{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ac19c62d-0304-40ef-9ce2-32f4f340488a",
   "metadata": {},
   "source": [
    "# Surrogate Modelling\n",
    "\n",
    "The following tutorial demonstrates how one may perform surrogate modelling of a simulation model via `calisim`. We will first import our required dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3a462176-a0ab-4cd7-93c4-7653db5b3292",
   "metadata": {},
   "outputs": [],
   "source": [
    "from calisim.data_model import (\n",
    "\tDistributionModel,\n",
    "\tParameterDataType,\n",
    "\tParameterSpecification,\n",
    ")\n",
    "from calisim.example_models import SirOdesModel\n",
    "from calisim.surrogate import (\n",
    "\tSurrogateModelMethod,\n",
    "\tSurrogateModelMethodModel,\n",
    ")\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.integrate import solve_ivp\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e8112ea-b09f-4cc9-8ad7-94f8cc927101",
   "metadata": {},
   "source": [
    "## SIR Model Parameters and Initial Conditions\n",
    "\n",
    "We next define our forward model. We will use an SIR (susceptible, infected, and recovered) compartmental model, combined with SciPy's solver for ordinary differential equations. The SIR model is expressed as a system of ordinary differential equations where:\n",
    "\n",
    "| Parameter | Value | Description |\n",
    "|-----------|-------|-------------|\n",
    "| β (beta)  | 0.4   | Infection rate: probability of transmission per contact per time unit |\n",
    "| γ (gamma) | 0.1   | Recovery rate: fraction of infected recovering per time unit |\n",
    "|           |       | **Average infectious period = 1 / γ = 10 time units** |\n",
    "\n",
    "With the following compartments:\n",
    "\n",
    "| Compartment | Symbol | Initial Value | Description |\n",
    "|------------|--------|---------------|-------------|\n",
    "| Susceptible | S0     | 999           | Individuals who can catch the disease (N - I0 - R0) |\n",
    "| Infected    | I0     | 1.0           | Individuals currently infected and can spread the disease |\n",
    "| Recovered   | R0     | 0             | Individuals recovered or removed; no longer infectious |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "adb3cf44-f6dd-4ddb-8152-f710902a07a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sir_simulate(parameters: dict) -> np.ndarray | pd.DataFrame:\n",
    "    def dX_dt(_: np.ndarray, X: np.ndarray) -> np.ndarray:\n",
    "        S, I, _ = X \n",
    "        dotS = -parameters[\"beta\"] * S * I / parameters[\"N\"]\n",
    "        dotI = (\n",
    "            parameters[\"beta\"] * S * I / parameters[\"N\"] - parameters[\"gamma\"] * I\n",
    "        )\n",
    "        dotR = parameters[\"gamma\"] * I\n",
    "        return np.array([dotS, dotI, dotR])\n",
    "\n",
    "    X0 = [parameters[\"S0\"], parameters[\"I0\"], parameters[\"R0\"]]\n",
    "    t = (parameters[\"t\"].min(), parameters[\"t\"].max())\n",
    "    x_y = solve_ivp(\n",
    "        fun=dX_dt, y0=X0, t_span=t, t_eval=parameters[\"t\"].values.flatten()\n",
    "    ).y\n",
    "\n",
    "    df = pd.DataFrame(dict(dotS=x_y[0, :], dotI=x_y[1, :], dotR=x_y[2, :]))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93a95b93-b79e-45b1-aa0e-760c380b686e",
   "metadata": {},
   "source": [
    "We will perform a simulation study with the following ground-truth parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "95c54892-4ce1-41bc-85c0-62f910ccabdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>N</th>\n",
       "      <th>I0</th>\n",
       "      <th>R0</th>\n",
       "      <th>S0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   beta  gamma     N   I0  R0     S0\n",
       "0   0.4    0.1  1000  1.0   0  999.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SirOdesModel()\n",
    "pd.DataFrame(model.GROUND_TRUTH, index=[0]) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b19c4fd-760a-4350-998b-1a1df38364f0",
   "metadata": {},
   "source": [
    "When supplied to our forward model, these ground-truth parameters will generate the observed data below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "97550d3b-4961-466d-aff2-f59ebd81c8af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dotS</th>\n",
       "      <th>dotI</th>\n",
       "      <th>dotR</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>999.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>998.534208</td>\n",
       "      <td>1.349201</td>\n",
       "      <td>0.116592</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>997.906105</td>\n",
       "      <td>1.819995</td>\n",
       "      <td>0.273899</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>997.059813</td>\n",
       "      <td>2.454180</td>\n",
       "      <td>0.486007</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>995.919926</td>\n",
       "      <td>3.308098</td>\n",
       "      <td>0.771976</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>994.385263</td>\n",
       "      <td>4.457212</td>\n",
       "      <td>1.157524</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dotS      dotI      dotR  day\n",
       "0  999.000000  1.000000  0.000000    0\n",
       "1  998.534208  1.349201  0.116592    1\n",
       "2  997.906105  1.819995  0.273899    2\n",
       "3  997.059813  2.454180  0.486007    3\n",
       "4  995.919926  3.308098  0.771976    4\n",
       "5  994.385263  4.457212  1.157524    5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observed_data = model.get_observed_data()\n",
    "observed_data.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c639dcdd-3dcd-47df-88ea-52033c479677",
