From 37a224297598227bb61d023bf07de2db9cc0a14e Mon Sep 17 00:00:00 2001 From: Anirban Chaudhuri <75496534+anirban-chaudhuri@users.noreply.github.com> Date: Wed, 12 Jul 2023 09:34:59 -0400 Subject: [PATCH] Updating from main (#230) * Added get_name on the mira parameter keys for safety (#218) * Added get_name on the mira parameter keys for safety * Removed test for SIDARTHE which fails due to https://github.com/indralab/mira/issues/195 * checked out scenario1 from main so that it doesn't clobber Vignesh's changes * Added test that confirms https://github.com/indralab/mira/pull/196 solves https://github.com/indralab/mira/issues/195 * Added pin to latest mira commit * bump mira (#227) * bump mira * bumped AMR file --------- Co-authored-by: Jeremy Zucker Co-authored-by: Sam Witty --- ...eling_Representation_Issues_Examples.ipynb | 220 +++- notebook/integration_demo/demo_ensemble.ipynb | 28 +- .../calibrated_sample_results.csv | 1000 ++++++++--------- setup.cfg | 2 +- src/pyciemss/PetriNetODE/base.py | 5 +- test/models/AMR_examples/scenario1_a.json | 292 +++++ test/test_mira/test_rate_law.py | 2 +- test/test_petrinet_ode/test_ode_interfaces.py | 9 +- 8 files changed, 1005 insertions(+), 553 deletions(-) create mode 100644 test/models/AMR_examples/scenario1_a.json diff --git a/notebook/Examples_for_TA2_Model_Representation/Modeling_Representation_Issues_Examples.ipynb b/notebook/Examples_for_TA2_Model_Representation/Modeling_Representation_Issues_Examples.ipynb index 757547d28..d3f5bf908 100644 --- a/notebook/Examples_for_TA2_Model_Representation/Modeling_Representation_Issues_Examples.ipynb +++ b/notebook/Examples_for_TA2_Model_Representation/Modeling_Representation_Issues_Examples.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "id": "ecbb71f2-4ac5-459f-8c3b-825b5f520bf3", "metadata": {}, "outputs": [], @@ -21,28 +21,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 2, "id": "8d9914c4", "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'requests'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 8\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmira\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodeling\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01maskenet\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpetrinet\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AskeNetPetriNetModel\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyciemss\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minterfaces\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m setup_model, calibrate, intervene\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyciemss\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mPetriNetODE\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minterfaces\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m load_petri_model, setup_petri_model\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\n", - "File \u001b[0;32m~/Projects/pyciemss/src/pyciemss/PetriNetODE/interfaces.py:5\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyro\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minfer\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Predictive\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyciemss\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mPetriNetODE\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 6\u001b[0m PetriNetODESystem,\n\u001b[1;32m 7\u001b[0m ScaledBetaNoisePetriNetODESystem,\n\u001b[1;32m 8\u001b[0m MiraPetriNetODESystem,\n\u001b[1;32m 9\u001b[0m get_name,\n\u001b[1;32m 10\u001b[0m )\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyciemss\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrisk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mouu\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m computeRisk, solveOUU\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyciemss\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrisk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrisk_measures\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m alpha_quantile, alpha_superquantile\n", - "File \u001b[0;32m~/Projects/pyciemss/src/pyciemss/PetriNetODE/base.py:8\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Dict, List, Optional, Union, OrderedDict\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrequests\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnetworkx\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'requests'" - ] - } - ], + "outputs": [], "source": [ "import mira\n", "from mira.metamodel import Concept, ControlledConversion, GroupedControlledConversion, Initial, NaturalConversion, Parameter, Template, TemplateModel\n", - "from mira.modeling.viz import GraphicalModel\n", "from mira.modeling import Model\n", "from mira.modeling.askenet.petrinet import AskeNetPetriNetModel\n", "import torch\n", @@ -56,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "id": "f6cb8d58-c4f1-4301-bd49-46875ba35571", "metadata": {}, "outputs": [], @@ -90,9 +75,9 @@ " 'gamma': Parameter(name='gamma', value=0.2), # recovery rate\n", " },\n", " initials={\n", - " 'susceptible_population': (Initial(concept=self.susceptible, value=total_pop-1)),\n", - " 'infectious_population': (Initial(concept=self.infectious, value=1)),\n", - " 'recovered_population': (Initial(concept=self.recovered, value=0))\n", + " 'susceptible_population': (Initial(concept=susceptible, value=total_pop-1)),\n", + " 'infectious_population': (Initial(concept=infectious, value=1)),\n", + " 'recovered_population': (Initial(concept=recovered, value=0))\n", " }\n", " )\n", " model=Model(template_model)\n", @@ -101,27 +86,198 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 8, "id": "6a1478f5-4a77-44e2-a729-1cf4f200eebd", "metadata": {}, + "outputs": [], + "source": [ + "model = make_model()" + ] + }, + { + "cell_type": "markdown", + "id": "a5ce19e8-d4f3-48e2-b970-bbec29c3d9e6", + "metadata": {}, + "source": [ + "## SIDARTHE" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bad9a01b-bf24-4dec-bf90-a90222dc2537", + "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'self' is not defined", + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/zuck016/Projects/Proposals/ASKEM/build/clean-build/src/pyciemss/PetriNetODE/base.py:330: UserWarning: Parameter (('Infected', ('identity', 'ido:0000511')), ('Healed', ('identity', 'ido:0000592')), 'NaturalConversion', 'rate') has value None and will be set to Uniform(0, 1)\n", + " warnings.warn(warnings_string)\n" + ] + } + ], + "source": [ + "from pyciemss.PetriNetODE.base import get_name\n", + "sidarthe_amr = '../../test/models/AMR_examples/BIOMD0000000955_askenet.json'\n", + "raw_sidarthe = load_petri_model(sidarthe_amr)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "032e3eef-65b1-4d1c-bd1f-c56cbb48c753", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['beta', 'gamma', 'delta', 'alpha', 'epsilon', 'zeta', 'lambda', 'eta', 'rho', 'theta', 'kappa', 'mu', 'nu', 'xi', 'tau', 'sigma'])\n", + "dict_keys(['beta', 'gamma', 'delta', 'alpha', 'epsilon', 'zeta', (('Infected', ('identity', 'ido:0000511')), ('Healed', ('identity', 'ido:0000592')), 'NaturalConversion', 'rate'), 'eta', 'rho', 'theta', 'kappa', 'mu', 'nu', 'xi', 'tau', 'sigma'])\n" + ] + }, + { + "ename": "AssertionError", + "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mmake_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[17], line 26\u001b[0m, in \u001b[0;36mmake_model\u001b[0;34m()\u001b[0m\n\u001b[1;32m 11\u001b[0m S_to_I \u001b[38;5;241m=\u001b[39m ControlledConversion(\n\u001b[1;32m 12\u001b[0m controller \u001b[38;5;241m=\u001b[39m infectious,\n\u001b[1;32m 13\u001b[0m subject\u001b[38;5;241m=\u001b[39msusceptible,\n\u001b[1;32m 14\u001b[0m outcome\u001b[38;5;241m=\u001b[39minfectious,\n\u001b[1;32m 15\u001b[0m rate_law\u001b[38;5;241m=\u001b[39m(beta\u001b[38;5;241m/\u001b[39mtotal_pop)\u001b[38;5;241m*\u001b[39mS\u001b[38;5;241m*\u001b[39mI\n\u001b[1;32m 16\u001b[0m )\n\u001b[1;32m 17\u001b[0m I_to_R \u001b[38;5;241m=\u001b[39m NaturalConversion(\n\u001b[1;32m 18\u001b[0m subject\u001b[38;5;241m=\u001b[39minfectious,\n\u001b[1;32m 19\u001b[0m outcome\u001b[38;5;241m=\u001b[39mrecovered,\n\u001b[1;32m 20\u001b[0m rate_law\u001b[38;5;241m=\u001b[39mgamma\u001b[38;5;241m*\u001b[39mI\n\u001b[1;32m 21\u001b[0m )\n\u001b[1;32m 