Fixes benchmarks

.github/workflows/test_on_push.yaml → .github/workflows/test_on_pull_request.yaml

@@ -105,6 +105,37 @@ 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

CHANGELOG.md

@@ -17,6 +17,7 @@
 
 ## Bug Fixes
 
+- [#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

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":{

benchmarks/benchmark_model.py

@@ -26,19 +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)
         )
@@ -58,7 +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):
@@ -69,7 +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):
         """

benchmarks/benchmark_optim_construction.py

@@ -28,7 +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),
@@ -41,7 +41,7 @@ def setup(self, model, parameter_set, optimiser):
                 bounds=[0.375, 0.625],
                 initial_value=0.51,
             ),
-        ]
+        )
 
         # Generate synthetic data
         sigma = 0.001
@@ -75,7 +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)
 
@@ -86,6 +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])

benchmarks/benchmark_parameterisation.py

@@ -29,7 +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()
@@ -45,7 +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),
@@ -56,7 +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
@@ -110,7 +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()
 

benchmarks/benchmark_track_parameterisation.py

@@ -45,7 +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),
@@ -56,7 +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

tests/unit/test_models.py

@@ -82,6 +82,12 @@ def test_predict_without_pybamm(self, model):
         ):
             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