examples/sampling/abc-smc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ABC-SMC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PINTS can be used to perform inference for stochastic forward models. Here, we perform inference on the [stochastic degradation model](../toy/model-stochastic-degradation.ipynb) using Approximate Bayesian Computation (ABC). This model has only a single unknown parameter -- the rate at which chemicals degrade. We use the \"ABC SMC\" method to estimate this unknown and provide a measure of uncertainty in it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we load the stochastic degradation model and perform 10 simulations from it. The variation inbetween runs is due to the inherent stochasticity of this type of model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pints\n",
    "import pints.toy as toy\n",
    "import pints.toy.stochastic\n",
    "import pints.plot\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(5)\n",
    "\n",
    "# Load a forward model\n",
    "model = toy.stochastic.DegradationModel()\n",
    "\n",
    "# Create some toy data\n",
    "real_parameters = model.suggested_parameters()\n",
    "times = np.linspace(0, 10, 100)\n",
    "\n",
    "for i in range(10):\n",
    "    values = model.simulate(real_parameters, times)\n",
    "\n",
    "    # Create an object with links to the model and time series\n",
    "    problem = pints.SingleOutputProblem(model, times, values)\n",
    "\n",
    "    # Create a uniform prior parameter\n",
    "    log_prior = pints.UniformLogPrior([0.0], [0.3])\n",
    "\n",
    "    # Set the error measure to be used to compare simulated to observed data\n",
    "    error_measure = pints.RootMeanSquaredError(problem)\n",
    "\n",
    "    plt.step(times, values)\n",
    "\n",
    "\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('concentration (A(t))')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit using ABC SMC\n",
    "\n",
    "The ABC SMC method can be used to perform parameter inference for stochastic models, where the likelihood is intractable. In ABC methods, typically, a distance metric comparing the observed data and the simulated is used. Here, we use the root mean square error (RMSE), and we accept a parameter value if the $RMSE<3$ for the first intermediate distribution, $RMSE<1.5$ for the second one and $RMSE<1$ for the last. The size of the intermediate distributions will be 200."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running...\n",
      "Using ABC-SMC\n",
      "Running in sequential mode.\n",
      "Starting t=0, with threshold=3.0\n",
      "Iter. Eval. Acceptance rate Time m:s\n",
      "1     2      0.5              0:00.0\n",
      "2     9      0.222222222      0:00.0\n",
      "3     13     0.230769231      0:00.0\n",
      "20    68     0.294117647      0:00.0\n",
      "40    126    0.317460317      0:00.1\n",
      "60    174    0.344827586      0:00.1\n",
      "80    225    0.355555556      0:00.1\n",
      "100   301    0.332225914      0:00.2\n",
      "120   349    0.343839542      0:00.2\n",
      "140   415    0.337349398      0:00.2\n",
      "160   473    0.338266384778   0:00.2\n",
      "180   546    0.32967033       0:00.3\n",
      "Starting t=1, with threshold=1.5\n",
      "200   608    0.328947368      0:00.3\n",
      "220   755    0.291390728      0:00.4\n",
      "240   861    0.278745645      0:00.5\n",
      "260   1003   0.259222333001   0:00.6\n",
      "280   1151   0.243266725      0:00.7\n",
      "300   1298   0.231124807396   0:00.8\n",
      "320   1411   0.226789511      0:00.9\n",
      "340   1604   0.211970074813   0:01.0\n",
      "360   1744   0.206422018      0:01.1\n",
      "380   1855   0.204851752      0:01.1\n",
      "Starting t=2, with threshold=1.0\n",
      "400   2010   0.199004975      0:02.3\n",
      "420   2646   0.158730159      0:02.7\n",
      "440   3300   0.133333333      0:03.2\n",
      "460   4348   0.105795768      0:03.7\n",
      "480   5075   0.0945812808     0:04.2\n",
      "500   5687   0.0879198171     0:04.5\n",
      "520   6377   0.0815430453     0:05.0\n",
