Add support in JAX backend for PEtab v2 #3115
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -32,7 +32,8 @@ | |
| "outputs": [], | ||
| "source": [ | ||
| "import petab.v1 as petab\n", | ||
| "from amici.importers.petab import *\n", | ||
| "from petab.v2 import Problem\n", | ||
| "\n", | ||
| "# Define the model name and YAML file location\n", | ||
| "model_name = \"Boehm_JProteomeRes2014\"\n", | ||
|
|
@@ -41,14 +42,20 @@ | |
| " f\"master/Benchmark-Models/{model_name}/{model_name}.yaml\"\n", | ||
| ")\n", | ||
| "\n", | ||
| "# Load the PEtab problem from the YAML file as a PEtab v2 problem\n", | ||
| "# (the JAX backend only supports PEtab v2)\n", | ||
| "petab_problem = Problem.from_yaml(yaml_url)\n", | ||
| "\n", | ||
| "# Import the PEtab problem as a JAX-compatible AMICI problem\n", | ||
| "pi = PetabImporter(\n", | ||
| " petab_problem=petab_problem,\n", | ||
| " module_name=model_name,\n", | ||
| " compile_=True,\n", | ||
| " jax=True,\n", | ||
| ")\n", | ||
| "\n", | ||
| "jax_problem = pi.create_simulator(\n", | ||
| " force_import=True,\n", | ||
| ")" | ||
| ] | ||
| }, | ||
|
|
@@ -75,6 +82,16 @@ | |
| "llh, results = run_simulations(jax_problem)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "6c5b2980-13f0-42e9-b13e-0fce05793910", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "results" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "415962751301c64a", | ||
|
|
@@ -90,11 +107,11 @@ | |
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# # Define the simulation condition\n", | ||
| "experiment_condition = \"_petab_experiment_condition___default__\"\n", | ||
| "\n", | ||
| "# # Access the results for the specified condition\n", | ||
| "ic = results[\"dynamic_conditions\"].index(experiment_condition)\n", | ||
| "print(\"llh: \", results[\"llh\"][ic])\n", | ||
| "print(\"state variables: \", results[\"x\"][ic, :])" | ||
| ] | ||
|
|
@@ -146,8 +163,8 @@ | |
| "import matplotlib.pyplot as plt\n", | ||
| "import numpy as np\n", | ||
| "\n", | ||
| "# Define the experiment condition\n", | ||
| "experiment_condition = \"_petab_experiment_condition___default__\"\n", | ||
| "\n", | ||
| "\n", | ||
| "def plot_simulation(results):\n", | ||
|
|
@@ -158,7 +175,7 @@ | |
| " results (dict): Simulation results from run_simulations.\n", | ||
| " \"\"\"\n", | ||
| " # Extract the simulation results for the specific condition\n", | ||
| " ic = results[\"dynamic_conditions\"].index(experiment_condition)\n", | ||
| "\n", | ||
| " # Create a new figure for the state trajectories\n", | ||
| " plt.figure(figsize=(8, 6))\n", | ||
|
|
@@ -172,7 +189,7 @@ | |
| " # Add labels, legend, and grid\n", | ||
| " plt.xlabel(\"Time\")\n", | ||
| " plt.ylabel(\"State Values\")\n", | ||
| " plt.title(experiment_condition)\n", | ||
| " plt.legend()\n", | ||
| " plt.grid(True)\n", | ||
| " plt.show()\n", | ||
|
|
@@ -187,18 +204,7 @@ | |
| "id": "4fa97c33719c2277", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "`run_simulations` enables users to specify the simulation experiments to be executed. For more complex models, this allows for restricting simulations to a subset of experiments by passing a tuple of experiment ids under the keyword `simulation_experiments` to `run_simulations`." | ||
| ] | ||
| }, | ||
| { | ||
|
|
@@ -384,8 +390,8 @@ | |
| "from amici.jax import ReturnValue\n", | ||
| "\n", | ||
| "# Define the simulation condition\n", | ||
| "experiment_condition = \"_petab_experiment_condition___default__\"\n", | ||
| "ic = 0\n", | ||
| "\n", | ||
| "# Load condition-specific data\n", | ||
| "ts_dyn = jax_problem._ts_dyn[ic, :]\n", | ||
|
|
@@ -397,7 +403,7 @@ | |
| "nps = jax_problem._np_numeric[ic, :]\n", | ||
