Update notebooks to avoid merge conflicts

ciemss · Jul 10, 2023 · 27af0a8 · 27af0a8
1 parent fe6f625
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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
@@ -24,7 +24,21 @@
  "execution_count": 13,
  "id": "8d9914c4",
  "metadata": {},
- "outputs": [],
+ "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'"
+ ]
+ }
+ ],
  "source": [
  "import mira\n",
  "from mira.metamodel import Concept, ControlledConversion, GroupedControlledConversion, Initial, NaturalConversion, Parameter, Template, TemplateModel\n",
@@ -1534,9 +1548,9 @@
  ],
  "metadata": {
  "kernelspec": {
- "display_name": "pyciemss-main",
+ "display_name": "Python 3 (ipykernel)",
  "language": "python",
- "name": "pyciemss-main"
+ "name": "python3"
  },
  "language_info": {
  "codemirror_mode": {

diff --git a/notebook/integration_demo/demo.ipynb b/notebook/integration_demo/demo.ipynb
@@ -9,8 +9,11 @@
  "import os\n",
  "from pyciemss.PetriNetODE.interfaces import (\n",
  " load_and_sample_petri_model,\n",
- " load_and_calibrate_and_sample_petri_model\n",
- ")"
+ " load_and_calibrate_and_sample_petri_model,\n",
+ " load_and_optimize_and_sample_petri_model,\n",
+ " load_and_calibrate_and_optimize_and_sample_petri_model\n",
+ ")\n",
+ "import numpy as np"
  ]
  },
  {
@@ -148,6 +151,162 @@
  " os.path.join(DEMO_PATH, \"results_petri/calibrated_sample_results.csv\"), index=False\n",
  ")"
  ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## load_and_optimize_and_sample_petri_model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Time taken: (2.19e-01 seconds per model evaluation).\n",
+ "Performing risk-based optimization under uncertainty (using alpha-superquantile)\n",
+ "Estimated wait time 1643.2 seconds...\n",
+ "Optimization completed in time 302.05 seconds.\n",
+ "Optimal policy:\t0.0\n",
+ "Post-processing optimal policy...\n",
+ "Estimated risk at optimal policy [0.7047186017036438]\n",
+ "Optimal policy: 0.0\n",
+ "Estimated risk at optimal policy [0.7047186017036438]\n"
+ ]
+ }
+ ],
+ "source": [
+ "num_samples = 100\n",
+ "timepoints = [0.0, 1.0, 2.0, 3.0, 4.0]\n",
+ "\n",
+ "# Run the calibration and sampling\n",
+ "OBJFUN = lambda x: np.abs(x)\n",
+ "INTERVENTION = [(0.1, \"beta\")]\n",
+ "QOI = (\"scenario2dec_nday_average\", \"Infected_sol\", 2)\n",
+ "ouu_samples, opt_policy = load_and_optimize_and_sample_petri_model(\n",
+ " ASKENET_PATH,\n",
+ " num_samples,\n",
+ " timepoints=timepoints,\n",
+ " interventions=INTERVENTION,\n",
+ " qoi=QOI,\n",
+ " risk_bound=10.,\n",
+ " objfun=OBJFUN,\n",
+ " initial_guess=0.02,\n",
+ " bounds=[[0.],[3.]],\n",
+ " verbose=True,\n",
+ ")\n",
+ "\n",
+ "# Save results\n",
+ "ouu_samples.to_csv(\n",
+ " os.path.join(DEMO_PATH, \"results_petri/optimize_sample_results.csv\"), index=False\n",
+ ")\n",
+ "print(\"Optimal policy:\", opt_policy[\"policy\"])\n",
+ "print(\"Estimated risk at optimal policy\", opt_policy[\"risk\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## load_and_calibrate_and_optimize_and_sample_petri_model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "iteration 0: loss = 37.5659122467041\n",
+ "iteration 25: loss = 35.41392183303833\n",
+ "iteration 50: loss = 33.67425513267517\n",
+ "iteration 75: loss = 32.595038652420044\n",
