pavement-r.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an R port of the [pomegranate](https://pomegranate.readthedocs.io/en/latest/)-based example of a [Bayesian network](https://en.wikipedia.org/wiki/Bayesian_network), using [reticulate](https://rstudio.github.io/reticulate/index.html).\n",
    "\n",
    "The [wet pavement](https://gist.github.com/bearloga/cded2130fddd673a4d034b6b1e20100c) model:\n",
    "\n",
    "![](pavement.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# My config:\n",
    "Sys.setenv(\"RETICULATE_PYTHON\" = \"~/envs/probabilistic/bin/python\")\n",
    "reticulate::use_virtualenv(\"~/envs/probabilistic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(reticulate)\n",
    "\n",
    "pg <- import(\"pomegranate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "raining <- pg$DiscreteDistribution(dict(\"Yes\" = 0.25, \"No\" = 0.75))\n",
    "\n",
    "sprinkler <- pg$ConditionalProbabilityTable(\n",
    "  list(\n",
    "    list(\"Yes\", \"On\", 0.001),\n",
    "    list(\"Yes\", \"Off\", 0.999),\n",
    "    list(\"No\", \"On\", 0.8),\n",
    "    list(\"No\", \"Off\", 0.2)\n",
    "  ),\n",
    "  list(raining)\n",
    ")\n",
    "\n",
    "pavement <- pg$ConditionalProbabilityTable(\n",
    "  list(\n",
    "    list(\"Yes\", \"On\", \"Wet\", 1.0),\n",
    "    list(\"Yes\", \"On\", \"Dry\", 0.0),\n",
    "    list(\"Yes\", \"Off\", \"Wet\", 1.0),\n",
    "    list(\"Yes\", \"Off\", \"Dry\", 0.0),\n",
    "    list(\"No\", \"On\", \"Wet\", 0.75),\n",
    "    list(\"No\", \"On\", \"Dry\", 0.25),\n",
    "    list(\"No\", \"Off\", \"Wet\", 0.01),\n",
    "    list(\"No\", \"Off\", \"Dry\", 0.99)\n",
    "  ),\n",
    "  list(raining, sprinkler)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 <- pg$Node(raining, name = \"raining\")\n",
    "s2 <- pg$Node(sprinkler, name = \"sprinkler\")\n",
    "s3 <- pg$Node(pavement, name = \"pavement\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pg$BayesianNetwork(\"wet pavement model\")\n",
    "model$add_states(s1, s2, s3)\n",
    "model$add_edge(s1, s2)\n",
    "model$add_edge(s1, s3)\n",
    "model$add_edge(s2, s3)\n",
    "model$bake()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[1] \"No\"\n",
       "\n",
       "[[2]]\n",
       "{\n",
       "    \"class\" :\"Distribution\",\n",
       "    \"dtype\" :\"str\",\n",
       "    \"name\" :\"DiscreteDistribution\",\n",
       "    \"parameters\" :[\n",
       "        {\n",
       "            \"On\" :0.9966777408637874,\n",
       "            \"Off\" :0.0033222591362126854\n",
       "        }\n",
       "    ],\n",
       "    \"frozen\" :false\n",
       "}\n",
       "\n",
       "[[3]]\n",
       "[1] \"Wet\"\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "probabilities <- model$predict_proba(list(\"No\", NULL, \"Wet\"))\n",
    "probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$On</dt>\n",
       "\t\t<dd>0.996677740863787</dd>\n",
       "\t<dt>$Off</dt>\n",
       "\t\t<dd>0.00332225913621269</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$On] 0.996677740863787\n",
       "\\item[\\$Off] 0.00332225913621269\n",
       "\\end{description}\n"
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      "text/markdown": [
       "$On\n",
       ":   0.996677740863787\n",
       "$Off\n",
       ":   0.00332225913621269\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$On\n",
       "[1] 0.9966777\n",
       "\n",
       "$Off\n",
       "[1] 0.003322259\n"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "probabilities[[2]]$parameters[[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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