examples/plotting/mcmc-histogram-plots.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inference plots - Histogram plots\n",
    "\n",
    "This example builds on [adaptive covariance MCMC](https://pints.readthedocs.io/en/latest/mcmc_samplers/adaptive_covariance_mc.html), and shows you how to plot the MCMC chain histograms, also known as the marginal posterior distributions.\n",
    "\n",
    "Other inference plots:\n",
    "* [Predicted time series](mcmc-predicted-time-series.ipynb)\n",
    "* [Autocorrelation](mcmc-autocorrelation.ipynb)\n",
    "* [Pairwise scatterplots](mcmc-pairwise-scatterplots.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up an MCMC routine\n",
    "\n",
    "See the adaptive covariance MCMC example for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pints\n",
    "import pints.toy as toy\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load a forward model\n",
    "model = toy.LogisticModel()\n",
    "\n",
    "# Create some toy data\n",
    "real_parameters = [0.015, 500]  # growth rate, carrying capacity\n",
    "times = np.linspace(0, 1000, 100)\n",
    "org_values = model.simulate(real_parameters, times)\n",
    "\n",
    "# Add noise\n",
    "noise = 50\n",
    "values = org_values + np.random.normal(0, noise, org_values.shape)\n",
    "real_parameters = np.array(real_parameters + [noise])\n",
    "\n",
    "# Get properties of the noise sample\n",
    "noise_sample_mean = np.mean(values - org_values)\n",
    "noise_sample_std = np.std(values - org_values)\n",
    "\n",
    "# Create an object with links to the model and time series\n",
    "problem = pints.SingleOutputProblem(model, times, values)\n",
    "\n",
    "# Create a log-likelihood function (adds an extra parameter!)\n",
    "log_likelihood = pints.GaussianLogLikelihood(problem)\n",
    "\n",
    "# Create a uniform prior over both the parameters and the new noise variable\n",
    "log_prior = pints.UniformLogPrior(\n",
    "    [0.01, 400, noise*0.1],\n",
    "    [0.02, 600, noise*100]\n",
    ")\n",
    "\n",
    "# Create a posterior log-likelihood (log(likelihood * prior))\n",
    "log_posterior = pints.LogPosterior(log_likelihood, log_prior)\n",
    "\n",
    "# Perform sampling using MCMC, with three chains\n",
    "xs = [\n",
    "    real_parameters * 1.1,\n",
    "    real_parameters * 1.15,\n",
    "    real_parameters * 0.9,\n",
    "]\n",
    "mcmc = pints.MCMCController(log_posterior, 3, xs)\n",
    "mcmc.set_max_iterations(6000)\n",
    "mcmc.set_log_to_screen(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histograms\n",
    "\n",
    "The plots below show the histograms of the chains generated by three independent runs of the adaptive MCMC routine (all from the same starting point). All three chains require an initial period before they converge to the same parameter values. The initial period is usually discarded as 'burn-in'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Running...\nDone!\n"
    }
   ],
   "source": [
    "print('Running...')\n",
    "chains = mcmc.run()\n",
    "print('Done!')\n",
    "\n",
    "# Discard 'burn-in'\n",
    "chains = chains[:, 3000:, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import pints.plot\n",
    "pints.plot.histogram(chains, parameter_names=['growth rate', 'carrying capacity', 'noise'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms with KDE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "pints.plot.histogram([chains[0]], kde=True, parameter_names=['growth rate', 'carrying capacity', 'noise'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.5 64-bit ('venv': venv)",
   "language": "python",
   "name": "python37564bitvenvvenv6fee2a6ea2a745d499a5a8b20d7765b4"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}