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PEtab v2 import via amici #1634

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baa723e
Prepare for amici>=1.0.0
dweindl Oct 7, 2025
fb8ee2c
PEtab v2 import
dweindl Oct 28, 2025
089aa30
..
dweindl Nov 6, 2025
848567d
fix
dweindl Nov 6, 2025
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342 changes: 153 additions & 189 deletions doc/example/amici.ipynb
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22 changes: 11 additions & 11 deletions doc/example/conversion_reaction.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -108,18 +108,18 @@
"# load amici module (the usual starting point later for the analysis)\n",
"sys.path.insert(0, os.path.abspath(model_output_dir))\n",
"model_module = importlib.import_module(model_name)\n",
"model = model_module.getModel()\n",
"model.requireSensitivitiesForAllParameters()\n",
"model.setTimepoints(np.linspace(0, 10, 11))\n",
"model.setParameterScale(amici.ParameterScaling.log10)\n",
"model.setParameters([-0.3, -0.7])\n",
"solver = model.getSolver()\n",
"solver.setSensitivityMethod(amici.SensitivityMethod.forward)\n",
"solver.setSensitivityOrder(amici.SensitivityOrder.first)\n",
"model = model_module.get_model()\n",
"model.require_sensitivities_for_all_parameters()\n",
"model.set_timepoints(np.linspace(0, 10, 11))\n",
"model.set_parameter_scale(amici.ParameterScaling.log10)\n",
"model.set_parameters([-0.3, -0.7])\n",
"solver = model.create_solver()\n",
"solver.set_sensitivity_method(amici.SensitivityMethod.forward)\n",
"solver.set_sensitivity_order(amici.SensitivityOrder.first)\n",
"\n",
"# how to run amici now:\n",
"rdata = amici.runAmiciSimulation(model, solver, None)\n",
"amici.plotting.plotStateTrajectories(rdata)\n",
"rdata = amici.run_simulation(model, solver, None)\n",
"amici.plotting.plot_state_trajectories(rdata)\n",
"edata = amici.ExpData(rdata, 0.2, 0.0)"
]
},
Expand Down Expand Up @@ -329,7 +329,7 @@
")\n",
"\n",
"# run prediction\n",
"prediction = predictor(x=model.getUnscaledParameters())"
"prediction = predictor(x=model.get_unscaled_parameters())"
]
},
{
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2 changes: 1 addition & 1 deletion doc/example/getting_started.ipynb
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Expand Up @@ -516,7 +516,7 @@
"outputs": [],
"source": [
"# Set gradient computation method to adjoint\n",
"problem.objective.amici_solver.setSensitivityMethod(\n",
"problem.objective.amici_solver.set_sensitivity_method(\n",
" amici.SensitivityMethod.adjoint\n",
")"
]
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110 changes: 55 additions & 55 deletions doc/example/petab_import.ipynb
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Expand Up @@ -17,21 +17,19 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# install if not done yet\n",
"# !apt install libatlas-base-dev swig\n",
"# %pip install pypesto[amici,petab] --quiet\n",
"# %pip install git+https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab.git@master#subdirectory=src/python --quiet"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from pprint import pprint\n",
Expand All @@ -43,7 +41,9 @@
"import pypesto.optimize as optimize\n",
"import pypesto.petab\n",
"import pypesto.visualize as visualize"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -68,11 +68,9 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# a collection of models that can be simulated\n",
"\n",
Expand All @@ -92,7 +90,9 @@
"\n",
"# create a petab problem\n",
"petab_problem = petab.Problem.from_yaml(yaml_config)"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -110,23 +110,23 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"importer = pypesto.petab.PetabImporter(petab_problem, simulator_type=\"amici\")\n",
"factory = importer.create_objective_creator()\n",
"\n",
"model = factory.create_model(verbose=False)\n",
"\n",
"# some model properties\n",
"print(\"Model parameters:\", list(model.getParameterIds()), \"\\n\")\n",
"print(\"Model const parameters:\", list(model.getFixedParameterIds()), \"\\n\")\n",
