): + problem_1 = pybop.FittingProblem(model, parameters, dataset, signal=signal) + + with pytest.raises( + ValueError, match="Make a new_copy of the model for each problem." + ): + pybop.MultiFittingProblem(problem_1, problem_1) + + # Generate a second fitting problem + model = model.new_copy() + experiment = pybop.Experiment( + ["Discharge at 1C for 5 minutes (1 second period)"] + ) + values = model.predict( + initial_state={"Initial SoC": 0.8}, experiment=experiment + ) + dataset_2 = pybop.Dataset( + { + "Time [s]": values["Time [s]"].data, + "Current function [A]": values["Current [A]"].data, + "Voltage [V]": values["Voltage [V]"].data, + } + ) + problem_2 = pybop.FittingProblem(model, parameters, dataset_2, signal=signal) + combined_problem = pybop.MultiFittingProblem(problem_1, problem_2) + + assert combined_problem._model is None + + assert len(combined_problem._dataset["Time [s]"]) == len( + problem_1._dataset["Time [s]"] + ) + len(problem_2._dataset["Time [s]"]) + assert len(combined_problem._dataset["Combined signal"]) == len( + problem_1._dataset[signal] + ) + len(problem_2._dataset[signal]) + + y = combined_problem.evaluate(inputs=[1e-5, 1e-5]) + assert len(y["Combined signal"]) == len( + combined_problem._dataset["Combined signal"] + ) + @pytest.mark.unit def test_design_problem(self, parameters, experiment, model): + with pytest.warns(UserWarning) as record: + problem = pybop.DesignProblem( + model, + parameters, + experiment, + update_capacity=True, + ) + assert "The nominal capacity is approximated for each evaluation." in str( + record[0].message + ) + + with pytest.warns(UserWarning) as record: + problem = pybop.DesignProblem( + model, + parameters, + experiment, + initial_state={"Initial open-circuit voltage [V]": 4.0}, + ) + assert "It is usually better to define an initial state of charge" in str( + record[0].message + ) + assert "The nominal capacity is fixed at the initial model value." in str( + record[1].message + ) + # Construct Problem problem = pybop.DesignProblem(model, parameters, experiment) - assert problem._model == model + assert problem.model == model assert ( - problem._model._built_model is None + problem.model.built_model is None ) # building postponed with input experiment + assert problem.initial_state == {"Initial SoC": 1.0} + + # Test evaluation + problem.evaluate(inputs=[1e-5, 1e-5]) + problem.evaluate(inputs=[3e-5, 3e-5]) - # Test model.predict - model.predict(inputs=[1e-5, 1e-5], experiment=experiment) - model.predict(inputs=[3e-5, 3e-5], experiment=experiment) + # Test initial SoC from parameter_set + model = pybop.empirical.Thevenin() + model.parameter_set["Initial SoC"] = 0.8 + problem = pybop.DesignProblem(model, pybop.Parameters(), experiment) + assert problem.initial_state == {"Initial SoC": 0.8} @pytest.mark.unit def test_problem_construct_with_model_predict( @@ -191,7 +317,7 @@ def test_problem_construct_with_model_predict( # Test problem evaluate problem_output = problem.evaluate([2e-5, 2e-5]) - assert problem._model._built_model is not None + assert problem.model.built_model is not None with pytest.raises(AssertionError): assert_allclose( out["Voltage [V]"].data, diff --git a/tests/unit/test_sampling.py b/tests/unit/test_sampling.py new file mode 100644 index 000000000..e410242e6 --- /dev/null +++ b/tests/unit/test_sampling.py @@ -0,0 +1,413 @@ +import copy +import logging +import re +from unittest.mock import call, patch + +import numpy as np +import pytest +from pints import ParallelEvaluator + +import pybop +from pybop import ( + DREAM, + MALAMCMC, + NUTS, + AdaptiveCovarianceMCMC, + DifferentialEvolutionMCMC, + DramACMC, + EmceeHammerMCMC, + HaarioACMC, + HaarioBardenetACMC, + HamiltonianMCMC, + MetropolisRandomWalkMCMC, + MonomialGammaHamiltonianMCMC, + PopulationMCMC, + RaoBlackwellACMC, + RelativisticMCMC, + SliceDoublingMCMC, + SliceRankShrinkingMCMC, + SliceStepoutMCMC, +) + + +class