From 238b49d7efc74434c8a7ec2422f90a22fafe0462 Mon Sep 17 00:00:00 2001 From: Anirban Chaudhuri <75496534+anirban-chaudhuri@users.noreply.github.com> Date: Mon, 10 Jul 2023 10:03:52 -0400 Subject: [PATCH] Update NB to resolve conflicts --- notebook/integration_demo/demo.ipynb | 163 +-------------------------- 1 file changed, 2 insertions(+), 161 deletions(-) diff --git a/notebook/integration_demo/demo.ipynb b/notebook/integration_demo/demo.ipynb index eeee36ec8..012bf4ee7 100644 --- a/notebook/integration_demo/demo.ipynb +++ b/notebook/integration_demo/demo.ipynb @@ -9,11 +9,8 @@ "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_optimize_and_sample_petri_model,\n", - " load_and_calibrate_and_optimize_and_sample_petri_model\n", - ")\n", - "import numpy as np" + " load_and_calibrate_and_sample_petri_model\n", + ")" ] }, { @@ -151,162 +148,6 @@ " 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": {