   "metadata": {},
   "source": [
    "Let's view the trajectory of infected individuals over time in days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "61b7585f-01c1-4303-9a9c-ad22e3fca433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='day', ylabel='dotI'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "observed_data.plot.scatter(\"day\", \"dotI\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6d122c0-2139-4b89-8293-33076561bd78",
   "metadata": {},
   "source": [
    "## Surrogate Modelling via Gaussian Processes\n",
    "\n",
    "Next, let's use `calisim` to perform surrogate modelling using the end-of-simulation number of infections. \n",
    "\n",
    "To start with, we'll need to define our `ParameterSpecification` parameter specification:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "849fa816-b458-4db3-9bb7-cd527cf9db87",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter_spec = ParameterSpecification(\n",
    "\tparameters=[\n",
    "\t\tDistributionModel(\n",
    "\t\t\tname=\"beta\",\n",
    "\t\t\tdistribution_name=\"uniform\",\n",
    "\t\t\tdistribution_args=[0.3, 0.5],\n",
    "\t\t\tdata_type=ParameterDataType.CONTINUOUS,\n",
    "\t\t),\n",
    "\t\tDistributionModel(\n",
    "\t\t\tname=\"gamma\",\n",
    "\t\t\tdistribution_name=\"uniform\",\n",
    "\t\t\tdistribution_args=[0.05, 0.15],\n",
    "\t\t\tdata_type=ParameterDataType.CONTINUOUS,\n",
    "\t\t),\n",
    "\t]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed4e07cc-5eee-4cd7-8a83-e990744e52cb",
   "metadata": {},
   "source": [
    "This contains information concerning the various parameter names, probability distributions, ranges, distribution parameters, and data types.\n",
    "\n",
    "We next need to create a wrapper function around our forward model to ensure there's compatibility with the `calisim` API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "efbf14e8-3c14-449a-bbee-6d55e7c2390d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def surrogate_modelling_func(\n",
    "\tparameters: dict, simulation_id: str, observed_data: np.ndarray | None, t: pd.Series\n",
    ") -> float | list[float]:\n",
    "    simulation_parameters = model.GROUND_TRUTH.copy()\n",
    "    simulation_parameters[\"t\"] = t\n",
    "    \n",
    "    for k in [\"beta\", \"gamma\"]:\n",
    "        simulation_parameters[k] = parameters[k]\n",
    "    \n",
    "    simulated_data = sir_simulate(simulation_parameters).tail(1).dotI.item()\n",
    "    return simulated_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e922ada-5b5c-4a41-a2cc-424d6f729671",
   "metadata": {},
   "source": [
    "The last step is to create a `SurrogateModelMethodModel` specification for the surrogate modelling procedure itself, which we then supply to a `SurrogateModelMethod` calibrator. We'll use a `Gaussian process` surrogate model via the [scikit-learn engine](https://scikit-learn.org/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0b1cf8b6-48df-409a-91fa-d156cd939068",
   "metadata": {},
   "outputs": [],
   "source": [
    "specification = SurrogateModelMethodModel(\n",
    "\texperiment_name=\"sklearn_surrogate_modelling\",\n",
    "\tparameter_spec=parameter_spec,\n",
    "\tmethod=\"gp\",\n",
    "\tn_samples=50,\n",
    "\toutput_labels=[\"Number of Infected\"],\n",
    "\tcalibration_func_kwargs=dict(t=observed_data.day),\n",
    ")\n",
    "\n",
    "calibrator = SurrogateModelMethod(\n",
    "\tcalibration_func=surrogate_modelling_func,\n",
    "\tspecification=specification,\n",
    "\tengine=\"sklearn\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d37e2a18-3ae0-4e12-b572-047b644b12c4",
   "metadata": {},
   "source": [
    "Finally, we'll run the calibration procedure. This is composed of 3 steps:\n",
    "\n",
    "1. **Specify**: Define your calibration problem: Parameter distributions, observed data, objective/discrepancy function, and calibration settings (like algorithm, directions, iterations)\n",
    "2. **Execute**: Run the actual calibration process (simulation + optimization/inference)\n",
    "3. **Analyze**: Process, summarize, and optionally save plots/metrics of the calibration results\n",
    "\n",
    "Or **SEA**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a2aad087-c580-488e-8424-39bc276be210",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<calisim.surrogate.implementation.SurrogateModelMethod at 0x7f0d37c27a60>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RIn3xxRduLQ4AAH/DXXsAAACAX7gcSr399tsaNWqUgoOD9c0336i6ulqSVFZWRu8pAACOgrv2AAAAAL9wOZR6+OGH9dJLL2nu3LkKDAx0tA8ePFjffPONW4sDAMAfcdceAAAA4BjmlNq6datSUlIatNtsNpWWlrqjJgAA/JotJFALJw3krj0AAABo01wOpaKjo5WTk6O4uDin9i+++EI9e/Z0V10AAPi9+EjCKAAAALRdLg/fu+mmm/T73/9eX3/9tSzL0q5du/Tmm29q+vTpuu222zxRIwAAAAAAAPyMyz2l7r77btXV1emCCy5QVVWVUlJSFBQUpOnTpystLc0TNQIAAAAAAMDPWMYYcyxPrKmpUU5OjioqKtS3b1+FhYW5uzavKS8vl81mU1lZmcLDw31dDgAAAIAWZlthhfJLqpgHEACaobk5i8s9pW688UY9++yz6tSpk/r27etor6ysVFpaml577bVjqxgAAAAAWpjSqhqlZ2ZpVXahoy0l0a6M1CTZQgKbeCYA4GhcnlPq9ddf188//9yg/eeff9bChQvdUhQAAAAAtATpmVlanVPk1LY6p0hpmRt8VBEA+I9m95QqLy+XMUbGGO3bt08dO3Z0LKutrdWHH36oqKgojxQJAAAAAN62rbDCqYdUvVpjtCq7ULlFlQzlA4Dj0OxQqnPnzrIsS5Zl6eSTT26w3LIsPfjgg24tDgAAAAB8Jb+kqsnlecWEUgBwPJodSq1YsULGGA0fPlxvv/22unTp4ljWoUMHxcbGqlu3bh4pEgAAAG0HE0r7l9b8+4ztEtLk8riI1nU8ANDSNDuUGjJkiCQpNzdXPXr0kGVZHisKAAAAbQ8TSvsXf/h99rSHKSXRrtU5Rao95KblAZalwQmRrS5kA4CWxuWJzpcvX64lS5Y0aF+8eLFef/11txQFAACAtocJpf2Lv/w+M1KTNDgh0qltcEKkMlKTfFQRAPiPZveUqjdr1iy9/PLLDdqjoqJ08803a/z48W4pDAAAAG0HE0r7F3/6fdpCArVw0kDlFlUqr7iyVQ5DBICWyuWeUtu3b1d8fHyD9tjYWG3fvt0tRQEAAKBtac6E0mg9/PH3GR8ZqmG9owikAMCNXA6loqKi9O233zZo37hxoyIiItxSFAAAANoWJpT2L/w+AQDN4XIolZqaqvT0dK1YsUK1tbWqra3V8uXL9fvf/17XXnutJ2oEAACAn6ufUDrgsJvpBFiWUhLt9E5pZfh9AgCaw+VQaubMmRo0aJAuuOACBQcHKzg4WCNHjtTw4cP1yCOPeKJGAAAAtAFMKO1f+H0CAI7GMuaQe5u64Pvvv9fGjRsVHBys/v37KzY21t21eU15eblsNpvKysoUHh7u63IAAADaNCaU9i/8PgGg7WluzuLy3ffqxcXFyRijXr16qX37Y94MAAAA4CQ+kvDCn/D7BAAcicvD96qqqjRp0iSFhISoX79+jjvupaWl6dFHHz2uYh599FFZlqWpU6c62vbv36/JkycrIiJCYWFhGjt2rPbu3ev0vO3bt2v06NEKCQlRVFSU7rjjDh08ePC4agEAAAAAAIDnuBxKzZgxQxs3btTKlSvVsWNHR/uIESP0t7/97ZgLWbdunV5++WWddtppTu233367/u///k+LFy/WZ599pl27dunKK690LK+trdXo0aNVU1OjL7/8Uq+//roWLFig++6775hrAQAAAAAAgGe5HEq9++67ev7553XeeefJOuRuGv369dMPP/xwTEVUVFTouuuu09y5c3XCCSc42svKyjRv3jw99dRTGj58uAYMGKD58+fryy+/1FdffSVJ+uijj7Rlyxa98cYbOuOMM3TxxRdr5syZeuGFF1RTU9Po/qqrq1VeXu70AwAAAAAAAO9xOZQqLCxUVFRUg/bKykqnkMoVkydP1ujRozVixAin9vXr1+vAgQNO7X369FGPHj20Zs0aSdKaNWvUv39/de3a1bHOqFGjVF5ers2bNze6v1mzZslmszl+YmJijqluAAAAAAAAHBuXQ6mzzjpLH3zwgeNxfRD16quvKjk52eUC/vrXv+qbb77RrFmzGizbs2ePOnTooM6dOzu1d+3aVXv27HGsc2ggVb+8flljZsyYobKyMsfPjh07XK4bANC2bSus0IqtBcotqvR1KQAAAECr5PJt8x555BFdfPHF2rJliw4ePKhnn31WW7Zs0ZdffqnPPvvMpW3t2LFDv//97/Xxxx87zU/laUFBQQoKCvLa/gAA/qO0qkbpmVlalV3oaEtJtCsjNUm2kEAfVgZ/sK2wQvklVYqL4G5lAADA/7ncU+q8885TVlaWDh48qP79++ujjz5SVFSU1qxZowEDBri0rfXr16ugoEBnnnmm2rdvr/bt2+uzzz7Tc889p/bt26tr166qqalRaWmp0/P27t2r6OhoSVJ0dHSDu/HVP65fB4Dv0JsE/iY9M0urc4qc2lbnFCktc4OPKoI/KK2q0bh5azX8yc80cf46DXtipcbNW6uyqgO+Lg0AAMBjmtVTatq0aZo5c6ZCQ0O1atUqnXvuuZo7d+5x7/yCCy7Qpk2bnNomTpyoPn366K677lJMTIwCAwP16aefauzYsZKkrVu3avv27Y6hgsnJyfrzn/+sgoICx1xXH3/8scLDw9W3b9/jrhHAsaE3CfzRtsIKp3O6Xq0xWpVdqNyiSnq34Jg0FXYunDTQR1UBAAB4VrN6SmVkZKiiokKSNGzYMJWUlLhl5506ddKpp57q9BMaGqqIiAideuqpstlsmjRpkqZNm6YVK1Zo/fr1mjhxopKTk3XOOedIkkaOHKm+ffvqhhtu0MaNG7Vs2TLdc889mjx5MkP0AB+iNwn8UX5JVZPL84rpEQjX1YedtcY4tR8adgIAAPijZvWUiouL03PPPaeRI0fKGKM1a9bohBNOaHTdlJQUtxb49NNPq127dho7dqyqq6s1atQozZkzx7E8ICBA77//vm677TYlJycrNDRU48eP10MPPeTWOgA0H71J4K9iu4Q0uTwugvMarmtO2MlnJgAA8EeWMYd9LdeId999V7feeqsKCgpkWZaO9BTLslRbW+v2Ij2tvLxcNptNZWVlCg8P93U5QKu3YmuBJs5fd8Tl8yeerWG9o7xYEeA+4+at1eqcIqdeLQGWpcEJkQyzwjHZVlih4U8e+WYxK6YPJZQCAACtSnNzlmYN37viiiu0Z88elZeXyxijrVu36qeffmrw465hfQBaN3qTwJ9lpCZpcEKkU9vghEhlpCb5qCK0dj3tYUpJtCvAspzaAyxLKYl2AikAAOC3mjV8r15YWJhWrFih+Ph4tW/v0lMBtCH1f2AdqTcJf2ChNbOFBGrhpIHKLapUXnGl4iJCOadx3DJSk5SWucFp6DNhJwAA8HfNGr53uLq6OuXk5KigoEB1dXVOy9w9p5Q3MHwPcL+yqgMN/sDi7nsA0DTCTgAA4A+am7O4HEp99dVX+s1vfqP8/PwGc0sxpxSAw/EHFgAAAAC0Lc3NWVweg3frrbfqrLPO0gcffKATTzxR1mHzHwDAoeIjCaMAAAAAAA25HEplZ2dryZIlSkhI8EQ9AAAAAAAAaAOadfe9Qw0aNEg5OTmeqAUAAAAAAABthMs9pdLS0vSHP/xBe/bsUf/+/RUY6Dxh8Wmnnea24gAAAAAAAOCfXJ7ovF27hp2rLMuSMYaJzgEAAAAAANo4j010npube1yFAQAAAAAAAC6HUrGxsZ6oAwAAAAAAAG1Is0Op9957r1nrXX755cdcDAAAAAAAANqGZodSV1xxxVHXaa1zSgEAAAAAAMC7mh1K1dXVebIOAAAAAAAAtCENb6UHAAAAAAAAeBihFAAAAAAAALyOUAoAAAAAAABe1+w5pQAAADxhW2GF8kuqFBcRqvjIUF+XAwAAAC9pVij13HPP6eabb1bHjh21fft2xcTEyLIsT9cGAAD8WGlVjdIzs7Qqu9DRlpJoV0ZqkmwhgT6sDAAAAN7QrOF706ZNU3l5uSQpPj5ehYWFR3kGAABA09Izs7Q6p8ipbXVOkdIyN/ioIgAAAHhTs3pKdevWTW+//bYuueQSGWP0448/av/+/Y2u26NHD7cWCAAA/M+2wgqnHlL1ao3RquxC5RZVMpQPAAC0KW1xSoNmhVL33HOP0tLSNGXKFFmWpbPPPrvBOsYYWZal2tpatxeJtqUtvhEBoK3JL6lqcnleMaEUAABoG9rylAbNCqVuvvlmpaamKj8/X6eddpo++eQTRUREeLo2tDFt+Y0IAG1NbJeQJpfHRRBIAQCAtqGpKQ0WThroo6q8o9l33+vUqZNOPfVUzZ8/X4MHD1ZQUJAn60Ib1JbfiADQ1vS0hykl0a7VOUWqNcbRHmBZGpwQSS8pAADQJrT1KQ2aNdH5ocaPH6+goCCtX79eb7zxht544w198803nqgNbUj9G/HQP0wk5zciAMC/ZKQmaXBCpFPb4IRIZaQm+agiAAAA72rOlAb+rNk9peoVFBTo2muv1cqVK9W5c2dJUmlpqYYNG6a//vWvstvt7q4RbQBziwBA22MLCdTCSQOVW1SpvOJK5hIEAABtTluf0sDlnlJpaWnat2+fNm/erJKSEpWUlOjf//63ysvLlZ6e7oka0Qa09TciALRl8ZGhGtY7ikAKAAC0OfVTGgRYllN7gGUpJdHu99dHLodSS5cu1Zw5c3TKKac42vr27asXXnhB//znP91aHNqOtv5GBAAAAAC0TW15SgOXh+/V1dUpMLDhndACAwNVV1fnlqLQNmWkJiktc4PTJG9t5Y0IAAAAAK3RtsIK5ZdUMQz/OLTlKQ0sYw6bWfooxowZo9LSUmVmZqpbt26SpJ07d+q6667TCSecoHfeeccjhXpSeXm5bDabysrKFB4e7uty2ry2+EYEAAAAgNaktKpG6ZlZTp0KUhLtykhNki2kYUcWtC3NzVlcHr73/PPPq7y8XHFxcerVq5d69eql+Ph4lZeXKyMj47iKBiTmFgEAAACAli49M0urc4qc2lbnFCktc4OPKkJr5PLwvZiYGH3zzTf65JNP9N1330mSTjnlFI0YMcLtxQEAAAAAgJZlW2GFUw+perXGaFV2oXKLuHs6msflUEqSLMvShRdeqAsvvNDd9QAAAAAAgBYsv6SqyeV5xYRSaJ5jCqUAAAAAAGitmJz7+MR2CWlyeVwErymah1AKAAAAANAmMDm3e/S0hykl0a7VOUWqPeTeaQGWpcEJkQR9aDaXJzoHALRt2wortGJrgXKLKn1dCgAAgEuYnNt9MlKTNDgh0qltcEKkMlKTfFQRWiOXekodPHhQf/nLXzRq1Ch17drVUzUBAFogvlkEAOD4MGTMt5ic271sIYFaOGmgcosqlVdcyXmNY+JSKNW+fXvdeuut+s9//uOpegAALVRT3ywunDTQR1UBANDy8cVOy8Dk3J4RH0kYhWPn8vC9gQMHKisrywOlAABaqvpvFg+dM0By/mYRAAA0jiFjLQOTcwMtj8sTnf/ud7/TtGnTtGPHDg0YMEChoc5v3NNOO81txQEAWga+WQQA4NgwZKzlYHJuoOVxOZS69tprJUnp6emONsuyZIyRZVmqra11X3UAgBaBbxYBADg2fLHTsmSkJiktc4NTUMjk3IDvuBxK5ebmeqIOAEALxjeLAAAcG77YaVmYnBtoWVwOpWJjYz1RBwCgheObRQAAXMcXOy0Tk3MDLYNlzGGz1jbDokWL9NJLLyk3N1dr1qxRbGysnnnmGcXHx2vMmDGeqNOjysvLZbPZVFZWpvDwcF+XAwAtGt8sAkDbsq2wQvklVXzuH4eyqgMNvtjh7nsA/FlzcxaXe0q9+OKLuu+++zR16lT9+c9/dswh1blzZz3zzDOtMpQCADQf3ywCQNtQWlWj9MwsghQ3YMgYADSunatPyMjI0Ny5c/WnP/1JAQEBjvazzjpLmzZtcmtxAAAAAHwjPTNLq3OKnNpW5xQpLXODjypq/eIjQzWsdxSBFAD8l8uhVG5urpKSGs4fEhQUpMrKSrcUBQAAAMB3thVWaFV2odMcSJJUa4xWZRcqt4jrfgDA8XM5lIqPj1dWVlaD9qVLl+qUU05xR00AAAAAfCi/pKrJ5XnFhFIAgOPn8pxS06ZN0+TJk7V//34ZY7R27VplZmZq1qxZevXVVz1RIwAAAAAviu0S0uTyuAiGnwEAjp/LodRvf/tbBQcH65577lFVVZV+85vfqFu3bnr22Wd17bXXeqJGAAAAAF7U0x6mlES7VucUOQ3hC7AsDU6IZE4kAIBbWMYcNlDcBVVVVaqoqFBUVJQ7a/K65t6qEAAAAGgryqoOKC1zA3ffAwC4rLk5i8s9peoVFBRo69atkiTLsmS32491UwAAAABaGFtIoBZOGqjcokrlFVcqLiKUHlIAALdyOZTat2+ffve73ykzM1N1dXWSpICAAP3617/WCy+8IJvN5vYiAQAAAPhGfCRhFADAM1y++95vf/tbff311/rggw9UWlqq0tJSvf/++/rXv/6lW265xRM1AgAAAAAAwM+4PKdUaGioli1bpvPOO8+p/fPPP9dFF12kysrWd3tY5pQCAAAAAABwD4/NKRUREdHoED2bzaYTTjjB1c0BAAAAbretsEL5JVXMgwQAQAvmcih1zz33aNq0aVq0aJGio6MlSXv27NEdd9yhe++91+0FAgAAAM1VWlWj9Mws7hgHAEAr0Kzhe0lJSbIsy/E4Oztb1dXV6tGjhyRp+/btCgoKUmJior755hvPVeshDN8DAADwD+PmrdXqnCLVHnKJG2BZGpwQqYWTBvqwMgAA2g63Dt+74oor3FUXAAAA4BHbCiucekjVqzVGq7ILlVtUyVA+AABakGaFUvfff7+n6wAAAACOS35JVZPL84oJpQAAaElcnlPqUBUVFaqrq3NqY/hb68akoAAAoLWK7RLS5PK4CK5tAABoSVwOpXJzczVlyhStXLlS+/fvd7QbY2RZlmpra91aILyDSUEBAEBr19MeppRE+xHnlOILNwAAWhaXQ6nrr79exhi99tpr6tq1q9ME6Gi90jOztDqnyKltdU6R0jI3MCkoAABoNTJSk5SWucHpi7bBCZHKSE3yYVUAAKAxLodSGzdu1Pr169W7d29P1AMfYFJQAADgLr6eCsAWEqiFkwYqt6hSecWVTEkAAEAL5nIodfbZZ2vHjh2EUn6ESUEBAMDxamlTAcRHEkYBANDSuRxKvfrqq7r11lu1c+dOnXrqqQoMdL7IOO2009xWHLyDSUEBAMDxYioAAADgKpdDqcLCQv3www+aOHGio82yLCY6b8WYFBQAABwPpgIAAADHwuVQ6sYbb1RSUpIyMzOZ6NyPMCkoAAA4VkwFAAAAjoXLoVR+fr7ee+89JSQkeKIe+AiTggIAgGPFVAAAAOBYtHP1CcOHD9fGjRs9UQtagPjIUA3rHUUgBQAAmq1+KoCAw3rQB1iWUhLtXFcAAIBGudxT6rLLLtPtt9+uTZs2qX///g0mOr/88svdVhwAAABaB6YCAAAArrKMOWRm62Zo1+7Inata60Tn5eXlstlsKisrU3h4uK/LAQAAaLWYCgAAADQ3Z3G5p1RdXd1xFQYAAAD/FR9JGAXA87YVVii/pIoAHGjlXA6lAAAAAADwhdKqGqVnZjkNFU5JtCsjNUm2kMAmngmgJXI5lHrooYeaXH7fffcdczEAAAAAABxJemaWVucUObWtzilSWuYGLZw00EdVAThWLodS77zzjtPjAwcOKDc3V+3bt1evXr0IpQAAAAAAbretsMKph1S9WmO0KrtQuUWVDOUDWhmXQ6kNGzY0aCsvL9eECRP0q1/9yi1FAQAAAABwqPySqiaX5xUTSgGtzZFvpeeC8PBwPfjgg7r33nvdsTkAAAAAcNhWWKEVWwuUW1Tp61LgQ7FdQppcHhdBIAW0Nm6b6LysrExlZWXu2hwAAACANo5JrXGonvYwpSTatTqnSLXGONoDLEuDEyLpJQW0Qi6HUs8995zTY2OMdu/erUWLFuniiy92W2EAAAAA2jYmtcbhMlKTlJa5wSmoHJwQqYzUJB9WBeBYWcYcEjE3Q3x8vNPjdu3ayW63a/jw4ZoxY4Y6derk1gK9oby8XDabTWVlZQoPD/d1OQAAAECbt62wQsOf/OyIy1dMH0rPmDYst6hSecWViosI5TwAWqDm5iwu95TKzc09rsIAAAAA4GiY1BpNiY8kjAL8gVsmOgcAAAAAd2JSawDwf83uKXXjjTcedR3LsjRv3rzjKggAAAAAmNS6ZdpWWKH8kiqGzQFwi2aHUj/99NMRl9XW1uqTTz5RdXU1oRQAAAAAt2BS65aDOyHCk/wl7PSX4/Amlyc6P9w//vEP/fGPf9SuXbt011136e6773ZXbV7DROcAAABAy8Wk1r43bt7aI/Za406IOFb+Enb6y3G4U3NzlmOeU2r16tU6//zz9Zvf/EaXXnqptm3b5nIg9eKLL+q0005TeHi4wsPDlZycrH/+85+O5fv379fkyZMVERGhsLAwjR07Vnv37nXaxvbt2zV69GiFhIQoKipKd9xxhw4ePHishwUAAHBMthVWaMXWAuUWVfq6FMDvxEeGaljvKAIpH9lWWKFV2YVOgZQk1RqjVdmFfO7hmKVnZml1TpFT2+qcIqVlbvBRRcfGX47DF1y++96WLVt01113aenSpRo3bpwyMzPVvXv3Y9p59+7d9eijjyoxMVHGGL3++usaM2aMNmzYoH79+un222/XBx98oMWLF8tms2nKlCm68sortXr1akm/DBscPXq0oqOj9eWXX2r37t0aN26cAgMD9cgjjxxTTQAAAK7g21EA/o47IcIT6sPOwx0adraG88pfjsNXmt1TaseOHZo4caJOP/10tW/fXt9++63mzZt3zIGUJF122WW65JJLlJiYqJNPPll//vOfFRYWpq+++kplZWWaN2+ennrqKQ0fPlwDBgzQ/Pnz9eWXX+qrr76SJH300UfasmWL3njjDZ1xxhm6+OKLNXPmTL3wwguqqak55roAAACai29HAfg77oQIT2hO2Nka+Mtx+Eqze0r17t1blmVp2rRpGjx4sLKzs5Wdnd1gvcsvv/yYCqmtrdXixYtVWVmp5ORkrV+/XgcOHNCIESMc6/Tp00c9evTQmjVrdM4552jNmjXq37+/unbt6lhn1KhRuu2227R582YlJTU+AWJ1dbWqq6sdj8vLy4+pZgAA0Lbx7SiAtoA7IcIT/CXs9Jfj8JVmh1L79++XJD3++ON6/PHHG13HsizV1ta6VMCmTZuUnJys/fv3KywsTO+884769u2rrKwsdejQQZ07d3Zav2vXrtqzZ48kac+ePU6BVP3y+mVHMmvWLD344IMu1QkAAHA4hrQAaCu4EyLczV/CTn85Dl9pdihVV1fnkQJ69+6trKwslZWVacmSJRo/frw+++wzj+yr3owZMzRt2jTH4/LycsXExHh0nwAAwP/w7SiAtsIWEqiFkwZyJ0S4lb+Enf5yHL7g8kTnzTV69Gi9+uqrOvHEE5tcr0OHDkpISJAkDRgwQOvWrdOzzz6rX//616qpqVFpaalTb6m9e/cqOjpakhQdHa21a9c6ba/+7nz16zQmKChIQUFBx3JYAAA/sa2wQvklVVxU47jw7SiAtiY+kn834T7+Enb6y3H4QrMnOnfVqlWr9PPPP7v8vLq6OlVXV2vAgAEKDAzUp59+6li2detWbd++XcnJyZKk5ORkbdq0SQUFBY51Pv74Y4WHh6tv377HfxAAAL9TWlWjcfPWaviTn2ni/HUa9sRKjZu3VmVVB3xdGlqpjNQkDU6IdGrj21EAAJovPjJUw3pHtfogx1+Ow5s81lOqOWbMmKGLL75YPXr00L59+/SXv/xFK1eu1LJly2Sz2TRp0iRNmzZNXbp0UXh4uNLS0pScnKxzzjlHkjRy5Ej17dtXN9xwg2bPnq09e/bonnvu0eTJk+kJBQB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      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "calibrator.specify().execute().analyze()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03c8cfda-c084-436b-b39c-06cf4f0f9fe5",
   "metadata": {},
   "source": [
    "We can retrieve the surrogate model using the `get_emulator()` method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "df88e83a-22ff-48f1-80c8-f8200c417047",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianProcessRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GaussianProcessRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html\">?<span>Documentation for GaussianProcessRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GaussianProcessRegressor()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GaussianProcessRegressor()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surrogate = calibrator.get_emulator()\n",
    "surrogate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40cc21f7-a93f-41c6-8622-b4354c00141a",
   "metadata": {},
   "source": [
    "We can generate new predictions by supplying the surrogate with newly sampled parameter values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "82ae32f8-35f1-44ae-9c21-ab897a7def8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='gamma', ylabel='Emulated Number of Infections'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = pd.DataFrame(calibrator.sample_parameters(1000), columns = [\"beta\", \"gamma\"])\n",
    "X[\"Emulated Number of Infections\"] = surrogate.predict(X)\n",
    "X.plot.scatter(\"gamma\", \"Emulated Number of Infections\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42b8bbaa-72bd-437b-94ae-d51511b4380e",
   "metadata": {},
   "source": [
    "There's a non-linear negative relationship between γ and the end-of-simulation number of infections, with greater variance as γ increases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd37c8a7-4a8a-4a22-9ea6-485eba7be4f6",
   "metadata": {},
   "source": [
    "## Surrogate Modelling via Random Forest\n",
    "\n",