TestPintsSamplers: + """ + Class for testing the Pints-based MCMC Samplers + """ + + @pytest.fixture + def dataset(self): + return pybop.Dataset( + { + "Time [s]": np.linspace(0, 360, 10), + "Current function [A]": np.zeros(10), + "Voltage [V]": np.ones(10), + } + ) + + @pytest.fixture + def parameters(self): + return pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.6, 0.2), + bounds=[0.58, 0.62], + ), + pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Gaussian(0.55, 0.05), + bounds=[0.53, 0.57], + ), + ) + + @pytest.fixture + def model(self): + return pybop.lithium_ion.SPM() + + @pytest.fixture + def log_posterior(self, model, parameters, dataset): + problem = pybop.FittingProblem( + model, + parameters, + dataset, + ) + likelihood = pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=0.01) + prior1 = pybop.Gaussian(0.7, 0.02) + prior2 = pybop.Gaussian(0.6, 0.02) + composed_prior = pybop.JointLogPrior(prior1, prior2) + log_posterior = pybop.LogPosterior(likelihood, composed_prior) + + return log_posterior + + @pytest.fixture + def x0(self): + return [0.68, 0.58] + + @pytest.fixture + def chains(self): + return 3 + + @pytest.fixture + def multi_samplers(self): + return (pybop.DREAM, pybop.EmceeHammerMCMC, pybop.DifferentialEvolutionMCMC) + + @pytest.fixture( + params=[ + NUTS, + DREAM, + AdaptiveCovarianceMCMC, + DifferentialEvolutionMCMC, + DramACMC, + EmceeHammerMCMC, + HaarioACMC, + HaarioBardenetACMC, + HamiltonianMCMC, + MALAMCMC, + MetropolisRandomWalkMCMC, + MonomialGammaHamiltonianMCMC, + PopulationMCMC, + RaoBlackwellACMC, + RelativisticMCMC, + SliceDoublingMCMC, + SliceRankShrinkingMCMC, + SliceStepoutMCMC, + ] + ) + def MCMC(self, request): + return request.param + + @pytest.mark.unit + def test_initialisation_and_run( + self, log_posterior, x0, chains, MCMC, multi_samplers + ): + sampler = pybop.MCMCSampler( + log_pdf=log_posterior, + chains=chains, + sampler=MCMC, + x0=x0, + max_iterations=1, + verbose=True, + ) + assert sampler._n_chains == chains + assert sampler._log_pdf == log_posterior + if isinstance(sampler.sampler, multi_samplers): + np.testing.assert_allclose(sampler._samplers[0]._x0[0], x0) + else: + np.testing.assert_allclose(sampler._samplers[0]._x0, x0) + + # Test incorrect __getattr__ + with pytest.raises( + AttributeError, match="'MCMCSampler' object has no attribute 'test'" + ): + sampler.__getattr__("test") + + # Test __setattr__ + sampler.some_attribute = 1 + assert sampler.some_attribute == 1 + sampler.verbose = True + assert sampler.verbose is True + + # Run the sampler + samples = sampler.run() + assert samples is not None + assert samples.shape == (chains, 1, 2) + + @pytest.mark.unit + def test_effectie_sample_size(self, log_posterior): + sampler = pybop.SliceStepoutMCMC( + log_pdf=log_posterior, + chains=1, + max_iterations=1, + ) + results = sampler.run() + summary = pybop.PosteriorSummary(results) + + with pytest.raises(ValueError, match="At least two samples must be given."): + summary.effective_sample_size() + + @pytest.mark.unit + def test_single_parameter_sampling(self, model, dataset, MCMC, chains): + parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.6, 0.2), + bounds=[0.58, 0.62], + ) + ) + problem = pybop.FittingProblem( + model, + parameters, + dataset, + ) + likelihood = pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=0.01) + log_posterior = pybop.LogPosterior(likelihood) + + # Skip RelativisticMCMC as it requires > 1 parameter + if issubclass(MCMC, RelativisticMCMC): + return + + # Construct and run + sampler = pybop.MCMCSampler( + log_pdf=log_posterior, + chains=chains, + sampler=MCMC, + max_iterations=3, + verbose=True, + ) + result = sampler.run() + summary = pybop.PosteriorSummary(result) + autocorr = summary.autocorrelation(result[0, :, 0]) + assert autocorr.shape == (result[0, :, 0].shape[0] - 2,) + + @pytest.mark.unit + def test_multi_log_pdf(self, log_posterior, x0, chains): + multi_log_posterior = [log_posterior, log_posterior, log_posterior] + sampler = pybop.MCMCSampler( + log_pdf=multi_log_posterior, + chains=chains, + sampler=HamiltonianMCMC, + x0=x0, + max_iterations=1, + ) + assert sampler._n_chains == chains + assert sampler._log_pdf == multi_log_posterior + + # Run the sampler + samples = sampler.run() + assert samples is not None + assert samples.shape == (chains, 1, 2) + + # Test incorrect multi log pdf + incorrect_multi_log_posterior = [log_posterior, log_posterior, chains] + with pytest.raises( + ValueError, match="All log pdf's must be instances of BaseCost" + ): + sampler = pybop.MCMCSampler( + log_pdf=incorrect_multi_log_posterior, + chains=chains, + sampler=HaarioBardenetACMC, + x0=x0, + max_iterations=1, + ) + + # Test incorrect number of parameters + likelihood_copy = copy.copy(log_posterior.likelihood) + likelihood_copy.parameters = pybop.Parameters( + likelihood_copy.parameters[ + "Positive electrode active material volume fraction" + ] + ) + new_multi_log_posterior = pybop.LogPosterior(likelihood_copy) + + with pytest.raises( + ValueError, match="All log pdf's must have the same number of parameters" + ): + sampler = pybop.MCMCSampler( + log_pdf=[log_posterior, log_posterior, new_multi_log_posterior], + chains=chains, + sampler=HaarioBardenetACMC, + x0=x0, + max_iterations=1, + ) + + @pytest.mark.unit + def test_invalid_initialisation(self, log_posterior, x0): + with pytest.raises(ValueError, match="Number of chains must be greater than 0"): + AdaptiveCovarianceMCMC( + log_pdf=log_posterior, + chains=0, + x0=x0, + ) + + with pytest.raises( + ValueError, match="Number of log pdf's must match number of chains" + ): + AdaptiveCovarianceMCMC( + log_pdf=[log_posterior, log_posterior, log_posterior], + chains=2, + x0=x0, + ) + + with pytest.raises( + ValueError, match="x0 must have the same number of parameters as log_pdf" + ): + AdaptiveCovarianceMCMC( + log_pdf=[log_posterior, log_posterior, log_posterior], + chains=3, + x0=[0.4, 0.4, 0.4, 0.4], + ) + + # SingleChain & MultiChain Sampler + @pytest.mark.parametrize( + "sampler", + [ + AdaptiveCovarianceMCMC, + DifferentialEvolutionMCMC, + ], + ) + @pytest.mark.unit + def test_no_chains_in_memory(self, log_posterior, x0, chains, sampler): + sampler = sampler( + log_pdf=log_posterior, + chains=chains, + x0=x0, + max_iterations=1, + chains_in_memory=False, + ) + assert sampler._chains_in_memory is False + + # Run the sampler + samples = sampler.run() + assert sampler._samples is not None + assert samples is None + + @patch("logging.basicConfig") + @patch("logging.info") + @pytest.mark.unit + def test_initialise_logging( + self, mock_info, mock_basicConfig, log_posterior, x0, chains + ): + sampler = AdaptiveCovarianceMCMC( + log_pdf=log_posterior, + chains=chains, + x0=x0, + parallel=True, + evaluation_files=["eval1.txt", "eval2.txt"], + chain_files=["chain1.txt", "chain2.txt"], + ) + + # Set parallel workers + sampler.set_parallel(parallel=2) + sampler._initialise_logging() + + # Check if basicConfig was called with correct arguments + mock_basicConfig.assert_called_once_with( + format="%(message)s", level=logging.INFO + ) + + # Check if correct messages were called + expected_calls = [ + call("Using Haario-Bardenet adaptive covariance MCMC"), + call("Generating 3 chains."), + call("Running in parallel with 2 worker processes."), + call("Writing chains to chain1.txt etc."), + call("Writing evaluations to eval1.txt etc."), + ] + mock_info.assert_has_calls(expected_calls, any_order=False) + + # Test when _log_to_screen