fehiepsi/pyro-forum-696.ipynb

## pyro-forum-696.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set(rc={\"figure.figsize\": (8, 6)})\n",
    "import torch\n",
    "\n",
    "import pyro\n",
    "import pyro.distributions as dist\n",
    "from pyro.infer.mcmc import MCMC, NUTS\n",
    "\n",
    "pyro.enable_validation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c155382149284ee5aec96e62b326e2d1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Warmup', max=1300, style=ProgressStyle(description_width='ini…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "count_data = torch.tensor([\n",
    "    13,  24,   8,  24,   7,  35,  14,  11,  15,  11,  22,  22,  11,  57,  \n",
    "    11,  19,  29,   6,  19,  12,  22,  12,  18,  72,  32,   9,   7,  13,  \n",
    "    19,  23,  27,  20,   6,  17,  13,  10,  14,   6,  16,  15,   7,   2,  \n",
    "    15,  15,  19,  70,  49,   7,  53,  22,  21,  31,  19,  11,  18,  20,  \n",
    "    12,  35,  17,  23,  17,   4,   2,  31,  30,  13,  27,   0,  39,  37,   \n",
    "    5,  14,  13,  22,\n",
    "], dtype=torch.float)\n",
    "\n",
    "def model(data):\n",
    "    alpha = (1. / data.mean())\n",
    "    lambda1 = pyro.sample(\"lambda1\", dist.Exponential(rate=alpha))\n",
    "    lambda2 = pyro.sample(\"lambda2\", dist.Exponential(rate=alpha))\n",
    "\n",
    "    tau = pyro.sample(\"tau\", dist.Uniform(0, 1))\n",
    "    lambda1_size = (tau * data.size(0) + 1).long()\n",
    "    lambda2_size = data.size(0) - lambda1_size\n",
    "    lambda_ = torch.cat([lambda1.expand((lambda1_size,)),\n",
    "                         lambda2.expand((lambda2_size,))])\n",
    "    \n",
    "    with pyro.plate(\"data\", data.size(0)):\n",
    "        pyro.sample(\"obs\", dist.Poisson(lambda_), obs=data)\n",
    "\n",
    "nuts_kernel = NUTS(model, jit_compile=True, ignore_jit_warnings=True, max_tree_depth=7)\n",
    "posterior = MCMC(nuts_kernel, num_samples=1000, warmup_steps=300)\n",
    "posterior.run(count_data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "marginal = posterior.marginal(sites=[\"lambda1\", \"lambda2\", \"tau\"])\n",
    "marginal_support = marginal.support(flatten=True)\n",
    "for site in marginal_support:\n",
    "    support = marginal_support[site]\n",
    "    if site == \"tau\":\n",
    "        support = (support * count_data.size(0) + 1).long()\n",
    "    sns.distplot(support, kde=False, axlabel=site)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('lambda1',\n",
       "              OrderedDict([('n_eff', tensor(52.6577)),\n",
       "                           ('r_hat', tensor(1.0053))])),\n",
       "             ('lambda2',\n",
       "              OrderedDict([('n_eff', tensor(46.5222)),\n",
       "                           ('r_hat', tensor(1.0179))])),\n",
       "             ('tau',\n",
       "              OrderedDict([('n_eff', tensor(17.9911)),\n",
       "                           ('r_hat', tensor(1.0381))]))])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marginal.diagnostics()"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}