22\u001b[0m template_model \u001b[38;5;241m=\u001b[39m TemplateModel(\n\u001b[1;32m 23\u001b[0m templates\u001b[38;5;241m=\u001b[39m[S_to_I, I_to_R],\n\u001b[1;32m 24\u001b[0m parameters\u001b[38;5;241m=\u001b[39m{\n\u001b[1;32m 25\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbeta\u001b[39m\u001b[38;5;124m'\u001b[39m: Parameter(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbeta\u001b[39m\u001b[38;5;124m'\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.55\u001b[39m), \u001b[38;5;66;03m# transmission rate\u001b[39;00m\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtotal_population\u001b[39m\u001b[38;5;124m'\u001b[39m: Parameter(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtotal_population\u001b[39m\u001b[38;5;124m'\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241m.\u001b[39mtotal_pop),\n\u001b[1;32m 27\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgamma\u001b[39m\u001b[38;5;124m'\u001b[39m: Parameter(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgamma\u001b[39m\u001b[38;5;124m'\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m), \u001b[38;5;66;03m# recovery rate\u001b[39;00m\n\u001b[1;32m 28\u001b[0m },\n\u001b[1;32m 29\u001b[0m initials\u001b[38;5;241m=\u001b[39m{\n\u001b[1;32m 30\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msusceptible_population\u001b[39m\u001b[38;5;124m'\u001b[39m: (Initial(concept\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msusceptible, value\u001b[38;5;241m=\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtotal_pop\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))),\n\u001b[1;32m 31\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minfectious_population\u001b[39m\u001b[38;5;124m'\u001b[39m: (Initial(concept\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfectious, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)),\n\u001b[1;32m 32\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrecovered_population\u001b[39m\u001b[38;5;124m'\u001b[39m: (Initial(concept\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrecovered, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m))\n\u001b[1;32m 33\u001b[0m }\n\u001b[1;32m 34\u001b[0m )\n\u001b[1;32m 35\u001b[0m model\u001b[38;5;241m=\u001b[39mModel(template_model)\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model\n", - "\u001b[0;31mNameError\u001b[0m: name 'self' is not defined" + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[17], line 7\u001b[0m\n\u001b[1;32m 5\u001b[0m model \u001b[38;5;241m=\u001b[39m mira\u001b[38;5;241m.\u001b[39mmodeling\u001b[38;5;241m.\u001b[39mModel(template_model)\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(model\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mkeys())\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlambda\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m model\u001b[38;5;241m.\u001b[39mparameters\n", + "\u001b[0;31mAssertionError\u001b[0m: " ] } ], "source": [ - "model = make_model()" + "from mira.sources.askenet import model_from_json_file, model_from_url\n", + "template_model = model_from_json_file(sidarthe_amr)\n", + "assert 'lambda' in template_model.parameters\n", + "model = mira.modeling.Model(template_model)\n", + "assert 'lambda' in model.parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "cd800b72-2a99-4060-b3e9-941af26da5ca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.parameters[(('Infected', ('identity', 'ido:0000511')),\n", + " ('Healed', ('identity', 'ido:0000592')),\n", + " 'NaturalConversion',\n", + " 'rate')].value is None" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "91089ff1-0618-4c35-bf3c-a8527799cbcf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "({'lambda'},\n", + " {(('Infected', ('identity', 'ido:0000511')),\n", + " ('Healed', ('identity', 'ido:0000592')),\n", + " 'NaturalConversion',\n", + " 'rate')})" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set(template_model.parameters.keys()) - set(model.parameters.keys()), set(model.parameters.keys()) - set(template_model.parameters.keys())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e883c6a6-6359-412b-8928-b127126874ed", + "metadata": {}, + "outputs": [], + "source": [ + "start_state = {\n", + " get_name(v): v.data[\"initial_value\"] for v in raw_sidarthe.G.variables.values()\n", + " }" ] }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1386a0a6-710b-4748-917d-d59df3522fc0", + "metadata": {}, + "outputs": [], + "source": [ + "sidarthe = setup_petri_model(raw_sidarthe, 0.0, start_state)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "94e0e2ed-9060-430b-a4a3-a99f2aa072de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'beta': ,\n", + " 'gamma': ,\n", + " 'delta': ,\n", + " 'alpha': ,\n", + " 'epsilon': ,\n", + " 'zeta': ,\n", + " (('Infected', ('identity', 'ido:0000511')),\n", + " ('Healed', ('identity', 'ido:0000592')),\n", + " 'NaturalConversion',\n", + " 'rate'): ,\n", + " 'eta': ,\n", + " 'rho': ,\n", + " 'theta': ,\n", + " 'kappa': ,\n", + " 'mu': ,\n", + " 'nu': ,\n", + " 'xi': ,\n", + " 'tau': ,\n", + " 'sigma': }" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sidarthe.G.parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90f760d5-c269-4906-aa92-46577b455f6b", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 7, @@ -1548,9 +1704,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "clean-build", "language": "python", - "name": "python3" + "name": "clean-build" }, "language_info": { "codemirror_mode": { diff --git a/notebook/integration_demo/demo_ensemble.ipynb b/notebook/integration_demo/demo_ensemble.ipynb index 3e84d9193..1f20a53d8 100644 --- a/notebook/integration_demo/demo_ensemble.ipynb +++ b/notebook/integration_demo/demo_ensemble.ipynb @@ -693,20 +693,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "iteration 0: loss = 66.16035544872284\n", - "iteration 25: loss = 42.31934851408005\n", - "iteration 50: loss = 33.17540234327316\n", - "iteration 75: loss = 27.15283751487732\n", - "iteration 100: loss = 21.151540756225586\n", - "iteration 125: loss = 17.88287889957428\n", - "iteration 150: loss = 19.490506768226624\n", - "iteration 175: loss = 16.146777868270874\n", - "iteration 200: loss = 16.498568773269653\n", - "iteration 225: loss = 15.53571480512619\n", - "iteration 250: loss = 15.07484495639801\n", - "iteration 275: loss = 14.595208406448364\n", - "iteration 300: loss = 14.831865012645721\n", - "iteration 325: loss = 15.439243197441101\n" + "iteration 0: loss = 63.51079273223877\n", + "iteration 25: loss = 40.8916078209877\n", + "iteration 50: loss = 37.46487545967102\n", + "iteration 75: loss = 25.852425813674927\n", + "iteration 100: loss = 21.470157742500305\n", + "iteration 125: loss = 17.58678960800171\n", + "iteration 150: loss = 15.267673671245575\n", + "iteration 175: loss = 16.10206639766693\n", + "iteration 200: loss = 15.993813276290894\n", + "iteration 225: loss = 15.860064268112183\n", + "iteration 250: loss = 13.152705788612366\n", + "iteration 275: loss = 16.014341235160828\n", + "iteration 300: loss = 15.707162857055664\n", + "iteration 325: loss = 12.456510186195374\n" ] } ], diff --git a/notebook/integration_demo/results_petri_ensemble/calibrated_sample_results.csv b/notebook/integration_demo/results_petri_ensemble/calibrated_sample_results.csv index 53d431de9..f8e752712 100644 --- a/notebook/integration_demo/results_petri_ensemble/calibrated_sample_results.csv +++ b/notebook/integration_demo/results_petri_ensemble/calibrated_sample_results.csv @@ -1,501 +1,501 @@ timepoint_id,sample_id,model_0/beta_param,model_0/gamma_param,model_1/beta_param,model_1/gamma_param,model_1/delta_param,model_1/alpha_param,model_1/epsilon_param,model_1/zeta_param,"model_1/(('Infected', ('identity', 'ido:0000511')), ('Healed', ('identity', 'ido:0000592')), 'NaturalConversion', 'rate')_param",model_1/eta_param,model_1/rho_param,model_1/theta_param,model_1/kappa_param,model_1/mu_param,model_1/nu_param,model_1/xi_param,model_1/tau_param,model_1/sigma_param,model_0_weight,model_1_weight,I_sol,R_sol,S_sol -0,0,0.02753741294145584,0.10808221995830536,0.009341917932033539,0.5192248821258545,0.009026715531945229,0.49792781472206116,0.16478514671325684,0.10939453542232513,0.4332796633243561,0.1232658326625824,0.02812117710709572,0.385309100151062,0.01723608747124672,0.01962575875222683,0.023481564596295357,0.01888166554272175,0.010141394101083279,0.015748940408229828,0.7408677935600281,0.2591322064399719,0.7408687472343445,8.007500912343701e-12,741.126953125 