    "We'll next demonstrate how to switch to another type of surrogate model using the same ` scikit-learn ` engine with minimal code changes. In this case, a `random forest` model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2cb92602-19b0-422f-9e76-f488275b643e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<calisim.surrogate.implementation.SurrogateModelMethod at 0x7f0e736bdff0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Wrl0b55133lAgFRGxYMGC6O3tjVdfffW4n/Oee+6JbDY79HbWWWedpEcHjLbZVeVx8dnTBFIAAABjXKqh1IUXXhiPPvpofP/734+vfe1rsWnTpvjt3/7teOONN2L79u0xadKkOP300/M+Zvr06bF9+/aIiNi+fXteIHVo/tDc8dx6663R09Mz9Pbaa6+N7gMDAAAA4IQmpvnJL7/88qE/z5s3Ly688MKYNWtWfPvb345TTz31pH3eyZMnx+TJk0/a3w8AAADAiaW+fe9wp59+erzzne+Mzs7OmDFjRuzfvz/27NmTd82OHTtixowZERExY8aMo07jO/T+oWsAAAAAKDwFFUr19fXFL37xi3jHO94RF1xwQZSWlsYzzzwzNL9hw4bYunVrNDQ0REREQ0NDvPzyy7Fz586ha1auXBkVFRUxd+7cxOsHAAAAYHhS3b538803x1VXXRWzZs2Kbdu2xZ133hklJSXR3Nwc2Ww2Fi9eHMuXL4+pU6dGRUVFLF26NBoaGuKiiy6KiIjLLrss5s6dG9dee23cd999sX379rjttttiyZIltucBAAAAFLBUQ6lf/vKX0dzcHLt27Yrq6up43/veF88991xUV1dHRMQDDzwQEyZMiIULF8a+fftiwYIF8dWvfnXo40tKSuLJJ5+MG264IRoaGqK8vDyuu+66uPvuu9N6SAAAAAAMQyaXy+XSLiJtvb29kc1mo6enJyoqKtIuBwAAAGDMGm7OUlA9pQAAAAAoDkIpAAAAABInlAIAAAAgcUIpAAAAABInlAIAAAAgcUIpAAAAABInlAIAAAAgcRPTLgBgPNrY1Rdbdg9ETWV5zK4qT7scAACAgiOUAhhFewb2x7LW9ljT0TU01lRXHS3N9ZEtK02xMgAAgMJi+x7AKFrW2h5tnd15Y22d3bG0dX1KFQEAABQmoRTAKNnY1RdrOrpiMJfLGx/M5WJNR1ds6u5PqTIAAIDCI5QCGCVbdg+ccH7zLqEUAADjz8auvli1YacXYXnL9JQCGCWzppadcL6mUsNzAADGD/1UebuslAIYJXOqp0RTXXWUZDJ54yWZTDTVVTuFD8YJrwYDwL/RT5W3y0opgFHU0lwfS1vX571a1FhbFS3N9SlWBYwGrwYDwH841E/1SIf3U/WiLG9GKAUwirJlpbFi8fzY1N0fm3f1R01luR/GME6c6NXgFYvnp1QVAKRjOP1U/T+YNyOUAjgJZlcJo2A88WowAOTTT5XRoKcUAMCbcLomAOTTT5XRIJQCAHgTXg0GgKO1NNdHY21V3ph+qrwVtu8BALyJQ68Gt3V2x2AuNzRekslEY22VV4MBKEr6qfJ2WSkFADAMXg0GgGObXVUeF589TSDFW2alFADAMHg1GABgdAmlAADeAqdrAgCMDtv3AAAAAEicUAoAAACAxAmlAAAAAEicUAoAAACAxAmlAAAAAEicUAoAAACAxAmlAAAAAEicUAoAAACAxAmlAAAAAEicUAoAAACAxAmlAAAAAEicUAoAAACAxAmlAAAAAEicUAoAAACAxAmlAAAAAEicUAoAAACAxAmlAAAAAEicUAoAAACAxAmlAAAAAEicUAoAAACAxAmlAAAAAEicUAoAAACAxAmlAAAAAEicUAoAAACAxE1MuwAAGOs2dvXFlt0DUVNZHrOrytMuBwAAxgShFACM0J6B/bGstT3WdHQNjTXVVUdLc31ky0pTrAwAAAqf7XsAMELLWtujrbM7b6ytszuWtq5PqSIAABg7hFIAMAIbu/piTUdXDOZyeeODuVys6eiKTd39KVUGAABjg1AKAEZgy+6BE85v3iWUAgCAExFKAcAIzJpadsL5mkoNzwEA4ESEUgAwAnOqp0RTXXWUZDJ54yWZTDTVVTuFDwAA3oRQCgBGqKW5Phprq/LGGmuroqW5PqWKAABg7JiYdgEAMFZly0pjxeL5sam7Pzbv6o+aynIrpAAAYJiEUgDwNs2uEkYBAMBbZfseAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQuIlpF0Bh29jVF1t2D0RNpePOAQAAgNEjlOKY9gzsj2Wt7bGmo2torKmuOlqa6yNbVppiZQAAAMB4YPsex7SstT3aOrvzxto6u2Np6/qUKgIAAADGE6EUR9nY1RdrOrpiMJfLGx/M5WJNR1ds6u5PqTIAYDzZ2NUXqzbs9H8LAChStu9xlC27B044v3lXv/5SAMCIaRMAAERYKcUxzJpadsL5mkqBFAAwctoEAAARQimOYU71lGiqq46STCZvvCSTiaa6aqukAIAR0yYAADhEKMUxtTTXR2NtVd5YY21VtDTXp1QRADAeDKdNAABQHPSU4piyZaWxYvH82NTdH5t39UdNZbkVUgDA26ZNAABwiFCKE5pdJYwCAEbPoTYBbZ3deVv4SjKZaKyt8v8OACgitu8BAJAobQIAgAgrpQAASJg2AQBAhFAKAICUaBMAAMVNKMWYt7GrL7bsHvAqKwAAAIwhQinGrD0D+2NZa3us6egaGmuqq46W5vrIlpWmWBkAAADwZjQ6Z8xa1toebZ3deWNtnd2xtHV9ShUBAAAAwyWUYkza2NUXazq68o6SjogYzOViTUdXbOruT6kyAAAAYDiEUoxJW3YPnHB+8y6hFMBYs7GrL1Zt2OmFBQCAIqGnFGPSrKllJ5yvqdTwHGCs0CMQAKA4WSnFmDSneko01VVHSSaTN16SyURTXbVT+ADGED0CAQCKk1CKMauluT4aa6vyxhprq6KluT6ligB4q/QIBAAoXrbvMWZly0pjxeL5sam7Pzbv6o+aynIrpADGmOH0CPS9HQBgfBJKMebNrhJGAYxVegQCABQv2/cAgNToEQjw1jipFBhPrJQCAFLV0lwfS1vX552+p0cgQD4nlQLjUSaXO6KzaBHq7e2NbDYbPT09UVFRkXY5AFCU9AgEOL5FDz8fbZ3deQdDlGQy0VhbFSsWz0+xMoCjDTdnsVIKACgIegQCHNuhk0qPdPhJpb5/AmORnlIAAAAFbDgnlQKMRVZKARSpjV19sWX3gK1SAFDgnFQKjFdCKYAio1EqAIwth04qPV5PKS8uAWOV7XsARWZZa3u0dXbnjbV1dsfS1vUpVQQAvJmW5vporK3KG3NSKTDWWSkFUEQ0SgWAsSlbVhorFs93UikwrgilgCF6DI1/w2mU6msPAIXLSaXAeFIw2/fuvffeyGQyceONNw6N7d27N5YsWRKVlZUxZcqUWLhwYezYsSPv47Zu3RpXXHFFlJWVxbRp0+LTn/50/OpXv0q4ehjb9gzsj0UPPx+X3L86rn/khbj4iz+MRQ8/Hz0DB9IujVGmUSoAAFAoCiKUeuGFF+LrX/96zJs3L2/8pptuiu9973vxne98J1avXh3btm2LD3/4w0Pzg4ODccUVV8T+/fvj2WefjW9+85vx6KOPxh133JH0Q4AxTY+h4nGoUWpJJpM3XpLJRFNdtVdeAQCAxKQeSvX19cXHPvaxeOihh+LXfu3XhsZ7enri4Ycfjr/6q7+KSy65JC644IJ45JFH4tlnn43nnnsuIiJ+8IMfxE9/+tP4n//zf8Zv/dZvxeWXXx6f/exn4ytf+Urs378/rYcEY8qhHkOHn+QSkd9jiPFFo1QAAKAQpB5KLVmyJK644oq49NJL88bXrVsXBw4cyBs/55xzYubMmbF27dqIiFi7dm2cd955MX369KFrFixYEL29vfHqq68e93Pu27cvent7896gWA2nxxDjy6FGqatu/kA8cv17Y9XNH4gVi+dHtqw07dIAAIAikmqj88cffzxeeumleOGFF46a2759e0yaNClOP/30vPHp06fH9u3bh645PJA6NH9o7njuueeeuOuuu95m9TA+6DFUvDRKBQAobA4iYrxLLZR67bXX4lOf+lSsXLkyTjnllEQ/96233hrLly8fer+3tzfOOuusRGuAQnGox1BbZ3feFr6STCYaa6v88AMAgITtGdgfy1rbY01H19BYU111tDTXW93OuJLa9r1169bFzp074/zzz4+JEyfGxIkTY/Xq1fHlL385Jk6cGNOnT4/9+/fHnj178j5ux44dMWPGjIiImDFjxlGn8R16/9A1xzJ58uSoqKjIe4NipscQAAAUDgcRUSxSWyn1wQ9+MF5++eW8seuvvz7OOeecuOWWW+Kss86K0tLSeOaZZ2LhwoUREbFhw4bYunVrNDQ0REREQ0NDfP7zn4+dO3fGtGnTIiJi5cqVUVFREXPnzk32AcEYdqjH0Kbu/ti8q9/yYAAASMmhg4iOdPhBRP6vzniRWih12mmnxbve9a68sfLy8qisrBwaX7x4cSxfvjymTp0aFRUVsXTp0mhoaIiLLrooIiIuu+yymDt3blx77bVx3333xfbt2+O2226LJUuWxOTJkxN/TDDW6TEEAADpGs5BRP7PzniRaqPzN/PAAw/EhAkTYuHChbFv375YsGBBfPWrXx2aLykpiSeffDJuuOGGaGhoiPLy8rjuuuvi7rvvTrFqAAAAGBkHEVFMMrncYZ2Ni1Rvb29ks9no6enRXwoAAIBULXr4+eMeRLRi8fwUK4PhGW7OklqjcwAAAOBoDiKiWBT09j0AAAAoNg4iolgIpQAAAKAAOYiI8c72PQAAAAASJ5QCAAAAIHFCKQAAAAASJ5QCAAAAIHFCKQAAAAASJ5QCAAAAIHFCKQAAAAASNzHtAhg9G7v6YsvugaipLI/ZVeVplwMAAABwXEKpcWDPwP5Y1toeazq6hsaa6qqjpbk+smWlKVYGAAAAcGy2740Dy1rbo62zO2+srbM7lrauT6kiAAAAgBMTSo1xG7v6Yk1HVwzmcnnjg7lcrOnoik3d/SlVBgAAAHB8QqkxbsvugRPOb94llAIAADjSxq6+WLVhpxfyi4Svd2HSU2qMmzW17ITzNZUangMAAByiJ29x8fUubFZKjXFzqqdEU111lGQyeeMlmUw01VU7hQ8AAOAwevIWF1/vwiaUGgdamuujsbYqb6yxtipamutTqggAAKDw6MlbXHy9C5/te+NAtqw0ViyeH5u6+2Pzrv6oqSy3QgoAAOAIw+nJ63ep8cPXu/AJpcaR2VXCKAAAgOPRk7e4+HoXPtv3AADGGScMARybnrzFxde78GVyuSM2Vxah3t7eyGaz0dPTExUVFWmXAwAwIk4YAnhzPQMHYmnret8ri4SvdzqGm7MIpUIoBQCMD4sefj7aOrvzGrqWZDLRWFsVKxbPT7EygMKjJ29x8fVO1nBzFj2lAADGgUMnDB3p8BOG/Ccc4D/oyVtcfL0Lk55SAADjwHBOGAIAKCRCKQCAccAJQwDAWCOUAgAYB5wwBACMNUIpAIBxoqW5Phprq/LGGmuroqW5PqWKAACOT6NzAIBxIltWGisWz3fCEAAwJgw7lKqvr4/MEcvBj+ell14acUEAALw9ThgCAMaCYYdSV1999dCf9+7dG1/96ldj7ty50dDQEBERzz33XLz66qvxiU98YtSLBAAAAGB8GXYodeeddw79+Y/+6I9i2bJl8dnPfvaoa1577bXRqw4AAACAcSmTy+Vyb/WDstlsvPjii1FXV5c33tHREe95z3uip6dn1ApMQm9vb2Sz2ejp6YmKioq0ywEAAACKzMauvtiye2Bc9IQcbs4yokbnp556arS1tR0VSrW1tcUpp5wykr8SIDXj6Zs/AAAwtuwZ2B/LWttjTUfX0FhTXXW0NNdHtqw0xcpOvhGFUjfeeGPccMMN8dJLL8X8+fMjIuJHP/pR/M3f/E3cfvvto1ogwMlSzN/8AQCAwrCstT3aOrvzxto6u2Np6/pYsXh+SlUlY0Tb9yIivv3tb8eXvvSl+NnPfhYREeeee2586lOfio985COjWmASbN+D4rTo4eejrbM7Bg/7NliSyURjbdW4/+YPAACkb2NXX1xy/+rjzq+6+QNjcjfHSd2+FxHxkY98ZEwGUAAR//bN//AVUocM5nKxpqMrNnX3j8lv/gAAwNixZffACec37xrfv5dMGOkH7tmzJ77xjW/Ef/2v/zV2794dEREvvfRS/Mu//MuoFQdwsgznmz8AAMDJNGtq2QnnayrHbyAVMcKVUj/5yU/i0ksvjWw2G5s3b44/+qM/iqlTp8Z3v/vd2Lp1a6xYsWK06wQYVcX+zR8AAEjfnOop0VRXfdy2IuN5lVTECFdKLV++PD7+8Y9HR0dH3ml7v/u7vxtr1qwZteIATpZD3/xLMpm88ZJMJprqqsf9N38AAKAwtDTXR2NtVd5YY21VtDTXp1RRcka0UuqFF16Ir3/960eN//qv/3ps3779bRcFkISW5vpY2ro+r7dUsXzzBwAACkO2rDRWLJ4fm7r7Y/Ou/qipLC+aF8lHFEpNnjw5ent7jxr/53/+56iurn7bRQEkoZi/+QMAAIVldlXx/T4you17H/rQh+Luu++OAwcOREREJpOJrVu3xi233BILFy4c1QIBTrbZVeVx8dnTiu4HAAAAQJpGFErdf//90dfXF9OmTYt//dd/jfe///1RW1sbp512Wnz+858f7RoBAAAoYhu7+mLVhp2xqdsJyTCejGj7XjabjZUrV0ZbW1v8+Mc/jr6+vjj//PPj0ksvHe36AAAAKFJ7BvbHstb2vB6gTXXV0dJcH9my0hQrA0ZDJpc77MzBYVqxYkV89KMfjcmTJ+eN79+/Px5//PFYtGjRqBWYhN7e3shms9HT0xMVFRVplwMAAEBELHr4+Wjr7I7Bw35tLclkorG2KlYsnp9iZcCJDDdnGdH2veuvvz56enqOGn/jjTfi+uuvH8lfCQAAAEM2dvXFmo6uvEAqImIwl4s1HV228sE4MKJQKpfLRSaTOWr8l7/8ZWSz2bddFAAAAMVty+6BE85v3iWUgrHuLfWUqq+vj0wmE5lMJj74wQ/GxIn/8eGDg4OxadOm+E//6T+NepEAAMCxbezqiy27B6KmsviOEmd8mzW17ITzNZWe7zDWvaVQ6uqrr46IiPb29liwYEFMmTJlaG7SpElRU1MTCxcuHNUCAQCAo2kAzXg3p3pKNNVVH7enlBAWxr4RNTr/5je/GR/96EfjlFNOORk1JU6jcwAAxppibwBthVhx6Bk4EEtb1wtfYYwZbs4yolBqvBFKAQAwlmzs6otL7l993PlVN39g3AY1VogVp03d/bF5V78QEsaIk3r63uDgYHzxi1+M+fPnx4wZM2Lq1Kl5bwAAwMlTzA2gl7W2R1tnd95YW2d3LG1dn1JFJGF2VXlcfPY0gRSMMyMKpe666674q7/6q/joRz8aPT09sXz58vjwhz8cEyZMiM985jOjXCIAAHC4Ym0AvbGrL9Z0dOVtWYyIGMzlYk1HV2zqHr9hHMB4NKJQ6lvf+lY89NBD8ad/+qcxceLEaG5ujm984xtxxx13xHPPPTfaNQIAAIc51AC6JJPJGy/JZKKprnrcriYp5hViAOPRiEKp7du3x3nnnRcREVOmTImenp6IiLjyyivj//7f/zt61QEAAMfU0lwfjbVVeWONtVXR0lyfUkUnX7GuEAMYryaO5IPOPPPMeP3112PmzJnxG7/xG/GDH/wgzj///HjhhRdi8uTJo10jAABwhGxZaaxYPL+oGkAfWiF2vFMHx/vjBxhvRrRS6vd///fjmWeeiYiIpUuXxu233x51dXWxaNGi+MM//MNRLRAAADi+YmsAXYwrxADGq0wud0SXwBFYu3ZtrF27Nurq6uKqq64ajboSNdyjCgEAgMJQTCvEAMaa4eYsoxJKjXVCKQAAAIDRMdycZdg9pf7P//k/w/7kH/rQh4Z9LQAAAADFZ9ih1NVXXz2s6zKZTAwODo60HgAAAACKwLBDqYMHD57MOgAAAAAoIiM6fQ8AAAAA3o5hr5Q63N13333C+TvuuGNExQAAAABQHEYUSj3xxBN57x84cCA2bdoUEydOjN/4jd8QSgEADNPGrr7YsnvAsfYAQNEZUSi1fv36o8Z6e3vj4x//ePz+7//+2y4KAGC82zOwP5a1tseajq6hsaa66mhpro9sWWmKlQEAJGPUekpVVFTEXXfdFbfffvto/ZUAAOPWstb2aOvszhtr6+yOpa1Hv/gHADAejWqj856enujp6RnNvxIAYNzZ2NUXazq6YjCXyxsfzOViTUdXbOruT6kyAGC4Nnb1xaoNO/3cfhtGtH3vy1/+ct77uVwuXn/99fgf/+N/xOWXXz4qhQEAjFdbdg+ccH7zrn79pQCgQNmCP3pGFEo98MADee9PmDAhqqur47rrrotbb711VAoDABivZk0tO+F8TaVACgAK1Ym24K9YPD+lqsamEYVSmzZtGu06AACKxpzqKdFUVx1tnd15W/hKMplorK2ySgoACtShLfhHOnwLvp/jwzeqPaUAABielub6aKytyhtrrK2Klub6lCoCAN7McLbgM3wjWim1d+/eaGlpiVWrVsXOnTvj4MGDefMvvfTSqBQHADBeZctKY8Xi+bGpuz827+qPmspyr6xSMDZ29cWW3QOelwBHsAV/dI0olFq8eHH84Ac/iGuuuSbmz58fmUxmtOsCACgKs6v80k/h0LwX4MRswR9dmVzuiLOIhyGbzcY//MM/RGNj48moKXG9vb2RzWajp6cnKioq0i4HAABSsejh54/7i5bmvQD/pmfgQCxtXS/AP4Hh5iwjWin167/+63HaaaeNuDgAAKCwaN4LMDy24I+eETU6v//+++OWW26JLVu2jHY9AABACjTvBXhrZleVx8VnTxNIvQ0jWin1nve8J/bu3Rtz5syJsrKyKC3NX562e/fuUSkOAABIhua9ACRtRKFUc3Nz/Mu//Et84QtfiOnTp2t0DgAAY5zmvQAkbUSh1LPPPhtr166Nd7/73aNdDwAAkJKW5vqjmvc21lZFS3N9ilUBMF6NKJQ655xz4l//9V9HuxYAjmNjV19s2T2giSIFxfMSxh/NewFI0ohCqXvvvTf+9E//ND7/+c/Heeedd1RPqRMd9wfA8O0Z2B/LWtsdN0tB8byE8W92lTAKgJMvk8sdtmF8mCZM+I9D+w7vJ5XL5SKTycTg4ODoVJeQ3t7eyGaz0dPTI1ADCsqih58/bm+PFYvnp1gZxczzEgCAExluzjKilVKrVq0acWEADM/Grr68lSiHDOZysaajKzZ193sVm8R5XgIAMFomvPklR3v/+98fEyZMiIceeij+7M/+LGpra+P9739/bN26NUpKSka7RmAM29jVF6s27IxN3f1plzLmbNk9cML5zbv8m5I8z0sAAEbLiEKp//2//3csWLAgTj311Fi/fn3s27cvIiJ6enriC1/4wqgWCIxNewb2x6KHn49L7l8d1z/yQlz8xR/Gooefj56BA2mXNmbMmlp2wvmaSqtRSJ7nJQAAo2VEodTnPve5ePDBB+Ohhx7Ka3Le2NgYL7300qgVB4xdy1rbo62zO2+srbM7lrauT6misWdO9ZRoqquOksN690X8W++eprpqW6RIheclAACjZUSh1IYNG6Kpqemo8Ww2G3v27Hm7NQFj3KGeM4NHnKNweM8ZhqeluT4aa6vyxhprq6KluT6lisDzksJlyziFyPMS4PhG1Oh8xowZ0dnZGTU1NXnj//RP/xRz5swZjbqAMWw4PWesphiebFlprFg8PzZ198fmXf1RU+mIbtLneUmh2TOwP5a1tuc14W+qq46W5vrIlpWe4CPh5PG8BHhzI1op9cd//MfxqU99Kn70ox9FJpOJbdu2xbe+9a24+eab44YbbhjtGoExRs+Z0Te7qjwuPnuaX/wpKJ6XFApbxilEnpcAb25EK6X+7M/+LA4ePBgf/OAHY2BgIJqammLy5Mlx8803x9KlS0e7RmCMOdRzpq2zO28LX0kmE421VX6BBWDUHNoyfqTDt4z7uUPSPC8BhmdEK6UymUz8+Z//eezevTteeeWVeO6556Krqys++9nPjnZ9wBil5wwASRjOlnFImuclwPCMaKXUIZMmTYq5c+eOVi3AOKLnDABJsGWcQuR5Wbw2dvXFlt0D/u8Lw/S2QimANzO7yg9kAE4eW8YpRJ6XxUdjexiZEW3fAwCAQmHLOIXI87K4aGwPI5PJ5Q6L7otUb29vZLPZ6OnpiYqKirTLAQBgBGwZpxB5Xo5/G7v64pL7Vx93ftXNH/C1p+gMN2dJdaXU1772tZg3b15UVFRERUVFNDQ0xFNPPTU0v3fv3liyZElUVlbGlClTYuHChbFjx468v2Pr1q1xxRVXRFlZWUybNi0+/elPx69+9aukHwoAACmbXVUeF589zS9/FBTPy/FPY3sYuVRDqTPPPDPuvffeWLduXbz44otxySWXxO/93u/Fq6++GhERN910U3zve9+L73znO7F69erYtm1bfPjDHx76+MHBwbjiiiti//798eyzz8Y3v/nNePTRR+OOO+5I6yEBAABQRDS2h5EruO17U6dOjb/8y7+Ma665Jqqrq+Oxxx6La665JiIifv7zn8e5554ba9eujYsuuiieeuqpuPLKK2Pbtm0xffr0iIh48MEH45Zbbomurq6YNGnSsD6n7XsAAACM1KKHnz9uY/sVi+enWBmkY0xs3zvc4OBgPP7449Hf3x8NDQ2xbt26OHDgQFx66aVD15xzzjkxc+bMWLt2bURErF27Ns4777yhQCoiYsGCBdHb2zu02upY9u3bF729vXlvAAAAMBIa28PITEy7gJdffjkaGhpi7969MWXKlHjiiSdi7ty50d7eHpMmTYrTTz897/rp06fH9u3bIyJi+/bteYHUoflDc8dzzz33xF133TW6DwQAAICilC0rjRWL52tsD29R6iulzj777Ghvb48f/ehHccMNN8R1110XP/3pT0/q57z11lujp6dn6O211147qZ8PAACA8U9je3hrUl8pNWnSpKitrY2IiAsuuCBeeOGF+NKXvhQf/ehHY//+/bFnz5681VI7duyIGTNmRETEjBkz4vnnn8/7+w6dznfommOZPHlyTJ48eZQfCQCMjo1dfbFl94BXWQEAGNdSXyl1pIMHD8a+ffviggsuiNLS0njmmWeG5jZs2BBbt26NhoaGiIhoaGiIl19+OXbu3Dl0zcqVK6OioiLmzp2beO0A8HbsGdgfix5+Pi65f3Vc/8gLcfEXfxiLHn4+egYOpF0aAACMulRXSt16661x+eWXx8yZM+ONN96Ixx57LH74wx/G008/HdlsNhYvXhzLly+PqVOnRkVFRSxdujQaGhrioosuioiIyy67LObOnRvXXntt3HfffbF9+/a47bbbYsmSJVZCATDmLGttj7bO7ryxts7uWNq63sk9AACMO6mGUjt37oxFixbF66+/HtlsNubNmxdPP/10/M7