+ "iteration 100: loss = 33.10420513153076\n",
+ "iteration 125: loss = 32.42216110229492\n",
+ "iteration 150: loss = 33.292160987854004\n",
+ "iteration 175: loss = 31.957093477249146\n",
+ "iteration 200: loss = 32.40791869163513\n",
+ "iteration 225: loss = 32.1630494594574\n",
+ "iteration 250: loss = 32.64620661735535\n",
+ "iteration 275: loss = 32.447582960128784\n",
+ "iteration 300: loss = 32.42112612724304\n",
+ "iteration 325: loss = 32.70798373222351\n",
+ "iteration 350: loss = 32.1295063495636\n",
+ "iteration 375: loss = 32.78153920173645\n",
+ "iteration 400: loss = 32.15399241447449\n",
+ "iteration 425: loss = 32.18069672584534\n",
+ "iteration 450: loss = 32.34887766838074\n",
+ "iteration 475: loss = 32.39561104774475\n",
+ "iteration 500: loss = 32.51458191871643\n",
+ "iteration 525: loss = 32.285157680511475\n",
+ "iteration 550: loss = 32.21916651725769\n",
+ "iteration 575: loss = 32.54395031929016\n",
+ "iteration 600: loss = 32.487563133239746\n",
+ "iteration 625: loss = 32.54312562942505\n",
+ "iteration 650: loss = 31.965022087097168\n",
+ "iteration 675: loss = 32.316070318222046\n",
+ "iteration 700: loss = 32.36382842063904\n",
+ "iteration 725: loss = 32.52105975151062\n",
+ "iteration 750: loss = 32.83406376838684\n",
+ "iteration 775: loss = 32.393730878829956\n",
+ "iteration 800: loss = 32.43019080162048\n",
+ "iteration 825: loss = 32.3959174156189\n",
+ "iteration 850: loss = 32.112430572509766\n",
+ "iteration 875: loss = 32.36790990829468\n",
+ "iteration 900: loss = 32.66983437538147\n",
+ "iteration 925: loss = 32.438520669937134\n",
+ "iteration 950: loss = 32.27924203872681\n",
+ "iteration 975: loss = 32.38777709007263\n",
+ "Time taken: (1.62e-01 seconds per model evaluation).\n",
+ "Performing risk-based optimization under uncertainty (using alpha-superquantile)\n",
+ "Estimated wait time 1214.8 seconds...\n",
+ "Optimization completed in time 325.88 seconds.\n",
+ "Optimal policy:\t0.0\n",
+ "Post-processing optimal policy...\n",
+ "Estimated risk at optimal policy [0.7005741119384765]\n",
+ "Optimal policy after calibration: 0.0\n",
+ "Estimated risk at optimal policy after calibration [0.7005741119384765]\n"
+ ]
+ }
+ ],
+ "source": [
+ "data_path = os.path.join(DEMO_PATH, \"data.csv\")\n",
+ "num_samples = 100\n",
+ "timepoints = [0.0, 1.0, 2.0, 3.0, 4.0]\n",
+ "\n",
+ "# Run the calibration and sampling\n",
+ "OBJFUN = lambda x: np.abs(x)\n",
+ "INTERVENTION = [(0.1, \"beta\")]\n",
+ "QOI = (\"scenario2dec_nday_average\", \"Infected_sol\", 2)\n",
+ "ouu_cal_samples, opt_cal_policy = load_and_calibrate_and_optimize_and_sample_petri_model(\n",
+ " ASKENET_PATH,\n",
+ " data_path,\n",
+ " num_samples,\n",
+ " timepoints=timepoints,\n",
+ " interventions=INTERVENTION,\n",
+ " qoi=QOI,\n",
+ " risk_bound=10.,\n",
+ " objfun=OBJFUN,\n",
+ " initial_guess=0.02,\n",
+ " bounds=[[0.],[3.]],\n",
+ " verbose=True,\n",
+ ")\n",
+ "\n",
+ "# Save results\n",
+ "ouu_cal_samples.to_csv(\n",
+ " os.path.join(DEMO_PATH, \"results_petri/calibrate_optimize_sample_results.csv\"), index=False\n",
+ ")\n",
+ "print(\"Optimal policy after calibration:\", opt_policy[\"policy\"])\n",
+ "print(\"Estimated risk at optimal policy after calibration\", opt_policy[\"risk\"])"
+ ]
  }
  ],
  "metadata": {