examples/plotting/customise-pints-plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An example of customising Pints plots\n",
    "\n",
    "This example builds on [adaptive covariance MCMC](https://pints.readthedocs.io/en/latest/mcmc_samplers/adaptive_covariance_mc.html) and [pairwise scatterplots](mcmc-pairwise-scatterplots.ipynb), and shows you how to plot the parameter distributions with examples of customising the plots."
   ]
  },
  {
   "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]\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 a single chain\n",
    "x0 = real_parameters * 1.1\n",
    "mcmc = pints.MCMCController(log_posterior, 1, [x0])\n",
    "mcmc.set_max_iterations(6000)\n",
    "mcmc.set_log_to_screen(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Pints' standard 1d histograms\n",
    "\n",
    "We can now run the MCMC routine and plot the histograms of the inferred parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "print('Running...')\n",
    "chains = mcmc.run()\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select chain 0 and discard warm-up\n",
    "chain = chains[0]\n",
    "chain = chain[3000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pints.plot\n",
    "\n",
    "# Plot the 1d histogram of each parameter\n",
    "pints.plot.histogram([chain])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customise the plots\n",
    "\n",
    "For example, here our [toy model](https://pints.readthedocs.io/en/latest/toy/logistic_model.html) is a logistic model of population growth\n",
    "$$f(t) = \\frac{k}{1 + (k/p_0 - 1)\\exp(-rt)},$$\n",
    "where $r$ is the growth rate, $k$ is the carrying capacity, and $p_0$ is the initial population (fixed constant). In this example, we have model parameters $r$ and $k$, together with a noise parameter $\\sigma$, to be inferred.\n",
    "\n",
    "We will do the following update to the plots to make it prettier:\n",
    "1. We may want to change the labels to the names of parameters;\n",
    "2. We will add lines for (i) the mean and (ii) the 95% credible interval;\n",
    "3. We will make the figure size a bit bigger."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x648 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the 1d histogram of each parameter\n",
    "fig, axes = pints.plot.histogram([chain])\n",
    "\n",
    "# Customise the plots\n",
    "parameter_names = [r'$r$', r'$k$', r'$\\sigma$']\n",
    "for i, ax in enumerate(axes):\n",
    "    # (1) Add parameter name\n",
    "    ax.set_xlabel(parameter_names[i])\n",
    "    # (2i) Add mean\n",
    "    ax.axvline(np.mean(chain[:, i]), color='k', label='Mean')\n",
    "    # (2ii) Add 95% credible interval\n",
    "    ax.axvline(np.percentile(chain[:, i], 2.5), color='C1', label='95% credible interval')\n",
    "    ax.axvline(np.percentile(chain[:, i], 97.5), color='C1')\n",
    "axes[0].legend()\n",
    "# (3) Update figure size\n",
    "fig.set_size_inches(14, 9)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}