{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compiled Sequential Importance Sampling\n",
    "\n",
    "Compiled sequential importance sampling [1], or inference compilation, is a technique to amortize the computational cost of inference by learning a proposal distribution for importance sampling.\n",
    "\n",
    "The proposal distribution is learned to minimise the KL divergence between the model and the guide, $\\rm{KL}\\!\\left( p({\\bf z} | {\\bf x}) \\lVert q_{\\phi, x}({\\bf z}) \\right)$. This differs from variational inference, which would minimise $\\rm{KL}\\!\\left( q_{\\phi, x}({\\bf z}) \\lVert p({\\bf z} | {\\bf x}) \\right)$. Using this loss encourages the approximate proposal distribution to be broader than the true posterior (mass covering), whereas variational inference typically learns a narrower approximation (mode seeking). Guides for importance sampling are usually desired to have heavier tails than the model (see this [stackexchange question](https://stats.stackexchange.com/questions/76798/in-importance-sampling-why-should-the-importance-density-have-heavier-tails)). Therefore, the inference compilation loss is usually more suited to compiling a guide for importance sampling.\n",
    "\n",
    "Another benefit of CSIS is that, unlike many types of variational inference, it has no requirement that the model is differentiable. This allows it to be used for inference on arbitrarily complex programs (e.g. a Captcha renderer [1]).\n",
    "\n",
    "This example shows CSIS being used to speed up inference on a simple problem with a known analytic solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.functional as F\n",
    "\n",
    "import pyro\n",
    "import pyro.distributions as dist\n",
    "import pyro.infer\n",
    "import pyro.optim\n",
    "\n",
    "import os\n",
    "smoke_test = ('CI' in os.environ)\n",
    "n_steps = 2 if smoke_test else 2000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specify the model:\n",
    "\n",
    "The model is specified in the same way as any Pyro model, except that a keyword argument, `observations`, must be used to input a dictionary with each observation as a key. Since inference compilation involves learning to perform inference for any observed values, it is not important what the values in the dictionary are. `0` is used here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(prior_mean, observations={\"x1\": 0, \"x2\": 0}):\n",
    "    x = pyro.sample(\"z\", dist.Normal(prior_mean, torch.tensor(5**0.5)))\n",
    "    y1 = pyro.sample(\"x1\", dist.Normal(x, torch.tensor(2**0.5)), obs=observations[\"x1\"])\n",
    "    y2 = pyro.sample(\"x2\", dist.Normal(x, torch.tensor(2**0.5)), obs=observations[\"x2\"])\n",
    "    return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### And the guide:\n",
    "\n",
    "The guide will be trained (a.k.a. compiled) to use the observed values to make proposal distributions for each unconditioned `sample` statement. In the paper [1], a neural network architecture is automatically generated for any model. However, for the implementation in Pyro the user must specify a task-specific guide program structure. As with any Pyro guide function, this should have the same call signature as the model. It must also encounter the same unobserved `sample` statements as the model. So that the guide program can be trained to make good proposal distributions, the distributions at `sample` statements should depend on the values in `observations`. In this example, a feed-forward neural network is used to map the observations to a proposal distribution for the latent variable.\n",
    "\n",
    "`pyro.module` is called when the guide function is run so that the guide parameters can be found by the optimiser during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Guide(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.neural_net = nn.Sequential(\n",
    "            nn.Linear(2, 10),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(10, 20),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(20, 10),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(10, 5),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(5, 2))\n",
    "\n",
    "    def forward(self, prior_mean, observations={\"x1\": 0, \"x2\": 0}):\n",
    "        pyro.module(\"guide\", self)\n",
    "        x1 = observations[\"x1\"]\n",
    "        x2 = observations[\"x2\"]\n",
    "        v = torch.cat((x1.view(1, 1), x2.view(1, 1)), 1)\n",
    "        v = self.neural_net(v)\n",
    "        mean = v[0, 0]\n",
    "        std = v[0, 1].exp()\n",
    "        pyro.sample(\"z\", dist.Normal(mean, std))\n",
    "\n",