| "\n", | ||
| "# Load parameters for the specified condition\n", | ||
| "p = jax_problem.load_model_parameters(jax_problem._petab_problem.experiments[0], is_preeq=False)\n", | ||
| "\n", | ||
| "\n", | ||
| "# Define a function to compute the gradient with respect to dynamic timepoints\n", | ||
|
|
@@ -431,13 +437,17 @@ | |
| "cell_type": "markdown", | ||
| "id": "19ca88c8900584ce", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Model training" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "7f99c046d7d4e225", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "This setup makes it pretty straightforward to train models using [equinox](https://docs.kidger.site/equinox/) and [optax](https://optax.readthedocs.io/en/latest/) frameworks. Below we provide barebones implementation that runs training for 5 steps using Adam." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
|
|
@@ -569,16 +579,20 @@ | |
| "from amici.sim.sundials.petab.v1 import simulate_petab\n", | ||
| "\n", | ||
| "# Import the PEtab problem as a standard AMICI model\n", | ||
| "pi = PetabImporter(\n", | ||
| " petab_problem=petab_problem,\n", | ||
| " module_name=model_name,\n", | ||
| " compile_=True,\n", | ||
| " jax=False,\n", | ||
| ")\n", | ||
| "\n", | ||
| "amici_model = pi.create_simulator(\n", | ||
| " force_import=True,\n", | ||
| ")\n", | ||
| "\n", | ||
| "# Configure the solver with appropriate tolerances\n", | ||
| "amici_model.solver.set_absolute_tolerance(1e-8)\n", | ||
| "amici_model.solver.set_relative_tolerance(1e-16)\n", | ||
| "\n", | ||
| "# Prepare the parameters for the simulation\n", | ||
| "problem_parameters = dict(\n", | ||
|
|
@@ -594,86 +608,65 @@ | |
| "outputs": [], | ||
| "source": [ | ||
| "# Profile simulation only\n", | ||
| "amici_model.solver.set_sensitivity_order(SensitivityOrder.none)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "42cbc67bc09b67dc", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%%timeit\n", | ||
| "amici_model.simulate(petab_problem.get_x_nominal_dict())" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "4f1c06c5893a9c07", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Profile gradient computation using forward sensitivity analysis\n", | ||
| "amici_model.solver.set_sensitivity_order(SensitivityOrder.first)\n", | ||
| "amici_model.solver.set_sensitivity_method(SensitivityMethod.forward)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "7367a19bcea98597", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%%timeit\n", | ||
| "amici_model.simulate(petab_problem.get_x_nominal_dict())" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "a31e8eda806c2d7", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Profile gradient computation using adjoint sensitivity analysis\n", | ||
| "amici_model.solver.set_sensitivity_order(SensitivityOrder.first)\n", | ||
| "amici_model.solver.set_sensitivity_method(SensitivityMethod.adjoint)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "3f2ab1acb3ba818f", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%%timeit\n", | ||
| "amici_model.simulate(petab_problem.get_x_nominal_dict())" | ||
|
Collaborator
Author
There was a problem hiding this comment. When the notebook runs there is an error from this cell that the FIM was not computed: https://github.com/AMICI-dev/AMICI/actions/runs/21985309084/job/63517900881?pr=3115 @dweindl can you advise on that? |
||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
|
|
@@ -691,7 +684,8 @@ | |
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.12.3" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
|
|
||
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