      "540   6955   0.0776419842     0:05.4\n",
      "560   7592   0.0737618546     0:05.8\n",
      "580   8053   0.0720228486     0:06.1\n",
      "600   8735   0.0686891815     0:06.6\n",
      "620   9380   0.066098081      0:07.1\n",
      "640   10165  0.0629611412     0:07.5\n",
      "660   10881  0.0606561897     0:08.0\n",
      "680   11510  0.0590790617     0:08.4\n",
      "700   12359  0.0566388866     0:09.0\n",
      "720   13093  0.0549912167     0:09.5\n",
      "740   13951  0.0530427926     0:10.0\n",
      "760   14770  0.0514556534     0:10.6\n",
      "780   15481  0.0503843421     0:11.0\n",
      "800   16148  0.0495417389     0:11.5\n",
      "820   16822  0.0487456902     0:11.9\n",
      "840   17485  0.0480411782     0:12.4\n",
      "860   18470  0.0465619924     0:13.1\n",
      "880   19275  0.0456549935     0:13.6\n",
      "900   20037  0.0449169037     0:14.1\n",
      "920   20750  0.0443373494     0:14.5\n",
      "940   21577  0.0435649071     0:15.0\n",
      "960   22095  0.0434487441     0:15.3\n",
      "980   23152  0.0423289565     0:16.0\n",
      "1000  23557  0.0424502271     0:16.3\n",
      "1020  24146  0.0422430216     0:16.7\n",
      "1040  24971  0.041648312      0:17.1\n",
      "1060  25615  0.0413820027     0:17.6\n",
      "1080  26414  0.0408874082     0:18.0\n",
      "1100  26868  0.0409408962     0:18.4\n",
      "1120  27780  0.0403167747     0:19.1\n",
      "1140  28498  0.0400028072     0:19.6\n",
      "1160  29137  0.039811923      0:20.1\n",
      "1180  29916  0.0394437759     0:20.7\n",
      "1200  30661  0.0391376667     0:21.1\n",
      "1220  31580  0.0386320456     0:21.7\n",
      "1240  32314  0.0383734604     0:22.1\n",
      "1260  33152  0.0380067568     0:22.5\n",
      "1280  33981  0.0376681086     0:23.0\n",
      "1300  34410  0.0377797152     0:23.2\n",
      "1320  35560  0.03712036       0:23.9\n",
      "1340  36489  0.0367233961     0:24.4\n",
      "1360  37079  0.0366784433     0:24.7\n",
      "1380  37809  0.0364992462     0:25.3\n",
      "1400  38428  0.0364317685     0:26.0\n",
      "Halting: target number of samples (1400) reached.\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "abc = pints.ABCController(error_measure, log_prior, method=pints.ABCSMC)\n",
    "\n",
    "# set threshold schedule\n",
    "abc.sampler().set_threshold_schedule(np.array([3,1.5,1]))\n",
    "\n",
    "# produce intermediate distributions of size 200\n",
    "abc.sampler().set_intermediate_size(200)\n",
    "\n",
    "# set target number of samples\n",
    "abc.set_n_samples(1400)\n",
    "\n",
    "# log to screen\n",
    "abc.set_log_to_screen(True)\n",
    "\n",
    "print('Running...')\n",
    "samples  = abc.run()\n",
    "print('Done')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now plot the ABC posterior samples versus the actual value that was used to generate the data. This shows that, in this case, the parameter could be identified given the data. We plot the first, second and last generation separately in order to observe how the samples are getting more accurate in later generations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([x[0] for x in samples[0:200]], label='First generation samples')\n",
    "plt.xlim(0, 0.3)\n",
    "plt.vlines(x=model.suggested_parameters(), linestyles='dashed', ymin=0, ymax=50, label='Actual value', color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([x[0] for x in samples[200:400]], label='Second generation samples')\n",
    "plt.xlim(0, 0.3)\n",
    "plt.vlines(x=model.suggested_parameters(), linestyles='dashed', ymin=0, ymax=50, label='Actual value', color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([x[0] for x in samples[400:]], label='Third generation samples')\n",
    "plt.xlim(0, 0.3)\n",
    "plt.vlines(x=model.suggested_parameters(), linestyles='dashed', ymin=0, ymax=250, label='Actual value', color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "62b8c3045b77e73a8aab814fbf01ae024ab075fc3f7014742f3a4c5a8ac43e7b"
  },
  "kernelspec": {
   "display_name": "Python 3.8.0 32-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}