is False + sampler._log_to_screen = False + sampler._initialise_logging() + assert mock_info.call_count == len(expected_calls) # No additional calls + + @pytest.mark.unit + def test_check_stopping_criteria(self, log_posterior, x0, chains): + sampler = AdaptiveCovarianceMCMC( + log_pdf=log_posterior, + chains=chains, + x0=x0, + ) + # Set stopping criteria + sampler.set_max_iterations(10) + assert sampler._max_iterations == 10 + + # Remove stopping criteria + sampler._max_iterations = None + with pytest.raises( + ValueError, match="At least one stopping criterion must be set." + ): + sampler._check_stopping_criteria() + + # Incorrect stopping criteria + with pytest.raises( + ValueError, match="Number of iterations must be greater than 0" + ): + sampler.set_max_iterations(-1) + + @pytest.mark.unit + def test_set_parallel(self, log_posterior, x0, chains): + sampler = AdaptiveCovarianceMCMC( + log_pdf=log_posterior, + chains=chains, + x0=x0, + ) + + # Disable parallelism + sampler.set_parallel(False) + assert sampler._parallel is False + assert sampler._n_workers == 1 + + # Enable parallelism + sampler.set_parallel(True) + assert sampler._parallel is True + + # Enable parallelism with number of workers + sampler.set_parallel(2) + assert sampler._parallel is True + assert sampler._n_workers == 2 + + # Test evaluator construction + sampler.set_parallel(2) + evaluator = sampler._create_evaluator() + assert isinstance(evaluator, ParallelEvaluator) + + @pytest.mark.unit + def test_base_sampler(self, log_posterior, x0): + sampler = pybop.BaseSampler(log_posterior, x0, chains=1, cov0=0.1) + with pytest.raises(NotImplementedError): + sampler.run() + + with pytest.raises( + ValueError, + match=re.escape("log_pdf must be a LogPosterior or List[LogPosterior]"), + ): + pybop.BaseSampler(pybop.WeightedCost(log_posterior), x0, chains=1, cov0=0.1) + + @pytest.mark.unit + def test_MCMC_sampler(self, log_posterior, x0, chains): + with pytest.raises(TypeError): + pybop.MCMCSampler( + log_pdf=log_posterior, + chains=chains, + sampler=log_posterior, # Incorrect sampler + ) diff --git a/tests/unit/test_solvers.py b/tests/unit/test_solvers.py new file mode 100644 index 000000000..3fe318378 --- /dev/null +++ b/tests/unit/test_solvers.py @@ -0,0 +1,75 @@ +import numpy as np +import pybamm +import pytest + +import pybop + + +class TestSolvers: + """ + A class to test the forward model solver interface + """ + + @pytest.fixture( + params=[ + pybamm.IDAKLUSolver(atol=1e-4, rtol=1e-4), + pybamm.CasadiSolver(atol=1e-4, rtol=1e-4, mode="safe"), + pybamm.CasadiSolver(atol=1e-4, rtol=1e-4, mode="fast with events"), + ] + ) + def solver(self, request): + solver = request.param + return solver.copy() + + @pytest.fixture + def model(self, solver): + parameter_set = pybop.ParameterSet.pybamm("Marquis2019") + model = pybop.lithium_ion.SPM(parameter_set=parameter_set, solver=solver) + return model + + @pytest.mark.unit + def test_solvers_with_model_predict(self, model, solver): + assert model.solver == solver + assert model.solver.atol == 1e-4 + assert model.solver.rtol == 1e-4 + + # Ensure solver is functional + sol = model.predict(t_eval=np.linspace(0, 1, 100)) + assert np.isfinite(sol["Voltage [V]"].data).all() + + signals = ["Voltage [V]", "Bulk open-circuit voltage [V]"] + additional_vars = [ + "Maximum negative particle concentration", + "Positive electrode volume-averaged concentration [mol.m-3]", + ] + + parameters = pybop.Parameters( + pybop.Parameter( + "Negative electrode conductivity [S.m-1]", prior=pybop.Uniform(0.1, 100) + ) + ) + dataset = pybop.Dataset( + { + "Time [s]": sol["Time [s]"].data, + "Current function [A]": sol["Current [A]"].data, + "Voltage [V]": sol["Voltage [V]"].data, + "Bulk open-circuit voltage [V]": sol[ + "Bulk open-circuit voltage [V]" + ].data, + } + ) + problem = pybop.FittingProblem( + model, + parameters=parameters, + dataset=dataset, + signal=signals, + additional_variables=additional_vars, + ) + + y = problem.evaluate(inputs={"Negative electrode conductivity [S.m-1]": 10}) + + for signal in signals: + assert np.isfinite(y[signal].data).all() + + if isinstance(model.solver, pybamm.IDAKLUSolver): + assert model.solver.output_variables is not None diff --git a/tests/unit/test_standalone.py b/tests/unit/test_standalone.py index 026692011..666d58b98 100644 --- a/tests/unit/test_standalone.py +++ b/tests/unit/test_standalone.py @@ -9,35 +9,75 @@ class TestStandalone: """ - Class for testing stanadalone components. + Class for testing standalone components. """ + @pytest.fixture + def dataset(self): + return pybop.Dataset( + { + "Time [s]": np.linspace(0, 360, 10), + "Current function [A]": 0.25 * np.ones(10), + "Voltage [V]": np.linspace(3.8, 3.7, 10), + } + ) + + @pytest.fixture + def parameter(self): + return pybop.Parameter( + "Positive electrode active material volume fraction", + prior=pybop.Gaussian(0.6, 0.02), + bounds=[0.58, 0.62], + ) + + @pytest.fixture + def model(self): + model = pybop.lithium_ion.SPM() + model.build(initial_state={"Initial open-circuit voltage [V]": 3.8}) + return model + + @pytest.fixture + def problem(self, model, parameter, dataset): + return pybop.FittingProblem( + model, + parameter, + dataset, + ) + + @pytest.fixture + def cost(self, problem): + return pybop.SumSquaredError(problem) + @pytest.mark.unit - def test_standalone_optimiser(self): - optim = StandaloneOptimiser() + def test_standalone_optimiser(self, cost): + # Define cost function + optim = StandaloneOptimiser(cost, maxiter=10, x0=[0.6]) assert optim.name() == "StandaloneOptimiser" - x, final_cost = optim.run() - assert optim.cost(optim.x0) > final_cost - np.testing.assert_allclose(x, [2, 4], atol=1e-2) + results = optim.run() + assert optim.cost(optim.x0) > results.final_cost + assert 0.0 <= results.x <= 1.0 # Test with bounds - optim = StandaloneOptimiser(bounds=dict(upper=[5, 6], lower=[1, 2])) + optim = StandaloneOptimiser( + cost, maxiter=10, x0=[0.6], bounds=dict(upper=[0.8], lower=[0.3]) + ) - x, final_cost = optim.run() - assert optim.cost(optim.x0) > final_cost - np.testing.assert_allclose(x, [2, 4], atol=1e-2) + results = optim.run() + assert optim.cost(optim.x0) > results.final_cost + assert 0.3 <= results.x <= 0.8 @pytest.mark.unit - def test_optimisation_on_standalone_cost(self): + def test_optimisation_on_standalone_cost(self, problem): # Build an Optimisation problem with a StandaloneCost - cost = StandaloneCost() + cost = StandaloneCost(problem) optim = pybop.SciPyDifferentialEvolution(cost=cost) - x, final_cost = optim.run() + results = optim.run() - initial_cost = optim.cost(cost.x0) - assert initial_cost > final_cost - np.testing.assert_allclose(final_cost, 42, atol=1e-1) + optim.x0 = optim.log["x"][0] + initial_cost = optim.cost(optim.x0) + assert initial_cost > results.final_cost + np.testing.assert_allclose(results.final_cost, 1460, atol=1) @pytest.mark.unit def test_standalone_problem(self): @@ -72,14 +112,14 @@ def test_standalone_problem(self): # Test the Problem with a Cost rmse_cost = pybop.RootMeanSquaredError(problem) rmse_x = rmse_cost([1, 2]) - rmse_grad_x = rmse_cost.evaluateS1([1, 2]) + rmse_grad_x = rmse_cost([1, 2], calculate_grad=True) np.testing.assert_allclose(rmse_x, 3.05615, atol=1e-2) np.testing.assert_allclose(rmse_grad_x[1], [-0.54645, 0.0], atol=1e-2) # Test the sensitivities sums_cost = pybop.SumSquaredError(problem) - x = sums_cost.evaluateS1([1, 2]) + x = sums_cost([1, 2], calculate_grad=True) np.testing.assert_allclose(x[0], 934.006734006734, atol=1e-2) np.testing.assert_allclose(x[1], [-334.006734, 0.0], atol=1e-2) diff --git