v/E5ERDzwwAMxYcKEWLhwYezbty8WLFgQX/3qV4c+vqSkJJ588sm44YYboqGhIcrLy+O6666Lu+++O62HBMAoKqZtbBu7+mJNR9dR44O5XKzp6IpN3f3j/t8AAIDiksnlcrm0i0hbb29vZLPZ6OnpiYqKirTLASh6ewb2x7LW9ryQpqmuOlqa6yNbVppiZSfPqg074/pHXjju/CPXvzcuPntaghUBAMDIDDdnKbieUgBwom1s49WsqWUnnK+ptEoKAIDxRSgFQEE5tI1t8IiFvIdvYxuP5lRPiaa66ijJZPLGSzKZaKqrtnUPAIBxRygFQEHZsnvghPObd43PUCoioqW5Phprq/LGGmuroqW5PqWKAADg5Em10TkAHKmYt7Fly0pjxeL5sam7Pzbv6i+KBu8AABQvoRQABeXQNra2zu68LXwlmUw01lYVRUgzu0oYBQDA+Gf7HgAFxzY2AAAY/6yUAqDg2MYGAADjn1AKgIJlGxsAAIxftu8BAAAAkDihFAAAAACJE0oBAAAAkDihFAAAAACJE0oBAAAAkDihFAAAAACJE0oBAAAAkDihFAAAAACJm5h2AVCINnb1xZbdA1FTWR6zq8rTLgcAAADGHaEUHGbPwP5Y1toeazq6hsaa6qqjpbk+smWlKVYGAAAA44vte3CYZa3t0dbZnTfW1tkdS1vXp1QRAAAAjE9CKfh3G7v6Yk1HVwzmcnnjg7lcrOnoik3d/SlVBoymjV19sWrDTvc0AACkzPY9+Hdbdg+ccH7zrn79pWAMsz0XAAAKi5VS8O9mTS074XxNpUAKxjLbcwEAoLAIpeDfzameEk111VGSyeSNl2Qy0VRXbZUUjGG25wIAQOERSsFhWprro7G2Km+ssbYqWprrU6oIGA3D2Z4LAAAkS08pOEy2rDRWLJ4fm7r7Y/Ou/qipLLdCCsYB23MBAKDwCKXgGGZXCaNgPDm0PbetsztvC19JJhONtVXudwCgaGzs6ostuwe8AE9BEEoBUBRamutjaev6vNP3bM8FAIqFk4gpRJlc7oiur0Wot7c3stls9PT0REVFRdrlAHAS2Z4LABSjRQ8/f9xV4ysWz0+xMsaj4eYsVkoBUFRsz4WRsd0DYOw6dBLxkQ4/idj3dtIglAIA4Lhs9wAY+4ZzErFQijRMSLsAAAAK17LW9mjr7M4ba+vsjqWt61OqCIC3yknEFCqhFAAAx3Rou8fgES1ID9/uAUDhO3QScUkmkzdekslEU121VVKkRigFAMAxDWe7BwBjQ0tzfTTWVuWNOYmYtOkpBQDAMdnuATB+ZMtKY8Xi+U4ipqAIpQAAOKZD2z2Od4S4X2YAxh4nEVNIbN8DAOC4bPcAAE4WK6UAADgu2z0AgJNFKAUAwJuy3QMAGG227wEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQuIlpF0Dx2djVF1t2D0RNZXnMripPuxwAAAAgBUIpErNnYH8sa22PNR1dQ2NNddXR0lwf2bLSFCsDAAAAkmb7HolZ1toebZ3deWNtnd2xtHV9ShUBAAAAaRFKkYiNXX2xpqMrBnO5vPHBXC7WdHTFpu7+lCoDAAAA0iCUIhFbdg+ccH7zLqEUAAAAFBOhFImYNbXshPM1lRqeAwAAQDERSpGIOdVToqmuOkoymbzxkkwmmuqqncIHAAAARUYoRWJamuujsbYqb6yxtipamutTqggAAABIy8S0C6B4ZMtKY8Xi+bGpuz827+qPmspyK6QAAACgSAmlSNzsKmEUAAAAFDvb9wAAAABInFAKAAAAgMQJpQAAAABInFAKAAAAgMQJpQAAAABInFAKAAAAgMQJpQAAAABInFAKAAAAgMQJpQAAAABI3MS0CwAAgGKysasvtuweiJrK8phdVZ52OQCQGqEUAAAkYM/A/ljW2h5rOrqGxprqqqOluT6yZaUpVgYA6bB9DwAAErCstT3aOrvzxto6u2Np6/qUKgKAdAmlAADgJNvY1RdrOrpiMJfLGx/M5WJNR1ds6u5PqTIASI9QCgAATrItuwdOOL95l1AKgOIjlAIAgJNs1tSyE87XVGp4DkDxEUoBAMBJNqd6SjTVVUdJJpM3XpLJRFNdtVP4AChKQikAAEhAS3N9NNZW5Y011lZFS3N9ShUBQLompl0AMDIbu/piy+6BqKks9+oqAIwB2bLSWLF4fmzq7o/Nu/r9DAeg6AmlYIzZM7A/lrW2x5qOrqGxprrqaGmuj2xZaYqVAQDDMbtKGAUAEbbvwZizrLU92jq788baOrtjaev6lCoCAACAt04oBWPIxq6+WNPRFYO5XN74YC4Xazq6YlO346QBAAAYG4RSMIZs2T1wwvnNu4RSAAAAjA1CKRhDZk0tO+F8TaX+FAAAAIwNQikYQ+ZUT4mmuuooyWTyxksymWiqq9Y0FQAAgDFDKAVjTEtzfTTWVuWNNdZWRUtzfUoVAQAAwFs3Me0CgLcmW1YaKxbPj03d/bF5V3/UVDpWGgAAgLEn1ZVS99xzT7z3ve+N0047LaZNmxZXX311bNiwIe+avXv3xpIlS6KysjKmTJkSCxcujB07duRds3Xr1rjiiiuirKwspk2bFp/+9KfjV7/6VZIPBRI3u6o8Lj57mkAKAACAMSnVUGr16tWxZMmSeO6552LlypVx4MCBuOyyy6K//z9OELvpppvie9/7XnznO9+J1atXx7Zt2+LDH/7w0Pzg4GBcccUVsX///nj22Wfjm9/8Zjz66KNxxx13pPGQAAAAABiGTC6Xy6VdxCFdXV0xbdq0WL16dTQ1NUVPT09UV1fHY489Ftdcc01ERPz85z+Pc889N9auXRsXXXRRPPXUU3HllVfGtm3bYvr06RER8eCDD8Ytt9wSXV1dMWnSpKM+z759+2Lfvn1D7/f29sZZZ50VPT09UVFRkcyDBQAAABiHent7I5vNvmnOUlCNznt6eiIiYurUqRERsW7dujhw4EBceumlQ9ecc845MXPmzFi7dm1ERKxduzbOO++8oUAqImLBggXR29sbr7766jE/zz333BPZbHbo7ayzzjpZDwkASNHGrr5YtWFnbOruf/OLAQBIVME0Oj948GDceOON0djYGO9617siImL79u0xadKkOP300/OunT59emzfvn3omsMDqUPzh+aO5dZbb43ly5cPvX9opRQAMD7sGdgfy1rbY01H19BYU111tDTXR7asNMXKAAA4pGBWSi1ZsiReeeWVePzxx0/655o8eXJUVFTkvQEA48ey1vZo6+zOG2vr7I6lretTqggAgCMVRCj1yU9+Mp588slYtWpVnHnmmUPjM2bMiP3798eePXvyrt+xY0fMmDFj6JojT+M79P6hawCA4rGxqy/WdHTF4BFtMwdzuVjT0WUrHwBAgUg1lMrlcvHJT34ynnjiifjHf/zHmD17dt78BRdcEKWlpfHMM88MjW3YsCG2bt0aDQ0NERHR0NAQL7/8cuzcuXPompUrV0ZFRUXMnTs3mQcCABSMLbsHTji/eZdQCgCgEKTaU2rJkiXx2GOPxd///d/HaaedNtQDKpvNxqmnnhrZbDYWL14cy5cvj6lTp0ZFRUUsXbo0Ghoa4qKLLoqIiMsuuyzmzp0b1157bdx3332xffv2uO2222LJkiUxefLkNB8eAJCCWVPLTjhfU1meUCUAAJxIqiulvva1r0VPT0984AMfiHe84x1Db3/7t387dM0DDzwQV155ZSxcuDCamppixowZ8d3vfndovqSkJJ588skoKSmJhoaG+IM/+INYtGhR3H333Wk8JAAgZXOqp0RTXXWUZDJ54yWZTDTVVcfsKqEUAEAhyORyRzRcKEK9vb2RzWajp6dH03MAGAd6Bg7E0tb1Tt8DgDFoY1dfbNk9EDWV5V5MGqOGm7Okun0PAOBkyJaVxorF82NTd39s3tXvP7UAMAbsGdgfy1rbvahURAri9D0AgJNhdlV5XHz2NIEUAIwBy1rbo62zO2+srbM7lrauT6kiTjahFAAAAJCqjV19saajKwaP6DA0mMvFmo6u2NTt9NzxSCgFAAAApGrL7oETzm/eJZQaj4RSAAAAQKpmTS074XxNpa3445FQCgAAAEjVnOop0VRXHSWZTN54SSYTTXXV+kOOU0IpAAAAIHUtzfXRWFuVN9ZYWxUtzfUpVcTJNjHtAgAAAACyZaWxYvH82NTdH5t39UdNZbkVUuOcUAoAAAAoGLOrhFHFwvY9AAAAABInlAIAAAAgcUIpAAAAABInlAIAAAAgcUIpAAAAABInlAIAAAAgcUIpAAAAABInlAIAAAAgcUIpAAAAABInlAIAAAAgcUIpAAAAABInlAIAAAAgcUIpAAAAABInlAIAAAAgcUIpAAAAABInlAIAAAAgcUIpAAAAABInlAIAAAAgcUIpAAAAABInlAIAAAAgcUIpAAAAABInlAIAAAAgcRPTLgAAAID0bOzqiy27B6KmsjxmV5WnXQ5QRIRSAAAARWjPwP5Y1toeazq6hsaa6qqjpbk+smWlKVYGFAvb9wAAAIrQstb2aOvszhtr6+yOpa3rU6oIKDZCKQAAgCKzsasv1nR0xWAulzc+mMvFmo6u2NTdn1JlQDERSgEAABSZLbsHTji/eZdQCjj5hFIAAABFZtbUshPO11RqeA6cfEIpAACAIjOneko01VVHSSaTN16SyURTXbVT+IBECKUAAACKUEtzfTTWVuWNNdZWRUtzfUoVAcVmYtoFAAAAkLxsWWmsWDw/NnX3x+Zd/VFTWW6FFJAooRQAAEARm10ljALSYfseAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAIkTSgEAAACQOKEUAAAAAImbmHYBAIwfG7v6YsvugaipLI/ZVeVplwMAABQwoRQAb9uegf2xrLU91nR0DY011VVHS3N9ZMtKU6wMAAAoVLbvAfC2LWttj7bO7ryxts7uWNq6PqWKAACAQieUAuBt2djVF2s6umIwl8sbH8zlYk1HV2zq7k+pMgAAoJAJpQB4W7bsHjjh/OZdQikAAOBoQikA3pZZU8tOOF9TqeE5AABwNKEUAG/LnOop0VRXHSWZTN54SSYTTXXVTuEDAACOSSgFwNvW0lwfjbVVeWONtVXR0lyfUkUAAEChm5h2AQCMfdmy0lixeH5s6u6Pzbv6o6ay3AopAADghIRSAIya2VXCKAAAYHhs3wMAAAAgcUIpAAAAABInlAIAAAAgcUIpAAAAABInlAIAAAAgcUIpAAAAABInlAIAAAAgcUIpAAAAABInlAIAAAAgcUIpAAAAABInlAIAAAAgcUIpAAAAABInlAIAAAAgcUIpAAAAABInlAIAAAAgcUIpAAAAABInlAIAAAAgcRPTLqAQ5HK5iIjo7e1NuRIAAACAse1QvnIobzkeoVREvPHGGxERcdZZZ6VcCQAAAMD48MYbb0Q2mz3ufCb3ZrFVETh48GBs27YtTjvttMhkMmmXM2K9vb1x1llnxWuvvRYVFRVplwNjhnsHRsa9AyPj3oG3zn0DI5PWvZPL5eKNN96IM844IyZMOH7nKCulImLChAlx5plnpl3GqKmoqPCNGkbAvQMj496BkXHvwFvnvoGRSePeOdEKqUM0OgcAAAAgcUIpAAAAABInlBpHJk+eHHfeeWdMnjw57VJgTHHvwMi4d2Bk3Dvw1rlvYGQK/d7R6BwAAACAxFkpBQAAAEDihFIAAAAAJE4oBQAAAEDihFIAAAAAJE4oNY585StfiZqamjjllFPiwgsvjOeffz7tkqCgrFmzJq666qo444wzIpPJxN/93d/lzedyubjjjjviHe94R5x66qlx6aWXRkdHRzrFQoG455574r3vfW+cdtppMW3atLj66qtjw4YNedfs3bs3lixZEpWVlTFlypRYuHBh7NixI6WKoTB87Wtfi3nz5kVFRUVUVFREQ0NDPPXUU0Pz7ht4c/fee29kMpm48cYbh8bcO3C0z3zmM5HJZPLezjnnnKH5Qr5vhFLjxN/+7d/G8uXL484774yXXnop3v3ud8eCBQti586daZcGBaO/vz/e/e53x1e+8pVjzt93333x5S9/OR588MH40Y9+FOXl5bFgwYLYu3dvwpVC4Vi9enUsWbIknnvuuVi5cmUcOHAgLrvssujv7x+65qabborvfe978Z3vfCdWr14d27Ztiw9/+MMpVg3pO/PMM+Pee++NdevWxYsvvhiXXHJJ/N7v/V68+uqrEeG+gTfzwgsvxNe//vWYN29e3rh7B47tN3/zN+P1118fevunf/qnobmCvm9yjAvz58/PLVmyZOj9wcHB3BlnnJG75557UqwKCldE5J544omh9w8ePJibMWNG7i//8i+Hxvbs2ZObPHlyrrW1NYUKoTDt3LkzFxG51atX53K5f7tPSktLc9/5zneGrvnZz36Wi4jc2rVr0yoTCtKv/dqv5b7xjW+4b+BNvPHGG7m6urrcypUrc+9///tzn/rUp3K5nJ85cDx33nln7t3vfvcx5wr9vrFSahzYv39/rFu3Li699NKhsQkTJsSll14aa9euTbEyGDs2bdoU27dvz7uPstlsXHjhhe4jOExPT09EREydOjUiItatWxcHDhzIu3fOOeecmDlzpnsH/t3g4GA8/vjj0d/fHw0NDe4beBNLliyJK664Iu8eifAzB06ko6MjzjjjjJgzZ0587GMfi61bt0ZE4d83E9MugLevu7s7BgcHY/r06Xnj06dPj5///OcpVQVjy/bt2yMijnkfHZqDYnfw4MG48cYbo7GxMd71rndFxL/dO5MmTYrTTz8971r3DkS8/PLL0dDQEHv37o0pU6bEE088EXPnzo329nb3DRzH448/Hi+99FK88MILR835mQPHduGFF8ajjz4aZ599drz++utx1113xW//9m/HK6+8UvD3jVAKABiWJUuWxCuvvJLXowA4vrPPPjva29ujp6cn/tf/+l9x3XXXxerVq9MuCwrWa6+9Fp/61Kdi5cqVccopp6RdDowZl19++dCf582bFxdeeGHMmjUrvv3tb8epp56aYmVvzva9caCqqipKSkqO6p6/Y8eOmDFjRkpVwdhy6F5xH8GxffKTn4wnn3wyVq1aFWeeeebQ+IwZM2L//v2xZ8+evOvdOxAxadKkqK2tjQsuuCDuueeeePe73x1f+tKX3DdwHOvWrYudO3fG+eefHxMnToyJEyfG6tWr48tf/nJMnDgxpk+f7t6BYTj99NPjne98Z3R2dhb8zxyh1DgwadKkuOCCC+KZZ54ZGjt48GA888wz0dDQkGJlMHbMnj07ZsyYkXcf9fb2xo9+9CP3EUUtl8vFJz/5yXjiiSfiH//xH2P27Nl58xdccEGUlpbm3TsbNmyIrVu3unfgCAcPHox9+/a5b+A4PvjBD8bLL78c7e3tQ2/vec974mMf+9jQn9078Ob6+vriF7/4RbzjHe8o+J85tu+NE8uXL4/rrrsu3vOe98T8+fPjv/23/xb9/f1x/fXXp10aFIy+vr7o7Owcen/Tpk3R3t4eU6dOjZkzZ8aNN94Yn/vc56Kuri5mz54dt99+e5xxxhlx9dVXp1c0pGzJkiXx2GOPxd///d/HaaedNtR7IJvNxqmnnhrZbDYWL14cy5cvj6lTp0ZFRUUsXbo0Ghoa4qKLLkq5ekjPrbfeGpdffnnMnDkz3njjjXjsscfihz/8YTz99NPuGziO0047bahn4SHl5eVRWVk5NO7egaPdfPPNcdVVV8WsWbNi27Ztceedd0ZJSUk0NzcX/M8codQ48dGPfjS6urrijjvuiO3bt8dv/dZvxfe///2jmjZDMXvxxRfj4osvHnp/+fLlERFx3XXXxaOPPhr/5b/8l+jv748/+ZM/iT179sT73ve++P73v6+nAUXta1/7WkREfOADH8gbf+SRR+LjH/94REQ88MADMWHChFi4cGHs27cvFixYEF/96lcTrhQKy86dO2PRokXx+uuvRzabjXnz5sXTTz8dv/M7vxMR7hsYKfcOHO2Xv/xlNDc3x65du6K6ujre9773xXPPPRfV1dURUdj3TSaXy+XSLgIAAACA4qKnFAAAAACJE0oBAAAAkDihFAAAAACJE0oBAAAAkDihFAAAAACJE0oBAAAAkDihFAAAAACJE0oBAAAAkDihFABAAj7wgQ/EjTfeOOKP37x5c2QymWhvbx+1mgAA0jQx7QIAAIrBd7/73SgtLU27DACAgiGUAgBIwNSpU9MuAQCgoNi+BwCQgMO379XU1MQXvvCF+MM//MM47bTTYubMmfHXf/3Xedc///zzUV9fH6ecckq85z3vifXr1x/1d77yyitx+eWXx5QpU2L69Olx7bXXRnd3d0RE/PCHP4xJkybF//t//2/o+vvuuy+mTZsWO3bsOHkPFABgmIRSAAApuP/++4fCpk984hNxww03xIYNGyIioq+vL6688sqYO3durFu3Lj7zmc/EzTffnPfxe/bsiUsuuSTq6+vjxRdfjO9///uxY8eO+MhHPhIR/xGCXXvttdHT0xPr16+P22+/Pb7xjW/E9OnTE3+8AABHsn0PACAFv/u7vxuf+MQnIiLilltuiQceeCBWrVoVZ599djz22GNx8ODBePjhh+OUU06J3/zN34xf/vKXccMNNwx9/H//7/896uvr4wtf+MLQ2N/8zd/EWWedFf/8z/8c73znO+Nzn/tcrFy5Mv7kT/4kXnnllbjuuuviQx/6UOKPFQDgWIRSAAApmDdv3tCfM5lMzJgxI3bu3BkRET/72c9i3rx5ccoppwxd09DQkPfxP/7xj2PVqlUxZcqUo/7uX/ziF/HOd74zJk2aFN/61rdi3rx5MWvWrHjggQdO0qMBAHjrhFIAACk48iS+TCYTBw8eHPbH9/X1xVVXXRV/8Rd/cdTcO97xjqE/P/vssxERsXv37ti9e3eUl5ePsGIAgNGlpxQAQIE599xz4yc/+Uns3bt3aOy5557Lu+b888+PV199NWpqaqK2tjbv7VDw9Itf/CJuuummeOihh+LCCy+M66677i0FXwAAJ5NQCgCgwPzn//yfI5PJxB//8R/HT3/60/iHf/iH+OIXv5h3zZIlS2L37t3R3NwcL7zwQvziF7+Ip59+Oq6//voYHByMwcHB+IM/+INYsGBBXH/99fHII4/ET37yk7j//vtTelQAAPmEUgAABWbKlCnxve99L15++eWor6+PP//zPz9qm94ZZ5wRbW1tMTg4GJdddlmcd955ceONN8bpp58eEyZMiM9//vOxZcuW+PrXvx4R/7al76//+q/jtttuix//+MdpPCwAgDyZXC6XS7sIAAAAAIqLlVIAAAAAJE4oBQAAAEDihFIAAAAAJE4oBQAAAEDihFIAAAAAJE4oBQAAAEDihFIAAAAAJE4oBQAAAEDihFIAAAAAJE4oBQAAAEDihFIAAAAAJO7/A+l4Pp39qqAzAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "specification.method = \"rf\"\n",
    "\n",
    "calibrator = SurrogateModelMethod(\n",
    "\tcalibration_func=surrogate_modelling_func,\n",
    "\tspecification=specification,\n",
    "\tengine=\"sklearn\",\n",
    ")\n",
    "\n",
    "calibrator.specify().execute().analyze()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e79e99a-6b26-4530-846c-ab3f752ca5e2",
   "metadata": {},
   "source": [
    "We can retrieve the random forest surrogate model using the `get_emulator()` method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6d16a785-8fa8-4be6-9172-50870cb5c37a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surrogate = calibrator.get_emulator()\n",
    "surrogate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ad30bd-b5e1-48f4-a776-8e70a09c5395",
   "metadata": {},
   "source": [
    "Again, we can generate new predictions by supplying the random forest model with newly sampled parameter values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eb864a52-18d9-49dc-8d47-799e05a221f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='gamma', ylabel='Emulated Number of Infections'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = pd.DataFrame(calibrator.sample_parameters(1000), columns = [\"beta\", \"gamma\"])\n",
    "X[\"Emulated Number of Infections\"] = surrogate.predict(X)\n",
    "X.plot.scatter(\"gamma\", \"Emulated Number of Infections\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e76104c-50b1-4c1a-aa94-76675bf006b3",
   "metadata": {},
   "source": [
    "There's a non-linear negative relationship between γ and the end-of-simulation number of infections, with greater variance as γ increases. When compared to the Gaussian process surrogate, the predictions appear forked and are not as smooth."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}