examples/dummy_test.ipynb

{
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  {
   "cell_type": "code",
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   "metadata": {},
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   "source": [
    "import pandas as pd\n",
    "import sib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes: 4\n"
     ]
    },
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      "text/plain": [
       "   i  j  t  lambda\n",
       "0  2  3  0    0.02\n",
       "1  3  2  0    0.02\n",
       "2  1  2  0    0.02\n",
       "3  2  1  0    0.02\n",
       "4  2  1  1    0.02\n",
       "5  1  2  1    0.02\n",
       "6  0  1  3    0.02\n",
       "7  1  0  3    0.02"
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     "execution_count": 28,
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   ],
   "source": [
    "#contacts are directed, add the two directions for bidirectional contacts\n",
    "df_contacts = pd.read_csv(\"./data/dummy/transmissions.csv\")[[\"i\", \"j\", \"t\", \"lambda\"]]\n",
    "contacts = list(df_contacts.to_records(index=False))\n",
    "N = df_contacts[[\"i\", \"j\"]].max().max() + 1\n",
    "print(f\"Number of nodes: {N}\")\n",
    "df_contacts"
   ]
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   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "      <th>t_test</th>\n",
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       "   i  s  t_test\n",
       "0  1  1       1\n",
       "1  2  1       2"
      ]
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     "execution_count": 29,
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   ],
   "source": [
    "#observation S=0, I=1, R=2\n",
    "df_observ = pd.read_csv(\"./data/dummy/observations.csv\")\n",
    "observ = list(df_observ.to_records(index=False))\n",
    "df_observ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate the factor Graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parameters of the SIR model used for inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "lambda_ = 0.04 #prob of infection\n",
    "mu = 1/50 #rate of recovery\n",
    "pseed = 1e-2 #probability to have sources\n",
    "params=sib.Params(prob_r=sib.Exponential(mu=mu), pseed=pseed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy observations for computing marginals\n",
    "We want to computer the marginal probabilities of each node to be S, I or R at time t=0 and t=3.\n",
    "\n",
    "We add dummy observations to the observations list.\n",
    "\n",
    "We force each node the have these times in the factor graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1 = 0\n",
    "t2 = 3\n",
    "observ += [(i,-1,t1) for i in range(N)]\n",
    "observ += [(i,-1,t2) for i in range(N)]\n",
    "observ = list(sorted(observ, key=lambda x: x[2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### factor graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = sib.FactorGraph(params=params,\n",
    "                    contacts=contacts, \n",
    "                    observations=observ\n",
    "                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iterate the BP equations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1.0\n",
      "1 0.6945823115881788\n",
      "2 0.0\n"
     ]
    }
   ],
   "source": [
    "sib.iterate(f, maxit=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Print the marginals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 node:0 -- Marginals (S,I,R): (0.980, 0.019, 0.000)\n",
      "1 node:1 -- Marginals (S,I,R): (0.249, 0.751, 0.000)\n",
      "2 node:2 -- Marginals (S,I,R): (0.502, 0.498, 0.000)\n",
      "3 node:3 -- Marginals (S,I,R): (0.976, 0.024, 0.000)\n"
     ]
    }
   ],
   "source": [
    "#marginal at time 0\n",
    "for n in f.nodes:\n",
    "    m = sib.marginal_t(n, t1)\n",
    "    print(n.index, f\"node:{n.index} -- Marginals (S,I,R): ({m[0]:.3f}, {m[1]:.3f}, {m[2]:.3f})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 node:0 -- Marginals (S,I,R): (0.980, 0.018, 0.002)\n",
      "1 node:1 -- Marginals (S,I,R): (0.000, 0.961, 0.039)\n",
      "2 node:2 -- Marginals (S,I,R): (0.000, 0.980, 0.020)\n",
      "3 node:3 -- Marginals (S,I,R): (0.966, 0.031, 0.002)\n"
     ]
    }
   ],
   "source": [
    "#marginal at time 3\n",
    "for n in f.nodes:\n",
    "    m = sib.marginal_t(n, t2)\n",
    "    print(n.index, f\"node:{n.index} -- Marginals (S,I,R): ({m[0]:.3f}, {m[1]:.3f}, {m[2]:.3f})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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