"print(\"Model outputs: \", list(model.getObservableIds()), \"\\n\")\n",
"print(\"Model states: \", list(model.getStateIds()), \"\\n\")"
]
"print(\"Model parameters:\", list(model.get_parameter_ids()), \"\\n\")\n",
"print(\"Model const parameters:\", list(model.get_fixed_parameter_ids()), \"\\n\")\n",
"print(\"Model outputs: \", list(model.get_observable_ids()), \"\\n\")\n",
"print(\"Model states: \", list(model.get_state_ids()), \"\\n\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -151,15 +151,15 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"importer = pypesto.petab.PetabImporter.from_yaml(\n",
" yaml_config, simulator_type=\"amici\"\n",
")\n",
"problem = importer.create_problem() # creating the problem from the importer. The objective can be found at problem.objective"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -170,11 +170,9 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import libsbml\n",
"\n",
Expand All @@ -185,10 +183,12 @@
"obj = factory.create_objective()\n",
"\n",
"# for some models, hyperparameters need to be adjusted\n",
"# obj.amici_solver.setMaxSteps(10000)\n",
"# obj.amici_solver.setRelativeTolerance(1e-7)\n",
"# obj.amici_solver.setAbsoluteTolerance(1e-7)"
]
"# obj.amici_solver.set_max_steps(10000)\n",
"# obj.amici_solver.set_relative_tolerance(1e-7)\n",
"# obj.amici_solver.set_absolute_tolerance(1e-7)"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -199,9 +199,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ret = obj(\n",
" petab_problem.x_nominal_scaled,\n",
Expand All @@ -210,7 +208,9 @@
" return_dict=True,\n",
")\n",
"pprint(ret)"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -221,12 +221,12 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"problem = importer.create_problem(obj)"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -237,13 +237,13 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(f\"{problem.x_fixed_indices=}\")\n",
"print(f\"{problem.x_free_indices=}\")"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -254,22 +254,20 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"objective = problem.objective\n",
"fval, gradient = objective(\n",
" petab_problem.x_nominal_free_scaled, sensi_orders=(0, 1)\n",
")\n",
"print(f\"{fval=}\\n{gradient=}\")"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"eps = 1e-4\n",
"\n",
Expand All @@ -291,7 +289,9 @@
"for i, (g, f) in enumerate(zip(gradient, fdval)):\n",
" print(f\"{i=}: {g=:9f},\\t{f=:9f},\\t{g - f=:9f}\")\n",
"print(f\"l2 difference: {np.linalg.norm(gradient - fdval):.2e}\")"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -309,11 +309,9 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"optimizer = optimize.ScipyOptimizer()\n",
"\n",
Expand All @@ -324,7 +322,9 @@
"result = optimize.minimize(\n",
" problem=problem, optimizer=optimizer, n_starts=10, engine=engine\n",
")"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -342,19 +342,19 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"problem = importer.create_problem(\n",
" startpoint_kwargs={\"check_fval\": True, \"check_grad\": True}\n",
")"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -372,12 +372,12 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"result.optimize_result.fval"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -388,17 +388,17 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ref = visualize.create_references(\n",
" x=petab_problem.x_nominal_scaled, fval=obj(petab_problem.x_nominal_scaled)\n",
")\n",
"\n",
"visualize.waterfall(result, reference=ref, scale_y=\"lin\")\n",
"visualize.parameters(result, reference=ref);"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -413,21 +413,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# we need to explicitly import the method\n",
"from pypesto.visualize.model_fit import visualize_optimized_model_fit\n",
"\n",
"visualize_optimized_model_fit(\n",
" petab_problem=petab_problem, result=result, pypesto_problem=problem\n",
");"
]
],
"outputs": [],
"execution_count": null
}
],
"metadata": {
Expand Down
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