a/tests/unit/test_transformations.py b/tests/unit/test_transformations.py new file mode 100644 index 000000000..daec0c8d6 --- /dev/null +++ b/tests/unit/test_transformations.py @@ -0,0 +1,237 @@ +import inspect + +import numpy as np +import pytest + +import pybop + + +class TestTransformation: + """ + A class to test the transformations. + """ + + @pytest.fixture + def parameters(self): + return pybop.Parameters( + pybop.Parameter( + "Identity", + transformation=pybop.IdentityTransformation(), + ), + pybop.Parameter( + "Scaled", + transformation=pybop.ScaledTransformation(coefficient=2.0, intercept=1), + ), + pybop.Parameter( + "Log", + transformation=pybop.LogTransformation(), + ), + ) + + @pytest.mark.unit + def test_identity_transformation(self, parameters): + q = np.asarray([5.0]) + transformation = parameters["Identity"].transformation + assert np.array_equal(transformation.to_model(q), q) + assert np.array_equal(transformation.to_search(q), q) + assert transformation.log_jacobian_det(q) == 0.0 + assert transformation.log_jacobian_det_S1(q) == (0.0, np.zeros(1)) + assert transformation.n_parameters == 1 + assert transformation.is_elementwise() + + jac, jac_S1 = transformation.jacobian_S1(q) + assert np.array_equal(jac, np.eye(1)) + assert np.array_equal(jac_S1, np.zeros((1, 1, 1))) + + # Test covariance transformation + cov = np.array([[0.5]]) + q = np.array([5.0]) + cov_transformed = transformation.convert_covariance_matrix(cov, q) + assert np.array_equal(cov_transformed, cov) + + @pytest.mark.unit + def test_scaled_transformation(self, parameters): + q = np.asarray([2.5]) + transformation = parameters["Scaled"].transformation + p = transformation.to_model(q) + assert np.allclose(p, (q / 2.0) - 1.0) + assert transformation.n_parameters == 1 + assert transformation.is_elementwise() + + q_transformed = transformation.to_search(p) + assert np.allclose(q_transformed, q) + assert np.allclose( + transformation.log_jacobian_det(q), np.sum(np.log(np.abs(2.0))) + ) + log_jac_det_S1 = transformation.log_jacobian_det_S1(q) + assert log_jac_det_S1[0] == np.sum(np.log(np.abs(2.0))) + assert log_jac_det_S1[1] == np.zeros(1) + + jac, jac_S1 = transformation.jacobian_S1(q) + assert np.array_equal(jac, np.diag([0.5])) + assert np.array_equal(jac_S1, np.zeros((1, 1, 1))) + + # Test covariance transformation + cov = np.array([[0.5]]) + cov_transformed = transformation.convert_covariance_matrix(cov, q) + assert np.array_equal(cov_transformed, cov * transformation.coefficient**2) + + # Test incorrect transform + with pytest.raises(ValueError, match="Unknown method:"): + transformation._transform(q, "bad-string") + + @pytest.mark.unit + def test_log_transformation(self, parameters): + q = np.asarray([10]) + transformation = parameters["Log"].transformation + p = transformation.to_model(q) + assert np.allclose(p, np.exp(q)) + assert transformation.n_parameters == 1 + + q_transformed = transformation.to_search(p) + assert np.allclose(q_transformed, q) + assert np.allclose(transformation.log_jacobian_det(q), np.sum(q)) + + log_jac_det_S1 = transformation.log_jacobian_det_S1(q) + n = transformation._n_parameters + assert log_jac_det_S1[0] == np.sum(q) + assert log_jac_det_S1[1] == np.ones(n) + + jac, jac_S1 = transformation.jacobian_S1(q) + assert np.array_equal(jac, np.diag(np.exp(q))) + jac_S1_def = np.zeros((n, n, n)) + rn = np.arange(n) + jac_S1_def[rn, rn, rn] = np.diagonal(jac) + assert np.array_equal(jac_S1, jac_S1_def) + + # Test covariance transformation + cov = np.array([[0.5]]) + cov_transformed = transformation.convert_covariance_matrix(cov, q) + assert np.array_equal(cov_transformed, cov * np.exp(q) ** -2) + + # Test incorrect transform + with pytest.raises(ValueError, match="Unknown method:"): + transformation._transform(q, "bad-string") + + @pytest.mark.unit + def test_composed_transformation(self, parameters): + # Test elementwise transformations + transformation = pybop.ComposedTransformation( + [parameters["Identity"].transformation, parameters["Scaled"].transformation] + ) + # Test appending transformations + transformation.append(parameters["Log"].transformation) + assert transformation.is_elementwise() is True + + q = np.asarray([5.0, 2.5, 10]) + p = transformation.to_model(q) + np.testing.assert_allclose( + p, np.asarray([5.0, ((q[1] / 2.0) - 1.0), np.exp(q[2])]) + ) + jac = transformation.jacobian(q) + jac_S1 = transformation.jacobian_S1(q) + np.testing.assert_allclose( + jac, + np.asarray([[1, 0.0, 0.0], [0.0, 0.5, 0.0], [0.0, 0.0, 2.202647e04]]), + rtol=1e-6, + ) + np.testing.assert_allclose(jac_S1[0], jac) + assert jac_S1[1].shape == (3, 3, 3) + np.testing.assert_allclose(jac_S1[1][2, 2, 2], 22026.4657948067) + np.testing.assert_allclose(jac_S1[1][0, :, :], np.zeros((3, 3))) + np.testing.assert_allclose(jac_S1[1][1, :, :], np.zeros((3, 3))) + + correct_output = np.sum(np.log(np.abs(2.0))) + np.sum(10) + log_jac_det = transformation.log_jacobian_det(q) + assert log_jac_det == correct_output + + log_jac_det_S1 = transformation.log_jacobian_det_S1(q) + assert log_jac_det_S1[0] == correct_output + np.testing.assert_allclose(log_jac_det_S1[1], np.asarray([0.0, 0.0, 1.0])) + + # Test composed with no transformations + with pytest.raises( + ValueError, match="Must have at least one sub-transformation." + ): + pybop.ComposedTransformation([]) + + # Test incorrect append object + with pytest.raises( + TypeError, match="The appended object must be a Transformation." + ): + pybop.ComposedTransformation( + [parameters["Identity"].transformation, "string"] + ) + + @pytest.mark.unit + def test_verify_input(self, parameters): + q = np.asarray([5.0]) + q_dict = {"Identity": q[0]} + transformation = parameters["Scaled"].transformation + assert transformation.verify_input(q) == q + assert transformation.verify_input(q.tolist()) == q + assert transformation.verify_input(q_dict) == q + + with pytest.raises( + TypeError, match="Transform must be a float, int, list, numpy array," + ): + transformation.verify_input("string") + + with pytest.raises(ValueError, match="Transform must have"): + transformation.verify_input(np.array([1.0, 2.0, 3.0])) + + +class TestBaseTransformation: + """ + A class to test the abstract base transformation class. + """ + + @pytest.fixture + def ConcreteTransformation(self): + class ConcreteTransformation(pybop.Transformation): + def jacobian(self, q): + pass + + def _transform(self, q, method): + pass + + return ConcreteTransformation() + + @pytest.mark.unit + def test_abstract_base_transformation(self): + with pytest.raises(TypeError): + pybop.Transformation() + + @pytest.mark.unit + def test_abstract_methods(self): + abstract_methods = ["jacobian", "_transform"] + for method in abstract_methods: + assert hasattr(pybop.Transformation, method) + assert getattr(pybop.Transformation, method).__isabstractmethod__ + + @pytest.mark.unit + def test_concrete_methods(self): + concrete_methods = [ + "convert_covariance_matrix", + "convert_standard_deviation", + "log_jacobian_det", + "to_model", + "to_search", + ] + for method in concrete_methods: + assert hasattr(pybop.Transformation, method) + assert not inspect.isabstract(getattr(pybop.Transformation, method)) + + @pytest.mark.unit + def test_not_implemented_methods(self, ConcreteTransformation): + not_implemented_methods = [ + "jacobian_S1", + "log_jacobian_det_S1", + ] + for method_name in not_implemented_methods: + with pytest.raises(NotImplementedError): + method = getattr(ConcreteTransformation, method_name) + method(np.array([1.0])) + + with pytest.raises(NotImplementedError): + ConcreteTransformation.is_elementwise()