    "guide = Guide()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now create a `CSIS` instance:\n",
    "The object is initialised with the model; the guide; a PyTorch optimiser for training the guide; and the number of importance-weighted samples to draw when performing inference. The guide will be optimised for a particular value of the model/guide argument, `prior_mean`, so we use the value set here throughout training and inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimiser = pyro.optim.Adam({'lr': 1e-3})\n",
    "csis = pyro.infer.CSIS(model, guide, optimiser, num_inference_samples=50)\n",
    "prior_mean = torch.tensor(1.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we 'compile' the instance to perform inference on this model:\n",
    "The arguments given to `csis.step` are passed to the model and guide when they are run to evaluate the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for step in range(n_steps):\n",
    "    csis.step(prior_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### And now perform inference by importance sampling:\n",
    "\n",
    "The compiled guide program should now be able to propose a distribution for `z` that approximates the posterior, $p(z | x_1, x_2)$, for any $x_1, x_2$. The same `prior_mean` is entered again, as well as the observed values inside `observations`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "posterior = csis.run(prior_mean,\n",
    "                     observations={\"x1\": torch.tensor(8.),\n",
    "                                   \"x2\": torch.tensor(9.)})\n",
    "marginal = pyro.infer.EmpiricalMarginal(posterior, \"z\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We now plot the results and compare with importance sampling:\n",
    "\n",
    "We observe $x_1 = 8$ and $x_2 = 9$. Inference is performed by taking 50 samples using CSIS, and 50 using importance sampling from the prior. We then plot the resulting approximations to the posterior distributions, along with the analytic posterior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.stats\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "with torch.no_grad():\n",
    "    # Draw samples from empirical marginal for plotting\n",
    "    csis_samples = torch.stack([marginal() for _ in range(1000)])\n",
    "\n",
    "    # Calculate empirical marginal with importance sampling\n",
    "    is_posterior = pyro.infer.Importance(model, num_samples=50).run(\n",
    "        prior_mean, observations={\"x1\": torch.tensor(8.),\n",
    "                                  \"x2\": torch.tensor(9.)})\n",
    "    is_marginal = pyro.infer.EmpiricalMarginal(is_posterior, \"z\")\n",
    "    is_samples = torch.stack([is_marginal() for _ in range(1000)])\n",
    "\n",
    "# Calculate true prior and posterior over z\n",
    "true_posterior_z = torch.arange(-10, 10, 0.05)\n",
    "true_posterior_p = dist.Normal(7.25, (5/6)**0.5).log_prob(true_posterior_z).exp()\n",
    "prior_z = true_posterior_z\n",
    "prior_p = dist.Normal(1., 5**0.5).log_prob(true_posterior_z).exp()\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [30, 15]\n",
    "plt.rcParams.update({'font.size': 30})\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(prior_z, prior_p, 'k--', label='Prior')\n",
    "plt.plot(true_posterior_z, true_posterior_p, color='k', label='Analytic Posterior')\n",
    "plt.hist(csis_samples.numpy(), range=(-10, 10), bins=100, color='r', density=1,\n",
    "         label=\"Inference Compilation\")\n",
    "plt.hist(is_samples.numpy(), range=(-10, 10), bins=100, color='b', density=1,\n",
    "         label=\"Importance Sampling\")\n",
    "plt.xlim(-8, 10)\n",
    "plt.ylim(0, 5)\n",
    "plt.xlabel(\"z\")\n",
    "plt.ylabel(\"Estimated Posterior Probability Density\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using $x_1 = 8$ and $x_2 = 9$ gives a posterior far from the prior, and so using the prior as a guide for importance sampling is inefficient, giving a very small effective sample size. By first learning a suitable guide function, CSIS has a proposal distribution much more closely matched to the true posterior. This allows samples to be drawn with far better coverage of the true posterior, and greater effective sample size, as shown in the graph above.\n",
    "\n",
    "For other examples of inference compilation, see [1] or <https://github.com/probprog/anglican-infcomp-examples>.\n",
    "\n",
    "## References\n",
    "\n",
    "[1] `Inference compilation and universal probabilistic programming`,<br />&nbsp;&nbsp;&nbsp;&nbsp;\n",
    "Tuan Anh Le, Atilim Gunes Baydin, and Frank Wood"
   ]
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}