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+4,99,0.026368681341409683,0.10807503759860992,0.011001638136804104,0.41776254773139954,0.009366938844323158,0.5717196464538574,0.2003476917743683,0.10241158306598663,0.5612789988517761,0.13055738806724548,0.03004145622253418,0.3264865279197693,0.017149850726127625,0.0202367901802063,0.0286702960729599,0.019304588437080383,0.009050717577338219,0.018475085496902466,0.1542626917362213,0.8457373380661011,0.11124610900878906,0.05688760429620743,155.0946044921875 diff --git a/setup.cfg b/setup.cfg index 4c365747d..0cdada8a1 100644 --- a/setup.cfg +++ b/setup.cfg @@ -18,7 +18,7 @@ install_requires = torchdiffeq networkx pandas - mira @ git+https://github.com/indralab/mira.git@3a3a931ee52c9e5b976ea849424b1a5c65f4c8d2 + mira @ git+https://github.com/indralab/mira.git@56bf4c0d77919142684c8cbfb3521b7bf4470888 xarray netcdf4 h5netcdf diff --git a/src/pyciemss/PetriNetODE/base.py b/src/pyciemss/PetriNetODE/base.py index 4088bc251..0831396c8 100644 --- a/src/pyciemss/PetriNetODE/base.py +++ b/src/pyciemss/PetriNetODE/base.py @@ -463,7 +463,9 @@ def deriv(self, t: Time, state: State) -> StateDeriv: # Get the current state states = {v: state[i] for i, v in enumerate(self.var_order.keys())} # Get the parameters - parameters = {k: getattr (self, k) for k in self.G.parameters} + parameters = {get_name(param_info): getattr (self, get_name(param_info)) + for param_info in self.G.parameters.values() + } # Evaluate the rate laws for each transition deriv_tensor = self.compiled_rate_law(**states, **parameters, **dict(t=t)) @@ -500,7 +502,6 @@ def mass_action_deriv(self, t: Time, state: State) -> StateDeriv: def param_prior(self): for param_info in self.G.parameters.values(): param_name = get_name(param_info) - param_value = param_info.value if isinstance(param_value, torch.nn.Parameter): setattr(self, param_name, pyro.param(param_name, param_value)) diff --git a/test/models/AMR_examples/scenario1_a.json b/test/models/AMR_examples/scenario1_a.json new file mode 100644 index 000000000..5b544c230 --- /dev/null +++ b/test/models/AMR_examples/scenario1_a.json @@ -0,0 +1,292 @@ +{ + "name": "Scenario 1a", + "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json", + "schema_name": "petrinet", + "description": "Scenario 1a", + "model_version": "0.1", + "properties": {}, + "model": { + "states": [ + { + "id": "S", + "name": "S", + "grounding": { + "identifiers": { + "ido": "0000514" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "I", + "name": "I", + "grounding": { + "identifiers": { + "ido": "0000511" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "E", + "name": "E", + "grounding": { + "identifiers": { + "apollosv": "0000154" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "R", + "name": "R", + "grounding": { + "identifiers": { + "ido": "0000592" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "D", + "name": "D", + "grounding": { + "identifiers": { + "ncit": "C28554" + }, + "modifiers": {} + }, + "units": { + "expression": "person", + "expression_mathml": "person" + } + } + ], + "transitions": [ + { + "id": "t1", + "input": [ + "I", + "S" + ], + "output": [ + "I", + "E" + ], + "properties": { + "name": "t1" + } + }, + { + "id": "t2", + "input": [ + "E" + ], + "output": [ + "I" + ], + "properties": { + "name": "t2" + } + }, + { + "id": "t3", + "input": [ + "I" + ], + "output": [ + "R" + ], + "properties": { + "name": "t3" + } + }, + { + "id": "t4", + "input": [ + "I" + ], + "output": [ + "D" + ], + "properties": { + "name": "t4" + } + } + ] + }, + "semantics": { + "ode": { + "rates": [ + { + "target": "t1", + "expression": "I*S*kappa*(beta_c + (-beta_c + beta_s)/(1 + exp(-k*(-t + t_0))))/N", + "expression_mathml": "ISkappabeta_cbeta_cbeta_s1kt_0tN" + }, + { + "target": "t2", + "expression": "E*delta", + "expression_mathml": "Edelta" + }, + { + "target": "t3", + "expression": "I*gamma*(1 - alpha)", + "expression_mathml": "Igamma1alpha" + }, + { + "target": "t4", + "expression": "I*alpha*rho", + "expression_mathml": "Ialpharho" + } + ], + "initials": [ + { + "target": "S", + "expression": "5599999.00000000", + "expression_mathml": "5599999.0" + }, + { + "target": "I", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "E", + "expression": "1.00000000000000", + "expression_mathml": "1.0" + }, + { + "target": "R", + "expression": "0.0", + "expression_mathml": "0.0" + }, + { + "target": "D", + "expression": "0.0", + "expression_mathml": "0.0" + } + ], + "parameters": [ + { + "id": "N", + "value": 5600000.0, + "units": { + "expression": "person", + "expression_mathml": "person" + } + }, + { + "id": "beta_c", + "value": 0.4, + "units": { + "expression": "1/(day*person)", + "expression_mathml": "1dayperson" + } + }, + { + "id": "beta_s", + "value": 1.0, + "units": { + "expression": "1/(day*person)", + "expression_mathml": "1dayperson" + } + }, + { + "id": "k", + "value": 5.0, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "kappa", + "value": 0.45454545454545453, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "t_0", + "value": 89.0, + "units": { + "expression": "day", + "expression_mathml": "day" + } + }, + { + "id": "delta", + "value": 0.2, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "alpha", + "value": 6.4e-05, + "units": { + "expression": "1", + "expression_mathml": "1" + } + }, + { + "id": "gamma", + "value": 0.09090909090909091, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + }, + { + "id": "rho", + "value": 0.1111111111111111, + "units": { + "expression": "1/day", + "expression_mathml": "day-1" + } + } + ], + "observables": [], + "time": { + "id": "t", + "units": { + "expression": "day", + "expression_mathml": "day" + } + } + } + }, + "metadata": { + "annotations": { + "license": null, + "authors": [], + "references": [], + "time_scale": null, + "time_start": null, + "time_end": null, + "locations": [], + "pathogens": [], + "diseases": [], + "hosts": [], + "model_types": [] + } + } +} \ No newline at end of file diff --git a/test/test_mira/test_rate_law.py b/test/test_mira/test_rate_law.py index a9d513218..ff1d6ef52 100644 --- a/test/test_mira/test_rate_law.py +++ b/test/test_mira/test_rate_law.py @@ -132,7 +132,7 @@ def test_symbolic_flux_to_numeric_flux(self): def test_time_varying_parameter_rate_law(self): """Test that the rate law can be compiled correctly.""" - url = 'https://raw.githubusercontent.com/indralab/mira/hackathon/notebooks/hackathon_2023.07/scenario1_c.json' + url = 'https://raw.githubusercontent.com/indralab/mira/56bf4c0d77919142684c8cbfb3521b7bf4470888/notebooks/hackathon_2023.07/scenario1_c.json' scenario1_c = load_petri_model(url, compile_rate_law_p=True) expected_rate_law_str = 'kappa*(beta_nc + (beta_c - beta_nc)/(1 + exp(-k_2*(-t + t_1))) + (-beta_c + beta_s)/(1 + exp(-k_1*(-t + t_0))))' expected_rate_law_symbolic = sympy.sympify(expected_rate_law_str) diff --git a/test/test_petrinet_ode/test_ode_interfaces.py b/test/test_petrinet_ode/test_ode_interfaces.py index 34b5a4b69..0de5ad731 100644 --- a/test/test_petrinet_ode/test_ode_interfaces.py +++ b/test/test_petrinet_ode/test_ode_interfaces.py @@ -91,7 +91,6 @@ def test_samples_dtype(self): for col_name in s.columns[2:]: self.assertEqual(s[col_name].dtype, np.float64) - class TestODEInterfaces(unittest.TestCase): @@ -294,9 +293,13 @@ def test_load_and_calibrate_and_sample_petri_model(self): actual_intervened_samples = load_and_calibrate_and_sample_petri_model(ASKENET_PATH, data_path, num_samples, timepoints, interventions = interventions, start_state=initial_state, num_iterations=2) assert_frame_equal(expected_intervened_samples, actual_intervened_samples, check_exact=False, atol=1e-5) + SCENARIO_1a_H2 = 'test/models/AMR_examples/scenario1_a.json' + scenario1a_output = load_and_sample_petri_model(SCENARIO_1a_H2, num_samples, timepoints) + self.assertTrue(isinstance(scenario1a_output, pd.DataFrame)) - - + SIDARTHE = 'test/models/AMR_examples/BIOMD0000000955_askenet.json' + sidarthe_output = load_and_sample_petri_model(SIDARTHE, num_samples, timepoints) + self.assertTrue(isinstance(sidarthe_output, pd.DataFrame))